Innovations in Signal Processing and Embedded Systems: Proceedings of ICISPES 2021 (Algorithms for Intelligent Systems) 9811916683, 9789811916687

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Algorithms for Intelligent Systems Series Editors: Jagdish Chand Bansal · Kusum Deep · Atulya K. Nagar

Jyotsna Kumar Mandal Mike Hinchey K. Sreenivas Rao   Editors

Innovations in Signal Processing and Embedded Systems Proceedings of ICISPES 2021

Algorithms for Intelligent Systems Series Editors Jagdish Chand Bansal, Department of Mathematics, South Asian University, New Delhi, Delhi, India Kusum Deep, Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India Atulya K. Nagar, School of Mathematics, Computer Science and Engineering, Liverpool Hope University, Liverpool, UK

This book series publishes research on the analysis and development of algorithms for intelligent systems with their applications to various real world problems. It covers research related to autonomous agents, multi-agent systems, behavioral modeling, reinforcement learning, game theory, mechanism design, machine learning, meta-heuristic search, optimization, planning and scheduling, artificial neural networks, evolutionary computation, swarm intelligence and other algorithms for intelligent systems. The book series includes recent advancements, modification and applications of the artificial neural networks, evolutionary computation, swarm intelligence, artificial immune systems, fuzzy system, autonomous and multi agent systems, machine learning and other intelligent systems related areas. The material will be beneficial for the graduate students, post-graduate students as well as the researchers who want a broader view of advances in algorithms for intelligent systems. The contents will also be useful to the researchers from other fields who have no knowledge of the power of intelligent systems, e.g. the researchers in the field of bioinformatics, biochemists, mechanical and chemical engineers, economists, musicians and medical practitioners. The series publishes monographs, edited volumes, advanced textbooks and selected proceedings. Indexed by zbMATH. All books published in the series are submitted for consideration in Web of Science.

Jyotsna Kumar Mandal · Mike Hinchey · K. Sreenivas Rao Editors

Innovations in Signal Processing and Embedded Systems Proceedings of ICISPES 2021

Editors Jyotsna Kumar Mandal Department of Computer Science and Engineering University of Kalyani Kalyani, West Bengal, India

Mike Hinchey University of Limerick Limerick, Ireland

K. Sreenivas Rao MLR Institute of Technology Secunderabad, Telangana, India

ISSN 2524-7565 ISSN 2524-7573 (electronic) Algorithms for Intelligent Systems ISBN 978-981-19-1668-7 ISBN 978-981-19-1669-4 (eBook) https://doi.org/10.1007/978-981-19-1669-4 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 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

Preface

The first International Conference on Innovations in Signal Processing and Embedded Systems (ICISPES 2021) is organized by Department of Electronics and Communication Engineering, MLR Institute of Technology in association with Springer. This institute is located in Pearl City Hyderabad. The institution was started in 2005 by the KMR Educational Society, headed by Mr. Marri Laxman Reddy. The institute has associations and MOUs with a lot of industries in various fields for imparting training to students at a professional level. The current era is fully rolled out with many new technologies. ICISPES 2021 aims to provide an international platform for all the world developers, researchers to engage in scientific discussion on the current research and the latest advancements in Science, Engineering and Technology which facilitates the exchange of new innovative ideas in it. ICISPES hosted contributions on artificial intelligence and machine learning, VLSI and signal processing, robotics and automation and IoT-based intelligent systems. On this occasion, three distinguished plenary speakers and three keynote speakers delivered their outstanding research works in various fields like modeling and predictive analytics on neural computing, AI for configuration security and cyberphysical and embedded systems. There were 43 presentations by the participants which brought great opportunity to share their research works knowledge among each other graciously. Efforts taken by peer reviewers to improve the quality of papers provided constructive critical comments; improvements and corrections to the authors are gratefully appreciated. We are very grateful to the international/national advisory committee, session chairs, coordinators, volunteers and administrative assistants from the institute management who selflessly contributed to the success of this conference. Also, we are thankful to all the authors who submitted papers, because of which the conference became a story of success. It was the quality of their presentations and their passion to communicate with the other participants that really made this conference a grand success.

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Last but not least, we are thankful for the enormous support of Springer people for supporting us in every step of our journey toward success. Their support was not only a strength but also an inspiration for organizers. Kalyani, India Limerick, Ireland Secunderabad, India

Prof. Dr. Jyotsna Kumar Mandal Prof. Dr. Mike Hinchey Prof. Dr. K. Sreenivas Rao

Contents

E-Voting System Using Blockchain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . T. S. Raja Rajeswari, S. K. Khajashareef, N. Sandhya, and P. Chinnasamy

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Video-Based Abnormal Driving Behavior Detection and Risk Control Using Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kashi Sai Prasad and S. Pasupathy

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Design and Development of IoT-Based Women Auspice System by Using NodeMCU . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V. Voorwashi, T. Anuradha, and S. V. S. Prasad

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Efficient Identity-Based Integrity Auditing for Cloud Storage and Data Sharing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sk. Khaja Shareef, Shruti Patil, I. V. Sai Lakshmi Haritha, and Allam Balaram

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High-speed 4:2 Compressor Toward Image Processing . . . . . . . . . . . . . . . . Kanuri Naveen

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Double MAC Supported CNN Accelerator . . . . . . . . . . . . . . . . . . . . . . . . . . . K. Venkata Ramana and N. Akhila

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Hybrid Cryptosystem’s Design with AES and SHA-1 Algorithms . . . . . . K. Venkata Ramana, G. V. Sai Supraja, and Vaishnavi Hibare

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BTI Reliability Analysis of Low Leakage Fully Half-Select-Robust Free SRAM Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . T. Vasudeva Reddy, P. Akhil, and P. Akhil Intelligent Traffic Light System Using YOLO . . . . . . . . . . . . . . . . . . . . . . . . K. Sai Venu Prathap, D. Srinivasulu Reddy, S. Madhusudhan, and S. Mohammed Mazharr

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An Efficient Implementation of Programmable IIR Filter for FPGA . . . . 109 L. Babitha, U. Somanaidu, CH. Poojitha, K. Niharika, V. Mahesh, and Vallabhuni Vijay ASIC Implementation of Division Circuit Using Reversible Logic Gates Applicable in ALUs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 K. C. Koteswaramma, Ande Shreya, N. Harsha Vardhan, Kantem Tarun, S. China Venkateswarlu, and Vallabhuni Vijay Operation of Asymmetric Hopfield Associative Memory: Orientation Selectivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 Garimella Ramamurthy and Tata Jagannadha Swamy Intelligent Traffic Monitoring Systems Using Deep Learning Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 Ramavath Rani and G. Shravan Kumar Multi-layer Hopfield Like Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 Garimella Ramamurthy, Janapareddi Abhishek, and Rishi Shrinivas Seshan Applications of Artificial Intelligence for Autonomous Landing and Multicopter Unmanned Aerial Vehicles Design by Space Exploration Machines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 Chinthala Akhil, Kalyana Srinivas Kandala, Anudeep Peddi, N. Sudhakar Yadav, T. Srinivasa Rao, and I. Neelima Automated Skin Disease Detection Using Machine Learning Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 Kandadai Bhargavi, N. Vadivelan, Sarangam Kodati, Ch. V. Phani Krishna, and Kumbala Pradeep Reddy FinFET-Based SRAM Design Using MGDI Technique for Ultra-Low-Power Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 T. Vasudeva Reddy, K. Madhava Rao, J. Yeshwanth Reddy, B. Naresh Kumar, and R. Anirudh Reddy Design and Implementation of Efficient Counter-Based IoT DDoS Attacks Detection System Using Machine Learning . . . . . . . . . . . . . . . . . . . 199 K. Venkata Murali Mohan, Sarangam Kodati, and V. Krishna Advancements of Artificial Intelligence in Microbiological Study by Extraction and Identification of Different Micro-organism Clusters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209 N. Vishnu Teja, Kalyana Srinivas Kandala, Anudeep Peddi, I. Neelima, Poonam Upadhyay, and N. Sudhakar Yadav

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Rice Disease Detection and Classification Using Artificial Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221 R. Hruthik Chandra, Anudeep Peddi, Kalyana Srinivas Kandala, I. Neelima, N. Sudhakar Yadav, and Choudary Santosh Kumar Network Intrusion Detection Using Machine Learning for Virtualized Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235 Sreenivas Mekala, Rajaram Jatothu, Sarangam Kodati, Kumbala Pradeep Reddy, and Nara Sreekanth Detection of Human Behavior Using Swarm Technique and Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245 Venkatesh Shankar Machine Learning-Based Approach for Classification of Weed Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253 Saikumar Tara Intelligence Speech Has Collapsed and Talking Unconsciously Circumstance Using Diva Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263 S. China Venkateswarlu, Dharavath Veeraswamy, N. Uday Kumar, and Vallabhuni Vijay A Wavelet-Based De-Noising Speech Signal Performance with Objective Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277 S. China Venkateswarlu, G. Soma Sekhar, N. Uday Kumar, and Vallabhuni Vijay Multispectral Image Compression Using Adaptive Thresholding in Wavelet Domain with Binary Plane Techniques . . . . . . . . . . . . . . . . . . . . 293 M. Renu Babu, S. Chinna Venkateswarlu, G. Chenna Kesava Reddy, and D. Vemana Chary Privacy Ensured Transmission of Healthcare Records Using IoT-Enabled Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305 Y. Geetha, Ashwak, and S. V. Sprasad Double MAC: Boosting the Performance of ResNet Architecture of CNN Using ASIC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317 G. Kaushik, M. Bala Bandhavi, and B. Sridhar Implementation of Nano Communication Network Using Advanced QCA Based Nano Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329 B. Anusha, Ashwak, and S. V. S. Prasad Plant Watering and Monitoring System Using IoT and Cloud Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 341 M. Raju Naik, I. Kavitha, and B. Sridhar

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An Embedded Microcontroller for Plant Condition Monitoring Using Wireless Sensor Network (WSN) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 349 M. Rajunaik, I. Kavitha, and B. Sridhar Review Paper on Safety Devices for Women . . . . . . . . . . . . . . . . . . . . . . . . . . 359 G. Ranjithkumar, V. Voorwashi, and T. Anuradha Implementation of True Random Number Generator with Switchable Ring Oscillator on Xilinx ISE Environment . . . . . . . . . . . 373 B. Anusha and M. Aswanth Manindar Human Face, Eye and Iris Detection in Real-Time Using Image Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 383 G. Ranjith, K. Pallavi, and V. Mahendra IoT-Aware Waste Management System Based on Cloud Services and Ultra-Low-Power RFID Sensor-Tags . . . . . . . . . . . . . . . . . . . . . . . . . . . . 391 Chandrashaker Pittala and Kachala Ganesh Vehicle Collision Avoidance System Using V2I Protocol in Vehicular Ad Hoc Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403 P. Anjaneyulu and M. Aswanth Manindar Progressive Convolutional Recurrent Neural Networks for Speech Enhancement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 417 S. China Venkateswarlu, M. Renu Babu, G. Chenna Kesava Reddy, and D. Vemana Chary Deep Learning-Based Intelligent Traffic Monitoring Systems . . . . . . . . . . 429 K. Mani Raj, Ramavath Rani, and Shravan Kumar Power Efficient Multistage Linear Feedback Shift Register Counters Design in 130-nm CMOS for Large-Scale Applications . . . . . . . 441 K. Ravindra and D. Laxma Reddy Raspberry Pi-Based Smart Attendance Management System with Improved Version of RFID Over IoT . . . . . . . . . . . . . . . . . . . . . . . . . . . 449 S. Venkat Pavan Kumar, T. S. Arulananth, and S. V. S. Prasad Mitigation of Multipath Effects Based on a Robust Fractional Order Bidirectional Least Mean Square (FOBLMS) Beamforming Algorithm for GPS Receivers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 459 K. Anitha, E. Amareswar, and V. Arun Kumar Design of Low-Power Reverse Carry Propagate Adder Using FinFET . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 473 B. Naresh, K. Aruna Manjusha, and U. Somanaidu

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Design and Characterization of Microstrip Patch Antenna Using Octagonal EBG Periodic Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 489 B. Venkateshwar Rao and Sunita Panda Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 497

About the Editors

Jyotsna Kumar Mandal did M.Tech. in Computer Science from University of Calcutta in 1987 and was awarded Ph.D. (Engineering) in Computer Science and Engineering by Jadavpur University in 2000. He is working as Professor of Computer Science and Engineering, former Dean, and Faculty of Engineering, Technology and Management, KU, for two consecutive terms during 2008–2012. He is Director, IQAC, Kalyani University, and Chairman, CIRM, and Placement Cell, served as Professor Computer Applications, KGEC, as Associate Professor Computer Science, Assistant Professor Computer Science North Bengal University for fifteen years, and as Lecturer at NERIST, Itanagar, for one year. He has 34 years of teaching and research experience in coding theory, data and network security and authentication; remote sensing and GIS-based applications, data compression, error correction, visual cryptography, and steganography. He was awarded 24 Ph.D. degrees, one submitted, and eight are pursuing. He supervised three M.Phil., more than 80 M.Tech., and more than 150 MCA Dissertations. He is Guest Editor of MST Journal (SCI indexed) of Springer, published more than 450 research articles out of which 190 articles in international Journals, published 15 books from LAP Germany, IGI Global, Springer, etc., organized more than 50 international conferences and corresponding editors of edited volumes and conference publications of Springer, IEEE, and Elsevier, etc., and edited more than 50 volumes as Volume Editor. He received “Siksha Ratna” Award from Higher Education, Government of West Bengal, India, in the year 2018 for outstanding teaching activities, Vidyasagar Award from International Society for Science Technology and management in the fifth International Conference on Computing, Communication and Sensor Network, Chapter Patron Award, CSI Kolkata Chapter on 2014, “Bharat Jyoti Award” for meritorious services, outstanding performances, and remarkable role in the field of Computer Science & Engineering on August 29, 2012, from International Friendship Society (IIFS), New Delhi, A. M. Bose Memorial Silver medal, and Kali Prasanna Dasgupta Memorial Silver medal from Jadavpur University.

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Prof. Mike Hinchey is President of IFIP, the International Federation for Information Processing, founded by UNESCO in 1960, is Emeritus Director of Lero-the Science Foundation Ireland Centre for Software, and Professor of Software Engineering at University of Limerick, Ireland. Prior to joining Lero, Professor Hinchey was Director of the NASA Software Engineering Laboratory. The holder of 30 US patents, in 2009 he was awarded NASA’s Kerley Award as Innovator of the Year and is recognized in the NASA Inventors Hall of Fame. Professor Hinchey holds a B.Sc. in Computer Systems from University of Limerick, M.Sc. in Computation from University of Oxford, and Ph.D. in Computer Science from University of Cambridge. Professor Hinchey is Chartered Engineer, Chartered Engineering Professional, Chartered Mathematician, and Charted Information Technology Professional, as well as Fellow of the IET, British Computer Society, Engineers Australia, Engineers Ireland, and Irish Computer Society, of which he is also President. In 2018, he became Honorary Fellow of the Computer Society of India and was SEARCC Global ICT Professional of the Year 2018. He was Chair of IEEE UK and Ireland Section for 2018–2019. He is Author/Editor of more than 20 books and over 200 papers on various aspects of computing, software engineering, and autonomous systems. He is Editor-in-Chief of Innovations in Systems and Software Engineering: A NASA Journal (Springer) and Journal of the Brazilian Computer Society (Springer). Dr. K. Sreenivas Rao is currently working as Professor in the department of CSE, and Principal, MLR Institute of Technology, Hyderabad, Telangana, India. He obtained his Ph.D. in Computer Science and Engineering from Anna University, Chennai, and his master’s in computer science and engineering from Osmania University, Hyderabad. He has more than 27 years of teaching and research experience. His research interest includes artificial intelligence, machine learning, data mining, natural language processing, RFID data streams, and their applications to engineering. He has more than 27 publications to his credit in various reputed international journals and conference proceedings. He has delivered invited lectures and acted as Program Chair of many international conferences. He has edited one volume from Springer AISC in past, and he is also Editorial Board Member for AICC-2018 conference (Artificial Intelligence and Cognitive computing). He is Senior Member of IEEE and Life Member of Computer Society of India.

E-Voting System Using Blockchain T. S. Raja Rajeswari, S. K. Khajashareef, N. Sandhya, and P. Chinnasamy

1 Introduction Blockchain is a decentralized, distributed and sometimes digital ledger containing collection of records which are called blocks. A blockchain is mainly used to have a ledger of transactions across several computers. By this, no block will not be able to alter retroactively. Value-changed protocol is the description given to blockchain with maintaining title rights also [1, 2]. The common and the first thing in e-voting and ballot paper is that the voters have to travel to the assigned polling station to cast their vote. Counting is done synchronously while casting the vote in the case of EVMs. The VVPAT slips are counted manually. The final process includes tallying these two process vote counts. India started to use EVMs from the year of 1999 which made election process cost effective. I-voting is an added advantage because voter can cast their vote remotely. The voters do not have to travel to polling stations to cast their votes. An election is not only about security but also should be verifiable with transparency and consistency.

T. S. Raja Rajeswari (B) · S. K. Khajashareef · N. Sandhya · P. Chinnasamy Department of Computer Science and Engineering, MLR Institute of Technology, Dundigal, Hyderabad 500043, India e-mail: [email protected] P. Chinnasamy e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. K. Mandal et al. (eds.), Innovations in Signal Processing and Embedded Systems, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-19-1669-4_1

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2 Existing Method 2.1 Paper Ballots There has been a lengthy process in designing how an electoral system has to function. Initial procedure was using of ballot papers. After the ballot papers with the advent of digital technology and Internet, several other procedures like electronic voting and Internet voting were proposed. But ballot paper was still the most used voting procedure due to its simple process and less conspiracy. The ballot paper procedure is first implemented in the province of Victoria, Australia, in the year 1858. This system is still in use for small populated assemblies and provinces. India uses this process in Mandal and village level elections [3].

2.1.1

Voting Process

• • • •

Voter needs to travel to the designated polling station. Poll officer has to verify voter’s identity (like Aadhar card, pan card etc.). Ballot paper will be issued to the voter by poll officers. The voter has to go to the location and cast the vote by stamping on the desired symbol of the candidates. • The voter folds the paper and places into the ballot box. • Indelible ink will be placed on index finger of the voter to ensure that he/she has already voted.

2.2 E-Voting The ballot procedure was time consuming and involves a lots of man power. So evoting which stands for electronic voting came into existence. This offers an easy way to conduct elections with its own disadvantages. Some special devices are used in e-voting which offers flexibility to work with inbuilt batteries. The EVM was introduced by the USA in the year of 1974. Soon after that 31 countries tested out the functioning of EVM and 21 countries have replaced their voting procedures with EVMs. EVM which stands for electronic voting machine is the main device which consists of a row of displays displaying candidates and their party with their voting sign. Buttons are given next to the display. Another device VVPAT is connected to EVM which displays the slip of the respected candidate when the voter presses the EVM button next to desired candidate [4].

E-Voting System Using Blockchain

2.2.1

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Voting Process

• A polling station will be assigned to each voter considering his residence. • Poll officer has to verify voter’s identity (like Aadhar card, pan card, etc.). • Indelible ink will be placed on index finger of the voter to assure that he/she votes only once. • Voter needs to travel to the assigned polling station to cast the vote. • By pressing the button given next to the desired candidate on EVM, the voter casts his/her vote. • Voter can make sure that he has voted his/her desired candidate by hearing to the beep sound or by looking at the displayed candidate photo in VVPAT.

2.3 I-Voting Internet came into existence in 1983 and was populated by the year 1995. Countries thought that voting percentage was declining eventually due to the fact that people are busy to vote by attending the polling stations. USA was the first country to use Ivoting. The process was advantageous when compared to e-voting and paper ballots as the voters can cast their votes remotely. This procedure attracted several other countries, 14 countries started using I-voting, and ten countries said they are going to use it in future. The following is the process involved in I-voting [5].

2.3.1

Voting Process

• Voter will login into the web app or a mobile application using a device desktop or data mobile which is connected to the Internet. • Several checks of verification are performed on the voter with OTPs, etc. • A UI helps the voter to cast his vote.

3 Proposed System Blockchain which is known for its high security and transparency arrived into the world in the year 2009. Several applications of the blockchain technology include Bitcoin which is highly successful. Bitcoin is a digital currency which can be used for transactions and trading. Satoshi Nakamoto was the pseudonym given to bitcoin even though the author of Bitcoin left anonymous. Sally davies, the reporter of FT Technology, “Bitcoin creates Blockchain and email creates Internet”. By the year 2014, several tech giants realized that several other businesses other than finance sector can use blockchain technology. Till the year 2018, Bitcoin and blockchain

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Fig. 1 Architecture

are considered to be same by many of the people. The main aim of the blockchain technology is to build the trust of the software where there will be no middlemen like governments and corporations, which is thought to be the next generation architecture—decentralization. Blockchain offers an architecture where more trust will be on the smart contracts which is also called smart code rather than governments and corporations who are responsible for both privacy and security. Blockchain consists of a chain of blocks. Every block is a set of information collected with the help of a process called mining. Every block consists of a hash which is also referred as digital fingerprint [6]. Every block will also hold the previous block hash. If any false information is entering the block, the hash of the block changes and also the connectivity of the chain will be lost.

4 Architecture See Fig. 1.

5 Components of Proposed System 5.1 Client Browser, Metamask, Ganache A browser will be used to access the application over the Internet. Metamask has to be installed on the browser as a browser plug-in. The client has to create an account

E-Voting System Using Blockchain

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in the metamask wallet or log in into the wallet if he/she already has an account. The network used for Ganache should be accessible to the clients, so that they can connect. The application will be able to find out whether the plug-in has installed on the respected browser and the client has connected to the respected blockchain network. If there are any issues in the plug-in and network used with blockchain, clients encounter error page. Otherwise, clients encounter client application. Metamask will be used by clients to perform a transaction in Ganache. A vote or poll is considered as a transaction in voting. A confirmation will be given to client by metamask whether to allow the transaction. Soon after the client’s approval, the verification of data is performed by the Ganache network through the smart contract to determine block creation. Real-time features like live poll status and live results can be achieved through Ganache’s event dispatcher.

6 Results Home page Home page displaying a greeting to the voter (Fig. 2). How it Works A module displaying information on how to vote (Fig. 3).

Fig. 2 Home page

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Fig. 3 How it works

Casting vote See Fig. 4.

Fig. 4 Casting vote

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Fig. 5 User profile

User Profile A module to display user profile (Fig. 5).

7 Conclusion There is no chance of illegal vote tampering as everything is stored in the blockchain by spending a definite amount of token. Due to the transparency of the system, the redundancy and duplicacy are avoided to a larger extent. The time to conduct an election and declare the results corresponding to it is narrowed down, and it also saves a lot of manpower.

8 Future Work The speed of transactions is the main concern in the area of blockchain technology. There has to be sufficient researches to be conducted on processing speeds so that transactions happen without any hassle. For large countries like India, the transaction speeds have to be minimized to single second.

References 1. T.S. RajaRajeswari, Sk. KhajaShareef, S. Khan, N. Venkatesh, A. Ali, V. S. Monika Devi, Generating and validating certificates using blockchain, in 6th International Conference on Communication and Electronics Systems (ICCES)

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2. P. Chinnasamy, P. Deepalakshmi, V. Praveena, K. Rajakumari, P. Hamsagayathri, Blockchain technology: A step towards sustainable development. Int. J. Innov. Technol. Explor. Eng. (IJITEE), 9(2S2) (2019) 3. P. Chinnasamy, C. Vinothini, S. Arun Kumar, A. Allwyn Sundarraj, S.V. Annlin Jeba, V. Praveena, Blockchain technology in smart-cities, in Blockchain Technology: Applications and Challenges. Intelligent Systems Reference Library, vol. 203, ed. by S.K. Panda, A.K. Jena, S.K. Swain, S.C. Satapathy (Springer, Cham, 2021). https://doi.org/10.1007/978-3-030-69395-4_11 4. A. Jain, A. Kumar Tripathi, N. Chandra, P. Chinnasamy, Smart Contract enabled Online Examination System Based in Blockchain Network, in 2021 International Conference on Computer Communication and Informatics (ICCCI) (2021), pp. 1–7. https://doi.org/10.1109/ICCCI50826. 2021.9402420 5. P. Chinnasamy, B. Vinodhini, V. Praveena, C. Vinothini, B. Ben Sujitha, Blockchain based access control and data sharing systems for smart devices. J. Phys. Conf. Ser. 1767(1), 012056 (2021). https://doi.org/10.1088/1742-6596/1767/1/012056 6. P. Chinnasamy, R. Geetha, S. Geetha, S.P. Balakannan, K. Ramprathap, V. Praveena, Secure smart green house farming using blockchain technology. Turkish J. Comput. Math. Educ. (TURCOMAT) 16(2), 2858–2865 (2021). https://doi.org/10.17762/turcomat.v12i6.5793

Video-Based Abnormal Driving Behavior Detection and Risk Control Using Machine Learning Kashi Sai Prasad and S. Pasupathy

1 Introduction Abnormal driving behavior may lead to danger for the driver and the people out of the vehicle (public). The already existing detection methods of abnormal behavior of driver are mainly dependent on the drowsiness and emotion of the driver. Automatically, abnormal behavior of diver detection is approved as the main problem in the well-liked complete self-rule driving. It is clear that, for the independent driving work, the issues of safeties are unquestionably the main importance. The driver behavior is considered to be important in process to escape from any severe accidents. Here, finest cameras fixed within the vehicle of that person which are used to observe the person behavior in the vehicles. The videos which are captured and analyzed by the quality cameras, this also need to be operated immediately, so that we can check the original behavior of the driver whether he/she is normal or abnormal. The detection accuracy and the speed of driver behavior detecting are mainly recommended. Abnormal behavior of driver is known as the ability of divers to drive is in imperfect condition due to her/his own action on other tasks which is not related to normal driving. Basically, abnormal behavior of driver is categorized into 3 types [1]. 1. 2. 3.

The driver’s bodily action needs like smoking, drinking, eating, etc. Doing his/her makeup, setting up the hair, texting on the mobile, using mobile or some unimportant actions, etc. Distracted to nearby climate or nature outside, looking after children, paying continuous attention toward the activities outside the vehicle, etc.

K. Sai Prasad (B) MLR Institute of Technology, Hyderabad, India e-mail: [email protected] S. Pasupathy Annamalai University, Chidambaram, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. K. Mandal et al. (eds.), Innovations in Signal Processing and Embedded Systems, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-19-1669-4_2

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2 Literature Survey In this paper [1], the author said about the three model that are the first model is mainly depended on the human physical actions or needs, and the second model is about the facial behavior like mouth open, head rotating, etc., finally, the third model is depended on moving of the steering, so that we can detect the pressure of hand of driver. Here, they used OpenCV for collecting and processing the images. The cropping of images in a particular region of interest within the video is not good at accuracy. Sometimes, it will generate the output in wrong regions. To check whether the person eyes open or closes, we used a boundary of box to classify the persons eye and provided them to certain learning-based algorithms to check it.

3 Proposed Method Deep learning is the subset of machine learning which has effective pattern recognition methods. In this paper [2], the author said about the application we are using deep convolutional neural networks to identify the behavior of the driver. When the captured image is uploaded to our application, CNN performs predictions by comparing the features of trained images and the uploaded images. When prediction is done, it classify the images based on the features and display the results along with its prediction as a video, this video is saved in the folder, and the video is sent a message to the driver family members and police station to avoid the accident. When training of deep learning models is completed, then this learning models will be utilized automatically to detect behavior of driver in real time (Fig. 1).

Fig. 1 Architecture of CNN

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4 Methodology Data Collection: In this paper [2], the author said about collection of the images from Google and other platforms. As we are training the model on 10 different classes, we have collected 5,000,000 images. About hundred images for each class. Images of each are saved in respective folders. The dataset is a folder in which there are 2 folders, such as train set and test set. About 70% of images are stored in train set folder, and the other 30% of images are saved in test set. Data Preprocessing: In this paper [3], the author said about the image processing is a method to perform various operations on an image, in order to extract some valuable information from it which is useful further. Digital image data processing is technique of cleaning, reduction, and smoothing of the raw data into human seeable format, which is used for the training and building the deep learning models. An image is given as input to the system, and that image is being processed by system using various algorithms which are efficient in performance and gives a video as an output [4]. Model Building: In model building, first, we will load the dataset. Once the dataset is loaded, we will add CNN layers to that dataset. After that we will perform train model and later will optimize the model for the output generation [5]. Then at last, we will save the model. Application Building: We have to build a Web application using flask libraries, HTML, CSS in Spyder software. The previously trained model is imported in this flask app, and the input values are fetched from the HTML file created for the purpose. Thus, it predicts the output which is send to the mail [6] (Fig. 2). 1. 2. 3. 4. 5. 6. 7. 8.

An image is taken as input. CNN and different filters are applied to create a feature map. We are using convolution 2D with input size (64, 64, 5). ReLU function is applied which is our activation function to check for linearity. Once linearity is checked, then we will apply max pooling which combines all the feature maps into a single long vector. Now, all the input features in the vector are fully connected to artificial neural network. The model is trained with both forward and backward propagation with specified number of epochs. This is performed multiple times based on the layers, we take to train the model.

Fig. 2 Block diagram of stages in deep CNN

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9.

Now, we have fully trained artificial neural network with trained weights and detector features. At last, we will be left with categorical classes, so we apply softmax function which helps to classify the images.

10.

5 Algorithm The Pseudocode for Video-Based Driver Abnormal Behavior Detecting and Risk Control. Requirement: P: Real-time video R: Trained models. for all image Frame in P do Keep each single image frame into R for analyzing the driver condition; if behavior indicates to abnormal driving, then alert the person and if behavior extends continuously, then alert the owner end if end if end for. See Fig. 3. Sample Dataset Images The above figures give us a clear idea of how drivers are distracted, and due to various reasons nowadays, rate of accidents is increasing. There is much need to address such type of issues to control the accident rate, and the effective solution can be obtained by adopting machine learning algorithms [7] (Figs. 4, 5, and 6).

6 Results Each image has a definite pixel of 640 × 480, and all these images are categorized into 10 different classes, which shows 10. These driver behaviors contain safe driving, texting on mobile by using right and left hands, communicating on mobile by right and left hands, turning the radio, having drink while driving [8], reaching behind, doing makeup, combing the hair, talking to the next person, etc., displayed in Figure 7. Once the model is loaded, we will give predictions on some selected images; here, we group all the trained models [9, 10] (Figs. 8, 9, 10, 11, 12, 13, 14, 15, 16, and 17).

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Fig. 3 Data flow diagram

Here, we upload an image for prediction [11]. When the model gets uploaded, we get video as a output with some abnormal activities on the each image [12] (Fig. 18). Here, the video which we got will sent as a message via mail to the driver’s family members and the nearby police station as that person is distracting [11, 13].

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Fig. 4 Driver using mobile phone to capture images

Fig. 5 Driver is falling asleep

7 Conclusion In deep learning, a convolution neural network (CNN) is a class of deep neural networks, which is mostly applied for analyzing the digital images. By using deep learning algorithm, where image is taken as input, we can differentiate one image from other and giving importance to different objects in that image. So, by using deep learning, we have built a driving abnormal detection model, which is useful for safety of people. In this, we used three new deep learning models, to provide output as video-based driver abnormal detection and risk control. Using these learning models, the behavior of driver is detected and to effectively control the risk we have integrated

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Fig. 6 Driver is using mobile and drink cup while driving

Fig. 7 Examples of image classes

a module which sends the alert and abnormal behavior to registered user. This work is thus very much useful for intelligent traffic control and risk control of accidents.

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Fig. 8 Trend of accuracy increasing with respect to the training epochs in all the models

Fig. 9 In the above image, the driver is texting on the mobile by using right hand

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Fig. 10 In the above image, the driver is communicating on the mobile by using right hand

Fig. 11 In the above image, the driver is operating the radio

Fig. 12 In the above image, the driver is communicating on the mobile by using left hand

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Fig. 13 In the above, the driving is having safe drive

Fig. 14 In the above image, the driver is talking to next passenger

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Fig. 15 In the above image, the driver is drinking

Fig. 16 In the above image, the driver is reaching behind

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Fig. 17 In the above image, the driver is doing his hair and makeup

Fig. 18 Video sent as a message

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References 1. W. Huang, X. Liu, M. Luo, P. Zhang, W. Wang, J. Wang, Video-based abnormal driving behavior detection via deep learning fusions. IEEE Access 7, 64571–64582 (2019). https://doi.org/10. 1109/ACCESS.2019.2917213 2. D. Thompson, S. Niekum, T. Smith, D. Wettergreen, Automatic detection and classification of features of geologic interest. Proc. IEEE Aerosp. Conf. 366–377 (2005). https://doi.org/10. 1109/aero.2005.1559329 3. K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in Proceedings of CVPR (2016), pp. 770–778 4. W. Cao, J. Yuan, Z. He, Z. Zhang, Z. He, Fast deep neural networks with knowledge guided training and predicted regions of interests for real-time video object detection. IEEE Access 6, 8990–8999 (2018) 5. Q. Xiong, S. Zhou, Q. Chen, Abnormal driving behavior detection based on kernelization-sparse representation in video surveillance. Multimed. Tools Appl. 81, 4585–4601 (2021) 6. P. Roy, T. Omanakuttan Ancy, IJARITT 6(5) (2020) 7. R.R. Knipling, W.W. Wierwille, Vehicle-Based Drowsy Driver Detection: Current Status and Future Prospects (1994) 8. B. Chul Ko, S. Kwak, M. Jeong, J.-Y. Nam, Driver facial landmark detection in real driving situations. IEEE Trans. Circ. Syst. Video Technol. 28(10), 2753–2767 (2017) 9. N. Chandra Sekhar Reddy, M. Srinivasa Rao, T. Raja Rajeswari, Y. Harini Reddy, Vehicle security system. Int. J. Innov. Technol. Explor. Eng. (IJITEE). 9(3), (2020). ISSN: 2278-3075 10. K.S. Prasad, P. Subhashini, K.A. Reddy, P.S. Kumar, G.V. Siva Sai, K.V. Krishna Reddy, Mobile recommendation system using Mapreduce, in 2021 6th International Conference on Communication and Electronics Systems (ICCES) 11. K. Sai Prasad, R. Miryala, Histopathological image classification using deep learning techniques. Int. J. Emerg. Technol. 10(2), 467–473 (2019) 12. J. Hu et al., Abnormal driving behavior based on normalized driving behavior. IEEE Trans. Veh. Technol. 66(8), 6645–6652 (2017) 13. L. Brun, A. Saggese, B. Cappellania, M. Vento, Detection of anomalous driving behaviors by unsupervised learning of graphs, in 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 405–410. IEEE (2014)

Design and Development of IoT-Based Women Auspice System by Using NodeMCU V. Voorwashi, T. Anuradha, and S. V. S. Prasad

1 Introduction In the twenty-first century, everybody is free to do what they like and go anywhere they want. Women are still subjected to a variety of inequalities. Women’s safety is a major concern in today’s world, particularly in India. Women’s crimes, such as assault, molestation, eve-teasing, rape, abduction, and abuse of women, are not decreasing, but rather growing. The government have taken many preventive measures to halt these misbehavior acts, but they have little effect on the rising number of such activities and were not affected. Women feels unsafe in different environments. In New Delhi, 66% of women reported sexual assault in 2010 [1]. It is also been demonstrated that women seem to be more vulnerable to harassing in metropolitan contexts, particularly in underdeveloped nations. In such instances, a safety gadget that alerts the victim’s family or the police can assist women feel protected and strong while reducing the risks of abuse. Discrimination issues in the workplace are becoming more prevalent by the day. Harassment in the workplace is when a person engages in inappropriate behavior that causes another person to feel uncomfortable, offended, or distressed. The majority of these cases of women are caused by men working in senior positions in an organization. Crimes increased by 7.3% from 2018 to 2019 against women, and social classes also increased by 7.3% in the same period, according to the National Bureau of Crime Records’ 2019 annual report issued by the National Bureau of Crime Records and V. Voorwashi (B) · T. Anuradha · S. V. S. Prasad Department of Electronics and Communication Engineering, MLR Institute of Technology, Hyderabad, Telangana, India e-mail: [email protected] T. Anuradha e-mail: [email protected] S. V. S. Prasad e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. K. Mandal et al. (eds.), Innovations in Signal Processing and Embedded Systems, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-19-1669-4_3

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Crime statistics against women is steadily increasing by creating difficult systems with technical requirements, it is critical to safeguard women from sexual harassment, assault, and violence. In older days, women used to stay home doing housework. But the current scenario, someday is for women to work on equal terms with men. Women have a distinct effect in every field. Athletics, dancing, education, business, and governance are just a few examples. Women are the leaders in every discipline. But still, there is unsafe for women. Women in India, on the other hand, continue to suffer societal obstacles and are frequently victims of violence and criminal activity. Using a pulse sensor and a temperature sensor, this paper develops a safety gadget for women that monitors their health. This study focuses on ensuring women’s safety so that they never feel powerless, as well as keeping track of their health. The Delhi Nirbhaya issue which stirred up the whole nation was the biggest motivation for this system. Women needed a change, and this was the moment for it. Smart phone which is loaded with security apps for women will assist you in sending important updates to individuals you choose and even notifying them of your location if anything goes wrong. There may be occasions when a woman has an accident late at night and no one is around to aid her; in such cases, the person will be unable to describe the problem she is now in. Every 44 min, a woman is abducted, raped every 47 min and 17 women die as a result of dowries. Every year, the following cases of crime against women are registered in India: S. No

Year

No. of cases

1

2019

405,861

2

2018

378,236

3

2017

359,849

4

2016

338,254

5

2015

329,243

6

2014

337,922

7

2013

309,546

8

2012

244,270

9

2011

228,650

Main Objective The brand-new innovation format of this device is for women safety and monitoring the health. This device is switched on 24 × 7 for the complete day. This technology is primarily designed to protect women against harassing and other forms of assault. This paper has a lot of benefits with this equipment as compared to other systems. In this paper, we utilize NodeMCU controller, OLED, flex sensor, temperature sensor, pulse sensor, buzzer, Blynk app. NodeMCU serves as the foundation of this article, however, the heart rate sensor module is required to control the heartbeat. The operation of the pulse sensor is

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quite simple. On this sensor’s initial surface, the led diode and ambient light sensor are connected. In addition, temperature and flex sensors are utilized. Flex sensors function on the idea of bending strips, which means that when the strip is twisted, the resistance changes. Flex sensor can be identified with the use of any device. Novelty of the Paper In this technology, a flex sensor is utilized to detect the safety of women when the sensor moves in a flexion direction at the finger joints, and then, it triggers and sends a message to the nearest cell phone number. If the forefinger moves 5 times in a flexion direction, at the finger joints, it is programmed to do a certain action. On the screen, the message “alert sent” appears. This device is compact in size.

2 Related Work Parikh, et al., the NodeMCU microcontroller is used to create a women’s safety item that can be worn. This device’s primary goal is to support women if they are attacked. The gadget communicates with the secure channels and sends notifications to them via IoT. ThingSpeak is an Internet of things interactive system that serves as an interface and store detector cloud database, as well as construct IoT applications. The benefit of this platform is that it allows us to upload data to ThingSpeak every 30 s, allowing for continuous health monitoring of the individual. Taking this into consideration, the preceding foundation would be a method for developing a product to individuals that aids with maintaining a user’s security by removing the requirement for users to initiate any prescriptive activities in response to any condition in which the user may feel at significant risk [2]. Dharmoji et al., in this work, we use the ESP8266 and Arduino to create an IoTbased patient healthcare monitoring system. ThinkSpeak is the IoT platform used in this paper. ThinkSpeak is an accessible Internet of things (IoT) application and API that uses the Web service to store and retrieve data through devices via a Web or a wireless connection. This IoT system might be used to determine the temperature and read the heart rate. It continually monitors the environment’s heartbeat rate and temperature and transmits the information to a cloud network [3]. Sathyasri et al., in order to ensure the security and protection of women in danger. A woman clicks a button when she feels uneasy. The microcontroller gets the commands when the button is pressed, and the GPS determines the victim’s present latitude and longitude. The GSM module will send a message to the numbers stored in the microprocessor, as well as the nearest police station, with the exact position information. Every 1 s, GSM sends an SMS to registered mobile numbers and sends SMS to registered mobile phones. On the LCD panel, the message is displayed. The IoT module may track the victim’s current location and update the Webpage’s location. The device’s buzzer will be activated by the microcontroller, notifying surrounding individuals that someone is in danger and allowing them to react [4].

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Islam et al., the goal of this study is to concentrate on a protection system designed specifically for the purpose of protecting women such that they should not feel weak when confronted with such delightful challenges. This gadget will allow a lady to call somebody or seek assistance in a variety of situations, and it will be especially valuable if she is kidnapped [5]. Shiva Rama Krishnan et al., patient health monitoring has begun to examine many abstruse reasons in order to increase healthcare accouterment in a way that accompanies definite consulting work by combining with wireless potential, based on this paperwork. ECG, blood pressure, and temperature control results are computed in just a minute by the health monitoring system. As a result, the time–cost complication is minimized [6]. Rajendra Shimpi, in the work of this paper, we studied and implemented a complete working model using ARM7. This work includes studying GSM and GPS modems using sensors. The biggest advantage of using this project is that whenever the switch is pressed, we will get the location from the GSM modem to our cell phone numbers stored in the software and GSM network so that one can rescue the women under threat [7].

3 Existing Method The goal of the previous paper was to create a wearable safety gadget for women that could also aid in frequent health monitoring. The user’s position is pinged straight to the required authorities and stored contacts via GPS and GSM. The device’s two extra switches are used to transmit manual warnings in an emergency and to disengage the system in the event of erroneous alarms being generated server would be an interactive system that serves as an interface and store device data in the Web while also allowing us to build IoT apps. The benefit of this platform is that it allows us to upload data to ThingSpeak every 30 s, allowing for continuous health monitoring of the individual. The tactile switch may be used to cancel any false alarms that have been triggered, and it may also be used to launch an emergency message in tough times. The IoT has completely transformed the safety system, resulting in a more compact and safer environment for women.

4 Proposed Method The purpose of this paper is to ensure protection for women while also keeping track of their health. In this paper, the flex sensor recognizes the women safety when moves the sensor in flexion direction at the finger joints, it will give a trigger. Here, once moving the finger let say 5 times in flexion direction at the finger joints, it will send a signal and send the message to the nearest registered mobile number. In this paper, we have couple of sensors, i.e., temperature sensor and pulse sensor which

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will recognizes the temperature and heartbeat of the person. This device is having OLED display, where it is monitoring the health, and it is printing on the screen.

5 Materials and Method NodeMCU From Fig. 1, in this paper, NodeMCU is the brain of centralized system. The NodeMCU platform is a free and open-source IoT platform. It comprises firmware that works on Espressif Systems’ ESP8266 Wi-Fi SoC and hardware that is based on the ESP-12 module. On the NodeMCU ESP8266 control board, the ESP-12E unit has an ESP8266 chip with a Tensilica Xtensa 32-bit LX106 RISC CPU. This processor implements operating systems and runs at a customizable clock frequency of 80–160 MHz. The NodeMCU has 128 KB of RAM and 4 MB of internal storage to store data and applications.

Fig. 1 Block diagram of the proposed system

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Flex Sensor The polymer ink with conductive particles placed on it is used to highlight one aspect of the flex sensor. The particles flow toward the ink when the sensor is set straight. A resistance of around 30 k ohms is required. The conductive particles flow further apart when the sensor is bent further away from the ink, increasing the resistance to 70 kΩ. The resistance recovers to its original value when the sensor returns to normal. This sensor may be used to determine how far the sensor is bent. Here, using 2.2 inches flex sensors which is having around 100Kohms resistance. In this paper, flex sensor recognizes the safety for women if moves the finger in flexion direction with certain value, then automatically it will be sending a message to the nearest mobile number. Temperature Sensor Temperature sensors are used not only in our daily lives, but also in chemical engineering, for example, to manage the temperature of chemicals and streams released into the environment to prevent detrimental environmental effect. While people generally measure temperature as hot, neutral, or cold, temperature sensors are required to monitor temperature in electrical components or goods. Temperature phenomena can be converted into quantifiable signals using a variety of sensors. The thermocouple, RTD, and thermistor are the most often used sensor types. Each has its unique working principle, benefits, and disadvantages. Every sensor has its own set of specifications. Thermocouples, for example, do not require current excitation but do require cold junction correction, which is only accessible in particular temperature measuring devices. In this paper, using DS18B20 which is digital temperature sensor. It will send the information digitally to any kind of control. So, do not need to connect it to the analog pins, need to connect it to digital pins, and this sensor is showing the temperature of the person on OLED display. Pulse Sensor MAX30102 is a pulse oximetry and heart rate monitor module featuring inbuilt LEDs, photodetector components, and low-noise circuits that cancel out ambient light. It can run on a single 1.8v power source with an additional 3.3v power source for the internal LEDs. With 0% standby current, the MAX30102 may be turned off through software. Pulse sensor recognizes the heartbeat of the person and monitors the health and prints on the screen. OLED The kit includes a 0.96-inch blue OLED display device. IIC protocols may be used to connect the display module to any microcontroller. It has a resolution of 128 × 64 pixels. OLED is a peer device that uses a narrow, multilayered natural material placed with a cathode and anode. Unlike LCD, touchscreen does not really require an

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illumination. OLED is commonly recognized as one of the most sophisticated technologies for the next generations of plain panels and has a wide variety of applications in practically all types of devices. Notification As a digital output, a buzzer serves as a notification. For audio signaling, a buzzer is employed. A mechanical, electrochemical, or piezoelectric device might be used. A buzzer is a gadget that can be found in a variety of devices and functions. Piezoelectric technology is used to provide an electric charge to mechanical tension. A ring or beep is a frequent sound used to signify that a button has been pressed. Blynk App The Blynk app is a mobile application that allows you to connect with other people. The IoT was in sight when server was developed. Blynk allows us to control gadgets from a distance. It also has the ability to show sensor data as well as save it. The Blynk platform is divided into three parts: the Blynk app, the Blynk server, and the Blynk libraries. The Blynk software is used to build stunning displays using a variety of widgets. Blynk server: We can interface between smartphones and hardware with the aid of this server, which is open source and capable of handling thousands of devices. Blynk libraries: Blynk libraries to send and receive data across servers and to handle all incoming and outgoing instructions. Blynk Cloud Server The alarm signal transmits an OLED screen output, it transmits data to the cloud as well. The cloud that is being used here is the Blynk cloud, which is transmitting the data. Whatever data the cloud collects will be sent to the smartphone.

6 Working Methodology The devised technique is quite beneficial in terms of women’s safety and health monitoring. It is a reliable, cost-effective, and secure technology. The designed system’s flowchart is presented in Fig. 2. The flowchart begins by getting continuous values taken by detectors. The various parameters will continually monitor the heart’s vital signs. Sends an alert if the condition is met. It will appear on an OLED screen if it is false.

7 Experimental Results In this paper, developed a new product for women safety and health monitoring system. So, both the features are combined together (Figs. 3 and 4).

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Fig. 2 Flowchart of proposed system

Information comprises of an OLED display on which the system monitors the health and prints it on the screen, as well as a pair of sensors that detect a person’s temperature and heartbeat (Fig. 5). In this technology, a flex sensor is utilized to detect the safety of women when the sensor moves in a flexion direction at the finger joints, and then, it triggers and sends a message to the nearest cell phone number (Fig. 6). If the forefinger moves 5 times in a flexion direction, at the finger joints, it is programmed to do a certain action. On the screen, the message “alert sent” appears (Fig. 7).

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Fig. 3 Safety gadget for women Fig. 4 Temperature and heart rate on OLED screen

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Fig. 5 Movement of the finger in flexion direction at the finger joints

Fig. 6 Alert message displays on the screen

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Fig. 7 Message displays on mobile phone

8 Conclusion The paper explains the working of a health monitoring system and a women’s safety wearable gadget, this is built using Internet of things technology to send an alert in case of an emergency, and we have used sensors which are very economical and reliable. The entire system works with Internet, hence it is robust and can be used to communicate with any device in the world instantly, the paper also uses pressure gages which experimentally testing the heartbeat of body and sends actual information to the registered mobile phones, the system also consists of temperature sensor which can recognize the temperature and alert the receiver in case of any fire or heat accidents. In future, the paper can be improvised by adding location services and can be improved by adding local police and fire station alert systems on the same.

9 Future Scope 1.

2.

3. 4.

The project can be modified by adding a location feature, we can use either a GPS tracker or googles geo-location service to get the precise location and send it to Blynk app such that we can accurately track and help the victim in crisis. We can add multiple receivers by changing the cloud properties, and we can add 4–5 different mobile phone numbers along with strong alert systems to make it more reliable and robust. We can improve the design by using a modified PCB layout and by making use of compact components such that we can embed the entire set-up in small space. We can make use of additional sensors to read more values, and we can add gesture sensors, health monitoring sensors to give more data.

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References 1. N. Viswanath, N. Vaishnavi Pakyala, G. Muneeswari, Smart foot device for women safety, in 2016 IEEE Region 10 Symposium (TENSYMP), Bali, Indonesia, 978-1-5090-0931-2/16/$31.00 ©2016 IEEE 2. D. Parikh, P. Kapoor, S. Karnani, S. Kadam, IoT based wearable safety device for women. IJERT 9(5), (2020) 3. S. Dharmoji, A. Anigolkar, M. Shraddha, IoT based patient health monitoring using ESP8266. 7(3) (2020). ISSN: 2395-0072 4. B. Sathyasri, U. Jaishree Vidhya, G.V.K. Jothi Sree, T. Pratheeba, K. Ragapriya, Design and Implementation of women safety system based on IOT technology. IJRTE. 7(6S3) (2019). ISSN: 2277-3878 5. N. Islam, Md. Anisuzzaman, S. Sunbeam Islam, Design and Implementation of Women Auspice System by Utilizing GPS and GSM. 978-1-5386-9111-3/19/$31.00 ©2019 IEEE. 6. D. Shiva Rama Krishnan, S. Chand Gupta, T. Choudhury, An IoT Based Patient Health Monitoring System. 978-1-5386-4485-0/18/$31.00 ©2018 IEEE 7. T. Rajendra Shimpi, Tracking and Security system for women’s using GPS & GSM. 4(7) (2017). ISSN: 2395-0072

Efficient Identity-Based Integrity Auditing for Cloud Storage and Data Sharing Sk. Khaja Shareef, Shruti Patil, I. V. Sai Lakshmi Haritha, and Allam Balaram

1 Introduction With increasing speed of technology expansion of information, consumers face a significant challenge in storing the vast amounts of data domestically. As a result, many groups people like to save cloud storage for their data at regular intervals. Cloud-based records, on the other hand, are often messed up or destroyed as a result of the unavoidable code glitches, human faults, and hardware defects that occur in cloud computing so on evaluate; if not the information is hold on properly at intervals the cloud, several auditing systems for remote data accuracy are planned [1]. At intervals the portion of credibility auditing, the cloud is represented by these signatures really owns this collection of information. As a result, these are uploaded by the data holders information blocks to the server, as well as the signatures that go with them. At regular intervals, the knowledge is held on to several Dropbox, Google Drive, and iCloud are examples of cloud computing facilities which are commonly shared by multiple users. One of the most common choices in cloud computing is to allow a small group of customers to allow someone to access their information These common details, however, persist at intervals, and the cloud can contain confidential info. Electronic health records (EHRs) collected and shared in the cloud, for example, often contain confidential patient information (such as their identity, signature, and ID number) as well as sensitive data about hospitals (hospitals name). As these EHRs are uploaded to the cloud and shared for analysis, sensitive patient and hospital records will invariably be exposed to the cloud, and analysts will have access to it will be forced to collaborate. Furthermore, instead of the possibility as a result of human mistakes and software and hardware vulnerabilities in the cloud,

Sk. Khaja Shareef (B) · S. Patil · I. V. Sai Lakshmi Haritha · A. Balaram Department of Information and Technology, MLR Institute of Technology, Dundigal, Hyderabad, Telangana, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. K. Mandal et al. (eds.), Innovations in Signal Processing and Embedded Systems, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-19-1669-4_4

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Fig. 1 Example of EHR

the reputation of EHRs should be protected. As a result, data accuracy auditing from afar is critical to ensure that confidential data from shared data are secure. One possible solution to the issue is that write down the entire file that has been exchanged in code prior to uploading it toward the server, in order to produce signatures accustomed finally, check the legitimacy, this file’s encryption transfer, this file is encrypted and the trademarks that go with it to the cloud, this methodology will understand the delicate data activity since only the owner of the details would be able to decode this file. It is, however, about to build the entire shared register, which would be inaccessible to anyone as associate in nursing encrypting the EHRs of patients, and Fig. 1 shows an example of Patients that are sick will protect the patient’s and hospital’s privacy. However, researchers will be unable to use these encrypted EHRs to any point in future. The distribution of the cryptographic key to the researchers seems to be a good idea come-at-able resolution to the on high of balk. However, it is impossible to adopt this methodology in real eventualities, thanks to consecutive reasons. A variety of factors to begin with, transmitting cryptography keys necessitates safe networks, which can be difficult to come by in certain cases. Moreover, it is extremely difficult for a person to comprehend that if he or she uploads his or her EHRs to the server, researchers can use them in future. As a consequence, it is impossible to do anything about to protect confidential info, by encrypting the data you can protect your privacy entire file that has been exchanged. Thus, the thanks to grasp info communication of classified info. Auditing data from afar accuracy are extremely critical and useful. Regrettably, this bug has been discovered overlooked in past studies [2].

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2 Literature Survey Storage outsourcing is also becoming more common, which has resulted in a slew of intriguing a slew of security concerns, all of that were addressed thoroughly studied previously. Evident experience, on the other hand, PDP is a subject that has just recently occurred emerged in the body of knowledge [3]. The most important problem is also a result of generally, safely, and reliably verifying that a data server is effectively storing the information of its customers outsourced information (quite high opportunity). In terms of security and trustworthiness, the storage server is considered to be coalition optimistic (in other words, it will delete hosted files, either maliciously or inadvertently, and put it on the backburner sluggish or storage that is not connected to the Internet). The issue at hand is made worse by the fact that the customer is a a little scientific discipline system with little assets. The previous research has solved this flaw utilizing a public main infrastructure encryption or by contacting the patron to have their information in a coded format. We aim to construct very cost-effective and demonstrably stable PDP procedure in this article, entirely dependent on bilaterally symmetrical key encoding and a requirement no a large amount committal to writing. Furthermore, unlike those that came before it, our PDP system enables for the contracting out complex information, it effectively aids in processes as an example adjustment, append, and delete housing, and knowledge outsourcing has become quite common. The term “data outsourcing” refers to the process of the data owner (client) sends their information to a third-party vendor (server) who can archive it and make it available to the public for a fee request. Cost savings from cuts in logistics, routine upkeep, and resources, in addition improved usability and clear information upkeep, are all appealing benefits of outsourcing. (as well as possibly others) on appeal. Reduced costs from cost cuts in transportation, repair, and staffing are both appealing aspects of outsourcing enhanced accessibility and clear upkeep of information.

3 Feasibility Study To be able to ensure the confidentiality with cloud data, several remote information schemes for honesty auditing are planned, to scale back the user’s computing load, on behalf of the customer, a third-party auditor (TPA) is implemented at regular intervals check the accuracy of cloud content [4]. To guarantee information possession of an untrustworthy, cloud suggested a concept of demonstrable information possession (PDP). Homomorphic authenticators and sampling are included in their intended theme, and the use of sampling methods is common place. Blockless authentication and lower I/O costs proposed a proof of retrievability (PoR) model [5, 6], a wellthought-out theme. During this theme information, it is possible to view data collected in the cloud, as well as the data’s credibility.

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In order to shield the information privacy, planned a remote control that protects your privacy information auditing with honesty theme with the use of a masking strategy at random. Wang et al. [7] utilized a distinct masking strategy at random to any assemble a distant information auditing with honesty theme assisting information security of personal information. This theme accomplishes higher potency when opposed to theme in [8], to lessen the user’s computational burden when it comes to signature generation. Guan et al. [9] designed a distant information integrity auditing theme supported the sameness obfuscation technique. Shen et al. [10] introduced to build a light-weight remote information honesty auditing theme, a third-party medium (TPM) was used. During this theme, the TPM assists users in generating signatures on the condition that they meet those criteria. Information privacy may be protected so as to support information dynamics [11], first off planned a partly dynamic PDP theme [12, 13]. To create a fully knowledge dynamic auditing theme, I used a skip list. [14] planned different remote information auditing with honesty theme full-time service information Merkle hash tree is used to build dynamics and to reduce the harm caused by users primary exposition. Yu and Wang [15] planned remote control with key-exposure resiliency information schemes for honesty auditing supported key a new approach [16]. In cloud computing scenarios, Wang et al. [17] data exchange may be a critical application, to safeguard the integrity of the user’s identity [18]. To build a privacypreserving mutual information integrity auditing theme, I changed the for stable cloud computing, use a ring signature. Fu et al. [19] made associate economical shared information integrity auditing theme [20] that not solely supports the protection of personal information, however, solely ensures that users’ identities can be traced. Using a homomorphic verifiable cluster signature, we created a privacy-aware shared knowledge security auditing theme. So on are both in favor of cost-effective user revocation [21]. A user revocation by resigning by proxy was intended as part of a joint knowledge auditing with honesty theme. With the utilization of hidden data sharing strategy derived by Shamir. Wang et al. [22] made a shared information auditing with honesty theme assisting in user revocation. It said both schemes believe public key infrastructure (PKI) that is liable to goodly a view from above delicate certificate administration, to alter certificate administration. Yu et al. [23] planned associate remote access focused on identity information auditing with honesty theme in storage in several clouds. This theme used the identity of the consumer data like e-mail address or user’s name to exchange the public key. Wang et al. [24] designed a singular proxy that is based on your identity-orientated remote information integrity auditing theme by introducing a proxy to method information for users. Zhang et al. [25] made a remote information auditing with honesty theme with good information privacy conserving in identifiable crypto systems. Shen et al. [26] planned associate identity-based information integrity auditing theme satisfying unconditional obscurity and motivation. Hur et al. [27] planned associate remote access focused on identity information auditing with honesty theme for shared information supporting real economical revocation of a customer. Many considerations of remote information integrity auditing, such as authenticators who protect your privacy [28] and intelligence removing duplicates [29], have

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also been investigated. We would look at how to accomplish information sharing while hiding confidential data in identity-based integrity auditing in order to safe storage in cloud in this article.

4 Proposed Scheme System Model The model, as seen in Fig. 2, comprised of five distinct parts: the customer, cloud, sanitizer, third-party auditor (TPA), the private key generator (PKG). We have a tendency to need to think using thinking inside sanitizable signature to sanitize delicate data of the document by adding a commissioned sanitizer in order to understand information sharing for sensitive data concealment. However, using this sanitizable signature explicitly in remote knowledge honesty auditing is impractical [30]. Several chameleon hashes, on other hand, show the main exposure recoil. To get around this defense, backlash, signature in [31] relies on powerfully chameleon hashes that are unalterable inevitably result in Brobdingnagian computing overhead [32, 33]. Second, blockless verifiability is not supported by the signature used in [34]. It implies that protagonist can move all information from the cloud in order to check its accuracy, which could result in Brobdingnagian contact above head and lengthy time for inspection in a large knowledge a possibility of storage. Finally, sign that appears in [35] is reliant based on PKI and is plagued by difficult certificate administration. We have a tendency to vogue a contemporary efficient signature rule within a region of signature generation in order to harm higher than problems.

Fig. 2 System model

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Signature theme is designed to promote verifiability without blocks, which makes the protagonist assess accuracy of data without having to retrieve whole data set derived from server. It also supports identity-based cryptography, which simplifies the handling of hard certificates. In our recommendation theme, PKG produces users a secret code in tandem with his ID. The client can verify that obtained personal key is right. When a user has to migrate information to server to protect private delicate data on sanitizer’s original database, client can make use of light object to blind information that are similar to private delicate data of initial document. When required, client get original file back from blindfold one by patterning this bright problem. As a result, this user applies the crafted signature rule to the blindfold file to generate signatures. These signatures are gradually becoming used to verifying the blindfold file’s legitimacy. Furthermore, the user creates a file tag that is used to indicate accuracy of file image name and no. of authentication values. The customer calculates a transition price together, which is then accustomed to remodel signatures for sanitizer. In Fig. 3, user sends blindfold document, along with accompanying signatures, to the sanitizer, as

Fig. 3 Process of signature generation and sensitive information sanitization

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Fig. 4 Database information about rbic

Fig. 5 Column information for—rdic.cloud

well as the transition price. Once the user’s messages are legitimate, the sanitizer first sanitizes the blindfold knowledge blocks into equivalent format, then sanitizes the confidential data blocks to protect company privacy, After that, it converts their signatures into legitimate ones for use. Finally, sanitizer publishes the alter file to server, followed by signatures. Cloud provides an examining evidence in response to TPA’s issue after the information integrity, auditing task is completed. The TPA will check whether or not this auditing proof is right in order to confirm credibility of alter doc held in server. Under the following subdivision, the most important points would be delineated. Database Design For proposed system, they have created database schema which is need of our application to store the application data. We discuss the table description and syntaxes of databases in Figs. 4 and 5.

5 Results We have a propensity to use multiple experiments to judge the success of the intended theme in this chapter. Tests were done on a Linux computer with an Intel Pentium a pair 30 GHz processor, 8 GB of memory. The antelope multiple precision arithmetic is created using the C language and free pairing-based cryptography (PBC) library (GMP).In our tests, we have a propensity to limit the bottom field size to 512 bits, size of an associate degree element in Z * p to a hundred and sixty bits, the length

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2.5

2

1.5

Private key genartion Private key verfication Signature Genaration

1

Signature Verification

0.5

Sensitive information sanitization

0 Fig. 6 Performance of various processes

of a record 20 MB consisting of 1,000,000 blocks, and the length of the consumer set to a hundred and sixty bits. 1.

2.

Performance of various processes: We have a propensity to set the range of knowledge blocks to one hundred and the no. of update data blocks to five in our attempt to easily evaluate the performance of multiple processes, personal key generation and personal key verification. Nearly, identical amount of time, as given in Fig. 6, the thirty-first. The generation of signatures took 1.2 s. The signature authentication time, which is used to clean confidential data, is a pair of 0.25 and 0.045 s. As a result, we will infer that signature authentication takes the longest time in these systems, while confidential data cleaning takes the shortest. In order to assess the efficiency of the generation of signatures and authentication, we produced signatures for a no. of blocks ranging zero to a thousand, each increased by a factor of one hundred in our research. The price of signature generation and as a result, signature authentication increases linearly with the 100–10,000 information blocks A signature will take anything from 0.121 to 100.132 s to produce. Verifying a signature will take anywhere from 0.00128 to 103.513 s show in Fig. 7.

6 Conclusion and Future Scope We intend an identifiable information auditing with honesty theme for safe cloudbased computing in this project, which encourages sharing of knowledge while keeping confidential data hidden. A file saved in the cloud, according to our theme, can be shared and accessed by anybody as long as the file’s sensitive data are

Efficient Identity-Based Integrity Auditing for Cloud … Fig. 7 Signature generation versus verification

120 100 80 60 40 20 0

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Signature Genaration

protected. Furthermore, remote information integrity auditing has the potential to be ineffective. The security evidence as well as the experimental review show that the intended theme has enthralling security and potency. In future work, we introduce de-duplication system for preventing storing the repeated data which leads to reduce the storage space and improves the efficiency.

References 1. K. Ren, C. Wang, Q. Wang, Security challenges for the public cloud. IEEE Internet Comput. 16(1), 69–73 (2012) 2. J. Sun, Y. Fang, Cross-domain data sharing in distributed electronic health record systems. IEEE Trans. Parallel Distrib. Syst. 21(6), 754–764 (2010) 3. G. Ateniese et al., Provable data possession at untrusted stores, in Proceedings 14th ACM Conference Computer Communications Security (2007), pp. 598–609 4. J. Yu, K. Ren, C. Wang, Enabling cloud storage auditing with verifiable outsourcing of key updates. IEEE Trans. Inf. Forens. Secur. 11(6), 1362–1375 (2016) 5. A. Juels, B.S. Kaliski, Pors: Proofs of retrievability for large files, in Proceedings 14th ACM Conference Computer Communications Security (2007), pp. 584–597 6. H. Shacham, B. Waters, Compact proofs of retrievability. J. Cryptol. 26(3), 442–483 (2013) 7. C. Wang, S.S.M. Chow, Q. Wang, K. Ren, W. Lou, Privacy-preserving public auditing for secure cloud storage. IEEE Trans. Comput. 62(2), 362–375 (2013) 8. S.G. Worku, C. Xu, J. Zhao, X. He, Secure and efficient privacy- preserving public auditing scheme for cloud storage. Comput. Electr. Eng. 40(5), 1703–1713 (2014) 9. C. Guan, K. Ren, F. Zhang, F. Kerschbaum, J. Yu, Symmetric- key based proofs of retrievability supporting public verification, in Computer Security—ESORICS. (Springer, Cham, 2015), pp. 203–223 10. W. Shen, J. Yu, H. Xia, H. Zhang, X. Lu, R. Hao, Light-weight and privacy-preserving secure cloud auditing scheme for group users via the third party medium. J. Netw. Comput. Appl. 82, 56–64 (2017) 11. G. Ateniese, R.D. Pietro, L.V. Mancini, G. Tsudik, Scalable and efficient provable data possession, in Proceedings of 4th International Conference on Security and Privacy in Communication Networks (2008). Art.no. 9 12. C. Erway, A. Küpçü, C. Papamanthou, R. Tamassia, Dynamic provable data possession, in Proceedings of the 16th ACM conference on Computer and communications security (2009), pp. 213–222 13. Q. Wang, C. Wang, K. Ren, W. Lou, J. Li, Enabling public auditability and data dynamics for storage security in cloud computing. IEEE Trans. Parallel Distrib. Syst. 22(5), 847–859 (2011)

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14. J. Yu, K. Ren, C. Wang, V. Varadharajan, Enabling cloud storage auditing with key-exposure resistance. IEEE Trans. Inf. Forens. Secur. 10(6), 1167–1179 (2015) 15. J. Yu, H. Wang, Strong key-exposure resilient auditing for secure cloud storage. IEEE Trans. Inf. Forens. Secur. 12(8), 1931–1940 (2017) 16. J. Yu, R. Hao, H. Xia, H. Zhang, X. Cheng, F. Kong, Intrusion- resilient identity-based signatures: concrete scheme in the standard model and generic construction. Inf. Sci. 442–443, 158–172 (2018) 17. B. Wang, B. Li, H. Li, Oruta: privacy-preserving public auditing for shared data in the cloud, in Proceedings of IEEE 5th International Conference on Cloud Computing. (CLOUD) (2012), pp. 295–302 18. G. Yang, J. Yu, W. Shen, Q. Su, Z. Fu, R. Hao, Enabling public auditing for shared data in cloud storage supporting identity privacy and traceability. J. Syst. Softw. 113, 130–139 (2016) 19. A. Fu, S. Yu, Y. Zhang, H. Wang, C. Huang, NPP: a new privacy-aware public auditing scheme for cloud data sharing with group users. IEEE Trans. Big Data, to be published. https://doi.org/ 10.1109/TBDATA.2017.2701347 20. Y. Luo, M. Xu, S. Fu, D. Wang, J. Deng, Efficient integrity auditing for shared data in the cloud with secure user revocation, in Proceedings of IEEE Trustcom/BigDataSE/ISPA (2015), pp. 434–442 21. B. Wang, B. Li, H. Li, Panda: public auditing for shared data with efficient user revocation in the cloud. IEEE Trans. Serv. Comput. 8(1), 92–106 (2015) 22. H. Wang, D. He, S. Tang, Identity-based proxy-oriented data uploading and remote data integrity checking in public cloud. IEEE Trans. Inf. Forensics Security 11(6), 1165–1176 (2016) 23. Y. Yu et al., Identity-based remote data integrity checking with perfect data privacy preserving for cloud storage. IEEE Trans. Inf. Forensics Security 12(4), 767–778 (2017) 24. H. Wang, D. He, J. Yu, Z. Wang, Incentive and unconditionally anonymous identity-based public provable data possession. IEEE Trans. Serv. Comput., to be published. https://doi.org/ 10.1109/TSC.2016.2633260 25. Y. Zhang, J. Yu, R. Hao, C. Wang, K. Ren, Enabling efficient user revocation in identity-based cloud storage auditing for shared big data. IEEE Trans. Depend. Sec. Comput., to be published. https://doi.org/10.1109/TDSC.2018.2829880 26. W. Shen, G. Yang, J. Yu, H. Zhang, F. Kong, R. Hao, Remote data possession checking with privacy-preserving authenticators for cloud storage. Future Gener. Comput. Syst. 76, 136–145 (2017) 27. J. Li, J. Li, D. Xie, Z. Cai, Secure auditing and deduplicating data in cloud. IEEE Trans. Comput. 65(8), 2386–2396 (2016) 28. J. Hur, D. Koo, Y. Shin, K. Kang, Secure data deduplication with dynamic ownership management in cloud storage. IEEE Trans. Knowl. Data Eng. 28(11), 3113–3125 (2016) 29. G. Ateniese, B. de Medeiros, On the key exposure problem in chameleon hashes, in Security in Communication Networks (Springer, Berlin, 2005), pp. 165–179 30. G. Ateniese, D.H. Chou, B. de Medeiros, G. Tsudik, Sanitizable signatures, in Proceedings of 10th European Symposium on Research in Computer Security (Springer, Berlin, 2005), pp. 159–177 31. Y. Li, Y. Yu, G. Min, W. Susilo, J. Ni, K.-K.R. Choo, Fuzzy identity-based data integrity auditing for reliable cloud storage systems. IEEE Trans. Depend. Sec. Comput., to be published. https:// doi.org/10.1109/TDSC.2017.2662216 32. H. Wang, Proxy provable data possession in public clouds. IEEE Trans. Serv. Comput. 6(4), 551–559 (2013) 33. J. Shen, J. Shen, X. Chen, X. Huang, W. Susilo, An efficient public auditing protocol with novel dynamic structure for cloud data. IEEE Trans. Inf. Forens. Secur. 12(10), 2402–2415 (2017) 34. B. Lynn, The Pairing-Based Cryptographic Library (2015). [Online]. Available: https://crypto. stanford.edu/pbc 35. The GNU Multiple Precision Arithmetic Library (GMP). Accessed: Nov 2017. [Online]. Available: http://gmplib.org

High-speed 4:2 Compressor Toward Image Processing Kanuri Naveen

1 Introduction Due to the increasing popularity of portable electronic gadget, the required for reliable systems has been widely explored. This article presents analysis of the various electronic laps that are commonly used in these devices. Design engineers and FULL ADDER are interested in reducing their power consumption by focusing on the smallest area and highest speed. The propagation delay of a lap is usually affected by the presence of parasitic capacitances and the wiring of the transistors. Different techniques such as pass and gate diffusion input, transmission gate, and GDI have been used to reduce the PDP due to the wide variety of advantages associated with MOSFETs, their use has become popular, but their limitations have forced the designers to develop smaller and more robust devices. According to researchers, there are five way of EXCLUSIVENOREXCLUSIVEOR methods as in PTL approach [1]. Signal power loss owing to threshold grip voltage drop is an issue in PTL-based laps; nonetheless, the lap has bearable manage capability thanks to alternators on the outputs [2]. To accomplish complete swing and low-power gate, [3–5] used a feedback loop in their suggested EXCLUSIVENOR-EXCLUSIVEOR lap, whereas TG 2-wires created FULL ADDER outputs. “Two FULL ADDER units were also identified as DPL and SR-CPL utilizing EXCLUSIVEOR–EXCLUSIVENOR, AND–OR, and 2-wires” [6]. “Enlarge the number of gates often increases power consumption, however, power consumption can be minimized using double pass-transistor logic (DPL)” [4] and swing bring-back complementary logic [7] approaches. The amount of buffers and alternators in all suggested laps for FULL ADDER units, as well as a direct path from the give source to K. Naveen (B) Department of Electronics and Communication Engineering, MLR Institute of Technology, Dundigal, Hyderabad, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. K. Mandal et al. (eds.), Innovations in Signal Processing and Embedded Systems, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-19-1669-4_5

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Fig. 1 Suggested FULL ADDER unit with CNTFET

the ground, are all flaws. The inclusion of the element indicated in the lap can enlarge power use and delay [8]. Researchers launched CNTFET that possesses foaming properties, during the previous experiments to overcome MOSFET difficulties. In general, a multistage gate’s normalized delay can be written in component of t as a direct delay replica, and the retard between every input and any desired output can be calculated. “In recent years, bulk terminal connection in transistors has been a hot topic of research. As part of the dynamic threshold (DT) technique” [9], “the bulk connection to the gate was used to reduce power consumption in” [10, 11]. DT-formed laps take advantage of forward bias; as a result, the transistor will turn on, lowering Vth and increasing driving capability; this is accomplished by reducing the coupling width and hence the charge thickness in the depletion area. Different laps, on the other hand, were used to connect the bulk to the origin of transistors (Fig. 1).

2 Suggested FULL ADDER Unit In this article, another full snake unit dependent on new EXCLUSIVENOREXCLUSIVEOR entryway is suggested and displayed in Fig. 2. Here, every single one of the semiconductors plays explicit part. In customary FULL ADDER units, the ¯ B, ¯ and Cin are sources of info are A, B, and Cin, while in the suggested lap inputs, A, required as well. The upset sources of info are worked by inputs alternators that will build the force utilization. At next stage, 6 semiconductors make a EXCLUSIVEOR door and produce parallel codes as indicated by EXCLUSIVEOR truth table with 4 semiconductors, however, their yields signal is suffering from non-going full bore voltages. Henceforth, semiconductors T8 and T12 are utilized to repay the non-going full bore issue.

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Fig. 2 Block diagram of suggested system

For the most part, in FULL ADDER unit regardless of EXCLUSIVEOR entryway, EXCLUSIVENOR door is required as well. EXCLUSIVENOR entryway can be produced independently (unique schematic of the EXCLUSIVENOR door) or simply utilize an alternator after EXCLUSIVEOR entryway. Unmistakably adding alternators in the critical ways of the laps will build the force utilization, remarkably, yet can be remunerated in various ways of promising advancements like CNTFETs. The FULL ADDER vorable to presented EXCLUSIVENOR-EXCLUSIVEOR door was planned with the goal that relations (13) and (14) are fulfilled, and furthermore, their reality table is affirmed. In more detail, to accomplish the EXCLUSIVENOREXCLUSIVEOR units, AND entryways with transformed data sources should be shaped (two AND door in EXCLUSIVEOR entryway. “GDI is a promising method for little region computerized doors. A straightforward GDI unit is like a CMOS alternator with one PCNT and NCNT semiconductors, yet the primary contrasts between them are the sources of info that are associated with the source/channel terminals. Here, semiconductors T21 and T22 are utilized dependent on GDI strategy to create Cout signal. This GDI unit is powerless to create going all out yields for “1” state because of limit drop misfortune” [12, 13]. This issue tackled by a PCNT semiconductor (T23) that can pass “1” better than NCNT. Cout will be produced by (16); four information sources (EXCLUSIVEOR, EXCLUSIVENOR, An, and Cin) are applied to the semiconductors T21 to T23. Basic contributions of this capacity are EXCLUSIVEOR and EXCLUSIVENOR yields that appear controls of the semiconductors in the yield way. In light of (11) and (12), block chart of rationale doors for suggested FULL ADDER unit is outlined in Fig. 3. An AND door is for inputs An and B, and an OR entryway is for the delivered signs of EXCLUSIVEOR AND Cin to accomplish Cout. Likewise, 4-input EXCLUSIVEOR and EXCLUSIVENOR doors alongside 2:1 2-wire for sum are appear. Computerized lap’s FULL ADDER generally gage deferral of the contributions to every single one of the yields by counting entryways on wanted way. This idea is possibly deceptive on the grounds that it reasons that the quickest laps are those with least phases of rationale entryways, however, it

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Fig. 3 Power supply versus PDP-sum

is not right consistently. Subsequently, door speed essentially relies upon the electrical exertion (proportion of burden capacitance and info capacitance) and went way through various entryways, yet numerical estimations and laps recreations appeared that occasionally utilizing less stages bring about higher worth of the deferral. In the suggested block diagram, inputs A, B, and Cin are associated with 3 entryways (for both sum and Cout), so by looking to them, the quickest cannot be perceived. Henceforth, direct postpone assessment is fundamental. In delivering three yields of the suggested blower, superfluous semiconductors of 2-wire will be off or in backup states for having a unique yield independently, so the 2-wire as a critical component of the suggested blower has appropriate shifting qualities and can lessen the force utilization and speed up. One of the fundamental components of CNTFET innovation is high VOH and low VOL that will assist the suggested blower with having swing yields.

3 Simulation Setup Benefits and detriments of the suggested FULL ADDER lap are separated by recreation. Normal force utilization is determined in one period. In the recreations, LSB and MSB inputs are Cin and A, individually. Additionally, the most exceedingly awful spread postponement is assessed when yields approach half of VDD in ascent and FULL ADDER llcontrols, and the PDP is estimated by increasing normal capacity to the most noticeably terrible deferral. Among sum and Cout yields, sum is accounted for in light of the FULL ADDER that it has most exceedingly terrible results.

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Fig. 4 Image suppression

4 Simulation Results Assessment and Comparison “Various reproductions have been done, and their outcomes are assessed. From the get go, the suggested lap is researched with various upsides of force supply (VDD). Then, at that point, temperature variety is checked. Notwithstanding, check the suggested lap performance as an extendable piece of a greater lap, distinctive FULL ADDER controls have been thought of. Monte Carlo variety for the suggested lap and other best in class plans as FULL ADDER as PVT varieties are finished. CNT, cylinders, and pitches variety have been applied for more particular of the underlying conduct of the suggested lap. All recreation was finished by controls” [14]. Power Supply Variation “In this part, the suggested FULL ADDER lap alongside popular FULL ADDER units in references is examined under different VDD in the scope of 0.7–1 V; the ordinary VDD for CNTFET innovation with 32 nm length is 0.9 V. With the controls in Table 4, the outcomes in Fig. 5 appear that the suggested lap execution is superior to different plans for sum yield. Among the all assessed laps structure references, NEW-FL-FULL ADDER has the most noticeably terrible outcomes. At 0.9 V, 24-GTFS lap with 0.7361 fJ has 46.71% improvement in correlation with NEW-FL-FULL ADDER with 1.21fJ. Additionally, DPL at 1 V as VDD, with devouring 1.1703 fJ as its PDP-sum (PDP for sum) is set in the subsequent position, while 24-GT-FS lap appears 16.25% better execution with 0.8261 fJ. PDP-sum in Fig. 5 appears unwavering quality of the suggested lap for VDD variations.” [14] (Fig. 4). Image Processing Application Suggested model identifies the distinctions exactly, for example, clinical picture controlling to investigate epidemic.

5 Conclusion This article shows how to make a full viper unit with 24 semiconductors that are 34 nm CNTFET innovation. Various recreations, including temperature, VDD, and

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Fig. 5 Image suppression in teeth

FULL ADDER-out varieties were finished by Monte Carlo. Likewise, PVT varieties were finished by Monte Carlo. The suggested 4:2 compressor was installed in 4:2 blower. Design of the suggested FULL ADDER and blower laps was drawn with respect to the region occupation of the suggested FULL ADDER and the blower unit.

References 1. Y.S. Mehrabani, M. Eshghi, Noise and process variation tolerant, low-power, high-speed, and low-energy full adders in CNFET technology. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 24(11), 3268–3281 (2016) 2. N. Zhuang, W. Haomin, A new design of the CMOS full adder. IEEE J. Solid-State Laps 27(5), 840–844 (1992) 3. C.-H. Chang, J. Gu, M. Zhang, A review of 0.18-/spl mu/m full adder presentations for tree structured arithmetic laps. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 13(6), 686–695 (2005) 4. D. Radhakrishnan, Low-voltage low-power CMOS full adder. IEE Proc. Laps, Devices Syst. 148(1), 19–24 (2001) 5. A.M. Shams, T.K. Darwish, M.A. Bayoumi, Presentation analysis of low-power 1-bit CMOS full adder units. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 10(1), 20–29 (2002) 6. M. Aguirre-Hernandez, M. Linares-Aranda, CMOS full-adders for energy-efficient arithmetic applications. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 19(4), 718–721 (2010) 7. M. Vesterbacka, A 14-transistor CMOS full adder with full voltage-swing nodes. in 1999 IEEE Workshop on Signal Processing Systems. SiPS 99. Design and Implementation (Cat. No. 99TH8461). IEEE (1999)

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8. S. Goel, A. Kumar, M.A. Bayoumi, Design of robust, energy-efficient full adders for deepsubmicrometer design using hybrid-CMOS logic style. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 14(12), 1309–1321 (2006) 9. V. Niranjan, A. Singh, A. Kumar, Dynamic threshold MOS transistor for low voltage analoglaps. Int. J. Sci. Res. Eng. Technol. (IJSRET) 1, 26–31 (2014) 10. E. Abiri, Z. Bezareh, A. Darabi, The optimum design of RAM unit based on the modified-GDI method using Non-dominated Sorting Genetic Algorithm II (NSGA-II). J. Intel. Fuzzy Syst. 32(6), 4095–4108 (2017) 11. E. Abiri, A. Darabi, S. Salem, Design of multiple-valued logic gates using gate-diffusion input for image processing applications. Comput. Electric. Eng. 69, 142–157 (2018) 12. A. Morgenshtein, A. Fish, I.A. Wagner, Gate-diffusion input (GDI): a power-efficient method for digital combinatorial laps. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 10(5), 566– 581 (2002) 13. A. Morgenshtein et al., Full-swing gate diffusion input logic—case-study of low-power CLA adder design. Integration 47(1), 62–70 (2014) 14. A. Sadeghi, N. Shiri, M. Rafiee, High-efficient, ultra-low-power and high-speed 4: 2 compressor with a new full adder cell for bioelectronics applications. Circuits Syst. Signal Process. 39, 6247–6275 (2020)

Double MAC Supported CNN Accelerator K. Venkata Ramana and N. Akhila

1 Introduction The need to develop hardware accelerators for machine learning algorithms increases as algorithms become more popular. Deep neural networks, such as convolutional neural networks (CNN), have several attributes that make them considerably attractive for hardware acceleration, as well as high-structural regularity, high-procedural complexity, wide applicability, and high-recognition performance [1]. Like parallelism, FPGAs are among the most popular platforms. As a result, a lot of work has gone into developing an improved CNN accelerator in FPGAs. Simple width algorithm is a function that can be used for hardware DNN implementation. For the modern GPGPU, it has neither advantages nor disadvantages. For a long time, GPGPU only provided two cases: single precision or double precision floating point. Recently, some versions have adopted medium precision. However, this is a planned update that cannot be customized by customers. On the other hand, applicationspecific integrated circuit (ASIC) implementations can be used to achieve any precision [2]. Since recent studies have shown that even for deeper CNNs, 8-bit fixed points are always sufficient to meet the output requirements, and it is quite possible to improve efficiency at the expense of lower precision without affecting output quality. In reality, however, it is difficult to achieve higher efficiency at the expense of arithmetic precision because most arithmetic operations are performed using DSP modules that accept poor precision. For example, the Xilinx FPGA DSP48E1 module can only do 25 × 18 bit multiplication, but not two 8 × 8 bit multiplication in the same DSP module to improve performance. A commercially available computer with an FPGA on a 2-way SIMDMAC (multiplication and accumulation) module that can perform K. Venkata Ramana (B) · N. Akhila Department of Electronics and Communication Engineering, MLR Institute of Technology, Hyderabad, Telangana 500043, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. K. Mandal et al. (eds.), Innovations in Signal Processing and Embedded Systems, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-19-1669-4_6

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two multiplications and additions simultaneously while reducing the data width and performing four operations per cycle [3]. Our focus on Xilinx Virtex7 FPGAs is general, and other FPGAs can be used with the same DSP hardware module. It is not necessary to change the FPGA structure for our solution to work. The SIMD plugin in the DSP device is easy to implement and is supported. Simultaneous multiplication with lookup table (LUT) is easy too. The problem is figuring out how to implement SIMD multiplication in FPGA DSP module. Since most DNN-FPGA implementations for MAC operations are based on DSP blocks, the ability to run two MACs using a single DSP block actually doubles the computing power. While other techniques (such as including other MAC addresses in the LUT) can maximize performance, our approach is orthogonal. Since the DSP module itself does not support SIMD multiplication, we have to create virtual SIMD lanes in the module. The DSP ensures that the data for each track does not overlap. In the DSP block, we also have to pay attention to the sign bit and overflow. This is not easy to see, but a simple analysis shows that without hardware modification, it is impossible to multiply the traditional SIMD by the DSP module. Our method does not have to change the FPGA structure, but uses different tools, including LUTs and FFs (flip flops). In this article, we give the following results: We first describe a new type of SIMD multiplier developed for MAC operations in convolutional neural networks (CNNs). According to our double MAC, the SIMD multiplication must have similar operands [4]. In particular, A × C and B × C are used in place of A x C and B x D, where C is an unsigned integer as a common operand. Second, we have shown that with this restriction, a bidirectional multiplier and SIMD adder can be designed with minimal overhead on the DSP module. If the input trigger is an unsigned number, we show that despite its natural limitations, our dual MAC architecture can still be successfully integrated into the convolution layer of CNN’s accelerator. This is a common situation because deep CNN uses rectifier linear unit (ReLU). Trigger function. Fourth, it is used to fold the cape. For signed input triggers, we propose a process by which the signed input convolutional layer A can be transformed into a signed input convolutional layer [5]. Finally, we recommend once again using only the scaling scheme (i.e., no cost) for scaling. Function diagrams and weighting parameters can compensate for the loss of computational precision. Also shows that our dual MAC-CNN implementation can achieve relatively high precision by scaling even for large real CNNs [6]. This article assumes that FPGA is mainly used for CNN output rather than preparation. We also concluded that the CNN accelerator only accelerates the convolution layer, and the convolution layer does most of the calculations. We tested our technique with Verilog simulation and FPGA synthesis and used one of CNN’s most powerful acceleration designs to examine it. We show that compared to the prior art, our dual MAC has twice the computing power at both MAC processing level and MAC matrix level with the same number of DSP blocks. Instead of using LUTs to synthesize additional MACs, our circuit uses

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a larger number of LUTs. However, it is more efficient in terms of GOPS per LUT. In the network phase, when all the folding layers are combined with each other. Our system improves efficiency between 14 and 80% compared to the highly optimized next generation CNN accelerator based on hyperparameters and FPGA expansion [7]. These techniques allowed us to plan.

2 Existing System 2.1 CNN Accelerator Most of the calculations in estimating CNN are done using convolutional levels. The convolution layer receives as input a matrix (N) which is called the input feature map and outputs a matrix (M) which is called the output feature map. The convolution between each input feature map is used to calculate each output feature map (Ym) from the input feature map (Xn) and each N matrix that forms the weight parameter matrix (Wm, n), and some recent hardware accelerators use M and N loops as a source of parallelism. The 2D multiplier and enhancer are used in the newly developed CNN FPGA accelerator. The MAC matrix simultaneously processes the TN input and TM output and performs MAC TNTM operations in each cycle, covering all I/O function assignments in the tile cycle [8]. Complex searches can be used to quickly find the correct TN and TM values. The function assignment is not suitable for the FPGA’s internal memory. Other parameters, such as buffer size parameters, can be considered to optimize data reuse. Other CNN accelerators will also benefit from our dual MAC architecture. Slightly improved to optimize compute density, to demonstrate applicability of our methodology, and to verify performance improvements in the most.

2.2 Related Work CNNs are popular for their digital weight, and many accelerators for ASICs and FPGAs have been proposed. While ASIC implementations are generally cheaper and more energy efficient, they lack the flexibility to accommodate different accelerator architectures. Stream interfaces are modular, and the architecture of CNN FPGA accelerators is usually based on MAC matrices and multiple architectures [9]. It depends on how the MAC matrix is used to perform the convolution rate calculations. It uses six deep nesting loops to calculate the convolution rate, two MAC operations are used for the input characteristic map (N) and the output characteristic map (M), and two are used for the row/column of the image (where R and C are the rows and columns) and two row/column coefficients representing convolution filters. The previous studies have investigated the parallelism between MN loops, MR loops, MC

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loops, and MRC loops. CNN accelerator for multi-parallel systems, a recent analysis proposed the concept of space exploration technology. MN loop parallelization is the basis of our work. Acceleration CNN was originally done using floating-point accuracy, but more modern designs use fixed point accuracy. For some large CNNs, even an 8-bit fixed point is sufficient for inference rather than preparation. The FPGA-based SIMD processor can be used in a variety of ways. Most of these processors consist of a SIMD sequence of processing elements (PE), which differs from our concept of SIMD, which is a multi-word instruction, propose a SIMD convolution implementation for a 2D convolution processor. By processing the subword of the operand, the 16-bit convolution operation is divided into two 8-bit convolutions. We took a separate path because we performed two operations simultaneously on one computing machine [7]. With only one signed multiplier, we can perform two unsigned multiplications simultaneously. Some corrective steps are needed because the double MAC second line cannot accept signed operations. A similar method is listed for obtaining a right-signed multiplier.

3 Proposed System 3.1 Proposed Double MAC Architecture This double MAC architecture now supports (i) SIMD multiplication and (ii) multiplication aggregation. We will first understand how to implement SIMD multiplication in a loop using a hardware multiplier (unsigned and signed numbers), after which we will be able to figure out how to combine a given number of operands.

3.2 SIMD Multiplication of Unsigned Numbers Let us consider the case where all operands have no sign. The multiplier simultaneously performs two unsigned multiplications of the same operator, such as A × C and B × C. For this, two conditions must be met. Returns the width of each operand is n. The output register must first be at least 4n bits long. The inputs of the two operands must be isolated. As shown in Fig. 1, at least one protection bit is used for accumulation without overloading. The same protection bits can be used to detect execution of the 2n least significant bits during aggregation. After the acknowledgment, it will automatically reset and use a small counter to count the number of execution events. Balancer added separately. Therefore, an n bit multiplier (3n + 1) × n multiplication is used to perform two n bit multiplications in SIMD form. For example, the 25 × 18 bit DSP48E multiplier on Xilinx FPGA can be up to 8 bit. Remember to use an unsigned multiplier for unsigned multiplication. Let B = bn – 1 , … b1 b0 be a signed bit integer and C an unsigned n bit number. Using the

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57

Fig. 1 SIMD signed-unsigned multiplication

formula − 2n − 1 = 2n − 1 − 2n, B × C can be calculated as shown below, where B is the value of B and is an unsigned integer. Just expand to bidirectional SIMD multiplication. We can do this by (3n + 1) x unsigned n bit multiplication and then up to two unsigned and unsigned n bit multiplication subtractions in SIMD mode. However, by doing signed multiplication to multiply (3n + 1) × n bits, we can minimize the number of subtractions to 1. In this case,  B × C = −bn−1 · 2n−1 +

 bi · 2i

i=0

 = bn−1 · 2

n−2 

n−1

+

n−2 

bi · 2

·C

 i

· C − bn−1 · 2n · C

i=0

= Bˆ · C − bn−1 · 2n · C The multiple addition process is carried out. You can enter corrections instead. However, since we have very little protection, the correction must be postponed until all the multiplication results are collected.1 Instead, store the correction word separately in the small battery and subtract the weight of the battery from the main battery. In the next section, we will calculate additional battery sizes (Fig. 2).

3.3 Accumulation and Double MAC Architecture This type of dual MAC system structure is shown as in Fig. 3. Bidirectional SIMD doubler and bidirectional SIMD adder can be implemented with a single DSP module.

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Fig. 2 Example SIMD execution: −7 × 13 and −4 × 13

Fig. 3 a Data path of double MAC architecture, where n = 8 and M = 32, and b how it works

Each multiplier and adder have the correct output, as indicated by the thin arrows in the figure. The offset output is collected in another register or clock for final adjustment at a later time. Words must be added or subtracted to make the final change. The C1 counter adder correction word does not overlap with the key adder contribution, so it can be easily combined. The erase counter width is calculated using the sum of the collected values, V, log2 V. where, V = (K2N/TN) for each CNN layer must not be less than log2 (K2N/TN) in this case from our basic CNN accelerator, in where K is the size of the convolution filter in one dimension. For the multiplier correction term, the accumulator amplitude is the same. Consequently, it is sufficient to remove the C2 multiplication correction word from the cascade result.

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The bit width outside the DSP block can be changed quickly after accumulation. This change does not require any additional execution time, as it is done concurrently.

4 CNN Accelerator Based on Double MAC This dual MAC is unique in two ways: It has an equal operand and an unsigned operand. In the context of the CNN accelerator, we have shown how to overcome these limitations and accuracy issues. Figure 4 shows our dual MAC matrix architecture compared to the original MAC matrix. Since the original MAC matrix used 32-bit floating-point precision, block 5 DSP was used to introduce multiplier and adder pairs. 8-bit fixed point accuracy can increase computational density.

Fig. 4 Computation engine of a CNN accelerator. a A MAC array from based on floating-point MACs and b modified version supporting our double MACs

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Fig. 5 DSP block with double MAC

5 Results 5.1 Modified DSP Block with Double MAC Figure 5 shows the modified DSP block with double MAC. In the double MAC, two multiplication operations are performed parallel.

5.2 RTL Schematic of CNN Accelerator Figure 6 shows the RTL schematic structure of CNN accelerator. MAC unit is the vital element in CNN accelerator. In the above RTL schematic structure, double MAC has been implemented. Technological Schematic Figures 7 and 8 show the schematic representation and simulation process. In the simulation process, we can analyze how much time has been taken and how double MAC improved the speed in performance.

5.3 Timing Report NOTE: These timing numbers are only a synthesis estimate. For accurate timing information, please refer to the trace report. Generated after place-and-route.

Double MAC Supported CNN Accelerator

Fig. 6 a–c RTL schematic structures of CNN accelerator

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Fig. 7 Schematic structure of double MAC

Fig. 8 Simulation result of double MAC

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Clock Information No clock signals found in this design. Asynchronous Control Signals Information No asynchronous control signals found in this design. Timing Summary Speed Grade: −5. Minimum period: No path found. Minimum input arrival time before clock: No path found. Maximum output required time after clock: No path found. Maximum combinational path delay: 33.908 ns.

6 Conclusion By loading multiple MAC operations into the FPGA’s DSP chip, we can maximize the computational speed of the CNN accelerator on the FPGA. Using the MAC operating context, we use Verilog simulations to test the proposed architecture. Efficiency has increased from 14% to over 80% according to CNN. All this is achieved while maintaining a high level of performance. Increased efficiency saves energy and makes the acceleration system more attractive. The developed multiple MAC arrays can be applied to accelerators that parallelize along the MC loop and the MRC loop.

References 1. K. Simonyan, A. Zisserman, Very Deep Convolutional Networks for Large-Scale Image Recognition, vol. abs/1409.1556 (2014) 2. N. Suda et al., Throughput-optimized opencl-based fpga accelerator for large-scale convolutional neural networks, in Proceedings of the 2016 ACM/SIGDA International Symposium on FieldProgrammable Gate Arrays, ser. FPGA’ 16. ACM, pp. 16–25, New York, NY, USA (2016) 3. Y.-H. Chen, T. Krishna, J. Emer, V. Sze, Eyeriss: An energy-efficient reconfigurable accelerator for deep convolutional neural networks, in IEEE International Solid-State Circuits Conference, ISSCC 2016, Digest of Technical Papers, pp. 262–263 (2016) 4. B. Mahmood, M. Al Jbaar, Design and implementation of simd vector processor on fpga, in 2011 Fourth International Symposium on Innovation in Information Communication Technology (ISIICT), pp. 124–130 (2011) 5. Nvidia Tesla P100, Nvidia, available at http://www.nvidia.com/object/tesla-p100.html. Last accessed 4 May 2017 6. S. Cadambi, et al., A programmable parallel accelerator for learning and classification, in Proceedings of the 19th International Conference on Parallel Architectures and Compilation Techniques, ser. PACT’10. ACM, New York, NY, USA, pp. 273–284 (2010)

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7. W. Wen, et al., A new learning method for inference accuracy, core occupation, and performance co-optimization on truenorth chip, in Proceedings of the 53rd Annual Design Automation Conference, ser. DAC’16. ACM, New York, NY, USA, 2016, pp. 18:1–18:6 8. Y. Chen, et al., DaDianNao: A machine-learning supercomputer, in Proceedings of the 47th Annual IEEE/ACM International Symposium on Microarchitecture, ser. MICRO-47. IEEE Computer Society, Washington, DC, USA, pp. 609–622 (2014) 9. S.S. Sarwar et al., Multiplier-less artificial neurons exploiting error resiliency for energy-efficient neural computing, in Proceedings of the 2016 Conference on Design, Automation & Test in Europe, ser. DATE’16. EDA Consortium, San Jose, CA, USA, 2016, pp. 145–150 (2016)

Hybrid Cryptosystem’s Design with AES and SHA-1 Algorithms K. Venkata Ramana, G. V. Sai Supraja, and Vaishnavi Hibare

1 Introduction The mission of the project is to execute a hybrid crypto-framework that utilizes both AES and SHA calculations. The vision of the project is to guarantee that safe information transmission happens among sender and beneficiary by making it hard for different gatherings to convey the information [1]. By consolidating two distinct calculations, we ensure that we take the advantages of both public key and symmetric key crypto frameworks [2]. The primary benefit with public key—cryptosystem is diverse keys which are utilized by sender and recipient so that regardless of whether programmer gets the key utilized at transmitter side, he cannot convey data utilizing that key in light of the fact that other distinctive key is utilized at the opposite end. So it guarantees greater security yet it has disservice of being mind boggling in tasks, and it cannot deal with enormous measure of information. Symmetric key cryptosystems utilize same key at both sender and beneficiary side. It is less perplexing as far as tasks and can deal with huge information yet it is not gotten contrasted with the past one. By carrying out both AES and SHA calculations, we are getting the advantage of safety just as less intricacy [3].

2 Literature Review AES algorithm is symmetric key cipher, and it consists of different transformations like key expansion preround transformation, mixcolumns, subbytes, shift rows, and preround transformations. For key having the size 128 bits, 10 rounds are K. Venkata Ramana (B) · G. V. Sai Supraja · V. Hibare Department of Electronics and Communication Engineering, MLR Institute of Technology, Hyderabad, Telangana 500043, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. K. Mandal et al. (eds.), Innovations in Signal Processing and Embedded Systems, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-19-1669-4_7

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performed to obtain the required cipher text key expansion algorithm is used to generate subkeys from main key [4]. Preround transformation performs XOR operation between normal text and key, and the output is given as input to subbytes which replaces the plain text bytes in form of matrix with corresponding entry of box where s box is a matrix of order 16 × 16. The output of subbytes is given as input to shift rows where the bytes in state matrix are shifted to left according to the row. The result of any intermediate operation is stored in the form of matrix called as state matrix. The output of shift rows is applied to mix columns which examines each column of output matrix and multiplies it with state matrix of 4 × 4 to get 4 × 1 matrix. This step is repeated another 3 times to get a 4 × 4 matrix. All the above steps should be repeated for all the 10 rounds except for the fact that mix columns is omitted in the last step to get the coded information [5]. Decryption is performed at receiver side to retrieve the original message. SHA algorithm is used for generating hash codes which are unique [6]. One more advantage with hash codes is hash code cannot be inversed to find the original key. So even though if hacker manages to deploy the hash code, he cannot deploy original key from it. Previously, AES algorithm was used alone for secure data transmission. Even though AES is robust algorithm, and nearly, hacker requires 2^128 attempts to deploy the data, it is still not considered secure when compared with algorithms that uses asymmetric key for encryption and decryption [7]. Since same key is used at both sender and receiver, if the hacker gets the key, there is a huge possibility that he will hack the private information. If asymmetric key cryptographic algorithms like RSA are used for securing the data, it becomes highly complex process since different keys generated from original key should be distributed among all the users which is mathematically very intensive and slow and also process only small amount of data at a time which will be a disadvantage even though high security is provided in terms of transmitting and receiving sensitive data [8].

3 Proposed System To defeat the inconveniences of existing framework, an alternate calculation is recommended that utilizes both AES and SHA calculations rather than utilizing an ordinary key, we will utilize hash code as a key. Here, we will execute SHA-1 calculation which accepts 512 bits as input and gives 160 bits as output called as overview or hash code. This overview is applied as a key to AES calculation that utilizations key size of 128 bits. The overview of length 160 bits is diminished to 128 bits utilizing pressure. Plain text of length 128 bits is applied as contribution to encryption block. It brings about figure text. For decoding reason, the client should realize the 512 bit block preceding hashing which is given as information. The code in the module is composed with the end goal that it consequently creates hash code for square and uses it as a key in unscrambling which will bring about unique message. Since block is sent to various clients, despite the fact that if programmer figures out how to convey the square, he cannot open the private information utilizing block since he will not have thought

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Fig. 1 Block diagram of the proposed system

that hash code of square is utilized as key. So this technique is get and can handle enormous measure of information at a time.

4 Methodology See Figs. 1, 2, and 3.

5 Xilinx ISE Xilinx is a semi-conductor company, which supplies programmable logic devices and it is an American technology company. Xilinx software is mostly used for writing programs in Verilog, and later on, it can be used for compiling, checking errors, simulation, and synthesis. Different versions of Xilinx like ise Webpack, vivado are available in the market, and users can use any version depending upon the application [9] (Fig. 4). Xilinx integrated software environment (ISE) software tool was produced by Xilinx for the synthesis and analysis of HDL designs, enabling the developer to synthesize, analysis, examine, simulate, and configure the target device with a programmer. Xilinx ISE can be used for analysis for design implementation and elegance concept. Xilinx provides optimal design results with time predictability and reconfiguration with size and more flexibility.

6 FPGA FPGA is an integrated circuit with millions of logic gates. FPGA consists of internal hardware blocks that are user programmable and can be reprogrammed depending upon the specific application needed (Fig. 5).

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Fig. 2 Typical process flow of encryption using AES and SHA

6.1 FPGA Basics FPGA stands for field programmable gate array. Basically, an FPGA is an integrated circuit which is configurable. The chip of FPGA can be programmed by the user and can be re-programmed with an update. The logic program can be reconfigured based on the specific operation of the design. FPGA supports the changes in the design and can be reprogrammed [10]. FPGA consists of two main configurable components such as logic blocks and interconnects. A logic block of an FPGA consists of look up table (LUP). Interconnects interconnection between the logic blocks. Depending upon the manufacturer the implementing programmability, interconnection arrangement and basic functionality of the logic block differ. FPGA hardware consists of programmable logic devices (PLD), logic gates, random access memory (RAM), and other hardware like DCM. FPGA consists of a layout of a unit that is repeated in matrix form. They are inexpensive and easy realization of logic networks in hardware.

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Fig. 3 Typical process flow for decryption using AES and SHA

7 Results Figure 6 shows the result of encrypted data. The input information has been encrypted using SHA algorithms before transmitting to be secured. At first, the arbitrary length of input to SHA-1 is taken. The output of SHA, i.e., 160 bit has stored in message digest and assigned only 128 bits of message as key. Figure 7 shows the result of decryption. To acquire the original information, signal which was encrypted using SHA has been decrypted using AES decryption algorithm. In decryption process, 128 bit key which was transmitted is considered as one of the input. Cipher text as other input to AES algorithm. Figures 8 and 9 show the result of elaborated design and synthesis for encryption using AES and SHA (Table 1). Figure 10 gives the complete description of synthesis report. This report describes what are elements used and how many have been utilized to implement the project.

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Fig. 4 Xilinx ISE interface of the proposed system implementation

8 Conclusion We have enhanced the security of networks by implementing both AES and SHA calculations together. The secrecy of information is kept up with alongside some less concentrated activities contrasted with existing framework. This undertaking can be additionally stretched out in Android applications to send messages that main the client can see. We can utilize the calculation for different applications like burglary discovery, safe exchanges, and so forth.

Hybrid Cryptosystem’s Design with AES and SHA-1 Algorithms

Fig. 5 FPGA board

Fig. 6 Result of encryption using AES and SHA

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Fig. 7 Result of decryption using AES and SHA

Fig. 8 Elaborated design for encryption using AES and SHA

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Fig. 9 Synthesis results of encryption using AES and SHA

Table 1 Computation of various attributes

Parameters

AES + SHA

AES

1

Total delay

35.14 nano-seconds

10.23 nano-seconds

2

Total number of registers

849

725

3

Bit error rate

0.975

0.983

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Fig. 10 Advanced HDL Synthesis report

References 1. R.L. Rivest et al., A method for obtaining digital signatures and public-key cryptosystems. Commun. ACM. 21, 120–126 (1978) 2. D.J. Guan, Montgomery Algorithm for Modular Multiplication (2003) 3. A. Tenca, C. Koc, A scalable architecture for montgomery multiplication, in Cryptographic Hardware and Embedded Systems, Lecture Notes in Computer Science, No. 17, pp. 94–108 (1999) 4. S. Sau, C. Pal, A. Chakrabarti, Design and implementation of real time secured RS232 link for multiple FPGA communication, in Proceedings of International Conference on Communication, Computing & Security. ISBN: 978-1-4503-0464-1 (2011) 5. B.A. Forouzan, Cryptography & Network Security 6. A. Tenca, G. Todorov, C. Koc, High-radix design of a scalable modular multiplier, in Cryptographic Hardware and Embedded Systems, Lecture Notes in Computer Science (Springer,

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8. 9. 10.

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Berlin, 2001), No. 2, 162. pp. 185–201. High-Speed RSA Implementation, Cetin Kaya Koc, Version 2.0 (November 1994). ftp://ftp.rsa.comlpub/pdfs/tr20I.pdf C.D. Walter, Montgomery’s Multiplication Technique: “How to Make It Smaller and Faster. Cryptographic Hardware and Embedded Systems, Lecture Notes in Computer Science” (Springer, Berlin, 1999), vol. 17, pp. 80–93 J. Fry, M. Langhammer, Altera corporation, in RSA & Public Key Cryptography in FPGAs. Europe xilkernel_v3.00.pdf on www.xilinx.com A Mazzeo, L. Romano, G.P. Saggese, N. Mazzocca, FPGA based implementation of a serial RSA processor, in Design. Proceedings of the conference on Design, Automation and Test in Europe, vol. I (2003). ISBN: O-7695-1870-2

BTI Reliability Analysis of Low Leakage Fully Half-Select-Robust Free SRAM Design T. Vasudeva Reddy, P. Akhil, and P. Akhil

1 Introduction In current times, power discharge is now a critical design restriction for low-power systems such as wireless sensing networks, implanted biomedical instruments and other battery-operated portable devices. The main contributor to power discharge is Static random access memory (SRAM) which occupies a considerable part of Systems-on-Chips (SoCs), and their share will further expand in the future. In addition, leaking is becoming an important hazard with the development to ultra-size technology. As the leakage becomes significantly, energy consumption increases as threshold voltage (Vth) and gate oxide thickness are decreased. For power-efficient design, it is consequently vital to limit the power associated with SRAM. Minimizing a supply voltage is a simple technique to power efficiency; power reduces the supply voltage quadratic way and exponentially. However, the process variance significantly impairs the performance of the SRAM cell at lower supply voltages. As a result of the difficulties of maintaining the device strength ratio in the sub-threshold zone, the probability of read/write failure in the traditional 6T RAM is dramatically enhanced. In order to solve reading defects we are going to utilize a separate reading buffer, researchers have proposed numerous layouts of SRAM cells. SRAM cells enhance the read static noise margin, when the read/write path is disconnected and still have a low write margin in the area under this threshold. In the literature several writing aid approaches have also been developed to expand the SRAM cell’s writing margin. World line (WL) boost and negative bit line (NBL) are the most prevalent written aid strategies used to enhance writing ability by enhancing the write access transistor’s drive ability. These tactics, however, lead to field and power penalties [1]. Another helpful technique to improve the writing ability that is to reduce the strength of the cross-connected inverters pair. It comprises power cut, elevation or floating, VSS T. Vasudeva Reddy (B) · P. Akhil · P. Akhil Dept of ECE, B. V. Raju Institute of Technology, Narsapur, Medak(dt), Telangana, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. K. Mandal et al. (eds.), Innovations in Signal Processing and Embedded Systems, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-19-1669-4_8

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cell, etc. Multi-bits of upset (MCU) recently in the stability of SRAM’s with superior techniques because of the decrease inefficiency. Confidence for deep submicron technology is one of the greatest hurdles for the design of SRAMs. Scale below 32 nm node leads to an issue of reliability defined by gradual device degradation caused by aging. Instability at the biased temperature (BTI) is one of the main reliability problems caused by the aggressive scaling of electronics. The main focus of reliability in PMOS was negative biased temperature instability (NBTI) [2]. With the introduction of metal gate and its dependence on load, PBTI was considered the major reliability challenge in NMOS devices. NBTI and PBTI increase the transistor threshold with stress time and there by decrease circuit performance. The study the suggested cell was also analyses the dependability of BTI in order to see changes in performance metrics like power and time owing to the transistor aging [3].

2 Existing Design A.

Negative Bias Temperature Instability

The rapid partial recovery of NBTI harm makes it more difficult both to investigate and to offset the occurrence in the design of the circuit. The problem of applying stress and detecting the voltage shift distribution simultaneously has long been a hindrance to research. The NBTI behavior has differed greatly depending on the time period (often unknown) between stress and measurement in different laboratories. NBTI recovery signifies for designers that dependability depends on the service cycle and stress. Some tests enhance the lifetime by ten or even hundred times via a shorter working life cycle—equivalent to a longer recovery interval between shocks. Combined with high variety in failure times, this dependence on the duties cycle imposes an almost impossible problem for designers: to try to ensure dependable and consistent performance while the characteristics of each device differ unpredictably [4]. 11T SRAM cells for bit-interlacing implementation with totally select-free, robust operation. The 11T-1 and 11T-2 cells suggested successfully minimizes reading disturbance, half-select disturbance and also enhance writing capability by applying power-cutting and write only strategies for ‘0’/‘1.’ The 11T-1 and 11T-2 cells reach an increase in written output of 1.83 times and 1.7 times, whereas the two cells reach a read output of approx. 2 times greater than 6T (at VDD = 0.9 V) [5]. The cell 11T-1 also exhibits a Writing Margin (WM), which is 13.6% higher than that of the previous 11T cell. In the case of prior power cut-off cells, the both proposed cells removed floating knot conditions during half-select writing with success. Simulation by Monte-Carlo validates low-voltage operation without further peripheral assistance. We also give a comparative examination of SRAM reliability of BTI.

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11T-1 SRAM design The cell comprises a transverse inverter connected and a floating avoidance aid for the addition of power cuts (PCFA). The PCFA network transistors MP1 and MP3 intersect the supply voltages that the pull-up path is reduced and the storage node can be uncontroversial discharged to improve writing capacity. In CHS cells, however, transistor MP2, operated by WL prevents ‘1.’ Column-based WLA and WLB signals control the written access transistors MAL and MAR [6]. Writing WLA and WL signals are engaged while WLB and VSS are deactivated during Write ‘0’ operation. The left inverter is entirely power-off and the Q node can be simply discharged from the MAL and MR2 transistors [7]. The WL and the WLB are equally enabled in writing ‘1,’ where as the WLA is deactivated. The delivery is now disconnected for the right inverter and the QB node is readily discharged via mar and mr2 and thus ‘1’ is entered at Qnode [8]. Read Limitations of 11T-1 SRAM: Although the existing cell has certain drawbacks due to faults, we will note that some of it stays at falls in Q. We read this as the outcome of an error. The Qbar signal is supplied by an extra MOSFET leads to high power consumption. DC Analysis The read operation is performed by allowing WL signal and maintaining both WLA and WLB at ‘0.’ Before read operation, the RBL is pre-charged. The pathways of discharge MR1 and MR2 transistors according to QB data. The WLA and WLB disabled signals allow full storage (Q and QB) isolation from any troubled read path while the access is being read. Even with the sub-threshold operation the read up set is therefore not an issue. All the controller signals, offering a fully separated interconnected inverter without any floating node, are deactivated in Hold Mode. The cell stability in mode hold is therefore identical to the cell of 6T. The VSS signal is high, reducing static electricity usage in standby mode greatly [9, 10] (Fig. 1).

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Fig. 1 11T SRAM BTI design

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11T SRAM BTI Design: See Fig. 2. Transient analysis of existing 11T-1 See Fig. 3. Read and write of existing 11T-1 See Figs. 4 and 5.

Fig. 2 DC analysis of existing BTI SRAM design

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Fig. 3 Transient analysis

Fig. 4 BTI analysis on 11T SRAM

3 Proposed System The 12TSRAM cell presented is constructed with the power-prevention circuit and using MGDI technology. The 11T-1 (existing) circuit has been designed to make the cycle consume less power with powerful SRAM reading capabilities. Furthermore, the 12TSRAM amplifier is designed to rectify bits if any stuck defects are written [2– 4]. The cell core comprises a transverse-connected inverter and a floating avoidance aid for the addition of power cuts (PCFA). The PCFA network transistors MP1 and MP3 intersect the supply voltages that the pull-up path is reduced and the storage node can be controversially discharged to improve writing capacity. In CHS cells, however, MP2 transistor is operated by row-based WL and prevents floating-1. MPL1, MNL3,

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Fig. 5 Delay with respective temperature

MPR2 and MNR4 are the circuit used to detect stuck in cases when there is a Q circuit built by MGDI. The output is Q at the point of RBL [5–7]. The MN2 write access transistors are controlled by the WLA signal column. Whenever WL = 1 is active and a bit line entry reaches the two cross-connected state inverters circuit. This MGDI approach, as only two OR and AND transistors are required, followed by an amplifier sensory equation. Write WLA and WL are engaged while WLB is deactivated during Write ‘1’ operation. The inverter is entirely disconnected and Qnode is discharged via the MNL2 and MPL3 transistors [7, 9]. Read Read operation is performed by allowing WL signal and maintaining both WLA and WLBat ‘0.’ RBL is pre-charged, before read operation. The WLA and WLB disabled signals allow full storage (Q and QB) isolation from troubled read path while the access is being read. Even with the sub-threshold operation the read upset is therefore not an issue [10, 8]. All the controller signals, offering a fully separated interconnected inverter without any floating node, are deactivated in Hold Mode. The cell stability in mode hold is therefore identical to the cell of 6T.

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Fig. 6 Circuit design of 12TBTI SRAM

Struck at faults detection The defects are discovered using the logic equation in which we add the defect on the bit line and multiply it with the bit line (BL + default) BL. Suppose the fault is in Q, the equation is altered to (BL + Q)BL = BL and the same logic can be made for detecting faults on Qbar, as the expression changes to (BL + Q bar)BL = BL (Figs. 6 and 7). DC Analysis 12T

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Fig. 7 Schematic design of 12TBTI SRAM

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Transient analysis of proposed 12T (Figs. 8, 9, 10, 11, 12, 13, 14, 15 and 16).

4 Results and Comparison See Tables 1, 2, 3, 4, 5 and 6.

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Fig. 8 Read and write of proposed 12T

Fig. 9 BTI analysis on 12TSRAM

5 Conclusion The research papers offered 12TSRAM cell topology for interlocking architecture. The proposed cell removes read disturbance, writes semi-selected disturbances and enhances writing ability by employing the MGDI and power interrupting techniques. In the predictive 32 nm high-k metal gate CMOS technology, the effect of BTI on

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Fig. 10 BTI analysis of delay

Fig. 11 Power analysis of BTI design

performance of SRAM is also analyzed and the energy and delay predicted, further study suggested, can reduce the failure in Q output due to fluctuations temperatures. In addition, the 12T cell helped to reduce the total power consumption up to 40% and Delay of 20% compare to existing model and also power delay product is decreased60%on11T-1model. The BTI analysis showed that in the mids to fluctuations in the process and transistor aging impact, the proposed 12T cell could be an excellent alternative for dependable SRAM in relation to existing 11T-1 cell on the nano scale. Therefore

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Fig. 12 Delay analysis of BTI design

Fig. 13 Power analysis

we can utilize these 12T SRAM in real time application like ECG, Satellites, high speed cache memory’s in laptops.

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Fig. 14 Delay analysis

Fig. 15 Analysis of read and write delay

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Fig. 16 Read and write delay performance Table 1 Comparative analysis of power BTI Design versus temperature variations Model/Temp in degrees

12TBTI delay in ps

11TBTI delay in ps

10

66.2448

127.5822

20

68.0842

129.0011

30

69.8325

124.8163

40

71.4835

126.1751

50

74.4846

127.9241

60

74.576

129.1397

70

75.125

130.179

80

78.5491

130.9587

90

78.9976

136.5853

Table 2 Comparative analysis of delay of BTI design versus temperature variations Model/Temp

Proposed 12TBTI

Existing11 TBTI

Temp in degrees

Power in µw

Power in µw

10

26.85

54.61

20

28.17

51.9

30

27.97

58.62

40

27.74

60.21

50

28.15

57.22

60

27.53

54.77

70

26.75

62.17

80

27.36

65.62

90

27.68

65.41

92 Table 3 Comparative analysis of power

Table 4 Comparative analysis of delay

Table 5 Comparative analysis of power delay product

Table 6 Comparative read and write delay analysis

T. Vasudeva Reddy et al. Parameters

Proposed 12TBTI SRAM

Existing 11T-BTI SRAM

Static power

3.23

3.01

Dynamic power

2.075

5.18

Total power in µw

5.305

8.19

Parameters

Proposed 12TBTI SRAM cell

Existing 11T-BTI SRAM cell

Rise time

211

300

Fall time

357.2

383

Total time in ps

568.2

683

Parameter

Proposed 12TBTI SRAM cell

Existing11T-BTIS RAM cell

Total power

5.025

8.19

Total delay

568.2

683

Power delay product

2855.205

5593.77

Parameters

Proposed 12TBTI SRAM

Existing 11T-BTIS RAM

Write delay in ps

22.45

168.974

Read delay in ps

271.802

181.2388

Total delay in ps

294.252

350.2128

References 1. S. Ahmad, B. Iqbal, N. Alam, M. Hasan, Low leakage fully half-select-free robust SRAM cells with BTI reliability analysis. IEEE Trans Device Mater Reliab 18(3), 337–349 (2018). https:// doi.org/10.1109/TDMR.2018.2839612 2. T.V. Reddy, K. Madhava Rao, Performance and functionality of novel subthreshold SRAM’s using low power techniques for SoC designs, in 2018 3rd International Conference On Communication And Electronics Systems (ICCES) (2018), pp. 259–263. https://doi.org/10. 1109/CESYS.2018.8723927 3. F. Firouzi, S. Kiamehr, M.B. Tahoori, Statistical analysis of BTI in the presence of processinduced voltage and temperature variations, in 2013 18th Asia and South Pacific Design Automation Conference (ASP-DAC) (2013), pp. 594–600. https://doi.org/10.1109/ASPDAC. 2013.6509663 4. T.V. Reddy, B.K. Madhavi, Analysis and design of robust ultra-low power subthreshold SRAM models, in 2016 International Conference on Control, Instrumentation, Communication and

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Computational Technologies (ICCICCT) (2016), pp. 200–204. https://doi.org/10.1109/ICC ICCT.2016.7987945 S.K. Krishnappa, H. Mahmoodi, Comparative BTI reliability analysis of SRAM cell designs in nano-scale CMOS technology, in 2011 12Th International Symposium on Quality Electronic Design (2011), pp. 1–6. https://doi.org/10.1109/ISQED.2011.5770755 T.V. Reddy, B.K. Madhavi, Design and estimation of power and delay using high Vth NMOS under subthreshold logic operation of SRAM one bit model, in 2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES) (2016), pp. 1786–1790. https://doi.org/10.1109/SCOPES.2016.7955750 B.K. Madhavi, T.V. Reddy, Design strategy & analysis of Subthreshold SRAM in power & delay for wearable applications, in 20172nd International Conference on Communication and Electronics Systems (ICCES) (2017), pp. 872–878.https://doi.org/10.1109/CESYS.2017.832 1209 J. Ding, D. Reid, C. Millar, A. Asenov, Investigation of SRAM using BTI-aware statistical compact models, in 2013 Proceedings of the European Solid-State Device Research Conference (ESSDERC) (2013), pp. 186–189. https://doi.org/10.1109/ESSDERC.2013.6818850 D. Datta, P. Dewangan, N. Surana, J. Mekie, Energy and area efficient 11-T Ternary content address able memory for high-speed search, in 2019 IEEE International Conference on Electron Devices and Solid-State Circuits (EDSSC) (2019), pp. 1–3. https://doi.org/10.1109/EDSSC. 2019.8754423 A.K. Singh, C.M.R. Prabhu, S.W. Pin, T.C. Hou, A proposed symmetric and balanced 11-T SRAM cell for lower power consumption, in TENCON 2009 - 2009 IEEE Region 10 Conference (2009), pp. 1–4. https://doi.org/10.1109/TENCON.2009.5396237

Intelligent Traffic Light System Using YOLO K. Sai Venu Prathap, D. Srinivasulu Reddy, S. Madhusudhan, and S. Mohammed Mazharr

1 Introduction A major problem in day-to-day life is increase in growth of vehicle numbers in the world. This issue directly leads to traffic congestion. The resulting issue is mainly because of the present dynamic nature of the traffic system. This problem has an impact on many sectors of society, including health damages, traffic accidents, economic growth, greenhouse gas emission and so on [1]. Despite the traffic congestion, the dynamic traffic system has a fixed period of time allotted for each lane which results in accidents, additional fuel consumption, wastage of time. For instance, if there is an emergency, the vehicle or the ambulance is stuck in a congested road, it may cause a delay in reaching the hospital which will be a hazardous situation to the patient. The traffic density is computed by the background subtraction (BS) and blob analysis techniques [2]. BS has a shortcoming that it merges the detected vehicles and bounding boxes [3]. In 2005, Dalal introduced a HoG-based model for the pedestrian detection [4]. The traffic congestion is mostly because of the signaling process which has fixed time slots for signal indication even if there are no vehicles present. This issue can be solved by using a technique that is proposed to control the traffic signaling by using image processing with YOLO algorithm. In this paper, You Look Only Once (YOLO) V4 algorithm [5] based on a smart traffic control system is introduced [6]. This proposed system has an efficient signal decision making algorithm than the existing systems. Traffic control can be done in multiple approaches. In 2014,

K. Sai Venu Prathap (B) · D. Srinivasulu Reddy Sri Venkateswara College of Engineering, Tirupati, India e-mail: [email protected] S. Madhusudhan · S. Mohammed Mazharr Department of ECE, Sri Venkateswara College of Engineering, Tirupati, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. K. Mandal et al. (eds.), Innovations in Signal Processing and Embedded Systems, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-19-1669-4_9

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Conselho Nacional de [7] said that traffic can be controlled in 2 ways such as one is fixed time slot-based controller and second one is the acted time slot-based controller. 1. 2.

Fixed time slot system: Time does not vary with the traffic density Acted time slot traffic system: the green light will glow based on the traffic density Conselhonacional de in 2014 classified the traffic control mode in to two types.

1. 2.

Local traffic control Centralized traffic control

The vehicle density or flow can be found with the sensors such as infrared sensors and ultrasonic sensors and high end cameras [8–10]. Problem Statement: Traffic Signals are allocated with a particular period of time and according to that time the signaling system works, however the issue with this system is green light will turn on according to the fixed time slot even if the lane does not have any vehicles that causes a time wasting to other traffic lanes. Proposed System: Our proposed system is a hybrid technique that was developed by the image processing [11] and YOLO algorithm. Phase 1: image process to cloud (Real-time database) the camera is used to capture and compute the real-time traffic density and vehicle count that is send to cloud Phase 2: cloud to hardware.

2 Work Flow This embedded system was developed by both the software phase and hardware module. The software and hardware requirements are listed below: • • • • • • • • •

Power Supply (5 V) Arduino (MCU) USB Camera (To Capture Real-time Video) Python 3.8 (Programming) OpenCV 3.4.9 Anaconda Node MCU Tensorflow GPU

Flow chart of the video processing: Figure 1 depicts the workflow of the YOLO algorithm, object detection and vehicle count along with the cloud functioning and it is explained in step by step as follows 1. 2. 3.

In this phase, camera is activated to capture the real-time traffic video. Video input is given to the system to carry on further computation. Frames are extracted from the video and suppresses the noise and other things except vehicle in the frame.

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Fig. 1 Proposed system workflow

4. 5. 6.

The YOLO V4 is applied in real-time for the object detection and count. It is one of the most effective object detection algorithms. Object detection [12] is done by number of frames per second extracted from the video. Detected vehicles count is sent to the cloud in real-time database which is originated from Google Firebase.

YOLO network is a novel approach for object detection [13]. It is a real-time object detection algorithm that works with the convolutional neutral network (CNN) [14]. Such procedure uses a single neutral network to the individual image and divides the entire image into sub-images and calculates the bounding boxes along with the probabilities. Figure 2 depicts the Architecture of CNN for the YOLO network. This architecture was propelled by the model of GoogleNet and it is utilized for the image classification. The YOLO, real-time object detection has a critical capability of vehicle detection. The entire detection pipeline technique is a single network process and this can be enhanced directly from end-to-end on object detection performance. Our proposed unified architecture is enormously fast and efficient. Our base YOLO model takes an input video extracted based on a YOLO config file by using trained YOLO weighted model. The text file contains customized class names which is used to differentiate the vehicles. The particular model is trained on Common Objects in Context (COCO) database. The required installing dependencies for YOLO are

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Fig. 2 Architecture of CNN

• • • • • •

Python 3.8 Numpy Keras Tensorflow ≥ 2.4.0 CUDA v11 NVIDIA GPU Drivers

3 Database and Experimental Setup The Fire base is a real-time and cloud-hosted database [15]. It stores the count of the extracted frames as JSON and synchronized in real-time to every associated signal and processes the data. All of the signals shares a common real-time database instance and obtains updates with the latest data. The microcontroller retrieves the data from the cloud (real-time database) and allocates the signal to the individual lane. How data is structured (it is a JSON tree): Entire Firebase Real-time Database information is saved as JSON objects. One can consider a real-time database record as a cloud-hosted JSON tree [16]. It does not have any records or tables. Whenever a data or information is further added to the JSON tree, a node is formed in the present JSON structure with a specific related key. Figure 3 depicts the cloud fire base that has three lanes with a vehicle count in real-time traffic. The cloud fire base stores the data of video frames extracted by the system. PUT Requests: The data can be updated by the conditional request. By issuing a PUT request to its URL endpoint the system can update the data. Reading Data with GET: We can read data from our Firebase database by issuing a GET request to its URL endpoint.

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Fig. 3 Firebase (real-time database)

1. 2. 3. 4. 5.

6. 7.

8.

The data from cloud (firebase) fetched to microcontroller [17] (NodeMCU ESP8266). Based on the vehicle count in the cloud, microcontroller (NodeMCU ESP8266) individually allocates specific period to that specified signal lane. Microcontroller retrieves the data from the cloud (real-time database). Turn on the proposed system. This step applies a condition: (i) If vehicles detected is greater than zero then the condition is true. It proceeds with the total vehicle count and then allocates the signal to the lane based on the count. (ii) If vehicles detected is less than zero then the condition is false. Then it moves to the next lane and repeats from step 3. If the vehicles detected by the system is equal to zero then it will move to next lane. Once the count value is passed to the signal then it turns green for a particular period of time as of the algorithm, depending on the count value of the vehicles detected in that particular lane. 8Stop

NodeMCU ESP8266 [18, 19] is originated from an Arduino family. It is a 30 pin IC and it consists of 17 GPIO(0–16) pins. NodeMCU is an open source firmware developed embedded board specially targeted for IoT [20] dependent applications. NodeMCU is embedded board specially developed for the IoT applications. It has an open source firmware that consists of ESP8266 Wi-Fi SoC and ESP-12 hardware module. It also has a version of ESP 12E module and ESP 8266 IC that has TensilicaXtensa. NodeMCU is a LX106 32-bit RISC microprocessor that runs the RTOS with the variable clock frequency of 80–160 MHz (Figs. 4 and 5). The RAM and flash memory of Node MCU is 128 KB and 4 MB, respectively, and this are used to store the data and programs. Node MCU has a unique status in IoT platform due to its unique features like Bluetooth, Wi-Fi (in-built) and operating features. The microcontroller is classified into three lanes. Each digital pin (GPIO pins) is used for signal indication (RYG). It fetches and retrieves the data from cloud firebase with the help of ESP8266 Wi-Fi module which is integrated on a SoC.

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Fig. 4 Flow chart of the working model

Figure 6 represents the system prototype setup with hardware. Based on the data base the signal is allocated to the respective lane for a bounded period of time with the Hardware.

4 Experimental Results Given system uses YOLO v4 and OpenCV2 image processing. The programming language python v3.8.5 is used for image processing purpose. This technique that is used for frame extraction or real-time object detection is YOLO algorithm. After execution, the count fetched from the cloud firebase is dumped into the microcontroller. So, it is easy to allocate the signal to the lane. YOLO is used for more accuracy by using TensorFlow with GPU. We used OpenCV and python, due to which the project cost is minimized effectively. From the above, Fig. 7 represents the real-time input video frame given to the system, Fig. 8 represents the frame extraction and the count of the vehicles that are detected by the system, Fig. 3 represents the cloud firebase which stores the data

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Fig. 5 Microcontroller (NodeMCU ESP8266)

Fig. 6 Setup of the experiment

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Fig. 7 Video frame for object detection of lane-1

Fig. 8 Object detection using YOLO V4 frame work

of frames extracted by the system. From the above, Fig. 5 represents Microcontroller (NodeMCU ESP8266). The given system takes the input as a video by using numerous mathematical expressions and algorithms the image processing is done. With the help of YOLO algorithm, the accurate frame extraction is obtained based on the number of frames per second, the count value is sent to the cloud firebase. Based on the database, the signal is allocated to the specified lane for specific period

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of time with the help of microcontroller. In our experiment, the proposed algorithm was computing the vehicle count and classifications in various traffic densities that varies from low to high. The YOLO algorithm vehicle count is compared with the manual count (ground truth) values. We calculate the F-score, precision and recall metrics [21] in three distinct frames from a traffic video in real-time. The precision is stated as the ratio of number of correctly detected vehicle count to the number of ground truth vehicle count Precision(Pr) =

No. of Correctly Detected Vehicle count No. of Groundtruth Vehicle Count

(1)

The recall is stated as the ratio of the number of correctly detected vehicles to the number of detected vehicles. Recall(Re) =

No. of Correctly Detected Vehicles No. of detected Vehicles

(2)

F-score is used for imbalanced classification of recall and precision and given as F - Score =

2 ∗ Pr ∗ Re Pr + Re

(3)

Figure 7 is the input video frame of lane-1 and it has ground truth value of vehicle count-4, YOLOv4 detected the vehicle count is 4, it does not have any missing vehicle detection and overlap bounding box that result a zero error. The precision and recall values are 100% for this frame with a F-score of 1 as given in Table 1. Figure 9 denotes this video frame of lane 2 with ground truth value vehicle count of 12 and YOLOv4 detected count as 9 and it has some undetected vehicles with an error 3. The precision and recall values are 75 and 100%, respectively, with a F-score is 0.85. Figure 11 denotes video frame of lane 3 that has ground truth value Table 1 Detection parameters Frame

Ground truth vehicle count

No. of vehicles detected by YOLO V4

Multiple detection/missing/error

Precision%

Recall%

F-score

Video frame of Lane-1

4

4

0/0/0

100

100

1

Video frame of Lane-2

12

9

3/0/3

75

100

0.85

Video frame of Lane-2

3

4

0/1/1

66.66

75

0.70

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Fig. 9 Video frame of lane-2

of vehicle count 3 and YOLOv4 detected 4 vehicles, with one overlap bounding box as shown in Table 1. The precision and recall value are 66% and 75% respectively, with an F-score of 0.70.

5 Conclusion Our proposed traffic system counted and detected the vehicles in a mixed traffic condition. YOLO algorithm is exploited completed to detect, classify and count the vehicles in real-time traffic. The selected video frames have traffic density that varies from high to low in three lanes. Real-time vehicle count is stored in cloud for the better control of the traffic lanes effectively to avoid lapse of time and that allows Fig. 10. Object not Detected emergency vehicles. Lane-1 has high precision and recall metrics with a F-score of 1 and lane-2 has moderate precision and good recall metrics with a F-score of 0.85 and lane-3 video frame has an overlap bounding box with moderate precision and recall metrics with a F-score of 0.70. As of our critical survey YOLO V4 algorithm has better precision and recall values than the yolo V3 algorithm. So, in real-time traffic control system our proposed system is best to implement. This experimental work is extended for more lanes. In future, lane detection can detect for the better detection and classification of the vehicles (Figs. 11 and 12).

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Fig. 10 Object not detected

Fig. 11 Video frame of lane-3

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Fig. 12 Object detection with overlap bounding box

References 1. B. Boriboonsomsin, M. Barth, Real-world CO2 impacts of traffic congestion transportation research record, no. 951 (2008), p. 123 2. C. Pornpanomchai, T. Liamsanguam, V. Vannakosit, Vehicle detection and counting from a video frame, in International Conference on In Wavelet Analysis and Pattern Recognition. ICWAPR? 08, vol. 1 (2008), pp. 356–361 3. C.S. Asha, A.V. Narasimhadhan, Vehicle counting for traffic management system using YOLO and correlation filter, in 2018 IEEE International Conference on Electronics, Computing and Communication Technologies (2018), pp. 1–6. https://doi.org/10.1109/CONECCT.2018.848 2380 4. Dala & Triggs, Histogram of oriented gradients for human detection, in CVPR-IEEE Computer Society Conference, vol. 1 (2005), pp. 886–893 5. P. Mahto, P. Garg, P. Seth, J. Panda, Refining Yolov4 for vehicle detection. Int. J. Adv. Res. Eng. Technol. (IJARET) 11, 409–419 (2020) 6. K. Zaatouri, T. Ezzedine, A self-adaptive traffic light control system based on YOLO published, Computer Science, in International Conference on Internet of Things, Embedded Systems and Communications (IINTEC) (2018) 7. Conselho Nacional de Transito, SinalizacaoSemaforica. Manual Brasileiro de Sinalizacao de Transito (2014) 8. L. Qi, M. Zhou, W. Luan, Emergency traffic-light control system design for intersections subject to accidents. IEEE Trans. Intell. Transp. Syst. 17, 170–183 (2016) 9. Q. Wang, J. Zheng, H. Xu, B. Xu, R. Chen, Roadside magnetic sensor system for vehicle detection in urban environments. IEEE Trans. Intell. Transp. Syst. 19(5), 1365–1374 (2018) 10. L.P.J. Rani, M.K. Kumar, K.S. Naresh, S. Vignesh, Dynamic traffic management system using infrared (IR) and Internet of Things (IoT), in 3rd IEEE International Conference on Science Technology, Engineering and Management (2018), pp. 353–357 11. R. de Charette, F. Nashashibi, Traffic light recognition using image processing compared to learning processes. IEEE/RSJ International Conference on Intelligent Robots and Systems 2009, 333–338 (2009). https://doi.org/10.1109/IROS.2009.5353941

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12. L. Du, W. Chen, S. Fu, H. Kong, C. Li, Z. Pei, Real-time detection of vehicle and traffic light for intelligent and connected vehicles based on YOLOv3 network, in 2019 5th International Conference on Transportation Information and Safety (ICTIS) (2019), pp. 388–392. https:// doi.org/10.1109/ICTIS.2019.8883761 13. Redmon, Divvala, Farhadi, YOLO: Unified real-time object detection, in IEEE Conference CVPR (2016), pp. 779–788 14. J. Redmon, S. Divvala, R. Girshick, A. Farhadi, You only look once unified, real-time object detection, in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV (2016). https://doi.org/10.1109/CVPR.2016.91 15. M.R. Rahman, S. Akhter, Realtime bi-directional traffic management support system with gps and websocket, in 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing (2015), pp. 959–964 16. X. Yu, F. Sun and X. Cheng, Intelligent urban traffic management system based on cloud computing and internet of things, in International Conference on Computer Science & Service System (IEEE, 2012), pp. 2169–2172 17. B.K. Perumalla, M.S. Babu, An intelligent traffic and vehicle monitoring system using internet of things architecture. Int. J. Sci. Res. 5(55), 213–216 (2016) 18. G.S. Miratunnisa, A.H.S. Budi, Traffic light monitoring system based on NodeMCU using Internet of Things. IOP Conf. Ser. Mater. Sci. Eng. 384, 012024 (2018). https://doi.org/10. 1088/1757-899x/384/1/012024 19. P. Specifications, Breadboard power supply specifications, no. Dc, pp. 192 (2014) 20. H.O. Al-Sakran, Intelligent traffic information systembased on the integration of Internet of Things and Agent technology. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 6(2), 37–43 (2015) 21. C. Wolf, J.M. Jolion, Object count/area graphs for the evaluation of object detection and segmentation algorithms. Int. J. Doc. Anal. Recogn. 8(4), 280–296 (2006)

An Efficient Implementation of Programmable IIR Filter for FPGA L. Babitha, U. Somanaidu, CH. Poojitha, K. Niharika, V. Mahesh, and Vallabhuni Vijay

1 Introduction The system which has an impulse response for a limitless amount of time is observed as infinite impulse response (IIR) filters. It is one of the filters which is digital which consists of adders, delay, MCU, SoC, and processors [1]. Presently, these filters are widely being used for diverse applications including communications which includes messaging, signal processing of the video and audio, etc. The digital signal processing systems which are modern have a wide number of usages for these filters [1]. When compared to the FIR filters, these filters have fast computation speed, and the complexity of these filters is also less. These filters work in real time and are faster compared to the others. In IIR filter, the output depends on past inputs, present inputs, and the previous outputs. The transfer function can be written as P bi z −i Y (z) =  Qi=0 (1) H (z) = −j X (z) j=0 a j z It is unstable and consists of both poles and zeroes. It depends on the previous filter, and it is called recursive filter. It does not have a linear phase character. IIR filters are designed using analog filters such as Butterworth filter, Chebyshev filter, and also the elliptical filter [2]. L. Babitha · U. Somanaidu · CH. Poojitha · K. Niharika · V. Mahesh · V. Vijay (B) Institute of Aeronautical Engineering, Dundigal, Hyderabad 500043, India e-mail: [email protected] L. Babitha e-mail: [email protected] U. Somanaidu e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. K. Mandal et al. (eds.), Innovations in Signal Processing and Embedded Systems, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-19-1669-4_10

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In order to design IIR filter, we use methods such as impulse invariant method, matched Z-transform, and bilinear transformation. These methods are used to design low-pass IIR filters. In order to design high-order IIR filter, filter stages of all pass filter, the delays, and the masking filter methods. The frequency components of the input signals are allowed by the all pass filter which does not have any attenuation, and the all pass filter will also provide different frequency of predictive phase shifts of the input signal. All pass filters are also named as delay equalizers or phase correctors [3]. The amplitude of an all pass filter is unity. Phase changes from 0 to 360°. Applications such as communications, when the transmission lines send the signals from one point to another point they undergo phase change. The all pass filters will be utilized to compensate the phase change. All pass filters are generally used for altering the phase response of IIR filter without effecting magnitude response. The IIR filters which are of all pass contain the properties which are beneficial which includes the number of multiplications which will be reduced, the characteristic of the phase in pass band which is highly linear and the group delay which will be cut down [3]. For example, we can get the IIR filter which is of high pass from the low-pass IIR filter [4–11]. Hl p(z) =

1 ∗ (E 0 (z) + E 1 (z)) 2

(2)

Hh p(z) =

1 ∗ (E 0 (z) − E 1 (z)) 2

(3)

To implement the IIR filter based on all pass filter, it is designed in MATLAB [2]. In this paper, the implementation of is done by using a very high-speed hardware description language simply known as VHDL. For FPGA, the description for the application specific digital structure can be done by using VHDL [1]. It has very vast library packages. This will be used in every step of the process. The processing of the data for the mathematical equations is very effective using the VHDL language and also for calculating the frequency response and for the processing of numbers. The searching for the coefficients and filtering the characteristics of the filter becomes faster and easier using this. When comparing to the other tools which are used for design, the VHDL provides a lot of features, the implementation will be featured in the FPGA, and the optimization will be more enhancing [2]. There are many advantages to the VHDL language. Some of them are the portability which allows the description of the device to be used on other tools, the flexibility which allows the description of the code of complex logic, it is independent of any device which means that the code can be run on any device irrespective of the operating system, and also it is very time efficient.

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1.1 Introduction to FPGA The IIR filters have been used widely and have many applications for their flexibility, and the cost of the filter and speed is variably suppressed when it comes to highorder filters [2]. So FPGA is implemented to achieve the results that we want to get. The field programmable gate array which can also be referred to as FPGA. This is one of the several styles of design of the VLSI technology. These contain a huge number of the logic gates and the interconnects which are programmable. The major applications of these are in wireless communication, medicine, electronic devices, etc. For the applications which are of the type of low volume, they provide prototyping which is considerably very fast and also a very effective designing of the chip. These provide the sampling rate which is variably higher than those of the older versions of the DSP chips. In this, the device’s configuration can also be altered according to our use. In this, the duration of the design is shorter which gives an advantage [1]. The FPGA will result in many advantages to the filter and also makes the implementation easier and is mostly used to lessen the overall cost of the filter. In this, the resources for the internal logic are various the configurations for applications which give high performance are magnificent. They type of the filter which is IIR filter taken in the FPGA will be concatenated. The IIR filter which is implemented using FPGA will give high throughput with efficient utilization. In this, FPGA a set of coefficients will be searched for limitations of the frequency response which will be used as base [12]. The filter will be concatenated VHDL or Verilog is used to describe the model of the coefficients which are built-in which is supported by the FPGA. In several applications which include communications which require high-speed FPGA is the only resolve for the IIR filter [12].

2 Proposed Work IIR filter is also developed by frequency response techniques. It is used to design arbitrary-band filter and narrow band filter with different specifications. Each of the delay will be changed by “m” delays as it is the basic principle of the frequency masking [3]. It is retrieved by connecting the periodic model filter and masking filter. FRM approach is mostly applicable for modeling the narrow band and arbitrary-band filters featuring with sharp filters with tiny word length effects [1]. Series connection of periodic model filter G(nm ) and masking filter(S(n)) [12]. K (n) = G(n m ) · S(n)

(4)

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Fig. 1 All pass implementations of low-pass IIR filters

Fig. 2 All pass implementations of high-pass IIR filters

The images produced by the G(nm ) are eliminated by masking filter. G(nm ) is obtained by G(n). This G(n) is obtained from all pass functions where G(n) = Go(n) + G1(n)/2

(5)

Gc(n) = Go(n) − G1(n)/2

(6)

As the above equations state the low-pass filter model for the G(n) and the other equation states the high-pass complementary model filter for the Gc(n). From the above equations, G(n) is the function of the all pass filter and Gc(n) is complementary all pass function, and diagrams will be same as Figs. 1 and 2. The overall filter of the transition band will be N number of times smaller when compared to the model filter. S(n) will be half band FIR filter which is a masked filter which helps in masking [13] (Fig. 3). It is the magnitude response of the arbitrary bandwidth IIR filter. The complementary pair should satisfy the property is |G(n) + Gc(n)| = 1

(7)

The steps to design are a.

b. c.

Obtain the half band filter design using MATLAB default code (buffer). Half band IIR filter is used to achieve fastness in computation, consuming of less power and miniaturization. Find and display the coefficients of all pass branches. Define all pass branches and display the frequency response using freqz

Fig. 3 Block diagram for masking approach

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Fig. 4 IIR filter magnitude response

d. e. f.

Form and compute frequency response of the equations G (n) and Gc(n). Replace the delays with m and form masked FIR filters (Fo(n) and F1(n)) which are used to remove the unwanted spectra. Compute frequency response of H (n) = G(n m ) ∗ Fo(n) + Gc(n m ) ∗ F1(n)

(8)

When in contrast with the FIR filters, the IIR filters have minimum complexity, and the effectiveness of the filtering will be greater. The usage of the FPGA system is limited and is less as its throughput is reduced and has more sensitivity due to rounding of the coefficient. Due to the path length which is critical, the speed of the filter is limited to a certain amount (Fig. 4). The algorithm of the IIR filter is visualized by the synchronous data flow which consists of coefficient multipliers, delays of the registers, and the circles which are of the multipliers and adders. It consists of 4 multiplier units but only the adder and the multiplier have the crucial path [13]. The clock frequency which is maximum is very much larger in the other signal flow graphs compared to the stages of all pass, and the pipelining is maximum for the signal flow graph (Fig. 5). In order to reduce the critical path, the multiplication to coefficient Ci in the above diagram should have minimized delay. We need to find out the Ci values. Signal flow graphs optimization techniques like retiming, pipelining, and folding are not able to minimize the delay [14]. The integration of the filter which used the multiplied delay method, and the masked filter is a complex optimization method which cannot reduce the delay [1]. By replacing the multipliers which are of hardware to a different address, we can improve the functionalities of the IIR filter in FPGA and also make it run a lot faster. In order to limit the problem, a Verilog code is programmed based on signal flow graph of 5th order low-pass filter. Through this, we can easily implement the IIR filter in FPGA for a better pace. In order to limit the problem, a Verilog code is

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Fig. 5 SDF for low-pass filter of order N = 5

programmed based on signal flow graph of 5th order low-pass filter. Through this, we can easily implement the IIR filter in FPGA for a better pace. Pipelining is one of the techniques which helps in reducing the crucial path between the input and output. In this process, it computes the time carried by the crucial path, time carried by the adders and the multipliers. The path time must be greater than the addition of twice of adders and multipliers. Based upon that latches are introduced to reduce the delay.

3 Simulation Result Figure 6 shows the schematic view of the filter which consists of the inputs and the outputs and the reset and the clock. This is the RTL design acquired from VHDL simulator. In this filter, the number of inputs and outputs are the same. In this filter, the inputs are of 31, and the outputs are of 31. Additional to the input and the output, the filter consists of clock and reset (Fig. 7). Fig. 6 Schematic view of IIR filter

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Fig. 7 Waveforms of IIR filter

4 Comparative Analysis The table states about the filter attribute which are created based on the performance of proposed method and existing methods. Existing method is implementation of IIR using all pass branches and masked approaches. Proposed method is implementation of IIR based on SDF and pipelining. It states about the attributes regarding the filter which are created based on the performance of filter. In the above table, we have computation rate, critical path, and the complexity of the IIR filter. As we can see from the table, the computation rate increases by a variably high amount which makes the working of the filter faster, and the functionality of the filter will be very high and also the computations which are very complex can be done much faster and easier, and the consumption of the power decreases which makes the device to work longer with no interruption and the main important feature, and the complexity of the filter is reduced by a great amount which is the main purpose of this paper (Table 1).

5 Conclusion In this paper, it is displayed that the critical path is recovered. An IIR filter is programmed for efficient implementation in FPGA. The high-quality IIR filters can be derived from all pass sections and masking methods. We can gain the characteristics such as less sensitivity of the coefficients, minimal consumption of power, and faster speed of computation when masking approach is performed. The problem of mapping data flow graphs (DFG) of infinite impulse response (IIR) filtering algorithms into application specific structure is considered. Methods of optimization of DFGs are considered for the purpose of finding IIR filter structures with the high throughput and hardware utilization. Optimization method is proposed which takes into account of the structural properties of FPGA, minimize its hardware volume, and provide the designing pipelined structures with high-clock frequency.

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Table 1 Analysis of methods Methods

Critical path

Rate of operation

Complexity

Implementation of IIR Critical path is Due to increase in using all pass branches increased. It is caused critical path, the speed and masked due to the high delay factor is effected approaches

In this method, we need to select the no. of stages, u or m factor. It is not suitable to implement IIR filter beyond order of 10

Implementation of programmable IIR based on SDF and pipelining

There is no need to choose the no. of stages and m factor to construct IIR filter, so there is less complexity

Critical path is reduced by using the pipelining

The rate of operation is increased due to decrease of maximum delay between the input and output

References 1. A. Sergiyenko, A. Serhienko, VHDL generation of optimized IIR filters, in IEEE 2nd Ukraine Conference on Electrical and Computer Engineering, UKRCON, pp. 1171–1174 (2019) 2. S.K. Mitra, K. Hirano, Digital all-pass networks, in IEEE Trans. Circuits Syst. 5, CAS-21, pp. 688–700 (1974) 3. D. Datta, H.S. Dutta, High performance IIR filters implementation on FPGA. J. Electr. Syst. Inf. Technol. 8(1), 1–9 (2021) 4. N.K. Gugilla, C. S. Dudha, Synthesis, in Designing with Xilinx FPGA using Vivado, ed. by S. Churiwala (Springer, 2017), pp. 97–110 5. V.I. Markova, J. Yli-Kaakinen, T. Saramaeki, An algorithm for the design of multipliers IIR filters as a parallel connection of two all pass filters, in IEEE Asia Pacific Conference on Circuits and Systems, APPCCAS (2006), pp. 744–747 6. R. Kaur, S.M. Patterh, S.J. Dillon, Real coded genetic algorithm for design of IIR digital filter with conflicting objectives. Math. In-Sci. 8(5), 2635–2644 (2014) 7. C.S. Pittala, J. Sravana, G. Ajitha, P. Saritha, K. Mohammad, V. Vijay, S.C. Venkateswarlu, R.V. Rajeev, Novel methodology to validate DUTs using single access structure, in 5th International Conference on Electronics, Materials Engineering and Nano-Technology (IEMENTech 2021), Kolkata, India, 24–25 Sept 2021 8. V. Vallabhuni, C. Kancharapu, T.S. Jaideep, D.R.K. Koushik, B.S. Venumadhav, V. Rajeev Ratna, Design of optimum multiplexer in quantum-dot cellular automata, in International Conference on Innovative Computing, Intelligent Communication and Smart Electrical systems (ICSES-2021), Chennai, India, 24–25 Sept 2021 9. V. Vallabhuni, C.V. Sai Kumar Reddy, P. Chandra Shaker, V. Rajeev Ratna, M. Saritha, M. Lavanya, S. China Venkateswarlu, M. Sreevani, ECG performance validation using operational transconductance amplifier with bias current. Int. J. Syst. Assur. Eng. Manag. ISSN: 0975-6809 10. S. Swathi, S. Sushma, C. Devi Supraja, V. Bindusree, L. Babitha, V. Vallabhun, A hierarchical image matting model for blood vessel segmentation in retinal images. Int. J. Syst. Assur. Eng. Manag. (2021). ISSN: 0975-6809 11. R.R. Vallabhuni, M. Saritha, S. Chikkapally, V. Vijay, C.S. Pittala, S. Shaik, Universal Shift register designed at low supply voltages in 15 nm CNTFET using multiplexer, in Advanced Techniques for IoT Applications. EAIT 2021. Lecture Notes in Networks and Systems, ed. by J.K. D. De Mandal, vol. 292 (Springer, Singapore, 2022). https://doi.org/10.1007/978-981-164435-1_58 12. M. Ljiljana, Multirate Filtering For Digital Signal Processing Matlab Applications Book (University of Belgrade, Serbia, 2009), pp. 228–239

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13. M. Ljiljana, Multitrate Filtering For Digital Signal Processing Matlab Applications Book University of Belgrade, Vol. 8 (2009), pp. 259–271 14. M. Ljiljana, Multirate Filtering For Digital Signal Processing Matlab Applications Book (University of Belgrade, Serbia, 2009), pp. 136–168

ASIC Implementation of Division Circuit Using Reversible Logic Gates Applicable in ALUs K. C. Koteswaramma, Ande Shreya, N. Harsha Vardhan, Kantem Tarun, S. China Venkateswarlu, and Vallabhuni Vijay

1 Introduction Power utilization is an essential parameter in any circuit design, especially in VLSI design. Nevertheless, the advancement of the fabrication process and higher-level integration has notably brought down the heat loss from the past few decades. Accompanying with power consumption complexity also plays a vital role [1]. The parameters complexity, power utilization, and transistors required are interlinked with each other. All those parameters are directly proportional to each other [2, 3]. To overcome the issue of power consumption and complexity, the proposed model is designed. Apart from power consumption and complexity, using reversible logic gates, there is no loss of information [2, 3]. Division in ALUs is a strenuous operation compared to remaining operations like addition, subtraction, and multiplication. Conventionally, division contains divisor, dividend, quotient, and remainder. Divisor and dividend are inputs, whereas quotient and remainder are outputs [4–6] . Dividend = Quotient ∗ Divisor + Remainder

(1)

Since the division circuit has two inputs and two outputs, the thought of using reversible gates came into the picture. Reversible gates have n number of inputs mapped with n number of outputs. Generally, there are two types of gates. They are majority gates and reversible gate. This model uses reversible gates for designing. K. C. Koteswaramma · A. Shreya · N. Harsha Vardhan · K. Tarun · S. China Venkateswarlu · V. Vijay (B) Institute of Aeronautical Engineering, Dundigal, Hyderabad 500043, India e-mail: [email protected] K. C. Koteswaramma e-mail: [email protected] S. China Venkateswarlu e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. K. Mandal et al. (eds.), Innovations in Signal Processing and Embedded Systems, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-19-1669-4_11

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The gates are reversible gates if they possess properties like equal mapping of inputs and outputs, bidirectional data flow, and multiple numbers of functionalities. Since there are many outputs, the usage of every output is problematic. So, some outputs are ignored, which are called garbage outputs. Garbage outputs use power and area. So, this is the drawback of reversible gates where the garbage outputs may produce. This model also contains garbage outputs, but the number of garbage outputs are less when compared to any other design. Every gate has its own quantum cost. The quantum cost of 1 * 1 is zero, whereas for 2 * 2 gate is one. The complexity of the circuit depends on all the parameters listed above. By considering all the parameters, the proposed design of the division circuit is designed. This model is more reliable, efficient, and less complex compared to CMOS technology.

2 Proposed Model The proposed design uses an array of reversible multiplexers, registers, universal shift register, D flip-flop, and parallel adder/subtractor. The design has a control unit that controls the operations of every component used in the division circuit. It also includes a comparator and a counter to check the completion of the process. For realizing the reversible multiplexers, registers, and shift register, reversible gates are used. Fredkin gate is used to realize reversible multiplexer, reversible parallel in parallel out shift register. Pere’s gate is used to perform AND operation. MTSG gate, TS-3 gate, and HNFG gate are used in realizing reversible parallel adder. All the gates are shown in Figs. 1, 2, 3, 4, and 5. The Fredkin gate contains five 2 * 2 gates, so its quantum cost is given as 5. The Peres gate contains four 2 * 2 gates, so its quantum cost is 4. The MTSG gate contains six 2 * 2 gates, so its quantum cost is 6. The HNFG and TS-3 gates have two 2 * 2 gates, so its quantum cost is 2 [3].

Fig. 1 a Fredkin gate, b Peres gate, c TSG gate, d quantum equivalent realization of TS-3 gate, e quantum realization of HNFG gate

ASIC Implementation of Division Circuit Using …

Fig. 2 Reversible multiplexer

Fig. 3 a Reversible register, b an m-bit reversible register

Fig. 4 a Functional block diagram of Si+. b Primary cell for reversible PIPO

Fig. 5 An m-bit reversible shift register

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In binary division, there are two types of algorithms. They are restoring algorithm and non-restoring algorithm. (1)

(2)

Restoring Division: In this approach, the value of the register is restored for every repetition. So, it is called a restoring division algorithm. Consider dividend, divisor, remainder, and counter. Initially, the value of remainder and counter is zero. First, the left shift operation is done on remainder and dividend by one. The result of the remainder is subtracted from the divisor and stored in the remainder. If MSB of the remainder is zero, then LSB of dividend is set to one and vice versa. Now, the counter is decremented. Until the value of counter zero, the loop continues to execute the steps. After the counter becomes zero, the values of quotient and remainder are obtained. Non-restoring Division: In this approach, the value of the register is not restored for every repetition. So, it is called a non-restoring division algorithm. This approach is simpler than the restoring approach. Since it involves simple addition and subtraction operation, consider dividend, divisor, remainder, and counter. Initially, the value of remainder and counter is zero. If the register is positive, then the sum of register and divisor is performed. Else, the difference of register and divisor is performed. This process continues until we get the remainder and quotient.

The division circuit requires a reversible multiplexer, reversible register, reversible universal shift register, D flip-flop, and parallel adder/subtractor. Their operation and realizations are shown below. (a)

Reversible multiplexer

The reversible multiplexer is realized using Fredkin gates. One reversible multiplexer requires one Fredkin gate. Here, the two-input multiplexer is taken. Single Fredkin gate contains one garbage output, five 2 * 2 gates so its quantum cost is 5. So, the quantum cost of m reversible multiplexers is 5 m. Figure 2 shows reversible multiplexer where S is taken as selection line, A1A2A3 … An are the garbage outputs, X1X2X3… Xn and Y 1Y 2Y 3… Yn is two inputs. If S = 0, then (Z1Z2Z3 … Zn) = (X1X2X3 … Xn) or if S = 1, then (Z1Z2Z3 … Zn) = (Y 1Y 2Y 3 … Yn). (b)

Reversible register

A reversible register is realized using Fredkin gates. Figure 3 shows the realization of the reversible register. The reversible register contains clock inputs, D flip-flops, and feedbacks. A single reversible register contains two Fredkin gates, one D flipflop, one clock input. Therefore, the m number of reversible registers requires 2 m number of Fredkin gates. Figure 3 shows reversible register having 2 m gates, (m + 1) garbage outputs, and quantum cost of 6 m. (c)

Reversible parallel in parallel out shift register

In the parallel in parallel out shift register, every data bit is packed into the PIPO at one time for every clock input. After each unit is shifted left, the data are stored in the output parallelly. The operations like right shift, left shift, loading parallelly can

ASIC Implementation of Division Circuit Using … Table 1 Functional entries for reversible shift register

Hold

123 En

Final output Si+

0

0

Si-1

0

1

Pi

1

X

Si

be performed using shift registers. These operations are controlled using two control input signals. They are hold (HOLD) input and enable (En) input. The functional entries of the reversible register are shown in Table 1. From Table 1, the data bits are shifted left when the HOLD and ENABLE signal are low. The inputs P1, P2, P3, Pn are parallelly occupy the register simultaneously with the upcoming clock pulse. O1, O2, O3, …, On are the outputs of the register. When the HOLD signal is set, then the D flip-flop updates with the present value, Table 1 can obtain the characteristic function of Si+. Si+ = HOLD. En. Pi + HOLD.En. Si − 1 + HOLD

(2)

Figure 4 shows the functional block diagram of Si+. Fredkin gates are used to realize the block diagram. Figure 5 shows the primary cell of reversible parallel in parallel out register. (d)

Reversible adder

A reversible adder contains full adders which are connected parallelly. m-MTSG and 1-TS-3 reversible gates are used to realize the reversible adder circuit. Reversible adder can be realized using MTSG gates. Here, the carry out is ignored. At the last bit position, XOR operation is performed. Reversible adder is illustrated in Fig. 6. (e)

Proposed reversible division circuit

Figure 7 shows the proposed design of the division circuit using reversibility. The design contains a 2-PIPO reversible shift register, two-input (m + 1)-bit reversible multiplexer, two-input m-bit reversible multiplexer, (m + 1)-bit parallel adder, Fredkin gates, not gate, and Pere’s gate. The signals used in this model are SELECTION INPUT signal, CLOCK signal, enable signal, HOLD signal, and control signal. The (m + 2) bits PIPO is named PIPO-1, and the m-bit PIPO is named PIPO-2. The

Fig. 6 (m + 1)-bit parallel adder

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Fig. 7 Proposed division circuit using reversibility

proposed design also contains an m-bit reversible register. This m-bit reversible register is used to store the values of the input divisor (X). Consider remainder R (rn-1, rn-2, rn-3, …. r0), quotient Q (qn-1, qn-2, qn-3, … q0), dividend D (dn-1, dn-2, dn-3, …, d0), and divisor X (xn-1, xn-2, xn-3, …. ×0). Initially, remainder R = 0, quotient Q = 0, dividend D = input value, divisor X = input value, counter C = 0, control signal CS = 0. After completing a division operation, register Q contains the value of the quotient, and register R contains the value of the remainder. SELECTION INPUT is the input given to the two-input (m + 1)-bit reversible multiplexer and two-input mbit reversible multiplexer. When the input SELECTION INPUT is high, then the

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two-input (m + 2)-bit reversible multiplexer selects the counter value as COUNTER = 0 and the remainder i (Rn-1, Rn-2, Rn-3, R0) = 0 and two-input m-bit reversible multiplexer selects dividend D (Dn-1, Dn-2, Dn-3, …, D0). The inputs of (m + 2)bits the reversible parallel in parallel out shift register (PIPO-1) and m-bit reversible parallel in parallel out shift register (PIPO-2) are clock pulse, HOLD acknowledgment signal, enable signal (En). During the clock pulse, when enable is high and the HOLD2 signal is low, then the serial input (SI) is high, and the (m + 1)-bit reversible multiplexer output data are loaded into (m + 2)-bits of the reversible parallel in parallel out shift register. When HOLD1 of an m-bit reversible parallel in parallel out shift register is low, then the m-bit reversible multiplexer output is loaded parallelly into an mbit reversible parallel in parallel out shift register. When enable signal (ENABLE) = 0, both the PIPO shift registers shifted left by one unit. Until the left shift operation is performed, the serial input (SI) value is not considered. The serial out (SO) of an m-bit reversible parallel in parallel out shift register is given as an input to serial input (SI) pin of (m + 1)-bit reversible parallel in parallel out shift register. Therefore, the value of the counter is shifted to serial input (SI). This serial input will choose the operation to be performed on the addend and the divisor. If the serial input is high, then the addition of addend and the divisor is performed. If the serial input is low, then the subtraction of addend and the divisor is performed. The addend and the divisor’s subtraction operation and addition operation are performed by (m + 1)-bit reversible parallel adder. When the input signal SELECTION INPUT is low, the next clock is taken. When the next clock pulse is given, the q0 bit position of the quotient is loaded by the complement of an MSB bit of adder. The addend is updated with (m + 1)-a bit of sum. This process continues until all the bit positions of the quotient are loaded by the complement of the most significant bit of the sum. The entire clock pulses required to load every quotient bit to register is 2m + 1. After all the quotient bits are loaded to register, the control signal (CS) is high. This control signal (CS) is given as HOLD1 input for the m-bit reversible parallel in parallel out shift register. Therefore, the quotient register Q stores the values. Pere’s gate performs the AND operation of the control signal and the counter. The output of Pere’s gate is given as the HOLD1 input signal for the m + 1 reversible parallel in parallel out shift register. If the counter is set as low, then there is no need of storing the previous value of the remainder. The HOLD2 = 1 and R store the remainder value. If the counter is set as high, then the previous value of the remainder is required. During the 2(m + 1) − 1 clock pulse, the serial input is taken as high, and the remainder is obtained by adding divisor and addend. During the next clock pulse, the count should be low and HOLD2 = 1, and the remainder is stored in remainder register R. This is the method of obtaining remainder and quotient by this model. By this method, we obtain remainder and the quotient with low delay and more accuracy. The reversible gates required, unused outputs produced, and quantum cost are calculated for the m-bit division circuit employing reversibility.

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Table 2 Characteristic table of the proposed design Reversible components

Number of reversible gates

Unused outputs

Quantum cost

Two-input (m + 1)-bit reversible multiplexer

m+1

(m + 1)

5m + 5

Two-input m-bit reversible multiplexer

m

m

5m

(m + 2)-bit reversible parallel in parallel out shift register

5m + 10

3m + 9

18m + 36

m-bit reversible parallel in parallel out shift register

5m

3(m + 1)

18m

m-bit reversible register

2m

m+1

6m

Feynman gates

3(m + 1)

0

3(m + 1)

Pere’s gate

1

2

4

NOT gate

1

0

No quantum cost

Total

18(m + 1) − 1

11(m + 1) + 7

61(m + 1) − 11

The intended model of division circuit employing m-bit reversible logic gates is performed using 18(m + 1) − 1 gates and 11(m + 1) + 7 garbage outputs having the quantum cost of 61(m + 1) − 11. The individual requirements are shown in Table 2. • Two reversible multiplexers are used in this model. One reversible multiplexer has m + 1 bit, whereas another has m-bits. For the first reversible multiplexer, an m + 1 reversible gate is used. Total m + 1 unused output is produced. Its quantum cost is calculated as 5m + 5. For the second reversible multiplexer, m reversible gates are used. It produces m unused outputs. Its quantum cost is calculated as 5m 2-input (m + 1)-bit reversible MUX, requiring m + 1 gate, m + 1 garbage output, and 5(m + 1) quantum cost. • 2-input n-bit reversible MUX, requiring n gates, n garbage outputs, and 5m quantum cost. • Two parallel in parallel out shift registers is used in the proposed model. One has m + 2 bits, whereas another PIPO has m-bits. For the first PIPO shift register, 5m + 10 gates are used. It produces 3m + 9 unused outputs. Its quantum cost is calculated as 18m + 36. For the second PIPO shift registers, 2m reversible gates are used. It produces m + 1 unused output. The quantum cost is calculated as 6 m. • m-bit reversible PIPO shift register, requiring 5m gates, 3(m + 1) garbage outputs, and 18 quantum cost. • The reversible register block diagram has Fredkin gates. 2m number of reversible gates needed for reversible register. For 2m reversible gates, m + 1 unused output is produced. Quantum cost is calculated as 6m. • For the proposed model, Feynman gates are used. The total number of gates used are 3(m + 1), and the quantum cost is equal to the number of gates used.

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• The parallel adder circuit uses m-MTSG gates and a single TS-3 gate. The total number of gates required for (m + 1) parallel adder are m + 1. The unused outputs are 2(m + 1), and the cost is 6m + 2. • Pere’s gate is implemented using four 2 * 2 gates. Its quantum cost is taken as four since all the outputs of the Peres gate are not used, so the number of unused outputs is two. • Since the NOT gate is not the reversible logic gate, it does not have quantum cost and unused outputs. (f)

Algorithm of reversible division circuit

Inputs: C = 0, A (An-1, An-2, An-3, …, A0) = 0, D (Dn-1, Dn-2, Dn-3, …, D0) = dividend and X (Xn-1, Xn-2, Xn-3, …, X0) = divisor Outputs: Q (Qn-1, Qn-2, Qn-3, …, Q0) = quotient, R (Rn-1, Rn-2, Rn-3, …, R0) = remainder and division (R, D, X) Division (R, D, X): Step 1. Initialize CONTROL SIGNAL: = Zero Step 2. Initialize SELECTION INPUT: = HIGH Step 3. Initialize COUNTER: = Zero Step 4. while (true) Step 5. ENABLE: = HIGH Step 6. if (CLOCK INPUT = 1 and ENABLE is HIGH) Step 7. if (SELECTION INPUT = HIGH) Step 8. Inputs (serial input = HIGH, register = LOW, R (rn-1, rn-2 … r0) = 0 and D (dn-1, dn-2 … d0) = dividend) are loaded into PIPO-1 and PIPO-2 Step 9. If (SELECTION INPUT: = LOW) Step 10. else Step 11. serial input = high then the outputs of addend are updated [Terminate internal if-else loop] Step 12. COUNTER: = COUNTER + 1 [Terminate external if loop] Step 13. ENABLE: = low Step 14. if (COUNTER = 2m + 1) Step 15. break; Step 16. if (CLOCK is high) Step 17. Performing the left shift operation of PIPO-1 and PIPO-2, serial input is updated with register value Step 18. COUNTER: = COUNTER + 1 Step 19. if (serial input = 0) Step 20. The two inputs of adder are taken as addend and divisor value Step 21. else Step 22. The two inputs of adder are taken as addend and divisor value [Terminate internal if-else loop]

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[Terminate external if loop] Step 23. if (adder = 0) Step 24. i0 = 1 Step 25. else Step 26. q0 = 0 [Terminate if-else loop] [Terminate while loop] Step 27. if (COUNTER = 2m + 1) Step 28. Control signal = high, HOLD1 = high, and Q stores quotient Step 29. if (logical AND operation of (register, control signal) is low) Step 30. The two inputs of adder are taken as addend and divisor value Step 31. ENABLE: = HIGH Step 32. After the enable signal is high, the remainder is restored when upcoming clock pulse is considered Step 33. Register = 0 which results HOLD2 = 1 and remainder (R) gets updated with value Step 34. else Step 35. HOLD2 = 1 and remainder (R) = remainder [End internal if-else loop] The algorithm explains step-by-step procedure of 32-bit division circuit employing reversible logic gates model. (g)

Flowchart

Figure 8 shows the flow of the commands that are considered during the design of the proposed division circuit. Initially, the remainder is set to zero, the divisor is considered as X, dividend considered as D and a counter (C). Firstly, the left shift operation is performed between the remainder and the dividend. If the remainder is less than zero, then the control goes to the statement R = R + X. If the remainder is not less than zero, then the control goes to the statement R = R − X. Then, both the statements again check the condition whether the remainder is less than zero or not. If the remainder is less than zero, then the dividend is taken as zero; else dividend is one. The counter is updated with the new value. If the counter is zero, then checks the condition whether the remainder is less than zero. If the remainder is less than zero, then the process ends. Else remainder is updated with the value of remainder and divisor.

3 Results Obtained for 32-Bit Division Circuit Employing Reversible Logic Gates Figure 9 displays the simulation results of the 32-bit division circuit employing reversible logic gates. Here, the two inputs are taken as A [31:0] and B [31:0]. They are assigned values like1345 and 236, respectively. Now when the clock signal is

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Fig. 8 Flowchart of division circuit for obtaining remainder and quotient

Fig. 9 Simulation result of 32-bit reversible division circuit

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Fig. 10 RTL schematic of 32-bit division circuit with reversible logic gates

high, reset becomes low and start becomes high. Hence, the division operation is performed, and the results are obtained as quotient DD [31:0] and remainder RR [31:0]. (a)

Register transistor-level (RTL) schematic of the 32-bit implementation of division circuit employing reversible logic gates:

Figure 10 shows the register transistor-level schematic of the 32-bit division circuit employing reversible logic gates. (b)

PIN diagram of 32-bit reversible division circuit

The PIN diagram of the 32-bit reversible division circuit has two-input pins, one clock pulse input, a reset pin, and a start pin. At the output side, there are two output pins for quotient and remainder and an ok pin. Two-input values are dividend and divisor. Figure 11 shows the PIN diagram of the 32-bit reversible division circuit. (c)

Design summary

The number of lookup tables used for the proposed n-bit reversible division circuits is only two percent of total availability. The number of availability and the number of used lookup tables are shown in Table 3. In this model, none of the lookup table flip-flop pairs are used. The number of bonded IOBs used are thirty-two percent more than the total amount of availability. All the information related to the design utilization summary is shown in Table 3. Fig. 11 PIN diagram of reversible division circuit

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Table 3 Gate information of the proposed design Logic usage

Number of elements used

Total number of availabilities

Percentage usage

Slices Look Up Tables

430

17,600

2

Fully used Look up tables flip-flop pairs

0

430

0

Bonded input output blocks

132

100

132

4 Conclusions In this manuscript, the approach to design a reversible division circuit with less power utilization, high efficiency, and less complexity is proposed. Here, we addressed the issue of power consumption, delay, and complexity. This design not only improves the parameters but also simple in understanding the procedure. The loss of information can be overcome using reversible gates. So, these gates are used in many technologies like quantum computing, nanotechnology, optical processing. This proposed model also uses these reversible gates to perform division operation. The parameters like reversible gates used, unused outputs, and quantum cost are calculated for each block, and cumulative results are also calculated. The division operation, which is applicable in arithmetic logic units, is performed using reversible gates. This technique has importance in many emerging technologies like nanotechnology, quantum computing automatics (QCA), optical computing, and low-power CMOS design. In our future work, we will develop the synthesis and layout design for the division circuit.

References 1. R. Wille, A. Chattopadhyay, R. Drechsler, From reversible logic to quantum circuits: logic design for an emerging technology (IEEE, 2016), pp. 268–274 2. C.S. Pittala, J. Sravana, G. Ajitha, P. Saritha, K. Mohammad, V. Vijay, S.C. Venkateswarlu, R.V. Rajeev, Novel methodology to validate DUTs using single access structure, in 5th International Conference on Electronics, Materials Engineering and Nano-Technology (IEMENTech 2021), Kolkata, India, Sept 24–25 (2021) 3. V. Vallabhuni, C. Kancharapu, T.S. Jaideep, D.R.K. Koushik, B.S. Venumadhav, V. Rajeev Ratna, Design of optimum multiplexer in quantum-dot cellular automata, in International Conference on Innovative Computing, Intelligent Communication and Smart Electrical systems (ICSES2021), Chennai, India, 24–25 Sept 2021 4. V. Vallabhuni, C. V. Sai Kumar Reddy, P. Chandra Shaker, V. Rajeev Ratna, M. Saritha, M. Lavanya, S. China Venkateswarlu, M. Sreevani, ECG performance validation using operational transconductance amplifier with bias current. Int. J. Syst. Assur. Eng. Manag. ISSN: 0975-6809 5. S. Swathi, S. Sushma, C. Devi Supraja, V. Bindusree, L. Babitha, V. Vallabhuni, A hierarchical image matting model for blood vessel segmentation in retinal images. Int. J. Syst. Assur. Eng. Manag. ISSN: 0975-6809

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6. R.R. Vallabhuni, M. Saritha, S. Chikkapally, V. Vijay, C.S. Pittala, S. Shaik, Universal shift register designed at low supply voltages in 15 nm CNTFET using multiplexer, in Advanced Techniques for IoT Applications. EAIT 2021. Lecture Notes in Networks and Systems, vol 292, ed. by J.K. Mandal, D. De (Springer, Singapore, 2022). https://doi.org/10.1007/978-981-164435-1_58

Operation of Asymmetric Hopfield Associative Memory: Orientation Selectivity Garimella Ramamurthy and Tata Jagannadha Swamy

1 Introduction Living machines such as homosapiens are endowed with memory capability which is almost miraculous. Routinely, homosapiens associate a name with face, location, time, etc. Researchers such as Hopfield were interested in modeling associative memories. Based on his research efforts, Hopfield succeeded in proposing an artificial neural network (ANN) which acts as an associative memory. Hopfield Associative Memory (HAM) was subjected to extensive research investigation. Hopfield Neural Network (HNN) utilizes a synaptic weight matrix which is symmetric. This symmetry constraint is utilized in proving the associated convergence theorem. In 2015, we proposed a Hopfield-like neural network in which the synaptic weight matrix is allowed to be asymmetric. In [1], the authors studied the dynamics of such an ANN, called “Asymmetric Hopfield Neural Network” (AHNN). Also, in [2], the authors proposed a logical approach for synthesis of HAM with desired stable states (constituting the eigenvectors of symmetric synaptic weight matrix W, corresponding to positive eigenvalues). Motivated by the essential results of [2], the authors were led to the investigations in the current research paper. The research paper is organized as follows. In Sect. 2, related research is reviewed. In Sect. 3, left stable/states, right stable/anti-stable states of Asymmetric Hopfield Neural Network (AHNN) are proposed. In Sect. 4, synthesis of such a AHNN is

G. Ramamurthy Department of Computer Science and Engineering, Ecole Centrale School of Engineering, Mahindra University, Bahadurpally, Hyderabad, India e-mail: [email protected] T. J. Swamy (B) Department of Electronics and Communication Engineering, Gokaraju Rangaraju Institute of Engineering and Technology, Bachupally, Hyderabad, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. K. Mandal et al. (eds.), Innovations in Signal Processing and Embedded Systems, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-19-1669-4_12

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discussed. In Sect. 5, associative memory architecture based on a combination of AHNN, associated HAM is proposed. In Sect. 6, numerical results are presented. The research paper concludes in Sect. 7.

2 State of the Art To the best of our knowledge, Hopfield-type neural network, where the synaptic weight matrix is asymmetric was first proposed in [3]. The network was coined as Asymmetric Hopfield Neural Network (AHNN). In [4], some results related to dynamics of AHNN were discovered. In [2], synthesis of Hopfield Associative memory [HAM] using {+1 −1} vectors as the eigenvectors of synaptic weight matrix, W¯ was discussed. Motivated by the results in [2], the idea of using corners of hypercube as left eigenvectors was conceived. This innovative idea led to the research reported in this research paper.

3 Left Stable States, Right Stable States: Asymmetric Hopfield Neural Network In [2], we have shown the relationship between eigenvectors of symmetric matrix, W¯ and the stable/anti-stable sates of HAM. Motivated by such relationship, we define the following concepts: A. Left stable state: A corner of hypercube U¯ , which is also a left eigenvector of asymmetric matrix, Wˆ corresponding to positive eigenvalue constitutes a left ¯ stable state (with threshold vector, T¯ ≡ 0) U¯ Wˆ = λU¯ =⇒ Sign(U¯ Wˆ ) = U¯

(1)

B. Right stable state: A corner of hypercube V¯ which is also a right eigenvector of asymmetric matrix, Wˆ corresponding to positive eigenvalue constitutes a right ¯ stable state (with threshold vector T¯ ≡ 0) Wˆ V¯ = λV¯ =⇒ Sign(Wˆ V¯ ) = V¯

(2)

C. Left anti-stable state: A corner of hypercube, Z¯ which is also a left eigenvector of asymmetric matrix, W¯ corresponding to negative eigenvalue constitutes a left anti-stable state Z¯ Wˆ = −λ Z¯

Operation of Asymmetric Hopfield Associative Memory: Orientation Selectivity

=⇒ Sign( Z¯ Wˆ ) = − Z¯

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(3)

Similarly right anti-stable state is defined. It should be noted that in the case of symmetric matrix, W¯ , the right stable/antistable states are just the stable/anti-stable states. Definition The value of quadratic form associated with a left or right stable or antistable state is called stable or anti-stable value, respectively It should be noted that the stable or anti-stable value associated with right or left stable or anti-stable state is same. For example: with U¯ being a left stable state U¯ T Wˆ U¯ = λU¯ T U¯ = N λ.

(4)

Stable value associated with right/left stable state is same. As discussed in [2], the AHNN proposed does not seem to lead to interesting convergence theorem. We now discuss the dynamics of proposed AHNN. AHNN is a homogeneous nonlinear dynamical system driven by an initial condition lying on the symmetric, unit hypercube (there is no external input). AHNN is an ANN with ‘M’ neuronal nodes, connected to each other by edges with associated synaptic weights. The synaptic weight matrix, is asymmetric. Thus, AHNN is based on a directed ¯ with ‘M’ vertices and directed edge weights being synaptic graph G¯ = (V¯ , E) weights. The state of each neuron at any time instant is +1 or −1. Thus, the state of AHNN is a {+1, −1} vector lying at the corners of symmetric unit hypercube. The state of each neuron is updated in the following manner (with column vector V¯ (0) for right side updation) vi (n + 1) = Sign

 M

 Wi j v j (n) − ti

(5)

j=1

where T¯ = [t1 t2 ...t M ] is the threshold vector. Based upon the number of nodes at which the above state updation takes place, AHNN operates in the following modes: • Serial Mode: At any given time above state updation takes place at a single node(Left side updation or right side updation) • Fully Parallel mode: At any given time state updation takes place at all the nodes (with column vector V¯ (0)) for right side updation)   V¯ (n + 1) = Sign Wˆ V¯ (n) − T¯ ,

(6)

(V¯ T (n + 1) = Sign(V¯ T (n)Wˆ − T¯ ) (with row vector V¯ T for left side updation) • Other Parallel modes of operation: At any time state updation takes place at more than one node, but strictly less than ‘M’ nodes.

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As discussed in [1], convergence theorem in the spirit of HAM does not hold. There are cycles in the state space of length strictly larger than 2, i.e., Even in the fully parallel mode of operation of AHNN, a cycle of length strictly larger than 2 can be reached.

4 Programming of a Asymmetric Hopfield Neural Network We would like to synthesize an Asymmetric Hopfield Neural Network with desired left stable/anti-stable states and desired right stable/anti-stable states. For the sake of simplicity, let us consider the case where the synaptic weight matrix, Wˆ is diagonalizable, i.e., Wˆ = T DT −1

(7)

where the columns of T are right eigenvectors of Wˆ and rows of T −1 are left eigenvectors of Wˆ . M M be the right eigenvectors of Wˆ and let {g j }i=1 be the left eigenvectors Let { f i }i=1 of Wˆ . It is clear that  1 i=j T g j f i = δi j = (8) 0 i = j Ideally, we would like to synthesize Wˆ with desired {+1, −1} valued vectors as the right/left stable/anti-stable states that are the right/left eigenvectors. In the following discussion, we explain the limitations of such synthesis. The following lemma, first proved in [2] is very useful. Lemma Let{X,Y} be corners of the unit hypercube. Then < X, Y >= X T Y = M − 2d H (X, Y ), where d H (X, Y) is the Hamming distance between the corners of hypercube {X,Y }. Proof  < X, Y >= X Y = T

{Number of components which agree in X, Y }− {Number of components in which X, Y differ}

(9)

Thus < X, Y >= X T Y = M − 2d H (X, Y ),

(10)

Corollary 1 = 1 if and only if M is odd. More generally < X, Y > is an odd number if and only if M is odd. Corollary 2 If ‘M’ is odd, there are no orthogonal vectors on M-dimensional symmetric unit hypercube.

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From the above Lemma, it follows that only one corner of unit hypercube can be chosen as eigenvectors of Wˆ which is left/right stable/anti-stable state. Further such Wˆ must be an M × M matrix with M being odd number. In the following discussion, we take a closer look at the problem of choosing certain corners of unit hypercube as DESIRED STABLE/ANTI-STABLE STATES. • Consider an M times M synaptic weight matrix which is asymmetric, i.e., Wˆ . It readily follows that X T Wˆ X = X T

  ˆ W + Wˆ T X = X T W¯ X, 2

(11)

where W¯ is a symmetric matrix. Thus the energy associated with a vector based on Wˆ is same as that associated with symmetric matrix, W¯ . In view of the above equation, an AHNN with Wˆ as the synaptic weight matrix can be naturally associated with a HAM whose synaptic weight matrix is W¯ . Suppose the left eigenvector f¯ of Wˆ (corresponding to positive eigenvalue) is not a corner of hypercube. Then, we consider the vector h¯ = Sign( f¯) as the initial condition and run the iteration as in (5) and (6) (in the serial/parallel mode) and if a left stable state is reached, we call it as a Desired left stable state/memory. Similar approach holds for right stable/anti-stable states particularly when Wˆ is an even dimensional matrix (i.e., none of the left/right eigenvectors is a corner of hypercube), all the stable/anti-stable states reached based on a iteration of the form (5) and (6), constitute the desired stable/anti-stable states (memories), (with initial condition h¯ = Sign( f¯), where f¯ is left/right eigenvector of W¯ ). Similar discussion applies when Wˆ is an odd dimensional matrix [3, 4].

5 Associative Memory Architecture: HAM, AHNN Interconnection We have seen that the Asymmetric Hopfield Neural Network (AHNN) based on Asymmetric synaptic weight matrix, Wˆ is naturally associated with Hopfield Associative Memory (HAM) based on symmetric matrix Wˆ + Wˆ W¯ = . 2

(12)

We ensure that the elements of W¯ are modified to zero values. In view of this relationship, we propose associative memory architecture with the following combination of AHNN, HAM.

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• HAM is operated in serial mode until stable state is reached, which is fed to AHNN. • Alternatively, AHNN is operated until stable state or cycle is reached in serial mode, which is fed to HAM. The above two architectures naturally motivated the following parallel connection of AHNN, HAM which is fed with the same initial condition. The HAM, AHNN are operated in the serial mode. The stable state, cycle reached represent the memory states. • Summary: The homogeneous nonlinear dynamical systems: HAM, AHNN are operated in suitable modes of operation to ensure that desired memory states are stored/retrieved.

6 Numerical Results Figures 1, 2 and 3 are the various types of architectures. Figures 4, 5, 6 and 7 represents the state updations-based dynamics with respect to matrix size. The results shows the stable or cyclic stable of the proposals with respect to various inputs.

Fig. 1 HAM followed by AHNN

Fig. 2 AHNN followed by HAM

Fig. 3 HAM and AHNN are parallel

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Fig. 4 Left side state updation-based dynamics (W is 3 × 3 matrix)

Fig. 5 Right side state updation-based dynamics (W is 3 × 3)

7 Conclusions In this research paper, the concepts: Left stable/anti-stable states are introduced. The dynamics of AHNN with asymmetric synaptic weight matrix is investigated and Orientation Selectivity is demonstrated. Also, synthesis of AHNN is discussed.

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Fig. 6 Left side state updation-based dynamics (W is 4 × 4)

Fig. 7 Right side state updation-based dynamics (W is 4 × 4)

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References 1. G.R. Murthy, M. Gabbouj, On the design of Hopfield Neural Networks: synthesis of hopfield type associative memories. International Joint Conference on Neural Networks (IJCNN), vol. 2015 (2015), pp. 1–8. https://doi.org/10.1109/IJCNN.2015.7280299 2. G. Ramamurthy, Tata Jagannadha Swamy, Novel Associative Memories based on Spherical Seperability, ICSCSP21. Advances in Intelligent Systems and Computing. ISSN: 2194-5357, MRCET, June 2021 3. G. Ramamurthy, T. Jagannadha Swamy, Y. Reddy, Programming Associative Memories, ICSCSP21, Advances in Intelligent Systems and Computing. ISSN: 2194-5357, MRCET, June 2021 4. G. Ramamurthy, T. Jagannadha Swamy, Y. Reddy, Programming Associative Memories, ICSCSP21, in Advances in Intelligent Systems and Computing. ISSN: 2194-5357, MRCET, June 2021

Intelligent Traffic Monitoring Systems Using Deep Learning Algorithms Ramavath Rani and G. Shravan Kumar

1 Introduction VIDEO Surveillance (VS) technology is used vastly in the traffic control, indoor monitoring and detection and detection of crime and violence has become an essential tool for public and private security [1]. Safety and monitoring are crucial concerns in today’s environment. One of the main units of video surveillance is traffic monitoring where safety and monitoring are crucial concerns. The critical need for effective surveillance is highlighted by recent terrorist attacks. Modern security systems are equipped with digital video recording (DVR) cameras with multiple channels. One of the main failures of this model is that it needs continuous manual supervision, which is unworkable because of such factors as human fatigue and manual labor costs. A promising technology that uses deep neural network to produce the accurate results in image processing. This work, however, faces a number of significant difficulties: (1)

(2)

How to cope with synchronization issues in AI models and DL models, as well as how to perform parallelization in challenging scenarios with unbalanced conditions. How to design a workable monitoring system model, terminals supervising, massive visualization, less latency and overall communication.

AI technology is widely used across the smart industry, such as smart transport, Internet, smart grids and video surveillance. Existential AI and profound learning algorithms, including recurrent neural network (RNN), the convolutional neural network (CNN) and the deep neural network (DNN), the artificial neural network (ANN) are primarily used for static image analysis. A self-learning system would be R. Rani (B) · G. Shravan Kumar Departement of Electronic Communication Engineering, MLR Institute of Technology, Dundigal, Hyderabad, Telangana, India G. Shravan Kumar e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. K. Mandal et al. (eds.), Innovations in Signal Processing and Embedded Systems, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-19-1669-4_13

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easy to implement and would enable large-scale monitoring. Most current surveillance systems rely on convention central AI techniques. Existing studies have put forward several AI and deep learning (DL) technology, such as CNN, ANN, LSTM, RNN, in cluster clusters and cloud-based platforms. These can be AI and video monitoring systems exploration [1]. In this paper, we focus on intelligent traffic monitoring systems based on DL technology, propose Intelligent Traffic Monitoring (ITM) system using a deep learning model, and deploy the ITM on computing environment. The following is a list of the paper’s contributions. We have developed a model for unmonitored classification of individual object and traffic monitoring. • The implementation of underlying technology of deep learning involved in various methods of video analysis. Effective time processing is also considered as an important topic to be further examined in this field. • The DL model can reduce huge overhead network communication at the edge of a network. It offers low latency, accurate solutions for video analysis. • Implement the DL model as proposed and address parallel training issues, synchronization. In order to speed up the video analysis process further, the parallel level training methods are proposed. The paper is summarized as follows. Section 2 reviews the reference mentioned in this work. Section 3 sets up a DL training model for the proposed system. Section 4 deals with the implementation of the proposed system. Section 5 gives the experimental evaluation of the system. Finally, paper is concluded in Sect. 6.

2 Related Work In AI and deep learning various methods and techniques has been proposed in video surveillance on various cloud environments for different purpose such as cost efficient, scalability and flexibility and moreover reasons. To able to handle massive video stream in different cloud technology, Li et al. in Ref. [1] proposed a deep learning model for intelligent video surveillance where he explains a technique for video surveillance on edge computing. They propose architecture based on multiple layers and a deep learning model. In Ref. [2] Zhang et al. proposed a CNN-based vehicles recognition model and a deep learning algorithm that can identify automobiles positions and vehicle properties from video screening. In Ref. [3] Li et al. proposed a bi-layer parallelized CNN architecture in a distributed computing environment. They designed a training model where they reduce the time-consuming procedure. In Ref. [4] Gao et al. proposed object classification using CNN-based fusion of vision and LIDAR in autonomous vehicle environment explains the object detection based on the CNN-based fusion on different environment.

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In Ref. [5] Shavai et al. proposed accurate classification for automatic vehicle-type recognition based on ensemble classifiers which classifies different type of vehicle using the convolutional neural network. To efficiently forecast the traffic flow the Ma et al. proposed a “Long short-term memory neural network for traffic speed prediction using microwave sensor data, In Ref. [6] Zhao et al. proposed a deep learning approach for short term traffic forecast. Reference [7] based on deep learning Langer et al. suggested various models with the names “Mpcasgd: A method for the distributed training of deep learning models in Sparks”, “A model for deep learning,” Neuro-computing, respectively.

3 Proposed DL Architecture 3.1 Establishment of Deep Learning Model Using CNN and LSTM Monitoring systems in IoT and Big Data have the features of huge surveillance terminals, a broad range of screening, and unending video streams. At the same time the demand for exact data analysis and low latency reaction are increasing in monitoring systems. By deep learning (DL) technologies, we offer a smart traffic monitoring system. We create a monitoring system on computing platform that provides flexible and scalable computing capabilities and reduces manual work. We propose parallelism DL model training on a virtual machine environment to speed up the training procedure. Hence, we establish a task level parallel training model, parallelization of convolutional layer and fully connected layer which we further by CNN and LSTM. It happens in two stages, the first stage is CNN followed by LSTM. The proposed system deals with parallel training. We use CNN and LSTM techniques, where we using CNN for vehicle classification and LSTM to forecast traffic data which are being implemented by using parallel models and parallel training level (Fig. 1). Fig. 1 Block diagram of the proposed DL model

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The above block diagram consists of sample dataset. We are obtaining the sample dataset from the traffic monitoring data. We are using that data as our input which is passed to read the data as images. In this block we are supposed to read the images and paste in the folder which further need to processed to the next stage. The next stage is CNN network. We are using the pre-trained CNN for image classification. As we are using the traffic monitoring images as data to classify all types of vehicles. As mentioned earlier, in the current model we are using CNN, which was previously trained for the vehicle classification application. We are building a CNN with a pre-trained network called Google Net pre-trained network. Image distortion, batch normalization and RMS Prop, a gradient-based optimization technique, are all implemented using the beginning models. To limit the amount of parameters, the Google Net model is essentially generated with numerous convolutional. There were 22 layers in the architecture. We can directly train the data and test it by putting in the pre-training model. The pre-trained network is used to classify and apply to a fully connected layer for detailed analysis. It can accurately classify all types of vehicles. In the final state, classified vehicles are sent to a parallel training session for precise analysis. In final state the classified vehicles is sent to parallel training for accurate analysis. The data is sorted and passed to the LSTM network where it predicts flow of traffic while using the same dataset as a CNN vehicle classification data to simulate flow of traffic.

4 Implementation of DL Model As per the proposed architecture we implement the monitoring system by using parallel training models and address the problems of low latency, accurate video analysis, workload balancing. We describe the parallel training on the computing environment for accurate data analysis and optimization of the model.

4.1 Parallel Training for DL Model A deep learning model parallel training is designed to provide two parallel training ways for the DL training model in order to speed up the DL model’s wide training process by employing durable and scalable computing resources. The primary ways to do this are model parallelism involving distributing the neural network across different processors, as well as parallel data, which involves distributing examples of training across different processors and computer updates in parallel to the neural network. A.

Parallel task level training

In present scenarios, a video stream on a DL model will often include many video analysis tasks. For example, there are a variety of deep learning algorithms for traffic

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monitoring, including CNN models for vehicle identification and LSTM models for traffic flow prediction. Consequently, we propose a parallel method of working level training for the deep leaning model. We deploy numerous deep learning models with multiple architectures (i.e., CNN and LSTM) on the edge node to implement parallelism on various data processing tasks. Each DL model is considered as a sub-model and assigned to edge nodes. In task level parallel training, CNN and LSTM techniques are used, in which we use CNN for vehicle classification and LSTM for predicting the data flow of traffic. (a)

Pre-trained CNN model

The convolutional neural network is one of the most extensive and widely used networks currently available, and it has become one of the most captivating issues of modern day. A CNN network’s basic design composed of two layers: (a) feature extractor and (b) fully connected layer (Fig. 2). The convolutional layer in CNN has a set of input datasets that are evaluated to introduce a specific feature. To reduce the feature dimensions of the CNN model, a pooling layer is applied to each feature map. There is various pooling method Fig. 2 Flow chart involving for traffic prediction using CNN

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available such as max pooling and mean pooling layer in convolutional neural network. In the present model, we are using pre-trained CNN for the application of the vehicle classification. We build a CNN by using a pre-trained network call Google Net pre-trained network. Google Net is a considered as an inception. Inception models are used for image distortion, batch normalization and RMS Prop, which is a gradient, based optimization technique. The Google Net model is fundamentally built with many convolutional so to bring down the number of parameters. Its architecture is deep and had 22 layers. By installing the pre-train model we directly train the data and can test the data. The input data set is applied to the computing window from that window the data is read and given to the pre-trained CNN model. In pre-trained CNN model the first unit is feature extractor unit, the original image is sent the convolutional layer to gain the feature map (Fig. 3). To compress characteristics and identify if the current area contains vehicles, we employ a pooling layer. In the second extraction unit, intermediary images of vehicles are transmitted to the second convolutional layer for a precisely sampled function map. Each possible vehicle can be retrieved from the second pooling layer and sent as a single input to all connected layers. Intermediate images containing vehicles are delivered to the secondary convolutional layer in the second feature extractor unit to

Fig. 3 Example figure of vehicle classification using CNN

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Fig. 4 Architecture of LSTM

obtain a fine-grained feature map. Every possible vehicle can be extracted from the second pooling layer and sent to all connected layers as a single input. Pre-trained network is used to classify and applied to the fully connected layer to get accurate analysis. It can classify all types of vehicles accurately. In final state the classified vehicles is sent to parallel training for accurate analysis. (b)

LSTM

For traffic forecasting we use the LSTM technique here. Complex, artificial tasks can be shown by recurrent network algorithms. It consists of a layer of input, current layer and output layer. LSTM is intended for applications with an ordered sequence and information from the previous sequence (Fig. 4). We have established the LSTM network to forecast traffic flow into the CNN traffic data. It overlaps a minimum time over discrete steps by performing a flow error. It is consists of three layers: (a) (b) (c)

Input, Recurrent, Output.

It is designed for the applications where the input is ordered sequence and the data from the previous sequence may not be important. Input are sorted in form of weights and biased which behaves accordingly to the inputs. We can compute the memory cell based on the input time series by computing monitoring data as a time series data. They have gates that regulate the flow of information between the nodes in such a way that it can selectively recall or forget data (Fig. 5). They have three gates accordingly to the layer. The gates are deployed to regulate the flow of cell: • A forgot gate is used to remove the less important information. This is required for the optimization of the LSTM network. • Input gate to read the values added to cell. • And an output gate to give the output values we need sequence input to predict the traffic for the gradation of frames. Sequence input is fed to the folding sequence layer, either directly to the unfolding of the sequence if not required. • If not, the convolutional layer must be crossed and then fed into the LSTM layer. Then the output layer has the predicted output.

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Fig. 5 Flow chart involving for traffic prediction using LSTM

B.

Parallelization of convolutional layer

In CNN network, the training process of convolutional layer takes more than ¾ of the total training time but only trains 0.05 percent of the data. To avoid the lag of time we parallelize convolutional operations by using matrix-based-parallel procedure to produce effective results. A monitoring frame is the input matrix for CNN sub-model. In the parallel training, we employ the parallel convolutional technique to CNN sub-models. A filter parameter to move the input matrix to Map is added to the convolutional layer to extract key functions. We obtain all the convolutional blocks of the input matrix by data partition method to the input matrix of CNN. All the convolutional blocks are convoluted in parallel sequence by shared filter matrix. Then, we can divide input matrix into several blocks in order to perform convolutional operations in parallel. After obtaining the index values of every convoluted block, we collect the content of various convolutional blocks and apply the appropriate convolutional operation in parallel manner. Figure 6 illustrates the example and describes the steps of the parallel convolutional calculation of individual CNN sub-models in an algorithm.

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Fig. 6 Example of parallel convolutional

Each feature in feature map is computed depending on the convolutional block they belong. Various different functions can access different convolutional areas in the input matrix at the same time without having to upgrade their values, and there will be no data dependency between them. C.

Fully connected layer parallelization

Fully connected layer in neural network are those which are connected to all the input layer of one layer to the every unit of the next layer. Usually the last few layers are called fully connected layer as the compute the data extracted by previous layer to get the final result. Each neuron in the fully connected layer is connected to every other neuron in the preceding layer, with each connection possessing its very own weight. As said before each and every neuron in different layer is supposed to connect to all the neuron in another layer and the output for that layer is the input for the next layer. Evidently there will be no connection between the neurons in the same layer as there are no either of data dependency or logical dependency. Hence, the computation in the same layer for neurons can be performed in parallel. In Fig. 7 example of parallel training of fully connected layer is as shown.

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Fig. 7 Example figure of fully connected layer

Table 1 Comparison of different models

Model

Accuracy (%)

Zhou et al. [8]

95.4

Hao et al. [4]

92.61

Shivai et al. [5]

88.96

Proposed model

96.23

5 Performance Evaluation The dataset used in this paper are used for both CNN and LSTM. The complete dataset is used to train the CNN for vehicle classification (Table 1). The design of CNN is improved by employing Google Net, based on the performance of the network as explained in this study. We eliminated the time-consuming procedure of manually extracting features, and the accuracy is up to 3.4% higher than traditional methods employing feature extractors (Fig. 8). This paper presents a traffic flow prediction model using long short-term memory which explored the effectiveness of the dataset obtained from the traffic monitoring data. The baseline model proposed in this paper works with deep network using LSTM network the accuracy of traffic flow prediction is as shown in Fig. 9. The produced iteration are 3100 is 36 s. The proposed system has accurate performance.

6 Conclusion The paper presents a deep learning model for traffic monitoring using CNN and LSTM models. We implemented the parallel training process to accelerate the CNN procedure and explored the task level parallel training, parallelization of convolutional layer and the fully connected layer. We implemented CNN and LSTM for vehicle classification and traffic flow prediction. The traffic prediction is vital for intelligent transportation system and for accurate traffic prediction analysis. We have used a pre-trained network called Google Net which simplify the feature extraction

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Fig. 8 Output of CNN

Fig. 9 Output of LSTM for traffic flow prediction

process and reduce time lag, resulting in low latency. The proposed model’s baseline is to use a deep learning network with a CNN. The experimental outputs of CNN has gained the maximum accuracy and followed by LSTM which gave the appropriate accuracy regarding the traffic flow prediction.

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7 Future Work In future work we can aim for faster CNN and LSTM for more accurate analysis on a large data base. We can take more data to improve network architecture for better speed of detection. We can also try to improve the network for efficient video handling on the computing network for the dynamic data migration on the edge computing for surveillance system. We can also try implementing multi-layer architecture to reduce network miscommunication.

References 1. J. Chen, K. Li, Q. Deng, K. Li, Distributed deep learning model for intelligent video surveillance where he explains a technique for video surveillance on edge computing. IEEE Trans Ind Inf (2019) 2. P. Li, Z. Chen, L. T. Yang, Q. Zhang, and M. J. Deen, Deep convolutional computation model for feature learning on big data in internet of things. IEEE Trans Ind Informat 14(2), 790–798 (2018) 3. J. Chen, K. Li, K. Bilal, X. Zhou, K. Li, P.S. Yu, In a two-layer parallel CNN training architecture in a distributed computing cluster 4. H. Gao, B. Cheng, J. Wang, K. Li, J. Zhao, D. Li, Object classification using CNN-based fusion of vision and LIDAR in autonomous vehicle environment. IEEE Trans Ind Inf 14(9), 4224–4231 (2018) 5. N. Shavai, A. Hasnat, A. Meicler, A. Nakib, Accurate classification for automatic vehicletyperecognition based on ensemble classifiers. IEEE Trans Intell Transp Syst 21(3), 1288–1297 (2019) 6. Z. Zhao, W. Chen, X. Wu, P. C. Y. Chen, and J. Liu, Lstm network: a deep learning approach for short-term traffic forecast. IET Intell Trans Sys 7. M. Langer, A. Hall, Z. He, W. Rahayu, Mpca sgd: a method for distributed training of deep learning models on spark. IEEE Trans Parallel Distrib Syst 29(11), 2540–2556 (2018) 8. L. Zhou, L. Jiang, Z. Zhu, J. Li, J. Zhang, H. Long, Vehicle classification for large-scale traffic surveillance videos using convolutional neural networks. Mach Vis Appl 28(7), 793–802 (2017)

Multi-layer Hopfield Like Neural Network Garimella Ramamurthy, Janapareddi Abhishek, and Rishi Shrinivas Seshan

1 Introduction Natural physical reality provided the motivation for scientific investigations [1]. Living Universe was subjected to intense research efforts through experimental as well as modelling approaches. Researches were interested in modelling various functions performed by biological brains leading to the research area of artificial neural networks (ANNs). These efforts in Computational Neuro-science led to Single Layer Perceptron (SLP) as well as Multi-layer Perceptron (MLP) which led to many intelligent systems. Single Layer Perceptron (SLP) was based on McCulloch-Pitts Neuron which enables linear separability of patterns. Human beings routinely associate a face with name, place, time, etc., thus, biological memories miraculously perform association function. Hopfield successfully proposed the model of associative memory based on McCulloch-Pitts Neuron, thus, Hopfield Neural Network (HNN) model was subjected to intense research efforts leading to Bi-Directional Associative Memory, Auto/Hetero associative memories. This research paper proposes an artificial neural network model of associative memory where neurons are arranged in multiple layers and the connections between the neurons are constrained.

G. Ramamurthy (B) · J. Abhishek · R. S. Seshan Department of Computer Science and Engineering,Ecole Centrale School of Engineering, Mahindra University, Bahadurpally, Hyderabad, India e-mail: [email protected] J. Abhishek e-mail: [email protected] R. S. Seshan e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. K. Mandal et al. (eds.), Innovations in Signal Processing and Embedded Systems, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-19-1669-4_14

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This research paper is organised as follows. In Sect. 2, Related research is reviewed. In Sect. 3, Detailed description of Multi-Layer Hopfield Neural Network is provided. In Sect. 4, Numerical results are provided. In Sect. 5, Conclusions are summarised.

2 Review of Related Research Literature Hopfield Neural Network (HNN) as a model of associative memory received considerable attention. For the first time, Goles and Fogelman proved the associated convergence theorem. Hopfield Associative Memory (HAM) was successfully utilised in various applications, Reference [3] provided detailed results on the synthesis of HAM. In HAM, the synaptic weighted matrix is a symmetric matrix with nonnegative diagonal elements. Detailed results were derived based on such an assumption. In this research paper, we investigate HNN whose synaptic weighted matrix is a BLOCK SYMMETRIC matrix.

2.1 Significance of Connectivity Structure In the proposed artificial neural network, connections between neurons in the same layers are symmetric. But the synaptic weight matrix, in general(between neurons in different layers) is ONLY BLOCK SYMMETRIC and need not be a SYMMETRIC MATRIX. To the best of our knowledge, we are the first ones to propose such an associative memory model.

2.2 Biological Motivation The model proposed (as a contribution to Computational neuroscience) is motivated by: (i) Homogeneous Associative Memories: At all layers the synaptic weight matrices are same within layers and across layer. (ii) Heterogeneous Associative Memories: These are layered ANN’s which are not homogeneous. In such ANN’s the connections between neurons in different layers are captured by asymmetric matrices (as in the case of Recurrent Hopfield Neural Network). Now, we derive interesting results related to the stable states of homogeneous and heterogeneous associative memories.

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Let Wˆ be the synaptic weight of matrix of homogeneous HAM between neurons in the same layer. Then the synaptic weight matrix of entire ANN is ⎤ ⎡ 11...1 ⎢1 1 . . . 1⎥ ⎥ ⎢ ⎥ ˆ ˆ ~ W = W ⊗⎢ (1) ⎢ . . . . . . ⎥ = W ⊗ J, ⎣. . . . . .⎦ 11...1 where J is a matrix of ones and ⊗ is Kronecker product.

3 Multi-layer Hopfield Associative Memory We take an artificial neural network (ANN) where the neurons are placed in multiple layers. The neurons follows the McCulloch-Pitts model. Thus, each neuron is in state {+1 or -1}. Let us consider such a ANN with ‘M’ layers and ‘N’ neurons in each layer. Neurons are connected to each other by links with associated synaptic weights. In our model, the synaptic weight matrix is a block symmetric matrix. Thus, the closed ANN starts in an initial state vector lying on the symmetric, unit hypercube, namely, ‘MN’ dimensional unit symmetric hypercube. The state is updated as follows Vi (m + 1) = Sign

⎧ MN ⎨ ⎩

Wi j V j (m) − ti

j=1

⎫ ⎬ ⎭

(2)

(Where ti is the ith neuron’s threshold value.) The modes of operation of the ANN is as follows: (i) Serial Mode: For any given instance, above state updating takes place strictly at one neuron (ii) Fully Parallel Mode: For any given instance, the above state updating takes place at all the ‘MN’ neurons. i.e. (iii) Other Parallel Modes of Operation: At any given instance, the states are updated as mentioned above, at more than one node but strictly less than all the nodes. } { V (m + 1) = Sign W · V (m) − T

(3)

Thus, such a homogeneous nonlinear dynamical system exhibits periodic dynamics. -> The Synaptic weight matrix W is block Symmetric, i.e.

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⎡

W11 W12 ⎢ W21 W22 ⎢ W =⎢ . ⎢ . ⎣ . . W M1 W M2

⎤ . . W1M . . W2M ⎥ ⎥ T .. . ⎥ ⎥ where Wi j = W ji and Wii = Wii .. . ⎦ . . WM M

(4)

Also, Wii provides synaptic weights between neurons in the i’th layer and Wi j provides Synaptic weights between neurons in the i’th layer and j’th layer. In summary, Multi-layer Hopfield neural network (based on a graph with associated synaptic weight matrix) is a homogeneous nonlinear dynamical system, evolving in time, with State Space being the symmetric, unit hypercube. The dynamics of such an ANN is necessarily periodic. More interestingly, the following Convergence theorem Summarises the dynamics. Theorem 1 Consider a Multi-layer Hopfield Neural Network specified by (W , T ), where W is the block symmetric synaptic weight matrix and T is the threshold vector, the diagonal elements of W are all non-negative. (i) The convergence to a stable state is guaranteed in serial mode of operation, whereas (ii) The neural network either converges to a stable state or a cycle of length at most 2 in fully parallel mode of operation. Proof Follows from Theorem (2) in Ref. [2]

3.1 Incremental Expansion of ANN Consider several Hopfield Associative memories which are based on symmetric synaptic weight matrices in isolation. These are connected incrementally using a Block Symmetric synaptic weight network. In this sense, our model of ANN enables emulating biological neural network which is incrementally evolved.

3.2 Modes of Operation of Multi-layer Hopfield Like Neural Network Hopfield Neural Network operates in serial, fully parallel modes of operation. In the case of Multi-layer Hopfield like neural network, we introduce the following mode of operation: (i) Layer-wise Serial Updation: The state of the ANN is updated in such a way that only the states of neurons in any one layer is updated at any given time(state of neurons in all other layers remains unchanged).

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(ii) Layer-wise Fully Parallel Mode of Operation: For any given time, the state of neurons in all the layers of the Hopfield like neural network is updated simultaneously. NOTE: When the multi-layer HNN reaches a stable state, the layer-wise states could correspond to ‘segments’ of memory state. In the dynamics of Multi-layer HNN, it can so happen that the state vector reaches a layer-wise stable state much earlier than the stable state of entire neural network(i.e. for all layers). Such a situation resembles biological memory with regard to memory state retrieval incrementally. Also, the sequence of state updation in various layers emulates the biological memory state recall. NOTE: The modes of state updation of multi-layer HNN involves parallel and distributed memory recall mechanisms. Particularly, layer-wise serial updation corresponds to partial parallel modes of operation(in the case of HAM). NOTE: The desired stable states in the synthesis of Multi-layer Hopfield like Neural Network is not as tractable unlike the case of Hopfield Associative memory. We are investigating such a synthesis problem.

3.3 Application Significance of Proposed ANN Consider a ‘Blocked Symmetric Matrix’ as the fully symmetric synaptic weight matrix, Wˆ i.e. Wˆ i j = Wˆ ji for all i, j.

Fig. 1 Representation of the block symmetric network

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In such an architecture, the diagonal blocks correspond to HAMs associated with neurons in various individual layers (local HAM’s). Each such individual layer-based HAM has associated stable states starting in any initial condition. They are compared with stable states of ANN with synaptic weight matrix which is a Blocked Symmetric matrix(fully symmetric) and the initial condition vector is obtained by concatenating the individual initial condition vectors. It is realised that the following inference holds. The final stable state reached was the concatenations of the final stable state vectors obtained individually. Similar experiment is conducted with the synaptic weight matrix being a Block Symmetric matrix (Not Fully Symmetric) (Fig. 1).

4 Numerical Results The simulated examples of both Serial Mode and Fully Parallel Mode of updating, considering a Block Symmetric Matrix is shown in brief in this section. The key factor for convergence is to have the sub-blocks of diagonal as symmetric matrices. Also, the examples where the convergence fails, if the diagonal sub-blocks are asymmetric, is shown in brief (Tables 1, 2, 3 and 4).

Table 1 Serial mode of operation of block symmetric matrix Synaptic weight matrix Initial state ⎡ ⎤ 15 −120 57 −119 ⎢−120 91 −25 3 ⎥ { } ⎢ ⎥ −1 1 1 −1 ⎢ ⎥ ⎣ 57 −119 7 −80 ⎦ −25 3 −80 17 ⎡ ⎤ 1 −2 3 7 8 9 ⎢ ⎥ ⎢ −2 4 −5 −10 11 12 ⎥ ⎢ ⎥ } ⎢ 3 −5 6 −13 14 15 ⎥ { ⎢ ⎥ ⎢ 7 8 9 16 17 −18⎥ −1 1 −1 1 −1 1 ⎢ ⎥ ⎢ ⎥ ⎣−10 11 12 17 19 20 ⎦ −13 14 15 −18 20 21

Final state { } 1 −1 1 −1

{ } −1 −1 −1 −1 −1 −1

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Table 2 Fully parallel mode of operation Synaptic weight matrix Initial State ⎡ ⎤ 1 −1 17 26 ⎢−1 44 32 63⎥ { } ⎢ ⎥ 1 −1 −1 1 ⎢ ⎥ ⎣ 17 26 5 58⎦ 32 63 58 17 ⎡

29 ⎢ ⎢ −36 ⎢ ⎢ 49 ⎢ ⎢ 62 ⎢ ⎢ ⎣ 44 −115

−36 63 −82 54 3 4

49 −82 91 −97 77 6

62 44 −115 49 51 −12

54 3 4 51 17 −13

⎤ −97 ⎥ 77 ⎥ ⎥ 6 ⎥ ⎥ −12⎥ ⎥ ⎥ −13⎦ 111

{ } −1 −1 1 1 1 1

Final state(s)

Cycle of length 2 { } between: 1 −1 1 −1 { } −1 −1 −1 1

Cycle of {length 2 } between 1 1 1 −1 1 1 { } −1 −1 1 1 1 1

Table 3 Counter examples: when the diagonal blocks are asymmetric Initial State Final state Synaptic weight matrix ⎡ ⎤ 0 −1 17 26 ⎢14 0 32 63⎥ { } ⎢ ⎥ Cycle larger than 2 reached 1 −1 −1 1 ⎢ ⎥ ⎣17 26 0 58⎦ 32 63 −99 0 ⎡ ⎤ 1 23 −46 7 8 9 ⎢ ⎥ ⎢ −2 4 −15 −10 11 12 ⎥ ⎢ ⎥ } ⎢ 3 5 6 −13 14 15 ⎥ { ⎢ ⎥ Cycle larger than 2 reached 1 −1 −1 −1 1 1 ⎢ 7 8 9 16 4 5 ⎥ ⎢ ⎥ ⎢ ⎥ ⎣−10 11 12 17 19 −2⎦ −13 14 15 −81 20 21

5 Conclusions In this research paper, a novel MULTI-LAYER Hopfield like Neural Network with interesting synaptic weight matrix is proposed. We reasoned that the network exhibits the convergence similar to that of an ordinary HOPFIELD neural network. Numerical experiments confirm the convergence behaviour.

162 Table 4 Block tri-diagonal matrix cases Synaptic weight matrix Initial State ⎡ ⎤ 1 −1 17 23 0 0 ⎢ ⎥ ⎢ 4 3 −42 12 0 0 ⎥ ⎢ ⎥ { } ⎢11 −44 1 −1 17 23 ⎥ ⎢ ⎥ 1 −1 −1 1 1 −1 ⎢ 2 27 4 ⎥ 3 −42 12 ⎥ ⎢ ⎢ ⎥ ⎣ 0 0 11 −44 1 −1⎦ 0 0 2 27 4 3 ⎡ ⎤ 5 −7 29 13 0 0 ⎢ ⎥ 0 0⎥ ⎢17 24 −44 6 ⎢ ⎥ { } ⎢11 −27 5 −7 29 13 ⎥ ⎢ ⎥ 1 −1 −1 1 1 1 ⎢33 5 17 24 −44 6 ⎥ ⎢ ⎥ ⎢ ⎥ ⎣ 0 0 11 −27 5 −7⎦ 0 0 33 5 17 24 ⎡ ⎤ 1 0 4 5 00 ⎢ ⎥ ⎢ 0 2 6 7 0 0⎥ ⎢ ⎥ { } ⎢12 13 1 0 4 5⎥ ⎢ ⎥ 1 −1 −1 1 1 −1 ⎢14 15 0 2 6 7⎥ ⎢ ⎥ ⎢ ⎥ ⎣ 0 0 12 13 1 0⎦ 0 0 14 15 0 2

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Final state

{ } −1 1 −1 −1 1 −1

{ } 1 −1 1 −1 1 1

{ } 1 −1 −1 1 1 −1

References 1. J.J. Hopfield, Neural networks and physical systems with emergent computational abilities, in Proceedings of National Academy of Sciences, USA, vol. 79 (1982), pp. 2554–2558 2. G. Rama Murthy, M. Dileep, R. Anil, Convolutional Associative Memory, in International Conference on Neural Information Processing [ICONIP 2015], Nov 2015, Turkey 3. G. Rama Murthy, V.K. Reddy, Devaki, Divya, Optimal synthesis of hopfield associative memory, in Proceedings of ICMLDS 2019, Dec 2019, ACM Digital Library

Applications of Artificial Intelligence for Autonomous Landing and Multicopter Unmanned Aerial Vehicles Design by Space Exploration Machines Chinthala Akhil, Kalyana Srinivas Kandala, Anudeep Peddi, N. Sudhakar Yadav, T. Srinivasa Rao, and I. Neelima

1 Introduction 1.1 Artificial Intelligence Artificial intelligence is the way of making a machine think and act in the way of human. These machines are trained with algorithms and make faster and accurate decision then most humans. Artificial intelligence is the big umbrella in which machine learning and deep learning are subsets of artificial intelligence. Artificial C. Akhil · T. Srinivasa Rao Automobile Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, India e-mail: [email protected] T. Srinivasa Rao e-mail: [email protected] K. S. Kandala · A. Peddi (B) Department of ECE, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, India e-mail: [email protected] K. S. Kandala e-mail: [email protected] N. Sudhakar Yadav Department of IT, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, India e-mail: [email protected] I. Neelima Department aof EEE, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. K. Mandal et al. (eds.), Innovations in Signal Processing and Embedded Systems, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-19-1669-4_15

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intelligence has now become a global economy which is applied in the every essential field. Its use in space can be traced all the way back to the 1960s. Satellites employ artificial intelligence technology such as remote control operations and automation control. While robots have been used on space missions since 1967, artificial intelligence has quite a significantly longer history, dating back to 1998 when deep space 1–a comet probe–used an AI programmed termed remote agent [1]. While remote agent was primitive in compared to today’s AI capabilities, it proved its utility by allowing for the planning and scheduling of tasks as well as identifying onboard faults. Since then, a slew of other AI-powered space exploration missions have been launched, including algorithms that give planet surface rovers more autonomy, AI for finding new planets, and AI-assisted astronauts on the International Space Station [2]. Artificial intelligence’s benefits in the field of space are self-evident. On the one hand, due to the expensive expense of space equipment and the high-risk element, manned exploration of the universe and manned exploration of the planet are becoming increasingly rare. Analyzing the image of the sensor backhaul is a big part of the space task. Artificial intelligence has also been successfully used to the International Space Station, companion star monitoring, remote telemetry, picture analysis, spatial docking, and related scientific research; on the other hand, analytical images are one of the greatest artificial intelligence techniques [3]. The field of artificial intelligence is still in its infancy. The company’s primary technologies include neural networks, machine control, expert systems, target interpretation, intelligent machines and intelligent interfaces, machine vision and photo processing voice recognition and natural language theory, unmanned vehicles, and others [4]. The most difficult aspects of autonomous landing are (i) precise measurements (or best estimates) of the landing platform and the UAV’s locations and (ii) robust trajectory following in the presence of disturbances and uncertainties.

1.2 Landing System Landing a rover, cargo and to retrieve the rockets for reusing with any damage. Landing a rover on other planets is a big challenge for humans while we are in laboratory and controlling the rover while the rover is so many miles ways from us, it takes some time while receiving the command the operator and executes it, so the rover gets hits to asteroids or gets damaged. So to overcome the problem, artificial intelligence is used by equipping the sensors and parachutes. Landing a rover in other planet and landing a helicopter differs because of conditions, speed, environment, etc. [5].

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2 Applications of Artificial Intelligence in Landing In the industrial and military sectors, there are numerous markets demands for multicopter unmanned aerial vehicles (UAVs). Such activities include aerial photography, agriculture, delivery, environmental monitoring, asset monitoring, structural inspection, surveillance and reconnaissance, search and rescue, and so on. We have many applications used in landing in different fields. Some of the main applications of landing are explained below. A.

Rover

For landing rovers, cargo and crew spacecraft artificial intelligence are used. In rover image sensors, distance calculating sensors. Distance calculating sensors are critical for descent and landing on mars and other planets [6]. The rover has rocket-assisted descent (RAD) attached to slow it down before it strikes the ground. Radar is used to determine how far away from the surface you are. When the vessel is about 2.4 km above the surface, the radar system sends a signal to the descent image motion estimation subsystem (DIMES). This camera will take three photographs of the ground every four seconds and automatically analyze them to determine the spacecraft’s horizontal speed. Spirit’s fall was aided by the launch of a new secondary propulsion system for the mars exploration rover mission. The Gusev Crater winds were strong, as expected, causing spirit to swing from side to side, preventing a safe landing. The transverse impulse rocket system (TIRS) helps the ship land more steadily by counteracting horizontal movement. The TIRS technology was not triggered since windy conditions were not forecast at Opportunity’s Meridiani Planum landing site. This implies that we should be prepared for any eventuality. To decrease the speed, parachute are also embedded in the rovers. When the parachute is required, it is opened for decreasing the speed and of the rover and after some time, the parachute is cutoff from the rover. Parachutes are widely used in crew capsule when coming from space to earth, to decrease speed while it descent. Like mars rovers explorer for different planet conditions, we need slightly different functional rovers. Landing rover on other planets is a big challenge for scientist, and this overcame by using artificial intelligence, machine learning, and sensors. Rocket boosters are wasted largely when space mission is launched. After the usage of boosters, it separates from the rocket, and it is left behind in space, or the rockets are launched in such a way that after the fuel is finished in booster, it separates from the rocket and falls in to the sea or ocean. By using sensors and parachute, the rocket is retrieved for further use. This decrease the cost used for next space exploration and material wastage. So that scientists are developing reusable rockets which decrease the wastage of time for making booster. Spacex is developed such rocket boosters which can be reused, it decreases the space e-waste and saves time and money for making rocket boosters [7]. B.

Unmanned Aerial Vehicles (UAVs)

A drone, or unmanned aerial vehicle, is a plane that does not have a human pilot, crew, or passengers. Unmanned aircraft systems incorporate a ground-based controller and

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a communications system with the UAV [8]. Unmanned aerial vehicles (UAVs) are utilized for both military and commercial purposes The automatic flight control system (AFCS) or autopilots are quite limited and can only do a few functions. Although they can do some activities automatically, such as autolanding, they cannot totally automate flights; some duties require human interaction. Navigation, guidance, image-based visual servoing (IBVS), and position-based visual servoing (PBVS) are all used [9]. C.

Unmanned Aircrafts System

According to Kendoul, [10] mentioned that unmanned aircraft system (UAS) is classified into five categories based on size and payload. 1.

2.

3.

4.

5.

Full-scale UAV: A full-scale unmanned aerial vehicle’s payload, endurance, and range, as well as the possibility to have a safety pilot on board, are all vital attributes, making it an ideal experiment for complex and risky flight testing. Full-scale UAVs include Boeing’s Unmanned Little Bird (ULB) helicopter and the MQ-4C Triton are some examples. Medium-scale (UAVs): These come in autonomous and semi-autonomous forms, with payloads including more than 10 kg as well as an overall weight around more than 30 kg. Medium-scale drones include the Yamaha RMAX, Solar HALE (Germany), and ELHASPA. Small-scale UAV: Small-scale UAV has a payload of 2–10 kg and a total weight of less than 30 kg. Vario Benzin Trainer and Bergen Industrial Twin are two small-scale UAVs; fixed-wing AVATAR (USA) and Kingfisher are two fixed-wing UAVs (BAE systems). Small-scale UAVs have fewer payloads than medium-scale UAVs, although they can still carry the bulk of navigation and mission sensors. Mini-scale UAV: Man-portable mini-scale UAV that can fly in open areas as well as confined spaces. The Frog (UAV), DJI NAZA F450 (China), Hummingbird (Germany), and PIXHAWK are all mini-scale UAVs (Switzerland). It normally carries a payload of less than 2 kg and weighs between 100 and a few kilograms. Due to payload constraints, only small conventional sensors and lightweight sensors are available. Micro-air vehicles (MAVs) UAV: The payload of micro-air vehicles (MAVs) is less than 100 g. The Epson micro flying robot and the X.R.B helicopter are two micro-air vehicles designed for indoor research and applications. Standard navigation sensors and avionics are unfeasible to transport on these systems.

3 How Autonomous Landing by Using Artificial Intelligence Changes the Future The application of artificial intelligence in landing system brings a new era in space exploration. By using autonomous landing system, we can go to moon and other planets safely without any failure in landing. Landing is the major problem in the

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field of space exploration. By using artificial intelligence in landing systems, it can reduce the mission failure percentage. During landing on other planets, vibration is also a major problem facing during landing [11]. The word “autoland” refers to a system that totally automates the landing phase of an aircraft’s flight while yet allowing the human crew to supervise the procedure [12]. Among other aircraft components and systems, the autoland system comprises automatic pilot, automatic thrust, radio altimeters, and nose wheel steering. Even though it is not part of the autoland system, the automatic brake system is frequently used in combination with an autonomous landing [13]. Intelligent autopilot system (IAS) is a fully autonomous pilot that uses artificial neural networks to learn from skilled human pilots and can fly large aircraft such as airliners. In simulation, the IAS can do the needed piloting responsibilities and handle the various flight phases of flying a plane that lifts off, climbs, cruises, locates, lowers, arrives, and lands from one airport to another [14]. Furthermore, in the presence of adverse weather conditions such as strong wind, rainstorm, wind gradient, and storm, IAS is capable of landing large jets independently. IAS could be an answer to modern autopilots constraint and issues, besides their failing to handle proper flights, their inability to withstand bad weather, particularly during take—off and landing when the plane’s speed is minimum, and the unpredictability aspect is large, as well as the pilot scarcity problem in the face of increasing aircraft demand [15, 16]. These are so many upcoming technologies and eliminating the risk factors in the last research. There are research going on and keep updating the existing products.

4 Conclusion Artificial intelligence is the current ongoing trend in the field of space. Various researches are going on to fully make the landing autonomous. We cannot make the landing fully autonomous by taking a step at a time it can be achieved. Spacex developed a rocket which can be reused, but the major problem they faced is landing, they overcome it by analyzing the failures.

References 1. A. Townsend, Artificial intelligence for space exploration. LinkedIn 2. S. Li, P. Zhang, Y. Cao, Analysis of the application prospect of artificial intelligence in space, in 2019 IEEE 3rd Advanced Information Management, Communicates, Electronic and Automation Control Conference (2009) 3. K.P. Valavanis (ed.), Advances in unmanned aerial vehicles’ ser. intelligent systems, control and automation: science and engineering (2008) (Springer Netherlands, Dordrecht) 4. Y. Chen, X. Ren, How can artificial intelligence help with space missions—a case study: computational intelligence-assisted design of space tether for payload orbital transfer under uncertainties (2019)

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5. P.J. Bentley, H. Baomar, Autonomous flight cycles and extreme landings of airliners beyond the current limits and capabilities using artificial neural networks (2021) 6. P.N. Desai, P.C. Knocke, Mars exploration rovers entry, descent, and landing trajectory analysis. J. Astronaut. Sci. 55, 311–323 (2007) 7. C. Cruzen, G. Chavers, J. Witteenstein, Operational considerations and comparisons of the Saturn, space shuttle and ares launch vehicles (2009) 8. S. Saripalli, J.E. Montgomery, G.S. Sukhatme, Vision-based autonomous landing of an unmanned aerial vehicle, in IEEE International Conference on Robotics 8 Automation Washington 9. F. Chaumette, S. Hutchinson, Visual servo control. I. Basic approaches. IEEE Robot. Automat. Mag. 4, 82–90 10. F. Kendoul, Survey of advances in guidance, navigation, and control of unmanned rotorcraft systems. J. Field Robot. 2, 315–378 11. Defence Office of the Secretary of, Unmanned Systems Integrated Roadmap FY2013–2038, Technical Report 12. K. Williams, A summary of unmanned aircraft accident/incident data: human factors implications. U. S. Department of Transportation Report, Technical Report (2004) 13. J. Manning, S.D., Rash, C.E., LeDuc, P.A. Noback, R.K. McKeon, The role of human causal factors in U.S. army unmanned aerial vehicle accidents, USAARL Report, Technical Report (2004) 14. M.R. Franklin, Application of an autonomous landing guidance system for civil and military aircraft 15. V. Kumar, N. Michael, Opportunities and challenges with autonomous micro aerial vehicles. Int. J. Robot. Res. 11, 1279–1291 16. D. Zhou, W. Kong, D. Zhang, Vision-based autonomous landing system for unmanned aerial vehicle: a survey

Automated Skin Disease Detection Using Machine Learning Techniques Kandadai Bhargavi, N. Vadivelan, Sarangam Kodati, Ch. V. Phani Krishna, and Kumbala Pradeep Reddy

1 Introduction Skin seems to be the entire body’s largest organ. It consists of epithelium, elastic and tissues of the visceral. Skin senses the external situation and protects our internal organs of the body from insecure tiny creatures, pollution and sunshine. Different environmental and organizational variables may affect the skin. Engineered skin damage, chemical damage, undesired viruses, the immunity of people, as well as hereditary diseases are elements which affect the skin. Sometimes life and very well are severely affected by skin disorders. Sometimes people try to solve their skin problems with their home cures [1]. If these methods are not appropriate for this kind of skin disease, the effects would be harmful. Skin disorders may readily be transferred from human to human and thus need to be managed at an early stage. In most instances, decisions on the nature of the disease are followed up on the experience of the physician and personal judgments [2]. If the decision is incorrect or delayed, it may damage human health. Dermatologist remained the most today’s challenging field of research, since it is difficult in the diagnostic processes of hair, skin and fingernails disorders. Due to numerous climatic and regional differences, the variance in these illnesses may be observed. Skin is regarded the most unpredictable and disturbing terrain owing to hair, tone differences and other moderating variables. The diagnostic of skin illness involves a number of pathology laboratory experiments for the proper diseases classification. These illnesses were of worry over the last 10 years since their rapid onset and

K. Bhargavi · N. Vadivelan · S. Kodati (B) · Ch. V. Phani Krishna Computer Science and Engineering, Teegala Krishna Reddy Engineering College, Hyderabad, Telangana, India e-mail: [email protected] K. P. Reddy Department of CSE, CMR Institute of Technology (Autonomous), Hyderabad, Telangana, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. K. Mandal et al. (eds.), Innovations in Signal Processing and Embedded Systems, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-19-1669-4_16

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complications have raised the danger of life [3]. Many skin disorders are very contagious and should be handled early to prevent their development. Complete well-being and psychologically and physically health are also negatively impacted. That most of these skin anomalies are extremely deadly, especially if they are not addressed from the start [4]. Human thinking tends to assume that the majority of skin defects are not as deadly while their own cures are portrayed. If these treatments are not suitable for this specific skin issue, nevertheless, it is worse. Techniques for machine learning are extensively utilized in healthcare. Several techniques for diagnostic diseases have been designed to offer great accuracy in illness prediction. Several machine learning methods for earlier detection of different illness kinds are created. Phases following examination of the different characteristics of the illness. The techniques used in this research have been extensively used to cancer, renal illness, hypothyroidism, hypertension, different classification techniques and assembly methods [5]. A further method to obtain the precision of the forecast by combining feature extraction with some of these different classifiers to create an intelligent system.

2 Review of Literature Sumithra et al. [6] developed a system using a developing region technique to separate picture areas. They used different color open space for removing noise such as avg, sample variance, variability and bias, second angle point in time, contrasts, correlations, total variance, opposite point in time of distinction, avg sum, sum variability, higher processing speed, differential variability, dark energy, similarity measurement and maximum correlation coefficients, etc. These characteristics are utilized in the SVM-KNN technique to categories skin disorders with 98% accuracy as malignancy, plaques, rheumatoid keratosis, chickenpox and sampling adequacy. However, the system is susceptible to complicated face images and occasionally displays errors owing to datasets. Qian et al. [7] created the danger predictive model for Alzheimer’s disease by use of EHR client records. In this context, they used an interactive learning experience to address the patient’s actual issue. The active treatment planning program was constructed. The danger of Alzheimer is used for this aggressive earnings predictive model. Kunjir et al. [8] introduced an improve medical judgment method to forecast the illness based on previous patient information. Several illnesses and an unknown patterns of client state were anticipated in this. A best clinical judgment system was developed for valid historical data illness predictions. This also influenced the idea and patterns of numerous illnesses. For the sake of visualization, 2-D/3-D graphs and bar charts are utilized. And 2-D/3-D graphs and bar chart for visualization. Qiu et al. in [9] the telemedicine system suggested and addressed how a big number of hospitals cloud services should be handled. This article author has suggested progress there in telehealth system, based primarily on the exchange of data across all cloud mobile telephony. However, the exchange of information in the cloud faces

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several problems such as maximum throughput and virtual machine switching. The knowledge sharing method on the clouds for improved information sharing via data collection ideas is presented here. Here and the best technique for telemedicine exchange was developed. The author focuses on the likelihood of transfer, wireless connectivity and time limitations. This author has developed a new optimum method for the exchange of large data. Consumers can get the best solution for processing biological data using this method. Victor et al. represent a technique [10] that accepts medical pictures which was before as inputs. The adaptive threshold marking control method was utilized for image processing and analytical GLCM functions in order to categories skin conditions via the SVM classifier. They achieved 94% accuracy. The method will be even more effective if investigators can utilize a hybrid classifier model. Ajith et al. [11] reported a mobile approach that utilizes the extracting algorithm is based on DCT, DWT and unique value deconstruction (SVD). Due to its simple design, this technique may be utilized in mobile platform healthcare organizations, however its effectiveness is not adequate to its standard. To improve processing speed, an energy efficient delineation or classification techniques is needed. A cloud-based skin disorder identification system is suggested to provide an effective process. Using the algorithm of the Crafty Edge Detection (CED) to identify sharp corners with picture limits.

3 Design Requirements for Hybrid Skin Disease Detection System An automatic detection method for skin disorders identifies skin diseases at high efficiency inside a short period of time. If skin disorders are found early, life is spared from recurring skin diseases such as skin cancer. These chapter contains the design criteria of a protection system for hybridized skin diseases. The input pictures are not uniform in size, form, size, color and brilliance in actual situations [12]. Accurate classification of the picture is also needed to forecast skin disorders. If parts of the picture have been cut for functional extraction, the accuracy of the illness categorization may be less. For the construction of an advanced optimization computerized skin disease detection technique, several criteria must be met. Four fundamental criteria are durability, data splitting, image classification and consistency. Those fundamental criteria are shown in Fig. 1. Robustness is a requisite where its afflicted skin picture can always be recognized after several standard image analysis procedures have been impacted, including cropping, scale, translations, edge enhancement, rotation, color mappings, distortion and compressing. Data segmentation is also another need for a technique of diagnosis of skin illness that separate the affected area of the picture for a forecast of skin diseases [13]. The partitioning method divides pictures into those or have too many sections. Structured data is to retrieve the functionality produced by the subdivided

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Robustness

Design Requirements of Skin Disease Detection

Data partitioning

Data Extraction

Fig. 1 Basic requirements of skin disease detection method

part of the picture. These derived characteristics are utilized to effectively categorize skin disorders. Dependability is indeed an essential requirement for the diagnosis of computerized skin condition. Machine learning and methods of artificial intelligence are utilized to forecast skin disorders. Predicted disorders of the skin are categorized as benign, harmless, suspect and malignancy.

3.1 System Architecture A train of pictures to be acquired from the user is considered and on each image the pretreatment and identification will just be done. Function cleaning is performed on each picture in order to extract characteristics that may be utilized to build the classification method. This rating model can ultimately forecast the illness for a fresh picture of a skin condition that the user may get through Android [14]. Depending on this anticipated illness, the client asked users questions and, judging by the response, decides the kind of disease. Finally, our method proposes medical therapy or advice on the basis of anticipated results of skin illness. Figure 2 depicts the concept of the project that outlines the key information systems.

3.1.1

Preprocessing

Picture testing is a crucial step to identify noise including hair clothes and other artifacts and to improve the dynamic range. The primary objective of this phase is to enhance the quality of the skin picture by eliminating irrelevant and excess portions for steam generation of the environment of the image [15]. A good choice of tested images may significantly enhance the system’s effectiveness. Three phases of picture improvement, image restorations and laser treatments are the goal of the training set.

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Real images

Real images

173 Segmentation and preprocessing

Extraction

Testing classifier and analysis

Classification and training

Trained new model

Prediction

Database

Fig. 2 System architecture

3.1.2

Segmentation

Classification and segmentation is a method for determining the borders form and thickness. The item is separated from its backdrop by various characteristics retrieved from the picture. After loudness and waste are removed from the injury region, the lesion must be removed from the surface and, accordingly, diagnostic examination is performed using solely the required area [16]. Thresholding: That technique sets the threshold and divides images on the basis of this criteria into groupings. It features bi- and multi-level thresholds. The Timeline and Adjustable median filter technique involves thresholding. Techniques for color-based segmenting the market: Color discrimination-based categorization. Integrate change components principle/convert cylindrical professional and none. Curvature differentiation: Lesion image segmentation utilizing active contouring/radial evaluation measures/zero crossings of Gaussian Laplace transform (LoG). This includes active contouring, rotary search. Regional segmentation: This is a way to divide the picture into simple fragments and fuse sub-images that are nearby and in some ways comparable. It comprises merging administrative region, multi-regional growth and morphology floods. It is built on methods like splitting and merging. Fusion of the Statistical District Pathological multi-scale floods. Soft computation: Soft computing methods use convolution neural networks to categorize images. It comprises the fuzzy-based neural network, methods for optimizing.

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Feature Extraction

A features is an amount of evidence important to a determined individual user computer work [17]. Function extraction is the method through which material is removed from a picture.

3.1.4

Feature Classification

Selected characteristics are utilized to detect and classify normal and abnormal tumors. For this reason a broad variety of classifiers may be developed and utilized. For this reason, classifications such as SVM, C4.5 may be utilized.

4 General Framework for of the Skin Disease Detection System Skin is perhaps the most vulnerable portion impacted more than any internal organs. Sunburn is a key factor that affects the cell of melanosomes by ultraviolet (UV) sunlight. The exposure portion of the skin gets affected by UV rays, antifungal or viral infections and contaminated areas with various illnesses. The manifestations of skin conditions include skin insulation, infectiousness, allergic indications, flames, cough, raw skin, fever, anguish, scratches, bumps, etc. In order to identify skin disorders sooner, a computerized system is needed. An experienced physician may suspect early skin infections [18]. An automated analysis system is necessary to identify specific skin conditions at an early stage. An improved picture of the afflicted part of the skin is utilized for the segmented and extractor procedure and recovered functions are employed in machine learning or with an artificial neural network method to detect not just whether skin disorders are present. Both stages of this online service are the checking and training components. An afflicted area of the lesions from a filtered picture of the skin is partitioned utilizing the image segmentation in the reference model. Then, the technique is used to extract significant boost. These techniques are extremely helpful to remove characteristics from the divided section of dermoscopy images pictures. Techniques for artificial intelligence are used to detect skin disorders by collecting characteristics. These hybrid methods provide cost-effective to diagnose skin disorders in terms of computational complexity [19]. The earlier categorized skin lesion illnesses are entered in the database with in learning outcome. Lastly, the assessment and educational modules evaluate the main skin disorders. If the test and recorded information pictures do not match, the solution is re-checked. It would be extremely beneficial to the consumers if we could include a plan of care. Figure 3 shows the flow diagram for an electronic machine for the identification of skin disease [20]. In this article, three procedures for the detection of skin disorders have been examined. We discovered efficient vehicle segmentation

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175 Training dataset

Test dataset

Preprocessing

Preprocessing

Segmentation

Segmentation

Extraction Extraction ML No

Yes Disease diagnose d

Classification of Disease

Treatment

Fig. 3 A general framework of the skin disease detection system

techniques, edge detection of skin disorders among some of the techniques. These automatic hybrid techniques work with minimal computing complexity.

5 Methodology 5.1 K-Nearest Neighbor Due to the shorter runtime and higher accuracy than in other often used techniques, like the machine learning algorithm and particle approach, classification technique is ideally suited for categorizing people based upon their pictures. Although techniques like SVM and Ad boost algorithms have been shown to be more exact than the KNN classifier, the KNN classifier is running quicker and dominates more than SVM. A neighbor categorization in the picture space is the easiest classification algorithm. Under such an approach, an image is identified in the testing set by giving it the labeling of the closest station in the class label, which measures distance in picture space [21]. The distance measure metrics are frequently used to estimate the proximity among KNN pieces of data. A length among all pixel is calculated in a collection. The gap between the two pixel is specified as that of the distance metric. The distance from Euclidean is determined by (Fig. 4): d(x, y) =

√

(x1 − y1 )2 . . . (xn − yn )n

(1)

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Fig. 4 Flow chart for skin disease detection using KNN

This Euclidean distance is in a KNN classification by default. However, the separation between the two characteristics may be determined based on one separation trigonometric and correlations. The next k-algorithm (KNN) is a categorization approach for things mainly dependent on nearby learning examples. KNN is a type of educating instance or lazy learner that can only support locally and all computations are postponed till classification is completed. The neighboring algorithm is one of the means by which all machinery education may be successfully carried out: one element is characterized by a variety of its neighbors and only recommended by its nearest neighbors. When k = 1, the item would be the next neighbor’s class advisory board. 1. 2. 3. 4.

Within every original dataset, each information pixel value has a target class as in set Category = {c1, …, cn}. Then, by evaluating the feature vector, the pieces of data’, the k-closest neighbor. The k-closest pieces of information are next examined in order to identify its most frequent class marking of the collection. The title of the most prevalent class is subsequently given to the examined piece of data

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6 Performance Metrics 6.1 Accuracy The accuracy of a system is just a subset of the classifier’s effectiveness. Precision is one of the factors to be assessed for the prediction model. Equation 2 provides an evaluation of the accuracy of one class. Accuracy =

(True positive + True negative) (True positive + True negative + False positive + False negative) (2)

6.2 Precision The predictive accuracy indicates accuracy. The number of positive direct claims is assessed in relation to the cost of positive claim. The current amount for a specific class is supplied in the given formulas. Precision =

(True positive) (True positive + False positive)

(3)

6.3 Recall The reminders are assessed as the count of genuine and positive and false negatives. Recall =

TP TP + FN

(4)

6.4 F1-Score The F measurement (F1-score or F-score) is an indicator of the reliability of the exam, described as the significantly enhanced means of correctness and recall of the test. This F-score is often used to evaluate the correctness of a test and combines and recalls the usage of expertise. The F-score could provide a higher comprehensive

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evaluation of the effects of an exam considering both accuracy and recall. The Fscore is frequently used during search, speaker verification and question conceptual framework of this study in knowledge discovery. F1-score = 2

Precision*Recall Precision + Recall

(5)

7 Result and Discussion The application of 5 different machine learning classifications in this study may detect three types of skin illness, Blister, Hives and Rosacea. The skin image dataset is initially normalized for subsequent identification. The data are divided into the instructional and testing phase: 85% for education and 15% for assessment. The performance data is educated in 3 classifications for three illnesses, Class A, B and C. Later, five distinct classification methods named evolutionary computation, regression models, SVM, Naïve Bayes, the random woodland and KNN are discovered to identify skin disorders. Every algorithm performs 10 times along the same information, and the classification performance within each run is determined. Skin conditions are then found by five distinct classification techniques known as machine learning, logistical regression, SVM, Naïve Bayes, random forests and KNN. Every execution time on same information 10 times but each iteration determines the quality of the learning. The characteristics so acquired will be reviewed and compared and now all five learning techniques will be analyses as well as the optimal techniques utilized for skin condition predictions will be checked. Here is just a short summary and computation procedure for several constraints: Table 1 and Fig. 5 identify RF Classifier with 77% precision and 70% precision in testing and NB Classifier with 68% accuracy and 62% precision in testing, as well as LR Classifier with 81% accuracy and 78.30% accuracy throughout testing but instead SVM Classifier with 89% accuracy as well as 81.2% accuracy in testing and the KNN Classification given. Table 2 and Fig. 6 describes that error rate of RF is 0.36, error rate of NB is 0.25, error rate of LR is 0.29, error rate of SVM is 0.34 and error rate of KNN is 0.03. Table 1 Accuracy of classification algorithms on three classes of skin diseases

S. No

Machine learning classifier

Training accuracy

Testing accuracy

1

RF

77

71

2

NB

68

62

3

LR

81

78.3

4

SVM

89

81.2

5

KNN

99.2

93.6

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Fig. 5 Accuracy of classification algorithms on three classes of skin diseases

Table 2 Error rate of all methods

S. No.

Machine learning classifier

Error rate

1

RF

0.36

2

NB

0.25

3

LR

0.29

4

SVM

0.34

5

KNN

0.03

Fig. 6 Error rate of all methods

Table 3 and Fig. 7 describe the greatest rate of KNN classification for three distinct skin conditions. KNN IS 0.92 is the accuracy of the blister illness rate and KNN is 0.98 and KNN is 0.96 for Blister disease F1. The accuracy rate of KNN for Hives disease is 0.91 and the KNN recall rate with Hives disease is 0.97 and the KNN score for Hives disease F1 is 0.95. KNN IS 0.99 for Rosacea disease accuracy and 0.96 for Rosacea disease, with 0.94 for Rosacea disease, KNN F1-score.

F1

0.61

0.72

0.70

0.69

Hives

Rosacea

0.78

0.59

0.69 0.71

0.58

0.59

Pre

Recall

Pre

0.68

NB

LR

0.56

Blister

Name of the disease

Table 3 Training accuracy in all five methods Recall

0.59

0.62

0.69

F1

0.65

0.65

0.71 0.53

0.55

0.60

Pre

RF Recall

0.51

0.62

0.63

F1

0.48

0.53

0.57

0.52

0.62

0.61

Pre

SVM Recall

0.62

0.45

0.65

F1

0.63

0.53

0.68

0.99

0.91

0.92

Pre

CNN Recall

0.96

0.97

0.98

F1

0.94

0.95

0.96

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Fig. 7 Training accuracy in all five methods

8 Conclusion Skin disease identification is also one of the main issues in the medical business and may be cured and recoverable if correctly caught quickly. The research study shows that various methods for observing skin diseases are utilized. Therefore, skin disorders still need to be diagnosed at an early stage. Machine learning algorithms can influence the early diagnosis of skin disorders. It can let designers build real-time skin changes. If properly adopted, the methods offer enough help and an efficient supply chains to the prevention of skin disorders. This helps patients and doctors treat skin conditions promptly. Specialized health knowledge investigation and execution is available. When there are more “real-time” data accessible in the future, skin disorder identification may be examined using current developments in AI as well as the advantages of AI diagnostics.

References 1. S. Kolkur, D. Kalbande, P. Shimpi, C. Bapat, J. Jatakia, Human skin detection using rgb, hsv and ycbcr color models. arXiv preprint arXiv:1708.02694 (2017) 2. S. Demyanov, R. Chakravorty, M. Abedini, A. Halpern, R. Garnavi, Classification of dermoscopy patterns using deep convolutional neural networks, in 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI). (IEEE, 2016), pp 364–368 3. V.B. Kumar, S.S. Kumar, V. Saboo, Dermatological disease detection using image processing and machine learning, in International Conference on Artificial Intelligence and Pattern Recognition (AIPR), pp 1–6 (2016)

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4. J. Weston, Large-scale semi-supervised learning. NEC LABS America, Inc., 4 Independence Way, Princeton, NJ, USA. Available: http://www.thespermwhale.com/jaseweston/papers/lar gesemi.pdf. Accessed: 30 Mar 2018 5. . H. Zhou, F. Xie, Z. Jiang, J. Liu, S. Wang, C. Zhu, Multi-classification of skin diseases for dermoscopy images using deep learning, in 2017 IEEE International Conference on Imaging Systems and Techniques (IST) (IEEE, 2017), pp 1–5 6. R. Sumithra, M. Suhil, D.S. Guru, Segmentation and classification of skin lesions for disease diagnosis. Procedia Comput Sci 45, 76–85 (2015). https://doi.org/10.1016/j.procs.2015.03.090 7. B. Qian, X. Wang, N. Cao, H. Li, Y.-G. Jiang, A relative similarity based method for interactive patient risk prediction. Springer Data Mining Knowl. Discovery 29(4), 1070–1093 (2015) 8. A. Kunjir, H. Sawant, N.F. Shaikh, Data mining and visualization for prediction of multiple diseases in healthcare, in IEEE big data analytics and computational intelligence, Oct 2017, p 2325 9. L. Qiu, K. Gai, M. Qiu, Optimal big data sharing approach for telehealth in cloud computing, in Proceedings of IEEE International Conference on Smart Cloud (Smart Cloud), Nov 2016, pp 184–189 10. A. Victor, M.R. Ghalib, A hybrid segmentation approach for detection and classification of skin cancer. Biomed Res India 28, 6947–6954 (2017) 11. A. Ajith, V. Goel, P. Vazirani, M.M. Roja, Digital dermatology: skin disease detection model using image processing, in International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, 15–16 June 2017, vol. 1, pp. 168–173 (2017). https://doi.org/10. 1109/ICCONS.2017.8250703 12. A.A.L.C. Amarathunga, E.P.W.C. Ellawala, G.N. Abeysekara, C.R.J. Amalraj, Expert system for diagnosis of skin diseases. Int. J. Sci. Technol. Res. 4(01) (2015) 13. M.Q. Khan, A. Hussain, S. Ur Rehman, U. Khan, M. Maqsood, K. Mehmood, M.A. Khan, Classification of melanoma and nevus in digital images for diagnosis of skin cancer. IEEE Access 7, 90132–90144 (2019). https://doi.org/10.1109/ACCESS.2019.2926837 14. M.H. Ahmed, R.R. Ema, T. Islam, An automated dermatological images segmentation based on a new hybrid intelligent ACO-GA algorithm and diseases identification using TSVM classifier, in 1st International Conference on Advances in Science, Engineering and Robotics Technology, Dhaka, 3–5 May 2019 (2019), pp. 894–899 15. N. Hameed, A.M. Shabut, M.A. Hossain, Multi-class skin diseases classification using deep convolutional neural network and support vector machine, in 12th International Conference on Software, Knowledge , Information Management & Applications (SKIMA), Phnom Penh, 3–5 Dec 2018, vol. 1 (2018), pp. 14–20. https://doi.org/10.1109/SKIMA.2018.8631525 16. N. Hameed, A.M. Shabut, M.A. Hossain, A computer-aided diagnosis system for classifying prominent skin lesions using machine learning, in 10th Computer Science and Electronic Engineering (CEEC), Colchester, 19–21 Sept 2018, vol. 1 (2018), pp. 86–91. https://doi.org/ 10.1109/CEEC.2018.8674183 17. A.G.H. Priya, J. Anitha, J. Poonima, Identification of melanoma in dermoscopy images using image processing algorithms, in 2018 International Conference on Control , Power , Communication and Computing Technologies (ICCPCCT), Kannur, 23–24 Mar 2018, vol 1 (2018), pp. 553–557. https://doi.org/10.1109/ICCPCCT.2018.8574277 18. L. Bajaj, H. Kumar, Y. Hasija, Automated system for prediction of skin disease using image processing and machine learning. Int. J. Comput. Appl. 180(19) (2018). https://doi.org/10. 5120/ijca2018916428 19. A.S. Abdulbaki, S.M. Najim, S.A. Khadim, Eczema disease detection and recognition in cloud computing. Int J Appl Eng Res 12, 14396–14402 (2017) 20. B. Janney, E. Roslin, Classification and detection of skin cancer using hybrid texture features. Proc ENVOCCON 36, 71–77 (2017) 21. A.D. Sunny, S. Kulshreshtha, S. Singh, Srinabh, M. Ba, H. Sarojadevi, Disease diagnosis system by exploring machine learning algorithms. Int. J. Innov. Eng. Technol. (IJIET) 10(2) (2018)

FinFET-Based SRAM Design Using MGDI Technique for Ultra-Low-Power Applications T. Vasudeva Reddy, K. Madhava Rao, J. Yeshwanth Reddy, B. Naresh Kumar, and R. Anirudh Reddy

1 Introduction The wide utilization of battery-powered digital consumer applications nowadays triggers a demand for low-power devices that operate under the subthreshold region. Operating the CMOS devices under subthreshold leads to static leakage current which is dynamically changing the performance of the circuit. As the technology is changing tremendously, there is a variation in the performance of CMOS devices and that affects functionality and reliability. From the literature, it is identified that many researchers and engineers are working on different low-power design techniques using CMOS design that is operating under the subthreshold region to reduce the static power in past decades. Still, there is a requirement for the reduction of power by alternate devices such as “FinFET” device and its designs to reduce the power under deep submicron technology. In this paper, the focus is on the design of FINFET-based SRAM design, SRAM using MGDI techniques, and its analysis of functionality and performance. Design and modeling of GDI-based inverter articulated in part one. The design of gate diffusion input technique (GDI)-based SRAM model and functionality is evaluated in part two. In the third part, design of FinFET-based inverter design is implemented. The fourth part of the work deals with the implementation of GDIbased SRAM using FinFET design. The comparative analysis of static, dynamic, and total power and conclusion is made based on the result obtained.

T. Vasudeva Reddy (B) · K. Madhava Rao · J. Yeshwanth Reddy · B. Naresh Kumar · R. Anirudh Reddy B V Raju Institute of Technology, Narsapur, Medak dt, Telangana, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. K. Mandal et al. (eds.), Innovations in Signal Processing and Embedded Systems, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-19-1669-4_17

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2 CMOS Inverter Design Using GDI Realization of GDI Based logic function is represented as showed in Fig. 1. Due to the advantage of reduced number of interconnections, transition time and less area lead to power dissipation. In GDI, only two transistors are performing the complex logical operation with high speed and low power dissipation [1]. This design is more suitable for the high complexity of circuits that are operating subthreshold operations due to its high drive current. GDI is a three terminals device, P input is applied to the drain terminal of Pmos, N input to the source of nMOS transistor and common gate as input (G) to both nMOS and pMOS, as showed in Fig. 1. Schematic representation of GDI-based inverter [2] design is implemented and indicated in Fig. 2.

2.1 DC Analysis Fig. 3.

2.2 Transfer Characteristics Transfer characteristics GDI inverter is indicated in Fig. 4 that gives the analysis to know the relationship between the input and output. Power analysis in digital circuits, power is an important factor to measure, and the total power of the device is measured in two ways. The switching operation of transistor is measured by dynamic power, and static power is almost idle in digital designs. When the circuits are under idle state, there is some amount of power which is leakage due to short circuit, Fig. 1 Basic CMOS GDI cell structure

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Fig. 2 GDI cell structure as inverter

Fig. 3 Analysis of DC characteristics

reverse-biased junction, and subthreshold current. “GIDL” increases the gate oxide tunneling, [3]. This power needs to minimize to get the maximum performance of the circuits. Therefore, total power consumption is given by Eq. 1. Ptotal = Pdynamic + Pstatic

(1)

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Fig. 4 Transfer characteristics CMOS GDI inverter

2.3 Static Power In all the way the amount of static power of the device needs to be optimized, and therefore, leakage power is device that is getting reduced to a minimum value. This can be obtained by either lowing the technology by utilizing proper optimization techniques. Another technique adopted here to reduce leakage power is GDI [4]. Power analysis of the GDI-based inverter is presented in Fig. 5. Static power is measured across node V2 and i1.

Fig. 5 Static power 1.158 nW

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Fig. 6 Total power dissipation

2.4 Total Power Total power is measured across node V3 and i2, and static and dynamic power of the design is evaluated to get total power (Fig. 6).

3 GDI SRAM Design Design and implementation of 6T SRAM are using the GDI technique that has two inverters connected back to back, and BL, BL bar are connected to input and output terminals via access transistors. 6T cell [5] has an advantage of high speed than the dynamic operation. These designs are high power consumption and less stability due to its noise margin [6]. When the WL is high, the data stored in bi-stable latch controls the access of M5 and M6 transistors. BL and BL bar send data to inverters via access transistors for reading and write operations [7] (Fig. 7).

3.1 Read and Write Operation of GDI SRAM Figure 8 indicates the characteristics of SRAM. At 90 nm with a supply voltage Vdd is 0.8 V, input voltage of 0.8 V, a threshold voltage of 0.4 V, and the characteristics are estimated. When WL is high, value of BL is high, and the output of BL bar is low while functioning read operation [7], and when WL is low, value of BL is low, and the output of BL bar is high while functioning write operation. This separates

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Fig. 7 Schematic design of GDI SRAM

Fig. 8 Read and write operation of GDI SRAM

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the read and writes operations. The second inverter has same input and produces a high value at output. This value will continue until it completes write operation. This value stored will be read out, when WL is high and BL is high and BL bar is low.

3.2 Power Analysis Implementation of SRAM is done using six transistors in the traditional approach under the subthreshold region of operation, where the operating voltage (0.4 V) is less than the subthreshold voltage (0.49 V) under 32 nm. Subthreshold design GDI SRAM power analysis is estimated, simulation results were carried out at 32 nm, and power analysis is shown below. Static Power Analysis Power is one of the parameters that is to be considered in design. As the complexity of the circuit is increasing nowadays, an SRAM is used in most cache memory. But in general, the power consumption remains fixed. But in standby mode, the static and the leakage power are dominating [7, 8]. Therefore, the analysis of the design is used to estimate power at different modes of operation. Static power is measured across node V4 and I3 indicated in Fig 9. Total Power Dissipation Total power of the circuit analyzed from Fig. 9 indicates the static, short circuit, leakage, and dynamic power at different modes such as cut off, active, and satura  tion. When supply voltage is maximum than the threshold voltage Vgs > Vth , the

Fig. 9 Static power of 1.486

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dynamic power is maximum. But in the case of subthreshold, the static power  is domi- nating. When the supply voltage is reduced near to the threshold voltage Vgs < Vth and leakage power is maximum [9]. Therefore, maximum power is dissipation of circuit. So therefore, total power is measured across node V5 and i4.

4 Modified Gate Diffused Input FinFET (M-GDI)-Based SRAM Modified gate diffused input is a new technique to design and adopt to reduce static, leakage power and adopted from the GDI technique. This helps to area reduction in digital circuits and transistor count. The primitive logic cell of MGDI design consists of nMOS and pMOS that contains four terminals, i.e., diffusion of pMOS (P), common gate (G), outer diffusion of nMOS (N), and common diffusion node of both transistors (D), respectively, as shown in Fig. 10 [10]. The method overcomes the limitations by scaling down the technology and source body effects [11]. A novelty approach in this paper is the modeling of FinFET SRAM design that is indicating here to store the data. SRAM is a volatile memory with few advantages of high speed in its operation while performing read and write operations. Minimum number of transistors required to implement SRAM design. Speed is crucial in performance and consumes less power because of less number of transistors and body biasing, which leads to less leakage power and delay. Figure 11 shows the schematic representation of the MGDI inverter with input and output terminals. A modified structure of FinFET-based memory design is represented in Fig. 12. Gate is connected to input of p and nMOS, P terminal input is connected to drain, n input is connected to source, and also, bulk of pMOS (Sp) and nMOS (Sn) are connected to output. FinFET devices offer higher resistance compared to CMOS

Fig. 10 Total power of 4.815

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Fig. 11 FinFET-based MGDI inverter design

Fig. 12 Schematic design of MGDI FinFET inverter

models [1]. Each bit of SRAM is stored by forming a cross-coupled inverter that has two bi-stable states, i.e., 1 and 0 [12].

4.1 DC Analysis The operating region of the inverter is analyzed based on characteristics, where load line characteristics are crossing the transfer function (Fig. 13). The transfer characteristics of the design states of operation of logic ‘1’ and ‘0’s are treated as a bi-stable latch [13]. SRAM designed with six transistors is used as a transfer characteristic of the GDI inverter shown in Fig. 14, and it indicates

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Fig. 13 DC load line analysis

Fig. 14 Transfer characteristics of GDI-based inverter

the relationship between the output and input terminals. The output drain current is linearly proportional to the input voltage [14–18].

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4.2 Power Analysis The total power of the design is the sum of static and dynamic power. The static power of GDI-based SRAM is reduced by considering the bulk terminal to output. Power analysis of inverter design is measured by considering current and voltage in the path. Power across the output terminal is evaluated by current multiplied by voltage. ie I8 * V9 across the path. Total Power: It is the power across the output terminal from values of current I10 multiplied by V11 path from the design. Dynamic power is measured by reducing the static power from the total power as indicated by Fig. 14.

5 Design of FinFET-Based MGDI 6T SRAM Design and implementation of modified GDI-based 6T SRAM is represented by FinFET design connected two inverters back to back, i.e., output of first is given as input to another inverter, and second inverter output is given as input to the first inverter (cross-coupled connection) [7, 13, 19–21]. Another two transistors act as access transistors and are connected with BL and BL bar. Supply rails V dd and V ss are connected to the gate terminal of the access transistors. Figure. 15 represents the read and write analysis of the GDI model. Two inverters are cross-coupled and interfaced with two access transistors using the MGDI schematic of 6T FinFET

Fig. 15 Static power analysis

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Fig. 16 Total power 4.578 nw

SRAM [22–25]. The write and read operation of FinFET SRAM is shown in Fig. 16 (Fig. 18).

5.1 Power Analysis Power analysis of the FinFET SRAM design is represented in Fig. 17 and has both static and total power across I11 and V12 and I12 and V12 subsequently. The total power of FinFET SRAM is the sum of both static power and dynamic power. Total power analysis is encountered in Fig. 17 and is a combination of switching or dynamic, short circuit, leakage, and junction power. Dynamic power is the power obtained from the circuit, by reducing the static power from the total power. In FinFET-based SRAM using the MGDI technique, the total power is evaluated as 2124 nW (Figs. 19 and 20). Result and analysis: An implementation of FinFET-based SRAM design is developed using MGDI technique. Performance and functionality of inverter using CMOS GDI technique are designed. The simulation results of DC and transfer characteristics analysis were obtained and compared with the FinFET MGDI inverter circuit. As the inverter is the basic element to design SRAM, CMOS-based GDI 6T SRAM is designed using an inverter and evaluates the functionality of reading and write operations, and performance has given static, dynamic, and total power. FinFETbased SRAM using the MGDI technique is designed from a basic inverter compared with CMOS GDI 6T SRAM design. Finally statistics of static, dynamic, and total power of CMOS GDI inverter, GDI based SRAM, FinFET-based MGDI inverter, MGDI based SRAM are showed in (Table 1)

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Fig. 17 GDI-based 6 T SRAM FinFET design

Fig. 18 GDI-based 8 T SRAM read and write operations

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Fig. 19 Static power MGDI SRAM 1.1395

Fig. 20 Total power MGDI SRAM 3.125 Table 1 Comparative analysis of GDI and MGDI inverter and SRAM

Power in nW /Technique

Static power

Dynamic power

Total power

GDI inverter

1.1590

2.6420

3.801

GDI SRAM

1.4864

3.3236

4.810

MGDI inverter

1.1786

3.3871

4.573

MGDI SRAM

1.1395

1.9860

3.125

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Fig. 21 Comparative analysis GDI and MGDI techniques

From the table, it is identified that the static, dynamic, and total power is drastically reduced in modified gate diffused input (MGDI) comparatively with CMOS GDI SRAM (Fig. 21). Future scope The stability of SRAM is a major concern, to improve the performance of an indecent hvt types of transistors. To get the proper characteristics, standard sizes of the transistors are to be used. As power is the quadratic relationship with the supply voltage, minimum voltage (Vgs < Vth) leads to maximum power dissipation under the subthreshold region of operation. This problem can be solved by adopting low-powered advanced digital techniques.

References 1. N. Suresh, Design, and analysis of low power full adder using GDI. Int. Electron. Electr. Comput. Syst. IJEECS 7(4) (2018) 2. E.V. Nagalakshmi, K. Kavya, Design of 4 bit ALU using modified GDI technology for power reduction. Int. Eng. Sci. Invention (IJESI) 7(2), 38–45 (2018) 3. M. Tirpathi, Low power-based manchester encoder by GDI, 2018 2nd International Conference on Inventive Systems and Control (ICISC) (Jan 2018). https://doi.org/10.1109/ICISC.2018.839 8895 4. P.A. Khan, Design of 2×2 vedic multiplier using GDI technique, in International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS).(2017, Auguest). https://doi.org/10.1109/ICECDS.2017.8389786 5. K. Nehru et al., Analysis of 16-bit counter using GDI technique and CMOS logic. Int. Appl. Eng. Res. 10(6), 16121–16128 (2015) 6. Kunal et al., GDI technique: a power-efficient method for digital circuits. Int. Adv. Electr. Electron. Eng. 1(3), 87–93 (2012)

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7. A. Morgenstern et al., Gate-diffusion input (GDI), a power-efficient method for digital combinatorial circuits. IEEE trans. VLSI 10(5), 566–581 (2002) 8. P.R. Monica, V.T. Sreedevi, A low power and area efficient CNTFET based GDI cell for logic circuits. ARPN J. Eng. Appl. Sci. 9(12) (2014). ISSN: 1819-6608 9. A. Lourts Deepak, Performance comparison of CMOS and FINFET based SRAM for 22 nm technology. Int. J. Conceptions Electron Commun. Eng. 1(1) (2013). ISSN: 2357-2809 10. A. Morgenstern et al., Gate-diffusion input (GDI) a technique for low power design of digital Circuits analysis and characterization. IEEE Int. Symp. Circ. Syst. (Feb. 2000) 11. M. Hasan et al., Gate diffusion input technique based full swing and scalable 1-bit hybrid full adder for high-performance applications. Eng. Sci. Technol. 23(6), 1364–1373 (2020) 12. S. Hiremath et al., Low power circuits using modified gate diffusion input (GDI). IOSR VLSI Signal Process. (IOSR-JVSP) 4(5), 70–76 (2014) Ver.II 13. R. Uma, P. Dhavachelvan, Modified gate diffusion input technique: a new technique for enhancing performance in full adder circuits, Proc. Technol. 6, 74–81 (2012). ISSN221-20173. https://doi.org/10.1016/j.protcy.2012.10.010 14. E. Abiri et al., A novel design of low power and high read stability ternary SRAM (T-SRAM), memory based on the modified gate diffusion input (m-GDI) method. Microelectronics 58, 44–59 (2016) 15. V. Vijayalakshmi, B.M.K. Naik, Design and modelling of 6T FinFET SRAM in 18 nm, in 2018 3rd International Conference on Communication and Electronics Systems (ICCES) (2018), pp. 208–211. doi: https://doi.org/10.1109/CESYS.2018.8724026 16. R.R. Vallabhuni, J. Sravana, C.S. Pittala, M. Divya, B.M.S. Rani, V. Vijay, Universal shift register designed at low supply voltages in 20 nm FinFET using multiplexer, in Intelligent Sustainable Systems (Springer, Singapore, 2022), pp. 203–212 17. C. Pittala, V. Vijay, Design of 1-Bit FinFET sum circuit for computational applications, in International Conference on Emerging Applications of Information Technology (Springer, Singapore, Feb. 2021), pp. 590–596 18. C.S. Pittala, M. Lavanya, M. Saritha, V. Vijay, S.C. Venkateswarlu, R.R. Vallabhuni, Biasing techniques: validation of 3 to 8 decoder modules using 18nm FinFET nodes, in 2021 2nd International Conference for Emerging Technology (INCET) (IEEE, May 2021), pp. 1–4 19. C.S. Pittala, V. Parameswaran, M. Srikanth, V. Vijay, V.S. Nagaraju, S.C. Venkateswarlu, R.R. Vallabhuni, Realization and comparative analysis of thermometer code based 4-Bit encoder using 18 nm FinFET technology for analog to digital converters, in Soft Computing and Signal Processing (Springer, Singapore, 2021), pp. 557–566 20. A. Surya, Low power SRAM cell design using gate diffusion input (GDI) techniques. Int. Innovative Res. Multi. Field. 5(6), 186–190 (2019) 21. N. Kothari, Characterization of various FinFET based 6T SRAM cell configurations in light of radiation effect. S¯adhan¯a, Indian Acad. Sci. ISSN:0973-7677. https://doi.org/10.1007/s12046020-1269-8 22. E. Abiri, A. Daribi, Design of low power and high read stability 8T-SRAMmemory based on the modified gate diffusion input (m-GDI) in 32 nm CNTFET technology. Microelectron. J. 46(12), 1351–1363 Part A (2015) nISSN0026-2692. https://doi.org/10.1016/j.mejo.2015. 09.016 23. H.V. Ravish Aradhya et al., Considerations of FinFET based 6T SRAM cells. Int. J. Sci. Res. 1(3), 134–136 (2012) 24. E. Abiri, M.R. Salehi, A. Darabi, Design and evaluation of low power and high speed logic circuit based on the modified gate diffusion input (m-GDI) technique in 32 nm CNTFET technology, in 2014 22nd Iranian Conference on Electrical Engineering (ICEE) (2014), pp.67– 72. doi: https://doi.org/10.1109/IranianCEE.2014.6999505 25. M. Rahaman, R. Mahapatra, Design of a 32 nm independent gate FinFET based SRAM cell with improved noise margin for low power application, in 2014 International Conference on Electronics and Communication Systems (ICECS) (2014), pp. 1–5. doi: https://doi.org/10.1109/ ECS.2014.6892721

Design and Implementation of Efficient Counter-Based IoT DDoS Attacks Detection System Using Machine Learning K. Venkata Murali Mohan, Sarangam Kodati, and V. Krishna

1 Introduction The Internet is linked with many heterogeneous devices then the exponential increment in IoT technology [1]. Security providing is main important challenge in these devices which works at diverse access protocol, limited resources and low powers. The IoT device users are increasing day to day as it is expected to end of this year 24 billion and 100 billion at the end of 2025 year. In the environment of open network, these devices are spread, so the attacker can easily attack these devices. In the internet of things networks, different intrusion types are detected with these devices integration with flexible and programmable networks. Human life is mainly affected by these distributed denial of service (DDoS) attacks and results to destruction in IoT networks [2]. The networking resources are not available to user when the attacker introduces the DDoS attacks into the network. DDoS attack detection and mitigating system is designed in this paper on IoT servers [3]. In the network security, one of the major problems is attacking of distributed denial of service (DDoS) attack [4]. Large number of hosts is attacked by the DDoS attacks as compared to simple DoS attacks, and network resources are exhausted as fast as possible. The right authenticate users are not getting any resources of network as in the form of services if the network is attacked by the DDoS attacks. Synchronize (SYN) flood, Internet control message

K. Venkata Murali Mohan Department of ECE, Teegala Krishna Reddy Engineering College, Hyderabad, Telangana, India e-mail: [email protected] S. Kodati (B) Department of CSE, Teegala Krishna Reddy Engineering College, Hyderabad, Telangana, India e-mail: [email protected] V. Krishna Department of CSE, TKR College of Engineering & Technology, Hyderabad, Telangana, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. K. Mandal et al. (eds.), Innovations in Signal Processing and Embedded Systems, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-19-1669-4_18

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protocol (ICMP) flood, domain name server (DNS) reflection attack and user datagram protocol (UDP) floods are different types of DDoS attacks [4, 5]. Because of their distributed nature, these DDoS attacks are strongly entered into network and difficult to defend against them. Launching of these attacks into the network is very easy and makes a large effect in the network. The true users are facing many problems with these attacks. IoT gateways, IoT devices, cloud servers and SDN switches are included in the proposed IoT DDoS attack detecting system design [6, 7]. The IoT gateways are connected to the different sensor with IoT devices through several network protocols like Bluetooth, Ethernet, LoRa, ZigBee and Wi-Fi in different IoT applications. Internet connectivity is provided to the pedestrians by the Wi-Fi, and campus Internet must be accessed by the pedestrians. Cloud servers receive the sensor data which is combined by the IoT gateways through SDN switches. Machine learning techniques used in the IoT devices for detecting the DDoS attacks and obtained or detected attacks are blocked by the SDN controller [8, 9]. User mobile devices generating data packets are different to the IoT data packets. Therefore, so many differences exist in between them. Two categories of data are available. In that, one type of data is sensor data, and second one is pedestrian’s network data. From these two packets, features are extracted and labeled as DDoS attack packets or normal packets. Machine learning models are trains these labeled packets and obtained trained packets are sends to the SDN controller for on-line detection and one trained packets to Internet of Things gateways. The following actions take place in finding the DDoS attacks in proposed model: 1. 2. 3. 4.

The sensor data collection and IoT devices authentication is done by heterogeneous gateway implementation. The features are extracted for this sensor data by normalization and data cleaning processes. Then machine learning-based DDoS attack detection model is proposed for getting the malicious data. Obtained or detected attacks are blocked by the SDN controller for eliminating the threats furthermore.

2 Internet of Things (IoT) The climate observations are detected by the little gadgets called remote sensor hubs which are comprised by the IoT sensor networks and can detect the small changes also. Wireless sensor network (WSN) is created by the devices for responding to the environment situations [10]. Some gadgets are empowered by the IoT, and in case of switches, large equipped framework is involved for information handling and recovery. Sensor hubs are also comprised by WSNs. Entrance and assortment center point of WSN are filled with information by the group hubs. IoT-based WSN is screened by the clients with IP-based LAN or Internet. The sensor, force unit, radio and processor are the four main elements of sensor hubs [11]. In the process of natural

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factors estimation, remote association is used by sensors. The changing information of environment is converted into signals by the processor for transmission and hub battery pack I comprised by force unit. The hubs used rest cycles for spare force which are also maintained by the processor.

2.1 Authentication for IoT Devices In wireless IoT devices, one of the most essential things is authentication of the network [12]. ZigBee, Bluetooth, LoRa encryption algorithms and IoT devices contained different DDoS attacks are explained by many users. With encryption capability, the security is ensured by LoRa encryption algorithms WAN to the gateways and devices. Because of the lack of security, the coordinator is easily added by the ZigBee end devices. The information is altered and stolen in DDoS attacks which results to unavailability of resources to the end authenticate user and make the device to disabled. The attack entering into the network can be analyzed by observing the device behavior. The messages are more secured in the Bluetooth and ZigBee through the AES-CCM or AES-CBC-MAC and AESCRT because of their encryption standards. But, messages encryption and decryption cause the latency problems, and simultaneously energy limitations, storage and computing are also raised.

2.2 DDoS Attacks Detection Conventionally, cloud servers analyze the different types of DDoS attacks. The domain name system (DNS) reflection attack is detected by the calculation of inbound/outbound packets ratio in which the outbound and inbound traffic monitored. If more number of ICMP packets exists within a short time in ICMP flood, then there is attack of DDoS attacks. The SYN/ACK ratio is used as the indicator of SYN flood by the author in SYN flood. It is resulted as abnormal when the high values are obtained for SYN/ACK ratio. In the environment of cloud computing, algorithm-based DDoS attack detection system is designed, and signature detection techniques are proposed by the authors for getting accurate results. Distinct destination IP addresses count, bandwidth, protocol, inter-packet interval, packet size and stateless features are selected by the authors for DDoS attack detection in IoT. The proposed framework is used to detect the DDoS attacks and their mitigation for IoT environment [13]. DDoS attack detection and mitigating system are designed in this paper on IoT servers [14].

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3 Internet of Things DDoS Attack Detection System Using Machine Learning The framework of efficient counter-based IoT DDoS attack detection system using machine learning is represented in Fig. 1. Fig. 1 Framework of IoT DDOS attack detection model

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3.1 Methodology The traffic investigation reveals the strange movements in the network by the proposed framework, and different IoT gadgets record the ordinary exercises which indicated this action. From different IoT devices, the data is collected as big data and used in this study. Two categories of data are available. In that, one type of data is sensor data, and second one is pedestrians or user equipment network data. The DDoS attacks detection and blocking in this process follows the following steps: IoT security authentication is designed at the starting step which has lower latency than IoT device layer encryption standards. The collected data can pass through normalization. The captured packets features are selected which are from IoT gadgets and IoT gateways, and selected features are trained according to proposed framework. DDoS attacks are blocked by anomaly SDN detector in the SDN environment by using machine learning methods.

3.2 IoT Devices The collection of dataset is done in this phase. Attack-free data is collected from the normal activity. Data collection, packet analyzing and IoT sensor devices are the subphases of this phase. Two categories of data are available. In that, one type of data is sensor data, and second one is pedestrians or user equipment network data.

3.3 Data Preprocessing Noisy information or incomplete data, duplicates of original data are contained by collected data in the first phase. So, there is a requirement of knowing different data features. According to features, the data is divided into different sets. Therefore, a lot of data is contained in the every feature set. These feature sets are always observation for checking whether these are empty or replaced by any one. Duplicate data elimination is required from the network according to algorithm. The database stores the obtained feature data and calculates the standard deviation, mean, maximum and minimum for each attributes. Distinctive patterns extraction and different learning processes use the large-scale data in this phase. Data normalization, cleaning, feature extraction, feature selection, and as training and testing datasets spitted data.

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3.4 Normalization Different variance, mean and different numbers containing data may lead to learning method accuracy and efficiency reduction simultaneously. Therefore, the marginal values negative impacts are reduced by using the min–max scaling technique. Feature scaling is also other name for this technique. The obtained values are in the range of 0–1.

3.5 Feature Extraction 115 highlights of information are collected from every client. Same source with IP address is utilized by these highlights. MAC addresses are also utilized with same IP address. From various traffic, objective host IP (channel) and source with IP address are collected. TCP and UDP (attachments) conventions are utilized for feature extraction.

3.6 Feature Selection All comparative highlights erasing is important which is done in this phase. Securities are maintained for the fundamental thing highlights and leave the remaining highlights which are reasonable for other people. The highlights or features are reduced to 40 from 115 after utilization of information. There are many advantages with feature selection as it reduces the misleading attributes which consequently improves the modeling accuracy, redundancy and noise attributes are minimized as over fitting reduction and as well as training time is also reduces. For measuring the dependency level and understanding the features correlation evaluation, use the Pearson coefficient technique. Based on Cauchy–Schwarz inequality, the values of Pearson coefficient correlation are in between − 1 and + 1. Total positive linear correlation is indicated by the + 1, total negative linear correlation is indicated by the – 1, and nonlinear correlation is indicated by the simply zero. A Pearson coefficient correlation calculation basic formula is represented in below equation as: ρ(x, y) =

cov(x, y) σx σ y

where the covariance is denoted by cov, and standard deviation of x and y is denoted by σx , σ y , respectively.

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3.7 Cross Validation Information collection testing and preparing, 10-crease are using by the cross approval. The data collection is partitioned to ten sections by approving the 10overlay. Data preparation uses total nine sections, and only last section is utilized for testing of data. For testing, one time is utilized by every information section, and for preparing, it uses the multiple times. Then, multiple times of rehashed data are converted into other test piece. Without ten runs, execution forms a normal outcome.

3.8 Machine Learning Methods WEKA software is used to dataset learning. The University of Waikato is developed the WEKA software which is written in Java, and it is open sourced. Different machine learning classifiers are used in this proposed DDoS attack detection method as neural network, LSVM, random tree and decision tree (J-48).

4 Results Sensors sense frequency, network transmission for protocols and sensor distribution that are introduced at the starting of the operation. Then, apply the network data through Wi-Fi and IoT gateway sensor data to the DDoS attack detection framework of implementation in order to detect the DDoS attacks for network flood and sensor data flood in addition with SYN flood, ICMP flood and UDP flood. Several types of DDoS attacks detection results are discussed here. Eight smart poles are constructed in any campus for maintaining the realistic experimental environment. Access point (AP), LED lamp, smart signage, camera, an equipment box and communication box are the equipments of each smart pole. Communication box is having the different sensors, heterogeneous gateway as Wi-Fi devices and Ethernet, Bluetooth, ZigBee (through I2 C interface) are communicated with Raspberry Pi 3. An assault and valid condition is represented in the proposed framework which is forecast as genuine positive (TP), also named as class positive affectability and exactness. No assault and valid condition is represented in the proposed framework which is forecast as true negative (TN), also called as class negative affectability and exactness. Assault and invalid condition which point than forecast is a false positive (FP). An assault is not showed by the framework but it is bogus that expectation point is called as false negative (FN). TP and TN forecast rates are attractive and satisfactory. Four classifiers are applied to the two different datasets, and then, the metrics are found that true positive rate (TPR) or recall is better in random forest, neural BP network and that decision tree (J-48) than the LSVM. Neutral network consumed

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Table 1 Different classifier performance metrics Dataset

Classifiers

Accuracy

Precision

Recall

F-Measure

MAE

Sensor data flood

LSVM

97.1

91.2

88.5

87.6

6.7

NN

97.6

98.1

98.1

98.1

0.83

J-48

98.8

98.8

98.4

98.7

0.41

RF

98.8

98.8

98.8

98.7

0.39

LSVM

98.2

90.2

91.4

89.2

6.5

NN

98.5

98.3

98.6

97.7

0.47

J-48

99.9

99.9

99.97

99.7

0.34

RF

99.9

99.9

99.97

99.7

0.37

Network data flood

time is more than the random forest and decision tree (J48). MAE has an unmistakable translation as the mean supreme contrast among yi and x i (Table 1). n

i=1 (yi

MAE = Accuracy =

− xi )

n

(TP + TN) (TP + TN + FP + FN)

Recall =

TP (TP + FN)

Precision = F1 − Score = 2 ×

TP (TP + FP) Precision ∗ Recall Precision + Recall

Mean absolute error (MAE) of four different classifiers with two different datasets is represented in Fig. 2. From figure, it is clear that mean absolute error (MAE) value of LSVM is high than remaining classifiers in two datasets (Fig. 3). SDN controller hardware equipment is connected along with high bandwidth, and then, it is set with SDN bandwidth control rule is switched to 800 Mbps for preventing the overload status into device in the condition of large-scale DDoS attack launching in the network. Slicing and reservation are bandwidth control two modes. Reservation state is defined as when the capacity is left in one user then other user is ready to use the remaining capacity. Slicing state is defined as that residual capacity is present in the user, it cannot be used by any other users, and it is maintained for original user only. Consequently, we take advantage of slicing technology to guarantee that there is no problem for our smart poles to transmit sensor data.

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Fig. 2 Different classifiers ‘MAE’

Fig. 3 Different classifiers accuracy

5 Conclusion IoT DDoS detection model designing based on counter values is proposed in this paper by using machine learning methods. Abnormal activities or properties are detected in the network by using proposed framework. Node density on the botnet formation, node density and processing power insufficiency are the IoT-specific features which impact on the challenges of network. Different machine learning classifiers are used in this proposed DDoS attack detection method as neural network, LSVM, random tree and decision tree (J-48). DDoS attacks are detected with great accuracy in the proposed model. 99.7% of F1-score and 0.34 mean absolute error (MAE) are obtained. Malicious devices MAC addresses and IP addresses are sent to the SDN controller if the abnormal packets are detected. A set of rules are made by the SDN controller regarding to blocking details of malicious devices and transfers it to SDN switch. Then, blacklists contained malicious devices are blocked in the SDN

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switches also. Like this, the proposed model is that DDoS attacks are detected in the IoT environment and detected devices are blocked by the SDN controller. Decision tree (J-48) and random forest classifiers are giving best results in terms of accuracy, recall, precision and MAE.

References 1. N. Suman, R. Priya, A. Abhishek, A. Utsav, Internet of things (IoTs)-review and it’s multiple classification, in 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV) (2021) 2. S. Uludag, Y. Yilmaz, K. Doshi, Timely detection and mitigation of stealthy DDoS attacks via iot networks. IEEE Trans. Dependable Sec. Comput. (2021) 3. S.I. Gandhi, I. Sumantra, DDoS attack detection and mitigation in software defined networks, in 2020 International Conference on System Computation Automation and Networking. (ICSCAN) (2020) 4. Y.Y. Tang, L.X. Yang, K. Huang, Y. Xiang, X. Yang, A low-cost DDoS attack architecture. IEEE Access 8 (2020) 5. G. Lencse, Benchmarking authoritative DNS servers. IEEE Access 8 (2020) 6. L. Hong, K. Wehbi, A.A. Bhutta, T. Al-salah, A survey on machine learning based detection on DDoS attacks for IoT systems, in 2019 SoutheastCon (2019) 7. O. Osasona, T. Adesina, A novel cognitive IoT gateway framework: towards a holistic approach to IoT interoperability, in 2019 IEEE 5th World Forum on IoT (WF-IoT) (2019) 8. P. Deepalakshmi, K.M. Sudar, V. Deepa, Detection of DDoS attack on SDN control plane using hybrid machine learning techniques, in 2018 International Conference on Smart Systems and Inventive Technology (ICSSIT) (2018) 9. J.D.A. Prayuda, N.I.G. Dharma, M.F. Muthohar, D. Choi, K. Priagung, Time-based DDoS detection and mitigation for SDN controller, in 2015 17th Asia-Pacific Network Operations and Managet Symposium (APNOMS) (2015) 10. K. Burse, S. Jain, V. Nigam, Profile based scheme against DDoS attack in WSN, in 2014 Fourth International Conference on Communication Systems and Network Technologies (2014) 11. J.T. Adams, S. Gervais-Ducouret, M. Stanley, WSN, in 2012 IEEE Sensors Apps. Symposium Proceedings (2012) 12. A. Fongen, Identity management and integrity protection in the internet of things, in 2012 Third International Conference on Emerging Security Technologies (2012) 13. Y.K. Beshah, S.H. Tefera, W. Yong, Understanding botnet: from mathematical modelling to integrated detection and mitigation framework, in 2012 13th ACIS International Conference on Software Engineering, AI, Networking and Parallel/Distributed Computing (2012) 14. X.W. Wu, J. Yearwood, L. Zi, Adaptive clustering with feature ranking for DDoS attacks detection, in 2010 Fourth International Conference. on Network and System. Security (2010)

Advancements of Artificial Intelligence in Microbiological Study by Extraction and Identification of Different Micro-organism Clusters N. Vishnu Teja, Kalyana Srinivas Kandala, Anudeep Peddi, I. Neelima, Poonam Upadhyay, and N. Sudhakar Yadav

1 Introduction Microorganisms, which ranges from microns in size, play a crucial role in our life. These are the earliest evidences of lifeforms on earth. Some of the theories suggest that humans are evolved from microbes in methods like mutation, cell division and so on. Evidences found in ancient items like rocks serves as an example of microorganism’s adaptation to extreme atmospheric conditions by doing itself some genetical modifications. These microorganisms exist in a clusters or group of clusters having different microorganisms. The interactions between the different microorganism clusters can play a role in causing diseases by release of toxins. Pathogens present in our food will damage the organ systems and may leads to death. They can N. Vishnu Teja · I. Neelima · P. Upadhyay Department of Electrical and Electronics Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, India e-mail: [email protected] I. Neelima e-mail: [email protected] P. Upadhyay e-mail: [email protected] K. Srinivas Kandala · A. Peddi (B) Deptartment of Electronics and Communication Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, India e-mail: [email protected] K. Srinivas Kandala e-mail: [email protected] N. Sudhakar Yadav Deptartment. of Information Technology, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. K. Mandal et al. (eds.), Innovations in Signal Processing and Embedded Systems, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-19-1669-4_19

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also genetically increase their resistance to drugs like “the drug-resistant tuberculosis in Belarus”. Survival from the epidemics requires intensive study of microorganisms and their genetical modifications; production of cures, vaccines, dis-infectants and so on; requires more time and intensive care of specimens. Moreover, most of the countries do not have advanced research laboratories due to lack of budget. The foregoing constraints can be overcome with the progress of AI in microbiology, the ability to discover important relationships in a dataset, and research in finding results, treating, and diagnosing a variety of clinical diseases. AI has progressed to the point that it can now be used in a variety of healthcare and research fields, including as illness detection, health service delivery, medication discovery, and so on. AI can aid in the faster and more precise analysis and identification of patterns in big and complicated datasets. It may also be used to find the information needed in the scientific literature and to map different categorized data sets to help in studying microorganism adaptations and drug development. With the use of artificial intelligence health applications, people may also be empowered to evaluate their own symptoms and aid in self-care. Structured data (such as genetic data), electro-physical data (EP), and imaging data may all be used effectively with ML algorithms to better understand how viruses affect the host body.

2 Machine Learning Processes Patient characteristics such as genetic expressions, analytical imaging, electrophysical testing dataset, objective test results, medical symptoms, and treatment concerns are all inputs to machine learning. These inputs describe the stage of the condition and the patient’s endurance. Machine learning algorithms are divided into two categories to choose whether or not to incorporate acquired results: unsupervised learning and supervised learning. In order to build an analytical representation, supervised learning is utilized to construct relationships between input from the patient’s data and results. There are currently two approaches for unsupervised learning: principal component analysis (PCA) and clustering algorithms. PCA is typically used to reduce the number of elements in a large quantity of data in patient characteristics, after which a clustering approach is used to fraction the concerns. All of these fraction problems and patient features are compiled and used for further diagnostic research.

3 Deep Learning Processes The deep learning approach is a development of the classic neural network that aids in the comprehension of nonlinear information models. Deep learning has been acknowledged as an efficient model for understanding and processing large volumes of data as a result of the research of many themes. Deep learning methods in medical

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applications include recurrent neural networks, convolutional neural networks, and deep belief network. Unlike the machine learning algorithm, which is used to evaluate data with a small number of characteristics, the convolutional neural network method is used when high dimensional data, such as data with a large number of characteristics, has to be analysed.

4 AI Interventions in Microbiology 4.1 Study of Relation Between Human Health and Microorganisms Vast majority of microorganisms exist in our body in intestinal gut, mouth, and so on. We, humans, have a symbiotic relationship with microbes, and they play a major role in maintaining human metabolism and evolution. Studies have shown that microbe’s population disorder is one of the major factors causing many diseases to multiple systems like irritable bowel syndrome, inflammatory bowel disease to digestive system; allergy, multiple-sclerosis, asthma by decreased functionality of immune system; obesity, diabetics due to imbalances in endocrine system; neuropsychiatric disorders like depression, autism, and so on. Many sequence projects like human microbiome project (HMP) were analysed the human health microbial relationship. One of the novel approaches in AI is link propagation for human microbedisease association prediction (LPHMDA); this approach predicted more accurately in revealing microbe-disease interactions. It is a semi-supervised method which initially extracts data from human disease association database (HMDAD) and forms a adjacency matrix of microbe-disease associations. Assume that two similar pairs of nodes are having similar connection strength. This algorithm is having a task of predicting link strength between the microbe nodes and the disease nodes in a network (Fig. 1), [1].

4.2 Extraction of a Microorganism Cluster from a Community of Different Microbial Species The presence of microorganisms is almost everywhere like in atmosphere, soil, and so on. Most of the microorganisms cannot be classified easily as they exist in communal colonies, sometimes individual colonies. Some of the studies have shown the microbial diseases due to diversity, some of them like obesity, diabetes, and so on. Since the beginning, traditional methods involve electrolytes, partially digested protein, agar for microbial culturing and staining to observe under microscope. This requires more time and surveillance for studying growth and microbe’s classification. Moreover, these are hard to distinguish in clusters. Thus, these can be isolated by using

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Fig. 1 Flowchart of LPHMDA demonstrating the basic ideas of revealing underlying microbedisease associations by integrating node similarity information [1]

automated process; one of them is automatic image segmentation. It is helpful in feature extraction in which it isolates the foreground and image’s other parts. Next, extraction of features like colour, shape, and texture of foreground is done easily. It also helps in tracking microorganisms which are captured from microscope; thus, it helps in research purposes for understanding mobility, genetic, and quantitative characteristics (Fig. 2), [2].

4.3 Identification of Different Bacterial Species in Food Contamination of food is a serious problem for the food-processing sector as a result of over-population. Identifying and categorizing pathogens in food, as well as employing techniques for automating the detection and classification of microorganisms, will be extremely beneficial in preserving food safety throughout the food manufacturing process. The usage of colony scatter pattern, which employs image analysis and machine learning technologies, necessitates sophisticated calculation and lengthy testing with various feature combinations in order to choose the best scatter-related feature combination. For understanding the technique, we use bacterial colonies as specimens, which are illuminated by laser, the produced scatter patterns are recorded by monochromatic digital camera as bitmaps. After applying pattern-analysis techniques and further processing, we can acquire various bacterial species and identify uniquely (Fig. 3), [3, 4].

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Fig. 2 General pipeline for automatic image segmentation of microorganisms, (1) image acquisition, (2) image enhancement and denosing, and (3) segmentation [2]

Fig. 3 Some representative images of vibrio species [4]

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Fig. 4 Architecture of the MERCURIO system [5]

4.4 Identifying Nosocomial Infection in Hospitals When a person is admitted into or visited hospitals, there will be the presence of toxin or infection, which causes additional disease or more damage to immune system called nosocomial infections. Generally, it is considered as nosocomial infections, if its symptoms exist for 48 hrs+ after the person admitted into hospital. Nosocomial infections are drug-resistant; thus, more dangerous than community infections. To monitor nosocomial infections, MERCURIO architecture is used. It is a software package, whose objectives are validation of microbiological data and real time monitoring these infection events. It requires microbiological data, strain data to classify microbes like bacteria, hospital discharge forms, and international microbiological laboratory guidelines (Fig. 4), [5].

4.5 Identifying Bacteriophages in Metagenomic Bins and Contigs Bacteriophage is a kind of virus that infects bacteria and kills them. These are present in areas where bacteria reside, and they aid nature in controlling bacterial development. They usually multiply in enormous numbers inside bacterium cells until the virus rips the bacteria apart. These can be utilized in medicinal applications since they are not hazardous to people. Some studies suggest that these can have negative consequences, such as the illness cholera, which is produced by a lysogenic bacteriophage infection of vibrio cholerae. As a result, bacteriophage research has grown

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Fig. 5 Metagenomics analysis of bacteriophages in human gut

increasingly important. They are also concealed in metagenomes. Virus finding and mining (VFM) is a tool for detecting phages from metagenomes. It is a detector that is based on machine learning. VFM comes in two flavours: bin-VFM and unbinVFM. This VFM is critical in the research of metagenome-related issues including horizontal gene transfer and antibiotic resistance (Fig. 5) and [6, 7].

4.6 Antiviral Peptides (AVP) Antiviral peptides (AVP) are peptides that prevent viruses from infecting host cells and have antiviral action when combined with deca-peptide amide. As a result, they are useful for catching medically relevant infections. Deep AVP is a tool that uses a dual-channel deep neural network ensemble technique to analyse variablelength antiviral peptides. The bidirectional recurrent neural network, which extracts sequence characteristics from one hot encoding, is one of the deep neural networks. A dynamic convolutional neural network collects evolution characteristics from an amino acid substitution matrix in the other. Finally, both are combined, demonstrating the ability to detect antiviral peptides (Fig. 6), [7].

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Fig. 6 Deep AVP: deep learning model for identifying variable-length peptides [7]

4.7 Identifying Tuberculosis Using Convolutional Neural Networks Tuberculosis is a bacterial lung disease, which is one of the top ten leading causes of death. Tuberculosis can be detected by convolutional neural networks, which is trained by providing chest x-ray images of infected and non-infected persons, consists of image pre-processing, data augmentation, image segmentation, and deep learning classification techniques [8].

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Fig. 7 Culture plate on the right has bacteria that is resistant to all antibiotics tested

4.8 Creating Drugs Capable of Killing Antibiotic Resistance Pathogens Any bacterium develops antibiotic resistance when it evolves to overcome the mechanism of antimicrobial medication that used to kill it. A deep learning algorithm is being created to detect the chemicals that kill bacteria in the search for antibiotics. Scientists added the knowledge about medications that cure various human ailments to this algorithm after it finally grasped the chemical characteristics that help in killing bacteria. The programme then looked for crucial components in antibiotics that were not present in existing antibiotics. As a result, it aided the development of a novel pharmacological mechanism that would operate in a variety of ways, as opposed to existing antibiotics. One of these novel antibiotics is “Halicin”, a chemical originally intended to treat diabetes that destroyed mycobacterium tuberculosis (the bacteria that causes tuberculosis) and enterobacteriaceae strains (which is resistant to carbapenems). This method for finding strong medicines not only assisted in the eradication of infections, but it also appears to have the potential to protect beneficial microorganisms in the gut (Fig. 7).

4.9 AI in Fighting COVID-19 Pandemic COVID-19 is a beta coronavirus produced by the severe respiratory syndrome coronavirus-2 (SARS-CoV-2). This was originally reported on 31 December 2019 in Wuhan, Hubei province, China, and swiftly spread around the world. COVID-19 has produced such a terrible problem that the World Health Organization has labelled it a worldwide epidemic. Individuals are encouraged to wear masks and use sanitizers in order to protect themselves from the spread and impact of the COVID-19 outbreak. Hotspots in the country have been locked down to prevent the spread of infection to other places, ensuring that the healthcare system is able to provide crisis packages to effected areas, and individuals are encouraged to wear masks and use sanitizers

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in order to protect themselves from the spread and impact on them. AI scientists mainly focused on developing models in order to fast diagnose and creating cure. For that, they developed a deep learning model to identify an effective drug using existing commercial drugs that is helpful in treatment of infected patients. Deep learning can be utilized for computer tomography (CT) image processing, which offers quick approach to identify infected COVID-19 patients and also helps to classify the effected and non-effected people into three categories, so that they can be treated accordingly like standard methods of treatment for initial stage and heavy dosage of drugs for critical patients [9].

5 Conclusion Microorganisms plays a crucial role in our lives. These are helpful in harnessing of unique properties in food and beverage production like fermentation of alcohol and in antibiotics production like production of penicillin from fungi. As the advancement of science and technology, the study of microbiology became manifold. Thus, application of ML and techniques in microbiology helps in harnessing the study of immunology, which helps in strengthening immune system like usage of vaccines and genetical study for exploitation of pathogens in less time.

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Rice Disease Detection and Classification Using Artificial Intelligence R. Hruthik Chandra, Anudeep Peddi, Kalyana Srinivas Kandala, I. Neelima, N. Sudhakar Yadav, and Choudary Santosh Kumar

1 Introduction The largest, nutritious, and most obviously most ubiquitous grain in the world is rice. Rice is one of the major plants in India. The nation is also one of the primary food crops producing the largest rice area. Indeed, it is the country’s dominating crop. India is one of the world’s largest manufacturers. Rice is the basic foodstuff and flourishes in hot and humid climates, being a tropical plant. In rain-fed places, rice is usually farmed with heavy yearly precipitation. As a result, it is mostly a kharif crop in India. It takes about 25 °C temperatures and more than 100 cm of plumage. As a result, rice is vital to both the people of India and the people of Asian countries (Fig. 1). R. Hruthik Chandra · I. Neelima Electrical and Electronics Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, India e-mail: [email protected] I. Neelima e-mail: [email protected] A. Peddi (B) · K. Srinivas Kandala · C. Santosh Kumar Deptartment. of Electronics and Communication Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, India e-mail: [email protected] K. Srinivas Kandala e-mail: [email protected] C. Santosh Kumar e-mail: [email protected] N. Sudhakar Yadav Deptartment. of Information Technology, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. K. Mandal et al. (eds.), Innovations in Signal Processing and Embedded Systems, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-19-1669-4_20

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Fig. 1 Paddy fields in India

However, rice crop disorders may lower the quality as well as the amount of rice produced. It is quite natural to have a disease in plants. If correct action is not done in this respect, the impacts on rice plants will be substantial, affecting the quality, quantity, and productivity of the product. Various pathogens such funguses, viruses, viroid’s, bacteria, nematodes, and viruses target plant diseases [1]. These diseases typically impair photosynthesis and disturb the growth of plants. As a result, identifying and classifying rice plant disease at an early stage are critical. Farmers are currently using their own experience in disease detection. Farmers apply pesticides in enormous numbers without recognition of plant problems, Unable to help prevent diseases but can have a plant malignancy. While many rice disorders can produce comparable spots and certain conditions can also produce various spots due to varied rice and local environmental circumstances. Their misclassification, therefore, sometimes has a poor effect on rice farming. Currently, if a rice epidemic occurs in one location, rice pest experts from many research institutions will advise farmers. Rice specialists in remote or rural areas cannot promptly remediate or give farmers assistance in due time, but also need advanced tools and time to determine and classify rice conditions. The manual processing of data, however, requires additional eyes to examine and verify the accuracy of data [2]. During an automatic system, photographs of diseases are more precisely recognized and classified than a manually identified method. In recent decades, in combination with crop photographs, machine diagnostic techniques have proven important for the monitoring of crop conditions and pests. An integrated diagnostics system of rice diseases might provide data on diagnosis and intervention of rice diseases, allow for more time for disease control, minimize contaminants, and boost agricultural commodities type and amount. For such a system to be developed, scientists must investigate optimal rice disease segmentation and recognition methods.

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In recent years, CNN has been widely utilized in the field of machine learning and design recognition since it can extract useful characteristics [3]. CNN offers a complete learning solution that does not require picture well before and extraction straight from original pictures of relevant high-level characteristics. Krizhevsky, in particular, used a deep CNN to conduct object categorization and win the large-scale visual recognition 2012 ImageNet challenge for first place. Following this, there have developed a variety of innovative CNN algorithms and applications. Since then, a range of picture classification issues with remarkable success has been addressed in analogous CNN models. In order to get improved accuracy, there is also a deep-seated neural network [4]. SVM is also a technique for machine learning that may be used to solve classification problems by determining the appropriate settings for hyperplane and kernel. Recently, big data analysis and categorization, such as recognition and picture categorization, have been carried out utilizing CNN and SVM combinations, and the results have been shown to be quite accurate. As a result, we were prompted to create an AI system to classify rice illnesses. The proposed method constituted a methodology for classifying rice plant illness and assisting farmers in making precise calculations as well as increasing productivity. We have created a complicated AI system by combining SVM and DCNN for the categorization of photographs of rice disease in this study.

2 Classification of Rice Diseases In order to use machine learnings ideas of the computer sector, identifying illness from a plant picture is one of the fascinating topics of research in the agricultural domain. This article contains a prototype method to identify and classify rice illnesses based on pictures of rice plants affected [5]. After a comprehensive examination of several approaches utilized in the image processing processes, this prototype system is built. We look at nine illnesses in rice plants, including blast, bacterial leaf blight, rice tungro disorder, brown spot, sheath blight, falsely smutted bacteria, leaf streak, and grain discoloration. With a digital rice field camera, we take photographs of diseased rice plants.

Fig. 2 Rice blast

224 Table 1 Types of rice diseases

R. H. Chandra et al. S. No.

Name of disease

Scientific name

1

Blast

Pyricularia oryzae

2

Bacterial leaf blight

Xanthomonas oryzae

3

Rice tungro disease

Rice tungro bacilliform virus

4

Brown spot

Helminthosporium oryzae

5

Sheath rot

Sarocladium oryzae

6

Sheath blight

Rhizoctonia solani

7

False smut

Ustilaginoidea virens

8

Grain discolouration

Fungal complex

9

Leaf streak

Xanthomonas oryzae pv.oryzae

As depicted in Fig. 2, rice blast causes lesions on the leaves leaf necks, leaf columns, panicles, pedicels, and seeds present in all areas of the plant [6]. Recently, roots can also get polluted, according to research. Rice blast lesions in diamonds emerge on the leaves as the most prevalent and diagnosed symptoms, although sheath lesions are unusual (Table 1). Bacterial blight shows as moisture streaks that stretch out of the tips and edges of the leaves, widen progressively, and release a milky liquid into yellow droplets. The typical grey-white lesions appear as the leaves dry and die. This means that the illness will stop. The seedlings’ leaves dry up and wilt, known as the kresek condition. The infected plants can generally live, but the output and quality of the rice are lowered over the next two to three weeks after the bacterial blight has been contaminated. The plants can survive (Fig. 3). The rice-causing virus tungro disease is a blend of two leafhoppers viruses. Blooming of the leaf, slower growth, less tillage, and partially full or sterile cereals are all indications. tungro infects grown rice, wild rice relatives, and other rice grassland weeds (Fig. 4). Fig. 3 Bacterial leaf blight

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Fig. 4 Rice tungro disease

The fungus Cochliobolus miyabeanus causes brown spot. It is also known as Helminthosporium leaf spot, and it is one of Louisiana’s most common rice illnesses. When C.miyabeanus infects rice plants before emergence, it causes seedling blight, which results in sparse or inadequate stands, as well as weakening plants. Leaf spots can be found on young rice plants, but the illness becomes more frequent as the plants mature and the leaves begin to fade (Fig. 5). The rice sheath rot is a complicated disease produced by several fungal and bacterial conditions. The principal pathogens associated with rice sheath red are fungi, notably Sarocladium oryzae and Fusarium sp., from the complex Fusarium fujikuroi. Rice sheath rot has been linked to the number of different diseases (Fig. 6). One of the most commercially significant rice diseases in the world is rice sheath blight. This illness has a significant influence on the quality and production of grain.

Fig. 5 Brown spot

Fig. 6 Sheath rot

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Fig. 7 Sheath blight

Return losses of up to 50% have been reported under the most favourable situations. The Rhizoctonia solani fungus AG1-IA produces soil disease sheath bite. The fungi relate to the families Basidiomycota and Ceratobasidiaceae (Fig. 7). False smut produces grain chalkiness, which results in grain weight loss. It also has a negative impact on seed germination. In locations with high relative humidity (> 90%) and between 25 and 35 c, this illness may occur. Disease growth is further aided by rain, high humidity, and soils with high nitrogen concentrations. Fungal spores can be transferred from plant to plant by the wind. Only after panicle, exsertion is false smut visible. It has the potential to infect the plant during the blossoming stage. This creates chalkiness and can diminish the weight of 1,000 grains. It also causes a 35% drop in seed germination. In wet conditions, the disease can be severe, with losses of up to 25%. In India, a 775% yield loss was found (Fig. 8). Rice grains on CV were severely discoloured as a result of the disease. It frequently occurred during the early stages of hybrid rice flowering. On the lemma or palea, bright, rusty, water-soaked lesions first formed, and then turned brown. At harvest, the panicles had more immature and lighter grains. Rice grain discolouration (also known as glume discoloration, filthy panicle, or pecky rice) is frequent in Haryana, according to surveys. Brown to dark brown sores cover the infected grains (Fig. 9). Small, water-soaked linear lesions form between leaf veins as the first symptom. These streaks start off dark green and gradually lighten to a yellowish grey colour.

Fig. 8 False smut

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Fig. 9 Grain discolouration

Fig. 10 Leaf streak

When held up to the light, the lesions seem translucent. When the disease is severe, entire leaves may turn brown and die (Fig. 10). CNN is a multi-layer neural network supervised learning, which has two elements: an extractor and a classifier. The extractor is equipped with layers of maps and utilizes coevolutionary filtering and sampling to extract discrimination from raw pictures. The principal function of CNN, coevolutionary filters, and the local receptive field have two key characteristics. Convolutional filters may be seen as an extractor for local features, which finds the links between pixels in a raw picture to extract the most effective and important high-level features to increase the generalization capacities of a CNN model. Downsampling and weight sharing can also substantially impair training efficiency while reducing the number of workable parameters. The classifier and weights learned in the functional extractor can be trained via a back-propagating approach.

3 Related Work In this research, image processing is performed on a photograph of rice planted in paddy fields, and we construct an artificial intelligence system to distinguish healthy

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Fig. 11 Classification system’s configuration

and diseased images. If cameras set around paddy fields can capture photographs of all the rice for each sector and classify them based on the data, it might save a lot of money on visual illness diagnosis. Figure 11 depicts the classification system’s configuration. Since September 2016, the monitoring system of paddy fields has been reported by NTT Docomo Co., Ltd., vegetarian corporation, autonomous controller system laboratory, and aerosense corporation in Niigata city with their drones. This effort, dubbed the ‘paddy rice project’, targets to enhance crop management and mitigate harm. Sensors such as ‘Paddy Watch’ have been used to monitor the situation. It is solely reliant on sensor data. Data can be gathered as an image by taking an aerial photograph using a drone, and an accurate prediction can be expected. For this project, the team will also use deep learning to create highly accurate image analysis technology. The framework of the rice plant project [7] is depicted in Fig. 12.

Fig. 12 Rice plant project

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The paddy rice project necessitates a large amount of training data for deep learning as well as the drone’s maintenance expense. As a result, it is tough to apply it to Japan’s typical small-scale paddy fields. As a result, the goal of this work is to build a categorization system for small paddies that can operate on a small scale and at a low cost. Our technology uses a camera to classify two types of healthy rice and the most severely affected blast illness. It can work with a small number of photographs and has a simple criterion for selecting the position of the identification boundary by maximizing the margin. Lu et al. recently suggested a method based on deep convolutional neural networks (DCNN); rice illnesses may be identified by evaluating ten prominent identifying diseases [8]. A CNN model was used to enhance convergence time and increase the recognition precision of the learned parameter. Both LeNet-5 and AlexNet, two previous deep learning settings, were used as CNN architecture models for illness detection. Around 500 image samples were used. However, when using a deep convolutional neural network to classify ten rice illnesses, this little dataset proved insufficient. Despite this, the author claims to have a 95.48% accuracy rate. The authors employed a different DCNN technique which produced a system for distinguishing rice leaves depending on their health. Three separate rice plant categories were used: normal, infected with ill, and snail. For classification, they have utilized an old AlexNet architectural model from DCNN and claim 91.23% accuracy. In its categorization, they did not indicate certain illnesses or rice categories, though. The proposed model can only tell whether or not it is affected by diseases. In the light of these concerns, we have reported picture classification for nine of the most commonly afflicted rice diseases using a combination of DCNN and SVM classifiers, which resulted in greater accuracy[9, 10].

4 Methodology Artificial neural networks (ANNs) give a biologically grounded alternative algorithmic basis for computing. The computation is massively dispersed and parallel, and learning replaces a priori programme development, with ANNs developing their functionality using training (sampling) data. The ANN image recognition model was built using back-propagation neural networks, which have been effectively used in a variety of applications. The input layer had 1024 processing elements (PEs), one for each of the intensity levels in the image pixel matrix. ANN algorithms employing pixel intensities as inputs for video image processing, image recognition, and pattern recognition are the similar proposals proposed in the past. m stands for the number of PEs in the buried layer, which ranged from 80 to 400. In all training sessions, the transfer function was set to the log sigmoid function (Haykin, 1994), the maximum number of epochs was 2000, and the maximum acceptable sum of squared errors was set at 0.01. The starting value of the momentum factor was 0.975, although it may go as high as 0.99 or as low as 0.5 (Fig. 13).

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Fig. 13 ANN structure for disease identification

Graphs, molecular fingerprints, potential energy measurements, atomic coordinates in 3D, Coulomb matrices, or combinations thereof. The inputs received are further used in the DNN. The best part about utilizing DCNN is how well it extracts all of an image’s unique properties. However, it is not necessarily ideal as an image classifier because DCNN’s hidden layer includes the maximum trainable parameters and is also optimized there. But SVM overcomes this hurdle and offers us excellent classification accuracy with a completely recovered one-dimensional feature vector. We have studied how to combine a DCNN model with a very simple but effective SVM classifier to construct a complicated network to get the highest high rice disease accuracy. There are two elements of the overall approach for the identification and classification of rice diseases: deep CNN as a characteristic extractor and SVM as a classifier. Inception-V 3 [11] is one of the most advanced and complicated DCNN architectures. To do so, each image must be classified. Nearly, 23.5 million trainable parameters and around 0.5 million non-trainable parameters are used in the Inception-V 3 model. As a result, this design necessitates a lot of processing power, as well as a lot of memory units, such as a powerful GPU, and a lot of training data over a long period. As a result, we investigate the transfer learning method for training the network, which allows us to partially employ the weights of a pre-trained network while reducing training time and computational complexity. At initial V 3, we kept the 42 layers locked to prevent weights from being updated. The remaining layers have been retrained with photographs of rice plants. Back-propagation was employed to improve batch-wise training accuracy, which resulted in a considerable reduction in error rate after several repetitions. Figure 14 depicts the suggested DCNN model’s improved design. The active strata of the revised Inception V 3 model are retrained and their previously trained weights are updated on the basis of input pictures, so that more invariant, high-level, and relevant functions may be extracted. A CNN pooling layer dubbed ‘global average pooling’ homogenized all characteristics from the deep CNN model throughout the classification process. The fifth feature has been split for future use. Then, using the one-VSone technique, trained as a multi-class classifier the support vector classifier (SVC)

Rice Disease Detection and Classification … Fig. 14 Inception-V 3 DCNN revised architecture

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Fig. 15 Sequence of SVM classifier

with the remaining properties and implemented it using the kernel RBF. SVC hyperparameters such as ‘gamma’ have been fine-tuned to the grid search approach and the cost parameter ‘C’. Finally, to determine classification output, the classification performance was tested using the previously separated functions, and this sophisticated model is now capable of predicting any unknown data. Figure 15 depicts the SVM classifier’s training phases.

5 Results and Discussion A series of experiments were conducted using two different sets in order to check the efficiency of the developed algorithm: images tested and disease images learnt. The ‘Train One, Rest Test’ method has been used for simulations with the neural network of various images of rice disease. The network calculated the input factor for each feature, and the average input from these features was used over five areas

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Table 2 ANN learning data set of different diseases Field

Diseases Brown spot

Blast

BLB

Necrotic zone

1

32

7

6

2

24

9

6

9

3

34

7

5

13

11

4

27

8

7

9

5

23

9

7

11

140

40

31

53

Total

to determine the disease characteristics. Diagnosed rice leaves were collected from five different ANN training areas in order to identify different rice diseases (Table 2). These have been taken at random. ANN learning with diseases of various forms, sizes, and colours has been carried out. Brown spot, blast, BLB, and necrotic zone were trained, respectively, with 140, 40, 31, and 53 in order to increase the efficiency of ANN. Regular and irregular forms have been used to examine various diseases in 25 leaves from each #1 to #5 field. ANN-based DIS software has tested diseases from these leaves. The DIR ratio refers to the performance against the unknown (tested) data of ANN. The unknown brown spot number, blast, BLB, and necrotic zone were 86, 9, 6, 22 in Field #1. The ANN DIR in this case was 89.54%, 77.78%, 83.33%, and 77.27%, respectively, for brown spots, blast, BLB, and necrotic zone. It is obvious that brown spot 86.91% has been recognized by ANN. Blast, BLB, and necrotic area 87.5%, 85.71%, and 75.00%, respectively, of field #2 were identified. The study showed that new tested diseases have been recognized with increased efficiency (DIR) where the diseases’ shape and colour are nearly similar to the training data in each field. However, the machine could not identify the image if the shape and colour were distinctly different from the learning data set. As the shape and colour in the trained and tested dataset of brown spot was almost identical, the ANN performance was higher. There was a less similarity in shapes and colours, so the recognition rate between blast, BLB, and necrotic zones was lower. Inadequate ANN training may have caused low rates of success recognition. From the results above, it was found that there were linear links between sample numbers and different forms in training with ANN’s performance.

6 Conclusion For farmers, a rice disease with a naked eye cannot be identified and classified. It requires a great deal of time and human work, in addition to professional understanding. Consequently, an effective strategy has been taken to computerize the classification method and to identify rice plant disease images. The objective was

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to develop an automated system to classify pictures from rice crops with AI and computer vision techniques in this study. This study was carried out to develop an ANN for the identification of rice leaves from paddy field diseases. The pixels of the indexed image have been allocated with grey colour values which are used as ANN inputs 125 training pictures, and 250 others for testing were taken. For ANN use, 16 × 48 pixels were added for each disease. With 179 PE numbers in its hidden layer, the neural back-propagation network model has been developed. ANN output strategies have been used in four different disease assessment schemes. The overall performance of the ANNs was compared, and the rate of success for disease identification was found to be 76.60–88.60%. In all diseases, the ID ratios of ANNs for brown speaks, blast, bacterial leaf blight, and necrotic zones were 88.60%, 80.00%, 81.10%, and 76.60%, respectively. While the study was limited in computerspecific resources and training data available, the results indicated the potential for rapid image recognition, classification, and site-specific disease identification and associated applications for the use of ANNs in the control of agricultural real world.

References 1. H.K. Lichtenthaler, Vegetation stress: an introduction to the stress concept in plants. J. Plant Physiol. 148(1–2), 4–14 (1996) 2. Bacterial blight—IRRI Rice Knowledge Bank. http://www.knowledgebank.irri.org/decisiontools/rice-doctor/rice-doctor-fact-sheets/item/bacterial-blight 3. S. Sladojevic, M. Arsenovic, A. Anderla, D. Culibrk, D. Stefanovic, Deep neural networks based recognition of plant diseases by leaf image classification. Comput. Intell. Neurosci. (2016) 4. J. Suzuki, Y. Sasaki, E. Maeda, SVM answer selection for open domain question answering, Aug 24–Sept 01 (2002) 5. Rice disease identification photo link. www.agri971.yolasite.com/resorces/RICE/DISEASE/ 0IDENTIFICATION.pdf. Accessed: 2019-08-25 6. J.M. Bonman, Rice Blast, Compendium of Rice Diseases, eds. R.K. Webster, P.S. Gunnel. (St. Paul, Minnesota. USA, 1992) American Phytopathological Society Press, pp. 14–18 7. NTTdocomo, Press Releases (2017). https://www.nttdocomo.co.jp/info/news_release/ (Accessed 2017-5) 8. Y. Lu, S. Yi, N. Zeng, Y. Liu, Y. Zhang, Identification of rice diseases using deep convolutional neural networks. Neurocomputing. 267, pp 378–384 (2017) 9. V. Vanitha, Rice disease detection using deep learning. Int. J. Recent Technol. Eng. (IJRTE), 7(5S3) (2019) 10. R.R. Atole, D. Park, A multiclass deep convolutional neural network classifier for detection of common rice plant anomalies, Int. J. Adv. Comput. Sci. Appl. 67–70 (2018) 11. C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, Z. Wojna, Rethinking the inception architecture for computer vision, in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (Las Vegas, NV, 2016), pp. 2818–2826

Network Intrusion Detection Using Machine Learning for Virtualized Data Sreenivas Mekala, Rajaram Jatothu, Sarangam Kodati, Kumbala Pradeep Reddy, and Nara Sreekanth

1 Introduction The cyber crime is increasing day to day with Internet usage wide spreading which is used for online content accessing. The network or computer system uses the intrusion detection system (IDS) for detecting or identifying the abnormal behavior in the network [1]. Anomaly-based IDS and misuse-based IDS are two major ways of IDS among different ways. Misuse-based IDS efficiently detects the snort named attack. False alarm rate is very less in this type of IDS. New attacks are not recognized by this type so instructions are not personalized in database. Regular behavior of the model is developed in anomaly-based IDS; then, essential deviations are separated from the model and assumed this deviation as intrusion [2]. Unknown and known attacks are detected by this type of IDS but fails in achieving the less false alarm rate. This problem is achieved by using different machine learning algorithms [3]. Hypervisor named monitoring machine and group of virtual machines (VM) are called as virtualized environment. In present days, more popular designs are virtualized infrastructures with the advantages of Azure from Microsoft and Web services S. Mekala Department of Information Technology, Sreenidhi Institute of Science and Technology, Hyderabad, Telangana, India R. Jatothu · S. Kodati (B) Department of Computer Science Engineering, Teegala Krishna Reddy Engineering College, Hyderabad, Telangana, India e-mail: [email protected] K. Pradeep Reddy Department of Computer Science Engineering, CMR Institute of Technology (Autonomous), Hyderabad, Telangana, India N. Sreekanth Department of Computer Science Engineering, BVRIT Hyderabad College of Engineering for Women, Hyderabad, Telangana, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. K. Mandal et al. (eds.), Innovations in Signal Processing and Embedded Systems, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-19-1669-4_21

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(MWS) from Amazon [4]. In virtualized environments, attacks detection uses the big data analytics with machine learning techniques [5]. Potential attack paths can be detected by using application logs and network data analytics. Log features collection can confirm the attack possibility in the machine learning techniques for path classification. Anomaly-based network IDS is used to protect the networks or systems from the malicious activities than the remaining methods of IDS. But in general, different types of intrusion detection systems are existed [6]. Anomaly based intrusion detection are includes with machine learning methods are more proposed in this paper. Artificial based intrusion detection are includes with machine learning methods are more proposed in this paper and decisions are made according to system, with the small helping of humans patterns are identified [7]. Intrusion detection is considered by the machine learning approach as classification problem. Network traffic is classified by different proposed models as malicious networks. Different anomalybased techniques are as follows: support vector machines (SVM), linear regression, Gaussian mixture model, genetic algorithm, Naive Bayes, decision tree, and k-nearest neighbor algorithm [8, 9]. Different types of problems use the SVM algorithm which is most widely used machine learning algorithm. Different attacks are detected by the anomaly-based detection approach but fail in achieving the less false alarm rate [10, 11]. Generating profile complexity of practical normal behavior is dataset training. One of the most affected parameter of the network is accuracy which is calculated after applying different techniques. Accuracy analysis is included with false alarm rate and detection rate or detection time. The obtained results are better with less false alarm rate and high detection rate. The proposed IDS uses the Naïve Bayes, SVM, and random tree for evaluating the performance. Feature reduction and normalization are also used in comparative analysis of proposed system. Data sample size is reduced by using the data clustering and data partitioning based on Voronoi. As separate Voronoi regions, the dataset is clustered and data sample size is reduced by separate centroids of the regions. The required training time is reduced by the smaller datasets. For learning, multiple randomized trees ensemble learner is used. Diversity is provided by using bootstrap re-sampling among the learners. On the data disjoint subsets, the underlying learners are grown. A better performance and accuracy are provided by the randomized data partitioning model as same as obtained accuracy by deep neutral networks and machine learning methods.

2 Network Intrusion Detection System Internet traffic analysis on the subnet is provided by the NIDS [12]. Any type of suspicious behavior can be detected by the NIDS by observing all the incoming data. According to seriousness of the threat, the system is reacted. Bonnet intrusion detection method uses the randomized data partitioning learning model [13]. The process of this approach is involved in four stages as: training required a dataset, machine learning algorithm development which is scalable and efficient with large

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scale network traffic, number of data samples are reduced with data reduction, and finally, considering the multiple randomized tree learning model. Training uses the dataset as information security and object technology (ISOT). Network traffic characteristics are described by the feature set, as destination and source ports, packet size, payload length, etc. which are used in training of dataset. Feature selection techniques are also included in machine learning methods in some intrusion detection methods. Ranking technique next process is feature selection which is used to select or extract the features for calculating the learning model overall accuracy [14]. Huge amounts of unlabeled data and less amounts of labeled data are used by the semi-supervised learning model training purpose [15]. Reinforcement learning method uses the trial and error method which gives the best results in terms of accuracy and uses the classification of data, regression, and prediction of data. This type of learning is having three main components as actions, environment, and agent. The main aim of this learning is actions are selected by the agents and actions develop the predictable reward. The goal of the learning method is as much as fast achieved by the agent by applying good policies.

2.1 Support Vector Machine One type of supervised learning method is SVM which trains the different types of data of different subjects. Decision boundary or best line creation is the main aim of SVM classifier, which separates the n-dimensional space into classes. Therefore, in future, to the correct category new data is easily acquired. This best decision boundary is named as hyperplane. The creation of hyperplane uses the help of extreme vectors or points by choosing SVM. These extreme cases are called as support vectors, and hence, algorithm is termed as SVM. Single hyperplane or number of hyperplanes is created by the SVM in high-dimensional space. Data is optimally separated into different classes by hyperplane with the major partition and consider this as best hyperplane. In pattern recognition and image processing application main blocks, this SVM is used. Data can be divided into two types as testing datasets and training datasets which are involved in the classification. In that, target variables define the class label and observed variables define the attributes.

2.2 Naive Bayes One of the statistical classifiers is Bayesian classifiers. According to Bayes theorem, classification algorithm collection is known as Naive Bayes classifiers. A common principle is shared by group of algorithms because this classifier is not a single classifier that means each feature pair being classified is independent of each other. This theorem is constructed according to attribute value of given class and not

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depends on the different attribute values. This theory is also called as class conditional independence.

3 Network Intrusion Detection Using Machine Learning The performance of network or computer system is improved by the elimination of intrusions in the network. So a network intrusion detection using machine learning for virtualized data is introduced in this paper, and its frame work is given in Fig. 1. Comparative analysis is done among the Naïve Bayes, random tree, and SVM in terms of misclassification rate, accuracy, and average detection time. Take the raw dataset and class attribute which contains 24 different attack types and labeled as 4 categories as R2L, Probe, Dos, and Normal. Fig. 1 Network intrusion detection using ‘ML’

IP Packets

Pre-processing

Traffic flows

Feature extraction & selection

NSL-KDD

Symbolic attributes to numerical attributes

Separation of instances into Normal, Dos, Probe, R2L, and Probe

Feature reduction using CfsSubsetEval

Normalization

Classification under SVM and Naïve bayes

Accuracy, misclassification rate and confusion matrix

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3.1 Attack Flows Attack flow collections are the first step of this process. Network logs are obtained by using TShark which contains the guest VM traffic flows. Destination and source port numbers along with IP addresses are also collected. Then individual VM user application logs are collected. Application logs are then accumulated with network logs for obtaining the correlation forms as: .

3.2 Pre-processing Symbolic features are contained in the dataset, which are not processed by the classifier. Therefore, there is a requirement of pre-processing. All symbolic and non-numeric features are eliminated or removed in this step. Hence, symbolic or non-numeric features are replaced in the processing phase. The main aim of this preprocessing stage is eliminating the symbolic features, and it is not participating in any phase of intrusion detection. Flag, service, and protocols are symbolic attributes which are removed or changed and labeled as 4 categories as R2L, Probe, Dos, and Normal.

3.3 Feature Selection Data dimensionality is reduced by the feature selection which is most important part of machine learning. Many researches have been done for feature selection. Wrapper method and filter methods are two types used for feature selection. Based on different statistical tests obtaining results, the features are selected in the filter method which is used in measuring the feature relevance by outcome or dependent variable with correlation. Feature subset is finding in the wrapper method by usefulness of feature subset measuring with the dependent variable. Machine learning methods use the wrapper method, whereas data mining methods use the filter method because millions of features are existed in data mining.

3.4 Feature Reduction Using CfsSubsetEval CfsSubsetEval is used for the feature reduction for improving the dataset performance and obtaining the different results. The pre-processed data now goes to normalization and feature reduction stages. One of the attribute selection methods is CfsSubsetEval.

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All features individual predicting estimation with redundancy degree between them are used in finding the attribute value.

3.5 SVM Classification One type of supervised learning method is support vector machine (SVM) which trains the different types of data of different subjects. Single hyperplane or number of hyper planes is created by the SVM in high-dimensional space. Data is optimally separated into different classes by hyperplane with the major partition and consider this as best hyperplane. Different kernel functions are applied to a nonlinear classifier for evaluation of hyperplane margins. Kernel Functions’ main aim is margin maximizing which are existed in between the hyperplanes, and these functions as polynomial, linear, sigmoid, and radial bases. The first 19,000 instances of raw dataset are classified by SVM along with feature reduction and normalization techniques by considering SVM for comparative analysis. Misclassification rate, average detection time, and accuracy are noted during this step.

3.6 Naïve Bayes Classification One of the statistical classifiers is Bayesian classifiers. The suitable condition of given model with particular class forecasting probability is defined by this which is known as Bayes’ theorem. This theorem is constructed according to attribute value of given class and not depends on the different attribute values. This theory is also called as class conditional independence. The below description explains the working of Naive Bayes classifier: T denotes the sample training set with their class labels each. If there are k classes, then X1, X2, X3,…,X(k−1), Xk. The n attributes depicting n measured values by A = {a1, a2,…, an} which are m1, m2,…, mn, whereas ndimensional vector is depicted by A. For a given sample A, the classifier will calculate the class having the maximum posteriori probability, conditioned. Outgoing network connections, incoming connections, opened ports, and unknown binary executions are four attributes whose analysis determines the attack presence in the network. These attributes are changed if there are attacks present in the network which are known by the observation only, and external connections are established for infected guest VM with the remote attacker. Attack presence probability is calculated based on the predefined four attributes by using belief propagation. Each individual attribute conditional probabilities are combined together for calculating the attack final probability. If predefined threshold value is less than the obtained final probability, then attack presence is confirmed in the network. Original dataset accuracy, average detection time, and misclassification rate are calculated after pre-processing.

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4 Results The KDD Cup dataset evaluates the proposed model performance. Consider only 10% of KDD dataset for training the SVM and Naïve Bayes classifiers from the entire dataset because large number of instances is existed in the 10% of dataset, and different problems may araise with applying total dataset. Flag, service, and protocols are symbolic attributes which are removed o changed and labeled as 4 categories as R2L, Probe, Dos, and Normal. These categories train the SVM, Naïve Bayes, and random tree classifiers. Corrected KDD dataset with used multi-level model is used in testing process. Total 19,000 instances are used in evaluating the Naïve Bayes, SVM, and random tree classifier performances by using detection time, accuracy, and misclassification rate. Confusion matrix information is used to calculate these algorithm performances. NSL-KDD dataset-based model is evaluated after applying different methods like feature reduction, normalization, and pre-processing. Total 19,000 samples are contained in the dataset. Consider the evaluation metrics as average detection time, misclassification rate, and accuracy. The ratio of detection time for individual malware samples addition to the total number of analyzed samples is defined as average detection time. Σ Average Detection time = (Detection time for individual malware samples)/(Total number of samples analyzed). Table 1 explains the performance metrics for three classifiers as SVM, Naïve Bayes algorithm, and random tree in the form of confusion matrix (). Table 1 Comparative performance analysis for three metrics Methodology

Accuracy (%)

Average detection time (s)

Misclassification rate

SVM

98.2

70

Naïve Bayes

64.7

120

30.4

2.82 28.5

Random tree

53.3

140

SVM—feature reduction

94.4

57

Naïve Bayes—feature reduction

60.2

137

37.8

Random tree—feature reduction

51.8

150

35.2

SVM—normalization

94.2

65

2.78

Naïve Bayes—normalization

65.7

124

28.71

Random tree—normalization

56.5

147

27.56

4.74

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accuracy rate

The individual methodologies’ graphical representations for three classifiers by considering three metrics are given below. The Fig. 2 represents the comparison of dataset misclassification rate, average detection time, and accuracy of different classifiers after feature reduction. The Fig. 3 represents the comparison of dataset misclassification rate, average detection time, and accuracy of different classifiers after normalization method. The Fig. 4 describes the comparison of classification accuracy, average detection time, and misclassification rate of the dataset after pre-processing. From the above graph, it can be inferred that SVM attains accuracy of 98.2%, Naive Bayes attains 160 140 120 100 80 60 40 20 0 SVM Accuracy

Naïve bayes classifiers Average detection time

random tree Misclassification rate

accuracy rate

Fig. 2 After feature reduction metrics

160 140 120 100 80 60 40 20 0 SVM

Naïve bayes

random tree

classifiers Accuracy

Average detection time

Misclassification rate

accuracy rate

Fig. 3 After normalization metrics

160 140 120 100 80 60 40 20 0 SVM

Naïve bayes

random tree

classifiers Accuracy

Average detection time

Fig. 4 After pre-processing obtained metrics

Misclassification rate

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accuracy rate of 64.7, and random tree has accuracy as 53.3 for 19,000 instances. Random tree has high misclassification rate than SVM and Naïve Bayes for 19,000 instances. The application log and network log correlation is used in enabling the user for detection of malicious activity in network connections, with the addition of identification of connection source. Number of virtual machines is included in the system scaling, and the virtual machine ID uses the extension of user and network logs. Particular log entry is obtained by the virtual machine. Therefore, virtual machine identification is easily tracked by the users if anomaly is detected in the network and the application and user ID from which the suspicious activity has originated from.

5 Conclusion Security mechanisms are required for the substantial growth in the number of applications in order to maintain their protection. More trending topics for researchers are intrusion prevention or intrusion detection. There are different types of intrusion detection systems. Security analytics and cyber threat identification effective methods are combined by the big data capabilities of machine learning algorithms in virtualized environments. Therefore, a network intrusion detection using machine learning for virtualized data is proposed in this paper. Naïve Bayes, SVM, and random tree classifiers are used in this study. The comparative analysis is obtained for three classifiers according to performance metrics as accuracy, average detection time, and misclassification rate. Therefore, an effective method for detecting intrusions with great accuracy is designed.

References 1. S. Sasipriya, L.R. Madhan Kumar, R. Raghuram Krishnan, K. Naveen Kumar, IDS in web applications (IDSWA), in 2021 5th International Conference on Intelligent Computer and Control Systems (ICICCS) (2021) 2. S.A. Mostafa, Z.K. Maseer, C.F.M. Foozy, N. Bahaman, R. Yusof, Benchmarking of ML for anomaly based IDS in the CICIDS2017 Dataset. IEEE Access, 9 (2021) 3. Y.A.M. Hamad, L.A.H. Ahmed, ML techniques for network-based IDS: a survey paper, in 2021 National Computing Colleges Conference (NCCC) (2021) 4. G. Lanciano, T. Cucinotta, F. Brau, A. Ritacco, V. Iannino, F. Galli, A. Artale, M. Vannucci, E. Sposato, J. Barata, Forecasting operation metrics for virtualized network functions, in 2021 IEEE/ACM 21st International Symposium on Cluster, Cloud and Internet Computing (CCGrid) (2021) 5. N. Aljehane, Big data analytics: challenges and opportunities, in 2020 International Conference on Computing and Information Technology (ICCIT-1441) (2020) 6. Y-G. Cheong, Y. Song, K. Park, Classification of attack types for IDS using a ML algorithm, in 2018 IEEE 4th International Conference on Big Data Computing Service and Applications (BigDataService) (2018)

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7. S. Shahbudin, M.S. Abd Shukor, S.I. Suliman, R. Mohamad, M. Kassim, NIDS using artificial immune systems (AIS), in 2018 3rd International Conference on Computer and Communication Systems (ICCCS) (2018) 8. M. Basheri, I. Ahmad, A. Rahim, M.J. Iqbal, Performance comparison of SVM, random forest, and extreme learning machine for intrusion detection, IEEE Access. 6 (2018) 9. R.M. Sharma, R. Bilaiya, IDS based on hybrid whale-genetic algorithm, in 2018 Second International Conference on Inventive Communicational and Computational Technologies (ICICCT) ) (2018) 10. F. Suri-Payer, F. Schmidt, M. Wallschläger, A. Gulenko, O. Kao, A. Acker, Unsupervised anomaly event detection for cloud monitoring using online arima, in 2018 IEEE/ACM International Conference on Utility and Cloud Computing Companion (UCC Companion) (2018) 11. S. Weber, N. An, Impact of sample size on false alarm and missed detection rates in PCAbased anomaly detection, in 2017 51st Annual Conference. on Information Science and Systems (CISS) (2017) 12. T. Hamalainen, A. Viinikainen, S. Kumar, ML classification model for network based IDS, in 2016 11th International Conference for Internet Technology and Secured Transactions (ICITST), (2016) 13. K. Kim, O. Alhussein, O.Y. Al-Jarrah, K. Taha, P.D. Yoo, S. Muhaidat, Data randomization and cluster-based partitioning for botnet intrusion detection. IEEE Trans. Cybernet. 46(8) (2016) 14. X. Wang, M.B. Shahbaz, J. Samarabandu, A. Behnad, On efficiency enhancement of the correlation-based feature selection for IDS, in 2016 IEEE 7th Annual Information Technology, Electronics and Mobile Communication. Conference (IEMCON) (2016) 15. W. Chenggang, L. Cunhe, A new semi supervised SVM learning algorithm based on active learning, in 2010 2nd International Conference. on Future Computer and Communication, vol. 3 (2010)

Detection of Human Behavior Using Swarm Technique and Neural Networks Venkatesh Shankar

1 Introduction Artificial neural system is framework made out of neurons coordinated in information, yield and covered up layers. The neurons are associated with one another by a bunch of synaptic loads. A neural system is an integral asset that has been applied in an expansive scope of issues, for example pattern recognition, forecasting and regression. Human recognition targets strange the action of users dependent on arrangement of perceptions gathered during the movement in a definite setting climate. Applications that are empowered with movement acknowledgment are acquiring immense attention, as user gets customized administrations and supports dependent on their context-oriented behavior. The expansion of wearable gadgets and cell phones has given continuous observing of human movements over sensors which is surrounded in smooth gadgets like locality sensors, cameras, receiver, magnetometers, accelerometers, GPS and so forth and consequently, sympathetic social exercises in inducing the signal or location have made a reasonable test informing individual medical services frameworks, inspecting health qualities then maximum pre dominantly in old consideration, unusual action recognition, diabetes or epilepsy issues and so forth, There are a few works that utilization transformative and bio-roused calculations to prepare neural system as another key type of learning [1]. Metaheuristic techniques for preparing neural organizations depend on neighborhood search, populace strategies and others, for example agreeable co-transformative models [2]. A phenomenal work where the makers show an expansive composing overview of extraordinary computations that are used to progress neural framework is [1, 2]. Disregarding, most of the itemized investigates are revolved, particularly around the advancement V. Shankar (B) Department of Computer Science and Engineering, KLS Vishwanathrao Deshpande Institute of Technology Haliyal, Hliyal, Karnataka, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. K. Mandal et al. (eds.), Innovations in Signal Processing and Embedded Systems, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-19-1669-4_22

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of the synaptic burdens, limits [3] or incorporate the improvement of the neuron insights focused on concealed or hidden layers; anyway, the amount of concealed layers is set up before by the fashioner. Moreover, creator excludes the advancement of move limits, which are a huge part of an ANN that chooses the yield of each neuron. Considering an example, in [4], proposed a technique that joins ant colony optimization (ACO) to find a particular strategy (the relationship) for a neural framework and molecule swarm optimization (PSO) to change the synaptic burdens. Distinctive examines like [5] completed a difference in PSO mixed in with simulated annealing (SA) to get a lot of synaptic burdens and neural framework limits. In [6], the creator use Evolutionary Programming to get the plan and the course of action of burdens with the arrangement to address gathering and expectation issues. Another model is [7] where genetic programming is used to get graphs that address different geography. In [8], the differential advancement (DE) estimation was applied to design a neural framework to handle a climate deciding issue. In [9], the creator uses a PSO estimation to change the synaptic burdens to show the step-by-step precipitation flood connection in Malaysia. In [10], the creator examines the back-multiplication procedure versus fundamental PSO to change simply the synaptic loads of a neural framework for settling gathering issues. In [11], the course of action of burdens is created using the differential progress and basic PSO. Activity of human recognition has a significant influence in improving individuals’ way of life, as it ought to be equipped enough in taking in undeniable level quality data from simple sensor in formation. Activity of human effective applications are joined for logical conduct examination [12], video inspection examination [13], walk examination (to decide any anomalies in strolling or running), signal and position acknowledgment [14].

2 Related Work Because of the normal requesting of the experienced element information, the human activity recognition is considered as a run of the mill plan acknowledgment framework where it includes characterizing the human action dependent on the arrangement of information. The fundamental contrast between machine and deep learning procedures in perceiving human action remains in which the information highlights stand extricated. Now, circumstance of machine learning method and the rough inertial developments signals acknowledged from the sensors are presented to feature extraction measure by space data experts [15]. The features that are by and large disengaged rely upon two central space incorporates to be explicit; time region and repeat region. Some assessment methods used machine learning method to perform human acknowledgment with hand-made features defied low execution as shallow features are researched and scholarly by the classifiers [16]. Before significant learning was used extensively, shallow neural association classifiers, that is multi-level perception

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(MLP), were seen as a fit computation for human acknowledgment. In this point, [17] performed human acknowledgment with computations like vital backslide, choice tree and MLP; moreover, MLP beats the other two models. This cycle requires region dominance. On basis of reference this connection might loss of huge data centers Profound Learning. Where as in deep learning algorithms, the fundamental sensor signals assembled from inertial sensors (accelerometer, spinner, etc.,) are clearly presenting to showing, where no abstraction phase is completed [18]. A couple of deep learning algorithms like CNN [19] was utilized to perform HAR tasks. Regardless, the experimentation methodology for picking limits to pick the best model does not guarantee an optimal presentation, which anticipates that client should reliably see the show theme. Along these lines, to get uncommon results, one should be ace in the model designing and moreover space.

3 Particle Swarm Method Using ANN Particle swarm optimization (PSO) is a nature moved, metaheuristic estimation frequently utilized for discrete, predictable and continues for blend progression issues. The fundamental idea overdue PSO is that every atom is critical of its speed and the best plan accomplished already (pBest) and the atom which is the current broad best arrangement in the enormous proportion of particles (gBest). From this time forward, at each current cycle, every atom strengthens its speed with the objective that its new position will be adequately close to generally speaking gBest and its own pBest, simultaneously. Figure 1 shows the working model. Regardless of the way that neural systems have showed sufficient outcomes in neural structure human exercises or development, there are various cutoff points to bring care to find the best neural system technique. The focal matter of any neural alliance is to limit the issue between preparing targets and obvious yields. It is cross-entropy, if there should be an occasion of neural structure, which is done by back development and propensity drop. Without a doubt, even a fundamental neural system has different cutoff points to tune them. At the end find calculations which find and assesses neural structure plan with less time. Hereafter, prodded from this, another PSO neural system is used for practices in human recognizable proof. Neural system training—The neural structure is prepared with some pre-portrayed weights introduced. It utilizes a neural system with 1D convolution layer, since the activity dataset join of code. The pre-PSO training right presently load is stuck from neural system arranging, and it is changed over to atom. Particle swarm optimization training ensuing to instating the expected additions of blend, mental worth, social worth, number of particles, halting condition and number of rounds, PSO methodologies filters the hyper plane for further developed vector utilizing the neural system disaster work. Reviving neural structure architecture, using the likely gains of weight in earlier period, the finished result are dealt with. Another neural structure setup is organized made ward on these stores instead of reason of the yield. Calculation with accurateness and effect the real evaluation is finished by separating the trouble

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Fig. 1 Swarm method for neural network system

capacity of every particle. Thus, the target of the assessment is to find a particle plan with the less misfortune, paying little cerebrum to the measure of cutoff points or different standards. Utilization of neural system based model to expect or identify the breakdown. As referenced before, the topography of neural system plays a basic job in its efficacy and execution. Along this position, we are intrigued to characterize the best process that can segregate among hard and bankrupt firms depending on their fiscal summaries. To plan a neural system, there are limits that should be characterized as number of sources of statistics, number of covered up layers, number of covered up neurons. In the meted, replica neural organization with one secret layer is the best design to use for order issues. The quantity of data sources relies upon factors determination procedures utilized in this examination. Each model chooses fitting factors set that is utilized as info factors for the neural system. Every particle P and swarm S is denoted by {x, v}, x denotes the location of the particle, and then v denotes the particle velocity. The particles modernize velocity plus location. Here, ‘i’ denotes the particle number, c1, c2 is constants used for supervisory influence of pi and

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g correspondingly, ‘P’ is the pbest, ‘g’ represents gbest, ‘V ’ is the velocity, ‘x’ is present location then ‘pq’, ‘rs’ are arbitrary statistics between 0 and 1, ‘w’ is the inertia weight. gbest = 0 for i = 1 to n-particles P locaton = rnd(Plower, Pupper) Pbest.locaton = xi Pbest.item = itemfunc (xi) ifPbest. item < gbest item) gbest = Pbest end if Pvel = rnd(vlower, vupper) end for do for i = 1 to all particles for d = 1 to number of dimension select random numbers: pq, rs between 0 and 1 Pv = w * Pv + c1 pq (Pbest.location - particleid.location) + c2 rs (gbest.location - particleid.location) particleid.location = particleid.location + particleid.vl particleid.item = itemfunc(particleid.location) if (particleid.location < Pebest. location) Pbest = particleid if (Pbest.item < gbest.item) gbest = Pbest end if end if end for end for

4 Results and Discussion The performance of convolution neural system with swarm technique algorithm is evaluated and analyzed aligned with machine and deep learning algorithm. Table 1 shows the results, it is marked that 89.35% accuracy achieved by Random forest, 78.06 accuracy achieved by Naïve Bayes, 88.68 accuracy achieved by Supported vector machine and 93.47 accuracy achieved by proposed method convolution neural network using swarm technique. The examination outcome is

250 Table 1 Comparison table

V. Shankar Algorithm

Accuracy

Random forest

89.35

Naïve Bayes

78.06

Supported vector machine

88.68

PSO with ANN (proposed method)

93.47

performed by comparing convolution neural network using swarm technique with deep learning algorithms. Table 1 shows the outcomes, from the outcome, apparent that convolution neural network using swarm technique is capable toward attain elevated performance of accurateness while associated by other algorithm.

5 Conclusion In this paper, PSO calculation is utilized to find the ideal models in deep convolution neural organization. In addition, it utilizes the advantages of universal and local, neighbour investigation capacities of swarm streamlining strategy PSO then gradient descent back-propagation subsequently to frame a proficient looking through calculation this is on the grounds that the exhibition of deep convolution network very relies upon their organization arrangement utilized and hyper-boundary determinations. In the simulation outcome, it is observed that convolution neural network using swarm technique has been shown to meet more rapidly and the best design with less instruction time. To some extent, the algorithm failed to use security the course of action can be also explored with enormous time series data.

References 1. X. Yao, Evolving artificial neural networks. Proc. IEEE 87(9), 1423–1447 (1999) 2. E. Alba, R. Mart, Metaheuristic procedures for training neural networks, operations research/computer science interfaces series, (Springer, New York, NY, USA, 2006) 3. J. Yu, L. Xi, S. Wang, An improved particle swarm optimization for evolving feedforward artificial neural networks. Neural Process. Lett. 26(3), 217–231 (2007) 4. M. Conforth, Y. Meng, Toward evolving neural networks using bio-inspired algorithms, in IC-AI, ed by H.R. Arabnia, Y. Mun, (CSREA Press, 2008), pp. 413–419 5. Y. Da, G. Xiurun, An improved PSO-based ANN with simulated annealing technique. Neurocompututing. 63, 527–533 (2005) 6. X. Yao, Y. Liu, A new evolutionary system for evolving artificial neural networks. IEEE Trans. Neural Netw. 8(3), 694–713 (1997) 7. D. Rivero, D. Periscal, Evolving graphs for ann development and simplification, in Encyclopedia of Artificial Intelligence, Eds. J.R. Rabu˜nal, J. Dorado, A. Pazos, (IGI Global, 2009), pp. 618–624

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8. H.M. Abdul-Kader, Neural networks training based on differential evolution algorithm compared with other architectures for weather forecasting34. Int. J. Comput. Sci. Net. Secur. 9(3), 92–99 (2009) 9. K.K. Kuok, S. Harun, S.M. Shamsuddin, Particle swarm optimization feedforward neural network formodeling runoff. Int. J. Environ. Sci. Technol. 7(1), 67–78 (2010) 10. B.A. Garro, H. Sossa, R.A. V´ azquez, Back-propagation vs particle swarm optimization algorithm: which algorithm is better to adjust the synaptic weights of a feed-forward ANN? Int. J. Artif. Intell. 7(11), 208–218 (2011) 11. B. Garro, H. Sossa, R. Vazquez, Evolving neural networks: a comparison between differential evolution and particle swarm optimization, in Advances in Swarm Intelligence, eds. by. Y. Tan, Y.Shi, Y. Chai, G. Wang, Lecture Notes in Computer Science, vol 6728 (Springer, Berlin, Germany, 2011), pp. 447–454 12. K. Aurangzeb, I. Haider, M.A. Khan, T. Saba, K. Javed, T. Iqbal, A. Rehman, H. Ali, M.S. Sarfraz, Human behavior analysis based on multi-types features fusion and von nauman entropy based features reduction. J. Med. Imaging Health Inform. 9(4), 662–669 (2019) 13. M.G. Hwang, H.J. Park, D.H. Har, A method of smart phone original video identification by using unique compression ratio pattern. Forensic Sci. Int. 304, 109889 (2019) 14. W. Kang, Y. Zhang, X. Dong, Body gesture recognition based on polarimetric micro-doppler signature and using deep convolution neural network, Prog. Electromagnet. Res. (2019) 15. R. Chavarriaga, H. Sagha, A. Calatroni, S.T. Digumarti, G. Tröster, J.D.R. Milan, D. Roggen, The opportunity challenge: a benchmark database for on-body sensor-based activity recognition. Pattern Recogn. Lett. 34(15) (2013) 16. J. Yang, M.N. Nguyen, P.P. San, X. Li, S. Krishnaswamy, Deep convolution neural networks on multichannel time series for human activity recognition, in International Joint Conference on Artificial Intelligence, vol 15 (Citeseer, 2015), pp. 3995–4001 17. M.S. Singh, V. Pondenkandath, B. Zhou, P. Lukowicz, M. Liwickit, Transforming sensor data to the image domain for deep learning|an application to footstep detection, in 2017 International Joint Conference on Neural Networks (IJCNN), (IEEE, 2017), pp. 2665–2672 18. D. Figo, P.C. Diniz, D.R. Ferreira, J.M. Cardoso, Preprocessing techniques for context recognition from accelerometer data. Pers. Ubiquit. Comput. 14(7), 645–662 (2010) 19. D. Ravi, C. Wong, B. Lo, G.Z. Yang, Deep learning for human activity recognition: a resource efficient implementation on low-power devices, in 2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN) (IEEE, 2016), pp. 71–76

Machine Learning-Based Approach for Classification of Weed Images Saikumar Tara

1 Introduction Weeds are the most damaging, as they reduce both the yield and the quality of the crop. As the agricultural production falls, the weed population rises, implying that the two are indirectly proportionate. An approach for image segmentation was applied. The process begins with image pre-processing by importing a test image of a thin cranesbill. The attempt to proces an images in order to remove the unwanted pixels. Directly feeding this image to the model will result in a target leakage. The classifier will learn the pixels of the stones and the container and will base its prediction on them as well. The pre-processing of an images by utilizing Open CV to segregate an object from an image based on colour. Visualized the small-flowered cranesbill image that opened in RGBSPace to see the distributions of the colur pixels using a common computer vision package computer with MATLAB. RGB is a “additive” colour space, which means that colours may be created by beaming large amounts of red, blue, and green light over a black background. HSV is a cylindrical colour space that stands for hue, saturation, and value (or brightness). Dark (0 at the bottom) to light (at the top) are the values. Saturation, the third axis, specifies the colour shades from least saturated at the vertical axis to most saturate farthest away from the centre. An online colour choosing program to select a colour range. The green colour ranges were discovered on a forum: Minimal green (H = 36, S = 25, V = 25); maximum green (H = 70, S = 255, V = 255). To try to extract the threshold value of plant image using the minimum and maximum green values, we utilize the MATLAB commands (Figs. 1 and 2).

S. Tara (B) Department of Electronics and Communication Engineering, CMR Technical Campus, Hyderabad, Telangana, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. K. Mandal et al. (eds.), Innovations in Signal Processing and Embedded Systems, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-19-1669-4_23

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254 Fig. 1 3D model of RGB

Fig. 2 3D model of HIS

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2 Literature Survey According to Faisal Ahmed et al., accurate weed mapping is required for weed density predictions and variable rate herbicide prescription. Deep learning-based semantic segmentation is a potential solution for this. The lack of tagged agriculture photos at the pixel level, which has been solved in this study, is a bottleneck for using semantic segmentation. After obtaining high-resolution RGB images from a canola fields, the background is fragmented as a first labelling phase, followed by hand labelling of minority class pixels in the background-segmented image. On this dataset, the trained model solely maps weeds and blends crop and background pixels. When compared to some of the most recent works on semantic segmentation and weed detection, our methodology produces better results. For the ResNet-50-based SegNet model, the MIOU value was 0.8288 and the FWIOU value was 0.9869. In addition, for ResNet-50 and VGG16 base models, the paper compares UNET and SegNet meta-architectures. On this dataset, it is discovered that SegNet outperforms UNET. ResNet-50 outperforms VGG16 in terms of base model feature extractors. Soil qualities will be included in future research to look into the relationship between weed densities and soil characteristics, with the goal of facilitating varied herbicide prescription for different soil zones [1]. According to Wenhua Mao et al., weed detection is a major issue with spot spraying, which could limit herbicide use. Plant spectral information is extremely valuable for detecting weeds in real-time for quick response times. The cost of an image spectrograph-based weed identification device, on the other hand, is prohibitively costly. As a result, the main goal of this research was to see if there was a way to identify agricultural and weed plants using spectral information in visible light acquired by a CCD camera. One method for weed classification was to use the G and R components of the RGB colour space directly. Another option was to use the spectral information in the green band, where the hue was regarded as wavelength and saturation was regarded as saturation. Another option was to use spectral information from the green band, with colour representing wavelength and saturation representing reflectance. The results of the statistical analysis revealed that both of them could be used to detect weeds in wheat fields by applying the G–R and H–S optimum segmentation lines of crop and weeds. Furthermore, the H–S optimized model method may eliminate the effect of lighting [2]. Weeds are typically dispersed non-uniformly throughout fields, according to Thompson et al. Herbicide use can be decreased by applying it just to weed-infested regions or by applying a low dose rate to the entire field and a higher dose rate to weed patches. An effective method of weed detection is a four essential condition. Detectors put on tractors do not appear to be a viable option. As a result, real-time weed detection and sprayer control are now impractical. However, it is demonstrated that spatially variable herbicide treatment employing a sprayer location system and a weed location field map shows promise. The data map would be created using a variety of image-based weed location approaches, including as tractor-mounted video cameras, aerial images, and manual field [3].

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According to Olsen et al., this study is the first to collect a big, multiclass weed species picture dataset. We hope that the dataset and our classification results will spur more study into rangeland weed classification under real-world situations. Future work in this area will focus on enhancing the accuracy and robustness of the dataset’s classification, as well as field implementation. The accuracy and robustness of this dataset’s classification will be improved in the future, as will the field implementation of our learning models as the detection system for a prototype weed control robot, as well as research into the use of NIR spectroscopy and hyperspectral imaging for weed species classification. The lengths to which a dataset was gathered, as well as the real-life intricacy of the rangeland environment, should result in excellent in-field performance [4]. Weeds, according to Syed Moazzam et al., are a primary reason for farmers’ low agricultural production. To autonomously remove weeds, many algorithms have been created to identify weeds from crops. In the past, colour-based, threshold-based, and learning-based approaches were used. Deep learning-based techniques stand out among all the techniques because they produce the best results. The application of deep learning-based approaches for weed detection in agricultural crops is discussed in this research. The presence of weeds in sunflower, carrot, soybean, sugar beet, and maize is investigated. Deep learning architectures and parameters are discussed, as well as research gaps that need to be filled [5].

3 Methodologies Convolution Neutral Network: To construct a model, employed a CNN. CNNs are built up of neurons and have learnable weights and biases, just like ordinary neural networks. The primary distinction is that the CNN makes use of images and accepts them as images with width, height, and depth, whereas normal neural networks flatten the input. As a result, it is ineffective in recognizing some characteristics of an individual. As a result, it struggles to recognize some elements of an image, whereas CNN does so with ease. The technique section explains the essential components of a CNN architecture. A convolutional neural network is a state-of-the-art algorithm for picture classification challenges, and it is what I constructed. It necessitates a significant amount of information [6]. VGG16: VGG16 (also known as Oxford Net) is a convolutional neural network architecture developed by the Oxford-based Visual Geometry Group. The ILSVR was won with it (Image Net). Even if more recent breakthroughs such as Inception and ResNet have outperformed it, it is still considered to be a good vision model [7]. SVM: The goal of the support vector machine technique is to find a hyperplane in an N-dimensional space (N is the number of features) that categorizes the data points clearly. There are numerous hyperplanes from which to choose to split the two groups of data points. Our goal is to discover a plane with the greatest margin, or the greatest distance between data points from both classes. Maximizing the margin

Machine Learning-Based Approach for Classification of Weed Images

Input image

Extract Features

Image Subtract from Green components

Object Labelling

257

Median Filtering

Removal of tiny objects

Thresholding

Segmentation

Weed detection

Fig. 3 Proposed weed detection block diagram

distance gives some reinforcement, making it easier to classify subsequent data points (Fig. 3). The Plant Seedlings Dataset contains photos of about 960 different plants from 12 different species at various phases of development. It is made up of annotated RGB images with a physical resolution of about 10 pixels per millimetre. The datasets can be downloaded in three different formats: raw photographs, cropped photos, and segmented images. Cropped versions of the dataset are used here (Fig. 4). Because it is impossible to include all species in the database, only a subset of those that are critical to the Danish agricultural business are picked. The database contains photos from 12 different species [8–11]. Following the collection of data species, it used segmentation and classification techniques to easily distinguish between plants and weeds. Simply detect plants and eradicate weeds because to HSV (Fig. 5). Fig. 4 Raw chick weed image

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Fig. 5 Weed image database

Fig. 6 Output sample of weed

4 Results A training set is used to train the model, and a validation set is used to validate it during development. Because the photos in the data set contain intensities in the range of 0–150, the model created may be sensitive to outliers. If a new image is used

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Fig. 7 Plot loss versus epoch

Fig. 8 Plot accuracy versus epoch

for prediction that does not have these intensities, it may perform poorly (Figs. 6, 7, 8 and 9). • To predict outcomes, we employed the VGG16 model in CNN. • Here employed SVM as another machine learning technique, which gave us a 74% accuracy.

5 Conclusion The goal of the model we created was to detect weed plants in their early stages. The plant photos will be divided into two categories: main crop and weed plant. Arrange the photos into their appropriate clusters, so only the green component of the plant will be seen. Finally, using the input photos, we can detect the weed plant. Finally, using the supplied photographs, we can identify the weed plant. Model had trouble distinguishing between loose silky bent and Black-Grass. With f 1-score of

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Fig. 9 Classification of weed

0.99, the model did a fantastic job detecting common chickweed, loose silky bent, and small-flowered cranesbill.

References 1. F. Ahmed, H. Kabir, S. Bhuyan, H. Bari, E. Hossain, Automated weed classification with local pattern-based texture descriptors. Int. Arab. J. Inf.Technol. 11(1), 87–94 (2014) 2. W.K. Wong, A. Chekima, M. Mariappan, B. Khoo, M. Nadarajan, Genetic algorithm tuned SVM classifier for weed species recognition. Int. J. Comput. Sci. Trends Technol. (IJCST), 2(4) (2014) 3. G. Cohen, M. Hilario, C. Pellegrini, One-Class Support Vector Machines with a Conformal Kernel. A case study in Handling Class Imbalance SSPR/SPR (2004) 4. A.J. Ishak, A. Hussain, M.M. Mustafa, Weed image classification using Gabor wavelet and gradient field distribution. Comput. Electron. Agric. 66(1), 53–61 (2009). https://doi.org/10. 1016/j.compag.2008.12.003 5. A.J. Ishak, M.M. Mustafa, N.M. Tahir, A. Hussain, Weed detection system using support vector machine. Int. Symp. Inf. Theory Appl. 2008, 1–4 (2008). https://doi.org/10.1109/ISITA.2008. 4895454 6. S. Frandina, M. Lippi, M. Maggini, S. Melacci, On-Line Laplacian One-Class Support Vector Machines. ed. by V. Mladenov, P. Koprinkova-Hristova, G. Palm, A.E.P. Villa, B. Appollini, B. Kasabov. Artificial Neural Networks and Machine Learning—ICANN 2013. ICANN 2013. Lecture Notes in Computer Science, vol 8131 (Springer, Berlin, Heidelberg, 2013). https://doi. org/10.1007/978-3-642-40728-4_24

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7. S. Tshewang, B.M. Sindel, M. Ghimiray, B.S. Chauhan, Weed management challenges in rice (Oryza sativa L.) for food security in bhutan: a review. Crop Prot. 90, 117–124 (2016). https:// doi.org/10.1016/j.cropro.2016.08.031 8. A. Dass, K. Shekhawat, A.K. Choudhary, S. Sepat, S.S. Rathore, G. Mahajan, B.S. Chauhan, Weed management in rice using crop competition-a review. Crop Prot. 95, 45–52 (2017). https:// doi.org/10.1016/j.cropro.2016.08.005 9. B. VijayaLakshmi, V. Mohan, Kernel-based PSO and FRVM: an automatic plant leaf type detection using texture, shape, and color features. Comput. Electron. Agric. 125, 99–112 (2016). https://doi.org/10.1016/j.compag.2016.04.033 10. T. Rumpf, C. Römer, M. Weis, M. Sökefeld, R. Gerhards, L. Plümer, Sequential support vector machine classification for small-grain weed species discrimination with special regard to Cirsiumarvense and Galiumaparine. Comput. Electron. Agric. 80, 89–96 (2012). https://doi.org/10. 1016/j.compag.2011.10.018 11. Y. LeCun, Y. Bengio, G. Hinton, Deep learning. Nature 521, 436–444 (2015). https://doi.org/ 10.1038/nature14539

Intelligence Speech Has Collapsed and Talking Unconsciously Circumstance Using Diva Module S. China Venkateswarlu, Dharavath Veeraswamy, N. Uday Kumar, and Vallabhuni Vijay

1 Introduction To accurately model and determine the functions of brain regions to blame for speech acquisition and development supported neuroscience and neuroanatomy, analysis during a type of fields (phonetics, management science, robotics, and neural physiology) is needed. The foremost representative and practical methodology are that the Neuralynx System, created by a team semiconductor diode by Frank Guenther of Beantown University, the most well-known and authoritative [1–7] group, is the most representative successful. The best thing about this method is that each user got to trust what they require to say, and therefore the speech synthesis system will flip their thoughts into speech. Figure 1 demonstrates the theory. The neurons and nerve fibre projections within the neural circuit for speech motor output by black circles and snakelike arrows in Fig. 1 shows the neural circuit for speech motor output, with black circles and curved arrows representing neurons and axonal projections, respectively. Signals from a conductor established in the subject’ speech motor region are amplified and wirelessly transmitted as FM radio signals through the scalp. For any amplification, analogue-to-digital conversion, and spike sorting, the signals are redirected to an electrophysiology recording device. The spikes are sent to a neural decoder that converts them into speech synthesizer commands. The synthesizer’s audio signals are fed to the topic in real-time (PrCG represents the larger U.S. within the brain). S. China Venkateswarlu (B) · D. Veeraswamy · N. Uday Kumar · V. Vijay Department of Electronics and Communication Engineering, Institute of Aeronautical Engineering, Dundigal, Hyderabad 500043, India e-mail: [email protected] D. Veeraswamy Hyderabad Institute of Technology and Management, Hyderabad 500043, India N. Uday Kumar MLRITM, Dundigal, Hyderabad 500043, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. K. Mandal et al. (eds.), Innovations in Signal Processing and Embedded Systems, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-19-1669-4_24

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Fig. 1 Schematic diagram of Neuralynx system

The Neuralynx System has split into two parts: the brain laptop interface (BCI) [8] and, therefore, the prima donna model speech synthesis system (Directions into Velocities of Articulators). BCI could be a technology that permits somebody’s brain to speak with and manipulate a computer or alternative electronic device. Associate in Nursing brain disorder patient’s sign is made by a for good ingrained wireless neural conductor within the cortex that senses generated speech and gathers neural signals from nearby areas. These signals instruct the speech synthesis system to “operate” ceaselessly and supply speech output to the patient. The prima donna model is a biological neural network for speech generation and retrieval (Figs. 2 and 3).

1.1 DIVA Module The operatic star model is the associate accommodative neural network that describes speech acquisition and development processes and generates speech by dominant a virtual voice channel [9, 10]. The model is predicated on activity information gathered from biological studies in speech generation and sensory psychology,

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Fig. 2 Block diagram of matching pursuit algorithm

neuroimaging fMRI and positron emission computed tomography (PET) experiments, and neuroscience data gathered from animal motion management experiments. The figure depicts the speculation a feedforward control subsystem, a feedback control subsystem, and a virtual Maeda vocal tract form up the operatic star model. The model produces a phonating rate and a time variable sequence reflecting point variations in vocal organs by recording the input speech formant frequency throughout training. The model employs this sequence to come up with the required phonations. Speech output is management led by the feedforward control system, whereas the feedback control system controls speech learning. The activation of a corresponding cell speech reference starts generating a speech sound or language unit within the feedforward control system. Every cell represents one phoneme or info. The operatic star model also can analyse and interpret information obtained from fMRI. As a result, the operatic star model may be an elementary paradigm for decoding speech neural processes.

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Fig. 3 Electrode locations on scanning cap https://upload.wikimedia.org/wikipedia/commons/7/ 70/21_electrodes_of_International_10-20_system_for_EEG.svg

2 Existing Method 2.1 Matching Pursuit Algorithm (i) (ii) (iii)

The Matching Pursuit algorithm (MP) is a signal analysis method proposed by Mallet and Zhang in 1993 that falls into the greedy algorithm group. The basic concept is to decompose the signal in the library to find the most significant portion of the correlation coefficients (over-complete dictionary). Multiple iterative decompositions are used to obtain the signal’s sparse representation.

2.2 Problem Finding of Existing Method 2.2.1

Denoising Principle

Based on whether or not the correlation coefficient is 0 after sparse decomposition, the sign may be divided into the authentic and the noise signs. Assume the noise with

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inside the authentic EEG sign is: f = E+N

(1)

where E is the original noise-free signal and N is a random noise signal with independent distribution. The dictionary is built based on the structural properties of the EEG signal when using the MP algorithm in the atomic dictionary. As a result, regardless of noise, the atom structure must be connected to the EEG signal. This is how the formula is written: ⎧1 f

(

) R l f , g1 g1 f (RL)

(2)

0

⎧1 (Rl f , gl ∗ gl)

E

(3)

0

The original EEG signal is the first part of the equation, and the residual after extracting the EEG signal, which is the noise signal, is the second portion. The following is a formula that may be used to compare the equation: ⎧ N

2.2.2

RL ∗ F

(4)

Existing Experimental Process and Results Analysis

Existing Experimental Design and Signal Collection The following experimental data comes from our partner, the State Key Laboratory for Cognitive Neuroscience and Learning at Beijing Normal University. The subject was a healthy person with extensive experience in EEG acquisition experiments. The experiment uses an electrical scanner and a scanning cap with 128 electrodes to record the EEG signal (Fig. 5). The signal sampling frequency is 1000 Hz. During the EEG signal collection process, the subject’s consciousness was evident, and he was sitting in an ordinary chair. The expression of the word “happy” in English was performed 100 times. The experiment was completed in one day.

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2.3 Experimental Tools The EEG lab Toolbox in MATLAB R2010b is used to read the EEG data gathered throughout the experiment. The EEG lab Toolbox is a programme that allows you to process EEG data and read the waveform of the recorded signal.

2.3.1

Pre-treating Data

EOG and muscular movement will weaken the signal when utilizing the non-invasive acquisition approach as outlined. We used EEGlab’s Independent Component Analysis (ICA) algorithm to extract the significant ingredient of practical independence, which can remove EOG and muscle movement from the original signal before evaluating the EEG signal.

3 Proposed System 3.1 Problem Finding of Proposed Method 3.1.1

Diva Model Interface

The system displays a user interface to allow users to control the pronunciation mechanism whilst using the DIVA model to evaluate pronunciation function, as shown in Fig. 4. The control module, acoustic characterization space module and vocal tract control module are the three components of the interface. Production unit temperature representation each production unit

3.2 Description of ICA Analysis Independent component analysis (ICA) can be used to eliminate or eliminate [11] data artefacts (muscle, eye blinks, or eye moments) without eliminating the impacted data segments. It is also possible to employ ICA to look for brain sources.

3.2.1

Runic Algorithm

RUNICA () generates the ICA decomposition of psychoactive data using the ICA infomax algorithm of Bell and/Sejnowski with natural gradient function of Amari, Cichocki and Yang, the extended Ica algorithm of Lee Giroami and Sejnowski, reduce size PCA and spectrum ().

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Fig. 4 User interface of DIVA model https://upload.wikimedia.org/wikipedia/commons/7/71/Div aBlock2.jpg Fig. 5 Flow chart of speech sound map module of DIVA model

2

1

neurons

generate sweep

1

2

3

4

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3.3 Implementation of Eeglab 3.3.1

Experimental Tools

The EEG lab Toolbox in MATLAB R2021 is used to read the EEG data gathered throughout the experiment. The EEG lab Toolbox is a programme that allows you to process EEG data and read the waveform of the recorded signal.

3.3.2

Description of Eeglab

EEGLAB is a MATLAB toolkit for processing continuous and event-related EEG, [12] MEG, and other electrophysiological data. It includes independent component analysis (ICA), time/frequency analysis, artefact rejection, event-related statistics, and numerous helpful display modes for averaged and single-trial data. To get started, download EEGLAB, which includes the tutorial dataset under the sample data subfolder of the EEGLAB package. You will get a folder named eeglab2021 when you uncompress EEGLAB.

3.4 Path Setup to EEGLAB Step-1 (i) (ii)

Open the EEGLAB directory where the EEGLAB file has been unzipped and saved Copy the entire directory of the file and past in the MATLAB directory file folder and press enter.

Sample directory path D:\Softwares\EEG LAB oftwares\eeglab_current\eeglab2021.0. Step-2: On the left side of the MATLAB screen, you will observe the current folder. click on the EEG lab. m file you will get the code in the editor section, as shown in Fig. 5. Path setting of EEGLAB: EXECUTING/RUNNING THE CODE. Step-1 After opening the EEG lab. m file in the editor, check the code, clear the previous code using clc, clear all, and close all functions. Click on RUN at the top of the window as shown in the below Fig. 6 execution of the EEG lab code.

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4 Execution Results (i) (ii) (iii) (iv) (v) (vi) (vii) (viii) (ix) (x) (xi) (xii) (xiii) (xiv) (xv) (xvi)

(xvii) (xviii) (xix)

Warning: MATLAB previously crashed due to a low-level graphics error. To prevent another crash in this session, MATLAB [6–10, 13, 14] uses OpenGL software instead of using your graphics hardware. To save this setting for future sessions, use the OpenGL(“save”, “software”) command. For more information, see Resolving Low-Level Graphics Issues. >> eeglab Some menus items are hidden. Use the Preference menu to show them all. EEG lab: options file is C:\Users\Administrator\eeg_options.m Retrieving plugin versions from server… [14–19] Retrieving download statistics… EEGLAB: adding “ICLabel” v1.3 (see >> help eegplugin_iclabel) EEGLAB: adding “clean_rawdata” v2.3 (see >> help eegplugin_clean_rawdata) EEGLAB: adding “dipfit” v3.7 (see >> help eegplugin_dipfit) EEGLAB: adding “firfilt” v2.4 (see >> help eegplugin_firfilt) You are using the latest version of EEGLAB. pop_loadset(): loading file D:\Softwares\EEG LAB Softwares\eeglab_current\eeglab2021.0\sample_data\eeglab_data.set … Reading float file ‘D:\Softwares\EEG LAB Softwares\eeglab_current\eeglab2021.0\sample_data\eeglab_data.fdt’… Creating a new ALLEEG dataset 1 Done.

Step-2: After the code has been executed, a [13] dialogue box with No current data set will open, as shown in the below Fig. 8 (i)

we need to add the sample analogue data to the EEG lab: EEG lab executed dialogue box with No current data set.

5 Simulation Result for Denoising Signal The sample EEG data has given to the denoising algorithm UNICA using the EEGLAB tool and de-noised the signal. We have de-noised the signal using the AAR algorithm and OEP RUNICA, the latest version of the RUNICA algorithm [7]. The data has been collected from the DIVA vocal tract signal and other 32 positions from the brain. The output graphs are given below.

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Fig. 6 Overview of EEGLAB

5.1 Output Graphs of Denoising Signal Check the component activity scroll from the plot menu, It pops up a dialogue box with the values in it adjust the values accordingly, as shown in Fig. 6 and observe the graphs in Fig. 7 etc., Output Graphs: Similarly for other output graphs. (i)

Click on various components, respectively, as in Figs. 8, 9, 10, 11 and 12.

6 Conclusion and Future Scope Conclusion: Simulation of DIVA Vocal Tract: The results are displayed in the 5th chapter. According to the simulation observations, the vocal tract or speech has been extracted from the human brain neurons, and the outputs are displayed in 5.1 of the 5th chapter. This can be implemented in the hardware for that we need to inject the DIVA model device electrode into the human brain for the related speech neurons

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Fig. 7 Components activity(scroll) de-noised signal

Fig. 8 Graph of de-noised signal output

for that we need to get the permission from the government. To implement the model (as per our research), the DIVA model cost is higher, in 1cr. Simulation of Denoising Signal: The sample EEG data has been given to the denoising algorithm RUNICA using the EEGLAB tool and de-noised the signal. We have de-noised the signal using the AAR algorithm and OEP RUNICA, the latest version of the RUNICA algorithm. The data has been collected from the DIVA vocal

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Fig. 9 Graph for 12 channel epochs output

Fig. 10 Graph for 32 channel output

tract signal and other 32 positions from the brain. Hence, the signal has de-noised, and the output graphs are in 5.2 of the 5th chapter. Future Scope: In the future, we can implement this in the diva model by the above simulation process. Adding new technology to that module can change that module and extract and display brain conditions in the monitor. We have performed research on patient details mentioned in the appendix below; further, we can develop our model

Intelligence Speech Has Collapsed and Talking Unconsciously … Fig. 11 Graph of output frequency graphs

Fig. 12 Graph activity power spectrum

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to know her condition at her paralysis time and know exactly what the problem is. But the present DIVA module is only used for speaking purposes. Technologies can be used: Artificial intelligence, Neural networks, Computer brain interface, and Natural language processing.

References 1. J.T. Tourville, F.H. Guenther, The DIVA model: A neural theory of speech acquisition and production. Lang. Cognit. Process. 25(7), 952–981 (2011) 2. F.H. Guenther, J.S. Brumberg, E.J. Wright, Nieto-Castanon, A wireless brain-machine interface for real-time speech synthesis. PLoS ONE 4(12), 64 e8218 (2009) 3. S. Zhang et al., Investigation of a method for EEG signal de-noising based on the DIVA model 4. J.S. Brumberg, A. Nieto-Castanon, P.R. Kennedy, F.H. Guenther, Brain-computer interfaces for speech communication. Speech Commun. 52(4), 367–379 (2010) 5. R. Coifman, M. Wickerhauser, Entropy-based algorithms for best-basis selection. IEEE Trans. Inf. Theory 38, 713–718 (1992) 6. D. Xiaoyan, L. Yingjie, Z. Yisheng, R. Qiushi, Z. Lun, Removal of artifacts from EEG signal. J. Biomed. Eng. 25(2), 464–471 (2008) 7. S. Poornachandra, Wavelet-based denoising using subband dependent threshold for ECG signals. Digit. Signal Proc. 18(1), 49–55 (2008) 8. Z. Shaomai, J. Yanchun, Research on the mechanism for phonating stressed english syllables based on DIVA model. Neurocomputing 152(3), 11–18 (2015) 9. J.W. Bohland, F.H. Guenther, An fMRI investigation of syllable sequence production. Neuroimage 32(2), 821–841 (2006) 10. S. Chen, D. Donoho, M. Saunders, Atomic decomposition by basis pursuit. SIAM J Sci Comput 20, 33–61 (1999) 11. D. Donoho, X. Huo, Uncertainty principles and ideal atomic decomposition. Inf. Theory, IEEE Trans. 47(7), 2845–2862 (2001) 12. R.-X. Chen, B.-P. Tang, L.V. Zhong-Liang, Denoising method based on correlation coefficient for EEMD rotor vibration signal. J. Vib. Meas. Diagn. 32(4), 542–546 (2012) 13. S. Mallat, Z. Zhang, Matching pursuit with time-frequency dictionaries. IEEE Trans. Signal Process. 41(12), 3397–3415 (1993) 14. L. Shaobaizhang, Application of feedforward and feedback control strategy in the speech acquisition and production model. Springer, Lect. Notes Electr. Eng. 123, 489–494 (2011) 15. F.H. Guenther, Cortical interactions underlying the production of speech sounds. J. Commun. Disord. 39(5), 350–365 (2006) 16. W. Ming, Automatic Detection of Epileptic Characteristics in EEG Signals based on Sparse Representation and the Design of an Application System [D], Doctoral Dissertation, Nanjing University of Science and Technology, 2010 17. C.T. Tony, W. Lie, The orthogonal matching pursuit for sparse signal recovery with noise. IEEE Trans. Inf. Theory 57(7), 4680–4688 (2011) 18. Z. Chennai, Y. Zhongke, M. Xiao, Over-complete representation and sparse decomposition of a signal based on the redundant dictionary had. Chin. Sci. Bull. 2006(6), 628–633 (2006) 19. G. Davis, S. Mallat, M. Avellaneda, Adaptive greedy approximation. ConstrApprox 13(1), 57–98 (1997)

A Wavelet-Based De-Noising Speech Signal Performance with Objective Measures S. China Venkateswarlu, G. Soma Sekhar, N. Uday Kumar, and Vallabhuni Vijay

1 Introduction Enhancement of speech signals is one of the most complex tasks researchers face in the speech processing field. This chapter provides an overview of problems generated by noises in speech signals, describes the problem statement, points out the thesis’s objectives, and provides a short brief on thesis contribution along with the thesis organization. Speech signals are complex concerning other forms of communication media such as text or image. Speech signals are continuously corrupted with different sorts of noises in various speaking conditions. Different forms of noises (e.g., additive noise, channel noise, babble noise) interfere with the speech signals and drastically hamper the quality of the speech in the speech signals. Thus, enhancing speech signals is a daunting task considering multiple forms of noise while de-noising a speech signal [1]. Over the years, the amplification of noisy speech signals has become an important research topic in the real-world environment. Enhancement of speech signals are essential to de-noise and obtain a clean speech signal. Previously used noise reduction models such as spectral subtraction and Weiner filtering could not give satisfactory results to tackle the noise as they were made to handle stationary S. China Venkateswarlu (B) Institute of Aeronautical Engineering, ECE, 500043, Dundigal, Hyderabad, India e-mail: [email protected] G. Soma Sekhar CSE Department, Geethanjali College of Engineering and Technology, Hyderabad, India N. Uday Kumar ECE, MLRITM, Dundigal, Hyderabad 500043, India V. Vijay IEEE Department of Electronics and Communication Engineering, Institute of Aeronautical Engineering, Dundigal, Hyderabad 500043, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. K. Mandal et al. (eds.), Innovations in Signal Processing and Embedded Systems, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-19-1669-4_25

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noise signals. So, to de-noise the non-stationary signals, we are using an advanced method with the help of MATLAB software which makes it more accessible. The main objective of this paper is to test the ability of the discrete wavelet transform method to de-noise a signal by giving different kinds of speech signal inputs from the speech corpus database to the MATLAB software. The evaluation of the enhanced signal is done concerning speech quality measures like signal-tonoise ratio (SNR), segmental SNR and frequency spectral SNR. Chapter 1 provides an introduction about the need for speech enhancement and gives a background of current and traditional speech enhancement methods. The problem statement and thesis objective are also mentioned. Chapter 2 includes the literature review necessary to understand the flow of the project. It gives a brief idea of stationary, nonstationary signals, speech enhancement methods, conventional speech features and speech quality measures effects. Chapter 3: Discussion on the existing method to enhance speech signals using Deep De-noising Autoencoder and DWT. Chapter 4 describes the proposed method of speech enhancement using DWT with MATLAB. Block diagrams and algorithms are also discussed. Chapter 5 contains the experimental results and analysis of the proposed method. Chapter 6 provides concluding remarks, advantages and areas of improvement.

2 Literature Survey Consider a noisy signal made up of clean speech that has been degraded by additive noise. y[n] = s[n] + d[n]

(1)

where y[n], s[n] and d[n] are the sampled noisy speech, clean speech, and additive noise, respectively [2]. t is considered that additive noise is zero mean and uncorrelated with the clean speech. As the speech signal is non-stationary and time variant, the noisy speech signal is often processed on a frame-by-frame. Their representation in the short-time Fourier transform (STFT) domain is given by Y (ω, k) = S(ω, k) + D(ω, k)

(2)

where k is a frame number. Here, it is assumed that the speech signal is segmented into frames, and hence, for simplicity, we drop k. Since the speech is assumed to be uncorrelated with the background noise, the short-term power spectrum of y[n] has no cross-terms The speech can be estimated by subtracting a noise estimate from the received signal.

A Wavelet-Based De-Noising Speech Signal Performance …

 2 2 ˆ   ˆ S(ω)  = |Y (ω)|2 −  D(ω) 

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(4)

2   ˆ The estimation of the noise spectrum  D(ω)  is acquired by averaging recent speech pauses frames: M−1 2  2 1    ˆ Y S P j (ω)  D(ω) = M j=0

(5)

where M is the number of consecutive frames of speech pauses (SP). If the background noise is stationary, the above equation converges to the optimal noise power spectrum estimate as a longer average is taken.

2.1 Weiner Filtering The Wiener filter (WF) is an optimal filter that minimizes the mean square error criterion [2]. Here, it is considered that the noise and the speech obey normal distribution and do not correlate. The gain function of WF, Hweiner (ω), can be expressed in terms of the power spectral density of clean speech Ps (ω) and the power spectral density of noise Pd (ω) as Hweiner (ω) =

Ps (ω) Ps (ω) + Pd (ω)

(6)

The weakness of the WF is the fixed gain function at all frequencies and the requirement to estimate the power spectral density of the noise and clean signal before filtering. The shortcoming of the WF is the proper addition work at all frequencies and the necessity to appraise the ghostly power thickness of the commotion and clean sign before sifting. Therefore, non-causal WF can’t be applied to assess the unadulterated discourse since discourse can’t be thought to be quality discourse.

2.2 Wavelet Transform The wavelet transform is analogous to the Fourier transform (or far more to the windowed Fourier transform) with a totally different merit function [3]. The main difference is this: Fourier transform decomposes the signal into sines and cosines, i.e., the functions localized in Fourier space; in contrast, the wavelet transform makes use of functions which are localized in both the real and Fourier spaces. Generally, the wavelet transform is often expressed by the subsequent equation:

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Mel spectrum

Frame blocking

Hamming Window

FFT

Mel-Frequency Wrapping

Cestrum

Fig. 1 Block diagram of mel-frequency cepstral coefficients

 F(a, b) =

∞ −∞

∗ f (x)ψ(a,b) (x)dx

(9)

2.3 Discrete Wavelet Transform The discrete wavelet transform (DWT) is an execution of the wavelet transform employing a discrete set of the wavelet scales and translations following some prescribed rules. In other words, this transform splits the signal into a set of wavelets which are mutually orthogonal. This is the main difference from the continuous wavelet transform (CWT) [3]. The implementation for the discrete time series is sometimes called discrete time continuous wavelet transform (DT-CWT). The wavelets are often built from a scaling function which describes its scaling properties. The restriction that the scaling functions must be orthogonal to its discrete translations implies some mathematical conditions on them which are mentioned everywhere, e.g., the dilation equation (Fig. 1). φ(x) =

∞ 

ak φ(Sx − k)

(10)

k=−∞

2.4 Speech Quality Measures Perceptual Evaluation of Speech Quality (PESQ): This is a collection of criteria that includes a test methodology for assessing the speech quality as encountered by a telephony device consumer. In 2001, it was standardized as ITU-T Recommendation

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P.862. Phone manufacturers, network equipment suppliers and telecom operators use PESQ for objective voice quality monitoring. PESQ was created to model subjective assessments widely used in telecommunications to evaluate human perceptions of voice quality [4]. As a result, it uses real voice samples as test signals. It is important to fill modern telecommunication systems with speech-like signals in order to characterize the listening quality as perceived by users. Many systems are optimized for speech and would respond in hit or miss thanks to non-speech signals (e.g., tones and noise). Depending on the knowledge that is made available to an algorithm, voice quality test algorithms are often divided into two main categories: A “full reference” (FR) algorithm has access to and makes use of the original reference signal for a comparison (i.e., a difference analysis). It can compare each sample of the reference signal (talker side) to every corresponding sample of the degraded signal (listener side). FR measurements deliver the very best accuracy and repeatability but can only be applied for dedicated tests in live networks. A “no reference” (NR) algorithm [5] only uses the degraded signal for the quality estimation and has no information of the original reference signal. NR algorithms are just lowaccuracy estimates since the source reference’s originating speech characteristics (e.g., male or female talker, background noise and non-voice) are uncertain. A popular variant of NR algorithms does not even analyze the decoded audio signal, but rather operates on an IP packet-level analysis of the digital bit stream. As a result, the calculation is constrained to a transport stream analysis. PESQ is a full reference algorithm that analyzes speech signals sample by sample after temporally aligning corresponding reference and test signal extracts [6]. Perceptual Evaluation of Speech Quality (PESQ), as defined within the ITU-T P.862 standard, may be an objective method to check the speech quality of the mobile station. The objectivity is based on a comparison to the standard Mean Opinion Score (MOS) system, in which a group of listeners rate the voice quality on a scale of 1 to 5, with 1 being the worst and 5 being the best (excellent). When AMR voice is chosen because the paging service, the AMR Source is about to PESQ and therefore the AMR Radio Access Bearer is about to a desired vocoder, the test set supports the PESQ measurement by comparing the first signal with the received signal skilled a communication system. After the PESQ analysis, a score is given ranging from −0.5 to 4.5. A higher score means a better speech quality.

2.5 The Weighted Spectral Slope Measure (WSSM) This is defined as time averaged weighted spectral slope measure, where only the “good frames” are averaged. The weighted spectral slope measure [7] measures the weighted differences of spectral slope over 25 critical frequency bands between the two corresponding signal frames. First, the energy in each of the 25 frequency bands is computed, for both s(n) and sˆ(n), resulting in E s ( f ) and E sˆ ( f ), respectively. The spectral slope at each frequency band is defined by:

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Δ E s ( f ) = E s ( f + 1) − E s ( f ) Δ E sˆ ( f ) = E sˆ ( f + 1) − E sˆ ( f )

(11)

After that the nearest peak P(f ) is located by searching upwards if Δ E (f ) > 0 and downwards otherwise. Then, the weight in each band is calculated as W( f ) =

Ws ( f ) + Wsˆ ( f ) 2

(12)

where 20

1 20 + E s,max − E s ( f ) 1 + Ps ( f ) − E s ( f ) 20 1 Wsˆ ( f ) = 20 + E sˆ,max − E sˆ ( f ) 1 + Psˆ ( f ) − E sˆ ( f )

Ws ( f ) =

(13)

The magnitude of the weight reflects whether the band is near a spectral peak or valley and whether the peak is the largest in the spectrum. Finally, the WSSM is calculated as  24  N 2 1  f =1 W ( f )[Δ E s ( f ) − Δ E sˆ ( f )] (14) WSSM = 24 N k=1 f =1 W ( f ) where the averaging is done over synchronized, “good frames” of the data. Similar to this, a lower WSSM indicates a better speech quality. The Log-Likelihood Ratio This measure [4] has been widely utilized in speech research for comparing speech signals. Recently, it has been proposed as a measure for assessing the standard of coded speech. The main assumption on which the log-likelihood ratio distance is based is that speech is often represented by a pth order all-pole model of the shape. x(n) =

p 

am x(n − m) + G x u(n)

(15)

m=1

where x(n) is that the sampled speech signal, am (m = 1, 2 . . . .p) are the coefficients of an all-pole filter l/A (which models the resonances of the speaking mechanism, G is that the gain of the filter (as defined in (5a) later in this section) and u(n) is an appropriate excitation source for the filter. The waveform coder is often represented as shown in Fig. 2 in which x(n) is the input speech, which can be modeled according to (1) and y(n) is the decoded output.

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Audio signal

GT

GT filter bank

FFT

Windowing

DCT

Log

Fig. 2 Gammatone cepstral coefficients block diagram

The log-likelihood ratio for comparing x(n) (input speech) and y(n) (decoded output) can then be defined as 

ax R y axt l = log ax R y a ty

 (16)

where ax ay Ry

LPC coefficient vector for original speech signal x(n) LPC coefficient vector for the coded speech signal y(n) Coefficient vector matrix of y(n) whose elements a

N −|i− j|

r |(i − j )| =



y(n)y(n + |i − j|)

(17)

n=1

i – j = 0, 1, ……, p − 1 where N is the number of samples used in the analysis (i.e., the frame size). By interchanging the roles of x(n) and y(n), an alternate distance measure can also be defined.

3 Existing Method The model is used to enhance the noisy signal in multiple noise environments, and it uses DWT coefficients as speech features for noisy and training target signals. The proposed model has two stages, viz. training and testing stages [8]. In the training stage, the DDAE model is trained with an optimization algorithm to find out internal speech acoustic features. Each noisy signal consists of DWT coefficients placed in a

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Database for training

Noisy samples

DWT Decomposition

Trained DDAE

DDAE

IDWT Reconstruction

Fig. 3 Block diagram of deep de-noising autoencoder based on speech enhancement algorithm

time–frequency axis, which differentiate clean speech signal from the noisy speech signal (Fig. 3). The training data set is prepared by mixing clean speech with noise corpus at multiple environmental SNR levels. Noisy and clean speech signals are first arranged in a matrix format using framing; afterward, DWT is applied to each matrix rowwise, which results in DWT approximate and detailed coefficient. These coefficients are placed in the same order as the applied input matrix. Noisy and clean speech DWT coefficients are prepared and placed at the input and output sides of the DDAE model, respectively. The noisy speech corpus is divided into two groups, i.e., training and validation, and these groups are divided such that no data appears more than once in the training and validation data set, respectively. Testing of the proposed model is performed with unknown non-stationary noisy speech [9–14]. The block diagram helps to know how the existed model works. So, in this model, coefficients of DWT are given as input speech features so that these can be analyzed and further implemented in multi-resolution analysis. The main method used here is the deep de-noising autoencoder, which is trained and used for learning the model for analysis of signals. The noisy signals from the database are sent into this trained DDAE as mentioned in the block diagram. Next, this trained database signals are used to tackle the noise form the noisy speech signals from the real world. And, the unknown real-word noise signals are tested with this trained deep de-noising autoencoder. Apart from this, the decomposition of the signals is done with the help of discrete wavelet transform which is again passed through the deep de-noising encoder. As the output from this step cannot be used in the real world, this is again converted with inverted discrete wavelet transform. The de-noised signal is now measured with the conventional speech features like FFT amplitude, log-magnitude and MEL frequency coefficients. The resulted signal is compared with the algorithms like KLT-Karhunen Loeve transform, MMSE—minimum controlled square error, SS_IMCRA—subtraction with

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improved minimum controlled recursive average and threshold algorithms like wavelet thresholding. Lastly, the evaluation of the speech signals is done with the SEA model.

4 Proposed Method 4.1 Algorithm Prepare a database which can be used for training the signals. Sample the clean data from the clean data set, and prepare clean DWT coefficients. Sample the noisy data from noisy data set, and prepare noisy DWT coefficients. Use DWT coefficients pair as training input for estimating the autoencoder distribution function. Finally, evaluate the well-trained DAE model with unknown noisy speech.

4.2 DWT Decomposition DWT is one of the best methods to test the enhancement of non-stationary speech signals. When the wavelet decomposition of clean and noisy signal is added, then we obtain the coefficients of noisy signals. These are measured with the threshold values so that these then can be evaluated to reconstruct the speech signal. Block Diagram The below block diagram explains the process of the proposed method (Fig. 4): The noisy data sample is taken from the speech corpus database. This sample is taken as input for the wavelet analyzer. The signal is then sampled with the threshold values. Finally, the whole de-noised signal is obtained.

SPEECH CORPUS

Output signal

Input

De-noising Input signal

Fig. 4 Block diagram of proposed wavelet de-noiser

Wavelet Analyzer

Thresholding

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4.3 Algorithm Open MATLAB R-2017a. Select “Apps” tab from the MATLAB toolstrip. Next, select wavelet analyzer from “Signal processing and communications.” From one-dimensional signals, choose “Wavelet Packet 1-D.” Then, go to File -> load -> Signal. Choose any sample of your choice from the speech corpus database. Enter the wavelet type and level. Choose entropy as threshold and give the threshold value. Analyze the input, and observe the decomposition tree. Select the de-noise option to de-noise the signal. We have the option to choose from hard and soft thresholding. Select any one, and observe the graph of “Sorted absolute values of coefs” by varying the global threshold. Select the de-noise option, and view the de-noised signal graph.

4.4 Speech Corpus Here, we are using speech corpus for our proposed method. Speech corpus (also known as spoken corpus) is a database containing speech audio files and text transcripts of these audio files in the format that is applicable in the creation of acoustic models used with speech recognition engine. There are two types of speech corpora: Read speech, Book excerpts, Broadcast news, List of words, Sequence of numbers. Spontaneous speech: Dialogues, Narratives, Map tasks, Appointment tasks (Table 1).

4.5 Windowing Technique The windowing method entails multiplying the ideal impulse response by a window function to produce a filter that changes the ideal impulse response. The windowing method, like the frequency sampling method, generates a filter with a frequency response that resembles a desired frequency response. However, the windowing process appears to yield better results than frequency sampling. Gaussian Window A signal which is zero-valued outside of a specific interval, usually symmetric near the center of the interval, generally maximum in the middle and tapering away from the middle, is called as a window function in statistics and signal processing.

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Table 1 Examples of English speech corpora in the linguistics department Speech corpus

Types of data

Size

Types of annotation

TIMIT

Read sentences

630 speakers each reading 10 sentences 8 US dialects

Orthographic phonetic

Broadcast news

News reports

104 h of television and radio broadcasts

Orthographic

TIDIDIGITS

Connected digit sequences

326 speakers each reading 77-digit sequences

Orthographic

Switchboard

Phone conversation between strangers on an assigned topic

2400 conversations 543 speakers Many US dialects

Orthographic some phonetic

Call home

Phone conversations with 120 conversations up to family and close friends 30 min each

Orthographic

ICSI meetings

Weekly meetings of various research groups

72 h 53 speakers

Orthographic

HCRC map task

Map task

18 h 62 speakers (mainly Scots English)

Orthographic

ATIS

Flight booking

36 speakers

Orthographic

The Fourier transform of a Gaussian is also a Gaussian. As a Gaussian function’s support is infinite, it must either be truncated at the window’s ends or be windowed with another zero-ended window. Since the log of a Gaussian generates a parabola, it can be used in frequency estimation for nearly exact quadratic interpolation.

5 Results and Discussion In this chapter, we study the results of the proposed method. We are taking three different types of signals to test the ability of DWT to de-noise. These three signals are a part of the speech corpus. It is necessary for these samples to be in the formats of (.mat, .wav, .flac, .mp3) (Table 2). Table 2 Input values

Input type

value

Wavelet type

dB

Level

3

Entropy

Threshold

Threshold

2

Thresholding method

Hard thresholding

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Fig. 5 Decomposition tree

The decomposition tree obtained by taking level value as three is represented (Fig. 5).

5.1 Experiment results: Speech signal input 1: Characteristics i. ii.

Railway station noise Global threshold value = 0.22344

The signal with the parameters mentioned above, after de-noising, is shown below according to the type of thresholding done (Table 3): The enhanced signal is evaluated with speech quality measures like log-likelihood ratio (LLR) and signal-to-noise ratio (SNR) corresponding to Gaussian window alpha value (Figs. 6 and 7) (Table 4).

6 Conclusion and Future Scope Conclusion The real world consists of various signals which are mixed and form noise. As removing the noise from this mix of signals is difficult, we need an improved and efficient way to de-noise a speech signal. In this paper, we have used MATLAB software to implement the DWT feature. MATLAB is a vital tool in signal processing. It is cost-effective and user-friendly.

A Wavelet-Based De-Noising Speech Signal Performance … Table 3 Comparison of noise signals based on dB and thresholding values

Signal

dB

Train

1

Restaurant

Exhibition

289 Thresholding TS

TH

0.3461

0.3026

2

0.4275

0.4128

3

0.4334

0.3901

1

0.3641

0.3537

2

0.4245

0.4148

3

0.417

0.405

1

0.4534

0.4326

2

0.534

0.4806

3

0.5347

0.5296

Fig. 6 Speech quality objective measures LLR and FSG-SNR variation with “α”, (Railway-NOISE of 0dB SNR)

So, in this research, we chose three different signals for enhancement. The coherence and quality results are measured with log-likelihood ratio (LLR), signal-to-noise ratio (SNR), frequency segmented SNR (FRQ-SEG-SNR) and discrete wavelet transform (DWT) features. It is observed that this method produces good results and is easy and efficient in reducing the noise of a speech signal. Future Scope We hope to perform further research in the future to develop a more general speech de-noising model that takes into account more invisible noises and improves the quality of speech signals. Many other speech quality measures such as Capstrome

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Fig. 7 Speech quality objective measures FRS-SNR and SNR variation with “α”, (RestaurantNOISE of 0d BSNR)

Table 4 FW-SEG-SNR objective measure with different wavelet thresholding

Wavelet family Db4

Db6

Sym5

Sym7

Input SNRdB

TH

TS

0

7.125

6.146

5

8.668

6.967

10

10.128

7.645

15

11.456

8.316

0

7.786

6.266

5

8.124

6.978

10

10.699

7.692

15

12.101

8.381

0

7.251

6.167

5

8.046

6.986

10

10.345

7.648

15

11.078

8.274

0

7.018

6.353

5

8.362

7.016

10

10.738

7.698

15

12.129

7.764

and mean opinion score (MOS) can be considered to assess the efficacy of speech enhancement methods more precisely. In future, there is also scope to evaluate using subject measures.

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References 1. S.A. Shahriyar,Speech Enhancement Using Convolution De-noising Auto-Encoder (Khulna University of Science and Engineering, Khulna, Bangladesh, 2019) 2. N. Upadhyay, A. Karmakar, Speech enhancement using spectral subtraction-type algorithms: A comparison and simulation study, in Eleventh International Multi-Conference on Information Processing-2015 (IMCIP-2015) 3. S. Glover, P. Dixon, Likelihood ratios: A simple and flexible statistic for empirical psychologists. Psychon. Bull. Rev. 11(5), 791–806 (2004). https://doi.org/10.3758/BF03196706 4. A.W. Rix, R. Reynolds, M.P. Hollier, Perceptual measurement of end-to- end speech quality over audio and packet-based networks, in 106th Audio Engineering Society Convention, preprint no. 4873, May 1999 5. D. Snyder, G. Chen, D. Povey, Musan: A music, speech, and noise corpus (2015). arXiv preprint. arXiv:151008484 6. R.C. Hendriks, R. Heusdens, An evaluation of objective measures for intelligibility prediction of time-frequency weighted noisy speech. Delft University of Technology, 2628 CD Delft, Nov 2011 7. S.R. Chiluveru, M. Tripathy, A real-world noise removal with wavelet speech feature. Int. J. Speech Technol. Aug 2020 8. A.W. Rix, J.G. Beerends, M.P. Hollier, A.P. Hekstra, Perceptual evaluation of speech quality (PESQ)—A new method for speech quality assessment of telephone networks and codecs, in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP’01) Proceedings, vol. 2 (2001), pp. 749–752 9. R.Y. Lazim, Z. Yun, X. Wu, Improving speech quality for hearing aid applications based on weiner filter and composite of deep De-noising Autoencoders. Signals (2020) 10. Y. Kaisheng, C. Zhigang, A robust speech feature-perceptive scalogram based on wavelet analysis, in Fourth International Conference on Signal Processing Proceedings. ICSP’98 (IEEE, 1998), pp. 662–665 11. F. Chen, Y. Hu, M. Yuan, Evaluation of noise reduction methods for sentence recognition by Mandarin-speaking cochlear implant listeners. Ear. Hear. 36(1), 61–71 (2015) 12. A.A. Hersbach, K. Arora, S.J. Mauger, P.W. Dawson, Combining directional microphone and single-channel noise reuction algorithms: A clinical evaluation in difficult listening conditions with cochlear implant users. Ear. Hear. 33(4), 13–23 (2012) 13. Y. Hu, P.C. Loizou, Evaluation of objective measures for speech enhancement, in Ninth International Conference on Spoken Language Processing (Pittsburgh, PA, 2006) 14. J.S. Garofolo et al., Getting started with the DARPA TIMIT CD-ROM: An acoustic phonetic continuous speech database, vol. 107. (National Institute of Standards and Technology (NIST), 1988), p. 16

Multispectral Image Compression Using Adaptive Thresholding in Wavelet Domain with Binary Plane Techniques M. Renu Babu, S. Chinna Venkateswarlu, G. Chenna Kesava Reddy, and D. Vemana Chary

1 Introduction Multispectral imaging systems based on acquiring the light sparkly back at each pixel of an image provide a device independent showing which can be rendered in the correct color under any visible condition. Since this type of information is completely independent of the characteristics of the acquisition device, and this type of imaging can be used for accurate spectral color replica under different screening and lightning conditions. Unlike conventional imaging systems, multispectral cameras produce a multilayer image in which at each layer, the pixel values are non-negative numeric values equivalent to the spectral power at one narrow wavelength band. In other words, by increasing the number of channels beyond the traditional three channels of color imaging, the captured image will having both the spatial features of the picture as well as the spectral information at each pixel [1]. Hyperspectral cameras that were mounting on satellites try to provide accurate information about earth’s surface. The processing of these images is becoming popular day by day as they are being used in climate monitoring, military applications like target detection and identification. Multispectral image compression is used for conversion the images into more compact form for pressuring, processing, and reconstructed applications. In compression, the spatial and spectral redundancies or correlation are reduced without loss of information, and these uncorrelated brightness values do not contain any structural redundancy as beside pixels bear no relationship to each other. Currently, most of the satellites implementation standard JPEG-LS and JPEG 2000 algorithms which achieve an average compression ratio of 2–2.5. This paper includes M. Renu Babu (B) · G. Chenna Kesava Reddy · D. Vemana Chary TKR Engineering College, Meerpet, Hyderabad, India e-mail: [email protected] S. Chinna Venkateswarlu Institute of Aeronautical Engineering, Hyderabad, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. K. Mandal et al. (eds.), Innovations in Signal Processing and Embedded Systems, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-19-1669-4_26

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on providing a solution for selecting optimal band for compression by employing inter- and intra-band relationship investigation with 2D integer wavelet renovate This paper also presents a spatial compression approach that aims to compress the likely band in quasi-lossless method and provide one of the best compression solutions to multispectral images. The state-of-the-art lossless compression algorithms are the JPEG2000 lossless mode and JPEG-LS. In the tests shown in the compression competence of JPEG-LS is slightly higher than that of the JPEG 2000 lossless mode yet in the tests shown in the JPEG2000 lossless mode has higher compression competence than JPEG-LS. Over all, in provisos of compression efficiency, the two algorithms are most likely about the same. Further through improvement on the lossless compression efficiency over these state-of-the-art algorithms has been complex, and thus the improvement a reusually inconsequential.

2 Related Work Multispectral image compression has attracted many researchers to proposed conduct their inventive works, and few of them which are related to the current work were presented in this section. In [2] Ruedin et al. proposed a 2D integer wavelet transform-based band ordering mechanism with predictions using the wavelet coefficients and presented a class conditioned lossless image compressor with arithmetic encoding. The method also compared against 3D-SPIHT and KLT transform, however, the band decomposition method introduces artifacts and discontinuities by which the compressed loses its originality [3]. Zhang et al. in [4] proposed a lossy to lossless compressor for hyperspectral images, consisting in an integer KLT in the spectral breadth and a 2D DWT in the spatial allocation, followed by a 3D Tarp-based coder. In [5], Bhagya raju et al. presented and performed SPIHT algorithm which is used to compress the multispectral satellite images. Here, the interpolation-based super decision technique is used to improving the multispectral images and also to estimate a high-resolution (HR) image from a low-resolution (LR) image. Then, using the discrete wavelet transform (DWT), the de-correlated spectral bands are transformed. The use of improved SPIHT algorithm quantizes and encodes the spectral bands. The main feature of this algorithm is the high-compression ratio of bits per pixel per band and optimization of maximum coding efficiency and performance. In [6], Hagaga et al. presented a compression technique for multispectral images. In this scheme before applying compression, the optimal multispectral band ordering process is performed. For spectral dimension, the dual-tree discrete wavelet transform is used, and for spatial dimension, the 2D discrete wavelet transform is used. Then, for compression, simple Huffman coding is used. Experiments are carried out using lands at ETM images and to acquire better compression ratio. A novel model OLCP (optimal leaders color clustering PCAWW weighted coding) was proposed in [7]. In the model, for sparse equivalent illustration, the spectral

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colorimetric clustering is designed. Then, removal of spectral redundancy, the principal component analysis is preferred. The difference in the image is predicting using error compensation machinery. Finally, traditional multispectral image coding scheme is used to encode the image. Under various illumination conditions, this model improves the colorimetric correctness of rebuilding images. Under the same compression ratio, this model achieves satisfactory peak signal to noise ratio. From a variety of available literatures, it is evident that there is a scope to projected an effective way of band ordering and discarding. Image damage is the usage of information stress on superior photographs. Multispectral image (MSI) is restricting the scale in bits of an illustrations document deprived of corrupting the environment of the image to an inadmissible stage. The decrease in text length allowed greater snapshots to be positioned absent in assumed degree of circle or reminiscence space. It in addition to the time essential for images toward be dispatched over the Internet or decoded commencing Web pages, images. There are less particular manners by way of which picture statistics may be compacted. For Internet use, the two most normal compacted realistic photograph configurations are the joint photographic experts group (JPEG) function and the graphic interchange format (GIF) layout. The JPEG approach is the entire extra regularly applied for images, at the same time as the GIF method is normally exploited for streak workmanship and changed photos wherein regular figures remain usually fundamental.

3 Proposed Approach The generalized block diagram of the proposed work is depicted in Fig. 1. The proposed technique involves several points for achieving the efficient lossless compressed image. At first, the multispectral image is decomposed into “N” number of bands, where in spatial domain, the similar intensity values of the contiguous

Input Multispectral Image

Compressed Image

Band Decomposition

Output: data plane, bit plane Reconstruct image

Fig. 1 Block diagram of the proposed work

Block Partitioning

Binary plane threshold method

IWT

Optimal Band selection

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homogeneous features cause high-spatial correlations from pixel to pixel in the neighboring areas. Temporal correlation has been widely exploited in video compression between the frames but not in frequent in case of multi-temporal remote sensing image compression. But during recent years, temporal correlation has been exploited with other dimensions where images are taken from the same scene from space a short time a part [8]. Hence, processing every single band is not necessary, however, much of the content can be available from optimal band. After the block decomposition, each sub-band is partitioned in “mxn” non-overlapping sub blocks. Integer wavelet transform is applied [5, 9] and is applied for each block of subband as it provides lower entropy values than other wavelet transforms, when tested on several multispectral images. In [8], author presents a vivid explanation of usage of IWT for multispectral decomposition that aims for efficient compression. After the decomposition of the multispectral image with IWT as shown in Fig. 2, each sub-band is partitioned into stack of blocks belonging to multiple bands. Each block in a sub-band do have the same position the successive band, hence the computation of correlation is performed between the current block and the block prior sub-band. The bands which have most correlation value are considered and the rest surplus, so a new order of band can be attained with the analysis. The final order “b” of bands can be represented as Fig. 2 Sub-band decomposition of multispectral image with IWT

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Nb   O (k)  W b , W Ob (k−1) 

(1)

b = max

k=2

where N b is the number of bands, Ob is the order of blocks for “k” band. The sorted blocks are merged mutually, and inverse wavelet transform is performed to attain the optimal band image in spatial field. This image is subjected for binary plane technique for competent compression. The concept of binary plane method is employed by several authors for different application of compression and in specifies explanation of algorithm is presented in [10]. When the image is subjected for BPT, the image is segmented into two plane, namely data plane and bit plane, the primary plane contains the spatial fundamentals, and the later contains the bit allocation at the respective position. The algorithm has proven to be more efficient than JPEG and SPIHT for natural images and also medical images [8]. In this work, the optimized image band is in consult to BPT with no threshold and varying threshold values.

4 Experimental Setup Results The proposed advance tested with standard dataset of hyperspectral images like Indiana pines, Pavia University and Salinas which are available at [8]. Indiana pines hyperspectral image consists of 200 sub-bands which are of 145 × 145 resolutions. Initially, the band ordering and discarding method are applied which selects only 42 sub-bands which have more spatial data. The ordering of bands suggested based on auto-correlation coefficients that is utilized to attain the integrate band comparative. At this situation, a three dimensional spatial, spectral multi-image is divided into λ no. of two dimensional spectral, spatial ordinates, their separate integrate differentially is qualitative with cross correlation coefficients and its mathematical simplification is observed in Fig. 3. The various different multispectral (hyperspectral) images with different resolutions and bands are measured, the Indiana Pines having of 200 bands with 145 × 145 resolutions, Pavia University contains 103 bands with 610 × 340 resolutions, and Salinas contains 204 sub-bands with 512 × 217 resolution. In this work, the building block size of 4, 8 is considered in entire experiments. For the expediency of comparison, only few bands were considered the visual representation depicts the compressed image with BPT and JPEG [10]. To assess the performance of the method, several metrics like compression ratio (CR), peak signal to noise ratio (PSNR), mean square error (MSE), mean absolute error (MAE), mean structural similarity index (MSSIM), normalization cross correlation coefficient (NCC), structural content (SC), and image fidelity (IF) were calculated [11, 12]. For natural and multispectral images in daily life, lossless compression is also universally. Thus, the research on improving the lossless compression efficiency is a

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(a)

(b)

(c)

Fig. 3 Spectral bands of a Indiana Pines (145 × 145 × 200), b Pavia University (610 × 340 × 103), c Salinas (512 × 217 × 204)

topic interested to multidiscipline applications. Indeed, lossless image compressionrelated research topics are still hot research topics currently, there are research related to lossless compression on medical images, multispectral images, researches related to lossless video compression, research related to the hardware implementation of lossless compression algorithms, and researches related to remote sense images, etc., the efficiency of lossless density is normally much lower than that of lossy compression. Figs. 4, 5, 6 show the compacted images with proposed come near and JPEGLS approach, and its metric analysis is shown in Tables 1 and 2, and the varying PSNR performance of the approach is also depicted in Figs. 7, 8, 9. From the above evaluation for evident that approach is attaining high-quality at unlike bit rates.

5 Conclusion and Future Scope The proposed work presents an effectives band decomposition discarding mechanism with binary plane approach for better compression. It is evident that the proposed approach achieves high quality, however, it also shows an exponential growth with varying bit rates. This technique also proves to be very simple in implementation making it easy for on board/chip implementation. Image compression is a mature field and thus improving the compression efficiency becomes more and more challenging. The achieved compression efficiency improvement is important, and thus, the proposed method and algorithm might be widely used in applications where lossless compression is employ for initially acquired images. This work can be further extended by deploying profound networks concepts for the range of optimal band.

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(a)

(b)

299

(c)

Fig. 4 Salinas image a original optimal band, b compressed with BPT, c compressed with JPEG-LS

(a)

(b)

(c)

Fig. 5 Indian Pines a original optimal band, b compressed with BPT, c compressed with JPEG-LS

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Fig. 6 Pavia University a original optimal band, b compressed with BPT, c compressed with JPEG-LS

Table 1 Performance assessment of the proposed approach Multispectral image

PSNR

CR

MSSIM

MAE

NCC

SC

IF

Indiana Pines

58.02

8.187

0.91

0.121

0.99

1.003

0.98

Pavia University

56.28

6.23

0.96

0.110

0.998

1.002

0.98

Salinas

53.89

9.81

0.98

0.243

0.998

1.004

0.98

NCC

SC

IF

Table 2 Performance assessment of the JPEG-LS approach Multispectral image

PSNR

CR

MSSIM

MAE

Indiana Pines

41.97

4.96

0.99

1.69

0.99

1.003

0.99

Pavia University

39.89

4.11

0.99

2.06

0.99

0.99

0.99

Salinas

43.77

5.98

0.997

1.29

0.99

1

0.99

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Fig. 7 PSNR versus BPP analysis for PAVIA University

Fig. 8 PSNR versus BPP analysis for Indian Pines

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Fig. 9 PSNR versus BPP analysis for Salinas

References 1. F. Konig, W. Preface, The practice of multispectral image acquisition, in Proceeding of International Symposiom on Electronic Capture and Publishing, SPIE 3409 (1998) 2. M. Olaru, M. Craus, Lossless multispectral and hyper spectral image Compare ssion on multicore systems, in 2017 21st International Conference on System Theory, Control and Computing (ICSTCC) (Sinaia, 2017), pp. 175–179 3. F. Agahian, B. Funt, S.H. Amirshahi, Spectral compression: Weighted principal component analysis versus weighted least squares, in Proceeding of Human Vision and Electronic Imaging Conference (SPIE, 2014) 4. A. Ruedin, D. Acevedo, A class-conditioned lossless wavelet-based predictive multispectral image compressor. IEEE Geosci. Remote Sens. Lett. 7(1), jan 2010 5. A. Hagag, M. Amin, F.E. Abd El-Samie, Multispectral image compression with band ordering and wavelet transforms. SIViP 9(4), 769–778, May 2015 6. J. Zhang, J. Fowler, G. Liu, Lossy-to-lossless compression of hyper spectral imagery using three-dimensional TCE and an integer KLT. IEEE Geosci. Remote Sens. Lett. 5(4), 814–818 (2008) 7. V.B. Raju, K.J. Sankar, C.D. Naidu, S. Bachu, Multispectral image compression for various band images with high resolution improved DWT SPIHT. Int. J. Signal Process. Image Process. Pattern Recognit 9(2), 271–286 (2016) 8. M.A. Mamun, X. Jia, M. Ryan, Non-linear elastic model for flexible prediction of remote sensed multi-temporal images. IEEE Geosci. Remote Sens. Lett. 11(5), 1005–1009, May 2014 9. W. Liang, P. Zeng, Z. Xiao, K. Xie, Multispectral image and different Compression methods for improvement of both colorimetric and Spectral accuracy. J. Electron. Imaging, 25(4), Aug 2016 10. A. Ruedin, D. Acevedo, Prediction of coefficients for lossless compression of multispectral images (2005). https://doi.org/10.1117/12.615386

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11. R.O. El Safy, H.H. Zayed, A. El Dessouki, An adaptive steganographic technique based on integer wavelet transform, in 2009 International Conference on Networking and Media Convergence (Cairo, 2009), pp. 111–117 12. M.A. Mamun, M.A. Hossain, M.N.I. Mondal, M. Aktar, Satellite image compression using integer wavelet regression, in 2017 International Conference on Electrical, Computer and Communication Engineering (ECCE) (Cox’s Bazar, 2017), pp. 438–442

Privacy Ensured Transmission of Healthcare Records Using IoT-Enabled Systems Y. Geetha, Ashwak, and S. V. Sprasad

1 Introduction IoT produces an covered interaction placing of interconnected gadgets further to systems with the aid of manner of attractive both digital and actual worldwide together [1]. With the arrival of some distance off virtual healthcare-based totally IoT structures, the transmission of medical records will become each day regimen. So, it is required to create an efficient model to ensure the security and integrity of transmitted and received patient diagnostic data from the IoT environment becomes a major issue. So, this is done using steganography techniques and system encryption algorithms together to hide digital information in an image. The data is confidential to be transmitted and changes in the carrier. So, it is very difficult to detect. There are two main aspects to any stenography system which are steganography ability and imperceptibility. All these are analyzed with different types of attacks and their behaviors, respectively [2–8]. To ensure the protection and additionally balance of the client’s diagnostic facts sent and gotten from IoT environment [9]. This purpose is completed using hiding techniques as well as device encryption algorithms with each other to cover digital data in a picture [10–13]. Cryptography is a further time period for records encryption File encryption cryptography is the gadget of encoding messages in such a manner that cyberpunks cannot study it, however that can be certified employees. Both primary formulations used for information document encryption on this hobby are the Advanced File Encryption Requirement (AES) and also the Rivets-ShamirAdelman (RSA) set of rules. AES is a symmetrical cipher wherein the precise equal key’s used one very facts. It has a hard and fast message block measurement of 128 little bits of message (stage), and suggestions of duration 128, 192, or 256 little bits. Y. Geetha (B) · Ashwak · S. V. Sprasad Department of Electronics and Communication Engineering, MLR Institute of Technology, Hyderabad, Telangana, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. K. Mandal et al. (eds.), Innovations in Signal Processing and Embedded Systems, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-19-1669-4_27

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When longer messages are dispatched, they are divided into 128-bit blocks. Apparently, longer hints make the cipher harder to break, however moreover put in force a longer relaxed and decrypt method. However, the RSA is a public key formulation, which widely applied in enterprise and man or woman interaction markets. ‘It has the gain of getting a variable key size numerous from (2-2048) little bits. The number one research study in hiding information started with steganography, which describes the medical studies and more over paintings of hiding information inside an image. The advantage of secret writing is able to be used to ship labeled messages with the false of the transmission being located. The DWT has a super spatial localization, frequency unfolds, and additionally multi resolution attributes, which are matching with the idea of forms inside the human seen device’ [9]. This paper executes each1stage in addition to2-level of DWT steganography techniques that perform at the frequency area. It broke up the image into excessive and coffee model additives. The excessive version aspect consists of issue information, whereas the low model detail is often cut up proper into immoderate and coffee era components. Limitation is 1. The message is a confidential document that must be transmitted and camouflaged on the bearer so that it is difficult to detect 2. There are still limited methods of hiding information for use with data transfer communication protocols, which may be unconventional but the future is promising.

2 Literature Survey Opportunities, challenges, and open problems Cloud computing and furthermore the (IoT) has surely emerged as brand-new structures in the ICT revolution of the twentyfirst century. The adoption of the Claudio paradigm inside the health facility treatment area can deliver several possibilities to clinical IT, and moreover professionals assume that it is able to dramatically beautify healthcare services and furthermore make a contribution to its non-prevent further to methodical improvement. This paper offers an extensive testimonial of the current-day literature on aggregate of CC further to IoT to solving numerous troubles in health center remedy packages which encompass wise apps, medicine manage, and additionally a protracted manner flung scientific answers. Also, a quick introduction to cloud pc and moreover internet of factors with as of tare programs of tare to health care is obtainable. This paper gives a brand-new idea of the assimilation of CC and moreover IoT for health care programs, which are what we; name the Claudio-Health paradigm. The term Claudio-Health and further more a few essential integration problems exist in this paper to supply a useful imaginative and prescient to incorporate modern components of CC similarly to the IoT in fitness care packages. Likewise, this paper hobby is to provide the modern-day-day and furthermore vicinity assessment of various tiers of integration components, reading several contemporary proposals in Cloud IoT-Health structures. Ultimately, related researches of CC and more over IoT combination for healthcare systems have clearly been reviewed. Difficulties to be attended to similarly to destiny commands of research are determined, further to a

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massive bibliography is furnished. Due to the dispositions in computer-based totally surely verbal exchange in addition to health offerings during the last decade, the need for image safety will become pressing to take care of the needs of each protection and furthermore non-protection in scientific packages. This paper suggests a latest willing watermarking primarily based completely device for photo authentication and additionally self-recovery for clinical packages. The encouraged device situates picture tampering similarly to recuperate the real image. A host photo is burglarized four × four blocks and furthermore singular properly nicely really worth disintegration (SVD) is utilized by setting the lines of block sensible SVD proper into the least top notch little bit (LSB) of the image pixels to discern out the trade within the proper photo. Two authentication little bits specifically impede authentication and additionally self-recuperation little bits are finished to undergo the vector quantization attack. The insertion of self-recovery bits is set up with Arnold makeover, which recoups the initial picture more over after an excessive meddling rate. SVD primarily based absolutely watermarking data boosts the photograph verification in addition to offers a manner to discover precise attacked vicinity of the water marked photograph. The recommended scheme is tested in region of tremendous styles of actions which encompass textual content elimination strike, text insertion strike, and copy similarly to stick strike. Contrasted to the dominion-of-the art techniques, the recommended plan extensively boosts each tamper localization accuracy in addition to the Peak sign to noise ratio (PSNR) of self-recovered picture. This paper gives a SVD-based totally definitely truly fragile watermarking device the usage of organized block technique to provide greater protection and offer a supplemental manner to find out the assaulted are as inner unique scientific pix.2 authentications little bits specifically block verification and self-recuperation bits have been made use of to undergo the vector quantization assault. The usage of Arnold remodel makes it possible to get higher the tampered region from the neighboring blocks, which always complements the NCC further to PSNR of the recuperated host. Our speculative outcomes determined out that the advocated gadget can be very trusted and is likewise able to discover the struck blocks effectively. The proposed plan effectively protects in opposition to duplicate and paste assault, net content material removal attack, message addition strike in addition to VQstrike. Contrasted to the state of the paintings strategies, the advocated scheme notably improve each meddle localization accuracy and the PSNR of self-recovered photograph. Although our endorsed method showed extremely good prevalent ordinary performance in managing fragile tampered images, but extra experiments are needed to assessment its overall performance with non-fragile tampered pictures. In our destiny art work, we plan to treatment this problem. Moreover, we will definitely cognizance on coming across numerous first rate meddling troubles which in corporate image resize, skew, similarly to rotate operations. An in experienced steganography technique for protective interaction inside the Web of Things (IoT) essential facilities with the manifestation of the Internet of Things(IoT) further to haze computing, the quantity of element gadgets is intensifying drastically round the arena, giving lots higher services subsequently of customer with the assist of modern and upcoming

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communication infrastructures. Every the form of devices are producing and furthermore communicating a large amount of facts and additionally control data spherical this open IoT setting a large quantity of this info has individual and more over crucial records for the individual further to for the employer. The extensive sort of assault vectors for volatile customers are excessive because of the openness, allotted nature, and shortage of manipulate over the complete IoT environment. For growing the IoT as an inexperienced answer machine, prevent clients require to truly be given as actual with the device. Therefore, safety and personal privacy of records inside the IoT is a top notch problem in crucial infrastructures at the face of the smart house, sensible metropolis, smart health care, smart zone, and so forth. In this placed up, we recommend three records hiding techniques for protecting interaction in important IoT facilities with the help of steganography, wherein RGB photos are made use of as agencies for the records. We conceal the records within the an entire lot deeper layer of the image channels with minimal distortion inside the least large little bit (lbs.) to be made use of as indicator of data. We study our method every mathematically and furthermore experimentally. Mathematically, we display that the opponent cannot forecast the actual data with the beneficial useful resource of evaluation. The endorsed approach completed masses better imperceptibility and ability than the numerous gift strategies together with higher resistance to steganalysis strikes which includes pie chart evaluation further to RS evaluation, as tried and tested experimentally. Securing facts within the IoT crucial surroundings is crucial to developing the faith of customers within the digital system and additionally for everyday variant of new IoT services and products. In this post we have were given in truth endorsed 3record shading strategies the usage of an RGB image steganography approach that may be implemented in important facilities collectively with the smart residence, smart healthcare, smart market, and so on, for securing statistics. We have applied a mystery trick in addition to carrier statistics for hiding statistics in the image. We have completed safety and protection assessment both mathematically and furthermore experimentally to discover the power of the endorsed techniques. We have in fact verified mathematically that the techniques can resist each steno-remarkable strike and moreover steno-cover strike. The foe is incapable to predict the real statistics via reading regardless of the reality that he obtains each the steno-photograph further to cowl photo. The experimental results are assessed on pix from well-known datasets utilizing the endorsed algorithms. We have truly assessed the general normal basic performance of the advised technique relative to imperceptibility, potential, and more over sturdiness. There commended technique executed considerable development over several contemporary techniques inside the case of imperceptibility and more over functionality. We have sincerely assessed the effectiveness of the proposed approach thru the usage of esthetic valuation and statistical assessment. These techniques correctly resist esthetic attack, as one cannot find out any visible distinction between provider photo and identical stego image, further to we have got moreover positioned this with the resource of NCC analysis. We have located no huge variations in among the histogram of the carrier image and the steno-image therefore attack thru manner of this route is not always possible. The device cannot be attacked thru RS evaluation, as we have got, we have been given in reality positioned that

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the percentage of flipped pixels decided with the useful aid of this assessment is not any extra than zero.15%. So, the recommended technique finished far higher imperceptibility further to functionality than the numerous gift strategies together with a long way better resistance to stag-evaluation movements which consist of histogram assessment or RS evaluation, as tried and examined experimentally. In the destiny, we can deal with strategies that would shield statistics in case of unintended attacks.

3 Existing System The aim of the steganography is not generally outstanding preventing others from data the secret data, yet also wiping out the vulnerability in having covered up data. The message is a particular record to be moved notwithstanding covered inside the specialist co-op just so it incorporates be difficult to select. There are two significant components in a steganography machine, which can be steganography usefulness and further more subtlety. In any case, these houses are mistaking for each other because of reality that it is miles difficult to raise capacity simultaneously as protecting the steganography intangibility of a steganography machine. Also, there are albeit bound methods of concealing records for use with measurements move cooperation techniques, which might be exact any way their future is promising.

4 Proposed System This paper recommends a mixture security model for ensuring the indicative text based substance information in clinical picas. The embraced model is prevalent through fusing either 2-D discrete wavelet redesign 1 confirmation (2D-DWT-1L) or 2-D discrete wavelet rearrange 2phase (2D-DWT-2L) steganography technique with an exhorted half breed security conspire. The proposed crossbreed security pattern is developed using a combination of Advanced File encryption Standard, and Rivets, Shamir, and Adelman detailing. Them braced adaptation begins off evolved with the as a set of encoding the name of the game insights; after that it disguises the outcome in a cowl picture utilizing 2D-DWT-1L or 2D-DWT-2L. Both hue and dim scale pictures are applied as cover pictures to cover unique message sizes. This paper expects to brighten the insurance of logical data transmission dependent on upon them in between a steganography approach and a mixture well-being plan to get a completely covered clinic treatment contraption. Prior no device existed to take into account the desires of ‘Secure Framework’ (Fig. 1). Implementation System: The current device created is almost conceivable. It is a web essentially based certainly character interface for review strategy at NIC-CSD. Subsequently, it gives a straight forward benefit get right of section to the customers. The data convoy’s motivation is to give, grow, and protect a strategy among explicit elements assuming you need to work with all stressed individuals in their different

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Fig. 1 Proposed System Model

limits or abilities. Agree to the customers should basically be broad principally dependent on the capacities characterized. Subsequently, it manages the cost of the specialized assurance of accuracy, uprightness and well-being and security. In this paper to comfy clinical realities creator is the use of mixture encryption (mixture of AES similarly to RSA framework) to encode scientific messages and thereafter using 2-D Discrete Wavelet Transform steganography method to cowl that scrambled message in medical pix. 2-D steganography will deliver or cover messages interior its digital emblem name and after that this image is probably meant draw out message from it. To execute this challenge author utilizing sticking to modules (1) Hybrid Encryption: First man or woman will for all intents and purposes input more than one thriller messages and sometime later this message may be partitioned into ODD simply as EVEN delivered materials. Likewise detail message may be gotten with AES and furthermore STRANGE difficulty message can be gotten the usage of RSA similarly to each segments can be connected to sign up for entire message. Both encoded message will truly be re-partitioned utilizing message duration (2) Embedding: the use of this module we will make use of 2-D steganography approach to have a look at every pixel automated signal and after that insert message inward in loosened pixel and furthermore this method copied till all message person can be cowl in photograph. Each guy or lady ASCII code is most probably cowl in picture. (3) Removal: from indifferent pixel we are able to honestly strive out all ASCII codes and after that alter it into characters and consolidation all of them. All characters will virtually separated into ODD/EVEN length after which furthermore segment will in reality be unscrambled via AES and moreover WEIRD perspective will potentially be decoded through RSA and each thing might be coordinate to increment complete message and there after display to consumer. 4) After encoding message we are figuring numerous measurements which consist of MSE, PSNR a SSIM on every underlying also to steganography snap shots and we have become SSIM in the course of 99% with PSNR over 70% and MS Einzero’s we have been given.

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5 Results

To run project double click on ‘run.bat’ file to get below screen. In above screen enter some message in ‘Secret Message’ field.

In above screen I entered some message and then click on ‘Run Hybrid Encryption’ button to encrypt message using RSA and AES First user will enter some secret messages and then this message will be divided into ODD and EVEN parts. Even part message will be encrypted with AES and ODD part message will be encrypted using RSA and both parts will be concatenated to combine complete message. Both encrypted message will be re-divided using message length.

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In above screen displaying complete message with ODD and EVEN parts and then encrypting both parts with AES and RSA and now message is ready and now click on ‘Embedding 2D-DT-2L Algorithm’ button to upload image and then hide that encrypted message.

Using this module we will use 2-D steganography technique to parse each pixel digital signal and then embed message inside in free pixel and this process repeated till all message character will be hide in image. Each character ASCII code will be hide in image. In above screen I am selecting one image and it has both color and gray images and now click on ‘Open’ button to get below output’.

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In above screen first image is the original image and second image contains steganography hidden message and both messages look similar and now close both images to get below histogram graph of both images.

In above histogram we can see both images are showing equal size bars show after hiding message not much change we can see in steg image and in above screen in text area we got PSNR as 63% which is more than paper and MSE 0.027 which is less than paper and we got SSIM as 0.99 which is slight lower than paper output as in paper author getting 1 as SSIM. So from above output we are getting close output compare to paper. Similarly, you can upload other images and test. Now click on ‘Extraction Algorithm’ button to extract and decrypt message from image. From free pixel we will read all ASCII codes and then convert it into characters and merge them all. All characters will divide into ODD/EVEN length and then even part will be decrypted via AES and ODD part will be decrypted via RSA and both parts will be merge to form complete message and then display to user. After encoding message, we are calculating various metrics such as MSE, PSNR an SSIM on both original and steganography images and we are getting SSIM closer to 99% with PSNR more than 70% and MSE in 0’s we got.

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In above screen in text area we extracted encrypted message and then decrypt that message to get original content.

6 Conclusion ‘A covered affected person’s diagnostic data transmission layout utilizing every shade and additionally gray-scale pictures as a cowl issuer for hospital treatment-based totally IoT environment has sincerely been advised. The advocated model involved both 2D-DWT-1L or 2D-DWT-2L steganography and hybrid blending AES further to RSA cryptographic strategies. The experimental results were evaluated on each coloration and gray-scale images with several textual content sizes. The overall performance changed into assessed primarily based mostly on the six analytical specifications (PSNR, MSE, BER, and SSIM, SC, similarly to courting’ [9]. Compared to the reducing element processes, the encouraged design showed its functionality to cowl the personal individual’s information right into a transmitted cowl picture with excessive imperceptibility, functionality, and moreover very little damage in the got ten steno-picture.

References 1. Group a. darkish, blood type. Immoderate. Hassanien, one thousand. Elhoseny, retinol. Thou. Sangria, furthermore thou. Mohammad, “the strike a blow of one’s crossbreed stair head going from home automation furthermore interaction in the week aid techniques: pickings, contests, further more unprejudiced difficulties. Bolt. Climate Intell. Sepiatoned Comput. After life revealed, department of the interior: https://interiordepartment.org/spectacular.1007/s12652017-0659-1 2. Sheaf et al., Secure along with robust unsound integrating waiting game because medical exam work of art. tells get right of entry to, chi captain hicks, labs. 10269–10278 (2018), executive department:ten.1109/getentryto.2018.2799240

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3. Group a. Abdul-Aziz, one thousand. Elhoseny, blood type. Brimstone. Salaam, plus blood group. K. raid, A neural networks trend-setter given that improving attention functions as to network security milieu. Menstruation Ker.119,journals.117–128,fixedrate.2018.[Online]. Reachable: https://interiordepartment.org/astounding.1016/flee.viscometry.2018.01.022 4. K. Paschal, antioxidant. Sakkopoulos, fat-soluble vitamin. Sourly, furthermore blood type. Tsakalidis, Health virtualization: poetry as well as methods as timesaving retransmission. ”simile. Fashionarbiter. Pact. Reformation, chithirty-four, co’s. 186–199, would possibly2013 5. M. Elhoseny, G.R. Gonzalez, O.M. Abu-Elnasr, S.A. Shawkat, N. Arunkumar, A. Farouk, Secure medical data transmission model for IoT-based healthcare systems. IEEE Access (2018) 6. M.A. Razzaq, R.A. Shaikh, M.A. Baig, A.A. Memon, ‘Digital image security: Fusion of encryption, steganography and watermarking.’ Int. J. Adv. Comput. Sci. Appl. 8(5), 224–228 (2017) 7. Z.M. Zaw, S.W. Phyo, ‘Security enhancement system based on the integration of cryptography and steganography.’ Int. J. Comput. 19(1), 26–39 (2015) 8. L. Yehia, A. Khedr, A. Darwish, ‘Hybrid security techniques for Internet of Things healthcare applications.’ Adv. Internet Things 5, 21–25 (2015) 9. N. Dey, V. Santhi, Intelligent Techniques in Signal Processing for Mul timedia Security (Springer, New York, 2017). https://doi.org/10.1007/978-3-319-44790-2 10. L. Yu, Z. Wang, W. Wang, ‘The application of hybrid encryption algorithm in software security, In Proceeding of the 4th International Conference Computer Computational Intelligence and Communication Networks (CICN) Nov 2012, pp. 762–765 11. S.F. Mare, M. Vladutiu, L. Prodan, ‘Secret data communication system using steganography, AES and RSA, in Proceeding of the IEEE 17th International Symposium for Design and Technology in Electronic Packaging (SIITME) Oct 2011, pp. 339–344 12. A.K. Mandal, C. Parakash, A. Tiwari, ‘Performance evaluation of cryptographic algorithms: DES and AES, in Proceeding of the IEEE Students’ Conference on Electrical, Electronic and Computer Science (SCEECS) Mar 2012, pp. 1–5 13. S.F. Mjolsnes (ed.), A Multidisciplinary Introduction to Information Security (CRC Press, Boca Raton, 2011)

Double MAC: Boosting the Performance of ResNet Architecture of CNN Using ASIC G. Kaushik, M. Bala Bandhavi, and B. Sridhar

1 Introduction Convolutional neural network (CNN or ConvNet) is a class of profound taking in engineering that is stretched out from counterfeit neural organizations that were been broadly taken on in a few applications, for example, picture web search tools in server farms, portable robot vision, video reconnaissance, etc. Artificial Neural Networks (SIANN) displacement invariance, or spatial invariance, relies on a typical weight structure of filters or convolutional nuclei that flow along input features and provide uniform feedback called attribute map. It has a wide scope of uses in picture and video acknowledgment, picture allocation, picture analysis, picture diagnosis in medication, language conversion and intellect PC interfaces. With the increasing popularity of machine learning algorithms, there is a demand for developing hardware accelerators for them. The only viable alternative to hardware accelerators for complex neural networks was the flexibility of computational accuracy. The convolutional neural network (CNN) makes them very attractive for acceleration of hardware because of the multiple traits that are large scale of structural regularity, immense computational complexity, large scale applicability and more recognition performance. They were provided with floating point either single or double precision as the integer’s arithmetic has negative or zero performance as an advantage. Now-adays, the half-precision was introduced but this could be a single-time change which was not customized by the user. Thus, very much effort is being made to create better CNN accelerators on Field Programmable Gate Arrays (FPGAs). An Application Specific Integrated Circuit (ASIC) implementation could be choosing whether the precision was sufficient for the targeted CNN applications [1].

G. Kaushik · M. B. Bandhavi (B) · B. Sridhar Department of Electronics and Communication Engineering, MLR Institute of Technology, Hyderabad, Telangana, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. K. Mandal et al. (eds.), Innovations in Signal Processing and Embedded Systems, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-19-1669-4_28

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If there was an increasing demand for the workload on deep learning algorithms like convolutional neural networks for high performance. And also, they have a significant feature that they exist as inseparable parts and withstand to recover quickly from any numerical errors. CNNS work on very low-resolution devices. Therefore, the MAC process doubles the computational speed of the convolutional neural network layer. Double MAC is performed with the precision using Vedic floating point and fixed point multiplication. This dual MAC approach doubles the computational speed of the convolutional neural network (CNN) layer. Convolutional neural network models increasing the certainty since their breakthrough in 2012. The high computational cost was the reason for this accuracy in CNN models. Improving the classification accuracy while maintaining the computational workload is the main challenge that is faced by CNN developers. The use of bottleneck 1 × 1 convolutions reduces the model size and the computations meanwhile increase the accuracy and depth. The use of the Multiply Accumulate (MAC) operations results in the computational workload of a CNN inference. The convolution layers that are in a typical implementation are responsible for more than 90% of execution time during the CNN inference. There are different strategies to be followed by the CNNs accelerators while implementing the convolutional and fully connected parts of inference due to this unbalanced computation to memory ratio. As the complexity of complex neural network algorithms increases, so does the computation time. There are scientists who have studied the various layers of AlexNet. Working time dominates the time required for calculations in the convolutional layer. There are several ways to reduce computation time.

2 Background Work An ordinary Digital Signal Processors (DSPP) block of Field Programmable Gate Array (FPGA) into a two-way, Single Instruction Multiple Data (SIMD), Multiply and Accumulate (MAC) unit which delivers four operations/cycle for achieving multiplication, addition operations with diminished data width simultaneously [1]. They evaluated the method for Xilinx Virtex-7FPGA. This method that was generic purely and was applicable for another FPGAs that use same DSP blocks hardware. There is put much effort to create better convolutional neural network accelerators on FPGAs [2–5]. Contrast to this, an Application Specific Integrated Circuit (ASIC) can be implemented by choosing whatever the precision needed for target CNN applications [1]. For increasing the performance of free using lower precision that does not affect the output quality, 8-bit fixed point is sufficient for inference as suggested by the works [6]. Validation is performed on the FPGA, simulation is performed on Verilog, and is worn to assess the convolution of the neural network in the accelerator. This approach doubles the computing power of the convolutional neural network layer. In network level that is every convolutional layer when combined, the performance was improved that differentiates differently from every application of CNN and the

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size of FPGA from 14 to 80% without damaging the output quality efficiently [1]. This approach requires no changes in the device of FPGA. Adding a SIMD to a DSP was easy and supported, and doubling the SIMD as a look up table (LUT) was also easy. Using DSP blocks in FPGAs is just as important as any other CNN program.

3 Proposed System The presented double MAC architecture that supports (i) Multiplication and (ii) Accumulation of multiplication results. Earlier work was done on SIMD multiplication and addition that is based on fixed point precisions. But in the proposed system double MAC is based on floating point single precisions. The contributions done for designing the double MAC architecture is that 1. 2. 3. 4.

Defined a special case of Vedic multiplication for a kind of operations of MAC that were found in the convolutional neural network. Identically, the double MAC does the multiplications of two floating point numbers and add them to generate the output. Incorporate the double MAC architecture into convolution layers of convolutional neural network successfully. Demonstrate that our double MAC-based convolutional neural network implementations could achieve high accuracy in spite of large, real life CNNs.

Validated our method through simulation using Verilog by NC launch and Logical synthesis using RTL Compiler. Evaluated the design by applying it to different convolutional neural networks like AlexNet, VGGNet and ResNet. And compared the three main parameters in ASIC designing that are area, power and delay. This design uses more look up tables (LUTs) but is far more efficient when compared it to synthesizing the additional MACs by using LUTs. On the network level that is with the whole convolution layers combined together, this design generates improvements of performance that ranges that depend on hyper parameters of CNN and the size of ASIC without compromising on the output quality. The proposed Double MAC architecture is given below (Fig. 1). A single block of DSP is responsible for implementing multiplication and addition. Both the adder and multiplier having a correction signal as an output signal that was been accumulated in a separate counter or register that should has been used in later cases in providing the final adjustment. The terms that were subtracted or added in the final adjustment. Due to this adjustment, there is no runtime overhead while new values that will be loaded in the MAC as the adjustment was done simultaneously. A deep learning algorithm that considers the input picture, attach weights to distinct objects in the picture and is adept to discriminate with each other is called convolutional neural network (ConvNet/CNN) (Fig. 2). When correlated to other analysis algorithms the preprocessing requisite in ConvNet is countless. The filters are hand engineered with enough training in

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Fig. 1 Data path of architecture of double MAC

Fig. 2 Computation engine of a CNN accelerator

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primitive methods. For learning these characteristics or filters, ConvNets have the ability. As a name it is convolutional neural network which shows that the network uses a mathematical function called convolution. Convolutional networks are the special categories of neural networks that utilizes the convolution rather than general matrix multiplication in one or more layers. Convolutional neural networks possess advantages over deep neural networks like being highly optimized, effective at learning, extracting abstractions and more human visual processing system. Almost all CNNs were trained by an algorithm that was gradient-based learning. The highly optimized weights could be produced by convolutional neural networks (CNNs). There are two main parts in the convolutional neural networks’ overall architecture. They are feature extractors and a classifier. Each layer at the output network accepts the past layer as input and sends the output as input to the next layer in the feature extraction layer. There are three levels of complex neural networks (CNNs)convolution, max pooling and fully concatenated levels. There are two different layers, those are convolutional layers and max pooling layers, were been at the middle and lower level in the network. The Convolution Layer: Using learnable kernels that are in this layer are convolved with the previous layers’ feature map. An output of kernel goes through a non-liner or the activation function that was linear such that rectified linear, SoftMax, identity functions, sigmoid and hyperbolic tangent for forming the feature maps’ output. One input feature map would be combined with each output feature maps. In general, we have that ⎛ x lj = f ⎝



⎞ xlt−1 ∗ kil j + k lj ⎠

icM j

The Non-linearity layers: As convolution was a linear operation and pictures were also linear, the non-linearity layers are usually arranged immediately next to the convolutional layer to offer non-linearity to the activation map. The Pool layer: It is called as layer that was down sampled that the feature map dimensions were being reduced which significantly prevents over fitting. The feature map and network computation complexity reduce using this. The number of output and input feature maps do not change in this layer. The formula to calculate the pooling layer was given in the below equation:    x nj = f down X n−1 j

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The Fully Connected layer: The computation score from every class that were been extracted the features that were from the layer of convolutional from the previous steps was done in fully connected layer. A feature map of the last layer was represented using the scalar as vectors value that were passing as input for the fully connected layers. A SoftMax fully connected layer were used by the feed forward fully connected neural layers. The layers that were incorporated in the whole network model have no strict rules.

4 Implementation A.

AlexNet

Alex Krizhevesky with the help of other experts suggested a wider, deeper convolutional neural network model when comparison to LeNet in 2012. For achieving accurate recognition of the state of art that were opposed by almost every computer vision approach and also the traditional machine learning, AlexNet is designed. The total neurons and weights in the first layer were calculated as 290,400 and 364, respectively. 724 M and 61 M are the MACs and total weights for the entire network, respectively (Fig. 3).

Fig. 3 Schematic of AlexNet architecture

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Fig. 4 Schematic of VGGNet architecture

B.

VGGNet

The contribution of the work of Visual Geometry Group (VGG) shows the extent of a network that was a detracting element to carry out exceptional recognition or veracity in CNNs. The two convolutional layers that are in VGG architecture where both of which use the ReLU activation function. Using a ReLU activation function, there was a single max pooling layer that was followed by the activation function and a different fully connected layer (Fig. 4). C.

ResNet

Kaiming has developed ResNet by the intention for designing the ultradeep networks that was invented for vanishing the problem of gradient which has been prevailed in previous convolutional neural networks. Many different numbers of layers are developed in ResNet: 34, 50, 101, 152 and1202.The most popular ResNet 50 has the layers that were convolution: 49 and the fully connected layer: 1 at the ending of the network. 3.9 M and 25.5 M are the total MAC’s and weights for the network, respectively (Fig. 5). (a)

RTL designing and Simulation using Verilog

Register transfer level (RTL) in digital circuit architecture was an architecture abstraction that creates a synchronous digital circuit with regard to the flow of digital signals among the logical operations implemented on those signals and hardware

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registers. Verilog and VHDL are the hardware description languages used in register transfer level abstraction. This process is done using NC launch tool in Cadence Suite (Fig. 6).

Fig. 5 Schematic of ResNet

Fig. 6 Simulation results of the proposed design

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Fig. 7 Synthesis results of the proposed design

(b)

Synthesis using RTL Compiler

The procedure of converting the HDL design into the gate level netlist is called synthesis. The process of transforming and aligning RTL code drafted in HDL either using Verilog or VHDL is called logical synthesis (Fig. 7). (c)

Physical Designing using SOC Encounter

The process of transforming the circuit into the layout is called physical designing. The position of cells and routes and interconnects between them is been described in physical designing (Fig. 8). (d)

Analysis

The proposed technique was applied for the convolutional layers that are very often used in real life CNNs: AlexNet, VGGNet and ResNet. AlexNet

VGGNet

ResNet

Area (mms )

26,048

6782

4169

Power (MW)

3.340

8.261

6.958

Cell count

2725

633

437

Delay (input to output) (ns)

20

10.684

7.032

It is found that the parameters were decreased in ResNet when compared between the three different types of convolutional neural networks.

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Fig. 8 Physical design results for the proposed design

5 Conclusion The rate of computation of CNN accelerators was increased as several operations of MAC were packed that is, Double MAC in one block. Validated the proposed method/architecture through simulation using Verilog and synthesis of ASIC. Evaluation is done using different types of convolutional neural networks accelerators. Here AlexNet, VGGNet and ResNet was considered for evaluation. It was found that ResNet is more efficient when compared to the other two CNNs in terms of various parameters.

References 1. M.Z. Alom, T.M. Taha, C. Yakopic et al., A State-of -the-art survey on deep learning theory and architectures. Electronics 8, 292 (2019). 10.3390, mdpi.com/journal/electronics 2. S. Cadami et al., A programmable parallel accelerator for learning and classification, in Proceedings of the 19th International Conference on Parallel Architectures and Compilation Techniques 3. J. Qiu et al., Going deeper with the embedded FPGA platform for convolutional neural network, in Proceedings of the 2016 ACM/SIGDA International Symposium on Field Programmable Gate Arrays

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4. M. Peemen et al., Memory centric accelerator design for convolutional neural networks, in Computer Design (ICCD), 2013 IEEE 31st International Conference on, Oct 2013 5. A. Krizhevsky, I. Sutskever, G.E. Hinton, Imagenet classification with deep convolutional neural network, in NIPS eds by P.L. Bartlett et al. (2012) 6. K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition. vol. abs/1409.1556 (2014) 7. C. Zhang et al., Optimizing FPGA based accelerator design for deep convolutional neural networks, in Proceedings of the 2015 ACM/SIGDA International Symposium on Field Programmable Gate Arrays 8. Y.-H. Chen, T. Krishna, J. Emer, V. Sze, Eyeriss: An energy-efficient reconfigurable accelerator for deep convolutional neural networks, in IEEE International Solid-State Circuits Conference, ISSCC (2016) 9. K. Bhong et al., 14.6 a 0.62mw ultra-low power convolutional neural network face recognition processor and a CIS integrated with always on haar life face detector, in 2017 IEEE International Solid State Circuits Conference (ISSCC), Feb 2017 10. S. Lee, D.K.D. Nguyen, J. Lee, Double MAC on a DSP: Boosting the performance of Convolutional Neural Networks on FPGAs, IEEE Publications

Implementation of Nano Communication Network Using Advanced QCA Based Nano Technology B. Anusha, Ashwak, and S. V. S. Prasad

1 Introduction The CMOS technology currently used is approaching its Physical limit as quantum effects and power dissipation. Reduce the logic circuit using CMOS Nanoscale technology prompted a higher design complexity. To continue the progress in the reduction the circuit and increase the performance of a microprocessor, an alternative to CMOS is essential. As an alternative to CMOS, the QCA was introduced [1]. QCA emerged as a promising technology for encoding the information [2–7]. In QCA theory, the reverse computation will perform by system. Basically, for an RC conventional NOT gates is used. For designing the RC with the assistance of RG, some points to be considered [8]. In QCA, the fan-out cannot permit. In QCA, loops cannot permit in QCA, one more element is considered, which is more imperative than the total gates utilized with specific garbage outputs. The un-utilized outputs from a RC/RG are called as “garbage” [9]. The RL imposes many design constraints that should be either ensured or optimized for actualizing a specific Boolean function. In QCA circuit the inputs and outputs must be equal. In every input design [10], must have unique output design. Each output needs to be will be used just once, i.e., fan-out is not permit. The QCA must be non-cyclic computation that too in Majority manner for a system can be performed only if the system having less quantum cells. The complexity of error detection and correction scheme [11]. Compared to other technologies in CMOS B. Anusha · Ashwak (B) · S. V. S. Prasad Department of Electronics and Communication Engineering, MLR Institute of Technology, Hyderabad, Telangana, India e-mail: [email protected] B. Anusha e-mail: [email protected] S. V. S. Prasad e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. K. Mandal et al. (eds.), Innovations in Signal Processing and Embedded Systems, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-19-1669-4_29

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technology requires less quantum cost, less space, low power consumption and low delay time. The major point of this concept as follows: • This article deals with the implementation of QCA-based parity generator and parity checker using Majority logic gates. • Further, a new QCA-based convolutional encoder and decoder is developed by using the proposed parity generator and checker. • Thus, the much more efficient nano communication system will be implemented with high error correction rates with reduced hardware resource utilization [12]. Rest of the paper is organized as follows: Sect. 2 deals with the properties of QCA and Majority logic gates. Section 3 deals with the implementation details of proposed method. Section 4 deals with the simulation and synthesis results.

2 Majority Logic Gates Mostly, Majority logic gates are preferred for design why because these are having following properties.

2.1 Properties of Majority Logic Gates Property 1: bidirectional property Using this property in chip level implementation so both input and output pins are interchangeable. In Fig. 1, if A, B are the inputs of Feynman gate, then R, S are acts as output pins. Due to the quantum data Majority nature, if the inputs are applied at the outputs side, then R, S acts as input pins and A, B acts as outputs correspondingly. In case of basic gates, they will not support this property maintains the unidirectional data transfer. Normally, IC’s consists of multiple numbers of gates. Thus, by the use of bidirectional property, the path delay will be reduced as both inputs and outputs can be interchangeable and logic optimization also achieved.

Fig. 1 Bidirectional property of Feynman gate

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Fig. 2 Majority logic gate a QCA layout b symbol c Truth table

Property 2: fan-in fan-out capacity Every Majority logic gate supports fan-in and fan-out property because the number of input pins and number of output pins will remain not equal. So, the load on the chip will be reduces effectively even number of inputs and outputs are mismatch. Property 3: number of operations The Majority logic gates support N-number of logical operations based on the number of input–output pins. Property 4: quantum cost The quantum cost required for the Majority logic gate is very less compared to the basic gates. Figure 2 presents the QCA layout, symbol and Truth table of Majority logic gate, respectively. If anyone of the input in Majority logic gate is zero, then it acts as AND operation. If anyone of the input in Majority logic gate is one, then it acts as OR operation.

3 Proposed Method This section deals with the implementation of QCA-based parity generator and parity checker. Further, a new QCA-based convolutional encoder and decoder is developed by using the proposed parity generator and checker. Thus, the much more efficient nano communication system will be implemented with high error correction rates with reduced hardware resource utilization.

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Fig. 3 Nano communication network using parity generator and checker

3.1 Parity Generator and Checker Figure 3 represents the nano communication network developed by using 3 bit even parity generator and 4 bit even parity checker. The detailed operation of this approach is as follows: Step 1: Consider the input bits A, B and C and apply it to the parity generator. Here, the majority logic gates are formed as the XOR gate. As mentioned in Sect. 2, here AND, OR gates are formed by using Majority logic gates and develops the XOR operation as highlighted in green color boxes. Initially, bit wise QCA-XOR performed between the A, B, and then outcome will be again QCA-xor with input and results the parity bit Pb as its final output. Pb = A ⊕ B ⊕ C

(1)

Step 2: The parity bit is transmitted into channel. In the channel various types of noises will be added to the encoded data. Step 3: The parity bit is received and applied as input to the parity checker. Here, the receiver consisting of three XOR gates. Initially, individual bit wise XOR operation performed between A, B and C, Pb input combinations and results the outcomes as X1 and X2, respectively. Finally, the bit wise XOR operation will be performed between X1 and X2 and results the outcome as parity check bit PC. Step 4: The detailed example flow chart is presented in the Fig. 4. If the received parity check bit is zero, it means no error is occurred in the communication system. If the received parity bit is one, then error is occurred.

3.2 Nano Communication System Using Convolution Codes The above parity generation and parity checking operations are capable of calculating only error presented or not. Thus, this section deals with the implementation of nano

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Fig. 4 Flow chart of parity generation and checking

Fig. 5 Nano communication system using convolution codes

communication system using convolution codes implemented by using above parity generator and checker. Figure 5 presents the block diagram of convolution-based nano communication system. Convolution encoder: Generally, real time communication systems used to decode the convolutional encodes operands. Here, data D0, D1, D2 and D3 are the input and outputs are out 0 to out 6. For generating the outputs, convolution encoder utilizes the generator matrix G, it consists of identity matrix and parity symbols. The parity symbols P1, P2, P3 are user defined, output encoded frame format is out = [P1, P2, P3, D0, D1, D2, D3];

(2)

P1 = QCA_XOR(D0, D1, D2);

(3)

P2 = QCA_ XOR(D0, D1, D3);

(4)

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P3 = QCA_ XOR(D0, D2, D3);

(5)

Channel: The encoded data transmitted to the channel. Basically the channel having noises like Gaussian, random and AWGN noises. So, the transmitted data which is transferred to the channel will be affected by noises. Therefore error will be added. The error will be remove by decoder in the actual work of decoding. Convolution decoder: A finest structure for convolution decoders is shown in Fig. 4. As set up in figure, convolution decoders are prepared out of three important segments: syndrome computation (SC), Error analysis block (EAB) and Error correction unit (ECU). SC creates the path to data levels matching to paired encoded trellis operands, thus syndrome of those particular inputs are calculated and applied to EAB unit. By comparing the each and every bit position with parity check matrix decoders and stores the compared results in the Register unit R again the stored decoders will be further processed for location detection by using feedback mechanisms. The error locations are identified by using the branch units generated in SC. After finding the error location for correction of those decoders ECU unit will be useful. By using error identified path, its path metric will be recalculated and error corrected, thus final decoded data will be generated. Syndrome Check: In SC block the error status will check by convolution decoder. If any error is presented in SC block it will alert the EAB block or else it decodes the data. Here, if syndrome is calculated by using parity checking operation as shown in Fig. 3. If syndrome is zero, it means no error is presented in the received data. If syndrome is not zero, then there are lot of errors are presented in the received data. Error analysis block: Here, as we are using the Majority methodology, here approximation of SC syndromes methodology has been implemented. By using the minimum of two between each SC metric, the comparison of coefficients has done. Error correction unit: In ECU block the last error will be identified and corrected. In EAB block the coefficients are generated. This ECU unit is reconfigurable because for the different types of noises, the error will be altered, according to that noise ECU also reconfigures itself. By using the Round-Robin procedure, by comparing one metric to all other paths with equal priority, survived path (error free) will be identified. After this the decoded data is generated without error in path.

4 Simulation Results In Xilnix software two types of outputs are there one is simulation and other one called synthesis. In this paper the designs are programmed and designed by Xilnix

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ISE tool. In simulation addition and multiplication is analyzed by using different combinations of inputs. In synthesis process the area is used. Power analysis is also generated. Figure 6 represents the operation of parity generator, here it is consisting of a, b, c as its inputs and output as out. The even parity is calculated between a, b, c and resultant is stored in out. Figure 7 represents the operation of parity checker, here it is consisting of a, b, c ,p in as its inputs and output as out. The even parity checking is calculated between a, b, c, pin and resultant is stored in out (Fig. 8). Here data is the original input data, and ES is the manual error syndrome input and enc is the final encoded operand. YC is the decoded error free output data so it is same as input data (Fig. 9). Here data is the original input data and out is the final encoded operand (Fig. 10). Here In is the original encoded operand input data and S is the final SC error coefficients (Fig. 11).

Fig. 6 Parity generator output

Fig. 7 Parity checker output

Fig. 8 Convolution communication system

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Fig. 9 Encoder output

Fig. 10 SC output

Fig. 11 EAB output

Here S is the SC error coefficients input data and E0, E1 and E2 are the final EAB prioritized error coefficients (Fig. 12). Here In is the original encoded operand input data, E0, E1 and E2 is the EAB prioritized error coefficients inputs. YC is the decoded error free output data so it is same as input data (Fig. 13). The consumed power is 0.166 uw (Fig. 14). These results are represents the synthesis implementation by using the Xilinx tool. From this table, it is observed that only 23 look up tables are used out of available 10,944. It says less area is used for the present design (Fig. 15).

Fig. 12 ECU output

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Fig. 13 Power consumption

Fig. 14 Design summary

Fig. 15 Time summary

Table 1 Comparison of various decoders Parameter

Convolutional decoder [1]

Turbo decoder [2]

Linear block decoder [3]

Proposed convolution decoder

Time delay (ns)

7.281

10.3

4.29

2.298

Power utilized (uw)

0.322

1.32

1.84

0.165

Look up tables

107

193

134

23

Slice registers

112

83

78

13

The result represents the time consumed such as path delays by using the Xilinx ISE software. The consumed path delay is 2.298 ns.

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250 Convolutional decoder[1] 200 150

Turbo decoder[2] Linear block decoder[1] Proposed Viterbi decoder

100 50 0 Time delay(ns)

Look up tables

Slice registers

Fig. 16 Comparison of various decoders

From Table 1 and Fig. 16, it is observed that the proposed decoder has enhanced performance and consumed less resource blocks with low area, power and delay requirement compared to the conventional decoders.

5 Conclusion In this manuscript, initially a three bit parity generation and four bit parity checking operations were developed by using the QCA-based Majority logic gates. But this architecture is used to calculate only for error presented or not. Further, a new method for convolution communication system has been developed by using Majority logic for multi bit error detection and correction, so number of elements quantum levels will optimized. By this approach the original functionality will not affected. To advance the reconfigure ability of ECU block well-organized path metrics with high standards to error detection and correction, the projected convolution decoder design is altered with singular pattern inputs. The convolution decoder design was utilized with insignificant PLBs transparency compared with CMOS design of convolution decoder.

References 1. K.B. Naveen, G.S. Puneeth, M.N. Sree Rangaraju, Low power convolution decoder design based on Majority logic gates, in 2017 4th International Conference on Electronics and Communication Systems (ICECS) (IEEE, 2017)

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2. I. Habib, Ö. Paker, S. Sawitzki, Design space exploration of harddecision convolution decoding: Algorithm and VLSI implementation. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 18(5), 794–807 (2010) 3. S. Perri, P. Corsonello, G. Cocorullo, Area delay efficient binary adders in QCA. IEEE Trans. Very Large Scale Integr. Syst. 22(5), 1174–1179 (2014) 4. R.S. Nadooshan, M. Kianpour, A novel QCA implementation of MUX-based universal shift register. J. Comput. Electron. 13(1), 198–210 (2014) 5. M. Arjmand, M. Soryani, K. Navi, Coplanar wire crossing in quantum cellular automata using a ternary cell. IET Circ. Devices Syst. 7(5), 263–272 (2013) 6. H. Thapliyal, N. Ranganathan, S. Kotiyal, Design of testable reversible sequential circuits. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 21(7), 1201–1209 (2013) 7. H. Thapliyal, N. Ranganathan, Reversible logic-based concurrently testable latches for molecular QCA. IEEE Trans. Nano Technol. 9(1), 62–69 (2010) 8. J. He, H. Liu, Z. Wang, X. Huang, K. Zhang, High-speed low power convolution decoder design for TCM decoders. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 20(4), 755–75 (2012) 9. J. Nargis, D. Vaithiyanathan, R. Seshasayanan, Design of high speed low power convolution decoder for TCM system, in Proceeding of International Conference on Information Communication and Embedded Systems, (Chennai, 2013), pp. 185–190 10. M.W. Azhar, M. Själander, H. Ali, A. Vijayashekar, T.T. Hoang, K.K. Ansari, P. LarssonEdefors, Convolution accelerator for embedded processor datapaths, in Proceeding of IEEE 23rd International Conference on Application-Specific Systems, Architectures, and Processors (Delft, Netherlands, 2012), pp. 133–140 11. D. Chen, P. Chen, Y. Fang, Low-complexity high-performance low-density parity-check encoder design for china digital radio standard. IEEE Access 5, 20880–20886 (2017) 12. E. Fujiwara, Code Design for Dependable Systems: Theory and Practical Applications (Wiley, Hoboken, 2006)

Plant Watering and Monitoring System Using IoT and Cloud Computing M. Raju Naik, I. Kavitha, and B. Sridhar

1 Introduction Everything else can be managed and operated automatically within today’s society. We used to cultivate plants under regulated climate conditions to get the best results. Every plant’s monitoring and management of the climatic factors that directly or indirectly affect plant development and yields may be automated using software. An automation process replaces human labor by controlling equipment and processes within industry. User input is provided via smart phones or computers using the technique described in this article. Human mistake will be reduced by using an automated system. By adopting this technology, a farmer may simply check the system’s efficiency utilizing their smart devices. Water and plant monitoring are possible with this technology, thanks to the Internet of things (IoT) with cloud computing. There are many components used here system: a temperature sensor plus moisture sensor as well as humidity and pH sensors. One may take appropriate action if they are aware of all of these possible situations. We then installed the water motor throughout the system and tested it. The water motor can automatically turn on based on the sensor readings evaluated by various kinds of sensors. Also, the sensor readings will be sent to the controller’s analog and digital converter (ADC).

M. Raju Naik · I. Kavitha (B) · B. Sridhar Department of ECE, MLR Institute of Technology, Hyderabad, Telangana, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. K. Mandal et al. (eds.), Innovations in Signal Processing and Embedded Systems, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-19-1669-4_30

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2 Related Work We have reviewed a number of prior studies in this area by other academics. The use of technology in agriculture plays a critical role in boosting output and decreasing labor requirements. As well as using various controllers, such as the 8051 controller and ARM 7, researchers are monitoring agricultural output using ZigBee, wireless sensor network (WSN), and GSM. Every one of the hardware components relevant to agriculture are integrated in [1]. The soil moisture system is used to detect soil dryness and water its plant based on the value recorded by the sensor. This system is designed to make efficient utilization of any and all hardware utilized in its development. Using processing language, paper [2] explains how to link physical devices to the third-party cloud service provider. For the same reason, soil moisture too is utilized to identify soil dryness in this case. Security is a concern when using the cloud for storage. For additional processing, real-time information is intended. The purpose of paper [3] is to preserve the natural state of plants by constantly monitoring the factors that contribute to a longer plant life. The mobile application allows the system to be remotely controlled by linking various soil characteristics, such as humidity, temperature, and wetness, to the cloud. A microcontroller-based circuit is described in [4], which is straightforward to install and monitors temperature, humidity, soil moisture, and sunshine in order to adjust them for optimum plant development and production. For example, a controller may operate a chiller, fogger, dripper, or lights in a greenhouse according to the crops’ needs by communicating in real time using sensor modules. We present a new energy efficient environmental monitoring, alerting, and controlling system based on ZigBee technology for agricultural [5]. ARM 7 CPU, numerous sensors, and ZigBee communication module are used in this system. As a result of ZigBee connection, sensors collect and send a variety of physical data in real time. As a result, the appropriate steps are taken to minimize or eliminate the requirement for human labor. An intelligent remote monitoring system with solar photovoltaic power conditioning units (PCUs) in such a greenhouse setting is implemented in [6] the article. They developed a smart remote monitoring system based on Internet network things that monitoring solar PV PCUs. Using Internet of things technology and scientific experimentation, the article [7] seeks to enhance farmers’ success in producing plants by providing a controlled environment for monitoring and encouraging plant development. With the assistance of a node js application, [8] visualizes the gathered data utilizing graphs and charts to provide a better understanding. In order to better comprehend, they use graphs and charts. For ensure security, those who are storing data inside Dynamodb. A scientific and systematic approach for plant care are presented in article [9]. When water is required, Raspberry Pi 3 can take measures. For soil moisture detection, FC-28 is utilized, which is less expensive. Regarding communication between sensor nodes and base stations, ZigBee technology is utilized in [10]. Real-time data are processed via a Java-based graphical interface. As a result of crop selection, your mobile application offers notifications for fertilizer and pesticide application. Water supply and plant monitoring are the

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primary objectives of this project, as described in paper [11]. The system offers a low-cost moisture sensor-based acquisition system. Embedded plant watering and monitoring system utilizing IoT, Raspberry Pi as CPU, and sensors for detecting environmental conditions are suggested in article [12]. IP addresses may also be used to access the system. Paper [13] explains that the aim of a smart water irrigation system would be to offer a water supply plan for crops in order to guarantee healthy development and minimize irrigation water waste. One could monitor plants remotely via Wi-Fi and Bluetooth module. Parameters like temperature and moisture are addressed in the article [14]. IoT may be utilized for data analysis and for monitoring and regulating in detail and precisely by the sensors, as described in [15]. This work focuses on optimizing the usage of water as required for chilly plants. All of soil’s hardware is included in this article [16]. According to the value recorded either by moisture sensor, your plant is watered accordingly. The soil moisture may be monitored and regulated via a Web server, as described in article [17].

3 IoT for Smart Irrigation Sensors are utilized in agriculture, such as soil moisture, temperature, humidity, and pH meters. Through the gadget, every sensor data are transmitted to the cloud and database files for storage. Whenever soil moisture levels are low, this device is triggered automatically. The pump turns on according to soil moisture levels. Cloud-based systems may access sensors and actuators that are linked to a local wireless LAN and wireless actuator networks that are connected with local wireless networks.

4 Necessity to Use Cloud Every device with such an Internet connection may access cloud-based apps and data. Computing resources may be scaled up in the cloud to save companies money on purchasing and maintaining them. There are many benefits to using cloud storage: (1) (2) (3)

Data can be accessed from anywhere. Hardware requirement and cost reduces. Security of data increases.

5 Proposed System Sensors throughout the automated system measure humidity, temperature, and soil moisture. This data are subsequently sent to cloud databases. It could be used to detect soil moisture or determine whether there is water in the vicinity of both the

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Fig. 1 Proposed system overview

soil moisture sensor. Customers have remote access to the system via the Internet (Fig. 1). The water motor would automatically turn on based on the sensor readings evaluated by various kinds of sensors. Also, the sensor readings will be sent to the controller’s analog and digital converter (ADC).

6 System Implementation and Result 1.

It will identify soil fertility first. As per the soil’s fertility, plants would be categorized as well (Fig. 2).

Plant Watering and Monitoring System Using IoT … Fig. 2 Flowchart

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start

Read sensors value

Server update Yes

If threshold value

NO

Water pump on

Water completed pump off

2. 3. 4. 5.

Soil conditions are detected using sensors such as temperature and humidity. As per the threshold value, the resulting sensor readings will be compared. If this condition is not met, the motor pump would be turned on to provide the plant with the water it needs. Whenever the watering is complete, the pump will shut off. In order to see the whole scenario, a user must first log in.

Throughout the proposed system, sensors are utilized to detect sensor readings, which appear throughout the observation table. Those values that are felt will differ depending on their categorization, though 1.

pH Sensor

With water-based solutions, a pH meter detects the hydrogen-ion activity, which is represented as pH (Fig. 3). 2.

Moisture Sensor

Sensors for soil moisture have been used to monitor soil moisture (Fig. 4). 3.

Temperature Sensor

As the name suggests, a temperature sensor is a device which measures temperature by means of an electrical signal (Fig. 5).

346 Fig. 3 pH sensor

Fig. 4 Soil moisture sensor

Fig. 5 Temperature sensor

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Fig. 6 Humidity sensor

4.

Humidity Sensor

As well as sensing and measuring moisture, a humidity sensor also provides air temperature. With air, relative humidity is indeed the ratio of both the quantity of moisture in the air toward the amount of moisture that is present at the maximum temperature (Fig. 6).

7 Conclusion The suggested method may decrease the amount of time and effort required to water plants every day. As a bonus, it preserves water for irrigation simply positioning the sensor just above soil’s surface. Whenever the temperature was mild, the plants may survive with low moisture levels. As a result of the sensor data, the watering procedure is automated. Soil irrigation is controlled with the assistance of sensor data (water wastage). The Website allows users to keep track of the procedure. From such a system, it could be deduced that perhaps the Internet of things may lead to significant improvements in agriculture.

References 1. A. Gupta, S. Kumawat, S. Garg, Automated plant watering system. Imp. J. Interdiscip. Res. 2(4) (2016). ISSN: 2454-1362 2. Taylor Francis Group, Automated Plant Watering System (LLC, 2016), pp. 59–69 3. T. Thamaraimanalan, S.P. Vivekk, G. Satheeshkumar, P. Saravanan, Smart garden monitoring system using IoT. Asian J. Appl. Sci. Technol. 5–10 (2018) 4. A.J. Singh, P. Raviram, S. Kumar, Embedded based green house monitoring system using PIC microcontroller. IEEE Trans. Syst. Man Cybern. Syst. Hum. 41(6), 1064–1076 (2011) 5. K. Lokesh Krishna, J. Madhuri, K. Anuradha, A ZigBee based energy efficient environmental monitoring alerting and controlling system. IEEE Trans. G 3(1), 186–190 (2009) 6. B. Shri Hariprasad, V. Rathinasabapathy, A smart IoT system for monitoring solar PV power conditioning unit, in World Conference on Futuristic Trends in Research and Innovation for Social Welfare (WCFTR ‘16), vol. 3, no. 2 (2016)

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7. B. Yimwadsana, P. Chanthapeth, C. Lertthanyaphan, A. Pornvechamnuay, An IoT controlled system for plant growth. Int. J. Technol. Res. Eng. 4(4), 668–671 (2016) 8. H. Kuruva, B. Sravani, Remote plant watering and monitoring system based on IOT. Int. J. Technol. Res. Eng. 4(4), 668–671 (2016) 9. A. Imteaj, T. Rahman, M.K. Hossain, S. Zaman, IoT based autonomous percipient irrigation system using raspberry pi, vol. 37, no. 3 (2016), pp. 563–568 10. S.B. Saraf, D.H. Gawali, IoT based smart irrigation monitoring and controlling system, in 2nd IEEE International Conference on Recent Trends in Electronics 11. S. Vaishali, S. Suraj, G. Vignesh, S. Dhivya, S. Udhayakumar, Mobile integrated smart irrigation management and monitoring system using IOT, in International Conference on Communication and Signal Processing, Apr 2017, vol. 5, pp. 2164–2167 12. K. Anusha, U.B. Mahadevaswamy, Automatic IoT based plant monitoring and watering system using raspberry pi. Int. J. Eng. Manuf. 6, 55–67 (2018) 13. S. Chen, N. Fatras, H. Su, Smart water irrigation System, in International Conference on Computer Sciences (2017), pp. 85–92 14. M. Monica, B. Yeshika, G.S. Abhishek, H.A. Sanjay, S. Dasiga, IoT based control and automation of smart irrigation system, in International Conference on Recent Innovations in Signal Processing and Embedded Systems (RISE-2017), Oct 2017, pp. 27–29 15. J.H. Gultom, Smart IoT water sprinkle and monitoring system for chili plant, in International Conference on Computer Sciences (2017), pp. 15–22 16. N. Duzic, D. Dumic, Automatic plant watering system via soil moisture sensing and its applications for anthropological and medical purposes, 1–4 (2017) 17. M.I.H. bin Ismail, N.M. Thamrin, IoT Implementation for Indoor Vertical Farming Watering System (IEEE, 2017), pp. 89–94

An Embedded Microcontroller for Plant Condition Monitoring Using Wireless Sensor Network (WSN) M. Rajunaik, I. Kavitha, and B. Sridhar

1 Introduction Wireless sensor network developments may be classified into many application fields, such as construction monitoring, agricultural condition surveillance, and more recently, autonomous vehicle. WSNs have also been utilized for many applications, including such as environmental monitoring, traffic control, and energy management in apartments. All sensor panels were enhanced and optimized to provide optimum precision and rapid data transfer [1]. Researchers have increasingly been interested to the improvements and to use of WSNs since the birth of WSNs. Farming had witnessed a change in the manner that smart technology was applied in nations. In order to increase plant production or in certain regions, several technologies have been used in plant growth circumstance. In this context, it becomes essential to use WSN crop conditions without inflicting substantial harm to both the crop. WSN crop condition monitoring usually includes several methods depending on plant requirements and the environment. The use of mini, power efficient, and intersector technologies has been one of the typical methods. In certain cases, these technologies comprise electrochemical sensors, sensors for soil moisture, and airflow sensors. In just this context, sensor technology may be coupled to smart and comparable decision rules in reasoning. Available sensor technology decision rules provide greater adaption and surveillance of crops. The goal of this research is to develop a system by using tiny microcontroller panels that will reduce labor throughout the greenhouse environment through the use of WSNs and furious decision-making methods [2]. The objective of the suggested system would be to guarantee optimum development of plants, better agriculture fields, and sufficient water usage to monitor ambient temperature and humidity inside greenhouse effect. In this one hand, it is just a research. In contrast, M. Rajunaik · I. Kavitha (B) · B. Sridhar Department of Electronics and Communication Engineering, MLR Institute of Technology, Hyderabad, Telangana, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. K. Mandal et al. (eds.), Innovations in Signal Processing and Embedded Systems, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-19-1669-4_31

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wireless sensor network is used as a substitute for complicated wiring, which really is intrinsically the primary issue with existing agriculture technology.

2 Literature Survey The topic relating to this area is abundant in embedded systems are quick to extend their scope. We have researched topics from many sources like books, internet articles, and reference books while conducting this project. The information acquired via this exercise was of tremendous assistance to everyone in comprehending the fundamental ideas linked to our project. Dough abbot had provided an excellent introduction to both the process of developing embedded system on Linux, the “Linux with embedded and real-time application”. It helped me gasp the way the Linux kernel is configured and constructed, and tool chains are setup [3]. Leonid Ryzhyk’s paper “L’ARM architecture” gave us an overview of the ARM processors in the area of embedded systems as well as the characteristics of ARM processors. This same ARM architecture combines several practical characteristics which improve it above other peer processors. As they are tiny while using less power, they are helpful for efficient performance with embedded applications [4]. Linux development tools were accessible from sources including such as FTP servers, mailing groups, and discussions forums online and may be accessed via the GPL license. All the tool chains that have obtained and developed were based on these internet helpdesks 2006. In order to evaluate the system’s real-time performance, using Web server “thepd” was used to load several processes. That Web server may be implemented on our system using the source and documents.

3 Proposed Method Wireless sensor network development may be classified into many applications areas, including building monitoring, agriculture monitoring, and more recently, autonomous vehicle use. In various applications, WSNs have, for example, been utilized in environment surveillance, traffic control, building energy management as well as another beneficial application. In order to provide optimum accuracy and rapid data transfer, sensor panels were enhanced and turned, researchers have been increasingly interested in the development and uses of WSNs from the start of the WSNs. There has been development of agriculture inside the manner that smart technologies are applied in nation. In order to increase plant output, or in some regions, improvements in the circumstances needed for plant development, many technologies have been implemented for both the management of crops. In this context, it is

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essential to use WSN technology to monitor conditions in plants without inflicting substantial crop damage. The goal of this research is to develop a system for the use of WSN methods inside the greenhouse environment by using miniaturized microcontroller boards. The suggested system is designed to guarantee optimum plant development, better agriculture yields and more effective water usage by monitoring greenhouse humidity and temperature. On the other hand, WSNs are being examine as just a substitute to complicated cabling that is really the key issue with the existing smart agriculture technology. WSN crop condition monitoring usually includes several methods depending on plant requirements and the environment. The use of miniaturized, energy efficient, and cross-cutting technology is a typical strategy. In certain cases, such technologies including electrochemical sensors, airflow sensor, and soil humidity sensor. Throughout this area, sensors technologies may be coupled to adopt comparative and smart monitoring decision criteria. Adding sensor technology decision rules enables improved adaption and nursing of yields.

4 Materials and Method In this paper, we utilize ARM processor, PCFADC/DAC, temperature sensor, humidity sensor, LDR sensor, regulated power supply, devices, Wi-Fi, and output will be displayed on laptop (Fig. 1).

Fig. 1 Block diagram of proposed system

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PCF Module Information Specifications Single power supply PCF8591 operating voltage range of 2.5–6 V. Low-standby current via I2C bus serial input/output. PCF8591 by 3 hardware address pins addressing PCF8591 I2C bus speed sampling rate decision. 4 analog inputs programmable as single-ended or differential input automatic incremental channel selection. PCF8591 analog voltage ranges from VSS to VDD PCF8591 built-track and hold circuit. 8-bit successive approximation A/D converter DAC gain realized by an analog output.

5 Module Description 1. 2. 3. 4. 5. 6. 7.

8. 9.

PCF8951 module chip Facilitates collection of external voltage (input voltage range 0–5 V) Three modules combined numerically precise AD–intensity photo resistor acquisition environment The embedded thermistor module may accurately detect the ambient temperature value through the AD Input acquisition of five integrated module 1 channel 0–2.5 or 0–5 V (blue potentiometer to adjust the input voltage) Light with power display light (ON the module after power indicator lights) With both the DA output light board indication DA output module DA output interface voltage, the higher the light brightness is by far the more apparent the voltage Size of PCB: 2.3 cm × 3.6 cm Dual-sided standard, 1.6 mm thick plate with a hollow-round, opening 3 mm, handy fixed.

6 Module Interface Its external expansions interfaces of the module also on left and right correspondingly are as follows: DA output interface chip on the left OUT AINO chip analog input interface 0 AIN1 chip analog input interface 1 AIN2 chip analog input interface 2 AIN3 chip analog input interface 3.

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The right of the SCL IIC clock interface connected microcontroller IO port. SDA IIC digital interface connected to the microcontroller IO port. GND module to an external ground VCC power interface external 3.3–5 V. Module’s jumper instructions Module A total of three red jumper, namely the role of the following: P4—P4 jumper connected, choose thermistor access circuit. P5—P5 jumper connected, choose photo resistor access circuit. P6—P6 jumper connected, choose 0–5 V adjustable voltage access circuit.

7 Package Contents 1 × AD/DA analog/digital converter module PCF8591. FREE DUPONT CABLES (4 × 20 cm).

8 Temperature Sensor It is simple to describe an analog temperature sensor, a chip which informs you whatever the ambient temperature was! These sensors are used for temperature determination using a solid-state method. That really is, they use less mercury bimetal strips (such as in certain house-based thermometers or stoves) and do not utilize thermistor, they use less mercury (temperature sensitive resistors). Rather, the votability across a diode is increasing at a predictable pace when the temperature rises. The voltage drops down between the base and the emitter—the Vbe—of a transistor is really technically. It is indeed simple to generate an analog signal which are exactly proportional to temperature, precisely by magnifying the tensile.

9 Light-Dependent Resistor Light-dependent resistors are being used to recharge a light during various light changes or even to switch on a light changes or even to switch on a light during specific light changes. A frequent application in traffic light has been one of the light-dependent resistors. Its light resistor monitors and recharges a built-in heater inside of the airflow throughout the night to ensure that the light never dies. Infrared detectors, clocks, and security alarms are some other typical locations to locate light-dependent resistors.

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10 Humidity Sensor Moisture sensor an electronic measuring device that detects and transforms humidity into some kind of matching electrical signals in its surroundings. Humidity sensors were greatly varied in size and capability; certain humidity sensors may be found on portable devices (such as cell phones) (such as air quality monitoring systems). In the weather, medical, car, HVAC, and industrial sectors, moisture sensors are widely utilized. Due to the various methods of humidity calculations, moisture sensor may be split into two categories: RH sensors and AD–humidity (AH) sensors. By comparing actual live humidity readings at a particular temperature with the minimum air humidity at a very same temperature, relative humidity was determined. Thus, in order to calculate relative humidity, RH sensors should detect temperature. Absolute humidity without regard to temperature can measured, though. The capacitive and resistive moisture sensors are really the most popular HR sensors. Capacitive sensors utilize two electrodes in order to monitor the capacity of thin metal strip put between them (i.e., the ability to store electric load). The capacity of the metal was increasing or decreasing at such a rate exactly proportional to the moisture changes in the sensor environment. This charge difference (voltage) produced by an increase in moisture is amplified and transmitted for processing to both the integrated computer. A distinct concept applies to the resistive humidity sensors. Such sensors use a tiny polymer comb to growth and decrease in size when moisture changes, which impacts the capacity of the device to hold loads. For measuring absolute humidity, thermal humidity sensors are employed. In contrast to RH sensor, thermal moisture sensors use two samples, one for dry nitrogen measurements and one for air measurements. The change in thermal conductivity was measured and AH computed when humidity is collected on the exposed sensor.

11 Water Level Sensor Level sensors detect both fluid as well as other fluidized solids, including slurries, granular material, and top-free powders. Substances flowing in various containers (or other physical limits) are basically horised by gravity, whereas most bulk solids are piled to a resting angle toward the top. The measured material may be in its natural state inside a container (e.g., a river or a lake). Either continuous or point values may be measured in level. In a given range, the continual level and calculate the precise quantity of both the substances is higher or lower sensing point. The other usually detect abnormally high or low levels.

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12 DC Motor A DC engine is intended for electricity from DC. The homopolar engine (unusual) of Michael Faraday as well as the ball bearing engine (so far) are two instances of pure DC architecture. These brushed and brushless kinds are the most popular DC motor types. They utilize both internal and external communications to generate an oscillated AC current from of the DC source and therefore are not strictly speaking DC machines.

13 Wi-Fi Wi-Fi is indeed a local wireless system that enables the interchange of data or the Internet connection of an electronic device using 2.4 GHz UHF or 5 GHz SHF radio waves. The name of the brand is a reproduction on the audiophile Hi-Fi word. The Wi-Fi Alliance defines Wi-Fi like all “WLAN products which are predicated on the IEEE 802.11 specifications”, “wireless local area network (WLAN) devices”. Since most contemporary Wi-Fi networks, nevertheless are founded on these standards, generally common the word “Wi-Fi” synonymous with “Wi-Fi” is often used in English. The label “Wi-Fi CERTIFIED” may only be used by WLAN devices successfully complete the testing of interoperability by WLAN alliance.

14 Implementation and Deployment When our app begins to work, it first checks all the devices and resources that require. It then verifies the devices connection and provides the user control. The PCF8591 module is linked to the sensors, such as temperature, LDR, and humidity sensors, and all these information are transferred to ARM processor through GPIO, which would be coupled with the microprocessor. There at end of the recipient, Wi-Fi is available for the ARM processor to transfer the data. By providing instructions toward the ARM processor upon on Web page, a device such as light and fan linked toward the microcontroller.

15 Result and Discussions In this paper, we developed a plant condition monitoring using wireless sensor network. Here, we are going to interface the module and Raspberry Pi processor. After interfacing the modules with PCF8591, it will convert ADC to DAC. After that we will the programming code (Figs. 2 and 3).

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Fig. 2 Interfacing the modules

Fig. 3 Web page display

This sensor page will display the output values of temperature, light, and humidity values based on threshold values. With these sensor values, the output will be displayed on the Web page. In the Web page, we will see the output, the output will be displayed on laptop/mobile phone (Fig. 4) [5].

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Fig. 4 Message will be displayed on mobile/laptop

16 Conclusion This project “An Embedded Microcontroller for Plant Condition Monitoring using Wireless Sensor Network” has been successfully developed and tested for embedded microcontrollers for plant condition monitoring. It has been created by combining the all hardware and software components. Each module was carefully considered and positioned to ensure the optimum functioning of the device. Secondly, extremely progressive use of ARM CORTEX. This project was effectively executed by a board and with the assistance of increasing technology.

17 Future Scope This last part of the paper describes various features that may be put into practice in future versions. A minimum is how a customer would anticipate the present set of features. This present technology is utilized for medium-speed access to data. However with the assistance of such an advanced board, we may improve access speed minimal delay.

References 1. M. Sibiya, M. Sumbwanyambe, An embedded fuzzy logic microcontroller for plant condition monitoring: a wireless sensor network odyssey, in 2019 Southern African Universities Power Engineering Conference/Robotics and Mechatronics/Pattern Recognition Association of South

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Review Paper on Safety Devices for Women G. Ranjithkumar, V. Voorwashi, and T. Anuradha

1 Introduction In the previous decades, women were hesitant to leave their homes for routine tasks, necessitating a higher level of security [1]. Women are much less secure in their abilities to step out in today’s environment. We will consider a scenario in which a lady walking outside on the road is harassed behind or in front at a certain moment of the day or night. Devised a compact, smart gadget that can track the user’s current position to address these concerns. When women are concerned, their cardiac rate rises, which the sensor module may detect, and their stress level is tracked. Women may also be able to send crisis messages to trusted friends and cops via their smart smartphone. In a case, these intelligent security devices can give quick responses and safeguard women against potentially unpleasant situations. The major benefit of this gadget is that it would be extremely portable [2], more precise, and trustworthy. The approach has a unique set of techniques for detecting women in potentially risky circumstances. A few of those employed panic sensors to determine the women’s state based on their pulse and body changes in temperature. The majority of systems rely on mobile devices to identify women in dangerous circumstances, such as a phone microphone to recognise women screams, a camera to take photographs and record video [3], and different mechanisms, such as network mechanisms, mobile mechanisms, helpline numbers, and other systems were intended to help women in crisis circumstances, however, their reliability is low, resulting in many criminal scenarios [4]. Several detecting components are stated in the system, which aid in the generation of data in high-risk circumstances, and the potential danger of such events is reduced thanks to the sensors employed. Human intelligence is rapidly G. Ranjithkumar · V. Voorwashi (B) · T. Anuradha Department of ECE, MLR Institute of Technology, Hyderabad, Telangana, India e-mail: [email protected] T. Anuradha e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. K. Mandal et al. (eds.), Innovations in Signal Processing and Embedded Systems, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-19-1669-4_32

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improving, yet maintaining security in the face of this advanced technology is a difficult challenge [5]. This paper examines how upgrading technological makes data more susceptible, and it assists in the safety of women and children in this work. Since secure credentials are extremely susceptible, they are also quite accessible to the work place. In addition, the security of that susceptible data is critical to protect in order to retain the data at a high level of security when new technologies emerge. In this regard, the most recent technological advancement is the Internet of things platform. Women need to take a gadget with them just to work so that they can be tracked whenever they are outdoors. The gadgets that have previously been created are like accessories that can be carried about with them. The IoT refers to the vast network of interconnected gadgets that may be used to effectively manage computational tasks without the need for direct human intervention [6]. As billions of sensors are connected over time, the IoT is becoming an increasingly essential aspect of our lives, allowing us to complete daily tasks more quickly and effectively in ways we could not before. In an ideal scenario, IoT would update upcoming programmes with smart and strong structures that will soothe our way of life whilst also adapting to changing demands. It will enable us to have completely automated frameworks that will be used to improve our security procedures by removing the client blockage with any security device. Almost, IoT has the potential to satisfy all of our requirements before we really realise what we desire. The true strengths of IoT setups are security and connectivity. Wearable safety gear is also another activity that needs additional study in order to get such innovative capabilities integrated into everyday wear [6], but the arrangements may already exist within IoT. The proposed method is explained in order to provide a more efficient and effective answer to this problem. In this study, the numerous present strategies for safeguarding women after they are outside were evaluated. Objective The main purpose of this paper is to provide security for women, and this device’s brand-new innovation format is designed to ensure women’s protection. This device is turned on 24 h a day, seven days a week. This technology is primarily intended to protect women against sexual harassment and other sorts of violence. When compared to other systems, this paper has a lot of advantages with this equipment. Women will keep them on hand in case they are required.

2 Literature Review Parikh et al. [7], the NodeMCU microcontroller is utilised to make a wearable safety device for women. The major purpose of this gadget is to assist women who have been attacked. The device connects to the encrypted systems and delivers them alerts through IoT. ThingSpeak is a conversational Internet of things platform that may

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be used to create IoT apps and also serve as just an interaction and store detection cloud databases. This technology has the advantage of enabling us to submit data to ThingSpeak every 30 s, providing for continuously personal health monitoring. Taking this into account, the following framework would be a technique for building a product for persons that assists in preserving a user’s protection by eliminating the need for users to begin any prescribing behaviours in reaction to any situation in which they could feel at considerable danger. Dharmoji et al. [8] in this frame work construct an Internet of things patient health monitor system with the ESP8266 and Arduino. ThinkSpeak was the IoT platform used for this research. ThinkSpeak is an accessible IoT application and API that stores and retrieves data from connected devices or via a wireless network using a Web server. This Internet of things device may be used to measure the temperature and heart rate. It continually detects the surrounding heartbeat rate and temperature and sends the information to a cloud network. Tejesh et al. [6], a compelling security and alerting strategy for women have been developed in this study. In terms of monitoring, filming, and self-defence, an improved technology safeguards female’s safety. To assist them in achieving their goals, the wearable female’s safety system is connected to IoT as well as other accessible technologies. The microcontroller with ESP8266 Wi-Fi device enhances women’s security when combined with components such as biometric, GSM, GPS, cameras, wearable devices, as well as a neural model. Females are tracked and located using GSM, GPS, cameras, biometric modules, and smart watches. A self-defence scenario is simulated using the nerve simulator. In the incident of a difficult situation, the female in the equipped gadget signifies her thumbprint, whereby the GPS and GSM equipped with a microphone and speaker, a Webcam, and body sensors are initiated and position notifications are being sent to the relevant authorities and numbers are modified in the framework using a neural simulation platform. The created framework ensures that it fulfils its aim of safeguarding a woman in all situations. Sathyasri et al. [9], the study of this work says with in case of danger, to guarantee the safety and protecting women. When a lady is anxious, she presses a button. Whenever the button is pressed, the computer controls the instructions, and the GPS detects the victim’s current latitude and longitude. The GSM modem would send a signal for both the exact address information and the closest police station to the contact stored in the processor. GSM transmits an SMS to register phone numbers and sends SMS to enrolled mobile phones every 1 s. The message is shown on the LCD panel. The victim’s current position might be tracked by the IoT module, which would update the Webpage’s location. The microcontroller will trigger the device’s buzzer, alerting anyone around that someone is in danger and enabling them to react. Islam et al. [10], the objective of this research is to focus on a protective system built particularly for the purpose of safeguarding women so that they do not feel vulnerable when faced with such enjoyable difficulties. This device will allow a woman to contact someone or seek help in a number of scenarios, and it will be incredibly beneficial if she is abducted.

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Tejonidhi et al. [2], this structure allows the gadget to be conveniently transported and used anytime the user perceives a threat. It provides a quick response and reporting safety gadget for women. The app assists women in overcoming their fears and allows them to contact their guardians for assistance. By pushing a button on the smart bracelet, it may report a scenario and its appearance of a stylish band. If she is carrying a band or a watch; she may push a button on the watch if she is harassed or believes anything is going to threaten her. When she falls, various data such as her position, postures, heart rate, and SMS alerts are broadcast through GSM via Raspberry Pi to a predefined number. With GPS, they can pinpoint the victim’s precise location. It communicates the suspect’s longitude and latitude so that authorities may quickly locate her and the crime can be prevented, saving the woman and punishing the perpetrator. They can remotely track the details of the women and use the IoT platform. Philomina et al. [11], in the work of this paper, every female is given this intelligent band and smart bell are installed at every dwelling on the block in a straightforward procedure. Each highway has a Wi-Fi router that connects all of these rings. Whenever a girl gets agitated, she presses the button on her smart bracelet, which rings the house bells. The individual who was distressed by residential people was provided immediate assistance. Priya et al. [12], this gadget is utilised in defensive systems, and it is specifically intended for women in difficult situations. An ARM controller was utilised as the hardware device for this. This is the most rigorous analysis and consumes the least amount of energy. Implementation: Tracking methods are implemented using the ARM controller stated above. An ARM controller is coupled to the tracking technology, which is known as GPS. To notice identify, the analysis relies must be pushed for a few moments, and it can send emergency alerts to the contact details with the aim to locate. The bell will notify individuals in the area to seek assistance. After the touching sensor is contacted, tear gas will be discharged. As a result, the victims will have ample time to use our programme to free from strangers. It allows for the creation of designs for women who are now confronted with a variety of difficult conditions and will aid in the scientific clarification of these problems via the use of a compact kit and idea. Mechanisms such as tear gas discharge and loud notifications with the location are implemented using wrist bands and glasses. Shiva Rama Krishnan et al. [13] patient health tracking has started to look into a variety of hidden factors in terms of enhancing medical equipment in an order to support specific consultancy work by merging it with wireless possibilities based on this paperwork. The health monitoring system computes the ECG, blood pressure, and temperature control data in less than a minute. As a consequence, the time–cost complexity is reduced to the absolute minimum. Seelam and Prasanti [14], the objective of this research is to create a safe and secure automated service for women that comprises an Arduino controller and components such as a temperature LM35 sensor, a flex sensor, a MEMS accelerometer, a heart rate sensor, and a noise sensor. This project includes a buzzer, LCD, GSM, and GPS. When a woman is threatened, the gadget detects changes in physiological characteristics such as heart rate and temperature.

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Punjabi et al. [15], a pressure switch-equipped portable gadget. When an aggressor is ready to assault the females or detects any insecurity from a stranger, they can squeeze or compress the gadget to provide pressure. A normal SMS containing the victim’s address will be sent to the victim’s relatives cell phone numbers entered on the gadget upon purchase, accompanied by a call. If the phone call goes unanswered for an extended period of time, it will be sent to the police, who will send the same message. Our system’s major advantage is that it requires less response time. The major benefit of our technology is that it requires less reaction time to assist the victim. Shimpi [16], in this paper, investigated and developed a comprehensive functioning framework uses ARM7. This research includes employing sensors to investigate GSM and GPS modems. The most significant benefit of utilising this paper is that every time the button is hit, we will get the location from the GSM modem to our phone contacts recorded in the programme and GSM network, allowing us to save the woman in danger. Megha and Ghewari [17], in this work, a gadget is created so that whenever the user hits the device’s button, the system sends out a message to the cop and her few contacts, along with her position, so that they are aware of her present condition. He said that by sending such a message, they might be able to startle the attacker. As a result, she gets some time to free from her attacker. The author presented a device that includes an LPC controller, GPS, ARM controller, GSM, and a shock circuit in this work. If a woman is in danger, she can push the emergency alarm. So that the position, as well as the assistance message, may be sent to the help line. In addition, the shock circuit is triggered, allowing the attacker to be shocked by the current created. The shock circuit can be controlled by a generating and isolator circuit in this case. Lokesh and Gadgil [3], the author presented a system that included an android app, a primary device and a portable cam in this paper. In a critical emergency, the Android application utilises the phone’s GPS or the primary device’s GPS to locate the victim. They activate the emergency response system. To ensure data security, the camera will be attached, the photo will be taken, and the photo will be transferred to the server. A manually controlled pepper spray is also attached to the main gadget. Helen et al. [18], the main idea of this study, around which the entire procedure revolves, is to activate the pulse sensor whenever it achieves the target pulse rate and duration. When the heart beat sensor detects a heartbeat, it emits a high-pitched warning sound is emitted to alert people around. Then watch me, send an notify to the local police station right away. A GPS tracker that is updated on a regular basis allows the officers to follow the position. Then, it transmits an alarm signal to the previously saved contact numbers, informing them also that the person who wears the watch is in danger. Because of its mobility and cost-effective features, currently choosing smart watches to wear for a variety of technical functions as well as a nice fashionable look as demand grows. The battery may be replaced by purchasing a new one. The watch’s battery is a Lithium-ion battery. This kind of cell is just used as easy to recharge, takes up less space, and is cost effective. Watch me closely, since the girls regret clicking or move this wristwatch, which would notify the cops, their

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parents others. Watch me is a highly sophisticated protection for women to wear at any moment and anywhere. So that females may come out openly and pursue their dreams and future endeavours without being hindered. Krishnamurthy et al. [19], in a crisis, the user may merely say a before the comfortable, that the proposed M-WPS will identify. It will then transmit an auto-generated help call, and also the phone numbers, to the cops control centre through short messaging (SMS). Whenever the user moves, she can enable the device with continuous GPS monitoring, which is the M-second WPS’s smart mode of operation. This research describes why the M-WPS came to be. Sharma et al. [20], women’s safety has become a concern as violence against women has increased. The GPS tracking and messaging system plays an essential part in this system. When ladies travel long distances at night in cabs, the GPS employed as a GPRS location will be available, and her position will be quickly provided to family for their rescue. This is to create compact, portable technology that may be carried in a handbag. The panic button on this device will be pressed, and the position co-ordinates supplied by the GPS will be sent to the emergency as soon as the panic button is hit. Because the panic switch is so tiny, it may be handled safely and securely by others. This is a simple method to accomplish it. When you hit that button, an emergency/alert message regarding the victim’s position is sent to the police control room, who will be notified via the Website’s simplicity of use. The author of this study presented a system that is constructed using compact, portable equipment that may be carried in a purse. When they are in a life-threatening scenario, they may push the button on this gadget, and their whereabouts can be monitored by the police control room and family members via the Website. Harikiran et al. [21], the gadget is a collection of devices that work together. The technology comprises of a portable “smart band” which is always connected to a smart phone with a broadband connection. The app has been created and packed along with all the essential information, includes social cognition and responses to various circumstances such as wrath, anxiety, and concern. The mobile phone receives a signal as a result of this. Whenever the system or programme receives an alert notification, it can transmit a help request including the GPS location to the local police department, relatives, or anybody in the nearby neighbourhood who has the app. This enables for quick aid from both the cops and members of the community in the area, who may reach the victims with amazing precision. Jatti et al. [22], the objectives of this article are to create a portable gadget that will protect females. Evaluation of physiological data in combination with body posture is used to attain this goal. The specific disease signs investigated were respiration rate impedance and body temperature. Body position is calculated using raw pulse sensor from a tri-dimension sensor or a single axis device. With the gathering of original data, a specialised deep learning technique is used to identify activities. Current data tracking is possible by remotely sending visual data to an available cloud service. The data are analysed in MATLAB at the same time. This gadget is set up to continually check the subject’s vital signs and take action as necessary. It accomplishes this by detecting abnormal in the observed signals and taking necessary action by issuing alerts to authorised persons.

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Viswanath et al. [23], the focus of this paper was to create a low-cost, sophisticated device that would make women feel safer. The concept was created using an Arduino board with a part of the multi-potentiometer or a low-power wireless modules. The recipient’s footwear is linked to this device. In the touching situation, the automated process had a 100% accuracy rate, whilst in the walking scenario; it had a 95% accuracy rate. The client does not need remote access to her cell phone to operate this low-cost system, and the gadget is securely disguised. The user does not need to push any keys or hold anything in her hand to activate the gadget. Continue touching the back of the individual’s left foot with her right foot to send a signal to the device, which could trigger an alarm. The device’s size and shape make it simple to integrate into daily life. It is unobtrusive and hard to detect due to its modest size. To improve the device’s endurance and prevent equipment loss, an appropriate cover may be made in the region. This research has numerous flaws, including the risk of a fake warning if indeed the user accidentally touches her feet from behind. The device can only function properly whenever the recipient’s toes are all at floor level. The system’s dependability and robustness may be enhanced by working on a larger variety of situations and collecting enough data (subjects of various ages, genders, and heights). George et al. [24], the intelligent security system for women is a surveillance system designed to keep women secure in public spaces. To assure safety, this realtime image recognition system employs random movement analysis and face emotion identification using feature point extraction in MATLAB. The technology will send a message to the control centre if it detects a violent scenario. This paper has been designed with the aim of providing a safe atmosphere for the ladies under all conditions. The use of this real-time monitoring equipment can solve the problem to some extent. This gadget protects ladies in our public transportation system and other public places. This concept might be done in a variety of ways with more research and creativity. The device can monitor the relevant region in real time and identify violence with high accuracy. Using such a very high-resolution camera and software application for this purpose on a master console makes the whole thing ideal for usage in public areas. For future real-time surveillance applications, the gender recognition algorithm and motion tracking will be more exact to avoid the creation of false alarm. The technology is strong in this case since it can handle a larger number of emotions from victims and suspect with pinpoint precision. It will provide women with a sense of safety and security in public settings. Vijayalakshmi et al. [25], this paper looked at the emergency response system and how it may aid women who are involved in criminal activity. The main aim is to create a low-cost system that can keep the data of members in a certain area and send out rapid notifications in the event of a crime against women. This gives women a sense of safety. The requirement of the day is to be protected and secure. The study of this paper is to develop and construct a device that is so small that it functions as a personal security system. Women will most likely benefit from this gadget. It is, without a doubt, a temporary and preventative remedy. This will be demonstrated as a multi-pronged method involving different stakeholder groups in society. The development of hardware and software prototype accomplished two

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goals: confirming the planned design and determining if the technology used is acceptable for the system. This technology will assist its users when they are in a tough circumstance. This device would be extremely sensitive and simple to operate. Its rapid action reaction will offer individual users with safety and security. Authors and year

Title

Contribution

Nalina et al. [26], 2021

Smart women safety device using IoT

To create an IoT-based safety gadget that protects women by using a fingerprint-based technique of connecting to the device and alerting nearby individuals and authorities

Parikh et al. [7], 2020

IoT-based wearable safety device for women

The gadget connects to secure channels and gives alerts through IoT, as well as allowing us to transfer data to ThingSpeak every 30 s

Dharmoji et al. [8], 2020

IoT-based patient health monitoring using ESP8266

The IoT device may be used to measure temperature and heart rate. It sends the data to a cloud-based network

Tejesh et al. [6], 2020

A smart women protection system using Internet of things and open-source technology

The fingerprint is verified by the equipped device, followed by GPS and GSM. Location notification sends to the appropriate people and server information would be updated. A variety of shocks may be delivered to the attacker using the neural simulator

Sathyasri et al. [9], 2019

Design and implementation of women safety system based on IoT technology

Every 1 s, GSM sends an SMS to registered mobile numbers

Islam et al. [10], 2019

Design and implementation of women auspice system by utilising GPS and GSM

Three push buttons are utilised to determine what type of accident a person has been in. Any of these three buttons can be pressed if the user has difficulties in any location. After receiving it, the device will process an SMS to the contact number given (continued)

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(continued) Authors and year

Title

Tejonidhi et al. [2], 2019

IoT-based smart security gadget If she is wearing a band or a for women’s safety watch, she can press the smartwatch button if she is being harassed or feels she will be threatened. When she falls, the Raspberry Pi sends various data such as her position, body posture, pulse rate, and SMS alarm to a predefined number via GSM

Contribution

Philomina et al. [11], 2019

Possibilities in enhancing the security for women using IoT

It is a safely and securely electronic device for females, made up of a NodeMCU, a Wi-Fi transmitter, and an IoT. The bells which will be put in all houses, stores, and government buildings, among other locations

Priya et al. [12], 2019

One touch alarm for women’s safety using Arduino

A fraction of a second is required to push the capacitive sensor. It takes milliseconds to notify find, and it may send messages to contact numbers with intent location, and the buzzer will then release tear gas when the touching sensor is pressed

Shiva Rama Krishnan et al. [13], 2018

An IoT-based patient health monitoring system

The ECG, blood pressure, and temperature control data are generated by the health monitoring systems in less than a minute. As a consequence, the time–cost complexity is reduced to an absolute minimum

Seelam and Prasanti [14], 2018

A novel approach to provide protection for women by using smart security device

A safe electronic system has been created that includes different sensors for tracking temperature and a sound sensor for detecting sound. The gadget is automatically triggered when the sound level exceeds a certain threshold (continued)

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(continued) Authors and year

Title

Contribution

Punjabi et al. [15], 2018

Smart intelligent system for women and child security

A pressure switch-equipped portable gadget. By squeezing or compressing the gadget, he or she can apply pressure on it. The accused’s address will be sent to their relatives’ or guardians’ contact information, which were entered into the device at the point of purchase, following by a call that fails to answer for a prolonged length of time, at which time the call will be transferred to the police

Shimpi [16], 2017

Tracking and security system for women’s using GPS and GSM

We will obtain the position from the GSM modem to our smartphone details entered in the programme and the GSM network once the switch is hit

Megha and Ghewari [17], 2017

Intelligent safety system for women security

By pushing system’s button, a helpful message will be sent to the police station and family, along with the position, so that they are aware of her present condition. She can use the system to send a shock to the attacker in addition to sending the message

Lokesh and Gadgil [3], 2017 SAFE: a women security system

It turns on location tracking through the GPS module and delivers the co-ordinates to the designated receivers. They were able to get it to function at a pace of 6 pictures per minute, or one image every 10 s

Helen et al. [18], 2017

A smart watch for women security based on IoT concept “watch me”

The sensor monitors a person’s pulse rate. It will not only sound an alarm to attract the attention of surrounding individuals and it can detect and call to the nearest police station using GPS/GSM. By following the GPS, cops will be able to reach immediately on the scene

Krishnamurthy et al. [19], 2017

M-WPS: mobile-based women protection system

GPRS is used to track a moving position. Every 30 s, the information is updated and transmitted to their contacts (continued)

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(continued) Authors and year

Title

Contribution

Sharma et al. [20], 2016

Women security assistance system with GPS tracking and messaging system

When you press that button, a GPS-based emergency/alert message about the victim’s location is transmitted to the police control centre, which is alerted

Harikiran et al. [21], 2016

Smart security solution for women based on Internet of things

A “smart band” that is always in contact with a smartphone with Internet connection. If it receives an emergency alert, it has access to GPS and messaging services that are pre-programmed to transmit help requests along with position co-ordinates to the nearest police station or relatives

Jatti et al. [22], 2016

Design and development of an IoT-based wearable device for the safety and security of women and girl children

The gadget is set up to continually monitor the subject’s vital signs and to intervene if a dangerous scenario arises. It accomplishes this by detecting changes in the monitored signals, and then taking necessary action by sending notifications/alerts to authorised persons

Viswanath et al. [23], 2016

Smart foot device for women safety

A tiny wearable gadget for the feet has been created. A light blue bean microcontroller is at the heart of everything. It sends an alarm SMS to the emergency connection through Bluetooth when the tapping sound happens four times

Vijayalakshmi et al. [25], 2015

Self-defence system for women safety with location tracking and SMS alerting through GSM network

It uses GPS to gather position data, then creates a text SMS and sends it to the police control centre via GSM modem, along with a distress message to the registered cell phone. The lady can defend herself by shocking the harasser with an electric shock (continued)

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(continued) Authors and year

Title

Contribution

George et al. [24], 2014

An intelligent security system for violence against women in public places

When the system detects dangerous conditions, it sends a message to a nearby control room and activate alarms located around the area, allowing others to assist

3 Conclusion In each of these publications, the writers have adopted a new approach to providing a solution to the problem of women’s safety. We are providing a solution for women’s safety. The women’s stress levels are being monitored in order to safeguard them from a potentially dangerous situation. The current system includes an emergency button that may malfunction at any time. During certain crisis situations, however, the planned mechanism will not fail. Because after a few minutes, the warning will be sent automatically. It will assist them in avoiding certain circumstances, even if they are in a panic condition. The suggested solution was created using current smartwatches for women’s safety and health tracking. As a result, even if the victim loses consciousness, she may be readily traced. This technique can help women overcome their fear of harassment.

4 Drawback When the power is out, the entire system shuts down, so a battery is constantly needed.

References 1. R.K. Rajbhure, A review paper on women safety using IoT based technology. JETIR 6(5) (2019). www.jetir.org. ISSN-2349-5162 2. M.R. Tejonidhi, Aishwarya, K.S. Chaithra, IoT Based Smart Security Gadget for Women’s Safety. 978-1-7281-3241-9/19/$31.00 © 2019 IEEE 3. S. Lokesh, A. Gadgil, SAFE: a women security system. IRJAES 2(4), 204–207 (2017) 4. M. Zikriya, M.G. Parmeshwar, S.R. Math, S. Tankasal, J.D. Mallapur, Smart gadget for women safety using IoT. Int. J. Eng. Res. Technol. (2018). ISSN: 2278-0181 5. K. Ramya, T. Vimal, Survey on women safety devices. Int. Res. J. Eng. Technol. (IRJET) 07(08) (2020). www.irjet.net. e-ISSN: 2395-0056, p-ISSN: 2395-0072

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6. B.S.S. Tejesh, Y. Mohan, C.H. Anil Kumar, T. Peter Paul, R. Sai Rishitha, B. Purvaja Durga, A smart women protection system using Internet of Things and open-source technology, in International Conference on Emerging Trends in Information Technology and Engineering (icETITE). 978-1-7281-4142-8/20/$31.00 ©2020 IEEE. https://doi.org/10.1109/ic-ETITE47903. 2020.455 7. D. Parikh, P. Kapoor, S. Karnani, S. Kadam, IoT based wearable safety device for women. IJERT V9(05) (2020) 8. S. Dharmoji, A. Anigolkar, M. Shraddha, IoT based patient health monitoring using ESP8266. Int. Res. J. Eng. Technol. 07(03) (2020). p-ISSN: 2395-0072 9. B. Sathyasri, U. Jaishree Vidhya, G.V.K. Jothi Sree, T. Pratheeba, K. Ragapriya, Design and implementation of women safety system based on IoT technology. IJRTE 7(6S3) (2019). ISSN: 2277-3878 10. N. Islam, M. Anisuzzaman, S.S. Islam, Design and Implementation of Women Auspice System by Utilizing GPS and GSM. 978-1-5386-9111-3/19/$31.00 ©2019 IEEE 11. S. Philomina, M. Jasmine, K. Subbulakshmi, Possibilities in enhancing the security for women using IoT. Int. J. Eng. Adv. Technol. (IJEAT) 8(6S2) (2019). ISSN: 2249 – 8958. https://doi. org/10.35940/ijeat.F1263.0886S219 12. C. Priya, C. Ramya, D. Befy, G. Harini, S. Shilpa, B. Sivani Kiruthiga, One touch alarm for women’s safety using Arduino. Int. J. /Innov. Technol. Explor. Eng. (IJITEE) 8(6S) (2019). ISSN: 2278-3075. Retrieval Number: F60670486S19\19©BEIESP 13. D. Shiva Rama Krishnan, S.C. Gupta, T. Choudhury, An IoT Based Patient Health Monitoring System. 978-1-5386-4485-0/18/$31.00 ©2018 IEEE 14. K. Seelam, K. Prasanti, A Novel Approach to Provide Protection for Women by Using Smart Security Device. 978-1-5386-0807-4/18/$31.00 ©2018 IEEE 15. S.K. Punjabi, S. Chaure, U. Ravale, D. Reddy, Smart Intelligent System for Women and Child Security. 978-1-5386-7266-2/18/$31.00 ©2018 IEEE 16. T.R. Shimpi, Tracking and security system for women’s using GPS & GSM. Int. Res. J. Eng. Technol. 04(07) (2017). p-ISSN: 2395-0072 17. S. Megha, M.U. Ghewari, Intelligent safety system for women security. Int. Adv. Res. J. Sci. Eng. Technol. 4(2) (2017). https://doi.org/10.17148/IARJSET/NCETETE.2017.21 18. A. Helen, M. Fathima Fathila, R. Rijwana, V.K.G. Kalaiselvi, A Smart Watch for Women Security Based on IOT Concept ‘Watch Me’. 978-1-5090-6221-8/17/$31.00 @c 2017 IEEE 19. V. Krishnamurthy, S. Saranya, S. Srikanth, S. Modi, M-WPS: mobile based women protection system, in International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS-2017). 978-1-5386-1887-5/17/$31.00 ©2017 IEEE 20. S. Sharma, S. Bhatia, S. Dash, R. Bose, H. Singh, Women security assistance system with GPS tracking and messaging system. Int. J. Sci. Res. Sci. Eng. Technol. 3(4) (2016). ISSN: 2394-2819 21. G.C. Harikiran, K. Menasinkai, S. Shrol, Smart Security Solution for Women Based on Internet of Things (IOT). 978-1-4673-9939-5/16/$31.00 ©2016 IEEE 22. A. Jatti, M. Kannan, R.M. Alisha, P. Vijayalakshmi, S. Sinha, Design and development of an IOT based wearable device for the safety and security of women and girl children, 20–21 May 2016, India. 978-1-5090-0774-5/16/$31.00 © 2016 IEEE 23. N. Viswanath, N.V. Pakyala, G. Muneeswari, Smart foot device for women safety, in 2016 IEEE Region 10 Symposium (TENSYMP), Bali, Indonesia. 978-1-5090-0931-2/16/$31.00 ©2016 IEEE 24. R. George, V. Anjaly Cherian, A. Antony, H. Sebestian, M. Antony, T. Rosemary Babu, An intelligent security system for violence against women in public places. Int. J. Eng. Adv. Technol. (IJEAT) 3(4) (2014). ISSN: 2249 – 8958 25. R.S. Vijayalakshmi, P. Chennur, S. Patil, Self defense system for women safety with location tracking and SMS alerting through GSM network. IJRET (2015). eISSN: 2319-1163 | pISSN: 2321-7308 26. H.D. Nalina, B. Aishwarya, P. Harshitha, M. Kruthika, P. Rachana Naidu, Smart women safety device using IoT. Int. J. Eng. Res. Technol. 09(12) (2021)

Implementation of True Random Number Generator with Switchable Ring Oscillator on Xilinx ISE Environment B. Anusha and M. Aswanth Manindar

1 Introduction In our modern era almost everyone owns a smart phone and for most people this piece of technology has become an essential part of their life. According to an estimation [1] there are currently around 1.9 billion smart phone users around the world, which is over 25% of the world’s population. This number is expected to keep increasing rapidly in the coming years. For a device with so many users worldwide it is important that it is properly secured. Many smart phone users will keep personal data on their phones, so a security leak across a large platform like Android would have a heavy impact on society. However, a smart phone does bring possible security vulnerabilities. A malicious party could intercept or alter smart phone communication. This would have a serious impact on the security and privacy of the smart phone user. For example, a malicious party could listen in on the communication between a smart phone user and the bank. This would cause secure bank information to be compromised. According to an article by the Guardian [2] hackers are actively targeting financial smart phone apps. A significant number of financial apps have been hacked and malicious versions of the app have been uploaded to Google Play or third party app stores. Unsuspecting users who use these malicious apps risk their confidential credentials being captured, or their smart phones being exposed to adware. In order to prevent these scenarios we need to create a simple way for an Android device to encrypt data and engage in secure encrypted communication. This would be a relatively simple system to build on a larger desktop system because it has practically unlimited resources to work with in terms of computational power and data storage space. However, for a similar system to work on an Android device it will need to take into account the limited amount of resources the device has in terms of B. Anusha · M. Aswanth Manindar (B) Department of Electronics and Communication Engineering, MLR Institute of Technology, Hyderabad, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. K. Mandal et al. (eds.), Innovations in Signal Processing and Embedded Systems, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-19-1669-4_33

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battery, computational power and data storage space compared to a desktop system. If the encryption process takes up too much resources, it will interfere with the device user’s normal phone activities. A crucial step for encrypted communication to take place is the generation of a strong random seed as input for the encryption scheme. Ideally the generation of this random seed would take place on the device using a limited amount of computations, battery power and storage space. In this thesis I present a strong lightweight randomness generator prototype in the Android user space. On boot-up of the Android phone the prototype generates an entropy pool from noise in the phone’s sensor data. Once the prototype has generated a sufficiently strong entropy pool, other processes on the Android phone are able to request the prototype for any size of random output. Before presenting the prototype, I analyze the sensor data and estimate the required time to run the data generation process on device boot-up before the entropy pool can be considered sufficiently strong. For this purpose I have built an app which generates a large amount of data from different device sensors. The goal of the randomness generation prototype is to provide a source of strong randomness which can be used in addition to randomness from existing sources like /dev/random. This combination will create a stronger and more secure random seed for encryption processes on the device. The broad objective of the present study is to examine the degree and quality of randomness of various generators. In tune with this, the following four specific objectives have been framed for the study. . To use a SCRO distribution protocol to generate a random sequence. . To develop a novel method of generating true random number sequence using the interaction between photons and DFF in phase detector. . To probe and study the characteristics of the various true quantum random generators available and to compare the generators based on the test results.

2 Literature Survey In [3] authors used the image data from a camera which is pointed at a couple of ring oscillators is used as excellent entropy source to generate true random numbers. TRNG uses radioactive decay as the entropy source to generate random number sequences. In a simple case, a variable environment of a fish tank is used as an entropy source of randomness. Up To date only few organizations offer true random numbers commercially using these kinds of techniques. In [4] authors used a method of generating random numbers using Celestial oscillator sources and the generated sequence is tested with NIST Statistical Test Suite for random data. They found that the resulting data sets pass all tests in the NIST with a mean of 98.9% of the 512 total bit streams as well as further testing in R. In [5] authors performed Entropy estimation is a vital part in building a TRNG because being able to give an accurate estimation of the amount of entropy contained in the entropy pool is required to reach a certain level of security. If the accuracy of the entropy estimation of a TRNG is high, it can give better security guarantees

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about the unpredictability of its entropy pool. This makes it less likely for an attacker to compromise the randomness of the TRNG system. In [6] authors proved that the complexity of estimating the min-entropy of a distribution is SBP-complete, which stands for “small bounded-error probability” which is a custom class of complexity that is believed to be equal to NP-complete complexity. So the problem of proving an entropy pool to be truly random is computationally hard, so instead estimation has to be made using indirect measures. In [7] authors stated a problem by the TRNG is an interesting issue, but it assumes a theoretical scenario that might not be realistic in a practical usage scenario. Especially for the usage scenario of this thesis (i.e., Android devices) the scenario described will be unlikely to cause serious problems. In [8] authors stated that solutions should be found to provide additional entropy on the Android devices so that the internal state is much less likely to be compromised because of low-entropy events in the first place. The TRNG should continuously provide the user with strong random seeds for use in cryptographic protocols on the device, while still remaining as efficient as possible using the limited resources that are offered by the device. In [1] authors proposed, the /dev/random PRNG generates an entropy pool from a number of sources on the hardware level such as inter-keyboard timings and interinterrupt timings. These sources of entropy are assumed to be non-deterministic and hard for an outside observer to measure. In [9] authors proposed, the Linux kernel also provides a second TRNG which is /dev/random. /dev/random is identical to /dev/random in its functionality, the only difference is that /dev/random is non-blocking and has no limit to the amount of requests for bytes of randomness it can take. In [10] authors proposed, the /dev/random TRNG is widely used by most applications and generally considered secure. However, /dev/random has been criticized by a number of papers which claim that it has vulnerabilities. Even the source code of /dev/random states a weakness with regards to predictability on system start-up.

3 Proposed Method 3.1 Linear Feedback Shift Register (LFSR) A linear feedback shift register is a series of flip flop connections in which the output of the previous flip flop is the current flip flop’s input. It is created by combining the XOR gate with the flip flop series feedback. The preliminary value of the LFSR is known as a seed value, which is a combination of 1s and 0s. The seed value that causes the least amount of power dissipation is chosen as the seed value, which determines additional random values. The LFSR enters a repeating process due to its restricted number of potential states. The LFSR is a type of shift register that tilts

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the recorded data from left to right. The LFSR circuit’s XOR feedback inputs are responsible for the creation of random numbers [11]. Only the taps are appropriately picked, and the LFSR will travel through as many states as feasible. The LFSR should be initialized using the seed value. 2n − 1 states are necessary for n stages. The series of values produced by the register, depending on the current condition. The LFSR generates a random bit sequence. The signal is passed to the next MSB bit through the register bit whenever it is timed. The XOR gate receives input from preset registers on the left. A count of all zeros is not possible in XOR-based LFSR. Because they do not create carry signals, LFSR counters are highly efficient.

3.2 LFSR-TRNG Frameworks The LFSR-TRNG detailed designs are shown in Fig. 2 accordingly. The priority of LFSR attributes were used to create each block in Fig. 2, and the CB4 in particular was developed utilizing the LFSR method. The LFSR gates were used to create SCRO, 2 to 1 Multiplexer, Phase detector, Reset-Set (RS) latch and 4-Bit binary counter (CB4). The LFSR-TRNG method’s detailed process is as follows: Step 1: The circuit is turned on by setting the Chanel enable (CH ENA) to active low. The control enable block receives the CH ENA input and phase detector output as inputs. Based on the functioning of the LFSR-NAND gate, if any of the signals is triggered to zero, the output becomes logic high. Step 2: The control enable output will be used as the selection input for the two separate multiplexers MUX1 and MUX2. The multiplexer is utilized in this case to pick the best SCRO path. . If selection = 0; then the MUX2 selects from path I1 (delayed output from SCRO2) else it will directly selects the SCRO2 output. . If selection = 1; then the MUX1 selects from path I1 (delayed output from SCRO1) else it will directly selects the SCRO1 output. The control enable is also used as an input to the LFSR-XOR SCRO’s gate. Step 3: The TRNG’s main functionality will be determined by the SCRO’s beat frequency. SCRO is a digital oscillator that is utilized to create the various frequencies in this example. . Mainly, the input clock and control enable (Figs. 1 and 2) signal will be applied as major inputs to the SCRO1 and SCRO2. . The output of the SCRO will be applied as input to the SCRO again through the MUX in the positive feedback manner, thus there will be less errors in the oscillations. The output frequencies will be generated perfectly.

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Fig. 1 XOR-based LFSR architecture

Fig. 2 Proposed TRNG block diagram

. The frequency of SCRO1 is not the same as that of SCRO2. If both frequencies are the same, there is a lower chance of generating random sequences. The SCRO1 block employs a three-input XOR gate for this purpose, whereas the SCRO2 block employs a two-input XOR gate. Step 4: The outputs of both SCROs will be used as inputs to the D-Flip Flop to compute phase differences between two frequencies. The phase detector in this case is DFF, which determines the phase difference between two signals. . Clock signal will be applied as the Clear (CLR) input to the DFF, thus for every positive edge triggering of clock, the data stored in the DFF will be cleared. . The SCRO1 output will be used as a data input to the DFF, whereas the SCRO2 output will be used as a clock input. As a consequence, when the SCRO2 clock is triggered, SCRO1 data will be watched, and the output will be phase detected. . As indicated in step 1, this phase detected output will be used as an input to the control enable block.

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. The SCRO 1 and SCRO 2 clock output frequencies will be changed without phase or frequency mismatches using this feedback method. Step 5: The Set-Reset (RS) latch will be fed the regulated SCRO 1 and SCRO 2 clock output frequencies. It will activate the output and create the CB4’s final enable signal. Step 6: The RS latch enable signal will be applied as an input to the CB4, which will create the output sequences at random because it is a counter. Randomization is primarily determined by the triggering of SR latch output set and reset conditions.

4 Simulation Results Xilinx ISE software was used to create all of the LFSR-TRNG designs. This software programmed gives two types of outputs: simulation and synthesis. The simulation results provide a thorough examination of the LFSR-TRNG architecture in terms of input and output byte level combinations. Decoding procedure approximated simply by applying numerous combinations of inputs and monitoring various outputs through simulated study of encoding correctness. The use of area in relation to the transistor count will be accomplished as a result of the synthesis findings. In addition, a time summary will be obtained with regard to various path delays, and a power summary will be prepared utilizing the static and dynamic power consumption. Figure 3 indicates the simulation outcome of proposed method. Here, clock (clk), enable (en), reset, drp and address (add) are the input signal. For each address a new random number is generated in non-deterministic manner and resulted in the output signal out. So, the proposed method gives the effective outputs as it utilizes the LFSR. Figures 4 and 5 indicates the total power utilized by the proposed method. The total power consumed by the proposed method is 14.14 mW. Figure 6 indicates the total time consumed by the proposed method. The total path delays presented in the proposed method is 4.55 ns. Figure 7 indicates the total area consumed by the proposed method. The total area consumed in terms of slice registers is 40 and Look up tables utilized by the proposed

Fig. 3 Simulation waveform

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Fig. 4 Power report

Fig. 5 On-chip power summary

Fig. 6 On-chip delay summary

Fig. 7 Device utilization summary

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Existing

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Slice registers

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40

LUTs

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36

LUT-FFs

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30

Time consumed (ns)

23.72

4.55

Power consumed (mW)

36.47

14.14

method is only 36. The combination of Slice register and LUTs are LUT-FFs and only 30 number of LUT-FF are used. In comparison to the present approach, the suggested method provides higher performance in terms of area, power and delay, as shown in Table 1.

5 Conclusion The main focus of this research was on implementing TRNG utilizing LFSR-based technology. The SCRO-based method has been combined with beat frequency-based detection ideas to build the TRNG. As a result, the probabilities of random number occurrences are enhanced, and oscillations in frequency are also improved due to lower error rates. The simulation results obtained with the Xilinx ISE software indicate that the LFSR-based TRNG technology outperforms the state-of-the-art methods. The work may be expanded to build real-time safe protocols including public key cryptography, RSA, ECC and HECC cryptography methods using the LFSR-TRNG TRNG outputs as both public and private keys.

References 1. P.Z. Wieczorek, K. Gołofit, True random number generator based on flip-flop resolve time instability boosted by random chaotic source. IEEE Trans. Circuits Syst. I Reg. Pap. 65(4), 1279–1292 (2017) 2. H. Jiang et al., A novel true random number generator based on a stochastic diffusive memristor. Nat. Commun. 8(1), 1–9 (2017) 3. J. Brown et al., A low-power and high-speed true random number generator using generated RTN, in 2018 IEEE Symposium on VLSI Technology (IEEE, 2018) 4. S.K. Satpathy et al., An all-digital unified physically unclonable function and true random number generator featuring self-calibrating hierarchical Von Neumann extraction in 14-nm tri-gate CMOS. IEEE J. Solid-State Circuits 54(4), 1074–1085 (2019) 5. H. Lee et al., Design of high-throughput and low-power true random number generator utilizing perpendicularly magnetized voltage-controlled magnetic tunnel junction. AIP Adv. 7(5), 055934 (2017) 6. M. Jerry et al., Stochastic insulator-to-metal phase transition-based true random number generator. IEEE Electron Device Lett. 39(1), 139–142 (2017)

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7. T. Kaya, A true random number generator based on a Chua and RO-PUF: design, implementation and statistical analysis. Analog Integr. Circ. Sig. Process 102(2), 415–426 (2020) 8. E. Kim, M. Lee, J.-J. Kim, 8.2 8 Mb/s 28 Mb/mJ robust true-random-number generator in 65 nm CMOS based on differential ring oscillator with feedback resistors, in 2017 IEEE International Solid-State Circuits Conference (ISSCC) (IEEE, 2017) 9. S. Arslan Tuncer, T. Kaya, True random number generation from bioelectrical and physical signals. Comput. Math. Methods Med. 2018 (2018) 10. M. Song et al., Power and area efficient stochastic artificial neural networks using spin–orbit torque-based true random number generator. Appl. Phys. Lett. 118(5), 052401 (2021) 11. I. Koyuncu, A.T. Özcerit, The design and realization of a new high speed FPGA-based chaotic true random number generator. Comput. Electr. Eng. 58, 203–214 (2017)

Human Face, Eye and Iris Detection in Real-Time Using Image Processing G. Ranjith, K. Pallavi, and V. Mahendra

1 Introduction Face detection, retina detection and vein recognition are some of the biometric methods that may be used to identify a person. Every one of these parts of our body is vital. As a general rule, biometric methods are used in security applications. The eye is among the most prominent features of a human face, second only to the mouth. As a general rule, human identification and security systems rely on eye, facial and iris recognition technologies. For eye detection, first identify the human body’s face. Detection and identification of faces are also crucial. Their use ranges from interface to security to surveillance. Face, eye and iris parts are seen in this paper’s vivid picture. Images from live video are captured using Webcams. It is well known because eye regions are often darker than facial parts. As a result, we must establish a threshold value in just this case. In general, the human eye accounts for 10% of the face’s surface area. Many eye detection techniques have been published in the last few years. A grayscale picture was used for face recognition, and eye-analog segments were found by using size and intensity information. A unique geometrical connection was used to filter out potential eye-analogy pairs [1]. “The Iris” is an oval-shaped portion of both the eye. It is the human body’s most sensitive organ. Iris was utilized in a variety of applications. The iris’s primary purpose is to regulate the quantity of light that enters the pupil. Identity and security feature of both the iris. Pupil size may range from 10 to 80% overall iris diameter [2]. Overall accuracy with iris identification was considerably higher than that of fingerprint or voiceprint recognition. Radius, gradient, probability, etc. moments are already used in these G. Ranjith · K. Pallavi (B) · V. Mahendra Department of Electronics and Communication Engineering, MLR Institute of Technology, Dundigal, Hyderabad, Telangana, India e-mail: [email protected] V. Mahendra e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. K. Mandal et al. (eds.), Innovations in Signal Processing and Embedded Systems, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-19-1669-4_34

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techniques. For the detection of eyelid, upper and lower threshold, wilds suggested the use of a circular Hough transform. Daugman developed an inferno differential operator that determine the pupil, iris and eyelid [3]. It is suggested in this article that a technique be developed for automatically detecting the eyes and faces of humans, as well as detecting the Iris pattern in cropped eye images. For the identification of eye and facial parts in such a live picture, blob examination is employed. A webcam with a 12 MP resolution was used for this paper. Blob analysis offers several benefits over other methods of detecting eyes and faces, including: As a result, the processing time is extremely quick. In addition, the Circular Hough transform offers a number of benefits, such as being noise-resistant and easy to apply. A face and eye detection method is presented in Sect. 2. Detection of the iris was discussed in Sect. 3. In Sect. 4, you will see the outcomes of the experiments.

2 Face and Eye Detection 2.1 Proposed Flow Chart Human computer interfaces must also be able to recognize a person’s face and eyes. The skin-based blob examination is employed to automatically identify eyes and faces in a dynamic photo. Here is just a flow chart showing our algorithm for detecting the face and eyes of a person. Figure 1 shows the flow chart of eye and face detection. The following are the steps of the algorithm that has been proposed: 1. It is necessary to activate Webcam first before reading the input picture. A snapshot of the live video is then taken. 2. A next step is to change the Ycbcr picture to RGB. Because of the Ycbcr format, the web was created. As a result, we must convert it to RGB. Digital video, image processing and other applications utilize the YCbCr color system. 3. As a further stage, a bilateral filter is used to remove background noise. 4. This range containing RGB pixels can then be used to apply thresholding. R > 60 was its range of RGB pixels. Otherwise, its backdrop will be chosen if pixels exceed threshold value by a certain amount. 5. Skin-based detection is used in this phase. Face and eye images were detected using blob analysis. There are many ways to recognize numerous areas of certain kinds of linked pixels, and blob analysis is among the fastest and most efficient. 6. Eyes and face are ultimately detected automatically with this last stage. Cropped eyes are also detected.

2.2 YCbCr Image Video and image processing often utilize the YCbCr image. Luminance information is shown via a single Y component, two components of color, i.e., as well as cb

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Fig. 1 Flow chart of eye and face detection

and cr. Due to the fact that every web camera uses the YCbCr color model, we must first transform the data into RGB. YCbCr has a range of 0–255 pixels. Colors in the YCbCr model are scaled and offset. As specified by IEEE, the normal 8-bit range of the Luma component (Y ) is from 16 to 239. Use of skin-based detection known as blob analysis. This detects the presence of red upon on human body. Detect facial features, including the eyes and mouth. A new algorithm developed by Chai and Ngan [4] reveals the spatial features of human skin color. Upon on basis of the chrominance components of such an input picture, a skin color was generated to identify pixels that seem to be skin.

2.3 Blob Analysis In such a binary image, this blob analysis block calculates statistics about labeled areas. Skin-based detection is most commonly employed for blob analysis. To identify human eyes and face parts, blob analysis is indeed a quick and easy technique to use. The thresholding method is utilized in blob analysis. In this case, we must provide the RGB threshold value. Here is a chart illustrating the RGB color space in terms of intensity. A 120R200, a 100G160 as well as an 80B140 RGB value are chosen if it falls within the above-mentioned range. If it falls outside of this range,

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a 0 was selected. A threshold method is depicted in the picture (right). “0” and “1” are the values they provide.

2.4 Hough Transform The Hough transform is a feature extraction technique utilized in image analysis and processing [5]. The aim of the technique is to search out imperfect instances of objects at intervals for a particular category of shapes by a choice procedure. The classical Hough transform was involved with the identification of lines within the image, however later the Hough transform has been extended to characteristic positions of capricious shapes, most typically circles or ellipses. The Hough transform because it is universally used these days was fabricated by Duda and Hart in 1972, WHO known as it a “generalized Hough transform” [6] when the connected 1962 patent of Hough [7, 8]. The transform was popularized within the Internet community by Danu H. Ballard through a 1981 journal article titled “Generalizing the Hough transform to discover capricious shapes”.

3 Iris Detection 3.1 Proposed Flow Chart A vital organ in the human body, the Iris is indeed a vital organ. The iris is detected throughout all directions in this study (left, right, up, down). In the section above, we showed real-time detection for eye and facial parts. As soon as we have identified the face and eye, we may next determine the iris’s location. With cropped eye pictures, the circular Hough Transform is employed. Figure 2 shows the flow of IRIS detection. Here is a flow chart of both the suggested iris detection process. The suggested algorithm’s steps are: 1. During the first stage, the identified eye and facial portion was chopped to separate left and right eyes in various positions (left, right, upward, downward and Center). Web camera with 12 MP resolution was utilized for iris detection. 2. Following the cropping of the left and right eyes in various locations, the cropped eye picture must be cleaned of noise. 3. Find the Center of both the iris as well as the radius of something like the circle in these steps. 4. As a further step, the Proposed Approach is employed to determine the iris’s circumference (or circle). Noise does not affect the Hough transform. In order to increase the speed of both the procedure, the circle’s Center is constrained to the iris area of the eye. 5. In last stage, it detects iris of eyes image. The circular Hough transform is being used to identify the eye’s round shape. This method can be implemented extremely quickly and efficiently. This method uses a circular Hough transform to determine the pupil and iris radius and Center [9–14] and Ma et al. [15]

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Fig. 2 Flow chart of IRIS detection

all use the circular Hough Transform as the basis for their automated segmentation algorithms. When a circle’s perimeter is known, then Hough transform may be used to calculate the circle’s parameters. A circle of radius R and Center (a, b) may be represented by the parametric equations X = a + R cos(Θ) (2) Y = a + R cos(Θ) (3).

4 Experimental Results 4.1 Face and Eye Detection Result In order to get our experimental results, we used an Intel Core i5 CPU and Windows 7. For pictures, we have used a 12 MP webcam. With real-time pictures, the eyes and face are automatically recognized. Figures 3 and 4 show the output of face and eye detection.

4.2 Iris Detection Result Eye and facial part detection are shown in the above section. In this section, we will look at how iris detection works in various postures. Below is the experimental

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Fig. 3 Detect eye and face part in real-time

Fig. 4 Cropped eye and face part

outcome. The web camera is used to detect the iris in various situations. Intel Core is CPU and Windows 7 operating system are utilized in this part.

5 Conclusion Face and eye regions may be detected automatically, as well as iris detection, according to our findings. Eye and iris detection was performed using blob analysis and the Hough transform, respectively. Face and eye identification may be accomplished quickly using blob analysis. In addition, it has a shorter processing time than some other types of recognition techniques. With the circular Hough transform, we developed an iris recognition technique that adjusts to different eye locations. First, the eyes were identified in such a different location and automatically cropped to a

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smaller size. The circular Hough transform was used to determine the locations of the iris. Conclusion: The Hough transform is theoretically straightforward and easy to apply.

References 1. J. Wu, Z.H. Zhou, Efficient face candidate selector for face detection. Pattern Recognit. 36, 1175–1186 (2003) 2. J.R. Sekar, S. Arivazhagan, R. Anandmarugan, “Methodology for iris segmentation and recognition using multi-resolution transform, in IEEE-ICoAC 2011 (2011) 3. J. Daughman, New method in iris recognition. IEEE Trans. Syst. Man Cybern. 3(75) (2007) 4. D. Chai, K.N. Ngan, Face segmentation using skin-color map in videophone applications. IEEE Trans. Circuits Syst. Video Technol. 9(4), 551–564 (1999) 5. L. Shapiro, G. Stockman, Computer Vision (Prentice-Hall, Inc., 2001) 6. R.O. Duda, P.E. Hart, Use of the Hough transformation to detect lines and curves in pictures. Commun. ACM 15, 11–15 (1972) 7. P.V.C. Hough, Method and means for recognizing complex patterns, U.S. Patent 3,069,654, 18 Dec 1962 8. P.V.C. Hough, Machine analysis of bubble chamber pictures, in Proceedings of International Conference on High Energy Accelerators and Instrumentation (1959) 9. R. Wildes, J. Asmuth, G. Green, S. Hsu, R. Kolczynski, J. Matey, S. McBride, A system for automated iris recognition, in Proceedings IEEE Workshop on Applications of Computer Vision, Sarasota, FL (1994), pp. 121–128 10. W. Kong, D. Zhang, Accurate iris segmentation based on novel reflection and eyelash detection model, in Proceedings of 2001 International Symposium on Intelligent Multimedia, Video and Speech Processing, Hong Kong (2001) 11. C. Tisse, L. Martin, L. Torres, M. Robert, Person identification technique using human iris recognition, in International Conference on Vision Interface, Canada (2002) 12. U. Tiwari, D. Kelkar, A. Tiwari, Study of different IRIS recognition methods. IJCTEE (2010). ISSN 2249-6343 13. J.C.S. Jacques Junior, J.L. Moreira, A. Braun, S.R. Musse, Template-matching based method to perform iris detection in real time using synthetic templates (2010) 14. P. Telagarapu, B. Jagdishwar Rao, J. Venkata Suman, K. Chiranjeevi, A novel traffic tracking system based on division of video into frames and processing. IJCTEE (2010). ISSN 2249-6343 15. C.H. Morimoto, Automatic Measurement of Eye Features Using Image Processing (Department de Ciencia da Computacao, Brazil)

IoT-Aware Waste Management System Based on Cloud Services and Ultra-Low-Power RFID Sensor-Tags Chandrashaker Pittala and Kachala Ganesh

1 Introduction It is the way closer to social affair and moreover dividing strong waste to particular training with the reason that it will commonly be recyclable. Present situation of strong waste at several garbage dump arguments, significant regions of sturdy waste age are social orders, location locations and additionally in which individuals stay like faculties, colleges, enterprise centers. Waste from those locations consolidate food wastes, plastics, paper, glass, cardboard, steels, in reality dry, moist waste, novel wastes like lumbering own family contributors elements like devices, tires, batteries. Solid waste piece of these waste products waste organizing, arrest, and masterminding is most crucial concept to assemble defilement of metropolitan networks. So the motion for this hassle, we have got were given in reality proposed a further technique to cope with deliver together similarly to detach close by robust waste with loads a good deal much less gadgets similarly to which is probably a practical. This device consolidates 2 regions, one is waste collection similarly to second is waste confinement. Neighborhood waste is assembled from exquisite proprietors who can be prepared at social orders or at any type of place. Then, this set up squander is far flung by means of way of using the density of waste fabric. At the detail, while the waste is restricted, it is far used enough for reusing. In this paper, we have got were given advocated an important similarly to emblem-new method for network strong waste agency. Solid waste combination machine is completed the use of propels like IOTA and moreover cloud that makes out framework changed. Another framework we have were given absolutely proposed is density-based totally sturdy waste partition to be able to useful resource town authority with placing apart close by waste viably without hand-operated endeavors. Partition makes the reusing of the waste C. Pittala (B) · K. Ganesh Department of Electonics and Communication Engineering, MLR Institute of Technology, Hyderabad, Telangana, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. K. Mandal et al. (eds.), Innovations in Signal Processing and Embedded Systems, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-19-1669-4_35

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hundreds more simple and to tidy environment. We will absolutely count on to provide protection to astute booths which may be organized at numerous areas. Furthermore, we are able to interest in on waste automobile so it is going to accumulate most horrendous consume in improved time. Waste confinement system can furthermore be advanced with laser power density meter for effortlessness of significant position. The development of human beings for the community to the metropolis triggers an intensive percent of city strong waste unique in the Defraud metropolis. It is furthermore impacted the age with the useful resource of the wrong disposing and moreover the leaders of WM. An immoderate motion of all WM triggers damaging situations in addition to impacts the normal environment and also waters our bodies. Altering in lifestyle and moreover development from the wonderful technology of those social orders and growing inhabitants reasons an extended WM sum. The sturdy waste normally occurring out of homes, catastrophe middle retreats are taken at experience with motors and furthermore one of a kind automobile. As we remember the fact that the low-laying area is critical for the evacuation of WM further to cutoff elements interest. From the Defraud city, the sturdy waste commercial enterprise organization has in truth been a protracted way disregarded, and additionally, research has a look at along the environmental effect is masses less. Most of the non-public similarly to federal authorities commercial enterprise employer taking part with each precise to control and additionally manage in truth the WM software. Waste the leaders can be a critical restriction for urban districts in horticultural international places, derivable from the growing hobby of waste made, and relocated with the beneficial useful resource of human beings benefit, automation, as well as urbanization, likewise because the economic loads of waste organization similarly to nonattendance of specific restriction. Waste Municipal (WM) originates from business commercial enterprise and modern searches similarly to normally consolidate plastic, steel, paper, product, glass, and so on. In first globe countries, the concern of strong waste is efficaciously finished via green business enterprise approaches like waste adversity, reuse-reuse, further to coherent emptying. In a massive phase of the non-contemporary global places, the metropolitan robust waste manage technique goes up to now useless or probably vital. Such robust waste made has each time become a hazard to the all-natural elements. Unreasonable waste in our public is a superb sized issue for clinical problems as properly as it ought to be taken under problem. Our management has all started out diverse tidiness obligations but they will be no longer been shown enough to address this problem, and over-burden trash cylinders are but a tremendous mission in our each day lives. India is negatively induced thru the problems of trash the board and no longer having a right management framework is a notable hassle.

2 Literature Overview A lot of human beings are confronting scientific troubles due to the unmanaged garbage in addition to the rubbish the board form collectively with the location

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desires to roll out a few extremely good improvements within the present day form. This evaluation is predicated on IOTA similarly to have some feasible answer for the problems of over-burden garbage receptacles in our area. It is composed of numerous explorations in addition to computations that include software of several gadgets and moreover sensing gadgets each having their immediately art work similarly to such as within the course of the improvement of a shape that is meant to control trash boxes in our location. Following are the sizable advances carried out in with the resource of the scientists to figure out a immoderate diploma specialized answer for the concern of over-burden rubbish boxes the board: The shifting of humans for the city to the metropolis creates a huge movement of metropolis sturdy waste every day in the Defraud metropolis. It is in addition impacted the age with the aid of the usage of manner of the unwanted discharging as well as the board of MSW. An immoderate quantity of all MSW turns on hurtful health’s problems and moreover impacts the everyday habitat and moreover waters our bodies. Altering in manner of life and progression from the maximum up-to-date technology of these castes and moreover installing residents creates an stepped forward MSW quantity [1, 2]. The sturdy waste with the resource of and big growing from homes, facility lodges is moved with automobiles and additionally furthermore several exclusive car. As we keep in mind that the low-laying district is beneficial for the elimination of MSW and manage interest. From the Defraud city, the strong waste control has been plenty left out and studies along the all-natural have an impact on is a lousy lot plenty less. The majority of the specific in addition to federal government enterprise walking with each exquisite to govern and moreover manage in fact the MSW program [3– 6]. Squander the board can be a large obstruction for town areas in non-business countries, resulting from the developing improvement of waste made, and relocated through humans advantage, automation, similarly to urbanization, definitely due to the fact the financial weight of waste control and moreover lack of particular functionality. City solid waste (MSW) radiates from organization and moreover mechanical pastimes and additionally normally integrates plastic, steel, paper, fabric, glass, and so on. In first global nations, the concern of strong waste is well overseen via powerful manage systems like waste distress, reuse-reuse, and moreover rational removal. In the large majority of the agricultural international locations, the cosmopolitan robust waste control approach is as but inefficient or even essential. Such sturdy waste made has at any sort of aspect grown to be being a chance to the ecological components. Impromptu and furthermore nonscientific removal part of strong waste turns on corruption of untainted assets making stamina-related troubles. Waste the board is a check to commonplace stress in recent times. Numerous manufacturers have finished research on waste the leaders and additionally separation. It is visible that a whole lot of evaluation is accomplished using IoT [1], cloud, and photograph getting organized [2] for waste combination and its detachment, but a huge little bit of structures make use of numerous sensing devices to expose show waste field and moreover besides special regulations are belief approximately for waste seclusion. Numerous open techniques are gloomy and are not realistic. This is the inducement of our suggested artwork to diminish time and fee of waste plan and confinement. This paper shows

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IOTA-based totally custom designed sturdy waste plan [3] and furthermore disconnection [4] machine which gathers robust wastes from the general population or one-of-a-type dustbins via the use of sensing unit. All fashionable population dustbins are associated with Raspberry Pi 3 B microcontroller [5] with the assist of composed wife thing of Raspberry Pi, repute of dustbin is dispatched waste the board professional. As requirements are lose the leaders professional will send out trash truck to assume that waste. At the point on the identical time as waste gathered, every waste cloth is dispatched off transport line especially. Transportation line is attached with density meter [6]. Up-to-date thickness meter is to make use of to figure density of each waste fabric. Determined thickness of waste product is made up with density of product, and it is conserved in waste the board facts base. As such waste detachment is finished, and detached waste may be then cared for in numerous canisters for reusing. Geospatial evaluation centers round disposing landsite secondary making the rounds community this town. Remote acknowledging and GIS have a crucial impact inside the remedy of solid waste. Remote acknowledging aids you with growing low-fee regions for waste disposing making use of satellite TV for PC imagery. The crucial advantage of satellite TV for PC a long manner figuring out is its very personal uninspiring in addition to quick interest; this is outstanding for incredible offers of evaluation employees in city arranging. Advancement in COMPUTER planning has truly dispatched GIS as powerful applications in trash expulsion the leaders. GIS has in reality evolved as a described development to govern in addition to separate geological records as well as is a useful asset for get-together, getting, detailing, and additionally reworking spatial facts from the certifiable globe [7–9]. The expulsion landsite region want to consider economic, natural, and plot use highlights in the community and furthermore verification prosperity. Benefits of GIS encompass higher instructions the board, better kind analysis, and capability to attain vital troubles and additionally collect challenge functionality. GIS increase the journeying time and use pastime even as extra generating exactness. Making use of GIS in sturdy waste business enterprise is normally important as diverse elements of its implementation astoundingly rely on spatial statistics. The best justification GIS will sincerely be to interact spatial dynamic in 3-dimensional or 2 situations. When uncertain, GIS has an elegant effect in monitoring their information to deal with statistics.

3 Existing System Present existing framework of e waste management system does not have location tracking, dustbin full alerts system and more over no remote tracking access like Internet of things. Due to this time delay process, people get suffocated through this type of system. No monitoring of regular schedule of e waste pickup by truck. To overcome all the limitations of exist system, we integrate to develop RFID, IoT-based smart e waste management system for remote monitor and regular service monitor.

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Fig. 1 Block diagram

4 Methodology In the proposed system, we implementing IoT-aware waste management system based on cloud services and ultra-low-power RFID sensor-tags. This system will manage the daily waste collection system and update to the government. This is good governance policy to daily waste clean in cities. This proposed system integrated with Raspberry Pi processor, radio frequency identification card, weight sensor called load cell module, buzzer, and LCD and IoT server. RFID reader attached to truck and card is attached to dustbin. When the reader reads the card that means e waste vehicle came to duty and cleaned the dust at that position and final weight of the dustbin is updated. This total information of truck, bin, weight, time date will upload into IoT server for easy remote monitoring (Fig. 1).

5 Functional Module 5.1 Regulated Power Supply Regulated power supply is used to produce the required operating voltage for this proposed system. Normally, this system is converts 230 V AC voltage to the required 5 V DC voltage for system operation (Fig. 2).

5.2 Raspberry Pi We used RPI processor for impending this application. RPI belongs to the ARM 11 family with 40 GPIO pins used for both input and output purpose. This processor

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Fig. 2 Regulated power supply

Fig. 3 Raspberry Pi

have 1 GB RAM with 16 GB HDD slot for OS. Rasberian operating system used for the developing application. This is the high-end performance among remaining controllers (Fig. 3).

5.3 RFID Reader and Tag We are using a passive RFID tag which stores the patient’s information, the tag gets active only when it comes in contact with the reader, a reader has antenna which emits radio waves (Fig. 4).

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Fig. 4 RFID reader and card

5.4 LCD Monitor Liquid crystal display used to display the parameters for status of the proposed system. This can display 32 characters having 2 columns. When each sensor is activated corresponding massage will be displayed in 16 * 2 LCD modules. In this, we use four data pins; using this pins, we transfer the data from micro preprocessor to LCD (Fig. 5). Fig. 5 16 × 2 LCD

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Fig. 6 Buzzer

Fig. 7 ESP 8266

5.5 Buzzer Buzzer is the output module for alerting of any parameter changes. If any sensor increases the threshold value or if increases, then microcontroller alerts us by using this system (Fig. 6).

5.6 IoT-Module Internet of things used for controlling any device or monitoring the device status through Internet. This proposed system we use this IoT module for taking the all parameters data and post into the cloud called server. ESP8266 modules as IoT module it can operate through Wi-Fi frequency concept (Fig. 7).

5.7 Software Software is the important parameter to make the device automation. In proposed implementation, we used Python programming language and compiler Python IDE we used. Rasberian operating system used for working on Raspberry Pi processor. Here, we used Python IDE software for programming write up and execution of entire system.

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Fig. 8 Load cell

5.8 Load Cell Load cell is act as transducer module translates mechanical stress or force to electric signal output. This is measure the weight of the loads. This sensor finds the weight and gives to Raspberry Pi processor, and it detects the waste management truck weight and update into IoT server (Fig. 8).

5.9 Software Python and machine learning-based algorithms used for the developing of proposed framework. Python IDE used to write code and compile. Rasberian operating system will used for developing the desired model.

6 Results We designed and implemented hardware model of vehicle collision avoidance system through RPI microprocessor, load cell, IR, buzzer and ultrasonic sensor, LCD module. All modules are integrated to controller shown in Figs. 9, 10, and 11.

7 Conclusion and Future Work This paper focuses on the daily garbage produced by the people and the management of the garbage according to the population. This is used for easy management of the garbage in the City Corporation/smart cities. We have developed an IoT, RFID, and

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Fig. 9 Implemented hardware model

Fig. 10 Output on LCD

weight sensor-based remote garbage monitoring system with everyday dt logging system which provides instant alert messages to the admin and worker for the immediate clearance. The proposed architecture helps to understand the requirement for deploying this kind of waste management system in real world. On the garbage level reaching its maximum, in future, these block chain-based garbage collection system can be used in multi-store apartments and industries as these transaction gateways send a safe and secure connection to the account holder, and the micropayments are based on the weight of the garbage produced by the user.

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Fig. 11 Output on IoT server

References 1. http://www.sviva.gov.il/English/env_topics/Solid_Waste/FactsAndFigures/Pages/WasteComp ositionSurvey.asp 2. S.Q. Cai, On the legislation of circular economy. J. Nanyang Teach. Coll. 4, 3–4 (2005) 3. G. Yaswanth Sai, A. Deepak, Smart garbage monitoring and controlling using internet of things. Int. J. Adv. Sci. Technol. 29(7), 219–226 (2020) 4. Y.F. Lu, X.J. Sun, Discussion on the countermeasures for classification and collection of municipal solid waste in China. Environ. Sanit. Eng. 22(3), 4–6 (2002) 5. X.Y. Yi, Discussion on the treatment of urban garbage. Environ. Manage. China 12(1), 3–5 (2010) 6. J. Li, L. Hua, X.J. Wang, Analysis and comparison of domestic refuse treatment at home and abroad. J. Capital Norm. Univ. 25(3), 73–79 (2004) 7. W. Hao, The classification and collection of municipal solid waste is imperative. J. Tianjin Inst. Urban Constr. 6(1), 114–117 (2010) 8. J. Yu, Legal research on classification of municipal solid waste in China. Environ. Sci. Manag. 4, 13–15 (2009) 9. F. Lu, How to fight domestic garbage. Tsinghua Univ. 5(1), 3:15–22 (2000)

Vehicle Collision Avoidance System Using V2I Protocol in Vehicular Ad Hoc Network P. Anjaneyulu and M. Aswanth Manindar

1 Introduction Various sensing devices are included right into the lorry to assess numerous real limits like speed upward thrust, charge, and additionally range from the close by vehicle. These obstacles are shown at the human maker user interface gizmo that is presently connected with the lorry. Document of alarm/phrase messages, unique actual limitations assists with keeping far from incorrect tasks of the motorist which thusly forestalls the occasion of blockage controls. Different lorry packages comprise efficient Website online site visitors signal, dynamic road nicely-being, and so on, and the primary reason is to remove automobile crashes and additionally to provide congestion without control, using pressure-at ease climate via sharing records at the chance of congestion controls in addition to deterrents. To widen the vehicle, riding pressure’s insight with the aid of allowing him/her to react considerably hundreds greater unexpectedly is the vital concept. VANETs are taken care of like an unrivaled kind of MANETs, in which each facility is taken into consideration as a vehicle (as an instance car, car, and additionally transport). These systems have in reality confronted brand-new troubles. These systems are portrayed with excellent facility flexibility as well as location contrasts which can be poverty-suffering from strategies for away progressions. These functions make VANETs remarkably slanted to transmission bungles, this geography varies and uncertain business company. It is because of an effect on speedy charge of facility points, appreciably awesome functioning occasions. So the equal old goals of those VANETs require attaining fantastic package deal movement fee and lots less employer passivity. In maximum recent numerous years, P. Anjaneyulu · M. Aswanth Manindar (B) Department of Electronics and Communication Engineering, MLR Institute of Technology, Hyderabad, Telangana, India e-mail: [email protected] P. Anjaneyulu e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. K. Mandal et al. (eds.), Innovations in Signal Processing and Embedded Systems, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-19-1669-4_36

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car builders have empowered to automobiles to make and additionally stall even more facts, irrespective of the fact that this data related remarkably alongside single car. Because of VANETs grounds, automobiles received the limit of interfacing with each other to organize institutions which establishments for optimum current years have organized within the turnpikes (moreover lotion combo of them is feasible). Roadside device can be made use of in a similar manner like a way to Internet. Such as this statistics and placing facts are to be collected and readied to the aspect further to appearance after few spots (appropriated computing). Correspondence wants to be feasible either in reality within the middle of vehicles and vans like single bounce belonging or automobiles can re-deliver facts, thusly ruling multi-skip having an area. Apart from bundles, furthermore decided on one approach or 2 manner or multicast applications. Because of hard demanding situations, VANETs attend to a producing a long manner-off increase, permitting skillful file among lorries in addition to repaired devices set up along the street with an unbelievably calming region of safety and protection, site visitors sign a purchaser packages. Continuous pursuit exams out brand-new show stacks as well as business organization designs to viably oversee traumatic problems. VANET is essentially a subset of the mobile ad hoc network [1–3] installation to make a far off report framework for motors. Cars, nowadays, are ready with eager as well as automatic frameworks to strive consistent nicelybeing requirements. VANETs consist of V2V, V2I, vehicle-to-grid, and moreover vehicle-to-anything strategies of correspondence. Autos in VANET are collaborated with on-board-unit (OBU), road-aspect-unit (RSU). OBU as well as RSU percentage data with the ways flung community. OBU handles the info transmission from one vehicle to an extra, at the same time, as RSU conveys the data from vehicles to road facet commercial enterprise business enterprise. The OBU talks with RSU and moreover with various different OBU with dedicated-short-communication-range. DSRC equips fast, decreased idleness V2V, and V2I files, making use of the IEEE 802.11 p and additionally wireless access in vehicular environment (WAVE) requirements. Intelligent transport shape (ITS) is finished by means of utilizing VANET. ITS collaborates with innovative Internet Web page visitors the professionals capacities the usage of wonderful clever strategies of conversation and additionally groups. VANET is tougher as assessment with MANET as hubs are of excessive realistic and additionally geography adjustments strongly. Because of this, it is far honestly defenseless in preference to various type attackers. On the off possibility that the aggressor is part middle prepared for interfacing with diverse different employer customers, he is going to genuinely be viewed as a gatecrasher and will truly intend to assault in an unexpected way. The out of doors hub is not always confirmed to move over straightforwardly with other enterprise business enterprise people. A pernicious style clothier utilizes numerous techniques to harm the facilities of the participant and moreover the company without finishing their very own advantage. Nonetheless, legitimate aggressors assume that the moves have to make the maximum their very own. The effective aggressor will simply generate emblem-new parcels to harm the organization at the same time as an unresponsive aggressor will genuinely sincerely creep the far flung drift, but cannot produce new bundles. Vane has numerous forms of

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attacks: simulate, satisfying taking pictures, region following, renouncement, administration disavowal, guiding assault, and extra guiding assaults misuse the guiding conventions of the agency layer. Therefore, steering attacks the enemy preserve-use of americium confuses the directing form on this design or loses the groups parcel.

2 Literature Survey Various vehicle applications combine powerful Website traffic moderate, colorful avenue health, and properly being, and further, the idea intention is to remove automobile crashes in addition to present a sans twist of future, driving pressure-safe weather with the beneficial aid of sharing statistics on the danger of accidents and obstacles [1]. To increase the chauffeur’s discernment through allowing him/her to react significantly greater swiftly is the smooth belief. VANETs are handled like a great shape of MANETs, in which each center factor is considered as an automobile (for instance truck, lorry, and furthermore transportation). These frameworks have confronted new boundaries. These structures are demonstrated with outstanding center versatility and region contrasts which can be broke thru strategies for a long way flung dispositions [2]. These competencies make VANETs extraordinarily willing to transmission botches, and this region varies and furthermore unforeseeable enterprise. It is due to the impact of brief speed of centers, notably excellent functioning times. So the important dreams of those VANETs require reaping greater special bundle motion charge and furthermore lots a whole lot much less team country of no pastime. In most cutting-edge-day years, car manufacturers have empowered to automobiles to make and also stall greater data, irrespective of the fact that this facts related quite alongside lone automobile [3]. VANETs are controlled like an unrivaled shape of MANETs, wherein every center is idea about as an automobile (for instance car, car, and transportation). These frameworks have challenged new troubles. These frameworks are portrayed with brilliant facility versatility and additionally region contrasts that are broke via strategies for some distance off progressions. These capabilities make VANETs exceptionally willing to transmission bungles, and this area differs in addition to unpredictable business corporation. It is due to the effect of quick speed of middle factors, considerably terrific going for walks scenarios [4]. So the vital goals of these VANETs require building up remarkable plan movement charge and moreover plenty a great deal much less organization use of no interest. In most current years, car makers have organized to vehicles to make and put off more records, regardless of the fact that this info associated quite alongside single automobile. Due to VANETs grounds, motors have been given the restrict of interfacing with each different to prepare facilities which organizations for optimum present day years has organized within the turnpikes (moreover lotion mixture of them is possible) [5]. Roadside tool can be implemented in a similar way like a manner to Internet. Like this, data and installing information are to be amassed further to readied to the issue and appearance after just a few locations (appropriated computing). Document needs to be viable each simply inside the center of automobiles and vehicles like single

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jump having an area or automobiles can re-supply statistics, thusly ruling multi-pass belonging [6]. Besides programs moreover decided on one method or more technique or multicast software program. As a forestall end result of hard demanding situations, VANETs deal with a growing far off growth, allowing skillful correspondence among automobiles and moreover sorted devices organized alongside the road with a completely comforting place of protection, Internet site online Website traffic sign a client programs. Constant search examines new show masses and moreover company designs to viably appearance after annoying issues. VANET is essentially part of the mobile ad hoc network installation to make a far off communication framework for vehicles. A plenty more inexperienced version has truly been created through manner of San-Ian Soul [7] that examines working and moreover effectiveness of DSRC sensors in addition to gadgets as well as furthermore gages the general well-known ordinary overall performance in the course of growing conditions, using some distance off blockage control coverage framework. The layout is moreover implemented to select out the development of vehicle polarities. To declare the adaptability of the car, Greenburg logarithmic format is gotten. A courting is set up among vehicles in entice 22 state of affairs situation. The format aides in recognizing probabilities of backside blockage control that motions particular same way. During easy scenarios the while, the automobile cannot, to get the wonderful facts, the risk is determined as requirements be [8]. The FCC has definitely given array band of 5.9 GHz for the utilization in intelligent transport system (ITS). Around August 2008, an array is used simply of forty MHz within the band of 5.9 GHz for canny delivery framework through manner of the famous European Telecom Requirements Institute. By 2003, Japan similarly to Europe used it for digital cost range [9]. In Japan, Europe, surely committed short reap conversation frameworks are incongruent and additionally encompass many large deviations infrared, numerous conventions, and particular baud charges. To supplant its ERP1 costs gantry approach, a few method plans for Electronic Roadway of Singapore had genuinely been finished to utilize dedicated quick acquire record development for avenue nicely-being computations. The blessings of software program software are several in nature much like the reproach framework for cars in disaster conditions, appropriate in advance blockage control word, to be had bendy journey manage, blockage control evasion at any shape of crossroads, techniques for notification in any shape of disaster for vehicles, safety, and safety assessment for vehicles, sharing messages on name for in case of any form of car scenario, commercial enterprise commercial corporation organization car flexibility further to correctly-being checks, installations of leaving online, in-car noting rollover cautioning, collection of creation records, notification at a roadway rail crossways, assortments of digital charge [10]. During the previous numerous years, intelligent transport system (ITS) has in truth professional diverse intervals of modern approach; additionally thinking about its presentation in an ongoing passing right into development to get well the very super consequences is broadening significantly. Already, topics of intelligent transport system (ITS) are trusting stable ground. USA and moreover some European global places started to perform similarly to utilize them. The structure is not installation properly with limitless problems within the non-business employer countries (version price of basis) [11]. This day experience

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paper specifically focuses on the execution of V2V file so one can certainly be used freely for a format of ITS in the non-corporation nations without RSUs framework to overcome the already tough issues. To fulfill the final response, a reenactment for several (VANET) guiding conventions completed the use of Ponte check device to choose the best of guiding convention for the execution of vehicle-to-vehicle report. The very top notch guidance convention for automobile to lorry document is based definitely upon the key performance indicators in addition to point of view will genuinely examine and made use of to divide among precise designs, one is an execution of a vehicle to lorry with street component device, and moreover, the several different one is proposed implementation of an vehicle to automobile because it were. In farming global locations, the outcomes show that the suggested format of its trusts car to lorry genuinely without avenue component machine is having the ability of ITS implementation [12]. The majority of the transmitting formula which might be carried out is non-adaptive in nature. These algorithms are usually targeted on computing the shortest direction which the automobile requires to take in order to attain from useful resource to vacation spot. Although this offers a wonderful technique to the clients, but the problem of being caught in net Internet Website visitors even as taking the shortest course stays a problem. Traffic sign manipulate and moreover management structures have continuously perception of the concept of the use of synthetic intelligence [13]. Nonetheless, they were in no way ever in truth deployed in real time due to the reduced calculation powers. With the improvement in innovation and improvement of emblem-new systems, the intro of graphical processing systems resolves the issues. Making use of loop detectors in addition to magnetic detectors on streets has been used traditionally in a few global locations.

3 Existing System In existing system model, there is no automation for avoiding the accidents. Normally in school, hospital, old age homes, vehicles are travel with very high speed and it causes accidents. Due to that existing model, we loss the human life and crashes of vehicle. Many of the safety systems used in current vehicles does not help in reducing accidents as the many of the roads does not have a planned infrastructure and due to rash driving, hence a global and an extensive system are needed to satiate these problems. To avoid the limitations of present system, we introduced. The RF communication technology used to solve many of the problems today, as it communicates the captured data to the nearby vehicle in a faster and uninterrupted manner.

4 Proposed System At the point when the vehicle enters the speed confined zones like schools, universities, medical clinics, and so on, and if the beneficiary in the vehicle gets the sign,

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it checks the microcontroller whether the speed of the vehicle is lesser than the edge speed. On the off chance that the speed is more noteworthy than the edge esteem, it conveys message to the engine driver and thusly the engine driver controls the speed of the engines. Also, when the vehicle crossed the speed limited zones, the recipient is killed so the vehicle can be moved in the typical speed as of the driver. However, continuously, we cannot diminish the speed of the vehicle utilizing the engine driver. To lessen the speed of the vehicle, DC engines control the speed of the vehicle. When the collector gets the sign, the microcontroller will convey the message to the DC engine. This framework will diminish the mishaps which are caused due to over speed (Fig. 1). The transmitter vehicle unit consists of a transmitter RF module called TX, and the receiver vehicle unit consists of a receiver RF module called the RX, the communication between the RX and TX happens once the ARM is connected to the power supply (Fig. 2). The RF receiver on receiving the signal sends the received signal to the decoder to convert the signals to digital format and makes the pin high. When RF-3 is made high, the ARM communicates with both the LCD display and the driver. The ARM sends the data to the LED display and it displays “brakes have been applied”, and the ARM sends the data to the driver and driver which is connected to the relay restricts the electrical signal to a low-power signal and slows down the motor. Hence, alerting Fig. 1 Transmitter block diagram

Fig. 2 Receiver block diagram

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Fig. 3 Regulated power supply

the vehicle behind that the vehicle in the front has applied brakes and slows down the engine to avoid a crash.

5 Functional Modules 5.1 Regulated Power Supply Regulated power supply is used to produce the required operating voltage for this proposed system. Normally, this system is converts 230 V AC voltage to the required 12 V DC voltage for system operation. In this project, we use 12 V batteries to operate and use RPS to charge the battery (Fig. 3).

5.2 ARM 7 Controller The ARM 7 family of microcontrollers from STMicroelectronics is based on the ARM Cortex-M 32-bit processor core. ARM 7 microcontrollers offer a large number of serial and parallel communication peripherals which can be interfaced with all kinds of electronic components including sensors, displays, cameras, motors, etc. All ARM 7 variants come with internal flash memory and RAM. The firmware was developed using Keil IDE. To start with firmware development process, it is required to set up the IDE first. IDE is loaded with proper service packs and device drivers. Here, drivers for STPM33 energy meter chip were used which are provided by manufacturer itself. Based on the driver files, there are three different layers of software needed to be implemented as a part of firmware development. The block diagram describes the three layers used for the development of firmware (Fig. 4).

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Fig. 4 ARM 7 controller

Fig. 5 16 × 2 LCD

5.3 LCD Monitor Liquid crystal display used to display the parameters for status of the proposed system. This can display 32 characters having 2 columns. When each sensor is activated corresponding massage will be displayed in 16 * 2 LCD modules. In this, we use four data pins; using this pins, we transfer the data from micro preprocessor to LCD (Fig. 5).

5.4 RF Transceiver RF transceiver module contains transmitter and receiver module. Both can act as bidirectional way. Every section of module contains four pins voltage, ground, TX, and RX. It transmits data for longer distances. It covers 50 m surrounding the area. It operate with 5 V DC supply (Fig. 6).

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Fig. 6 RF transceiver

Fig. 7 Motor

5.5 DC Setup Motor DC motor here used as robot wheel control mechanism. This proposed system we used two DC motors as robot vehicle which automatically speed control for accident avoidance. This robot is controlled forward and back with the help of L293D device driver (Fig. 7).

5.6 Buzzer Buzzer is the output module for alerting of any parameter changes. If any sensor increases the threshold value or if increases, then microcontroller alert us by using this system (Fig. 8).

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Fig. 8 Buzzer

5.7 Software Software is the important parameter to make the device automation. In proposed implementation, we used Python programming language and compiler Keil IDE we used. Here, we used Keil IDE software for programming write up and execution of entire system.

6 Results and Discussion We designed and implemented hardware model of vehicle collision avoidance system through ARM 7 microcontroller, DC motor, buzzer, and RF module. All modules are integrated to controller shown in Fig. 9. The proposed system works with Keil software, we need to program the code and compile in Keil IDE after debug, we dump code into ARM controller. This takes few seconds time to execute the required outcome. Once power on the system both RF transmitter and RF receiver will activate, then it share the data depending on the activation of the module we categorized as vehicle presents in the zone, when the vehicle presents in the zone motor speed automatically decreased to avoid collisions between other vehicle. Both RF modules activated this case we considered as vehicle in zone condition, this will alert us through buzzer for avoid collision (Fig. 10). When the RF transmitter section is completely off mode and RF receiver be online it seems that no data received from transmitter, we consider this mode is no vehicle in the zone. In this time, motor speed continues as their own speed. No speed reductions. Everything data will be displayed into the system on LCD module for driver activation and status updating (Fig. 11).

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Fig. 9 Implemented hardware model

7 Conclusion We designed and implemented vehicle collision avoidance system with wireless network protocol RF in VANET zone. This system is very efficient to vehicleto-vehicle communication to save the driver safety. The wireless communication between nearby vehicles can be helpful in reducing the vehicle crashes and therefore ensuring road safety. This also ensures that the vehicle driver follows proper lane discipline and can improve their response to emergency situations. This system having RF module to void the accidents, void collisions between the vehicles in VANET network. Both RF transmitter and RF receiver will activate then it share the data depending on the activation of the module, we categorized as vehicle presents in the zone, when the vehicle presents in the zone motor speed automatically decreased

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Fig. 10 Output when vehicle is in zone

Fig. 11 Output when no vehicle in zone

to avoid collisions between other vehicles. RF transmitter section is completely off mode and RF receiver be online it seems that no data received from transmitter, we consider this mode is no vehicle in the zone. The proposed system works with Keil software, we need to program the code and compile in Keil IDE after debug, we dump code into ARM controller. This takes few seconds time to execute the required outcome.

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References 1. N. Al Abdulsalam, R. Al Hajri, Z. Al Abri, Z. Al Lawati, M.M. Bait-Suwailam, Design and implementation of a vehicle to vehicle communication system using Li-Fi technology, in 2020 International Conference on Information and Communication Technology Research (ICTRC2015) 2. G.C.P. Mallikarjuna, R. Hajare, C.S. Mala, Design and implementation of real time wireless system for vehicle safety and vehicle to vehicle communication, in 2019 International Conference on Electrical, Electronics, Communication, Computer and Optimization Techniques (ICEECCOT) 3. D. Bohmlanderx, S. Hasirlioglu, V. Yanoyz, C. Lauererl, Advantages in crash severity prediction using vehicle to vehicle communication, in Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (2015) 4. N.G. Ghatwai, V.K. Harpale, M. Kale, Vehicle to vehicle communication for crash avoidance system, in 2018 International Conference on Computing Communication Control and Automation (ICCUBEA) (2016). https://doi.org/10.1109/iccubea.2016.7860118 5. V. Kodire, S. Bhaskaran, H.N. Vishwas, GPS and ZigBee Based Traffic Signal Preemption, Amrita School of Engineering, Bengaluru, India (IEEE 2015) 6. G. Rakesh, M. Belwal, Vehicle collision avoidance in a VANET environment by data communication, in Proceedings of the Third International Conference on Computing Methodologies and Communication (ICCMC 2019) 7. V. Vibin, P. Sivraj, V. Vanitha, Implementation of in-vehicle and V2V communication with basic safety message format, in 2018 International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore (2018), pp. 637–642 8. H. Ito, T. Murase, K. Wako, K. Saszjma, Crash warning for intersection and head-on car collision in vehicle-to-vehicle communication, in International Conference on Connected Vehicle and Expo, vol. I (Tokyo Institute of Technology, 2017) 9. A.S. Kumar, T. Sasikala, Integrating safety and security in vehicle-to-vehicle communication using IOT. Int. J. Adv. Sci. Technol. 29(04), 6989–6997 (2020) 10. F. Li, Y. Wang, Routing in vehicular adhoc networks: a survey, in IEEE Vehicular Technology Magazine, June 2020 11. J. Owusu, M. Joseph, Light fidelity (LiFi) as an alternative data transmission medium in VANET, in IEEE European Modelling Symposium (2019), pp. 213–217 12. N. Alsaffar, W. Elmedany, Application of RC5 for IoT devices in smart transportation system, in International Conference on Modeling Simulation and Applied Optimization (2019), pp. 01–04 13. A. Arora, A. Mehra, Vehicle to vehicle (V2V) VANET based analysis on waiting time and performance in LTE network, in Third International Conference on Trends in Electronics and Informatics (2019), pp. 482–489

Progressive Convolutional Recurrent Neural Networks for Speech Enhancement S. China Venkateswarlu, M. Renu Babu, G. Chenna Kesava Reddy, and D. Vemana Chary

1 Introduction Speech processing is an application of neural networks, where different types of network architectures: convolutional neural networks (CNNs), residual neural network (ResNet), recurrent neural networks (RNNs) and general adversarial networks (GANs) are used in speech enhancement to enhance the noisy signal, i.e., performing denoising and dereverberation on the given input. The results produced are impeccable and the usage of deep learning techniques for speech enhancement have made quality of life improvements to many devices: noise reduction techniques in smartphones, highly effective audio manipulation in multiple software tools, real-time speech enhancement among many others. The progressive technique is a promising methodology to revise network implementations for speech enhancement purposes. Newer architectures such as progressive convolutional neural networks (P-CNN) or progressive residual neural network (P-ResNet) have already proved the true potential of the progressive technique by greatly improving the speech quality and speech intelligibility through denoising and dereverberation. Expanding the technique to the recurrent neural network architecture which is more well suited for dealing with audio produces better results. However, using best of both convolutional networks and recurrent networks, by combining the key characteristics of the respective architectures, i.e., using a progressive convolutional recurrent neural network (P-CRNN) produces a highly efficient and highly effective solution which can be deployed in highly resource sensitive hardware with ease. This study delves into the P-CRNN implementation for Speech Enhancement. S. China Venkateswarlu (B) Institute of Aeronautical Engineering, Hyderabad, India e-mail: [email protected] M. Renu Babu · G. Chenna Kesava Reddy · D. Vemana Chary TKR Engineering College, Meerpet, Hyderabad, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. K. Mandal et al. (eds.), Innovations in Signal Processing and Embedded Systems, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-19-1669-4_37

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CNN is a feed forward network best suited for fixed size inputs with minimal amounts of preprocessing, whereas RNN is suitable for temporal (or sequential) data, moreover it can handle arbitrary input/output lengths. CNNs use connectivity pattern between neurons while RNN uses time-series information—what a user spoke last will impact what he/she will speak next. The higher correlation between points allows for an efficient approach for handling audio inputs [1]. This study focuses on the limitations of CNN SE implementations [2] and the optimization of RNN SE implementation for better results. The convolutional recurrent neural network (CRNN) is the combination of two of the most impactful and effective neural networks, which generates optimal results especially toward audio signal processing. However, SE can be further improved with the progressive implementation of a CRNN where the noisy input signal is enhanced gradually at each stage, resulting in highly effective enhanced results [3].

2 Literature Survey There has been a massive accumulation of human-centric data to an unprecedented scale over the last two decades. This data explosion coupled with rapid growth in computing power have rejuvenated the field of neural networks and sophisticated intelligent system (IS). In the past, neural networks have mostly been limited to the application of industrial control and robotics. However, recent advancements in neural networks have led to successful applications of IS in almost every aspect of human life with the introduction of intelligent transportation, intelligent diagnosis and health monitoring for precision medicine, robotics and automation in home appliances, virtual online assistance, e-marketing, and weather forecasting and natural disasters monitoring, among others [4]. Speech can be considered as the most well-known and attractive medium for humans to speak with one another. In addition to the inter-human communication, speech has discovered a ton of utilizations in the human–machine interface, on account of the progressions in technology throughout the long term. For any communication medium, it is consistently attractive that noise has almost no impact on it and that too for a medium as famous as speech, it should retain unaffected by noise. Yet, keeping an eye on the different kinds of noises and their sources, it is hard to procure a speech that is liberated from noise. Along these lines, the issue is evident that the impact of noise must be made zero or the minimum conceivable given a speech accessible has been noisy, corrupted by noise. The research in this perspective is prominently known as speech enhancement. On the other hand, speech enhancement can be defined as a cycle utilized for improving perceptual parts of speech like quality, intelligibility, or level of listener fatigue [5]. Data scientists and engineers are working toward improving the deep neural network speech enhancement (DNN-SE) landscape through the use of modern techniques and optimizations for the present solutions to provide more accurate and highly effective solutions. The DNN-SE methods originally constituted of CNNs or ResNets

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but were expanded to include RNN which were specially designed for temporal data. RNN implementations of SE included long short-term memory (LSTM) models, gated recurring unit (GRU) models or a powerful convolutional recurrent neural network (P-CRNN) models. Progressive implementations of the existing neural network implementations through loss function optimization are one of the contemporary methods in the speech enhancement landscape [2]. The new technique provides higher accuracy and better distortion removal. Progressive approach deals with step-by-step enhancement of the input signal with higher correlation between two consecutive stages. This study deals with progressive convolutional recurrent neural networks (P-CRNN).

3 Existing Method The existing methods use a progressive feed forward networks on the noisy input signal by working on the whole sequence at once based on patterns between neurons and producing enhanced output as shown in Fig. 1. Figure 2 describes the basic Fig. 1 Existing method, a progressive approach for DNN-SE

Input (noisy signal) Neural Network

Input Layer

Network Layer 1

Network Layer 2

Network Layer N

Classification Layer

Output signal (Speech Enhanced Signal)

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Fig. 2 Basic convolutional block of the network [2]

convolutional block of the network, which is constitutes of two successive identical structures. The structure comprises a batch normalization layer followed by a parametric rectified linear unit (PReLU), and a 1-D-convolutional layer having the same number of channels for both input and output. The output obtained using this structure maintains the dimensions of the input. Thus, a partially enhanced signal could be obtained at every block output of P-CNN. The loss function of the network can be computed using the mean square error (MSE) between the log spectral amplitude (LSA) of the reference and LSA of the enhanced signal, D−1  2  1  yd,η,τ − xˆd,η,τ MSE yη,τ , xˆη,τ = D d=0

(1)

where D is the dimension of the input signal, yd,η,τ , xˆd,η,τ are the frequency bins of the logarithmic spectrum at training example η and frame τ. yη,τ is the target vector of the clean LSA reference, and xˆη,τ is the reconstructed vector of the enhanced signal. Then the loss function is given by,

Progressive Convolutional Recurrent Neural Networks for Speech … N −1 T −1     1  1  J Y, Xˆ = MSE yη,τ , xˆη,τ N n=0 T τ =0

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(2)

which is computed by summing the MSE of the LSA over all the examples and sequence length of an update step, where Y and Xˆ are the LSA representation of the training update [2].

4 Problem Identification Most of the existing DNN-SE implementations work by producing an enhanced signal by working on the input signal at once, due to the feed forward nature of the architecture of the network. Speech is temporal data and is usually not fixed in size. Basic DNN architectures are neither suited nor well optimized for temporal data and provide inadequate enhancement to the input signal as a result of their limitation. The progressive implementations perform better than their traditional counterparts but still carry the aforementioned limitation.

5 Proposed Method This study proposes an application of progressive approach to the neural network architecture: convolutional recurrent neural network (CRNN), which are well suited for working with temporal data due to its two key characteristics: 1. 2.

Convolutional Section: A convolutional neural network is useful for finding the patterns in the data and applying the knowledge from the patterns to given input Recurrent Section: The recurrent nature of this architecture makes it effective for use with temporal data such as audio (or speech).

The CRNN is vastly superior to its constituent network architectures because it can handle temporal data while using the patterns in the data for effective and efficient operation on the given signal. Speech, as we know is highly correlated: what was spoken before determines what could be spoken next. Using a highly correlated network which is good at pattern recognition will vastly improve the quality of the output of speech enhancement (Fig. 3). A progressive implementation of CRNN, allows for the optimal speech enhancement due to pattern recognition and high correlation between consecutive stages of the network, coupled with the gradual enhancement process of the progressive method. Figure 4 describes a CRNN. In Fig. 4, a convolutional neural network stage is followed by a recurrent neural network stage.

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Input (noisy

Progressive Convolutional Neural Network

Progressive Recurrent Neural

Output signal (Speech Enhanced Signal)

Fig. 3 Proposed block diagram, a progressive approach using convolutional recurrent neural network (CRNN)

Fig. 4 Convolutional recurrent neural network (CRNN) [3]

Fig. 5 Proposed architecture of the progressive implementation of convolutional recurrent neural network (CRNN) [6]

Progressive Convolutional Recurrent Neural Networks for Speech … Table 1 Detailed structure of the network-I

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Layer name

Input dimensions

Hyper parameters

Output dimensions

Reshape_1

Tx 161

–

1x Tx161

Cascade_1

1x Tx161

–

qx Tx161

Conv2d_1

qxTx 161

2 × 3, (1, 2), 16

16xTx 80

Conv2d_2

16xTx 80

2 × 3, (1, 2), 16

16xTx 39

Reshape_1

Tx 161

–

1x Tx161

Cascade_1

1x Tx161

–

qx Tx161

Cascade_1

1x Tx161

–

qx Tx161

Conv2d_1

qxTx 161

2 × 3, (1, 2), 16

16xTx 80

Conv2d_2

16xTx 80

2 × 3, (1, 2), 16

16xTx 39

Cascade_1

1x Tx161

–

qx Tx161

6 Methodology As shown in Fig. 5, using a combination of convolutional encoding/decoding and an long short-term memory (LSTM) layer, the noisy spectrum of the input signal is enhanced, and the clean spectrum is produced as output. Table 1 describes the setup of the architecture where each stage comprises approximately the same structure, except varying for the input stages. Multiple layers and their corresponding hyperparameters, input dimensions and output dimensions can be observed in Table 2.

7 Algorithm Step 0: Step 1: Step 2: Step 3: Step 4: Step 5: Step 6: Step 7: Step 8:

Start Datasets for training, evaluation and testing need to be selected covering a wide range of SNR. Preprocessing of the acquired data, which includes noise concatenation or other relevant processes to aid the deep learning process. Setup the proposed architecture of the P-CRNN with the layers and hyper parameters as specified in Table 1. Monitor the training process, i.e., the gradient descent Perform the testing on the model and repeat Step 4 if necessary till required result is obtained Deploy the model and test on a noisy input Observe the changes in the output from the noisy input Stop

424 Table 2 Detailed structure of the network-II

S. China Venkateswarlu et al. Layer name

Input dimensions

Hyper parameters

Output dimensions

Conv2d 4

16x Tx19

2 × 3, (1, 2), 32

32x Tx9

Conv2d_5

32xTx 9

2 × 3, (1, 2), 64

64xTx 4

Reshape_2

64x Tx4

–

Tx 256

LSTM1

Tx256

256

Tx256

LSTM2

Tx256

256

Tx256

Reshape_3

Tx 256

–

64x Tx4

Skip_1

64x x4

–

128xTx4

Deconv2d_1

128xT x4

2 × 3, (1, 2), 32

32x Tx 9

Skip_2

32x x9

–

64x Tx9

Deconv2d_2

64x Tx 9

2 × 3, (1, 2), 16

16x Tx 19

Skip_3

16xTx 19

–

32xTx 19

Deconv2d_3

32x Tx 19

2 × 3, (1, 2), 16

16x Tx 39

Skip_4

16x Tx39

–

32x Tx39

Deconv2d_4

32x Tx 39

Skip_5

16xTx 80

–

32xTx 80

Deconv2d_5

32x Tx 80

2 × 3, (1, 2), 1

1x Tx 161

Reshape_4

1x Tx161

–

Tx 161

Conv2d_4

16x Tx19

2 × 3, (1, 2), 32

32x Tx9

Conv2d_5

32xTx 9

2 × 3, (1, 2), 64

64xTx 4

Reshape_2

64x Tx4

–

Tx 256

LSTM1

Tx256

256

Tx 256

LSTM2

Tx256

256

Tx256

Reshape_3

Tx 256

–

64x Tx4

Skip_1

64x x4

–

128xT x4

Deconv2d_1

128xT 4

2 × 3, (1, 2), 32

32x Tx 9

Skip_2

32x x9

–

64x Tx9

Deconv2d_2

64x Tx 9

2 × 3, (1, 2), 16

16x Tx 9

Skip_3

16xTx 19

–

32xTx 19

16x Tx 80

(continued)

Progressive Convolutional Recurrent Neural Networks for Speech … Table 2 (continued)

425

Layer name

Input dimensions

Hyper parameters

Output dimensions

Deconv2d_3

32x Tx 19

2 × 3, (1, 2), 16

16x Tx 9

Skip_4

16x Tx 39

–

32x Tx 9

Deconv2d_4

32x x39

–

16x Tx 0

8 Flow Chart of Algorithm See Fig. 6.

9 Software Used Python is used for all the aforementioned tasks: data augmentation (or preprocessing) [7], setup of the neural network architecture using Keras framework [8]. With the use of additional libraries such as Pydub [9], audio files can be manipulated to suit the speech processing [10, 11] (or enhancement) process. Other notable library used is matplotlib [12] facilitating the required vectors for the neural network and complex plots or spectra related to the audio.

10 Final Results and Conclusion The perceptual evaluation of speech quality (PESQ) [13] and the signal-to-distortion ratio (SDR) [14] in the case of P-CRNN implementations have improved on comparison with the conventional approach, i.e., P-DNN implementations. The spectrograms of the input and output when compared support the aforementioned point. Table 3 describes the PESQ and SDR data of the P-CRNN for a noisy input having average SNR of 1.96 dB. Figure 7 describes the input and output spectrograms, respectively. The P-CRNN has done speech enhancement very well as seen in Fig. 7. The noise present in the signal as seen in the spectrogram has been cleaned up very well as shown in the output. Therefore, it can be concluded that the proposed P-CRNN implementation works better than the existing P-DNN implementation. The recurrent nature of the network architecture makes it best suitable for temporal data such as audio.

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Start

Datasets preparation

Pre-processing of data

Setup the proposed P-CRNN network

Monitor the training process

No

Is the model good?

Perform testing Yes Deploy the model

Observe output

Stop Fig. 6 Flow chart of the algorithm

Table 3 Results and discussions

Metrics

PESQ

SDR (in dB)

P-DNN

2.49

10.58

P-CRNN

2.83

12.88

Progressive Convolutional Recurrent Neural Networks for Speech …

427

Fig. 7 Left: input spectrogram (noisy); right: output spectrogram (clean)

References 1. TensorFlow—CNN and RNN Difference, Tutorialspoint, https://www.tutorialspoint.com/tensor flow/tensorflow_cnn_and_rnn_difference.htm 2. J. Llombart et al., Progressive loss functions for speech enhancement with deep neural networks. EURASIP J. Audio Speech Music Process. 2021(1), 1–16 (2021) 3. C.C. Chatterjee, An Approach Towards Convolutional Recurrent Neural Networks (Towards Data Science, 2019), https://towardsdatascience.com/an-approach-towards-convolutional-rec urrent-neural-networks-a2e6ce722b19 4. M. Alam et al., Survey on deep neural networks in speech and vision systems. Neurocomputing 417, 302–321 (2020) 5. D.C. Naik et al., A literature survey on single channel speech enhancement techniques. Int. J. Sci. Technol. Res. 9(03) (2020). http://www.ijstr.org/final-print/mar2020/A-Literature-Sur vey-On-Single-Channel-Speech-Enhancement-Techniques.pdf 6. A. Li et al., Speech enhancement using progressive learning-based convolutional recurrent neural network. Appl. Acoust. 166, 107347 (2020) 7. A. Deis, Data Augmentation for Deep Learning (Towards Data Science, 2019), https://toward sdatascience.com/data-augmentation-for-deep-learning-4fe21d1a4eb9 8. The Keras Ecosystem, Keras, https://keras.io/getting_started/ecosystem/ 9. Jiaaro [James Robert], Manipulate audio with a simple and easy high level interface, GitHub, https://github.com/jiaaro/pydub 10. S.C. Venkateswarlu, N.U. Kumar, A. Karthik, Speech enhancement using recursive least square based on real-time adaptive filtering algorithm, in 6th International Conference for Convergence in Technology, I2CT 2021 (2021) 11. S. Venkateswarlu, A. Karthik, D. Naveen Kumar, Performance on speech enhancement objective quality measures using hybrid wavelet thresholding. Int. J. Eng. Adv. Technol. 8(6), 3523–3533 (2019) 12. API Overview, Matplotlib, https://matplotlib.org/stable/api/index.html 13. Perceptual Evaluation of Speech Quality, Wikipedia, https://en.wikipedia.org/wiki/Percep tual_Evaluation_of_Speech_Quality 14. J. Le Roux et al., SDR—half-baked or well done?, in ICASSP 2019–2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (IEEE, 2019)

Deep Learning-Based Intelligent Traffic Monitoring Systems K. Mani Raj, Ramavath Rani, and Shravan Kumar

1 Introduction VIDEO Surveillance (VS) technology is used vastly in the traffic control, indoor monitoring and detection and detection of crime and violence has become an essential tool for public and private security [1]. Safety and monitoring are crucial concerns in today’s environment. One of the main units of video surveillance is traffic monitoring where safety and monitoring are crucial concerns. The critical need for effective surveillance is highlighted by recent terrorist attacks. Modern security systems are equipped with digital video recording (DVR) cameras with multiple channels. One of the main failures of this model is that it needs continuous manual supervision, which is unworkable because of such factors as human fatigue and manual labor costs. A promising technology that uses deep learning neural network to produce effective results in image analysis. This work, however, faces a number of significant difficulties: (1) (2)

How to cope with synchronization issues in AI models and DL models, as well as how to perform parallelization in unbalanced conditions. How to design a workable monitoring system model, terminals supervising, massive visualization, less latency and overall communication. AI technology is widely used across the smart industry, such as smart transport, Internet, smart grids and video surveillance. Existential AI and profound learning algorithms, including recurrent neural network (RNN), the convolutional neural network (CNN), and the deep neural network (DNN), the artificial neural network (ANN) are primarily used for static image analysis. A self-learning system would be easy to implement and would enable large-scale monitoring.

K. Mani Raj · R. Rani (B) · S. Kumar Department of Electronics and Communication Engineering, MLR Institute of Technology, Hyderabad, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. K. Mandal et al. (eds.), Innovations in Signal Processing and Embedded Systems, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-19-1669-4_38

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Most current surveillance systems rely on convention central AI techniques. Existing studies have put forward several AI and deep learning (DL) technology, such as CNN, ANN, LSTM, RNN, in cluster clusters and cloud-based platforms. These can be AI and video monitoring systems exploration [1]. In this paper, we focus on intelligent traffic monitoring systems based on DL technology, propose Intelligent Traffic Monitoring (ITM) system using a deep learning model, and deploy the ITM on computing environment. The following is a list of the paper’s contributions. We have developed a model for unmonitored classification of individual object and traffic monitoring. . The implementation of underlying technology of deep learning involved in various methods of video analysis. Effective time processing is also considered as an important topic to be further examined in this field. . The DL model can reduce huge overhead network communication at the edge of a network. It offers low latency, accurate solutions for video analysis. . Implement the DL model as proposed and address parallel training issues, synchronization. In order to speed up the video analysis process and for appropriate prediction of data, the parallel level training methods are proposed. The paper is divided as following. Section 2 reviews the reference mentioned in this work. Section 3 sets up a DL training model for the proposed system. Section 4 deals with the implementation of the proposed system. Section 5 gives the experimental evaluation of the system. Finally, paper is concluded in Sect. 6. In Sect. 7 we address future work of this paper.

2 Related Work In AI and deep learning various methods and techniques has been proposed in video surveillance on various cloud environments for different purpose such as cost efficient, scalability, and flexibility and moreover reasons. To able to handle massive video stream in different cloud technology, Li et al. in [1] proposed a deep learning model for video surveillance where he explains a technique for video surveillance on edge computing platform They propose architecture based on multiple layers and a deep learning model to reduce the huge network overhead from center edge to the different edges where mostly latency problems arises. This paper has proved the model can be ecstatic and have a scalable computing environment. They have implemented the model using parallel training on multi-layer architecture. In Refs. [2, 3], Turchini et al. “Deep Learning-Based Surveillance System for Open Critical Areas”. In this paper they proposed an object tracking system and employed with the developed tracking detection model designed by them. This is further used for wide surveillance units. Sreenu and Saleem Durai “Intelligent video

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surveillance”. This paper includes a deeply viewed survey on various crowd analysis such as action recognition, object recognition, crowd activity. Zhao et al. in [4] proposed a “LSTM network: a deep learning model for shortterm traffic prediction”. It is a short-term traffic prediction model for travels routes which makes it easy for transportation and has accurate forecast. In Ref. [5] Espinosa et al. proposed a “Vehicle Detection Using Alex Net and Faster R-CNN Deep Learning Model”. In this model the two deep learning technologies, Alex Net and faster R-CNN are evaluated based on the quality, failure rate, time taken for completion of task. And concluded that pre-trained Faster R-CNN model achieves the better results.

3 Proposed DL Architecture 3.1 Establishment of Deep Learning Model Monitoring systems in IoT and Big Data have the features of huge surveillance terminals, a broad range of screening, and unending video streams. At the same time the demand for exact data analysis and low latency reaction are increasing in monitoring systems. By deep learning (DL) technologies, we offer a smart traffic monitoring system. We create a monitoring system on computing platform that provides flexible and scalable computing capabilities and reduces manual work. We propose parallelism DL model training on a virtual machine environment to speed up the training procedure. Hence, we establish a task level parallel training model, parallelization of convolutional layer and fully connected layer which we further by CNN and LSTM. It happens in two stages, the first stage is CNN followed by LSTM (Fig. 1). The proposed system deals with parallel training. We use CNN and LSTM techniques, where we using CNN for vehicle classification and LSTM to forecast traffic data which are being implemented by using parallel models and parallel training level.

Fig. 1 Block diagram of the proposed DL model

sample dataset

Read Images

Pre-Trained CNN

Vehicle classification

LSTM Network

Predicted Output

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The block diagram consists of sample dataset. We are obtaining the sample dataset from the traffic monitoring data. We are using that data as our input which is passed to read the data as images. In this block we are supposed to read the images and paste in the folder which further need to processed to the next stage. The next stage is CNN network. We are using the pre-trained CNN for image classification. As we are using the traffic monitoring images as data to classify all types of vehicles. The data is sorted and passed to the LSTM network where it predicts flow of traffic while using the same dataset as a CNN vehicle classification data to simulate flow of traffic.

4 Implementation of DL Model As per the proposed architecture we implement the monitoring system by using parallel training models and address the problems of low latency, accurate video analysis, workload balancing. We describe the parallel training on the computing environment for accurate data analysis and optimization of the model.

4.1 Parallel Training for DL Model A deep learning model parallel training is designed to provide two parallel training ways for the DL training model in order to speed up the DL model’s wide training process by employing durable and scalable computing resources. The primary ways to do this are model parallelism involving distributing the neural network across different processors, as well as parallel data, which involves distributing examples of training across different processors and computer updates in parallel to the neural network.

4.1.1

Task Level Parallel Training

In present scenarios, a video stream on a DL model will often include many video analysis tasks. For example, there are a variety of deep learning algorithms for traffic monitoring, including CNN models for vehicle identification and LSTM models for traffic flow prediction. Consequently, we propose a parallel method of working level training for the deep leaning model. We deploy numerous deep learning models with multiple architectures (i.e., CNN and LSTM) on the edge node to implement parallelism on various data processing tasks. Each DL model is considered as a sub-model and assigned to edge nodes. In task level parallel training, CNN and LSTM techniques are used, in which we use CNN for vehicle classification and LSTM for predicting the data flow of traffic.

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(a)

433

Pre-trained CNN for vehicle classification

The convolution neural network is one of the most extensive and widely used networks currently available, and it has become one of the most captivating issues of modern day. A CNN network’s basic design composed of two layers: (a) feature extractor and (b) fully connected layer. The convolutional layer in CNN has a set of input datasets that are evaluated to introduce a specific feature. To reduce the feature dimensions of the CNN model, a pooling layer is applied to each feature map. There is various pooling method available such as max pooling and mean pooling layer in convolutional neural network. In the present model, we are using pre-trained CNN for the application of the vehicle classification. We build a CNN by using a pre-trained network call Google net pre-trained network. Google net is a considered as an inception. Inception models are used for image distortion, batch normalization, and RMSProp, which is a gradient, based optimization technique. The Google net model is fundamentally built with many convolutions so to bring down the number of parameters. Its architecture is deep and had 22 layers. By installing the pre-train model we directly train the data and can test the data. The input data set is applied to the computing window from that window the data is read and given to the pre-trained CNN model. In pre-trained CNN model the first unit is feature extractor unit, the original image is sent the convolutional layer to gain the feature map (Fig. 2).

Fig. 2 Example of vehicle classification using CNN

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Fig. 3 Architecture of LSTM

To compress characteristics and identify if the current area contains vehicles, we employ a pooling layer. In the second extraction unit, intermediary images of vehicles are transmitted to the second convolutional layer for a precisely sampled function map. Each possible vehicle can be retrieved from the second pooling layer and sent as a single input to all connected layers. Intermediate images containing vehicles are delivered to the secondary convolutional layer in the second feature extractor unit to obtain a fine-grained feature map. Every possible vehicle can be extracted from the second pooling layer and sent to all connected layers as a single input. Pre-trained network is used to classify and applied to the fully connected layer to get accurate analysis. It can classify all types of vehicles accurately. In final state the classified vehicles is sent to parallel training for accurate analysis. (b)

LSTM for DL model

For traffic forecasting we use the LSTM technique here. Complex, artificial tasks can be shown by recurrent network algorithms. It consists of a layer of input, current layer and output layer. LSTM is intended for applications with an ordered sequence and information from the previous sequence (Fig. 3). We have established the LSTM network to forecast traffic flow into the CNN traffic data. It overlaps a minimum time over discrete steps by performing a flow error it is consists of three layers: (a) input, (b) recurrent and (c) output. It is designed for the applications where the input is ordered sequence and the data from the previous sequence may not be important. Input are sorted in form of weights and biased which behaves accordingly to the inputs. We can compute the memory cell based on the input time series by computing monitoring data as a time-series data. They have gates that regulate the flow of information between the nodes in such a way that it can selectively recall or forget data (Fig. 4). They have three gates accordingly to the layer. The gates are deployed to regulate the flow of cell: a forgot gate to remove the less important information. This is required for the optimization of the LSTM network. Input gate to read the values added to cell. And an output gate to give the output values we need sequence input to predict the traffic for the gradation of frames. Sequence input is fed to the folding sequence layer, either directly to the unfolding of the sequence if not required. If not,

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Fig. 4 Blocks involving for traffic prediction using LSTM

the convolutional layer must be crossed and then fed into the LSTM layer. Then the output layer has the predicted output.

4.1.2

Parallelization of Convolutional Layer

In CNN network, the training process of convolutional layer takes more than ¾ of the total training time but only trains 0.05% of the data. To avoid the lag of time we parallelize convolutional operations by using matrix-based-parallel procedure to produce effective results. A monitoring frame is the input matrix for CNN sub-model. In the parallel training, we employ the parallel convolutional technique to CNN sub-models. A filter parameter to move the input matrix to Map is added to the convolution layer to extract key functions. We obtain all the convolutional blocks of the input matrix by data partition method to the input matrix of CNN. All the convolutional blocks are convoluted in parallel sequence by shared filter matrix. Then, we can divide input matrix into several blocks in order to perform convolution operations in parallel. After obtaining the index values of every convoluted block, we collect the content of various convolution blocks and apply the appropriate convolutional operation in parallel manner. Figure 5 illustrates the example and describes the steps of the parallel convolution calculation of individual CNN sub-models in an algorithm. Each feature in feature map is computed depending on the convolutional block they belong. Various different functions can access different convolution areas in the input matrix at the same time without having to upgrade their values, and there will be no data dependency between them.

4.1.3

Parallelization of Fully Connected Layer

Fully connected layer in neural network are those which are connected to all the input layer of one layer to the every unit of the next layer. Usually the last few layers

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Fig. 5 Example of parallel convolution

are called fully connected layer as the compute the data extracted by previous layer to get the final result. Each neuron in the fully connected layer is connected to every other neuron in the preceding layer, with each connection possessing its very own weight. As said before each and every neuron in different layer is supposed to connect to all the neuron in another layer and the output for that layer is the input for the next layer. Evidently there will be no connection between the neurons in the same layer as there are no either of data dependency or logical dependency. Hence, the computation in the same layer for neurons can be performed in parallel. In Fig. 6 example of parallel training of fully connected layer is shown. Fig. 6 Example of fully connected layer

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5 Performance Evaluation The dataset used in this paper are used for both Conan LSTM. The complete dataset is used to train the CNN for vehicle classification (Table 1). The network’s performance is increased by using Google net, as mentioned in this study. We have avoided the complicate process of manually extracting features and the accuracy is up to maximum, which is 3.4% higher than the conventional methods using features extractors (Fig. 7). This paper presents a traffic flow prediction model using long short-term memory which explored the effectiveness of the dataset obtained from the traffic monitoring data. The baseline model proposed in this paper works with deep network using LSTM network the accuracy of traffic flow prediction is as shown in Fig. 8. The produced iteration are 3100 is 36 s. The proposed system has accurate performance. Table 1 Accuracy for different CNN models

Model

Accuracy (%)

Zhuo et al. [6]

95.4

Cho et al. [7]

92.61

Shvai et al. [8]

88.96

Proposed model

100

Fig. 7 Output of the CNN vehicle classification

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Fig. 8 Output of LSTM for traffic flow prediction

6 Conclusion The paper presents a deep learning model for traffic monitoring using CNN and LSTM models. We implemented the parallel training process to accelerate the CNN procedure and explored the task level parallel training, parallelization of convolutional layer and the fully connected layer. We implemented CNN and LSTM for vehicle classification and traffic flow prediction. The traffic prediction is vital for intelligent transportation system and for accurate traffic prediction analysis. The experimental outputs of CNN has gained the maximum accuracy and followed by LSTM which gave the appropriate accuracy regarding the traffic flow prediction.

7 Future Work In future work we can aim for faster CNN and LSTM for more accurate analysis on a large data base. We can take more to improve network architecture for better speed of detection. We can implement this on different computing environments like cloud computing, edge computing where large data analysis can be done and have huge communication issue.

References 1. J. Chen, K. Li, Q. Deng, K. Li, Distributed deep learning model for intelligent video surveillance where he explains a technique for video surveillance on edge computing 2. M. Langer, A. Hall, Z. He, W. Rahayu, MPCA SGD: a method for distributed training of deep learning models on spark. IEEE Trans. Parallel Distrib. Syst. 29(11), 2540–2556 (2018)

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3. F. Turchini, L. Seidenari, T. Uricchio, A. Del Bimbo, Deep learning based surveillance system for open critical areas 4. Z. Zhao, W. Chen, X.M. Wu, P.C.Y. Chen, J. Liu, LSTM network: a deep learning approach for short-term traffic forecast 5. J.E. Espinosa, S.A. Velastin, J.W. Branch, Vehicle detection using Alex Net and faster R-CNN deep learning models: a comparative study, in Advances in Visual Informatics. Lecture Notes in Computer Science, vol. 10645 (2017), pp. 3–15 6. L. Zhuo, L. Jiang, Z. Zhu, J. Li, J. Zhang, H. Long, Vehicle classification for large-scale traffic surveillance videos using convolutional neural networks. Mach. Vis. Appl. 28(7), 793–802 (2017) 7. D. Cho, Y.-W. Tai, I.S. Kweon, Deep convolutional neural network for natural image matting using initial alpha mattes. IEEE Trans. Image Process. 28(3), 1054–1067 (2019) 8. N. Shvai, A. Hasnat, A. Meicler, A. Nakib, Accurate classification for automatic vehicle-type recognition based on ensemble classifiers. IEEE Trans. Intell. Transp. Syst. 21(3), 1288–1297 (2019) 9. Zhang et al., CNN-based vehicles detection and annotation algorithm that can identify vehicle positions and extract vehicle properties from video stream 10. J. Chen, K. Li, K. Bilal, X. Zhou, K. Li, P.S. Yu, A bi-layered parallel training architecture for large-scale convolutional neural networks. IEEE Trans. Parallel Distrib. Syst. 1 (2018) 11. X. Ma, Z. Tao, Y. Wang, H. Yua, Y. Wang, Long short-term memory neural network for traffic speed prediction using remote microwave sensor data 12. Z. Zhao, W. Chen, X. Wu, P.C.Y. Chen, J. Liu, Deep learning approach for short term traffic forecast. IET Intell. Transp. Syst. 11(2), 68–75 (2017) 13. G. Sreenu, M.A. Saleem Durai, Intelligent video surveillance: a review through deep learning techniques for crowd analysis 14. M.A. Butt, A.M. Khattak, S. Shafique, B. Hayat, S. Abid, K.-I. Kim, M.W. Ayub, A. Sajid, A. Adnan, Convolutional neural network based vehicle classification in adverse illuminatus conditions for intelligent transportation systems 15. Z. Zhou, H. Liao, B. Gu, K.M.S. Huq, S. Mumtaz, J. Rodriguez, Robust mobile crowd sensing: when deep learning meets edge computing. IEEE Netw. 32(4), 54–60 (2018)

Power Efficient Multistage Linear Feedback Shift Register Counters Design in 130-nm CMOS for Large-Scale Applications K. Ravindra and D. Laxma Reddy

1 Introduction After continuous progress in applications such as single-photon identification, the execution of countless counters in small territories has become important, which involve flight time (TOF) running on and off cameras, which include counters to test clock cycles, plus photon tally cameras with the number of photons in the range? Decreasing the region deviated by the counter for such applications are necessary if the amount of pixels in cameras is to be increased because every camera pixel includes a specific counter. Whereas direct critical movement registers (LFSRs) are generally used as pseudorandom number generators, they are also demonstrated to be an efficient way to update simultaneously displayed counters and are suitable for large displays, since the moving register can be implemented as a sequential read-out system [2–4]. Throughout the CMOS pixel structure and the single-photon labeling clusters, LFSR counters were included. An LFSR’s clock speed is independent of the number of bits in the counter, and all systems traverse the counter except for increasing null condition. Be that as it might, the LFSR count submission is pseudorandom, and it needs special care to view the LFSR condition of repeated queries. In three separate techniques are studied to divide the LFSR system into two: loop method, immediate query table (LOOK UP TABLE) approach, and time memory mismatch measurement. The loop technique stresses the whole LFSR control class and analyzes each to the counterpoint. For an n-bit LFSR, this includes roughly 2n−1 checks in total. The direct LOOK UP TABLE technique uses an n to n LOOK UP TABLE to decode the LFSR state lawfully. The calculation of the time memory compromise set out under consolidates both strategies by putting 2(N/2) LFSR on the table, emphasizing the values in the LFSR group until the estimates are included K. Ravindra · D. Laxma Reddy (B) Department of Electronics and Communication Engineering, MLR Institute of Technology, Hyderabad, Telangana, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. K. Mandal et al. (eds.), Innovations in Signal Processing and Embedded Systems, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-19-1669-4_39

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in the table. The sum of importance is then removed from the opportunity to obtain decoded recognition. Additional measurements were provided in based on distinct logarithms and were modified for the usage of periodic ring generator counters in. For applications with broad clusters, each cell in the display must be decoded through two additional requests, and it is necessary to carry out the breakdown on the chip for system plans. This precondition demands that the definition justification be integrable and quick since several improvements will take place. In either scenario, all the strategies described above grow exponentially in time or zone with the scale of the LFSR. In the case of single-photon recognition applications, there are a few cases of structures seen that cannot be modified with LFSR counters without broad organized LOOK UP TABLEs.

2 Literature Survey The impact of the kind of LDPC codes on the translating execution is likewise examined utilizing recreations. The disentangled deciphering calculations introduced in this paper, and the related exhibition assessments are either new or have just been proposed by the creators. Their unification, however, is novel and permits us to separate and select a fitting unraveling plan as per execution, inactivity, computational intricacy, and memory prerequisites. Utilization of region and force is vital to be dealt with while ideating any electronic hardware for present mechanical development. A generously more use of force and region prompts a disadvantage while circuit utilization [1]. Comparators are the most broadly utilized part in the electronic circuits. Essentially, the worldwide interest in planning of electronic frameworks is toward assembling the frameworks which will have precision, high velocity, and furthermore with low region. A voltage comparator analyzes the momentary upsides of info signal with reference signal subsequently creating the computerized yield. The most well-known and significant application for rapid voltage comparator happen in analog to digital converters. In the analog to digital change measure, examining of info signal is required. The information signal which is examined is applied to a bunch of comparators. This interaction gives what might be compared to simple sign. In view of this correlation, the twofold yield is acquired. In present day telecom frameworks, rapid low-powers ADCs are the key structure blocks. Heightening of comparator has the most extreme significance as the presentation of analog to digital converter relies upon the exhibition of a comparator. In this current world, there is a quick development of the compact electronic frameworks like customer hardware, remote specialized gadgets. Due to this quick development, there is an expanded interest for growing low-force circuits. Simple to digital converters are the one such application where this low-force circuits are required. And furthermore in this day and age, there is an expanded interest for the compact battery gadgets, so there happens the need for dynamic hooked comparators with low-force utilization and rapid. Dynamic comparators are utilized in plan of fast ADCs in view of their lowforce utilization and rapid. In this venture, power utilization is of the unmistakable

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fascination to accomplish the general superior of the analog to digital converters [5]. A few components, including the LDPC code itself, impact the decision of an unraveling plan. Other than the traditional LDPC codes, different geometric LDPC codes have been concentrated in. These two classes of LDPC codes vary in the number of equality checks utilized in interpreting just as in the number of short cycles in their tanner diagrams. The previous 2 impacts the disentangling multifaceted nature, the last the deciphering execution dependent on the measure of the relationship presented between messages. Besides, the decision of code rates and lengths additionally influences the deciphering execution. Here, we unite a few of the main methodologies for disentangling LDPC codes and analyze them from an algorithmic and auxiliary perspective. We examine their presentation just as their usage multifaceted nature. Beginning with numerically exact adaptations of the registration update calculation, different diminished unpredictability variations are inferred in which the decrease in multifaceted nature depends on enlarging the BP-based estimate with either standardization or an added substance counterbalance term. A considerable lot of the deciphering calculations are broke down by methods for DE, a useful asset for upgrading key boundaries of the calculations just as for assessing limited quantization impacts [6–8].

3 Existing System Reduced-complexity decoding of low-thickness equality checks (LDPC) codes convey excellent execution when decoded with the conviction spread (BP) or the whole item calculation. As LDPC codes are being considered for use in a wide scope of utilizations, the quest for effective executions of deciphering calculations is being sought after seriously. The BP calculation can be disentangled utilizing the purported BP-based approximation (also known as the “min-whole” guess), which incredibly decreases the usage of multifaceted nature, however, acquires a debasement in interpreting execution. This has lead to the advancement of many diminished unpredictability variations of the BP calculation that in any case convey close ideal deciphering execution. Work on LDPC deciphering has for the most part centered on drifting point math or boundless accuracy. Be that as it may, equipment usage of the disentangling calculations for LDPC codes must address quantization impacts in a fixed-point acknowledgment. The impact of quantization on interpreting dependent on probability proportions was considered in. Unraveling dependent on log probability proportions (LLRs) and limited accuracy was concentrated somewhat in. The exhibition of these calculations has likewise been assessed utilizing thickness development (DE). DE for the BP-based calculation, or min-entirety estimation, was created in. A changed BP-put together calculation based concerning restorative terms was dissected utilizing DE in. Further, alterations of the BP-based calculation utilizing a standardization term and a balance modification term have likewise been assessed. As of late, streamlined the determination of quantization go (cutting limits) and the number of quantization bits for short- and medium-length codes utilizing

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broad recreations and proposed a changed BP-based calculation that is appealing when utilizing coarse quantization.

4 Proposed System This paper provides a particular counter strategy focused on various LFSR phases, which can be decoded with a justification that logarithmically grows with the counter scale instead of exponentially. While a clear link between the LFSR counters would cause a critical presentation decrease, like double wave counters, this paper knows how to scatter the wave signal in a timely fashion and summarizes it in an explicitly unsalable rationale. This paper further provides proof that this counter structure is being implemented and depicted in a 130-nm CMOS method. An n-bit LFSR is referred to in this article as an n-LFSR. The general arrangement of the counter as found in the outline. M comparable n-LFSR squares are worked by a sign initiated. On the off chance that the n-LFSR (m−1) is poor upon a certain level, the authorization signal is reaffirmed with a definitive target that the n-LFSR Mth progresses a certain condition. It makes the entire district in the M n-bit state to be crossed. The counter will in like way fill in as a brisk back to back reader chain in wide packs. This is created by minimal extra logic which dodges the LFSR investigation and ripple carrying squares. The multistage control devise confines counter to M individual units, enabling n-LFSR to be independently decoded by a tube bit (LOOK UP TABLE) instead of an (M circle) circle (M tube) bit. This can be steadily executed on a chip for tiny n.

4.1 LFSR Block A couple of LFSR input styles exists, including many-to-one, the one-to-a considerable lot (obviously known as Fibonacci and Galois LFSRs, individually) and ring generators. Ring generators (depicted in Fig. 1) are dependably seen as the perfect system to execute an LFSR, where the move register shapes a ring and taps structure sub-floats inside the ring. For any circumstance, the course of action improvement requires additional resistance in the LFSR, commanding the essential way. Or then again maybe, many-to-one style LFSRs (Fig. 1) are used, permitting the input procedure for reasoning and the grouping enhancement protection to be combined into a single reason hinder side interest minimization as showed up in Fig. 1. The multistage counter allows adaptability in choice of the size of the n-LFSR, with the objective that little single-tap LFSRs are extraordinarily picked. A singular tap many-to-one LFSR is topologically sketchy from the comparing ring generator.

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Fig. 1 Block diagram multistage LFSR counter

5 Experimental Results A single shot timing circuit execution has been intended to examine the introduction and zone of the multistage LFSR to customary LFSRs. The measures of pixels in the exhibit also as the fill factor of the photosensitive territory are fundamental parts in the imagined one-photon distinguishing confirmation applications in the course of action. These contemplations award the counter in the locale to be diminished while retaining high ability. Schematic See Fig. 2.

Fig. 2 Schematic diagram of the proposed system

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Fig. 3 Output waveform of the proposed design

Waveform See Fig. 3. Area Device and node counts MOSFETs

37

MOSFET geometries

3

Voltage sources

5

Subcircuits

1

Model definitions

2

Computed models

2

Independent nodes

30

Boundary nodes

3

Total nodes

36

Delay Parsing

0.04 s

Setup

0.04 s

DC operating point

0.06 s

Transient analysis

0.03 s

Overhead

1.00 s

Total

1.17 s

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Power Power results VVoltageSource_7 from time 0 to 100 Average power consumed −> 1.188705e−015 W Max power 5.240852e−005 at time 5e−009 Min power 0.000000e + 000 at time 0

When introduced in ordinary logic innovation, the tear carrying logic proposed will permit an extra memory work notwithstanding the logic for detecting the transitional status to maintain the state edge exposure. This will on a very basic level raising the counter’s field limit. Dynamic logic ought to also be utilized to sensibly combine the state edge disclosure logic and the memory estimation into a single logic square. Dynamic logic in like way will when everything is said in done improve transistor packaging density, which recommends that the entire undertaking was orchestrated by various logic techniques to minimize the general counter district.

6 Conclusion This article introduces an overall specification of multistage LFSR counters and the functional realization in 130 nm CMOS as well as the decoding logic needed to convert the count series in binary order. The counter proposed consists of several smaller LFSR phases, which are triggered by a single state transition from the previous stage. This design enables the decoding logic, rather than needing the LOOK UP TABLE, to scale with the counter size to be centered on a constant LOOK UP TABLE for a variety of levels. The decryption principle of the proposed counter scales is proportional to the number of stages logarithm rather than uniformly to the number of bits needed by traditional LFSR decoding methods. The LFSR cross-stage counter maintains much of the same benefits as the LFSR counters, such as good accuracy, irrespective of the number of bits in the counter with a limited amount of extra logic. The proof of the principle used in the integrated TOF camera application in 130 nm CMOS was developed and tested for a 0.84 LSB max-mum timing error of 800 MHz as predicted. The multistage LFSR may include efficiency and region advantages of LFSR controllers for all applications, which include a range of event detectors, such as one-photon imaging sensors. An extension of this paper is to generalize this multistage counter design to permit various counter types to use the same wing-carrying technique in various stages. The high output of the LFSR counter could be extended to a generic multistage counter in the first stage while the discrete counter for subsequent stage might theoretically require future counter designs for capacity, region, or electricity consumption.

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References 1. Y.G. Praveen Kumar, B.S. Kariyappa, M.Z. Kurian, Implementation of power efficient 8-bit reversible linear feedback shift register for BIST, in 2017 International Conference on Inventive Systems and Control (ICISC) (IEEE, 2017) 2. R. Landauer, Irreversibility and heat generation in the computing process. IBM J. Res. Dev. 5(3), 183–191 (1961) 3. Y.P. Kumar, B.S. Kariyappa, M.Z. Kurian, Implementation of power efficient 8-bit reversible linear feedback shift register for BIST, in 2017 International Conference on Inventive Systems and Control (ICISC) (IEEE, 2017), pp. 1–5 4. C.H. Bennett, Logical reversibility of computation. IBM J. Res. Dev. 17(6), 525–532 (1973) 5. S.B. Tang, J. Cheng, Exploration on blunder remedy calculation of fast QKD framework dependent on FPGA. Worldwide J. Quantum Inf. 17(02), 1950013 (2019) 6. R.J. Milliken, J. Silva-Martinez, E. Sanchez-Sinencio, IEEE Trans. Circuits Syst. I: Regul. Pap. 54, 1879 (2007). https://doi.org/10.1109/TCSI.2007.9025 7. H. Martínez-García, in Proceedings of the 2014 IEEE Emerging Technology and Factory Automation (ETFA) (IEEE, Barcelona, Spain, 2014), p. 1. https://doi.org/10.1109/etfa.3 8. A. Hicham, H. Qjidaa, in Proceedings of 2012 IEEE International Conference on Complex Systems (ICCS) (IEEE, Agadir, Morocco, 2012), p. 1–4

Raspberry Pi-Based Smart Attendance Management System with Improved Version of RFID Over IoT S. Venkat Pavan Kumar, T. S. Arulananth, and S. V. S. Prasad

1 Introduction In the existing system, we used keypad-based security for attendance system. Due to that system, there is no accurate security for the existing system. There are many limitations due to the existing system. To overcome the existing system limitations, we integrate to produce the latest technology called RFID-based attendance system. In this proposed system, we implement using RFID method to increase personal security. The present proposed system is implemented using Raspberry Pi processes approach of Python programming. With that, efficiency of the system increases with accurate results. Existing informational structures in colleges and schools underline the vital need for recording the day-by-day participation of understudies. Be that as it may, the current paper-based method of recording student attendance is somewhat problematic. Apart from being time-devouring, the understudies can purposely decide to dismiss the participation sheet, forget their signatures or sign on behalf of absent classmates. Additionally, losing the attendance sheet would mean losing the attendance recordable together. The late application of RFIDs is the edge level, and it conveys a few stars in a different field. RFID library [1], RFID healthcare [2], RFID ATM systems [3], and RFID door locking security frameworks are probably the best instances of that. Participation of the student from kindergarten to higher examination is still a risky angle. It is an unsolvable puzzle from some time in the past. A few gatherings and studies present numerous arrangements. However, those are still not a perfect solution. Educational systems are the backbone of most countries. Time S. Venkat Pavan Kumar (B) · T. S. Arulananth · S. V. S. Prasad MLR Institute of Technology, Hyderabad, Telangana, India e-mail: [email protected] T. S. Arulananth e-mail: [email protected] S. V. S. Prasad e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. K. Mandal et al. (eds.), Innovations in Signal Processing and Embedded Systems, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-19-1669-4_40

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and participation are fundamental parts of any instructive activities. However, the drawbacks of the system are sad and unbearable. It needs a better system as soon. This study zeroed in on creating RFID-based understudy participation framework to avoid several issues switch face by the existing framework.

2 Literature Survey Olanipekun and Boyinbode have presented RFID-based intelligent attendance systems [4]. This product was created utilizing VB.net and Microsoft Access information base. Every understudy’s RFID tag is associated with their student ID card. There is a sequential connection among gadgets, and RFID per user is setup for correspondence with RFID and PC organization. The RFID per user is at the auditorium entrance. When understudies arrive at the RFID per user auditorium, read the RFID tag and store all understudy data (passage date, title, and so forth) into the worker through sequential connection and hold measure. This framework head will get to all records utilizing the product interface by recovering information from the worker with no dislike to customary systems [5]. Srivignessh and Bhaskar discovered a framework for robotized cooperation, RFID, and position invariant face verification. It is capable with two-factor checks. In the primary stage, understudies should utilize RFID label read by RFID client. In the event that the initial step works, it advances to the second step for affirmation, if not, the individual is unnoticed. The subsequent advance is face verification. In the event that the face fits a particular RFID mark, it shows the worker’s participation. Without the two readings over, the program perceives bamboozling members. A two-factor programmed framework disposes of wholesale fraud and maltreatment of attendance [6]. Thein et al. fostered a RFID and fingerprint reader-based understudy interest the board structure. It besides fills in as a two-factor affirmation. In this strategy, RFID per user is appended to machine, gadget has explicit programming to figure out programmed understudy participation worked by Microsoft Visual Studio, and a SQL information base is likewise settled in this interaction. At first, every scholar necessity registers his/her RFID mark and impressions and stores them in the record. When understudies arrive at the study hall, they need to utilize the RFID tag. This will be perused by the RFID per user, which checks the worker to confirm the mark. On the off chance that it is right, the following test will start. Understudy’s subsequent unique finger impression is affirmed. When matched, information base presence will be enlisted. Administrator will get to, alter, and erase program. Instructors additionally have encryption to utilize this gadget, and they might fill in as administrator. This is extremely simple (since RFID) and secure gadget (unique finger impression). This project mainly aimed to create an integrated system to reduce time and manual work for tracking attendance. This is one of the particular methods that can be used in educational institutes as a standard, fully automated attendance process. Such databases as a whole system for managing attendance process in university focused on the automatic attendance framework. In this situation, tag can likewise be utilized by a few; however, two-factor mitigates the

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issues and participation put away naturally just as misrepresentation free actions [7]. “Arduino Integrated Intelligent RFID Protection and Attendance Module with Audio Acknowledgment” was made by Mishra et al. RFID-marked SD card module with different voice codes is used in this module. Tag ID and voice welcoming codes are put away in the SD module. As an understudy moves toward the homeroom entryway, his/her RFID tag is perceived. Assuming the label ID coordinates with the put away information in the SD card, a specific individual should utilize the voice welcoming. When viable, the entryway will be opened and the participation will be recorded in Excel sheet. Understudies can see participation subtleties utilizing Arduino’s LCD. Here, Arduino goes about as a microcontroller to join LCD, RFID per user, SD card, and so forth. It likewise fills in as a two-factor check measure. Also, this framework is extremely basic schematics because of exceptionally basic segments and plans. Here, as well, we get a speedy exactness response [7].

3 Existing System We do not have an integrated student attendance management system that manually controls participant power and reports, resulting in time-consuming and less attendance reliability. Many approaches used barcode scanning, which is high power usage and less reliable and no information tracking in some other areas utilizing wireless technology. Due to high power, reduced reliability, and no wireless data transmission, we introduced the new RFID web-based attendance program.

4 Proposed System Figure 1 shows the proposed system of RFID attendance system, we used RFID passive cards and EM18 reader. Students have card, and college has reader module. When the student shows card to the reader module, it automatically detects and stores in Raspberry Pi and then sends to server using the IoT module. Status of the work will be displayed in LCD module, and buzzer will alert you if unauthorized card is detected. The RFID server is a device-based connectivity program that provides consumers with Internet-based software and other web services to several RFID readers. Students and teachers receive RFID tags in teaching and learning settings (e.g., classrooms and laboratory) to monitor their presence. That classroom session (e.g., teaching) starts reading the teacher’s RFID tag. The RFID tags of the students are then loaded into the same school. The program logs all RFID labels of users. By using your RFID tag, you can trigger various network actions based on an asset configuration. Proposed system was developed using RFID technologies. In this system, RFID cards show RFID readers giving data to Raspberry Pi processing unit and sending data to Internet of things (IoT) module, posting the students’ total attendance on web

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Fig. 1 Proposed system of RFID attendance system

Fig. 2 Regulated power supply

page. It is a wirelessly monitoring method. This project mainly aimed to create an integrated system to reduce time and manual work for tracking attendance. This is one of the particular methods that can be used in educational institutes as a standard, fully automated attendance process. Such databases as a whole system for managing attendance process in university focused on the automatic attendance framework.

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Fig. 3 Raspberry Pi

5 Functional Modules 5.1 Regulated Power Supply Regulated power supply shown in Fig. 2 is used to produce the required operating voltage for this proposed system. Normally, this system converts 230 V AC voltage to the required 5 V DC voltage for system operation (Fig. 1).

5.2 Raspberry Pi In this work used a RPI processor for impending this application. RPI belongs to the ARM 11 family with 40 GPIO pins used for both input and output purposes. This processor has 1 GB RAM with 16 GB HDD slot for OS. Rasberian operating system is used for the developing application. This is the high-end performance among remaining controllers (Fig. 3).

5.3 RFID Reader and Tag Using a passive RFID tag stores the Student’s information. The tag gets active only when it comes in contact with the reader, and a reader has antenna which emits radio waves (Fig. 4).

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Fig. 4 RFID reader and card

5.4 LCD Monitor Liquid crystal display is used to display the parameters for status of the proposed system. This can display 32 characters having 2 columns. When each sensor is activated, corresponding message will be displayed in 16 * 2 LCD modules. In this, we use four data pins. Using these pins, we transfer the data from micro-preprocessor to LCD (Fig. 4). Fig. 5 16 × 2 LCD

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Fig. 6 Buzzer

Fig. 7 ESP8266

5.5 Buzzer Buzzer is the output module for alerting of any parameter changes. If any sensor increases the threshold value or if increases, then microcontroller alerts us by using this system (Fig. 5).

5.6 IoT Module Internet of Things is used for controlling any device or monitoring the device status through Internet. In this proposed system, we use this IoT module for taking the all parameters data and post into the cloud called server. ESP8266 modules as IoT module can operate through Wi-Fi frequency concept (Fig. 6).

5.7 Software Software is the important parameter to make the device automation. In proposed implementation, we used Python programming language and compiler Python IDE . Rasberian operating system is used for working on Raspberry Pi processor. Here, we used Python IDE software for programming write up and execution of entire system.

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Fig. 8 Implementation of the proposed RFID attendance system

6 Results Figure 8 shows the implementation of the proposed RFID attendance system is done with LCD, RFID module RFID cards and buzzer. All operations initiated and processed by Raspberry Pi processing unit. Figure 9 indicates that the record of the attendance system datasheet via RFID card in an excellent web server IoT sheet. Figure 10 shows the Comparison of manually recording attendance and using RFID. The figure shows that using RFID, naming 11 students attendance took just 20 s, whereas the manual process requires twice as long as 40 s.

7 Conclusion In the enhanced edition of RFID-based student attendance management system, we incorporated both input modules and output modules. This paper mainly aimed to create an integrated system to reduce time and manual work for tracking attendance. This is one of the particular methods that can be used in educational institutes as a standard, fully automated attendance process. Such databases as a whole system for managing attendance process in university focused on the automatic attendance framework. The program eliminates teacher’s manual work, as it also produces performance data. We successfully developed web-based attendance program. In the future, we can also have two-way protections in the future by submitting OTP for attendance tracking and attaching fingerprint for high-priority safety entry to attendance management system.

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Fig. 9 Record of the attendance system datasheet via RFID card in an excellent web server IoT sheet

Table 1 Comparison of attendance management system Method

Security

Reliability

Performance

Cost

Manual

Very low

Low

Low

Low

Barcode

Medium

Low

Medium

Low

RFID

High

High

High

Low

Fig. 10 Comparison of manually recording attendance and using RFID

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References 1. K. Mohammed, A.S. Tolba, M. Elmogy, Multimodal student attendance management system (MSAMS). Ain Shams Eng. J. 9, 2917–2929 (2018) 2. G. Ostojic, S. Stankovski, D. Vukelic, M. Lazarevic, J. Hodolic, B. Tadic, S. Odri, Implementation of automatic identification technology in a process of fixture assembly/disassembly. StrojniskiVestnik/J. Mech. Eng. 57, 819–825 (2011) 3. S. Stankovski, G. Ostojic, I. Senk, M. Rakic-Skokovic, S. Trivunovic, D. Kucevic, Dairy cow monitoring by RFID. Scientia Agricola 69, 75–80 (2012) 4. S. Bhattacharya, G.S. Nainala, P. Das, A. Routray, Smart attendance monitoring system (SAMS): a face recognition based attendance system for classroom environment, in 2018 IEEE 18th International Conference on Advanced Learning Technologies (ICALT), pp. 358–360 (2018) 5. A.A. Olanipekun, O.K. Boyinbode, A RFID based automatic attendance system in educational institutions of Nigeria. Int. J. Smart Home 9(12), 65–74 (2015) 6. P.S.S. Srivignessh, M. Bhaskar, RFID and pose invariant face verification based automated classroom attendance system, in International Conference on Microelectronics, Computing and Communication, MicroCom 2016 (2016) 7. M.M.M. Thein, C.M. Nwe, H.M. Tun, Students’ attendance management system based on RFID and fingerprint reader. Int. J. Sci. Technol. Res. (2015) 8. M. Pandiselvi, M. Renuka, S.S.P. Hussaima, B. Shenbagam, P. Dhivya, RFID based smart class attendance system with absentees using face verification. Asian J. Appl. Sci. Technol. (AJAST) 1, 108–110 (2017) 9. P. Patil, S.G. Chaudhari, Online attendance management system using RFID with object contradict. Int. J. Comput. Sci. Mob. Appl. 5, 104–107 (2017) 10. D. Mijic, O. Bjelica, J. Djurdjic, D. Jankovic, RFID-based system for automated registration of students’ attendance, teachers’ work and classroom use, in XI International SAUM Conference on Systems, Automatic Control and Measurements, pp. 68–71 (2012) 11. M.B. Srinidhi, R. Roy, A web enabled secured system for attendance monitoring and real time location tracking using biometric and radio frequency identification (RFID) technology, in 2015 International Conference on Computer Communication and Informatics (ICCCI), pp. 1–5 (2015) 12. B.O. Oyebola, K.O. Olabisi, O.S. Adewale, Fingerprint for personal identification: a developed system for students attendance information management. Am. J. Embedded Syst. Appl. 6, 1–10 (2018) 13. Y.K. Hooi, K.S. Kalid, S. Tachmammedov, Multi-factor attendance authentication system. Int. J. Softw. Eng. Comput. Syst. 4, 62–79 (2018) 14. Y. Mishra, G.K. Marwah, S. Verma, Arduino based smart RFID security and attendance system with audio acknowledgement 4(01), 363–367 (2015) 15. M.B. Chaniago, A. Junaidi, Student presence using RFID and telegram messenger application, in 8th Widyatama International Seminar on Sustainability (WISS 2016) (Widyatama University, IEEE, 2016), pp. 1–5

Mitigation of Multipath Effects Based on a Robust Fractional Order Bidirectional Least Mean Square (FOBLMS) Beamforming Algorithm for GPS Receivers K. Anitha, E. Amareswar, and V. Arun Kumar

1 Introduction Radio communication and moreover TV broadcasting is pleasant times for articulating the vital role achieved with the useful resource of cordless interplay in our lives. Both such applications use present day generation wherein they devise lots of symptoms the usage of the very same community. Such a strategy is referred to as multiplexing of frequency department. This is a manner which separates the whole records transfer into a group of some regularity sub-bands which might be non-overlapping in addition to sporting a few man or woman indicators. Frequency Division Multiplexing (FDM) emerges as very well used for the advancement of voice communiqué for the number one generation cordless systems. In the second one technology systems in which information processing is also made it viable for, time branch multiplexing has truly been made software for the dependable use of conversation channel. The want for transmission of records, multimedia net web site traffic, and additionally incorporated voice brought on the improvement of one/3 generation systems. The channel capacity is constricted. Tests live in development to limit the amount of facts without affecting the amazing of acquired signal. The essential benefits of GENERAL PRACTITIONER receivers encompass, the assist of overlapping, GPS receivers makes green use the spectrum, revealing immoderate resistance closer to frequency discerning fading contrasted to the single carrier device with the department of the community right into a narrowband flat fading below channel. It receives rid of ISI utilizing a cyclic prefix. Recovery of out of place symbols is viable with the use of the right network coding and moreover interleaving taking into account networks are frequency selective channels. Addictiveness inside the direction of excessive channel hassle with none complicated time-vicinity K. Anitha · E. Amareswar · V. Arun Kumar (B) Department of Electronics and Communication Engineering, MLR Institute of Technology, Hyderabad, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. K. Mandal et al. (eds.), Innovations in Signal Processing and Embedded Systems, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-19-1669-4_41

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equalization is possible. It offers safety toward co-channel interferences and moreover spontaneous parasitical noises. Perfect facts of the channel for equalization while studying GENERAL PRACTITIONER receivers device overall performance is assumed. Making use of the suitable network information, the better restrict of the GPS receivers tool overall performance may be understood. However, this is not always effortlessly to be had in a real situation. It wishes to be adjusted. There are diverse techniques in which channel equalization is completed, along facet blind or pilot (education)-based, bendy or now not, with the help of the parametric version, the usage of frequency (and/or) time correlation residential homes of the wireless channel. Equalization is a way accomplished to make up the signal distortion on the receiver. An honest one tap equalizer is generally made use of in GPS receiver’s structures, to deal with flat fading indicators on every sub-provider. Nonetheless, now and again at the same time as the version of the multipath channel is pretty rapid, the use of the channel cannot be considered as unchanged with one icon length. In the ones conditions, inter corporation disturbance or the disturbance the various subcarriers is precipitated. ICI on the receiver should be eliminated. There are three varieties of channel equalization in GPS receivers. The Choice Directed Network Equalization (DDCE): Here, the equalization of the channel of an earlier GENERAL PRACTITIONER receiver’s signal is used for the discovery of records in its equalization. This discovery is based certainly upon the records preference wherein the factor of the constellation is made use of and additionally a smooth facts preference wherein a chunk realistic preference achieved. The critical constraint in that is that it uses an obsolete equalization of network and additionally the presumption of the detection of correct records. The difficulty does no longer present a massive problem in which the channel has sincerely been differing gradually. In case the channel is changing quickly, the discovery of records may be incorrect because the channel coefficient has been obsolete and the proliferation of mistakes takes region causing this incorrect choice. Blind Channel Equalization: There is not any want for pilots, i.e., recognized icons. Blind equalization is made use of for retaining off the bandwidth used by the pilot signal. Hidden mathematical houses of statistics dispatched are carried out. It wishes a good deal greater amount of reminiscence at the receiving stop to have prolonged statistics report. In this paper, we are fostering a unique fashion of BLMS calculation via remembering the several analytics idea for bidirectional LMS calculation referred to as FOBLMS. The weight alteration equation within the BLMS is in part modified the use of the factional math idea, therefore the noteworthy hundreds of the beam forming calculation is also remembered for the moderate emission making which turns on more compelling lower of the multipath influences inside the GPS beneficiary for course utility. The GENERAL PRACTITIONER receivers are of three sorts which can be based on the plans of block transmission. These are the Cyclic Prefix GPS receivers (CP-GPS receivers), the Absolutely no Padding GPS receivers (ZP-GPS receivers) similarly to the straight away Domain call Simultaneous GENERAL PRACTITIONER receivers (TDS-GPS receivers).

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Fig. 1 Types of GPS receivers scheme. a The CP-GPS receivers b The ZP-GPS receivers c The TDS-GPS receivers d The DPN-GPS receivers

A MIMO apparatus having NT communicate receiving wires and furthermore the NR get hold of radio wires are thought roughly Fig. 1. Three is the square example of MIMO style. So as to win over powerful demanding duct audio frequency thanks to epithetical retinol self-opinionated broadband operating room group a juiceless ducts deferens by means consisting of thinking about the general integrity consisting of epithelial duct that’s above sensational transmission capacity containing powerful radiate. Also, powerful duct antiquated pretended given that sitting at powerful time in reference to the general process going from vex for blood type chase away. So, pondering sensational bar lord Rayleigh dying trend-setter anyway consisting of the general unstimulating memo airports, sensational tropism in reference to the overall ducts deferens are going to be frozen within just unspecified ward off. And the general duct reaction Old Testament Occidentalize indiscriminately delight in a well-known consisting of some chase away to a different haphazardly. At sensational time containing training, sensational alarum fell upon on this urogenital can be shown for specified in the overall equating nominative (Fig. 2).

Fig. 2 General architecture of a MIMO

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2 Literature Survey In [1] builders have detailed at the method for reestablishing the cyclist of a procured signal for a coded MIMO-GPS recipient’s gizmo. This recommended cyclic prefix entertainment which restricts the efficiency bandwidth as a result of the usage of the CP. In [2] designers factor bent at the pilot provider work impacting channel leveling. The paper has contemplated the MSE pilot regions of this community gage for the general mostly implementation of the framework. The author has furthermore proposed other heuristic attempt to locate computations specifically, the slope alpinism computation in addition to the certain arrangement of guidelines, for figuring out the pilot management scenario which cuts down the MSE of network progressing. It became closed reduction complexity and also may not intend to simplify the limits. Xiang et al. [3] incorporated one additional associative technique for enhancing the system. The give up results display that this remedy is having one more cyclic technique of enjoyment that completes a preferred exhibition of image bungles charge that is sincerely a lower bound with a decrease certain without penance of phantom execution. In [4] developers made a proposal of absolutely no Cushioning for a multicarrier as some different choice to the conventional CP-GPS beneficiaries. In this, both the CP/ZP-GPS lovers frameworks have been examine for acquiring every one of their advantages within the wireless frameworks and also their ZP-GPS recipients. The FAST-LMS equalizers may be produced for the ZP-GPS beneficiaries for the presentation of the opportunity offs nearby the complexity of implementation. Better, the outperformance of CP-GPS beneficiaries fashionable with the assessment of the ZP-GPS lovers and FAST-LMS equalizer has definitely been proven. However, an surroundings-pleasant synchronization in each the time and also the reoccurrence space asks for take a look at substantially quicker than bringing them into attention for the multicarrier structures of report. In [4] creators attempted that subtracting the PN array from that of the TDS-GPS recipients signal that influences in Zero Padding of the GPS fans indication. This CP-GPS recipients uses the prefix as a protect C programming language sign for keeping far away from the impacts of all the attaching photographs as well as additionally sends out more substantial than about 10% in their information picas. These CP-GPS receivers and also their prep work markers collaborate with lower within the throughput of the channel decreasing the unearthly efficiency. Instead of the usage of the CP within the DVB-T, the TDS-GPS series organizations is pseudo-commotion grouping padding which is the watchman C program language period that changed into applied in area of the CP that is made use of because the tutoring photographs. This TDS-GPS receiver additionally cuts down the transmission overhead close by a development in its performance someplace inside the range of 5 and also 15%, a ways higher than the CP-GPS fanatics. Channel nighttime out is fashionable for details and is also fundamental just as intense within the fashionable implementation of the MIMO-GPS receivers systems. It has been accumulated right into the daze network stability, the pilot helped in channel leveling, in addition to focusses in an esthetically damaged trade of network, and the

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pilot aided equilibrium of the community. In [5] designers similarly have truly tested the Blind Channel evening out that goals a protracted info record for assessing at the enthusiast as well as is furthermore realistic for a progressively unique community. The Semi visually hindered community equilibrium makes use of an aggregate of the visually impaired adjustment approach as well as the fixed houses of this got indicator was used, and also inside the event of the deterministic method each warning signs which can be gotten and the network coefficients had been used. In [6] developers have without a doubt cultivated a bendy setup of policies for dazzle network ID in the ZP-GPS recipient’s frameworks. Specifically, the ones Recursive Least Squares and additionally the Least Mean Square form of calculations correctly supply responses to the issues of esthetically impaired channel progressing. In the uncertainty beneath become the association of autocorrelation grid of the obtained facts for managing problems inside the personality of channels. As consequently a subspace technique, the usual concept is connected with right deterioration of realities in the indication and also the commotion subspace. The definition of space follows this, motivating an outright final adjustment of the inspiration response of the channel. These diagnosed estimations were finished as well as furthermore tried in numerous conditions of flagging that encompass device no matter unreasonable SNR in fixed and obscuring channels. In every such instance, the computationally exorbitant techniques collaborated with the ones of a activate technique for the SVD. Garin and Rousseau [7] has clearly provided a justification of the network change which is essentially founded on plan of examination valued for an incredibly reduced pass addition and carrying out suitably the use of network stability estimations. This brush kind pilot affiliation permits in following of rapid blurring networks. In [8] makers has certainly supplied techniques of 2 channel stability specifically the MLE and LMS leveling for the GPS recipients frameworks. A few critiques have really performed the perceptions of MLE appreciating a gain of currently not desiring community statistics and the SNR. Therefore, it is additional massive than the LMS night out as well as the MLE which is simple in execution. On the event that the channel info comes the LMS stability will have an advanced phase of exactness as well as contains out higher than the MLE at a discounted as well as SNR that gives the amount of pilots in an effort to be large than the CIR length. In [9] makers have surely used a paper which proposes a fundamental example for transmission for path the squares for this device that has 4 exceptional ship radio cords. A serviced design empowers saving four GENERAL PRACTITIONER recipients periods for sending out the schooling obstructs and additionally their examples and also the 4 GPS recipients extends to the schooling blocks working with the maker’s occasion. This additionally gives a comparably pinnacle show off associated a habitual one beneath the reduced SNR and further plays nicely in a high SNR. In [10] makers have proven a customer novel and periodic succession of making ready this is blanketed mathematically at a discounted degree of uniformity to the realities range of every consumer earlier than the tweak. The networks that use the first request insights in quite some time community can be equalized in the simple improve. The immediately equalizer and also the Iturbi sign had been utilized for the progressing of the series of facts, and a deterministic loads of probability method that relies upon a Iturbi locator

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or perhaps a immediately LMS equalizer-based totally technique has been made use of in the resulting undertaking for an iterative amendment of the MIMO community and the preparations of dimensions. The paper finally ends up showing its trendy presentation that is a bit less than the same old methods.

3 Existing System Normalized LMSA standard drawback epithetical adaptation white noise canceller in response to CNA plus ALMS breakthrough serves as the overall enceinte fear in reference to spare mean square erratum which results palm alarum tortuosity palm spectacular noise-canceled alert. Booming the overall ECLMS procedure sensational time-varying step-size that will be compared to sensational sloped pattern in reference to the general quarrel betwixt 2 the next stimulant matrices rather than powerful stimulus data line as prospering spectacular alms. The current system supplies advancements prospering ritenuto mean squared typographical error (else) plus therefore lowering signaling warping. The burden enlighten married person because ELMS set of rules will be thus and so, the way of sound expulsion from GPS signal utilizing ECLMS-based versatile separating gadget exists. For this, the center and the supported movement markers are effectively settled on in this kind of way that the unmistakable out result is the pleasant the exceptionally least made even statement of the fundamental GPS sign. The proposed treatment abuses the progressions inside the weight update strategy and thus speeds up over the comparing LMS and ECLMS-based totally acknowledge. Our recreations, notwithstanding the way that check that the presentation of the ECLMS is an awful part higher than the LMS set of rules as far as SNR notwithstanding maladjustment. For this rationale ECLMS-based absolutely totally bendy clamor canceller might be utilized in all valuable bundles. Using adaptation downloads because cutting short sensational white noise wit ground the general assumption that the general frequency wit going from sensational upshot will probably be unequaled from spectacular background at. This is often promptly legitimate because spectacular wallet during which powerful background disturbance is continuing in addition to powerful outcome will be ephemeral. The overall ephemeral properness signifies that sensational radio frequency with epithetical spectacular effect will be open over almost frequency packing containers because of owned spontaneous mundane features. In addition, because many resources containing background interference, spectacular ghost like humor are often quite low-pitched. Given that locomotive supported at, the overall warning signal are often intrinsically fitful successful natural world primarily based along powerful primary hair-raiser styles containing the overall moving constructions. Sensational oftenest humor enclosed by railway locomotive styles as well as modes will diversify blood group great deal. Powerful apparitional signature tune in reference to type a widowed twin cam in the week type an oral exam tripod will be greatly less complicated compared to group a steam turbine beaver state sixteen volumetric

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curve diesel motor and trying to deal worm gear lanes. Powerful frequency ingredients because the particular assets could be fitful and coupled up to befittingly scooped conifer American state air strainer lenders that one may reduce abundance. Powerful fertilize impudent mixture (fir) formulation tender offer retinol higher degrees consisting of stability other than require more tunes that one may realize type a nominative frequency response. Least mean squares (LMS) estimations are a class of Adaptive channel used to mimic an ideal channel through running over the channel coefficients that become mindful of with becoming the most un-suggest squares of the blunder signal (assessment among the right and the genuine sign). It is a stochastic tendency recourse procedure in that the channel is essentially changed ward on the bumble at the current time. While the above Wiener-Hop affiliation immaculately takes care of the trouble, it required an astute structure substitute (breaking the issue into two areas) with an end goal to normally no longer artworks for an adaptable fuss losing trouble. Moreover, the right technique cannot be acted continually, considering reality that the entire sign should be outfitted all aggregately for the response for artistic creations. The LMS affiliation avoids every one of those inconveniences through changing the channel loads on the grounds that the talk is being played. The eventual outcomes be that as it can, is not generally close to the idea of the Wiener-Hop game plan. To use the LMS computation, we need to begin with choose a direct solicitation and in some time a respectable reinforce size. This ought to be conceivable through way and-botch, or through finding the autocorrelation of the reference signal and the move-association between the reference and vital signs and side effects. Since, we recently did this for the right channel, we understand the channel demand need not be any bigger than 60. Be that as it can, whereas expanding the channel demand neglected to ruin the display of the right channel, it will degrade the introduction of LMS. This is for the explanation that the greater channel demand, the bigger the eigenvalue unfurl of the autocorrelation organization of the reference signal (we do not substitute the autocorrelation work, yet we incorporate more expressions from that potential into the autocorrelation grid).

4 Proposed Method Overall discovering system (GENERAL PRACTITIONER) is a satellite television for PC TV for PC-based totally radio way shape made by utilizing the use, Department of Protection [1]. The GPS balance depends ordinarily upon positional accuracy which diminishes with the select misstep property being partaken in a GPS signal assessments, similar to satellite television for PC TV for PC actuated slip-ups satellite TV for PC clock, ephemeris appropriate mix-ups, every day putting climatic blunders, as an example, ionosphere keep-up, decline environmental factors discard, natural mix-ups multipath, staying, etc. A piece of the blunders referred to is presumably denied through utilizing differential methods by virtue of their area pursuing [2], aside from the multipath and moreover staying botch assets [3, 4]. Multipath, wherein

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a brand name turns up the a few way is a core value goofs supply in a couple of extreme precision GPS programs, restricting the exactness thus influencing the pseudo increase and supplier degree assessment [5]. In a multipath issue, the sign includes a receiving link utilizing a couple of procedures on account of sign refection or diffraction. Also, GPS is normally unprotected to staying with the affectability of beneficiary. The GPS beneficiaries are sensitive to get an inclined sign from the orbiting circular satellites. Definitely, a jammer with decreased strain can stifle the legitimate GPS tells over an extensive spot [6]. The hotshot of the GPS might be updated through walking around the managing of GPS assortment association to work of art in thick multipath circumstances. The social event of multipath could make a decent estimated curving to the realm of the association craftsmanship, which initiates mix-ups inside the capacity size of recipients. At last, to diminish the effects of multipath on a lover, the multipath issue has unquestionably been cared for through certain methods the request calculations had been utilized for multipath solace in GPS frameworks. Extensive contemporary action occupations had been better than light the multipath influences, and in addition more than one systems have without a doubt been outfitted [7–11]. Multipath balance approaches are of 3 orders; radio twine affiliation, sign getting prepared and furthermore records adapting. Sun et al. [12] have showed a receiving wire ordinarily based absolutely totally multipath solace strategy taking increase of the beam forming estimation albeit the fast day venture way cannot be decreased, beam forming-based certainly totally procedure is limit of the advantageous response for the multipath little amounts cure [13]. To ease the multipath influences of satellite television for PC television for PC educates, an adaptable local area is brought inside the back of each receiving wire component and furthermore the succeeding arrangement of stations are pointed as bendy making prepared. In beam forming, flaunt of receiving strings at the GPS aficionado are ventured forward with a tons to impact the radio twine factors with a gathering a specific strategy for the required sign through which benefit of the receiving twine is advanced [14]. Fragmentary examination (FC) [15] is a sub-division of mathematical exploration that loosens up to the genuine or plausible confounded numbers the request the differential further to worked together supervisors.

5 Proposed System Model In this part, an issue through detail shape rendition for a GENERAL PRACTITIONER authority show is added. Think about a GPS beneficiary width ‘s’ decision of radio rope factors gets a sign S(n) from the satellite TV for PC. They have been given sign might be made as; X k (n) =

M−1  k=0

Sk (n)aθk (n) + Ik (n)aϕk (n) + V (n)

(1)

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V (n) are the satellite television for computer beamed gotten with the aid of antenna elements in the GPS sensing unit array, guiding vector of the signal Ski, steerage vector of the interference sign, interference sign that’s a jammer sign and moreover white Gaussian sound and not the use of a median and variance. The Gaussian noise is mixed with the antenna components from the sign area. The ‘adequate’ price tiers from ‘0’ and also ‘−1’ represents first and additionally the ultimate antenna factors with a GPS linear antenna preference. The steerage vector of a corresponding signal is the signals instructions vector which publications the signal in a delegated route. By steering the course vector, antenna elements in a GPS sensing unit range increases its radiation pattern in commands alongside the popular sign. The sign steerage vector is stood for as; For S(n) signal, the guidance vector is obtainable via; aθ (n) = exp(− j K o kd sin(θ )); 0 ≤ k ≤ M − 1

(2)

For I(n) signal, the steering vector is given by; aϕ (n) = exp(− j K o kd sin(ϕ)); 0 ≤ k ≤ M − 1

(3)

The steering vector for the array of the antenna elements can be expressed as (Fig. 3);

Fig. 3 Proposed system model for multipath mitigation for FOBLMS beam former

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⎡

⎤

1

   ⎢ ⎥ ⎢ exp − j K o d sin θ ⎥ ⎢ ⎥ ϕ ⎢ ⎥ ai = ⎢ . ⎥ ⎢ .. ⎥ ⎢ ⎥ ⎣    ⎦ exp − j K o sin θ ϕ (M − 1)d

(4)

5.1 Proposed Fractional Order Bidirectional LMS Beamforming Algorithm Flourishing this neck of the woods, powerful characterization about sensational expected broken club asynchronous amps beam forming procedure sit. The general films beam forming set of rules lightness voicing software are often delineated prospering common fig tree. Two. ‘x(n)’, ‘w(n)’, ‘s*(n)’, ‘e(n)’, ‘d(n)’ encapsulates spectacular air traffic got hold of alarm, beam former lightness, turnout the beam former, misprint alarum a craved reaction since ‘nth’ sample distribution highlighted common fig tree. Twain. Given that every beam former printout, misprint signaling may be calculable in line with that powerful coefficient can be up-to-date to attenuate the overall literal error subroutine. By means of cutting spectacular erratum subroutine, moderation in reference to type a multipath effect may be possible prospering projected films procedure. Sensational beam former outturn signaling may well be spoken given that; S ∗ (n) = W H (n) · X (n)

(5)

In which WH = T is the Hermitical network of weight vector as well as the signal accessed the radio cable series of GPS sensing unit exhibition is X(n) = X 1 , X 2 , … X M for the referring to receiving cable parts from ‘0’ to ‘m−1’ at GPS recipient. The blunder sign e(n) is the assessment between the optimal reactions and additionally go back S*(n). It is given by means of (Fig. 4); e(n) = d(n) − S ∗ (n)

(6)

The one in question equalization is termed powerful crosspiece direction lightness in sensational expected films algorithmic rule whatever increases powerful soma. That process became well-meant to discover spectacular climbs through reducing spectacular error. Sensational fresh fueled lifts have a tendency to be used to get the overall better arithmetic mean beliefs. So, sensational improved joining of the one in question weightiness may well devolve on the overall data derives since weather forecasting.

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Fig. 4 FOBLMS algorithm for weight adjustment

6 FOBLMS Weight Adjustments ‘FOBLMS weight adjustments depends based on cyclic wiener filter and its adjustment wiener filter’.

6.1 Cyclic Wiener Filter ‘Cyclic wiener filter exploits spectral coherence theory it is mainly necessary to introduce coherence theory based on second order firstly’ (Fig. 5).

Fig. 5 Cyclic wiener filter

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Fig. 6 Weight vector errors

6.2 Adjustment for Cyclic Wiener Filter See Fig. 6.

6.3 Weight Vector Errors ‘That the weight vector errors happens to weight of beam former and follows statistics and mean output power’.

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6.4 Steering Vector Errors ‘Steering vector error happens that the array system of platform cannot be steady all the time mainly in high altitude radar’.

7 Conclusion In this paper, a robust beam forming gadget for multipath discount inside the GPS receiver became endorsed. The advised Fractional Order Bidirectional Least Mean Square algorithm is set up thru incorporating the fractional calculus idea similarly to Bidirectional Least Mean Square formulation. The fraction calculus modifications the load on the beam former, considering the historic weights. The encouraged FOBLMS steers the sensing unit form of GENERAL PRACTITIONER such as antenna factors in the path of the instructions of the satellite TV for PC signal with the useful resource of creating up the multipath effects and also jammer effects. The overall performance of endorsed FOBLMS beam forming set of regulations in GPS receiver is showed over the winning beam forming formula which includes Wiener, LMS, in addition to BLMS. The trial and errors consequences confirmed the effectiveness of the counseled FOBLMS beam forming components in multipath mitigation of GPS receiver with closing antenna advantage of 10.09 dB in the favored route of the signal with little or no bit mistake fee.

References 1. P. Enge, P. Misra, Special issue on global positioning system. Proc. IEEE 87(1), 3–15 (1999) 2. Y. Yang, R.R. Hatch, R.T. Sharpe, GPS multipath mitigation in measurement domain and its applications for high accuracy navigation, in Proceedings of the ION GNSS, pp. 1–6 (2004) 3. F. Xiang, G. Liao, C. Zeng, W. Wang, A multipath mitigation discriminator for GPS receiver. Int. J. Electron. Commun. (2013) 4. L.R. Weill, Multipath mitigation using modernized GPS signals: how good can it get? in Proceedings of ION-GPS, pp. 493–505 (2002) 5. M. Mukhopadhyay, B.K. Sarkar, A. Chakraborty, Augmentation of anti-jam GPS system using smart antenna with a simple DOA estimation algorithm. Proc. Prog. Electromagn. Res. 67, 231–249 (2007) 6. D. Dong, M. Wang, W. Chen, Z. Zeng, Mitigation of multipath effect in GNSS short baseline positioning by the multipath hemispherical map. J. Geo-Inform. 90, 255–262 (2016) 7. L. Garin, J.M. Rousseau, Enhanced strobe correlator multipath rejection for code and carrier, in Proceedings of the Tenth International Technical Meeting of the Satellite Division of the Institute of Navigation ION GPS-97 (1997) 8. F. Xiang, G. Liao, C. Zeng, W. Wang, A multipath mitigation discriminator for GPS receiver. Int. J. Electron. Commun. (2013). https://doi.org/10.1016/j.aeue.2013.04.007 9. R. Moradi, W. Schuster, S. Feng, A. Jokinen, The carrier-multipath observable: a new carrierphase multipath mitigation technique. GPS Solution (2014). https://doi.org/10.1007/s10291014-0366-8

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10. D. Bétaille, P.A. Cross, H.J. Euler, Assessment and improvement of the capabilities of a window correlator to model GPS multipath phase errors. IEEE Trans. Aerosp. Electron. Syst. 42(2), 707–718 (2006) 11. M. Bhuiyan, E. Lohan, M. Renfors, Code tracking algorithms for mitigating multipath effects in fading channels for satellite-based positioning. EURASIP J. Adv. Signal Process. (2008) 12. L. Sun, J. Chen, S. Tan, Z. Chai, Research on multipath limiting antenna array with fixed phase center. GPS Solution (2014) 13. M. Sahmoudi, M.G. Amin, Optimal robust beamforming for interference and multipath mitigation in GNSS arrays, in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (2007) 14. Y. Yapıcı, A.O. Yılmaz, An analysis of the bidirectional LMS algorithm over fast-fading channels. IEEE Trans. Commun. 60(7), 1759–1764 (2012). https://doi.org/10.1109/tcomm.2012. 050812.110116 15. K.S. Miller, B. Ross, An Introduction to the Fractional Calculus and Fractional Differential Equations (Wiley, New York, 1993)

Design of Low-Power Reverse Carry Propagate Adder Using FinFET B. Naresh, K. Aruna Manjusha, and U. Somanaidu

1 Introduction Many modern sign handling frameworks are being planted on a VLSI chip as the scale of integration continues to expand. These signal processing applications not only require a lot of computing power, but they also consume a lot of energy. Although execution and size are going to be the two most significant design criteria, power drawing factor has become an increasingly crucial consideration in today’s VLSI systems. Two factors drive the demand for low-power VLSI systems. Initially, with the stable and consistent increase in working recurrence and taking care of breaking point per chip, big currents should be delivered and the energy generated by highpower utilization must be removed using appropriate methods of cooling [1]. Next, the longevity of batteries in small electronic devices is limited. Architecture of lowwattage devices inevitably leads to a longer lifespan. Current technological trends aim to decrease characteristic area and voltage source value. Decreasing V DD results in longer circuit being slow and as a result, lower operational throughput. Deferral of an entryway is conversely identified with the square of the channel length of the gadgets, subsequently more modest elements lessen door delay. Furthermore, for reliability concerns, less width gate oxides impose restrictions to voltage. As a result, for smaller geometries, the supply voltage should be reduced. Despite the fall in V DD , the overall impact is there is an improvement in the performance of the circuit in CMOS technology. As a result, a new technology

B. Naresh (B) · U. Somanaidu Department of Electronics and Communication Engineering, Institute of Aeronautical Engineering, Hyderabad, India e-mail: [email protected] K. Aruna Manjusha Department of Electronics and Communication Engineering, MLR Institute of Technology, Hyderabad, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. K. Mandal et al. (eds.), Innovations in Signal Processing and Embedded Systems, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-19-1669-4_42

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has enabled the contradictory requirements of low power and high throughput to be met. Computing the innovation and the gadgets dependability, compromise for space for less wattage in designing manner, and misusing the simultaneousness ability in algorithmic changes are some of the current strategies used to scale the supply voltage. As a result, the voltage scaling is compelled by V th, the limit voltage. In applications where throughput is more important than speed, such as digital processing, architecture can be designed to lower voltage. FINFET technology was developed as a result of the ever-increasing degree of integration. Moore’s law has been true for many years, dating back to the early days of integrated circuit technology. The count of transistors on a particular silicon width becomes twice biennially, as expected. Even though they were advanced for the time, few milestone chips of the generally early incorporated circuit period had a very few transistors. For example, the 6800 CPU has only 5000 transistors. Many orders of magnitude more are available nowadays. Many parameters have altered in order to obtain the huge increases in integration levels. Fundamentally, feature sizes have shrunk to allow for the fabrication of more devices in a given space. Other numbers, like as power dissipation and line voltage, have decreased as frequency performance has improved [2]. Individual device scalability has its limits, and as process technologies shrank closer to 20 nm, it has become hard to accomplish correct the increment of many device factors. The power supply voltage, which is the most important aspect in determining dynamic power, was especially affected. Optimizing for one variable, such as performance, led to unintended tradeoffs in other areas, such as power. As a result, additional more revolutionary solutions, such as a shift in transistor structure from the typical planar transistor, have to be considered (Fig. 1). Fig. 1 Structure of FinFET

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Fig. 2 16-bit carry select adder

2 Existing Method 2.1 Carry Select Adder In MSB adders, one full adder performs expansion with convey input set to one, while the other snake performs expansion with MSB input set to zero. A multiplexer is utilized to make the pick. This technique for parting the snake into stages expands the measure of room utilized while accelerating the expansion interaction (Fig. 2).

2.2 Multiplexer Two-input multiplexer has n select lines that are used to select which has data line to deliver at the output is the one of the input. Multiplexers are basically used to enhance proportion of data that can be sent over an association in a given proportion of time and speed (Figs. 3 and 4). Z = A·S+B·S A 4-to-1 multiplexer’s Boolean equation is F = A · S0 · S1 + B · S0 · S1 + C S0 · S1 + D · S0 · S1 Fig. 3 Multiplexer logic circuit

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Fig. 4 4-to-1 mux

2.3 Delay and Area Evaluation of Basic Blocks Figure 5 shows the XOR gate for evaluating the area and delay. The total number of AND OR Invert logic gates needed for each logic block is counted to determine the location (Table 1). Fig. 5 Area and delay evaluation of XOR gate

Table 1 Delay and area count of the basics blocks of CSLA

Adder blocks

Delay

Area

XOR

3

5

2:1 MUX

3

4

Half adder

3

6

Full adder

6

13

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Fig. 6 Group 2 for delay and area evaluation

Table 2 Delay and area count of regular SQRT CSLA groups

Group

Delay

Area

Group 2

11

57

Group 3

13

87

Group 4

16

117

Group 5

19

147

2.4 Area and Evaluate Delay of Regular CSLA The 16-bit standard SQRT CSLA is classified into five categories, each with a different size RCA. Each group’s delay and area evaluation are described in Fig. 6, here the numerals inside determine the defer values [3]. For example, sum-2 needs ten gate delay. The following are the steps that lead to the assessment. There are two arrangements of 2-bit RCA in Table 2. In group 2, one bunch of 2-bit RCA has two FA for, and the other set has one full adder and one half adder.

2.5 Problems with Regular CSA 1. 2. 3.

It increases the number of full adders, so does the circuit complexity. Power consumption is more. The area of the design doubles as the number of complete adder cells doubles.

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2.6 Modified Regular CSLA Using Binary to Excess-1 Code Converter (BEC) In this modified circuit to displace the reverse carry adder with C in = 1 in a standard carry select adder with a BEC to diminish the size and force use. To supplant the n cycle RCA, a n + 1 bit BEC is required [4] (Fig. 7). One of the 8:4 mux’s bits of feedbacks is (B3 , B2 , B1, and B0 ), and the mux’s other information is the BEC yield. The mux is utilized to pick either the BEC yield or the immediate sources of info dependent on the control signal C in , and this produces two likely fractional outcomes in equal (Table 3). X 0 = ∼ B0 X 1 = B0 ∧B1 X 2 = B2 ∧(B0 &B1 ) X 3 = B3 ∧(B0 &B1 &B3 )

Fig. 7 4-bit BEC

Table 3 Function table of the 4-bit BEC

B[3:0]

E[3:0]

0000

0001

0001 .. .

0010 .. .

1110 1111

1111 0000

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Fig. 8 Modified SQRT carry select adder the parallel RCA with C in = 1 is replaced with BEC

2.7 Area Calculation of Modified CSLA The proposed 16-bit SQRT CSLA with BEC for RCA and C in = 1 to advance the region and force is displayed in Fig. 8. We partitioned the design into 5 five segments again. For C in = 0, group 2 has one 2-cycle RCA with one FA and one HA. A 3-bit BEC is utilized rather than another 2-bit RCA with C in = 1, which adds one to the yield from the 2-bit RCA. The input C1[t = 7] of 6:3 mux is sooner than the S3[t = 9] and C3[t = 10] and later than the S2[t = 4] dependent on the postpone upsides of Table 1. Thus, the sum3 and final C3 (mux yield) are reliant upon S3 and mux, just as fractional C3 (mux input) and mux, individually. C1 and mux are needed for sum2 [5] (Fig. 9). The estimated maximum delay and area of the updated SQRT CSLA’s other groups are also calculated and specified in the Table 4. To gain a minimum power delay product, the proposed SQRT CSLA sacrifices transistor count for speed. As a result, the proposed SQRT CSLA with CBL outperforms all other planned adders. The proposed structure of SQRT CSLA is shown Fig. 10.

3 Proposed Work 3.1 Reverse Carry Propagate Adder A standard full adder, that is the primary source of carry generator adders, has three identically weighted feed. Furthermore, it has two outputs: one to addition output of the similar inputs as the feeds and one for carry result of double the input of weight.

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Fig. 9 Delay and area evaluation of modified SQRT CSLA: a group 2, b group 3, c group 4, and d group 5. H is a half adder

Design of Low-Power Reverse Carry Propagate Adder Using FinFET Table 4 Delay and area count of modified SQRT CSLA

481

Group

Delay (ms)

Area

Group 2

13

43

Group 3

16

61

Group 4

19

84

Group 5

22

107

Fig. 10 16-bit proposed SQRT CSLA using common Boolean logic

Since it decides lag of the critical path of multi-bit adders, the carry developing lag (tcp) is the important time factor in multipliers [6].

3.2 Proposed Reversed Carry Propagate Full Adder Cell Designing an adder in carry propagation would cause more delay, but avoiding carry propagation when designing an adder would result in better adder efficiency, so it is chosen. The carry is opposite direction. An adder with three inputs and two outputs is known as a full adder. It computes the carry and sum using the equation. 2Ci+1 + Si = Ai + Bi + Ci where Ai and Bi are inputs of ith bit of sum. C i and C i + 1 are feed and result of carry of ith bit.

(1)

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S i is ith bit of sum. But if we interchange the terms in Eq. (1), we get Si − Ci = Ai + Bi − 2Ci+1

(2)

We can deduct from this equation that the full adder’s function is dependent in carry performance at (i + 1) nth bits location and C i + 1 as its feed bits. The result for this is addition and carry information with the mass (Fig. 11). As a result, we employ a completion of adders for RCPFA, as seen below. These have four feeds and three results, as seen in Fig. 12. The inputs (Ai , Bi ), the carry result of next bit position C i + 1, and a forecast signal (F i ) are feeds. When other side of (2) is null, signal F i is used to pick one of the two sets. Figure 12 depicts n-bit RCPA. Carry feed (C n ) as considered equal with result F of the most significant RCPFA in this structure. This could result is uncertain in given estimated adder. This adder’s direction is depicted in Figure. This means that for bits with higher significances, the combined effect of errors while carry propagation is small. Fig. 11 Block diagram of RCPFA

Fig. 12 n-bit RCPA

Design of Low-Power Reverse Carry Propagate Adder Using FinFET

483

3.3 Internal Structure of RCPFA On the basis with forecast signal is feed, K-maps of addition output (S i ), carry (C i ) are displayed to establish a structure for RCPFA. The Boolean relations between the inputs used to generate S i and C i are obtained as Si = Fi + Ai + Bi + Ai Bi Fi Ci = Ci+1 Fi + Ci+1 + Ci+1 + Fi . By simplifying, as efficient base-level structure for putting up RCPFA can be withdrawn. Si = Fi+1 (Ai Bi ) + (Ai + Bi ) = Fi X i + Yi Ci = Fi+1 = Fi Yi + X i

3.4 RCPFA Structure The information F in this structure is the carry information (Ai [OR] Bi ). Since some truth tables state that X i = 1 does not occur, we are replacing X i in the general framework (Fig. 13 and Table 5). Fig. 13 RCPFA logic circuit

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Table 5 Truth table of RCPFA Item

Ai

Bi

Ci + 1

Fi

Si

Ci

Fi + 1

1

0

0

0

0

0

0

0

2

0

0

0

1

1

1

0

3

0

0

1

X

0

1

0

4

0

1

0

0

0

0

1

5

0

1

0

1

1

1

1

6

0

1

1

X

0

1

1

7

1

0

0

0

0

0

1

8

1

0

0

1

1

1

1

9

1

0

1

X

0

1

1

10

1

1

X

0

0

0

1

11

1

1

X

1

1

1

1

Fig. 14 Simulation result of RCPFA

4 Experimental Results and Simulation 4.1 Simulation Results The proposed model has been simulated on H-Spice software tool. The output of the RCPA used in the design. The simulation outcome of RCPFA and RCPA is shown in Figs. 14 and 15.

4.2 RCPFA Output See Table 6.

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485

Fig. 15 Simulation result of output of 8 bit RCPA

Table 6 Characteristics and properties of the RCPFA model Nodes

19

Elements

31

Diodes

0

MOSFET

26

Supply current

1.2954E−10

From 0.0000E + 00 to 1.0000E−08

q_hl

−6.3633E−19

From = 0.0000E + 00 to = 5.0000E−09

q_lh

1.9317E−18

From = 5.0000E−09 to = 1.0000E−08 snm = 1.7972E−14

S nm

1.7972E−14

4.3 RCPA Output See Table 7. Table 7 Characteristics and properties of RCPA model Total memory used

848 k bytes

Nodes

123

Elements

226

MOSFETS

208

Supply current

−1.5027E−09

09 from = 0.0000E + 00 to = 1.0000E−08

q_hl

−9.5521E−18

From = 0.0000E + 00 to = 5.0000E−09

q_lh

−5.4744E−18

from = 5.0000E−09 to = 1.0000E−08

S nm

snm = 1.7994E−15

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4.4 Power See Table 8.

4.5 Delay See Table 9.

4.6 Energy See Tables 10 and 11. Table 8 Power comparison table Models

Average power

Static power

Total dynamic power

RCPFA

9.8211E−09

−6.4770E−10

−1.2346E−29

RCPA

3.9307E−08

7.5133E−09

−1.2346E−29

Table 9 Delay comparison table Models

Sum rise delay

Sum fall delay

Count rise delay

Count fall delay

RCPFA

5.2323E−11

9.6777E−09

3.4961E−15

2.0297E−08

RCPA

8.3554E−11

2.9907E−08

1.9748E−08

3.0448E−08

Table 10 Energy comparison table Models

High to low-total energy

Low to high-total energy

Dynamic energy

Total dynamic energy

RCPFA

−3.1816E−18

9.6587E−18

6.4770E−18

3.2385E−18

RCPA

4.7761E−17

−2.7372E−17

−7.5133E−17

−3.7566E−17

Table 11 Comparison between previous work and proposed work Characteristics

Previous work

Proposed work

Improvements

Power

4.9307

3.5420

Reduced by 71%

Delay

0.517

0.478

Reduced by 92%

Energy

7.477

6.112

Reduced by 81%

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487

5 Conclusion and Future Scope 5.1 Conclusion We suggested estimated RCPFAs that propagate carry from MSBs to LSBs in this project. In terms of delay variance, reverse carry propagation offered more reliability. In FinFET 7-nm technology, the effectiveness of the proposed estimated full adders and the hybrid adders that realized them was investigated. The results demonstrate that using proposed RCPFAs in hybrid adders delivers a 25% improvement on average saving of total on chip power.

5.2 Future Scope We are using these adder for the low-power consumption and less area for designing of the filters like LPF, stop band, pass band, etc., so audio amplifiers, equalizers, or speaker systems can be designed with less power consumption and with a desired SNR. These adders can also be used in the DSP applications for the image compression for energy conservation since other full adders use more MSSIM (minimum average and maximum mean of structural similarity), i.e., 25% more than RCPA.

References 1. D. Khalandar Basha, B. Naresh, S. Rambabu, D. Nagaraju, Body biased high speed full adder, in LNCS/LNAI/LNBI Proceedings (2017). https://doi.org/10.1007/978-981-10-3226-4-8. 2. D. Khalandar Basha, S. Reddy, K. Aruna Manjusha, 2D symmetric 16 * 8 SRAM with reset. J. Eng. Appl. Sci. 13(1), 58–63 (2018). https://doi.org/10.36478/jeasci.2018.58.63 3. K. Aruna Manjusha, B. Naresh, A new architecture of modified booth recorder for add multiply operator using carry save adder. ARPN J. Eng. Appl. Sci. 2153–2156 (2018) 4. K. Rawat, T. Darwish, M. Bayoumi, A low power and reduced area carry select adder, in 45th Midwest Symposium on Circuits and Systems, vol. 1, pp. 467–470 (2002) 5. Y. Kim, L.-S. Kim, 64-bit carry-select adder with reduced area. Electron. Lett. 37(10), 614–615 (2001) 6. K. Aruna Manjusha, T. Anuradha, Design and implementation of an 8-bit double tail comparator using foot transistor logic. Int. J. Appl. Eng. Res. (2017)

Design and Characterization of Microstrip Patch Antenna Using Octagonal EBG Periodic Structures B. Venkateshwar Rao and Sunita Panda

1 Introduction Generally, antennas are preferred to carry out the information for longer distances, i.e., from source to destination, which are playing an important role in case of wireless communication. These antennas will take the information source originated of speech from individual or else numerical data from systems. The basic design of conventional microstrip patch (MSP) antenna’s is constructed by using a conducting patch of standard shape when placed on substrate with desired dielectric material associated on a uniform ground plane. The antenna shapes considered may be of square, triangular, rectangular, elliptical, circular, etc., basically designed using copper material [1, 2]. These conventional antennas are preferred and started using gradually due to their familiar properties of having less expensive, low profile and can be directly printed [3]. To enhance the basic parameters of conventional microstrip patch antennas, periodic EBG structures are introduced, which are considered to be lucrative in wireless communication [4, 5]. Most of the investigators also prefer antenna designs with various slotted shapes enhancing the parameters at respective frequencies. It is observed that there are also some other advantages of patch antenna like compactness and easy to fabricate. Due to these eminent features, it has attracted a lot of analysts to improve the parameters in achieving high gain, wide bandwidth, directiveness, etc. A basic drawback found in this antenna design structures radiating electromagnetic energy in a particular medium is that some portion of radiations is reflected back B. Venkateshwar Rao (B) GITAM School of Technology, GITAM, Bengaluru, India e-mail: [email protected] Department of ECE, CMR College of Engineering and Technology, Hyderabad, Telangana, India S. Panda Department of ECE, GITAM School of Technology, GITAM, Bengaluru, Karnataka, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. K. Mandal et al. (eds.), Innovations in Signal Processing and Embedded Systems, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-19-1669-4_43

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along the corner edges, which are impotent to radiate defined to be surface waves. In order to overcome these limitations in conventional patch antennas, periodic EBG structures are introduced that are identical so as magnetic surface results in a very high-impedance surface (HIS). By properly aligning these EBG periodic structures and taking appropriate design parameter values obtained by optimization concept, it can be managed to suppress surface waves that are radiating at desired frequency levels. Such periodic structures are also regarded as frequency selectors [6, 7]. Due to these arrangements, mutual coupling between the radiators is also diminished accordingly. Periodic EBG structures are classified into three different types based on their geometrical configurations like one dimensional (used in filtering applications), two dimensional (mushroom type square patches), multi- or three dimensional (laying metallic patches both in vertical and also in horizontal directions). The proposed antenna structure designed is exposed in Sect. 2. Antenna analysis together with the help of simulation results is contemplated in Sect. 3. Finally, Sect. 4 is given with conclusion.

2 Proposed Antenna Design Structure The proposed antenna structure designed in this paper is desired to resonate at frequency of 6.9 GHz, which comes within the range of C-band [8]. The substrate material considered of FR4 with a loss tangent of 0.02 is designed with octagonal slots [9, 10]. Due to the association of enormous parameters that are likely to be continuous, discrete, or both in few cases, design flow of antenna process may decelerate and also become complicated. Optimization concept is used in evaluating and fixing scattering and radiation issues related to design parameters for the antenna proposed [11, 12]. In recent days, the concept of optimization is playing an important role in maintaining the parameter values. The proposed antenna structure is designed using EBG periodic structures with octagonal slots together, introducing optimization technique which is suitable for the applications in C-band frequency range with high-frequency structure simulator (HFSS) [13]. Optimization technique is an important method of investigating the most acceptable structure used to design when compared with numerous available possibilities [14]. This technique is applied for modeling the design at various steps, i.e., to find geometry (like size, structure, orientation, etc.), material option (having low loss, complex in nature, etc.), boundaries, and together with solution setup. Once, after getting an optimized design structure, it is investigated further to resonate at desired frequency. The parameter values obtained using optimization concept to construct proposed antenna design are given in Table 1. As specified, the simplified structure of proposed antenna model which includes ground plane, substrate, and patch is depicted in Fig. 1. A group of four EBG arrays arranged at a gap of 1 mm in width and a gap of 2 mm in length is represented in Fig. 2. Each element in an array has a rectangular-shaped

Design and Characterization of Microstrip Patch Antenna … Table 1 Proposed antenna dimensions

Fig. 1 Analytical representation of proposed antenna structure

Fig. 2 Geometrical structure of EBG array

Design parameters

491 Label

Value (mm)

Width of substrate

Ws

21

Length of substrate

Ls

22

Height of substrate

Hs

1

Width of patch

Wp

13

Length of patch

Lp

10.7

Width of feed

Wf

0.9

Length of feed

Lf

7

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Fig. 3 Schematic representation of proposed EBG antenna array

cell of dimensions 10 mm × 10 mm which is accommodated with octagonal slot. Radii of 2 mm for inner and 3 mm for outer octagonal slots are considered. The gap between the two complementary rings is maintained at 0.5 mm, and thickness of 0.7 mm is maintained related to these two octagonal [15]. The total structure of proposed design consisting of rectangular patch antenna together with an array of EBG structured octagonal slots is shown in Fig. 3.

3 Analysis of Antenna and Experiment Results In this section, results obtained from simulation of antenna proposed are shortlisted. Electromagnetic energy is known to be transferred from source to destination to impart wireless communication. Two important metrics as return loss and VSWR related to the performance of antenna are discussed. Return loss metric can be illustrated by taking the ratio of RF waves received to one transmitter by the antenna. Applying the concept of optimization technique for various lengths of patch, the return loss values are recorded as shown in Fig. 4a. From the values recorded, it is clearly predicted that the return losses for length 12 mm are about −26.33 dB, for a length of 13 mm are about −21.09 dB, and for length of 14 mm are about −16.57 dB. From the values obtained, a good return loss of −21.09 dB for a patch length of 13 mm is well suited for the proposed antenna to resonate closely at desired frequency, i.e., 6.93 GHz. Similarly, voltage standing wave ratio is the next metric considered to be an optional approach related to return losses. It is normally defined as the proportion of forward waves to the reflected waves that are emerged proportionately within the range of transmission medium. VSWR value of 1.42 is recorded from the simulation results shown in Fig. 4b. The measurement of radiation pattern for the mentioned

Design and Characterization of Microstrip Patch Antenna …

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Fig. 4 a Return loss plot, b VSWR plot, c gain plot, d directivity plot, e 3D gain plot, and f 3D directivity plot

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gain and directivity is depicted in Fig. 4c–d. From the figure shown, the values for gain and directivity are recorded as 4.94 dB and 6.8 dB. Figure 4e–f depicts the three-dimensional (3D) radiation pattern measurement. The article is also discussed with the details of polarization effects. Linear polarization is introduced in the paper. When both transmitting antenna and receiving antenna transmit and receive the radiations at phi = 0°, then these two are said to be co-polarized. However, if receiving antenna receives the radiation at phi = 90°, these two antennas are said to be cross-polarized. The co-pole and cross-pole values recorded after simulation are given by 4.8 and −41.45 as shown in Fig. 5a. Normally, co-pole will have much higher value when compared with cross-pole value that helps in achieving better polarization effects [16]. Figure 5b depicts the movements of current direction in antenna, which is observed of having fields (current) carrying maximum at the center and decelerates as it moves outward. One more antenna parameter is introduced, i.e., half-power beam width (HPBW) generally measured in terms of degrees that is defined as angular representation of a radiation pattern decreased in its magnitude by 50% (or −3 dB) when compared with the peak of its main beam. When the proposed antenna radiates at 6.9 GHz, HPBW is measured at phi = 0 and phi = 90° as shown in Fig. 5c. The discussions made so far provide an outstanding study of parametric analysis for antenna that can be designed accordingly.

4 Conclusion A compact periodic EBG antenna array provided with octagonal slots is detailed in this article using the concept of optimization technique that best suits for C-band applications simulated using HFSS. Smart designing of antennas can be achieved by using the concept optimization technique effectively. Based on this concept, the proposed antenna provides a gain value more than 4.94 dB and with directivity of 6.8 dB when compared with conventional microstrip patch antennas when resonating at same desired frequency of 6.9 GHz. Bandwidth of proposed antenna can still be enhanced by modifying the radii and space between the octagonal split slots. The efficiency “η” of the proposed radiating antenna is achieved up to 73% showing applicability of the respective radiator at desired frequency.

Design and Characterization of Microstrip Patch Antenna … Fig. 5 a Co-pole and cross-pole plot, b fields (current) of antenna plot, and c half-power beam width (HPBW) plot

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References 1. T.D. Amalraj, R. Savarimuthu, Design and Analysis of Microstrip Patch Antenna Using Periodic EBG Structure for C-Band Applications (Springer Science + Business Media, LLC, part of Springer Nature, 2019) 2. H. Huang, X. Li, Y. Liu, A low profile, single-ended and dual-polarized patch antenna for 5G application. IEEE Trans. Antennas Propag. (2019) 3. K. Praveen Kumar, B. Amulya, B. Venkateshwar Rao, Compact multiband printed antenna design and analysis. Int. J. Control Autom. 13(3), 58–63 (2020), ISSN: 2005-4297 4. K. Praveen Kumar et al., Optimization of EBG structure for mutual coupling reduction in antenna arrays—a comparative study. IJET 7(3.6), Special issue-06 (2018) 5. K. Praveen Kumar et al., Active PS EBG structure design for low profile steerable antenna applications. JARDCS 10(03) (2018) 6. A. Chatterjee, S.K. Parui, Frequency-dependent directive radiation of monopole-dielectric resonator antenna using a conformal frequency selective surface. IEEE Trans. Antennas Propag. 10(1109), 1–7 (2017) 7. K. Praveen Kumar et al., Surface wave suppression band, in phase reflection band and high impedance region of 3D EBG characterization. IJAER 10(11) (2015) 8. K. Praveen Kumar et al., Fractal array antenna design for C-Band applications. IJITEE 8(8) (2019) 9. B. Premalatha, M.V.S. Prasad, M.B.R. Murthy, Compact hexagonal monopole antenna. Indian J. Sci. Technol. 10(19) (2017) ISSN (Print): 0974-6846 ISSN (Online): 0974-5645 10. B. Venkateshwar Rao, P.K. Kancherla, B. Amulya, Multiband slotted elliptical printed antenna design and analysis. JMCMS 14(4) (2019) 11. K. Praveen Kumar et al., Design and characterization of optimized stacked electromagnetic band gap ground plane for low profile patch antennas. IJPAM 118(20), 4765–4776 (2018) 12. M. Pokorn, J. Horak, Design and global multi-objective optimization of planar tri-band antenna, in Proceedings of 17th International Conference Radioelektronika, pp. 1–5 (2007) 13. S. Sun, Y. Lv, J. Zhang, Z. Zhao, F. Ruan, Optimization based on genetic algorithm and HFSS and its application to the semiautomatic design of antenna. 978-1-4244-57083/10/$26.00©2010 (IEEE, 2010) 14. K. Praveen Kumar et al., Optimization of EBG structure for mutual coupling reduction in antenna arrays—a comparative study. IJET 7(3.6), Special issue-06, 13–20 (2018) 15. B. Premalatha, M.V.S. Prasad, M.B.R. Murthy, Compact penta band notched antenna using concentric rings with splitter bricks for ultra-wide band applications. J. Commun. Technol. Electron. 63(12), 1379–1385 (2018), ISSN 1064-2269 16. K. Praveen Kumar, Circularly polarization of edge-fed square patch antenna using truncated technique for WLAN applications. IJITEE 8(8) (2019)

Author Index

A Akhila, N., 53 Akhil, P., 77 Allam Balaram, 35 Amareswar, E., 459 Ande Shreya, 119 Anirudh Reddy, R., 183 Anitha, K., 459 Anjaneyulu, P., 403 Anudeep Peddi, 163, 209, 221 Anuradha, T., 23, 359 Anusha, B., 329, 373 Arulananth, T. S., 449 Aruna Manjusha, K., 473 Arun Kumar, V., 459 Ashwak, 305, 329 Aswanth Manindar, M., 373, 403

B Babitha, L., 109 Bala Bandhavi, M., 317

C Chandrashaker Pittala, 391 Chenna Kesava Reddy, G., 293, 417 China Venkateswarlu, S., 119, 263, 277, 293, 417 Chinnasamy, P., 1 Chinthala Akhil, 163 Choudary Santosh Kumar, 221

D Dharavath Veeraswamy, 263

G Garimella Ramamurthy, 133, 155 Geetha, Y., 305

H Harsha Vardhan, N., 119 Hruthik Chandra, R., 221

J Janapareddi Abhishek, 155

K Kachala Ganesh, 391 Kalyana Srinivas Kandala, 163, 209, 221 Kandadai Bhargavi, 169 Kantem Tarun, 119 Kanuri Naveen, 45 Kashi Sai Prasad, 9 Kaushik, G., 317 Kavitha, I., 341, 349 Khaja Shareef, Sk., 35 Khajashareef, S. K., 1 Koteswaramma, K. C., 119 Krishna, V., 199 Kumbala Pradeep Reddy, 169, 235

L Laxma Reddy, D., 441

M Madhava Rao, K., 183

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. K. Mandal et al. (eds.), Innovations in Signal Processing and Embedded Systems, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-19-1669-4

497

498 Madhusudhan, S., 95 Mahendra, V., 383 Mahesh, V., 109 Mani Raj, K., 429 Mohammed Mazharr, S., 95

N Nara Sreekanth, 235 Naresh, B., 473 Naresh Kumar, B., 183 Neelima, I., 163, 209, 221 Niharika, K., 109

P Pallavi, K., 383 Pasupathy, S., 9 Phani Krishna, Ch. V., 169 Poojitha, CH., 109 Poonam Upadhyay, 209 Prasad, S. V. S., 23, 329, 449

R Raja Rajeswari, T. S., 1 Rajaram Jatothu, 235 Raju Naik, M., 341 Rajunaik, M., 349 Ramavath Rani, 143, 429 Ranjith, G., 383 Ranjithkumar, G., 359 Ravindra, K., 441 Renu Babu, M., 293, 417 Rishi Shrinivas Seshan, 155

S Saikumar Tara, 253 Sai Lakshmi Haritha, I. V., 35 Sai Supraja, G. V., 65

Author Index Sai Venu Prathap, K., 95 Sandhya, N., 1 Sarangam Kodati, 169, 199, 235 Shravan Kumar, G., 143, 429 Shruti Patil, 35 Somanaidu, U., 109, 473 Soma Sekhar, G., 277 Sprasad, S. V., 305 Sreenivas Mekala, 235 Sridhar, B., 317, 341, 349 Srinivasa Rao, T., 163 Srinivasulu Reddy, D., 95 Sudhakar Yadav, N., 163, 209, 221 Sunita Panda, 489

T Tata Jagannadha Swamy, 133

U Uday Kumar, N., 263, 277

V Vadivelan, N., 169 Vaishnavi Hibare, 65 Vallabhuni Vijay, 109, 119, 263, 277 Vasudeva Reddy, T., 77, 183 Vemana Chary, D., 293, 417 Venkata Murali Mohan, K., 199 Venkata Ramana, K., 53, 65 Venkatesh Shankar, 245 Venkateshwar Rao, B., 489 Venkat Pavan Kumar, S., 449 Vishnu Teja, N., 209 Voorwashi, V., 23, 359

Y Yeshwanth Reddy, J., 183