Innovations in Electronics and Communication Engineering: Proceedings of the 9th ICIECE 2021 (Lecture Notes in Networks and Systems, 355) 9811685118, 9789811685118

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Lecture Notes in Networks and Systems 355

H. S. Saini · R. K. Singh · Mirza Tariq Beg · Ravibabu Mulaveesala · Md Rashid Mahmood   Editors

Innovations in Electronics and Communication Engineering Proceedings of the 9th ICIECE 2021

Lecture Notes in Networks and Systems Volume 355

Series Editor Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Advisory Editors Fernando Gomide, Department of Computer Engineering and Automation—DCA, School of Electrical and Computer Engineering—FEEC, University of Campinas— UNICAMP, São Paulo, Brazil Okyay Kaynak, Department of Electrical and Electronic Engineering, Bogazici University, Istanbul, Turkey Derong Liu, Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, USA Institute of Automation, Chinese Academy of Sciences, Beijing, China Witold Pedrycz, Department of Electrical and Computer Engineering, University of Alberta, Alberta, Canada Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Marios M. Polycarpou, Department of Electrical and Computer Engineering, KIOS Research Center for Intelligent Systems and Networks, University of Cyprus, Nicosia, Cyprus Imre J. Rudas, Óbuda University, Budapest, Hungary Jun Wang, Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong

The series “Lecture Notes in Networks and Systems” publishes the latest developments in Networks and Systems—quickly, informally and with high quality. Original research reported in proceedings and post-proceedings represents the core of LNNS. Volumes published in LNNS embrace all aspects and subfields of, as well as new challenges in, Networks and Systems. The series contains proceedings and edited volumes in systems and networks, spanning the areas of Cyber-Physical Systems, Autonomous Systems, Sensor Networks, Control Systems, Energy Systems, Automotive Systems, Biological Systems, Vehicular Networking and Connected Vehicles, Aerospace Systems, Automation, Manufacturing, Smart Grids, Nonlinear Systems, Power Systems, Robotics, Social Systems, Economic Systems and other. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution and exposure which enable both a wide and rapid dissemination of research output. The series covers the theory, applications, and perspectives on the state of the art and future developments relevant to systems and networks, decision making, control, complex processes and related areas, as embedded in the fields of interdisciplinary and applied sciences, engineering, computer science, physics, economics, social, and life sciences, as well as the paradigms and methodologies behind them. Indexed by SCOPUS, INSPEC, WTI Frankfurt eG, zbMATH, SCImago. All books published in the series are submitted for consideration in Web of Science. For proposals from Asia please contact Aninda Bose ([email protected]).

More information about this series at https://link.springer.com/bookseries/15179

H. S. Saini · R. K. Singh · Mirza Tariq Beg · Ravibabu Mulaveesala · Md Rashid Mahmood Editors

Innovations in Electronics and Communication Engineering Proceedings of the 9th ICIECE 2021

Editors H. S. Saini Managing Director Guru Nanak Institutions Ibrahimpatnam, Telangana, India Mirza Tariq Beg Department of Electronics and Communication Engineering Jamia Millia Islamia New Delhi, Delhi, India

R. K. Singh Professor and Associate Director Guru Nanak Institutions Technical Campus Ibrahimpatnam, Telangana, India Ravibabu Mulaveesala Department of Electrical Engineering Indian Institute of Technology Ropar Rupnagar, Punjab, India

Md Rashid Mahmood Department of Electronics and Communication Engineering Guru Nanak Institutions Technical Campus Ibrahimpatnam, Telangana, India

ISSN 2367-3370 ISSN 2367-3389 (electronic) Lecture Notes in Networks and Systems ISBN 978-981-16-8511-8 ISBN 978-981-16-8512-5 (eBook) https://doi.org/10.1007/978-981-16-8512-5 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Committees

Editorial Board Members Chief Patron Sardar Tavinder Singh Kohli, Chairman, GNI

Patron Sardar Gagandeep Singh Kohli, Vice-Chairman, GNI

Conference Chair Dr. H. S. Saini Managing Director, GNI

Conference Co-chairs Dr. M. Ramalinga Reddy Director, GNITC Dr. R. K. Singh Associate Director, GNITC Dr. S. V. Ranganayakulu Dean (R&D), GNITC Dr. Pamela Chawla, Associate Director and Dean (ECE), GNITC

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Steering Committee Dr. S. Sreenatha Reddy Principal, GNITC Dr. P. Parthsaradhy Joint Director, GNITC Dr. Rishi Sayal Associate Director, GNITC Dr. Sanjeev Shrivastava Professor (CSE) and Dean (ARD), GNIT

Conveners Dr. Md Rashid Mahmood, Professor, GNITC Dr. B. Kedarnath, HOD-ECE, GNITC Dr. S. P. Yadav, HOD-ECE, GNIT Prof. S. Maheswara Reddy, ECE, GNITC

Committee Members Dr. Vikas Maheshwari, Professor, GNITC Prof. Anitha Swamidas, GNITC Prof. A. Mohan, GNITC Dr. Binod Kumar, Associate Professor, GNITC Dr. Harpreet Kaur, Associate Professor, GNITC Dr. Sandeep Patil, Associate Professor, GNITC Dr. K. Shashidhar, Associate Professor, GNITC Dr. Ch. Raja Rao, Associate Professor, GNITC Mr. D. Surendra Rao, Associate Professor, GNITC

Conference Committee Members Budget Dr. R. K. Singh Dr. Md Rashid Mahmood Mr. Sandeep Patil

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Email Campaigning Dr. Binod Kumar Prasad Ms. K. Ramyasri Ms. Md. Anees Ms. S. Aparna Mr. U. Shivanna

Receiving Papers and Acknowledgment, Attending Queries of Authors Dr. Md Rashid Mahmood Dr. Vikas Maheshwari Mrs. C. Sailaja Mrs. N. Ramya Teja

Conversion of Papers Dr. Md Rashid Mahmood Dr. Vikas Maheshwari Mrs. C. Sailaja Mrs. N. Ramya Teja

VIP Committee Dr. B. Kedarnath Dr. Vikas Maheshwari

Invitation Preparation and Distribution Dr. Md Rashid Mahmood Dr. Vikas Maheshwari

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Registration (Online/Spot) Dr. Md Rashid Mahmood Dr. Vikas Maheshwari Mrs. C. Sailaj Mr. Ch. Raja Rao Mrs. N. Ramya Teja

Reception and Certificate Mr. Naresh Mr. V. Sai Babu

Conference Office Dr. Binod Kumar Prasad

VC, MD, JNTUH Messages Prof. A. Mohan

Banners Mr. K. Krishna Kumar

Proceedings Dr. Binod Kumar Prasad

Recording Committee Prof. Maheswara Reddy

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Dr. Sandeep Patil Mr. D. Surendra Rao Mr. N. V. S. Murthy

Program Committee Prof. A. Mohan Prof Anitha Swamidas Dr. Harpreet Kaur Dr. Binod Kumar Prasad Dr. Sandeep Patil Prof. S. Maheswara Reddy Mrs. C. Sailaja Mrs. S. Swetha Dr. K. Shashidhar Mrs. K. Nadiya Mrs. B. Anitha Mr. D. Surendra Rao Mr. R. Gopinath Mr. Chinna Narasimhulu Ms. S. Aparna

Purchase Committee Dr. Md Rashid Mahmood Prof. A. Mohan

Anchoring Dr. Harpreet Kaur Ms. Manpreet Kaur

Inauguration Dr. R. K. Singh Dr. Shatrughna Prasad Yadav Prof. S. Maheswara Reddy

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Dr. Sandeep Patil Dr. Md Rashid Mahmood

Photos, Videos, Press Release Media and Conference Report Mr. O. Ravinder Mrs. B. Mythily Devi Mrs. K. Ramya Sree

Keynote Speech Arrangements Dr. Vikas Maheshwari

Food Committee Dr. Md Rashid Mahmood Prof. A Mohan

After Conference Attending Queries of Authors Dr. Md Rashid Mahmood Dr. Vikas Maheshwari Mrs. C. Sailaja Mrs. N. Ramya Teja

Certificate Posting Mr. O. Ravinder Mrs. S. Swetha

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International Advisory Board Dr. Mohd Rizal Arshad, Academic and International Universiti Malaysia Perlis, Malaysia Dr. Ali O Abid Noor University of Technology, Iraq Dr. Wan Zuha Bin Wan Hasan, Univesity Putra Malaysia Dr. Razali Ngah, Universiti Teknologi Malaysia, Skudai Dr. Shamimul Qamar, King Khalid University, Saudi Arabia Prof. Korhan Cengiz,Trakya University, Turkey Dr. Dhananjay Singh, University of Foreign Studies, South Korea Dr. Xiao Z. Gao, Lappeenranta University of Technology, Finland Dr. Osamah Ibrahim Khalaf, Al-Nahrain University, Baghdad, Iraq Dr. Ghanshyam Singh, University of Johannesburg, Gauteng, South Africa Dr. Saman Halgamuge, The Australian National University, Canberra Prof. Akhtar Kalam, Victoria University, Australia Dr. Alfredo Vaccaro, University of Sannio Benevento (Italy) Prof. Kim, Hannam University, South Korea Prof. Nowshad Amin, The National University of Malaysia Dr. Ganesh R. Naik, FEIT-UTS, Sydney, Australia Dr. Ahmed Faheem Zobaa, BU, UK Dr. Nesimi Ertugrul, UA, Australia Dr. Naim Ahmad, King Khalid University, Abha, Kingdom of Saudi Arabia

National Advisory Board Prof. Vikas Singh, Vice Chancellor, ITM University Raipur (CG) Prof. Mirza Tariq Beg, Jamia Millia Islamia, New Delhi Dr. Nirmala Devi, College of Engineering, Osmania University, Hyderabad Dr. Mohammed Zafar Ali Khan, Associate Professor and Head, IIT, Hyderabad Dr. Gulam Mohammed Rather, Professor, ECE, NIT, Srinagar Dr. N. Bheema Rao, Professor and Head, ECE, NIT, Warangal Dr. Deepak Mathur, Director, IEEE Region 10, Asia Pacific Region Dr. Ravibabu M., Associate Professor, Indian Institute of Technology, Ropar Dr. Niranjan Prasad, Scientist G, DLRL, Governing Council, IETE, New Delhi Dr. M. N. Giriprasad, Professor, JNTU, Anantapur Dr. Asha Elizabeth, Professor, Cochin University of Science and Technology, Cochin Dr. Sanjay Sharma, Professor, Thapar University, Patiala Dr. Ajaz Hussain Mir, Professor, NIT, Srinagar

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Reviewers List External Reviewers Dr. Rohit Raja, GGV Central University, Bilaspur Dr. Ashish Gupta, Sharda University, Greater Noida, Uttar Pradesh Dr. Korlapati Keerti Kumar, Vaageswari College of Engineering, Karimnagar, Telangana Dr. Md Ehtesham, Maulana Azad National Urdu University, Cuttack Campus, Odisha Dr. Ashish Gupta, MITS Gwalior, Madhya Pradesh Dr. Khushboo Pachori, Oriental Institute of Science and Technology, Madhya Pradesh Sanjay Dubey, BVRIT, Narsapur, Telangana Agha Asim Husain, I. T. S. Engineering College, Greater Noida, Uttar Pradesh Kasiprasad Mannepalli, KL University, Vaddeswaram, Andhra Pradesh Dr. Rebelli Shashank, SR University, Warangal, Telangana Dr. D. Jackuline Moni, Karunya Institute of Technology Sciences, Tamil Nadu Dr. M. Aravind Kumar, GVV Institute of Technology, Bhimavaram, Andhra Pradesh Dr. Srinivas Bachu, Marri Laxman Reddy Institute of Technology and Management, Telangana Dr. B. Anil Kumar, Gokaraju Rangaraju Institute of Engineering and Technology, Telangana Dr. V. A. Sankar Ponnapalli, Sreyas Institute of Engineering and Technology, Telangana Dr. V. Deepika, Vignana Bharathi Institute of Technology, Telangana Anuj Singal, Guru Jambheshwar University of Science and Technology, Haryana Hemlata Dalmia, St. Peters Engineering College, Telangana Dr. Amit Sehgal, Sharda University, Greater Noida, Uttar Pradesh Dr. Satish Saini, RIMT University Mandi, Gobindgarh, Punjab Dr. Vimlesh Kumar Ray, Gautam Buddha University Greater Noida, Uttar Pradesh Dr. Navaid Z. Rizvi, Gautam Buddha University, Uttar Pradesh Dr. Subhashish Tiwari, Vignan’s Foundation for Science, Technology and Research, Andhra Pradesh Dr. Mukesh Yadav, SAGE University, Madhya Pradesh Dr. Nidhi Tiwari, SAGE University, Madhya Pradesh Dr. D. Rama Krishna, University College of Engineering, Osmania University, Telangana Dr. Hemant Patidar, Oriental University, Madhya Pradesh G. Ahmed Zeeshan, Global Institute of Engineering and Technology, Telangana Dr. Somashekhar Malipatil, Malla Reddy Engineering College and Management Sciences, Telangana Dr. Santosh Kumar Agrahari, Poornima University, Rajasthan Dr. Santosh Kumar Sahoo, CVR College of Engineering, Telangana Dr. Abhishek Mehta, Parul University, Gujarat

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Dr. B. Suneela, Lords Institute of Engineering and Technology, Telangana Farooque Azam, REVA University, Karnataka Dr. Arun Kumar, Bharat Engineering College, Telangana Dr. Ravi Kumar Kandagatla, Lakireddy Bali Reddy College of Engineering, Andhra Pradesh Dr. Sandeep Kumar, Sreya’s Institute of Engineering and Technology (SIET), Telangana Sumita Gupta, K. C. College of Engineering and Management Studies and Research, Maharashtra Banavath Lalitha, Chadalawada Ramanama Engineering College, Andhra Pradesh Dr. Sanjay Kumar Suman, Bharat Institute of Engineering and Technology, Telangana Thakurendra Singh, A. P. J. Abdul kalam Technical University, Uttar Pradesh Navneet Kumar, I. T. S. Engineering College, Greater Noida, Uttar Pradesh Ghanshyam Singh, Feroze Gandhi Institute of Engineering and Technology, Uttar Pradesh Dr. Pratima Manhas, Manav Rachna International Institute of Research and Studies Faridabad, Haryana K. Tulasi Krishna, Srisairam College of Engineering and Technology, Tamil Nadu Dr. Ranjit Varma, Guru Gobind Singh Indraprastha University, Delhi Dr. Madhusudan, Malineni Lakshmaiah Womens Engineering College, Andhra Pradesh Dr. M. Shoukath Ali, Geethanjali College of Engineering and Technology, Telangana Dr. Bhaskar, Central university of Allahabad, Uttar Pradesh Dr. Mohit Agarwal, Sharda University, Uttar Pradesh Dr. Sudipta Das, NIT Sikkim, Sikkim Sai Krishna Karthik V., Sreyas Institute of Engineering and Technology, Telangana Fahmina Taranum, Muffakham Jah College of Engineering and Technology (MJCET), Telangana Rahul Agarwal, Raja Balwant Singh Engineering Technical Campus, Uttar Pradesh Dr. Jaishanker Prasad Keshari, IIMT College of Engineering, Uttar Pradesh Manjusha Kalekuri, Muffakham Jah College of Engineering and Technology (MJCET), Telangana S. Fouzia Sayeedunnisa, Muffakham Jah College of Engineering and Technology (MJCET), Telangana Maniza Hijab, Muffakham Jah College of Engineering and Technology (MJCET), Telangana Dr. L. Bhagyalakshmi, Rajalakshmi Engineering College, Tamil Nadu Reetika Kerketta, MIT School of Engineering, MIT ADT University, Pune M. Renu Babu, JNTUH, Hyderabad Ashwini N. Mandale, Dr. Daulatrao Aher College of Engineering, Karad, Maharastra K. Saritha, MallaReddy Engineering College and Management Sciences, Telangana Dr. Narbada Prasad Gupta, Lovely professional University, Punjab

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Internal Reviewers Dr. R. K. Singh, Associate Director, GNITC Dr. Pamela Chawla, Associate Director, Dean ECE, GNITC Dr. S. V. Ranganayakulu, Dean R&D, GNITC Dr. Md Rashid Mahmood, Professor, ECE, GNITC Dr. B. Kedarnath, HOD-ECE, GNITC Dr. S. P. Yadav, HOD-ECE, GNIT Dr. Vikas Maheshwari, Professor, ECE, GNITC Prof. Maheshwar Reddy, Professor, ECE, GNITC Prof. A. Mohan, Professor, ECE, GNITC Prof. Anitha Swamidas, Professor, ECE, GNITC Dr. Binod Kumar Prasad, Associate Professor, GNITC Dr. Harpreet Kaur, Associate Professor, GNITC Dr. Sandeep Patil, Associate Professor, GNITC Dr. K. Shashidhar, Associate Professor, GNITC Dr. Raja Rao, Associate Professor, GNITC

Committees

Preface

The 9th International Conference on “Innovations in Electronics and Communication Engineering” (ICIECE 2021) was organized by the Department of Electronics and Communication Engineering, Guru Nanak Institutions Technical Campus, Hyderabad, India, during August 13–14, 2021. More than 300 papers were received from India and across the globe including Saudi Arabia, Australia, Iraq, Malaysia, Bangladesh, Oman, etc. Sixty-five papers have been selected by reviewers for publication in Springer “Lecture Notes in Networks and Systems.” The research contributions cover a wide range in the domain of electronics and communication engineering, which includes five tracks: communication engineering, signal/image processing, embedded system, VLSI and miscellaneous. Distinguished professors from India and abroad joined as keynote speakers and shared their valuable ideas for innovation and integration in the field of electronics and communication engineering. We acknowledge keynote speakers Dr. Milan Simic, School of Engineering, RMIT University, Australia; Prof. Xavier Fernando, Professor and Director, Ryerson Communications Lab, Ryerson University, Toronto, Canada; and Prof. Yu-Dong Zhang, School of Informatics, University of Leicester, Leicester, UK. The papers selected were presented by the authors during the conference, in the presence of session chairs and delegates in online mode. Five parallel sessions were conducted for paper presentation, and ample time is given to authors to discuss their research findings. Selection of papers was done by a team, checking the plagiarism using Turnitin software. All papers were subjected to two blind reviews from all over the country and abroad. This is the ninth conference of this series, and since last five years, it is supported by Springer Nature. The conference has grown exponentially over the years and has become an excellent platform for scientists, researchers and academicians to present their ideas and share their cutting-edge research in various emerging fields of electronics and communication engineering. This conference was funded by Defence Research and Development Organisation (DRDO), Delhi. We would like to thank all the keynote speakers, participants, session chairs, committee members, reviewers and international and national advisory board members, Guru Nanak Institutions xv

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Management, and all the people who have directly or indirectly contributed to the success of this conference. We would also like to thank Springer Editorial Team for their support and for publishing the papers as part of the “Lecture Notes in Networks and Systems” series continuously since last five years. Ibrahimpatnam, India Ibrahimpatnam, India New Delhi, India Rupnagar, India Ibrahimpatnam, India

H. S. Saini R. K. Singh Mirza Tariq Beg Ravibabu Mulaveesala Md Rashid Mahmood

Contents

Communications An Efficient Cooperative Communication Technique for Multiuser Wireless Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Satish Kumar Gannamaneni and Jibendu Sekhar Roy

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Performance of MQC Code-Based SAC-OCDMA in FSO System . . . . . . Nabamita Das and Md. Jahedul Islam

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Resource Management in 5G . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Komal Raghavendra Kulkarni, Mayuri Manage, M. R. Kiran, and R. M. Banakar

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PAPR Reduction for FBMC-OQAM Signals Using PSO-Based JPTS Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. Rajendra Prasad, S. Tamil, and Bharti Chourasia

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Intrusion Detection System (IDS) for Security Enhancement in Wireless Sensing Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bharat Bhushan

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Design and Analysis of Wideband Circularly Polarized Modified Cylindrical Dielectric Resonator Antenna Array . . . . . . . . . . . . . . . . . . . . . P. Kondalamma, R. Sindhura, L. Naga jyothsna, K. Tarun, M. VamsiKrishna, and Ch. Raghavendra

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Design and Performance Analysis of Tri-band Monopole Planar Antenna for Wireless Communications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . T. Ravi Kumar Naidu and M. Susila

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Performance of Circular Patch Antenna Without and with Varying Superstrates Height . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V. Saidulu

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Contents

A Metasurface-Based Patch Antenna with Enhanced Gain and Frequency for X-Band Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nitish Kumar Pagadala, Anudeep Allamsetty, Suman Bulla, Vineetha Mukthineni, and K. Sneha Design and Simulation of Slot Antenna for Energy Harvesting . . . . . . . . . Ashima Sharma, Shrishti Singh, Paurush Dhawan, and Dinesh Sharma Design of Planar Slot Antenna Based on SIW Technology for Wireless LAN Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lokeshwar Bollavathi, Ravindranadh Jammalamadugu, and Murali Krishna Atmakuri

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Embedded Systems A Novel Approach for the Measurement of pH of Body Fluids at Various Temperatures Using Compensation Technique . . . . . . . . . . . . . 101 M. Sameera Fathimal and S. Jothiraj Design of Low-Cost Active Noise Cancelling (ANC) Circuit Using Ki-CAD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 Mehaboob Mujawar and D. Vijaya Saradhi An Approach for Designing of Low-Noise Bandgap Reference Circuit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 Anushree and Jasdeep Kaur Robotic Interactive Companion: Human–Robot Interaction for Wellness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 Alan Jacob, Manju Singh, and Agha Asim Husain Design and Fabrication of Automatic Oxygen Flow Controller for COVID Patient . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 S. J. Sugumar, Divya Sugathan, Bhagyalaxmi S. Patil, S. Chandana, S. Harsha, and V. Karthik Kumar Analysis and Testing of Geophone for Different Soil Conditions for Elephant Intrusion Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 S. J. Sugumar, D. Jeevalakshmi, S. Shreyas, R. Vishnu, M. S. Suryakotikiran, and B. Kushalappa Implementation of Goods Monitoring System Using Cloud . . . . . . . . . . . . 155 V. Arulkumar, R. Lathamanju, V. Sundari, and K. Thaiyalnayaki An Inventive and Frugal IoT-Based System for Unmanned Railway Crossings and Real-Time Train Collision Prediction Pertaining to Indian Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 Shriram K. Vasudevan, Prashant R. Nair, and Juluru Anudeep

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Signal and Image Processing Robust Blind Source Separation of Maternal and Fetal ECG Signals—Application to Instantaneous Heart Rate Calculation . . . . . . . . 179 El-Mehdi Hamzaoui A Pilot Study on Detection and Classification of COVID Images: A Deep Learning Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187 R. K. Chandana Mani, Bharat Bhushan, Vankadhara Rajyalakshmi, Jothiaruna Nagaraj, and T. Ramathulasi Ai-Based Online Hand Drawn Engineering Symbol Classification and Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195 Alikapati Keerthi Priya, N. Gaganashree, K. N. Hemalatha, Janaki Sutha Chembeti, and T. Kavitha Flower Detection Using Advanced Deep Learning Techniques . . . . . . . . . 205 Kolla Bhanu Prakash, Ch. Sreedevi, Pallavi Lanke, Pradeep Kumar Vadla, S. V. Ranganayakulu, and Suman Lata Tripathi A Hybrid Framework for Efficient Detection of Fake Currency Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213 M. V. B. T. Santhi, S. Hrushikesava Raju, S. Adinarayna, V. Lokanadham Naidu, and Saiyed Faiayaz Waris A Systematic Review Literature on Computer-Aided Detection Methods for COVID-19 Detection in X-Ray and CT Image Modalities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227 R. Brindha, A. Kavitha, and Bharat Bhushan Apple Fruit Classification and Damage Detection Using Pre-trained Deep Neural Network as Feature Extractor . . . . . . . . . . . . . . . 235 Gurucharan Kapila, B. Vandana, Ayush Khaitan, A. Francis Avinash, and C. H. Ajay Kumar Power Quality Event Classification Using Wavelets, Decision Trees and SVM Classifiers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245 M. Venkata Subbarao, Chinimilli Pravallika, D. Ramesh Varma, and M. Prema Kumar Comparative Analysis of Different Models for Diabetic Retinopathy Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253 Lavanya Bagadi, E. Pavankumar, A. Likitha, K. Niranjan, and B. Nani Convolutional Neural Network-Based Tomato Plant Leaf Disease Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259 V. Tejashwini, Shubha Suresh Patil, Shweta S. Mali, M. S. Salina, and Jyothi S. Nayak

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Virtual Assistant Based on Facial Emotions Using Convolution Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271 J. N. V. R. Swarup Kumar, D. N. V. S. L. S. Indira, R. Abinaya, and D. Suresh Detection of Arrhythmia Using Adaptive Boosting Algorithm . . . . . . . . . . 283 Harpreet Kaur, Shruti Bhargava Choubey, Abhishek Choubey, K. Sai Deekshith, B. Veeranna, and Y. Santhosh Reddy Classification of EEG Signal Based on DTCWT and Neural Network Classifier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293 Manpreet Kaur, M. Sugadev, Harpreet Kaur, and V. G. Siva Kumar Feature Set Analysis of Linear Predicted Code and Its Extended Algorithms for Speech Signal Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303 M. Shoukath Ali, P. Sandeep, and C. Sailaja Hybrid Metaheuristic-Based Thresholding and Faster Region-Convolutional Neural Network for Object Detection in Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311 Santosh Kumar Sahoo Breast Ultrasound Image Segmentation to Detect Tumor by Using Level Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319 G. R. Byra Reddy and H. Prasanna Kumar COVID-19 Testing Under X-ray Images and Web App Development Using Python Flasks Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327 B. Likhith, B. Praveen Nayak, and K. R. Suneetha Implementation of Novel High Performance FIR Filter Design Using Wallace Tree Multiplier with 7–3 and 8–3 Compressor . . . . . . . . . . 337 Saher Jawaid Ansari, Priyanka Verma, and Surya Deo Choudhary An Revolutionary Fingerprint Authentication Approach Using Gabor Filters for Feature Extraction and Deep Learning Classification Using Convolutional Neural Networks . . . . . . . . . . . . . . . . . . 349 N. R. Pradeep and J. Ravi Automatic Attendance System Using AI and Raspberry Pi Controller . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 361 Harpreet Kaur, Manpreet Kaur, Md Rashid Mahmood, Subham Badhyal, and Sarabpreet Kaur Miscellaneous DDoS Mitigation in SDN Using MTD and Behavior-Based Forwarding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 373 M. Udhaya Prasath, B. Sriram, P. Prakashkumar, and V. Vetriselvi

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NutriChain: Secure and Transparent Midday Meals Using Blockchain and IoT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 381 Ayasha Malik, Rekha Kashyap, Karan Arora, and Bharat Bhushan Association Rule-Based Routing Protocol for Opportunistic Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 391 Ayasha Malik, Bharat Bhushan, and Abhijit Kumar A Framework for Secured Dissemination of Messages in Internet of Vehicle Using Blockchain Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 401 Farooque Azam, Sunil Kumar, and Neeraj Priyadarshi A Study of Bridge Tap Effects on DSL Channel . . . . . . . . . . . . . . . . . . . . . . 413 Ajay Ashok Ovhal and Shweta Prakash Gaikwad A View of Virtual Reality in Learning Process . . . . . . . . . . . . . . . . . . . . . . . . 423 Ghaliya Al Farsi, Ragad M. Tawafak, Sohail Iqbal Malik, Roy Mathew, and Mohammed Waseem Ashfaque Study and Analysis of Zoning in Meitei Mayek Recognition . . . . . . . . . . . 429 Sanasam Chanu Inunganbi Study on Influence of Outliers on the Performance of Various Classification Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 437 Lingam Sunitha, Shanthi Makka, Sailaja Madhu, and J. Bheemeswara Sastry COVIBOT: A ChatBot for Covid-19 Related Information . . . . . . . . . . . . . 447 Farooque Azam, P. V. Bhaskar Reddy, Neeraj Priyadarshi, Md Rashid Mahmood, A. Laxmikanth, and M. Siddappa Design of 15 Holed Cobra Type Probes for Subsonic Wind Tunnel Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 461 Akhila Rupesh, J. Rakshija, Pratap, S. Sushanth, and S. M. Shanth Kumar Optimizing Memory in Opportunistic Sensor Networks . . . . . . . . . . . . . . . 469 Salman Arafath Mohammed, Shamimul Qamar, Khaleel Ur Rahman Khan, and K. V. N. Sunitha Students’ Satisfaction with Technology-Assisted Learning: An Empirical Analysis of Female University Students in Saudi Arabia Using Telecourse Evaluation Questionnaire . . . . . . . . . . . . . . . . . . . 479 Najmul Hoda, Naim Ahmad, and Md Rashid Mahmood Electric Vehicle Charging Technology: Recent Developments . . . . . . . . . . 487 S. Paul Sathiyan, Santo Jensen, and Jothi Abirami An Efficient Algorithm for Evaluate Routing Metric Parameters for RIoT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 495 Aakanksha Tyagi, Kanika Chauhan, and Gaurav Indra

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Mining Top-K Competitors by Eliminating the K-Least Items from Unstructured Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 505 Mahendra Eknath Pawar and Satish Saini Comparative Analysis of GLDAS and CWC Data of Wardha Basin . . . . 515 Yukta Chikate, Atharva Konge, and Asheesh Sharma Optimal Designing of FIR Filter with Hybrid Bat Optimization Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 523 Harmandeep Kaur, Satish Saini, and Amit Sehgal Amazon Product Alexa’s Sentiment Analysis Using Machine Learning Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 543 Ayesha Naureen, Ayesha Siddiqa, and Pothereddypally Jhansi Devi VLSI Design of Low Voltage Improved Current Mirror . . . . . . . . . . . . . . . . . . . . . 555 P. Anil Kumar, S. Tamil, and Nikhil Raj Design of High-Gain Operational Transconductance Amplifier . . . . . . . . 563 Rajesh Durgam, S. Tamil, and Nikhil Raj Design of Low-Power Bit Swapping BIST for IC Self-testing . . . . . . . . . . . 569 Kanika Gupta and Ashish Raman Design of Novel Low Area Decoder Using Quantum Cellular Automata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 579 Nancharaiah Vejendla, J. Priyanka, V. Y. S. S. Sudir Patnaikuni, S. Suresh Kumar, and M. Ravindra Kumar An Area-Efficient JK Flip-Flop-Based Phase Detector for Phase Measurement System Based on FPGA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 589 S. S. Kerur, Veeresh, and Shrikanth K. Shirakol Design and Analysis of Power-Efficient Carbon Nanotube-Based Parity Checker Circuits for High-Data Transmission Rate . . . . . . . . . . . . . 597 Imran Ahmed Khan and Md Rashid Mahmood Design of Low-Power CNTFET Parity Generators for High-Speed Data Transmission . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 605 Imran Ahmed Khan, Md Rashid Mahmood, J. P. Keshari, and Mirza Tariq Beg Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 613

Editors and Contributors

About the Editors Dr. H. S. Saini is Managing Director for Guru Nanak Institutions with Ph.D. in the field of Computer Science. He has over 24 years of experience at University/College level in teaching UG/PG students and has guided several B.Tech., M.Tech. and Ph.D. projects. He has published/presented more than 30 high quality research papers in International, National Journals and proceedings of International Conferences. He is Editor for Journal of Innovations in Electronics and Communication Engineering (JIECE) published by Guru Nanak Publishers. He has two books to his credit. Dr. Saini is Lover of innovation and is Advisor for NBA/NAAC accreditation process to many Institutions in India and abroad. Dr. H. S. Saini is also Chief Editor of JIECE, JICSE and many others reputed international journals. Dr. R. K. Singh is Associate Director, Guru Nanak Institutions Technical Campus, Hyderabad, is Alumina of REC (Now MNIT Jaipur), did his M.Tech. in Communication Engineering from IIT Bombay and Ph.D. from GITAM University, Visakhapatnam, with specialization in Radar Signal Processing. Dr. R. K. Singh has served Indian Army in the core of Electronics and Mechanical Engineering for 21 years before hanging his uniform as Lt. Col. He has rich industrial experience as Army Officer Managing Workshops and has been teaching faculty for more than six years while in service. He started his career as Teaching Assistant at MNIT Jaipur

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for one year before joining Army. As Professor, he has served for more than twelve years after premature retirement from the army services. He has served as HOD, Vice Principal and principal of various engineering colleges before being approved as Associate Director of this institute. Dr. R. K. Singh had hands on experience on high-tech electronic equipments and has done many courses on radars and simulators. He has published several papers on Microstrip Antennas, VLSI and Radar Signal Processing in International/National Journal and symposia of conferences. He also served as Editor for the Scopus Indexed Springer Lecture Note. Dr. R. K. Singh is Editor of Journal of Innovation in Electronics and Communication Engineering, published by GNI. Prof. Mirza Tariq Beg is Professor, Department of Electronics and Communication Engineering, Faculty of Engineering and Technology, Jamia Millia Islamia, New Delhi. He received Ph.D. degree from Jamia Millia Islamia New Delhi in the year 2003, M.Tech. from Delhi University Delhi in the year 1987 and B.Tech. from Aligarh Muslim University Aligarh in 1985. He started his career as Assistant Professor in the Department of Electronics and Communication Engineering from Jamia Millia Islamia New Delhi in 1987. Now, he is working as Professor since 2003 in the same organization. He was also Director of Centre for Distance and Open Learning (CDOL), Jamia Millia Islamia, New Delhi. His research area includes Microwave and Communication Engineering. He has guided several Ph.D. students and authored and co-authored more than 50 research papers in peer-reviewed, international journals.

Editors and Contributors

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Dr. Ravibabu Mulaveesala received M.Tech. from National Institute of Technology (NIT), Tiruchirapalli, in 2000 and Ph.D. from Centre for Applied Research in Electronics, Indian Institute of Technology Delhi (IITD), India, in 2007. He started his carrier as Assistant Professor, IIITDM Jabalpur. Currently, he is working as Associate Professor, Department of Electrical Engineering, Indian Institute of Technology, Ropar. His research interests include thermal, acoustical and optical methods for non-invasive/non-destructive imaging technologies. He has more than 100 research papers, two books, three book chapters and three patents in his account. He serves as Editorial or Advisory Boards of the several refereed journals of Institute of Physics, Institute of Electrical and Electronics Engineers (IEEE), Institution of Engineering and Technology (IET), Elsevier, etc., and also to several peer-reviewed conferences. He has completed four sponsored research grants under Sponsoring Agency of Science and Engineering Research Board (SERB), Ministry of Defense (AR&DB), Science and Engineering Research Board (SERB) and Global Innovation and Technology Alliance, respectively, in the capacity of Principal Investigator/Co-Principal Investigator/partner. He is Editor/Associate Editor of more than ten international journals. He has given several invited talks in various workshop, conferences in India as well as abroad. Dr. Md Rashid Mahmood received his Ph.D. degree from Jamia Millia Islamia, New Delhi, M.Tech. from Maharishi Dayanand University, Rohtak, Haryana, and B.E. from Jamia Millia Islamia, New Delhi. He started his carrier as Lecturer and currently working as Professor in the Department of Electronics and Communication Engineering, Guru Nanak Institutions Technical Campus, Hyderabad. Dr. Rashid Mahmood has more than sixteen year of teaching experience in reputed Engineering Colleges in India. He is Keen Researcher, and he has authored and co-authored more than 50 research papers in international/national journals and proceedings of symposia. He has eight patents in his account (published in India as well as abroad), and two patents have been granted. His areas of interest include image and video processing, wireless sensors

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networks (WSNs), propagation and scattering of transmission lines, design and application of microwave filters and antennas. He is Editor and Reviewer of several internationally refereed journals.

Contributors R. Abinaya Department of CSE, Gudlavalleru Engineering College, Gudlavalleru, Andhra Pradesh, India Jothi Abirami Department of Electrical and Electronics Engineering, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India S. Adinarayna Department of CSE, Raghu Institute of Technology, Visakhapatnam, India Naim Ahmad Department of Information Systems, King Khalid University, Abha, Kingdom of Saudi Arabia C. H. Ajay Kumar Department of ECE, Lendi Institute of Engineering and Technology, Vizianagaram, India Anudeep Allamsetty Department of Electronics and Communication Engineering, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, Andhra Pradesh, India P. Anil Kumar Department of ECE, SRK University, Bhopal, India Saher Jawaid Ansari Department of Electronics and Communication Engineering, Noida Institute of Engineering and Technology, Greater Noida, India Juluru Anudeep Amrita Vishwa Vidyapeetham, Vengal, India; Department of Electronics and Communication Engineering, Amrita School of Engineering, Coimbatore, India Anushree Indira Gandhi Delhi Technical University for Women, Delhi, India Karan Arora Inderprastha Engineering College, Ghaziabad, India V. Arulkumar Sri Sivasubramaniya Nadar College of Engineering, Chennai, India Mohammed Waseem Ashfaque Al Buraimi University College, Al Buraimi, Oman Murali Krishna Atmakuri Department of ECE, R.V.R. & J.C. College of Engineering, Guntur, India Farooque Azam Department of Computer Science and Engineering, Sangam University, Bhilwara, Rajasthan, India;

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Department of Computer Science and Engineering, REVA University, Bangalore, India Subham Badhyal Department of Physical Therapy and Rehabilitation Science, School of Medicine, University of Maryland Baltimore, Baltimore, USA Lavanya Bagadi Department of ECE, MVGR College of Engineering (A), Vizianagaram, India R. M. Banakar Department of Electronics and Communications, KLE Technological University, Hubli, Karnataka, India Mirza Tariq Beg Jamia Millia Islamia, New Delhi, India P. V. Bhaskar Reddy Department of Computer Science and Engineering, REVA University, Bangalore, India J. Bheemeswara Sastry Koneru Lakshmaiah Education Foundation, Deemed to be University, Hyderabad, Telangana, India Bharat Bhushan Department of CSE, School of Engineering and Technology (SET), Sharda University, Greater Noida, India Lokeshwar Bollavathi Department of ECE, R.V.R. & J.C. College of Engineering, Guntur, India R. Brindha Department of EEE, Sethu Institute of Technology, Kariapatti, India Suman Bulla Department of Electronics and Communication Engineering, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, Andhra Pradesh, India G. R. Byra Reddy Vemana Institute of Technology, Bengaluru, Karnataka, India S. Chandana Department of Electronics and Communication Engineering, MVJ College of Engineering, Bengaluru, India R. K. Chandana Mani School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India Kanika Chauhan Indira Gandhi Delhi Technical University for Women, New Delhi, India Janaki Sutha Chembeti Electronics And Communication Engineering, AMC Engineering College, Bengaluru, India Yukta Chikate William O’Neil India, Bengaluru, India Abhishek Choubey Electronics and Communication Engineering, Sreenidhi Institute of Science and Technology, Ghatkesar, Hyderabad, India Shruti Bhargava Choubey Electronics and Communication Engineering, Sreenidhi Institute of Science and Technology, Ghatkesar, Hyderabad, India

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Surya Deo Choudhary Department of Electronics and Communication Engineering, Noida Institute of Engineering and Technology, Greater Noida, India Bharti Chourasia SRK University, Bhopal, India Nabamita Das Department of Electrical and Electronic Engineering, Khulna University of Engineering and Technology, Khulna, Bangladesh Pothereddypally Jhansi Devi Department of Computer Science and Engineering, B V Raju Institute of Technology, Narsapur, Telangana, India Paurush Dhawan ECE Department, CCET (Degree Wing), Chandigarh, India Rajesh Durgam Department of ECE, SRK University, Bhopal, India Ghaliya Al Farsi College of Graduate Studies, Universiti Tenaga Nasional, Kajang, Malaysia; Al Buraimi University College, Al Buraimi, Oman A. Francis Avinash Department of ECE, Lendi Institute of Engineering and Technology, Vizianagaram, India N. Gaganashree Electronics And Communication Engineering, AMC Engineering College, Bengaluru, India Shweta Prakash Gaikwad Indira College of Engineering and Management, Parandwadi, Pune, Maharashtra, India Satish Kumar Gannamaneni School of Electronics Engineering, Kalinga Institute of Industrial Technology (KIIT) Deemed To Be University, Bhubaneswar, Odisha, India Kanika Gupta Department of Electronics and Communication, Dr. B R Ambedkar National Institute of Technology, Jalandhar, India El-Mehdi Hamzaoui Technical Division, National Centre for Nuclear Energy Science and Technology (CNESTEN), Rabat, Morocco S. Harsha Department of Electronics and Communication Engineering, MVJ College of Engineering, Bengaluru, India K. N. Hemalatha Electronics And Communication Engineering, AMC Engineering College, Bengaluru, India Najmul Hoda Department of Business Administration, College of Business, Umm Al-Qura University, Makkah, Kingdom of Saudi Arabia S. Hrushikesava Raju Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Guntur, India Agha Asim Husain I.T.S. Engineering College, Greater Noida, Uttar Pradesh, India D. N. V. S. L. S. Indira Department of IT, Gudlavalleru Engineering College, Gudlavalleru, Andhra Pradesh, India

Editors and Contributors

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Gaurav Indra Indira Gandhi Delhi Technical University for Women, New Delhi, India Sanasam Chanu Inunganbi Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Greenfields, Vaddeswaram, Guntur, India Alan Jacob I.T.S. Engineering College, Greater Noida, Uttar Pradesh, India Md. Jahedul Islam Department of Electrical and Electronic Engineering, Khulna University of Engineering and Technology, Khulna, Bangladesh Ravindranadh Jammalamadugu Department of ECE, R.V.R. & J.C. College of Engineering, Guntur, India D. Jeevalakshmi Department of Electronics and Communication Engineering, MVJ College of Engineering, Bangalore, India Santo Jensen Department of Electrical and Electronics Engineering, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India S. Jothiraj Department of Biomedical Engineering, Rajalakshmi Engineering College, Chennai, Tamilnadu, India Gurucharan Kapila Department of ECE, Lendi Institute of Engineering and Technology, Vizianagaram, India V. Karthik Kumar Department of Electronics and Communication Engineering, MVJ College of Engineering, Bengaluru, India Rekha Kashyap Noida Institute of Engineering Technology (NIET), Greater Noida, India Harmandeep Kaur School of Engineering (Electronics and Communication Engineering), RIMT University, Mandi Gobindgarh, Punjab, India Harpreet Kaur Department of Electronics and Communication Engineering, Guru Nanak Institutions Technical Campus, Ibrahimpatnam, Hyderabad, Telangana, India Jasdeep Kaur Indira Gandhi Delhi Technical University for Women, Delhi, India Manpreet Kaur Department of Electronics and Communication Engineering, Guru Nanak Institutions Technical Campus, Ibrahimpatnam, Hyderabad, Telangana, India Sarabpreet Kaur Electronics and Communication Engineering, Chandigarh Engineering College, Mohali, Chandigarh, India A. Kavitha Department of ECE, M. Kumarasamy College of Engineering, Karur, India T. Kavitha Electronics And Communication Engineering, AMC Engineering College, Bengaluru, India

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Alikapati Keerthi Priya Electronics And Communication Engineering, AMC Engineering College, Bengaluru, India S. S. Kerur Department of Electronics and Communication Engineering, SDM College of Engineering and Technology, Dharwad, Karnataka, India J. P. Keshari IIMT College of Engineering, Greater Noida, India Ayush Khaitan Department of ECE, Lendi Institute of Engineering and Technology, Vizianagaram, India Imran Ahmed Khan Jamia Millia Islamia, New Delhi, India Khaleel Ur Rahman Khan Department of Computer Science and Engineering, ACE College of Engineering, Hyderabad, India M. R. Kiran Department of Electronics and Communications, KLE Technological University, Hubli, Karnataka, India P. Kondalamma Department of Electronics and Communication Engineering, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, Andhra Pradesh, India Atharva Konge Yeshwantrao Chavan College of Engineering, Nagpur, Maharashtra, India Komal Raghavendra Kulkarni Department of Electronics and Communications, KLE Technological University, Hubli, Karnataka, India Abhijit Kumar Noida Institute of Engineering Technology (NIET), Greater Noida, India Sunil Kumar Department of Computer Science and Engineering, Sangam University, Bhilwara, Rajasthan, India B. Kushalappa Department of Electronics and Communication Engineering, MVJ College of Engineering, Bangalore, India Pallavi Lanke Department of Computer Science and Engineering, BV Raju Institute of Technology, Narsapur, Medak, Telangana, India R. Lathamanju ECE, SRM Institute of Science and Technology, Ramapuram, Chennai, India A. Laxmikanth Department of Computer Science and Engineering, Sri Indu College of Engineering and Technology, Hyderabad, India B. Likhith Department of Computer Science and Engineering, Bangalore Institute of Technology, Bangalore, Karnataka, India A. Likitha Department of ECE, MVGR College of Engineering (A), Vizianagaram, India

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V. Lokanadham Naidu Sree Vidyanikethan Engineering College (Autonomous), Tirupathi, India Sailaja Madhu Department of Computer Science and Engineering, Malla Reddy Engineering College for Women, Hyderabad, Telangana, India Md Rashid Mahmood Department of Electronics and Computer Engineering, Guru Nanak Institutions Technical Campus, Hyderabad, India; Electronics and Communication Engineering, School of Engineering and Technology, Guru Nanak Institutions Technical Campus, Ibrahimpatnam, Hyderabad, Telangana, India Shanthi Makka Department of Computer Science and Engineering, Geethanjali College of Engineering and Technology, Hyderabad, Telangana, India Shweta S. Mali B.M.S College of Engineering, Bengaluru, India Ayasha Malik Noida Institute of Engineering Technology (NIET), Greater Noida, India Sohail Iqbal Malik Al Buraimi University College, Al Buraimi, Oman Mayuri Manage Department of Electronics and Communications, KLE Technological University, Hubli, Karnataka, India Roy Mathew Al Buraimi University College, Al Buraimi, Oman Salman Arafath Mohammed Department of Electrical Engineering, Computer Engineering Section, King Khalid University, Abha, Kingdom of Saudi Arabia Mehaboob Mujawar Goa College of Engineering, Goa, India Vineetha Mukthineni Department of Electronics and Communication Engineering, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, Andhra Pradesh, India L. Naga jyothsna Department of Electronics and Communication Engineering, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, Andhra Pradesh, India Jothiaruna Nagaraj School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India Prashant R. Nair Department of Computer Science and Engineering, Amrita School of Engineering, Coimbatore, India; Amrita Vishwa Vidyapeetham, Vengal, India B. Nani Department of ECE, MVGR College of Engineering (A), Vizianagaram, India Ayesha Naureen Department of Computer Science and Engineering, B V Raju Institute of Technology, Narsapur, Telangana, India

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Jyothi S. Nayak B.M.S College of Engineering, Bengaluru, India K. Niranjan Department of ECE, MVGR College of Engineering (A), Vizianagaram, India Ajay Ashok Ovhal Department of EIE, M S Ramaiah Institute of Technology (Affiliated to VTU), Bengaluru, India Nitish Kumar Pagadala Department of Electronics and Communication Engineering, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, Andhra Pradesh, India Bhagyalaxmi S. Patil Department of Electronics and Communication Engineering, MVJ College of Engineering, Bengaluru, India Shubha Suresh Patil B.M.S College of Engineering, Bengaluru, India S. Paul Sathiyan Department of Electrical and Electronics Engineering, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India E. Pavankumar Department of ECE, MVGR College of Engineering (A), Vizianagaram, India Mahendra Eknath Pawar RIMT University, Punjab, India N. R. Pradeep Department of ECE, Channabasaveshwara Institute of Technology, Gubbi, Tumakuru, Karnataka, India Kolla Bhanu Prakash Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India P. Prakashkumar Anna University, Guindy, Chennai, India H. Prasanna Kumar University Visvesvaraya College Of Engineering Karnataka, Bengaluru, India Pratap Department of Aeronautical Engineering, Mangalore Institute of Technology and Engineering, Moodbidri, Karnataka, India Chinimilli Pravallika Department of ECE, Shri Vishnu Engineering College for Women, Bhimavaram, India B. Praveen Nayak Department of Computer Science and Engineering, Bangalore Institute of Technology, Bangalore, Karnataka, India M. Prema Kumar Department of ECE, Shri Vishnu Engineering College for Women, Bhimavaram, India Neeraj Priyadarshi Department of Business Development and Technology, CTiF Global Capsule, Aarhus University, Herning, Denmark J. Priyanka ECE, Lendi Institute of Engineering & Technology, Vizianagaram, India

Editors and Contributors

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Shamimul Qamar Faculty of Science and Arts, Dharan Al Janub King Khalid University, Abha, Kingdom of Saudi Arabia Ch. Raghavendra Department of Electronics and Communication Engineering, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, Andhra Pradesh, India Nikhil Raj Department of ECE, The LNM Institute of Information Technology, Jaipur, India D. Rajendra Prasad SRK University, Bhopal, India Vankadhara Rajyalakshmi School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India J. Rakshija Department of Aeronautical Engineering, Mangalore Institute of Technology and Engineering, Moodbidri, Karnataka, India Ashish Raman Department of Electronics and Communication, Dr. B R Ambedkar National Institute of Technology, Jalandhar, India T. Ramathulasi School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India D. Ramesh Varma Department of ECE, Shri Vishnu Engineering College for Women, Bhimavaram, India S. V. Ranganayakulu Guru Nanak Institutions Technical Campus (Autonomous), Khanapur, Telangana, India J. Ravi Department of ECE, Global Academy of Technology, Bengaluru, Karnataka, India T. Ravi Kumar Naidu Department of ECE, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India M. Ravindra Kumar ECE, Lendi Institute of Engineering & Technology, Vizianagaram, India Jibendu Sekhar Roy School of Electronics Engineering, Kalinga Institute of Industrial Technology (KIIT) Deemed To Be University, Bhubaneswar, Odisha, India Akhila Rupesh Department of Aeronautical Engineering, Mangalore Institute of Technology and Engineering, Moodbidri, Karnataka, India Santosh Kumar Sahoo Department of Electronics and Instrumentation Engineering, CVR College of Engineering, Hyderabad, Telangana, India K. Sai Deekshith Electronics and Communication Engineering, Sreenidhi Institute of Science and Technology, Ghatkesar, Hyderabad, India V. Saidulu Department of ECE, Mahatma Gandhi Institute of Technology, Hyderabad, Telangana, India

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Editors and Contributors

C. Sailaja Guru Nanak Institutions Technical Campus, Hyderabad, India Satish Saini School of Engineering (Electronics and Communication Engineering), RIMT University, Mandi Gobindgarh, Punjab, India M. S. Salina B.M.S College of Engineering, Bengaluru, India M. Sameera Fathimal Department of Electronics and Communication Engineering, Anna University, College of Engineering Guindy, Chennai, Tamilnadu, India P. Sandeep Guru Nanak Institutions Technical Campus, Hyderabad, India M. V. B. T. Santhi Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Guntur, India Y. Santhosh Reddy Electronics and Communication Engineering, Sreenidhi Institute of Science and Technology, Ghatkesar, Hyderabad, India Amit Sehgal School of Engineering and Technology, Sharda University, Greater Noida, Uttar Pradesh, India S. M. Shanth Kumar Department of Aeronautical Engineering, Mangalore Institute of Technology and Engineering, Moodbidri, Karnataka, India Asheesh Sharma NEERI, Nagpur, Maharashtra, India Ashima Sharma ECE Department, CCET (Degree Wing), Chandigarh, India Dinesh Sharma ECE Department, CCET (Degree Wing), Chandigarh, India Shrikanth K. Shirakol Department of Electronics and Communication Engineering, SDM College of Engineering and Technology, Dharwad, Karnataka, India M. Shoukath Ali Geethanjali College of Engineering and Technology, Hyderabad, India S. Shreyas Department of Electronics and Communication Engineering, MVJ College of Engineering, Bangalore, India M. Siddappa Department of Computer Science and Engineering, SSIT, Tumkur, India Ayesha Siddiqa Department of Computer Science and Engineering, Shadan Womens College of Engineering and Technology, Khairtabad, Telangana, India R. Sindhura Department of Electronics and Communication Engineering, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, Andhra Pradesh, India Manju Singh I.T.S. Engineering College, Greater Noida, Uttar Pradesh, India Shrishti Singh ECE Department, CCET (Degree Wing), Chandigarh, India

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V. G. Siva Kumar Department of ECE, Vidya Jyothi Institute of Technology, Hyderabad, India K. Sneha Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, Andhra Pradesh, India Ch. Sreedevi Department of Computer Science and Engineering, BV Raju Institute of Technology, Narsapur, Medak, Telangana, India B. Sriram Anna University, Guindy, Chennai, India V. Y. S. S. Sudir Patnaikuni ECE, Lendi Institute of Engineering & Technology, Vizianagaram, India M. Sugadev Department of ECE, Sathyabama Institute of Science and Technology, Chennai, India Divya Sugathan Department of Electronics and Communication Engineering, MVJ College of Engineering, Bengaluru, India S. J. Sugumar Department of Electronics and Communication Engineering, MVJ College of Engineering, Bengaluru, India V. Sundari Meenakshi Sundararajan Engineering College, Chennai, India K. R. Suneetha Department of Computer Science and Engineering, Bangalore Institute of Technology, Bangalore, Karnataka, India K. V. N. Sunitha BVRIT Hyderabad College of Engineering for Women, Hyderabad, India Lingam Sunitha Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Deemed to be University, Hyderabad, Telangana, India D. Suresh Department of IT, Annamalai University, Chidambaram, Tamil Nadu, India S. Suresh Kumar ECE, Lendi Institute of Engineering & Technology, Vizianagaram, India M. S. Suryakotikiran Department of Electronics and Communication Engineering, MVJ College of Engineering, Bangalore, India S. Sushanth Department of Aeronautical Engineering, Mangalore Institute of Technology and Engineering, Moodbidri, Karnataka, India M. Susila Department of ECE, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India J. N. V. R. Swarup Kumar Department of CSE, Gudlavalleru Engineering College, Gudlavalleru, Andhra Pradesh, India S. Tamil Department of ECE, SRK University, Bhopal, India

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Editors and Contributors

K. Tarun Department of Electronics and Communication Engineering, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, Andhra Pradesh, India Ragad M. Tawafak Al Buraimi University College, Al Buraimi, Oman V. Tejashwini B.M.S College of Engineering, Bengaluru, India K. Thaiyalnayaki ECE, SRM Institute of Science and Technology, Ramapuram, Chennai, India Suman Lata Tripathi School of Electronics and Electrical Engineering, Lovely Professional University, Phagwara, Punjab, India Aakanksha Tyagi Indira Gandhi Delhi Technical University for Women, New Delhi, India M. Udhaya Prasath Anna University, Guindy, Chennai, India Pradeep Kumar Vadla Department of Computer Science and Engineering, BV Raju Institute of Technology, Narsapur, Medak, Telangana, India M. VamsiKrishna Department of Electronics and Communication Engineering, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, Andhra Pradesh, India B. Vandana Department of ECE, Lendi Institute of Engineering and Technology, Vizianagaram, India Shriram K. Vasudevan Intel IoT Innovator, Project Manager, MNC Services Company, Bengaluru, Tamilnadu, India B. Veeranna Electronics and Communication Engineering, Sreenidhi Institute of Science and Technology, Ghatkesar, Hyderabad, India Veeresh Department of Electronics and Communication Engineering, SDM College of Engineering and Technology, Dharwad, Karnataka, India Nancharaiah Vejendla ECE, Lendi Institute of Engineering & Technology, Vizianagaram, India M. Venkata Subbarao Department of ECE, Shri Vishnu Engineering College for Women, Bhimavaram, India Priyanka Verma Department of Electronics and Communication Engineering, Noida Institute of Engineering and Technology, Greater Noida, India V. Vetriselvi Anna University, Guindy, Chennai, India D. Vijaya Saradhi Malineni Perumalu Educational Society Group of Institutions, Guntur, Andhra Pradesh, India R. Vishnu Department of Electronics and Communication Engineering, MVJ College of Engineering, Bangalore, India

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Saiyed Faiayaz Waris Department of Computer Science and Engineering, Vignan’s Foundation for Science, Technology and Research, Vadlamudi, Guntur, India

Communications

An Efficient Cooperative Communication Technique for Multiuser Wireless Network Satish Kumar Gannamaneni and Jibendu Sekhar Roy

Abstract By improving spectrum efficiency, power, reliability, and network access, in wireless networks, cooperative communication enhances the link quality in wireless communication. It also allows communication tools to be used by enabling the nodes and paths in a network to cooperate with each another through data transmissions. This reduces the life spans of both the nodes, and the network itself as data is transmitted at a larger power. Thus, to obtain a sufficient level of bit error rate (BER) output at the receiver, control over the transmission of power is necessary. By reducing power consumption, relay nodes will enhance device efficiency. In this paper, the system model having the source, destination, and relay nodes is examined using decode and forward method. Results show the transmitted power savings when relay nodes are used. Keywords Cooperative communication · BER · Relay nodes · Decode and forward

1 Introduction Cooperative communication (CC) in cellular networks has recently been an attracting subject for study. Wireless networks of next generations for point-to-point or pointto-multi-point have facilitated the latest demand for CC research [1]. Such inspirations focus on exchanges between nodes in which they have to work together to enhance their ability [2]. CC using relay nodes based on network coding can improve the capacity of the wireless networks [3]. In order to enhance energyefficient performance, management of power control plays a prominent part in mobile networks [4]. Transmitted power management has greatly strengthened a range of extreme wireless communication network constraints, such as reducing the use of energy and improving the use of network resources. Throughput can be enhanced by optimal energy harvesting using DF relaying [6]. For transmitting information, the S. K. Gannamaneni (B) · J. S. Roy School of Electronics Engineering, Kalinga Institute of Industrial Technology (KIIT) Deemed To Be University, Bhubaneswar, Odisha 751024, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_1

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S. K. Gannamaneni and J. S. Roy

source is using 16-QAM in a DF system, and every relay identifies and transmits the successfully decoded signal to the destination [7]. Aim of this research work is the power transfers of mobile networks using DF protocol for cooperation. Coding is developed using MATLAB to calculate SNR during the use of network relay nodes with lower power consumption using DF protocol. This improves the BER as a consequence of power saving. Simulated results are compared with published results to show the better performance of the proposed method.

2 Cooperative Communication 2.1 System Model There are three nodes in the DF relay system model in general: source (S), relay (R), and destination (D). The transmission mechanism is split into two stages in a DF relay: First of all, the source transmits data to the relay and destination, and the data is decoded by the relay. Later, to the destination, the relay transmits the data. The destination finally integrates the signal sent from S and from R and decodes the data. Transmission power can be conserved during the use of the R between the S and D. This is the main objective of CC in mobile networks (Fig. 1). However, as in the general model, the present paper examines a three-node device model. In course of transmission, sends the data to R, and the R decodes the data. The relay then re-transmits and forwards the data to the D node. As depicted in Fig. 2, for the source relay (SR) and relay destination (RD) channels, AWGN is predicted. The system model considers a DF cooperative relay protocol. The signal transmitted from a source with an energy unit is supposed to be x. It is considered that S and R transmissions have fixed power. Furthermore, Y S, R is the received signal from S at R, and Y RD is the received signal at D from R. nSR, nRD are the additive noises in the SR link, RD link with σ R 2 , σ D 2 as respective variances. Fig. 1 General system model

An Efficient Cooperative Communication Technique for Multiuser …

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Fig. 2 Proposed system model

In addition, for the SR link, X SR is the distance, and for the RD link, X RD is the distance. The additive noise is zero, with a variance of N 0 = σ R 2 = σ D 2 , indicating a complex Gaussian random variable. Y SR and Y RD , referring to the signals received at the relay and destination, are given by [5, 8]. YSR = YRD =

√ √

P

k + n SR X SR

(1)

P

k + n RD X RD

(2)

where P is the power transmitted. γ = P/N 0 is the SNR at the transmitter. The SNR for the SR link is γ SR = (P/N 0 )|hSR |2 . The BER of cooperation for the current model is given by: Pe−DF = Pe1 + (1 − Pe1 ) × Pe2

(3)

Pe1 is BER for the first cooperative link, given by: 3 Pe1 = Q 8



2γSR 5

 (4)

where γ SR is SNR of the SR link and Q (·) is given by: 1 Q(x) = √ 2π

∞

t2

e− 2 dt

(5)

x

Pe2 is BER for the second cooperative link 3 Pe2 = Q 8



2(γSR + γRD ) 5

 (6)

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where γ RD is SNR of the RD link. In this case, to enhance system execution, a cooperative relay is required. MATLAB is the environment, which is used to simulate the current system model.

2.2 Implementation Direct Link System The direct link is constructed as the starting stage of the system. It just requires a source and a destination. The modulation scheme can then be set up using the modem.qammod() function as a 16-QAM modulation. The signal constellation size must be set at 16 when the modulation is set up, as needed in this paper. It will pass through the channel after the signal is modulated. Users can place the channel in additive white Gaussian noise mode by using the awgn() function. In order to generate the received signal, modem.qamdemod() function is used. The biterr() function is used to compute BER. The program is analyzed and evaluated using the BER theoretical formula of QAM, given by   3 erfc k ∗ 0.1 ∗ (10∧ (E b N0 /10)) 2k

(7)

Single-Relay System (SRS) Later, the system is extended by placing a relay midway between S and D with the same SNR. Then, to compare with the direct link, the BER is computed. Here, it is necessary to change the input stream into a matrix. For both links, BER must be calculated by integrating the first input stream and the second link output stream before the graph is plotted (Fig. 3). The single-relay connection BER can be seen to be worse than the BER of direct link. Therefore, to receive same BER, SNR must be larger in the SRS than the direct link system. Next, by marginally increasing the SNR, the shift in the SRS is altered. This gives the same BER as in direct link, and it confirms the saving of power by relay system. Using 100.06 to measure the transmitter power is increased by 1.14 times, and the SNR is enhanced by 0.6 dB. Therefore, the cumulative power of the transmitter is 1.14P/2 = 0.57P. Fig. 3 Bit error rate for SRS

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Fig. 4 Bit error rate for multiple relay

Just in Fig. 4, the performance of the BER can be observed and compared with that of earlier plot. The BER plot displayed the nature of direct link as shown in Fig. 4. Multiple-Relay System In multiple-relay system, two relays are used. In addition, the required power to obtain a BER plot equivalent to that of direct connection is determined. SNR is raised by 0.9 dB by using 100.09 = 1.23 which must be translated to the power ratio. Since there are three links, each of which is one third of its way across the direct link. The transmitted power amount is 1.23P/3 = 0.41P. The cumulative transmitted power can be found to be lower than the SRS. Therefore, it would be easier to conserve power by using more relay nodes. It is visible in Fig. 4 that the BER for direct communication is the same as the BER since it includes more than one relay node, each of which saves the power transmitted. The below equation is used for the analysis of power relations in both single-relay and multiple-relay SNRs [9]. SNR =

k × d12 × power noisepower

(8)

Here, d is the distance between the nodes of S and R. The power transmitted is power, and the constant variable is k. The use of this formula shows that in the direct link, the overall BER is same as BER in the SRS. On the other hand, the power of single-relay networks is studied to confirm that in CC relay nodes saves power.

3 Results and Analysis The goal was to determine how to minimize transmission power by a relay node in CC and boost total efficiency by reducing BER. The first phase in the direct link simulation using 16-QAM modulation has been completed. Using the theoretical BER formula, this relation was checked. The connection worked at the end and ran properly. It is visible from Fig. 5 that at 13 dB SNR, the BER is 3.1 × 10−5 . Later SRS

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Fig. 5 Direct link system (SNR = 13 dB)

was introduced. It is noted that the BER output overall was poor. Therefore, the SNR must be greater than SNR in the direct connection system to enhance overall BER efficiency. The total BER is 8.9 × 10–5 at 13 dB SNR (Fig. 6), which is comparable

Fig. 6 SRS with SNR = 13 dB

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Fig. 7 BER comparison

to direct link performance. The BER output generated in the single relay is the same as the direct link BER as compared to the direct link system result. As is visible in Fig. 7, which contrasts the two systems, achieves the same BER output as the SRS, and generates lower transmitter power for two transmitters. In terms of energy efficiency and power management, SRS performs better than the direct link system. It generates the same general BER, but its performance is improved by power saving management. In a single-relay device, one quarter power is sufficient for S and R transmissions, as R is located at the center. For both transmitters, the SNR is greater than the direct connection SNR. The proposed work for multiple-relay system for 16-QAM is shown in Fig. 8. In Fig. 9, results are compared with published reports [10–12].

4 Conclusion Current paper explored cooperative methods and strategies in mobile networks designed by implementing the system model to conserve transmitted power. The relay system model uses the DF cooperative relay protocol. A direct connection simulation between S and D is given, and the final output of BER is measured. In addition, it is evident that SRS using DF is simulated for saving power and delivering BER output same as direct links. These goals are successfully accomplished in this

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Fig. 8 BER comparison (16-QAM)

Fig. 9 BER comparison for the proposed work

S. K. Gannamaneni and J. S. Roy

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paper, and the findings show that the SNR is enhanced to generate a BER output close to that seen in direct links. These simulations show that relay nodes conserve transmitted power and that a perfect BER would directly boost system performance.

References 1. A.S. Shah, A survey on cooperative communication in wireless networks. Mecs Press J. 6(7), 66–78 (2014) 2. W. Saad, Coalitional game theory for distributed cooperation in next generation wireless networks. Ph. D. dissertation, University of Oslo, Oslo (2010) 3. X. Bu, C. Liu, Q. Yu, L. Yin, F. Tian, Optimization on cooperative communications based on network coding in multi-hop wireless networks, in 2020 International Wireless Communications and Mobile Computing (IWCMC) (Cyprus, 2020), pp. 384–387 4. Z. Sheng, Z. Ding, K. Leung, Cooperative wireless networks: from radio to network protocol designs. IEEE Commun. Mag. 49(5), 64–69 (2011) 5. M.A. Alamri, An efficient cooperative technique for power-constrained multiuser wireless network. Telecommun. Syst. 69, 263–271 (2018) 6. N.B. Halima, H. Boujemaa, Cooperative communications with optimal wireless energy harvesting. Signal Image Video Process. 14, 1405–1412 (2020) 7. J. Garg, A review on cooperative communication protocols in wireless world. Int. J. Wirel. Mobile Netw. 5(2), 107 (2013) 8. M. Hossain, Decode-and-forward cooperative communications: performance analysis with power constraints in the presence of timing errors, in Mobimedia conference (2010) 9. P. Dananjayan, A non-cooperative game theoretical approach for power control in virtual mimo wireless sensor. Int. J. UbiComp (IJU) 1(3), 44 (2010) 10. M. Ju, I. Kim, ML performance analysis of the decode-and-forward protocol in multihop networks, in 29th IEEE International Conference on Distributed Computing Systems Workshops (Montreal, QC 2009), pp. 499–503 11. I. Lee, D. Kim, BER analysis for decode-and-forward relaying in dissimilar Rayleigh fading channels. IEEE Commun. Lett. 11(1), 52–54 (2007) 12. Y.G. Kim, N.C. Beaulieu, Relay advantage criterion for Multihop decode-and-forward relaying systems. IEEE Trans. Wireless Commun. 13(4), 1988–1999 (2014)

Performance of MQC Code-Based SAC-OCDMA in FSO System Nabamita Das and Md. Jahedul Islam

Abstract In this paper, the address sequence of user-modified quadratic congruence (MQC) codes is applied to evaluate the performance of bit error rate (BER) of a free space spectral-amplitude-coding optical code division multiple access (SACOCDMA) scheme. Due to the fixed in-phase cross-correlation value of MQC codes, a balanced detection is needed to decrease multiple access interference (MAI). The BER estimation of this system includes phase-induced intensity noise (PIIN), thermal noise, and shot noise. The BER performance of the proposed system is evaluated using a variety of system parameters such as transmitted power, transmission length, divergence angle, and the simultaneous user’s number. In this paper, the dependency of the system’s BER performance on various weather conditions, for example, haze, snow, mist, rain, and fog, has been investigated. It has been seen that the system gives comparatively better performance in hazy weather than foggy weather. Keywords BER performance · SAC-OCDMA · MQC code · FSO

1 Introduction Free space optical (FSO) communication has a bright future due to low cost and superior transmission efficiency. The benefits of FSO include low infrastructure requirements, ease of deployment, relatively cheap, minimal signal interference, and a license-free band [1]. It offers bandwidth of several GHz in case of transmitting data. Optical communication systems, in general for telecommunication networks and in particular access networks, have played an important role. It has been suggested that multiple access techniques, for example, time division multiple access (TDMA), wavelength division multiple access (WDMA), and optical code-division multiple access (OCDMA), are allowed in case of using the enormous bandwidth which is N. Das · Md. Jahedul Islam (B) Department of Electrical and Electronic Engineering, Khulna University of Engineering and Technology, Khulna 9203, Bangladesh e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_2

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offered by optical fiber [2]. OCDMA has been considered as a potential aspirant in the field of optical network access as it can be accessed by multiple users asynchronously and at the same time with a high degree of transmission protection [3]. In the presence of other users, the data can be extracted by a user, or in additional, the existence of multiple access interface (MAI) is a primary intension for designing of the OCDMA systems [4]. Since MAI is the most common cause of degradation in case of OCDMA system, good design and detection scheme of the code sequences is critical in case of reducing the impact of MAI. The detection method has an effect on designing of the transmitters and the receivers in OCDMA systems [5–7]. Coherent and incoherent detections are the two basic detection techniques in general. Although coherent detection indicates the signal detection with information of the carriers’ phase facts, incoherent detection indicates the signal detection which does not involve such information. The schemes use coherent processing to allow bipolar orthogonal codes to be used for reducing MAI includes close-to-zero cross-correlation functions. In incoherent optical signal processing, the signature code belongs to a family which has unipolar sequences means 0 and 1 [8]. There are two renowned unipolar codes families named OOCs and prime codes that are specifically intended for incoherent OCDMA. In case of local area network (LAN), OCDMA is well suited where traffic is usually burst and asynchronous because of its asynchronous land. In this paper, a free space SAC-OCDMA system has been proposed which is based on modified quadratic congruence (MQC) codes having stabled cross-correlation significance. Here, the optical source is an LED having 1550 nm spectral wavelength. The balanced detection is carried out using two photo-detectors.

2 System Description The schematic illustration of the suggested free space SAC-OCDMA system is represented in Fig. 1 for k simultaneous users. Here, as the sequence of address, MQC codes are used. For convenience, only one transmitter and receiver are represented in detailed. At the transmitting station, binary data modulates an optical pulse which is released by a high-speed optical source. For a data bit of ‘1’, an optical pulse with a particular spectral range is transmitted to the encoder and delivered nothing for a data bit of ‘0’. A combiner combines all the transmitter where signals are optically encoded. The combined signal is then transmitted by optical fiber to all the receivers. A splitter is employed which divides the signal and transmits them to the particular receivers. At the receiving end, a 1:2 splitter splits the signal received into two ratios. The split portion is carried to a decoder and C-decoder. Here, the C-decoder receives all of the frequencies that the decoder has refused. The decoded signal is then converted to photocurrent by using the photo-detection technique. The photo-detection is made up of two photodiodes linked in the opposite electrical.

Performance of MQC Code-Based SAC-OCDMA in FSO System

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Fig. 1 Block diagram of MQC code based SAC-OCDMA in FSO system

3 System Analysis 3.1 Design of FSO Link In designing the FSO link, the link budget equation of the FSO system is significant and described by general empirical formulas,  Prv =

D ϕL

2

Pt e−αL

(1)

where Prv is the power received (watt), Pt the power transmitted (watt), D is receiver effective diameter (m), ϕ is the divergence of the beam (rad), L is the transmission length (m), and α is the atmospheric absorption loss (m−1 ).

3.2 The BER Calculation In the investigation of the system which is proposed, the outcome of phase-induced intensity noise, along with shot noises as well as thermal noises in PD1 and PD2, has been considered. In case of identification of an unpolarized thermal light which is considered as ideal, the variance of photocurrent, which is caused by spontaneous emission, can be uttered as 2 2 2 2 = σPIIN + σSHOT + σTHR σTP

(2)

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2 2 2 Here, σPIIN , σSHOT , and σTHR are the variance caused by PIIN noise, shot noise, and thermal noise correspondingly. Thermal noise does not rely on the PD’s photocurrents, which rely on both PIIN noise and shot noise. We can express thermal noise as follows: 2 = σTHR

4kb T0 Be RLoad

(3)

Here, Be is the electrical bandwidth, RLoad is the load resistor at receiving end, kb is the constant of Boltzmann, and T0 is the receiver’s absolute noise temperature. Now, we can express shot noise as, 2 = 2E(IPD1 + IPD2 )Be σSHOT   p − 1 + 2k = 2E Be e Prv p2 + p

(4)

Here, IPD1 and IPD2 are the photocurrents measured at PD1 and PD2 correspondingly, p is the prime number of MQC codes, e is the responsivity, and E is the electron’s charge of the photodiodes and is expressed as: e =

ηE hϑ0

(5)

Here, η is the quantum efficiency of photo-detector, ϑ0 is the original broad-band optical pulse’s central frequency, and h is the constant of plank. The PIIN noise can be calculated as   2 2 2 = Be IPD1 tc1 + Be IPD1 tc2 σPIIN   k−1 Be 2e Prv2 k + p+k =  f ( p + 1) p 2 p

(6)

Here,  f is the thermal source’s line width, at PD1 and PD2, and the coherent times of the source are denoted by tc1 and tc2 , respectively. Suppose, bit ‘1’ is transmitted by all users. The cumulative noise power in (2) can be determined by replacing (3), (4), and (6). Each consumer has a 50% chance of transmitting ‘1’. Thus, full noise power can be expressed finally, 2 σTP

    k−1 p − 1 + 2k 4kb T0 Be Be 2e Prv2 + pk + 2E Be e Prv + = 2 2  f ( p + 1) p p p +p RLoad (7)

Now, the average of the received photocurrent obtained at the output of the PD’s can be evaluated as,

Performance of MQC Code-Based SAC-OCDMA in FSO System

17

Prv d p

(8)

IPD = IPD2 − IPD1 = e

Here, for a particular user, d is the bit value of ‘1’ or ‘0’. It has been recognized that, SNR in an optical system can be written as, SNR =

2 IPD 2 σTP

(9)

Thus, the system SNR can be calculated by replacing (7) and (8) in (9). So finally, considering Gaussian approximation, a system’s BER of can be written as, 

1 SNR BER = erfc 2 8

(10)

4 Results and Discussion For the numerical calculation of the proposed system, the source’s line width  f is 3.75 THz, the photo-detectors’ responsivity e is 0.8 A/W, electrical bandwidth Be which is 80 MHz, the absolute noise temperature of the receiver T0 is 300 K, transmission wavelength λ is 1550 nm, the load resistance of the receiver RLoad is 50 , the receiver’s PD quantum efficiency η is 0.6, divergence angle of transmission beam ϕ is 32°, Boltzmann constant kb is 1.38 × 10–23 J/K, and the electron charge E is 1.6 × 10–19 C. Figure 2a represents the BER versus transmitted power curve assuming the simultaneous user’s number, k = 50, divergence angle, ϕ = 30°, the prime number, p = 11, and the transmission length, L = 0.1 km. From Fig. 2a, it can be observed that

a

b

Fig. 2 a BER versus transmitted power. b BER versus number of simultaneous user at different weather conditions

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N. Das and Md. Jahedul Islam

the BER decreases with the increase of transmitted power. The performance is also investigated in different conditions of weather, including haze, rain, mist, snow, and fog. The results vary depending on the weather, because different weather conditions have different attenuation. It can also be seen that transmitted power requirements are lower in the case of haze, rain, mist, and snow than in the case of fog. For 10–9 BER, the transmitted power required for haze, rain, mist, snow, and fog are −12.12 dBm, −11.62 dBm, −10.62 dBm, −9.62 dBm, and −6.64 dBm, respectively. Thus, it can be observed from the figure that in haze, the power requirement is the least to achieve BER of 10–9 compared to foggy weather condition. Figure 2b characterizes the BER versus simultaneous user’s number curve at the power which is transmitted, Pt = 5 dBm, transmission length, L = 0.1 km, divergence angle, ϕ = 30°, and prime number, p = 11. We have included the BER variation of different weather like haze, rain, mist, snow, and fog for comparison. It has been obtained that the value of BER rises due to the increasing simultaneous user’s number. The result behind this is increased MAI. Despite the fact, a great amount of users can be handled while preserving a preferred BER. For 10–9 BER, the simultaneous user’s number for haze, rain, mist, snow, and fog are 78.6, 78.5, 78, 77, and 68, respectively. It is noted that the maximum simultaneous user’s number, k will be equal to the square of the prime number of the MQC codes. Here, the maximum simultaneous user’s number is 121 as the prime number is 11. As of Fig. 2b, it has been obtained that the performance of BER becomes poor for foggy weather than the hazy weather. Figure 3a represents the changes of BER regarding transmission length at the power which is transmitted, Pt = 5 dBm, the prime number, p = 11, divergence angle, ϕ = 30°, and simultaneous user’s number, k = 50. The BER increases dramatically as the transmission length increases. So it can be specified that the performance of the system largely relies on the transmission length. The value of BER of 10–9 is attained at 179 m, 245 m, 284 m, 344 m, and 388 m in foggy, snow, mist, rain, and hazy condition, respectively. In hazy weather, the system is capable of covering a much greater distance while retaining a BER of 10–9

(a)

(b)

Fig. 3 a BER versus length. b BER versus divergence angle at different weather condition assuming the simultaneous user’s number = 50

Performance of MQC Code-Based SAC-OCDMA in FSO System

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than foggy weather. This is because fog is denser than snow, mist, rain, and haze which causes even more attenuation and reduces efficiency in the transmission medium. Figure 3b represents the changing characteristics of BER regarding divergence angle considering various weather condition. Here, transmitted, Pt = 5 dBm, transmission length, L = 0.1 km, the prime number, p = 11, and simultaneous user’s number, k = 50. The BER rises with the increasing divergence angle. For 10–9 BER, the value of divergence angle are 6 mrad, 8.5 mrad, 9.5 mrad, 10.8 mrad, and 11.2 mrad in foggy, snow, mist, rain, and hazy condition, respectively. The performance degrades in foggy weather comparing to other condition because of high density.

5 Conclusion In this paper, a free space SAC-OCDMA system centered on MQC codes is designed, and the BER output of the system is investigated for various system parameters while accounting for various noises like phase-induced intensity noise (PIIN), shot noise, and thermal noise. The exploration is performed in several conditions of weather, which includes haze, rain, mist, snow, and fog. The BER performance has been found to be remarkably affected by atmospheric conditions. Since fog is denser than snow, mist, rain, and haze, the performance degrades significantly in foggy conditions. For foggy weather, more transmitted power is needed compared to other weather conditions. For example, the transmitted power needed in haze, rain, mist, snow, and fog with a given BER value of 10–9 are −12.12 dBm, −11.62 dBm, −10.62 dBm, − 9.62 dBm, and −6.64 dBm. Finally, it has been concluded that the BER performance degrades with increasing density of the weather.

References 1. N.T. Dang, A.T. Pham, Performance improvement of FSO/CDMA systems over dispersive turbulence channel using multiwavelength PPM signaling. Opt. Express 20(24), 26786–26797 (2012) 2. Z. Wei, H.M.H. Shalaby, H. Ghafouri-Shiraz, Modified quadratic congruence codes for fiber Bragg-grating-based spectral-amplitude-coding optical CDMA systems. J. Lightwave Technol. 19(9), 1274–1281 (2001) 3. A.H. Yousif, M. Zeghid, W.A. Imtiaz, T. Sharma, A. Chehri, P. Fortier, Two-dimensional permutation vectors’ (PV) code for optical code division multiple access systems. Entropy 22(5), 576 (2020) 4. S. Gupta, A. Goel, Advance method for security enhancement in optical code division multiple access system. IETE J. Res. 64(1), 17–26 (2017) 5. S. Chaudhary, X. Tang, A. Sharma, B. Lin, X. Wei, A. Parmar, A cost-effective 100 Gbps SAC-OCDMA–PDM based inter-satellite communication link. Opt. Quant. Electron. 51, 148 (2019) 6. H.A. Fadhil, S.A. Aljunid, R.B. Ahmad, Performance of random diagonal code for OCDMA systems using new spectral direct detection technique. Opt. Fiber Technol. 15(3), 283–289 (2019)

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7. M. Noshad, K. Jamshidi, Bounds for the BER of codes with fixed cross correlation in SACOCDMA systems. J. Lightwave Technol. 29(13), 1944–1950 (2011) 8. H. Sarangal, A. Singh, J. Malhotra, Construction and analysis of a novel SAC-OCDMA system with EDW coding using direct detection technique. J. Opt. Commun. 40, 265–271 (2019)

Resource Management in 5G Komal Raghavendra Kulkarni, Mayuri Manage, M. R. Kiran, and R. M. Banakar

Abstract 5G networks consist of many devices like cell phones and Internet of Things equipment that generate a huge number of requests. The network should be able to deal with the traffic from these devices with no congestion. Radio resource management helps in avoiding congestion and enables quality of service to prevail, thereby improving the overall system performance. Radio resource management is a fuzzy logic algorithm with a preemption queuing feature that accounts for better system design. In this real-time, delay intolerant connections are given high priority and are processed first. During congestion, the non-real-time, delay-tolerant connections are processed in edge clouds. Edge clouds are responsible for handling the non-real-time connections when congestion occurs. Simulation results show that the objective of reducing the call blocking probability is completely achieved. The realtime requests are fully catered to, thus providing better quality of service, latency, and efficient resource utilization. Keywords Radio resource management · Quality of service · Fuzzy controller · Call admission control

1 Introduction Present-day users want data speeds that are faster and service, which is more reliable. The fifth generation networks (5G) guarantee to deliver that, with 5G, users should be able to download files ranging from megabytes to gigabytes of data in seconds. It also boosts the development of technologies such as autonomous vehicles [1], the Internet of Things [2], and virtual reality [2]. There are a few key enablers that make 5G possible. Millimeter waves (mm Waves) and small cells stand out. Millimeter waves operate at frequencies between 30 GHz and 300 GHz compared to below 2.3 GHz which is evident in LTE. The wavelength of mm waves ranges from 1 to 10 mm. However, these waves cannot travel further than 250 m which brings K. R. Kulkarni (B) · M. Manage · M. R. Kiran · R. M. Banakar Department of Electronics and Communications, KLE Technological University, Vidya Nagar, Hubli, Karnataka, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_3

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propagation to small cells [3]. In miniature base stations which are portable, minimal power is required. These low-power base stations are housed throughout the city in large numbers. Improvement inefficient data delivery is achieved by beamforming. Beamforming identifies the route for efficient delivery and reduces the interference for nearby users in the process. This is commonly assisted by massive MIMO which is a cluster of hundreds of ports and antennas. In order to deploy 5G using the aforementioned technologies, the cost mainly depends on the base station installation sites and the frequency spectrum allocation fees. Radio resource management (RRM) plays an important role in 5G deployment. The main design features of RRM are the utilization of bandwidth, the total number of calls, and the total number of packets received per unit time. Conventional RRM algorithms suffer from abnormal behavior due to the busty nature of requests. Thus, the new generation of RRM using fuzzy logic is popular because their decisions are based on various inputs. This paper attempts to improve the efficiency and response time of such existing algorithms.

2 Resource Management Design Unit The block diagram of the fuzzy system that is developed is shown in Fig.1. The incoming user equipment (UE) requests a connection to the nearest base station (gNB). The effective capacity and service type, i.e., real-time or non-real-time, are specified. The resource estimator then estimates the capacity of the gNB that is available. It is added as a parameter to the fuzzy controller. The fuzzy controller gives the admittance decision (Ad) as its output response. On accepting, the resources will be allocated to the request. During preempt, some low priority connection will be preempted and sent to be served by the public cloud. Finally, the appropriate response is sent to the UE. There is a request transfer and a response receive between the UE and the base station. This communication protocol establishes the design platform during resource management scenarios.

3 Software-Specific Requirement The description of the software that is necessary to develop the intended system is given by software requirement specifications (SRS). After the requirements of a particular business are specified, the system is modeled. The requirements: both functional and non-functional are laid out. For an effective interaction between the users and the software, the use case diagrams have to be presented. Establishing an agreement between the customers and the suppliers is essential. This assessment is rigorous, and it helps to scrutinize the requirements before the system design is carried out. This reduces the burden of problem identification in the later stages. The SRS will list all the critical software requirements that aid in the software development

Resource Management in 5G

23

Fig. 1 Proposed fuzzy system

process. The software developer needs to have sufficient knowledge and the ability to extract the details and design the software according to the need.

3.1 Software Requirements 1. 2. 3.

NS3 simulator. Millimeter wave and amp; Fuzzy lite modules. Netanim.

Figure 2 represents the different service blocks that form the basic architectural structure in the network design. The connection request from the UE is received by the access and mobility function (AMF). The service gateway (SGW) and the packet gateway (PGW) together form the user plane function (UPF). The authentication server function (AUSF) performs the authentication function. Application function

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Fig. 2 Architecture of the 5G network

(AF) provides application influence on traffic routing. Data network or DN represents the rest of the internet backhaul. In Fig. 3, when a new request is placed, the system sends the connection request to the nearest gNB. The resource availability is checked before the request is processed. The gNB parameters are passed to the fuzzy controller for the computation of output. A response is sent accordingly to the UE.

Fig. 3 Detailed data flow of the system

Resource Management in 5G

25

Fig. 4 Trapezoidal and triangular functions

Membership function is used in fuzzy controller. The following inputs are given to the controller: (i) Effective capacity: Under a certain statistical model, it examines the performance of the wireless networks; (ii) available capacity. Figure 4 determines the user count that is trying to access the network simultaneously. It also deals with the effective utilization of bandwidth; (iii) Service-type: real-time and non-real-time requests and outputs of the admittance decision.

4 Implementation and Simulation The experiment is carried out on the NS3 network simulator. A topology is created and traffic is generated accordingly. Different UEs request for different types of loads (real-time or non-real time). On the gNB side, these requests are received and processed by the fuzzy controller, and a decision is taken on the admittance criteria of the request. A.

Algorithm Results

The result of the fuzzy controller, i.e., the admittance decision against the combinations of input parameters on applying the fuzzy rules, is shown in Fig. 5. In Fig. 5, Ec , St, and Ad are the three axes that have been defined for plotting the response. Initially, it is an NRT case, so the calls are preempted. When the service type (St) is switched to RT requests and if the available capacity (Ac) is less than effective capacity (Ec), the calls are rejected, hence the downfall in the graph. The observation derived from the graph is that no matter at what state the Ec is at, if the Ac is low that is less than Ec, the RT requests will be rejected.

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Fig. 5 Plot of St , Ec versus Ad with low Ac

This particular scenario in Fig. 6 is considered for NRT connections Ec , Ac, and Ad which are the axes in the represented graph. The NRT requests are preempted if the Ac is low. Here, the downfall in the graph is seen because high priority is given to RT requests. If Ac is sufficient, they are preempted toward the cloud irrespective of their effective capacity (Table 1).

Fig. 6 Plot of St, Ec versus Ad for NRT connections

Resource Management in 5G Table 1 Tabular representation of the graph

27 ST

AC

Ec

Ac

RT

0.2

Not enough

Outsource

RT

0.1

Not enough

Reject

RT

0.1

Not enough

Reject

NRT

0.2

Enough

Accept

NRT

0.8

Enough

Accept

NRT

0.8

Enough

Accept

5 Conclusion A fuzzy logic-based resource control algorithm in the fifth-generation (5G) networks is developed here. The fuzzy logic helps in handling the busty nature of data traffic and requires low computation time. An edge cloud preemption technique has been suggested in which loss tolerant NRT connections are processed by an edge cloud service. The unique automation of providing the input real-time parameters to the 5G network using the NS3 design platform (100 nodes) reduces the development and testing time. As 5G is still far from deployment, ISPs constantly improve the architecture. With those changes, modifications to the existing systems are unavoidable. Further advancements in the field of machine learning can make way for the development of improved algorithms to obtain slightly better results.

References 1. N.A. Greenbalt, Self-driving cars will be ready before our laws are, in IEEE Spectrum on Self-Driving Cars and the Law (2016), pp. 46–51 2. N. Jadhav, The democratization of innovation for the internet of things, in IEEE Spectrum (2017), pp. 346–357 http://spectrum.ieee.org/computing/networks/the-democratizationof-inn ovation-for-the-internet-of-things 3. A. Bliecher, A surge in small cell sites, in IEEE Spectrum on A Surge in Small Cells (2012), pp. 195–201

PAPR Reduction for FBMC-OQAM Signals Using PSO-Based JPTS Scheme D. Rajendra Prasad, S. Tamil, and Bharti Chourasia

Abstract Filter bank multicarrier (FBMC) was the contender for the next-generation broadband wireless communication networks with offset quadrature amplifier modulation (OQAM). FBMC has the inherent problem of the high peak-to-average power ratio (PAPR) in a several multicarrier system especially in FBMC-OQAM. In order to solve the high PAPR problem that can achieve better PAPR performance, a partial transmission sequence (PTS) scheme is adopted used to solve in FBMC-OQAM system. This paper, therefore, examines the FBMC-OQAM signal PAPR reduction with the help of a particle swarm optimization (PSO) with joint PTS (JPTS). The PAPR of the FBMC-OQAM method has proven stronger through simulations of PSO-JPTS than the initial FBMC-OQAM. The simulation findings also show the complementary cumulative distribution function various populations may gain CCDF. Keywords PAPR reduction · FBMC-OQAM · PTS · PSO

1 Introduction The persistent growing needs for higher data speeds, supports for multimedia content and bandwidth introduces unparalleled barriers for potential mobile communications networks. The forthcoming 5G radio access technology (RAT) appears to be widely agreed for much better results than the fourth generation (4G) networks of today [1, 2]. Moreover, through the use of offset quadrature amplitude modulation (OQAM) in filter bank multicarrier (FBMC) systems, the highly localized frequency location will attain greater spectral efficiency and no guard interval must be inserted. According to convenient channel environments, we may use multiple filters for various characters [3, 4]. Then, the FBMC-OQAM systems can achieve higher frequency dispersion in environments and have improved side lobe characteristics. But the FBMC-OQAM system’s disadvantages are a multicarrier strong PAPR technology. In recent years, a D. Rajendra Prasad (B) · S. Tamil · B. Chourasia SRK University, Bhopal, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_4

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number of PAPR reduction strategies in multicarrier networks have been suggested [5, 6]. The technique of the partial transmission sequence (PTS) was highly attractive because no interference between neighboring data signals was straightforward and successful. The FBMC-OQAM method cannot use traditional PAPR reduction techniques. Many who measure the optimum PAPR reduction on the new symbolic would then work to understand the past symbols of the FBMC-OQAM signals. In addition to past symbols, the dispersive SLM is used to minimize PAPR of FBMC-OQAM signals. The optimal phase pattern calculated for traditional schemes is not effective for dispersive SLM schemes under the current symbol. In addition, in comparison with traditional phase pattern determination, the trellis coded modulation is useful. The literature proposes various PAPR reduction methods for OFDM with significant achievements in PAPR reduction. The FBMC/OQAM signal PAPR cannot be lowered based on the PAPR reduction methods first suggested for the OFDM system, because of its overlapping signal structure. FBMC/OQAM’s essential components are the SFB on the transmitter, as illustrated in Fig. 1. The synthesis filter bank, it must be emphasized that the SFB is efficient to construct with polyphase filtering structures FFT and IFFT size M. To reduce the PAPR values of FBMC-OQAM schemes, the author proposed some hybrid-based PTS schemes with few costumes and low complexity in [7]. First of all, by means of double-layer analysis, the suggested algorithm processes PTS step variables. The difficulty of the search is then minimized for optimum step variables. In order to further minimize computational complexity,

Fig. 1 Block diagram of the FBMC/OQAM transmitter

PAPR Reduction for FBMC-OQAM Signals Using PSO-Based JPTS …

31

an effective algorithm is often used for PAPR values approximation of a given FBMCOQAM signals. We suggest a popular PTS scheme to increase JPTS in the FBMCOQAM signals as a JPTS scheme in this paper. The suggested PAPR optimization method for the JPTS system is updated by the traditional PTS scheme, with the overlapping of signals known as the optimal PAPR value by the previous symbol signals. Because of the enormous computer complexity in the traditional PTS system, particularly when the cluster size is large, the particle swarm optimization (PSO) algorithm is used in the PAPR optimization method to reduce the computational complexity.

2 Background Works After observing, the simulation outcomes of hybrid-based PTS (H-PTS) schemes outperform other conventional PTS algorithms (C-PTS) and a segmented PTS algorithm (S-PTS) in terms of larger PAPR reduction and less computational complexities. In [8], the authors took into account the FBMC-OQAM signal’s decrease in PAPR. Their study completely focused on the artificial bee colony (ABC), which is an optimization technique which is combined with overlapping PTS. The salient characteristic of it is switching from the traditional PTS to an OPTS scheme and thus making the PAPR optimization less computationally complicated using the ABC algorithm. The authors of [9] proposed that the cuckoo search optimization algorithm (CSOA) optimizes PTS schemes with different categories. In addition, the NIM concept is used for improving and improving search results. This demonstrates that FBMC-OQAM schemes using NIM-CSOA reductions for initial traditional system solutions using an MBJO-PTS scheme. The numerical complexity of the system was also greatly decreased, when the algorithm of iterations was applied with regard to the amount of real multiplications. With an optimal timing and frequency synchronization, FBMC symbols are transmitted via the frequency selective fading channel (FSFC) channel. This can be put in the mathematical form as Flk = Rlk + j I lk , 0 ≤ k ≤ K − 1, 0 ≤ l ≤ L − 1

(1)

where the kth symbol in the lth subcarrier is represented, respectively, by Rlk and Ilk . Let u k,l be an effective symbol, which in the lth subcarrier, is a real and imaginative part of the QAM symbol. T /2, where T is the time interval stagger the components of step and quadrature. The interpretation of OQAM system independently can be put in expression as     u 2k,l = Re Flk , u 2k+1,l = Im Flk

(2)

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x(t) =

K −1  L−1 

vk,l (t)u k,l

(3)

k=0 l=0 (t) where pulse vk,l = p(t − kT ) e j2πl C(t − kT ) e jθk , l merely a variant of the filter p prototype shifting time and frequency (t). The C shows the distance between frequencies. The expression “first”, means an extra step term as θ k,l = (π /2)(k + l). The continuous time domain sampling rate provided the FBMC signal in realistic use. It can, therefore, transmit the data of the FBMC method as

x(i) =

L−1 K −1  

u k,l v(i − l K /2) e j K m (i− 2 ) e jθ k,l 2λ

D

(4)

k=0 l=0

For i ∈ / 0, 1, …, D. Equation (4) can easily be re-written x(i) =

K −1  L−1 

u k,l vk,l (i)

(5)

k=0 l=0

where vk,l (i) denotes in time and frequency the changed v(i) variants. The demodulated symbol [10] on the receiver, considering the perfect channel with no noise, can be written as 

u k,l =

−1  L−1 +∞ K  

∗ u k,l vk,l (i) vk,l (i)

(6)

i=−∞ k=0 l=0

where (·) denotes the kth interval and lth subcarrier, respectively. They build the prototype filter to meet the true state of orthogonality  

+∞ 

 vk∗ ,l  (i) vk,l (i)

= δk  ,l  δk,l

(7)

i=−∞

The PAPR corresponds to the relationship between a sample’s maximal powers in a transmission symbol, separated by the average power of the OFDM symbol. PAPR occurs if the various subcarriers are out of phase in a multicarrier environment. They vary at each moment in various step values in relation to each other. This leads the output power to immediately increase as all the values reach concurrently the highest peak value, leading to a “threshold” in the output power. The system’s maximum value may be very high as opposed to the average of the system because of including a wide number of separate subcarriers that are supposed to present in the FBMC system. This ratio of maximum and average power is called the peak-to-average power ratio (PAPR). The OFDM signal PAPR is about 12 dB in the LTE system.

PAPR Reduction for FBMC-OQAM Signals Using PSO-Based JPTS …

33

They, thus, described the PAPR as follows for the lth OFDM symbol: PAPRlFBMC =

max |u l [k]|2 , 0≤k ≤ K −1 e |u l [k]|2

(8)

where ul [k] is the lth FBMC, the predicted value is e|ul [k]|. Equation (8) for OFDM systems is appropriate. Owing to the conflicting structure of the FBMC system, the signal duration of FBMC-OQAM may, however, be greater than the signal transmission length. The PAPR of an FBMC-OQAM signal cannot be calculated. The PAPR would not clarify the PAPR sequence distribution. The cumulative distribution complementary function (CCDF) is then adopted, and it commonly used this measure for the evaluation of PAPR reduction techniques. It described the PAPR of the discreet time signal as the likelihood of the excess of a certain threshold β:   CCDF(PAPR{u l [k]}) = Pr PAPR{s(m)} > PAPRβ

(9)

where PAPRβ is the threshold of PAPR.

3 Proposed Work Because the computational complexity of the standard PTS method is too high, particularly, if the number of clusters increases as a result of phase optimization finding W V −1 . In order to address this issue, the JPTS technique is used as the PSO/PTS method to optimize the particle swarm. Joint PTS (JPTS) Approach Joint PTS is critical [11, 12] and useful in resolving larger PAPR, which appears to be a greater challenge in multicarrier systems. Generally, standard PTS works on the principle of splitting modulation data into g clusters. After this, every cluster gets multiplied by an equivalent weighting factor that can be formulated as x(k1 , m 1 ) =

g 

{b(k1 , g, m 1 ) · x(k1 , g, m 1 )}

⎧ ⎫ ⎫  ⎪ T ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ x k2 −1, m 2 + ⎪ ⎪ ⎪  ⎪ g ⎪ ⎨ ⎬ 2 ⎬ g b = min max ⎪ ⎪  ⎪ ⎪ bg ⎪ ⎪ g=1 ⎪ ⎪ ⎪ ⎪ ⎪ {b(k2 , g, m 2 ) · x(k2 , g, m 2 )}⎪ + ⎪ ⎪ ⎪ ⎭ ⎩ ⎩ ⎭ ⎪ g=1 ⎧ ⎪ ⎪ ⎪ ⎪ ⎨

and

(10)

k=1

(11)

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⎫ ⎧ ⎬ g ⎨  {b(k1 , g, m 1 ) · s(k1 , g, m 1 )} b = min max bg ⎩ ⎭ g=1

(12)

As mentioned above, x(k1 , m 1 ) denotes the time domain signal after undergoing PAPR optimization along with PTS during k1th symbol and m th 1 sample. Similarly, b(k1 , g, m 1 ) denotes the gth cluster weighting factor. From Eqs. (10 and (11), it is evident that each symbol k1th and k2th are optimized its PAPR values independently. The symbolic overlap of the FBMC system causes the average power to overlap between the symbols of FBMC/OQAM. The substantial amount of energy for the symbol FBMC/OQAM also resides in the two following intervals of the symbol. Therefore, directly using PTS in PAPR optimization is challenging. In this way, in the FBMC problem in operation, we used an up-sampling factor of 4 modified joint PTS (JPTS) for PAPR optimization, as shown in Fig. 2. We use a factor of 4 to raise the sampling rate to 3 per sample rate. Therefore, the spectrum would include the image frequencies initially focused on the various frequency sampling rates without applying the interpolation filter.  

v T {b(k2 , g, m 2 ) · x(k2 , g, m 2 )} + x(k2 , m 2 ) = x k2 −1, m 2 + 2 k=1

(13)

From Eq. (13), it is evident that x(k2 , m 2 ) denotes overlapped time domain signal after undergoing PAPR optimization along with OPTS during k2th symbol and m th 2 sample. Now, the proposed OPTS put in mathematical form with weighting factor as

Fig. 2 Block diagram of proposed JPTS scheme with clustering block

PAPR Reduction for FBMC-OQAM Signals Using PSO-Based JPTS …

⎧ ⎫ ⎫  ⎪ T ⎪ ⎪ ⎪ ⎪ ⎪ m x −1, + ⎪ ⎪ ⎪ k 2 2  ⎪ ⎪ ⎬ ⎬ ⎪ 2 g ⎨ g b = min max  ⎪ ⎪ bg ⎪ ⎪ g=1 ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ {b(k2 , g, m 2 ) · s(k2 , g, m 2 )}⎪ + ⎪ ⎪ ⎪ ⎭ ⎩ ⎩ ⎭ ⎪

35

⎧ ⎪ ⎪ ⎪ ⎪ ⎨

(14)

g=1

Particle swarm optimization (PSO) algorithm for PAPR optimization From the family of metaheuristic algorithms that rely on the dynamics to direct particles to look for optimal solutions globally, based on the social behavior of birds. However, it does not depend on the gradient of the constraint being optimized. This algorithm is easy to apply to any kind of problem, and it has faster convergence. Each particle is called the basic search agent in PSO, and we put it in a feature search space to calculate its objective function. From among the group of particles (potential solutions), every particle looks for the best position in the search space by adjusting the speed of flight. PSO evaluated the fitness function of the particles. In this current study, the optimized variable is the PAPR as reflected in Eq. (8) and is the target feature (Fig. 3).

Fig. 3 PSO flowchart based on PSO-JPTS

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For instance, consider the group (swarm)with P particles,   with two corresponding vectors xi = xi1 , xi2 , ..., xiD and vi = vi1 , vi2 , ..., viD in which D denotes the solution space dimension for every particle i that comprises it. Each of these particles moves at a speed which allows to stochastically adjust their position vector to obtain the global optimum. In the search region, the particles were already randomly dispersed. They must then initialize their velocity. The velocity vector is again randomized by speed in the direction.     vid (t + 1) = wv id (t) + c1 ri1 (t) pbestid (t) − xid (t) + c1 ri1 (t) gbestid (t) − xid (t) (15)   where pbestid (t) = pi1 , pi2 , ..., piD denotes the previous best position of the particle   i, gbestid (t) = pi1 , pi2 , ..., piD denotes the best position of all the particles, r i 1 and r i 2 denote random terms which are in the range of (1, 0), c1 and c2 denote cognitive and social values, respectively, and w denotes the inertia weights for particle momentum. To see the influence of the coefficient w (inertia weight) is deciding factor for exploration and the exploitation for best candidate solutions, the lower the value of w, the better the convergence. For minimization problem, pi is given by  pi =

βi , if f (βi ) < f ( pi−1 ) βi , if f (βi ) ≥ f ( pi−1 )

(16)

Both two coefficients can be seen as having an impact. To that end, we have chosen a very low W coefficient deliberately and forced the two extremes c1 and c2, respectively. Conversely, when c2 is high, swarm particles are more strongly influenced by others. The process and working of the PSO algorithm are depicted in Fig. 4. In this current work, the chosen updating coefficient w, it is observed that over the iterations reduced linearly, is linearly reduced to exploit to obtain the better outcome that reaches global optimum with faster convergence. w = w max −

wmax − wmin × iter itermax

(17)

For better convergence set, the values of c1 and c2 to balance the best results with progress in the iterations.

4 Results and Discussion In this paper, all the simulations are carried out on MATLAB 2018a to investigate the PAPR reduction techniques with standard and PSO-based PTS schemes that run for different population sizes and number of iterations. The complementary cumulative

PAPR Reduction for FBMC-OQAM Signals Using PSO-Based JPTS …

(a )

37

(b)

Fig. 4 Performance metrics a comparison of original FBMC-OQAM and proposed PSO for different PAPR values, b PAPR CCDF using PSO for different population sizes (n)

distribution function (CCDF) and PAPR are the performance metrics to decide the efficiency of the system. From Fig. 4a, it is clear that the traditional FBMC-OQAM has 11.2 dB, where our proposed PSO-based JPTS scheme has 7.6 dB PAPR values that show the greater reduction of PAPR and improvement of 16% in the CCDF for our system. From Fig. 4b, it is clear that the PAPR for PSO-based JPTS with a population size (n) of 40 has PAPR of 3.8 dB, when n = 20, it is 8.2 dB and n = 10 it is 11.2 dB shows the greater reduction in PAPR values with the population size of the particles. From Fig. 5, it is clear that the PAPR for FBMC-based JPTS with a population size of 40 has PAPR of 7.4 dB, 8 dB, 8.9 dB, and 10.5 dB shows the greater reduction in PAPR values of OFDM system of 7.8 dB, 8.2 dB, 9.2 dB, and 10.8 dB with the same population size of the particles.

5 Conclusions The paper proposes the joint PTS with the PSO-JPTS algorithm to reduce PAPR signals in the PSO-JPTS system. It is important that the PAPR by JPTS is improved, and the PAPR optimization mechanism reduces computational complexities by utilizing the PSO algorithm. These simulation results will show that, with lower computational complexity for the FBMC-OQAM signal, the proposed PSO-based JPTS scheme improved greatly the PAPR values.

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Fig. 5 PAPR CCDF using PSO for population size of for FBMC and OFDM systems

References 1. P. Banelli, S. Buzzi, G. Colavolpe, A. Modenini, F. Rusek, A. Ugolini, Modulation formats and waveforms for the physical layer of 5G wireless networks: who will be the heir of OFDM? (2014) 2. Q. Bodinier, F. Bader, J. Palicot, On spectral coexistence of CP-OFDM and FB-MC waveforms in 5G networks. IEEE Access (2017). https://doi.org/10.1109/ACCESS.2017.2723822 3. R. Kobayashi, T. Abrao, FBMC prototype filter design via convex optimization. IEEE Trans. Veh. Technol. 1–1 (2018). https://doi.org/10.1109/TVT.2018.2879856 4. J. As, H. Zamiri-Jafarian, Prototype filter design for FBMC systems via evolutionary PSO algorithm in highly doubly dispersive channels. Trans. Emerg. Telecommun. Technol. 28 (2016). https://doi.org/10.1002/ett.3048 5. Er. Sukhjinder, Singh, D. Jalandhar, Er. Malhotra, Er. Sareen, The performance analysis of PAPR reduction using a novel PTS technique in OFDM–MIMO system (2021) 6. S. Randhawa, D. Bajwa, PAPR reduction in OFDM using PTS technique. Int. J. Eng. Res. Technol. (2017). https://doi.org/10.17577/IJERTV6IS070250 7. Z. He, L. Zhou, Y. Chen, X. Ling, Low-complexity PTS scheme for PAPR reduction in FBMCOQAM systems. IEEE Commun. Lett. 1–1 (2018). https://doi.org/10.1109/LCOMM.2018.287 1263 8. T. Mata, P. Boontra, A. Dataesatu, K. Mori, P. Boonsrimuang, A PAPR reduction for FBMCOQAM signals using ABC-OPTS scheme (2019). https://doi.org/10.23919/ICACT.2019.870 2051 9. Y. Yan, X. Pingping, PAPR reduction of FBMC-OQAM signals based on cuckoo search optimization algorithm. 1–5 (2017). https://doi.org/10.1109/ICSPCC.2017.8242399 10. H. Nam, M. Choi, S. Han, C. Kim, S. Choi, D. Hong, A new filter-bank multicarrier system with two prototype filters for QAM symbols transmission and reception. IEEE Trans. Wireless Commun. 15, 1–1. https://doi.org/10.1109/TWC.2016.2575839 11. M. Bellanger, Physical layer for future broadband radio systems. 436–439 (2010). https://doi. org/10.1109/RWS.2010.5434093 12. T. Jelta, A. Nordbotten, M. Annoni, E. Scarrone, S. Bizzarri, L. Tokarchuk, J. Bigham, C. Adams, K. Craig, M. Dinis, Future broadband radio access systems for integrated services with flexible resource management. Commun. Mag. IEEE. 39, 56–63 (2001). https://doi.org/ 10.1109/35.940036

Intrusion Detection System (IDS) for Security Enhancement in Wireless Sensing Applications Bharat Bhushan

Abstract Intrusion detection has been defined as the system to identify the anomalous, incorrect, or inappropriate moving attackers. Moreover, intrusion detection system (IDS) when used along with wireless sensor networks (WSNs) is the main point of interest in various applications such as ambience intelligence and battlefield surveillance. Therefore, the characterization of WSN parameters like sensing range and node density is a fundamental issue. Furthermore, IDS is used in WSNs owing to its ability to detect the alien attacks which might lead to severe consequences. In this paper, we have described the existing security attacks that occur in WSNs and also highlight the related IDS-based proposed countermeasures. This paper discusses the need of IDS in WSNs and explores the various types of IDSs. Further, the work analyzes WSNs from the perspective of its network structure and explores various types of network-based IDS mechanism. Finally, the paper presents the recent advancement in IDS-enabled applications and enumerates the future course of research in order to benefit the expert as well as general readers. Keywords Wireless sensor network · Security · Intrusion detection system · Attacks · Privacy · Anomaly

1 Introduction WSNs have emerged as a vowing area of research in recent years. These are used in ambient intelligence applications such as building monitoring applications and assisted living [1]. In such applications, some sensors are mobile and some are deployed statically in the concerned place. However, in battlefield surveillance [2], unguarded wireless communication mediums are used and the area where the sensors are deployed in restricted for human accessibility. Regardless of application areas, various environmental parameters are collected and transmitted by numerous sensors. One of the key issues in this transmission and communication is security. Actually, B. Bhushan (B) School of Engineering and Technology, Sharda University, Greater Noida, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_5

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in ambient intelligent systems, numerous confidential data about the emotional or physiological state of the users are collected by the sensors and any type of modification in these data may lead to severe circumstances [3]. Especially, when the WSN applications are designed for commercial and hostile environments, security is utmost concern [4]. Different types of security solutions such as key management, cryptography, or authentication can enhance the WSN’s security at a certain extent. However, these are not the solution to all the attacks in WSNs. Therefore, there is a need for second line of defense such as IDS. When an IDS is implemented in wired networks, it can keep track on nodes and in case of any type of misbehavior of nodes, it broadcast this information to other nodes so that they can take the appropriate action. On the contrary, when designing IDS for WSN, there is the need to keep certain characteristics of wireless network into consideration such as battery, memory, and limited processing power. Specially, IDS plays an important role as a security provider from both external as well as internal threats [5]. Various IDS-based WSNs have been proposed by many researchers in last few years [6, 7]. This paper aims to present a survey addressing various security attacks in WSNs and their corresponding countermeasures in IDS enabled WSNs. In summary, the major contributions of the paper are as follows. • This work presents a survey on WSN technology and explores various security threats that are encountered and to be faced while communicating in WSNs. • This work presents the types of IDS that can serve as an effective security enhancement scheme in WSNs. • This work redefines various IDS schemes such as data anomalies schemes, misuse detection schemes, and specification-based schemes. • Finally, this work identifies avenues for future research by concluding state-ofthe-art work. This article is organized in the following manner. Section 2 discusses the security attacks encountered in WSNs. Section 3 gives the brief overview about the need of IDS in WSN along with the types of WSN based on different features. Section 4 explores the anomalies detection schemes associated with IDS in WSNs. Section 5 adds the misuse detection schemes followed by specification-based schemes in Sect. 6. Finally, Sect. 7 concludes the paper.

2 Security Attacks in WSN Ensuring the security in WSN applications to expected level is more complex than other wireless systems owing to several reasons such as unattended environment, limited resources of sensor node, and wireless broadcasting. Various types of security attacks in WSNs are explored in the subsections below.

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2.1 Sybil Attack Several applications may use the collaboration of sensor nodes with other nodes to perform any specific task. Thereafter, the applications can also design several management policies for distributing the subtasks to separate nodes. In this attack, the attacker node uses the identities of more than one legitimate user thus pretends to be more than one node simultaneously [8]. This attack can target the data aggregation process and routing mechanisms. As possible countermeasures, a logically centralized authority can be used as the cluster head or the base station in the network [9].

2.2 HELLO Flood Attack This attack utilizes HELLO packets, which are used to discover one hop neighbors, as a tool to entice sensor node. Any attacker having enough processing power and large radio range, specifically can send HELLO packets to numerous sensor nodes in the network and thus flooding any specific section of the network. The packet receiving node can be confused assuming that the sender/attacker is within the radio range and adversary can hence persuade the sensor node to be its neighbor. The proposed countermeasures use multiple base stations, secure multipath routing, and bidirectional verification of links [10].

2.3 The Node Replication Attack In this attack, the invader adds one or more malicious nodes in the network acting on same secret keys as any legitimate node. The impacts of this type of attacks can be such as disconnection and data corruption in the network. Some protocols, such as distributed detection techniques and neighboring-voting mechanism, have been proposed by some researchers in order to detect these attacks [11].

2.4 Selective Forwarding The basic assumption for communication in multi-hop WSNs is that all nodes are faithful to forward the messages whenever the request is made for message forwarding to the base station. However, whenever the malicious node exists in between, acting as the normal node, it intentionally drops the sensitive and relevant packets that are not easily detectable if missed. Though this attack does not depend upon the blackhole/sinkhole attacks, the attacker node can utilize them in order to

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increase its bad impact in the network. Some secure routing techniques have been suggested by some researchers in order to detect these types of attacks [12].

2.5 Denial of Service (DoS) Attack DoS attack can harm the network in three ways, by physically altering or destructing the network resources, by altering or destructing the configuration information or by consuming the limited or scarce non-renewable resources from the network. In WSNs, DoS attack can be implemented by targeting the most significant resources such as hardware of sensor. Generally, sensor nodes are vulnerable to jamming attack [13, 14]. Moreover, actual sensor hardware is vulnerable to tampering attacks.

3 Need for IDS in WSNs and Types of IDS It is really difficult to design an attack free network which is not vulnerable to attack in any way. In fact, there is the need to integrate fault tolerance capabilities and self-awareness in the networks. This will not only assume the possible ways for problem to be occur but also it will identify and lessen the effect of detected threat. The solution to this problem can be given by IDS that is responsible for detecting the misbehavior of nodes. IDS agent in IDS-based mechanism is responsible for detecting the abnormal behavior of the node and analysis of network. Though it is not feasible to apply some types of IDSs in wireless sensor networks owing to the vast variation in their network features such as limited mobility, deployment location, long lifetime, self-configurability, autonomy, and specificity, some are used in ad hoc and wired networks as a prevention mechanism. Moreover, power resources and computing are more constrained in WSNs than that in ad hoc networks. Therefore, there is the need of lightweight and novel design of IDS in WSNs. Based on the platform on which an IDS is deployed and input data collected from distinct sources such as network traffic, application process, system and user activities, audit log, and system call, there are several types of IDS. When IDS is deployed in wireless network, an IDS agent uses some detection mechanism to identify the misbehavior of malicious nodes, initially, data from the network are collected and based upon the detection mechanism, processing is done on the collected data. There can be several types of anomalies occurred in WSNs, such as data anomalies, network anomalies, and node anomalies. Based upon the detection mechanism that an IDS uses to detect the false nodes, there are further several types of network-based IDS. Figure 1 shows various types of detection mechanism that are used in network-based IDS. Most of the network anomalies cope with the connection issues in WSNs and there may be fluctuations in signal connectivity which aids to decide whether there is loss in the network on not. The anomalies that occur due to hardware or software

Intrusion Detection System (IDS) for Security Enhancement …

43

Fig. 1 Detection taxonomy of IDS in WSNs

problems in the sensors are called node anomalies. This may occur on account of power issues and solar panels failure. Data anomalies may mainly occur owing to the datasets disorder as well as some irregularities due to environmental and sensor problems. Misuse-based anomaly is also called signature-based IDS and utilizes the misuse detection to detect the known attacks. The only constraint to this technique is the absence of any predefined rules due to which no new attack can be detected using this technique. To use this technique in WSNs is a difficult, less effective, and tedious task. One algorithm utilizing this mechanism is watchdog-based clonal selection, which is used to detect and check the abnormal behavior of nodes at the time of data forwarding. An IDS is capable of detecting misbehaving nodes as well as informing neighbor nodes to take appropriate countermeasures [15, 16]. IDS implementation is relatively easier in the case of wired and ad hoc networks in comparison with the wireless networks due to varied network characteristics of the wireless networks. Several characteristics such as specificity, autonomy, mobility, lifetime, deployment location, and self-configurability make the design of security techniques complicated. Therefore, requirement for lightweight IDS design is necessary. The node’s behavior is checked and labeled as normal or anomalous. IDS decides the node to be malicious if it does not act as per the defined protocol specification. The node’s behavior is compared to known attack patterns which are predefined and feed to the system in case of misuse detection. This cannot detect novel attacks to perfection as this scheme requires to build attack patterns, and there is a necessity for someone to continuously

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update the attack database. The specification-based scheme integrates the anomaly and misuse detection mechanisms.

4 Anomaly Detection Schemes It relies on analyzing the nodes behavior and labeling them as normal or malicious based on certain metrics and assumptions. In order to finalize the actual normal behavior, the anomaly detection schemes employ several assumptions. Firstly, the packet payload must not be modified or altered and packet retransmissions occur after some time quantum only. Secondly, sending rate of the packets must be within some specified limit. The various types of anomaly detection schemes are as follows.

4.1 Statistical Model-Based Technique This technique requires each node to build a statistical model based on their neighbor’s behavior. The neighbor statistics are calculated only on the basis of last few packets received from that particular neighbor. The arriving packets are compared with existing statistics and if it satisfies the neighbor statistics then it gets accepted. However, it could not detect several attacks such as wormhole or selective forwarding attack as they use only the simple statistics.

4.2 Clustering-Based Technique This technique is useful in detecting the anomalous traffic pattern and works in two phases. First, the training phase in which set of clusters are built in the feature space. Second is the testing phase. As there are no communication required between the SNs, the overall power consumption is, therefore, reduced.

4.3 Centralized Anomaly Detection Technique In such intrusion detection schemes, the detection agent is situated within the base station for collecting node status information and management information. The management information includes the node id, number of hops counts, and the number of packets dropped due to failure. The node status information includes the normal, abnormal, duplicated, and unavailable nodes states. This information can be collectively analyzed to identify the type of threat possibilities.

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4.4 Isolation Table-Based Technique It has a three-tier hierarchical WSNs comprising of secondary cluster heads (CHs), primary CHs, and the base station. Here, the anomaly information is recorded by the isolation table and the same information is also used by the detection agents for isolating the nodes from the network. All the CHs generate the table and forward them to the base station (BS).

4.5 Game Theory-Based Technique Several models based on game theory were applied for intrusion detection and these even proved to be efficient for a wired network in terms of security. In case of WSNs, the performance of this mechanism degrades. This is because SNs in WSNs have constrained resources and as the number of nodes in network tends to increase, the overall network performance degrades. Two techniques, namely Markov decision process as well as intuitive metric technique, are used for future behavior prediction of the attacker. Table 1 compares the above-mentioned schemes in terms of different metrics. Table 1 Comparison of various anomaly detection schemes Anomaly detection schemes

Accuracy

Energy efficiency

Network structure

Memory requirement

Statistical model-based technique

Intermediate

No detail

Normal

No detail

Clustering-based technique

High

High

Clustered

High

Centralized anomaly detection technique

High

Low

Normal

Low

Artificial immune system

Intermediate

No detail

Normal

No detail

Isolation table-based technique

Low

Low

Clustered

Intermediate

Game theory-based technique

Intermediate

Low

Normal

Intermediate

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5 Misuse Detection Schemes This technique is also called rule-based detection scheme where knowing the motive of the attacker is very difficult. The network administrator models various attack patterns on the basis of attacks that might occur in the near future. As these detection schemes needs to store signatures of the attack, the constrained nature of WSNs makes this difficult scheme ineffective. Watchdog approach, the most common misuse detection scheme is described below.

5.1 Watchdog Approach This technique basically relies on the broadcast and open nature of the communication media and assumes densely deployed sensors. Every broadcasted packet is received by both the receiver as well as the neighboring nodes. These packets are used by neighbor nodes for intrusion detection. Therefore, a node can monitor the neighbor packet by overhearing them by activating its IDS agent. In order to increase the accuracy of attack detection, only one monitor node is not sufficient. This scheme requires nodes to overhear each and every packet which is not even destined to them, therefore increasing the overall energy consumption of the network.

6 Specification-Based Schemes In this scheme, the protocol specification or the attack development is done by the administrator. The network administrator manually defines the specification and monitors any deviation in the behavior on the basis of specified constraints. Various types of specification-based schemes are described below.

6.1 Decentralized Approach This includes three different phases. The first phase, namely the data acquisition phase, involves collection of packets in promiscuous mode and filtering the data before storage. The second phase is the rule application phase, and the final phase is the detection phase that includes the comparison between the number of expected occasional failures and the raised failures.

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6.2 Predefined Watchdog Approach This approach includes three basic modules. Firstly, local detection and monitoring engine for analysis on the basis of predefined rules. Secondly, the co-operative detection engine that facilitates accurate decision-making, and thirdly, the local response module that facilitates taking proper action in case the network verifies any intrusion.

6.3 Hybrid System Approach This approach integrates the concept of both the anomaly and misuse detection techniques. Here, it allows both the detection schemes to coexist and interaction between them is based on detection agent. Thus, detection agents are automated training-based ADT and artificial rule-based MDT. The misuse detection module of the hybrid approaches uses various types of predefined rules such as integrity rule, radio transmission range rule, packet interval rule, and packet delay rule. This method uses knowledge of the two-hop neighbor for preventing the routing attacks. This neighbor information is used in broadcasting protocols for reducing the number of packets being transmitted. Even though this technique detects multiple intruder attacks, there is an increase in energy consumption as well as cost. Furthermore, the mechanism is not tested for real-time environment. Figure 2 depicts the taxonomy of IDS approaches in WSNs.

7 Conclusion Owing to security features, IDS implementation in WSNs has gained much interest in the field of communication. Along with that, for future interactive systems, it promises to play an important role due to its various features that provide a secure way of communication in wireless networks. In this context, this paper presented a survey on various security attacks that are faced when performing communication, data sharing, and storing in WSN. This paper also laid out the need of IDS in WSN systems and also presents classification of threat detection mechanisms in IDSs such as anomaly, misuse, and specification-based methods. We hope that this paper would give usable insights into IDS-enabled WSN system for real-life secure applications.

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Fig. 2 Taxonomy of IDS approaches in WSNs

References 1. I. Khan, F. Belqasmi, R. Glitho, N. Crespi, M. Morrow, P. Polakos, Wireless sensor network virtualization: a survey. IEEE Commun. Surv. Tutorials 18(1), 553–576 (2016). https://doi.org/ 10.1109/comst.2015.2412971 2. J. Liu, Z. Zhao, J. Ji, M. Hu, Research and application of wireless sensor network technology in power transmission and distribution system. Intell. Converged Netw. 1(2), 199–220 (2020). https://doi.org/10.23919/ICN.2020.0016 3. B. Bhushan, G. Sahoo, Recent advances in attacks, technical challenges, vulnerabilities and their countermeasures in wireless sensor networks. Wireless Pers. Commun. 98(2), 2037–2077 (2017). https://doi.org/10.1007/s11277-017-4962-0 4. B. Bhushan, G. Sahoo, Requirements, protocols, and security challenges in wireless sensor networks: an industrial perspective, in Handbook of Computer Networks and Cyber Security, pp. 683–713 (2020). https://doi.org/10.1007/978-3-030-22277-2_27 5. H. Xie, Z. Yan, Z. Yao, M. Atiquzzaman, Data collection for security measurement in wireless sensor networks: a survey. IEEE Internet Things J. 6(2), 2205–2224 (2019). https://doi.org/10. 1109/JIOT.2018.2883403 6. B. Bhushan, G. Sahoo, ISFC-BLS (intelligent and secured fuzzy clustering algorithm using balanced load sub-cluster formation) in WSN environment. Wireless Pers. Commun. (2019). https://doi.org/10.1007/s11277-019-06948-0 7. B. Bhushan, G. Sahoo, $$Eˆ{2} SRˆ{2}$$ E 2 S R 2: an acknowledgement-based mobile sink routing protocol with rechargeable sensors for wireless sensor networks. Wireless Netw. 25(5), 2697–2721 (2019). https://doi.org/10.1007/s11276-019-01988-7

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8. J. Newsome, E. Shi, D. Song, A. Perrig, The sybil attack in sensor networks: analysis and defense, in IEEE/ACM IPSN’04, pp. 259–268 (2004) 9. H. Yu, M. Kaminsky, P.B. Gibbons, A. Flaxman, SybilGuard: defending against sybil attacks via social networks. ACM SIGCOMM, 267–278 (2006) 10. M.A. Hamid, M. Mamun-Or-Rashid, C.S. Hong, Routing security in sensor network: HELLO flood attack and defense, in IEEE ICNEWS 2006 (Dhaka, Bangladesh, 2006), pp. 77–81 11. C.E. Loo, M.Y. Ng, C. Leckie, M. Palaniswami, Intrusion detection for routing attacks in sensor networks. Int. J. Distrib. Sens. Netw. 2(4), 313–332 (2006) 12. S. Kaplantzis, A. Shilton, N. Mani, Y.A. Sekercioglu, Detecting selective forwarding attacks in wireless sensor networks using support vector machines, in ISSNIP 2007 (Melbourne, Australia, 2007), pp. 335–340 13. N. Ahmed, S. Kanhere, S. Jha, The holes problem in wireless sensor networks: a survey. ACM SIGMOBILE Mobile Comput. Commun. Rev. 9(2), 4–18 (2005) 14. M. Cagalj, S. Capkun, J.-P. Hubaux, Wormhole-based antijamming techniques in sensor networks. IEEE Trans. Mobile Comput. 6(1), 100–114 (2007) 15. D.S. Mantri, N.R. Prasad, R. Prasad, Random mobility and heterogeneity-aware hybrid synchronization for wireless sensor network. Wireless Pers. Commun. 100(2), 321–336 (2017). https://doi.org/10.1007/s11277-017-5072-8 16. O.O. Ogundile, A.S. Alfa, A survey on an energy-efficient and energy-balanced routing protocol for wireless sensor networks. Sensors 17(5), 1084 (2017)

Design and Analysis of Wideband Circularly Polarized Modified Cylindrical Dielectric Resonator Antenna Array P. Kondalamma, R. Sindhura, L. Naga jyothsna, K. Tarun, M. VamsiKrishna, and Ch. Raghavendra Abstract In this paper, a design approach for aperture-coupled ring dielectric resonator antenna (Ring DRA) array is presented. Ring DRA is obtained by removing a central cylindrical section of radius ‘b’ from a cylindrical dielectric resonator antenna of radius ‘a’. Though the increase in b/a ratio increases the q factor, to maintain the same resonant frequency, the outer radius ‘a’ must be increased. For an approximate triple increase in the outer radius, Q factor reduces by about 14 times providing wider impedance bandwidth. The dual ring-shaped ceramic blocks placed with a small gap act as a radiating element. This design is simulated using HFSS 13.0. The proposed antenna with aperture and cylindrical cut section produces circularly rotating electromagnetic waves in the frequency range of 4.6–6.4 GHz making it more appropriate for WLAN (5.25 GHz) and WiMAX (5.5 GHz) applications. Keywords Ring DRA · Circular polarization · HFSS · Antenna array

1 Introduction DRA was first proposed by Richtmyer in the year 1939, and it was later headed by S.A. Long who was the first one to design and test the dielectric resonator antennas (DRAs). DRA is being greatly focused of its various attractive features which allows the designers to meet many requirements. Different resonator shapes can excite various modes to produce desired radiation patterns within the DRA element. DRAs offer low losses, easy fabrication, high dielectric strength, wider impedance bandwidth, and higher power handling capacity. The two main research areas which are mainly focused in the field of DRA’s are (i) DRA array (ii) circularly polarized (CP) DRA. Antenna arrays can be used to increase the performance when compared to that of single antenna. Circular polarization is desired because it gives greater flexibility in terms of orientation angle and has the ability to minimize multipath interference P. Kondalamma (B) · R. Sindhura · L. Naga jyothsna · K. Tarun · M. VamsiKrishna · Ch. Raghavendra Department of Electronics and Communication Engineering, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, Andhra Pradesh, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_6

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compared to linear polarization. It also reduces the problem of cross polarization. In our proposed design, to achieve circular polarization, Wilkinson power divider is used. It is a three-port device which divides the signal into two equal splits (3 dB) and in phase between the output ports and 90° phase difference between input and output port. Analytical studies carried out on CDRAs have illustrated that the Q factor could be reduced by removing a central portion of the dielectric material there by increasing the impedance bandwidth performance [1]. For most of the practical applications of the DRA, bandwidth enhancement is the major consideration. This can be achieved by changing either the shape of the DRA or by optimizing the feeding mechanism.

2 Literature Survey In the open literature survey, Chair et al. [2] proposed a circularly polarized rectangular stair-shaped DRA which is excited by a narrow rectangular slot and rotated 45° concerning the sides of the DRA to generate circular polarization. This design provides 3 dB axial ratio bandwidth of 10.6% when the length to the width ratio is 1.9 in the frequency range of 7.8–11.3 GHz. Pan and Leung [3] proposed that a trapezoidal-shaped DRAs yield better circular polarization. In spite of the fact that this design methodology is simple, it is quite hard to fabricate such type of design. Zou et al. [4] researched a wideband circularly polarized rectangular DRA in which an Archimedean slot is used to excite the DRA. It has axial ratio bandwidth of 25.5% in the frequency range of 1.95–2.52 GHz. Sarkar [5] presented a new compound ground technique to overcome the limitations of traditional bonding by chemical glue. This proposed configuration includes a secondary ground plane with strategic perforations and clipping arrangements to achieve required mechanical stability along with accurate DRA positioning. This technique finds many applications up to the operating frequency range of 25 GHz. CP antenna makes the transmitter and the receiver orientation independent, but it has limitations like excessive power loss which results in reduced gain. The combination of CP antenna with array arrangement gives us the desired results. So, in the proposed design, circularly polarized ring DRA with an array arrangement is used to achieve circular polarization along with increased gain and bandwidth.

3 Geometrical Structure of the Proposed Design Figure 1 shows the schematic diagram of the proposed antenna array design. In the proposed design, the FR-4 substrate is used from which rectangle aperture along with two vertically placed slots in opposite direction has been etched out. 3D view of proposed antenna is shown in Fig. 1c. It comprises of four ring-shaped ceramic blocks in which each combination of two blocks is fed by one dual L-shaped aperture.

Design and Analysis of Wideband Circularly Polarized Modified …

(a)

53

(b)

(c)

Fig. 1 (a) Substrate’s top view, (b) substrate’s bottom view, (c) substrate’s 3D view

Fig. 2 CDRA having cylindrical cut inside

These ceramic blocks are made of alumina, and the gap between them is 2.0 mm. Each block contains a cylindrical cut section of diameter 1 mm and height 16 mm as shown in Fig. 2. It is expected that by removing central portion of the DRA, its bandwidth can be increased. The size of the substrate used is 105 * 54 mm2 . All the optimized dimension information is provided in Table 1. On the beneath of the same substrate, 3 dB Wilkinson power divider is designed. ‘Wilkinson power divider provides isolation between the output ports, is capable of being matched at all ports, and becomes lossless when the output ports are matched’ (Table 2).

4 Working Principle of Proposed Antenna Simple rectangle placed on the substrate acts as a horizontally placed magnetic dipole and thus creates HEM11 δ mode at 5.2 GHz. Slots placed in opposite directions behave as vertically placed magnetic dipole and thus creates TE01 δ mode at 6.2 GHz.

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Table 1 Performance assessment of proposed design with other existing designs based on shape, impedance bandwidth, gain, overall size, and frequency range Shape of radiator

Impedance bandwidth(%)

Gain (dBi)

Overall size (mm2 )

Frequency range (GHz)

Stair-shaped DRA [2]

36.6

5.0

55 × 55

7.8–11.3

Trapezoidal-shaped DRA [3]

33.5

5.28

100 × 100

1.91–3.23

Rectangular DRA [4]

25.5

5.5

75 × 75

1.95–2.52

Cubic DRA [6]

35.35

1.5

40 × 40

3.3–3.7

Elliptical DRA [7]

35

5.0

80 × 80

8–13.5

Rectangular DRA [8]

17

12.0

135 × 48

6.01–6.92

CDRA array [9]

30.9

10.0

105 × 54

4.5–5.7

Proposed Design

32.25

10.3

105 × 54

4.64–6.47

Table 2 Proposed antenna parameters with optimized dimensions

Parameter Dimension in mm Parameter Dimension in mm L8

2.5

L3

16.5

WG

54.0

W1

1.25

LS

105.0

W3

1.25

LG

105.0

W7

2.5

D

16.0

W5

2.0

H

8.0

W6

1.0

W2

1.75

WF

3.0

B

1.6

L5

28.5

A

2.0

L7

11.0

L1

9.5

L6

48.0

L2

8.25

d

1.0

Figure 3a, b shows the E field lines frequencies 6.2 GHz and 5.25 GHz, respectively, where red color indicates the highest value and blue indicates the least value. These values can be noted down using the E field scale placed beside the E field lines. The operating frequency range of the proposed antenna is 4.64–6.47 GHz. These values are shown in Fig. 4. From Fig. 5, the value of axial ratio at θ = 0 is less than 3 dB, so it is undergoing circular polarization. Due to the cylindrical cut inside each DRA, the maximum gain in the entire operating frequency range is obtained as 10.3 dB which occurs at frequency 6.22 GHz. Figure 6 shows the gain plot for all the frequencies in the operating frequency range. Gain is also measured in the broadside direction. From Fig. 6, it can be observed that gain increases as frequency increases because the effective area of radiator increases with respect to wavelength [9]. Figure 8 displays the simulated LHCP and RHCP patterns at 6.2 GHz. Axial ratio is plotted toward broadside direction (∅ = 0, θ = 0). Figure 7 shows the

Design and Analysis of Wideband Circularly Polarized Modified …

Fig. 3 (a) E field lines at 6.22 GHz, (b) E field lines at 5.25 GHz

Fig. 4 S 11 plot where m1 and m2 are the markers on the curve at −10 dB

Fig. 5 Axial ratio beam width plot where m1 is the marker on the curve at −3 dB

55

56 Fig. 6 Total gain plot in the entire frequency range

Fig. 7 Polar plot at all angles of F, θ, and at frequency 6.22 GHz

Fig. 8 RHCP and LHCP patterns at 6.2 GHz

P. Kondalamma et al.

Design and Analysis of Wideband Circularly Polarized Modified …

57

Fig. 9 Radiation patterns at all angles of θ and at all frequencies 6.22 GHz and 5.25 GHz

radiation polar plot measured at all angles of θ and ∅ at frequency 6.22 GHz where the maximum gain occurs. Similarly, Fig. 9 shows the radiation pattern at all angles of θ and ∅ equal to 0 and 90° at two frequencies 6.22 GHz and 5.25 GHz.

5 Conclusion In this paper, design methodology of aperture-coupled ring dielectric resonator antenna (Ring DRA) array has been explained and shown the results obtained using HFSS. The main purpose of this antenna design is to increase bandwidth with central cylindrical cut portion, gain due to antenna array, and also to generate circular electromagnetic wave (4.64–6.4 GHz). Two vertical slots placed in opposite direction along with the rectangular aperture excite the dual mode pattern. Modified ring DRA is designed in such a way that it gives lower Q factor. The proposed antenna design operates in the frequency range of 4.64–6.4 GHz and is best suited for WLAN (5.2 GHz), WiMAX (5.5 GHz), and future 6G Wi-Fi band applications.

References 1. R. Kumari, S.K. Behra, Ring DRA for broad band applications, in IEEE International Conference

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on Computational Intelligence and Communication Networks (2010) 2. R. Chair, S.L.S. Yang, A.A. Kishk, K.F. Lee, K.M. Luk, Aperture fed wideband circularly polarized rectangular stair shaped resonator antenna. IEEE Trans. Antennas Propag. Lett. 54(4), 1350–1352 (2006) 3. Y.M. Pan, K.W. Leung, Wideband circularly polarized trapezoidal dielectric resonator antenna. IEEE Antennas Wireless Propag. Lett. 9, 588–591 (2010) 4. M. Zou, J. Pan, Z. Nie, A wideband circularly polarized rectangular dielectric resonator antenna excited by an Archimedean spiral slot. IEEE Antennas Wireless Propag. Lett. 14, 446–449 (2015) 5. C. Sarkar, Glue-less compound ground technique for dielectric resonator antenna and arrays. IEEE Antennas Wireless Propag. Lett. (2017) 6. R. Kumar, R.K. Chaudhary, A wideband circularly polarized cubic dielectric resonator antenna excited with modified micro-strip feed. IEEE Antennas Wireless Propag. Lett. 15, 1285–1288 (2016) 7. S.-L.S. Yang, R. Chair, A.A. Kishk, K.-F. Lee, K.-M. Luk, Study on sequential feeding networks for sub arrays of circularly polarized elliptical dielectric resonator antenna. IEEE Trans. Antennas Propag. Lett. 55(2), 321–333 (2007) 8. B. Rana, S.K. Parui, Micro-strip line fed wideband circularly polarized dielectric resonator antenna array for microwave image sensing. IEEE Sensors Lett. 1(3) (2017). Art no. 3500604 9. M. Khalily, R.K. Gangwar, S. Gupta, A. Sharma, Wideband circularly polarized dielectric resonator antenna array with polarization diversity. IEEE Antennas Wireless Propag. Lett. 7 (2019)

Design and Performance Analysis of Tri-band Monopole Planar Antenna for Wireless Communications T. Ravi Kumar Naidu and M. Susila

Abstract A novel tri-band monopole planar antenna is proposed which covers various applications such as digital cellular system, WLAN, and WiMAX. The proposed antenna is designed on a Rogers RO3003 substrate with relative permittivity εr = 3.0 and tan δ = 0.0010. The height of the substrate is 1.6 mm, and the designed antenna exhibits good radiation characteristics and moderate gain. The proposed antenna resonates at three different frequencies, i.e., 1.8 GHz with an impedance matching bandwidth (B.W) of 19 MHz, 2.4 GHz with a B.W of 48 MHz and 3.35 GHz with a B.W of 269 MHz which serves for personnel communication system (PCS)/digital cellular system (DCS) (1.8 GHz), Wi-Fi (2.4 GHz ISM band), and WiMAX (3.35 GHz) applications. Keywords Tri-band antenna · Planar monopole antenna · Antenna · Wi-Fi · WiMAX

1 Introduction The communication industry has a significant growth in improving the performance of antenna. Antenna designing for multi-band with high gain, high radiation efficiency with low cost is a challenging task. The most popular antenna design for multi-band applications is planar antennas due to its benefits such as miniaturization, multi-band performance, cost efficiency, and the easiness to merge with other elements in the system. The authors [1] proposed an ACS-fed antenna operated at 2.4 GHz and 1.8 GHz which covers WLAN and DCS applications. The authors Varadhan et al. [2] introduced RFID reader and tag antennas by deploying fractal geometry which operates at 8.2 GHz, 5.8 GHz, and 3.6 GHz frequencies. Sami et al. [3] fabricated rectangular tri-band patch antenna which operates at 5.7 GHz, 3.5 GHz, and 2.4 GHz frequencies for wireless communication applications. The authors [4] in their study, they presented a ACS-fed monopole antenna with two T. Ravi Kumar Naidu (B) · M. Susila Department of ECE, SRM Institute of Science and Technology, Kattankulathur Campus, Kattankulathur, Tamil Nadu, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_7

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T. Ravi Kumar Naidu and M. Susila

inverted L-shape branches for WLAN and WiMAX applications. Afzal et al. [5] presented an H-shaped antenna for DCS and WLAN applications. Various types of planar antennas with different geometrical shape are extensively examined as they exhibit the vital features of DCS, WLAN, WiMAX applications [6–10]. In this work, planar antenna is designed for PCS/DCS/Wi-Fi/WiMAX applications. This research article is ordered as follows: the antenna configuration with its parametric study in Sect. 2. Results and discussions in Sect. 3 and the work are concluded in Sect. 4.

2 Antenna Configuration with Its Parametric Study 2.1 Design Parameters The parameters required for the design of this tri-band monopole planar antenna are as follows: Operating frequency: Antenna is designed to operate for band of frequencies ranging from 1 to 4 GHz with a resonant frequency of 1.8 GHz, 2.4 GHz, and 3.35 GHz. Substrate used: Roger’s RO3003 substrate (εr = 3.0) with thickness of 1.6 mm and tan δ = 0.0010) is chosen for its excellent stability of dielectric constant over temperature and low dielectric loss.

2.2 Antenna Structure The tri-band monopole planar antenna geometry and parameters are illustrated in Fig. 1. The optimized dimensional values of the tri-band monopole antenna are tabulated in the given Table 1. Figure 2 demonstrates the progression of the designed antenna. First, Antenna-I is designed which operates at 1.8 GHz frequency, and then added L-shaped patch to the Antenna-I to get the second radiating element (Antenna-II) which operates at two frequencies, i.e., 2.4 GHz and1.8 GHz. Finally, U-shaped patch is introduced to the Antenna-II to get the proposed radiating element (Antenna-III) which operates at three frequencies, i.e., 3.35 GHz, 2.4 GHz, and 1.8 GHz. It is observed that the overall performance of the antenna is improved after introducing all the resonant bands. S-parameters comparison for the three antennas is shown in Fig. 3. And results are shown in Table 2.

Design and Performance Analysis of Tri-band Monopole Planar …

(i)

61

(ii)

(iii)

Fig. 1 Proposed tri-band monopole planar antenna: (i) Patch (ii) partial ground, and (iii) geometry of the antenna with all dimensions in mm Table 1 Dimensions of the designed tri-band monopole planar antenna Parameter’s L W LG WG HT HS Length Length substrate substrate ground ground copper substrate L1 L2 length width length width thickness thickness 50

10.5

1.6

17

32

Parameter’s Length L3

Value [mm]

67

Length L4

Length Length Length L5 L6 L7

Length L8

Width W1

Width W2, W3

Value [mm]

23.5

8

10

3.5

5.25

18

Antenna-I

50

28

Antenna-II

Fig. 2 Progression of the tri-band monopole antenna

0.035

56.5

Antenna-III

62

T. Ravi Kumar Naidu and M. Susila

Fig. 3 S11-comparison of different stages of antenna

Table 2 Operating frequencies and its corresponding return loss of the designed radiating elements

S. No.

Radiating element

Operating bands in GHz

Return losses in dB

1

Antenna-I

1.8

11.72

2

Antenna-II

1.8, 2.4

14.65, 11.32

3

Antenna-III

1.8, 2.4 and 3.35

13.12, 14.94 and 16.67

3 Results The simulation studies of the designed tri-band monopole planar antenna are carried out by using CST MWS. The impedance matching measured bandwidths (B.W) for S11 ≤ −10 dB are about 19 MHz B.W (1.790–1.809 GHz) resonated at 1.8 GHz with a return loss (RL) of 13.115 dB, 43 MHz B.W (2.378–2.421 GHz) resonated at 2.4 GHz with a RL of 14.941 dB, and 269 MHz B.W (3.249–3.519 GHz) resonated at 3.35 GHz with a RL of 16.67 dB and VSWR is less than two as shown in the Fig. 4. Table 3 shows the parametric results of the antenna proposed, which is well suitable for applications PCS/DCS (1.790–1.809 GHz), WLAN (2.378–2.421 GHz), and WiMAX (3.249–3.519 GHz). Figures 5 and 6 present simulated radiation efficiencies and radiation patterns of Antenna-III. The efficacy of the designed planar tri-band monopole antenna is compared with comparable type of antennas which are reported in literature and tabulated the metrics in Table 4. It has high value of efficiency and gain compared

Design and Performance Analysis of Tri-band Monopole Planar …

63

Fig. 4 a Simulated return losses (−S11) and b Simulated VSWR of the Antenna-III Table 3 Parametric results of the designed antenna (Antenna-III)

Parameters

Frequency in GHz 1.8 GHz

2.4 GHz

3.35 GHz

Return loss (dB)

13.115

14.941

16.67

VSWR

1.5671

1.4361

1.3439

Gain (dBi)

3.035

3.141

3.714

Directivity (dBi)

3.39

3.518

4.038

Radiation efficiency (%)

92.16

91.68

92.82

Total efficiency (%)

87.66

88.74

90.82

Fig. 5 Antenna-III simulated radiation efficiencies and total efficiencies

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T. Ravi Kumar Naidu and M. Susila

(a)

(b)

(c)

Fig. 6 Antenna-III simulated 3D-radiation patterns at a 1.8 GHz frequency, b 2.4 GHz frequency and c 3.35 GHz frequency Table 4 Comparison of designed antenna with comparable type of antennas which are reported in literature Reference/Year

Substrate parameters

Ashkarali et al. [1]

Operating bands in GHz

Peak gain in dBi

Radiation efficiency in %

Antenna purpose

Substrate = FR4 1.81 Relative 2.42 permittivity = 4.4 and h = 1.6 mm

1.08 1.21

73 83

Dual band

Liu et al. [4]

Substrate = FR4 2.4 Relative 3.5 permittivity = 4.4 5.8 and h = 1.6 mm

1.73 1.93 2.39

–

Triple band

Afzal et al. [5]

Substrate = FR4 1.8 Relative 2.45 permittivity = 4.4 5.2 and h = 1.6 mm tan δ = 0.02

1.6 1.9 2.1

–

Triple band

Anil Kumar et al. [8]

Substrate = FR4 1.89 Relative 3.5 permittivity = 4.4 5.5 and h = 1.6 mm tan δ = 0.02

0.136 2.12 3.55

34.5 80 83.9

Triple band

Upadhyaya et al. [9]

Substrate = FR4 0.868 Relative 2.4 permittivity = 4.4 and h = 1.6 mm tan δ = 0.02

1.18 2.1

69.2 81.3

Dual band

Anil Kumar et al. [10]

Substrate = FR4 2.4 Relative 3.5 permittivity = 4.4 5.5 and h = 1.6 mm tan δ = 0.02

1.56 1.16 4.9

–

Triple band

Proposed antenna

Substrate = 1.8 Rogers RO3003 2.4 Relative 3.35 permittivity = 3.0 h = 1.6 mm and tan δ = 0.0010

3.035 3.141 3.714

87.66 88.74 90.82

Triple band

Design and Performance Analysis of Tri-band Monopole Planar …

(a)

(b)

65

(c)

Fig. 7 Far-field gains of designed planar tri-band monopole antenna (Antenna-III) at a 1.8 GHz, b 2.4 GHz and c 3.35 GHz frequencies

to the cited references. The far-field gain at constant pi (=90°) for the resonant frequencies is shown in Fig. 7.

4 Conclusion In this work, a tri-band monopole radiating element is proposed for wireless communication. By adding three patches with different shapes and dimensions to the monopole, the designed antenna can operate at three frequencies. The vital parameters of the designed antenna are discussed and presented in detail. Simulated results show that the designed antenna attained good Omni-directional radiation pattern, high radiation efficiency, moderate gains at desired bands which found it suitable for PCS/DCS, WLAN, and WiMAX applications.

References 1. P. Ashkarali, S. Sreenath, R. Sujith, R. Dinesh, D.D. Krishna, C.K. Aanandan, A compact asymmetric coplanar strip fed dual-band antenna for DCS/WLAN applications. Microw. Opt. Technol. Lett. 54(4), 1087–1089 (2012) 2. C. Varadhan, J.K. Pakkathillam, M. Kanagasabai, R. Sivasamy, R. Natarajan, S.K. Palaniswamy, Tri-band antenna structures for RFID systems deploying fractal geometry. IEEE Antennas Wirel. Propag. Lett. 12, 437–440 (2013) 3. G. Sami, M. Mohanna, M.L. Rabeh, Tri-band micro-strip antenna design for wireless communication applications. NRIAG J. Astron. Geophys. 2013(2), 39–44 (2013) 4. Y.-F. Liu, P. Wang, H. Qin, A compact tri-band ACS-fed monopole antenna employing invertedl branches for WLAN/WiMAX applications. Prog. Electromagnet. Res. C 47, 131–138 (2014) 5. W. Afzal, U. Rafique, M.M. Ahmed, A tri-band H-shaped micro-strip patch antenna for DCS and WLAN applications, in MIKON 2012 19th International Conference on Microwaves, Radar and Wireless Communications (Warsaw, Poland, 2012), pp. 256–258 6. R. Dehariya, N. Gunavthi, A compact tri-band antenna for WLAN and WiMAX applications, in IEEE International Conference On Recent Trends In Electronics Information Communication Technology 2016 (India, 2016)

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7. J. Puskely, A.G. Yarovoy, A.G. Roederer, Planar tri-band antenna element in L/S/C-bands, in 2017 11th European Conference on Antennas and Propagation (EUCAP), pp. 1153–1157 (2017) 8. C.V. Anil Kumar, B. Paul, P. Mohanan, Compact tri-band dual F-shaped antenna for DCS/WiMAX/WLAN applications. Prog. Electromagn. Res. Lett. 78, 97–104 (2018) 9. T. Upadhyaya, A. Desai, R. Patel, Design of printed monopole antenna for wireless energy meter and smart applications. Prog. Electromagn. Res. Lett. 77, 27–33 (2018) 10. C.V. Anil Kumar, B. Paul, M. Mani, P. Mohanan, A CPW-fed tri-band antenna for 2.4/3.5/5.5 GHz applications. Prog. Electromagn. Res. Lett. 92, 75–83 (2020)

T. Ravi Kumar Naidu is currently working as Assistant Professor in Department of Electronics and Communication Engineering at Sree Vidyanikethan Engineering College. He received his M. Tech. degree in Embedded Systems in the year 2012 and currently pursuing Ph. D. at SRM Institute of Science and Technology (Deemed to be University). His research interest includes antenna design, radio wave propagation, and device-to-device communications.

M. Susila is currently working as Associate Professor in Department of Electronics and Communication Engineering, SRM Institute of Science and Technology (formerly known as SRM University). She received her Ph. D. degree in Telecommunication Engineering in the year 2019. Her research interest includes antenna design, radio wave propagation, and device-todevice communications.

Performance of Circular Patch Antenna Without and with Varying Superstrates Height V. Saidulu

Abstract The objective of this paper is studies on the performance of circular microstrip patch antenna with multiple dielectric superstrates varying the heights and thickness of the dielectric constants of the superstrates. The antenna is designed at the center frequency of 2.4 GHz. This frequency is used in wireless and Bluetooth applications. The cavity model analysis and coaxial probe fed technique are used for designing of the patch antenna. The antenna is fabricated on low loss dielectric constant substrate. The antenna is simulated using electromagnetic simulator such as HFSS. Comparing the performance of circular patch antenna with varying the height of the superstrates above the patch antenna as increasing the height of the superstrates, the antenna performance is improved, and at particular optimum height, the performance of patch antenna is matched with antenna without dielectric superstrate (single antenna). Keywords Dielectric cover · Dielectric superstrates · Optimum height · Dielectric constant · Bandwidth · Radiation patterns

1 Introduction Microstrip antenna is used in high speed vehicles, missiles, military, RADAR applications, etc. The major characteristic of antenna is low profile [1–5]. The microstrip antenna often covered with dielectric cover (or superstrate) for protection to the antenna from various environmental conditions, such as heat, rain, snow, and physical damage. When placing the dielectric cover above the patch which changes the characteristics of antenna. The circular patch is one of the most widely used antenna in now days in various applications. This antenna is suitable for planar and non-planar geometry applications. The advantages of these antennas are low profile, light weight, low volume, and easy fabrications on PCB board [6–8]. The limitation of this antenna V. Saidulu (B) Department of ECE, Mahatma Gandhi Institute of Technology, Gandipet , Hyderabad, Telangana, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_8

67

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V. Saidulu

is low power handling capacity, lower gain, and antenna radiates into half plane. The surface wave loss is the major problem in this antenna [2, 9–13]. Some of these limitations are overcome by taking care of antenna design, in this paper, comparing the single antenna characteristics and antenna with varying different heights of dielectric superstrates.

2 Specifications The circular patch antenna is designed at operating frequency of 2.4 GHz. The coaxial probe fed technique method is used to excitation to the antenna. The substrate material which is used for designing patch antenna is Arlon DiClad and having dielectric constant (∈ r1 ) = 2.2, and the thickness of the substrate is 1.6 mm. The superstrates materials which are Arlon AD880, Arlon AD320, FR4, and Arlon AD1000 and having thickness and dielectric constants are 1.6 mm, 3.2 mm, 1.6 mm, 0.8 mm and 2.2, 3.2, 4.8, and 10.2, respectively. These all the substrate materials are low loss and low loss tangent and stand for high temperature. If the high dielectric constant substrates materials are reduce the efficiency and increase the losses. Whereas, low dielectric constant substrate material is reduce the antenna losses and the increase the efficiency.

3 Patch Antenna Design The circular patch antenna designed geometry is shown in Fig. 1. The designed dimensions of the patch such as diameter, radius, and effective radius were calculated using the cavity model analysis. The actual radius (a), effective radius (ae ), and mode of center frequency ( fr ) of the patch are determined using the Eqs. (1–3) Fig. 1 Circular microstrip patch antenna

Performance of Circular Patch Antenna Without and with Varying …

a=

where the value F =

1+

2h πεr F

ln

F πF  2h

+ 1.7726

1/2

69

(1)

9 8.791×10 √ fr εr



1/2 2h   πa + 1.7726 ln ae = a 1 + π εr 2h ( fr )110 =

1.8412vo √ 2πae εr

(2) (3)

where v0 is the velocity of light. The antenna fed point location is determined using trial and error method. The feed point location f x =10.5 mm and f y =0 mm. The designed dimensions of CPMA are substrate width (W s ) = 81.3 mm, substrate length (L s ) = 72.3 mm, patch radius (R) = 23.75 mm.

4 Effect of Superstrate (or Dielectric Cover) Superstrate (or dielectric cover) is remarkable effect on circular microstrip patch antenna. The dielectric cover is providing the protection to the antenna from various environmental conditions such as rain and snow. The superstrate or dielectric cover has changed the properties of the antenna characteristics. By placing the dielectric cover above the square patch antenna at height (H) = 0 mm, all the antenna parameters are slightly degraded their performance. The antenna designed frequency is decreased to 2.3 GHz from 2.4 GHz and all other parameters changed their performance as comparing the performance of patch antenna without superstrate. As increasing the height of the superstrates, the performance is improved. The circular patch antenna with optimum height of the superstrate is shown in Fig. 2. The changing of the resonant frequency (or center frequency) is given in Eqs. (4–5)  fr = fr

√ √ ∈e − ∈eo √ ∈e

(4)

If ∈e =∈eo + ∈e and  ∈e ≤ 0.1 ∈eo , then  ∈e ∈ eo  fr =1 2 fr 1 + 1 2 ∈e ∈ eo

(5)

70

V. Saidulu

Fig. 2 Circular patch antenna with optimum height of superstrate

where ∈e ∈ eo  ∈e  fr fr

Effective dielectric constant of the substrate with dielectric cover Effective dielectric constant of the substrate without dielectric cover Change in dielectric constant of the substrate due to dielectric cover Change in center frequency Center frequency.

5 Results and Discussion 5.1 Effect of Superstrate at Height (H) = 0 mm The superstrate (or dielectric cover) height at H = 0 mm, all the antenna parameters are decreased as comparing the patch antenna without superstrate. The antenna center or designed frequency is decreased to 2.19 GHz from 2.43 GHz. Bandwidth is decreased to 0.03 GHz from 0.05 GHz. RL is decreased from −24.6 dB to −24.44 dB. It is observed that the HPBW is decreased to 50.6° from 79.9° in azimuth plane and increased in elevation plane from 79.3°–108.2°. Further, it is also observed that the gain is decreased in both elevation and azimuth plane, from 8.07 dB to 6.81 dB and 8.67 to 6.93 dB, respectively. The comparison of return loss plot with and without superstrate for ∈ r1 =1.0 is shown in Fig. 3 and corresponding the measured and simulated data with superatrate height H = 0 mm is shown in Tables 1 and 2.

5.2 Effect of Superstrate at Optimum Height (H = H optimum ) The effect of superstrate or dielectric cover on the circular patch antenna is a remarkable effect. The superstrate above the patch at height H = 0 mm, all antenna parameters such as gain, bandwidth, and antenna designed center frequency are slightly

Performance of Circular Patch Antenna Without and with Varying …

71

Fig. 3 Comparison of return loss with and without superstrate at dielectric constant ∈ r1 = 1.0

Table 1 Simulated and measured results CMPA with and without dielectric superstrates at height H = 0 (mm) Dielectric constant (∈r 2 )

1a

2.2

3.2

4.8

10.2

2.43

2.38

2.39

2.31

2.19

Center frequency (fr), GHz

Simulated Measured

2.43

2.38

2.37

2.33

2.20

Bandwidth (GHz)

Simulated

0.05

0.04

0.04

0.03

0.03

Measured

0.05

0.04

0.04

0.03

0.03

Simulated

−24.6

−28.98

−30.13

−23.75

−24.44

Measured

−23.3

−27

−29.10

−25.20

−23.5

Return loss (dB) a

without dielectric superstrate

decreased and decreased parameters can improved as increasing height of the superstrate. The superstrate at the optimum height means patch antenna with superstrate at height H = H optimum above the patch, the antenna parameters are increased and the increased results matched with antenna without superstrate. At optimum height, the antenna gain is increased to 8.8 dB from 8.57 dB in azimuth plane and also increased from 7.87 dB to 8.55 dB in elevation plane. The return loss is increased −30.13 dB from −24.6 dB, and beam width is increased in elevation plane from 79.3° to 108.2° and decreased in azimuth plane 50.6° from 79.3°. The measured and simulated return loss and radiation patterns characteristics are shown in Figs. 3, 4 and 5 and corresponding the data are shown Tables 1, 2, 3 and 4.

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V. Saidulu

Table 2 Simulated and measured results HPBW and gain for CPMA with and without dielectric superstrates at height H = 0 (mm)

a

Dielectric constant of superstrate (∈r2 )

Antenna half power beam width (deg.)

Antenna gain (dB)

Simulation

Simulation

1a

79.7

79.3

79.79

2.2

65.3

82.1

3.2

60.9

87.10

4.8

56.4

10.2

50.6

Measured

Measured

Azimuth Elevation Azimuth Elevation Azimuth Elevation Azimuth Elevation plane plane plane plane plane plane plane plane 79.64

8.67

8.07

8.6

8

65.93

83.05

8.65

7.84

8.6

7.83

61.79

87.21

8.41

7.5

8.35

7.4

96.1

57.32

96.21

7.94

7.15

7.8

7

108.2

51.01

108.36

6.93

6.81

6.95

6.80

without dielectric superstrate

Fig. 4 Comparison of simulation and measurement of return loss with and without superstrate at dielectric constant ∈ r2 = 10.2

6 Conclusion Circular microstrip patch antenna is designed with 2.4 GHz frequency and analyzed using the cavity model analysis. Initially, the simulated and measured parameters of circular patch antenna without superstrate as observed that the performance is good. But when keeping the superstrate above the patch, it is observed, all parameters are degraded at the superstrate height H = 0. As increasing the height of the

Performance of Circular Patch Antenna Without and with Varying …

(a) Height (H) = 0mm

73

(b) Height (H) = 21.07mm

Fig. 5 Comparison of simulation and simulation radiation pattern with and without superstrate a H = 0 mm, phi = 0°, b H = 21.07 mm, phi = 90° in E-plane for dielectric constant ∈ r1 = 1.0

Table 3 Antenna parameters are measured and simulated the height of superstrate at optimum height H (mm) Dielectric constant of superstrate (∈r 2 ) Optimum height (H), mm Center frequency (fr), GHz Bandwidth (GHz) Return loss (dB)

1a

2.2

3.2

4.8

10.2

–

21.07

17.46

14.26

9.78

Simulated

2.43

2.43

2.43

2.44

2.44

Measured

2.43

2.43

2.43

2.44

2.44

Simulated

0.05

0.05

0.05

0.05

0.05

Measured

0.05

0.05

0.05

0.05

0.05

Simulated

−22.92

−25.39

−22.83

−21.19

−15.43

Measured

−21.1

−26.75

−22.1

−22.5

−14.2

a

without dielectric superstrate Bold signifies the dielectric constants of the superstrates

superstrate from H = 0 mm to H = 20.07 mm, the performance is improved. The performance characteristics are antenna without superstrate which is shown in Table 1 and antenna with varying the height of the superstrates antenna performance characteristics are improved but at optimum height the performance of antenna is same as antenna without superstrate. The return loss and VSWR are increase as height of the superstrate is increased but gain is decreased. The antenna characteristics are obtained at frequency of 2.4 GHz and is most widely used in wireless and bluetooth applications.

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V. Saidulu

Table 4 Antenna parameters are measured and simulated the height superstrate at optimum height H (mm) above the patch Dielectric Antenna half power beam width (deg.) constant Simulation Measured (∈r 2 ) Azimuth Elevation Azimuth Elevation plane plane plane plane

Antenna gain (dB) Azimuth Elevation Azimuth Elevation plane plane plane plane

1a

79.9

79.9

80.74

80.59

8.75

7.87

8.7

7.7

2.2

79.9

81

73.28

81.48

8.76

8

8.65

8

3.2

69.9

82.8

70.44

83.12

8.46

7.8

8.4

7.7

4.8

67.5

87.30

68.18

87.51

8.25

7.67

8.15

7.60

10.2

78.9

78.9

79.09

79.80

8.8

8.55

8.8

8.50

Simulation

Measured

a

without dielectric superstrate Bold signifies the dielectric constants of the superstrates

Acknowledgements The author expresses his gratitude to Mahatma Gandhi Institute of Technology, Department of ECE, for their encouragement during this work.

References 1. Rajeswari Chatterjee, Dielectric and Dielectric- Loaded Antennas Hardcover (1985). ISBN. 978-0863800344 2. C.A. Balanis, Antenna Theory: Analysis and Design, 4th edn. (2016). ISBN. 978-1-118-642061 3. R.S. Yaduvanshi, G. Varshney, Nanodielectric Resonator Antenna for 5G Applications (2020) 4. I.J. Bahl, P. Bhartia, P. Bhartia, Microstrip Antennas (Artech house, 1980) 5. R. Garg, P. Bhartia, I.J. Bahl, A. lttipiboon, Microstrip Antenna Design Hand Book (1980) 6. V. Saidulu, Design a hybrid feed square patch stacked antenna at 3 GHz. Int. J. Eng. Adv. Technol. (IJEAT) 10(3), 201–205 (2021). ISSN: 2249–8958. https://doi.org/10.35940/ijeat. C2279.0210321. IF: 5.97 (Scopus Indexed) and UGC Approved. 7. V. Saidulu, Design inset fed microstrip patch antenna for L-band applications. Int. J. Innov. Technol. Explor. Eng. (IJITEE) 10(5), 4–7, IF: 4.7 (2021). ISSN: 2278–3075. https://doi.org/ 10.35940/ijitee.D8595.0210421 (Scopus Indexed) and UGC Approved 8. V. Saidulu, Dielectric cover layer thickness effect on circular microstrip antenna parameters. Int. J. Recent Technol. Eng (IJRTE) 9(6), 257–261 IF: 1.0 (2021). ISSN. 2277–3878. https:// doi.org/10.35940/ijrte.F5578.039621, (Scopus Indexed) and UGC Care list Approved 9. V. Saidulu, Experimental analysis of rectangular and circular microstrip patch antenna with superstrate. Recent Trends Electron. Commun. Syst. @ STM J. 7(3), 32–43 (2021). ISSN: 2393–8757 (UGC Care list) 10. P.K. Agrawal, M.C. Bailey, An analysis technique for feed line microstrip antennas. IEEE Trans. Antennas Propagat. AP 25, 756–758 (1977) 11. R.K. Yadav, R. Lal Yadava, Superstrate loaded rectangular microstrip antennas—an overview. (JIIK) 3(2), 19–36 (2011) 12. J. Bahl, S.S. Stuchly, Analysis of microstrip covered with a lossy dielectric. IEEE Trans. MTT 28, 104–109 (1980) 13. I.J. Bahl, P. Bhatiya, S.S. Stuchly, Design of microstrip antenna covered with a dielectric layer. IEEE Trans. AP 30, 314–318 (1982)

A Metasurface-Based Patch Antenna with Enhanced Gain and Frequency for X-Band Applications Nitish Kumar Pagadala, Anudeep Allamsetty, Suman Bulla, Vineetha Mukthineni, and K. Sneha

Abstract The patch antennas are used for low profile applications which are operating at a frequency band of 100 MHz with low gain. But, the patch antennas are every often used for their ease of fabrication and their compactness. The current paper deals with the designing of a patch antenna with improvement in the parameters with attracting compactness. The method followed to improve the gain is allowing the antenna to have the superstrate in the form of metasurface (MS) which is mounted on the FR4 dielectric with unit cells of C-shape centered with a tiny rectangle and surrounded by double inverting L-shapes. All unit cells are arranged in the form of 5 × 5 array which is embraced with a rectangular grid. The conventional patch is made to have some T-shaped slots which are placed on inexpensive FR4 dielectric substrate with some rectangular openings in the ground plane. The gap between the primary substrate and superstrate is filled with Teflon. This antenna operates at a frequency band of 9.4–11.1 GHz with dual-band performance with the same compactness. At 10.4 GHz, the obtained return loss is 20 dB with a fractional bandwidth of 16.82%. The highest gain of 9.7 dBi is observed in the operating band of frequency (10.1 GHz). The designed antenna operates in the X-band region for which it can be used in satellite communication, modern radars, defense applications, and space craft applications. Keywords Low profile applications · Metasurface · Superstrate · Rectangle openings · Dual-band performance

N. K. Pagadala (B) · A. Allamsetty · S. Bulla · V. Mukthineni Department of Electronics and Communication Engineering, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, Andhra Pradesh, India K. Sneha Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, Andhra Pradesh, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_9

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1 Introduction In the recent times, patch antennas are much popular for their compactible design. To achieve compactness in the antenna design, different normalization procedures in microstrip antennas have been investigated such as optimizing the electrical dimension of antenna by reforming its shape, employing resistive or reactive loading, and utilizing high dielectric substrates [1–3]. MS structures are typically made up of sub-wavelength metallic shapes or dielectric etchings in varied layer configurations [1–4]. The metasurface layers are mostly used in the optical domain to manipulate the incoming light. With a controlled phase profile of the metasurface, the light beam can be twisted, focused, etc. All these techniques are used in the microwave imaging process. To enable the beam shaping, the metasurface is executed in proper manner to have a surface impedance by arranging the unit cells in the MS layer [5–8]. It is also investigated that the metasurface can increase the effective refractive index, thereby providing a high gain [9]. In this work, the method preferred is using the high dielectric substrate to achieve high gain, good return loss, and nice fractional bandwidth. The placement of slots in upper region of patch and in ground plane improves the impedance bandwidth. An array of 5 × 5 cells in the metasurface is used for gain improvement by acting as a superstrate configuration. All the proposed work is analyzed in the Ansys HFSS simulator, and results obtained are accurate. The antenna’s simulation results demonstrate a gain of 9.7 dBi at 10.1 GHz, and a fractional bandwidth of 16.82% is observed at 10.4 GHz frequency.

2 Antenna Design In the present work, antenna has a square patches in fractal manner and shorting is provided in the bottom portion of conventional patch. Several small arrays of patch elements are incorporated at the head of the antenna to get more directivity. The rectangular slots provided in the bottom portion are useful for the improvement of impedance bandwidth of the antenna. The ground layer and patch are separated by the 1.6 mm thickness FR4 substrate, which is of low cost and easily available (Fig. 1).

(a)

(b)

(c)

Fig. 1 a 3D view of designed antenna. b Top view of the conventional patch. c Backside view of the conventional patch

A Metasurface-Based Patch Antenna with Enhanced …

Case-1

Case-2

Case-3

77

Case-4

Fig. 2 Design steps of patch with different modifications. a Conventional patch. b Patch with T-shapes. c Rectangular slots in ground plane. d Final patch with tiny square patches

The proposed antenna has 28 × 28 mm2 area with a 6 mm feed line is excited with a external AC source. The entire antenna is made with a compact dimensions to have a high gain and good bandwidth.

2.1 Basic Design of Patch Firstly, a 16 × 16 mm2 patch was made over low-cost FR4 dielectric which of 1.6 mm thickness shown in Case-1 of Fig. 2. The deployed basic antenna has an operating frequency of 8.4 GHz, and the realized gain at the same frequency is 4.07 dBi with an impedance bandwidth of 4.7% at 8.4 GHz frequency.

2.2 Customization of the Patch On the next stage, the designed patch is structured in such a way that it can exhibit the good impedance bandwidth by having a T-shaped four different slots which are illustrated at the Case-2 of Fig. 2. Variations shown in q and n parameters are shown in Fig. 3. It is observed that the gaps t and s are depend on q. At this stage, the antenna exhibits a gain of 5.23 dBi at an operating frequency of 10.7 GHz and has an impedance bandwidth of 4.3%.

2.3 Implementation of Rectangular Shorting in Ground Plane The poor nature of having low bandwidth in patch antennas can be removed by defective ground structure (DGS). In the proposed antenna, two notches in ground plane are planned with the dimensions of 0.4 × 10 mm2 in rectangular shape to provide a DGS concept as shown in the Case-3 of Fig. 2. The variation in the slot

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Fig. 3 S11 (dB) plot by varying q-parameter

(a)

(b)

Fig. 4 S11 (dB) plot showing frequencies by varying, a variation of c when l1 = 10 mm, b variation of l1 when c = 0.4 mm

width (c) and length (l1) is illustrated in Fig. 4a, b in terms of impedance bandwidth. The results express an impedance bandwidth of 11.71% at 10.5 GHz, and obtained gain is 6.64 dBi.

2.4 Placement of Tiny Square Patches In last stage of patch design, there is an incorporation of square patches of 1 × 9 array in the top region of the patch as shown in the Case-4 of Fig. 2. The case study by varying the gap (t g ) and dimensions of the small patch (t s ) is shown in Fig. 5. Implementation of small patch elements in array format produces a fractional bandwidth of 11.4% at 11.4 GHz, and the realized gain is 6.02 dBi at 10.8 GHz frequency.

A Metasurface-Based Patch Antenna with Enhanced …

79

Fig. 5 Graphical illustration of frequency through S11(dB) plot by varying t g and t s

2.5 Designing of Metasurface (MS) Layer The MS is designed by arranging 5 × 5 unit cells in periodic manner. Each unit cell is obtained by forming a square of 0.5 × 0.5 mm2 which is surrounded by a C-shape of length spread equally as 2.1 mm, and the separation between two shapes is 0.5 mm, the entire structure is again surrounded by double L-shapes which is of length (ls ) = 4 mm and width (ws ) = 2.5 mm in order to achieve maximum gain in the outer cross section as illustrated in Fig. 6a, b. The whole area is again embraced by a rectangular shape in order to achieve the maximum current flow in the antenna. The fabricated antenna is shown in Fig. 6c, d. Each unit cell is exited through HFSS by providing periodic boundary conditions. Now, a gain of 7.9 dBi is obtained at 10.3 GHz and an impedance bandwidth of 7.61% is achieved. In the further considerations, the air spacing is filled with Teflon and a 9.7 dBi gain is achieved at 10.1 GHz and an impedance bandwidth of 16.82% is obtained at 10.4 GHz.

(a)

(b)

(c)

(d)

Fig. 6 a Unit cell in MS layer, b unit cells in array with a rectangular separation. c Top view of the fabricated antenna. d Bottom view of the fabricated antenna

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3 Results The proposed antenna from Fig. 1 operates at the frequency region (9.4–11.15) GHz by having an impedance bandwidth of 16.82% at 10.44 GHz frequency. A maximum return loss of 20 dB is obtained at 10.44 GHz as illustrated graphically in the Fig. 7a, and the measured result is shown in Fig. 7b. On further analysis, it is investigated that the allotment of rectangular slot in inner region of C-shape and double L-shapes improves gain. The gain 3D plots are shown in Fig. 8a, b with corresponding frequencies. The overall gain is good across the frequency band, and high gain is observed at 9.7 dBi at 10.1 GHz. The surface current distributions are shown for different surfaces in Fig. 9. Surface currents are found to be much higher in the MS layer and around antenna apertures. Because there are more unit cells in the MS layer, there is a longer current distribution path. The proposed antenna’s radiation characteristics at two different frequencies are shown in Fig. 10a, b. It is observed that at 10.3 GHz, the radiation pattern is cross polarized and at 10.1 GHz frequency is quietly co-polarized.

Fig. 7 a Graphical illustration of frequency through S11 (dB) plot of the designed antenna. b Measured return loss plot

(a)

(b)

Fig. 8 3D polar plots of proposed antenna at a 10.3 GHz and at b 10.1 GHz

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(a)

81

(b)

(c)

Fig. 9 Surface current distributions at 10.4 GHz of a top view of conventional patch, b rear view of patch, c top view of MS layer

(a)

(b)

Fig. 10 Radiation pattern at a 10.1 GHz and at b 10.3 GHz

4 Conclusion The proposed antenna with metasurface as superstrate configuration has been analyzed by changing unit cells in periodic manner. The gain of the antenna is improved by providing 5 × 5 array elements in MS layer which are embraced with a rectangular grid which improves the current flow in the antenna. The fractal patch and defected ground structure (DGS) enhance the impedance bandwidth. The designed antenna has an operating frequency of 10.4 GHz, and a fractional bandwidth of 16.82% and a maximum gain of 9.7 dBi is occurred at the 10.1 GHz frequency. In the far-field region, the antenna exhibits the unidirectional pattern which illustrates that the antenna is suitable to work for X-band applications like satellite communication, modern radars, defense applications and space craft applications, and so on.

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References 1. J. Wang, H. Wong, Z. Ji, Y. Wu, Broadband CPW-Fed aperture coupled metasurface antenna. IEEE Antennas Wirel. Propag. Lett. 18(3), 517–520 (2019) 2. D. Samantaray, S. Bhattacharyya, A gain-enhanced slotted patch antenna using metasurface as superstrate configuration. IEEE Trans. Anten. Propag. 1–1 (2020). https://doi.org/10.1109/TAP. 2020.2990280 3. G. Feng, L. Chen, X. Xue, X. Shi, Broadband surface-wave antenna with a novel non-uniform tapered meta-surface. IEEE Antennas Wirel. Propag. Lett. 1–1 (2017). https://doi.org/10.1109/ LAWP.2017.2751621. 4. K. Konstantinidis, A.P. Feresidis, P.S. Hall, Broadband subwavelength profile high-gain antennas based on multi-layer metasurfaces. IEEE Trans. Antennas Propag. 63(1), 423–427 (2015) 5. C. Caloz, T. Itoh, Electromagnetic Metamaterials: Transmission Line Theory and Microwave Applications (Wiley, 2006) 6. H. Li, G. Wang, X. Gao, J. Liang, H. Hou, A novel metasurface for dual-mode and dual-band flat high-gain antenna application. IEEE Trans. Antennas Propag. 66, 3706–3711 (2018) 7. N.S.S. Syed, W. Liu, Z.N. Chen, Wide bandwidth and enhanced gain of a low-profile dipole antenna achieved by integrated suspended metasurface. IEEE Trans. Antennas Propag. 66, 1540– 1544 (2018) 8. R. Quarfoth, D. Sievenpiper, Artificial tensor impedance surface waveguides. IEEE Trans. Antennas Propag. 61, 3597–3606 (2013) 9. W. Liu et al., Miniaturized wideband metasurface antennas. IEEE Trans. Antennas Propag. 65(12), 7345–7349 (2017)

Design and Simulation of Slot Antenna for Energy Harvesting Ashima Sharma, Shrishti Singh, Paurush Dhawan, and Dinesh Sharma

Abstract This study presents two slot antennas with basic design having small footprint (15 mm × 17 mm × 1 mm) which is designed for energy harvesting applications. The flexible material used for substrate is a cost-efficient polyester material with relative permittivity (εr ) of 3.2 and having dielectric loss tangent, tan δ = 0.003. Patch and ground are fabricated with a finite conductive material. To achieve a comparative study, two antennas are designed, one with a defected ground structure and another without DGS. It offers radiation characteristics with −22 and − 26.5 dB return loss (with and without DGS) and radiates in omnidirectional patterns. The antenna radiates at three bands of frequencies 5.1, 6.1, and 7.5 GHz. Further at 5.1 GHz, a gain of 2.74 dB without DGS and 1.2 dB with DGS was obtained. The antennas consist of slot antenna radiations fed through a micro-strip line. Ansys HFSS software is used to design and simulate the proposed antenna. Keywords Energy harvesting · Slot antenna · High frequency structure simulator (HFSS) · Defected ground structure (DGS)

1 Introduction Utilization of the sustainable power sources to control electronic devices is not a recent development. As the need for electricity has grown in recent years, alternative energy sources have become increasingly important. Energy harvesting, or gathering energy from the environment to generate electricity, has grown in popularity as a way to provide limitless energy to the lifetime of the electronic equipment. We know that “the energy can neither be created nor be destroyed but can be transform from one form to another.” So, the whole idea behind energy harvesting is using energy transmitted by various wireless networks to directly charge low-voltage electrical devices [1]. The antenna should have a high gain, high directivity, omnidirectional radiation pattern, big bandwidth, and high efficiency for energy harvesting systems. A. Sharma · S. Singh (B) · P. Dhawan · D. Sharma ECE Department, CCET (Degree Wing), Chandigarh 160019, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_10

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To achieve the requirements, two antenna types have been developed in this paper that can gather all the feasible energy which can effectively send that received energy to the voltage doubler. Many researchers have been harvesting from several frequency bands using high gain antennas utilizing multiband antennas. In the paper, two models of slot antennas with and without defected ground structure that can meet the demands for energy harvesting are presented. Defected ground structure refers to carved flaws or slots on the ground plane of micro-strip antenna circuits. DGS can be utilized in the area of micro-strip antennas not only to enhance the bandwidth and gain of the micro-strip antenna by increasing the efficiency of the antenna model along with the radiation characteristics, but also to cut off the higher mode vibrations, mutual coupling involving neighboring elements, and cross-polarization. The slot microstrip antenna uses the DGS to increase the bandwidth up to 300 MHz. The energy harvested around us can be used to power a rectifying antenna, also known as a rectenna. Rectenna or often called rectifying antenna is employed to change the surrounding electromagnetic power into electrical power. Careful planning is highly required to harvest available signal energy and further converting it to DC power for practical purposes. The most important component required for this procedure is an effective antenna design that maximizes the reception of ambient energy signals. Keeping these facts in focus, two antenna designs have been proposed, and their efficiency along with other parameters is compared. This research paper proposes two compact 5.1 GHz antennas for energy scavenging or energy harvesting purposes. The proposed antennas are the crucial elements because they are in charge of gathering energy from neighboring radiating sources. Because antenna properties such as gain, radiation pattern, polarization, and impedance bandwidth can affect the quantity of harvestable energy, an appropriate antenna design is critical. The HFSS software environment is accustomed to design and simulate the antenna with and without DGS. For 5.1 GHz, a slot antenna with micro-strip line and polyester fabrication is proposed. There is less interference while using the 5.1 GHz band. Higher frequency, on the other hand, results in a smaller antenna, which is inefficient for energy scavenging. There is an impact on the flow of current on the ground plane structure of the antenna due to the slots present on it, which can further affect the transmission line’s characteristics. These characteristics can be altered by adding some slot parameters to the same line parameters including resistance, inductance, and capacitance. Alternatively, adding slot parameters to each imperfection on the ground plane under the lumped port affects the effective capacitance as well as the inductance of the micro-strip line. Additionally, the interaction between slot elements can improve forward radiation [2]. The antenna is designed to radiate at a frequency of 5.1 GHz and achieves a gain of 2.74 dB without defected ground structure whereas 1.2 dB with defected ground structure. The principal design procedures through examination of both the structure’s impedance, radiation properties, and other parameters. A comparative study has been carried out on how efficient the antenna is when constructed with and without the defected ground substrate.

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2 Design of Antenna A novel slot antenna to maximize the amount of energy that can be harvested is designed and simulated. For efficient results, two models have been proposed where one uses defected ground structure and the other does not.

2.1 Antenna Without DGS Figure 1a displays the proposed antenna’s geometry. The length, breadth, and height (L, W, H) of the antenna are measured in mm. The antenna is fed through a microstrip feed line using lumped port. The antenna’s size is proportional to the operating frequency which adds to its importance and makes it a vital component. A costefficient polyester substrate with a relative permittivity (εr ) of 3.2, a thickness of 1 mm, and a dielectric loss tangent (tan δ) of 0.003 is used upon which the antenna is mounted. On the top of the substrate are eight rectangular slots and three circular slots that make up the radiation element. A ground plane is constructed opposite to the patch. Also, resonant frequency for a micro-strip patch antenna is given as: f =

C √ 2 ∗ L εr

(1)

where C gives the light’s speed; L describes the antenna’s patch length, and εr gives the grounded microwave substrate’s relative permittivity. The substrate’s dielectric constant has a major influence on the antenna’s overall performance, as well as the length, breadth, and resonant frequency; thus, a suitable polyester material is chosen. Materials having a finite conductivity of the value of 5.81 × 106 S/m can be used to surround the slots and form the ground. The antenna has been designed and optimized to absorb energy from the surrounding environment in the Wi-Fi band of 5 GHz radio frequency spectrum. The antenna covers an area of 15 × 17 mm2 . The most vital thing

Fig. 1 a Antenna geometry without DGS. b Antenna parameters. c Antenna using HFSS (without DGS)

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Table 1 Antenna specifications (in mm) L1

15

L15

3.5

L8

6

W5

0.8

L2

7.5

L16

6.9

L9

1.6

W6

0.5

L3

7.6

L17

2.4

L10

0.8

W7

4

L4

5.3

W1

17

L11

1.1

W8

2

H1

1

W2

0.6

L12

7

W9

0.4

L6

2.1

W3

0.8

L13

9

R1 = R2 = R3

0.5

L7

8.3

W4

1.7

L14

4.5

while designing the antenna was the optimal choice of the relative permittivity (εr ) of the substrate which is polyester in our proposed scenario, antenna size, and ground plane. The suggested antenna features and performance are estimated and augmented using the Ansys HFSS software environment. Slot antennas are omnidirectional microwave antennas that are widely used. In comparison to micro-strip antenna, this antenna provides omnidirectional gain, a larger bandwidth, smaller dispersion, and lower radiation loss. The polyester substrate comprises a radiating patch (with slots) and a ground plane on either side. On the substrate, the radiating patch and feed lines are photoetched. The main radiator primarily comprises slots 1 and 3. The lengths of both these slots are measured to be 26.4 mm. To make the antenna more compact, slot 3 is twisted. As the total length of slots 1 and 3 is decreased, the resonant frequency changes to some higher frequency. Breadth of the straight vertical part of the third slot is determined with the help of micro-strip feed line (W9). When the slots width W3 is adjusted, the resonance frequency moves, but the bandwidth remains relatively the same [2]. Since bandwidth is inversely linked to the range of smallest circle which can fully encompasses the antenna. At a distance of L11, slot 4 is constructed, which is identical to slot 3. Above slot 3, three circular slots with a radius of 0.5 mm each are formed. The second slot is shaped like a split ring. Slots 5 and 6 are built within the space bounded by slot 2 for efficient results. Also, Q-factor is relative to the radius of the smallest circle that altogether encompasses antenna; the adjustment of W3 has no impact on the quality factor. Hence, any change in W3 will only lead to a change in frequency. The radiation element functions similarly as two antennas with similar radiation properties. As a result, the total radiation is enhanced. It is observable that backward radiation increases with an increase in the forward radiation, and the shift is appropriate when compared to the anticipated direction improvement. Overall, the antenna without DGS gives an efficiency of 52% (Table 1).

2.2 Antenna with DGS The second model proposed is constructed on defected ground structure. The technique of implanting some geometrical and compact slots over the surface of ground plane in microwave antenna structures as well as circuit refer to as defected ground

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Fig. 2 a Geometry of antenna with DGS. b Ground slot parameters. c Slot antenna using HFSS (with DGS)

Table 2 DGS parameter (in mm)

L1

6

L2

6

W1

1

W2

0.975

W3

2.025

L4

17

W4

15

structure (DGS). In simpler words, it is a defect or slot on the substrate’s ground plane that is purposely generated. DGS proves beneficial as it aids in enhancing the gain and bandwidth of the antenna. Increasing inductance while decreasing the effective capacitance, suppressing coupling between neighboring elements, cross-polarization to improve the impedance of the antenna and its radiation characteristics is the underlining feature of this technique. The antenna here is designed with DGS as shown in Fig. 2a. The antenna is designed similarly as described for the above model excluding the three circular slots. The striking difference between them is ground plane and has been created with some defects or slots to reduce the size to obtain better efficiency. The ground plane here is constructed as shown in Fig. 2b. The use of DGS leads to an increased efficiency which is up to 70% (Table 2).

3 Simulation Results We have used electromagnetic simulation software Ansys HFSS to focus on design development and simulation characterization of slot antenna with the goal of energy harvesting antenna, at the desired frequency of 5.1 GHz. There are two proposed slot antennas for the purpose served—one having a defected ground structure and the other without defected ground structure. The return loss (S1, 1) of the simulated antenna at 5.1 GHz, without and with DGS, is shown in Fig. 3a, b. The return loss of

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Fig. 3 a Antenna without DGS S parameter plot. b Antenna with DGS S parameter plot

−26.5 and −22 dB is achieved, respectively. The VSWR can be then observed from the graph plotted in Fig. 4a, b, which is 1.09 and 1.17 for antenna without and with DGS, respectively. The gain of antenna decreases with the increase in dielectric constants εr (s), while it increases with increase in thickness of the substrate, which is 1 mm here [3], and when flexible conductive fabric is used instead of copper, there is a greater loss [4]. The gain of the slot antenna for energy harvesting is 2.74 dB without a defected ground substrate (DGS) and 1.2 dB with a DGS, as shown in Figs. 5a, b.

Fig. 4 a Antenna without DGS VSWR plot. b Antenna with DGS VSWR plot

Fig. 5 a Gain of slot antenna (without DGS). b Gain of slot antenna (with DGS)

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Figure 6a, b illustrate the radiation patterns which are the distributions of energy emitted in space for both antennas. Although this is not universal, but the beam width within the elevation and azimuth planes level are comparable that can result in an objectively circular beam shape. The beam widths might be adjusted to harvest energy using the antenna with a greater or a lower gain, which depends up on the antenna requirements [5]. A high gain of the antenna can be additionally needed to provide maximum feasible wireless energy, particularly when the source’s position is acknowledged [6]. Below shown are the tabulated results of the proposed slot antennas (Table 3).

Fig. 6 a Radiation pattern (without DGS) of slot antenna. b Radiation pattern (with DGS) of slot antenna

Table 3 Results after simulation of the proposed antennas

Parameter

Proposed antenna Without DGS

With DGS

Return loss

−26.5 db

−22 db

VSWR

1.09

1.17

Gain

2.74 db

1.2 db

Bandwidth

Less than 100 MHz

300 MHz

No. of bands

3

1

Efficiency

52%

70%

E-field

26,620 v/m

21,808 V/m

H-field

50 A/m

100 A/m

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4 Conclusion Two slot antennas, one with a defected ground structure and the other without DGS, are designed in the paper. The simulated results attained specifies that proposed antennas give decent performance in the required Wi-Fi frequency range of 5 GHz. The obtained efficiency of the slot antenna without DGS is 52% which is less than that obtained with DGS that is 70%. As a result, this paper provides the comparative study for design and simulation of both proposed slot antennas for energy harvesting. For energy harvesting over the 5G Hz Wi-Fi band, both antennas with and without DGS can be used.

References 1. C.R. Valenta, G.D. Durgin, Harvesting wireless power: survey of energy-harvester conversion efficiency in far-field, wireless power transfer systems. IEEE Microwave Mag. 15(4), 108–120 (2014) 2. Y.J. Li, Z.Y. Lu, L.S. Yang, CPW-fed slot antenna for medical wearable applications. IEEE Access 7, 42107–42112 (2019) 3. S.D. Gupta, A. Singh, Design and analysis of multi dielectric layer micro-strip antenna with varying superstrate layer characteristics. Int. J. Adv. Eng. Technol. 3(1), 55–68 (2012) 4. D. Yamanaka, M. Takahashi, 5.2 GHz Band textile antenna for biological information monitoring. IEICE Trans. Commun. J101(7), 584–591 (2018) 5. B.S. Taha, H.M. Marhoon, A.A. Naser, Simulating of RF energy harvesting micro-strip patch antenna over 2.45 GHZ. Int. J. Eng. Technol. 7(4), 5484–5488 (2018) 6. A. Bakkali, J. Pelegri Sebastia, T. Sogorb, V. Llario, A. Bou Escriva, A dual-band antenna for RF energy harvesting systems in wireless sensor networks. J. Sens. 8 (2016)

Design of Planar Slot Antenna Based on SIW Technology for Wireless LAN Applications Lokeshwar Bollavathi, Ravindranadh Jammalamadugu, and Murali Krishna Atmakuri

Abstract A cavity-backed square ring-slot antenna is presented in this article, and the intended design depends on substrate-integrated waveguide (SIW) technology. The proposed antenna consists of dielectric substrate, SIW cavity, square ring slot, ground plane, and micro-strip feeding line. The antenna is designed for 5.5 GHz frequency, extensively used for WLAN applications. The simulation results of intended antenna have a fractional bandwidth of 5.8%. The intended antenna has a return loss and gain of −22.94 dB and 6.68 dBi, respectively, at 5.52 GHz and −12.95 dB, 7.34 dBi, respectively, at 5.74 GHz. Results show that the designed antenna has promising characteristics like high gain, wider bandwidth, unidirectional radiation pattern, low-profile, easy integration, and easy fabrication for WLAN applications. Keywords Cavity-backed antenna · SIW · Square ring slot · WLAN

1 Introduction Microstrip line slot antenna turns up to be quite useful in numerous fields of application due to its fraternal traits like easy integration with other planar components, savings in cost, lightweight and low-profile [1]. Researchers in cavity-backed slot antenna (CBSA) are also investigated substantially to enhance the directional properties of slot antenna. Anyhow, conventional CBSAs are troublesome to organize with the curved surfaces, and also, they might complicate the antenna structure while fabricating [2, 3]. The substrate-integrated waveguide (SIW) technology received a good attention to realize non-planar rectangular waveguide in planar form. The properties of SIW incorporate high power capacity, easy integration and fabrication with planar technology, low-loss and low-profile and convenient fabrication [4, 5]. L. Bollavathi (B) · R. Jammalamadugu · M. K. Atmakuri Department of ECE, R.V.R. & J.C. College of Engineering, Guntur, India M. K. Atmakuri e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_11

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First planar slot antenna backed by SIW cavity described in [6], where the antenna resonates at 10 GHz. In [7], properties of the cavity TE110 mode are exploited to obtain the smallest possible cavity at 2.4 GHz, using SIW technique. SIW ring slot antenna is reported in [8] for single frequency response at 5.8 GHz with an impedance bandwidth of 2.6%. Single-band response was obtained by using a square ring-shaped slot antenna for X-band applications in [9]. In [10], a circular-shaped ring slot is incised on a SIW cavity to attain single frequency response. In [11], a circular ring slot antenna in which all components of the antenna are manufactured from textile materials for wireless body area networks (WBAN) applications for single frequency resonance. Bandwidth is enhanced by using bilateral slots [12], by balanced metallized vias [13], by perturbing TE210 mode [14]. Dualband response is achieved by bilateral slots in [15]. A size reduction SIW antenna is reported in [16] for WBAN applications. In this paper, the proposed antenna utilizes unilateral slot, that is, square ring slot for designing. This unilateral slot in SIW cavity is useful to generate a double resonance in C band.

2 Antenna Geometry The design of the intended antenna involves many phases. The first phase of the designing involves the construction of SIW cavity as depicted in Fig. 1a, and the second phase of the construction involves the insertion of slot on the cavity as shown in Fig. 1b. A square ring shaped slot is inserted onto the cavity. The representation of the intended dual-resonance antenna is shown in Fig. 2. Dielectric substrate with permittivity 2.2 is employed to configure the antenna for generating the required frequency. The vertical walls of the SIW cavity were orderly organized by arrays of metallic vias along the edges of the substrate. Perfect electric conductor is used for ground, cavity and patch. In order to reduce the leakage losses between the neighboring vias, diameter d and pitch p are chosen in such a way that they must meet the guidelines (s/d ≤ 2 and d/λ0 ≤ 0.1). A square ring slot is placed on the cavity with a distance ds from the vertical wall of the cavity. The operating frequency in which dimensions of the SIW cavity can be calculated by the equation is given: 1 fr (TE210 ) = √ 2 μ0 ε0 εr



m L eff

2

 +

n Weff

2 +

 p 2 h

(1)

L eff = L − d 2 /0.95 ∗ s

(2)

Weff = W − d 2 /0.95 ∗ s

(3)

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Fig. 1 Evolution phases of the designed antenna, a SIW cavity, b dual resonance antenna

where d—diameter of the cylinder, and s—spacing between the neighboring vias (pitch).

3 Principle of Operation The intended antenna is designed for 5.5 GHz using Rogers RT/duroid 5880 dielectric material of thickness 1.6 mm with the use of Ansys HFSS. The reflection coefficient (S 11 ) of the antenna is shown in Fig. 3. The bandwidth for the scattering parameter (S 11 ) under −10 dB is 5.8%. The parametric study is done to understand the antenna’s behavior and determine the consequences of antenna configurational parameters on the impedance matching. Throughout this inquiry, a single variable is altered and the remaining parameters are maintained as fixed. On varying the outer square length of the ring slot, the reflection coefficient is interpreted as shown in Fig. 4a. The outer square length has very high impact on resonant frequency of the antenna. On increasing the length of the slot, there is an increase in operating frequency. As it is a square slot, both length and width will equally influence operating frequency. Figure 4b conveys the effect of increasing inner length of the square slot on operating frequency. We notice that on increasing values of this quantity, impedance matching is not happened. This parameter has

94 Fig. 2 Geometry of the proposed dual-resonance antenna (W sub = 25, L sub = 50, W = 25, L = 42, L 1 = 21, L 2 = 16, 3, d s = 4, L m = 5, gm = 1.3, W ms = 3.4, L ms = 6.5, d = 1, s = 1.5, h = 1.57) (unit mm)

Fig. 3 Response of reflection coefficient of the proposed antenna

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Fig. 4 Effect of chosen antenna specifications on scattering coefficient: a Ring-slot length L 1 , b Ring-slot length L 2 , and c distance ds between square ring slot and sidewall

great impact on operating frequency of the antenna. Figure 4c exhibits the effect of distance ‘d s ’ between the square ring slot and SIW cavity walls. On decreasing its value, the operating frequency also decreases. Thus, we can conclude that this have directly proportional relation and operating frequency has effect due to inner square slot length, outer square slot length, and also distance between slot and cavity. Using

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the conventional sequence of values of all specifications, the aerial can be adjusted for a preferred frequency.

4 Results The simulated results of the reflection coefficient (S 11 ) confirms the double resonance as depicted in Fig. 5a. The simulated reflection coefficient evident dual resonance at 5.52 and 5.74 GHz. The return losses are −22.94 and −12.95 dB, at 5.52 and 5.74 GHz, respectively. The voltage standing wave ratio of the proposed antenna is as displayed in Fig. 5b. It is less than 2 within the operating band. The 3D polar plots of designed antenna at 5.52 and 5.74 GHz are as shown in Fig. 5c, d. The simulated gain of the proposed antenna is above 6 dBi in the whole operating band.

Fig. 5 a S 11 of the intended antenna, b VSWR of the intended antenna, c 3D polar plot at 5.52 GHz, and d 5.74 GHz

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5 Conclusion In this paper, a dual-resonance cavity-backed structure with unilateral slot using SIW technology is presented, where square ring slot etched on the top of the cavity is used to perturb the accepted modes of the cavity. The results of the proposed antenna confirms that a dual resonance with an impedance bandwidth of 5.8%. The aerial exhibits good performance with a gain of 6.68 dBi at 5.52 GHz and 7.34 dBi at 5.74 GHz in the operating band. In conclusion, with improved bandwidth and sufficient gain, the antenna is suitable for C band applications.

References 1. Y. Yoshimura, A microstrip slot antenna. IEEE Trans. Microw. Theory Tech. 20(11), 760–762 (1972) 2. A. Arlon, Flush mounted rectangular cavity slot antennas. IEEE Trans. Antennas Propag. 15(3), 342–351 (1967) 3. V. Andrea, B.G. Guido, Microstrip-fed slot antennas backed by a very thin cavity. Microw. Opt. Technol. Lett. 49(1), 247–250 (2006) 4. B. Maurizio, G. Apostolos, W. Ke, Review of substrate integrated waveguide circuits and antennas. Microw. Antennas Propag. 5(8), 909–920 (2011) 5. D. Dominic, W. Ke, Design consideration and performance analysis of substrate integrated waveguide components, in 32nd European microwave conference, pp. 1–4 (2002) 6. G.Q. Luo, Z.F. Hu et al., Planar slot antenna backed by substrate integrated waveguide cavity. IEEE Antenna Wirel. Propag. Lett. 7, 236–239 (2008) 7. C.B. Juan, A.F.P. Humberto et al., Planar substrate integrated waveguide cavity backed antenna. IEEE Antennas Wirel. Propag. Lett. 8, 1139–1142 (2009) 8. V. Petr, L. Jaroslav, Circularly polarized rectangular ring-slot antenna with chamfered corners for off-body communication at 5.8 GHz ISM band. Radioengineering 26(1), 85–90 (2017) 9. L. Jaroslav, Circularly polarized SIW square ring-slot antenna for X-band applications. Microw. Opt. Technol. Lett. 54(11), 2590–2594 (2012) 10. J. Lacik, T. Mikulasek, Z. Raida, T. Urbanec, Substrate-integrated waveguide monopolar ringslot antenna. Microw. Opt. Technol. Lett. 56, 1865–1869 (2014) 11. G.Y. Hong, J. Tak, J. Choi, An all-textile SIW cavity-backed circular ring-slot antenna for WBAN applications. IEEE Antennas Wirel. Propag. Lett. 15, 1995–1999 (2016) 12. B. Lokeshwar, D. Venkatasekhar, A. Sudhakar, Wideband low-profile SIW cavity-backed antenna bilateral slots antenna for X-band application. Prog. In Electromagn. Res. M. 97 (2020) 13. L. Bollavathi, V. Dorai, S. Alapati, Wideband planar substrate integrated waveguide cavitybacked amended dumbbell-shaped slot antenna. AEU-Int. J. Elect. Commun. Eng. 127, 153489 (2020) 14. B. Lokeshwar, D. Venkatasekhar, A. Sudhakar, Bandwidth-enhanced of SIW cavity-backed slot antenna by perturbing TE210 cavity mode. Biosci. Biotech. Res. Commun. 13(14), 320–324 (2020) 15. B. Lokeshwar, D. Venkatasekhar, A. Sudhakar, Dual-band low profile SIW cavity-backed antenna by using bilateral slots. Prog. Electromagn. Res. C., 100, 263–273 (2020) 16. D. Chaturvedi, S. Raghavan, Circular quarter-mode SIW antenna for WBAN application. IETE J. Res. 1–7 (2017)

Embedded Systems

A Novel Approach for the Measurement of pH of Body Fluids at Various Temperatures Using Compensation Technique M. Sameera Fathimal and S. Jothiraj

Abstract The pH is the negative base 10 logarithm of the hydrogen ion concentration. The hydrogen ion (H+ ) activity is measured and an electrical potential is produced by the pH electrode. The pH electrode is based on the principle of voltage that is developed as two liquids having different pH at either sides of a thin glass membrane is contacted. The most chemical and biological reactions are governed by the hydrogen ion activity. The pH of the body fluids in human plays an important role. Either increase or decrease in its value leads to many diseases such as respiratory alkalosis, brain injury, lung cancer, and kidney stone. The practically used electrodes will not be ideal and has true output that deviates from zero mV. The variation is called the electrode offset error. Also, the pH of any solution will rely upon temperature. Therefore, a pH electrode and temperature compensator have been designed. The electrode output varies linearly to the changes in pH of the solution. pH-based applications require temperature compensation to provide accurate pH values. Keywords pH · Temperature · Body fluids · Compensation technique

1 Introduction Many natural processes are highly dependent on the pH value. The pH plays an important role in many fields such as in food industry, dye industry, and pharmaceutical. The pH of the biological fluids in the living organism is maintained within a narrow pH range. The pH values of the fluids range between 0 and 14. The fluid is considered acidic, if it contains more H+ ions and alkaline, if it contains more OH− ions. The reason for the pH value being between 0 and 14 is the molar (M) of the solution or fluid used for measurements. Many applications do not have a molar M. Sameera Fathimal Department of Electronics and Communication Engineering, Anna University, College of Engineering Guindy, Chennai, Tamilnadu, India S. Jothiraj (B) Department of Biomedical Engineering, Rajalakshmi Engineering College, Chennai, Tamilnadu, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_12

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Fig. 1 pH Scale

concentration that exceeds 1 M. Hence, the pH with 1 M of H+ is 0 and pH with 1 M of OH− ions is 14. The pH scale varies from 0 to 14 (see Fig. 1). Water has a pH of 7 which is neutral [1]. The pH of urine or saliva or blood is measured for disease diagnosis. Human blood pH is slightly alkaline with the pH of 7.4. A variation from this range indicates a disease. An acidic pH results from food intake, stress, immune responses, and toxicity [2]. The body possess compensation mechanisms that restores the normal pH. Acid accumulation occurs if the food consumed does not contain appropriate alkaline minerals [3, 4]. The acidity will lead to decrease in the capacity of the body to absorb minerals, ability of cell to yield energy, capacity to restore the injured cells, and may result in tumor cell formation. Even a slight acidic pH of 6.9 could lead to unconsciousness and mortality. The pH value is provided by the equation, pH = −log[H+ ]. It is appropriate to mention at this point that any change in normal pH of body fluids results in disease condition. A theoretical electrode at standard room temperature of 25 °C will provide an output of zero (0 mV) when it is positioned in a solution that has a pH of 7. Also, electrodes employed practically will not be ideal and that will not show 0 mV [5]. This alteration is termed as electrodes off set error. At room temperature, the sensitivity of a theoretical electrode is 59.16 mV per pH unit. Deviations from this value is expressed as the electrodes span error. The system should be calibrated if high system accuracy is required. The design of electronic circuit for pH electrode which eliminates the standard errors such as electrode offset error and electrode span error is required. Also, temperature is being compensated by the technique of ATC. The design challenges are dealt with level shifting and temperature compensation approaches, and it should be interfaced with LCD for reading the values. The circuit is compact and has the flexibility according to the user requirements. The pH values obtained by using the designed circuit and are interpolated using MATLAB.

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2 Block Diagram and Methodology The pH of the solution depends on the temperature. The National LM35 precision center temperature sensor in the circuit is used to measure the temperature of the solution and adjust the sensitivity change with temperature. This allows accurate temperature-compensated pH measurements. The modern pH electrode for a greater convenience consists of both glass electrode and reference electrode. pH is determined by measuring the variation in the voltage in both the electrodes. The electrode terminal has a thin film of a certain sort which can exchange ions. This element determines the concentration of hydrogen ions in the test solution. The potential of the reference electrode is constant and is created by the internal elements of the reference electrode. LM35 senses the temperature produced at the electrode and values are digitally displayed. The schematic and circuit diagram of the proposed system consists of temperature sensor, pH electrode, ADC, microcontroller, and LCD (see Fig. 2). Analog to digital conversion of measured values is performed. AD0809 which is used for acquiring data is CMOS component inbuilt with an ADC (8-bit), multiplexer and control logic. The encrypted inputs of the integrated addressable multiplexer and the configured TTL TRI status outputs provide a simple interface to the microprocessor. It is possible to interface AT89S52 with a microcontroller directly. The liquid crystal display is used as an electronic visual display of the values obtained (see Fig. 3). The pH values of the chemical compounds have been listed (see Table 1). The chemical substances having pH from 0 to 14 has been listed. Additionally, the biological fluids having different pH values have been tabulated (see Table 2). The human body consists of two-thirds fluid [6]. Potassium, phosphate, and magnesium ions are present in the intracellular fluid and oxygen, glucose, sodium, chloride, and carbon dioxide in the extracellular fluid [7]. Homeostasis is maintained by the acid–base balance in the body. A change in this balance may lead to severe complications and disease [8, 9]. Renal organs plays a major role in maintaining acid–base balance [10]. Temperature Sensor

Analog to Digital Converter

pH Electrode

Liquid Crystal Display

Fig. 2 Block diagram of the proposed system

ATMEL AT89S52

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Fig. 3 Circuit diagram for proposed system Table 1 pH values of chemical compounds Color

pH

Compound

0

0— Hydrochloric Acid (HCl)

1

1 – Sulfuric Acid (H2SO4)

2

2 – Citric Acid (C6H8O7), 2.2 – Acetic Acid (CH3COOH)

3

3 – Soda

4

4.5 – Lycopene (C40H56)

5

5 – Coffee Extract (C25H28N6O7)

6

6.1 to 6.4 – Butter (C15H8C18O2)

7

7 – Pure Water (H2O)

8

8.3 – Sodium Bicarbonate (NaHCO3)

9

9 – Sodium Fluoride (NaF)

10

10.3 – Magnesium Hydroxide (Mg (OH)2)

11

11 – Ammonia (NH3)

12

12.4 – Calcium Hydroxide (CaOH2)

13

13 – Lye 14 – Sodium Hydroxide (NaOH)

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Table 2 pH values of biofluid Color

pH

Biofluid

1

1 – Gastric Acid

4

4.5 – Lysosomes, 4.7 – Human Skin

5

5.5 – Chromaffin Cells Granules

6

6 - Urine

7

7.2 – Cytosol, 7.3 – Cerebrospinal, 7.4 – Human Blood & Tears

8

8.1 – Pancreas Secretions

3 Results and Discussion The biological fluids such as ophthalmic, pulmonary, vaginal, parenteral, buccal and sublingual, and oral fluids were simulated, and their applications were explored with dissolution test [11]. The electrically connected pH electrode was tested by titration with NaOH versus HCl at varying temperature of 27, 40, and 50 °C. The pH values were obtained from the circuit, where pH = V out + 512 mV. The pH versus V out (mV) obtained at temperatures of 27, 40, and 50 °C (see Fig. 4). Also, the values taken were interpolated in MATLAB and tabulated for every temperature considered (see Table 3). The changes in ions dissociation constants and glass electrode resistance are the causes for the variation in temperature. This affects the measured pH. LM35 senses the solution at any temperature, and the driver IC heats and cools the medium to give the required output. The crystal oscillators send the program to microcontroller and generate the clock pulse the ATMEL microcontroller performs analog counter parts and transmits the information to ADC. The output is displayed using 2 * 16 LCD. The voltage obtained from the circuit is equivalent to pH values at various temperatures, and the graph of the above values is almost linear. Thus, perfect linearity is achieved when precise pH meter probe is used. Fig. 4 pH versus V out (mV) at different temperatures

106 Table 3 pH versus V out (mV) at different temperatures

M. Sameera Fathimal and S. Jothiraj pH values

V out (mV) 27 °C

40 °C

50 °C

2

−192

−237

−132

3

−159

−130

−206

4

−155

−119

−134

5

−122

−92

−72

6

59

−16

−5

7

12

32

64

8

73

67

92

9

132

128

135

10

161

151

172

11

208

169

234

12

270

252

311

13

310

287

401

4 Conclusion pH plays vital role in our life and in all fields, so better awareness of it is to be accounted. The advanced techniques such as measuring pH of pleural fluid, wireless transmission of pH values using pH sensor for agriculture purpose and many more define the importance pH in our life [12]. The pH electrode which eliminates the standard errors such as electrode offset error and electrode span error has been designed. An electronic circuit for obtaining absolute pH values was designed and the basic errors and maintain temperature was eliminated. Also, device is built to display the exact pH value during the measurement at any temperature and at designed at low cost and meet a wide range of end-user needs. Also, temperature is being compensated by the technique of ATC. It has been designed to be compact, flexible. The pH values obtained are interpolated using MATLAB and linear graph results and show that sensitivity is linearly proportional to the temperature. Accuracy of the measurement can be still improved by using precise pH meter and probe. Thus, the device can be interfaced with computer to meet a wide range of end-user needs.

References 1. E.W. Slessarev, Water balance creates a threshold in soil pH at the global scale. Nature 540(7634), 567–569 (2016) 2. M. Nanang, N. Faud, R. Didik, S. Topo, J. Panuwun, Effect of alkaline fluids to blood pH and lactic acid changes on sub maximal physical exercise, in The 2nd International Conference on Biosciences. IOP Conf. Series: Earth and Environmental Science, vol. 197, no. 012049, pp. 1–7 (2018)

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3. A. Wataru, Z. Xiaobu, B. Jian, Y. Xiao, Y. Marunaka, Body fluids pH balance in metabolic health and possible benefits of dietary alkaline foods. eFood 1(1), 12–13 (2020) 4. S. Karastogianni, S. Girousi, S. Sotiropoulos, pH: principles and measurement, in Encyclopedia of Food and Health, pp. 333–338 (2016) 5. F.C. de Abreu, P.A. de L. Ferraz, Goulart, M.O.F. Goulart, Some applications of electrochemistry in biomedical chemistry. Emphasis on the correlation of electrochemical and bioactive properties. J. Braz. Chem. Soc. 13(1), 19–35 (2002) 6. P. Saikia, D. Choudhury, B. Bora, Role of acidic pH of intravenous fluids in subsequent development of metabolic acidosis—may not be what it seems. Indian J. Critic. Care Med. 18(7), 484 (2014) 7. J.L. Seifter, H.-Y. Chang, Extracellular acid-base balance and ion transport between body fluid compartments. Physiology 32(5), 367–379 (2017) 8. J. Clancy, A. McVicar, Short-term regulation of acid-base homeostasis of body fluids. Br. J. Nurs. 16(16), 1016–1021 (2007) 9. W. Aoi, Y. Marunaka, Importance of pH homeostasis in metabolic health and diseases. Crucial role of membrane proton transport. BioMed Res. Int. 1–8 (2014). Article ID 598986 10. C.J. Lote, Renal regulation of body fluid pH, in Principles of Renal Physiology, pp. 121–140 (2012) 11. R.C.M. Margareth, L. Raimer, A. May, Simulated biological fluid with possible applications in dissolution testing. Dissolut. Technol. 18, 15–28 (2011) 12. Z. Zulkarnay, S. Shazwani, B. Ibrahim, A.J. Jurimah, A.R. Ruzairi, S. Zaridah, An overview on pH measurement technique and application in biomedical and industrial process, in 2nd International Conference on Biomedical Engineering (2015)

Design of Low-Cost Active Noise Cancelling (ANC) Circuit Using Ki-CAD Mehaboob Mujawar and D. Vijaya Saradhi

Abstract In this developing world, there is an increase in pollution, mainly noise pollution due to industrial machineries, vehicles, construction activities, aircrafts and various other forms of man-made noise leading to anxiety, headache, noiseinduced hearing losses, heart diseases, stress and various others problem for humans. Therefore, the ANC technology can help to reduce this unwanted noise based on acoustic-based solutions. But still, it can be said that ANC is not a complete solution to this noise, but it can reduce the noise at the personal level. In our ANC circuit, we have made use of simple adder, amplifiers, delays and certain filtering components to cancel noise of wideband range, and it does not deal with other forms of noise. The ANC is used in the present world appliances like mobile phones, ear phones, aircrafts and in the industrial applications. Keywords Active noise cancelling circuit · Adder · Amplifiers · Delays · Phones · Ear phones · Pre-amplifier · Summing amplifier · Attenuation of noise

1 Introduction The main theme behind our project is the use of FEEDFORWARD technique in the noise cancellation process. This technique is employed by placing the mic inside the ear cup and the active noise cancelling circuit which would normally be in the headphones will invert the noise wave by 180° which is then mixed with audio playback to create noise cancellation. Active noise cancellation widely differs with passive noise cancellation in the sense that design of passive noise cancellation technology involves mainly with physical design of ear cuffs, and it cannot cancel noises of higher frequencies thereby giving active noise technology a much better advantage. This technology could have greater importance in coming future. However, the ANC M. Mujawar (B) Goa College of Engineering, Goa, India D. Vijaya Saradhi Malineni Perumalu Educational Society Group of Institutions, Guntur, Andhra Pradesh, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_13

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is still used in the present world appliances like mobile phones, ear phones, aircrafts and in the industrial applications. In this project, we have implemented ANC in the stereo-based earphones with left and right channel having independent ANC circuit to cancel noise at each ear with the independent ANC circuitry. Quality of the speech is one of the important concerns in sound and acoustics. ANC ensures high quality of the speech/music signal with minimum noise. ANC can be achieved by three different methods, i.e. feedforward, feedback form and hybrid methods. In feedforward microphone, for capturing noise is placed in such a way that it collects external noise only which is then inverted and fed so as to nullify the noise. This method can cancel wideband frequencies. In feedback form, microphone will be placed close to the speakers which captures speaker output and compares it with the music input, and any unwanted signals are treated as noise which is further inverted and fed to cancel noise. This method is effective in cancelling narrow band frequencies. Hybrid form is an advanced form of ANC having feedback and feedforward technology running in sync; thus, hybrid form is capable of cancelling narrow and wide band frequencies. Our particular project involves feedforward technique, wherein a microphone is placed outside of each ear cup to collect the external noise, also inverting the output of microphone by shifting its phase of signal and finally adding the output with audio. Entire circuit setup has three important blocks, namely the pre-amplifier, all-pass filter/delay block and summing amplifier, wherein microphone-captured noise has been amplified by the pre-amplifier as per the music signal input and delay block ensures synchronisation in noise and music as there exist some time difference in noise and music, and finally, the summing amp performs the crucial task of adding music input and inverted noise input thus resulting into cancellation of noise.

2 Literature Survey From paper [1], he authors of this research paper, in particular, want to highlight the issue of environmental distress caused due to noise pollution including physical and psychological effects caused by working in noisy areas such as construction sites leading to effects such as problems in hearing or partial hearing loss, anxiety, insomnia and various other problems. The authors also want to point out the fact that passive noise cancellation technique isn’t completely a full proof way of limiting background noise due to physical design of ear cups. Therefore, the author’s device is a concept of ANC technique to manage noise in construction sites. This concept of active noise control tries to reduce the level of sound pressure by transmitting a sound of reversed phase but of same amplitude. The major limitation which was observed in this research paper was the use of feedback method of ANC. The feedback method could only cancel noise of lower frequencies and hence, was ineffective in cancelling noises of frequencies above 1000 Hz. However, our project uses a different kind of a method, namely FEEDFORWARD technique in which the circuit inverts the noise to anti-noise by inverting the noise wave by 180°. Thereby, our project can cancel noises of higher frequencies greater than 1000 Hz. In paper [2], a new design of

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active noise cancellation capability is verified and referenced for headset using VSSNLMS algorithm. The VSS-NLMS algorithm is examined in combined audio as well as the feedback active noise cancellation system. Here, in this method of ANC system, the author has proposed to fix the microphone closer to loudspeaker in the ear cup of headphone which will pick up the noise reaching ear more efficiently thus by attenuating the required noise. Also, combined audio and feedback system are introduced between the audio source (pre-recorded music, live call from mobile) and special headphone (proposed ANC design circuit). Author has carried out necessary experiments to access the performance of the least mean square, normalised least mean square and variable step size-least mean square algorithm under various noisy environments. From the performances of all three algorithms, it can be observed that the VSS-NLMS algorithm is able to reduce the level of noise signals. The convergence rate for VSS-NLMS algorithm is much faster than LMS and NLMS. Also, informal listening test affirms the quality of the audio signal received is possibly better. This particular method of ANC using VSS-NLMS algorithm can be used to improve our project to get more efficient noise cancellation effect. In paper [3], the author focuses on how the normal ANC setup can be ineffective in cancelling the noise for small frequency but significant noises like of fan, air conditioners, vacuum cleaner and so on due to frequency sensitivity of summation point. To deal with above problem, in normal ANC, author suggests the use of an improved algorithm, namely filtered-x LMF algorithm dependent on the principle of mean square weight. In other words, it is a kind of adaptive ANC technique where depending on algorithm; the noise cancelling filter so designed can have different gain factor, amplification factor, etc. It has been observed FX-LMF algorithm reduces the residual errors in noise cancellation systems. In the particular example, author has considered filtered-x LMF for a fan, where its adaptiveness depends on the fan rotation speed. From paper [4], conference and seminar rooms need to free from noise. The outside high/medium frequency noise can be easily eliminated using passive noise reduction techniques. But, low frequency noise from fan, air conditioning system, cough, whispering, etc. can be very annoying whilst a seminar or conference is going on. The author of this research paper designed a model to make a seminar room silent using active noise cancellation (ANC) method using filtered-x least mean square (FxLMS) algorithm. Since LMS algorithm doesn’t work very well for quickly varying environments, therefore FxLMS algorithm is used to design and enhance the ANC system which is a modified algorithm of least mean square algorithm. The different noise signals along with the desired signal is given to ANC system, and finally, the output obtained on the loudspeaker is the desired signal without noise. The error microphones are placed ten feets away from each other, and speakers are placed in between error microphones. Total six microphones and four speakers in two rows are used which reduces noise up to −50 db. For wavelength of 3 m, working frequency is 120 Hz.

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3 Circuit Design The entire circuitry can be divided into two identical section with having the common base as 3.5 mm 3 pole audio jack. This identical section is separate for left channel and the right channel. Every channel has pre-amplifier, delay block, summing amplifier and a power supply unit, with the independent mic and speaker assembly. The below mentioned Table 1 shows the components used to design the circuit. However, for the testing purpose, a power supply may be necessary for supplying power to mic arrangement and to track the signal characteristics at every stage an oscilloscope might be needed, and the optional signal could be generated through signal generator and can be applied as input through mic and music input. Input section: Input for the circuit is the music input which is fed through the 3.5 mm audio jack, since we are concerned about dual channel stereo music, so we are required to have three pole jack having ground, left channel, right channel inputs. Respectively, input of left channel and right channel is given to the summing amplifiers of each section. Pre-amplifier: Fig. 1 shows implementation of pre-amplifier circuit on breadboard. The pre-amplifier block consists of an op-amp which is used for amplifying the signal from the mic. The gain provided by the amplifier to the signal which is to be received is actually the ratio of particular resistances. The gain is given to the received signal because they do not have much strength and hence cannot travel longer distances. This particular pre-amplifier block is also used for reducing the offset as well as the DC components present in the circuit. All-pass filter/delay: The circuit delays the noise signal which was amplified from the mic. The pre-amplified noise is therefore subjected to some delay before summing amplifier so that there is precise nullifying anti-noise over the noise. However, delay so introduced is in micro-seconds but can play a key role in cancelling the noise. Amount of delay depends on frequency and phase lag in sound so accordingly the capacitors need to be manipulated so as to achieve required delay. Table 1 Components used to build the circuit Name of the component

Specification

Operational amplifier

LT1056 (6)

Electric microphone

ECM-60PC-R (2)

Audio Jack (Aux Cable)

3.5 mm (1)

Speaker

CVS-1708 (2)

Power supply

8 V/30 mA (1)

Capacitors

0.01 uF (2), 1nF (4), 10 uF (8)

Resistors

500 K  (2), 100 K  (2), 1 K  (2), 2.2 K  (2), 4.7 K  (2), 10 K  (8), 13 K  (2), 22 K  (2), 1 M  (2)

Male connectors

01 × 04 Connectors (2)

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Fig. 1 Implementation of pre-amplifier circuit on breadboard

Summing amplifier: This unit performs the crucial task of superimposing the delayed noise signal with the incoming music. Addition occurs in such a manner that the final output is the sum of music and 180° phase shifted noise. Potentiometer should be adjusted precisely such that external noise and internal anti-noise should match in amplitude. Final signal is then amplified with some gain value for driving the output unit (Fig. 2). Power supply filter: The power supply filter operates as a low-pass filter which allows signals of lower frequencies and blocks or attenuates high frequencies. This is done so as to avoid aliasing of frequency. Resistors are used for providing a DC bias for microphone. The capacitor components remove DC offset and pass the noise through the microphone. Output section: Output for this circuitry can be simple speaker. Speakers of lower amp rating can be directly driven by the summing amplifier, but in case of speakers with higher amp rating can increase load on the circuit, so a driver circuit can be used as per the requirements. Practical circuit applications: This technology finds application in the industrial, mining environments to reduce the effect of industrial machinery on the workers working on the machine. Workers are employed with ANC earplugs. Vehicle manufacturers also use ANC in multiple speaker and microphone configuration to cancel

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Fig. 2 Implementation of delay, op-amp and summing amplifier circuit on breadboard

tyre, engine, aerodynamic, traffic noise so that the passengers can be devoid of unwanted sounds. Aircraft pilots also use ANC tech to improve communication amongst themselves in extremely loud engine sound in the cockpit. Modern auditoriums, theatres, conference room use ANC as the passive form of noise cancellation can be costly and not completely effective too. This technology is widely used in modern mobile phones, ear phones and sound system so as to effectively reduce the effect of noise and improve sound quality (Figs. 3 and 4).

4 Conclusion This project demonstrates the application simple of ANC technology in the stereobased earphones. This technology can improve the quality of music, and in case with no music playing, the headphones were able to attenuate the level of ambient noise thus creating a noise ridden environment. Noise-free environment can serve the mankind to reduce the effect of noise at the personal level, thus ensuring the healthy environment.

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Fig. 3 Schematic diagram of the circuit in Ki-CAD

Fig. 4 PCB model of the ANC in Ki-CAD

References 1. N. Kwon, M. Park, H.-S. Lee, J. Ahn, M. Shin, Construction noise management using active noise control techniques. J. Constr. Eng. Manage. 142(7), 04016014 (2016). https://doi.org/10. 1061/(ASCE)CO.1943-7862.0001121 2. T.N. Senthilkumar, C. Averty, Active noise cancellation headset’ circuits and systems, 2005. ISCAS 2005, in IEEE International Symposium on Circuits and Systems (2005). https://doi.org/ 10.1109/ISCAS.2005.1464576 3. K.-S. Lee, J.-C. Lee, D.-H. Youn, A new algorithm for active cancellation of fan noise, in Proceedings of International Conference on Consumer Electronics (1995), pp. 426–427. https:// doi.org/10.1109/ICCE.1995.518048

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4. Md. Makid Hasan, Md. Al-Amin Howlader, A new application of FxLMS algorithm and designing of a silent seminar room using active noise cancellation’ 2018, in International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2) (2018). https://doi.org/10.1109/IC4ME2.2018.8465677

An Approach for Designing of Low-Noise Bandgap Reference Circuit Anushree and Jasdeep Kaur

Abstract This work presents an approach for designing of low-output noise bandgap reference circuit using passive RC filter with larger area and an area efficient active low-pass filter. When no such technique for noise reduction is introduced, circuit produces noise voltage of 57.236E−12 (V2 /Hz). On inclusion of passive filter, noise voltage is reduced to 62.24E−18 (V2 /Hz); further, it reduces to 39.66E−18 (V2 /Hz) when active low-pass filter is applied at the BGR output. The proposed lownoise bandgap reference circuit is operating with 1.8 V supply voltage. It produces a of 1.25 V reference voltage for temperature coefficient of lower value such as 7.49 ppm/°C and −40 to 120 °C temperature range. Further, it includes study of different approaches such as feedback mechanism, CDS approach, and chopping technique that can be adopted for designing of low-noise bandgap reference circuit. All the simulation results have been obtained using OrCAD PSPICE software at TSMC 180 nm CMOS process. Keywords Bandgap reference circuit · Noise reduction · Active filter · Passive filter · Temperature coefficient (TC)

1 Introduction Bandgap reference circuits are building main component of many analog, digital, and mixed signal-integrated circuits. Ideally, a reference circuit produces a current or voltage that does not depends on temperature, process parameters, and supply voltage. Being an essential building block of integrated circuit designing, BGR block should be either immune or very less affected due to noise. Noise is one the serious problem that affects bandgap reference circuit’s performance. This noise is further increased in amplifiers when it passes from input to the output due to their amplifying Anushree (B) · J. Kaur Indira Gandhi Delhi Technical University for Women, Delhi, India J. Kaur e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_14

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characteristics. In CMOS circuits, with large noise densities, this noise reduction is possible only at the cost of increasing overall area occupied by the circuit. This all minimizes the integration of designed low-noise reference block in very complex systems-on-a-chip (SoCs). Therefore, reducing the noise without increasing the area and minimally affecting the other parameters of the circuit is a challenging task for most of the circuit designers.

2 Different Approaches for Noise Reduction in BGR 2.1 Feedback Mechanism In [1], value of noise can be reduced without using any technique such as chopping or auto-zeroing. In this, value of noise and offset is reduced by increasing the value of feedback coefficient which is achieved using suppression of offset and noise.

2.2 CDS Approach Techniques discussed in [2–4] use CDS approach. In [2], CDS approach is implemented using switched capacitor architecture. In this work, noise of the bandgap reference circuit is filtered using low-pass transfer function of system. In [3], noise in charge coupled devices is reduced and described using AZ operation followed by a S/H. The effect of CDS approach on the amplifier noise and offset is alike AZ process. Similarly, Chen et al. [4] reduces errors produced by offset voltage, 1/f noise, clock feedthrough, and finite op-amp gain using correlated double sampled (CDS) technique.

2.3 Chopping Technique In [5], two-level chopping technique reduces the non-linearities caused by op-amp. As result of this, input offset referred voltage and 1/f noise produced due to transistors in minimized. Jiang and Lee [6] shows improvement in 1/f noise level as compared to untrimmed techniques. Similarly, technique discussed in [3] minimizes noise effects produced at the virtual ground stage of operational amplifier because of narrow band noise sources.

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2.4 Using Active RC Filter In techniques discussed in [7–9], active and passive filters are used for reduction of noise. These active or passive filters are connected at the output of proposed bandgap reference circuit. In [7], on using passive filter for noise reduction, capacitor of large value is required which can be reduced by using a current mirror of which utilizes power in ultra-low range in active filter. Magod et al. [8] uses sample and hold switched-RC filter for reduction of noise effect in bandgap reference circuit. Similarly, in [9], for reducing the value of output noise, current mode chopped error amplifier is utilized. Along with this, a switch capacitor notch is introduced for obtaining ripple-free output reference voltage from bandgap reference circuit.

2.5 Other Techniques In technique discussed in [10], common mode voltage of operational amplifier is shifted toward ground rail with the help of voltage dividers; as a result of this, value of 1/f noise is reduced when sizes of transistors are changed. Sudha and Timothy Holman [11] presents a principle where two voltages are combined which results in reference voltage of zero temperature coefficient, and low-noise performance is achieved without increasing the value of power consumed.

3 Designing of Low-Noise BGR Circuit A curvature-corrected BGR [12] is presented in Fig. 1. The output of folded cascade op-amp is biasing all the pMOS used in the bandgap reference circuit for generation of complementary bias voltage. This complementary bias voltage is produced Fig. 1 Bandgap reference circuit without any filter [12]

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using diode-connected nMOS devices. The the resistor is controlled by pMOS and nMOS devices. The two bias voltages are complementary in nature which helps in limiting the current through a resistor. As a result of this, obtained bandgap reference circuit has improved temperature coefficient. This proposed bandgap reference circuit produces higher value of output noise voltage. The value of this noise voltage is 57.236E−12 (V2 /Hz). For reducing this noise voltage, two techniques are proposed with passive and active RC filters presented in Fig. 2 are connected at the output of bandgap reference circuit which results in noise reduction where Fig. 2a is representing passive RC filter and Fig. 2b is representing active RC filter. The expression for reference voltage of bandgap reference circuit of Fig. 1 is given by, VREF = (ICTAT + IPTAT )RNN

(1)

The current ICTAT is across resistor Rc by the built-in-potential difference of diode-connected BJTs. Its expression is given by, ICTAT =

VBE RC

(2)

Similarly, IPTAT is produced due to difference in built-in-potential of diodeconnected MOSFETs. The expression for IPTAT is given by, IPTAT =

VT ln (K ) RP

(3)

where Q2 is K-times sized to Q1 , and V T is representing thermal voltage. Therefore, the final expression for VREF is given by,  VREF = RN

Fig. 2 a Passive RC filter. b Active RC filter

VBE VT ln (K ) + RC RP

 (4)

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All the simulation have been performed using TSMC 180 nm CMOS technology. Thermal voltage V T is considered to be 26 mV here. The value of process parameters K p  and K n  are 405.36 * 0.4211 = 170.7 μA/V2 and 85.73 * 0.4211 = 36.1 μA/V2 . where ROUT is total resistance of the circuit which is the addition of equivalent output resistance (Req ) seen at point V REF of circuit, and R is the resistance of passive RC filter or equivalent resistance of active RC filter’s MOSFET. Similarly, C is representing capacitance of the active or passive RC filter. The cut-off frequency of passive RC filter is given in Fig. 2a, and active RC filter of Fig. 2b is given by f c . The expression for f c is given by, 1 2π (Rout C)

(5)

1   2π R + Req C

(6)

fc = fc =



3.1 Simulation Results All results of proposed low-noise bandgap reference circuit are obtained using Orcad Pspice 180 nm CMOS process. The proposed BGR produces a reference voltage of 1.25 V for the temperature range of −40 to 120 °C and TC of 7.49 ppm/°C. In Fig. 3, reference voltage versus temperature curve of proposed BGR circuit is shown. Similarly, Fig. 4 represents reference voltage versus supply voltage curve. Figure 5 represented below is plotted between noise voltages at various frequencies. From figure, it is clear when active or passive low-pass filter is added BGR circuit presented in Fig. 1. The value of noise voltage obtained is reduced without affecting the outputs produced by the circuit. When passive RC filter of cut-off Fig. 3 Reference voltage versus temperature curve

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Fig. 4 Reference voltage versus supply voltage curve

Fig. 5 Simulated noise of BGR circuit with passive and active filters

frequency 5 kHz is added at the output of BGR, noise voltage is reduced to 62.24E−18 (V2 /Hz) in comparison to the noise voltage obtained without introduction of any filter 57.236E−12 (V2 /Hz). The value of obtained noise voltage can further reduced if in place of passive RC filter active RC filter of cut-off frequency 5 kHz is added at the output of bandgap reference circuit. Table 1 indicates the comparative study between two discussed approaches, i.e., value of different parameters such as temperature coefficient, temperature range, squared noise and voltage when either passive RC filter of active RC filter are included in proposed bandgap reference circuit.

An Approach for Designing of Low-Noise … Table 1 Simulation results of BGR circuit with active and passive filter

123

Parameter

BGR with passive filter

BGR with active filter

Supply voltage

1.8

1.8

Reference voltage (V)

1.25

1.25

Temperature range −40−120 °C (C)

−40−120 °C

Temp. Coeff. (ppm/°C)

6.3

6.3

Squared output noise (V2 /Hz)@5 kHz

62.24E−18

39.66E−18

Cutoff frequency of filter (KHz)

5

5

Output capacitor

10 pF

10 pF

Output resistor

1K

–

Output MOSFET (W/L)

–

10 µm/5 µm

4 Conclusion A low-noise curvature-corrected bandgap reference circuit with passive RC filter, and active RC filter is presented in this study. The bandgap reference circuit is designed in a standard 180 nm CMOS process using Orcad Pspice software at 1.8 V supply voltage. The simulation results obtained with passive and active RC filter are compared. After comparison, we concluded that active RC filter shows better noise reduction than passive RC filters. Therefore, results prove that the active RC filter for the BGR circuit can effectively reduce the high frequency noise than passive RC filter. Along with this technique, different other techniques that can be adopted for noise reduction are discussed. The BGR circuit consists of BJTs, MOSFET, and resistors. The simulation results explain presented BGR generates reference voltage of 1.25 V for −40 to 120 °C temperature range and having temperature coefficient of 7.49 ppm/°C. Then multiple types of start-up circuits for bandgap reference circuits are discussed.

References 1. L. Liu , X. Liao, J. Mu, A 3.6 µVrms Noise, 3 ppm/°C TC bandgap reference with offset/noise suppression and five-piece linear compensation. IEEE Trans. Circuits Syst.-I: Regul. Pap. 66(10), 3786–3796 (2019) 2. A.N. Longhitano, F. del Cesta, P. Bruschi, R. Simmarano, A compact low-noise fully differential bandgap voltage reference with intrinsic noise filtering, in IEEE 10th Conference on Ph.D. Research in Microelectronics and Electronics (PRIME) (2014)

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3. C.C. Enz, G.C. Temes, Circuit techniques for reducing the effects of OP-amp imperfections: autozeroing, correlated double sampling, and chopper stabilization, in Proceedings of The IEEE, vol. 84, No. 11 (I996) 4. J. Chen, G. Li, Y. Cheng, Low-power offset-cancellation switched-capacitor correlated double sampling bandgap reference. Electron. Lett. 48(14), 821 (2012) 5. B. Wu, S.U. Ay, Low-noise CMOS bandgap reference generator using two-level chopping technique, in IEEE Workshop on Microelectronics and Electron Devices (WMED) (2015) 6. Y. Jiang, E.K.F. Lee, A low voltage low 1/f noise CMOS bandgap reference, in IEEE International Symposium on Circuits and Systems (2005) 7. P. Liu, Q. Duan, Z. Meng, J. Sun, S. Huang, Y. Ding, L. Han, A novel low-noise bandgap reference with an active RC filter, in IEEE 4th International Conference on Integrated Circuits and Microsystems (2019) 8. R. Magod, N. Suda, V. Ivanov, R. Balasingam, B. Bakkaloglu, A low-noise output capacitorless low-dropout regulator with a switched-RC bandgap reference. IEEE Trans. Power Electron. 32(4), 2856–2864 (2017) 9. R. Magod, N. Suda, V. Ivanov, R. Balasingam, B. Bakkaloglu, A 14.8 µVRMS integrated noise output capacitorless low dropout regulator with a switched-RC bandgap reference, in IEEE Custom Integrated Circuits Conference (CICC) (2015) 10. Y. Jiang, E.K.F. Lee, A low voltage low 1/f noise CMOS bandgap reference, in IEEE International Symposium on Circuits and Systems, pp. 3877–3880 (2005) 11. M. Sudha, W. Timothy Holman, A low noise sub-bandgap voltage reference, in Proceedings of 40th Midwest Symposium on Circuits and Systems. Dedicated to the Memory of Professor Mac Van Valkenburg, vol. 1, pp. 193–196 12. R. Kaushik, J. Kaur, Anushree, Design of folded cascode Op amp and its application—bandgap reference circuit. Circuit World J. (2020)

Robotic Interactive Companion: Human–Robot Interaction for Wellness Alan Jacob, Manju Singh, and Agha Asim Husain

Abstract Due to increasing mental illness and loneliness among children and adults, they need something that could help them cope with it. Owning a pet is a good step, but people who can’t afford or are allergic to it need something similar, i.e., a pettype robot, and thus introduced Robotic Interactive Companion (R.I.C.). R.I.C. is a pet robot that can interact with the user via Talk-Bot not only to entertain with its joking ,playing and mood-lifting music features but also to help the user cope with mental health issues by giving advice. The Talk-Bot dialogs are predefined and thus only respond to a limited number of questions which can be increased with regular updates. It can also follow the hand of the user like a pet animal would do, and every time, it detects the user it will start wagging its tail. The hand-follower will only work, whenever the user satisfies the predefined distance condition. It rotates its head (sensor) in a given interval of time to detect the hands of the user to follow. It also has a tracking system that will help the user to find the robot if lost and an entertainment system to play music. The result shows that the loneliness of adult people decreased and is educating and entertaining for children. The prototype of the robot is found to be effective. Keywords IoT · Companion robot · Talking robot · Mental illness

1 Introduction Today, robotics is used everywhere for the manufacturing of products, space exploration, medicine, weaponry, etc. Robots are also introduced for commercial uses like Roomba which is used to clean the floor. There is also research going on robots for house plants like Potpet developed by Kawakami et al. [1] which is used to help users grow plants more effectively and enjoyably. Socially engaging robots A. Jacob (B) · M. Singh · A. A. Husain I.T.S. Engineering College, Greater Noida, Uttar Pradesh 201308, India A. A. Husain e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_15

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researched by Brown et al. [2] are shown to be effective in education, as a health coach, as a play partner, etc. But, when it comes to any emotional healing or mental healing, there is only a little research done on robotics. Mental health problems are one of the leading health issues in India. According to WHO, India’s mental health workforce is inadequate, and there is a significant lack of psychiatrists and psychologists in the nation when compared to the number of individuals suffering from mental illnesses [3]. Loneliness is one of the causes of mental health problems which has been increased especially during the COVID-19 pandemic. Human beings need something to interact with or to play with, something that is capable of helping them cope with mental health issues, and to overcome these, I introduce Robotic Interactive Companion (R.I.C.), the robot which will help the user with mental health issues by interacting with the user via its Talk-Bot feature. For children, especially, autistic children R.I.C. could be used as a tutor to educate them about embedded robotics, as a toy to play with. Research by Robins et al. [4] that autistic children show positive results when interacting with the robot (Fig. 1). R.I.C. is designed to act and work like a robotic dog that can follow the hand of the user using its Hand-Following feature and also wags its tail as a real dog would do. R.I.C. could be used as a substitute for animal-assisted therapy in a children’s ward or ICU as it doesn’t carry any diseases and also doesn’t pee. One of the advantages of being a robot is that it couldn’t feel any pain or injuries when left with the children. According to a report, many people love animals and have pets, but 15% of the population is allergic to dogs and cats, and 30% of people with asthma is allergic to Fig. 1 Robotic Interactive Companion (R.I.C.) front view

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pets. People with pet allergies have over-sensitive immune systems. R.I.C. doesn’t mean to replace living pet animals, but to act as a substitute for the people who have allergies as they can react to harmless proteins in the pet’s urine, saliva, or dander. Also, people who have anger issues or lack of time to care about a pet animal or can’t handle grief over an animal’s death are some more reasons to go for R.I.C. A research survey was conducted by Kanamori et al. [5] which shows responses of the people when interacting with pet-type robots, a 68-years-old women’s loneliness scale value goes from 4 to 1, a 74-year-old women’s loneliness scale value goes from 5 to 2; an 84-year-old man says that his mental health issues are decreasing; his loneliness scale value during the first session was six which decreases to 1 by the final session. A pet robot PARO was developed by Bennett et al. [6] for adult people suffering from depression. The result suggests that the use of the PARO robot by the participants shows reduced symptoms of depression for a majority of patients [6]. The R.I.C. is made of both hardware components such as Arduino Uno, ultrasonic sensor, servo motor, and many more, and software components such as— Robotic Interactive App (R.I.A.) and Blynk android applications. R.I.C. has four features - Hand-Following, Entertainment System, GPS tracking, and Talk-Bot.

2 Literature Review Only a few researches are being done in the field of the pet-type robot. Artificial intelligence robot (AIBO) is a series of robotics dogs designed and manufactured by Sony. The first prototype was made in mid-1988. The first consumer model was introduced to the market in 1999. A companion robot should be able to help the user with mental health issues through speech or emotions. It should be able to educate the younger generations and should increase their curiosity toward the future. Some of the research done in human-following robots and mental health-healing robots are discussed below (Table 1). R.I.C. is not as accurate and responsive as compared to the robot projects in S. No.1, 2 above as it is only meant to follow the hands of the user. But, the inspiration is taken from it. R.I.C. doesn’t have any touch sensing capabilities nor cries out but has a different user-bot interacting feature that might be compensated for. From the above research, the R.I.C. has its own ways to deal with mental health and has its own entertaining methods. All these robots are contributing to the knowledge and curiosity of the younger generation toward the future embedded system and robotics and for the betterment of life.

3 Methodology The prototype can be divided into two parts: hardware and software.

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Table 1 Related work comparison S. No.

Project

Description

1

Minion [7]

is a robot that follows humans and carries heavy goods. The sole purpose of this robot is to carry goods, and the user has to carry the transmitter with him to make the robot follow him. It does use an Ultrasonic sensor but only to avoid objects in its path

2

Köylüoglu [8]

Developed a robot called Stalk-E that will also follow humans and carry goods. This is similar to the minion [7] except it uses a pixy camera for following the human

3

Buddy developed by [9]

is a family companion robot that can move around the house on its own. It has a display to show emotional reactions, but neither it can help with mental health problems nor interact as a pet robot. The project is still useful as a house robot that moves around

4

PARO developed by Japan’s National Institute of Advanced Industrial Science and Technology [10]

is a companion robot that looks like a small seal was designed for the care and support of old individuals, especially those living on their own. The robot reacts to the touch and also cries out like a seal [11]. PARO is one of the favorite robots chosen by adult people

3.1 Hardware The hardware is consisting of an Ultrasonic Sensor, DC motor, Arduino UNO module, speaker, Servo motors, motor controller, amplifier, Ublox Neo 6m, NodeMcu esp8266, lithium-ion battery, Bluetooth module HC-05, SD-card module. An Ultrasonic Sensor is a sensor that detects the distance of any object by sending ultrasonic sound waves. The ultrasonic sensor emits ultrasonic sound waves and converts the reflected sounds into electrical signals. Ultrasonic sound waves always travel faster than audible sounds. An experiment done by Latha et al. [12] shows that the ultrasonic sensor is effective at calculating the distance. An Arduino Uno is a microcontroller that can be programmed to do various functions. It is based on ATmega328p. Arduino Uno runs on the code that is uploaded by the user and does the action according to it. A Servo motor is a rotary actuator which can regulate position, velocity, and acceleration accurately. It consists of a suitable motor coupled to a sensor for position feedback. A DC motor is a rotary electrical motor that will convert electrical energy into mechanical energy. A speaker is a transducer that converts the electrical audio signals into corresponding sound waves.

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A Motor controller or driver is a device module that allows the user to control the speed and directions of the multiple motors simultaneously. A NodeMcu ESP8266 module is an open-source Lua-based firmware. It is a developmental board specially used for IoT-based applications. An Ublox Neo 6m is a GPS-receiving module with a built-in ceramic antenna, which has fairly strong satellites search capabilities. A Bluetooth serial port protocol (SPP) module like the HC-O5 is simple to use. It communicates with the microcontroller through serial communication, which makes it easier to interface with. A MicroSD Card Adapter is a microSD card reader module that establishes an SPI interface via the file system driver and the microcontroller system to accomplish microSD card read and write files [13].

3.2 Software Two android applications are used to carry out the various processes named Talk-Bot and Blynk.

4 Prototype Implementation The prototype implementation processes are discussed below.

4.1 Hand-Following The Hand-Following feature will enable the bot to follow the user’s hand, whenever it will satisfy the range of the condition. An Ultrasonic Sensor in this is not used to avoid objects but to follow the object or hand whenever it satisfies the condition. The ultrasonic is only able to detect the data coming from its front, but can’t detect anything that is lying in its left or right. To overcome this, we will mount the sensor on top of the servo motor that will rotate the Ultrasonic Sensor from 0° to 180° and allow the robot to detect the object from its left and right side. The Servo motor will rotate at a defined time interval enough to allow the sensor to perform its task. The received signal from the Ultrasonic Sensor is fed to the Arduino UNO; it processes it and sends the required commands to the motor driver/controller. The motor controller is used to control the direction with its H-bridge principle and speed of the DC motors. Here, we are using the L298N motor driver which works on the H-bridge principle. Four DC motors are used in this robot for four-wheel drive purposes. The motor controller will supply power to the motor according to the received command from Arduino, i.e., for going forward, all four

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Fig. 2 Hand-following block diagram

DC motors are rotated; for turning left, both right side DC motors are rotated, and for turning right, both left side DC motors are rotated. The motor controller or driver requires a minimum 12V DC supply to run all four motors, but due to dissipation, the motors only get 10V DC; thus, we are supplying 14V DC to compensate for the loss. For the DC power supply, we are using lithium-ion batteries that are rechargeable (Fig. 2). A servo motor is added with a tie to the backside of the robot to work like a wagging tail. Whenever the robot follows the hand, the tail of the robot also starts wagging.

4.2 GPS Tracker The GPS tracker is used to find the robot’s location using an android application named Blynk. The robot can be easily trackable via the Internet, thus involves IoT. GPS tracker developed by Kanani et al. [14] for critical health patients also uses Arduino and Ublox neo 6 m, and the results show that the tracker is effective (Fig. 3). NodeMCU ESP8266 module is used to make the GPS tracker. NodeMCU is connected to an android application via the Internet and will receive the location data such as latitude and longitude from the Ublox Neo 6 M module which uses satellites to provide the location. This location data are sent to the NodeMCU ESP8266 and NodeMCU ESP8266 will transmit that data back to an android application which will use longitude and latitude to locate the robot on the Google map. The NodeMCU module is also powered by lithium-ion batteries. The voltage is reduced using a voltage regulator and will only apply the required voltage. GPS tracking is done via the Internet and thus could be counted as an IoT-based application.

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Fig. 3 GPS tracking

4.3 Entertainment System The robot consists of a speaker and a Bluetooth system that enables the user to play music or watch movies on speaker and can use it as a personal music system. The Bluetooth module makes a connection between the user and the robot.

4.4 Talk-Bot The Talk-Bot feature enables the user to interact with the bot through an android application made using MIT app inventor. Users can ask some questions, and the bot will respond to them. It can give an introduction, tell jokes, play music, and respond to some of the predefined questions asked by the user. A chatbot developed by Jagtap et al. [15] to interact with the user has a limitation when it comes to user-bot dialogs as it only tells the humidity, distance, and temperature (Table 2). New questions and their responses will be added to the Talk-Bot with every update. When the user asks a question using an android application, it will send the data from the app to the Bluetooth module HC-05 which will pass the received data to Arduino UNO. Arduino will compare the received data speech with the predefined speech, and if it matches, the stored response will be given to the user via a speaker. The signal from the Arduino needs to be amplified, and thus, we will use the PAM-8403 amplifier circuit. The Arduino UNO is limited by memory space; the amount of memory we need for Talk-Bot is not available; thus, we are storing our responses on a secure digital-card using the secure digital-card module (Fig. 4).

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Table 2 User’s question and their responses Question

Response

“What is your name?”

“My name is Robotic Interactive Companion but you can call me R.I.C.”

“What are your features?”

“I can talk to you, I can follow your hand, you can listen to music through me, you can locate me through GPS.”

“Can you help me with my depression?”

“Yes, I can help you with all types of mental illness.”

“How to cope with stress?”

Stress_audio.mp3 is played

“Can you tell me a joke?”

The bot will tell a joke

“Can you tell me another joke?”

The bot will tell another joke

“Play my favorite song?”

Hall of fame.mp3 is played

“How to deal with depression?”

Depression_audio.mp3 is played

Fig. 4 Talk-Bot’s hardware block diagram

4.5 App Development The development of an android application, namely Talk-Bot is made using MIT app inventor. MIT app inventor is a Web-based application that is used to develop android or iOS applications. It was initially created by Google but now is maintained and run by the Massachusetts Institute of Technology. The app is made using block codes. The app inventor offers a solid, friendly, and rewarding entree into manipulating technological machines like mobile devices and robots [16]. The app developed for the R.I.C. is called Robotic Interactive App (R.I.A.) which contains—Talk-Bot and Hand-Following user guide (Figs. 5 and 6).

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Fig. 5 Flowchart of Talk-Bot application

5 Result A Robotic Interactive Companion prototype is developed. R.I.C. can follow the user’s hand accurately. The user-bot interaction is established and is found helpful for both childrens and adults to overcome the issues of loneliness and mental illness. The robot is found to be both entertaining as well as educating. The GPS tracking system lacks some accuracy, but all the features of the robot are easy to use, and thus, the prototype is found to be effective.

6 Discussion Mental illness or loneliness is a major problem for both children and adults, and it could somewhat be overcome by the use of Robotic Interactive Companion (R.I.C.) interactive features. The loneliness scale value of the people went from high to low when left to play with companion robots. This means that the Talk-Bot feature and Hand-Following feature are effective and could be upgraded in the future for more efficiency. The result is somewhat similar to the PARO where the participants were more comfortable owning a robotic pet instead of a real pet. R.I.C. doesn’t make any mess and also doesn’t require any food which is an advantage for the people who couldn’t afford a real pet animal for emotional support or have allergies. No response

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Fig. 6 Talk-Bot application

to questions that aren’t defined is the major limitation of the prototype which could be resolved in the future using machine learning.

7 Conclusion and Future Scope of Work In conclusion, a Robotic Interactive Companion is designed and developed which is found to be effective for both childrens and adults to overcome mental health issues using various features, especially the Talk-Bot feature. It is educating as well as entertaining for the younger generation. It works as a depression coping device for adults. In the future, this robot could be upgraded with an A.I. system that no longer requires the user to ask predefined questions to get a response. It could be upgraded from Hand-Following to Human-Following. Some new home safety features could be introduced to protect the house from burglars or fire.

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References 1. A. Kawakami, et al., Potpet: pet-like flowerpot robot, in Proceedings of the Fifth International Conference on Tangible, Embedded, and Embodied Interaction (2010) 2. L.V. Brown, A.M. Howard, Engaging children in math education using a socially interactive humanoid robot, in 2013 13th IEEE-RAS International Conference on Humanoid Robots (Humanoids). IEEE (2013) 3. A. Bhatia, World Mental Health Day 2020: In Numbers, The Burden of Mental Disorders in India (2020). https://swachhindia.ndtv.com/world-mental-health-day-2020-in-numbers-theburden-of-mental-disorders-in-india-51627/ 4. B. Robins, et al., Robotic assistants in therapy and education of children with autism: can a small humanoid robot help encourage social interaction skills? Univers. Access Inform. Soc. 4(2), 105–120 (2005) 5. M. Kanamori, M. Suzuki, M. Tanaka, Maintenance and improvement of quality of life among elderly patients using a pet-type robot. Nihon Ronen Igakkai zasshi. Jpn J. Geriatr. 39(2), 214–218 (2002) 6. C.C. Bennett, et al., A robot a day keeps the blues away, in 2017 IEEE International Conference on Healthcare Informatics (ICHI). IEEE (2017) 7. I.R. Abir, I. Sharmim Shanim, N. Ahmed, MINION: a following robot using ultrasonic wave, in Progress in Advanced Computing and Intelligent Engineering (Springer, Singapore, 2018), pp. 387–395 8. T. Köylüoglu, E. Lindbergh, Stalk-e: Object Following Robot (2017) 9. G. Milliez, Buddy: A companion robot for the whole family, in Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction (2018), pp. 40–40. 10. Paro (Robot). https://en.wikipedia.org/wiki/Paro_(robot) 11. A. Sharkey, N. Wood, The Paro seal robot: demeaning or enabling, in Proceedings of AISB, vol. 36 (2014) 12. N. Anju Latha, B. Rama Murthy, K. Bharat Kumar, Distance sensing with ultrasonic sensor and Arduino. Int. J. Adv. Res. Ideas Innov. Technol. 2(5), 1–5 (2016) 13. Interfacing Micro SD Card Module with Arduino (2020). https://lastminuteengineers.com/ard uino-micro-sd-card-module-tutorial 14. P. Kanani, M. Padole, Real-time location tracker for critical health patient using Arduino, GPS Neo6m and GSM Sim800L in health care, in 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE (2020) 15. N.S. Jagtap, P. Shevatekar, R. Mudholkar, Cloud Based Chat Bot using IoT and Arduino (2019) 16. S.C. Pokress, J.J. Dominguez Veiga, MIT App Inventor: Enabling Personal Mobile Computing. arXiv preprint arXiv:1310.2830 (2013)

Design and Fabrication of Automatic Oxygen Flow Controller for COVID Patient S. J. Sugumar, Divya Sugathan, Bhagyalaxmi S. Patil, S. Chandana, S. Harsha, and V. Karthik Kumar

Abstract In this ongoing pandemic situation, an acute shortage of oxygen due to number of patients required the pure oxygen supply becoming high, showed us the level of importance of this gas. People getting affected by COVID-19 are suffering from low saturation level which needs to be increased by the supply of pure oxygen. The oxygen is used to bring the saturation level to 94%. Any drop below 84% lead to serious respiratory failures. We propose a system where the oxygen is supplied to the patient as per the requirement preventing the wastage of oxygen. This is done by constantly monitoring the oxygen level of the patient and releasing the exact amount of oxygen needed by the patient, using a microcontroller device, to control the flow rate of oxygen. This helps automatically to control the oxygen flow and also alert the doctor if the patient goes into a critical condition. Keywords Oxygen · Saturation level · Oximetry · Monitoring vitals · Alert notifications

1 Introduction We are well aware of the situation outside today. The world is suffering with an unwanted and very sudden attack of a virus, which although has been around us for ages, was never seen as big of a threat; it is today to the whole human kind. After knowing about this, potential virus set out to suffocate the whole world was a pandemic; the world was then faced with a lack of doctors and hospital staff to treat all these patients [1]. Then, India started to suffer from the acute shortage of oxygen. Oxygen saturation (SpO2 ) is a specification used to direct the proportion of oxygen level in hemoglobin blood cells, and it can be determined without an invasive method by applying a device called an oxygen pulse meter. The oxygen level determination is done by connecting the sensor of the oxygen pulse meter onto a patient’s fingertip. S. J. Sugumar (B) · D. Sugathan · B. S. Patil · S. Chandana · S. Harsha · V. Karthik Kumar Department of Electronics and Communication Engineering, MVJ College of Engineering, Bengaluru, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_16

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Patients with regular lung function have SpO2 levels in the range of 98 and 100%. If a patient has a SpO2 level below 90%, it indicates that patient is suffering from hypoxia (inadequate oxygen supply for the body). Administering this oxygen to the patients is a task out of the hands of the doctors and nurses working day and night, when the number of patients is far more than the count of the hospital staff working for the sake of patients since the levels of oxygen need to be constantly monitored by the staff and the amount of oxygen that need to be given to the patient should be set by them thus taking out their time from attending the much more needful patients. Hence, by this project, we are proposing a way to automate this whole process so as to help the patients by administering the proper volume of oxygen needed by them by continuously monitoring the SpO2 levels in their blood [2]. This also reduces the wastage of oxygen happening due to continuous flow even when the saturation levels have been recovered.

2 Method 2.1 Components Used NodeMCU is a micrcontroller which has inbuild Wi-Fi solutions to connect remote locations with open-source software for prototyping embedded application designs. The MAX30100 is an integrated pulse oximeter and heart-rate monitor sensor solution. The MG995 is a better option for high torque, digital metal-geared servo. The oxygen switch shut-off valve which turns on and off or sets the amount of oxygen to be supplied. An oxygen cylinder is a high-pressure, non-reactive, whole hardened steel cylinder for storing compressed gas (O2 ) used for medical, therapeutic, or diagnostic applications.

2.2 System Setup The whole system can be split into two sections, first the hardware part where all the sensors and motors and the oxygen supply system work as a whole to supply oxygen to the patient. Then, there is the backend which registers all the values read through the sensors and works to alert the doctor in case of emergencies or gives the values read when the doctor wants to know about the condition of the patient, through the mobile application [3]. This helps the doctor to monitor the patient anytime from anywhere without coming in contact to the patient unless emergency and be free from the worry of being infected and also reduces the number of in person visits made to the patient thus reducing the work load of the front line warriors and be present to attend the patients in dire need (Fig. 1).

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Fig. 1 Block diagram of the system

The system uses Max301000 sensor module for detection of SiO2 level in blood. It makes use of principle of absorption of RED light and IR light by blood. By calculating the amount of light absorbed by the blood, we can calculate the SiO2 level present in the blood. The setup will mainly consist of a IR LED and a red led of a particular wavelength which will be placed on the soft side of the finger, and a photodiode will placed on the nail side of the finger; so, when both lights are transmitted from one side of the finger, some light will get absorbed by the blood itself, so the amount of light which will pass through the finger will be detected by the photodiode, and intensity of light will indicate the SiO2 level in the blood [4]. The sensor is connected to NodeMCU via cables which in turn is connected to Internet via the inbuilt WI-FI module as shown in Fig. 2. The NodeMCU is attached to google sheets where all the readings of the sensor is stored acting as cloud database for the project. The google sheet is programmed to take the arguments from the NodeMCU and store it in the sheet in right format as shown in Fig. 4. In the backend, Python program will be running which acts as an intermediate between the app and NodeMCU, thus enabling M2M communication for the system. It uses Google API for communicating with the Google sheets. It is responsible for all the calculations and alerts which the doctor gets on his mobile app [5]. The mobile app is developed on java platform for better working of app.

2.3 Working of the System This device employs M2M communication for efficient communication and uses Internet as medium of computation. It uses low cost and efficient MAX30100 for obtaining accurate reading of the blood pressure and oxygen level in blood. The reading is then transmitted to the NodeMCU board which analyses the readings and

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Fig. 2 The hardware setup

adjusts the flow rate of the oxygen accordingly with respect to the SiO2 readings from the sensor via a valve which is controlled by a servo on the microcontroller’s command. The readings are then updated to the Google sheets via the NodeMCU board [6]. The Python program which runs in the backend downloads the reading from the Google sheets and analyses the readings and then raises an alert when the SiO2 level goes below a certain threshold set by the doctor. It updates the information in the emergency section in the Google sheets. When the app encounters the alert in the Google sheet, it gives a notification to the doctor in the form of alarm and also displays the information of the patient like patient ID, name, bed no. and his SiO2 level to the doctor after which the doctor can attend the patient. The alarm will go off only after the patient oxygen level increases above the threshold. In order to monitor the health of the patient, the doctor can use his mobile and access the readings from anywhere in the globe via a link provided.

2.4 Outputs Seen The outputs are mainly observed in the form of the servo motor rotating, as shown in Fig. 3 whenever there is a need for the supply of oxygen and the values being updated, whenever the mobile application is opened and the alert received by the doctor in case of emergencies. However, a serial output is seen when the system runs observed on the Arduino IDE gives us the insights about how the system is working and is represented in (Figs. 4 and 5).

3 Results The device after being connected to the patient’s body was able to continuously monitor the patient’s SiO2 level and blood pressure and control the oxygen level

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Fig. 3 The valve with servo motor attached

Fig. 4 Serial output on Arduino IDE when a finger is not placed and placed on MAX30100

according to the command given by the microcontroller, which is NodeMCU ESP8266 which works on the program written on Arduino IDE. The values read from the sensor MAX30100 are further updated constantly onto a Google sheet which takes Python backend to determine the amount of oxygen to be transferred to the patient which gets updated to the NodeMCU ESP8266 so that it can further gives the command to the servo motor to turn the valve. The values being updated on the spreadsheet are also relayed online so that the doctor can be updated on the patient’s condition at any point of time without being actually present with them all the time. The values are shown to the doctor or user via the mobile application developed in this project. The mobile app is also able to notify the doctor about any emergency situations, like the saturation level going beyond safe levels or saturation levels not changing even after the continuous supply of oxygen to the patient, and update the readings to them.

4 Conclusion and Future Scope A system to monitor the conditions of a patient continuously and keep track on their condition is developed. This system was developed by keeping the ongoing pandemic in mind, but this acts as a helpful way to automate the oxygen supply to the patients with hypoxemic respiratory issues on an everyday basis even after this pandemic

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Fig. 5 Mobile application screen 1 and 2 showing the condition of the patient and alert notification respectively

period will be over. This reduces the amount of visit a doctor or any other hospital staff has to do to a patient further keeping them available and able to attend patients who are in more need of them. In future scope, this can be further developed to handle multiple patients and have an allotting system where a number of doctors can be assigned to a set number of patients. This can also be developed to other areas other than just the oxygen flow to the patients. It can also be developed to check the purity of the oxygen supplied and also empty cylinder alert.

References 1. E.F. Hansen, J. Dahlgaard, H. Charlotte, S. Bech, J.-U. Stæhr Jensen, T. Kallemose, J. Vestbo,

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3. 4.

5.

6.

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Automated oxygen control with O2matic® during admission with exacerbation of COPD. Int. J. COPD (2021) P. Chanyagorn, P. Kiratiwudhikul, Automatic control of fraction of inspired oxygen in neonatal oxygen therapy using fuzzy logic control. IEIE Trans. Smart Process. Comput. 5(2), 107–116 (2016) E.D. Dijemeni, R. Dickinson, Portable mobile real time oxygen monitoring auto-ventilation system, in The 4th IEEE International Conference (2013) E.F. Hansen, C. Sandau Bech, J. Vestbo, O. Andersen, L.M. Kofod, Automatic oxygen titration with O2matic® to patients admitted with COVID-19 and hypoxemic respiratory failure. Eur. Clin. Respir. J. (2020) J.-M. Roué, J. Delpeut, A. d‘Hennezel, T. Tierrie, A. Barzic, E. L’Her, P. Cros, Automatic oxygen flow titration in spontaneously breathing children: an open-label randomized controlled pilot study. Novel Therap. (2020) S.J. Sugumar, S. Madiraju, T.G. Chowhan, T. Anurag, S.A. Ahmed, Detection of inadequate growth of early childhood and development of adult disease alert via embedded IoT systems using cognitive computing, in Innovations in Electronics and Communication Engineering, ed. by H. Saini, R. Singh, V. Patel, K. Santhi, S. Ranganayakulu. Lecture Notes in Networks and Systems, vol. 33 (Springer, Singapore, 2019). https://doi.org/10.1007/978-981-10-8204-7_18

Analysis and Testing of Geophone for Different Soil Conditions for Elephant Intrusion Detection S. J. Sugumar, D. Jeevalakshmi, S. Shreyas, R. Vishnu, M. S. Suryakotikiran, and B. Kushalappa

Abstract The human-elephant conflict is a key problem in forest border areas that ways agriculture damage, human, and elephant loss. As a solution to HEC, an early warning is provided to humans as an alert or to raid the elephants back to the forest. To detect the elephant intrusion, a geophone seismic vibration sensor is used in this work. As the forest border is prone to get rain frequently, the sensitivity of geophone output can be affected and may not detect the vibration due to the dampness in the soil. To improve the performance, the sensing system is designed to adapt variable soil conditions and thereby adjusts the level of threshold as per the noise variance influenced by the ground sources at the geophone output. We explored the variance in the spectral parameters and the signal strength of the elephant foot fall signal and arrived at a design as solution to avoid problems related to the noise and variable soil circumstances. Keywords Human elephant conflicts · Seismic sensors · Early warning system · NodeMCU · ThingSpeak cloud

1 Introduction Elephants’ incursion into human habitat has expanded dramatically in the last few years. This is mostly due to the loss of elephant habitat [1]. Elephant living areas are now converted to human encroachment for agriculture, resorts, tourisms, etc. Elephants are moving into the living places of human, and there exists humanelephant conflict. As a result, elephants raid the crops cultivated by the farmers, and the loss due to the destruction is high. The elephants are also been to hurt by humans substantial in risk to the life of elephants and humans by elephants. Though it is mostly the fringes of forest are to be taken care that are the crucial facts for S. J. Sugumar (B) · D. Jeevalakshmi · S. Shreyas · R. Vishnu · M. S. Suryakotikiran · B. Kushalappa Department of Electronics and Communication Engineering, MVJ College of Engineering, Bangalore, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_17

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Elephant Death 20

Elephant

Human

2019

2020

10

0 2016

2017

2018

human-elephant conflict (HEC) [2]. The amount of HEC incidents in the forest borders with amount of people affected in elephant attack in Coimbatore and the number of elephants killed by such conflicts [3] is shown in Fig. 1.

2 Literature Survey To mitigate the HEC, several approaches have been trailed like electric fences, trenches, using sounds, beehives, and GPS collars. But, all these methods have been unsuccessful due to their shortcomings like cost, high maintenance, and trouble to the living conditions of elephants. Elephant movement sensing methods like camera, microphones, geophones, and remote satellite where been tried [4]. Out of these, geophones provide better results compared to the other methods. Researchers have made efforts to put on geophones as sensing method either for elephant population senses or for early warning systems. Jason D. Wood et. Al made an evaluation of the amplitude generated by transitory of elephants that can be used to determine the amount of elephants passing the geophone for population census [5]. Mortimer et al. proposed methods from seismology to convert the geophone footages into basis roles—the time-varying seismic patter produced with elephant seismic vibrations for diverse landscapes and noise ranges [6]. Mandal et al. devised a process to spots the incidence of elephants adjacent the railway tracks and instantaneously triggers the trainer to raid away elephants from the railway tracks using geophone [7]. Parihar et al. proposed the possibility training for the recognition of elephants by means of seismic sensors within a forest location and added characterization of the seismic signals [8]. This research work analyzes the convention of geophone sensors in detecting the presence of elephants, especially in the forest border areas in variable ground soil conditions. Because forest border areas are prone to frequent rains, the performance of the detecting system stated in the connected activities may be delayed. Therefore, we have proposed a model to have a better detecting mechanism with variable ground soil condition there by avoid the ambiguity and reducing false alarm in this paper.

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3 Modeling of Geophones In the framework added to this paper, seismic sensors are built in hardware for the embedded network. The majority of footstep vibration energy is dispersed in the ten to a hundred Hz frequency range. Human steps and motors touring with ultralow amplitude responses are detected via way of means of the geophone sensor (in microvolts). For sign processing hardware, the amplitude of the geophone sensor sign needs to be greater with a benefit large than 1000. Geophones are utilized in mirrored image seismology to generate the electricity alerts meditated from the earth floor [9]. Any motion or vibration upon the floor is transformed to a small electric pulse as proven in Fig. 2. The geophone is primarily based totally on a coil suspended via way of means of springs in a magnetic field, inside a metallic case. When the vibration of any type movements the case, the coil stays solid because of its inertia. The case movements relative to the constant coil, producing an electrical voltage proportional to the coil’s speed with admiration to the case [10]. Measuring versions within side the electric voltage presents the information to decide the frequency and depth of the vibration. When the dynamic hundreds are resulting from floor movement, forcing at the shape is the inertial pressure resisting the floor acceleration, which equals the mass of the shape instances the floor acceleration given [11] by, F(t) = −m y¨ (t)

(1)

The movement of the floor and the movement of the evidence mass inside a seismic sensor variety are associated with the aid of using the easy harmonic oscillator equation, m x¨ + k(x − y) + c(x˙ − y˙ ) = 0

(2)

Let the movement of the bottom be denoted through y(t) and the reaction of the mass through x(t) as proven in Fig. 3, and assuming that the bottom has the harmonic Fig. 2 Excitation with an one degree of freedom

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Fig. 3 Elephant stepping on geophone

movement of the form y(t) = Y sin(ωb t). Using the assumed shape for the motion, you could replace for y and its derivative, ensuing in Eq. (3): ¨ = cY ωb cosωb t + kY sin(ωb t) mx + c x˙ + kx

(3)

which when divided through by the mass, yields. This equation exhibits plenty of approximately the mass’s motion. First, we will see that the precise answer represents the steady-nation response, while the homogeneous answer is the brief response, where    ω2 + (2ζ ωb )2 2ζ ωωb ω ,∅1 = tan−1 2 , ∅2 = tan−1 (4) A0 = ωY    2 2 2 2 2 2ζ ωb ω − ω ω − ωb − (2ζ ωωb ) b In a geophone, voltage is given by transfer function product by sensitivity in Vs/m [12]. We can substitute time derivatives with iω by using the Fourier transform of the simple harmonic oscillator equation, for a geophone gives VG = SG

∂X −iω ∂ 2U = SG ∂t −ω2 + 2ζ ωωb + ω02 ∂t 2

(5)

where V G is the information from the geophone, and S G is the sensitivity, where, x(t) = base displacement, y(t) = coil displacement, m = shifting mass, k = suspension stiffness, c = suspension damping, f = frequency, ω = cyclic frequency, ς = damping ratio and ωd = ω0 sqrt (1−ς 2).

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4 Proposed Work The proposed system contains linear array of geophone with sensing range of 75 m of 5 sensors placed at 15 m spacing. The linear array end of senor strings is connected to an amplifying circuit to improve the signal level and fed to a Schmitt trigger to check the signal crossing a variable threshold level. The Schmitt trigger input fed to an interrupt input NodeMCU controller [13]. The variable threshold level is determined by the readings of the moisture sensor measurement which determined by the ADC unit of NodeMCU. Because the forest boundary receives a lot of rain, the sensitivity of the geophone output can be harmed, and the ground vibrations may not be detected. To avoid the ambiguity, the sensing system is designed to adapt variable soil conditions by switching the gain factor of the amplifier to a high gain response output if a wet condition is detected. The threshold value in the software is also updated depending upon the measured moisture sensor readings. The threshold is set low if the moisture sensor detects a wet condition, and high if the moisture sensor detects a dry condition [14]. To determine the moisture sensor’s volumetric water content, we first calculate the gravimetric water content, W W = (m w /m m )

(6)

where m is the mass and the subscripts w and m refer to water and minerals. Now, calculate the bulk density, ρ, where V t is the total volume of the sample. ρb = (m m /Vt )

(7)

Now, calculate the volumetric water content, θ. The density of water, ρw, is 1 g/cm3 . θ = w ∗ ( ρb / ρw )

(8)

The volumetric water content (VMC) measured for dry soil is 0.183 and for wet soil is 0.411, when the threshold for wet and dry circumstances crosses the configured value, a warning message is updated in a thing talk cloud. The ThingSpeak cloud module updates the date and time of the event occurrence and recorded. The data from the cloud can be sent to a base station to produce alarm to evacuate the humans from the respective areas by alarming system or sending messages to respective mobile number using GSM. In this work, we use the geophone SM24, then buried geophone underground with 15 cm depth to detect any ground vibrations or a seismic action at the indicator position and produces an electrical voltage. With one seismic sensor, users may monitor a small region or trail of about 15 m (SM24). The output of geophone produces the voltage in millivolts; the amplifying circuit is used to increase the value of voltage produced from geophone with a gain of 10 or 80 db. With multiple detectors (SM24) connected in a string, users can monitor a specific area of perimeter. Here, in this work, a linear array of five geophone sensor arrays

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is kept to detect a specific intrusion of elephant in a pocket of area where elephant enters and exits from the forest to enter in to the village areas where the cultivations of firmest taking place. Intrusion detection range depends on numerous variable quantity, such as: type of ground, sensor accuracy, number/type of intruders, and condition of soils (wet/dry). It detects the signal all through 24 × 7 because the geophone doesn’t require any external power supply to detect.

5 Result The system is designed such a way the soil moisture sensor senses the soil moisture condition, and the threshold for detecting elephant is altered. The volumetric water content (VMC) of the moisture sensor for dry soil is 0.183 and for wet soil 0.411. The signal produced by the geophone for an elephant of mass 3000 kg was tested with trained elephant as shown in Fig. 3. The geophone amplitude for dry soil is 0.5 v with time period of 2 s is shown in Fig. 4, and 0.04 v for wet soil with time period of the signal is 1.5 s for wet soil is shown in Fig. 5. Geophone output of elephant in different soil conditions is recorded in Tables 1 and 2; it is clear that elephant possesses high amplitude, and the time periods are different. The amplitude and response time period for elephants in both dry and wet soil are chosen as thresholds to identify elephant entrance in forest boundary areas. Once the geophone output signal crosses the threshold, the processor sends the signal to a base station, and an early warning signal is sent to the forest officers via GSM message, and an alarm is generated. The system is designed and tested to adapt variable soil condition and can even function during rainy season. Hence, the problem of ambiguity and false alarm is reduced. The output of the geophone for human being and elephant is tested in both dry and wet soil is recorded. The amplitude and time period differences are the factors that distinguish elephants from humans. Fig. 4 Geophone output for dry soil

Dry Soil 0.4 0.3

Amplitude

0.2 0.1 0 -0.1 -0.2 -0.3 -0.4 -0.5 2.39

2.395

2.4

2.405

2.41

2.415

2.42

No of Samples

2.425

2.43

2.435

2.44 6

x 10

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Fig. 5 Geophone output for wet soil

Wet Soil 0.03

Amplitude

0.02 0.01 0 -0.01 -0.02 -0.03 1.44

1.445

1.45

1.455

1.46

1.465

1.47

No of Samples

Table 1 Geophone output of elephant and human dry conditions

Table 2 Geophone output of elephant with dry and wet soil

x 106

Object (dry soil)

Time period

Amplitude (volts)

Human being (75 kg)

0.5 s

0.003

Elephant (3000 kg)

2s

0.026

Soil condition

Time period

Amplitude (volts)

Dry soil

2s

0.5

Wet soil

1.5 s

0.04

The response of geophone output is captured for analysis using audacity software in the PC as shown in Table 1. The output of the geophone for human being and elephant is tested in both dry and wet soil and recorded in Table 1. The output of the geophone for human being and elephant is tested in both dry and wet soil and recorded in Table 2. The event detected by the system is recorded and shown in the ThingSpeak clod port that is created to store the events and display, the time and date of arrival of elephant are specific pockets and is shown in Fig. 6 to visualize the elephant information with a mobile application. MathWorks manages the ThingSpeak service. To sign in for ThingSpeak, you need to produce a brand new MathWorks Account or log in to your existing MathWorks as shown in Fig. 6.

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Fig. 6 ThingSpeak output

6 Conclusion Analysis and testing of geophone in different soil conditions for elephant infiltration, the goal is to provide early warning and reduce false alarms so that no human’selephant conflict occurs. The geophone sensors are tested in different soil conditions to know the variation of signal produced by geophone while detecting the elephant intrusion. By measuring the volumetric water content of the soil, this model works in every climatic state. The simulation and hardware results obtained are compared both in amplitude and time period. The voltage and time period obtained are set as threshold to detect elephant intrusion in forest border areas. This model developed helps in elephant conservation and reduces the human-elephant conflict. The geophone amplitude for dry soil is found to be 0.5v and 0.04v for wet soil. The time period of the signal is 2 s for dry soil and 1.5 s for wet soil.

References 1. H.N. Kumara, S. Rathnakumar, M. Ananda Kumar, M. Singh. Estimating Asian elephant, Elephasmaximus, density through distance sampling in the tropical forests of Biligiri Rangaswamy Temple Tiger Reserve, India. J. Trop. Conserv. Sci. 5(2), 163–172 (2012) 2. D.T.S. Wijesekera, M.T. Amarasinghe, P.N. Dassanaike, T.H.H. De Silva, N. Kuruwitaarachchi, Modern solution for human elephant conflict, in 2021 2nd International Conference for Emerging Technology (INCET). IEEE (2021, May), pp. 1–6 3. K. Ramkumar, et al.Human and elephant (Elephas maximus) deaths due to conflict in Coimbatore Forest Division, Tamil Nadu, India. ZOO’s PRINT XXIX(8), 12–19 (2014) 4. S.J. Sugumar, R. Jayaparvathy, An early warning system for elephant intrusion along the forest border areas. Curr. Sci. 104, 1515–1526 (2013) 5. J.D. Wood, C.E. O’Connell-Rodwell, S.L. Klemperer, Using seismic sensors to detect elephants and other large mammals: a potential census technique. J. Appl. Ecol. 42, 587–594 (2005) 6. B. Mortimer, W.L. Rees, P. Koelemeijer, T. Nissen-Meyer, Classifying elephant behaviour through seismic vibrations. Curr. Biol. 28(9), R547–R548 (2018)

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7. R.K. Mandal, D.D. Bhutia, A proposed artificial neural network (ANN) model using geophone sensors to detect elephants near the railway tracks, in Advanced Computational and Communication Paradigms, ed. by S. Bhattacharyya, N. Chaki, D. Konar, U. Chakraborty, C. Singh. Advances in Intelligent Systems and Computing, vol. 706 (Springer, Singapore, 2018). https:// doi.org/10.1007/978-981-10-8237-5_1 8. D.S. Parihar, R. Ghosh, A. Akula, S. Kumar, H.K. Sardana, Seismic signal analysis for the characterisation of elephant movements in a forest environment. Ecol. Inform. 64, 101329 (2021) 9. A. Barzilai, T. Van Zandt, T. Kenny, Technique for measurement of the noise of a sensor in the presence of large background signals. Rev. Sci. Instrum. 69(7), 2767–2772 (1998) 10. R. Brincker, T. Lago, P. Andersen, C. Ventura, Improving the classical geophone sensor element by digital correction, in Proceedings of the International Modal Analysis Conference, Orlando, FL, USA (2005) 11. H.P. Gavin, Vibrations of Single Degree of Freedom Systems (CEE 201L, 2014) 12. M. Hons, et al., Ground motion through geophones and MEMS accelerometers: sensor comparison in theory modeling and field data, in 2007 SEG Annual Meeting. Society of Exploration Geophysicists (2007) 13. S. Thangavel, C.S. Shokkalingam, The IoT based embedded system for the detection and discrimination of animals to avoid human–wildlife conflict. J. Ambient Intel. Human. Comput. 1–17 (2021) 14. K.O. Daffallah, G.M. Abdellattif, A.I. Adam, Development of a soil moisture control system with PC monitoring. Gezira J. Eng. Appl. Sci. 5(1), (2010)

Implementation of Goods Monitoring System Using Cloud V. Arulkumar, R. Lathamanju, V. Sundari, and K. Thaiyalnayaki

Abstract There is a significant proportion of loss of goods like meat and fragile items which while delivering caused by erroneous technical aspects as well as natural conditions. It is observed that specific types of vehicles are dedicated to performing certain types of delivery procedures, i.e., a meat carrying truck can deliver only meat since it possesses coolant and the temperature monitors, and fragile material carrying trucks have monitors to ensure that there are no vibrations or cracks resulted in carrying the goods. Statistically, it is seen that goods are broken and stolen during transportation. This solution, which utilizes the microcontroller and a sensor technology to monitor the temperature, vibration (using accelerometer), and an LDR sensor to check the integrity of the delivered goods. Using cloud technologies, the system is monitored that helps to implement an alert system for the management and the users in the event of mishaps. Keywords Internet of Things · Goods monitoring · Cloud

1 Introduction The growth of gadgets with transmit-activating capabilities is bringing the concept of an Internet of Things closer to reality, where sensing and actuating functions seamlessly blend away from view, and new capabilities are enabled by access to rich new data sources. Many IoT frameworks make use of many sensors to collect data and then make intelligent decisions. Using the cloud is important for gathering data and extracting knowledge from it. The IoT structure alone without cloud service V. Arulkumar Sri Sivasubramaniya Nadar College of Engineering, Chennai, India R. Lathamanju (B) · K. Thaiyalnayaki ECE, SRM Institute of Science and Technology, Ramapuram, Chennai, India e-mail: [email protected] V. Sundari Meenakshi Sundararajan Engineering College, Chennai, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_18

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can result in difficulties of data aggregation and access process, which would make it hard for people to make decision process, viewing data from long connectivity ranges and hence destabilizing monitoring process of real updates and activity result. Therefore, integrating cloud service with IoT can bring convenience and drastic growth in improvising result of real-time management in certain fields and allowing customers/clients to understand the management’s decisions to deal with continuous data. By bringing this methodology, we can save a cost by utilizing cloud services and protocols with IoT structure instead of building complex structure to deal with connectivity ranges and data mobility. This work involves with three stages. 1.

2.

3.

Building IOT structure—building an IoT structure with Arduino programmable microchips ESP8266 connected with compatible sensors DHT-11, SW-420, LDR. Utilizing cloud service—registering Google’s cloud service firebase and establishing ESP8266’s connection to real time for making use of data aggregation and mobility processes. Developing mobile application—developing mobile application to control, view, and manage decisions corresponding to the respective ESP8266 chips.

The following is how the rest of the article is structured. Section 2 discusses previous relevant efforts as well as existing systems that address a similar problem. Section 3 contains a full description of each module. The dataset specifics, as well as the implementation approaches and techniques used, are presented in Sect. 4. The implementation sets and comments are presented in Sect. 5. Section 6 summarizes the findings of this study and suggests ways to improve it in the future. Section 7 provides conclusion and future work.

2 Literature Review We now live in a technologically advanced society where everything is digital. People, on the other hand, see the need for automation in resource management and the necessity to track resource utilization daily. The Internet of Things (IoT), which relies on Bluetooth and individual WI-FI connections, has never been able to bring automation to a user-acceptable level for dealing with multiple resources and management. As a result, we’ve reached a point in the development of IoT models with various sensors, where the WI-FI modules are subsequently linked to cloud services. We have considered “Cloud-Based Goods Monitoring System” as the appropriate way to sustain numerous resources and management while considering multiple sensors and administration without Bluetooth [1]. This concept allows the customer to effortlessly automate and monitor commodities in containers in various sorts of trucks without causing any disturbances, as well as safeguard various types of goods. The data can be shared in a distributed fashion via cloud services, allowing other IOT sets to read the values and so execute a decision process. With cloud services, it is

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simple to automate a choice process among IOT sets to improve the result while maintaining human convenience. During shipping, the Cargolog transport monitoring system [2] is utilized to keep track of high-value items. The communication and alert system will immediately alert the driver, and the driver will be able to stop and inspect and report any damages to your items as you receive a real-time warning. Monitoring the cargo is critical when transporting sensitive, heavy, valuable, and large technological equipment. With the Cargolog® transport monitoring system, you can track the shipment via our cloud service, Cargolog® Online, while the carrier’s driver is alerted to any accidents or shocks and may immediately stop to inspect and respond before any products are harmed. The Cargolog transport monitoring system is directly attached to the vehicle and is used to monitor a wide range of goods, including wind turbines, lithium batteries, and a variety of other fragile and sensitive items. In your industry, learn how to use the transport monitoring system. In the Cargolog system [3], the system monitors only the impacts or shocks and alerts the driver to any necessary actions, whereas in our system, we not only monitor the impacts or shocks, but also the temperature and humidity of certain products such as meats, fisheries, and certain edible or consumable products, as well as the theft of the goods. In addition to the various types of monitoring described above, we suggest a system in which the vehicle can transport many types of commodities in the same container and be monitored all at once, eliminating the need for separate sensors to monitor similar types of resources in a single container.

3 Descriptions of Each Module The structure to be modeled and constructed requires the following components to be connected in such a way that they can capture data readings and send them to cloud services, which can then be accessed by the user in mobile applications. The following are the components:

3.1 ESP8266-NodeMCU The ESP8266 is a user-friendly and low-cost Internet connectivity device that can act as both an access point (to create a hotspot) and a station (to connect to Wi-Fi), allowing it to simply collect data and publish it to the Internet, making Internet of Things as simple as possible.

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Fig. 1 NodeMCU pin layout

3.2 DHT-11 This sensor is used to determine the room’s temperature and humidity. This has VCC/VDD, GND, and DATA pins and runs on 3.3–5.5 v. Its job in this project is to sense the room’s temperature and humidity. The sensor includes a separate negative temperature coefficient (NTC) for temperature measurement and an 8-bit microprocessor for temperature and humidity as serial data output. The sensor is factory calibrated, making it simple to connect to other microcontrollers. With an accuracy of 1 °C and 1%, the sensor can measure temperature from 0 to 50 °C and humidity from 20 to 90% (Figs. 1 and 2).

3.3 LDR The resistivity of a light-dependent resistor (also known as a photoresistor or LDR) is a function of the incident electromagnetic radiation. As a result, they are photosensitive devices. Photoconductors, photoconductive cells, and simply photocells are other names for them. When the illumination is high, the photocell resistance value decreases, and when the illumination is low, the photocell resistance value increases.

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Fig. 2 DHT-11 (Temperature and humidity sensor) pin layout

3.4 SW-420 The vibration sensor (SW-420) is a non-directional vibration sensor with a high sensitivity. The circuit is turned on, and the output is high once the module is stable. The circuit will be briefly severed, and the output will be low when the movement or vibration happens. At the same time, you can change the sensitivity to meet your specific requirements (Figs. 3 and 4).

4 Implementations and Development of Mobile Application The vibration sensor, light sensor, temperature, and humidity sensor set are all part of the proposed system. Each of these sets will have an ESP8266-NodeMCU that will be used to write and receive data sets into firebase. The well-developed mobile Fig. 3 Light dependent resistor (LDR) workflow

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Fig. 4 SW-420 (vibration sensor) pin layout

Fig. 5 Workflow of the structure of cloud based goods monitoring system

application will be connected to the firebase for examining data and informing the driver in the event of an emergency (Fig. 5). The above diagram shows that output data from each of the structure’s sensors is given to NodeMCU, which can then be seen in a mobile app via cloud services. The ESP8266 reads the data from the sensors and writes and updates it to the firebase.

4.1 Development of Mobile Application The UI components used in the mobile application are (i) Layouts: Relative layouts are used to allocate the children in a relative order to each other within the layout. The linear arrangement is frequently used to assign children in a chronological order. (ii) Text views: These are representations of strings and characters that the user can read. Styles such as italic, bold, and underlined can be used to represent them.

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4.2 Tools and Environment Used (i) Arduino IDE, (ii) Firebase [4–6], (iii) Android studio, (iv) Languages and libraries used: C, C++, JAVA, XML, Android-firebase library, Arduino-firebase library.

5 Implementation of Sets LDR−Light-Dependent Resistors (DTH-11 LDR) pushing data to Firebase (SW420).

6 Results and Discussions 6.1 Output from Sensors When light enters the container, the LDR sensor activates. For the set to trigger notification, a threshold is set. Temperature and humidity sensors keep an eye on the meat to see if it is in excellent shape. The notification is triggered when the value exceeds a certain threshold. The vibration sensor detects the vibrations inside the container on a continual basis. It sends out an alarm when the pilot’s speed exceeds a certain limit (Figs. 6 and 7).

6.2 Firebase and Mobile Application The dataset contains the values that are shared between the three sensors that were installed, as well as a registered mobile application for seeing the data in a userfriendly and understandable format for automating and monitoring the process via the generated mobile application (Figs. 8 and 9).

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Fig. 6 Values read from the three different sensors

7 Conclusion and Future Work Conclusion The goal of this project is to address difficulties like long-range communication and collaboration across various IoT sets in a connected state. The older IoT devices were discovered to employ Bluetooth, which lacks long-range connectivity and multiple administration with human intervention between IoT. It is used in conjunction with a cloud service to share datasets in a similar format between NodeMCU sets, ensuring proper processing and decisions without the need for user participation in the automation of a process. It provides for the cost-effective and mobile management of various IoT sets through a single cloud service.

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Fig. 7 The values can be observed in the FireBase

The dataset is kept in such a way that it allows for better management and improves the outcome with the help of the user. Efficient delivery monitoring enables business owners to rethink their routine procedures, eliminate unnecessary steps, and improve customer experience while working smarter, faster, and more efficiently. The cutting-edge technology of the items monitoring system enables superior operational administration and quicker deliveries, resulting in higher consumer delight reduces the expense of unexpected delays and lost deliveries.

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The Application Interface No Notifications

Fig. 8 Android Application with no notification interface

Future Work Building a large-scale commodity monitoring system that can track all types of goods, as well as a delivery system that can track the goods’ condition and position. Creating a better user interface and enhancing the system’s performance can result in a revolutionary shift in delivery methods that benefits everyone in the company. When connected with warehouse logistics and other corporate systems like purchase order systems, shipment tracking solutions provide optimal production efficiency. By utilizing information about the shipment’s status, potential delivery delays can be communicated to clients more smoothly. Furthermore, business owners will be able to quickly evaluate which means of communication or carrier is the most dependable.

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Notifications when threshold limit exceeds

Fig. 9 Android Application with notification interface

References 1. International Research Journal of Engineering and Technology (IRJET) e ISSN: 2395-0056 2(3) 2. Mobitron Cargolog System. https://mobitron.com/cargolog-transport-monitoring-system/ 3. Y. Tsang, K. Choy, C. Wu, G. Ho, H. Lam, P. Koo, An IoT-based cargo monitoring system for enhancing operational effectiveness under a cold chain environment. Int. J. Eng. Bus. Manage. 9, 184797901774906 (2017). https://doi.org/10.1177/1847979017749063 4. L. Moroney, Firebase cloud messaging, in The Definitive Guide to Firebase, pp. 163–188. https:// doi.org/10.1007/978-1-4842-2943-9_9 5. W.-J. Li, C. Yen, Y.-S. Lin, S.-C. Tung, S. Huang, Just IoT internet of things based on the firebase real-time database, in 2018 IEEE International Conference on Smart Manufacturing, Industrial & Logistics Engineering (SMILE) (2018). https://doi.org/10.1109/smile.2018.835 3979 6. Pyrebase Library for Manipulating Firebase Database [online] https://github.com/thisbejim/Pyr ebase 7. V. Dao, V. Hoang, A smart delivery system using internet of things, in 7th International Conference on Integrated Circuit, Design, and Verification (ICDV), Hanoi (2017), pp. 68–63

An Inventive and Frugal IoT-Based System for Unmanned Railway Crossings and Real-Time Train Collision Prediction Pertaining to Indian Conditions Shriram K. Vasudevan, Prashant R. Nair, and Juluru Anudeep

Abstract Trains are the major means of transport in India. On an average, they carry 234 million passengers per day. According to the statistics of Indian railways, on a given day, 12,617 trains run on the Indian tracks which pass through 28,607 railway crossings of which 9340 are unmanned crossings that are posing a major threat to the public. Many accidents occur at unmanned railway crossings resulting in casualties of public and cattle due to lack of alerts about the arrival of trains. To avoid this situation, we have designed a system that monitors the trains passing through the railway crossing and sends a soft signal for closing the gate automatically. This system is also capable of monitoring multiple trains that travel on a single track to prevent collisions between the trains. This system is built with the power of IoT, cloud computing, and sensors. Keywords Unmanned railway crossings · IoT · Cloud computing · GPS · Single track multiple train detection

1 Introduction Trains are the major means of transport in India. On an average, they carry 234 million passengers per day [1]. According to the statistics of Indian railways, on a given day, S. K. Vasudevan (B) Intel IoT Innovator, Project Manager, MNC Services Company, Bengaluru, Tamilnadu, India P. R. Nair Department of Computer Science and Engineering, Amrita School of Engineering, Coimbatore, India e-mail: [email protected] P. R. Nair · J. Anudeep Amrita Vishwa Vidyapeetham, Vengal, India J. Anudeep Department of Electronics and Communication Engineering, Amrita School of Engineering, Coimbatore, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_19

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12,617 trains run on the Indian tracks which pass through 28,607 railway crossings of which 9340 are unmanned crossings which are posing a major threat to the public [2]. The train accidents at railway crossings are the biggest killer, they account for 40% of train accidents and 66% of fatalities, reflecting the railways’ failure in implementing better means to avoid them [3]. The deaths at unmanned railway crossing may be caused due to the negligence of railway personnel in installing caution boards, human negligence, or lack of awareness of arrival of trains. In 2014–2015, the number of unmanned railway crossing accidents stood at 50 and 130 people died from them [4]. Not only at the unmanned crossings, but there are also a notable number of accidents which occurred at manned crossings due to human errors that can only be prevented by implementing an automated system that can automatically detect the arrival of trains. The train collisions on a single track constitute the next major part of train accidents. They are of two types, namely head-on collisions and rear-end collisions [5]. To prevent these accidents, we have proposed a system that makes use of GPS sensors, IoT, and cloud computing to measure the distance between two trains and also the distance from the train to the railway crossing junction using Google distance matrix API.

2 Existing Systems IoT coupled with machine learning algorithms and image processing is finding wide application in a variety of use-cases. These include diverse domains such as smart education [6], smart spaces [7], retail and shopping recommendation systems [8, 9], emotion detection [6], security [10] and temperature prediction [11]. There have been several attempts made toward achieving the task of reducing the casualties due to the unmanned railway crossings. Banuchandar and Kaliraj had come up with a design that makes use of IR sensors that are placed on either side of the tracks which gives a digital signal when an object is placed in between them [12]. Keith L. Shirke and Casella have designed a system in order to detect how far the train (proximity of the train) is using the RF signal waves. Firstly, they send a modulated RF wave which carries the data of speed and position of the train. They make use of pre-installed transceivers at the side of tracks that transmit the information that the train has crossed predetermined coordinates which makes the alarms at the railway crossings to turn on indicating its arrival [13]. There is one more invention by Hobson and Wootton toward the abovementioned problem. They have used the power of video analysis and image processing to process the video frames and searches for any abandoned objects that may cause damage to the train and is potential for an accident [14].

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3 Proposed System Architecture The objective of the system is to build an IoT-based system to track and alert the people at unmanned railway crossing and to prevent accidents between trains that run on single tracks. The proposed system is designed so as to detect the trains approaching railway crossings or approaching each other on the same track using Google distance matrix API, Adafruit cloud, and GPS sensors. Each train will have a GPS module installed on it which will be updating its respective location data into the cloud and are accessed by other trains, railway stations, and railway crossings. Figure 1 shows the flowchart of the proposed system. Proposed system architecture of railway crossing and single line tracks are shown in Figs. 2 and 3, respectively. Various components of the system include: Google Distance Matrix API: Google distance matrix API is an open-source service by Google to calculate the distance between two places. It returns the distance between two places if latitude and longitude of the origin and destination are provided. The following URL is used to query the Google maps for returning the distance between the two coordinates. https://maps.googleapis.com/maps/api/distancematrix/json?units=imperial&ori gins=17.2251,78.3615&destinations=17.5333,78.5153&key=YOUR_API_KEY.

Fig. 1 Flowchart of the proposed system architecture

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Fig. 2 Proposed system architecture at level crossing

Fig. 3 Proposed system architecture for trains on single line tracks

Adafruit Cloud: Adafruit is a cloud service provider that can be used to create feeds for storing information. We have used Adafruit for creating three types of feeds, i.e., private feed, particular feed, and access feed. A private feed is created with the train number as its feed name that is used to upload the geolocation of the train along with the timestamp. These are accessed by the railway crossings, trains on the same single line track to calculate the distance between them to avoid accidents. The next one is a particular feed which is created for every railway station with the station code as its feed name. These feeds store the information of trains leaving from that railway station along with the time. Access feeds are created only for the railway stations which have a single line track between its adjacent stations. The information in access feeds is used to know about the trains moving on the single track shared with its adjacent stations.

4 Experimental Approach The process of detecting trains approaching a railway crossing can be understood with the following example. Consider two railway stations, A and B which has a railway crossing between them. The railway crossing will continuously access the particular feed of A and B so that it can know the trains starting from these railway stations. If a train starts from either of the railway stations, then the server at the railway

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crossing starts accessing the private feeds of the train started from the railway station to know the distance between them using Google distance matrix API. If this distance between them is less, than six kilometers then a soft signal is sent for closing the railway gates. In the scenario shown in Figs. 4 and 5, a train with number 16318 has departed from a railway station toward a railway crossing. The train number of the departing train is uploaded to the particular feed of the railway station as well as the GPS data of the departed train is being uploaded to the private feed of the train in real time. The server at the railway crossing computes the distance of the train from it; as the distance is too long, no signal is sent for closing the gates. If the calculated distance is less than the threshold, then immediately a soft signal is sent for closing the gates automatically. The process of detecting trains on a single line railway track is a bit complicated and involves the use of three different types of feeds which can be understood from the following example. Let us assume the track between the three railway stations,

Fig. 4 Train departed from the station and approaching the crossing

Fig. 5 Train traversing the railway crossing and moving toward the adjacent station

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A, B, and C are a single line track, and B is located in between A and C. As railway station B has single line track on both sides, it will have an access feed that will collect the data from its adjacent stations’ particular feed and store in it. This data are used by the trains starting from railway station B to know about the trains traveling on the same single track. The train starting from railway station B will calculate the distance between other trains traveling on the same single track using the data in their private feeds. If the distance between them is less than three kilometers, then a soft signal is sent for automatically applying the brakes. If there is no access feed in a particular railway station that means there is no single line track after that railway station. From Figs. 6 and 7, the stations A, B, and C upload the train numbers departing from them to their respective particular feeds, whereas access feed of station B is updated with the train numbers present in the particular feeds of its adjacent stations. Trains 16,318 and 17,229 are uploading their geolocation data into their respective private feeds which are accessed by the trains traveling on the same single line track. This makes the trains to know the distance between the approaching trains. Both the trains have

Fig. 6 Trains entering the single track from two different stations

Fig. 7 Trains present in close proximity where an accident is imminent

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safer distance between them, so brakes are not applied. If the trains approach each other closely causing a potential situation for an accident, a soft signal is sent to the trains for applying brakes automatically preventing an accident.

5 Experimental Setup In this investigation, we have used a Raspberry Pi as a server in the railway station and crossing junctions to upload and access data from the Adafruit feeds. It is also used to calculate the distance between the trains with the help of GPS sensors. In the train, we used NodeMCU and GPS sensor for uploading the data to the Adafruit feeds. The connection diagram of the proposed system is shown in Fig. 8. The GPS sensor can connect to a maximum of nine satellites and works as expected only when it connects with at least three satellites. It returns data in NMEA format as shown in Fig. 9 which contains velocity, timestamp, latitude, longitude, and many more details of the GPS module. In our system, we only need latitude and longitude data for locating the train, and the remaining data can be neglected. To get latitude and longitude data, it is sufficient to parse the $GPRMC line, highlighted in Fig. 9.

Fig. 8 Connection diagram of proposed system

Fig. 9 NMEA data fed to Python script

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$GPRMC,144916.000,A,1054.1073,N,07653.7800,E,0.23,180.03, 050418„,A*6F

From the $GPRMC line, the text in bold is considered separately. The numbers in the text will be of the format xxxyy.zzzz. Latitude or longitude can be easily obtained by using the formula xxx + yy.zzzz/60. If the letter next to the number is N or S, then the number can be used for finding the latitude. Similarly, if the letter next to the number is E or W, then it is used for finding the longitude. Latitude: 10 + 54.73/60 = 10.91216. Longitude: 076 + 53.7800/60 = 76.8963. So, the location of the train having this GPS module is 10.91216, 76.8963.

6 Results The results of this investigation are by testing the whole experimental setup in trains, railway crossings, and stations. We have placed a server with GPS sensor at the crossing junction and a NodeMCU setup as shown in Fig. 8 inside the train. Figure 10 shows the distance between the railway crossing and approaching train toward the crossing. As the distance between them is less than 1.3 km, the gate has been closed which is indicated by a red circle. As we cannot make two trains travel in opposite direction on a single track. So, for prototype and testing purpose, we have placed GPS sensors connected with NodeMCU in two buses separated by 70 km on the same road moving opposite to each other. Figure 11 shows the distance between the two buses with time. When the distance is less than 2.5 km, brakes are applied by both the buses, and they are stopped which is indicated by the red circle. Fig. 10 Distance between train and crossing junction versus time plot

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Fig. 11 Distance between two buses versus time plot

7 Conclusion and Future Enhancements Indian railways are on the plan of introducing smart passenger coaches which are Wi-Fi enabled. Smart railway engines can aid our systems effectively. This can render uninterrupted Internet connection to our system for uploading the geolocation values to cloud relentlessly, and along with this, the railway crossings present are also needed to be Wi-Fi enabled in order to read the data from the cloud and perform the data analytics. By installing this system, which is a one-time investment for the government, it can reduce the manpower and human errors at the railway crossing and can economize the railway budget. Nowadays, almost every railway station in India is Wi-Fi enabled which is an added advantage to our system, and no extra expenditure is required to maintain our system.

References 1. https://www.quora.com/On-average-how-many-people-are-on-the-go-in-a-train-every-dayin-India. Last accessed 30 Dec 2020 2. https://economictimes.indiatimes.com/industry/transportation/railways/railways-eliminates1503-unmanned-level-crossings-in-2016-17/articleshow/57993823.cms. Last accessed 30 Dec 2020 3. https://timesofindia.indiatimes.com/india/66-of-accident-fatalities-at-level-crossings/articl eshow/38984972.cms. Last accessed 30 Dec 2020 4. http://indianexpress.com/article/india/uttar-pradesh-kushinagar-school-bus-accident-unm anned-railway-crossing-5152402/. Last accessed 30 Dec 2020 5. https://en.wikipedia.org/wiki/List_of_Indian_rail_accidents. Last accessed 30 Dec 2020 6. S.R. Thangavel, T. Rajevan, Deep learning based emotion analysis approach for strengthening teaching-learning process in schools of tribal regions. J. Adv. Res. Dyn. Control Syst. 11(8), 621–635 (2019) 7. P.A. Paresh, L. Parameswaran, Vision-based algorithm for fire detection in smart buildings, in Lecture Notes in Computational Vision and Biomechanics, vol. 30 (Springer, Netherlands, 2019), pp. 1029–1038

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8. P. Subathra, S.V. Ghanapathy, A.R.V. Chidambaram, K.R. Ganesh, Exploratory data analysis and predictive analysis on grocery shopping. J. Adv. Res. Dyn. Control Syst. 10(5), 1803–1809 (2018) 9. R.C. Jisha, R. Krishnan, V. Vikraman, Mobile Applications Recommendation Based on User Ratings and Permissions in International Conference on Advances in Computing, Communications and Informatics (ICACCI), Bangalore, India (2018) 10. A. Baskar, K.T. Gireesh, Facial expression classification using machine learning approach: a review, in Advances in Intelligent Systems and Computing, vol. 542 (2018), pp. 337–345 11. N.M. Dhanya, S. Veerakumar, Performance analysis of various regression algorithms for time series temperature prediction. J. Adv. Res. Dyn. Control Syst. 175–194 (2018) 12. J. Banuchandar, V. Kaliraj, P. Balasubramanian, S. Deepa, N. Thamilarasi, An automated unmanned railway level crossing system. Int. J. Modern Eng. Res. (IJMER) 2, 458–463 (2012) 13. K.L. Shirkey, B.A. Casella, Hughes Aircraft Co. Wireless Train Proximity Alert System. U.S. Patent 5,554,982 (1996). https://patents.google.com/patent/US5554982A/en 14. G. Hobson, J.R. Wootton, Esco Technologies Inc. Video Detection Apparatus for Monitoring a Railroad Crossing. U.S. Patent 5,825,412 (1998). https://patents.google.com/patent/US5825 412A/en

Signal and Image Processing

Robust Blind Source Separation of Maternal and Fetal ECG Signals—Application to Instantaneous Heart Rate Calculation El-Mehdi Hamzaoui

Abstract Blind Source Separation approaches have proved their efficiency to solve problems dealing with recovering a set of underlying sources from recoded observations without any a priori knowledge on the mixture process and sources. For this reason, we propose to use them to extract the true fetal ECG signal and consequently to calculate its instantaneous heart rate. Thus, we aim the application of the Robust Second-Order Blind Identification (RSOBI) algorithm, which exploits nonstationarity properties and second-order statistics, to a set of ECG mixtures recorded on pregnant mother. The obtained results show that we can separate original mixtures into 3 main sources which can be considered as the fetal ECG, the maternal ECG and noise. The recovered fetal ECG signals were found very clean and have permitted to perform fetal instantaneous heart rate calculation with a high precision. Keywords Blind source separation · Fetal ECG · Instantaneous heart rate · RSOBI

1 Introduction The purpose of fetal monitoring is to detect any signs of fetal distress during all periods of pregnancy so that early intervention can be achieved. The well-being of the fetus depends mainly on the placental exchange of oxygen between the fetal circulation and the maternal circulation [1]. In the presence of certain risk factors, fetal electronic monitoring is necessary to save the fetus. To perform this monitoring, there are several techniques for calculating the parameters characterizing the state of the fetus. Amongst these parameters, the variability of the heart rate of the fetus is currently the most used to evaluate fetal health and help the decision to extract a fetus with intrauterine pain. The fetal heart rate varies according to various internal and external events which can significantly affect the fetal heart rate interpretation [1]. E.-M. Hamzaoui (B) Technical Division, National Centre for Nuclear Energy Science and Technology (CNESTEN), B.P.: 1382 RP, Rabat, Morocco © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_20

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The variability of the basic heart rate, which is considered to be one of the most significant features in the detection of abnormalities, explains the reason why many researchers have tackled its analysis [1, 2]. Thus, automatic fetal heart rate analysis was developed by Professor Dawes and Redman at Oxford University in the United Kingdom in 1977 using a database containing 8000 cases of pathological pregnancy [1, 3]. Digital data acquisition and processing systems are designed to make automatic fetal heart rate interpretation and simplify fetal monitoring. However, the fetal ECG signal is very weak and corrupted by a background noise. This makes difficult its extraction and analysis. For this reason, various algorithms for fetal ECG signal analysis and R-peak detection have been proposed in the literature [1]. Several scientific works have addressed this problem using blind source separation (BSS) methods [4, 5]. This problem has been considered as a “source separation” one for the first time by De Lathauwer et al. [6]. The authors assume that the electrical activity of the mother’s heart can be likened to a rotating field (3-dimensional) from a distance. Thus the mother’s ECG (mECG) is therefore seen as a 3-component signal. Also they consider that the fetal ECG (fECG) signals are statistically independent of the mother’s ones [6]. Choi et al., have implemented the Flexible Independent Component Analysis (FICA) algorithm which uses the natural gradient method, introduced in [4] to achieve the fECG separation task [7, 8]. However, the authors propose an estimation of the probability densities of the sources which takes into account, in the progression of the algorithm, the nature of over-Gaussianity or subgaussianity of the sources. They then compared their results to those of Bell and Sejnowski [9] and showed that their method improves the signal-to-noise ratio on the components representing the fECG [8]. Zarzoso et al. proposed a comparative study between the adaptive filtering technique presented in [10] and a higher order statistics BSS techniques. Tests conducted in this study showed that the BSS methods are more robust and provide better results than those obtained by adaptive filtering, thus reinforcing the interest they have in BSS [10]. In this paper, the proposed to use a BSS technique without any a priori information on the way in which the two signals, mECG and fECG, are mixed nor on the nature of the noise which affect the recording. The proposed algorithm exploits the nonstationarity of the signals and the second-order statistics to achieve fECG extraction. The objective is to get a very clean signal so that we can achieve automatic location of the R-peak and thus to compute the instantaneous heart rate of the fetus.

2 Materials and Methods 2.1 ECG Data In this work, we used ECG signals recorded on 8 different pregnant women (38– 41 weeks of gestation) using 8 leads; three thoracic and five abdominal leads. Each

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Fig. 1 ECG signal’s power spectral density (lead 1)

ECG signal is sampled at Fs = 250 Hz and contains 1000 samples. The original data have been whitened to remove the baseline in order to get better results. The power spectral densities’ plots of the recorded ECG signals show that these signals have a coloured and non-stationary character as Fig. 1 illustrates. Indeed, the whitening processing technique consists of transforming a coloured stochastic signal into a white one. This transformation aims inverting the parameter that contains the colour information. This information is included in the signal energy, its correlation, or simply in the signal’s 2nd order statistics [4].

2.2 The BSS Formalism The BSS methods are used to solve the problem of recovering unknown and mutually independent original sources S from a set of recorded observations X (mixture sources): X = HS + N

(1)

where N denotes the noise. The principle of BSS methods consists of the computation of the separating matrix W which approximates the inverse transfer function of the mixing system H −1 , using only the information contained in the observed/recorded signals x(k). The process undergoes until obtaining estimated sources y(k) which are as independent as possible [4]. Many BSS methods exploit the second-order moments. These require the sources to be temporally correlated (coloured sources). Fety [11] and Tong et al. [12], are the first to propose such approaches based on the exploitation of the algebraic properties

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of two observation covariance matrices, associated respectively with a zero and nonzero delay. Based on these works, the Second-Order Blind Identification (SOBI) algorithm has been developed by Belouchrani et al. [4, 13, 14]. The SOBI algorithm exploits no longer one but more covariance matrices of observations associated with non-zero delays. More precisely, its authors show that after bleaching of observations and a joint diagonalization of the considered covariance matrices, it is possible to estimate the mixture with a trivial matrix [4, 13, 14]. The RSOBI algorithm consists of the SOBI method combined to a robust orthogonalization steps. Indeed, in several BSS methods, the robust orthogonalization is an important pre-processing step which is used to be sure that the global mixing matrix G = WHG = WH (where H is mixing matrix and WW is dimixing or separating matrix) is orthogonal. It aims to find a linear arrangement of several symmetric timedelayed covariance matrices in order to minimize the effect of the noise [4]. Thus, the RSOBI algorithm can be summarized as follows [4]: Step 1 Perform robust orthogonalization as in [4]: x(k) = Qx(k)

(2)

Step 2 Estimate the set of covariance matrices for a preselected set of time lags (p1 , p2 , …,pL ) or bandpass filters: 

R x ( pi ) =

N 1  x(k)x T (k − pi ) = Q R x Q T N k=1 

(3)

Step 3 Perform a joint approximate diagonalization using one of the available numerical algorithms [15]: Rx ( pi ) = U Di U T ; ∀i

(4)

Step 4 Estimate the independent source signals as: 

s (k) = U T Qx(k)

(5)

3 Results and Discussion We process five ECG signals (mixtures) at each iteration, so the algorithm was run 56 times to process all possible combinations without repetition of the 8 mixture. The application of the RSOBI algorithm then results in two main separated sources. This result is confirmed by the value of the signal to interference ratio (SIR) which

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is computed using according to the column of the original mixing matrix as the following Fig. 2 shows. The two extracted sources have the highest SIR values. We assume that one of them corresponds to the mECG and the other to the fECG. We also notice that the mean SIR value is about 75 dB which indicates how much the applied BSS algorithm can be robust and efficient to extract the original independent sources of the recorded signal. In addition, there is no need to have knowledge about the noise nature nor about how the mixing process. Figure 3 illustrates the plots of the separated sources. We can confirm the hypothesis cited above and we can state on the good visual quality of the signals plots. In order to identify the extracted sources, we plotted each estimated component with an original ECG signal keeping the same scale (time and amplitude). The following Fig. 4 shows that the 1st extracted source corresponds to maternal ECG whereas the 3rd one match the fetal ECG waves in the original recordings. The 2nd source is assumed to be the noise that affects the recorded signals. This comparison can be achieved automatically and in an easiest way using the computation of cross correlation between extracted sources and pure mECG and fECG. As said before, one of our main objective of extracting fetal ECG signals is to achieve an automatic rate heart monitoring. Indeed, we have used a MATLAB® (R2018b) code which incorporates both RSOBI method to achieve the separation task and an R-wave detection method based on the findpeaks() subroutine. This last aims to locate the highest peaks in the fECG and thus permits to compute the fetal instantaneous heart rate (IHR). Since a threshold value is necessary to discriminate all the peaks of the fECG signal, we have chosen it to be 80% of the height of the R-peak. The obtained results allowed us to calculate the fetal heart beat rate defined as [16]: beat_rate = 300/average(IRH)

(6)

Since the IRH values are {2.1739; 2.1739; 2.2124; 2.2124; 2.2124; 2.2321; 2.2124; 2.2321}, we find that the mean fetal beat rate in our case is 135.88 heart Fig. 2 Computed values of SIR using the estimated mixing matrix columns

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Fig. 3 Results of the RSOBI algorithm

Fig. 4 Visual identification of the three extracted sources

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beat per minute (bpm). This value is within the normal fetal beat rate range which defined to be 110–160 bpm.

4 Conclusion Recording fetal heart rate remains the most important clinical way to monitor the fetus health. However, to get the heart rate information, it is first necessary to extract the fetal ECG signal which is very noisy and weak signal. In our work, we have tackled this problem as a BSS one. We have found that the RSOBI method is the most efficient one amongst 4 tested BSS techniques to extract the fECG. The obtained results showed that the recorded ECGs are composed by 2 main independent sources; the 3rd independent component corresponds to the fECG whereas the 1st one matches the mECG. In addition, we have succeeded to automatically detect the R-wave peaks whilst analysing the extracted fECG. This has allowed us to compute automatically the instantaneous heart rate of the fetus. The average value of the obtained IHR is about 2.2077 Hz which corresponds to normal fetal beat rate (110 bpm ≤ 135.88 bpm ≤ 160 bpm).

References 1. S. Oudjemia, Analysis of Biomedical Signals by Multi-fractal and Entropic Approaches: Application to the Variability of the Foetal Heart Rate. PhD thesis, University Mouloud Mammeri, Algeria (2015) 2. B.M. Saykrs, Analysis of heart rate variability. Ergonomics 16(1), 17–32 (1973) 3. G. Boog, Practical applications of computerized analysis of foetal heart rate by the sonicaid oxford 8002 system during pregnancy and delivery. French J. Gynecol. Obstetr. Reprod. Biol. 30(1), 2841 (2001) 4. A. Cichocki, S.I. Amari, Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications, vol. 1 (Wiley, 2002) 5. A. Kachenoura, L. Albera, L. Senhadji, Blind source separation in biomedical engineering. IRBM 28(1), 20–34 (2007) 6. L. De Lathauwer, D. Callaerts, B. De Moor, J. Vandewalle, Fetal Electrocardiogram Extraction by Source Subspace Separation, Girona, Spain (1995), pp. 134–138 7. S. Choi, A. Cichocki, S. Amari, Fetal electrocardiogram data analysis via flexible independent component analysis, in Proceedings of APCMBE’99, Seoul, Korea (1999) 8. S. Choi, A. Cichocki, S. Amari, Flexible independent component analysis. J. VLSI Sig. Proc. 26, 25–38 (2000) 9. A.J. Bell, T.J. Sejnowski, An information maximization approach to blind separation and blind deconvolution. Neural Comput. 7, 1129–1159 (1995) 10. V. Zarzoso, A.K. Nandi, E. Bacharakis, Noninvasive fetal electrocardiogram extraction: blind separation versus adaptative noise concellation. IEEE Trans. Biomed. Eng. 48(1), 12–18 (2001) 11. L. Fety, Méthodes de traitement d’antenne adpatées aux radiocommunications, Ph.D. dissertation, Ecole Nationale Supérieure des Télécommunications (ENST) (1988) 12. L. Tong, V. Soon, Y. Huang, R. Liu, Amuse: A New Blind Identification Algorithm (IEEE, New Orleons LA, 1990), pp. 1784–1786

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13. L. Tong, R. Liu, V. Soon, Y. Huang, Indeterminacy and identifiability of blind identification. IEEE Trans. Circ. Syst. 38(5), 499–509 (1991) 14. A. Belouchrani, K. Abed-Meraim, J.-F. Cardoso, E. Moulines, Second-order blind source separation of correlated sources, in International Conference on Digital and Signal, Nicosia, Cyprus (1993), pp. 346–361 15. A. Belouchrani, K. Abed-Meraim, J.-F. Cardoso, E. Moulines, A blind source separation technique using second-order statistics. IEEE Trans. Signal Process. 45(2), 434–444 (1997) 16. E.S. Prakash, Madanmohan: how to tell heart rate from an ECG? Adv. Physiol. Educ. 29(2), 57–57 (2005)

A Pilot Study on Detection and Classification of COVID Images: A Deep Learning Approach R. K. Chandana Mani, Bharat Bhushan, Vankadhara Rajyalakshmi, Jothiaruna Nagaraj, and T. Ramathulasi

Abstract Outbreak of COVID-19 disease has been identified as huge pandemic for last ten decades. It threatened the global population as it has direct impact on the respiratory system. This virus includes lengthy RNA genome sequence of about 120 nm. CT-scan and X-rays are the widely used image modalities for the identification of COVID-19 disease till now. Manual diagnosis is time taking and tedious task to identify existence of COVID-19. To improve the performance and decreasing the time complexity, deep learning methods have been used. This paper reviews the deep learning systems developed for detection of COVID-19 disease. Further this paper discusses the available databases regarding COVID-19. This paper also explores the existing challenges and future directions of deep learning methods in field of diagnosing COVID-19. The ultimate goal of this paper is to illustrate the significance of deep learning methods in identification of COVID-19 disease. Keywords COVID-19 · Deep learning · Image modalities · Detection · Diagnosis

1 Introduction To the entire world, prevalent coronavirus (COVID-19) respiratory syndrome has become a serious pandemic caused by coronavirus 2 (SARS-CoV-2) [1, 2]. In addition to deaths, the rate of infections is also rising rapidly. Till today, more than 175,686,814 people have been infected with COVID-19, and 3,803,593 have died and 10,670,000 R. K. Chandana Mani (B) · V. Rajyalakshmi · J. Nagaraj School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India J. Nagaraj e-mail: [email protected] B. Bhushan School of Engineering and Technology, Sharda University, Greater Noida, India T. Ramathulasi School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_21

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have recovered from COVID-19. This transmission globally had impact on every sector of industries and placed a huge financial crisis for the entire population. The first COVID-19 case was identified in December 2019 in China, which was sourced by animals and followed a rapid spread to all the mankind. Physical contact and air are the possible medium for virus transmission. This virus inoculates into cells of lungs deeply by entering into respiratory system, and quickly multiply and produce a greater damage to lungs. Due to its nature of multiple mutations, it became a tough job to detect the virus and treat the affected persons. The familiar symptoms corresponding COVID-19 are fever, shortness of breath, cold, dizziness, headache, and muscle aches. The COVID-19 virus became very ferocious and terrific to total population of the world, as it weakened the entire immune system of the infected persons. Many virologists and medical experts are still working for efficient way of treatment to the deadly virus. It is important to identify COVID-19 at an early stage and its basic characteristics. Various blood tests (CBCs), PCR, and medical imaging tests are currently available to diagnose this process. However, according to WHO guidelines, coronavirus diagnoses should be made by “Reverse-Transcription Polymerase Chain Reaction (RTPCR)” [3]. But taking more time to fit this test can make this problem more dangerous for COVID-19 sufferers. Medical imaging is very useful for solving this problem and for early-stage COVID-19 detection. The diagnosis is first made by medical imaging followed by a clinical and RT-PCR test for an accurate final diagnosis. Two methods of medical imaging, CT-scan and X-ray, are used to diagnose COVID-19. Among these methods, X-ray is considered as cheaper and avoids the impact of high radiations to human body. But this method is still leaving the radiologists ambiguous and often leading to miss-diagnosis of tuberculosis and pneumonia as COVID positive. The main purpose of this paper is to collect the working methods of diagnostic systems working on COVID-19 disease due to the data collected through in-depth practice-based methods of image samples by medical imaging methods. Before the classification of these approaches, the classification was demonstrated by in-depth customized transfer learning methods based on trained models.

2 Related Works There have been wide research studies going on to create computerized detection of COVID-19 using some common deep learning methodologies. They focus on methods that are relied on CNN approaches which are extensively used to solve image classification problems. Narin et al. [4] proposed a hybrid approach that comprises three different models of CNN, namely ResNet50, Inception V3, and InceptionResNetV2, which successfully worked out in detection of COVID-19. Khan et al. [5] developed a DCNN model namely CoroNet that performs X-ray image analysis which outcomes the binary classification. Hemdan et al. [6] have introduced a novel model of CNN that is COVIDX-NET. It is combination of many previously developed neural networks like modified VGG-19, DCNN, and revised

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version of MobileNet. Lie et al. [7] proposed COVID-MobileXpert model that is relied on mobile and very lightweight neural network architecture. Mahdy et al. [8] developed a novel method for detection of COVID-19 infection that uses CXR images. Maghdid et al. [9] introduced a new strategy that helps medicos and radiologists in quick and easy analysis of COVID-19 cases. This model uses transfer learning and deep learning methods to construct a huge dataset, which is mixture of both X-ray and CT images and resulted in COVID-19 detection. Afshar et al. [10] utilized capsule networks instead of CNN models and developed COVIDCAPS that was very sophisticated system which gives accurate results for smaller sized datasets. Apostolopoulos et al. [11] also proposed a model to evaluate the performance of CNN architectures, using transfer learning techniques. He developed MobileNet that helped in feature extraction of COVID-19 images.

3 Deep Learning Methods Used for COVID-19 Images Recent methods of deep learning have ability to solve complicated issues through easier depictions. Usage of several layers in a step-by-step manner makes the deep learning models very familiar. These models have ability to learn features, thus retrieve accurate representations [12]. In the leaflet of medical images, the deep learning models are mostly used in the fields like smart healthcare, biomedicine, drug discovery, and medical image processing [13]. The rapid growth of deep learning models leads to their extensive utilization in detecting COVID-19 automatically in virus-affected patients. Generally, collection of data, data preprocessing, feature extraction, image classification, and evaluating the performance of the model are considered to be steps of deep learning pipeline. As an initial step, the patient data is collected in the hospital premises, and the patient is identified as participant. Although data have different categories, coming to COVID images, they use the image modalities such as X-ray and CT which are considered. Further step is preprocessing of the collected data where the collected data is converted to specific format for easier analysis. This step may include several image operations such as resizing, noise removal, data augmentation, and so on. Cross-validation is the popular technique used for partitioning of data. In next step, the given data is divided into training, testing, and validation set of given images, and this process is called data partitioning. To develop the specific model, train set data is used, which is further given to validation set of data to evaluate. At last, the model performance is evaluated by test set of data. For COVID-19 detection, feature extraction followed by image classification is the important steps. At final step, the deep learning model eventually retrieves the feature maps, by processing various operations again and again; thus, COVID-19 image classification is performed with the help of obtained feature maps with labels as COVID positive or negative. There are several models of deep learning models, which are successfully used for different image classification systems in previous studies which are discussed below.

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Fig. 1 CNN model for COVID-19 image classification

3.1 Standard 2D-CNN Limited training models are limited, the learning parameters are high, the gap between these numbers causes concern in the training of overfitting proposed models, and this has become a major problem. As a solution, research has been done to overcome this with the introduction of convolutional networks with convolutional layers. Twodimensional images are considered input to CNN, which requires minimal preprocessing. It is therefore designed to retain information about the structure between the pixel and the neighboring pixels and make it usable again. Structurally a sequence of layers, a different function is used to convert one volume action through each layer. The structure of the three neural layers for a normal working procedure in computer vision is as shown in Fig. 1.

3.2 VGGNET VGG architecture was developed by a layered structure with some convincing layers. Each of these membrane activation functions is utilized by ReLU. But we will use SoftMax for classification in the last layer of this network structure. A filter with a size equal to 3 × 3 was selected for the layers in the convolution structure. Two stride structure can be seen in VGG-E. The three variants VGG-11, VGG-16, and VGG19 are constructed of layers 1, 16, and 19, respectively. All types of models with VGG-E architecture are finished with FC layers. In addition, the different models of the above models have different convolution layers, i.e., the numbers 16 VGG-19, 13 VGG-16, and 8 VGG-11. The building block developed by the VGG network is described for diagnosis of COVID-19.

3.3 AlexNet AlexNet has already gained the attention of many researchers as the first well-known and first in-depth learning-structure network in many studies. The structure of this

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Fig. 2 AlexNet model for COVID-19 image classification

network can be seen in Fig. 2. Network learning has been facilitated by the introduction of new perspectives such as local response normalization (LRN) and drop out in the construction of this network. The drop out is first applied to the last used FC layers in the structure. In addition, LRNs used in two ways are first applied by selecting the NN patch by generalizing the feature maps based on their neighborhood values. Second, LRN feature maps can be used throughout the build [14].

3.4 Xceptionnet The Xception architecture is a structure based on Inception V3. Exception architecture is a structure consisting of linear stacks, with residual connections of deep and detachable convolution layers. The network consists of 36 numbered convolution layers operated by 14 numbered modules. All modules except the first and last exhibit a simple residual connection. Due to the residual connection, it was finally possible to obtain the best and fastest work with the help of Xception architecture [15].

3.5 DenseNet The DenseNet formed by the dense interconnection of all the CNN layers made it possible for each layer in the network to be produced by the dense interconnection of their inherited layers [16]. It is popularly known as DenseNet due to its dense connectivity. A significant reduction is possible due to effective feature reuse in network parameters. The DenseNet is formed by a variety of transitions and dense blocks. These were assembled between two adjacent dense blocks.

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4 Challenges and Future Directions Implementation of CAD systems to predict and classify the COVID-19 made the researchers to overcome many issues, since the spread of the disease is very speedy globally. The major problems incorporated with COVID-19 are structuring deep learning models, data availability, and hardware requirements. Current research in diagnosis of COVID-19 suffers with many challenges in application with deep learning methods. Despite deep learning, relied models for detection of COVID-19 derived from X-ray and CT exhibit the satisfactory results, yet over spread of the disease still fighting with many technical issues. As most of deep learning methodologies are designed as automatic, they require huge datasets for developing an efficient and powerful tool for the purpose of disease diagnosis. Coming to COVID-19, it is a known fact that it is a developing novel research, so it suffers with lack of sufficient and adaptable datasets. Also the existing image datasets of the virus affected patients are noisy, incomplete, inaccurate, and ambiguous in some cases. Training a deep learning model with diverse and massive datasets is very tedious job and involves many issues such as sparsity, data redundancy, and missing values. This paper reviews number of datasets relying on the methodology indulged. Hence, it is very complicated task to retrieve the best system that acquires promising results for diagnosis of COVID-19. Hence, this pandemic is very new all over the world, medical data could not be exactly monitored and collected sincerely. This lead to very less amount of datasets. Also existing datasets presents a minimum number of cases. Hence, many studies reported datasets of very small in size [17]. The absence of phenotypic data like gender and age in the reports is the issue. Usage of such data can extend the performance of deep learning models. The complete characteristics of the virus is still unpredictable, and its mutational probability is a major challenge.

5 Conclusion The entire population of the world is suffering due to this pandemic disease. In less period, it creates a dangerous situation to the health of common people. Experts can detect the COVID-19 on working with CT or X-ray images along with RT-PCR reports. This study carried out a detailed survey of the detection of COVID-19 disease by incorporating deep learning models. This study represents the currently available databases that help in predicting COVID-19. The lack of huge database is very big problem for performing deep learning models accurately on disease diagnosis. In future, model fusion concepts may give exact prediction and diagnosis of the COVID-19. The study suggests that the fusion of machine learning and deep learning architectures will help in obtaining a good accurate diagnostic model. In future, medical experts take advantage of improvised CAD systems for accurate diagnosis.

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References 1. D. Cucinotta, M. Vanelli, WHO declares COVID-19 a pandemic. Acta Bio Medica: Atenei Parmensis 91(1), 157 (2020) 2. F. Wu, S. Zhao, B. Yu, Y.M. Chen, W. Wang, Z.G. Song, Y. Hu, Z.W. Tao, J.H. Tian, Y.Y. Pei, M.L. Yuan, Y.Z. Zhang, A new coronavirus associated with human respiratory disease in China. Nature 579(7798), 265–269 (2020) 3. C. Sohrabi, Z. Alsafi, N. O’Neill, M. Khan, A. Kerwan, A. Al-Jabir, C. Iosifidis, R. Agha, World health organization declares global emergency: a review of the 2019 novel coronavirus (COVID-19). Int. J. Surg. 76, 71–76 (2020) 4. A. Narin, C. Kaya, Z. Pamuk, Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks. Pattern Anal. Appl. 1–14 (2021) 5. A.I. Khan, J.L. Shah, M.M. Bhat, CoroNet: a deep neural network for detection and diagnosis of COVID-19 from chest x-ray images. Comput. Methods Progr. Biomed. 196, 105581 (2020) 6. E.E.D. Hemdan, M.A. Shouman, M.E. Karar, COVIDX-NET: a framework of deep learning classifiers to diagnose COVID-19 in x- ray images (2020). arXiv:2003.11055 7. L. Li, L. Qin, Z. Xu, Y. Yin, X. Wang, X.J. Kong, Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT. Radiology (2020) 8. A.E. Hassanien, L.N. Mahdy, K.A. Ezzat, H.H. Elmousalami, H.A. Ella, Automatic x-ray covid-19 lung image classification system based on multi-level thresholding and support vector machine (2020). medRxiv 9. H.S. Maghdid, A.T. Asaad, K.Z. Ghafoor, A.S. Sadiq, M.K. Khan, Diagnosing COVID-19 pneumonia from X-ray and CT images using deep learning and transfer learning algorithms (2020). arXiv:2004.00038 10. P. Afshar, S. Heidarian, F. Naderkhani, A. Oikonomou, K.N. Plataniotis, A. Mohammadi, COVID-CAPS: a capsule network-based framework for identification of COVID-19 (2020) 11. I. Apostolopoulos, S. Aznaouridis, M. Tzani, Extracting possibly representative COVID19 biomarkers from X-ray images with deep learning approach and image data related to pulmonary diseases. J. Med. Biol. Eng. 14, 1–8 (2020) 12. Y. LeCun, Y. Bengio, G. Hinton, Deep learning. Nature 521(7553), 436–444 (2015) 13. A. Esteva, A. Robicquet, B. Ramsundar, V. Kuleshov, M. DePristo, K. Chou, C. Cui, G. Corrado, S. Thrun, J. Dean, A guide to deep learning in healthcare. Nat. Med. 25(1), 24–29 (2019) 14. L. Zhang, M. Wang, M. Liu, D. Zhang, A survey on deep learning for neuroimaging-based brain disorder analysis. Front. Neurosci. 14 (2020) 15. K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition (2014). arXiv:1409.1556 16. M.Z. Alom, T.M. Taha, C. Yakopcic, S. Westberg, P. Sidike, M.S. Nasrin, B.C. Van Esesn, A.A.S. Awwal, V.K. Asari, The history began from alexnet: A comprehensive survey on deep learning approaches (2018). arXiv:1803.01164 17. G. Huang, Z. Liu, L. Van Der Maaten, K.Q. Weinberger, Densely connected convolutional networks, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 4700–4708

Ai-Based Online Hand Drawn Engineering Symbol Classification and Recognition Alikapati Keerthi Priya, N. Gaganashree, K. N. Hemalatha, Janaki Sutha Chembeti, and T. Kavitha

Abstract The Covid-19 pandemic situation transformed the education system across the world to the virtual mode. Due to this, the process of teaching learning becomes virtual which made various domains in the education problematical to carry out. Consequently, there is an increase in the rate of purchasing the additional hardware like writing pads and pens for teaching, which is not practicable in the case of deprived. Also, there is an ever-increasing inquisitiveness in crafting systems to automatically recognize free hand drawn sketches as it includes the challenge of recognizing various diverse patterns of sketch and the diagrams in different directions. In this work Deep learning (DL)-based Convolutional Neural Networks with VGG16 architecture is proposed to classify the hand drawn electronic components and digitize them for better legibility. It is most preferable during online classes and presentations. For training and testing, custom online hand drawn dataset is given, which consists of 15 different symbols each of 1100 symbols. In this approach a user-friendly GUI is provided for drawing circuit symbols which is very helpful for the user rather than picking placing and drawing the diagram. The custom hand-made dataset is trained, tested and accuracy is calculated. With this approach along with the individual symbol recognition, complete circuit is also reconstructed and gives 99.2% accuracy. Keywords Symbol · GUI · Convolutional neural networks · Recognition · Classification · Circuit · Free hand sketching · Deep learning

1 Introduction Sketch is a most impressive way of delivering ideas to the real world. Due to this covid-19 pandemic, circumstances around the world changed drastically. Prominently, the education domain across the world had experienced several changes shifting from classroom teaching to virtual online mode of teaching, as a result A. Keerthi Priya (B) · N. Gaganashree · K. N. Hemalatha · J. S. Chembeti · T. Kavitha Electronics And Communication Engineering, AMC Engineering College, Bengaluru, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_22

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quite a lot of domains underwent trouble in handling classes through online mode. Online platforms need an upgrade in drawing tools to enhance learning experience in order to recognize and directly convert hand drawn engineering symbols to a digitized form, as it is difficult for a teacher to teach diagrammatic subjects in the online platforms without using additional hardware like writing pads or pens. The learner experiences trouble in recognizing these hand drawn sketches due to the problem in drawing diagrams with mouse as it is not so legible with it to draw. Considering Electronics and Electrical domain in Engineering, it mainly deals with the devices and components which are solely built underlying with circuits and its various circuit components interconnected. This proposed paper presents a unique approach of circuit and its components recognition and reconstruction using Convolutional Neural Networks (CNN) which is a DL-based approach. The significant facet of the study is that dataset used for the execution is the custom online hand drawn dataset exclusively created by the authors which consists of symbols like resistor, capacitor, inductor, voltmeter, ammeter, switch, diode etc. Related work is mentioned in Sect. 2. The general information about the architecture and its details are mentioned in the Sect. 3. Further, the dataset used, approach and algorithm are explained step by step in Sect. 4. Next, in Sect. 5, the training and testing aspect of the work is detailed where the obtained accuracy and performance metrics are elucidated along with that, the results achieved in the study are detailed. To the end, this study is concluded with Conclusion and Future work in the Sects. 6 and 7.

2 Related Work Deep learning (DL) obeys the principle saying “learning from experience”. Through constant exposure to the environment or situation to be learnt, good experience is accomplished [1]. Paper [2] explains the conversion of hand drawn symbols to CAD models with tablet data entry. CNN approach for electronic component recognition is done in MATLAB obtaining 95% accuracy [3]. Paper [4] describes the usage of Artificial Neural network approach of classifying digits and three different electric symbols. Paper [5] explains about the visual-based stroke method for classifying three types of drawing such as digits, electrical signs and power point shapes and not extended to complete sketch recognition. Blurred shape model using Ada-boost technique is used to classify the hand written public database in work [6]. Using C# language, handwritten circuit identification on online via Tablet PC is explained [7]. Hidden Markov model-based hand drawn electric circuit diagram recognition is explained with the use of Viterbi algorithm [8]. Circuit schematic simulation and detection is explained with computer vision approach which required to present with highly discriminate features like average height, inclination and other parameters [9]. Network template matching, syntactic recognition and Geometric transform statistic method are used in circuit diagram recognition [10]. Four different CNN models are used for performance comparison for Basic circuit components classification

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showing accuracy of 84.41% [11]. Image based recognition methods are compared with feature extraction approach in Paper [12]. Logic circuits simulation and recognition is performed on Tablet PC limited to certain functionalities [13]. Hand drawn flow chart and flexible relation diagrams are recognized with Natural correction and editing process [14]. Online stroke positioning for detection of arrows is used to increase accuracy but this approach has lower exactness [15]. The method to modify electronic documents taking gesture and speech as an input id explained [16]. Hand written graphic recognition introduced to understand and differentiate inter-related mathematical and complex flowchart of 1D structures [17]. Segmentation-based approach for online complex freehand drawings are described [18]. Online sketched flowchart recognition is explained based on spatial and temporal information Logical structure and graph grammar producing 70.4% accuracy [19].

3 Proposed Methodology The basic operation of the proposed project work is the integration of all the different modules in the project like Data Processing, Training and Classification. Data processing contains three subblocks inside i.e., the raw custom data is collected by the author, then it is loaded into the model, where different image processing activities which are inbuilt in the model are applied. Further this model is used for training with the given dataset which is split into 2 portions namely training and testing dataset. Later after training, this model is used for testing with the user given data. Further considering classify image block, it displays the digitized output symbol to the user on the screen.

3.1 Architecture The proposed work employed in the project is the CNN model which is a Deep learning (DL)-based approach. In Convolutional Neural Network, VGG16 architecture is used in proposed work.

3.2 Data Preprocessing In this project, a new custom data set of various electronic components are created which are hand drawn by the authors of this study. It consists of 12 different electronic symbols like resistor, capacitor, inductor etc., each consisting of 1100 symbols. In which few of the sample symbols of dataset are displayed in (Table 1). A python code is written for dataset creation, where each symbol is stored in a separate folder in the process, which will be further used in the training stage. Here the made dataset

DC supply

Capacitor

Component

Diode

Symbol

Resistor

Component

Table 1 Sample images of dataset Symbol

Inductor

Ammeter

Component

Symbol

Voltmeter

Component

Symbol

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is preprocessed in the neural network, where first the image is resized and then raw pixel intensities of the image are scaled to [0,1] range. Further color transformation and thresholding operation is applied on the images.

4 Classification After data preprocessing model training is done, then it is loaded and testing is performed on it with various symbols and circuits. To begin with the testing of proposed model different packages are initialized and the trained model gets loaded as in Fig. 1. This loads the user-friendly GUI and hence drawing space is displayed for the user to test the model with different symbols. There, five buttons will be available for the user to select appropriate one either to test the individual symbol or to test the complete circuit by clicking on change mode button and change color. Then user can draw and click on predict symbol or predict circuit button appropriately. Then if user needs to check with the different symbol, drawing space can be cleared by clicking on clear space button else quit button is pressed to close the drawing space.

A START

IniƟalize the packages/library

Load GUI and drawing space

Load trained model

Get predicted results

Display output name

Clear drawing space

Draw image on the drawing space

B

Convert to image

If Quit buƩon pressed

Pass the image to loaded model

Break from loop

Yes

A

Fig. 1 Model flowchart

END

No

B

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5 Results 5.1 Individual Symbol Recognition Using Convolutional neural networks, firstly the model is designed such a way that it performs symbol recognition task. It displays a user-friendly GUI so that, for a user, there is screen available to draw the symbol and when clicked the button to predict, it classifies the drawn symbol and recognizes it correctly. As shown in Figs. 2 and 3 symbols like inductor and ac supply are classified and recognized. Fig. 2 Recognition of resistor symbol

Fig. 3 Recognition of Ac supply symbol

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Fig. 4 Reconstruction of circuit consisting symbols of capacitor and resistor

Fig. 5 Reconstruction of circuit consisting symbols of bulb, ac supply, resistor

5.2 Complete Circuit Reconstruction In this study, as in further part, there is also possibility to reconstruct the complete circuit basis on the symbols drawn in the circuit neatly so as to get legible digitized circuit. All the symbols in the circuit are recognized and reconstructed well using the CNN approach. Here, if circuit symbols drawn are very obscure by the user also produces the precise results (Figs. 4 and 5). These are not only the complete symbols which actually can be predicted but in total 12 different electronic circuit symbols can be accurately predicted in this project.

5.3 Evaluation Metrics This project achieved an all-time high accuracy with the electronic symbols drawn with free hand as in individual and also as a complete circuit. The final recall, precision and f 1-score got in this project is 99%. The validation accuracy is 99.2% and

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Fig. 6 Performance measure

the final step accuracy is 94.61% which is the best accuracy till now. The performance measure obtained for each component is displayed clearly in Fig. 6. The model success rate for the components like diode, fuse, ground, resistor and switch is 100% accurate and the remaining all predicted with the slight difference giving 98 and 99% accuracy. As precision depicts the relevancy of the output and recall depicts the actually relevant results, the proposed model’s overall precision and recall is 99% which clearly describes that our model which got high precision and high recall yields mostly all the results categorized correctly. Figure 7 demonstrates that loss kept on decreasing with the increase of dataset as well with good batch size and epoch and finally validation loss reached a best minimum value of 0.0253. Fig. 7 Training and validation loss

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6 Conclusion As the covid19 circumstances led the education domain move toward the fully virtual mode, there is lot of scope globally to online education system which most possibly become the choice for many education systems all over the world to conduct pedagogy process both in offline and online i.e., in the blended mode. This will increase the demand in the technology to be updated with all possible ways to make the virtual mode of education easier and here this study plays a vital role in revolutionizing the way of teaching diagrammatic subjects via online mode. This satisfies the user requirement of teaching sketch related topics easily without any additional hardware.

7 Future Enhancement This particular area of the research has a good scope in developing and enhancing the features provided to the user via the user interface. There is a wide range of opportunities to be extended to various other domains of education as well as in the other areas such as for all the virtual events, presentations, conferences happening online, book publishers etc. The forthcoming works will focus on model training using even more number of symbols possible in all different directions, with including more number of features in the user interface so as to make it an abundant tool in each and every feasible way. Also, the future work will put a spotlight on integrating with virtual conference tools so as to be helpful in meetings to sketch and explain the concept. Acknowledgements This work is supported and encouraged by the “Karnataka State Council for Science and Technology, 44th Series of Student Project Programme: 2020–21” providing financial and academic support. It is accredited with Project Reference No. 44S_BE_2464.

References 1. M. Sivaanandh, S. Surya, G. Priyanka, Hand written Indian numeral character recognition using deep learning approaches, in International Conference on Recent Innovations in Electrical, Electronics & Communication Engineering-(ICRIEECE), 2018. 978-1-5386-5995-3/18 2. A.K. Mishra, J.A. Eichel, P.W. Fieguth, D.A. Clausi, VizDraw: a platform to convert online hand-drawn (2009). Springer-Verlag, Berlin, Heidelberg 3. H. Wang, T. Pan, Hand-drawn electronic component recognition using deep learning algorithm, China. Int. J. Comput. Appl. Technol. 62(1) (2020) 4. M. Rabbania, R.H.S. Nagendraswamya, M. Contib, Hand drawn optical circuit recognition, in 7th International conference on Intelligent Human Computer Interaction, IHCI 2015. https:// doi.org/10.1016/j.procs.2016.04.064

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5. T.Y. Ouyang, R. Davis, A visual approach to sketched symbol recognition, in Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence (IJCAI-09), 2009, pp. 1463–1468 6. A. Fornés, S. Escalera, J. Lladós, G. Sánchez, J. Mas, Hand Drawn Symbol Recognition by Blurred Shape Model Descriptor and a Multiclass Classifier, GREC 2007, LNCS 5046, pp. 29– 39, 2008. Springer-Verlag, Berlin, Heidelberg 7. O. Ejofodomi, S. Ross, A. Jendoubi, M. Chouikha, J. Zeng, Online handwritten circuit recognition on a tablet PC, in 33rd Applied Imagery Pattern Recognition Workshop (AIPR’04), Washington, DC, USA, 2004, pp. 241–245. https://doi.org/10.1109/AIPR.2004.35 8. Y. Zhang, C. Viard-Gaudin, L. Wu, An online hand-drawn electric circuit diagram recognition system using hidden Markov models, in 2008 International Symposium on Information Science and Engineering. 978-0-7695-3494-7/08 9. M. Angadi, N.R. Lakshman, handwritten circuit schematic detection and simulation using computer vision approach. IJCSMC 3(6), 754–761 (2014). ISSN 2320-088X 10. Q. Li, N. Xiao, D. Liang, Y. Li, Improved Algorithm for Circuit Diagram Image Recognition. CSSE’19, May, 2019, Xi’an, China.https://doi.org/10.1145/3339363.3339387 11. Mihriban, Classification of Hand-Drawn Basic Circuit Components Using Convolutional Neural Networks. IEEE (2020). 978-1-7281-9352-6/20 12. R. Sinan Tumen, M. Emre Acer, T. Metin, Feature extraction and classifier combination for image-based sketch recognition, in 2010, EUROGRAPHICS Symposium on Sketch-Based Interfaces and Modeling. https://doi.org/10.2312/SBM/SBM10/063-070 13. M. Liwicki, L. Knipping, Recognizing and Simulating Sketched Logic Circuits (2010). https:// doi.org/10.1007/11553939_8 14. J. Wu, C. Wang, L. Zhang, Y. Rui, Sketch Recognition with Natural Correction and Editing (2014). Association for the Advancement of Artificial Intelligence 15. Q. Yu, Y. Yang, F. Liu, Y.-Z. Song, T. Xiang, T.M. Hospedales, Sketch-a-net: a deep neural network that beats humans. Int. J. Comput. Vis. Sketch-a-Net Deep Neural Netw. Beats Humans 16. J.A. Oberteuffer, J. Willbanks, K.-H. Loken-Kim, W. Kania, Processing handwritten and handdrawn input and speech input. US 6,438,523 B1 17. F. Julca-Aguilar, H. Mouchère, C. Viard-Gaudin, N.S.T. Hirata, A general framework for the recognition of online handwritten graphics. Int. J. Document Anal. Recogn. (IJDAR). https:// doi.org/10.1007/s10032-019-00349-6 18. W. Guanfeng, W. Shuxia, Classified segmentation of online complex freehand sketching, in 2011 International Conference of Information Technology, Computer Engineering and Management Sciences 19. Q. Chen, D. Shi, G. Feng, X. Zhao, B. Luo, On-line Handwritten Flowchart Recognition Based on Logical Structure and Graph Grammar

Flower Detection Using Advanced Deep Learning Techniques Kolla Bhanu Prakash, Ch. Sreedevi, Pallavi Lanke, Pradeep Kumar Vadla, S. V. Ranganayakulu, and Suman Lata Tripathi

Abstract In nature, we have found different types of flower plants. It is difficult to identify and recognize which flower species it is. Since the recent growth of deep learning in computer vision, identification of objects is extended through various fields. In this paper we aim to detect the flowers on Oxford17 flower dataset. Due to the wide variety of flower species with varying colors, shapes, and sizes, as well as their surroundings with leaves, shrubs, and other objects, flower recognition is the most difficult task in the subject of object detection. We present a formal contract Yolo object detection model in this research for rapid and accurate detections. The proposed model is a novel single-step object detection method for differentiating flowers from a wide variety of species. This system performs both localization and object recognition in the image automatically. The flower region is automatically split to enable for the creation of the smallest bounding box feasible around it, and the items in the image are then marked. We use advanced measures throughout the training stage to improve classification stability, precision, and speed. We evaluated our method on Oxford17 dataset and Google images dataset. The experimental study results have shown better results and exceed 98% on the dataset which is effective than the others. Keywords Flower detection · Object detection · Object classification · Computer vision · YOLO K. B. Prakash (B) Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India e-mail: [email protected] Ch. Sreedevi · P. Lanke · P. K. Vadla Department of Computer Science and Engineering, BV Raju Institute of Technology, Narsapur, Medak, Telangana, India S. V. Ranganayakulu Guru Nanak Institutions Technical Campus (Autonomous), Khanapur, Telangana, India S. L. Tripathi School of Electronics and Electrical Engineering, Lovely Professional University, Phagwara, Punjab, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_23

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1 Introduction In nature we find various types of plants species with flowers. Most people have no knowledge about these flowers so people generally have to use flowers reference books or use related pages on the internet, to browse the details using keywords in order to learn about them. Typically for a lot of people this keyword checking isn’t realistic. It is understood that it is difficult to classify an object against the background. Such problem existed for many reasons, such as; the conflict that occurs between the features of the objects and the context, the entity that is supposed to be identified over the objects in the context (rest of image) may be massive [1]. And the matching mechanism that might address such a big question as body orientation. Because there are so many different flower classes with similar traits, detecting and classifying flowers is a difficult undertaking. Several flower types have the same color, form, and look as one another [2]. Furthermore, photos of different flower species frequently include comparable elements such as stems, trees, and shrubs. This similarity and dissimilarity makes the flower identification process very difficult, with a highly incorrect output. To collect information about the flower at least one keyword relevant to that flower should be identified. Although there is a system of input image searching, derived results are mostly unrelated to what we want. One of the primary sources of characteristics widely used to differentiate between the distinct flower species is color, pattern, structure, and some descriptive detail. Due to the availability of large-scale annotated datasets and technology (GPUs) capable of processing massive volumes of data, several computer vision tasks have lately seen significant advances in performance. We propose a novel robust real-time flower detection system based on YOLOv3 object detection in this study. YOLOv3 is a cutting-edge real-time object identification system that employs a model with 53 convolutional layers [3] and five maxpooling layers [4]. As a result, YOLOv3 is significantly faster and produces better results. A number of computers are available due to the availability of largescale annotation datasets and hardware (GPUs) [5]. A number of computer vision tasks have lately shown considerable performance gains. On floral datasets, our robust strategy is tested, and the results reveal that the implemented methodology achieves at least 98% classification accuracy. The detection of flowers is proposed in this research utilizing YOLO Real Time Object Detection. This article’s material is structured as follows. The second section discusses relevant work in the field of flower detection utilizing various object detection algorithms. The YOLO real-time object detection system is introduced in Sect. 3. The proposed plan’s experiment findings are provided in Sect. 4. Finally, in Sect. 5, the conclusion and future research are discussed.

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2 Related Work This section focuses on the research investigations that have been conducted in order to cover all phases of any recognition system. Arje [6] proposed automatic monitoring system with time lapse flower is detected using light weight convolutional neural network and performed localization and classification of flower using sliding window approach. Three time lapse cameras were set up over separate Dryad integriafolia flower beds to collect data. They achieved accuracy of flower detection but training time is huge with sliding window approach. In [7] paper they recognize flower based on 4 steps. image enhancement is done by cropping image and extracting the colored image of the Blue Layer of Our Flowers (RGB). From the rest of the image, segmented flowers object (foreground) (background). To refine and improve the feature extraction procedure, the Chan-Vese image processing segmentation technique is employed. Extracting flowers of features such as texture using Gray Level Co-occurrence Matrices (GLCM) [8], color with (HSV) [9] Moments and shape with Hu Moments. In [10] they presented a system that classifies flower from the input image using Grab Cut in the segmentation phase to segment foreground (flower) from background. RGB Histogram was used as feature points of each flower in the training set and input set. Random Forest algorithm was used for classification process. Tanakorn Tiay developed image processing program [11], by taking flower photos from any smartphone or digital camera for analysis and identification. The graph cutting approach is used to extract the feature, and the closest neighbor algorithm is utilized for classification k. Having too many characteristics, on the other hand, may result in poor classification performance.

3 Proposed System 3.1 Yolo Architecture Yolo is one of the object detection systems that is extremely fast and produces more accurate results. Compared to other object detection systems such as fast-rcnn, rcnn it has very low Computational [12] overhead. Yolo detects images over 45 fps per second to make flower detection, YOLO categorizes the image into s × s grid cells to estimate bounding boxes using cluster sizes like Anchor boxes. The grid cell will be responsible for detecting the object if the major part of the object falls into it. The confidence score should otherwise be the intersection of the anticipated box and the ground truth. The five predictions x, y, w, h, and confidence are used to represent each bounding box (Fig. 1). A boundary box and a confidence box are predicted by each grid cell. These confidence levels indicate how confident the model is that the box contains an item, as well as how accurate the box believes it is in its predictions. We formally define

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Fig. 1 Dimension priors and location prediction inside the bounding boxes

confidence as Prb (Object) predated by IOUTruth. Each grid cell predicts likewise the probabilities of C conditional class, Pr (Classij Object). These probabilities specify the grid cell that holds the entity. Regardless of the number of boxes B, we only forecast the probability of class per grid cell. At the time of test we subtract the odds of conditional class   Prb Classi j Object _Prb(Object)_IOU Truth_pred = Prb(Classi ) _ IOUTruth_pred Regardless of the number of boxes B, we only predict class probabilities [13] per grid cell. At the test time, we deduct the probability of conditional class (Fig. 2).

Fig. 2 YOLO system architecture [14]

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3.2 Class Prediction To identify which categories the bounding box can contain, each box uses multi-label classification. We don’t utilize a softmax because we think it’s unnecessary for good results; instead, we use only independent logistic classifiers. During preparation, we apply conditional cross-entropy loss for class predictions. The dataset has multiple overlapping classes. When you use a softmax, you’re assuming that each box has only one class, which isn’t always the case. A multi-label solution is the best way to model the data.

3.3 Predictions Across Scales YOLOv3 predicts boxes in three different scales. Our system uses a similar approach to the pyramid networks to remove features from certain scales [15]. We’re adding multiple convolutional layers from our base function extractor. The last of these forecasts bounding frame, objectness, and class predictions in a 3-d tensor encoding.

3.4 Feature Extraction We use a new network to perform extraction of features [16]. The network employs three average and one average convolution layers in succession, but it now has some shortcut links and is significantly larger. There are 53 levels of convolution in it.

4 Experiment Results 4.1 Dataset We used two different datasets for implementing proposed system. OXFORD17 and dataset where images are collected from Google image. Oxford17 is a freely available dataset with collection of widely-used flowers. The photos, which include 1360 flower photographs from 17 categories with 80 images in each category of various image sizes, show massive scale, posing, and light variations. The Google Images collection is divided into four categories, each with a different floral image (Table 1).

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Table 1 Results of both datasets with accuracy Dataset

No. of classes No. of images Method

Accuracy (%)

Oxford17

17

1360

CNN

94.5

YOLOv3

98.2

Flower identification system 15

100

Image processing 81

Ours

50

YOLOv3

5

98.7

Fig. 3 Comparison of different dataset (method) with accuracy

4.2 Performance Results We’re gathering samples to see if the proposed flower detecting technology is feasible and effective. Using cuda (cuDNN) and the Darknet53 framework, all of the experiments were run on GOOGLECOLAB with GPU. We consider correct detections with IoU ≥ 0.5. Both datasets are used to test the proposed system. Our system’s accuracy is greater than 98%, according to the testing results on both datasets [17– 23]. The amount of time it takes to train is determined on the speed of the Internet and the size of the dataset (Fig. 3).

5 Conclusion and Future Work We’ve used YOLO to recognize flowers on two separate datasets throughout this project. The real-time object detection YOLO model was trained to detect the object from the image. The results of the trials demonstrate that based on the training

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duration, the predictability of spotting flowers is greater than 98%. Since flowers in nature hold similar color combinations and patterns, it is impossible to distinguish all kinds of flowers by flower shades or shapes. In future work we will aim to detect large dataset images of various dimensions, low light, etc.

References 1. K.B. Prakash, M.D. Rangaswamy, A.R. Raman, ANN for multi-lingual regional web communication, in International Conference on Neural Information Processing (Springer, Berlin, Heidelberg, 2012), pp. 473–478 2. K.B. Prakash, M.D. Rangaswamy, A.R. Raman, Statistical interpretation for mining hybrid regional web documents, in International Conference on Information Processing (Springer, Berlin, Heidelberg, 2012), pp. 503–512 3. K.B. Prakash, Mining issues in traditional Indian web documents. Indian J. Sci. Technol. 8(32), 1–11 (2015) 4. K.B. Prakash, Content extraction studies using total distance algorithm, in 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT) (IEEE, 2016), pp. 673–679 5. K.B. Prakash, M.D. Rangaswamy, T.V. Ananthan, V.N. Rajavarman, Information extraction in unstructured multilingual web documents. Indian J. Sci. Technol. 8(16), 1–8 (2015) 6. J. Ärje, D. Milioris, D. Tran, A. Iosifidis, J. Raitoharju, Automatic flower detection and classification system using a light-weight convolutional neural network, in EUSIPCO Workshop on“Signal Processing, Computer Vision and Deep Learning for Autonomous Systems (2019) 7. H. Almogdady, S. Manaseer, H. Hiary, A flower recognition system based on image processing and neural networks. Int. J. Sci. Technol. Res. 7(11), 166–173 (2018) 8. K.B. Prakash, A. Rajaraman, Mining of bilingual Indian Web documents. Proc. Comput. Sci. 89, 514–520 (2016) 9. M. Ismail, K.B. Prakash, M.N. Rao, Collaborative filtering-based recommendation of online social voting. Int. J. Eng. Technol. (UAE) 7(3), 1504–1507 (2018) 10. W. Pardee, P. Yusungnern, P. Sripian, Flower identification system by image processing, in 3rd International Conference on Creative Technology CRETECH, vol. 1, pp. 1–4 (2015). 11. W. Liu, Y. Rao, B. Fan, J. Song, Q. Wang, Flower classification using fusion descriptor and SVM, in 2017 International Smart Cities Conference (ISC2) (IEEE, 2017), pp. 1–4 12. Google. Google Image Search. Retrieved from https://images.google.com 13. D. Babitha, M. Ismail, S. Chowdhury, R. Govindaraj, K.B. Prakash, Automated road safety surveillance system using hybrid cnn-lstm approach. Int. J. Adv. Trends Comput. Sci. Eng. 9(2), 1767–1773 (2020) 14. J. Wu, Complexity and accuracy analysis of common artificial neural networks on pedestrian detection. MATEC Web Conf. 232, 01003 (2018) 15. Y. Liu, F. Tang, D. Zhou, Y. Meng, W. Dong, Flower classification via convolutional neural network, in 2016 IEEE International Conference on Functional-Structural Plant Growth Modeling, Simulation, Visualization and Applications (FSPMA) (IEEE, 2016), pp. 110–116 16. D. Babitha, T. Jayasankar, V.P. Sriram, S. Sudhakar, K.B. Prakash, Speech emotion recognition using state-of-art learning algorithms. Int. J. Adv. Trends Comput. Sci. Eng. 9(2), 1340–1345 (2020) 17. J. Redmon, A. Farhadi, YOLO9000: better, faster, stronger, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 7263–7271 18. J. Redmon, S. Divvala, R. Girshick, A. Farhadi, You only look once: unified, real- time object detection, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 779–788

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19. J. Redmon, A. Farhadi, Yolov3: an incremental improvement (2018). arXiv:1804.02767 20. Y.S.S. Bharadwaj, P. Rajaram, V.P. Sriram, S. Sudhakar, K. Prakash, B: Effective handwritten digit recognition using deep convolution neural network. Int. J. Adv. Trends Comput. Sci. Eng. 9(2), 1335–1339 (2020) 21. K.B. Prakash, M.D. Rangaswamy, T.V. Ananthan, Feature extraction studies in a heterogeneous web world. Int. J. Appl. Eng. Res. 9(22), 16571–16579 (2014) 22. H. Hiary, H. Saadeh, M. Saadeh, M. Yaqub, Flower classification using deep convolutional neural networks. IET Comput. Vis. 12(6), 855–862 (2018) 23. N. FatihahSahidan, A.K. Juha, N. Mohammad, Z. Ibrahim, Flower and leaf recognition for plant identification using convolutional neural network. Indonesian J. Electr. Eng. Comput. Sci. 16(2), 737–743 (2019)

A Hybrid Framework for Efficient Detection of Fake Currency Notes M. V. B. T. Santhi, S. Hrushikesava Raju, S. Adinarayna, V. Lokanadham Naidu, and Saiyed Faiayaz Waris

Abstract The monetary system of any country should be properly secured that make effective administration. The terrorists or outside underground riots may generate false notes that would appear like genuine notes that affect any country’s loss of economy. To provide more security to the country’s currency, efficient methodologies are reviewed and are mixed in order to provide more accuracy and increase security in protecting the country’s policies of currency notes. Although the characteristics are given by the concerned government, the determination of note is fake or genuine depends on the adapted double-check process in a very quick time. The two techniques (reinforcement learning + recurrent convolution neural network) along with the image processing technique that is used in determining the note’s transparency are applied one after the other. If any segment is missed according to the policy but passes through the first phase, that might be covered in the second phase technique. This way would help to the detection of the currency if fake or real. Keywords Hybrid approach · Accuracy · Double check process · Re-inforcement learning · Recurrent convolution neural network · Efficiency · Currency · Economy

M. V. B. T. Santhi (B) · S. Hrushikesava Raju Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Guntur 522502, India e-mail: [email protected] S. Adinarayna Department of CSE, Raghu Institute of Technology, Visakhapatnam, India V. Lokanadham Naidu Sree Vidyanikethan Engineering College (Autonomous), A.Ramgampeta, Tirupathi, India S. F. Waris Department of Computer Science and Engineering, Vignan’s Foundation for Science, Technology and Research, Vadlamudi, Guntur, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_24

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1 Introduction Any nation’s economy may be degraded because of many factors in which one significant factor is fake notes. The counterfeit of notes may cause corruption and degrade the value of currency in that country. Not only machine learning approaches, the image processing orientation is also required. There are many factors that are useful in judging the note is genuine or not. Those factors are identification mark, security thread, watermarking, micro printing, slanting the color portion, edge features, and etc. play a key role. The specific approaches are applied as analysis and somehow resulted the note is genuine or not. Other than those, the two techniques that accurately guide the note are valid or not. The validity of the note is assessed by the two machine learning techniques such as reinforcement learning and recurrent convolution neural network. In this process, if the note to be verified is to be examined by reinforcement learning. If it is passed through the first step, then only second step recurrent convolution neural network is applied. The working of reinforcement learning and RCNN are elaborated in the objective of proposed ideology. Along with these, the image processing techniques are applied at the initial stage for making this task simpler and extract the output in the further steps of reinforcement and RCNN. The features of image processing technique that should be presented here as Table 1. Along with the above features, the steps that need to be performed in order to achieve functionality of proposed system are: Image is decomposed into required portions in order to compute intensities Table 1 Features to be considered from image of the note Feature

Purpose

Serial number

Every note is identified by a unique number based on regulations of the country. They would appear in a specific format based on the policy

Security thread

It is embedded between the layers of the note and are metallic which is difficult to forge

Watermarking

It represents art drawn or static context as the surface of the paper as high level of transparency and further fibers are compressed by a kind of wired stencil against the pulp before the impression is dried. It is also difficult to forge

Micro printing (Intaglio press printing)

The intaglio ink would set out offset printing and create a print using feathering effect. The edge line border is also drawn intelligently based on design and is oil printing

Edge detection

The captured note image is scanned for edges using Hough transform method and optical character recognition methods where checking the gaps in the lines over the edges

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(2) (3)

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For each denominate of a note, there should be a standard threshold of values for each feature that could be verified in the first technique called reinforcement learning. If first phase is passed, then recurrent neural network is applied in order to judge the note is genuine or not. Assess the accuracy of the intended ideology for determining the note is real or not.

2 Literature Review Most of studies that were existed in the market are useful for fake note detection but they are ways required to still improve the process. Regarding the demonstration of [1], the ideology mentioned is combination of Single Shot Detector (SSD) and Convolution Neural Network (CNN) is applied over currency in order to detect the front and back denomination and extract the features. The accuracy of this approach is raised to 97% in detecting the currency. In the view of description of [2], the approach is used to count the number of interruptions in the thread line determine the currency note is real or fake. The efficiency is determined using entropy of the note and the software proposed is MATLAB. As per the principle of source mentioned in [3], the fake note which is circulating in the country is to be detected with factors such as compatibility and mobility using image processing technique. This fake note would create a loss to the society and defame the innocent persons. Regarding the demonstration from [4], the various detection techniques are proposed over the fake currency. This detection depends on the features of currency note and the specific country. These mentioned techniques are compared and contrasted. In the view of description of [5], the sequence of the operations that are proposed in determining the fake or genuine note accurately although many methodologies are there in the market. Among the operations, classification and selection are significant and are used at final stage. With respect to source of [6], the various techniques are expressed and are analyzed in finding the fake note based on defined features of a country. As per the observation of [7], the various image processing techniques are applied for currency paper detection and those techniques merits and demerits are analyzed. With respect to the demonstration of [8], the canny edge detection algorithm is applied over the decomposed images of a bank note and would be faster than original note using bit plane slicing technique. In the view of description in [9], the statistical classification technique called Logistic regression and linear discriminate analysis is used to accurately detect the fake currency effectively and is considered as better model for authenticate the currency. Regarding the work denoted in [10], the image processing techniques are applied in order to detect the Indian currency notes as fake or real based on three significant fields such as imprint, intaglio, and threads. As per the approach specified in [11], the image processing technique and machine learning approach is helpful in determining the fake currency although there are many approaches are existed. The study is taken because fake currency may defame

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the country in many ways. In regard to the source mentioned in [12], the mobile app called BLaDE is proposed to use barcode reader in order to scan the items especially for visually impaired persons. The few samples are taken for study and feedback is taken as audio. With the regard of description provided from [13], the food packets are labeled with barcodes and are obtained using featuristic barcode scanners. It is designed for blind people that would help to scan the barcodes on food packets. As per the description from [14], the computerized barcode reader scans the object and sends a code that is decoded to URL and output the verbal description of the object finally that would help to the visually impaired people. As per the methodology specified in [15], the policies of the country are considered in converting local currency into required currency and payment to be done and alert the merchant and customer accounts simultaneous time. Here, reading the product and asking for making payment is done according to customer local currency against the merchant currency. Regarding the demonstration of data given in [16], the certain countries are taken and money to be converted accurately in to the authorized countries currency. The international currency is restricted to few countries but not limited to all countries. Here, the categories are made into over, balanced, and under type customizers. With respect to the source given in [17], the classifiers such as naïve and KNN are applied one after the other in order to detect the attack in intrusion detection system is trusty or un-trusty depending on the deepest packet inspection. Regarding the study in [18], the AI and ML technique called SVM is applied over the nonlinear data and forecast the stock prediction in more accurately when started to compare against such approaches, in which SVM proves the better approach. As per the principle specified in [19], the currency to be scanned in order to decide that is real or fake that could be done using AFCRS. The machine learning method called deep learning is used in predicting the note is fake or real rather than precious image processing techniques. With respect to the source mentioned in [20], the currency note detection to the blind people is done using image processing technique there steps carried in such a way that scan the note, convert that into the text and that converted into the audio that really helps as a guide to the blind people. With respect to the source provided in [21], the currency here is bit coin that was in circulation in most of the countries, the transfer of bit coin from one person to other person is maintained as history of that coin, hence there is no chance of fake currency. The methodology used here is RCNN and LSTM are applied along with python’s library tensor flow for accurate prediction of the currency. With respect to the source mentioned in [22], the measures like accuracy, recall, efficiency are considered for fake note detection and enhanced SVM is applied over the note image that was scanned. The result would be note is valid note or not. As per the demonstration of information in [23], the denomination of note and determination of note is genuine or fake based on ID and serial number are considered using advanced image processing approach. The steps that are involved are feature extraction, edge detection, image segmentation and compare the images for determining the note is genuine or not. In the view of information specified in [24], the botanical plants raw materials are assigned mini-barcodes so that customers could have a trust on the plants that purchase original type of plant o specific characteristic or not. The kind of testing here considered is DNA testing over botanical items. As per

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direction of extracted information in simple words from [25], the eyesight is scanned and generates a report on the eye sight that is of shortage w.r.to threshold values and get notification on the order of item from the nearest the merchant. With respect to the data represented in [26], the digital mask is designed in order to detect the surroundings about the affected or healthy and alert the user about the infected area, and suggests the prescription to set-right the environment. Regarding of work dumped in [27], the specific currency notes are trained and their denominations are verified using convolution neural network. The feature maps of every note from the online database are compared against the note given for verification, for judging the note is fake or not in less computation time. In the juncture made from [28], the application is designed there note is scanned and allowed to apply defined features over that note. The image processing technique is used internally to extract the features that help to judge the note is fake or real. The future scope given in this is foreign currency may be considered with modification in the implemented ideology. From the view of depiction from [29], the Bangladesh currency is to be verified and produce the note is fake or not based on specific features like hologram, water marking, and printing style quality as well as methodology suggested is support vector machines. The obtained results are verified and proved that SVM is effectively proved than other existing methods. With respect to the source specified in [30], the sequences of steps are performed in order to determine the note is fake or genuine. The characteristics of image processing technique are analyzed and computed intensities are compared against the threshold intensities for the defined features. Based on satisfaction, the result is announced. As per description noted from [31], the measures assumed such as serial number, fitness, authenticity, and denomination are computed for note based on efficient processing machines. These would detect the note is real or not based on sensors loaded with the dataset. In the view of demonstration of information from [32], the preparation of anti-counterfeiting applied to the notes w.r.to three main factors ink, printing, and substrate. These reflect security for the note against duplicate or fake currencies. In the principle raised from [33], the policies and guidelines are framed by the elected governing body of a country in order to make currency note more secure. Some of studies mentioned above are representing the various ML, AI and Image processing techniques in determining accurately, and others are used for scanning and further analysis of decision making.

3 Proposed Ideology In this, the description is to be provided in terms of ER diagram of intended ideology, flow chart of modules which are identified in this objective, and elaboration of pseudo procedures of the identified modules. Hence, the visual diagram that denotes the interaction of use cases and actors as objects involved, modules interaction in a flow chart, and description of algorithms of those defined modules (Fig. 1).

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Fig. 1 ER diagram of hybrid framework for currency authentication

The modules identified here are: (A)

Image processing technique: It uses segmentation and uses OCR in which scanned image is divided according to required portions (substrates) like serial number part, denomination parts, gaps count in each line of edges, and etc. This segmentation leads to extraction of content from individual portions, that would be further analyzed.

Pseudo_Procedure Image_Processing_Technique (): Step1: Read the number of partitions. Step2: Divide the scanned note into required number of parts. Step3: Forward them to the next module. (B)

Reinforcement Learning: It takes previous knowledge of verified knowledge of denominated notes. This knowledge could be helpful in bringing note to second phase of verification. Otherwise, it rejects the note which saves time for further analysis.

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Fig. 2 Functionality of RCNN Pseudo_Procedure Reinforcement_learning(): Step1: Use OCR approach to extract the characters from the partitions obtained from Image Processing Technique Step2: if denominate == 500 if 500.serialnumber in list(500.serialnumbers in onlinedb): continue else break if denominate == 2000 if 2000.serialnumber in list(2000.serialnumbers in onlinedb): continue else break Step3: If break is encountered, report fake report else go to RCNN() Step4: Observe the features of trained notes, that knowledge applies to the other note is to be verified

(C)

Recurrent convolution neural Network: It consists of functionality of both recurrent neural network and convolution neural network. It consists of features of both RNN and CNN. The below is the functionality of RCNN (Fig. 2).

Pseudo_Procedure RCNN(): Step1: Setup the design that considers output one layer as input to other layer Step2: Increase the power of max pooling layer Step3: if predicted_denomination.taglio_color_scan == standard.taglio_denominate_color: continue; else break; if predicted_denomination.threads_count == standard. denominate.threads_count continue; else break; if predicted_denomination.watermarking_template == standard. denominate. watermarking continue; else break; if predicted_denomination.miscellaneous==standard.denominate.miscellaneous_ threshold: continue; else break;

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Fig. 3 Flow of modules w.r.to hybrid framework for fake currency detection

The below is the flow graph of these modules in the hybrid framework proposed for fake currency detection (Fig. 3).

4 Results The order of events is demonstrated in the following diagrams in which Fig. 4 demonstrates on machine that verifies the note, the portions of a note, extraction of required content, compare such extracted entities against the standard thresholds from the online database mapping. Figures 5 and 6 depict the case if the note won’t pass the

Fig. 4 Cash machine with scanner to detect the fake note

A Hybrid Framework for Efficient Detection of Fake Currency Notes

Fig. 5 Two phases exploration for detecting the fake note Fig. 6 Scanner light turns red for a fake note

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Accuracy of approaches against the Hybrid Framework Fig. 7 Accuracies of considered approaches and their graph

scrutinization and announces the fake note with audio and in red color. From Fig. 7, the accuracy of intended ideology against the other existing approaches is depicted. From Fig. 4, the 500 note is taken as consideration where few portions are taken for scrutinization. The portions numbered 1 and 2 denote number of stripes, portion 3 denotes denomination, portion 4 denotes security threads, 5 denotes latent image that is hidden as 500, 6 denotes 500, portion 7 denotes security threads with integrated colors in layers and specific content, portion 8 represents micro letters with specific content, portion 9 denotes watermarking with specific image as hidden, and portion 10 denotes specific image. In this Portions 1 and 2 counts to be same that should satisfy standard threshold values, portion 10 and portion 9 consist of same images in which 9 part is hidden and 10 part is explored. The similar policy is applied for the backside of a note and verifies the details but the main focus is on front side of a note which guarantees the security. The same policies for different note are loaded and the same procedure is applied for that intended kind of ideology. The below diagram denotes the two phases demonstration: From Fig. 6, the note is genuine; the machine works fine but if the note found invalid would alert through red light and audio and stops counting until receives the input from the user. From the literature survey studies, the accuracies of RNN, SVM are extracted from the sources and tabulated in Fig. 7 and accuracy of the proposed technique against RNN, SVM approaches is also depicted in same Fig. 7 (Table 2).

A Hybrid Framework for Efficient Detection of Fake Currency Notes Table 2 Accuracies of approaches for fake currency detection

RNN

SVM

Hybrid framework (RL + RCNN)

60

80

99

223

5 Conclusion There exist two approaches proposed in this study where first is reinforcement learning which when applied for preliminary check. It suppose passes preliminary check needs to go to further step the second phase recurrent neural network where in detail analysis is performed in judging the note is real or not. The objective is to minimize the issues of the country that influence the economy and its currency value in the world. As compared to traditional approach, the proposed study focuses more on accuracy which is number of correct detections by the total number of trails over the note. The double check verification process for scrutinizing the note would benefit to avoid the down fall of the economy, increase faith and trust in the global market, and would identify the forgery and make it invalid instantly. In future, the determination of currency note is valid or invalid through introducing novel methods as well as novel features over the note.

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30. T. Agasti, G. Burand, P. Wade, P. Chitra, Fake currency detection using image processing, ICSET-2017. IOP Conf. Ser. Mater. Sci. Eng. 263, 052047 (2017). https://doi.org/10.1088/ 1757-899X/263/5/052047 31. J.W. Lee, H.G. Hong, K.W. Kim, K.R. Park, A survey on banknote recognition methods by various sensors. MDPI Sens. 17, 313 (2017).https://doi.org/10.3390/s17020313 32. A.B. Centeno, O.R. Terrades, J.L. Canet, C.C. Morales, Identity document and banknote security forensics: a survey, Oct 2019. https://arxiv.org/pdf/1910.08993.pdf 33. J. Chambers, W. Yan, A. Garhwal, M. Kankanhalli, Currency security and forensics: a survey. https://core.ac.uk/download/pdf/56364077.pdf

A Systematic Review Literature on Computer-Aided Detection Methods for COVID-19 Detection in X-Ray and CT Image Modalities R. Brindha, A. Kavitha, and Bharat Bhushan

Abstract The novel coronavirus was spreading all over the world and causes Severe Acute Respiratory Syndrome coronavirus2 (SARS-CoV2). Failing to identify this syndrome in the early stage of infection leads to death. Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) testing procedure is used to detect infection of SARS-CoV2, but the false-negative rate of the RT-PCR is up to 61% in the early stage of testing. To improve the detection accuracy, Lung Computed Tomography (L-CT) and chest radiograph (CXR) image modalities are used along with RT-PCR clinical procedure. This paper presented a comprehensive survey of the recently used methods and its techniques are used in the computer-aided analysis of L-CT images and CXR images for SARS-CoV2 detection. The survey result might help overcome the limitations of the existing computer-aided image analysis methods and identify research opportunities in lung CT and chest X-Ray image analysis for SARS-CoV2 detection. Keywords Covid-19 · Deep learning · Lung CT images · X-Ray images

1 Introduction The covid-19 outbreak started in Wuhan, China, and has since stretched across over 130 countries worldwide. At the time of writing this review article, cases of covid-19 are reaching nearly 173.3 million worldwide. Cases in India reached 29.1 million, with 1 million cases still active. RT-PCR is a primary testing procedure used to detect the covid-19 infection, but the system suffers from a high false-negative rate R. Brindha (B) Department of EEE, Sethu Institute of Technology, Kariapatti, India A. Kavitha Department of ECE, M. Kumarasamy College of Engineering, Karur, India B. Bhushan Department of CSE, School of Engineering and Technology, Sharda University, Greater Noida, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_25

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and time delay [1]. To cover these issues, image modalities such as chest CT and chest X-Ray are used along with RT-PCR for early detection of Covid-19 infection. Different Computer-Aided Image analysis methods are proposed to detect the Covid-19 pneumonia CXR images [2]. The symptoms of Covid-19 pneumonia and other lung diseases such as viral pneumonia, and tuberculosis in CXR images show a high degree of inter-class similarities and low degree intraclass similarities [2]. Figure 1 shows the CXR images of Covid-19 pneumonia, bacterial pneumonia and viral pneumonia abnormalities. Figure 2 shows the Lung CT images with normal and ground-glass opacities. Different automatic computer-based detection methods were proposed to detect the above-specified abnormalities in CXR and Lung CT images using image processing, deep learning (DL) and machine learning (ML) techniques [3]. Different methods were proposed using handcrafted techniques and Fig. 1 Lung CT scans-normal and abnormal image respectively (from left to right)

Fig. 2 CXR images, first row (normal CXR images), second row (CXR with Covid-19), third row (CXR with bacterial pneumonia), fourth row (CXR with viral pneumonia)

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deep learning techniques, these methods use CXR, CT and both (fusion of CXR and Lung-CT). Handcrafted feature representation methods use different texture feature learning techniques such as HOG, SIFT and LBP to learn and represent anomaly pattern in CXR and Lung-CT images [3]. Various feature classification methods such as SVM, K-NN and Random forest were used to classify anomaly patterns. Deep learning methods use different existing models such as AlexNet, ResNet-50 and VGG-16 to learn and classify anomaly patterns in CXR and Lung-CT images. Deep models performed well in natural image processing applications such as classification, segmentation and detection task because natural images have high discriminative boundaries and information, but medical image modalities such MRI, CT and X-RAY images complex in nature. The success of finding discriminative features in medical images lies in representing anomaly areas in an efficient representation technique. One of the major problems with deep learning is block-box nature of the model, highly depends on the labelled dataset, model suffers from trustworthiness because of its low explain-ability [4]. • Comparison of various deep model proposed for Covid-19 detection other than model performance • Impact of various dataset and dataset quality used in deep learning model performance assessment • Multi-Modal feature interaction to improve detection accuracy • Quantitative analysis of various proposed models used in Covid-19 detection.

2 Systematic Review Literatures Computer-Aided Detection or Classification in X-Ray and CT images helps in early diagnosing of Covid-19 Detection. Many of the existing methods recently proposed are deep learning-based feature learning and detection methods. In recent years deep learning models show their promising performance in the field of medical image analysis at thecost of a huge number of training data [5]. Very few handcrafted feature learning methods were also proposed using various texture and statistical features [3, 6]. Saygili et al. [3] suggested handcrafted texture features extracted using Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG) methods for Covid-19 pattern representation in CXR and CT images. To reduce the dimensionality Principal Component Analysis method was adopted by the author and different feature classifiers such as K-Means Neural Network (K-NN), Support Vector Machine (SVM) were used to classify covid-19 patterns in CT and CXR images. For covid-19 image classification in chest CT images [7] proposed adaptive feature selection method using the random forest to learn high-level discriminative image features such as volume features, infected lesion number, histogram distribution and surface area. The general architecture diagram of the deep learning-based model. To classify and localize the covid-19 infection in CT images [8] proposed weakly supervised deep Convolutional model, a pre-trained UNet model generates ground truth mask.

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The generated mask along with the original image was fed into AlexNet-based classifier model. In [6], prior-attention residual learning is used to screen CT images. Instead of lung segmentation, the author used lobe segmentation using a pre-trained UNet segmentation model and the segmented models were fed into the 3D-ResNet model for CT image classification. To train the model three different losses are calculated such as detection loss, classification loss and combined loss. In [9] author used the deep learning model EfficientNet to classify Covid-19 CT images, different augmentation techniques such as flipping, rotating and shifting used to enhance the size of the training dataset. A stacked Auto Encoder and Decoder-based deep model was proposed for content-based Covid-19 feature extraction in [10]. Both chest XRay and Lung CT images are transferred into latent representation using auto encoder in the feature extraction phase. In the similarity comparison phase, the queried features are compared by using the K-NN classifier model. The semi-supervised deep model architecture was proposed by H. Jiang, S. et al. using Cycle Generative Adversarial Network to classify GGO-based Covid-19 CT images and Lung Nodule pattern-based Lung cancer CT images. Y. Jiang et al. proposed conditional GAN for CT image synthesis to help improve the dataset size in deep learning-based CT image applications. A simple supervised deep learning model was proposed by using VGG-169 as backbone network to classify Covid-19 infected CT images. Silva et al. proposed supervised DL model using EfficientNet as a backbone network to classify covid-19 CT images. In this work, authors made cross-dataset analysis using SARS-CoV-2 CT-scan dataset and COVID-CT dataset. The author proposed a deep learning-based segmentation method using a modified ResNet-50 model for chest X-Ray image analysis. The author proposed an ensemble deep learning model using pre-trained AlexNet, GoogleNet and ResNet, here the relative majority voting method is used to ensemble prediction of the three pre-trained models (Table 1).

3 Research Findings The analysis of various computer-aided detection methods proposed for Covid-19 detection in CXR and CT images exposes the challenges of domain expert knowledge consideration in DL models, the trustworthiness of the model and the quality of the dataset used for model training. The review was narrowed to investigate the feasibility of developing interpretable deep models for Covid-19 detection by incorporating expert knowledge into the model training process. To overcome the issue of the shortage of the labelled data, the deep model can be trained by using both labelled and unlabelled data. Most of the existing proposed models are tested against the different custom datasets and there are no standard performance evaluation guidelines for covid19 detection from CXR and CT image modalities. The quantitative analysis of the existing methods in the aspect of tasks, type of image modalities used and Quality of the dataset is shown in Fig. 3.

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Table 1 Summary of formerly proposed Covid-19 detection methods in CT and C-RAY images References

Method used

Task

Image modality

Dataset size

Sun et al. [6]

Handcrafted feature and adaptive feature selection Texture features

Detection and classification

Chest CT images

1495 images

Zhang et al. [8]

Weakly supervised Segmentation UNet &AlexNet

Chest CT images

500 images

Wang et al. [6]

Supervised model 3D-ResNet model

Segmentation

Chest CT images

2406 images

Anwar et al. [9]

Supervised model EfficientNet-64

Classification

Chest CT images

740 images

Benyelles et al. [10]

Stacked auto encoders

Classification

Both X-Ray and CT images

760 images

Jiang et al.

Semi-supervised model CycleGAN

Classification and segmentation

Chest CT images

300 images

Jiang et al.

Semi-supervised model Conditional GAN

Image synthesis

CT images

2000 images

H et al.

Supervised model VGG-16

Segmentation and Classification

CT images

144,167 images

Ma et al.

Attention model VGG-16

Detection and localization

CT images

7250 images

Ri et al.

Supervised model VGG-19

Classification

CT images

591 images

Silva et al.

Supervised model EfficientNet

Classification

CT images

COVID-CT

Tabik et al.

Disease detection ResNet-50

Segmentation

X-Ray images

COVIDGR dataset

4 Conclusions This systematic literature review of computer-aided detection methods proposed for covid-19 detection in CXR and CT was studied. The impact of using handcrafted models and deep models for CXR and CT also reveals the dataset quality, size of the dataset and standard guidelines for model evaluation. It has been discovered that existing deep models used for CXR and CT analysis are supervised learning models and they use a minimal amount of annotated data for model training. By using unlabelled data also the model can learn some useful features from the images. The model’s trustworthiness is also a factor considered for clinical usage of that model. The model trustworthiness can be improved by adopting domain specific knowledge in model decision making process. We believe that this review further explores research opportunities in computer-aided detection from CXR and CT images.

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Fig. 3 Quantitative analysis of the existing proposed model based on application-wise, dataset used, Image modalities and features used

References 1. T. Anwar, S. Zakir, Deep learning based diagnosis of COVID-19 using chest CT-scan images, in Proceedings of 2020 23rd IEEE International Multi-Topic Conference on INMIC 2020, pp. 6–10. https://doi.org/10.1109/INMIC50486.2020.9318212 2. G. Jia, H.K. Lam, Y. Xu, Classification of COVID-19 chest X-Ray and CT images using a type of dynamic CNN modification method. Comput. Biol. Med. 134, 104425 (2021). https://doi. org/10.1016/j.compbiomed.2021.104425 3. A. Saygili, A new approach for computer-aided detection of coronavirus (COVID-19) from CT and X-ray images using machine learning methods. Appl. Soft Comput. 105, 107323 (2021). https://doi.org/10.1016/j.asoc.2021.107323 4. J. Ker, L. Wang, J. Rao, T. Lim, Deep learning applications in medical image analysis. IEEE Access 6, 9375–9379 (2017). https://doi.org/10.1109/ACCESS.2017.2788044 5. P. Muruganantham, S.M. Balakrishnan, A survey on deep learning models for wireless capsule endoscopy image analysis. Int. J. Cogn. Comput. Eng. 2, 83–92 (2021). https://doi.org/10.1016/ j.ijcce.2021.04.002 6. J. Wang et al., Prior-attention residual learning for more discriminative COVID-19 screening in CT images. IEEE Trans. Med. Imaging 39(8), 2572–2583 (2020). https://doi.org/10.1109/ TMI.2020.2994908 7. L. Sun et al., Adaptive feature selection guided deep forest for COVID-19 classification with chest CT. IEEE J. Biomed. Heal. Inform. 24(10), 2798–2805 (2020). https://doi.org/10.1109/ JBHI.2020.3019505

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8. J. Zhang, Y. Chu, N. Zhao, Supervised framework for COVID-19 classification and lesion localization from chest CT. Ethiop. J. Heal. Dev. 34(4), 235–242 (2020) 9. T. Anwar, S. Zakir, Deep learning based diagnosis of COVID-19 using chest CT-scan images. IEEE Explor. (2021) 10. F.Z. Benyelles, A. Sekkal, N. Settouti, Content based COVID-19 chest X-Ray and CT images retrieval framework using stacked auto-encoders, in 2020 2nd International Workshop HumanCentric Smart Environment Healing Well-Being, IHSH 2020, 2021, pp. 119–124

Apple Fruit Classification and Damage Detection Using Pre-trained Deep Neural Network as Feature Extractor Gurucharan Kapila , B. Vandana, Ayush Khaitan, A. Francis Avinash, and C. H. Ajay Kumar

Abstract The specific fruit category identification is necessary for the industries, retail stores, and markets. The whole process needs surveillance of a certain number of people, which takes time and requires the most investment in hiring the labor. The industrial intellectuals are looking forward to a technological solution for the above problems. These labor-intensive tasks can be automated by the use of Computer Vision and Machine Learning. This paper proposes a classification model for different apple fruit varieties by support vector machine (SVM) classifier using hierarchical features extracted from the fully connected layer of the Pre-trained deep convolutional neural network. These extracted features are fed to different classifiers like SVM, Random Forest, Linear Regression, and K-nearest Neighbor. The performance metrics of the proposed classification model are assessed in terms of Accuracy, F1-score, Precision, and Recall. The evaluation metrics show that the SVM classifier provides better results than other classifiers. The SVM classifier trained on the features extracted by ResNet 50 Pre-trained deep neural network achieves Accuracy and precision of 99.1%, F1-score of 95.4%, Recall of 98.6%. The Proposed Results are also compared with the related works in the same domain. Keywords Deep learning · Convolutional neural network · ResNet-50 · Support vector machine

1 Introduction Fruit classification is a difficult task in supermarkets, and it takes lots of labor cost and time to classify the fruit and to determine the price of the fruit. The fuzzy model [1, 2] is the method introduced to identify the ripening level of the pineapple. Particle swarm optimization [2] is utilized for changing the boundaries of the fuzzy model. MUSA data set is used for the analyzing the aging of the fruit pineapple at different stages. The swarm advancement method was utilized to respond to the G. Kapila (B) · B. Vandana · A. Khaitan · A. Francis Avinash · C. H. Ajay Kumar Department of ECE, Lendi Institute of Engineering and Technology, Vizianagaram, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_26

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asset’s quick reaction, which is processing. Various classifiers, for example, quadratic discriminant examination, K-Nearest Neighbor [3], and LDA were explored on the ghostly reluctance information of a multiband sensor framework. Deep neural networks are similar to the neural system of living beings. As the nervous system of beings tries to recognize, classify, identify, and stimulate a response by training the layers that are in the model. The input of image or text or sound was provided at the input layer. The output of it passed to the hidden layers; finally, the output layer receives the information from the production of the last hidden layer. Convolution neural network shifts its works to classification after the feature extraction. Vector maintains the probabilities of each class when the image is being classified. The final layer of the CNN is the softmax function to get the classification output of the data given.

2 Literature Survey Fruit grading is based on the steps like image acquisition and preprocessing, segmentation, feature extraction, knowledge-based comparison, and decision making. The image is acquired using the image capturing device. Then filtered are applied for preprocessing purposes. The preprocessing was performed by the methods like split and merged [4–7]. It is used to clear the background to have a focus on the desired object. Subsequent spectral measurements of the fruits and the leaves were corrected by using a spectrophotometer [8]. Local preprocessing [1], Gradient operators suppress [1], the small fluctuations and the noise in an image. Image segmentation is followed by partitioning the image into small parts for easy analysis. The machine learning algorithms are used for the knowledge base comparison and decision making such as k-nearest neighbor [1], support vector machine, fuzzy logic [1, 2], principal component analysis [1, 4, 5, 7] for classification and grading. Further features were extracted from an image that is the shape, color, size, texture for object recognition and classification which reviews the different part removing methods which are helpful in the classification process. The ways include color histogram [3, 7], histogram intersection [3], the color histogram fork-means [3], color correlogram, color co-occurrence matrix, chromaticity, dominant color descriptor, and other methods [9]. HSV [2] color space is considered for its correlation and CIELa*b* for the external color detection using particle swarm optimized fuzzy model [2]. Principal Component Analysis reduces images dimensionally can achieve a variance of 95% concerning the original image.

3 Methodology Different varieties of apples can be classified by using the deep neural networking procedure. It includes the following steps (Fig. 1).

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Fig. 1 Block diagram of classification using support vector machine

1.

2. 3.

Take a data set, Fruit-360, which contains the number of fruits images along with the damaged fruit images. These images are helpful for the model training and the testing purpose. The images are loaded into the pre-trained neural network to extract features such as size, shape, color, height, etc. Then, the results are feed into the support vector machine for the best classification.

3.1 ResNet 50 The main objective of the residual function is to reflect the output as the same as input. For achieving the main objective of the function F(X) is equal to zero. Then, the resultant output is equal to the input. Input image: An image is provided as the input for the processing of classification of elements in the image to the 7 * 7 convolutional layer. Convolution of 7 * 7: The filter of size 7 * 7 with 64 such a filter of stride 2. The stride is helpful for the reducing the size of image. Convolutional layer (CONV): The convolution layer in Fig 3, performs the convolutional operations by using filters of different sizes. The size of the filter is less than that of the image size to perform the convolutional operation. The filter of size F with the stride of S. Activation map is obtained as a result. Max pool: The max pool in Fig. 3, is of window size 3 * 3 of stride of 2. After the convolution, down sampling process is desired to perform for some spatial variance as pooling layer is used. It may be maximum or average pooling (Fig. 4).

Fig. 2 Residual neural network

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Fig. 3 Convolutional filter operations

Fig. 4 Convolution, max pool, average pool, fully connected layer

Stage 1: In the first stage we have 1 * 1 convolution layer of 64 such layers, then having 3 * 3 filter of 64 such size, 1 * 1 of convolutional filter of 256 of such layers. stage 2: In the first stage we have 1 * 1 convolution layer of 128/2 such layers, then having 3 * 3 filter of 128 such size, 1 * 1 of convolutional filter of 512 of such layers Stage 3: In the first stage we have 1 * 1 convolution layer of 256/2 such layers, then having 3 * 3 filter of 256 such size, 1 * 1 of convolutional filter of 1024 of such layers Stage 4: In the first stage we have 1 * 1 convolution layer of 512/2 such layers, then having 3 * 3 filter of 512 such size, 1 * 1 of convolutional filter of 2048 of such layers Average pool: Average pooling in Fig. 4, is the down sampling process of taking the maximum of the given pixels of the image, it is followed and creates new block of reduced size. Each pooling operation averages the values of the current view. Fully connected layer: FC in Fig. 4, each input is connected to neurons. FC layers are present at the end of CNN architectures and can be used to optimize objectives such as class scores.

3.2 Classifiers Support Vector Machine is a text labeling learning model which was used for the classification. By using the Kernel trick, the data is transformed and finds the boundary

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points for the performing task of categorizing. Svc radial essential function uses the transformations for the study of variety to support the vector classifier. It works on the Fourier Transformation of the kernel. Training the non-linear models by producing the vectors can be allowed by using the Svc polynomial kernel. In the k-nearest neighbor model, the data is segregated by finding the distance between the data elements. Here the K value can be estimated by the trail and the error method. Random forest is another algorithm in the different decision trees extracted and merged as a forest to make predictions accurate. This algorithm works on the different conditions to check the category of the fruit to which it belongs. In the case of analyzing the complex data, a logistic function can make the binary classification with the help of a logistic regression function. Classifiers are mainly used to identify the type of the fruit [10] that is provided as the input to the model. There are different classifiers available in the present days. This paper mainly works on the finding the efficient classifier among the available models to classify. For that comparison is made among the classifiers such as Support Vector Classifier Kernel, SVC Radial Polynomial, SVC Linear, Random Forest and others. Python is used in identifying the accuracy, precision, f 1 score, recall for finding the effectively working classifier.

4 Results and Discussion The present work describes the classification of apples, identifying the damaged fruits, and labeling the input image with its name. These tasks are performed with the help of the dataset fruit 360 pictures at the possible directions with 360° rotation. All the species that are taken for the classification are shown in Fig. 5. It consists of 26 different types of the apple fruits and a damaged fruit for training. The fruits available are follows: Apple Red 1, Apple Red 2, Apple Red 3, Apple Golden 1, Apple Golden 2, Apple Golden 3, Apple Red Yellow 1, Apple Red Yellow 2, Indian Apple Golden, Apple Crimson Snow, Apple Red Delicious, Apple Early Fuji, Apple

Fig. 5 All the species used for classification, preprocessing steps

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Granny Smith, Apple Gala, Apple Ore Gun spur, Indian Apple Red Delicious, Apple Mcintosh, Apple Cam spur Auvli, Apple Fuji, Apple Scarlet Gala Indian, Apple Granny Smith, Apple Ambrosia, Apple Pink Lady, Apple Golden spur, Apple Green, Damaged Apple, Apple Braeburn. Preprocessing in Fig. 5, involves different measures to perform the task of classification. Initially all the available images are inserted into the model to reduce the size for the further processing. In the following steps the noise in the image is removed. The images are reduced to extract the features using the ResNet 50 architecture. Support vector machine is helpful for the classification of the fruits with the concept of boundary line making. The confusion matrix generated during the process is mainly for comparing the performance of the trained model and estimating its accuracy. This paper generates the results by comparing the working of the classifiers using different type of algorithms namely K-Nearest Neighbor, Random Forest, Logistic Regression, Support Vector Machine Kernel, SVC linear, SVC polynomial. The comparison among the different classifiers helps to find the working of the variety of the classifiers and it also helpful to identify the best among them. Figure 6, describes the metrics such as accuracy, precision, F1 Score, and Recall of different classifiers. The model proposed in this paper is better when compared to other works in all the parameters with accuracy and precision of 99.1%, 0.991 respectively using the svc linear. The model in this paper is performing effectively than the k-nearest neighbor, Random Forest, Logistic Regression in the F1 Score and Recall metrics. The content in Table 1, compares the performance of the proposed algorithm with the other works. Previously existed algorithms exhibit accuracy 94% for CNN,

Fig. 6 Accuracy, precision, F1 score, recall

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Table 1 Comparison of results with other works S. No.

Algorithm

Accuracy (%)

Precision

Recall

F score

1

CNN [11]

94

–

–

–

2

HPA-SLFN [7]

89.5

–

–

–

3

Fuzzy Model [2]

94.85

0.95

0.87

0.91

4

BPNN [12]

93.89

–

–

–

5

BBO-FNN []

89.11

–

–

–

6

FSCBBN-FNN []

89.1

–

–

–

7

SVM (proposed)

99.1

0.991

0.986

0.954

89.5% for HPA-SLFN, 94.85% for Fuzzy Model, 93.89% for BPNN. The accuracy of SVM is 99.1% which is better than the previous works. The other metrics such as precision, recall, f score also perform well than the Fuzzy model. The fuzzy model having precision, recall, f score of 0.95, 0.87, 0.91 whereas the SVM having 0.991, 0.986, 0.954. The predictions which are missed are shown in Fig. 7. The results include here are used for labeling the classified fruits and also finding the damaged ones. In Fig. 8, the results of our work as shown. Each part of the image shows the predicted and the actual outputs. It is available with the labels. The classifier we used predicts which type of apple is provided as input to the model using the trained information in the model.

Fig. 7 Wrongly predicted values

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Fig. 8 Actual and predicted values

5 Conclusion This work is about fruit classification and damage fruit identification with the help of the pre-trained neural network as a feature extractor, support vector machine along with other classifiers such as svc basic radial essential function, svc polynomial kernel, k-nearest neighbor, Random Forest, and logistic regression. The 26 different apple fruits along with a damaged fruit are trained into the model using the pretrained neural network. The images provided are used for training the model and helpful in the process of labeling the varieties which are available. During the preprocessing, the images are resized, denoised, contracted, and pass through the ResNet 50 architecture. The following step involves the use of different varieties of classifiers to classify the fruits, to label them, and to detect the damaged one. The results of the proposed models provide accuracy of 99.1%, Precision of 0.991, Recall of 0.986, and F1 Score of 0.954. This model performance is better than the considered algorithms.

References 1. S. Naik, B. Patel, Machine vision based fruit classification and grading—a review. Int. J. Comput. Appl. 170(9) (2017) 2. M. Senthilarasi, S. Mohamed Mansoor Roomi, Particle swarm optimized fuzzy model for the classification of banana ripeness. IEEE Sens. J. 17(15) (2017) 3. D. Srivastava, R. Wadhvani, M. Gyanchandani, Color feature extraction methods for content based image retrieval, in 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), 2016, pp. 2259–2263 4. Y. Zhang, S. Wang, G. Ji, P. Phillips, Fruit classification using computer vision and feedforward neural network. J. Food Eng. 143, 167–177 5. L. Yudong Zhang, Classification of fruits using computer vision and a multiclass support vector machine 12(9), 12489–12505 (2012)

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6. Y. Zhang, Z. Dong, X. Chen, W. Jia, S. Du, K. Muhammad, S. Wang, Image Based Fruit Category Classification By 13-Layer Deep Convolutional Neural Network and Data Augmentation (Springer, 2017) 7. S. Lu, Z. Lu, P. Phillips, S. Wang, J. Wu, Y. Zhang, Fruit classification by HPA-SLFN, in 2016 8th International Conference on Wireless Communications & Signal Processing (WCSP), 2016, pp. 1–5 8. C. Yang, W.S. Lee, J.G. Williamson, Classification of blueberry fruit and leaves based on spectral signatures. Biosyst. Eng. 113(4), 351–362 9. R. Raja, S. Kumar, M.R. Mahmood, Color object detection based image retrieval using ROI segmentation with multi-feature method. Wirel. Personal Commun. 1–24. Print ISSN 09296212, Online ISSN 1572-834. https://doi.org/10.1007/s11277-019-07021-6 10. K. Gurucharan, S.S. Kiran, K. Babburu, L. Vadda, Computer vision based fruit recognition and classification system. Int. J. Future Gener. Commun. Netw. (IJFGCN) 13(3), 1–14 (2020). ISSN:2233-7857 (Print); 2207-9645 (Online), NADIA 11. Z.M. Khaing, Y. Naung, P.H. Htut, Development of Control System for Fruit Classification Based on Convolutional Neural Network (IEEE, Moscow and St. Petersburg, Russia) 12. B.J. Samajpati, S.D. Degadwala, A survey on apple fruit diseases detection and classification. Int. J. Comput. Appl. 130, 25–32 (2015) 13. L. Raja Sekar, N. Ambika, V. Divya, T. Kowsalya, Fruit classification system using computer vision: a review. Int. J. Trend Res. Dev. 5(1). ISSN: 2394-9333 14. J.L. Rojas-Aranda, J.I. Nunez-Varela, J.C. Cuevas-Tello, G. Rangel-Ramirez, Fruit classification for retail stores using deep learning. Pattern Recogn. 3–13 (2020) 15. A. Bhargava, A. Bansal, Fruits and vegetables quality evaluation using computer vision. J. King Saud Univ. Comput. Inf. Sci. 33(3), 243–257 (2021) 16. J. Naranjo-Torres, M. Mora, R. Hernández, R.J. Barrientos, C. Fredes, A. Valenzuela, A review of convolutional neural network applied to fruit image processing. Appl. Sci. 10(10), 3443 17. Y. Zhang, P. Phillips, S. Wang, G. Ji, J. Yang, J. Wu, Fruit classification by biogeography-based optimization and feedforward neural network. Entropy 17, 5711–5728 (2015) 18. P.K. Sethy, Indian Fruits-40, Mendeley Data, V1 (2020)

Power Quality Event Classification Using Wavelets, Decision Trees and SVM Classifiers M. Venkata Subbarao, Chinimilli Pravallika, D. Ramesh Varma, and M. Prema Kumar

Abstract Recently the usage of voltage-sensitive devices around the world is growing rapidly. These devices are not ideal and may get affected by Power Quality (PQ) events such as Sag, Swell, Harmonics, and Interruptions, etc. In IoT applications, failure of these sensitive devices may cause serious damages. Existing methods classify the disturbances 90–97% accurately with a large feature set. To avoid complexity, this paper deals with the classification of PQ events with a set of wavelet decomposed & direct signal features such as Shannon entropy, mean energy, and total harmonic distortion (THD), etc. Machine Learning (ML) approaches such as Decision Tree (DT) and Support Vector Machine (SVM) classifiers are used for classification. By considering the different training rates, the performance analysis is carried out. Simulation results indicate the supremacy of the proposed DT & SVM classifiers when compared with the already existing methods. Keywords PQ events · WT · Decision trees · SVM classifiers

1 Introduction Nowadays, the usage of sensitive devices is tremendously increasing. Sometimes, these devices are easily affected by PQ disturbances. Any deviation in the power signals from the normal values is depicted as a PQ disturbance [1]. PQ disturbances are due to the sudden variations in the voltage and the frequency in the pure sinusoidal signals [2]. The reasons for these variations are non-linear loads, utility switching, and fault clearing. These PQ disturbances can cause the failure of devices, interrupting the continuation transmission of power in the power signals and disturbing the whole network [3]. This section contains the information about various existing approaches and discussed about the pros and cons of the existing approaches. Some of the existing techniques are Probabilistic Neural Network (PNN), Artificial Neural Networks (ANN) and extracted features using Discrete Wavelet Transform, S-transform and M. Venkata Subbarao (B) · C. Pravallika · D. Ramesh Varma · M. Prema Kumar Department of ECE, Shri Vishnu Engineering College for Women, Bhimavaram, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_27

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Hilbert Transform etc. proposed by various authors. Ucar [4] have proposed the W-ELM-based features extraction method and derived 3 features from the signals and ELM is used for classifying the type of disturbance. This method obtained 96% correct classification rate out. Biswal [5] have proposed Discrete S-transform-based feature extraction and derived 6 features and Adaptive Particle Swarm Optimization (APSO) and fuzzy C-means algorithm are used for classifying the PQ disturbance. This method obtained 96% correct classification rate out. Mishra et al. [6] have proposed S-transform-based feature selection and used Probabilistic Neural Network (PNN) classifier for classifying the PQ disturbance. This method obtained 96% correct classification rate out. Bhavani and Rathina Prabha [7] have proposed Wavelet Packet-Based feature extraction and used Artificial Neural Networks (ANN) for classifying the PQ disturbance. As a result, this method obtained 95.25% correct classification rate out of 200 actual events tested. Masoum et al. [8] have proposed Wavelet Network-Based Algorithm for classifying the PQ disturbances. As a result, this method obtained 98.18% correct classification rate. Most of the existing models considered only limited disturbance. For training and testing, they have considered large numbers of features and analysis carried at fixed training rates. Further, if they have added some more classes then there would have been a chance of decrease in accuracy. In order to overcome these limitations, a certain number of combinations have been considered and they were classified using DT & SVM Classifiers. The arrangement of the rest of the paper is as follows. Section 2 deals with the WT decomposition and feature extraction. Section 3 deals with the DT & SVM Classifiers. Section 4 deals with Performance Analysis of DT & SVM Classifiers by considering different training rates. Finally, Sect. 5 shows the major conjectures of the research work done which are obtained through simulation.

2 Feature Extraction This section deals with the WT decomposition of PQ disturbances and different features extractions from detailed, approximated, and normal time domain signals. The wavelet decomposition of a PQ disturbance is shown in Fig. 1 [9]. Here discrete Meyer WT is applied for decomposition.

HPF PQ Disturbance

Detailed Signal (D)

DWT

LPF Fig. 1 DWT decomposition of PQ disturbance

Approximated Signal (A)

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The features which are extracted from a PQ disturbance are Shannon wavelet packet entropy of disturbance, detailed and approximated, mean energy of the signal, THD and maximum energy percentage. These features are used to train the classifier. The same features of unknown disturbance are used for classification in testing phase.

3 Methodology This section deals with the methodology of the of the proposed DT, SVM-based PQ classifier. Figure 2 represents the proposed PQ classifier [10].

3.1 DT Classifiers DT classifier has the ability to classify the different classes. It forms a tree like structure while training the model. Internal nodes represent the features of a dataset, branches represent the conditions, and leaf node represents the output. Based on the conditions, it will take the decision to produce the output using various features. Basically, DT has the ability to think like a human being. It is easy to understand the function of this algorithm as it forms a tree like structure. Based on the number of leaves, Decision Tree is further classified into three types they are Fine Tree (FT), Medium Tree (MT) and Coarse Tree (CT). It has the ability to think all the possible outcomes for all problems. Training Phase Different classes of Power Quality Signals

Signal Preprocessing

Features Extraction

Testing Phase Unknown Power Quality Signal

Signal Preprocessing

Fig. 2 Proposed PQ classifier

Features Extraction

DT/ SVM Classifier

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3.2 SVM Classifiers SVM is mostly used for classification problem. It is a representation of different classes in multidimensional space. The main aim of SVM is to find out a best decision boundary or hyper plane that helps to classify the classes in n-dimensional space. The dimensions of the hyper plane depend upon the number of features. Different kernel functions are considered for the decision functions and there is a possibility of specifying the custom kernel. Hyper plane is created with in n-dimensional space with extreme vectors.

4 Simulation Results Table 1 contains the simulation information. It consists of 10 classes of PQ disturbances i.e., pure signal, interruption, swell, sag, impulsive transient, oscillatory transient, flicker, harmonics, swell + harmonics, sag + harmonics. The size of dataset if 10,000 * 6 in which each class of size 1000 copies and 6 features by considering different training and testing rates. The dataset consists of 6 features and one label i.e., PQ disturbance. The training rates considered for training the model are from 90 to 50% and the dataset for testing is 10–50%. Performance analysis of the classifier can be done by calculating the accuracy of the model. Tables 2 and 3 represent the performance of all DT and SVM classifiers for different training rates. Figures 3 and 4 represent the performance of DT and SVM classifiers at different training rates. The proposed classifiers have an accuracy more than 97% even at 50% training rate. From these simulations it is observed that the accuracy with WT features have higher than that of existing approaches. Table 1 Simulation information Parameters

Description

Type of disturbances

Pure signal, interruption, swell, sag, impulsive transient, oscillatory transient, flicker, harmonics, swell + harmonics, sag + harmonics

Features

Shannon entropy of approximated coefficient, Shannon entropy of detailed coefficient, Shannon entropy of a signal, mean energy, max-percentage and total harmonic distortion

Dataset

10,000 * 6

Training rate (%)

50–90

Testing rate (%)

10–50

Performance indices

Accuracy

Classifiers

SVM and decision tress

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Table 2 Accuracy of DT classifiers Classifier

Hold out validation 90

Cross validation

Noise

80

70

60

50

10-fold

20 dB

20-fold

FT

98.3

98.2

97.9

97.8

97.5

98

98

79.9

MT

97.0

96.5

96.4

96.3

96.2

96.2

96.1

77.2

CT

49.9

49.6

49.6

49.7

49.7

49.7

49.7

49.6

Table 3 Accuracy of SVM classifiers Classifier

Hold out validation 90

80

70

60

50

Cross validation

Noise

10-fold

20 dB

20-fold

Linear

98.8

98.7

98.2

98.1

98

98

98

91.4

Quadratic

99.5

99

98.8

98.7

98.6

98.8

98.9

92.6

Cubic

99.5

99.5

99.3

99.1

99.1

99.3

99.3

94.6

Fine Gaussian

99.2

99.2

98.9

98.6

98.6

98.9

98.9

85.2

Medium Gaussian

99.1

99.1

98.5

98.6

98.4

98.6

98.6

88.7

Coarse Gaussian

97.8

97.8

97.2

96.9

96.7

97.2

97.3

83.7

Fig. 3 Performance of DT classifiers

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Fig. 4 Performance of SVM classifiers

5 Conclusion In this paper, different DT and SVM classifiers are developed to classify the PQ disturbances. Extracted 6 features from the signals using Discrete Wavelet Transform. Analyzed the performance of each classifier at different training rates from 90 to 50% and testing rates from 10 to 50% by considering the combination of disturbances. From the simulation results, it is observed that the highest accuracy of 99.5% is obtained from quadratic and cubic SVM and remaining classifiers accuracy ranges from 98 to 99% except Coarse Tree classifier and the accuracy is more even if the training rate is low. It is also proved that accuracies of proposed approaches are better than the existing approaches.

References 1. C.Y. Lee, Y.X. Shen, Optimal feature selection for power-quality disturbances classification. IEEE Trans. Power Delivery 26(4), 2342–2351 (2011) 2. M.V. Subbarao, S.K. Terlapu, V.V.S.S.S. Chakravarthy, S.C. Satapaty, Pattern recognition of time-varying signals using ensemble classifiers, in Microelectronics, Electromagnetics and Telecommunications. Lecture Notes in Electrical Engineering, vol. 655, eds. by P. Chowdary, V. Chakravarthy, J. Anguera, S. Satapathy, V. Bhateja (Springer, Singapore, 2021). https://doi. org/10.1007/978-981-15-3828-5_76

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3. P.K. Dash, M.V. Chilukuri, Hybrid S-transform and Kalman filtering approach for detection and measurement of short duration disturbances in power networks, in IEEE Transactions on Instrumentation and Measurement, vol. 9. 2018 6th International Istanbul Smart Grids and Cities Congress and Fair (ICSG), Apr 2018, pp. 39–43 4. F. Ucar, O.F. Alcin, B. Dandil, F. Ata, J. Cordova, R. Arghandeh, Online power quality events detection using weighted extreme learning machine, in 2018 6th International Istanbul Smart Grids and Cities Congress and Fair (ICSG), 2018, pp. 39–43. https://doi.org/10.1109/SGCF. 2018.8408938. 5. B. Biswal, P.K. Dash, B.K. Panigrahi, Power quality disturbance classification using fuzzy Cmeans algorithm and adaptive particle swarm optimization. IEEE Trans. Ind. Electron. 56(1), 212–220 (2009) 6. S. Mishra, C.N. Bhende, B.K. Panigrahi, Detection and classification of power quality disturbances using S-transform and probabilistic neural network. IEEE Trans. Power Del. 23(1), 280–287 (2008) 7. R. Bhavani, N.R. Prabha, A hybrid classifier for power quality (PQ) problems using wavelets packet transform (WPT) and artificial neural networks (ANN), in 2017 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS) (2017). https://doi.org/10.1109/itcosp.2017.8303073M 8. A.S. Masoum, S. Jamali, N. Ghaffarzadeh, Detection and classification of power quality disturbances using discrete wavelet transform and wavelet networks. IET Sci. Meas. Technol. 4(4), 193–205 (2010) 9. M.V. Subbarao, P. Samundiswary, Time-frequency analysis of non-stationary signals using frequency slice wavelet transform, in 2016 10th International Conference on Intelligent Systems and Control (ISCO), 2016, pp. 1–6. https://doi.org/10.1109/ISCO.2016.7726999 10. M. Venkata Subbarao, T. Sudheer Kumar, G.R.L.V.N.S. Raju, P. Samundiswary, Power quality event recognition using cumulants and decision tree classifiers, in Innovations in Electronics and Communication Engineering. Lecture Notes in Networks and Systems, vol. 107, eds. by H.S. Saini, R.K. Singh, M. Tariq Beg, J.S. Sahambi (Springer, Singapore, 2020). https://doi. org/10.1007/978-981-15-3172-9_55

Comparative Analysis of Different Models for Diabetic Retinopathy Classification Lavanya Bagadi, E. Pavankumar, A. Likitha, K. Niranjan, and B. Nani

Abstract The Diabetic Retinopathy (DR) is a medical condition of eye in people with diabetes that may lead to vision loss. The retina blood vessels in the eye get affected by DR. For diabetic patients, the eye condition has to be examined frequently once in a year to identify the symptoms at early stage. Early detection is essentially a challenge and is important for treatment success. Manual detection of DR by ophthalmologist takes lot of time and patients need to suffer at this time. An automated system can help detect DR quickly and can take follow-up action easily to avoid further affects to eye. The main task is to classify the different stages of DR using Deep Learning (DL). So, different models based on CNN architecture are used for training the model with modified hyper parameters and compared with each other. This paper focuses on different stages of DR classification using APTOS dataset on Kaggle and their comparison. Keywords Convolutional neural network · Classification · Deep learning · Diabetic retinopathy

1 Introduction Diabetes occurs due to change in the metabolism of human body caused by the high blood sugar values. The early signs are frequent urination, high amount of increase in the intake of food and water. Treatment of diabetes is very important to avoid any health complications. Diabetic ketoacidosis, hyperosmolar hyperglycemic state is some of the normal complications while cardiovascular disease, foot ulcers, cognitive impairment, damaging the nerves and eyes are some severe complications of diabetes. Diabetic Retinopathy is an eye damage condition occurs due to the retinal blood vessels get affected because of diabetes, and further leading to Blindness [1]. Diabetic

L. Bagadi · E. Pavankumar (B) · A. Likitha · K. Niranjan · B. Nani Department of ECE, MVGR College of Engineering (A), Vizianagaram, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_28

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condition increases the patient’s chances of having glaucoma and even cataract conditions. Generally, diabetic patients are advised to visit ophthalmologist frequently to avoid this Diabetic Retinopathy condition. So, it is essential to develop an efficient and accurate model for detecting DR condition. Diabetic retinopathy affects a large percentage of people who are suffering from diabetes with more than 15–20 years. This percentage of people can be reduced by monitoring properly and providing required treatment. The amount of time a patient is suffering from diabetes increases risks for DR. The DR stages are divided into five types as, No DR, Mild non-proliferative diabetic retinopathy (NPDR), Moderate NPDR, Severe and Proliferative diabetic retinopathy (PDR) [2]. Related studies reveal the recent advancements in deep learning, particularly CNN gained success in image classification and even solved many medical problems [3]. Before the discovery of CNNs, the hand-crafted feature extraction tools are used by different experts for processing of digital images. But, currently CNNs achieve the task of automatic feature extraction by making use of a batch of convolution and pooling layers in the training phase itself. Mishra et al. [4] trained CNN to classify DR by comparing VGG16 with DenseNet121 architecture and resulted in accuracies of 73.2 and 96.1% resp. using APTOS dataset available on Kaggle. Huang et al. [5] used DenseNet to train deeper layers with few parameters and dense connections and achieved high accuracy compared with ResNet. Wang et al. [6] studied the pretrained CNN architectures performance on available Kaggle dataset having 166 images only for AlexNet, VGG16, and InceptionV3 models, to detect the five stages of DR. The images are resized during preprocessing and specified an average accuracy of 37.4%, 50.03%, and 63.3% for AlexNet, VGG16, and InceptionV3 respectively, although the networks were trained with limited number of images. Harangi et al. [7] integrated the hand-crafted features with the available pretrained AlexNet model to classify DR into five stages. Kaggle dataset is used to train the CNN and IDRiD for testing. Mansour [8] used transfer learning for feature extraction to train a deep CNN using Kaggle dataset and made a computer assisted diagnosis for DR. Pratt et al. [9] used a large Kaggle dataset and evaluated their 13-layer CNN performance to classify DR. So, early detection and diagnosis can help patients to provide with better medical treatments and prevent long-term vision impairment effects caused by DR. Dutta et al. [10] selected 2000 fundus images from Kaggle dataset and used 300 images as test dataset to train a shallow feed forward neural network, deep neural network and VGG16 model. The accuracies stated are 42%, 86.3%, and 78.3% respectively.

Comparative Analysis of Different Models … Defining Hyper parameters

Training Images

Testing Images

255 CNN model used

Predicted output

Validation Images

Fig. 1 Block diagram of model

2 Methodology The features of fundus images are extracted using deep CNN architecture to classify DR into 5 different classes. Generally, any CNN model has two parts, feature extraction and classification. Feature extraction task is performed by the convolutional layers present with the CNN and Fully connected layers or Dense connections take care of classification part. Now, based on the collected feature characteristics, the classifier then classifies into required number. The transfer learning method is used for the models considered to train the convolutional part from the database of ImageNet to extract distinguished features. So, to train with a deep CNN model here, the weights of the pretrained architectures of different models like ResNet, DenseNet, VGGNet and Xception are taken into consideration in this paper for analysis purpose. Initial layers are freezed for all these considered models and last few layers are changed by adding an activation function like softmax and taking SGD and ADAM optimizer along with a 5-layer classifier at the Full connected layer of output. On Comparison, DenseNet model with Adam optimizer shows better performance compared to other models taken, Fig.1 represents block diagram of the proposed model.

3 Experimental Results The proposed model is tested and evaluated on APTOS 2019 dataset along with retinopathy 2015 dataset that includes about 38,774 retinal images [11]. So, class 0 includes 6349 images, class 1 has 2812 images, class 2 has 6287 images, class 3 has 1066 images, and class 4 has 1003 images. This dataset is separated into three folders as training, testing, and validation. The accuracy of classification models is observed and compared with other models. The training and validation accuracy, validation loss is considered to find efficiency of proposed classification models. The proposed models are considered by selecting various CNNs with different optimizers and total parameters (weights and biases), the details are shown in Table 1. Model 1 is taken as ResNet50 with 48 Convolutional layers followed by maxpool and Average Pool layer having Floating points operations of 3.8 × 109 . Model 2 is

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Table 1 Details of various proposed classification models

Model name

CNN used

Optimizer used

Total parameters (w, b)

Model 1

Resnet50

Adam

23,597,957

Sgd

23,597,957

Model 2

Densenet

Adam

7,042,629

Model 3

Vgg16

Adam

14,717,253

Sgd

14,717,253

Model 4

Xception

Adam

20,871,725

Sgd

20,871,725

DenseNet-121 with four dense blocks having [6, 12] layers in each block. Model 3 is VGG-16 with 16 layers deep convolutional neural network, The Xception architecture is model 4 with 36 convolutional layers forming the feature extraction base of the network [12, 13]. As these models are evaluated for Image classification task, each convolutional base is followed by a Softmax layer. Model 2 is best as it shows 95% classification accuracy among all other models. So, the confusion matrix for the proposed model 2 for the 5 classes with Adam optimizer is shown in Fig. 2. The performance of classifier is evaluated using confusion matrix. Confusion matrix offers the performance of a classification model by taking a dataset of 5080 test images. In confusion matrix there are four entities, they are True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN). For better performance, False Negative values must be minimum. Table 2 represents hyper parameters of proposed models with different optimizers used to train the model by varying learning rate. The model trains well for low learning rate.

No DR

569

193

225

8

3

Mild DR

181

596

168

67

34

201

170

554

73

26

6

89

115

544

244

4

41

76

155

738

Moderate DR Severe DR Proliferative DR

Fig. 2 Confusion matrix of proposed model 2

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Table 2 Hyper parameters of proposed models with % accuracy Model name

CNN used

Learning rate

Optimizer

Accuracy (%)

Model 1A

Resnet50

0.00005

Adam

61.71

Model 1B

0.00005

SGD

85.70

Model 1C

0.00001

SGD

65.43

Model 2

Densenet

0.0001

Adam

95

Model 3A

Vgg16

0.00005

SGD

85.66

0.000001

Adam

79.68

0.00005

Adam

74.18

0.000001

SGD

85.31

Model 3B Model 4A

Xception

Model 4B

Table 3 Performance metrics and regression accuracy metrics of proposed classification models Model name

(TPR)

(TNR)

(PPV)

(NPV)

R-squared

MAE

MSE

Model 1A

0.64

0.91

0.65

0.9

0.67

0.44

0.65

Model 1B

0.27

0.81

0.42

0.82

0.28

0.96

1.45

Model 1C

0.2

0.8

0.3

0.6

0.21

2.05

6.22

Model 2

0.73

0.93

0.73

0.92

0.69

0.58

0.97

Model 3A

0.31

0.80

0.39

0.81

0.58

0.62

0.85

Model 3B

0.49

0.87

0.53

0.87

0.57

0.61

0.84

Model 4A

0.65

0.91

0.65

0.90

0.66

0.45

0.67

Model 4B

0.70

0.90

0.68

0.91

0.60

0.40

0.63

In this classification task, there are more than two output classes; hence the entities are 5 values for 5 classes in each model instead of single values. So, the average values of these entities are mentioned in Table 3. Table 3 shows the performance metrics computed from the confusion matrix. They are True Positive Rate (TPR), True Negative Rate (TNR), Positive Predicted Value (PPV), and Negative Predicted Value (NPV) along with regression accuracy metrics R-squared, MAE and MSE. R-squared value lies between 0 and 1. ‘1’ indicates that the model explains all the variability of the response data around its mean. Mean Absolute Error (MAE) denotes the deviation of predicted values from original and are extracted by averaging the absolute difference over the data set. Mean Squared Error (MSE) denotes deviation of predicted values from original extracted by squaring the average difference over the data set.

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4 Conclusion and Future Scope The deep learning method for classifying Diabetic Retinopathy is discussed using various models. Four different models are proposed to categorize the 5 stages of DR, and to train model well. The accuracy results obtained using Adam optimizer in model 2 is much higher than those obtained in all other models. An accuracy of 95% is obtained by the proposed model 2. The Proposed model 2 is best among all other models. The testing accuracy may be increased on using better augmentation methods, on increasing number of layers, and on increasing the epochs. An advantage of using augmentation is, the training dataset can be increased so that the model will be trained well. The Proposed models can be used to classify fundus retinal images, blood cells images, neuroimaging, skin cancer images, plant diseases, and animals/birds images and so on.

References 1. W.L. Alyoubi et al., Diabetic retinopathy detection through deep learning techniques: a review. Inform. Med. Unlocked 1–11 (2020) 2. S. Qummar et al., A deep learning ensemble approach for diabetic retinopathy detection. IEEE Access (2019) 3. M.A. Syben et al., A gentle introduction to deep learning in medical image processing. Zeitschriftfür Medizinische Physik 29(2), 86–101 (2019) 4. S. Mishra et al., Diabetic retinopathy detection using deep learning, in 2020 International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE) (2020) 5. G. Huang et al., Densely connected convolutional networks, in IEEE Conference on computer Vision and pattern recognition (CVPR), pp. 4700–4708 (2017) 6. X. Wang et al., Diabetic retinopathy stage classification using convolutional neural networks, in International Conference on Information Reuse and Integration for Data Science, pp. 465–471 (2018) 7. B. Harangi et al., Automatic screening of fundus images using a combination of convolutional neural network and hand-crafted features, in 41st annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2699–2702 (2019) 8. R.F. Mansour et al., Evolutionary computing enriched computer-aided diagnosis system for diabetic retinopathy: a survey. IEEE Rev. Biomed. Eng. 10, 334–349 (2017) 9. H. Pratt et al., Convolutional neural networks for diabetic retinopathy. Proc. Comput. Sci. 90, 200–205 (2016) 10. S. Dutta et al., Classification of diabetic retinopathy images by using deep learning models. Int. J. Grid Distr. Comput. 11(1), 99–106 (2018) 11. Kaggle dataset [online]. Available https://kaggle.com/datasets 12. L. Tiwari, R. Raja, V. Awasthi, R. Miri, G.R. Sinha, M.H. Alkinani, K. Polat, Detection of lung nodule and cancer using novel Mask-3 FCM and TWEDLNN algorithms. Measurement 172, 108882 (2021). ISSN 0263-2241. https://doi.org/10.1016/j.measurement.2020.108882 13. R. Raja, S. Kumar, M.R. Mahmood, Color object detection based image retrieval using ROI segmentation with multi-feature method. Wirel. Personal Commun. 1–24. Print ISSN 09296212, online ISSN 1572-834. https://doi.org/10.1007/s11277-019-07021-6

Convolutional Neural Network-Based Tomato Plant Leaf Disease Detection V. Tejashwini, Shubha Suresh Patil, Shweta S. Mali, M. S. Salina, and Jyothi S. Nayak

Abstract The evolution of the human race was identified based on agricultural advancements. The nomadic humans settled in a particular land and started cultivating the land. Hence, most of the countries are agricultural based, and India is one such country. The need for identifying the problems faced in the agricultural sector needs to be addressed in a more efficient way. One of the problems faced is plant diseases. Plant diseases have been shown in several studies to affect the quality of agricultural goods. In plants, diseases can be identified in various parts such as stem, leaf, fruit, and flower. These diseases can be caused due to bacteria, fungi, and viruses. The objective of this research is to find an efficient way of identifying tomato plant diseases through the pattern of the plant leaves since tomato is the most regularly used and affordable vegetable in India. The identification of patterns in tomato leaves is done using convolutional neural network (CNN). This approach can help farmers in identifying the diseases in the early stage and increase the yield, minimizing financial loss. In addition to this, the requirement of pesticide per hectare is reduced, which adds to the health benefits to the consumer and improves financial returns to the farmers. Keywords Convolution neural network · Leaf pattern · Plant-village dataset

1 Introduction The diseases affecting plants constitute 10–30% of the overall crop loss. In a country like India which is an agro-based economic country, this percentile constitutes a huge loss. Plants are susceptible to a variety of illnesses. Tomatoes are the most widely consumed vegetable in India. India’s tomato crop cultivation area is projected to be 350,000 hectares, with annual output quantities of about 52,00,000 tons, making V. Tejashwini · S. S. Patil (B) · S. S. Mali · M. S. Salina · J. S. Nayak B.M.S College of Engineering, Bengaluru, India J. S. Nayak e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_29

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it the world’s third highest tomato producer. Due to the sensitivity of crops and environmental conditions, diseases are prevalent in the tomato crop at all stages of growth. Convolutional neural networks (CNNs), a subset of deep learning techniques, have recently risen to prominence as the preferred approach. CNN is the most widely used image recognition classifier, and it has demonstrated exceptional capacity in image processing and classification. There are many proven pros in feature extraction and recognition with the advancement of deep learning, such as the CNN that trains the network automatically and extracts the features from the input data by introducing local connections and sharing weights. Tremendous progress has been made in the identifying diseases of banana, coffee, cucumber, and tomato. However, due to differences in the structure of different models used for recognition and the quality influence of the natural environment on the appearance of tomato leaves, the recognition impact varies. As a result, the study’s main concerns are as follows: (i) Poor image quality; when a laboratory-based algorithm is applied to real-life scenarios, the picture quality acquired in the real-life is easily influenced by a range of factors in the receiving environment. The neural network structure may learn automatically the extracted features from the training set when the original images of the tomato leaf is directly given as input to the network. However, noise and improper features in the actual image of tomato leaves may result in erroneous feature extraction and accumulation, as well as picture ambiguity in the leaves’ backdrop reduces network recognition efficiency. (ii) After adjusting the model, the tomato leaf illnesses show a lot of overlap in certain categories, making it difficult for the neural network to distinguish them. Because the early disease’s region is limited, the symptoms are not obvious, more comprehensive information from the network layer must be retrieved, and the early stage and later stages picture features of the same disease may vary significantly. This paper is mainly concerned with application of neural networks in tomato plant disease detection based on plant leaf image classification. This approach is a unique, quick, and easy approach that is implemented in practice. The implemented model has the ability to predict nine different classes of tomato plant disease and is capable of distinguishing the tomato plant leaf from its surroundings.

2 Literature Survey Detection of tomato plant disease has been studied by many researchers. It is critical to diagnose disease at an early stage in order to boost agricultural growth. The studies showed that: According to the study, late blight and early blight are two diseases that harm potato plants. For identification, image segmentation was utilized [1]. Picture segmentation and soft computing techniques were used to detect the diseases, Berry spot and Quick wilt in pepper plants [2]. Early and exact detection of the stresscausing factor is possible with combination of computer vision and deep learning

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techniques. In computational testing, the suggested system, which employs the ResNet50 architecture, achieved a biotic stress classification accuracy of 95.24% and a severity estimation accuracy of 86.51% [3]. An smart irrigation management system based on IoT technology utilizes machine learning and open source technologies which presents an IoT idea to serve as inspiration for the future vision of the “Internet of Things,” and to make the irrigation decision, image processing techniques are combined with IoT sensors and machine learning technology [4]. The availability of sensors for the detection of plant-emitted volatile organic compounds (VOCs) including ethylene (which signals general stress), esters of jasmonic acid (which signal pest attack), and esters of salicylate (which signal pathogen attack) would allow the issue of infection alerts and actuate a pathogen or pest-specific response as appropriate [5]. Thermal imaging is used in combination with other image processing and data analytic techniques to reduce agricultural water stress and offer irrigation scheduling [6]. Deep learning advances allow for unsupervised feature extraction and prediction on large datasets and have accelerated the development of visual plant phenotyping systems [7, 8]. Sensor networks are used with AI systems like artificial neural networks and the multilayer perceptron to build a system for assessing the suitability of agricultural land. The image was captured using an infrared video sensor, and the gadget was 97.5% accurate [9].

3 Proposed Methodology The proposed method has four key stages: data acquisition, tomato leaves image enhancement, image background segmentation, and disease identification of tomato leaves. The model architecture with all the stages is shown in Fig. 1. i.

Data acquisition: A large volume of data will improve the model’s performance and prevent over fitting issues. Hence, The New Plant Diseases Dataset (Augmented) is considered, which contains a large number of healthy plant leaf images as well as various diseased leaf images that will be used to train the model. Augmented dataset is generated by performing augmentation on dataset, which refers to strategies for increasing the quantity of data by doing

Fig. 1 Model architecture

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slight changes to the copies of current data or creating new artificial data from existing data. When training a model, it works as a regularizer and helps reduce overfitting. Some of the strategies used for creating augmented dataset include rotating images, flipping images horizontally and/or vertically, etc. Image enhancement of tomato leaves: External variables such as dust and dew can inevitably affect photographs after they have been acquired; as a result, the final image may be of poor quality, with inaccurate picture edge modulus and details, lowering the accuracy of disease identification in later stages of development. Hence, the images are enhanced to increase the content of the data. Image background segmentation: The photographs are recorded in a complicated environment, and just a portion of the plant, namely the leaf picture, is used as data. The image is segmented to eliminate the background noise. Tomato leaf disease identification: Many of the symptoms of several tomato leaf illnesses are identical, but the symptoms of the leaf in the early and late stages of the disease vary. If the disease is still in its early stage, its location is difficult to determine. Early detection and control of the disease, on the other hand, is more practicable. For extracting more detailed information, we use a deep neural network. The deeper the network, the more likely gradient degeneration will develop.

CNNs are a form of deep neural network. Shift-invariant or space-invariant artificial neural networks are so named because of its shared weights design and translation invariance properties. Convolution is a mathematical operation employed in this network. The term “convolution” refers to a subset of linear operations. The sole distinction between this network and others is that at least one of its layers uses convolution instead of general matrix multiplication. In a word, CNN is a machine learning system that takes a tomato leaf image as input and assign weights and biases which are learnable, to various features in the image, as well as discriminate between them. CNN’s role in the proposed model is compressing the input images into a more manageable format while maintaining essential features for accurate prediction. A CNN model consists of an input layer and many hidden layers. Convolutional layers convolve with dot product and are commonly used as hidden layers. The most widely used activation function is the rectified linear unit activation function (ReLU), which is followed by pooling layers, fully connected layers, and normalizing layers. Figure 2 depicts the detailed design of the implementation. The diagram is intended to show the whole process of, model training and testing. Once the data is collected in the data acquisition phase, the images are enhanced and the background is removed for efficient prediction. Then, the images are gone through a series of convolution layers to extract features from the tomato leaves and train the model accordingly. Then, the dense layers classify the disease using activation functions—ReLU and Softmax.

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Fig. 2 Detailed design of the proposed model

4 Proposed Model The proposed system employs a sequential model. A sequential approach is mainly used when there are layers, which can be simply stacked with one input and one output tensor for each layer. During implementing of the model, sequential constructor is used to generate a sequential model by giving a list of levels to it. The summary of the built model is shown in Fig. 3.

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Fig. 3 Summary of the sequential model layers

The convolution layer: The conv2d layer’s fundamental goal is to extract highlevel patterns from the picture. When creating the neural network, more than one convolution layer is used. The first convolution layer is in charge of collecting gradients, while the second and third layers are in charge of catching edges. ReLU is used as an activation function in the proposed model because it is used to enhance the nonlinearity of a network without changing the receptive fields of convolution layers. ReLU allows for faster data training. The Max-Pooling Layer’s Function: The activation maps are the result of the pooling layer’s non-linear down-sampling of the convolved feature. This is primarily to minimize the computational burden of processing the massive amount of data associated with an image. Pooling may be divided into two forms, however, in the proposed approach, Max-Pooling is employed, because it delivers the greatest value from the region of the picture covered by the Pooling Kernel. Image Flattening: After the output has been pooled, it is transformed to a tabular format that is utilized by a neural network to conduct classification. Using a Sequential model to extract features: Once constructed sequential model acts as functional API model, which can handle models with shared layers, nonlinear topology, and even multiple inputs and/or multiple outputs. Every layer, thus, has an input and output property. These characteristics may be used to easily create a model that extracts the outputs of all intermediate levels in a sequential model. Figure 4 shows the features that are extracted by the convolutional layer. The advantage of a drop-out layer: To prevent the algorithm from overfitting, drop-out layers are included. While training the data, dropouts discard a few of the activation maps, but utilize all of them for testing. By lowering the correlation between neurons, it avoids overfitting. Dense layers are the ones that are most frequently utilized as output layers. The ‘Softmax’ activation function is employed, which generates the probability for each of the class that sums up to one. The Softmax

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Fig. 4 Feature extraction

activation function is the activation function used in the output layer of the model that predicts a multi-class probability distribution. The model’s predictions will be based on the class with the highest likelihood.

5 Dataset Description The New Plant Diseases Dataset (Augmented) from kaggle was used to develop the suggested method. There are roughly 22,930 photographs organized into ten categories in this collection. A total of 18,345 photographs were utilized to train the model, while 4585 photographs were preserved for testing. Data augmentation techniques were used to increase the dataset by randomly rotating the photographs by 20 degrees, horizontally flipping the images, and vertically and horizontally shifting images. With a batch size of 32, the model was trained for 50 epochs. Figures 5, 6, and 7 show the dataset images. Fig. 5 Septoria leaf spot

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Fig. 6 Yellow leaf curl

Fig. 7 Healthy leaf

6 Results and Analysis To assess the proposed model’s performance, a range of activation functions and CNN layers were used. Table 1 presents a summary of the findings. They show the greatest values of performance measures, including the epoch number. Table 1 Accuracy of the model trained with different activation functions and number of epochs

Activation function

No. of epochs

Accuracy (%)

ReLU and Softmax

50

98.31

Sigmoid and Softmax

50

10.57

ReLU and Softmax

25

97.09

ReLU and Softmax

10

94.57

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Over 50 epochs of training, the highest validation accuracy of 98.32% was achieved, while the highest training accuracy of 98.25% was achieved. On average, a validation accuracy of 98.25% was reached. Table 2 summarizes the accuracy of the model when trained with different ratios and different number of epochs. The average accuracy achieved here is about 97.3%. This is a useful metric for determining how well a deep learning model can classify data. Figure 8 shows the graphs of training against testing accuracy and training loss versus testing loss. The model appears to have stabilized at 50 epochs, and the metrics have not considerably improved in the last 10 epochs. Unlike complex neural networks, which often demand a lot of processing resources or the usage of a graphics processing unit, the implementation procedure does not require any complex model and processing. Because there are fewer layers, the training parameters are reduced. Smaller train sizes and filter sizes are used in these images. As a result, the model provides a simple and effective solution for tomato leaf disease detection. The model is tested using a simple Web application as shown in Fig. 9. The model through GUI has predicted the correct result when an image of tomato leaf affected by “Spider mites” disease is given as input. Table 2 Accuracy of model trained with different ratios of the dataset and number of epochs Dataset ratio

No. of epochs

Accuracy (%)

80%—Train, 20%—Valid

50

98.32

80%—Train, 20%—Valid

25

97.09

80%—Train, 20%—Valid

10

94.57

70%—Train, 30%—Valid

50

98.23

60%—Train, 40%—Valid

50

98.29

Fig. 8 Model trained with 10, 25, and 50 epochs

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Fig. 9 Front end of the developed web application

7 Conclusion and Future Works New technology, such as early detection and prevention of plant diseases, can be used to increase the production of healthy food. CNN models can be used to detect plant diseases with high accuracy. Many pattern recognition methods exist in the problem-solving world, but CNN is effective because it can be used in both theory and practice. The main goal of the project is to detect common tomato plant diseases from the leaves. Working with images of varying resolution, size, and orientation, the system can find accurate results after finalizing the raw data, completing the training, and validating the problem. Experimenting with newer architectures to enhance the model’s performance on the train set might potentially be part of it. As a result, the above model might be used as a deciding tool to assist and advise farmers to identify diseases that harm tomato plants. The suggested approach has a 97–98% accuracy in detecting leaf diseases. In future, the system can be improved such that the system can classify a number of other plants and diseases. A mobile app can be developed for easier access. A smart Web application can be developed that can recommend plant nutrients based on disease identified.

References 1. M. Islam et al., Detection of potato diseases using image segmentation and multiclass support vector machine, in 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE) (IEEE, 2017) 2. J. Francis, B.K. Anoop, Identification of leaf diseases in pepper plants using soft computing techniques, in 2016 Conference on Emerging Devices and Smart Systems (ICEDSS) (IEEE, 2016) 3. J.G.M. Esgario, R.A. Krohling, J.A. Ventura, Deep learning for classification and severity estimation of coffee leaf biotic stress. Comput. Electron. Agriculture 169, 105162 (2020) 4. A. Goap, et al., An IoT based smart irrigation management system using Machine learning and open source technologies. Comput. Electron. Agriculture 155, 41–49 (2018) 5. Z. Zhai et al., Decision support systems for agriculture 4.0: survey and challenges. Comput. Electron. Agriculture 170, 105256 (2020) 6. C. Goumopoulos, B. O’Flynn, A. Kameas, Automated zone-specific irrigation with wireless sensor/actuator network and adaptable decision support. Comput. Electron. Agric. 105, 20–33 (2014) 7. A.L. Chandra et al., Computer vision with deep learning for plant phenotyping in agriculture: a survey (2020). arXiv:2006.11391

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8. A.K. Sahu, S. Sharma, M. Tanveer, R. Raja, Internet of Things attack detection using hybrid deep learning model. Comput. Commun. 176, 146–154 (2021). ISSN 0140-3664. https://doi. org/10.1016/j.comcom.2021.05.024 9. D.R. Vincent et al., Sensors driven AI-based agriculture recommendation model for assessing land suitability. Sensors 19(17), 3667 (2019)

Virtual Assistant Based on Facial Emotions Using Convolution Neural Networks J. N. V. R. Swarup Kumar, D. N. V. S. L. S. Indira, R. Abinaya, and D. Suresh

Abstract The theme of this paper is to build a virtual assistant whose actions are customizable based on basic emotions theory which consists of seven prototype basic emotions. Since facial emotions play an important role in communication, we need a digital assistant which can be able to detect the human facial emotions and adapt its behavior itself accordingly. The idea is to build a neural network which is able to understand the facial emotions to determine the psychological tendencies in humans and integrate this model with the digital avatars. The facial emotions have a strong interconnection with our behavior. There are seven basic emotions which includes happy, sad, anger, terror, surprise, hatred, as well as neutral. This paper is all about to build a machine using state-of-the-art tools which is capable of detecting facial emotions. Nowadays, human–computer interaction (HCI) makes machines to predict emotions which is a difficult task. Current facial emotions recognition (FER) systems are lacking reliable dataset to train and not good in accuracy as well. Finally, incorporate these psychological AI-driven model with a virtual assistant to make our lives more sparkling. Keywords Facial emotion recognition · Expression detection · Deep learning · Convolution neural network · Virtual assistant

J. N. V. R. Swarup Kumar (B) · R. Abinaya Department of CSE, Gudlavalleru Engineering College, Gudlavalleru, Andhra Pradesh, India e-mail: [email protected] D. N. V. S. L. S. Indira (B) Department of IT, Gudlavalleru Engineering College, Gudlavalleru, Andhra Pradesh, India D. Suresh Department of IT, Annamalai University, Chidambaram, Tamil Nadu, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_30

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1 Introduction Tech monster organizations like Google, Microsoft, Apple, Amazon, and Facebook are attempting to cause their remote helpers to show up more like people. Each tech organization attempts to adapt their AI usefulness of its menial helpers. The thought is to join advanced associates with a mental AI model which is equipped for recognizing human facial feelings. Since facial articulations and feelings are basic perspectives in human correspondence and connections which assist us with understanding the sentiments and the aims of individuals for detecting emotions of humans, [1, 2] we place a sensor, Web camera which screens the client looks and gives it as a predicate to the AI model. Looks pass on non-verbal signs which assume a fundamental part in interchanges. Face feeling acknowledgment is probably the best achievement in profound learning. People can perceive feelings instinctually and effectively as a result of their example acknowledgment power; however, for machines, it is troublesome and testing. So facial feeling recognition is a moving errand for the machines to perceive distinctive expressive facial examples to recognize person feelings. It attracts consideration of scientists to fabricate a model which is fit for perceiving facial feelings and ready to comprehend passionate insight. This exploration prompts a few headways in machine to human associations. People are adapted to be enthusiastic. We have an extraordinary design in our cerebrum which can feel and mind the feelings of other. The thought is to develop a remote helper which can have the option to identify client feelings and act passionate to make it more close to home to the client. It improves client experience and connection with the remote helper. This methodology can likewise be an initial step to assemble humanoids. So first we develop a numerical model to characterize human facial feelings into seven essential feelings utilizing condition of craftsmanship devices. The methodology here is to assemble the convolution neural organization model, and the testing result will be evaluated dependent on precision of the model. Finally, we incorporate this model with a symbol, for example, Siri, Alexa, and so forth, to make them more close to home. This methodology can likewise be an initial step to fabricate humanoids.

2 Literature Survey Nowadays, advanced emotion AI expertise can detect, analyze, and process the emotional states of humans through facial patterns and coding, gestures, body language, and tonality of voice. A comprehensive research on the facial emotion recognition shows the characteristics of the dataset and facial emotion recognition classifier [3]. We can build a mathematical model to understand the emotional state of person using state of art tools. The idea is to build a classifier which is able classify the emotions using visual features. Some classification machine learning models like KNN and random forest

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are used to predict emotions. Deep neural network comes into picture to solve these problems in real life. Deep recurrent neural network like LSTM and bi-directional LSTM is designed for audio and speech datasets. Various range of CNN models are used to detect and extract visual features in an image [4]. Emotion is recognized from facial images using filters and extractors, and deep CNN which gives high accuracy rate gives an interpretation that deep learning can be used for emotion detection for better results. Some facial algorithms like LHBP algorithms able to identify facial patterns and edges and detect the emotions of the person. It works on any optical sensor, like a simple Web cam since these algorithms are light invariant [5, 6]. Emotion recognition through speech also gains momentum in research field. Various deep learning models can be used such as CNN and LSTM for emotion recognition. Generally, we use MFCC format for audio data to train the models [2, 7]. This paper collects the dataset released by Kaggle used for facial emotion recognition. The primary layers in CNN used to select features and classifiers are used to categorize different range of emotions. As the amount of data increases, then the use of traditional machine learning models become bottleneck. Long short-term memory (LSTM) can also use for emotion recognition through speech which is temporal data. Different facial images were scrutinized for recognizing emotions from the facial expressions using different classifiers such as convolution neural network and Xception [8, 9]. Building a deep learning model to classify the emotions consists of following steps (Fig. 1). A.

Preprocessing

Preprocessing involves scaling, resizing, transforming, converting into greyscale, etc. It reduces computations and increases performance of the model. B.

Face Detection

To feed the face to the model, we have to detect the facial portions in the image. The face is detected by some pretrained models which are stored in xml files. C.

Facial Components

The detection of facial components involves the parts of the individual parts of the face for recognizing the emotion. Some of the most common feature selection techniques are principal component analysis, independent component analysis, and local binary pattern. D.

Facial Extraction

This step involves extracting only necessary information from the image and removes unnecessary details in the image. It improves efficiency of the model. E.

Classifier

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Fig. 1 Deep learning model

It involves building a mathematical model and trained it with the preprocessed data to predict the facial emotions of the individual in the image.

3 Database Preparation There are different information bases which are utilized for facial feeling recognition [2]. Among them, we chose facial emotion recognition delivered by Kaggle. In numerous investigations, facial emotion acknowledgment (FER) Kaggle dataset which contains seven feeling classes that are Disgust, Fear, Happy, Sad, Surprise, and Neutral is utilized to upgrade human PC collaboration. The feeling classes are encoded into some numeric code going from 0 to 6 (Disgusting = 0, Fear = 1, Anger = 2, Joyful = 3, Gloomy = 4, Surprise = 5, and Unbiased = 6). The information comprises of 48 × 48 pixel grayscale pictures of appearances (Fig. 2). Numerous information researchers, AI specialists, and analysts have utilized this dataset with various deep learning models and calculations and accomplished great accuracy [8, 10]. To arrive at great precision, deep learning models ought to be prepared with enormous measure of dataset. Execution of the model increments alongside the dataset. To defeat the issue of restricted size and assortment of the information, we create our own information from the left information. This system is called data increase. Distinctive inherent bundles are accessible in various

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Fig. 2 Comparison on emotion classes

systems. In this investigation, we use ImageDataGenerator module which is accessible in Keras framework. Information augmentation procedures includes translation, rotation, flipping, adding noise, and transformation.

4 Proposed Technique This article proposes a CNN engineering for characterizing facial feelings dependent on information base delivered by Kaggle lastly, and we think about the exactness consequences of model prior to performing data augmentation and in the wake of performing data augmentation. We fabricated a design with seven convolutional layers, three normal pooling layers, one worldwide normal pooling layer, two fully associated layers, and SoftMax function as yield layer. First completely associated layer has 256 neurons, while second completely associated layer has 128 neurons. The completely associated layers comprise of dropout layer, a cycle which diminishes the danger of the organization overfitting. Here, rectified linear unit (ReLU) was utilized as activation work for this model. For the most part, the framework works in three classes: train dataset, approving, and test dataset.

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4.1 Convolutional Layer The convolutional layer is utilized to remove facial highlights like edges, milestones, and intriguing focuses from the information dataset. It learns picture highlights and examples utilizing little square boxes of info picture and makes an element stack by protecting spatial connection between pixels. Subsequent to offering contribution to the convolutional layer, convolution is performed among input and the highlights learned by the organization. Convolution is a straight component, wherein component insightful grid tasks are done and yield convolutional layer has same goal as info. Convolutional layer consists of following attributes • Convolutional kernels width and height • Number of input and output channels • Depth of convolutional depth. The output of the convolution layer can be determined as follows: G[m, n] = ( f ∗ h)[m, n] =

 j

h[ j, k] f [m − j, n − k]

k

where h is kernel, f is image, and m and n are rows and columns of the matrix, respectively.

4.2 Rectified Linear Unit Each convolutional layer yield or the element guide of FER dataset is gone through ReLU layer initiation work. RELU is a non-direct capacity used to standardize facial element guide of convolutional layer. It is applied for each pixel in the picture and substitute all adverse pixel esteems in the component load with nothing (Fig. 3). R(X ) = max(0, X )

4.3 Pooling Pooling is utilized to lessen dimensionality of each component map by eliminating all superfluous data in the identified appearances. Pooling includes max pooling, normal pooling, and worldwide pooling. Nearby pooling joins little bunches into single worth, while worldwide pooling consolidates whole group in a solitary pixel. Max pooling thinks about the greatest

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Fig. 3 ReLU function

worth of the bunches. Normal pooling takes the mean of the upsides of the pixels present in the groups. In our architecture, we use average pooling to take the mean of all the pixel values. Pooling decreases the computations of the model.

4.4 Full Connected Layer The yield of pooling layer is given as contribution to full associated layer. It interfaces with each neuron in one layer to another layer. It imitates the conventional multifacet neural organization. We utilize two full associated layers in our design, and SoftMax initiation work is utilized as conclusive grouping layer to foresee the feelings. SoftMax work is utilized for multi-arrangement which gives yield dependent on likelihood dispersions.

4.5 Convolution Neural Network Architecture Since we are utilizing looks outlines, convolutional neural organization over plays out all customary neural organizations. Convolution neural organization helps us in grouping, restriction, and location of highlights in the picture. It is generally used on image dataset. It is also well known as space-invariant artificial neural network, Convolution neural organization is a specific form of multi-facet insights. The completely associated neural organization is inclined to overfitting information. We add some additional term which is alluded as regularization term. It is motivated

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Fig. 4 Block diagram of working of CNN in facial emotion classification

by the visual cortex. The convolution neural organization contains arrangement of convolutional layers followed by RELU layers and is trailed by pooling layers and full associated layers. To start with, we perform standardization methods on look pictures, for example, resizing, reshaping, changing over into greyscale, and so forth. This standardized FER dataset is vectorized and leveled to take care of to the CNN design. In each layer, the yield is given as contribution to the accompanying layer. In each layer, we characterize misfortune capacity and initiation work. We general utilize straight out misfortune work in our design. The working of convolutional organization to recognize facial feelings is as per the following (Fig. 4).

5 Experimental Results and Discussions We characterized and executed convolutional neural organization models with FER Kaggle information with information expansion. Also, the models were prepared with 200 ages with learning rate 0.001. We applied batch normalization and average pooling. At last, SoftMax work is utilized to give most extreme likelihood dispersion in the yield layer. At the yield layer of the CNN, in light of greatest likelihood worth, we perform one hot encoding to get required yield. The aftereffects of the proposed look classifier utilizing convolutional neural organization is as follows (Fig. 5).

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Fig. 5 Proposed model output

The model can distinguish facial feelings with an exactness of 61%. The precision of the model is proportionate to the quantity of ages. The underneath chart shows how the preparation information and approval exactness increments with number of cycles (Fig. 6). The training and validation loss reduces as number of epochs increases. The neural network adjusts itself in such a way that it reduces error, its rate, and increases its accuracy (Fig. 7).

Fig. 6 Training and validation accuracy of the model

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Fig. 7 Training and validation loss of the model

6 Conclusion and Future Scope We proposed CNN model to anticipate the facial feelings. For the engineering, exactness versus ages diagrams are drawn and we made after ends: deep learning architectures accomplish significant accuracy rate when it is trained with augmented data. Deep neural networks get more accuracy than the shallow neural networks. Performance of a network increases with number of epochs. Any exploration work or review has its own importance just when it is helpful, all things considered, applications. This framework assists us with contemplating the human conduct. It additionally empowers machines to comprehend passionate knowledge of people. Later on, we need to construct an individual AI associate whose activities are adjusted dependent on our facial feelings. These AI controlled advanced aids can feel all the more agreeable and more close to home and improves our lives.

References 1. Z. Yu, C. Zhang, Image based static facial expression recognition with multiple deep network learning, in Proceedings of the 2015 ACM on International Conference on Multimodal Interaction (ICMI‘15) (Association for Computing Machinery, New York, NY, USA, 2015), pp. 435–442. https://doi.org/10.1145/2818346.2830595 2. S. Ebrahimi Kahou, V. Michalski, K. Konda, R. Memisevic, C. Pal, Recurrent neural networks for emotion recognition in video, in Proceedings of the 2015 ACM on International Conference on Multimodal Interaction (ACM, 2015), pp. 467–474 3. L. Tan, K. Zhang, K. Wang, X. Zeng, X. Peng, Y. Qiao, Group emotion recognition with individual facial emotion CNNs and global image based CNNs, in Proceedings of the 19th ACM

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International Conference on Multimodal Interaction—ICMI 2017 (ACM, 2017), pp. 549–552 4. M. Li, H. Xu, X. Huang, Z. Song, X. Liu, X. Li, Facial expression recognition with identity and emotion joint learning. IEEE Trans. Affective Comput. 12(2), 544–550 (2021). https://doi. org/10.1109/TAFFC.2018.2880201 5. S. Jain, M. Mahmood, R. Raja, K. Laxmi, A. Gupta, Multi-label classification for images with labels for image annotation. SAMRIDDHI J. Phys. Sci. Eng. Technol.12(SUP 3), 122–127 (2020) 6. M.R. Mahmood, R.K. Patra, K. Mehta, S. Jain, A. Mohan, A modified system for facial expression recognition and head pose estimation techniques. SAMRIDDHI J. Phys. Sci. Eng. Technol. 12(3) (2020) 7. A. Dhall, R. Goecke, S. Lucey, T. Gedeon, Static facial expressions in tough conditions: data, evaluation protocol and benchmark, in First IEEE International Workshop on Benchmarking Facial Image Analysis Technologies BeFIT, IEEE International Conference on Computer Vision ICCV2011 (Barcelona, Spain, 6–13 Nov 2011) 8. P. Ekman, W.V. Friesen, Constants across cultures in the face and emotion. J. Pers. Soc. Psychol. 17(2), 124–129 (1971) 9. A. Mollahosseini, D. Chan, M.H. Mahoor, Going deeper in facial expression recognition using deep neural networks, in 2016 IEEE Winter Conference on Applications of Computer Vision (WACV) (Lake Placid, NY, 2016), pp. 1–10 10. A. Dhall, R. Goecke, S. Lucey, T. Gedeon, Collecting large, richly annotated facial-expression databases from movies. IEEE Multimedia 19(3), 3441 (2012)

Detection of Arrhythmia Using Adaptive Boosting Algorithm Harpreet Kaur, Shruti Bhargava Choubey, Abhishek Choubey, K. Sai Deekshith, B. Veeranna, and Y. Santhosh Reddy

Abstract Electrocardiography (ECG) is a test at that checks the electric activity of the coronary heart. Arrhythmia is a sort of coronary heart ailment characterized through abnormal heartbeats. The prognosis is primarily based totally at the hobby of the R top withinside the ECG sign. The maximum not unusual place sort of arrhythmia is atrial fibrillation. The heartbeat will become abnormal and fast because of this. In order to stay a healthful life, it is far important to repair a everyday coronary heart rhythm. The present-day technique of detecting arrhythmia is to connect the tool to a lead and ship an ECG sign to a health practitioner, while the occasion is occurring. The uncooked ECG sign acquired from the present-day database is preprocessed the use of the FFT filter. The diploma of the polynomial equation is decided through the wide variety of factors that have to match in an effort to create an easy curve that replicates the HRV sign A polynomial diploma of n = 6 equation yields the highquality outcomes in becoming the HRV sign. Statistical and wavelet parameters are mixed right into an unmarried set of parameters in hybrid with curve becoming. The performance of the proposed set of rules is in comparison with that of different algorithms and evaluated on an MIT database. The proposed offline technique has an accuracy of 94%. Keywords Signal · Features · Classifiers · FFT

H. Kaur (B) Electronics and Communication Engineering, Guru Nanak Institutions Technical Campus, Ibrahimpatnam, Telangana 501506, India S. B. Choubey · A. Choubey · K. Sai Deekshith · B. Veeranna · Y. Santhosh Reddy Electronics and Communication Engineering, Sreenidhi Institute of Science and Technology, Ghatkesar, Hyderabad, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_31

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1 Introduction To study the moves of diverse body parts, diverse indicators are used. The electric pastime of the coronary heart is measured the usage of electrocardiography (ECG). Arrhythmia is a coronary heart circumstance that consequences in an abnormal heartbeat rhythm. To diagnose it, the operation of the R top withinside the ECG sign is used. Atrial traumatic inflammation is the maximum not unusual place kind of arrhythmia. As a result, the heart beat will become abnormal and rapid. The modern-day technique of detecting arrhythmia is to implant a gadget with leads that transmit the ECG sign to a health practitioner, while the occasion occurs. The usage of EDBD learning rule with hidden layers, those charges are 93% and 91%, respectively [1]. The paper applies a discrete wavelet rework to every heartbeat to acquire the morphological features. The common precision is 84%, and the common accuracy is 75%. To describe the arrhythmia, the hybrid features were also combined [2]. Electrocardiogram (ECG) signal variations are too tiny to be apparent with the naked eye. The main benefit of this work is that the number of classifications would minimize the need to detect and segment the QRS complex. Wavelets have proven to be effective in displaying various features in ECG signal classification, with an accuracy of 98.30% [3]. Wavelets are the most accurate representation of ECG beats. It employs the polyphone representation of the wavelet filter bank [4].

2 Background of Arrhythmia The information is split into databases, the primary of that is used to choose the classifier configuration and the second one of which offers unbiased output. It has a two-dimensional array, and this is digitally implemented [5, 6]. The precision is extra than 90%. A new approach called supervised noise artifact is used to categorize strong coronary heart arrhythmia fusion. It includes structural classifiers. As a noise discount approach, wavelet transforms [7] are used. For ECG class [8], employs ANN and SVM. In this study, several types of deep learning techniques such as the convolutional neuronal network (CNN), the network of serious beliefs (DBN), longterm memory (LSTM), the recurrent neuronal network (RNN), and the reheating unit of door (GRU) are used. Between everyday and arrhythmia conditions, the ECG sign traits alternate nonlinearly and significantly. The nonlinear traits display a median precision of 95% and 80% sensitivity, respectively, and 100% specificity [7]. Table 1 displays exceptional one of the techniques for detecting the ECG in which, every heartbeat’s electric sign is made from waveforms of motion impulses produced withinside the coronary heart via way of means of diverse cardiac tissues [8]. In this work, the Kalman clear out is used to dispose of noise. The best information set is used to optimize ECG sign denoising and carry out multilayer perceptron [9]. The ECG sign is disintegrated into exceptional sub-bands withinside the first step the usage of wavelet transformation. The precision become envisioned to be approximately

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Table 1 Polynomial curve fit from n = 2 to10 for accuracy, sensitivity, specificity, and F-measure n=2 n=3 n=4 n=5 n=6 n=7 n=8 n=9 n= 10 ADABOOST Accuracy

SVM

ANN

46

77

80

84

90

94

94

94

94

Sensitivity 80

87

74

87

87

94

94

94

94

Specificity 31

73

82

85

91

94

94

94

94

F-measure 48

71

68

79

84

91

91

90

90

Accuracy

77

81

83

92

90

92

88

85

85

Sensitivity 94

97

100

97

97

94

97

94

94

Specificity 40

47

47

80

74

87

67

67

67

F-measure 85

88

90

94

93

94

92

90

90

Accuracy

90

90

92

88

85

92

90

88

88

Sensitivity 88

85

91

88

85

91

88

85

85

Specificity 94

100

94

87

87

94

93

94

93

F-measure 93

92

94

91

89

94

92

90

90

99.65% [10]. The ECG section is converted the usage of a wavelet transform [11]. Naive Bayes set of rules has been observed to have a better accuracy of 99.7% [15]. The goal of this paper is to create an offline computerized device that could hit upon arrhythmia from an ECG sign. Section 3 discusses the proposed method and the effect of curve becoming for exceptional order, Sect. 4 discusses the findings and discussions, and Sect. 5 concludes the paper.

3 Methodology A causal convolution neural network is used to alleviate the problem of the recurrent neural network frame network not being accelerated by hardware. Convolution network is divided into four levels. Peak detection, feature extraction, and classifiers are all part of the preprocessing process. The following steps are used for process of data. Step 1 ECG signal: The FFT of raw ECG signal is done for preprocessing to remove noise as shown in Fig. 1. Figure 2 shows the filter’s output, which is a noise-free ECG signal. Step 2 Windowing techniques: The extraction of R peaks, as proven in parent 3, is the second one step in detecting arrhythmia. Only the R peaks are present withinside the HRV sign (heart rate variability). For extraction, a two-byskip filter method combining a square window and a threshold clear out is used. The fundamental aim of making a window is to discover the region’s maximum t value.

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ECG Signal

Preprocessing FFT method

HRV signal Extraction Curve Fitting

Feature extraction DWT Adaboost

Normal

Abnormal

Fig. 1 Block diagram of proposed method

Fig. 2 Raw ECG signal

Step 3 Curve Fitting: Curve fitting with a seventh order polynomial is used to suit the extracted HRV signal. Step 4 Feature Extraction: Mathematically, the numerical values of capabilities are derived. The overall performance of curve becoming yields higher capabilities. The maximum typically used capabilities are statistical and wavelet capabilities.

Detection of Arrhythmia Using Adaptive Boosting Algorithm Table 2 Polynomial curve fitting at N ≥ 7

287

Parameters

ADABOOST

ANN

SVM

Accuracy

94

92

92

Sensitivity

93

91

94

Specificity

94

93

87

F-measure

90

94

94

Step 5 The mathematical representation of that signal is derived from the statistical analysis of that signal. It shows the parameters that have been derived by mathematical analysis. Step 6 Discrete wavelet features: The discrete wavelet rework translates the time– amplitude representation of a sign into a time–frequency illustration given as a set of wavelet coefficients. A sign’s wavelet coefficients must be exactly mirrored. The frequency and time records provided by these factors can be used to interpret a sign. Step 7 ADABOOST is a time period used to explain adaptive boosting. It is a gadget getting to know algorithmic norm that improves overall performance. The ADABOOST algorithmic rule is touchy to noise. The ADABOOST training technique selects most effective alternatives that enhance the model’s predictive potential even as minimizing spatiality and computation time. ADABOOST is a method for education a boosted classifier. ADABOOST is famous for having the nice classifier. A polynomial equation of diploma n = 6 produced the nice outcomes in becoming the HRV sign. To healthy extracted R height sign, polynomial curves are used. Curve fitting is done on the extracted HRV signal using different degrees of polynomial. The signal order was varied from 2 to 10, and the output was assessed using accuracy, sensitivity, specificity, and F-measure metrics. Table 1 shows the polynomial curve appropriate for different performance measures such as accuracy, sensitivity, specificity, and F-measure for different order from 2 to 10. Lower-order polynomials were found to be unstable, and feature extraction was unclear. The classifier’s output was also influenced by the polynomial’s orders. Curve fitting is very likely to remove features from the signal after order N ≥ 7. Consequently, curve fitting is done using a polynomial of order N = 7 (Table 2).

4 Results The proposed system uses an ECG signal from the MIT-BIH database, and the output is assessed as both ordinary and extraordinary. Filters which include low pass, excessive pass, and Butterworth filters are used to pre-technique the enter signal. The category output’s performance is measured the use of four exclusive parameters. (1)

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Table 3 Results of features extracted from HRV signal without curve fitting Parameters Statistical features

Wavelet features

Hybrid feature extraction with ADABOOST classifier

ADABOOST SVM ANN ADABOOST SVM ANN ADABOOST SVM ANN Accuracy

82

81

84

85

85

88

94

91

67

Sensitivity 93

76

88

94

82

83

93

90

55

Specificity 76

93

81

81

94

94

94

93

93

F-measure 76

85

90

81

89

91

91

93

70

Accuracy—Accuracy is described because the ratio of general categorized indicators to general enter indicators. tp + tn/N = Accuracy (2) Specificity—The charge at which ordinary indicators are categorized is known as specificity. tp/p = specificity (3) Sensitivity—The charge at which extraordinary indicators are categorized is known as sensitivity tn/n is the sensitivity. (4) F- measure—F- measure, that considers both precision and recall [12–14]. Precision is the degree of the charge of effective outcomes. Recall is the degree of effective a part of the outputs, where p = quantity of ordinary indicators in real enter. n = quantity of extraordinary indicators in real enter. N = p + n (general quantity of indicators). tp = sum of the ordinary indicators which can be expected as identical because of the real entered indicators. tn = sum of the extraordinary indicators which can be expected as identical because of the real entered indicators. Table 3 indicates that the classifier’s output is unaffected with the aid of using the usage of curve becoming (Figs. 3, 4 and 5). The classifier performance is superior in phrases of accuracy and precision, bear in mind with the aid of using integrating curve becoming withinside the automatic technique, as proven in desk nine. Curve becoming is used to enhance the automatic system’s accuracy. Curve becoming the HRV signal, concatenated function extraction, and the ADABOOST classifier also are used to enhance the system’s performance. When in comparison with different category techniques, the ADABOOST classifier has an excessive degree of accuracy. The ADABOOST classifier is likewise superb at classifying extraordinary indicators. The ADABOOST classifier has

Fig. 3 FFT filtered signal

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Fig. 4 R peak detection

Fig. 5 HRV signal with curve fitting

an excessive precision, bear in mind cost as well. Figure eight depicts the automatic system’s results. As a result, an automatic technique is proposed that classifies arrhythmia sickness higher than different algorithms (Figs. 6, 7, 8 and 9).

5 Conclusion The improvement of an offline computer based algorithm for detecting arrhythmia disorder is proposed. The enter sign preprocessed the use of the FFT approach to dispose of noise from the ECG sign. In the proposed set of rules, curve is becoming completed with a polynomial of order N ≥ 6. The mathematical and wavelet capabilities had been extracted later, and the later capabilities had been extracted the use of a concatenation set of rules. The ADABOOST category set of rules, which has a 94% reliability, is used to categorize the arrhythmia disorder. The ADABOOST

290 Fig. 6 Polynomial curve fitting accuracy performance on HRV signal

Fig. 7 Polynomial curve fitting sensitivity performance on HRV signal

Fig. 8 Polynomial curve fitting specificity performance on HRV signal

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Fig. 9 Polynomial curve fitting F measure performance on HRV signal

classifier has the very best accuracy while as compared to different classifiers. The database incorporates about eighty arrhythmia and non-arrhythmia signals, and the proposed set of rules has been tested. The first-class set of rules with the proposed HRV extraction, function extraction, and category changed into determined to be the first-class among all of the different implementation techniques on this research.

References 1. S. Sahoo, A. Subudhi, M. Dash, S. Sabut, Automatic classification of cardiac arrhythmias based on hybrid features and decision tree algorithm. Int. J. Autom. Comput. 17, 551–561 (2020) 2. S.M. Anwar, M. Gul, M. Majid, Arrhythmia classification of ECG signals using hybrid features 18, 1–8 (2018). Article Id:-1380348 3. S.K. Pandey, R.R Janghe, Automatic detection of arrhythmia from imbalanced ECG database using CNN model with SMOTE 42(4), 1129–1139 (2019) 4. G. de Lannoy, D. François, J. Delbeke et al., Feature relevance assessment in automatic interpatient heart beat classification, in Proceedings of the Third International Conference on Bioinspired Systems and Signal Processing (Valencia, Spain, 2010), pp. 13–20 5. S. Osowski, T.H. Linh, ECG beat recognition using fuzzy hybrid neural network. J. IEEE Trans. Biomed. Eng. 48(11), 1265–1271 (2001) 6. K.S. Park, B.H. Cho, D.H. Lee et al., Hierarchical support vector machine based heartbeat classification using higher order statistics and hermite basis function, in Computers in Cardiology (IEEE, Bologna, Italy, 2008), pp. 229–232 7. T. Li, M. Zhou, ECG classification using wavelet packet entropy and random forests. J. Entropy 18(8), 285 (2016) 8. Z.F.M. Apandi, R. Ikeura, S. Hayakawa, Arrhythmia detection using MIT-BIH dataset: a review, in Proceedings of the 2018 International Conference on Computational Approach in Smart Systems Design and Applications (ICASSDA) (Kuching, Malaysia, 2018) 9. H.I. Bulbul, N. Usta, M. Yildiz, Classification of ECG arrhythmia with machine learning techniques, in Proceedings of the 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) (Cancun, Mexico, 2017) 10. F. Ma, J. Zhang, W. Liang, J. Xue, automated classification of atrial fibrillation using artificial neural network for wearable devices. Math. Probl. Eng. 2020, 9159158 (2020)

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11. M.A. Al-antari, S.M. Han, T.S. Kim, Evaluation of deep learning detection and classification towards computer-aided diagnosis of breast lesions in digital X-ray mammograms. Comput. Methods Programs Biomed. 196, 105584 (2020) 12. R.J. Martis, U.R. Acharya, H. Adeli, Current methods in electrocardiogram characterization. J. Comput. Biol. Med. 48, 133–149 (2014). S. Celin, K. Vasanth, ECG signal classification using various machine learning techniques 42(12), 2412018 (2014) 13. T. Mar, S. Zaunseder, J.P. Martínez et al., Optimization of ECG classification by means of feature selection. J. IEEE Trans. Biomed. Eng. 58(8), 2168–2177 (2011) 14. S. Islam, N. Ammour, N. Alajlan, Atrial fibrillation detection with multiparametric RR interval feature and machine learning technique, in Proceedings of the 2017 International Conference on Informatics, Health and Technology (ICIHT) (Riyadh, Saudi Arabia, 2017)

Classification of EEG Signal Based on DTCWT and Neural Network Classifier Manpreet Kaur, M. Sugadev, Harpreet Kaur, and V. G. Siva Kumar

Abstract Medical research, cognitive science, neuroscience, and brain–computer interfaces are just a few of the scientific and practical fields that employ brain signals today. The analysis of brain signals has several constraints, including short sample sizes, high complexity, and noisy signals. The identification of EEG brain signals in a patient to distinguish between normal and abnormal, such as tumor and epilepsy, is a difficult task that necessitates a thorough examination of the whole length of the EEG data. DTCWT and PNN classifier are used in this work to automatically classify EEG data for the identification of normal and abnormal (epilepsy and malignancy). EEG signals are used to extract a feature. EEG signals were used to test the suggested algorithm’s performance. The findings indicated that the suggested classifier is capable of efficiently classifying EEG data. Keywords ROI extraction · Texture and shape features · Probabilistic neural network · Wavelet transform · And MATLAB

1 Introduction EEG (electroencephalogram) is a compilation of electrical potential changes that convey information about human brain function. Mistreatment sensors put on the scalp or mistreatment intracranial electrodes will be used to acquire these measurements. EEG signals will be useful for a variety of applications, including feeling recognition and brain–computer interfaces (BCIs). One of the most important uses

M. Kaur · H. Kaur (B) Department of ECE, Guru Nanak Institutions Technical Campus, Hyderabad, Telangana, India M. Sugadev Department of ECE, Sathyabama Institute of Science and Technology, Chennai, India V. G. Siva Kumar Department of ECE, Vidya Jyothi Institute of Technology, Hyderabad, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_32

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of EEG signal processing is in neurobiology to detect illnesses and brain disorders. Convulsions are one of the most frequent medical conditions across the world. It is frequently detected by doctors via a visual scan of the graph signals, which is a time-consuming and incorrect approach. A frequent medical disease is associate degree convulsion, which is a form of chronic seizure. They account for about one-hundred and twenty-five percent of the world’s population who are afflicted with this disease. There are two types of epilepsy: focal and non-focal convulsions. When a brain illness targets a specific confined region within the brain, such as a little low portion of one of the temporal lobes, it results in a very significant disadvantage called advanced partial seizure focus. The operation is a potentially dangerous step since it might harm vital regions associated with language, primary motor, and vision. As a result, it is critical to determine whether or not the patient has focus regions before surgery. Computational complexity was a concern for the MRBF and PCA methods [1]. The author [2] utilized an adaptive wavelet packet method to differentiate between normal and epileptic seizure-affected EEG data. Seizure and non-seizure EEG data were analyzed using the fast PHA technique [3]. To categorize EEG signals during normal and epileptic phases, the author used MAR and the expectation–maximization method [4]. The visibility graph tool [5] was a one-of-a-kind method for predicting epilepsy. For categorizing epileptic seizure-affected and normal EEG data, a method based on nonlinear analysis and time–frequency was proposed [6]. The categorization of non-focal and focal EEG data was the emphasis of the author [7]. On the other hand, these approaches had flaws, and some of them were erroneous. In this work, we present a DTCWT transform for signal pictures, as well as features extraction (shape and texture). The feature extraction detects the signals and divides the signal area into equal-sized blocks. The instances are then classified as normal, tumor, or epilepsy using a neural network classifier.

2 Literature Survey A new technique termed quaternion-valued singular spectroscopic analysis is developed for multichannel analysis of EEG data (SSA). However, in the case of the Social Security Administration, the solitary price decomposition entails tight orthogonality of the rotting parts, which makes it impossible to properly reproduce sources with identical neural activity. For the first time, the figure domain is taken into account while creating the SSAs enhanced statistics, allowing for co-channel coupled analysis. We show that even at a signal/noise ratio of −10 dB, the augmented quaternionvalued Social Security Administration (AQSSA) could wish to extract the sources as an associate degree application. We tend to utilize the intended SSA for sleep, an analysis was performed to obtain applied math descriptors and maybe because of the quality of our QSSA in an exceeding rehabilitation context [8].

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Seizure fragments are separated from non-seizure parts using segmentation. The higher-order values (specifical variance) computed in multiple sub-bands inside the ripple remodel domain are used as a distinguishing feature by the support vector machine (SVM) classifier. When the method was tested on 175 h of continuous graph knowledge from five patients, it averaged 99% accuracy with extremely high specificity and sensitivity values. Additionally, the idea of the figure of merit, for his or her outstanding performance for all the patients, for patient-invariant seizure detection, seven channels were chosen, which may make EEG’s job easier by minimizing the arduous task of monitoring graph knowledge across all channels [9]. When the DNN is trained with different coaching epoch settings (TEs), a SoftMax classifier is used to differentiate between focal and non-focal EEG. The EEG signals in non-heritable form graph work for five brain disease patients which were used to investigate the anticipated theme in this work. Overall, the projected categorization technique performs substantially better than the current progressive approaches with good classification accuracy [10].

3 Block Diagram See Fig. 1.

3.1 Data Acquisition EEG signal is acquired as the input signal for the proposed system. There are 30 signals collected from different patients to analyze brain conditions. The database is created for three different conditions.

Fig. 1 Proposed work in block diagram

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3.2 Pre-processing In the proposed method, the input EEG signal is preprocessed using a median filter. As it retains edges during noise reduction in few cases, which improves the outcomes of later processing. In signal processing, a “window” refers to a set of neighbors that slide access by access over the whole signal. When dealing with 1D signals, the most typical window is the first few leading and following accesses; however, when dealing with 2D signals, such as pictures, more sophisticated window patterns (such as “box” or “cross”) are explored. When a window has an odd number of accesses, the median may be readily calculated as the middle value after all of the window’s accesses have been numerically organized. The denoised signal value is obtained by a weighted average of all samples in its neighborhood, and it is defined by Rs = (1/C) ∗ sum(w ∗ Is ) where C is the normalization constant: sum(w), W = it is determined by similarity of neighborhood, and I s = Noisy input signal. The final restored signal will be reshaped into a two-dimensional vector for analyzing the compression effects on signals.

3.3 Dual-Tree Complex Wavelet Transformations (DTCWT) The DTCWT is used to compute a signal’s complex transform by separating discrete wavelet transform (DWT) decompositions (tree a and tree b). A DWT will yield real coefficients, while the other will create imaginary coefficients, only if filters on both trees are designed separately. The redundancy of two not only boosts processing power but also provides additional information used for analysis. In the fusing of noisy signals, DWT has numerous uses, but shift-invariance is crucial for strong sub-band fusion. To achieve this, the DTCWT is redesigned to allow for the fusing of noisy signals. In terms of first-class metrics peak signal-to-noise ratio (SNR), pass correlation, and also a visual outcomes, the DTCWT approaches outperform the DWT methodology.

3.4 GLCM Feature Extraction A gray-level co-occurrence matrix (GLCM) is created by computing the frequency with which a certain pixel with the intensity value I appears in a specified spatial relationship to a pixel with the value j. The amount of gray levels in an image determines the size of the GLCM. The gray co-matrix restricts the amount of intensity values in a picture to 8 by default, but it may change the scale of gray levels using the num levels and gray limits options.

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3.5 Probabilistic Neural Network (PNN) The PNN is trained using the ‘newff’ and ‘train’ commands. In this, for dataset1, target one is considered, and for dataset2, target two is considered, whereas for dataset3, target three is considered [11]. The modified weight problem and biases for different networking parameters are kept in mind when input alternatives are simulated after training. Examine the picture selections made during the classification stage to simulate with a trained network model using the ‘sim’ command. Finally, it produces a 1, 2, or 3-digit pricing, indicating whether the choice is normal, tumor case, or epilepsy case [12].

4 Results We use the K-nearest utilizing classifier and the SVM classifier in the current system. Only 75% of the time, the output result should be accurate. We use GLCM features (texture and shape) to extract the value of the features, and then, we use the deep learning backpropagation neural network approach to categorize the output as normal, tumor, or epilepsy. The output result should become 99.9% of accurate.

4.1 Preprocessing and Filtering EEG signals from different lobes of both the hemisphere are recorded and filtered to remove spike noise using a median filter (Figs. 2, 3 and 4).

4.2 Statistical Parameters Various statistical parameters obtained are mean, entropy, variance, standard deviation, kurtosis, and skewness (Figs. 5, 6 and 7).

4.3 Classification Results Table 1 shows the classification results for all the three cases.

298 Fig. 2 EEG signal of normal case

Fig. 3 EEG signal of epilepsy case

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Fig. 4 EEG signal of tumor case

Fig. 5 Normal case

299

300

Fig. 6 Epilepsy case

Fig. 7 Tumor case

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Classification of EEG Signal Based on DTCWT and Neural Network … Table 1 Classification result of all three subset

Parameters (%)

301

Different cases Normal (Subset A)

Tumor (Subset B)

Epilepsy (Subset C)

Sensitivity

99.9

99.8

99.9

Specificity

99

99

99

Accuracy

99.9

99.99

99

Misclassified accuracy

0.1

0.11

1

Selectivity

99

99

100

5 Conclusion In this work, we have developed a method for detecting seizures and brain illness in EEG data. The extraction of texture and shape options from EEG data served as the idea for this approach. The use of EEG is prompted by the fact that signals are nonstationary, and information-based methodologies have a far greater capacity to deal with non-stationary in EEG signals. Wherever we have utilized NNs for classification, the projected feature set is integrated in a pattern recognition framework (results mistreatment numerous classifiers are bestowed for comparison). For our investigations, we used a publicly available EEG data collection. From a clinical standpoint, we have considered five possible scenarios, each with its own set of goals. Our tests have demonstrated that when we use hybrid alternatives for extracting important data from graphical record signals, we gain significant performance improvements in three situations that suggest a three-category pattern identification problem (Cases I and II, and Case III). When compared to the different benchmark techniques for these situations, the proposed methodology will improve classification performance by over 100% for Cases I, II, and III (when utilizing a NN classifier) under our experimental setup. For the other three cases, our technique performs similarly to the alternative solutions considered in this paper. In comparison with the other feature extraction techniques, the projected choices in conjunction with the 1NN classifier produce significantly different outcomes in the classification trials connected with Case V.

6 Advantages 1. 2. 3. 4.

Detecting accuracy is high due to extracting features of the image. Light pixels have a high SNR, whereas dark pixels have a low SNR and can be accustomed to detect tumors in a short amount of time. It can segment tumor regions from the EEG signal accurately. It is useful to classify the normal or tumor or epilepsy case detection from EEG signals for accurate detection.

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References 1. A. Ahmadi, V. Shalchyan, M.R. Daliri, A new method for epileptic seizure classification in EEG using adapted wavelet packets, in IEEE Conference 2017 on Electric Electronics, Computer science, Biomedical Engineering’s Meeting (EBBT), pp. 1–4 (2017) 2. K. Fujiwara et al., Epileptic seizure prediction based on multivariate statistical process control of heart rate variability features. IEEE Trans. Biomed. Eng. 63(6), 1321–1332 (2015) 3. S. Belhadj et al., Whole brain epileptic seizure detection using unsupervised classification, in 8th International Conference on modelling, Identification and control (ICMIC), pp. 977–982 (2017) 4. R. Hussein et al., Energy efficient EEG monitoring system for wireless epileptic seizure detection, in 15th IEEE International Conference on ICMLA, pp. 294–299 (2017) 5. K. Rai, V. Bajaj, A. Kumar, Features extraction for classification of focal and non-focal EEG signals, in Information Science and Applications. Lecture Notes in Electrical Engineering, vol. 339, pp. 599–605 (2015) 6. C. Hao et al., Analysis and prediction of epilepsy based on visibility graph, in 3rd IEEE Conference on ICISCE, pp. 1271–1274 (2016) 7. D. Gajic, Z. Djurovic, J. Gligorijevic, S.D. Gennaro, I. Savic-Gajic, Detection of epileptic form activity in EEG signals based on time-frequency and non-linear analysis. Front. Comput. Neurosci. 9(38) (2015) 8. S. Enshaeifar, S. Kouchaki, C. Cheong Took, S. Sanei, Quaternion singular spectrum analysis of electroencephalogram with application in sleep analysis. IEEE. Neural Syst. Rehabil. Eng. 4320(c), 1–1 (2015) 9. A.B. Das, J.H. Pantho, M. Imamul, H. Bhuiyan, Discrimination of scalp EEG signals in wavelet transform domain and channel selection for the patient-invariant seizure detection. pp. 4–6 (2015) 10. A.M. Taqi, F. Al-Azzo, M. Mariofanna, J.M. Al-Saadi, Classification and discrimination of focal and non-focal EEG signals based on deep neural network, in 2017 International Conference on Current Research in Computer Science and Information Technology (ICCIT) (2017) 11. Z. Zainuddin, L.K. Huong, O. Pauline, On the use of wavelet neural networks in the task of epileptic seizure detection from electroencephalography signals, in Proceedings of the 3rd International Conference on Computational Systems—Biology and Bioinformatics, vol. 11, pp. 149–159 (2012) 12. Y. Li et al., Epileptic seizure classification of EEGs using time-frequency analysis based multiscale radial basis functions. IEEE J. Biomed. Health Inf. 22(2), 386–397 (2017)

Feature Set Analysis of Linear Predicted Code and Its Extended Algorithms for Speech Signal Processing M. Shoukath Ali, P. Sandeep, and C. Sailaja

Abstract Speech is an important communication for human. Processing of this speech signal plays a vital role in the present era. Extracting features from the speech signal reduce the complexity of processing the complete data. Different feature extraction techniques are available in the state of art; the most simple and used feature extraction technique are LPC algorithm. In this paper, linear predicted code algorithm and its extended versions performance analysis are discussed and the results show that as the improved version of linear predicted code gives better results over the basic model. Keywords Feature extraction · Speech signal processing · Speech recognition · LPC · LPCC · HXLPS

1 Introduction Many speech signal processing applications require spectrum analysis [1]. Linear predictive coding is the most commonly used algorithm for feature extraction of speech signal for further processing in most of the speech signal processing applications such as speaker recognition, speech recognition, source separation, speaker diarization, etc. [2]. Feature extraction is an important technique to process the speech signal in many of the applications such as speech recognition, speaker recognition, speaker diarization and speaker spoofing. There are two different techniques to extract feature from speech signal, parametric method and nonparametric method [3]. Linear prediction coding based on cepstrum is one of the best examples for parametric method and nonparametric method such as mel-frequency cepstral coefficients [4]. In this paper, parametric method linear prediction coding is discussed in detail. The linear M. Shoukath Ali Geethanjali College of Engineering and Technology, Hyderabad, India P. Sandeep · C. Sailaja (B) Guru Nanak Institutions Technical Campus, Hyderabad, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_33

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prediction coding technique is extended with different methods to get more accurate features from the speech signal. In this paper, we evaluate the different linear prediction models [5].

2 Related Work In general, the speech generation mechanism is composed of two main blocks such as excitator which can produce a constant noise or sinusoidal excitation and the second one is the resonator, this represents the cavities in the sound production system, here we can produce the sound by changing the cavities into different shapes [6]. It is an early method generally used for speech data. It can also estimate spectral envelope of speech signal. In this method, each sample of signal in time domain is represented as linear combination of its P preceding values. For each predicted value, the error is minimised. Linear Prediction Coding (LPC): In linear prediction model, the next speech sample can be estimated using the previous speech samples [2]. Sn =

p 

ak Sn−k

k=1

From Fig. 1, we can analyse that input speech signal is passed through preprocessing stage where the signal is divided into frames and windowing technique is applied. Many of the algorithms follow hanning windowing technique, then the resultant signal is passed through auto-correlation analysis and cepstrums are calculated. Linear Prediction Cepstral Coding (LPCC): Linear prediction cepstral coefficients (LPCC) are cepstral coefficients derived using LPC calculated spectral envelope. LPCC is calculated from the coefficients of frequency domain transformation of the logarithmic magnitude spectrum of LPC [7]. In the field of speech processing, cepstral analysis is commonly used as it is easy to represent speech waveforms with limited number of features. In speech signal processing, LPCC is also similar to LPC algorithm. The block diagram of LPCC is as follows, Fig. 1 LPC analysis for speech signal

Input signal

Framing

Windowing

Auto

LPC

LPC Output

Feature Set Analysis of Linear Predicted Code and Its Extended …

305

Fig. 2 LPCC analysis for speech signal

From Fig. 2, we can analyse that the input signal is passed through the preprocessing stage of framing and windowing stages, the resultant signal is passed through auto-correlation analysis and then cepstrums are calculated. LPCC has less exposure to noise [8]. These features provide lower error rate as compared with the features extracted from LPC algorithm. Holo-entropy Linear Prediction Snapshot (HXLPS): The HXLPS proposed extracts the feature information for the speech signal. The function space usually displays a substantial audio signal recognition [9]. The speaker activity detection is carried out on segmented voice signals as per the feature information. By incorporating the holo-entropy mechanism into the XLPS method, the proposed feature extraction is established. The XLPS extraction functionality is interpreted as follows: both the extended linear prediction (XLP) and the weighted linear prediction (WLP) minimise error energy with the partial weight values [10]. The linear forecast is an expansion that consists of both linear predictions. The benefit of LP is that it has less bandwidth usage and also reduces the size of the transmission signal whilst raising the quantity of speakers [11]. WLP has the advantages for meaningless sounds of robustness test. The XLP model is developed by integrating these benefits. This leads to the diagnosis failure using XLP:

F

XLP

=



 a p B p,0 −

d 

p

2 αsXLP a p−s B p,s

(1)

s=1

where B p,0 and B p,s are denoted as partial weights for XLP. Thus, the equation for XLP is described as d 

αsXLP



B p,s a p−s B p,q a p−q =



p

s=1

B p,0 a p B p,q a p−q 1 ≤ q ≤ d

(2)

p

 If the partial weight is B p,s = R p , the WLP forecast model is obtained whilst the LP is reached by B p,s = g. For each delayed sample speech signal, the weight is used independently at each time. The model is called the XLP. Thus, the application of the normal XLP equation below reduces the energy of the defect. d  s=1

αs

 p

H p,s,q a p−s a p−q =

 p

H p,q,0 a p a p−q 1 ≤ q ≤ d

(3)

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where the XLP model weights H p,s,q and H p,q,0 which are determined by a lagging sample speech signal multiplication of weight. Then, weighting is displayed as vector using snapshot vectors for autocorrelation and term α. It is demonstrated by  

    X Hp⊗ a pa p α = H p,q,0 a p a p

p

(4)

p

The weight and the autocorrelation snapshot vectors along with the term α = (α1 , α2 , . . . αd ) X are multiplied in the above equation. Because of snapshot vectors autocorrelation, error energy decreases well, which renders the audio signal of the laptop segmentation the most important component. In comparison with the time, the snapshots of each lagged signal are explicitly weighted. The performance of XLP is decreased due to some low bit rates of the acoustic signal. We implement the best extraction method called HXLPS in order to solve this problem. The suggested HXLPS method extraction plays a significant role in this weighting scheme. By combining holo-entropy with the XLPS method, the latest function extraction approach is developed. The holo-entropy essentially tests the audio signal worldwide, minimising even the device sophistication. The functional information is then modelled on the XLPS based on holo-entropy. Holo-entropy is used to predict the new signal properties for the identification of speech activity. Consequently, the mathematical method for extraction of features using proposed HXLPS is described as follows. Z = L O ∗ H p,q

(5)

where L O denotes the holo-entropy function and H p,q symbolises the weighting function absolute value sum (AVS), which is primarily used to minimise the energy of errors whilst extracting the feature information. Specifies the AVS equation of the form: H p,q =

 d −1 1    H p−1,q + a p  + a p−q  d d

(6)

Therefore, the holo-entropy function is anticipated by entropy measurement and weight function component. Because of the novel weighting-based extraction of holoentropy XLPS, it achieves the low diarization error rate that virtually guarantees good efficiency in diarizing. The entropy and weight functions alter every characteristic vector of the speech signal. L O(t) = Tr(t) × R(t)

(7)

The holo-entropy of the tth function vector is initially calculated by entropy value variable and by weights. The measurement of entropy and the weight is then described by

Feature Set Analysis of Linear Predicted Code and Its Extended … Fig. 3 HXLPS analysis for speech signal

Input signal

Framing

307

XLPS

Windowing

Feature Holoentrophy

Tr(t) = −

n 

D(t) × log2 P(t)

d=1



R(t) = 2 × 1 −

Output

1 1 − exp(−1 ∗ Tr(t))

(8)  (9)

where D(t) field determines the tth characteristic vector probability index [12]. In the same way, the n number of function vectors for holo-entropy is calculated. Thus, the holo-entropy test is used to approximate any XLP system function vector, contributing to the n number of acoustic signal characteristics [13]. The speaker signal is partitioned from the input audio signal based on its characteristics. The identification of speech movement is used to separate the signal (Fig. 3).

3 Implementation The methods discussed are implemented in MATLAB 2015a is used using the English language speech database for speaker recognition (ELSDSR) [24]. The metrics for feature extraction techniques such as accuracy, false alarm rate and miss rate are calculated for LPC, LPCC and HXLPS methods (Table 1). Table 1 Performance evaluation of the LPC, LPCC and HXLPS algorithms with added noise in the speech signal Algorithm Category Added noise No. of features Accuracy in False alarm Miss rate (dB) extracted predicting next sample LPC

Male

Female

5

16

87

0.78

0.68

10

16

85

0.74

0.64

15

16

78

0.72

0.62

5

16

88

0.90

0.1

10

16

82

0.82

0.18

15

16

80

0.70

0.3 (continued)

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Table 1 (continued) Algorithm Category Added noise No. of features Accuracy in False alarm Miss rate (dB) extracted predicting next sample LPCC

Male

Female

HXLPS

Male

Female

5

15

88

0.84

0.16

10

15

86

0.80

0.2

15

15

77

0.76

0.24

5

15

88

0.88

0.12

10

15

85

0.82

0.18

15

15

82

0.77

0.23

5

15

90

0.92

0.08

10

15

89

0.84

0.16

15

15

84

0.75

0.25

5

15

91

0.95

0.05

10

15

85

0.84

0.16

15

15

82

0.76

0.24

4 Results and Discussion See Figs. 4, 5 and 6. 10 -4

5

Original signal LPC estimate

4 3

Amplitude

2 1 0 -1 -2 -3 -4 0

10

20

30

40

50

60

70

Sample Number

Fig. 4 Predicted signal over original signal using LPC algorithm

80

90

100

Feature Set Analysis of Linear Predicted Code and Its Extended …

309

10-4

5

Original signal LPCC signal

4 3

Amplitude

2 1 0 -1 -2 -3 -4 0

10

20

30

40

50

60

70

80

90

100

Sample Number Fig. 5 Predicted signal over original signal using LPCC algorithm 10-4

5

Original signal HXLPS signal

4 3

Amplitude

2 1 0 -1 -2 -3 -4 -5

0

10

20

30

40

50

60

70

Sample Number

Fig. 6 Predicted signal over original signal using HXLPS algorithm

80

90

100

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5 Conclusion In this paper, we analysed different feature extraction models and compared their performance by means of different parameters such as accuracy, false alarm and miss rate. From the above analysis, we can conclude that HXLP feature extraction is showing good results compared with the remaining algorithms.

References 1. K. Daqrouq, A. Morfeq, M. Ajour, A. Alkhateeb, Wavelet LPC with neural network for speaker identification system. WSEAS Trans. Signal Process. 9(4), 216–226 (2013) 2. Thiang, S. Wijoyo, Speech recognition using linear predictive coding and artificial neural network for controlling movement of mobile robot, in International Conference on Information and Electronics Engineering, vol. 6 (IACSIT Press, Singapore, 2011), pp. 179–183 3. A.M. Soe, M.M. Latt, H.M. Tun, Z. Mi, Electronic control system of home appliances using speech command words. Int. J. Sci. Technol. Res. 4(6), 323–329 (2015) 4. B. Abinayaa, D. Arun, B. Darshini, C. Nataraj, Voice command based computer application. Int. J. Innov. Res. Sci. Eng. Technol. 4(4), 57–63 (2015) 5. I.a.-R. al-Jazari, The Book of Knowledge of Ingenious Mechanical Devices. Trans and annotated by D.R. Hill Dordrecht (Reidel, 1974) 6. W.S.M. Sanjaya, Z. Salleh, implementasi pengenalan pola suara menggunakan mel_frequency cepstrum coefficients (MFCC) dan adaptive neuro-fuzzy inferense system (ANFIS) sebagai kontrol lampu otomatis. Al-Hazen J. Phys. 1(1), 44–54 (2014) 7. B. Kulji, S. János, S. Tibor, Mobile robot controlled by voice, in International Symposium on Intelligent Systems and Informatics, vol. 5, pp. 189–192 (2007) 8. Srishti, P. Jain, Shalu, S. Singh, Design and development of smart wheelchair using voice recognition and head gesture control system. Int. J. Adv. Res. Electr. Electron. Instrum. Eng. 4(5), 4790–4798 (2015) 9. Y.-m. Koo, G.B. Kim, S.C. Jang, W.S. Lee, H.-G. Kim, S.H. Han, A study on travelling control of mobile robot by voice commend, in International Conference on Control, Automation and System, vol. 15, pp. 13–16 (2015) 10. A. Punchihewa, Z.M. Arshad, Voice command interpretation for robot control, in International Conference on Automation, Robotics and Applications, vol. 5, pp. 90–95 (2011) 11. D. Rudrapal, S.D. Smita Das, N.D.N. Kar, Voice recognition and authentication as a proficient biometric tool and its application in online exam for P.H people. Int. J. Comput. Appl. 39(12), 6–12 (2012) 12. K.S. Jadhav, S.M. Gaikwad, Writing robotic arm by speech recognition. Int. J. Adv. Res. Electr. Electron. Instrum. Eng. 4(6), 4983–4990 (2015) 13. M. Varalakshmi, N.N. Raju, Design of speech controlled pick and place robot with wireless Zigbee technology. Int. J. Sci. Eng. Technol. Res. 3(20), 4062–4066 (2014)

Hybrid Metaheuristic-Based Thresholding and Faster Region-Convolutional Neural Network for Object Detection in Images Santosh Kumar Sahoo

Abstract The main intent of this proposal is to develop the intelligent image object detection using the modified segmentation and classification models. Initially, the segmentation of the image will be performed by the hybrid metaheuristic-based thresholding with multi-objective function concerning the maximization of between class variance of Otsu method, Kapur’s entropy, and segmentation accuracy. Initially, the segmentation of objects has done optimally, then for detecting, the modified faster region-convolutional neural network (FR-CNN) is applied for detection. In modification stage, the number of hidden neurons of faster R-CNN’s hidden layer nodes are tuned through proposed hybrid metaheuristic algorithm, in such a way that accuracy and precision should attain maximum. Keywords Faster region-convolutional neural network · Entropy · R-CNN · Ostu method

1 Introduction An object in a specified image can easily detected by computer vision or machine vision scheme. In this process, the number of objects can be counted with details of their location, position as well as proper labeling if required. The huge variety in object scales and the arbitrary orientation of objects is the most difficult aspects of this endeavor to overcome. Most order approaches rely on handcrafted features and the sliding window method; however, several advancements have been made to improve performance. These techniques, however, have several flaws. For starters, the created features have sufficient representational strength to detect objects accurately. Simultaneously, the sliding window approach analyzes the image at all scales in all places, resulting in high computing expenses. Several approaches have been proposed to handle this problem, ranging from crude detectors to utilizing segmentation for providing a small number of object possibilities. The fact that gathering S. K. Sahoo (B) Department of Electronics and Instrumentation Engineering, CVR College of Engineering, Hyderabad, Telangana 501510, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_34

311

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ground truth data (i.e., object-level annotations) for training is frequently far more time consuming and expensive than collecting image-levels for object classification is a well-known challenge in object identification. This difficulty is worsened in the context of today’s deep networks, which require vast amount of data to be trained or fine-tuned. Much progress has been made in the field of object detection by an application of Conv-*Nets. There are two types of deep network-based object detectors like region-free approaches and region-based methods. Liu et al. [1] worked on salient object recognition for RGB-D images in 2019, aiming to use color and indepth information to automatically pinpoint objects of human interest in the scene and reduce visual analysis complexity. However, its performance is still lesser. In 2020, Fua et al. [2] developed a unified framework for arbitrarily oriented and multiscale object recognition in remote sensing pictures based on a region-based convolutional neural networks. In 2020, Yu et al. [3] have proposed a new easy-to-hand learning strategy based on unsupervised spatio-temporal analysis to progressively enhance object detection in picture sequences/videos for next-iteration training. Both in [2, 3] though the result have high accuracy but cannot handle randomly fluctuations of features, splitting or combine problems, and heavy inter-occlusions. In 2020, Kim and Ha [4] established a reliable foreground particle recognition system. Their proposed design gives more stable detection even in diverse conditions and enhances the performance. But it offers inferior detection in ghost objects. In 2019, Sangineto et al. [5] have planned a self-paced learning paradigm-based training regimen. The basic idea was to employ an iterative process to pick up the most reliable subset of photos and boxes for training. However, this model reduces the accuracy when huge dataset is processed. In 2016, Diao et al. [6] established an effective object recognition system that integrated the strength of deep belief network’s (DBNs) unsupervised feature learning and visual saliency. As proposed scheme improves the efficiency and accuracy and has better detection performance merely, it cannot detect an object with wide size variation. In 2020, Awan and Shin [7] have presented an end-to-end WSOD system based on discriminative feature learning. To get early proposals from the photographs, the objectness technique was employed. In their proposed model, some of the thresholds are less efficient for detection of outlier clusters. Based on freely available sen¸tinel-2 optical imagery (10 m spatial resolution), sen¸tinel-1 interferometric coherence data, and digital elevation model, in 2020, Robson et al. [8] have presented a novel method that combines deep learning (convolutional neural networks or CNNs) and object-based image analysis (OBIA) into one workflow (DEM). CNNs and OBIA enhance the accuracy and attain better detection rate. However, it also requires the refined identification for better results. A major research issue in computer vision is object detection. Unfortunately, in a variety of situations, fine-grained manually annotated objects are not available. Generally, there is a possibility of obtaining deficient assumed detections through a few weak object detectors with some weak supervision such as earlier knowledge of size, shape, or motion. These challenges are motivated the researchers for implementing the new object detection model using deep learning approaches.

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313

2 Proposed Model Diabetic object recognition is perhaps one of the most hotly debated topics in computer vision and picture analysis. It has a variety of uses, including video surveillance, medical imaging, and robot navigation. Background subtraction, temporal differencing, optical flow, Kalman filtering, support vector machine, and contour matching are some of the algorithms that can be utilized for this purpose. Aside from the aforementioned techniques, deep learning is the most recent method for object detection. Deep learning models based on convolutional neural networks (CNNs) might be used to solve this challenge, however, training or fine-tuning would necessitate a large number of manually labeled objects. Unfortunately, in many circumstances, finegrained manually annotated objects are not available. Using some weak supervision, such as prior knowledge of shape, size, or motion, it is usually possible to acquire imperfect initialized detections by some weak object detectors. The main intent of this proposal is to develop the intelligent image object detection using the modified segmentation and classification models. Initially, the segmentation of the image will be performed by the hybrid metaheuristic-based thresholding with multi-objective function concerning the maximization of between class variance of Otsu method, Kapur’s entropy, and segmentation accuracy. Once, the segmentation of objects is done optimally, the modified faster region-convolutional neural network (FR-CNN) has applied for detection. During modification, hidden layer of faster RCNNs has tuned through hybrid metaheuristic algorithm, in such a way that accuracy and precision should attain maximum. The enhancement of both segmentation and classification will be done by the two metaheuristic algorithms like sailfish optimizer (SFO) [6] and deer hunting optimization algorithm (DHOA) [7]. On conducting the complete experimentations on a publicly available data’s toward diverse pattern classifier, the proposed model achieves higher rate over cutting-edge performance through which the efficacy of the proposed scheme can be validated. The proposed model shown in Fig. 1 represents insight view of metaheuristic-based thresholding and faster region-convolutional neural network for object detection in images. The convolution layer is the key component of R-CNN, which is used for extracting feature maps from the input. The two steps of regular convolution are sampling and summation. The output generated from the deformable features using region convolution is formulated in Eq. (1). cy(itnt ) =

NT 

wcit E f s ex f s (it)

(1)

it=1

Here, wcit is the kernel weight. This convolution considering fractional data locations and also the interneuron positions, which are not considered in regular convolution. In existing convolution method, the output feature cy for each time instant it0 is defined as shown in Eq. (2).

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Image dataset

Hybrid meta-heuristicbased thresholding for segmentation Hybrid SFO+DHOA Modified FasterRCNN-based detection

Detection output

Fig. 1 Proposed model

cy(it0 ) =



wc(itnt )E f s ex f s (it0 + itnt )

(2)

itnt

Here, it denotes the time instants of the sampling grid SG. The regular grid SG is attached with offsets itnt |nt = 1, 2 . . . T N . cy(it0 ) =



wc(itnt )E f s ex f s (it0 + itnt + itnt )

(3)

itnt

In Eq. (3), the term itnt +itnt refers to indicate the changeable sampling locations. The term itnt is typically fractional, and the linear interpolation method is used to find the new location. The equation is denoted in Eq. (4). E f s ex f s (it) =



G S(v, it)E f s ex f s (v)

(4)

v

3 Outcome Analysis The proposed object detection in image is realized in MATLAB 2019a with experimental assessment. To evaluate this projected work by two metaheuristic algorithms

Hybrid Metaheuristic-Based Thresholding and Faster Region …

315

Table 1 Proposed model’s performance compared with existing models SVM [6]

EMCD + DPE-EWA-LWT [7]

0.90755

0.93683

0.94737

0.94737

Specificity

0.90227

Precision

0.5625

Performance measures

NN [3]

Type I measures

Accuracy Sensitivity

Type II measures

DCN [8]

Proposed method (hybrid SFO + DHOA)

0.90293

0.92142

0.94453

0.92105

0.93421

0.95105

0.93543

0.90052

0.91972

0.94764

0.66055

0.55118

0.60684

0.7

NPV

0.90227

0.93543

0.90052

0.91972

0.94764

F1-score

0.70588

0.77838

0.68966

0.73575

0.79545

MCC

0.68658

0.75934

0.66588

0.71426

0.77369

FPR

0.097731

0.064572

0.099476

0.080279

0.052356

FNR

0.052632

0.052632

0.078947

0.065789

0.078947

FDR

0.4375

0.33945

0.44882

0.39316

0.3

like sailfish optimizer (SFO) [6] and deer hunting optimization algorithm (DHOA) [7]. On conducting the wide-ranging experimental test on a public dataset intended for diverse object detection, the proposed model achieves higher rate over state-of-the-art performance, which validates the efficiency of suggested techniques. When a classifier prototype is established for a particular class of object by applying suggested approach, the identical unit can be utilized continually for other applications, as a result, the monitoring activity will reduce. In this proposed work, an EEG signal dataset (University of Bonn) is considered for the classification purpose. The proposed model’s performance was compared to that of traditional replicas with respect to Type-I and Type-II measurements as per Table 1. Type I metrics include accuracy, sensitivity, specificity, precision, negative predictive value (NPV), F1-score, and Mathews correlation coefficient (MCC), whereas Type II comparisons include false positive rate (FRR), false negative rate (FNR), and false discovery rate (FDR). Analysis of ocular artifacts of the proposed model and performance analysis over existing method is presented in Fig. 2 and 3, respectively, from which it is concluded that the hybrid SFO + DHOA performs the state-of-the-art response analogy to existing scheme.

4 Conclusions The proposed approach used for detection and improvement of ocular artifacts from EEG signals was introduced as this performs a very important role during diabetic detection. Presented model has two phases such as detection phase and mitigation

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Fig. 2 Analysis of ocular artifacts of the proposed model

Fig. 3 Analysis of proposed model’s performance over existing algorithms

phase. In detection part, the input EEG signals were decomposed through 5-levelDWT and Pisarenko harmonic decomposition techniques. The features of decomposed signals were extracted by hybrid metaheuristic-based thresholding for segmentation. Then, the extracted features were given to the modified faster R-CNN-based detection, in which the optimization was done by hybrid SFO + DHOA algorithm. The optimized hybrid SFO + DHOA classified the signals into signal with artifacts and without artifacts. The performance analysis on the proposed hybrid SFO + DHOA algorithm ensures the enhanced results over the existing metaheuristic algorithms in terms of MAE, RMSE, and correlation coefficients. From the overall

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317

analysis, the specificity evaluation of the proposed SFO + DHOA model has achieved 5.03% better than NN, 0.93% better than SVM, 2.84% better than EMCD + DPEEWA-LWT, and 3.23% better than DCN. Thus, it is concluded that the developed SFO + DHOA model achieves better performance in detection and mitigation of ocular artifacts from EEG signals.

References 1. Z. Liu, S. Shi, Q. Duan, W. Zhang, P. Zhao, Salient object detection for RGB-D image by single stream recurrent convolution neural network. Neurocomputing 363, 46–57 (2019) 2. K. Fua, Z. Changa, Y. Zhanga, G. Xu, K. Zhang, X. Sun, Rotation-aware and multi-scale convolutional neural network for object detection in remote sensing images. ISPRS J. Photogramm. Remote Sens. 161, 294–308 (2020) 3. H. Yu, D. Guo, Z. Yan, L. Fu, J. Simmons, C.P. Przybyla, S. Wang, Weakly supervised easy-tohard learning for object detection in image sequences. Neurocomputing 398, 71–82 (2020) 4. J.-Y. Kim, J.-E. Ha, Foreground objects detection using a fully convolutional network with a background model image and multiple original images. IEEE Access 8, 159864–159878 (2020) 5. E. Sangineto, M. Nabi, D. Culibrk, N. Sebe, Self-paced deep learning for weakly supervised object detection. IEEE Trans. Pattern Anal. Mach. Intell. 41(3), 712–725 (2019) 6. W. Diao, X. Sun, X. Zheng, F. Dou, H. Wang, K. Fu, Efficient saliency-based object detection in remote sensing images using deep belief networks. IEEE Geosci. Remote Sens. Lett. 13(2), 137–141 (2016) 7. M. Awan, J. Shin, Weakly supervised object detection using complementary learning and instance clustering. IEEE Access 8, 103419–103432 (2020) 8. B.A. Robson, T. Bolch, S. MacDonell, D. Hölbling, P. Rastner, N. Schaffer, Automated detection of rock glaciers using deep learning and object-based image analysis. Remote Sens. Environ. 250 (2020). Appendix: Springer-Author Discount

Breast Ultrasound Image Segmentation to Detect Tumor by Using Level Sets G. R. Byra Reddy and H. Prasanna Kumar

Abstract Common cancer among the women is breast cancer that develops in either the lobules or the ducts of the breast. Identifying the tumor shapes in ultrasound images is still a challenging job because of speckle noise, poor contrast, and image intensity variations. A multiphase level set strategy is proposed in this research to efficiently segment the ultrasound image. Speckle noise of ultrasound images is reduced by using speckle reducing anisotropic diffusion (SRAD) filter. This proposed model demonstrates that it outperforms the Chan-Vese (CV) method and handles noisy, low contrast images better. This proposed approach is more robust to intensity inhomogeneities. Experiments show that the suggested method extracts more precise tumor boundaries than the CV method. This proposed approach is validated with different performance measure metrics such as Jaccard coefficient, Dice coefficient, and Hausdorff distance. Keywords Breast cancer · Hausdorff distance · Jaccard similarity · Level set · Ultrasound

1 Introduction Breast cancer is the most common cancer among women worldwide [1, 2]. Early identification of signs and symptoms of breast cancer helps to decrease the mortality [3, 4]. Breast ultrasound (BUS) imaging is popular since it is non-invasive and does not use radiation [4]. However, in order to achieve the correct diagnosis, clinical knowledge and competence are required [5]. Currently time-consuming and tedious manual segmentation methods are replaced by automated segmentation process that requires little or no user intervention. Automatic segmentation of the BUS image remains a difficult task for two primary reasons. First, the BUS images show speckle G. R. Byra Reddy (B) Vemana Institute of Technology, Bengaluru, Karnataka, India H. Prasanna Kumar University Visvesvaraya College Of Engineering Karnataka, Bengaluru, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_35

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noise, local intensity changes, and low contrast. Secondly, breast tumors vary widely in shape, size, and location. Numerous researchers have segmented the image of the BUS using active contour methods. In this paper, the preprocessing of ultrasound images has been done by using speckle reducing anisotropic diffusion (SRAD) to minimize speckle noise. Most frequently used region-based active contour method has been shown homogeneous regions. However, they fails with inhomogeneity of the image. To overcome this, paper proposes multiphase level set formulations to segment the inhomogeneous images in order to detect the more precise tumor boundaries in low contrast regions. The following is the structure of the paper: Sect. 2 describes related works for ultrasound image segmentation. Section 3 explains the preprocessing, Chan-Vese (CV), and level set approaches, and Sect. 4 describes data collection, experimental results, and discussion accompanied by the works conclusions.

2 Related Work Many popular segmentation methods rely on [6–8] intensity homogeneity but they are ineffective for inhomogeneous images. Based on task specific constraints, a quantitative survey of low-quality ultrasound image segmentation is conducted [9]. Region-based active contour model with Ostu thresholding technique can be applied for homogeneous images [10]. The threshold will be selected by minimizing the variance. The snake model [11, 12] was widely used for BUS images. The snake deformation procedure is a very time-consuming and manual generation of initial contour. The smoothing process [13] to reduce the effect of noise will make weak edges to disappear. To handle the speckle noise robustly, phase-based level segmentation [14] is proposed. This method considers the local orientation and phase from the monogenic signal. An efficient despeckling method called Bayesian non-local means filter (OBNLM) [15] is used to ultrasound images to minimize speckle noise. The speckle noise will be removed by using SRAD filter for sonography images. Edge-based active contour model [16] which will robustly mange the speckle noise and uses the phase information for better edge map.

3 Proposed Multiphase Level Set Approach Ultrasound images are generally affected by speckle noise [17, 18]. SRAD filter is used to reduce the speckle noise and the preservation and enhancement of the edges. In the Chan-Vese active contour [6, 19] method, for given US image f (x, y) in domain , the energy functional given by (1) F(c1, c2, C) = μ · length(C) + v · Area(inside(C))

Breast Ultrasound Image Segmentation to Detect Tumor …

321

 (| f (x, y) − c1|)2 dx, dy

+ λ1 · inside(C)



(| f (x, y) − c2|)2 dx, dy

+ λ2

(1)

outside(C)

where μ, v, and λ1λ2 are fixed parameters having the value greater or equal to zero. Smoothness controlled by μ, propagation speed increased by v, and inside and outside forces of the image contour controlled by λ1λ2. Inside and outside of the C, image intensity are approximated by c1 and c2 energy functions, respectively. CV method requires a complex differential method for numerical stability and relays on intensity homogeneity. This proposed technique overcomes the segmentation difficulty for intensity heterogeneous images. Multiphase level set method is used in this technique to segment the US image into 2m sections. The US images can be segmented with more than two objects. For two phase segmentation, image domain  is divided into two 1 and 2 sections and can be shown as membership functions represented by M1 (φ) = H (φ) and M2 (φ) = 1 − H (φ). Hence, the level set formulation [20] for the energy is given by Eq. (2) ε=

  N 

 K (y − x)| f (x, y) − b(y)ci | Mi ((φ)(x))dx dy 2

(2)

i=1

Here, K (y − x) is positive window function. Such that K (y − x) = 0, b(y) is the slowly varying bias field. By simplification, Eq. (2) can written as Eq. (3) ε=

  N 

 K (y − x)| f (x, y) − b(y)ci | dy Mi ((φ)(x))dx 2

(3)

i=1

Hence, rewriting the energy ε((φ), c, b) for multiphase level set Eq. (4) ε((φ), c, b) =

  N

ei (x)Mi ((φ)(x))dx

(4)

i=1

Multiphase level set approach detects the region of interest in inhomogeneous better than the CV method, and also the position of the initial contour is independent of the object need to be detected in the given image. The multiphase level set not only gives us intensity information, but it also tells us where and how picture features are located.

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4 Results and Discussion Experiments are performed on the ultrasound (US) image dataset of Baheya [21] hospital. MATLAB 2018b is used to implement the proposed approach and validated with CV method results for the dataset of 100 images. In Fig. 1a, it shows four despeckeled ultrasound images are illustrated to evaluate the effectiveness of the method. The segmented results of CV method and proposed approach are shown in Fig. 1b and c. The CV method’s accuracy is determined by the initial contour’s location. The CV method curves boundaries are not smooth, as can be seen in the figure, but the proposed strategy can detect the tumor boundary with very smooth contours, independent of the starting contour’s position. Experiments show that the proposed method extracts more precise tumor boundaries than the CV method. Figure 1d shows the segmented part of the tumor for the proposed approach.

Fig. 1 a Despeckled image, b CV method contour, c proposed method contour, d segmented

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Table 1 Performance analysis of two segmentation methods S. No.

Ultrasound database images

Performance metrics Jaccard

Dice

HD

Jaccard

Dice

HD

1

Benign

0.864

0.901

1.949

0.9264

0.9171

1.641

2

Benign

0.844

0.897

3.241

0.8894

0.9071

2.692

3

Benign

0.837

0.893

2.242

0.8935

0.9325

1.644

4

Benign

0.873

0.878

2.132

0.8777

0.8981

1.667

5

Malignant

0.906

0.907

2.193

0.9364

0.8967

1.949

6

Malignant

0.899

0.897

2.262

0.841

0.943

2.041

7

Malignant

0.873

0.831

2.126

0.885

0.9125

2.001

8

Malignant

0.837

0.847

2.671

0.877

0.9178

1.679

Avg of 100 images

0.861

0.895

2.542

0.891

0.9254

1.962

CV method

Proposed method

4.1 Performance Metrics To evaluate the accuracy of the proposed segmentation approach, the performance measures [22] Dice coefficient (DC), Jaccard coefficient (JC), and Hausdorff distance are represented in Eqs. (5), (6), and (7), respectively, were used. The Hausdorff distance (HD) [23] measures the max distance between two contours. Comparison of performance metrics for CV and proposed method is shown in Table 1. For tumor region between two label sets L and S, let L is the ground truth and S is the automated contour from segmentation. J(L, S) and D(L, S) get values ranging from 0 to 1. Larger value implies the better segmentation. Optimal value of HD(L, S) is 0. DC measures the segmentation result’s overlap with the ground truth. |L ∩ S| |L| + |S|   L ∩ S   JC = J (L , S) =  L ∪ S   HD(L , S) = max max min a − b max min b − a DC = D(L , S) = 2

a∈L b∈S

b∈S a∈L

(5) (6) (7)

5 Conclusion Currently, ultrasound is the best imaging modality in conjunction with mammography for detecting and diagnosing breast abnormalities. In this paper, level set-based

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US image segmentation is proposed. First, low contrast and speckled US images are denoised by SRAD filter and proposed method has been validated by performing experimentation on 100 images. The experiments demonstrate that the CV method is fails in case inhomogeneity images, and boundaries are not smooth. In contrast, this proposed approach can detect the tumor boundary with very smooth contours. The metrics such as JC, DC, and Hausdorff distance have been used to assess the recommended method’s efficacy for better segmentation. Further, these findings can be used in feature extraction and classification of ultrasound images to detect breast cancer.

References 1. R.L. Siegel, K.D. Miller, A. Jemal, Cancer statistics. Cancer J. Clin. 65(1), 5–29 (2015) 2. L. Fan, K. Strasser-Weippl, J.-J. Li, J. St Louis, D.M. Finkelstein, K.-D. Yu, Z.-M. Shao, P.E. Goss, Breast cancer in China. Lancet Oncol. 15(7), 279–289 (2014) 3. H.D. Cheng, X.J. Shi, R. Min, L.M. Hu, X.R. Cai, H.N. Du, Approaches for automated detection and classification of masses in mammograms. Pattern Recogn. 39(2006), 646–668 (2006) 4. B.O. Anderson, R. Shyyan, A. Eniu, R.A. Smith, C.H. Yip, N.S. Bese, L.W. Chow, S. Masood, S.D. Ramsey, R.W. Carlson, Breast cancer in limited-resource countries: an overview of the breast health global initiative 2005 guidelines. Breast J. 12(2006), 3–15 (2006) 5. H.D. Cheng, J. Shan, W. Ju, Y.H. Guo, L. Zhang, Automated breast cancer detection and classification using ultrasound images a survey. Pattern Recogn. 43(2010), 299–317 (2010) 6. T. Chan, L. Vese, Active contours without edges. IEEE Trans. Image. Process 2(10), 266–277 (2001) 7. R. Ronfard, Region-based strategies for active contour models. Int. J. Comput. Vis. 13(2), 229–251 (1994) 8. C. Samson, L. Blanc-Feraud, G. Aubert, J. Zerubia, A variational model for image classification and restoration. IEEE Trans. Pattern Anal. Mach. Intell. 22(5), 460–472 (2000) 9. J. Nobel, D. Boukerroui, Ultrasound image segmentation: a survey. IEEE Trans. Med. Imaging 25(8), 987–1010 (2006) 10. C. Li, Distance regularized level set evolution and its application to image segmentation. IEEE Trans. Image Process. 19(12), 3243–3254 (2010) 11. M. Kass, A. Witkin, D. Terzopoulos, Snakes: active contour models. Int. J. Comput. Vision 1, 321–331 (1996). https://doi.org/10.1007/BF00133570 12. R.F. Chang, W.J. Wu, W.K. Moon, W.M. Chen, W. Lee, D.R. Chen, Segmentation of breast tumor in three-dimensional ultrasound images using three-dimensional discrete active contour model. Ultrasound Med. Biol. 29(2003), 1571–1581 (2003) 13. G.R. Byra Reddy, H. Prasanna Kumar, Smoothing of mammogram using an improved gradient based technique. Adv. Biomed. Eng. 9, 202–208 (2020). https://doi.org/10.14326/abe.9.202 14. A. Belaid, D. Boukerroui, Y. Maingourd., J.-F. Lerallut, Phase-based level set segmentation of ultrasound images. IEEE Trans. Inf. Technol. Biomed. 15(1), 138–146 (2011) 15. J. Kang, J.Y. Lee, Y. Yoo, A new feature-enhanced speckle reduction method based on multiscale analysis for ultrasound b-mode imaging. IEEE Trans. Biomed. Eng. 63(6), 1178–1191 (2016) 16. L. Gao, X. Liu, W. Chen, Phase and GVF-based level set segmentation of ultrasonic breast tumors. J. Appl. Math. 1–22 (2012) 17. Y. Yu, S.T. Acton, Speckle reducinganisotropic diffusion. IEEE Trans. Image Process. 11(11), 1260–1270 (2002) 18. Y. Guo, H.D. Cheng, J. Tian, Y. Zhang, A novel approach to speckle reduction in ultrasound imaging. Ultrasound Med. Biol. 35(4), 628–640 (2009)

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19. H. Prasanna Kumar, S. Srinivasan, Fast automatic segmentation of polycystic ovary in ultrasound images using improved chan-vese with split Bregman optimization. J. Med. Imag. Health Inf. 5, 57–62 (2015) 20. G.G.N. Gewied, M.A. Abdallah, Novel approach for breast cancer investigation and recognition using M-level set based optimization functions. IEEE Access 7(1), 136343–136357 (2019) 21. W. Al-Dhabyani, M. Gomaa, H. Khaled, A. Fahmy, Dataset of breast ultrasound images. Data in Brief (2020) 22. A.A. Taha, A. Hanbury, Metrics for evaluating 3d medical image segmentation: analysis, selection, and tool. BMC Med. Imag. 15(1), 29–39 (2015) 23. D. Karimi, S.E. Salcudean, Reducing the hausdorff distance in medical image segmentation with convolutional neural networks. IEEE Trans. Med. Imag. 39(2), 499–513 (2020)

COVID-19 Testing Under X-ray Images and Web App Development Using Python Flasks Model B. Likhith, B. Praveen Nayak, and K. R. Suneetha

Abstract The testing of X-rays images using existing modules is measured by considering the affected COVID-19 chest X-rays and normal X-rays images of the patient trained under Web app to get the best accuracy percentage based on the epochs size. Whereas, in the other part of the proposed model is used to deploy the previously trained convolutional neural network (CNN) in order to develop the Web application by using various scripting languages as Python backend, basics of HTML to host a Web and Flasks model. To discover the model in a high-level version of deep learning, which uses artificial neural networks as CNN to track the amount of data and to form a vast discovery technique for learning is so-called deep neural networks. In this paper, the state of learning CNN algorithm to have best accurate method which is going to serve a positive result to our model and gives great impact to understand and deliver the supremacy of prediction and noticed that the training loss and validity loss decreases gingerly after every 20 epochs. In turn driving, a good model in overall reports training the neural networks. Keywords COVID-19 · Chest X-rays · Deep learning · Convolutional neural network

1 Introduction Coronavirus is a general term for a very common type of virus that exists widely in nature. The proportion of severe illness and death is low, and their transmission and pathogenicity are currently lower than the SARS epidemic in 2003. The new coronavirus is more common in winter, spring and can be distributed or outbreak [1]. The main clinical manifestations are fever, soreness, dyspnea in a small part, and lung infiltration. The virus is related to the virulence, the route of infection, and the age with immune status of the host. The viruses that cause viral pneumonia are B. Likhith · B. Praveen Nayak · K. R. Suneetha (B) Department of Computer Science and Engineering, Bangalore Institute of Technology, Bangalore, Karnataka 560004, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_36

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common with influenza viruses. Others are parainfluenza virus and coronavirus. To spread the attack of virus, avoid closed, airless public places and crowded places. During 2006, the discovery of techniques for learning in deep neural networks is known as the subset of deep learning on which multilayered neural networks learn from vast amount of data and have been developed further. Considering the present aspect, deep neural networks and deep learning achieve outstanding performance on many import problems in computer vision, speech recognition, and natural language processing [2]. In a neural network without user human assistance, the system learns from observational data, figuring out its own solution to the problem at hand. Deep learning algorithm allows the computers learn automatically without human intervention or assistance and adjust actions accordingly [3]. The actual target in this work is to know how to train a convolutional neural network and also how to integrate trained neural network model into a Web application using Python Flasks model. In beginning, the model has to be preprocessed into three main codes, which are using in this proposed work stated as 1. 2. 3.

Creating the datasets Preprocessing the amount of data need to normalize the pixel array of X-rays Training the CNN model by applying deep learning algorithm. Further using with the help of two main datasets, that is, COVID-19 affected chest X-rays and normal pneumonia X-rays. Then, create the datasets into customized datasets.

This paper is organized in form of seven sections, Sect. 1 gives an introduction to deep learning, Sect. 2 provides system design, Sect. 3 delivers implementation and its proposed methods, Sect. 4 as model building, Sect. 5 analyzes on experimental results, Sect. 6 comes up with related work, and conclusions are drawn in Sect. 7.

2 System Design The structure of the model is designed based on the deep learning theory. The following functional stages are considered to manufacturing the neural network are Pace 1: Retrieving the chest X-ray images from the dataset of COVID-19 patients and normal patients Pace 2: Normalize chest X-ray images by illustrating the data preprocessing Pace 3: Organize the X-rays in a feature space extraction and apply deep learning algorithm Pace 4: Split both the datasets into two sets: a training set and a validation set to have the overall accuracy Pace 5: Measure the accurate performance of the detection of coronavirus on the validation dataset To introduce the above stages into the proposed model initially, the X-ray images of both categories need to be classified and illustrate the respiratory of affected COVID-19 patient’s datasets and then perform the data augmentation.

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Fig. 1 Generative the adversarial networks from the input model

Deep Learning (Feature Extraction + Classification)

2.1 Classification of Both Chest X-Rays Classification indicates that the data have discrete class label. Classification predictive modeling is the task of approximating a mapping function (f ) from input variables (x) to discrete output variables (y) or classes [4]. The output variables are often called labels or categories. The mapping function predicts the class or category for a given observation as shown in (Fig. 1).

2.2 Data Illustration and Data Augmentation In this stage, the two main datasets are extracted. The actual X-ray images need to filter the COVID-19 positive images and other pneumonia normal X-ray images. Hence, two categories of image datasets help to train the model [5]. Data augmentation is a process of mobilizing the data in which the images will be loaded and saved in the augmented layer resulting in order to get two neurons such as one is COVID-19 positive and another as COVID-19 negative. The respiratory datasets need for data illustration is drawn from affected COVID-19 patient’s datasets.

2.3 Importing the Necessary Libraries To preprocess and normalize the X-rays images, various libraries are required, among all the libraries the most frequently used are TensorFlow, Keras, Python, and many other modules. These modules enhance the build performance to have the best accuracy result. TensorFlow TensorFlow is an end-to-end open-source platform for deep learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that let to work as state of the art in deep learning and early to build deploy the previously trained CNN in our model.

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Keras Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow model. It was developed and organized with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay. Python It allows for easy and fast prototyping through user friendliness, modularity, and extensibility. It supports convolutional neural network and runs seamlessly on CPU.

3 Implementation and Its Proposed Methods In this work, implementation details are provided in the form of six steps as mentioned below: 1. 2. 3. 4. 5. 6.

To deploy the previously trained CNN model Trained it into a Web application Application used Python backend and other modules Within turn a Flask Web development framework The frontend of the Website is created with HTML and JavaScript Loading the Keras models and OpenCV for preprocessing

Initially, read the data and find the X-ray images which are COVID-19 positive from the respiratory datasets. Later, originate or transfer the data from the dataset containing positive images. Similarly, find in the second dataset containing pneumonia normal images. Once data reading is over, check the model and if the label is normal that images will be loaded and saved it in the dataset file having COVID-19 negative images. Next step is to perform preprocessing for these datasets of both the categories and create the actual datasets that need to apply for the convolutional neural network. Fill the label array where the labels will be [0, 1]. Since the data have two kinds of information so it is declared as 0 and 1. Declaring or creating the dictionary by saying COVID-19 negative as 0 and COVID-19 positive as 1.

3.1 Normalizing the Image Data Once the image data are loaded, the data will be normalized by converting it into the pixel range intensities of [0, 1] of RGB channel by resizing the image data into 100 × 100 converting the image to gray color using convert color command in OpenCV. The empty list called data [] is used to append to the next image data. Sometimes, the images may be corrupted and then it will not terminate. Further, the image will leave the exception and then it is going to continue further. Finally, normalizing the images

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Fig. 2 Normalizing and loading the chest X-ray images within the data

to know all the pixels of single images having the range of [0–255]. Converting it to 0 and 1 by dividing with 255 later reset it to the data shape (Fig. 2).

3.2 Splitting the Dataset The 10% of data is split into testing. Where training the neural network for the 90% of data. Model checkpoint is in order to monitor the validation loss saving all the neural networks models over there. If the condition of validation loss is less than the previous epoch then only the particular models will be saved to maintain the minimum validation loss. The neural network will be framed for 20 epochs later on increasing the epoch size can be done in order to select or to choose the best accuracy. From the among 90% training data, 10% data are used for validation history to increase the accuracy to have the best validation result. Hence, in this proposed model, observing 99% of validation accuracy is driven which is a good model in overall report taken in training model.

4 Model Building Here, convolutional neural network model is built. CNN is a deep learning algorithm is used take the input by feature extraction and classify the various input of the data into several layers of CNN to generate the adversarial networks based on the input. Finally, in the proposed model, reducing the input into required output as two output neurons such as one as COVID-19 positive and another one as COVID-19 negative. A convolutional neural network functions into various neural layers as (Fig. 3): 1. 2.

3.

Input Layer—Extracting the input as an image within the window size set by normalizing and which is loaded into the data frame. Other Convolution Layers—Passing through the remaining layers among the size of an image set to parallel 128 at 3 × 3 pixel range of the previous passing from input layer. Concatenating Layer—Here, the images are drawn into this layer to reduce the resolution of the parallel image that has been set. Once passing this stage, the convolution will be goes on reducing under preprocessing the images to pass through the next flattening the layer.

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Fig. 3 CNN architecture for classification and detection of COVID-19

4. 5.

6.

7.

Flatten Layer—X-rays have reduced their resolution will be flattened and bring back to the original layer and starts polling. Dense Layer- It is most common and frequently used layer, where pooling called down sampling, it is the function to reduce the size of the representation and number of parameters and computation in the neural network. Fully Connected Input Layer—The main objective is to take the results of all the previously trained layers and keep it ready to classify the image into the next label. Fully Connected Output Layer—The final layer to get two neurons at this stage and they are giving as COVID-19 positive and COVID-19 negative by reducing the resolution from 64 to 2 final neurons. Hence, the probability of both the categories will be derived.

5 Experimental Results The result analysis is done under using CNN algorithm from varying its epochs value and deploying the previously trained neural network to host under Web application. Below is the experimental put up required to establish and deduce the final results in this work.

5.1 Experimental Setup To train the proposed deep learning model, the minimum software requirements required are Tool: Jupyter Operating System: Windows XP/7, 8, 10 Scripting Language: Python, HTML, Flasks, JavaScript

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Fig. 4 illustrates training, validation loss, and accuracy after every 20 epochs

5.2 Results The used here is CNN network with weights pre-trained on ImageNet and added our own layers to construct the final model. The optimizer used is Adagrad with binary cross entropy as the loss function and accuracy as the metrics. Experiments performed to detect and classify COVID-19 confirmed cases using X-ray images and train the models in two divisions: COVID-19 positive and COVID-19 negative. The model was evaluated using 90% of X-ray images for training, and the rest of the 10% is used for testing or validation. Moreover, the loss function is highly essential to understand the excellence of the prediction and observed that the training loss and validity loss is decreased gradually after every 20 epochs. Categorical cross entropy loss which is commonly use loss for classification type problems and for optimizer. SoftMax as the activation of the output layer gives the probability of COVID-19 positive and COVID-19 negative. Overall, the loss and accuracy graph (Fig. 4) not showing evidence for overfitting, where the loss is decreasing with the epochs that have used in the model. Totally, 10% of data on testing gives the probability of 0.99% and the loss mean squad error of 0.02% as the overall result. Hence, it is driven to be a good promising result that has obtained in our model. Figure 5 demonstrates the various chest X-rays by selecting random images trained on the Web application is able to predict the presence of COVID-19 positive and negative also gives probability measure of the input in terms of percentage.

6 Related Work Studies diagnosed with among various researches on COVID-19 using chest X-rays have binary or multiple classifications. The drawback of COVID-19 diagnosis model over several study in this work was detecting COVID-19 using deep learning theory with the help of CNN network was found to be observed with having high accuracy, sensitivity, and precision metrics based on the epochs size. According to the existing work in this proposed system, it attains a class of high overall accuracy and precision which gives the outbreak of COVID-19 allows for an appropriate work

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Fig. 5 Demonstration of various sample images is shown here. X-rays that show the model forecast the sample as COVID-19 positive, whereas the actual class as COVID-19 negative

balance and wider search range benefits diversity and inclusion. Communication and identification of coronavirus become easy to the individuals by considering and testing the chest X-ray images during this pandemic.

7 Conclusion This proposed model “COVID-19 testing using X-ray images and Web app development using python flasks model” will act as an assistive technology for healthcare workers to take up rapid decision in admitting patients and saving life and reducing mortality rate. Easy deployment of Web-based app will enable healthcare providers to adopt the low-cost proposed system. The optimistic results obtained using this proposed CNN models suggest that chest X-ray images will be useful for early detection of the coronavirus as compared to the time-consuming persistent test or costly CT-scan. Everyday, people lose their life due to COVID-19. Early detection of COVID-19 with old age patients and early care may reduce fatality rate. Proposed solution aims at early detection of lung damage status of patients. Low-cost Webbased solution will act as an assistive technology for healthcare workers to take up rapid decision in admitting patients and saving life with reducing their mortality rate. This study was done under the consultation of doctors of respiratory diseases, pulmonary experts, and related healthcare providers. This study consists of very small data images for model training, so it will be interesting to see the effect of large data and the use of other pre-trained models in future work. Easy deployment of Web-based app will enable healthcare providers to adopt the low-cost proposed system.

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References 1. A. Narin, C. Kaya, Z. Pamuk, Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks (Zonguldak Bulent Ecevit University, 67100, Zonguldak, Turkey, 2020) 2. E.F. Ohata, G.M. Bezerra, J.V.S. das Chagas, A.V.L. Neto, A.B. Albuquerque, V.H.C. de Albuquerque, P.P.R. Filho, Automatic detection of COVID-19 infection using chest X-ray images through transfer learning (2020) 3. A. Makris, K. Tserpes, I. Kontopoulos, COVID-19 detection from chest X-ray images using deep learning and convolutional neural networks (2020). https://doi.org/10.1101/2020.05.22. 20110817 4. L. Brunese, F. Mercaldoa, A. Reginellic, A. Santone, Explainable deep learning for pulmonary disease and coronavirus COVID-19 detection from X-rays (2020) 5. N. Wang, H. Liu, C. Xu, Deep learning for the detection of COVID-19 using transfer learning and model integration (Beijing Key Laboratory of Information Service Engineering Beijing Union University Beijing, 100101, China, 2020)

Implementation of Novel High Performance FIR Filter Design Using Wallace Tree Multiplier with 7–3 and 8–3 Compressor Saher Jawaid Ansari, Priyanka Verma, and Surya Deo Choudhary

Abstract In this paper, a technique has been proposed for designing of FIR filter using multiplier based on compressor. This proposed FIR filter is simulated and synthesized using Xilinx ISE 14.7 navigator. This technique effectively minimizes the delay. This proposed design of FIR filter has been implemented using (8 * 8) Wallace tree multiplier, with lesser no. of partial products using compressors of different order. Since, this proposed FIR filter design multiplication operation has been obtained in less number of steps as compared to conventional filter. In this, number of stages are reduced from 16-12-8-6-4-3-2. This proposed 8-bit FIR filter with Wallace tree multiplier using 7–3 and 8–3 compressor requires a delay of 4.202 ns and 3.861 ns which is 29% and 34% reduced as compared to the conventional FIR filter. The result also proves that the transistor count has also been reduced to 19%, and power consumption is reduced to 29% with the proposed design, which in turn leads to lesser area consumption for the implementation. Certainly, the function advancement of the proposed multipliers is ratify by implementing a higher order FIR filter using WTM. The result indicates better performance, low latency, and overall efficiency of compressors which can be used for image processing application. Keywords FIR filter · Wallace tree multiplier · Compressor · High performance · Latency

1 Introduction Elevation of computer system work has flattened out as fiction technology is touching its physical deadline to a great extend. Generally, compressors can be used in super geared addition of numbers and multiplication design layout in microprocessor unit. Speed and area are two major motives which combat each other. Therefore, the speed of the multiplication operation is achieved using various compressors, and WTM is one of them [1–3]. S. J. Ansari (B) · P. Verma · S. D. Choudhary Department of Electronics and Communication Engineering, Noida Institute of Engineering and Technology, Greater Noida, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_37

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In general, a compressor consists of half and full adders [4–7]. This paper shows higher order compressors (7–3, 8–3) design was performed and implemented in VERILOG language using XILINX navigator tool 14.7 to verify and compare their performances in terms of power, delay, and number of transistors. From the achievement resemblance, an excellent design would be endorsed for implementation in FIR filter.

2 m:3 Compressors The main design of m:3 is shown in Fig. 1 given, [8, 9] it produces one more than main output, i.e., sum3 as compared to m:2 compressor. This additional output (sum3) gives higher length of output values. Therefore, the m:3 compressor needs less carry signals than m:2 compressor [10, 11]. 3  i=1

outputs +

p 

output carries =

i=1

m 

partial products +

i=1

p 

input carries

i=1

  2k + 2k+1 + 2k+2 + p ∗ 2k+2 = m + p ∗ 2k

(1)

(4 p + 7) ∗ 2k = (m + p) ∗ 2k

(2)

p = [m − 7/3] p ≥ 0

(3)

Fig. 1 Structure of m:3 compressor

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2.1 7:3 Compressor A 7:3 compressor apparently composes of a connective logic circuit with seven inputs and three outputs as shown in Fig. 2. It acquires a seven bit input strand as input and produces its sum as output. The connective architecture of a 7:3 compressor is based on the elongated design of a conventional 4–2 compressor. Equations 4, 5, and 6 can be acquired from Eqs. 7, 8, and 9 [8, 12]. h0 = (T 0 ⊕ T 1)T 0 + (T 0 + T 1)T 2

(4)

h1 = (T 0 ⊕ T 1 ⊕ T 2 ⊕ T 3)T 3 + (T 3 + T 4)T 5

(5)

h2 = (T 0 ⊕ T 1 ⊕ T 2 ⊕ T 3 ⊕ T 4 ⊕ T 5)T 4 + (T 0 ⊕ T 1 ⊕ T 2 ⊕ T 3 ⊕ T 4 ⊕ T 5)T 6

(6)

Fig. 2 (7:3) compressor design

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out0 = T 0 ⊕ T 1 ⊕ T 2 ⊕ T 3 ⊕ T 4 ⊕ T 5 ⊕ T 6

(7)

out1 = h0 ⊕ h1 ⊕ h2

(8)

out2 = (h1 ⊕ h0)h0 + h1 ⊕ h0)h2

(9)

2.2 8:3 Compressor Solla and Vainio [22] recommended an 8:3 compressor structure that is embellished in Fig. 3, which is a basic diagram of m:3 compressor. The simulation results are listed in Table 1 which indicates IOs, logic level, no. of transistors used, no. of LUTs, and slices used in connections compared to 8:2 compressor. The equations administrating the outputs in proposed 8–3 design is given below [9, 13]:Z 1 = g∧ h ∧o

(10)

  Z 2 = ((g h)|(g o)|(h o))∧ J ∧ k ∧l

(11)

  ∧ Z 3 = (g h)|(g o)|(h o) J ∧ k ∧l ((J k)|(J l)|(k l))

(12)

Fig. 3 (8:3) compressor design

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Table 1 Specification of FIR filter with 7–3, 8–3 compressor and without compressor (area analysis, power analysis, and delay analysis) Parameters

Conventional FIR filter

FIR filter using 8–3 compressor multiplier

FIR filter using 7–3 compressor multiplier

IOs

35

42

36

Level of logic

32

16

16

No. of transistors

225

185

180

Number of occupied slices

134

153

121

Number of external IOBs

28

16

8

Dynamic power (W)

0

0

0

Static power (W)

1.033

0.723

0.733

Leakage power (W)

2.02

1.06

1.28

Total power (W)

3.053

1.783

2.013

  Z 4 = ((gh)|(go)|(ho))& J ∧ k ∧l &((J k)|(Jl)|(Jl))

(13)

g = P0∧ P1

(14)

h = P0 &P1

(15)

o = P2∧ P3∧ P4

(16)

j = ((P2 &P3 )|(P2 &P4 )|(P3 &P4 ))

(17)

k = P5∧ P6∧ P7

(18)

l = ((P5 &P6 )|(P5 &P7 )|(P6 &P7 ))

(19)

where,

3 8-BIT FIR Filter The finite impulse response filter states the same number or finite number taken as input values from one end of the system, hence we receive the output at the other end of the system called receiver’s end of the system. The series in the figure is showing sample value x whose index value is n−k, where k is some integer (k > 0). The filter

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coefficients h(k) are the FIR filter’s impulse response. The main benefit of the FIR filter as compared to other filters like the infinite impulse response (IIR) filter lies in the proportional to symmetrical essence of the delay imported into the signal which do not cause any phase shifting at the receiver’s end [4, 14, 15].

4 FIR Filter Implementation Using Compressors The FIR filters mainly associate in information technology which is digitally transmitted to the intervening frequency of the receiver. For detail, a digital audio signal transmitted via radio recipient converts the analog signal to the intervening frequency and then converts it back to digital using with a d/a or digital to analog converter [4]. Designs of FIR filter mainly include delay component mainly combinational and logic delay, multiplier, and adder. The simulation is done using Xilinx 14.3 software. The conventional FIR filter consumes more area and delay. This proposed FIR filter with WTM using compressors decreases the delay, when compare to conventional FIR filter. Figure 4 shows the block diagram of a simple digital filter. The three basic work operations of a digital filter are delay of time, multiplication of digits, and addition at the final summation of the process, which helps in

Fig. 4 (8-bit) FIR filter

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Fig. 5 FIR filter design

identification of filter. In Fig. 5, x(n) is the input series which is sampled or delayed according to the impulse response and sampling rate of the x(n) [5]. m:2 compressors, Fig. 5, are more popular than m:3. However, the conventional ones require several carry signals. To mention precisely, a conventional m:2 compressor requires [p = m−3], input–output carries. Their inner structures are shown in Fig. 6. Table 1 demonstrates the specifications of these compressors. An m-bit binary number can be represented as it is shown in 7, where ak e {0, 1}2 (k = 0, 1, 2, … m−1) is the kth bit. An m:3 compressor, since there are three output signals and they represent the addition of input variables, this arithmetical component is able of taking seven input variables (2 k + 2 k+1 + 2 k+2 = 7 × 2 k ). However, it is possible to increase the number of inputs by involving carry signals. Generally, an m:3 compressor requires [p = (m−7/3)] input–output carries [6]. However, the inner structure of compressors prevents additional latency. However, the inner structure of compressors prevents additional dormancy. Either the output carry is not fed by the input one or it takes the exact equal time to prepare other intra signals by the time carry arrives [7, 16]. (am−1 . . . a2 a1 a0 )2 ⇔ am−1 × 2m−1 + · · · + a1 × 21 + a0 × 20

(20)

The inner structure of the conventional m:3 compressors is shown in Fig. 6 [30]. These conventional units are composed of single-bit HAs and FAs. In order to estimate, how fast the compressors are a simple power analysis and delay analysis is provided in this paper. The delay parameters of HA and FA are normalized by .

5 Simulation See Fig. 7, 8, 9, 10 and 11.

Fig. 6 Inner structure of the conventional m:3 compressors. a 4:3 [27], b 5:3 [28], c 6:3 [29], d 7:3 [29], e 8:3 [30], f 9:3 [30]

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6 Results and Discussion The implementation of FIR filter is verified using Xilinx ISE 14.7 navigation tool using VERILOG HDL language. The conventional FIR filter is compared with the proposed WTM with 7–3, 8–3 compressor with different logic styles. The total power is reduced 29% as compared to conventional FIR filter. The total delay of conventional and proposed FIR filter states reduction of 41% in delay as compared to conventional FIR filter. The transistor count comparison for conventional multipliers with proposed FIR filter shows a reduction of 19% reduction in transistor count. The delay FIR filter is less as compared to conventional FIR filter, which means FIR filter using proposed

Fig. 7 a RTL schematic diagram of conventional FIR filter, b waveform simulation of conventional FIR filter

Fig. 8 a RTL schematic diagram of FIR filter with 7–3 compressor, b wave simulation of FIR filter with 7–3 compressor

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Fig. 9 a RTL schematic diagram of FIR filter with 8–3 compressor, b wave simulation of FIR filter with 8–3 compressor

a

b 14

7 6

12 Delay(ns)

5

10

4

8

3

6

2

4

1

convenƟonal mulƟplier

using 8:3 compressor

2

0 using 7:3 convenƟonal using 8:3 FIR Filter compressor compressor FIR Filter FIR Filter

0

using 7:3 compressor

Fig. 10 a Delay analysis of conventional and proposed FIR filter, b total delay analysis of conventional and proposed FIR filter

WTM with higher order compressors is faster and more efficient. The area in terms of number of transistors and power consumption in terms of leakage, dynamic, and static power for the proposed FIR filter is less as compared to conventional FIR filter, which means proposed FIR filter using WTM with compressor is area efficient, less complex, and thus cheap in terms of manufacturing cost.

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a 3.5 3 2.5 2 1.5 1 0.5 0

b

Total Power(W)

250 200 150 100 50 0

no of transist or counts

Fig. 11 a power analysis of conventional and proposed FIR filter, b transistor count comparison of conventional and proposed FIR filter

7 Conclusion Conventional FIR filter using Wallace tree multiplier uses higher order compressors [7–3, 8–3] in the partial product reduction stage which provides irregular signal conversion to the multiplier used for the multiplication. Higher order compressors are used to reduce the critical path and also reduces the number of stages. Thus, the performance of FIR filter is verified using Xilinx ISE 14.7 synthesis tool using VERILOG HDL language. All the results are synthesized and simulated using Xilinx 14.7 navigator Tool. The conventional FIR filter consumes larger area and delay. In practice, all DSP filters can be used using finite-precision arithmetic, which is a set of possible numbers (finite). The use of finite-precision arithmetic in IIR filters can cause significant problems due to the use of feedback, but in FIR filters, there is no feedback. Hence, it can be implemented using less number of inputs. The modified FIR filter with compressor reduces the delay, when compared to conventional FIR filter. The results prove that the proposed architecture is more efficient than the existing one in terms of delay, power, and area. This approach may be well applicable for multiplication of numbers with 8-bit size or more for high speed multiplication operations. The power of the proposed FIR filter can be used to implement high performance multipliers like WTM using high order compressors in VLSI applications [17].

References 1. H.R. Mahdiani, A. Ahmadi, S.M. Fakhraie, C. Lucas, Bio-inspired imprecise computational blocks for efficient VLSI implementation of soft-computing applications. IEEE Trans. Circuits Syst. (IEEE) 5, 850–862 (2020)

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2. V.G. Oklobdzija, D. Villeger, S.S. Liu, Method for speed optimized partial product reduction and generation of fast parallel multipliers using an algorithmic approach. IEEE Trans. Comput. (IEEE) 4, 249–306 (2020) 3. S. Vaidya, D. Dandekar, Delay-power performance comparison of multipliers in VLSI design circuit. Int. J. Comput. Networks Commun. (IJCNC) 2, 440–690 (2020) 4. S.S. Demirsoy, I. Kale, A.G. Dempster, Efficient implementation of digital filters using novel reconfigurable multiplier blocks, in Proceedings—38th Asilomar Conference on Signals, Systems and Computers (Nov. 2004), vol. 1, pp. 461–464 (2012) 5. R.S. Waters, E.E. Swartzlander, A reduced complexity wallace multiplier reduction. IEEE Trans. Comput. 59(8), 1134–1137 (2012) 6. S. Mehrabi, R.F. Mirzaee, S. Zamanzadeh, A. Jamalian, A new hybrid 16-bit × 16-bit multiplier architecture by m: 2 and m:3 compressors. Int. J. Inf. Electron. Eng. 6, 79 (2012) 7. S. Mehrabi, R.F. Mirzaee, S. Zamanzadeh, K. Navi, O. Hashemipour, Design, analysis, and implementation of partial product reduction phase by using wide m:3 (4 m 10) compressors. Int. J. High Perform. Syst. Architect. 4, 231–241 (2012) 8. R.V.K. Pillai, D. Al-Khalili, A.J. Al-Khalili, Energy delay analysis of partial product reduction methods for parallel multiplier implementation, in International Symposium on Low Power Electronics and Design (ISLPED), vol. 23, pp. 201–204 (2014) 9. R. Mahesh, A.P. Vinod, New reconfigurable architecture for implementing FIR filters with low complexity. IEEE Trans. Comput. Aided Des. Integr. Circ. Syst. 29, 102–220 (2014) 10. M. Margala, N.G. Durdle, Low-power low-voltage 4–2 compressors for VLSI applications, in Proceeding—IEEE Alessandro Volta Memorial Workshop Low-Power Design, vol. 3 (IEEE, 2018), pp. 2600–3200 11. Y.-H. Chen, An accuracy adjustment fixed-width array multiplier based on multilevel conditional probability. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. (IEEE) 23, 1800–1890 (2018) 12. N. Nagdeve, V. Moyal, A. Fande, A simulation based evaluation of different compressors for fast multiplication. Int. J. Sci. Eng. Res. (IJSR) 3, 250–156 (2015) 13. T. Solla, O. Vainio, Comparison of programmable FIR filter architectures for low power, in Proceeding—28th European Solid-State Circuits Conference (Firenze, Italy, 2014), pp. 759– 762 14. E.M.K. Lai, A.P. Vinod, An efficient coefficient-partitioning algorithm for realizing low complexity digital filters. IEEE Trans. Comput. Aided Des. Integr. Circ. Syst. 24(12), 1936–1946 (2013) 15. T. Zhangwen, J. Zhang, H. Min, A high-speed, programmable, CSD coefficient FIR filter. IEEE Trans. Consum. Electron. 48(4), 834–837 (2013) 16. R. Nirlakalla, R.T. Subba, T. Jayachandra-Prasad, Performance evaluation of high speed compressors for high speed multipliers. Serb. J. Electr. Eng. 8, 293–306 (2011) 17. N. Pokhriyal, H. Kaur, D.N. Prakash, Compressor based area-efficient low-power 8 × 8 Vedic multiplier. Int. J. Eng. Res. Appl. 3, 1469–1472 (2011) 18. V. Gupta, D. Mohapatra, S.P. Park, A. Raghunathan, K. Roy, IMPACT: IMPrecise adders for low-power approximate computing, in Low Power Electronics and Design (ISLPED) International Symposium, vol. 2, pp. 450–553 (2019) 19. D. Baran, M. Aktan, V.G. Oklobdzija, Energy efficient implementation of parallel multipliers with improved compressors. IEEE Trans. Electron. Des. (IEEE) 2, 147–152 (2019) 20. Muhammad H. Rashid, Power Electronics Circuits Devices, and Applications, vol. 5 (IEEE, Pearson Education Inc., 2019), pp. 1600–2400

An Revolutionary Fingerprint Authentication Approach Using Gabor Filters for Feature Extraction and Deep Learning Classification Using Convolutional Neural Networks N. R. Pradeep

and J. Ravi

Abstract Biometrics is a field of research that focuses on the analysis of an individual’s identity which is based on a feature vector that contains characteristics derived from behavioural or physical properties. The identification and authentication of a personal fingerprint are one of the most famous and reported biometrics. Fingerprint recognition in various civil, defence and commercial applications has long been tested with technological progress and safety. Studies have been conducted since the second millennium when fingerprints were used for signature applications on different aspects along with properties of fingerprints. This paper presents a Gabor filter for feature extraction, as well as CNN for deep learning. A preprocessing stage covers histogram equalization, Gabor filter enhancement and fingerprint thinning to extract features from fingerprints. To classify the fingerprints that have been preprocessed, a deep convolutional neural network classifier is used. This work will introduce a Gabor-based CNN network that works extremely efficiently in comparison with the deep learning networks. Another challenge of CNN is the fact that rotation is not robust. Although CNN has already suggested the concept of a Gabor filter, this paper presents a completely new and very simple Gabor-based CNN that produces highly acknowledged data effectiveness and works invariantly for rotation. For 64 epochs, the proposed method for a training accuracy of 100% achieved a validation accuracy of 99.33%. The accuracy achieved is considerably higher than the results reported on the same dataset. Keywords Feature extraction · Gabor filter · Convolutional neural network (CNN) · Deep learning · Fingerprint enhancement · Biometrics

N. R. Pradeep (B) Department of ECE, Channabasaveshwara Institute of Technology, Gubbi, Tumakuru, Karnataka, India J. Ravi Department of ECE, Global Academy of Technology, Bengaluru, Karnataka, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_38

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1 Introduction The biometrics is the physical or behavioural assessment of the identity of the individuals [1]. Security concerns are a major concern today and they are growing in severity and complexity. Organizations, educational institutions, political and government agencies all need protection in order to deal with personality and extortion issues. In the event of a new threat, biometrics aims at preventing unauthorized access to control and fundamental information systems, organizations have their security programme implementing and updating periodically [2]. Authentication of persons which is safe and reliable is a fascinating goal and as security needs increases, it is becoming increasingly important on the basis of at least one of its characteristics. The use of biometric technology has the ability to simplify the process of identifying individuals. These are mechanisms that demonstrate customer’s ability to control a particular trademark [3]. Due to its complexity and singularity, biometrics has become indefensible part of human existence. As a consequence, in the light of the biometric highlights, a different form of system and strategy for client identification and control appears to be notable to each person [4]. Gabor filters have gained a lot of publicity because they have the best spatial and frequency domain localization properties. In several fields, deep convolution neural networks have excelled, including recognition of voice and computer vision. These networks can learn extremely complex and broad models without the need for numerous human design decisions. However, it costs to achieve these valuable properties. As a result of the large number of variables, these systems are highly computational and a broad training set is needed for the extraction of sufficient information to optimize the parameters. Several attempts to speed up CNN by minimizing the set of indicators have been made in recent years. Although attempting to preserve the trained model’s efficiency and precision, these approaches successfully minimize the number of parameters. Nonetheless, we believe that managing smaller training sets in such complex and large models are still needed. One approach is to guide the training process in order to preserve the useful features of the components during training. The properties of Gabor filters are used in this paper to collect more details in the image, which aids in the extraction of more distinguishing features from an image.

1.1 Biometric Database Databases are readily accessible with separate images of same individuals and individual databases inside the cyber world. We used the 2006 FVC database in the proposed research. The type of sensor used is a thermal sweeping sensor. With 150 people in the database and each of them having 12 images, the total number of images is 1800.

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1.2 Contribution This paper offers a new architecture for recognition of fingerprints consisting of fingerprint preprocessing phase based on Gabor, for classification, accompanied by a deep convolution neural network. Finally, the findings show that the suggested solution has a validation accuracy of 99.33% and a training accuracy of 100% for 64 epochs.

1.3 Organization This research work is formatted in this way, Sect. 2 sums up the survey, Sect. 3 provides a description of the proposed model, as well as explanation and information on the dataset used. The algorithm is explained in Sect. 4. Section 5, discusses the study and effects of the experiments, Sect. 6 contains review and recommendations for future studies.

2 Literature Survey Kucken and Newell investigated the idea behind epidermal ridge formation [5], they spoke about how (i) buckling instability in the epidermis basal layer causes the primary ridges to form. (ii) The hitch process behind the development of the fingerprint is controlled by stresses in the basement and not the skin surface curves. (iii) At creases and nail furrows, boundary forces operate, natural displacements influence the stress that dictates ridge direction that is most pronounced close to the ridge. To protect sensitive data with fingerprint data, Moon et al. [6] introduced the fuzzy fingerprint vault framework. The decoders used are geometric hashing and RS algorithms. A system for the online identification of fingerprints was developed and compiled by Jain et al. [7]. This goes through two stages, thorough extraction and careful matching. The use of an elastic alignment algorithm is suggested. The algorithm can compensate for non-linear deformations and inaccurate changes between fingerprints. Two methods for fingerprint image enhancement were proposed by Greenberg et al. [8]. The first one uses local histogram equalizations, Wiener filtering and image binarization. A single filter for direct greyscale improvement is employed for the second method. The Hong improvement tool can extract the orientation of the local ridge and frequency of the ridge is used for the extraction of global characteristics and fingerprint enhancements in the work Gabor filter enhancements fingerprint verification suggested by Lavanya et al. [9]. It was compared to existing algorithms, the sensitivity and specificity values are higher. Pradeep and Ravi [10] proposed

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utilizing the DTCWT algorithm to recognize fingerprints. In comparison with modern algorithms, the performance terms have been strengthened.

3 Model We define parameters of performance, modelling, databases and classifications in this section.

3.1 Definitions (i) (ii) (iii) (iv)

Minutia: Bifurcations and ends of ridges in a fingerprint image [9]. Training: During the planning phase, these are added to the network and the network is modified based on the errors found [11]. Validation: It is used for assessing network generalization and stop training if it no longer enhances generalization [11]. Epochs: A period of time distinguished by specific changes, one loop through the entire training dataset is referred to as an epoch.

3.2 Model Suggested The suggested algorithm uses the FVC2006 DB 3_A database to identify an individual more precisely, using Gabor-based features, followed by CNN. The block diagram is shown in Fig. 1. (i)

(ii)

FVC2006 DB 3_A fingerprint database: Database FVC 2006 was made available to those who took part in the fingerprint verification competition 2006. Since there are 150 people in the database, each of whom has 12 images, the total number of images is 1800. In order to select a quality index for the hardest fingerprints, images from this database have been selected from a large database. Subsets in DB3 are more difficult and demanding than those in DB1. Whereas, DB2 and DB4 are considered simple. Volunteers ranged in age and experience, from manual labourers to senior citizens as they were heterogeneous. Data collection took place intentionally without unnecessary distortions, rotations and on the acquisition unit, volunteers were instructed to position their fingers. Fingerprint Preprocessing: This phase improves the contrast of the fingerprint images to provide certain information during the subsequent fingerprint recognition stage. The more preprocessing that is done, the more efficient recognition processes would be.

An Revolutionary Fingerprint Authentication Approach … Fig. 1 Gabor-based feature extractor followed by CNN for deep learning classification block diagram

353

Database Fingerprint

Database Testing

Preprocessing

Preprocessing

Feature Extractor by Gabor Deep learning with CNN Classification

Feature Extractor by Gabor Deep learning with CNN Classification

Decision Block Accept / Reject

Each block in the fingerprint image will be classified as recoverable or unrecoverable regions to create the area mask. An agreed-upon image is passed through the filtering step. Gabor filters in both space and frequency areas have the best joint resolution and they are characterized by frequency and orientation [9]. Thus, it is suitable to remove noise and maintain real valley structures with band-pass Gabor filters. Binarization is used for transforming an enhanced grey image to a binary image [9]. Processing complexity increases as the number of grey levels grows. The image is turned into a binary image. The binarized image is thinned morphologically. The image is subjected to a morphological operation known as dilation until no further changes occur [9]. (iii)

Fingerprint Features based on Gabor: Harmonic function multiplied by Gaussian results as Gabor filter impulse response [12]. To strengthen the ridges and smooth, the valleys are the primary motives of Gabor. In areas such as computer vision, imaging and model recognition, Gabor has very large applications. Spectral spatial information as well as fingerprint image resistance to changing contrast or luminosity are provided by Gabor filtering, Eq. 1 gives the overall form of the Gabor filter.  2 2 s +t − 12δ21

G (δ, f,θ ) (s, t) = e

{cos(2π f (s1 )) + j sin(2π f (s1 ))}

(1)

where s1=s cosθ + t sinθ and t1=t cosθ −s sinθ, along the s1 and t 1 axes. The width of the envelope of Gaussian δ, f is the x-axis sinusoidal plane frequency and the Gabor filter’s x-axis orientation is θ. Before filtering,

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three variables (δ, f, and θ ) should be set. The orientation of the Gabor filter is determined by the predicted direction of the filter. The Gabor filters are symmetrical, because a rotated 180° Gabor filter corresponds with the 0° rotational angle Gabor filter. Equation 2 is used for computing filter orientations (θ n). n θn = π , where n = [0 . . . (k − 1)] k

(iv)

(2)

The orientation is θ n, and the number of filter directions is k. As a result, for eight filter orientations, θ takes values of 0°, 22.5°, 45°, 67.5°, 90°, 112.5°, 135° and 157.5° Proposed CNN Architecture: This paper suggests the following components for a modern fingerprint recognition architecture: (1) (2)

Fingerprint feature extraction in the form of preprocessing, after that To perform the classification task, a deep convolutional neural network is used.

Figure 2 illustrates the proposed architecture, user’s fingerprints were scanned with a thermal fingerprint scanner and for experimentation, a dataset was created. Processing actions included image orientation, image improvement, pore detection and selection of region of interest (ROI). After the above operations, the fingerprint ROI measures 480 × 320 pixels. (a)

Histogram Equalization: By applying the cumulative density operation to the histogram, flattening it with histogram equalization increases the dynamic spectrum

Fig. 2 Proposed CNN architecture

An Revolutionary Fingerprint Authentication Approach …

(b)

(c)

(d)

355

of intensity values. The histogram equalization is a procedure for transforming an image histogram into a single histogram. The transformation is carried out by importing all grey levels across an images histogram with a median value, which may be in the middle of the grey levels. As a result, when the discrete implementation is reached, the output images mean brightness is mostly in the middle or near to it. When it comes to images with low and high brightness values, the enhanced image can be found as an important innovation. Secondly, histogram equalization enhances an image based on its overall content. Essentially, the edges and borders of different objects are highlighted by histogram equalization. Gabor Enhancement: Gabor filters are frequency and orientation selective band-pass filters. In both the spatial and frequency sectors, they have the highest common resolution. By applying correctly tuned Gabor filters to an image of the fingerprint, the real crushing structure can be strongly emphasized. The ridges and furrow structures that have been emphasized effectively reflect a fingerprint image. Fingerprint Thinning: The technique of thinning fingerprints involves extracting pixels from the ridge line edges by reducing the width of the ridge line. The process of thinning is done through models and binary images compared to such templates, to see if pixels can be extracted at specific locations. A binary image is used to thin a fingerprint image, and the characteristics of the binary image have a significant effect on the thinned image. Small splits, bridges between ridges and other objects can be allowed even with a good binary algorithm. As a result, the most difficult part of this phase is deciding on a suitable pattern. The number of spikes that can lead to incorrect data extraction can be reduced by thinning fingerprint. CNN Architecture Model: Fingerprint image of 350 × 223 pixels is fed into the CNN. The convolutional layer is the first, with 32 filters of 5 × 5 dimension. The image is reduced by the maximum pool layer with a filter dimension of 2 × 2. Furthermore, to avoid the issue of overfitting neural networks, a dropout feature is used. The weight relations between two layers are severed when dropout occurs. Dropout (0.2), for example, it means that 20% of weights are removed between convolution and maximum pool layer. A ReLU-triggering mechanism and a convolution layer with 32 filters of 3 × 3 filter sizes are also included. Then, to further down sample in the image, a Max pool layer was formed. The network’s main building block is the convolutional layer, Fig. 3 depicts convolution performed using kernel filter. It is made up of a series of learnable filters with small receptive fields that span the entire input volume. As the input passes through the convolution layer, each filter is slid over the input volume’s width

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Fig. 3 Calculating the contribution of a convolutional layer is illustrated in this diagram

and height. At that particular location, dot product is computed between the input and filter entries. The activation maps are stacked along the depth dimensions for every filter, and the output is produced for the convolution layer. The filter moves to the right with a certain step value until the whole width is checked. It continues with the same step value to the beginning (left) of the image and continues the process until the whole image is crossed. (v)

Matching: Fingerprint matching is calculated using an assessment of the distance of prominent vectors of Euclidean distance.  d(r, s) = d(s, r ) =

(r1 − s1 )2 + . . . + (rn − sn )2

(3)

where, ‘r’ ‘s’

Test image features. The database image features.

When the Euclidean distance between two vectors falls below a threshold, both images come from the same finger, according to the results. If not, two images are taken from separate fingers.

4 Algorithm The suggested Gabor specific feature extractor algorithm can be used to effectively identify individuals and assess the accuracy of the CNN model proposed for inside database with respect to training and validation accuracy against number of epochs. Table 1 lists fingerprint recognition algorithm for extracting Gabor-based features.

An Revolutionary Fingerprint Authentication Approach … Table 1 Suggested algorithm

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Input: Image of fingerprint Output: Individual identification based on Gabor features Step 1: A fingerprint database was used to create the image Step 2: The image is changed to a dimension of 2x × 2y dimension Step 3: To boost the image quality, fingerprint enhancement is done Step 4: Using binarization, a greyscale image is converted to binary Step 5: The ridges of the binarized image are thinned to a pixel width Step 6: Implementation of the feature extraction algorithm based on Gabor Step 7: Calculate the Gabor-based features f , δ and θ from Eqs. (1) and (2) Step 8: Combine all of the features into a feature map to generate the final distinctive feature vectors Step 9: Repeat steps 1 to 8 for a test image Step 10: The image is confirmed by the Euclidean threshold value

5 Analysis and Experimental Findings Due to their high capacity of complex pattern recognition, deep neural network (DNN) has attracted much attention by the scientific community in the last few years. A broad variety of problems can be solved using their applications, including classification and fusion, digit recognition, feature extraction and diagnostic image classification, to name a few. One benefit of DNN is that neurons extract the information implicitly from raw input patterns. This enables a generic learning mechanism that is not reliant on features that have been specifically selected or extracted previously. DNN and CNN stand out as potentially promising fingerprint classification models because of all of these characteristics. The key results of the Gabor features in the form of f , δ and θ followed by deep learning classification, training and validation consistency for the number of epochs are the parameters used to assess the results.

5.1 Experimental Results Training accuracy, validation accuracy for the number of epochs are the metrics used to assess the results (Table 2).

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Table 2 CNN model recognition rate with gabor filter for inside database

Training accuracy

Validation accuracy

Epochs

99.10

91.03

16

99.70

92.33

32

100

99.33

64

Recognition Accuracy (Inside Database) Accuracy %

105 100 95 90 85

16

32

64

Training Accuracy

99.1

99.7

100

Validation Accuracy

91.03

92.33

99.33

Fig. 4 Percentage accuracy of CNN model with different number of epochs inside database

(i)

Recognition Accuracy (Inside Database): For 100 persons, considering 1 image per person makes up 100 images inside the database.

A prototype with MATLAB was developed for the proposed CNN model. The models are built using different layers and parameters after loading the training and testing datasets. The size and number of kernels (filters) used in the CNN model have an effect on the models consistency and accuracy. We chose a 5 × 5 kernel based on our observations, we have checked the CNN-based model’s accuracy using various epoch counts. The optimum validation accuracy of 99.33% for a training accuracy of 100% is obtained with 64 epochs, as shown in Fig. 4, there is no substantial change with lesser epochs. We used an early stopping technique during model training to prevent over fitting that can occur when using too many epochs or under fitting that can occur when using a small number of epochs. When no substantial performance change is observed for a period of time, this technique stops model training.

5.2 The Proposed Algorithm Performance Compared with Other Existing Algorithms Table 3 illustrates proposed CNN-based model and the current algorithms [12–14]. The accuracy of recognition is exceptional with the proposed method, unlike contemporary algorithms. Compared to other traditional methods, Gabor-based CNN model is more efficient. The error rejection rate is decreased as there are more sectors and

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Table 3 For FVC2006 database, comparison of recognition accuracy of different methods with proposed CNN model Method

Features

Accuracy (%)

Chavan et al. [12]

Gabor coefficients

82.95

Pushpalatha and Gautham [13]

Gabor coefficients

96.308

Pandya et al. [14]

Gabor filter-based deep convolution neural network

98.21

Proposed Gabor-based CNN model

Gabor filter followed by CNN for deep learning classification

99.33

orientations and thus performance increases. The validation accuracy of recognition in the proposed algorithm is 99.33%.

6 Conclusion Fingerprint authentication is a convenient, easy to use, safe and strong way to identify and verify individually. This paper examines authentications of fingerprints using a Gabor CNN model and a thorough understanding of feature extraction from fingerprint images. FVC 2006, database is used to validate the algorithm. Body part modalities can be used as a biometric identifying password. This paper presents an extensive architecture for deep learning recognition of fingerprint that covers a Gabor-based feature extractor preprocessing step before applying the neural network classification. The feature extraction process included histogram equalization, Gabor filtering and fingerprint thinning. CNN outperformed all of the methods compared, according to the findings CNN outperformed any mix of feature extractors and classifiers in terms of recognition accuracy. Furthermore, CNNs do not reject any fingerprints, but they also outperform feature extractors with a certain rejection rate in terms of accuracy. The runtime required by CNN is also very competitive and superior to the most exact approaches. Training and evaluation of a wider dataset would be included in the future work. Furthermore, future work will involve the building of multi-modal biometric identification using deep learning architectures, including data that combine fingerprint features with keystroke, signature, iris, face features, to build biometric recognition systems that are more reliable.

References 1. N. Dalal, B. Triggs, Histograms of oriented gradients for human detection, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 868–893 (2005)

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2. K. Nir, Cybercrime and cyber-security issues associated with China: some economic and institutional considerations. Electron. Commer. Res. 13(1), 41–69 (2013) 3. G. Jayavardhana, R. Buyya, S. Marusic, M. Palaniswami, Internet of things (IoT): a vision, architectural elements and future directions. Futur. Gener. Comput. Syst. 29(7), 1645–1660 (2013) 4. J.A. Unar, W.C. Seng, A. Abbasi, A review of biometric technology along with trends and prospects. Pattern Recogn. 47(8), 2673–2688 (2014) 5. M. Kucken, A.C. Newell, Fingerprint formation. J. Theor. Biol. 71–83 (2005) 6. D. Moon, S. Lee, Y. Chung, Implementation of automatic fuzzy fingerprint vault, in Proceedings of International Conference on Machine Learning and Cybernetics, pp. 3781–3786 (2008) 7. A. Jain, L. Hong, R. Bolle, On-line fingerprint verification. IEEE Pattern Anal. Mach. Intell. 19, 302–314 (1997) 8. S. Greenberg, M. Aladjem, D Kogan, Fingerprint image enhancement using filtering techniques, in National Conference on Real-Time Imaging, pp. 227–236 (2002) 9. B.N. Lavanya, K.B. Raja, K.R. Venugopal, Fingerprint verification based on Gabor filter enhancement. Int. J. Comput. Sci. Inf. Secur. 6(2) (2009) 10. N.R. Pradeep, J. Ravi, Fingerprint recognition model using DTCWT algorithm. Int. J. Inf. Technol. (2021). https://doi.org/10.1007/s41870-021-00700-3 11. J. Rajharia, P.C. Gupta, A new and effective approach for fingerprint recognition by using feed forward back propagation neural network. Int. J. Comput. Appl. 52(10) (2012) 12. S. Chavan, P. Mundada, D. Pal, Fingerprint authentication using gabor filter based matching algorithm, in International Conference on Technologies for Sustainable Development (2015) 13. K.N. Pushpalatha, Gautham AK, Fingerprint verification in personal identification by applying local Walsh Hadamard transforms and Gabor co-efficients. Int. J. Image Video Process. (ICTACT) 7(4) (2017) 14. B. Pandya, G. Cosma, A.A. Alani, A. Taherkhani, V. Bharadi, T.M. McGinnity, Fingerprint classification using a deep convolutional neural network, in 4th IEEE International Conference on Information Management (2018)

Automatic Attendance System Using AI and Raspberry Pi Controller Harpreet Kaur, Manpreet Kaur, Md Rashid Mahmood, Subham Badhyal, and Sarabpreet Kaur

Abstract In the present-day scenario, educational institutions, industries, and all the organizations use person face detection algorithms for the daily attendance of the employees and students. For the purpose of automatic attendance marking system, various technologies like face recognition, iris detection, and RFID-based algorithms have been used. Among all these techniques, facial recognition is considered to be most efficient. In this paper, IoT-based facial recognition has been used for attendance marking has been proposed. A camera captured the images and the attendance of the students will be marked by the system automatically by comparing the faces with the saved database. In this algorithm, detection of the faces is accomplished with the help of Haar cascade classifier. Further, the images of the students entering the classroom are compared with the images in the saved dataset with the help of local binary pattern histogram algorithm. Also, students are notified when they are marked present with a message. Raspberry Pi 3B hardware has been used to implement this system. Keywords Face recognition · OpenCV · Raspberry Pi · Haar cascade classifier

1 Introduction In a classroom with a large number of students, keeping track of attendance is a difficult and time-consuming task [1]. To avoid this, an automated attendance marking system can be developed so that the class time can be effectively utilized for teaching. H. Kaur · M. Kaur · M. R. Mahmood Electronics and Communication Engineering, Guru Nanak Institutions Technical Campus, Ibrahimpatnam, Telangana 501506, India S. Badhyal (B) Department of Physical Therapy and Rehabilitation Science, School of Medicine, University of Maryland Baltimore, Baltimore, USA S. Kaur Electronics and Communication Engineering, Chandigarh Engineering College, Mohali, Chandigarh, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_39

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As face recognition [2] is the best technique to identify any person, so this technique has been used in this work. RFID-based and fingerprint biometric approaches are currently being employed for attendance monitoring. The advantage of automated techniques for attendance marking as compared to the manual technique is that fake attendance can be avoided. The main motive of this paper is to develop an efficient face recognition system which automatically captures images and marks them in the database along with sending a message to students as well as to their parents to avoid any miscommunication.

2 Literature Survey All educational institutions, organizations, and companies use different manual and automated techniques for attendance marking. These include manual registers, RFIDbased attendance markers, and biometric fingerprint algorithms [3]. Radio frequency identification (RFID) technique makes use of electromagnetic fields to track the persons by identifying the tags attached to the persons. The RFID environment can be easily attacked by hackers, and this is the limitation of this algorithm. If there is no proper synchronization between the RFID reader and receiver, then the read rate is less and hence the cards and the reader must be under constant check to avoid any inconvenience. Fingerprint biometric technique [4] employs fingerprint as unique identification but it is time-consuming process. It is one of the efficient algorithms but the limitation is that external temperament can affect the individual’s fingerprints. Iris recognition method is also a secure type of identification algorithm in which the iris patterns of the individuals are saved in the database, and then, the captures iris patterns are compared with the database to mark the attendance [5]. This method is a time-consuming process, and hence, there is a need of a fast and efficient recognition system using face recognition. Face detection and recognition are the two primary stages of the face recognition-based attendance monitoring system [6]. This is accomplished by installing a camera in the classroom at a position that efficiently covers the entire classroom. The system receives this image of the classroom as an input. Image enhancing techniques were applied to improve the system’s efficiency. The students in the last rows were recognized using the histogram equalization approach [7].

3 Methodology This proposed system for attendance marking will be used in schools, colleges, offices, and other workplaces. The block diagram shown in Fig. 1 shows a Web camera attached with Raspberry Pi 3B module at allocation in the classroom where the camera can capture every person present in the room.

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Power supply Display Web camera

Raspberry Pi 3B

16 GB memory

Database

Fig. 1 Proposed work

The various steps involved in the implementation of the proposed system are summarized below:

3.1 Student Enrollment With the use of a camera, all of the students’ photographs will be taken. Histogram equalization and noise filtering will be used to improve the photos. The photos’ features will also be extracted, and the extracted features will be kept in a database. Each person will be assigned a unique ID.

3.2 Image Acquisition In the classroom, a high-definition camera will be placed in a location that allows the entire classroom to be seen. The system receives the captured image as input.

3.3 RGB Image into Grayscale Image The brightness of the image taken by the camera device may need to be removed in order to attain the desired outcome. As a result, for improvement, the captured picture is transformed to grayscale.

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3.4 Histogram Normalization A technique for enhancing contrast is histogram normalization. The image will then be equalized to remove the contrast, allowing children in the rear rows to be easily seen and identified. It then creates the equalized image’s histogram.

3.5 Classification of Skin All pixels in the skin classification approach are colored black, with the exception of those that are intimately associated to the skin. Those pixels will turn white. After skin categorization, the face identification algorithm’s accuracy improves.

3.6 Face Detection After the picture has been enhanced, it is sent to the face detection module. This module will use an image to recognize students’ faces. Face detection is accomplished using the Viola and Jones algorithm. It was designed by Viola P. and M. 1. Jones and is also known as the AdaBoost method for face identification (Fig. 2).

3.7 Face Recognition Then, using OpenCV’s library files, face recognition is performed and it automatically compares the results to the existing database. Other techniques are less sophisticated and efficient than face recognition [8]. Face recognition involves two stages: feature extraction and categorization. These are performed by the cascade classifiers which are present in OpenCV source library CV2 [9]. CascadeClassifier(“haarcascade_frontalface_default.xml”). These are compared to characteristics collected in real-world settings such as face expressions and lighting conditions [10]. The accuracy of the face recognition system starts to grow as it considers more than 30 real-time photos, and the work performance improves as a result of continual training.

3.8 Attendance Marking The attendance will be recorded on the server after the verification of faces and successful identification [11].

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Fig. 2 Flowchart of proposed work

3.9 Automatic Generated Message to Students To guarantee that all students present on a given day are awarded attendance, an automatically produced message is delivered to them.

4 Results For evaluating the algorithm, the database has been created by capturing image of 56 students in a class. To train the dataset, the image of individual student was captured using high-definition camera. Image is enhanced, and the features were extracted. The unique features were then stored in the face database, and each student was assigned a unique ID.

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Fig. 3 GUI for face recognition attendance system

The user interface for the face recognition attendance system is shown in Fig. 3. In front of the classroom, a high-definition camera was installed. The camera equipment took a picture of the entire classroom. The system was given the captured image as an input (Fig. 4). Image is tracked using AdaBoost algorithm used for face detection. Skin classification was performed in order to determine the no. of faces in the image. Skin classification face is recognized and matched with the images in the existing database through library files present in OpenCV. Figure 5 represents how the person is being identified in the console. Further, image of the individual image recognized was enhanced by converting RGB image to gray and by improving the contrast of the image referred as histogram normalization (Fig. 6). The proposed algorithm has boost the accuracy of the detection algorithm procuring better results. Once the person is identified in the console, the candidate is marked as present. Here, the candidate name “Adarsha” is matched with the existing database and being marked present. Candidate those who are marked present gets the marked present notification on their registered mobile number.

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Fig. 4 Face detection

Fig. 5 Person being identified in the console

5 Conclusion In institutes, the smart and automated attendance system has been proven to be an efficient solution for classroom attendance. The likelihood of fake attendance and

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Fig. 6 Attendance being updated automatically in the database

proxies is considerably minimized when this approach is used. There are a variety of biometric systems that may be used to monitor attendance, but face recognition has the highest performance. As a result, we must design a reliable and efficient classroom attendance system that can handle numerous face recognition at the same time. As a result, face recognition attendance systems have been proven to be safe and effective. The Haar cascade classifiers outperform other algorithms in real-time circumstances and are deemed to be suitable for use in this project. The dlib is based on Linux, but because the Raspbian OS is based on Linux, it could be applied directly, resulting in a higher recognition rate with a lower false rate. Using a Raspberry Pi on its own promotes work mobility and allows it to operate as a stand-alone piece of hardware (Fig. 7).

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Fig. 7 Message received by the present students

References 1. E. Varadharajan, R. Dharani, S. Jeevitha, B. Kavinmathi, S. Hemalatha, Automatic attendance management system using face detection, in IEEE International Conference on Green Engineering and Technologies (IC-GET) (2016) 2. R. Raja, R.K. Patra, Md. Rashid Mahmood, Image registration and rectification using background subtraction method for information security to justify cloning mechanism using high-end computing techniques. Adv. Intell. Syst. Comput. 1090 (2020) 3. S. Khan, A. Akram, N. Usman, Real time automatic attendance system for face recognition using face API and OpenCV. Wirel. Personal Commun. 113, 469–480 (2020) 4. O. Sanli, B. Ilgen, Face detection and recognition for automatic attendance system, in Advances in Intelligent Systems and Computing (2018) 5. H.M. El Barkey, Face detection using fast neural networks and image decomposition. Neurocomputing 11(3), 1039–1046 (2002) 6. A.K. Jain, A. Ross, S. Prabhakar, An introduction to biometric recognition. IEEE Trans. Circuits Syst. Video Technol. 14(1), 4–20 (2004) 7. M.H. Yang, N. Ahuja, D. Kriegmao, Face recognition using kernel eigenfaces, in IEEE International Conference on Image Processing, vol. 1, pp. 10–13 (2000) 8. M. Turk, A. Pentland, Face recognition using eigenfaces, in Proceeding—IEEE Conference on Computer Vision and Pattern Recognition (1991) 9. O. Shoewn, Development of attendance management system using biometrics. Pac. J. Sci. Technol. 13(1) (2012) 10. C. Gomes, S. Chanchal, T. Desai, D. Jadhav, Class attendance management system using facial recognition, in International Conference on Automation, Computing and Communication 2020 (ICACC-2020) (2020) 11. S. Kumar, Md. Rashid Mahmood, A study on smart electronics voting machine using face recognition and aadhar verification with IOT, Springer Lecture Notes Network and System, vol. 65 (2019)

Miscellaneous

DDoS Mitigation in SDN Using MTD and Behavior-Based Forwarding M. Udhaya Prasath, B. Sriram, P. Prakashkumar, and V. Vetriselvi

Abstract DDoS attacks target computing resources and they are usually launched from a large number of distributed devices. Due to its distributed nature, it is hard to trace and stop the DDoS attack. Also the attacks are more mature and varied due to the development of sophisticated tools and technology. Reactive defense mechanisms, such as IDS, have tried to secure systems and networks against such attacks in the past. But attackers cannot be prevented just by using reactive defense mechanisms. Moving target defense (MTD) is a “proactive defense mechanism” which is aimed at defeating the efforts of an attacker. Software defined networking is gaining large attention, and its popularity is increasing rapidly. Hence, it is essential to secure systems in the SDN environment against network attacks such as DDoS. We use a moving target defense mechanism called random host mutation and behavior-based forwarding which varies the quality of service provided to the clients based on their behavior to mitigate DDoS attacks in software defined networking environments. Keywords DDoS · Moving target defense · SDN

1 Introduction SDN is an adjustable architecture that demarcates the network forwarding and control planes. It enables centralized network control and they can be programmed directly [5]. The control plane uses SDN controllers to acquire the entire network status from the data plane and make dynamic adjustments to the network. Denial of service (DoS) is aimed at making the attacked resources unavailable for normal users. While the purpose of a DDoS attack is the same, DDoS attack comes from multiple sources. A lot of techniques has been proposed for minimizing the effect of DDoS attacks. Stochastic analysis is one such technique that has been used to detect anomalous events. Machine learning techniques are used to detect and classify attack traffic [1]. M. Udhaya Prasath (B) · B. Sriram · P. Prakashkumar · V. Vetriselvi Anna University, Guindy, Chennai 600025, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_40

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Moving target defense (MTD) [4] is based on the idea of security through diversification. MTD techniques focus on changing the configurations of a target, dynamically and randomly, thereby increasing the complexity and uncertainty of the attack surface. MTD techniques make the system less homogeneous, static, or deterministic and can defend against a number of cyberattacks. The adaptability of SDN makes it suitable for applications like MTD which require continuous flexibility. We use the MTD mechanism in SDN to mitigate DDoS attacks.

2 Literature Survey Software defined networking (SDN) is used to abstract and demarcate the data or forwarding plane from the control plane to make network management efficient [5]. A centralized controller configures and controls the network by issuing flow rules to switches. The switches use flow rules for handling the incoming data packets. The controller populates these rules in them using a standard interface such as OpenFlow. Moving target defense (MTD) is a technique that is specifically designed to make it difficult for an attacker to gain information about a system and exploit vulnerabilities using the gained information. This technique works by continuously changing the environment [4]. One such MTD technique is the host mutation wherein virtual IP addresses are used instead of the real IP addresses. Since allocating predictable or contiguous IP addresses defeats the aim of MTD, a randomized selection algorithm has to be used [2]. The virtual IP addresses are usually short-lived, and they are changed either periodically or in response to an event. The SDN controller is responsible for performing this mutation across the network. The real IP-virtual IP translation is performed at the gateway of the network. The hosts that are highly exploitable can have higher mutation rate. Jia et al. [7] proposed an MTD technique for dealing with DDoS attacks by securing data transmission between the server and authenticated clients. They use proxy servers with their IP addresses known to the authenticated clients alone. They use a filter ring around the application server, consisting of numerous high speed routers. It allows inbound traffic from valid proxy servers alone. The authentication server is used to authenticate clients and link legitimate ones with individual proxy nodes. Client-to-proxy mapping is shuffled to continuously move secret proxies and isolate the attackers. Behavior and reputation scores have been used to provide quality-of-service to systems proportional to their behavior score calculated over a period of time [10]. Han et al. [6] used a hidden semi-Markov model (HSMM)-based anomaly detection method to secure cloud storage systems from application-based attacks. This model is based on the fact that the request rate follows Poisson distribution and the interarrival request time forms a Markov process. Proof of work schemes is used for detection and mitigation of DDoS attacks. Almohri et al. [3] proposed a protocol for client bootstrapping in proxy-based MTD systems in the cloud. It uses a high-capacity cloud service (cloud notification service)

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Fig. 1 Overall architecture

to handle client bootstrapping. To access the application served by the target network, clients should register with the notification service. While there are numerous systems that only use reactive defense mechanisms, they are not enough to mitigate the DDoS attacks effectively. A combination of proactive and reactive defense mechanisms is needed. Our proposed system incorporates both reactive and proactive defense mechanisms to counteract DDoS attacks. Machine learning models cannot be directly trained to accurately predict the probability that a traffic flow is benign. The model has to be calibrated for more accurate predictions.

3 System Design 3.1 Architecture Diagram See Fig. 1.

3.2 Proposed System The aim is to protect online services against DDoS (ICMP flooding) attacks. In the server side, apart from the main application server, a set of proxy servers and a lookup server work in tandem to ensure that the online service is available to legitimate clients. The application server is the one that provides service to the clients. Proxy servers are a set of servers that relay communication between the main application server and clients. The lookup server is the initial point of contact for the clients and

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is responsible for linking legitimate clients with proxy servers [7]. After authorizing the client, the server assigns it to a proxy server. The lookup server sends the proxy’s virtual IP to the client for further communication and also informs the proxy server about the upcoming connections from clients. This enables the proxy servers to serve only the clients assigned to them. Every client IP will be assigned an initial behavior score. The traffic data are collected periodically from the switches, and the behavior score is periodically updated by calculating the probability that the flow is legitimate traffic. Based on the behavior score, the future packets from the client will be assigned to a quality-ofservice queue in switches. Each QoS queue has a predefined packet forwarding rate. This limits the bandwidth provided to the clients making it complex for attackers to set up an attack, as traffic from malicious users is served at a lower rate. Anomaly detection module runs periodically to detect the attacks that bypass the proactive preventive measures in place. When an anomaly is detected, host mutation is done. The mutation causes the attack setup to fail as the traffic generated by the attackers cannot reach the servers (since the destination IP will become invalid). The clients must contact the lookup server to get the new virtual IPs of the proxy servers.

3.3 Detailed Module Design Authorization using POW The clients are authorized by the lookup server before they are served by the proxy servers. Lookup server is protected from DDOS attacks using a proof of work mechanism. When the client contacts the lookup server, it sends a cryptographic puzzle to the client. The client should find the solution for the puzzle and send it to the server. The server will check the solution. If the solution is correct, it assigns the client to a proxy server and then sends the proxy’s virtual IP to the client for further communication. The lookup server also informs the proxy server about the upcoming connection from the client. This prevents unauthorized clients from sending traffic to the proxy servers. The difficulty of a proof of work puzzle given to the users on subsequent requests, depends on their behavior score calculated by the IP Profiling module. IP Profiling Each IP’s traffic data are periodically collected from the switches by the controller. It then calculates the IP’s behavior score for the current flow and updates the overall behavior score of the client. The behavior score of a client is calculated by determining the probability that the traffic flow belongs to a normal flow. ML techniques can be used to model the normal traffic flow. As far as we know, there is no dataset that explicitly relates traffic statistics with the behavior score. So the models can only discriminate legitimate traffic data from the attack traffic which is not helpful. However, they can be calibrated using techniques such as Platt scaling and isotonic regression [9] to predict the probability that a given instance belongs to a particular class. Isotonic regression, which uses the weighted least-squares regression model to transform the predicted probabilities, is used to calibrate the model.

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Queuing QoS functionality can be used to control bandwidth provided to the clients. The parameters such as port name, queuing discipline, global maximum rate or minimum rate, and queue array with maximum and minimum rate for each queue are used to create a queue. QoS rules are created with the following match conditions: priority, source IP, destination IP, source port, destination port, protocol type. When the packets match these conditions, they will be placed in the corresponding queue. Client IPs are assigned to various priority queues based on their behavior score. So the rates and bandwidth of the IPs with bad behavior are limited in the switches, thus making it hard for the attacker to exhaust the available bandwidth. This also ensures that legitimate users are not affected during the attack. Anomaly Detection A SDN specific dataset [11] is used for the anomaly detection module. Since the fields like IP source, IP destination are specific to the dataset, they are not used for training. A random forest classifier is built using the training data. Random forest algorithm is preferred as it does not require feature scaling. This is desirable in our case because the live data collected from switches for realtime classification cannot be normalized properly due to the irregularity in data. The traffic flow data are periodically collected from the switches, and per flow features are extracted and given to the trained model. The model predicts whether the traffic is legitimate or malicious. When an anomaly is detected, the proxy server being attacked is identified and the vIP mutation module will be triggered. vIP Mutation Once an attack is detected, the anomaly detection module will trigger this module. Random IPs are selected from the available pool of IPs and are assigned to the proxy servers as virtual IPs. All the virtual IP information is then broadcasted to the lookup server, controller, and switches. As the proxy server’s virtual IPs are

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changed, the attack setup will fail. The attackers need to contact the lookup server again to get the new virtual IP [2]. Virtual IPs are also mutated after a timeout, which means the proxy cannot be accessed with the old IP. When a packet is received, IP translation is done as follows.

4 Results and Discussion Mininet, a network simulator is installed in the virtual machine (Ubuntu server 18.04 LTS). We used the Ryu controller which enables us to write applications to monitor and manage the network. We used open vSwitches which are the software implementation of a network switch. OpenFlow protocol is used for communication between the switches and the controller.

4.1 Response Time 2000 pings packets are sent per second by the attackers while a file transfer is taking place. The graph shows the response time, that is, the time required to transfer files of various sizes under DDoS attack with and without DDoS mitigation. Without DDoS mitigation, the response time is very high. It increases drastically for very large files. Whereas with DDoS mitigation, there is a drastic decrease in the response time (Fig. 2).

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Fig. 2 Comparison of response time with and without DDoS mitigation

4.2 Anomaly Detection Performance The random forest classifier has an accuracy of 99%, precision of 97%, recall of 99%, and F1 score of 98%.

4.3 Brier Score Loss The Brier score loss is given by N 1  B= ( p t − a t )2 N t=1

(1)

where pt is the probability predicted by the model, at is the actual outcome and N is the total number of instances. Random forest classifier is calibrated to predict the probabilities for IP profiling. The Brier score loss of the calibrated model is 0.002.

5 Conclusion This work proposes a solution for mitigating ICMP flood (DDoS) attacks on a server in the SDN environment. The approach involves both proactive and reactive defense mechanisms. The behavior scoring system provides proactive defense, and anomaly detection is a reactive defense mechanism. The system uses the behavior score to provide different quality-of-service guarantees to client IPs. To detect attacks, it

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employs an anomaly detection mechanism and when an attack is detected, the IP address of the server is mutated. This work can be extended to identify other types of flooding attacks. The vIP mutation module can be extended to support proxy servers that are geographically distributed. Port hopping is another MTD mechanism in which the ports of the application will be changed periodically. It is simpler than IP mutation but the efficiency of the method needs to be studied. However, both port hopping and IP mutation can be used together to defend against DDoS attacks.

References 1. T. Liang, P. Yue, W. Jing, Z. Jianguo, J. Hao, D. Yuchuan, A new framework for DDoS attack detection and defense in SDN environment. IEEE Access 8, 161908–161919 (2020) 2. J.-H. Jafarian, E. Al-Shaer, Q. Duan, OpenFlow random host mutation: transparent moving target defense using software defined networking, in SecureComm 2012: Security and Privacy in Communication Networks, pp. 127–132 (2012) 3. H. Almohri, M. Almutawa, M. Alawadh, K. Elish, A client boot-strapping protocol for DoS attack mitigation on entry point services in the cloud. Secur. Commun. Netw. 2020, 1–12 (2020) 4. J.-H. Cho, D.-P. Sharma, H. Alavizadeh, S. Yoon, B.-A. Noam, T.-J. Moore, D.-S. Kim, H. Lim, F.-F. Nelson, Toward proactive, adaptive defense: a survey on moving target defense. IEEE Commun. Surv. Tutorials 22(1), 709–745 (2020) 5. N. McKeown, T. Anderson, H. Balakrishnan, G. Parulkar, L. Peterson, J. Rexford, S. Shenker, J. Turner, OpenFlow: enabling innovation in campus networks. ACM SIGCOMM Comput. Commun. Rev. 38, 69–74 (2008) 6. D. Han, K. Bi, B. Xie, L. Huang, R. Wang, An anomaly detection on the application-layer-based QoS in the cloud storage system. Comput. Sci. Inf. Syst. 13, 659–676 (2016) 7. Q. Jia, K. Sun, A. Stavrou, MOTAG: moving target defense against internet denial of service attacks, in International Conference on Computer Communications and Networks, pp. 1–9 (2013) 8. A. Juels, J. Brainard, Client puzzles: a cryptographic countermeasure against connection depletion attacks, in Proceedings of the Network and Distributed System Security Symposium (1999) 9. A. Niculescu-Mizil, R. Caruana, Predicting good probabilities with supervised learning, in Proceedings of the Twenty-Second International Conference Machine Learning (ICML 2005), pp. 625–632 (2005) 10. S.-D. Kamvar, M. Schlosser, H.-G. Molina, The EigenTrust algorithm for reputation management in P2P Networks, in WWW ’03: Proceedings of the 12th international conference on World Wide Web, pp. 640–651 (2003) 11. N. Ahuja, G. Singal, D. Mukhopadhyay, DDOS attack SDN dataset, in Mendeley Data, V1. https://doi.org/10.17632/jxpfjc64kr.1

NutriChain: Secure and Transparent Midday Meals Using Blockchain and IoT Ayasha Malik, Rekha Kashyap, Karan Arora, and Bharat Bhushan

Abstract The midday meal program is an initiative by the government and NonGovernmental Organization (NGO) to tackle malnutrition among younger generations, enrolled in educational institutions aiming to provide a nutritious meals. It is a large scale food assistance program with an overwhelming budget. Despite government authorities trying their best to make it a success, many problems ranging from poor food quality, meals not being served as per guidelines, and food not delivered are encountered. The present implementation can be improved if there is transparency in the system with proper distribution of powers from a handful to all stakeholders involved in the process. Blockchain being an immutable, shared, and distributed ledger without the control of any single centralized authority can address these shortcomings. The proposed work NutriChain has implemented the idea of blockchain and Internet of things (IoT)-based distribution network by storing meal preparation and distribution data on Ethereum-based permissioned blockchain. Each batch of meal will be identified by unique identity and every successful and unsuccessful transfer of a batch of meal from one stakeholder to another will be recorded as a transaction on blockchain. The nutrition value along with the temperature, photographs, and weight of the meal is recorded by the IoT sensors as IoT metrics score. All meal details are stored on the database, and its hash along with aggregated IoT metrics score is stored as append-only blocks of transaction on blockchain. The initial research suggests that the blockchain and IoT-based approach in solving the chosen use case is both feasible and promising. Keywords Blockchain · Midday meals · Supply chain · Internet of things · NutriChain

A. Malik · R. Kashyap (B) Noida Institute of Engineering Technology (NIET), Greater Noida, India K. Arora Inderprastha Engineering College, Ghaziabad, India B. Bhushan School of Engineering and Technology (SET), Sharda University, Greater Noida, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_41

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1 Introduction The history of blockchain roots back to invent of bitcoin, the cryptocurrency used in trading without using a centralized trusted third party. Blockchain can be described as an append-only, incorruptible digital ledger of blocks containing transactions that are designed to record not just financial transactions but virtually any data of value. When a new transaction is executed, it is sent to all other nodes in the network in order to update their ledgers. Thus, every node has the same copy of the ledger; this transparency level presents the idea of decentralized technology. Anything that happens on the blockchain is a function of the network as a whole. Hence, it can eliminate the frauds or fake transactions that can happen in a centralized version, which is under the full control of a single party or entity [1]. In this paper, our focus is on the challenges faced in implementing midday meals, food assistance programs and we further discussed the implementation of blockchain for improvement of existing system. The midday meal scheme was introduced in India a long ago, but the issues and problems it faced from the very beginning have still not been sorted out. Now and then, meal-related accidents or mismanagement problems are figured out. With the help of blockchain and the Internet of things (IoT), the entire journey of a meal can be tracked from the preparation to its endpoint which is serving food to the beneficiaries [2]. In the current work, we have proposed a solution for securing the supply chain of midday meals by establishing trust and integrity with the help of permissioned blockchain and IoT sensors. Figure 1 shows the history of the midday meal concept in a summarized view. The motivation of this work is enumerated as follows: • The proposed work NutriChain has implemented the idea of Ethereum-based permissioned blockchain with IoT-based distribution network for storing meal preparation and distribution to the needy people. • This work highlights some previous work provided by researchers related to this domain. • The initial work suggests that the blockchain and IoT-based approach, the chosen use case should be feasible and promising. The remainder of the paper is organized as follows, Sect. 2 discussed the related work that has been done by many researchers in this domain. Furthermore, Sect. 3 presented the detailing of proposed work where blockchain network, IoT sensors,

Fig. 1 History of midday meal concept

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various required role creations, and meal preparations with proper transportations plus feedback are well-defined via flowcharts. In addition, audit requirements and verifications are stated. Moreover, Sect. 4 highlights the performance analysis of the proposed model based on two attributes named gas-cost analysis and execution time analysis. Finally, Sect. 5 concludes with the conclusion.

2 Related Work Blockchain reduces the risks associated with single point of failure, centralized control, and network related attacks. In the current practice of midday meal distribution, there are provisions of strict security guidelines and protocols but problem lies in its implementation and transparency. Figure 2 shows the supply chain of midday meal program. Perboli et al. [3] highlighted the use of blockchain to improve efficiency, transparency, and reliability of supply chain. Kumar and Mallick [4] focused on blockchain security, along with issues and challenges in the IoT systems. Qian et al. [5] discussed the various security and privacy issues related to IoT devices and their possible solutions with the adoption of blockchain technology. Tian [6] suggested the use of blockchain for entire food supply chain for enhancement of food safety. Hofmann et al. [7] said the IBM, Nestle, and Walmart have formed a consortium for studying the use of blockchain addressing food safety problems. The million meals project (MMP) by Accenture [8] addressed a similar use case related to the quality of midday meals served to students by implementing a pilot project using blockchain and artificial intelligence (AI) along with sensor enabled devices. The main focus of this work was on optimization of the cooking process, revolutionizing their supply chain operations, and future predictions [9]. The government’s midday meal scheme is successfully running with the partnership of the Akshaya Patra Foundation, serves

Fig. 2 Supply chain of midday meal program

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students of 16,956 schools of around 12 states and 2 union territories of India, i.e., 1.9 million children per day. The focus of our research is specific to the midday meal program, by empowering all stakeholders in the recording and visibility of data.

3 Proposed Work This paper has proposed NutriChain as a trusted and transparent midday meal solution. The solution had used blockchain and IoT as its underlying technology. We developed a proof of concept (PoC) implementation of the food chain traceability system and further performed tests along with evaluations. The most critical challenge for realizing on-chain storage and tracking is the limitation of gas fee on Ethereum Mainnet: executing a single instruction of smart contracts consumes a certain amount of gas, called gas fee, with the average of about 10 thousand. Whereas, Ethereum has a parameter block gas limit, usually about 10 million, which determines the total gas that can be consumed within a block. That is, the total gas fee for invoking a smart contract cannot exceed block gas limit, because the corresponding transaction can only be included in one block, which means that number of instructions cannot exceed 10 million/10 thousand = 1000, otherwise the transaction will be refused.

3.1 Blockchain Network The chosen blockchain technology for implementation is Ethereum. The prototype is developed in a private instance of Ethereum network under a generic genesis block using Geth. The new block is constructed after receiving 100 transactions each time wherein all pending transactions received are included in this newly constructed block.

3.2 IoT Sensors We have used temperature and global positioning system (GPS) sensors in prototyping our idea. The sensors are connected to Raspberry Pi, and data are handled by invoking REST APIs. IoT metrics score lies in the range from 0 to 1 and is calculated using Eqs. (1) and (2) as shown below: n IoT Metrics Score = 1 −

i=1

n

i

,

(1)

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i =

VEi − VOi VEi

385

(2)

where i = Deviation of observed value of sensor i from the expected value, i ∈ [0, 1], n = total number of sensors used, VEi = Value expected from sensor I and VOi = Value observed from sensor i.

3.3 Role Creations CatererAdmin, TransporterAdmin, SchoolAdmin are registered and assigned unique IDs with proper access rights through Web-based interfaces. Database of mealPlans to be shared between CatererAdmin, TransporterAdmin, and SchoolAdmin is created, which is used to share the assignment of a batch of meal from caterer to school, delivered via transporter. Each batch of meal is identified by its unique mealID and assigned CatererID, mealQuantity, transporterID, schoolID along with referred MealCode, where the preferredMealCode is assigned in accordance with the decision and balanced meal plan for beneficiaries. The database of mealPlans is regularly monitored and updated. It is the responsibility of all stakeholders to get details about assignedMeal and plan accordingly.

3.4 Meal Preparations, Transportations, and Feedback In this step, the meal gets prepared, transported to the end destination and is served to beneficiaries in stages, with recorded feedback of the beneficiaries. The process is further elaborated.

3.4.1

Meal Preparation

The catererAdmin will view the meals assigned to him for the next day, along with the transporter assigned, quantity of meals to prepare and preferredmealCode from the mealPlans database. Meals are prepared accordingly. For each batch of meals prepared, preparation details are submitted in the prescribed format by the caterer which is further evaluated by IoT sensors and IoT metrics score is derived. The submitted and evaluated mealDetails for each MealID are stored in the off-chain database: catererMealDetails.

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Meal Handover to Transporter

The transporterAdmin is provided a Web-based interface to view the shipments assigned to him for the next day, along with the assigned caterer, assigned school, and quantity to pick up from the mealPlans database. If the information provided in the interface matches, the actuals at the pickup site transporter submit the successful or unsuccessful pickup report through the web interface accordingly. For all successful pickups, mealDetails submitted by catererAdmin are hashed and concatenated with IoT metrics score to be submitted to blockchain as a successful pickup transaction. Unsuccessful pickups are also submitted as transactions on blockchain for recording purposes. All submitted transactions are appended to the ledger after mining, and txHash is issued and stored as catererTxHash along with mealID in the catererVerificationHash off-chain database.

3.4.3

Meal Handover to SchoolAdmin

The SchoolAdmin is also provided a Web-based interface to view the deliveries assigned for the next day, along with assigned transporter, quantity to be delivered, etc., from mealPlans database. Transporter ships the meals to assigned school. If the information provided in the interface matches/mismatches with the actual batch received through transporter, SchoolAdmin submits the successful/unsuccessful delivery report accordingly through the Web interface. For all successful delivery, schoolAdmin submits received mealDetails and an updated IoT metrics score is calculated and stored along with the mealDetails in the schoolMealDetails offchain database. Hash of the same data, after concatenating with mealID and updated IoT metrics score, is submitted as a successful delivery transaction on blockchain. Unsuccessful deliveries are also submitted as transactions on blockchain for recording purposes. All submitted transactions are appended to ledger after mining, and txHash is issued and stored as schoolTxHash along with mealID in the schoolVerificationHash off-chain database, and meals are served to beneficiaries.

3.4.4

Feedback Submission

For each batch of meal served, ten percent of randomly selected beneficiaries submit their feedback in the range from 1 (lowest) to 10 (highest), about the meal served through the Web-based interface after authentication. Same beneficiary is not allowed to record feedback more than once in at least 7 and ideally 10 consecutive meal cycles, to ensure randomness. The schoolAdmin is responsible to select random beneficiaries in such a way to ensure that the above criteria are followed and any discrepancy can be detected as beneficiaries are authenticated before submitting feedback. The feedback is stored in a buffer storage on an off-chain database, and once adequate feedback is

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received, an aggregated feedback score is calculated and is stored along with mealID on the ledger as a feedback submission transaction. Feedback is also aggregated in a range from 1 to 10 using formula in Eq. (3). n Aggregated feedback =

i=1

n

Ri

(3)

where Ri = feedback rating by student i, Ri ∈ [1, 10] and n = total number of students eligible for submitting feedback. Feedback is aggregated for easy audit and verification, as the meal size and number of beneficiaries recording feedback can differ for every mealID. Submitted transaction is appended to the ledger after mining and txHash is issued and stored as feedbackTxHash along with mealID in feedbackVerificationHash off-chain database.

3.5 Audit and Verification For verification and audit requirements, an application is developed with a userfriendly interface wherein anyone can search for mealIDs by filtering from schools and caterers and verify the midday meal details by entering mealID. The detailed verifying/auditing process can be further defined using the following steps. • Search for mealID: Search for mealID can be done by filtering through the schoolID and/or catererID. The application displays all mealIDs concerned with the respective schoolID and/or catererID entered. Individual details of meals served can be searched with obtained mealID, as discussed below. • Search by mealID: Anyone can audit/verify/view all the details of meals served to beneficiaries by entering the respective mealID. • Verification of Caterer Submitted mealDetails: mealDetails submitted by caterer and catererTxHash is fetched through mealID from catererMealDetails and catererVerificationHash database, respectively. From catererTxHash, caterer’s data hash and IoT metrics score can be fetched from the blockchain ledger. Caterer mealDetails from the off-chain database are hashed again and compared to the caterer’s data hash fetched from blockchain. If matches, caterer mealDetails are displayed, along with fetched IoT metrics score verifying integrity of the off-chain caterer submitted mealDetail. • Verification of Feedback: From the feedbackVerificationHash database, feedbackTxHash is fetched by submitted mealID. From feedbackTxHash, feedback rating of respective mealID is directly fetched from blockchain and displayed.

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Table 1 Average gas used and execution time in milliseconds for all processes/actions generating transactions Action

Average gas used

Average time in milliseconds

catererMealDetailsSubmitToLedger

53,309

1246

schoolMealDetailsSubmitToLedger

50,247

1508

feedbackDataSubmitToLedger

28,963

652

VerifyByMealID

Null

1287

4 Performance Analysis The prototype was tested using Geth implementation of Ethereum-based private blockchain. Analysis was done on 25 complete cycles of meal distribution, wherein each cycle constituted meal preparation, meal shipment, meal distribution, and feedback submission. For performance analysis, 5 schools, 2 caterers, and 3 transporters were registered, and a total of 5 meals were shipped to each registered school, with different combinations of caterer and transporter assigned. We sent a total of 75 transactions, for 25 cycles of distribution. The transactions were mined after every 5 min to include them in the new block.

4.1 Gas-Cost Analysis The prototype is developed using a private instance of Ethereum, using Geth. An analysis of average gas used per operation is presented below. For verification or auditing, no gas cost was incurred as we performed only read operation on ledger.

4.2 Execution Time Analysis Table 1 outlines the timing analysis measurements for tasks in the NutriChain. Execution time is calculated for each of the processes listed in Table 1, excluding manual data entry time, i.e., only time taken between function call and function execution is considered.

5 Conclusion This proposed idea is to secure midday meals/food assistance program by storing IoT metrics score, feedback, and hash of mealDetails on distributed and shared ledger is to bring transparency and trust to the public distribution system (PDS).

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Mining distributes the powers from handful to many and ensures the participation of all the stakeholders involved. In this work, we have proposed and implemented NutriChain, which uses the application of blockchain and IoT to store real-time sensor captured data along with mealDetails entered by all stakeholders from preparation to the endpoint of delivery on immutable and distributed ledger. This ensures proper accountability in the system and any discrepancy can be detected instantly. The feedback mechanism ensures the fair working of the program. The proposed idea can be secured further by integrating biometric sensors and replacing manual login with biometric login. The model can be trained by supervised learning to detect the meal type, instead of manually entering mealCode. The manual data entry can be completely digitized to avoid possible human errors while submitting mealDetails. The current prototype is working on private blockchain with limited sensors, but the end goal is to shift to permissioned blockchain with addition of sufficient sensing devices.

References 1. N. Nakamoto, Centralised bitcoin: a secure and high performance electronic cash system. SSRN Electron. J. (2017). https://doi.org/10.2139/ssrn.3065723 2. K. Christidis, M. Devetsikiotis, Blockchains and smart contracts for the Internet of things. IEEE Access 4, 2292–2303 (2016). https://doi.org/10.1109/access.2016.2566339 3. G. Perboli, S. Musso, M. Rosano, Blockchain in logistics and supply chain: a lean approach for designing real-world use cases. IEEE Access 6, 62018–62028 (2018). https://doi.org/10.1109/ access.2018.2875782 4. N.M. Kumar, P.K. Mallick, Blockchain technology for security issues and challenges in IoT. Procedia Comput. Sci. 132, 1815–1823 (2018). https://doi.org/10.1016/j.procs.2018.05.140 5. Y. Qian, Y. Jiang, J. Chen, Y. Zhang, J. Song, M. Zhou, M. Pustišek, Towards decentralized IoT security enhancement: a blockchain approach. Comput. Electr. Eng. 72, 266–273 (2018). https://doi.org/10.1016/j.compeleceng.2018.08.021 6. F. Tian, An agri-food supply CHAIN TRACEABILITY system for China based on RFID & blockchain technology, in 2016 13th International Conference on Service Systems and Service Management (ICSSSM) (2016). https://doi.org/10.1109/icsssm.2016.7538424 7. E. Hofmann, U.M. Strewe, N. Bosia, Concept—where are the opportunities of blockchain-driven supply chain finance?, in Supply Chain Finance and Blockchain Technology (2017), pp. 51–75. https://doi.org/10.1007/978-3-319-62371-9_5 8. Accenture Labs and Akshaya Patra, Disruptive technologies to enhance efficiency in mid-day meal program for school children (2017). [Online]. Available: https://newsroom.accenture.com/ news/accenture-labs-and-akshaya-patra-use-disruptivetechnologies-to-enhance-efficiency-inmid-day-meal-program-for-school-children.html 9. A. Malik, S. Gautam, S. Abidin, B. Bhushan, Blockchain technology-future of IoT: including structure, limitations and various possible attacks, in 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), Kannur (2019), pp. 1100– 1104. https://doi.org/10.1109/ICICICT46008.2019.8993144

Association Rule-Based Routing Protocol for Opportunistic Network Ayasha Malik, Bharat Bhushan, and Abhijit Kumar

Abstract Opportunistic network or OppNet is a network of wirelessly interconnected mobile nodes that are temporarily formed without any need of predefined infrastructure, it is the advancement of mobile ad hoc networks (MANETs), in which source and destination nodes are unable to communicate due to non-existence of route, and if the route exists, then it is available for a very small amount of time, so far the successful transmission of messages between nodes or mobile devices is a difficult and challenging task due to intermittent nature of nodes. The nodes are frequently disconnected and moving from their location, for these nodes, OppNet works in store, carry, and forward fashion for transmission of messages. Therefore, the biggest concern in this type of network is to decide which neighbor nodes will be a good and genuine messenger for transmission of a message in the current scenario or not. Hence, this paper proposed a novel routing protocol called association rule-based routing protocol which selects the neighbor nodes on the basis of some parameter values and to determine the next node for the carrier, for implementation, the one simulator is used. Keywords Opportunistic network · Source · Destination · OppNet · Routing

1 Introduction For many years, there has been a clear paradigm shift from the fixed networks to wireless networks. A wireless network is a type of network that uses wireless communication between network devices for the transference of data/information [1]. Wireless communication or wireless networking is a process by which costly installation of cables is avoided. Wireless communication networks are implemented and controlled using a radio communication controller that placed at physical layer of A. Malik · A. Kumar (B) Noida Institute of Engineering Technology (NIET), Greater Noida, India B. Bhushan School of Engineering and Technology (SET), Sharda University, Greater Noida, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_42

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network infrastructures. Wireless local area network (WLAN), wireless metropolitan area network (WMAN), wireless wide area network (WWAN), wireless personal area network (WPAN), wireless sensor network, mobile ad hoc network (MANET), vehicular ad hoc network (VANET), delay tolerant network (DTN), opportunistic network (OppNet), and satellite communication network are some example of wireless network. Some of these networks are based on fixed infrastructure mode, and some are based on infrastructure less mode [2]. But in most situations like disaster recovery operations, emergency relief operations, etc., the use of this type of network may not be feasible because of high cost and worst communication facilities. OppNet is used to tackling these types of problems. Furthermore, a summary of the involvement of this effort is enumerated as below: • The work discusses the background of wireless networks and their application. • The work highlights the background, features, application, challenges, architecture, and routing protocols of OppNet for a better understanding of the actual need of OppNet while design protocol for it. • The work redefines the inspiration for developing new protocol that eases the routing facilities. • The work explores some parameters to analyze the results that come after the simulation process. The remainder of the paper is organized as follows. Section 2 elaborates the background, features, application, challenges, architecture, and routing protocols of OppNet. Moreover, Sect. 3 described the proposed protocol named association rule-based routing protocol (ARBRP) where working of ARBRP is shown via an algorithm. Furthermore, Sect. 4 defined a comparative analysis of ARBRP based on some input/output parameters. Section 5 deliberates simulation setting and process by using one simulator. In addition, Sect. 6 discusses the obtained results from proposed work. Finally, the paper concludes with Sect. 7.

2 Opportunistic Network It is a type of DTNs where nodes/links are intermittent (not continuous/fixed), it means, there is no fixed (end-to-end) path exists between destination node and source node. Kevin Fall the first person who proposed the concept of OppNet in 2007 [3]. It was an extension of DTN. These networks are designed with an assumption of unpredictable mobility of network devices. The network devices appear opportunistically without any previous information. OppNet is part of DTN that is why this network inherits most of the features of DTN like intermittent connectivity, frequent change in network topology, and no end-to-end path exist. Due to this, delay between sending and receiving of message between source and destination node is longer as compared to other networks employed by MANETs [4].

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2.1 Architecture of Opportunistic Network OppNet is divided into various network partitions that are called regions. Conventional routing protocols that are proposed for wireless networks are not fit for OppNets, because they suppose that there exists a path (end-to-end) between destination and source node. To handle this issue, OppNets use store-carry-forward fashion. This procedure is implemented with the help of bundle layer which is new protocol layer in this network [5]. Table 1 shows the protocol stack used in OppNet. In OppNet, every node is set up with a layer called bundle layer, in OppNet, a single node can work in three different ways such as host, router, and gateway so bundle layer acts differently according to the type of node which is explained in Table 2 [6].

3 Proposed Protocol, Association Rule-Based Routing Protocol ARBRP uses the association rules for making the decision for routing of messages. Working of association-based rule mining helps in discovering the best node from the list of nodes, and how this node is better than other nodes before describing the whole procedure. Association rule learning or association rule mining is a rulebased machine learning technique for uncovering exciting relations, finding frequent patterns, correlations between variables in big databases. It is a data mining process of discovering the rules that may govern associations and causal objects between sets of items [7]. In this paper, the association rule learning is used as follows: Table 1 Protocol stack

Application layer Bundle layer Transport layer A

Transport layer B

Network layer A

Network layer B

Link layer A

Link layer B

Physical layer A

Physical layer B

Table 2 Bundle layer working at different node Node

Bundle layer working

Host

Store data and transfer messages to another node. It must be persistent, max storage capacity, and custody transferring

Router

Store, carry, and forward whole fragments (bundles) between nodes in same region

Gateway

Transfer messages (bundles) across different regions. Able to broadcasting

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Table 3 Calculating confidence (X → Z)

Total E N

Confidence (X → Z) (%)

Node

Encounter with Z

R

12

24

50

S

20

30

66.6

T

10

20

50

U

30

100

30

V

5

6

W

25

50

83.3 50

Let, I = (i1 , i2 , i3 , … in ) be number on nodes. M = (m1 , m2 , m3 , … mn ) number of messages which is transferred. Each message has a unique ID and contains a subset of nodes. A rule is defined as X → Z, where X, Y, and Z are a subset of I and X is the source node, Y is the neighboring node of X and Z is the destination node. • Support: It is an indication of how frequently a node visits another node. It defines the level of interaction between nodes. X→Z Support (X U Y ) can find several encounters (E N ) with the destination node (Z). • Confidence: It is an indication of how reliable the rule is. It gives the percentage value which shows the reliability of the rule Confidence (X → Z ) = Support (X ∪ Y ) ∗ 100/Total E N (Y ) An example scenario having X is the source node R, S, T, U, V, and W is the neighboring node of X, and Z is the destination node. Table 3 describes how the confidence factor is calculated for Z. A threshold value T is calculated as average confidence factor that will decide which K neighbor nodes are selected from the N neighbor nodes where K < N. The message is then transmitted to K neighbor nodes whose confidence factor is ≥T. By taking into consideration an average threshold value, the ABRP selects an optimal set of nodes that will be the best next sender of the message to the destination.

3.1 Algorithm Working of our proposed routing protocol ARBRP via algorithm

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//getEncounter(x,y) returns the number of encounters of x with y. //totalEncounter(x) returns the sum of all the encounters of x with other nodes. Begin While each message do While each neighbor n of the source/interm.node do EN = getEncounter(n,dest) Total EN = totalEncounter(n) End while End while While each neighbor do Confidence=(EN*100)/Total EN IF Confidence(of neighbor) >=threshold add node in the list //this loop will be given the confidence factor for proposed scheme End if End while While each node in the list do Transfer message from source/intermediate node to node in the list

End while End

4 Comparative Analysis Based on Input/Decision Parameters In this section, analysis of proposed routing algorithm is done on the basis of some attributes such as message size, drop time, hop count, and number of encounter nodes.

4.1 Message Size In OppNet, a node can store two parameters related to buffer space available named BufferTotal which defines the total capacity of a node and BufferUsed which defines how much buffer of a node is used. On the basis of two parameters, the available buffer space of a node is calculated as BufferAvailable = BufferTotal − BufferUsed .

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4.2 Time-to-Drop (TTD) Due to limited and fixed storage at a node, whenever the buffer of node gets full, the message should be dropped. So the transmission of a message from one node to another node is not affected due to the limited buffer size. For this, the node maintains two parameters named TimeMax will define the maximum time for a message that can reside in a buffer and TimeCount will define the maximum limit of a message that has been sending from a node. A message should not be sent after the limit of TimeCount is reached. Message drop is calculated as TTD = Max (TimeMax ,TimeCount ). If the TimeMax or TimeCount of a message reached its limit its means, the message should be dropped from the buffer because it reaches its maximum limit for residing in a buffer and the buffer should be available for the arrival of new messages.

4.3 Hop Count In OppNet, delivering of message between source and destination occurs node to node (hop by hop). So there should be a parameter that keeps the track of sequence of nodes visited by the messages. This parameter can be very useful for delivering the acknowledgment to source from destination. Node count is a parameter that contains a list of nodes visited by the messages in the same sequence as it visited and after reaching the destination node this helps in sending. The successful and fast delivery of acknowledgment will increase the reliability of OppNet because the source node will know that the message is received by the destination.

4.4 Encounter Nodes This parameter is very crucial for this algorithm which keeps track of how many times a node interacts with other nodes. Every node maintains a list which contains node and number of encounter as shown in Table 4. Table 4 For node A

Node

Encounters (E N )

B

10

C

5

D

9

E

3

F

7

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Table 4 shows that node A interacts 10 times with B, 5 times with C, 9 times with D, 3 times with E, and seven times with F. Total E N (A) = Encounters with B + C + D + E + F = 39.

5 Simulation: The One Simulator The one [20] is a type of tool which generally provides an environment to run DTN-based routing protocol, especially for OppNet. It provides various types of movement models and scenarios to conduct simulations. One tool provides both graphic user interface (GUI) and command line-based interface to run algorithms that are designed by various users. One can import mobility data from real-world traces or other mobility generators. One tool provides various types of reports of message delivery, failures, dropped, etc. This tool is based on object oriented language known as Java which provides vast variety of simulations for various protocols of DTN. In one simulator, each node acts as an agent. And node agent is not fixed, it is always moving like car, pedestrian, etc.

6 Result After simulation, performances of protocols are evaluated then various comparisons are done between new protocols (ARBRP) and existing protocols (epidemic, spray and wait, MaxProp). During evaluation, number of nodes varied from 40 to 120. Evaluation and comparison are done on the basis of average latency time, number of messages dropped, average hop count, and overhead ratio at each instance. In Fig. 1, it is clearly shown that less amount of message is dropped in ARBRP as compared to other existing protocols. Massage drop is directly depending on number of nodes. It is inferred that the rate of drop in messages directly depends on the number of intermediate nodes, the more the intermediate nodes the more message drop. In Fig. 2, it is clearly shown that less amount of node is required to provide communication in ARBRP as compared to other existing protocols. Average hop count is directly depending on number of nodes but when the number of nodes is large, hope count raises extensively, which causes misfiring in ARBRP. Fig. 1 Dropped message

No. of message dropped

1500 Epidemic

1000

Spray & wait

500

MaxProp

0 40 80 100 120

No. of Nodes

ARBRP

Fig. 2 Average hop count

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Average hop count

398 6 4

Epidemic

2

Spray & wait

0

MaxProp 40

80

100

120

Fig. 3 Time-to-drop

Time-to-Drop

No. of Nodes

8000 6000 4000 2000 0

Epidemic Spray & wait MaxProp 40

80

100

120

Overhead Ratio

No. of Nodes

Fig. 4 Overhead ratio

ARBRP

ARBRP

150 100

Epidemic Spray & wait

50

MaxProp

0 40

80

100

120

ARBRP

No. of Nodes

In Fig. 3, it is clearly shown that time-to-drop is less in ARBRP as compared to other existing protocols. Time-to-drop (delay) does not depend on number of nodes. In Fig. 4, it is clearly shown that overhead ratio is less in ARBRP as compared to other existing protocols. It is not depending on number of nodes.

7 Conclusion This paper starts with a small description of why we need OppNet, and then a brief introduction of OppNet is given with its challenges, applications, architecture, and routing protocols. Additionally, we have implemented some pre-existing (epidemic, spray and wait and MaxProp) protocols by using one simulator tool and calculated some decision parameters (message size, hop count, time-to-drop, encounter nodes, dropped message, and overhead ratio). Furthermore, a novel routing protocol for OppNets is proposed called ARBRP, which uses the data mining concept of association rule for message forwarding process. An algorithm is stated to elaborate the flow of ARBRP. Moreover, a comparison is done between pre-existing protocol and proposed protocol on the basis of some decision parameters (dropped message, average hop count, Time-to-drop, and overhead ratio). Based on simulation results,

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it is concluded that the performance of ARBRP is far better in comparison with epidemic, spray and wait, and MaxProp as shown in the result. In future, we can also test this protocol against the various network attacks like blackhole attack, gray hole attack, worm hole attack, etc.

References 1. A. Lindgren, A. Doria, E. Davies, S. Grasic, Probabilistic routing protocol for intermittently connected networks (2012). https://doi.org/10.17487/rfc6693 2. Z. Li, L. Sun, E.C. Ifeachor, WSN10-5: adaptive multi-copy routing for intermittently connected mobile ad hoc networks, in IEEE Globecom 2006 (2006). https://doi.org/10.1109/glocom.200 6.950 3. A. Malik, V. Gupta, Comprehensive survey on blackhole attack with various detection/prevention techniques in ad-hoc network. Int. J. Appl. Eng. Res. 14(8), 2009–2017 (2019). https://www.rip ublication.com/ijaer19/ijaerv14n8_35.pdf 4. A. Malik, S. Gautam, Comparative analysis of AODV routing protocol vs. nodes in MANET. J. Emerg. Technol. Innov. Res. (JETIR) 6(3), 53–61 (2019). http://www.jetir.org/papers/JETIR1 903610.pdf 5. A. Malik, S. Gautam, N. Khatoon, N. Sharma, I. Kaushik, S. Kumar, Analysis of black-hole attack with its mitigation techniques in ad-hoc network, in Deep Learning Strategies for Security Enhancement in Wireless Sensor Networks, ed. by K. Sagayam, B. Bhushan, A. Andrushia, V.C. Albuquerque (IGI Global, 2020), pp. 211–232. https://doi.org/10.4018/978-1-7998-50687.ch011 6. B. Bhushan, G. Sahoo, Recent advances in attacks, technical challenges, vulnerabilities and their countermeasures in wireless sensor networks. Wireless Pers. Commun. 98(2), 2037–2077 (2017). https://doi.org/10.1007/s11277-017-4962-0 7. N. Sharma, I. Kaushik, V.K. Agarwal, B. Bhushan, A. Khamparia, Attacks and security measures in wireless sensor network, in Intelligent Data Analytics for Terror Threat Prediction (2021), pp. 237–268. https://doi.org/10.1002/9781119711629.ch1

A Framework for Secured Dissemination of Messages in Internet of Vehicle Using Blockchain Approach Farooque Azam, Sunil Kumar, and Neeraj Priyadarshi

Abstract Emergence of Vehicular communication in Intelligent Transportation Systems (ITS) pave the way for communication among connected vehicles where a Certificate Authority (CA) is responsible for certifying and authenticating the vehicles for exchange of information in most of the cases. As, the growth of connected vehicle is going to boost further in the Internet of Vehicle (IoV) scenario where huge number of vehicles will be connected to share safety as well as non-safety messages. Hence, the reliance on the CA may lead to a single point of failure. Thus, in this research work, a Blockchain based approach has been proposed to provide data immutability and a decentralized vehicular Ad-hoc environment to overcome a single point of failure. The informal security analysis reveals its robustness to provide anonymity with other security requirement such as to thwart insider and outsider attacks. Keywords Blockchain · CA · IoV · ITS · VANET

1 Introduction Past decades have seen an increase in the connected autonomous and smart vehicle. Internet of Vehicle is the interconnection of vehicles through the internet also referred as Vehicular Ad-hoc network that uses dedicated short range communication (DSRC) and Wireless Access in Vehicular Environment (WAVE) for vehicular communication. VANET provides safety services along with infotainment and entertainment services using a beacon (small chunk of information). These beacons should reach F. Azam (B) · S. Kumar Department of Computer Science and Engineering, Sangam University, Bhilwara, Rajasthan, India S. Kumar e-mail: [email protected] N. Priyadarshi Department of Business Development and Technology, CTiF Global Capsule, Aarhus University, 7400 Herning, Denmark © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_43

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securely in a stipulated time during communication in VANET. VANET are susceptible to various security and privacy challenges apart from trust. Various researches have been carried out in the recent past to mitigate the challenges using public key cryptography where a certificate authority (CA) issues certificate and verify the authenticity of the communicating vehicles’ onboard unit (OBU) and the road side unit (RSU). Thus, in PKI based system, CA is overall in-charge and hence if it is compromised, then whole VANET system will collapse [1–3]. A vehicle sends safety message every 100–300 ms as per the DSRC protocol [3]. In a PKI based system, a message is signed before sending and the verification for these messages were mitigated in previous research work. But, when we consider a heavily dense traffic where 60–200 vehicles share safety message, the generation and verification of 600–2000 signed messages poses challenges in terms of communication and computational overhead. Also, PKI based system needs the public key and certificate to be appended to the message which may increase the message size and incur message delay. Even a small delay can lead to a disaster and hence causes chaos. Also, for the success of smart city, a decentralized approach is the need. For this, Blockchain can provide a promising solution. The proposed scheme employs a public Blockchain based consensus approach where an RSU calculates the trust value of a node and regularly update it to the Blockchain to avoid misbehaving node who are trustworthy in beginning but later become malicious. This approach uses message as transaction in similarity to bitcoin to create new block and link the hashes of consecutive blocks to build the Blockchain. The schemes is constrained to create and share blocks limited to a geographical area as it is meaningless to share traffic information of one country with other. RSU is responsible for ensuring both entity trust and message trust and thus the scheme provides highly secure communication.

2 Related Work Several key management schemes using PKI based system has been proposed. Vijayakumar et al. [4] have proposed a dual authentication scheme where the trusted authority (TA) divides the perspective user as primary, secondary and unauthorized user. In this paper, a group key management scheme along with dual authentication has been implemented to safeguard the VANET from malicious user. The author claims the effectiveness of the scheme as computationally efficient against the other schemes compared in the paper. The scheme is highly dependent on the authentication of the vehicle and doesn’t provide location privacy. Tangade et al. [5] have proposed a trust management scheme using hybrid cryptography. Trust is an important concept to access the trustworthiness of the communicating peer. Trust is of three type viz. entity trust, content trust and hybrid trust. In this context, hybrid cryptography using asymmetric identity based digital signature with symmetric hash message authentication code have been used. The RSU calculates the trust value and thus the proposed scheme is able to provide privacy

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preservation, node trust and content trust and thus facilitate secured communication between nodes. However, the scheme is highly reliant on the TA. Several researchers have preferred Blockchain as a promising technology to increase its efficiency in terms for fast communication, low overhead and privacy preservation. In this context, Shrestha et al. [6] have proposed the dissemination of critical event messages using Blockchain. They claimed the integrity of the message by storing the event messages into the Blockchain. However, no simulation has been conducted. In [7], authors have adopted Blockchain to enhance the security and privacy in automotive environment. Blockchain based consensus mechanism provides integrity of messages. Also, problem of centralization can be solved through it. In [8], authors have employed message authentication code and public/private key for secured authentication. In this, the proof of work (PoW) and Practical Byzantine Fault Tolerance (PBFT) consensus algorithm have been combined to provide secure communication in vehicular communication. In [9], a new kind of Blockchain has been introduced to tackle critical message dissemination. Here, concept of local Blockchain has been introduced based on the geographical boundary and used public Blockchain to store the trustworthiness of the node and message. In this way, vehicle will not hesitate in exchanging messages after knowing the trustworthiness. The RSU acts as the validator and the verifier for trustworthiness. Edge computing based framework has been proposed for further improving efficiency and scalability. In this, no simulation and security analysis was conducted.

3 Preliminaries 3.1 Blockchain Basics Nakamoto [10] first introduced Blockchain which have attractive features as discussed [1]. The block header of a block consists of the following fields as depicted in Fig. 1. Each block comprises of block header and block body. The fields in block header are previous hash value, nounce, hash value, timestamp and the Merkle root. However, the body comprises of transactions along with other information as per requirement of the Blockchain. The size of each block is 80 bytes. Genesis block is the first block and it is the origin of all the blocks in the Blockchain. It consists of hashes of all records and each block has the knowledge of previous block’s hash to form the chain as depicted in Fig. 1.

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Fig. 1 Structure of a blockchain [10]

3.2 Consensus Algorithm Consensus algorithm accomplishes required agreement between nodes in a distributed network. Consensus provides the monopoly and set rules between the communicating nodes. The database is shared publicly and hence requires an efficient reliable and real-time approach to guarantee transactions in the network that are trustworthy and communicating nodes must adapt the particular consensus algorithm [11]. Based on the type of Blockchain, i.e. public Blockchain consensus algorithm are of different types. For public Blockchain Proof of Work (PoW) and Proof of Stake (PoS) are commonly used. • PoW: In this, a miner is responsible to compute the previous hash value of the block header and the Merkel root chooses different nonce value until the resulting hash become less than the difficulty target [12]. This algorithm resembles a cryptographic puzzle which is tough to solve but it is easy to verify once all inputs are known. • PoS: A miner is chosen based on its wealth or stake [13]. Here, miner stake their claim in terms of currency or coins and verify without requiring the owner to prove its authenticity for each transaction. It is cheaper and greener distributed consensus algorithm. Authors from [15–19], discuss other categories of consensus algorithm like Byzantine Fault Tolerant-based Proof-of-Stake, Practical Byzantine Fault Tolerance (PBFT), Casper the Friendly Ghost, Casper the Friendly Finality Gadget, Delegated Proof of Stake (DPoS), Federated Byzantine Agreement (FBA) etc.

3.3 System Architecture Figure 2 shows the proposed system architecture consisting of RSU, vehicles’ OBU, VANET message, Blockchain network (BN), blocks which are described as follows:

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Fig. 2 Architecture of proposed blockchain based VANET system

RSU: RSU are deployed across the city and have high computational power and storage capability. RSU is an important component which is responsible for calculation and updation of the trust value of a legitimate vehicle on to the public Blockchain and hence enhances the trustworthiness of the vehicle. The RSU generates genesis block based on an event. OBU: OBU are present in each vehicle and consist of cryptographic information and sensors for collecting the data. Also, OBU is responsible for communication with other vehicle’s OBU and the RSU. Apart from this it generates messages and mine the current blocks and update its trust value through the RSU onto the Blockchain to prove its trustworthiness. Vehicles are allotted trust value ranging from 0.0 to 1.0 in the beginning based on their presence and behavior during the communication. VANET messages: We have categorized messages which are periodically broadcasted to inform about traffic status to the communicating vehicle in the RSU range as beacon and the message to intimate about a road accident and catastrophe as critical safety messages. All these trigger an event message. Beacons are periodically broadcasted by an RSU at regular intervals. BN: The Blockchain network is built by the RSU and it is a P2P network. RSU runs the mining function and sends transactions. All miners have to solve proof of work (PoW) for creating new blocks. Block: Each block consists of a header and a body as discussed above in Sect. 3.2. Location_Certificate: Location certificates are provided by the RSU to the vehicle. The process to generate the location certificate is as shown using Algorithm 1.

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4 Proposed Algorithm Following assumptions have been made to conduct this research. • All vehicles have their public/private key pair during the offline registration with the Transport Authority of the country. • Each RSU in a region is self-capable and have high computational power. Also, each RSU are highly trusted and cannot be compromised in any circumstances. • RSU are connected to the Blockchain through the RSU server. • Vehicle can become the part of the VANET only when it is trustworthy. The trustworthiness of each vehicle is improved based on its trust value which is periodically updated by the RSU. RSU calculates its public, private and secret key using the following equations. RSUprk := key1(RIDRSU , Ts)

(1)

  RSUpubk := key2 RSUprk , Ts

(2)

The RSU generates its’ secret key using the following relation.   RSUSrk := key3 RIDRSU , RSUprk , Ts

(3)

  Thus, RSU have the following parameters RIDRSU , RSUprk , RSUsrk , RSUpubk . Each vehicle pseudo identity (PIDV ) is calculated as follows:

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PIDV := Hash(IDV ||TsV )

407

(4)

Initially, the trust value of a vehicle is zero and is represented as TvalVcurr = 0

(5)

RSU then generates the trust code of a vehicle using vehicle’s initial trust value as follows:   TcodeV = HMACRSU RSUsrk , TvalVcurr ||PIDV ||TsV

(6)

 After, this RSU uploads following system parameters to its database IDV , PIDV , Vpub , RSUprk , RSUpubk , TvalVcurr , TcodeV , RSUsrk , TsV . The algorithm to compute and update the trust value of a vehicle consists of four phases viz. Phase-1: In this phase, vehicle generate request for updating its’ trust value in the Blockchain. Phase-2: In this phase, on receipt of the trust update request by the vehicle, RSU first authenticates the vehicle and then checks the integrity of the request. If the request is valid, then the trust value is updated else the request is dropped by the RSU and the vehicle’s trust value is reduced to 0.0 level and is discontinued from the VANET communication by alerting the nearby RSU and vehicles in the BN. Phase-3: In this phase, the vehicle gets the acknowledgment receipt with new trust value calculated in Algorithm 5. Phase-4: In this phase, the RSU calculates the new trust value of the vehicle. The detailed description of all the phases in terms of an algorithm is as shown below.

408 Table 1 Trust table required for calculating trust level (TL) of a node

F. Azam et al. Range of trust level (CTL) [0.0, 0.1]

Reward points as per trust level (RPTL ) 50

[0.1, 0.2]

90

[0.2, 0.3]

140

[0.3, 0.4]

180

[0.4, 0.5]

250

[0.5, 0.6]

290

[0.6, 0.7]

340

[0.7, 0.8]

400

[0.8, 0.9]

480

[0.9, 1.0]

600

Table 2 shows different alert message generated by a vehicle, its alert type like, accident prone, traffic related, road priority and its assumed reward points. Table 1 depicts the trust range and its associated reward point being in that trust level (TL) range. Table 2 Different safety road alerts, their type and assumed reward points for valid message broadcast Alert messages

Alert type

If a vehicle sends a “Blind Spot” message

Accident related 20

Reward points (RP)

If a vehicle sends a “Forward collision” message

Accident related 20

If a vehicle sends a “Do not pass” message

Accident related 20

If a vehicle sends an “Intersection Collision avoidance” Accident related 20 message If a vehicle sends an “Emergency vehicle”

Road priority

15

If a vehicle sends a “Vehicle safety inspection” message Traffic rule

10

If a vehicle sends a “Transit or emergency vehicle signal” message

Road priority

15

If a vehicle sends a “Parking and toll payments” message

Traffic rule

10

If a vehicle sends a “Commercial vehicle clearance and Traffic rule safety inspection” message

10

If a vehicle sends a “In-vehicle signing” message

Traffic rule

10

If a vehicle sends a “Roll over on road” message

Accident related 20

If a vehicle sends a “Traffic and travel condition data” message

Traffic rule

10

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5 Conclusion In this research work, a Blockchain based approach has been proposed to provide data immutability and a decentralized vehicular Ad-hoc environment to overcome a single point of failure. Because the scheme rate the node as valid or invalid node on calculating trust based on its message type, it is highly efficient to defend insider attack. Second, outsider can’t manipulate the block as it is immutable and thus thwart outsider attack. As a future endeavor, the proposed algorithm will be simulated to prove its effectiveness against other state of art algorithm.

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References 1. F. Azam, S. Yadav, N. Priyadarshi, S. Padmanaban, R.C. Bansal, A comprehensive review of authentication schemes in vehicular ad-hoc network. IEEE Access 9, 31309–31321 (2021). https://doi.org/10.1109/ACCESS.2021.3060046 2. F. Azam, N. Priyadarshi, H. Nagar, S. Kumar, A.K. Bhoi, An overview of solar-powered electric vehicle charging in vehicular adhoc network, in Electric Vehicles, Green Energy and Technology (Springer, Singapore, 2021), pp. 95–102 3. F. Azam, S. Kumar, S. Yadav, N. Priyadarshi, S. Padmanaban, An outline of the security challenges in VANET, in Proceedings of 2020 IEEE 7th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) (2020), pp. 1–6 4. P. Vijayakumar, M. Azees, A. Kannan, L. Jegatha Deborah, Dual authentication and key management techniques for secure data transmission in vehicular ad hoc networks. IEEE Trans. Intell. Transp. Syst. 17(4), 1015–1028 (2016). https://doi.org/10.1109/TITS.2015.2492981 5. S. Tangade, S.S. Manvi, P. Lorenz, Trust management scheme based on hybrid cryptography for secure communications in VANETs. IEEE Trans. Veh. Technol. 69(5), 5232–5243 (2020). https://doi.org/10.1109/TVT.2020.2981127 6. R. Shrestha, R. Bajracharya, S.Y. Nam, Blockchain-based message dissemination in VANET, in Proceedings of the 2018 IEEE 3rd International Conference on Computing, Communication and Security (ICCCS), Kathmandu, Nepal, 25–27 Oct 2018, pp. 161–166 7. A. Dorri, M. Steger, S.S. Kanhere, R. Jurdak, Blockchain: a distributed solution to automotive security and privacy. IEEE Commun. Mag. 55, 119–125 (2017) 8. N. Jaewon, J. Sangil, C. Sunghyun, Distributed blockchain-based message authentication scheme for connected vehicles. Electronics 9(74), 1–20 (2020) 9. R. Shrestha, R. Bajracharya, A.P. Shrestha, S.Y. Namb, A new type of blockchain for secure message exchange in VANET. Digit. Commun. Netw. 6(2), 177–186 (2020) 10. S. Nakamoto, Bitcoin: a peer-to-peer electronic cash system (2008). [Online]. Available: https:// bitcoin.org/bitcoin.pdf 11. Consensus mechanism in cryptocurrency (2018). [Online]. https://www.investopedia.com/ terms/c/consensus-mechanism-cryptocurrency 12. A.M. Antonopoulos, Mastering Bitcoin, 1st edn. (OReilly Media, Inc., United States of America, 2015) 13. S. King, S. Nadal, PPCoin: peer-to-peer crypto-currency with proof-of-stake [Online] 14. https://peercoin.net/assets/paper/peercoin-paper.pdf (2012). Accessed 14 Mar 2021 15. E. Zhang, A Byzantine fault tolerance algorithm for blockchain (white paper) (2018). [Online]. http://docs.neo.org/en-us/basic/consensus/whitepaper.html. Accessed 15 Mar 2021 16. O. Moindrot, C. Bournhonesque, Proof of stake made simple with casper (2017). [Online]. https://www.scs.stanford.edu/17au-cs244b/labs/projects/moindrot_bournhonesque. pdf. Accessed 17 Mar 2021 17. V. Buterin, V. Griffith, Casper the friendly finality gadget. CoRR J. 110 (2017); D. Larimer, Delegated proof of stake. Bitshares.org (2014). [Online]. Accessed 18 Mar 2021 18. D. Mazieres, The stellar consensus protocol: a federated model for internet-level consensus, in Proceedings of Marzires2015TheSC (2015), pp. 1–45 19. M. Castro, B. Liskov, Practical Byzantine fault tolerance Miguel, in Proceedings of the Third Symposium on Operating Systems Design and Implementation (2002), p. 114

A Study of Bridge Tap Effects on DSL Channel Ajay Ashok Ovhal and Shweta Prakash Gaikwad

Abstract Digital subscriber line (DSL) is a well-known technology used for voice as well as data communication. In this technology, the copper pair wires are used as a communication channel. In DSL technology Bridge taps are used to serve multiple customers. With the use of bridge taps, the same channel (one cable pair) can be used to provide connections to multiple users. If the user is disconnected from the line and it is not terminated, the line will be open-circuited at one end. This causes adverse effects on the performance of the main channel. In this paper, the impact of the open-circuited bridge taps on the main DSL channel (one cable pair) is discussed with the use of a simple mathematical model. The effect of different type of copper pair gauge on the channel response is presented. The Matlab simulation results are compared with the loop simulator (DLS8131/8132). Keywords Bridge taps · Digital subscriber line (DSL) · Very high-speed digital subscriber line (VDSL) · Channel response\and insertion loss · Transmission line

1 Introduction Asymmetric digital subscriber line (ADSL) is a DSL technology that enables fast data communication over copper telephone lines [1]. ADSL uses high bandwidth and provides a high bit rate for downstream data transmission (towards the customer side) compared to upstream data transmission. In ADSL frequency band of 26.07– 137.82 kHz is used for upstream communication while 138 kHz–1.10 MHz is used for downstream communication. The speed provided by ADSL is 8 Mbits/s (downstream) and 1 Mbits/s (upstream). Very high-speed digital subscriber line (VDSL) and Very high-speed digital subscriber line 2 (VDSL2) technology provide data A. A. Ovhal (B) Department of EIE, M S Ramaiah Institute of Technology (Affiliated to VTU), Bengaluru 560054, India e-mail: [email protected] S. P. Gaikwad Indira College of Engineering and Management, Parandwadi, Pune, Maharashtra 410506, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_44

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Fig. 1 A simple connection diagram with Bridge tap

transmission faster than ADSL technology [2, 3]. VDSL2 utilizes the bandwidth up to 30 MHz with a downstream data rate of 200 Mbits/s and an upstream data rate of 100 Mbits/s. The Bridge tap is a commonly used wiring method in DSL technology. It is used to provide the connection to different subscribers located in the same geographical area as shown in Fig. 1 [4]. In Fig. 1, the MODEM1 and MODEM2 represent two different subscribers connected to the main DSL line. The DSLAM is a digital subscriber line access multiplexer located in the telephone exchange and connected to multiple DSL lines. Bridge taps are the “T” or the branch in the cable without any impedance matching device. Unused or unconnected bridge taps are analogous to open-circuited transmission lines. Due to impedance mismatch at the other end, the signal reflects and interferes with the signal in the main channel [5]. This causes disturbances in the channel response. It is observed that the channel response is smooth without a Bridge tap. Due to the insertion of the bridge tap multiple nodes (nulls) are observed in the channel response. A basic mathematical model for the DSL channel with a single bridge tap was first proposed in [6]. The propagation loss caused by the channel is discussed in [7]. This paper presents the study on channel response of a DSL line with multiple bridge taps extending the mathematical model presented in [6] and loop simulator (DLS8131/8132) [8].

2 Modelling of the Channel 2.1 Channel Transfer Function The VDSL standard utilizes a bandwidth up to 30 MHz [2]. So, the signal is transmitted up to the frequency range of 30 MHz. Because of the channel, there is attenuation in the signal at the receiver end. This attenuation is prominent at higher frequencies. The channel transfer function of the line represented in Fig. 2 is given by the following equation:

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Fig. 2 Transmission line

H( f ) =

VL VS

(1)

where, V L is load voltage, V S is source voltage, Z L is load impedance and Z S is source impedance.

2.2 Transmission Matrix The DSL channel can be represented as a transmission line as shown in Fig. 2. The transmission line can be represented as a two-port network, shown in Fig. 3, where A, B, C and D are the transmission parameters (constants in the transmission matrix) of the two-port network. In Fig. 3, V 1 , I 1 is the input voltage and current to the two-port network and V 2 , I 2 be the output voltage and current of the two-port network. With the use of transmission matrix, the relation between the input voltage, current and output voltage, current can be written as, 

V1 I1



 =

A B C D



V2 I2

 (2)

Using (1), (2) and with a mathematical simplification, the transfer function can be written as, H=

(Z S + Z L ) AZ L + B + C Z L Z S + D Z S

Fig. 3 Representation of a transmission line as a two-port network

(3)

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Fig. 4 Channel with transmission matrix

Similarly, the input impedance can be calculated as, 

VS IS

 = Z in =

(AZ L + B) (C Z L + D)

(4)

2.3 Transmission Matrix for the Channel With the use of transmission line theory, the transmission matrix for the channel (shown in Fig. 4) is given as [9] 

A B C D





cosh(γ l) Z 0 sinh(γ l) = sinh(γ l)/Z 0 cosh(γ l)

 (5)

where Z 0 is the characteristic impedance and γ is the propagation constant of the transmission channel.

2.4 Model of the Transmission Line Figure 4 shows different parts of the channel with their transmission matrix. It can be observed that the source impedance can be represented as a simple matrix followed by the ABCD matrix for the channel.

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2.5 Modelling of the Channel with a Single Bridge Tap Figure 5 shows the connection of the transmission channel with a single bridge tap, where d 1 is the length of the channel before and d 2 is the length of the channel after the connection of a Bridge tap. Abr , Bbr , C br and Dbr are the transmission parameters of the Bridge tap. The input impedance offered by the bridge tap at connection points A and B is given as [9], Z in = Z 0

(Z br + Z 0 tanh(γbr dbr )) (Z 0 + Z br tanh(γbr dbr ))

(6)

As the Bridge tap is open-circuited (Z br ≈ ∞), (6) can be written as [10], Z in =

Z0 (tanh(γbr dbr ))

(7)

The Bridge tap is represented by its admittance, as shown in Fig. 6, and the transmission matrix of the same is, 

Abr Bbr Cbr Dbr



 =

1 0 1/Z in 1

 (8)

Figure 7 represents the equivalent two-port network for the entire channel with bridge tap (given in Fig. 5). In this network each two-port matrix has three dimensions, the third dimension corresponds to the frequency (for VHDL it is 30 MHz). The equivalent matrix for the entire network is written as, Fig. 5 Channel with a Bridge tap and the transmission matrix for each section

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Fig. 6 Equivalent circuit for the Bridge tap

Fig. 7 Equivalent two-port presentation of the complete channel



Aeq Beq Ceq Deq



 =

1 ZS 0 1



A1 B1 C 1 D1



Abr Bbr Cbr Dbr



A2 B2 C 2 D2

 (9)

The channel transfer function can be calculated by (9). Similar to the above presentation, for two bridge taps (refer Fig. 8) equivalent matrix can be calculated as, Fig. 8 Channel connection with two Bridge taps

A Study of Bridge Tap Effects on DSL Channel



Aeq Beq Ceq Deq





1 ZS = 0 1



A1 B1 C 1 D1



Abr1 Bbr1 Cbr1 Dbr1

419



A2 B2 C 2 D2



Abr2 Bbr2 Cbr2 Dbr2



 A3 B3 C 3 D3 (10)

3 Results and Discussions The transmission (DSL) channel with the bridge taps is modelled with the technique discussed in Sect. 2 using Matlab. The loop simulator DLS8131/8132 is used to compare the simulation results. The copper wires with gauge awg24 (0.0201 in.) and awg26 (0.0159 in.) available in the loop simulator are used. The line parameters (R, L, C and G) for awg24 and awg26 are set in the Matlab simulation using [11]. The simulation is carried out for the channel transfer function (H) with a single Bridge tap attached and two Bridge tap attached.

3.1 Single Bridge Tap The single open-circuited Bridge tap is attached to the DSL channel as shown in Fig. 5. The mathematical model is simulated using Matlab and loop simulator. The line and Bridge tap parameters are as follows: d 1 = 900 ft. (awg26), d br = 50 ft. (awg26) and d 2 = 50 ft. (awg24). Z S and Z L is set as 100 . Figure 10 shows the combined result of Matlab and loop simulator. It shows attenuation in dB (y-axis) offered by the channel for each frequency in Hz (x-axis). It can be observed that Matlab simulation results are closely matched to the loop simulator results.

3.2 Two Bridge Taps In this case, two open-circuited Bridge taps are attached to the DSL channel as shown in Fig. 8. The simulation is carried out using Matlab and loop simulator. The line and Bridge tap parameters are as follows: d 1 = 900 ft. (awg26), d br1 = 60 ft. (awg26), d 2 = 50 ft. (awg26), d br2 = 50 ft. (awg24), d 3 = 50 ft. (awg26) and Z S = Z L = 100 . Similar to the single Bridge tap results, Matlab simulation results are closely matched to the loop simulator results for two Bridge taps as shown in Fig. 9.

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Fig. 9 Channel response in dB for single Bridge tap

Fig. 10 Channel response in dB for two Bridge tap

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Fig. 11 Response for the two different types of Bridge taps (awg24 and awg26)

3.3 Effect of Change in the Gauge of Bridge Tap To study the effect of different type of wire gauge used in the bridge tap we carry out the following simulation. The loop parameters for the experiments are as follows (refer Fig. 5): d 1 = 900 ft. (awg26), d br = 50 ft. (awg26) and d 2 = 50 ft. (awg26). The loop type of dbr is changed to awg24 and the Matlab simulation is repeated (Fig. 10). Figure 11 shows the result of the simulation. The node frequencies are the same for both Bridge taps. But, the difference in the depth of the nodes can be observed in the figure. The difference in depth is found to be ≈ 2 dB.

4 Conclusions The complete DSL channel with bridge tap is modelled and studied in this article. The results of the simulation are compared with the loop simulator results (DLS8131/8132). The Matlab simulation results match well with the loop simulator results. To study the impact of the different Bridge tap gauge, the experiment is conducted for two different types of bridge taps (awg24 and awg26). The nodes in the response appear at the same frequencies, but the difference of depth (≈ 2 dB) is observed.

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References 1. J.M. Cioffi, Asymmetrical digital subscriber lines, in Communications Handbook, ed. by J.D. Gibson (CRC Press Inc., 1997), pp. 450–479 2. ITU-T recommendation G.993.1-2004: very high speed digital subscriber line, in SERIES G: Transmission Systems and Media, Digital Systems and Networks (2004) 3. ITU-T recommendation G.993.2-2019: very high speed digital subscriber line transceivers 2 (VDSL2), in SERIES G: Transmission Systems and Media, Digital Systems and Networks (2019) 4. W. Tomasi, Introduction to Data Communications and Networking (Pearson Education Inc., 2009) 5. T. Anwar, T.S. Yong, Performance analysis of ADSL. ELEKTRIKA J. Electr. Eng. 10(1), 32–42 (2008) 6. J. Werner, The HDSL environment. IEEE J. Sel. Areas Commun. 9(6), 785–800 (1991) 7. G.-H. Im, Performance of a local distribution system for interactive multimedia TV, in Global Telecommunications Conference 1995. GLOBECOM ’95, vol. 1 (IEEE, 1995), pp. 178–182 8. DLS 8130 Operating Manual (2005), https://support-kb.spirent.com/resources/sites/SPI RENT/content/live/DOCUMENTATION/10000/DOC10408/enUS/DLS%208130%20Rev1. 0.pdf 9. M.N.O. Sadiku, Elements of Electromagnetics (Oxford University Press, New York, 2005) 10. E. Kreyszig, Advanced Engineering Mathematics (Wiley, 2006) 11. 3MTM Cable, https://multimedia.3m.com/mws/media/22419O/3mtm-cable-properties-int ernal-wiring-ts0731.pdf

A View of Virtual Reality in Learning Process Ghaliya Al Farsi, Ragad M. Tawafak, Sohail Iqbal Malik, Roy Mathew, and Mohammed Waseem Ashfaque

Abstract VR has a great deal of promise, and its application to education has recently seen a great deal of research interest. However, there is currently little systematic work on how researchers have used immersive VR for higher education purposes, despite the use of both high-end and budget head-mounted displays (HMDs). Therefore, to classify design elements of current research dedicated to VR in higher education, we recommend using systematic mapping. The papers reviewed collected through the extraction of critical information from documents indexed in digital scientific libraries. The settings of VR also represent 3D space, which may be real or fictional, macroscopic or microscopic and based on natural physical laws of physics, or imaginary dynamics. The multiple examples that can use to represent VR make it widely applicable to education in different fields. A primary characteristic of VR is that it allows multi-sensory contact with space to simulate. This paper explores and discusses the view about the virtual reality, the problems, and the challenges faced in the learning process. Keywords Virtual reality environment · Technology · Simulator

1 Introduction During the instructive interaction, because of the trouble and need for consistent reasoning and ideas, understudies experience challenges with comprehension. An ever increasing number of instructive focuses are beginning to consolidate incredible new innovation-based assets all throughout the planet that can oblige the requests of an assorted gathering of understudies [1]. Virtual reality (VR) propels out of core interest. Schooling had the option to stay up with these turns of events and unrests G. Al Farsi (B) College of Graduate Studies, Universiti Tenaga Nasional, Kajang, Malaysia e-mail: [email protected] G. Al Farsi · R. M. Tawafak · S. I. Malik · R. Mathew · M. W. Ashfaque Al Buraimi University College, Al Buraimi, Oman © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_45

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that happened in data innovation sciences, and among these advanced advances are the virtual simulation innovation that showed up toward the start of the eighties of the century past, which is another sort of PC learning [2]. Artificial intelligence (AI) is one of the core drivers of industrial improvement and a vital aspect in promoting the integration of emerging technologies, such as graphic processing unit, Internet of things, cloud computing, and the blockchain, in the new technology of significant records and Industry 4.0 [3, 4]. In this paper, we assemble an extensive survey over the previous years of technology and deep learning. The lookup presents a valuable reference for researchers and practitioners thru the multi-angle systematic analysis of technology, from underlying mechanisms to practical applications [5–7].

2 Literature Review In various settings, researchers have investigated the advantages and applications of virtual reality (VR). VR has a great deal of promise, and its application to education has recently seen a great deal of research interest [8]. However, there is currently little systematic work on how researchers have used immersive VR for higher education purposes, despite the use of both high-end and budget head-mounted displays (HMDs). Therefore, to classify design elements of current research dedicated to VR in higher education, we recommend using systematic mapping [9]. The papers reviewed collected through the extraction of critical information from documents indexed in four digital scientific libraries that were systematically filtered using exclusion, inclusion, semi-automatic, and manual methods. Our analysis highlights three key points: the existing domain framework as a basis for effective VR-based learning in learning content, VR design features, and learning theories [10–12]. 5 million virtual and virtual reality products in 2016. In a CCS survey, it planned to sell 24 million VR devices by 2018 [13]. Even if this figure is petite relative to the number of smartphone users, this degree of development is impressive, given how recently this technology has entered mainstream consumerism [14].

3 VR Advantages on Learning Process Provides outstanding visualizations that are not possible in the traditional classroom. Virtual reality is perfect because it helps us to explore and alternate our encounters with various facts. By wearing a VR headset, you experience high-quality visualizations that can positively describe you [15]. Students would always love to sit and watch something instead of reading it no matter what age they are [16]. Take medication, for instance. Innovative physicians are taking advantage of VR technology in 2016 to discover new medicine areas and better educate others [17] (Table 1).

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Table 1 Summarized studies on VR applications No

Author

Problem and method

Contribution to knowledge

1

Lippert-1987

It found that there is a need for an in-depth analysis of the subject required to develop expert systems Method: Survey

The in-depth and decisive analysis of the topic helps to develop as much as possible and understand the field and is necessary to solve problems

2

Martin and Oxman-1888

Literature review organizing knowledge facts and reasoning to solve problems that need the expertise of humanity Method: Experimental method

The technology is a model for a knowledge-based approach, and the primary goal is to adopt knowledge at the expert level to the level of beneficiaries

3

Durkin-1990

The technology defined as a This system can help the computer program developed expert when solving problems to model its ability to solve a human expert’s problem Method: Survey

4

Jonassen-2005

Solve the issues of intelligence software Method: Survey

The writer mentioned that expert systems computer intelligence programs designed to simulate expert logic to support decision-making for any problem

5

Chakraborty-2010

The technology comprises expert knowledge about a problem area Method: Experimental method

Technology is a computer-based interactive tool that contains facts in addition to reasoning

4 Problems and Challenge VR is the future of the learning process but still has many challenges to implement it on our learning process due to the price is too high, and the price is one of the main roadblocks facing the industry. Plain and simple—the bulk of the audience concerned cannot afford to purchase VR equipment [18]. On the other hand, since virtual reality gear production is expensive, competition on the market is practically non-existent and no excessive demand from customers, industry players cannot afford to reduce the price to an acceptable level. The second point is the content is lacking. Provide an action piece. It is a reasonably obvious matter. Innovative and entertaining content is also the perfect way to bring modern, cutting-edge innovations to the mass market [19]. Third point is lack of viable business models. Digital reality tech is in a strange place right now. Another significant problem currently facing the VR industry is concern regarding potential impacts on consumer wellbeing. The VR can view as an industry-crushing obstacle in some way. If there is even the slightest risk that VR will

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somehow put its users in the line of fire, public sentiment and eventual legislation will conflate the industry. Also, the security users are at an all-time high of digital immersion; they are also at an all-time high in exposure to incoming dangers [20]. Cybersecurity and data privacy are a sensitive issue in every industry. Because of its novice state, it is especially critical for the VR industry to develop an effective solution. Finally, the batteries a power-based constraint is the most obvious obstacle for VR applications. While that may not be much of an issue for a desktop-based gear, it is certainly something to deal with in the mobile domain. The epistemological position of AI studies what sorts of information about the world is accessible to an observer with given opportunities to observe, how this information can represent a computer’s memory, and what regulations permit reputable conclusions to draw from these facts [21]. It leaves apart from the heuristic issues of searching spaces of chances and how to match patterns [22].

5 Discussion and Conclusion Discusses VR technology remains in a particular space of exploration, the principal kind of VR stand is fundamentally used to show a condition of comprehension, helping understudies with the learning of hypothetical information, like phrasing, times, information, laws or logical hypotheses [23–25]. The most un-vivid air, for example, divider-based or screen-based projection with unique goggles or HMD with essential information gadgets like a console, cursor, touchscreen or regulator, is in this manner ordinarily required. These circumstances ordinarily comprise of 3D representation. Risky condition preparing [10], just as flight and space travel. There are extremely solid references by [12] where he sums up the impact of VR on training in culture. As indicated by his examination, VR exercises incorporate the capacity for understudies to “move on schedule” with their own eyes to notice verifiable occasions just as to experience chronicled destinations, design, dress, and activities of individuals. As indicated by recently educated insight, the second classification of VR stage is utilized to show practical capacity. Such circumstances are part into a hypothetical data introduction (as a manual/necessity introduction). This part would eventually be imitated/duplicated in the way of a reasonable occupation by the student. This type of use may include a more significant feeling of submersion and force.

References 1. J. Fernandez-Rio, J. Cecchini, G. Méndez, A. Mendez, J. Prieto, Self-regulation, cooperative learning, and academic self-efficacy: interactions to prevent school failure. Front. Psychol. 22(8) (2017)

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2. G. ALFarsi, J. Jabbar, M. ALSinani, Implementing a mobile application news tool for disseminating messages and events of Al Buraimi University College. Int. J. Interact. Mob. Technol. (iJIM) 12(7) (2018) 3. M.M. Alamri, Undergraduate students’ perceptions toward social media usage and academic performance: a study from Saudi Arabia. Int. J. Emerg. Technol. Learn. 14(3) (2019) 4. G. AlFarsi, R.M. Tawafak, A. ElDow, S.I. Malik, J. Jabbar, A. Al Sideiri, Smart classroom technology in artificial intelligence: a review paper (2021) 5. R.M. Tawafak, A. Romli, S.I. Malik, G. Alfarsi, Learning management systems for accreditation approval: a review paper. AIP Conf. Proc. 2339(1), 020073 (2021) 6. G. Alfarsi, R.M. Tawafak, A. ElDow, S.I. Malik, J. Jabbar, A. Al Sideiri, R. Mathew, General view about an artificial intelligence technology in education domain (2021) 7. G. Alfarsi, J. Jabbar, R.M. Tawafak, A. ElDow, S.I. Malik, A. Al Sideiri, Modern education and an artificially intelligent world (2021) 8. W. Dai, L. Riliskis, P. Wang, V. Vyatkin, X. Guan, A cloud-based decision support system for self-healing in distributed automation systems using fault tree analysis. IEEE Trans. Ind. Inf. 14(3), 989–1000 (2018) 9. R. Mathew, S.I. Malik, R.M. Tawafak, Teaching problem solving skills using an educational game in a computer programming course. Inform. Educ. 18(2), 359–373 (2019) 10. W.S. Alhalabi, Virtual reality systems enhance students’ achievements in engineering education. Behav. Inf. Technol. 35(11), 919–925 (2016) 11. J. Jabbar, S.I. Malik, G. AlFarsi, R.M. Tawafak, The impact of WhatsApp on employees in higher education, in Recent Advances in Intelligent Systems and Smart Applications (Springer, Cham, 2021), pp. 639–651 12. R.M. Tawafak, A. Romli, S.I. Malik, G. Alfarsi, J. Jabbar, Examining continuous integrating of technology acceptance model with task-technology fit. IOP Conf. Ser. Mater. Sci. Eng. 1088(1), 012061 (2021) 13. G. AlFarsi, R.M. Tawafak, A. ElDow, S.I. Malik, J. Jabbar, A. Al Sideiri, General view about games based learning: literature review (2021) 14. D. Dajani, A.S.A. Hegleh, Behavior intention of animation usage among university students. Heliyon 1–10 (2019) 15. M. Fernandez, Augmented-virtual reality: how to improve education systems. High Learn. Res. Commun. 7(1), 1–15 (2017) 16. J. Martín-Gutiérrez, C.E. Mora, B. Añorbe-Díaz, A. González-Marrero, Virtual technologies trends in education. EURASIA J. Math. Sci. Technol. Educ. 13(2), 469–486 (2017) 17. R. Tawafak, G. AlFarsi, J. Jabbar, S.I. Malik, R. Mathew, A. AlSidiri et al., Impact of technologies during COVID-19 pandemic for improving behavior intention to use E-learning (2021) 18. S.I. Malik, R.M. Tawafak, G. Alfarsi, A model for enhancing algorithmic thinking in programming education using PAAM. Int. J. Interact. Mob. Technol. 16(9) (2021) 19. R.M. Tawafak, G. Alfarsi, J. Jabbar, Innovative smart phone learning system for graphical systems within COVID-19 pandemic. Contemp. Educ. Technol. 13(3), ep306 (2021) 20. M.J. AlSinani, A. Kayed, G. Al Farsi, Improving data security using modified LSB-based image steganography technique. J. Hunan Univ. Nat. Sci. 47(8) 21. R. Tawafak, S. Malik, R. Mathew, M. Ashfaque, J. Jabbar, M. AlNuaimi et al., A combined model for continuous intention to use E-learning system (2021) 22. S. Iqbal Malik, R. Mathew, R.M. Tawafak, G. Alfarsi, A web-based model to enhance algorithmic thinking for novice programmers. E-Learn. Digit. Media 20427530211026988 (2021) 23. R.M. Tawafak, S.I. Malik, G. Alfarsi, Development of framework from adapted TAM with MOOC platform for continuity intention. Development 29(1), 1681–1691 (2020) 24. R.M. Tawafak, S.I. Malik, G. Alfarsi, Impact of technologies during the COVID-19 pandemic for improving behavioral intention to use E-learning. Int. J. Inf. Commun. Technol. Educ. (IJICTE) 17(3), 137–150 (2021)

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25. R.M. Tawafak, A. Romli, A. Al Sideiri, S.I. Malik, J. Jabbar, Concepts of e-learning performance on system use outcomes in Omani Universities. IOP Conf. Ser. Mater. Sci. Eng. 1088(1), 012008 (2021)

Study and Analysis of Zoning in Meitei Mayek Recognition Sanasam Chanu Inunganbi

Abstract Handwritten character recognition is an exciting and challenging problem in computer vision due to its vast variation in writing style. The accuracy of a recognition system largely depends on what features have been used and in what manner they are extracted. So, selection of zoning for feature extraction is another challenge in mapping an unknown object (character) to give a correct label. In this paper, feature extraction, such as background directional distribution (BDD), uniform local binary pattern (ULBP), zone density (ZD) and profile features considering various decomposition of an image in the form of zones have been studied and analyzed. Keywords Handwritten character recognition · Meitei Mayek · Zoning · BDD · ULBP · ZD · Profile feature

1 Introduction One of the challenging and interesting problems in the computer vision and pattern recognition community is Handwritten Character Recognition. The handwritten character suffers largely from massive variation in writing style and similar-looking character. Handwritten character recognition system finds specific application in postal automation for finding addresses, bank cheque processing, data entry form, etc. Feature extraction plays a vital role in building a recognition system. The feature must promote distinguishable descriptor to maximize the performance of the recognition model. The problem becomes further intense if the set of characters considered for recognition contains close resemblance images. Variation in different people writing styles and the occurrence of noise at the time of data acquisition stimulates further challenges.

S. C. Inunganbi (B) Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Greenfields, Vaddeswaram, Guntur 522502, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_46

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A popular and successful approach to feature extraction of non-uniform shapes as in the instance of unconstrained handwritten character is the regional or spatial decomposition of images in the form of small zones. Each zone separately contributes to the formation of feature vectors. In this work, recognition of handwritten Meitei Mayek alphabets is presented based on BDD, ULBP, ZD and profile feature into the various decomposition of zones of the character image. The analysis has been experimented on self-collected Meitei Mayek character Dataset. 35 classes of character group is considered in this paper (27 Eeyek Eepee and 8 Lonsum Eeyek). Fourfold cross-validation is performed using k nearest neighbor (KNN) classifier, the average is considered as overall recognition rate. The variation observed in recognition rate based on the several decompositions of images into zones have been studied and analyzed in this paper.

2 About Meitei Mayek Meitei Mayek is the script of the official and commonly spoken language of Manipur, which is termed Manipuri. Research on this script is at primitive phases due to the unavailability of a standard dataset and being a regional language. Manipuri script is composed of the following components, as illustrated in Fig. 1. • 10 numerals known as Cheising Eeyek. • 27 primary characters that is constituted by 18 initial letters known as Eeyek Eepee and 9 additional letters known as Lom Eeyek. • 8 derived characters from Eeyek Eepee known as Lonsum Eeyek. • 8 associating symbols known as Cheitap Eeyek. • 4 punctuation marks called Khudam Mayek. The Meitei Mayek script has been reinstated recently, and hence very few researches have been accomplished on this script leaving an enormous area to be explored by Natural Language Processing (NLP) or research community.

Fig. 1 Meitei Mayek alphabets

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3 Literature There exist several character recognition systems across the world based on numerous languages. On the other hand, there is very little research conducted for recognition based on Meitei Mayek or Manipuri script, and they are studied in details as follows. In [1], the authors have proposed a recognition system based on an artificial neural network (ANN) for the identification of handwritten Meitei Mayek characters. In their paper, they have collected 594 sample images for experimentation analysis. Out of the total sample images, 495 are utilized for the purpose of training the system, while the remaining 135 are used for testing. The authors have utilized fuzzy features and probabilistic features for the experimental analysis; and their combination for their work. The accuracy or recognition rate obtained in this work is reported to be 85.92% for probabilistic features, 88.14% for the fuzzy features and 90.3% when they are combined. In [2], an OCR system for printed Meitei Mayek documents is proposed with the help of support vector machine (SVM) classifier. In this work, printed documents from textbooks, newspapers and magazines have been collected and scanned. Local features such as directional distribution and chain code; and global features such as longest vertical run and aspect ratio have been used for recognition of the printer characters. They have reported an accuracy of 96% for their recognition system. Further, a neural network (NN) with backpropagation is presented in [3] for the classification of the Meitei Mayek script. In their work, around 1000 images were used; half each has been utilized for testing and training purpose. The features they have used for developing their system is the binary pattern and achieved a recognition rate of 80%. A recognition system for Meitei Mayek digits called Cheising Eeyek is presented in [4] using a neural network (NN). In this work, the authors have utilized binary patterns and pixel density as feature vectors. The experiments have utilized 1000 sample images of Meitei Mayek numerals and classified them by the NN approach. Their work has reported a recognition rate of 85%. Another work for the recognition of Meitei Mayek numerals is presented in [5]. In this paper, the authors have employed the Gabor filter along with the machine learning classifier which is called SVM for the classification of 800 samples of numeral images. The authors have proclaimed a recognition rate of 89.58%. Further, digit identification with the help of SVM RBF kernel is reported in [6]. In this work, numerous features such as diagonal feature, background directional distribution and histogram of order gradient (HoG) for building the system by the authors. A recognition rate of 95.16% is reported for their work.

4 Feature Extraction In this paper, the subsequent features are used for our recognition model which are BDD, ULBP, ZD and profile feature. The operation for the extraction of feature is

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performed on the normalized character image of size 40 * 40 and on the various decomposition of the image into zones or sub-images. Sub-images that have been considered in our experiment are 4, 16, 25 and 64 zones where each sub-image sizes are 20 * 20, 10 * 10, 8 * 8 and 5 * 5 each respectively of the 40 * 40 normalized character image.

4.1 Background Directional Distribution (BDD) Feature The directional distribution is calculated for every foreground pixels concerning a specific mask on the background. Here, 8 directional distribution features are computed. The weight for each direction is calculated using a specific mask in the particular direction representing collective fractions of background pixels in the fixed direction. The mask for each direction has been described in Fig. 2. The center pixel marked as ‘X’ is the foreground pixel in the process for calculation of directional distribution values of background. The BDD features are estimated for the entire image and sub-divided images. When the entire image is considered, 8 BDD features have been computed. While considering sub-images, after obtaining all directional distribution values for each foreground pixel, all the similar directional distribution values are summed up for all pixels in each sub-division or zone. Thus, finally the 8 directional distribution feature values for each zone is computed and produced, 32 (4 zones * 8), 128 (16 zones * 8), 200 (25 zones * 8) and 512 (64 zones * 8) values of BDD features for each sample image.

Fig. 2 The mask for calculating background directional distribution in different directions

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4.2 Uniform Local Binary Pattern (ULBP) Feature The Local Binary Pattern (LBP), presented by Ojala et al. [7], has been widely used to examine and study the texture of an image. It may be described as the correlation of a pixel with its neighbor. The correlation is formulated as an ordered set of binary numbers generated by thresholding between the center and its neighbor pixels. The order set of binary number thus generated is then multiplied by the weighted power of 2 and converted into decimal. Further, it was noticed that in natural images 90% of the local binary patterns are uniform. A local binary pattern is said to be uniform if its uniformity measure is at most 2. Uniformity may be defined as the number of transitions between 0 and 1 or vice versa. In Uniform LBP (ULBP), all non-uniform patterns are accredited to a single label while each uniform pattern is stored to a separate label. Generally, there is 58 ULBP features of an image. If decomposition of the image into zones is considered, then each zone contributes 58 values, and final ULBP feature is estimated by concatenating the feature that is generated from each zone. Thus, it is computed with 58 (entire image), 232 (4 zones of 20 * 20), 928 (16 zones of 10 * 10), 1450 (25 zones of 8 * 8) and 3712 (64 zones of 5 * 5) values of ULBP feature vectors for each image.

4.3 Zone Density (ZD) Feature In zone density feature calculation, the character image is divided into N zones. The primary objective of zoning is to obtain the local characteristics instead of global characteristics. The following zones are created: 4 zones of 20 * 20 size, 16 zones of size 10 * 10, 25 zones of size 8 * 8 and 64 zones of size 5 * 5 each out of our 40 * 40 normalized samples by horizontal and vertical division. By dividing the number of foreground pixels by the cumulative number of pixels in each zone, i.e., 400, 100, 64 and 25 the density of each zone is obtained. Thus, 4, 16, 25 and 64 values of zone density features for each image are received. Zone density feature for the whole character image has not been considered as it will yield only one value.

4.4 Profile Feature Profile feature keeps track of the distance from the bounding box of the character till the first occurrence of foreground pixel. The length can be horizontal—left and right, vertical—top and bottom. In this paper, all the four profiles are used. It is calculated by counting the number of pixels in all the direction to the outer edge of the character (a foreground pixel) occurs. That means, left and right profile have been calculated by tracing the pixels from the left side of the bounding box in forwarding direction and

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from right in reverse direction respectively until the outer edge of character occurs. Similarly, bottom and top profiles are traced by vertical traversing of pixels from the bottom in the upward direction and from top in the downward direction respectively till the outer edges of character. Here, 160, 320, 640, 800 and 1280 values of profile features are computed for experimentation considering the various decomposition of images.

5 Experimental Results and Analysis The analysis and experiments are conducted on the self-collected dataset of 4900 sample images of 35 classes (Eeyek Eepee and Lonsum Eeyek). Preprocessing has been carried out on the collected samples to acquire isolated character and minimum bounding rectangle. First, the character table is separated from the rest of the filled form and sample characters have been isolated by dividing into appropriate rows and columns. Once each character is separated, using morphological operations and analysis on connected component, the character is obtained with the least bounding box or minimum bounding box. A sample of the collected dataset and preprocessing performed on it is illustrated in Fig. 3. The preprocessing step presented in this work that is illustrated in Fig. 3 is adopted from the previous work presented in [8, 9].

Fig. 3 A sample dataset form and preprocessing performed to obtain each character

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Table 1 Recognition rate (in %) Feature

1 zone

4 zones

16 zones

25 zones

64 zones

BDD

43.24

82.08

89.26

90.30

82.34

ULBP

40.22

67.39

83.40

85.26

83.08

ZD

–

35.28

84.61

92.85

95.06

Profile

88.81

93.34

93.94

94

92.67

Bold values indicate the best recognition rate among the different zones

Fourfold cross-validation has been performed using 1225 samples as test images and 3675 as training sample using KNN classifier with distance metric ‘Cityblock’ and value of k as one. Then, the average of them is treated as a recognition rate in this paper. The experimental results have been summarized in Table 1. It can be realized from the table that the achieved recognition rate from the decomposition of an image into various zones yields better results as compared to the feature extracted from the whole image. It can be stated that dividing an image into zones and extraction feature from zones contributes more distinguishable feature. These distinguishable features increased the interclass patterning difference and minimized the intraclass variance. Further analysis and conclusion from the experiment is that the recognition rate tends to increase if the size of the feature vector increases, i.e., as decomposition of the image into zones takes place. But, the trend is limited to a certain extent, for instance, BDD, ULBP and profile feature achieved the highest accuracy at 25 zones while ZD obtained the highest accuracy at 64 zones. Decomposing an image into zones or sub-images indeed help to explore local feature in abundance and hence produced higher accuracy. In the problem of character recognition local feature return superior recognition rate as compared to the global feature.

6 Conclusions and Future Works In this paper, several techniques for feature extraction are presented at different level of decomposition of the image into zones. The formulation of the character recognition system in identifying the optimal decomposition of images into zones has been resulted. The aggregation of features from each zone has maximized the recognition rate as compared to the feature obtained from the entire image alone. So, it can be concluded that the local feature is efficient and effective in the character recognition system and accomplished better performance in terms of accuracy. Further, it can be investigated to build an efficient and robust classification model with less time complexity and better accuracy.

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References 1. T. Thokchom, P. Bansal, R. Vig, S. Bawa, Recognition of handwritten character of Manipuri script. JCP 5(10), 1570–1574 (2010) 2. S. Ghosh, U. Barman, P. Bora, T.H. Singh, B. Chaudhuri, An OCR system for the Meetei Mayek script, in 2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG) (IEEE, 2013), pp. 1–4 3. R. Laishram, P.B. Singh, T.S.D. Singh, S. Anilkumar, A.U. Singh, A neural network based handwritten Meitei Mayek alphabet optical character recognition system, in 2014 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) (IEEE, 2014), pp. 1–5 4. R. Laishram, A.U. Singh, N.C. Singh, A.S. Singh, H. James, Simulation and modeling of handwritten Meitei Mayek digits using neural network approach, in Proceedings of the International Conference on Advances in Electronics, Electrical and Computer Science Engineering-EEC (2012), pp. 355–358 5. K.A. Maring, R. Dhir, Recognition of Cheising Iyek/Eeyek-Manipuri digits using support vector machines. IJCSIT 1(2) (2014) 6. C.J. Kumar, S.K. Kalita, Recognition of handwritten numerals of Manipuri script. Int. J. Comput. Appl. 84(17) (2013) 7. T. Ojala, M. Pietikäinen, D. Harwood, A comparative study of texture measures with classification based on featured distributions. Pattern Recogn. 29(1), 51–59 (1996) 8. S. Inunganbi, P. Choudhary, Recognition of handwritten Meitei Mayek script based on texture feature. Int. J. Nat. Lang. Comput. (IJNLC) 7(5), 99–108 (2018) 9. S. Inunganbi, P. Choudhary, K.M. Singh, Local texture descriptors and projection histogram based handwritten Meitei Mayek character recognition. Multimed. Tools Appl. 79(3), 2813– 2836 (2020)

Study on Influence of Outliers on the Performance of Various Classification Algorithms Lingam Sunitha, Shanthi Makka, Sailaja Madhu, and J. Bheemeswara Sastry

Abstract Detection of outliers, also called outlier mining, is subject to prior treatment. Outliers are a significant milestone in many machine learning algorithms, data analysis and research. Outliers are complicated and exciting. The outliers detection has two purposes. First and foremost, in the KDD process for data cleanup. The second case is the detection of abnormal data detection or fraud behavior. Failure to identify and delete all outliers may result in the wrong classification. Data mining tasks are divided into three types: unsupervised, supervised and semi-supervised. In any of these algorithms, the detection of outliers is a compulsory process for maintaining good accuracy. Outliers strongly influence logistic and linear regression. Especially for any classification application that does not require outliers, these mislead classification. Medical diagnosis desires an accuracy of 100%. Wrong diagnosis leads to improper treatment; in this paper, before and after eliminating outliers, the classification performance was observed and compared. Keywords High leverage · Influence · Performance · Novelties · IQR

L. Sunitha (B) Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Deemed to be University, Hyderabad, Telangana 500075, India S. Makka Department of Computer Science and Engineering, Geethanjali College of Engineering and Technology, Hyderabad, Telangana, India S. Madhu Department of Computer Science and Engineering, Malla Reddy Engineering College for Women, Hyderabad, Telangana, India J. Bheemeswara Sastry Koneru Lakshmaiah Education Foundation, Deemed to be University, Hyderabad, Telangana 500075, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_47

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1 Introduction In the real world, datasets contain errors due to misrecorded observations or reading errors. There are pretty many definitions for an outlier. However, the most accurate being: An outlier is an object which is much more deviated from other items. Outlier detection has multiple applications in various areas, such as military surveillance to detect the activities of terror groups and ensure safety. In cybersecurity, for intrusion detection [1–3] and malicious attack. In finance, for fraud detection, credit card monitoring and any abnormal activity. The biomedical domain for detecting physical defects in essential body organs such as tumors, fractures, using CT, MRI [4–8] and much more. Identification of irregular network traffic pattern which can indicate any hacking activity. Outlier detection has multiple applications in various areas, such as military surveillance to detect the movements of terror groups and ensure safety. Transfer of sensitive data to non-authorized sources. Industrial applications [9, 10] for security alarms in the event of an accident or detection of manufacturing failures. Dependent and independent variables may be extreme values. Extreme values should not have an impact on supervised learning methods, but when any object impacts on the classification such objects are ‘Influential’ objects.

1.1 Outlier Detection Extremes are points high score or ‘high leverage’. In one-dimensional space, so the extreme value is a variable, which may be high or low. Many predictors work in large space, so the extreme value is simply multivariate, this may be high or low. Types of classification: Here we have two kinds of extremes. 1.

2.

Outliers: For instance, consider an image classification issue where we try to find given image is either dog or cat. One of the pictures of the training set has a tiger or any other category that does not belong to the goal of the problem. In that case, the image of the tiger is considered as ‘noisy’. Since this is not the category we are working on, it makes no sense to look for outliers. Because that’s not the category we’re working on, detecting outliers here makes no sense. Novelties: Some times while performing classification new things or unseen objects are observed, and the problem is commonly referred to as supervised fault detection. Here, we try to detect abnormalities in new observations, and the aim is not to eliminate outliers or reduce their impact. But it is specifically used to detect fraud.

Noise and outliers can be present in each machine learning algorithms, the presence of noise and outliers affects the generated model. We need to learn the algorithms to treat noisy and outliers to generate useful models. A general approach that contains the simplest hypothesis that fit data tend to be the best, there is a compromise between the accuracy of the drive assembly and the

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complexity of the model. As a result, we generally prefer a simpler model without overloading training data. Another method of prevention is to make sure that noise and outliers are not included during training and testing. Some learning algorithms have an integrated method to eliminate suspected outliers, like isolation forest builds decision trees based on a drive dataset using the concept of information entropy. This prunes out that are considered not significant. Other approaches only address noise and outliers when they are identified. However there is no specific definition of outliers or how to distinguish the sound of an extreme and are application-specific. Outlier detection is intended to detect anomalies in the dataset and can be performed using various methods that includes statistical methods, rule mining and clustering techniques. Proposed work is not focusing on one algorithm. There is no algorithm which is suitable for all the data sets.

2 Proposed Work Present calculation uses the preparation datasets for two purposes, thus developing the dataset by recognizing and developing abnormalities. The dataset is also used to decide on the correctness of the model produced from the pruned dataset. To get more and more accurate results, the readiness dataset is divided into two sections: 1. A set of generators of training. 2. Set of studies to estimate the accuracy of the model, acquired from the reduced dataset called test data (Fig. 1). Fig. 1 Proposed frame work for comparison of classification algorithms

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2.1 Algorithm for Outlier Detection Input: Dataset Output: Dataset without outlier (Normal objects) Step 1: For each attribute Step 2: Sort the attribute in ascending order Step 3: Calculate the median for every attribute Step 4: Separate the data into two portions based on median above and Q1 and Q3 Step 5: Calculate mean of Q1 and Q3 Step 6: Find the inter quartile range (IQR) is difference between Q3 and Q1 Step 7: Calculate X = 1.5 * IQR; Step 8: If (Q1 − X) < attribute Or (Q3 + X) > attribute Mark as an outlier Step 9: Remove the entire instance Step 10: Repeat the above process for every numeric attribute

3 Experimental Results i.

Naive Bayes

In machine learning, a classifier model is used for segregating different objects on the basis of certain features of variables. This is a sort of classifier which operates on the Bayes theorem. The probability of membership is predicted for each class, and the probability of the object mapped with a particular class. The class having high probability is considered as the most suitable class. The probability for a hypothesis is: P(A) = max P(A/B) P(A) = max(P(A/B) ∗ P(A)/P(B)) P( A) = max(P( A/B) ∗ P(H )) P(B) = probability that event B has occurred, and it is used to normalize the result. The result (of P(A)) will not be affected by removing P(B). Naive Bayes is well suitable for binary class as well as multiclass classification and very fast. It is scalable, working on large datasets. Being a fast learning used to make predictions in real-time, text classification, sentiment analysis. The drawback of the Naïve Bayes is it considers probability of all the independent variables.

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ii.

iii.

iv.

v.

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Support vector machines/Sequential minimal optimization is an algorithm used in support-vector machines (SVM). Vladimir Vapnik discovered SVM in 1963. SMO is extensively used for training, it solves smallest optimization problems. Decision tree: ID3 is classification algorithm which depends on decision tree. ID3 uses the measures gaining index and entropy. J48 is advanced version of ID3. In the WEKA tool, J48 is implemented in Java. Random forest (RS) is one of the machine learning algorithm for classification, different size subsamples are considered for ‘N’ and number of trees ‘T ’, and uses an average which improvise the accuracy of prediction. RS prevents overfitting. Regression: Linear regression can be used for numerical predictions and logistic regression is for classification of categorical data.

3.1 Classification Metrics The performance of algorithms is identified with suitable measures. Run Time is calculated as sum of training time and testing time. Metrics like validation of the model are used to evaluate accuracy depending on behavior of the dataset. Confusion Matrix summary of method gives a two-by-two matrix which validates the type of classification errors by classifier. The advantage with this matrix is how many are misclassified and which type of misclassification (Table 1). No. of objects classified correctly = True Positives (TP). No. of objects incorrectly labeled as negative = False Negatives (FN). No. of negative instances incorrectly labeled as positive = False Positives (FP). No. of negative instances correctly classified = True Negatives (TN). Sensitivity known as true positive rate. Specificity describes the true negative rate. Accuracy = (True Positives (TP) + True Negatives (TN)) /(True Positives (TP) + True Negatives (TN) +False Positives (FP) + False Negatives (FN)) i.

Credit Dataset: Data acquired from weka tool. This dataset organizes individuals represented by many properties. It includes two class labels negative and positive credit dangers. Also comes with a price in a Matrix format. It is from the multi-varied dataset, the number of attributes is 24 and it consists of 30,000 instances.

Table 1 2 by 2 matrix (Confusion)

Actual Predicted

True Positive

False Negative

False Positive

True Negative

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Table 2 Classification performance before and after removing outliers Method

Correctly classified (before)

Correctly classified (after)

Time to build Time to build model (before) model (after) (s) (s)

Decision tree (J48)

70.5

100

0.14

0.01

Regression

75.9

100

1.3

0.7

Sequential minimal optimization

75.1

100

0.9

0.2

Decision tree (random forest)

76.4

100

0.52

0.06

Naïve Bayes

75.4

100

0.03

0.01

120 100 80 60 40 20 0

Naïve Bayes

SMO Correctly classified before

J48

Random Forest

Regression

Classified correctly aer

Fig. 2 Performance of classification for the credit data set

Outlier instances from the dataset that are not required for classification. From Table 2 after removing the outlier instances the accuracy of classification has increased. The percentage of correctly classified has increased, and almost all the cases time to build a model has reduced (Fig. 2). Dataset 2: Breast Cancer Diagnostic data [11] consists of 286 records and has 10 attributes. Characteristics computed from in the form of an amount of mass a patient in breast (Fig. 3, Table 3). Dataset 3: Diabetes data [12] stores the blood glucose levels. Understanding records were obtained from manual and auto generated in periodically i.e., after breakfast, after lunch, late evening or early night (supper), bed time (sleeping time). Readings are collected four times a day; fixed sessions were distributed for breakfast (8 a.m.), lunch (12 p.m.), dinner/supper (6 p.m.) and bedtime (10 p.m.). It is composed by 9 attributes and 768 instances (Fig. 4, Table 4).

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Comparison with Breast Cancer Data 78 76 74 72 70 68 66 Naïve Bayes

SMO

J48

Correctly classified before

Random Forest Classification via regression Classified correctly after

Fig. 3 Comparison of classification with breast cancer data Table 3 Performance before and after removing outliers Method

Correctly classified (before)

Time to build mode Correctly (before) (s) classified (after)

Time to build model (after) (s)

Naïve Bayes

71.678

0.01

75.874

0.01

SMO

69.58

0.05

75.874

0.05

J48

75.52

0.01

75.874

0.01

Random forest

69.58

0.13

75.874

0.13

Classification via regression

71.321

0.12

70.279

0.12

Comparison with Diabetes Data 85 80 75 70 65 Naïve Bayes

SMO

Correctly classified before

J48

Random Forest Classification via regression Classified correctly after

Fig. 4 Comparison of classification with Diabetes data

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Table 4 Classification performance before and after outliers Method

Correctly classified (before)

Time to build model (before) (s)

Correctly classified (after)

Time to build model (after) (s)

Naïve Bayes

76.302

0.01

79.42

0.01

SMO

77.34

0.04

84.11

0.11

J48

73.828

0.01

84.11

0.05

Random forest

75.78

0.31

84.11

0.48

Classification via regression

76.69

0.19

84.11

0.17

3.2 Key Observations i.

ii.

iii.

For credit dataset, before removing outliers, random forest algorithm has performed best among the rest with 76.4% correctly classified percentage. After removal of outliers, we observe improved results. All the five algorithms namely, Naïve-Bayes, J48, SMO, RF, Classification via Regression resulted in 100% performance. For Breast cancer dataset, prior to removal of outliers, J48 algorithm has performed with the highest accuracy of 75.52%. After removing outliers, it is observed that the algorithms Naïve-Bayes, J48, SMO, RF have depicted improved results. All the algorithms resulted in same accuracy of 75.874%. We recommend any one of the four considered. The performance of Classification via Regression algorithm has slightly reduced from 71.321 to 70.279%. In case of Diabetes dataset, before eliminating outliers, SMO algorithm is the best performed with the highest accuracy of 77.34%. After removing outliers, we observed more improvement in algorithms: J48, SMO, Random Forest and Classification via Regression. All of them produced same accuracy of 84.111%. The accuracy of Random Forest algorithm has improved to 79.42%.

4 Conclusion Present work identified the influence of outliers on the accuracy of the applied algorithms. This paper demonstrates how classification could be used effectively. Five machine learning algorithms namely J48, Naïve Bayes, SMO, Random Forest and classification via Regression. Algorithms are tested with 3 datasets. For credit dataset after removing outliers all the algorithms are given 100% accuracy and time is also reduced to build a model. There is no fixed algorithm for classification. For each dataset some algorithm is suitable and in some cases more than one algorithm is also suitable.

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References 1. D. Pahuja, Outlier detection for different applications: review. Int. J. Eng. Res. Technol. (IJERT) 2(3), 1–13 (2013) 2. L. Akoglu, H. Tong, B. Meeder, C. Faloutsos, PICS: parameter-free identification of cohesive subgroups in large attributed graphs, in Proceedings of the 2012 SIAM International Conference on Data Mining 3. H.J. Shin, D.-H. Eom, S.-S. Kim, One-class support vector machines—an application in machine fault detection and classification. Comput. Ind. Eng. 48(2), 395–408 (2005) 4. M. Ahmed, A.N. Mahmood, M.R. Islam, A survey of anomaly detection techniques in financial domain. Future Gener. Comput. Syst. 55, 278–288 (2016) 5. P.V.V.S. Srinivas, L.V.N. Pavan Sai Sujith, P.M. Sarvani, D.S. Kumar, D. Parasa, Prediction of hospital admission using machine learning. Int. J. Sci. Technol. Res. 8(12) (2019) 6. S. Rizwana, K. Challa, S. Rafi, S.S. Imambi, Enhanced biomedical data modelling using an unsupervised probabilistic machine learning 7. P. García-Teodoro, J.E. Díaz-Verdejo, G. Maciá-Fernández, E. Vázquez, Anomaly-based network intrusion detection: techniques, systems and challenges. Comput. Secur. 28(1–2), 18–28 (2009) 8. C. Baur et al., Deep autoencoding models for unsupervised anomaly segmentation in brain MR images, in Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (2018), pp. 161–169 9. S.A. Yasin, P.V.R.D.P. Rao, A framework for decision making and quality improvement by data aggregation techniques on private hospitals data, July 2018 10. S.A. Shinde, P.R. Rajeswari, Intelligent health risk prediction systems using machine learning: a review. IJET 7(3) (2018) 11. R.B. Vallabhaneni, V. Rajesh, Performance analysis of total variant techniques for efficient segmentation of medical images. Int. J. Mech. Eng. Technol. (IJMET) 8(12), 768–780 (2017) 12. K. Bache, M. Lichman, UCI machine learning repository (2013)

COVIBOT: A ChatBot for Covid-19 Related Information Farooque Azam, P. V. Bhaskar Reddy, Neeraj Priyadarshi, Md Rashid Mahmood, A. Laxmikanth, and M. Siddappa

Abstract Covid-19 was declared as a pandemic by World Health Organization (WHO) on March 11, 2020, creating chaos among all the countries around the world. Millions of people have lost their lives with this outbreak. It’s been a year and still countries like India are fighting this global pandemic. Under such circumstances, having the right information about the cases and facilities around would be of great use. In this context, we aim to develop a chatbot for the people of Bangalore to gain the necessary information about the situation. The proposed application includes statistics about the covid cases in Karnataka, Bruhat Bengaluru Mahanagara Palike (BBMP) Help-Line Number for specific zones, Home Intensive Care Unit (ICU) Service Providers, Online Doctor consultation, Oxygen Providers Information and availability of vaccine slots. In this way, our bot can help people gain better understanding of current condition and take informed decisions. Keywords BBMP · Covid-19 · Home · ICU service · WHO

F. Azam (B) · P. V. Bhaskar Reddy Department of Computer Science and Engineering, REVA University, Bangalore, India N. Priyadarshi Department of Business Development and Technology, CTiF Global Capsule, Aarhus University, 7400 Herning, Denmark M. R. Mahmood Department of Electronics and Computer Engineering, Guru Nanak Institutions Technical Campus, Hyderabad, India A. Laxmikanth Department of Computer Science and Engineering, Sri Indu College of Engineering and Technology, Hyderabad, India M. Siddappa Department of Computer Science and Engineering, SSIT, Tumkur, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_48

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1 Introduction COVID-19 is an ailment which is caused by SARS-CoV-2 that leads to a respiratory tract infection. As a result, it affects upper respiratory tract organs like nose sinuses, and throat etc. or lower the windpipe and lungs which are called as lower respiratory organ. The spread of COVID-19 is through person-to-person or touching a surface or through the droplets available in the air [1]. Infections range from mild to severe resulting to death. In the early 2020, after December 2019 outbreak in China, WHO called SARS-CoV as a novel Corona virus. The outbreak quickly spread around the world. As of May 21st, 2021, in India, 26,031,991 active cases have been added to the total count. India is having a huge population after China with more than 1.34 billion and is undergoing difficulty in controlling the transmission of this virus among its population. The Ministry of Health and Family Welfare of India have started increasing the awareness on the recent outbreak and have taken immediate and necessary actions to control the spread of COVID-19. The central and state governments are taking several wartime protocols to achieve this goal. This outbreak is directly or indirectly associated to the economy of the nation, as it has dramatically impeded industrial sectors because people worldwide are currently cautious in doing business in the affected area. So during these difficult times spreading awareness among people with the correct information is important in order to protect themselves and others from this pandemic. Chatbots are becoming popular in the modern world with the advancements in artificial intelligence and technology. Hundreds of bots are being developed every year in different fields say Retail and e-commerce, Travel and hospitability, Healthcare, Media and entertainment, Education and so many others. But when we already have websites to provide this information, why do we need chatbots? Though every organization will have its own website, navigating and getting simple information is quite hard. On the other hand, chatbots are designed to make things simpler and give users personalized experience. One click and the information the users are looking for is displayed. In this context, we have developed a chatbot which is designed as an add-on to the Telegram Application (App). Telegram App is among the most popular and highly used social networking applications between communities. Thus, we have developed chatbot as an add-on to provide COVID-19 related information for the people of Bangalore.

1.1 Our Contributions Our bot is mainly designed for the users to get all the necessary information on a single platform. Some of the main features of the bot are: • Bot helps people find information on covid cases in Bengaluru Rural and Bengaluru Urban.

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• Avail information about BBMP Help-Line Numbers, availability of oxygen cylinders, Online Doctors, information about Home ICU Suppliers. • Get Details about available vaccine slots.

1.2 Organization of the Paper The paper is organized as follows: Sect. 1, describes some of the recent chatbots created in various fields using artificial intelligence. Section 2 gives the objectives and features of our bot. Section 3 briefs about system design and methodology employed to develop our bot. Section 4 discusses the result and finally Sect. 5 concludes the work.

2 Related Work In the recent years, people have started gaining interest in developing chatbots for various purposes to make work easy and less complicated. With respect to this, the authors in [1] have discussed a vision-based emotion tracking system where the emotional state of the user can be identified by tracking the facial expression during the conversation. In future they aim to create a bot more human like to increase user experience. In [2] the authors have introduced an advanced thought where they have designed a chatbot for farmers selling their product directly to consumers who need it at the right price. They ran the program in Slack using open source software called HUBOT and Raspberry Pi 3. This application is useful only for local farmers and does not connect with farmers nationally. In [3], a Telegram based chatbot is implemented for foreigners in Japan to get real-time disaster related information like earthquakes, torrential rains so that they do not face language related issues and take precautionary measures immediately. An education related AI based bot has been designed by the authors of [4] for Bengali speakers. With the help of this bot, the users who speak only Bengali language can get answers for their queries by the bot in the Bengali language itself. The bot is implemented using machine learning algorithms and Bengali Natural Language Processing (BNLP). In [5], the authors have developed a voice identification based chatbot using Automatic Speaker Recognition (ASR) algorithm to enable authorized access to lab facilities. The bot allows only recognized people inside the lab.

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3 System Design As mentioned earlier, a pandemic is on the outbreak and the situation is critical in India. Methods are available to spread COVID-19-related information through radio, television and smartphone. However, there is still room for improvement in the provision of information on various platforms. Thus, in this research, we have developed a COVIBOT: A COVID-19 information sharing system for the people of Bangalore. This application is used as a chatbot running on Telegram. Getting all the information on a single platform is difficult and hence our bot is designed for that purpose. Users can get the required information using simple commands guided by the bot. Though, getting all COVID-19 related information is not possible, the major ones are covered in this application. In this way users can have basic information in their finger tip. The bot is user friendly with no prior knowledge required as the bot guides you through simple steps. Figure 1 shows the components and basic requirement for the development of chatbot. In order to develop our bot we have used BotFather which is a system chatbot within Telegram App. Once the necessary details are given, the BotFather will issue a token to access the HTTP API. This token is unique for every bot created. The entire coding was done on Visual Studio Code platform using the Python Libraries pyTelegramBotApi—for interaction between the bot and the user, covid.org Api—for extracting district wise covid cases. To host our bot we used two platforms, Heroku and Amazon Web Services. Heroku is used to host the main bot which gives all the mentioned information whereas AWS is used to host the sub bot for getting only vaccine related information. Figure 2 shows the overall working of our COVIBOT. Once the bot is loaded, user can send messages in the form of commands. If a valid command say /help is captured by the bot, it will load the list of commands available for the bot and displays it. The user can now send any command from the list. If the command message sent by the user matches with the commands in the system the bot extracts the data from the particular website through web scrapping and displays the required information by calling the specific method implemented for that particular command.

Fig. 1 Architecture diagram to develop the chatbot

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Fig. 2 Working flowchart of the chatbot

The /start and /help commands guided by the bot will display all the available options for the bot so that the users can get their desired information.

4 Results and Discussions Out of the options provided, the users can get the information they are looking for by clicking on the specific commands as shown in Fig. 3. Some of the symptoms and preventions identified by World Health Organization and doctors is displayed as shown in Fig. 4. Here the total covid cases recorded recently is displayed as shown in Fig. 5. BBMP helpline numbers of certain zones obtained from official websites is available as shown in Fig. 6. A list of oxygen suppliers details in Bangalore is provided for users in the form of an excel sheet as shown in Fig. 7. Here the information about some of the Home ICU suppliers available in Bangalore is given as shown in Fig. 8. The available doctors’ details provide online consultation for Covid and Non-Covid patients is given as shown in Fig. 9. Clicking on @C19VinBot will take the users to a new bot designed specifically for vaccine slot availability as shown in Fig. 10. Every week the bot checks for vaccine slots available in Bangalore for people above 18 and 45 is as shown in Fig. 11.

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Fig. 3 Options available in COVIBOT

Fig. 4 Symptoms and preventions

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Fig. 5 Covid statistics in COVIBOT

Fig. 6 BBMP helpline in COVIBOT

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Fig. 7 Oxygen supply attribute in COVIBOT

Fig. 8 Home ICU suppliers attribute in COVIBOT

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Fig. 9 Online doctors

Fig. 10 Vaccine status for slot availability

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Fig. 11 Vaccine slot query

4.1 Commands and Description Figure 4 shows how users can get information about the most common symptoms and simple preventions that can be practiced to avoid getting infected by using a simple command /Covid19. Figure 5 gives the statistical data of the cases in Bangalore Urban which comprises of total confirmed cases, deceased, recovered and tested. Similarly users can get information about Bangalore Rural as well by giving commands /BengaluruUrban or /BengaluruRural. Figure 6 shows how users can get BBMP Helpline Numbers for various queries. These numbers are zone specific which include the areas of Yelahanka, Mahadevpura, Bommanahalli, RR Nagar, Dasarahalli, East, West and South Zones. In this way users can use these numbers to contact the nearby helpline centers. Figure 7 gives oxygen supplier details for emergency situation. /Oxygen command gives a spreadsheet link which contains details about nearby oxygen cylinder

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suppliers and cost along with the availability status. This spreadsheet is updated every day to get valid results. Figure 8 gives information about Home ICU suppliers with name, location, contact number and last updated details. There are four supplier details mentioned for the users to access using the commands /HIC1, /HIC2, /HIC3, /HIC4. Figure 9 gives the details for consulting doctors online with their name, contact number and consultation charges. A list of doctors and related information is available for the users to choose from by giving /doctor1 command and so on. Figures 10 and 11 gives vaccine related information. Here the command /Covaccine takes the users to a new additional bot (@C19VinBot) designed to give only vaccine related information. The users can check for open slots in particular regions by specifying the age constraint and pin-code of the desired area. Table 1 shows the comparison of some of the recent chatbot developed in the recent past. Our COVIBOT is lightweight and efficient as it is robust enough to be downloaded and works on a PC also apart from working on as an add-on application on Telegram.

5 Conclusion Chatbot is a software that simulates personal conversations with users via text and voice messages in a chat. Its main function is to assist users by providing answers to their questions. In this research work, we have developed a Telegram-based chatbot known as COVIBOT that provides information related to COVID-19 like online doctor consultations, Oxygen cylinders and Home ICU service providers with their contact numbers along with BBMP helpline numbers of particular zones. Users can also get vaccine slot related details through another bot linked to the main bot. The proposed system takes advantage of social networking services that can disseminate information in real time and enable users to access a variety of Telegram applications, which is one of the most popular social media platforms. Our bot is developed with only text based application for the people of Bangalore. In future we aim to add voice detecting feature and expand this application for obtaining information about the entire country.

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Table 1 Comparison table of some of recent chatbots developed Paper id

Use

Platform used

[6]

Conversational chatbot for educational remediation

Moodle Learning Students can Environment self-train and improve their skills, teachers can direct their remediation based on the results of the assessments

[7]

Chatbot for Discord Chat expert Service recommendation

The quality of communication is increased as the engineers have the ability to know who to contact and when to contact while facing problems

The bot must respond to wider variety of questions

[8]

Chatbot for financial transactions

Blockchain

Different user creation transactions (subscriptions), balance query and transfer

The current model does not support transactions for multiple banks

[9]

Chatbot for Facebook network security Messenger

Important information about the camera, video and photos of the found person will be sent to the user via Facebook messenger, the user can also contact and get information about the camera by sending a message

There is only one feature about human detection that is analyzed for use via Facebook messenger

[10]

Chatbot for sharing disaster related information

Latest reports about the disaster, nearby reports evacuation places, disaster based hospitals

Does not provide relevant details and exit routes depending on user status and current location

Twitter

Features

Remarks The bot doesn’t remind the key concepts to the learner based on their performance

(continued)

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Table 1 (continued) Paper id

Use

Platform used

Proposed work Chatbot for Telegram Covid-19 related information

Features

Remarks

Latest update regarding ICU bed availability, vaccine availability, oxygen etc.

It is specific to Covid-19 which is need of hour and performs well with all platforms including Android and Windows operating system

References 1. K.A.L.R. Carranza, J. Manalili, N.T. Bugtai, R.G. Baldovino, Expression tracking with OpenCV deep learning for a development of emotionally aware chatbots, in 2019 7th International Conference on Robot Intelligence Technology and Applications (RiTA) (2019), pp. 1–4. https://doi.org/10.1109/RITAPP.2019.8932852 2. U. Kiruthika, S.K.S. Raja, V. Balaji, C.J. Raman, E-agriculture for direct marketing of food crops using chatbots, in 2020 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS) (2020), pp. 1–4. https://doi.org/10.1109/ICPECTS49113.2020. 9337024 3. S.E. Ahmady, O. Uchida, Telegram-based chatbot application for foreign people in Japan to share disaster-related information in real-time, in 2020 5th International Conference on Computer and Communication Systems (ICCCS) (2020), pp. 1–4. https://doi.org/10.1109/ICC CS49078.2020.9118510 4. M. Kowsher, F.S. Tithi, M. Ashraful Alam, M.N. Huda, M.M. Moheuddin, M.G. Rosul, Doly: Bengali chatbot for Bengali education, in 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT) (2019), pp. 1–6. https://doi.org/10. 1109/ICASERT.2019.8934592 5. F. Patel, R. Thakore, I. Nandwani, S.K. Bharti, Combating depression in students using an intelligent ChatBot: a cognitive behavioral therapy, in 2019 IEEE 16th India Council International Conference (INDICON) (2019), pp. 1–4. https://doi.org/10.1109/INDICON47234.2019. 9030346 6. J. Cerezo, J. Kubelka, R. Robbes, A. Bergel, Building an expert recommender chatbot, in 2019 IEEE/ACM 1st International Workshop on Bots in Software Engineering (BotSE) (2019), pp. 1–5. https://doi.org/10.1109/BotSE.2019.00022 7. T. Van Cuong, T.M. Tan, Design and implementation of chatbot framework for network security cameras, in 2019 International Conference on System Science and Engineering (ICSSE) (2019), pp. 1–5. https://doi.org/10.1109/ICSSE.2019.8823516 8. K. Gaglo, B.M. Degboe, G.M. Kossingou, S. Ouya, Proposal of conversational chatbots for educational remediation in the context of covid-19, in 2021 23rd International Conference on Advanced Communication Technology (ICACT) (2021), pp. 1–5. https://doi.org/10.23919/ICA CT51234.2021.9370946 9. M. Kosugi, O. Uchida, Chatbot application for sharing disaster-information, in 2019 International Conference on Information and Communication Technologies for Disaster Management (ICT-DM) (2020), pp. 1–2. https://doi.org/10.1109/ICT-DM47966.2019.9032901 10. F. Clarizia, F. Colace, M. De Santo, A context-aware chatbot for tourist destinations, in 2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) (2020), pp. 1–7. https://doi.org/10.1109/SITIS.2019.00063

Design of 15 Holed Cobra Type Probes for Subsonic Wind Tunnel Calibration Akhila Rupesh , J. Rakshija, Pratap, S. Sushanth, and S. M. Shanth Kumar

Abstract Flow measurement is an important parameter in the arena of Aerodynamics. In the techniques and methods available for flow measurement, wind tunnel test setup is the most appropriate tool. But the main problem faced while using wind tunnel is the existence of flow angularity in the test section. Determination of flow angularity is a crucial requirement for maintaining a laminar flow inside the test section. In order to figure out the flow angularity, calibration of the wind tunnel is a necessity. There are a wide range of instruments for the calibration of wind tunnel among which the Pitot static tube is the most commonly used one where accurate results are not expected. Hence, in the present work a new instrument is designed. The instrument consists of 15 holes distributed on 3 cobra shaped probe attached to a single wedge for the calibration of the subsonic wind tunnel which will provide grid wise results for the flow parameters. Keywords Wind tunnel · Aerodynamics · Pitot tube

1 Introduction Scrutiny of flow patterns around scale models and the measure of aerodynamic forces or pressure upon or the velocity around these structures is the primary purpose of a wind tunnel [1]. Determination of mean values and also the uniformity of various flow parameters say, stagnation pressure, temperature, velocity, Mach number and flow angularity in region where the model is tested is the major step involved in wind tunnel calibration. However, determination of these flow parameters by currently available methods, demands the flow to be maintained laminar which has been one of the major problems faced during calibration. Determination of flow angle is a crucial requirement for maintaining a laminar flow inside the test section and the operation involves wind tunnel calibration. Though there are wide ranges of instruments available for the calibration of wind tunnel, accurate results are not guaranteed A. Rupesh (B) · J. Rakshija · Pratap · S. Sushanth · S. M. Shanth Kumar Department of Aeronautical Engineering, Mangalore Institute of Technology and Engineering, Moodbidri, Karnataka, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_49

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[2, 3]. The major part of calibration is achieved through the measurement of pressures and temperatures and in general, the most basic and useful tool in the calibration of wind tunnels is the measurement of pressures which are sensitive to hole size and geometry of the probes. Basically, pressure probes are instruments used to determine the associated pressures and also the flow angle within a fluid stream. Design of pressure probes for fluid measurements, involves the consideration of the factors, blockage effects, hole geometry and size, frequency response, local Mach numbers and Reynolds numbers, etc. Better accuracy is obtained when smaller probes are used however, they require longer time to respond and also faces greater problems regarding contamination. Pressure probes must be designed to be susceptible to the flow direction and in consequence the probes are essentially calibrated to determine the effects when they are aligned to the flow field which when rotated about their axis can also give the effects of pitch and yaw [4]. A simple cylindrical tube with some pressure taps placed parallel to the flow can be a static pressure probe which can also be worked with different nose configurations say wedge, cone, disc, etc. The most commonly used probes are cobra, wedge, cylindrical with different hole configurations for two-dimensional and three-dimensional flow examinations. A cobra probe is a multi-hole pressure probe for measuring the associated pressures and velocity components of the moving fluid. It gives a measure of the varying three component velocities, Reynolds stresses also the pitch and yaw details. A cobra probe normally comes in 1-, 3-, 5-, or 7-hole configurations for twodimensional and three-dimensional flow measurements. Cobra probes are designed to have low tunnel blockage and hence it is for the study of flow angle, a cobra probe is extensively used for and also it is comparably easy to fabricate also, it provides accurate measurements comparatively [5]. Wind tunnel applications may comprise frequently changing fluid flow conditions or it may require purposeful alterations in fluid flow parameters thus affecting the accuracy of the instrument used. With the aim of measuring flow parameters with minimum error, we have designed an instrument to be competent to reach desired accuracy and be least sensitive to errors and the parameter being measured to not be affected by the instrument design. Taking into account of such cases we have come with the design of three cobra type probes each having five holes, mounted on a single wedge, the pressure taps in turn connected to pressure transducers to provide multiple measurements at a single time by serving as a single instrument, saving time eliminating the need to remount the instrument each time a new measurement is required ameliorating the accuracy of the instrument [6]. Flow measurement is an important parameter in the arena of Aerodynamics. In the techniques and methods available for flow measurement, wind tunnel test setup is the most appropriate tool. But the main problem faced while using wind tunnel is the existence of flow angularity in the test section. Determination of flow angularity is a crucial requirement for maintaining a laminar flow inside the test section. In order to figure out the flow angularity, calibration of the wind tunnel is a necessity. There are a wide range of instruments for the calibration of wind tunnel among which the Pitot static tube is the most commonly used one where accurate results are not expected. Hence, in the present work a new instrument is designed. The instrument consists

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of 15 holes distributed on 3 cobra shaped probes attached to a single wedge for the calibration of the subsonic wind tunnel which will provide grid wise results for the flow parameters.

2 Methodology The 15 holed cobra type probes are a pressure measurement sensor designed to be capable of providing significant information about the three components of flow velocity, flow angularity as well as all the associated pressures. This multi-hole probe is designed according to the required specifications and considering the various factors affecting the design. It is composed of three probes attached on a single wedge, all having a conical extremity, where on each probe 5 holes are drilled, one at the center and the remaining packed around it, each hole serving as a pressure tap connected to the measuring device. The major parts include the adapter, wedge, cone probe and the probe adopter. Each probe is effectively connected to the wedge through the probe adopter, the wedge is in turn held by the adapter as depicted in Fig. 1.

2.1 Adapter The figure depicts the detailed view of the adapter which is designed to hold the wedge. Adapter is a cylindrical structure featuring a length of 115 mm and a diameter of 30 mm offering an inner diameter of 20 mm up to 85 mm length after which a 15 mm * 10 mm rectangular section is projected further which includes 2 holes of 3 mm diameter and modified internally as shown in the figure to fit the wedge. The right view of the adapter shows clearly that four holes of 3 mm diameter which are at 90° to each other are cut for the purpose of including tubing. The holes are located such that the distance between the hole center and the cylinder edge is 15 mm. After 30 mm distance, a slot of dimensions 5 and 45 mm is cut on either sides as shown in Fig. 2.

2.2 Wedge As demonstrated in Fig. 3, the wedge attached to the adapter is a structure designed to hold the probes. It features a length of 60 mm and a width of 140 mm. In the figure, the side view of the wedge, clearly shows that the structure is a convex polygon with one included angle to be 30° as depicted. The depth of the structure is 20 mm and on the rear side, three holes of diameter 10 mm out of which the center hole is further modified in the interior so as to fit the adapter. These holes that are designed to fit the

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Fig. 1 15-hole cobra probe

conical probes through the probe adopter are located at a distance of 35 mm from the edges and also, they are 35 mm apart from their respective centers. The top view of the wedge which includes two holes on either side at a distance of 15 mm and one at the center at a distance of 20 mm, all of 3 mm diameter. The holes on either sides and the hole at the center are at a distance of 15 mm and 20 mm from the length respectively. The holes are along the center line of the width.

2.3 Probe Adopter The figure displays the details of the probe adopter that holds the cone probe connecting the wedge. As depicted in the figure, it’s an L shaped cylindrical structure with an outer diameter of 10 mm. The bent portion is designed such that the radius of curvature is 8.37 mm. The upper end of the probe adopter follows an inner radius

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Fig. 2 Adapter

Fig. 3 Wedge

of 7.5 mm up to 10 mm length with some allowance to fit the cone probe and further the inner radius is 7 mm throughout. Three probe adopters in total are required to hold the three five-holed probes (Fig. 4).

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Fig. 4 Probe adopter

2.4 Conical Probe The figure displays the detailed design of the probe with the conical tip featuring 5 holes to act as pressure taps. As described in the side view, the length of the section in total is 30 mm with a diameter of 7.5 mm up to 10 mm length and further the diameter is 10 mm up to the whole length. With an inner radius of 2.75 mm up to 21 mm length. The structure is cut into a cone with the main angle to be 40° and the height to be 13.7 mm. The conical section is provided with 5 holes in total out of which one lies at the center and the other four surrounding it, whose axis is such that it’s perpendicular to the face of the cone. The holes are 1.10 mm in diameter and the holes excluding the one at the center, are 90° apart. Three probes are designed to lead 15 holes in total (Fig. 5).

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Fig. 5 Conical probe

3 Conclusion The proposed instrument, according to the design considered, can be proven to work effectively in wind tunnel calibrations seeing to that the instrument design don’t affect the parameters being measured. It is designed to meet desired accuracy in measurements by being least sensitive to errors. Since the instrument uses three cobra probes that are mounted on a wedge, the pressure taps connected to pressure transducers can give multiple accurate measurements at one time, making the process time saving.

References 1. A.R. Paul, R.R. Upadhyay, A. Jain, A novel calibration algorithm for five-hole pressure probe. J. Flow Meas. Instrum. 3(2), 89–95 (2002). https://doi.org/10.4314/ijest.v3i2.68136 2. S.J. Lien, N.A. Ahmed, An examination of suitability of multi-hole pressure probe technique for skin friction measurement in turbulent flow. J. Flow Meas. Instrum. 22, 153–164 (2011). https://doi.org/10.1016/j.flowmeasinst.2011.01.004 3. M. Yasar, O.C. Melda, A multi-tube pressure probe calibration method for measurements of mean flow parameters in swirling flows. J. Flow Meas. Instrum. 9, 243–248 (2011). https://doi. org/10.15680/IJIRSET.2016.0504040 4. J. Main, C.R.B. Day, G.D. Lock, M.L.G. Oldfield, Calibration of a four-hole pyramid probe and area traverse measurements in a short-duration transonic turbine cascade tunnel. Int. J. Innov. Res. Sci. Eng. Technol. 21, 302–311 (2016). https://doi.org/10.1007/BF00190681

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5. A. Rupesh, J.V. Muruga Lal Jeyan, S. Uthaman, Design and analysis of five probe flow analyser for subsonic and supersonic wind tunnel calibration. IOP Conf. Ser. Mater. Sci. Eng. 715(01), 1–7 (2020). ISSN 1757-899X. https://doi.org/10.1088/1757-899X/715/1/012083 6. A. Rupesh, J.V. Muruga Lal Jeyan, J. Lal, Dynamic characterization of single lap joints in composite laminate over experimental and computational approach. Int. J. Eng. Technol. IJET 07(03), 1062–1070 (2018). ISSN 2227-524X. https://doi.org/10.14419/ijet.v7i3.12.17633

Optimizing Memory in Opportunistic Sensor Networks Salman Arafath Mohammed, Shamimul Qamar, Khaleel Ur Rahman Khan, and K. V. N. Sunitha

Abstract Most of the world today is connected. Now, data security plays a vital role in a connected world where data are sent in an open medium. If the data are sent in its simple form and the medium is shared, it is very easy for the intruders to read your data, thereby resulting in an attack on confidentiality. Most of the techniques are used to secure the data such as cryptography and data hiding. In this process even in resource constraint environments, memory is very limited but very few papers focus on this area where memory usage can be optimized. This paper employed the existing elliptic curve cryptography (ECC) cryptography which is very much suitable for resource constraint environments, upon it, the concept of group key and data hiding is implemented. The group key is implemented through security nodes in path protected hop by hop (PPHH) protocol, and data hiding was achieved by XOR operations. Finally, the packets are routed in an optimal and secure manner. This work is an extension on the PPHH protocol. The comparative analysis of AES versus proposed shows interesting improvements with respect to memory usage. Keywords OSN · Security · Cryptography · AES · Memory · IoT

S. A. Mohammed (B) Department of Electrical Engineering, Computer Engineering Section, King Khalid University, Abha, Kingdom of Saudi Arabia S. Qamar Faculty of Science and Arts, Dharan Al Janub King Khalid University, Abha, Kingdom of Saudi Arabia e-mail: [email protected] K. U. R. Khan Department of Computer Science and Engineering, ACE College of Engineering, Hyderabad, India K. V. N. Sunitha BVRIT Hyderabad College of Engineering for Women, Hyderabad, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_50

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1 Introduction Memory and time are the two trade-offs right from the inception of the computer world. This trade-off is consistently persisting from the decades. Therefore, the researchers are trying to gain advantage on memory usage efficiency by sacrificing time or achieving time efficiency by sacrificing memory usage using their innovative techniques. However, these techniques are application dependent. If the application is tracking-based such as military, time is important. If the application is monitoringbased, the memory usage if of high concern and plays a vital role. Opportunistic sensor networks (OSN) are defined as application oriented wireless sensor networks [1, 2]. These networks are delay tolerant in nature. The intermittent connection is a rule here rather an exception. It is always assumed that there is no “continuous” path between sender and receiver. The networks are assumed to be highly dynamic, and the topology is, thus, extremely unstable and sometimes completely unpredictable [3, 4]. This paper is an attempt to target an application in monitoring area of OSN where the data have to be collected on a timely basis and usage of memory should be less for data gathering as these nodes have limited memory, battery life, and speed. Also, it can be used in tacking applications where memory is a constraint because the results show that with the existing approaches amount of time consumed and memory usage has wide time and memory trade-off gap. The proposed technique, of course, has a trade-off between time and memory with less time and memory tradeoff gap. In fact, this paper is an extension of work carried out by Arafath et al. [5, 6] for ensuring security in OSN. This technique is employed in the scenarios discussed in Arafath et al. [5, 6] at data security level in OSN by encapsulating cryptographic technique and stenographic technique without using images but by concealing data to provide next level data security.

2 Literature Review It is agreed that advance encryption standard (AES) is one of the best data encryption algorithms available. However, it cannot be used in sensor networks in its original form because it requires high processing and memory consumption. In order to use the power of AES in constraint-limited environments, AES has been tailored by many researchers such as Arafath et al. [7], Banu and Velayutham [8], Panait and Dragomir [9] and Thangarajan and Kanchana Bhaaskaran [10]. Arafath et al. [7] have proposed the AES with reduced number of rounds and key size and analyzed its strength which is as strong as the earlier version of AES with more number of rounds. At certain, point of time increasing number of rounds is superfluous. The author has found that threshold of rounds beyond it performing rounds only increasing processing time without actually increasing the security. The main focus was on time efficiency, memory storage was not optimized. Banu and Velayutham [8] proposed levels of security in OSN depending on the traffic paths. The proposed

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system has two traffic paths and they are using the MAC medium depending on the uplink and downlink. For traffic path calculation, breadth first search and colored connected dominant set data structure techniques are used to perform level by level offset schedules. Here, smart scheduling is performed to achieve time efficiency. Panait and Dragomir [9] have proposed an optimized implementation of AES-128 in hardware using system on chip ATmega128RFA1 CPU with 16 MHz frequency. The radio transceiver which is compatible with it is IEEE 802.15.4 which itself offers low energy consumption. Here also, the focus was time-efficient implementation of AES and memory usage was ignored. Thangarajan and Kanchana Bhaaskaran [10] have attempted to provide tailored AES by implementing in hardware using SoC TSMC180 technology library. The optimization is achieved in S-Box by calculating multiplicative inverse on the input on Galois field of 8. Here, another optimization is done by repeating the hardware 10 times for parallel processing which is of course reduces time but take lot more of memory.

3 Research Gap It is apparent from the literature review that most researchers are focused on the timeefficient implementation of the AES, either by tailoring the current AES to various modules or by implementing its modification in hardware. The findings also reveal that there is a substantial amount of improvement made by completely avoiding AES memory usage. The results also show that there is considerable amount of improvements achieved by completely ignoring the memory usage of AES. This paper finds this gap that memory efficiency can be improved using data hiding in the encryption process. This reduces not only the use of memory in resource constraint environments, but also reduces the time and memory trade off gap. The proposed time and memory trade-off gap are characterized as the difference between the highest peaks reached in time and in memory, respectively, by the two comparable techniques (AES vs. proposed). If the value is negative, then it is an improvement and if the value is positive, then meaning is this metric performance is poorer.

4 System Overview As the opportunistic sensor network field has been deployed randomly with 30 sensor nodes. All these sensor nodes can start taking part in the OSN only after registering with the security node. The proposed system has 4 such security nodes. In this implementation, every sensor node is authenticated after registering process, using the in-build library. This paper has not focused on any new authentication mechanism but rather used the existing libraries to achieve it. Routing is done in this network using path protected hop by hop (PPHH) protocol as implemented by Arafath et al. [5]. Due to security nodes, malicious nodes are avoided. Any node willing to take part in the

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network without registering process can be easily eliminated using PPHH. The secure and optimal path are selected from source to destination using PPHH. The protocol ensures that all the intermediate nodes are the nodes that are already registered with any one of the security node otherwise those nodes are avoided as intermediate nodes. The data sent through these nodes is encrypted/decrypted at sender/destination side, respectively, using the technique proposed in subsequent sections. In addition, the network is implemented in OMNeT++. A secret key is provided to each sensor node by a security node that helps in the encryption and decryption process. The security nodes, here, have added responsibility for providing secret keys and keeping track of these secret key details for all security nodes, i.e., a global list of secret sensor id keys is kept in every security node.

5 Proposed Algorithms The first step, here, is to encrypt the data using elliptical curve cryptography (ECC) by using a secret key. The encryption process is shown in the below section.

5.1 Algorithm for Data Hiding in Encryption Process The actual AES takes minimum 128 bits of block size but keeping in view of our target networks, we set this same minimum block size of 16 bytes. We get secret key from the LightWeightKeyGeneration algorithm used in [6]. A secret key is generally a string that will be used to encrypt or decrypt our data. Algorithm Data Hiding in Encryption Input: Secret Key, Message, Secure Route provided by PPHH Output: Partially, hidden and fully encrypted data eliminate malicious node 1. Begin Initialize the algorithm 2. For All sensor nodes in an OSN (Ns belongs to N) All source nodes will perform the steps from step 3 3. Select the secure route calculated by path protected hop by hop protocol Selecting the secure route by PPHH protocol 4. Detecting attackers using authentication mechanism used in PPHH protocol Eliminating the malicious node 5. Encrypt data Encryption is done using ECC public key cryptography 6. Obtain first 8-bits Obtaining first 8-bits of encrypted data from the cipher text generated 7. Hiding bits

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Hiding the 8-bits by using group key 8. Send the packet Sending partially hidden and fully encrypted data to destination (sink node) 9. End.

In the above algorithm, if the sender nodes want to send the data to the destination node, they can first get the path to the destination using the PPHH protocol. The PPHH protocol selects only those intermediate nodes that have a secret key and are registered with the security node. After having the paths to the destination, pick the reliable path as specified in [5] but before forwarding to these nodes, encrypt the data using the above algorithms. The secret key issued to the security nodes is used in the encryption process. The encryption algorithm being used is ECC, which is a public key cryptography and is suitable for resource constraint conditions [3, 5, 7]. The ECC algorithm provides encrypted cipher text data: As seen in the flowchart, the first 8-bit cipher text is converted to binary values using the corresponding ASCII code. These binary bits are hidden using the group key. The group key is generated by XORing the group member secret keys, which means that the binary value is hidden in another text that is the group key. Figure 1 displays the activity diagram at a glance of the operations undertaken during the encryption process. For an instance, the sender node wants to send “network” to the destination node using the intermediate nodes selected by PPHH protocol. Here plaint text = “Network” and from the simulation, one instance of secret key generated by security node as follows: = “MF0wEwYHKoZIzj0CAQYIKoZIzj0DABADRgAEMU6HXBIiOJHxJlFrvC7 SJ+ipSSrtENuFXF3KREDMHFnYtYb4z9vKDqBKI28kWzJUlp8VAWOj92LgpS UpmbgRzApEjKA=” Plain text = “Network” Cipher Text = T8ETgOVwQzwJXx2pWVvZhOp0/f/2FZyYVxu6EfZZ6WkeqY VjWBj9. This cipher text converted into binary values. Now, the cipher text is converted into binary values using corresponding ASCII codes. If the group key is 10,000 (say), then the entire data are XORed with group key in order to hide the cipher text. Thus, the only first 8-bits are hided and the entire binary value is transmitted to destination. Thus, entire data XORed with group key in order to hide cipher text. This is the binary value that gets transmitted with first 8-bits hided.

5.2 Algorithm for Data Unhiding in Decryption Process Now, the cipher text is converted into binary values using corresponding ASCII codes. If the group key is 10,000 (say), then the entire data are XORed with group key in order to hide the cipher text. Thus, the only first 8-bits are hided and the entire

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Start Obtain data for encryption

Encrypt data by secret key using ECC

Convert first 8-bits of cipher text into binary Hide binary value into group key Obtain encrypted data

Send to destination through optimal route End

binary value is transmitted to destination. Thus, entire data XORed with group key in order to hide cipher text. This is the binary value that gets transmitted with first 8-bits hided. Algorithm Decryption Process for Data Unhiding Input: Partially hidden and fully encrypted data Output: Unhided data in plain text format, decrypted data not shared with malicious nodes (secure reception of data) 1. Begin Initialize the algorithm 2. Receive the packet Destination node receives partially hidden and fully encrypted data 3. Unhide bits The 8-bits hided are recovered by using the same group key. 4. Obtain first 8-bits Obtaining first 8-bits of unhide data from the text generated 5. Decrypts data Decryption is done using ECC public key cryptography.

Optimizing Memory in Opportunistic Sensor Networks Fig. 2 Data unhiding using decryption process

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Start Destination receives the cipher text Obtain Encrypted Data

Unhide binary value using same group key Convert first 8-bits of binary to ASCII

Decrypt data by secret key using ECC Obtained Plain text

End

6. Since, attackers are detected using PPHH protocol, the decrypted data will never be shared with the malicious nodes. The data are safe. 7. End

It is clear that E (plaintext) = cipher text, i.e., if the encryption method is applied to plain text, it gives cipher text. On the same lines, if D (ciphertext) = plaintext, i.e., if the decryption method is applied to the cipher text, it provides plain text. This implies that the “E” and “D” processes are mathematically inverse to each other. Figure 2 displays the activity diagram at a glance of the operations undertaken during the encryption process.

6 Results and Discussion While the PPHH protocol chooses a secure path, the proposed data security algorithms add a higher degree of security since they incorporate both ECC cryptography and data hiding using the group key principle. In addition, group keys are created

476 Fig. 3 Comparative results for AES and proposed method in terms of time and memory usage

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300 200 100 0 Time consumption (ms) Memory usage (bytes)

periodically by security nodes. The simulation results showed that the suggested approach is effective in terms of memory usage. However, it lacks in time efficiency due to the reason that in the proposed method, time consumption is including secret key generation time and encryption time required by it also it performs two encryption processes using two different keys. With AES there is no secret key generation. It has only round keys which are the byproducts of the encryption process. Memory usage refers to amount of memory required to encrypt the data. Even with two different keys, the memory usage is less in this proposed technique. The existing AES provides with minimum time but increases memory consumption. The time consumption of proposed technique is 180 ms, whereas the time consumption of AES is 93 ms. The trade-off gap between proposed and AES techniques is +87 ms. This implies that the proposed technique is lagging due to two encryptions done one with ECC and other with group key (Fig. 3). The memory consumption of proposed technique is 210 bytes, whereas the memory usage of AES technique is 324 bytes. The memory trade-off gap is −114. So, there is an improvement in memory usage even it requires two different keys. The reason that AES is lacking is due to its large key size.

7 Conclusion This paper employed the existing ECC cryptography which is very much suitable for resource constraint environments, upon it, the concept of group key and data hiding is implemented. The group key is implemented through security nodes in PPHH protocol, and data hiding was achieved by XOR operations. Next, the packets are routed in an optimal and secure manner. This paper presents achieving the data security keeping focus on memory usage. Many existing techniques are focusing on time consumption with no attention on memory usage. Since there is a trade-off between time and memory, the proposed technique results in improved memory usage with little sacrifice on time consumption. In sensor networks, applications can be classified

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into two categories, namely tracking-based and monitoring-based. In monitoringbased applications memory usage plays a prominent role in order to gather environmental data on regular schedules. If techniques are employed keeping memory usage in view, they will be more suitable for monitoring-based sensor network applications. This paper is a successful attempt in achieving memory optimization. It may result in a subtle impact on the research scholars to give attention to the memory optimization techniques. Most of the techniques are used to secure the data such as cryptography and data hiding. In this process even in resource constraint environments, memory is very limited but very less papers focus on this area where memory usage can be optimized. The comparative analysis of AES versus proposed shows interesting improvements with respect to memory usage. However, with respect to time, AES is superior. Finally, the results show the trade-off between time and memory. Even with the two encryption keys, the memory usage of the proposed technique is better than the existing AES. AES lacks with the existing technique due to larger key size. The researchers can utilize this research gap to put their effort in coming out with new approaches or techniques to decrease the memory usage in resource constraint environments.

References 1. M.S. Arafath, K.U.R. Khan, Opportunistic sensor networks: a survey on privacy and secure routing, in 2017 2nd IEEE International Conference on Anti-Cyber Crimes (ICACC), 26–27 Mar 2017 (IEEE), pp. 41–46 2. M.S. Arafath, S. Qamar, K.U.R. Khan, K.V.N. Sunitha, Analysis of power in medium access control code division multiple access protocol for data collection in a wireless sensor network (2020). https://doi.org/10.1007/978-981-15-3172-9_4 3. M.S. Arafath, K.U.R. Khan, K.V.N. Sunitha, Pithy review on routing protocols in wireless sensor networks and least routing time opportunistic technique in WSN, in 10th International Conference on Computer and Electrical Engineering, Edmonton, Canada, 11–13 Oct 2017. J. Phys. Conf. Ser. 933(1), 012016. ISSN: 1742-6596. https://doi.org/10.1088/issn.1742-6596; Online ISSN: 1742-6596/Print ISSN: 1742-6588 4. S. Qamar et al., Fault analysis for lightweight block cipher and security analysis in wireless sensor network for Internet of things, in Innovations in Electronics and Communication Engineering, ed. by H.S. Saini, R.K. Singh, M. Tariq Beg, J.S. Sahambi. Lecture Notes in Networks and Systems, vol. 107 (Springer, Singapore, 2020). https://doi.org/10.1007/978-981-15-317 2-9_1 5. M.S. Arafath, K.U.R. Khan, K.V.N. Sunitha, Incorporating privacy and security in military application based on opportunistic sensor network. Int. J. Internet Technol. Secur. Trans. (IJITST) (2017). ISSN: (Print), ISSN: 1748-5703 (Online) 6. M.S. Arafath, K.U.R. Khan, K.V.N. Sunitha, Incorporating security in opportunistic routing and traffic management in opportunistic sensor network. Int. J. Adv. Intell. Paradig. (IJAIP) 16(3/4) (2020). ISSN print: 1755-0386, ISSN online: 1755-0394. IJITST is indexed in SCOPUS (Elsevier). ACM Digital Library. https://doi.org/10.1504/IJAIP.2020.10028397 7. M.S. Arafath, K.U.R. Khan, K.V.N. Sunitha, Security in opportunistic sensor network and IoT having sensors using light weight key generation and cryptographic algorithm, in 2018 International Conference on Recent Innovations in Electrical, Electronics & Communication Engineering (ICRIEECE), Bhubaneswar, India (2018), pp. 3427–3433

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8. A.J. Banu, R. Velayutham, Secure communication in wireless sensor networks using AES algorithm with delay efficient sleep scheduling, in 2013 IEEE International Conference on Emerging Trends in Computing, Communication and Nanotechnology (ICECCN), Tirunelveli, India (2013), pp. 706–711. https://doi.org/10.1109/ICE-CCN.2013.6528596 9. C. Panait, D. Dragomir, Measuring the performance and energy consumption of AES in wireless sensor networks, in 2015 Federated Conference on Computer Science and Information Systems (FedCSIS), Lodz, Poland (2015), pp. 1261–1266. https://doi.org/10.15439/2015F322 10. S. Thangarajan, V.S. Kanchana Bhaaskaran, High speed and low power implementation of AES for wireless sensor networks. Procedia Comput. Sci. 143, 736–743 (2018). ISSN 18770509. https://doi.org/10.1016/j.procs.2018.10.440. https://www.sciencedirect.com/ science/article/pii/S1877050918321380

Students’ Satisfaction with Technology-Assisted Learning: An Empirical Analysis of Female University Students in Saudi Arabia Using Telecourse Evaluation Questionnaire Najmul Hoda, Naim Ahmad, and Md Rashid Mahmood Abstract Technology-assisted learning (TAL) is one of the key factors to realize the goals of sustainable development, being exemplified in the form of smart cities in urban areas. Student satisfaction is an important predictor of success and achievement in all modes of education including TAL. In the unique circumstances created by COVID-19 pandemic, online education became the new normal. However, remote online education was already being used in educational institutions in some countries, including Saudi Arabia, for imparting education to female students by male instructors. The current study, based in Saudi Arabia, aims to measure the perception of female students about the interactive televised classes (ITV), a type of TAL. The survey was conducted using the standard scale called the telecourse evaluation questionnaire (TEQ). This instrument measures students’ satisfaction with ITV classes on three main dimensions namely; instructor, technology and course management. A total number of 108 valid responses were received. The results show that the students perceive the quality of such classes to be just above average on each item as well as in total. The findings of this study should offer better insights into the pedagogical research dealing with TAL. Further, each stakeholder engaged in education would find the results of this study useful in improving the quality of TAL that became a new normal, and is set to persist in future especially in the context of smart cities.

N. Hoda (B) Department of Business Administration, College of Business, Umm Al-Qura University, Makkah, Kingdom of Saudi Arabia e-mail: [email protected] N. Ahmad Department of Information Systems, King Khalid University, Abha 62529, Kingdom of Saudi Arabia e-mail: [email protected] M. R. Mahmood School of Engineering and Technology, Guru Nanak Institutions Technical Campus, Ibrahimpatnam, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_51

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Keywords Technology-assisted learning · Interactive televised classes · Online classes · COVID-19 · Student satisfaction · Biner’s TEQ · Saudi Arabia

1 Introduction To create the best environment for innovation and sustainability in the urban areas, the concept of smart cities has come out to be very promising. Many countries across the globe, including Saudi Arabia, have established top governmental entities to address the challenges for the development of smart cities. Smart people, being one of the key dimensions of smart city, require establishment of modern educational infrastructure including support for technology-assisted learning (TAL). With this purpose, all Saudi universities have made hefty investments in TAL infrastructures. And there also exists a dedicated online university by the name Saudi Electronic University. The current pandemic situation caused by COVID-19 created an impromptu space for distance learning embedded in TAL technology [1, 2]. Education institutions at all levels had to adopt online learning in order to cope with the abrupt and prolonged lockdown that continues even till date in many countries [3]. Due to cultural reasons, teaching remotely using interactive televised classes or ITV classes (a type of TAL technology) has already been in practice in many Arab countries, including Saudi Arabia, in situations where female students needed to be taught face-to-face by male instructors [4, 5]. Recent studies on the experience of students and teachers with online classes during lockdown show mixed results of satisfaction surveys [6, 7]. The current study aims to measure and analyse the factors that affect student satisfaction with ITV classes using the Biner’s telecourse evaluation questionnaire model [8]. Such a study focusing on female students’ experience with ITV classes are very few [4, 5]. The findings do have the potential to be generalized and extrapolated to all online/distance learning for all places and student categories. The rest of the paper includes a literature review in Sect. 2, description of research methodology in Sect. 3. Results are discussed in Sect. 4 followed by conclusion and limitations in the last section.

2 Literature Review 2.1 Student Satisfaction Student satisfaction has been a popular area of research mainly in the face-to-face technologies. It has been described as a complex construct that could include varying dimensions [9] and various connotations such as “success and enjoyable experience” [10]; or “students’ perception of favorability” [11]. Student satisfaction is a critical measure of success in learning [12], included as a pillar in the Sloan Consortium’s quality framework [13].

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2.2 Student Satisfaction with TAL Previous studies determining student satisfaction in TAL environments [14, 15], have considered factors like technology characteristics [10]; course management [2]; role of instructors [16]; non-verbal communication [3]; classroom interaction [17]; environmental factors such as a “professional learning environment” [7]; students’ characteristics [18, 19]. An important demographic factor, gender, has been found to be significantly influencing students’ satisfaction with various forms of TAL technologies [4, 5, 20].

2.3 Instruments to Measure Satisfaction in TAL Environment Different scales have been developed to measure student satisfaction in general and for TAL specifically. Based on the perception that student satisfaction is similar to the consumers’ perception regarding services, researchers used SERVQUAL to measure student satisfaction [21]. Developed by Parasuraman et al. [22], this instrument measures satisfaction on five dimensions namely: “reliability; responsiveness; empathy; assurance; and tangibles”. Honey and Mumford’s learning style questionnaire [23] is also a measure of student experience that may be applied to TAL. It has 80 questions in total with 20 for each of the 4 learning styles. Some other notable instruments are the “motivated strategies for learning questionnaire (MSLQ)” originally created by Pintrich and De Groot [24]; the net promoter score (NPS) [6]; the online course satisfaction survey (OCSS) [25]. Biner et al. [8] developed and validated the telecourse evaluation questionnaire (TEQ) that include both “affective and cognitive factors” and is specially targeted at ITV classes. It measures satisfaction as a function of three dimensions namely: instructor characteristics, technology characteristics and course management characteristics. The TEQ comprises of total 34 items measuring the three dimensions (Fig. 1). The scale has been used in several studies that measured satisfaction with TAL [26, 27].

3 Research Methodology The current study is an extension of the research carried out by the same authors [4]. The survey instrument was the Biner’s TEQ scale, explained in Sect. 2 above. The instrument was found to be highly reliable, well above the recommended Cronbach’s alpha value of 0.7 for all items [28] (Table 1). The sampling method used is convenient but is systematic in the way the survey was administered. The questionnaire was translated into Arabic before sharing with respondents. The sample is concentrated more in two Saudi universities that represent a significant proportion of students of the western region of Saudi Arabia, both in

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Fig. 1 The TEQ framework. Source Biner et al. [8]

Table 1 Instrument validity

Dimension

Cronbach’s alpha

Instructor characteristics

0.946

Technology characteristics

0.911

Course management characteristics

0.875

terms of size and diversity. Majority of the students belonged to one university (75.9%), in the age group of 20–25 years and specialized in business (80.6%). The students taught one course only and more than one courses through ITV were equal (50%). Data analyses were conducted using SPSS 20.0. The analysis mainly used descriptive statistics of each item for each dimension of satisfaction with ITV classes. Further, the cumulative satisfaction score was also computed for each dimension and all dimensions together.

4 Results and Discussion The results of satisfaction survey conducted using TEQ are presented in Table 2. The results have been arranged in order of decreasing mean score for each item.

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Table 2 Satisfaction with ITV on TEQ scale Instructor characteristics

Technology characteristics

Course management characteristics

Item code

Mean

S.D.

Item code

Mean

S.D.

Item code

Mean

S.D.

INST1

3.8

1.109

TECH1

3.64

1.211

COUR1

3.71

1.136

INST2

3.69

1.106

TECH2

3.53

1.226

COUR2

3.57

1.078

INST3

3.58

1.112

TECH3

3.45

1.114

COUR3

3.52

1.089

INST4

3.57

1.087

TECH4

3.42

1.033

COUR4

3.46

1.218

INST5

3.56

1.044

TECH5

3.36

1.089

COUR5

3.44

1.016

INST6

3.56

1.097

TECH6

3.02

1.111

COUR6

3.33

1.127

INST7

3.51

1.115

COUR7

3.3

1.202

INST8

3.5

1.063

COUR8

3.21

1.12

INST9

3.5

1.089

COUR9

3.2

1.281

INST10

3.42

1.193

COUR10

3.13

1.136

INST11

3.41

1.059

COUR11

3

1.297

INST12

3.36

1.279

INST13

3.29

1.086

INST14

3.2

1.19

INST15

3.18

1.142

INST16

3.07

1.213

4.1 Analysis of Students’ Satisfaction with Instructor Characteristics The satisfaction scores on the instructor dimension show that the students’ satisfaction are just above average on each item (ranging from 3.07 to 3.8). Overall satisfaction on this dimension is 3.45 implying an average level of satisfaction. The students’ rating for items like, the instructor’s teaching ability in ITV classes, overall instructor performance, instructor’s level of enthusiasm was perceived more satisfying to students than other items. The least score in this dimension was attributed to the distractions in room.

4.2 Analysis of Students’ Satisfaction with Technological Characteristics The satisfaction scores on the technology dimension also show an average on each item (ranging from 3.02 to 3.64). Overall satisfaction on this dimension is 3.38 implying an average level of satisfaction. The students’ rating for items like, the quality of the television sound was perceived to be more satisfying than other items.

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The least score in this dimension was attributed to the confidence that the classes will not be interrupted.

4.3 Analysis of Students’ Satisfaction with Course Management Characteristics The satisfaction scores on the course management dimension again are average on each item (ranging from 3 to 3.71), with overall mean score of 3.35 on this dimension. The students’ rating for items like, class enrolment and registration procedure were perceived to be more satisfying than other items. The least score in this dimension was attributed to the confidence that the classes will not be interrupted.

5 Conclusion, Implications and Limitations The current study aimed at analysing female students’ satisfaction with ITV classes using the TEQ scale. It may be inferred that the students’ satisfaction with ITV classes where they are taught by male instructors remotely, is just average across all the dimensions. This calls for a serious consideration for improvement by the universities on all the dimensions. More studies should also be conducted to confirm the findings of this study, as prescribed by Tratnik et al. [16]. As reported in the earlier study by the authors [4], program type or course type do not affect student satisfaction with ITV classes. However, the number of courses taught through ITV in one semester does have significant impact on student satisfaction. Student satisfaction is an important predictor of student learning and performance. It is therefore imperative that the universities work on improving the students’ experience on each dimension considered in this study. It is quite clear that the future of education needs a mix of online and F2F learning, which is more commonly called blended learning. Higher education institutions need to adapt and develop the policies considering the factors that are critical to achieving the best outcomes. Students should be made to feel comfortable in communicating and expressing themselves. Instructors must be trained to acquire “techno-pedagogical skills” [20] in order to excel in this mode of learning. Researchers prescribe different preparation standards while delivering a program/course/module via online medium, suggesting more collaborative and supportive methodologies while using TAL technologies. The findings of this study should offer better insights into the pedagogical research dealing with TAL technologies. Further, each stakeholder engaged in education would find useful implications of results in upgrading the quality of online education that became a new normal, and is set to persist in future.

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References 1. G. El-Sayad, N.H. Md. Saad, R. Thurasamy, How higher education students in Egypt perceived online learning engagement and satisfaction during the COVID-19 pandemic. J. Comput. Educ. (2021). https://doi.org/10.1007/s40692-021-00191-y 2. S. Kim, D.-J. Kim, Structural relationship of key factors for student satisfaction and achievement in asynchronous online learning. Sustainability 13(12), 6734 (2021) 3. R. Khalil, A.E. Mansour, W.A. Fadda, K. Almisnid, M. Aldamegh, A. Al-Nafeesah et al., The sudden transition to synchronized online learning during the COVID-19 pandemic in Saudi Arabia: a qualitative study exploring medical students’ perspectives. BMC Med. Educ. 20(1), 1–11 (2020) 4. N. Hoda, N. Ahmad, Q.N. Naveed, Evaluating student satisfaction with interactive television classes in Saudi universities, in 2018 IEEE 5th International Conference on Engineering Technologies and Applied Sciences ICETAS 2018 (2019), pp. 1–5 5. K. Alasfor, Impact of showing a male instructor’s face on female students’ perceived social presence, satisfaction, and comprehension in distance education in a conservative, gendersegregated culture. Educ. Technol. Res. Dev. (2021). https://doi.org/10.1007/s11423-021-100 13-8 6. J. Oblitas, J. Jorge, Differences in student satisfaction in online learning and remote teaching courses during the COVID-19 adaptation stage, in Future of Engineering Education: Current Challenges and Opportunities. EDUNINE 2021—5th IEEE World Conference on Engineering Education (2021), pp. 9–13 7. H. Baber, Determinants of students’ perceived learning outcome and satisfaction in online learning during the pandemic of COVID19. J. Educ. e-Learn. Res. 7(3), 285–292 (2020) 8. P.M. Biner, R.S. Dean, A.E. Mellinger, Factors underlying distance learner satisfaction with televised college-level courses. Am. J. Distance Educ. (1994) 9. M.M. Navarro, M.P. Iglesias, P.R. Torres, A new management element for universities: satisfaction with the offered courses. Int. J. Educ. Manag. (2005) 10. E. Alqurashi, Predicting student satisfaction and perceived learning within online learning environments. Distance Educ. 40(1), 133–148 (2019). https://doi.org/10.1080/01587919.2018. 1553562 11. K.M. Elliott, D. Shin, Student satisfaction: an alternative approach to assessing this important concept. J. High Educ. Policy Manag. (2002) 12. H.L. Harvey, S. Parahoo, M. Santally, Should gender differences be considered when assessing student satisfaction in the online learning environment for millennials? High Educ. Q. 71(2), 141–158 (2017) 13. J.C. Moore, The Sloan consortium quality framework and the five pillars (2005). [Internet]. Available from: https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.115.4238&rep= rep1&type=pdf 14. Y.C. Kuo, A.E. Walker, B.R. Belland, K.E.E. Schroder, A predictive study of student satisfaction in online education programs. Int. Rev. Res. Open Distrib. Learn. 14(1), 16–39 (2013) 15. I.M.K. Ho, K.Y. Cheong, A. Weldon, Predicting student satisfaction of emergency remote learning in higher education during COVID-19 using machine learning techniques. PLoS One (2021) 16. A. Tratnik, M. Urh, E. Jereb, Student satisfaction with an online and a face-to-face Business English course in a higher education context. Innov. Educ. Teach. Int. 56(1), 36–45 (2019). https://doi.org/10.1080/14703297.2017.1374875 17. F. Li, F. Jhang, The relationship between interaction and student satisfaction with online learning in social work undergraduates in China, in ICSSHE, vol. 505 (2020), pp. 23–27 18. B. Landrum, J. Bannister, G. Garza, S. Rhame, A class of one: students’ satisfaction with online learning. J. Educ. Bus. 96(2), 82–88 (2021). https://doi.org/10.1080/08832323.2020.1757592 19. A.M. Nortvig, A.K. Petersen, S.H. Balle, A literature review of the factors influencing e-learning and blended learning in relation to learning outcome, student satisfaction and engagement. Electron. J. e-Learn. (2018)

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20. W. Elshami, M.H. Taha, M. Abuzaid, C. Saravanan, S. Al Kawas, M.E. Abdalla, Satisfaction with online learning in the new normal: perspective of students and faculty at medical and health sciences colleges. Med. Educ. Online 26(1) (2021). https://doi.org/10.1080/10872981. 2021.1920090 21. K. Devinder, B. Datta, A study of the effect of perceived lecture quality on post-lecture intentions. Work Study (2003) 22. A. Parasuraman, V.A. Zeithaml, L.L. Berry, SERVQUAL: a multi-item scale for measuring customer perception of service quality. J. Retail. (1988) 23. P. Honey, A. Mumford, The Manual of Learning Styles (1992) 24. P.R. Pintrich, E.V. De Groot, Motivational and self-regulated learning components of classroom academic performance. J. Educ. Psychol. (1990) 25. F. Inan, E. Yukselturk, M. Kurucay, R. Flores, The impact of self-regulation strategies on student success and satisfaction in an online course. Int. J. E-Learn. Corp. Gov. Healthc. High Educ. (2017) 26. N. Ayub, S. Iqbal, Student satisfaction with e-learning achieved in Pakistan. Asian J. Distance Educ. (2011) 27. V. Stone, Student satisfaction with and perceptions of relationship development in counselor education videoconferencing courses, Tesis posgrado, 2006 28. J. Nunnally, I. Bernstein, Psychometric Theory, 3rd edn. (McGraw-Hill, New York, 1994)

Electric Vehicle Charging Technology: Recent Developments S. Paul Sathiyan, Santo Jensen, and Jothi Abirami

Abstract With the rise in popularity of electric vehicles (EVs), its more important than ever to build an EV charger that meets performance, safety, and grid integration rules. On a lighter note, the standards for EV charging power levels, charging architecture, various converters and their classifications, design concerns such as converter power rating, and design target concerning on-board chargers (OBC) have been examined. The article also compares commercially available OBCs with the set targets to identify design deficiencies in OBCs that can be addressed in the future. Keywords Electric vehicle charger · On-board charger · Charging specifications · Charger performance

1 Introduction Chargers provide the necessary charging power at the appropriate profile to the on-board batteries by establishing a communication link between the EV’s battery management system (BMS) and the electric vehicle supply equipment (EVSE) [1]. The configuration of EVSE with AC and DC charging at Level 1, 2, and 3 (Table 1) are presented in Fig. 1. EVs may charge with either single phase or three-phase AC (up to 7.7 kW) (up to 22 kW). EVs can charge their batteries with on-board chargers (OBCs) using single phase AC power at Levels 1 and 2, and three-phase AC power at Level 3, whereas all levels of DC charging must be done with external chargers (ECs) located at public charging stations (PCS) in malls, highways, and other public areas. When the vehicle is connected to AC and the EVSE is turned on, the power supplied to the EV is converted to unregulated DC, which is then regulated by a DC–DC converter to deliver the necessary charging power to the batteries. When an EV is linked to DC–EVSE, a power factor correction (PFC) is used to filter out the harmonics injected into the grid by the charger, however when an EV is connected to S. Paul Sathiyan (B) · S. Jensen · J. Abirami Department of Electrical and Electronics Engineering, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu 641114, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_52

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Table 1 Summary of charging power levels Source type

Level

Usage location

Power level (kW)

Charger type

AC

1

Home

1.4–3.3

OBC

2

Home

3.7–7.7

OBC

3

Public

22

OBC/EC

1

Public

40

EC

2

Public

90

EC

3

Public

240

EC

CHAdeMO

Public

62.5

EC

IEC Standard DC fast charging

Public

100–200

EC

Tesla—DC super charger

Public

136

EC

DC

Fig. 1 EVSE system configuration

DC–EVSE, power is delivered to the EV directly, bypassing all on-board converters. The converters inside the EVSE regulate charging in conjunction with the BMS inside the EV. EVs can be charged at Level 3 EVSE in PCS at a maximum power level. DC chargers are 10–20 times more expensive than Level 2 units, making them too expensive for the average person to acquire [2, 3]. At PCS, charging power ranges from 1.4 kW slow residential AC to 350 kW fast DC charging [3, 4] Slow (below 22 kW), fast (between 22 and 150 kW), and ultrafast charging (beyond 150 kW) are all possible with PCS. AC three-phase power is used for sluggish charging, while DC power ranging from 22 to 350 kW is used for fast and ultrafast charging. Slow charging is done with a three-phase AC supply, whereas fast charging is done with a DC source ranging from 22 to 350 kW.

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2 EV Charger Design Considerations Chargers with a wide input voltage range and variable grid frequency are compatible with the worldwide EV market and EVSE units. The charging power of batteries is limited by (i) battery type, (ii) charging rate, and (iii) OBC and EC power ratings. The maximum rating and availability of the charging source effect the charging time of batteries at higher power levels. Efficiency and cost considerations are especially important [5] because current conduction losses can be substantial at high power levels, and the most cost-effective semiconductor devices on the market have limited power handling capacity (Table 2). As the power level rises, the charging power is divided between multiple converters linked in parallel (Table 3). At higher power levels, efficiency, and cost considerations are especially essential since currentrelated conduction losses may be substantial, necessitating paralleled switching devices. The United States has established a goal to reach by 2025 in terms of OBCs (Table 4). The specific power and power density of Ficosa’s OBC [6] (Table 4) are 4.9 kW/kg and 5.1 kW/L, respectively [6]. While others are getting close to the goal, this is significantly higher (Table 5). Commercial OBCs capable of handling power up to 22 kW with a three-phase AC input are being developed by companies such as Ficosa, Hella, and Infineon. Table 2 Maximum rating of semiconductor devices

Table 3 Rating of charging power and number of parallel converters

Table 4 OBC design target

Device

Voltage

Current

Power diode

8.5 kV @ 1.2 kA

9.6 kA @ 1.8 kV

Thyristor

12 kV @1.5 kA

5 kA @ 0.4 kV

GTO

6 kV @ 6 kA

6 kA @ 6 kV

GCT/IGCT/SGCT

10 kV @ 1.7 kA

5 kA @ 4.5 kV

HV-IGBT

6.5 kV @ 0.75 kA

2.4 kA @ 1.7 kV

Charging power rating (kW)

Number of converters

Power rating (kW)/Converter

3.3

1

3.3

6.6

2

3.3

11

3

3.3

22

3

6.6

Targets

2025

Cost, $/kW

35

Specific power, kW/kg

4

Power density, kW/L

4.6

Efficiency

98%

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Table 5 Comparison of industrial grade OBCs OBC

Supply

AC V in (V)

DC V out (V)

Rated power (kW)

Efficiency (%)

Power density (kW/L)

Specific power (kW/kg)

NLG667

1φ 3φ

200–250 360–440

360–750 570–750

6.5 20.75

>90 >94

1.89

1.73

AVIDTP

1φ 3φ

110–260 360–440

450–850

7.3 22

>90

Innolectric OBC42

1φ 3φ

120–240 380−480

210–510

22

>94

0.91

1.22

Innolectric OBC82

1φ 3φ

120–240 380−480

400–850

22

>94

0.91

1.22

Ficosa

1φ 3φ

3.6 22

≤96

5.1

4.9

Evolve

1φ

230–275

180–430

3.5

>93

0.85

0.92

Vitesco

1φ

100–240

220–475

7.2 22

G-Power EV33-336/9

1φ

187–253

200–420

3.3

>94

0.7

0.6

1.8

3 High Voltage Electric Powertrain Due to physical constraints, any vehicle with a powertrain rating more than 12 kW must operate at high voltage, with current limited to a maximum of 250 A. The smaller the current flow required for the same power output, the higher the DC voltage used, allowing for maximum power transfer at peak voltage. Reducing the DC voltage reduces the transmittable power and raises the expenses per kW correspondingly. Higher operating voltages provide increased performance with fewer cooling requirements as the current reduces for the same power. The AC voltage specification in the vehicle sector ranges from 30 to 1 kV, whereas the DC voltage specification is from 60 to 1.5 kV [7] and most EVs operate at greater voltage levels. Though these converters take up a small amount of space, they are nevertheless important, and efforts are being made to enhance their power-to-weight ratio [7]. The high-performance EVs’ battery packs are approximately 350–400 V, which meets the 600 V IGBT industrial standard, and the suggested minimum voltage level is around 230 V. The PFC is designed to sustain an output voltage range of 750 V [5] to allow a wide voltage range. Modern EVs have higher operational voltage and current needs than conventionally powered vehicles. The car makes use of high-capacity batteries, inverters, and regenerative braking technologies. Because they have low switching loss and perform better at higher layer temperatures at the barrier with improved thermal conductivity, higher switching frequency operation of converters, and inverters are most welcoming and advantageous to EV application. Because IGBT-based power converts can run at a greater switching frequency, they are more popular in EV applications [8]. These higher switching frequencies help minimize noise and losses at

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the motor side by lowering the rating of passive inductive components in the power converter. In the meantime, the charger’s total harmonic distortion (THD) should be kept below 5% [9]. A 12 V DC network is also required for the EV system’s accessories and switching converter circuits. It has both fixed and floating fields, and modules with varying voltages create galvanic isolation. When information is exchanged, galvanic isolation prevents current flow between various functional parts without creating noise-generating ground loops. Circuits and people are protected from high voltages by galvanic isolation. The EV system’s accessories and switching converter circuits both require a 12 V DC network. It features both fixed and floating fields, and galvanic isolation is achieved by using modules with variable voltages. Galvanic isolation limits current flow between distinct functional sections without forming noise-generating ground loops while information is transmitted. Galvanic isolation protects circuits and persons from excessive voltages. Despite the fact that most of the proposed chargers in [10] for an FC topology [5], are non-isolated, investigation reveals that (i) there is a common mode voltage between the battery and the field, and (ii) due to the minimal parasitic capacitance between the EV chassis and the battery, BMS malfunction and high-current flow through the earth safety conductor [11]. In the absence of a mitigation measure, this current exceeds the safe limit. Off-board isolation is provided via the EVSE integrated transformer [12]. This type of charger is detailed in [13], however it requires a higher initial investment.

4 Converter Topology for EV Charging Application The interleaved boost converter is gaining prominence in the PFC area. The total volume of the input electromagnetic interference (EMI) filter and the boost inductor are reduced as the output current is increased while the input current is reduced [14, 15]. The circuit complexity and cost increase as a result of interleaving. The transient response and power density of the front-end boost rectifier are both affected by switching frequency. While increasing the switching frequency reduces the size of the reactive components utilized in the converter, it also causes greater harmonics with EMI exceeding 150 kHz [16], as well as increased switching losses and a reduction in efficiency [17]. Figure 2 lists the many EV charger configurations.

5 Conclusion Some of the important aspects that influence the penetration of the EV industry include on-board battery charging time and charging facility. Charger design from an industrial standpoint has been discussed. Because OBCs are more flexible than ECs, the focus is on the OBC design target, which is discussed and compared to the commercial version of OBC currently available on the market to discover the research

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Fig. 2 Classification of EV chargers

potential. The interleaved boost converter is reviewed out of the various available converters for charging applications, and the resulting difficulties are noted.

References 1. A. Khaligh, M. D’Antonio, Global trends in high-power on-board chargers for electric vehicles. IEEE Trans. Veh. Technol. 68(4), 3306–3324 (2019) 2. M. Gjelaj, C. Træholt, S. Hashemi, P.B. Andersen, Cost-benefit analysis of a novel DC fastcharging station with a local battery storage for EVs, in 2017 52nd International Universities Power Engineering Conference (UPEC) (2017), pp. 1–6 3. S. Khan, S. Shariff, A. Ahmad, M. Saad Alam, A comprehensive review on level 2 charging system for electric vehicles. Smart Sci. 6(3), 271–293 (2018) 4. V.D.B. Peter, T. Tom, O. Noshin, V.M. Joeri, Developments and challenges for EV charging infrastructure standardization. World Electr. Veh. J.8(2), 557–563 (2016) 5. L.D. Sousa, B. Silvestre, B. Bouchez, A combined multiphase electric drive and fast battery charger for electric vehicles, in 2010 IEEE Vehicle Power and Propulsion Conference (2010), pp. 1–6 6. On Board Charger. https://www.ficosa.com/products/emobility/on-board-charger/. Accessed Jan 2021 7. ZVDI, Voltage Class for Electric Mobility (ZVEI—German Electrical and Electronic Manufacturers’ Association, 2013) 8. A. Sharma, S. Sharma, ‘Review of power electronics in vehicle-to-grid systems. J. Energy Storage 21, 337–361 (2019) 9. H. Ramakrishnan, J. Rangaraju, Power Topologies in Electric Vehicle Charging Stations (Texas Instruments, 2020) 10. S. Liu, S. Hahlbeck, T. Schoenen, K. Hameyer, An integrated on-board charger with direct grid connection for battery electrical vehicle, in Automation and Motion International Symposium on Power Electronics Power Electronics, Electrical Drives (2012), pp. 335–340 11. A.S. Abdel-Khalik, A. Massoud, S. Ahmed, Interior permanent magnet motor-based isolated on-board integrated battery charger for electric vehicles. IET Electr. Power Appl. 12(1), 124– 134 (2018) 12. C. Shi, Y. Tang, A. Khaligh, A three-phase integrated onboard charger for plug-in electric vehicles. IEEE Trans. Power Electron. 33(6), 4716–4725 (2018)

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13. I. Subotic, N. Bodo, E. Levi, M. Jones, V. Levi, Isolated chargers for EVs incorporating sixphase machines. IEEE Trans. Ind. Electron. 63(1), 653–664 (2016) 14. K. Fahem, D.E. Chariag, L. Sbita, On-board bidirectional battery chargers topologies for plugin hybrid electric vehicles, in 2017 International Conference on Green Energy Conversion Systems (GECS) (2017), pp. 1–6 15. H. Wang, S. Dusmez, A. Khaligh, Design and analysis of a full-bridge LLC-based PEV charger optimized for wide battery voltage range. IEEE Trans. Veh. Technol. 63(4), 1603–1613 (2014) 16. M. Najjar, A. Kouchaki, M. Nymand, evaluation of active common mode filter utilization for size optimization of a 20 kW power factor correction, in 2019 IEEE 13th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG) (2019), pp. 1–5 17. A. Ali, M.M. Khan, J. Yuning, Y. Ali, M.T. Faiz, J. Chuanwen, ZVS/ZCS Vienna rectifier topology for high power applications. IET Power Electron. 12(5), 1285–1294 (2019)

An Efficient Algorithm for Evaluate Routing Metric Parameters for RIoT Aakanksha Tyagi, Kanika Chauhan, and Gaurav Indra

Abstract An Internet of things (IoT) is a fastest growing area in wireless sensor network. Now-a-days IoT is used in many different areas. It structure consist of different connectivity devices, nodes, wireless objects and different routers. The connectivity device routers plays vital role to find an efficient route for packet delivery without any packet loss. The process of find route is known as routing. It perform with the help of router and it maintain routing table with all necessary fields. The routing convention for low-power and lossy organizations (RPL) is a standard directing structure for Internet of things (IoT). It supports multi point-to-point (MP-to-P), highlight point (P-to-P) and highlight multi point (P-to-MP) correspondences. It is realized that RPL’s control overhead can bring about the convention’s comes in P-to-P and P-to-MP communication. Previous research shows that a routing protocol for the Internet of things (RIoT) can supports MP-to-P, P-to-P and P-to-MP correspondences. The convention/protocol can develop P-to-P and P-to-MP courses with moderately lower control overhead. Base paper outcomes show that either with or without RDC RIoT demonstrates measurably fundamentally better packet delivery ratio, end to end delay and control overhead contrasted with the RPL-based convention. We ascertain a routing measurements parameters (hop count, latency, path bandwidth) in this paper. Keywords Computer network · Hop count · RIoT · Minimum spanning tree · WSN

1 Introduction A computer network system is a group of associated have PCs. There are on a very basic level two kinds of systems namely, open system and private system. An open system is where each host can access and share an information and assets which are accessible in system while in a private system just an approved host can get to an information and assets. A. Tyagi (B) · K. Chauhan · G. Indra Indira Gandhi Delhi Technical University for Women, New Delhi, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_53

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IoT is a significant part information sharing. So maintain an integrity of data we need a secure algorithm. WSN is a quickly developing field. Simultaneously, the capacities of a specific single gadget is least, the arrangement of several gadgets offers basic new mechanical conceivable outcomes [1, 2]. The main objective of this paper is to analysis different parameters of routing. Apart this we calculate minimum spanning route for wireless sensor network. The remainder of this paper is broken down into six sections which shows literature survey, problem definition, proposed algorithm and results generated by proposed algorithm. At last in this paper conclusion and future work is defined for further research work.

2 Literature Survey In this literature survey, an extensive and up-to-date survey of the existing security techniques of public and private network are presented. This section reviews the various studies carried out using existing public and private networks provisioning and restoration algorithms that have been applied to the networks to improve their performance and quality of service. Remote sensor networks (WSNs) is the one of the arising networks which works under intrinsic asset limitations. Topological of these types of organizations can transform change powerfully dependent on the area, number of sensor hubs and application. It is key to develop viable distributed calculations to deal with the energy, data transfer capacity limits of WSNs [1]. Because of the appearance of a few network organization innovations, distributed algorithm calculations have ended up being a fundamental and quickly developing field. The significant reason for distributed calculation is to communicate an enormous number of messages, which in a indirectly can use a relatively huge amount of energy and time. Consequently, this requires the organization to be reconfigured to dynamic routing consistently and quickly. In paper [2] author recommended that AAL-IoT applications. To perform this particular task, wireless sensor network might be a non-intrusive choice to collect and evaluate information for inferring something about the physical or intellectual status of a noticed individual users. The most utilized directing conventions in sensor network may have a few disadvantages that diminish the exhibition of the applications. With this, it is significant the utilization of elective techniques to expand the routing proficiency planning to offer a superior assistance to an AAL-IoT application. Subsequently, this paper proposes a composite routing metric as a choice to hop count at AODV with the target of upgrading the directing process. Accordingly, a few improvement are still required. A MST is characterized as a sub-diagram of any chart that have all the vertices and a bunch of edges from the given diagram for conceivable interconnection between each pair of vertices and this sub-diagram is additionally a tree. A few spreading over trees are conceivable of a given diagram. On the off chance that we dole out

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weight to each edge of the diagram and make crossing tree of that specific chart then here comes a term that is least traversing tree (MST). A base spreading over tree is a crossing tree whose absolute load of edges is either not exactly or equivalent to the all-out weight of edges of each and every other conceivable traversing tree of that diagram. Shikha et al. proposed RAY calculation depends on just two basic system followed by certain means in both of the method that neither necessities arranging of edges nor require examination between ways at every current hub to reach to the following nearby hub in the diagram. Beam calculation checks just for cycles or circles that are happening during the way toward creating a base crossing of the given chart. The whole method of deciding the base traversing tree by utilizing RAY calculation can be more justifiable from the model which is trailed by the stepwise working of each interaction of the RAY calculation to deliver the base spreading over tree in an appropriate characterized and reasonable way. Akpan et al. this work fixated on the transportation issue in the shipment of link box for an underground link establishment from three stockpile finishes to four areas at a building site where they are required; in which case, we looked to limit the expense of shipment. The issue was displayed into a bipartite organization portrayal and settled utilizing the Kruskal strategy for least traversing tree; after which the arrangement was affirmed with TORA optimization programming form 2.00. The outcome showed that the expense acquired in delivery the link box under the use of the strategy, which was AED 2,022,000 (in the United Arab Emirate Dollar), was more compelling than that got from simple heuristics when looked at. Charles applied Prim’s algorithm for minimal spanning problem by designing a local area network in Chuka University, Kenya. He was able to minimize the total cost of the various university buildings interconnection which was represented by nodes with fiber-optic.

3 Problem Definition Remote sensor networks (WSNs) are the one of the developing systems which works under characteristic asset limitations. Topological of these sorts of systems can likewise change powerfully dependent on the area, number of sensor hubs and application. It is crucial to develop powerful dispersed calculations to deal with the vitality, data transfer capacity impediments of WSNs. Because of the landing of a few confused system advances, dispersed calculations have ended up being a fundamental and quickly developing field of research. The real reason for dispersed calculation is to transmit an enormous number of messages, which in a roundabout way can devour a nearly huge measure of vitality and time. Subsequently, this requires the system to be reconfigured to dynamic setting normally and rapidly. We evaluate RIoT using different routing metrics and objective functions.

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Additionally, reconfiguration is fundamental to consistently appropriate the vitality utilization among every one of the hubs and in this manner to upgrade the system lifetime. The minimum spanning tree (MST) issue is one of the most wellknown issues in the field of disseminated processing and an oftentimes happening crude in the plan and activity of WSN. Remote sensor systems are made out of low-vitality, little size and low-run unattended sensor hubs. As of late, it has been seen that by intermittently killing on and the detecting and correspondence capacities of sensor hubs, we can altogether diminish the dynamic time and therefore drag out system lifetime. However, this obligation cycling may bring about high organize inactivity, directing overhead and neighbor disclosure delays because of no concurrent rest and allocation. These restrictions require a countermeasure for obligation cycled remote sensor systems which ought to limit directing data, routing traffic burden and vitality utilization. So, the main objective of this paper design a modified minimum spanning tree for wireless sensor network and routing parameters.

3.1 Motivation As we realized that in computer network, a private systems transversely an open system and guarantees clients to move and get data/information crosswise over open or shared systems. They find that a proper network traffic classification is necessary. In previous algorithm, RIoT is used for different communication (P-to-P, P-toMP, MP-to-P), but routing metric does not calculate for RIoT. So, we proposed an extensible algorithm which removes all mention issues.

3.2 Objective of Proposed Work Generate a secure path for send a data in wireless sensor network. a. b. c. d. e. f.

Balance network traffic in WSN. Maintain load balancing of ever node. To investigate the exhibition of private systems model utilizing another security calculation. Minimize time sequence graph parameter. To give ensured transfer speed wireless sensor network administrations and cost enhancement. To guarantee administration accessibility and consistent recuperation in a wireless sensor network.

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4 Proposed Algorithm Input: Wireless sensor arrange dataset Output: Analysis of routing and network parameters, generate MST (minimum spanning tree) Begin: Step: Stage 1: Select the ideal sending of WSN; Stage 2: Initialize arrange hubs and parameters; Step 1: Calculate separation between two hubs and store them in network. Step 2: Generate directed acyclic graph (DAG) for wireless network utilizing datavalue. Step 2.1: For every network node with capacity and distance parameter, if the distance between node and sensor device is minimum. then Form NFR < (sensor device, node); end if end for Calculate the total cost C new C new > {C iter,1 ; C iter,2 ; …; C iter,n }; end while if {C iter1 ; C iter2 ; …; C itern } not equal to null then Compare elements, and then C min Min {C; C iter,1 ; C iter,2 ; …; C iter,n }; end if Calculate minimum spanning tree Step 3: Balance traffic shaping/congestion control using congestion control algorithm. Step 3.1: Set the buffer size; Step 3.2: Set the output rate; Do Transmit the packets such that there is no overflow. For each packet do Repeat the process of transmission until all packets are transmitted. If pack_size > buffer_size then stop

5 Result Analysis In this research work, we have evaluated deployment of wireless sensor network and minimum spanning tree using proposed algorithm and calculate all routing parameters. We present here load distribution graph of WSN, execution time of algorithm,

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Fig. 1 WSN deployment with 50 nodes by proposed algorithm

load balancing and throughput of the proposed algorithm. To measure these performance parameters, we have deploy WSN and sensor data set. The main purpose of the proposed algorithm is to improve traffic and make a minimal spanning tree which reduce time parameter. All the experiment execute on MATLAB R2021a (Figs. 1, 2, 3 and 4).

6 Conclusion and Future Work IoT is a fastest growing field. Basically wireless sensor networks are used in IoT infrastructure. As mention above router plays vital role in WSN. RIoT so an extensive routing metric algorithm is necessary for maintain routing table and evaluate its different parameters. We proposed an algorithm which provide better results in many terms. A system security or data security is one of the main issue in data communication. A data encryption is one of the solution for security issues in data communication. The experimental study shows that proposed algorithm gives accurate result for security in terms of routing parameters, cost, execution time, authentication and secure key generation. In future network packet and routing matric can visualize on MATshark software.

An Efficient Algorithm for Evaluate Routing Metric … Fig. 2 DAG and PATH generation by proposed algorithm

Fig. 3 Distance of nodes in WSN

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Fig. 4 WSN network with malicious node

Bibliography 1. Y. Gu, Y. Ji, J. Li, F. Ren, B. Zhao, EMS: efficient mobile sink scheduling in wireless sensor networks. Ad Hoc Netw. 11(5), 1556–1570 (2013) 2. A.J. Garcia-Sanchez, F. Garcia-Sanchez, F. Losilla, P. Kulakowski, J. Garcia-Haro, A. Rodríguez, J.V. López-Bao, F. Palomares, Wireless sensor network deployment for monitoring wildlife passages. Sensors 10(8), 7236–7262 (2010) 3. S. Samarah, M. Al-Hajri, A. Boukerche, A predictive energy-efficient technique to support object-tracking sensor networks. IEEE Trans. Veh. Technol. 60(2), 656–663 (2011) 4. L. Yu, N. Wang, X. Meng, Real-time forest fire detection with wireless sensor networks, in International Conference on Wireless Communications, Networking and Mobile Computing, 2005. Proceedings 2005 (IEEE, 2005), pp. 1214–1217 5. I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, Wireless sensor networks: a survey. CompNet 38(4), 393–422 (2002) 6. C. Tunca, S. Isik, M.Y. Donmez, C. Ersoy, Distributed mobile sink routing for wireless sensor networks: a survey. IEEE Commun. Surv. Tutor. 16(2), 877–897 (2014) 7. K. Almi’Ani, A. Viglas, L. Libman, Energy-efficient data gathering with tour lengthconstrained mobile elements in wireless sensor networks, in 2010 IEEE 35th Conference on Local Computer Networks (LCN) (IEEE, Denver, Colorado, USA, 2010), pp. 582–589 8. S. Preetha, S. Nagarathinam, Weighted rendezvous planning for energy efficient mobilesink path in wireless sensor networks, in 2015 2nd International Conference on Electronics and Communication Systems (ICECS) (IEEE, Karpagam College of Engineering, Othakalmandapam, Coimbatore, India, 2015), pp. 695–698 9. L. Bagheri, M.D.T. Fooladi, A rendezvous-based data collection algorithm with mobile sink in wireless sensor networks, in 2014 4th International eConference on Computer and Knowledge Engineering (ICCKE) (IEEE, Ferdowsi University of Mashhad, 2014), pp. 758–762 10. W. Liang, J. Luo, X. Xu, Prolonging network lifetime via a controlled mobile sink in wireless sensor networks, in Global Telecommunications Conference (GLOBECOM 2010), vol. 2010 (IEEE, Miami, Florida, USA, 2010), pp. 1–6 11. R.C. Shah, S. Roy, S. Jain, W. Brunette, Data mules: modeling and analysis of a three-tier architecture for sparse sensor networks. Ad Hoc Netw. 1(2), 215–233 (2003) 12. L. Tong, Q. Zhao, S. Adireddy, Sensor networks with mobile agents, in Military Communications Conference, 2003. MILCOM’03, vol. 1 (IEEE, Monterey, CA, 2003), pp. 688–693

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13. Y. Shi, Y.T. Hou, Theoretical results on base station movement problem for sensor network, in INFOCOM 2008. The 27th Conference on Computer Communications (IEEE, Phoenix, AZ, USA, 2008)

Mining Top-K Competitors by Eliminating the K-Least Items from Unstructured Dataset Mahendra Eknath Pawar and Satish Saini

Abstract In today’s merciless business environment, victory is entirely dependent on the potential to create something that is maximum engaging to consumer than the competitiveness. ‘Huge information’ is a popular statement that refers to large amounts of information that can be divided into three categories: organized information, semi-organized information, and unstructured information (see Fig. 1). The volume and variety of significant amounts of information are so vast that it is nearly impossible to collect, process, and store it using normal database administration frameworks and programming methods. As a result, breaking down massive amounts of information necessitates the use of a variety of approaches and instruments. In this work, we attempted to address the issue of entrepreneurial practices, often known as the identification of the k-most competitive products (k-MCP). Keywords Itemset mining · Web mining · Data mining · Information retrieval · Association rules

1 Introduction The strategic significance of discovering and analyzing company competitors is an unavoidable research that is prompted by a number of business issues. In previous research, the monitoring and identification of a firm’s competitors were investigated. For mining competitors, data mining is the most efficient method of dealing with such large amounts of information. Item reviews forms available online provide valuable information about consumer’ opinions and interests, which can be used to gain a general understanding of competition. To the contrary, it is often impossible to read all of the reviews on different websites for competing brands and to receive useful ideas in this manner manually. M. E. Pawar (B) RIMT University, Punjab, India S. Saini ECE, RIMT University, Punjab, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_54

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Because of the strong competition, everyone is preoccupied with attracting the greatest possible amount of attention from consumers. In order to do this, the company must have products that meet the wants of customers. In this topic, extensive research is being conducted on a large scale. In such circumstances, the needs of the consumer are quite crucial. The effectiveness of a manufacturing process can be represented mathematically as a function that represents the company’s contact with various stake holders, among other things. Suppose the situation of rentable housing multishops in a town, where the distance between a multishops and a hospital is one of the most important requirements for customers seeking a rentable property to meet their needs. For the purpose of making a marketing decision, a rental firm has acquired the necessary information from its customers about the distance between them and the nearest market and hospital. Consider the following scenario: a rental corporation demands a collection of properties. The management of the said organization must select k residences in order to compete with the current rentable dwellings available for rental in the area. A way for obtaining the greatest possible advantage is to increase the estimated number of customers for each of the k properties selected. Let E represent the sets of present products. C represent sets of potential products. Furthermore, kC is the considered of k picked from C, C refer consumer product from kC, U refer set of users needs are met by cp which is candidate product select from the set of candidate product, as shown in the example. According to the formula, the chance of U select cp is inversely correlated to the number of products, comprising E, kC that fulfill C. The predicted figure of users cp is affected by the figure of consumers met cp as well as the whole count of items fulfill the similar number of users as illustrated in Fig. 1. In this system, the goal is to provide instructions on how to develop a competent and appropriate algorithm for understanding the problem of k-MCP finding.

Fig. 1 An illustration for k-MCP mining

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The prime benefaction of this system: limitation in k-MCP is calculated to be an application development issue of required things. It is then propound that two algorithms be used in conjunction with pruning approaches to determine the suitable solution to the task. One approach for determining the k-least number of goods is presented, which is also significant for production planning. Following the completion of k-least, the items/products are removed from the list, resulting in a reduction in the time required for k-most competitive products. The organization of the paper: firstly discuss about the complication that addressing the possible solution in Sect. 1. In Sect. 2, discuss about the existing mechanism and about the various models to be used in the proposed system. Section 3 presents the implementation details like the structure of dataset, evaluation of performance parameters of the system, experimental environment and obtained outcome. In Sect. 4, present the explicit scrutiny of the result obtain from the experiment followed by the conclusion.

2 Literature Review 2.1 Related Work Data evaluation is necessary to determine whether or not associations will downplay the obvious. In reality, no firm can make decisions without first consulting publicly available data [1]. When it comes to making purchasing selections for goods, customer inclination is a critical aspect to consider, which transforms into one of the most important stresses in microeconomics. A wide range of frameworks, incorporating quantitative evaluation and machine learning-based methods, are being demonstrated as a portion of the rapid manufacturing industry. The rationale for conducting these types of investigations is to enable relationships to develop new goods that meet the needs of clients in the target market, which is, in other words, the purpose of this research report. Recognizing that there are various relationships, each with their own additional benefit restrictions and a plan of treatment of consumer requirements, and taking into cogitation competition, the goal of [2] is to explore one stuff with the best anticipated percentage of consumers for every bonding, while also satisfying the advantage fundamental of each one bonding. Conclusion, the goods revealed in [2, 3] are required to complete the advantage requirement of the affiliation; nonetheless, they are difficult to determine. A number of different studies, such as [4], have worked with the potential consumers locating other types of requests. It is impossible to distinguish between the considerations of these works on the basis of their practicality. The investigation in [5] advances the cause of uncovering heuristics of a thing by which the rank of the particular item is the most astounding fundamental motivation each and every one of the products. Because it does not take into account client requirements, it is possible that customers will not be satisfied with the items discovered as a result

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of the theory’s aims. Miah et al. [6] suggest a technique to find k tricks of a given product that please a large number of consumers, while taking the client criteria into consideration. Using the found advantages to promote the product should have a greater chance of attracting the ideas of more buyers than other methods. Vlachou [7] predicted that a top-k request will be published as the first issue of significance. Besides that they started developing compartmentalization plans that are based on area partitioning, which creates switch top-k views, which are then used to shift pivot top-k query activity through and via other compartments. They make it possible for thoroughgoing check evaluations to be performed, which reveals the experience of their figuring’s. RTA consistently increases one’s response to solicitations of varying sizes from the unaware approach. Measure collection of captivating measure for future work is available in the square measure collection. This is especially true for larger measurements. Miah et al. [8] anticipated the problem of selecting and combining the best qualities of the most recent tulle, and determined that this tuple is extremely hierarchical when provided a dataset, a cal log, or both. However, started working on refining the annoyance for mathematicians by depicting, composing material; which means that ideal figures may be obtained from modest data sources. Aside from that they revealed inexhaustible counts, which square measure offered to build magnificently erroneous extents. Eventually, in the midst of this debate, they were concentrated on slanting toward whatever group of qualities should be removed in order to resolve the issue.

2.2 Bit Map Index Structure In this section, the number of consumers in C for a kCP of k goods, i.e., E(kC, U), is obtained by adding E(C, U). For each user U in C, it is necessary to number of the existing items that satisfy U in order to obtain the value of the variable N(EP, U). This paper proposes the use of a BM index structure dubbed BMI to efficiently compute N(EP, U), while simultaneously maintaining the fulfilling information between the problems pertaining of users in C and the grade values of current items in EP. In this case, qv[x] values on the xth (Tables 1 and 2).  qv[x] =



U ∈C



⎛

U [x] ∪ ⎝



⎞ ep[x]⎠

(1)

ep∈EP

It follows that ep1 satisfies the conditions c1 , c3 , c4 , c5 , c6 , c8 and c9 . All of the existing rental properties’ satisfaction bit strings are presented in Table 3 along with their satisfaction bit strings. Similarly, in Table 4, the matching N{EP, U} is depicted.

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Table 1 BMI structure for all the users wrt existing rental properties Customers/Existing Quality describer rental properties Distance to school

Distance to sub way

9

8

7

6

5

4

3

2

1

10 9

8

7

6

5

4

3

2

1

c1

0

0

0

0

0

0

1

1

1

0

0

0

0

1

1

1

1

1

1

c2

0

0

0

0

0

0

0

0

1

1

1

1

1

1

1

1

1

1

1

c3

0

0

0

0

1

1

1

1

1

0

0

0

0

1

1

1

1

1

1

c4

0

0

0

0

1

1

1

1

1

0

0

1

1

1

1

1

1

1

1

c5

0

0

0

1

1

1

1

1

1

0

0

0

1

1

1

1

1

1

1

c6

0

0

0

1

1

1

1

1

1

0

0

0

0

0

1

1

1

1

1

c7

0

0

1

1

1

1

1

1

1

0

0

0

0

0

0

0

1

1

1

c8

0

1

1

1

1

1

1

1

1

0

0

0

0

1

1

1

1

1

1

c9

1

1

1

1

1

1

1

1

1

0

1

1

1

1

1

1

1

1

1

ep1

0

0

0

0

0

0

0

1

1

0

0

0

0

0

0

1

1

1

1

ep2

0

0

0

0

0

1

1

1

1

0

0

0

0

0

0

0

0

1

1

ep3

0

0

0

0

1

1

1

1

1

0

0

0

0

0

0

0

0

0

1

Table 2 Satisfaction bit string wrt existing properties Existing rental properties

Customers c1

c2

c3

c4

c5

c6

c7

c8

c9

ep1

1

0

1

1

1

1

0

1

1

ep2

0

0

1

1

1

1

1

1

1

ep3

0

0

1

1

1

1

1

1

1

Table 3 N{EP, U} for the example EP

c1

c2

c3

c4

c5

c6

c7

c8

c9

1

0

3

3

3

3

2

3

3

Table 4 Computation time on varying number of customers

No. of customers

UBP

SPG

25

510

535

50

747

778

75

932

913

100

1185

1198

125

1377

1392

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3 Implementation and Experiment Analysis The overall flow of the system is shown in Fig. 2. Firstly we calculate the BMI Index on the input dataset as discussed in previous section. The satisfaction bit string is than calculated and N Vector for each instance is obtained. Based on this we implement the two Greedy algorithms, SPG greedy (single post greedy) Algorithm and Upper Bound Pruning (UBP) Algorithm for determining the k-most competitive products. Thus we propose to obtain the k-least important products from the dataset and eliminate it from the algorithm workspace. Data Collection: A dataset from TripAdvisor is utilized to calculate the k-most competitive items for the purposes of the trials. Several quality describers are evaluated on the legal resources to evaluation some parameters. The user comments with low rating scores “average” were selected from the data sets in order to emulate the consumer requirements. Data Preprocessing: If the data used for the analysis is not normalized, it might affect the accuracy of the system. Setup and Techniques Used: The proposed system was programmed in Java in order to assess their efficacy, efficiency and memory requirements. This allowed us to compare the results. The

Fig. 2 Flow of the system

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studies were carried out using a computer equipped with an Intel Core i5 processor, 8 GB of system memory and the Microsoft Windows 10 operating system. Optimal Solutions and Performance Analysis: Let M, be a system such that, K = {IP, P, Algo, O} Input IP = {DI, CR} Where, DI = Dataset. DI = {di1, di2, …, din} Where, D is set of input data and di1, di2 … din are number of data in set DI CR = {cr1, cr2, …, crn} cr1, cr2 … crn are number of Customer Requirements. Preprocessing P = {BMI, SI, N} Where, P = set of Data Preprocessing BMI = BMI index Structure SB = Satisfaction Bit String N = N-vector(EP, C) Algorithms for obtaining k-Most Competitive Products and k-Least Competitive Products Algo = {SPG, UBP, k-LEAST} Where, SPG = Single Product Based Greedy. UBP = Upper Bound Pruning Algorithm. TOP-K = Exact Top k. Output O = k-Most Competitive Products and k-Least Competitive Products.

Following Table 4 compares the computation time of the UBP method and the k-LEAST algorithm when the number of clients is varied. The following numbers of customers are used: 25, 50, 75, 100 and 125 customers. The results of the computational cost of UBP and SPG are shown in the following Fig. 3. It seems that the UBP algorithm takes the same amount of time to execute as the SPG algorithm. This is seen in the accompanying graph, which shows the differentiation memory. Coordinate axis, the numerous algorithms listed, including UBP, SGP and a few more. As an additional step, we implement the procedure both before and after deleting the k-least product, and the results are displayed in Fig. 4. That is, number of memory bytes will gradually expand. Memory comparisons of UBP and k-least algorithms are both among best of all the algorithms are fulfill in terms of execution. Using the coordinate axis, it is possible to compute the amount of RAM in bytes, which ranges from 0 to little below 52,000. The time comparison is illustrated in Fig. 5, which is seen below. On the X-axis, algorithms that are similar to those used in the first graph are employed. The time in milliseconds is displayed on the Y-axis, which is used to estimate the computational cost of the algorithms. Time is calculated in milliseconds.

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Computation Time on Number of Customers UBP

SPG

1600 1400 1200 1000 800 600 400 200 0

25

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Fig. 3 Computation time analysis

Memory Comparison Graph 52000 51000 50000 49000 48000 47000 46000 45000 44000 43000 42000

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SPG

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46179

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Fig. 4 Memory utilization analysis

4 Conclusions This work presents a formulation of the k-MCP discovery issue, which is concerned with identifying the k-most competitive goods with the highest predicted number of users. System initially computed BM index structure, satisfaction N-vector (EP, C), bit string which were then used as inputs. In the same way, two algorithms, one of which being the SPG algorithm, are described for get an appropriate solution to the problem. Additionally, the UBP algorithm is introduced for the purpose of

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Time Comparison Graph 30000

Time in ms

25000 20000 15000 10000 5000 0

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SPG

9318

10206

11025

13241

UBP

22612

23948

24338

24580

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2296

2343

2788

3279

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4659

6483

7893

8118

Algorithm

Fig. 5 Time utilization analysis

identifying optimal solutions. In order to prune products that are unable to become optimal solutions, these two algorithms use lower and upper bound pruning techniques. System has also developed an algorithm for identifying the least demanding items, which can be useful in the planning of manufacturing operations. The levels of quality attributes may have an impact on the likelihood that a product will be purchased by a customer. In a similar vein, notional qualities are sometimes used to represent the characteristics of a product in some applications. These are the kinds of questions that can be addressed through research.

References 1. A.N. Paidi, Data mining: future trends and applications. Int. J. Mod. Eng. Res. (IJMER) 2(6), 4657–4663 (2012) 2. Z. Zhang, L.V.S. Lakshmanan, A.K.H. Tung, On domination game analysis for microeconomic data mining. ACM Trans. Knowl. Disc. Data 2(4), 18–44 (2009) 3. X. Lian, L. Chen, Monochromatic and bichromatic reverse skyline search over uncertain databases, in Proceedings of 27th ACM SIGMOD International Conference on Management of Data (2008), pp. 213–226 4. F. Korn, S. Muthukrishnan, Influence sets based on reverse nearest neighbor queries, in Proceedings of 19th ACM SIGMOD International Conference on Management of Data (2000), pp. 201–212 5. T. Wu, D. Xin, Q. Mei, J. Han, Promotion analysis in multi-dimensional space, in Proceedings of 35th International Conference on Very Large Data Bases (2009), pp. 109–120 6. M. Miah, G. Das, V. Hristidis, H. Mannila, Determining attributes to maximize visibility of objects. IEEE Trans. Knowl. Data Eng. 21(7), 959–973 (2009) 7. A. Vlachou, C. Doulkeridis, Y. Kotidis, K. Norvag, Reverse top-k queries, in Proceedings of 26th International Conference on Data Engineering (2010), pp. 365–376

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8. Y. Tao, D. Papadias, X. Lian, Reverse kNN search in arbitrary dimensionality, in Proceedings of 30th International Conference on Very Large Data Bases (2004), pp. 744–755 9. W. Wu, F. Yang, C.Y. Chan, K.L. Tan, FINCH: evaluating reverse k-nearest-neighbor queries on location data, in Proceedings of 34th International Conference on Very Large Data Base (2008), pp. 1056–1067 10. C. Li, B.C. Ooi, A.K.H. Tung, S. Wang, DADA: a data cube for dominant relationship analysis, in Proceedings of 25th ACM SIGMOD International Conference on Management of Data (2006), pp. 659–670 11. C.-Y. Lin, J.-L. Koh, A.L.P. Chen, Determining K-most demanding products with maximum expected number of total customers. IEEE Trans. Knowl. Data Eng. 25(8) (2016) 12. E. Achtert, C. Bohm, P. Kroger, P. Kunath, A. Pryakhin, M. Renz, Efficient reverse k-nearest neighbor search in arbitrary metric spaces, in Proceedings of 25th ACM SIGMOD International Conference on Management of Data (2006), pp. 515–526 13. E. Dellis, B. Seeger, Efficient computation of reverse skyline queries, in Proceedings of 33rd International Conference on Very Large Data Bases (2007), pp. 291–302 14. J. Kleinberg, C. Papadimitriou, P. Raghavan, A microeconomic view of data mining. Data Min. Knowl. Disc. 2(4), 311–322 (1998) 15. Q. Wan, R.C.-W. Wong, I.F. Ilyas, M.T. Ozsu, Y. Peng, Creating competitive products, in Proceedings of 35th International Conference on Very Large Data Bases (2009), pp. 898–909 16. W.C. Wang, E.T. Wang, A.L.P. Chen, Dynamic skylines considering range queries, in Proceedings of 16th International Conference on Database Systems for Advanced Applications (2011) 17. M. Miah, G. Das, V. Hristidis, H. Mannila, Standing out in a crowd: selecting attributes for maximum visibility, in Proceedings of 24th International Conference on Data Engineering (2008), pp. 356–365 18. S. Borzsonyi, D. Kossmann, K. Stocker, The skyline operator, in Proceedings of 17th International Conference on Data Engineering (2001), pp. 421–430

Comparative Analysis of GLDAS and CWC Data of Wardha Basin Yukta Chikate, Atharva Konge, and Asheesh Sharma

Abstract The Global Land Data Assimilation System (GLDAS) takes satellite and ground-based observation and data products. This project aims to obtain the surface runoff data from GLDAS for a particular period of time of a specific region to study the rainfall and reasons of flood occurrences. The observed data of Wardha region, India, obtained from Central Water Commission (CWC) is compared with the extracted and calculated data from GLDAS to find the co-relation around 0.6–0.7. The images were filtered according to the clipped region found with the help of a shape file obtained from Bhuvan website of India. Total 9 co-ordinates are altered using the Arc-GIS application. The extracted data was clipped from the region according to the coordinate got from the Arc-GIS by filtering the images of (6–9) a.m. from 2000 to 2015 for each day. The GLDAS data is in kg/m2 /3 h and the observed data from CWC is m3 /s, i.e., volumetric flow. This project converted the obtained GLDAS data into desired units for comparing it with the observed data and represent it in the form of graph to show the values of both in pictorial representation and observe the heavy rainfall and flood occurrences during the period of 2000–2015. Keywords GLDAS · CWC · Runoff · Wardha basin · Bamni · Arc-GIS · Python · Water management

1 Introduction Dams become hazardous when they are over-flowed due to heavy rainfall. This can be prevented if we can find the amount of rainfall received in that region according to various seasons [1]. For this, we have developed a module which fetches the data Y. Chikate (B) William O’Neil India, Bengaluru, India A. Konge Yeshwantrao Chavan College of Engineering, Nagpur, Maharashtra, India A. Sharma NEERI, Nagpur, Maharashtra, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_55

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from Google Earth engine and GLDAS surface runoff is received and converted to appropriate values. Moreover, we have calculated runoff of particular regions of India to find out the co-relation between the observed data and the data obtained from CWC and to study the high and low surface runoff pattern according to the seasons. The results of this analysis are useful for understanding regional surface runoff and their relationship in various seasons over India [2, 3]. The scope of our project is to analyze the data using Google Earth engine to extract GLDAS data in order to get the runoff of a particular location for a particular time period [4]. We are displaying the output in the form of graphs, maps, and statistical data. Also for better understanding, we are using a software named Arc Maps which helps in analyzing the output maps. We have extracted the GLDAS runoff data from 2000 to 2015 for every day and then we filtered the (6–9) a.m. image identities so that we get the approximate correct value. Data was in kg/m2 /3 h. We applied unit conversion and converted the data into volumetric flow, i.e., m3 /s. We have done this for a particular point, i.e., Bamni. The final co-relation between the observed data and the data extracted and converted to proper units is 0.61593. Hence, we are able to find the pattern of increase and decrease in the rainfall at Bamni region over 15 years, i.e. 2000–2015 and hence this module can be used for any other regions in the world.

2 Methodology To extract the dataset from the GLDAS model, we create a session with Google Earth engine (GEE). In GEE, we need to create an account by creating a unique identity and password to authenticate and link with GEE’s API. The observed data was received from the Bhuvan website which included the river and shape file of the catchment area of Bamni region [5, 6]. For the catchment boundary of all the regions, projecting a shape file in Arc-GIS can obtain a clear view. The grid will be created by using Fishnet or polygon grid tool to get the x and y co-ordinates. The grid size should be 27,700 × 27,700 m2 according to the GLDAS dataset. Hence values of every day runoff is fetched using GLDAS dataset from 2000 to 2015 of Bamni region (India). Data in GLDAS is updated every 3 h in a day, so if we are fetching every day’s data then for 1 day, it will give us 8 images of 3 h each starting from 12:00 a.m. We filter out the identifications of all the images which are having the time slot of (6–9) a.m. which in our case is the closest values we can get. The images are getting exported to the Google drive for future usages. Further, by using a function which will return all the 36 bands values of the required time period and of the given location along with the geometry defining the longitude and latitude of each point. From this, the .csv file will be read in order to get the latitude and longitude of all the points that are fetched with the help of Arc-GIS using the shape file of the catchment area of Bamni region. Total points the Bamni area received is 9. We store the unique ids in a .csv file and then for its usage extract them in a list. Then, iterate over those image ids and append the GLDAS dataset id in another list for storing purposes and save the

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output in a csv file. The calculation of converting the units from kg/m2 to m3 /s, i.e., volumetric flow. We needed to convert the values from density area to volumetric flow and stored the calculated values. We plotted the graph showing the difference in observed and calculated data and also displayed the table consisting of calculated and converted data values from 2000 to 2015.

3 Results and Discussion In the study area, the shape file of Wardha basin’s data was obtained by CWC through Bhuvan, and was observed in the Arc-GIS software. The shape file includes data of catchment of Wardha region, various river streams, dam, and Bamni region point. We can see the tributaries on the map area and also we have displayed the centroids of each grid. Now, by layering the catchment area with the grid built and observing 9 points under it. Hence, successfully created the grid for the catchment area (Fig. 1). Fig. 1 The built grid on catchment area

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The surface runoff dataset is in kg/m2 for 3 h each. It is the density area given for every 3 h’ time period for a particular location. For getting the desired values, we have converted the density area into volumetric flow for every seconds, i.e., m3 /s, i.e., GLDAS runoff is in kg/m2 /3 h. Kg of water is equal to 0.001 m3 and the area of 1 pixel in m2 is 767,290,000. Total number of pixels in the catchment area is 9 and the catchment area is 3,720,000,000 m. So in calculation of Bamni, 9 co-ordinate values of surface runoff of 02/01/2000 are displayed total of 9 values: 0.049699999857. After getting the desired points of the catchment region through the analysis and plotting of files in Arc-GIS, the values of every day runoff are fetched using Global Land Data Assimilation System (GLDAS) dataset from 2000 to 2015 of Bamni region (India) [7]. The region is clipped to Bamni point and the data is obtained for 2000–2015. In GLDAS, data fetched for every 3 h and hence we obtained the images of only 3 h, i.e., (6–9) a.m. Though most of the observed values came around 8:30 a.m. to get more accurately calculated values. The data shown in the table is then saved as .csv on the local computer for the further calculations and storage purposes. Along with runoff data, other data such as precipitation, soil moisture, evaporation, evapotranspiration and snow is also present in the .csv file as per observation (Fig. 2). It consists of 5843 data values (2000–2015) of every day. In Fig. 3, we are comparing the observed data given from CWC and the values which we obtained from GLDAS and filter and converted them to the desired units. The co-relation came around 0.6–0.7 through which it can be said that the two sets of data are strongly linked together. From Table 1, positive co-relation is seen. The data obtained after calculating corelation for each year is near to 0.7 and is positive which means both the quantity, i.e., observed data and the data obtained through calculated GLDAS can be considered as mostly the same. If Bhuvan data is not there, GLDAS data can be depended on. From Figs. 4, 5 and 6, we have segregated some values for each year between 2000 and 2015 for comparing the observed data and the calculated data we got. The

Fig. 2 Final GLDAS calculated values with year from 2000 to 2015

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Fig. 3 Scattered comparison of observed and final data Table 1 Yearly co-relation observations

Year

Final value

Year

Final value

2000

0.74077727

2008

0.7877

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0.600833

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0.7607

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0.605027

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0.7833

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0.57927

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0.7362

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0.4502

2015

0.779

2007

0.6693

2016

0.6132

Fig. 4 Summer season data 2000–2015

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Fig. 5 Rainy season data 2000–2015

Fig. 6 Winter season data 2000–2015

observed data has been taken from the CWC website from 2000 to 2015 of Wardha area of Maharashtra, India. We can observe that the surface runoff value was highest in the month of August. In 2013, the runoff increased around 3475.879 m3 /s which led to rise in water level and hence caused floods at Irai river which resulted in rise of water level sharply which immersed the Chandrapur area. Similarly, the highest water level of 175.89 m recorded during the deluge of August 2006. We can see, the surface runoff is very minimal around January to May. As June approaches, the surface runoff increases and thus water level rises. By observing this, we can find the pattern of rainfall and thus prevent the floods by opening and closing the dam’s door at the appropriate time [8]. We find the co-relation of the observed and the calculated GLDAS data from 2000 to 2015. It came as 0.615930 units of runoff more than 10,000 cusec in observed values is due to dam flow in 2013 when flood happened. In August 2016, the flood

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level of the Irai river rose sharply, so the water level of Bamni rose eventually. Water level at Bamni was recorded at 172.92 m at Wardha river in the afternoon. It is expected to rise up to 175.57 m, which is very close to the highest water level of 175.89 m recorded during the deluge of August 2006. In August 2006, half of the city was submerged due to flooding for nearly four days. Around September 2019, in Nagbhid Tehsil of MP district, 13 people were rescued from flood waters near Bamni village.

4 Conclusion and Future Scope The need for this model is to extract the runoff data which is essential as it can greatly contribute in the controlling of any natural calamities and various research analyses. Here, we have fetched GLDAS data of the river surface runoff of Bamni region in India from the 2000–2015 time period in the form of bands and co-ordinates. We also performed calculation of unit conversion to volumetric flow so that the final output received is in a single data type. The final co-relation between the observed data and the data extracted was found to be 0.61593 units. For further analysis, we compare the final values with the data provided by CWC by Bhuvan website. The main objective of the project was to observe and study the runoff according to the precipitation as per seasons so that we can solve the water management problem of any required region. Using this model, systems can notify the opening time of dam gates by looking at the average water level of the water reservoir. This could be helpful in flood management of various regions.

References 1. Y. Deng, J.P. Wilson, Gallant, Chap. 23, in The Handbook of Geographic Information Systems, vol. 417. 2. B.F. Zaitchik, M. Rodell, F. Olivera, Evaluation of the global land data assimilation system using global river discharge data and a source-to-sink routing scheme. Water Resour. Res. 46, W06507 (2010). https://doi.org/10.1029/2009WR007811 3. M. Rodell, P. Houser, U.E.A. Jambor, J. Gottschalck, K. Mitchell, J. Meng, K. Arsenault, C. Brian, J. Radakovich, M.G. Bosilovich, J. Entin, J. Walker, D. Lohmann, D.L. Toll (2004) The global land data assimilation system. BAMS 85, 381–394. https://doi.org/10.1175/BAMS-853-381 4. N. Gorelick, M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau, R. Moore, Google earth engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017). ISSN 0034-4257 5. K.S. Rawat, S.K. Singh, Estimation of surface runoff from semi-arid ungauged agricultural watershed using SCS-CN method and earth observation data sets. Water Conserv. Sci. Eng. 1, 233–247 (2017) 6. B. Hao, M. Mingguo, L. Shiwei, Q. Li, D. Hao, J. Huang, Z. Ge, H. Yang, X. Han, Land use change and climate variation in the three gorges reservoir catchment from 2000 to 2015 based on the Google Earth Engine. Sensors 19, 1–24 (2019). https://doi.org/10.3390/s19092118

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7. W. Qi, J. Liu, H. Yang, X. Zhu, Y. Tian, X. Jiang, X. Huang, L. Feng (2020) Large uncertainties in runoff estimations of GLDAS versions 2.0 and 2.1 in China. Earth Space Sci. 7, e2019EA000829 (2020) 8. M. Lv, H. Lu, K. Yang, Z. Xu, M. Lv, X. Huang, Assessment of runoff components simulated by GLDAS against UNH–GRDC dataset at global and hemispheric scales. Water 10, 969 (2018)

Optimal Designing of FIR Filter with Hybrid Bat Optimization Algorithm Harmandeep Kaur, Satish Saini, and Amit Sehgal

Abstract In the present article, a unique optimization innovation is implemented for designing finite impulse response (FIR) filter with wanted parameter details. A new optimization technique is proposed in this work using the hybrid of bat and seeker optimization algorithms and filter coefficients are designed using particle swarm optimization (PSO), bat, seeker and hybrid optimization technique. Using these algorithms, the optimum coefficients of the impulse response for the variant filters are determined to fulfil their ideal response features. In hybrid optimization, the parameters of both the bat and seeker are combined to explore the merits of both the techniques. Initially, the echolocation capability of bat is utilized and in accordance with that the approximate location of the target is found. Immediately after this to reach at the optimal value, the search direction property of seeker is taken into effect. This designed technique proved its efficiency in terms of results and implementation. In comparison to traditional optimization algorithms, these techniques do not stuck on local optimal solution. Simulative results of these techniques are presented and compared on the base of their pass-band as well as stop ripples. Keywords FIR filter · PSO · Bat · Seeker algorithm

1 Introduction In this burgeoning era, an intensive work is being done on the designing of digital filters due to the advent of latest technology. In the field of electronics, filters play an indispensable role and are considered to be an essential part in almost every field of engineering. In the most emerging field of signal processing, a channel is a gadget or procedure that expels some undesirable part or a component from the H. Kaur (B) · S. Saini School of Engineering (Electronics and Communication Engineering), RIMT University, Mandi Gobindgarh, Punjab, India A. Sehgal School of Engineering and Technology, Sharda University, Greater Noida, Uttar Pradesh, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_56

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given signal [1]. Categorization of filters is performed on the base of distinctive criteria’s, i.e. the type of the signal given at the input, frequency characteristics and the duration of impulse response. Depending upon the impulse response duration, these are categorised as FIR and IIR filters [2]. FIR filter proves to be more efficient in terms of linearity and stability whereas IIR filters requires less coefficients and less memory. FIR filter has impulse response of limited continuance, since it sets to negligible value in bounded interval. Whereas impulse response of IIR filters has boundless continuance. The design procedure for any filter requires a particular set of parameters to be defined known as filter specifications. Umpteen number of research papers have been recorded by filter designing researchers for projecting FIR filters. Different design methods are: window design method [2–5], frequency sampling method [2, 4] weighted least squares design [2, 4] and Parks-McClellan method [6]. The main idea behind these optimal design procedures is to incur the filter coefficients again and again until the desired outcome is obtained or as specified in the programme. A diverse variety of algorithms have been put forward by researchers for optimization. Different researchers have introduced plethora of new concepts leading to a number of optimization techniques [7]. Kennedy and Eberhart introduced particle swarm for optimizing the nonlinear functions [8] and thus particle swarm optimization (PSO) has eventually turned apparent as a very prominent technique for providing a solution to a variety of optimization issues [9, 10]. The variants of PSO are also presented by many researchers [11–13]. Differential evolution (DE) method was introduced by Price and Storn for optimizing the continuous functions [14] and used in [15–17]. Similarly, teaching learning based optimization (TLBO) [18–20] cuckoo search (CS) [21, 22], artificial bee colony (ABC) optimization [23], grasshopper optimization algorithm [24] find their use in optimization problems. Seeker optimization algorithm (SOA) [25] mime the behaviours of anthropoid hunt population to resolve optimization problems of real-parameters [26, 27] and bat algorithm [28] is inspired form bats behaviour in foraging for food. Nowadays, it is being used in almost every field of engineering, such as optimization [28, 29], filter designing [30], image processing [31, 32], data mining, feature selection, fuzzy logic [33, 34], artificial neural networks [35, 36], frequency control problems [37] and many more. Modifications of bat algorithm has also been presented by many as directional bat algorithm [38], Taguchi binary bat in [39] and so on. This research article is arranged in the under mentioned manner. Section 1 presents the introduction. In Sect. 2 PSO, bat, seeker and their hybrid optimization techniques are discussed, respectively. Simulation results obtained by both the techniques and their comparison with some other algorithms are presented in Sect. 3. Eventually, Sect. 4 provides the conclusion for this article.

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2 Optimization Algorithms Utilized 2.1 Particle Swarm Optimization PSO algorithm is a swarm dependent technique based upon the swarm of birds or school of fish or any other living creatures that follow the path by learning from their fellow mates. Kennedy and Eberhart [8] established it in 1995. After its origin, it has gained very popularity in almost every field of optimization as it has the advantage of not getting trapped in the local optimal solution but solves the problem at a global approach. Every individual varies their parameters in accordance with the group of individuals (known as swarm) especially position which has two values-personal or local best (pbest) and group (global) best (gbest) where local best is the finest position (location) of individual particle and global best is the best position of that particle among the entire group.

2.2 Bat Optimization Algorithm Bat optimization is a search algorithm depending upon the behaviour of bats and their echolocation capability. It is proposed by Yang [28] and works according to the search behaviour of bats for their food. This technique is almost similar to PSO but senses the distance using echolocation property and takes advantage of frequency equation. To utilise this algorithm for any optimization problem, initialised values for velocity, position, minimum frequency, wavelength and loudness (speech intensity) values are set to find out the target. Bat motion and variations of loudness and pulse rates Each bat has its initialized velocity vi , position pi and pulse frequency qi in a solution space. qi = qmin + (qmax − qmin )β

(2)

where qmax and qmin are maximal and minimal emission frequencies which are assigned a value uniformly. Initially, value of a frequency is haphazardly assigned for each bat and is drawn consistently from [qmax , qmin ]. The values for the vector β ∈ [0, 1] are considered to be the random value taken from a uniform distribution. The new velocity position vi t and position zi t of the bats are updated at every time step t in accordance with the velocity and position equations as follows:   vit = vit−1 + z it−1 − z ∗ f

(3)

where z* states the present best position (result) globally that is taken after the comparison of the entire solutions of n count of bats.

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z it = z it−1 + z it

(4)

A fresh solution is bring forth locally for each bat utilizing arbitrary walk after the selection of a solution amongst the present best solutions. z new = z old + εL t

(5)

where ε symbolizes random count selected within − 1 and 1, i.e. ε ∈ [− 1, 1] L it+1 = αL it

(6)

  pit+1 = pi0 1 − exp(−γ t)

(7)

assuming L i and pi as the loudness value and pulse rate and are needs to be updated in accordance with the proximity of the prey with an increase in the number of iterations. On reaching near the prey, the decrease in loudness value is seen, but on the contrary the speed of emitted pulse rises. The values of loudness are specified as per the convenience of the problem to be solved, usually, L 0 = 1 and L min = 0 are chosen for any problem where L min = 0 indicates that the search of a bat for its prey is successful and is currently not sending any pulses for further processing. α and γ are constant values and generally for almost all simulative analysis these are taken to be equal, i.e. α = γ .

2.3 Seeker Optimization Algorithm Seeker optimization algorithm (SOA) mimes the doings of individual hunt populace to resolve actual optimization problems. It works on the basis of human searching to reach at the desired optimum solution. A group of human known as population is taken. Each individual in this are known as seekers. Each seeker has its own centre position, search radius, trust degree and search direction and in accordance with these parameters, each seeker updates its position [25–27]. The final decision making is done while considering these four parameters. Each seeker is initialized with a random value and afterwards their positions are changed as per the following equation: Z id (t + 1) = Z id (t) + αid (t)βid (t)

(8)

where αid (t) and βid (t) are the step length and search direction of the ith seeker and dth dimension or variable. βid = 1 indicates that the ith seeker moves in the positive direction on the dimension whereas βid = −1 indicates its movement in negative direction and βid = 0 shows that no movement of the ith seeker. Step length and search direction are updated at each iteration depending upon the following factors.

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2.4 Hybrid Optimization Algorithm A new method known as BATSOA based on the hybrid of bat and seeker optimization techniques is created in this work which gives better results than those of the individual bat and seeker methods. In hybrid optimization algorithm, initially echolocation capability of bat algorithm is utilized and in accordance with that a pulse of a particular frequency is emitted. On the basis of this pulse, velocity and position of the bats are varied and reach at the optimal solution. Each bat has its initialized velocity vi , position zi and pulse frequency qi in a solution space. The new velocity position vi t and position zi t of the bats are updated at every time step t in accordance with the velocity and position equations as for BAT algorithm using the Eqs. (2), (3), (4) and (5). However the final solution is not selected using bat algorithm. Before selecting the final solution seeker algorithm is initiated and it is selected using the following equation. Moreover the search direction and step length of the individuals are altered as in the case of seeker using four parameters: an egotistic term, two altruistic terms and a pro-activeness term. Each seeker is initialized with a random value taken from bat algorithm and afterwards their positions are changed as per the Eq. (8). Calculation of search direction (hunt trajectory)—(βid (t)) The variation in the actual search direction depends upon the four parameters: an egotistic term (Eq. 9), two altruistic terms (Eqs. 10 and 11) and a pro-activeness term (Eq. 12). βi,ego (t) = sign(Pi (t) − Z i (t))

(9)

where Pi represents the best experience of ith seeker and sign (.) represents signum function.   βi,alt1 (t) = sign Pg (t) − Z i (t)

(10)

where Pg represents the best experience of all seekers in the corresponding neighbourhood.   βi,alt2 (t) = sign l g (t) − Z i (t)

(11)

where l g represents the current best of all seekers in the corresponding neighbourhood. βi,pro (t) = sign(Z i (t1 ) − Z i (t2 )) Calculation of step length—(αid (t)) Step length for every variable, d is calculated by the following equation:

(12)

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 αid = δd − ln(μid )

(13)

where δ is a parameter which is common for all the seekers and is given by: δ = ω ∗ abs(Z best − Z rand )

(14)

In this the parameter ω helps in improving the search precision by minimizing the step length with an increase in the time step. Z best and Z rand are the best seeker and arbitrarily chosen seekers, respectively, taken from the similar populace to which ith seeker belongs. Where μid is a term formed from the linear membership function which introduces the randomness in the value of each variable and is mainly used to improve the search capability in the local domain. μid = RAND(μi , 1)

(15)

μi represents the linear membership function and is given by μi = μmax −

S − Ii (μmax − μmin ) S−1

(16)

where Ii represents sequence count of zi (t) acquired subsequent to screening out the fittingness terms. μmax represents the maximal value for the membership degree which needs to be initiated at the onset and is usually taken to be equivalent to or minute below 1.0 and μmin is minimum value of membership degree. S is population size.

3 Simulative Results This section illustrates the simulative results performed on MATLAB software for the designing of all categories of FIR filters. The actual algorithm deals with the minimization of the fitness function. The error function of FIR filter is considered to be the fitness function that is bound to be diminished by the utilization of optimization algorithms to design an optimal filter with better filter coefficients. The filter parameters are as: filter order = 40 thus filter coefficients = 41, sampling frequency = 1 Hz. Respective algorithm is operated for 100 times to incur the optimal results. The specification variables of the filter to be projected using optimization techniques are taken as: select band ripple (Dp ) = 0.1, reject band ripple (Ds ) = 0.01. For low pass filter cut off frequency is taken as 0.7. For high pass filter cut off frequency (normalized) is taken as 0.3. For band pass lower-level and higher-level edge frequencies are 0.3 and 0.7. Similarly for band stop lower-level and higher-level edge frequencies (normalized) are same. Figures 1, 2, 3 and 4 compares the amplitude responses of actual filter and the filters projected with PSO, bat, seeker and hybrid optimization algorithms for all types of FIR filters.

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Fig. 1 Comparison of actual filter and the filters projected with PSO, bat, seeker and hybrid technique for low pass filter

Fig. 2 Comparison of actual filter and the filters projected with PSO, bat, seeker and hybrid technique for high pass filter

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Fig. 3 Comparison of actual filter and the filters projected with PSO, bat, seeker and hybrid technique for band pass filter

Fig. 4 Comparison of actual filter and the filters projected with PSO, bat, seeker and hybrid technique for band stop filter

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Figures 5, 6, 7 and 8 demonstrates the comparison of convergence profile, i.e. the error function for FIR-LP, FIR-HP, FIR-BP and FIR-BS filters successively designed with all the four types of optimization techniques, i.e. PSO, bat, seeker

Fig. 5 Evaluation of convergence profile for FIR low pass filter with PSO, bat, seeker and hybrid

Fig. 6 Evaluation of convergence profile for FIR high select filter using PSO, bat, seeker and hybrid

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Fig. 7 Evaluation of convergence profile for FIR band pass filter using PSO, bat, seeker and hybrid

Fig. 8 Evaluation of convergence profile for FIR band stop filter using PSO, bat, seeker and hybrid

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and hybrid algorithm. This clearly indicates that the error function for hybrid algorithm comes out to be less than others, i.e. followed by SOA, then bat and PSO at the last order for all the types of filters depicting that it can best approximate the ideal filter characteristics. Tables 1, 2, 3 and 4 sequentially presents the various other comparative data for performance variables of all techniques for 40th order FIR. These tables illustrate the numerical data of the above pictorial representation. The top-notch maximal reject band attenuation attained for all the four kinds of filters with hybrid technique are − 50.86, − 51.77, − 50.86 and − 51.62 dB sequentially. Almost lowest select band ripple and reject band ripple have been achieved for hybrid optimization algorithm while designing all the four kinds of filters. For low pass, high pass, band select and band reject filters hybrid technique demonstrates the bottom-most, or near to bottommost reject band ripples of 0.002864, 0.00258, 0.002926 and 0.002628 successively. Similarly it also presents lower or almost similar values of pass-band ripples for all categories of filters in comparison to other algorithms discussed in this work and these are 0.0032, 0.0014, 0.005 and 0.0041 consequently. Execution times (enactment duration) and error fitness values for these are also lowest among all. Hybrid design converges to the least value of error fitness value amongst all techniques. For low pass filter, it assembles to min error value of − 2.0058 in 81.6797 s in 7 iterations. For HP filter, it converges to min error value of − 2.0058 in 81.6650 s in 14 iterations. For BP filter, it converges to min error value of 90.4856 in 59.0494 s in 2 iterations. Table 1 Analysis of comparative variables for FIR-LP filter of order 40 projected with various techniques Method

Max reject band Max select band Max reject band Enactment Error fitness attenuation (dB) ripple ripple duration for 100 (normalized) (normalized) runs (s)

PSO

−52.5

BAT

0.0027

0.002322

66.917039

56.96

−53.76

0.0029

0.002053

76.146732

47.37

SEEKER −53.76

0.0029

0.002051

83.304865

12.67

HYBRID −50.86

0.0032

0.002864

81.679766

−2.0058

Table 2 Analysis of comparative variables for FIR-HP filter of order 40 projected with various techniques Method

Max reject band Max select band Max reject band Enactment Error fitness attenuation (dB) ripple ripple duration for 100 (normalized) (normalized) runs (s)

PSO

−53.12

BAT

0.0028

0.002208

75.014381

47.83 20.07

−53.73

0.0029

0.002053

60.033561

SEEKER −53.73

0.0029

0.00201

82.96129

13.49

HYBRID −51.77

0.0014

0.00258

81.665071

−2.0058

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Table 3 Analysis of comparative variables for FIR-BP filter of order 40 projected with various techniques Method

Max reject band Max select band Max reject band Enactment Error fitness attenuation (dB) ripple ripple duration for 100 (normalized) (normalized) runs (s)

PSO

−49.71

0.0056

0.003268

61.851479

BAT

−9.87

0.0044

0.002461

79.033561

91.68

SEEKER −52.18

0.0061

0.003426

82.4027

91.13

HYBRID −50.81

0.0052

0.002926

59.04949

90.4856

94.78

Table 4 Analysis of comparative variables for FIR-BS filter of order 40 projected with various techniques Method

Max reject band Max select band Max reject band Enactment Error fitness attenuation (dB) ripple ripple duration for 100 (normalized) (normalized) runs (s)

PSO

−52.97

0.0019

0.002241

60.91101

BAT

−53.73

0.005

0.002059

70.15104

110

SEEKER −53.73

0.9997

0.003353

88.93257

102.1

HYBRID −51.62

0.0041

0.002628

67.227915

86.965

124.5

For BS filter, it converges to min error value of 86.9625 in 67.2279 s in 69 iterations. Thus it performs best among all the design approaches discussed in this work. Statistical or analytical data for all types of FIR filters obtained by this procedure of various optimization algorithms are remarked under the Tables 5, 6, 7 and 8 accordingly. Various analytical results of LP, HP, BP and BS filters such as maximum (max) value, mean (average), variance and standard deviation (SD) of select band ripple (normalised) obtained from hybrid-based design are 0.00295, 3.5E−8, 1.87E−4; 0.000562, 2.327E−7, 4.82E−4; 0.0043, 6.1E−7, 7.81E−4; and 0.00345, 3.233E−7, 5.69E−4, respectively. Furthermore, it presents the maximum values, mean, variances and standard deviation of reject band attenuation too as −61.192, 36.0029, 6.00243 for FIR-LP; −54.02, 2.2812, 1.5103 for HP; −53.19, 9.6116, 3.10025 for BP; −53.2867, 2.3899, 1.5459 for BS filters consequently. These values are sometimes lowest or almost same or nearly lower in relative to all the other design approaches discussed above. Even with some of the higher values of pass-band ripple and their corresponding statistical values, hybrid technique performs absolutely well due to the top-notch maximum reject band attenuation.

Max

0.0027

0.0029

0.0029

0.0032

Algorithm

PSO

BAT

SOA

HYBRID

0.00295

0.002414

0.001614

0.001

Average

0.000000035

0.0000000881

0.0000003481

0.000000776

Variance

Select band ripple (normalised)

Table 5 Statistical data for FIR low pass filter with distinct algorithms

0.000187

0.000297

0.00059

0.000881

SD −54. 086 −54. 954 −57.2325 −61. 192

−52.5 −53.76 −53.76 −50.86

Average

Reject band attenuation (dB) Max

36.00292

7.588291667

2.41288

6.40958

Variance

6.000243

2.754685

1.553345

2.531715

SD

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Max

0.0028

0.0029

0.0029

0.0014

Algorithm

PSO

BAT

SOA

HYBRID

0.000562

0.001643

0.001867

0.002486

Average

0.0000002327

0.0000003329

0.0000002667

0.000000048095

Variance

Select band ripple (normalised)

Table 6 Statistical data for FIR high pass filter with distinct algorithms

0.000482

0.000577

0.000516

0.000219

SD

Average −58.176 −54. 938 −54. 938 −54. 02

−53.12 −53.73 −53.73 −51.77

Reject band attenuation (dB) Max

2.281266667

2.46017

2.46017

10.48643

Variance

1.510386

1.568493

1.568493

3.238276

SD

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Max

0.0056

0.0044

0.0061

0.0052

Algorithm

PSO

BAT

SEEKER

HYBRID

0.0043

0.005433

0.0039

0.005

Average

0.00000061

0.0000009733

0.0000003333

0.00000063

Variance

Select band ripple (normalised)

0.000781

0.000987

0.000577

0.000794

SD

Table 7 Statistical data for FIR band select filter with distinct algorithms

−58.1544 −53.1988 −59.4488 −53.19

−49.71 −9.87 −52.18 −50.81

Average

Reject band attenuation (dB) Max

9.6116

25.94118393

332.4233268

26.0031278

Variance

3.100258

5.093249

18.23248

5.099326

SD

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Max

0.0019

0.005

0.9997

0.0041

Algorithm

PSO

BAT

SEEKER

HYBRID

0.00345

0.749125

0.003422

0.0014

Average

0.0000003233

0.248754

0.000001701920

0.0000001533

Variance

Select band ripple (normalised)

0.000569

0.498753

0.001305

0.000392

SD

Table 8 Statistical data for FIR band reject filter with distinct algorithms

−55.7717 −57.6767 −57.6767 −53.2867

−52.97 −53.73 −53.73 −51.62

Average

Reject band attenuation (dB) Max

2.389906667

35.29783

35.29782667

11.43669

Variance

1.545932

5.941197

5.941197

3.381819

SD

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4 Conclusion In this research article, a novel hybrid optimization technique is employed for designing FIR filter which is created by the hybridization of bat and seeker optimization algorithms. As per the output variable and figures, all the techniques were found efficient for this processing. Earlier optimization techniques could not appropriately design an FIR filter, as those techniques usually struck on the local optimal solution. In order to overcome that PSO, bat, seeker and hybrid optimization algorithms are used in this paper and efficiently proved their results. However in comparison to other techniques, the proposed hybrid technique possesses the best optimum result hence achieves the cracking convergence profile, i.e. it attain the minimal error with this. Moreover as seen from the simulation results, Hybrid algorithm achieves the best performance characteristics on the account of least select band and reject band ripples and apart from this the magnitude responses are very feasible. It also attains the least error in lesser number of iterations. As a consequence, it makes this technique competitive and efficient as contrasted with the other optimization methods for the use of various optimization problems as it approximates the design conditions of ideal filter.

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Amazon Product Alexa’s Sentiment Analysis Using Machine Learning Algorithms Ayesha Naureen , Ayesha Siddiqa, and Pothereddypally Jhansi Devi

Abstract One of the most important elements of NLP is emotion analysis. We used machine learning algorithms like SVM, random forest as well naive bayes to solve the problem of sentiment analysis on any amazon product in this paper. Sentiment analysis is a data analysis concept in which a list of reviews are considered, these reviews are analyzed, process, and suggested to the consumer. The knowledge is obtained from the product websites. If the preprocessing is completed, the qualified datasets are categorized using the naive bayes and SVM algorithms to delete unnecessary data stop sentences, be verbs, punctuation, and conjunctions are examples. These current algorithms provided accuracy that was inadequate. In this paper, naive bayes, random forest and SVM algorithms were used. The accuracy of a product is determined by the number of reviews it receives. Keywords Machine learning · SVM · Random forest · Naive bayes’ · Text mining

1 Introduction Text mining includes sentiment analysis, which means that dataset that will analyzed soon after can be source from comments column, upon a specific product, or from people’s opinions or else sentiments on topic. Sentiment analysis is often extracted from the evaluation of different data in the form of perceptions or views. The sentiment analysis results could expressed as a percentage of a positive, negative, or else neutral sentiment. A sentiment analysis is very valuable for a number of problems that concern practitioners and researchers in human–computer interaction, sociology, marketing and advertising, psychology, economics, and political science. A. Naureen (B) · P. J. Devi Department of Computer Science and Engineering, B V Raju Institute of Technology, Narsapur, Telangana 502313, India A. Siddiqa Department of Computer Science and Engineering, Shadan Womens College of Engineering and Technology, Khairtabad, Telangana 500004, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_57

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2 Literature Review Soni and Patel [1] employed aspect level sentiment analysis, which recognizes that documents contain opinions on numerous aspects of one or more of the document’s objects. Sentiword net extracts domain-specific properties that can be used to build a dictionary with senti. Fang and Zhan [2] used a naive bayes classifier to extract subjective content and solve the polarity categorization problem using sentimental analysis on product review results. The analysis of online product reviews on amazon datasets was used in this research. Both stages of categorization experiments, such as sentence level and state level, produce positive results. As compared to SVM classifier, the benefit of this paper is that it provides medium accuracy, which aids the consumer in making a fast-buying decision and provides additions to classifier’s values. This method cannot recommend to consumer for a decision making because accuracy is poor [2]. Sentiment classification is a methodology for collecting the text of written consumer feedback for particular goods or services by categorizing them as depending on the polarity of the grade, it may be positive or negative [3]. As a result, algorithm is unable to decide if a review is positive or negative when the accuracy is low. As a result, when the accuracy is poor, the algorithm is unable to assess if a review is positive, negative, or else neutral, and these reviews be unable to be used for advance recommendation. Each algorithm takes a short amount of time to classify the data [4]. This paper describes a sentiment analysis experiment conducted on amazon reviews written by female customers [5]. Three classification models were used to classify reviews after balancing the data with a nearly equal ratio of positive and negative reviews.

2.1 Preprocessing Module After data has been compiled, it must be pre-processed to delete any unnecessary phrases, words, or symbols.. Initially, symbols such as @ and # will be eliminated. The next step included marking parts of expression [6]. Data Acquisition We collected our datasets in three different JSON formats and named our data sets manually because we have a large number of reviews to mark. As a result, any three-star review is eliminated from our dataset, and the remaining reviews are used to mark the dataset in the following phase [7].

Amazon Product Alexa’s Sentiment Analysis Using Machine …

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2.2 Sentiment Analysis

In this research only positive and negative sentiment orientation has been considered for classification of reviews. Based upon the reviews on amazon product alexa reviews we have shown in the form of graphical representation of sentiment analysis.

3 Methodology 3.1 Naive Bayes Naive is a probabilistic ML algorithm base upon bayes theorem that use in wide range of a classification task [8]. Naïve Bayesian Classifier The following is how the naive bayes classifier works: consider set of training data, D, where every tuple is represent thru n-dimensional trait vector, A = a1 , a2 , a3 , … an , indicate n measurement performed upon tuple using n attributes or else features. suppose there is b class in total, D1 , D2 , D3 , … If and only if, the classifier will predict that tuple A belongs to C i : P(Dk |A) > P(Dl |A), where k, l [1, b] and k is not equal to l. the formula for P(Dl |A) is computed as [9] P(Dk |A) =

n 

P(ak|Di )

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3.2 Random Forest Random forest algorithm is supervised, and it creates a forest out of a collection of decision trees that are typically trained through the bagging process. Main premise behind the bagging strategy is that combining many learning models improves overall performance [10]. Random forest is a classifier that averages the results of a number of decision trees on distinct subsets of a dataset to improve the dataset’s predictive accuracy [11]. Learning algorithms are used in ensemble classification methods that build a group of classifiers rather than a single classifier as well then classify latest data points by voting on their prediction. The majority commonly use ensemble classifier be bagging, boosting, as well random forest [12]. It is a more sophisticated version of bagging that integrates randomness [13]. Instead of using top split among every variable, RF splits every node using best split among subset of predictor randomly selected at the node [14]. RF classification and regression ensemble learning approach that produces number of decision trees during training plus outputs a class that is approach of classes output via distinct trees [15]. To make prediction at latest point z: Regression :

c=1 

g c (z) = 1/C C

rg

Classification : Sb(Z ) Let Dp (y) be class prediction of both random forest tree then Da rg (y) = majority vote{Dc (x)}c 1

3.3 SVM—Support Vector Machines Support vector machine is another popular algorithm has proved and demonstrated excellent results in a variety of real-world applications. SVM classification algorithm aims at separating two groups using a hyper plane-a linear classifier, and its foundation dates back to relatively early times. The problem is that if such a function exists, one can create an infinite number of such boundaries for a given set of training [3]. SVM stands for supervised machine learning and could be used to solve a classification as well regression problems. It is, however, mainly used in classification problem [16]. SVM is used in regression analysis and classification to collect training sets of different types and categories. As a result, during training phase, here we select a subset of training points from which we compute test point similarity. Now that we have chosen our support vectors, we will assign each one a weight, which indicates how much weight we want to give it when making our decision [6].

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3.4 N-Gram It is a method of testing a sequence of text or speech for “n” continuous words or sounds. n gram model is used in sentiment analysis to evaluate the sentiment of a text or document. We will use the simplification P to describe probability of random variable X i take on top of value “the” or P(X i = “the”) (the) sequence of N words will be represents w1 , …, w1:n… , so the expression w1:n−1 refers to the string w1 , w2 , …, wn−1 . We will use P(w1 , w2 , w3 , …, wn ) for joint likelihood of every world in series having exacting value P(X = w1 , Y = w2 , Z = w3 , W = wn ). How do we measure the probabilities of whole sequences alike P(W 1 , W 2 , …, W n ) Now? Using a chain of probabilities, we may decompose this probability for example P(X 1 , . . . , X n ) = P(X 1 )P(X 3 |X 1 : 2), . . . , P(X n |X 1:n−1 ) = P(X k |X 1:k−1 ) We get the following results when we apply chain rule to words: P(w1 : n) = P(w1 )P(w2 |w1 )P(w3 |w1 : w2 ), . . . , P(wn |w1:n−1 ) = P(Wk |W1:k−1 ) N-gram model’s assumption is that substitute of calculating a word’s likelihood based on its entire history, we can estimate it using only the last few terms.

3.5 Word Frequency Counting the frequencies of each n-gram is the first step in this method. Any occurrence of each unigram and bigram in the training set will be counted for each possible classification category if you are using a bigram model. Each unigram and bigram count will be used as a feature in the resulting model. Assume we are attempting to determine whether or not anyone is trustworthy based on information that has been made public about them [17].

3.6 Word Network We create a network plot using n grams, which links words in a graph. To use the package, we must first construct a graph with the graph package. The graph from data frame() function in the graph package will help you do this quickly [17].

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3.7 Word Cloud The frequency and importance of words are represented visually in a word cloud. It can be used to quickly acquire a summary of the most essential terms in your results. This visual also allows you to customize the appearance of the work cloud, including the room’s size and security, as well as how the data is managed. Stop words may also be allowed to delete popular terms from the word cloud, stopping it from being cluttered [18].

3.8 Confusion Matrix It is used to evaluate the output of a classifier using test datasets where we already have actual values. The uncertainty matrix is another name for the error matrix. It contains the total number of correct and wrong values for each class. As a result, a confusion matrix is a technique for evaluating the performance of a classifier model that outputs two or more classes. It is a table that has four types of true and anticipated values. Terminologies in confusion matrix: Confusion matrix is useful not only for evaluating prediction errors, but also for determining important metrics such as accuracy, recall, precision, and F-measure. The accuracy value of this metric shows how accurate our classifier is at predicting outcomes. Accuracy = (TP + TN)/(TP + TN + FP + FN) Precision: proportion of real positives to expected positive values. Precision = TP/(TP + FP) Recall: Out of all real positive values, the percentage of positive values that are correctly predicted is calculated. Recall = TP/(TP + FN) F-measure: It is difficult to equate classification models with high recall but low precision. So we use F-measure-score to compare the two classifier models. Instead of using integer mean, harmonic mean is used. F-measure = 2 ∗ recall ∗ precision/(Recall + Precision)

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4 Results and Discussion Negative 299 positive 75,892 values have a predicting accuracy of 0.915. SVM parameters are as follows: SVM form of C-classification has a linear SVM kernel, a cost of 1, and a total of 166 support vectors. Precision of this calculated actual negative positive values is 0.886, with 338 support vectors, for this predicted real negative positive accuracy of 0.907. In this research, we used SVM, naive bayes, and random forest. Outputs of alexa amazon product are depicted in the diagrams below.

4.1 Output of the Word Cloud of Amazon Reviews of Alexa In Fig. 1, it shows output of emotions of customers review on amazon product alexa how they feel about the product it has showed in the form of word cloud (Fig. 2). Here in this Fig. 3, it shows the reviews of a word in frequency like how many words has given in frequency level which word has how much frequency for the word Fig. 1 Output of the emotions of customers on alexa

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Fig. 2 Analysis of amazon product alexa in the form of bar plotting

Fig. 3 Reviews of alexa in word frequency

product there is a frequency of 1683. Every emotional word has frequency. Here are the results of emotion mining, survey sentiment analysis of the alexa amazon product. Customers’ feelings about the product anger, anticipation, disgust, fear, joy, sorrow, surprise, as well trust are just some of the emotions alexa has experienced.

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5 Conclusion Sentiment analysis is a branch of psychology that examines people’s feelings, behaviors, and emotions toward specific objects. We perform sentiment analysis by means of SVM accuracy is 91.5%, random forest 88.6%, naive bayes 90.7% algorithm as well achieve accuracy of about 91.5%. In this paper, we have used SVM, term frequencies, word cloud, bar plot. The results of the emotion of customers are shown in the above figure of survey sentiment. In future we can use other methods for this sentiment analysis.

References 1. V. Soni, M.R. Patel, Unsupervised opinion mining from text reviews using sentiwordnet. Int. J. Comput. Trends Technol. 11(5), 234–238 (2014) 2. X. Fang, J. Zhan, Sentiment analysis using product review data. J. Big Data 2(1), 1–14 (2015) 3. K. Dave, S. Lawrence, D.M. Pennock, Mining the peanut gallery: opinion extraction and semantic classification of product reviews, in Proceedings of the WWW, 20–24 May (ACM Press, Budapest, Hungary, 2003), pp. 519–528 4. J. Sadhasivam, R.B. Kalivaradhan, Sentiment analysis of Amazon products using ensemble machine learning algorithm. Int. J. Math. Eng. Manag. Sci. 4(2), 508–520 (2019). https://doi. org/10.33889/IJMEMS.2019.4.2-041 5. A. Noor, M. Islam, Sentiment analysis of women’s e-commerce reviews using machine learning algorithms, in 10th International Conference on Computing Communication and Networking Technologies, July 2019. https://doi.org/10.1109/icccnt45670.2019.8944436 6. Sentiment analysis of amazon products using ensemble machine learning algorithm. Int. J. Math. Eng. Manag. Sci. 4(2), 508–520 (2019) 7. T.U. Haque, N.N. Saber, F.M. Shah, Sentiment analysis on large scale amazon product reviews, in 2018 IEEE International Conference on Innovative Research and Development (ICIRD) (IEEE, 2018) 8. Naive Bayes Algorithm model for machine learning classification, KDnuggets 9. X. Fang, J. Zhan, Sentiment analysis using product review data. J. Big Data 2(5) (2015) 10. Complete Guide for Random Forest Algorithm, Data Science 11. Random Forest Algorithm, Java Point, Machine Learning 12. Ö. Akar, O. Gungor, O. Güngör, Classification of Multispectral Images Using Random Forest Algorithm. View project 3D mapping View project Classification of multispectral images using Random Forest 13. L. Breiman, Random Forests (2001) 14. R. Genuer, Forêtsaléatoires: aspect théoriques, sélection de variables et applications. Thèse de DoctoratMathématiques, Université de Paris-Sud XI (2010) 15. Y. Al Amrani, M. Lazaar, K.E. El Kadiri, The First International Conference on Intelligent Computing in Data Sciences Random Forest and Support Vector Machine Based Hybrid Approach to Sentiment Analysis 16. I.H. Witten, E. Frank, Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann (Elsevier, Amsterdam, June 2005), pp. 1–560 17. D. Jurafsky, J.H. Martin, N-gram language models, in Speech and Language Processing 18. Text Mining Word Frequency Models from Data Science Mosaic

VLSI

Design of Low Voltage Improved Current Mirror P. Anil Kumar, S. Tamil, and Nikhil Raj

Abstract Current mirror being a fundamental design block has applications in almost every analog system design. A high performance design of low voltage current mirror is proposed. The improved performance parameters includes reduced input and increased output resistances. The input signal is fed to low impedance node of design. This results in reduced input resistance by a half, whereas at the output the regulated cascode stage is used which provides a significant impedance boost of resistance from kilo to mega ohm range. The resistances input and output is 400  and 32M , respectively. Also this improvement is achieved without significant increase in power. The analysis is carried on MOSFET 0.18 µm model technology at a voltage supply of ± 0.5 V. Keywords Current mirror · Regulated cascode · Input resistance · Output resistance · Bandwidth

1 Introduction Any analog system performance is a strong function of components used in building the system. Among such, current mirror is one of those components which has numerous advantage [1]. The current mirror basically generates the output current same like input current. The ideal requirements of current mirror includes wide dynamic range and bandwidth along with low input and high output resistances. But at reduced supply these requirements are not easily met which is due to MOSFET threshold voltage. To overcome various approaches used in literature [2] and related current mirrors in [3–10].

P. Anil Kumar (B) · S. Tamil Department of ECE, SRK University, Bhopal, India N. Raj Department of ECE, The LNM Institute of Information Technology, Jaipur, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_58

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In this paper, the current mirror with reduced input compliance voltage is proposed. The input section uses the shunt feedback which reduces the minimum required voltage for its operation. This results in reduced input resistance. For boosting the performance in terms of output resistance regulated stage is used at output. However these changes does not much impact the bandwidth. Few such low voltage current mirror recently reported can be found in [11–15]. The proposed current mirror paper has wide dynamic range, giga hertz range bandwidth, reduced input and high output resistances compared to its conventional design. The paper is detailed as: Sect. 2 details on proposed current mirror which is also supported by mathematical analysis. The simulations are shown in Sect. 3 and conclusion in Sect. 4.

2 Proposed Design The conventional current mirror design operating at low voltage is shown in Fig. 1a which uses N-type MOS transistors (M 1 –M 4 ). Here as the drain current of M 3 is fixed to I B1 , any change in the input is sensed by M 1 and accordingly produces suitable gate-to-source voltage (Vgs,M1 ) which modulates the output current (I out ). The V bias is the DC voltage applied to maintain M 3 and M 4 in saturation. The input and output resistances are given as (1/gm 1 ) and (gm 4 r04 r02 ), respectively, where gm i and r 0i denote the transconductance and output resistance. While the output resistance is given as (gm 4 r04 r02 ). However, in many applications these values does not provide optimum performance. In the proposed circuit shown Fig. 1b, the input signal instead of drain of M 3 it is fed to drain of M 1 . Since the drain of M 1 experience a low impedance node, it results in reduced input resistance. For the output impedance Iin Iout

VDD M3

M4

M1

M2

VSS

(a) Fig. 1 a Conventional; b proposed FVF current mirror

(b)

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improvement a regulated stage is used by using MOSFET M 5 and current source IB2 . The voltage headroom of M 2 is regulated via M 4 and M 5 transistors. The presence of feedback loop amplifier implemented using M 5 and IB2 prevents changes in V DS of M 2 ensuring better stability. The working is similar to that of cascode however this configuration yields an additional multiplying factor of (gm r 0 ) in output resistance. The effective output resistance compared to conventional FVF current mirror design is boosted by (gm r 0 ) which turn out in the range of mega ohms.

2.1 Small Signal Model During analysis the symbols shown have their usual meaning and matches to spice model parameters of MOS transistors. The operating region of all MOS transistors is assumed in saturation region.

2.1.1

Calculation of Rin

The small signal equivalent model for Rin of proposed circuit is shown in Fig. 2. At node 2 i in −

V1 V2 − gm 1 V1 − =0 R1 r01

(1)

At node 1 Fig. 2 Calculation of input resistance

1 gm3V2

I in Vin

r

R1

03

2

gm1V1

r

01

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V1 ≈ gm 3 (r03 /R1 )V2

(2)

From (1) and (2) Rin =

V2 1 ≈ i in gm 1 gm 3 (r03 /R1 )

(3)

For an ideal current source, R1 = ∞ Rin,prop. ≈ 

1  gm 3 r03 gm 1

(4)

1 gm 1

(5)

whereas for conventional FVF is given by Rin,conv. ≈

From (4) and (5), it can be observed that compared to conventional there is a scaling factor of (gm r 0 ) in the input resistance.

2.1.2

Calculation of Rout

The small signal equivalent model for Rout of proposed circuit is shown in Fig. 3. At node 4 i out = gm 4 V53 +

V4 − V3 r04

(6)

At node 3 V3 = i out r02 Fig. 3 Calculation of output resistance

(7)

5 gm5 V3

I out

4

r

gm4V 53

r

05

3 r

02

04

Vout

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At node 5 V5 = −gm 5 r05 V3

(8)

   Rout,prop. ≈ gm 4 r04 gm 5 r05 r02

(9)

From (6), (7) and (8)

whereas for conventional FVF current mirror it is given as   Rout,conv. ≈ gm 4 r04 r02

(10)

Comparing (9) with (10), a multiplying factor of (gm r 0 ) is observed in output resistance. The effective resistance of proposed gets enhanced from kilo ohm to mega ohm range.

3 Simulations The proposed circuit is simulated MOS model of 0.18 µm technology at 0.5 V supply. The (W/L) of MOSFET M 1 –M 4 is kept as (25 µm/0.24 µm) while for M 5 it is (10 µ/0.24 µ). The biasing currents is taken as IB1 = 10 µA while IB2 as 30 µA. The output characteristic shown in Fig. 4 where input current is swept from 0 to 200 µA in steps of 50 µA. The input resistance plots is shown in Fig. 5 and output resistance plots is shown in Fig. 6. As seen in Fig. 5, the input resistance gets drastically reduced to 400  from 850 . Also improvement in output resistance can be seen in Fig. 6 which for

Fig. 4 Output characteristics

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Fig. 5 Input resistances

Fig. 6 Output resistances

proposed it is found to be 32M  and for conventional it is 880K . The bandwidth for both the designs remains same of about 2.4 GHz.

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4 Conclusion A low voltage design of current mirror having reduced input and increased output resistance has been shown in this paper. The current has reduced input resistance due to feedback path and similarly boosted output resistance in mega ohm with a giga hertz range bandwidth. The input resistance in range of ohms and output resistance in mega ohms range suits its applicability in precise amplifiers. The micro watt power dissipation encourages its applications in low power VLSI circuits.

References 1. M. Akbari, A. Javid, O. Hashemipour, A high input dynamic range, low voltage cascode current mirror and enhanced phase-margin folded cascode amplifier, in Iranian Conference on Electrical Engineering (2014), pp. 77–81 2. F. Khateb, S. Bay, A. Dabbous, S. Vlassis, A survey of non-conventional techniques for lowvoltage low-power analog circuit design, in Radioengineering (2013), pp. 415–427 3. X. Zhang, E. El-Masry, A regulated body-driven CMOS current mirror for low-voltage applications. IEEE Trans. Circ. Syst. II Expr. Briefs 571–577 (2004) 4. P.S. Manhas, S. Sharma, K. Pal, L.K. Mangotra, K.K.S. Jamwal, High performance FGMOSbased low voltage current mirror. Indian J. Pure Appl. Phys. 355–358 (2008) 5. F. Esparza-Alfaro, A.J. Lopez-Martin, J. Ramírez-Anguloa, R.G. Carvajal, Low-voltage highlylinear class AB current mirror with dynamic cascode biasing. Electron. Lett. 1336–1338 (2012) 6. N. Raj, A.K. Singh, A.K. Gupta, Low power high output impedance high bandwidth QFGMOS current mirror. Microelectron. J. 1132–1142 (2014) 7. F. Esparza-Alfaro, A.J. Lopez-Martin, R.G. Carvajal, J. Ramirez-Angulo, Highly linear micropower class AB current mirrors using quasi-floating gate transistors. Microelectron. J. 1261–1267 (2014) 8. N. Raj, A.K. Singh, A.K. Gupta, Low-voltage bulk-driven self-biased cascode current mirror with bandwidth enhancement. Electron. Lett. 23–25 (2014) 9. N. Raj, A.K. Singh, A.K. Gupta, Low voltage high output impedance bulk-driven quasi-floating gate self-biased high-swing cascode current mirror. Circ. Syst. Signal Process. 2683–2703 (2016) 10. N. Raj, A.K. Singh, A.K. Gupta, Low voltage high performance bulk-driven quasi-floating gate self-biased cascode current mirror. Microelectron. J. 124–133 (2016) 11. N. Raj, A.K. Singh, A.K. Gupta, Low voltage high bandwidth self-biased high swing cascode current mirror. Indian J. Pure Appl. Phys. 1–7 (2017) 12. Y. Bastan, E. Hamzehil, P. Amiri, Output impedance improvement of a low voltage low power current mirror based on body driven technique. Microelectron. J. 163–170 (2016) 13. L. Safari, S. Minaei, A low-voltage low-power resistor-based current mirror and its applications. J. Circ. Syst. Comput. 175–180 (2017) 14. M.S. Doreyatim, M. Akbari, M. Nazari, A low-voltage gain boosting-based current mirror with high input/output dynamic range. Microelectron. J. 88–95 (2019) 15. N. Raj, Low voltage FVF current mirror with high bandwidth and low input impedance. Iranian J. Electr. Electron. Eng. 1–7 (2021) 16. I. Ahmed Khan, M. Rashid Mahmood, J.P. Keshari, Analytical comparison of power efficient and high performance adders at 32 nm technology, in Innovations in Electronics and Communication Engineering. Lecture Notes in Networks and Systems, vol. 107 (Springer, Singapore, 2020). https://doi.org/10.1007/978-981-15-3172-9_62

Design of High-Gain Operational Transconductance Amplifier Rajesh Durgam, S. Tamil, and Nikhil Raj

Abstract A multifunction filter is presented in this paper whose design is done using the low-voltage operational transconductance amplifier. The filtering type includes low, high, and band pass responses. The operational transconductance amplifier is a three current mirror-based topology. The amplifier use bulk driven to operate the amplifier with minimal power dissipation. However, the low transconductance of bulk-driven approach limits the gain for which its hybrid form bulk-driven QFG MOSFET is adopted. This improved the parameters like gain and bandwidth without increasing the power dissipation. The complete design and analysis have been done using 0.18 µm at supply of ± 0.5 V. Keywords OTA · QFG MOSFET · Bulk-driven · Gain · Transconductance

1 Introduction The increasing growth of low-power demand has pushed industry to re-design the circuits using non-conventional approaches. With conventional techniques, the circuit working at low supply does not fulfill the goal. The main obstable is due to threshold voltage of MOSFET which does not scale in proportion to supply trend. In context to this, few low-power approach are body/bulk driven [1], floating and quasifloating gates, and bulk/body-driven quasi-floating gate (BDQFG) [2–6]. Though among these BD-gained potential interest for low-voltage design due to its simple architecture, the issue with BD is low transconductance and poor frequency response. The poor transconductance is visible in low gain and low bandwidth. Few recent articles based on BD for realizing LP circuits can be found in [7–10] and based on BDQFG which results in improved transconductance and better bandwidth circuits R. Durgam (B) · S. Tamil Department of ECE, SRK University, Bhopal, India N. Raj Department of ECE, The LNM Institute of Information Technology, Jaipur, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_59

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[11–19]. In the paper, design of operational transconductance amplifier (OTA) is shown which is based on BDQFG technique. The amplifier is basically a three current mirror topology. Further, its application in multifunction filter realization is shown in this paper. The paper is discussed as follows: Sect. 2 describes proposed operational transconductance amplifier followed to realization of multifunction filter in Sect. 3. Simulation results are shown in Sect. 4 while conclusion Sect. 5.

2 Proposed OTA The OTA choosen is current mirror-based OTA [20, 21]. The combination of N-type CM (M 7 , M 8 ), and P-type CM (M 3 , M 5 ) and (M 4 , M 6 ) are the three basic CM’s used to build OTA. The N-type CM (M 9 , M 10 ) acts as a tail current source biased using a constant current source I bias . The BDQFG-based schematic of OTA design is shown in Fig. 1. In general, the fourth terminal of MOSFET, the bulk which can also be used for signal processing. The DC gain and unity gain bandwidth is given by DC gain,  A V = G m Rout = Unity-gain bandwidth, Fig. 1 N-channel CM-based OTA using LP technique

 µn Cox

W L



 I10 2

1 gds6 + gds8

 (1)

Design of High-Gain Operational Transconductance Amplifier

Gm UGB = = CL





µn Cox

W L

565





I10 /C L

(2)

2

  As seen, the DC gain is dependent on the input gate transconductance of M 2 gm 2 and effective output resistance (Rout ) of the amplifier. The BDQFG technique offers the high transconductance, low output impedance, and high DC gain.

3 Multifunction Filter The 2nd order Gm-C multifunction filter realization based on BDQFG OTA shown is shown in Fig. 2 [22]. In the architecture, the OTAs are used having transconductances as G m 1 and G m 2 along with capacitors C 1 and C 2 to realize the LP, BP, and HP filter response. Using standard analysis yields the output as

Vout =

G sVin,BP + C1mC12 Vin,LP

G G G s 2 + Cm22 s + Cm11 C2m2

s 2 Vin,HP +



1 C2

(3)

Here, Case (i): when Vin,BP = Vin,HP = 0 the architecture is 2nd order LP filter Vout G m 1 /C1 C2    = 2  Vin,LP s + G m 2 /C2 s + G m 1 G m 2 /C1 C2

(4)

Case (ii): when Vin,LP = Vin,HP = 0 the architecture is 2nd order BP pass filter Vout (1/C2 )s    = 2  Vin,BP s + G m 2 /C2 s + G m 1 G m 2 /C1 C2

(5)

Fig. 2 2nd order multifunction filter Vout

Gm2 V1 Gm1 C2

Vin, LP C1

Vin,HP Vin,BP

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Case (iii): when Vin,LP = Vin,BP = 0 the architecture is 2nd order HP filter Vout s2    = 2  Vin,HP s + G m 2 /C2 s + G m 1 G m 2 /C1 C2

(6)

4 Simulations The CM OTA of Fig. 1 is simulated on 0.18 µm at ± 0.5 V. The dimensions of MOSFET and assumed parameters for simulation are shown in Table 1. The DC gain analysis result is shown in Fig. 3; where for BD, it is 21 dB, and for BDQFG, it is 39 dB. The filter has a cut-off frequency of 1 KHz. The magnitude plots of filters are shown in Fig. 4. Table 1 Width and length of MOSFETs MOSFETs

W (µm)

L (µm)

MOSFETs

W (µm)

L (µm)

M1

15.12

1.26

M6

15.12

0.72

M2

15.12

1.26

M7

15.12

1.26

M3

15.12

1.98

M8

15.12

1.26

M4

15.12

0.72

M9

15.12

1.98

M5

15.12

1.98

M 10

15.12

1.98

MP1

0.24

0.24

MP2

0.24

0.24

C 1 = C 2 = 1 pf, CL = 1 pf, I bias = 10 µA

Fig. 3 Frequency response of CM OTA

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Fig. 4 Magnitude plots

5 Conclusion A design of operational transconductance amplifier working at low voltage is presented which is further used in multifunction filter realization. The CM-based OTA uses BDQFG MOSFET which improved the bandwidth of amplifier. The observed gain gets doubled compared to BD-based design. The filter showed three responses as low, high, and band pass.

References 1. B.J. Blalock, P.E. Allen, G.A. Rincon-Mora, Designing 1-V op amps using standard digital CMOS technology. IEEE Trans. Circ. Syst. II Analog Digital Signal Process. 769–780 (1998) 2. M. Gupta, R. Pandey, Low-voltage FGMOS based analog building blocks. Microelectron. J. 903–912 (2011) 3. J.M.A. Miguel, A.J. Lopez-Martin, L. Acosta, J. Ramirez-Angulo, R.G. Carvajal, Using floating gate and quasi-floating gate techniques for rail-to-rail tunable CMOS transconductor design. IEEE Trans. Circ. Syst. I Regul. Pap. 1604–1614 (2011) 4. C. Garcia-Alberdi, A. Lopez-Martin, L. Acosta, R.G. Carvajal, J. Ramirez-Angulo, Tunable class AB CMOS Gm-C filter based on quasi-floating gate techniques. IEEE Trans. Circ. Syst. I: Regul. Pap. 1300–1309 (2013) 5. N. Raj, A.K. Singh, A.K. Gupta, Low power high output impedance high bandwidth QFGMOS current mirror. Microelectron. J. 1132–1142 (2014) 6. A. Guzinski, M. Bialko, J.C. Matheau, Body driven differential amplifier for application in continuous-time active C-filter, in Proceedings of ECCD, Paris, France (1987), pp. 315–319 7. F. Khateb, Bulk-driven floating-gate and bulk-driven quasi-floating-gate techniques for lowvoltage low-power analog circuits design. AEU-Int. J. Electron. Commun. 64–72 (2013) 8. N. Raj, R.K. Sharma, Modeling of human voice box in VLSI for low power biomedical applications. IETE J. Res. 345–353 (2011) 9. H. Khameh, H. Shamsi, On the design of a low-voltage two stage OTA using bulk-driven and positive feedback techniques. Int. J. Electron. 1309–1315 (2012) 10. L. Zuo, S.K. Islam, Low-voltage bulk-driven operational amplifier with improved transconductance. IEEE Trans. Circ. Syst. I: Regul. Pap. 2084–2091 (2013)

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11. J. Gak, M.R. Miguez, A. Arnaud, Nanopower OTAs with improved linearity and low input offset using bulk degeneration. IEEE Trans. Circ. Syst. I: Regul. Pap. 1–10 (2013) 12. N. Raj, A.K. Singh, A.K. Gupta, Low voltage high output impedance bulk-driven quasi-floating gate self-biased high-swing cascode current mirror. Circ. Syst. Signal Process. 2683–2703 (2015) 13. N. Raj, A.K. Singh, A.K. Gupta, Low voltage high performance bulk-driven quasi-floating gate self-biased cascode current mirror. Microelectron. J. 52, 124–133 (2016) 14. N. Raj, A.K. Singh, A.K. Gupta, Low voltage high bandwidth self-biased high swing cascode current mirror. Indian J. Pure Appl. Phys. 55, 245–253 (2017) 15. N. Raj, Low voltage FVF current mirror with high bandwidth and low input impedance. Iranian J. Electr. Electron. Eng. 1–7 (2021) 16. F. Khateb, W. Jaikl, M. Kumngern, P. Prommee, Comparative study of sub-volt differential difference current conveyors. Microelectron. J. 1278–1284 (2013) 17. N. Raj, A.K. Singh, A.K. Gupta, Low-voltage bulk-driven self-biased cascode current mirror with bandwidth enhancement. Electron. Lett. 23–25 (2014) 18. N. Raj, A.K. Singh, A.K. Gupta, High performance current mirrors using quasi-floating bulk. Microelectron. J. 11–22 (2016) 19. F. Khateb, The experimental results of the bulk-driven quasi-floating-gate MOS transistor. AEU Int. J. Electron. Commun. 462–466 (2015) 20. P.E. Allen, D.R. Holberg, CMOS Analog Circuit Design (Oxford University Press, USA, 2002) 21. S. Ali, A power efficient gain enhancing technique for current mirror operational transconductance amplifiers. Microelectron. J. 183–190 (2015) 22. F. Khateb, N. Khatib, P. Prommee, W. Jaikla, L. Fujcik, Ultra-low voltage tunable transconductor based on bulk-driven quasi-floating-gate technique. J. Circ. Syst. Comput. 1–13 (2013)

Design of Low-Power Bit Swapping BIST for IC Self-testing Kanika Gupta and Ashish Raman

Abstract In nanotechnologies, IC performance and complexity grow at an exponential rate. It makes evaluating IC products for long-term use extremely difficult. It causes ICs to age and deteriorate over time. The paper shows how to use the integrated circuit’s self-test (BIST) system for self-generated pseudo-random testing using the bit swapping methodology. It will assist in lowering the cost of IC testing and maintenance. Because of the high switching activity required for test pattern production, the approach reduces transition power. The suggested circuitry is a one-of-a-kind scanning approach employed in the BIST controller to enable default techniques such as launch-on-shift and launch-on-capture. Keywords Integrated circuit (IC) · Built-in self-testing (BIST) · Bit swapping (BS) · Linear feedback shift register (LFSR) · Multiple input shift register (MISR) · Test pattern generator (TPG) · Circuit under test (CUT) · Launch-on-shift (LOS) · Launch-on-capture (LOC)

1 Introduction The complexity of nanoscale electronic systems has increased in recent years, resulting in a single chip with enhanced embedded device functionality. Integrated circuits (ICs) have a wide range of performance applications in the networking, banking, aviation, military, automobile, electronics, telecommunications, and health industries, with rising usage and complexity [1]. It is critical to properly monitor defectiveness in the test setting to ensure a constant standard of product consistency. The increased complexity of today’s integrated circuits has an impact on both design and production. Furthermore, given its varied range of dedication to design, the K. Gupta (B) · A. Raman Department of Electronics and Communication, Dr. B R Ambedkar National Institute of Technology, Jalandhar, India A. Raman e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_60

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requirement to maintain a high level of trust in IC operations in line with demand for lower prices and shorter production periods is obviously a major challenge [2]. These two design elements are always improbable. Traditional options for high confidence are governed by advanced testing equipment known as automated test equipment (ATE). Traditionally, the cost of ATE has been computed solely using a computerized price pin approach, which has a disproportionate impact on the cost per examination [3]. In recent years, more calculations and proposed changes have been made to raise traditional cost evaluation of experiments, including the cost of core systems for hardware devices, central instruments, and useful scale of pin counts. BIST is one of these methods, and it is currently a key design factor for DFT methods [4]. BIST has various advantages. The reliance on external test equipment can be significantly minimized. If the company has additional research equipment, it can help reduce total costs and time to the point where test equipment can be shifted to other instruments in the present design if necessary. Furthermore, because ATE typically lags the most modern technology items a few years ago, new technology products do not always require high-speed tests. Testing is an important cost driver in the manufacturing process (it accounts for up to 70% of total expenses [1, 5]). It gives the correct research plan a major competitive advantage in an industry with billions of electronic components and systems. Another notable advantage is that BIST allows circuit evaluations not only during manufacture but also throughout their lifespan, which is critical when long-term degradation affects begin to limit the life cycle of projected nanoscale ICs. This would usher in a new era of system reliability and research, as well as a new paradigm. BIST can also overcome pin restrictions and effectively employ the existing greater chip surface due to the packaging, offering more fault knowledge [1].

2 Related Work 2.1 Bit Swapping LFSR • It composed of 2 × 1 MUX and LFSR. It is used for generating random test patterns for BIST-based IC scanning, and in turn, it reduces the number of transitions. • It generates an identical number of 1 and 0 at the output of MUX creating a bit exchange technique by exchanging two adjoining cells. This creates an equal probability of 1 or 0 prior to applying the scan chain to the test vector. • From Fig. 1 shown below, we observe a bit swapping is done between register C 1 and C 2 , similarly C 3 with C 4 and so on. Finally, C n−2 is swapped with C n−1 , and C n is having a connection with a select line of MUX. In this scenario, the test vector which is generated is like traditional LFSR, but the order is different due to bit swapping, and the overall transition in primary input gets reduced by 25%. Let number of transitions, n = 8.

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Fig. 1 Bit swapping linear feedback shift register shows bit swapping between register resulting into overall transition in primary input reduces by 25% [6]

The number of transitions saved by swapping = T saved = 26 . The number of transitions without swapping 2 × 27 = 28 . Therefore, by swapping bits, T saved = 26 /28 = 25% saved [4].

2.2 Scan Chain Reordering The test power may be reduced using the appropriate order of the scan chain cells to get the sequence of the deterministic test vectors with the corresponding output responses. This will provide us the minimum test power of the ordered scan chain at the output. BS-LFSR provides good performance to reduce average power consumption in the generation of test vectors. Peak power is also minimized during scan of a new test vectors, but it can’t reduce overall peak power due to any components which occur during scanning or while applying the test vector and capturing the response of the test cycle [2]. To address these issues, BS-LFSR combines with the cell order algorithm that reduces the transition into the scan chain when it captures the response. This will lower the overall average power, including peak power. This can be achieved by rearranging the ordered scan cell during the test cycle [7]. Following are the steps of the scan chain reordering algorithm: • Using BS-LFSR, simulate CUT to produce test patterns. • Identify the test vectors that increase peak power. • By identifying the test vector, identify the corresponding cells that modify their value during the test cycle, thus increasing the peak power. • For each cell obtained in step (3), search for all cells, which play a significant role in the value of this cell in the test cycle. • If both cells have the same value in the applied test vector, then that cell has no transition in the test cycle, hence connecting those cells [2]. If these two cells have a different value, the cell in question has no transition in the test cycle, thus connecting these cells with an inverter [2].

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If no test vector increases the peak power, it is not necessary to carry out the above phases [2]. If we proceed to reorganize the scan chain in all cells of the scan chain, the effect of the algorithm will be destroyed, and the computation time will be increased [2].

3 Proposed Model 3.1 BIST Architecture The BIST architecture shown in Fig. 2 consists of MUX, three LFSR chains used for input bits, to generate test vectors, and counter scan. The rectangular non-defined box is the comparator circuit getting input from the BIST controller and LFSR used for

Fig. 2 Proposed built-in self-test architecture having 3 LFSR, 2 comparators, MUX, CUT, and multiple input scan register

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counter scan and LFSR used for the input sequence and provides the single output. The BIST controller is the main brain of the system. CUT consists of scan flip-flops.

3.2 MUX Block MUX acts as the switch between input and the test vector which is generated from the first LFSR (LFSR for I/P) shown in Fig. 2. There is n (2 × 1) MUX, where n is the number of inputs.

3.3 LFSR for Input Block This block generates the pseudo-random patterns for CUT. When the BIST controller receives the switching information from the test vector, it resets the block to logic “0” and supplies the initial seed values to LFSR. These initial seed values will define the flip-flop configuration as when the seed-specific bit is “0” that flip-flop will RESET, whereas when seed bit is “1” that flip-flop will SET. Enable signal used in LFSR is to pause the operation of the LFSR.

3.4 LFSR for Scan Block This LFSR block is like the above but is used to generate a test vector for the CUT scan chain using BS-LFSR. We can generate test vectors and random sequences in one LFSR block, but separate LFSR blocks deliver better test results and high-test coverage. When this block is sending data to CUT, the select line from controller must be high so that all the internal flip-flop in the CUT must be in scan mode.

3.5 LFSR for Scan Counter Block This block is used to count the number of clocks to scan into CUT’s flip-flop through test vectors generated in the previous LFSR block. Therefore, the flip-flop used in this block should be a round-off value of log2 (k). Where k specifies the number of flip-flops used in the CUT scan chain. When the count ends, it will receive the information to suspend the process until any new instruction is given. Whenever the new instruction is provided, it will start again from the initial position. This process will continue several times depending upon the first LFSR block pseudo-random test patterns.

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3.6 Multiple Input Shift Register (MISR) Block This block is like LFSR with multiple input bits connected to flip-flops by XOR gates. The input of this block is the output of CUT input bits smaller than the flip-flops present in this block to prevent aliasing in the test sequence.

3.7 BIST Controller Block This block is the brain of the architecture. It checks the instructions for each block to provide a self-test function. It provides the selection line to MUX to transition from normal to test mode. The start signal is responsible for the self-test process. When the start is logic “1”, the circuit will enter test mode, and FSM will change its state from IDLE to RESET state. Enable signal is used to disable LFSR for an input block during scan state and enable the internal CUT’s flip-flop to go into scan mode. The output of the LFSR scan block provides serial data, and the LFSR scan counter block starts counting and notifies the controller after loading. At this point, the FSM state changes to LAUNCH and deactivates the enable signal. This will enable the LFSR for input block. This block is important for fault coverage.

4 Results and Discussion The ISE Xilinx 14.7 design suite is used to obtain the RTL view and the Xilinx power estimator to calculate power. Figure 3 illustrates the RTL view of BS-LFSR-based BIST consisting of I/P, clock, reset, and start bit as input and O/P, MISR O/P, done as output. Figure 4 illustrates the simulation of BS-LFSR-based BIST. A seed value of “0110” is fed to LFSR for input, LFSR for scan, MISR, and seed value of “01” is fed to LFSR for scan counter. Done represent the state of FSM. Table 1 shows static, dynamic, and total power, the proposed technique achieves 1090.16 mW, 24.14 mW, and 1114.30 mW, whereas existing dual threshold BS-LFSR achieved 3062.99 mW, 26.33 mW, and 3089.32 mW [6] and for efficient BS-LFSR with MUX realization achieves 3062.09 mW, 26.17 mW, and 3089.21 mW [6]. By comparing the values of power, total power is reduced by 63.93% in the proposed technique. Table 2 provides the information regarding delay and frequency. For the existing model, delays are 1.488 ns for dual threshold BS-LFSR [6] and 0.94 ns for efficient BS-LFSR with MUX realization [6] which get reduced to 0.904 ns in the proposed technique. In addition, the proposed technique obtained the frequency

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Fig. 3 RTL view of proposed built in self-test architecture

Fig. 4 Timing simulation of proposed built-in self-test architecture when a = 1, b = 0, reset = 1, and BistStart = 1

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Table 1 Power comparison between dual threshold BS-LFSR, efficient LFSR with MUX realization, and proposed BS-LFSR-based BIST [6] Parameters

Dual threshold BS-LFSR [6]

Efficient BS-LFSR with Proposed MUX realization [6] BS-LFSR-based BIST

Static power (mW)

3062.99

3062.99

1090.16

26.33

26.17

24.14

3089.32

3089.21

1114.30

Dynamic power (mW) Total power (mW)

Table 2 Delay and frequency comparison between dual threshold BS-LFSR, efficient LFSR with MUX realization, and proposed BS-LFSR-based BIST [6] Parameters Delay (ns) Frequency (MHz)

Dual threshold BS-LFSR [6]

Efficient BS-LFSR with MUX realization [6]

Proposed BS-LFSR-based BIST

1.488

0.94

0.904

672.043

1063.83

430.923

430.923 MHz relative to the dual threshold BS-LFSR and the efficient BS-LFSR with MUX achievement of 672.043 and 1063.83 MHz [6].

5 Conclusion A new approach for dynamic scanning BIST addition is presented in this paper. The fundamental notion is that BIST searches are based on LOS and LOC techniques. However, the system included three novel scan BIST methods for the development of patterns for architectural failure. LOC-based scanning of BIST, LOS-based scanning of BIST, and mixed scanning (which mixes two other techniques with one BIST test) are the three forms. The current architecture requires the introduction of new elements; in contrast, with the standard scan BIST controller, and the BIST controller has one additional state as a finite state machine (FSM). LOS is achieved when the control signal “BistStart” is high. During low levels, the LOC procedure is used. Although this hardware is different, the total number of transistors and pin counts is roughly identical. The output losses in the CUT incurred by the BIST injection are like those caused by the classic BIST scan. There is no major difference in the additional delays caused by the MUX feedback and replacement of the CUT flip-flops with scan flip-flops. When comparing the power of the proposed model with dual threshold LFSR and efficient BS-LFSR with MUX realization, the power consumption for the proposed design is very low. The static power consumed by dual threshold BSLFSR is 3062.99 mW; while for efficient BS-LFSR with MUX realization, it was 3062.99 mW, but for the proposed model, it is 1090.16 mW. The dynamic power consumed by dual threshold BS-LFSR is 26.33 mW; while for efficient BS-LFSR

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with MUX realization, it was 26.17 mW, but for the proposed model, it is 24.14 mW. The total power consumed by dual threshold BS-LFSR is 3089.32 mW; while for efficient BS-LFSR with MUX realization, it was 3089.21 mW, but for the proposed model, it is 1114.39 mW. By comparing the values of power, the proposed model shows the low power consumption of 63.93% as compared to ideal models for BIST.

References 1. R.G. Bennetts, C.M. Maunder, G.D. Robinson, CAMELOT: a computer-aided measure for logic testability. IEE Proc. Pt. E 128(5), 177–189 (1981) 2. A.A. Awad, S. Hamdioui,Reducing test power for embedded memories, in 2011 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (2011), pp. 112–119. https://doi.org/10.1109/DFT.2011.59 3. C. Banupriya, S. Chandrakala, A low power built in self repair technique for word-oriented memories, in 2014 International Conference on Electronics and Communication Systems (ICECS) (2014), pp. 1–5. https://doi.org/10.1109/ECS.2014.6892679 4. Y. Bonhomme, P. Girard, C. Landrault, S. Pravossoudovitch, Power driven chaining of flipflops in scan architectures, in Proceedings. International Test Conference (2002), pp. 796–803. https://doi.org/10.1109/TEST.2002.1041833 5. R.M. Williams, Keynote address: IBM perspectives on the electrical design automation industry, in 23rd ACM/IEEE Design Automation Conference (1986), pp. 1–1. https://doi.org/ 10.1109/DAC.1986.1586060 6. S.V. Murugan, B. Sathiyabhama, Bit-swapping linear feedback shift register (LFSR) for power reduction using pre-charged XOR with multiplexer technique in built in self-test. J. Ambient. Intell. Humaniz. Comput. (2020). https://doi.org/10.1007/s12652-020-02222-5 7. K. Jamal, P. Srihari, Low power TPC using BSLFSR. Int. J. Eng. Technol. 8(2), 759–767 (2016) 8. P. Girard, L. Guiller, C. Landrault, S. Pravossoudovitch, J. Figueras, S. Manich, P. Teixeira, M. Santos, Low energy BIST design: impact of the LFSR TPG parameters on the weighted switching activity, in Proceedings of International Symposium on Circuits and Systems, June 1999, pp. 110–113 9. L. Jie, Y. Jun, L. Rui, W. Chao, A new BIST structure for low power testing, in 5th International Conference on ASIC 2003. Proceedings, vol. 2 (2003), pp. 1183–1185. https://doi.org/10.1109/ ICASIC.2003.1277425 10. C.R. Shankar Reddy, V. Sumalatha, A new built in self-test pattern generator for low power dissipation and high fault coverage, in 2013 IEEE Recent Advances in Intelligent Computational Systems (RAICS) (2013), pp. 19–25. https://doi.org/10.1109/RAICS.2013.6745440 11. V. Suryanarayana, K. Miranji, Multi-degree smoother for low power testable digital system design using BS-LFSR and scan-chain ordering techniques. Int. J. Electron. Signals Syst. (2014). https://doi.org/10.47893/ijess.2014.1193 12. S. Wang, A BIST TPG for low power dissipation and high fault coverage. IEEE Trans. Very Large-Scale Integr. (VLSI) Syst. 15(7), 777–789 (2007). https://doi.org/10.1109/TVLSI.2007. 899234 13. V.N. Yarmolik, S. Hellebrand, H. Wunderlich, Self-adjusting output data compression: an efficient BIST technique for RAMs, in Proceedings Design, Automation and Test in Europe (1998), pp. 173–179. https://doi.org/10.1109/DATE.1998.655853 14. P. Dhanesh, A.J. Balaji, Dual threshold bit-swapping LFSR for power reduction in BIST, in 2015 International Conference on Advanced Computing and Communication Systems (2015), pp. 1–3. https://doi.org/10.1109/ICACCS.2015.7324061

Design of Novel Low Area Decoder Using Quantum Cellular Automata Nancharaiah Vejendla, J. Priyanka, V. Y. S. S. Sudir Patnaikuni, S. Suresh Kumar, and M. Ravindra Kumar

Abstract The most advanced technology in the propelling nanotechnology field that at last comes down the impediments of the CMOS innovation is the quantum cellular automata. With this nanotechnology, very fast operations are possible, and the power consumption is also low. This system is relying upon the association of electrons inside quantum spots. This paper reflects an effective three-to-eight decoder which was planned and successfully implemented in QCA with the aid of one-to-eight demultiplexer. The proposed architecture of three-to-eight decoders can be used to implement a n-to-2n decoder. The outcomes show that the proposed decoder circuit performs similarly all around contrasted with existing decoder plans and performs better if there should be an occurrence of past co-planar decoder structures with empower input usefulness in all perspectives. The software that has enables us in designing the QCA circuits in a single level by means of taking full gain of the unique capabilities of it is QCA designer. Keywords Decoder · Demultiplexer · Quantum cellular automata (QCA)

1 Introduction An impulse closer to nanotechnology is because of the constraints of cutting down the form of MOS in CMOS processing; due to this fact, the scaling down of MOS ends in a boom in gain of power and latency [1]. The suggested alternate technology that is intended to excel in solving the punch through effects, tunneling effects, etc., and taking care of the CMOS circuit architecture is QCA. Compared to existing circuit designs based on CMOS technologies, this platform provides fast speed of automated circuits and low-power action. Decoders are critical digital circuits which are typically used to manage primary-volatile memory arrays. A variety of QCA decoders were developed and implemented with the assistance of various majority voters. The field-programmable logic array (FPGA) part of the N. Vejendla · J. Priyanka (B) · V. Y. S. S. Sudir Patnaikuni · S. Suresh Kumar · M. Ravindra Kumar ECE, Lendi Institute of Engineering & Technology, Vizianagaram, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_61

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combination logic block (CLB) has been built using majority voters with three inputs during the set-up of look up table. A two-to-four decoder utilizing majority voter having five entries was planned to reduce the difficulty of the layout structure [2, 3]. Vetteth et al. [4] suggested architectures for the QCA-based barrel shifter and the carry-look-ahead adder. Ravichandran et al. [5] developed the first cell-level positioning of QCA circuits, where the given circuit is presumed to be divided into four-phase asynchronous QCA timing zones. Lantz and Peskin [6] proposed the specification, configuration, and efficient simulation of the field-programmable gate array (FPGA) architecture for a QCA-based configurable logic block (CLB). Roy and Saha [7] suggested a new merging architecture that uses minority gate as the fundamental building block, with more developed QCA. Bubna et al. [8] presented a comprehensive schematic of some of the standard CA rules applied using QCA. Amiri et al. [9] have introduced a FPGA CLB that is based on MUX, with three multiplexers for a CLB and 34 QCA cells for each multiplexer. Moein and Nadooshan [2] proposed the architecture, configuration, and efficient simulation of a two-to-four decoder using majority voter gate having five entries in which the circuit consists of minimum number of cells. Moein and Nadooshan [10] proposed QCA defining, applying, and simulating the FPGA for CLB. Lent et al. [1] show that very lowpower operation is also feasible by precisely computing the motion equations for the individual circuits coupled to the thermal environment.

2 Materials and Methods 2.1 QCA Basics The cell is the most basic element in the quantum cellular automata. QCA has array of cells with which the designs are usually constructed. The shape of the cell is basically square, and every cell has four points called dots which are positioned either within the corners of the square, i.e., the cell, or within the center of the edges of the cell [8]. The quantum dots are separated by the tunneling barriers, and they are controlled by the clocks. These cells serve four functions. A cell can be a normal cell, an input cell, an output cell, or a fixed cell. Two electrons are accommodated in every cell. These two electrons, due to the columbic force of repulsion, are always resided at particular positions such that the distance between them is always the maximum, i.e., at the opposite corners. They represent two polarizations of the cells. The electrons move from one dot to other through the tunneling junctions [9]. Figure 1 shows the cell of QCA and its polarizations. If the polarity of any cell is changed, then all the adjacent cells will adapt the same polarization [11].

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Fig. 1 QCA cells

2.2 QCA Clocking In QCA technology, clocking plays a major role that it controls the flow and transfer of data and makes the cell polarized from an un-polarized state. In QCA, there are a total of four clocks. Every clock has four phases through which a cell goes through [5]. They are switch, hold, release, and relax [10]. The clocks and the clock phases are depicted in Fig. 2. In the first phase, i.e., switch phase, the potential barrier at the tunneling junctions will start to rise. So, the electrons find it difficult to tunnel from one dot to other. The second phase hold is reached when the potential barrier at the tunneling junctions has reached its maximum extent so that it can completely prevent the tunneling of electrons to other places. The potential barriers start to lower in release phase which it leads to the tunneling of electrons. Finally, the electrons can route freely without any barrier as the potential barrier in relax phase is totally disappeared. Then again, in the next phase, the barriers start to rise. To monitor the all cells in their respective region, the QCA clock signals are involved. The output of that zone can behave as a Fig. 2 Clock zones

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Fig. 3 Majority voter gate

reference to the next zone [4]. We have therefore managed to move the data just as in pipeline mode by implementing this partitioning concept.

2.3 Majority Gate The functionality of the primary logic gates within the CMOS era is carried out via the majority gates in the QCA generation. A schematic of a majority gate QCA with three inputs is represented in Figure, and Eq. (1) shows the basic functionality, whereas T, O, and P are inputs given to the majority voter having three feeds (Fig. 3). M(T, O, P) = T O + O P + P T

(1)

The device cell’s propensity to shift into a low state means it considers the polarity of most of the cells closest neighbors [7, 9]. A unit cell can now monitor the polarity of most as it correlates to the minimum energy level.

3 Decoder A very important and useful digital circuit that is used for accessing the memory array (decoder circuit) in QCA is being discussed in this section. It is implemented with the aid of AND gate that is made up of three input majority voter, which will be effective in all respects, compared to previous QCA decoder designs. The primary aim of the decoder is that it activates any one of the outputs, while keeping the other output lines deactivated. This is mostly used for addressing large number of devices using a single device at different times. The basic operation of the decoder will not change when the enable is logic ‘1’. But, if the enable is allowed to be ‘0’ binary logic, then all the values of the output are limited with zero which means they are deactivated.

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Fig. 4 a Two-to-four decoder using majority voters having 5 entries b two-to-four decoder using three clocks

Some designs of two-to-four decoder that were proposed previously are depicted in Fig. 4. The three-to-eight decoder was introduced using a logical circuit that chooses a single output lines among the eight outputs. It is represented in Fig. 5.

4 Proposed Decoder Models In the earlier section, we have come across different designs of the decoders, i.e., two-to-four decoders and three-to-eight decoders. They were implemented based on the basic logic of the decoder. But, none of the designs is perfect and efficient. They occupy large area; cell count is high, and the latency is also high. This has driven us to tweak these models which would be economical in all respects. A new decoder concept was proposed in this work. Here, the architecture is depended upon demultiplexer. Demultiplexer is the basic part of our proposed system. Basically, a demultiplexer takes a single input along with some selector lines and will represent the input only at a single output, while all the others remain zero. When the input of the demultiplexer is set to logic ‘1’, then it acts as a decoder. Figure 6 represents the proposed designs of two-to-four decoder and three-toeight decoder, respectively, using one-to-four demultiplexer and one-to-eight demultiplexer. A new design of 4-to-16 decoder using one-to-eight demultiplexer has been

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Fig. 5 Three-to-eight decoder

Fig. 6 a Proposed model of two-to-four decoder using demultiplexer b proposed model of threeto-eight decoder using demultiplexer

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Fig. 7 Proposed model of novel 4-to-16 decoder

proposed and depicted in Fig. 7. In this design, the data from the inputs flow in opposite directions as a pipeline [6]. This 4-to-16 decoder is made up of three-to-eight decoders. When the enable E = 0, the decoder that is in the top of the circuit is enabled, and the other decoder which is in the bottom part is disabled. The outputs of the lower decoder are all zeros and that of the upper part will be the minterms 1000 to 1111. The opposite happens to both the decoders when E = 1. The most productive decoder provided with a very strong response in this paper can thus easily be deployed in QCA, where the E input of the converter permits us to manipulate the data fully.

5 Simulation Results The prototypes proposed in this paper were explained by the use of the simulation software QCA designer version 2.0.3. Figure 8 shows the performance of the twoto-four decoders. Figure 9 shows the simulated output graphs of the proposed threeto-eight decoder. A comprehensive report on the QCA hardware cost of implementing the various decoder circuit designs in terms of area and cell count is given below in Table 1.

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Fig. 8 Output of two-to-four decoder

Fig. 9 Simulated output of three-to-eight decoder Table 1 Detailed report on hardware of implemented structures S. No. Circuit

Cell count Area occupied (µm2 )

1

Two-to-four decoder using five entries (Fig. 4a)

237

2

Two-to-four decoder design using clocks (Fig. 4b) 71

0.10

3

Three-to-eight decoder (Fig. 5)

0.37

4

Proposed two-to-four decoder

196

0.22

5

Proposed three-to-eight decoder

613

0.92

6

Proposed 4-to-16 decoder

1251

2.13

262

0.32

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6 Conclusion In this, paper proposed an optimized architecture of two-to-four, three-to-eight, and four-to-sixteen decoding circuits, which is most useful in all respects relative to the various layouts discussed within this article. We visualized our model with aid of the modeling equipment ‘QCA designer-2.0.3’. The suggested circuit designs are most efficient in many ways opposed to the previous two input decoding circuit designs. In the aspects of something like regional intricacy, the configuration proposed demands a higher quantity of cells, and that this decline in cell number helps to make the network quite flexible and also strong that relishes ones advancement toward different traits such as RAM and enhances its profitability. Regardless, future research could continue to explore the power consumption and the latency of the design proposed. It is desirable to use these designs in the efficient SRAM designing.

References 1. C.S. Lent, M. Liu, Y. Lu, Bennett clocking of QCA and the limits to binary logic scaling. J. Comput. Electron. 17, 4240–4251 (2017) 2. K. Moein, R.S. Nadooshan, A novel modular decoder implementation in quantum-dot cellular automata, in 2011 International Conference on Nanoscience, Technology and Societal Implications, Bhubaneswar (2011), pp. 1–5 3. K. Moein, S. Nadooshan, A conventional design for CLB implementation of a FPGA in quantum-dot cellular automata (QCA), in Nanoarch (2012), pp. 36–42 4. A. Vetteth, K. Walus, V.S. Dimitrov, G.A. Jullien, Quantum cellular automata carry-look ahead adder and barrel shifter, in Proceedings of the IEEE Emerging Telecommunications Technologies Conference (2002), pp. 2–4 5. R. Ravichandran, S.K. Lim, M. Niemier, Automatic cell placement for quantum-dot cellular automata. Integration 2005(38), 541–548 (2005) 6. T.D. Lantz, E.R. Peskin, A QCA implementation of configurable logic block for an FPGA, in Proceedings of the Third International Conference on Reconfigurable Computing and FPGAs, Sept 2006, pp. 132–141 7. S. Roy, B. Saha, Minority gate oriented logic design with quantum-dot cellular automata, in Lecture Notes in Computer Science, vol. 4173 (2006), pp. 646–656 8. M. Bubna, S. Mazumdar, S. Roy, R. Mall, Designing cellular automata structures using quantum-dot cellular automata, in 14th Annual IEEE International Conference on High Performance Computing (2007) 9. M.A. Amiri, M. Mahdavi, S. Mirzakuchaki, A QCA implementation of a multiplexerbased FPGA CLB, in Proceedings of the International Conference on Nanoscience and Nanotechnology, ICNN 2008 10. K. Moein, R.S. Nadooshan, A novel quantum-dot cellular automata CLB of FPGA. J. Comput. Electron.13, 709–725 (2014) 11. C.C. Tung, R.B. Rungta, E.R. Peskin, Simulation of a QCA-based CLB and a multi-CLB application, in FTP 2009

An Area-Efficient JK Flip-Flop-Based Phase Detector for Phase Measurement System Based on FPGA S. S. Kerur, Veeresh, and Shrikanth K. Shirakol

Abstract Phase measurement is necessary condition where electronics applications used synchronous operation. Many existing methods are based on ADC sampling which leads to extra hardware utilization, high power consumption, and bigger in size. The majority of high-speed transceivers in FPGA circuits not maintaining the same circuits delay following each reset, power cycle, interconnect delay, and firmware upgrade. This leads to unreliability with phase difference between the signals at receivers. To identify smallest phase difference at recovered signals with area efficient and low-power consumption proposed a VLSI digital logic system to provide phase difference in the range of few picoseconds. Proposed system operates in the frequency that supports with field-programmable gate array (FPGA). This paper implements phase measurement based on JK flip-flop-based phase detector (PD) and CLA for an area efficient and provides a comparative evaluation in terms of both area and power consumption with existing system. Keywords JK flip-flop-based PD · Phase measurement system · FPGA · CLA

1 Introduction Phase shift information is required for to calibrate and synchronous operations in electronics applications. Advantages of reconfigurable devices such as FPGA, designers can design and obtain all digital architectures for compact implementation. The majority of high-speed Serializer Deserializer (SerDes) chips of the FPGAs require the preservation of phase shift information between the circuits clocks during run time of the program because they don’t maintain same circuit delay at the receiver following power cycle, combinational and sequential delay, reset and upgrade of firmware. Many existing methods such as Lu et al. [1], Trebbels et al. [2], and Rong et al. [3] are based on ADC sampling which leads to external hardware utilization. Without use of external hardware utilization, a phase measurement approach S. S. Kerur · Veeresh (B) · S. K. Shirakol Department of Electronics and Communication Engineering, SDM College of Engineering and Technology, Dharwad, Karnataka 580002, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_62

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proposed by Hidvégi et al. [4], and this method achieved less resolution. Without use of external hardware utilization proposed another phase measurement approach by Mitra and Nayak [5] and this method provides ± 90° nominal lock point. Employed JK flip-flop-based phase detector (PD) and carry look-ahead adder (CLA) in the proposed system for systematic sampling to provide ± 180° static phase shift with nominal lock point [6], area efficient, and low power consumption. JK flip-flopbased PD is not sensitive to the power cycle of the reference clock signal. Proposed system operates in the frequency that is supported within the FPGAs firmware. JK flip-flop-based phase detector is usually used in digital PLL. The proposed technique is suitable for the construction of a logical core, and the phase shift measurement between the signal clocks that have frequencies supported with FPGA can give values with the range of picoseconds. This paper implements phase measurement based on JK flip-flop-based PD and CLA to provide a comparative evaluation in terms of both area and power consumption with existing system.

2 Related Work Another form of the phase detector is said to be edge-triggered JK flip-flop-based PD. It is sequential-based circuit, and it accepts J and K inputs during active edge of the clock. The proposed circuit has benefit that which gives phase shift is between ± 180°, low power consumption and area efficient compared to existing system. During active edge of clock, if positive rising edge comes at J input, then output of JK flip-flop is logic level high, and if positive rising edge comes at K input, then output of JK flip-flop is low.

2.1 Existing Phase Measurement System The block diagram and VLSI architecture of existing phase measurement system based on FPGA are shown in Fig. 1a and b, respectively. Synchronizer Module: Four D flip-flops will be used to create the synchronizer, together with Clk1, Clk2, and the sampling clock. SAMPLE CLK uses asynchronous clock domain crossing to sample CLK1 and CLK2, which can result in metastable output. Synchronizer used for metastable signals to be settled down. Phase Detector Module: In the existing phase, measurement system used an XORbased phase detector. Subsamples were recorded by systematic sampling over an XOR-based phase detector and which gives less circuit delay. The XOR-based phase detector is sensitive to reference clock duty cycle. It is going to be locked up when a phase error occurs when the input reference clock duty cycle is not 50%.

An Area-Efficient JK Flip-Flop-Based Phase Detector for Phase …

(a)

591

(b)

Fig. 1 Existing phase measurement system. a Block diagram. b VLSI architecture

Duty Cycle Computation Module: Counts N number of XORed and reference clock duty cycle signals. Phase Value Computation Module: With the number of samples obtained, it will calculate phase value. In this paper for area-efficient analysis purposes, adder/subtractor is implemented with carry look-ahead adder (CLA) technique. Phase Polarity Detection Module: Reference clock is continuously sampled with phase-shifted reference clock to identify the polarity.

2.2 Proposed Phase Measurement System The block diagram and architecture of VLSI of proposed phase measurement system based on FPGA are shown in Fig. 2a and b, respectively. The proposed system used the existing method as it is except for the phase detector and adder/subtractor part. In the proposed system, JK flip-flop-based PD is employed as shown in Fig. 3; it is not sensitive to the power cycle of the reference clock signal. JK flip-flop-based PD gives exact DC output for harmonic signals, leading to potential locking point to harmonics. Waveforms analysis of JK flip-flop-based PD is shown in Fig. 4, and its overall response is shown in Fig. 5. JK phase detector angle is ± 180° static phase shift. The symmetry of CLK1 and CLK2 is unimportant because it is edge triggered. If the loop filter does not have a pole at zero, then both the XOR and the JK flip-flop phase detectors have a deeply limited pull-in range. CLA transfers carry bits before the sum calculation which reduces delay time to get results for number of bits. Delay order for CLA is O(log (n)).

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(a)

(b)

Fig. 2 Proposed phase measurement system. a Block diagram. b VLSI architecture Fig. 3 JK flip-flop-based PD in proposed VLSI architecture

Fig. 4 Waveform analysis of JK flip-flop-based PD

Fig. 5 JK flip-flop-based PD response

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3 Results 3.1 Measurement Setup CLA implemented for addition/subtraction operation. The JK flip-flop-based PD provides phase value nominal lock point with ± 180° phase shift. We have used reference clock period (T ) is 8000 ps, and phase shift t θ is 3 ps. To record the smallest phase shift of t θ , the least quantity of samples needed in the subsample cyclic vector is given by Eq. (1). ≥

T 2

(1)

tθ

Proper choice of sampling frequency calculated as follows:  > 4000(= (T /2)/tθ ),  ≥ 1/2Ftθ , gcd(, ) = 1,

 = 



> 1, Oversampling ≤ 1, Undersampling

Chosen values:  = 69,999,  = 8000 Ts =

 ∗T 

(2)

Sampling frequency can be calculated with the help of Eq. (2). Therefore, sampling frequency T s is 69999 ps. The output waveform is shown in Fig. 6.

Fig. 6 Output waveform for proposed phase measurement system based on FPGA

594 Table 1 Area comparison analysis with device utilization summary for XOR-based PD and JK flip-flop-based PD

Table 2 Power comparison analysis from power summary report for XOR-based phase detector and JK flip-flop-based phase detector

Fig. 7 XOR and JK flip-flop-based PDs area comparison

S. S. Kerur et al. Device utilization parameters Parameters

XOR-based PD with inbuilt adder

JK Flip-flop-based PD with CLA

No. of slice LUTs

688

557

No. of LUT flip-flop pairs used

766

625

Power supply analysis Parameters

XOR-based PD with inbuilt adder

JK flip-flop-based PD with CLA

Supply power in mW

62.31

52.40

1000

No. of Slice LUTs

500 0

XOR-based PD with JK Flip-flop based inbuilt adder PD with CLA

Fig. 8 XOR and JK flip-flop-based PDs power comparison

No. of LUT Flip-flop pairs used

Supply Power in mW 70 60 50 40

Supply Power in mW XOR-based PD with JK Flip-Flop based inbuilt adder PD with CLA

3.2 Simulation Result Analysis Simulation results comparisons are indicated in Tables 1 and 2 of Figs. 7 and 8.

4 Conclusion In this paper implemented phase measurement based on area-efficient JK flip-flopbased phase detector (PD), and it is insensitive to duty cycle of reference clock signal. It provides area improvisation and less power consumption compared to the existing phase measurement approach. This method produce phase shift measurement in the range of a few picoseconds. The designers can change architecture

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for more robustness of the system by replacing phase detector with other phase detector advanced technology adders like approximation adders. Sampling method for collecting subsample can replace with other complex analog or digital methods.

References 1. S. Lu, P. Siqueira, V. Vijayendra, H. Chandrikakutty, R. Tessier, Real-time differential signal phase estimation for space-based systems using FPGAs. IEEE Trans. Aerosp. Electron. Syst. 49(2), 1192–1209 (2013) 2. D. Trebbels, D. Woelki, R. Zengerle, High precision phase measurement technique for cell impedance spectroscopy. J. Phys. Conf. Ser. 224(1), 012159 (2010) 3. L. Rong et al., FPGA-based amplitude and phase detection in DLLRF. Chin. Phys. C 33(7), 594 (2009) 4. A. Hidvégi et al., FPGA based phase detector for high-speed clocks with pico-seconds resolution, in Proceedings IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), Oct/Nov 2013, pp. 1–4 5. J. Mitra, T.K. Nayak, An FPGA-based phase measurement system, IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 26(1) (2018) 6. B.B. Purkayastha, K.K. Sarma, A Digital Phase Locked Loop Based Signal and Symbol Recovery System for Wireless Channel (Springer Publications, Berlin, 16 June 2014)

Design and Analysis of Power-Efficient Carbon Nanotube-Based Parity Checker Circuits for High-Data Transmission Rate Imran Ahmed Khan and Md Rashid Mahmood

Abstract Due to physical, material, technological, power-thermal, and economical challenges, scaling of CMOS technology is going to end. Due to speed and power efficiency as compared to CMOS technology, carbon nanotube technology is one of the best suitable emerging technologies for electronic circuit designing. In this paper, CNTFET-based two parity checker circuits have been proposed. CNTFETbased even parity checker and odd parity checker have been compared with CMOS counterpart. These circuits are used to check the errors in the data transmission. SPICE has been used for simulating the parity checker circuits in 32 nm process node. Simulation results confirm usefulness of the proposed error-detecting digital circuits. Keywords Nanotechnology · CMOS technology · Data rate · Combinational circuit

1 Introduction The data transmission’s rate is rising regularly, so data transmission without error is mandatory. Fast data transfer rate is 1.125 Tbps. During the transmission of big data, errors may occur due to noise, crosstalk, interference due to EM waves, etc. [1]. If the error is not detected and corrected in due time, wrong transmission of information may take place. Thus, implementation of error-detecting codes is necessary. Parity generators and checkers are the most common solution for error detection [2]. The combinational parity checker validates the parity bit at the receiver end [3, 4]. The scaling of the size of the transistor has made significant progress in the silicon industry throughout the world. However, as the performance improves as a result of scaling, the increase in the power density takes place because of higher integration I. A. Khan Jamia Millia Islamia, New Delhi, India M. R. Mahmood (B) Guru Nanak Institutions Technical Campus, Hyderabad, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_63

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density [5]. Further due to physical, material, technological, power-thermal, and economical challenges, scaling of CMOS technology coming to an end. The new material, and a related device, which has potential to replace Si and CMOS, and has the scalability property of the devices lower than 22 mm technology, is carbon nanotube (CNTFET (carbon nanotube field effect transistor (CNTFET)) [6, 7]. Also, CNTFET is a promising element through which Moore’s prediction can be further extended [7–9]. The researchers have a strong interest in this field, and many digital circuits have been proposed using CNTFET such as adder/subtracter [10–13], flipflops [14–17], decoder/encoder [18, 19], and comparator [20]. Parity checker is a combinational circuit which validates the parity bit at the receiver. In this paper, CNTFET-based two circuits have been proposed. These are odd parity checker and even parity checker. These circuits can check the errors in the data transmission.

2 Even Parity Checker For three-bit data transmission, one-bit even parity along the three data bits is sent to the receiver. The parity checker at the receiver examines the errors during transmission. In this case, four bits obtained have to be even number of 1’s. If these four bits are found an odd number of 1’s, the circuit gives an error [21]. It basically checks the total number of “one” in the received data; thus, to design the parity checker, XOR gate and XNOR gate can be used [2]. The implementation of parity checker is shown by expression (1) [22]. Pevc = A(XOR)B(XOR)C(XOR)Pev

(1)

Figure 1 represents proposed CNTFET-based three-bit even parity checker. Table 1 presents power, speed, and power-delay-product for even parity checker, while temperature is varied from 0° to 100 °C. Table shows that in CNT variation with temperature is negligible, and at all temperatures, proposed circuit has better power, speed, and power-delay-product as compared to the rival circuit. Table 2 presents power, propagation delay, and power-delay-product of even parity checker with variation in power supply from 0.7 to 1.3 V. Table shows that at all voltages, proposed circuit have better power, speed, and power-delay-product as compared to the rival circuit.

3 Odd Parity Checker For three-bit data transmission, three data bits and one-bit odd parity are transmitted to the receiver. The parity checker at the receiver examines the errors during transmission. For three-bit data transmission, one-bit even parity along the three data

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Fig. 1 Proposed CNTFET even parity checker Table 1 Power, propagation delay, and power-delay-product for three-bit even parity checker as a function of temperature Temp. (°C)

Power consumption

Delay

Existing CMOS (nW)

Proposed circuit (nW)

Existing CMOS (pS)

PDP Proposed circuit (pS)

Existing CMOS (10−21 J)

Proposed circuit (10−21 J)

0

85.70

3.49

53.17

15.77

4556.67

55.04

25

147.59

3.49

61.65

15.77

9098.92

55.04

50

238.22

3.49

69.00

15.77

16,437.18

55.04

75

362.98

3.49

78.72

15.77

28,573.79

55.04

100

526.58

3.49

90.44

15.77

47,623.90

55.04

600

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Table 2 Power, propagation delay, and power-delay-product of three-bit even parity checker as a function of supply voltage Supply voltage (V)

Existing CMOS (nW)

Proposed circuit (nW)

Existing CMOS (pS)

Proposed circuit (pS)

Existing CMOS (10−21 J)

Proposed circuit (10−21 J)

0.7

40.30

2.65

109.26

18.11

4403.18

0.9

147.59

3.49

61.65

15.77

9098.92

47.99 55.04

1.1

571.74

4.36

43.94

13.56

25,122.26

59.12

1.3

2599.60

5.28

37.85

8.09

98,394.86

42.72

bits are sent to the receiver. The parity checker at the receiver examines the errors during transmission. In this case, the four bits obtained have to be odd number of 1’s. If these four bits are found an even number of 1’s, the circuit gives an error. The implementation of odd parity checker is shown by expression (2) [22]. Podc = [A(XOR)B](XNOR)[C(XOR)Pod ]

(2)

Figure 2 represents proposed CNTFET-based odd parity checker of three bit. Table 3 presents power, propagation delay, and power-delay-product of odd parity checker, while temperature is varied from 0° to 100 °C. Table shows that at all temperatures, proposed circuit has better power, speed, and power-delay-product as compared to the rival circuit. Table 4 presents power consumption, propagation delay, and powerdelay-product of odd parity checker with variation in power supply from 0.7 to 1.3 V. Table shows that at all voltages, proposed circuit has better power, speed, and power-delay-product as compared to the rival circuit.

4 Conclusion Parity generator/checker circuits are generally preferred for error-free transmission between digital devices. Parity checker circuits have been proposed in this work. Due to physical, material, technological, power-thermal, and economical challenges, scaling of CMOS technology is going to end. Due to speed and power efficiency as compared to CMOS technology, carbon nanotube technology is one of the best suitable emerging technologies for electronic circuit designing. So, carbon nanotube technology has been used in designing the circuits in the paper. The proposed circuits are odd parity checker and even parity checker. These circuits are compared with CMOS counterpart. The bases of comparison are power, performance, and power-delay-product (PDP). SPICE has been used for simulating the parity checker circuits in 32 nm process node. Although the fabrication of the proposed circuits is complex as compared to CMOS counterparts, but these circuits have high performance and lesser power consumption.

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Fig. 2 Proposed CNTFET odd parity checker

Table 3 Power, propagation delay, and power-delay-product of three-bit odd parity checker as a function of temperature Temp. (°C)

Existing CMOS (nW)

Proposed circuit (nW)

Existing CMOS (pS)

Proposed circuit (pS)

0

87.34

3.42

52.9

11.76

25

150.91

3.42

59.46

50

243.96

3.39

70.065

75

372.01

3.42

100

539.93

3.39

Existing CMOS (10−21 J)

Proposed circuit (10−21 J)

4620.29

40.22

11.76

8973.11

40.22

3.59

17,093.06

12.17

80.11

11.76

29,801.72

40.22

92.18

3.59

49,770.75

12.17

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Table 4 Power, propagation delay, and power-delay-product of three-bit odd parity checker as a function of supply voltage Supply voltage (V)

Existing CMOS (nW)

Proposed circuit (nW)

Existing CMOS (pS)

Proposed circuit (pS)

Existing CMOS (10−21 J)

Proposed circuit (10−21 J)

0.7

39.53

2.59

108.47

11.32

4287.62

29.32

0.9

150.91

3.42

59.46

11.76

8973.11

40.22

1.1

632.07

4.28

44.62

9.21

28,199.80

39.40

5.18

38.03

8.46

120,006.90

43.80

1.3

3156.0

References 1. K.B. Lakshmi, S. Tejaswi, S.C. Vamsi, B. Jeevanarani, A novel area efficient parity generator and checker circuits design using QCA, in IEEE Fifth International Conference on Inventive Computation Technologies (2020), pp. 1108–1113 2. B. Han, J. Xu, P. Chen, R. Guo, Y. Gu, Y. Ning, Y. Liu, All-optical non-inverted parity generator and checker based on semiconductor optical amplifiers. Appl. Sci. 11, 1499 (2021) 3. E. Deniz, K. Aksoy, S. Tahar, Y. Zeren, Design and verification of parity checking circuit using HOL4 theorem proving. Sigma J. Eng. Nat. Sci. 10(2), 245–252 (2019) 4. V. Shukla, O.P. Singh1, G.R. Mishra, Optimized even/odd parity generator/checker circuits with reversible logic approach. Int. J. Adv. Sci. Technol. 29(03), 12076–12085 (2020) 5. I.A. Khan, M.T. Beg, Design and analysis of low power master slave flip-flops. Informacije Midem-J. Microelectron. Electron. Compon. Mater. 43(1), 41–49 (2013) 6. R. Saito, G. Dresselhaus, M. Dresselhaus, Physical Properties of Carbon Nanotubes (World Scientific Publishing Co. Inc., 1998) 7. D.S. Bethune, C.H. Kiang, M. Devries, G. Gorman, R. Savoy, R. Beyers, The discovery of single-wall carbon nanotubes at IBM. Nature 363, 605–607 (1993) 8. Y.B. Kim, Integrated circuit design based on carbon nanotube field effect transistor. Trans. Electr. Electron. Mater. 12(5), 175–188 (2011) 9. A. Raychowdhury, K. Roy, Carbon nanotube electronics: design of high-performance and low power digital circuits. IEEE Trans. Circ. Syst. I: Regul. Pap. 54(11) (2007) 10. P. Keshavarzian, R. Sarikhani, A Novel CNTFET-based ternary full adder. Circ. Syst. Signal Process. 33, 665–679 (2014) 11. A. Mohammaden, M.E. Fouda, L.A. Said, A.G. Radwan, Memristor-CNTFET based ternary full adders, in IEEE Conference (2020), pp. 562–565 12. F. Zahoor, F.A. Hussin, F.A. Khanday, M.R. Ahmad, I. Mohd Nawi, C.Y. Ooi, F.Z. Rokhani, Carbon nanotube field effect transistor (CNTFET) and resistive random access memory (RRAM) based ternary combinational logic circuits. Electronics 10(1), 79 (2021) 13. I. Ahmed Khan, M. Rashid Mahmood, J.P. Keshari, Analytical comparison of power efficient and high performance adders at 32 nm technology, in Innovations in Electronics and Communication Engineering. Lecture Notes in Networks and Systems, vol. 107 (Springer, Singapore, 2020). https://doi.org/10.1007/978-981-15-3172-9_62 14. D.M. Badugu, S. Sunithamani, J.B. Shaik, R.K. Vobulapuram, Design of hardened flip-flop using Schmitt trigger-based SEM latch in CNTFET technology. Circ. World 47(1), 51–59 (2020) 15. A. Karimi, A. Rezai, M.M. Hajhashemkhani, Ultra-low power pulse-triggered CNTFET-based flip-flop. IEEE Trans. Nanotechnol. 18, 756–761 (2019) 16. M. Shaveisi, A. Rezaei, Analysis of reversible sequential circuits based on carbon nanotube field effect transistors (CNTFETs). J. Electr. Comput. Eng. Innov. 6(2), 167–178 (2018)

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17. K. Swami, R. Sharma, Implementation and Optimization of CNTFET Based Ultra-Low Energy Delay Flip Flop Designs (Research Square, 2021) 18. C. Vudadha, S. Rajagopalan, A. Dusi, P.S. Phaneendra, M.B. Srinivas, Encoder-based optimization of CNFET-based ternary logic circuits. IEEE Trans. Nanotechnol. 17(2), 299–310 (2018) 19. R.A. Jaber, A. Kassem, A.M. El-Hajj, L.A. El-Nimri, A.M. Haidar, High-performance and energy-efficient CNFET-based designs for ternary logic circuits. IEEE Access 7, 93871–93886 (2019) 20. C. Vudadha, P.S. Phaneendra, G. Makkena, V. Sreehari, N.M. Muthukrishnan, M.B. Srinivas, Design of CNFET based ternary comparator using grouping logic, in 2012 IEEE Faible Tension Faible Consommation (2012), pp. 1–4 21. M.G. Waje, P.K. Dakhole, Implementation and performance analysis of single layered reversible parity generator and parity checker circuits using quantum dot cellular automata paradigm, in International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT) (2015), pp. 175–180 22. S. Santra, U. Roy, Design and optimization of parity generator and parity checker based on quantum-dot cellular automata. Int. J. Nucl. Quantum Eng. 8(3), 491–497 (2014)

Design of Low-Power CNTFET Parity Generators for High-Speed Data Transmission Imran Ahmed Khan, Md Rashid Mahmood, J. P. Keshari, and Mirza Tariq Beg

Abstract Nowadays, digital data transmission is the most common method applied in communication systems. Transmission of data without error is needed from the source device to the destination device, odd/even parity generators/checkers is applied to ensure this. Due to better characteristics as compared to CMOS technology, carbon nanotube technology is one of the most emerging technologies for replacement of CMOS in designing of electronic circuits. Therefore, in this work, CNTFET odd and even parity generators have been proposed. Simulation results confirm that these proposed designs are efficient in terms of power, speed, and power-delay-product (PDP). Keywords Transmission error · Nanotechnology · Wireless communication · Error detection · Propagation delay

1 Introduction To handle the rising demand on bandwidth, there is tremendous growth in wireless data transmission. The data rates have increased beyond 1 Gbps due to advanced developments in wireless [1]. Due to high data transfer rate, the persons consulted a medical doctor without stepping out of their home specially in COVID-19 pandemic and lockdowns [2]. During transmission, data may be corrupted, and we may obtain the data at the receiver which is different from original data. To detect this error, extra bit is added that is called parity [3, 4]. There may be even parity or odd parity depending upon

I. A. Khan (B) · M. Tariq Beg Jamia Millia Islamia, New Delhi, India M. R. Mahmood Guru Nanak Institutions Technical Campus, Hyderabad, India J. P. Keshari IIMT College of Engineering, Greater Noida, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5_64

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number of ones in transferring information. With this parity, one can identify the errors during transmission [5–7]. The scaling of the size of the transistor has made significant progress in the silicon industry throughout the world. However, as the performance improves as a result of scaling, the increase in the power density takes place because of higher integration density [8, 9]. The deep-sub-micron CMOS technology faced many challenges because of scaling. The further downscaling is becoming more and more difficult [10, 11]. The limitations of traditional CMOS technology will lead to a shift of the researchers in the direction of nanotechnology [12]. The researchers have proposed various digital circuits using CNTFET such as adder/subtracter [13–16], flip-flops [17–20], and multiplexer [21–23]. Parity generator is an integrated circuit which creates a parity bit at transmission end in data transfer. Parity checker is a combinational circuit which checks the parity bit at the receiver end [24, 25]. The parity generators are inherent part for error detection and correction in the transceiver system [26–30]. In this paper, CNTFETbased two circuits have been proposed. These are odd parity generator and even parity generator. These designs can investigate odd number of errors during data transmission.

2 Even Parity Generator In a three-bit even parity generator circuit, the three-data bits are forwarded with an even parity bit. The three-data bits and the output of even parity generator circuit (Pev ) are transmitted. Even parity generator produces parity bit Pev = 1 when there are odd numbers of “ones” in data inputs, otherwise Pev = 0. The expression of even parity generator is represented in Eq. 1 [31]. Pev = A(XOR)B(XOR)C

(1)

An optimized CNTFET-based XOR gate is proposed in this work, and optimized parity generator circuits have been designed. Figure 1 illustrates proposed CNTFET three-bit even parity generator. Proposed CNTFET design is compared with the rival circuit. The bases of comparison are power, speed, and PDP. Table 1 shows power, speed, and power-delay-product for even parity generator, while temperature is varied from 0° to 100 °C. This table lucidly shows at all temperatures, proposed CNTFET design has better power, speed, and power-delay-product as compared to the rival circuit. Table 2 presents power and power-delay-product of even parity generator with variation in power supply from 0.7 to 1.3 V. Table shows that for all voltages, proposed circuit has better power and PDP than rival circuit.

Design of Low-Power CNTFET Parity Generators for High-Speed …

607

Fig. 1 Proposed CNTFET three-bit even parity generator design Table 1 Power, propagation delay, and PDP for even parity designs as a function of temperature Temp. (°C)

Existing CMOS (nW)

Proposed circuit (nW)

Existing CMOS (pS)

Proposed circuit (pS)

Existing CMOS (10−21 J)

Proposed circuit (10−21 J)

0

57.13

2.33

57.98

8.60

3312.11

20.03

25

98.39

2.33

67.12

8.60

6603.44

20.03

50

158.81

2.31

76.67

14.32

12,175.17

33.07

75

241.98

2.31

87.60

14.50

21,197.45

33.48

100

351.05

2.31

99.19

14.32

34,820.65

33.08

Table 2 Power and power-delay-product for even parity generators as a function of supply voltage Supply voltage (V) 0.7

Existing CMOS (nW)

Proposed circuit (nW)

26.87

1.76

0.9

98.39

1.1

381.15

1.3

1732.90

Existing CMOS (10−21 J)

Proposed circuit (10−21 J)

3192.02

32.00

2.33

6603.44

20.03

2.91

18,339.03

38.11

3.52

69,662.58

29.13

608

I. A. Khan et al.

3 Odd Parity Generator In a three-bit odd parity generator circuit, the three-data bits are forwarded with an odd parity bit. The three-data bits and the output of odd parity generator circuit (Pod ) are transmitted. Odd parity generator produces parity bit Pod = 1 when there are even numbers of “ones” in data inputs, otherwise Pod = 0. The expression of this generator is represented in Eq. 2 [31]. Pod = [A(XOR)B](XNOR)C

(2)

Figure 2 illustrates proposed CNTFET odd parity generator. Table 3 shows power, propagation delay, and PDP of odd parity generator, while temperature is varied from 0° to 100 °C. For all temperatures, proposed circuit has better power, speed, and PDP than the rival circuit. Table 4 shows power consumption, propagation delay, and PDP of odd parity generator with variation in power supply from 0.7 to 1.3 V. For all voltages, proposed circuit has better power, speed, and PDP than the rival design.

Fig. 2 Proposed CNTFET odd parity generator design

Design of Low-Power CNTFET Parity Generators for High-Speed …

609

Table 3 Power, propagation delay, and PDP for three-bit odd parity generator as a function of temperature Temp. (°C)

Existing CMOS (nW)

Proposed circuit (nW)

Existing CMOS (pS)

Proposed circuit (pS)

Existing CMOS (10−21 J)

Proposed circuit (10−21 J)

0

58.77

2.25

52.40

16.46

3079.25

37.04

25

101.72

2.25

60.00

16.46

6103.20

37.04

50

164.55

2.24

69.58

10.00

11,448.57

22.40

75

251.02

2.24

78.57

16.40

19,721.39

36.74

100

364.40

2.24

89.50

15.21

32,613.80

34.07

Table 4 Power, propagation delay, and PDP of three-bit odd parity generator as a function of supply voltage Supply voltage (V)

Existing CMOS (nW)

Proposed circuit (nW)

Existing CMOS (pS)

Proposed circuit (pS)

Existing CMOS (10−21 J)

Proposed circuit (10−21 J)

0.7

26.10

1.71

109.69

16.43

2862.78

28.09

0.9

101.72

2.25

60.00

16.46

6103.20

37.04

1.1

441.49

2.82

43.75

14.43

19,312.98

40.69

1.3

2289.60

3.41

37.09

7.88

84,909.82

26.87

4 Conclusion Due to many challenges, scaling of CMOS technology is going to end. Due to better characteristics as compared to CMOS technology, carbon nanotube technology is one of the most emerging technologies for replacement of CMOS in designing of electronic circuits. Therefore, CNTFET odd parity generator and even parity generator have been proposed in this work. The proposed circuits have been compared to rival design on bases of power, speed, and power-delay-product (PDP). For all temperatures and supply voltages, proposed circuit has better power, propagation delay, and power-delay-product than the rival circuit. Thus, proposed CNTFET generators are speed and power efficient.

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Author Index

A Abinaya, R., 271 Abirami, Jothi, 487 Adinarayna, S., 213 Ahmad, Naim, 479 Ajay Kumar, C. H., 235 Allamsetty, Anudeep, 75 Anil Kumar, P., 555 Ansari, Saher Jawaid, 337 Anudeep, Juluru, 167 Anushree, 117 Arora, Karan, 381 Arulkumar, V., 155 Ashfaque, Mohammed Waseem, 423 Atmakuri, Murali Krishna, 91 Azam, Farooque, 401, 447

B Badhyal, Subham, 361 Bagadi, Lavanya, 253 Banakar, R. M., 21 Bhaskar Reddy, P. V., 447 Bheemeswara Sastry, J., 437 Bhushan, Bharat, 39, 187, 227, 381, 391 Bollavathi, Lokeshwar, 91 Brindha, R., 227 Bulla, Suman, 75 Byra Reddy, G. R., 319

C Chandana Mani, R. K., 187 Chandana, S., 137 Chauhan, Kanika, 495

Chembeti, Janaki Sutha, 195 Chikate, Yukta, 515 Choubey, Abhishek, 283 Choubey, Shruti Bhargava, 283 Choudhary, Surya Deo, 337 Chourasia, Bharti, 29

D Das, Nabamita, 13 Devi, Pothereddypally Jhansi, 543 Dhawan, Paurush, 83 Durgam, Rajesh, 563

F Farsi Al, Ghaliya, 423 Francis Avinash, A., 235

G Gaganashree, N., 195 Gaikwad, Shweta Prakash, 413 Gannamaneni, Satish Kumar, 3 Gupta, Kanika, 569

H Hamzaoui, El-Mehdi, 179 Harsha, S., 137 Hemalatha, K. N., 195 Hoda, Najmul, 479 Hrushikesava Raju, S., 213 Husain, Agha Asim, 125

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. S. Saini et al. (eds.), Innovations in Electronics and Communication Engineering, Lecture Notes in Networks and Systems 355, https://doi.org/10.1007/978-981-16-8512-5

613

614 I Indira, D. N. V. S. L. S., 271 Indra, Gaurav, 495 Inunganbi, Sanasam Chanu, 429

J Jacob, Alan, 125 Jahedul Islam, Md., 13 Jammalamadugu, Ravindranadh, 91 Jeevalakshmi, D., 145 Jensen, Santo, 487 Jothiraj, S., 101

K Kapila, Gurucharan, 235 Karthik Kumar, V., 137 Kashyap, Rekha, 381 Kaur, Harmandeep, 523 Kaur, Harpreet, 283, 293, 361 Kaur, Jasdeep, 117 Kaur, Manpreet, 293, 361 Kaur, Sarabpreet, 361 Kavitha, A., 227 Kavitha, T., 195 Keerthi Priya, Alikapati, 195 Kerur, S. S., 589 Keshari, J. P., 605 Khaitan, Ayush, 235 Khan, Imran Ahmed, 597, 605 Khan, Khaleel Ur Rahman, 469 Kiran, M. R., 21 Kondalamma, P., 51 Konge, Atharva, 515 Kulkarni, Komal Raghavendra, 21 Kumar, Abhijit, 391 Kumar, Sunil, 401 Kushalappa, B., 145

L Lanke, Pallavi, 205 Lathamanju, R., 155 Laxmikanth, A., 447 Likhith, B., 327 Likitha, A., 253 Lokanadham Naidu, V., 213

M Madhu, Sailaja, 437 Mahmood, Md. Rashid, 361, 447, 479, 597, 605

Author Index Makka, Shanthi, 437 Malik, Ayasha, 381, 391 Malik, Sohail Iqbal, 423 Mali, Shweta S., 259 Manage, Mayuri, 21 Mathew, Roy, 423 Mohammed, Salman Arafath, 469 Mujawar, Mehaboob, 109 Mukthineni, Vineetha, 75

N Naga jyothsna, L., 51 Nagaraj, Jothiaruna, 187 Nair, Prashant R., 167 Nani, B., 253 Naureen, Ayesha, 543 Nayak, Jyothi S., 259 Niranjan, K., 253

O Ovhal, Ajay Ashok, 413

P Pagadala, Nitish Kumar, 75 Patil, Bhagyalaxmi S., 137 Patil, Shubha Suresh, 259 Paul Sathiyan, S., 487 Pavankumar, E., 253 Pawar, Mahendra Eknath, 505 Pradeep, N. R., 349 Prakash, Kolla Bhanu, 205 Prakashkumar, P., 373 Prasanna Kumar, H., 319 Pratap, 461 Pravallika, Chinimilli, 245 Praveen Nayak, B., 327 Prema Kumar, M., 245 Priyadarshi, Neeraj, 401, 447 Priyanka, J., 579

Q Qamar, Shamimul, 469

R Raghavendra, Ch., 51 Rajendra Prasad, D., 29 Raj, Nikhil, 555, 563 Rajyalakshmi, Vankadhara, 187 Rakshija, J., 461

Author Index

615

Raman, Ashish, 569 Ramathulasi, T., 187 Ramesh Varma, D., 245 Ranganayakulu, S. V., 205 Ravi Kumar Naidu, T., 59 Ravi, J., 349 Ravindra Kumar, M., 579 Roy, Jibendu Sekhar, 3 Rupesh, Akhila, 461

Suneetha, K. R., 327 Sunitha, K. V. N., 469 Sunitha, Lingam, 437 Suresh, D., 271 Suresh Kumar, S., 579 Suryakotikiran, M. S., 145 Sushanth, S., 461 Susila, M., 59 Swarup Kumar, J. N. V. R., 271

S Sahoo, Santosh Kumar, 311 Sai Deekshith, K., 283 Saidulu, V., 67 Sailaja, C., 303 Saini, Satish, 505, 523 Salina, M. S., 259 Sameera Fathimal, M., 101 Sandeep, P., 303 Santhi, M. V. B. T., 213 Santhosh Reddy, Y., 283 Sehgal, Amit, 523 Shanth Kumar, S. M., 461 Sharma, Asheesh, 515 Sharma, Ashima, 83 Sharma, Dinesh, 83 Shirakol, Shrikanth K., 589 Shoukath Ali, M., 303 Shreyas, S., 145 Siddappa, M., 447 Siddiqa, Ayesha, 543 Sindhura, R., 51 Singh, Manju, 125 Singh, Shrishti, 83 Siva Kumar, V. G., 293 Sneha, K., 75 Sreedevi, Ch., 205 Sriram, B., 373 Sudir Patnaikuni, V. Y. S. S., 579 Sugadev, M., 293 Sugathan, Divya, 137 Sugumar, S. J., 137, 145 Sundari, V., 155

T Tamil, S., 29, 555, 563 Tariq Beg, Mirza, 605 Tarun, K., 51 Tawafak, Ragad M., 423 Tejashwini, V., 259 Thaiyalnayaki, K., 155 Tripathi, Suman Lata, 205 Tyagi, Aakanksha, 495

U Udhaya Prasath, M., 373

V Vadla, Pradeep Kumar, 205 VamsiKrishna, M., 51 Vandana, B., 235 Vasudevan, Shriram K., 167 Veeranna, B., 283 Veeresh, 589 Vejendla, Nancharaiah, 579 Venkata Subbarao, M., 245 Verma, Priyanka, 337 Vetriselvi, V., 373 Vijaya Saradhi, D., 109 Vishnu, R., 145

W Waris, Saiyed Faiayaz, 213