Microelectronics, Circuits and Systems: Select Proceedings of Micro2021 9819904110, 9789819904112

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Lecture Notes in Electrical Engineering 976

Abhijit Biswas Aminul Islam Rishu Chaujar Olga Jaksic   Editors

Microelectronics, Circuits and Systems Select Proceedings of Micro2021

Lecture Notes in Electrical Engineering Volume 976

Series Editors Leopoldo Angrisani, Department of Electrical and Information Technologies Engineering, University of Napoli Federico II, Naples, Italy Marco Arteaga, Departament de Control y Robótica, Universidad Nacional Autónoma de México, Coyoacán, Mexico Bijaya Ketan Panigrahi, Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, Delhi, India Samarjit Chakraborty, Fakultät für Elektrotechnik und Informationstechnik, TU München, Munich, Germany Jiming Chen, Zhejiang University, Hangzhou, Zhejiang, China Shanben Chen, Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, China Tan Kay Chen, Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore Rüdiger Dillmann, Humanoids and Intelligent Systems Laboratory, Karlsruhe Institute for Technology, Karlsruhe, Germany Haibin Duan, Beijing University of Aeronautics and Astronautics, Beijing, China Gianluigi Ferrari, Università di Parma, Parma, Italy Manuel Ferre, Centre for Automation and Robotics CAR (UPM-CSIC), Universidad Politécnica de Madrid, Madrid, Spain Sandra Hirche, Department of Electrical Engineering and Information Science, Technische Universität München, Munich, Germany Faryar Jabbari, Department of Mechanical and Aerospace Engineering, University of California, Irvine, CA, USA Limin Jia, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Alaa Khamis, German University in Egypt El Tagamoa El Khames, New Cairo City, Egypt Torsten Kroeger, Stanford University, Stanford, CA, USA Yong Li, Hunan University, Changsha, Hunan, China Qilian Liang, Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA Ferran Martín, Departament d’Enginyeria Electrònica, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, Spain Tan Cher Ming, College of Engineering, Nanyang Technological University, Singapore, Singapore Wolfgang Minker, Institute of Information Technology, University of Ulm, Ulm, Germany Pradeep Misra, Department of Electrical Engineering, Wright State University, Dayton, OH, USA Sebastian Möller, Quality and Usability Laboratory, TU Berlin, Berlin, Germany Subhas Mukhopadhyay, School of Engineering and Advanced Technology, Massey University, Palmerston North, Manawatu-Wanganui, New Zealand Cun-Zheng Ning, Electrical Engineering, Arizona State University, Tempe, AZ, USA Toyoaki Nishida, Graduate School of Informatics, Kyoto University, Kyoto, Japan Luca Oneto, Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genova, Genova, Genova, Italy Federica Pascucci, Dipartimento di Ingegneria, Università degli Studi Roma Tre, Roma, Italy Yong Qin, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China Gan Woon Seng, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore Joachim Speidel, Institute of Telecommunications, Universität Stuttgart, Stuttgart, Germany Germano Veiga, Campus da FEUP, INESC Porto, Porto, Portugal Haitao Wu, Academy of Opto-electronics, Chinese Academy of Sciences, Beijing, China Walter Zamboni, DIEM—Università degli studi di Salerno, Fisciano, Salerno, Italy Junjie James Zhang, Charlotte, NC, USA

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Abhijit Biswas · Aminul Islam · Rishu Chaujar · Olga Jaksic Editors

Microelectronics, Circuits and Systems Select Proceedings of Micro2021

Editors Abhijit Biswas Department of Radio Physics and Electronics University of Calcutta Kolkata, West Bengal, India Rishu Chaujar Department of Engineering Physics Delhi Technological University New Delhi, India

Aminul Islam Department of Electronics and Communication Engineering Birla Institute of Technology Ranchi, Jharkhand, India Olga Jaksic Institute of Chemistry, Technology and Metallurgy University of Belgrade Belgrade, Serbia

ISSN 1876-1100 ISSN 1876-1119 (electronic) Lecture Notes in Electrical Engineering ISBN 978-981-99-0411-2 ISBN 978-981-99-0412-9 (eBook) https://doi.org/10.1007/978-981-99-0412-9 © 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

Contents

Highly Reliable PMOS Pass Transistor-Based Radiation Tolerant 12T SRAM Cell for Deep Space Applications . . . . . . . . . . . . . . . . . . . . . . . . . Ravi Teja Yekula, Monalisa Pandey, and Aminul Islam

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Majority PFET-Based Radiation Tolerant Static Random Access Memory Cell . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Monika Rani, G. Sai Namith, Shashank Kumar Dubey, and Aminul Islam

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Comparison of Snapback Phenomenon and Physics in Bottom and Top Body Contact NMOS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pragati Singh, Niladri Pratap Maity, Rudra Sankar Dhar, and Srimanta Baishya A Review on Optimal Power Flow Problem . . . . . . . . . . . . . . . . . . . . . . . . . . Naveen Kumar, Ramesh Kumar, and Ram Kumar A Study on Zn/Cu-Based Pandan Leaf (Pandanus Amaryllifolius) Electrochemical Cell . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Salman Rahman Rasel, K. A. Khan, Md. Sayed Hossain, Shahinul Islam, M. Hazrat Ali, and Rajada Khatun Gate All Around 22 nm SOI Schottky Barrier MOSFET with High I ON /I OFF Current Ratio for Low-Power Digital and Analog Circuit Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Amit Saxena, R. K. Sharma, Manoj Kumar, and R. S. Gupta Studies on Synthesis, Characterization, and Monitoring of Ag NPs for Power Production Using Tomato . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Farhana Islam, K. A. Khan, Md. Sayed Hossain, Salman Rahman Rasel, and Shirin Akter Man–Machine Interface in Designing Through Simulation in Solar Power Development in India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shreya Karmakar and Pradip Kumar Sadhu

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Contents

Optical Cryptography Using Reversible Logic Gate . . . . . . . . . . . . . . . . . . . Goutam Kumar Maity, Tarak Nath Bera, Aranya Manna, Pradip Ghosh, and Subhadipta Mukhopadhyay

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Red Spinach-A New and Innovative Power Source . . . . . . . . . . . . . . . . . . . . 113 K. A. Khan, Farhana Islam, Md. Sayed Hossain, Salman Rahman Rasel, and Md. Ohiduzzaman Heart Disease Risk Prediction Using Supervised Machine Learning Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 Madhumita Pal, Smita Parija, and Ranjan K. Mohapatra Implementing Machine Learning Algorithms for Predicting Roof Fall Statistics in UG Coal Mines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 Jitendra Pramanik, Singam Jayanthu, Abhaya Kumar Samal, and Surendra Kumar Dogra Synthesis, Characterizations of Silver Nanoparticles (AgNPs) and Monitoring for Power Production Using Drumstick Leaves . . . . . . . . 145 K. A. Khan, Mohammad Tofazzal Haider, Md. Sayed Hossain, and Salman Rahman Rasel Extract of Green Chili—A New Source of Electricity . . . . . . . . . . . . . . . . . . 159 Md. Ohiduzzaman, K. A. Khan, Shahinul Islam, Md. Sayed Hossain, and Salman Rahman Rasel Graphene-Based Biosensor: Physics and Technology . . . . . . . . . . . . . . . . . . 171 Rupanwita Das Mahapatra, Sulagna Chaterjee, and Moumita Mukherjee Graphene Sensors for Application in Defence and Healthcare Sector: Present Trends and Future Direction . . . . . . . . . . . . . . . . . . . . . . . . . 183 Rupanwita Das Mahapatra, Sulagna Chaterjee, and Moumita Mukherjee Design and Simulation of a High Power LED Bulbs with Different Array of Fins in Passive Mode of Cooling . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197 Nitin Namdeo Pawar and Kiran K. Jadhao An Experimental Investigation of Production of Plastic Fuel and Blend with Diesel Fuel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211 Nitin Namdeo Pawar and Kiran K. Jadhao Bivariate Frequency Analysis of Drought Using Copulas for Telangana Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221 Ashutosh Chaturvedi and Gore Vikas Sudam Influence of AlN Spacer Layer on SiN-Passivated AlGaN/GaN HEMT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233 Santashraya Prasad and A. Islam

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Design and Analysis of Fractal Type MIMO Radiator for the Applications of Sub 6-GHz 5G Systems . . . . . . . . . . . . . . . . . . . . . . . 243 K. Vasu Babu, R. Tejaswini, N. Sowjanya, B. Sujitha, T. Vineela, and B. Durga Prasad Histopathology Cancer Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251 Kolluri Paul Wilson, Muntala Srinivasa Reddy, Suyadevara Chakravarthy Karthik, Venkata Vamsi Kolluru Anudeep, and Kande Giri Babu Contingency Analysis Study for a 39 Bus System in a Micro-grid . . . . . . . 263 Dipu Mistry, Bishaljit Paul, and Chandan Kumar Chanda Applications of Green Energy Storage Systems Using PKL Battery . . . . . 275 K. A. Khan, Md. Sayed Hossain, Salman Rahman Rasel, and Mehedi Hasan Comparative and Robustness Study of 3-Bit Adder . . . . . . . . . . . . . . . . . . . 287 Md. Faizan Khan, Subham Chowdhury, Ravi Kumar, Shashank Kumar Dubey, Santashraya Prasad, and Aminul Islam Brain Tumor Detection and Classification from MRI Images Using Cascaded Deep Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 301 Pallavi Priyadarshini, Abdul Kayom Md. Khairuzzaman, and Priyadarshi Kanungo Development of Low-Cost Intelligent Alert System for Underground Coal Mines Using GSM . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313 Vijaya Bhasker Reddy, Suneetha Ghatikanti, and Falguni Sarkar Analysis of an IoT-Based SDN Smart Health Monitoring System . . . . . . . 325 M. Tejeswara Kumar, N. V. R. Vikram G, and Punyaban Patel Internet of Things in Agriculture Industry: Implementation, Applications, Challenges and Potential . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335 Kiran Jot Singh, Divneet Singh Kapoor, Anshul Sharma, Khushal Thakur, Tanishq Bajaj, Ashwin Tomar, Sparsh Mittal, Baljap Singh, and Raghav Agarwal Assessing Dynamic RAM Technology with Contrast Era of Megabit, Gigabit, and Merged Dynamic RAM/Logic . . . . . . . . . . . . . . . 349 Sanchit Yadav, Ritika Rattan, and Tripti Sharma Analysis of Computational Complexity in Interference Mitigation with 3D MIMO Beamforming Techniques in 5G Networks . . . . . . . . . . . . 361 Ashutosh Tripathi and Ranjeet Yadav Emotion Recognition: A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 371 Bhavesh Gandhi, Sandeep Saxena, and Pulkit Jain

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Smart Shirt: A Leap into Technological Fashion . . . . . . . . . . . . . . . . . . . . . . 381 Alok Barwal and Pulkit Jain Design of Domino Logic-Based NOR Gate Circuit for Reduction of Charge Sharing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 391 Saumya Srivastava and Sangeeta Shekhawat Design of Dynamic Logic Circuit-Based NOR Gate for Low Power . . . . . 399 Saumya Srivastava and Sangeeta Shekhawat Modeling of MQW Transistor Laser Using Group IV Materials . . . . . . . . 405 Jaspinder Kaur and Rikmantra Basu Driver Drowsiness Detection Using OpenCV and Machine Learning Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 413 K. Radhika, N. V. Krishna Rao, N. Shalini, V. Divya Vani, and B. Geetavani The Power Density of PKL, Aloe Vera, Myrobalan, Lemon, and Tomato Electrochemical Cell—An Observation . . . . . . . . . . . . . . . . . . 423 K. A. Khan, Md. Sayed Hossain, Salman Rahman Rasel, and Mehedi Hasan Modeling and Design of FPGA-Based Power Quality Analyzer . . . . . . . . . 433 M. Balasubbareddy, Kondapalli Venkata Sri Ram, and Ravindra Sangu Design of Hardware Unified Power Quality Conditioner to Mitigate Sag and Swell . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 445 Mallala Balasubbareddy and Ravindra Sangu Complementing Biometric Authentication System with Cognitive Skills . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 457 B. Sindhu and B. Kezia Rani An Interactive Method to Predict Thyroid Disease . . . . . . . . . . . . . . . . . . . . 467 Sai Jyothi Bolla, Kalavathi Alla, and Bhanu Supraja Grandhe A Brief Study of Designing a 10KWP Grid Connected Photovolatic System Using PVSYST . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 475 Amrita Chanda, Sagar Bera, Ramanuj Bhowmick, and Susovan Dutta Automatic Plant Watering System Using Moisture Sensor and Arduino Uno_An Experimental Validation . . . . . . . . . . . . . . . . . . . . . . . 485 Dhanaraju Athina, Kolli Vittal, Swapna Revuri, Kulsum Shireen, and Karnati Keerthi Lung Cancer Detection Using Computer-Aided Diagnosis (CAD) . . . . . . 501 Ashok Kumar Nanduri, Jeevan Ratnakar Kondru, M. Rambhupal, and Nutalapati Ashok

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Deep Learning Based Real-Time Object Detection on Jetson Nano Embedded GPU . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 511 Pardha Saradhi Mittapalli, M. R. N. Tagore, Pulagam Ammi Reddy, Giri Babu Kande, and Y. Mallikarjuna Reddy Density-Based Scanning to Provide Effective Medical Emergency System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 523 Sai Jyothi Bolla, C. M. Suvarna Varma, and G. Shireesha Phytochemicals as an Active Pharmaceutical Ingredient of Ocimum Sanctum and Azadirachta Indica:A Theoretical Screening Study . . . . . . . 535 Sourav Patanayak, Grishma Ninave, Moumita Mukherjee, Jayanta Mukhopadhyay, V. Ragavendran, B. B. Paira, Sukhendu Samajdar, Saumya Dasgupta, Debosreeta Bose, and Madhumita Mukhopadhyay DAAM: WSN Data Aggregation Using Enhanced AI and ML Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 547 Sanjay Gandhi Gundabatini, Suresh Babu Kolluru, C. H. Vijayananda Ratnam, and N. Nalini Krupa Dual-Strip Flag Microstrip Patch Antenna for Millimeter-Wave Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 557 Purnima K. Sharma, Dinesh Sharma, E. Kusuma Kumari, V. S. D. Rekha, and Vivek Garg A Cost-Effective Tracking and Health Monitoring System for Suspected COVID-19 Patient in Quarantine . . . . . . . . . . . . . . . . . . . . . . 569 Jhilam Jana, Sayan Tripathi, Akash Bhattacharya, Ritesh Sur Chowdhury, Deep Ranjan, and Jaydeb Bhaumik

About the Editors

Abhijit Biswas received his B.Tech., M.Tech., and Ph.D. (Tech.) degrees from the University of Calcutta. In 1999, he joined the University of Calcutta as a lecturer in the Department of Radio Physics and Electronics where he is currently working as a professor. He was associated with Jadavpur University as a lecturer in Electronics Science during 1997–1999 and as a reader in the Department of Electronics and Telecommunication Engineering during 2006–2008. His research interests include semiconductor physics, modeling, simulation, and characterization of electronic, optoelectronic and photovoltaic devices and circuits. He has published 81 technical papers in international SCI journals and over 130 papers in international conferences/workshops. He produced 11 Ph.D. scholars under his supervision and conducted many research projects funded by Govt. of India. He received UGC research award during 2012– 2014. He served as a Guest Editor of Microsystem Technologies, Springer several times, and acted as a reviewer of a large number of SCI journals including IEEE Electron Device Letters, IEEE Transactions on Electron Devices, Superlattices and Microstructures, Microelectronics Reliability, and many more.

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About the Editors

Aminul Islam received the B.Tech. in Computer Engineering from IE(I), in 2001, M.Tech. in ECE from BIT, Mesra, in 2006, and a Ph.D. degree from Aligarh Muslim University, India, in 2013. Until November 2006, he was with Indian Air Force. Since November 2006, he has been in the Department of ECE, BIT, where he is currently an associate professor. His research interest area is VLSI/CAD design for emerging technologies. He has over 276 journal publications, 135 conference papers, 32 book chapters, and 2 Indian patents published to his credit.

Rishu Chaujar is presently working as a professor in the Department of Applied Physics and Associate Dean (Acad-PG), Delhi Technological University (DTU). Her doctoral research involves modeling, design, and simulation of Sub-100 nm Grooved Gate/Concave MOSFETs, FinFETs, Tunnel FETs, Nanowires, HEMT structures modeling for high-performance sensing, biomedical, and wireless applications. She has over 305 papers in various reputed international and national journals and conferences. She has supervised 7 Ph.D. students, and 08 scholars are currently working with her. She has supervised several national and international research projects. She is a reviewer of various reputed international journals. She is a fellow of IETE, a life member of NASI, and a member of various international professional societies. Olga Jaksic received her Dipl.-Ing. and her Mag. Sci. degree in electrical engineering from the School of Electrical Engineering, University of Belgrade. She received her Ph.D. degree in physical chemistry from the Faculty of Physical Chemistry, University of Belgrade. Since 1993, she has been with the Institute of Chemistry, Technology and Metallurgy, University of Belgrade, currently as an associate research professor. She has been working with noise and fluctuations in MEMS sensors and their characterization; thermopile devices; resonant and micro-cantilever-based MEMS structures; photonic crystals; plasmonics, metamaterials, and 2D materials. Her main research interests include stochastic processes and random phenomena. She is a founding member of the Optical Society of Serbia, a member of

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the Society of Physical Chemists of Serbia, an editorial board member of several journals, and a reviewer of several SCI journals. She authored 135 publications in journals and conferences of international repute.

Highly Reliable PMOS Pass Transistor-Based Radiation Tolerant 12T SRAM Cell for Deep Space Applications Ravi Teja Yekula, Monalisa Pandey, and Aminul Islam

Abstract This paper proposes a highly reliable PMOS pass transistor-based radiation-tolerant 12T (PPTRT 12T) SRAM cell for deep-space applications. The proposed SRAM cell achieves 17.88% improvement in critical charge (QC ), 2.19× improvement in read static noise margin (RSNM), and 1.24× improvement in read access time (T RA ) as compared to QUCCE 10T SRAM cell at the expense of marginal (1.04×) degradation in write access time (T WA ). The read operation of the proposed circuit is highly stable (read upset proof) because of its higher RSNM. It is also highly reliable in radiation environments because of its higher QC . The theoretical design of the proposed SRAM cell has been validated with extensive simulations on PrimeSim HSPICE using 16-nm high-performance CMOS technology. Keywords RSNM · Critical charge · Access time · SET · SEU

1 Introduction Due to technology scaling, there is a decrease in supply voltage and other node capacitances for controlling the power consumption in ICs, but it increases the chance of being vulnerable to the SET (single event transient) which can eventually increase the probability of single event upset (SEU), which are also known as ‘Soft Errors’ [1–4]. SETs are caused due to Cosmic rays (neutrons), alpha particles originating from extra-terrestrial rays, and packaging materials, respectively [5]. These particles can either directly or indirectly ionize (generate electron-hole pairs) the semiconductor materials like silicon substrates used in the ICs. The generated electron/hole drifts toward the reverse-biased drain diffusion regions of NMOS/PMOS transistors causing an increase in charge. If this extra charge is gained by the sensitive nodes it causes voltage transients and if the amplitude and time duration of these transients R. T. Yekula · M. Pandey · A. Islam (B) Department of ECE, BIT, Mesra, Ranchi, Jharkhand 835215, India e-mail: [email protected] R. T. Yekula e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Biswas et al. (eds.), Microelectronics, Circuits and Systems, Lecture Notes in Electrical Engineering 976, https://doi.org/10.1007/978-981-99-0412-9_1

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are strong enough they lead to change in the state of the node. This phenomenon is termed as single event upset or Soft Error [5, 6]. These do not cause permanent damage, hence, they are also regarded as temporary errors but they can potentially cause a malfunction in the circuit and, hence, protection is needed against such SET [6]. Aggressive technology scaling has placed the transistors close to each other and due to the less spacing between transistors, more than one transistor can be affected in a single event because of charge sharing causing Single Event Multi-Node Upset (SEMNU), which can easily cause a malfunction in the circuit. These SEUs are possible in both logic and memory circuits but memory circuits are more susceptible due to their compact design, dense packing, and lack of recovery or error masking mechanisms [7]. Various alternate circuits were proposed in place of the standard 6T SRAM cell to have protection from SEU. However, a differential read Quarto 10T cell was introduced in [8] which is much robust to radiation strike compared to 6T SRAM and 1/2 rate ECC protected 6T. Taking the time penalty also into account Quarto 10T is preferred, but the high write failure probability is a major concern for Quarto 10T SRAM cells [8]. Therefore, a write enhanced Quarto 12T SRAM cell was introduced to boost the write ability compared to the Quarto 10T SRAM but with a tradeoff of large area overhead. Later, NMOS stacked 10T SRAM cell (NS10T) and PMOS stacked 10T SRAM cell (PS10T) were introduced but they provide only partial protection against the SEU, i.e., either a 0–1 or a 1–0 transient is only protected according to whether the NMOS or PMOS is used in the stacking structure [9]. In view of the above issues, this paper proposes a 12T SRAM cell, which uses a non-differential read technique to provide a shorter read access time and higher RSNM (read static noise margin), which makes it read upset tolerant in addition to single event upset tolerant at the cost of slightly increased write delay. The rest of the paper is organized as follows. Section 2 briefly describes the prior work. Section 3 presents the proposed SRAM cell. Section 4 consists of the simulation of SRAM cells. Section 5 provides the conclusion.

2 Prior Work The QUCCE 10T SRAM cell proposed in [10] has two storage nodes named Q and QN in addition to its internal node pair A and B. Each node is present between a series of PMOS and NMOS transistors as shown in Fig. 1. Both the bit lines, namely BL and BLB connect the two storage nodes Q and QN via NMOS access transistors N5 and N6, respectively. These access transistors are controlled through word line WL which is connected to the gate of these two access transistors. Considering ‘0’ is stored in the cell, the node values are as follows—A is ‘1’, Q is ‘0’, QN is ‘1’, and B is ‘0’, respectively. With these the hold, read, and write operations are explained as follows:

Highly Reliable PMOS Pass Transistor-Based Radiation Tolerant 12T …

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Fig. 1 Quadruple cross-coupled storage cells (QUCCE) 10T SRAM cell [10]

2.1 Hold Operation In this mode, the WL is grounded making sure the N5 and N6 access transistor are switched off, the transistors P1, N2, P3, and N4 are in ‘ON’ state and N1, P2, N3, and P4 are in ‘OFF’ state.

2.2 Read Operation In this operation, initially both the bit lines BL and BLB are pre-charged to supply voltage V DD and then WL is made high switching ON the access transistors N5 and N6 but only N2 is in ON state and N3 is in OFF state, hence, there is a discharge path only for BL through N5 and N2 leading to a voltage difference between BL and BLB. With the help of a sense amplifier, read operation is carried out. During read operation zero storing node, Q gets disturbed from its state due to the voltage division between the resistance of N5/6 and N2/3. While the discharge of BL a voltage bump is developed at Q, which should not be high enough to flip the state of the cell. Hence, the N2/3 should be sized stronger than the N5/6, i.e., the cell ratio (W N2 /L N2 )/(W N5 /L N5 ) or (W N3 /L N3 )/(W N6 /L N6 ) must be chosen properly to avoid flipping of the cell and ensure proper read operation.

2.3 Write Operation In this operation, for writing an opposite value to the already stored value, initially the bit line associated with the node storing ‘1’ is driven to GND and the other bit line is driven to V DD . That is, since QN was previously storing ‘1’, hence, the BLB is grounded and BL is driven to V DD ; then WL is made high switching ON both the access transistors. Therefore, the QN node storing ‘1’ is pulled down by BLB

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through transistor N6 such that the potential at QN falls below the threshold voltage (V th ) of N2 putting it in an OFF state; on the other side the BL tries to pull up the potential of storage node Q and the cross-coupled structure of N2 and N3 transistors amplify the difference between Q and QN aiding in flipping the stored voltages. For this to happen, we should ensure N6 (N5) transistor is stronger compared to the P3 (P2) so the pull-up ratio (W P2 /L P2 )/(W N5 /L N5 ) or (W P3 /L P3 )/(W N6 /L N6 ) should be chosen properly. The cross-coupled structure of P1 (P3) and P2 (P4) helps in amplifying the difference between Q (QN) and A (B) [10]. To pull up the potential of node, B the P4 should be stronger than the N4 and, hence, the pull-up ratio, i.e., (W P4 /L P4 )/(W N4 /L N4 ) or (W P1 /L P1 )/(W N1 /L N1 ) should be chosen adequately. Since the mobility of holes is lesser than that of free electrons a larger PMOS is required here for proper write operation this causes more area overhead [10]. After the write operation, the transistors P2, N3, P4, and N1 are ‘ON’ and the transistors N4, P1, N2, and P3 are in ‘OFF’ state and, hence, the contents of the nodes are flipped to A is ‘0’, Q is ‘1’, QN is ‘0’, and B is ‘1’, respectively.

3 Proposed PMOS Pass Transistor-Based Radiation Tolerant 12T 3.1 Cell Structure and Behavior The proposed circuit, as shown in Fig. 2, is similar to the QUCCE 10T with the exception that PMOS transistors (P5 and P6) are used as access transistors instead of NMOS access transistors (N5 and N6) and a separate NMOS stack containing two NMOS transistors are used to carry out the read operation separately without influencing the present state of the cell. The Hold and Write operations are similar to that of QUCCE 10T SRAM cell as explained in Sect. 2, only the read operation differs from that of the QUCCE 10T SRAM and it is as follows:

Fig. 2 PMOS pass transistor-based radiation-tolerant 12T (PPTRT 12T) SRAM cell

Highly Reliable PMOS Pass Transistor-Based Radiation Tolerant 12T …

5

Read Operation is carried out with help of the NMOS stack connected to RBL, which is pre-charged before the read operation is initiated. To perform read operation, read line (RL) is activated. The lower NMOS in the stack is already in ON state due to storage node QN, which has been assumed to hold a ‘1’ previously. Therefore, as soon as the read line (RL) is raised high the read bit line (RBL) starts discharging through the NMOS stack, and a sense amplifier (not shown) senses the decrease in RBL with respect to a reference voltage. The non-differential read operation is completed once a potential difference between the read bit line (RBL) and the reference voltage becomes 50-mV because a sense amplifier can decipher the stored content only if the potential difference is at least 50-mV [10]. The stacked NMOS sizing is done conveniently as they do not influence the cell and there is no risk of flipping the contents of the cell [10]. Hence, a higher value of RSNM is achievable, which makes the cell robust and stable in the presence of noise thereby making the circuit highly reliable and robust against read upset.

3.2 Cell Sizing The transistors in a cell are to be sized in such a way that the cell content is not altered during a read operation and the cell content is flipped during a write operation. Phrased in a different way—read upset and write failure should not occur due to sizing problems. Unlike QUCCE 10T, the proposed cell state is not influenced in a read operation, hence, only write operation is considered for cell sizing. The pull-up ratio (W P2 /L P2 )/(W P5 /L P5 ) or (W P3 /L P3 )/(W P6 /L P6 ) for a stable write operation is set as 2 and the pull-up ratio (W P4 /L P4 )/(W N4 /L N4 ) or (W P1 /L P1 )/(W N1 /L N1 ) for ensuring proper write operation is set to be 2.5. As the read operation does not disturb the content of the cell, the cell ratio CR (W N2 /L N2 )/(W P5 /L P5 ) or (W P3 /L P3 )/(W P6 /L P6 ) can be taken conveniently.

3.3 SEU Recovery Mechanism This subsection describes the SEU recovery mechanism of the proposed cell referring to Fig. 2 and with the nodes A, Q, QN, and B holding the values ‘1’, ‘0’, ‘1’, and ‘0’, respectively. If node A is affected by a SET, the state of node A is changed from ‘1’ to ‘0’. This immediately switches N4 transistor OFF and P2 to turn ON [10]. Now both the transistors P2 and N2 are in an ON state causing the node Q present in between them to be in an unstable state, at the same time since N4 is OFF, node B enters into a high impedance state so its state ‘0’ is retained. Since the P3 transistor is unaffected and continues to be in an ON state the node QN retains its state ‘1’. Hence, the node QN helps the node Q to regain its original state ‘0’ due to the persistent signal at the gate terminal of transistor N2 and then node Q itself ensures that node A is brought

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back to its original state ‘1’ as the node Q controls the transistor P1. The analysis is similar in the case of node QN due to the symmetric cross-coupled structure of the SRAM cell. If node Q is affected by a SET, the node Q state is changed from state ‘0’ to ‘1’ which causes transistor N3 to switch ON and transistor P1 to switch OFF immediately, this causes node QN to be in an unstable state, simultaneously we can notice that node A is in high impedance state and, hence, it retains its value without any fluctuation, and in turn, it makes sure that node B’s state is also unchanged.

4 Simulation Results and Discussions of SRAM Cells 4.1 Read Access Time or Read Delay Analysis The read access time (T RA ) or read delay is calculated during the read operation; it is estimated as the time from which the WL goes high to the time when BL or BLB discharges by 50-mV from its pre-charge (V DD ) value; this difference is sufficient for the sense amplifier to detect a successful read [10]. The estimated (T RA ) is reported in Table 1, from which we can observe the Read Access Time (T RA ) is less in the case of the proposed SRAM cell than the QUCCE 10T. This is because the NMOS stack has been sized conveniently for shorter T RA since the read operation does not affect the contents of the cell. From Fig. 3, the variation of T RA with respect to variation of V DD for both the cells has been reported. It can be observed that the proposed PPTRT 12T SRAM cell shows 1.24× shorter read delay compared to QUCCE 10T SRAM cell at a nominal supply voltage of 0.7 V. The spread (standard deviation) of T RA of QUCCE 10T SRAM cell is 1.25× wider than that of the proposed cell at nominal supply voltage 0.7 V. This implies the robustness of the proposed cell against voltage variation compared to QUCCE 10T SRAM cell. Table 1 Comparison of read delay and its variation with V DD

Cell QUCCE10T

PPTRT12T

Std. Dev. of T RA (ps)

Mean of T RA (ps)

V DD (mV)

61.55

319.2

770

62.11

346.7

735

65.93

376.8

700

63.18

408.9

665

64.22

445.0

630

51.29

254.7

770

52.05

278.1

735

52.91

303.8

700

53.95

332.3

665

54.71

363.9

630

Highly Reliable PMOS Pass Transistor-Based Radiation Tolerant 12T … 440

7 QUCCE 10T Proposed 12T

420 400

T

RA

(ps)

380 360 340 320 300 280 260 640

660

680

700

720

740

760

V DD (mV)

Fig. 3 Variation of read access time (T RA ) or read delay with V DD is plotted for both QUCCE 10T and the proposed 12T SRAM cells

4.2 Write Access Time or Write Delay Analysis The write access time (T WA ) or write delay is measured during the write operation. It can be estimated as the time from when the WL line is activated to the time when the node Q is flipped from ‘0’ to ‘1’ state [11]. Table 2 shows the comparison between QUCCE 10T SRAM cell and the proposed PPTRT 12T cell in terms of write access time (T WA ) and its variation with V DD . From the table, we can observe that the T WA of the proposed cell is longer compared to QUCCE 10T SRAM cell. This is due to the use of PMOS access transistors, which have lower drive current due to lower mobility of holes compared to electrons in NMOS transistors in QUCCE 10T. From Fig. 4, the variation of T WA with respect to V DD for both QUCCE 10T SRAM cell and the proposed 12T SRAM cell can be observed. Table 2 Comparison of write delay and its variation with V DD

Cell

Std. Dev. of T WA (ps)

V DD (mV)

QUCCE10T

763.7

770

790.2

735

819.8

700

851.2

665

886.9

630

802.3

770

831.3

735

851.3

700

885.6

665

PPTRT12

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QUCCE 10T Proposed 12T

900

T WA (ps)

880 860 840 820 800 780 640

660

680

700 V DD (mV)

720

740

760

Fig. 4 Variation of write access time (T WA ) or write delay with V DD is plotted for both QUCCE 10T and the proposed 12T SRAM cells

4.3 Read Stability Analysis Static noise margin can be defined as the minimum voltage caused by the noise which is sufficient to flip the contents of the cell [10]. It is the most extensively used design metric for estimating the stability of the cell. The length of the side of the largest square that can be fitted into the smallest wing of a butterfly curve gives the RSNM of the corresponding cell. From Fig. 5, we can observe that the RSNM of the proposed SRAM cell is higher compared to the QUCCE 10T SRAM cell (RSNM of QUCCE 10T is 80 mV and that of the proposed PPTRT 12T cell is 175 mV). Whereas in QUCCE 10T SRAM cell the read operation affects the contents of the cell and thus can reduce the read stability of the cell. Hence, the RSNM of the proposed cell is 2.19× higher compared to that of the QUCCE 10T SRAM cell.

4.4 SEU Robustness Comparison SEU robustness can be analyzed using a metric called critical charge (QC ), which is defined as the minimum charge collected at a sensitive node that can cause an upset to the state of the cell. By using an exponential current source on the LT spice simulation environment we can create a SET (Single Event Transient) at the sensitive node, and using (1), we can calculate the charge that is accumulated at the sensitive node due to the exponential current source [10]. By increasing the current through the current source, we are increasing the charge induced at the nodes to which this source is connected; as we increase the current steadily we can find the critical charge (QC ) of the circuit, i.e., by measuring (Q0 ) corresponding to the current which causes the SEU using (1), where τ α and τ β are rise and fall time delay constants, respectively.

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0.7 QUCCE 10T

Storage Node QN (V)

0.6

PPTRT 12T

0.5

RSNM QUCCE 10T @ 0.7V = 80mV RSNM PPTRT 12T @ 0.7V = 175mV

0.4 0.3 0.2 0.1 0

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Storage Node Q (V)

Fig. 5 Butterfly curve for estimating RSNM of QUCCE 10T SRAM cell and the proposed 12T SRAM cell

I (t) =

−t  Q o  −t e τα − e τβ τα − τβ

(1)

The proposed 12T SRAM cell and the QUCCE 10T SRAM cell are robust against SET (Single Event Transient) until the charge induced by the SET is less than the critical charge (QC ). From Figs. 6 and 7 we can observe that a 1 → 0 SET at node QN of both QUCCE 10T SRAM cell and the proposed 12T SRAM cell flip its stored content. Extensive simulations are done using PrimeSim HSPICE, where an exponential current source is used for mimicking the SET at node QN. The minimum charge to flip the contents of the cell, i.e., (critical charge, QC ) in the case of QUCCE 10T SRAM cell is 2.8 pC and the QC in the case of the proposed 12T SRAM cell is 3.3 pC. These are calculated for the 1 → 0 SET in both circuits. The proposed PPTRT 12T SRAM cell exhibits 17.88% increase in critical charge (QC ) compared to QUCCE 10T SRAM cell. This is because the access transistors used in the proposed cell are PMOS unlike the NMOS transistors used in QUCCE 10T cell. The drain diffusion regions of PMOS access transistors in PPTRT 12T are not subjected to reverse-biased conditions, there is only a slight rise in potential beyond ‘1’ at node QN due to the strike of energetic particle. This positive spike of potential does not flip the cell content. In the case of QUCCE 10T SRAM cell, strike-generated electrons are collected by the node QN due to reverse-biased condition at the drain diffusion regions of N3 and N6 and, hence, a negative spike is generated resulting in a possibility of SEU.

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Fig. 6 Flipping of cell contents due to 1 → 0 SET at node ‘QN’ for the QUCCE10T

Fig. 7 Flipping of cell contents due to 1 → 0 SET at node ‘QN’ for the proposed 12T SRAM cell

5 Conclusion The proposed 12T SRAM cell is more robust against SEU caused by the energetic particle. It shows shorter read delay and higher read static noise margin (RSNM) compared to QUCCE 10T SRAM cell. The proposed PPTRT 12T SRAM cell exhibits a higher critical charge compared to the QUCCE 10T SRAM cell thereby proving its radiation hardness against energetic particles. The proposed design is, therefore, an attractive choice as cache memory in the processor for deep-space applications.

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References 1. B. Narasimham, S. Gupta, D. Reed, J.K. Wang, N. Hendrickson, H. Taufique, Scaling trends and bias dependence of the soft error rate of 16 nm and 7 nm FinFET SRAMs, in Proceedings of IRPS (2018), pp. 4C.1–1–4C.1–4 2. M.T. Bohr, I.A. Young, CMOS scaling trends and beyond. IEEE Micro 37(6), 20–29 (2017) 3. R.C. Baumann, Radiation-induced soft errors in advanced semiconductor technologies. IEEE Trans. Device Mater. Reliab. 5(3), 305–316 (2005) 4. V. L. Ferlet-Cavrois, W.P. Gouker, Single event transients in digital CMOS—A review. IEEE Trans. Nucl. Sci. 60(3), 1767–1790 (2013) 5. P.E. Dodd, L.W. Massengill, Basic mechanisms and modeling of single-event upset in digital microelectronics. IEEE Trans. Device Mater. Reliab. 50(3), 583–602 (2003) 6. D. Krueger, E. Francom, J. Langsdorf, Circuit design for voltage scaling and SER immunity on a quad-core Itanium processor, in Proceedings of International Solid-State Circuits Conference (2008), pp. 94–95 7. T. Karnik, P. Hazucha, J. Patel, Characterization of soft errors caused by single event upsets in CMOS processes. IEEE Trans. Dependable Secure Comput. 1(2), 128–143 (2004) 8. S.M. Jahinuzzaman, D.J. Sachdev, M. Sachdev, A soft error tolerant 10T SRAM bit-cell with differential read capability. IEEE Trans. Nucl. Sci. 56(6), 3768–3773 (2009) 9. I.S. Jung, Y.B. Kim, F. Lombardi, A novel sort error hardened 10T SRAM cells for low voltage operation, in Proceedings of IEEE 55th International MWSCAS (2012), pp. 714–717 10. J. Jiang, Y. Xu, W. Zhu, J. Xiao, S. Zou, Quadruple cross-coupled latch-based 10T and 12T SRAM bit-cell designs for highly reliable terrestrial applications. IEEE Trans. Circuits Syst. I Regul. Pap. 66(3), 967–977 (2019) 11. J. Guo et al., Design of area-efficient and highly reliable RHBD 10T memory cell for aerospace applications. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 26(5), 991–994 (2018)

Majority PFET-Based Radiation Tolerant Static Random Access Memory Cell Monika Rani, G. Sai Namith, Shashank Kumar Dubey, and Aminul Islam

Abstract Our article presents a Majority PFET-based Radiation Tolerant (MPRT) Static RAM (SRAM) cell. The benefit of using a greater number PFETs is its high radiation tolerance. The leakage currents in NMOS access transistors increase rapidly by the radiation bombardment, whereas they are not affected in the case of PMOS access transistors. The proposed MPRT SRAM cell achieves 1.2× higher value of QCri (stands for critical charge) in comparison with the We-Quatro SRAM bit cell under parametric variations of 16-nm CMOS technology. Proposed circuit MPRT consumes ≈6% lower hold power in comparison with the We-Quatro. It exhibits higher read stability by showing 1.5× improvement in RSNM. The proposed cell achieves these improvements at the expense of 1.26× longer read delay and 1.22× longer write delay at nominal supply voltage. Keywords Radiation-hardened SRAM · Soft error · We-Quatro · Current margin · Critical charge · Read static noise margin (RSNM) · Hold power

1 Introduction Research in radiation hardening covers a broad subject because radiation originates from various sources that exist all over the universe. Higher density and lower power in SRAM are in high demand. To satisfy those demands, dimensions of devices and operating voltages of SRAM are reduced. Cache memory is made up of SRAM, which covers 90% of the chip area. In standby mode, while performing data retention operation, SRAM cell consumes static power due to leakage current of the nanoscale devices. In the nanoscale regime, memory cells of SRAM are more susceptible to radiation particles because their nodal capacitance is smaller. Less storage node charge and decreased noise margin make the nanoscale integrated circuits (ICs), M. Rani · G. Sai Namith · S. K. Dubey · A. Islam (B) Department of ECE, Birla Institute of Technology, Mesra, Ranchi 835215, India e-mail: [email protected] G. Sai Namith e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Biswas et al. (eds.), Microelectronics, Circuits and Systems, Lecture Notes in Electrical Engineering 976, https://doi.org/10.1007/978-981-99-0412-9_2

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particularly SRAM circuits, tremendously prone to energetic hit-induced SET (stands for single-event upset) [1, 2]. Packaging materials and intergalactic rays produce alpha particles and cosmic neutrons which cause SETs. The soft error occurs when an energetic particle strike causes the cell content to flip. It affects the data state of memories such as SRAM cells and other sequential elements. This error is known as ‘soft error’ and not hard error or permanent error because due to radiation, the circuit is not damaged forever. If new data are stored, then cells store them without any problem. This phenomenon is variously called single-event transient (SET) or SET single-event upset. There are two types of radiation hardening techniques occurred: • Radiation Hardening by Process (RHBP): Radiation hardening by process (RHBP) is a method to harden a device to SEE using certain features in the fabrication process. This is done by modifying a current fabrication process. • Radiation Hardening by Design (RHBD): Radiation hardening by design (RHBD) approach uses common methods through constructing various topologies of transistor connections inside cells to achieve circuit-level protection. In this paper, RHBD approach has been adopted for hardening of memory cell to avoid bit flipping. To reduce the leakage power, increase the read stability and soft error problems, many general and radiation-hardened bit cells are presented [3–17]. Results have illustrated that in the radiation environment standard 6T SRAM cell may not convey adequate reliability. To deliver high soft error resilience, researchers are strongly inspired to explore other SRAM cells. The authors in [3] compared 6 T, the Quatro, and the We-Quatro through parametric variations. These make the Quatro more favorable than the 6 T. However, in scaled technologies, Quatro suffers from poor write ability. To address this problem, the author exhibited different radiation-hardened SRAM cell called as We-Quatro [3]. The write ability of Quatro is highly affected by the strength of the access pass gate. For enhancing the write ability, the author in [3] added two more access pass gates in Quatro to make it We-Quatro. Access pass gates were realized as NMOS transistors. We present a new Majority PFET-based Radiation Tolerant (MPRT) SRAM cell, which has 8 PMOS transistors out of 12. After radiation bombardment, leakage currents and charge sharing in NMOS transistors increase however PMOS transistors remain almost unaffected. For higher radiation tolerance, PMOS transistors are used as major devices. Extensive simulations on SPICE have been carried out to verify our design and its superiority compared to the previously proposed We-Quatro in [3]. In SRAM design, the current margin and critical charge should be considered in addition to general design metrics namely RSNM, etc. Hence, the proposed bit cell’s metrics have been compared with those of We-Quatro. The remaining portion of the article is arranged in the following manner. Section 2 briefly describes the previously presented We-Quatro bit cell. Section 3 presents the proposed MPRT SRAM cell. The values from the simulation are discussed in Sect. 4. We conclude the article in Sect. 5.

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Fig. 1 Schematic of writability-enhanced Quatro (We-Quatro) SRAM cell [3]

2 A Brief of Previously Proposed We-Quatro SRAM Cell Due to process, voltage, and temperature (PVT) variation in advanced CMOS technology, Quatro SRAM cell experiences inferior writability so authors go for proposing We-Quatro which is described in this section. We-Quatro SRAM cell exhibits SEU tolerance with reasonable silicon area over budget. Figure 1 illustrates the schematic of the 12T We-Quarto cell [3]. It contains four PMOS transistors specified as MP1, MP2, MP3, and MP4, four NMOS transistors named as MN1, MN2, MN3, and MN4, and four NMOS access transistors (MN5, MN6, MN7, and MN8). Node ‘D’ and ‘A’ are connected to bit line bar (BLB) by two access transistors (MN7 and MN5), and another two nodes (‘B’ and ‘C’) are attached to the bit line (BL) via two access devices (MN6 and MN8). NMOS (MN4, MN5, MN6, and MN7) access transistors have been replaced with PMOS transistors to improve the radiation hardness of our proposed circuit. We have followed the transistor sizing strategies of We-Quatro discussed in the Quatro proposed in [4] for carrying out Monte Carlo simulations. For achieving a good read static noise margin (RSNM), cell ratio (CR) of 1.5 is maintained, i.e., MN1/3–MP5/6 ratio is 1.5 [3]. Pull-up ratio 1 (PR1) = MP1/3–MP5/6) ratio = 1 and pull-up ratio 2 (PR2) = MP2/4 to ratio MN2/4 ratio = 0.67 are maintained for reliable writing [3].

3 Description of Proposed MPRT SRAM Cell Figure 2 shows the outline of the MPRT SRAM cell. It also consists of 12 transistors like We-Quatro. It contains four PMOS transistors specified as MP1, MP2, MP3, and MP4 and four NMOS transistors named MN1, MN2, MN3, and MN4. There are four PMOS access transistors specified as MP5, MP6, MP7, and MP8. Access

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transistors (MP5 and MP7) control the link between BLB and the node ‘A’ and ‘D.’ Access transistors (MP6 and MP8) control the link between BL and the node ‘B’ and ‘C.’ The nodes ‘D’ and ‘C’ are redundant nodes of storage nodes ‘A’ and ‘B.’ In the case of hold, read, and write operations, there are two kinds of storage possibilities. For ‘0’ stored bit, the node values are A = 0, B = 1, C = 1, and D = 0. The node values are A = 1, B = 0, C = 0, and D = 1 for the stored bit of ‘1.’ The analyses of the nodes (A, B, C, and D) are demonstrated as follows. Case 1 (A = 0, B = 1, C = 1, and D = 0): Assuming when node A stores ‘0,’ then MN1 and MP1 are ON and OFF, respectively. The Gate of MN1 is attached to node B. For allowing node A to have a path to discharge to logic ‘0.’ Since node B is at logic ‘1,’ MN4 is ON pulling node D down to logic ‘0,’ which turns MP2 ON, raising node C to logic ‘1.’ The Gate of MP1 is attached to node C. Hence, the logic value at node C is maintained at ‘1’ to cutoff MP1. That is, the nodes A, B, C, and D maintain their chronological order (0, 1, 1, 0), respectively. Case 2 (A = 1, B = 0, C = 0, and D = 1): Assuming when node A stores ‘1,’ the path of charging to V DD by the MP1 is available and the path to discharge to the ground is cutoff. That is, MP1 and MN1 are turned ON and OFF respectively. The Gate of MP1 is connected to node C. Since the logic value at node C is ‘0,’ it switches MP1 ON which allows node A to charge to logic ‘1.’ Since node C is logic ‘0,’ MP4 is ON pulling node D up to logic ‘1,’ which turns MP3 OFF and since the gate of MN3 is connected to node A, it is ON, thereby pulling down node B to logic ‘0,’ The Gate of MN1 is connected to B; hence, it is OFF. So, the logic value of node A is maintained at ‘1.’ For any ON PMOS, its gate should be ‘0.’ That is, the nodes A, B, C, and D maintain their chronological order (1, 0, 0, 1), respectively. The transistor sizing of the proposed MPRT 12T SRAM cell for carrying out Monte Carlo simulations is as follows. For a fair comparison, the (W/L) of pull-down devices (MN1, MN3) and (MN2, MN4) are 36 nm/16 nm and 24 nm/16 nm, respectively. W/L of pull-up transistors (MP1, MP3) and (MP2, MP4) are 20 nm/16 nm and Fig. 2 Proposed majority PFET-based radiation tolerant (MPRT) static random access memory cell

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16 nm/16 nm, respectively. For successful read and write operations, access devices are made stronger than pull-up devices and weaker in strength than pull-down devices and for that W/L of access devices (MP5, MP6, MP7, MP8) are 24 nm/16 nm.

3.1 Write and Read Operation For a write operation, we suppose that node A is ‘0’ and node B is ‘1.’ Both the bit lines BLB and BL are set to ‘1’ and ‘0,’ respectively. Word line (WL) goes low. Hence, all four access transistors are ON. In these circumstances, node B is pulled down by MP6, and node C is pulled down by MP8. They are fighting against the weaker pull-up devices MP3 and MP2, that is, BL forcibly flip node B and C to ‘0.’ At the same time, the access transistors MP5 and MP7 help to pull up the nodes ‘A’ and ‘D,’ respectively, and the write operation is successfully performed. In a read operation, both bit lines (BL and BLB) are precharged to V DD . We assume that A = ‘0,’ B = ‘1,’ C = 1, and D = 0 before read operation. For carrying out read operation, WL is lowered. MN3 and MP1 are turned OFF, and MN1 and MP3 will be turned ON. Hence, BLB starts discharging through MP5 and MN1 because it provides a ground path. On the other hand, BL does not discharge because there is no conducting path to the ground. Due to discharging, BLB voltage decreases, and when the voltage difference of both bit lines BLB and BL come to be 50 mV, a sense amplifier (not shown) which is connected in the middle of bit lines can sense and decipher the stored content of the cell.

3.2 Error Tolerance Analysis Assuming that B = ‘1,’ A = ‘0,’ C = ‘1,’ and D = ‘0’ in Fig. 2, in this subsection we analyze the SEU recovery behavior at the circuit level. Case 1. (+ve spike at Node A): If drain diffusion region/n-well of OFF transistor MP1 is hit by an energetic particle, it collects all the strike generated holes or positive charge, and a positive spike is generated at node A (that is, node A changes from ‘0’ to ‘1’). Consequently, transistors MN2 and MN3 are turned ON. Although, the state of other transistors cannot affect by a positive transient pulse. As a result, nodes B and C remain unaffected. We know, C is a redundant node of B so it stores ‘1.’ Node C affects the MP1. As a result, for an instant of time node, A changes its value but after sometimes the nodal logic level is recovered. Case 2. (−ve spike at Node B): When the drain diffusion region of ON transistor MP3 is hit by an energetic particle, it collects all the strike generated electrons or negative charge, and a negative spike is generated at node B (that is, node B changes from ‘1’ to ‘0’). Consequently, transistors MN4 and MN1 are turned OFF. Although, the state of other transistors cannot affect by a negative transient pulse. As a result,

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nodes A and D remain unaffected. We know, D is a redundant node of A so it stores ‘0.’ Node D affects the MP3. As a result, for an instant of time node B changes its value but after sometimes the nodal logic level is recovered. Case 3. (−ve spike at Node C): When the drain diffusion region of ON transistor MP2 is hit by an energetic particle, it collects all the strike generated electrons and negative charge and a negative spike is generated at node C (that is, node C changes from ‘1’ to ‘0’). Consequently, transistors MP1 and MP4 are turned ON. Although, the state of other transistors cannot affect by a negative transient pulse. As a result, nodes A and D remain unaffected. We know, D is a redundant node of A so it stores ‘0.’ Node D affects the MP2. As a result, for an instant of time node C changes its value but after sometimes the nodal logic level is recovered. Case 4. (+ve spike at Node D): When the drain diffusion region of OFF transistors MP4 is hit by an energetic particle, it collects all the strike generated holes or positive charge, and a positive spike is generated at node D (that is, node D changes from ‘0’ to ‘1’). Consequently, transistors MP2 and MP3 are turned OFF. Although, the state of other transistors cannot affect by a positive transient pulse. As a result, nodes C and B remain unaffected. We know, C is a redundant node of B so it stores ‘1.’ Node C affects the MP4. As a result, for an instant of time node D changes its value but after sometimes the nodal logic level is recovered.

4 Simulation Results and Discussions The focus of this work is to achieve a higher critical charge, which signifies improved radiation hardness of the circuit. We carry out SPICE simulation using a 16-nm PTM at a nominal voltage of 0.7 V for comparison with We-Quatro.

4.1 Soft Error Robustness In this paper, soft error robustness is studied by estimating QCrit (critical charge). To perform the soft error tolerance analysis, the transient injection at the B node is simulated, by the double-exponential current source. The double-exponential current is modeled by Jung et al. [5]    −t −t/ Q τ τ f r . −e I (t) = e τf − τr

(1)

Here, Q = ± ve charge created and by the particle strike, which is collected by sensitive node, τ f (fall time) = time constant for the collection of charges at the p-n junction, and τ r (rise time) = time constant for ion-track establishment [5]. τ f and τ r

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19

are process-dependent. If an energetic particle strikes at or near a sensitive node and the minimum amount of charge generated and collected for changing cell content is known as critical charge [6].   −τCric/ τ QCrit = Q 1 − e

(2)

We determine QCrit using SPICE-based simulation. An exponential current is injected in the form (1) at node B. We put the value of τr = 10 ns and τf = 15 ns and delay time = 2 µs. Now, we increase the value of current amplitude until the cell flips. After that for evaluating the value of QCrit injected exponential current is integrated from delay time to τCric until node voltages intersect. It has been observed from Figs. 3 and 4, τCric of MPRT cell is 2.01234 µs and τcric of We-Quatro cell is 2.00997 µs, respectively. When we enter the value of τCric in (2), the value of QCrit is determined. QCrit of We-Quatro is 132.7094 fC and Qcrit of MPRT is 158.0576 fC. From the above value, it is observed that the value of QCrit is approximately 1.2× higher in the MPRT circuit compared to We-Quatro. This observation shows that cell become more radiation hardened by using PMOS access transistors. The reason behind this is the leakage current in the PMOS access Fig. 3 Non-recovery of the MPRT cell for an injected exponential current imitating at storage node B

Fig. 4 Non-recovery of the We-Quatro cell for an injected exponential current imitating at storage node B

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transistors do not get affected by the radiation. This is because in the case of PMOS transistor due to the use of holes as majority charge carriers are slower and carry less current, whereas in the case of NMOS transistor electrons are used as majority charge carriers. Since, mobility of electron is higher than holes so it has higher conductivity hence leading to lower Rds (dynamic resistance). Due to this even small radiation can energizes NMOS compare to PMOS hence making PMOS as an access transistor makes our circuit more radiation hardened.

4.2 Analysis of Read Access Time (TRA ) and Write Access Time (TWA ) The T RA is evaluated from the moment when the word line (WL) is activated. It is evaluated up to when BL/BLB is dropped by 50-mV from V DD . This difference in potential between BL and BLB can be easily detected with the help of a sense amplifier, thereby avoiding misread. T RA shows more dependency on the I READ (read current) flowing via access devices. The bit line capacitance and cell ratio determine I READ [7]. In the We-Quatro bit cell, NMOS devices are used as access transistors, whereas in MPRT cell, PMOS devices are used as access transistors. Electrons are more mobile as compared to holes, that is why the T RA of MPRT cell is longer than the We-Quatro. Figure 5 depicts the graph of T RA comparison between We-Quatro and MPRT at different voltage levels. Monte Carlo simulations are run with a sample size of 3000 for estimation of various design metrics in this work. The required time for storing ‘0’ or ‘1’ to from the time when WL is activated to the time when the storage node rises to 90% of its full swing from its initial low level or when the storage node falls to 10% of its initial high level (that is, its 90% swing) is known as T WA (write delay or write access time). Figure 6 shows the graph of T WA comparison between We-Quatro and MPRT at different voltage levels using 3000 sample size during Monte Carlo analysis. Fig. 5 Read access time (T RA ) or read delay of MPRT and We-Quatro SRAM cell at various V DD

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Fig. 6 Write access time (T WA ) or write delay of proposed MPRT and We-Quatro SRAM cell at various V DD

4.3 Hold Power Analysis SRAM bit cells remain mainly in hold mode. For long data retention during hold mode, the word line (WL) is disabled. BL and BLB are precharged, and the partial cross-coupled inverters are tightly connected [6]. Hold power has been estimated for both the cells varying the V DD from 630 to 770 mV. Figure 7 shows the hold power of MPRT and We-Quatro cell. PMOSFETs exhibit an order of magnitude smaller I G (gate leakage current) compared to NMOSFETs [8]. Therefore, the proposed design consumes ≈ 6% lower hold power compared with that of the We-Quatro SRAM bit cell. Total standby power or hold power is given by   PHold = VDD × Isub + Ig + IJN

(3)

where leakage current includes subthreshold leakage current (I sub ), the gate leakage current (I g ), and junction leakage current (I JN ) through device. Fig. 7 Hold power of MPRT and We-Quatro cell at various V DD

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Fig. 8 Read static noise margin (RSNM) of the proposed MPRT and We-Quatro SRAM cells

4.4 Read Static Noise Margin (RSNM) Static noise margin (SNM) is the minimum DC noise voltage that is required to change the cell content [5]. It measures the stability of the cell. Figure 8 shows the butterfly curve of RSNM. The stability of the SRAM bit cell during read mode is determined by RSNM. The side length of the biggest square that can be inscribed in the smaller wing of the butterfly curve is used to estimate the noise margin [9]. To estimate the noise margin initially bit lines are precharged in both bit cells. WL is biased at supply voltage in the We-Quatro cell and in the case of MPRT, WL is biased to the ground. Two voltage sources N1 and N2 are connected to the gate of MN3 and MN1 to introduce the DC noise at both storage nodes A and B. N1 and N2 voltages are swept from 0 V to V DD to calculate the voltages of storage nodes. Estimated voltage values are used to plot the butterfly curve. The critical strategy of our design to improve read stability (RSNM) is to decide the cell ratio (CR) by properly sizing the access devices and the pull-down devices. The proper dimensioning has resulted in 1.5× higher read SNM of our design in comparison with that the existing We-Quatro bit cell.

5 Conclusion This paper presented a new SRAM cell that is more radiation-hardened than the We-Quatro SRAM cell. Although the We-Quatro SRAM bit cell is one of the good radiation-hardened SRAM bit cells, by proper design and sizing of the FETs, we could achieve improved results compared with the We-Quatro SRAM bit cell. In addition, we compare Read Time and Write Time of MPRT and We-Quatro through

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appropriate simulations. The proposed MPRT design shows improvement in critical charge, RSNM, and standby power.

References 1. Y.S. Dhillon, A.U. Diril, A. Chatterjee, A.D. Singh, Analysis and optimization of nanometer CMOS circuits for soft-error tolerance. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 14(5), 514–524 (2006) 2. R. Baumann, Soft errors in advanced computer systems. IEEE Des. Test Comput. 22(3), 258– 326 (2005) 3. L.D. Trang Dang, J.S. Kim, I.J. Chang, We-Quatro: Radiation-hardened SRAM cell with parametric process variation tolerance. IEEE Trans. Nucl. Sci. 64(9), 2489–2496 (2017) 4. S.M. Jahinuzzaman, D.J. Rennie, M. Sachdev, A soft error tolerant 10T SRAM bit-cell with differential read capability. IEEE Trans. Nucl. Sci. 56(6), 3768–3773 (2009) 5. I.-S. Jung, Y.-B. Kim, F. Lombardi, A novel sort error hardened 10T SRAM cells for low voltage operation, in 2012 IEEE 55th International Midwest Symposium on Circuits and Systems (MWSCAS) (2012) 6. S. Ahmad, N. Alam, M. Hasan, Pseudo differential multi-cell upset immune robust SRAM cell for ultra-low power applications. AEU-Int. J. Electron. Commun. 83, 366–375 (2018) 7. J. Jiang, Y. Xu, W. Zhu, J. Xiao, S. Zou, Quadruple cross-coupled latch-based 10T and 12T SRAM bit-cell designs for highly reliable terrestrial applications. IEEE Trans. Circuits Syst. I Regul. Pap. 1–11 (2018) 8. W. Zhang, L. Li, J. Hu, Design techniques of p-type CMOS circuits for gate leakage reduction in deep submicron ICs, in 52nd Midwest Symposium on Circuits and Systems—MWSCAS 2009 (2009), pp. 551–554 9. S. Pal, A. Islam, 9-T SRAM cell for reliable ultralow-power applications and solving multibit soft-error issue. IEEE Trans. Device Mater. Reliab. 16(2), 172–182 (2016) 10. L.D. Trang Dang, M. Kang, J. Kim, I.-J. Chang, Studying the variation effects of radiation hardened Quatro SRAM bit-cell. IEEE Trans. Nucl. Sci. 63(4), 2399–2401 (2016) 11. L.D. Trang Dang, D. Seo, J. Han, J. Kim, I.-J. Chang, A 28mn FDSOI 4KB radiation-hardened 12T SRAM macro with 0.6 ~ 1V wide dynamic voltage scaling for space applications, in Proceedings of 2018 Asian Solid-State Circuits Conference (A-SSCC) (2018), pp. 133–134 12. R.C. Baumann, Radiation-induced soft errors in advanced semiconductor technologies. IEEE Trans. Device Mater. Reliab. 5(3), 305–316 (2005) 13. A. Islam, M. Hasan, Variability aware low leakage reliable SRAM cell design technique. Microelectron. Reliab. 52(6), 1247–1252 (2012) 14. A. Islam, M. Hasan, Leakage Characterization of 10T SRAM Cell. IEEE Trans. Electron Devices 59(3), 631–638 (2012) 15. S. Pal, A. Islam, Variation tolerant differential 8T SRAM cell for ultralow power applications. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 35(4), 549–558 (2016) 16. A. Islam, M. Hasan, T. Arslan, Variation resilient subthreshold SRAM cell design technique. Int. J. Electron. 99(9), 1223–1237 (2012) 17. A. Islam, M. Hasan, A technique to mitigate impact of process, voltage and temperature variations on design metrics of SRAM Cell. Microelectron. Reliab. 52(2), 405–411 (2012)

Comparison of Snapback Phenomenon and Physics in Bottom and Top Body Contact NMOS Pragati Singh, Niladri Pratap Maity, Rudra Sankar Dhar, and Srimanta Baishya

Abstract This paper compares the features and physics of snapback involved in 2D NMOS structures having body/substrate contact at bottom and adjacent to source under the application of high current ramp at drain and zero gate voltage. We analyzes the S-shaped current–voltage characteristics of two structures for understanding the snapback phenomenon and operational window of compact memory devices. This work also evaluates the carrier electrostatics involving the electron–hole carrier build up and ambipolar current flow in the body of the structures. We also investigate the formation of memory cell in the body of NMOS under zero gate bias and ramp of high current stress at drain terminal due to bipolar turn. Keywords ZRAM · Snapback · SOIFED-RAM · TRAM

1 Introduction As we are entering into modern era of nanoscale semiconductor industry, the memory has become one of the crucial elements for high performance integrated circuits. Scaling of MOSFET has reached to saturation having gigantic advancements, while semiconductor-based memories are still facing severe challenges in scaling. In standard DRAM cell, bulky capacitor accounts for considerable area which is very tough for designer to shrink the size. The newly introduced zero capacitor RAM (ZRAM) has only a transistor, and it does not have any capacitor that’s why it shows 1T/0C unlike the standard DRAM cell which is having one transistor and one capacitor shown by 1T/1C DRAM cell. Silicon on insulator (SOI)-based multi-gate device is used for designing of snapback-based memories having bipolar transistor formed in P. Singh (B) · R. S. Dhar National Institute of Technology Mizoram, Aizawl 796012, India e-mail: [email protected]; [email protected] N. P. Maity Mizoram University, Aizawl, Mizoram 796004, India S. Baishya National Institute of Technology, Silchar, Assam 788010, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Biswas et al. (eds.), Microelectronics, Circuits and Systems, Lecture Notes in Electrical Engineering 976, https://doi.org/10.1007/978-981-99-0412-9_3

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body of the FET [1]. Impact ionization takes place as the drain voltage increases, which results in generation of the majority carriers. In p-type substrate, holes moves toward the body, and minority electrons get collected by the drain as the drain voltage increases to cause the breakdown near the drain–body depletion region. The modified threshold voltage due to the storage of the charges provides bistability to bipolar transistor having two states (high and low current). The ZRAM scalability has been shown in multi-gate with 10 nm thin body [2], and it also expresses the two states of the memory cell for bipolar transistor formed in the body of the device. The advancement in vertical gate all around, double gate junction-less, thyristor RAM (TRAM), silicon on insulator field effect diode RAM (SOIFED-RAM) and zero-ionization, zero-swing FET (Z2FET) RAM has led the future generation of the ZRAM [3–6]. Memories based on charge storage have reached to a limit of scalability. Therefore, the concept of new memory technologies has been introduced. These new memory technologies must have the smaller size, longer retention time, higher performance, lower operating voltage and of course the simple structure. So far TRAM, SOIFED-RAM and Z2FET-RAM have been analyzed to form the compact capacitor-less snapback-based memory having better retention time and lower power dissipation [4–7]. TRAM exhibits two stable states due to fast gate switching, and it relies on the majority carrier concentration in the gated structure. The majority carrier concentration due to high and low switching of the gate decides the high and low current states in the thyristor-based RAM. Accumulation of holes (majority carriers) in p-base thyristor shows high current level, whereas depletion of holes shows low current level [7]. As per the newer studies, TRAM- and SOI-based FED-RAM’s high and low current levels defined by the depletion and accumulation of majority carries [5], which is the opposite of the previously believed studies. The Z 2 FET-RAM is also a snapback-based memory shown by the S-shaped hysteresis characteristics, and its high and low states are governed by the high and low drain voltage [8]. Double gate junction-less device has also been analyzed for bipolar snapback-based memories [9]. Previously, studied capacitor-less memories were designed by thyristor or SOI. Insights of TRAM, SOIFED-RAM, Z2FET-RAM and all other snapback-based memories show that the clearer conception can be developed for the apprehension of the low and high states of the memory depending on the carrier electrostatics. Structure of the device and switching conditions also play an important role to decide the memory states of capacitor-less RAM cells depending on carrier concentrations. However, bulk MOS has not been introduced in the designing of capacitor-less memories prominently. Therefore, a better insight is needed in designing of capacitor-less memory cell using different structures of bulk MOS. This research is applicable for non-planar devices like FinFets, multi-gate structures and gate all around structures (GAA). Double gate junction-less transistor and vertical transistors are also applicable for the snapback. However, we are looking in to the bulk NMOS as there is no comparison has been done for bottom body contact NMOS and side body contact NMOS. Most importantly, we found many untouched facts in this research which have never been explored in any of the research so far. However, we are intended to do more research using the non-planar device in the field

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of snapback to design capacitor-less memory cells. We are more concern about bulk oriented ZRAM as bulk technology is still formidable in the field of semiconductor memory. These devices present new concept of capacitor-less memory as compared to the existing 1T-DRAM. Drain current increases rapidly due to increase in drain to source voltage. Increase in the drain current causes onset of avalanche multiplication, wherein newly generated carriers can participate in generating more carriers causing breakdown. Hole current is significantly increasing in substrate causing voltage drop across the resistance of the substrate to forward bias the source–substrate pn junction. Due to forward biasing of source–substrate pn junction, electrons are injected from source to substrate giving rise to parasitic bipolar turn on. The effect of the bipolar action shown in I D –V D characteristic (Fig. 1) is termed as snapback. In general, MOSFETs are not operated in the snapback region, whereas it can be used at input/output of the chips to provide ESD (electro-static discharge) protection [10]. MOS transistors under zero gate bias are prominently used as ESD protection devices. Behavior of snapback phenomenon is explained under zero gate bias, but carrier electrostatics are missing [11, 12]. Gate Grounded NMOS (GGNMOS) under high current applied at the drain terminal shows the I–V characteristic of the device in Fig. 1. Snapback phenomenon in these devices are dependent on bipolar turn on in the body of the device [13, 14]. Explanation of the carrier electrostatics is not clearly understood in the previous studies. Formation of the memory cell in the body of the device relies on snapback characteristics, and its retention time is dependent on parameters (Fig. 1) like (V t1 , I t1 ), V h and (V t2 , I t2 ) [15–19]. These parameters are the prominent candidates for transforming in to the circuit models. In this paper, snapback-based capacitor-less memory using two structures of bulk MOS has been presented. We further emphasized on analysis of snapback in top and bottom body contact bulk MOS. The formation of the memory cells using the parameters like (V t1 , I t1 ), V h and (V t2 , I t2 ) in bulk MOS has also been focused.

Fig. 1 Bipolar formation inside the body of NMOS under zero gate bias and stress of high current ramp at drain showing the snapback in I–V characteristics

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2 Device Structures and Simulation Setup Figure 1 shows bipolar formation in the body of NMOS at zero gate bias and high current ramp at the drain terminal. The device simulation setup uses well calibrated mobility model and hydrodynamic transport model. In order to capture the accurate results, Fermi–Dirac model, high field saturation, avalanche generation models and Shockley–Read–Hall recombination/generation are included for MOS operating biases. In this paper, two structures have been simulated. Structure one has the body contact at the bottom (Fig. 2), and the structure two has the body contact at the top adjacent to the source contact (Fig. 3). In the both of the structures, gate terminal is grounded, and a high current ramp has been applied at the drain terminal. In the top body contact structure, depth of the substrate is 10 times of the gap between midpoint of source and body contact to have better analysis of flow lines and carrier electrostatics. The device characteristics are simulated using Sentaurus two dimensional (2D) Technology Computer Aided Design (TCAD) using its default parameters available in the simulator. Here, we performed 2D transient device simulations on device structures and examined the mechanism of snapback by applying the zero gate voltage and high current at the drain terminal. The schematic of GGNMOS under applied high current bias at the drain is shown in Fig. 1. This structure is the basic building block of the capacitor-less snapbackbased memories having formed BJT in the body of the structure. The drain voltage– current characteristics have also been shown, which form the memory cell. Fig. 2 Contour of hole current density for bottom body contact GGNMOS structure

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Fig. 3 Contour of hole current density and flow lines of hole current for side body contact GGNMOS structure

3 Results and Discussions Substrate, source and drain terminals represent base, emitter and collector of BJT formed in the body of GGNMOS under stress of high current at drain terminal (Fig. 1). High voltage appears at drain terminal due to applied current ramp causes impact ionization, which results in generation of the carriers. I–V characteristics show (V t1 , I t1 ) as first snapback point. The generated carriers act as initiating current as they flow in to the substrate (base), which results in bipolar transistor turn on [20]. Subsequently, collector to base voltage deceases to V h (holding voltage) due to flow of initiating current in the base (shown in Fig. 1). The bipolar action ceases to exist due to decrease in collector voltage caused by collector–emitter current. The bottom body contact structures shown in Fig. 2 provide the storage of the node, whereas top body structures shown in Fig. 3 govern the sensing of the current presenting the two states of the memory cell formed in body of the structures. The majority carrier holes are generated due to band to band tunneling or impact ionization in MOS transistor [21, 22]. The generated holes stay in top body structure under zero gate bias. Under these conditions, the vertical field under grounded gate is screened due to majority carriers, and it has less effect on minority carriers. Current I t1 (shown in Fig. 1) flowing through the p-substrate of the bottom body structure reads state “1” and reads state “0” under depleted hole condition (Figs. 2 and 4), whereas in the top body contact structure, the picture is exactly opposite [23]. In top body structure,

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buildup of electrons (absence of holes) represents state “1” and depletion of electron (presence of holes) represents state “0” (Figs. 4 and 5). The important differences between bulk and SOI-based capacitor-less memory also between the top and bottom body bulk MOS structures are as follows: 1. Drain current flows due to majority carrier (holes) in bottom body contact, whereas current flow is determined by electrons in top body contact GGNMOS structure. Fig. 4 Contour of electron current density for bottom body contact GGNMOS structure

Fig. 5 Contour of electron current density and flow lines of the electron current for side body contact GGNMOS structure

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2. Flow of holes and electron determines the state of the memory cells. 3. Buildup of holes takes place in p-substrate bottom body contact, whereas electrons buildup in p-substrate top body contact structure. 4. Bulk MOS can be utilized for the formation of the memory cells. Buried oxide is not necessary. 5. Coupling of carriers plays an important role as both electron and hole presence are crucial in determining the memory states due to bipolar turn on in the body. Equation (1) shows the relation between the carrier generation/recombination due to impact ionization and current gain of the device, which is manifested in Figs. 2, 3, 4 and 5. α·M =1

(1)

Current gain α and impact ionization multiplication factor “M” maintain the balance between formation of BJT in the body of the MOS and carrier generation– recombination.

4 Conclusion In this paper, the memory cell formation inside the body of the bulk MOS structure has been explained. We found that high and low level of memory states are dependent on BJT formed inside the MOS. We examined majority carrier (holes) accumulates in NMOS (bottom body) structure which decides the high level of the memory cell and absence of the holes relates to the low state of memory cell. We also demonstrated the memory states relating to the minority carrier (electron) in the top body bulk MOS structure. The memory state dependency related to carrier electrostatics is exactly opposite in bottom and top body contact MOS. Acknowledgements Authors would like to thank Special Manpower Development Program for chip to system design (SMDP-C2SD) sponsored by Ministry of Electronics and Information Technology (MeiTy) Govt. of India and National Institute of Technology Mizoram, India, for providing the machine and tools required to simulate the devices and carry out the research work.

References 1. S. Okhonin, M. Nagoga, E. Carman, R. Beffa and E. Faraoni, New generation of Z-RAM. In 2007 IEEE International Electron Devices Meeting, Washington, DC, USA, (2007) pp. 925– 928. https://doi.org/10.1109/IEDM.2007.4419103 2. V. Sverdlov, S. Selberherr, Scalability of a second generation Z-RAM Cell: A computational study. In Proceedings of the International Conference on Computational & Experimental Engineering and Sciences (ICCES) (2010) (pp. 232–247). http://hdl.handle.net/20.500.12708/ 71386

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A Review on Optimal Power Flow Problem Naveen Kumar, Ramesh Kumar, and Ram Kumar

Abstract Operation of power system with systematic planning plays an important role in the growth of the economy of the country. The optimal power flow has the utmost duty of maintaining reliable, safe, and finest functioning of the power system. OPF consists of complicated, non-convex, non-linear, non-constant as well as a multichannel problem that contains both discrete and constant variables. Traditionally, classical optimization methods were used to solve the OPF problem. But with the incorporation of FACTS devices and deregulation of the power sector, the traditional concepts and practices are superimposed by economic market management. Now, different objective functions such as minimization of fuel cost, minimization of emission, improving voltage profile, enhancement of voltage stability, reducing active power loss, and minimization of transmission cost have to achieve. Techniques that are used to solve OPF problems are the arithmetic programming method, analytical approach, and meta-heuristic optimization algorithm. This work gives focus a review of different optimization methods used for OPF in power systems. Keywords Optimal power flow · Objective functions · Constraints · Approaches for OPF · Merits and demerits of different approaches for OPF solution

1 Introduction Optimal power flow (OPF) starts from generating plants, transmission lines, and distribution lines up to the customer’s end in the power system. That is why a power system network is a very complicated and complex network. For its complex nature,

N. Kumar (B) · R. Kumar Department of EE, National Institute of Technology, Patna 800005, India e-mail: [email protected] R. Kumar e-mail: [email protected] R. Kumar Department of EEE, Katihar Engineering College, Katihar 854109, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Biswas et al. (eds.), Microelectronics, Circuits and Systems, Lecture Notes in Electrical Engineering 976, https://doi.org/10.1007/978-981-99-0412-9_4

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planning, operation, and management of power systems are a great challenge for operators. Availability of electrical energy with quality, reliability, security, and economic to the customers is the prime duty of the operators. The increasing load on generating plants, establishment of new station and substation, and seasonal and climatic condition causes variation in load demand. Hence, operator must have to avoid voltage deviation with proper maintaining voltage throughout the network as well as minimized active power loss in generation, transmission, and distribution network [1, 2]. These objectives can be achieved from OPF by using reactive power compensation devices as generator voltage setting, shunt capacitor bank, synchronous condenser, tap changing transformer, and VAR devices while fulfilling a set of physical as well as operating regularity and irregularity constraints. Optimal power flow (OPF) was for the first time introduced by Carpentier in 1962 [3]. Later on, OPF was further developed by Dommel and Tinney [1]. A first survey related to optimal power flow was presented by Happ [4] and after that IEEE working group [5] given a bibliography survey of economic security functions in 1981. Carpentier [6] presented a survey and classified the OPF algorithm according to their solution methodology in 1985. Chowdhury and Rahman [7] presented a survey on economic dispatch problems in 1990. Momoh et al. [8] presented a review on some selected OPF techniques in 1999. Pandya and Joshi [9] given a survey on traditional and artificial intelligence optimization methods in 2008. This OPF problem is classified into two subcategories. The first is recognized as economic load dispatch (ELD), and the second is recognized as optimum reactive power dispatch (ORPD). The subcategories of the OPF problem are given in Fig. 1.

Minimization of Power Loss

Optimal Reactive Power Dispatch (ORPD)

Optimal Power Flow (OPF)

Improvement of Voltage Profile Enhancement of Voltage Stability

Minimization of Operating Cost

Minimization of Fuel Cost

Economic Load Dispatch (ELD) Minimization of Emission

Fig. 1 Subcategories of optimal power flow

A Review on Optimal Power Flow Problem

37

2 Mathematical Modeling of OPF The main task of the OPF problem is the solving of complicated and non-continuous functions by nourishing both equality and inequality constraints. Control variables for OPF problems are alternator voltage, transformer tapping, and reactive power delivered by reactors and capacitors.

2.1 Objective Function Minimization of Fuel Cost F1 = MinFuelcost =

N ∑

| | | α j + β j PG j + γ j PG2 j + |e j sin f j (PGMin j − PG j )

(1)

j=1

α j , β j , and γ j are fuel cost coefficient of j th generator, PG j is the power delivered by j th generator, e j and f j are the fuel cost coefficients of the j th generator due to the valve point effect. Minimization of Emission 2 F2 = MinEmission = ak + bk PGk + ck PGk + εk (exp(δk ∗ Pk ))

(2)

where ak, bk, εk, and δk are the emission coefficients of k th generating unit. Minimization of Active Power Losses F3 = MinPowerLoss =

N ∑

[ ] G B (VK )2 + VL2 − 2VK VL cos θKL

(3)

B=1

where VK = Voltage of bus K, VL = Voltage of bus L, B = Branch number, N = Number of lines, G B = Conductance, θKL = Voltage angular difference. Improvement of Voltage Profile Regulation of voltage at buses is also an important objective of ORPD. The general equation for the improvement of voltage profile is given below. F4 = MinVoltageDeviation

( N ) ∑| sp || | VMb − VMb =

(4)

b=1 sp

b = Bus number, VMb = Actual bus voltage, VMb = Specific bus voltage, N = Number of load bus

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Enhancement of Voltage Stability F5 = Min(L Max ) = Min[Max(L M )]

LM

M = 1, 2, 3 . . . . . . , N

(5)

| | N | | ∑ Vu | | = |1 − f uv < {θuv + (δu − δv }| | | V v m=1 f uv = − [Yuv ]−1 [Yuv ]

where L M = Voltage stability indicator, Y = Admittance, θ = Phase angle, N = Number of buses. Minimization of Transmission Cost [ F6 = MinTran Loss = Cgpj Q gj = Cgpj Sgj max − Cgpi

(/ (

Sgj2 max − Q 2gj

)

)] kgj (6)

A quadratic function 2 Cgpi Pgk = a Pgk + b Pgk + c

where Pgk is the active power output of gk and a, b, and c are cost coefficients.

2.2 Constraints The OPF needs to satisfy with power balance and with system operational limits. These constraints are divided into two categories, i.e., equality constraints and inequality constraints. Equality Constraints Equality constraints are generally shown by power balance equations which ensure that total power generation must satisfied total load demands and power loss in transmission lines. Active Power Flow Balance Equation Pgs − PLs − Vs

∑ k∈Ns

Vk (gsk cos θsk + Bsk sin θsk ) = 0

(7)

A Review on Optimal Power Flow Problem

39

Reactive Power Flow Balance Equation Q gs − Q Ls − Vs

∑

Vs (gsk sin θsk + Bsk cos θsk ) = 0

(8)

k=Ns

where Bsk is the susceptance. Inequality Constraints In OPF, inequality constraints are of two types—control variables and state variables. The control variables consist of transformer output setting, generator bus voltages, and shunt capacitors reactive power generation. The state variables include load bus voltages, PV buses reactive power, flow limit of line, and generation of the active power of slack bus. These constraints are given as follows: Load bus voltage magnitude is given by VsMin ≤ Vs ≤ VsMax , s ∈ Nb

(9)

where s = Bus number and Nb = Total number of buses. PV buses reactive power generation limit is given by Max Q Min gs ≤ Q gs ≤ Q gs , s ∈ Ng

(10)

s = Bus number and Ng = Total generators number. Compensator reactive power output is given by Max Q Min cj ≤ Q cj ≤ Q cj ,

j ∈ Nc

(11)

where s = Bus number and Nc = Total capacitors number. Transformer tap setting is given by TlMin ≤ Tl ≤ TlMax , l ∈ Nt

(12)

where l = Branch number and Nt = Total connection number. Transmission line power flow limit is given by S J ≤ S Max j where S Max = Maximum value of apparent power of jth line. j

(13)

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Fig. 2 Flowchart of optimal power flow problem

3 Formulation of OPF Problem The OPF problem aims to set up a network with proper planning at minimum cost for satisfying desired objectives. First of all, it is required to define system data, allocation of generators, and reactive power sources [10]. After that control variables are optimized to find out certain objective functions considering equality and inequality constraints. Control variables include terminal voltages of generator bus, reactive power generation of VAR sources, and transformer tapping [11]. The dependent variables include voltage magnitude of load bus, active power generation at stack bus, power flows through transmission lines, and reactive power output of the generators (Fig. 2).

4 Challenges in OPF # Due to a large number of constraints and non-linearity, it has become a great challenge for engineers as well as for mathematicians to get optimum solutions. # Due to deregulated market of electricity, optimal power flow faces problems in adjusting the various type of participants of the market, requirements in data modeling, and processing in real-time. # Optimum power flow has to face challenges in dealing with time requirements for external modeling such as loop flow, simultaneous sending, and sensitivity in using lines.

A Review on Optimal Power Flow Problem 8%

Renewable sources

Small Hydro

8.90%

Bio power Geothermal Solar thermal

41

1% 0.50% 39.60%

Solar PV Wind

41.60%

Percentage Fig. 3 Contribution of renewable sources in energy generation

# Optimum power flow faces challenges in providing control locally and globally for minimizing voltage deviation as well as for maintaining angular stability. # Optimum power flow faces challenges in providing future generation scopes, unbundled services in transmission, and allocation for other resources.

5 Optimal Power Flow with Renewable Energy Source The role of renewable energy sources is very important for optimal power flow as it reduces the cost of generation as well as minimizes the emission of harmful and toxic gases. But due to its intermitted nature, it has become a challenging issue to incorporate renewable energy sources with the electric grid. Therefore, a hybrid system is developed in which renewable energy sources are run with thermal power plants to avoid fluctuations during generation and supply. The contribution of renewable energy sources in energy production and its objectives in optimal power flow is given in Fig. 3 and Table 1, respectively.

6 Optimal Power Flow with FACTS Devices FACTS are solid-state flexible devices, which play an important role in enhancing the power handling capacity of transmission lines, system stability, and control of power flow. Solution of optimal power flow problems using FACTS devices attracts the interest of researchers for secure and economical functioning of power systems. Classification of FACTS devices and their applications is given in Fig. 4 and Table 2, respectively.

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Table 1 Contribution and objectives of renewable source in optimal power flow according to prior studies [12] Hybrid system

Objective

Algorithm

Author

Thermal and wind

Minimization of cost

Numeric optimization

[13]

Thermal and wind

Minimization of emission

Numerical optimization

[14]

Thermal and wind

Minimization of cost and emission

Gravitational search algorithm

[15]

Diesel and photovoltaic, battery

Minimization of cost and emission

Optimization toolbox

[16]

Thermal and wind

Dispatch of reactive power Modified bacteria foraging and cost minimization algorithm

[17]

Wind, battery, and photovoltaic

Maximization of reliability Iterative Pareto fuzzy and cost minimization technique

[18]

Thermal and wind

Enhancement of voltage security and cost minimization

[19]

Thermal and wind

Minimization of cost, loss, Hybrid algorithm and emission

[20]

Thermal

Cost and emission minimization

Flower pollination algorithm

[21]

Wind, solar, and hydro

Reallocation of effective energy

Markowitz portfolio theory

[22]

Wind, photovoltaic, and hydro

Maximization of generation

Non-dominated sorting genetic algorithm

[23]

Thermal, photovoltaic, and Minimization of cost wind

Adaptive differential evolution

[24]

Runoff river and photovoltaic

Minimization of variability and energy demand

Mixed-integer mathematical modeling

[25]

Thermal, wind, and hydro

Minimization of cost and enhancement of voltage security

Modified bacteria foraging algorithm

[26]

Wind, photovoltaic, and diesel

Enhancement of reliability Self-adaptive differential and cost minimization evolution

[27]

Thermal and wind

Modeling and estimation of uncertainty

Direct search and IGDT

[28]

Diesel and wind and photovoltaic

Minimization of cost and emission

HOMER software

[29]

Thermal and wind

Minimization of cost and computational time

A modified cuckoo search algorithm

[30]

Hydro and photovoltaic

Minimization of cost and loss

Genetic algorithm

[31]

Hydropower, photovoltaic, and wind

Energy and demand variability minimization

Mixed-integer mathematical modeling

[32]

Modified bacteria foraging algorithm

(continued)

A Review on Optimal Power Flow Problem

43

Table 1 (continued) Hybrid system

Objective

Diesel generator and photovoltaic

Minimization of cost, loss, Crow search algorithm and emission

Algorithm

Author [33]

Photovoltaic

Parameter estimation of solar cell

Sine cosine algorithm

[34]

Photovoltaic, battery, and wind

Minimization of cost and emission

Fuzzy model and chance-constrained programming

[35]

Brick plant

Minimization of emission

TORO and integer linear programming

[36]

Thermal, battery, and photovoltaic

Minimization of fuel Chao mutation whale consumption and pollution optimization algorithm

Regenerative boiler, wind, and gas turbine

Maximization of revenue and minimization of risk

GAMS software with ILOG [38] solver

Diesel, battery, and wind

Minimization of cost and emission

Mixed-integer linear programming

[39]

Hybrid system

Renewable energy consumption rate, emission reduction

RKC hypothesis

[40]

Cold chain logistics

Minimization of cost and emission

Modified ant colony optimization

[41]

Wind system

Estimation of short-term wind power

Improved dragonfly algorithm

[42]

Thermal, hydro, wind, and solar

Minimization of cost, emission, power loss, and voltage deviation

Modified bacteria foraging algorithm and fuzzy membership approach

[43]

[37]

FACTS DEVICES

SERIES COMPENSATOR

TCSC

TSSR

SSS

TSSC

Fig. 4 Classification of FACTS devices

SHUNT COMPENSATOR

TCSR

SVC

STATCOM

SERIES SHUNT COMPENSATOR

UPFC

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N. Kumar et al.

Table 2 Application of FACTS devices in optimal power flow problem Facts devices

Applications

Thyristor controlled series compensator (TCSC)

It is used to increase the stability of the system, mitigation of sub-synchronous resonances, minimization of damping power oscillation, and load flow control

Thyristor switched series reactor (TSSR)

It is used for voltage regulation, smooth variable inductive reactance, and to achieve stepped series inductance

Static synchronous series compensator (SSSC)

It is used to control line inductance, series compensation, current, voltage, and damping oscillation of power

Thyristor switched series capacitor (TSSC)

It is used to reduce line inductive reactance and control capacitive reactance

Thyristor switch reactor (TSR)

It is used as a VAR absorber, limiting short circuit current, and exchange of inductive and capacitive current

Thyristor controlled reactor (TSC)

It is used as harmonic controller and eliminator, suppression of synchronous resonance, and as power factor improver

Static VAR compensator (SVC)

It is used for controlling dynamic reactive power, increasing system stability, and improvement of voltage quality

Static synchronous compensator (STATCOM)

It is used to increase the transient stability of the grid, reducing line loss, voltage regulation, and power factor improvement

Unified power flow controller (UPFC)

It is used as a two-way switch for controlling power flow, reactive/active current compensators, transmission angle as well as impedance controller

7 Method for Solution of OPF Optimum power flow problem is a complex, non-linear, convex, non-continuous, and multi-objective optimization problem with a large number of uncertainties. Traditionally, conventional methods are used to solve OPF [44]. The classical methods are based on mathematical programming and are weak in handling qualitative constraints. They have poor convergence capability and become too slow if some variables are large. To overcome the shortcoming of conventional methods, artificial intelligence (AI) has been developed in the recent past. The major advantages of artificial methods are that they are relatively versatile in controlling various qualitative constraints. So, they are quite suitable for handling multi-objective optimization problems. In most cases, they can find globally optimal solutions. But due to a large number of control parameters, tuning is needed which slows the convergence rate and more time is consumed. The approaches for solving OPF problems are classified

A Review on Optimal Power Flow Problem

45

into three categories as arithmetic programming approach, analytical approach, and meta-heuristic approach. Different optimization algorithms under these approaches are given in Fig. 5 (Fig. 6). Analytical Approaches Analytical approaches are very helpful to find out the effects and benefits of location and size of reactive power sources [45]. These approaches give a clear vision of economic and technical benefits under different scenarios. It helps design future planning for optimal power flow management and pricing in the deregulated market [46]. But these approaches are time-consuming and fail to give a novel solution for large-scale power plants. Arithmetic Programming Approaches Arithmetic programming approaches are also known as conventional optimization algorithms [46]. Arithmetical programming is a mathematical representation aimed

NUMBER OF PUBLICATION

250 200 150 100 50

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

0

YEAR

NUMBER OF PUBLICATIONS

Fig. 5 Statistics of publications of optimal power flow per year as per Web of science since last 25 years

300 250 200 150 100 50 0

300295 220 158 9580

60585550464443424140 363432 302826

COUNTRY Fig. 6 Country-wise contribution for optimal power flow in last 25 years

2422 20

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N. Kumar et al.

at programming or planning the best possible allocation of resources. Under arithmetic programming, linear programming is one of the developments and most useful. It concerns the optimization allocation of limited resources under a set of constraints. Other conventional methods are gradient programming, quadratic programming, Newton–Raphson method, nonlinear programming, and interior point method. Besides excellent results, these optimization approaches have a poor convergence rate and weak in handling qualitative constraints [47]. Meta-heuristic Optimization Approaches Meta-heuristic optimization approaches are very helpful in solving multi-objective optimization problems because they can find out multiple optimal solutions. The performances of meta-heuristic optimization are highly dependent on the setting of parameters [48]. Meta-heuristic algorithms are inspired by nature. On the basis of source of inspiration, they can be bio-inspired, science inspired, art inspired, and social inspired. The bio-inspired algorithm follows collective behavior of animals. The science inspired algorithm follows nature or law of physic and chemistry. The art inspired algorithm follows artists behavior to create artistic stuffs. The socially inspired algorithm is simulated by social behaviors (Figs. 7, 8 and 9). Approach used for OPF problem

Arithetic Programming Approach Conic Programming [34]

Interior Point Method [8]

Analytical Approach

Meta-heuristic Approach

Swarm Based Meta-heuristic Optimization Seeker Optimization [13]

Newton Method [9] Branch and Bound Method [10,11]

Mixed Integer non-linear Programming [7] Non-linear Programming [6]

Ant-colony Optimization [14]

Immune Algorithm [15] Harmony Search Algorithm [16]

Artificial Bee Algorithm [12] Firefly Algorithm [17] Teaching Learning Algorithm [188]

Evolution Based Meta-heuristic Optimization Genetic Algorithm [23]

Differential Evolution Algorithm [24] Differential Search Algorithm [25]

Hybrid Metaheuristic Optimization PSO-GSA [27]

PSO-GA [26] FAPSO [33] PSO-DE [29]

AIACA [28] HEP [30] EPSO [31, 32]

Fig. 7 Categories and subcategories of different approaches for solution optimal power flow of problem

A Review on Optimal Power Flow Problem Fig. 8 Percentage of considered fitness evaluation functions for optimal power flow

47

Power Factor Improvement 5% Power Loss Minimization 16% Service Quality 8%

System Reliability 14%

Fig. 9 Percentage of computational techniques for optimal power flow

Simulated Pareto Annealing Optimization 5% 10% Ant Colony Optimization 8% Fuzzy Logic 5% MINLP 5% Evolutionary Algorithm 8%

Voltage Deviation Index 22% Cost Minimization 11% Distributed Generation 5% Optimal Allocation 19%

Non Linear Programming 10%

Metaheuristics 39%

Genetic Algorithm 10%

8 Conclusion This survey presents different categories of optimal power flow problems (OPF) and different optimization techniques used to solve OPF problems. These techniques are categorized as arithmetic programming, analytical approach, and meta-heuristic approach. The advantages and disadvantages of different optimization algorithms are also discussed here. The challenges which are faced by a mathematician in modeling OPF problem are focused in this. The objectives of the OPF problem as fuel cost minimization, emission minimization, active power loss minimization, improvement of voltage deviation, enhancement of voltage stability, and transmission loss minimization with their equality and inequality constraints are highlighted here. This survey will help researchers in selecting the right and appropriate OPF method to find optimal solutions considering different objectives and constraints.

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A Study on Zn/Cu-Based Pandan Leaf (Pandanus Amaryllifolius) Electrochemical Cell Salman Rahman Rasel, K. A. Khan, Md. Sayed Hossain, Shahinul Islam, M. Hazrat Ali, and Rajada Khatun

Abstract At present, there is a dire need of electricity around the world. There are a lot of remote areas where grid electricity is absent. So that some innovations are needed there for electricity production. The nonrenewable energy sources are limited. But we have a plenty of renewable energy sources. Although renewable energy sources are more safer for use in human life and the environmental pollutions. After finishing the renewable energy sources, people can use sun and plants for electricity generation. In our research paper, an idea has been taken for electricity production from pandan leaves because plants and leaves grow everywhere. The current and voltage have been generated from the pandan leaves which have been further used to light the LED lamp. Lighting the LED bulb proved that the idea was successful for electricity generation from pandan leaf. It is not limited to LED bulb; further, this idea will be used for other electric appliances. Pandan leaf (PL) has been used for getting electricity. The scientific name of the pandan leaf is Pandanus amaryllifolius. The pandan leaf extract has been used for electricity production. A comparative study has been done for both with and without secondary salt. CuSO4 has been used as a secondary salt. It has been studied the several parameters like open circuit voltage, load voltage, short circuit current, load current, maximum power, load power, and internal resistance. These parameters have been developed for both with and without secondary salt. It is shown that the performance for with secondary salt is better than S. R. Rasel Local Government Engineering Department (LGED), Sherpur Sadar, Sherpur, Bangladesh K. A. Khan (B) Department of Physics, Jagannath University, Dhaka 1100, Bangladesh e-mail: [email protected] Md. S. Hossain Center for Research Reactor, Bangladesh Atomic Energy Commission, Dhaka, Bangladesh S. Islam Department of Physics, Uttara University, Dhaka, Bangladesh M. Hazrat Ali Department of EEE, European University of Bangladesh (EUB), Dhaka, Bangladesh R. Khatun Medical Physics Division, Atomic Energy Centre, Dhaka 1000, Bangladesh © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Biswas et al. (eds.), Microelectronics, Circuits and Systems, Lecture Notes in Electrical Engineering 976, https://doi.org/10.1007/978-981-99-0412-9_5

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the without secondary salt. The internal resistance was less for using the secondary salt. After doing this research work, it can be concluded that result is positive for electricity generation from pandan leaf. Keywords Pandan leaf (Pandanus amaryllifolius) · Zn/Cu electrodes · Pandan leaf extract · Electricity · Secondary salt

1 Introduction The basic needs like home, money, water, food, and electricity are growing day by day due to rise in population [1–3]. In this article, it has been focused on one of the basic needs electricity [4, 5]. The renewable energy sources like solar energy, wind energy, biogas energy, biomass energy, geothermal energy, water energy, tidal energy, wave energy, and OTEC energy are not limited source and echo friendly [6– 8]. Furthermore, environment will not polluted day by day for using these renewable energy sources. It can be generated again and again and will never run out. On the other hand, nonrenewable energy sources like oil, gas, and coal does have a limited source. It will be finished within short time [9, 10]. Use of electricity using plants is a safeguard for our future and earth by reducing greenhouse effect [11]. If it is shifted the use of electricity from nonrenewable energy to renewable energy sources, then it will be more economic viable as well as environment friendly strategies [12]. In this research paper, it is found that pandan leaf produces voltage and current which leads to light a LED bulb [13]. There are a lot of plants, but it has been used here pandan leaf because it can grow well in this subcontinent under this climatic condition [14]. It can also harvest energy from the sun, and it can also transform that energy into food and oxygen [15].

2 Methods and Material The pandan leaf grows in Bangladesh well. It is found almost everywhere in the country. It is cultivated by the farmers specially for medicinal purposes. Figure 1 shows the pandan plant (PP). Anyone can harness the pandan plant and pandan garden for getting pandan leaf. It grows almost everywhere. Firstly, it has to collect from the garden of the pandan plant. Then after, it has to be blended for making pandan leaf extraction. The extraction was filtered by Whatman filter paper 41 and Whatman filter paper 42. It is shown in Fig. 2 that the process of PL extract by a blender machine. Firstly, it has been cut by a cutter and then into a several small piece. The small pieces were put into the blender and make extract for electricity generation. The extract was filtered by Whatman filter paper 41 and 42.

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53

Fig. 1 Pandan plant for getting pandan leaf

Fig. 2 Preparation of pandan leaf extract by a blender machine

It is shown in Fig. 3 that the pandan leaf extract by the dried method. Firstly, the leaf was dried and then make powder and then after finally mixed it with water. The extract was filtered by Whatman filter paper 41 and 42. Figure 4 shows the experimental setup of the basic principle of Zn/Cu-based PKL electrochemical cell. Here, Cu-plate acts as a cathode, and Zn-plate acts as an anode. Pathor Kuchi Leaf (PKL) acts as an electrolyte.

Fig. 3 Preparation of pandan leaf extract by a dried leaf

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Voltmeter

LED Load Ammeter Cu-Plate

Zn-Plate

Pandan leaf sap

Fig. 4 Experimental setup of the basic principle of Zn/Cu-based pandan leaf (Pandanus amaryllifolius) electrochemical unit cell

Figure 5 shows that 4 unit electrochemical cells have made a module. Each unit cell has one copper plate and one zinc plate which act as electrodes. The zinc plate acts as a cathode, and copper plate acts as an anode. The unit cells are connected in series combination. Figure 6a shows the pandan leaf electric unit module made by 4 unit cell. Each cell is made by a copper and zinc plate. Figure 6b shows the pandan leaf electric lamp. Figure 6c shows the cycle of the finished product of the pandan leaf electric lamp. Figure 7 shows the complete cycle of the finished product of pandan leaf electricity. A LED bulb is used a load. The summery of the working principle is given by the following:

Fig. 5 Experimental setup of the basic principle of Zn/Cu-based pandan leaf (Pandanus amaryllifolius) electrochemical module

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(a) Pandan leaf electric unit module.

(b) Pandan leaf electric lamp

55

(c) Finish product of the Pandan leaf lamp

Fig. 6 Experimental methods of a pandan leaf lamp production

Fig. 7 Cycle of the finished product of pandan leaf electricity

(a) 30 g of fresh pandan leaves were collected from the garden and then washed thoroughly with clean water, and a fine extract was made using a grinding stone. (b) The extract was kept in a bottle for about a week being sealed. (c) A Zn/Cu bio-electrochemical cell is designed where Zn and Cu plates were used as electrodes, pandan leaf (Pandanus amaryllifolius) extract as electrolyte. (d) An LED bulb was attached to ensure that the bio-electrochemical cell is functional. (e) Each of the four falcon tubes was filled with pandan leaf extract of 30 ml. (f) Readings of different parameters such as open circuit voltage, load voltage, short circuit current, and load current were taken at an interval of 3 h using a calibrated multimeter. (g) Again after adding 1 ml CuSO4 · 5H2 O in the tubes, readings of the same parameters were taken at an interval of 3 h using a calibrated multimeter.

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3 Chemical Reactions and Generation of Cell Potential At Anode—On account of its high electrolytic pressure, zinc passes into the solution as zinc ions, each ion leaves two electrons at the electrode. The process will continue until an equilibrium is reached between the electrode and the solution. At equilibrium, there will be potential difference between the electrode and the solution. Zn → Zn2+ + 2e− (Oxidizing Electrode)

(1)

At Cathode—At this electrode, copper ions gain electrons and are reduced to metallic copper which is deposited at the copper electrode: Cu2+ + 2e− → Cu(Reducing Electrode)

(2)

When the two electrodes are connected by a wire, the excess electrons on the electrode flow along the wire in order to neutralize the positive charge on the copper electrode. This movement of electrons from zinc to copper produces a current in the circuit. The net cell reaction can be represented as follows: Zn + Cu2+ → Zn2+ + Cu

(3)

To measure the cell potential at any moment during a reaction or at condition other than standard state, Nernst equation is used which can be expressed as bellow: E = E 0 − (RT /n F) ln Q c

(4)

where E = Cell potential under specific conditions E 0 = Cell potential at standard state condition (known) R = Ideal gas constant (8.314 J/mol-K) T = Temperature in Kelvin (0 °C = 273 K) n = Number of moles of electrons transferred in the balanced equation F = Faraday’s constant (i.e., charge of one mole of electrons, 95,484.56 C/mol) ln Qc = Natural logarithm of the reaction quotient at the moment in time. Qc = reaction quotient = [Product Ions]/[Reactant Ions] = [Zn2+ ]/[Cu2+ ][H+ ], Where [Zn2+ ] = Product ion concentration [Cu2+ ] = Reaction concentration ion concentration [H+ ] = Hydrogen ion concentration, which is measured by pH meter. Before going to the calculation using Nernst equation, it is very much needed to find out the reaction quotient (Qc ).

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4 Results and Discussion Most of the results have been tabulated and graphically discussed. The tables and graphs are given by the following: Case-I: Graphical Analysis of LED Bulb Lighting System Using Pandan Leaf (Pandanus amaryllifolius) Extract Without CuSO4 Table 1 shows the data for LED bulb lighting system using pandan leaf (Pandanus amaryllifolius) extract without CuSO4 . The data was open circuit voltage Voc , load voltage (V ), short circuit current, Isc (mA), load current, IL (mA), maximum power Pmax = Voc Isc (mW), load power PL = VL IL (mW), and internal resistance, rin = VIscoc (Ω) with the variation of time. Figure 8 shows the variation of open circuit voltage versus time duration. It is shown that the open circuit voltage was almost constant up to 27 h. Figure 9 shows the variation of load voltage versus time duration. It is shown that the load voltage was almost constant up to 27 h. Figure 10 shows the variation of short circuit current versus time duration. It is shown that the short circuit current was almost constant up to 18 h. Then after, it was decreased from 3.7 to 3.6 mA, and then finally, it was almost constant up to 27 h. Figure 11 shows the variation of load current versus time duration. It is shown that the short circuit current was almost constant up to 9 h. Then after, it was decreased from 3.6 to 3.5 mA, and then finally, it was almost constant up to 25 h, and then finally it decreased from 3.5 to 3.4 mA, and then, it was almost constant. Figure 12 shows the variation of short maximum power versus time duration. It is shown that the maximum power was almost constant up to 18 h. Then after, it was Table 1 Data for LED bulb lighting system using pandan leaf (Pandanus amaryllifolius) extract without CuSO4 Time duration (hr)

Open circuit voltage Voc (V)

Load voltage VL (V)

Short circuit current, Isc (mA)

Load current, IL (mA)

Maximum power Pmax = Voc Isc (mW)

Load power PL = VL IL (mW)

0

3.5

2.5

3.7

3.6

12.95

9.00

0.945

3

3.5

2.5

3.7

3.6

12.95

9.00

0.945

6

3.5

2.5

3.7

3.6

12.95

9.00

0.945

9

3.5

2.5

3.7

3.6

12.95

9.00

0.945

12

3.5

2.5

3.7

3.5

12.95

8.75

0.945

15

3.5

2.5

3.7

3.5

12.95

8.75

0.945

18

3.5

2.5

3.7

3.5

12.95

8.75

0.945

21

3.5

2.5

3.6

3.5

12.60

8.75

0.972

24

3.5

2.5

3.6

3.4

12.60

8.5

0.972

27

3.5

2.5

3.6

3.4

12.60

8.5

0.972

Internal resistance, rin = VIscoc (Ω)

58

Open circuit Voltage vs Time duration

Open circuit voltage (V)

Fig. 8 Open circuit voltage versus time duration

S. R. Rasel et al.

4 2 0

0

10

20

30

Fig. 9 Load voltage versus time duration

Load Voltage (V)

Time duration (hr) 3 2 1 0

Load Voltage vs Time duration

0

10

20

30

Fig. 10 Short circuit current versus time duration

Short circuit current (mA)

Time duration (hr) Short circuit current vs Time duration

3.75 3.7 3.65 3.6 3.55

0

10

20

30

Fig. 11 Load current versus time duration

Load current (mA)

Time duration (hr)

Load current vs Time duration

3.7 3.6 3.5 3.4 3.3

0

10

20

30

Time duration (hr)

Fig. 12 Maximum power versus time duration

Maximum power (mW)

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13

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Maximum power vs Time duration

12.8 12.6 12.4

0

10

20

30

Fig. 13 Load power versus time duration

Load power (mW)

Time duration (hr)

Load power vs Time duration

9.2 9 8.8 8.6 8.4

0

10

20

30

Time duration (hr) decreased from 12.95 to 12.6 mW, and then finally, it was almost constant up to 27 h. Figure 13 shows the variation of load current versus time duration. It is shown that the load power was almost constant up to 9 h. Then after, it was decreased from 9 to 8.75 mW, and then finally, it was almost constant up to 25 h, and then finally, it decreased from 8.75 to 8.5 mW, and then, it was almost constant. Figure 14 shows the variation of short internal resistance versus time duration. It is shown that the internal resistance was almost constant up to 18 h. Then after, it was increased from 0.945 to 0.972 Ω, and then finally, it was almost constant up to 27 h. Case-II: Graphical Analysis of LED Bulb Lighting System Using Pandan Leaf (Pandanus amaryllifolius) Extract with CuSO4 Figure 15 shows the variation of open circuit voltage versus time duration. It is shown that the open circuit voltage was almost constant up to 27 h. Figure 16 shows the variation of load voltage versus time duration. It is shown that the load voltage was almost constant up to 27 h (Table 2). Figure 17 shows the variation of short circuit current versus time duration. It is shown that the short circuit current was almost constant up to 9 h. Then, it decreases from 10.6 to 10.5 mA, and finally, it decreased from 10.5 to 10.2 mA. Figure 18

Fig. 15 Open circuit voltage versus time duration

Internal resistance vs Time duration

0.98 0.96 0.94

0

5

10

15

20

25

30

Time duration (hr)

Open circuit voltage (V)

Fig. 14 Internal resistance versus time duration

S. R. Rasel et al.

Internal resistance (ohm)

60

Open circuit Voltage vs Time duration

4 2 0

0

10

20

30

Time duration (hr)

Load Voltage vs Time duration Load Voltage (V)

Fig. 16 Load voltage versus time duration

3 2 1 0

0

10 20 30 Time duration (hr)

shows the variation of load current versus time duration. It is shown that the short circuit current was almost constant up to 9 h. Then, it decreases from 10.1 to 0.98 mA, and finally, it decreased from 0.98 to 0.96 mA. Figure 19 shows the variation of maximum power versus time duration. It is shown that the load power was almost constant up to 9 h. Then, it decreases from 39.22 to 38.85 mW, and finally, it decreased from 38.85 to 37.37 mW. Figure 20 shows the variation of load power versus time duration. It is shown that the load power was almost constant up to 9 h. Then, it decreases from 26.26 to 25.48 mW, and finally, it is decreased from 25.48 to 24.96 mW.

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Table 2 Data for LED bulb lighting system using pandan leaf (Pandanus amaryllifolius) extract with CuSO4 Open circuit voltage Voc

Load voltage VL

0

3.7

3

3.7

Time duration (hr)

Short circuit current, Isc (mA)

Load current, IL (mA)

Maximum power Pmax = Voc Isc (mW)

Load power PL = VL IL (mW)

2.6

10.6

2.6

10.6

Internal resistance, rin = VIscoc

10.1

39.22

26.26

0.349

10.1

39.22

26.26

0.349

(Ω)

3.7

2.6

10.6

10.1

39.22

26.26

0.349

3.7

2.6

10.6

10.1

39.22

26.26

0.349

12

3.7

2.6

10.5

9.8

38.85

25.48

0.352

15

3.7

2.6

10.5

9.8

38.85

25.48

0.352

18

3.7

2.6

10.5

9.8

38.85

25.48

0.352

21

3.7

2.6

10.1

9.6

37.37

24.96

0.377

24

3.7

2.6

10.1

9.6

37.37

24.96

0.377

27

3.7

2.6

10.1

9.6

37.37

24.96

0.377

Fig. 17 Short circuit current versus time duration

Short circuit current (mA)

6 9

Short circuit current vs Time duration

10.8 10.6 10.4 10.2 10

0

10

20

30

Fig. 18 Load current versus time duration

Load current (mA)

Time duration (hr) Load current vs Time duration

10.2 10 9.8 9.6 9.4

0

10 20 30 Time duration (hr)

S. R. Rasel et al.

Maximum power (mW)

62

Maximum power vs Time duration

40 39 38 37

0

10

20

30

Time duration (hr)

Load power (mW)

Fig. 19 Maximum power versus time duration

Load power vs Time duration

27 26 25 24

0

10

20

30

Time duration (hr) Fig. 20 Load power versus time duration

Figure 21 shows the variation of short internal resistance versus time duration. It is shown that the internal resistance was almost constant up to 18 h. Then after, it was increased from 0.352 to 0.377 Ω, and then finally, it was almost constant up to 27 h.

5 Conclusions The pandan leaf can be used for electricity generation like Bryophillum pinnatum leaf. It is a good biomass energy source. The conversion efficiency, voltage efficiency, columbic efficiency, power density, energy density, voltage regulation, capacity, discharge characteristics, and self-discharge characteristics should be studied properly in next time. It is finally decided that the pandan leaf can generate electricity. It will create a new era and a new revolution in environment friendly and pocket

Internal resistance (ohm)

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0.39 0.38 0.37 0.36 0.35 0.34

63

Internal resistance vs Time duration

0

5

10

15

20

25

30

Time duration (ohm)

Fig. 21 Internal resistance versus time duration

friendly electricity. Although it can be said that apart from using pandan leaf for medicinal uses only, it can also be used for electricity production.

6 Future Prospects The next step of our research team to light energy bulb and other electric appliances from pandan leaves that may be lighten up the people who are living in the remote and rural complex areas all over the world.

References 1. C.D.D. Angel, Introduction of electrochemistry, Georgetown University. http://bourman.chem. georgetown.edu/S02/lect25/lect25.htm. Accessed 16 Oct 2012 2. M. Hasan, K.A. Khan, Dynamic model of Bryophyllum pinnatum leaf fueled BPL cell: a possible alternate source of electricity at the off-grid region in Bangladesh. Microsyst. Technol. (2018). Microsystem Technologies Micro - and Nanosystems Information Storage and Processing Systems, Springer, ISSN: 0946-7076. https://doi.org/10.1007/s00542-018-4149-y 3. K.A. Khan, L. Hassan, A.K.M. Obaydullah et al., Bioelectricity: a new approach to provide the electrical power from vegetative and fruits at off-grid region. Microsyst. Technol. (2018). https://doi.org/10.1007/s00542-018-3808-3 4. K.A. Khan, S.R. Rasel, M. Ohiduzzaman, Homemade PKL electricity generation for use in DC fan at remote areas. Microsyst. Technol. 25(12) (2019). Microsystem Technologies Microand Nanosystems Information Storage and Processing Systems, ISSN: 0946-7076. https://doi. org/10.1007/s00542-019-04422-2 5. M. Hasan, K.A. Khan, Experimental characterization and identification of cell parameters in a BPL electrochemical device. SN Appl. Sci. 1, 1008 (2019). Springer. https://doi.org/10.1007/ s42452-019-1045-8 6. L. Hassan, K.A. Khan, A study on harvesting of PKL electricity. Microsyst. Technol. 26(3), 1031–1041 (2020). Springer Journal. https://doi.org/10.1007/s00542-019-04625-7 7. K.A. Khan, M.A. Mamun, M. Ibrahim, M. Hasan, M. Ohiduzzaman, A.K.M. Obaydullah, M.A. Wadud, M. Shajahan, PKL electrochemical cell: physics and chemistry. SN Appl. Sci. 1, 1335 (2019). Springer Journal.https://doi.org/10.1007/s42452-019-1363-x

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8. K.A. Khan, M. Hazrat Ali, A.K.M. Obaydullah, M.A. Wadud, Production of candle using solar thermal technology. Microsyst. Technol. 25(12) (2019). Microsystem Technologies Micro- and Nanosystems Information Storage and Processing Systems, Springer, ISSN: 0946-7076. https:// doi.org/10.1007/s00542-019-04390-7 9. K.A. Khan, M.S. Bhuyan, M.A. Mamun, M.Ibrahim, L. Hasan, M.A. Wadud, Organic electricity from Zn/Cu-PKL electrochemical cell, in Contemporary Advances in Innovative and Applicable Information Technology, Advances in Intelligent Systems and Computing, vol. 812, ed. by J.K. Mandal et al. (Springer Nature Singapore Pvt. Ltd., 2018), Chapter 9, pp. 75–90 10. A.K.M. Atique Ullah, M. Mahbubul Haque, M. Akter, A. Hossain, A.N. Tamanna, M. Mottaleb Hosen, A.K.M. Fazle Kibria, M.N.I. Khan, M.K. AKhan, Green synthesis of Bryophyllum pinnatum aqueous leaf extract mediated bio-molecule capped dilute ferromagnetic α-MnO2 nanoparticles. Mater. Res. Express 7(1), 015088 (2020). IOP publishing Ltd. 11. K.A. Khan, M. Hazrat Ali, M.A. Mamun, M. Mahbubul Haque, A.K.M. Atique Ullah, M.N. Islam Khan, L. Hassan, A.K.M. Obaydullah, M.A. Wadud, Bioelectrical characterization and production of nanoparticles (NPs) using PKL extract for electricity generation. Microsyst. Technol. (2020). Springer Journal. https://doi.org/10.1007/s00542-020-04774-0 12. T. Subramani, M. Nallathambi, Mathematical model for commercial production of bio-gas from sewage water and kitchen waste. IJMER 2(4), 1588–1595 (2012) 13. C. Ngo, J.B. Natowitz, in Our Energy Future; Resources, Alternatives and the Environments (Wiley, 2009). R.A. Hinrichs, M.H. Kleinbach, in Energy: Its Use and the Environment, 4th ed. (Thomson Brook/Cole, 2006) 14. V.R. Prakash, I.K. Bhat, Energy, economics and environmental impacts of renewable energy systems. Renew. Sustain. Energy Rev. 13(9), 2716–2721 15. M. Mani Teja, M. Basha, N. Balanaidu, Green electricity from aloe vera. Int. J. Mag. Eng. Technol. Manag. Res. (2011). APENG/2011/47294

Gate All Around 22 nm SOI Schottky Barrier MOSFET with High I ON /I OFF Current Ratio for Low-Power Digital and Analog Circuit Applications Amit Saxena, R. K. Sharma, Manoj Kumar, and R. S. Gupta

Abstract The design of low-power and high-speed circuits must require a high I ON /I OFF ratio. In the present work, SOI Schottky barrier MOSFET is investigated for low-power digital and analog circuits applications. Static power consumption with I ON /I OFF ratio is computed for gate all around basic Schottky barrier, depletion pocket Schottky barrier, and silicon on insulator Schottky barrier MOSFETs with a channel length of 22 nm. Further, CMOS inverter for digital circuit application and resistive load single-stage voltage amplifier for analog circuit application is implemented using all three devices. Keywords SOI SB-MOSFET · I ON /I OFF ratio · Low-power circuit · Static power consumption

1 Introduction Today’s modern communication technology uses Internet of things (IoT) and longterm evolution (LTE) systems and demands digital and analog circuits having very high-speed and low-power consumption [1, 2]. Advanced body implant bioelectronic components require very low static power consumption [3–5]. As we are now approaching to end of Moore’s law, the further shrinks in the channel length A. Saxena (B) USICT, Guru Gobind Singh Indraprastha University, Sector-16C, Dwarka, New Delhi, India e-mail: [email protected] R. K. Sharma Department of Electronics and Communication Engineering, Netaji Subhas University of Technology East Campus, New Delhi, India M. Kumar Department of Electronics and Communication Engineering, USAR, Guru Gobind Singh Indraprastha University, East Delhi Campus, 110092 Delhi, India R. S. Gupta Department of Electronics and Communication Engineering, Maharaja Agrasen Institute of Technology, New Delhi, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Biswas et al. (eds.), Microelectronics, Circuits and Systems, Lecture Notes in Electrical Engineering 976, https://doi.org/10.1007/978-981-99-0412-9_6

65

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of MOSFET will increase the short channel effects (SCEs). Silicon on insulator (SOI) Schottky barrier (SB) MOSFET is recently purposed [6]. Due to the novel architecture of SOI SB-MOSFET, it is highly immune to SCEs at 22 nm technology node. Analog/RF parameters analysis is performed for basic SB-MOSFET, depletion pocket (DP) SB-MOSFET, and SOI SB-MOSFET at 22 nm technology node in Silvaco TCAD. Static power consumption and I ON /I OFF ratio comparison for all the three MOSFET architectures are performed. For digital application, a 22 nm CMOS inverter circuit is simulated, and static power consumption analysis is performed for all the three MOSFETs. Also for analog/RF application, single-stage resistive load voltage amplifier is simulated, and vital parameters are compared for basic SB-MOSFET, DP SB-MOSFET, and SOI SB-MOSFET architectures.

2 Device Structure The 3D and 2D architectures of SOI SB-MOSFET are shown in Fig. 1a, b, respectively. Figure 2a, b shows the 2D architecture of basic SB-MOSFET and DP SB-MOSFET, respectively. The SOI SB-MOSFET includes SiO2 insulator piler of 2 nm radius. Table 1 shows the different device parameters used for the Silvaco TCAD simulation.

Fig.1 a Three-dimensional architecture of SOI SB-MOSFET, b two-dimensional architecture of SOI SB-MOSFET

Gate All Around 22 nm SOI Schottky Barrier MOSFET with High …

67

Fig. 2 a Two-dimensional architecture of basic SB-MOSFET, b two-dimensional architecture of dielectric pocket SB-MOSFET

Table 1 Device structural parameters used for numerical simulation

Parameter

SB

DPSB

SOISB

Length of channel (nm) (L)

22

22

22

Doping of channel (Nd) (cm−3 )

1015

1015

1015

Thickness of gate oxide (nm)

2

2

2

Diameter of silicon pillar (nm)

10

10

10

Work-function of metal (eV)

4.8

4.8

4.8

Length of dielectric pillar (nm)

–

4

22

Radius of dielectric pillar (nm)

–

2

2

3 Analog/RF Parameter Analysis Basic SB-MOSFET has a drawback of ambipolarity effect at low gate bias voltage, as shown in Fig. 3. The SOI SB-MOSFET and DP SB-MOSFET show the improvement by suppresses the ambipolarity effect of basic SB-MOSFET. The plot of linear and log scale drain current with applied gate bias at V DS = 1 V which is shown in Fig. 3 shows that among all the three MOSFET architectures, SOI SB-MOSFET highly suppresses the unwanted ambipolarity effect. Figure 4 shows the plot of drain current with drain voltage at constant gate to source voltage. In analog/RF applications, MOSFET

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biased in saturation region must have constant drain current, i.e., independent to drain to source voltage. Figure 4 shows that SOI SB-MOSFET has minimum variation in drain current as compared to basic SB-MOSFET and DP SB-MOSFET architecture. At zero gate bias, the drain current for basic SB-MOSFET, DP SB-MOSFET, and SOI SB-MOSFET architectures is 7.56nA, 1.94pA, and 0.13pA, respectively. The I ON /I OFF ratios for basic SB-MOSFET, DP SB-MOSFET, and SOI SB-MOSFET architectures are 1.71 × 104 , 2130 × 104 , and 26,700 × 104 . Hence, SOI SBMOSFET architecture has minimum OFF state drain current and the highest I ON /I OFF ratio as compared to basic SB-MOSFET and DP SB-MOSFET architectures.

Fig. 3 Linear and log scale drain current plot with gate bias at fixed drain to source voltage

Fig. 4 Drain current with applied drain bias at constant gate to source voltage

Gate All Around 22 nm SOI Schottky Barrier MOSFET with High …

69

4 CMOS Inverter CMOS inverter is used as a standard benchmark for designing and analyzing all digital circuits. Figure 5 shows the circuit of CMOS inverter, implemented using basic SB-MOSFET, DP SB-MOSFET, and SOI SB-MOSFET architectures. The output waveform for SOI SB-MOSFET with capacitive load (C L ) of 1fF, V DD = 0.5 V at 10 GHz input frequency is shown in Fig. 6. Table 2 shows the vital parameters measured of CMOS inverter for basic SB-MOSFET, DP SB-MOSFET, and SOI SB-MOSFET architectures. The power consumption plot for basic SB-MOSFET, DP SB-MOSFET, and SOI SB-MOSFET architectures CMOS inverter circuit with V DD = 0.5 V is shown in Fig. 7. For SOI SB-MOSFET architecture, the static power consumption is minimum as compared to Fig. 5 CMOS inverter circuit with capacitive load (C L ) of 1fF and V DD = 0.5 V

Fig. 6 Input and output waveform for SOI SB-MOSFETCMOS inverter circuit with capacitive load of 1fF and V DD = 0.5 V

70 Table 2 The CMOS inverter vital parameters for SB, DP and SOI SB MOSFETs

A. Saxena et al. Parameter

SB

DPSB

SOISB

Max. input voltage, V IH (V)

0.3207

0.3209

0.321

Min. input voltage, V IL (V)

0.1792

0.1791

0.1791

Max. output voltage, V OH (V)

0.4494

0.4498

0.4498

Min. output voltage, V OL (V)

0.0518

0.0507

0.0506

Fig. 7 Plot of power consumption for CMOS inverter circuit at V DD = 0.5 V for all the three MOSFET architecture

basic SB-MOSFET and DP SB-MOSFET architectures. The static power consumptions of SOI SB-MOSFET are 0.347pW and 1.645pW less as comparison with DP SB-MOSFET and basic SB-MOSFET, respectively.

5 Voltage Amplifier Circuit for Analog/RF Applications Common source NMOS voltage amplifier widely used in analog/RF applications and the self-bias common source NMOS voltage amplifier is shown in Fig. 8. Supply voltage V DD = 1.V and a gate bias voltage V GS = 0.225 V are derived using R1 = 10 MΩ and R2 = 2.9 MΩ, and drain resistance RD = 2 MΩ and load resistance RL = 10 MΩ are used. For amplification, an input sinusoidal voltage signal of amplitude 5 mV and frequency of 100 GHz is used. The amplified output signal for basic SB-MOSFET, DP SB-MOSFET, and SOI SB-MOSFET architectures is shown in Fig. 9. The measured voltage gain (AV ) for basic SB-MOSFET, DP SB-MOSFET, and SOI SB-MOSFET common source NMOS voltage amplifier for Fig. 8 is 1.376, 1.481, and 1.503, respectively.

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Fig. 8 Common source NMOS resistive load voltage amplifier for 100 GHz sinusoidal input signal

Fig. 9 NMOS resistive load voltage amplifier for 100 GHz input signal

The analog/digital circuit parameters compression for all three devices is shown in Table 3.

6 Conclusion CMOS inverter of digital circuit applications and common source NMOS self-bias voltage amplifier for analog/RF application are simulated for basic SB-MOSFET, DP SB-MOSFET, and SOI SB-MOSFET architectures. Different digital and analog/RF circuit parameters are measured as shown in Table 3. From all the three MOSFET

72 Table 3 Analog/digital circuit parameters compression for all three devices

A. Saxena et al. Parameter

SB

DPSB

SOISB

OFF state drain current (pA)

7560

1.94

0.13

I ON /I OFF current ratios (104 )

1.71

2130

26,700

Max. input voltage, V IH (V)

0.3207

0.3209

0.321

Min. input voltage, V IL (V)

0.1792

0.1791

0.1791

Max. output voltage, V OH (V)

0.4494

0.4498

0.4498

Min. output voltage, V OL (V)

0.0518

0.0507

0.0506

Static power Cons.(µW)

0.1772

0.1759

0.1756

Voltage gain, AV

1.376

1.481

1.503

architectures, SOI SB-MOSFET circuits show the highest I ON /I OFF current ratio, voltage gain (AV ), and minimum I OFF current, static power consumption. Acknowledgements The authors are thankful to the Director, Maharaja Agrasen Institute of Technology for providing the research facilities.

References 1. L. Caruccio, O. Piazza, G. Polese, G. Tortora, Secure IoT analytics for fast deterioration detection in emergency rooms. IEEE Access 8, 215343–215354 (2020). https://doi.org/10.1109/ACCESS. 2020.3040914 2. H. Bhamra, Y.-W. Huang, Q. Yuan, P. Irazoqui, An ultra-low power 2.4 GHz transmitter for energy harvested wireless sensor nodes and biomedical devices. IEEE Trans. Circuits Syst. II Express Briefs 68(1), 206–210 (2021). https://doi.org/10.1109/TCSII.2020.3005332 3. B.D. Deebak, F. Al-Turjman, Smart mutual authentication protocol for cloud based medical healthcare systems using internet of medical things. IEEE J. Sel. Areas Commun. 39(2), 346–360 (2021). https://doi.org/10.1109/JSAC.2020.3020599 4. G.S. Aujla, A. Jindal, A decoupled blockchain approach for edge-envisioned IoT-based healthcare monitoring. IEEE J. Sel. Areas Commun. 39(2), 491–499 (2021). https://doi.org/10.1109/ JSAC.2020.3020655 5. O. Postolache, D.J. Hemanth, R. Alexandre, D. Gupta, O. Geman, A. Khanna, Remote monitoring of physical rehabilitation of stroke patients using IoT and virtual reality. IEEE J. Sel. Areas Commun. 39(2), 562–573 (2021). https://doi.org/10.1109/JSAC.2020.3020600 6. A. Saxena, M. Kumar, R.K. Sharma et al., SOI Schottky barrier nanowire MOSFET with reduced ambipolarity and enhanced electrostatic integrity. J. Electron. Mater. 49, 4450–4456 (2020). https://doi.org/10.1007/s11664-020-08164-0

Studies on Synthesis, Characterization, and Monitoring of Ag NPs for Power Production Using Tomato Farhana Islam, K. A. Khan, Md. Sayed Hossain, Salman Rahman Rasel, and Shirin Akter

Abstract In this research paper, tomato extract has been synthesized and characterized for Ag NPs. This synthesized Ag NPs have been used for power production. This produced Ag NPs using tomato extract are very useful and eco-friendly. Different characterizations have been conducted UV-visible spectroscopy, FTIR, XRD, and FESEM. It is found that the absorption peak at around 428 nm for UV-visible spectrum. It is also found that the biomolecule compounds were responsible for the reduction and capping material of silver nanoparticles using FTIR spectra. It is also found that the particles to be crystalline in nature by XRD study, with a face-centered cubic (FCC) structure. The power production activity of Ag NPs was assessed to find their potential use in electrochemical cell. It is found that the open circuit voltage (V oc ), short circuit current (I sc ), and maximum power (Pmax ) were better for using Ag NPs from the tomato extract of a single electrochemical cell. Keywords Ag NPs · Synthesis · Characterizations · Monitoring · Power production

F. Islam Department of Physics, Uttara University, Dhaka, Bangladesh K. A. Khan (B) Department of Physics, Jagannath University, Dhaka 1100, Bangladesh e-mail: [email protected] Md. S. Hossain Center for Research Reactor, Bangladesh Atomic Energy Commission, Dhaka, Bangladesh S. R. Rasel Local Government Engineering Department (LGED), Sherpur Sadar, Sherpur, Bangladesh S. Akter Medical Physics Division, Atomic Energy Centre, Dhaka 1000, Bangladesh © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Biswas et al. (eds.), Microelectronics, Circuits and Systems, Lecture Notes in Electrical Engineering 976, https://doi.org/10.1007/978-981-99-0412-9_7

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1 Introduction Nanotechnology is an important technology nowadays [1, 2]. The application of nanotechnology is very attractive [3]. Ag NPs have been synthesized using tomato extract. Ag NPs are using various sectors [4]. In this case, Ag NPs are using for power production [5]. It has been designed and developed electrochemical cell [6]. Tomato extract was used as an electrolyte. It has also been prepared the liquid forms of Ag NPs in the laboratory [7, 8]. This liquid forms of NPs have to add with the tomato extract [9]. The performance of the electrochemical cell with and without Ag NPs has been studied [10, 11]. This technology is very innovative all over the world. The applications of Ag NPs for power production are very viable and feasible [12–14]. The traditional sources of energy like oil, gas, and coal are diminishing day by day. So that we have to depend on renewable energy sources like solar energy, wind energy, biogas energy, geothermal energy, water energy, biomass energy, tidal energy, wave energy, and ocean thermal energy conversion (OTEC) immediately [15].

2 Methodology Ag NPs are synthesized via a facile green synthesis route where silver nitrate AgNO3 was used as precursor and tomato extract was used as a source of reducing and capping agents. Ag NPs have been synthesized using tomato extract and then perform the characterization and finally monitoring for power production.

2.1 Materials Chemicals, Reagents, Instruments, and Biological Samples The chemicals and reagents used in this work were of analytical grade and were used without further purification. De-ionized water with resistivity 18 MΩ-cm was used as solvent in order to prepare the solutions required in this work. The chemicals and reagents used in this work are listed below: (i) Silver nitrate(AgNO3 ), (ii) tomato extract, and (iii) de-ionized water. The following equipment and instruments were used for the synthesis, characterization, and electricity generation applications of the AgNO3 NPs: Grindstones (traditional manual grinder), measuring flux calibrated beaker, volumetric flux, falcon tube (50 mL), falcon tube (15 mL), sample holder, vial tube, wash bottle, thermometer, Whatman filter paper (41 and 42), magnetic stirrer with hot plate, digital balance (78-1 magnetic stirrer, China), centrifuge machine (ABT-028C, USA), tissue paper, tape, magnetic bar, freeze/refrigerator, X-ray diffractometer (Philips, Expert Pro, Holland), Fourier transform infrared spectrophotometer, field emission scanning electron microscopy, UV-visible machine.

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2.2 Synthesis of Ag NPs Using Tomato Extract The detailed procedure for the synthesis of Ag NPs is summarized below: Figure 1 shows the block diagram of the procedure of synthesis of Ag NPs from tomato extract. It is shown (Fig. 1) that raw silver nanoparticles (Ag NPs) have been produced. The healthy tomatoes were collected from the local market. The collected tomatoes were gently washed to remove dust. Washed several times with tap water along with de-ionized water to remove the dust particles and then dried at room temperature under ceiling fan. The tomatoes were chopped with a knife and then mashed for making extract with grindstones. The process is shown in Fig. 2. From Fig. 3, it is shown that it was mixed 20 g of the extract with 100 mL deionized water and then shaking with magnetic stirrer with hot plate heated at 60 °C temperature for 1 h (60 min).

Fig. 1 Block diagram of the procedure of synthesis of Ag NPs from tomato extract

Fig. 2 Collecting, washing, chopping, and making extract of tomato

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Fig. 3 Extract and shaking with magnetic stirrer with hot plate

It is shown (in Fig. 4) that after making it cool at normal (room) temperature, the tomato extract was obtained by filtering the mixture twice with Whatman-41 and Whatman-42 to eliminate residual solids and stored at 4 °C for 60 min for further analysis. Highly pure AgNO3 precursor is used in this experiment, purchased from Sigma Aldrich. For the synthesis of silver nanoparticles, 5 mL of tomato filtered extract was added with 45 mL, 1 mM AgNO3 solution which is shown in Fig. 5. It is shown that it is put under light for further analysis. Then, the colorless mixture turned yellow to light orange within three hours. After couple of days, it was finally been formed a dark-orange color solution with some black sediments at the bottom of the flask, which in turn affirms the formation of Ag NPs. The color changing diagram is shown in Fig. 6. After, synthesized the raw Ag NPs were obtained by centrifugation at 3000 rpm 7 times for 70 min. Then, the centrifuged particles were washed with de-ionized water and again subjected to centrifugation at 3000 rpm for 10 min. Separated Ag NPs

Fig. 4 Filtering with Whatman-41 and Whatman-42 paper

Fig. 5 Making 45 mL, 1 mM AgNO3 solution and adding with 5 mL filtered tomato extract

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Fig. 6 Color changing diagram of Ag NPs from initial to final stage

Fig. 7 Process of dried Ag NPs

were dried through hot plate (showed in Fig. 7). The calcined Ag NPs have been applied for other analytical techniques.

3 Results and Discussion 3.1 X-ray Diffraction (XRD) Analysis The structural characterization of tomato extract mediated green synthesized silver nanoparticles (Ag NPs) was carried out using X-ray diffraction (XRD) technique, where the diffractograms were recorded in the 2θ range of 0–80º as shown in Fig. 8, and the exact values are illustrated in Fig. 9. The diffraction patterns for the synthesized Ag NPs were obtained at Bragg’s angle of 38.20º, 44.40º, 64.60º, 77.60º, 81.76º, 98.17º, 110.88º, 115.32º, and 135.50º which were indexed to (111), (200), (220), (311), (222), (400), (331), (420), and (422) planes, respectively. The XRD patterns obtained for the synthesized Ag NPs in the present study showed a good agreement with the reported values [1, 2]. The broadening of the Bragg’s peaks indicates the formation of silver nanoparticle. The average crystal size of Ag NPs is calculated from the XRD data using Debye– , where λ is the X-ray wavelength (λ = 1.54060 Å), θ is Scherrer formula, D = β0.89λ cos θ Bragg’s diffraction angle, and β is the full width at half maximum (FWHM) [3–5].

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Fig. 8 XRD patterns of synthesized Ag NPs using tomato extract and AgNO3

Fig. 9 Intensity versus 2θ

3.2 UV–Visible Technique Figure 10 shows the UV-visible spectra of tomato extract (blue line), AgNO3 salt solution in DI water (red line), and synthesized Ag NPs (green line). Blue line and red line did not appear any particular peak from total 300 to 800 nm wavelengths. On the other hand, the green line showed a clear peak from the range of 400 to 500 nm wavelength, which in turn affirms the presence of Ag NPs. A clear diagram of UV-Vis spectra of Ag NPs is given below. Figure 11 shows the change of absorbance versus UV-Vis spectra of Ag NPs. The UV-visible spectrum, Fig. 11 reveals a sharp peak at around 330 nm which is appeared due to the band emission of silver nanoparticles. The maximum absorption is observed with a strong broad peak at around 440 nm due to the transformation of Ag0 from Ag+ which is corresponded to the surface plasmon absorption of silver nanoparticles. The absorption peak at the higher wavelength is originated due to the larger particle size. Moreover, color change of the solution is occurred due to the

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Fig. 10 UV-spectra for filtered tomato extract, AgNO3 , and Ag NPs

Fig. 11 UV-Vis spectra of Ag NPs

radiation absorption in the visible region of the electromagnetic spectrum due to the localized surface plasmon of silver nanoparticles [6–9].

3.3 FTIR Analysis Figure 12 represents the FTIR spectra of filtered tomato extract. The spectra revealed several peaks at around 1219.01, 1371.39, 1643.35, 2133.27, and 3381.21 cm−1 . The peak at around 1219.01 cm−1 may associate for the stretching of strong C–O bond of alkyl aryl ether. The bending of O–H bond may be responsible for the peak at

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Fig. 12 FTIR of filtered tomato extract

around 1371.39 cm−1 . Furthermore, the peak at around 1643.35 cm−1 was appeared due to the stretching vibration of C = C. Another sharp peak at around 2133.27 cm−1 is originated due to the stretching vibrations of C ≡ C bond. Moreover, the peak at wave number 3381.21 cm−1 may be appeared due to the O–H and N–H stretching both [10]. Figure 13 narrates the FTIR spectra of AgNO3 dissolved in de-ionized water. The spectra revealed several peaks at around 1093.64, 1255.66, 1641.42, 2094.69, and 3383.14 cm−1 . The peak at around 1093.64 cm−1 may be associated for the stretching vibration of C-N bond, whereas the peak at around 1255.66 cm−1 was appeared due to the stretching of C-O bond. A significant peak at 1641.42 cm−1 is assigned the C = C stretching. Furthermore, the peak at around 2094.69 cm−1 was appeared due to the stretching vibration of N = C = S bond, whereas the peak at around 3383.14 cm−1 was associated for the stretching vibration N–H and O–H bonds both [10]. Figure 14 illustrates the FTIR spectra of tomato extract mediated Ag NPs. The spectra revealed several peaks at around 1217.08, 1367.53, 1514.12, 1641.42, 1726.29, 2129.41, 2891.30, and 3379.29 cm−1 . Another sharp peak at around 2129.41 cm−1 is originated due to the stretching vibrations of C ≡ C bond of alkyne. Moreover, the peak at wave number 2891.30 cm−1 may be appeared due to the O–H stretching and C–H of alkane [10–12].

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Fig. 13 FTIR of AgNO3 dissolved in de-ionized water

Fig. 14 FTIR of tomato extract mediated Ag NPs

3.4 FESEM Analysis The morphology of Ag NPs was analyzed using the field emission scanning electron microscopy (FESEM) images JSM-7610F equipped at 15 keV. Figure 15a, b represent high resolution FESEM image of Ag NPs with different scale. The formation of large sizes nanoparticles was clearly observed from the FESEM images.

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Fig. 15 FESEM analysis

3.5 Applications of Ag NPs for Power Monitoring Application Techniques The synthesized Ag NPs can be used for power production [1]. The longevity of the produced power has been increased [13, 14]. The reading was taken for a unit cell where there is single Zn plate and single Cu plate. Figure 16a shows the experimental setup for the application of Ag NPs in electricity production with digital multimeter for the measurement of different electrical parameters like current, voltage, and power. Figure 16b shows the experimental set up for the application of Ag NPs in electricity production with micrometer for the measurement of different electrical parameters like current, voltage, and power.

(a) With digital multimeter

(b) With microcontroller

Fig. 16 Experimental setup for the application of Ag NPs in electricity production

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(a): open circuit voltage against time (with NPs)

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(b): open circuit voltage against time (without NPs)

Fig. 17 Graphical representation of open circuit voltage against time (with and without NPs)

3.6 Open Circuit Voltage (Voc ) From Fig. 17a, b, it is shown that the variation of open circuit voltage (V oc ) with the variation of time duration. We have observed the difference between the V oc for with and without NPs of the tomato extract electro chemical cell. From Fig. 17a, it is shown that V oc is decreasing gradually with time up to 90 min where in Fig. 17b, it is represented that V oc is decreasing gradually up to 1 h. After 1 h, it remains almost constant.

3.7 Comparison Between the Cells for with NPs and Without NPs It is found that the maximum and minimum short circuit current for with Ag NPs are 0.935 V and 0.901 V, respectively, and whereas the maximum and minimum short circuit current for without Ag NPs are 0.918 and 0.895 V, respectively. The difference between the highest and lowest values of Fig. 17a is 0.034 V where in Fig. 17b is 0.023 V.

3.8 Short Circuit Current (Isc ) From Fig. 18a, b, it is shown that the variation of short circuit current (I sc ) with the variation of time duration. It is observed that the difference between the I sc for with and without Ag NPs. From the Fig. 18a, it was found that the current was found that the short circuit current decreases gradually and slowly up to 90 min. From the Fig. 18a, it was found that the current was found to be same up to 40 min and then decreased up to 90 min.

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(a): Short circuit current against time (with NPs)

(b): Short circuit current against time (without NPs)

Fig. 18 Graphical representation of short circuit current against time (with and without NPs)

Comparison between the without NPs and with NPs It is found that the maximum and minimum short circuit current for with Ag NPs are 0.003A and 0.0022A, respectively, and whereas the maximum and minimum short circuit current for without Ag NPs are 0.0024A and 0.0015A, respectively.

3.9 Maximum Power (Pmax ) It is shown from Fig. 19a, b that the changes of maximum power (Pmax ) change with the variationation of time. From the Fig. 19a, it was found that the maximum power was found to slightly decreased up to 60 min and then remain almost constant. The difference between the highest value and the lowest value of maximum power is approximately 8 mW. Figure 19b demonstrated that the maximum power was found to be almost same up to 40 min and then decreased linearly. The difference between the highest value and the lowest value of maximum power is approximately 9 mW.

(a): Maximum Power against` time (with NPs)

(b): Maximum Poweragainst time (without NPs)

Fig. 19 Graphical representation of maximum power against time duration (with and without NPs)

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Comparison between the without NPs and with NPs It is found that the maximum and minimum short circuit current for with Ag NPs are 0.002754 W and 0.001969 W, respectively, and whereas the maximum and minimum short circuit current for without Ag NPs are 0.002244W and 0.001351W, respectively.

4 Conclusions It is shown that the performance has been increased for using Ag NPs in the tomato based electro chemical cell. So that this technology is feasible electricity generation system in near future. Use of microcontroller to measure the current and voltage simultaneously is very interesting. The process of synthesis and characterization is also very interesting.

References 1. J. Huang, Q. Li, D. Sun, Y. Lu, Y. Su, X. Yang, H. Wang, Y. Wang, W. Shao, N. He, J. Hong, Biosynthesis of silver and gold nanoparticles by novel sundried Cinnamomum camphora leaf. Nanotechnology 18(10), 105104 (2007) 2. S. Basavaraja, S.D. Balaji, A. Lagashetty, A.H. Rajasab, A. Venkataraman, Extracellular biosynthesis of silver nanoparticles using the fungus Fusarium semitectum. Mater. Res. Bull. 43(5), 1164–1170 (2008) 3. S. Nath, D. Chakdar, Synthesis of CdS and ZnS quantum dots and their applications in electronics, nanotrends 4. S.S. Nath, D. Chakdar, G. Gope, D.K. Avasthi, Effect of 100 MeV nickel ions on silica coated ZnS quantum dots. J. Nanoelectron. Optoelectron. 3(2), 180–183 (2008) 5. B.D. Hall, D. Zanchet, D. Ugarte, Estimating nanoparticle size from diffraction measurements. J. Appl. Crystallogr. 33(6), 1335–1341 (2000) 6. C. Wu, X. Zhou, J. Wei, Localized surface plasmon resonance of silver nanotriangles synthesized by a versatile solution reaction. Nanoscale Res. Lett. 10(1), 1–6 (2015) 7. N.M. Ishak, S.K. Kamarudin, S.N. Timmiati, Green synthesis of metal and metal oxide nanoparticles via plant extracts: an overview. Mater. Res. Express 6(11), 112004 (2019) 8. A.J. Kora, S.R. Beedu, A. Jayaraman, Size-controlled green synthesis of silver nanoparticles mediated by gum ghatti (Anogeissus latifolia) and its biological activity. Org. Med. Chem. Lett. 2(1), 1–10 (2012) 9. M.R. Shaik, M. Khan, M. Kuniyil, A. Al-Warthan, H.Z. Alkhathlan, M.R.H. Siddiqui, J.P. Shaik, A. Ahamed, A. Mahmood, M. Khan, S.F. Adil, Plant-extract-assisted green synthesis of silver nanoparticles using Origanum vulgare L. extract and their microbicidal activities. Sustainability 10(4), 913 (2018) 10. S. Iravani, H. Korbekandi, S.V. Mirmohammadi, B. Zolfaghari, Synthesis of silver nanoparticles: chemical, physical and biological methods. Res. Pharm. Sci. 9(6), 385 (2014) 11. K.A. Khan, S.R. Rasel, M. Ohiduzzaman, Homemade PKL electricity generation for use in DC fan at remote areas. Microsyst. Technol. 25(12). Microsystem Technologies Micro- and Nanosystems Information Storage and Processing Systems, ISSN: 0946-7076. https://doi.org/ 10.1007/s00542-019-04422-2

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12. M. Hasan, K.A. Khan, Experimental characterization and identification of cell parameters in a BPL electrochemical device. SN Appl. Sci. 1, 1008 (2019). Springer https://doi.org/10.1007/ s42452-019-1045-8 13. L. Hassan, K.A. Khan, A study on harvesting of PKL electricity. Microsyst. Technol. 26(3), 1031–1041 (2020). Springer Journal. https://doi.org/10.1007/s00542-019-04625-7 14. K.A. Khan, M.A. Mamun, M. Ibrahim, M. Hasan, M. Ohiduzzaman, A.K.M. Obaydullah, M.A. Wadud, M. Shajahan, PKL electrochemical cell: physics and chemistry. SN Appl. Sci. 1, 1335 (2019). Springer Journal. https://doi.org/10.1007/s42452-019-1363-x 15. I. De Leersnyder, H. Rijckaert, L. De Gelder, I. Van Driessche, P. Vermeir, High variability in silver particle characteristics, silver concentrations, and production batches of commercially available products indicates the need for a more rigorous approach. Nanomaterials 10(7), 1394 (2020)

Man–Machine Interface in Designing Through Simulation in Solar Power Development in India Shreya Karmakar and Pradip Kumar Sadhu

Abstract The history of evolution of mankind is the evolution of man–machine interface. In recent past, in Indian Banking sector, users have availed multiple benefits through banking at user’s convenience. This has happened through core banking system, Internet banking, app-based payment system, etc. Such man–machine interfaces, through mobile phone and Internet connectivity, have replaced the traditional physical movement and cash intensive transactions. For mitigating the climate change, the Government of India has taken large-scale renewable energy [RE] program which has resulted rapid capacity addition in RE in India, especially in solar PV and wind energy sector. Thus, from an intermediate power supply system to the main driving force of Indian Power Sector, solar energy development in India has received a quantum jump during 2009–2021. The, then, manual exercises for site survey to design-engineering have transformed into simulation-based man–machine interfaces. In the present article, a sincere attempt is made to study, analysis, and recommend on the evolution, adaptation, and future scope of further propagation of man–machine interface for designing through simulation. Keywords Man–machine interface · Modeling · Simulation

1 Introduction The principles of dynamic analysis of man–machine interface [MMI] address reliability analysis in respect of system performance which includes both the performance of man and machine. It helps to develop rational thinking process through reasoning. It supports pragmatic and efficient decision making processes, both at high and low level through an integrated ergonomic model. Now, man–machine interface has become more users friendly. The interface between the smartphone and smartphone users through android operating systems has brought revolution in the man–machine interface dynamics. It intends to provide S. Karmakar (B) · P. K. Sadhu Indian Institute of Technology, Dhanbad 826004, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Biswas et al. (eds.), Microelectronics, Circuits and Systems, Lecture Notes in Electrical Engineering 976, https://doi.org/10.1007/978-981-99-0412-9_8

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the non-specialist user, the ability to perform specialist’s task with intelligent system interfaces. The evolution of such interface is like: semi-automatic system interface, automatic system interface, Internet-enabled system interface, and IoT-based system interface. In present day life, there exist broadly two types of interfaces: fully automated interfaces and not fully automated interfaces including man–machine interactions. After introduction of Windows operating system in desktops and laptops, the computer–computer user interface has become more user friendly, than all of its previous occasions. The most ‘interfaced’ machine of human civilization on today is smartphones with Android and iOS operating systems. Now, more and more user friendly man–machine interface in respect of smartphone intends to provide support with system interfaces to the semi-skilled or not so skilled user, so that the user can perform specialist’s tasks. In recent days, the climate change has surfaced as the worst threat against the very existence of human civilisation in this planet earth. The Indian Meteorology Department in its ‘Climate of India during 2020’ report as released on 04.01.2021 says that the year 2020 was the 8th warmest year in India since 1901. Extreme weather has killed some 1400 people in India in 2020.The cyclone Amphan has emerged as the deadliest of its kind in this century. The Ministry of Health and Family Welfare; Government of India in its report in December, 2020 has shown that some 15 lacs of people have died in 2020 in India from air pollution. In this background, the country has adopted strategies to reduce its greenhouse gas (GHG) emissions. These are through capacity addition through renewable energy sources and phase-wise replacing the fossil fuel driven transport system by electric vehicles [E.V]. After signing of the Kyoto Protocol, in 2008, the Government of India had introduced major initiatives for climate mitigation. The National Action Plan on Climate Change was launched. This had 8 National Missions including two energy-related missions. One such national mission was linked with solar energy, and another mission was linked with energy efficiency. Later on, in 2011, a third energy-related national mission was introduced in the name of electric mobility. After Kyoto regime [2007–2012], the Paris Climate Accord was signed in 2015. It has given India the conditional target to have its 40% of power generation capacity (Nationally Determined Contribution) in GW from non-conventional energy sources like solar energy, wind energy, small hydro system, etc., by 2030. These exercises are aimed to limit the global atmospheric temperature rise within 1.5 degree Celsius by 2100 [1]. To meet the commitment in accordance to the Paris Accord, in 2015, India had fixed up the target to add 175 GW of renewable energy [RE] in its grid. Out of this, 100 GW is targeted from solar. By December, 2021, India has already achieved 150 GW+ from renewables. Observing enthusiastic achievements, the Government of India has upgraded its RE target from 175 to 220 GW by 2022. [Convocation: Pandit Deendayal Upadhyaya Petroleum University: Speech of the Hon’ble Prime Minister

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of India: November, 2020]. The further target is 450 GW of RE by 2030 which is to accomplish India’s goal to enhance its RE penetration to its cumulative power generation capacity to 40% from RE sources, by 2030 [2]. Before the introduction of solar mission, the solar energy was a device-based ready to install system like a small capacity (say 18, 37, 74, and 100 Wp, etc.,) solar home system, street lighting system, water purifier, water heating system, etc. At that time, solar systems were designed as per market demand and availability of the then technology options. Customization was rarely practiced. Then, such systems were not integrated with the grid. Previously, solar systems were used at un-electrified areas for limited hours’ of operation with battery storage. Now, Gigawatt level utility scale solar parks are established at different corners of this country. Megawatt level rooftop solar power plants are also installed at big shades of factory, housing, and commercial places [3]. In spite of industrial down turn due to SARS Cov 2 induced global pandemic, 3.2 GWp of capacity addition in Indian grid from solar energy took place in 2020, about 11.1 GW capacity additions in 2021, and the cumulative installation capacity has reached at 48+ GWp. Such achievements are through more penetration of man–machine interface in design and simulation, starting from the site survey to performance analysis of the systems as installed in grid connected mode. Enhancement of overall system efficiency, reduced completion time, customization, optimization, grid compatibility, forecasting, etc., is some key benefits, as received by the solar power users in India for its rapid capacity addition even in the prevailing complicated economic and grid environment.

2 Overall Classification of Man–Machine Interface (MMI) The man–machine interaction (MMI) can be defined as data exchange among a human, a computer system, and its software. The information exchange or dialog exchange between man and machine consists of a two part. One is validation (input) part, and another is demonstration (output) part [4] (Fig. 1). According to human psychology, interaction with computer involves three types of activities: perception, cognition, and interaction. The process of a perception is in receiving and transmitting the data from computer system to the human brain. Through cognition process, user can estimate the perceiving data and also make decision in correspondence to data which has already stored in mind. The interaction part is that where user is ready to handle situations like how to receive, recognize, decide, and respond to the present information and performs an input action. A quality user interface reduces the time spent on perception, cognition, and interaction. So, in brief, MMI is the quality user interface to carry out the intended work with minimum sensible consideration to its tools. It can attain maximum task efficiency [6].

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Fig. 1 MMI human process [5]

3 Man–Machine Interface in Solar Energy Development: Transformation from Intermittent System to Main Driving Force in Indian Power Sector 3.1 Man–Machine Interface in Solar Design and Engineering Site Survey and Capacity Fixation Situation Before Man–Machine Interface: Before introduction of Jawaharlal Nehru National Solar Mission (JNNSM), widely used solar energy products and devices where solar lantern, solar PV home lighting systems, solar PV street lighting systems, etc. For such systems, site survey was required only to identify shadow-free installation place and quantitative requirement of the user [7]. Man–Machine Interface: Situation: After introduction of JNNSM, MW label power plants have come into the picture. Here, the capacity is not predetermined like solar home lighting systems and solar street lighting systems. Here, the capacity is site specific. Home and street lighting systems are systems with battery backup and for limited hours of operations. After JNNSM, grid connected solar PV power plants are introduced. These plants are broadly of two types in respect of grid connectivity: systems with net-metering arrangement (here, the user/consumer utilizes the generated solar energy first for his/her self-consumption, and if there is any excess, the excess amount goes to the grid.), systems with grid integration through Power Purchase Agreement (PPA: through such arrangement, the entire solar power feeds to the grid). So, balancing various factors like: fixing true shadow-free installation space, ensuring optimal generation, and ensuring optimal ‘excess push’ to the utility grid of the distribution company (DISCOM) at optimal tariff period are to be balanced to fix up the optimal capacity of the PV power plant. A simulation tool is needed to carry out such exercises with an appropriate accuracy level [8].

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3.2 Future Scope of Intervention App-based interface is needed to introduce for one window solution for an interested consumer/user for solar PV installation. Through such interface, potential users can avail information on available technologies, pricing, source of materials, grid compatibility, tariff, etc. It can guide the potential user to have informed decision toward solar energy installation [9] (Fig. 2). Designing steps for the grid tied solar PV system in simulating platform: PV Array Loss Factors The daily differences in the input/output profiles of energy (Figs. 3 and 4): [above] For different years, probability distribution of the system production forecast is basically dependent on the metrological data used for the simulation and on the following choices: Metrological data, year-to-year variability variance 2.5%, some system parameters, uncertainties, specified deviation, PV module modeling/parameters 1.0%, inverter efficiency uncertainty 0.5%, soiling and mismatch uncertainties 1.0%, degradation uncertainty 1.0%, global variability variance 3.1 [10] (Fig. 5). Annual production probability variability 2.73 MWh P50 88.59 MWh P90 85.08 MWh P95 84.10 MWh. Fig. 2 PVSYST methodology

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Fig. 3 Designing procedure

Fig. 4 PVSYST analysis of daily variation of energy profile

3.3 Identification Appropriate Installation Place Situation Before Man–Machine Interface: Before such interface, people made physical survey through measuring tape and compass. Through such exercises, it is very difficult to identify true shadow-free installation space. Situation After Man–Machine Interface: The solar PV modules must be installed in the shadow-free area for obtaining maximum generation. Shading means any kind of object (near or far) obstructing direct sunlight falling on the solar module. Shading in the power plant is mostly a result of inappropriate designing considerations.

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Fig. 5 Performance forecasting to assess techno-commercial feasibility of a PV power plant

It is preferred that the shadow analysis of the roof space will be done through relevant software tool to ensure a proper shadow-free area for installation. So, simulation has become an integral part of solar designing. Future Scope of Intervention: App-based interface is needed to introduce to have one window smart (intelligent) support system for an interested designer and/or engineer in execution for solar PV job. Through such interface, designer an engineer can avail information, references on prevailing and upcoming technologies, trend, pricing, source of materials, grid compatibility, tariff, regulations, etc. It can guide the potential engineer to have more informed, accurate, and updated decision toward solar energy installation (Fig. 6).

4 Online Performance Monitoring of the Power Plant 4.1 Through Remote Monitoring Systems Situation Before Man–Machine Interface: Solar PV home lighting systems and solar PV street lighting systems were installed in distributed manner at different locations. To know the regular performance of such systems, there were two options: physical verification at sites or collecting information over phone. Through such process, fault detection was difficult, and in case of restoration of such systems from defunct condition was time taking.

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Fig. 6 Shadow analysis process to optimize effective shadow-free space for PV application

Situation After Man–Machine Interface: After introduction of grid connected PV power plant, the optimal generation from the PV plant and optimal push to the grid has become very important. Here, the remote monitoring systems (RMS) have started playing a pivotal role in respect of performance of the plant. For example, ideally, in non-hilly areas of West Bengal, 1200–1350kwh (unit) of solar electricity should be available from each kilowatt of PV installation in a year. So, through RMS (a mechanism through which both online and offline data can be retrieved from the inverter or energy meter of the power plant), [11] the performance of the specific plant can be known, and the said performance can be compared with the bench marking. For smaller capacity plants, inverter-based remote monitoring systems are used, and for larger capacity plants, SCADA-based remote monitoring. System is used. The RMS can be extended for other than performance analysis, like fault detection also.

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4.2 Future Scope of Intervention Through fully owned smart meters, Internet gateways, and cloud computing, a smart monitoring system can be developed. Through this Internet of things (IoT)-based application, the solar EPC can support its user in real-time monitoring of installed solar systems. From such systems, the solar generation of the plant can be optimized through faster interventions. Simulation for Continuous Monitoring for Electricity Bill Reduction The main objective of grid connected PV power plant in net-metering mode is to reduce the electricity bill of the user/consumer. In such PV systems, generally, two meters are used: energy meter to know the total solar generation from the plant, as provided by the solar installer/solar EPC and the export–import meter (net meter), as provided by the concerned Electricity Service Provider or the Distribution Company (DISCOM). These DISCOM meters are not functioning properly in many occasions. In case of reverse phase sequence condition, export energy (solar generation) is not being recorded in the said meter. Secondly, in case of unbalance drawl of energy by the consumer, the export energy (solar generation) is being added with the import energy. So, in case of such type of functioning, instead of reducing the electricity bill of the consumer, the said bill of the consumer is enhanced. For such cases, the very purpose of the installation of grid connected PV power plant in net-metering mode is lost [12].

4.3 Future Scope of Intervention It is very difficult for a consumer to understand and diagnose the meter functioningrelated situation as mentioned above. In such situations, through RMS or through physical verifications, it can be observed that the solar plant is in operation. But in reality, in spite of appropriate functioning of the PV power plant, the electricity bill of the user/consumer is increasing [13]. Ideally, the consumer does not have any access to diagnose the DISCOM export– import meter. DISCOM is the only authority to check the functionality of such meters. The local ground level DISCOM offices do not know the said efficacy of such meters in many occasions. Even after testing before the consumer, they say that meter is functioning properly. So, an appropriate simulation mechanism is required to be adopted to pass on the real benefit to the consumer that is the reduction of electricity bill of the consumer through appropriate adjustment of generated solar power. Here, a smart simulation tool is needed clubbed with block chain technology to ensure real-time settlements for the consumer with optimal accuracy level (Fig. 7).

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Fig. 7 Remote monitoring process for a solar PV power plant

5 Conclusion From an intermittent source of power supply to the major driving force of Indian power sector, solar energy has made a big jump in last three four years in spite of COVID pandemic. Manual designing exercises to simulation software-based analysis; standardization, risk analysis, fore casting, performance-based proto-typing, and modeling; are some noticeable changes. IoT-based performance monitoring has enabled both installer and user to work hand in hand to avail optimum system performance. More advanced and user friend IoT-based man–machine interface in cloud computing, block chain is on the way to make solar power more cheaper and more reliable source of energy in coming days. App-based guidance to an investor for obtaining an informed and appropriate decision toward investing a solar power plant is already at the introduction stage. Through such interface, potential users avail information on technologies, pricing, quality standard for materials, regulation, tariff, grid compatibility, capacity of the plant, RoI, feasibility, etc. This rapid evolution of man– machine interface like data availability to data imagination via data interaction will make the solar energy propagation in India, faster, especially in grid environment. This, in turn, will support to fulfill India’s ambitious target of 500 GW of RE installed capacity by 2030.

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References 1. File Name: India Solar compass Q4. Website, www.bridgetoindia.com (2019). Accessed 18 Jan 2020 2. File Name: Mercom India Clean Energy Magazine, vol. 03. Website: www.mercomindia.com (2021). Accessed 22 Feb 2021 3. X. Wang, Research on human-machine interface optimization design of numerical control machine tools in ocean engineering based on visual communication perception field. J. Coast. Res. (115), 322–326 (2020) 4. Y. Man, Human-machine interface considerations for design and testing in distributed sociotechnical systems, vol. 3 (2015), pp. 2674–2681. Department of Shipping and Marine Technology 5. A.J. Borstad, Man machine interface design for modeling and simulation software. MIC 7(3), 129–144 (1986). Statoil A/S, Research and Development, N-7000, Norway 6. Z. Ahmed, Intelligent human machine interface design for advanced product life cycle management systems. Association for Computing Machinery, New York, United States, ACM 978-1-60558-642-7/09/12 (2009) 7. S. Karmakar, J. Chakraborty, Rooftop solar power in West Bengal under New Regulation Regime. Power Genxt Mag. 9, 12–15 (2021). Engineers’ Welfare Forum 8. P.M. Jones, R.W. Chu, C.M. Mitchell, A methodology for human-machine systems research: Knowledge engineering, modeling, and simulation. IEEE Trans. Syst. Man Cybern. 25(7), 1025–1038 (1995) 9. S. Karmakar, J. Chakraborty, For quantitative and qualitative growth of employment in Indian RE Sector: A repeat of ‘Maruti Story’ is the need of the hour: Institution of Engineers (India), IEC 35 (2020) 10. G.V. Kondraske et al., Measuring human performance: concepts, methods, and applications, in SOMA: Engineering for the Human Body (ASME) (1988), pp. 6–13 11. G. Grote, J. Weyer, N.A. Stanton, Beyond human-centred automation—Concepts for humanmachine interaction in multi-layered networks. Ergonomics 57(3), 289–294 (2014). https://doi. org/10.1080/00140139.2014.890748 12. R. Sinha, C.J.J. Paredis, P.K. Khosla, V.-C. Liang, Modeling and simulation methods for design of engineering systems. J. Comput. Inf. Sci. Eng. 1(1), 84–91 (2000) 13. Doctor Narvik, Man-machine and intermachine interaction in flexible manufacturing systems. Thesis for the degree of Philosophiae (2013)

Optical Cryptography Using Reversible Logic Gate Goutam Kumar Maity, Tarak Nath Bera, Aranya Manna, Pradip Ghosh, and Subhadipta Mukhopadhyay

Abstract This technology deals with scale back to the facility utilization of the digital circuit. This style is the most promising system that condenses of warmth and power consumption. Otherwise, energy reduction in digital system is mostly probable to zero. Nowadays, cryptography depends on exploitation of digital system. Our paper presents a resolution for cryptography by exploitation of optical digital system. In our proposition, it is indicated that the fundamental diagram of cryptography has four input terminals, and it is made of Fredkin and Toffoli gate. It is worked on primarily on MZI based reversible digital circuit. Keywords Cryptography · MZI · Fredkin gate · Toffoli gate etc.

1 Introduction It is one in all the foremost rising system larger than before the high recital computing as per the G. Moor’s law, some semiconductor device counts to be integrated per unit space in device can all most double in one and 0.5 year. Cryptography can reformat and remodel our information, creating it safer on its trip between networking systems. The technology relies on the necessities of secret codes, increased by fashionable

G. K. Maity (B) Department of Physics, Raipur Block Mahavidyalaya, Bankura, West Bengal 722134, India e-mail: [email protected] T. N. Bera · P. Ghosh Department. of Computer Science, Midnapore City College, Paschim Medinipur, West Bengal 721129, India A. Manna Department of Physics, Sabang S.K. Mahavidyalaya, Paschim Medinipur, West Bengal 721140, India S. Mukhopadhyay Department of Physics, Jadavpur University, Kolkata 700032, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Biswas et al. (eds.), Microelectronics, Circuits and Systems, Lecture Notes in Electrical Engineering 976, https://doi.org/10.1007/978-981-99-0412-9_9

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arithmetic that protects our information in powerful ways in which modern cryptography is heavily supported mathematical theory and engineering application; cryptography algorithms are designed around computational hardness assumptions, creating such algorithms laborious to interrupt in actual observed by any mortal, whereas it’s in theory potential to interrupt into a well-designed system, and it’s impracticable in actual observe to try and do thus [9]. Such computationally secure scheme, theoretically advanced, which enhances integer factorization algorithms and results in quicker computing process [2]. This scheme is more secure and can’t be broken by even with unlimited computing powers. The growth of cryptographic technology has raised variety of legal problems within the modern era [1–3]. Cryptography’s potential to be used as a tool for sedition and security measure using semiconductor diode and several governments classify it as a weapon and to limit or perhaps interdict its use and export. In some jurisdiction wherever utilization of cryptography is lawful, laws allow the researchers to require the discloser of suspicious text keys for credentials significant to an analysis. Cryptography additionally acting a serious character in digital human rights administration and copyright infringement dispute in relation to digital media. Several reversible logic gates are planned that embody Fredkin gate (FG), Richard Philips Feynman gate, Toffoli gate (TG) and Richard Philips Feynman double gate. A gate is conservative if the playacting weight of its input is equal to the playacting weight of its output. Varity of physical implementation for the universal Fredkin gate are planned supported nonlinear optical bistable components, interferometers, linear optics, complementary metal oxide semiconductor. All optical implementation of Fredkin gate has conjointly been incondensable by experimentation with semiconductor optical amplifier and recently with optical fiber-based nonlinear optical loop mirror.

2 Some Definition Now, we have taken into account the subsequent definition. Definition-1: There is n input and n output digital operate update n * n operate square measure reversible if its input and output is equal. Definition-2: There is n input and n output system reversible if its n * n reversible operate. Definition-3: If β (0, 1) for i = 1, 2, 3, and let A* denote the negation of A. 1 * 1 NOT gate presents the operation. 1 * 1 NOT gate (×1) performs the operation (A1) − (A1 ⊕ 1), 2 * 2 CNOT (A1, A2) gate performs the operation (A1, A2) − (A1, (A1 ⊕ C1) ⊕ A2). 3 * 3 Toffoli (A1, A2, A3) gate performs the operation. (A1, A2, A3) − (A1, A2, (A1 ⊕ C1) (A2 ⊕ A2) ⊕ A3).

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Fig. 1 Basic structure of MZI

Fig. 2 MZI-based optical switch

4 * 4 Toffoli (A1, A2, A3, A4) gate performs the operation. (A1, A2, A3, A4) − (A1, A2, A3, (A1 ⊕ C1) (A2 ⊕ C2) (A3 ⊕ C3 ⊕ A4). 3 * 3 Fredkin gate (A1, A2, A3) performs the operation. (A1, A2, A3) − (A1, A1 * A2 + A1 A3, A1 A2 + A1 * A3).

3 Working Principle of MZI The operational principle of MZI by the primary coupling purpose induces coupling of a fraction of the incident lightweight propagation within the core mode to the facing mode and also performs the opposite function, generating the interference of sunshine propagating in several optical ways [4–7]. A collimated beam is split by a halfsilvered mirror. The second ensuing beam (the simple beam and also reference beam) square measures every mirror by a mirror beams then passes a second half-silvered mirror and enters two detectors (Figs. 1 and 2; Table 1).

4 MZI-Based Toffoli Gate The MZI-based Toffoli gate has three inputs as example (A, B, C), and three outputs (P, Q, R) which satisfy the relation as follows (Table 2):

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Table 1 Truth table of Fig. 1 Incoming signal

Control signal

Output port-1

Output port-2

0

0

0

0

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1

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Table 2 Truth table of Toffoli gate [8] Outputs

Inputs A

B

C

P

Q

R

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1

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1

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1

1

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0

P=A Q=B R =C⊕ A·B

⎫ ⎪ ⎬ ⎪ ⎭

As the truth table is a fact for a gate, here it is shown for understanding.

5 MZI-Based Fredkin Gate Fredkin gate is a 3 * 3 conservative reversible logic gate. It has three inputs (A, B, C) and three outputs (X, Y, Z) that satisfy the relation as follows (Table 3): X=A Y = A· B + A·C Z = A· B + A·C

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Table 3 Truth table of Fredkin gate Outputs

Inputs A

B

C

X

Y

Z

0

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1

6 Implementation of Cryptography Using All Optical Reversible Logic Gate Cryptographic hash function (CHF) is a mathematical algorithm that maps data of encrypted size (often known as the “message”) to a bit array of another set of size (the “hash value,” “hash,” or “message digest”). It’s a one-way forward operator which operates impracticable ways to invert the original message. It is hard to realize a message after creating CHF, that manufactures a given hash, is not detectable to a brute-force search for matching, or not detectable by a rainbow table for matching or tracing the hash function. Cryptanalytic hash functions square measures a basic tool of recent cryptography. The main properties of cryptographic hash functions are: It is deterministic, which means that similar message continuously leads to a similar hash. It is fast to cipher the hash price for any given message. It’s impracticable to get a message that yields a given hash price. It’s impracticable to search out two completely different message. It’s a little amendment to a message ought to amendment the extensively that brand new hash price seems unrelated with the recent hash price. There are certain properties of cryptography hash function impact security of password storage. One-Way Function: It generates strong hash which is not possible to reconstruct the original password after working on the output or on the hash. Avalanche Effect: Changing a bit of the original message generates keys of unpredictably. Determinism: Same text will generate the same hash key or enciphered text. Collision Resistance: The digest generated will never collide with each other meaning that hash key is unique.

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A brute force has the highest power to generate keys to reproduce the original message. It is used mostly for cryptographic hacking and on guessing possible combinations of a targeted password until the original and correct password is discovered. The longer the password with critical combinations of characters which needs more iteration of brute-force searching.

7 Simulation and Results The simulated results of optical Toffoli gate using Opti-system software are shown in Fig. 3c. The simulated results of optical Fredkin gate using Opti-system software is shown in Fig. 4c. We investigate the simulated results of optical cryptography using reversible logic gate that is shown in Fig. 5 (Figs. 6, 7 and 8).

8 Conclusion To comprehend the ability of quantum PC, contemplate RSA-640, variety with 193 digits, which might be factored by eighty a pair of 0.2 GHz computers over the span of five months, one quantum PC would consider but seventeen seconds. Numbers that will generally take billions of years to work out may solely take matter of hours or perhaps minutes with a totally developed quantum PC. In view of those facts, fashionable cryptography can get to seek for computationally devise utterly new techniques of archiving the goals presently served by fashionable cryptography.

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a

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Fig. 3 a Basic diagram of Toffoli gate, b MZI-based Toffoli gate, c simulation diagram of Toffoli gate using Opti-system software

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a

X= A Y = Ā.B + A. C Z = A . B + Ā. C X

A B C

FG

Y Z

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Fig. 4 a Schematic diagram of Fredkin gate (FG), b MZI-based Fredkin gate (FG), c simulation diagram of Fredkin gate using Opti-system software

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Fig. 5 Schematic diagram of cryptography

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Fig. 6 a Simulated input of optical Toffoli gate, b simulated output of optical Toffoli gate

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Fig. 7 a Simulated input of optical Fredkin gate, b simulated output of optical Fredkin gate

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Bit sequence: (00001111)

Bit sequence: (00001111) Fig. 8 a Simulated input of optical cryptography, b simulated output in cryptography

References 1. H. Thapliyal, M. Zwolinski, Reversible logic to cryptographic hardware, in Proceedings of 49th International Midest Conference on Circuit and SYSTEMS (2006) 2. N.M. Nayeem, L. Jamal, H.M.H. Babu, Efficiently reversible multiplier and its application to hardware cryptography. J. Comput. Sci. (2009) 3. A. Banerjee, Reversible cryptography hardware with optimized quantum cost and delay, in Proceedings of Annual IEEE India Conference (2010), pp.1–4 4. T. Chattopadhyay, All-optical modified fredkin gate. IEEE J. Sel. Top. Quantum Electron. PP(99), 1–8 (2012) 5. S. Agrawal, Metaphorical study of reversible logic gate. Int. J. Innov. Res. Comput. Commun. Eng. 1(4) (2013)

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6. S. Kotiyal, H. Thapliyal, N. Ranganathan, Mach-Zehnder interferometer based design of alloptical reversible binary adder, in IEEE (2012) 7. P.R. Yelekar, S.S. Chiwande, Design of sequential circuit using reversible logic, in IEEE ICAESM (2012) 8. C. Padmini, J.V.R. Ravindra, PEARL: Performance analysis of ultra low power reversible logic circuits against DPA attacks, in 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) (2016) 9. R. Latha, R. Premkumar, S. Anand, An efficient wavelet transform based steganography technique using chaotic map, in 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT) (2018)

Red Spinach-A New and Innovative Power Source K. A. Khan, Farhana Islam, Md. Sayed Hossain, Salman Rahman Rasel, and Md. Ohiduzzaman

Abstract Different plants extracts have been proven that they are the potential for electricity generation. By immerging two electrodes into the extract to allow flow of ions and then electricity is produced. Different types of electrodes and plant leaves suggested that current and voltage are produced greater or lesser extents where Zn/Cu-based electrodes and PKL extract generates the maximum power output. The results confirmed that electrochemistry is the responsible for electricity generation. LED bulb lighting system using red spinach (Amaranthus dubius) extract with and without copper sulphate solution (CuSO4 ·5H2 O). Bio-electrochemical systems show the process of electrical power generation or achieve the reduction reaction with a certain potential poised through electron transfer between the electron acceptor and electron donor. In a Zn/Cu bio-electrochemical cell, the zinc plate losses the electrons, the copper plate gains those electrons and the electrons react with H+ and Cu2+ ions and eventually convert into H2 and Cu atoms. Finally, H2 releases from the cell and copper atoms are deposited onto the Cu plate. The experimental was conducted with and without adding copper sulphate solution CuSO4 · 5H2 O with red spinach extract. A comparative study has been done for with and without secondary salt (CuSO4 · 5H2 O). It is found that the internal resistance has been decreased for K. A. Khan (B) Department of Physics, Jagannath University, Dhaka 1100, Bangladesh e-mail: [email protected] F. Islam Department of Physics, Uttara University, Dhaka, Bangladesh Md. S. Hossain Center for Research Reactor, Bangladesh Atomic Energy Commission, Dhaka, Bangladesh S. R. Rasel Local Government Engineering Department (LGED), Sherpur Sadar, Sherpur, Bangladesh Md. Ohiduzzaman Department of Physics, Jashore University of Science and Technology, Jashore 7408, Bangladesh e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Biswas et al. (eds.), Microelectronics, Circuits and Systems, Lecture Notes in Electrical Engineering 976, https://doi.org/10.1007/978-981-99-0412-9_10

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using copper sulphate as a secondary salt and it was from 0.347 to 0.33 Ω. As a result current has been increased from 1.8 to 7.7 mA and obviously power has been increased from 5.76 to 20.2 mW. Keywords Red spinach · Extract · Electricity · Secondary salt · Performance · Electrochemistry

1 Introduction The global economy depends on the production rate of the fossil fuels (oil, gas and coal) [1]. But it is diminishing day by day rapidly [2]. As per the present consumption situations, the reserved oil will be finished within 40 years and gas will be finished within 60 years [3–5]. A lot of researchers proved an inverse relationship between the oil price and activity of the economy. Fossil fuels are the engine of economic activities. The extensive use of fossil fuels are raising the CO2 in the atmosphere [6–8]. As a result the greenhouse effect is becoming seriously. The greenhouse gases increases the earth’s temperature gradually and therefore we are getting a warmer atmosphere and it is collapsing the global environment [9, 10]. It is clear that climate change depends on us. After finishing the fossil fuels Nuclear power may be the alternative sources [11]. But it is very risky and expensive. Now renewable energy may be the alternative energy sources [12]. The meaning of renewable energy sources mean solar energy, wind energy, biogas energy, geothermal energy, biomass energy, water energy, tidal energy, wave energy and OTEC [13]. In the meantime a team of engineers from Washington University has developed low power production device from living plants. Furthermore, different developed and developing countries are harvesting electricity production from different plants and plant leaves [14]. It is mentioned that the role of electrochemistry is the main responsible of electricity production. This paper has expressed the some fundamental parameters to harness electricity from red spinach. The red spinach is one kind of biomass energy sources. It has a great medicinal value. Side by side it has also power value. This paper presents that it is possible to generate electricity using the red spinach. This research work may be the guide line for electricity generation in near future.

2 Methods and Materials In this section it has been discussed about the selection of electrodes as an anode and cathode and energy source and then after finally understanding the basics of power generation.

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2.1 Copper Electrode The used copper should be highly pure a (above 95%).

2.2 Zinc Electrode Zinc is called the sacrificial element. It donates the electron.

2.3 Experimental Details The experimental details are given below: (a) Twenty-five grams of fresh red spinach leaves were collected from the field and then washed thoroughly with clean water and a fine extract was made using a grinding stone. (b) The extract was kept in a bottle for about a week being sealed. (c) A Zn/Cu bio-electrochemical cell is designed where Zn and Cu plates were used as electrodes, red spinach (Amaranthus dubius) extract as electrolyte. (d) An LED bulb was attached to ensure that the bio-electrochemical cell is functional. (e) Each of the four falcon tubes was filled with pandan leaf extract of 30 ml (f) Readings of different parameters such as V oc (open circuit voltage), V L (load voltage), I sc (short circuit current) and I L (load current) were taken at an interval of 3 min using an ammeter and voltmeter. (g) Again after adding 1 ml CuSO4 · 5H2 O in the tubes readings of the same parameters were taken at an interval of 3 min using an ammeter and voltmeter. For falcon tubes were taken for this research work. Copper is used as an anode and Zinc is used as a cathode. The electrodes are connected in series combinations to flow the electrons for electricity generation.

2.4 Chemical Reactions At Anode: Zn → Zn2+ + 2e− At Cathode: Cu2+ + 2e− → Cu Resulting Net Reaction: Zn + Cu2+ → Zn2+ + Cu, Where, Cu2+ = Reactant ions and Zn2+ = Product ions.

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2.5 Nernst Equation (Generation of Potential) E = E 0 −(RT /n F) ln Q c where E E0 R T n F ln Qc

Cell potential under specific conditions Cell potential at standard state condition (known) Ideal gas Constant (8.314 J/mol-K) Temperature in Kelvin (0 °C = 273 K) Number of moles of electrons transferred in the balanced equation Faraday’s Constant (i.e. charge of one mole of electrons, 95,484.56 C/mol) Natural logarithm of the reaction quotient at the moment in time.

2.6 Reaction Quotient (Qc ) For the reaction: Zn + Cu2+ → Zn2+ + Cu. We know, Qc = [Zn2+ ]/[Cu2+ ] In Nernst equation R and T are constant. If we further consider T = 298 K (i.e. 25 °C), then we have, E = E 0 − (0.02568/n) ln Qc. There is a transfer of 2 electrons. So, n = 2. Putting the value we get, E = E 0 − (0.02568/2) ln Qc. At equilibrium, E = 0. Therefore, 0 = E 0 − (0.02568/2) ln Qc . Therefore, E 0 = 0.01284 ln Qc. Finally, 0 = E 0 − (RT /nF) ln Qc . Therefore, at equilibrium, we have, E 0 = (RT /nF) ln Qc.

2.7 Necessary Materials Red spinach (Amaranthus dubius), CuSO4 solution, 4 falcon tubes, 8 Zn plates, 8 Cu plates, LED bulb, copper wires, crocodile clips, ammeter, voltmeter. Figure 1 shows the cycle of the red spinach leaf electricity. It shows the final product from the initial red spinach plant. Figure 2 shows the 4 unit cells connected in series combination to flow electrons for electricity production.

3 Results and Discussion In this section, findings of red spinach electrochemical cell with and without secondary salt have been studied. The CuSO4 5H2 O has been used as a secondary salt.

Red Spinach-A New and Innovative Power Source

Fig. 1 LED bulb lighting system using Amaranthus dubius extract electrochemical cell

Fig. 2 An experimental set up of an LED bulb lighting system using Red spinach extract

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3.1 Graphical Analysis of LED Bulb Lighting System Using Red Spinach (Amaranthus Dubius) Extract Without CuSO4 Table 1 shows that the performance of data for LED bulb lighting system using red spinach (Amaranthus dubius) extract without CuSO4 . The collected parameters were V oc , V L , I sc , I L , Pmax , PL and internal resistance. Figure 3 shows that the open circuit voltage remains constant at a peak value of 3.2 V with respect to time duration. Figure 4 shows that the load voltage also tends to remain constant at a peak value of 2.5 V with respect to time duration. Figure 5 shows the short circuit current versus time duration curve. It is shown that the I sc (short circuit current) was constant at 1.8 mA up to 15trs and then it Table 1 Data for LED bulb lighting system using red spinach (Amaranthus dubius) extract without CuSO4 Time V oc (Open VL (Load duration circuit voltage) (hr) voltage) (V) (V)

Isc (Short IL (Load circuit current) current) (mA) (mA)

Maximum power, Pmax = Voc Isc (mW)

Load Internal power, resistance, PL = VL IL rin = Voc Isc (mW) (Ω)

0

3.2

2.5

1.8

1.6

5.76

4.0

1.78

3

3.2

2.5

1.8

1.6

5.76

4.0

1.78

6

3.2

2.5

1.8

1.6

5.76

4.0

1.78

9

3.2

2.5

1.8

1.6

5.76

4.0

1.78

12

3.2

2.5

1.8

1.6

5.76

4.0

1.78

15

3.2

2.5

1.8

1.6

5.76

4.0

1.78

3.2

2.5

1.8

1.6

5.76

4.0

1.78

21

3.2

2.5

1.7

1.5

5.44

3.75

1.88

24

3.2

2.5

1.7

1.5

5.44

3.75

1.88

27

3.2

2.5

1.7

1.5

5.44

3.75

1.88

Fig. 3 V oc (open circuit voltage) versus time duration

Open circuit voltage (V)

18

Open circuit Voltage vs Time duration 4 3 2 1 0

0

10

20

Time duration (hr)

30

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Load Voltage vs Time duration Load Voltage (V)

Fig. 4 Load voltage versus time duration

119

3 2 1 0

0

10

20

30

Time duration (hr)

Fig. 6 Load current versus time duration

Short circuit current vs Time duration 1.85 1.8 1.75 1.7 1.65

0

10

20

30

Time duration (hr)

Load current vs Time duration Load current (mA)

Fig. 5 Short circuit current versus time duration

Short circuit current (mA)

decreases up to 22 h and then after it was constant up to 27 h. Figure 6 shows the load current versus time duration curve. It is shown that the short circuit current was constant at 1.6 mA up to 15trs and then it decreases up to 22 h and then after it was constant up to 27 h. Figure 7 shows the maximum power versus time duration curve. It is shown that the short circuit current was constant at 5.76 mW up to 15trs and then it increases up to 18 h and then after it decreases up to 22 h and then after it was almost constant up to 27 h. Figure 8 shows the maximum power versus time duration curve. It is shows the load power was same at 4 mW up to 15trs and then it increases up to 18 h and then after it decreases up to 22 h and then after it was almost constant up to 27 h. It is shown from Fig. 9 that the internal resistance was almost constant up to 18 h and then it increases up to 1.88 Ω.

1.65 1.6 1.55 1.5 1.45

0

10

20

Time duration (hr)

30

120

Maximum power vs Time duration Maximum power (mW)

Fig. 7 Maximum power versus time duration

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6 5.8 5.6 5.4 5.2

0

10 20 Time duration (hr)

30

Load power vs Time duration

Fig. 8 Load power versus time duration

Load power (mW)

4.1 4 3.9 3.8 3.7

0

10

20

30

Fig. 9 Internal resistance versus time duration

Internal resistance (ohm)

Time duration (hr)

1.9

Internal resistance vs Time duration

1.85 1.8 1.75

0

10

20

30

Time duration (hr)

3.2 Graphical Analysis of LED Bulb Lighting System Using Red Spinach (Amaranthus Dubius) Extract with CuSO4 Table 2 shows that the performance of data for LED bulb lighting system using red spinach (Amaranthus dubius) extract with CuSO4 . The collected parameters were V oc , V L , I sc , I L , Pmax , PL and internal resistance. Figure 10 shows that the open circuit voltage was 2.7 V which remains constant up to 27 h. Figure 11 shows that the load voltage was 2.4 V up to 27 h. It is found

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Table 2 Data for LED bulb lighting system using red spinach (Amaranthus dubius) extract with CuSO4 V oc (Open VL (Load Time voltage)(V) duration circuit voltage) (hr) (V)

Isc (Short IL (Load circuit current) current) (mA) (mA)

Maximum power Pmax = Voc Isc (mW)

Load Internal power resistance, PL = VL IL rin = Voc Isc (mW) (Ω)

0

2.7

2.4

7.7

7.5

20.2

18.0

0.347

3

2.7

2.4

7.7

7.5

20.2

18.0

0.347

6

2.7

2.4

7.7

7.5

20.2

18.0

0.347

9

2.7

2.4

7.7

7.5

20.2

18.0

0.347

12

2.7

2.4

7.6

7.4

19.0

17.02

0.339

15

2.7

2.4

7.6

7.4

19.0

17.02

0.339

18

2.7

2.4

7.6

7.4

19.0

17.02

0.339

21

2.7

2.4

7.5

7.3

18.0

16.06

0.33

24

2.7

2.4

7.5

7.3

18.0

16.06

0.33

27

2.7

2.4

7.5

7.3

18.0

16.06

0.33

Fig. 11 Load Voltage versus time duration

Open circuit voltage (V)

Fig. 10 Open circuit voltage versus time duration

Open circuit Voltage vs Time duration

Load Voltage (V)

that after adding secondary salt as CuSO4 5 H2 O, the voltage has been decreased for both open circuit and load voltages. Figure 12 shows the short circuit current versus time duration curve. It is shown that the short circuit current has been increased than the normal red spinach leaf extraction electro chemical cell. The short circuit current varies from 7.7 to 7.5 mA.

3

3 2 1 0

0

10

20

30

Time duration (hr)

Load Voltage vs Time duration 2 1 0

0

10

20

Time duration (hr)

30

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Fig. 13 Load current versus time duration

Short circuit current vs Time duration 7.75 7.7 7.65 7.6 7.55 7.5 7.45

0

10 20 Time duration (hr)

30

Load current vs Time duration Load current (mA)

Fig. 12 Short circuit current versus time duration

Short circuit current (mA)

Whereas for the normal red spinach leaf extraction electro chemical cell short circuit current varies from 1.8 to 1.7 mA. Figure 13 shows the Load current versus time duration curve. It varies from 7.5 to 7.3 mA. Whereas for the normal red spinach leaf extraction electro chemical cell short circuit current varies from 1.6 to 1.5 mA. Figure 14 shows the maximum power versus time duration curve. It is shown that the maximum power has been increased than the normal red spinach leaf extraction electro chemical cell. The maximum power varies from 20.2 to 18.0 mW. Whereas for the normal red spinach leaf extraction electro chemical cell maximum power varies from 5.76 to 5.44 mW. Figure 15 shows the Load power versus time duration curve. It varies from 18 to 16.06 mW. Whereas for the normal red spinach leaf extract electro chemical cell short circuit current varies from 4.00 to 3.75 mW.

7.55 7.5 7.45 7.4 7.35 7.3 7.25

0

10

20

30

Fig. 14 Maximum power versus time duration

Maximum power (mW)

Time duration (hr)

Maximum power vs Time duration 21 20 19 18 17

0

10

20

Time duration (hr)

30

Red Spinach-A New and Innovative Power Source

Load power vs Time duration Load power (mW)

Fig. 15 Load power versus Time Duration

123

19 18 17 16 15

0

10

20

30

Fig. 16 Internal resistance versus time duration

Internal resistance (ohm)

Time duration (hr)

Internal resistance vs Time duration 0.35 0.34 0.33 0.32

0

10

20

30

Time duration (hr)

It is shown from Fig. 16 that the Internal resistance versus Time duration curve. It is shown that it varies from 0.47 to 0.33 Ω. Whereas for the normal red spinach leaf extraction electro chemical cell maximum power varies from 1.78 to 1.88 Ω. It is found that the Internal resistance decreases and the power also increases after using Copper sulphate and the current also increases.

4 Conclusions In this research work, plants leaves have been used as a renewable energy source. It has been presented a method of electricity production using red spinach leaf extract. Previously, different vegetative, fruits and leaves have been used for power production. But red spinach is different from them. It can be used for power production at remote areas all over the world. Acknowledgements The authors are grateful to the Grant of Advanced Research in Education (GARE) project, Ministry of Education, GoB for providing the financial support during the research work (Project/User ID: PS2019949).

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References 1. ScienceDaily, Electrical circuit runs entirely off power in trees, University of Washington, (2009) 2. C.D.D. Angel, Introduction of Electrochemistry, Georgetown University, http://bourman.chem. georgetown.edu/S02/lect25/lect25.htm. Accessed 16 Oct 2012 3. Thermo Elemental, AAS, GFAAS, ICP or ICP-MS? Which technique should I use? An elementary overview of elemental analysis (2001) 4. M. Garg, J. Singh, Quantitative AAS estimation of heavy metals and trace elements in marketed ayurvedic chuma preparations in India. Int. J. Pharm. Sci. Res. 3(5), 1331–1336 (2012) 5. G.D. Christian, J.E. O’Reilly, Instrumental analysis, in Atomic Absorption Spectroscopy, 2nd edn. (Allyn and Bacon, Boston, 1986) 6. K. Sukender, S. Jaspreet, D. Sneha, G. Munish, AAS estimation of heavy metals and trace elements in Indian herbal cosmetic preparations. Res. J. Chem. Sci. 2(3), 46–51 (2012). International Science Congress Association 7. K.A. Khan, S.R. Rasel, M. Ohiduzzaman, Homemade PKL electricity generation for use in DC fan at remote areas. Microsyst. Technol. 25(12) (2019). Microsystem Technologies Microand Nanosystems Information Storage and Processing Systems, ISSN: 0946-7076. https://doi. org/10.1007/s00542-019-04422-2 8. M. Hasan, K.A. Khan, Experimental characterization and identification of cell parameters in a BPL electrochemical device. SN Appl. Sci. 1, 1008 (2019). Springer. https://doi.org/10.1007/ s42452-019-1045-8 9. L. Hassan, K.A. Khan, A study on harvesting of PKL electricity. Microsyst. Technol. 26(3), 1031–1041 (2020). Springer Journal. https://doi.org/10.1007/s00542-019-04625-7 10. K.A. Khan, M.A. Mamun, M. Ibrahim, M. Hasan, M. Ohiduzzaman, A.K.M. Obaydullah, M.A. Wadud, M. Shajahan, PKL electrochemical cell: Physics and chemistry. SN Appl. Sci. 1, 1335 (2019). Springer Journal. https://doi.org/10.1007/s42452-019-1363-x 11. L. Cindrella, H.-Z. Fu, Y.-S. Ho, Global thrust on fuel cells and their sustainability–an assessment of research trends by bibliometric analysis. Int. J. Sustain. Energy 33(1), 125–140 (2014) 12. K.A. Khan, L. Hassan, A.K.M. Obaydullah, S.M. Azharul Islam, M.A. Mamun, T. Akter, M. Hasan, S. Alam, M. Ibrahim, M.M. Rahman, M. Shahjahan, Bioelectricity: a new approach to provide the electrical power from vegetative and fruits at off-grid regions. Microsyst. Technol. 26, 3161–3172 (2018) 13. T. Sorey, V. Hunt, E. Balandova, B. Palmquist, A new twist on the old lemon battery (NSTA press, New York, 2012), pp. 91–98 14. C.H. Synder, in The Extraordinary Chemistry of Ordinary Things, 4th edn. (Wiley, Hoboken, 2004)

Heart Disease Risk Prediction Using Supervised Machine Learning Algorithms Madhumita Pal, Smita Parija, and Ranjan K. Mohapatra

Abstract Due to increase in population early diagnosis of chronic disease become a major problem in recent medical fields. Heart disease is one of the most dangerous chronic diseases and 17.9 million people die every year around the world due to this chronic disease. Due to the unhealthy life style most of the people in the world suffered from this chronic disease. Early detection of the disease can save the life of human being. In this paper we have presented a comparative analysis of five supervised machine learning model such as logistics regression, naïve Bayes, KNN, decision tree and random forest for prediction of heart disease. The proposed model is used to predict and classify whether a patient suffered from heart disease or not. The performance of each of the model is measured in terms of accuracy, precision, recall, f 1-score and support. We obtained highest prediction accuracy of 89% using random forest algorithm followed by other four algorithms such as logistics regression, naïve Bayes, decision tree and K-NN with accuracy of 85%, 85%, 82%, 69%, respectively. The dataset was taken from UCI repository site. It contains 303 samples and 14 features. All these algorithms were implemented on the dataset using python software and operated in jupyter notebook. Keywords Heart disease · Machine learning · Chronic disease · Logistics regression · Random forest · Decision tree · K-nearest neighbors

M. Pal · S. Parija (B) Electronics Science and Engineering, C. V. Raman Global University, Bidyanagar, Odisha Bhubaneswar-752054, India e-mail: [email protected] M. Pal e-mail: [email protected] M. Pal Department of Electrical Engineering, Government College of Engineering, Keonjhar, Odisha 758002, India R. K. Mohapatra Department of Chemistry, Government College of Engineering, Keonjhar, Odisha 758002, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Biswas et al. (eds.), Microelectronics, Circuits and Systems, Lecture Notes in Electrical Engineering 976, https://doi.org/10.1007/978-981-99-0412-9_11

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1 Introduction Heart disease is the major cause of death and disability of people in US [1]. Around 610,000 people will die from this chronic disease in every year around the globe. There are different types of heart disease such as cardiopathy, coronary artery disease, Arrhythmia, congenital heart problem, infection in heart. Coronary artery heart disease is the most common heart problem occurs in people and caused due to narrowing or blockage of the coronary arteries that supply blood. Other kinds of heart problems may happen to the valves in the heart or the heart may not pump well and cause heart failure. The risk of heart disease can be reduced by controlling the following factors [1]. • • • •

blood pressure cholesterol smoke doing exercise The signs of heart attack are-

1. 2. 3. 4. 5. 6.

irregular heartbeat breathing problem weakness chest pain pain in neck and jaw discomfort in chest

Early diagnosis of the disease can save the life of numerous people. In the present paper we have implemented machine learning models for prediction of the disease at early stage. Machine learning is the ability to learn automatically and gain knowledge from its past experience without human interference [2]. Machine learning model may be supervised, unsupervised or reinforcement depending upon the type of data used for training the model. In our proposed model we used five supervising algorithms for diagnosis of the disease at early stage and compared the performances in terms of accuracy, precision, recall, f -score, support. Rajesh and coworkers [3] have taken the dataset from UCI repository site consisting of 300 patient record and implemented naïve Bayes and decision tree algorithms for prediction of heart disease. According to the study, naive Bayes gives better accuracy result compared to decision tree for small data set. Garg and coworkers [4] have predicted the heart disease by using two supervised ML algorithms (K-NN and random forest). The prediction accuracy obtained by K-NN is 86.885% and by random forest is 81.967%. Pal and Parija [5] have reported the prediction of heart disease by using random forest algorithm and obtained accuracy of 86.9% with sensitivity value 90.6% and specificity value 82.7%. Shah and coworkers [6] have compared the use of various ML algorithms for the prediction of heart disease by using naïve Bayes, K-nearest neighbor, decision tree and random forest techniques with the dataset obtained from Cleveland database of UCI repository. Latha and Jeeva

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[7] used ensemble classifier for improving the prediction accuracy of heart disease. They have used bagging and boosting ensemble classifier for early prediction of the disease and also improved the prediction accuracy by 7% as compared to other weak classifiers. Beunza and coworkers [8] compared different supervised machine learning algorithms such as decision tree, support vector machine, random forest, neural network, logistics regression using two software named as R-studio and RapidMiner for prediction of clinical event. They used Framingham open heart database for their study which contains 4240 observations. They found that neural network gave highest AUC of 0.71 using R-studio and they got maximum AUC of 0.75 by using RapidMiner software with support vector machine algorithms. Dinesh and coworkers [9] compared naïve Bayes, support vector machine, gradient boosting, random forest, logistics regression machine learning algorithms in terms of their accuracy in prediction of cardiovascular disease. They used R software for their experiment and got 86% prediction accuracy using logistics regression algorithm. Kumar and coworkers proposed a three tire IOT-based architecture for determining the feature responsible for heart disease. From the experiment they found that respiratory rate of 50 and 12 creates higher probability of person suffering from heart disease. Application of machine learning in medical field helps in analysis and prediction of huge amount of patient data and helps the doctor for diagnosis of the remote area patient at early stage as the mortality rate of the people around the world due to heart disease increases day by day. It motivates many researchers to do research in this area for early detection of the chronic disease by the doctor and give proper treatment to the patient at proper time. With the use of machine learning algorithms early prediction of the chronic disease is possible and also machine learning helps in prediction of other diseases at early stages which helps to save the life. Our objective of the study is to design an automated system for early prediction of the chronic disease using five supervised machine learning models. Early detection of the disease can help to give proper treatment to the patient and prevent the life loss. The organization of the paper as follows. Section 2 of the article describes the material and methods used for the study. Section 3 explains the experimental setup of the study. Section 4 gives the result part of the study. Section 5 conclude the article with future work.

2 Materials and Methods For this study, the dataset used as “Cleveland Data Set” with 303 data samples and 14 attributes. This data set was used to diagnose a patient suffering from heart disease or not (0 = no heart disease, 1 = heart disease). The dataset is located [10] https:// archive.ics.uci.edu/ml/datasets/Heart+Disease. We have implemented five machine learning model on the above dataset for prediction of the heart disease namely logistics regression, naïve Bayes, K-NN, decision tree and random forest. The features and operation of each of the model has explained below. The proposed model of the

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Fig. 1 Proposed model of the work

work is shown in Fig. 1. 80% data we have taken for training the model and 20% of the data we have taken for testing the model.

2.1 Proposed Methods 2.1.1

Logistic Regression

It is the commonly used supervised machine learning algorithm commonly used for classification problem. It is implemented for solving classification problem

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containing one or more than one independent variables. In this model result obtained in binary discrete form for a given number of independent variables. The logistics regression model can be represented by the following equations. Y = d0 +

m 

djyj

(1)

R(Y ) = e y /(1 + e y )

(2)

j=1

Y represents the no. of participated variables yi (i = 1 ,..., m), di is the regression coefficient with maximum probability. R(Y ) ≥ 0.5, which indicates sample classified as an excitement [11]. For classifying two class problem 0 and 1, a hypothesis (z) is created by considering a threshold level φz(x) at 0.5. If hypothesis φz(y)is greater than 0.5 then classifier will predict y = 1 indicating that the person has heart disease and if φz(y) < 0.5, then classifier will predict y = 0 which indicates that the person doesn’t suffered from heart disease. So in this way logistic regression predict whether a person suffered from heart disease or not. For 0 ≤ φz(y) ≤ 1. Logistics regression used sigmoid function for classification task. The sigmoid function can be written as φz(y) = p(y) P(y) =

1 1 + e−y

(3)

Sigmoid function is used by logistics regression algorithm for performing classification task.

2.1.2

Random Forest

It is the most powerful ensemble classifier which creates forest of decision trees for a given set of features [11]. This model uses ensemble learning for solving a particular problem of artificial intelligence [12, 13]. For classification the following steps are followed by random forests. 1. 2. 3. 4.

From the given training sample choose L data samples arbitrarily. From these data samples create decision trees. From the generated trees pick M-Tree and repeat the process Create the M-tree that classify the new data sample having highest probability.

2.1.3

K-Nearest Neighbors

It is the commonly used supervised machine learning classifier for pattern recognition. It classifies the new data point which is nearest to it. In this model all the data samples are present in a metric space and in the training phase all the attributes of the data samples are stored. Then the neighbors of K data point was found out which

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is regular among K data samples [14]. If the number of neighbors is represented by P in this model, then P samples are determined by the following distance formula Dist(m, n) =

 p 

1/B mi − ni

(4)

z=1

Distance metric = Manhattan if B = 1, and B = 2 if it is Euclidean distance and if B = 3 it is Chebyshev distance. Among these distance metric Euclidean distance is commonly used.

2.1.4

Decision Tree

This is the simplest nonparametric machine learning model used to handle numerical and categorical features and used for solving classification and regression problem. This model predicts the target from the dataset with less amount of data preparation. Depending upon the number of data samples used for training target variable is predicted. This model generally suffers from overfitting problem. A small variation in the data sample can create another decision tree which misled the prediction ability of the model.

2.1.5

Naïve Bayes

It is another supervised machine learning model used for classification problem. Prediction of the model depends upon Bayes theorem.

3 Experimental Setup For prediction of whether a person suffering from heart disease or not we have used five machine learning techniques such as logistics regression, K-NN, decision tree, random forest, naïve Bayes by using an Intel core i5 powered computer with 32 GB RAM for processing purposes. All the algorithms are operated in jupyter notebook in python open-source web application programming language using scikitlearn, numpy, pandas, matplotlib, seaborn packages. Table 1 describe the parameters value used for the study. The Cleveland dataset contains 14 features as mentioned in Table 2. The performance of each of the model is evaluated through different performance measure metric such as accuracy, precision, recall, f-score and support which is described in Table 3.

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Table 1 Data samples used for the study Sl. Age Sex Cp trestbps chol fbs restecg thalach Ex oldpeak Slope ca thal Target ang No 0

63

1

3

145

233 1

0

150

0

2.3

0

0

1

1

1

37

1

2

130

250 0

1

187

0

3.5

0

0

2

1

2

41

0

1

130

204 0

0:,

172

0

1.4

2

0

2

1

3

56

1

1

120

236 0

1

178

0

0.8

2

0

2

1

4

57

0

0

120

354 0

1

163

1

0.5

2

0

2

1

Table 2 Parameters for data analysis Sl. No Features Types of feature

Description

1

Age

Numerical

Age of patient in years

2

Sex

Categorical

Sex of patient as male or female

3

Cp

Numerical

Types of chest pain

4

trestbps

Numerical

Resting blood pressure

5

chol

Numerical

Cholesterol level of patient

6

Fbs

Numerical

Fasting blood sugar of patient < or > 120 mg/dl

7

restecg

Numerical

Resting cardiograph Heart rate max value

8

thalach

Numerical

9

exang

Categorical Exercise induces angina Exercise induces angina

10

old peak Numerical

ST depression comparison during rest and workout period

11

Slope

Slope of exercise

Numerical

12

Ca

Categorical

No. of major vessels

13

Thal

Categorical

Defect category

14

NUM

Categorical

Types of disorder

Table 3 Performance measure metrics Accuracy Precision Recall f -score Support

TP1 +TN1 TP1 +TN1 +FP1 +FN1 TP1 TP1 +FP1 TP1 TP1 +FN1 2TP1 2TP1 +FP1 +FN1

Actual predicted class to total no. of class Actual positive samples to all the predicted positive samples from all data samples Actual predictive class to total instance Harmonic mean of precision and recall It is a quantity of samples containing actual target value

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Fig. 2 Accuracy comparison of machine learning models

4 Results and Discussion The Cleveland data set contains 303 data samples which contain 165(54.46%) patients with heart disease, 138(45.54%) patients without heart disease. Figure 2 compared accuracy of five machine learning model which clearly shows that random forest gives superior performance of accuracy of 88.52% followed by logistics regression and naïve Bayes with an accuracy of 85.24%, decision tree with accuracy of 81.96% followed by K-NN with an accuracy of 68.85%. Performance metrices value of each ml model is described in Table 4.

5 Conclusion The proposed study compared five machine learning techniques in terms of accuracy for the prediction of heart disease named as logistics regression, naïve Bayes, Knearest neighbors, decision tree, random forests. Features and working operation of each of the five machine learning model were discussed. The highest prediction

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Table 4 Classification report of ML models ML models

Accuracy

Precision

Recall

f 1-score

Support

Logistics regression

0.85

0.85

0.85

0.85

61

Naïve Bayes

0.85

0.86

0.84

0.85

61

K-NN

0.69

0.69

0.69

0.69

61

Decision tree

0.82

0.82

0.82

0.82

61

Random forest

0.89

0.88

0.89

0.88

61

accuracy obtained by random forest is 89%, whereas the lowest accuracy obtained from the K-NN is 69%. The proposed method eliminates the expenditure and time of the patient during diagnosis of the chronic disease and also helps the medical professional to diagnosis heart disease at early stage. The proposed algorithms can also be used for forecasting of other chronical disease at early stage. From the above study we can conclude that using random forest model we obtained an automatic diagnosis system with highest accuracy.

References 1. Centers for Disease Control and Prevention. Underlying Cause of Death, 1999–2018. CDC WONDER Online Database. Atlanta, GA: Centers for disease control and prevention (2018). Accessed 12 March 2020 2. J. Soni, U. Ansari, D. Sharma, S. Soni, Predictive data mining for medical diagnosis: an overview of heart disease prediction. Int. J. Comput. Appl. 17(8), 43–8(2011) 3. N. Rajesh, T. Maneesha, S. Hafeez, H. Krishna, Prediction of heart disease using machine learning algorithms. Int. J. Eng. Technol. 7(2), 363–366(2018) 4. A. Garg, B. Sharma, R. Khan, Heart disease prediction using machine learning techniques. IOP Conf. Ser. Mater. Sci. Eng. 1022, 012046(2021) 5. M. Pal, S. Parija, Prediction of heart diseases using random forest. J. Phys: Conf. Ser. 1817, 012009 (2021) 6. D. Shah, S. Patel, S.K. Bharti, Heart disease prediction using machine learning techniques. SN Comput. Sci. 1, 345 (2020) 7. C.B.C. Latha, S.C. Jeeva, Improving the accuracy of prediction of heart disease risk based on ensemble classification techniques. Inf. Med. Unlocked 16, 100203 (2019) 8. J.-J. Beunza, E. Puertas, E. García-Ovejero, G. Villalba, E. Condes, G. Koleva, C. Hurtado, M.F. Landecho, Comparison of machine learning algorithms for clinical event prediction (risk of coronary heart disease). J. Biomed. Inform. 97, 103257 (2019) 9. K.G. Dinesh, K. Arumugaraj, K.D. Santhosh, V. Mareeswari, Prediction of cardiovascular disease using machine learning algorithms. IEEE Xplore 18290642 (2018) 10. https://archive.ics.uci.edu/ml/datasets/Heart+Disease 11. P. Scanlon, I.O. Kennedy, Y. Liu, Feature extraction approaches to RF finger printing for device identification in femtocells. Bell Labs Tech. J. 15(3), 141–151 (2010) 12. G. James, D. Witten, T. Hastie, R. Tibshirani, An introduction to statistical learning. 1st ed. (Springer;New York, 2013) 13. S. Guido, A.C. Mller, Introduction to machine learning with python (O’Reilly Media Inc., Sebastopol, 2016)

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Implementing Machine Learning Algorithms for Predicting Roof Fall Statistics in UG Coal Mines Jitendra Pramanik, Singam Jayanthu, Abhaya Kumar Samal, and Surendra Kumar Dogra

Abstract Roof falls are the most hazardous events that occur in underground mines that inflict damage to machinery and loss of human life. Therefore, the use of tools and techniques to ensure the safety of miners as well as machines is of utmost priority. Though there are many factors responsible for the occurrence of such catastrophic events, many of these factors are neither precisely nor accurately measurable, nor are well defined, making the issue of assessment quite uncertain. Machine learning methods have been proved to be a successful tool to provide trustworthy solutions to many mission-critical real-life problems. In this paper, we attempt to explore the extent of applicability of machine learning techniques to adequately predict the occurrences of such catastrophic roof fall events in UG coal mines due to strata control problems. This work has conducted a comparative study of the potency of varied machine learning techniques on the roof convergence data taken GDK-10 underground coal mine. Keywords Underground Coal Mining · Strata control problem · Roof fall · Machine learning techniques

1 Introduction The roof fall in the UG mine is considered to be one of the most hazardous events that make UG mine work unsafe. A sudden collapse of the UG mine roof can inculcate injury, disability or fatality on mine workers and create downtime, interruptions in mining operations and breakdown of equipment imposing a huge financial loss on mining companies. There are several reasons which can inflict an unforeseen J. Pramanik (B) · S. Jayanthu · S. K. Dogra Department of Mining Engineering, National Institute of Technology, Rourkela, Odisha, India e-mail: [email protected] J. Pramanik Centurion University of Technology and Management, Gajapati, Odisha, India A. K. Samal Trident Academy of Technology, Bhubaneswar, Odisha, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Biswas et al. (eds.), Microelectronics, Circuits and Systems, Lecture Notes in Electrical Engineering 976, https://doi.org/10.1007/978-981-99-0412-9_12

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strata failure in UG mines. Some of them can be loading conditions, lack of support system, geological defects and degradation due to moisture. An accurate prediction of roof fall can help to formulate preventive measures to decrease the incidence of roof failure. For several decades, thorough research has been made on the factors affecting ground control operations. Still, many miners get injured or killed because of roof stability problems. Due to the sporadic behaviour mining industries heavily relies on the empirical formula for analysis and design of roof behaviour of UG mines. In those cases, to solve intricate problems proficient information is looked at from the field of mining. In the wake of the information age, since the last couple of years, machine learning (ML) approaches have gained popularity among researchers due to their scope of application in a variety of real-life fields. Machine learning techniques are a conglomeration of many methods and algorithms that enable computers to support data-driven decision-making processes. Broadly, ML techniques learn from historical data to successfully provide a future prediction with varying degrees of accuracy depending upon the algorithm deployed to implement the machine learning technique. ML techniques have paved their application in a variety of domains, ranging from the industrial applications to many commercial applications. Machine learning methods have been evolved as a successful tool to provide trustworthy solutions to many mission-critical real-life problems. In this paper, we attempt to explore the scope of applicability of various machine learning algorithms to adequately predict the occurrences of possible catastrophic roof fall events in UG coal mines due to strata control problems. This work has laid out a comparative study of the potency of varied machine learning techniques on the roof convergence data taken from GDK-10 incline. The rest of the manuscript is worked out as: Sect. 2 presents review of the literatures being referred to concerning the proposed study, Sect. 3 presents a brief on the problems pertaining to strata control, Sect. 4 discusses the different algorithms pertaining to machine learning, Sect. 5 presents the results and associated discussion on the study being conducted, Sect. 6 presents concluding remarks on the work.

2 Related Work The unevenness in the geotechnical parameters combined with the complexity of the geotechnical situation arouses dubiousness in the prediction of roof fall in UG mines. Several researchers tried numerous modern methods like using regression, neural network, fuzzy logic, etc. to unravel the relationship between different parameters involvement in UG roof failure. Hobbs [1] reported the measurement of convergence of roof and floor rock strength and applied pressure with an empirical formula. Ashwin et al. [2] reported the relationship between maximum convergences of coal roof as a function of height. It reported formulating an empirical relationship to computer maximum convergence as a function of extraction height used in British coal mines. Barczak et al. [3] reported the application of empirical design procedures towards the development of longwall roadways in reducing the frequency of

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major roof falls in longwall mining in US. Palei and Das [4] studied and analysed the effect of geotechnical parameters like RMR, rock mass density, bolts per row, bolts spacing, bolt strength, the width of gallery on stability and likelihood of the roof fall in UG mines. Palei and Das [5] reported the application of a binary logistic regression model to assess the vitality of contributing factors causing roof fall accidents in UG coal mines. Javanshir et al. [6] reported the modelling and classification of roof behaviour in coal mines using geotechnical parameters like Tensile-Strength (TS), UCS, bedding thickness, moisture sensitivity and surface of the working area. Tobabi et al. [7] reported an empirical formula for the prediction of underground coal mine roof rock strength with the help of unconfined compressive strength (UCS). It substantiated the relationship between Schmidt rebound number and UCS of rock mass under a particular geological circumstance. Janusze et al. [8] reported the development of an ensemble regression model to assess the effect of seismic activity the UG coal mines.

3 Strata Control Problem in Underground Coal Mines For underground collieries, geological discontinuity and defect is the prime factor behind the strata movement problems. The most common factors behind the roof fall index are faults bedding, fault slips and slicken sides followed by bedding planes, joints and cleats. The tension failure causes bedding faults in thinly bedded strata. The poor cohesion/adhesion between the strata control parameters creates discontinuities which further lead to roof instability. Unseen breaks in the otherwise solid roofs may provide little warning of impending failure. It is a rare coal mine that has never experienced some type of geologic roof disturbances. The depositional conditions of the site also play a major role in estimating the performance of mining structures. In fact, the behaviour of underground structures during depillaring are generally considered to be site-specific due to the wide variation of different geo-mining parameters of different mine sites. This is the reason we find that most of the strata control norms are based on empirical formulations developed by measurement/testing of different parameters at different mines. But, for development of empirical formulations require quite dense field measurements of different strata control parameters.

4 Machine Learning Approaches The machine learning (ML) algorithms are regarded as a subset of artificial intelligence and in some perspective even a subset of computer science. The study of ML algorithms helps to forge precise predictions/classification and reactions in specific situations. ML algorithms can be classified into the following paradigms:

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• Supervised learning • Unsupervised learning • Reinforcement learning The supervised algorithms which are part of our proposed system are being laid out below.

4.1 Naive Bayes Classifier (NB) The NB is generally a probabilistic classifier derived from Bayes theorem. For a given dataset, it extracts a sample set of data and finds out the prior and conditional probabilities. It is called a naive since all the variables taken are considered to be equally weighted with all features having independence of interest. Being an probabilistic algorithm, if the population is more probable, it correctly allocates the crania in comparison to any other algorithm. In other words, seriousness in violation of in modelling assumption can be ignored if the overall cross-validation classification accuracy is found to be high [9, 10]. The basis of the NB classifier is based on the Bayes theorem, represented as: P(y|X ) =

P(X |y)P(y) P(X )

(1)

As per the Bayes theorem, we can find the probability of y happening, given that X has occurred, i.e. P(y|X), here y is the hypothesis and X is the evidence, assuming the predictors are independent, such that the presence of a specific feature do not alter the other, and hence, termed as naive. Thus, if variable X represent the parameters or features, given as: X = (x 1 , x 2 , x 3 , …, x n ), then by substituting the value for X and expanding using chain rule, we obtain: P(y|x1 , x2 , x3 , . . . , xn ) =

P(x1 |y)P(x2 |y)P(x3 |y) . . . P(xn |y)P(y) P(x1 )P(x2 )P(x3 ) . . . P(xn )

(2)

4.2 Support Vector Classifier (SVC) It is mostly suited for applications like pattern recognition, non-linear function approximation. It manifests important properties like low sensitivity to dataset dimensionality, good generalization capability in comparison to other ML techniques. With an ability to obtain global classification solution, SVM is basically preferred over other ML algorithms [11]. In the SVM algorithm, the approach is to increase the

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margin between the data points and the hyperplane. Here, hinge loss is the loss function that maximizes the margin. The representation of the cost function is given as:  c(x, y, f (x)) =

0, if y × f (x) ≥ 1 1 − y × f (x), otherwise

c(x, y, f (x)) = (1 − yi xi , w)+

(3) (4)

4.3 Gradient Boost (GB) It is one type ensemble-based learning technique which assimilates different predictors in a sequential manner. The execution speed and approximation accuracy of GB can be enhanced with incorporation of randomization of procedure. During each iteration, subset of a training data is drawn randomly from the entire dataset. The imported dataset is feed to the base learner for further computation. The overall process enhances the robustness against overcapacity of the base learner [12]. For each case in the dataset, the target outcome relies on the changing of the case prediction: • If a huge decline in error occurs due to a slight change in prediction of a case, then the target outcome of the next case is on a higher side. The error will be reduced for a new model that are closer to its target. • If there is no change in error for a slight change in prediction of a case, then target outcome for the next case is zero. There is no decrement in error for changing the prediction.

4.4 Logistic Regression (LR) Concept of probability is foundation of Logistic Regression classification technique. The linkage between the dependent variable and independent variables (one or many) is being affirmed by LR, with help of probabilities using its underlying logistic function which is also called sigmoid function. [9]. A sigmoid function takes real valued number and maps it within range of 0–1. It is further converted into either 0 or 1 using threshold classifier. The classification problems with two classes, the LR is regarded as befitting method for solving it. Logistic regression models the data using the sigmoid function (a non-linear function): y(x) =

1 1 + e−x

(5)

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Assuming that we have a training sample as (X, y) = {(x(1), y(1)), (x(2), y(2)), (x(3), y(3)), …, (x(n), y(n))}, where n is the number of training samples, and ⎧ x(i )belongs to a negative class i.e., ⎪ ⎪ ⎨ 0, where the hypothesis does not hold y(i ) = x(i )belongs to a positive class i.e., ⎪ ⎪ ⎩ 1, where the hypothesis holds In a linear regression, a straight line equation is used for predicting the target variable y as: y(i) = b0 + b1 X 1 + εi in which the intercept is b0 and the slope of the straight line is b1 , X 1 is the input value of the feature variable and the residual term is εi . In its simplest form, the probability of Y is computed for one predictor variable using the logistic regression equation given as: P(Y = yes) =

1 1+

(6)

e−(b0 +b1 X 1 +εi )

5 Implementation and Observation In proposed research study, the roof convergence data of GDK-10 Incline station C3 at location 59ALN/42D is being taken. Total 106 roof convergence data is used and labelled. The dataset is then divided into two subsets: 70% of the data was considered towards ‘training’ and 30% of the data was considered towards ‘testing’. The ML algorithms are being educated with the help of training subset. Once trained, the ML models performances are being assessed via testing subset. In the suggested work, four varied ML algorithms are applied and the risk associated with fall of a roof is being discerned into 3 levels, i.e. as risk levels ‘0’ or ‘Low Risk, level ‘1’ or ‘Medium Risk’ and level ‘2’ or ‘High Risk’. Table 1 represent the performance of the classifiers. Table 1 presented above is a representation of the performance in terms of various quality metric parameters, such as score, precision, recall and f 1-score of various machine learning classifiers used in the proposed prediction model based on 106 numbers of roof convergence data taken from the field study conducted at station C3 Table 1 Analysis metric comparison Name of classifiers

Score

Precision

Recall

F1-score

Gaussian Naïve Bayes

0.740741

0.740741

0.740741

0.740741

Logistic regression

0.925926

0.925926

0.925926

0.925926

Gradient boost

1

1

1

1

Support vector classifier

0.777778

0.777778

0.777778

0.777778

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of GDK-10 Incline at location 59ALN/42D. From the findings, it is clearly visible that Logistic Regression and Gradient Boost have exhibited very good classification performance, at a level above 92.6% and 100%, respectively, while Gaussian Naïve Bayes and Support Vector Classifier have exhibited average performance, at a classification level around 75%.

5.1 The Confusion Matrix In the presented work, confusion matrix is conceived to envision the accomplishment of the ML models. In general, our study is a case of multi-class ML-based classification problem, specifically, a 3-class classification problem. Here, for each classification model as an entry in the confusion matrix, there would be appropriate value or level to classify our prediction. In order to have a better understanding of the performance of the classification model, what the classification model is doing right and what types of errors the models are making, can be accomplished with the help of the confusion matrix, and therefore here we have adopted confusion matrix technique to summarize the performance of the classification algorithms under study. The number of correct predictions and incorrect predictions are summarized with corresponding count values. The key data element responsible for materializing the risk level classification is the rate of convergence as already available from the historical data collected through traditional manual collection practise. As the problem is comprised of 3-classes, the performance metric) perceives the risk level as ‘Low Risk’, ‘Medium Risk’ or ‘High Risk’. Performance of the different machine learning-based prediction models summarized in the confusion matrix presented in the Figs. 2, 3, 4 and 5 as follows: The above presented work implemented ML algorithm to classify the given input dataset with respect to the mentioned risk levels by taking the rate of convergence as the most influencing parameter. The Fig. 1 exemplify the performance of the classifiers and Table 1 set out the classification findings.

5.2 Analysis of Result Presented in Confusion Matrix From the confusion matrix in Fig. 2 presenting the performance of the Gradient Boost (GB) algorithm shows the cleanest prediction since all the prediction values appear across the diagonal only. In other figures presenting the performance of the Gaussian Naive Bayes (GNB) algorithm in Fig. 3, Logistic Regression (LR) algorithm in Fig. 4 and Support Vector Classifier (SVC) algorithm in Fig. 5, non-zero entries in the lower diagonal of the confusion matrix reflects possibilities of predicting false positive cases during prediction process. Therefore, from our study, the behaviour of the Gradient Boost (GB) algorithm is found to outperform other three algorithms.

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Fig. 1 Performance assessment of the ML models

Fig. 2 Performance of the Gradient Boost (GB) algorithm

6 Conclusion and Future Work The plethora of information present in a UG mine constitutes an unquestionable appeal to the various sectors. With the rapid expansion of machine learning algorithms, the field of digital analysis is getting revolutionized day by day. This article explores the usage of machine learning techniques in the field of roof fall classification. In this paperwork, a few key machine learning-based classification techniques are implemented to detect different risk levels in underground coalmines (Low Risk Level, Medium Risk Level and High Risk Level). The performance score of our

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Fig. 3 Performance of the Gaussian Naive Bayes (GNB) algorithm

Fig. 4 Performance of the Logistic Regression (LR) algorithm

proposed approach is clearly evident from the performance plot presented in Figure1 as per the abstract of findings in Table 1, as well as the confusion matrix presented in Fig. 2 through Fig. 5. The efficiency of the model is assessed in terms of true positive, false positive, precision, recall, f 1-score, support and accuracy. The experimental result revealed that the preprocessing strategies and the classifiers effectively provide the highest performance to appropriately detect the desired risk level.

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Fig. 5 Performance of the Support Vector Classifier (SVC) algorithm

References 1. D.W. Hobbs, Scale model studies of strata movement around mine roadways—III: roadway shape and size. Int. J. Rock. Mech. Min. Sci. 5, 245–251 (1968) 2. D.P. Ashwin, S.G. Campbell, J.D. Kibble, J.D. Haskayne, J.F.A. Moore, R. Shepherd, Some fundamental aspects of face powered support design. Min. Eng. 129(119), 659–675 (1970) 3. T.M. Barczak, Longwall tailgates: the technology for roof support has improved but optimization is still not there. in Proceeding of the longwall USA, international exhibition and conference, (Pittsburgh, PA, 2003), pp. 105–130 4. S.K. Palei, S.K. Das, Sensitivity analysis of support safety factor for predicting the effects of contributing parameters on roof falls in underground coal mines. Int. J. Coal Geol. 75, 241–247 (2008) 5. S.K. Palei, S.K. Das, Logistic regression model for prediction of roof fall risks in bord and pillar workings in coal mines: an approach. Saf. Sci. 47, 88–96 (2009) 6. M. Javanshir, M. Ataei, S.R. Torabi, Modeling and classification of roof behavior in coal mines. Int. J. Indust. Eng. Prod. Manage. 19(9), 45–53 (2009) 7. S.R. Torabi, F. Sereshki, M. Zare, M. Javanshir, An empirical approach in prediction of the roof rock strength in underground coal mines. in Coal Operators’ Conference. (The AusIMM Illawarra Branch, 2008), pp. 132–136 ´ ezak, Predicting seismic 8. A. Janusz, M. Grzegorowski, M. Michalak, Ł Wróbel, M. Sikora, D. Sl˛ events in coal mines based on underground sensor measurements. Eng. Appl. Artif. Intell. 64, 83–94 (2017) 9. J.T. Hefner, K.C. Linde, Chapter 20 - analytical methods. J.T. Hefner, K.C. Linde (eds), Atlas of Human Cranial Macromorphoscopic Traits, (Academic Press, 2018), pp. 287–292. https:// doi.org/10.1016/B978-0-12-814385-8.00020-3. ISBN 9780128143858 10. A. Pe´rez, P. Larran˜aga, I. Inza, Supervised classification with conditional Gaussian networks: increasing the structure complexity from naive Bayes. Int. J. Approx. Reason 43, 1–25(2006) 11. T. Jiang, J.L. Gradus, A.J. Rosellini, Supervised machine learning: a brief primer. Behav. Ther. (2020). https://doi.org/10.1016/j.beth.2020.05.002 12. O. González-Recio, J.A. Jiménez-Montero, R. Alenda,The gradient boosting algorithm and random boosting for genome-assisted evaluation in large data sets. J. Dairy Sci. 96(1), 614– 624(2013). https://doi.org/10.3168/jds.2012-5630. ISSN 0022-0302

Synthesis, Characterizations of Silver Nanoparticles (AgNPs) and Monitoring for Power Production Using Drumstick Leaves K. A. Khan, Mohammad Tofazzal Haider, Md. Sayed Hossain, and Salman Rahman Rasel

Abstract The drumstick fresh leaves in aqueous extract form have been used (Moringa oleifera) for green synthesis of silver nanoparticles (AgNPs). The green synthesis method has been conducted for the production of AgNPs for the application in power production. The different methods are developing for synthesis of AgNPs for multiple applications. It is found that green synthesis from Moringa oleifera leaves is cost-effective and echo-friendly. The synthesized AgNPs have been characterized through ultraviolet–visible spectroscopy (UV–Vis), Fourier transform infrared (FTIR), powder X-ray diffraction (XRD), field emission scanning electron microscope (FESEM). It is found that the UV–visible spectrum contains AgNPs in the aqueous medium, and it is also found that absorption peak was at around 332 nm. It is again found from the FTIR analysis that the bimolecular compounds were responsible for the reduction and capping material of AgNPs. It is also found that from the XRD study that the particles to be crystalline in nature, with a face-centered cubic (fcc) structure. The power production activity of AgNPs was assessed to find their potential use in an electrochemical cell. Most of the results have been tabulated and graphically discussed. Keywords AgNPs · Characterization · Drumstick leaf · Power production · Green synthesis · Electrochemical cell

K. A. Khan (B) Department of Physics, Jagannath University, Dhaka 1100, Bangladesh e-mail: [email protected] M. T. Haider Department of Physics, Uttara University, Dhaka, Bangladesh Md. S. Hossain Center for Research Reactor, Bangladesh Atomic Energy Commission, Dhaka, Bangladesh S. R. Rasel Local Government Engineering Department (LGED), Sherpur Sadar, Sherpur, Bangladesh © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Biswas et al. (eds.), Microelectronics, Circuits and Systems, Lecture Notes in Electrical Engineering 976, https://doi.org/10.1007/978-981-99-0412-9_13

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1 Introduction Nano-green technology is an important part of the nanotechnology [1]. AgNPs as a noble metal NPs are gaining importance due to their applicability in the field of physics, chemistry, medical science, engineering, agriculture, material science, and biology [2]. Researchers proved that the metal NPs have large surface area and surface atoms because of their chemical properties, electronic properties, optical properties, thermal properties, magnetic properties, and antibacterial properties [3]. Metal synthesis is enormous due to their huge and potential application in the field of energy, electronics, chemistry, medical science development [4, 5]. Metal NPs are called noble particles because of the strong optical absorption in the visible region of the free electrons [6, 7]. AgNPs have their a large number of applications like: good electrical conductors, nonlinear optics, selective black coating in solar thermal conversion, bio-labeling, catalyst in chemical reactions, chemically stable materials, electrical batteries[8–10]. Researchers have developed novel method for green synthesis of AgNPs [11–13]. In chemical and medical industry, silver is widely used [14, 15]. The green and biosynthesis method uses the renewable materials, eco-friendly solvent, and nontoxic chemicals [16]. In the medical science, Ag and AgNPs have been used as skin ointments, creams to avoid infection of open wounds and burns. AgNPs can be synthesized by different chemical and physical methods like electrochemical reduction, photochemical reduction, chemical reduction, microemulsion micelles [17]. There are a lot of biological approaches for green synthesis of leaf extract. Presently, it has been investigated the drumstick leaf for the green synthesis of AgNPs for power production using electrochemical cell.

2 Methods and Materials 2.1 A Chemicals, Reagents and Biological Samples The required chemicals, reagents, and biological samples are as follows: silver nitrate, AgNO3 , drumstick leaf extract, and de-ionized water.

2.2 Instruments The required instruments are as follows: grindstones (Traditional manual grinder), measuring flux, conical flux, calibrated beaker, volumetric flux, magnetic bar, Falcon tube (50 ml), Falcon tube (15 ml), sample holder, thermometer, Whatman filter paper 41, Whatman filter paper 42, vial tube, tissue paper, Scotch tap, wash bottle, refrigerator, spatula, weigh machine, centrifuge machine (ABT-028C, USA), magnetic stirrer with hot plate, X-ray diffractometer (Philips, Expert Pro, Holland), Fourier

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transform infrared spectrophotometer (IRPrestige21 Kyoto, Japan), field emission scanning electron microscopy, UV–visible spectrometer (UV-1700, Kyoto, Japan).

2.3 Synthesis of AgNPs Figure 1 shows the collecting, washing, smashing, and making extract of drumstick (Moringa oleifera) leaves for green synthesis. It has been firstly collected from the garden. Then, after it was washed by the dI water and then after it has been blended and fully it was made extract and put it in a bottle. Figure 2 shows the magnetic stirrer with hot plate for shaking the produced drumstick extract. After shaking the extract, it was shown fine in solvent form with dI water. Figure 3 shows the method of preparation of raw of 45 mL, 1 mM AgNO3 solution. It is needed an electronic balance and a measuring flux for preparation of raw of 45 mL, 1 mM AgNO3 solution. Figure 4 shows the color changing process of AgNPs from the raw AgNPs in liquid form. It is shown that color is becoming deeper with time duration. Figure 5 shows the filtering process of the raw AgNPs with Whatman paper 41 and Whatman paper 42. The color change becomes after filtration of the Whatman paper 41 and 42.

Fig. 1 Collecting, washing, smashing, and making extract of Moringa oleifera leaves

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Fig. 2 Extract shaking by magnetic stirrer with hot plate

Fig. 3 Preparation of 45 mL, 1 mM AgNO3 solution

Fig. 4 Color changing diagram of AgNPs

Fig. 5 Filtering with Whatman paper 41 and Whatman paper 42

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Fig. 6 Process of dried AgNPs

Figure 6 shows the method of preparation of dried AgNPs from liquid form. The FTIR, XRD, and FESEM has been conducted using dried AgNPs.

3 Results and Discussion for Characterizations of Synthesized AgNPs In this section, it has been discussed the characterization of silver nanoparticles (AgNPs).

3.1 UV–Visible Spectral Studies of AgNPs Figure 7 shows the UV–visible spectra of without NPs (blue line) and synthesized AgNPs (red line). Metal nanoparticles have free electrons, which yield a surface plasmon resonance (SPR) absorption band, due to the mutual vibration of electrons of metal nanoparticles in resonance with light wave. The appearances of the peaks show the characteristics of surface plasmon resonance of silver nanoparticles.

3.2 X-Ray Diffraction (XRD) Analysis Figure 8 shows the structural characterization of Moringa oleifera leaves extractmediated green synthesized silver nanoparticles (AgNPs) was carried out using Xray diffraction (XRD) technique, where the diffractograms were recorded in the 2θ range of 0º–80º as shown in Fig. 2, and the exact values are illustrated in Fig. 3. The diffraction patterns for the synthesized AgNPs were obtained at Bragg’s angle of 27.58° 29.63°, 31.92°, 38.28°, 39.52°, 44.53°, 46.58°, 49.20°, 54.87°, 64.54°, 66.98°, and 77.59°. The XRD patterns obtained for the synthesized AgNPs in the present study showed a good agreement with the reported values. These agree well with those reported standard JCPDS file No. 04-0783 [2, 3]. The broadening of the Bragg’s peaks indicates the formation of silver nanoparticle. The average crystal size

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Fig. 7 UV–Vis spectra of synthesized AgNPs

of AgNPs is calculated from the XRD data using Debye–Scherrer formula, D=

0.89λ β cos θ

(1)

where λ is the X-ray wavelength (λ = 1.54060 Å), θ is Bragg’s diffraction angle, and β is the full width at half maximum (FWHM) [4–6].

Fig. 8 XRD patterns of AgNPs synthesized using Moringa oleifera leaves extract and AgNO3

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Fig. 9 High-resolution FESEM image of AgNPs with different scale

3.3 Field Emission Scanning Electron Microscopy (FESEM) Figure 9 shows the SEM technique was employed to visualize the size and shape of Ag nanoparticles. Dried powder of the silver was placed on carbon-coated copper grid. The SEM characterizations of the synthesized Ag nanoparticles are shown in Fig. 9a and b. The formation of silver nanoparticles from the silver ions was analyzed by the morphological characterization by using field emission electron microscopy (FESEM) at 15 keV (Model number JSM-7610F). Figure 9a and b has shown the FESEM images of Ag NPs which indicates the formation of nanoparticles by the bio-reduction of Ag+ ions. The large size of AgNPs is found in the FESEM images.

3.4 Fourier Transform Infrared Spectrophotometer Analysis Figure 10 represents the FTIR spectra of filtered drumstick leaf extract. The spectra revealed several peaks at around 1018.41, 1093.64, 1215.15, 1263.37, 1639.49, 2115.91, 3363.86 cm−1 . The peak at around 1018.41 cm−1 may be associated for the stretching of strong C–F bond of fluoro compound whereas the peak at around 1093.64 cm−1 is appeared due to the stretching of C–O bond of aliphatic ether. The C–O stretching bond of alkyl aryl ether may be responsible for the peak at around 1215.15 and 1263.37 cm−1 both. Furthermore, the peak at around 1639.49 cm−1 was appeared due to the stretching vibration of C = C. Another sharp peak at around 2115.91 cm−1 is originated due to the stretching vibrations of C ≡ C bond. Moreover, the peak at wave number 3363.86 cm−1 may be appeared due to the O–H and N–H stretching both [9]. Figure 11 illustrates the FTIR spectra of Moringa oleifera leaves extract-mediated AgNPs. The spectra revealed several peaks at around 1020.34, 1095.57, 1261.45, 1641.42, 2100.48, and 3358.07 cm−1 [9].

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Fig. 10 FTIR of filtered Moringa oleifera leaves extract

3.5 Applications of AgNPs Synthesized from Drumstick Leaf Figure 12 shows the complete cycle of different steps of LED bulb lighting system using drumstick leaf extract. It shows from the initial to final steps of the application of AgNPs using drumstick leaf extract. Figure 13 shows a drumstick leaf electrochemical module made by 4 unit cell. There is a copper plate and zinc plate in each unit cell. Copper is used a cathode, and zinc is used an anode. They are connected by the connecting wires to flow the electrons.

3.6 Results and Discussion for Practical Applications of AgNPs Figure 14 shows the variation of open circuit voltage with the variation of time duration (h) for both with and without AgNPs. It is shown that the open circuit voltage has been decreased with the applying of AgNPs in a unit drumstick electrochemical

Synthesis, Characterizations of Silver Nanoparticles (AgNPs) and …

Fig. 11 FTIR of filtered Moringa oleifera leaves extract-mediated AgNPs

Fig. 12 Different steps of LED bulb lighting system using drumstick leaf extract

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Fig. 13 An experimental setup of an LED bulb lighting system using drumstick leaf extract

cell. The maximum open circuit voltage was 1.00 V and 0.87 V for with and without Ag AgNPs, respectively. The difference was 0.13 V. Similarly, the minimum open circuit voltage was 0.87 V and 0.84 V for with and without AgNPs, respectively. The difference was 0.03 V. So, it can be said that the rate of decreasing of the open circuit voltage for without AgNPs is better than the rate of decreasing of open circuit voltage for with AgNPs.

Fig. 14 Open circuit voltage versus time duration curve

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Fig. 15 Short circuit current versus time duration curve

Figure 15 shows the variation of short circuit current with the variation of time duration (h) for both with and without NPs. It is shown that the short circuit current has been decreased with the applying of AgNPs in a unit electrochemical cell. The maximum short circuit current was 5.80 mA and 1.40 mA for with and without AgNPs, respectively. The difference was 4.40 mA. Similarly, the minimum short circuit current was 4.40 mA and 1.20 mA for with and without AgNPs, respectively. The difference was 3.30 mA. So, it can be said that the rate of decreasing of short circuit current for with AgNPs is better than the rate of decreasing of short circuit current for without AgNPs. Figure 16 shows the variation of maximum power with the variation of time duration (h) for both with and without NPs. It is shown that the maximum power has been decreased with the applying of AgNPs in a unit electrochemical cell. The maximum Maximum power was 5.10 mW and 1.40 mW for with and without AgNPs, respectively. The difference was 3.70 mW. Similarly, the minimum power was 4.60 mW and 1.10 mW for with and without AgNPs, respectively. The difference was 3.50 mW. So, it can be said that the rate of decreasing of maximum power for with AgNPs is better than the rate of decreasing of power for without AgNPs. Figure 17 shows the variation of internal resistance with the variation of time duration (h) for both with and without AgNPs. It is shown that the internal resistance has been increased with the applying of AgNPs in a unit electrochemical cell. The maximum internal resistance was 0.77 Ω and 0.19 Ω for without and with Ag NPs, respectively. The difference was 0.58 Ω. Similarly, the minimum internal resistance was 0.52 Ω and 0.15 Ω for without and with AgNPs, respectively. The difference was 0.37 Ω. So, it can be said that the rate of increasing of internal resistance for without AgNPs is more than the rate of increasing of internal resistance for with AgNPs.

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Fig. 16 Maximum power versus time duration curve

Fig. 17 Internal resistance versus time duration curve

Figure 18 shows the variation of power density with the variation of time duration (h) for both with and without AgNPs. It is shown that the power density has been increased with the applying of AgNPs in a unit electrochemical cell. The maximum power density was 50 mW/CC and 14 mW/CC for with and without AgNPs, respectively. The difference was 36 mW/CC. Similarly, the minimum power density was 37 mW/CC and 10 mW/CC for with and without AgNPs, respectively. The difference

Synthesis, Characterizations of Silver Nanoparticles (AgNPs) and …

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Fig. 18 Power density versus time duration curve

was 27 mW/CC. So, it can be said that the rate of increasing of power density for without AgNPs is better than the rate of power density for with NPs.

4 Conclusions The green thesis method is eco-friendly and cost-effective. The drumstick leaf extract has been worked as both stabilizing and reducing agent. The synthesized AgNPs have been characterized by UV–Vis, FTIR, XRD, and FESEM analysis. It is confirmed the plasmon surface resonance of green synthesized AgNPs by UV–Vis analysis. The FTIR measurement confirmed that biomolecules were responsible of Ag NPs for reducing and capping agent. FESEM analysis expressed the size and shape of Ag NPs. It is found that the shape of the AgNPs was spherical and uniform shaped, and the size of the AgNPs was 25–60 nm. By XRD analysis, it is found that the crystal structure was face-centered cubic (fcc). The green synthesized AgNPs using drumstick leaves were found to have a power production capacity by using an electrochemical cell. Acknowledgements The authors are grateful to the GARE (Grant of Advanced Research in Education) project, Ministry of Education, GoB for providing the financial support during the research work (Project/User ID: PS2019949).

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References 1. . K. Anandalakshmi, J. Venugobal, V. Ramasamy, Characterization of silver nanoparticles by green synthesis method using Pedalium murex leaf extract and their antibacterial activity (2015) 2. J. Huang, Q. Li, D. Sun, Y. Lu, Y. Su, X. Yang, H. Wang, Y. Wang, W. Shao, N. He, J. Hong, Biosynthesis of silver and gold nanoparticles by novel sundried Cinnamomum camphora leaf. Nanotechnology 18(10), 105104 (2007) 3. S. Basavaraja, S.D. Balaji, A. Lagashetty, A.H. Rajasab, A. Venkataraman, Extracellular biosynthesis of silver nanoparticles using the fungus Fusarium semitectum. Mater. Res. Bull. 43(5), 1164–1170 (2008) 4. S. Nath, D. Chakdar, Synthesis of CdS and ZnS quantum dots and their applications in electronics. Nanotrends (2007) 5. S.S. Nath, D. Chakdar, G. Gope, D.K. Avasthi, Effect of 100 MeV nickel ions on silica coated ZnS quantum dots. J. Nanoelectron. Optoelectron. 3(2), 180–183 (2008) 6. B.D. Hall, D. Zanchet, D. Ugarte, Estimating nanoparticle size from diffraction measurements. J. Appl. Crystallogr. 33(6), 1335–1341 (2000) 7. N.M. Ishak, S.K. Kamarudin, S.N. Timmiati, Green synthesis of metal and metal oxide nanoparticles via plant extracts: an overview. Mater. Res. Express 6(11), 112004 (2019) 8. A.J. Kora, S.R. Beedu, A. Jayaraman, Size-controlled green synthesis of silver nanoparticles mediated by gum ghatti (Anogeissus latifolia) and its biological activity. Org. Med. Chem. Lett. 2(1), 1–10 (2012) 9. https://www.sigmaaldrich.com/technical-documents/articles/biology/ir-spectrum-table.html 10. K.A. Khan, S.R. Rasel, M. Ohiduzzaman, Homemade PKL electricity generation for use in DC fan at remote areas, Microsystem Technologies Micro- and Nanosystems Information Storage and Processing Systems. Microsyst. Technol. 25(12), (2019). https://doi.org/10.1007/s00542019-04422-2. ISSN 0946-7076 11. M. Hasan, K.A. Khan, Experimental characterization and identification of cell parameters in a BPL electrochemical device. Springer, SN Appl. Sci. 1, 1008(2019). https://doi.org/10. 1007/s42452-019-1045-8 12. L. Hassan, K.A. Khan, A study on harvesting of PKL electricity. Springer J. Microsyst. Technol. 2020(26), 1031–1041 (2019). https://doi.org/10.1007/s00542-019-04625-7,26(3),PP: 1032-1041 13. K.A. Khan, M.A. Mamun, M. Ibrahim, M. Hasan, M. Ohiduzzaman, A.K.M. Obaydullah, M.A. Wadud, M. Shajahan PKL electrochemical cell: physics and chemistry. Springer J. SN Appl. Sci. 1, 1335(2019). https://doi.org/10.1007/s42452-019-1363-x 14. L. Cindrella, F. Hui-Zhen, Y.-S. Ho, Global thrust on fuel cells and their sustainability–an assessment of research trends by bibliometric analysis. Int. J. Sustain. Energ. 33(1), 125–140 (2014) 15. K.A. Khan, L. Hassan, A.K.M. Obaydullah, S.M. Azharul Islam, M.A. Mamun, T. Akter, M. Hasan, S. Alam, M. Ibrahim, M.M. Rahman, M. Shahjahan, Bioelectricity: a new approach to provide the electrical power from vegetative and fruits at off-grid regions. Microsyst. Technol. 26, 3161–3172 (2018) 16. T. Sorey, V. Hunt, E. Balandova, B. Palmquist, A new twist on the old lemon battery. (NSTA press. New York, 2012), pp. 91–98 17. C.H. Synder, The extraordinary chemistry of ordinary things. 4th ed. (John Wiley and sons. Hoboken, New Jersey, 2004)

Extract of Green Chili—A New Source of Electricity Md. Ohiduzzaman, K. A. Khan, Shahinul Islam, Md. Sayed Hossain, and Salman Rahman Rasel

Abstract Green chili is abundantly available for everywhere in the world. It is cultivating for cooking in curry. It can be used a green source of energy. Previously, it has been generated electricity from different vegetative and fruits. Electrochemistry is the responsible of mechanism for electricity generation from vegetative and fruits. In this paper, it has been used as a source of electricity using electrochemical cell. The Zn/Cu-based electrodes were used, and the extract of the green chili was used as an electrolyte. The green-electrochemical systems achieve the reduction reaction with a certain voltage for which electrons transfer between the cathode and anode. Here, cathode means gain of electron, and anode means loss of electrons. In this research paper, zinc is used as an anode, and copper is used as a cathode. The electrons react with Cu2+ and H+ . The Cu2+ and H+ eventually convert into Cu and H2 atoms. Finally, it is shown that Cu atoms were deposited onto the copper plate, and H2 releases from the green chili electrochemical cell. The experimental results were observed with and without adding copper sulfate solution CuSO4 · 5H2 O as a secondary salt. The outcome of the research work improvises a better understanding on the electricity generation technology of the system. Keywords Green chili · Extract · Electrochemical cell · Electricity generation · Performance · Zn/Cu electrodes

Md. Ohiduzzaman Department of Physics, Jashore University of Science and Technology, Jashore 7408, Bangladesh e-mail: [email protected] K. A. Khan (B) Department of Physics, Jagannath University, Dhaka 1100, Bangladesh e-mail: [email protected] S. Islam Department of Physics, Uttara University, Dhaka, Bangladesh Md. S. Hossain Center for Research Reactor, Bangladesh Atomic Energy Commission, Dhaka, Bangladesh S. R. Rasel Local Government Engineering Department (LGED), Sherpur Sadar, Sherpur, Bangladesh © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Biswas et al. (eds.), Microelectronics, Circuits and Systems, Lecture Notes in Electrical Engineering 976, https://doi.org/10.1007/978-981-99-0412-9_14

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1 Introduction Renewable energy is the safeguard of our future and the Earth [1]. From the power production point of view, renewable energy sources will be the beneficiary management strategies for electricity production [2]. Renewable energy sources will be the environment-friendly and eco-friendly [3]. Renewable energy sources means that does not have a limited source; it can be generated again and again and will never run out [4]. The examples of renewable energy sources are as follows: solar energy, wind energy, biogas energy, geothermal energy, biomass energy, water energy, tidal energy, wave energy, and OTEC [5, 6]. Fruits-based energy means electricity generation from fruits [7]. The production of electricity from fruits is cost-effective, environment-friendly, and locally abundant [8, 9]. The renewable energy sources are inexhaustible and literally around us may be converted into usable power production [10, 11]. The traditional energy sources like oil, gas, and coal are exhaustible and diminishing day by day rapidly [12]. Researchers expected that nuclear power could be the alternative sources of energy [13]. But, due to the high cost and risk point of view, it is not feasible and viable for practical utilization [15]. Recently, a group of researchers have developed electricity generation mechanism from various vegetative and fruits under the leadership of Prof. Dr. Md. Kamrul Alam Khan [16]. It is found that electrochemistry is the responsible of the origin of power production. It converts chemical energy to electrical energy by immerging of electrodes into the green chili extract [17]. This electrochemical cell is called fuel cell because H2 gas is released from this cell. This idea will create a new era and revolution for electricity production from vegetative and fruits [18]. The finding of the research suggests that the origin of the power production followed as per the rules of electrochemistry. Green chili extract as an electrolyte succeeded in producing electricity through the working principle of voltaic cell [19]. To enhance the performance of the system, copper sulfate solution (CuSO4 · 5H2 O) was added [20]. The performances of the parameters like open circuit voltage, load voltage, short circuit current, load current do not increase. The maximum power, load power is decreased. The internal resistance showed a higher value than the ideal value. During 27 h of conducting the experiment, the parameters varied after short intervals. But, after the addition of copper sulfate solution (CuSO4 · 5H2 O), the performance of the parameters did not develop like PKL electricity [21]. It will be needed more R & D work for practical utilization. This work may the guide line for further study.

2 Methods and Materials The necessary materials for electricity production using green chili are given: green chili (Capsicum annuum) extract, CuSO4 solution, 4 falcon tubes, 8 Zn plates, 8 Cu plates, LED bulb, copper wires, crocodile clips, ammeter, voltmeter.

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Fig. 1 Steps diagram of LED bulb lighting system using Capsicum annuum extract

Figure 1 shows the electricity production using green chili. It is shown that the outcomes of the findings of the research work from the starting. It acts as a graphical abstract. Figure 2 shows an experimental setup of an LED bulb lighting system using green chili extract. It is shown that there are 4 unit cells. Each cell has one copper plate and one zinc plate. There are 4 unit cells where 4 copper and 4 zinc plates. The unit cells are connected by copper wire in series combination. As a result, electrons can flow easily through the circuit. It was bought newly Zn and Cu plates those were used as electrodes. Calibrated digital multimeter was used for accurate measurement. It was ensured the same amount of filtered extract among the 4 falcon tubes. The quantity of the equipments is given below: (a) 500 g of fresh green chili were collected from the garden and then washed thoroughly with clean water, and a fine extract was made using a grinding stone. (b) The extract was kept in a bottle for about a week being sealed. (c) A Zn/Cu bio-electrochemical cell is designed where Zn and Cu plates were used as electrodes, green chili (Capsicum annuum) extract as electrolyte. (d) An LED bulb was attached to ensure that the bio-electrochemical cell is functional. (e) Each of the four falcon tubes was filled with green chili extract of 30 ml (f) Readings of different parameters such as open circuit voltage, load voltage, short circuit current, and load current were taken at an interval of 3 h using an ammeter and voltmeter. (g) Again after adding 1 ml CuSO4 · 5H2 O in the tubes, readings of the same parameters were taken at an interval of 3 h using an ammeter and voltmeter.

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Fig. 2 An experimental setup of an LED bulb lighting system using green chili extract

In this experiment, CuSO4 , 5H2 O was used as a secondary salt. Figure 3 shows the methods of the measurement of the current and voltage collection for both use of with and without CuSO4 , 5H2 O. A digital multimeter has been used to take the reading. Chemical reactions: According to electrochemistry, a few typical standard electrode potentials are given below:

Fig. 3 Experimental setup of a unit cell for both with and without CuSO4 solution

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Metal/Metal ion combination

E°(V)

Mg2+ /Mg

2.37

Zn2+ /Zn

− 0.76

Cu2+ /Cu

+ 0.34

Ag+ /Ag

+ 0.80

The potential at Zn, E Zn = −0 .76 V and the potential at Cu, E Cu = + 0.34 V. So that the cell potential, E cell = E Cu −E Zn = + 0.34−(− 0.76) = 1.10 V, which is considered as an ideal value. But practically, it is fluctuated due to various reasons like due to impurity of the Zn and Cu materials, distance between two electrodes, concentration of the extract, temperature of the extract, pH of the extract, area of the electrodes, density of the electrodes, etc.

3 Results and Discussion III. A Graphical analysis of LED bulb lighting system using Green Chili (Capsicum annuum) extract without CuSO4 . Table 1 shows the collected data for LED bulb lighting system using green chili (Capsicum annuum) extract without CuSO4 . It has been collected different parameters like open circuit voltage, load voltage, short circuit current, load current, maximum power, load power, and internal resistance using a calibrated multimeter. Figure 4 shows the open circuit voltage versus time duration curve. It is shown that the maximum open circuit voltage was 3.60 V, and the minimum open circuit Table 1 Data for LED bulb lighting system using green chili (Capsicum annuum) extract without CuSO4 Time Voc ( Open VL (Load Isc (Short I L (Load Maximum Load power Internal PL = duration circuit voltage) circuit current) power resistance, (V) Pmax = VL I L (mW) rin = Voc (hr) voltage) current) (mA) Isc Voc Isc (mW) (V) (mA) (Ω) 0

3.27

2.5

0.7

0.6

2.3

1.5

4.67

3

3.14

2.4

0.8

0.6

2.5

1.44

3.93

6

3.22

2.5

0.7

0.5

2.2

1.25

4.60

9

3.30

2.2

0.8

0.5

2.6

1.1

4.12

12

3.40

2.3

0.7

0.6

2.4

1.38

4.85

15

3.50

2.4

0.8

0.4

2.8

0.96

4.38

18

3.40

2.4

0.9

0.6

3.0

1.44

3.70

21

3.40

1.9

0.8

0.5

2.7

0.95

4.25

24

3.60

1.9

0.7

0.6

2.5

1.14

5.14

27

3.40

2.1

0.8

0.4

2.7

0.84

4.25

Fig. 4 Open circuit voltage versus time duration curve

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Open circuit voltage (V)

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4 3 2 1 0

0

5

10

15

20

25

30

Time duration (hr)

Fig. 5 Load circuit voltage versus time duration curve

Load Voltage (V)

voltage was 3.14 V. The difference between two cases was 0.46 V. It was also shown that the open circuit voltage change was in almost sinusoidal up to 27 h. Figure 5 shows the load voltage versus time duration curve. It is shown that the maximum load voltage was 2.50 V, and the minimum load voltage was 1.90 V. The difference between two cases was 1.60 V. It was also shown that the load voltage change was in almost sinusoidal up to 27 h. Figure 6 shows the short circuit current versus time duration curve. It is shown that the maximum short circuit current was 0.9 mA, and the minimum short circuit current was 0.7 mA. The difference between two cases was 0.2 mA. It was also shown that the short circuit current change was in almost sinusoidal up to 27 h. Figure 7 shows the load current versus time duration curve. It is shown that the maximum load current was 0.60 mA, and the minimum load current was 0.40 mA. The difference between two cases was 0.2 mA. It was also shown that the load current change was in almost sinusoidal up to 27 h. 2 1 0 0

10

20

30

Fig. 6 Short circuit current versus time duration curve

Short circuit current (mA)

Time duration (hr)

1 0.5 0 0

10

20

Time duration (hr)

30

Fig. 7 Load current versus time duration curve

Load current (mA)

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0.8 0.6 0.4 0.2 0 0

5

10

15

20

25

30

25

30

Fig. 8 Maximum power versus time duration curve

Maximum power (mW)

Time duration (hr)

3 2 1 0 0

5

10

15

20

Time duration (hr)

Fig. 9 Load power versus time duration curve

Load power (mW)

Figure 8 shows the maximum power versus time duration curve. It is shown that the maximum power was 3.00 mW and the minimum maximum power was 2.30 mW. The difference between two cases was 0.70 mW. It was also shown that the maximum power change was in almost sinusoidal up to 27 h. Figure 9 shows the load power versus time duration curve. It is shown that the maximum load power was 1.50 mW, and the minimum load power was 0.84 mW. The difference between two cases was 0.66 mW. It was also shown that the load power change was in almost sinusoidal up to 27 h. Figure 10 shows the internal resistance versus time duration curve. It is shown that the maximum internal resistance was 5.14 Ω, and the minimum internal resistance was 3.70 Ω. The difference between two cases was 1.44 Ω. It was also shown that the internal resistance change was in almost sinusoidal up to 27 h. But, this sinusoidal behavior is in opposite phase to open circuit voltage, short circuit current, load current, maximum power, load power. But also, this sinusoidal behavior is in same phase to load voltage. Table 2 shows the collected data for LED bulb lighting system using green chili (Capsicum annuum) extract with CuSO4 . It has been collected different parameters

1 0.5 0 0

10

20

Time duration (hr)

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Fig. 10 Internal resistance versus time duration curve

Internal resistance (ohm)

166

10 5 0 0

10

20

30

Time duration (hr)

Table 2: Data for LED bulb lighting system using green chili (Capsicum annuum) extract with CuSO4 Voc (Open VL (Load Isc (Short I L (Load Time voltage) circuit current) duration circuit voltage) (V) current)(mA) (mA) (h) (V)

Maximum Load power Internal power PL = VL I L resistance, (mW) Pmax = rin = VIscoc Voc Isc (mW) (Ω)

0

3.0

1.5

0.7

0.6

2.10

0.90

4.29

3

2.8

1.6

0.4

0.3

1.12

0.48

7.0

6

3.0

1.5

0.5

0.3

1.50

0.45

6.0

9

2.8

1.7

0.6

0.4

1.68

0.68

4.6

12

2.8

1.5

0.4

0.3

1.12

0.45

7.0

15

3.2

1.6

0.6

0.4

1.92

0.48

5.3

18

3.0

1.6

0.5

0.4

1.50

0.48

6.0

21

2.9

1.4

0.4

0.3

1.16

0.42

7.25

24

3.0

1.5

0.6

0.4

1.80

0.60

5.0

27

2.9

1.4

0.4

0.3

1.16

0.42

7.25

Fig. 11 Open circuit voltage versus time duration curve

Open circuit voltage (V)

like open circuit voltage, load voltage, short circuit current, load current, maximum power, load power, and internal resistance using a calibrated multimeter. Figure 11 shows the open circuit voltage versus time duration curve. It is shown that the maximum open circuit voltage was 3.00 V, and the minimum open circuit voltage was 2.80 V. The difference between two cases was 0.20 V. It was also shown that the open circuit voltage change was in almost sinusoidal up to 27 h.

4 3 2 1 0 0

10

20

Time duration (hr)

30

Fig. 12 Load voltage versus time duration curve

Load Voltage (V)

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2 1 0 0

10

20

30

Time duration (hr)

Short circuit current (mA)

Figure 12 shows the load voltage versus time duration curve. It is shown that the maximum load voltage was 1.60 V, and the minimum load voltage was 1.40 V. The difference between two cases was 0.20 V. It was also shown that the load voltage change was in almost sinusoidal up to 27 h. But, the phase is opposite to the open circuit voltage. Figure 13 shows the short circuit current versus time duration curve. It is shown that the maximum short circuit current was 0.7 mA, and the minimum short circuit current was 0.4 mA. The difference between two cases was 0.5 mA. It was also shown that the short circuit current change was in almost sinusoidal up to 27 h. It is same phase to open circuit voltage but opposite to load voltage. Figure 14 shows the load current versus time duration curve. It is shown that the maximum load current was 0.60 mA, and the minimum load current was 0.30 mA. The difference between two cases was 0.3 mA. It was also shown that the load current change was in almost sinusoidal up to 27 h. It is same phase to open circuit voltage but opposite to load voltage. Figure 15 shows the maximum power versus time duration curve. It is shown that the maximum maximum power was 2.10 mW, and the minimum maximum power was 1.20 mW. The difference between two cases was 0.90 mW. It was also shown

Fig. 14 Load current versus time duration curve

Load current (mA)

Fig. 13 Short circuit current versus time duration curve

1 0.5 0 0

10 20 Time duration (hr)

30

1 0.5 0 0

5

10

15

20

Time duration (hr)

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Maximum power (mW)

168 Fig. 15 Maximum power versus time duration curve

3 2 1 0 0

10

20

30

Time duration (hr)

Fig. 16 Load power versus time duration curve

Load power (mW)

that the maximum power change was in almost sinusoidal up to 27 h. It is same phase to open circuit voltage but opposite to load voltage. Figure 16 shows the load power versus time duration curve. It is shown that the maximum load power was 0.90 mW, and the minimum load power was 0.42 mW. The difference between two cases was 0.48 mW. It was also shown that the load power change was in almost sinusoidal up to 27 h. It is same phase to open circuit voltage but opposite to load voltage. Figure 17 shows the internal resistance versus time duration curve. It is shown that the maximum internal resistance was 7.25 Ω, and the minimum internal resistance was 4.29 Ω. The difference between two cases was 2.96 Ω. It was also shown that the internal resistance change was in almost sinusoidal up to 27 h. But, this sinusoidal behavior is in opposite phase to open circuit voltage, short circuit current, load current, maximum power, load power. But also, this sinusoidal behavior is in same phase to load voltage.

1 0.5 0 0

10

20

30

Fig. 17 Internal resistance versus time duration curve

Internal resistance (ohm)

Time duration (hr)

10 5 0 0

5

10

15

20

Time duration (hr)

25

30

Extract of Green Chili—A New Source of Electricity

169

4 Conclusions In this research work, electricity is produced on the principle of mechanism of electrochemistry. So that the electrochemistry is the responsible of the production of electricity green chili extract. Two electrodes have been immerged into the green chili extract and then connected it to the load by copper wire to flow the electrons in the circuit. Based on the obtained results, it is found that performances have been decreased after applying the copper sulfate as a secondary salt. When the load voltage is kept half of the source voltage, then the more conductive electrons can flow through the external load for making a non-saturated effect. It is found that oxidation has been occurred successfully. Acknowledgements The authors are grateful to the GARE (Grant of Advanced Research in Education) project, Ministry of Education, GoB for providing the financial support during the research work (Project/User ID: PS2019949).

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Md. Ohiduzzaman et al. and processing systems, Springer, ISSN 0946–7076, Microsyst Technol, 25(12). DOI (2019). https://doi.org/10.1007/s00542-019-04390-7 K.A. Khan, S.R. Rasel, M. Ohiduzzaman, Homemade PKL electricity generation for use in DC fan at remote areas, Microsystem Technologies Micro- and Nanosystems Information Storage and Processing Systems, ISSN 0946–7076, Microsyst. Technology, 25(12). DOI (2019). https:// doi.org/10.1007/s00542-019-04422-2 M. Hasan, K.A. Khan, Experimental characterization and identification of cell parameters in a BPL electrochemical device. Springer, SN Appl. Sci. 1, 1008(2019). https://doi.org/10. 1007/s42452-019-1045-8 L. Hassan, K.A. Khan, A study on harvesting of PKL electricity. Springer J. Microsyst. Technol. 2020(26), 1031–1041 (2019). https://doi.org/10.1007/s00542-019-04625-7,26(3),PP: 1032-1041 K.A. Khan, M.A. Mamun, M. Ibrahim, M. Hasan, M. Ohiduzzaman, A.K.M. Obaydullah, M.A. Wadud, M. Shajahan, PKL electrochemical cell: physics and chemistry. Springer J. SN Appl. Sci. 1, 1335(2019). https://doi.org/10.1007/s42452-019-1363-x M. Hazrat Ali, U. Chakma, D. Howlader, M. Tawhidul Islam, K.A. Khan, Studies on performance parameters of a practical transformer for various utilizations, microsystem technologies (Springer, 2019), https://doi.org/10.1007/s00542-019-04711-w. Accepted 03 Dec 2019 K.A. Khan, L. Hassan, A.K.M. Obaydullah et al., Bioelectricity: a new approach to provide the electrical power from vegetative and fruits at off-grid region. Microsyst. Technol. (2018). https://doi.org/10.1007/s00542-018-3808-3 K.A. Khan, M.S. Bhuyan, M.A. Mamun, M. Ibrahim, L. Hasan, M.A. Wadud, Organic electricity from Zn/Cu-PKL electrochemical cell, in Contemporary Advances in Innovative and Applicable Information Technology, Advances in Intelligent Systems and Computing, vol. 812, eds. by J.K. Mandal et al. (Springer Nature Singapore Pvt. Ltd., 2018), Chapter 9, p. 75–90 A.K.M.A. Ullah, M.M. Haque, M. Akter, A. Hossain, A.N. Tamanna, M.M. Hosen, A.K.M.F. Kibria, M.N.I. Khan, M.K.A. Khan, Green synthesis of Bryophyllum pinnatum aqueous leaf extract mediated biomolecule capped dilute ferromagnetic α-MnO2 nanoparticles. Mater. Res. Express 7(1), 015088 (2020), IOP publishing Ltd. K.A. Khan, M. Hazrat Ali, M.A. Mamun, M. Mahbubul Haque, A.K.M. Atique Ullah, M.N. Islam Khan, A.K.M. Lovelu Hassan, M.A.W. Obaydullah, Bioelectrical characterization and production of nanoparticles (NPs) using PKL extract for electricity generation, received: 31 July 2018/Accepted: 4 February 2020. Microsyst. Technol. Springer J. (2020). https://doi.org/ 10.1007/s00542-020-04774-0

Graphene-Based Biosensor: Physics and Technology Rupanwita Das Mahapatra, Sulagna Chaterjee, and Moumita Mukherjee

Abstract Recently, two-dimensional materials have showed the interest of scientific society because of the broad range of exclusive characteristics at nanometerscale width. Graphene is ultra-light, a good conductor of electricity and optically transparent. These properties are extremely convenient for biosensing purposes. In this chapter, physics of graphene sensors and different properties of 2D materials are discussed. Graphene is a sophisticated nanomaterial and also has exceptional mechanical characteristics, good conductivity, and elasticity. These characteristics of graphene offer huge potential for its utilization in different field. Graphene has been at the frontline of materials study in current years because of their unique optical and electrical properties and attractive mechanical characteristics obtaining from their atomically lean dimensions. Graphene has produced an explosion in the area of sensor-related research globally because of its advantages in extraordinary electrical, thermal, and mechanical characteristics. The technology used in biosensor is based on electrochemical property of graphene. This material has an outstanding electron transfer capacities, big exact surface region, and powerful mechanical stability which is necessary for building biosensors. As a part of human health observation systems and the interface with the body of human, biosensors are able to identify and calculate various signals. Keywords Multilevel Inverter · Space Vector PWM · Cascade H-bridge (CHB) · Harmonic Analysis · Voltage Source Converter

R. Das Mahapatra (B) · S. Chaterjee Department of Electronics and Communication Engineering, Adamas University, Kolkata, India e-mail: [email protected] M. Mukherjee Department of Physics, Adamas Universit, IEEE Member, Kolkata, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Biswas et al. (eds.), Microelectronics, Circuits and Systems, Lecture Notes in Electrical Engineering 976, https://doi.org/10.1007/978-981-99-0412-9_15

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1 Introduction For the last few decades, the excellent properties of single-layer graphene material have motivated aggressive investigation of additional 2D materials, at first providing attention on transition metal dichalcogenides [1, 2], but currently lengthening to the bigger family of covered materials[3, 4]. Along with the exceptional chemical and physical features of 2D materials, optical and electrical characteristics present particular chances in electronic, optoelectronic devices, graphene sensors together with photo-detectors, light-emitting diodes, and solar cells. Research says that graphene and sensors are a usual combination. Graphene, with different properties like outstanding electrical conductivity [5], like ultrahigh carrier mobility, exceptional mechanical flexibility, and its exclusive electronic band structure, has occupied a wonderful role in the electronic biomedical sensor groups. Graphene has large surface area (2630 m2 /g) among obtainable nanomaterials [6] and also accessible in a varied choices of biomolecules. More precise surface location and the graphene layers atomic width take out all carbon atoms in touch with analytics, so grapheneoriented biosensors occupy larger sensitivity rather than silicon [7]. Graphene has also emerged quickly as capable materials for use in the biomedical field, like tissue engineering, biological sensors, drug delivery, bioimaging [8]. There are basically two points on uses of graphene. One point depends on charge-bimolecular connections at π –π areas, charge replaces leading to electrical deviations in pure graphene. Graphene electric field sensor is the membrane for developing advanced level sensor identifying specific molecules.

2 Materials Based on Graphene: Properties and Manufacture Process In medical diagnosis and biomedical applications, graphene-based sensors have received significant role. It is essential to study the effect of graphene, its selectivity, sensitivity, repeatability, and bio-conformability along with its possible hazards to the atmosphere prior to graphene is incorporated with human body. The amalgamation of graphene-oriented materials in an occurrence of variety methods can be handled to confer properties for specific and desirable applications. It must be observed that the term–graphene in usually defines a sequences of graphene-based nanomaterials (GBN) including GO and also rGO [9]. The main types of materials based on graphene have been helpful for technical biosensors shown here (see Fig. 1). As graphene-oriented sensors are recently used for neuroscience, so, we highlight the consequences of GBNs to the essential nervous arrangement. Due to its improved solubility and stability in organic fluids, GO is chosen to original graphene for medical diagnosis. The arrangement of pure graphene is categorized as the range of 2D and sp2-hybridization of clean carbon atoms structured in a hexagonal pattern by covalent bonds. In the meantime, functionalized graphenes are accomplished

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Fig. 1 Graphene structure: a Graphene, b Graphene oxide, c Reduced graphene oxide, d Graphenebased quantum dot

by its amalgamation and preparation. Interestingly, various types of the graphene quantum dots (GQDs) have involved attention because of their exceptional optical features in the existence of photoluminescence concerning exact binding ability to a wide array of biomolecules. Such intrinsic and morphological features must provide the investigative transduction of biological sensors on the sensitivity, selectivity, repeatability, limit of detection (LOD), and biocompatibility. GQDs are very small graphene nanoparticles with a dimension less than 100 nm. To give an perceptive of subjects biochemistry, physics and technical information of graphene-related configuration, we have essential impact.

3 Pure Graphene Pure graphene is a one-atom-thick carbon layer self-possessed a mono crystalline graphitic film. It may be equipped by skinning it out of little thrombocyte of highly oriented pyrolytic graphite in a approach of physically removing dead skin cells using Scotch tape [see Fig. 2]. Physically removing dead skin cells is the process using that a vertical pressure is deployed under the plane, toward defeat the Van der Waals unbreakable force of just about 0.02 eV/Å [10]. The equation of Van der Walls force is. F(r ) = −

A R1 R2 (R1 + R2 )6r 2

where F = van der Waals force, R1 and R2 = radii of spherical bodies, r = the distance between the surfaces, and A = Hamaker constant.

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Fig. 2 Some processes of graphene amalgamation

While graphite layer is manufactured as the carrier within a field-effect transistor, the extraordinary electronic characteristics of the GFET relying upon the carrier movement and electron conductivity in energy band configuration. A diversity of realistic uses now become possible due to originality of graphene, and 2D nano-substances based on latest graphene are applied in the construct of newly invented electronic models. The effectiveness of sensors, biological-based sensors made by graphene are expected to exist considerably quicker rather than especially semiconductor-oriented products evolving in further competent computers. A. Chemical Vapor Displacement For industry-oriented manufacture of graphene, chemical vapor displacement is another famous techniques at present. This technique is very excellence standard and the total surfaces may be accurately inhibited, because of structural and electrical characteristics has stayed weaker rather than Scotch-based graphene. Outstanding utilizations of chemical vapor displacement (CVD) graphene have been achieved in different area, like electronic transistors [23], corrosion-abstraction layers [11], highly conductive, transparent and flexible, and high dedicated sensing device. CVD is another path to melt a gassy incitement, for instance, ethane, methane, or propane, into a warmed up material like copper or nickel. The metal or polymer may be utilized as transferred substrate when the shifting procedure will create scratches from splitting with the development of crumples. Utilizing less-layer pieces with the clarity 97.3% comparative to low pieces resistance of 125 Ω/sq, as-prepared graphene films

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made which is applied in touch monitor displays with the touch section monitor sector. Otherwise, big-dimension chemical vapor deposition graphene is initiated against probable biosensor stages to maintain Au nanomaterials and finally to build biomolecule receptor prototypes [12]. To improve the susceptibility and quality of sensors and biosensors, graphene is offered while the nanomaterials enhance the exterior area to fix the analyte. B. Liquid Depilation Techniques Liquid depilation is an inexpensive top–bottom procedure for manufacturing graphene in a mixture by an ultrasonic force resource toward produce small cavitations. This technique is used to explosion the unprocessed mass graphite into lesser particles and smaller sheets of graphene. The proposal of the liquid depilation is accepted out in organic substances like γ-butyrolactone (GBL), Nmethylpyrrolidone (NMP), 1,3-dimethyl-2-imidazolidinone (DMEU), and N,Ndimethylacetamide (DMA) [13, 14]. It has been studied that as a substitute of those organic substances on the diffusion of graphite were moved in water/liquid with their modification has been illustrated by DLVO and Hamaker law [15]. Colloid can be formed by the surfactant-coated exfoliated graphene. The blasted and exfoliated scattering of graphite arranged from end to end ultrasonication and gentle centrifugation. This is very famous technique because of the easy interpolation and cosmetology method. The high throughput will be shown by delaminated graphite surfaces between the production phase. The application of graphene inside fluid delaminating gives a significant standard in the growth of costly graphene-based translucent electrodes and sensing devices along with high plane area and electro-catalytic movement, mechanical reinforcing stimulus for polymer-oriented compositions, and optical limiters. C. Epitaxial Development on Silicon Carbide Epitaxial development on silicon carbide using thermal decomposition is a technique to construct major-scale few-layer graphene (FLG). Because of its different properties, graphene is one of the most talented nanomaterials for the future prospect. This technique begins along with silicon carbide (SiC) warmed up to 1240–1470° C with especially ultrahigh vacuum (UHV) when found in Fig. 3. The silicon molecules will afterward redirect, separating following the carbon molecules on top of the plane of silicon carbide. As a sample, epitaxial-based graphene reaches the shift by utilizing a fine gold plane along with polyimide to flake-off from silicon carbide into silicon slab. The consequential graphene sheet anyway has more quantities of faults with especially small mobility of 110 cm2 /Vs [16]. Graphene exhibits a minimum conduc2 tivity on the order of 4eh where h is Planck’s constant and e is the elementary electric charge. This technique has complexity in handling the amount of stages developed within the change of temperature and the occurrence of errors or disarray in the hexadic system. The Fermi level of intrinsic graphene is E F0 = 4.57eV. The energy band separation size is also depends on the width of graphene. Most of the researchers have learned and studied intensively the arrangement of energy band gap, expansion mechanism, and at the graphene-silicon carbide interface how uniformity breaking

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Fig. 3 Illustrative arrangement of chemical amalgamation supported on Lerf–Klinowski process

is happening. To explain the gap opening incidence between the two bands, there are two possible origins. To blend the electronic conditions by the Dirac levels is one, whatever needs the transformation uniformity must be fragmented. The second one is to split the equality between molecular sublayers in graphene, without any transformation uniformity changing. Biosensing devices supported on epitaxial growth (EG) are established to decide four nucleic acid supports and are capable to differentiate lithic and ascorbic acid at physiological pH where the electrochemical enforcements supply to the existence of border and plane flaws. For industrial fabrication even though the price of Si carbide slice residues a preventive factor, EG proposes simple incorporation into real electronic measures.

4 Functionalized Monolayer Graphene Chemical amalgamation be a high price up-down nano-approach which uses decomposition and depletion responses to incidentally create graphene. To acquiesce a one-layer hexagonal arrangement by carbon, chemical amalgamation was the initial technique, but the continuation of 2D coating has never been informed. Graphene may be promoted through the method connecting nitrification of thin graphite to graphene oxide; it again flaked off and condensed thermally or lightened to reduced graphene oxide. Below arrangement (see Fig. 3) explains the how the chemical amalgamation procedure of graphene happens using actual graphite material, if the level of fundamental proportions differs supported by the unlike chemical response rules. Some most important techniques and constructional prototypes are analyzed in subsequently.

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A. Chemical Amalgamation Process of Graphite Material and GrapheneBased Oxide Particularly, three important techniques used to amalgamate graphene. These are as follows: Brodie technique [17], the graphene oxide was arranged by Ceylon like a unprocessed substance consequential in a refinement to provide 99.97% carbon. A steamed combination of intense sulfuric and nitric acids labeled as carbonic acid was utilized as an oxidizer. The oxidized Ceylon graphite incorporated carbon:hydrogen:oxygen values as 67.78:1.83:31.37, and the carbon-to-oxygen proportion was 2.22 from the observation of fundamental inspection. So, the substance was called graphic acid, and it became the first example of graphite oxide arranged analytically. 1. Staudenmaier technique [18], this technique is almost same with Brodie’s. A combination of intense sulfuric and concentrated nitric acid used to prepare the graphite oxide. Along with this, potassium chlorate oxidizing means is too additionally mixed and responded over around 4 days. Sulfonate ions were eliminated, with cleaning inside water and dissolving in mitigated hydrochloric acidic liquid. Lastly, the graphite-based oxide was dehydrated up almost at 61 °C for 2 times. Graphite-based oxide arranged with this technique was checked to have an essential structure of carbon:hydrozen:oxigen of 58.72:17.98:23.27. The oxygento-carbon proportion was 2.51, which shown the minimum amount of loss of electrons during a reaction by a molecule. 2. Hummers and Offeman technique [19], this method was formed when it was accomplished that the utilization of nitric acid need more time to oxidize graphite has the capability for burst and remove of extremely erosive vapor. The Hummers and Offeman technique is also a little risky path while oxidizing graphite material. The oxidant is a combination of few chemicals like potassium permanganate, condensed sulfuric acid and sodium nitrate. The whole procedure requires about 1–3 h to finish the chemical reaction. Finally, carbon-to-oxygen proportion between 2.1 and 2.8 will be achieved by graphitic oxide. The commodity supplies a dazzling yellowish shade toward maximum corroded graphite when the green to black shade means to minimum oxidized graphite with more carbonto-oxygen proportion. Nowadays, the Hummers and Offeman technique is the typically applied and is normally recognized as the Hummers technique. There is an increment in the intermediate layer gaping as a consequence of the corrosion process of graphite. For uncorroded graphite material, the intermediate layer spacing of 0.334 nm expands to 0.563 nm after 1 h of corrosion process and again expands to 0.738 nm with 24 h of corrosion process. The quantity of chemical potassium permanganate be converted to double, and also, chemical sodium nitrate was exchanged with orthophosphoric acid in a magnitude toward sulfuric acid of (1:8). The modified technique shows a large quantity of deliquescent GpO being prepared contrast with primary Hummers technique. Mainly, the GpO is configured of carbon, oxygen, and hydrogen particles where for absolute oxidation the C-to-O proportion is in a scale of 1.5–2.5. To represent the

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GpO design, few research peoples have argued and discovered the accurate design with help of complicated and further processes. Basically, seven planned designs have been forecasted. 1,3-alkaline oxide was arranged at 1/4th of the hexamethylene. Regeneration happened by hydroxyl and ketone units along with utilizing daily quinoidal stands. A functional prototype related with the 2-type graphite fluorine (C 2 F)n was planned by Nakajima and Matsuo in 1988. This framework was deducted from the substance analysis, diffraction of chemical sensitivity, and X-ray studies. The most familiar and broadly used structure was described from the 13 C and 1 H magnetic-declination revolving nuclear magnetic field resonance (NMR) information of graphene oxide proposed by Lerf and Klinowski in 1996. The information recommended acyclic chemical compound having a ring with 6 members incorporating hydroxyl, dual joined carbon, and alkylene oxide which tells a radical plane composition made by fragrant chains. The radical carbon composition was simply not detached by the carbon–oxygen–hydrogen connections directing to covering, except the graphite-based oxide construction was reorganized to be polyhedral. This shows to the powerful interconnection through water particles. In this framework, the hydroxyl and 1,2 alkylene oxide-based chemical groups are at the radical surface when the hydroxyl and carboxyl units are largely at the border of the surface. Some other structure was projected by Dékány as newly as 2006. Particularly, the graphite oxide includes trans-related cyclohexyl easter marks, along with 1,3-alkylene oxide and cardinal hydroxyls, and furrowed systems of keto/quinoidal units. Along with that phenolic parts were putted into this structure to clarify the acerbity of graphite-based oxide. In 2008, Ruoff and colleagues produced and resolute the constructional uniqueness of 13 C-categorized graphite oxide. Hydroxyl and alkylene oxide parts were seen tied to the carbon arrangement using 13 C-MAS nuclear magnetic resonance analysis. Even the carbonylic categories were established frequently connected with the boundary of faults. The carbon composition had non-hydronated utility as perceived close to 110 ppm. The structural units on graphite oxide enclosed the 116 (hydroxyl along with epoxide group), the 63 (sp2 alloyed carbon), the 3 (lactol functional group, O-C-O), the 10 (lactol functional group:citrate:acid anhydride), and 9 (acetone carbonyl). Furthermore, the GpO and GO are following the acidic characteristics because of the ketones on the border of carbon surface into an occurrence of inadequately disintegrate equivalent sulfates, including monooctadecyl ester sulfuric acid. In comparison with common acid characteristics, it develops from the existence of carboxylic classes. B. Description of Declining or Lessening of Graphene-Based Oxide Material The chemical combination carries the possibility to make graphene on a large-scale point from graphite oxide or graphene oxide in compound. The GpO is generally scaled off via manual stirring otherwise by applying sound energy to stir up particles in a sample or ultrasonication. It is an efficient and rapid exfoliation technique, but it might split at a distance and extricate the GO plates toward little particles. The limiting was passed out at 100 °C during a single-step flagging procedure at night. The GO transferred into a black concrete and impulsive slowly after the reduction. Afterward, RGO is regularly soaked by water with ethanol and finally refined, next

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air dehydrated to obtain the hard outcome. In water, the exfoliated GpO or GO shows hydrophilic performance because of the oxygen filling as declared before, yet the RGO collective together because of the actuality that the chemical associations within the interlayers are eliminated, so the reduced graphene oxide slips become more hydrophilic and reduce in size jointly. More prominently, the constructional categorization can be established by Raman microspectroscopy meant in favor of the first-class spreading of E2 g method and the widespread corrosion in-plane sp2 -field. It must be noted that the hydrazinium hydroxide is not only utilized in the decrease of graphite oxide, other than the amount of falling factors known to date has enhanced also. The decreasing factors can be classified for making best assistance and used for sedating intention. As example, aluminum, hydride, and sodium borohydrides are decreasing factors which are utilized for best-support intention when hydrazine, Lascorbic acid, and isopropanol chemical are utilized as doping intention. The database of these decreasing factors used for chemical lessening of graphite-based oxide. With respect to the chemical changes of graphene oxide, the benefits of this techniques are such that: (1) the method is a high price and easy procedure utilizing inexpensive chemical substances; (2) this technique gives a high issue of graphene spreading heavily for commercial enterprise; (3) this chemically extracted graphene sorts highly steady mixtures; and (4) bigger-size graphene sheets can be made, and this helps macroscopic production.

5 Details of Graphene-Based Quantum Dots Graphene quantum dots are very small dimensional member in carbon group and is basically measured as a sliced fragment in graphene page. They are comparatively new set of material, which are derived from both graphene and carbon dots. Another new substances is known as “carbon-oriented dots.” The exploit of carbonoriented occurred unexpectedly through refinement of single-barriered carbon nanomaterial based tubes (SWNTs) from arc-release residue. These carbon molecules were afterward titled carbon-based quantum dots. For uniformity, the term “dot” relating “quantum” is differentiated from the spheroidal semiconductors. Basically, dots be acquired because of the size change of radial-plane carbon compositions to some nanometers coming to of the dots with quantum incident. Most significantly, the new substantials are fewer noxious and are congenial for cellular incorporation as substantiated in more units. Fluorescence carbon-oriented dots (see Fig. 4) classified into graphene quantum dot (GQD), carbon quantum dot (CQD), and carbon-based nanodot (CND) as distinguished to the semiconductor quantum dot. The well semiconductor quantum dots exhibit significant tunneling along with distinct concentration about positions. When decided, the carbon-oriented dots are supported on arrangements by sp3 -sp2 alloyed areas of carbon substances where the arrangement of graphene survives as a foundation. Furthermore, the concept of quantum dot

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Fig. 4 Illustrative images and photo-fluorescent systems of carbon-oriented dots, together with graphene-based quantum dot (GQD), carbon-based nanodot (CND), carbon-based quantum dot (CQD), evaluated to the quantum dots completed of semiconductor (SQD)

may be kept for the carbon-oriented dots that assure the nanometer-dimension and organized arrangements beside quantum imprisonment. The graphene-based quantum dots (GQDs) can be ended up with a sole molecule of graphite-based coating or graphene sliced into segments in a space of 2–22 nm. Mainly, the chief substance predecessors are not just the unlimited graphene piece and the organized graphene, but a few organic particles are too relevant. With top-down techniques, the hydrological trimming makes numerous nano-volumes pieces when the bottom-up techniques utilize straight pyrolysis and carbonization of the chemical free predecessor. The nano- toward micro-dimensioned particles of graphene oxide from hydrological trimming were activated by putting -carbon = oxygen, oxygen hydrogen, and -carbon oxygen oxygen hydrogen positions on top of the plane. Nano-typed GO − similar to arrangements produced from humic acid materials in the occurrence of nitrogen-consists of sets like -NH2 and carbon = nitrogen on the exterior of carbon-oriented dots creating an development during quantum acquiesce found by Dong. Normal and unnatural fragrant carbon materials have also been examined allowing a fine correction of vital visual characteristics, like adjustable fluorescence. These fragrant carbon materials can be worked as efficient tagging atoms as informed currently, for instance diethylamine (DEA), chloroform (CHCl3 ) and orange juice, potato, citric acid and polymer, polycyclic aromatic hydrocarbon (PAH), linear-structured polyethyleneimine (LPEI). The many color discharge spectrum were because of unlike dimensions and life of plane functional sets, i.e., carbon

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= oxygen and carbon = nitrogen. To attain convenient quantum outcomes, the dimension may be compact downward to a little nanometers, and the carbon basic arrangement be able to become chemically customized. So, the as-composed carbon-oriented dots forever hold sp3− and sp2− blended constructions dominating them for being customized by the nitrogen/oxygen organized sets and protein alteration. The characteristics of fluorescence along with their machinery occur as of the specific positions of hole-electron (h-e) pairs which generate excitons equivalent to predictable quantum dots. The policies of chemical mixture are utilized to steady the commodities be an inquest for precise purposes in in vivo biological imaging and biological markers, to enhanced medicine release, and to become incorporated for biological sensing devices.

6 Conclusion During the assessment, we talk about our important points of analysis on the inherent characteristics of graphene plus its plane functionalization concerned with the natural action method in medical electronics purposes. We try to make clear numerous familiar methods utilized for the amalgamation of graphene-oriented resources and their characteristics. A different types of graphene-oriented equipments have been prepared belongs to natural graphene with the operation of graphene-based oxide, reduced graphene-based oxide, and also graphene-based quantum dot. The methods are decided through the most current biosensing instruments for medicine escape, medical diagnosis sensing devices, biological sensing devices, biological imaging, and other narrative procedures. Graphene is most famous 2-dimensional substances. On the other hand, to continue those characteristics, graphene will be entirely correct. Another techniques by chemical amalgamation which give out for ambient situations are offered too. In case of artificial measures, the graphite is customized and operated by different oxygen-oriented sets. Along with this, the plasma functionalized graphene material is all the time polluted by, deficiencies and disarray. Therefore, the arrangement of graphene like to be significantly altered; particularly, the electronic features must be imprecise. Graphene-oriented gas sensing element operated by occupying a gas molecule over the surface of graphene, which acts as acceptors or donors of electrons. Sensing properties of graphene can be achieved due to their outstanding large precise surface area, amazing electronic features, electron transport abilities, and ultrahigh elasticity. Graphene has been observed as a perfect material for preparing highly flexible and stretchable sensors. This releases up a broad variety of prospects and acting a critical responsibility in sensor and biological sensor purposes operates in the largest plane area. In the forthcoming days, these 2D new substances will be more expanded and modified for particularity of biological receptors. These substances can be engaged and incorporated in variety types of sensor and bio-related sensor stages giving a very high susceptibility and can give a resolution to few demands, like initial stage cancer recognition.

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Graphene Sensors for Application in Defence and Healthcare Sector: Present Trends and Future Direction Rupanwita Das Mahapatra, Sulagna Chaterjee, and Moumita Mukherjee

Abstract As advanced electronic-based medical diagnosis is growing rapidly, suitable studies have been carried out to examine biocompatible sensors. Nanomaterials like graphene and graphene oxide have expanded wide interests in various research due to their exclusive physiochemical features. As twin T-graphene is having two-dimensional allotropic structure, so, it is used in various biological fields. In this chapter, prospect of graphene-based devices in various fields like defence and medical sector and use of graphene biosensor to detect biological system are discussed. Technology of biological sensor and its recent development, technology of intact graphene, manufacturing of graphene-based biosensor are also discussed here. An electronic graphene-based sensor which is wearable is considered to be a vital asset of computational intelligence-based smart personnel electronics. Recently, sprain and pressure-sensible devices are one of the major trending research areas and are the typical mechanism of stretchy and elegant electronics. Various characteristics of graphene provide huge prospective for its exploitation in automation, man–machine interfaces, computational intelligence-based robotics, defence application, etc. Electro-optical properties, thermal conductivity and ballistic properties of a material are useful in the defence field. Keywords Graphene · Devices · Weight or pressure sensor · Computational intelligence · Defence · Bioimaging

1 Introduction Medical diagnosis techniques have become increasingly cost-effective. There is a requirement to find methods to identify the most suitable medical care without increasing healthcare costs. Protective medical methods, which change with the R. Das Mahapatra (B) · S. Chaterjee Department of Electronics and Communication Engineering, Adamas University, Kolkata, India e-mail: [email protected] M. Mukherjee Department of Physics, Adamas University, IEEE Member, Kolkata, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Biswas et al. (eds.), Microelectronics, Circuits and Systems, Lecture Notes in Electrical Engineering 976, https://doi.org/10.1007/978-981-99-0412-9_16

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health situation, can be then recognized and identified. Risk to life can too be estimated with overwhelming challenges by improving the rate of treatment while lowering the overall medical treatment cost. A biocompatible sensor is an investigative apparatus that can simply identify a biological molecule-based area by means of the preferred transducer to produce a computable signal as of the sample value. The biological sensing device-oriented methods exhibited in below diagram (see Fig. 1) illustrates a classic display place. It belongs to a bio-receptor besides a transducer device. The signal related to biological can be determined in a variety of measures are transduced through visual, thermal otherwise electrochemical measures into visible information and examined. In the biomedical area, the utilization of biocompatible sensors in medical diagnosis became crucial for human health development in sort to examine diseases through patient, detect and to diagnose bimolecular samples [1–5] and to incorporate with medicine delivery [6]. After long time study, it has been observed that main necessities of biocompatible sensors are that the receptor must be highly discriminating along with the transducer must be ultrasensitive for consistent real-life calculations. To get more accuracy, the biological specificity is present in a branded technique to confirm that the branded biotic behaviour gives a suitable and efficient signal. Sensors, as a serious part of fitness measurement systems, including implantable and wearable sensors, are capable to identify and calculate various signals [7]. To design biocompatible sensors for medical diagnosis, these extraordinary graphene properties are exploited and incorporated. In this evaluation, we will give attention on the material possessions based on graphene due to their fabrication procedure, the photo-luminescent graphene and surface chemistry. These graphene possessions are exploited and incorporated in biosensors for biomedical purposes. Currently, different sensors which can be wearable have been broadly expanded in fitness supervising, individual movement recognition, man–machine interfaces, machine scheme incorporation and machine intelligence [8–11]. Graphene exhibits

Fig. 1 Schematic diagram of biosensor system

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exceptional uniqueness like great mechanical force, suitable electrical characteristics, more heat conductivity and chemically balanced [12, 13] which are overall necessary for the sensible objects of wearable electromechanical sensing devices. Because of very high reactivity, graphene is also one of the most excellent nanoparticles for weight or pressure and also sprain-sensing purposes [14, 15]. Graphene and nanotechnology is start to be used for the help of the arms production. This raises the possibility of terribly effective and devastating new weapons. Surveillance drone, which is used in defence, will clearly profit from graphene growths for many of their components: electronics, lightweight construction, cameras or sensors.

2 Impact of Graphene in Various Fields Newly developed graphene-oriented sprain and weight or pressure-sensing devices provides different applications as well as device scheme incorporation, fitness supervising, man movement recognition and computational intelligence [16, 17]. Lastly, we sum up current growth tendency and functionalities forecasts, particularly the issues in arrangement with respect to computational intelligence [18–20]. Special characteristics of graphene provide great potential for application in robotics, human– machine interface, automation in industry, etc. Pressure sensor made by graphene shows different applications which includes health monitoring, human motion detection and artificial intelligence. At last, we summarize current growth trends and applications predict, particularly the challenges along with artificial intelligence. In addition, military arms would be manufactured with a small amount of graphene metal, so it would be more complicated to trace them by radar. These techniques would activate the use of graphene in army field. Graphene is material formed by pure carbon particles arranged in a hexagonal arrangement. It is very lightweight, a sheet of 1 square metre weigh only 0.75 mg. It is considered approximately 200 times harder than steel, and its density is same like carbon fibre, being about 6 times lighter than steel. One of the most popular application of graphene in military sector is the possibility of manufacturing robot outfits (Fig. 2).

3 Technology of Biological Sensor Devices with Graphene-Oriented Substantial and Recent Developments Initially, the construction and prominent characteristics of graphene-oriented substantials by different amalgamation techniques have been made use of in biological sensing utilization as graphene be a semi-conductor through extreme-big charge movability providing superior electronic characteristics, having big surface domain,

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Fig. 2 Graphene in defence application

being able of being operated on its plane. There are few manageable approaches to invent the anatomical structure for referencing biological molecules. In case of biomedical area, new graphene is alone not favoured towards like an oxide-less graphene performing π –π mound, non-covalent operation and large electrostatically power, but it too provides a boundless plane on an atomic plane. Graphene supplies for charge bio-atomic actions because of the large limited plane region directing to a detection improvement and also encouraging the wanted functionalism to focus biomolecules to modify the discrimination. Below diagram shows the positions of scene of the achievable interconnections of the particles based on graphene or metal particles and also interactions at a vacancy imperfectness [see in Fig. 3]. The functionalized graphene region is capable of instantly observe the biomolecules by its own oxide elements because of the amalgamation where lots of carboxyl, hydroxyl and epoxide units are defined on top of the border and surface sites. GO has exclusive inherent physical as well as chemical properties. The chemical property permits it to be used in bio-sensing, bioimaging capabilities. GO also has specific optical and biological characteristics, which permits it to be utilized for biomolecule recognition and little molecular drug delivery. Along with that the operationalized graphene permits constricting of quantum dots (QDs), heteroatoms, nanoparticles (NPS), proteins, antibodies, antigens, DNA and other particular particles [21–23]. GO is utilized for immobilizing numerous proteins. Horseradish peroxidise and lysozyme enzymes can be shattered unexpectedly on the surface of GO, and a microbial protein is also used in the functionalization of graphene oxide. Customized GO is utilized for the reason of gene release. Polyethylenimine changes the exterior of GO sheets and so makes it prepared for cellular gene release through covalent bonding and electrostatic interface for plasmid DNA.

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Fig. 3 Illustrative diagram of the graphene-oriented resources that know how to be inactivated with biological molecules like receptor

For biomedical field, graphene-related nanostructures were currently described with extremely susceptible and particular achievement as biological sensors and scheduled in Table 1.

4 Technology of Intact Graphene-Biomolecule-Oriented Biological Sensors In case of graphene-oriented biological sensors, graphene is capable to improve the susceptibility and LOD and also the presentation of the biological sensor appliance by recovering the electron transport linking graphene and the biological molecules because of its unexpected characteristics. For instance, as observed in image, a tagfree and transferable aptamer-based biosensor works on intact graphene like the terminal inside a field-effect transistor apparatus (see Fig. 4a). The GFET biological sensor is utilized towards distinguish the Pb2+ atoms in blood of children, where the template of blood is fully complex. The method of differentiating Pb2+ atoms from ordinary ions inside blood, along with Ca2+ , K+ , Mg2+ and Na+ at under 0.2 M/L, is essentially p material-based doping at CVDgraphene with the exterior engineering by G-quadruple, thrombin aptamer (TBA). Aptasensor based on graphene is having smallest concentration of 37.5 ng/L, that was roughly one thousandth of protection restriction (101 µg/L) of Pb2+ inside blood cells. A GFET structure demonstrates a alike Pb2+ senses stage but with G-quadruplex like the receptor (see Fig. 4b) [35]. The technique is because of the electrostatic power shift following the direct merges to the double sheet of CVD/DNA graphene terminals directing to the change in Dirac end in the energy band construction. Using the apparatus, the limit of detection is just 163.7 ng/L for initial signal strength authentication of DNA/GFET. Recently,

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Table 1 Recent invention of graphene-oriented biosensors Techniques

Receptor plan

Goal biological molecules

Limit of detection

References taken

Electron transfer technique

MoS2 /graphene oxide

Glucose presence in individual serum

65 nM

[24]

ECHEM

D-dimer monoclonal antigen/graphene nanomaterials

Fibrin degradation product

1 µM

[25]

ECHEM 1

AuNPS/GO

MCF-7

0.0375 µg/mL

[26]

SPR made by Ag-molybdenum Fibre optic disulphide (MoS2 )-Graphene

DNA

1 µM

[27]

GSPR 6

Biotin-SA 17/GO

DNA

–

[28]

SPR

Graphene oxide/(N-) HCG 23 PPLRINRHILTR(-C) 22

0.065 nM

[29]

GLSPR 5

Ni/graphene

3-NT 19

0.13 pg/mL

[30]

RGO 4 FET

Urease/PEI 14 RGO

Urea

1 µM

[31]

GO FET

GO/pentacene

Artificial DNA

0.1 pM

[32]

GFET2

Graphene/Tris–HCl

Pb2+

< 37.5 ng/L

[33]

FRET7

Boron-based doped Ce3 + graphene quantum Dots molecules in MCF-74 cells

0.4 mM in 10 + −5 cell/mL

[34]

3

Comments: (1) ECHEM: electrochemistry, (2) GFET: graphene field-effect transistor, (3) GO: graphene oxide, (4) RGO: reduced grapheme oxide, (5) GLSPR: graphene localized surface Plasmon resonance, (6) GSPR: graphene-based surface plasmon resonance, (7) FRET: fluorescence resonance energy transfer

it has been tested that inside blood of a cancer patient, carcinoembryonic antigen (CEA) protein can be calculated. A label-free immunosensor related to the agglutininchanged graphene field effect transistor was informed (see Fig. 4c) [36]. The plane alteration is used through a non-covariant functionalization with π-stacking by a beta-pyrene, and an automatic dicarboximide ester sets to cooperate by graphene. The graphene FET biological sensor illustrates the precise checking of the carcinoembryonic antigen protein in instantaneous time with high susceptibility of lower than 100 pg/mL. In accurate quantitative quantity of DNAattention along with obligatory similarities and kinetics of interbreeding of DNA, a collection of FETs with six chemical vapour deposition graphene was manufactured with a single multiplied sensing devices for DNAinvestigation (see Fig. 4). The absorption of single nucleotides may be calculated as small like 11 pM where the single to single oriented alteration can be examined within present time.

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Fig. 4 Illustrating design of graphene-oriented biological sensors: a Pb2+ inside blood biological sensor depended on graphene FET; b Pb2+ biological sensor related on DNA/graphene; c CEA protein made biological sensor related on anti-CEA/graphene; d actual-time obligatory kinetics and empathy of hybridization of DNA supported on graphene FET; e paper-oriented biological sensor meant for individual papillomavirus (HPV) recognition; and f a lipid-related customized graphene-based electrochemical biorelated sensor

Utilization of chemical characteristics of graphene substance, a narrative paperoriented biological sensor for human papillomavirus recognition had been descripted (see Fig. 4e). The graphene-polyaniline terminal is customized with help of an anthraquinone-based pyrrolidine peptide nucleic acid (acpcPNA) survey (AQ-PNA) and written by inkjet marking process. On occurrence of surface manufacturing technique of amino acid with negatively charge on graphene electrode with respect to the electrostatic appeal, an artificial 14-base single-nucleotide goal along with progression consequent near papillomavirus of human kind 16 DNA is calculated the electrochemical indicator retort of the AQ brand to recognize the primary phases of collar cancer disease. With the rapid growth of chemical knowledge, graphene small electrodes incorporated by bilayer lipid membranes (BLMs) have exposed hopeful consequences in stagnant and stimulated testing see Fig. 4f [37]. In addition, because of help prepared by lipoid film, the biological sensor realizes a good recyclability, duplicability, faster reply times, long-shelf existence and highly sensible. This allows a straight potentiometric calculation. The utilization of the graphene little electrodes in sensing corruptive, i.e. carbamate pesticides in fruit [38], has also described by Nikolelis et al. saxitoxin [39] and for identification of D-dimers which is a fibrin degradation product, carbamide and cholesterin as observed in Fig. 4f.

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5 Manufacturing of Biological Molecules-Organized Graphene-Oriented Biosensors and Use Biological sensors related to the part of graphene substances or organized graphene GO, GQD and RGO are mostly applied in medical sectors, biomedical fields and biological imaging systems. This is possible because of their enormously big plane area and capability to cooperate with different kinds of molecules. Along with that the exceptional characteristics of biocompatibility, solubility and functionalization provide a significant part in sensing techniques. Presently, as per diagram (see Fig. 5.), the FET oriented on GO has just indicated glucose recognition in the absence of an enzymatic glucose explanation [40]. In the particular appliance, GO is applied like discriminatory substance with glucose when the susceptibility for sensor is improved downwards to 1 µM by adding up copper and silver nanoparticles. Excitingly, operationalized graphene oxide (GO) ink done drawing on a polycyclic aromatic hydrocarbon FET for identifying unnatural DNA and mingling tumour units [41]. Based on capture the DNA along with inorganic phosphate set, negative electric charge draws holes at the grain-boundary of the polycyclic aromatic hydrocarbon covering and encourages the impacting or disintegrating in the section with the pentacene coating. So, the movability for the FET varies tremendously attaining a more susceptibility of 0.11 pM with this would be enhanced for large-ratio manufacture of marked biorelated sensors. In the stage for ultra-delicate carbamide recognition, the reduced graphene oxide plane is painted with the new structure of stage-by-stage congregation of polyethylenimine and also urease [31]. The reduced graphene oxide FET of carbamide recognition may be identified as the shift of pH in fluid entry, through which changing the Dirac location at the least voltage of greater than 510 mV. The edge of urea recognition is behind to 1 µM along with extremely rapid reply and good lasting solidity. In total, the initiation the Cu2+ progresses the limit of detection downwards to 0.01 µM. In current complex policies, FET biorelated sensors using reduced graphene oxide joined with brain natriuretic peptide-sensing device at initial stage point [42]. The brain natriuretic peptide is a familiar biological marker, and it is extremely significant during heart stoppage detection and projection. The reduced graphene oxide FET realizes the least of recognition at 110 fM within a human total blood specimen. Broadly applied method, surface plasmon resonance is used to explore biochemical responses in systematic examine and health analysis [43]. Particularly, SPR supplies non-marking biological sensing and immediate observation of biological molecule connections. Anyway, for tinny atoms or on small absorptions of the destinations, the SPR motion is deficient to be examined. For developing the SPR motion and biorelated sensing production, connecting sheets of graphene oxide was established inside the SPR-sensing element system. The sensor-integrated chip belongs to gold sensor chips using PMMA as an in-between film, a single-layered CVD graphene on peak of peak and the biotin-SA joined, correspondingly. The SA atoms permit the biosensor to choose the inactivated biological molecules having vitamin H. The connecting layers of graphene oxide in the scheme offers an amount of active sites for biological molecules because of the big plane region. Anyway, a

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graphene oxide width of greater than 10 nm powerfully restricted the optical assimilation leading to a susceptibility depletion. Currently, there has been an explanation viewing the development and management of the plasmalyte pairing method in graphene oxide SPR-oriented immunoadhesin biological sensors by summing up carboxyl category. The carboxyl-GO-based surface plasmon resonance chip be able to accomplished four times above the SPR phase change and reached smallest antibody exposure restriction of 0.02 pg/mL. More effort has informed the decline of graphene oxide-oriented SPR manufactured with thermal diminution at more temperature, the reduced graphene oxide SPR, by means of a thickness of 8.1 nm. The achievement of the reduced graphene oxide SPR biological sensor illustrates a feedback to rabbit immune gamma globulin G with a limit of detection of 0.0624 µg/mL. Recently, fluorescence biological sensors supported on the graphene-based quantum dots have expanded much concentration as a substitute option due to their relieve of the amalgam, better solidity, fast substance internalisation and bio-stability. The luminance biological sensing devices depends on the energy shifting in between the electron acceptor and donor that is dominant for medicine liberation and biological modelling connections on the nano-range. Actually, fluorescence resonance power transport is a process near relate the energy transmission linking two luminous atoms, when one is a donor with a thrilled condition and prepared for shifting to towards another one or an acceptor through dipole–dipole blending [44]. This easy method was informed in an original FRET supported on PEG-GQD aptamer or antibody/MoS2 for the recognition of somatic cell hold particle; it is a biological layer protein with glycosylation articulated on the plane of blood circulating tumour cells (CTCs). In this method, particularly, GQD is utilized as the fluorescence resonance energy transfer donor that releases phosphorescence at 465 nm under a stimulation of 362 nm. MoS2 possesses a good quality satiating ability is the

Fig. 5 Illustrative figure of organized graphene-oriented biosensors: a a glucose recognition related to GOFET (Republished by consent after); b DNA recognition supported on printing graphene oxide/FET made by pentacene; c urea stage biological sensor supported on RGO FET/ urease/ PEI; d heart stoppage recognition based on RGO FET/Pt NPS; e Graphene oxide SPR/SA–biotin flake; f BSA-based biological sensor supported on COOH-GO improved SPR; g Isotype control rabbit IgG polyclonal recognition related to reduced graphene oxide SPR; h FRET biological sensor oriented on MoS2/PEG-GQD aptamer

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acceptor or receiver. While the PEG is linked against graphene quantum dot, the PEGIntron Sylatron displays a powerful phosphorescence emission as of the quantum incarceration. After that PEG-Intron Sylatron is mixed along with the aptamer or antibody through van der Waals fastening producing a closeness of graphene quantum dots and MoS2 and satiating graphene quantum dots. While the EpCAM protein accompanied by powerful requisite similarity is initiated, the graphene quantum dot would brand on EpCAM aptamer and renovate its phosphorescence. Consequently, the EpCAM objective protein can be observed by the luminescence secretion.

6 Recent Applications of Graphene with Computational Intelligence Graphene-based biomedical sensors are presently being tracked in two main regions: invasive and noninvasive sensors. Invasive sensors improve the sensing accuracy which also near to the target tissues. It is mostly used in health checking, diagnosis, administration of diseases with treatment, which shows its prospective in various medical applications. Even though graphene-based invasive sensors have been established in restricted features, like neural stimulation, glucose checking and EMG/ECG signals monitoring, great quantity of work on implantable sensing device for health checking has been completed. The practicability of graphene-oriented transplants has been proved in vivo in biology-oriented arrangements, including the circulatory system, sense organs, digestive tube. On-invasive sensors for health checking, particularly wearable sensors and electronic skin (e-skin) detect vibrant signals and biomarker without penetrating and disrupting in the skin or tissue. Noninvasive sensors are so important and modern testified sensors for regular actual-time checking of complicated values for human medical issues. For active potential, it is formed while the cell is stirred and agitated from the outer surface. An electrocardiogram (ECG), electroencephalogram (EEG), electro-retinogram (EOG) generated by collecting electrodes with bioelectric signal after suitable filtering and amplification. To find precise, consistent, instantaneous bioelectric signals, electrodes must have exceptional electrical and mechanical characteristics. All the characteristics of graphene and its results connect the achievement necessities of electrodes related to bioscience. For example, graphene is the tinniest current carrying channel; it is biodegradable, vigorous and steady electrochemically. Graphene and its by-products have been usually used in different pressure sensors as their important piezo-resistive presentation and cool management into different constructions. Recently, graphenebased pressure sensors with good flexibility and different combined resources and constructions have been attained with their outstanding sensing properties. Scientists established a liquid-shape-oriented sensor along with a graphene/potassium chloride ionic conducting material as the recognizing essentials and Ecoflex as encapsulate. An ultra-conformal, decomposable strain sensor also established with graphene as the

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substrate and active materials. This sensor displayed a robust adhesion, a high sensitivity, a rapid response, skin-level stretchability which abled to display the important biological signals along with vocal folds activities, jugular vein beats and brachial artery waves, permitting man–machine communication. The thermal properties of graphene have apprehended the care of researchers. Scientist Trung planned wearable thermal sensors invented by self-supporting particular reduced graphene oxide (rGO), where the resistance changes on change of heat. The sensing devices solve numerous difficulties like real-time checking, lucidity, thermal electricity and anyway complicated manufacturing phases and high price restriction its extensive utilization. Recently, computational intelligence is slowly incoming the business application phase, and the significant point for the outbreak is being escorted in. Artificial intelligence skill is now broadly used in different fields of communal life and manufacture, changing or even challenging our usual cognition near the prospect of manufacturing, cultivation and rule enforcement [45]. Stretchable and conformable matrix network (SCMN) extended the use of wearable instruments in the area of computational intelligence [46]. SCMN has numerous tasks along with distinguishing sprain, force, moisture, temperature, luminosity, closeness and magnetic field. Integrated through computational intelligence, the stretchable and conformable matrix network has a broad scope of utilization in man–machine interactions, fitness checking technology and diagnosis devices. In detail, there are lots of questions still in executing computational intelligence applications for graphene-oriented sprain and weight and pressure sensors. For example, how to trace a huge quantity of data and finally use them with computational intelligence like deep learning process is a type to the utilization of graphene-oriented sprain and weight and pressure sensors. Now, how to select the suitable model control algorithm to compute the constraints is another tricky point. In addition, handful of utilization of graphene-oriented sprain and weight and pressure sensors are in exercise, and we have to think how to suitably join the graphene-oriented sprain and weight and pressure sensors along with computational intelligence.

7 Conclusion It is important to extend graphene-oriented sprain and weight and pressure sensors to realize potential utilization specially in the existing development of computational intelligence. But, we require to upgrade performance with respect to both technological advancement and cost. On the whole, graphene-oriented sprain and weight and pressure sensors have a brilliant research latent and broad usefulness. Machine learning-based approach is also becoming important to calculate positions of unidentified vacancies in graphene. Still there are lots of challenge in executing artificial intelligence applications for graphene-based sensors and different devices.

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Design and Simulation of a High Power LED Bulbs with Different Array of Fins in Passive Mode of Cooling Nitin Namdeo Pawar and Kiran K. Jadhao

Abstract In this paper, a completely unique cooling tactic through ventilating at atmospheric condition air to the upper surface of the modern chips of the high-power light-emitting diodes (LEDs) was proposed to tackle the ever tough issues in front of the conventional thermal management approaches. For commercialization of LED, we mostly focus on five parameter such as weight, temperature, luminosity, life of light, design, and most important is cost. In the existing system, rectangular fins are used in between square shape fins are also used. As per the current demand more heat dissipation than existence. So, we are introducing in the literature is square fins in porous matter. By using porous square fins, we reduce the weight of existence fins and increase in the parameter of temperature reduction and cost reduction. Temperature decreases them automatically luminosity of light. Thermal resistance is reduced then automatically life of bulb increases. Overall we increase the overall performance of LED bulb. Keywords Waste plastic fuel · Compression ratio · Ethanol · Thermal efficiency · Load · Blend · Diesel · Engine speed

1 Introduction The durability of LED bulbs is an important factor in the power world with the minimization of the materials. The new technology developed within the space {of lightweight|of sunshine} for utilization normally illumination applications is highpower light-emitting diode (LED). During the lighting, heat is directly proportional to a luminous. Thermal management technique is an important aspect of increasing N. N. Pawar (B) Department of Mechanical Engineering, Alamuri Ratnamala Institue of Engineering and Technology Shahapur, Thane, India e-mail: [email protected] K. K. Jadhao Department of Mechanical Engineering, Baba Saheb Naik College of Engineering Pusad, Yavatmal, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Biswas et al. (eds.), Microelectronics, Circuits and Systems, Lecture Notes in Electrical Engineering 976, https://doi.org/10.1007/978-981-99-0412-9_17

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the lifetime of the LED bulb with better performance. For minimizing the lightemitting diode temperature, we have got to scale back the temperature at junction of light-emitting diode. For reducing the junction temperature, cut back the thermal resistant of a system. This could be achieved by well outline heat flow path by physical phenomenon through a conductor and anon by convection heat surface. The on top of development is habitually applied on electronics for cooling purpose. Features of light-emitting diode should be considered during the design of LED bulb such as size, form, and building codes with lighting. Circular heat sinks with conical/cylindrical shrouds are less than 7% of square heat sinks with pyramidal/square shrouds. 5W LED bulb has 300K of surface temperature with a pin fin circular heat sink. It found that thermal resistance in the range of 0.4 to 0.35 K/W has been reduced with an inclination angle of 71.8º to 65.3º, respectively. Whereas maintaining identical extent because the pin fin circular sink, a circular sink with solely straight radial fins was created. The thermal resistance was found 0.32 K/W in a circular sink with solely straight radial fins for an identical pumping power. Likewise, a hybrid sink with each pin fins and straight radial fins had a thermal resistance of 0.33 K/W for those conditions. The best-case shroud geometries for these 2 cases were cylindrical. At the side of the marginally lower thermal resistance, the ‘straight radial fin only’ configurations would be preferred to the hybrid fin configurations as a result of producing concerns and compactness. By increasing the extent, straight radial fin atomic number 13 heat sinks in cylindrical shrouds were shown to be capable of thermal resistances as low as 0.19 K/W for the established conditions. Major distinction between a fluorescent and light-emitting diode lightweight sources mercury vapor is employed in lamp for lighting purpose while not mercury vapor lamp can’t emit lights and additionally needed a high voltage arc needed for lighting purpose, while not mercury vapor lightweight won’t glow is that the main downside in potency of fluorescent lightweight. Light-emitting diode lightweight is higher potency than fluorescent light to eightieth. The fluorescent lightweight is accessible following 2 sorts. (1) Linear fluorescents: Lifetime of this sort of sunshine is ten thousand to 20,000 h. (2) Compact fluorescent: Lifetime of this sort of sunshine is 120000–20,000 h. An international customary for checking AN intensity of sunshine is aglow potency. Unit of mensuration is lumens per watt. The performance of illumination areas follow (i) Beam Distribution (ii) Hue and (iii) Brightness (whole lumen output). The parameter contemplate for enhancing the industrial facet of lighting areas follows. . . . .

Cost Environmental impact Application skillfulness Weight

Design and Simulation of a High Power LED Bulbs …

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2 Review Stage It has been discovered from this investigation that as a results of the vary of fins and aspect ratio relation of cylinder will increase the warmth transfer constant having vary from a range of 9.1639–9.2336 W/m–K and potency having vary thirty a pair of .9–49.5% will increase with decrement on thermal resistance having vary 7.789–5.69 of CW one, and then, the arithmetic having eighteen fins with 1/3H/D relation with base temperature 298 K shows best and optimum results among all of them relating to trustiness and quality aspect [1]. To improve free convection at intervals, the tall cylinder-based semiconductor diode lamp, a chemical mixture of gas was introduced at intervals the bulb, and its mixture was obtained victimization the mathematical equation. It had been established that the temperature increase of a semiconductor diode bulb crammed with the optimum gas mixture (mixture of seventy four half and twenty sixth xenon) was 30.7% below that crammed with air at an identical power input [2]. The present demand for energy consumption is created problems concerning saving energy and up potency a priority in many nations [3]. Lighting accounts for 20–25% of complete electricity uses at intervals the business countries [4], and constitutes up to 25–38% of the complete energy consumption in housing purpose [5]. Light-emitting diodes (LEDs) use 1/60th less energy and have 8–11 times longer lives than mercury light [4], and hence, they are mortal thought-about as a results of resulting generation of lighting. However, the operative heating range of a semiconductor diode affects considerably its performance and long that sometimes mustn’t exceed 100–110 °C [6]. Heating will cause performance hazards, reduction in value, shifts at intervals the emitted wavelength, and lower operational potency, whereas not applicable thermal management, the potency of LEDs square measure planning to be reduced from a theoretical worth of ninetieth to values as low as a combine of hundredth [7]. Varied advanced cooling principle equally as liquid cooling [7, 8], cooling liquid [9], liquid metal [10], and transient different state cooling [11, 12] are modified to thermally manage fattening LEDs. Petroski [7] analyze a combination of liquid and heat absorbing devices for gettable applications in the thermal organization of high-power semiconductor diode arrays. Cheng et al. [8] investigate an energetic liquid cooling answer of bright LEDs for automobile headlights appliance. Ramos-Alvarado et al. [9] incorporated refrigerant at intervals an unreal example and settled that the cooling liquid reduced the joint temperature @ Lai et al. [10] planned and by experimentation analyzed the thermal performance of an energetic heat reliving system. Victimization of low-density metal as a fluid increases the heat transfer of fins. Faranda et al. [11] and Deng and Liu [12] used heatcarrying cylinder to quickly dissipate intense heat per unit area from semiconductor diode strips and incontestable that the warmth pipes exaggerated the potency and low-weight outcomes of LEDs. Compared to force cooling schemes, free air cooling can be plenty of compact, many reliable and stable, and quiet. Lee and his cluster [13] developed a multi-disciplinary improvement to at identical time minimize the heat resistance and mass of a point-fin radial sink for semiconductor diode lighting

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Fig. 1 Rectangular LED bulb parren

purpose. These authors applied an identical technique to get the better result of the fin-height profile that reflects a 3D long cylinder flow pattern [14].

2.1 Rectangular Pattern In this pattern, we use a rectangular fin on sink, i.e., back side of LED bulb is shown in Fig. 1.

2.2 Cross Shape Patterns In this pattern, we use a cross shape fin on sink, i.e., back side of LED bulb is shown in Fig. 2.

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Fig. 2 Cross shape of LED bulb parren

2.3 Cross Shape with Porosity Patterns In this pattern, we use a cross shape with porous fin on sink, i.e., back side of LED bulb is shown in Fig. 3.

3 Result and Discussion 3.1 Rectangular Fin Mathematical calculation of rectangular fins by using iterative method on basis the following Table 1 is formed. He shows a value of convective heat transfer coefficient 9.317 w/m2 s and surface temperature 372.17 °C. Meshing Result of Rectangular Fins By using a software of HYPERMESH 14.0 for meshing a rectangular fins pattern, tetrahedral shape of meshing is done. 17,261 elements were generated as well as 32,186 nodes are generated by using above-mentioned toll, i.e., HYPERMESH 14.0 for the ANSYS simulation work (Fig. 4).

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Fig. 3 Cross shaped with porosity LED bulb parren

Table 1 Iterative calculation of rectangular fins pattern Sr. No.

Tw (assume)

Gr *105

Nu

1

313

0.6267

7.8853

2

403.1623

1.3698

9.5218

3

366.9938

1.3443

9.4988

H 6.6637 10.019 9.2094

Tw

△T

403.1623 366.9938

36.1685

373.3233

6.2395

4

373.3233

1.3515

9.5078

9.3418

372.2133

5

372.2133

1.3345

9.4778

9.3123

372.4571

6

372.4571

1.3382

9.4844

9.3188

372.4031

− 1.11 0.2438 − 0.054

7

372.4031

1.3374

9.483

9.3174

372.405

0.0019

8

372.405

1.3376

9.4833

9.3177

372.4124

0.0074

9

372.4124

1.3376

9.4832

9.3176

372.413

0.0006

10

372.413

1.3376

9.4832

9.3177

372.4129

− 0.0001

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Square fin array pattern with a meshed structure

Simulated result of rectangular plate fin array

Fig. 4 Rectangular fins pattern LED bulb pattern

3.2 Cross Fin Mathematical calculation of rectangular fins by using iterative method on accuracy basis the following Table 2 is formed. He shows a value of convective heat transfer coefficient 9.307 w/m2 s and surface temperature 370.17 °C.

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Table 2 Iterative calculation of cross shape fins pattern Sr. No

Tw (assume)

Gr *105

Nu

H

Tw

△T

1

353

0.10782

8.999

8.8583

375.3419

2

375.3419

0.11664

9.1626

9.50

369.8464

5.4955

3

369.8664

0.1176

9.1859

9.3818

370.8585

1.0121

4

370.8585

0.1171

9.1757

9.3898

370.7985

0.06

5

370.8945

0.1170

9.1739

9.3880

370.8087

0.0102

6

370.8087

0.1170

9.1743

9.3884

370.8056

0.003

7

370.8056

0.1170

9.1742

9.3883

370.8062

8

370.8062

0.1170

9.1743

9.3883

370.8061

9

370.8061

0.1170

9.1743

9.3883

370.8061

0.00060 − 0.0001 0

Meshing Result of Cross Fins By using a software of HYPERMESH 14.0 for meshing a cross fins pattern, tetrahedral shape of meshing is done in a three-dimensional module. 31,006 elements were generated as well as 69,491 nodes are generated by using above-mentioned toll, i.e., HYPERMESH 14.0 for the ANSYS simulation work (Fig. 5).

3.3 Cross Pattern with Porous Fin Mathematical calculation of rectangular fins by using iterative method on accuracy basis the following Table 3 is formed. He shows a value of convective heat transfer coefficient 9.247 w/m2 s and surface temperature 365.38 °C. Meshing Result of Cross Fins with Porous By using a software of HYPERMESH 14.0 for meshing a cross fins pattern, tetrahedral shape of meshing is done in a three-dimensional module. 45,209 elements were generated as well as 96,235 nodes are generated by using abovementioned toll, i.e., HYPERMESH 14.0 for the ANSYS simulation work (Fig. 6). Table 4 shows a transient analysis in ANSYS software with different time in which cross fin with porous shows better result. The temperature difference of various shape fins on the same cross-sectional heat sink is shown in the given figure. The cross shape type of fins gives better cooling effect than other fins.

Design and Simulation of a High Power LED Bulbs …

Cross shape fin array with a meshed structure

Simulative result of Cross fin

Fig. 5 Rectangular fins pattern LED bulb pattern

205

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N. N. Pawar and K. K. Jadhao

Table 3 Iterative calculation of cross shape fins pattern with porous Sr. No.

Tw (assume)

Gr*105

Nu

H

Tw

1

353

0.1078

8.999

8.8583

368.22

△T − 3.923

2

368.22

0.1172

9.1792

9.3598

364.30

3

364.30

0.1135

9.1092

9.2064

365.45

1.1546

4

365.4

0.1139

9.1150

9.239

365.20

− 0.2534

5

365.2

0.1135

9.1077

9.232

365.2

0.0565

6

365.2

0.1136

9.1093

9.233

365.24

0.0126

7

365.2

0.1136

9.1090

9.233

365.24

0.0028

8

365.2

0.1136

9.1090

9.233

365.24

0.0006

9

365.2

0.1136

9.1090

9.233

365.2

0.0002

10

365.2

0.1136

9.1090

9.233

365.24

0

Minimum

tem-

00 0 0

h Rec-

Sq

C

Cross

3.4 Weight of Various Fins By using a ANSYS software, we can also measure a weight of various pattern if fins in which we find that cross shape fins with porous pattern. Its weight is 0.699 kg. which is cost-effective (Table 5). In this work, numerical and experimental study is carried out for the heat transfer enhancement parameters for company’s existing rectangular finned plate (heat sink) and optimized or newly proposed plus sign type finned plate. Temperatures with respect to time, convective heat transfer coefficient and Nusselt no. are calculated from the experimental results. Experimental results obtained are discussed below.

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Simulative result of Cross fin with porous

Fig. 6 Cross shape fins pattern with porous LED bulb parren

3.5 Temperature Readings with Respect to Time (Numerical) Figure 7 shows the Numerical temperature readings with respect to time. It shows that the company’s original rectangular reaches near about 51 °C after 8 h but with newly proposed Plus signed fins it reaches near about 47 °C.

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N. N. Pawar and K. K. Jadhao

Table 4 Camparision of all fins pattern Type of fin Max Rectangle

3600

7200

10,800

14,400

18,000

81.56

84.777

85.227

85.274

85.28

Min

78.777

82.883

83.324

83.368

83.374

Max

84.931

87.948

88.147

88.163

88.165

Square

Min

83.04

85.785

85.961

85.975

85.976

Max

79.779

81.525

81.637

81.648

81.649

Cross

Min

77.122

79.261

79.402

79.416

79.416

Max

67.029

67.666

67.682

67.683

67.683

Min

64.39

65.266

65.289

65.29

65.29

Cross with porous

Table 5 Weight of various fins pattern Sr. No.

Name of pattern

Weight (Kg)

01

Rectangular fins

0.791

02

Cross fins

0.752

03

Cross pattern with porous fin

0.699

Fig. 7 Simulated variation of temperature with time

3.6 Temperature Readings with Respect to Time (Experimental) Figure 8 shows the numerical temperature readings with respect to time. Transient analysis shows that company’s original rectangular fin reaches near about 58 °C after 8 h running in ANSYS 14.5, but with newly proposed plus signed fins, it reaches 51 °C.

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Fig. 8 Experimental variation of temperature with time

4 Conclusion Steady-state natural convection heat transfer for rectangular fin and cross fin is experimentally presented. ANSYS simulation is done on software ANSYS workbench version 14.5 release. After analyzing many types of defined fins, we found that pin fin such as cross pin fin is also good for particular this case, but cross with porous fins has better heat transfer coefficient. It is observed that by changing the geometry of conventional fin by proposed cross fin, convective heat transfer coefficient increases as well as the material required for fins is less over rectangular fin; hence, the proposed fin is cost-effective. Fin efficiency, fin effectiveness, and overall fin effectiveness of proposed cross with porous finned heat sink found better theoretically on comparison with original rectangular finned heat sink.

References 1. S. Kaushik, S.Singh, N. Kanojia, R. Naudiyal, R. Kshetri, A.R. Paul, R. Kumari, A. Kumar, S. Kumar, Effect of introducing varying number of fins over LED light bulb on thermal behavior. Int. Mech. Eng. Conf. (2019) 2. S. Feng, Optimum composition of gas mixture in a novel chimney-based LED bulb. Int. J. Heat Mass Transfer 115, 32–42(2017) 3. L. Pérez-Lombard, J. Ortiz, C. Pout, A review on buildings energy consumption information. Energy Build. 40(3), 394–398 (2008) 4. W.R. Ryckaert, C. Lootens, J. Geldof, Criteria for energy efficient lighting in buildings. Energy Build. 42(3), 341–347 (2010) 5. N. Khan, N. Abas, Comparative study of energy saving light sources. Renew. Sustain. Energy Rev. 15(1), 296–309 (2011) 6. B.L. Ahn, C.Y. Jang, S.B. Leigh, S. Yoo, H. Jeong, Effect of LED lighting on the cooling and heating loads in office buildings. Appl. Energy 113, 1484–1489 (2014)

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7. J. Petroski, Advanced natural convection cooling designs for light-emitting diode bulb systems. J. Electron. Packag. 136(4), 041005 (2014) 8. T. Cheng, X. Luo, S. Huang, Thermal analysis and optimization of multiple LED packaging based on a general analytical solution. Int. J. Therm. Sci. 49(1), 196–201 (2010) 9. B. Ramos-Alvarado, B. Feng, G.P. Peterson, Comparison and optimization of single-phase liquid cooling devices for the heat dissipation of high-power LED arrays. Appl. Therm. Eng. 59(1), 648–659 (2013) 10. Y. Lai, N. Cordero, F. Barthel, Liquid cooling of bright LEDs for automotive applications. Appl. Therm. Eng. 29(5), 1239–1244 (2009) 11. R. Faranda, S. Guzzetti, G.C. Lazaroiu, Refrigerating liquid prototype for LED’s thermal management. Appl. Therm. Eng. 48, 155–163 (2012) 12. Y. Deng, J. Liu, A liquid metal cooling system for the thermal management of high power LEDs. Int. Commun. Heat Mass Transfer 37(7), 788–791 (2010) 13. H. Ye, M. Mihailovic, C.K.Y. Wong, Two-phase cooling of light emitting diode for higher light output and increased efficiency. Appl. Therm. Eng. 52(2), 353–359 (2013) 14. X.R. Tang, B.H. Ding, Z.T. Yu, B. Li, Liu, A high power LED device with chips directly mounted on heat pipes. Appl. Therm. Eng. 66, 632–639 (2014)

An Experimental Investigation of Production of Plastic Fuel and Blend with Diesel Fuel Nitin Namdeo Pawar and Kiran K. Jadhao

Abstract The world is developing very fastly. During the development stage main problem in front of any city or country is disposed of plastic waste. There are near about 30% of expenditure is invested on disposal of Municipal waste particularly and it hazards a lot on nature. In this literature we are going to find the best alternative for C12 range fuel. Therefore work on various blend of diesel with ethyl alcohol and plastic fuel for a trial taken a fifteen hundred fix speed single cylinder Diesel Engine with compression ratio, thermal efficiency, varying load. The preeminent running engine condition at a load, 20% waste of plastic fuel and 20% ethanol with 60% diesel fuel with a ratio of total volume to clearance volume, i.e., compression ratio load at a high value.in this study, we know for Diesel engines waste plastic fuel is a suitable alternative. Keywords Waste plastic fuel · Compression ratio · Ethanol · Thermal efficiency · Load · Blend · Diesel · Engine speed

1 Introduction The demand for energy and plastic consumption is continuously growing because the population of the world is also increasing very rapidly. Due heavy use of plastic in daily life and industrial work create a very huge problem of plastic deposal. For deposal, high energy is required. This has caused the two most problems of today, elevated energy consumption and waste generation rate is drastically increased, the plastic contains elements like carbon, nitrogen, chlorine, and sulfur, can be degraded N. N. Pawar (B) Department of Mechanical Engineering, Alamuri Ratnamala Institue of Engineering and Technology Shahapur, Thane, India e-mail: [email protected] K. K. Jadhao Department of Mechanical Engineering, Baba Saheb Naik College of Engineering Pusad, Yavatmal, India

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Biswas et al. (eds.), Microelectronics, Circuits and Systems, Lecture Notes in Electrical Engineering 976, https://doi.org/10.1007/978-981-99-0412-9_18

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using a thermal process called pyrolysis. And the resultant oil is and the resultant oil is blended with diesel fuels to reduce the stress of ethanol. In the plastic scenario in India, demand for plastic for plastics is per year of 80 lakh tons. Per day creation of plastic waste India is more than 10,000 metric tons. Import of India is 10% of developing countries near about 43% of plastic (polyethylene) is not recycled due to lack of availability, most of them export to countries.

1.1 Plastic Types Municipal solid waste (MSW) is the main problem due to their usage and production increases day by day. Currently, MSW is the third-largest plastic waste due to paper and food wastage in metro cities. In economically slow-growing cities use of plastic material demand increases as well as users also. Plastic material such as packaging, materials, bottles and electrical appliances, and shopping bag. Major cities which are very very slow economically developing countries also produce a very large amount of plastic less called pyrolysis and the resultant waste. For reducing this problem many researchers find out a technique, i.e., produce plastic fuel from waste plastic. Plastic fuel is generated from plastic waste by the pyrolysis process. In this process in the absence of oxygen at high temperature by thermal degradation to produce useful fuel. At 2750C the process is non-catalytic. The middling yield of the pyrolytic lubricate (48.6%), wax (40.7%), pyrogens (10.1%), and char (0.6%) from pyrolysis of PW. Municipal waste in terms of solid–milk cover, food container, house plastic, disposal glass and cup. Electronics equipment like CD, DVD, Box of cassatas, electronic cases. Cold drink bottles, drainage pipe, Fridge liner, guttering and plumbing pipes. Method of Pyrolysis is used to convert all the above waste into plastic fuel by thermal degradation of Plastic waste.10 tons of plastic cover waste in every single Taluka per year. This high range of waste is covering land pollution and creates soil pollution. The main type of plastic is as follows: . . . .

Industrial waste Household waste Plastic Toys Medicine cover Waste.

1.2 Blends Ethanol and diesel is the famous blend now a day’s used in diesel vehicles. Ethanol contains a high content of moisture is adversely affected the Calorific value of fuel. It time needs to find some alternative to ethanol to reduce its stress. It has different viscosity, alcohol fumigation emission problems, moisture generation, and duel injection system required. The best replacement for ethanol is biofuel and plastic fuel.

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Alcohol-diesel blends and emulsions are close to most diesel engines. During new blending most efficient ways are without major modification in the engine and it will not hamper on an emission norm of pollution.

1.3 E-Diesel Diesel and ethanol blend is refereed by a name of E-Diesel or E-Diesel. Sometimes is also called a “oxygenate diesel”. Oxygenated diesel term is due to contain an additive which is contaminated with oxygen like Methyl Ester or other additives with oxygen. The type of E-Diesel blend is classified as per the concentration of ethanol in the blend. The most used blend of E-Diesel is as follows: . . . . .

E5 E15 E10 E85 Electronic Waste.

1.4 Procedure of Pyrolysis Process Feeding—Applied a west plastic in boiler, i.e., feeder. Waste plastic is used as raw material. Heating—Heat the raw material up to a melting point or up to the vaporization point of plastic. Vaporization temperature is depending upon the size of plastic. Condensing—Evaporate the plastic at high temperature and then conduces by using a suitable condenser, i.e., straight and spiral tube condensers. Liquid collection—After a condensing processes the output liquid is stored in a liquid collector. Output is divided into two types, i.e., condensable and non-condensable fuel. Condensable liquid storage and avoid non-condensable fuel cyclone separator is used. Pyrolysis units use these non-condensable fuels for reheat. Water wash—This process takes 5–7 h to complete. An equal quantity of water and fuel is required. After some time water and crystal settle down at the bottom. Plastic fuel will come up in its pure form. Purification–With the help of filter paper purification is done. pH Test—for finding out the pi value of a fuel pH test will be taken. Most of the time when fuel washes by water. It shows pH value is nearer to 7.

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2 Review Stage So it’s continuously cheering to look to find an alternate supply of C12 fuel like Diesel. On the opposite hold, production of plastic product and succeeding large generation of wastes plastic confront a serious dumping drawback and different nature problems. Oils obtained waste plastics will be best option for C12 liquid compound and conjointly mentioned by completely various researchers [1, 2] however, most important outcome of an analysis is so as to it can’t be used in a straight line in diesel motor thanks to its more consistence that results not in good spraying characteristics, injection, and burning issues throughout running of vehicle engine [3]. And more, the employment of this fuel provides bad emission character [4]. Additionally, videocassette recorder engine evoke a prime resolution for solid uncomplete combustions because it permits a inconsistent compression magnitude relation to revive sympathetic situation for the combustion thanks to not as much of likelihood of unbern fire and speedy flame circulation [5]. Similarly, addition of various varieties of ventilated fuel have conjointly been found matched to enhance the combustion behavior well so the performance as well as unwanted gases. The foremost well-known fuel additives utilized by completely different investigators square measure diethyl ether (DEE) (Bridjesh [6]; straight chain pentanol [7], etc. and has given away betterment within on the whole performance of diesel motor. Of these ventilated improver content like additives reported minimum emission of various gases together with particulate pollutant and conjointly improve smoothness of the mix. The intrinsic atomic number 8 gift in this molecules assists more burning quality and so better’s combustion potency as well as ignition quality [8]. Pyrolysis that is the method of thermally degrading lengthy chain compound molecules into small chain, minimum complicated molecules through high temperature and high pressure, appear to be feasible for the utilization of waste of plastic. This method is in a position to provide better quantity of plastic fuel up to eighty World Trade Center at a reasonable temperature around (Three hundred to five hundred) °C. Shift doesn’t turn out abundant environmental pollution, so once the intake plastic waste is while not sulfur and chlorine content, still therewith, work will going to improving the technology and converted in to inexperienced technology. The organic oil created will be utilized in numerous applications like Automobile engine like diesel, power plant application as well as power generation, etc., besides the aerosolized byproduct has substantial hot worth so as to it will be reused to balance the general power demand of a shift plant. Also, the method management is additionally abundant easier as well as versatile; Associate in nursing intense sorting isn’t extremely needed as. The speed of an engine is 1500 rpm on a compression ratio of 16 to 18. The single-cylinder diesel engine is tested the author concluded that the performance characteristics are at the best level at 17 is a compression ratio [9]. Diethyl ether is used as an additive in a compression engine with waste plastic fuel diesel blend in the range of 5–15%. The result notes that combustion delay, high level of smoke, and NOX [10]. The paper deals with a blend of diesel and methanol with an additive (1-dodecanol) in various concentrations and found that methanol labeled is more

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215

efficient as compared to baseline mineral diesel [11]. The authors deal with plastic use in highway construction with a density of plastics is 9300 Kg/m3 [12]. The paper deals with the equilibrium in which without the need for kinetic. The working temperature is 500 °C with low-density polyethylene [13]. This paper deals with the increase in the characteristics of fuel in the pyrolysis method in which two type of feedstock is used. Characteristics are the oil yield, water content, and caloric value of the fuel [14].

3 Production of Plastic Fuel 3.1 Feed of Plastic Fuel The lot of types of plastics taken for the pyrolysis process include; PP, HDPE, LDPE. Plastic wastes are almost all over and can be available in huge quantities from houses, roadsides, etc. For this work, the plastic wastes were collected from the plastic waste stream of the University of Cape Coast, Ghana. The plastics collected range from; bottle and bottle caps, drinking straws, yogurt sachet and containers, plastic plates, detergent bottles, shower curtains, etc.

3.2 Preparation of Sample The mixture collected of plastic waste, were clean with cleaning agent like detergent and natural liquid to wash any unwanted materials. Fuel could be a hydrocarbon, and thus it’s capable to have an effect on the chemical property fuel. The waste plastic was water free and was withdraw items to appropriate dimensions with zero to two in exploitation nail clippers.

3.3 Transmutation of Waste Plastic Thermo chemical changes of the compound of waste plastics to plastic oil, was conduct employing on minute range outwardly giving a temperature secure bed transmutation reactor of batch type. Prime elements of the heater include; transmutation cabinet; temperature sensor like thermostat, cooling device, thermometer, a coil of heating, thermal resister, vessel, and a vaporized fuel line with an together with this diameter and totally long of fifteen cm and a thirty-eight cm, severally. It fastened bed reactor is transportable and is a straightforward style, poor responsive

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Fig. 1 Plastic fuel preparation set up using method of pyrolysis

to variation in experimental sizes and not mark up to required quality of feedstock, at exit gas temperature is low, high burnout, and thermal potency. The investigational trial model is illustrated in Fig. 1. Heater with assorted forms of plastic was het to three hundred and fifty °C. Element of chemical property was more matured true|gone through|had|undergone|saw|felt|responded to|suffered} the transmutation the rate of chamber is two hundred ml/min to supply steady state heating diagonally the crosswise of the chamber of reactor as well as make an atmosphere within the transmutation condition of chamber. After a while reactor is achieved a suitable temperature condition. The way of work embarked on and lasted for concerning two hours and forty minute; as temperature reduces output was not optimum. Gas made as a results of chemical changes w.r.t temperature was condensed water as plastic fuel; the high carbon fuel oil was separated. The heater lid was again start once the temperature was less than forty °C to get rid of the waste material in the form of solid.

3.4 Classification of Oil from Beginning to End Determination of Kinematic Oil Structure The tag measuring instrument is utilized in that work out. Instant that was live for a hard and fast quantity of flow liquid below mass to volume ratio through the capillary of tag measuring instrument below a duplicable pouring space and a well proscribed as well as notable temperature limits of (Forty, fifty, seventy, and eighty only) °C. Beneath in equivalent is that the universal equation for determinant the viscosity without gravity effect; therefore, standardization constant are obtained. V = Constant × t where; V —the viscosity of kinematic in mm2 /s

(1)

An Experimental Investigation of Production of Plastic Fuel and Blend … Table 1 Temperature, time, and kinematic viscosity reading

Temperature (°C)

Time (s)

Kinematic viscosity

80

7

0.7252

70

8

0.8288

50

9

0.9325

40

10

1.036

217

T —the time in second, C—is the constant in mm2 /s2 .

3.5 By Using FT–IR Analyzer Analys the Performance of Plastic Waste FT-IR investigation was led to recognize the different characteristics-spasm utilitarian gatherings or substance functionalities there in the rough fuel oil. A Perkin Elmer FT-IR spectrometer (Spectrum 0.4 k) utilized for investigation. The examples were canned in the scope of 4 k–0.4 k cm−1 with an unearthly goal of 4 per cm.

4 Result and Discussion 4.1 Kinematic Viscosity of the Plastic Fuel The viscosity of kinematic of the waste plastic fuel sample at different temperature. The kinematic viscosity for temperatures at forty, fifty, seventy, and eighty °C were found to be 1.036, 0.9324, 0.8288, and 0.7252 mm2 /s, respectively; as a result the temperature is working inversely proportional to kinematic viscosity of waste plastic fuels. Kinematic viscosity plays a very important role in engine running condition. Optimize value of kinematic viscosity gives high range of efficiency. For incomplete combustion high viscosity is responsible also low value of viscosity affect adversely on combustion of fuel (Table 1 and Fig. 2).

4.2 Brake Thermal Efficiency with Various Compression Ratios Finally, L 9-OA explains the response values with their corresponding S/N ratio values for brake thermal efficiency as mentioned in Table 2.

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Fig. 2 Temperature versus kinematic viscosity

Table 2 Break thermal efficiency with different blend Sr. No.

Blend (%)

Load (%)

Compression ratio

Brake thermal efficiency

S/N ratio

1

20

50

17.5

17.23

24.72

2

20

75

17.5

21.13

26.49

3

20

100

17.5

27.09

28.7

Various type blend of diesel, ethanol, and waste plastic fuel with different ratio of total volume to clearance volume, i.e., compression ratio noticeable Brake Thermal efficiency is increased. 60D20E20W shows better value of efficiency and other performance than any other blend and such increment in thermal efficiency is obtained most substantial at a high value of ratio of total volume to clearance volume, i.e., compression ratio and full load situation (Fig. 3).

4.3 Test Fuel Preparation and Its Characterization In our experiment is prepared by blending waste plastic oil and ethyl alcohol in appropriate proportions of each WPO and ethyl alcohol in diesel. The analysis blends are level as D90W10E10, D70W15E15, and D60W20E20, where D for diesel, W for waste plastic and E for ethyl alcohol, and the number indicates the % volume of the individual liquid. After preparing the required composition, the prepared composition was stirred by well instrument to get a homogeneous mixture after that used in the engine. The physical characteristics of the various different blends are reported in Table 3. This data is the different fuel properties are measured by ASTM standard procedures.

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Fig. 3 Effect of ratio of total volume to clearance volume, i.e., compression ratio on BTE

Table 3 Properties of various blend Properties

DIESEL

WPO

ETHANOL

D80W10E10

D70W15E15

D60W20E20

Density @ 15 °C, (g/m3 )

835

793

757

830.9

817

811

Flashpoint (°C)

52

5

13

47.4

39.1

34.8

Fire point (°C) 57

9

16

52.3

43.6

39.2

GCV (MJ/kg) 45

46

30

45.2

42.9

42.2

Cetane number

31

5

41

34.8

32.4

42

5 Conclusion The analysis of Diesel engine is evaluated on the basis of emission, performance, and various blending of diesel, ethanol, and waste plastic fuel. Various oxygenated additives with varying compression ration at a constant engine speed and torque, i.e., load. With various blend quantity and different ratio of total volume to clearance volume, i.e., compression ratio by Taguchi methods experiments is carried out. At ratio of total volume to clearance volume, i.e., compression ratio 18:1.100% load and quality of fuel is 60% diesel, 20% ethanol, and waste plastic fuel each. So the Taguchi method is robustly recommended for achieving the best state for engine performance and emission (Table 4).

220 Table 4 Relation among load, blend, and CR

N. N. Pawar and K. K. Jadhao Level

Load

Blend

CR

1

166.33

95

140.33

2

114

108

111

3

51

128.33

80

From the study of production of waste plastic fuel, it’s been proved that the transformation technology will be employed in changing the waste plastic waste in best energy fuel. The subsequent observation is created. . It’s a cyclic compound. . At 40 °C Kinematic consistence of the heating oil was found to be 1.036 mm2 s−1 . Carbon contain in waste plastic fuel within the vary of 12–24 variety of carbon. Waste plastic isn’t the complete drawback; however, we have a tendency to handle it conjointly tally. Therefore, the responsibility is on U.S. to be good on however we have a tendency to handle plastics; as a waste matter or as and resource in economical.

References 1. R.D. Arjanggi, J. Kansedo, Recent advancement and prospective of waste plastics as biodiesel additives: a review. J. Energy Inst. Attri. (2019) 2. R.D. Arjanggi, J. Kansedo, Recent advancement and prospective of waste plastics as biodiesel additives: a review. J. Energy Inst. (2019) 3. I. Kalargaris, G. Tian, S. Gu, Combustion, performance and emission analysis of a DI diesel engine using plastic pyrolysis oil. Fuel Process Technol. 157, 108–115 (2017) 4. A. Pakiya Pradeep, S. Gowthaman, Combustion and emission characteristics of diesel engine fuelled with waste plastic oil–a review. Int. J. Amb. Energy 1–19 (2019) 5. S. Milojevi´c, R. Peši´c, Determination of combustion process model parameters in diesel engine with variable compression ratio. J. Combust. 1–11 (2018) 6. P. Bridjesh, P. Periyasamy, A.V. Krishna Chaitanya, N.K. Geetha, MEA and DEE as additives on diesel engine using waste plastic oil diesel blends. Sustain. Environ. Res. 28, 142–147 (2018) 7. M. Dhanasekaran, P. Saravana Bhavan, N. Manickam, R. Kalpana, Physico-chemical characteristics and zooplankton diversity in a perennial lake at Dharmapuri (Tamil Nadu, India). J. Entomol. Zool. Stud. 5(1), 285–292 (2017) 8. Knothe, Van Gerpen, The Biodiesel Handbook, 1–328 (2005) 9. V.K. Sharma, S.K. Singh, M. Saraswat, Effect of ratio of total valuem to clearance volume i.e. compression ratio on performance and emissions of diesel on a single cylinder four stroke VCR engine. Int. J. Emerg. Technol. Adv. Eng. 5, 94–102 (2015) 10. V.K. Kaimal, P. Vijayabalan, An investigation on the effects of using DEE additive in a DI diesel engine fuelled with waste plastic oil. Fuel 180, 90–96 (2016) 11. A.K. Agarwal, N. Sharma, A.P. Singh, V. Kumar, D.P. Satsangi, C. Patel, Adaptation of methanol–dodecanol–diesel blend in diesel genset engine. J. Energy Res. Technol. 141(10), (2019) 12. E.N. Wami, F.C. Emesiobi, V.I.P. Ugoha, Suitability of recycled waste plastic bags as aggregate for highway construction: the Nigerian experience. Nigerian Soc. Chem. Eng. (2004) 13. I.A. Njiribeako, E.G. Kathleen, Management of non-biodegradable wastes in Nigeria. J. Eng. Manage. 4(2), 9–12 (2003) 14. F. Abnisa, W.M.A. WanDaud, A review on co-pyrolysis of biomass: an optional technique to obtain a high-grade pyrolysis oil. Energy Convers. Manage. 87, 71–85 (2014)

Bivariate Frequency Analysis of Drought Using Copulas for Telangana Region Ashutosh Chaturvedi and Gore Vikas Sudam

Abstract Drought is a natural phenomenon which occurs when the availability of water is less than normal condition, for a particular duration and at a specific region. Meteorological Drought is defined as the significant reduction in rainfall from normal. Due to change in climatic condition and rise in global temperature there is a significant requirement for the study of drought and its various properties. These changes in environment cased the variation in the frequency of many natural events, like drought. The two main attributes of drought are drought severity and duration, and they both require to study together to clearly define the drought. Thus, a bivariate frequency analysis is carried out for drought, using copula functions. The copulas are the functions that are used for linking more than one functions, which describes the relation between the random variable. The one-parameter Archimedean copula family is usually more applicable in the field of hydrology, and three copulas from that family are used under this study which are Clayton, Gumbel and Frank copula. Initially Standardized Precipitation Index is used as an index to quantify drought and to calculate its attributes. K-means clustering analysis is carried out and three homogeneous regions in the study area found. Univariate analysis is also carried out and distribution fitting is done using maximum likelihood and the best fit is selected on the basis of Akaike’s Information Criteria value. For estimating copula and to find correlation between the attributes of drought Kendall’s Tau non-parametric statics is used. The empirical process comparing the empirical copula with a parametric estimate of the copula derived under the null hypothesis is used as goodness of fit measure. Univariate and bivariate frequencies are estimated. The return period is set for 5, 10, 50 and 100 years and then the corresponding duration and severity are found for univariate and two cases of bivariate analysis. For cluster with average monthly rainfall around 52 mm the return period in joint distribution case is of order of 200 years, for region with average rainfall of 75 mm its about 350 years and with rainfall of about 95 mm it is 400 years which means area with higher rainfall having less frequency of drought and lower average rainfall zones getting frequent once. The results reveal the importance of bivariate studies. A. Chaturvedi (B) · G. V. Sudam Department of Civil Engineering, Alamuri Ratnamala Institute of Engineering and Technology Thane, Thane, Maharashtra 421601, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Biswas et al. (eds.), Microelectronics, Circuits and Systems, Lecture Notes in Electrical Engineering 976, https://doi.org/10.1007/978-981-99-0412-9_19

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Keywords Standard Precipitation Index · K-means clustering · Copula function

1 Introduction Drought is a natural phenomenon which occurs when the availability of water is less than normal condition, for a particular duration and at a specific region. Due to change in climatic condition and rise in global temperature there is a significant requirement for the study of drought and its various properties. These changes in environment cased the variation in the frequency of many natural events, like drought, flood, etc. Drought affects many sectors throughout the world, which includes agriculture, water supply, industries and most importantly our ecosystem. Estimation of how often a particular drought event occur is important for water resources studies and its management. The current study deals with the frequency analysis of the bivariate aspect of drought. The two important variables which exhibits the bivariate nature of drought are drought severity and duration. Separate analysis of drought duration and severity cannot reveal the significant associations between them. A. Standard Precipitation Index (SPI) To quantify the impact of drought there are many indices available in literature, in present study the Standardized Precipitation Index is used which is based only on the precipitation values. The calculation of SPI requires a long series of precipitation data. The precipitation data if fitted to either of Gamma or Pearson type III distribution. Once the fitting is done then the values are transformed to normal distribution having a mean as zero and standard deviation as 1. Now the estimated values are termed as SPI. From SPI values the drought duration and severity which are nothing but the period of SPI values lower than any threshold which is set as zero in current case and the sum of SPI values corresponding to that period, respectively. B. K-Means Clustering Working with large number of data which is spread over a specific study region is a difficult task. So as to work on such a large number of data the better idea is to cluster the area into homogeneous regions and study the representative points or point of interest from each homogeneous group. There are multiple methods available in literature to group homogeneous regions. In current study we are going to use Kmeans clustering algorithm. In K-means clustering algorithm the distance from the center of each clusters are found out and points are listed under a particular cluster based on its distance from the each cluster center, minimum distance from cluster center is the criteria. This method is called Euclidean distance as a measurement tool. C. Bivariate Analysis Using Copula For the bivariate frequencies, the copula functions are used and parameters are estimated using inversion of Kendall’s tau. Copulas are functions that link univariate distribution functions to form multivariate distribution functions. Copula is a Latin

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word having a meaning as “Link”. The copulas are the functions that are used for linking more than one functions, so as to narrate the relation between the random variable. Thus, we can say that the copulas are the joint distribution of random variable. According to Sklars’ theorem any multivariate joint distribution can be written in terms of univariate marginal distribution functions and a copula which describes the dependence structure between the variables. If F is a joint distribution function having margins as F1 . . . Fd then there will be a copula such that for all x1 . . . xd in [− ∞, ∞]. F(x1 . . . xd ) = C(F1 (x1 ) . . . Fd (xd ))

(1)

Let us consider FD (d) and FS (s) are the marginal Cumulative Density Function for drought duration and Severity, respectively FD,S (d, s) = C[FD (d), FS (s)] = C(u D , u S )

(2)

Here, U D = FD (d) and U S = FS (s). The one-parameter Archimedean copula family is usually more applicable in the field of hydrology. Frequency analysis includes finding out the frequency by which a certain event or events repeats itself. Using the method of Shiau [1], the return periods are found out for bivariate case. After that the return periods are fixed for 5, 10, 50 and 100 years and the severity and duration are found out. From the best fit copula using by [1] equations given in Eqs. (11) and (12), the joint return periods for the case “D ≥ d and S ≥ s” and “D ≥ d or S ≥ s” are found out. Here “D ≥ d or S ≥ s” means that the probability of a drought having a drought severity ‘s’ or a duration ‘d’. The literature is revised and analyzed and it can be concluded from it that, the frequency analysis for multivariate case can be classified broadly in four main steps. Which are, first to quantify the drought using an index, then Clustering to analyze homogeneous regions, univariate analysis and multivariate analysis and then finding the return periods. Different literature uses different methods to analyses the drought. For defining drought there are methods like SPI and SPEI and peak over threshold. For clustering analysis there are Hosking and Wallis, fuzzy C-means clustering and K-means clustering, etc. Now coming toward the analysis of drought variables, for univariate estimation there is maximum likelihood method is used, and for bivariate analysis copula function is used and to estimate the copula parameter there are method like IFM, Kernel density function, Kebdall’s tau parametric estimate, multivariate L-moments. The goodness of fit tests used are AIC, BIC, SIC, chi square, RMSE, etc. and return period calculation using Q–Q plot or equations defined by Shiau and Shen (2001) and Shiau [1] from literature.

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2 Methodology To implement methodology, it is required to have a study area, in current study Telangana State is selected as the study area. Telangana having coordinates as 18° N 79° E, the 29th state of India, is situated in peninsular India, the center-southern part of India, on the high Deccan plateau. It receives rainfall mostly from south-west monsoon, which is around 4 months. The states is classified under semi-arid region, with a dominant hot and dry climate. In this study, properties of rainfall are taking into consideration which are, mean precipitation, variance of precipitation, standard deviation of precipitation, kurtosis of precipitation, for K-means clustering. For this initially any of the random k points are selected as the cluster centers with their parameters and the distance from each of the k cluster is found out for each individual points for example in this study the value of k is set as 4 and for each 442 mandals the distance is calculated from those four points (centers) (Fig. 1). Distance from region 1(D1 ) = (X 1 − xi ) + (Y1 − yi ) + (Z 1 − z i ) + (S1 − si ) (3) Here, X 1 = mean precipitation, Y1 = variance of precipitation, Z 1 = Standard deviation of precipitation and S1 = Kurtosis of precipitation; for cluster center 1. Similarly D2 , D3 andD4 are calculated. Now based on the minimum distance from cluster centers, each mandal is assigned a homogeneous region. After this for the defined regions, average for each region is calculated and termed as the new cluster center of that region, and again the same process is repeated until the two consecutive cluster centers will become same. To compute the SPI, a software naming DrinC (Drought Indices Calculator) is used. For this the data from excel is firstly arranged in as per the guidance given under DrinC, then the data is uploaded in software. After this the distribution is set as gamma distribution and time scale as 1 month and now the SPI values are calculated by the software. In order to calculate this feature of drought, time period for which the SPI values are lower than a particular threshold value, zero in this current study is used. The time period is months. It can be expressed as the cumulative values of SPI below a threshold value (zero in current case) for a consecutive period. It gives an idea about the magnitude of drought. S=

D 

SPIi

(4)

i=1

here D = drought duration and S = drought Severity. According to Sklar’s theorem the joint behavior of random variables (X, Y ) with continuous marginal u = F X (x) = P(X ≤ x) and v = F Y (y) = P(Y ≤ y) can be characterized uniquely by its associated dependence function or copula C.

Bivariate Frequency Analysis of Drought Using Copulas for Telangana …

225

Fig. 1 Study area for this research work

FX,Y (x, y) = C(FX (x), FY (y))

(5)

Let us consider FD (d) and FS (s) are the marginal Cumulative Density Function for drought duration and Severity, respectively, then for 2-dimensional case, for all (u, v) ε [0, 1]2 the relationship can be written as in the Eq. (2). The bivariate Clayton copula in the closed form can be given as, Clayton copula, which is defined as: −1/  −θ θ C(u D , u S ) = u −θ D + uS − 1

(6)

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Here in the above equation θ is the copula parameter which is restricted in the closed interval (0, ∞) The Gumbel copula in its bivariate form is given as, Gumbel copula, in its bivariate form is given as:   1  θ θ /θ C(u D , u S ) = exp − (− log u D ) + (− log u S )

(7)

Here in the above equation θ is the copula parameter which is restricted in the interval [1, ∞). The Frank copula allows the maximum range of dependence, which is not likely in Clayton copula as well as Gumbel copula, for bivariate case:  

 −θ u e D − 1 e−θ u S − 1 1   C(u D , u S ) = − log 1 + θ e−θ − 1

(8)

Here in the above equation θ is the copula parameter which is restricted within all real values. So from the earlier estimated marginal of drought duration and severity the univariate return periods are found out using following relations, which are based on or defined by Shiau and Shen (2001). TD =

E(L) 1 − FD (d)

(9)

TS =

E(L) 1 − FS (s)

(10)

In current study the return periods are estimated for the probability of D ≥ d or S ≥ s and D ≥ d and S ≥ s, which means having a return period for either a drought of magnitude ‘s’ or a duration ‘d’, using the following equation developed by [1]. ' TDS =

TDS =

E(L) E(L) E(L) = = P(S ≥ sorD ≥ d) 1 − FDS (d, s) 1 − C(FD (d) + FS (s))

(11)

E(L) E(L) = P(S ≥ sandD ≥ d) 1 − FD (d) − FS (s) + C(FD (d) + FS (s))

(12)

3 Results of the Study First the K-means clustering is performed on the precipitation data. Initially the kvalue is set for 4, which is to create 4 homogeneous regions in the Telangana State, and I got 155 points in 1st cluster, 154 in second one, only 9 in third and 124 in 4th

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227

Fig. 2 Cluster results for Telangan region

cluster. As only 9 points are there in cluster 3, so the k-value is changed again and set as 3. Now for the new formed regions in cluster 1, 185 points are there, in cluster 2 there are 181 points and in cluster 3, there are 79 points. Now after the clustering analysis, the analysis of drought properties is stared. One point from each clustered region is selected as the representative points based on their average precipitation value. The selected regions are: from Cluster (1) Mahabubnagar-DAMARAGIDDA, Cluster (2) Rangareddy-SHANKARPALLE and Cluster (3) Nizamabad-KAMAREDDY (Fig. 2). From SPI value analysis it is found that the most severe occurred from May 1995 to oct 1995 with a magnitude to 8.47 and a duration of roughly 6 months. The longest duration encountered is of 7 months that too twice once from May 2011 to Nov 2011 with a severity of 5.74 and from May 1996 to Nov 1996 with a severity of 5.95. This clearly shows that the most severe drought is not with higher duration or vice versa. The drought severity and durations are found and their analysis is done. The three types of distribution selected and fitted to duration and severity and best fit is decided based on the values of AIC and BIC. The estimated distributions and the values of AIC and BIC are based on minimum AIC and BIC values one distribution is selected out of Gamma, Weibull and lognormal. The best fitted distribution which is selected along with its estimated parameters for drought duration and severity is given in Table. Here the parameters are shape and scale, respectively (Table 1). Once the parameter of the individual distributions for both drought duration and drought severity series are estimated, then after that copula functions are required to be selected and, parameters are need to be found out, of the selected copula function. So three copula functions from Archimedean copula family are taken for the study, which are usually used in the field of hydrology. The following (Table 2) gives the

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Table 1 Estimated parameters of best fit margins Sn.

Distribution

Duration

1

Log-normal

0.535

0.799

− 0.548

Severity 1.01

Nizamabad-KAMAREDDY

Region

2

Log-normal

0.521

0.540

− 0.295

0.984

Rangareddy-SHANKARPALLE

3

Log-normal

0.449

0.565

− 0.697

1.29

Mahabubnagar-DAMARAGIDDA

Table 2 Parameters of Copula Function (with Kendall’s tau correlation) Sn

Copula Function

Parameter

Statistic

p-value

Tau (τ, Data)

Tau (τ, Simul-ated)

1

Gumbel

2.31

0.035

0.1078

0.567

0.571

–

–

–

–

0.571

0.568

Nizamabad-KAMAREDDY

2

Clayton

2.63

0.049

0.0098

3

Frank

7.13

0.036

0.0683

Rangareddy-SHANKARPALLE 1

Gumbel

2.33

0.056

0.0098

2

Clayton

2.67

0.042

0.0063

3

Frank

7.21

0.042

0.0098

Mahabubnagr-DAMARAGIDA 1

Gumbel

3.39

0.025

0.364

0.705

0.701

2

Clayton

4.79

0.051

0.288

–

–

3

Frank

11.66

0.028

0.343

parameters of three copula functions with their p-values, which is used as an index for selecting the better fit. After the parameter estimation of the copula functions is done now, the best fitted copula is selected out of Gumbel, Clayton and Frank copula belonging to Archimedean copula family, for the three regions, based on the higher p-value. The univariate return period are calculated using the individual fit from the individual distribution followed by duration and severity series based on minimum AIC values, is calculated. Bivariate return periods “D ≥ d or S ≥ s” and “D ≥ d and S ≥ s” which is based on the joint distribution given by the copula function are calculated using Eqs. (3) and (4), respectively. The joint return periods for the case “D ≥ d and S ≥ s” and “D ≥ d or S ≥ s” are found out. Here “D ≥ d or S ≥ s” means that the probability of a drought having a drought severity ‘s’ or a duration ‘d’. In the following Table 3 the value of severity and durations are given for return periods of 5, 10, 50 and 100 years (Table 4). The following figures shows the variation of Return period with drought duration and severity. From Figs. 3, 4 and 5 contains for cluster 1–3, the return period given is individual for both the drought variables. From Figs. 3, 4 and 5 contains for cluster

Bivariate Frequency Analysis of Drought Using Copulas for Telangana …

229

Table 3 Severity and duration values with respect to return period Cluster region (3) Nizamabad-KAMAREDDY

Cluster region (2) Rangareddy-SHANKARPALLE

(1) Mahabubnagar-DAMARAGIDDA

Return period

Severity (SPI)

Duration (Months)

5

1.34

3.35

10

2.11

4.77

50

4.63

9.1

100

6.3

9.88

Return Period

Severity (SPI)

Duration (Months)

5

1.62

2.65

10

2.61

3.36

50

5.67

5.06

100

7.48

5.91

5

1.47

2.52

10

2.6

3.24

50

6.99

5.07

100

9.85

5.85

Table 4 Severity and duration values with respect to joint return period Cluster region

(3) Nizamabad-KAMAREDDY

(2) Rangareddy-SHANKARPALLE

(1) Mahabubnagar-DAMARAGIDDA

Return period

Severity or duration

Severity and duration

(SPI)

(SPI)

(Months)

(Months)

5

2.72

3.02

1.96

1.53

10

5.63

4.05

2.59

1.76

50

9.83

6.23

5.86

5.70

100

10.4

7.5

6.87

7

5

1.77

3.87

1.25

2.26

10

3.91

4.13

2.83

3.51

50

5.15

8.12

4.35

5.21

100

7.89

8.92

5.87

6.17

5

3.24

2.81

1.35

2.62

10

4.07

4.13

3.42

3.13

50

8.05

6.7

6.68

5.68

100

9.2

7.3

8.06

6.81

1–3, the return period is for both duration and severity as a joint, with a probability of D > d and S > s.

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NizamabadKAMAREDDY(Joint)

NizamabadKAMAREDDY(Individual)

100

100

80

80

60

60

40

40

20

20

0

Return Period

120

Return Period

120

0

0

5

10

0

15

Severity(SPI) & Duration(Months) Severity

2

4

6

8

Severity(SPI) & Duration(Months) Severity

Duration

Duration

Fig. 3 Individual and joint return period versus drought duration and severity for cluster 3

RangareddySHANKARPALLE(Joint)

RangareddySHANKARPALLE(Individual) Return Period

100

Return Period

120

120

100

80

80

60

60

40

40

20

20

0

0 0

2 4 6 Severity(SPI) & Duration(Months) Severity

Duration

8

0

2

4

6

Severity(SPI) & Duration(Months) Severity

Duration

Fig. 4 Individual and joint return period versus drought duration and severity for cluster 2

8

Bivariate Frequency Analysis of Drought Using Copulas for Telangana …

MahabubnagarDAMARAGIDDA(Joint)

MahabubnagarDAMARAGIDDA(Individual)

100

100

80

80

60

60

40

40

20

20

0 0

Return Period

120

Return Period

120

231

2

4 6 8 10 Severity(SPI) & Duration(Months) Severity

Duration

12

0

0

2 4 6 8 Severity(SPI) & Duration(Months) Severity

10

Duration

Fig. 5 Individual and joint return period versus drought duration and severity for cluster 1

4 Conclusion In this study, bivariate nature of drought is analyzed using the copula function and the frequency analysis is carried out. The report consists of mainly four processes to carry out frequency analysis of drought. First of all, K-means clustering is used to divide the study region into sub region. The precipitation-based parameters of points are used to classify them under a homogeneous group, which results in 3 homogeneous sub regions. After that SPI values are calculated for the precipitation series so as to quantify drought, although the sub region 1 have least average monthly rainfall but the severe drought events are observed more in region 2 and 3. This is shows droughts can be severe in good rainfall zones also. Also, the months with SPI = 0 and the drought events are least in less region 3. But all these analyses are from SPI values that too for the given dataset. In bivariate analysis the asymmetric Gumbel copula having higher dependency in positive tail results in best fit copula for sub region 1 and 3, and for region 2 symmetric frank copula is resulted as best fit. The correlation between the given variables and the simulated once are almost same which reveals that the correlation between the drought duration and severity is captured properly with the fitted copula. Estimation of how often a particular drought event occur is important for water resources studies and its management. The frequency analysis results reveal that for a fixed return period the univariate frequencies of both drought severity and duration are unrelated with that of joint frequencies in both the cases. This means that the bivariate nature of drought is significantly different from that of univariate. In some cases the high magnitude of severity in bivariate have lesser return period when it

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compared to the univariate case because that high level severity may be associated with lesser duration. For example, Mahabubnagar-DAMARAGIDDA have a severity of 9.85 with a return period of 100 years and in bivariate case for 100-year return period the severity is 9.20 in case of “or” and 8.06 in “and” case. Taking into account the two bivariate frequencies case which are “D ≥ d or S ≥ s” and “D ≥ d and S ≥ s”, it can be concluded that for a fixed return period the severity and duration values are more in case of “or” than “and”. Drought affects many sectors throughout the world, which includes agriculture, water supply, industries and most importantly our ecosystem. In accessing water supply and reservoir operations under drought condition, the frequency of drought plays an important role and is a significant tool in managing such schemes. Only the knowledge of one of them does not reveal the characteristics properly. Bivariate analysis gives results for both the variables together, this helps in studying drought with its variables and their correlation.

References 1. J.T. Shiau, Fitting drought duration and severity with two-dimensional copulas, Springer 2006. Water Resour. Manage. 20, 795–815 (2006) 2. M. Mirakbari, A. Ganji, S.R. Fallah, Regional bivariate frequency analysis of meteorological droughts. J. Hydrol. Eng. HE.1943–5584(2010) ASCE 3. R. Mirabbasi, A. Fakheri-Fard, Y. Dinpashoh, Bivariate drought frequency analysis using the copula method. Springer (2011). https://doi.org/10.1007/s00704-011-0524-7 4. J. Yoo, H.H. Kwon, T.-W. Kim, J.-H. Ahn, Drought frequency analysis using cluster analysis and bivariate probability distribution. J. Hydrol. 420, 102–111(2012) Elsevier 5. L. Chen, V.P. Singh, S. Guo, A.K. Mishra, J. Guo, Drought analysis using copulas. J. Hydrol. Eng. (2013) 6. S. Sadri, D.H. Burn, Copula-based pooled frequency analysis of droughts in the Canadian Prairies ASCE. J. Hydrol. Eng. 1943–5584.0000603(2014) ASCE 7. F. Tosunoglu, I. Can, Application of copulas for regional bivariate frequency analysis of meteorological droughts in Turkey. Nat. Haz. 82, 1457–1477–26(2016) Springer 8. O.O. Ayantobo, Y. Li, S. Song, Multivariate drought frequency analysis using four-variate symmetric and asymmetric archimedean copula functions. Water Res. Manage. (2018) Springer. https://doi.org/10.1007/s11269-018-2090-6 9. L. Hangshing, PP. Dabral, Multivariate frequency analysis of meteorological drought using copula. Water Res. Manage. (2018). Springer. https://doi.org/10.1007/s11269-018-1901-0 10. Climate Data Guide. https://climatedataguide.ucar.edu/climate-data/standardized-precipita tion-index-spi 11. Drought Prone Areas in India. http://nihroorkee.gov.in/rbis/India_Information 12. India Meteorological Department. http://imd.gov.in/section/nhac/wxfaq.pdf 13. K means Clustering – Introduction. https://www.geeksforgeeks.org/k-means-clustering-introd uction/ 14. Package copula. https://cran.r-project.org/web/packages/copula/copula.pdf

Influence of AlN Spacer Layer on SiN-Passivated AlGaN/GaN HEMT Santashraya Prasad

and A. Islam

Abstract This paper has reported the improved reliability of AlGaN/GaN-based HEMT devices operating at unity gain frequency (f T ) of 125.89 GHz and maximum frequency (f MAX ) 251.18 GHz using AlN spacer layer and highly conductive SiC substrate layer. The introduction of the spacer layer between AlGaN and GaN is used to tune the device characteristics such as 2DEG density, mobility, and drain current. AlGaN/GaN HEMT exhibit a subthreshold slope of 62.5 mV/decade. The simulations were carried out using the Silvaco tool that showed the increase in drain current with an increase in spacer layer thickness from 0.5 to 2 nm, attaining a maximum drain current of 14 mA. In addition to this, the proposed model showed improvement in the current collapse effect due to field-effect charge control by field plate (FP), resulting in quick recovery of partial depletion of the channel in the gateto-drain access region. SiN passivation is used to forbid the formation of a virtual gate. Keywords Spacer layer · Field plate · Substrate layer · Passivation · Virtual gate · Current collapse

1 Introduction With the advent of new technologies in this trending era, high electron mobility transistor (HEMT) has been developed and is widely used due to its high-speed switching operation exhibiting high frequency and hence valuable for high power application [1–5]. Besides this, these devices have also shown high breakdown voltages, lowloss and high-temperature tolerance that operates at microwave and low frequencies [6, 7]. The superior material properties of AlGaN/GaN HEMT such as high electron

S. Prasad · A. Islam (B) Birla Institute of Technology, Mesra, Ranchi 835215, India e-mail: [email protected] S. Prasad e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Biswas et al. (eds.), Microelectronics, Circuits and Systems, Lecture Notes in Electrical Engineering 976, https://doi.org/10.1007/978-981-99-0412-9_20

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mobility, high electron saturation velocity, high breakdown voltage [2], large electric breakdown field (1.5 × 107 V/m, as compared to 2.5 × 105 V/m of GaAs), high electron density, and large bandgap energy (3.42 eV, as compared to 1.4 eV of GaAs) are the critical reasons for outstanding performance of AlGaN/GaN-based HEMT over other device models [5, 8–11]. To date, AlGaN/GaN HEMT has exhibited highquality performances owing to good material quality and device processing; however, its material, and device properties are yet to be studied and discovered, hence makes its use critical than other heterojunction devices. HEMT-based devices are formed by combining heterojunctions (i.e. the junction between two materials with different bandgaps with nearly equal lattice constants). It is due to the presence of spontaneous polarization [12] that occurs when AlGaN and GaN materials are brought close [4, 13]. Both these materials possess nearly equivalent lattice constants, thus causes the formation of the potential well (i.e. 2DEG–two dimensional electron gas) at the interface without doping [14]. The potential well forms channel in which electrons flow. The 2DEG in AlGaN/GaN HEMT has an electron density above 1013 cm−2 , five times larger than that of GaAs. However, since AlGaN/GaN-based HEMT devices are not doped, they show high mobility due to low scattering phenomena [15]. Despite high-frequency performances, HEMT devices encounter specific issues such as current collapse [16], variation of material properties at high temperature, temperature increase due to large thermal resistance, thus leading to a detrimental increase in the conduction losses. Other addressable issue includes AlGaN/GaN HEMT devices’ temporary reduction in drain current due to trapping effect also called current collapse effect. HEMT exhibits different dynamic current–voltage (I– V) characteristics during fast switching due to trapping effects [17]. It is due to the formation of the virtual gate [18] and buffers trap that causes current collapse. Current collapse causes a temporary increase in the channel resistance in the sourcegate and gate-drain region of HEMT, leading to a pernicious increase in conduction losses [19, 20]. A shift (increase) in the threshold voltage is observed in the Id Vg characteristic curve due to virtual gate formation. The transconductance (gm ) decreases as the trapping of electrons increases [3]. The electrons from 2DEG occupy metal contact between gate and drain, such that some electrons from gate occupy surface state, which forms a virtual gate. Thus the two gates are present in series in between the source and drain. This leads to controlling the virtual gate’s potential by the total amount of trapped charges in the gate-drain access region. To overcome this defect, passivation techniques have been introduced. Thus, the use of the SiN passivating layer reduces or eliminates the surface effects responsible for limiting both the breakdown voltage and the RF current of AlGaN/GaN-based HEMTs [21]. The other type of defect that was overcome while using AlGaN/GaN HEMT is trapping electrons in the buffer region [20]. A high electric field gets established under the high drain-source voltage condition, and the electrons moving in the 2DEG channel get injected into the buffer traps. These trapped electrons have long trapping time constants which makes them unable to flow higher frequency-based signals. As the electron density in the 2DEG region decreases, so does the current in the channel. Electrons in the buffer region possess a negative charge, which causes the

Influence of AlN Spacer Layer on SiN-Passivated AlGaN/GaN HEMT

235

depletion of electrons in the 2DEG region. Hence a spacer layer is introduced. The spacer layer is introduced to prevent the low activation energy of Aluminium from moving around the next layers, where it could affect the band levels [22]. Besides this, it gives the benefit of a strain relief layer towards other crystals. The substrate type also determines the output. Our simulations have used SiC substrate because of its high thermal conductivity and low lattice mismatch (only 4%) than Sapphire or Si [23]. The high-frequency devices are used in wireless communications (broadband wireless connection, TV broadcasting, transmitter, satellite communication, mobile communication, base station amplifier) and various other militaries (missile, radar seeker) applications [5, 11, 13].

2 Structure Description and Device Modelling The primary issue limiting power-switching performances in AlGaN/GaN HEMTs is the current collapse due to trapping effects [4–11]. Several techniques have been introduced as a practical approach to suppressing current collapse [12–17]. In this manuscript, drain current dispersion has been studied for AlGaN/GaN HEMTs passivated with SiN and those of SiO2 . The authors in [10] have demonstrated their simulations that FP was an effective way to reduce the current collapse effect, thus limiting the tunnelling injection of electrons into surface traps located in the gate-to-drain region. The model of the explored device has been represented in Fig. 1. The sequences of epilayers of the structure are as follows: a 1.8 µm Al0.18 Ga0.82 N thick back-barrier layer, a 150 nm undoped GaN channel layer, a 2 nm AlN layer, and a 4 nm thin Al0.45 Ga0.55 N top barrier layer. The structure was passivated with a 50 nm SiN layer. After that, a 5 nm silicon dioxide (SiO2 ) layer was then layered over the SiN layer followed by applying Ni/Au (95 nm) gate metal and the simulations were carried out at an ambient temperature of 300 K. In the simulation code, the affinity parameter follows the affinity rule. The affinity parameter has been manually adjusted with the conduction band offset. By the norms of affinity rule, the discontinuity of the conduction band equals the difference between the electron affinities of two materials. Here the gate is placed asymmetrically. When a high drain voltage is applied, a high electric field builds up between the gate and drain with a peak at the drain side. The gate is shifted away from the drain so that the field plate of the gate is away from the drain, reducing the field hence increasing the breakdown voltage at the cost of reducing the maximum cut-off frequency. Figure 2 represents the formation of heterojunctions between two materials. The following formulas are used for aligning heterojunctions (E g1 > E g2 and χ 1 < χ 2 ), △E C = χ1 − χ2 and

(1)

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Fig. 1 Schematic diagram of the proposed structure with AlN spacer layer produces high electron density in 2DEG. The structure was uniformly doped at 2 × 1019 cm−2 . Owing to the fragile AlGaN barrier (4 nm), removing the SiN (in-situ) from the base of the gate electrode causes depletion in the 2DEG channel [12]

Fig. 2 It represents the energy band diagram of the formation of 2DEG at the heterojunction formed by two materials of different bandgaps with nearly equal lattice constants position of aluminium in AlGaN

Influence of AlN Spacer Layer on SiN-Passivated AlGaN/GaN HEMT

237

) ( △E C = E g2 − E g1 x

(2)

where χ 1 is the electron affinity of material 1, χ 2 is the electron affinity of material 2, E g1 is the energy band gap of material 1, E g2 is the energy band gap of material 2, and x is the alignment parameter [24]. The metal work function necessary for simulation was calculated using the formula scripted below using parameters such as electron affinity and Schottky barrier height. φ m = φ b + χs

(3)

where ϕ m is metal work function, ϕ b is Schottky barrier height, and χ s is semiconductor electron affinity [24]. These variations in mole fractions set the AlGaN material’s energy bandgap following the formula mentioned below. E g (x) = (3.42 + 2.86x − x(1 − x)1.0)eV

(4)

E g , is the energy bandgap, and x is the mole concentration of Al in AlGaN [24].

3 Result and Discussion This section discussed the simulation results in detail about the AlGaN/GaN HEMTs passivated using SiN and AlN as a spacer layer. Using the experimental data in Table 1 and solving the set of equations mentioned in Silvaco User Guide [25], the simulation of device characteristics of the proposed model was validated. The poor thermal conductivity of sapphire AlGaN/GaN HEMT on sapphire has a considerable accumulation of heat in the channel near the gate edge of the drain side. However, for HEMT on SiC, a notable improvement in device characteristics has been obtained [23]. The minimum lattice mismatch has resulted in higher Table 1 List of experimental data for simulation of proposed model

Parameter

Values

Band Gap of Al0.18 Ga0.82 N

3.79 eV

Band Gap of GaN

3.42 eV

Band Gap of AlN

6.2 eV

Electron saturation velocity of AlGaN

1.1 × 107 cm/s

Electron saturation velocity of GaN

2.5 × 107 cm/s

Electron saturation velocity of AlN

1.6 × 107 cm/s

Interface charge

1.01 × 1012 cm−2

The work function of the gate electrode

5.1 eV

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conductivity hence preferable for high power devices [6]. Chakroun et al. [3] have obtained a breakdown voltage of 573 V, while our simulations of AlGaN/GaN-based HEMTs have reported an impressive device performance such as breakdown voltage of 1156.4 V shown in Fig. 3 with a current gain cut-off frequency of 125.89 GHz in Fig. 4, and power gain maximum frequency of 269.15 GHz in Fig. 5. The maximum drain to source current was achieved at 8 mA at V gs = 3 V in [3], whereas the same structure, when simulated using an AlN spacer layer, produced a maximum drain current of 14 mA. The plot for Ids -Vds at different values of V gs has been shown in Fig. 6. The maximum drain current obtained at V gs = −1 V, 0 V, 3 V are 12.101 mA, 14 mA, 19.462 mA, respectively. The dynamic resistance (Ron ) was thus obtained to be 4.19 Ω. Fig. 3 Breakdown voltage reported at V ds = 1156.4 V

Fig. 4 Obtained a unity gain frequency (f T ) of 125.89 GHz

Influence of AlN Spacer Layer on SiN-Passivated AlGaN/GaN HEMT

239

Fig. 5 The power gain maximum frequency (f MAX ) notably achieved at 269.15 GHz

Fig. 6 Obtained the maximum was drain to source current of 19.46 mA

The maximum drain to source current against the gate to source voltage at a constant drain voltage of 10 V was obtained to be 14.3 mA, which is better than those obtained in [3] by the authors. The plot for I ds –V gs characteristic curve has been shown in Fig. 7. By our simulations, we have obtained a subthreshold value of 62.5 mV/decade. The plot for the same has been shown in Fig. 8. The formula used for the calculation has been mentioned below in Eqs. (5) and (6). The subthreshold slope was calculated using the formula mentioned below. S=

△ log(Ids ) △Vgs

(5)

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Fig. 7 Ids –Vgs characteristic curve shows the maximum I DS of 14.3 mA at V ds = 10 V

Fig. 8 It represents the characteristic curve of 62.5 mV/dec

Subthreshold_Slope =

1 S

(6)

The transconductance of the explored model of the HEMT device was obtained to be 238.33 mS/mm. Thus, HEMT devices have reported significantly impressive performance with more negligible current collapse effect and maximum drain current.

4 Conclusion The model suggested for the HEMT device in this paper displays excellent subthreshold characteristics and increased current flow in on-state. The subthreshold slope approaches up to 62.5 mV/dec. An enhancement in switching characteristics of the device was reported when subthreshold slopes are estimated lower than the

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traditional value. Besides this, the paper also investigates fluctuations in drain current due to variation in the AlN spacer layer and the prevention of unwanted virtual gate formations at the surface. With the increase in the spacer layer, the drain current also increases. Thus the device features as a promising candidate in the future for high switching and high power applications.

References 1. T. Mizutani, Y. Ohno, M. Akita , S. Kishimoto, K. Maezawa, A study on a current collapse in AlGaN/GaN HEMTs induced by bias stress. IEEE Trans. Electron Devices 50(10), Oct.(2003). https://doi.org/10.1109/TED.2003.816549 2. F. Aziz, M.J. Siddiqui, Optimization of d-doped AlInAs/InGaAs HEMT performance using spacer layer and d-doping, in 2011 International Conference on Multimedia, Signal Processing and Communication Technologies (Aligarh, 2011), pp. 236–239 3. Chakroun et al., AlGaN/GaN MOS-HEMT device fabricated using a high-quality PECVD passivation process. IEEE Electron Device Lett. 38(6), 779–782(2017). https://doi.org/10.1109/ LED.2017.2696946 4. S.K. Dubey, K. Sinha, P.K. Sahu, R. Ranjan, A. Pal, A. Islam, Characterization of InP-based pseudomorphic HEMT with T-gate. Microsyst. Technol. 26(7), 2183–2191 (2019) 5. K. Sinha, S.K. Dubey, A. Islam, Study of high Al fraction in AlGaN barrier HEMT and GaN and InGaN channel HEMT with In0.17Al0.83N barrier. Microsyst. Technol. 26(7), 2145– 2158(2019) 6. S.K. Dubey, M. Mishra, A. Islam Characterization of AlGaN/GaN based HEMT for low noise and high frequency application. Int. J. Numer. Model. Electron. Networks Devices Fields 1–12(2021) 7. H. Hasegawa, T. Inagaki, S. Ootomo, T. Hashizume, Mechanisms of current collapse and gate leakage currents in AlGaNÕGaN heterostructure field effect transistor. J. Vacuum Sci. Technol. B, Microelectron. Nanometer Struct. Process. Measur. Phenomena 1844–1855(2003) 8. S. Kumar, V. Kumar, A. Islam, Characterization of field plated high electron mobility transistor. in 2016 International Conference on Microelectronics, Computing and Communications (Micro Com), (Durgapur, 2016), pp. 1–3. https://doi.org/10.1109/MicroCom.2016.7522455 9. R. Swain, K. Jena, T.R. Lenka, Modeling of forwarding gate leakage current in MOSHEMT using trap-assisted tunneling and Poole-Frenkel emission. IEEE Trans. Electron. Devices 63(6), 2346–2352 (2016) 10. https://doi.org/10.1109/TED.2016.2555851(2016) 11. M.T. Hasan, T. Kojima, H. Tokuda, M. Kuzuhara, Effect of sputtered SiN passivation on current collapse of AlGaN/GaN HEMTs. in MANTECH Conference (New Orleans, Louisiana, USA, 2013), pp. 131–134, May 13th - 16th 12. S. Prasad, A.K. Dwivedi, A. Islam, Characterization of AlGaN/GaN and AlGaN/AlN/GaN HEMTs in terms of mobility and subthreshold slope. J. Comput. Electr. 15(172–180), 2015 (2016). https://doi.org/10.1007/s10825-015-0751-8 13. R. Vetury, N.Q. Zhang, S. Keller, U.K. Mishra, The impact of surface states on the DC and RF characteristics of AlGaN/GaN HFETs. IEEE Trans. Electr. Devices 48, 560–566 (2001). https://doi.org/10.1109/16.906451 14. G. Amarnath, G. Srinivas, T.R. Lenka, 374GHz cut-off frequency of ultra-thin InAlN/AlN/GaN MIS HEMT. in 2015 International Conference on Computer Communication and Informatics (ICCCI), (Coimbatore, 2015), pp. 1–4 15. T. Sreenidhi, G.A. Das, N.D. Gupta, Temperature and bias dependent gate leakage in AlInN/GaN high electron mobility transistor. in 2012 International Conference on Emerging Electronics, (Mumbai, 2012), pp. 1–4. https://doi.org/10.1109/ICEmElec.2012.6636260

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16. T. Palacios, U.K. Mishra, AlGaN/GaN high electron mobility transistors, nitride semiconductor devices: principles and simulation. Journal Piprek, ed. (Wiley-VCH Verlag & Co., 2007), ch. 10, pp. 211–233 17. J.A. Mittereder, S.C. Binari, P.B. Klein, J.A. Roussos, D.S. Katzer, D.F. Storm, D.D. Koleske, A.E. Wickenden, R.L. Henry, Current collapse induced in AlGaN/GaN high-electron-mobility transistors by bias stress. Appl. Phys. Lett. 83(8 25), 1650–1652(2003) 18. M. Islam, G. Simin, Compact model for current collapse in GaN-HEMT power switches. Int. J. High Speed Electron. Syst. 25(01, 02), 1640001(2016) 19. M.J. Anand, G.I. Ng, S. Vicknesh, S. Arulkumaran, K. Ranjan, Reduction of current collapse in AlGaN/GaN MISHEMT with bilayer SiN/Al2O3 dielectric gate stack. Phys. Status Solidi 1421–1425(2013) 20. Y. Sakaida, H. Tokuda, M. Kuzuhara, Improved current collapse in AlGaN/GaN HEMTs by O2 plasma treatment. in CS MANTECH Conference (Denver, Colorado, USA, 2014), pp. 197–200, May 19th - 22nd 21. W.-C. Liao, Y.L. Chen, Z.X. Chen, J.I. Chyi, Y.M. Hsin, Gate leakage current induced trapping in AlGaN/GaN Schottky-gate HFETs and MISHFETs. Nanoscale Res. Lett. (2014) 22. F. Sacconi, M. Povolotskyi, A.D. Carlo, Strain effects in SiN-passivated GaN-based HEMT devices. J. Comput. Electr. 115–118(2006) 23. Y. Cao et al., High-mobility window for two dimensional electron gases at ultrathin AlN/GaN heterojunctions. Appl. Phys. Lett. 90, 182112 (2007) 24. V. Hoel, N. Defrance, J.C.D. Jaeger, H. Gerard, C. Gaquiere, H.R. Lahreche, A. Langer, W.M. Lijadi, S. Delage, First microwave power performance of AlGaN/GaN HEMTs on SopSiC composite substrate. Electr. Lett. 44, 238(2008) 25. Device Simulation Software, Silvaco, Version 1.8.20.R. (2010)

Design and Analysis of Fractal Type MIMO Radiator for the Applications of Sub 6-GHz 5G Systems K. Vasu Babu, R. Tejaswini, N. Sowjanya, B. Sujitha, T. Vineela, and B. Durga Prasad

Abstract In this structure, a fractal type-based MIMO radiator is designed with a size 55 mm × 30 mm using the substrate of FR-4. The S-parameters of the current design ranging 2.8–4.5 GHz with wider impedance bandwidth 1.7 GHz. The return loss and isolation parameters are − 40 dB and − 42 dB at the resonating frequency. In order to minimizing the isolation among the patches spacing is considered as 3.0 mm. By using, various parametric approaches identified that greater reduction in mutual coupling the element spacing is optimized. The technique of monopole ground plane improved the enhancement in the bandwidth and improve antenna parameters. The MIMO parameters of the proposed system are within the acceptable values to use in real time applications. Keywords Return loss · Coupling · ECC · TARC · Bandwidth · Optimizer

1 Introduction The MIMO technology is an immense edge in the contemporary communication systems of wireless devices demand usability urban scenarios and good exploitation about channel capacity causes the multipath fading. In [1] designed L and ohm symbols for improving the isolation among patches, minkowski shaped MIMO radiator with multi-band applications designed [2], wearable MIMO radiator of compact design [3] to reduce mutual coupling, EBG structure integrated in microstrip design for applications of arrays [4], using the technique of Defected Ground Structure designed UWB MIMO system to reduce the isolation among pair of patches [5], for LTE and Wi-Fi wideband system with dual-polarization [6], graphene-based MIMO antenna designed for THz applications [7], for satellite multimode navigation PIFA antenna with circular polarization [8], for defense system, and WLAN and WiFi system applications tri-band MIMO design [9], Wang shape neutralization with diversity parameters [10], two-port high isolation UWB shared structure [11] and for K. Vasu Babu (B) · R. Tejaswini · N. Sowjanya · B. Sujitha · T. Vineela · B. Durga Prasad Department of ECE, Vasireddy Venkatadri Institute of Technology, Guntur, Andhra Pradesh, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Biswas et al. (eds.), Microelectronics, Circuits and Systems, Lecture Notes in Electrical Engineering 976, https://doi.org/10.1007/978-981-99-0412-9_21

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Table 1 Comparison with other existing methods Literature

Area (mm2 )

Operating frequency (GHz)

Spacing distances

Isolation value (dB)

Efficiency (%)

[1]

3400

3.57

0.80λ0

23

68

[3]

2916

2.85

0.54λ0

29

72

[4]

5000

[6]

13,125

2.38

0.08λ0

28

79

3.5

0.12λ0

31

71

[8]

8836

5.2

0.31λ0

27

64

[10]

2400

2.78

0.47λ0

18

69

[12]

12,540

3.5

0.09λ0

35

71

3500

3.1

0.04λ0

40

74

Fractal MIMO

enhancing the isolation among the radiating patches for the UWB operating MIMO systems [12]. The novelty of the current fractal-based MIMO design compact structure, better MIMO parameters, less interference, wide impedance bandwidth and better matching network (Table 1).

2 Antenna Geometry and Analysis Figure 1 shows geometry of fractal-based MIMO system fed with two-elements. The designed constellation radiator has been designed on FR-4 substrate having the area of 55 × 30 mm2 . Initially, the development of antenna is considered a circular shape and then it is converted into the fractal-based structure by varying various positions to develop the current structure. The dimensions of ground plane are chosen monopole ground provide good matching of impedance from 2.8 to 4.5 GHz. In the first stage of the design, rectangular fractals are used on both sides of the patch and full ground plane is considered. In the second stage circular slots are introduced improving the isolation to form final structure of the MIMO structure (Table 2).

3 Results Discussion Analysis Figure 2 shows the S-parameters comparison of fractal type-based MIMO radiating system. The S 11 and S 21 of the current design resonating at 3.1 GHz which varies from ranging 2.8 GHz–4.5 GHz ≤ − 10 dB. Similarly within the range of frequency isolation is ≤ − 35 dB which produces the wider impedance bandwidth. Figure 3 shows the impedance of the fractal-based MIMO radiating system with real as well as imaginary part. The real part indicates the impedance of 48 Ω and imaginary part impedance is zero impedance which can be act as a filter at the resonant frequency.

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(a) Front-view

(b) Back-view Fig. 1 Geometry of fractal MIMO system Table 2 Parameters in mm of fractal-based MIMO system Parameter

L

W

a

b

c

d

e

f

g

h

i

j

k

Value (mm)

30

55

8

2.2

8

2

4

9

6

2.5

4

2

8

Parameter

l

m

n

o

p

L g1

L g2

L g3

L g4

W g1

W g2

W g3

εr

Value (mm)

2.2

12

3

20

16

18

18

10

18

1.7

8

27

4.3

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Fig. 2 S-parameters comparison

Fig. 3 Impedance of the fractal MIMO design

Figure 4 shows the distribution of surface current at port_1 of the component excited, port_2 of the component is terminated and vice versa. The maximum current flowing at the edges of the strip and then distributed equally by modifying the full ground plane convert to monopole ground plane for to increase impedance bandwidth as well as isolation depending on distribution of surface currents. Figure 5 shows the radiation pattern at 3.1 GHz with co and cross polarized simulation which is in the acceptable limit of the design. Figure 6 indicates efficiency of the design which is 74%.

4 MIMO Analysis In order estimate better data rates and increasing the channel capacity of the wireless systems go for the analysis of MIMO design. The various MIMO parameters related to the fractal-based MIMO design are Envelope Correlation Coefficient (ECC), Total Active Reflection Coefficient (TARC), Diversity Gain (DG), Channel Capacity Loss (CCL) are evaluated the following mathematical expressions from Eqs. (1) to (6).

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Fig. 4 Distribution of surface waves at 3.1 GHz

E-field

co polarization simulated

H-field

cross polarization simulated

Fig. 5 Radiation pattern at 3.1 GHz

Fig. 6 Efficiency of MIMO design

Figure 7 represents the ECC of fractal-based MIMO system with its value is 0.015. Figure 8 shows the diversity gain of the radiating system which is 9.996 evaluated from the ECC. Figure 9 indicates the channel capacity loss of the fractal MIMO design, which is evaluated for the 2-port system using S-parameters, is 0.1558. All the MIMO parameters of the current design are within the acceptable limit for designed

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MIMO system. The designed system is well suited for the applications of lower sub 6-GHz systems. For compatible radiator system ECC is an important figure value. The minimum required value for ECC 0.5. There are actually many methods are there to calculate ECC in the form of S-parameters and 3-D radiation pattern are most commonly Fig. 7 MIMO design ECC

Fig. 8 MIMO design diversity gain

Fig. 9 CCL of fractal MIMO design

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used. S-parameter method is easy to calculate ECC between the two-elements which is faster compared to the radiation pattern method. | | | ˜ | ∗ | | ∗ | | | | 4π S11 S21 + S21 S22 ECC = | [( | | 1 − (| S11 |2 + | S21 |2 ))(1 − (| S22 |2 + | S12 |2 ))]1/2 | / / DG = 10 1 − |ρe |2 = 10 1 − |ECC|2 [ |2 | 2 || 2 | √ |∑ | ∑ | | Si1 + Sin e j θ n−1 | / 2 TR = | | |

(1)

(2)

(3)

n=2

i=1

( ) Closs = − log2 det  R (  = R

ρ11 ρ12 ρ21 ρ22

(4)

)

( | |2 ) ) ( ρii = 1 − |Sii |2 + | Si j | , and ρi j = − sii∗ si j +si∗j s j j , for i, j = 1 or 2

(5) (6)

For any system of conventional method, CCL increases number of radiator elements linearly used without variation in transmitting power or increasing bandwidth which occurs specified assumption. The correlation among MIMO channel systems produces capacity loss.

5 Conclusions In the present research, a fractal-based MIMO radiating system has been designed with a size 55 mm × 30 mm for lower sub 6-GHz applications. A fractal-based MIMO system with spacing among the patches has been placed 3.0 mm to reduce the parameter of isolation. A monopole ground plane considered for to improve the isolation and impedance bandwidth among the patch elements. For this fractal MIMO designed structure analysis got better results in terms of its MIMO parameters like CCL, TARC, DG and ECC at the resonating frequency of the current fractal MIMObased radiating system. The simulate results conformed for fractal MIMO structure was isolated with − 42 dB and reflection coefficient − 40 dB at 3.1 GHz.

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References 1. K.V. Babu, B. Anuradha, Design of inverted L-shape and ohm symbol inserted MIMO antenna to reduce the mutual coupling. AEU-Int. J. Electron. Commun. 105, 42–53 (2019) 2. K.V. Babu, B. Anuradha, Design of multi-band minkowski MIMO antenna to reduce the mutual coupling. J. King Saud Univ. Eng. Sci. 32(1), 51–57 (2020) 3. A.K. Biswas, U. Chakraborty, Compact wearable MIMO antenna with improved port isolation for ultra-wideband applications. IET Microwaves Antennas Propag. 13(4), 498–504 (2019) 4. F. Yang, Y. Rahmat-Samii, Microstrip antennas integrated with electromagnetic band-gap (EBG) structures: a low mutual coupling design for array applications. IEEE Trans. Antennas Propag. 51(10), 2936–2946 (2003) 5. K.V. Babu, B. Anuradha, Design of UWB MIMO antenna to reduce the mutual coupling using defected ground structure. Wirel. Pers. Commun. 118(4), 3469–3484 (2021) 6. A. Moradikordalivand et al.,Wideband MIMO antenna system with dual polarization for WiFi and LTE applications. Int. J. Microwave Wirel. Technol. 8(3), 643 (2016) 7. K. Vasu Babu et al., A micro-scaled graphene-based tree-shaped wideband printed MIMO antenna for terahertz applications. J. Comput. Electron. 1–15 (2022) 8. Y. Yao et al., Novel diversity/MIMO PIFA antenna with broadband circular polarization for multimode satellite navigation. IEEE Antennas Wirel. Propag. Lett. 11, 65–68 (2012) 9. K.B.V. Babu, B. Anuradha, Tri-band MIMO antenna for WLAN, WiMAX and defence system and radio astronomy applications. Adv. Electromagnet. 7(2), 60–67 (2018) 10. K.V. Babu, B. Anuradha, Design of Wang shape neutralization line antenna to reduce the mutual coupling in MIMO antennas. Analog Integr. Circ. Signal Process. 101(1), 67–76 (2019) 11. L.Y. Nie et al., High isolation two port UWB antenna based on shared structure. IEEE Trans. Antennas Propag. 68(12), 8186–8191 (2020) 12. S.-Y. Lin, H.-R. Huang, Ultra-wideband MIMO antenna with enhanced isolation. Microw. Opt. Technol. Lett. 51(2), 570–573 (2009)

Histopathology Cancer Detection Kolluri Paul Wilson, Muntala Srinivasa Reddy, Suyadevara Chakravarthy Karthik, Venkata Vamsi Kolluru Anudeep, and Kande Giri Babu

Abstract Histopathology deals with studying of tissues of patients suffering from tumor and involves examining tissues or cells under a microscope to detect diseases. For this paper, we are focused on detecting cancer. Generally, this process of examining and detecting cancer is done by a trained doctor but now it can be detected by computer vision achieved by deep learning through Python by examining medical images of the person. Detection of cancer cells in tissues through lymph nodes is discussed in this paper. Keywords Deep learning · Convolutional neural networks · Histopathology cancer detection · Neural networks

1 Introduction Cancer is a one of the major health problems in the world. According to reports, most common type of cancer found are breast cancer, lung and bronchus cancer and prostate cancer, etc. Cancer becomes so danger because of its nature of spreading. Cancer cells start at one place in body and moves to other place in the body making it difficult to find. These cells transform their shape and characteristics completely. K. Paul Wilson (B) · M. Srinivasa Reddy · S. C. Karthik · V. V. K. Anudeep Vasireddy Venkatadri Institute of Technology, Namburu, Guntur, Andhra Pradesh, India e-mail: [email protected] M. Srinivasa Reddy e-mail: [email protected] S. C. Karthik e-mail: [email protected] V. V. K. Anudeep e-mail: [email protected] K. Giri Babu Ph.D, Professor and Dean of Academics, Vasireddy Venkatadri Institute of Technology, Guntur, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Biswas et al. (eds.), Microelectronics, Circuits and Systems, Lecture Notes in Electrical Engineering 976, https://doi.org/10.1007/978-981-99-0412-9_22

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The type of cancer found at the initial stage is primary cancer. There are different stages of cancer based on its toxicity to the body. The last stage of cancer is metastatic cancer, and it is the dangerous phase of the cancer. In this paper, we find a way of detecting cancer using modern methods. If cancer cells are found at a part of body and show the different characteristics, then the cancer cells are not originated at the part of body they are found. Hence, this type of cancer is found at the last stage, i.e., metastatic cancer and the name of the cancer found will have the name of where it is originated rather than where it is found [1]. In this paper, we discuss about a way to deal with examining cancer cells of one of a type of cancer, i.e., breast cancer. Histopathology is a combination of three Greek words, namely, histos, pathos, logia where histos meaning tissue, pathos means suffering and logia means study of, i.e., this paper deals with the tissues suffering with breast cancer. Persons who study these tissues are meant to be histopathologists. These are responsible for diagnosing the disease. Work of histopathologists is to have keen observation of the tissues under a microscope. Whole Slide Imaging (WSI) is a technique of scanning the glass slides obtained from the real world and converting them into digital slides which is the most recent imaging modality that is being used by pathologists worldwide. Initially, these images are captured by using camera but now this has evolved to Whole Slide Imaging (WSI). WSI is commonly referred to as “virtual microscopy” [2]. As discussed earlier, metastatic cancer spreads from one part of the body to the other so in our case for breast cancer this spreads to nearby lymph nodes which is the integral part of lymphatic system which is responsible for immunity of a person [3]. There are two ways lymph nodes can be checked. They are sentinel lymph nodes and axillary lymph nodes. The lymphatic system plays an important role in body’s immune system. There are two ways lymph nodes can be checked. They are sentinel lymph biopsy and axillary lymph node dissection. With the development in the field of artificial intelligence (AI), the techniques of AI can be used to mine critical data which has become a trend in medical industry [4]. Machine learning is a branch of artificial intelligence and also a technique for recognizing patterns that can be applied to medical images. But when it comes to dealing with images, deep learning is most used because it has vivid applications and techniques to work with images. Deep learning is inspired by human brain and possess a structure like neural system of our brain. It has neural networks connected which is to be trained [5].

1.1 Computational Methods for Histopathology Convolutional neural networks (CNNs) are used for feature extraction, image segmentation and image classification. The techniques used by CNNs make the job of pathologists easy working with the images of histopathology. When we work with images and deep learning, CNN is the most approachable framework. CNN consists of different layers, namely convolutional layer, pooling layer and fully connected layer.

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Fig. 1 Figure showing some random images from the Patch Camelyon dataset annotated by 0 and 1 representing presence and absence of cancer tumor cells

2 Dataset The field of pathology is growing in a rapid way since the advancements in microscopic imaging hardware. Our dataset is PatchCamelyon (PCam) dataset which is a large scale dataset derived from Camelyon 16 dataset. We need to prepare the dataset as input for CNN to train the model. This dataset is a new dataset which can be used for mage classification especially this dataset is used for classification of medical histopathological images. This dataset consists of 57.5k test images and 220k train images. All these images are taken from histopathological scans of tissues having lymph node which has tumor cells. Here each image is indicated by a binary label denoting the presence or absence of tumor cell. This dataset is not as small as CIFAR-10 and not as large as Imagenet. Dataset is split into validation and training data. We find it difficult to work with images but it is easier to work with binary labels. And this does the same instead of working with images it turns this task into a binary image classifying task which classifies images like having tumor or not. There is exact split in training and testing images of positive and negative label. A positive label is an image containing tumor tissue at the center of image, i.e., 32 × 32 px range. If the tumor is outside the center (32 × 32) pixel range, it is indicated as a negative sample [6] (Fig. 1).

3 Methods 3.1 Neural Networks Neural networks also known as artificial neural networks (ANNs) which are used for computation are composed of a structure which is similar to human brain. Neural

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Fig. 2 Below figure depicts the basic structure of a neural network

networks are composed of layers where each layer consists of nodes. Each node from one layer is connected to every other node in another layer. These connections are associated with some weights. There are different types of layers like input layer, output layer, and in between input layer and output layer, there are a series of hidden layers. Input layer takes the input to be trained. Output layer determines the output, and hidden layer is the interconnected layers. The function of hidden layers is to take the weighted inputs and produce an equivalent output with the help of activation function and these are called hidden layers because they are no directly visible from the inputs and outputs of the network (Fig. 2). Convolutional Neural Networks Convolutional neural networks automatically learn the best possible way to extract features from the raw data instead [7]. A typical convolutional neural network consists of a series of special layers in the hidden layer, namely convolutional layer, activation layer, pooling layer and fully connected layers. The convolution layer is the heart of a CNN. It contains different filters which are to be learned throughout the training; in this layer, each image is convoluted and an activation map. The function of activation layer is to describe how well the model is learning the training dataset. The fully connected layer will connect all the neurons in the previous layer. The pooling layer will summarize the features. In neural networks, inputs are provided with some weights. There are some pretrained models which can be used for fast training. We copy the weights from these pre-trained models and use it to train the model for our input. This technique is often referred to as transfer learning. The input to the CNN is 96 × 96 × 3 images, i.e., 96 × 96 color image. This input is then fed to hidden CNN in the network. The hidden network is composed of three sets of layers where each layer consists of three convolutional layers and activation layers. Every set of these layers is terminated by a pooling and dropout layer. The last layer is then flattened and acts like a dense layer which is an output layer (Fig. 3).

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Fig. 3 Below figure depicts the convolutional neural network used for our model

4 Challenges Faced In this study, we have developed algorithm for deep learning model which is used to classify images present in our dataset, which is better in terms of accurate results when compared to health care professionals. However, some parameters are to be considered for a better reliable and consistent output. The parameters may be like “large image size”, “color variants” and “insufficient data”.

4.1 Images of Larger Size Greater image size is one of the biggest issues we have faced while developing this deep learning model, generally small size images are used as an input to the network for classifying. If a large image is encountered, then we have to make it smaller in order to fit it to the network. If large images are not resized, then it requires large amount of power to extract features from the data which has been provided as an input to the network and also require more memory. The classification of Whole Side Images (WSI) is a tricky process and difficult to examine, but resizing would lead to decrease the information present in the image. So while performing this analysis, we have divided these large size images into smaller size patches, such that each patch is computed individually and the result is combined after performing the analysis. This will enhance the performance and image classification capability of the deep learning model.

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4.2 Images with Different Color Contrast Color difference in the images of dataset is the second issue we have faced while developing this model. Due to different color contrast in images, it is difficult for the model to learn. We can also train our models without considering but it may decrease the performance of deep learning models. There are many methods to work with color variation of the image like color balance, color swapping, match color and color normalization. The process of changing the color of a pixel by pixel is known as color normalization. This uses methods like color transfer and color deconvolution. When the images have same tissue compositions, then color normalization is the best method to perform. The color normalization process should be applied properly or else the efficiency and performance of the deep learning model may degrade. Lack of Required Data When developing this model, the third challenge we have faced is insufficient data. Due to privacy firms like healthcare will only provide little amount of data to make predictions, as the data is too low the model may predict the output inaccurately. Due to small amount of data, the CNN models will lead to overfitting problem. Here the model will possess low training errors and high test errors, and the model won’t work well in the market. There are some methods do deal with insufficient data, they are . Model Complexity: This refers to developing a model with few parameters as it is less affected to insufficient data. . Transfer Learning: This refers to reuse a trained network that has the capability to withstand with insufficient data. This method will be useful when the data is small and not sufficient to train the deep learning model. . Data Augmentation: This method is used to make slight changes to get a new image from older images. It performs some operations like scaling, rotation, Transforms, after this process the images are used by the model. . Synthetic Data: This method refers to create an artificial data which possess the same features which the original data consist of, it is possible only when there is a good command on the features. Algorithm 1. If the images are of large size we resize or divide them with suitable methods and perform the evaluation for individual images. 2. If the images are of different color contrast, we use different techniques like color swapping, color balance and make them equal. 3. After taking images from dataset, we divide them into training set and validation set. 4. Training set is used to train the model. 5. After that we perform the test on these images and specify the images as malignant and benign.

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6. This gives the performance of our model with different elements like confusion matrix and AUC_ROC.

5 Experimental Results 5.1 Confusion Matrix After getting the data, we need to separate it in to training and testing data/validating data, and then we train our model with the training data. In order to calculate how well the model is performing after getting trained, we test our model with the testing/validating data and performance is described by a matrix known as confusion matrix. A confusion matrix is used to access the performance of a model. Better the effectiveness, better the performance, in order to evaluate the performance of a model we use different metrics. A confusion matrix produces results in a simple manner and gives a clear idea about the model understandable manner (Fig. 4). The different metrics produced by the matrix are . Accuracy, Precision, Recall, Macro average and Weighted average Precision

Recall

F1-score

No_tumor

0.91

0.94

0.93

Has_tumor

0.94

0.91

0.92

8000

0.92

16,000

Accuracy

Support 8000

Macro avg.

0.92

0.92

0.92

16,000

Weighted avg.

0.92

0.92

0.92

16,000

And the equations to calculate these metrics are given by, Precision = Recall = Accuracy =

(TP) . (TP + FP)

(TP) . (TP + FN)

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

. Macro average (macro avg) is the average of recall and precision . F1-score is the harmonic mean of combination of both precision and recall and if the value of f 1-score is high, then the model will produce good results. In general, it ranges from 0 to 1, where 1 is an indication for correct classification and 0 is an indication for incorrect classification or incapable classification.

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Fig. 4 Confusion matrix obtained for our model

. Weighted average is an average in which each quantity to be averaged is assigned a weight, and is given by W = x or W n wi xi

w1 x1 + w2 x2 + w3 x3 + · · · + wa x y w1 + w2 + w3 + · · · + wn

Weighted average Number of terms to be averaged Weights applied to x values Data values to be averaged

where TP, FP, TN and FN are the positive and negative, correct and false predictions made by the model while testing the data.

5.2 ROC Curve ROC curve which is known as Receiver Operating Characteristics Curve is an important metric which is used to calculate how well the model is performing. It is a plot between TPR and FPR, which gives the sensitivity of our model. An ideal model will be having an ROC of 100% true positive rate. The model classify the true and false positive rates based on an applied threshold value. The two main parameters of this curve are TPR and FPR. TPR which is abbreviated as True Positive rate where the actual positive value is predicted as true and the value is positive. It is defined as the ratio of true positive value to the sum of true positive and false negative and is mathematically given as

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Accuracy curve

Loss Curve

Fig. 5 Below figure accuracy curve and loss curve obtained after running our model for a number of epochs

TPR =

TP TP + FN

FPR which is abbreviated as False Positive rate is a measure of how many original negatives the model shown incorrect and is defined as the ratio of false positive to the sum of true positive and false positive FPR =

FP FP + TN

If we choose a low threshold value, then the model will classify more items as positive which in turn increases the True and False positives.

5.3 Area Under Roc Curve (AUC_ROC) The Area Under Roc Curve is a metric used for the evaluation of the deep learning model and is defined as how well the model is able to differentiate between classes. If AUC is more, the model is effective in differentiating between positive and negative classes. ROC AUC Score = 0.9774432656250001 The accuracy of the model is evaluated based on the Area Under the ROC curve, if the AUC is near to one then it is considered as best case and the system will classify the images perfectly. If the AUC of the model is near to zero, then it is considered as worst case and the model can’t classify the images effectively. If AUC is an average of best case and the worst case, then the model can’t classify the images perfectly (Fig. 6).

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Fig. 6 Figure showing the ROC curve for the model

6 Conclusion This study is based on breast cancer and deep learning. We drew conclusions based on the model generating predictions of a medical image on whether it consists a malicious cancer tumor or not. Breast cancer has been a major health problem for women. This is the area where more no of deaths has being recorded. The study on breast cancer using deep learning is being done from the start to till now. Our main study is on breast cancer, deep learning and convolutional neural networks. We trained our model for a number of epochs where it did not get either overfitted or underfitted to the data to make our predictions to accurate. We had also ran the model for validation data to verify whether our model is doing well for new images before it is tested. We achieved 91.72% of accuracy with our model. We also performed regularization to negotiate overfitting of data to the model. Over the past years, convolutional neural networks show a better result for processing images when compared to artificial neural networks. We have done a lot of research on the CNN methods and trained our model in such a way that efficiency of image classification to detect the malignant and benign images in the data is more. Acknowledgements We thank GIRI BABU GARU for guiding us to perform the paper.

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References 1. National Cancer Institute, https://www.cancer.gov/types/metastatic-cancer. Last accessed 10/11/2020 [website] 2. N. Farahani, A. Parwani, L. Pantanowitz, Whole slide imaging in pathology: advantages, limitations and emerging perspectives (2015) [website] 3. P.K. Sanjeev, Review on breast cancer detection using deep learning (2021) [website] 4. Arizona Oncology, https://arizonaoncology.com/breast-cancer/detection-diagnosis/lymphnodes/ [website] 5. X. Liu, K. Gao et al., Advances in deep learning based medical image analysis [website] 6. Patchcamelyon dataset github link. https://github.com/basveeling/pcam. Last accessed 08/04/2020 [website] 7. A. Krizhevsky, I. Sutskever, G.E. Hinton, Advanced deep convolutional neural network approaches for digital pathology image analysis (2012) [website]

Contingency Analysis Study for a 39 Bus System in a Micro-grid Dipu Mistry, Bishaljit Paul, and Chandan Kumar Chanda

Abstract Day by day there is a high uses of renewable energy sources to meet the clean energy demand. Interest in micro-grids where a group of electricity sources and loads are interconnected by a system of power transmission line is growing because of their ability to incorporate renewable energy sources to power system. Outage of one transmission line, transformer or power source may cause to over loads in other branches power system followed by voltage fluctuation. Contingency analysis is an important tool used to calculate violations and overcome voltage instability. Contingency analysis is also necessary for power system security and protection purpose. In power system, source of contingencies may be lighting and over loading of equipment or due to failure of the internal components. This paper explores predefined contingency cases through 3 a 39 bus system. Effect of outage of pre-defined generators, transmission lines and transformers has been studied and the results are analyzed. Generator outages considered for the present study. Newton Raphson power flow method has been incorporated to calculate the contingencies ranking based on active power and voltage performance index in the power system due to the line outage. The aim of this study is to find out the accurate load composition and security checking for power system planning. Keywords Contingency analysis · Outage · Transmission line · Voltage instability · Security checking

1 Introduction To fulfill increasing demand of power, power transmission system is becoming more congested and hence transmission capability in the transmission lines reaches to its maximum limit. It has a large impact on power transmission around the whole world D. Mistry (B) · B. Paul Department of Electrical Engineering, Narula Institute of Technology, Kolkata, India e-mail: [email protected] C. K. Chanda Department of Electrical Engineering, IIEST Shibpur, Kolkata, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Biswas et al. (eds.), Microelectronics, Circuits and Systems, Lecture Notes in Electrical Engineering 976, https://doi.org/10.1007/978-981-99-0412-9_23

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[1]. It needs to maintain security in the power system operation. The system must be operated at all times in such a way that the system will not be left in a dangerous condition should any credible initiating event occur. Different outages like failures of equipment, transmission line, etc., are there in power system. Only a selected outage may lead to severe conditions in power system security [2]. One of the major security checking functions in the power system is the contingency analysis (CA). Contingency is a hypothetical outage which defines the outage components or the ‘base’ components only. It does not includes outages due to breakers that isolate the components, nor specifically list any additional equipment that would be outage [3]. CA is a ‘what if’ scenario that evaluates, provides and prioritizes the impacts on an electric power system whenever typically unplanned problems or outages occur. It is a very important part of any modern power system analysis effort. By this analysis, operators are informed about the static security and protection [4]. In order to rank the severity of outage of each component with respect to power system, the results of CA analysis are used to calculate power performance indices and voltage performance indices. A case study through CA has been carried out by Venkata Kirthiga et al. [5]. The study proposes an algorithm for forming controlled islands on occurrence of line contingencies in micro-grids, using the information about accidentally formed islands and line contingency ranking. They have formulated a methodology for forming controlled islands in an autonomous micro-grid on occurrence of line contingencies. Various contingency cases are studied and it is predicted how the in higher bus systems, that the energy prices, at every nodes are analyzed by the locational marginal prices (LMPs) and the prices for the reserved generators by ancillary service marginal price (ASMPs) [6]. Analysis of the contingency is useful to solve and plan the power system operation by identifying the weak components that helps to minimize the impact of failures that cause the release of components [7]. In the literature [8] and [9], fast decoupled power flow method is used for the power system contingency ranking for the line outage based on the active power and voltage performance index. In order to make the contingency analysis easier, contingency analysis comprises three basic steps as contingency creation, contingency selection and contingency evaluation. Roy and Jain [10] selected contingency by calculating two kinds of performance indices: active power performance index (PIP) and reactive power performance index (PIV) for single transmission line outage. They have been done the analysis with the help of Fast Decoupled Load Flow (FDLF) in MATLAB environment.

2 System Under Study In the present work, a 39 bus system interconnected network contingency analysis is performed. During outage as shown in Fig. 1 and Table 1 of a 39 bus system, the contingency analysis of a generator is performed in the paper. Generator 2 and generator 5 go out of order which is connected to bus 31 and 39, respectively. The change of voltages at the corresponding buses was recorded.

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Fig. 1 Single line diagram of 5-generator 39 bus model

Table 1 Details of 39 bus system

Components

Number(s)

Buses

39

Generators

10

Slack bus

1

PV generators

4

Compensators (synchronous condenser)

5

Loads

21

Lines

45

3 Mathematical Formulation The mathematical statement of the power flow by a bus admittance matrix is [IBus ] = [YBus ][VBus ]

(1)

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where IBus is bus current injected vector, YBus is admittance bus matrix as in VBus voltage bus profile vector [13]. As buses are interconnected where YBus is n × n square matrix. ⎡

Ybus

Y11 Y12 . . . Y1 j . ⎢ ⎢ Y21 Y22 . . . .. ⎢ ⎢ . ⎢ Y31 Y32 . . . .. ⎢ ⎢ .. .. =⎢ ⎢ . . . . . Yi j ⎢ . ⎢ .. Yi2 . . . ... ⎢ ⎢ . . ⎢ .. .. . . . ... ⎣ . Yn1 Yn2 . . . ..

. . . Y1n

⎤

⎥ . . . Y2n ⎥ ⎥ ⎥ . . . Y3n ⎥ ⎥ .. ⎥ ... . ⎥ ⎥ .. ⎥ ... . ⎥ ⎥ .. ⎥ ... . ⎥ ⎦

(2)

. . . Ynn

By denoting the elements in row j and column k of the Ynn matrix which is the element of the admittance matrix. Let us consider the self-admittance of any bus k is represented by the Ykk that is a diagonal admittance on the other hand mutual admittance within any two buses i and j is denoted by Yi j . If there is no direct connection between the buses i and j, then the value of the element of the admittance matrix is considered to be zero. YBus is a complex matrix and it is also a thin matrix. If Ik is the net injected current at any bus k then Ik can be represented as Ik =

n 

Yk j V j

(3)

j=1

where V j is the complex voltage at bus j. The net injection power can be described at any bus k Sk = Vk .Ik

(4)

Substituting the value of the net injected current from Eq. 3 in Eq. 4 we have the Substituting bus net injected current from Eq. 3 in Eq. 4, obtain the residue form of the equation for each bus k as ⎛ Sk = Vk ⎝ ⎛ Sk − Vk ⎝

n  j=1

n  j=1

⎞ Yk j V j ⎠ ⎞ Yk j V j ⎠ = 0

(5)

Contingency Analysis Study for a 39 Bus System in a Micro-grid

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Here if bus number is 1, then it will be written as k = 1 and then Eq. 5 will be re-written as, ⎛ ⎞ n  S1 − V1 ⎝ Y1 j V j ⎠ = 0 or f 1 (X ) = 0 (6) j=1

If bus number is 2, then it will be written as k = 2 and then Eq. 5 will be re-written as, ⎛ ⎞ n  S2 − V2 ⎝ Y2 j V j ⎠ = 0 or f 2 (X ) = 0

(7)

j=1

If bus number is n, then it will be written as k = n and then Eq. 5 will be re-written as, ⎛ ⎞ n  Sn − Vn ⎝ Yn j V j ⎠ = 0 or f n (X ) = 0

(8)

j=1

Here, X is the bus voltage vector and x1 , x2 , . . . xn , is the V1 , V2 , . . . , Vn . ⎡

⎤ x1 ⎢ x2 ⎥ ⎢ ⎥ X =⎢ . ⎥ ⎣ .. ⎦

(9)

xn Equation 6 can be rearranged as f 1 (X ) = f 1 (x1 , x2 , . . . xn ) = 0 Substituting initial velocity vector input X 0 values are, V10 , V20 , . . . , Vn0 , or x10 , x20 , . . . , xn0 ⎤ x10 ⎢ x20 ⎥ ⎥ ⎢ X0 = ⎢ . ⎥ ⎣ .. ⎦ ⎡

xn0 while writing the solution of g(x) in Taylor series at x = x0 .

(10)

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Then x = x0 + △x g(x) = g(x0 + △x) = g(x0 ) + △x + higher order terms

∂g(x0 ) ∂x (11)

Comparing Eq. 6 with Eq. 11 g(x) = f 1 (X ) g(x0 ) = f 1 (X 0 ) X = X 0 + △X f 1 (X ) = f 1 (X 0 + △X )

∂ f 1 (X )

X0 ∂X ∂ f 1

= f 1 (X 0 ) + △X X ∂X 0 = f 1 (X 0 ) + △X

(12)

Expanding Eq. 12, about a guess solution X 0 , we have ∂ f 1

X ∂ x1 0 ∂ f 1

+ · · · + △xn X ∂ xn 0

f 1 (X ) = f 1 (x10 , x20 , . . . , xn0 ) + △x1 + △x2

∂ f 1

X ∂ x2 0

(13)

where △x1 = x1 − x10 ; △x2 = x2 − x20 and △xn = xn − xn0 . The compact form of Eq. 13 is given below n  ∂ f 1

f 1 (X ) = f 1 (X 0 ) + X △x j ∂ xj 0 j=1

(14)

f 2 (X ) = f 2 (X 0 ) +

n  ∂ f 2

X △x j ∂x j 0 j=1

(15)

f n (X ) = f n (X 0 ) +

n  ∂ f n

X △x j ∂x j 0 j=1

(16)

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For all bus that is bus number 1 to n, above equation (Eqs. 14 to 16) can be written in a matrix form ⎡ ∂ f1 ∂ f1 ⎤ ⎡ ⎤ . . . ∂∂xf1n △x1 ∂ x1 ∂ x2 ⎢ ∂ f2 ∂ f2 ∂f ⎥ ⎥ ⎢ ∂ x1 ∂ x2 . . . ∂ x2n ⎥ ⎢ ⎢ △x2 ⎥ ⎢ (17) F(X ) = F(X 0 ) + ⎢ . . . . ⎥ ⎢ . ⎥=0 ⎥ ⎣ .. .. .. .. ⎦ ⎣ .. ⎦ ∂ fn ∂ fn △xn . . . ∂∂ xfn ∂x ∂x 1

2

n

|X 0

The above matrix form as in Eq. 17 is known as Jacobian matrix [J ]. The compact form of the above equation is given below F(X 0 ) + [J ]|X 0 [△X ] = 0

(18)

For the bus k, Eq. 18 is f k (X 0 ) = Sk − Vk

n  

 Yk j V j = 0

(19)

j=1

As Eq. 18 is a power equation so, the term F(X 0 ) is the power mismatch at each bus and the power flow solution will be obtained by if the value of the quantity f k (X 0 ) is very small. Sk(calculated) = Vk

n  

Yk j V j



(20)

j=1

where Sk(calculated) is the calculated power flowing away from bus k to all the other buses j. From Eq. 18, n  

 Yk j V j = 0

(21)

j△Sk = Sk(Scheduled) − Sk(Calculated)

(22)

△Pk = Pk(Scheduled) − Pk(Calculated)

(23)

△Q k = Q k(Scheduled) − Q k(Calculated)

(24)

f k (X 0 ) = Sk − Vk

j=1

The contingency analysis using Newton Raphson method is given with a flow chart as shown in Fig. 2 (Table 2).

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Fig. 2 Flow diagram

The analysis has been carried out using power system analysis toolbox in MATLAB. Three shunt capacitors have also been added in the circuit to compensate reactive power (Q). The data for the injected Q due to shunt capacitors is given in Table 3.

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Table 2 Generator and condenser power capacity Generator/Condenser

Voltage magnitude

MW

MVAR

Qmin

Qmax

Generator 2

1.04

40

0

−40

40

Generator 3

1.06

40

0

−40

40

Generator 4

1.01

40

0

−40

40

Generator 5

1.00

40

0

0

0

Condenser 1

1.00

0

0

−40

40

Condenser 2

1.01

0

0

−10

40

Condenser 3

1.00

0

0

−6

24

Condenser 4

1.00

0

0

−6

24

Condenser 5

1.00

0

0

−6

24

Table 3 Injected q due to capacitor

Bus No.

MVAR

10

10

16

10

28

5

Newton Rapson method is used for power flow computation. We have assumed the following data for four generator and condenser. Generator 1 is connected to slack bus and its data is not given.

4 Results and Analysis Line flow data is obtained from the output is given in Table 4. Here line flow data without any outage is denoted by ‘line flow’. Table 3 also gives the power flow through all lines after outage of generator 2 and generator 5. Without any generator outage line 2 carries maximum power which is 61.8 MW. Capacity of all transmission line is assumed to be 70 MW which is greater than 61.8.

5 Conclusion Contingency analysis has been carried out in an interconnected power system when generators 2 and 5 were in outage. In our study, the disturbances occurred due to the outage of the generators and the power flow in few lines exceeded, but the voltage of the buses remained within acceptable limits. The effect of line security is reflected in this paper as the line number 2, 5 and 13 crossed their limits due to the outage of generator 2 and generator 5. The contingency study plays an active role in maintaining system security.

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Table 4 Line flow data Line

Fr

To

Line flow (MW)

Line flow for generator 2 outage

Line flow for generator 5 outage

L1

1

L2

2

2

58.1

173.9

144.5

3

61.8

126.9

L3

2

98.3

25

−11.0

29

21.4

L4 L5

2

30

6.7

37.4

45.0

3

4

40.3

90.3

68.0

L6

3

18

17.4

31.4

25.5

L7

4

5

17.1

56.2

38.9

L8

4

14

14.7

24.4

19.9

L9

5

6

−13.6

58.5

23.0

L10

5

8

0.5

5.5

12.5

L11

6

7

9.4

−2.9

8.5

L12

6

11

14.3

19.5

18.6

L13

6

31

−39.6

79.6

39.6

L14

7

8

−13.3

0.9

12.5

L15

8

9

−38.4

10.5

43.0

L16

9

39

−44.2

16.2

48.8

L17

10

11

4.4

12.0

8.1

L18

10

13

−4.5

3.1

0.8

L19

10

32

0.1

0.1

0.1

L20

11

12

−4.0

0.6

2.2

L21

12

13

−4.1

−6.4

−1.3

L22

13

14

−15.0

−3.6

5.6

L23

14

15

−7.0

−2.4

2.4

L24

15

16

−15.2

5.7

L25

16

17

−11.5

10.6

0.7

L26

16

19

−28.4

30.5

29.6

L27

16

21

21.0

30.9

27.0

L28

17

18

−13.8

0.1

5.9

L29

17

27

−6.8

−1.9

2.2

L30

19

20

0.1

0.0

0.0

L31

19

33

−38.2

38.2

38.2

L32

20

34

0.0

0.0

0.0

L33

21

22

3.4

13.4

9.2

L34

22

23

3.2

13.3

9.4

L35

22

35

0.0

0.0

0.0

10.7

(continued)

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Table 4 (continued) Line

Fr

To

Line flow (MW)

Line flow for generator 2 outage

Line flow for generator 5 outage

L36

23

24

3.2

3.2

L37

23

36

0.0

0.0

0.0

L38

25

26

24.3

43.0

35.0

L39

25

37

−35.6

35.6

35.6

L40

26

27

10.0

19.9

15.3

L41

26

28

8.2

11.5

10.2

L42

26

29

0.5

0.8

0.7

L43

28

29

−05

0.3

0.3

L44

29

38

0

0.0

0.0

L45

30

39

4.2

32.2

39.6

3.2

References 1. B. Paul, C.K. Chanda, J. Pal, M.K. Pathak, Congested power transmission system in a deregulated power market, in Computational Advancement in Communication Circuits and Systems. Lecture Notes in Electrical Engineering 575 2. M.P. Singh, P. Arora, Comparison of different methods of contingency analysis in power system, in Advanced Research in Electrical and Electronic Engineering, vol. 2, no. 10 (2015), pp. 72–75 3. M. Chen, A. Grid, Dynamic contingency re-definition in power system security analysis (IEEE, 2011), pp. 63–67 4. J. Venkateswaran, K. Vinothini, B.T. Monisha Shree, R. Jayabarathi, Contingency analysis of an IEEE 30 bus system, in 3rd IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, May 2018, pp. 328–333 5. M. Venkata Kirthiga, P. Muppiddi, A case study for controlled islanding based on line contingency ranking in autonomous micro-grids. Department of Electrical and Electronics Engineering, National Institute of Technology, Tiruchirappalli 6. B. Paul, C.K. Chanda, M.K. Pathak, J. Pal, Effective scheduling of spinning reserve services and cost of energy in a deregulated power market 7. D. Fauziah, Y. Mulyadi, Contingency analysis of south Bandung electric power system, in 1st Annual Applied Science and Engineering Conference (2017), pp. 1–9 8. N.S. Swaroop, M. Lekshmi, Contingency analysis and ranking on 400 kV Karnataka network by using mipower. Int. J. Sci. Dev. Res. 1(10), 287–294 (2016) 9. G.D. Rohini, B. Kantharaj, R.D. Satyanarayana Rao, Transmission line contingency analysis in power system using fast decoupled method for IEEE-14 bus test system. Int. J. Innov. Sci. Eng. Technol. 2(4), 991–994 (2015) 10. A.K. Roy, S.K. Jain, Improved transmission line contingency analysis in power system using fast decoupled load flow. Int. J. Adv. Eng. Technol. 6(5), 2159–2170 (2013) 11. R. Singh, J. Singh, R. Singh, Power system security using contingency analysis for distributed network. Int. J. Eng. Res. Technol. (IJERT) 2(4), 2159–2170 (2013) 12. S. Gusev, V. Oboskalov, Recursion based contingency analysis of an electrical power system. IEEE (2016) 13. Y. Sun, J. Overbye, Visualizations for power system contingency analysis data. IEEE Trans. Power Syst. 19(4), 1859–1866 (2004)

Applications of Green Energy Storage Systems Using PKL Battery K. A. Khan, Md. Sayed Hossain, Salman Rahman Rasel, and Mehedi Hasan

Abstract Renewable energy is an alternative energy source which will help after finishing the traditional energy sources like oil, gas and coal. Pathor Kuchi Leaf (PKL) extract has been used as an electrolyte, whereas Zn and Cu plates have been used as an electrode. Copper plate is used as a cathode and the zinc plate is used as an anode. AgNPs have been synthesized using green synthesis (PKL extract) and used in PKL module as a liquid form to enhance the performance. Different circuit parameters have been studied like open-circuit voltage, load voltage, voltage efficiency and energy efficiency of the PKL module for both with and without AgNPs. It is seen that the open-circuit voltage, load voltage, voltage efficiency and energy efficiency have been increased for using AgNPs. It can be concluded that the PKL module can be used long time as an energy source using AgNPs in the PKL module. It was found that the maximum open-circuit voltages were 5.93 V (without AgNPs) and 6.11 V (with AgNPs), the maximum load voltages were 5.47 V (without AgNPs) and 5.90 V (with AgNPs), the maximum voltaic efficiencies were 92.24% (without AgNPs) and 96.56% (with AgNPs) and the maximum voltage regulations were 29.73% (without AgNPs) and 5.37% (with AgNPs). It was also found that the minimum open-circuit voltages were 5.62 V (without AgNPs) and 6.05 V (with AgNPs), the minimum load voltages were 4.38 V (without AgNPs) and 5.77 V (with AgNPs), the minimum voltaic efficiencies were 77.08% (without AgNPs) and 95.05% (with AgNPs) and the minimum voltage regulations were 8.41% (without AgNPs) and 3.56% (with AgNPs). Finally, it is shown that the performances of the PKL module have been increased for using AgNPs.

K. A. Khan (B) Department of Physics, Jagannath University, Dhaka 1100, Bangladesh e-mail: [email protected] Md. Sayed Hossain Center for Research Reactor, Bangladesh Atomic Energy Commission, Dhaka, Bangladesh S. R. Rasel Local Government Engineering Department (LGED), Sherpur Sadar, Sherpur, Bangladesh M. Hasan General Education Department, City University, Dhaka, Bangladesh © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Biswas et al. (eds.), Microelectronics, Circuits and Systems, Lecture Notes in Electrical Engineering 976, https://doi.org/10.1007/978-981-99-0412-9_24

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Keywords Green energy · Energy source · PKL module · AgNPs synthesis · Electrochemical cell

1 Introduction Green technology means that electric power comes from solar, wind, water, geothermal wave, tidal, biogas, biomass and Ocean Thermal Energy Conversion (OTEC). It has low gas emissions and preserving energy and natural resources [1]. It is environmentally friendly. It does not have a limited source and will never run out. Traditional sources like oil, gas and coal are the limited source [2]. It will be finished after a certain period [3]. Nuclear power may be the alternative source but it is very expensive and risky. At this moment, the renewable energy is the utmost important for our daily life [4]. The use and demand of electricity are increasing day by day across the world. The source of power needs constant supply without any interruption. It is shown that 3rd world countries around the world need more electricity to develop their economy [5]. It is found that in Bangladesh around 89% electricity comes from the fossil fuels [6–8]. The fossil fuels are not eco-friendly [9–11]. But the renewable energy sources are eco-friendly [12]. It has been already technically studied that the electricity generation storage systems using AgNPs [13, 14]. This green technology is a new technology and a very portion of the people know about this technology. Green energy can be harvested from PKL using such kind of technology and this technology does not require a high cost. Such technology can generate electricity without disturbing any ecosystem. This technology can help to improve the fresh air system for specific areas [15]. This new and green innovative research can help the electricity generation as well as storage system.

2 Methodology Preparation of Electric Modules This study was conducted to determine the electricity generation and storage system by the PKL using AgNPs at the Department of Physics, Jagannath University, Dhaka, Bangladesh. Using this electricity generation and storage system, a lighting system has been designed and developed. Figure 1 shows the cultivation of PKL trees for electricity generation. Figure 2 shows the PKL sap after blending by a blender. The sap was filtered by Whatman paper 41 and 42. Figure 3 shows a PKL module with 6 compartments. Zinc and copper plates are used as an anode and cathode, respectively. The zinc and copper plates are connected in series connection by plastic clips. The filtered PKL extract has been used as an electrolyte. Figure 4 also shows a PKL module with AgNPs.

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Fig. 1 PKL tree

Fig. 2 PKL extract

2.1 Synthesis of Ag Nanoparticles (AgNPs) In this experiment, highly pure AgNO3 was used as precursor which purchased from Sigma Aldrich. 1.0 mM aqueous solution of silver nitrate (AgNO3 ) was prepared and used for the synthesis of silver nanoparticles. 10 mL of BPL (Bryophyllum Pinnatum Leaves) sap was mixed with 90 mL of aqueous solution of 1 mM silver nitrate for the reduction into Ag+ ions and incubated overnight at room temperature in dark. After shaking by magnetic stirrer with hot plate heated at 60 °C temperature for 1 h (60 min), the mixture was kept in dark and then the colorless mixture turned yellow to brown within three hours. After a couple of days, it was finally formed a darkbrown color solution with some black sediments at the bottom of the flask, which in turn affirms the formation of AgNPs. Figure 5 shows the steps diagram of AgNPs synthesis using BPL plant extract and Fig. 10 represents the schematic diagram of the formation of AgNPs.

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Fig. 3 PKL module with 6 compartments

Fig. 4 PKL module with AgNPs

Fig. 5 Green synthesis mechanism of Ag nanoparticle formation

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It is shown in Fig. 6 that the steps diagram of green synthesis of AgNPs using Bryophyllum pinnatum leaves extract. It is shown in Fig. 7 that the schematic diagram of color changes during the formation of AgNPs.

Fig. 6 Steps diagram of green synthesis of AgNPs using Bryophyllum pinnatum leaves extract

Fig. 7 Schematic diagram of color changes during the formation of AgNPs

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3 Results and Discussion To study the performance of PKL electrochemical cell for the generation and storage of electricity using AgNPs and without AgNPs, some parameters have been studied for 220 h and the measured parameters have been graphically presented. Here, voltaic efficiency, ηV = (V L /V oc ) (%) and voltage regulation, V R = (V NL − V FL )/V FL (%), ηV = voltaic efficiency, V L = load voltage, V oc = open-circuit voltage, V R = voltage regulation, V NL = no load voltage and V FL = full load voltage. Figure 8 shows the variation of load voltage (V L ) (without AgNPs) versus time duration (h). It is shown that the maximum load voltage is 5.47 V and the minimum load voltage is 4.31 V. So that, the difference between the maximum and minimum voltage is 1.16 V. It is also shown that the load voltage decreases linearly up to 80 h and then the load voltage was almost constant up to 220 h. Figure 9 shows the variation of open-circuit voltage (V OC ) (without AgNPs) versus time duration (h). It is shown that the maximum open-circuit voltage is 5.93 V and the minimum open-circuit voltage is 5.17 V. So the difference between the maximum and minimum voltage is 0.76 V. It is also shown that the open-circuit voltage decreases linearly up to 20 h. Then it increases up to 40 h. Again it decreases linearly up to 200 h and then the load voltage was almost constant up to 220 h. Figure 10 shows the variation of voltaic efficiency, ηV (without AgNPs) versus time duration (h). It is shown that the maximum voltaic efficiency is 92.24 and the minimum voltaic efficiency is 77.08. So the difference between the maximum and minimum voltaic efficiency is 15.16. It is also shown that the voltaic efficiency decreases linearly up to 80 h. Then it almost was almost constant up to 120 h. Again it increases linearly up to 220 h. Figure 11 shows the variation of voltage regulation, V R (without AgNPs) versus time duration (h). It is shown that the maximum voltage regulation is 29.73 and the minimum voltage regulation is 8.41. So the difference between the maximum and

Fig. 8 Load voltage (V L ) versus time duration (h) [without AgNPs]

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Fig. 9 Open-circuit voltage (V OC ) versus time duration (h) [without AgNPs]

Fig. 10 Voltaic efficiency, ηV versus time duration (h) [without AgNPs]

minimum voltage regulation is 21.32. It is also shown that the voltage regulation increases linearly up to 80 h. Then it almost was almost constant up to 120 h. Again it decreases exponentially up to 220 h. Figure 12 shows the variation of load voltage (V ) (with AgNPs) versus time duration (h). It is shown that the maximum load voltage is 5.9 V and the minimum load voltage is 5.34 V. So the difference between the maximum and minimum voltage is 0.56 V. It is also shown that the load voltage decreases linearly up to 20 h and then increases up to 40 h. Finally, the load voltage was almost constant up to 220 h. Figure 13 shows the variation of open-circuit voltage (V ) (with AgNPs) versus time duration (h). It is shown that the maximum open-circuit voltage is 6.11 V and the minimum open-circuit voltage is 6.03 V. So the difference between the maximum and minimum voltage is 0.08 V. It is also shown that the open-circuit voltage decreases

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Fig. 11 Voltage regulation, V R versus time duration (h) [without AgNPs]

Fig. 12 Load voltage (V L ) versus time duration (h) [without AgNPs]

linearly up to 80 h then the load voltage was almost constant up to 160 h. Then it decreases linearly up to 220 h. Figure 14 shows the variation of voltaic efficiency, ηV (with AgNPs) versus time duration (h). It is shown that the maximum voltaic efficiency is 95.56 and the minimum voltaic efficiency is 95.19. So the difference between the maximum and minimum voltaic efficiency is 0.37. It is also shown that the voltaic efficiency decreases linearly up to 40 h. Then it almost was almost constant up to 160 h. Again it increases linearly up to 220 h. Figure 15 shows the variation of voltage regulation, V R (with AgNPs) versus time duration (h). It is shown that the maximum voltage regulation is 5.37 and the minimum voltage regulation is 3.55. So the difference between the maximum and minimum voltage regulation is 1.82. It is also shown that the voltage regulation

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Fig. 13 Open-circuit voltage (V OC ) versus time duration (h) [without AgNPs]

Fig. 14 Voltaic efficiency, ηV versus time duration (h) [without AgNPs]

increases linearly up to 60 h. Then, it was almost constant up to 160 h. Again it decreases exponentially up to 220 h.

4 Conclusions It is shown that the electric voltage depends on the AgNPs applying in the PKL extract as an electrolyte. Biosynthesis of silver nanoparticles is more beneficial to human use due to the elimination of hazardous chemicals and the reduced capital cost involved in the production. However, biosynthesis of silver nanoparticles by plant systems is still at its infant stage and currently still underexploited. Applications

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Fig. 15 Voltage regulation, V R versus time duration (h) [without AgNPs]

of silver nanoparticles for power monitoring in the electrochemical cell are found effective for practical utilizations. Acknowledgements The authors are grateful to the Grant of Advanced Research in Education (GARE) project, Ministry of Education, GoB for financing during the research work (Project/User ID: PS2019949).

References 1. K.A. Khan, S.M. Zian Reza, The situation of renewable energy policy and planning in developing countries. IJARIIE 5(4), 557–565 (2019) 2. K.A. Khan, S.R. Rasel, S.M.Z. Reza, F. Yesmin, Electricity from living PKL tree. Published in the Open Access book, “Energy Efficiency and Sustainability in Outdoor Lighting: A Bet for the Future”, ed. by M.J. Hermoso-Orzáez (IntechOpen, London, UK, 2019) 3. Gunawan et al., Energy storage system from galvanic cell using electrolyte from a plant as an alternative renewable energy. IOP Conf. Ser. Mater. Sci. Eng. 509, 012045 (2019) 4. M. Nedaei, Wind energy potential assessment in Chalus county in Iran. Int. J. Renew. Energy Res. 2(2), 338–347 (2012) 5. E. Tzen, M. Papapetrou, Promotion of renewable energy sources for water production through desalination. Desalin. Water Treat. 39(1–3), 302–307 (2012) 6. C.Y. Ying, J. Dayou, Modelling of the electricity generation from living plants. Jurnal Teknologi 78(6), 29–33 (2016) 7. F.P. Chee, C.A. Chen, J.H.W. Chang, Y.Y. Choo, J. Dayou, Data acquisition system for in situ monitoring of chemoelectrical potential in living plant fuel cells. J. Biophys. 2016 (2016) 8. A.G. Volkov, J.C. Foster, E. Jovanov, V.S. Markin, Anisotropy and nonlinear properties of electrochemical circuits in leaves of Aloe vera L. Bioelectrochemistry 81(1), 4–9 (2011) 9. Y.Y. Choo, J. Dayou, N. Surugau, Origin of weak electrical energy production from livingplants. Int. J. Renew. Energy Res. 4(1), 198–203 (2014)

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10. K.A. Khan, M.S. Bhuyan, M.A. Mamun, M. Ibrahim, L. Hasan, M.A. Wadud, Organic electricity from Zn/Cu-PKL electrochemical cell, in Contemporary Advances in Innovative and Applicable Information Technology, Advances in Intelligent Systems and Computing, vol. 812, chap. 9, ed. by J.K. Mandal et al. (Springer Nature Singapore Pvt. Ltd., Singapore, 2018), pp. 75–90 11. K.A. Khan, S.R. Rasel, S.M. Zian Reza, F. Yesmin, Energy efficiency and sustainability in outdoor lighting—a bet for the future, in Energy Efficiency and Sustainable Lighting—A Bet for the Future, ed. by M.J. Hermoso-Orzáez, A. Gago-Calderón (IntechOpen, 2020). http:// doi.org/10.5772/intechopen.89413 12. K.A. Khan, F. Yesmin, Md. Abdul Wadud, A.K.M. Obaydullah, Performance of PKL electricity for use in television (NAROSA Publisher, 2019) (accepted as a book chapter) 13. M.N.F. Rab, K.A. Khan, S.R. Rasel, M. Hazrat Ali, L. Hassan, M. Abu Salek, S.M. Zian Reza, M. Ohiduzzaman, Voltage cultivation from fresh leaves of air plant, climbing spinach, mint, spinach and Indian pennywort for practical utilization, in Energy Systems, Drives and Automations, Proceedings of ESDA 2019. Lecture Notes in Electrical Engineering, vol. 664 (Springer, Singapore, 2020), pp. 150–160. http://doi.org/10.1007/978-981-15-5089-8. eBook ISBN: 978-981-15-5089-8. Hardcover ISBN: 978-981-15-5088-1. Series ISSN: 1876-1100 14. P. Khademi-Azandehi, J. Moghaddam, Green synthesis, characterization and physiological stability of gold nanoparticles from Stachys lavandulifolia Vahl extract. Particuology (2014). https://doi.org/10.1016/j.partic.2014.04.007 15. K.A. Khan, M.A. Saime, M. Hazrat Ali, S.M. Zian Reza, N. Alam, Md. Afzol Hossain, M.N.F. Rab, S. Islam, A study on PKL electrochemical cell for three different conditions. Lecture Notes in Electrical Engineering, vol. 664, pp. 374–386 (Springer, Singapore, 2020). http://doi.org/10. 1007/978-981-15-5089-8. eBook ISBN: 978-981-15-5089-8. Hardcover ISBN: 978-981-155088-1. Series ISSN: 1876-1100

Comparative and Robustness Study of 3-Bit Adder Md. Faizan Khan, Subham Chowdhury, Ravi Kumar, Shashank Kumar Dubey, Santashraya Prasad, and Aminul Islam

Abstract A 1-bit full adder is a circuit that is omnipresent in Digital Signal Processor (DSP), all kinds of microprocessor (µP), all kinds of microcontroller (µC), and any component that perform data processing operation. This paper performs the comparative study of various adders. Adders that are under consideration in this paper are transmission gate (TG)-based full adder, mirror adder, and carry bypass adder. The simulations of a full adder that can add two numbers of three bit each at 16-nm technology are carried out in this paper using the SPICE simulation tool. Performance analysis of the same has been done based on different design metrics such as average power dissipation (Pavg ), average delay or propagation delay (t p ) ((t p = t pLH + t pHL )/2), and the product of average delay and average power dissipation (PDP). We have analyzed all these circuits for their robustness by carrying out variability analysis in terms Pavg , t p , and PDP. The average power for transmission gate, mirror, and carry bypass adder at 16-nm technology are 0.20 µW, 0.22 µW, and 0.39 µW, respectively. The t p for transmission gate, mirror, and carry bypass adder at 16-nm technology is 0.13 ns, 0.09 ns, and 0.02 ns, respectively, at V DD = 0.7 V. It is observed that the t p of the carry bypass adder is very less as compared to other logic styles and Pavg of transmission gate-based full adder is less as compared to other logic styles. TG-based full adder has the lowest variability for t p as compared to other logic styles at V DD = 0.7 V. Keywords Full adder · TG · Mirror adder · Carry bypass adder · PDP · Variability · Propagation delay · Average power

Md. Faizan Khan · S. Chowdhury · R. Kumar · S. K. Dubey · S. Prasad · A. Islam (B) Department of ECE, Birla Institute of Technology, Mesra, Ranchi 835215, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Biswas et al. (eds.), Microelectronics, Circuits and Systems, Lecture Notes in Electrical Engineering 976, https://doi.org/10.1007/978-981-99-0412-9_25

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1 Introduction Every µP, DSP, Network on Chip (NoC), System on Chip (SoC), or any other Application Specific Integrated Circuit (ASIC) uses data path. At the heart of every data path and addressing units, there are arithmetic units, such as comparators, adders, subtracters, and multipliers. All arithmetic operations must be performed consuming low-power, area-efficient circuits operating at higher speed. The summing operation is the most basic operation; 1-bit full adder is the most basic summing circuit of any kinds of processor [1]. Adders play a vital role in this digital era. Adders are an integral part of the arithmetic and logic unit (ALU) which performs various operations like addition, subtraction, division, multiplication, and logical operations. As the demand for very-large-scale integration (VLSI) circuits is increasing day by day and adders are an important part of it so to improve the performance of the overall circuit, a circuit designer needs to know which topology of adder is well suited for his intended design [2, 3]. The most important design metrics to be kept in consideration are propagation delay, and power dissipation which affects the performance and usefulness of any VLSI circuit. Both, the design metrics are calculated and compared in all the full adders under consideration. The full adders under analysis are (1) transmission gate full adder, (2) mirror adder, and (3) carry bypass adder. The remainder of this article is organized as mentioned below. Section 2 briefly outlines the various full adder topologies investigated in this paper. Section 3 consists of simulation results of the full adders mentioned in this paper. Section 4 compares design metrics such t p , Pavg and PDP, and their variability. Section 5 concludes this paper.

2 Circuit Description of the Adders 2.1 Transmission Gate A transmission gate (TG) is a combination of two MOSFETs—one NMOSFET and another PMOSFET. Since a single NMOSFET cannot pass a good “1” and a PMOSFET cannot pass a good “0”, but an NMOSFET pass a good “0” and a PMOSFET pass a good “1”. These good properties of both the MOSFETs are utilized in transmission gate (TG) by connecting them in parallel. Hence, the TG acts a switch, which can pass good “1” and good “0”. The connection of MOSFETs in transmission gate is shown in Fig. 1. When control input C is high (and hence C ' is low), the input applied at node A gets path through the TG and the value of the input is available at node B without degradation. The total 20 transistors are required to realize TG-based full adder circuit as shown in Fig. 2.

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Fig. 1 Transmission gate

Fig. 2 1-bit full adder realized with TG logic

2.2 Mirror Adder The mirror adder consists of total 24 transistors (see Fig. 3). The pull down and pull up networks are logical implementation of propagate/delete/generate function. The silicon area required to layout the mirror adder is less than that of conventional static CMOS 1-bit full adder, which requires 28 transistors [4].

2.3 Carry Bypass Adder Since the carry out from a stage ripple through the various stages, the final carry out and sum bit are not settled quickly. An additional bypass (skip) mechanism introduced to speed up the operation of the adder. Therefore, a carry bypass adder

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Fig. 3 Transistor level schematic of 1-bit mirror adder

is such an adder, which implements the bypass circuitry [5]. The working of carry bypass adder is explained as follows: Figure 4 shows a 3-bit carry bypass adder. Here, the incoming carry bit is denoted by CO and the final output carry is CARRY3. The propagate conditions are generated using XOR gates (pi = ai xor bi ). The CO gets path to COUT (that is, COUT is the carry-in bit, i.e., CO) if all the propagate conditions are true. Otherwise, the COUT is obtained via the normal route. That is, CARRY3 gets path to COUT. In other words, when block propagates, i.e., (p1 p2 p3 ) = 1, then output carry, COUT = CO otherwise, COUT is generated following the chain of full adders [6]. This paper implements and analyzes a 3-bit carry bypass adder for carrying out the comparative study with other adders. The carry bypass adder has been implemented using TG logic. Figure 2 shows the 1-bit full adder using TG logic. The carry skip adder is implemented using generate and propagate signals, Boolean expressions of which are as follows: pi = X i ⊕ Yi gi = X i · Yi

(1)

The output carry from the ith adder cell is expressed as: Ci+1 = gi + pi Ci The generate and propagate signals are given by:

(2)

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Fig. 4 3-bit conventional carry bypass adder, which is also called carry skip adder

G j:i = g j + p j g j−1 + p j p j−1 g j−2 + · · · + p j p j−1 p j−2 . . . pi+1 gi P j:i = p j p j−1 p j−2 . . . pi .

(3)

The output carry of the entire group is given by C j+1 = G j:i + P j:i Ci .

(4)

The first 1-bit full adder is the starting point of the critical path in a carry skip adder, which traverses via all other 1-bit full adders. The end point of the critical path the sum bit sn−1 . Carry skip adders are chained to reduce the overall critical path, since a single n-bit carry skip adder has no real speed benefit compared to a n-bit ripple-carry adder. τCSA (n) = τCRA (n)

(5)

A m input AND gate and one multiplexer is used skip carry. TSK = TAND (m) + TMUX

(6)

For conditional skip, the critical path consists only of the delay imposed by the multiplexer [7].

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TCSK = TMUX = 2D

(7)

3 Simulation Setup and Comparative Analysis of Design Metrics Two CMOS inverters are added to all inputs of the full adder and one CMOS inverter is added to each of the output of the full adder for getting more realistic simulation and for analyzing power and delay performance under different loads Fig. 5. But they are not included in delay and power analysis. Same footing is there for all the adders, i.e., pulse width, pulse period, step time, and stop time. The transistor count in the transmission gate, mirror circuit, and carry bypass circuit adder is 60, 72, and 76, respectively. MOSFET’s width and length specification for transmission gate, mirror and carry bypass adder style for 16-nm technology are as follows: Width of NMOS = 16 nm, Length of NMOS = 16 nm, Width of PMOS = 32 nm, and Length of PMOS = 16 nm. All the simulations are performed in SPICE at 16-nm CMOS process. After verifying the functionality, various performance parameters such as power, delay, and power-delay product (PDP) have been analyzed.

3.1 Average Delay (tp ) Estimation Average delay or propagation delay (t p ) is calculated by averaging the low-to-high and high-to-low delays. The t p can be written as tpLH + tpHL CL VDD = tp = 2 4 where C L is the load capacitance. Fig. 5 Simulation test bench

(

1 IPDN,avg

+

1 IPUN,avg

) (8)

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Fig. 6 Propagation delay versus supply voltage for various 1-bit full adder circuit

Estimated values of t p have been Plotted in Fig. 6 for displaying the results. It is found that carry bypass adder shows the least t p (of 0.019 ns) among all the three adders considered at nominal V DD of 0.7 V, which is 6.84 × (4.68 × ) shorter than TG-based (Mirror) full adder [8, 9].

3.2 Average Power Dissipation Analysis Average Power dissipation ((Pavg )) by the supply voltage (V DD ) is obtained for comparison. Average power Pavg is expressed as

Pavg

1 = T

T 0

VDD p(t)dt = T

T i VDD (t)dt

(9)

0

Statistical mean (μ) and standard deviation (σ ) values are obtained by varying V DD by 10% round the nominal supply voltage = 0.7 V. The mean values have been plotted in Fig. 7 with respect to V DD for making the comparison clearly visible. It is found that TG-based adder shows the least average power dissipation (of 0.205 µW) among all the three adders considered at nominal V DD of 0.7 V, which is 1.1 × (1.9 × ) lower than mirror (carry bypass) adder [8, 9].

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Fig. 7 Average power versus V DD for full adder cells considered for comparison

3.3 PDP Estimation and Discussion PDP is an importance metric. This is because, PDP signifies a trade-off between Pavg and t p . It is calculated by multiplying Pavg and t p [8, 9]. The PDP is expressed as PDP = Pavg ∗ tp .

(10)

The statistical mean (μ) and standard deviation (σ ) values of PDP have been obtained by varying V DD by 10% around its nominal of 0.7 V. The mean values have been plotted as shown in Fig. 8 for visual and quick inspection. It is found that carry bypass adder shows the least PDP (of 0.742 × 10−17 J) among all the three adders considered at nominal V DD of 0.7 V, which is 2.67 × (3.58 × ) lower than mirror (TG-based) adder. The carry bypass adder exhibits the lowest propagation delay followed by the mirror adder and TG logic adder because the carry bit for each block can be skipped over blocks with group propagate signal set to logic 1 in carry bypass adder. The mirror adder has a moderate propagation delay. The TG logic has minimum average power consumption when compared to carry bypass and mirror adder. The carry bypass shows minimum PDP when compared to TG logic and mirror adder. The carry bypass adder has the low propagation delay, high average power consumption, and lowest power-delay product (PDP) among the three followed by the mirror adder and TG logic adder.

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Fig. 8 PDP versus V DD for all full adder cells considered for comparison

4 Variability Analysis of Design Metrics Two CMOS inverters are added to all inputs of the full adder and one CMOS inverter is added to each of the output of the full adder for getting more realistic simulation and for analyzing power and delay performance under different loads Fig. 5. But they are not included in delay and power analysis. Same footing is there for all the adders, i.e., pulse width, pulse period, step time, and stop time. Variability analysis has become an important design analysis [10–18]. This subsection of this article performs variability analysis of the design metrics such Pavg , t p , and PDP. The variability (expressed by ratio of standard deviation (σ ) to mean (μ) of a design metric) has become gradually critical with aggressive technology scaling, and hence, it is imperative that the variability analysis must be carried out.

4.1 Average Delay (tp ) Variability Estimation The variability analysis of t p has been carried out by estimating the mean and standard deviation of the t p . The estimated values of standard deviation have been divided by the mean values of the t p . The estimation of t p -variability has been carried out by varying supply voltage (V DD ) from 0.63 to 0.77 V (the variation is 10% about the nominal V DD of 0.7 V). The t p -variability has been plotted with respect to V DD in Fig. 9. The value of t p -variability has been normalized with respect to that of TGbased full adder at nominal V DD of 0.7 V. It can be observed that the TG-based full

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Fig. 9 Average delay (t p ) variability versus V DD of full adder cells considered for comparison

adder exhibits the lowest t p -variability. This implies that the TG-based full adder is robust so far propagation delay is concerned.

4.2 Average Power (Pavg ) Variability Analysis The variability analysis of average power dissipation has been carried out by estimating the mean and standard deviation of the Pavg . The estimation of Pavg -variability has been carried out by varying supply voltage (V DD ) from 0.63 to 0.77 V (the variation is 10% about the nominal V DD of 0.7 V). The Pavg -variability has been plotted with respect to V DD in Fig. 10. The value of Pavg -variability has been normalized with respect to that of mirror full adder at nominal V DD of 0.7 V. It can be observed that the mirror full adder exhibits the lowest Pavg -variability. This implies that the mirror full adder is robust so far average power dissipation is concerned.

4.3 PDP Variability Analysis The variability analysis of power-delay product (PDP) has been carried out by estimating the mean and standard deviation of the Pavg . The estimated values of standard deviation have been divided by the mean values of the t p . The estimation of PDP variability has been carried out by varying supply voltage (V DD ) from 0.63 to 0.77 V

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Fig. 10 Pavg -variability versus V DD of all full adder circuits considered for comparison

(the variation is 10% about the nominal V DD of 0.7 V). The PDP variability has been plotted with respect to V DD in Fig. 11. The value of PDP variability has been normalized with respect to that of mirror full adder at nominal V DD of 0.7 V. It can be observed that the mirror full adder exhibits the lowest Pavg -variability. This implies the mirror full adder is robust so far average power dissipation is concerned. The variability of all the adder circuits involved in this paper is analyzed to check the robustness of the adder circuits as the variability has emerged to be a key factor in designing nanoscale circuits. For estimating variability, a Monte Carlo simulation has been carried out at 16-nm technology by varying the supply voltage by 10% about the nominal V DD of 0.7 V. Standard deviations from its respective mean values are estimated. The propagation delay variability, average power variability, and PDP variability all the three full adders have been estimated and reported as explained above.

5 Conclusion This paper has examined the performance of three adders in the deep sub-micron (16-nm) CMOS process. Some design parameters are compared among these adders. Carry bypass adder has the lowest propagation delay followed by mirror adder and TG logic adder. The average power (Pavg ) of the TG logic adder is the lowest followed by the mirror adder and carry bypass adder. Carry bypass has the lowest power-delay product as compared to mirror and TG logic adder. The variability of all the full

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Fig. 11 PDP variability versus V DD of all full adder circuits considered for comparison

adders involved in this paper is also analyzed which helps the designers to find an appropriate full adder circuit that makes their system fast, robust, and more simplified. It is concluded that carry bypass adder is the most robust design since its compound metrics PDP shows least variability.

References 1. A.A. Mathew, P.R. Sreesh, Comparative analysis of full adder circuits. IOP Conf. Ser. Mater. Sci. Eng. 396, 012041 (2018). https://doi.org/10.1088/1757-899x/396/1/012041 2. J.-F. Jiang, Z.-G. Mao, W.-F. He, Q. Wang, A new full adder design for tree structured arithmetic circuits, in 2010 2nd International Conference on Computer Engineering and Technology (IEEE, Chengdu, 2010), pp. V4-246–V4-249 3. J.M. Rabaey, A. Chandrakasan, B. Nikolic, Digital Integrated Circuits: A Design Perspective, 2nd edn. (Prentice-Hall of India Pvt. Ltd, 2009) 4. S. Aphale, K. Fakir, S. Kodagali, Analysis of various adder circuits for low power consumption and minimum propagation delay, in Advances in Intelligent Systems Research, Proceedings of the International Conference on Communication and Signal Processing 2016 (ICCASP 2016) (2016), pp. 349–357. http://doi.org/10.2991/iccasp-16.2017.54 5. R. Uma, V. Vijayan, M. Mohanapriya, S. Paul, Area, delay and power comparison of adder topologies. Int. J. VLSI Des. Commun. Syst. 3(1), 153–168 (2012) 6. K. Nagori, S. Nehra, Design of a high speed and low power 4 bit carry skip adder. Int. J. Eng. Res. Appl. 7(3) (Part-5), 66–69 (2017) 7. B. Parhami, Computer Arithmetic: Algorithms and Hardware Designs, 2nd edn. (Oxford University Press, New York, 2010) 8. A. Kumar, A. Islam, Multi-gate device and summing-circuit co-design robustness studies @ 32-nm technology node. Microsyst. Technol. 23, 4099–4109 (2017)

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9. V. Dokania, A. Imran, A. Islam, Investigation of robust full adder cell in 16-nm CMOS technology node, in IMPACT-2013, Aligarh, India (IEEE, 2013), pp. 207–211 10. S.R. Nayini, K. Sai Kushal Gella, S.K. Dubey, M. Guduri, A. Islam, Robust design of noise tolerant 2-phase non overlapping clock generating circuit, in 2021 Devices for Integrated Circuit (DevIC), Kalyani, India (IEEE, 2021), pp. 211–215 11. Aakansha, G.S. Namith, A. Dinesh, A.S. Ram, S.K. Dubey, A. Islam, A highly reliable and radiation-hardened majority PFET-based 10T SRAM cell, in Microelectronics, Circuits and Systems. Lecture Notes in Electrical Engineering, vol. 755, ed. by A. Biswas, R. Saxena, D. De (Springer, Singapore, 2021), pp. 113–122 12. S. Koushik, P.K. Sahu, S.K. Dubey, A. Islam, Radiation immune SRAM cell for deep space applications, in Microelectronics, Circuits and Systems, ed. by A. Biswas, R. Saxena, D. De. Lecture Notes in Electrical Engineering, vol. 755 (Springer, Singapore, 2021), pp. 147–156 13. P.K. Sahu, S. Koushik, S.K. Dubey, A. Islam, Radiation tolerant memory cell for aerospace applications, in Microelectronics, Circuits and Systems, ed. by A. Biswas, R. Saxena, D. De. Lecture Notes in Electrical Engineering, vol. 755 (Springer, Singapore, 2021), pp. 101–110 14. S.K. Dubey, A. Reddy, R. Patel, M. Abz, A. Srinivasulu, A. Islam, Architecture of resistive RAM with write driver. Solid State Electron. Lett. 2, 10–22 (2020). https://doi.org/10.1016/j. ssel.2020.01.001 15. S.K. Dubey, A. Islam, Design of resistive random access memory cell and its architecture. Microsyst. Technol. 26, 1325–1332 (2020) 16. S. Saha, S.K. Dubey, S. Banerjee, I. Pal, A. Islam, Nonvolatile write driver for spin transfer torque memory and logic design, in Social Transformation—Digital Way. CSI 2018. Communications in Computer and Information Science, vol. 836, ed. by J. Mandal, D. Sinha (Springer, Singapore, 2018), pp. 156–166 17. K. Agrawal, S. Chowdhury, S.K. Dubey, A. Islam, Robustness study of Muller C-element, in Social Transformation—Digital Way. CSI 2018. Communications in Computer and Information Science, vol. 836, ed. by J. Mandal, D. Sinha (Springer, Singapore, 2018), pp. 131–139 18. N. Gupta, P. Thakur, S.K. Dubey, A. Islam, Design of nonvolatile MRAM bitcell, in 2017 7th International Symposium on Embedded Computing and System Design (ISED), Durgapur, India (IEEE, 2017), pp. 1–4

Brain Tumor Detection and Classification from MRI Images Using Cascaded Deep Neural Networks Pallavi Priyadarshini, Abdul Kayom Md. Khairuzzaman, and Priyadarshi Kanungo

Abstract A brain tumor is one of the most serious causes of death worldwide. Early detection of the tumor is a challenging task for the radiologist and also for the researchers so that many valuable lives can save. Therefore, diagnosis and classification of tumors from MRI images are more important to assist radiologists and can become a gold standard non-invasive method of diagnosis in comparison with hazardous invasive methods like tissue biopsy. Both computer-aided diagnosis systems and deep neural network architecture are the most prevalent methods that have shown outstanding performance in brain tumor classification and segmentation tasks. In this chapter, attempts have been taken purposefully in designing efficient brain tumor detection techniques for the classifications of the tumor using the cascaded method. Two types of training and validation schemes have been proposed (i.e., 70% and 30%) for the datasets giving the best simulation results of 0.9804, 1.00, 0.99, and 0.98 in terms of accuracy, precision, recall, and F1-score, respectively. Two CNN models for MR image classification have shown the robustness of our system using VGG-19 and ResNet-50. ResNet-50 gives better accuracy, i.e., 0.99, and VGG-19 gives an accuracy of 0.98 in detecting and classifying brain tumors when compared with other pre-trained deep learning models. Keywords CNN · MRI · VGG-19 · ResNet-50

1 Introduction Brain tumors are defined as abnormal growth or proliferation of normal brain tissues. These are broadly classified into benign or malignant tumors. Benign tumors are slow-growing with a low grade of invasion and metastasis, whereas malignant brain P. Priyadarshini (B) · A. K. Md. Khairuzzaman · P. Kanungo Department of Electronics and Communication Engineering, C. V. Raman Global University, Bhubaneswar, Odisha, India e-mail: [email protected] P. Kanungo e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Biswas et al. (eds.), Microelectronics, Circuits and Systems, Lecture Notes in Electrical Engineering 976, https://doi.org/10.1007/978-981-99-0412-9_26

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tumors are rapidly growing with high grades of invasion and metastasis. Brain tumors can be broadly classified into two types: primary and secondary. Primary brain tumors are originated from the primary tissues and the secondary brain tumors spread from other parts of the body such as the lungs, colon, breasts, and skin in a process called metastasis [1]. Based on histology and anatomical location, primary brain tumors are further classified as glioma, astrocytoma, ependymoma, meningioma, medulloblastoma, ganglioglioma, schwannoma, chordoma, etc. [2]. A brain tumor is one of the deadliest and major causes of death in the world based on the National Brain tumor Foundation (NBTF) [3]. Various imaging modalities have been implemented for the automatic detection and classification of brain tumors such as ultrasound, CT scan, and MRI [4]. MRI is non-invasive, uses unionization radiation, and gives a more detailed picture of brain tissues with high resolution [5]. Brain MRI produces images from 3 directions axial, coronal, and sagittal [6]. MRI brain captures different modalities of image sequence such as T1, T2, and FLAIR [7]. T1 gives a good idea for the segmentation of tumors from healthy brain tissue. T1 has high accuracy in detecting tumor boundaries. T2 detects edema (fluid) around the brain tumor. And Flair visualizes the edema region from cerebrospinal fluid. Enhancing the tumor gives a hyperintensity picture in T1. Non-enhancing and necrotic tumors give a hypointense picture in T1 weighted [7]. Diagnosis, classification, and management by manual segmentation are very complex and time-consuming. To minimize human errors, automatic brain tumor segmentation of MRI images can be a revolutionary idea in diagnosing and managing brain tumors. Nowadays, CNN has become the state of art in medical image analysis. CNN is used for segmentation and classification in brain image analysis [8]. The objective of this study is to find out a robust CNN cascade model which can be precisely utilized for the brain tumor classification from MRI images. There are several deep learning pre-trained models such as AlexNet, GoogleNet, VGGNet, ResNet, and Inception V3. All our proposed work shows baseline CNN along with VGG-19 and ResNet-50 cascaded together and gives higher performances in comparison with other existing works in the prediction of accuracy and loss [9]. The effectiveness of deep learning methods depends upon the number of available samples during the training process. In medical applications, we usually have a very limited number of images. Hence, the major challenges in the application of deep learning in medical image analysis are due to the limited number of training samples used to build deep models to avoid overfitting [10]. The popularity of deep learning is mostly due to the two major reasons first is advanced CPUs and GPUs, and the second is the big dataset availability [11]. For better outcomes, deep neural networks require a huge amount of data which can take a large training time. To overcome this problem, transfer learning can be used. Transfer learning is a DL technique that uses a pre-trained model that facilitates training of the model for a specific application with less number of training samples [12]. Image generation is also another important application of computer vision, which has been a widely researched area worldwide. CNN has the capabilities of extracting image features itself and has gained popularity in the field of image generation [13]. The research’s key contribution is developing a more accurate method using the concept of transfer learning for better MR image analysis and performance [14].

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2 Convolutional Neural Network CNN is extremely popular and has given successful results for several computer vision applications such as face recognition, object detection, image classification, and semantic segmentation [15]. A convolutional layer is a layer that plays an important role in the detection of the pattern by using varieties of filters. Filters are useful to detect various objects [16]. Convolutional neural networks have three building blocks: First is a convolutional layer, the second is a subsampling (max-pooling) layer specially used to reduce the dimensions of an image and the third is a fully connected (FC) layer used for classification [17]. There are various traditional methods for the detection of a brain tumor which are very time-consuming. The input feature map and the layer activation computation in CNN are represented as: ⎛ ⎝ S (l) j = f

(l−1) M 

⎞ ⎠ ∗Si(l−1) ∗ ki(l)j + b(l) j

(1)

i=1

ki(l)j represents input M (l−1) denotes number of fesubj maps in l − 1, b(l) j is the bis and f (.) is a nonlinear activation function as mentioned [10].

3 Nonlinear (RELU) Layer Nonlinearity is achieved by a separate which is activation function. It helps in simplifying and accelerating the training process and avoiding the vanishing gradient problem. It has the property to mitigate vanishing gradient problems and is more advantageous in weight updating in different layers of CNN which provides nonlinearity to a neural network for better accuracy and faster computation as compared to sigmoid and tanh. It can be represented as: F(z) = Max(0, z)

(2)

There are many other activation functions such as sigmoid, tanh, leaky RELUs, RRELUs, and PRELUs [18]. The final layer of the proposed model contains a softmax layer for the classification of images and is formulated as: ez X z = K j=1

ez j

(3)

where X z is softmax activation function, z is input value, and ez is standard value of exponential function, K is number of classes used in the binary classifier and ez j is standard exponential function (Fig. 1).

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Fig. 1 Block diagram of proposed VGG-19 EfficientNet convolutional neural network

Due to the limitations of traditional methods, deep neural network has gained popularity in computer vision and image recognition applications. In this paper, the sigmoid activation function is used for binary image classification which gives whether the tumor is benign or malignant. In this section, we have presented the workflow of our proposed work in detail. We worked on how to detect brain tumors using pre-trained VGG-19 EfficientNet model, and ResNet-50 model performance of accuracy, loss, precision, sensitivity, and specificity has been evaluated and compared with other proposed models of the referred works. This proposed VGG-19 EfficientNet model gives better accuracy in training and testing phases as compared to other existing works (Fig. 2). Before preprocessing MR images, it is required to resize images in the dataset to 224 * 224 and to extract different multiresolution features of the images through the feature extraction process using CLAHE, adaptive Gaussian filtering, and Otsu’s thresholding method and then apply the preprocessing techniques to the images which give our required ROI cropped image. Two pre-trained CNN models are used here. The first one is pre-trained VGG-19 and the second is ResNet-50. The first step is to do normalization. The selected brain portion is cropped from the images [16]. In Fig. 3 step, 1 represents the input image needed to be pre-processed, and step 2 represents the second step of preprocessing in which we need to find the higher contrast image using contrast limited adaptive histogram equalization (CLAHE) for more image clarity. Step 3 represents the output of the adaptive Gaussian filter threshold image we need to identify to crop the brain out of the image carefully. Step 4 is the desired pre-processed segmented image. Figure 4 shows the original MR

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Fig. 2 Workflow of the proposed methodology

brain tumor, non-tumor image, and their segmented image at a threshold value of 50, 100, 150, and 200, respectively. Data augmentation is a process used in deep learning to increase the number of datasets if there is a limited amount of data in the required datasets [19]. In training process, it is used to increase the training samples and prevent overfitting. We have applied this technique to train the model by doing minor modifications such as flipping, rotation, and brightness for getting better results in our images. So that our training data will increase in size and the model can perform well on limited amount data also [20] (Fig. 5). Initially, we need to import an OpenCV package and with the help of that package, we implement cv2.threshold and cv2.dilate functions. Dilation and erosion are mostly

Fig. 3 Finding the CLAHE method, adaptive Gaussian filter, and segmented tumor images of the brain

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Fig. 4 T1 contrast MR images after thresholding

Fig. 5 Dataset after data augmentation

used as morphological operators to perform morphological operations. These operations work on images based on image structure [16]. After normalization, total dataset splits into 70% for training and 30% for testing purposes.

4 VGG-19 Model We have used a pre-trained VGG-19 model which is one of the popular CNN pretrained models. It has 19 layers out of which 16 are convolution layers and 3 fully connected dense layers along with ReLU nonlinear activation function. The input image size is 224 * 224 * 3. VGG-19 model used in our work has 16 convolution layers with a fixed 3 * 3 filter size and 5 max-pooling layers of 2 * 2 sizes connected all through the model. In the output layer, it has 2 fully connected layers with a softmax activation function to generate binary classification output. VGG-19 model is an advanced pre-trained CNN model which works on diverse images of 138 million parameters [9].

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5 Res Net 50 Architecture ResNet-50 is a 50-layer Residual Network which has 26 million parameters and is trained on millions of images. It can use the skip connection to propagate information across layers and capable to mitigate the vanishing gradient problem. ResNet-50 has the ability to do the skip connection so that the input layer can be connected directly to output layer because some layers are skipped and hence the values are not reaching to small value [9] (Fig. 6).

5.1 Simulation and Output Results Figure 7 indicates the result of classification accuracy and loss generated after training the proposed CNN model up to 20 and 25 epochs.

Fig. 6 ResNet-50 architecture for image classification

Fig. 7 Plot of training and validation accuracy and loss using baseline CNN

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Fig. 8 Plot of prediction/actual/loss/probability using ResNet-50

5.2 Result and Discussion When we create train and validation data loaders to feed to the neural network: We do it by using the high-level API provided by ResNet-50, which automatically performs the image preprocessing and the label medicalization in the classification model. Figure 8 shows the different classification and simulation results used in the ResNet-50 model for the first 25 epochs. Using this CNN model, we show how to train a convolutional neural network that can identify the presence of a tumor in a brain scan with an overall accuracy of 0.99. In comparison with the other results, the DNN model achieved an overall accuracy of 0.9143 (Fig. 9 and Table 1). Figure 10 highlights the result of the confusion matrix for the true and predicted label shows where the accuracy value is 1.0000 and misclassification value is 0.0000. The few errors are spread evenly among the two labels: The unbalanced dataset does not appear to have induced any bias in the model. In Fig. 8, we can see the seven misclassified images out of the 1021 validation set. The figure prediction/actual/loss/probability results of the misclassified image using the ResNet-50 model. Figure 9 shows the accuracy and loss performance of VGG-19 which gives 0.98.

6 Conclusion Brain tumor detection is one of the major topics in medical image analysis. With this method of deep learning, binary classification is possible and also it can improve the possibility of a patient’s survival rate so that it can be helpful for medical professionals. CNN model is used to improve the accuracy of tumor diagnosis and classification. Both computer-aided diagnosis system and deep neural network architecture

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Fig. 9 Plot of training and validation concerning loss and accuracy in the VGG-19 EfficientNet model

Table 1 Comparison of accuracy, precision, and F1-score among different pre-trained deep learning-based techniques Model

Precision

F1-score

Recall

Accuracy

VGG-19 EfficientNet

0.97 (Yes) 1.00 (No)

0.98 (Yes) 0.97 (No)

1.00 (Yes) 0.95 (No)

0.98

CNN

0.85 (Yes) 0.87 (No)

0.83 (Yes) 0.84 (No)

0.87 (Yes) 0.85 (No)

0.85

RESNET-50

0.99 (Yes) 0.99 (No)

0.99 (Yes) 0.99 (No)

0.99 (Yes) 0.99 (No)

0.99

are the most prevalent method which has shown outstanding performance in brain tumor classification. Due to the ability to mitigate the vanishing gradient problem, ResNet-50 has shown a higher performance of accuracy which is 0.99 among all pre-trained CNN models. It can be helpful as there is a lack of medical knowledge in rural areas and also a shortage of health experts to detect the exact tumor location within a shorter period. In the future works, we will explore multiple classification models of brain MRI images and design an automatic tumor detection system.

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Fig. 10 Confusion matrix for tumor classification in the VGG-19 model

References 1. N.B. Bahadure, A.K. Ray, H.P. Thethi, Image analysis for MRI-based brain tumor detection and feature extraction using biologically inspired BWT and SVM. Int. J. Biomed. Imaging 2017 (2017) 2. A. Wadhwa, A. Bhardwaj, V.S. Verma, A review on brain tumor segmentation of MRI images. Magn. Reson. Imaging 61, 247–259 (2019) 3. E.S.A. El-Dahshan, H.M. Mohsen, K. Revett, A.B.M. Salem, Computer-aided diagnosis of human brain tumor through MRI: a survey and a new algorithm. Expert Syst. Appl. 41(11), 5526–5545 (2014) 4. K.G. Khambhata, S.R. Panchal, Multiclass classification of a brain tumor in MR images. Int. J. Innov. Res. Comput. Commun. Eng. 4(5), 8982–8992 (2016) 5. E.I. Zacharaki, S. Wang, S. Chawla, D. Soo Yoo, R. Wolf, E.R. Melhem, C. Davatzikos, Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Magn. Reson. Med. Official J. Int. Soc. Magn. Reson. Med. 62(6), 1609–1618 (2009) 6. J. Juan-Albarracín, E. Fuster-Garcia, J.V. Manjon, M. Robles, F. Aparici, L. Martí-Bonmatí, J.M. Garcia-Gomez, Automated glioblastoma segmentation based on a multiparametric structured unsupervised classification. PLoS One 10(5), e0125143 (2015) 7. S. Bauer, R. Wiest, L.P. Nolte, M. Reyes, A survey of MRI-based medical image analysis for brain tumor studies. Phys. Med. Biol. 58(13), R97 (2013) 8. D. Daimary, M.B. Bora, K. Amitab, D. Kandar, Brain tumor segmentation from MRI images using hybrid convolutional neural networks. Procedia Comput. Sci. 167, 2419–2428 (2020) 9. H.A. Khan, W. Jue, M. Mushtaq, M.U. Mushtaq, Brain tumor classification in MRI image using convolutional neural network. Math. Biosci. Eng. 17(5), 6203–6216 (2020) 10. D. Shen, G. Wu, H.I. Suk, Deep learning in medical image analysis. Annu. Rev. Biomed. Eng. 19, 221–248 (2017)

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11. H.H. Sultan, N.M. Salem, W. Al-Atabany, Multi-classification of brain tumor images using deep neural network. IEEE Access 7, 69215–69225 (2019) 12. N. Noreen, S. Palaniappan, A. Qayyum, I. Ahmad, M. Imran, M. Shoaib, A deep learning model based on concatenation approach for the diagnosis of brain tumor. IEEE Access 8, 55135–55144 (2020) 13. D. Liu, Y. Liu, L. Dong, G-ResNet: improved ResNet for brain tumor classification, in International Conference on Neural Information Processing (Springer, Cham, 2019), pp. 535–545 14. R. Mehrotra, M.A. Ansari, R. Agrawal, R.S. Anand, A transfer learning approach for AI-based classification of brain tumors. Mach. Learn. Appl. 2, 100003 (2020) 15. A.K. Mondal, J. Dolz, C. Desrosiers, Few-shot 3D multi-modal medical image segmentation using generative adversarial learning (2018). arXiv preprint arXiv:1810.12241 16. K. Saratha Chandra, A. Sai Priya, S. Durga Maheshwari, B.R. Naidu, Detection of brain tumor by integration of VGG-16 and CNN model. IJCRT 8(7) (2020). ISSN: 2320-2882 17. J. Kang, Z. Ullah, J. Gwak, MRI-based brain tumor classification using ensemble of deep features and machine learning classifiers. Sensors 21(6), 2222 (2021) 18. J. Ker, L. Wang, J. Rao, T. Lim, Deep learning applications in medical image analysis. IEEE Access 6, 9375–9389 (2017) 19. C. Shorten, T.M. Khoshgoftaar, A survey on image data augmentation for deep learning. J. Big Data 6(1), 1–48 (2019) 20. Keras, Image data preprocessing Keras API. Keras documentation 21. M.A.B. Siddique, S. Sakib, M.M.R. Khan, A.K. Tanzeem, M. Chowdhury, N. Yasmin, Deep convolutional neural networks model-based brain tumor detection in brain MRI images, in Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics, and Cloud) (I-SMAC) (IEEE, 2020), pp. 909–914 22. Chakrabarty, Brain MRI images for brain tumor detection datasets. Available online https:// www.kaggle.com/navoneel/brain-MRI-images-for-brain-tumor-detection. Accessed on 1 Aug 2020 23. P. Ghosal, L. Nandanwar, S. Kanchan, A. Bhadra, J. Chakraborty, D. Nandi, Brain tumor classification using ResNet-101 based squeeze and excitation deep neural network, in Second International Conference on Advanced Computational and Communication Paradigms (ICACCP) (IEEE, 2019), pp. 1–6 24. S. Minaee, Y.Y. Boykov, F. Porikli, A.J. Plaza, N. Kehtarnavaz, D. Terzopoulos, Image segmentation using deep learning: a survey. IEEE Trans. Pattern Anal. Mach. Intell. (2021) 25. M. Nazir, S. Shakil, K. Khurshid, Role of deep learning in brain tumor detection and classification (2015 to 2020): a review. Comput. Med. Imaging Graph. 91, 101940 (2021) 26. S. Iqbal, M.U. Ghani, T. Saba, A. Rehman, Brain tumor segmentation in multi-spectral MRI using convolutional neural networks (CNN). Microsc. Res. Tech. 81(4), 419–427 (2018)

Development of Low-Cost Intelligent Alert System for Underground Coal Mines Using GSM Vijaya Bhasker Reddy, Suneetha Ghatikanti, and Falguni Sarkar

Abstract Workplace safety and environment management in coal mine are major concern in present days, as we need more production. The main aim of this proposed paper is to design an intelligent system for monitoring environmental aspects like hazardous gasses, workplace temperature, humidity and safety aspect in form of proximity detection by integrating different sensors, ultrasonic distance measuring system and GSM technology, etc. In this context, a hazard detection and alert system has been configured in which the analogue quantities that need to be recorded on a regular basis are taken and converted into digital values using an analogue to digital converter. For detecting hazardous gasses in coal mine faces, a gas sensor is connected to the controller. The Ultrasonic sensor has been used to measure the certain distance which provides an alert to a person through a buzzer, when the mine trucks/haulage carts are nearing to the person. In case of configuring alert and communication system, Arduino UNO and Global System for Mobile Communication (GSM) module have been used to establish an Short Message Service (SMS) dispensing alert system for communicating the chance of dreadful happenings to mine operators, so that the mine management can take necessary proactive measures to moderate the situation. The prototype model of designed system was tested for the purpose and effectiveness of the system will be examined in an underground Mine coal mine. Keywords Coal mines · Intelligent system · GSM · Sensors · Arduino UNO

V. B. Reddy (B) Department of Electronics and Communication Engineering, KG Reddy College of Engineering & Technology, Hyderabad, Telangana, India e-mail: [email protected] S. Ghatikanti Usharama College of Engineering and Technology, Vijayawada, Andhra Pradesh, India F. Sarkar Department of Mining Engineering, National Institute of Technology, Rourkela (NIT-Rourkela), Rourkela, Odisha 769008, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Biswas et al. (eds.), Microelectronics, Circuits and Systems, Lecture Notes in Electrical Engineering 976, https://doi.org/10.1007/978-981-99-0412-9_27

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1 Introduction Safety management through proper workplace environment monitoring and proximity detection (for presence of human near moving transportation machineries) in underground coal mines have gradually evolved into major areas of concern for the regulators, mine management; people employed in mines, general public and the government. The critical effect of any hazard can be substantially reduced to cause minimum lives by quickly communicating the information regarding the hazards to the proper concerns [1]. In this context, there is a need to develop active detection and quantification systems concerning workplace temperature, noxious gases (CO2 , CO, NOx and SO2, etc.) and inflammable gas (CH4 ) to quickly analyse the underground work environment and accurately provide early warning to evacuate workers from the hazardous workplace. Digital temperature-humidity compound sensor with advanced sensitivity, first sensing capacity and long-term stability is utilized in various systems. Methane, oxygen, CO2 , CO, NOX and SO2 concentration sensors (readers) had easily been connected to ZigBee nodes to sense the environment [2]. Sensors-based proximity detection system also can help to reduce the underground haulage and transportation related accidents. Latest proximity application and systems presented in mines include collision avoidance of vehicle systems and Strata Proximity Systems [3–5]. In this paper, application is developed using wireless sensor network all over the world for real-time monitoring system for inside regulated and hazardous mining environment [6]. This paper represents an instrumentation plan comprising temperature sensor, gas sensor, infrared sensor, ultrasonic sensor (for proximity detection) and remote advancements like Global System for Mobile Communication (GSM), etc. to ensure the workplace safety in underground coal mines. This framework screens encompassing ecological boundaries like temperature, moistness and various poisonous gases and so forth [7]. This framework likewise gives us an early caution note, which will also useful for all mine workers inside the operating mine to save their life prior to any misfortune happens. The organization of paper is as follows; starting with introduction of the system to be designed along with methodologies used and their principles. Next parts comprise proposed methodology with efficient sensing techniques along with information about necessary modules used for system design, testing and verification of an individual sensing element with specific samples. Finally results of system and conclusion have been appended at the end.

2 Literature Review Wu et al. [7] designed a coal mineshaft observing framework utilizing Bluetooth wireless media-based remote transmission framework. Bluetooth-based set up was configured with standard low-power minimal expense remote wireless media interface and controllable programme opening framework. This paper portrays the

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advanced foundation, specialized elements and the construction of the convention heap of Bluetooth technology innovation, and proposed the arrangements based on the Bluetooth having regulator interface network correspondence for the purpose of intricacy of its turn of events. Ahalya et al. [8] Developed DCS-based Coal Mine-Monitoring System based on RS485 Bus, RS485 transport structure which upholds multipoint and two-way correspondence. Thus, this kind of framework can be created utilizing normal 8bit microcontrollers. It enjoys the benefits of straightforward circuit design and low expenses. Muduli et al. [9] proposed a Wireless Sensor Network-based fire and gas monitoring system for underground coal mines by using computational facilities of fuzzy logic approach for enhancing the reliability for rapid decision-making to alleviate the mine fire hazard. Ramya and Palaniappan [10] designed microcontroller-based toxic gas, which is generated naturally in underground mines, detecting and alerting system. The hazardous gases like LPG and propane were sensed properly and displayed each and every second in the monitoring display unit. Vastava et al. [11] discussed about utilization of Arduino Uno controller board for various wireless sensor network applications also provided detail description of ports and connectivity of sensors and other input–output devices. Bo et al. [12] proposed a mechanism of a Web of Things (WoT)-based remote observing framework that exploits remote sensor networks in blend with the CAN transport correspondence procedure that abstracts the underground sensor information and capacities into WoT assets to offer administrations utilizing authentic state move (REST) style. We likewise present three distinctive carried out situations for WoT-based remote checking frameworks for coal mineshaft wellbeing, for which the framework execution has been estimated and investigated. At last, we depict our decisions and future work. Xia et al. [13] developed a new calculation procedure for multi sink WSNs dependent on transmission power control. Initially, a transmission power control calculation is utilized to arrange the ideal correspondence span and transmission force of each sink. Second, the non-uniform bunching thought is taken on to advance the group head determination. Li and Liu [14] proposed a remote sensor networks dependent on RSSI calculation. In the light of the full investigation of the underground remote signal transmission weakening model, RSSI calculation was used for distance estimations. The main objective of this proposed methodology is to provide a robust and high secure low cost-efficient monitoring system for coal mine worker with the help of high-end sensing elements which can sustain in such harsh environment.

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3 System Design and Description A hazard alert system is a device working together to detect the probable danger and hazards to the people through audio and visual appliances on site (through different ways) as well as can be communicated to a remote location on the surface or to the authority via some transmission system. The designed system is simply a oneway wireless alert system designed to alert the upsurge of the impacts of various environmental aspects such as noxious gasses, work place temperature, humidity and collision hazard associated with underground transportation system. The hazards are detected using different sensors for noxious gasses, temperature, humidity and ultrasonic sensors for proximity detection. The whole system runs at an operating voltage less than 12 V. The block model of the system is shown in Fig. 1. Flow chart for alert signal processing and SMS sending system is depicted at Fig. 2. The system is based on a distinctive system modules and different components such as: . Sensors for sensing physical phenomena . Audio and visual Alert module . Microcontroller and GSM module

Fig. 1 Block model of the proposed system

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Fig. 2 Flow chart for alert signal processing and SMS sending

. Liquid crystal display (LCD) . Receiver/cell phone (for receiving SMS).

3.1 Sensors To sense gasses, temperature and humidity gas sensors, temperature sensors and infrared sensors were used. In the event of rise in temperature due internal combustion of coal, the temperature around the sensor will increase which will be sensed by the sensor. In the event of rise of temperature beyond the threshold limit, the circuit activates the AVA. The alert mechanism is shown in Fig. 3. Same mechanism of alert systems becomes activated while over concentration of noxious gasses in coalmine

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air. Similarly, ultrasonic sensor integrated in system was used for detecting human presence near the moving transportation vehicles. An infrared sensor is an electronic instrument that is utilized to detect certain qualities of its environmental elements. Infrared sensors are additionally fit for detecting the excess humid condition at mine work face (Table 1).

Fig. 3 Temperature detection

Table 1 Threshold limits of various noxious gas sensors chosen as per DGMS(S&T)/Tech. No 1 of 2018 S. No.

Gas

Maximum allowable concentration Percentage (%) by volume

PPM 5000

1

Carbon dioxide

0.5

2

Carbon monoxide

0.005

50

3

Nitric oxide (NO)

0.0025

25

4

Nitrogen dioxide (NO2 )

0.0005

5

5

Sulphur dioxide (SO2 )

0.0005

5

6

Hydrogen sulphide (H2 S)

0.0005

5

7

Aldehydes

0.001

10

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3.2 Audio Visual Alert Module (AVA) On response to gasses, temperature, humidity and human presence the Audio (buzzer) Visual Alert modules will be activated and consequently persons can act. This AVA module can be placed at conspicuous locations at mine working face.

3.3 Liquid Crystal Display (LCD) Liquid Crystal Display (LCD) is the innovation utilized for displaying readings continuously in note pads, calculators and other more modest PCs. LCD showcases a lot slenderer than cathode beam tube (CRT) monitors. Small size LCD monitor is connected with the circuit displays the temperature and gas readings continuously.

3.4 Microcontroller and GSM Module In this study Arduino UNO has been used as microcontroller. The Arduino UNO is a board comprise of microcontroller ATmega328. It has an aggregate of 14 computerized information or yield pins, 6 simple data sources, a 16 MHz artistic resonator, a force jack, an ICSP header, a USB association and a reset button. It comprises of all that expected to help the plan of microcontroller-based framework; with which it can be basically associated to a PC with a USB link or force it with an AC-toDC connector or battery to begin [11, 15]. It is comparatively cheap, very easier to programme and feasible technically. The Global System for Mobile Communications is an open, computerized cell technology that is used to provide portable voice and data services. Global System for Mobile Communication (GSM) is a computerized mobile phone system that is widely used in Europe and other parts of the world. GSM is the most widely used of the three computerized distant phone developments, and it employs a variety of Time Division Multiple Access (TDMA) techniques (TDMA, GSM and CDMA). GSM digitizes and packs data before sending it down a channel with two different surges of client data, each time allowing enough room. It may operate in the recurrence bands of 900 MHz or 1800 MHz, and it upholds voice calls and information move velocities of up to 9.6 kbit/s, along with the transmission of Short Message Service (SMS) [16].

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Fig. 4 Noxious gas detection

4 System Configuration In order to establish an effective underground Coalmine environmental impact alert/communication system, proper integration of sensors, LCD, microcontroller (Arduino UNO has been used) and GSM module is essential. Figure 3 depicts a system configuration for alert signal processing and SMS sending system in case of rise in temperature. Similarly, noxious gas detection and object movement detection (proximity detection of human) issues have been examined by the application of the configurations presented in Figs. 3 and 4.

5 Experimentations and Results Arduino IDE is software tool used to work with ATMEGA microcontrollers. The application in IDE developed and simulated using C++ language and cross compilers of the respective language.

5.1 Temperature Monitoring Temperature measured continuously and updated the same on to LCD display shown, whenever temperature exceeds the threshold given, automatically buzzer will start and alert the people to safe guard themselves.

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5.2 Gas Detection Whenever any toxic gases release within the coal mine workplaces or in the region of working, this particular sensor configuration (Fig. 4) will detect and give information to control unit and make buzzer/AVA to alerts all the nearby people working to ensure the problem arises and get it rectified or move away.

5.3 Object Sensing Using Ultrasonic Sensors Any object like track train which carries coal also will be detected within the working range of people and let them alert and move away from track to save themselves. The schematic diagram developed for simulation and testing of various sensor operations are shown in Fig. 5. The main objective of this project is to reduce the risk at the coal mines by using intelligent techniques which are very useful and very modern. This is achieved by using a low-cost alerting system through GSM. It is used to monitor the gas, temperature and humidity every time. It also produces a buzzer sound when there is breach in threshold limits. An LCD display is connected in which the output is displayed. Such advanced technology is developed by using GSM. The various data sensing information from sensors are sent to authorize person through GSM.

Fig. 5 Schematic diagram for simulations

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6 Conclusion A low-cost system with measurement of multiple mine environmental parameters ensures the changes in state of parameters with respect to given threshold level and alert the people. The proposed system uses various sensors to measure temperature, poisonous gases and also detects the movement of objects like mine vehicles and trains used for coal and material transportation and alert the miners who are working in that particular area. It also sends a message to the authorized person to take necessary precautions to safeguard the miners. Acknowledgements The authors of this paper are very much thankful to the all-laboratory staffs for providing supports during all installations and setup of equipment’s required for this study. The concepts, theoretical or practical, mentioned in this paper are of the authors and not necessarily of the organizations to which they serve. For this research, there are no funding agencies were involved.

References 1. F. Sarkar, P.K. Adhikari, A. Mangal, Development of a hybrid type mine hazard alert system (MHAS) for inhibiting the disastrous incidences due to fire hazards, water inrush and strata failure in underground mines: an experimental trial. J. Inst. Eng. India Ser. D 102, 39–46 (2021). https://doi.org/10.1007/s40033-021-00260-7 2. M.A. Moridi, Y. Kawamura, M. Sharifzadeh, E.K. Chanda, M. Wagner, H. Jang, H. Okawa, Development of underground mine monitoring and communication system integrated ZigBee and GIS. Int. J. Min. Sci. Technol. 25, 811–818 (2015). https://doi.org/10.1016/j.ijmst.2015. 07.017 3. D. Kumar, Application of modern tools and techniques for mine safety and disaster management. J. Inst. Eng. Ser. D 97, 77–85 (2016). https://doi.org/10.1007/s40033-015-0071-y 4. D. Kent, Digital networks and applications in underground coal mines, in 11th Underground Coal Operators’ Conference, University of Wollongong, The Australasian Institute of Mining and Metallurgy (2011), pp. 181–188 5. W.H. Schiffbauer, An active proximity warning system for surface and underground mining applications, in ME Annual Meeting, Denver, 26–28 Feb 2001 6. Y.S. Dohare, T. Maity, P.S. Das, P.S. Paul, Wireless communication and environment monitoring in underground coal mines—review. IETE Tech. Rev. 32, 140–150 (2015). https://doi.org/10. 1080/02564602.2014.995142 7. Y. Wu, F. Guo, Z. Meng, The study on coal mine using the bluetooth wireless transmission, in 2014 IEEE Workshop on Electronics, Computer and Applications (2014), pp. 1016–1018 8. G. Ahalya, P. Suresh Babu, P. Prabhakar Rao, Development of coal mine safety system using wireless sensor networks. Int. J. Eng. Sci. Adv. Technol. 3(3), 74–78 (2013) 9. L. Muduli, P.K. Jana, D.P. Mishra, Wireless sensor network based fire monitoring in underground coal mines: a fuzzy logic approach. Process Saf. Environ. Prot. 113, 435–447 (2018). ISSN 0957-5820. http://doi.org/10.1016/j.psep.2017.11.003 10. V. Ramya, B. Palaniappan, Embedded system for hazardous gas detection and alerting. Int. J. Distrib. Parallel Syst. (IJDPS) 3(3), 287–300 (2012) 11. S.S.S. Vastava, B. Vandana, M. Bhavana, R. Gongati, Automatic movable road divider using Arduino UNO with Node Micro Controller Unit (MCU). Mater. Today Proc. (2021). https:// doi.org/10.1016/j.matpr.2021.05.622

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12. C. Bo, C. Xin, Z. Zhongyi, Z. Chengwen, C. Junliang, Web of things-based remote monitoring system for coal mine safety using wireless sensor network. Int. J. Distrib. Sens. Netw. (2014). https://doi.org/10.1155/2014/323127 13. X. Xia, Z. Chen, H. Liu, H. Wang, F. Zeng, A routing protocol for multisink wireless sensor networks in underground coalmine tunnels. Sensors 16, 2032 (2016). https://doi.org/10.3390/ s16122032 14. J. Li, H. Liu, A new weighted centroid localization algorithm in coal mine wireless sensor networks, in 2011 3rd International Conference on Computer Research and Development (2011), pp. 106–109. http://doi.org/10.1109/ICCRD.2011.5764256 15. Functionality of arduino and PIN structures. https://engineering.eckovation.com/arduino-arc hitecture-explained/ 16. GSM module and its working with specifications. https://www.elprocus.com/gsm-architecturefeatures-working

Analysis of an IoT-Based SDN Smart Health Monitoring System M. Tejeswara Kumar, N. V. R. Vikram G, and Punyaban Patel

Abstract Health specialists and scientists advocated for the use of IoT-based remote health monitoring devices to track the health of the senior citizens and critically challenged patients. However, such technologies may generate massive amounts of data, necessitating the security and privacy of that data. Various health monitoring system’s (HMS) safety and privacy issues propose the management of a safety IoTbased health monitoring system, privacy, and dependable service delivery to patients and seniors to minimize and avoid health risk. High quality of life is dependent on good health. A continuous health care monitoring system is required for healthcare, and the data is stored in the cloud via a network, allowing doctors and nurses to provide patients with rapid treatment. IoT is the most recent developing technology for healthcare that is associated with electronic devices such as sensors and smart phones that have become part of everyday life. Sensors and wireless sensor networks (WSN) are used by the Internet of Things to interact with patients to recognize medical concerns and communicate with them. Sensors collect a vast amount of data that is physically linked to the patient. Integrate data in the cloud with the aid of Software Defined Networks (SDNs). SDN keeps track of sensitive information and ensures the safety of medical records. It will assist not just patients but also health care professionals in safeguarding particular patient data. SDN not only secures data but also prevents traffic from flowing from source to destination. This study intends to build a smart health care system with SDN by utilizing an IoT application to collect data via sensors and then stores it in the cloud. Keywords HMS · SDN · WSN

M. Tejeswara Kumar · N. V. R. Vikram G (B) Department of ECE, Vignan’s Foundation for Science, Technology and Research (Deemed to be University), Guntur District, Andhra Pradesh, India e-mail: [email protected] P. Patel Department of Computer Science and Engineering, CMR Technical Campus, UGC Autonomous, Kandlakoya, Medchal, Hyderabad, Telangana 501401, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Biswas et al. (eds.), Microelectronics, Circuits and Systems, Lecture Notes in Electrical Engineering 976, https://doi.org/10.1007/978-981-99-0412-9_28

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1 Introduction The Internet of Things (IoT) is a fast-expanding architecture that intends to connect millions of things and smart devices to the Internet with little to no human intervention [1, 2]. A wide network of smart network devices generates tremendous traffic, which is becoming increasingly troublesome due to the usage of traditional network design [3], a vast network of smart network devices creates massive traffic and is becoming increasingly problematic through the use of conventional network design [3]. IoT applications include HVAC. These systems include intelligent houses, industrial automation, and sanitary equipment, but are not limited to these. To function successfully with low-power devices, the heterogeneity of IoT devices necessitates common communication technologies and protocols in a noisy, loss-free communication environment. IEEE 802.15.4, Zigbee, Bluetooth, and Wi-Fi [1] are the IoT communication technologies. Because of how healthcare is moving toward the end of IoT-enabled equipment [4], these gadgets are interconnected via a network. Although there are certain advantages, obstacles also arise. A network that can handle the problems is necessary for such an IoT-enabled network [1]. One of the important facts on the health of each patient is hence safety. In addition, this network might be made up of several networks. When a device is utilized for the patient record for the connected IoT-enabled medical photography, maximum bandwidth from the relevant hospital network and hence flexibility is needed. The traditional networks need innovation and the ability to address the above difficulties. In this study, smart health services are deployed on top of the SDN environment. The IoT uses many sensor types in different areas and a full analysis has been conducted [2]. This research work mostly contributes to this, . SDN technology in the cloud is designed to develop a smart health care system. . The built-in smart health care app ensures the end-users receive reliable health reports. . Smart healthcare gives smart warnings to end-users when the threshold values change. In this study, three types of traffic generated in a WBAN are investigated. The first category is physiological data, which is further separated into emergency and routine data. The other and third modes of transportation are sensor health and environmental data. We use an SDN capable of boosting WBAN performance in order to manage such heterogeneous traffic. Our compensation increases when we send data to a PDA managed by SDN and acting as an SDN switch. The PDA serves as the data plane for categorizing and transferring various types of traffic to their appropriate servers.

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2 The Impact of SDN and IoT in the Data Rich Health Care Industry Needless to say, healthcare, like every other business, is at the forefront of change, from network infrastructure to virtual treatment, digitalizing everything it requires. Our analog temperatures and laboratory results have been reviewed for hours outside of doctor’s offices in the past. Technology is still evolving, and there is a lot of promise in healthcare to lessen the risk of patient and practitioner burnout. The health business is data-rich, and a number of IoT and Big data Analytics applications are available to provide patients with the best possible care. In the field of healthcare nowadays, enormous groups of fragmented data are delivered globally through numerous systems, networks, and data centers [5]. The biggest difficulty is to add a steady yet healthy dose of next-generation communication technologies. “Software Defined Networking” helps to drive the Internet by providing optimal resource utilization, on-demand bandwidth, suited uptime, flow monitoring, security, and quality of sustainable service [6]. We are still early though, and it is reasonable to say that everyone is seeking SDN or IOT scenarios.

3 System Model and Testbed The end-to-end system design is displayed in Fig. 1. The architecture of the system consists of a patient, PDA, SDN, servers at home. There are WBAN sensors on your body and IoT sensors in the same room. The climatic data is transmitted to the SDN device (PDA). The SDN device (PDA), via the communication services such as WiFi or 3G/4G cellular networks, transfers the traffic to the backend server based on the flow tables set by the SDN controller.

3.1 Smart Health Care Traffic Types The PDA is ideally set up to fulfill the expectations and a large number of monitored and remote-controlled smart health care recipients, as an SDN switch that is configured by a remote SDN controller. Flows can be classified in smart health care systems in the form of data on physiological, environmental, and sensor health. Physiological or vital sign data can alternatively be classified as routine physiological and emergency physiological data [7]. Data can be acquired indoor or outdoor. The health of the sensors indicates the sensor state, like battery level, RSSI, the quality of the link, etc. The PDA can be set up for classification and transmission of traffic flows generated by the various sensors to their respective destinations. Figure 2 shows the traffic flow from the PDA sensors to the associated terminal systems.

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Fig. 1 System architecture and testbed

3.2 Wireless Sensor Network With Crossbow MICAz motes running Tiny OS embedded operating system, both WBAN and IoT sensors are implemented. The WBAN is implemented with 10 sensors representing the WBAN’s key sign sensors. To measure environmental data, the IoT network is installed using 5 sensors. The PDA is responsible for the transmission of data from the backend servers in both networks, as shown in Fig. 2. The main goal of this testbed is to undertake numerous experiments under different situations to examine the functioning of the WBAN and IoT network and to collect server datasets for future server processing. The test bed was put inside an academic unit building in an office environment. The office has a wireless IEEE 802.11ac network and works at a range of roughly −43 dBm at the 5 GHz band with RSSI. The MICAz mote works with a band of 2405 MHz and employs the CC2420 Chipcon. Compliant with the ready-to-use radio frequency transceiver IEEE 802.15.4 and ZigBee integrated with the Atmega128L. For the WBAN, the IoT network, and the gateway, we used varied RF transmission strengths. All motes other than the base are powered by an

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Fig. 2 IoT and SDN architecture

AA battery (sometimes referred to as the gateway or node 0). The base has a USBbased communication, system programming, and power connection to the MIB520. The motors run between 2.7 VDC and 3.6 VDC. You can tune the RF power transmission from 0 dBm (1mW) to −25 dBm (0.003 mW). Lower interference could be an advantage for low power of 17.5 mA at full power to 8.5 mA.

3.3 SDN Configuration SDN use is based on simplifying the operation of PDAs and shifting the control functions at the heart of this architecture. This is understood by the large-scale deployment of the smart health system. WBAN and IoT generate information on health and management which is communicated to the PDA. In addition to the additional capabilities that the WBAN and IoT networks demand, the PDA of this architecture also takes over the functions of the SDN switch. The PDA flow tables are created by the SDN controller. The SDN controller performs the management functions of the WBAN and the IoT networks [8]. Based on the traffic type of each sensor within the WBAN and the IoT network, the SDN controller manager determines

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the final objective. The PDA will be pushed as flow tables for this information. The server management functions define defects, settings, accounts, performance, security, mobility, and ID management (FCAPS).

4 IOT and Security in Healthcare The SDN-based, smart health care system collecting patients’ health records in realtime is displayed in Fig. 2. Cloud-based patient health information includes many IoT services including medicine guidance, sensor calibration, accuracy, and confidence. The right disease is also diagnosed and alert services are provided to the patient and doctors. The MQTT and CoAP are used to enhance communication. MQTT and CoAP are used. It starts to felt and it is broadcast into the cloud when sensors are connected to the human body. Many cloud services directly support healthcare and others provide the administration of safety and storage [5]. These are the most important IoT health services in Fig. 3. The suitable sensor, which best corresponds to the ailment and condition diagnosed, can successfully choose all these services. Many useful sensor functions with a threshold value simply displayed. The alerts are delivered or action is done based on the threshold value. The following Fig. 4 exhibits revenue statistics for numerous applications such as industrial, medical, automotive, consumer electronics, infrastructure, and other uses of IoT products [9]. This graphic depiction emphasizes the relevance of sensors and the need for future medical sensors. In the area of healthcare and its applications, this technology has remarkable progress. We should expect more sensors to be used tremendously in the next generation.

5 Results and Discussion Identification of Leading Researchers in IoT Security See Fig. 5. Keyword Clustering and Evolution of Research on IoT Security The minimum number of keyword occurrences for a 5 was set at 147 from the 3142 keywords from the 1365 studies, and the keyword co-occurrence network analysis conducted on 146 keywords, with the exception of IoT, present during all the studies given the use of Boolean Operation AND. The generated co-occurrence network of 10 clusters is shown in Fig. 6 and the network and cluster information is summarized. In Fig. 6, the size and thickness of the node is proportional to the weight of the connection between keywords. The number of keyword events is the same. The node color shows the cluster of the node.

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Fig. 3 Primary services of IOT in healthcare

Fig. 4 Revenue statistics

As demonstrated above, the data plane works as a router. The Raspberry pi in this situation is our data plane that generates streaming data. The chart below shows the temperature ranging from five minutes to the Thingspeak cloud readings (Fig. 7).

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Fig. 5 Co-citation network of 52 publications with 8 clusters, the most often cited (one color per cluster). CitNetExplorer was used to create the network

Fig. 6 Keyword co-occurrence network obtained using VOS viewer

6 Conclusion With sensors and network connectivity management, the Internet of Things and SDN develop as a thriving technology. These are intertwined and must overcome

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Fig. 7 Temperature data from Thingspeak

challenges like as programming and data management in order to satisfy the needs of customers. There are several challenges in the traditional health care system that can be addressed by combining two technologies. SDN is a novel concept to revolutionize network infrastructure. The SDN controller and transmission device were simulated using the Mininet software in a virtual machine [10]. The results show that the approach is simple, trustworthy, and efficient, and that it may be used to smart health systems with minimal overhead. More management functions, particularly the management of network and device problems, will be the focus of future study. Our research delivers IoT keyword clusters, keyword trends, subject classifications, and these trends to interested researchers [5]. There, we summarize and explain the evolution of keywords and issues that have a growing influence. We suggest that research be undertaken to establish a safe, decentralized framework that integrates edge computing, ML-based SDNs, and blockchain, and research into vehicles and UAVs as smart M-IoT objects.

References 1. R.K. Kodali, G. Swamy, B. Lakshmi, An implementation of IoT for healthcare, in 2015 IEEE Recent Advances in Intelligent Computational Systems (RAICS), Trivandrum, 10–12 Dec 2015 2. Internet of Things, European research cluster on the internet of things [Online]. http://www. internet-of-thingsresearch.eu/aboutiot.htm 3. P. Gupta, D. Agrawal, J. Chhabra, P.K. Dhir, IoT based smart healthcare kit, in 2016 International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT) 4. S. Shaikh, V. Chitre, Healthcare monitoring system using IoT, in 2017 International Conference on Trends in Electronics and Informatics (ICEI) (2017), pp. 374–377

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5. S.R. Islam, D. Kwak, M.H. Kabir, M. Hossain, K.S. Kwak, The internet of things for health care: a comprehensive survey. IEEE Access 3, 678–708 (2015) 6. S. Bera, S. Misra, A.V. Vasilakos, Software-defined networking for internet of things: a survey. IEEE Internet Things J. 4, 1994–2008 (2017) 7. D. Kreutz, F.M.V. Ramos, P.E. Verissimo, C.E. Rothenberg, S. Azodolmolky, S. Uhlig, Software-defined networking: a comprehensive survey. Proc. IEEE 103, 14–76 (2015) 8. C.W. Zhao, J. Jegatheesan, S.C. Loon, Exploring IoT application using raspberry pi. Int. J. Comput. Netw. Appl. 2(1), 27–34 (2015) 9. G. Keramidas, N. Voros, M. Hübner, Components and Services for IoT Platforms (Springer International Pu, Cham, 2016) 10. H. Kim, N. Feamster, Improving network management with software defined networking. IEEE Commun. Mag. 51, 114–119 (2013) 11. N. Bizanis, F.A. Kuipers, SDN and virtualization solutions for the internet of things: a survey. IEEE Access 4, 5591–5606 (2016) 12. O. Salman, I. Elhajj, A. Chehab, A. Kayssi, IoT survey: an SDN and fog computing perspective. Comput. Netw. 143, 221–246 (2018) 13. D.V. Dimitrov, Medical internet of things and big data in healthcare. Healthc. Inform. Res. 22(3), 156–163 (2016)

Internet of Things in Agriculture Industry: Implementation, Applications, Challenges and Potential Kiran Jot Singh, Divneet Singh Kapoor, Anshul Sharma, Khushal Thakur, Tanishq Bajaj, Ashwin Tomar, Sparsh Mittal, Baljap Singh, and Raghav Agarwal Abstract The Internet of Things (IoT) is allowing agriculture, particularly arable farming, to become more data-driven, resulting in more timely and cost-effective agricultural production and management while reducing environmental impact. In comparison with other agricultural systems, this study would involve an empirical evaluation of current and prospective IoT applications in arable farming where spatial details, vastly diverse landscapes, mission variety, and mobile devices provide unique challenges to overcome. The analysis describes the state-of-the-art in terms of deployed technologies. It discusses present and future applications and provides an overview of current and possible applications, as well as problems, viable alternatives, and implementations. Finally, it explores any potential IoT applications in arable agriculture. Smart phones, intelligent sensor control, middleware platforms, integrated field management systems across the supply chain, and automated vehicles and robots all stand out as present issues preventing arable farming from becoming smart. Interoperability is a key obstacle across all layers of an IoT system’s design, which may be solved through shared standards and protocols, and it arises during implementation. The paper examined has identified and addressed in detail difficulties such as data privacy, cost, power consumption, network latency, data analytics, and security, among others. Different resolutions to all recognized challenges are presented by technologies such as artificial intelligence, intelligent data managing, and further addressed in depth issues such as availability, device power usage, latency of network, big data processing, data protection, and protection, among others. Keywords Internet of things · IoT · Agriculture · Machine learning

K. J. Singh · D. S. Kapoor (B) · A. Sharma · K. Thakur · T. Bajaj · A. Tomar · S. Mittal · B. Singh · R. Agarwal Embedded Systems and Robotics Research Group, Chandigarh University, Mohali, Punjab 140413, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Biswas et al. (eds.), Microelectronics, Circuits and Systems, Lecture Notes in Electrical Engineering 976, https://doi.org/10.1007/978-981-99-0412-9_29

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1 Introduction The world has seen a rapid growth in its population and food utilization, while worldwide environmental change impacts the guaranteeing of food security in an economical way. Information-driven farming is one of the most techniques and thoughts proposed to broaden creation effectively while weakening its natural effect [7]. Information-driven advances by and large are rapidly progressing with the occasion of the Internet of Things (IoT) and ought to turn into a pivotal piece of the more drawn-out term of cultivating [5, 9, 27, 28]. Savvy farming, likewise called Agriculture 4.0 or computerized cultivating [6], is creating past the elegant idea of accuracy horticulture, which puts together its administration rehearses with respect to spatial estimations to a great extent due to Global Positioning System (GPS) signals. Brilliant cultivating puts together its administration undertakings likewise with respect to spatial information yet is upgraded with setting mindfulness and is enacted by continuous occasions, improving the presentation of heretofore accuracy horticulture arrangements [25, 28]. Moreover, smart farming typically joins insightful administrations for applying and overseeing information and communication technologies (ICT) in cultivating and permits cross-over combination all through the whole agri-evolved way of life with respect to sanitation and detectability [25]. The IoT idea was presented by Kevin Ashton in 1999 regarding connecting radio-frequency identification (RFID) for supply chains to the web [2], however, has no authoritative definition. It infers, nonetheless, the association of an organization of “things” to or through the web without direct human mediation. “Things” are frequently any item with sensors as well as actuators that is particularly addressable, interconnected, and open through the overall organization, for example, the web. The apparatus of IoT in agribusiness is profitable because of the probability to watch and control numerous different boundaries in an interoperable, adaptable, and open setting with expanding utilization of heterogeneous mechanized segments [10], moreover to the inescapable necessity for recognizability. As a consequence of IoT, farming is turning out to be informationdriven, for example, settling on educated constant choices for dealing with the ranch, decreasing vulnerabilities, and shortcomings, and as a result, diminishing its ecological effect. The effect of the Internet of Things (IoT) and associated gadgets during this current world is evident. Today, it is reached all over the place, from home to wellbeing area, savvy urban communities, wellness, to the modern area. Its quality is regularly seen in many enterprises, and consequently, the space of agribusiness is not unique. In the past a while, several technological advancements have taken place in farming, and thus, today it is far more industrialized and technology-driven because it was decades before. Today, we will see the ranchers effectively using brilliant cultivating devices, which empower them to have better power over this method of developing yields and raising animals. There are cows’ global positioning frameworks, mechanized farming technology, and savvy nurseries. This makes it more efficient

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and predictable. To be specific, COVID-19 emphatically affects IoT in the agribusiness piece of the pie. Interruptions in the inventory network, and the lack of qualified specialists, have moved its CAGR to 9.9%. Indeed, according to ongoing reports, the savvy outlining piece of the pie is set to reach $6.2 billion by 2021. Simultaneously, the worldwide savvy horticulture market size is relied upon to significantly increase by 2025, coming to $15.3 billion (contrasted with being marginally more than $5 billion back in 2016). Since the market is as yet creating, there is as yet abundant freedom for organizations willing to participate. Building IoT items for agribusiness inside the coming years can separate you as an early adopter, and accordingly, help you make ready to progress. The literature was studied [1, 3, 8, 11–23, 26, 29, 30] extensively, and the common practices for IoT and its implementation in agriculture sector has been discussed in the subsequent sections.

2 IoT Implementation in Agriculture Industry As the Internet of Things (IoT) spreads, associated gadgets are advancing into brilliant urban areas, savvy homesteads, and shrewd horticulture. As associated gadgets become more common, the Internet of Things (IoT) is turning into a part of smart cities, smart agriculture, and smarter agriculture. The Internet of Things (IoT) helps the rural business in accomplishing its maintainable improvement objectives by giving organized arrangements that permit ranchers to boost yields while devouring less common assets. IoT in agribusiness is utilized by consolidating ceaselessly propelling AI and scientific devices to screen crops, map fields and give information to ranchers to levelheaded homestead the executives plan. Notwithstanding the information gathered by canny sensors for agribusiness, a key segment is mechanized equipment. IoT arrangements and rural use are developing consistently, which is the reason BI intelligence predicts that the quantity of horticultural IoT gadgets will arrive at 75 million by 2020 and develop by 20% yearly. The improvement of IoT applications is turning out to be progressively well known, and IoT’s portion of the overall industry in agribusiness will arrive at 5.6 billion by 2021. With the 5G-speed data transfer capacity, further applications will be made that will take the IoT and agribusiness higher than ever. Before this occurs, it is critical to see how IoT is utilized in agribusiness and whether the shrewd cultivating industry is reacting rapidly enough to steady development. IoT sensors can be utilized to gather information on plant wellbeing, water quality, soil dampness, and soil wellbeing. Programmed keen water system highlights sensors that help give constant data on the state of the dirt and water supply, just as the accessibility of water. The information gathered by these sensors is utilized to know the constant status of a harvest and to do it with the assistance of programmed water system. A portion of the conspicuous chances recorded in our IoT and farming use cases highlight the capability of brilliant agrarian arrangements

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in horticulture and their expected effect on rural efficiency and profitability development. As appeared, this incorporates savvy ranches, canny water system frameworks, and insightful plant the board.

2.1 Advantages of Using IoT in Agriculture As in different businesses, utilization of Internet of Things in horticulture guarantees beforehand inaccessible productivity, decrease of assets and value, computerization, and information-driven cycles. In agribusiness, nonetheless, these advantages do not go about as enhancements, yet rather the answers for the whole business standing up to an assortment of risky issues. Dominated effectiveness. The present horticulture is during a race. Ranchers need to develop more item in falling apart soil, declining land accessibility, and expanding climate variance. IoT-empowered horticulture permits ranchers to watch their item and conditions progressively. They get bits of knowledge quick, can foresee issues before they occur and settle on educated choices while in transit to stay away from them. Moreover, IoT arrangements in agribusiness present robotization, for example, request-based water system, preparing and robot reaping. Diminished assets. Numerous IoT arrangements are centered around enhancing the usage of assets—water, energy, and land. Exactness cultivating utilizing IoT depends on the information gathered from different sensors inside the field which assists ranchers with apportioning assets to inside one plant. Cleaner measure. A comparable has pertinence to pesticides and manures. Not exclusively do IoT-based frameworks for exactness cultivating help makers save water and energy and, subsequently, make cultivating greener, yet additionally altogether downsize on the usage of pesticides and manure. This methodology permits getting a cleaner and more natural end result contrasted with customary farming strategies. Readiness. One among the upsides of utilizing IoT in farming is that the expanded nimbleness of the cycles. Due to ongoing checking and forecast frameworks, ranchers can rapidly answer any critical change in climate, mugginess, air quality additionally in light of the fact that the strength of each yield or soil inside the field. Inside the states of most extreme climate changes, new capacities help horticulture experts save the harvests.

2.2 Current Implementation of IoT in Agriculture To exploit IoT, it is imperative to design early and to embrace every one of the fundamental gadgets and administrations. The advantages are copious, including customizations, capacity to arrange, and ceaseless criticism on the general execution

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of the framework. To lessen costs, IoT network can likewise be utilized to make custom web applications on top of an IoT arrangement, along these lines disposing of the substantial information base administration measure. This can possibly essentially lessen the expense of entering the information. Horticulture needs particular Internet of Things (IoT) sensors that can gather data about the ranch. To make an IoT answer for horticulture and agribusiness, one ought not to fail to remember the rudiments of choosing sensors and gear. Given the expected advantages of IoT applications in horticulture, it is reasonable that a few ranchers are going to the utilization of the innovation on their homesteads. In the agrarian area, we are searching for profits by the IoT in zones like food security, bug control, water the board, crop assurance, water system, and so forth. From our experience, there are different manners by which the Internet of Things can improve horticulture and farming. Beneath we layout a portion of the likely advantages of IoT in agribusiness and how it will help ranchers meet worldwide food needs in the years ahead. Here are a couple of instances of how the Internet can be utilized—of—things to bring ranchers the best advantages as far as food security, bother control, water the executives, water system, plant wellbeing and water system frameworks, and different parts of agribusiness, here and here. The advancement of IoT on ranches has prompted an expansion in the time and energy required for seeds to turn out to be completely developed plants. Simultaneously, the joining of the Internet—the things that are innovatively conceivable— has empowered ranchers to utilize these assets all the more proficiently and cost— successfully—more viably than any time in recent memory. Advances in IoT innovation in farming have diminished the quantity of steps in an agrarian cycle in which a seed needs time or assets to turn into a completely mature vegetable, and the development of IOT innovation for agribusiness has decreased this. Uses of IoT in horticulture help ranchers screen the water tank level, making the water system measure more productive. Machine orders accelerate far off observing continuously and limit the requirement for manual work and mechanization. This will make it simpler for ranchers to gather precise information on climate conditions. Moreover, the diminishing expenses of horticulture and IoT sensors, just as the accessibility of ease innovations, are required to urge more ranchers to utilize IoT in farming during the recuperation period of COVID-19. Brilliant sensors for agribusiness gather huge loads of information, and surprisingly the farming area has its offer. Regardless of whether it is shrewd sensors or IoT innovation, the utilization of prescient information examination can help ranchers settle on educated choices about what they can best use later on. Brilliant cultivating, for instance, utilizes sensors associated with fields and harvests to give information to agrarian designers and laborers to acquire knowledge and settle on better profitability choices. By utilizing shrewd horticultural sensors to screen the state of harvests, ranchers can correctly characterize the number of pesticides and manures they need to use to accomplish ideal productivity. While IoT applications offer continuous advantages that address recent concerns, maybe their most noteworthy benefit is the information that IoT-empowered sensors gather.

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The brilliant horticulture area is additionally expected to develop fundamentally to address these issues, driven by the normal expansion in the quantity of web associated things (IoT) for rural purposes. IoT arrangements and farming use are developing consistently, and BI intelligence predicts that almost 12 million horticultural sensors will be introduced worldwide by 2023. We currently realize that not exclusively is the selection of IoT arrangements in horticulture consistently expanding, yet the size of the worldwide market for “keen agribusiness” is likewise quickly expanding. The quantity of horticultural IoT gadgets will arrive at 75 million by 2020 and develop by 20% yearly. The market will stay dynamic as long as there are buyers associated with these gadgets. Yet, in the event that they are not as famous as they used to be, markets will in any case be exceptionally unique. It is essential to see how IoT is utilized in horticulture and whether the brilliant cultivating industry is responding rapidly enough to consistent advancement. Notwithstanding, there will be a great deal of advancement in the years to come, in IoT as well as in brilliant horticulture. To build up an IoT answer for farming, you need to choose a sensor gadget or make a custom one. To create IoT answers for agribusiness and horticulture, it is fundamental that you select the sensor gadgets. To fabricate an IoT arrangement in horticulture, we need a sensor gadget that is chosen or a custom gadget is made. On the off chance that you are building an IoT answer for agribusiness, you need a sensor gadget that you have picked or made yourself. For sensors, equipment support is a test that is of essential significance for IoT items in horticulture, as sensors are commonly utilized in the field and can without much of a stretch be harmed. For sensors, equipment support is vital, as they are not normally being used for significant stretches of time and can undoubtedly be harmed, yet for sensors this is certifiably not an auxiliary test, as they are ordinarily being used for a brief timeframe. Horticulture needs particular Internet of Things (IoT) sensors that can gather data about the homestead. Horticultural IoT sensors that can be associated with livestock and observed are very much like checking crops. Figure 1 illustrates the types of communication technologies used for data transmission in IoT-based agriculture applications.

Percentage

89 72

2G/3G/4G

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61

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48 ZigBee

45 LPWA

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Fig. 1 Communication technologies used for data transmission in percentage

18 NB-IoT

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Software implantation and benefits: Software as a Service (SaaS) . . . .

Not necessarily physical equipment on farms; No equipment on farms necessary; Each year or month, low-cost low-risk subscription plans available. Satellite images with high resolution for tracking, GPS geotagging, and weather analysis; . Control of the entire supply chain; . Data on chemical use and labor logs are available. . It is possible to combine it with existing devices and IoT’s. Hardware implementation and benefits: The Internet of Things (IoT) . Sensors, robots, drones, and cameras must be installed on farms to track and control them. . Equipment is both costly and delicate. . Hardware recurring repair costs; . Large initial investments are needed. . There is no supply chain management. . Since it is not scalable, each farm’s data must be handled separately. . There is no log information. . Integration with already installed systems is difficult. Agribusiness utilizes IoT to acquire bits of knowledge and screen ranches by consolidating robots, robots, sensors, and PC vision with insightful applications. Actual gear introduced on ranches screens and records information, which is then broke down. As the chances in IoT are expanding each day, various associations are equipping to exploit the IoT. For example, ranchers are utilizing IoT for checking crop wellbeing and to settle on educated choices, improving the harvest wellbeing the executives and supplement application to meet yield objectives. IoT is the base that opens up new entryways in horticulture. The incorporated framework is required to diminish the expense and dangers of cultivating, improved the yields, and give a superior.

3 Applications of IoT in Agriculture Industry IoT assumes part in the improvement of horticultural area. As of now, the main point of contention in the ebb and flow area is usage of assets like labor and water which is deficient in numerous pieces of the country. IoT is an organization of interconnected gadgets which can be move information productively without human inclusion. All cultivating area should be possible utilizing advanced mobile phones and IoT gadgets. The applications of IoT in the agriculture industry have been highlighted in Fig. 2. Precision agriculture. Precise farming, also known as precision agriculture, can be considered something that makes farming more regulated and precise when it comes

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Animals Monitoring & Tracking 11%

Water Monitoring & Controlling 7% Fertilization Monitoring 4%

Air Monitoring Humidity 5% Monitoring 11%

Temperature Monitoring 12% Precision Farming 16%

Soil Monitoring 13% Irrigation Monitoring & Controlling 16%

Fig. 2 Sector-wise applications of IoT in agriculture industry

to livestock raising and cultivation. The use of IT and different elements, such as sensors, control systems, robotics, self-sufficient cars, hardware automation, variable rate technology, and so on, are a key component in this farming management strategy. A number of primary developments characterizing the precision farming pattern are the introduction of connections by the producer to high-speed Internet, handheld devices, and secure low-caste satellites (for imagery and placement). Precision farming is one of the most renowned agricultural IoT applications and various organizations around the world are using this technique. Intelligent greenhouses. Greenhouse cultivation is a technique to improve the production of tomatoes, fruits, plants, etc. The environmental parameters of the greenhouses are controlled by manual action or proportional control. These approaches are less efficient since manual action leads to loss of manufacturing, energy loss, and labor costs. With the aid of IoT, an intelligent greenhouse can be built, which intelligently monitors and regulates the environment, removing the need for manual interaction. Different sensors to monitor the temperature according to plant needs are used to regulate the environment inside a clever greenhouse. If it is linked via IoT, we can build a cloud server to remotely access the device. This prevents the need for continuous manual surveillance. The cloud server also allows data collection and a control action inside the greenhouse. This architecture provides farmers with low manual operation with cost-effective and optimal solutions. It produces new and inexpensive greenhouses through the use of IoT sensors with solar power. In the greenhouse, IoT sensors provide information about lighting, pressure, humidity, and heat. These sensors will automatically control the actuators by opening a window, activating light, controlling a heater, switching on a mister or fan. Management of crops. IoT crop control systems are a further form of IoT processing and another part of precision farming. Similar to meteoric stations, they must be installed on the farm for the collection of basic crop statistics, from rainfall, and

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temperature to leaf efficiency and general crop health. So, to avoid infestations or diseases that could harm your return, you also track crop growth and other abnormalities. Semiosis and Arab, for example, represent the right way to implement this utility case. Intelligent farming predictive analytics. Agriculture precision and data mining go together. The use of predictive data analysis allows farmers to understand it and have key expectations: the probability of infestations/diseases, yield and plant harvesting, etc. While smart sensor technology and IoT technology are vital to the extremely applicable real-time data. Predictive data analysis systems help make agriculture internally more reliant and more stable and manageable on weather conditions. The consistency and quantity of yields will foresee, as can exposures to unfavorable weather conditions such as droughts and flooding, for example, from a seed portal. It enables farmers to pick yield features to maximize the provision of water and nutrients to each crop and increase production.

4 Challenges and Solutions 4.1 Challenges The overall investment volume in agriculture increased by about 80% in the last five years or so. According to experts, precision agriculture—an input and nutrient optimisation method, tillage equipment, fields and crops to maximize control, and farm yield calculation—has the ability to play an important role in fulfilling the increasing world population’s increased food requirements [24]. The size of the global precision agriculture industry at the end of this decade was calculated in one latest survey at around $4.6 billion—with the CAGR being just a little less than 12% between 2015 and 2020. The US alone is expected to save more than 14% between now and 2022 on the demand for smart farming applications. However, precise agricultural growth and proliferation were not as vigorous as previously predicted [31]. The sector faces a number of important problems, and in this article, we turn our attention to: Poor Internet Connectivity in Farms. Most farms are located in remote places with insufficient Internet access for fast transmitting speed. In addition, channels of contact can be obstructed by crops, canopies, and other physical obstacles. He states that these conditions increase the cost of transmitting and have led to the poor adoption of agricultural accuracy technologies. This cost will increase exponentially with the introduction of big data. With the help of empty TV frequencies, the farm beats allows farmers to conquer this obstacle. Experts noted that in rural areas this is particularly useful as inadequate television coverage frequently results in the existence of white spaces in television broadcasting frequencies licensed for use.

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Disrupted Connectivity to the Cloud. Today’s IoT technology in agriculture is like every other IoT framework, based on a cloud computing system—in this case, the Azure Platform of Microsoft. But Internet access is always insufficient in the farmers’ houses to stream large data sets into the cloud. Furthermore, there may be intrusion sources on farms, which also have negative consequences for cloud connections. Experts clarified that farmers need to use data-driven technology to increase yields, minimize business costs, and ensure environmental sustainability. Experts will support farmers in solving the challenges of using IoT in rural areas to understand all the advantages of agriculture using info. Developers concluded by saying that these and other functionality are yet to be developed but enable farmers to use precision farming to operate a more productive and efficient business.

4.2 Solutions Agriculture must scale up to meet its requirements in view of the unfavorable environmental and climate circumstances for a prosperous world population expected to reach 9.6 billion by 2050. Agriculture would continue to use creative approaches to meet the emerging demands of people in order to achieve a much-needed advantage. Internet of Things (IoT) agricultural applications would allow the industry’s efficiency improvement, costs reduction, waste reduction, and quality enhancement. IoT-based intelligent agriculture is a device designed with sensor-based monitoring and automation of the irrigation methods for agricultural land (soil, humidity, light, temperature, etc.). Land conditions can be observed by farmers everywhere. For example, when soil humidity is poor, it alerts the farmer; for the irrigation process, the farmer will be using sensors. In comparison with conventional approaches, IoT-based smart farming is highly effective [4]. Irrigation systems that are fitted with IoT not only save water, but also ensure that crops receive the right water for optimal growth. Instead of a pre-determined period dependent irrigation, the irrigation system depends on moisture levels. This technology is used by many organizations worldwide. In India, Sat Sure is a data analytics firm integrated with the agricultural sector in terms of satellite, weather, and IoT analysis to support farmers in their financial protection and crop insurance. The company has smartphone apps to provide information on crop and crop stress statistics in its region. It helps you decide whether to sow, irrigate, apply, or prepare fertilizer. The Government of Andhra Pradesh is now using the start-up solutions. Queens employs IoT as a comprehensive IoT solution in order to handle fresh foods, to provide quick and accurate grading solutions for fresh foods by capturing insights on spoilage, shelf-life and maturity, which enable farms achieve greater margins from the same product. The deployment of this technology presents unique challenges in India, however, particularly for small-scale farmers in rural areas with a lack of decent Internet connectivity and proper infrastructure without which progressive monitoring systems are useless. Farmers may also be discouraged by the high costs of IoT equipment and

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the difficulty thereof. The adoption and deployment of this intelligent agricultural technology is expected by strategic and comprehensive IoT application strategies and supporting government plans. With success, the economy of the farming industry would certainly be boosted. In India, therefore, it should be of primary importance to promote agriculture through digital transformation. The Internet of Things allows farmers to make educated decisions to boost crop yields, increase productivity, and improve operational efficiency along with better management tools which lead to minimal waste in good time. IoT technology allows intelligent agriculture to save farmers water using IoT technology. IoT apps and technology are used most prominently at the first level, as we allow farmers to determine what to harvest in the season to achieve their best yield by using GPS and a weather tracker. As never before, IoT transforms agriculture by rising agricultural production and enabling farmers to cope with the tremendous difficulties they face. Up till now, farmers have struggled to find new water conservation options and their response is today’s IoT technology. In addition to the importance of responsible environmental management, IoT supports agriculturists by reducing water use by using IoT technologies, networks and data collection to up to 25% in accordance with state and federal water legislation.

5 Conclusion To meet the demands of a rising population, more smarter, better, and more effective harvesting techniques are required. Innovation, security and nutrition labeling, and collaboration between farmers, suppliers, merchants, and buyers are all important factors in the development of innovative ways for increasing harvest production. This study considered all of these factors and highlighted the role of many innovations, notably the Internet of Things, in making agribusiness more bright and capable of meeting future expectations. As a result, remote sensors, unmanned aerial vehicles (UAVs), cloud computing, and communication improvements are all thoroughly investigated. In addition, several IoT-based designs and phases for farming applications are provided. A list of the business’s ebb and flow issues, as well as future assumptions, is compiled to guide specialists and architects. Internet of Things (IoT) concerns are becoming increasingly prominent, and they are posing challenges in a variety of areas. As a result of this advancement, a slew of new mechanical advances have emerged, as well as a slew of new problems. Apart from the increase in the number and types of devices in use, the main IoT development areas are advancement of organizational advances specie to IoT, security, scaling down and device mix, limiting energy requirements, programming usability backing and ease of use, and the use of open-source software and open hardware gadgets.

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Assessing Dynamic RAM Technology with Contrast Era of Megabit, Gigabit, and Merged Dynamic RAM/Logic Sanchit Yadav, Ritika Rattan, and Tripti Sharma

Abstract DYNAMIC RAM or dynamic random-access memory (Dynamic RAM) is a type of random-access semiconductor memory in which a bit of data get stored into the memory cells which consist of capacitors and transistors. In digital electronics DYNAMIC, RAM technology is widely used with the requirement of low cost and higher memory capacity. With the newly emerging techniques, technology, challenges in DYNAMIC RAM technologies problem of high energy consumption, leak aging, etc., also rise. In this research paper, we will be able to know about DYNAMIC RAM, various fields of DYNAMIC RAM, such as merging of logic and DYNAMIC RAM, various trends of megabit, the era of gigabits, and newly advanced 3D DYNAMIC RAM technology. In this review, we learn about various techniques used in the above mentions topics their problems and effective solution, advantages disadvantages, few analyses, and its application. Keywords DYNAMIC RAM · Logic technologies · Merging · Megabit · Gigabits · Bandwidth

1 Introduction In view of the fact that the genuine memory transmission capacity of the framework is restricted by off-chip interconnects, and the time for accessing the memory is improved significantly by using the transmission capacity accessible from inside clusters. Regularly, there are under two dozen off-chip interconnects accessible on a DYNAMIC RAM part, and for the densest Dynamic RAM advancements, the interconnects give regularly 50 MB/s of transmission capacity per part despite the fact that the real transfer speed is a lot higher. This is rather than the 12 GB/s S. Yadav (B) · R. Rattan · T. Sharma Electronic and Communication Engineering, Chandigarh University (University Institute of Engineering), Mohali, Punjab, India e-mail: [email protected] T. Sharma e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Biswas et al. (eds.), Microelectronics, Circuits and Systems, Lecture Notes in Electrical Engineering 976, https://doi.org/10.1007/978-981-99-0412-9_30

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transmission capacity expected to help the center of the most current chips. The transfer speed prerequisite on the illustrations chip is significantly higher [1]. Indeed, the real transfer speed is higher inside the part of memory. Just a little level of the genuine number of memory bits read from these interior memory clusters is really made accessible on the chip. The vast majority of the data transmission really present inside the memory chip is disposed of on the framework level in view of the restricted I/Os and afterward agonizingly recaptured through replication [2]. The restricted transmission capacity at that point causes originators of much better CPU chips to spend a greater amount of their central processor silicon chip zone and outside paste rationale on transmission capacity speeding up and memory subsystem upholds circuits [3]. Measure/logic consolidated innovation new allows a very substantial measure of rationale to be set on DYNAMIC RAM chips, implying that the transfer speed accessible from inner memory exhibits can be used straightforwardly by at least one CPU set straightforwardly on the chip. Another merit is heat expulsion. In the event that memory transport can be disposed of by incorporating DYNAMIC RAM and center processor into a solitary chip, the greater part of the memory transport-related force dissemination which is a significant part of the complete force dispersal of the framework can be eliminated. Key circuit plan boundaries of the 1T cell [2, 3] are summed up assign to-clamor proportions, power dissemination, and speed. They are summed up along these lines due to the accompanying innate 1T cell attributes, one is a little understood sign another is charging and releasing the profoundly capacitive information (bit) lines at the same time with a high voltage, which perpetually presents numerous sorts of clamors and cause high force scattering. A high-voltage information line activity gets from both dangerous readout qualities and the necessity of a revive activity for 1T cells. Thinking about DYNAMIC RAM as a LSI innovation driver, speed is additionally a prime concern. Past and the present DYNAMIC RAM’s have been progressed by fundamentally zeroing in on the most proficient method to make memory cells little to figure it out high-thickness DYNAMIC RAM’s. In this methodology, memory cell innovation is the main goal and it is pushed for the most part by the “recoil innovation.” The consistent “contract innovation” up to gigabit thickness uncovered numerous difficulties. The most fundamental difficulties in GB thickness DYNAMIC RAM’s are the yield misfortune due to enormous bite the dust size and little component size, backup current disappointment brought about by huge chip size, and little information maintenance times owing to diminished charge parcel in the memory cell [4]. To improve the exhibition, the memory transport and idleness should be improved concerning memory access, which limits framework execution. Likewise, the memory limit gave in such frameworks is increasing as the framework execution improves. Notwithstanding, the structure factor of the memory is restricted, especially in versatile frameworks. Hence, there has been a solid interest for stacked kick the bucket bundling with through-silicon-by means of (TSV) innovation (3D-LSI) to improve the exhibition, speed up, and limit the volume [1, 4].

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2 Dynamic RAM/Logic Merged Technology The world of incorporating logic/memory circuits in the DYNAMIC RAM technology desires to improve the bandwidth of memory with high performances and low cost, and because of high cost many merging on memories are still unsuccessful and complicated because of the different nature of the technologies used for high performance [4]. After numerous efforts, many industries, organizations, and universities are the part of DYNAMIC RAM-logic era but still do not have any proper solution for the merging of these two technologies in the single integrated circuits, by focusing on low cost and high performance, higher density with high-performance DYNAMIC RAM in the memory logic technology [5]. As the bandwidth of memory in the system is limited and to improve the access time by using the available bandwidth in the internal structure, then the DYNAMIC RAM and the core processor must be integrated on a single silicon chip [4]. Less than two dozen interconnect off chips are available for DYNAMIC RAM, and about 50 megabits of bandwidth are provided by the in connects although it is very much lesser than an actual requirement of bandwidth which is needed about twelve gigabits per sec for the support of most microprocessor of the modern-day [6]. Approximately most bandwidth of the system is present inside the memory of a chip cast out due to limited input/output. The merged technology of DYNAMIC RAM-logic allows to place of a very significant amount of logic into the chips due to that many CPUs can directly use the available bandwidth from the internal structure of the memory. With the increasing demand for inherited graphics, the resolution of the pixels also increasing and for most applications of the graphics few chips (one-two) have that amount of capability just because the bits/chip rate of DYNAMIC RAM is so large [7]. Both the technologies we are using are just different from each other, as for the performance of high speed we use logic, and for better density and reliability, DYNAMIC RAM is used and with the help of few parameters like leakage current, threshold voltage, and other issues get to know the actual differences of DYNAMIC RAM processes and logic.

2.1 Leakage Current In DYNAMIC RAM, it is necessary to low down the leakage current and by applying substrate bias increase the threshold voltage to reduce leakage. In the logic process for the high speed as well as low threshold voltage, the voltage of substrate bias is kept at zero [1]. The current leakage causes a big problem during the implementation of DYNAMIC RAM along with logic and to reduce it the reduction of the electric field of source-drain junction plays an important role in the transistors. Also to decrease the leakage current, it is worthy of considering some more aspects like tunneling leakage current and sub-threshold leakage current, by the reference of [3] it comes out that

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the tunneling leakage current has not satisfied the conditions and sub-threshold has domination over it. As the 10% leakage of the total amount of charge considers as a failure so refresh cycle of 3.19 ms is required in the logic process also it is faster than the usually available DYNAMIC RAMs and also the power consumption gets increased as the frequency is directly proportional to it and by taking a few others steps leakage can be minimized.

2.2 Threshold Voltage In the DYNAMIC RAM process, it is necessary to keep lowering down the leakage current till it is possible. The high threshold voltage process is very much effective and helpful to keep lowering down the leakage current. After applying the substrate bias affect, the threshold voltage can be increased [8]. The doping concentration of DYNAMIC RAMs substrate is much more than the logic during scaling and so the DYNAMIC RAM faces the increment of 40% in threshold voltage, due to this shift in logic circuits the problem of degradation arises if they implemented on DYNAMIC RAM. Instead of using the memory array, the logic transistors of different levels can be used and the complexity of Dynamic RAM could be increased due to the participation of different voltages and biasing effects and may it would have to face the negative impact on cost and other related fields. There are many other issues in the logic process, one of the significant reasons why DYNAMIC RAM not implemented using the logic just because the leakage in DYNAMIC RAM should be minimum of 1 fA/lm2 [8]. By increasing the capacitance/unit area, the density of the DYNAMIC RAM gets increased and instead of planar technology, this capacitor requires a 3D capacitor to increase the density. The capacitance of bit lines is the last problem in DYNAMIC RAM and to secure the sensing merging, not more than a 15:1 ratio of capacitance to storage capacitance is required. From the above-mentioned issues, it is clear that merging of DYNAMIC RAMlogic IC can build on a DYNAMIC RAM technology.

3 Dynamic RAM Leading Technologies (Gigabit Era) 3.1 Cell Structure and Its Technology As it is notable, that building block plot created out of 1 semiconductor and one electrical device is the main boundary to describe DYNAMIC RAM for the expire size even as execution. The building block will be opened up in two distinct manners relying on piece line game arrange. First is that the folded bit line cell (FBLC) engineering and therefore the difference is open piece line cell style. The littlest building

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block dimensions, square measure in open piece line and FBLC style engineering, on an individual basis, where signifies the bottom part size that is commonly controlled by the process of lithography. FBLC style always has been the quality cell for the memory, format due to its boss commotion invulnerability irrespective expanded zone [9]. The folded bit line cell (FBLC) style also has been advanced in two numerous structures of cells utilizing the channel and stacked cells from the tabular cell structure. Stacked cells and each channel have their own favorable circumstances. The capacitors of the channel cell, square measure framed at the first stage within the channel cell DYNAMIC RAM creation [10]; on these lines, it will give a superior cycle scope for elite DYNAMIC RAM semiconductors. Low heat payment arranges for semiconductors in channel cell measure’s will be staggeringly valuable in up the semiconductor execution. Channel cell DYNAMIC RAMs have naturally higher geography on the grounds that the memory cell electrical device is roofed beneath the semiconducting material surface. In spite of the actual fact that channel cell DYNAMIC RAMs have various great highlights which are must in gigabit DYNAMIC RAM, growth of channel cell DYNAMIC RAM into gigabit DYNAMIC RAM does not look like straightforward thanks to hassling in accomplishing adequate capacitance of memory cell. The problem comes from the huge deep channel carving and therefore the absence of potential to execute high dielectric electrical devices. Later on, channel cell DYNAMIC RAM has additional focal points in consolidated the applications of DYNAMIC RAM during which the thickness is not the primary goal. Then again, a stacked cell electrical device includes a superior little bit of leeway in the death penalty a high non-conductor electrical device that is one of the foremost basic advances in gigabit thickness DYNAMIC RAM’s [8, 10].

3.2 Technology of Lithography Lithography innovation has been a serious force for the fourfold increment of DYNAMIC RAM thickness at regular intervals for the most recent twenty years. A good survey regarding the development of lithography can be seen away [11]. During this segment, the central points of contention of lithography within the gigabit time are examined. The central points of interest square measure goal and arrangement exactitude in printing little part size styles decreased than the frequency of the presentation device. The concealment of example size varieties over the big chip is another worry. These problems become substantially additional basic because of the part size contracts [12]. The instance size decreased than the frequency of the lithography device requires high-goal lithography methods, as for example, off-axis brightening, stage moving cowl, and optical neighborhood control. At the same time, the diminished profundity of center (DOF) in the goal upgrade technique ought to be overpowered by giving a level surface before each lithography step. The level surface will be accomplished by compound mechanical cleansing (CMP) planarization.

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The lithography innovation within the gigabit time is relied upon to be sent as follows. The 248-nm lithography is accepted to be primarily used afoot of 0.25μm generation since it was bestowed for the 0.30-μm generation. There are three extraordinary type approaches that have shown up to fathom the contact opening printing issue. One depends on the advance of lithography, significantly the photograph opposes measures. The fundamental plan driving these procedures is that the “size contracting strategies” square measure applied once contact gap examples bigger than needed examples square measure written. The “size acquiring strategy” in photograph oppose stream methods is that the streaming of photographs opposes at simply beneath glass temperature of photographs get up to. Measures a result of lacking mass quantity of photograph gets up to around the contact openings. The multilayer opposes life is made out of a pile of prime photograph oppose/oxide/base photoresist. The “size acquiring method” during this method is the development of contact openings in the compound layer decreased than those within the prime photograph get up to [13]. The decreased example estimates in oxide square measure are frequently accomplished by incline drawing ways. The muddled cycle impedes applying this procedure in mass creation. Another is that the utilization of sq. composing utilizing EB (Electron pillar). It will likewise be a solid challenger for contact opening with size and house at a lower place 0.16 μm within the gigabit time. Notwithstanding, the outturn is so far a serious issue to be improved. The other one is an additional essential arrangement obsessed with the new inventive memory cell live plot during which contact gap printing is not, at now elementary. New cell live plans use diverse carving rates among SiO and SiN throughout contact opening scratching [14].

3.3 Technology of Metallization The necessity of the metallization method of Dynamic RAM has been self-addressed because the density of DYNAMIC RAM is enhanced. As chip stiffness will increase, device rating supports the concept of measurement to improve the performance of devices such as MOSFET and electronic equipment, but the delay of connection within a bigger chip exceeds the value of a device rating. Delay will not be used if the correct measuring theme to measure back the maximum connection, and it leads to affect the performance of the chip [15]. The metal theme built into DYNAMIC RAM’s was transformed from a single metal theme used for four generations of Mb DYNAMIC RAM to a double metal theme introduced by sixteen megabits Dynamic RAM. It has been used continuously for up to sixty-four generations with 256 Mb DYNAMIC RAM. Any expansion of the double metal theme in the GB era is no longer available due to performance limitations and processing durability due to the beautiful patterns and international topology.

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The triple metallization method introduced in one generation of GB DYNAMIC RAM [16] will solve the difficult process due to the exhausted DOF and provide a flexible style yet still improved functionality. Outside of this generation, metallizationlevel metallization is required for good performance. In multi-level metallization [17], conductor technology and low technology become larger to reduce resource utilization, speed delays, detailed speech, and reduced IR. These results are a good concern for the high performance and high gigabit technology of DYNAMIC RAM’s, low material providing very small reductions in durable materials such as SiOF [18]. Therefore, this technology is being developed over the next decade to become the standard gigabit model.

4 Logical and Conceptual Dynamic RAM Chip Architecture and Megabit Dynamic RAM Circuit Design The thickness of dynamic arbitrary access recollections (measures) has quadrupled like clockwork since their coming around 20 years back. Building up this higher thickness has made them less expensive than different kinds of recollections. Low power distribution and rapid detection time have been found with the increasing the size of chip over each continuous year [17] As per given Fig. 1, this has too added to the measure advantage. Therefore, 1Mb DYNAMIC RAMs have now arrived at development underway and are being trailed by the advancement of 4 and 16 Mb measure. The resultant measure innovation has been applied to other effective items, for example, RAM’s used for videos, pseudo-static RAM, ASIC recollections, and recollections of low-power document [16]. Fig. 1 Dynamic RAM capacity

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4.1 Signal-to-Noise Ratio Escalated examines and cautious plan are needed to accomplish. A dependable detecting activity, DYNAMIC RAM is viewed as low memory S/N proportion as depicted prior [18]. The cell edge can be generally communicated as appeared in Fig. 2 briefly discussed in [15], utilizing the amount of the different successful commotion charges: linear data charge, CDvN, delivered by power interconnection in data lines from various conveyers, the electrical misalignment between a few information lines, and power which strengthens the balance breakdown charge, IL tREF max, critical charge Q, due to molecular strikes. Adding a signed fee, C, (V, VP) while minimizing the cost of successful debate is essential to achieving sustainable memory function [15, 16]. Coming up next is improvements to meet this prerequisite.

4.1.1

Signal Charge

Much effort was put forth to build advanced memory cells get a signal charge Qsig as extensive as could be expected under the circumstances. New cells, vertically arranged cells provide a higher C, for example, capacitor cells integrated with channel cells are becoming more important in megabit DYNAMIC RAM [16].

4.1.2

The Critical Charge

The α-molecule incited delicate blunders are turning into a significant issue. Luckily, in any case, it has been found that in cell mode the primary Q is reduced by reducing the amount of space used in the memory cell [19]. Fig. 2 Cell edge

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Leakage Charge

Smothering the expansion in intersection temperature T, just as improving the nature of the manufacturing cycle, is basic. This is a result of the long tREFmax required for increasing memory limit, as revealed over time. Accordingly, low force dispersal and a low warm obstruction bundle are critical to diminish Tj [20].

4.2 Power Dissipation A decrease in force dissemination is fundamental for megabit DYNAMIC RAM plans concerning the need for a more extended tREFmax and regulation of inner commotions. Be that as it may, there is a positive pattern toward expanded power dissemination with an expansion in memory limit with respect to a given outside flexibly voltage V cc and for a given cycle season of 230 ns [19].

4.3 Speed It will be difficult to achieve high speeds such as memory limits because an increase in chip size causes an increase in wiring strength and cable resistance. Because of this trouble, a fast has been acknowledged as appeared in Fig. 1 [15] utilizing the procedures which decrease RC defer time as talked about before the high speed of the BiCMOS measure results chiefly from the high manageability of the driver and the exceptionally touchy enhancer BiCMOS measure innovation will be appealing in terms of rapid access time, ease potential, and plan adaptability, even though the measure downsides of more process duration and higher force scattering remain unsolved [21].

4.4 Perspectives Persistent advancement out and about toward a gigabit measure will clearly require creative circuit advances, just as astounding memory cells to illuminate the already talked about issues. Future improvement endeavors ought to likewise be centered on the quest for imaginative testing and repetition advancements. These future advancements must be created thinking about the accompanying two issues: possible DYNAMIC RAM innovation enhancement and force flexibly normalization. It has been shown how the S/N ratio, power distribution, and speed are nearly linked to the on-going development of DYNAMIC RAM. According to DYNAMIC RAM trends, it was estimated that existing DYNAMIC RAM mechanics could be subdivided in

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the future so that high-tech memory technologies could merge with high-speed ones. The measurement of the power supply brings with it an on-going concern [20, 22].

5 Conclusion In this research paper, we concisely describe the DYNAMIC RAM which is a type of random-memory in the semiconductor and its analysis with various advancements topics where it tells how they are responsible for the different functions in the DYNAMIC RAM technologies and each topic has their own importance and requirement. On the basis of threshold and leakage, the chips of merged DYNAMIC RAM-logic are implemented into DYNAMIC RAM instead of logic process, challenges faced by the gigabits in this era are discussed with proper key solutions, in megabits it shows how SNR, speed, and other parameters are responsible for the nonstop progress in DYNAMIC RAM and finally, new technology has been introduced the packaging technique with three-dimensions stacked DYNAMIC RAM. As the technology is getting improved and inherited, various researches reported about the problems so many concepts, applications, and techniques are proposed with the help of them effective solutions also proposed to overcome with it.

References 1. K.N. Kim, J.Y. Lee, K.H. Lee, B.H. Noh, S.W. Nam, Y.S. Park, Y.H. Kim, H.S. Kim, J.S. Kim, J.K. Park, K.P. Lee, K.Y. Lee, J.T. Moon, J.S. Choi, J.W. Park, J.G. Lee, Highly manufacturable 1-Gb SDYNAMIC RAM, in VLSI Technology Digest of Technical Papers, June 1997, pp. 9–10 2. G. Bronner, H. Aochi, M. Gall, J. Gambino, S. Gernhardt, E. Hammerl, H. Ho, J. Iba, H. Ishiuchi, M. Jaso, R. Kleinhenz, T. Mii, M. Narita, L. Nesbit, W. Neumueller, A. Nitayama, T. Ohiwa, S. Parke, J. Ryan, T. Sato, H. Takato, S. Yoshikawa, A fully planarized 0.25-m CMOS technology for 256-Mbit DYNAMIC RAM and beyond, in VLSI Technology Digest of Technical Papers, June 1995, pp. 15–16 3. K. Itoh, R. Hori, H. Masuda, Y. Kamigaki, H. Kawamoto, H. Katto, A 5-V-only 64-K dynamic RAM, in ISSCC Digest of Technical Papers, Feb 1980, pp. 228–229 4. Y.-B. Kim, T.W. Chen, Assessing merged DYNAMIC RAM/logic technology. Integr. VLSI J. 27, 179–194 (1999) 5. P.M. Kogge, T. Sunaga, H. Miyataka, K. Kitamura, Combined DYNAMIC RAM and logic chip for massively parallel systems, in Proceedings of 16th Conference on Advanced Research in VLSI, Mar 1995, pp. 4–16 6. A. Kanuma, The best DYNAMIC RAM approach for graphics application, in ISSCC95/Evening Discussion Session/WE2, Feb 1995, pp. 96–97 7. M.D. Weir, J.F. Kita, T.J. Cockerill, P.F. Hynek, T.M. Lepsic, T.K. Ta, B.K. Wong, S.R. Woodham, R.R. Young, A 250K-circuit ASIC family using a DYNAMIC RAM technology, in Proceedings of IEEE 1990 CICC (1990), pp. 4.6.1–4.6.5 8. K.P. Lee, Y.S. Park, D.H. Ko, C.S. Hwang, C.J. Kang, K.Y. Lee, J.S. Kim, J.K. Park, B.H. Roh, J.Y. Lee, B.C. Kim, J.H. Lee, K.N. Kim, J.W. Park, J.G. Lee, A process technology for 1-gigabit DYNAMIC RAM, in IEDM Technical Digest (1995), pp. 907–910

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9. H. Hada, T. Tatsumi, K. Miyanaga, S. Iwao, H. Mori, K. Koyama, A self-aligned contact technology using anisotropical selective epitaxial silicon for gigabit DYNAMIC RAM’s, in IEDM Technical Digest (1995), pp. 665–668 10. J.K. Park, J.Y. Lee, B.H. Hwang, S.Y. Jo, B.C. Kim, S.K. Jang, S.D. Kwon, D.H. Kim, H.S. Kim, K.N. Kim, J.W. Park, J.G. Lee, Isolation merged bit line cell (IMBC) for 1-Gb DYNAMIC RAM and beyond, in IEDM Technical Digest (1995), pp. 911–914 11. N. Hayasaka, H. Miyajima, Y. Nakasaki, R. Katsumata, Fluorinedoped SiO2 for low dielectric constant films in sub-half-micron ULSI multilevel interconnection, in SSDM’95 Technical Digest, pp. 157–159 12. Y. Hu, C.W. Teng, T.W. Houston, K. Joyner, T.J. Aton, Design and performance of SOI pass transistors for 1-Gbit DYNAMIC RAM’S, in VLSI Technology Digest of Technical Papers, June 1996, pp. 128–129 13. J.H. Bruning, Optical lithography—thirty years and three orders of magnitude: the evolution of optical lithography tools. Proc. SPIE 3051, 14–27 (1997) 14. C.J. Willson, Photoresist materials: a historical perspective. Proc. SPIE 3051, 28–41 (1997) 15. K. Itoh, Trends in megabit DYNAMIC RAM circuit design. IEEE J. Solid-State Circ. 25(3) (1990) 16. T. Mano et al., Circuit technologies for 16 Mb DYNAMIC RAM’s, in ISSCC Digest of Technical Paper, Feb 1987, p. 22 17. M. Inoue et al., A 16 Mb DYNAMIC RAM with an open bit-line architecture, in ISSCC Digest of Technical Papers, Feb 1988, p. 246 18. S. Watanabe et al., An experimental 16 Mb CMOS DYNAMIC RAM chip with a 100 MHz serial read/write mode, in ISSCC Digest of Technical Papers (1988), p. 248 19. K. Arimoto et al., A 60 ns 3.3 V 16 Mb DYNAMIC RAM, in ISSCC Digest of Technical Papers, Feb 1989, p. 244 20. S. Fujii et al., A 45 ns 16 Mb DYNAMIC RAM with triple-well structure, in ISSCC Digest of Technical Papers, Feb 1989, p. 248 21. M. Aoki et al., An experimental 16 Mb DYNAMIC RAM with transposed data-line structure, in ISSCC Digest of Technical Papers, Feb 1988 22. M. Kawano, N. Takahashi, Y. Kurita, K. Soejima, M. Komuro, S. Matsui, Three-dimensional packaging technology for stacked DYNAMIC RAM with 3-Gb/s data transfer. IEEE Trans. Electron Devices 55(7) (2008) 23. J. Shaw, E. Babich, M. Hatzakis, J. Paraszczak, Polysiloxanes for optical lithography. Solid State Technol. 83–89 (1987)

Analysis of Computational Complexity in Interference Mitigation with 3D MIMO Beamforming Techniques in 5G Networks Ashutosh Tripathi and Ranjeet Yadav

Abstract Multiple-input multiple-output (MIMO) systems are examined as the future entitled technologies in 5G communication networks. The wireless communication undergoes a rigorous change in the mobile communication, IoT, smart devices, smart antenna system with the advent of 5G. New smart multi-antenna technologies like beamforming BF along with fifth generation 5G are commencing with supporting of a heterogeneous service with its individual comprehensive requirements. It predominantly supports a very enormous count of independently controllable smart antennas at the gNB and thereby achieves a considerable amplification about the energy and spectral efficiency. However, interference in the small and macrocell has to be reduced properly to make optimum use of spectral efficiency and bandwidth. These papers present a comprehensive analysis of computational complexity of lion with new cub generation (LA-NCG) algorithm over the other traditional approaches used in 3D MIMO beamforming techniques for interference mitigation between the adjacent cells and illustrate various methodologies, and its features and challenges of ultra-modern models in 3D MIMO beamforming technologies used in 5G systems. Keywords Multiple-input multiple-output · 3D MIMO · 5G systems · Beamforming · LA-NCG · Wireless communication

1 Introduction Multiple-input multiple-output (MIMO) [1–4] has noticeable among the numerous important advances in ongoing remote applications due to its amazing capacity to improve the data transmission effectiveness and execution, for example, through building up its special spatial multiplexing ability and spatial variety pick up. The A. Tripathi (B) Department of ECE, UIE, Chandigarh University, Gharuan, Mohali, Punjab, India e-mail: [email protected] R. Yadav Department of ECE, Amity School of Engineering and Technology, Noida, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Biswas et al. (eds.), Microelectronics, Circuits and Systems, Lecture Notes in Electrical Engineering 976, https://doi.org/10.1007/978-981-99-0412-9_31

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cell networks with fifth generation (5G) are presented with the decided point of supporting an extremely heterogeneous help combination, everybody with its individual definite prerequisites. Massive MIMO (mMIMO) [5–7] is one among them, which is considered as a difficult hypothesis, created for improving the remote organization’s exhibition, which incorporates the inescapable 5G innovation. It generally uses an extremely colossal check of freely controllable receiving wires at the BS, and along these lines achieve more noteworthy improvement with respect to the energy and otherworldly effectiveness. The imperative omnipresent duties in MIMO radars are the waveform plan and adaptive beamforming [4, 8–11]. The radar framework execution gets affected according to the decision of waveform in the perspective on goal and discovery. The waveforms that communicated should present better symmetry for giving better objective and confinement identification, attributable to the high aim, and low side lobes. An ever increasing number of approaches are abused on planning the symmetrical waveforms like frequency, time, and code division different access (CDMA). In addition, adaptive beamforming can be sent for the impedance relief and for boosting the signal to interference plus noise ratio (SINR). The fundamental testing angle in adaptive beamforming is on keeping up the huge signal-to-noise ratio (SNR) in the vulnerability presence on account of the divergence among the genuine and the supposed directing vectors. Without fail, the jumbling of a model may happen, this thus brings about the debasement on the exhibition of a conventional adaptive beamforming technique. In the cell correspondence framework plan, the interference reduction has arisen as the center issue. The inter-cell interference has jump up as vital issue. Besides, interference the board [12–15] has arisen as an urgent factor with the appearance of heterogeneous organization that included pico-cells, macrocells, and femtocells. To solve the interference issue, the parameters like throughput along with the computational complexity of the LA-NCG algorithm are examined to determine the efficiency over the existing algorithms.

2 Literature Review In 2015, Sun et al. [16] have proposed a novel sort of corresponding code which was labeled as 3D complementary code 3DCC that joined the code division different access along with space time coding on top of bound together activity for various info numerous yield MIMO framework working. In understanding to the ideal connection valuable, the 3DCC has ensured symmetrical transmissions among radio wires and users. The mathematical assessments have demonstrated that the actualized 3DCCMIMO framework has better capacity on supporting the two multiplex and communicate variety at comparable stage. The trial investigation has additionally approved the improvement of the executed MIMO framework with respect to its MUI free execution.

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In 2014, Zhang et al. [17] have examined interference cooperation for antenna array ordering in three-measurement (3D) inside MIMO and OFDMA wireless networks. The administering of cell-center user in addition to cell-edge user explicit downtilts was made appropriately in the 3D MIMOOFDM correspondence frameworks through unique vertical beamforming. Additionally, the interference coordination subject has been addressed by building up a novel fractional joint processing (JP) synchronized multiple point (COMP) based on dual decomposition strategy (JC-DDM), which commonly advances the resource block (RB) feature, power task (RAPA), and down-angle change. The outcomes accordingly clarified the adequacy of executed JC-DDM. In 2016, Li et al. [18] have examined a calculation for single-cell multiuser frameworks having two-dimensional (2D) colossal scale antenna array line up at the base station (BS) based on downlink scattering on top of Rican blurring channels. At first, view (LOS) segment of the channel was removed, contingent upon this and utilizing the impression of factual channel state information (CSI), Rican K-factor alongside the LOS part, every end users ideal beamforming vector has been inferred. From that point forward, they proposed the three-dimensional (3D) beamforming downlink transmission technique in the premise of this every users measurable CSI. Inferable from this, an exact scientific of a framework articulation has been extricated and picked up barely any important experiences. The trial result has demonstrated the adequacy of the proposed calculation. In 2018, Grassi et al. [19] have analyzed the coordination and design of the difficult transmission draws near. Moreover, from the framework level perspective, it further figured their conduct in assorted working conditions. Initially, the picked up result has shown the prevalent execution pick up, which offered by this new methodologies with respect to the traditional 4G innovation. Furthermore, this method further found the productivity of these arising procedures in fulfilling the pre-characterized quality requirements that clarified for the 5G organization. In 2018, Song et al. [20] have do the two-dimensional (2D) precoding calculation that was based on domain selective interference cancelation (DSIC). This has been made for indicating the crossing out of interference, azimuth space, and the heights in the current methods. The principle point on proposing this new method was to increase the received signal-to-noise ratio (SNR). The test investigation has subsequently approved that this precoding approach has accomplished preferable execution over the existing 2D precoding with respect to spectral proficiency. In 2018, Zhang et al. [21] have presented a novel radio antenna quieting approach for beamforming by considering the qualities of spatial multipath spread over the cellular channels. At first, the spatial reception gain (SRG) has been characterized by coordinating the channel precise force spectra and the receiving antenna cluster radiation design at the BS. Also, a 3D MIMO sounder was utilized to play out the field estimation crusade on the 3.5 GHz rural area macrocell (RMa) channels. Further, Laplace dispersion model has been developed for the spectra by estimating the azimuth and elevation power spectra (APS and EPS). As far as possible, the impact of the SRG edge and channel boundary measurements has been examined over the

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energy productivity of the executed structure and hence figured the presentation in the RMa discernment based on the spatial channel strategies. In 2016, Fan et al. [22] have embedded a three-dimensional (3D) beamforming approach for the massive multiple-input multiple-output (mMIMO) framework, which utilized a uniform rectangular cluster of array (URA) inside the base station (BS). In this, a pair stage beamforming approach has been utilized for keeping away from the enormous computational multifaceted nature that engaged with huge dimensional channel lattices, in which the second-stage beamforming was considered as a Kronecker result of elevation and azimuth discrete Fourier transform (DFT) beamforming. Further, a low-intricacy users gathering calculation has been created based on factual channel state information at the transmitter (CSIT) to distributed users. The examination result has hence clarified that the SINR discernments were tight and indicated the outcome of the executed 3D beamforming approach. In 2014, Peng et al. [23] have introduced a confined input and transmission approach for 2D antenna array exhibit MIMO frameworks based on traditional 2D codebook. The mobile station (MS) has expected with unsuitable channel information, and the base station (BS) has hence just received the fragmented data partner the channel initialization. The criticism of two channel state information (CSI) cases has been accounted for by MS, i.e., the vertical and flat CSIs. Once after the achievement of the two CSI occurrences, a novel vertical precoding vector has been added utilizing BS by conveying the vertical CSI. By utilizing vertical and flat precoding vectors, the BS re-developed a 3D beamforming vector and adjusted the detailed level channel quality pointer. 3DCC-MIMO [16] offers a better diversity gain and supports transmit diversity and multiplex modes. Still needs continuing analysis of implemented model over a multipath fading channel and the implementation of this is difficult. JCDDM [17] has better SINR performance, and total computation complexity is lesser, yet the computationally high complexity. 3D beamforming transmission algorithm [18] attains ergodic sum rate closer to the ergodic sum rate and requires less CSI. However, lacks in short of capturing the fading variations and inaccuracy of CSI. Massive MIMO and JSDM [19] have large throughput and reduced interference, but needs diverse antenna array structures and parameters and needs heterogeneous network deployments with small cells. DSIC algorithm [20] poses less complexity and avoids interference. However, the designing of algorithm based on many users is still a challenge. Antenna muting algorithm [21] has improved energy efficiency and better determination of antennas, still needs extension in current channel modeling specification and supports the development of the MIMO technologies. 2D-FQUG algorithm [22] achieves same sum-rate performance and has lower complexity. Yet, the SLNR metric results in a decoupled optimization problem. 3D MIMO [23] has improved system performance and has reduced quantization error, still difficult on estimating the perfect SINR.

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3 System Model 3D MIMO situations use a planar two-dimensional (2D) active antenna system (AAS) [24] that not just permits an enormous number of receiving antenna components to be set inside practical base station (BS) structure factors, yet in addition gives the capacity of elevation beamforming. We review a downlink 3D MIMO framework, where the BS is outfitted with NBS directional Tx antenna ports and the mobile station (MS) has NMS Rx antenna ports. In our design, there are NBS antenna ports put in the horizontal plane, along the ˆKy course. Every reception apparatus port is planned to M vertically stacked antenna components that decide the successful port. The equivalent Tx signal is taken care of to all components in a port with relating loads wk’s, k = 1, 2, …, M, to accomplish the ideal directivity. In the current action around 3D channel demonstrating by the 3GPP, the proposed strategy for 3D beamforming is to change the loads applied to the components in a port to get the ideal downtilt plot for that port. The equivalent is accomplished for different ports. The MS sees every antenna port as a solitary antenna since all the components convey the equivalent signal. Consequently, we are keen on the channel between the ending reception apparatus port and the collector side.

3.1 LA-NCG Algorithm The presented work deploys the improved LA for optimizing the factors in 5G network. Here, the existing LA approach has problems, such as local optimum and slow convergence, and it is improved so that it could resolve the more complex optimization issues. In general, self-improvement is proven to be promising in traditional optimization algorithms [24–28]. The LA [11] was inspired from the living nature of lion species. The LA-NCG algorithm [26] encompasses four phases such as proposed cub generation, generation of pride, territorial takeover, and territorial-defense (Table 1). Table 1 LA-NCG parameters and their values

Parameters

Values

Maximum generations

10

Mature age

03

Max_strength

03

Max_age

03

Mutation rate

0.15

Male_rate

0.15

Female_rate

0.15

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Using meta-heuristics methods, LA-NCG [26] can guarantee convergence to a local optimal solution. In LA-NCG, the computational complexities is O(2m ) where indicates the number of objectives. Here, the proposed system attains two best solutions for male  and female cub. In summary, total computational complexity of LANCG is O 2m S cubs (i ), S new (i ) . Therefore, LA-NCG effectively optimizes the total transmission throughput in a 3D MIMO system.

4 Results and discussion The proposed 3D MIMO beamforming and interference using LA-NCG was carried out in MATLAB and the throughput, and time complexity outcomes were obtained and compared with the traditional techniques.

4.1 Throughput Analysis The throughput investigation of the embraced LA-NCG calculation over the current models is indicated by Fig. 1 for changed number of cycles. On noticing the beneath charts, the introduced approach has accomplished a higher throughput over the looked at models for all number of cycles.

Fig. 1 Throughput analysis of the presented scheme over the existing schemes

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4.2 Analysis on Computation Time Table 2 presents the computation time of presented LA-NCG scheme over JC-DDM, FP, and LA models for the four cases with BS4, BS6, BS8, and BS10. The computation time of the LA-NCG scheme for BS10 is 15.073 s, which is higher than that of JC-DDM, FP, and LA. Thus, the presented LA-NCG scheme [26] takes less computation time even with an increased number of BS, which proves its efficacy. The computational complexity of the proposed LA-NCG is shown in Fig. 2. Here, the proposed system has a minimal computational complexity than the existent JC-DDM [17], FP [27], and LA [28]. Table 2 Evaluation on computational time Methods

Computation time in second BS4

JC-DDM

5.3163

BS6

BS8

BS10

11.85

19.136

28.668

FP

15.822

17.584

24.962

19.811

LA

12.83

13.816

17.317

15.953

LA-NCG

11.305

14.903

16.732

15.073

Fig. 2 Computational complexity analysis

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5 Conclusion This paper proffers a comprehensive evaluation of unique sorts of BF strategies which is an essential piece of the MIMO frameworks. This thinks about some current activities toward adaptable, green, and for the most part prevailing 5G versatile correspondence guidelines. It audits the flow research on different kinds of BF and inspects which are more reasonable for use in MIMO systems. This paper expands a far reaching appraisal of assorted sorts of BF methods which is a critical piece of the MIMO frameworks. These analyze some current capacity toward serviceable predominant 5G portable correspondence guidelines. It will be propelling in the forthcoming years to look at the presentation of the 3D beamforming methods.

References 1. N. Hatami, J. Nourinia, C. Ghobadi, M. Majidzadeh, B. Azarm, High inter-element isolation and WLAN filtering mechanism: a compact MIMO antenna scheme. AEU Int. J. Electron. Commun. 109, 43–54 (2019) 2. M. Rihan, L. Huang, Non-orthogonal multiple access based cooperative spectrum sharing between MIMO radar and MIMO communication systems. Digit. Sig. Process. 83, 107–117 (2018) 3. S. Hiltunen, P. Chevalier, T. Petitpied, New insights into time synchronization of MIMO systems without and with interference. Sig. Process. 161, 180–194 (2019) 4. R. Yadav, A. Tripathi, A survey on hybrid, 3D, interference mitigation and secure data beamforming techniques for 5G system. Wireless Pers. Commun. 114, 883–900 (2020). https://doi. org/10.1007/s11277-020-07397-w 5. S. Ghosh, R. Chopra, Training for massive MIMO systems with non-identically aging user channels. Phys. Commun. 35 (2019) 6. M.J. Azizipour, K. Mohamed-Pour, A.L. Swindlehurst, A burst-form CSI estimation approach for FDD massive MIMO systems. Sig. Process. 162, 106–114 (2019) 7. S. Chinnadurai, P. Selvaprabhu, X. Jiang, H. Hai, M.H. Lee, Worst-case weighted sum-rate maximization in multicell massive MIMO downlink system for 5G communications. Phys. Commun. 27, 116–124 (2018) 8. C. Xiang, D.-Z. Feng, H. Lv, J. He, Y. Cao, Robust adaptive beamforming for MIMO radar. Sig. Process. 90(12), 3185–3196 (2010) 9. J. Ahmadi-Shokouh, H. Keshavarz, RF beamforming for MIMO cognitive user. AEU Int. J. Electron. Commun. 67(12), 1079–1085 (2013) 10. K. Gao, H. Shao, H. Chen, J. Cai, W.-Q. Wang, Impact of frequency increment errors on frequency diverse array MIMO in adaptive beamforming and target localization. Digit. Sig. Process. 44, 58–67 (2015) 11. Y.-H. Pan, K.B. Letaief, Z. Cao, Dynamic spatial subchannel allocation with adaptive beamforming for MIMO/OFDM systems. IEEE Trans. Wireless Commun. 3(6), 2097–2107 (2004) 12. B.M. Zaidel, R.R. Müller, On adjacent channel interference mitigation for rotating MIMO receivers. IEEE Trans. Wireless Commun. 14(10), 5763–5779 (2015) 13. Y.J. Chun, S.W. Kim, Log-likelihood-ratio ordered successive interference cancellation in multi-user, multi-mode MIMO systems. IEEE Commun. Lett. 12(11), 837–839 (2008) 14. G. Nauryzbayev, E. Alsusa, Enhanced multiplexing gain using interference alignment cancellation in multi-cell MIMO networks. IEEE Trans. Inf. Theory 62(1), 357–369 (2016)

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15. D.M. Blough, P. Santi, R. Srinivasan, On the feasibility of unilateral interference cancellation in MIMO networks. IEEE/ACM Trans. Netw. 22(6), 1831–1844 (2014) 16. S. Sun, C. Chen, S. Chu, H. Chen, W. Meng, Multiuser-interference-free space-time spreading MIMO systems based on three-dimensional complementary codes. IEEE Syst. J. 9(1), 45–57 (2015) 17. W. Zhang, Y. Wang, F. Peng, Y. Yuan, Interference coordination with vertical beamforming in 3D MIMO-OFDMA networks. IEEE Commun. Lett. 18(1), 34–37 (2014) 18. X. Li, S. Jin, H.A. Suraweera, J. Hou, X. Gao, Statistical 3-D beamforming for large-scale MIMO downlink systems over Rician fading channels. IEEE Trans. Commun. 64(4), 1529– 1543 (2016) 19. A. Grassi, G. Piro, G. Boggia, M. Kurras, W. Zirwas, R.S.S. Ganesan, K. Pedersen, L. Thiele, Massive MIMO interference coordination for 5G broadband access: integration and system level study. Comput. Netw. 147, 191–203 (2018) 20. Y. Song, C. Liu, Y. Zou, The precoding scheme based on domain selective interference cancellation in 3-D massive MIMO. IEEE Commun. Lett. 22(6), 1228–1231 (2018) 21. R. Zhang, J. Wang, Z. Zhong, C. Li, X. Du, M. Guizani, Energy-efficient beamforming for 3.5 GHz 5G cellular networks based on 3D spatial channel characteristics. Comput. Commun. 121, 59–70 (2018) 22. L. Fan, S. He, Y. Huang, L. Yang, A low-complexity 3D massive MIMO scheme jointly using statistical and instantaneous CSIT. EURASIP J. Wirel. Commun. Netw. (2016) 23. Y. Peng, W. Yang, Y. Zhu, X. Chen, Transmission scheme for 2D antenna array MIMO systems with limited feedback. Wirel. Pers. Commun. 75(1), 759–774 (2014) 24. R. Yadav, A. Tripathi, Design of a scheduling algorithm in 3D MIMO beamforming 5G systems for interference mitigation, in 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS), pp. 1690–1694. https://doi.org/10.1109/ICACCS51430. 2021.9441770 25. 3GPP TR 36.873 V12.0.0, Study on 3D channel model for LTE, Sept 2014 26. R. Yadav, A. Tripathi, Enhanced optimization assisted interference mitigation with vertical beamforming in 3D MIMO-OFDMA for 5G wireless communication network. Int. J. Pervasive Comput. Commun. 19(2), 191–210 (2021). https://doi.org/10.1108/IJPCC-01-2021-0024 27. S.K. Moghaddam, S.M. Razavizadeh, Joint tilt angle adaptation and beamforming in multicell multiuser cellular networks. Comput. Electr. Eng. 61, 195–207 (2017) 28. R. Boothalingam, Optimization using lion algorithm: a biological inspiration from lion’s social behaviour. Evol. Intel. 11(1–2), 31–52 (2018) 29. Z. Hu, J. Peng, K. Luo, T. Jiang, Parameter identifiability of space-time MIMO radar. Digit. Sig. Process. 90, 10–17 (2019) 30. J. Yu, J. Krolik, MIMO adaptive beamforming for nonseparable multipath clutter mitigation. IEEE Trans. Aerosp. Electron. Syst. 50(4), 2604–2618 (2014)

Emotion Recognition: A Review Bhavesh Gandhi, Sandeep Saxena, and Pulkit Jain

Abstract The attracting field of emotion identification has obtained a good interest from both industrial and research point of view. There are various applications of emotion recognition in human–computer interaction, video gaming, 24 × 7 monitoring of infants and patients suffering from diseases like Parkinson’s, Alzheimer, depression, falls, etc. This paper discusses the approaches towards recognizing the emotions of human beings and is aimed at finding out the best and cost-effective method to recognize the emotions. The techniques with an accuracy of about 80– 90% comparable to EEG, ECG signals have also been discussed. From the review, it has been found that by using the low power RF signals, a person’s emotional condition can be determined without any wearable on his/her body and that too just by extracting out features like heart rate, breath rate, etc. Keywords RF signals · ECG · EEG · SER technology · Sensors · Wearable health devices

1 Introduction There are various applications of emotion recognition in human–computer interaction, video games, and medicine. One of the methods for the detection of facial emotions is that the image of the human face from a video clip is captured. Various algorithms are used to identify the facial expressions and then gather the underlying emotional state. The two main problems for methods to detect the emotional state of a person are uniqueness of expressions and gestures. The expressions and the gestures are unique and depend from person to person which makes it difficult to detect the B. Gandhi (B) · S. Saxena · P. Jain Department of Mechatronics Engineering, Chandigarh University, Mohali, India e-mail: [email protected] S. Saxena e-mail: [email protected] P. Jain e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Biswas et al. (eds.), Microelectronics, Circuits and Systems, Lecture Notes in Electrical Engineering 976, https://doi.org/10.1007/978-981-99-0412-9_32

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Fig. 1 Various facial expressions [1]

emotions based on facial expressions only. Also, there are methods in which the emotions are detected based upon voice signals [1]. An example of a different set of expressions of different people is shown in Fig. 1. As the facial expressions are unique and depend on person to person, it is not necessary that the new person’s facial expressions will be the same as those of the test subjects. Therefore, in testing states, there are insufficient examples to detect the emotions of a new individual. However, if we need more accuracy there should be more and more testing done so that there would be more availability of the expressions of different persons so that computers can evaluate the exact expression based on the programmed algorithms. There are many factors which affect facial emotion recognition such as various intra-class variations caused by the change in the position of the head, different build of face muscles, and many more [2]. An example of a different set of expressions of different people is shown in Fig. 1.

2 Literature Review There are many researches done till now to determine the emotional state of the person either by reading their facial expressions or by analysing their voice signals. In [2], sparse illustration tool is used for reconstruction, representation, and compression of noisy data, like images and videos and the data derived from them. This tool has an ability to unwrap essential data from the audio and video signals. Also the

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distributed illustration approach is flexible enough to reinforce noisy data which creates a database from the information recorded. This is not enough as our end goal is to detect the emotional state of the individuals. The sparse representation-based classification task has both the representational ability and discriminative power to easily detect emotions of the person [2]. Also, the author has proposed a method which jointly creates a database and detects the emotional state of the person. Some of the other methods to detect emotions of the person are given below.

2.1 Emotion Recognition System Using Multiple Model Approach Emotion recognition system using multiple model approach as presented in [3] consists of two sub-parts namely video and audio excitation. Section relying on video input contains. (i)

A module that identifies the facial region of the given video file, known as face detection module, (ii) A module called subspace creation module, which creates a subspace from extracted face images to encode the emotional state, and (iii) A module which compares the subspace created from the video file to prototypic subspaces of the emotional state categories, called as matching module. Section relying on audio input contains. (i) An element that extracts features from each sample file named as feature extraction module, (ii) An element providing scores as per SVM, generally called as matching module. At the end, both the sections are merged using a technique based on weighted sums to predict the emotional state of the individual as shown in the video input [3]. Electrocardiogram (ECG) Waves or Signals. Emotional health and emotionrelated physiology and mental disease can be monitored using emotion recognition through ECG signals. Emotions can be expressed not only through psychological behaviour but also through a series of physiological changes. Figure 2 shows breathing rate and heart rate (ECG signals) of an individual. The physiological changes are not consciously managed by humans. As compared to heart rate, the acquisition of breathing rate is more easy and comfortable. In today’s world, heart rate or ECG signal is determined by devices which need to be in direct contact with the human body which may lead to infection. In [4], the author has used a shoulder-worn heart sound collector which is easy to wear and also does not directly touch the human body, therefore, the above stated problems are solved, and the test is done comfortably.

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Fig. 2 Breathing rate and heart rate (ECG signals) of an individual [4]

3 RF Signals for Emotion Recognition The RF signal method used to recognize emotions is one of the best methods as it easily determines our emotion in a very effective way. Moreover, it does not require any sensor implant on the human body. Basically, the RF signals get reflected back by the human body and get modified with gestures. Studies have shown that reflections from radio waves can obtain a person’s respiration rate and heart rate. Means that the information about the person’s pulse can be configured with the help of the RF signals. Acquiring a person’s heartbeat from radio frequency signals may provoke multiple barriers which is shown in Fig. 3. As breathing rate is a highly accelerating signal as compared to heart rate so in order to separate the two for further processing frequency domain techniques have been used in the previous literature [5].

Fig. 3 Relation between breathing rate and heart rate [5]

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Fig. 4 Sensors used to record EEG and ECG signals [6]

4 Affect Emotion Recognition In [6], visual and audio stimuli in the form of video files were used to elicit emotions to the participants and the EEG and ECG signals were recorded. A dataset containing 18 short video files were selected and used for detecting emotions. The EEG headset used to detect emotions is shown in Fig. 4. Each video file was targeted to one of the given nine emotions; anger, fear, surprise, excitement, calmness, amusement, sadness, and happiness. The EEG headset used to detect emotions is shown in Fig. 4. For the acquisition of data, a 45'' TV-monitor was used with the embedded speakers for audio playback in a dark room. The readings were recorded at a sampling rate of 128 Hz with the help of Emotiv EPOC wireless EEG headset. The detail of emotions in the selected video file is shown in each clip which is shown in Table 1. In [6], the data acquired from the audio-visual stimuli is given in the form of a DREAMER database. The readings of the heart rate and brain wave patterns were taken using wireless, low-cost, portable devices and are stored in a database that allows integration of computing technology and algorithms into a wide range of applications.

5 Design of Music System Using Emotions of Input Speech Signals In [7], speech emotion recognition (SER) technology has been used which has enhanced the field of HMI interface, like in the areas of online learning, gaming, voice search, and observing and administering aircraft cockpits. The speech emotion recognition system mainly focuses on perceiving the emotional state of human beings based on their voice signals. In this approach, the way of speaking and the background of the individuals play an important role. The signs used for emotion detection depend

376 Table 1 Film clip and the targeted emotion [6]

B. Gandhi et al. ID

Film clip

Target emotion

1

Searching for Bobby Fischer

Calmness

2

D.O.A.

Surprise

3

The hangover

Amusement

4

The ring

Fear

5

300

Excitement

6

National Lampoon’s Van Wilder

Disgust

7

Wall-E

Happiness

8

Crash

Anger

9

My girl

Sadness

10

The fly

Disgust

11

Pride and prejudice

Calmness

12

Modern times

Amusement

13

Remember the titans

Happiness

14

Gentlemans agreement

Anger

15

Psycho

Fear

16

The Bourne identity

Excitement

17

The Shawshank redemption

Sadness

18

The departed

Surprise

upon the source individual, his/her personality, age, religion, and other related parameters. It is also possible that more than one emotion is detected by the system at the same time and it becomes very important to detect this type of emotion. 81% accuracy results [7] are obtained using the SVM classifier based upon the seven commonly used emotions which are stored in the Berlin emotional database (Emo-dB). The dataset in [7] consists of five emotions namely happiness, sadness, anger, fear/anxiety, and boredom. The system is divided into two sets as training set and testing set as shown in Fig. 5. The system contains a preprocessor block which is used for feature extraction and silence removal from the voice signal. During the training phase, pre-determined vectors are given as input to the classifier for generating its model. During the testing phase, final emotions of the individual are predicted based on totally new input vectors. Now, the system will automatically select a song from the database and play the music based upon the emotion detected.

6 Wearable Health Devices The rise of chronic diseases and an ageing population in today’s world has led to an interest in wearable physiological measurement devices. The development in

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Fig. 5 Block diagram of SER system [7]

this area has resulted in the manufacture of small wearable devices which are costeffective and are also easy to operate. So basically, wearable devices are the best way of configuring the information related to the human body. Some of the wearable health devices which can be worn by individuals are shown in Fig. 6.

Fig. 6 Examples of wearable health devices [8]

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The day-to-day activities can be monitored continuously using ambiance sensors to examine environmental conditions that the human body is exposed to during various sports and recreation exercises, like temperature and humidity to analyse dehydration. By using ambiance sensors, we can easily estimate the metabolic rate of the individuals, generally indoors due to lack of external factors. Wearable health devices are used to monitor the human body which helps in early detection of medical diseases, anomaly detection and diagnosis. But one drawback of these devices is that they are generally designed for monitoring only one physiological parameter. Some of the WHDs are under development like smart watches for the past few years and are also termed as an accessory for human beings. AMON was the first WHD designed in 2002 which can monitor several bodily parameters including heart rate, body temperature, and auto-concentration in blood. Lately, modern watches have come up with inbuilt Wi-Fi and other communication related services for monitoring the vital parameters of the body for a long period of time. They have a comfortable design which allows them to be worn constantly. They also have a feature to track various activities like calories burnt, distance travelled, and heart rate and also sleep monitoring system. Like, PEAK is the first smart watch which is able to track sleeping cycles. Also, Moov is a wearable bracelet which is basically an activity tracker and can be worn on either wrist or legs or other parts depending upon various sports activities like running, cycling, and swimming [8].

7 Review Analysis and Discussion The paper discusses different techniques through which we can detect emotions. They can be facial, speech, affect, and many more. The paper describes different methods to detect emotions like a multi-modal system which is based on artificial intelligence in which the emotions are being recognized by using audio and video inputs and using facial detection to detect the face and then matching the face image to detect the emotion. Similarly, the audio is extracted to recognize the emotion of the individual. There is also a system in which ECG signals are used to detect the emotions of the individual. Emotion detection using RF signals is the best suited system as it does not require any sensor to be attached to the body to detect the emotion of the person. There is also a system in which the emotions are detected using SER technology and then the emotion is recognized by the system and then the song is played from the database to cheer up the person. The problem in this system arises when there is more than one person in the room and speech samples of all the persons are taken, then the system has to differentiate between the persons and which song is to be played from the database. The affect emotion recognition system is basically the detection of the emotion of the individual after something happens to him/her in life, like the person saw a movie or an event happened in his life. To detect the affected emotions, they have used a

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system in which short video clips were shown to the volunteers and their emotions were recognized using EEG and ECG signals. Also, we have discussed some of the wearables or sensors which are attached to the body to detect the emotions.

8 Conclusion This paper presents the idea of understanding the emotions of individuals. It describes some of the methods that can be used to detect the emotions which can be detected either from facial expressions or from speech samples or by the information received by the radio frequency that contain the information related to the individual’s heartbeat. In this, we have also described the devices which can be used to gather the information of an individual’s emotion. The best suited method to detect various emotions is by using RF signals as in this method the person does not need to wear any external equipment and the accuracy is about 82–87%. There are various applications of emotion recognition in human– computer interaction, video games, and medicine. There are various wearable devices like smart watches (PEAKTM, Moov), Vital Jacket (t-shirt), Google Contact Lens, etc., which are used to sense the data and then analyse it to detect the emotions of the person.

References 1. A.C. Cruz, B. Bhanu, N.S. Thakoor, One Shot Emotion Scores for Facial Emotion Recognition: Center for Research in Intelligent Systems, vol. C. University of California, Riverside (2014), pp. 1376–1380 2. S. Shojaeilangari, W. Yau, Robust representation and recognition of facial emotions using extreme sparse learning. IEEE Trans. Image Process. 7149(c) (2015) 3. V. Struc, Multi-modal Emotion Recognition Using Canonical Correlations and Acoustic Features, vol. i (2010), pp. 4141–4144 4. C. Xiefeng, Y. Wang, S. Dai, P. Zhao, Q. Liu, Heart sound signals can be used for emotion recognition. Sci. Rep. 9(1) (2019) 5. M. Zhao, F. Adib, D. Katabi, Emotion Recognition Using Wireless Signals 6. S. Katsigiannis, N. Ramzan, DREAMER: a database for emotion recognition through EEG and ECG signals from wireless low-cost off-the-shelf devices. IEEE J. Biomed. Health Inform. 14(8) (2017) 7. S. Lukose, Music Player Based on Emotion Recognition of Voice Signals (2017), pp. 1751–1754 8. D. Dias, Wearable Health Devices—Vital Sign Monitoring, Systems and Technologies (2018)

Smart Shirt: A Leap into Technological Fashion Alok Barwal and Pulkit Jain

Abstract Clothing is one of the basic necessities of life but now with modern era of technological and fashion developments, it has taken the form of smart clothing or particularly smart shirt. A major advantage of smart shirt is its ability to provide an effective way to monitor human body in a discreet manner. This paper provides the basic information, needs, requirements, methods and developments that have taken place related to this field during the span of roughly two decades since 1990s when the concept of smart shirt was first realized. The topic smart shirt has been illustrated which provides us with its innovative and lifesaving applications in the field of health care, sports, military, space exploration, public safety, consumer fitness and research. Keywords Smart shirt · MEMSWear · SQUID · PDMS · Smart sensing

1 Introduction Today in this modern and progressive world, everything just seems to be getting smarter day by day whether it is computer, home, car, phone or even your shirt. The concept of smart shirt or textile has been around for roughly two decades now ranging from primarily health care to battlefield and now paving its way into our daily lives. The title—smart textiles—has been obtained from the smart or the intelligent materials. In the year 1989, the idea of—smart material was first determined in Japan. The textile material which was first classified a smart textile was a thread of silk possessing shape memory. One of the first-generation smart textiles namely Georgia Tech Wearable Motherboard (GTWM) was in the form of mobile and effective information system. This intelligent wearable system could be developed as per the requirements of individual to make use of the advancements in the field of

A. Barwal (B) · P. Jain Department of Mechatronics Engineering, Chandigarh University, Mohali, India e-mail: [email protected] P. Jain e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Biswas et al. (eds.), Microelectronics, Circuits and Systems, Lecture Notes in Electrical Engineering 976, https://doi.org/10.1007/978-981-99-0412-9_33

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GTWM Requirements • Functionality • Wearability • Durability • Maintainability

GTWM Properties • Sensing Properties • Comfort Properties

GTWM Materials & Fabrication Technologies • Materials • Design Parameters

Fig. 1 GT wearable motherboard: design and development framework

information processing and telemedicine. It serves following two tasks: (a) an information system which is flexible and can provide ubiquitous computing and (b) a monitoring system for control and monitor of numerous vital signs of individuals in a cost-effective and efficient manner. In 1996, the objective of Georgia Tech’s research project was to develop a system that would meet the two major requirements: projectile’s penetration detection and soldier’s vital signs monitoring. The requirements of the wearable motherboard are wear ability which entails that the GTWM should be breathable, durable, easy to wear and remove, easier detection of wounds and light in weight. These are some of the important combat conditions’ requirements so that the performance of soldier is not affected by the smart wear. It should provide a high wear life and should be flexible, able to resist abrasion, which is very important in any combat situation [1]. Similarly, smart shirts having different applications may have different requirements but some common ones are its flexibility, ability to withstand laundering, weight and wearability. The resulting GTWM design and development framework, which acts as base and can be modified to achieve any specified application of smart shirt is depicted in Fig. 1.

1.1 Smart Shirt Smart shirt is able to sense, react and adapt to stimuli from the surrounding with the help of its numerous functionalities. The nature of stimulus and response can be thermal, electrical, magnetic, chemical or some other nature. According to this definition, advanced materials like ultra-strong or fire proof fabrics are not considered as intelligent. There are basically three categories of smart shirts: (a) passive shirts— sensing only, (b) active shirts—sense and react accordingly and (c) third category refers to very smart textiles which can even adapt to circumstances in addition to sensing and reaction. By principle, three components are must in a smart shirt: a primary sensing element, an actuator and a processing unit to coordinate between the sensor and the actuator [2].

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1.2 Need of Smart Shirt The need of a smart shirt can be understood through the diversity of its applications in fields like health sector, military, sports, astronomy, etc. Health sector—nowadays, every person is becoming more and more conscious about his health and the idea of monitoring vital signals of his body and connecting with doctor through a distance is arising as a great need. Also, as our population is aging and elder people are more prone to diseases and health problems, it becomes very important for a way to reduce the cost but increase the efficiency of medical attention. Military—the firstgeneration smart shirt was in fact designed and fabricated for the military purpose of detecting projectile penetration and to detect the vital signs of soldier [1]. Similarly, there arises a need for smart shirts in field of sports to monitor the athlete and in astronomy to tackle various problem faced by astronauts in outer space.

1.3 Functions of Smart Shirt Five main functions which can be seen in smart shirt, are sensing, data processing, actuators, storage and communication [2]. Sensing: To convert physical to electrical signal, a transducer consisting of primary sensing element is required. Sensors can be present for vital signs’ monitoring like body temperature, heart rate, electrocardiogram (EKG), respiration rate and pulse oximetry (SpO2 ). Additionally, a microphone may also be plugged in to record the voices. So, considerably various sensors are used to fulfill this need of vital sign detection and even some other sensors may also be present like accelerometer, gyroscope, etc., for the purpose of movement, posture or fall detection. Data Processing: Data processing is an important part of active smart shirts and requires an electronic component to do so as, no true textile material can provide smart shirt with this functionality. Actuators: Actuators respond to a signal from sensor after data processing has taken place. Actuators as the name suggests are like motors to provide motion and performs the reverse operation of a transducer like electrical, hydraulic and pneumatic actuation. Data and Energy Storage: Two kinds of storage capacity namely for data storage and energy storage. Usually, electrical power is required for performing various functions like sensing, data processing, actuation and communication. Significant amount of work has also been done on developing smart shirts and battery backups which may last for several days. In 2014, a low power SoC shirt was also prototyped which could achieve the continuous transmission all day with the help of only a small coin cell battery tailored in the shirt. The whole system was integrated onto a single chip making it cost-effective [3].

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Communication: Communication is a very important function of a smart shirt as it makes the data acquired by shirt available to wearer or send it to the designated places. T-connectors—which looks like button clips connected to yarns are used for information relay with sensor which transmits it to smart shirt controller. This processed data can then be transmitted wirelessly [4]

1.4 Requirements of Smart Shirt Figure 2 displays the analysis of user requirements for a smart shirt.

2 Data Collection In 2002, the ability of fabrics to have piezoresistive properties when coated with polymer such as polypyrrole (PPy) or with rubber and carbon mixture was shown. These properties can be used to create strain sensors which can be useful in man– machine interfaces. The fabrics can be easily tailored into smart shirts with no discomfort to the subject [5]. In 2012, SMI technology was developed and found to be more convenient as compared to the conventional flexible and rigid circuit boards in context of textile integration. SMI modules were laminated by a thin layer of PDMS followed by screen printing them onto woven and knitted fabrics. The fabric and the modules’ Fig. 2 User requirements for a smart shirt Wearability

Performance Metrics

Durability

Functionality

Maintainability

Usability in Field

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adhesion were enough to withstand washing as it was tested for 50 washing cycles [6]. The design principle of SMI technology has been shown in Fig. 3. Yarns or fibers which can be woven into smart shirts can have electrical, electronic, mechanical, electrochemical properties. Conductive yarns can also be made by twisting of silver or copper threads with natural or artificial fibers. All these conductive yarns can be sewn, embroidered, knitted or woven using standard machines because of their elastic properties [7]. Recent research shows that polybutylene terephthalate (PBT) thread coated with carbon resistive ink can be developed into a thread-based strain sensor. The sensor can be further coated with PDMS layer for protection from the environment [8].

Fig. 3 SMI technology design principle

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3 Applications of Smart Shirt There are numerous applications of smart shirt ranging from medical field to battle field. The various fields of applications—public safety, health monitoring, battlefield, sports and fitness, etc., where smart shirt can be used have been illustrated in Fig. 4.

3.1 Developments in Various Applications of Smart Shirt One of the first-generation smart textiles is the Georgia Tech Wearable Motherboard (GTWM) developed in 1999 [1]. In 2003, a research shows the use of knowledge that the involuntary activities of nervous system (subconscious) in real conditions are associated with sensorial, emotional and cognitive activities and responses to develop a smart shirt named Marsian. Smart shirt was used to integrate respiration sensors and EKG electrodes which played an important role to evaluate the emotions of a person [9]. In 2005, a novel system for detection of falls in human beings which was able to send notification via SMS or email was developed. To differentiate between fall and normal activities, the absolute peak values among three-dimensional acceleration signals were considered which were tailored into the smart vest. The major benefit is that it raises the alarm on its own to get assistance as soon as possible [10]. Later, MEMSWear was also developed which could provide the same feature of fall detection through the use of gyroscopes and accelerometers. MEMSWear used its algorithms to distinguish between the normal human activities and fall [11].

Race Car Driver

Child

First Responders

Soldiers

Fig. 4 Applications of smart shirt

Mountain Climber

Smart Shirt

Off Site Monitoring

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Mathematical Model

Dynamic Sensors

Physiological Sensors

Fall Prediction

Muscle Constriction

Airbag Deployment

Injury Prevention

Fig. 5 MEMSWEAR II model

MEMSWear II was further developed from MEMSWear in 2006, incorporating various physiological and physical sensors connected with processor to use the algorithm which can even determine the probability of whether a fall is imminent or not. The processor also decides whether an injury minimization device should be activated [12]. The embedded real-time architecture for MEMSWear2 has been shown in Fig. 5. In 2005, another research showed the development of health care and disease prevention through smart shirt. Smart shirt can empower chronic patients to understand and handle the disease state better. For this purpose, systems have to monitor the vital parameters, sensors and take the appropriate measurements at the appropriate time. The stated concept is referred as smart sensing. Key components of smart shirt can be connected together through the use of local area networks. This acts as the base for personal area network (PAN). PAN’s aim is to interconnect the intelligence of all the components of smart shirt so that they work as a unified unit [7]. In 2008, smart vest was also designed and fabricated specifically for professions in which individual may be prone to dangers like a soldier, fire fighters, deep-sea divers, miners, law enforcement personnel and astronauts [13]. In year 2013, an innovative technological development namely SQUID was introduced which further automated and facilitated health care at home. SQUID integrated various data from sensors to develop a biofeedback which guides the patient to follow therapist prescribed procedure. The readings of workout sessions carried out by the patient can be transmitted wirelessly to smart phone application available along with online database which holds the therapy records and makes it possible for therapist to supervise the rehabilitation remotely [14]. The SQUID system block diagram and its components have been illustrated in Fig. 6. Lastly, a 2018 research [15] shows a little broad perspective about not just smart shirt but wearable health devices and developments that has taken place in field of personal health monitoring.

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Mobile Phone

Server and Receiver

Fig. 6 SQUID model

Considerable amount of advancement has taken place in field of smart healthcare wearable, thus resulting in ample opportunities to capitalize over it. Hence, a number of smart wearable have been developed and marketed for the economic purposes over the period of time. Various smart wearable and their diversity varying from smart shirts to bands to patches made available for variety of needs has been displayed in Fig. 7.

•HR + Muscle Activity •HR •T-Shirts Fitness/Sport •Chest Straps

•ECG •Adhesive Patches Medical/Health

Fig. 7 Smart wearables’ categories

Smart Shirt: A Leap into Technological Fashion

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4 Conclusion Conclusively, the paper states the importance and potential smart shirt holds as a technological and economic advancement. Smart shirt as an emerging and innovative technology holds the applicability to drastically change the practice of health care. The smart shirt uses the new sense of smart fashion along with being comfortable as well as stylish with a little intelligence which can be developed to meet the innovative and modern applications in the field of military, sports, health care, public safety, space exploration and consumer fitness fields. The paper discussed the basic information, needs, requirements, methods, applications and developments that have taken place in this upcoming industry known as smart shirts which holds the potential to change our lifestyles and fashion industry as we know it today.

References 1. C. Gopalsamy, S. Park, R. Rajamanickam, S. Jayaraman, The wearable motherboardTM : the first generation of adaptive and responsive textile structures (ARTS) for medical applications. Virtual Real. 4(3), 152–168 (1999) 2. L. Van Langenhove, C. Hertleer, Smart clothing: a new life. Int. J. Cloth. Sci. Technol. 16(1–2), 63–72 (2004) 3. T. Morrison, J. Silver, B. Otis, A single-chip encrypted wireless 12-lead ECG smart shirt for continuous health monitoring, in IEEE Symposium on VLSI Circuits, Digest of Technical Papers (2014), pp. 6–7 4. S. Park, S. Jayaraman, Enhancing the quality of life through wearable technology. IEEE Eng. Med. Biol. Mag. 22(3), 41–48 (2003) 5. A. Mazzoldi, D. De Rossi, F. Lorussi, E.P. Scilingo, R. Paradiso, Smart textiles for wearable motion capture systems. Autex Res. J. 2(4), 199–203 (2002) 6. T. Vervust, G. Buyle, F. Bossuyt, J. Vanfleteren, Integration of stretchable and washable electronic modules for smart textile applications. J. Text. Inst. 103(10), 1127–1138 (2012) 7. F. Axisa, P.M. Schmitt, C. Gehin, G. Delhomme, E. McAdams, A. Dittmar, Flexible technologies and smart clothing for citizen medicine, home healthcare, and disease prevention. IEEE Trans. Inf. Technol. Biomed. 9(3), 325–336 (2005) 8. A. Sadeqi, H. Rezaei Nejad, F. Alaimo, H. Yun, M. Punjiya, S.R. Sonkusale, Washable smart threads for strain sensing fabrics. IEEE Sens. J. 18(22), 9137–9144 (2018) 9. F. Axisa, A. Dittmar, G. Delhomme, Smart clothes for the monitoring in real time and conditions of physiological, emotional and sensorial reactions of human. Annu. Int. Conf. IEEE Eng. Med. Biol. Proc. 4, 3744–3747 (2003) 10. F.E.H. Tay, M.N. Nyan, T.H. Koh, K.H.W. Seah, Y.Y. Sitoh, Smart shirt that can call for help after a fall. Int. J. Softw. Eng. Knowl. Eng. 15(2), 183–188 (2005) 11. S.N.C. Po, G. Dagang, M.D.B.M. Hapipi, F.T.E. Hock, MEMSWear—biomonitoring—incorporating sensors into smart shirt for wireless sentinel medical detection and alarm. J. Phys. Conf. Ser. 34(1), 1068–1072 (2006) 12. S.N.C. Po et al., Overview of MEMSWear II—incorporating MEMS technology into smart shirt for geriatric care. J. Phys. Conf. Ser. 34(1), 1079–1085 (2006)

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13. P.S. Pandian et al., Smart vest: wearable multi-parameter remote physiological monitoring system. Med. Eng. Phys. 30(4), 466–477 (2008) 14. A.B. Farjadian, M.L. Sivak, C. Mavroidis, SQUID: sensorized shirt with smartphone interface for exercise monitoring and home rehabilitation, in IEEE International Conference on Rehabilitation Robotics (2013) 15. D. Dias, Wearable health devices—vital sign monitoring, systems and technologies. Sensors 18(8), 2414 (2018)

Design of Domino Logic-Based NOR Gate Circuit for Reduction of Charge Sharing Saumya Srivastava and Sangeeta Shekhawat

Abstract In this paper, NAND gate circuit is designed by the help of domino logic circuit to improve the performance of NAND gate circuit. Domino logic circuit is introduced to resolve the problem of dynamic logic circuit. Domino logic circuit will help to remove the problem of charge sharing in dynamic logic circuit. This NAND gate circuit is the universal logic gate and by the help of this any digital circuit block can be easily design. In this modern era, all the things are digital. Here, power dissipation of the circuit is improved approx. 50–60% and delay of the circuit is reduced. Speed of the device will improve and the charge sharing problem is also resolved. All the simulations are carried out on electronic design automation (EDA) tool on LTSPICE software. Domino logic is one of the best technique when try to reduce power consumption as well as speed, delay of the device and delay of the circuit is reduced. Keywords Domino logic · Time delay · Digital circuits · Electronic design automation tool

1 Introduction In the field of digital electronics circuit, all the things are now based on digital, and in the digital circuit, two factors that is ‘1’ and ‘0’ binary numbers are basically used. When gray codes are compared with binary numbers or if possible to replace binary number with gray code number than power consumption problem will be resolved [1–4]. Now binary numbers are replaced with gray code numbers. Power dissipation is very essential parameters, nowadays due to the charge sharing issue in dynamic logic circuit, power problem will be resolved by the domino technique. S. Srivastava (B) Department of Electronics and Communication Engineering, Chandigarh University, Gharuan, Mohali, Punjab, India e-mail: [email protected] S. Shekhawat Department of Electronics and Communication Engineering, Amity University, Jaipur, Rajasthan, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Biswas et al. (eds.), Microelectronics, Circuits and Systems, Lecture Notes in Electrical Engineering 976, https://doi.org/10.1007/978-981-99-0412-9_34

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When low power consumption or power becomes the essential term so in place of domino logic, all the digital electronic circuits are designed by the help of domino logic circuit. All the digital electronic circuit like NAND NOR XNOR half adder full adder decoder encoder multiplexer and de-multiplexers can be easily designed by the help of CMOS logic [5, 6]. CMOS is very good for low power and full swing purpose but when power factor becomes one of the more essential parameters, then dynamic logic circuit domino logic circuit will plays essential role. Figure 1 shows the domino logic circuit for the particular expression, i.e., V out = A . B. In the domino logic circuit inverter, circuits are used on the output side and help to remove the charge sharing problem by frequently switching the clock. Power consumption is one of the essential methods which will be take care using the dynamic and domino logic methodology. Power consumption can be resolved by the help of this circuit [7–11]. Here, in this figure, two expressions A and B are used as input and at the out A . B expression will be find out but due to charge sharing issue by the dynamic logic circuit one extra inverter circuit has to be added on the output side. On the another stage, one input C has been added here, and on the second stage, again it is used as input and output will be in the inverted form and finally, V out will be occurs. Figure 2 is showing the domino logic circuit with an additional pull up network, the reason behind this pull up network circuit is to reduce the noise leakage and charge sharing problem as minimum as possible. Here, in this Fig. 2, domino logic circuit with additional pull up network in the first NMOS network, three inputs are applied and one clock is used after the result all the signals are passed through and output is calculated by the inverted output form and clock is basically worked on the pre-charge and evaluation mode [12–16]. Figure 3 shows the clock period of the domino logic circuit in both the mode like pre-charge and evaluation phase. These pre-charge and evaluation modes are basically worked on the clock cycle when the clock cycle is low, then automatically

Fig. 1 Domino logic circuit

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Fig. 2 Domino logic circuit with an additional pull up network

Fig. 3 Clock mode in pre-charge and evaluation phase

pre-charge mode will be activated when the clock cycle is high, then evaluation mode will be activated [17–21].

In this paper, NAND gate circuit is designed by the help of domino logic circuit to remove the charge sharing problem of the circuit as well as power wastage [12, 21–23]. Area of the device will be very high because numbers of the transistors are more in the dynamic logic circuit. Table 1 shows the truth table of the NAND gate circuit basically this truth table express the logic formula of the circuit. For example, when both the input A and B are ‘0’ the output will be ‘1’, when one input is ‘0’ and other is ‘1’, then output will be ‘1’ in a similar way when another input is ‘1’ and ‘0’, then output is when and when finally both the input is ‘1’ then output is ‘0’. So through this truth table it can be conclude that when any one of the input is logic ‘0’

394 Table 1 Truth table

S. Srivastava and S. Shekhawat A

B

Out

0

0

0

0

1

0

1

0

0

1

1

1

then output will be ‘1’ [5–8]. This logic will help to design the truth table of OR gate circuit by the help of domino logic circuit.

2 Simulation Results In this section, all the simulation results of the corresponding NAND gate using domino logic circuit will be discussed here. All the experimental results are carried out one of the EDA, i.e., electronic design automation tool [8]. There are many EDA tools available like mentor graphics synopsis and cadence all are used basically on the industries purpose. These all EDA tools cadence virtuoso mentor graphics, synopsis are paid software for their license money have to pay on another side LT spice, and NG spice are free software easily available and easily downloadable [9–11]. This domino-based NAND gate circuit for low power and remove the problem of charge sharing is designed on the LT spice simulation portion. Simulation results are carried out on LT SPICE with the technology node 90 nm. After designing, the circuit result and simulations are carried out on wave form analysis window of LT SPICE. Table 2 expresses the tabular way for different input values and clock values so that overall performance of the domino-based circuit is analyzed here. In this table, first column represents the different parameters like V initial means express the initial value of the signal in terms of voltage source. Von expressed that maximum amount of the voltage when the signal is in the on condition. Here, V on time is basically considered as 2 V. Table 2 Tabular method for different parameters of domino logic-based NAND gate

Parameter

Input (A)

Input (B)

Clock

V initial (V)

0

0

0

V on (V)

2

2

1

T delay (s)

2 ns

0

1

T rise (s)

0.5 ns

0.5 ns

1

T fall (s)

0.5 ns

0.5 ns

1

T on (s)

20 µs

20 µs

1

T period (s)

41 µs

41 µs

1

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395

T delay is one of the parameter which expressed the maximum delay to the circuit so that power dissipation problem will be resolved. Here, delay is 2 ns is used for simulation. Another parameters are T rise as well as T fall time which is approx. 0.5 ns are taken here for simulation [13–15]. Ton expressed the total amount of time when the device will be in the on condition. Here, T on is 20 µs is considered here. T period express the double time of the total T on time. T period is 41 µs used here. These all parameters will help the circuit for simulation. All the analysis of the result is carried out on when clock is ‘0’ then pre-charge mode and when the clock is ‘1’ then evaluation mode takes place. Figure 4 presents the schematic diagram of NAND gate circuit for domino logic circuit. Here, two inputs are A and B both are pulse and supply voltage value is 5 V which is use to initiate the expression [14]. One clock pulse is used here and accordingly pre-charge and evaluation phase takes place after synchronization of the clock period. Here, in the domino logic circuit-based NAND gate, total number of transistors is eight [12]. A and B express the input signals pulse type input is applied here. On the output side one inverter circuit is used here. Number of PMOS is used here, four as well as number of NMOS is also four. Figure 5 expressed the output waveform of the signal according to the input waveform. Here, all the simulations are carried out on LT SPICE software. In the output waveform, X-axis indicates the voltage value and Y-axis indicates the time. This NAND gate circuit is basically used for low power application because domino logic will reduce the power consumption of the circuit approx. 50–60% of the conventional value. Here, through this circuit it is expressed when one input is 0

Fig. 4 Schematic diagram of domino-based NAND gate

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Fig. 5 Simulation result of domino-based NAND gate circuit

other one is 1 then output is 1 only. So the logic behind domino-based NAND gate circuit is low power but here area got affected.

3 Conclusion The main focus of this paper is low power consumption less delay and high-speed device circuit. Power consumption is now very important term when the cost factor plays important role in this era. Here, domino-based NAND gate circuit is designed to reduce the power consumption but one of the drawbacks of this circuit is high area. Power consumption is reduced here approx. 50–60% as compared to conventional as well as dynamic logic circuit. The major difficulty of the domino logic circuit is clock synchronization. This application is very useful for low power devices. The affiliated institutions, including town/city and country, are to be listed directly below the names of the authors. Multiple affiliations should be marked with superscript Arabic numbers, and they should each start on a new line. Including your postal code is optional. Please place an envelope icon (or any other pointer) next to the name of the corresponding author, whose email address is mandatory, in the header of the paper. Email addresses should start on a new line directly under the corresponding affiliation.

References 1. S. Sakoda, Optical Properties of Photonic Crystals (Springer, Berlin, 2001) 2. Z. Wu, K. Xie, H. Yang, Band gap properties of two dimensional photonic crystals with rhombic lattice. Optik 123, 534–536 (2012)

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3. Z.J. Li, Z.W. Chen, B.J. Li, Optical pulse controlled all optical logic gates in SiGe/Si multimode interference. Opt. Express 13, 1033–1038 (2005) 4. H. Alipour-Banaei, S. Serajmohammadi, F. Mehdizadeh, All optical NAND and NOR gates based on nonlinear photonic crystal ring resonators. Optik 125, 5701–5704 (2014) 5. H. Alipour-Banaei, F. Mehdizadeh, Significant role of photonic crystal resonant cavities in WDM and DWDM communication tunable filters. Optik 124, 2639–2644 (2013) 6. M. Djavid, M.S. Abrishamian, Multi-channel drop filters using photonic crystal ring resonators. Optik 123, 167–170 (2011) 7. H. Alipour-Banaei, M. Jahanara, F. Mehdizadeh, T-shaped channel drop filter based on photonic crystal ring resonator. Optik 125, 5348–5351 (2014) 8. M.Y. Mahmoud, G. Bassou, A. Taalbi, Z.M. Chekroun, Optical channel drop filter based on photonic crystal ring resonators. Opt. Commun. 285, 368–372 (2012) 9. H. Alipour-Banaei, F. Mehdizadeh, S. Serajmohammadi, A novel 4-channel demultiplexer based on photonic crystal ring resonators. Optik 124, 5964–5967 (2013) 10. M. Djavid, F. Monifi, A. Ghaffari, M.S. Abrishamian, Heterostructure wavelength division multiplexers using photonic crystal ring resonators. Opt. Commun. 281, 4028–4032 (2008) 11. H. Rostami, A. Banei, F. Nazari, A. Bahrami, An ultra-compact photonic crystal wavelength division demultiplexer using resonance cavities in a modified Y-branch structure. Optik 122, 1481–1485 (2011) 12. T. Ahmadi-Tame, B.M. Isfahani, N. Granpayeh, A.M. Javan, Improving the performance of all optical switching based on nonlinear photonic crystal micro ring resonator. Int. J. Electron. Commun. (AEU) 65, 281–287 (2011) 13. S. Serajmohammadi, H. Alipour-Banaei, F. Mehdizadeh, All optical decoder switch based on photonic crystal ring resonators. Opt. Quant. Electron. (in press) (2015) 14. H. Alipour-Banaei, F. Mehdizadeh, S. Serajmohammadi, M. Hassangholizadeh-Kashtiban, A 2 * 4 all optical decoder switch based on photonic crystal ring resonators. J. Mod. Opt. 62, 430–434 (2015) 15. X. Zhang, Y. Wang, J. Sun, D. Liu, D. Huang, All-optical AND gate at 10 Gbit/s based on cascaded single-port-coupled SOAs. Opt. Express 12, 361–366 (2004) 16. J. Wang, J. Sun, Q. Sun, Proposal for all-optical switchable OR/XOR logic gates using sumfrequency generation. IEEE Photon. Technol. Lett. 19, 541–543 (2007) 17. Y. Fu, X. Hu, Q. Gong, Silicon photonic crystal all-optical logic gates. Phys. Lett. A 377, 329–333 (2013) 18. N. Saidani, W. Belhadj, F. Abdel Malek, Novel all-optical logic gates based photonic crystal waveguide using self imaging phenomena. Opt. Quant. Electron. (in press) (2015) 19. B.M. Isfahani, T. AhamdiTameh, N. Granpayeh, A.M. Javan, All-optical NOR gate based on nonlinear photonic crystal microring resonators. J. Opt. Soc. Am. B 26, 1097–1102 (2009) 20. A. Taalbi, G. Bassou, M.Y. Mahmoud, New design of channel drop filters based on photonic crystal ring resonators. Optik (2012). https://doi.org/10.1016/j.ijleo.2012.01.045 21. F. Mehdizadeh, H. Alipour-Banaei, S. Serajmohammadi, Channel-drop filter based on a photonic crystal ring resonator. J. Opt. 15, 075401 (7pp) (2013) 22. G.V. Prakash, M. Cazzanelli, Z. Gaburro, L. Pavesi, F. Iacona, G. Franzò, F. Priolo, Linear and nonlinear optical properties of plasma-enhanced chemical-vapour deposition grown silicon nanocrystals. J. Mod. Opt. 49, 719–730 (2002) 23. S.D. Gedney, Introduction to Finite-Difference Time-Domain (FDTD) Method for Electromagnetics (Morgan and Claypool, Lexington, KY, 2010)

Design of Dynamic Logic Circuit-Based NOR Gate for Low Power Saumya Srivastava and Sangeeta Shekhawat

Abstract In this paper, NOR gate is designed by the help of dynamic logic circuit. As NOR gate is one of the universal logic circuits, it means any digital circuit can be easily designed using NOR gate. The main purpose of this paper is to design NOR gate using dynamic logic circuit for power reduction the power consumption and enhance the speed of the device. All the simulation is carried out on LTspice software, and power consumption is reduced approximately 50% as compared to conventional CMOS design. Here, both pre-charge and evaluation modes are very useful for dynamic logic circuit. The drawback of this circuit area increased slightly by increasing the number of transistor, and power dissipation is almost half to the conventional circuit. This paper will help to reduce the power consumption issue, and for low power application, this is one of the best techniques and power reduced as well as speed of the device will be increased. Keywords Dynamic circuit · Digital logic · Pre-charge · Evaluation · Electronic automation design

1 Introduction In the modern era, all the things are digital, and it totally depends on logic ‘0’ and logic ‘1’. Digital logic gates like NOT, AND, OR, NOR, NAND, XOR and XNOR gates are very essential circuit to design different multiplexers, adders, decoders and encoders. Half adder, full adder, all the combinational and sequential circuits are designed by the help of digital circuit. Now in the modern era, all the techniques are surrounded by digital logic 1 and logic 0 [1–4]. Designers and researchers are now focusing to reduce the power consumption of the circuit so that cost of the S. Srivastava (B) Department of Electronics and Communication Engineering, Chandigarh University, Gharuan, Mohali, Punjab, India e-mail: [email protected] S. Shekhawat Department of Electronics and Communication Engineering, Amity University, Jaipur, Rajasthan, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Biswas et al. (eds.), Microelectronics, Circuits and Systems, Lecture Notes in Electrical Engineering 976, https://doi.org/10.1007/978-981-99-0412-9_35

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circuit is reduced, and it will be easily affordable. In the static circuit, every point output response is conducted where as in dynamic circuits response will be counting continuously. In dynamic logic circuits, all the process is done through charging and discharging of capacitor [5–7]. This technique will work in two modes: pre-charge and evaluation mode. In the dynamic circuit once the capacitor discharge at output side, it will be again charged when the device will be in pre-charge mode. Dynamic transistor technique is basically worked in transition between ‘0’ and ‘1’. Dynamic logic circuit is better than static logic circuit in various aspects like it will be very useful to avoid the duplicating logic by separate indication of N-Tree and P-Tree. By avoiding circuit duplicating, dynamic logic circuit helps to reduce the overall power dissipation [7–11]. This dynamic circuit is mainly used for high performance device because to design the dynamic circuit synchronization of the clock is very essential and very difficult task to do. Dynamic logic circuit is useful for low power as well as high-capacity device. So, the main function of the dynamic logic circuit is to reduce power consumption. Dynamic logic circuit is designed by pull-up and pull-down network only. Basically, this circuit uses the M + 2 number of transistor if the circuit is designed through CMOS logic. This circuit is also very useful for full swing network. Dynamic logic circuit has very high speed, and it has less delay. In the present era, designer’s main focus is all about power dissipation. Dynamic logic circuit is one of the best techniques among for low power application [3, 12–14]. In this technique, extra two transistor one pull-up and pull-down network is connected with clock and provide the details accordingly. Figure 1 shows the basic dynamic logic circuit diagram. Two extra NMOS and PMOS are used for dynamic logic circuit performance. Here, clock is connected in the pull-up and pull-down network. This clock is mainly used to synchronize the circuit and minimize the power consumption [5–7]. Table 1 shows the truth table of NOR gate. Two inputs are A and B which help to produce the output. Figure 2 represents the pre-charge and evaluation mode, and these two modes are working on the basis of different clock pulse that’s why clock synchronization is the major issue of this dynamic logic circuit.

Fig. 1 Basic circuit of dynamic logic circuit

Design of Dynamic Logic Circuit-Based NOR Gate for Low Power Table 1 Truth table

A

B

401 Out

0

0

1

0

1

0

1

0

0

1

1

0

Fig. 2 Pre-charge and evaluate mode for different clock pulse

2 Simulation Results In this portion, simulation results are discussed. All the experiments are performed on LTspice software. This is one of the electronic design automation (EDA) tools, and it is very useful to design digital as well as analog circuits. The design of NOR gate circuit using dynamic logic technique helps to reduce the power consumption. [1–4]. NOR gate is one of the universal logic gates in digital circuit; it means by the help of NOR gate, any complex circuit can be easily designed. Here, dynamic logic circuit is used to reduce the power consumption and enhance the speed of the device. Table 2 shows the different parameters value for both the input A and B. Here, V initial value is ‘0’ and V on is 5 V given. Both input A and B has different delay provided here like for A 5 µs and for B 0 delay is provided during simulation. Both the inputs have provided same fall time and rise time of 1 ns and make complete on time till 10 µs. T period is basically double of the total on time so for both the input 21 µs T period is considered. Clock cycle is represents 0 is pre-charge mode and 1 in evaluation mode. Figure 3 shows the schematic diagram of NOR gate using dynamic logic circuit. Here, two inputs are used as A and B; both are pulse inputs: one supply voltage with 5 V and one clock pulse to synchronize the circuit as per application [10–13]. Here, both the transistors M1 and M2 are connected parallel which is NMOS type, and M4 and M5 are connected in series as they are PMOS type. Two extra transistors

402 Table 2 Tabular presentation of different parameters of input

S. Srivastava and S. Shekhawat Parameter

Input (A)

Input (B)

Clock

V initial (V)

0

0

0

V on (V)

5

5

1

T delay (s)

5 ns

0

1

T rise (s)

1 ns

1 ns

1

T fall (s)

1 ns

1 ns

1

T on (s)

10 µs

10 µs

1

T period (s)

21 µs

21 µs

1

added here, M3 and M6, for clock pulse Ck. Here, M6 is pull-up network and M3 is pull-down network. Figure 4 represents the two input waveforms and one output waveform. All the simulations are carried out on LTspice software. Here, as per results, NOR gate truth table is followed by the circuit. This NOR gate results are designed by the use of dynamic logic circuit, and after simulation, it is observed that after dynamic logic circuit power consumption and delay of the circuit is reduced, and speed of the device is improved. In the figure, when the clock is 0, so circuit will work in pre-charge mode, and when the clock is 1 means high, then it will evaluate the circuit. All the dynamic logic operation is performed in the pre-charge and evaluation mode [3–6]. Pre-charge mode occurs due to the capacitor charge, and evaluation mode occurs due to discharging of the capacitor.

Fig. 3 Schematic design of NOR gate using dynamic logic technique

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Fig. 4 Output wave of dynamic logic circuit-based NOR gate

In this output diagram, X-axis indicates the time and Y-axis indicates the voltage level. When both the input A and B are logic 0 so output will be high and when both the input is logic 1 so output will be zero [1–3]. For first waveform A is high and B is low so the output is low only. All the conditions of the waveform represent the truth table of NOR gate. Schematic design of NOR gate by the help of dynamic logic circuit will save 50% of the total power consumption, and delay of the circuit will be reduced according to rise and fall time of the circuit.

3 Conclusion In this paper, dynamic logic circuit-based NOR gate is designed. Dynamic logic circuit is used for low power consumption, low delay and high speed of the device. This technique is very useful for digital circuits; all the universal logic gates and special gates can be designed using this dynamic logic circuit. In the analysis, it is observed that power consumption of the circuit is approximately half to the conventional CMOS circuit. Here, two extra transistors are added to synchronize the clock pulse so the overall area of the device increased little bit, and circuit becomes more complex as compared to conventional CMOS circuit. This is one of the drawbacks of the circuit, and synchronization of clock pulse is slightly complex. Overall, it can be concluded for low power application and high-speed performance of the circuit dynamic logic is one of the best techniques.

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References 1. X. Sun, Z. Mao, F. Lai, A 64 bit parallel CMOS adder for high performance processors, in Proceedings of the IEEE Asia-Pacific Conference on ASIC (2002), pp. 205–208 2. T. Thorp, D. Liu, P. Trivedi, Analysis of blocking dynamic circuits. IEEE Trans. VLSI Syst., 744–749 (2003) 3. F. Mendoza-Hernandez, M. Linares-Aranda, V. Champac, Noise tolerance improvement in dynamic CMOS logic circuits. Proc. IEE Circ. Dev. Syst. 153(6), 565–573 (2006) 4. F. Tang, A. Bermark, Z. Gu, Low power dynamic logic design using a pseudo dynamic buffer. Integr. VLSI J. 45, 395–404 (2012) 5. K. Murali, S. Sinha, W.L. Ditto, A.R. Bulsara, Reliable logic circuit elements that exploit nonlinearity in the presence of a noise floor. Phys. Rev. Lett. 102, 104101 (2009) 6. K. Murali, S. Sinha, I.R. Mohamed, Chaos computing: experimental realization of NOR gate using a simple chaotic circuit. Phys. Lett. A 339, 39–44 (2005) 7. H. Peng, Y. Yang, L. Li, H. Luo, Harnessing piecewise-linear systems to construct dynamic logic architecture. Chaos 18, 033101 (2008) 8. V. Kursun, E.G. Friedman, Domino logic with variable threshold voltage keeper. IEEE Trans.VLSI Syst. 11(6), 1080–1093 (2003) 9. Y. Lih, N. Tzartzanis, W.W. Walker, A leakage current replica keeper for dynamic circuits. IEEE J. Solid State Circuits 42(1), 48–55 (2007) 10. D. Kuo, (2005) Chaos and its computing paradigm. IEEE Potentials 24, 13–15. T. Munakata, S. Sinha, W.L. Ditto (2002) Chaos computing: implementation of fundamental logical gates by chaotic elements. IEEE Trans. Circuits Syst. I Fundam. Theory Appl. 49, 1629–1633 11. J.-S. Wang, C.-R. Chang, C. Yeh, Analysis and design of high-speed and low-power CMOS PLAs. IEEE J. Solid State Circuits 36(8), 1250–1262 (2001) 12. M. Anders, S. Mathew, B. Bloechel, S. Thompson, R. Krishnamurthy, K. Soumyanath, S. Borkar, A 6.5 GHz 130 nm single-ended dynamic ALU and instruction-scheduler loop. IEEE ISSCC (2002), pp. 410–411 13. N.H.E. Weste, D. Harris, Principles of CMOS VLSI Design: A System Perspective, 3rd edn. (Addison-Wesley, 2004) 14. K. Murali, A. Miliotis, W.L. Ditto, S. Sinha, Logic from nonlinear dynamical evolution. Phys. Lett. A 373, 1346–1351 (2009)

Modeling of MQW Transistor Laser Using Group IV Materials Jaspinder Kaur and Rikmantra Basu

Abstract One of the active study fields in Si photonics nowadays is the use of materials from Group IV in optoelectronic devices. In this paper, a Group IV materialbased multiple quantum well (MQW) transistor laser (TL) is studied for various applications in the mid-infrared range. When compared to the bulk structure, the insertion of several quantum wells into the base area improves carrier confinement. Furthermore, with the insertion of Sn (tin) > 8% into the Ge (germanium), the group IV semiconductor, i.e., GeSn exhibits direct bandgap behavior, ensuring that the population inversion condition is met. The proposed GeSn transistor laser structure is interesting for various applications in the spectrum of mid-infrared range due to its low value of base threshold current and higher value of modulation bandwidth. Keywords GeSn alloy · Multiple quantum well · TCAD simulation

1 Introduction GeSn, which is one of the most promising materials, has several unique optoelectronic, electronic, and optical properties that make it attractive in many applications including photonics, optoelectronic, and nanoelectronics [1]. The research interest on Si photonics is immensely growing with the introduction of high-performing devices, such as laser diodes, electro-optical modulators, and photodetectors [2]. Till date, researchers have considered Group III–V and II–IV systems to fill this need based on their linear interpolation of composition with bandgap and lattice constant [3]. With the development of indirect-to-direct bandgap materials compatible with economical substrates [1], it is becoming more interesting to investigate the possibility of having a transistor laser using the Group IV materials as well as to look at J. Kaur (B) Department of Computer Science and Engineering, National Institute of Technology, Delhi, India e-mail: [email protected] R. Basu Department of Electronics and Communication Engineering, NIT Delhi, New Delhi, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Biswas et al. (eds.), Microelectronics, Circuits and Systems, Lecture Notes in Electrical Engineering 976, https://doi.org/10.1007/978-981-99-0412-9_36

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the performance and characteristics of such TLs. The authors report on the design and analysis of a Ge0.84 Sn0.16 –Si0.09 Ge0.8 Sn0.11 transistor laser with MQWs in the base in this research study. The calculated values are compared with the values for existing InGaAs-based multiple and single quantum wells’ TLs, and the proposed structure has significantly enhanced performance. Estimated results emphasizing on base current threshold reduction (~ 2.658 mA) as well as modulation BW improvement (~ 53 GHz), which confirms the proposed GeSn-based MQW TL structure, can be better choice as compared to III–V semiconductor-based TL. The structure of GeSn MQW TI-TL is shown in Sect. 2. The design of group IV MQW TL along with parameters are defined in Sect. 2.

2 Design of a Group IV MQW TL Figure 1 shows the design of proposed Ge0.84 Sn0.16 –Si0.09 Ge0.8 Sn0.11 MQW TL structure. The device parameters are shown in Table 1. The basic structure is a n–p– n heterojunction bipolar transistor, in which top Si0.08 Ge0.78 Sn0.14 layer forms the emitter, followed by Si0.09 Ge0.8 Sn0.11 base layer, while the next SiGeSn layer acts as the collector. The base region incorporates three Ge0.84 Sn0.16 multiple quantum wells, each having 16 nm width, and four thin barriers each of 9 nm width. The lattice-matched collector layer is grown on fully strain-relaxed GeSn buffer layer (400 nm), which is helpful in the subsequent growth of well and barrier regions. The substrate is silicon.

Fig. 1 Layered structure of Ge0.84 Sn0.16 –Si0.09 Ge0.8 Sn0.11 MQW TL

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Table 1 Parameters of the proposed structure Various regions

Semiconductors

Width (nm)

Doping (cm−3 )

Type

Emitter layer

SiGeSn

150

1 × 1020

n-type

1×

p-type

Barrier layer

SiGeSn

Well layer

GeSn

Collector layer

SiGeSn

Virtual substrate layer Substrate layer

9 16

1019

–

i

200

1 × 1017

n-type

GeSn

400

–

i

Si

400

–

i

3 Results and Discussion The base current threshold value for InGaAs and GeSn-based MQW TLs located at positions 39, 59, and 79 nm, respectively, is estimated in Fig. 2. The value of threshold base current for QW closer to the EB junction appears to be lower, as seen in Fig. 3. As QW position approaches the BC junction, the value of threshold current of base increases rapidly. Such behavior in single quantum wells (SQWs) has already been observed in [4]. A comparison of InGaAs- and GeSn-based TL is made. When comparing GeSn TL to InGaAs TL, the smallest value of threshold base current is reached for GeSn-based TL as illustrated in Fig. 3. In comparison to InGaAs-based TL, GeSn-based TL has a lower value of threshold base current. In comparison to InGaAs-based TL, the suggested GeSn-based TL achieves a minimum value of base threshold current, i.e., 2.658 mA at quantum well width equal to 16 nm, as shown in Table 2.

Fig. 2 InGaAs and GeSn TL threshold base currents are compared

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Fig. 3 Light output power with respect to base current for InGaAs and GeSn-based MQW TLs (for QW thickness = 16 nm and QW position = 59 nm)

Table 2 InGaAs and GeSn TL threshold base currents are compared Proposed structure (GeSn TL)

Theoretical value (InGaAs TL) [4]

Theoretical value (InGaAs TL) [5]

Experimental value (InGaAs TL) [6]

MQW

MQW

SQW

SQW

t qw (nm)

I bth (mA)

t qw (nm)

I bth (mA)

t qw (nm)

I bth (mA)

t qw (nm)

I bth (mA)

16

2.658

16

7.06

16

21.5

16

22

Figure 3 depicts the predicted light output power with respect to base current for InGaAs and GeSn MQW TL for QW positioned at 59 nm. Light emission occurs only once the base value of current goes beyond the threshold value of current, as seen in Fig. 3. It illustrates the linear relationship between the light power output and the base current. As the base threshold current density drops, the current density rises, and more coherent light is emitted early. Above a threshold current, the light output power P linearly increases along the base current, and the relationship is expressed as [5] P = (w/q)[αm /α + αm ][Ib − Ibth ].

(1)

Here, q parameter tells about the electronic charge, m parameter denotes the mirror loss, and h signifies the Planck’s constant. Figure 4 compares the modulation BW of InGaAs and GeSn TL. It may be interesting to compare the optical modulation BW of the GeSn TL suggested with the

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InGaAs using MQW TL. Table 3 shows the result of comparison of optical modulation BW for the QW width (16 nm). As may be seen, the GeSn MQW TL suggested is capable of providing modulation BW, ~ 53 GHz, which is much better than the InGaAs-based MQW TL. It is observed that when photon energy increases, the spontaneous emission spectrum rises over the threshold, as shown in Fig. 4. Obviously, this sudden rise in spontaneous emission rate density (TE) contributes to the TE optical gain attaining the threshold laser condition, and the peak of the spontaneous spectral emission is obtained at 0.49 eV (emission wavelength = 2.883 µm). Therefore, Ge0.84 Sn0.16 MQW TL is interesting in today’s world for high-speed optical communication applications (Fig. 5).

Fig. 4 Comparison of the modulation BW for InGaAs and GeSn

Table 3 Comparative analysis of modulation BW (InGaAs and GeSn TL) Proposed structure (GeSn TL)

Theoretical value (InGaAs TL) [7]

Theoretical (InGaAs TL) [8]

Experimental (InGaAs TL) [6]

MQW

MQW

Single QW

Single QW

t qw (nm) 16

I bth /BW

t qw (nm)

2.658 mA

16

52.29 GHz

I bth /BW

t qw (nm)

7.08 mA

16

29 GHz

I bth /BW

t qw (nm)

21.5 mA

16

21 GHz

I bth /BW 23 mA 13.5 GHz

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Fig. 5 Spontaneous emission spectra with respect to photon energy (for collector voltage = 2 V, Sn concentration = 16%, doping of base of 1 × 1019 cm−3 )

4 Conclusion Authors have calculated and compared the GeSn-based performance of TL with MQWs. Predicted results indicate that the minimum threshold base current will result in ~ 2.658 mA and the modulation BW of ~ 53 GHz has been estimated. Possessing CMOS compatibility, lower threshold base current, and high modulation bandwidth, we conclude that the proposed GeSn-MQW TL may be used as a highperformance laser for various applications in mid-infrared range of 2.883 µm such as chemical process monitoring, gas-sensing systems, and molecular spectroscopy. The proposed structure will encourage researchers to fabricate the GeSn TL compatible with Si CMOS technology. Acknowledgements Ms. Jaspinder Kaur is grateful to the National Institute of Technology, Delhi, for encouraging and supporting her research work.

References 1. S. Zaima et al., Growth and applications of GeSn-related group-IV semiconductor materials, in 2016 IEEE Photonics Society Summer Topical Meeting Series, SUM 2016 (2016), pp. 37–38 2. M. Oheme, E. Al, GeSn p–i–n detectors integrated on Si with up to 4% Sn. Appl. Phys. 101(14), 2–6 (2012) 3. J. Kaur, R. Basu, A.K. Sharma, Effect of separate confinement hetero-structure layer on tunnel injection transistor laser-based transmitter for high-speed optical communication networks. Opt. Laser Technol. 115 (2019), pp. 268–276

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4. P.K. Basu, B. Mukhopadhyay, R. Basu, Analytical model for threshold-base current of a transistor laser with multiple quantum wells in the base. IET Optoelectron. 7(3), 71–76 (2013) 5. R. Basu, B. Mukhopadhyay, P.K. Basu, Estimated threshold base current and light power output of a transistor laser with InGaAs quantum well in GaAs base. Semicond. Sci. Technol. 26(10) (2011) 6. M. Feng, N. Holonyak, A. James, K. Cimino, G. Walter, R. Chan, Carrier lifetime and modulation bandwidth of a quantum well AlGaAs/InGaP/GaAs/InGaAs transistor laser. Appl. Phys. Lett. 89(11), 1–4 (2006) 7. N. Kumar, B. Mukhopadhyay, R. Basu, Tunnel injection transistor laser for optical interconnects. Opt. Quant. Electron. 50(3), 1–12 (2018) 8. R. Basu, B. Mukhopadhyay, P.K. Basu, Modeling resonance-free modulation response in transistor lasers with single and multiple quantum wells in the base. IEEE Photonics J. 4(5), 1571–1581 (2012)

Driver Drowsiness Detection Using OpenCV and Machine Learning Techniques K. Radhika, N. V. Krishna Rao, N. Shalini, V. Divya Vani, and B. Geetavani

Abstract Driving in fatigue is a severe issue in current times. Lorry drivers, car drivers, and bus drivers drive long distance during day and night. Driving even when drowsy is the main cause of road accidents and loss. It is very much significant to have a system to monitor the person’s drowsiness and its demonstration. The system developed is a behavioral system. Supervised learning algorithm is used for drowsiness detection. In the established system, a webcam is used to record the video, and the driver’s facial frontal features like eye and mouth are detected, and to each frame, machine learning techniques are used. The eye aspect ratio and mouth opening ratio are the main significant values to be detected, and dependent on their values, drowsiness is identified. To detect the facial images, OpenCV library is used for detection. This works on the visual face features of the person driving. The OpenCV can easily detect the face of the person, and an alarm sound is given to the person if there is any slight indication of any sleep in the driver. This helps in decreasing many road accidents forehand, thus saving lots of lives. Keywords Open CV · Drowsiness · Fatigue · Visual monitoring · SVM

1 Introduction At present, driving has changed into a significant part of our everyday life, exclusively in downtown. Driver lethargic is dark despair toward travelers of India. On annual basis, a massive sum of harms and demises arise on highways which are K. Radhika Department of Information Technology, VNR Vignana Jyothi Institute of Engineering and Technology, Bachupally, India e-mail: [email protected]; [email protected] N. V. K. Rao (B) · N. Shalini · V. D. Vani · B. Geetavani Department of Computer Science and Engineering, Institute of Aeronautical Engineering, Dundigal, India e-mail: [email protected] V. D. Vani e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Biswas et al. (eds.), Microelectronics, Circuits and Systems, Lecture Notes in Electrical Engineering 976, https://doi.org/10.1007/978-981-99-0412-9_37

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accompanying to lethargy issue of the person driving. If the driver’s face shows any signs of fatigue, we can infer that he or she is drowsy using modern technology and cameras. This method is unique, and implementation in real life is very much successful. This can help reduce accidents vastly and can protect lots of lives. The method is designed by a live web camera that monitors the condition of the person driving endlessly and alarms the person who is driving if there is a fatigue condition by giving a caution signal. This method also detects with the yawning of the mouth whether the mouth is open or closed to say if the driver is yawning or not. To create a model for detecting driver drowsiness that focuses on precisely and continuously monitoring the circumstances of the driver’s senses over an extended length of time to determine whether they are open or closed for longer than a specific period of stretch. The main problem is to check the tiredness of the driver and stop him from sleep which will help to stop an accident to occur.

2 Literature Survey Drowsiness associated accidents are happening infrequently [1, 2]. Due to drowsiness of the driver, an accident may occur; this can be prohibited by accommodating a deformity discovering system inside the vehicle [3]. Sleepiness could be caused by a number of occurrences like psychosocial, physiological, and health factors [4]. Drowsiness detection can be conceded out by two methods. The principal method is intrusive, and the following is non-intrusive [5]. The intrusive method comprises heartbeat rate, assessing of mind wave, etc. The non-intrusive method is suitable to observe facial looks for tiredness detection. Mouth dozing and eye closing are the common signs of drowsiness detection [6]. The non-intrusive method comprises eye-flickering rate, head pose, mouth yawn, and eye closure detection [7]. One more non-intrusive method to detect fatigue can be split into three circumstances: driving performance, physiological and visual cues measurements, visual cues and physiological comprise straight computation, while driving performance comprises collateral computation [8]. It is appropriate for instantaneous application, since inessential for sensing electrodes. The eye region is calculated by adaptive thresholding, frame-to-frame intensity deviation [9].

3 Techniques Drowsiness of a driver can be detected in multiple ways like eye aspect ratio (EAR) and facial expression measurements. Every individual blinking pattern is dissimilar. The pattern gets changed based on the rate of closing, opening, and blinking of the eye [9]. The planned method is implemented using Haar Cascade Classifiers.

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3.1 Haar Cascade Classifiers Like and unlike images are trained to identify the fatigue of the driver using Haar Cascade Classifiers. OpenCV provides computer vision library and tools; it includes a detector and also a trainer. To train the face and eyes, a separate database is kept for each set of facial images with a specific set of negative and positive streams that show the eyes closing and opening [10]. Support Vector Machine and Haar Cascade Classifiers are used to detect drowsiness in 2013, Patil et al. [11].

3.2 System Model Data Acquisition Image recognition is done by taking videos of the person. This is the first step, and this takes place using an inbuilt webcam or camera attached. These frames which are extracted by webcam are preprocessed. Right after an image has been clicked, the image extraction takes place using a technique that is applied to the two-dimensional images. These 2D images are converted to gray scale, and a region of interest is created. After creating ROI, it is fed to a classifier. Now, driver data has been generated. In evaluating, the mathematical functions are Circularity = (4 * π * Area)/Perimeter2 Area = (Distance (p2, p5)/2)2 * π Perimeter = Distance (p1, p2) + Distance (p2, p3) + Distance (p3, p4) + Distance (p4, p5) + Distance (p5, p6) + Distance (p6, p1). Here, p1, p2, …, p6 are the boundary points of eyes. This is called pupil circularity (PUC). This denotes the roundness of the pupil. The area contains the pixel count. The total pixel count is the distance covered by the eye. The perimeter is the total distance around the eyes. This is also calibrated. The circumference is the shape factor for the roundness of the eyes. This is an indication of the shape of an eye. If this is closer to one, then it is in a perfect circle. Circle has a greater perimeter ratio. The reciprocal value of circularity is the area of roundness.

3.3 The Drowsiness Detector Algorithm Figures 1 and 2 describes the functionality of system. An embedded device generates the alert sound to awaken the driver from drowsy. The drowsiness of the driver is calculated by using OpenCV. This works based on machine learning algorithms and visual behavior models.

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Fig. 1 Block diagram

4 Drowsiness Detection Algorithm 4.1 Face Detection To detect the frame after extraction of an image first, the human face is to be detected. Support Vector Machine and a histogram of directed gradients are the primary techniques used for face detection. This technique is applied when positive examples of descriptors are found and evaluated. Necessary positive values, negative samples are also calculated, and these samples do not detect a human face. Once a face is detected, the positive samples of descriptors are calculated first.

4.2 Facial Land Marking Now after all the features are captured, normalization process takes place. Normalization is done for reduction of illumination from a camera; this helps in having a distance effect from the webcam to vary different image resolutions. A normalization process is a method of diminishing the width of the face to compress pixel values. All the normal images are converted into a gray scale image.

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Fig. 2 Drowsiness detection algorithm

4.3 Feature Extraction The facial landmarks of the face are completely detected. Now the EAR is estimated as the proportion of height and width of the retina from all the edges. The EAR is the eye aspect ratio that is calculated using the formula: EAR = || p2 − p6|| + || p3 − p5||/2|| p1 − p4|| Here, p1, p2, …, p6 are the boundary points of eyes. Nominator and denominator denote vertical and horizontal landmarks of eye.

4.4 Classification After all these three features are calculated, the foremost step and main assignment for the system is detecting sleepiness for the frames extracted. Adaptive thresholding

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Fig. 3 Framework of behavioral pattern-based approach

is well-thought-out for the arrangement. The classification of frames is done using a Navies Bayes classifier, and a Haar classifier is used to classify frontal face features. Machine learning algorithm makes it simpler for the classification of the data. SVM technique is used for the classification of data (Fig. 3).

4.5 Support Vector Machine Machine learning involves different algorithms for forecasting and categorizing statistics. According to the direction of the dataset reserved, we use different algorithms of machine learning techniques. Support Vector Machine (SVM) is an undeviating ideal of organization and progression problems. It’s mainly used for cracking linear and nonlinear glitches. It even works fine for several practical hitches. The foremost indication of the Support Vector Machine algorithm is to create a line or a hyperplane which split up the data into modules.

5 Results Set up the camera in a car so that it can simply detect face and relate facial landmark localization to monitor eyes. Take the camera and attach it to the top inside a car using tape that monitors a stream for faces (Fig. 4). If a face is identified, by using facial landmark detection, extract the eye regions (Fig. 5).

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Fig. 4 Step 1—identify the face in the input video stream

Fig. 5 Step 2—use facial landmark localization to extract the eye regions

Now consider eye regions and calculate the eye aspect ratio to check whether the eyes are closed (Fig. 6). If the eye aspect ratio indicates that the eyes have been closed for an adequately long enough amount of time, sound an alarm to wake up the driver (Figs. 7 and 8). Fig. 6 Step 3—evaluate the eye aspect ratio to check whether the eyes are closed

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Fig. 7 Step 4—sound an alarm if the eyes are closed for longer time

Fig. 8 Step 5—sound an alarm if the eyes are closed and yawning

A webcam stream which tracks all frames and shows whether or not the eyes are open. If eyes are open so the message is given as eye open. EAR and MAR which are being displayed are the eye aspect ratio and mouth aspect ratio; when the maximum threshold is reached, the driver will wake up by alarm sound.

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Test Cases Test case

Eye open

Eye close

Mouth open

Mouth closed

Test result

Case 1

Yes

No

No

Yes

No message

Case 2

No

Yes

No

Yes

Alert message

Case 3

Yes

No

Yes

No

Alert message

Case 4

No

Yes

Yes

No

Alert message

6 Conclusion Driver drowsiness detection is intended primarily to awake the driver during driving, to keep away from accidents due to drowsiness. An embedded device generates the alert sound to awaken the driver from drowsy. The drowsiness of the driver is calculated by using OpenCV. This work is based on machine learning algorithms and visual behavior models. This is an intrusive method, and this doesn’t distract the driver while driving. This is even budget-friendly. In this method, visual aspects like eyes and mouth are taken to calculate the drowsiness and detect the drowsiness of the driver. Haar Cascade Classifier is used to measure the fatigue by detecting eye and face. Euclidean method is used to calculate eye aspect ratio (EAR), and facial landmarks are detected using shape-predictor. EAR reaches maximum threshold, and it indicates that the driver is drowsy so an alarm is generated to awaken the driver. In the future, the enhancement can be carried out for the individuals with dark skin, different lighting conditions, and if there is drowsiness detected, it controls the hydraulic braking system, slows down the accelerator speed, and gives an alarm sound. As speed decreases, there will be even lesser chances for accidents to happen.

References 1. H. Mårtensson, O. Keelan, C. Ahlström, Driver sleepiness classification based on physiological data and driving performance from real road driving. IEEE Trans. Intell. Transp. Syst. 20(2), 421–430 (2018) 2. L. Jia, D. Zhao, K. Zheng, Z. Sun, G. Li, F. Zhang, Smartphone-based fatigue detection system using the progressive locating method. IET Intell. Transp. Syst. 10(3), 148–156 (2016) 3. J.S. Jayasenan, P.S. Smitha, Driver drowsiness detection system. IOSR J. VLSI Sig. Process. (IOSR-JVSP) 4(1), 34–37 (2014) 4. V. Triyanti, H. Iridiastadi, Challenges in detecting drowsiness based on driver’s behavior. IOP Conf. Ser. Mater. Sci. Eng. 277 (2017) 5. J. Gwak, M. Shino, A. Hirao, Early detection of driver drowsiness utilizing machine learning based on physiological signals, behavioral measures, and driving performance, in Proceedings of the 21st International Conference on Intelligent Transportation Systems (ITSC) (2018), pp. 1794–1800

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6. O. Khunpisuth, T. Chotchinasri, V. Koschakosai, N. Hnoohom, Driver drowsiness detection using eye-closeness detection, in 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) (2016), pp. 661–668 7. L.F. Ibrahim et al., Using Haar classifiers to detect driver fatigue and provide alerts. Multimedia Tools Appl. 71(3), 1857–1877 (2014) 8. T. Soukupová, J. Cech, Real-time eye blink detection using facial landmarks, in 21st Computer Vision Winter Workshop (2016) 9. G.J. Al-Anizy, M.J. Nordin, M.M. Razooq, Automatic driver drowsiness detection using Haar algorithm and support vector machine techniques. Asian J. Appl. 8(2), 149–157 (2015) 10. B. Sivakumar, K. Srilatha, A novel method to segment blood vessels and optic disc in the fundus retinal images. Res. J. Pharm. Biol. Chem. Sci. 7(3), 365–373 (2016) 11. N.L. Fitriyani, C.K. Yang, M. Syafrudin, Real-time eye state detection system using Haar cascade classifier and circular Hough transform, in IEEE 5th Global Conference on Consumer Electronics. GCCE (2016), pp. 5–7

The Power Density of PKL, Aloe Vera, Myrobalan, Lemon, and Tomato Electrochemical Cell—An Observation K. A. Khan, Md. Sayed Hossain, Salman Rahman Rasel, and Mehedi Hasan

Abstract Green energy means the energy which comes from nature. Solar, wind, biogas, biomass, water, geothermal, wave, tidal, and Ocean Thermal Energy Conversion (OTEC) are examples of green energy sources. These energy sources are also called renewable energy sources as they are not limited and will never run out. They are sustainable and environmentally friendly. This study aims to determine and compare the power density for different leaves and vegetative like Pathor Kuchi Leaf (PKL), Aloe Vera, Myrobalan, Lemon, and Tomato extracts using electrochemical cell technique. All the extracts were prepared by a manual blender and then filtered by Whatman papers 41 and 42. The zinc and copper electrodes were used as anode and cathode, respectively. It is found that the power density is better for PKL than the others. It is also shown that the PKL electric system can be used as a source of electricity at the off-grid areas instead of the solar photovoltaic system. This research output will help to develop electrochemical cell-based new techniques to generate electricity in a large amount in near future. Keywords Power density · PKL · Aloe Vera · Myrobalan · Lemon · Tomato · Electrochemical cell

K. A. Khan (B) Department of Physics, Jagannath University, Dhaka 1100, Bangladesh e-mail: [email protected] Md. S. Hossain Center for Research Reactor, Bangladesh Atomic Energy Commission, Dhaka, Bangladesh S. R. Rasel Local Government Engineering Department (LGED), Sherpur Sadar, Sherpur, Bangladesh M. Hasan General Education Department, City University, Dhaka, Bangladesh © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Biswas et al. (eds.), Microelectronics, Circuits and Systems, Lecture Notes in Electrical Engineering 976, https://doi.org/10.1007/978-981-99-0412-9_38

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1 Introduction In the twenty-first century, the demand for electrical energy is very high, and it is increasing day by day with the advancement of technology. As the natural energy resources are limited, new energy harvesting techniques have been a matter of interest to face the energy crisis [1]. To keep it in mind, this research work has been conducted [2, 3]. This technology is very easy and eco-friendly and is based on renewable energy sources [3]. This type of energy source can provide electrical power day and night, unlike solar energy [4, 5]. This study used the source of energy extracted from the PKL, Aloe Vera, Myrobalan, Lemon, and Tomato. The sources are new, renewable, sustainable, and pollution-free. The juice of PKL, Aloe Vera, Myrobalan, Lemon, and Tomato are used as electrolytes in the electrochemical cell where the chemical energy is converted into electrical energy. Both chemical and electrical processes induce the flow of electrons [6]. The rate of electricity depends on several parameters like concentration of the extract, the distance between the two electrodes, power density, energy density, specific power density, specific energy density, the internal resistance of the cell, voltage regulation of the cell, the capacity of the cell, energy efficiency of the cell, and the temperature of the extract [2, 6]. The voltage difference which is generated between the two plates can be explained by the basics of electrochemistry [7]. However, electrical conduction differs from electrodes to electrodes and from electrolytes to electrolytes as their different constituents [8]. The most promising electrodes pairs are zinc and copper, and the most promising electrolyte is found PKL extracts for longer periods [4]. The PKL is succulent and can contain water-retaining. This plant stores water in its stems, roots, and leaves to survive in a complex environment [9]. Previous research works have been conducted by different researchers for different vegetative and fruits using different mechanisms [10–12]. An electrochemistry process occurs there to convert electrical energy from chemical energy via oxidation and reduction reactions. The oxidation process occurs at the anode, and the reduction process occurs at the cathode and which is responsible for the electron to flow from anode to cathode to generate electricity [13]. With this method, the weak organic acid of the extracts is functioning as an electrolyte in the cell. This method is termed Leaf and Vegetative-Based Cell (LVBC) in this research work. Using this process, we get the DC voltage and current directly to operate the DC appliances [14–16].

2 Method and Materials 2.1 Preparation of Leaves and Vegetative Extract and Experimental Setup In each of the cases, all the elementary samples were blended and then filtered using Whatman papers 41 and 42 to get the extracts or juices only (Fig. 1).

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Fig. 1 Juice/extract preparation process

After having the juice for all the five specimens, they were put in a unit electrochemical cell containing one copper and one zinc plate as electrodes. In this study, the load, the dimensions of electrodes, water-extract ratio, the distance between the electrodes, and juice amounts were kept the same. To get the power density, the current and voltage were measured by a calibrated multimeter. The zinc and copper plates are used as an anode and cathode, respectively, and the filtered juices were used as electrolytes.

2.2 Power Density (W/L) It is defined as the amount of power per unit volume. Mathematically, it can be defined as Power density (W/L) = VIt/L, where V = Voltage, I = Current, t = time, and L = Volume of the extract.

2.3 Chemical Reactions The schematic diagram of a unit electrochemical cell used in this study has been shown in Fig. 2. When zinc plates come into contact with acids, they go into solution and generate 0.762 V relative to the standard hydrogen electrode; when copper plates come into contact and go into solution, they generate − 0.345 V [13]. As a result, in the presence of copper in the juice, the reaction in each electrode will be Zn → Zn2+ + 2e− Cu2+ + 2e− → Cu

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Fig. 2 Experimental setup

And the generating net electromotive force of these reactions in a cell or a copper– zinc pair electrodes in an acidic solution is 0.762 V − (− 0.345 V) = 1.107 V. The total reactions in the presence of electrolyte Zn + Cu2+ + H+ = Zn2+ + Cu + H2 where Cu2+ = Reactant ion, H+ = Reactant ion, and Zn2+ = Product ion.

2.4 Theory for Voltage Generation To measure the cell potential at any moment during a reaction or a condition other than standard state, Nernst equation is used which can be expressed as below E = E0 −

RT ln Q c nF

where E = Cell potential under specific conditions, E 0 = Cell potential at standard state condition (known), R = Ideal gas Constant (8.314 J/mol-K), T = Temperature in Kelvin (0 °C = 273 K), n = number of moles of electrons transferred in the balanced equation, F = Faraday’s Constant (i.e., charge of one mole of electrons, 95,484.56 C/mol), ln Qc = Natural logarithm of the reaction quotient at the moment in time. The mathematical product of the concentrations of the reaction product divided by the mathematical product of the concentrations of the reactants is the reaction quotient. For the reaction: Zn + Cu2+ → Zn2+ + Cu. The quotient constant (Qc ) is defined by the following equation:

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 2+  Zn Q c =  2+  +  Cu H where [Cu2+ ] = Reactant ion Concentration, [H+ ] = Reactant ion Concentration, and [Zn2+ ] = Product ion Concentration.

3 Results and Discussion The obtained results of different vegetative and fruits like PKL, Aloe Vera, Lemon, Tomato, and Myrobalan have been discussed graphically. It is shown (Fig. 3) that the power density of PKL electrochemical cell versus time duration. From Fig. 3 that the power density varies from 2.26 to 0.29 W/L. The change of the power density was almost exponential. The difference of the maximum and minimum power density was 1.97 W/L. The study period was 4620 min. It is shown (Fig. 4) that the power density of Lemon electrochemical cell versus time duration. From Fig. 4 that the power density varies from 1.42 to 0.17 W/L. The change of the power density was almost exponential. The difference of the maximum and minimum power density was 1.25 W/L. The time duration was 4620 min. It is shown (Fig. 5) that the power density of Aloe Vera electrochemical cell versus time duration. From Fig. 5 that the power density varies from 0.59 to 0.03 W/L. The change of the power density was almost exponential. The difference of the maximum and minimum power density was 0.56 W/L. The time duration was 4620 min. It is shown (Fig. 6) that the power density of Tomato electrochemical cell versus time duration. From Fig. 6 that the power density varies from 0.71 to 0.06 W/L. The change of the power density was almost exponential. The difference of the maximum and minimum power density was 0.56 W/L. The time duration was 4620 min.

Fig. 3 Power density of PKL electrochemical cell versus time duration

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Fig. 4 Power density of Lemon electrochemical cell versus time duration

Fig. 5 Power density of Aloe Vera electrochemical cell versus time duration

Fig. 6 Power density of Tomato electrochemical cell versus time duration

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It is shown (Fig. 7) that the power density of PKL electrochemical cell versus time duration. From Fig. 7 that the power density varies from 0.68 to 0.09 W/L. The change of the power density was almost exponential. The difference of the maximum and minimum power density was 0.59 W/L. The time duration was 4620 min. From Fig. 8, it is clearly seen that the power density of various extracts is decreasing with time. The power density of PKL was found higher than all the others throughout this study under the same conditions. However, variations may be observed if any of the parameters of this electrochemical cell or this process is changed [2]. The study period was for 4620 min.

Fig. 7 Power density of Myrobalan electrochemical cell versus time duration

Fig. 8 Power density of PKL, Lemon, AL, Tomato and Myrobalan electrochemical cell versus time duration

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4 Conclusion It is concluded that the PKL, Aloe Vera, Myrobalan, Lemon, and Tomato can produce electricity. This electricity can power the DC appliances directly, and after using an inverter with this, we can use AC appliances. It is found that PKL cells can generate the highest amount of voltage, current, and power. Finally, it is found that the power density is the maximum for PKL cells than the other Aloe Vera, Myrobalan, Lemon, and Tomato electrochemical cells. Acknowledgements The authors are grateful to the Grant of Advanced Research in Education (GARE) project, Ministry of Education, GoB, for financing during the research work (Project/User ID: PS2019949).

References 1. R. Timmers, Electricity Generation by Living Plants in a Plant Microbial Fuel Cell (2012). ISBN: 978-94-6191-282-4 2. M. Hasan, K.A. Khan, Experimental characterization and identification of cell parameters in a BPL electrochemical device. SN Appl. Sci. 1, 1008 (2019) 3. M. Hasan, K.A. Khan, Dynamic model of Bryophyllum pinnatum leaf fueled BPL cell: a possible alternate source of electricity at the off-grid region in Bangladesh. Microsyst. Technol. 25, 2481–2492 (2019) 4. K.A. Khan, L. Hassan, A.K.M. Obaydullah et al., Bioelectricity: a new approach to provide the electrical power from vegetative and fruits at off-grid region. Microsyst. Technol. 26, 3161–3172 (2020) 5. K.A. Khan, S.R. Rasel, S.M.Z. Reza et al., Electricity from living PKL tree, in Published in the Open Access Book, Energy Efficiency and Sustainability in Outdoor Lighting A Bet for the Future, ed. by M.J. Hermoso-Orzáez (London, UK, 2019) 6. G. Gunawan, D.S. Widodo, A. Haris et al., Energy storage system from galvanic cell using electrolyte from a plant as an alternative renewable energy. IOP Conf. Ser. Mater. Sci. Eng. 509, 012045 (2019) 7. C. Ying Ying, J. Dayou, Modelling of the electricity generation from living plants. J. Teknol. 78(6) (2016) 8. F.P. Chee, C.A. Chen, J.H. Chang et al., Data acquisition system for in situ monitoring of chemoelectrical potential in living plant fuel cells. J. Biophys. 2016, 6108056 (2016) 9. A.G. Volkov, J.C. Foster, E. Jovanov et al., Anisotropy and nonlinear properties of electrochemical circuits in leaves of Aloe vera L. Bioelectrochemistry 81(1), 4–9 (2011) 10. M. Bhardwaj, Neelam, The advantages and disadvantages of green technology. J. Basic Appl. Eng. Res. 2(22), 1957–1960 (2015) 11. M.F. Lee, M.N.N.M. Zain, C.S. Lai, Lighting system design using green energy from living plants. J. Phys. Conf. Ser. 1019, 012019 (2017) 12. L. Bird, K. Cardinal, Trends in utility green pricing programs (2004). Technical report. NREL/TP-640-40777 (2005) 13. R.V. Kumar, T. Sarakonsri, Introduction to Electrochemical Cells 14. M. Hasan, K. Alam, Bryophyllum pinnatum leaf fueled cell: an alternate way of supplying electricity at the off-grid areas in Bangladesh, in 4th International Conference on the Development in the in Renewable Energy Technology (ICDRET) (2016), pp. 1–5.

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15. Y.Y. Choo, J. Dayou, A method to harvest electrical energy from living plants. J. Sci. Technol. 5(1), 79–90 (2013) 16. D.P.B.T.B. Strik, H.V.M. Hamelers (Bert), J.F.H. Snel et al., Green electricity production with living plants and bacteria in a fuel cell. Int. J. Energy Res. 32(9), 870–876 (2008)

Modeling and Design of FPGA-Based Power Quality Analyzer M. Balasubbareddy, Kondapalli Venkata Sri Ram, and Ravindra Sangu

Abstract This paper deals with the modeling and design of power quality analyzer. The power quality analyzer (PQA) is essential in today’s world to give quality power uninterruptedly. We discussed about the hardware design of PQA and the software used. It is used to measure all the powers (P, Q, and S) in three phases. Voltage profiles, %THD, power factor, harmonic profile, and load curves can be obtained with this type of analyzer at cheaper cost. This equipment can be used to analyze the output of any machine like alternators transformers and transmission lines. Through the harmonic profile, we can measure which order of harmonic is more in the output of these machines. It uses Hall sensors (HE055T01) for current sensing and PT (230/9 V) for voltage stepping down and 7840 IC voltage sensing. These sensed along signals are converted to digital signals with help of ADC (AD7366). These digital signals are given to Field Programmable Gate Array (FPGA)-SPARTAN 6 model. FPGA processes these signals based on FIFO algorithm, and through USB port, it is connected to personal computer. Key sight VEE pro is the software used to analyze the power signals. We can measure active, reactive and apparent powers, THD of all three phase and also live load variations tracking through this software. This analyzer costs very less when compared to Fluke power quality analyzer. Keywords Power quality analyzer · VEE software · Hall sensors · Real and reactive power · FPGA

M. Balasubbareddy (B) · K. V. Sri Ram Department of Electrical and Electronics Engineering, Chaitanya Bharathi Institute of Technology, Hyderabad, India e-mail: [email protected] K. V. Sri Ram e-mail: [email protected] R. Sangu Department of Electrical and Electronics, Vasireddy Venkatadri Institute of Technology, Namburu, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Biswas et al. (eds.), Microelectronics, Circuits and Systems, Lecture Notes in Electrical Engineering 976, https://doi.org/10.1007/978-981-99-0412-9_39

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1 Introduction In [1], authors proposed novel algorithm for voltage transient detection and isolation for power quality monitoring. In [2], FPGA-based multichannel analyzer has designed, and its performance has evaluated. In [3], in this authors proposed case study of harmonic analysis of power system at Nuclear Research Reactor. In [4], cloud-based intelligent power quality monitoring system has proposed for industrial drive application. In [5], authors proposed the power quality measurements and real-time monitoring in distribution feeders. In [6], authors proposed a smart energy meter to overcome the glitch based on Internet of Things (IoT). This meter calculates the energy consumption and controls the consumption using Fast Fourier Transform (FFT). In [7], authors investigated power quality assessment at common coupling point of a steel mill to grid. In [8], authors proposed neural network based on admittance estimation strategy (NN-ADES) approach to improve dynamic performance of unified power quality conditioner (UPQC). In [9], this proposed novel control technique for PV integrated universal active power filter and also proposed instantaneous power balance theory (IPBT) to extract the reference signals for shunt and series active power filters. A power quality analyzer is used to measure electric power signals to determine load’s ability to function properly with that electric power. Power quality analyzer (PQA) is essential in today’s world to give quality power uninterruptedly. It is used to measure all the powers (P, Q, and S) in three phases. Voltage profiles, %THD, power factor, harmonic profile, and load curves can be obtained with this type analyzer at cheaper cost.

2 Block Diagram of Power Quality Analyzer (PQA) The block diagram consists of two main sensing circuits which are voltage sensing circuit with IC 7840 and current sensing circuit with hall sensor which is HE055T01. The heart of the setup is FPGA which is having ADC and a processor called SPARTAN 6. The block diagram is given in Fig. 1.

3 Components of PQA Power supply, front panel, back panel, voltage sensing board, current sensing board, FPGA board, other than this we need a PC where “Key Sight VEE Pro” software is installed. Power supply: In this PQA, we need three power supplies. Those are isolated power supply of + 5 V to HV side of voltage sensing board. Power supply

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Fig. 1 Block diagram of power quality analyzer

of ± 15 V is used for current sensing board and LV side of voltage sensing board. Power supply of ± 12 V and + 5 V are used for FPGA board. A. Power supply—There are two types: One is isolated power supply and another is other board power supply; both are required for the boards operation. Those are given in Fig. 2. This supply + 5 V is given to HV side of voltage sensing board. The other supply is given in Fig. 3.

B.

C.

D. E.

F.

So this uses several IC like IC 7815, IC 7915, IC 7812, IC 7912 for generation of required voltages for board operation. Voltage sensing board—It is used to sense the voltage signal coming from the source. It has IC 7840 for each phase. It requires two power supplies which are + 15 V and − 15 V on LV side and isolated power supply of + 5 V on HV side. It is shown in Fig. 4. Current sensing board—It uses hall sensors HE055T01 in each phase for current sensing and requires power supply of + 15 V and −15 V. It is given in Fig. 5. Front panel—It consists of MFM—Multi-function meter, which probes for oscilloscope and USB ports. It is shown as in Fig. 6. Back panel—It has probes for source and load connections. This probes are internally connected to voltage and current sensing circuits. The back panel has a provision for power card. It is shown in Fig. 7. FPGA board—It requires power supply of + 12 V, − 12 V, and + 5 V for board

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Fig. 2 Isolated power supply

Fig. 3 Other supply board

operation. The sense analog d signals from the sensing boards are converted into digital with the help of ADC and given to processor SPARTAN 6. This analyzes the source signal and gives the results displayed in MFM. It is shown in Fig. 8.

4 Results and Analysis We use a software called Key sight VEE pro through which some VEE files are created, and waveforms are obtained.

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Fig. 4 Voltage sensing board

Fig. 5 Current sensing board

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Fig. 6 Front panel

VEE files—we can see the V and I waveforms of each phase, individual harmonic data, THD, live load tracing, etc. 1. BAR.vee—Values of each harmonic up to 50th harmonic. 2. NUMERIC.vee—To get the actual P, A and S, PF, THD, and frequency of each phase. 3. WAVE_3PH.vee—To see the V and I waveforms of each phase. 4. POWER_WAVE.vee—To get the live load tracing and P, Q, and S waveforms of each phase. A test is conducted on alternator, and results are given in Fig. 9. The results are given in Fig. 10. Live load tracing in each phase is given in Fig. 11.

5 Conclusion FPGA-based PQA is a compact analyzer for analyzing the input signals compared to other power quality analyzers. It is cheaper in cost when compared to Fluke power quality analyzer. Design is a simple and can be used to analyze signals of every electrical machine. When this FPGA-based PQA combined with FPGA-based UPQC, all the power quality problems are analyzed and mitigated. So that end user can get uninterrupted quality power.

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Fig. 8 FPGA board

Fig. 9 Test setup on alternator

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Fig. 10 Test results—values

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P

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Fig. 11 Live load tracing

Acknowledgements The authors are thankful to All India Council for Technical Education (AICTE), New Delhi, India, for funding the project in the research promotion scheme.

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References 1. R. Moreno, N. Visairo, C. Núnez, E. Rodríguez, A novel algorithm for voltage transient detection and isolation for power quality monitoring. Electr. Power Syst. Res. 114, 110–117 (2014) 2. A. Garcia-Duran, V.M. Hernandez-Davila, H.R. Vega-Carrillo, O.O. Ordaz-Garcia, I. BravoMuñoz, R. Solís-Robles, FPGA embedded multichannel analyser. Appl. Radiat. Isot. 141, 282– 287 (2018) 3. S.R. Nassar, A.A. Eisaa, A.A. Saleha, M.A. Farahat, A.F. Abdel-Gawad, Evaluating the impact of connected non linear loads on power quality—a nuclear reactor case study. J. Radiat. Res. Appl. Sci. 13(1), 688–697 4. R.R. Singh, S.M. Yash, S.C. Shubham, V. Indragandhi, V. Vijayakumar, P. Saravanan, V. Subramaniyaswamy, IoT embedded cloud-based intelligent power quality monitoring system for industrial drive application. Future Gen. Comput. Syst. 112, 884–898 (2020) 5. C.S. Kumar, P. Ramesh, G. Kasilingam, D. Ragul, C. Bharatiraja, The power quality measurements and real time monitoring in distribution feeders. Mater. Today Proc. 45(Part 2), 2987–2992 (2021) 6. L.A. Kumar, V. Indragandhi, R Selvamathi, V. Vijayakumar, L. Ravi, V. Subramaniyaswamy, Design, power quality analysis, and implementation of smart energy meter using internet of things. Comput. Electr. Eng. 93, 107203 (2021) 7. E. Ugwuagbo, A. Balogun, A. Olajube, O. Omeje, A. Awelewa, S. Abba-Aliyu, Experimental data on power quality assessment at point of common coupling of a steel mill to an electric power grid. Data Brief 39, 107681 (2021) 8. M. Aryanezhad, E. Ostadaghaee, Robustness of unified power quality conditioner by neural network based on admittance estimation. Appl. Soft Comput. J. 107, 107420 (2021) 9. M. Golla, K. Chandrasekaran, S.P. Simon, PV integrated universal active power filter for power quality enhancement and effective power management. Energy Sustain. Dev. 61, 104–117 (2021)

Design of Hardware Unified Power Quality Conditioner to Mitigate Sag and Swell Mallala Balasubbareddy and Ravindra Sangu

Abstract The custom power devices will provide P, Q and V compensation, and also a quality power is delivered. These enhance quality and reliability of power delivered to the customer. Power systems should ensure rated sinusoidal voltage to voltage sensitive loads. Examples are hospitals, industries need voltage without any voltage sags, swells and harmonics. They need perfectly balanced sinusoidal voltage. In this paper, designed unified power quality conditioner (UPQC) to mitigate sag and swell compensation to supply quality of power to consumers. Keywords UPQC · Sag · Swell · Power quality · DVR · DSTATCOM · Power quality issue introduction

1 Introduction The primary objective of any utility system is to supply uninterrupted pure sinusoidal voltages and currents to the consumers at PCC at point of coupling. Every electrical equipment is designed based on sinusoidal currents and voltages along with nominal frequency. However, sue to faults, short circuits, lightenings and other interruptions, the power available at consumer end may not be pure sinusoidal with nominal frequency. These causes sags, swells and voltage interruptions. Since today almost all the consumer equipment is designed with chips, micro-controllers and sensitive circuits, we need to deliver a quality power constantly.

M. Balasubbareddy (B) Department of Electrical and Electronics Engineering, Chaitanya Bharathi Institute of Technology, Hyderabad, India e-mail: [email protected] R. Sangu Department of Electrical and Electronics Engineering, Vasireddy Venkatadri Institute of Technology, Namburu, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Biswas et al. (eds.), Microelectronics, Circuits and Systems, Lecture Notes in Electrical Engineering 976, https://doi.org/10.1007/978-981-99-0412-9_40

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Fig. 1 Block diagram of VFD

Ray et al. [1] proposed mitigation of power quality issues in distribution system using UPQC. Poongothai and Srinath [2] explained use of UPQC based on a unit vector template control algorithm with grid integration of photovoltaic, such as voltage sags/swell. In this [3], they focused the implementation of control strategies like SRF theory and instantaneous power (p − q) for the operation of UPQC that includes both series and shunt active power filter operating at the same time and thereby improves all the current and voltage related problem. In [4], a detailed mathematical analysis of the power flow through the two converters of the UPQC using its dual control strategy (iUPQC) is developed. Vinnakoti and Kota [5] proposed ANN-based control strategy for a three-level converter-based unified power quality conditioner. References [6–13] explained different control techniques to mitigate voltage and current related issues. In this paper, hardware implementation of UPQC has been carried out, and results are analyzed.

2 Unified Power Quality Conditioner (UPQC) Power systems should ensure rated sinusoidal voltage to voltage sensitive loads. Examples are hospitals, industries need voltage without any voltage sags, swells and harmonics. They need perfectly balanced sinusoidal voltage. In industries where motor applications are there, they generally use variable frequency drives (VFD) or adjustable speed drives (ASD). These drives themselves act as source for power quality problems. The block diagram for a general VFD is given in Fig. 1.

3 UPQC Block Diagram The general block diagram for UPQC is given in Fig. 2. It consists of DVR and DSTATCOM which are connected through a common DC link. Here, DVR is connected in series with the line, whereas the DSTATCOM is a

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Fig. 2 UPQC basic structure

shunt connected device. Here at injection transformer the voltage, “V f ” is compensating voltage, and the current passing through the inductive link is “if” which is compensating current. Whenever there is voltage dip or swells are there, the voltage stored in the DC link capacitor is used as compensating voltage, and through injection transformer, it is injected into the line. And the harmonic current will be suppressed by injecting the same current in magnitude but in phase opposition so that they are mitigated. These currents are called compensating currents and will be injected by DSTATCOM. So UPQC is combination of DVR and DSTATCOM.

3.1 Left Shunt UPQC The block diagram for left shunt UPQC is given in Fig. 3. Here, the DSTATCOM is left of the DVR. That is why it is called left shunt UPQC. The rating of DVR and DSTATCOM is high when compared right shunt UPQC.

3.2 Right Shunt UPQC The block diagram for right shunt UPQC is given in Fig. 4. In this, DSTATCOM is right side of the DVR. That is why it is called right shunt UPQC. Generally, right shunt UPQC is preferred over left shunt UPQC because the DSTATCOM is nearer to the load, and it effectively compensates the load unbalances, harmonics and other power quality problems at load without requiring large rating of DSTATCOM.

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Fig. 3 Left shunt UPQC

Fig. 4 UPQC-P—sag condition

4 Classification of UPQC Based on Power Rating This classification depends upon DVR voltage injection techniques such as: UPQCQ, UPQC-P and UPQC-S.

4.1 UPQC-P Sag and Swell Condition In this type UPQC, the DVR will inject only active power. As P stands for reactive power, it is named as UPQC-P. Let us understand this with sag and swell conditions. Let us consider the phasor diagram Fig. 4 for better understanding. Here at pre-sag condition V L = V S as there is no voltage sag; both the ends will have same voltage. The source current I S is in phase with source voltage V S , this is because this is UPQC where the DSTATCOM also in working condition, which ensures the reactive power compensation and maintains the source voltage in phase with the source current (UPF

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Fig. 5 UPQC-P—swell condition

condition). The load current I L is lagging at an angle ØL with respect to load voltage V L. Here, V t is terminal voltage or voltage at PCC. Let us consider sag condition where the load voltage is less than V L and equal to V t . Now the DVR will inject compensating voltage V f into the line which makes the load voltage (V L l = V L ) again equal to source voltage (V S ). The injected voltage by DVR is in phase with the current which means that only active power is injected in to the line, which is the reason this is called UPQC-P. It will supply active power from the source because the compensated voltage is positive. In this type UPQC-P, the DVR cannot be capacitor supported to inject active power. There should be energy supported BESS—Battery Energy Storage System. The same UPQC-P we consider for swell condition. Let us see the phasor diagram Fig. 5. Here, the initial condition is same as in sag condition. Pre-swell condition source V S and load voltages V L are equal. Source current I S being in phase with the source voltage V S due to DSTATCOM compensation. And load current I L lags behind load voltage V L with an angle ØL . V t is the terminal voltage which is greater than pre-swell voltage due to voltage swell. Now the DVR injects the compensating voltage—V f in phase to the current but in opposite direction so again the pre-swell condition is achieved that is load and source voltages are again equal. In this case, the active power is drawn from the supply because compensating voltage is negative, and no reactive power is involved in this type. So UPQC-P can compensate both type of sag and swell condition. The amount of active power required for compensation is very less so DVR rating is very less, but we need BESS to inject active power.

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Fig. 6 UPQC-S—sag condition

4.2 UPQC-S Sag and Swell Condition In this type UPQC, the DVR will inject both active and reactive power. As S stands for combination of active and reactive power which we call as apparent power, it is named as UPQC-S. Let us understand this with sag and swell conditions. Let us consider the phasor diagram Fig. 6 for better understanding. Here at pre-sag condition V L = V S as there is no voltage sag, both the ends will have same voltage. The source current I S being in phase with source voltage V S , this is because this is UPQC where the DSTATCOM also in working condition, which ensures the reactive power compensation and maintains the source voltage in phase with the source current (UPF condition). The load current I L is lagging at an angle ØL with respect to load voltage V L . Here, V t is terminal voltage or voltage at PCC. Let us consider sag condition where the load voltage is less than V L and equal to V t . Now the DVR will inject compensating voltage V f into the line which is neither in phase nor in perpendicular to the current, but it is at an angle with the current which says that it will inject both active and reactive power or simple it will inject reactive power. The same UPQC-S we consider for swell condition. Let us see the phasor diagram Fig. 7. Here, the initial condition is same as in sag condition. Pre-swell condition source V S and load voltages V L are equal. Source current I S being in phase with the source voltage V S due to DSTATCOM compensation. And load current I L lags behind load voltage V L with an angle ØL . V t is the terminal voltage which is greater than pre-swell voltage due to voltage swell. Now the DVR injects the compensating voltage V f which is neither in phase or nor perpendicular to current, but it is at angle to the current which means the apparent power is injected or drawn.

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Fig. 7 UPQC-S—swell condition

So UPQC-S will compensate both sag and swell condition by injecting a small amount of apparent power. This case also we need BESS across DC bus to inject small amount of active power.

5 UPQC Hardware The UPQC we used is left shunt UPQC so the left side VSI is used for DSATCOM. This VSI generates compensating currents to balance the load, to mitigate harmonics and for reactive power compensation. The right side VSI is used in DVR for injecting compensating voltages. These compensating voltages will be used to make load voltage is equal to source voltage. Here, two parallel loads are used: one is 3-phase nonlinear load and second one is 3-phase linear unbalanced load. Description about each part is discussed in later sections of this document (Fig. 8; Table 1). This section clearly analyzed hardware results in different conditions (Figs. 9, 10, 11, 12 and 13).

6 Conclusions Using UPQC power quality issues are mitigated and analyzed with existing literature. The performance of the proposed UPQC shows better results. Voltage and current related issues are successfully eliminated with UPQC.

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Fig. 8 Lateral view of UPQC hardware Table 1 Apparatuses S. No.

Apparatus

Qty

1

Multi-function meter (MFM)

2 No’s

2

3 POLE MCB

5 No’s

3

2 POLE MCB

2 No’s

4

LCD display

1 No

5

40 PIN PWM signals from controller

2 No’s

6

20 PIN feedback signals from sensors

2 No’s

7

IR switchs

2 No’s

8

DC voltmeter

2 No’s

Design of Hardware Unified Power Quality Conditioner to Mitigate Sag …

Fig. 9 R phase synchronization waveform

Fig. 10 Y phase synchronization waveform

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454

Fig. 11 B phase synchronization waveform

Fig. 12 Before compensation waveforms

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Design of Hardware Unified Power Quality Conditioner to Mitigate Sag …

455

Fig. 13 Waveforms after shunt compensation

Acknowledgements The authors are thankful to All India Council for Technical Education (AICTE), New Delhi, India, for funding the project in the research promotion scheme.

References 1. P.K. Ray, S.K. Dash, B. Subudhi, S.K. Korkua, Mitigation of power quality issues using UPQC. Int. J. Emerg. Electr. Power Syst. 21(5), 20200040 (2020) 2. S. Poongothai, S. Srinath, Power quality enhancement in solar power with grid connected system using UPQC. Microprocess. Microsyst. 79, 103300 (2020) 3. K.S. Ali, M. Sashikanth, Power quality improvement using unified power quality conditioner (UPQC). J. Inf. Comput. Sci. 10(3) (2020) 4. S.M. Fagundes, F.L. Cardoso, E.V. Stangler, F.A.S. Neves, M. Mezaroba, A detailed power flow analysis of the dual unifed power quality conditioner (iUPQC) using power angle control (PAC). Electr. Power Syst. Res. 192, 106933 (2021) 5. S. Vinnakoti, V.R. Kota, ANN based control scheme for a three-level converter based unified power quality conditioner. J. Electr. Syst. Inf. Technol. 5, 526–541 (2018) 6. A. Patel, H.D. Mathur, S. Bhanot, An improved control method for unified power quality conditioner with unbalanced load. Electr. Power Energy Syst., 129–138 (2018) 7. S. Shen, H. Zheng, Y. Lin, W. Zhao, UPQC harmonic detection algorithm based on improved p − q theory and design of low-pass filter, in Conference Paper. 8. M. Yavari, S.H. Edjtahed, S.A. Taher, A non-linear controller design for UPQC in distribution systems. Alex. Eng. J. 57, 3387–3404 (2018) 9. J. Ye, H.B. Gooi, B. Wang, X. Zhang, A new flexible power quality conditioner with model predictive control. IEEE Trans. Ind. Inform. 15(5) (2019) 10. R. Rajarajan, R. Prakash, A reformed adaptive frequency passiveness control for unified power quality compensator with model parameter ability to improve power quality. Microprocess. Microsyst. 73, 102984 (2020)

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11. P.V. Kumar, J. Jayaraj, S. Chacko, Performance analysis of SRF-based UPQC on polluted electric utility grid. Mater. Today Proc. (2021) 12. M.A. Aminia, A. Jalilian, Modelling and improvement of open-UPQC performance in voltage sag compensation by contribution of shunt units. Electr. Power Syst. Res. (2020) 13. K. Srinivas, N. Rani, Instantaneous reactive power theory control scheme for reactive power compensation using DSTATCOM. Int. J. Rec. Technol. Eng. 8(6) (2020)

Complementing Biometric Authentication System with Cognitive Skills B. Sindhu

and B. Kezia Rani

Abstract The current era is totally digitalized making all of us lead a digital life. There can be merely no imagination of how future would be without this ever blooming technology. As a fair side of technological advents, every common man has access to mobile phones, gadgets and has access to the Internet. On the dark side of technological boom lies the Dark web, privacy invasion, online frauds, piracy, hacking, online gambling etc. Protecting ourselves form the odd effects lies primarily in user authentication phase. The robust the Authentication phase is, the more cyber safe you are. Most of the current automated authentication schemes rely on biometrics. Biometrics has given a major breakthrough in robust user authentication. Essence of security and privacy lies in choosing a perfect combination of biometrics called as multi biometrics. Combining biometrics with cognitive skills makes the scheme more interactive and attack resilient. Keywords Biometrics · Computerized authentication schemes · Security · Cognitive skills · Interactive authentication

1 Introduction Biometrics footprints in the field of personnel authentication have originated in the nineteenth century when French Police officer named Alphonse Bertillon [1] used to record anthropomorphic (measurable human attributes) measurements of prisoners to identify them in unique fashion. The biometric systems have flourished that almost every law enforcement agency utilizes Automated Fingerprint Identification System (AFIS) and also used in every border crossing security systems.

B. Sindhu (B) · B. K. Rani Department of Computer Science and Engineering, Adikavi Nannaya University, Rajamahendravaram, Andhra Pradesh, India e-mail: [email protected] B. K. Rani e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Biswas et al. (eds.), Microelectronics, Circuits and Systems, Lecture Notes in Electrical Engineering 976, https://doi.org/10.1007/978-981-99-0412-9_41

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1.1 Biometrics and Its Types The term biometric is derived from Latin, ‘Bio’ is for ‘Life’ and ‘Metrics’ is for ‘Measure’, i.e., ‘Life Measurement’. Crucial study of biometrics has its inception in the 1970s [2, 3]. As authentication systems became crucial for utilizing every resource, technical advents in the area have become enormous paving the way for fool proof authentication techniques. There lies a huge scope for biometrics in user authentication. Distinguishing factor of biometrics is that it is unforgettable [4]. Biometric authentication is a method of identifying and distinguishing the users depending on their biometric traits [5]. Biometrics can be categorized into two sorts, i.e., Behavioral and Physiological biometrics. This categorization has been done depending on its nature of origin. Biometrics can also be categorized into soft and hard biometrics. Physiological biometrics as the name implies refers to the user’s physical measurements. Most focused physiological biometrics are fingerprint, iris, ear shape, DNA, palm print, hand veins, hand geometry, retina, etc. Following Table 1 depicts prominent features of these biometrics. Behavioral biometrics as the name implies refers to the user’s measurable behavior. Most used behavioral biometrics are signature, mouse dynamics, keystroke dynamics, game playing behavior, gaze, gait, voice, etc. Following Table 2 depicts prominent features of these biometrics. Soft biometrics refers to personal attributes of a user like eye color, heartbeat, MRI images, ethnicity, gender, tattoos, scars, marks, height, mole, voice accent, etc. Soft biometrics solely cannot uniquely identify the user but only complement the Table 1 Types of physiological biometrics DNA

Structure and segments of Deoxyribonucleic acid (DNA) are used to recognize the user

Face

Components and placement different facial features are used to recognize the user [6]

Iris

Iris pattern inside the eyeball is used to recognize the user

Finger print

Ridges, grooves, minutiae pattern on fingers are used to recognize the user [7]

Palm print

Ridges’ and grooves’ patterns on whole palm are used to recognize the user [8]

Retina

Eye vein pattern at back of the eye is used to recognize the user [9]

Ear shape

Shape of the ear and its components are used to recognize the user

Finger geometry

Shape, height, width, and other features of fingers are used to recognize the user [10]

Hand geometry

Geometrical shape of hands, fingers is used to recognize the user

Vein pattern

Internal vein pattern in the fingers or palms is used to recognize the user

Odor recognition

Unique natural odor of a human is used to recognize the user

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Table 2 Types of behavioral biometrics Keystroke dynamics Keying or typing behavior of using the keyboard keys is used to recognize the user [11] Mouse dynamics

Mouse movement and click patterns are used to recognize the user [12]

Signature

Style of delivering signature and its pressure points are used to recognize the user

Voice

Voice or style of speech delivery is used to recognize the user [13]

Gait

Unique walking style is used to recognize the user

Geometrics

Cognitive skills and game playing behavior are used to recognize the user [14]

Gaze

Eye movement patterns are used to recognize the user [15]

uniqueness provided by primary biometrics like fingerprint, voice, iris, etc. Hard biometrics refers to the major part of person’s identity attributes such as fingerprint, signature, face, etc. [16].

1.2 Eligible Biometric Traits Not every biometric detail can be used as a trait for identification. A practical biometric system should possess the following features: • Universality—every individual must possess the specific trait. • Distinctiveness—any two individuals can be identified sufficiently different with the selected trait. • Permanence—trait must not be easily perishable and should last long. • Collectability—the trait must be measurable quantitatively. • Performance—performance of the trait must be good such that its speed and accuracy are satisfactory. • Acceptability—the end user should be willing to present the trait whenever needed to be authenticated. • Circumvention—represents the capability of system how it can withstand when it is prone to fraudulent attacks [17].

1.3 Limitations of Unimodal Biometrics Systems Biometric authentication system that employs only one trait to recognize the user is called as unimodal biometric system. Following are the limitations of unimodal biometric systems. • Spoofing attacks: The biometric system is susceptible to circumvention such as spoofing attacks. Especially, signature and voice biometrics are prone to spoofing.

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•

•

•

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Physiological biometrics is also prone to attacks like sensor level attacks. It is easy to break the system if it relies only on only one trait [18]. Non-universality: Not all users possess quality and recognizable biometrics. There is a possibility that some users not at all possess it or vanish as age increases. For example, in fingerprint biometric, it is estimated that approximately 4% of the population does not possess identifiable fingerprints. This will result in Fail To Enroll (FTE) error. FTE rate may be high if the authentication system makes use of only one biometric trait. Distinctiveness: Biometric trait is only selected if it is distinctive. But, they may face several interclass similarities during feature extraction and may fail to distinguish between two different users. If application system makes use of only one biometric trait, it may fail to recognize the users appropriately. Intraclass variations: The template generated during enrollment phase might vary from the template generated during authentication in spite of belonging to the same user. This might occur because of aging, differences in sensor specifications, differences in makeup, health issues, etc. Noise inclusion: While enrollment, the biometric data might consist of noise such as dirty fingers, smudged sensors, usage of cosmetics on face, scars on hands, health issues. Noisy templates may fail to authenticate even if the user is legitimate.

1.4 Multimodal Biometric Systems In order to overcome the limitations of unimodal biometric system, systems are built such that multiple biometric traits are utilized at once. These are termed as multimodal biometric systems [19]. These are said to be comparatively more trustworthy because there exist independent, several different evidences at once. These systems provide a solution to problems such as non-universality as selection of multiple modalities confirms maximum target population handling. Also the system will be anit-spoof, as it is hard to spoof multiple traits simultaneously.

1.5 Operating Modes Multimodal biometric system can operate in three modes: • Serial mode. • Parallel mode. • Hierarchical mode. In serial mode, output of one trait will be fed to another trait. This feeding will narrow the possibilities of identities before the next trait decides. All the traits converge to a single identity.

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In parallel mode, all the traits will work simultaneously in order to recognize the user. The major difference between serial and parallel workings is that all the required biometrics are to be acquired parallelly in parallel mode of operation but not in the case of serial mode of operation. In hierarchical mode, the selected traits are combined to form a tree structure in which some work in parallel and some in serial.

1.6 Levels of Fusion Multimodal biometric systems should integrate the information generated by several biometric sensors. The information generated by used sensors can be integrated at different levels, either before template matching or after template matching is done. Sensor data and feature data can be fused before template matching; score, rank, decisions can be fused after template matching is done (Fig. 1). Major fusions are depicted in three different ways: • Feature extraction level fusion. • Rank level fusion. • Decision level fusion. All sorts of fusion techniques are depicted diagrammatically in Fig. 2. Feature extraction level fusion: Data extracted from every biometric trait will be used to generate a feature vector. If used biometrics is independent from each other, all the feature factors can be fused to form a new feature vector with higher dimensionality with help of feature reduction technique. Rank level fusion: When a biometric match takes place, a similarity score will be calculated which indicates the proximity between input template and stored template; based upon this score, user is authenticated or rejected. Fusion at rank level means calculating mean of all similarity scores and makes a decision depending on the score.

Fig. 1 Types of biometric fusion techniques

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Fig. 2 Different levels of biometric fusion techniques

Decision level fusion: Every selected biometric system will decide depending on its feature vector. Final decision can be made by employing schemes like majority voting scheme applied to all the feature vectors [20, 21].

1.7 Integration in Multimodal Biometric Systems Multiple sensors: Multiple biometric sensors are deployed to extract multiple biometric data of the same trait, e.g., deploying ultrasound, solid state, and optical sensors for fingerprints. Multiple biometrics: Multiple biometric sensors are deployed to extract multiple biometric data of different traits. These systems are said to yield better accuracy, e.g., face and fingerprint recognition systems. Multiple snapshots of same biometric: Several instances of single biometric are captured simultaneously, e.g., multiple images of face, multiple impressions of a fingerprint, multiple voice samples. Multiple representations and matching for one biometric: Utilizing different approaches and algorithms for every step of recognition.

1.8 Need of Multimodal Biometric Systems Banking and majority of the online applications need security toward authenticity. Lack of uniqueness, quality, and individuality of biometrics are the reasons for their failure. Missing or poor-quality biometric trait in the end user community may also cause problems. The employed biometric modality should possess high quality and

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universality in the population. Report says that 2% of the population has unusable fingerprints [22]. Therefore, in order to achieve the target level of flexibility and security, combination of different biometric modalities is mandatory. The biggest advantage of multimodal biometrics is that they are potentially strong enough against spoofing because it is extremely difficult to simultaneously spoof different modalities at once [23].

2 Proposed Methodology The proposed system authenticates the user depending on behavioral biometric traits and cognitive skills. The duo of multi biometrics with cognitive skills makes the approach novel and also more user-friendly. During the whole authentication phase, the user will be in constant interaction with the computer which will not make the user feel boredom or irritated. The user is authenticated based on keystroke dynamics, mouse dynamics, and cognitive skills dynamics. All these are dynamic and unique in nature for every human which makes user authentication system a robust one. Keystroke dynamics refer to the user’s unique style of hitting the keyboard keys. Mouse dynamics refers to the user’s unique style of moving and using the mouse. Cognitive skill dynamics refers to the user’s unique style of solving problems. It is the way a user solves simple puzzles or problems, and this is also termed as game playing behavior or problem-solving techniques. All these biometric traits are collected with help of a crossword puzzle. The reason behind selecting Crossword Puzzle as the game is that almost everyone is aware of solving it. While the user is involved in solving the puzzle, his cognitive skills will be recorded throughout the game. The puzzle consists of answers where some are to be typed and some are to be dragged and dropped. While the user solves drag– drop answers, mouse behavior will be recorded. While user solves typed answers, keystroke behavior will be recorded. A template will be created and stored which consists of all these behaviors in the form of numerical values and is saved in the database. During the registration phase, the user will face numerous puzzles to solve which will be generated randomly. Numerous puzzles are to be solved so as to record all the behaviors appropriately without any bias. Random puzzles are to be solved because randomness increases security against attacks. During authentication phase, the user has to solve a single random puzzle. All the behavior will be cross checked with that of the stored template and the decision is taken whether or not to authorize the user.

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3 Future Work Implementation of any one biometric trait in the authentication mechanism will not suffice against latest cyberattacks. Performance of biometric system that applies one sort of biometric modality will be often got caught up by several factors like nonuniversality, limited scalability, and poor-quality templates. Fusion of same sort of traits will face similar deficiencies. So, fusion has to be done on cross modalities such between Physiological biometrics and Behavioral biometrics such that authentication mechanism will be robust. Authentication schemes must be built such that it incorporates both behavioral and physiological traits which will outperform single biometric systems.

4 Conclusion This paper provides a coherent summary of biometrics, biometrics in the field of computerized user authentication and its different sorts. A novel approach has been proposed for user authentication which works on multi biometrics with the combination of intellectual abilities. This approach is first of its kind and it can become a breakthrough in the field of robust authentication.

References 1. H.T.F. Rhodes, Alphonse Bertillon, Father of Scientific Detection (Abelard-Schuman, 1956) 2. A. Kumar, K.V. Prathyusha, Personal authentication using hand vein triangulation and knuckle shape. IEEE Trans. Image Process. 38(9), 2127–2136 (2009) 3. R. Gaines, W. Lisowski, S. Press, N. Shapiro, Authentication by keystroke timing: some preliminary results. Technical report. Rand Corporation (1980) 4. P. Bours, C.J. Fullu, A login system using mouse dynamics, in 2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (2009), pp. 1072–1077. https://doi.org/10.1109/IIH-MSP.2009.77 5. A. Iyer, J. Karthikeyan, R. Khan, B. Mathew, An analysis of artificial intelligence in biometricsthe next level of security. J. Crit. Rev. https://doi.org/10.31838/jcr.07.01.110 6. K. Bonsor, How Facial Recognition Systems Work. Retrieved 02 June 2008 7. N. Zaeri, Minutiae-based fingerprint extraction and recognition, in Biometrics, ed. by J. Yang. ISBN 978-953-307-618-8 8. D. Zhang, W. Shu, Two novel characteristic in palmprint verification: Datum point invariance and line feature matching. Pattern Recognit. 32(4), 691–702 (1999) 9. A.N. Mazher, A. Noori, J. Waleed, Retina based glowworm swarm optimization for random cryptographic key generation. Baghdad Sci. J. 19(1), 0179 (2022) 10. History of Fingerprinting, in Fingerprinting. http://www.fingerprinting.com/history-of-finger printing.php. Accessed 30 Jan 2010 11. F. Monrose, A.D. Rubin, Keystroke dynamics as a biometric for authentication. Future Gen. Comput. Syst. 16(4), 351–359 (2000). ISSN 0167-739X 12. B. Sayed, I. Traoré, I. Woungang, M.S. Obaidat, Biometric authentication using mouse gesture dynamics. IEEE Syst. J. 7(2), 262–274 (2013). https://doi.org/10.1109/JSYST.2012.2221932

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13. B. Sindhu, B. Sujatha, Voice recognition system through machine learning. Int. J. Innov. Technol. Explor. Eng. (IJITEE) 8(10) (2019). ISSN: 2278-3075 14. M. Mohamed, N. Saxena, Gametrics: towards attack-resilient behavioral authentication with simple cognitive games, in Proceedings of the 32nd Annual Conference on Computer Security Applications (Association for Computing Machinery, New York, NY, USA, 2016), pp. 277–288. https://doi.org/10.1145/2991079.2991096 15. C.D. Holland, O.V. Komogortsev, Complex eye movement pattern biometrics: the effects of environment and stimulus. IEEE Trans. Inf. Forensics Secur. 8(12), 2115–2126 (2013) 16. A. El Kissi, N.E.B Amara, Soft and hard biometrics for the authentication of remote people in front and side views. Int. J. Appl. Eng. Res. 11, 8120–8127 (2016) 17. K.R. Badhiti, Biometric authentication an introduction, in Third International Conference on Advances in Computing, Communication and Information Technology—CCIT 2015 (2015). https://doi.org/10.15224/978-1-63248-061-3-17 18. T. Matsumoto, H. Matsumoto, K. Yamada, S. Hoshino, Impact of artificial gummy fingers on fingerprint systems. Proc. SPIE 4677, 275–289 (2002) 19. L. Hong, A.K. Jain, S. Pankanti, Can multi biometrics improve performance? in Proceedings of the AutoID’99 (Summit, NJ, 1999), pp. 59–64 20. Y.A. Zuev, S. Ivanon, The voting as a way to increase the decision reliability, in Proceedings of the Foundations of Information/Decision Fusion with Applications to Engineering Problems (Washington, DC, 1996), pp. 206–210 21. G. Kaur, S. Bhushan, D. Singh, Fusion in multimodal biometric system: a review. Indian J. Sci. Technol. 10, 1–10 (2017). https://doi.org/10.17485/ijst/2017/v10i19/114382 22. NIST Report to the United States Congress, Summary of NIST Standards for Biometric Accuracy, Tamper Resistance and Interoperability (2002) 23. A.K. Jain, A.M. Kumar, Biometrics of Next Generation: An Overview 1.1 Introduction (2010)

An Interactive Method to Predict Thyroid Disease Sai Jyothi Bolla, Kalavathi Alla, and Bhanu Supraja Grandhe

Abstract Thyroid makes and produces hormones that plays a vital role throughout our body. There are different types of thyroid disease, namely hyperthyroid, hypothyroid, euthyroidism, etc. In the realm of clinical data analysis, predicting endocrine illness is a crucial challenge. Thyroid gland produces two hormones T3 and T4. Machine learning (ML) has proved to be successful in generating decisions and making predictions from vast amounts of data collected in the healthcare industry. In several studies, machine learning algorithms have been employed to predict thyroid disease. The dataset used to create and assess the random forest classifier was obtained from the machine learning repository. Hence, this system proposes an approach to predict the type of thyroid disease that occurs mostly in patients, and this also identifies which age group it affects the most. Further, the model can be enhanced by employing feature selection methods to improve thyroid disease prediction performance. Keywords Thyroid disease · Prediction · Machine learning · Random forest · Hyperthyroid · Hypothyroid · Euthyroidism

1 Introduction Along with diabetes mellitus, the most prevalent endocrine issue is thyroid disease. According to the research study, TD affects women more than men. The thyroid secretes, stores, and releases T3 and T4 hormones. The thyroid gland absorbs iodine from food and uses it to make hormones. Hyperthyroidism and hypothyroidism, two of the most prevalent thyroid disorders, are caused by aberrant thyroid activity. Hypothyroidism is a disorder in which the thyroid gland does not produce adequate hormones, which can lead to a variety of health issues, including obesity, joint discomfort, infertility, and heart disease if not treated properly. When the thyroid gland generates too much of the hormone thyroxin, it is known as hyperthyroidism. S. J. Bolla (B) · K. Alla · B. S. Grandhe Department of Information Technology, Vasireddy Venkatadri Institute of Technology, Guntur, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Biswas et al. (eds.), Microelectronics, Circuits and Systems, Lecture Notes in Electrical Engineering 976, https://doi.org/10.1007/978-981-99-0412-9_42

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The bodies’ metabolism is considerably accelerated in this case, resulting in fast weight loss, rapid or irregular heartbeat, perspiration, and anxiety or irritation. The most difficult and arduous task is to diagnose medical diseases more accurately at an early stage. The right way to analyze the thyroid disease condition dataset, as well as clinical analysis, is a major issue in thyroid diagnosis. Thyroid prediction algorithms will aid in reducing the number of characteristics required to categorize thyroid illness. Random forest algorithm is used in this paper to predict the type of thyroid disease that occurs mostly to the people and also predicts which age is affected mostly with the type of thyroid disease.

2 Literature Review Roshan Banu D gave detail information that how data mining techniques are utilized to forecast TD accuracy [1]. In the past, many data mining techniques are used for the prediction of thyroid disease. The dataset with SVM and decision tree is used; SVM had the precision at its best, with a 99.63 percent accuracy [2]. In another research, Pal and Yadav [3] discovered that using individual classification methods such as random forest classifier, decision tree is produced results with the accuracy of 99%, 98%, respectively. Then, using the same dataset, created a bagging ensemble technique combines the three fundamental tree classifiers and achieves a greater accuracy of 100%. In another study, Patel [4] found that the multiclass classifier method was found to be the most accurate, with a 99.5% accuracy rate. Soft computing strategies for thyroid prediction were proposed by Ataide et al. [5]. The MLP’s accuracy was 97.4, which was lower than previous findings. Sidiq et al. [6] determined that decision tree had the greatest accuracy of 98.89 in comparison with other algorithms. On a dataset gathered from UCI, Razyia et al. [7] used SVM, MLR, and decision tree. Out of the all, decision tree was found to be the most accurate one with 99.23%. Gurram et al. [8] utilized a UCI dataset to evaluate logistic regression with SVM for thyroid illness prediction. Results showed that the former outperformed the latter, with an accuracy of 98.82%. Ammulu et al. [9] used random forest classifier for thyroid prediction and collected hypothyroid data of UCI machine learning repository. The results obtained from the WEKA instrument were 70.51% accurate. To determine the best classifier for thyroid prediction, Ionita et al. [10] examined Naive Bayes, MLP, and decision tree classifier. Data from the website providing Romanian data, UCI learning repository, were collected for the testing and validation of classifications. During the trial, the best accuracy was determined to be 97.35%. Dhash et al. [11] suggested a Naive Bayes classifier for thyroid illness prediction utilizing Ranker Search as a feature optimization strategy. The dataset retrieved from the UCI repository was trained and evaluated using a ten-fold cross-validation procedure. The accuracy of the results was 95.38%. A classifier with fuzzy rules was created. In terms of accuracy, this model’s predictive ability was compared to that of a MLR model. The results showed that the designed fuzzy rule-based approach predicts thyroid disorders with roughly 97% accuracy [12]. In another study by Pandey [13] et al. described classification

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of hyperthyroid disease using data mining techniques [13]. R. Banu predicted the thyroid disease using random forest with SVM resulting in the accuracy of 70% when random forest is taken alone [14].

3 Proposed Method In this paper, we used the random forest method to forecast the type of thyroid disease that most commonly affects individuals, as well as the age group it affects the most. And, we have forecasted the top five age groups that are afflicted by thyroid disease, and we have achieved 91% as the model accuracy.

3.1 Random Forest Approach Random forest is a collection of unpruned classification or regression-like bootstrapping algorithms with decision trees in big numbers. When fresh input data are provided, the algorithm creates trees out of those data and sets them in a forest. Random forest is frequently superior to single tree classifiers such as CART and C4.5. The following are the key benefits of the random forest algorithm: (1) (2) (3) (4)

Its accuracy is on par with, if not better than, AdaBoost It is applicable to both classification and regression problems It helps to enhance decision tree accuracy by reducing overfitting It is simple and uncomplicated to parallelize. The random forest algorithm involves the following steps:

. Selecting the count of trees to grow (Tn) . Choose the Vm number of unpredictables that will be utilized to separate each node. The number of input variables is denoted by Vm – Make trees grow (decisions), and with each tree, perform the following: – Construct an S size sample from the N training cases and let it grow – Select Vm variables at random from M while constructing a tree at each node to determine the optimal split – Grow the tree to its full potential . To decide the class label, the point K collects votes from every tree in the forest and then utilizes majority voting.

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3.2 Dataset Description It is designed to employ machine learning in its supervised learning paradigm, in particular, in order to be able to classify a person’s risk of thyroid issues assertively and rapidly based on data collected from them. It is important to have a data collection that contains a history of the disease in order to carry out the proposal and the development of the system. In our case, the dataset to be used comes from UCI collection. General information of dataset is shown in the Table 1. It has a total of 25 features, which are distributed as follows: . 7 continuous variables . 18 categorical variables. Table 1 Dataset features Attribute

Value

Value F

Sex

M

Age

Continuous

On_thyroxine

F

t

Thyroxine_query

f

t

f

t

Thyroid_surgery

f

t

Query_hypothyroid

f

t

Query_hyperthyroid

f

t

Sick

f

t

Pregnant

f

t

Lithium

f

t

Tumor

f

t t

Goiter

f

TSH

Continuous

TSH_measured

n

y y

T3_measured

n Continuous

Measured_TT4

n

TT4

Continuous

Measured_T4U

n

T4U

Continuous

Measured_FTI

n

FTI

Continuous

Measured_TBG

n

TBG

Continuous

? ?

Medication_on_antithyroid

T3

Missing

?

? y ? y ? y ? y ?

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3.3 Experimental Results We plotted the seaborn pair grid among the classes using age, gender, TSH, T3, TT4, and t4u as variables, as indicated in Fig. 1. Figure 2 illustrates that we are predicting the type of thyroid disease that most usually affects individuals, as well as the age group that is most affected. As a result, we created a graph that takes into account the relationship between age and classes (Figs. 3 and 4).

Fig. 1 Pairwise relationships between classes

Fig. 2 Pair plot between age and classes

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Fig. 3 Graph displaying the top five age groups affected with thyroid disease

Fig. 4 Bar graph displaying most affected thyroid type

4 Conclusion Thyroid forecasting is a critical topic for medical scientists to study. According to medical statistics, the majority of the population suffers from severe thyroid dysfunction, with women being more impacted than men. In this paper, we have explored the random forest algorithm. The predictions can be further enhanced in future by adding more examples to the dataset which will result in stronger findings. In addition, several function selection strategies can be applied to improve the model to enhance the performance of the thyroid disease prediction.

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References 1. B.D. Roshan, K.C. Sharmili, A study of data mining techniques to detect thyroid disease. Int. J. Innov. Res. Sci. Eng. Technol. 6(11), 549–553 (2017) 2. T. Ankita, M. Ritika, Interactive thyroid disease prediction system using machine learning technique, in 5th IEEE International Conference on Parallel, Distributed and Grid Computing, pp. 689–693, 2018 3. D.C. Yadav, S. Pal, Prediction of thyroid disease using decision tree ensemble method. Hum. Intell. Syst. Integr. 2, 89–95 (2020) 4. P. Hetal, An experimental study of applying machine learning in prediction of thyroid disease. Int. J. Comput. Sci. Eng. 7(1), 130–133 (2019) 5. M.L.D. Ataide, A. Dessai, Thyroid disease detection using soft computing techniques. Int. Res. J. Eng. Technol. 6(5), 8015–8016 (2019) 6. U. Sidiq, S.M. Aaqib, R.A. Khan, Diagnosis of various thyroid ailments using data mining classification techniques. Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol. 5(1), 131–136, 2019 7. R. Shaik, P.S. Prathyusha, N. Krishna, N. Sumana, A comparative study of machine learning algorithms on thyroid disease prediction. Int. J. Eng. Technol. 7(2.8). pp. 315–319, 2018 8. G. Deepthi, M.A. Rao, Comparative study of support vector machine and logistic regression for the diagnosis of thyroid dysfunction. Int. J. Eng. Technol. 7, pp. 326–328, 2018 9. V. Ammulu, Thyroid data prediction using data classification algorithm. Int. J. Innov. Res. Sci. Technol. 4(2), 208–212 (2017) 10. I. Ionita, L. Ionita, Prediction of thyroid disease using data mining techniques. BRAIN-Broad Res. Artif. Intell. Neuron Sci. 7(3), 115–124 (2016) 11. S. Dash, M.N. Das, B.K. Mishra, Implementation of an optimized classification model for prediction of hypothyroid disease risks, in International Conference on Inventive Computation Technologies (ICICT). Coimbatore, pp. 226–229, 2016 12. N.A. Sajadi, S. Borzouei, H. Mahjub, M. Farhadian, Diagnosis of hypothyroidism using a fuzzy rule-based expert system. Clin. Epidemiol. Global Health 7(4), pp. 519–524 (2019) 13. S. Pandey, R. Miri, S.R. Tandan, Diagnosis and classification of hypothyroid disease using data mining techniques. Int. J. Eng. Res. Technol. 2013 14. R. Banu, Classification model using random forest and SVM to predict thyroid disease, in International Conference on Advancements in Computing Technologies—ICACT 2018 ISSN: 2454-4248 4(2), 85–87. 2018

A Brief Study of Designing a 10KWP Grid Connected Photovolatic System Using PVSYST Amrita Chanda, Sagar Bera, Ramanuj Bhowmick, and Susovan Dutta

Abstract Sources like solar, wind, and biomass are more often used for the generation of electricity as renewable items because they are within our easy reach and there is no greenhouse gas emission. It is gaining popularity day by day together with its climate-friendly characteristic. The present paper aims at the aspects of design of such a grid connected basically photovoltaic system. A 10 KWp photovoltaic array is considered an essential necessary for general under consideration of usual condition of temperature. It will be considered and simulated the same in PVSYST software environments. This paper, however, throws a flood of light on different aspects that develop Sankey diagram of loss caused by power output of energy of photovoltaic plant installed and at the sometimes takes note on the performance of a particular photovoltaic module. It fixes the angles tilt and also the distribution-related curves taking into their consideration their temperature through simulation of related software curves. Keywords Grid connected solar plant · PVsyst software · Performance ratio · Power loss

1 Introduction At present times, PV systems are the one of the premier systems of electricity in the field of power generation [1]. In order to combat with the growing demand for electrical energy, the solar system has come third in the rank of power generation [2]. Solar panels are now being used in industries, institutions, and households. Solar systems are developed by scientists with better technology for energy production as maximum as possible. The performances of the PV systems are always characterized A. Chanda (B) · S. Bera · R. Bhowmick · S. Dutta Department of Electrical Engineering, Guru Nanak Institute of Technology, Panihatisodepur, West Bengal, India e-mail: [email protected] S. Dutta e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Biswas et al. (eds.), Microelectronics, Circuits and Systems, Lecture Notes in Electrical Engineering 976, https://doi.org/10.1007/978-981-99-0412-9_43

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by various parameters like geographical location panel orientation, PV modules, inverter, tilt angle, etc.[4]. This system of grid connected types are beneficial in as much they are easy to install without any need for battery hazard. The relevant losses are associated with a PV plant and can be classified into two folds–system losses and capture losses. System losses mean wiring losses, transformer losses, and inverter losses.

2 PVSYST The PVSYST is simulation software for study, sizing, and data analysis of complete PV system. In order to cope with the requirements the user shall have the liberty to fall backup on a selected pattern (design area) and mitigate for necessary solution [1]. This is the total detailed design of a solar PV plant, and one solar installer can easily set up a solar PV plant upon the simulation’s result [2] (Fig. 1).

3 Design Description A. Geographical Data Guru Nanak Institute of Technology is an engineering college located in panihati, North 24 parganas city in West Bengal (India). The location is 22.70° N latitude and 88.38°source from PVSYST software is shown in Fig. 2. B. PV Module The PV module model is PM245P00-250 Open circuit voltage (Voc) of 37.40 V and short circuit current (Isc) 8.69 A. The efficiency of the solar panel is 17.12%. Manufacturer specifications are shown in Table 1. C. PV Inverter PV inverter converts the direct current (DC) into alternating current (AC) [3]. Here in PV module RPI M8A inverter is used manufacture by delta energy. Manufacturer specifications are shown in Table 2 (Figs. 3 and 4). The array voltage sizing can also be depicted as follows: D. Tilt and Azimuth Angle The tilt angle can be modified depending upon the place if installation. The tilt angle is kept around 23°, and Azimuth angle is 0° (Fig. 5). Performance curve and plane orientation are shown in Figs. 6 and 7.

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Fig. 1 Simulation flowchart

4 Simulations Result After applying all the steps, the model should be simulated. The whole simulation is done in this software, and it creates many outputs. We show all of these below. If anyone sees those outputs, its make easy to understand. Many outputs like horizon line diagram, Sankey diagram, performance ratio, normalized power production, daily input/output diagram, etc.

5 Daily Input/Output Diagram See (Fig. 8)

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Fig. 2 Monthly geographical data Table 1 Manufacturer specification and other parameters Of PV module

Table 2 Manufacture’s specifications of PV inverter

Model

ELPS CS6P—250 MM

Manufacture

Canadian solar Inc

No. of modules

20

Maximum power

250Wp

Short circuit current

8.74A

Open circuit voltage

37.50 V

Maximum current

8.22A

Maximum voltage

30.4 V

Input voltage

200–800 V

Output voltage

21.74–28.50 A

No. of inerter

1

No. of phases

3

Output voltage

230 V

Efficiency

95.6%

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Fig. 3 Power sizing of inverter output

Fig. 4 PV array voltage sizing

Fig. 5 Tilt angle and Azimuth

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Fig. 7 Plane orientation

Fig. 8 Daily input/output diagram

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Fig. 9 Horizon line diagram

6 Horizon Line See (Fig. 9)

7 Normalized Power Production See (Fig. 10)

8 Performance Ratio P. R = (Actual energy from plant/Calculated output) ∗ 100 PR is the relation between the practical energy and theoretical energy output of a plant. In our paper the yearly average PR is 81.1%.From Table 1. We can see that in the month of January the PR is maximum, 84.9% and minimum PR is in the month of May that is 78.6%. We also can observed that the PR is high in the month of winter (November-February) as the modules surfaces are cool in that time.

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Fig. 10 Normalized power prodution

For the installed PV system at Guru Nanak Institute of Technology, the PR is 81.1% which means 81% solar energy is converted into electrical power or we can say that approx 19% energy is lossed during the losses. In the Fig. 11, we show the yearly performance ratio.

Fig. 11 Performance ratio analysis

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9 Sankey Diagram From performance ratio, we can understand that 19% energy is not converted to solar energy that means there are some losses. In the Fig. 12, we show all of the losses throughout a year. Here the nominal array energy is 8.78 MWh, and the efficiency is 15.55%.The virtual array energy is 7.36 MWh. After all the losses, we get 6.99 MWh energy which is injected into grid.

Fig. 12 Sankey diagram of losses

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10 Conclusion However, a conclusion may be arrived at on the study that it has basis on photovoltaic power plant, and all the required steps are explained like PV module, inverter power sizing, area selection, tilt angle, etc. If anyone has any interest in grid connected PV system then they can take it as a reference. They also find all the losses with the help of this PVSYST software. In this software, one can generate the full installation report.

References 1. A. Aryal, N. Bhattarai, Modeling and simulation of 115.2 kwp grid-connected solar PV system using PVSYST 2. B.K.K. Prasad, K.P. Reddy, K. Rajesh, P.V. Reddy, Design and simulation analysis of 12.4kwp grid connected photovoltaic system by using PVSYST software 3. O.A. Ahmad, W.H. Habeeb, D.Y. Mahmood, K.A. Jalal, H. Sayed, Design and performance analysis of 250 kW grid connected photovoltaic system in Iraqi environment using PVsyst software 4. P. Suresh, J. Thomas, A.G. Anu, Performance analysis of stand-alone PV systems using PVSYST

Automatic Plant Watering System Using Moisture Sensor and Arduino Uno_An Experimental Validation Dhanaraju Athina, Kolli Vittal, Swapna Revuri, Kulsum Shireen, and Karnati Keerthi

Abstract The day-to-day operations are associated with gardening or farming. During farming and gardening, the fundamental practice was the watering of plants. Irrespective of the weather conditions, the principal theme of this project is to limit the wastage of water that reaches plants. Advance watering structures need to be correctly pre-owned to water and save the life of plants on every occasion there is a requirement of watering. In general, the manual method contains a human required to water plants. But by using the necessary components, we designed a system by which we can water only the needed amount of water. Here, an automated plant watering system is created to replace the manual methods. It reduces human efforts and makes work easier, required, and at the right time. An automated plant watering system reduces water consumption. An advanced system in gardens, farms, or big agriculture fields all the vegetation attain its fullest would-be. Relay, soil moisture sensor, DC pump motor, and Arduino UNO were components used in this project to implement the automatic plant watering system. This system backend contains the code used to sense the moisture levels of soil. If the moisture content is less than the specified programmed value’s whichever had predefined in the program according to the particular plant requirements then the desired amount of water is supplied till it reaches the programmed value. Humans can water their plants without being worried about the absence of forgetfulness by using this prototype. Keywords Arduino UNO · Automatic · Farming · Field · Modern · Sensor · Soil · System

1 Introduction The scarcity of the groundwater is predominant problem these days in which 89% of water is used mainly for agriculture (Fig. 1). D. Athina (B) · K. Vittal · S. Revuri · K. Shireen · K. Keerthi Electronics and Communication Engineering, Vignan Institute of Technology and Science, Deshmukh, Nalgonda 508284 TS, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Biswas et al. (eds.), Microelectronics, Circuits and Systems, Lecture Notes in Electrical Engineering 976, https://doi.org/10.1007/978-981-99-0412-9_44

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Fig. 1 Pie chart of water withdrawal [1]

Agriculture is the main source of food for most of the living beings. So, if the scarcity of water is more than the scarcity of the food also takes place (Fig. 2). As we can see, the main occupation farming seen mainly in India, so there is a huge amount of scarcity of water mainly in India. The remaining part has less scarcity of water. The huge amount of scarcity of water is mainly seen in the areas of India, Africa, and some parts of America. The above map provides the levels of scarcity of water in various parts of the countries present in the world map (Fig. 3).

Fig. 2 World map about water scarcity [2]

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Fig. 3 Article taken from the newspaper the Hindu

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From the world map, we came to know the scarcity of water is more in India and from the news article taken from Hindu, we came to know the scarcity of water mainly in different states of India. They also provided us the information about the demand and supply of water mainly in India, and by seeing the article, we can also predict the future conditions if the situations proceed in the same way. So we came up with idea based on the human beings present in the modern era of advanced electronics and technology. The automated system has reduced the human efforts in their daily activities for which life became easy, convenient. The automated system which was introduced for watering plants is named as the automatic plant watering system, and this model contains controlling irrigation facilities using sensor technology. The sensor technology is used to sense soil moisture with an IC to make intelligent switching devices to help millions of people. Unbalanced irrigating mechanism leads to rock crystal loss in the soil and may also result in the decomposition of the plants [3]. In the daily life criteria, we can see so many aspects in which the plants are beneficial to human beings. The plant had serviced by keeping the environs at fine fettle by cleaning air naturally and producing oxygen. Especially in big cities, people are habitual for growing plants in their backyard and gardens. For technological developments, civilization, deficiency of habitation, lots of people cast off to raise vegetation in a mold, vessel, pots and sited them on the window ledges, walls, and rooftops. These plants require conservative breeding, wetting, and make available the precise amount of sunlight to increase the life and growth. In the hectic schedule of daily life, several times individuals overlook watering their plants. Because of this reason, plants undergo syndromes and plants die. The world’s leading problem in current society scinerio is the scarcity of water. The farming sector and industries are consuming large amounts of water. Humans must make use of water resources properly. Accordingly, an arrangement is essential to handle this job robotically [4]. The current moisture level of the soil had detected by an automated plant watering system through moisture sensors. Based on the given data, it supplies the required amount of water to plants. It will reduce extra water consumption and keep plants healthy [5, 6]. Food production technology improves rapidly due to the continuously increasing demand for food. India is a highly populated country, and the economy mainly depends on the farming sector and here we can see isotropic climatic conditions. But still, people wereunable to make use of farming resources. The key criteria are un sarcastic weather conditions, which cause unbalanced rainfall leads to scarcity of water for cultivation and drinking water. Most of the lands had filed as unirrigated land zones due to the continuous extraction of groundwater. Another reason for water scarcity is water wastage. In the modern days, the plants had monitored by an automatic plant irrigation system using available advanced technology. For implementation, a DC motor and Arduino Uno were used [3].

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2 Components and Materials In this modern era, we can see so many companies selling plant watering systems made in diverse ways, even though there is an easy way to build the plant watering system. If any individual wants to build a system, they require few components and little knowledge of electronics. For the fabrication of a plant watering system, there is the requirement of both software and hardware. A. Hardware Requirements (See Tables 1 and 2). 2 Software Requirements Arduino IDE: This is that the code can be easily written and upload to the I/O board. It runs on Windows, Mac, and Linux. The environment is written in Java, Python, and C language based up on the processing, A VC-GCC, as well as other open-source software. Soil moisture sensor, pin diagram of Arduino UNO, and relay are shown below. As shown, Fig. 4 shows the soil moisturizer sensor, Fig. 5 shows the Arduino Uno board, and Fig. 3 shows the relay which all are the hardware components (Fig. 6). Table 1 Specifications of the required hardware components Components name

Specifications

Arduino Uno

Operating voltage = 5 V, digital I/O pin = 14, clock speed = 16 MHz

Moisture sensor

Operating voltage = 3.3–5 V, dual output mode.(analog and digital)

Relay

10 A, 30 V Dc with operating frequency = 50/60 Hz

Pump motor

Max operating voltage = 12 V

Table 2 Smart plant watering system (plant moisture testing)

Equipment

Cost

Arduino UNO

345/-

LCD 16 * 2 display

140/-

Pump motor

100/-

Diode (IN4007)

2/-

Transistor (BC 547)

127/-

Resistors—1 kΩ * 2, 330 Ω * 2, 10 kΩ * 2

24/-

Soil moisture sensor

110/-

Led (red, blue)

2/-

Power supply (5v)

20/-

Connecting wires

5/-

Total

875/-

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Fig. 4 Soil moisture sensor

Fig. 5 Relay

The software Arduino Uno IDE was written and uploaded into the microprocessor. This software runs in the system having an operating system windows, Mac, and Linux. The software that we will be using is open-source software that can be preowned by anyone as it is free of cost (Fig. 7).

3 Working Principle The block diagram or algorithm of the plan watering system is presented in Fig. 8. It outlines the step–by–step process involved in the functioning of the system. The system starts with the start block, which signifies the beginning of the process. Initially, a power supply is provided to activate the mico-controller, which controls the system. Once the microcontroller is activated, it triggers the activation of the pins of the sensors connected to it. These sensors play a crucial role in the system as they provide information about the environmental conditions surrounding the plant, such as the moisture level, temperature, and light intensity. The information obtained by the sensors is then processed by the microcontroller, which uses this data to decide whether the plant requires watering. The micro controller activates the water pump

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Fig. 6 Pin diagram of the Arduino UNO

Fig. 7 Software of Arduino Uno [7]

to irrigate the plant if the moisture level falls below a specific threshold. Hence, the moisture sensors get activated and check the moisture of the earth near the plant. Here, moisturizer sensor checks the moisture of the soil and compares it with the threshold value. So, the threshold block is the decision-making block that decides

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whether to sprinkle water or not. If the threshold value is greater than the moisture value given by the moisturizer sensor, than it means the soil is in dry condition. If the soil is dry, the LED lights gets activated. LED light is provided to indicate that water sprinklers are going to be ON. The sprinklers will be ON state until soil reaches the required threshold value. If the threshold value of soil is less than moisture value given by the moisturizer sensor than it means that soil is in wet condition. If the soil is in wet condition the LED light will not get activated. As the LED light is in OFF, the sprinklers will also be in inactivating condition. The sprinklers and LED light will not be activated until the soil is wet. Below Fig. 9 shows the pin connections of the system to connect the sensor’s pins to the pins of the micro-controller; motor to the pins of the micro-controller; and follows the steps shown in Fig. 6. The circuit diagram of the Automatic Plant Watering System is presented in Fig. 10. The objective of this system is to irrigate plants automatically when the moisture level in the soil falls below a certain threshold. This is attained through the use of a moisture sensor embedded in the soil, which is connected to the microcontroller Arduino Uno. The moisture sensor continuously monitors the soil moisture level and sends the data to the micro-controller. If the moisture level is below the set

Fig. 8 Flowchart of an automatic plant watering system

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Fig. 9 Pin diagram of an automatic plant watering system

threshold, the micro-controller activates the water pump, which is also connected to it. The water pump then delivers water to the plant until the moisture level reaches the desired level. The circuit diagram illustrates the various components that make up the Automatic Plant Watering System, including the Arduino Uno Micro-Controller, the moisture sensor, the water pump, and other electronic components. By using this system, plant owners can ensure that their plants receive adequate moisture levels, even they are not physically present to water them manually. Arduino-Uno and soil moisturizer sensor modules have perfect required dimensions with the satisfactory working performance in which the moisturizer sensor helps to check the water level of the soil. A necessary code had written to examine the threshold value. The analog output gives a threshold value that is present in the back end of the program and compares with the values provided by a sensor and decided whether to sprinkle water or not based on the condition of soil near the plant.

4 Results and Discussions Figure 11 gives the arrangement of components based on the pin diagram. Firstly, we can see the soil moisturizer value shown in the LCD. The soil moisturizer sensor is resistive which contains three probes. We get the moisture value of the soil by placing the probes of sensor in contact with the ground. The current flows in the network and gives the resistive value based on the moisture present in the ground.

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Fig. 10 Circuit diagram of an automatic plant watering system

The percentage of moisture level is shown on LCD, and it also gives the condition of the soil (i.e.) whether the soil is dry or wet. The soil moisture shown in LCD is 8% which means that the soil is in the dry condition. Figure 12 illustrates that when the sensor detects the soil condition, it sends a message to the phone indicating whether the soil is wet or dry, along with the threshold values. The owner can then decide whether to turn the system on and irrigate the plants. This manual operation lets the owner control the system and avoid water wastage. Furthermore, the LCDs the threshold values, providing the owner with more information to make an informed decision about whether to irrigate the plants or not.

Fig. 11 Resistive soil moisture sensor value while soil is in dry state

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Fig. 12 Dry condition of soil and we are manually trying to water plant

By having this control in the owners’ hands, the system can be operated efficiently without wasting water. From Fig. 13, since the soil is dry, then electricity will pass through the sensor due to the resistive nature of probes, and the percentage of moisture of the earth near the plant has displayed on the LCD, and the pumping of the water takes place until the moisture value is attained, and water has been sprinkled until the earth takes require content of water. The LED is also in the triggered state. From Fig. 14, if the soil is wet condition then the electricity will pass through the sensor due to the resistive nature of probes, and the percentage of moisture of the earth near the plant has displayed on the LCD screen. If the moisture level present in the soil is greater than the threshold value, then LED light is not triggered, and water has not sprinkled until the earth takes require content of water. From Fig. 15, we can say that we have stopped the watering of the plants manually by using phone, as we can see the soil moisture attained to the required threshold value, and we came to know the threshold value from the LCD which is present to the circuit. The application of this project is done either manually or automatically. Manual mode mainly used to indicate the moisture content present in the soil at

Fig. 13 Watering of the plant takes place as the soil is in dry condition

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Fig. 14 Resistive soil moisture sensor value while the soil is in a wet state

initial state, and in the automatic mode it will take information of soil and decide to sprinkle water or not to sprinkle water. By using this system, we can grow plants healthy and we can even also save time, we can also stop over usage of water and use water whenever it is necessary. Fig. 15 Sprinkling of water stopped manually

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Firstly, after designing the components based on the pin diagram. We take the information from the moisture sensor and compare it with the threshold value present in the back end of the micro-controller, then decide to sprinkle the water or not. So, according to the application of our project, in the first few results, we can see that soil is in wet condition. LCD has shown how much percentage moisture is present in the earth where we placed our sensor. After a few hours when we took readings. LCD gave the percentage of moisture present in the earth. LED light is also activated as moisture content is less. After activation of LED, the water sprinklers got to turn ON. The LED and water sprinkler are ON until it reaches the required threshold value; the value differs in the LCD when the sprinkler is in ON condition. The prototype which we have used having many kinds of advantages such as less wastage of water, required less maintenance, low cost, and easy to build. So our project can be used in various kinds of the irrigation systems. Farming, gardening, drip irrigation, sprinkler irrigation, surface irrigation, and lawn irrigation. The main irrigation system seen in India is lawn and the surface irrigation, so these prototype can be made by any kind of electronic student and can be used by the any kind of farmer that is big or small (Figs. 16 and 17). In this paper, we have discussed the condition of water scarcity in our country and state and also discussed what can be the consequences if the situation continues in a same way, and we came up with the solution to reduce wastage of water during the agriculture. We also discussed how the soil moisture is taken and learned how the moisture sensor works, and we also gained the knowledge of how the watering takes place based on the crop and the soil condition, and the working of the automatic plant watering system is explained in a detailed way.

Fig. 16 Different types of irrigation systems [8]

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Fig. 17 Surface irrigation system [9]

5 Conclusion and Future Scope An automatic plant watering system is the prototype that we have designed. Our project will be beneficial for the farmers and gardeners. Several companie’s have implemented a similar kind of design, but in a compound way, and selling it for more price. Farmers who cultivate the little area and get little profit cannot afford the system provided by companie’s. So, we have decided to design our project on a low budget which can be affordable for anyone. Our design is person-friendly which means any individual can construct our project by taking the help of our information if they have prior knowledge of electronics. This paper gave the possibility to design the prototype with the low budget and in a very efficient way. In the future, we can do advancement or extension by providing not only the moisture content but also it should give information about which plant is to be cultivated based on the moisture content. There are many ways to do the future development of the project to get accurate results by providing more number of sensors and using by using scalable and supporting software, and we can also use the automatic system in future generations where we can completely operate the system using phone from anywhere.

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References 1. A. Thakur, A. Kumar, B. Vanita, G. Panchbhai, N. Kumar, A. Kumari, P. Dogra, Water footprint a tool for sustainable development of Indian dairy industry. Int. J. Livestock Res. 8(10), 1–18 (2018) 2. Water Scarcity. Retrieved March 24, 2023, from https://en.wikipedia.org/w/index.php?title= Water_scarcity&oldid=1144952309 (n.d.) 3. T.K. Shifa, Moisture sensing automatic plant watering system using Arduino Uno. American J. Eng. Res. (AJER) 7, 326–330 (2018) 4. N. Ðuzi´c, D. Ðumi´c, Automatic plant watering system via soil moisture sensing by means of suitable electronics and its applications for anthropological and medical purposes. Coll. Antropol. 41(2), 169–172 (2017) 5. P. Divani, Punjabi. Automated plant watering system, 2016 6. K.T. Tun, H.M. Oo, C.T. Nwe, Automatic plant watering system using arduino. Int. J. All Res. Writings 2(3), 178–186 (2019) 7. Installing Arduino IDE—SparkFun Learn. Retrieved March 24, 2023, from https://learn.spa rkfun.com/tutorials/installing-arduino-ide/all (n.d.) 8. Different types of Irrigation System. Retrieved March 24, 2023, from https://heeraagro.com/dif ferent-types-of-irrigation-system/ (n.d.) 9. Surface irrigation. Retrieved March 24, 2023, from https://en.wikipedia.org/w/index.php?title= Surface_irrigation&oldid=1145577655 (n.d.)

Lung Cancer Detection Using Computer-Aided Diagnosis (CAD) Ashok Kumar Nanduri, Jeevan Ratnakar Kondru, M. Rambhupal, and Nutalapati Ashok

Abstract Computer-aided diagnosis (CAD) plays a significant part in the automatic segmentation of distinct anatomical structures in chest radiography, and it is becoming more popular (CXR). Our research focuses on the segmentation of the lung section from a chest radiograph using a deformable active shape model with an adaptive approach, since the lung border shape may be used to identify the majority of abnormalities seen on chest radiographs. Using a combination of Grey Level Cooccurrence Matrix (GLCM) and Gabor feature extraction, it is possible to extract the features needed to assess lung abnormalities after segmentation. When GLCM is used alone, the accuracy of the analysis is reduced. Support vector machine (SVM) classifier is used to classify the lung pictures as normal or abnormal based on the characteristics that have been retrieved from them. Keywords Lung cancer · Segmentation · x-ray images · CAD

1 Introduction When it comes to chest radiography, lung segmentation is crucial. Segmentation of the lung from CXR pictures produces decent results, but it is a time-consuming operation to examine the large number of images available. For the purpose of speeding up the segmentation process, multiple methods for automated segmentation of lung images were created using CAD [1]. It is dependent on the segmentation technique used for lung boundary extraction which determines the kind of defect that may be detected in the lungs. If you compare a posterior anterior (PA) chest radiograph to A. K. Nanduri (B) · J. R. Kondru · M. Rambhupal · N. Ashok Department of InformationTechnology, Vasireddy Venkatadri Institute of Technology, Gunturu, India e-mail: [email protected] J. R. Kondru e-mail: [email protected] M. Rambhupal e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Biswas et al. (eds.), Microelectronics, Circuits and Systems, Lecture Notes in Electrical Engineering 976, https://doi.org/10.1007/978-981-99-0412-9_45

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an Anterior Posterior (AP) chest radiograph, you will see that the lung abnormalities may be seen more clearly in the PA chest radiograph [2]. In the lung section of the CXR, the following abnormalities may be identified: consolidation, interstitial, single nodule, mass, lymphadenopathy, cyst/cavity, and pleural abnormalities [3]. Different types of anomalies have distinct characteristics. The segmentation of a lung picture is primarily dependent on three factors: shape, edge, and texture. Other criteria are taken into account during the segmentation process; however, these three are the most often employed in lung image segmentation applications. Lung segmentation is achieved in paper [4] with the use of a mix of pixel-based classification and postprocessing techniques. In study [5], lung segmentation for TB screening is achieved by combining a rule-based segmentation algorithm with a pixel-based segmentation method in a hybrid scheme. Lung segmentation is accomplished using the graph-cut approach in the article [6]. Lung segmentation is achieved in paper [7] with the use of the Gaussian derivative. A new algorithm for lung segmentation based on the adaptive active shape model (AASM) is proposed in this paper, and the feature extraction from the segmented lung region is accomplished by combining the GLCM and the Gabor feature, resulting in an increase in the accuracy of feature extraction when compared to using the GLCM alone. On the basis of the feature retrieved using the GLCM and Gabor algorithms, the SVM classifier is used to categorize the CXR as normal or abnormal. With this suggested model, we get an accuracy of 96.5%. The following is the structure of the paper: Sect. 2 discusses the materials and methods that were used. Sect. 3 discusses the findings and conclusions of the planned research project. Section 4 is devoted to a conclusion as well as future work.

2 Methods Used The Montgomery County Chest X-ray set (MC) database [8] was used in this study, and it comprises 135 chest X-rays, with 58 cases afflicted by tuberculosis and 80 cases with normal chest X-rays (TB). The technique used in the suggested system is divided into the following steps: (Fig. 1). A. B. C. D. E.

Hybrid median filter is used to restore images in Part A. (HMF). The adaptive mean adjustment technique is used to improve images in B. (AMA). Image analysis using the adaptive active shape model (AASM). Extraction of features via the use of the GLCM and the Gabor filter. Classification using a support vector machine classifier.

A. Hybrid median filter is used to restore images in Part A. (HMF) A hybrid median filter is used to restore images in Part A. By maintaining edge information, hybrid median filtering is an upgraded version of the median filter [9], as it contains three kinds of ranking operators that rank the pixels from various spatial directions individually while still keeping the edge information. When using methods for noise filtering to automated lung segmentation, it is critical to maintain

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Fig. 1 Block diagram of the proposed system

Fig. 2 Flow diagram for hybrid median filter

the borders of the lung segments. The initial phase in our approach is to filter out background noise with the assistance of a hybrid median filter. With the help of this hybrid median filter, the CXR picture is recovered from the noise, and the flow diagram for this process is illustrated in Fig. 2. B. Image enhancement using Mean Adjustment When doing image enhancement, the histogram is sliced at a certain threshold, and then equalization is done. The adaptive contrast histogram equalization approach [10] for enhancing the contrast of a CXR picture is called contrast image enhancement with mean adjustment. The contrast of a CXR image is boosted by applying the algorithm to tiny data sections rather than the complete image. The flow of the algorithm is shown in Fig. 3. C. Adaptive Active Shape Model In the CXR image, an adaptive active shape is one of the deformable models that is used for lung boundary segmentation, which is accomplished by using the minimum cost path algorithm [11]. When compared to the use of ASM alone, the segmentation of the lung region from CXR using this method results in a more accurate segmentation of the lung region.

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Fig. 3 Image enhancement

D. Extraction of features from a dataset using the GLCM and the Gabor filter (D) The texture characteristics are calculated using the statistical distribution of observed combinations of intensities at defined points in relation to one another in the picture, and they are shown in the image. It has been shown that the Grey Level Co-Occurrence Matrix (GLCM) method [12, 13] can be used to extract second-order statistical texture features. It is a statistical strategy that takes into account the spatial connection of pixels in a segmented lung area when making a diagnosis. Because the accuracy of the features generated by GLCM is low near the edges, it is necessary to combine the Gabor feature [14] with GLCM to overcome this limitation. According to our findings, the following GLCM characteristics were retrieved from the segmented lung picture obtained from the CXR: The formula that was used to calculate the GLCM characteristics is detailed further below. The number of grey levels employed is denoted by the letter M. The mean value of P is denoted by the symbol. The averages and standard deviations of Px and Py are represented by the variables x, y, x, and y. When the rows of P are added together, the result is Px I which is the ith entry in the marginal probability matrix (i, j). An image’s angular second moment (ASM), for example, is a measure of how well each pixel is organized. When the pixels in the lung picture are in the same sequence as each other, the ASM value will be high. The square root of ASM yields the Energy, which is employed in the lung area as a textural metric to distinguish between different textures. ASM =

M−1 ∑ i, j=0

Pi,2 j

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. Contrast This is a measure of contrast or local intensity variation in the segmented lung region. Contrast =

M−1 ∑

n2

{∑ M ∑ M i=1

i=0

j=1

} p(i, j ) , |i − j| = n

. Local Homogeneity IDM =

M−1 ∑ M−1 ∑ i=0 j=0

1 p(i, j ) 1 + (i − j)2

The quantity of similarities in the lung area is shown by the measure of local homogeneity. It represents the amount of ordered and contrasting texture characteristics present in the picture and is expressed as a percentage. . Correlation is the relationship between two things Correlation is a measure of grey level linear dependency in the segmented lung between the pixels at the given places in respect to one another measured at the grey level. Correlation =

M−1 ∑ M−1 ∑ i=0 j=0

{i × j} × P(i, j) − {μx × μ y } σx × σ y

. Variance

Variance =

M−1 ∑ M−1 ∑

(i − μ)P(i, j )

i=0 j=0

. Sum Average

Sum Average =

2M−2 ∑ i=0

i Px+y (i)

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. Sum Entropy

SumEntropy =

2M−2 ∑

i Px+y (i) log (Px+y (i )

i=0

. Cluster Shade

Inertia =

M−1 n ∑∑

{i − j}2 × P(i, j)

i=0 i=0

. Cluster Prominence Cluster Prom =

M−1 ∑ M−1 ∑

{ }4 i + j − μx − μ y × P(i, j)

i=0 j=0

Lung image characteristics are generated and placed in a matrix; these features are then utilized for training and testing in order to classify lung abnormalities in the picture. E. SVM Classification SVMs [15] are a collection of similar supervised learning approaches that were applied in our study for the categorization of lung abnormalities. When there are precisely two classes in the data, SVM is utilized. SVMs are strong in that they can approximate any training data and generalize better when given new data sets. This job consists of the training and testing of the CXR image in the MC database in order to complete the task. The feature derived from the segmented lung area using the GLCM and the Gabor filter is used as input for the classification of lung abnormality in this study.

3 Results and Discussion Simulations were performed using Mat Lab version 2011 to simulate the results of our Sect. 2 work, and the results for the example picture (MUCXR 006. png-Fig. 4) from the MC database are detailed in the following paragraphs. The initial phase in our study is picture restoration using a hybrid median filter, as shown in Fig. 5, followed by image enhancement using adaptive mean adjustment, as shown in Fig. 6. The last step is image enhancement using adaptive mean adjustment. Following preprocessing, the next stage is a segmentation of the lung area, which is accomplished

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Fig. 4 Input image (MUCXR_006.png)

by the use of AASM, as seen in Fig. 7. In order to extract features for the segmented lung area, GLCM and Gabor filters are used in conjunction with a Gabor filter. The features collected by this approach are then fed into an SVM classifier during the training and testing procedures. In our research, we found that the categorization of photos in the MC dataset was 96.5% accurate on average.

4 Conclusion and Future Work When it comes to detecting abnormalities in the lung area, lung segmentation, feature extraction, and classification are all critical tasks to complete. The output of these stages will be very beneficial as a second opinion tool for medical professionals, and the output of these stages will be highly useful for medical experts. In our study, we addressed several methods for the categorization of lung abnormalities, as mentioned above. In our testing of the method on the MC dataset, we found that the accuracy gained for classification was about 96.5 percent. In this work, we use a spatial domain approach for feature extraction, and in the future, we will experiment with a different multiresolution approach for feature extraction in order to increase the classification rate, which will be extremely beneficial for medical experts in providing accurate diagnosis results.

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Fig. 5 Output of hybrid median filtered image

Fig. 6 Output of contrast-enhanced Image

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Fig. 7 Output of AASM

References 1. M.M. Theresa, S.V. Bharathi, A survey on CAD technique for various abnormality classification in chest radiography. R. J. Pharmaceutical Biol. Chem. Sci. 7(4) (2016) 2. http://www.radiologyinfo.org/en/info.cfm?pg=chestrad 3. http://radiologymasterclass.co.uk 4. B. van Ginneken, R.H. Philipsen, L. Hogeweg, P. Maduskar, J.C. Melendez, C.I. Sánchez, R. Maane, B. dei Alorse, U. d’Alessandro, I.M. Adetifa, Automated scoring of chest radiographs for tuberculosis prevalence surveys: a combined approach, in Fifth International Workshop on Pulmonary Image Analysis, pp. 9–19. 2013 5. B. Van Ginneken, B.M. ter Haar Romeny, Automatic segmentation of lung fields in chest radiographs. Med. Phys. 27(10), 2445–2455 (2000) 6. S. Candemir, S. Jaeger, K. Palaniappan, S. Antani, G. Thoma, Graph-cut based automatic lung boundary detection in chest radiographs, in IEEE Healthcare Technology Conference: Translational Engineering in Health and Medicine, pp. 31–34. 2012 7. W.S.H.M.W. Ahmad, M.F. Fauzi, W.M.A. Zaki, Abnormality detection for infection and fluid cases in chest radiograph, in Electronics Symposium (IES), 2015 International. IEEE, pp. 62– 67, 2015 8. Jaeger S, Candemir S, Antani S, Wáng YX, Lu PX, Thoma G, Two public chest Xray datasets for computer-aided screening of pulmonary diseases. Quant. Imaging Med. Surg 4(6), 475–477 9. A.B. Rakesh, A.R. Mohan, Int. J. Adv. Res. Comput. Sci. Softw. Eng. 10 (2013) 10. Q. Wang, R.K. Ward, Fast image/video contrast enhancement based on weighted thresholded histogram equalization. IEEE Trans. Consum. Electron. 53(2), 757–764 (2007) 11. S. Guo, B. Fe, A minimal path searching approach for active shape model (ASM)-based segmentation of the lung, in Proceedings of SPIE 7259 (2009)

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12. F. Albregtsen, Statistical texture measures computed from gray level coocurrence matrices. Image processing laboratory, department of informatics, university of oslo 5 (2008) 13. C.J. MacLeod, P.A. Tyler, P.A. Tyler, C.L. Walker (eds.), Tectonic, magmatic, hydrothermal and biological segmentation of midocean ridges. Geological Society of London, 1996 14. G. Amayeh, A. Tavakkoli, G. Bebis, Accurate and efficient computation of gabor features in realtime applications, in International Symposium on Visual Computing, pp. 243–252. Springer Berlin Heidelberg, 2009 15. Y. Tang, Deep learning using linear support vector machines, in 2016 International Conference on Engineering and Technology (ICET) vol. 4. arXiv preprint arXiv:1306.0239 (2013)

Deep Learning Based Real-Time Object Detection on Jetson Nano Embedded GPU Pardha Saradhi Mittapalli, M. R. N. Tagore, Pulagam Ammi Reddy, Giri Babu Kande, and Y. Mallikarjuna Reddy

Abstract Object classification, detection, and tracking have been regarded as the most difficult problem domains for the last two decades. This area of research is important in a variety of sectors, including anomaly detection in medical imaging, driving in autonomous vehicles, biometrics, anomaly detection, and so on. The model accuracy has become very important parameter in computer vision. The rise of edge computing has opened the door to a plethora of exciting and complex applications in computer vision. An ‘edge AI’-based implementation for object detection persuading from deep convolutional networks SSD Mobilenet, SSD Inception V3 using embedded GPU platform Jetson Nano is proposed. This research demonstrates that the NVIDIA Jetson is a low-power embedded computing device suited to accelerate deep learning applications. When compared to the other two models, the experimental findings reveal that the SSD Inception V3 model delivers the maximum accuracy. Keywords Embedded GPU · MobileNet · Inception

1 Introduction The basic idea of object detection is simple, but there are many influential factors which make object detection a real challenge. The major among them is variety of application perspectives, variability among objects like different colors, textures and shapes, illumination changes, occlusions, presence of shadows, and image noise. Equally important challenge is limitations of computational infrastructure.

P. S. Mittapalli · M. R. N. Tagore (B) · P. A. Reddy · G. B. Kande · Y. M. Reddy Department of Electronics and Communication Engineering, Vasireddy Venkatadri Institute of Technology, Nambur, Guntur, Andra Pradesh, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Biswas et al. (eds.), Microelectronics, Circuits and Systems, Lecture Notes in Electrical Engineering 976, https://doi.org/10.1007/978-981-99-0412-9_46

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1.1 Literature Review Object detection in deep learning refers to classifying various things in a given image with precise classifications and surrounding each object with a bounding box. In the early 2000s, academics were looking for data, which is no longer the case because data is available today. NVIDIA has produced unique computing devices that are based on Compute Unified Device Architecture (CUDA) and have an underlying deep learning framework called cuDNN (CUDA deep learning neural network library). CUDA [1] GPU’s are typically built upon 1000’s of cores exceptionally suitable for neural network training. In an implementation [2], the performance development kits of Jetson Nano with Jetson TX2 are compared. Per this approach, when tested on a variable image, TX2 is unquestionably faster. The extensive utilization of deep convolutional neural networks (CNNs) is primarily due to advancements in multicore architectures [3] in computer vision applications. R-CNN (Region-proposal CNN belongs to a two-stage objection method which has two different networks, namely, region proposing network and classification network. The R-CNN which is proposed in 2014 [4] can be considered as path breaking system in object detection challenge. This system successfully uses CNNs and achieved a mAP of 66%. Though this method has provided better accuracy over previous methods, it has suffered with poor efficiency. These problems are well addressed in SPPnet [5] in the very next year. The last convolution layer is succeeded by spatial pyramid pooling. The SPPnet is responsible for feature extraction at once from entire image. Single Shot Detector (SSD) and You Only Look Once (YOLO) are the popular one stage methods [6–8]. YOLOV1 is simple yet innovative method [6] where the input is complete image and output is an image with bounding box around object. This method is an instant success where the speed of detection has touched 45 fps with few background errors. To improve the model, Redmon has proposed YOLOV2 [7] and YOLOV3 [8] in the years 2016 and 2018. The YOLOV2 has 19 convolutional layers and five pooling layers aided by batch normalization. After removing dropout mechanism, multiscale training is employed. This makes YOLOV2 more accurate compared to YOLOV1, but it produces high overlap in tracing small targets. The YOLO V3 is considered to be most stable object detection with high speed and accuracy. The single labels in earlier versions are replaced by multi-label classification. The fundamental idea in SSD is regression and usage of anchor box. The Single Shot Detector (SSD) model [9] is developed in the year 2016 by integrating the YOLO and R-CNN. For better accuracy the SSD relies on bottom and high level features. The model has VGG with last two fully connected layers discarded. When tested on VOC dataset, the SSD recorded 74.3% of mAP. In addition to confidence scores. Finding an ubiquitous solution [10, 11] for autonomous driving is really complex if not impossible due to several factors such as presence of roads with no markings, ill-defined road edges. The deep learning models cannot contemplate tracking as the object detection has to be done first [12]. An MOT stands for multiple object tracking, usually very complex when compared to a single object system but has a lot of practical usage [13, 14]. This method uses piecewise linear interpolation to successfully mitigate

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Table 1 Description of SDK tools NVIDIA JETPACK S. No

Module

Description

1

TensorRT

High-end SDK that provides deep learning inference

2

CUDA

Toolkit for C and C++ developers suitable for GPU-accelerated applications

3

cuDNN

Deep learning library for GPU acceleration

4

Multimedia API

Library for video encoding

5

Computer vision

Deep learning library for computer vision

the high variance problem, resulting in a naturally summarized series of frames. In order to speed up the training process Luo et al. [15] have proposed FPGAs with the help of defect classification. Simple image processing techniques, machine learning techniques, and deep learning approaches can be used to solve computer vision challenges. Some benchmark databases [16, 17] used in deep learning are Caltech, KITTI, VOC, COCO, IMAGENET, FAT, and V6.

1.2 NVIDIA Jetpack (Jetson Nano Modules) NVIDIA offers its latest edge computing device Jetson Nano for AI and machine learning applications. Additionally, a software module known as JETPACK that includes samples, many libraries, operating system images, APIs, and documentation is also provided by NVIDIA. These modules and developer tools are listed in Table 1. The prime motto of present work is to analyze three models that are going to be implanted on JETSON NANO computer. The results will be well correlated in order to evaluate suitability of the models in autonomous systems. The remaining part of the paper is organized as follows: Sect. 2 provides brief descriptions of models and materials, the results of the present implementation are provided in Sect. 3, and concluding remarks are given in Sect. 4.

2 Devices, Models, and Materials 2.1 The Device All the object detection models are implemented in JETSON NANO device shown in Fig. 1. It is small yet powerful GPU machine that can be used to train models with extremely huge dataset.

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Fig. 1 Jetson nano device (This is an integrated accelerator hardware, which is widely used with automatic learning algorithms)

Table 2 Key features of Jetson nano Features

Descriptions

Size

Miniatured in size with dimensions of 69.6 mm × 45 mm

Processor

A 4-core ARM Cortex-A57 CPU (capable of providing 472 gigaflops). Builtwith Maxwell GPU of 128 CUDA cores

Memory

Available in 2 GB/4 GB of RAM, 16 GB of storage

Ports

Has 4/1 USB/HDMI ports, supports 1/2 camera modules

AI frameworks

TensorFlow, PyTorch, Caffe, Keras, MXNet, etc

This device can be used to perform parallel execution of many machine learning algorithms pertaining to image segmentation, image classification, regression, video processing, object detection, and speech processing. The key features of Jetson Nano are listed below in Table 2. Essentially, the device is light weight and consumes less power.

2.2 Materials The two datasets of 1209 RGB 3D depth images captured by oneplus six phone, and CCTV are used for training, the data augmentation [19] is used to generate sufficient images for training. The first dataset is of 750 images (4608 × 3456) images encloses images of different traffic scenarios captured under different lighting conditions. The second dataset is of 459 (1280 × 70) images having routine human activities.

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2.3 The Method Figure 2 depicts the features of the suggested object detection approach. It demonstrates that the process is divided into two stages: the training stage and the detecting stage. The steps for both stages are organized as follows, with three major stages: (i) input, (ii) training, and (iii) detection or output. The popularization of ImageNet[20], the CNN’s have become the underlying architecture to implement image net projects and to identify solutions to computer vision problems. TensorRT on GPU will provide a high-performance environment for Jetson Nano. It is specifically developed to accelerate the training of CNNs on GPUs. By optimizing and merging the layers, TensorRT improves latency and performance. CUDA Deep Neural Network (cuDNN) is a GPU [21] accelerated library developed by NVIDIA to enhance system performance for deep convolutional network implementations. This library helps in accelerating the implementations such as convolution blocks, batch normalization, activations, optimizations, and pooling layers. For the current work, three CNN models—Single Shot Detector MobileNet V1, Single Shot Detector MobileNet V2, and Single Shot Detector Inception V3— are used as indicated in Fig. 3 Perhaps the Single Shot Detector (SSD) model uses the information [22] to speed up the network. The MobileNet [23] uses depth-wise separable convolutions.

Fig. 2 Methodology

Fig. 3 llustration of the proposed method implemented on Jetson Nano onboard GPU

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3 Experiments, Results, and Discussions The three deep learning models are examined after conducting many experiments on Jetson Nano onboard GPU for the target detection. All the models utilize the TensorFlow and Keras libraries using Python. The deep learning object detection networks are optimized to run on NVIDIA GPU to run kernels.

3.1 Training the Model The three models—SSD MobileNetV1, SSD MobileNetV2, and SSD InceptionV3— are trained with pre-trained weights from COCO [24], will be deployed in Jetson Nano for object detection. Out of 1206 images acquired 1100 image are used for training the model. For training purpose a computer with the following specifications: i7 9700 3.0Ghz Processor@3 GHz, 32 GB RAM, 240 GB SSD, and 4 GB NVIDIA GRAPHIC CARD, is used. To optimize the loss function, ADAM optimizer is used with learning rate 0.005 and batch size 2. The average training duration is about 7 h for each model. Each model is trained for 200 epochs.

3.2 Metrics for Evaluation The competence of the object detection method is further enhanced by training and testing with 22 randomly selected synthetic images with annotations acquired from FAT dataset [25]. The actual FAT has about 60 k annotated synthetic images. These 22 images have nearly 212 annotated instances. The presence of large number of object instances in every image leads to effective training and testing. The models are evaluated with key parameters like average precision, recall, and accuracy, thereby proving robustness of the proposed method. The evaluation of these parameters need following indices: False Negative (FN), False Positive (FP), True Negative (TN), and True Positive (TP). Three lesion-based measures viz., accuracy, sensitivity, and specificity, are determined by using the above indices, thereby proving robustness of the proposed method. The accuracy is an indicator of the fraction of all correctly bounded pixels, whereas precision indicates accurate rate indication of true positives, and recall is an indicator of reliability: Accuracy(A)=

TP + TN TP + FP + TN + FN

(1)

TP TP + FP

(2)

Precision(P)=

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Recall(R)=

TP TP + FN

517

(3)

3.3 Results and Discussions Object detection research has made significant progress in solving several difficult challenges. The authors are obligated in the current work to analyze three key object detection algorithms, namely, SSD Inception V3, SSD MobileNet V1, and SSD MobileNet V2, to identify human faces and moving automobiles on highways on the Jetson Nano Platform while doing performance analysis. Two databases are employed for this purpose. The TensorFlow and Keras APIs are used to build the prototype models, and the prototype model is built utilizing Python programming. Following extensive testing with private datasets (datasets 1 and 2) and FAT (dataset 3). Frames per second are also computed to assess the viability of the current arrangement for real-time emergency applications. Figure 4 shows the object detection findings, while Table 2 shows the performance metrics acquired. Figure 4a depicts five cars, four of which are visible while the fifth is nearly invisible. In its object detection output, the SSD Inception V3 may also select the fifth automobile. At the same time, as seen in Fig. 4d, SSD MobileNet V2 is less accurate than the inception network. Table 3 shows that the Inception V3 model performs best in terms of accuracy, precision, and recall. In the inception model, ordinary convolution is used; however, in the MobileNet model, depth-wise separable convolution blocks are used which makes MobileNet lightweight model in terms of weights. However, as seen in Table 2, model accuracy will be diminished. To determine the resilience of these models running on Jetson Nano, more experimentation is carried out to establish the impact of the number of training cases.. For this purpose, we have selected image with only fewer object instances to train three designated models (Fig. 5). All the three models are trained purposefully with images having less number of annotations. After training, the three models are validated in terms of average object instances detected. It is discovered that 130 instances are picked up by these models when trained with only 42 instances. While conducting this experiment, the number of training instances are initially taken as 10 and then increased to 20, 30, 40 and to 70. As indicated in Fig. 6, very interesting revelation is found. When the training instances are increased to 40, the accuracy of detection attained maximum. Finally, the results of the proposed work are compared with few state of the art methods as tabulated in Table 4.

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Fig. 4 Results of SSD inception V3 and SSD MobileNet V2. Here, a and c are original images, and b and d are object detection results of SSD inception V3 and SSD MobileNet V2, respectively

Table 3 Results on Jetson nano Parameter A

SSD MobileNet V1

SSD MobileNet V2

SSD inception V3

DataSet 1, 2

DataSet 3

DataSet 1, 2

DataSet 3

DataSet 1, 2

DataSet 3

82.51

78.11

91.12

90.23

96.5

89.02

P

81.23

92.03

88.66

90.21

88

87.23

R

62.56

58.11

79.12

73.87

80.79

78.99

FPS

2.33

4.12

4.56

4 Conclusions Object recognition is a major subject in computer vision, and it has received a lot of attention from researchers. Nonetheless, the majority of research is conducted on

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Fig. 5 Results of human detection. a original image, b results of SSD MobileNet V1

Fig. 6 Effect of number of training instances on the predicted objects Table 4 Result comparision Method

Model

Dataset

Average precision

Girschik et al. [26]

VGG 16

VOC 7 (Traina and test)

59

Redmon et al. [27]

DARKNET 53

VOC 7 (Train) VOC12 (Train and test)

70.7

Girschik et al. [28]

VGG 16

VOC 7 (Train) VOC12 (Train and test)

66.9

Liu et al. [29]

RESNET 101

VOC 7 (Train) VOC12 (Train and test)

79

Pramanik et al. [30]

G-Alexnet

VOC 7 (Train) VOC12 (Train and test)

81

Proposed Jetson Nano system

Mobilenet V2

Private (Train and test) FAT (Train and test)

89

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costly computing environments, and many solutions have been developed. Our first effort is to be able to use a dataset of images of rural roads. This dataset is acquired under versatile lighting and environmental conditions consisting of automobiles, humans, and buildings. The three object detection models are validated using these datasets, and found that performance of object detection models embedded on Jetson is consistent. The NVIDIA Jetson Nano is very competent as high-performance hardware, while also supporting the research with low cost. According to the results of the tests, Jetson Nano has a good performance and allows the implementation of complicated models to create a wide range of applications. Furthermore, Jetson Nano is regularly upgraded in an efficient and practical manner with new deep neural network libraries with the goal of boosting the performance of computer vision workloads.

References 1. A. HajiRassouliha, A.J. Taberner, M.P. Nash, P.M. Nielsen, Suitability of recent hardware accelerators (DSPs, FPGAs, and GPUs) for computer vision and image processing algorithms. Signal Process. Image Commun. 68, 101–119 (2018) 2. A. Basulto-Lantsova, J. Padilla-Medina, F. Perez-Pinal, A. Barranco-Gutierrez, Performance comparative of OpenCV template matching method on Jetson TX2 and Jetson Nano developer kits, in Proceedings of the 2020 10th Annual Computing and Communication Workshop and Conference (CCWC), pp. 0812–0816 (January 2020). 3. A. Mauri, R. Khemmar, B. Decoux, N. Ragot, R. Rossi, R. Trabelsi, R. Boutteau, J. Ertaud, X. Savatier, Deep learning for real-time 3D multi-object detection, localisation, and tracking: application to smart mobility. Sensors 20(2), pp. 532 (2020) 4. S. Ren, K. He, R. Girshick, J. Sun, Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. vol. 39, pp. 1137–1149 (2017) 5. K.M. He, X.Y. Zhang, S.Q. Ren, J. Sun, Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. vol. 37, pp. 1904–1916 (2015) 6. J. Redmon, S. Divvala, R. Grishick, A. Farhadi, You only look once: unified, real-time object detection, in Computer Vision and Pattern Recognition. Las Vegas, pp. 779–788 (2016) 7. J. Redmon, A. Farhadi, YOLO9000: better, faster, stronger, in Computer Vision and Pattern Recognition. Hawaii., pp. 7263–7271 (2017) 8. J. Redmon, A. Farhadi, Yolov3: an incremental improvement. arXiv: Computer Vision, (2018) 9. W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Fu, A.C. Berg, Ssd: single shot multibox detector, in Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands, 8–16, Springer: Cham, Switzerland, pp. 21–37 (2016) 10. G. Krasner, E. Katz, Automatic parking identification and vehicle guidance with road awareness, in Proceedings of the 2016 IEEE International Conference on the Science of Electrical Engineering (ICSEE), Eilat, Israel, 16–18, pp. 1–5 (2016) 11. M. Heimberger, J. Horgan, C. Hughes, J. McDonald, S. Yogamani, Computer vision in automated parking systems: design, implementation and challenges. Image Vis. Comput., vol. 68, pp. 88–101 (2017) 12. Y. Seo, R. Rajkumar, Detection and tracking of boundary of unmarked roads, in Proceedings of the 17th International Conference on Information Fusion (FUSION), Salamanca, Spain, pp. 1–6 (2014) 13. N. Wang, D-Y Yeung, Learning a deep compact image representation for visual tracking, in Advances in neural information processing systems, pp 809–817 (2013)

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Density-Based Scanning to Provide Effective Medical Emergency System Sai Jyothi Bolla, C. M. Suvarna Varma, and G. Shireesha

Abstract Even though our ambulance management services are effective in India to provide emergency services, things are manual, and there is still a need for technology involvement. When someone meets with a major accident in a remote area or frequently accident-prone areas, still we need someone to identify the scene and dial for the services, or the scene is identified at a later stage, we end up losing a precious life in spite of emergency arrival. Keeping such problems in purview and assuming signal points every at k-distance, the density points are searched for unusual image captured by GPS and matches such images with EXIF data and alerts the services without any manual dialing. To perform this, we introduced a DBS_PM algorithm, where it has two phases with first being density-based clustering, where we identify the density points, e.g. “accident prone areas” and second phase, we check for unusual images of GPS at real time with EXIF data and nearby signal point can send immediate alert to emergency services. It is a novel idea of using DBSCAN clustering with GPS images and can also use a microcontroller at signal points for the alerts. Keywords Density-based clustering · EXIF data · GPS images · DBSCAN · Microcontroller

S. J. Bolla (B) · C. M. S. Varma · G. Shireesha Vasireddy Venkatadri Institute of Technology, Guntur, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Biswas et al. (eds.), Microelectronics, Circuits and Systems, Lecture Notes in Electrical Engineering 976, https://doi.org/10.1007/978-981-99-0412-9_47

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1 Introduction The medical emergency or ambulance management has been supporting with a vision and building capability to respond and save number of lives. While transporting the victim from the scene, premedical care can be given to stabilize before the victim reaches the hospital. However, the above is possible only when a dial or alternate helplines are given an alert message by the person near the accident scene. If the victim is not identified by anyone for long time being a remote area, the victim might lose his precious life. The focus of such management is only in increasing the services to tribal areas/others but not on automation. Identification of the accident scene irrespective of dialing is very important. Using various resources of road management like traffic signal points, GPS tracks, and it can be made possible to identify the victim scene without a manual dialing. An integration of technologies like GPS, signals, videos, and sensors are a very costly affair and quite challenging task. Clustering algorithms can be used to identify density and image matching algorithms can match the scene/image with existing data to predict the real time scene and economical microcontrollers can be used to alert the services. Researchers are working with various algorithms on such integrated technologies to bring an optimal solution for similar kind of applications and there has been minimal initiation on 108 services. Therefore, an attempt of such economical and optimal solution is made using the existing system with new methods to make this possible in a complete automatic real time and effective emergency management.

1.1 Density-Based Algorithms Density-based algorithms are used for unsupervised learning methods where we are unaware of distinct groups and nonlinear in nature. We have DBSCAN [3, 5], DENCLUE [4] algorithms that are used based on density of neighborhood objects or a density function. DBSCAN algorithm is a density algorithm used for nonlinear shape structure or a planar space and is based on the density neighborhood, and it is widely used density-based algorithm in machine learning, and especially to identify outliers and work on data with non-discrete points. A Density-Based Spatial Clustering of Applications with Noise (DBSCAN) works on the assumption that clusters are dense regions in space that are separated by spaces/regions with low densities. It groups such “densely populated data points” into a group and works on very large datasets to identify such distinct groups. Moreover, it doesn’t need the knowledge of number of clusters to be made like K-means & Hierarchical clustering. It uses two parameters minimum_points, required to classify a core point and the range_epsilon, to check the density of each data point for distinguishing clusters. It is also effective because of n-dimensionality and robust noise detection.

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1.2 Image Matching Algorithms GPS is a global positioning system that captures images at real time and the coordinates are noted. EXIF data (Exchangeable image file format) is a metadata that contains millions of images of all categories. Using the help of these images we can process the images for detecting unusual images. There are many image matching algorithms to locate the image and identify keypoints like image matching, scene/object detections, few such algorithms are SURF [6], ORB [8], SIFT [7, 9], etc. SIFT is a Scale Invariant Feature Transform, image matching algorithm that describes or detects and matches local features in images and this technique is very useful in image processing to find the existence of a new image with a source image in metadata. In our proposal, we can (1) Find the density data points that are accident prone; (2) Find the density data points of critical time; (3) Analyze the unusual images of GPS on such data points with EXIF data; and (4) Alert the nearest signal to dial emergency.

2 Literature Review The related works of various authors in density-based clustering are compared with its comparative algorithms from [3–5], also compared various image matching algorithms [6–9] in Table 1. And regarding the application of GPS tracking and ambulance services, the previous works of authors [1, 2] are compared for supporting our proposal. Table 1 lists various density based and image matching algorithms and the application used and the problems in the system.

3 Proposed System 3.1 Problem Statement To identify an accident or any unusual scenes at the places that are threatful, remote and not regular mobile areas, our existing system still uses manual support and there is need of automation to identify such accident and auto-alert to the emergency services. Previous problems in this application focused on traffic signaling [1] and ambulance diversion [2], but not on the above discussed problem. Our proposal uses density-based algorithms to identify the most accident-prone, remote, highthreatening areas, and to automate this, we need three concepts: firstly, to identify the above-mentioned areas and critical times; and secondly, identify any unusual

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Table 1 Literature review of algorithms and applications used Title

Description

Problems

Density based traffic signal system [1]

The traffic signal timing is dynamic and automated on sensing the traffic density at junctions using image processing and density based scanning

Density-based scan is done on the pictures and converts them to gray scale and sensors used to work on it, the response time is not quick and needs fast algorithm

GPS tracking system coupled with image processing in traffic signals to enhance life security [2]

Tracking and calculating position of ambulance from signal using image capturing technology

Only GPS track and image conversion can be done, but predicting ambulance calculation is not efficient with this method

Fast density-based clustering with R [3]

The Fast DBSCAN using OPTICS algorithm, it uses spatial indexing to speed up computation in R. Various comparisons are made using different tools for experimental results

OPTICS is an extension of DSCAN and is computationally fast but doesn’t assign cluster memberships and stores in order of points processed, which might not be very useful to applications used in [1, 2]

DENCLUE-IM: a new approach for big data clustering (Hazar et al., 2016)

To work on very large databases, with heterogeneous data and complex data, clustering is useful. DENCLUE-IM is one such clustering algorithm that uses density and also hill climbing concept to get efficient results

DENCLUE is an extension of density based clustering that uses hill climbing concept and is fast compared to DBSCAN, but it is very complex because it uses distribution functions

ORB-SLAM: a versatile and accurate monocular SLAM system [Rual et al., 2015]

ORB-SLAM is a feature-based monocular SLAM system that works on indoor-outdoor, small-large and real-time environments. It uses loop closing and relocalization techniques, the results compared to traditional monocular SLAM, ORB-SLAM is very effective

ORB is fast and robust local feature detector. Unlike SIFT, it is a partial scale invariant. The keypoints are more efficient compared to SURF and SIFT algorithms

G-DBSCAN: A GPU accelerated algorithm for density-based clustering [5]

G-DBScan is a Graphics Processing Unit based DBSCAN uses data indexing and parallelization opportunities

G-DBSCAN is faster than sequential systems but not very effective parallelization method because of its complexity in dealing GPUs (continued)

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Table 1 (continued) Title

Description

Problems

A comparative study of SIFT and its variants [9]

SIFT is compared to variants of SIFT like PCA-SIFT, GSIFT, CSIFT, SURF, and ASIFT. Of all SURF performs the worst but runs faster whereas, CSIFT is very effective under scaling and rotation change, GSIFT is best for blur images, ASIFT, and PCA-SIFT affine changes

A comparison study of various SIFT varieties are made, and SURF proves to be performing worse but runs faster compared to SIFT

SIFT flow: dense correspondence across scenes and its applications [7]

The SIFT-flow algorithm has matching samples that are dense and compares two images in preserving spatial discontinuities. Of all computer vision algorithms, SIFT is efficient for pattern matching

SIFT works on very large databases and be it image synthesis or object transfer, satellite images to face recognition, the SIFT -flow is effective image matching algorithm, but it is complex as the data key points area as compared to SURF and finds difficulty in descriptor featuring if keypoints are deviated

Speeded-up robust features (SURF), [6]

SURF works on robust features and is a fast algorithm and uses invariant detectors for rotation, scaling features

SURF uses Hessian matrix based measure for detector and distribution based descriptor, even though complexity is less, SIFT still performs better

scenes in such areas and match to the metadata; and finally, a match identification, alerts to the services automatically. The existing systems used RF Transmitter–Receiver modules at every identified area, which is very expensive affair to the govt, and it is only to identify the image and not the alerts because alert again needs a pattern matching setup to be done. So, the alternate method is to replace Transmitter–Receiver with GPS system and make use of metadata like EXIF data. The usage of the GPS system is not only economical, but also solves the distance constraints, and last but not the least is the usage of sensors is another expensive way of improving efficiency to algorithms, we can also avoid such sensors with a simple microcontroller just to alert the services. So, our problem solution not only works on the problem but also the economic factors. For the identified problem, we use density-based scan DBSCAN algorithm for the first step, secondly use a SIFT algorithm for pattern image matching, and finally a microcontroller for alerting automatically.

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3.2 Proposed Methodology Our proposed method as explained in the problem Sect. 3.1, is set in three actions. Firstly, whenever an accident occurs or any scene of murder or any image which is unusual, the GPS tracks the images in a regular period of time and compares these images with the metadata available with Google maps or similar metadata that captures images that are millions and zillions in number. To perform these steps, at stage 1 (see Fig. 1), a density-based scanning is done to identify places which need a GPS track to avoid too many scans and tracks, the density scan is done by finding the core points, border points, and noise points for a given minimum no. of points in an epsilon range (see Fig. 3). In the figure, the min_points are taken as 4 points and within an epsilon range. If the point in the epsilon range meets all 4 points, then the point is a core point and if the point is in epsilon range and doesn’t meet minimum required points, then it is a border point and the remaining are noise points that are not dense or useful. Secondly, when any image in these density areas looks suspicious, immediately the image is compared and matched to already existing metadata in stage 2 of the figure using image matching algorithm called Scale invariant feature Transform algorithm which helps in matching the key points or local features in the image that is shared by GPS. In step 3, the keypoints, scales, and octaves identify the pattern and confirms if the reference is unusual, and is signaled to microcontroller, and immediately the microcontroller alerts the emergency services in the final step.

Fig. 1 DBS_PM system architecture

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The system flow (see Fig. 2) of DBS_PM clearly explains the behavior of both density-based algorithm, where the data points are found, and density-based scanning is performed and on those data points, how a GPS matches the image of data point through EXIF data for image matching using SIFT algorithm and on pattern matching, the microcontroller alerts the nearest signal point and alarms emergency services (Fig. 3). The above steps are divided into two phases: where the density-based scanning is done and image is sent to the metadata for comparison is step 1, and this phase is called DBS, density-based scanning phase. The input for this phase is data points, GPS

Data points based on accident areas

Find Density Data points

DBSCAN

Data points based on critical time

Density Based Scanning

Fig. 2 DBS_PM system flow

Fig. 3 DBS_core_border_noise

GPS

Nearest Signal Point

SIFT

EXIFDATA

Image Matching

Micro controller

ALERT EMERGEN CY

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Fig. 4 PM_octaves and scaling process

provides with locations (data points), and with the core point and epsilon measure, the points are dense based on data points at accident areas or the data points at crucial times we identify the clusters that are dense. And in the second phase (see Fig. 2), this takes the reference image which is unusual and converts this image into octaves and scales as pattern matching (see Fig. 4), it uses Guassian functions and produces key points with difference and it is the Difference of this Guassian function with the compared image and subsequently it is further converted to a description vector generator and this helps the image in metadata finds the match of unusual image by GPS and immediately send the alert using microcontroller. The nearest signal point is any near by traffic signal point that has a microcontroller which has “alert emergency” on pattern matching. The second phase is called PM phase, pattern matching phase and our proposed model is DBS_PMalgorithm density-based scanned pattern matching algorithm. The microcontroller can be an economical device instead of an expensive sensor, to avoid too much of data to capture every now and then by a sensor but a control where it alerts when the algorithm finds a pattern matching. The first phase is called DBS algorithm, the second phase is called the PM algorithm, and the whole procedure of the two phases is collectively called the DBS_PM algorithm. Proposed Algorithm The proposed measure and minimum data points as input and process the densitybased scan and identifies data points and their unusual images. The image will be the output and input to the second phase. In second phase, this image is compared to exif data by using SIFT method of pattern matching and on match, to alert the emergency service using microcontroller.

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Phase 1: DBS algorithm Input: GPS_dataset (GD), epsilon_measure (ep), min_data_points For every unvisited point x in GD, set P → Visited Nb(x) → Get Neighbours of x in epsilon ep range Classify x as core, border, noise datapoints Cl → 0 /* no.of clusters Cl/* For every x e GD, x is core and not visited do Create new cluster Cl = Cl + 1 ExpandCl (x, Cl) Add x to cl Return Cluster {GDi }cl , where GDi = { x e GDi , x has label i} i=0 ref_img → capture image in x datapoint For every y e Nb(x) do Add y to current cluster cl If y is core_datapoint then Expand Cl(y,cl)

Phase 2: PM algorithm Input: ref_img, exif_data Feature detection in exif_data for ref_img Feature matching using guassian method with difference of scaling & octaves of both images Find the key points Transform the model for estimation from the Difference_of_guassian Image is resampled and transformed by descriptor vector generation If ref_image matches exif_data_image then Alert Emergency services

4 Experimental Results The landmarks of south Indian state Andhra Pradesh, Guntur district is considered for testing, and the comparisons of SIFT, SURF, and ORB are compared on accuracy levels with respect to landmarks using these algorithms. SIFT shows significant accuracy in the Fig. 5, and next to it is SURF and ORB is less accurate. The density is estimated for landmarks plotted at dataspace and identified, and the clusters for the estimated density at epsilon measure. (See Fig. 6), the landmarks of Guntur districts are considered in the data space and corresponding density estimation curve is estimated to the density, based on the estimate and epsilon measure, clusters are formed.

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Fig. 5 Accuracy comparison of SIFT, SURF, ORB on landmarks

Fig. 6 Density-based clustering on landmark density estimate

Each cluster of different regions in Guntur districts are mentioned in different shapes (See Fig. 6), the cluster 1 is mentioned as circle, the second cluster with rectangle, the third cluster as triangles, and the remaining areas where clusters are not formed are marked as diamonds showing the presence of noise.

5 Conclusion We proposed a concept of density-based clustering to estimate the dense points and corresponding unusual images of GPS track is compared to SIFT algorithm of EXIF

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data and using a microcontroller, the alert is sent to emergency services. Various proposals are in this vertical, yet this is very economical and efficient algorithm as SIFT is powerful among the image matching algorithms, and performance speed is very high and density-based algorithm finds clusters which are very efficient in the given measures. The experiments suggests that the scene captured or landmark identified using DBS_PM algorithm is useful and helps in saving lives during emergencies compared to previous application researches.

References 1. K. Vidhya, A. Bazila Banu, Density based traffic signal system 3(3), 2319–8753(O), 2347– 6710(P) (2014) 2. K.M. Prabhakar, S.M. Kumar, GPS tracking system coupled with image processing in traffic signals to enhance life security 5(4) (2013) 3. M. Hansler, M. Piekenbrock, D. Doran, dbScan: fast density-based clustering with R, 91(1) (2019) 4. H. Rehioui, A. Idrissi, M. Abourezq, F. Zegrari, DENCLUE_IM: a new approach for big data clustering, 83, 560–567 (2016) 5. G.Andrade, G. Ramos, D. Madeira, R. Sachetto, R. Ferreira, L. Rocha, G-DBSCAN: a GPU accelerated algorithm for density-based clustering 18, 369–378 (2013) 6. H. Bay, E.S.S. Andreas, T. Tuytelaars, L. Van Gool, Speeded-up robust features (SURF). Comput. Vision Image Understanding 110, 346–359 (2008) 7. C. Liu, J. Yuen, A. Torralba, SIFT flow: dense correspondence across scenes and its applications, pattern analysis and machine intelligence, 978–994 (2010) 8. R. Mur-Artal, J.M.M. Montiel, D.J. Tardos, Member, IEEE, ORB-SCAM: a versatile and accurate monocular SCAM system (2015) 9. J. Wu, Z. Kui, S.S. Victor, P.P. Zhao, D. Su, S. Gong, A comparative study of SIFT and its variants 13(3) (2013)

Phytochemicals as an Active Pharmaceutical Ingredient of Ocimum Sanctum and Azadirachta Indica: A Theoretical Screening Study Sourav Patanayak, Grishma Ninave, Moumita Mukherjee, Jayanta Mukhopadhyay, V. Ragavendran, B. B. Paira, Sukhendu Samajdar, Saumya Dasgupta, Debosreeta Bose, and Madhumita Mukhopadhyay Abstract Plants, also known as Phyto, are the most abundant source of medications in traditional medicine, contemporary medicine, nutraceuticals, pharmaceutical intermediates, dietary supplements, and artificial synthesis reagents. Medicinal plants are a great source of nutrition and are bestowed by nature, and their variability is diverse in different parts of the world. Alkaloids, tannins, flavonoids, and phenolic chemicals are plants’ most significant chemically active (biologically active) S. Patanayak · G. Ninave · S. Dasgupta (B) · D. Bose (B) Department of Chemistry, Amity Institute of Applied Sciences, Amity University, Kolkata 700156, India e-mail: [email protected] D. Bose e-mail: [email protected] S. Patanayak e-mail: [email protected] M. Mukherjee Department of Physics, Adamas University, Kolkata, West Bengal 700126, India e-mail: [email protected] J. Mukhopadhyay Energy Materials and Devices Division, CSIR-Central Glass and Ceramic Research Institute, Kolkata, India e-mail: [email protected] V. Ragavendran Department of Physics, Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya, Kanchipuram, Tamilnadu 631561, India B. B. Paira Maulana Abul Kalam Azad University of Technology, BF—142, Sector—ISalt Lake City, Kolkata, West Bengal 700064, India S. Samajdar · M. Mukhopadhyay (B) Department of Materials Science and Technology, School of Applied Science and Technology, Maulana Abul Kalam Azad University of Technology (MAKAUT), Haringhata, West Bengal 741249, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Biswas et al. (eds.), Microelectronics, Circuits and Systems, Lecture Notes in Electrical Engineering 976, https://doi.org/10.1007/978-981-99-0412-9_48

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elements. Flavonoids, tannins, terpenoids, and saponins are termed phytochemicals and are present in most wild plants’ leaves and stems. A simple but exciting domestic plant is Ocimum sanctum, while alkaloids are absent in it. In comparison, Neem (Azadirachta indica) has a wide range of active pesticide compounds known as “triterpenes,” or more precisely, “limonoids.” The present research article aims at theoretical DFT analyzes of selected phytochemicals like Epicatechin and Quercetin to study their reactivity pattern. Compared to other known phytochemicals in neem and Tulsi, these two are selected based on FMO formalism. This is followed by their molecular docking analyzes to study the interaction with BSA and CDK2 protein. Detailed hydrophobic analyzes are reported using these phytochemicals which enable effective prescreening prior to experimentation to establish them as an active pharmaceutical ingredient. Keywords Phytochemical · Active pharmaceutical ingredient · Docking · Density functional theory

1 Introduction Phytochemicals are the chemicals produced by plants which are found abundantly in fruits, vegetables, beans, seeds, and other plant parts. They are known to influence the activity and responses of the plants effectively. Phytochemicals are also known to hold immense medicinal importance. In India, since ancient times, people have relied on plants products for many purposes, majorly for the prevention and cure of diseases. It has been found in recent years; it has been discovered that they are also effectively preventing normal cells from turning into cancerous cells. In addition to this, scientists have also mentioned that consumption of certain specific phytochemical-containing fruits and vegetables can reduce the risk of cancer by 40%. A few examples of such phytochemicals are carotenoids, resveratrol, polyphenols, and isothiocyanates [1]. Neem and Tulsi are well-known medicinal plants whose working mechanism needs to be understood well for tailoring their action and application as a drug. As per reports, experimental initiation on the basis of drug applicability could be screened using theoretical density functional theory (DFT) followed by molecular docking [2]. Regarding the experimental outcome, “nanotechnology is mainly concerned with the synthesis of nanoparticles of various sizes, shapes, chemical compositions, and controlled dispersity and their potential use for human benefits.” Although synthesis procedures effectively produce clean and specific nanoparticles, they are costly and possibly hazardous to the environment. In this regard, green synthesis using plantbased phytochemicals, microbes, etc., is environment friendly and advantageous over chemical synthesis for large-scale production [3, 4]. In the modern arena, plant sources are generally applied as a bio-reducing and capping agent to synthesize metal, metal oxide, or bimetallic nanoparticles. It is reported that the phytochemicals present

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within the plant sources are responsible for such activity. Furthermore, such a bioreducing process is an environment-friendly process and prevents the accumulation of nanoparticles by the self-capping process [5]. The present context intends to study the role of selective phytochemicals of Neem and Tulsi plants, respectively. DFT or density functional theory helps study ground-state geometries, FMO or frontier molecular orbital energy diagrams (HOMO and LUMO), and the electrostatic potential maps (EPM) of the given compounds. Ocimum sanctum (Tulsi) has been employed in Ayurveda for its many therapeutic effects for hundreds of years. Tulsi helps in the adaption of stress by harmonizing numerous processes in human body. Colds, headaches, stomach issues, inflammation, heart disease, poisoning, and malaria are all treated with Tulsi extracts in Ayurvedic medicine. O. sanctum is traditionally consumed in various forms, including herbal tea, dried powder, and fresh leaf. Tulsi leaves have been mingled with stored grains for millennia to keep insects away. Neem has been a popular plant due to its robust properties. It belongs to the family Meliaceae in the plant kingdom. It also has a large number of traditional uses. In addition, it has various benefits and it holds enormous economic importance. The oil extracted from the Neem plant repels pests, mosquitoes, termites, and moths. It also serves as an antioxidant and an anti-inflammatory agent. Neem is also effective against bacteria, viruses, and fungi. Due to its effectiveness against mosquitoes, it is also presumed to have anti-malarial properties. Both the plants are reported to have a chemical composition of a number of chemicals termed as phytochemicals which acts as a natural bio reductant and caping agent. In the present context, two phytochemicals are selected, viz., Epicatechin from Tulsi and Quercetin from Neem. The flora has been an essential part of human life because of its extensive uses as various drugs, antioxidants, pain killers, etc. Phytochemicals like Epicatechin and Quercetin are well known for their properties and applications in health, fitness, well-being, and sanitation, and hence are widely researched. This work aims to analyze the following phytochemicals using density functional theory along with the help of Gaussian software, which is applied for the Optimization, Frequency Calculations, HOMO–LUMO analysis, etc. Based on the performed analysis, calculations are made to obtain the energy differences between the HOMO and LUMO. The reactivity pattern of these two systems is analyzed in terms of DFT. On the basis of such study, molecular docking analysis of such phytochemicals is carried out initially using Bovine serum albumin (BSA). Since serum albumins are primary source of proteins responsible for circulatory system, they are initially selected as the model for studying protein interaction with the targeted system. In addition to BSA system, the targeted phytochemicals are docked with another set of protein kinase family, viz., CDK2. This is of the background that, kinase-based proteins are one of the important targets for cancer treatment since they could control various for certain important processes like progression of cell cycle, relocation, etc. Hence, the initial screening is undertaken for the selected phytochemicals with aforementioned two proteins.

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2 Computational Details The electrical structure of atoms, molecules, and solids may be calculated using density functional theory (DFT). Its purpose is to derive a quantitative knowledge of material qualities from fundamental quantum mechanics rules [6–9]. With the help of Gaussian 09 (a quantum chemical program), the calculations on the ground as well as excited states were carried out [10] The ground state geometry of Epicatechin and Quercetin are acquired through complete optimization of the structural parameters with the help of DFT employing 6-31G* basis set. Using Becke’s three parameter hybrid functional (B3), with nonlocal usage of Lee–Yang– Parr, LYP, (commonly referred to as B3LYP), the optimizations in the geometry of 2H3NTS were then implemented. The hydrophobic interaction among the phytochemicals and proteins was studied using, molecular docking was performed using Auto Dock tools 1.5.7 (Auto Dock version 4.2.6) following the method described earlier [11]. The results were visualized using Chimera. For molecular docking calculation, the structure of Epicatechin and Quercetin were studied using DFT employing B3LYP/6–311++ G(d,P) level of calculation. On the other hand, the 3D structures of BSA (PDB ID:4F5S) and C-reactive protein (PDB ID:1B38) were possessed from Protein Data Bank (www. rcsb.org). The initial blind docking was performed. This is followed for the ligand as conversion of pdb to pdbqt format. Finally, specific docking was performed at the binding region. The number of docking runs was set to 10.

3 Results and Discussion 3.1 Structural Optimization of Phytochemicals in Tulsi (Ocimum Sanctum) and Neem (Azadirachta Indica) Using DFT Epicatechin C15H14O6, has a (2R, 3R)–configuration. Epicatechin (flavon-3-ol monomer units) (Fig. 1) is a subclass of flavonoids known as flavanol monomers present in green tea, grape, and cocoa. It has a therapeutic function in treating cancer. Recent investigations suggest a modification of nitric oxide and reactive nitrogen species metabolism, which leads to the maintenance and enhancement of the endothelial function of arterial arteries [12, 13]. After optimization of the structure, the system is allowed to study regarding the bonding pattern. In Tulsi herb, generally, Catechin, Epicatechin, and Theaflavin bear drug like traits. A comparative FMO of these systems is given in Fig. 2. Figures 2 and 3 show the comparative reactivity pattern of three phytochemicals of Tulsi, viz., Catechin, Epicatechin, and Theaflavin in a particular range.

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Fig. 1 Optimized structure of Epicatechin, a phytochemical of Tulsi herb

Fig. 2 Comparative HOMO–LUMO diagrams of the phytochemicals of Tulsi (Ocimum sanctum) along with the energies of MOs and their differences

It could be seen from the figures, the Epicatechin is the reactive species with reported anti-inflammatory behavior. The electron distribution cloud in Theaflavin is concentrated within the naphthalene-like cage compared to other two. Whereas, in Epicatechin, with reference to the electronegative oxygen sites, the ring carbons acquires either electronegativity or electron releasing trend. Owing to the fact that Epicatechin is selected as the targeted phytochemical, the FMO energy band is shown

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Fig. 3 Comparative representation of the energy gaps (E) for various phytochemicals of Tulsi (Ocimum sanctum)

in Fig. 4. The figure reveals the electron occupancy state, and the feasibility of unoccupied orbitals is also reflected. Similar to the phytochemicals of Tulsi, Quercetin is a polyphenolic flavonoid that is reported to have anti-cancer properties. Quercetin is found in a wide variety of plant foods and is a prominent bioflavonoid in the human diet, may have antiproliferative effects by modulating either the estrogenreceptor-mediated signal transduction pathways. Although the mechanism of action is unknown, this drug has been shown to have the following effects in vitro: reduced expression of mutant p53 protein and p21-ras oncogene, activation of G1 phase cell cycle arrest, and suppression of heat shock protein production. When coupled with chemotherapeutic medicines, this chemical also shows synergy and reverses the multidrug-resistant phenotype in vitro. In addition, Quercetin has anti-inflammatory and anti-allergy properties because it inhibits the lipoxygenase and cyclooxygenase pathways, which prevents the synthesis of pro-inflammatory mediators [14]. MO energ y levels

Fig. 4 FMO energy band of Epicatechin computed from DFT

1,500

1,500 1,400 1,300

439A (U)

439B (U)

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440B (U)

1,400 1,300

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22B (O) 241B 242B (U)

23A 24A 25A 26A 27A (O)

23B 24B 25B 26B 27B (O)

7A (O)

7B (O)

-500

1A (O)

1B (O)

-500

-600

2A (O) 3A 4A 5A

2B 3B 4B 5B (O)

-600

100 0 -100

0 -100 -200

-200 -300

100

-300 -400

-400

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Quercetin is a phytochemical that is present in various foods and plants and is an integral part of a healthy diet. Quercetin extracts have been tried to cure or prevent various ailments, including cardiovascular disease, infections, and cancer. Still, they have yet to be shown successfully in clinical studies for any medical condition. Quercetin is an example of dietary supplement that hasn’t been connected to elevated blood enzymes or instances of clinically apparent liver impairment [15]. Quercetin (Fig. 5) is a pentahydroxyflavone with five hydroxy groups. It’s a 7-hydroxyflavonol and a pentahydroxyflavone [16]. Selection of Quercetin is based on the comparative FMO study on three phytochemicals of Neem as shown in Fig. 6. The greater the electron mobility in large conjugated orbital systems, the more energy is distributed throughout the molecule, stabilizing it. Consequently, smaller HOMO–LUMO gaps indicate more stability. Figure 6 clearly shows the reactivity of Quercetin, wherein the electron cloud distribution is distributed throughout the system. In contrast, in other two phytochemicals, Nimbolin A and Sitosterol, major portion of the system does not bear significant reactivity in terms of negative charge distribution. The orbital contribution at ground level is only restricted to a part of the system as could be observed from the HOMO diagram as well. The similar observation is highlighted from Fig. 7 as well, wherein the reactivity and selection of Quercetin are well validated. The FMO energy band of selected phytochemical of neem is shown in Fig. 8. On the basis of such findings the selected phytochemical systems are allowed to undergo blind docking with two selected protein systems as exemplified in the next section. Fig. 5 Optimized structure of Quercetin, a phytochemical of Neem herb

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Fig. 6 Comparative HOMO–LUMO diagrams of the phytochemicals of Neem (Azadirachta indica) along with the energies of MOs and their differences

Fig. 7 Comparative representation of the energy gaps (E) for various phytochemicals of Neem (Azadirachta indica)

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Fig. 8 FMO energy band of Quercetin computed from DFT

1,500

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450 (U) 454 453 452 451 (U)

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3.2 Molecular Docking of Selected Phytochemicals The aforementioned section signifies the selection criteria for Epicatechin and Quercetin using FMO of DFT. This section intends analyze the interaction of such phytochemicals with specific protein in a very fundamental manner. The functionalization of protein study as reported primarily analyze the role of serum albumin which acts as the primary soluble constituents of the circulatory system [17]. For invitro study, thereby these are most opted for the study. These serum albumins enable reversible binding of the bioactive small molecules like the phytochemicals in the present case. In addition, they allow the solubilization of the hydrophobic active pharmacological ingredient (API) for in vivo delivery into cellular receptor. Table 1 reports the details on the significant docking parameters like free energy involved and intermolecular energy upon interaction of Epicatechin and Quercetin with proteins. The results have been reported based on 10 conformations during interaction of the phytochemicals with BSA. The conformations with positive free energy of binding are discarded and one with maximum negative magnitude is represented in Fig. 9. Irrespective of the type of phytochemicals (from either sources), thermodynamic affinity of interaction is found to be stronger with CDK2 species. Serine/threonineprotein kinase is reported to be involved in the control of the cell cycle and is essential for meiosis. It is dispensable for mitosis process. CDK2 is found to have activated interaction with cyclin E while DNA is in the initial stage of synthesis. CDK2 thereby is reported to prevent the oxidative stress-mediated Ras-induced senescence through phosphorylation. During the consequent damage of DNA, double strand breakage tends to start recombination by reduction of CDK2-mediated phosphorylation. Hence, CDK2 holds a key role during any anti-inflammatory or uncontrolled growth process. Compared to Quercetin, Epicatechin shows significant hydrophobic interactions with the amino acids of CDK2 wherein the OH group play a prime role. Quercetin shows selective interaction PRO254 and VAL197 residues maintaining other residues at a distant. However, quite significant number of hydrophobic interactions are visible among Quercetin and BSA. Hence, Quercetin is more selective toward serum albumin proteins with less feasible interactions with CDK2 system. The respective intermolecular energy comprising of Van der Wal, electrostatic interaction probability can be observed from Table 1 for both the phytochemical system. Hence,

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Table 1 Salient parameters regarding binding interaction of phytochemicals with proteins Phytochemicals with

BSA Epicatechin

Estimated free energy of binding (Kcal/mol)

Final intermolecular energy (Kcal/mol)

Cyclin—dependent kinase 2 Quercetin

Epicatechin

Quercetin

Conformations

− 6.89

− 7.15

− 11.6

− 7.22

1

− 6.44

5.36

− 9.52

− 6.77

2

− 5.57

− 7.11

− 9.80

− 6.55

3

− 6.06

− 6.01

− 9.89

− 7.67

4

− 5.09

− 7.64

− 7.53

− 6.89

5

− 5.53

− 7.35

− 7.13

− 8.03

6

− 7.83

− 6.6

− 7.54

− 7.37

7

− 5.57

7.69

− 6.53

− 6.08

8

− 7.75

− 5.42

− 7.37

8.06

9

− 7.95

− 5.85

− 9.17

− 6.36

10

− 8.68

− 8.94

− 12.85

− 9.0

1

− 8.23

− 7.15

− 11.31

− 8.56

2

− 7.36

− 8.9

− 11.59

− 8.34

3

− 7.85

− 7.8

− 11.68

− 9.46

4

− 6.88

− 9.43

− 09.32

− 8.68

5

− 7.32

− 9.14

− 8.92

− 9.82

6

− 9.62

− 8.39

− 9.33

− 9.16

7

− 7.36

− 9.48

− 8.32

− 7.87

8

− 9.53

− 7.21

− 9.16

− 10.39

9

− 9.74

− 7.64

− 10.96

− 8.15

10

Fig. 9 Hydrophobic interaction of BSA and CDK2 with: a, c Epicatechin and b and d Quercetin

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this method enables technical screening of the probable binding interaction of phytochemicals with protein system. Hence, in prior to the initiation of experimentation, it could be wise method to study such theoretical interaction.

4 Conclusion In a nutshell, the present investigation intends to study a theoretical screening study using density functional theory and molecular docking to analyze the reactivity of selected phytochemicals of Tulsi and Neem plants. This is followed by Molecular docking studies of Epicatechin and Quercetin with Bovine serum albumin and CDK2. The initial DFT analyzes enable the selection of he mentioned phytochemicals from the ensemble of others in either Tulsi and Neem. Quercetin is found to be more selective toward serum albumin proteins with less feasible interactions with CDK2 system. Compared to Quercetin, Epicatechin shows significant hydrophobic interactions with the amino acids of CDK2 wherein the OH group play a prime role. Quercetin shows selective interaction PRO254 and VAL197 residues maintaining other residues at a distant. However, quite significant number of hydrophobic interactions are visible among Quercetin and BSA. Hence, Quercetin is more selective toward serum albumin proteins with less feasible interactions with CDK2 system. Such theoretical pre-screening methodology could be applied in a selective manner so as to experimentally initiate the selectivity of the phytochemicals. Acknowledgements The authors ‘intend to thank Amity University and Maulana Abul Kalam Azad University of Technology for infrastructural and technical support.

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DAAM: WSN Data Aggregation Using Enhanced AI and ML Approaches Sanjay Gandhi Gundabatini, Suresh Babu Kolluru, C. H. Vijayananda Ratnam, and N. Nalini Krupa

Abstract Wireless sensor network (WSN) has been merged with a data aggregation platform in the present development, which includes a variety of modern advancements in AI and ML. Deep learning network and fuzzy based data aggregation approaches are used in a wide range of research projects to analyse wireless sensor Circumstances. In order to transmit data efficiently across wireless sensor networks, which have additional resource constraints, a robust routing system is required. Energy consumption, scalability, and QoS optimisation with overhead all play a role in WSN data processing efficiency. Propose a Machine Learning Data Aggregation Model for WSN improvement (EML-DA). Wireless Sensor Networks (WSNs) are networks of low-cost, low-energy sensor nodes that collect and transmit data about their surroundings to a sink or coordinator node. Thus, effective techniques should be implemented to conserve energy and extend network lifetime by reducing network load, since WSNs are power-constrained. Clustering can be used to partition the network into clusters, each of which has a cluster head where data is collected. It is possible that sensor nodes within a cluster will collect data that is redundant, increasing the overall quantity of data packets that must be delivered to the cluster head. Consequently, there is a requirement to consolidate cluster-head data so that redundant data can be eliminated and data can be compressed with fewer packets to be transferred across the network. The suggested research article aims to assess the current focus on AI-accompanied data aggregation paradigms in wireless communication by elaborating the integration framework. Through this proposed data aggregation and AI wireless sensor system, advancements in data analysis and interpretation have been made with high empowering pillars. Providing better security and encryption approaches to pre-processed data storage and streaming to bandwidth channels improves the data transmission rate as well.

S. G. Gundabatini · S. B. Kolluru (B) · C. H. V. Ratnam · N. N. Krupa Department of Computer Science and Engineering, Vasireddy Venkatadri Institute of Technology, Guntur, Andhra Pradesh, India e-mail: [email protected] S. G. Gundabatini e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Biswas et al. (eds.), Microelectronics, Circuits and Systems, Lecture Notes in Electrical Engineering 976, https://doi.org/10.1007/978-981-99-0412-9_49

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Keywords Artificial intelligence (AI) · Artificial neural network (ANN) · Back-propagation neural networks (BPN) · Central force optimisation (CFO) · Decision tree (DT) · Deep learning (dl) · Fuzzy logic (FL) · Gravitational search algorithm (GSA) · Intelligent water drops (IWD) · Machine learning (ML) · Multi-layer perception (MLP) · Principal component analysis (PCA) · Radial basis function (RBF) · Reinforcement learning (RL) · Recurrent neural network (RNN) · Swarm intelligence (SI)

1 Introduction Combining data from multiple sources is an important aspect of sensor network wireless routing. Additionally, aggregation of data can eliminate redundancy, reduce transmissions, and so reduce the amount of energy required. In order to save energy, data aggregation aims to reduce the amount of transmission required at various levels. Data aggregation can save energy if it consumes less energy than transmitting raw data to the next level. The aggregation protocol took redundancy and energy consumption out of consideration [1, 2]. In WSN, a clustering-based aggregation mechanism is used to minimise communication and maximise network longevity. By aggregating network data and minimising the sending distance, clustering reduces direct transmission to the base station. Hierarchical clustering improves aggregation for large numbers of nodes [3–5]. As the name suggests, wireless sensor networks (WSNs) are networks with many sensors deployed in a specified area. In addition to environmental monitoring, military applications, health care applications, industrial process control, home intelligence, and security and surveillance, WSNs play a critical role in a wide range of industries. Short range radio waves are used by sensor nodes to communicate and collaborate [6]. Sensor nodes, on the other hand, have a limited bandwidth, power, memory, and processing capacity. When a sensor node detects a target phenomenon, such as heat or light, it sends a query response to the host controller or sink. As opposed to data transmission, processing in WSNs uses less power. Instead of transmitting the sensed data to the sink node one at a time, aggregate functions like sum(), average(), and others can be used to collect data and then sent it to the sink, saving a significant amount of energy. An efficient data aggregation method can improve energy efficiency and network longevity [6].

2 Why Data Aggregations A data warehouse is the most common place to find aggregated data. Whereas there, it can answer analytical questions and drastically minimise the time needed to query massive data sets. Data aggregation is primarily motivated by a desire to conserve resources and reduce the amount of network bandwidth required. The sensor readings can be aggregated using the data aggregation technique presented in Fig. 1 [6].

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Fig. 1 Data aggregation process

The first step is to gather data from a variety of nodes. The sensor data is aggregated using several techniques, such as LEACH, centralised approach, and Tiny Aggregation (TAG). An aggregation method takes sensor signals from multiple nodes and aggregates them into a single value. The aggregated data is then transferred to the sink node through an efficient channel [7]. In order to deliver data from a source node to the sink, a routing protocol is used. A routing protocol that is resource efficient (energy resources are the primary concern) and that performs data aggregation by discovering various paths between the source and the destination is therefore required. When the sensor network is deployed in a hostile environment, the security challenges, latency, data confidentiality, energy consumption, integrity, and data aggregation become critical.

2.1 Tools Used in Data Aggregation in WSN See (Table 1 and Fig. 2).

3 Data Aggregation, Collection and Dissemination As part of WSNs, the data collecting and broadcasting process tries to collect data from nodes and provide it to the BS/Sink in the most efficient manner possible. as well as extending the useful life of the WSN in the process. Basic techniques such as aggregation and compression may have been used to process the data. Data aggregation is the process of bringing together data from numerous nodes and transferring it to the BS for further investigation. Long-term data collection and transmission from nodes to sink (or BS) via WSN increases communication burden because of the possibility of highly redundant data in the gathered data. The amount of redundant data generated by many sensors is beyond the BS’s processing capacity. Data aggregation is accepted out to deal with this difficulty, and the BS attains only the significant evidence. The major goal of data synthesis is to prevent replication of data from numerous sources and also reduces the transmissions’ count and preserve resources [8]. However, data distribution relates to the practise of disseminating information

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Table 1 Various useful tools for data aggregation Tool

Description

UWSim

For underwater sensor networks, UWSim serves as a simulator (UWSN). As a simulator for UWSN situations, UWSim can handle conditions such as restricted bandwidth, low memory, and high transmission energy

OMNeT++ Distinct events based on public source components can be simulated, and it can be used to model both wireless as well as fixed-wire IP communication networks. Using this technology, real-time data can be seen in a graphical style Castalia

Use it to test low-power embedded devices in a network. Power consumption can be measured with the use of Castalia’s sensor model

Prowler

In order to create an event-driven WSN for MATLAB, this simulator is used. Using Prowler’s user interface, you can quickly see how the routing protocol works and what it can do

MATLAB

In WSNs, various modulation and encoding methods, physical layer characteristics, and communication channels can be simulated using MATLAB (Simulink). In MATLAB (Simulink), end nodes and sensors can be added and removed from the WSN

Qualnet

A more advanced version of GlomoSim, it is utilised primarily for defence projects and distributed and heterogeneous network simulations

TinyPEDS

Data aggregation in WSN is protected by the use of tinyPEDS, which encrypts the data. After aggregation, the data is safeguarded using symmetric privacy holomorphic encryption

NS2

For discrete events in WSNs, NS2 is an open-source simulation tool. It is compatible with wireless MACs of the 802.14.4 and 802.11 types. WSN energy modelling can be done efficiently using NS-2

Fig. 2 String formation using query search

through a network as well as searches for data. In WSN, a source node is a node that generates data about an event, whereas a sink node is one that seeks out data. As opposed to interest, an event is a node’s description of what it is interested in learning about.

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Fig. 3 WSN data aggregation, collection, and dissemination challenges addressed by AI methods

4 Enhanced Artificial Intelligence Techniques in Data Aggregation AI is the ability of a computer system to do tasks that need human intelligence while emulating human thoughts and ideas. Many complicated challenges, such as security, finance, health care, transportation, and so on, can be solved by AI because of its ability to handle inadequate and noisy data, deal with nonlinear issues, and be used in prediction and faster generalisation once taught [9]. As illustrated in Fig. 3, we have examined and categorised several AI approaches utilised in WSNs. The following is a quick rundown of each of these methods (Tables 2 and 3).

5 Enhanced ML Approaches for Data Aggregations WSN designers must address concerns such as data aggregation and dependability; node clustering; energy-aware routing; event scheduling and fault detection; and security. Machine learning is typically referred to as an assortment of tools and techniques that are used to build prediction models by sensor network designers. Experts in machine learning, on the other hand, see it as a diverse discipline with a wide range of themes and patterns worth exploring. WSN machine learning practitioners will do well to familiarise themselves with such concepts. Many WSN applications can benefit greatly from machine learning algorithms.

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Table 2 Enhanced AI approaches in data aggregation Method

Description

Metaheuristics

When it comes to solving difficult issues, metaheuristics are the most prevalent sort of algorithm that employs a grade of haphazardness. It is possible to classify metaheuristic algorithms in a variety of ways. Trajectory and population-based techniques are two examples of classification schemes [10]. Piecewise movement in the design space is often the goal of trajectory-based algorithms in search of a single optimal solution. Population-based systems, on the other hand, employ a variety of approaches to find a solution and work together to do so

Learning methods Learning is the ability to learn new knowledge and enhance it via experience without the need for any programming. AI includes methods for teaching and learning, such as ANN, RL, DL. With the potential to simulate BNN and human qualities, ANNs have been efficacious in handling extremely hard situations. Prediction, function approximation, and validation, optimisation, clustering, and time series analysis are just a few of the many applications of this algorithm. These include RBF networks, MLP, BPN, and RNN architectures Fuzzy logic

AI technology FL mimics human decision-making in a similar way to other AI methods. It is used to deal with ambiguity and uncertainty in reasoning. Either it’s true (T) or it’s not (F). A ‘truth-value’ between 0 and 1 [11] is used in FL. Any number between 0 and 1 can be used to represent fuzzy set membership. Defuzzification of the centroid, the maximum, and the mean-of-maxima are a few possible examples

An introduction to ML for WSNs is provided here. ML algorithms fall into three categories: supervised, reinforcement, and unsupervised learning [20]. A labelled training data set is delivered to machine learning algorithms in the first category. This set is used to develop a system model that represents the learnt relationship between input, system parameters, and output. Unsupervised learning algorithms, in contrast to supervised ones, do not get labels. When using an unsupervised learning algorithm, the objective is to sort the input samples into distinct groups based on their degrees of similarity. Third-category models include those that learn by interacting with their environment, such as reinforcement learning algorithms (Fig. 4 and Table 4).

6 Conclusion Many characteristics of WSN are distinct from those of traditional networks, demanding protocols and tools that are tailored to the specific problems and constraints they present. WSNs, as a result, need unique solutions for real-time routing and energy aware scheduling, security, location, node clustering, fault detection, data aggregation, and data integrity. The ability of a wireless sensor network

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Table 3 Enhanced AI approaches with performance in data aggregation AI technique

Algorithm

Optimisation criteria

Performance parameters

Swarm intelligence

ABC [12], DAACA, ACOPSO

Minimizing energy consumption

Energy difference, average energy cost WSN lifetime, time complexity, average path length

Fuzzy logic

TTDFP [13], CTEEDG, DFHFE [14]

Energy consumption, Average end-to-end delay, load balancing network lifetime, half nodes die, first node die

Evaluation computation

MTS-LASC, ETDMA-GA

Min. data delivery time, low delivery latency

Delay, duty cycle, average latency, packet delivery ration, routing overhead

Q—learning

Q-DAEER, RINA [15], STAC

Less data redundancy, high aggregation ratio

Communication range, failure, network load, lifetime and scalability, average hop count, number of nodes with dead status

Nature inspired

DA-MOMLOA [16], BAT [17], CUCKOO

Network lifetime optimised, data collectors path findings

Energy consumption and network lifetime, Area size, cost, throughput, scalability, network efficiency

Combined SI and FL

FCOABC [18], MDF-FBCHS [19]

Rise in network Complexity analysis, alive lifespan, high energy nodes, average delay, efficiency packet delivery ratio, energy consumption, network life time

Fig. 4 Nonlinear SVM

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Table 4 Enhance ML approaches for data aggregation ML technique

Approach

Description

Supervised learning

KNN

K-nearest neighbour is a suitable distributed learning approach for WSNs because of this factor and the linked readings of surrounding nodes. High-dimensional problems might lead to erroneous results when using the k-NN approach, as the distance to various samples becomes invariant. It is the query processing subsystem in WSNs where the k-nearest neighbour algorithm is most commonly used [21]

Decision tree

It is a classification method that uses a learning tree to predict the labels of data [22]. In the literature, there are numerous examples of WSN design problems that can be solved using the DT algorithm which measures a few key characteristics like loss rate, corrupted-data rate, MTTF, and MTTR. A disadvantage of linearly separable data is that DT can only work with NP-complete learning trees

Neural networks

Due to the high processing needs for learning the network weights and the considerable administration overhead, distributed use of neural networks in WSNs is still rather uncommon. Because they can simultaneously learn many outputs and determination boundaries in centralised solutions, neural networks are well-suited to solve several network difficulties with the same model

SVM

Using labelled training examples, it is a machine learning system that learns to classify data points. As an illustration, one method of detecting SVM is used to evaluate temporal and geographical correlations of data in order to identify malicious behaviour in a node. Using WSN observations as points in a feature space, SVM creates a subset of the feature space. These components are separated by as many gaps as possible, and new ones are added in between as seen in Fig. 3

Bayesian statistics Most machine learning techniques require a large number of training examples, but Bayesian inference requires only a few. Without overfitting, Bayesian approaches use probability distributions to learn uncertain ideas more quickly and effectively (continued)

DAAM: WSN Data Aggregation Using Enhanced AI and ML Approaches

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Table 4 (continued) ML technique

Approach

Description

Unsupervised learning k-means clustering Labels are not given to unsupervised learners. The primary purpose of an unsupervised learning algorithm is to categorise a sample set into distinct groups by comparing the similarities between them. Data aggregation and node clustering problems make extensive use of learning methods PCA

As a multivariate technique for reducing the amount of information in a dataset, the components with the lowest standard deviation can be deleted. So, PCA lowers the data transferred among sensor nodes

to adapt to changes in its environment is enhanced by the use of machine learning algorithms.

References 1. C. Yang, Z. Yang, K. Ren, C. Liu, Transmission reduction based on order compression of compound aggregate data over wireless sensor networks, in 6th International Conference on Pervasive Computing and Applications (ICPCA), pp. 335–342 (2011) 2. B.S. Mathapati, S.R. Patil, V.D. Mytri, Energy efficient reliable data aggregation technique for wireless sensor networks, in International Conference on Computing Sciences, Phagwara, pp. 153–158 (2012) 3. K.S.B. Yaeghoobi, S.S. Tyagi, M.K. Soni, E.O.M. Ebadati, SAERP: an energy efficiency realtime routing protocol in WSNs, in 2014 International Conference on Optimization, Reliabilty, and Information Technology (ICROIT), pp. 249–254 (2014) 4. A. Sabri, K. Al-Shqeerat, Hierarchical cluster-based routing protocols for wireless sensor networks–a survey. IJCSI Int. J. Comput. Sci. 11(1) (2014) 5. M. MehdiAfsar, H. Mohammad, N. Tayarani, Clustering in sensor networks: a literature survey. J. Netw. Comput. Appl. 46, pp 198–226 (2014) 6. S.B. Pourpeighambar, M. Aminian, M. Sabaei, Energy efficient data aggregation of moving object in wireless sensor networks, in Australasian telecommunication networks and applications conference, pp. 1–8 7. M. Dagar, S. Mahajan, Data aggregation in wireless sensor network: a survey. Int. J. Inf. Comput. Technol. 3(3), 167 (2013) 8. S. Randhawa, S. Jain, Data aggregation in wireless sensor networks: previous research, current status and future directions. Wirel. Pers. Commun. 97, 3355–3425 (2017) 9. O. Bello, J. Holzmann, T. Yaqoob, C. Teodoriu, Application of artificial intelligence methods in drilling system design and operations: a review of the state of the art. J. Artif. Intell. Soft Comput. Res. 5, 121–139 (2015) 10. X.S. Yang, Nature-Inspired Metaheuristic Algorithms (Luniver Press, Bristol, UK, 2010) 11. P. Boucher, How Artificial Intelligence Works; Scientific Foresight Unit; EPRS | European Parliamentary Research Service, European Union: (2019) 12. Y. Chen, Y. Qin, Y. Xiang, J. Zhong, X. Jiao, Intrusion detection system based on immune algorithm and support vector machine in wireless sensor network, in Information and Automation, ser. Communications in Computer and Information Science. Springer Berlin Heidelberg vol. 86, pp. 372–376 (2011)

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13. S. Macua, P. Belanovic, S. Zazo, Consensus based distributed principal component analysis in wireless sensor networks, in 11th International Workshop on Signal Processing Advances in Wireless Communications, pp. 1–5 (2011) 14. D. Feldman, M. Schmidt, C. Sohler, D. Feldman, M. Schmidt, C. Sohler, Turning big data into tiny data: constant size coresets for k-means, PCA and projective clustering, in SODA, pp. 1434–1453 (2013) 15. J. Wang, R. Ghosh, S. Das, A survey on sensor localization. J. Control Theory Appl. 8(1), 2–11 (2010) 16. H. Malik, A. Malik, C. Roy, A methodology to optimize query in wireless sensor networks using historical data. J. Ambient. Intell. Humaniz. Comput. 2, 227–238 (2011) 17. M. Duarte, Y. Eldar, Structured compressed sensing: from theory to applications. IEEE Trans. Signal Process. 59(9), 4053–4085 (2011) 18. K. Kalaikumar, E. Baburaj, Fuzzy enabled congestion control by cross layer protocol utilizing OABC in WSN: combining MAC, routing, non-similar clustering and efficient data delivery. Wirel. Netw. 26, 1085–1103 (2020) 19. S. Kumar, N. Lal, V.K. Chaurasiya, A forwarding strategy based on ANFIS in internet-of-thingsoriented wireless sensor network (WSN) using a novel fuzzy-based cluster head protocol. Ann. Telecommun. 73, 627–638 (2018) 20. Y.S. Abu-Mostafa, M. Magdon-Ismail, H.-T. Lin, Learning from data. AMLBook, (2012) 21. P.P. Jayaraman, A. Zaslavsky, J. Delsing, Intelligent processing of k-nearest neighbours queries using mobile data collectors in a location aware 3D wireless sensor network, in Trends in Applied Intelligent Systems. Springer, pp. 260–270 (2010) 22. T.O. Ayodele, Types of machine learning algorithms, in New Advances in Machine Learning. In Technology (2010)

Dual-Strip Flag Microstrip Patch Antenna for Millimeter-Wave Applications Purnima K. Sharma, Dinesh Sharma, E. Kusuma Kumari, V. S. D. Rekha, and Vivek Garg

Abstract Here, a dual-strip flag microstrip patch antenna operating in the millimeter waveband for future 5G wireless communications is proposed. The ground plane, which is constructed on a FR4 epoxy base with a relative permittivity of 4.4 and a dielectric constant of 4.6, measures 30 mm × 100 mm × 1.6 mm. The simulation of proposed antenna design has been carried by HFSS 13.0. The antenna has a return loss of − 28 dB, − 26 dB, − 17 dB, and − 25 dB at 22 GHz, 24 GHz, 35.5 GHz, and 38.5 GHz, respectively. This makes the antenna suitable for the upcoming generation of wireless communication, i.e., 5G. This paper discusses the antenna structure as well as other factors such as radiation pattern, return loss, current distribution, VSWR, and Smith chart. The practical return loss which we have obtained is also shown in comparative analysis. Keywords Millimeter waveband · 5G · High-frequency structure simulator (HFSS) · Microstrip patch antenna · Voltage sanding wave ratio (VSWR)

1 Introduction In today’s wireless technology, we have several generations of cellular communication where each possesses several advantages over the previous generations, but one of the major drawbacks of the presently available generations is the lack of P. K. Sharma (B) · E. K. Kumari Department of Electronics and Communication Engineering, Sri Vasavi Engineering College, Tadepalligudem, India e-mail: [email protected] D. Sharma · V. Garg Department of Electronics and Communication Engineering, Chandigarh College of Engineering and Technology, Chandigarh, India e-mail: [email protected] V. S. D. Rekha Department of Electronics and Communication Engineering, PVP Siddhartha Institute of Technology, Vijayawada, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Biswas et al. (eds.), Microelectronics, Circuits and Systems, Lecture Notes in Electrical Engineering 976, https://doi.org/10.1007/978-981-99-0412-9_50

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frequency resources available and the continuous increase in the demand of wireless services. To overcome this, research has been started in the next generation of wireless communication, i.e., 5G at the millimeter waveband (20–300 GHz) that indicates an abundant of bandwidth for high data transmission rate. The mostly used frequency range for 5G is 24–60 GHz [1] which is expected to be launched globally by 2020. As compared to 4G, the difference in 5G cellular systems is that the frequency is shifted to higher frequency range, i.e., above 20 GHz where it becomes easier to achieve wider spectrum [2]. The key features of 5G which makes it better than other present generations are—higher throughput, reduced latency, and capability to connect a many more devices at the same time, communication services such as ultra HD video streaming (UHDV) and high-definition television (HDTV), larger capacity, etc. [3]. Wireless communication technology is advancing significantly toward 5G in reaction to the development of the Internet of Things (IoT) and the increasing demand for access to many multimedia services over wireless broadband [4]. Advancements in 5G are: . . . . .

High data rates. Round trip delay of about 1 ms. Low battery consumption. High connection density irrespective of the location of the device. Cheaper infrastructure development [4]. These advancements in 5G result in the following three main benefits:

. Increased throughput: Compared to 4G, data rate in 5G is about 10 times to that in 4G. . Reduced delay time: The time delay between the cause and effect is shortened, and hence, it is possible to watch LIVE videos without and lag. . Higher connectivity: 5G technology provides a reliable and a better connection for the users as compared to 4G/LTE [4]. The microstrip patch antennas comprises a fine sheet of metal on the top of the surface called as patch which is separated from the ground plane by a substrate of dielectric material. The performance of these antennas depends on the thickness and the substrate’s dielectric constant. Antenna performance can be improved by increasing the thickness of the substrate and decreasing its dielectric constant [5]. Microstrip patch antennas offer several advantages over the conventional antennas such as smaller size, light weight, cheaper, easy to integrate on the substrate of PCB [3]. Microstrip patch antennas are mostly utilized in microwave and millimeter frequency bands. The proposed striped flag-shaped antenna consists of metallic strips of different sizes, which can be varied to obtain different frequency bands. A conducting strip is connected between the metallic patch and the ground in this antenna’s microstrip line feed. The antenna operates at 26 GHz and 38 GHz which makes it useful in the coming years where 5G is going to replace the present generations available for cellular communications.

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2 Literature Survey E. Anna Lakshmi et al. demonstrated an H-shaped microstrip patch antenna for 5G applications with measurements of 15. 8 mm X 13.36 mm X 1.6 mm. The substrate is RT-Duroid with a permittivity of 4.3. The antenna has a return loss of -14 dB and a gain of 7.4040 dB at 28.5 GHz [2]. For 5G wireless communication, T. Kiran et al. introduced a microstrip patch antenna. The antenna’s dimensions, including the ground plane, are 11 mm × 8 mm × 0.5 mm, and it is made of Rogers-RT/Duroid 5880 with a dielectric constant of 2.2. The antenna has a return loss of -21 and -31 dB at 28 and 50 GHz, respectively. The antenna has a gain of 2.6 dB [1]. Raju R. Rathwa et al. presented a slotted microstrip patch antenna design for 5G applications. The FR4 substrate is 22.22 mm × 26.19 mm × 1.6 mm in dimension and has a dielectric constant of 4.6. The antenna has a resonance frequency of 5.4 GHz, a gain of 2.65 dB, and a return loss of -10.97 dB [5]. For the next generation of 5G wireless communication devices, Adamu Mohammed Jajere suggested a rectangular patch antenna operating at 38 GHz with a return loss of -31 dB and a voltage standing wave ratio (VSWR) of 1.10. The structure’s overall dimensions are 5 mm X 4 mm × 0.64 mm, using a Taconic RF-60(tm) substrate [3]. R Siri Chandana et al. introduced an antenna for 5G wireless communication with Rogers-RT 5880 substrate having permittivity 2.2. The dimensions of the substrate are 8 mm X 8 mm X 1.6 mm. The antenna resonates at 59 GHz with return loss of -42.4383 dB and VSWR 1.0152 [6]. Fatima Gharnati et al. created a 19 mm X 19 mm X 0.787 mm Rogers-RT 5880 substrate for a small double-band patch antenna for 5G applications. The operational frequencies are 10.15 GHz and 28 GHz, respectively, with gain of 5.51 dB and 8.03 dB [7]. Hedi Ragad et al. investigated the design of rectangular patch antenna arrays fed by microstrip and coaxial lines for future 5G applications, proposing a 28 GHz antenna. Rectangular 4*1 and 2*2 patch antenna arrays were built using Rogers-RT/Duroid 5880 substrate. A 4*1 antenna array serviced by microstrip line performs better than a 2*2 antenna array provided by coaxial cable, according to simulation data [8]. Amin Rida and colleagues developed a planar and low-profile microstrip patch array antenna for millimeter-wave applications with high gain. A matching network or its power divider/splitter with a gain of 27 dBi at 79 GHz and 26 dBi at 80 GHz is developed for a sub-array of 16 × 1 elements, and a corresponding matching network or its power divider/splitter with a gain of 27 dBi at 79 GHz and 26 dBi at 80 GHz is developed for 32X16 elements [9]. For future high-speed wireless communication systems, Shahbaz Khan et al. developed an inset feed 60 GHz millimeter-wave microstrip patch antenna. In comparison to the other antenna variants, the millimeter-wave antenna that uses the cross-shaped EBG offers improved gain. Due to surface suppression by the ground plane, the 60 GHz antenna based on the mushroom-type EBG performed better. The antennas that have been proposed could be employed in future high-speed wireless applications. These antennas are ideal for medical implants that operate in the unlicensed millimeter-wave range because of their small size [10].

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Fig. 1 Proposed antenna structure or top view

3 Antenna Design The physical construction of the antenna, as well as the size and materials employed, is covered in this section. The proposed antenna is made up of three layers: the top surface has a patch, which is a radiating element, and the internal layer is a dielectric substrate that delivers mechanical sustenance for the patch elements. The substrate also aids in achieving the requisite precise spacing between the two conducting surfaces, while the ground plane supports the antenna and aids in its bandwidth expansion [3]. The dielectric substrate is made of FR4 epoxy, which has a dielectric constant of 4.6. The antenna’s entire dimensions, including the ground plane, are 30 mm × 100 mm × 1.6 mm. The dual-stripped flag-like structure of the radiating metallic patch is made up of two flag-shaped sheets that are linked together. These consist of four metallic strips of the same thickness in between them with uniform spacing of 0.76 mm. The size of these strips can be varied to achieve different resonant frequencies. The antenna has a thickness of 1.6 mm. A 50-Ω microstrip transmission line is utilized for feeding, and the feed type is microstrip line feed. Figure 1 displays the proposed antenna structure as well as a top view of the proposed antenna. The dimensions of the proposed antenna are presented in Table 1 of Fig. 1. Figure 2 depicts the antenna’s side perspective, whereas Fig. 3 depicts the antenna’s bottom view, which includes the entire ground plane. In the practical aspect of the antenna, a SMA connector is connected at the point of feeding connecting the patch and the ground plane, as illustrated in Fig. 4.

4 Results and Simulations The radiation pattern, return loss, gain, current distribution, VSWR, and other parameters of the planned antenna are discussed in this section. These are used to assess the enactment of the antenna. The simulation is done using the Ansoft HFSS software.

Dual-Strip Flag Microstrip Patch Antenna for Millimeter-Wave … Table 1 Dimensions of the antenna structure

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Antenna parameter

Value (mm)

L1

43.25

L2

12.8

L3

9.8

L4

1.5

L5

3.15

L6

12.5

L7

3.65

L8

9.2

Fig. 2 Side view of the proposed antenna

Fig. 3 Bottom view of the proposed antenna (ground plane)

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Fig. 4 Practical view of the antenna

Fig. 5 Return loss

4.1 Return Loss The return loss is calculated (in dB) at the resonant frequency, i.e., 22 GHz, 24 GHz, 35.5 GHz, and 38.5 GHz, and it is found to be less than − 10 dB, which implies that 90% of the existing power is supplied to the antenna. The return loss obtained at 22 GHz is − 28 dB, at 2 4 GHz is − 26 dB, at 35.5 GHz is − 17 dB, and at 38.5 GHz is -28 dB. The return loss plot (simulated and practical) is presented in Fig. 5.

4.2 Voltage Standing Wave Ratio It measures the radio frequency power sent through a transmission line from a power source to a load (antenna). The VSWR for microstrip patch antennas should be less

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Fig. 6 VSWR

than 2. The VSWR at peak obtained at 26 GHz is 1.10 and at 38 GHz is 1.081. The VSWR at all the operating frequencies is less than 2. The VSWR plot is presented in Fig. 6.

4.3 Radiation Pattern The radiation pattern of an antenna refers to its directionality, i.e., the radiation pattern tells the strength of the radiations emitted in different directions. The main lobe, rear lobe, and side lobes make up the radiation pattern. The main lobe depicts the strength of the emitted radiations in the desired direction; the rear lobe depicts the amount of radiation in the direction opposite the desired paths; and the side lobes portray the radiation in other directions. Two-dimensional radiation pattern of the proposed antenna at the resonant frequencies is shown in Figs. 7 and 8, whereas the 3D radiation pattern is presented in Figs. 9 and 10 at 26 GHz and 38 GHz, respectively. The gain of the antenna is quite low because high gain cannot be obtained by using a lossy material like FR4 as the substrate. To increase the gain, effective area of the antenna should be increased somehow. This can be done by using parasitic elements, meandering of edges, etc.

4.4 Surface Current Distribution It represents the flow of current in the antenna. Figures 11 and 12 show the distribution of the current in the designed antenna at the operating frequencies. It can be inferred that nearer the conducting strips of the antenna to the feed, more is the current flow in the antenna.

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Fig. 7 Two-dimensional radiation pattern at 26 GHz

Fig. 8 Two-dimensional radiation pattern at 38 GHz

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Fig. 9 Three-dimensional radiation pattern at 26 GHz

Fig. 10 Three-dimensional radiation pattern at 36 GHz

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Fig. 11 Current distribution at 26 GHz

Fig. 12 Current distribution at 38 GHz

4.5 Smith Chart Smith chart is developed by Phillip H. Smith. We can see the transmission line impedance and antenna system as a function of frequency using the Smith chart. Figure 13 depicts the planned antenna’s Smith chart.

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Fig. 13 Smith chart

5 Conclusion In this paper, the advancements and the benefits in coming 5G networks are discussed over present technologies available, and a microstrip patch antenna is designed for 5G applications in the mm-wave band. The antenna is designed to work at 22, 24, 35.5, and 38.5 GHz with a return loss of -28, -26, -17, and -25 dB, respectively, and VSWR of 1.1 and 1.081 is obtained at peak. The antenna has good performance as it can be seen from return loss and VSWR obtained. Hence, this antenna is best suited for 5G wireless communications.

References 1. T. Kiran, N. Mounisha, Ch. Mythily, D. Akhil, T.V.B. Phony Kumar, ‘Design of Microstrip Patch Antenna for 5g Applications’ IOSR Journal of Electronics and Communication Engineering (IOSR-JECE) Volume 13, Issue 1, Ver. I (Jan.- Feb. 2018), PP 14–17. 2. E. Annalakshmi, D. Prabakaran, A Patch Array Antenna for 5G Mobile Phone applications. Asian Journal of Applied science and Technology (AJAST) 1(3), 48–51 (April 2017) 3. Adamu Mohammed Jajare Institute of Antena and Microwave Techniques Tianjin University of Technology and Education Tianjin, P.R. China, ‘Millimeter Wave Patch Antenna Design Antenna for future 5G applications’ International Journal of Engineering Research and Technology (IJERT) ISSN: 2278–0181 Vol. 6 Issue 02, February-2017. 4. Kelechi G. Eze, Matthew N. O. Sadiku, Sarhan M. Musa, ‘5G Wireless Technology: A Primer’ International Journal of Scientific Engineering and Technology Volume No. 7.

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5. Nikita M. Tarpara, Raju R.Rathwa, Dr. NIrali A. Kotak, ‘Design of Slotted Microstrip patch Antenna for 5G application’ International Research Journal of Engineering and Technology(IRJET) Volume: 05 Issue: 04 | Apr-2018. 6. R. Siri Chandana, P. Sai Deepthi, D. Sriram Teja, N. Veera Jaya Krishna, M. Sujatha, ‘Design of a single band Microstrip patch antenna for 5G applications’ International Journal of Engineering and Technology. 7. Fatima Gharnati, Yassine Jandi, Ahmed Oulad Said, ‘Design of a Compact Dual Band Patch Antenna for 5G applications’ 2017 International Conference on Wireless Technologies, Embedded and Intelligent Systems (WITS). 8. Hedi Ragad, Mourad Menif, Rim Barrak, IIhem Gharbi, ‘Design of Patch Array Antennas for future 5G applications’ International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA) 2017 9. Amin Rida, Manos Tentzeris, Symeon Nikolaou, ‘Design of Low-Cost micro-strip Antenna rrays for mm-wave applications’ 2011 IEEE International Symposium on Antennas and Propagation (APSURSI). 10. Sana Ullah, Sadiq Ullah, Shahbaz Khan, ‘Design and analysis of a 60GHz Millimeter Wave Antenna’ Department of Telecommunication Engineering, University of Engineering and Technology Peshawar. 11. Shrishti Sharma, Ira Joshi, ‘Comb Like Hexaband Micro-strip Antenna For S, C and X Band Applications’ International Journal of Electronics, Electrical and Computational system (IJEECS) ISSN 2348–117X Volume 7, Issue 2 February 2018.

A Cost-Effective Tracking and Health Monitoring System for Suspected COVID-19 Patient in Quarantine Jhilam Jana, Sayan Tripathi, Akash Bhattacharya, Ritesh Sur Chowdhury, Deep Ranjan, and Jaydeb Bhaumik

Abstract Recently, COVID-19 pandemic is the most serious issue across the world. Experts and scientists are still searching for an effective vaccine and medicine to thwart this highly transmissible disease. Till date, in order to prevent the spreading of this disease, people are relying on wearing masks, sanitizing hands, quarantine and maintaining social distancing. Academicians, healthcare units and the government of all countries are trying to figure out the best way to curb down the spread of COVID19. So, in this critical situation, it is the utmost important to design new mobile applications and hardware systems to track and monitor the health of COVID-19affected patients. In this paper, a smart embedded system for movement tracking and health monitoring of suspected COVID-19 patient is proposed. The proposed system can be employed to prevent the community transmission. The estimated cost of the proposed system at component level is around rupees two thousand. This system will also be helpful for the hospital and local authorities. Keywords COVID-19 · Quarantine · Tracking · Health monitoring · Arduino UNO · Pulse sensor · GPS · GSM · DS18B20 · Voice frequency detector

J. Jana (B) · S. Tripathi · A. Bhattacharya · R. S. Chowdhury · J. Bhaumik Department of ETCE, Jadavpur University, Kolkata, India e-mail: [email protected]; [email protected] S. Tripathi e-mail: [email protected] A. Bhattacharya e-mail: [email protected] R. S. Chowdhury e-mail: [email protected] J. Bhaumik e-mail: [email protected] D. Ranjan Indian Institute of Technology (ISM), Dhanbad, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Biswas et al. (eds.), Microelectronics, Circuits and Systems, Lecture Notes in Electrical Engineering 976, https://doi.org/10.1007/978-981-99-0412-9_51

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1 Introduction Nowadays, COVID-19 has become a serious problem all over the world. Governments from all the countries are trying to curb down the virus spreading by imposing lockdown, travel restrictions, quarantining suspected patients and suspending all social activities. In spite of these measures, precaution and self-awareness are very important for the prevention of coronavirus. In order to restrict the spreading of this infectious virus and track the quarantined patients, several researchers and institutions have already developed some mobile applications. A team of researchers have developed an app GoCoronaGo [1]. This app helps in identifying whether a person has crossed the paths with any COVID-19 patient or not. The app uses Bluetooth signals and GPS tracking for doing the same. This app also provides Geo-fencing for quarantined people. But, this mobile application does not monitor any symptoms of the people infected with COVID-19 [1]. COVID-19 Quarantine Monitor [2] is another app which can track the location of an asymptotic carrier with the help of GPS. This app also uses the technique of Geo-fencing and will report the authorities if the quarantined person leaves the specified zone of movement. This application uses AI and Geo-tagging for tracking the quarantined person. If any person violates the rules, then an alert signal will be generated. This application will fail if the quarantined person uses proxy for the entries. There is no ensuring algorithm which will make the local authorities sure that he/she is maintaining rules of quarantine. For example, if he/she leaves the phone in the house and roams freely, then authority cannot track [2]. Also, a mobile application has been developed which uses Geo-fencing system for tracking and surveillance. The quarantined person is allowed to share photograph and location on a Google Map [3, 4]. It becomes very difficult to upload photograph every thirty minutes as while sleeping, it becomes impossible to take photograph. This application requires continuous and moderate speed of internet connectivity and does not ensure about the user privacy and data security [4]. Governments of the UK, Canada and others introduced mobile applications for COVID-19 monitoring [5, 6]. An IoT system to track down both infected and uninfected patients was developed by Benreguia et al. The goal was to make the authorities alert [7]. Vedaei et al. proposed an IoT-based framework for health monitoring in post-pandemic situations. They have used apps and Fuzzy mechanism to calculate the risk of spreading of infections [8]. A design approach for wearable headset which would monitor different health parameters and will help in tracking of COVID-19 symptoms has been introduced by Radovan et al. [9]. This design approach describes how to monitor and measure the vital symptoms related to COVID-19. But, this wearable device is quite tough to make it feasible, and also, prolonged use of the headset may create other health problems and this device is only based on temperature sensing. However, the study shows that temperature is not the sole indicator for COVID-19 detection [9]. Dong et al. have described an IoT and cloud-fog computing-based framework which can be used for the prevention and control of COVID-19 [10]. Alsaeedy et al. have introduced a strategy for detecting

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high-risk regions for spreading COVID-19 with the help of existing cellular network functionality via UE mobility management protocols [11]. During the critical lockdown period in Italy, most of the people got information related to COVID-19 on online social media around them. So, a system for detecting and tracking relevant topics from Italian Tweets during COVID-19 has been introduced by Santis and his co-workers [12]. Roy et al. have developed an IoT framework for the contact tracing and monitoring. This approach has been used to identify the infected COVID-19 individuals [13]. The opportunities and challenges of emerging IoT technologies for COVID-19 have been analyzed in [14, 15]. In a related survey, Javaid et al. have presented a review of industry 4.0 technologies for COVID-19 [16]. Also, the dronebased technology for COVID-19 monitoring has been presented by Kumar et al. [17]. Additionally, an architecture for handling pandemic situation by social interaction tracking and health monitoring has been introduced in [18]. These existing systems require proper data transmission and internet connectivity which cannot be afforded by most of the people. Considering all these limitations of mobile applications and hardware devices, a smart embedded system for movement tracking and health monitoring of COVID19 patients has been proposed in this paper. The proposed design has the following objectives: (i) tracking the movement of the quarantined persons, (ii) monitoring the health parameters and (iii) updating the local authorities and hospital about patients. Employing proposed system, we can track the location of quarantined people by using Global Positioning System (GPS) module (Neo-6m GPS) and Global System for Mobile Communication (GSM) module. Other three modules of the proposed system are voice frequency detector, body temperature sensor and pulse sensor. Voice frequency detector is used to identify any unwanted person who is close to the quarantined person. Pulse sensor module is required to monitor the health condition of the quarantined people by measuring the heart rate. It has been observed that COVID-19 can lead to stroke [6]. During stroke, the pulse rate falls drastically. Hence, this device will alert concerned authority about the abnormal variation of pulse rate. These modules are connected with the Arduino UNO microcontroller. Also, a body temperature sensor module (DS18B20) records the body temperature of the person. The location, voice frequency, body temperature and heart rate of this person are sent to the registered mobile of local authority or monitoring hospital using a GSM module. The proposed tracking and monitoring system comes with the following features: (i) the local authorities can easily track and monitor quarantined patients by using this device, (ii) quarantined patients can reside at any place of their comfort, (iii) the hospital management can monitor the health parameters of the person by getting information such as body temperature, heart rate, (iv) this device can also detect whether any person comes in close contact with the quarantined person, (v) there is a reusability feature of our proposed model, (vi) the proposed model will be helpful for the suspected COVID-19 patients who cannot visit the hospital regularly for their health checkups and (vii) this model is also cost-effective, simple and easy to handle. This paper is organized for remainder portion as follows. Section 2 provides the overview of proposed design. Section 3 presents working principle of proposed

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design. Section 4 provides the PCB design and layout. Section 5 presents the experimental results and finally the paper is concluded in Sect. 6.

2 Proposed Design The proposed tracking and monitoring system for quarantined people has been designed and implemented in this paper. In proposed design, GPS, voice frequency detector, pulse sensor, body temperature sensor and GSM modules are connected with Arduino UNO microcontroller. The GSM module is used to transfer the monitoring data to local or hospital authority. The basic block diagram of the proposed model is presented in Fig. 1. The proposed model is user-friendly, compact, cost-effective and simple. The necessary components of proposed model are described in the following: 1. Arduino UNO: The main controlling device for the proposed system is Arduino UNO (IC 1136). The best features are low power consumption and high performance. Its operating voltage is within the range of 6–20 V. It is basically a microcontroller board which is based on Atmega328P. This microcontroller provides a profitable and easy solution for many complex embedded applications. 2. Neo-6m GPS module: It is a GPS receiver with complete facilities. The antenna provided in this module has a strong search capability and made of ceramic. It has an in-built EPROM to save configuration parameter data. 3. GSM module: The GSM module supports communication in a specified band only. The power requirement of GSM varies from model to model. Here, GSM module requires 5 V power supply. This module is used for sending SMS alerts to the concerned mobile phones.

Fig. 1 Block diagram of proposed model

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4. Voice frequency detector: LM567 is a tone decoder IC which is used for identifying a particular frequency band. Using this IC and a microphone, the frequency of the voice signal will be acquired which will be used to detect whether any other person has come close to the quarantined person or not. 5. Buzzer: Buzzer is basically an audio signaling device. It may be mechanical, electromechanical or piezoelectric. The buzzer is used to alert the person. 6. Pulse sensor: Pulse sensor module is used to measure the heart rate of the user continuously. 7. Body temperature module: DS18B20 temperature module is used for measuring the body temperature in Celsius scale, and temperature has a precision from 9 bits to 12 bits.

3 Mechanism of Proposed Model Figure 2 presents the circuit diagram of proposed design. Arduino UNO microcontroller is the central processing module of the proposed system. The Arduino UNO is connected with GPS module, GSM module, pulse sensor, temperature sensor module and voice frequency detector. The GPS module is used for tracking the location of the person in quarantine. An initial location of the person is stored in the microcontroller memory before providing the device to the concerned person. The Rx and Tx pins of the GPS module are connected to pins 4 and 3 of the Arduino UNO, respectively. Through these pins, data exchange with the microcontroller takes place. If the quarantined person violates the restrictions by moving beyond the specified range,

Fig. 2 Circuit diagram of proposed model

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the location data will be captured by the GPS module and will be provided to microcontroller. The microcontroller will alert the person by ringing the buzzer and transfer the location data to the GSM module. The voice frequency detector is used to detect whether any other person comes in close contact with the quarantined person by tracking the voice signals. The voice frequency of a male is ranging between 85 and 180 Hz, while for a female, it is from 165 to 255 Hz under normal condition. A voice sample of the quarantined person is loaded in the device initially. The input signal is taken using microphone sound sensor and the output is connected with the A1 pin of the Arduino UNO. The voice frequency detector is equipped with a microphone. The microphone captures the voice signal and compares it with the pre-stored voice signal of the concerned person. If the two signals match, then no further process will be initiated. If a mismatch is detected, then the buzzer rings to make the person alert. Meanwhile, the GPS module extracts the location information and transfers it to GSM module. Pulse sensor module is used to monitor the heart rate of the quarantined person. The heart rate has a direct relationship with the respiratory system. At the time of providing the system, the heartbeat of a normal person is pre-stored in the device. The output of this module (Vout) is connected to A0 pin of the microcontroller. The data are collected from the pulse sensor module and displayed on OLED display and compared. If any abnormality is found in the heart rate, then the data are transferred to GSM module. DS18B20 module is used for the measurement of the body temperature of the person. It has three pins, GND, VDD and data pin. Data collected from the sensor are displayed on the OLED display. If any unexpected temperature variation is found, then the data are sent to GSM module. GSM module is connected with the microcontroller through its Tx and Rx pins which are connected to pins 2 and 5 of microcontroller, respectively. The GSM module transfers the data provided by other four modules in the form of message to the local authorities. After making the device turned on, it monitors data from the four modules, namely voice frequency detector, body temperature sensor module, GPS module and pulse sensor module for first 5 min; then, the average value of the respective modules is set as a threshold value. The microcontroller will record the data from different modules and compare them with their respective threshold value. If there is any abnormal change compared to the respective threshold value, then a warning message is sent to the local authority or monitoring hospital. The proposed device can be reprogrammed using Arduino codes. The working flow of proposed model is described in Fig. 3.

4 PCB Layout and Design of Proposed Model Printed Circuit Board (PCB) is the physical realization of electronic circuit. In this work, PCB Wizard is used to design our proposed model. At first, the components from PCB component gallery are taken, and then, circuit is connected in the PCB Wizard. Ultimately, PCB solder side artwork of proposed design is developed. The PCB design of proposed model is shown in Fig. 4. Also, the PCB layout of the proposed model is presented in Fig. 5.

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Fig. 3 Working flow of proposed system

5 Results In this section, the analysis of the experimental results has been presented. Arduino IDE simulator has been used for testing the proposed system, where outputs have been observed in serial plotter and serial monitor. After testing the Arduino IDE simulation, we have developed the hardware of proposed architecture. Figure 6 represents the hardware implementation of proposed cost-effective system. In our implemented system, the heartbeat rate and body temperature have been measured by touching the fingertip on these modules. From the nearest hospital, dataset of actual heartbeat rate and body temperature are collected for comparison with the experimental results. The system records the body temperatures using DS18B20 module in degree Celsius (°C), and the results are converted into degree Fahrenheit (°F). The measured heartbeat rate and body temperature of the particular user are compared with the dataset values. The voice frequency and the GPS module have also been investigated and tuned in a similar manner. If the data received from respective modules deviate from the expected value, then it is notified to registered mobile user through GSM module. Functionalities of whole system are investigated and outcomes show that the proposed system is working efficiently. The experimental outcomes of the proposed system have been observed in module wise such as body

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Fig. 4 PCB design of proposed model Fig. 5 Solder side artwork PCB layout of the proposed model

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Fig. 6 Hardware implementation of proposed model

temperature module, heartbeat rate, voice frequency and GPS location. The GPS location output within the serial monitor of Arduino IDE is shown in Fig. 7. Figure 8 shows the comparison between experimental results and the actual dataset for body temperature module, pulse sensor module and voice frequency module, respectively.

Fig. 7 Output of GPS location using Arduino IDE serial monitor

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Fig. 8 a Actual and measured body temperature values (°F) b measured voice frequency (Hz) c actual and measured heart rate (BPM)

Functionalities of whole system are observed by doing several numbers of trails and outcomes show that the proposed system is working efficiently.

6 Conclusion The proposed cost-effective tracking and health monitoring system is designed for tracking and monitoring of quarantined people hence to curb down the spread of COVID-19. The proposed model has a social impact and can make the local authorities alert if any abnormality is identified in terms of heart rate, temperature and location of any COVID-19 patient. The health condition of the quarantined people can be monitored remotely by measuring the heart rate and body temperature. This will also make a doctor alert beforehand and will help them to respond quickly. The future goal of this proposed design is to improve the performance and efficiency. Also, IR sensor can be employed to identify whether the quarantined person has met with other person or not.

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