Nanoelectronics, Circuits and Communication Systems: Proceeding of NCCS 2018 (Lecture Notes in Electrical Engineering, 642) 9811528535, 9789811528538
This book features selected papers presented at the Fourth International Conference on Nanoelectronics, Circuits and Com
136 32 27MB
English Pages 616 [586] Year 2020
Committees
Patron:
Chairman Committee:
Co-chairman Committee:
Organizing Secretary:
General Chair:
Convener:
International Advisory Committee:
National Advisory Committee:
Technical Program Committee:
Joint Secretary:
Treasurer:
Preface
Editorial Acknowledgements
Contents
About the Editors
Broad Band Capacitive Coupling Antenna for C-Band Applications
1 Introduction
2 Methodology
3 Optimization
4 Conclusions
References
Energy-Efficient Waste Management System Using Internet of Things
1 Introduction
2 Literature Review
3 Proposed System
3.1 IR Sensor
3.2 Wi-Fi
3.3 ATmega 328P-PU
3.4 Web Page and Mobile App
4 Conclusions
5 Future Scope
References
Detection of Lesions in Mammograms Using FRFT-Enhanced Homomorphic Filter and Adaptive Thresholding
1 Introduction
2 Materials and Methods
2.1 Homomorphic Filtering
2.2 Fractional Fourier Transform
3 Proposed Methodology
4 Implementation and Results
5 Conclusion
References
A Novel Super-Resolution Reconstruction from Multiple Frames via Sparse Representation
1 Introduction
2 Background
2.1 Imaging Model
2.2 Sparse Representation of the Image
2.3 Dictionary
3 Proposed Super-Resolution Algorithm
3.1 Registration
3.2 Feature Extraction
3.3 Noise Filtration
3.4 Weight Estimation
3.5 Dictionary Training
3.6 Sparse Coding
4 Experimental Results
5 Conclusions
References
Design and Performance Analysis of Different Structures for Low-Frequency Piezoelectric Energy Harvester
1 Introduction
2 Mathematical Modeling
3 Designing Parameters of Cantilever Beam
3.1 Designing Parameters of the Tapered-Shaped Cantilever Structure
3.2 Designing Parameters of the Angled-T-Shaped Cantilever Structure
3.3 Designing Parameters of the A-Shaped Cantilever Structure
4 Results and Discussion
4.1 Modal Analysis
4.2 Dynamic Analysis
4.3 Piezoelectric Analysis
4.4 Stress Analysis
5 Conclusion
References
Fault Management Testing in Optical Networks Using NMS
1 Introduction
2 Fault Management Testing Setup
3 Alarm Types
4 Alarm Heirarchy
5 Test Scenario
6 Conclusions
References
Effects of Fin Height on Digital Performance of Hybrid p-Si/n-InGaAs C-FinFETs at 25 nm Gate Length
1 Introduction
2 FinFET Description and Simulation
3 Model Validation
4 Results and Discussion
5 Conclusion
References
A Takagi-Sugeno Based Fuzzy Inference System for Predicting Electrical Output in a Combined Cycle Power Plant
1 Introduction
2 Related Work
3 The Takagi-Sugeno Fuzzy Model
4 Fuzzy Inference Model Representation for CCPP
4.1 Establishment of Membership Function
4.2 Formation of Fuzzy Rules
5 Experimental Results
6 Conclusion
References
Smart Irrigation System Using Custom Made PCB
1 Introduction
2 Proposed System
3 Challenges
4 Conclusion and Future Scope
References
Efficient Advanced Encryption Standard with Elevated Speed and FPGA Implementation
1 Introduction
2 Related Work
3 Proposed Work
4 Experimental Results
4.1 RTL View of AES
4.2 About our Used Inputs
4.3 Output
5 Conclusion
References
Multi-class Cancer Classification in Microarray Datasets Using MI-Based Feature Selection and Artificial Neural Network
1 Introduction
2 Literature Survey
3 Proposed Approach for Cancer Classification
3.1 Mutual Information Based Gene Selection
3.2 Artificial Neural Network
4 Results and Discussion
5 Conclusion
References
Smart Communication in Coal Mines
1 Introduction
2 Historical View of Communication on Coal Mines
3 Need for Voice Communication
4 Modern Means of Communication in Coal Mines
4.1 Extraction’s Method
4.2 Communication
5 Personal Emergency Device
6 Impact Tracking System and Its Core Components
6.1 Wi-Fi Access Points as a Readers
6.2 Zone Display Units
6.3 Collision Avoidance Systems
7 Conclusion
References
Challenges of WSN in the Fossil Fuel Industry with Recommended Sensor Node Architecture
1 Introduction
2 Oil Industry Scenario
2.1 Leakage Detection with Traditional Monitoring
2.2 Obligation of WSN
3 Concerning Issues for WSN
3.1 Wireless Ad Hoc Sensor Network Dependencies
3.2 Contemplation Aspects of WSN in the Oil Industry
4 Recommended Sensor Node Architecture for Oil Industry
5 Conclusion and Future Work
References
FPGA-Based Smart Irrigation System
1 Introduction
2 Significance of Irrigation
3 Irrigation Procedure
3.1 Channel System
3.2 Sprinkler System
3.3 Drip System
3.4 Smart Irrigation System
4 System Components
4.1 Soil Moisture Sensor
4.2 Temperature Sensor
4.3 Humidity Sensor
4.4 Field Module
4.5 Control Module
5 Conclusion
References
Smart Healthcare System Using IoT
1 Introduction
2 Related Work
3 System Overview
4 Architectural Details
4.1 Nordic nRF52832 Microcontroller
4.2 Sensor Network
4.3 Piezoelectric Buzzer
4.4 User Interface
4.5 Ambulance Tracking Device
5 Conclusion
References
An Area-Optimized Architecture for LiCi Cipher
1 Introduction
1.1 Contributions
1.2 Framework of This Chapter
2 Algorithm Overview
3 Proposed Architectures
3.1 Round-Based Architecture
3.2 Modified Round-Based Architecture with a Single S-Box for the Plaintext
3.3 Modified Round-Based Architecture with Two S-Boxes Common for the Plaintext and the Key
4 Assessment Environment
5 Hardware Comparison
6 Conclusion
References
A Novel Hardware Architecture for Rectangle Block Cipher
1 Introduction
2 The Rectangle Cipher
2.1 AddRoundKey
2.2 Substitution
2.3 ShiftRow
2.4 Round Constant
2.5 Key Scheduling
3 Hardware Architecture of Rectangle Cipher
3.1 Proposed Novel Hardware Architecture of RECTANGLE Block Cipher
3.2 Finite State Machine
4 Experimental Evaluation
4.1 Platforms
4.2 Results
5 Discussion of Results
6 Conclusion
References
Design and Implementation of an Accurate 3-Bit System-on-Chip (SoC) Flash ADC for Aerospace Application in 90 nm CMOS Technology
1 Introduction
2 Methodology
2.1 Threshold Inverter Comparator
2.2 Wallace Tree Encoder
2.3 Multiplexer Based Encoder
2.4 Proposed Design
3 DNL and INL Analysis
3.1 Differential Non-Linearity (DNL)
3.2 Integral Non-Linearity (INL)
4 Process Corner of Flash ADC Analysis Using Proposed Encoder
5 Conclusion
References
Performance Evaluation of PV Module with Battery Storage in a Microgrid
1 Introduction
2 Configuration of Whole System
2.1 Photovoltaic System
2.2 DC-DC Boost Converter
2.3 Battery
2.4 Bidirectional Buck-Boost Converters
2.5 Voltage Source Inverter (VSI)
2.6 Filter
2.7 Transformer
2.8 Utility Grid
3 Maximum Power Point Tracking (MPPT)
3.1 Maximum Power Point (MPP)
3.2 MPPT Technique
4 Simulink Models
4.1 Implementation of MPPT
4.2 PV Array with MPPT Function
4.3 Simulink Model of Whole System
5 Results and Discussions
5.1 PV Array with MPPT
5.2 Grid-Tied Mode
5.3 Island Mode
6 Conclusions
References
Selection on Various Traditional Image Encryption Techniques: A Study
1 Introduction
1.1 Traditional Cryptographic Algorithm
1.2 Pseudorandom Number Generator
2 Literature Review
3 Research Methodology
4 Comparative Study of the Traditional Encryption Algorithm for Image
5 Conclusion
References
Selection on Traditional Cryptographic Algorithm for Real-Time Video Transmission and Storage: A Study
1 Introduction
1.1 Traditional Cryptographic Technique
1.2 File Scheduling Algorithm
2 Literature Review
3 Research Methodology
4 Comparative Study of Traditional Algorithm for Video Encryption
5 Discussion and Conclusion
References
Gini Index and Entropy-Based Evaluation: A Retrospective Study and Proposal of Evaluation Method for Image Segmentation
1 Introduction
2 Related Works
2.1 Image Segmentation
2.2 Evaluating Image Segmentation
2.3 Gini Index
2.4 Gini Based Segmentation
2.5 Entropy
2.6 Entropy-Based Segmentation
2.7 Entropy-Based Evaluation
2.8 Gini Index and Entropy
2.9 Gini Entropy
3 Proposed Method
4 Experimental Analysis
5 Discussion and Future Work
6 Conclusion
References
Image Encryption Scheme Using Chaotic Ring Oscillator
1 Introduction
2 Chaotic Ring Oscillator
3 Encryption Algorithm
4 Experimental Result and Security Analysis
4.1 Key Space Analysis
4.2 Histogram Analysis
4.3 Correlation Analysis
4.4 Information Entropy Analysis
4.5 Differential Attack Analysis
5 Conclusions
References
Wavelet Transform-Based Fusion of SAR and Multispectral Images
1 Introduction
2 Wavelet Transform for Image Fusion
3 Data Used
4 Methodology
4.1 Rule 1: Simple Averaging
4.2 Rule 2: Absolute Maximum of Coefficients
4.3 Rule 3: Based on Local Energy of the Neighbourhood
4.4 Rule 4: Maxima Over a Local Neighbourhood
5 Quality Assessment
5.1 Bias
5.2 Standard Deviation Difference (SDD)
5.3 Variance (VAR)
5.4 Correlation Coefficient (CC)
5.5 Entropy
6 Results and Discussion
7 Conclusion
References
Intelligent Streetlight System Using GSM
1 Introduction
2 Methodology
2.1 Basic Model
3 Literature Review
3.1 History
3.2 From IEEE Reference
3.3 Design of New Intelligent Street Light Control System
3.4 WSN for Intelligent Street Lighting System
3.5 Company Working on Similar Technology IoTcomm Technologies, China
4 PCB Design
4.1 Microcontroller Section
5 Future Works
5.1 Smarter Hospitals
5.2 Innovative Transportation
References
Mitigating Effect of Communication Link Failure in Smart Meter-Based Load Forecasting
1 Introduction
2 The Load Classification Method
2.1 K-Nearest Neighbor Classification Method (KNN)
2.2 Random Forest Method
2.3 Support Vector Classification Method
3 Load Forecasting Using Load Classification
4 The NIT Patna Campus Smart Grid
5 The Results
6 Comparison with Other Methods
7 Conclusion
References
Internet of Things (IoT) Based Green Logistics Operations for Sustainable Development in the Indian Context
1 Introduction
2 Fundamentals
2.1 Green Logistics
2.2 Internet of Things
3 Related Studies
4 Indian Logistics Scenario
5 IoT in INDIA
6 Application Areas of IoT
7 IoT Application in Logistics for SD
8 Conclusion and Future Scope
References
A Scientific Exploration of the Time Theory in Hindustani Ragas
1 Introduction
2 Related Work
3 Feature Extraction
4 Methodology
5 Results
6 Conclusion
References
Frequency-Based Passive Chipless RFID Tag Using Microstrip Openstub Resonators
1 Introduction
2 Chipless RFID System
3 Design of Multiresonator Circuit and Disc Monopole Antenna
4 Result and Discussion
5 Conclusion
References
Automatic Bird Species Recognition Based on Spiking Neural Network
1 Introduction
2 Proposed System
3 Result
4 Conclusion
References
Learning Discriminative Classifier Parameter for Visual Object Tracking by Detection
1 Introduction
2 Proposed Parameter Learning Method
3 Experimental Results
4 Conclusion
References
A 46.8 µW/1.12 GHz 7th Stage New Ring Voltage Controlled Oscillator
1 Introduction
2 Prior Research
3 Working Principle of Proposed VCO
4 Performance Metrics of Proposed VCO
4.1 Transient Analysis
4.2 Effect of Control Voltage Sweep on Output Frequency
4.3 Temperature Variation Versus Oscillation Frequency
4.4 Noise Analysis
4.5 Power and Delay Analysis
5 Physical Layout
6 Performance Comparison
7 Conclusion
References
A Current Mode VCO Design Approach for Higher Oscillation Frequency
1 Introduction
2 Description of the Proposed VCO
3 Simulation Results and Analysis
3.1 Power Delay and PDP Analysis
3.2 Phase Noise Analysis
3.3 Post-layout Analysis
4 Performance Comparison
5 Conclusion
References
Internet of Things Based Smart Lighting System Using ESP8266 and Zigbee
1 Introduction
2 Components
2.1 ESP8266 Chip
2.2 Zigbee
2.3 Monitoring Station
2.4 Presence Detector
2.5 Light Sensor
3 System Description
4 Methodology
5 Conclusion
References
Simulating and Designing of High-Order LPF with DFI
1 Introduction
2 Higher Order Filters
3 Simulation Results
4 Conclusion
References
Spelling Correction of OCR-Generated Hindi Text Using Word Embedding and Levenshtein Distance
1 Introduction
2 Related Works
3 Proposed Technique
3.1 Error Detection
3.2 Error Correction
3.3 Levenshtein Distance
3.4 WX Converter
3.5 Word Embeddings
3.6 Continuous Bag of Word Model
3.7 Error Correction With the Proposed Model
4 Result and Discussion
4.1 Working with an Example
4.2 Experimental Setting
4.3 Experimental Results
5 Conclusion and Future Scope
References
NLIIRS: A Question and Answering System for Unstructured Information Using Annotated Text Segment Comparison
1 Introduction
2 Utility
3 Advantages
4 Related Work
5 Proposed Model
6 Prerequisites
7 Results and Conclusions
8 Prerequisites
9 Limitations
10 Conclusions
References
A French to English Language Translator Using Recurrent Neural Network with Attention Mechanism
1 Introduction
2 Utility
3 Related Work
4 Proposed Model
5 Algorithms
6 Result
7 Conclusion
8 Prerequisites
9 Limitations
10 Abbreviation Table
References
Energy-Aware Clustering in Wireless Sensor Networks
1 Introduction
2 Related Work
3 WSN Clustering
4 Proposed Scheme
5 Simulation and Results
6 Conclusion
References
Glucometer: A Survey
1 Introduction
2 Type of Glucometer
3 Working Principle of Glucometer
3.1 Test Strip of Glucometer
3.2 Digital Glucometer
4 Glucometer’s Features
5 Advantages of Glucometer
6 Result and Conclusion
References
Performance and Comparison Analysis of Image Processing Based Forest Fire Detection
1 Introduction
2 Fire Detection Method
3 Simulation and Results
4 Conclusion
References
A Novel Hybrid Precoding Technique for Millimeter Wave
1 Introduction
2 Related Work
3 Proposed Work
4 Simulation and Results
5 Conclusion
References
K-NN Classification for Large Dataset in Hadoop Framework
1 Introduction
2 Background
3 Methodology
4 Results
5 Experimental Setup
6 Concluding Remarks
References
Performance Analysis of Novel Energy Aware Routing in Wireless Sensor Network
1 Introduction
2 Related Work
3 Proposed Model
4 Methodology
5 Simulation and Results
References
IoT-Based Moisture Sensor in Cement Industry
1 Introduction
2 Networking IoT
3 Programming IoT
4 Securing IoT
5 Equipment Monitoring
6 Predictive Maintenance and Quality
7 Connected Logistics
8 Conclusion
References
Internet of Things for Smart Class Rooms: A Review
1 Introduction
2 Smart Classroom Concept
3 Previous Work
4 Requirements
5 Primary Analysis
6 Practical Implementation
7 Limitation of IoT
8 Conclusion
9 Result and Discussion
References
Study and Design of Smart City with Internet of Things: A Review
1 Introduction
2 Methodology
3 Conclusion
References
Development of Heart Rate Monitoring Controller Using Fingertip Sensors
1 Introduction
2 System Hardware
2.1 Fingertip Sensor
2.2 Amplification and Finger Stage
2.3 Physical Properties
3 Results and Discussion
4 Conclusions
References
Bluetooth-Controlled Electronics Home Appliances
1 Introduction
2 Methodology
2.1 Android Phone
2.2 Bluetooth Module
2.3 Arduino Uno
2.4 IC L293DNE
2.5 Relay Switch
3 Working of the Process
4 Results and Conclusion
References
RFID (MF-RC522) and Arduino Nano-Based Access Control System
1 Introduction
2 Methodology
2.1 RFID RC522
2.2 Arduino Nano
2.3 16 * 2 LCD Screen
3 Working Principle
4 Advantages of Using RFID
5 Applications of Access Control System
References
Smart Irrigation System: A Review
1 Introduction
2 Proposed System
3 Working
4 Automatic Sensing Options
5 Type of Soil and System Controls
6 Components Required
7 Conclusion
References
Enactment of Advanced Traffic Control System Using Verilog HDL
1 Introduction
2 Infrared Sensors
3 System’s State Diagram
4 Conclusions and Results
References
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Lecture Notes in Electrical Engineering 642
Vijay Nath J. K. Mandal Editors
Nanoelectronics, Circuits and Communication Systems Proceeding of NCCS 2018
Lecture Notes in Electrical Engineering Volume 642
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 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 & 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 Federica Pascucci, Dipartimento di Ingegneria, Università degli Studi “Roma Tre”, Rome, Italy Yong Qin, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China Gan Woon Seng, School of Electrical & 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 Junjie James Zhang, Charlotte, NC, USA
The book series Lecture Notes in Electrical Engineering (LNEE) publishes the latest developments in Electrical Engineering - quickly, informally and in high quality. While original research reported in proceedings and monographs has traditionally formed the core of LNEE, we also encourage authors to submit books devoted to supporting student education and professional training in the various fields and applications areas of electrical engineering. The series cover classical and emerging topics concerning: • • • • • • • • • • • •
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Vijay Nath J. K. Mandal •
Editors
Nanoelectronics, Circuits and Communication Systems Proceeding of NCCS 2018
123
Editors Vijay Nath Department of Electronics and Communication Engineering Birla Institute of Technology Mesra Ranchi, Jharkhand, India
J. K. Mandal Department of Computer Science & Engineering University of Kalyani Kalyani, West Bengal, India
ISSN 1876-1100 ISSN 1876-1119 (electronic) Lecture Notes in Electrical Engineering ISBN 978-981-15-2853-8 ISBN 978-981-15-2854-5 (eBook) https://doi.org/10.1007/978-981-15-2854-5 © Springer Nature Singapore Pte Ltd. 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore
Committees
Patron: Dr. K. T. V. Reddy, President IETE New Delhi
Chairman Committee: Sh. K. K. Thakur, CGMT Jharkhand State Circle, ARTTC BSNL Ranchi Sh. V. B. Pandey, DOT BSNL Term Cell1 & Chairman ISVE Ranchi Sh. Ajay Kumar, AGM(HR) BSNL & Chairman IETE Ranchi Prof. P. S. Neelakanta, C. Engineering, Fellow IEE, Florida Atlantic University (FAU) USA Prof. J. K. Mondal, Professor, Kalyani University, W.B. Prof. C. K. Sarkar, Chairman IEEE Kolkata Section, W.B Prof. Vinay Gupta, Professor, Delhi University, Delhi Sh. Sanjay Kumar Jha, Immediate Past Chairman IETE Ranchi & Chief Executive Engineer Government of Jharkhand Prof. Subir Sarkar, Professor, Jadavpur University, W.B.
Co-chairman Committee: Dr. Sh. Dr. Dr.
Umesh Yadav, DDU GU. Rajan Kumar Ram, ARTTC BSNL Ranchi R. K. Singh Founder Chairman IETE Ranchi Anshuman Sarkar, Kalyani Government Engineering Colleges, Kalyani, W.B.
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vi
Committees
Organizing Secretary: Dr. Anand Kr. Thakur, Ranchi University & Treasurer IETE Ranchi
General Chair: Dr. Vijay Nath, BIT Mesra & Secretary IETE Ranchi
Convener: Dr. Raj Kumar Singh, Ranchi University & Executive Mem IETE Ranchi
International Advisory Committee: Prof. Bernd Michel, Micro Materials Centre (MMC) Berlin, Germany Prof. Bharath Bhushan, Ohio Eminent Scholar & The Horward D. Winbigler Professor, Director NBLL, The Ohio State University, Columbus, Ohio USA Dr. A. K. S. Chandelle, Immediate Past President IETE New Delhi Smt. Smriti Dagur, Former President IETE, New Delhi Dr. A. A. Khan, Former VC, Ranchi University, Ranchi Dr. M. K. Mishra, VC, BIT Mesra, Ranchi Dr. Ramgopal Rao, Professor & Director IIT Delhi Dr. P. K. Barhai, Former VC, BIT Mesra, Ranchi Dr. S. Pal, Professor & SD, Satellite Navigation, ISRO Bangalore Dr. M. S. Kori, Chairman IETE, TPC, New Delhi Sh. R. K. Gupta, Former President, IETE, New Delhi Dr. Rajendra Prasad, Professor, IIT Roorkee Dr. Labh Singh, Former CGM, ARTTC, BSNL, Ranchi Sh. R. Mishra, Former CMD, HEC, Ranchi Dr. Yogesh Singh Chauhan, Associate Professor, Nano Lab, Department of EE, IIT Kanpur Dr. S. N. Verma, Former CMD, EDC, Ltd. Jharkhand Sh. S. C. Thakur, Chief Engineer, Rural Electrification Energy Distribution Corporation Limited Jh. Sh. Ravindra Kr. Rakesh, Editor, Dainik Bhasker, Jharkhand Sh. Gopal Jha, Journalist, New Delhi Dr. A. N. Mishra, VC, Central University, Jharkhand
Committees
vii
Dr. R. Pandey, VC, RU, Ranchi Dr. A. Chakrabarty, Professor, IIT Kharagpur Dr. S. Banerjee, Professor, IIT Kharagpur Dr. Nandita Das Gupta, Professor, IIT Chennai Dr. L. K. Singh, Former Professor, Dr. RML AU, Faizabad Dr. B. S. Rai, Former Professor, MMMUT, Gorakhpur Dr. D. Samathanam, Former Adviser & Head TDT, DST New Delhi Dr. P. Chakrabarty, Professor, IIT BHU Dr. G. A. Murthy, Scientist-G, DRDO Hyderabad Dr. M. Srinivasa, Scientist-G, DRDO Hyderabad Dr. S. C. Bose, Scientist-G, CEERI Pilani Dr. Jamir Akhatar, Sr. Scientist, CEERI Pilani Dr. Arokiaswami ALPHONES, Vice–Chairman, IEEE Singapore Section & Professor, NTU Singapore Dr. K. Rajasekhar, Dy. Director General, NIC, DEIT, Mo CIT, Govt. of India, Hyderabad Dr. N. V. Kalyankar, VC, Marathwara University (MS) Dr. R. P. Panda, Professor, VSSUT, Burla, Odissa Dr. Allen Klinger, Professor, University of California Dr. Hisao Ishibuchi, Professor, Osaka Prefecture University, Japan Dr. T. K. Bhattacharya, Professor, IIT Kharagpur Dr. N. Gupta, Professor, BIT Mesra, Ranchi & Fellow Member IETE New Delhi Dr. V. R. Gupta, Professor, BIT Mesra, Ranchi & Fellow Member IETE New Delhi Dr. M. Chakrabarty, Professor, IIT Kharagpur Dr. A. S. Dhar, Professor, IIT Kharagpur Dr. D. K. Sharma, Professor, IIT Bombay Dr. Nandita Das Gupta, Professor, IIT Chennai Dr. B. Mishra, Professor, BIT Mesra, Ranchi Dr. Swaroop Gosh, Assistant Professor, University of South Florida Dr. S. P. Maity, Professor, IIEST, Shibpur Dr. S. K. Ghorai, Professor, BIT Mesra, Ranchi & Executive Member IETE Ranchi Centre Dr. M. Bhuyan, Professor, Tejpur University, Assam Dr. S. Hosimin Thilangar, Professor, Anna University, Chennai Dr. V. N. Mani, Senior Scientist, CMET, Hyderabad Dr. V. Kumar, Professor, ITT-ISM Dhanbad Dr. S. K. Paul, Professor, ISM Dhanbad Dr. J. P. Gupta, Former Pro-VC, DDU Gorakhpur University Dr. H. C. Prasad, Former Professor, DDU Gorakhpur University Dr. S. Bhaumik, Associate Professor, NIT Tripura Dr. P. D. Kashyap, Professor, NIT Arunanchal Pradesh Dr. J. Akhatar, Senior Scientist, CEERI Pilani Dr. S. Ahmad, Former Director, CEERI Pilani Dr. P. Kapoor, Former Director, CSIO, Chandigarh Dr. Uma Maheshwari, Professor, Anna University
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Committees
Dr. Sandip Rakshit, Professor, Kaziranga University, Assam Dr. Abhijit Biswas, Professor, Institute of Radio Physics & Electronics, Calcutta University Dr. Vikash Patel, SAC ISRO Ahmedabad Dr. Parul Patel, SAC ISRO Ahmedabad Dr. S. Pal, Professor, BIT Mesra Ranchi Dr. V. Bhattacharya, Professor BIT Mesra Ranchi
National Advisory Committee: Dr. Gaurav Trivedi, Assistant Professor, IIT Guwhati. Dr. B. K. Kaushik, Professor, IIT Roorkee Dr. K. K. Khatua, Professor, NIT Rourkela Dr. M. Bhaskar, Associate Professor, NIT Tirchi Dr. P. Kumar, Associate Professor, IIT Patna Dr. Soumya Pandit, Assistant Professor, Kolkata University Dr. Soma Berman, Assistant Professor, University of Cacutta Dr. K. B. Raja, Professor, Bangalore College of Engg, Bangalore Dr. R. P. Panda, Professor, VSSUT, Burla, Odisa Dr. P. R.Thakura, Professor, BIT Mesra, Ranchi Dr. S. S. Solanki, Professor, BIT Mesra, Ranchi Dr. Mahesh Chandra, Professor, BIT Mesra, Ranchi Dr. D. K. Malik, Associate Professor, BIT Mesra, Ranchi Dr. Nutan Lata, Associate Professor, BIT Mesra, Ranchi Dr. K. K. Senapati, Assistant Professor, BIT Mesra, Ranchi Dr. K. K. Patnaik, Associate Professor, IIIT Gwalior Dr. M. Goswami, Associate Professor, IIIT Allahabad Dr. Sukalayam Chakraborty, Assistant Professor, BIT Mesra Ranchi Dr. Lallan Yadav, Professor, DDU University Gorakhpur Dr. S. Chakrabarty, Professor, BIT Mesra, Ranchi Dr. D. Devaraj, Professor, Kalasalingam University, Tamilnadu Dr. J. S. Roy, Professor, KIIT Bhubneshwar Dr. N. K. Kamila, Professor, CV RCE, Bhubneshwar, Odisa Dr. B. K. Ratha, Associate Professor, Utkal University, Odisa Dr. A. Srinivasulu, Professor, Vignan University, Andhra Pradesh Dr. Manish Prateek, Professor, University of Petroleum & Energy Studies, Dehradun Dr. Vijay Laxmi, Professor, BIT Mesra, Ranchi Dr. V. K. Jha, Associate Professor, BIT Mesra, Ranchi Dr. R. K. Lal, Associate Professor, BIT Mesra, Ranchi Dr. L. B. Singh, Professor, RPSIT Patna Dr. H. S. Gupta, Senior Scientist ISRO, Bangalore Dr. N. S. Rao, Associate Professor, MECS, Hyderabad
Committees
Dr. Sh. Sh. Sh.
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Usha Mehta, Professor, Nirma Institute of Tech. Ahmedabad Manish Mehta, Senior Scientist, SAC ISRO Ahmadabad Vishvanath Vedam, Senior Scientist, SAC ISRO Ahmadabad Vijay Kumar Verma, Sceintist, ISRO Bangalore
Technical Program Committee: Dr. Kota Solomon Raju, Scientist-F, CEERI Pilani Dr. Amalin Prince, Associate Professor, BITS Pilani Goa Campus Dr. M. Mishra, Assistant Professor, DDU University Gorakhpur Dr. J. B. Sharma, Associate Professor, Rajasthan Technical University Kota Dr. Prabir Saha, Assistant Professor, NIT Meghalaya Dr. S. P. Tiwari, Assistant Professor, IIT Jodhpur Dr. Santosh Vishvakerma, Associate Professor, IIT Indore Dr. S. N. Shukla, Professor, Dr. RML Avadh University, Faizabad Dr. B. N. Sinha, Associate Professor, SSMC, Ranchi Dr. V. S. Rathore, Assistant Professor, BIT Mesra, Ranchi Dr. Manish Kumar, Assistant Professor, NERIST Dr. A. N. Jadhav, Professor, Y.M, R.T. Marathwara University, Nanded Dr. Sudip Sahana Assistant Professor, BIT Mesra Ranchi Dr. S. Pushkar, Assistant Professor, BIT Mesra Ranchi Dr. P. Pal, Assistant Professor, BIT Mesra Ranchi Dr. Amritanjali, Assistant Professor, BIT Mesra Ranchi Dr. Rishi Sharma, Assistant Professor, BIT Mesra Ranchi Dr. K. Bose, Assistant Professor, BIT Mesra Ranchi Dr. B. Achrya, Assistant Professor, NIT Raipur Dr. Anandi, Associate Professor, Department of ECE, Ramaiah Institute of Technology Bangalore Dr. Shylashree N, Assost. Prof., Department Of ECE RVCE Bengaluru-59 Karnataka Sh. Pramod Bhagawat Borole, Associate Professor, Veermata Jijabai Technological Institute, Mumbai Sh. Samuel Tensingh, Divya Nilayam Riverview colony, Anna nagar, Chennai.
Joint Secretary: Prof. Prof. Prof. Prof. Prof.
D. Acharya, President ISTM Kolkata Rajeev Ranjan, ISM, Dhanbad Amar Prakash Sinha, BIT Sindri Jayant Pal, NIT, Agarpara, Kolkata Adesh Kumar, Energy & Petroleum University, Dehradun
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Committees
Prof. J. Dinesh Reddy, BMS College of Engineering, Bangalore Prof. N. Srinivasa Rao, BMS College of Engineering, Bangalore Prof. P. Kumar, CIT Ranchi Sh. Ramkrishna Kundu, IBM, Bangalore Sh. Dipayan Gosh, GM Aircel, Kolkata Sh. S. Chakrabarty, IBM, Bangalore Prof. Jyoti Kumari, RBS, Bangalore Sh. Rahul Kumar Singh, ST Microelectronics, Noida Sh. Suraj Kumar, NIT Agartala Prof. Anand Kr. Singh, GNIT, Gaziabad Prof. Abhishek Pandey, Usha Martin University Ranchi & Secretary ISVE Ranchi Sh. Bidyut Mahto, IIT ISM Dhanbad
Treasurer: Dr. Anand Kr. Thakur, IETE Ranchi Sh. S. K. Saw, NCCS-2018 Smt. Saroj, ISVE Ranchi
Preface
Our conferences focus on the development of new technologies such as 5G and beyond which is going to take place in 2020. For this purpose, our mission is to focus on research to prepare software, compatible hardware, innovative products to meets its feasibility/requirements. 5G denotes a new era in which networks will adapt to applications and performance will be tailored precisely to the needs of users, according to the trade body GSM Association. The International Telecom Unit, the UN agencies shepherding development of the so-called IMT2020 standard for 5G technologies has said the upcoming universal specification will support a million connected devices per square kilometre, 1 millisecond latency or the amount of time a packet of data takes to set one point to another, higher energy and spectral efficiency a real data downloading speed upto 20 Gbps. 5th Generation of connections which capacity will be 10 Tbps per square kilometre and much higher density than the existing set-up about one million nodes per square kilometre. According to technology consulting firm iRunway worldwide more than five core companies are working in this field such as Qualcomm, Samsung Electronics, Intel, Ericsson of Sweden and Finland’s Nokia, Huawei and ZTE, etc. Maybe in 2019 5G tailored will be launched and fully functional in the year 2020. According to the telecom operator information deployment will be easier than 3G or 4G. Aerials will be able to cover larger areas and more devices and it will twist as user density. In 5G and beyond no cables are needed to ensure high speed, almost instantaneous communications, and stable connections. As a result, a new scenario of communication between smartphones and M2M (Machine to Machine) is also opened. According to Intel, it is estimated that there will be 50 billion connected devices by 2020 that is major benefit of 5G. In 5G all the agriculturerelated information will be delivered to their mobile without any high cost. By which former can know the information of soil, seeds, weather, fertilizers and market of crops time to time without any interference. There are number of jobs will be created in all major sectors. In 5G no need to download any agricultural app on your systems it will be in cloud and you can access directly without any interruption. So it would be in think of agricultural app with desktop power that could be played on smartphones, since processing would not be on the device itself, but in xi
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the cloud. The image would be reaching the phone in almost game real time. Some important research areas highly needed to enable the 5G in reality, e.g. (i) Future Radio Networks (FRN), (ii) Internet of Things (IoT), (iii) Cloud Services Oriented Networks (CSON), (iv) Security, Privacy and Internet Governance (CPIG), (v) Green Technologies (GT), (vi) Nanoelectronics, Circuits & Systems (NCS), (vii) smart grids (SG), etc. Benefits of technologies are: (i) It will help in decreasing crime if applied on whole, (ii) It will help in getting things done easily, (iii) It will help in decreasing documentation, (iv) Some of the services which will be provided through this desired effort are Digital Locker, e-education, e-health, e-sign and nationwide scholarship portal, etc. Really, this technology helpful to digitalization of India and worldwide and it will give the path to develop smart national & international connectivity. The growth rate in populations can only be facilitated when the new technology will be successfully launched time to time. 5G will give the new communication revolution in the country like India & worldwide. Surely, this technology is challenging and opening a lot of research options for research. To seeing the issues researchers, professors and industrialists are thinking to develop robust, low power consumable, high speed, high efficiency, low cost items for societies such as in medical devices, irrigation, aerospace, communications, transportations, military applications, developing new weapons, satellite, etc. Production of electronics goods provides major facility to world class market for high level digital portability, connectivity, safety and security in all directions of human and machinery needs. Nowadays development in electronics goods provides major facility to world class market for high level digital portability, connectivity, safety and security. Designs of complex ICs are possible due to EDA software such as Cadence, Mentor Graphics, Synopsis, Xilinx and Active HDL. As a design engineer we observe that up to 2015 in desktop more than 1 billion of transistors were exist. For its complete design having more than 500 rules and its in statistical nature. Now, three types of advanced techniques are playing major role in ICs design and manufacturing, such as full custom ICs design, semicustom ICs design and ASIC design. As per the demand of market for sophisticated systems designers/engineers are playing with automated EDA software tools for efficient and bulk design. These tools consider the length of transistor in nanoscale and lesser than that. For these range designed ICs required good support of nanomaterials and its related chemicals. Testing of ICs is also major issue in sophisticated systems. Testing should be in each step of design from SPICE level, schematics level, layout level and fabrication level, packaging level, etc. As level of design increases the testing cost of ICs also increases. Without efficient software support testing of ICs is also difficult. SoC (System on Chip) provides an excellent platform where analog and digital ICs layout can make together and fabricate on a single wafer. With the execution of such recommended facility the number of ICs/chips accommodated in single wafer, fabrication cost decreases and product quality increases. Nanotechnology allowed the integration of purely electronics devices, chips, circuits and systems. The nanoscale dimensions of nanoelectronics components for systems for giga-scale complexity are measured on a chip or in a package. Nanotechnology improve the
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capabilities of electronics components by reducing the size of transistors in ICs, increase the density of memory chips, reduce the power consumption and improve the display screen, thickness, etc. In global automation, control and functional environment IoT is playing a major role today. Now, you can imagine a single example of cashless world. The computing systems which are playing behind the cashless world are more reliable, robust, correct and highly accurate. Now this system is going to adopt for smart education system for making a global education hub. It is also adopted in global factory operation, control and monitoring for perfect and optimized products. This conference provides an excellent forum to young researchers, engineers and professors to work together and share their knowledge. It also gives ideas how to work in electronic media safely and securely. Manufacturing companies/industries and Education Universities are well contributed in the development of any country. However, it is facing several challenges such as rapid product development, flexibility, and low to medium volume, transportation and low cost, etc. Many advanced/ unconventional technologies/tools/ software are being developed worldwide to face these challenges. Among these technologies IC design and manufacturing, Internet of Things has become more popular due to ability of precise work. For the research, development, sharing knowledge and exchange ideas in current trends the 4th International Conference on Nanoelectronics, Circuits & Communication Systems (NCCS-2018) has been organized by Indian Society of VLSI Education (ISVE) Ranchi & Institution of Electronics & Telecommunication Engineers (IETE), Ranchi Centre at Advanced Regional Telecom Training Centre (ARTTC) BSNL near Jumar River Hazaribag Road Ranchi from 3–4th November 2018 This conference cover advancement in NEMS & Nanoelectronics, Wireless Communications 5G and beyond, Optical Communication, Instrumentation, Signal Processing, Internet of Things, Image processing, Bioengineering, Green Energy, Hybrid Vehicles, Environmental Science, Weather Forecasting, Cloud Computing, Renewable Energy, RFID, CMOS Sensors, Actuators, Transducers, Telemetry Systems, Embedded Systems, Sensors Network Applications in Mines, etc. In another hand, providing training to prepare manpower for electronic system design & manufacturing for upcoming technology like 5G and beyond which have opened market a lot of scope in all directions in the coming era. Density of human of the countries are increasing day by day; therefore, in global market product should be prepared in enough amount by which serve worldwide. It is possible with the help of research and development only. As the country and worldwide people are moving in digital era where knowledge of safety, security, performance, flexibility, feasibility are very-very important that comes through technical meetings, conferences, workshops, seminars on current trends of technology. NCCS-2018 is one part of them that provides forum to young researches for developing their knowledge in current trends and provide the solutions to upcoming technologies 5G and beyond as per need of world market. In this conference around 400 papers have been received, in which 63 peer blind reviewed, registered and presented papers have accepted for publication in conference proceeding of Springer Scopus book series Lecture Notes in Electrical
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Engineering (LNEE) and 05 outstanding papers were selected for SCI Journals like Microsystem Technologies and IETE Technical Review & Technical Research, etc. In parallel conference, excellent reviewer’s team guided to authors for extended version of the research articles, i.e. 70–80% improvement/changes with new content and titles forward for publication in listed SCI and Scopus journals. All these articles are blindly peer-reviewed by at least two to three reviewers and detailed comments were passed to concerned authors with decisions. We are also restricting the plagiarism articles for publications more than 10–15% plagiarism articles are not accepted for book/journal publications. In the presentation session six to ten expert reviewers’ teams evaluated their work. They also guided authors for IPR/patents and new innovative project for funding in their area of research. These series of conferences (NCCS-2018) organized by ISVE & IETE Ranchi in support of ARTTC BSNL Ranchi that accelerates the unique platform for young researchers, scientists, engineers and professors for exploring their work worldwide. NCCS-2018 conference expert committee guided to authors how articles will improve? And how it will be published in high impact journal and book series? This unique conference is leading by a group of experts that provides guidance to authors for enhancing or updating their articles of contents. This forum also provides outcome-based learning & research strategy. 4th International Conference on Nanoelectronics Circuits & Communication Systems (NCCS-2018) was organized by IETE, ISVE & BSNL at ARTTC BSNL Ranchi on 3–4th Nov. 2018. In the conference consent received from dynamic and visionary Chief Minister Shri Raghubar Das and Honourable Governor of Jharkhand Smt. Draupadi Murmu and President IETE New Delhi Dr. K. T. V. Reddy and their message have published in the conference souvenir with all other dignitaries for grand success of this two day events. Dignitaries present on dais Shri Vinay Kumar Choubey, Principal Secretary IT, Government of Jharkhand as chief guest; Prof. K. T. V. Reddy President IETE New Delhi as guest of honour; Prof. S. Pal, Former President IETE New Delhi and Former VC Defence Institute of Technology Pune as guest of honour; Lt. Gen(Dr.) A. K. S. Chandel, PVSM, AVSM(Retd), Immediate Past President IETE New Delhi as guest of honour; Shri K. K. Thakur, CGMT BSNL Jharkhand Circle Ranchi as Chairman IETE Ranchi Centre; Dr. Abhijit Biswas, Professor Institute of Radio Physics & Electronics Calcutta University (WB) as Keynote Speaker; Dr. Mahesh Chandra, Professor & Dean, BIT Mesra Ranchi as guest of honour; Dr. S. S. Solanki, Professor and Director University Polytechnic BIT Mesra as guest of honour; Dr. Vijay Nath, Honorary Secretary IETE Ranchi and faculty BIT Mesra as General Chair of NCCS-2018; Dr. Anand Kumar Thakur, honorary treasurer and faculty Ranchi University as Organizing Secretary NCCS-2018; Dr. R. K. Singh, Former Professor and Founder Chairman IETE Ranchi as guest of honour; Dr. Raj Kumar Singh, Faculty Ranchi University & Executive member IETE Ranchi as Convenor NCCS-2018 and Smt. Saroj Jha as companion Durdarshan Ranchi for demonstrating the function. Program started by welcome address by dynamic and visionary chairman of IETE Ranchi centre Shri K. K. Thakur CGMT BSNL Government of Jharkhand. He welcomes all the participants and delegates who came from across the country
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to present their views. General chair NCCS-2018 Dr. Vijay Nath demonstrates the whole technical activities and blind review procedure and publications of outstanding articles in SCI Journals and Scopus book series by world-renowned publisher Springer and Taylor Francis. Dr. K. T. V. Reddy presides the meeting and explain the importance of SCI & Scopus publications. He appreciates the activities of IETE Ranchi centres and also expresses his views to open IETE University in Ranchi in near future. He says, here people are laborious, committed to their work and so on. Dr. S. Pal expresses their experience in ISRO and explains the role of developed CMOS devices for defence in long back approx1955–60. When other countries were hesitating to instal these devices at that time he developed and successfully utilized in his mission. We are really glad to find such dignitaries on the dais. He also expresses the challenges of technologies. Dr. A. K. S. Chandel explains the needs of such international conference to the growth of state and country. Without research any country cannot develop independent environment. They appreciate efforts of IETE Ranchi centre and its unique team. Dr. Mahesh Chandra andDr. S. S. Solanki appreciate such wonderful activity of IETE Ranchi centre where every part of country renowned scientists, professors and research scholars, stake holders from IITs, NITs and other reputed technical institute/ University/industry of the country are available to present their research articles and express their views. This is good sign for development of Jharkhand where continuous growth is taking place. Dr. Abhijit Biswas delivered his keynote lectures on Nanoelectronics and explain the importance of Nanoelectronics in near future. Dr. Raj Kumar Singh explains the technical session activities and other hospitality. Shri Vinay Kumar Choubey, IAS, Principal Secretary Government of Jharkhand release the book “Nanoelectronics Circuits & Communication Systems” edited by Dr. Vijay Nath (BIT Mesra) and Dr. J. K. Mandal (Kalyani University West Bengal). They express their views how to develop the state of Jharkhand and as sample of Digital State in the country. Where such events are essential to discuss together nationally and internationally challenges and its solutions. He appreciates such wonderful International forum where across country eminent personalities are eager to present their views and discuss the problems. He also agreed to support such society activities today as well as near future which have national and international importance. We would get the moral support from our dynamic and visionary Chief Minister Government of Jharkhand Shri Raghubar Das. We also get support from our Honourable Governor of Jharkhand Smt. Droupadi Murmu for such activities and her message/blessing has published in our souvenir NCCS-2018. IETE, ISVE & BSNL team always support to enhance the quality of papers, direction of research, capability to write article to SCI & Scopus levels. This is a great achievement of our team. We also express our thanks to IITs, NITs, BITs, and other top technical university of India for supporting as reviewers, authors, expert lectures, etc. The authors/ reviewers/ technical committee members who have served once and know the complete procedure of the forum they become fan and ready to serve the society work forever. After several review, scrutiny, comments, papers meets the quality of SCI Journals and Scopus book series and at last, it will be published if authors are ready to incorporate the comments and suggestions.
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Within four years of journey more than 500 Scopus and more than 100 SCI articles have been published through this platform. We are proud to say that due to our quality of articles editors of journals and books demanding the articles and publishing in their outstanding journals and book series. Shri Ajay Kumar Im. Past chairman IETE Ranchi explains the activities of IETE and its contribution to society such as ESDM, Digital India, Prime Minister Mission, women education and e-learning. Dr. Anand Kumar Thakur express the vote of thanks to all the participants, delegates, Government Officials, media persons and core team members who worked day and night to make program grand success. This conference covers four major technical sessions in which more than 80 papers have been presented by authors and reviewed by expert committees of each technical session. First Technical session: Nanoelectronics and VLSI Design starts with technical lecture of Prof. Abhjit Biswas (Calcutta University). After their technical lecture he take the charge of Chairman of the first technical session and ten other co-chairpersons: Dr. Mahesh Chandra, Dr. S. S. Solanki, Dr. D. Acharya, Dr. Rishi Sharma, Dr. Amritanjali, Dr. R. K. Lal, Dr. S. Chakraborty, Dr, Raj Kumar Singh, Dr. S. Pushkar and Dr. Madhu Priya evaluate the presentation of authors and individually write comments and at end of presentation handover comments to authors for incorporation in the research articles. In this session Bidyut Mahato et al. present Design and development of a Single-phase Multilevel Inverter, Prototype model implementation of a new Multilevel Inverter in Laboratory and Verification of new MLI in laboratory set-up using lesser power switches. Nukala Srinivasa Rao et al. explain Broad band Capacitive Coupling Antenna For C-Band Applications and Energy Efficient waste management system using Internet of Things. Jankiballabh Sharma et al. demonstrate Detection of lesions in Mammograms using FRFT Enhanced Homomorphic Filter and Adaptive Thresholding and Efficient VHDL Implementation of Turbo Coded MIMO OFDM Physical Layer for Wireless Communication. M. K. Mandal et al. present A Novel Super-Resolution Reconstruction from Multiple Frames via Sparse Representation. Namrata Saxena et al. explain Design and Performance Analysis of Different Structures for Low-Frequency Piezoelectric Energy Harvester. Dabhade, Sandeep et al. demonstrate the Fault Management Testing In Optical Networks using NMS. Kallolini Banerjee et al. demonstrate the Effects of Fin Height on Digital Performance of Hybrid p-Si/n-InGaAs C-FinFETs at 25 nm Gate Length. Shamama Anwar et al. explain A Takagi-Sugeno based fuzzy inference system for predicting electrical output in a combined cycle power plant. Sanjay Kumar Surshetty et al. explain the Smart Irrigation System Using Custom Made PCB. Pushpinder Kaur et al. define Efficient Advanced Encryption Standard with Elevated speed and FPGA implementation. M. Jansi Rani et al. illustrate Multi-class Cancer Classification in Microarray Datasets using MI-based feature selection and Artificial Neural Network. Shruti Garg et al. explain modelling and Forecasting of Bitcoin Prices using Bayesian Regularization Neural Network. Sanjay Kumar Surshetty
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et al. demonstrate the Smart Communication in Coal Mines. Devesh Mishra et al. demonstrate the Challenges of WSN in Fossil Fuel Industry with Recommended Sensor Node Architecture. Sanjay Kumar Surshetty et al. demonstrate the FPGA based Smart Irrigation System. Second Technical session: Advanced Signal Processing & Challenges: Chairman Dr. B. Acharya (NIT Raipur) and eight co-chairpersons: Dr. V. K. Jha, Dr. Sanjeet Kumar, Dr. Raj Kumar Singh, Dr. M. K. Mukul, Dr. Sanchita Pal, Dr. P. Pal, Dr. Anand Kumar Thakur and Dr. S. S. Sahu evaluate the presentation of authors and individually write the comments and at end of presentation handover comments to authors for incorporation in their research articles. In this technical session, Rahul Priyadarshi et al. explain Diamond Search Algorithms for Motion Estimation: A Review. Deepak Prasad et al. demonstrate the Smart Healthcare System using IoT. Bibhudendra Acharya et al. demonstrate An Area-optimized Architecture for LiCi Cipher. Rupesh Kumar et al. explain Performance evaluation of PV module with Battery Storage in a Microgrid. Keshav Sinha et al. demonstrate Selection on Various Traditional Image Encryption Techniques: A Study and Selection of Traditional Cryptographic Algorithm for Real-Time Video Transmission and Storage: A Study. Anuja Negi, et al. explains Gini Index and Entropy Based Evaluation: A Retrospective Study and Proposal of Evaluation Method for Image Segmentation. Manali Kedar et al. illustrate Wavelet Transform Based Fusion of SAR and Multispectral Images. Deepak Prasad et al. demonstrate Intelligent Streetlight System using GSM. Vibhas Kumar Vats et al. define Mitigating Effect of Communication Link Failure in Smart Meter based Load Forecasting. Garima Gupta et al. explain Video Watermarking—A Method Based on Key with Improved PSNR. Shailendra Kumar Singh et al. define Internet of Things (IoT) based Green Logistics Operations for Sustainable Development in the Indian Context. Janki Ballabh Sharma et al. demonstrate the Efficient VHDL Implementation of Turbo Coded MIMO OFDM Physical Layer for Wireless Communication. Sanjay Kumar Surshetty et al. define the Design and Implementation of an Accurate 3-bit System on Chip (SoC) Flash ADC for Aerospace Application in 90nm CMOS Technology. Third Technical session: Embedded System & Signal Processing: Chairman Dr. M. Bhaskar (NIT Tirchi) and eight co-chairpersons: Dr. G. Pal, Dr. K. Bose, Dr. Vijay Nath, Dr. K. K. Senapati, Dr. I. Mukherjee, Dr. Anil Kumar, Dr. S. K. Sahana and Dr. Arun Kumar evaluate the presentation of authors and individually write the comments and at end of presentation handover the comments to authors for incorporation in the research articles. In this technical session Saurabh Sarkar et al. explain A Scientific Exploration of the Time Theory in Hindustani Ragas. V. Karthikeyan et al. define the Frequency Based Passive Chipless RFID Tag Using Microstrip Open stub Resonators. Ricky Mohanty et al. explain the Automatic Bird Species Recognition based on Spiking Neural Network. Yashwant Kurmi et al. define Medical Image Segmentation by Key Points and Contour Enhancement. Vijay K. Sharma et al. demonstrate Learning Discriminative Classifier Parameter for Visual Object Tracking by Detection. Amar Prakash Sinha et al. define Adder and Subtractor Design Suitable for Quantum-dot cellular automata. Madhusudan
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Maiti et al. explain a 46.8 µW/1.12 GHz 7th Stage New Ring Voltage Controlled Oscillator. Suraj Kumar Saw et al. define A Current Mode VCO Design Approach for Higher Oscillation Frequency. Vidushi Goel et al. demonstrate the Internet of Things based Smart Lighting System using ESP8266 and Zigbee. Meena Singh et al. explain Design of Higher Order Low Pass Filter with Differential Floating Inductor. Shashank Srigiri et al. explain the Spelling Correction of OCR Generated Hindi Text Using Word Embedding and Levenshtein Distance. Banerjee Partha Sarathy et al. explain the NLIIRS: A Question & Answering based Information Retrieval from Unstructured Information without Using SQL. Akshay Sharma et al. define A French to English Language Translator using Recurrent Neural Network with Attention Mechanism. Randheer et al. illustrate Energy Aware Clustering in Wireless Sensor Networks. Pragati Prerna et al. design Gulcometer. Fourth Technical session: Green Energy & Robotics: Chairman: Dr. P. R. Thakura (BIT Mesra) and Co-chairperson: Dr. D. Acharya, Dr. Abhijit Biswas, Dr. S. S. Tripathy, Dr. Om Prakash, Dr. Mamta Singh, Dr. I. Mukherjee and Dr. S. K. Mahapatra were present and evaluate the presentation of authors and individually write the comments and at end of presentation handover comments to authors for incorporation in the research articles. In this technical session Lucky Singh et al. explain Performance & Comparison Analysis of Image Processing Based Forest Fire Detection. Tarun Gupta et al. define A Novel Hybrid Precoding Technique for Millimeter Wave. Ritesh Jha et al. demonstrate the K-NN classification for large dataset in Hadoop Framework. Sanjeev Kumar et al. shows the Performance Analysis of Novel Energy Aware Routing in Wireless sensor Network. Vidushi Goel et al. demonstrate the IoT based Moisture Sensor in Cement Industry and also explain the Internet of Things for Smart Classrooms: A Review. V. Nath et al. demonstrate the Study & Design of Smart City with Internet of Things: A Review. V. Nath et al. explain the Heart Rate Monitor controller using Fingertip Sensors and also explain Aurdino based Voice Controlled Robot. Abhishek Pandey et al. demonstrate Bluetooth Controlled on Electronics Home Appliances. Abhishek Pandey et al. shows RFID (MF-RC522) and Arduino—Nano based Access Control System. Abhishek Pandey et al. demonstrate Smart Irrigation System: A Review. Deepak Prasad et al. explain the Enactment of Advanced Traffic Control System using Verilog@HDL. In valedictory session IETE Chairman Shri K. K. Thakur, CGMT BSNL Jharkhand Circle Ranchi facilitate with mementos and certificates to all the session chairpersons. He also facilitatesall presenters with presentation certificate and wishes them grand success in future. In the end of valedictory session, Dr. Anand Kumar Thakur expresses the vote of thanks to all the participants, delegates, Government Officials, media persons, BSNL and core team members: Shri Ajay Kumar, Dr. Vijay Nath, Dr. Raj Kumar Singh, Mr. Deepak Prasad, Mr. Abhishek Pandey, Ms. Neelam Yadav, Mr. Rajanish Yadav, Mr. Shril Nair, Mr. Deepak Kumar, Mr. Abhay Kumar, Shri G. Majhi, Shri. N. Choudhary, Shri Mahendra Singh and Shri Neelesh Singh who worked day and night to make program grand success. We also thank Shri Ramashray Prasad, GM Marketing BSNL; Shri Sujeet
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Kumar GM BSNL Mobile, Shri V. K. Maurya Principal ARTTC BSNL and other BSNL officials for providing infra-structure facility and supports. In this conference, authors are invited for the original papers submission and quality of papers has been selected for presentation. Authors are described their article in above-mentioned domain very well. Authors and editors have taken utmost care in presenting the information and acknowledging the original sources whenever necessary. Editors express their gratitude towards the authors, organizers of IC-NCCS and staff of Springer (India) for publication of this research book/proceeding possible. Readers are requested to provide their valuable feedback on the quality of presentation and inadvertent error or omission of information if any. We expect that the book will be welcomed by students as well as practicing engineers/researchers/professors. Ranchi, India
Vijay Nath
Editorial Acknowledgements
We extend our thanks to all the authors for contributing to this book/proceeding by sharing their valuable research findings. We specially thank a number of reviewers for promptly reviewing the papers submitted to the conference. We are grateful to the volunteers, invited speakers, session chairs, sponsors, sub-committee members, members of International advisory committee, and members of national advisory committee, members of technical program committee, members of joint secretary and members of scientific advisory committee for successful conduct of conference. The editors express their heartfelt gratitude towards Dynamic & Visionary Chief Minister Shri Raghubar Das of Jharkhand and Honourable Governor of Jharkhand Smt. Droupadi Murmu. The editors express their heartfelt gratitude towards Dr. K. T. V. Reddy President IETE New Delhi, Dr. A. K. S. Chandel Immediate Past President IETE New Delhi, Smt. Srimati Dagur Former President of IETE New Delhi, Prof. S. Pal Former VC IAT Pune, Sri Vinay Kumar Choubey Principal Secretary IT Government of Jharkhand, Sh. Rakesh Sharma Principal Secretary Higher Education Government of Jharkhand, Sh. Sudhir Tripathy Chief Secetary Government of Jharkhand, Sh. Sanjay Kumar Jha Former Chairman IETE Ranchi& Executive Engineer Government of Jharkhand, Prof. Bernd Michel Micro Materials Centre (MMC) Berlin Germany, Prof. Bharath Bhushan Ohio Eminent Scholar & The Horward D. Winbigler Professor & Director NBLL The Ohio State University Columbus Ohio USA, Prof. P. S. Neelakanta C. Engineering Fellow IEE Florida Atlantic University (FAU) U.S.A., Sh. K. K. Thakur Chairman IETE Ranchi & CGMT BSNL Jharkhand Circle Ranchi, Sh. Prasad Vijay Bhushan Pandey DTO Term Cell1 BSNL Ranchi & Chairman ISVE Ranchi, Prof. A. A. Khan Former VC Ranchi University, Prof. M. K. Mishra VC BIT Mesra, Prof. Gopal Pathak VC Jharkhand Technical University Ranchi, Prof. R. K. Pandey VC Ranchi University, Prof. P. K. Barhai Former VC BIT Mesra, Sh. R. Mishra Former CMD HEC Ranchi, Dr. M. Chakraborty Professor IIT Kharagpur, Dr. Ramgopal Rao Professor IIT Bombay & Director IIT Delhi, Dr. P. Chakraborty Professor IIT BHU & VC BESU Kolkata, Dr. S. Jit Professor IIT BHU, Dr. J. K. Mandal Professor Kalyani University, Dr. Abhijit Biswas Professor Kolkata University, Dr. Subir
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Editorial Acknowledgements
Kumar Sarkar Professor Jadavpur University, Dr. Gaurav Trivedi Associate Professor IIT Guwahati, Dr. Y. S. Chauhan Associate Professor IIT Kanpur, Dr. B. K. Kaushik Professor IIT Roorkee, Dr. Shree Prakash Tiwari Assistant Professor IIT Jodhpur, Dr. P. Kumar Associate Professor IIT Patna, Dr. M. Bhaskar Professor NIT Tirchy, Dr. Adesh Kumar Assistant Professor UPES University Dehradun, Dr. Manish Kumar Associate Professor MMMUT Gorakhpur, Dr. Manish Mishra Professor DDU University Gorakhpur, Dr. Umesh Yadav Professor DDU University Gorakhpur, Prof. D. Acharjee President ISTM Kolkata, Dr. N. Gupta Professor BIT Mesra Ranchi, Dr. S. Pal Professor BIT Mesra, Dr. S. Konar Professor BIT Mesra, Dr. Vibha Rani Gupta Professor BIT Mesra, Dr. B. K. Mishra Principal Jumeritelaya Government of Jharkhand, Dr. V. K. Jha BIT Mesra, Sh. Ajay Kumar AGM(admin) ARTTC BSNL Ranchi & Imm past Chairman IETE Ranchi, Dr. P. R. Thakura Executive Member of IETE & ISVE Ranchi & Professor of BIT Mesra Ranchi, Dr. M. Chandra Executive Member of IETE Ranchi & Professor of BIT Mesra Ranchi, Dr. S. K. Ghorai Executive Member of IETE Ranchi & Professor of BIT Mesra Ranchi, Dr. B. Chakraborty Executive Member of IETE Ranchi & Executive Engineer Mecon Ranchi, Dr. S. Chakraborty Executive Member IETE Ranchi & Professor BIT Mesra Ranchi, Dr. Vandana Bhattacharya Professor BIT Mesra Ranchi, Dr. D. Mohanta Professor BIT Mesra, Dr. S. S. Solanki Professor BIT Mesra Ranchi, Dr. S. Pal Professor BIT Mesra Ranchi, Dr. B. Acharya Professor NIT Raipur, Dr. Chandrashekhar NIT Jamshedpur, Dr. S. Kumar Executive Member IETE Ranchi & Associate Professor BIT Mesra Ranchi. Dr. B. K. Bhattacharya Professor NIT Agartala, Dr. Anand Kumar Thakur Treasurer of IETE Ranchi & Assistant Professor & Director FM Ranchi University, Dr. Raj Kumar Singh Executive Member IETE Ranchi & Assistant Professor & Coordinator UGC Refresher Course Ranchi University, Dr. R. K. Lal Associate Professor BIT Mesra Ranchi, Dr. Sudip Sahana, Mesra, Dr. P. Pal, Dr. Amritanjali, Dr. Rishi Sharma, Dr. K. K. Senapati, Dr. S. S. Sahu, Dr. Aminul Islam, Dr. Sudip Kundu, Dr. S. Pushkar, Dr. M. K. Mukul, Dr. K. Bose of BIT Mesra, Smt. Saroj Treasurer of ISVE Ranchi, Prof. Jyoti Singh Joint Secretary of ISVE Ranchi, Dr. A. K. Pandey Secretary ISVE Ranchi, Sh. Suraj Kumar Saw, Sh. Subro Chakraborty Executive Engineer IBM Bangalore, Sh. Dipayan Ghosh Executive Engineer Aircell, Sh. Ramkrishna Kundu Executive Member of ISVE Ranchi & Design Engineer IBM, Sh. Deepak Prasad, Mohd Javed Khan, Ms. Namrata Yadav, Sh. Sumit Singh, Sh. H. Kar, Sh. Rajanish Yadav, Sh. Anup Tirkey for their endless support, encouragement, motivation to organize such prestigious event that paved the way for this book on Proceeding of Fourth International Conference on Nanoelectronics, Circuits & Communication Systems (NCCS-2018). At last, we express our sincere gratitude towards the staff members of Springer (India) who helped in publishing this book.
Contents
Broad Band Capacitive Coupling Antenna for C-Band Applications . . . Nukala Srinivasa Rao, Deepak Prasad and Vijay Nath
1
Energy-Efficient Waste Management System Using Internet of Things . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Gopala Sharma, Gaddam Sarath Chandu and N. Srinivasa Rao
7
Detection of Lesions in Mammograms Using FRFT-Enhanced Homomorphic Filter and Adaptive Thresholding . . . . . . . . . . . . . . . . . . Shubhi Agarwal and Jankiballabh Sharma
15
A Novel Super-Resolution Reconstruction from Multiple Frames via Sparse Representation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . J. Karmakar, A. Kumar, D. Nandi and M. K. Mandal
33
Design and Performance Analysis of Different Structures for Low-Frequency Piezoelectric Energy Harvester . . . . . . . . . . . . . . . . Namrata Saxena, Ritu Sharma, K. K. Sharma and Varshali Sharma
47
Fault Management Testing in Optical Networks Using NMS . . . . . . . . . Sandeep Dabhade, Sumit Kumar, Pravas Mohanty, Manas Kumar, Abinash Das, Pranesha Bhat, Sushil Desai and Samapika Padhy
57
Effects of Fin Height on Digital Performance of Hybrid p-Si/n-InGaAs C-FinFETs at 25 nm Gate Length . . . . . . . . . . . . . . . . . Kallolini Banerjee and Abhijit Biswas
65
A Takagi-Sugeno Based Fuzzy Inference System for Predicting Electrical Output in a Combined Cycle Power Plant . . . . . . . . . . . . . . . Nandini Kumari, Shamama Anwar and Vandana Bhattacharjee
75
Smart Irrigation System Using Custom Made PCB . . . . . . . . . . . . . . . . Sanjay Kumar Surshetty, Saad Anwer, Yash Deep, Manish Gandhi and Vijay Nath
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Efficient Advanced Encryption Standard with Elevated Speed and FPGA Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pushpinder Kaur Chana, Preeti Gupta and Sanjiv Kumar
95
Multi-class Cancer Classification in Microarray Datasets Using MI-Based Feature Selection and Artificial Neural Network . . . . . 107 M. Jansi Rani and D. Devaraj Smart Communication in Coal Mines . . . . . . . . . . . . . . . . . . . . . . . . . . 117 Sanjay Kumar Surshetty, Jaya Anand, Sneha Chowdhury and Vijay Nath Challenges of WSN in the Fossil Fuel Industry with Recommended Sensor Node Architecture . . . . . . . . . . . . . . . . . . . . 129 Devesh Mishra, K. K. Agrawal, R. S. Yadav, Naresh Agrawal and J. P. Mishra FPGA-Based Smart Irrigation System . . . . . . . . . . . . . . . . . . . . . . . . . . 139 Sanjay Kumar Surshetty, Alisha Oraon, Renuka Kumari, Shradha Shreya and Vijay Nath Smart Healthcare System Using IoT . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 Rashi Patel, Nishu Sinha, Kuhu Raj, Deepak Prasad and Vijay Nath An Area-Optimized Architecture for LiCi Cipher . . . . . . . . . . . . . . . . . 157 Nigar Ayesha, Pulkit Singh and Bibhudendra Acharya A Novel Hardware Architecture for Rectangle Block Cipher . . . . . . . . . 169 Nivedita Shrivastava, Pulkit Singh and Bibhudendra Acharya Design and Implementation of an Accurate 3-Bit System-on-Chip (SoC) Flash ADC for Aerospace Application in 90 nm CMOS Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 Vidushi Goel, Sanjay Kumar Surshetty, Deepak Prasad and Vijay Nath Performance Evaluation of PV Module with Battery Storage in a Microgrid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197 Rupesh Kumar, R. P. Gupta, Matta Mani Sankar and Vijay Nath Selection on Various Traditional Image Encryption Techniques: A Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219 Rishabh Chaddha, Abhishek Kumar, Keshav Sinha, Partha Paul and Amritanjali Selection on Traditional Cryptographic Algorithm for Real-Time Video Transmission and Storage: A Study . . . . . . . . . . . . . . . . . . . . . . . 229 Nikhil Verma, Sagar Sharma, Keshav Sinha, Partha Paul and Amritanjali Gini Index and Entropy-Based Evaluation: A Retrospective Study and Proposal of Evaluation Method for Image Segmentation . . . . . . . . 239 Anuja Negi, Shubham Sharma and J. Priyadarshini
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Image Encryption Scheme Using Chaotic Ring Oscillator . . . . . . . . . . . 249 H. Mondal, J. Karmakar, D. Nandi and M. K. Mandal Wavelet Transform-Based Fusion of SAR and Multispectral Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261 Manali Kedar and Priti P. Rege Intelligent Streetlight System Using GSM . . . . . . . . . . . . . . . . . . . . . . . . 277 Aman Kumar, Akash Oraon, Siddharth Agarwal, Deepak Prasad and Vijay Nath Mitigating Effect of Communication Link Failure in Smart Meter-Based Load Forecasting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289 Vibhas Kumar Vats, Sneha Rai, Suddhasil De and Mala De Internet of Things (IoT) Based Green Logistics Operations for Sustainable Development in the Indian Context . . . . . . . . . . . . . . . . 301 Shailendra Kumar Singh and Supriyo Roy A Scientific Exploration of the Time Theory in Hindustani Ragas . . . . . 315 Saurabh Sarkar, Sandeep Singh Solanki and Soubhik Chakraborty Frequency-Based Passive Chipless RFID Tag Using Microstrip Openstub Resonators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323 V. Karthikeyan, U. Saravanakumar and P. Suresh Automatic Bird Species Recognition Based on Spiking Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343 Ricky Mohanty, Bandi Kumar Mallik and Sandeep Singh Solanki Learning Discriminative Classifier Parameter for Visual Object Tracking by Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 355 Vijay K. Sharma, Bibhudendra Acharya, K. K. Mahapatra and Vijay Nath A 46.8 µW/1.12 GHz 7th Stage New Ring Voltage Controlled Oscillator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 371 Madhusudan Maiti, Suraj Kumar Saw, Vijay Nath, Swarnendu Kumar Chakraborty and Alak Majumder A Current Mode VCO Design Approach for Higher Oscillation Frequency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 383 Suraj Kumar Saw, Madhusudan Maiti, Vijay Nath and Alak Majumder Internet of Things Based Smart Lighting System Using ESP8266 and Zigbee . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 395 Animesh Sharma, Ashish Deopa, Anushka Ahuja, Vidushi Goel and Vijay Nath Simulating and Designing of High-Order LPF with DFI . . . . . . . . . . . . 405 Suman Nehra, P. K. Ghosh and Meena Singh
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Spelling Correction of OCR-Generated Hindi Text Using Word Embedding and Levenshtein Distance . . . . . . . . . . . . . . . . . . . . . 415 Shashank Srigiri and Sujan Kumar Saha NLIIRS: A Question and Answering System for Unstructured Information Using Annotated Text Segment Comparison . . . . . . . . . . . 425 Banerjee Partha Sarathy, Chakraborty Baisakhi, Tripathi Deepak, Gupta Hardik and Sourabh S. Kumar A French to English Language Translator Using Recurrent Neural Network with Attention Mechanism . . . . . . . . . . . . . . . . . . . . . . 437 Akshay Sharma, Partha Sarathy Banerjee, Akshit Sharma and Akshansh Yadav Energy-Aware Clustering in Wireless Sensor Networks . . . . . . . . . . . . . 453 Randheer, Surender Kumar Soni, Sanjeev Kumar and Rahul Priyadarshi Glucometer: A Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 463 Pragati Prerna and Sanjay Shankar Tripathy Performance and Comparison Analysis of Image Processing Based Forest Fire Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 473 Lucky Singh, Ashok Kumar and Rahul Priyadarshi A Novel Hybrid Precoding Technique for Millimeter Wave . . . . . . . . . . 481 Tarun Gupta, Ashok Kumar and Rahul Priyadarshi K-NN Classification for Large Dataset in Hadoop Framework . . . . . . . 495 Ritesh Jha, V. Bhattacharya, Vinit Singh and I. Mukherjee Performance Analysis of Novel Energy Aware Routing in Wireless Sensor Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 503 Sanjeev Kumar, Surender Kumar Soni, Randheer and Rahul Priyadarshi IoT-Based Moisture Sensor in Cement Industry . . . . . . . . . . . . . . . . . . 513 Raghav Charan, Aditya Kumar, Bhavya Harika, Vidushi Goel and Vijay Nath Internet of Things for Smart Class Rooms: A Review . . . . . . . . . . . . . . 523 Dev Gupta, Prashant Kumar Nayak, Yashwi Parashar, Vidushi Goel and Vijay Nath Study and Design of Smart City with Internet of Things: A Review . . . 533 Gaurav Chaudhary, Harsh Agrawal, Sneha Tirkey and Vijay Nath
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Development of Heart Rate Monitoring Controller Using Fingertip Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 547 Sai Namith Garapati, K. Venkatesh, Kushwanth Reddy, Sahithi Unkili, Deepak Prasad and Vijay Nath Bluetooth-Controlled Electronics Home Appliances . . . . . . . . . . . . . . . . 553 Prashant, Ritesh, Hrithik, Vishal, Abhishek Pandey and Vijay Nath RFID (MF-RC522) and Arduino Nano-Based Access Control System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 561 Saurav Kumar Agarwal, Abhishek Singh, Vatsalya Aniraj, Abhishek Pandey, Deepak Prasad and Vijay Nath Smart Irrigation System: A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . 569 Shivani Bitla, Sai Santhan, Shreya Bhagat, Abhishek Pandey and Vijay Nath Enactment of Advanced Traffic Control System Using Verilog HDL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 579 Rohan Kumar, Master Abz, Abhishek Sinha, Abhishek Pandey and Vijay Nath
About the Editors
Dr. Vijay Nath received his bachelor’s degree in Physics and master’s degree in Electronics from DDU Gorakhpur University, India, in 1998 and 2001. He also completed his PGDCN (GM) at MMMUT Gorakhpur in 1999. He received his Ph.D. degree in VLSI Design & Technology from Dr. RML Avadh University Ayodhya in association with CEERI Pilani in 2008. From 2000 to 2001, he was a project trainee at the IC Design Group CEERI Pilani under the guidance of Dr. K. S. Yadav (Senior Scientist). From 2002 to 2006, he severed as faculty at the Department of Electronics, DDU University Gorakhpur. In 2006, he joined the Department of ECE, BIT Mesra, Ranchi (JH), India. Presently, he is the Professor In-charge of the Embedded System Design Lab, an Adviser of NAPS (News & Publication Society), member of BIT Mesra Brand Management, Faculty Adviser to the IEI Student Chapter (ECE) BIT Mesra, and Honorary Secretary of the IETE Ranchi Centre. He has been honored with the Vivekananda Techno Fiesta Award-2002, Young Scientist Award-2004, CCSN Best Paper Award-2013, First Prize in the IEI Technical Contest-2013, and Cadence Design Contest-2014 and 2016. His research interests include microelectronics, nanoelectronics, analog, digital and mixed CMOS VLSI circuits, low-power VLSI circuits, ultra-low-power CMOS temperature sensors, ASICs, embedded systems designs, smart cardiac pacemakers, smart grids, the Internet of Things, and machine learning. He has to his credit over 150 publications in reputed Scopus & SCI journals and Scopus books and conference proceedings. He has successfully completed two R&D projects funded by the DST New Delhi jointly with the DRDL Hyderabad and MHRD New Delhi, and a third project is currently underway, funded by RESPOND SAC ISRO Ahmadabad, Government of India. He has edited 5 books published by Springer. He is a member of several professional societies and academic bodies including IETE, ISTE, ISVE, and IEEE. Prof. J. K. Mandal received his M.Sc. in Physics from Jadavpur University in 1986 and his M.Tech. in Computer Science from the University of Calcutta, and was subsequently awarded a Ph.D. in Computer Science and Engineering by xxix
xxx
About the Editors
Jadavpur University in 2000. Currently, he is a Professor of Computer Science and Engineering and was Dean of the Faculty of Engineering, Technology and Management, Kalyani University, West Bengal, for two consecutive terms. He started his career as a Lecturer at NERIST, Arunachal Pradesh, in 1988, and has 30 years of teaching and research experience. His focus areas include coding theory, data and network security, remote sensing and GIS-based applications, data compression, error correction, visual cryptography, steganography, security in MANET, wireless networks, and unified computing. He is a life member of the Computer Society of India and a fellow of the IETE. He has edited more than 30 proceedings volumes, 360 articles, and 6 books. He is one of the editors for the Springer AISC and CCIS Series.
Broad Band Capacitive Coupling Antenna for C-Band Applications Nukala Srinivasa Rao, Deepak Prasad and Vijay Nath
Abstract In this paper, we present the design and simulation of co-axial fed microstrip antenna for C-Band (4–8 GHz) application with capacitive coupling technique. The antenna is designed from antenna theory (hand calculation) for centre frequency 5.6 GHz and later optimized for broadband using HFSS software. Dielectric substrate FR-4 proxy having dielectric constant 4.4 is used. The proposed antenna exhibits a much higher impedance bandwidth 53% (S11 < −10 dB).
1 Introduction With tremendous increase in research in the field of wireless technology has proved its importance in the world of communication. Due to vital role of antenna in the field of communication, it has drawn attention of many researchers in recent times [1, 2]. To meet the expectation of all the applications, these antennas are supposed to work at different frequency bands. Therefore, antennas capable of operating at these bands are suitable candidates to meet such requirements. Hence, in this paper one such effort has been made to design and develop broadband antenna.
N. S. Rao (B) Department of ECE, Matrusri Engineering College-Hyderabad, Hyderabad, India e-mail: [email protected] D. Prasad · V. Nath Department of Electronics and Communication Engineering, Birla Institute of Technology, Mesra, Ranchi 835215, JH, India e-mail: [email protected] V. Nath e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 V. Nath and J. K. Mandal (eds.), Nanoelectronics, Circuits and Communication Systems, Lecture Notes in Electrical Engineering 642, https://doi.org/10.1007/978-981-15-2854-5_1
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2 Methodology This paper demonstrates the design and optimization of co-axial fed microstrip antenna for broadband operation. The planned antenna is made to work by a co-axial feeding and the technique of capacitive coupling is being used. The SMA connector has been utilized to connect the rectangular patch which couples the energy to a radiating patch by co-axial feed. By adopting HFSS simulation software, the planned antenna is optimized as shown in Fig. 1. Circular loop dimension of the patch is being considered. Therefore, the true radius of the patch is specified by [3] a=
F 21 2h 1 + πε,F ln π2hF + 1.7726 F=
(1)
8.791 × 109 √ fr εr
In above Eq. (1), fringing effect has not been considered as it makes the patch electrically large. The effective radius of patch used is given by [4–7] 21 2h πa ln + 1.7726 ae = a 1 + π εr a 2h For TMz 110, the resonant frequency is given as [3] ( fr )110 =
1.8412ν0 √ 2πae εr
where free space speed of light is shown by v0 .
Fig. 1 Feed structure a top view, b side view
(2)
Broad Band Capacitive Coupling Antenna for C-Band Applications
3
3 Optimization The antenna is designed from antenna theory (hand calculations) for centre frequency 5.6 GHz and later optimized for broadband using HFSS software. These parameters should be taken for optimization [8–11] (a) (b) (c) (d) (e)
Circular patch’s radius (R) Feed strip’s length (s) Feed strip’s width (t) Gap between feed strip and patch. Air gap between substrates (g).
Optimized results are shown in Figs. 2, 3 and 4. Table 1 shows the initial values and optimized values for the proposed design.
4 Conclusions In the current paper, a coplanar capacitive coupled circular patch antenna with simple geometry along with compact size is presented. Return loss of almost below −10 dB has been obtained. An impedance bandwidth of 53% (BW < −10 dB) has also been received after making its dimension optimum.
Fig. 2 Return loss for variation of d (gap between feed strip and patch)
4
Fig. 3 Return loss for variation of t (feed strip’s width (t))
Fig. 4 Return loss for variation of s (length of feed strip)
N. S. Rao et al.
Broad Band Capacitive Coupling Antenna for C-Band Applications
5
Table 1 The initial values and optimized values for proposed design S. no
Parameters
Calculated values (in mm)
Optimized values (in mm)
1
Circular patch’s radius (R)
8.0
8.0
2
Feed strip’s length (s)
1.4
1.4
3
Feed strip’s width (t)
3
4
4
Gap between feeds strip and patch (d)
0.5
0.5
5
Air gap between substrates (g)
4
4
6
Thickness of substrate (h)
1.6
–
7
Dielectric constant
4.4
–
8
Loss tangent (tan d)
0.02
–
References 1. R. Garg, P. Bhartia, I. Bahl, A. Tttipiboon, Microstrip Antenna Design Handbook (Artech House, Norwood, MA, 2001) 2. G. Kumar, K.P. Ray, Broadband Microstrip Antennas (Artech House, 2003) 3. C.A. Balanis, Antenna Theory, 2nd edn. (Wiley, 2004) 4. X. Li, R. Xueshi, Y. Yingzeng, L. Chen, Z. Wang, A wideband twin-diamond-shape circularly polarized patch antenna with coupled feed. Progr. Electromag. Res. 139, 15–24 (2013) 5. P. Wang, J. Wen, Y. Huang, L. Yang, Q. Zhang, Wideband circularly polarized UHF RFlD reader antenna with high gain and wide axial ratio beam width. Progr. Electromag. Res. 129, 365–385 (2012) 6. Wu, X. Ren, Z. Li, Y. Yin Tiang, Modified square slot antennas for broad-band circular polarization. Progr. Electromag. Res. C 38, 1–14 (2013) 7. F. Xu, X.-S. Ren, Y.-Z. Yin, S.-T. Fan, Broadband single-feed single-path circularly polarized microstrip antenna. Progr. Electromag. Res. C 34, 203–213 (2013) 8. J. Deng, L. Guo, T. Fan, Z. Wu, Y. Hu, J. Yang, Wideband circularly polarized suspended patch antenna with indented edge and gap-coupled feed. Progr. Electromag. Res. 135, 151–159 (2013) 9. Y.-M. Cai, S.-F. Zheng, Y.-Z. Yin, l-J. Xie, K. Li, Single-feed circularly polarized annular slot antenna for dual-broadband operation. Progr. Electromag. Res. C 39, 91–101 (2013) 10. K. Ding, T.-B. Yu, Q. Zhang, A compact stacked circularly polarized annular-ring microstrip antenna for GPS applications. Progr. Electromag. Res. Lett. 40, 171–179 (2013) 11. Z. Deng, W. Jiang, S. Gong, Y. Xu, Y. Zhang, A new method for broadening bandwidths of circular polarized microstrip antennas by using DGS and parasitic split-ring resonators. Progr. Electromag. Res. 136, 739–751 (2013)
Energy-Efficient Waste Management System Using Internet of Things A. Gopala Sharma, Gaddam Sarath Chandu and N. Srinivasa Rao
Abstract Nowadays, waste management is a huge problem in the world. The key issue involved in waste management is waste collection within time, without overflowing the dustbins in public places. This paper presents an energy-efficient waste management system using IoT. This system measures the level of dustbin for every 30 min and sends the level to the cloud or database. Whenever the dustbin is full, it will send the location (longitude and latitude values) of dustbin to nearest garbage truck driver’s application. An application is developed for showing dustbin level and to track the location of the dustbin for truck drivers. This system will operate for every 30 min to increase the battery life and remaining time system will be in sleep mode. Wi-Fi technology is used for sending the dustbin level to database. Keywords IoT · Waste management · Wi-Fi · Location · Database
1 Introduction As years go on, population and wastage generation keep on increasing. India generates more than 1,00,000 metric tons of wastage/day. As discussed in [1], Urban India generates 31.6 million tons of wastage in 2001, 47.3 million tons in 2011, 71.15 million tons in 2021, 107.01 million tons in 2031 and 160.96 million tons in 2041. Figure 1 explains about the population and waste generation in urban India from year 2001 to 2041. The main problems with waste management are overflowing of dustbins at public places which cause emission of gases from the dustbins and it leads to many health problems. There are four types of wastages solid, liquid, hazardous and A. Gopala Sharma · G. Sarath Chandu (B) Department of ECE, Stanley College of Engineering & Technology for Women, Hyderabad, India e-mail: [email protected] A. Gopala Sharma e-mail: [email protected] N. Srinivasa Rao Department of ECE, Matrusri College of Engineering & Technology, Hyderabad, India © Springer Nature Singapore Pte Ltd. 2020 V. Nath and J. K. Mandal (eds.), Nanoelectronics, Circuits and Communication Systems, Lecture Notes in Electrical Engineering 642, https://doi.org/10.1007/978-981-15-2854-5_2
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Fig. 1 Graph for population versus waste generation from years 2001–2041
medical. Storing of these wastage leads to emission of carbon dioxide and methane gases. A huge amount of money is spent for waste collection and separation process. In [2], the author explained smart cities developed by doing different services like Structural health, Waste management, Air Quality, Noise monitoring, Traffic congestion, Smart parking, city energy consumption and Smart lighting, etc. Waste management is one of the main important service which has to be done to develop smart cities. Nowadays waste collection system is spending so much of effort and money to collect the waste from each home and each dustbin; each and every aspect of world technology is improved so, waste management system also has to be improved. Many researchers already worked on waste management system and proposed many techniques to eliminate waste management problems. These techniques are explained in Sect. 2. This paper is structured as 4 sections: Sect. 2 explains Literature Survey. Section 3 discusses about proposed system, hardware modules and working. Section 4 gives conclusion of the paper.
2 Literature Review In IoT-enabled dustbins [1], authors sahil mirchandani, sagar wadhwa, preeti wadhwa, Richard Joseph have proposed a system containing ultrasonic sensor for measuring dustbin level, air quality sensor is used to determine gas emissions, RFID is used for opening the lids of dustbins, measures the weight of wastage for every user and sends all these data to cloud. In this system, authors used GPS for finding location of the dustbins but in public places the location of dustbins will be static so,
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no need for the use of GPS and the total system can be ruggedized to safeguard the modules. In Smart Waste Management using Internet of Things [3], authors Gopal Krishna syam, Sunilkumar S. Manvi, Priyanka Bharti have designed a system with ultrasonic sensor for measuring dustbin level and sends data to server using Internet. The same system was also proposed for efficient waste collection system [4] by authors saurabh Dugdhe, Pooja shelar, Sajuli jure and Anuja apte. In IoT-based solid waste management system for smart city [5] by Krishna nirde, Prashant S. Muley, Uttam M. Cheskar used ultrasonic sensor, PIC and Arduino controllers. PIC is used for data collection from sensor, send it to Arduino using RF transmitter and Receiver module. Arduino received the data and uploaded it to server. This system is mainly used where Wi-Fi is not available at dustbins but it takes more power compared to other techniques. In [6], the authors have designed a smart waste bin for smart waste management using IoT. The designed system uses strain gauge load cells to measure the weight of wastage and tested this load sensor using brass weight set with fibre waste-bin 60L, stainless waste-bin 40L, iron sheet waste-bin 45L and plastic waste-bin 30L. Similarly HC-SR04 is used for wastage level measurement and is also tested by four kinds of waste bins. Weight and level of the bins are sent to server using GSM technology. Mobile application and web server are created for monitoring this information. In [7], the main objective of proposed system is segregation of different types of wastes like plastic, biodegradable and metal. The waste is dropped at the sensor panel, it segregates the type of waste with the help of different sensors and dump it into the corresponding segment. It has gas and bacteria sensor for detection of gas emissions and developed a odour controller for minimizing the unpleasant odour. Level of dustbin is measured and sent to server using STM-32 controller and Wi-Fi module. In [8], authors designed a system for checking the garbage container status and sending this status to server. System contains a ZigBee network for data acquisition from sensors, data processing by MQTT broker to the main server and Telegram Bot is used for sending notification to truck drivers. A Gateway is designed for collecting data from ZigBee coordinator and sending it to MQTT broker. From the above literature review, all researches explained about measuring the dustbin level, detection of gas emissions from the dustbin, weight of the dustbins, segregations of waste and this data is sent to server using various communication techniques like Wi-Fi, GSM and ZigBee. Our proposed system measures the dustbin level and sends the data to application and server with less power consumption. Our system also creates a provision for tracking the dustbin for the garbage vehicle driver using GPS technology.
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3 Proposed System The main objective of this paper is designing a waste management system with less power consumption and providing facilities like sending notification to garbage vehicle driver application when bin is full, tracking dustbin using app for fast cleaning of dustbins. The Block diagram of proposed system is shown in Fig. 2. It consists.
3.1 IR Sensor Proposed system consists of three IR sensors to detect waste in dustbins at three levels, i.e. LOW, MEDIUM and FULL. IR sensor has two parts one is transmitter and other receiver; transmitter is placed at one side of bin and receiver is placed exact opposite to transmitter. IR sensor placement and levels are shown in Fig. 3.
Fig. 2 Block diagram of proposed system
Fig. 3 IR sensor placement and measuring levels
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3.2 Wi-Fi ESP-8266 Wi-Fi module is used to send data from controller to web server and mobile application. Web page and mobile application are created for monitoring the level and track the dustbin route. Wi-Fi module will connect to the nearest router by providing username and password of the router.
3.3 ATmega 328P-PU ATmega 328P-PU is the controller used to collect data from IR receiver, checking whether dustbin is filled or not and sending static GPS coordinates to web page and mobile application using Wi-Fi module when dustbin is full. It will keep the total system in sleeping mode whenever required. An algorithm is created in controller for checking the nearest free vehicle and sending notification to that vehicle when dustbin is full.
3.4 Web Page and Mobile App Web page and mobile app are created for showing the level of dustbin and to track the location of dustbin using Google map. The complete process of the proposed system is shown in Fig. 4. Initially, IR sensors measure the waste level, sends the waste level to server using controller and Wi-Fi module. Controller will check whether the dustbin is full or not and if the dustbin is full, it will find the nearest vehicle and send the longitude and latitude values of dustbin to vehicle drivers mobile application. If the dustbin is not full, controller keeps the total system in sleep mode for 30 min to reduce the power consumption and after 30 min system will start from initial stage. To reduce the power consumption and cost of the system, GPS module is not used and the longitude and latitude values of dustbin are kept fixed because the dustbin used in public places is static. Google map is linked to the web page and mobile application to track the location of the dustbin using longitude and latitude values. After sending notification to garbage vehicle driver, driver will track the dustbin and clears it. Then the IR sensors detect no waste in dustbins and sends signal to controller like process is completed. Controller keeps the total system in sleeping mode for 30 min and start again after completion of time period. The above complete process flow can be explained in four steps: 1. 2. 3. 4.
Level measurement Data acquisition Data processing Tracking.
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Fig. 4 Process flow
START
Dust-bin level Measurement Wait for 30 minutes
Sends level to Server
Is Dustbin Full?
NO
YES Finding nearest Vehicle
Sends GPS Coordinates to driver application
Driver Tracks the dust-bin & clear it
4 Conclusions In this paper, we proposed a system to collect the waste in dustbins within time to avoid overflow of dustbins in public places. The system is designed mainly to use less power consumption by keeping system in sleeping mode for 30 min and easy
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way of interaction between the garbage vehicles with dustbins. Also, this system uses pre-fixed location of dustbins to reduce the power consumption and cost of the system.
5 Future Scope In IoT technology, energy-efficient communication technologies are playing a key role. Communication technologies like LoRa, LoRaWAN, sigfox and NB-IoT use very less power to transmit the data long ranges. In the proposed system, Wi-Fi can be replaced with above communication technologies to reduce the power consumption.
References 1. S. Mirchandani, R. Joseph, IoT enabled dustbins, in 2017 International Conference on Big Data, IoT Data Science (BID), Dec 20–22 (2017), pp. 73–76 2. A. Zanella et al., Internet of things for smart cities. IEEE Int Things J 1(1), 22–32 (2014) 3. G.K. Shyam, S.S. Manvi, Smart waste management using internet-of-things (IoT), in 2017 Second International Conference on Computer and Communication Technologies (2017), pp. 199–203 4. S. Dugdhe, P. Shelar, S. Jire, A. Apte, Efficient waste collection system, in 2016 International Conference on Internet Things Applications (IOTA), 22 Jan–24 Jan 2016, pp. 143–147 5. K. Nirde, P.S. Mulay, U.M. Chaskar, IoT based solid waste management system for smart city, in International Conference on Intelligent Computing and Control System ICICCS (2017), pp. 666–669 6. M. Niswar, Design a smart waste bin for smart waste management, in 2017 5th International Conference on Instrumentation, Control ad Automation (ICA), August 9–11 (2017), pp. 62–66 7. S.S. Lokeshwari, Eco-friendly IOT based waste segregation and management, 2017 International Conference on Electrical, Electronics and Computing Optimization Technologies (2017), pp. 297–299 8. S. Karthikeyan, G.S. Rani, IoT enabled waste management system using zigbee network, in 2017 2nd International Conference on Recent Trends on Electronics, Information and Communication Technology (RTEICT), May 19–20 (2017), pp. 2182–2187
Detection of Lesions in Mammograms Using FRFT-Enhanced Homomorphic Filter and Adaptive Thresholding Shubhi Agarwal and Jankiballabh Sharma
Abstract In this paper, a novel scheme for multilevel detection and segmentation of suspicious lesions from mammograms is proposed. First, non-decimated wavelet transform (NDWT) of selective scale is applied to input mammograms. In coarse segmentation, NDWT sub-images go through decimated wavelet transform, and thereafter multiscale analysis of image probability distribution function is used. In order to improve the morphological feature of coarse segmented image, a morphological filter is applied to NDWT transformed sub-image and convolved with it. Fractional Fourier transform (FRFT) domain homomorphic filter is applied on convoluted image for improved fine segmentation. FRFT domains are used to enhance the intensity gradient by controlling the reflectance slope with the help of fractional order ω. Second level local adaptive thresholding is used to obtain final segmented image. Efficiency of the proposed scheme is verified by simulating mammograms in the mammographic image analysis society (MIAS) database. The proposed algorithm obtains improved detection with 100% sensitivity and 0.667 false positive per image for CIRC and MISC lesions and comparable results for other masses in comparison to the nearest schemes. Keywords Segmentation · Adaptive thresholding · Homomorphic filter · Mammograms · FRFT
S. Agarwal · J. Sharma (B) Department of Electronics Engineering, Rajasthan Technical University, Kota, India e-mail: [email protected] S. Agarwal e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 V. Nath and J. K. Mandal (eds.), Nanoelectronics, Circuits and Communication Systems, Lecture Notes in Electrical Engineering 642, https://doi.org/10.1007/978-981-15-2854-5_3
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1 Introduction In women, an increase in mortality rate is caused due to breast cancer. Mammography is an important radiographic method to detect breast cancer. Mammograms are categorised as normal, benign and malignant [1, 2]. These mammograms are classified on the basis of lesions or masses as circumscribed (CIRC), speculated (SPIC), micro classification (MISC), architectural distortion (ARCH) and asymmetric (ASYM) [3, 4]. Shape features are mainly used to detect CIRC lesions, the MISC and ASYM lesions are detected by using gray-value and brightness features while texture features are used to detect the SPIC and ARCH lesions [5–9]. Different schemes for detection and segmentation of these lesions using the aforementioned features have been reported in [5–12]. Morphological filtering is a common image processing tool to maintain shape features, therefore many schemes employing morphological filters have been reported [5, 6, 10, 13–15]. Detecting and calculating the number of suspicious lesions have also been reported in [10, 13, 16]. Due to intensity variation in mammograms, adaptive thresholding is the most commonly used technique for detecting masses in mammograms [10, 16]. Wavelet-based adaptive thresholding has been applied in [5, 10, 16], because wavelet-based analysis of the mammograms made gray-level probability distribution function (PDF) to approach Gaussian distribution [5, 10, 16]. In PDF-based schemes, adaptive threshold value is chosen by global local minima of PDFs curves, hence these schemes suffer when target and background image having minute gray level difference [5–9]. To overcome this, linear transformation filter is applied in [6] for enhancing the intensity gradient of a mammogram and an improved local adaptive thresholding has been reported. But this algorithm gives empty area in segmentation results, and also not consider masses of smaller size in comparison to the window used for feature (local threshold) extraction [5, 17]. A scheme combining adaptive, global and local thresholding in the wavelet domain for segmentation of mammograms has been reported in [5]. Moreover, many schemes for contrast enhancement of mammogram images using homomorphic filter in wavelet domain have also been reported [14, 15, 18]. In order to analyse mammograms in multiple domains with different features and classification methods, schemes have also been reported [7–9, 11, 12]. In [11, 12], WFRFT coefficients are used as features for classification using support vector machine and feed-forward neural network (FNN) based classifier. The basic problem with these classifiers based schemes is that they require very high computation cost [11]. Different types of abnormalities in a mammogram are detected by different features, hence all abnormalities cannot be optimally detected by using a single algorithm. Second, due to the low contrast of mammogram images detection and segmentation methods have a trade-off between false positive detection and sensitivity [5, 6, 17]. Therefore, in this paper, an improved multistage segmentation algorithm for
Detection of Lesions in Mammograms …
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detection and segmentation of lesions in mammograms, using FRFT-enhanced homomorphic filter and local adaptive thresholding is presented. Course segmentation is performed by multiresolution analysis based adaptive thresholding, while for fine segmentation, FRFT-enhanced homomorphic filter along with window-based local adaptive thresholding reported in [6] is used. FRFT homomorphic filter improves the intensity and contrast of image and hence the detection of masses in highly dense mammograms [11]. In order to achieve optimal value of false positive rate and sensitivity, variation in the local adaptive threshold with respect to FRFT order ω is also studied, and selected accordingly. FRFT order ω also provides flexibility in optimising the results against the variations in capturing the mammograms. Due to better localisation of pixels in non-sparse sampling grid [8, 18], and improved multiresolution characteristics NDWT is applied in the proposed algorithm. Mammographic Image Analysis Society (MIAS) database images are tested using the proposed algorithm. FROC analysis curve, false-positive rate (FPR) and sensitivity performance parameters are compared with conventional schemes and show the superiority of the proposed scheme. Rest of this paper is organised as follows. Section 2 describes the materials and methods. In Sect. 3, proposed methodology is presented in detail. In Sect. 4 implementation results are shown and discussed. Section 5 concludes the work.
2 Materials and Methods 2.1 Homomorphic Filtering Homomorphic filter is used for controlling specifications such as illumination and reflectance. High and low-frequency components are dependent on Fourier transform in different ways. The image I h(y, z) is the product of reflectance and illumination component as shown in Eq. (1). I h(y, z) = i(y, z)r (y, z)
(1)
Both exp and ln are exponential operator and logarithmic operator, respectively. Illumination and reflectance components turned out to be additive by using log function. The frequency domain image is processed through homomorphic filtered function Hm. 2 −u L HL H(y,z) 0 +l (2) H m = (h − l) 1 − e
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where u is constant and controls the slope sharpness and its transitions between h and l. LH0 and LH are the cut-off frequency and distance from the centre frequency coordinates (y, z). For final enhanced image, convert filtered image into spatial domain. The details about homomorphic filtering technique can be found in [12, 15, 18].
2.2 Fractional Fourier Transform The Fourier transform (FT) represents an image into the frequency domain by decomposing time signal into frequencies. But, FT cannot analyse non-stationary signals. To overcome this problem, the novel variant of FT has been introduced, i.e. Fractional Fourier transform (FRFT). FRFT domains are intermediate domains between time and frequency [11, 12]. Let y(t) function be in the time domain and its ω-angle FRFT spectrum Yω ( f ) is defined as ∞ y(t)Wω ( f, t)dt (3) Yω = −∞
where f denotes frequency, t denotes time and W represents the transform kernel with the form of ⎛ ⎛ ⎞⎞ t 2 cot(ω)− Wω (t, f ) = 1 − j cot(ω) × exp⎝ jπ ⎝ 2 × f × t × csc(ω)+ ⎠⎠ (4) f 2 × cot(ω) As ω reaches to the multiple of csc and cot operators would diverge. This problem is overcome by taking the limits and the following expression is obtained: ⎧ ⎛ ⎛ 2 ⎞⎞ ⎪ t × cot(ω)− ⎪ ⎪ ⎪ ⎪ ⎨ exp⎝ jπ ⎝ 2vt csc(ω)+ ⎠⎠ω = kπ Wω (t, f ) = v 2 cot(ω) ⎪ ⎪ ⎪ δ(t − f ) ω = 2kπ ⎪ ⎪ ⎩ δ(t + f ) ω = (2k + 1)π
(5)
In proposed algorithm, 2D-FRFT is applied to the mammograms with two angles ωx and ω y . Here if ωx = 0 and ω y = 0, then FRFT is termed as identity operator. If ωx = 1 and ω y = 1, then FRFT is termed as standard FRFT or FT.
Detection of Lesions in Mammograms …
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3 Proposed Methodology In this section, the proposed algorithm for detection and segmentation of circumscribed lesions in mammograms using FRFT-enhanced homomorphic filter and local adaptive thresholding is described. Block diagram of the proposed algorithm is shown in Fig. 1. FRFT technique is used in fine segmentation to extract distinguish local features and to analyse an image in a unified time–frequency domain. Wavelet domain multiresolution analysis is applied for course segmentation. The following are the steps involved in the process of detection and segmentation of the number of CIRC lesions in mammograms using the proposed method: 1. For better localisation of pixels and improved multiresolution characteristics due to non-sparse sampling grid, non-decimated wavelet transform is applied on normalised mammogram image N0 . Here the scale is selected, considering the computation complexity and segmentation performance. NDWT sub-image at scale S = 2 is selected for 2D discrete wavelet transform (DWT). Original Mammogram Image
0
Coarse Segmentation
Non-Decimated Wavelet Transform
2-D Discrete Wavelet transform
Adaptive Thresholding based on PDF ′
Mapping
Fine Segmentation
Convolution Non-Linear Gray Value Scaling
FRFT Homomorphic Filtering h i Local Adaptive Thresholding
Detected Image
Fig. 1 Block diagram of proposed algorithm
Morphological Filtering Technique
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2. Adaptive thresholding based on PDF curve: The transformation of input image N0 in wavelet domain made gray-level probability distribution function (PDF) approaches to Gaussian distribution as discussed in [16]. Lesions have higher gray-level than normal masses. Background region would have lower gray-level than lesions. PDFs of background Pb (n) and lesions Pl (n) are as follows [16]: 1 (n − μb )2 Pb (n) = √ × exp − 2σb2 2π σb 1 (n − μl )2 , μl > μb × exp − Pl (n) = √ 2σl2 2π σl
(6)
where μl and μb are the means of background and lesion of image, pixel value is n and standard deviation of background and lesion of the image is σl and σb , respectively. Let Im(n) is the PDF of image and pb and pl be the prior probabilities of background and lesion image. Where Im(n) is given by [16] Im(n) = pb Pb (n) + pl Pl (n)
(7)
Here, Bayes threshold cannot be computed directly, as prior probabilities information of both classes are unknown. Therefore, it is selected by looking at the largest local minima of PDFs curve (histogram) of wavelet-transformed sub-images. Since the functioning of the Bayes threshold is comparable to the adaptive threshold TH1 , hence, coarse segmentation takes place in accordance with the adaptive threshold TH1 . Let BI be the coarse segmented binary image [16] B I (k, l) =
0, Y (k, l) < T H1 1, Y (k, l) ≥ T H1
(8)
where (k, l) is the coordinates of BI image and BI(k, l) is the pixel value compared to threshold TH1 . j Here, N1 is the sub-image of 2D-DWT. Nseg [16] is the segmented areas of transformed image at scale S by using a selected threshold value, then mapped from wavelet domain to original domain. Coarse segmentation gives a rough idea for localisation of lesion in mammograms. 3. For higher resolution [5], coarse segmentation Fir st1 corresponds to 2×2 matrix in Fir st1 as shown in Eq. (9).
Fir st1 (2ee + rr, 2dd + f f ) = Fir st1 (e, d)
Detection of Lesions in Mammograms …
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where rr, f f ∈ {−1, 0}
(9)
4. For improving morphological characteristics of NDWT sub-image N2 , morphological filter technique is applied [13]. After many experiments, optimal value of structural element disk size = 140 is selected for good results. 5. Filtered image Mor ph 1 is obtained from morphological enhancement technique. After enhancement, image Con 1 is obtained by convolution of Fir st1 and Mor ph 1 . Con 1 = conv(Fir st1 , Mor ph 1 )
(10)
6. Non-linear gray values scaling is applied to a convoluted image. This technique enhances the image in accordance with λl and A , details are discussed in [6]. For better results λl = 0.3 and A = 10,000 are selected to obtain enhanced image. A and B are two real number and v is said to be maximum gray-value, 255.
B =
1 − exp
v A
v
(11)
At λl = 0.3 pixel of convoluted image is modified to obtain augmented image as follows [6] EI =
A · log 1 + B · Con 1 (k1 , l1) , Con 1 (k1 , l1 ) > λl exp[Con 1 (k1 , l1 )/λl ] − 1 , Con 1 (k1 , l) < λl
(12)
The logarithm function enhances dark areas and the inverse of it enhances bright areas in above Eq. (12). 7. FRFT Homomorphic filtering technique is applied to non-linear gray-value scaled image E I . This filtering technique enhances the boundaries of lesion by increasing the high frequency and lowering the low frequency and also normalises the intensity and increases the contrast of image. Using fractional order, ω can transform the image in intermediate domain between time–frequency. In order to analyse detection results, FROC curve is implemented using fractional order ω. H1 ← ex p ← i f r f t ← H m(k, l) ← f r f t ← EI
(13)
8. For fine segmentation, local adaptive thresholding is implemented on complemented filtered image of H1 .
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Local Adaptive Thresholding: Low contrast mammograms contain fuzzy edges and complex background which made it difficult for segmentation. Subtracted image S(k, l) is obtained by subtracting convoluted image from filtered image as shown in following Eq. (14) S(k, l) = H1 (k, l) − Con 1 (k, l)
(14)
To obtain smaller and larger window, symmetric method is used for padding of boundary pixel of S(k, l). Threshold T (k, l) is used for fine segmentation. Threshold T (k, l) is calculated for each pixel of breast area S(k, l) in order to classify it as lesion pixel or normal mass pixel as shown below [6] I f Avg > η1 · Sdiff (k, l) and
|Avg − S(k, l)| Avg
< 1 − η1
then S(k, l) belong to lesion area and threshold is calculated as T (k, l) =
η1 · Avg, if Avg > S(k, l) Avg, otherwise
(15)
else T (k, l) = Avg + κ1 · sdiff
(16)
Sdiff = Smax (k, l) − Smin (k, l)
(17)
with
Here Avg is the average intensity of pixel of small neighbourhood around S(k, l) pixel. η1 and κ1 are thresholding bias coefficients. For better results κ1 is set to 0.5. Smax and Smin are maximum and minimum intensity of S(k, l). The fine segmentation provides more improved results for detection of lesion in mammograms.
Detection of Lesions in Mammograms …
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4 Implementation and Results In this section, simulation results of the proposed algorithm, implemented using MATLABR2016b are presented. CIRC, MISC, ASYM, ARCH, SPIC mammogram images of size 1024 × 1024, taken from Mini-MIAS database [4], are used to validate the efficacy of the proposed scheme. The testing images include 10 normal images and 20 shape occupying lesion images with 20 total lesions. Original mammogram images are shown in Figs. 2, 3, 4, 5 and 6a. Respective coarse segmentation, Homomorphic
Fig. 2 Results detection of CIRC lesions at ω = 0.4. The cases from top to bottom mdb002, mdb015, mdb021 and mdb025, respectively. a Original mammogram. b Coarse segmentation. c FRFT homomorphic filtered image. d Fine segmentation
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Fig. 3 Results detection of MISC lesions at ω = 0.4. The cases from top to bottom, mdb013, mdb030 mdb032, respectively. a Original mammogram. b Coarse segmentation. c FRFT homomorphic filtered image. d Fine segmentation
filtered and fine segmentation results are shown in Figs. 2, 3, 4, 5 and 6b, c, d for each mammogram. The results of the proposed scheme are also compared with [5, 17], with the same ground truth images and depicted in Tables 1, 2 and 3. Table 1 shows the optimal optimum value of sensitivity, obtained for proposed scheme with fractional order ω, and reported in [5]. In Table 2 average sensitivity and average FP/I of state-ofthe-art schemes are compared. Table 3 shows the experimental results of proposed algorithm, [5, 17] for lesion detection in mammograms. FROC analysis curve is also used for comparison and shown in Fig. 7. In Fig. 7b, FROC curve of the proposed algorithm is implemented between FPPI (FP/I) and true positive fraction (TPF) computed with a change in ω from 0 to 1 at 0.1 difference.
Detection of Lesions in Mammograms …
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Fig. 4 Results detection of ASYM lesions at ω = 0.4. The cases from top to bottom mdb072 mdb083 mdb081, respectively. a Original mammogram. b Coarse segmentation. c FRFT homomorphic filtered image. d Fine segmentation
Discussion: It is observed from Tables 1 and 3 that obtained sensitivity for CIRC MISC and SPIC lesions are close to 100% because these types of lesions depend upon the shape and gray-level features. The obtained sensitivity is low in the case of other types of lesions because they are sensitive to other texture features [5], and which are not used in the proposed algorithm for classification. It is evident from Table 2 that the proposed scheme outperforms the state-of-the-art schemes [5, 17, 11 and 12]. The detection accuracy of the proposed scheme can be further improved by using more features and classifiers [11, 12]. In this paper, NDWT (Daubechies 6) is applied on normalised original mammogram N0 for better localisation of pixel and multiresolution characteristics due to non-sparse sampling grid. For removing singularities and making it suitable for coarse segmentation, DWT (Daubechies 6) is applied on transformed sub-image of NDWT. The adaptive threshold is selected by histogram (PDF curves) of wavelettransformed sub-images. Wavelet transform of PDF curves are aligned at every scale according to their largest local minimum and weighted to select final threshold value for coarse segmentation. Using Eq. (9) higher resolution, mapping is validated on results of coarse segmentation.
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Fig. 5 Results detection of ARCH lesions at ω = 0.7. The cases from top to bottom mdb117 mdb120 mdb125, respectively. a Original mammogram. b Coarse segmentation. c FRFT homomorphic filtered image. d Fine segmentation
Table 1 Optimal detection results of proposed algorithm, (with Fractional order ω) and [5] Class of abnormality
Proposed methodology
[5]
Fractional order ω
Sensitivity (%)
Sensitivity (%)
CIRC
0.4
100
95.8
MISC
0.4
100
93.3
ASYM
0.4
85.71
93.3
ARCH
0.7
85.71
94.7
SPIC
0.7
100
78.9
94.28
91.3
Total
Table 2 Comparison of average sensitivity (TPF) and FP/I Scheme
Proposed method
[5]
[17]
[11]
[12]
Sensitivity (%)
94.28
91.3
90.7
92.22
91.5
–
–
FP/I
0.667
0.71
2.57
Detection of Lesions in Mammograms …
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Table 3 Detection of number of lesions per mammogram of proposed method, [5, 17], ground truth provided by Mias Mammogram
Class of lesion
Our method
[5]
[17]
Ground truth
mdb002
CIRC
1
1
1
1
mdb015
CIRC
1
4
8
1
mdb021
CIRC
1
1
7
1
mdb023
CIRC
1
4
4
1
mdb013
MISC
1
2
7
1
mdb030
MISC
1
2
10
1
mdb032
MISC
1
6
10
1
mdb264
MISC
1
1
1
1
mdb072
ASYM
2
1
2
1
mdb081
ASYM
1
1
1
1
mdb083
ASYM
1
1
9
1
mdb090
ASYM
1
2
5
1
mdb117
ARCH
1
1
3
1
mdb120
ARCH
1
2
2
1
mdb121
ARCH
3
1
2
1
mdb125
ARCH
2
2
4
1
mdb181
SPIC
1
1
1
1
mdb186
SPIC
1
1
1
1
mdb199
SPIC
2
3
7
1
mdb202
SPIC
1
1
1
1
To extract morphological characteristics morphological filter is applied on NDWT transformed sub-image N2 with a disk size of 140. Non-linear gray scale technique is performed on convoluted image of morphological filter and high resolution mapped image. This technique enhances the intensity gradient and also increases the contrast of convoluted image. This enhanced image is passed through the FRFT homomorphic filter which analyses the image in unified time–frequency domain and which also normalises the intensity and increases the contrast of image without losing details. This filter uses approximation coefficients h = 0.5 and l = 1. Fractional order ω is varied to detect lesions in mammogram. For better results, the value of fractional power ω of FRFT homomorphic filter is varied between and selected as shown in Table 1 for different types of lesions. Since the adaptive threshold value of each pixel is a function of fractional order ω, therefore, investigations are also performed to
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Fig. 6 Results detection of SPIC lesions at ω = 0.7. The cases from top to bottom mdb181, mdb186, mdb199, respectively. a Original mammogram. b Coarse segmentation. c FRFT homomorphic filtered image. d Fine segmentation
observe the variation in threshold with respect to ω. In this paper, T (k, l) is described as adaptive threshold which is used for fine segmentation. For simplicity and space constraint the mean value of threshold T (k, l) is calculated and shown in Fig. 8. It is observed that the mean adaptive threshold for each mammogram is different and varies with ω in approximately similar patterns. This justifies the superiority of proposed scheme in providing the additional degree of freedom in optimising the segmentation results for variation in mammograms and the fractional order ω can be used to fine-tune the algorithm.
Detection of Lesions in Mammograms … Fig. 7 a FROC analysis of [5]. b FROC analysis of proposed algorithm
Fig. 8 Variation of mean adaptive threshold with ω
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5 Conclusion This paper presents the automatic detection and segmentation of mammogram images using improved FRFT homomorphic technique. This detection algorithm uses NDWT and DWT for enhancing the multiresolution characteristic. The improved FRFT homomorphic filter is used to analyse enhanced mammograms in unified time–frequency domain. Gray-value and shape feature are used for detection and segmentation of lesions in mammograms. Morphological filter enhances shape feature whereas improved FRFT homomorphic filter enhances gray features. Simulation results of proposed algorithm outperform the other schemes. Proposed scheme is also having high sensitivity and low computation cost in comparison to classifier-based schemes [11, 12].
References 1. A.G. Hauss, M.J. Yaffe, A categorical course in physics: technical aspects of breast imaging, in 78th Scientific Assembly and Annual Meeting of RSNA (Radiological Society of North America) (1994) 2. K. Bovis, S. Singh, Detection of masses in mammograms using texture features, in Proceedings of the 15th International Conference Pattern Recognition (Vol. 2, 2002), pp. 267–270 3. G. Cardenosa, Mammography: an overview, in Proceedings of the 3rd International Workshop Digital Mammography, Chicago, IL, June 9–12 (1996), pp. 3–10 4. J. Suckling, S. Astley, D. Betal, N. Cerneaz, D.R. Dance, S.-L. Kok, J. Parker, I. Ricketts, J. Savage, E. Stamatakis, P. Taylor, Mammographic Image Analysis Society Mini-Mammographic Database (2005) 5. H. Kai, X. Gao, F. Li, Detection of suspicious lesions by adaptive thresholding based on multiresolution analysis in mammograms. IEEE Trans. Instrum. Meas. 60(2), 462–472 (2011) 6. G. Kom, A. Tiedeu, M. Kom, Automated detection of masses in mammograms by local adaptive thresholding. Comput. Biol. Med. 37(1), 37–48 (2007) 7. J.Y. Choi, D.H. Kim, K.N. Plataniotis, Y.M. Ro, Computer-aided detection (CAD) of breast masses in mammography: combined detection and ensemble classification. Phys. Med. Biol. 59(14), 3697 (2014) 8. S. Liu, C.F. Babbs, E.J. Delp, Multiresolution detection of speculated lesions in digital mammograms. IEEE Trans. Image Process. 10(6), 874–884 (2001) 9. B. Kurt, V.V. Nabiyev, K. Turhan, A novel automatic suspicious mass regions identification using Havrda & Charvat entropy and Otsu’s N thresholding. Comput. Methods Programs Biomed. 114(3), 349–360 (2014) 10. X.P. Zhang, M.D. Desai, Segmentation of bright targets using wavelets and adaptive thresholding. IEEE Trans. Image Process. 10(7), 1020–1030 (2001) 11. Y.-D. Zhang, S.-H. Wang, G. Liu, J. Yang, Computer-aided diagnosis of abnormal breasts in mammogram images by weighted-type fractional Fourier transform. Adv. Mech. Eng. 8(2), 1–11 (2016) 12. S. Wang, R.V. Rao, P. Chen, Y. Zhang, A. Liu, L. Wei, Abnormal breast detection in mammogram images by feed-forward neural network trained by Jaya algorithm. Fundam. Inf. 151(1-4), 191–211 (2017) 13. H. Li, Y. Wang, K.J. Ray Liu, B.L. Shih-Chung, M.T. Freedman, Computerized radiographic mass detection. Part I: lesion site selection by morphological enhancement and contextual segmentation. IEEE Trans. Image Process. 20(4), 289–300 (2001)
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14. J.H. Yoon, Y.M. Ro, Enhancement of the contrast in mammographic images using the homomorphic filter method. IEICE Trans. Inf. Syst. 85(1), 298–303 (2002) 15. P. Gorgel, A. Sertbas, O.N. Ucan, A wavelet-based mammographic image denoising and enhancement with homomorphic filtering. J. Med. Syst. 34(6), 993–1002 (2010) 16. J. Tang, X. Liu, Q. Sun, A direct image contrast enhancement algorithm in the wavelet domain for screening mammograms. IEEE J. Select. Top. Signal Process. 3(1), 74–80 (2009) 17. A.Z. Cao, Q. Song, X.L. Yang, Robust information clustering incorporating spatial information for breast mass detection in digitized mammograms. Comput. Vis. Image Understand. 109(1), 86–96 (2008) 18. R. Gonzales, R. Woods, Digital Image Processing (Prentice Hall, USA, 2002), pp. 191–193. Total page 793, Chapter 4
A Novel Super-Resolution Reconstruction from Multiple Frames via Sparse Representation J. Karmakar, A. Kumar, D. Nandi and M. K. Mandal
Abstract In this paper, we have put forward a novel technique of multiple frame super-resolution (SR) reconstruction based on sparse representation. The available SR reconstruction techniques are used to generate a high-resolution (HR) image either from single low-resolution (LR) frame or multiple LR frames of the same scene. But these techniques require some modification to provide desired SR frame when LR inputs are contaminated by the high amount of noise. The proposed multiframe based reconstruction technique overrates the SR part as well as noise removal simultaneously to achieve better outputs. Multiple frames of a noisy low-resolution (LR) image of the same scene with a sub-pixel shift or rotation are used as input of the proposed algorithm. The registration technique of these images results in a single HR frame having more information than any one frame from the set of multiple frames. Later noise removal, as well as edge reservation, is done by applying a median filter to the HR frame. Then sparse representation technique is used to reconstruct the super-resolution frame of the filtered HR frame. To ensure the qualitative goodness, some well-known quality metrics like PSNR, SSIM, and BLUR are measured for different image inputs and the results are compared with other techniques to confirm the claims of the authors. Keywords Super-resolution · Sparse representation · Adaptive weight estimation · Median filter · Registration J. Karmakar · M. K. Mandal (B) Department of Physics, National Institute of Technology Durgapur, Durgapur, India e-mail: [email protected] J. Karmakar e-mail: [email protected] A. Kumar · D. Nandi Department of Computer Science and Engineering, National Institute of Technology Durgapur, Durgapur, India e-mail: [email protected] D. Nandi e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 V. Nath and J. K. Mandal (eds.), Nanoelectronics, Circuits and Communication Systems, Lecture Notes in Electrical Engineering 642, https://doi.org/10.1007/978-981-15-2854-5_4
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1 Introduction Super-resolution reconstruction from low-resolution image frames is one of the most important tasks in the area of image processing and enhancement. This provides an expedient to overcome the profound resolution limitation of the low-cost imaging system or non-availability of the low-cost high-resolution camera. Also, the existing LR image can be converted into an HR image where there is no other option to capture it again. SR reconstruction is an inverse problem where a high-resolution image is generated based on some prior knowledge about the observation model that maps the high-resolution image to a low-resolution image [1, 2]. The performance of the reconstruction-based SR algorithm degrades rapidly when the desired magnification factor is large and the number of available input images is not adequate. This causes loss of important high-frequency details resulting in a very smooth image. On the other side, the interpolation-based SR generally uses a bi-cubic or bilinear interpolation technique which may provide ringing and jagged artifact [3]. However, some other techniques like Papoulis-Gerchberg extrapolation [4], Locally Linear Embedding [5], and Machine-based learning [6] have been proposed for SR reconstruction by different authors. Recently, Yang et al. [7] proposed a new approach that uses only one frame as input to generate an HR image via sparse representation. To reconstruct an SR image, they used a joint dictionary which is trained using the same sparse vector for LR and HR image patches. Ahmed and Shah in [8], used single frame LR for SR reconstruction where they designed a dictionary using clusters from training data. They proposed nine compact dictionaries which cover eight orientations of image feature space. Another single input single output image SR reconstruction is presented in [9]. Here, feature extraction is done using Hilbert phase congruency to preserves edges. In the work of [10], two different dictionaries for LR and HR image patches are trained separately to apply more flexibility of expressions. In [11], the single image SR is done using discrete wavelet transform (DWT) to preserve the high-frequency details. The main advantage of these algorithms is that it needs only one frame to generate an SR image. Moreover, it gives better results as compared to conventional interpolation-based image zooming. Wang et al. [12] proposed another algorithm where multiple sub-pixel shifted LR inputs were used for reconstruction of an SR image. They also used the joint dictionary training algorithm for SR reconstruction from multiple frames via sparse representation. In this technique, multiple frames are registered using non-local mean (NLM) approach to gather the information from all the LR frames onto the resulting HR image. The advantage of this method is that more information can be obtained in the SR image than that of single frame input single frame output method. However, it is found that both of these two techniques are not suitable if a higher amount of noise is present in the LR frames. The shortcoming of sparse representation based SR reconstruction algorithms for noisy LR image input have been addressed in this paper and a novel multi-frame based super-resolution algorithm using sparse representation is proposed. The novelty of this work is that here patch-based noise removal is done by using a median filter. This filter suppresses the noise part in the patches by preserving the edge parts too.
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An adaptive weightage factor is multiplied with the feature vector to improve the feature part in the SR image. Here, the weighted vector is calculated from the variance between the noisy and noiseless patch individuals. This work has used an LR image as input which has been created by registration with grid interpolation using multiple sub-pixel shifted LR frames of the same scene instead. The paper is arranged in the following manner, a preliminary idea about imaging model, sparse representation of an image, and dictionary is given in Sect. 2, a basic theoretical overview of the proposed method with explanation of registration mechanism, weight estimation technique, dictionary training, and sparse coding is described in Sect. 3. The experimental results are discussed in Sect. 4. Section 5 gives the conclusion of the work.
2 Background 2.1 Imaging Model An observed low-resolution image is a degraded version of the high-resolution image according to the following observation model: Y = AX + η,
(1)
where A in (1) includes down-sampling, blurring, spatial distortion, and may be given as A = D · B · S; where D, B and S matrices represent the down-sampling, blurring, spatial distortion, respectively and η indicates the noise part. X ∈ R P is the HR image which is to be recovered from the noisy LR frame Y ∈ R Q of the scene. We may reconstruct X using the formula given below: X = A−1 (Y − η).
(2)
Hence, the reconstruction of the HR frame X from the noisy LR frame Y using (2) is an inverse and ill-posed problem and it is difficult to obtain unique solution X. However, regularization or learning technique may be employed to obtain best SR (or HR) solution.
2.2 Sparse Representation of the Image In the sparse representation technique, an image can be represented with the help of a sparse matrix and an over-complete dictionary as follows: X = Dα
(3)
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where D ∈ R M×N is a dictionary and α is a sparse matrix. To achieve the sparsity in α the dictionary D should be over-complete, i.e., N > M.
2.3 Dictionary In (3), the dictionary D is trained by random image patches. In [7], Yang et al. trained a joint dictionary consisting of both LR as well as HR patches. Thus, total dictionary has two parts, the LR dictionary Dl ∈ R M1 ×N generated from LR patches of a random image set and HR dictionary (Dh ∈ R M2 ×N ) from HR patches of the random image set which is named as training image set. This joint dictionary training method of Yang et al. is adopted in our proposed work which is discussed in Sect. 3.5.
3 Proposed Super-Resolution Algorithm The proposed work of this paper has been concentrated on image super-resolution using multiple noisy frames of the same scene based on sparse coding with the noise filtering and adaptive weight estimation technique. Basically, the conventional superresolution method and super-resolution via sparse representation used by Yang et al. [7] are taken into consideration in this proposed work. The multiple low-resolution frames with a sub-pixel shift are used in order to create HR input of the same size as the required in SR image. In this article, we have applied a frequency domain approach for registration of aliased images. The detailed registration mechanism is given in the next sub-section where the sub-pixel shift is estimated among the input LR images by considering the method proposed by Vandewalle et al. [13].
3.1 Registration Registration is a technique to align multiple frames of the same scene with a small shift or slight rotation into one coordinate system. With respect to first or any one frames, other frames are aligned by intensity consideration or feature consideration. In our proposed algorithm, only linear shift on frames with respect to a reference frame is applied. This shift is calculated by the method given below. For registration purposes, the images are first transformed into Fourier domain in polar coordinates. As an example, two images are taken for registration. The Fourier ρ ρ ρ transformed form of two image frames f 1x and f 2x are named as F1 and F2 where F1 is considered as a reference frame. For the linear shift estimation between a frame and the reference frame can be expressed in the frequency domain by the following (4):
A Novel Super-Resolution Reconstruction … ρ
F2 =
¨
f 2x e− j2πρ x d x = T
x
¨
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f 1(x+x) e− j2πρ x d x = e− j2πρ T
x
F
T
x
ρ
F1 .
(4)
ρ
The translation parameters x can be determined by F2ρ . However, if the LR 1 frames are aliased, we cannot apply the above method for estimating the precise translation parameters due to the presence of different frequency content in the LR frames. In this case, we use the equation given below: ρ
F2 =
K
e− j2π(ρ−iρs )
T
x
F1 (ρ − iρs )
(5)
i=−K
where ρs is the sampling frequency and 2K + 1 overlapping copies at frequency ρ. According to the registration parameter values, the pixel values of the LR frames are projected on an HR frame and the method of grid interpolation technique is used to construct the HR input.
3.2 Feature Extraction To extract the most important features from the HR input image, we have used four derivative functions same as used by Yang et al. [7] which are given below: f 1 = [−1, 0, 1] and f 3 = [1, 0, −2, 0, 1]
f 2 = f 1T , f 4 = f 3T
where T represents the transpose operation. f 1 and f 2 are first-order and f 3 and f 4 are second-order derivatives for feature extraction in both vertical and horizontal direction. A feature function F is thus formed by concatenating the four feature vectors. These feature vectors reserve the high-frequency components of the grid interpolated frame. But this feature extraction will not perform well while the frame contains noise. Thus, we have introduced an adaptive weight estimation mechanism to suppress the noise while conserving the required high-frequency components also. The weight is estimated between the noisy unfiltered HR frame and a filtered HR frame.
3.3 Noise Filtration In our proposed work, we have used a median filter to remove or minimize the noise part and to preserve the edges of the HR frame. The median filter is a well-known nonlinear digital filter which removes noises with edge preservation. This filtered
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image is used in the adaptive weight estimation technique as discussed in the next sub-section.
3.4 Weight Estimation The weight estimation technique is used to reduce the magnitude of the noise frequency components that are extracted from the feature part. This operation improves the output HR image quality significantly. The adaptive weight is estimated by the following mathematical equation. 2 − PCk − P k (i, j) exp w(i, j) = √ , 2σ 2 2π σ 2 1
(6)
where P k (i, j) is the pixel value of a kth patch at (i, j) position of the noisy image, σ is the variance of that noisy image patch and PCk is the central pixel value of kth patch of filtered image. This weight matrix will be multiplied to the feature function (F) which will result in a weighted feature function Fw of the HR input frame for sparse coding.
3.5 Dictionary Training In our proposed work, the over-complete joint dictionary is trained for SR reconstruction considering the work of Yang et al. [7]. For local model, two dictionaries Dl and Dh are generated by randomly sampled raw images patches of high-resolution image and their corresponding low-resolution image patches from a set of training images. Consider Th = th 1 , th 2 , . . . , th t is a set of HR training images and the correspond ing LR images downsampled from these HR training set is Tl = tl1 , tl2 , . . . , tlt . The following equations are used to construct the dictionaries: D = [Dh , Dl ]
Dl = arg min Dl ,α X l − Dl α22 + λα1 , Dh = arg min D h ,α X h − Dh α22 + λα1
(7)
s.t. Di 22 ≤ 1, i = 1, 2, . . . , K for both LR and HR dictionary. X l and X h are the LR and HR patches from the training image set and λ is the Lagrange multiplier. The first term in (7) is a fitting term and the second term is used for the regularization to enforce sparsity. In the above equation, both Dl or Dh , i.e., the dictionary and the sparse vector cannot be convex simultaneously, but one of them will be convex by keeping the other one fixed. The optimization is done by the following steps:
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1. Initialize dictionary (Dl or Dh ) with a Gaussian random matrix and the columns of that matrix are normalized. 2. Update sparse vector by keeping dictionaries fixed with the following equation:
α = arg minα X l − Dl α22 + λα1 α = arg minα X h − Dh α22 + λα1
(8)
3. Update dictionary by keeping α fix with the following equation:
arg min Dl X l − Dl α22 , arg min Dh X h − Dh α22
(9)
s.t. Di 22 ≤ 1, i = 1, 2, . . . , K 4. Steps 2 and 3 are repeated until convergence is reached. The joint dictionary learning enforces the high-resolution training images and their low-resolution counterparts to share the same sparse codes solving the following optimization problem (10) min
D h , Dl ,α
1
X t − D h α 2 + 1 X t − Dl α 2 + λ 1 + 1 α1 , h l 2 2 m n m n
(10)
where m and n represent the respective dimensions of the HR and LR image patches in vector form. This joint dictionary decreases the computational load and makes the sparse representation algorithm efficient.
3.6 Sparse Coding In the proposed algorithm, the registered and grid interpolated LR frame is taken as input. To reduce the computational complexity, this input frame is divided into a number of patches of the same size by the method of the raster scan, from left to right and top to bottom. Thus, from the LR frame Y we have got a number of patches named as y. The corresponding sparse vector α is calculated for each of these patches with the help of the work of Lee et al. [14]. We consider the reconstruction of an HR image patch x ∈ R p by using the corresponding LR image patch y ∈ R q . The high-resolution and low-resolution image patches x and y have same sparse α. The proposed algorithm is given below. The corresponding flowchart of the proposed work is given in Fig. 1. Input: Four LR frames of the same scene with the sub-pixel shift and two learned dictionaries Dh and Dl . 1. Registration: Multiple frames are registered into a single LR frame Y by estimating sub-pixel shift.
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Mean m
Input LR noisy frames
Registration Parameter estimation
Grid Interpolation
Weight estimation
HR patches
W
Feature Extraction
F
Dl
Dh
Reconstructed HR image
Fig. 1 Flowchart of the proposed algorithm
2. Grid interpolation: A frame is formed by HR grid and with the help of registration parameters and LR frame a new LR frame Y is formed of the same size of HR image. 3. Ym is formed from Y by passing it through a median filter. 4. Features are extracted from the patches of Y using f 1 , f 2 , f 3 and f 4 . a. Patches y and ym of size 5 × 5 are extracted from both Y and Ym . b. Calculate the mean value m of each of the patch y. c. Weight vector w for each patch is estimated by considering (6) from each patch y and ym . d. Multiply the weight vector with feature vectors. Solve the optimization problem for α:
− y˜ 2 + λα1 min Dα 2 α
(11)
FW y F Dl and y˜ = . Dh w e. High-resolution patches for every LR patches are reconstructed as
= with D
x H = Dh α + m f. HR image X H is generated from these HR patches x H . g. The final SR image X S that is close to X H using the reconstruction constraint: X S = arg minY − dbX 22 + λ0 X − X H 22 X
(12)
4 Experimental Results The proposed algorithm is tested on the MATLAB software in Windows-8 with 64-bit OS and 2 GB RAM. The dictionary with 512 atoms is trained by 100,000 image patches of size 5 × 5 randomly chosen from a set of different images. To
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Fig. 2 Original images of a Lena, b Barbara, c House
test the proposed algorithm some standard high-resolution images are taken which are shown in Fig. 2. In the proposed work, the noisy low-resolution frames are generated from the original high-resolution images. Initially, some sub-pixel shifted frames are generated from the original image. Then, the frames are downsampled to generate low-resolution frame. Thereafter, the Gaussian noise is added with these low-resolution frames to generate noisy low-resolution input. The amount of noise is varied with the different values of standard deviation (σ). In this work, four frames of noisy images of the same scene with small translation are considered as multiple frames and these are used for registration. The effectiveness of any image processing algorithm relies on the quality of the output image. According to image processing literature, a number of quality metrics are used to measure the overall quality of an image quantitatively. Since different quality matrices focus on the different kind of features of the image, therefore, some specific quality matrices may be more useful than others depending on the field of applications. In this article, to quantify the super-resolution image, we use the following three main quality matrices: (i) Peak signal to noise ratio (PSNR), (ii) Structural similarity index metrics (SSIM), and (iii) BLUR [15]. The results of these quality matrices of the proposed work are given in the Table 1. In Table 2, a comparison on the quality of our proposed outputs is shown with other works. The better values of PSNR, BLUR, and SSIM in the comparison Table 2 ensure the reliability of the proposed algorithm properly. In Table 3, the variation of quality metrics values are shown for different standard deviation (σ ) 5, 10, 15, 20, and 25. In Figs. 3, 4 and 5, one low-resolution frame and the output of the proposed work for that corresponding frame are shown with a zoomed portion of each pictures. Figures 6, 7 and 8 show a comparison of the outputs of other comparative Table 1 Results of PSNR, SSIM, and BLUR for σ = 20
Image
Quality metrics PSNR
SSIM
BLUR
Lena
26.0345
0.5565
0.2794
Barbara
21.6836
0.5158
0.2980
House
25.3655
0.4965
0.2538
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Table 2 Comparison of the results of PSNR, SSIM, and BLUR of different images for σ = 20 Image
Quality metrics
Yang et al. [7]
Wang et al. [12]
Proposed
Lena
PSNR
23.6299
24.0761
26.0345
SSIM
0.4835
0.4723
0.5565
BLUR
0.3627
0.3158
0.2794
PSNR
20.5433
21.0439
21.6836
SSIM
0.4595
0.4919
0.5158
BLUR
0.3734
0.3214
0.2980
PSNR
23.9103
24.3868
25.3655
SSIM
0.4460
0.4543
0.4965
BLUR
0.3290
0.2910
0.2538
Barbara
House
Table 3 Variation of PSNR, BLUR, and SSIM values with a variation of σ for Lena image
Standard deviation (σ)
PSNR (dB)
BLUR
SSIM
5
28.9897
0.4337
0.8270
10
28.0982
0.3662
0.7327
15
27.4550
0.3121
0.6392
20
26.0345
0.2794
0.5565
25
24.4059
0.2510
0.4689
Fig. 3 Lena image: a LR input, b proposed output
methods with the output of our proposed method. A comparative study is done on a talpa image given in Fig. 9 where, all the outputs of the bi-cubic interpolation and the outputs of Yang et al. [7], Wang et al. [12] and our proposed work are shown. A possible application of the proposed work is shown with an air-plane image in Fig. 10. Here, a small portion of the LR image is specified by zooming which is not recognizable. But, after performing SR by our proposed work, it is readable. This shows that, the proposed work is efficient for surveillance purpose or in defense. These figures shows the visually goodness of the proposed output images.
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Fig. 4 Barbara image: a LR input, b proposed output
Fig. 5 House image: a LR input, b proposed output
Fig. 6 Lena image, outputs of a bi-cubic, b Yang et al. [7], c Wang et al. [12], d proposed method
Fig. 7 Barbara image, outputs of a bi-cubic, b Yang et al. [7], c Wang et al. [12], d proposed method
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Fig. 8 House image, outputs of a bi-cubic, b Yang et al. [7], c Wang et al. [12], d proposed method
(a)
(b)
(c)
(d)
Fig. 9 Image of Talpa, outputs of a bi-cubic, b Yang et al. [7], c Wang et al. [12], d proposed method
Fig. 10 Image of a plane, a low-resolution image, b output of the proposed method
5 Conclusions This paper presents a novel approach for multi-frame super-resolution based on sparse with the help of adaptive weight estimation technique and median filter. We have used several noisy low-resolution images with different noise variations for testing purposes. From the given figures, it can be concluded that the output images of the proposed work are visually as well as qualitatively good. The proposed approach
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gives better results as a comparison to another single image SR as well as multiimage SR, both in terms of numerical values and visual perception as mentioned in the experimental result section. The proposed method is also useful in video surveillance, defense, etc. as it is efficient to deal super-resolution as well as noise removal simultaneously.
References 1. S.C. Park, M.K. Park, M.G. Kang, Super-resolution image reconstruction: a technical review. IEEE Signal Process. Mag. 20, 21–36 (2003) 2. J.C. Ferreira, Algorithms for super-resolution of images based on sparse representation and manifolds. Image Processing, Université Rennes 1, 29–34 (2016) 3. S. Farsiu, M.D. Robinson, M. Elad, P. Milanfar, Fast and robust multi-frame super-resolution. IEEE Trans. Image Process. 13(10), 1327–1344 (2004) 4. E. Salari, G. Bao, Super-resolution using an enhanced Papoulis-Gerchberg algorithm. Image Processing, IET 6, 959–965 (2012) 5. V. Nguyen, C.C. Hung, X. Ma, Super resolution face image based on locally linear embedding and local correlation. Appl. Comput. Rev. 15(1), 17–25 (2015) 6. Y.N. Zhang, M.Q. An, Deep learning and transfer learning based super-resolution reconstruction from single medical image. J. Healthc. Eng. 2017 (2017) 7. J. Yang, J. Wright, T.S. Huang, Y. Ma, Image super-resolution via sparse representation. IEEE Trans. Image Process. 19(11), 2861–2873 (2010) 8. J. Ahmed, M.A. Shah, Single image super-resolution by directionally structured coupled dictionary learning. EURASIP J. Image Video Process. (Springer) (2016). https://doi.org/10.1186/ s13640-016-0141-6 9. R.R. Kumar, D. Mishra, Improved Sparse Representation based Super-Resolution (ICEEOT, IEEE, 2016), pp. 2265–2270 10. W. Yang, T. Yuan, W. Wang, F. Zhou, Q. Liao, Single-image super-resolution by subdictionary coding and kernel regression. IEEE Trans. Syst. Man Cybern. Syst. 47(9), 2478–2488 (2017) 11. S. Ayas, M. Ekinci, Single image super resolution based on sparse representation using discrete wavelet transform. Multimed Tools Appl. (Springer) 77, 16685–16698 (2018) 12. P. Wang, X. Hu, et al., Super-resolution reconstruction via multiple frames joint learning, in IEEE Conference on Multimedia and Signal Processing (CMSP) (2011) 13. P. Vandewalle, S. Susstrunk, M. Vetterli, A frequency domain approach to registration of aliased images with application to super-resolution. EURASIP J. Appl. Signal Process. 1–14 (2016), Article ID 71459 14. H. Lee, A. Battle, R. Raina, A.Y. Ng, Efficient sparse coding algorithms, in Proceedings of NIPS’06 (2006), pp. 801–808 15. F. Crete, T. Dolmiere, P. Ladret, M. Nicolas, The blur effect: perception and estimation with a new no-reference perceptual blur metric, in SPIE Electronic Imaging Symposium Conference, Human Vision and Electronic Imaging, San Jose: ÃL’tats Unisd’Am Ãl’rique (2007)
Design and Performance Analysis of Different Structures for Low-Frequency Piezoelectric Energy Harvester Namrata Saxena, Ritu Sharma, K. K. Sharma and Varshali Sharma
Abstract This paper describes the performance comparison of various cantilever structures (tapered-shaped, angled-T-shaped, A-shaped) that can be utilized in a piezoelectric energy harvester (PEH) applications. For proper selection of the material, a comparison is made among different piezoelectric materials and it is observed that for capturing low-frequency range Lead Zirconate Titanate (PZT-5H) is a better choice. All cantilever structures discussed in the paper are designed using same materials, i.e., PZT-5H as the piezoelectric layer, single crystal silicon for the structural layer and the proof-mass, and gold as the top and bottom electrode. All the structures possess equal thicknesses for these layers. The performance of all the PEHs is evaluated on the basis of parameters such as resonant-frequency, piezoelectric voltage, maximum displacement, and von-Mises stress. The angled-T-shaped cantilever beam structure generates a piezoelectric voltage of 4.63 V at the resonant-frequency of 419.36 Hz on the application of 1 g acceleration and proves to be the most suitable cantilever beam among all the three designs for acquiring ambient low-frequency. Keywords Piezoelectric energy harvester (PEH) · Cantilever beam · PZT-5H
N. Saxena (B) · R. Sharma · K. K. Sharma Department of Electronics & Communication Engineering, Malaviya National Institute of Technology, Jaipur, India e-mail: [email protected]; [email protected] R. Sharma e-mail: [email protected] K. K. Sharma e-mail: [email protected] V. Sharma Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 V. Nath and J. K. Mandal (eds.), Nanoelectronics, Circuits and Communication Systems, Lecture Notes in Electrical Engineering 642, https://doi.org/10.1007/978-981-15-2854-5_5
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1 Introduction Currently, a lot of research is going on across the globe to find out the novel genre of MEMS-based micro-cantilever for energy harvesting from surrounding resources to energize wireless gadgets and modules that have been achieving importance. Chemical fuel cells were employed conventionally to power these wireless electronic gadgets [1–3]. The strenuous task of replacing a battery and its reduced life-span triggers scientists to be able to harvest ambient mechanical energy from the surroundings. The foremost environmental energy sources are mechanical vibrations, solar energy, acoustic, wind, thermoelectric, and effluents [4–6]. There are four different techniques available for the harvesting of ambient mechanical vibrations, electromagnetic, electrostatic, magnetostrictive, and piezoelectric [7–9]. However, the electromagnetic transducers are complicated to fabricate using MEMS technology because planar magnets have complex properties, intricacies in integration and the quantity of coil turns should be minimum. In the electrostatic transducers, the high output impedance and the existence of parasitic capacitance restrict the output current and consequently degrades the efficiency of the generator. Magnetostrictive generator offers high flexibility, ultra-high coupling coefficient, viability for vibrations at high-frequency, and not any loss of polarization drawback. Though, the requirement of pick-up coil, non-linear effects, and complexity in the fabrication using MEMS, confine their uses for wireless applications [10, 11]. The PEH proves as the most promising device to produce electric potential from sustainable energy providers [12, 13]. The intention of this project is to design different structures of PEHs for the harvesting of ambient vibrations in the low-frequency range below 2 kHz. The designing of the cantilevers is accomplished using single crystal silicon for the structural layer and the proof-mass, PZT-5H as the piezoelectric layer. For proper selection of the material, analysis of different piezoelectric materials is carried out and it is observed that for capturing low-frequency range PZT-5H is the most suitable choice. In this work, analyses are classified into four categories. The modal analysis for the calculation of the resonant-frequency. The dynamic analysis in order to compute the maximum displacement through the zaxis. For the evaluation of the piezoelectric potential, the piezoelectric analysis is done. The stress analysis is performed for the determination of the von-Mises stress at the resonant-frequency.
2 Mathematical Modeling The majority of vibration-based micro-cantilevers are spring-mass-damper systems that yield optimum potential when the frequency of ambient vibration matches the resonant-frequency of the energy harvester. The resonant-frequency ( fr ) of a fixed-free cantilever beam can be defined by the Eq. (1)
Design and Performance Analysis of Different Structures …
1 fr = 2π
k m
49
(1)
where m is a mass of the attached proof-mass. The spring constant (k) of the cantilever is specified by the Eq. (2) k=
3E I 3Ewt 3 = 3 l 12l 3
(2)
where E is the Young’s modulus, I is the moment of inertia, l is the length, w is the width and t is the thickness of the cantilever beam. The resultant formula of the resonant-frequency is derived as Eq. (3) 1 fr = 2π
Ewt 3 4l 3 m
(3)
It reveals that the resonant-frequency depends on the cantilever dimensions and the mass of the proof-mass. The Stoney’s equation provides the relation between the cantilever end deflection δ and the applied force F as given by [14–17] δ=
3F(1 − υ) 2 E lt
(4)
where υ is the Poisson’s ratio. So, the applied force can be calculated as: 2 δ E lt F= 3(1 − υ)
(5)
To ascertain if a material will yield or fracture the von-Mises stress (σ ) can be calculated as given by Eq. (6). If the value of von-Mises stress of any material subjected to the loading condition is higher than or even equal to the maximum yield limitation of the same material under simple tension, then the material will certainly yield. σ =
F A
where A is the surface area of the cantilever beam.
(6)
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3 Designing Parameters of Cantilever Beam In this paper, three different structures of cantilever beams have been simulated using structural mechanics module of COMSOL Multiphysics, i.e., tapered-shaped, angled-T-shaped, A-shaped. In all these structures we have modified the conventional geometry of the cantilever beam along with the shape of the proof-mass. All the structures are designed to work as PEH for the harvesting of low-frequency ambient vibrations. In all the designs, both electrical and mechanical boundary conditions have been applied. In electrical boundary conditions, the ground potential is employed to the bottom electrode and the floating terminal voltage is applied to the top electrode along with the resistive load. In mechanical boundary conditions, one end of the beam is fixed. Some of the important material properties are listed below in Table 1.
3.1 Designing Parameters of the Tapered-Shaped Cantilever Structure The length, thickness, width (at the fixed-end), and the width (at free-end) of the tapered-shaped cantilever beam is 2000 µm, 6 µm, 600 µm, and 200 µm, respectively. The thickness of the piezoelectric material is taken to be 5 µm. In this structure, the shape of the proof-mass is similar to the free end of the cantilever structure. All the dimensions of the structure are illustrated in Fig. 1. The resonant-frequency obtained from the tapered-shaped cantilever beam is 739.75 Hz, with a piezoelectric potential generated at the fixed-end of the cantilever beam is 4.51 V. Table 1 Material properties
Material properties Density
(Kg/m3 )
Materials Silicon
PZT-5H
Gold
2329
7500
19,300
Young’s Modulu (E) (GPa)
170
150
70
Poisson’s ratio (υ)
0.28
0.31
0.44
Relative permittivity (ξ T )
11.7
{1704.4, 1704.4, 1433.6}
6.9
where ξ T is three-by-three dielectric constant matrix at constant stress, i.e., in stress-charge form
Design and Performance Analysis of Different Structures …
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Fig. 1 Dimensional view of the tapered-shaped cantilever structure
3.2 Designing Parameters of the Angled-T-Shaped Cantilever Structure The length, thickness, width (at the fixed-end), and the width (at free-end) of the angled-T-shaped cantilever beam is 1800 µm, 6 µm, 500 µm, and 200 µm, respectively. The thickness of the piezoelectric material is also taken to be 5 µm. In this structure, the shape of the proof-mass is similar to the free end of the cantilever beam. All the dimensions of the structure are illustrated in Fig. 2. The resonant-frequency obtained from the angled-T-shaped cantilever beam is 419.36 Hz, with a piezoelectric potential generated at the fixed-end of the cantilever beam is 4.63 V.
Fig. 2 Dimensional view of the angled-T-shaped cantilever beam
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3.3 Designing Parameters of the A-Shaped Cantilever Structure The length, thickness, width (at the fixed-end), and the width (at free-end) of the A-shaped cantilever beam is 2200 µm, 6 µm, 500 µm, and 200 µm, respectively. In this structure, the shape of the proof-mass is similar to the free end of the cantilever beam. All the dimensions of the structure are demonstrated in Fig. 3. The resonant-frequency obtained from the A-shaped cantilever beam is 521.9 Hz, with a piezoelectric potential generated at the fixed-end of the cantilever beam is 4.89 V.
4 Results and Discussion For PEHs discussed in the paper, four types of analyses have been done. (1) The modal analysis for the calculation of the resonant-frequency. (2) The dynamic analysis for the measurement of the maximum displacement through the z-axis. (3) The piezoelectric analysis to compute the piezoelectric potential. (4) The stress analysis to evaluate the von-Mises stress at the resonant-frequency. For the analysis of PEHs, the 1 g (g = 9.8 m/s2 ) acceleration is applied along the z-axis. The performances of these piezoelectric energy harvesters are evaluated on the basis of parameters such as resonant-frequency, piezoelectric voltage, maximum displacement, and von-Mises stress.
Fig. 3 Dimensional view of the A-shaped cantilever beam
Design and Performance Analysis of Different Structures …
53
4.1 Modal Analysis The modal analysis is carried out for the calculation of resonant-frequency among the first six modes of the frequency of the PEHs and its resultant shape of bending. The first eigen-frequency produces the maximum displacement and the piezoelectric potential, and thus considered as the resonant-frequency since its deformation behavior is appropriate for energy harvesting purposes, which is demonstrated in Fig. 4. The comparison between analytical resonant-frequency fr (analytical) obtained from Eq. (4) and the simulated resonant-frequency fr (simulation) of all the three Piezoelectric
Fig. 4 Eigen-frequency of a tapered-shaped, b angled-T-shaped, c A-shaped cantilever beams respectively
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Table 2 Comparison between analytical and simulated resonant-frequency of (a) tapered-shaped, (b) angled-T-shaped, (c) A-shaped cantilever beams Design
fr (analytical) (HZ)
fr (simulation) (Hz)
Error (%)
Tapered-shaped
714.6295
739.75
0.25
Angled-T-shaped
473.96
419.36
11.52
A-shaped
500.125
521.9
4.35
Energy Harvesters are enumerated in Table 2. It can be noticed that the percentage error in the analytical and simulation study is very low.
4.2 Dynamic Analysis For all the structures discussed in the paper, when the boundary load of 1 g acceleration is employed across the z-axis, the cantilever beam vibrates leading to the deflection of the beam [18–20]. It has been observed that the maximum displacement occurs at the free-end and the zero displacements at the fixed-end of the cantilever beam.
4.3 Piezoelectric Analysis It is well-known that the PEH generates an electric potential when it is subjected to a load. The open-circuit voltage of all three PEHs can be computed using FEM simulator, which is proportional to the stress and also depends on the material properties. It has been noticed that the maximum electric potential appears at the fixed-end of the cantilever beam.
4.4 Stress Analysis The stress analysis is important for the cantilever beam as it should be lower than the yield strength of the structure to verify if the material will yield or fracture. For this purpose von-Mises stress is calculated and it has been examined that it is maximum at the fixed-end of the cantilever beam. All these analyses have been carried out and the results are summarized in Table 3. A comparative analysis of the present work and the work reported in various literature on the basis of resonant-frequency, device volume, displacement at the fixed-end, and the generated piezoelectric voltage is summarized in Table 4.
Design and Performance Analysis of Different Structures …
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Table 3 Performance of (a) tapered-shaped, (b) angled-T-shaped, (c) A-shaped cantilever beams Design
von-Mises stress (N/m2 )
Volume (µm3 )
600
1.82 × 108
2.755 × 107
600
7.8 × 107
5.87 × 108
1400
3.8 × 108
9.24 × 107
Maximum displacement (µm)
Tapered-shaped Angled-T-shaped A-shaped
Table 4 Comarative analysis References
Maximum displacement (µm)
Piezoelectric voltage (V)
Resonant frequency (Hz)
Volume (µm3 )
[1]
–
–
< 200
1.1 × 109
[2]
2770
–
229.25
24.566 × 1010
[4]
(15–25)
(24–58) mV
1550–1900
–
[18]
4.6 × 10−3
–
0.63 M
–
This work
(600–1400)
(4.51–4.89)
(400–700)
(2.755 × 107 )–(5.87 × 108 )
From Table 4, it can be concluded that the designs reported in the paper are capable of generating an appreciable piezoelectric voltage for low-frequency range with reasonably less volume.
5 Conclusion This paper modeled the implementation of three cantilever beam structures proposed to yield electrical potential from ambient mechanical vibrations. The geometrical sizes are optimized predicated on the mathematical modeling, choice of the material and FEM analysis of an energy harvester. The resonant frequencies of all the PEHs are below 1 kHz, which makes them suitable for the harvesting of low-frequencies. All three designs produce approximately the same open-circuit voltage. The A-shaped PEH generates maximum displacement. Thus, the angled-T-shaped beam seems to be the better choice for the low-frequency energy harvesting application. Acknowledgements The authors wish to gratefully acknowledge DRDO, New Delhi for providing financial support to this project.
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References 1. H.-C. Song, P. Kumar, D. Maurya, M.-G. Kang, W.T. Reynolds, Jr., D.-Y. Jeong, C.-Y. Kang, S. Priya, Ultra-low resonant piezoelectric MEMS energy harvester with high power density. J. Microelectromech. Syst. 26(6) (2017) 2. Md. N. Uddin, Md. S. Islam, J. Sampe, M.S. Bhuyan, S.H. Md. Ali, Design and analysis of a T-shaped piezoelectric cantilever beam at low frequency using vibration for biomedical device (2016), ISSN 1992-1454 3. M. Domínguez-Pumar, J. Pons-Nin, J.A. Chávez-Domínguez, MEMS Technologies for Energy Harvesting (Chapter 2, Springer International Publishing Switzerland, 2016) 4. A. Iqbal, F. Mohd-Yasin, Comparison of seven cantilever designs for piezoelectric energy harvester based on Mo/AlN/3C-SiC, in RSM Proceedings edited by K. Terengganu (Malaysia, 2015) 5. J. Zhang, S. Ma, L. Qin, Analysis of frequency characteristics of MEMS piezoelectric cantilever beam based energy harvester, in Symposium on Piezoelectricity, Acoustic Waves, and Device Applications, Oct 30–Nov 2, Jinan, China (2015) 6. H. Li, C. Tian, Z. Daniel Deng, Energy harvesting from low frequency applications using piezoelectric materials. Appl. Phys. Rev. 1, 041301 (2014) 7. S.B. Kim, H. Park, S.H. Kim, H. Clyde Wikle, J.H. Park, D.J. Kim, Comparison of MEMS PZT cantilevers based on d31 and d33 modes for vibration energy harvesting. J. Microelectromech. Syst. 22, 26–33 (2013) 8. T. Guo, Z. Xu, L. Jin M. Hu, Optimized structure design of a bridge-like piezoelectric energy harvester based on finite element analysis, in IEEE Conference (2017) 9. H.-U. Kim, W.-H. Lee, H.V. Rasika Dias, S. Priya, Piezoelectric microgenerators-current status and challenges. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 56(8) (2009) 10. H.S. Kim, J.H Kim, J. Kim, A review of piezoelectric energy harvesting based on vibration. Int. J. Precis. Eng. Manuf. 12(6), 1129–1141 (2011) 11. A.Z. Abbasi, N. Islam, Z.A. Shaikh, A review of wireless sensors and networks applications in agriculture. Comp. Stand. Int. 36(2), 263–270 (2014) 12. S. Roundy, P.K. Wright, A piezoelectric vibration based generator for wireless electronics. Smart Mater. Struct. 13, 1131–1142 (2004) 13. L. Wang, F.G. Yuan, Vibration energy harvesting by magnetostrictive material. Smart Mater. Struct. 17(4), 045009 (2008) 14. U.M. Jamain, N.H. Ibrahim, R. Ab Rahim, Performance analysis of zinc oxide piezoelectric MEMS energy harvester, in IEEE-ICSE Proceedings, Kuala Lumpur, Malaysia (2014) 15. P. Graak, A. Gupta, S. Kaur, P. Chhabra, D. Kumar, A. Shetty, Design and simulation of various shapes of cantilever for piezoelectric power generator by using COMSOL, in Excerpt from the Proceedings of the 2015 COMSOL Conference in Pune 16. E. Varadrajan, M. Bhanusri, Design and simulation of unimorph piezoelectric energy harvesting system, in Excerpt from the Proceedings of the COMSOL Conference in Bangalore (2013) 17. H. Yu, J. Zhou, L. Deng, Z. Wen, A vibration-based MEMS piezoelectric energy harvester and power conditioning circuit. Sensors (2014) 18. S. Arora, Sumati, A. Arora, P.J.George, Design of MEMS based microcantilever using COMSOL multiphysics. IJAER 7(11) (2012), ISSN 0973–4562 19. M. Jambunathan, R. Elfrink, R. Vullers, R. van Schaijk, M. Dekkers, J. Broekmaat, Pulsed laser deposited-PZT based MEMS energy harvesting devices, in Proceedings of the 2012 International Symposium on Piezoresponse Force Microscopy and Nanoscale Phenomena in Polar Materials, Aveiro, Portugal, 9–13 July 2012, pp. 1–4 20. E.E. Aktakka, R.L. Peterson, K. Najafi, Thinned-PZT on SOI process and design optimization for piezoelectric inertial energy harvesting, in Proceedings of the 2011 16th International IEEE Solid-State Sensors, Actuators and Microsystems Conference (TRANSDUCERS), Beijing, China, 5–9 June 2011, pp. 1649–1652
Fault Management Testing in Optical Networks Using NMS Sandeep Dabhade, Sumit Kumar, Pravas Mohanty, Manas Kumar, Abinash Das, Pranesha Bhat, Sushil Desai and Samapika Padhy
Abstract In the telecommunication domain, various alarms are shown in Network Management Systems and Element Management Systems to monitor the network from remote. Alarm monitoring system helps in providing QoS (Quality of Services) to customers. This paper covers how fault management testing is done in the optical networks domain, various types of alarms. Hence, alarms shown on nodes should be reflected in NMS for providing uninterrupted traffic (payload) to customers. Keywords EMS · NMS · LOS · LOF · Alarm · OTN
S. Dabhade (B) · S. Kumar · P. Mohanty · M. Kumar · A. Das · P. Bhat · S. Desai · S. Padhy NSM NFM-T Department, NOKIA, Bangalore, India e-mail: [email protected] S. Kumar e-mail: [email protected] P. Mohanty e-mail: [email protected] M. Kumar e-mail: [email protected] A. Das e-mail: [email protected] P. Bhat e-mail: [email protected] S. Desai e-mail: [email protected] S. Padhy e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 V. Nath and J. K. Mandal (eds.), Nanoelectronics, Circuits and Communication Systems, Lecture Notes in Electrical Engineering 642, https://doi.org/10.1007/978-981-15-2854-5_6
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1 Introduction There are five basic alarms are setup for detection, isolation, and correction of fault management system such as (i) alarm for communication, (ii) alarm for quality of service, (iii) alarm for processing error, (iv) alarm for equipment, and (v) alarm for environment [1–4]. Perceived severity demonstrates the six levels of severity which explain the object affection managerial capability. Severity affection orders are critical, major, minor, and warming. This order defined as cleared, intermediate, critical, major, minor, and warming.
2 Fault Management Testing Setup Our testing setup is created using simulators. Various unprotected and protected connections were created using automation. Figure 1 indicates Mesh Setup used for our testing. Figure 2 indicates OTN with Network Adapters.
Fig. 1 Mesh setup
Fault Management Testing in Optical Networks Using NMS
59
Fig. 2 OTN with network adapters
Table 1 Probable cause
Probable cause name
Value reference
Version mismatch
Version mismatch
Underlying resource unavailable
Underlying resource unavailable
Transmitter failure
Transmitter failure
Threshold crossed
Threshold crossed
Software error
Software error
Out of memory
out of memory
Loss of signal
Loss of signal
Loss of frame
Loss of frame
Framing error
Framing error
Degraded signal
Degraded signal
3 Alarm Types There are various types of alarms used in optical networks. Whenever fiber is cut, loss of signal is raised indicating to operator to take appropriate action [5–8]. Table 1 indicates probable cause while Table 2 indicates alarm types.
4 Alarm Heirarchy Figure 3 indicates Alarm Tree wherein LOS is the highest priority alarm. Alarm Hierarchy is indicated by Fig. 4 which explains Upstream and Downstream alarms (Table 3 and Fig. 5).
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Table 2 Alarm types Alarm type
Probable problems
Communications alarm
Signal loss Frame loss Framing error Error in local node transmission Error in remote node transmission Signal degradation Failure in communications subsystem Error in communications protocol
Quality of service alarm
Excessive in response time Exceeded the queue size Reduction in bandwidth Excessive in transmission rate Crossed threshold Degradation in performance
Processing error alarm
Problem in storage capacity Mismatch version Data corruption Exceed the CPU cycles limit Error in software Program error in the software Abnormally terminated in software program Error in file Memory overdriven Unavailable underlying resource Failure in application subsystem Error in configuration or customization
Equipment
Problem in power Problem in timing Problem in processor Error in dataset or modem Problem in multiplexer Failure of receiver Failure of transmitter Failure in receive Failure in transmit Error in output device Error in input device Error in I/O device Malfunction in equipment Error in adapter
Environmental
Unacceptable temperature Unacceptable humidity Problem in cooling system/heating/ventilation Detection of fire Detection of flood Detection of toxic leak Detection of leak Unacceptable in pressure Excessive vibration Exhausted in material supply Failure of pump Door open enclosure
Fault Management Testing in Optical Networks Using NMS
Fig. 3 Alarm tree
Fig. 4 Alarm hierarchy Table 3 AS (alarm surveillance) page
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Fig. 5 Alarm dashboard
5 Test Scenario We simulate various alarms and test its propagation from Nodes to NMS and their correlation to the various layers of OTN [9–11]. Use Case 1: Remove the fiber Through this, LOS alarm is generated which will be correlated on the connection level. Use Case 2: Inject the Alarm through Optical Test Set Connections will be created using Optical Test Set and different types of alarm will be injected to test like real-time scenarios. Use Case 3: Clear Alarms Clear the alarms and verify that alarms are getting cleared from the Nodes as well as NMS.
6 Conclusions Alarms were raised and cleared on multiples nodes and these alarms got co-related to various connections successfully. We found that during our Fault Management testing, alarms were raised without any delays which are a must in field scenario. If there is a fiber cut, Loss of Signal is reported immediately so that NOC operator
Fault Management Testing in Optical Networks Using NMS
63
can take appropriate action. We successfully tested the alarm burst on simulators. Hence, we conclude that Fault Management testing is critical to deliver better QoS to customers in optical networks domain.
References 1. 2. 3. 4. 5.
6.
7.
8. 9.
10.
11.
International Telecommunication standard, X.733 Uyless Black, Optical Networks, Third Generation Transport Systems M. Eiselt, Programmable modulation for high-capacity networks, in ECOC (Geneva, 2011) O. Aboul-Magd, B. Jamoussi, Automatic switched optical network (ASON) architecture and its related protocols, https://shiraz.levkowetz.com/pdf/draft-ietf-ipo-ason-02.pdf L.-K. Chen, M.-H. Cheung, C.-K. Chan, From optical performance monitoring to optical network management: research progress and challenges, http://www.lightwave.ie.cuhk.edu.hk/ publication/document/Conference/ICOCN/2004/lkchen_icocn04.pdf P. Samadi, H. Wang, An optical programmable network architecture supporting iterative multicast for data-intensive applications, http://lightwave.ee.columbia.edu/files/Samadi2014a. pdf L. Gifre, L. Velasco, Experimental assessment of a high performance backend PCE for flexgrid optical network re-optimization, http://personals.ac.upc.edu/lvelasco/docs/research/2014OFC-4.pdf Y. Okumura, M. Kato, K. Fujii, Signal shaping to achieve OOK and PSK co-existence for improved optical access network performance. Am. J. Netw. Commun. 3(4), 56–62 (2014) L.-K. Chen, M. Li, S.C. Liew, Optical physical-layer network coding–another dimension to increase network capacity? http://www.lightwave.ie.cuhk.edu.hk/publication/document/ Conference/IPC/2014/2014_Chen_IPC.pdf M. Xia, M. Shirazipour, Y. Zhang, Howard Green, Attila Takacs, SDN and optical flow steering for network function virtualization, https://www.usenix.org/system/files/conference/ons2014/ ons2014-paper-xia.pdf M. Prabazhini, Performance improvement of optical network using dynamic bandwidth allocation. IJAICT 1(1) (2014)
Effects of Fin Height on Digital Performance of Hybrid p-Si/n-InGaAs C-FinFETs at 25 nm Gate Length Kallolini Banerjee and Abhijit Biswas
Abstract We report effects of Fin height on the logic application of hybrid complementary FinFETs (HC-Fins) composed of n-FinFETs and p-FinFETs employing InGaAs and Si channel, respectively, at gate length of 25 nm. Using extensive numerical analysis, we calculate rise time tr and fall time tf of NAND gate based inverter built with HC-Fins. Furthermore, the propagation delay per inverter and oscillation frequency of a three-stage ring oscillator constructed with HC-Fins are computed and compared with their corresponding Si value. Our findings reveal that tf of the proposed hybrid inverter exhibits a significant reduction of 63.1% relative to its equally sized Si inverter value with aspect ratio (AR) of 7. Moreover, the oscillation frequency of a three-stage ring oscillator built with hybrid NAND inverters exhibits 21.7% increment compared to its Si counterpart for AR = 7. Keywords Fall time · Hybrid FinFETs · Frequency of oscillations · Logic performance
1 Introduction The FinFET, a manufacturable version of multigate transistors, has become an industry-standard in the sub-30 nm technology node owing to its excellent control of short channel effects (SCEs), lower leakage current, and high packing density [1–3]. In addition, a thin semiconducting body of the FinFET makes the channel doping optional, thus resulting in enhancement of carrier mobility as well as drive current while lowering OFF-current [2]. The drive current can further be augmented using high mobility channel materials like InGaAs. Earlier experimental findings reported effective electron mobility as high as 2200 cm2 V−1 s−1 [4, 5] in InGaAs. It K. Banerjee (B) · A. Biswas Institute of Radio Physics and Electronics, University of Calcutta, 92 Acharya Prafulla Chandra Road, Kolkata 700009, India e-mail: [email protected] A. Biswas e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 V. Nath and J. K. Mandal (eds.), Nanoelectronics, Circuits and Communication Systems, Lecture Notes in Electrical Engineering 642, https://doi.org/10.1007/978-981-15-2854-5_7
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is well-established that CMOSFET configuration suits well for designing integrated circuits while dissipating less power. In order to utilize the benefits of high mobility channel material we, therefore, propose a hybrid complementary (C)-Fin configuration featuring an InGaAs channel n-FinFET and a Si channel p-FinFET. In addition, since all kinds of Boolean function can be implemented using universal logic gates such as NAND gate, they are economical and more preferred logic gate for fabrication in a chip. While previous investigations have only reported the performance of Si C-FinFETs (SC-Fins) on the analog and logic domains [6–8], there has been no study on the logic performance of hybrid C-FinFETs except our earlier findings on p-Ge/n-Si FinFETs in [9]. In this paper, we investigate the effects of Fin height HFin expressed in terms of aspect ratio AR (= HFin /WFin ) while maintaining a fixed fin width WFin on the logic performance of hybrid inverters built with InGaAs n-FinFETs and Si p-FinFETs at gate length of 25 nm. Furthermore, we construct a NAND-based ring oscillator (RO) using HC-Fin and determine propagation delay per inverter stage (tp ), and also the frequency of oscillations (fosc ) of the oscillator. Finally, we compare the various parameters such as fall time, propagation delay per inverter, frequency of oscillations of RO obtained using HC-Fin and SC-Fins.
2 FinFET Description and Simulation The schematic of our proposed HC-Fin device is illustrated in Fig. 1. The process flow of the Si p-FinFET and InGaAs n-FinFET is narrated at length in [10, 11], respectively. Both the Si p- and InGaAs n-FinFETs are processed on the same Si substrate using the direct wafer bonding technique, as demonstrated by Takagi et al. [12]. Both n-InGaAs and p-Si FinFETs feature the identical gate stack of Al2 O3 /HfO2 /TiN producing a capacitance equivalent thickness (CET) of 9 Å. The other important device design parameters and supply voltage of the proposed hybrid FinFETs are adopted following the descriptions listed in the International Roadmap for Devices and Systems [13] and entered in Table 1. We employ the value of interface-trapped charge Fig. 1 Cross-sectional view of the hybrid complementary FinFET (HC-Fin) having Lg = 25 nm
Effects of Fin Height on Digital Performance … Table 1 Device design parameters of hybrid C-FinFET
67
Sl. no.
Parameters
Values
1
Gate length (Lg )
25 nm
2
Fin width (WFin )
12 nm
3
Fin height (HFin )
12–84 nm
4
Capacitance equivalent thickness (CET)
0.9 nm
5
Bandgap (Eg ) of In0.53 Ga0.47 As
0.74 eV [16]
6
Electron affinity of In0.53 Ga0.47 As
4.5 eV [5]
7
Dielectric constant of In0.53 Ga0.47 As
13.9 [5]
8
Supply voltage (VDD )
0.8 V
density Dit as ~1.4 × 1012 eV−1 cm−2 at the InGaAs/high-k interface in accordance with prior experimental results recorded in [4]. In this study, we utilize BSIM-CMG model [14] for FinFETs to determine transfer characteristic curves of both InGaAs and Si FinFETs employing HSPICE simulation [15] and compare them with corresponding measured characteristics [10, 11] in order to extract a number of model parameters.
3 Model Validation In order to validate various models used in our simulation set-up we consider measured transfer characteristics of Si n-Fin and p-Fin, and InGaAs n-FinFETs described in [10, 11], respectively. The Si p- and n-FinFETs in [10] comprised a Si Fin featuring 12-nm width, 30-nm height and 25-nm gate length. These transistors were fabricated on a Si body kept on a buried oxide layer. The gate first approach was used to form the metal gate electrode followed by a HfO2 gate with dielectric constant of 25, producing a CET of 1.5-nm and the corresponding gate work function is optimized to achieve target threshold voltages. The In0.53 Ga0.47 As Fin used for our calibration [11] was grown directly on 300 nm Si substrate, having Lg = 120 nm, WFin = 25 nm, and HFin = 45 nm. The composite gate stack comprised of a metal gate having optimized work function to achieve the target threshold voltage, followed by scaled Al2 O3 /HfO2 dielectric stack producing an EOT of 9 Å. Figure 2a compares the experimental drain current vs. gate voltage characteristics of Si n- and p-FinFETs [10] with our simulated curves while Fig. 2b demonstrates similar comparative representation for the InGaAs n-FinFET [11]. From Fig. 2a, b, one can easily observe that simulated characteristics of Si as well as InGaAs FinFETs exhibit good concordance with the corresponding measured characteristics. The pretty good agreement validates our model and so does the simulation framework.
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Fig. 2 Plot of experimental and simulated transfer (ID –VG ) characteristics of a Si n- and p-Fins, and b InGaAs n-Fin. The measured results are collected from [10, 11], respectively
4 Results and Discussion We intend to investigate the impact of Fin height on the logic performance of the proposed NAND-based HC-Fin inverter with respect to its equivalent SC-Fin inverter in terms of maximum gain of the voltage transfer characteristic curve gm , rise time tr , and fall time tf . We vary the Fin height while keeping the Fin width constant in order to maintain the same value of footprint so that the package density in the chip remains unaltered. The variation of Fin height is expressed in terms of aspect ratio AR throughout our study. In this analysis, we consider the load capacitance (CL ) value as 0.6 fF including the wire capacitance [17]. The threshold voltage of the nand p-Fins are fixed at 0.27 V and −0.27 V, respectively by properly setting the work function of the gate. Figure 3a, b displays the transistor drain current vs. gate voltage characteristics of Si n-, Si p-, and InGaAs n-FinFETs used to construct SC-Fin and HC-Fin inverters, in linear as well as logarithmic current scales for Lg = 25 nm and drain voltage VDD = 0.8 V. It is to be noted that the drive current per µm is computed taking into account the effective fin width (Weff ) to be equal to (2 × HFin
Fig. 3 Variation of drain current as a function of gate voltage of InGaAs n-Fin, Si n-Fin and Si p-Fin in a linear and b log current scale at the gate length (Lg ) = 25 nm, HFin = 30 nm and WFin = 12 nm
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Fig. 4 Circuit diagram of NAND-based inverter
+ WFin ) as followed usually for the tri-gate FinFET structure [3]. Figure 4 depicts the circuit diagram of the proposed NAND-based-HC-Fin inverter comprising two Si p-FinFETs (M1 and M2 ) connected in parallel for the pull-up network, and two InGaAs n-FinFETs (M3 and M4 ) connected in series for the corresponding pulldown network. On the other hand, the equivalent SC-Fin also has a similar circuit configuration implemented with all Si FinFETs. For constructing the inverter circuit, the gates of M1 and M3 are shorted together to apply the input voltage (Vin ), and the gates of M2 and M4 are connected to the supply voltage (VDD ). Figure 5 compares the voltage transfer characteristic (VTC) curves of HC-Fin and equivalent SC-Fin inverters and the gain of the inverters, being equal to the slope of the VTC curve, as displayed in the inset of Fig. 5. From the figure, it is evident that the HC-Fin inverter produces a higher peak gain showing an increment of 6.53% over its equivalent SC-Fin inverter with AR = 5 at VDD = 0.8 V. This improvement is ascribed to the enhanced effective electron mobility in InGaAs FinFET compared to that in Si device. Next, for the evaluation of transient analysis, we apply a 5 GHz pulse train with an amplitude of 0.8 V at the input of the NAND-based inverters. Figure 6a, b shows the input pulse waveform and output response of the HC-Fin and SC-Fin inverters, which are utilized to extract both the rise time (tr ) and fall time (tf ) of the output response for AR = 5 at VDD = 0.8 V. Notably, the extracted value of tf for the proposed HC-Fin inverter is 5.05 ps, compared to tf of 8.04 ps, showing a
20
0.8
H C Fin S C Fin
Peak Gain (gm)
15
0.6
VOUT (V)
Fig. 5 The comparative representation of VTC curves for NAND-based HC-Fin and SC-Fin inverters. The variation of gain with Vin between HC-Fin and SC-Fin inverters is shown in the inset. Lg value is maintained at 25 nm throughout the analysis and aspect ratio (AR) = 5
0.4
10
5
0
0.2
0.2
0.4
0.0 0.0
0.2
0.6
Vin(V)
H C Fin S C Fin 0.4
0.6
0.8
Vin(V)
1.0
1.2
1.4
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Fig. 6 Comparison of output waveforms to extricate a rise and b fall time between HC-Fin and SC-Fin inverters with AR = 5 at VDD = 0.8 V
significant decrement of 59.3%, relative to the equivalent SC-Fin inverter with AR = 5 at VDD = 0.8 V. Such a considerable reduction in tf occurs due to outstanding effective electron mobility in the InGaAs channel. However, the obtained value of tr for both above-noted inverters remain almost equal for each value of AR, as the identical Si p-FinFET is employed in both the cases. Moreover, the average delay for switching from logic 0 to logic 1 and vice versa, is calculated as tdl = (tr + tf )/2 and is found to be 4.13 ps and 5.70 ps, respectively for HC-Fin and SC-Fin inverters, yielding a decrement of 38.3%. The lower value in tdl occurs due particularly to the reduced value of tf as explained earlier. Finally, we compare electrical behavior of a three-stage-ring oscillator comprising 3 NAND-based HC-Fin (H3RO) and 3 NAND-based SC-Fin (S3RO) inverters. Figure 7a delineates the circuit of such a three-stage ring oscillator along with the load capacitance CL of 0.6 fF. Figure 7b Fig. 7 a Circuit diagram of a three-stage NAND-based ring oscillator, b comparison of output responses from the third stage of ring oscillator to extract propagation delay (tp ) and frequency of oscillations between H3RO and S3RO at AR = 5 and VDD = 0.8 V
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Fig. 8 Comparison of a propagation delay (tp ) and b frequency of oscillations (fosc ) for different aspect ratio with supply voltage, VDD = 0.8 V for H3RO and S3RO constructed using NAND-based inverter
compares the output responses of the 3rd stage of H3RO and S3RO having AR = 5 powered with VDD = 0.8 V. Figure 8a, b shows the comparison of propagation delay (tp ) per inverter stage and frequency of oscillation (fosc ) as a function of AR with VDD = 0.8 V for H3RO and S3RO, respectively. Our findings reveal that as AR increases, tp decreases while the corresponding fosc increases. Moreover, this trend is maintained for AR in the range 1–5. However, the variation of both tp and fosc slows down with AR for its values beyond 5 as may be noticed from Fig. 8a, b. Importantly, tp and fosc show the performance improvement of 18.34% and 22.53%, respectively, for the proposed H3RO over its Si counterpart with AR = 5 at VDD = 0.8 V. The logic performance of HC-Fins, SC-Fins is compared in Table 2 at Lg = 25 nm. It can be easily found from the table that the fall time tf of output pulse and average switching delay tdl for logic 0 to 1 and vice versa for proposed HC-Fin is reduced by 63.06% and 38.67%, respectively, compared to the traditional SC-Fin. Furthermore, the value of propagation delay tp per inverter stage reduces by 17.82% for the proposed HC-Fin. Notably, the frequency of oscillations fosc is increased by 21.71%, for HC-Fin with respect to equivalent Si counterpart. The obtained results show that for all values of AR our proposed HC-Fin outperforms the equivalent SCFin. The augmented electron mobility in InGaAs accounts for the lower tp as well as higher fosc for HC-Fin compared to SC-Fin. Table 3 shows the comparison of propagation delay (tp ) per inverter stage and frequency of oscillations (fosc ) of three-stage ring oscillator, between our proposed Table 2 Comparison of logic performance of HC-Fin and SC-Fin for AR = 7 at VDD = 0.8 V Sl. no.
Parameters
HC-Fin
SC-Fin
1 2
Fall time (tf ) of output pulse (ps)
4.90
7.99
Average switching delay (tdl ) (ps)
4.06
5.63
3 4
Propagation delay per inverter stage (tp ) (ps)
17.29
21.04
Frequency of oscillations (fosc ) of RO (GHz)
9.64
7.92
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Table 3 Comparative study of ro features of our proposed HC-Fin with other reported data in [18–21], at multiple gate lengths Sl. no.
Parameters
Our proposed HC-Fin based 3-stage RO
3-stage RO [18]
3-stage RO [19]
41-stage RO [20]
3-stage RO [21]
1
Gate length (Lg in nm)
25
22
14
35
50
2
Propagation delay per inverter stage (tp in ps)
17.29
24.55
28.42
57.60
55.44
3
Frequency of oscillations (fosc in GHz)
9.64
6.79
5.86
0.212
3.01
H3RO and other experimental as well as simulation works [18–21]. To make multiFin structures in Refs. [18–21] comparable with our proposed single fin structure, we have calculated the propagation delay per inverter stage tp using the expression Cox VDD /Ion , where Ion refers to the ON-current. We have scaled down the ON-current for multi-Fin structures to determine the equivalent Ion for a single Fin transistor. In doing so the effective width of multi-Fin is calculated as n × (2 × HFin + WFin ), where n is the multiplication of number of Fin (NFin ) and number of finger (Nfinger ). In addition, Cox and VDD are accordingly scaled to fit the corresponding quantity of our proposed device in order to make proper comparison of performance parameters. The obtained results show that our proposed hybrid C-FinFET exhibits significant reduction of 29.6% for tp , and enhancement of 42% for fosc as compared to the best-reported value in [18] listed in the Table 3.
5 Conclusion A novel NAND-based hybrid C-FinFET (HC-Fin) inverter, consisting of a Si pFinFET and an InGaAs n-FinFET, is proposed for nanoscale CMOS technology. Our proposed NAND-based HC-Fin inverter outperforms the equivalent Si complementary FinFET (SC-Fin) inverter, in terms of various logic performance parameters viz. peak inverter gain, fall time, average switching delay, and propagation delay at gate length of 25 nm. Furthermore, our findings unveil that the oscillation frequency of a three-stage ring oscillator formed with 3 HC-Fin inverters is considerably higher compared to the corresponding Si value. A higher Fin height producing an aspect
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ratio of ~7 is beneficial for obtaining reduced inverter delay and enhanced oscillation frequency of the ring oscillator. Hence our suggested hybrid FinFET could be used as a multigate CMOS building block at more advanced technology nodes. Acknowledgements The first author acknowledges CSIR, HRDG, India for providing fellowship for SRF vide File No. 09/028(1030)/2018-EMR-I dtd. 16.04.2018. Also, she would like to thank TEQIP-III, University of Calcutta for providing financial support to participate in the NCCS-2018. The last author wants to thank SERB for providing financial support through the project bearing FILE NO. EEQ/2017/000634 dtd. 17.03.2018.
References 1. E.J. Nowak et al., Turning silicon on its edge. IEEE Circuits Devices Mag. 20(1), 20–31 (2004) 2. C. Hu, Thin body FinFET as scalable low voltage transistor, in Proceedings of IEEE International Symposium VLSI-TSA, April 2012, pp. 1–4 3. R. Saha, B. Bhowmick, S. Baishya, Statistical dependence of gate metal work function on various electrical parameters for an n-Channel Si step-FinFET. IEEE Trans. Electron Devices 64(3), 969–976 (2017) 4. Y. Xuan, Y.Q. Wu, H.C. Lin, T. Shen, P.D. Ye, Submicrometer inversion-type enhancementmode InGaAs MOSFET with atomic-layer-deposited Al2 O3 as gate dielectric. IEEE Electron Device Lett. 28(11), 935–938 (2007) 5. M. Poljak, V. Jovanovic, D. Grgec, T. Suligoj, Assessment of electron mobility in ultrathinbody InGaAs-on-insulator MOSFETs using physics-based modeling. IEEE Trans. Electron Devices 59(6), 1635–1643 (2012) 6. S.A. Tawfik, V. Kursun, Work-function engineering for reduced power and higher integration density: an alternative to sizing for stability in FinFET memory circuits, in Proceedings of the IEEE International Symposium on Circuits and Systems, June 2008, pp. 788–791 7. C.G. Martin, E. Oruklu, Performance evaluation of FinFET pass-transistor full adders with BSIM-CMG model, in Proceedings of the IEEE International Symposium on Circuits and Systems, September 2014, pp. 917–920 8. X. Zhang, D. Connelly, P. Zheng, H. Takeuchi, M. Hytha, R.J. Mears, T.J.K. Liu, Analysis of 7/8-nm bulk-Si FinFET technologies for 6T-SRAM scaling. IEEE Trans. Electron Devices 31(4), 1502–1507 (2016) 9. K. Banerjee, S. Tewari, A. Biswas, Impact of aspect ratio of nanoscale hybrid p-Ge/n-Si complementary FinFETs on the logic performance, in Microsystem Technologies (Springer, 2017), pp. 1–8 10. V.S. Basker, et al., A 0.063 µm2 FinFET SRAM cell demonstration with conventional lithography using a novel integration scheme with aggressively scaled fin and gate pitch, in Proceedings of the IEEE Symposium on VLSI Technology Digest, August 2010, pp. 19–20 11. M.L. Huang, et al., High performance In0.53 Ga0.47 As FinFETs fabricated on 300 mm Si substrate, in Proceedings of the IEEE Symposium on VLSI Technology Digest, September 2016, pp. 16–17 12. S. Takagi, R. Zhang, S.-H Kim, N. Taoka, M. Yokoyama, J.K. Suh, R. Suzuki, M. Takenaka, MOS interface and channel engineering for high-mobility Ge/III-V CMOS, in IEEE International Electron Devices Meeting, December 2012, pp. 505–508 13. International Roadmap for Devices and Systems (2016), http://public.itrs2.net 14. S. Khandelwal, et al., BSIM-CMG107.0.0 Multi-Gate Compact MOSFET Model Technical Manual (2015, Dec. 26), http://bsim.berkeley.edu/models/bsimcmg/latest-release 15. Synopsys HSPICE User Guide: Simulation and Analysis, Version E-2010.12 (Dec 2010), http:// synopsys.com
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16. S. Sant, A. Schenk, Methods to enhance the performance of InGaAs/InP heterojunction tunnel FETs. IEEE Trans. Electron Devices 63(5), 2169–2175 (2016) 17. K. Nayak et al., CMOS logic device and circuit performance of Si gate all around nanowire MOSFET. IEEE Trans. Electron Devices 61(9), 3066–3074 (2014) 18. E.S. Avila, J.C. Tinoco, A.G.M. Lopez, M.A.R. Barranca, A. Cerdeira, J.P. Raskin, Parasitic gate resistance impact on triple-gate FinFET CMOS inverter. IEEE Trans. Electron Devices 63(7), 2635–2642 (2016) 19. J. Lacord et al., Comparative study of circuit perspectives for multi-gate structures at sub-10 nm node. Solid-State Electron. 74, 25–31 (2012) 20. T. Chiarella et al., Benchmarking SOI and bulk FinFET alternatives for PLANAR CMOS scaling succession. Solid-State Electron. 54(9), 855–860 (2010) 21. N. Collaert et al., Low-voltage 6T FinFET SRAM cell with high SNM using HfSiON/TiN gate stack, fin widths down to 10 nm and 30 nm gate length, in Proceedings of the IEEE International Conference on Integrated Circuit Design and Technology Tutorials, June 2008, pp. 59–62
A Takagi-Sugeno Based Fuzzy Inference System for Predicting Electrical Output in a Combined Cycle Power Plant Nandini Kumari, Shamama Anwar and Vandana Bhattacharjee
Abstract The work highlighted in this paper presents a system for speculating the electrical output for a combined cycle power plant based on the input attributes: temperature, pressure, humidity, and vacuum. An accurate prediction is essential to maximize the electrical output by adjusting the input for optimal results. The paper uses the Takagi-Sugeno fuzzy inference system. The dataset used for the purpose is the UCI machine learning datasets for combined cycle power plant. The dataset consists of 9568 data points accumulated from a combined cycle power plant over a span of 6 years ranging from 2006 to 2011. Keywords Combined cycle power plant · Takagi-Sugeno · Fuzzy inference
1 Introduction Combined cycle power plant (CCPP) is growing rapidly as a major solution for electricity inefficiency and environmental issues such as global warming and pollution. A CCPP comprises of gas and steam turbines along with a heat recovery steam generator. In these power plants, both the gas and steam turbines generate electricity. These are combined in one cycle and is transferred from one turbine to another. The electrical energy production will be influenced by the environment variables, such as temperature or humidity [1]. The goal of this research is to introduce a Fuzzy Logic Controller for a CCPP. Fuzzy logic deals with uncertainty which has a tremendous capability of reasoning in the language. Fuzzy inference system consists of four major steps: (i) Fuzzification: It converts crisp input into fuzzy input for inference mechanism; (ii) Fuzzy rules formation: formation of fuzzy IF-THEN rules, using knowledge base. Each rule connects antecedents and consequents with fuzzy N. Kumari · S. Anwar (B) · V. Bhattacharjee Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi 835215, Jharkhand, India e-mail: [email protected] V. Bhattacharjee e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 V. Nath and J. K. Mandal (eds.), Nanoelectronics, Circuits and Communication Systems, Lecture Notes in Electrical Engineering 642, https://doi.org/10.1007/978-981-15-2854-5_8
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implication; (iii) Fuzzy inference controller which basically implements the fuzzy reasoning; and (iv) Defuzzification: conversion of fuzzy outputs into crisp values. There are mainly two types of FIS: Mamdani and Takagi-Sugeno. The UCI machine learning datasets for CCPP is considered here [2, 3]. The dataset consists of 9568 data tuples accumulated from a combined cycle power plant over a span of 6 years ranging from 2006 to 2011. Here, the elements that influence the energy output for CCPP were first identified, then the membership function for each variable is calculated followed by fuzzy rule formation. Finally, by use of testing data, the deviation from actual output is measured in terms of error. The paper is organized into four sections including the current section. Section 2 presents a summary of the related work, followed by the description of the proposed Takagi-Sugeno FIS for CCPP in Sect. 3. Section 4 discusses the experimental results followed by the conclusion in the next section.
2 Related Work Since the conception of Fuzzy logic in 1965 by Lotfi Zadeh, immense research has been done on its application. Fuzzy logic is a mathematical tool for dealing with uncertainty. Fuzzy controllers were developed that were rule-based models. These models used linguistic variables to link the input variable to the output. The rules are basically IF-THEN statements and help in deducing the output [4]. The standard fuzzy model is called the Mamdani controller developed by E. Mamdani in 1973 [5]. The other models such as the Sugeno controller [6] and Takagi-Sugeno (T-S) fuzzy control system [7] were proposed later. In 1985, Takagi and Sugeno proposed a widely applicable fuzzy model using implications and reasoning for mapping the input to the output [7]. The authors suggested multidimensional fuzzy reasoning [8], which reduced the number of implications drastically. The functionality of the proposed FIS was illustrated with two applications for water cleaning and steel-making process and control. In another application, the Zero-level Takagi-Sugeno fuzzy logic method was used to estimate the production of salt-affected by some attributes such as wind speed, solar radiation, rainfall, and the amount of production. These terms were considered as input to the inference system and a set of 27 rules were generated. The predicted output was then compared to the actual output and the error values were almost close to zero [9]. Yet another application includes the prediction of the nutritional status of patients. The paper studied the Body Mass Index threshold category for Indonesia. For each nutritional status, a BMI value is computed, based on which the membership function is calculated for the variables weight, height, and nutritional value. The system was tested for a set of 27 patients and the accuracy level was approximately 81.48% [10]. The aforementioned papers described the working principle of Takagi-Sugeno FIS and its effectiveness over the traditional crisp system. The same principle can be extended for application in a power station. A power station is an industrial establishment for the production of electric power. Most power stations comprise of
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one or more generators, rotating machine, boiler, power networks, and loads. A fuzzy model for Soot blowing optimization and Drum level control was proposed in [11]. For this purpose, a Mamdani FIS was used for fuzzification and centroid method for defuzzification. The environmental sustainability index of Biomass using TakagiSugeno fuzzy inference system was evaluated in [12]. It deals with the uncertain and imprecise data. They used four different variables to represent energy output, energy output/input ratio, use of fertilizers and the use of pesticides. 81 if-then rules were generated, and the Fuzzy Sustainable Biomass Index was used to represent the output. Further applications of fuzzy inference system include optimization of thermal plant output using single input—multi output model [13] and photovoltaic power control. The system considers two input variables current and voltage which are categorized in five linguistic terms namely ZE (Zero), S (Small), M (Medium), B (Big), and VB (Very Big). Takagi-Sugeno FIS is then implemented to generate a model for Maximum Power Point Tracking using the partial derivatives of control output [14]. A comprehensive study on some machine learning techniques for power generation prediction has also been done which aids in the formation of a speculative model for the same. The model was capable of predicting full load electrical power output of a CCPP per hour. The input variables used for the system are ambient temperature, atmospheric pressure, relative humidity, and exhaust steam pressure. These variables influence electrical power production. Based on these variables, the preeminent subset of the dataset is examined. Then, the different machine learning regression method is adopted for predicting full load electrical power output. Based on the experiments conducted in the paper, the Bagging algorithm with REPTree was found to be a suitable method. The calculated mean absolute error was 2.818 and the Root Mean-Squared Error was reported to be 3.787 [3].
3 The Takagi-Sugeno Fuzzy Model Lofti Zadeh presented the “fuzzy logic” or “fuzzy set theory”, in 1965. Fuzzy logic is used in accordance with problems that have uncertainty or vagueness; termed as fuzziness. The degree of membership defines the amount of belongingness to a particular fuzzy set. It is defined in the range [0, 1]; 0 signifying totally not in the set and 1 specifying totally in the set [15]. The membership function of a fuzzy set A can be denoted as A(x):x ∈ X . All the membership functions associated to the fuzzy for sets are linear. The mat of a fuzzy implication R is written as R : I f f x1 is Ai1 , . . . , xn is Ain then variable y = a0i + a1i x1 + · · · + a1i x1 + ani xn where y is the consequence whose value is hypothesized, (x1 , . . . , xn ) is the premise variable, Ai1 , . . . , Ain is the fuzzy set with linear membership functions representinga fuzzy subspace in which the impli cation R can be implemented for reasoning, a0i , . . . , ani is the coefficient of the premise variable and f is the logical function that connects the proposition in the premise [16].
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The weight of ith rule can be defined for a set of input (x1 , . . . , xn ) as follows: wi = µiA1 (x1 ) ∗ µiA2 (x2 ) ∗ · · · ∗ µiAn (xn )
(1)
where µiA1 , µiA2 , . . . , µiAn are membership function values of linguistic term used to represent the input variables. Thus, the combined fuzzy control action is determined as n wi y i y = i=1 n i i=1 w
(2)
where n indicates the total number of rules.
4 Fuzzy Inference Model Representation for CCPP The paper proposes a Takagi-Sugeno fuzzy inference system for CCPP. Four variables, namely, Temperature (T), Ambient Pressure (AP), Relative Humidity (RH), and Exhaust Vacuum (V) and considered as input. The electrical energy production (EP) is the output which is calculated using multiple regression method. The diagrammatic representation of proposed FIS is depicted in Fig. 1.
4.1 Establishment of Membership Function The first process is to determine the membership function (MF) for input variables. Each variable has three fuzzy sets, “LOW”, “MEDIUM”, and “HIGH”. Here trapezoidal MF is used for LOW, HIGH, and triangular MF is used for MEDIUM variable.
Temperature
Vacuum
: :
Pressure
Humidity
Fig. 1 T-S fuzzy controller for CCPP
Takagi Sugeno System for Combined cycle power plant
Electrical Energy Output
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Fig. 2 Membership function and their values
The representation of membership function distribution and its corresponding value is tabulated in Fig. 2.
4.2 Formation of Fuzzy Rules The fuzzy variable T, AP, RH, and V are the input variables. The number of the control rules generated is 34 = 81. The output is calculated based on multiple linear regression method. Consequence is implemented in SPSS by using the observation. For each implication, the observation data is selected according to the constraints from the given data set. With the help of these observational data, the coefficients p0 , p1 , p2 , . . . , pk are generated by performing multiple regression. For example, in implication, (temperature) is “low”, (Vacuum) is “Medium”, (pressure) is “Medium”, and (Humidity) is “High”, the corresponding row of data from the whole observation data is selected which satisfy
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Table 1 Output of coefficient if (Temperature) is “low”, (Vacuum) is “Medium”, (Pressure) is “Medium”, and (Humidity) is “High”
Variables
Coefficients
(Constant)
530.491
Temperature
−2.316
Vacuum
−0.271
Pressure
−0.006
Humidity
−0.157
the constraints. Table 1 represents the output of the coefficients for this implication. Table 2 represents the summary of the rules generated.
5 Experimental Results The dataset consists of 9568 tuples, out of which 7499 are used for training the FIS using MATLAB and the remaining 2069 tuples are used for testing. An example is presented here for a random tuple from the training dataset containing the attribute as Temperature = t = 8.34 Vacuum = v = 40.77 Pressure = p = 1010.81 Humidity = h = 90.01 The steps are as follows: Step 1: The degree of membership value of each variable is calculated as: a. Temperature: µ L O W [t] = 1, µ M E D I U M [t] = 0, µ H I G H [t] = 0 b. Vacuum µ L O W [v] = 0.9, µ M E D I U M [v] = 0.099, µ H I G H [v] = 0 c. Pressure µ L O W [ p] = 0.225, µ M E D I U M [ p] = 0.785, µ H I G H [ p] = 0 d. Humidity µ L O W [h] = 0, µ M E D I U M [h] = 0, µ H I G H [h] = 1
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Table 2 Rules generated for the FIS Rule
Temperature
Vacuum
Pressure
Humidity
Energy output
R1
LOW
LOW
LOW
LOW
zlow,low,low,low = (−1.661 ∗ x1 ) + (−0.927 ∗ x2 ) + (0.399 ∗ x3 ) + (−0.97 ∗ x4 ) + 133.883
R2
LOW
MEDIUM
LOW
HIGH
zlow,medium,low,high = (−2.215 ∗ x1 ) + (−0.447 ∗ x2 ) + (0.111 ∗ x3 ) + (−0.100 ∗ x4 ) + 413.602
R3
LOW
MEDIUM
MEDIUM
HIGH
zlow,medium,medium,high = (−2.316 ∗ x1 ) + (−0.271 ∗ x2 ) + (−0.006 ∗ x3 ) + (−0.157 ∗ x4 ) + 530.491
R4
MEDIUM
HIGH
HIGH
LOW
z medium,high,high,low = (−1.724 ∗ x1 )+ (−0.279 ∗ x2 ) + (0.204 ∗ x3 ) + (−0.130 ∗ x4 ) + 304.715
R5
MEDIUM
MEDIUM
LOW
LOW
z medium,medium,low,low = (−2.028 ∗ x1 ) + (−0.118 ∗ x2 ) + (0.294 ∗ x3 ) + (−0.116 ∗ x4 ) + 211.985
R6
MEDIUM
LOW
LOW
HIGH
z medium,low,low,high = (−1.724 ∗ x1 ) + (−0.279 ∗ x2 ) + (0.204 ∗ x3 ) + (−0.130 ∗ x4 ) + 304.715
. . .
. . .
. . .
. . .
. . .
. . .
R79
HIGH
LOW
MEDIUM
HIGH
z high,low,medium,high = (−1.925 ∗ x1 )+ (−0.386 ∗ x2 ) + (0.587 ∗ x3 ) + (−0.214 ∗ x4 ) + (−66.185)
R80
HIGH
LOW
LOW
MEDIUM
z high,low,low,medium = (−1.885 ∗ x1 ) + (−0.191 ∗ x2 ) + (0.428 ∗ x3 ) + (−0.128 ∗ x4 ) + 77.815
R81
HIGH
MEDIUM
LOW
LOW
z high,medium,low,low = (−1.491 ∗ x1 ) + (−0.152 ∗ x2 ) + (0.452 ∗ x3 ) + (−0.122 ∗ x4 ) + 41.513
Step 2: According to the membership values rules are fired. For the above example four rules are fired which are as follows: a. R1 : IF Temperature is LOW, Vacuum is LOW, Pressure is LOW and Humidity is HIGH =⇒ z 1 = (−2.229 ∗ t) + (−0.432 ∗ v) + (0.75 ∗ p) + (−0.099 ∗ h) + 44.185 = 479.88 b. R2 : IF Temperature is LOW, Vacuum is MEDIUM,
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Pressure is LOW and Humidity is HIGH =⇒ z 2 = (−2.215 ∗ t) + (−0.447 ∗ v) + (0.111 ∗ p) + (−0.1 ∗ h) + 413.602 = 480.11 c. R3 : IF Temperature is LOW, Vacuum is MEDIUM, Pressure is MEDIUM and Humidity is HIGH =⇒ z 3 = (−2.316 ∗ t) + (−0.271 ∗ v) + (−0.006 ∗ p) + (−0.157 ∗ h) + 530.491 = 479.93 d. R4 : IF Temperature is LOW, Vacuum is LOW, Pressure is MEDIUM and Humidity is HIGH =⇒ z 4 = (−2.308 ∗ t) + (−0.231 ∗ v) + (−0.014 ∗ p) + (−0.128 ∗ h) + 534.144 = 479.80 Step 3: The weight of each rule is calculated as in Eq. (1): w1 = µ L O W [t] ∗ µ L O W [v] ∗ µ L O W [ p] ∗ µ H I G H [h] = 1 ∗ 0.9 ∗ 0.225 ∗ 1 = 0.2025 w2 = µ L O W [t] ∗ µ M E D I U M [v] ∗ µ L O W [ p] ∗ µ H I G H [h] = 1 ∗ 0.099 ∗ 0.225 ∗ 1 = 0.0223 w3 = µ L O W [t] ∗ µ M E D I U M [v] ∗ µ M E D I U M [ p] ∗ µ H I G H [h] = 1 ∗ 0.785 ∗ 0.0.99 ∗ 1 = 0.0777 w4 = µ L O W [t] ∗ µ L O W [v] ∗ µ M E D I U M [ p] ∗ µ H I G H [h] = 1 ∗ 0.9 ∗ 0.785 ∗ 1 = 0.7065 Step 4: Defuzzification is then performed using the weighted average method as in Eq. (2) and the values obtained in Step 2 and Step 3. w1 z 1 + w2 z 2 + w3 3 + w4 z 4 w1 + w2 + w3 + w4 = 479.82
y=
The results are calculated for 2069 testing data set in the same way as the preceding example. The test results are compared with the actual result (Table 3). For the preceding example, the error value calculated is 0.66 (480.48 (actual value) − 479.82 (expected value)), which indicates the accuracy of the proposed system.
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Table 3 The error value for selected data tuples S. no.
Temperature
Vacuum
Pressure
Humidity
Expected result
Actual result
Error value
1
8.34
40.77
1010.81
90.01
479.82
480.48
0.66
2
9.72
39.66
1018.31
76.07
479.1788
480.05
−0.8712
3
18.9
47.83
1005.31
77.79
455.5118
455.94
−0.4282
4
5.49
40.81
1014.43
90.41
486.4357
487.36
−0.9243
5
18.96
50.16
1010.72
100.09
451.2078
451.2
0.0078
6
13.44
39.39
1012.93
84.29
469.3782
469.67
−0.2918
7
17.54
58.49
1012.35
91.56
453.5685
453.02
0.5485
8 .. .
17.58 .. .
44.9 .. .
1020.52 .. .
71.48 .. .
460.2623 .. .
459.78 .. .
0.4823 .. .
2066
16.52
44.88
1017.61
90.08
459.82
459.73
0.09
2067
12.31
44.03
1007.78
94.42
469.1783
468.13
1.0483
2068
11.65
41.54
1020.09
67.96
475.462
476.4
−0.938
2069
13.27
42.28
1008.3
86.2
468.7166
468.86
−0.1434
6 Conclusion The paper presents the process of designing a Takagi-Sugeno fuzzy inference model for the application in the field of power plant. Sugeno fuzzy method can be implemented as an alternative solution for calculating electricity energy production. Differences in the calculation of output obtained by the fuzzy Sugeno controller and actual result shows the accuracy of the fuzzy controller for CCPP. The proposed system is tested for input variables temperature (low), vacuum (medium), pressure (medium), and humidity (high). The coefficients are generated using multiple linear regression and the final output is calculated by the designed Takagi-Sugeno controller. The tabulated error value indicates the accuracy of the proposed system. Acknowledgements We would like to show our gratitude to Dr. Jaya Pal, Assistant professor, Birla Institute of Technology, who provided insight, guidelines, and expertise that greatly assisted this research.
References 1. R. Lopez, Combined cycle power plant. https://www.neuraldesigner.com/learning/examples/ combined_cycle_power_plant 2. H. Kaya, P. Tüfekci, F.S. Gürgen, Local and global learning methods for predicting power of a combined gas & steam turbine, in Proceedings of the International Conference on Emerging Trends in Computer and Electronics Engineering ICETCEE, pp. 13–18 (2012)
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3. P. Tüfekci, Prediction of full load electrical power output of a base load operated combined cycle power plant using machine learning methods. Int. J. Electr. Power Energy Syst. 60, 126–140 (2014) 4. Zimmermann, http://www.faqs.org/docs/fuzzy/, 2003, 2001. http://www.faqs.org/docs/fuzzy/, 2003 5. E.H. Mamdani, S. Assilian, An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Man Mach. Stud. 7(1), 1–13 (1975) 6. M. Sugeno, An introductory survey of fuzzy control. Inf. Sci. 36(1), 59–83 (1985) 7. T. Takagi, M. Sugeno, Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man Cybern. SMC-15(1), 116–132, Jan–Feb 1985 8. M. Sugeno, T. Takagi, Multi-dimensional fuzzy reasoning. Fuzzy Sets Syst IEEE 9(1–3), 313–325 (1983) 9. Y. Tony, K. Siti, U. Nurita, Application of fuzzy inference system by Sugeno method on estimating of salt (American Institute of Physics, Pamekasan, 2017) 10. S. Dinur, S. Tulus, The accuracy of fuzzy Sugeno method with antropometry, in International Conference on Information and Communication Technology (IconICT), Indonesia (2017) 11. T.K. Sai, K.A. Reddy, Fuzzy application in a power station. Int. J. Soft Comput. 1–16 (2015) 12. C. Fausto, A Takagi-Sugeno fuzzy inference system for developing a sustainability index of biomass. Sustainability 7(9), 12359–12371 (2015) 13. M. Salama, S.A. Yusri, Thermal plants optimization using fuzzy logic controller, in 2006 1st IEEE Conference on Industrial Electronics and Applications (2006) 14. N. Harrabi, M. Souissi, A. Aitouche, Maximum power control for photovoltaic power based on fuzzy Takagi-Sugeno model. J. Electr. Eng. 15(02), 196–207 (2015) 15. J. Hines, L.H. Tsoukalas, R.E. Uhrig, MATLAB Supplement to Fuzzy and Neural Approaches in Engineering (Wiley, N.Y., 1997) 16. T. Lujiao, Takagi-Sugeno and Mamdani Fuzzy Control of a Resort Management System (2011)
Smart Irrigation System Using Custom Made PCB Sanjay Kumar Surshetty, Saad Anwer, Yash Deep, Manish Gandhi and Vijay Nath
Abstract A computerized water system framework for efficient water administration has been anticipated. Soil parameters like soil wetness, stickiness zone units are estimated. The mystic marvels module has been acclimated to set up a correspondence interface between the hub and in this manner the field. The present field standing is suggested to the agriculturist through the remote system and moreover refreshed inside the server that is made inside the system framework. This will work though having native area neighborhood field WLAN or nearby working range. The agriculturist will get to the server with respect to the segment condition whenever, and from wherever in this manner diminishing the individual power and time.
1 Introduction Horticulture is the foundation of every single created nation. It utilizes eightieth of open water assets worldwide and this offer keeps on prevailing in water utilization on account of increment and duplicated nourishment request. Along these lines, sparing water administration is the significant worry in a few editing frameworks in dry and semi-dry zones is correct one. A programmed water system framework is required to improve water use for farming yields. The necessity of a programmed S. K. Surshetty (B) · S. Anwer · Y. Deep · M. Gandhi · V. Nath VLSI Design Group, Department of ECE, B.I.T. Mesra, Ranchi 835215, JH, India e-mail: [email protected] S. Anwer e-mail: [email protected] Y. Deep e-mail: [email protected] M. Gandhi e-mail: [email protected] V. Nath e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 V. Nath and J. K. Mandal (eds.), Nanoelectronics, Circuits and Communication Systems, Lecture Notes in Electrical Engineering 642, https://doi.org/10.1007/978-981-15-2854-5_9
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water system framework is to beat over water system and underneath water system. Over water system happens because of poor conveyance or administration of wastewater, concoction that outcomes in contamination. Underneath water system results in increased soil saltiness with resultant development of dangerous salts on the dirt surface in regions with high vanishing. To beat these issues and to curtail the individual power, sensible water system framework has been utilized.
2 Proposed System These days rural field is confronting the store of issues on account of absence of water assets. In order to help the agriculturists to beat the challenges, sensible water system framework has been utilized. Amid this framework, various sensors like soil wet, DHT11 zone unit implanted inside the bespoke PCB. The distinguished qualities from the sensors region unit showed in alphanumeric showcase. In the event that the identified cost goes on the far side the edge esteem set inside the program, the siphon is mechanically exchanged ON/OFF by the transfer circuit and it’s associated with the intention compel circuit that changes the voltage. The agriculturist is suggested in regards to this field condition through Wi-Fi module and conjointly refreshed inside the site. By abuse this system, the rancher will get to the little print with respect to the state of the circle wherever whenever. Step I: Prototyping In this step, the components are placed on the breadboard and connected via wires. Laptops are used as power sources. DTH with MCU board diagram is shown in Fig. 1. The DHT sensor is connected to the node MCU which is ESP8266. DTH
Fig. 1 Circuit diagram of PCB
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and MCU are separately shown in Figs. 6 and 7. The readings are taken in the serial monitor of the Laptop in the Arduino IDE. Step II: PCB Software Selection Efficient software selection is shown in Fig. 2. There are many softwares in which PCB can be made like Proteas, Eagle, Altium, etc. Eagle was chosen in our case because of the following reasons: (a) Free license of 3 years for students.
Fig. 2 Software selection for PCB
Fig. 3 Schematic of PCB
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Fig. 6 DHT sensor
Fig. 7 Node MCU
(b) Easy to use and understand. (c) Supports up to 16 layers. (d) Used in industries. Step III: PCB Layout Once our software is selected for PCB designing, the layout [1–8] is made using all the capacitors and resistors using prototype as the reference. It also includes selecting the footprints of components as well [9]. The layout of schematic PCB Fig. 3 is shown in Fig. 4. Step IV: Board Design Once the layout is created, the board dimensions are chosen and components are placed on it depending on the design rules and less sophistication. Then routing is done, either manually or automatically based on the routing rules and DRC. The board dimensions are important to select because of bigger board, costlier the PCB. After the PCB, Board is designed, the Gerber files are created which is then sent
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to the PCB manufacturing companies. These Gerber files are files that contain the different information of different layers of the PCB [10]. 1. Custom-Made PCB PCB stands for printed circuit board. Rules of PCB design [1–3, 11–15] are as follows: (a) (b) (c) (d) (e) (f) (g) (h) (i) (j) (k) (l)
The tracks should not be acute in angle. The tracks are preferred to have obtuse angles. The tracks shouldn’t overlap each other. The overlapping tracks should go beneath each other in different layers using vias. The track width of power lines should be more. The track width of data lines should be less. The data lines should be as small as possible. The layers should be grounded properly. There shouldn’t be airlines left after routing. The routing should be done manually. The routing should be checked using DRC after each step [12–14]. The advantages of having a PCB are as follows:
(a) (b) (c) (d) 2.
Compact Size and Saving of Wire. Ease of Repair and Diagnostic. Low Electronic Noise. Ease in Diagnostics and Repair. SENSORS
(a) Sensor for Measuring Soil Moisture: A sensor that is utilized to quantify the dampness content present in the dirt is known as Soil Moisture Sensor. There are two levels are identified groundlevel/low-level dimensions identified that is called (0 V) level or logic ‘0’ at the optimum end identified by 5 V that is represented at logic ‘1’. The computerized stick is utilized to straightforwardly peruse current soil dampness incentive to check whether it is above limit or not. The edge voltage can be controlled with the help of a potentiometer. (b) Sensor for Measuring Temperature and Humidity (DHT11 Sensor): A sensor capability to measure low-cost digital temperature and humidity together is called DHT11. For the construction of such sensors required thermistor as well as capacitive humidity sensor which are able to measure surrounding air and also convert signal in digital form. These sensors are low cost and handy systems which grabs the data perfectly. These sensors are giving perfect data every two seconds.
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3. Wi-Fi Module The ESP8266 Wi-Fi module is an independent SOC (System on Chip) with a coordinated TCP/IP (Transmission Control Protocol/Internet Protocol) convention stack that can give any microcontroller access to any Wi-Fi organize. Each ESP8266 module comes pre-modified significance; it very well may be just snared to Arduino gadget to get Wi-Fi capacity. This module has a sufficiently amazing on-boarding procedure and high stockpiling limit that enables it to be coordinated with the sensors and other application particular gadgets. Node of MCU is shown in Fig. 7 (Figs. 5 and 6). 4. Simulation Result of Code for PCB See Figs. 8 and 9.
Fig. 8 Code for PCB
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Fig. 9 Coding result of PCB structure
3 Challenges The difficulties confronted are primarily the lost cost PCB fabricating. The IoT [16–19] interface additionally acquires a much favorable position over traditional frameworks and represents a more noteworthy test soon. The power additionally represents a test since we require a framework where we require more power and more deplete time. Consequently, we require a source with a trade off between deplete time and power. It’s conceivable with the utilization of sun-oriented boards which could also be introduced. Other choice being the utilization of guard dog clock within the microcontroller. It keeps the microcontoller in the rest state utilizing the power in miniaturized scale amps and henceforth spares control. The other test that is in our direction is the correspondence between a various hub and the principle server. The IOT can be utilized and the information can be sent to the primary hub with every one of the readings for the individual to think regardless of whether to open the water lines [11].
4 Conclusion and Future Scope With the majority of the mechanical progressions we’ve seen these recent years, the possibility of a savvy city isn’t as unrealistic as it once appeared. A supportable urbanscape will be soon into the scene by the approach of IOT associated gadgets and the enormous information [15].
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It’s energizing to imagine that we can before long be living in the mechanical future that has been depicted so often in the motion picture industry. What will this keen city resemble, however? What would we be able to hope to see in 2018 and past? Albeit keen urban areas aren’t far-reaching yet, here’s a sneak see of how savvy your city could progress toward becoming. Shrewd city’s fundamental reason for existing is to fill the need of everybody and everything in a, in fact, propelled way. The objective is to deal with the assets found in an urban zone in a way that is both supportable and cheap, profiting both the general population and the world on the loose. As IoT tech turns out to be increasingly actualized into the ordinary articles we utilize, and these items speak with each other, a more extensive arrangement of huge information can be investigated. Some potential employments of huge information are included. City water frameworks can be observed and estimated by sensors to check whether there are any breaks or blockages that will influence water weight and stream. Water defilement can likewise be distinguished and made mindful of to the experts that can help. Keen vitality frameworks can likewise proficiently utilize assets because of the gathering of huge information. Furthermore, information researchers can determine how to enhance the economy, wrongdoing and medicinal services by finding designs from the information accumulated by IoT. The above said results are suitable for current PCB design which helps to setup all devices on a single board. Complete fabricated PCB structure is shown in Fig. 5 and interconnected DTH sensors are shown in Fig. 6. Acknowledgements We are thankful to the Department of ECE, BIT Mesra, Ranchi for providing the laboratory facilities. We are also thankful to our VC, Dr. M. K. Mishra and our HOD ECE, Dr. S. Pal for their constant inspiration and encouragement. Without their support, this research article would not have been possible.
References 1. K. Mitzner, PCB Orac D Capture and PCB Editor (Newnes, 2007) 2. International Journal of Engineering Science Invention, https://scholar.google.co.in/scholar? q=International+Journal+of+Engineering+Science+Invention+ISSN+(Online)&hl=en&as_ sdt=0&as_vis=1&oi=scholart 3. A. D. Udai, Make your own USB data acquisition system, in Electronics for You, Jan 2015 4. Atmel, AVR040: EMC Design Considerations, Nov 2016 5. Hard Kernel, Odroid XU-4, www.odroid.com/dokuwiki/doku.php?id=en:odroid-xu4 6. A. Thakur Engineers Garage: PCB Designing, https://www.engineersgarage.com/…/printedcircuit-boards-pcb-design-manufacturing? 7. Atmel, AT90USB1287 datasheet, Sept 2012 8. Jimbo, Capacitors, www.learn.sparkfun.com, Jan 2017 9. High Speed PCB Designing by tata Mcgraw Hills—2015 edition 10. PCB integrity by Pearson edition 2001 11. PCB Design Rules, 6th ed. (McGraw-Hill, 2007) 12. Microelectronics “IC package”, http://www.fujitsu.com/downloads/MICRO/fma/pdfmcu/ a810000112e.pdf
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13. Printed Circuit Design Magazine, Dec 2000. http://www.speedingedge.com/PDF-Files/busses. pdf 14. K.J. Knack (ed.), High Speed PCB and System Design (Speeding Edge, 2007) 15. D. Brooks, Signal Integrity Issues and Printed Circuit Board Design. Prentice Hall, 2003 16. N. Nidhi, D. Prasad, M. Kumari, A. Pandey, S. Solanki, A. Kumar, K.K. Thakur, V. Nath, Design of low power 3-Bit CMOS flash ADC for aerospace application, in Proceedings of the Third International Conference on Microelectronics, Computing and Communication Systems, Lecture Notes in Electrical Engineering, vol. 556, ed. by V. Nath, J. Mandal (Springer, Singapore, 2019) pp. 585–594 17. R. Agarwal, D. Prasad, N. Kumari, A. Pandey, V. Nath, An assessment of advanced transportation research opportunities, in Proceedings of the Third International Conference on Microelectronics, Computing and Communication Systems, Lecture Notes in Electrical Engineering, vol. 556, ed. by V. Nath, J. Mandal, pp. 631–640 18. A. Lakra, K. Murmu, D. Prasad, V. Nath, Study and development of solar-powered water pumping system, in Proceedings of the Third International Conference on Microelectronics, Computing and Communication Systems, Lecture Notes in Electrical Engineering, vol. 556, ed. by V. Nath, J. Mandal (Springer, Singapore, 2019), pp. 655–660 19. U. Anchalia, K.P. Reddy, A. Modi, N. Kumari, D. Prasad, V. Nath, Study and design of biometric security systems: fingerprint and speech technology, in Proceedings of the Third International Conference on Microelectronics, Computing and Communication Systems, Lecture Notes in Electrical Engineering, vol. 556, ed. by V. Nath, J. Mandal (Springer, Singapore, 2019), pp. 577–584
Efficient Advanced Encryption Standard with Elevated Speed and FPGA Implementation Pushpinder Kaur Chana, Preeti Gupta and Sanjiv Kumar
Abstract Cryptography assumes an imperative part in the security of information transmission. Cryptography can be defined as exploration of scientific strategies recognized with divisions of information security, for example, secrecy, data honesty, substance validation, and also verification of data sources. Communicating information through any media as cryptography is vital when passing on over any conflicting medium, which integrates any framework particularly the web. Advanced Encryption Standard can be customized in programming or worked with unadulterated equipment. Anyway, Field Programmable Gate Arrays (FPGAs) offer a snappier, more adjustable arrangement. Security is of principle concern while transmitting information over the web. This research work study and analyze various advance encryption standard techniques. The current prospective of our work is to design and implement the proposed framework for improving the security of data transmission. FPGA kits are used to validate the results. Keywords Advanced encryption standard · Authentication · Confidentiality · Cryptography · Field programmable gate array · Security · Integrity
1 Introduction Internet correspondence is assuming the imperative part to exchange substantial measure of information in different fields. Some of the information may be transmitted through a shaky channel from sender to collector. Distinctive procedures and techniques have been utilizing by private and open parts to shield touchy information from gatecrashers due to the security of electronic information is a significant P. K. Chana (B) · P. Gupta · S. Kumar UIET, PU, Chandigarh, India e-mail: [email protected] P. Gupta e-mail: [email protected] S. Kumar e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 V. Nath and J. K. Mandal (eds.), Nanoelectronics, Circuits and Communication Systems, Lecture Notes in Electrical Engineering 642, https://doi.org/10.1007/978-981-15-2854-5_10
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Fig. 1 General framework of data transmission [2]
issue. Cryptography is a standout amongst the most huge and well-known systems to anchor the information from aggressors by utilizing two crucial procedures that are Encryption and Decryption. Encryption is the way toward encoding information to keep it from gatecrashers to peruse the first information effortlessly. This stage can change over the first information (Plaintext) into unintelligible organization known as Cipher content. Current cryptography gives the secrecy, honesty, nonrepudiation, and validation. Nowadays, there are various calculations have been accessible to encode and unscramble sensitive information [1]. General framework of data transmission is shown in Fig. 1. The Advanced Encryption Standard (AES) is a standard that is used for encoding of electronic data [3]. The National Institute of Standards and Technology, (NIST), asked for the proposition for Advanced Encryption Standard. It is adopted by a US standard known as Federal Information Processing Standard; (FIPS) that is cryptographic calculation that is applied to defend the electronic data. Calculation is symmetric square figure of AES also that can be scuttling, (encipher), and decode, (unravel), information. Encryption changes more than information to incomprehensible shape called as figure content. Substance of figure which is unscrambling that amends over the data once again into its distinctive frame, which is known as plain text. Estimation of AES is prepared for using for different cryptographic key lengths of 128, 192, and 256 bits to encode and decode the data in squares of bits [4]. Figure 2 represents the block diagram of AES.
2 Related Work Alghazzawi et al. [5] talks about the Cryptanalysis investigation being done on the AES and examine the distinctive procedures being utilized to set up the upsides of the calculation being utilized as a part of Security frameworks. AES has been the focal point of Cryptanalysis since it was discharged in 2001, November. The exploration increased more critical when AES as proclaimed as the Type-1 Suite-B Encryption Algorithm, in 2003, by the NSA, which makes it regarded appropriate for being used for encryption of both Classified and Unclassified refuge records and framework. It would finish up by attempting to evaluate the term in which AES can be successfully utilized as a part of the National Security Applications.
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Fig. 2 AES block diagram [3]
Abed and Jawad [6] proposed a strategy to augment the security level of AES calculation. The proposed calculation, which depends on standard AES, expands many-sided quality of the encryption procedure prompting more trouble against assaulting and unscrambling of the original message without utilizing right encryption key. Examination explores the AES calculation as Field Programmable Gate Array (FPGA) and VHDL is known as Very High-Speed Integrated Circuit Hardware Description Language. Model Sim-Altera Starter Edition Software for Quartus II is utilized for reenactment and advancement of the basic VHDL code. All the mandatory changes of the encryption (encoding) and unscrambling forms are finished utilizing a pipelined cyclic outline technique to limit equipment utilizations. The pipelined configuration is executed on Altera Cyclone IV group of FPGA gadgets and decent throughput is accomplished with the negligible zone. Trang and Loi [7] proposed FPGA-based usage of AES calculation. This usage is contrasted with different works with demonstrating the proficiency. The plan utilizes a variable circling approach with square and key length of 128 bits, query table usage of S-box. The author tells this gives us low many-sided quality engineering and effectively accomplishes low idleness and in addition high throughput. Recreation comes about, execution comes about are given and thought about past revealed plans. Pitchaiah and Philemon Daniel [8] introduced a Rijndeal computation of AES of 128-bit piece in which both processes encryption as well as decryption is done using Verilog code that can be effortlessly executed on FPGA kit. Cryptography is the research of various procedures defines with the parts of data security, for illustration, classification, information trustworthiness, substance confirmation and data starting point validation. In information also in broadcast communication, cryptography is essential while imparting through any questionable intermediate, which integrates any arrangement. This calculation is done depending upon three standard divisions: figure, reverse figure and Key Expansion. Figure modifies larger than information to an incomprehensible shape called as plain text. Key Expansion as third division produces a Key timetable that is exploited as a part of figure and converse figure
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methodology. Figure and opposite figure are made out of exceptional number of times the rounds. Estimation of AES measures the digit of rounds to be completed in the midst of the calculation execution using a round capacity which is made out of four distinctive situated changes: Sub Bytes, Shift Rows, Mix sections and Add Round Key. Kaminsky et al. [9] give a review of momentum cryptanalysis inquire about on the AES cryptographic calculation. Since its discharge in November 2001, at that time (AES) NIST FIPS-197 had been teaching this subject of broad as cryptanalysis examine. The significance of this exploration had strengthened because AES was named, in 2003, by NSA as a Type-1 Suite B Encryption Algorithm (CNSSP-15). Accordingly, AES has currently approved to ensure arranged and uncertain national defense frameworks and data. Exchange has given on the effect by every system to the quality of the calculation in nationwide defense applications. The paper was closed with an estimate of the practical existence of AES in these applications. Bajaj and Gokhale [10] displayed an audit on the outline of AES calculation utilizing FPGA. Expanding need of information insurance in PC systems prompted the improvement of a few cryptographic calculations, subsequently sending information safely, finished a transmission connect is fundamentally imperative in numerous applications. AES speaks a calculation for Advanced Encryption Standard comprising of various activities required in the means of encryption and decoding. The AES calculation utilizes cryptographic keys having different sizes of 128, 192, and 256 bits to decode and encode data in squares of 128 bits. This paper speaks the outline of AES calculation of 128 pieces. The product Xilinx ISE venture guide is utilized for the combination and recreation of these proposed calculation reasons.
3 Proposed Work To accomplish an ideal number and the finest arrangement procedure to arrange registers in pipelining method, authors Oukili and Bri [11] set primary, pipeline enrols in every single conceivable situation in the S-box design. At that point, in every single round, the pipeline arrange which includes the most minimal of frequency is evacuated until the point that the ideal number and positions are accomplished of the pipeline registers. Figure 3 demonstrates five-stage pipelined plan of S-box utilizing combinational rationale. Remember that expansion of pipeline registers necessitates an increment in slices, as the registers are requiring storing moderate outcomes. Our proposed fast effective AES architecture meant to build throughput and utilize equipment assets least as it would be feasible. That is why we have taken up this algorithm. We have improved the critical path by adopting parallel pipelining technique. We have embedded registers after rounds where required. As a result, it enhances the throughput of the algorithm also improvement in slices.
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Fig. 3 Flowchart of proposed technique
Proposed work follows the following steps: • Implement advanced encryption standard using HDL language (VHDL/VERILOG). • Verify functionality on field-programmable gate array (FPGA). • Generate netlist from HDL code. • Use the generated netlist from backend design of advanced encryption standard hardware. These steps are shown in the flowchart of the proposed technique in Fig. 3. We are using a five-stage pipeline structure for further improvement in our algorithm. Figure 4 shows the basic five-stage pipeline structure.
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Fig. 4 Five-stage pipeline structure [11]
4 Experimental Results This section presents the experimental results of the proposed technique. First of all, we have used some data and key and then encrypted that data that is not in its original form means in coded form. Using decryption, we decrypt that data into its original form again using advanced encryption algorithm. To validate the results, hardware implementation on vertex-5 FPGA kit is done. We are sending some information from one person, company or work place to other one. We are assuming that the data or the information we have to send is as given: 7a1eaad867788568900d489da778520f. Using some secret key we encrypted the information using AES algorithm so that no one other can read this information except the one who knows about the key. So that encrypted data is as shown in Fig. 3, section presents the experimental results of the proposed technique.
4.1 RTL View of AES It is registered transfer level view which shows the basic gate level structure of our coding. This must be in the form of combinational logic or in form of sequential logic. Figure 5 shows the RTL view of some multiplexers used. If there are some logic paths from register to register which does not include any cycle then this is called pipelined. After implementation, we can check the RTL view showing the overall structure of AES algorithm contains input and output. Figure 6 is showing encrypted data in the form of a test bench waveform.
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Fig. 5 RTL view of AES
Fig. 6 Encrypted data waveform
Fig. 7 Decrypted data waveform
Using that secret key and the encrypted data we can decrypt the data using an algorithm just opposite to the encryption and read the information as shown in Fig. 7.
4.2 About our Used Inputs Reset: First step is to do reset. When reset = 1 previously stored data is cleared. So i/p that we will give must be correct as there is no previously stored data. Reset is one of the essential steps. Clock: Clock is required for any and every operation to be done. In this case, rising edge of the clock will change the output. Encryption: It is done to encrypt the data which is in the coded form called as cipher text. Decryption: It is used to decrypt data in its original form called plain text.
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Fig. 8 Write data or key
Wr_enable: To enable the wiring of register and after enable = 1 process going to start. Wr_data_or_key: When wr-data_or_key equal to one then write data is to be done from data and key. When wr-data_or_key equal to zero then write key is start as shown in Fig. 8. When enable is equal to one and wr_data_or_key is equal to one then at different location write the stored data. When all the four bits of wr_address “0000” then input data stored at [7:0] location is written to wr_data. When at “0001” input data stored at [15:8] location is written to wr_data and so on up to 128-bit locations. When all the four bits of wr_address “0000,0001,0010….. up to 1111” data is written from different key locations from o to 127-bit locations. Wr_address [3:0]: There are 4-bits with different locations from 0000 to 1111
4.3 Output When o/p valid is equal to one only then output data give correct output of o/p_data of 128 bit. In this algorithm, we give some information. This information in the form of words while sending this info to other person we are changing it in the form of coded words. These coded words are called as ciphertext. So we change cipher text to plain text while decryption. This all encryption and decryption process is done to gain the original information on. When o/p valid is equal to one only at that time o/p data gives the valid output. To validate the results implementation is done on Vertex-5 FPGA kit. As shown in Fig. 8 LEDs represent 8 bits. These 8 bits change 16 times as our input is of 128 bit. So 16 × 8 = 128 bit. These 8 LEDs gives output as shown in Fig. 9 is 0001 1110 in hexadecimal value is 1e which is third and fourth letter of information that we have to send. In our proposed AES we are working mainly on speed and throughput. Speed can be increased if operation time of AES is decreased and if this time is decrease than efficiency will be increased. To calculate, the efficiency we should know that the throughput and slices.
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Fig. 9 FPGA implementation
E f f iciency =
T hr oughput Ar ea (slices)
To calculate Efficiency we should know the value of throughput and to calculate throughput we should know no of output bits and delay of critical path as shown in equation. T hr oughput =
N umber o f out put bits Delay o f critical path
In this given five-stage architecture, we are placing the register and the best placement of registers are done to achieve maximum efficiency in terms of area of AES. To increase the speed of algorithm we can use parallel pipeline AES structure that means we are using pipelined AES algorithm in parallel using combinational logic circuits.
5 Conclusion This research work study and analyze various advance encryption standard techniques. The main purpose of our work is to design and implement the proposed framework for improving the security of data transmission. FPGA kits are used to validate the results (Table 1). Advanced Encryption Standard (AES) calculation is one of the mainly well-known and broadly symmetric square figure calculation utilized as a part of around the world. This calculation has a claim specific structure to encode and decode delicate information and is connected in equipment and programming everywhere throughout
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Table 1 Comparison References
Tech. used
Devices
Output parameter Slices
Throughput (Gbps)
Max-Freq. (MHz)
Efficiency
[12]
Pipeline
Vertex
6784
28.7
283.2
4.2
[13]
Pipeline
Vertex-6
2597
59.59
450.045
22.94
[14]
Pipeline
Vertex-6
3121
64.1
501
20.54
[15]
Pipeline
Vertex-5
5759
68.12
532.19
11.82
[16]
Pipeline
Vertex-6
3900
73.39
573.39
18.81
[17]
Pipeline2
Vertex-6
5613
78.22
611.06
13.9
[11]
Pipeline
Vertex-6
4830
79
617.627
16.36
Ourwrk
Parallel pipeline
Vertex-5
4179
116.89
913.069
27.97
the world. It is to a great degree hard for programmers to get genuine information while scrambling by AES calculation. Till date, there isn’t any proof to crake this calculation. AES can manage three distinctive key sizes, for example, AES 128, 192, and 256 pieces and every one of these figures has 128-piece square sizes.
References 1. A.M. Abdullah, Advanced encryption standard (AES) algorithm to encrypt and decrypt data. Cryptogr. Netw. Secur. (2017) 2. U. Sawant, K. Wane, Analysis of the effective advanced encryption standard algorithm. Int. J. Adv. Res. Comput. Commun. Eng. 5(3), 965–967 (2016) 3. P. Kaur, P. Gupta, S. Kumar, A review on high speed advance encryption standard (AES). JETIR 5(8) (2018) 4. A.M. Borkar, R.V. Kshirsagar, M.V. Vyawahare, FPGA Implementation of AES Algorithm (IEEE, 2011), pp. 401–405 5. D.M. Alghazzawi, S.H. Hasan, M.S. Trigui, Advanced encryption standard—cryptanalysis research, in IEEE, International Conference on Computing for Sustainable Global Development, pp. 660–667 (2014) 6. A.A. Abed, A.A. Jawad, FPGA implementation of a modified advanced encryption standard algorithm, in International Conference of Electrical, Communication, Computer, Power and Control Engineering (2013) 7. H. Trang, N.V. Loi, An efficient FPGA implementation of the Advanced Encryption Standard algorithm (IEEE, 2012) 8. M. Pitchaiah, P. Philemon Daniel, Implementation of Advanced Encryption Standard algorithm. Int. J. Sci. Eng. Res. 3(3), 1–6 (2012) 9. A. Kaminsky, M. Kurdziel, S. Radziszowski, An overview of cryptanalysis research for the advanced encryption standard, pp. 1–8 10. R.D. Bajaj, U.M. Gokhale, Review on design of AES algorithm using FPGA. Int. Res. J. Eng. Tech. 2(9), 2380–2383 (2015) 11. S. Bri, S. Oukili, High speed efficient AES implementation, High school of Tech., Moulay Islamil Univ. Mekn., Morocco (2017)
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12. Y. Wang, A. Kumar, Y. Hay, FPGA-based high throughput XTS-AES encryption/decryption for storage area network. Natio. Univ. of Sing., Singapore (2014) 13. K. Rahimunnisa, P. Karthigaikumar, N.A. Christy, S.S. Kumar, J. Kumar, PSP: parallel subpipelined architecture for high throughput AES on FPGA and ASIC. Cen. Eur. J. Comp. Sci. 3, 173–186 (2013) 14. Q. Liu, Z. Xu, Y. Yuan, A 66.1 Gbps single-pipeline AES on FPGA, in Proceedings of international conference on field programmable technology (FPT) (IEEE, Kyoto, 2013), pp. 378–381 15. V.K. Sharma, S. Kumar, K.K. Mahapatra, Iterative and fully pipelined high throughput efficient architectures of AES in FPGA and ASIC. J. Circ. Syst. Comp. 25, 1650049 (2016) 16. L.Q. Liu, Z. Xu, Y. Yuan, High throughput and secure advanced encryption standard on field programmable gate array with fine pipelining and enhanced key expansion. IET Comp. Dig. Tech. 9, 175–184 (2015) 17. Y. Wang, Y. Ha, High throughput and resource efficient AES encryption/decryption for SANs, in Proceedings of International Symposium on Circuits and Systems (IEEE, Montreal, 2016), pp. 1166–1169
Multi-class Cancer Classification in Microarray Datasets Using MI-Based Feature Selection and Artificial Neural Network M. Jansi Rani and D. Devaraj
Abstract Recent developments in microarray data analysis and improvements in soft computing techniques have played a major role in the field of cancer classification. Classification and identification of cancer are very important as early cancer identification is highly useful for its treatment. Cancer diagnosis is challenging as microarray data has large number of genes. Many gene selection algorithms have been applied to effectively select the most promising gene subsets that can accurately identify cancer samples. In this paper, Mutual Information is used to select the informative genes and Feed Forward Neural Network is used to classify the genes. The proposed MI-ANN based gene selection and ANN classification are applied to classify multi-class microarray datasets. The obtained results show that the proposed method provides higher classification accuracy compared to existing methods. Keywords Data mining · Cancer classification · Gene selection · Microarray dataset · Soft computing
1 Introduction Cancer classification [1] or identification is a critical task that requires a lot of information related to the cancer cells and in this case the genes that correspond to particular cancer. Identifying cancer samples during the early stages is not possible using traditional medical techniques as these techniques cannot obtain deeper information related to cancer cells. After the development of microarray data [2] few years back, a major change has been seen in the field of bioinformatics and especially in the M. Jansi Rani (B) Assistant Professor, Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, Virudhunagar, India e-mail: [email protected] D. Devaraj Dean, School of Electronics & Electrical Technology, Kalasalingam Academy of Research and Education, Krishnankoil, Virudhunagar, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 V. Nath and J. K. Mandal (eds.), Nanoelectronics, Circuits and Communication Systems, Lecture Notes in Electrical Engineering 642, https://doi.org/10.1007/978-981-15-2854-5_11
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cancer classification [2] research. By representing cancer samples in the form of a microarray and then by analyzing the microarray data, it is feasible to extract and understand about the deeper information of the genes. Based on this information, only the genes that correspond to the given cancer can be selected and using this early cancer identification can be done. Thus, the process of cancer classification is divided into two stages such as (1) Gene Selection (2) Cancer Classification. Gene selection [3, 4] is the process of selecting the most promising subset using which the cancer samples can be classified accurately. Two variants of gene selections algorithms can be used such as the Filter variant and the Wrapper variant. The Filter approach [5] uses an evaluation or ranking based approach that can select genes based on gene-to-class information. On the other hand, the Wrapper approach [6] used a classification model to identify the most promising gene subset by using geneto-gene and gene-to-class information. In certain cases of filter approaches also the gene-to-gene information is used. The development of soft computing techniques also led to the improvement in the field of cancer classification. Soft computing techniques such as Genetic Algorithm (GA), Fuzzy Logic (FL) [7], Neural Network (NN), Bayesian Network (BN), etc. play a major part in cancer identification classification.
2 Literature Survey The most challenging task for any gene selection algorithm is to handle the multiplicity of classes within a cancer dataset. Usually, a microarray cancer dataset contains only binary classes that say 1 or 0 for Benign and Malignant. But certain cancer datasets also contain multiple classes that contain more than two classes. Chen et al. [8] introduced data classification using artificial neural network based on samples filtering. Leukemia data sets were used for evaluation. Artificial neural network and support vector machines are used for classification. Han et al. [9] proposed a gene selection approach using Binary Particle Swarm Optimization (BPSO). Gene-to-Class Sensitivity (GCS) is identified by the BPSO and this sensitivity value is used for gene selection. This is a hybrid approach as it uses both swarm optimization and sensitivity. The GCS is extracted from microarray data using Extreme Machine Learning (EML). EML-based gene selection was tested using some classifiers for better accuracy. Guyon et al. [11] proposed a Gene Selection algorithm for Cancer Classification. In this paper, they have introduced a method namely Recursive Feature Elimination (RFE). The results indicated that their method is more accurate and more stable. Tabakhi et al. [10] proposed an unsupervised filter-based feature selection technique that makes use of the Ant Colony Optimization (ACO). This method approach uses a fully connected weighted graph to represent the population space and then each edge is associated with a pheromone value and heuristic information. The MGSACO approach can select genes with maximum relevance and minimum redundancy by using an efficient fitness function to choose the path for ants. Better results were
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obtained in multiple microarray cancer datasets and the algorithm had a much lesser complexity compared to MIM-AGA. Also, this model can evaluate genes using the fitness function without a learning model. The major drawback of the MGSACO is that it requires more manual calculation and the number of selected genes is fixed. Alshamlan et al. [12] proposed a unique Genetic Bee Colony (GBC) algorithm for selecting informative genes for classification by combining the Genetic Algorithm (GA) and Artificial Bee Colony (ABC) algorithms. By combining the characteristics of both the algorithms, the GBC algorithm was able to handle both binary class and multiple-class cancer datasets with better accuracy in both. Here also we have used 3 class dataset called Lymphoma. That contains 3 categories of cancer namely DLCL, BCL and BL. So the true flexibility of an efficient gene selection algorithm also depends on how it can handle the number of classes and can still provide an efficient result.
3 Proposed Approach for Cancer Classification Cancer classification or in general classification is associated with two stages: the gene selection stage and then the classification stage. The efficiency in selecting the most promising genes decides the accuracy in the classification. The proposed cancer classification approach uses the MI approach for gene selection and ANN for classification [13]. The gene selection algorithm uses the characteristics of the Mutual Information to effectively select the most prominent gene subset to obtain maximum classification accuracy. The obtained gene subset from the gene selection algorithm is used for the final classification stage [14]. Classification of cancer samples is a critical process as it depends on the combination of genes used for the classification [15]. ANN-based classifier is proposed here, which is used to identify the type of class for each sample within the microarray data (Fig. 1).
3.1 Mutual Information Based Gene Selection Given a microarray cancer data M(S, G, C), the proposed MI-based gene selection algorithm identifies the most promising and informative genes using Mutual Information (MI). Let the number of top informative genes identified using Mutual Information be h, using which a new gene subset G mi ∈ G is formed such that G mi = {mi 1 , mi 2 , . . . , mi h } contains all the selected informative genes. By reducing the original microarray cancer data M(S, G, C) with only the genes in the set G mi , the reduced microarray cancer data Mmi (S, G mi , C) is generated. In this case, the value of genes within any given sample Si is represented as mi 1i , mi 2i , . . . , mi hi where i = 1, 2, . . . , n.
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Fig. 1 Proposed feature selection and classification system
According to Eq. (2), the initial entropy of the dataset M(S, G, C) is calculated as H (C) using the set of class label as represented in Eq. (1), H (C) = −
k P c j ∗ log P c j
(1)
j=1
Equation (1) provides the total initial entropy of the microarray cancer class C = {c1 , c2 , . . . , ck } and this measure defines the amount of uncertainty about the specific cancer class of the microarray dataset which depends on the number of occurrences of each of the class labels. Higher probability of one or more class labels decreases the uncertainty whereas class labels having almost equal probabilities lead to higher uncertainty. According to Eq. (3), the conditional entropy of gene mi j (where j = 1, 2, . . . , h) within the set G mi is calculated using the conditional probability of the gene with that of the class C as in Eq. (2), ⎤ ⎡ z k H mi j |C = − P(mi i z ) ∗ ⎣ P c j |mi i z ∗ log P c j |mi i z ⎦ i=1
j=1
(2)
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Here, z denotes the number of unique values of the gene mi j which is different for each geneand mi j z represents the zth value of the gene. The conditional probability H mi j |C for each gene mi j is calculated and substituted in Eq. (3) to calculate the Mutual Information I mi j ; C as denoted in Eq. (3), I mi j ; C = H (C) − H mi j |C
(3)
The Mutual Information (MI) value for each gene within the microarray data is calculated and they are ranked based on decreasing value of MI. The required number of genes with high and positive mutual information is selected as the final gene subset G mi . Then the reduced microarray dataset Mmi (S, G mi , C) is generated that contains samples with only the genes from the subset G mi . The newly generated microarray data Mmi (S, G mi , C) is given as input to the classifier.
3.2 Artificial Neural Network An Artificial Neural Network (ANN) is an information processing model that is modelled based on biological nervous system, which is composed of a large number of highly interconnected processing elements (neurons). ANNs have been recently applied to healthcare problems, especially for cancer identification. Figure shows multilayer Feed Forward Neural Network with three layers. In this feed forward neural network, all neurons in the input layer are connected to the neurons which are present in the hidden layer and those neurons are connected in the neurons which are present in the output layer via unidirectional links. These links can process the information in forward direction only (Fig. 2). Feed Forward Neural Network is trained by Backpropagation algorithm. The backpropagation algorithm is based on the minimization of an energy function indicating the instantaneous error for training the network. To minimize an energy function, Fig. 2 Feed forward neural network
Hidden layer Input layer
Output layer
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E(m) = 1/2
n dq − yq
(4)
q=1
where d q = desired network output for the qth input pattern yq = the actual output of the neural network. Each weight is changed according to the rule: wi j = −k
dE dwi j
(5)
where k = constant of proportionality E = Error function W ij = Weights of the connection between neuron j and neuron i. The adjustment of weight is repeated until the difference between the target output and actual output values are within some acceptance.
4 Results and Discussion The proposed MI-ANN approach uses the MI for gene selection and ANN for classification. The implementation has been done using the MATLAB environment installed in a system with Intel Core i5 Processor, 8 GB of RAM, 1 TB of Hard Disk and having 64-bit Windows 10 Operating System. When executing the proposed cancer classification algorithm, apart from calculating the calculation accuracy the execution times of the algorithms are also captured. For experimentation purpose, different cancer datasets have been taken that has varying parameters such as the number of genes and number of instances. Here binary class and multi-class dataset have been taken as shown below in Table 1. In Table 2, the classification accuracy has been given for each class of Lymphoma and Colon datasets. When setting the hidden node as 12, we can get the maximum accuracy. The selected genes from the Lymphoma and Colon datasets using Mutual Information is shown below in Table 3. Figures 3 and 4 show that the Mutual Information Value for each gene. The highest Mutual Information values are plotted. The top 50 genes which have high Mutual Information values are given as input to the ANN. Table 1 Microarray cancer dataset taken for implementation Dataset
Number of samples
Number of genes
Number of classes
Training samples
Testing samples
Colon
62
2000
2
30
32
Lymphoma
66
4026
3
30
36
Multi-class Cancer Classification in Microarray Datasets … Table 2 Classification accuracy with gene subset of 50 genes
Dataset
Class
Accuracy
No. of hidden nodes
Colon
0
55.55
12
1
72.7273
DLCL
91.3043
BCL
100
BL
100
Lymphoma
Table 3 Top 10 genes from selected 50 genes
113
12
S. no.
Colon
Lymphoma
1
143
3878
2
1771
3739
3
1671
3670
4
1582
2939
5
1414
2913
6
1286
2874
7
1042
2863
8
897
2858
9
493
2841
10
391
2762
Fig. 3 Selected 50 mutual information value for lymphoma dataset
5 Conclusion Gene selection is an important task in cancer classification as this determines the accuracy of the overall classification process. Selecting genes with high information with respect to the cancer class increases the chance of identifying the cancer class.
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Fig. 4 Selected 50 mutual information value for colon dataset
Gene index
The Mutual Information selects the most promising gene subset from the top genes that can obtain maximum classification accuracy. According to the experimental results, the proposed MI-ANN attains higher classification accuracy in binary class as well as multi-class microarray datasets compared with the existing gene selection algorithms.
References 1. M. Dashtban, M. Balafar, Gene selection for microarray cancer classification using a new evolutionary method employing artificial intelligence concepts. Elsevier, Genomics 109(2), 91–107 (2017) 2. E. Lotfi, A. Keshavarz, Gene expression microarray classification using PCA-BEL. Elsevier, Comput. Biol. Med. 54, 180–187 (2014) 3. H. Motieghader, A. Najafi, B. Sadeghi, A. Masoudi-Nejad, A hybrid gene selection algorithm for microarray cancer classification using genetic algorithm and learning automata. Elsevier, Inf. Med. Unlocked 9, 246–254 (2017) 4. W. Zhong, G. Altun, R. Harrison, P.C. Tai, Y. Pan, Improved k-means clustering algorithm for exploring local protein sequence motifs representing common structural property. IEEE Trans. NanoBiosci. 4(3), 255–265 (2005) 5. G. Ditzler, R. Polikar, G. Rosen, A sequential learning approach for scaling up filter-based feature subset selection. IEEE Trans. Neural Netw. Learn. Syst. PP(99), 1–15 (2017) 6. L. Ma, M. Li, Y. Gao, T. Chen, X. Ma, L. Qu, A novel wrapper approach for feature selection in object-based image classification using polygon-based cross-validation. IEEE Geosci. Remote Sens. Lett. 14(3), 409–413 (2017) 7. T. Nguyen, A. Khosravi, D. Creighton, S. Nahavandi, Medical data classification using interval type-2 fuzzy logic systems and wavelets. Elsevier J. Appl. Soft Comput. 30, 812–822 (2015) 8. W. Chen, H. Lu, M. Wang, C. Fang, Gene expression data classification using artificial neural network ensembles based on samples filtering, in 2009 International Conference on Artificial Intelligence and Computational Intelligence, Date Added to IEEE Xplore: 12 Jan 2010, INSPEC Accession Number: 11058898
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9. F. Han, C. Yang, Y.-Q. Wu, J.-S. Zhu, Q.-H. Ling, Y.-Q. Song, D.-S. Huang, A gene selection method for microarray data based on binary PSO encoding gene-to-class sensitivity information. IEEE/ACM Trans. Comput. Biol. Bioinf. 14(1), 85–96 (2017) 10. S. Tabakhi, P. Morad, F. Akhlaghian, An unsupervised feature selection algorithm based on ant colony optimization. Elsevier, Eng. Appl. Artif. Intell. 32, 112–123 (2014) 11. I. Guyon, J. Weston, S. Barnhill, Gene selection for cancer classification using support vector machines. Mach. Learn. 46, 389–422 (2002), Kluwer Academic Publishers. Manufactured in The Netherlands, Springer, pp. 389–422 12. H.M. Alshamlan, G.H Badr, Y.A. Alohali, Genetic Bee Colony (GBC) algorithm: a new gene selection method for microarray cancer classification. Elsevier, Comput. Biol. Chem. 56, 49–60 (2015) 13. B.A. Garro, K. Rodriguez, R.A. Vazquez, Classification of DNA microarrays using artificial neural networks and ABC algorithm. Elsevier, Appl. Soft Comput. 38, 548–560 (2016) 14. A.M. Abdel-Zaher, A.M. Eldeib, Breast cancer classification using deep belief networks. Elsevier, Expert Syst. Appl. 46, 139–144 15. M. Jansi Rani, D. Devaraj, A combined clustering and ranking based gene selection algorithm for microarray data classification, in IEEE International Conference on Computational Intelligence and Computing Research
Smart Communication in Coal Mines Sanjay Kumar Surshetty, Jaya Anand, Sneha Chowdhury and Vijay Nath
Abstract This paper demonstrates personnel safety and maximizing the mining process communication system. Environment monitoring in underground coal mines has been a mandatory requirement to ensure safe working conditions in the mines. Hence, active communication and information network need to be developed, which can accurately provide locational references and evacuate workers from the danger zone. Many of the technologies are common on the surface of the Earth, but have limitation issues underground. This paper outlines the major implications for wireless communication systems in underground coal mines and also addresses the recent technological developments in the area of communication and environment monitoring in underground coal mines.
1 Introduction Mining is one of the oldest endeavors of mankind along with agriculture. Underground coal mining is a dangerous job, as the miners work thousands of feet below the ground underneath tones of rocks. They are surrounded by very high voltage electric lines, darkness and dust. Mine disasters lead to trapping of survivors underground. Accurate knowledge of underground communications, environmental conditions and actual location and conditions of victims must be known [1]. Some method of knowing the location of trapped miners and means of communicating with them S. K. Surshetty (B) · J. Anand · S. Chowdhury · V. Nath Department of Electronics and Communication Engineering, Birla Institute of Technology, Mesra 835215, India e-mail: [email protected] J. Anand e-mail: [email protected] S. Chowdhury e-mail: [email protected] V. Nath e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 V. Nath and J. K. Mandal (eds.), Nanoelectronics, Circuits and Communication Systems, Lecture Notes in Electrical Engineering 642, https://doi.org/10.1007/978-981-15-2854-5_12
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is essential. Although the technology involved in removing materials from below the Earth’s surface has a long history, communication systems in underground workings are relatively new to the industry. Communication equipments did not begin appearing in underground mines till the early 1990s. During the early days, phones were used in underground mines. The only difference between these phones and the ones that were used on the surface of the Earth was that these phones were enclosed in cast iron boxes as protection against moisture, acid fumes and gases. In the 1950s, the Chesapeake and Potomac Telephone Co. of West Virginia introduced a telephone set for use in the explosive atmospheres that was designed around the philosophy of explosion containment [2]. In the early 1970s, work began on the use of modern communication system for use in the underground environments and the following requirements were established. The communication system must meet the intrinsic safety standards, be compatible with the environment, must be rugged in structure and flexible in design. It must be a total system in design and must be able to work with the modern telephone systems. It must be reasonably priced and must offer full-service support. In recent history, the most common method of underground mine communication consisted of loud-speaking or paging-type telephones. Early shaft communications consisted of bells or whistles signaling systems. Today, these systems have been replaced by radio and carrier current systems that allow two-way voice communication to and from personnel in the mines. The most important reason for developing the systems is the safety of coal miners [3, 4]. They work under such harsh conditions so certain devices have been invented to ensure their safety. The equipments are so developed in order that it can work in the environmental conditions of high pressure and high temperature in the underground mines. These devices may not work on the surface as the specifications of the devices may vary.
2 Historical View of Communication on Coal Mines The communication system has evolved a lot in the past few years. The Bureau of Mines along with the Clinch field Coal Co-operation in the year 1976 signed a consensus for the growth of the systems used for the purpose of communication which can be used in the field of mines [5]. For the purpose of expulsion of the coal, which is produced to an out by portage conveyor, a segmented mobile conveyor was incorporated in a mine of that kind. An ultra-high-frequency radio system has been developed by the Bureau of Mines and this system is currently in operation at the Clinch field’s Splash dam mine which is situated near Haysi, Va. At the Clinch field’s Splash dam, three ambulatory aqueduct conveyors are tethered as one segment by two ambulatory aqueduct bearer. This segment is responsible for the unburdening of the coal onto the extendible prolonged John Belt. Each aqueduct bearer requires one machine minder which is similar to the one used by the persistent miner [6]. The region of operation of the persistent miner is performed
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several feet away from the machine and this operation is conducted by the help of remote dominant box which consists of wired system. The depth of the coal deposition varies from 30 to 48 inches. The equipments are so developed so that it can work in the environmental conditions of high pressure and high temperature in the underground mines. These devices may not work on the surface as the specifications of the devices may vary. The detonation of gas or coal dust is one of the most dangerous things that can take place in underground coal mining. In a survey, it has been observed that during the year 1911–1969, about 5000 plus miners have been killed in more than 200 paramount cataclysms which involved detonations. The main important things after such kind of accidents are to save as many lives as possible, to rescue as many miners. Keeping these views in mind, the government has started taking various steps towards the advancement of the rescue team and their paraphernalia. There have been several instances which have been surveyed where the rescue teams faced a number of problems as a result of mine disasters [1]. One of the tedious and dangerous jobs is to search a mine after it has been exploded or caught in a fire. One of the famous examples of the rescue team killed was the incident from the Scotia mine where the entire team of 13 members was killed because of the mine disaster. In spite of the danger of losing their lives, the rescue team must decide where to begin its search operation from, because generally the area to be searched is quite large. The miners who manage to escape the explosion can inform the rescue team to those parts where the other trapped miners are present. The entire techniques of locating the survivors are based on the heat and trial method. Thus, there is a need for accurate knowledge about the position of the trapped miners so that the rescue team can rescue them with minimal hazards. Without any sort of accurate knowledge, it would be very difficult for the team to search in such dangerous areas. An electromagnetic system has been developed, the basic structure of which has been completed and the equivalent hardware is in the phase of testing [7]. The system consists of a transmitter system that is carried on by the miner and a receiver system that is present on the surface and receives the signals from the earth or from the mines. Two long wall sections are present in the working sections which consist of room and pillar in the mine which is run by Robinson near Shinnston. A world record was made in January 1973 when 23 workers who were working on the long wall sections produced 12,387 tons of coal in only 24 h time. There are a number of longwall systems in Europe but on the other hand, there are only around 75 longwall systems in the United States of America and about less than 5% of the country’s coal production is produced through the longwall systems. Majority of the equipments are shipped from other counties and about $1 to $2 million per machinery are invested by these countries. Overall, the importance of the longwall systems is very small in the USA, but they represents the country’s expertise role in underground coal mining [3]. Despite of the not so impressing stats, the Federal Bureau of Mines and the US mining industry are undertaking various steps for the promotion of the longwall mining in the country. There are mainly three reasons behind these impressive steps undertaken by these two bodies of national importance. The first reason being the
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very high potential of this longwall mining to produce large amount of coals and because of this, there is a belief that this technique will see very rapid growth in the upcoming years. As discussed in the above sections that the machinery for the longwall mining requires the equipments to be shipped from other countries as a result of which there has been a decrease in the use of this technique as it was generating more economical burden on the state, but now the US companies are starting to produce the equipments in their own country [6]. Thus, this is one of the major reasons why everyone is encouraging longwall mining. Third reason being automation in this field. And this is a very important reason as it can reduce human interference and can reduce the hazards. The Federal Bureau of mines investigated that the longwall mining consists of the three different categories of communication and they discovered that the Europeans integrates all these communication into a single system. There were basically five aims of the Bureau which they had to achieve. The first objective was to make a survey of the tools required for communication along with the need for the same. The second one focuses on observing the methods, equipments, experience of the European market because they had been dominating in this field for a very long period of time. Third objective states the determination of the areas where the problem lies and what further research needs to be taken to resolve them. The fourth objective was to design the rules and regulations regarding the setting up of the systems along with its maintenance [4]. The last objective was to recommend various further ideas which could be incorporated to make the longwall systems more advanced. Communication is one of the indispensable parts of the mining process. Without any sort of communication process, it’s very hard to imagine any mining process. The main focus of this research article is to discuss the Communication systems which have been incorporated in the mining process.
3 Need for Voice Communication There is an immense need for voice communication in mining areas. The main aim of the communication in these regions is to transmit information between the trapped miners and the rescuers and this information could be well enough to save somebody’s life [1, 7]. The information that can be transmitted are any unusual situations being faced by the trapped minors or any sort of advice that could be provided by the rescuers to the trapped miners until their arrival, would be very useful. Thus, communication plays an important role in this field as it can be used to track the location of the trapped miners so that they can be located and rescued. The most important reason for developing the systems is the safety of coal miners [3, 4]. They work under such harsh conditions so certain devices have been invented to ensure their safety. The equipments are so developed in order that it can work in the environmental conditions of high pressure and high temperature in the underground mines. These devices may not work on the surface as the specifications of the devices may vary. The equipments are so developed in order that it can work in the environmental
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conditions of high pressure and high temperature in the underground mines. These devices may not work on the surface as the specifications of the devices may vary.
4 Modern Means of Communication in Coal Mines The process of coal extraction from the coal deposition depends on the depth at which the coals are present, also upon the quality of the deposition and various other factors like geographical factors along with the environmental factors. The various process of extraction of coal has been distinguished from one another by whether they are performed on the surface or underground [6]. The coals which are extracted from either surface or underground mines need washing in a plant where the preparation of coals takes place. The two methods which are the most common ones for the extraction of coals are surface mining and deep underground mining methods for surface mines and underground mines respectively. The decision for the mining methods is done on the basis of the depth at which the coals are present, their density and their extent.
4.1 Extraction’s Method 4.1.1
Modern Surface Mining
An economical and convenient method for the extraction of the coals deposited near the surface is the method of open-cut which is also known as methods of open cast or open strip. Dynamos and explosives are used in this mining method in order to break the surface and achieve access to the inside of the surface. Once the coal deposition gets exposed then the process of drilling is performed which is followed by fracturing and which is further followed by thorough mining of the coals in strips. These extracted coals are then loaded onto large vehicles for the purpose of transportation to either the plant where the preparation of coals takes place or to the place where the direct consumption of coal takes place.
4.1.2
Underground Mining
Majority coals are penetrated deep inside the ground and as a result of which opencast mining seems to be a difficult process and thus their extraction requires the process of underground mining which is at present responsible for 60% of world’s coal production. The process of deep mining incorporates the method of room and pillar or pillar or board along with it and the support to the mine roof is provided by the pillars and timbers. Today’s section of pillars makes use of equipment which is
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Fig. 1 Automatic telephone exchange
controlled using remote and these also include huge hydraulic supports for the roof. These hydraulic supports are responsible for preventing cave-ins as long as the area designated for work has been left by the miners along with their machineries [4].
4.2 Communication There are various systems which find application in the mining industries. Some of the equipments have been discussed in the following section.
4.2.1
Automatic Telephone Exchange
It is evident from the name itself that it is an automatic dialing type telephone which is indistinguishable from the one used on the surface. The telephone line can be protracted to the surface as well as to the standpoints which are situated underground. The extension to the underground points can be done with the help of interfaces [5]. These interfaces are similar to the one used in C.D.S. systems. The automatic telephone exchange system is shown in Fig. 1.
4.2.2
Telephone System Powered By Sound
Short distances communication between point to point such as loading bounder and loading faces in mines which are relatively small in size can be performed with the help of telephone system which is powered by sound. These telephone systems operate without the use of battery. Electronic impulses are generated by the sound which strikes the microphone [4]. These generated impulses are responsible for facilitating the transmission of signals over very short distances. Figure 2 shows the telephone system powered by sound.
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Fig. 2 Sound powered telephone system
4.2.3
Telephone System Powered By Battery
This type of telephone system shows resemblance to its sound powered counterpart and they are also used for the point to point communications. The difference between the sound powered and battery-powered telephone system lies in their range of operation, the sound powered can be used for small distance because signal attenuation takes place for large distance which makes the communication inefficient whereas the battery-powered telephone system can be used for large distance without much attenuation [2]. The reason for the transmission of the signals without much attenuation is the use of battery in these systems which provide a sufficient amount of energy for the transmission of the signals. In order to achieve more versatility the several point to point communication systems can be connected through the means of exchange. Figure 3 shows the battery-powered telephone system.
4.2.4
Radio Paging System
The radio paging system is mainly used at the surface. These systems are used for communications between key personnel situated at different points on the surface itself. There are two versions of the paging system. The first one being a simpler version which helps in the transmission of only coded messages which notifies the users to talk from the nearest telephone exchange [3]. The other one being a complex version, which helps in the transmission of messages which consists of data that is present in the form which can easily be understood. It also facilitates the movement to the nearest telephone exchange through the means of message that can be transmitted from the user itself. The component diagram for the radio paging system has been shown in Fig. 4.
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Fig. 4 Radio paging system
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VHF Communication System
Very low-frequency systems, as the name signifies can be used in very low-frequency operations and these can be utilized to locate the position of the trapped miners, as these systems can generate radiations which can pass through rigid bodies. This system is under development at the Indian Institute of Technology, Kharagpur and as of now, penetration of about 150 meters has been achieved and researches are been carried out to enhance this figure. Communication is one of the vital components of mining industries. It is essential from protection point of view as well as for maximizing the productivity in the field of mining. Thus, in order to connect to the demands of the mines like the infrastructure of the communication systems, an impact suite has been developed in the twenty-first century which is known as the Mine Site Technologies. Most of the precious and important resources can be obtained in the underground regions. Thus, locating these underground resources is very essential and keeping this in mind a tracking system has been developed. This tracking system makes use of Wi-Fi, for the purpose of tracking the resources and miners present in the underground regions. The sprightly dockets, which are carried by the miners or are associated with some equipments, can be tracked with the help of these systems. In underground mines, low-frequency signals are used because they can easily pass through the grime and rocks [6, 7]. These low-frequency signals can be transmitted with the help of antennas, repeaters and reticulation alignment. The communications through these elements are the line of sight communication. Such radio signaling having low frequency signals are known as the Through-The-Earth (TTE) signaling. In order to increase the penetration of the signals through the hard rocks, ultralow frequency signals can be used which can penetrate to a greater depth than the low-frequency signals.
5 Personal Emergency Device Among various mines disasters, the incident from the mines of Australia at Moura number 4 coal mine, 1986 was a famous one because it leads to the initial stage development of a device which came to known as the PED (Personal Emergency Device). This device finds further development after the Moura number 2 coal mine. PED is a device that finds its application in the one way communication which supports text message forwarding. This device is widely used in Australia, United States, Argentina, Norway, Bermuda, Sweden etc. The development of this device began in 1987 by an Australian firm, “Mine site technologies”. This device was made available to the personals associated with the mining industry in 1991 by Health Administration (MSHA). All these developments in the field of PED came after the ignition of coal mines in 1994 (Fig. 5).
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Fig. 5 Personal emergency device
6 Impact Tracking System and Its Core Components 6.1 Wi-Fi Access Points as a Readers In order to meet the draconian necessity [8–10] of the industries carrying out underground mining, a system has been designed which is known as the Impact tracking system. This system has proven its reliability on every grounds which are necessary for a system to be used successfully in these industries. This system is very manageable, pitted and meet most of the safety caliber of underground mining [3]. In case of any exigency, such systems plays a very vital role in this field. Besides providing augmentation to safety, this system also improves the coherence in the working of any underground drudgery.
6.2 Zone Display Units The tagging particulars, if necessary are exhibited at definite sites with the help of colossal LED screens [11, 12] which are situated in the stockade made up of stainless steel. These colossal LED screens are known as the Zone Display Units (ZDU’s). Some of the definite sites could be cardinal elocutionist locations, entrances and exits of mine, etc.
6.3 Collision Avoidance Systems Any mine whether it is an underground mine or surface mine, requires tracking system for monitoring the location of the trapped miners. Thus, one such tracking system is Collision Avoidance System and this is also known as the Proximity Detection System or the Collision Avoidance Technology. These systems can also be utilized
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for the purpose of tagging. Such systems can be utilized to minimize the threat of the people advancing into association with automobiles in an untrammeled approach. The investment in the cognitive chattels and the consummate received with the existing impact tracking and tagging technology paved the road for the invention of the Collision Avoidance System [1, 3, 7]. The crucial unit of the Collision Avoidance System consists of the Vehicle per spaciousness system which also allows the real-time verdict observation of the vehicles. The system discussed above finds reinforcement from the Australian Coal Association Research Program (ACARP). This system is also responsible for receiving feed in from large amount of coal and mines situated on hard rocks in Australia. The communications are necessary from the individual’s safety point of view and their efficiency. The safety can be ensured by the messages which are sent by the trapped miners. These messages may be in the form of short text messages, voice over internet protocol phone, radio frequency identification tags. These various forms of messages are received by devices which are capable of receiving it [3]. Personal emergency device and Personal Pager [8, 13–15] are an example of such devices.
7 Conclusion The conclusion which can be drawn from this research article is how the communication systems evolved over the period of time. In earlier days, they were not efficient enough, they were not advanced, and there were many limitations as a result of which they were unable to rescue the lives of the miners. But with the advancement in the field of communication systems, several advanced techniques have been invented, several advanced equipments have been generated which are capable enough to rescue a large number of trapped miners and can minimize the amount of hazards. Acknowledgements We are thankful to the Department of Electronics and Communication Engineering BIT Mesra, Ranchi for providing the laboratory facilities. We are also thankful to our ViceChancellor, Dr. S. Konar and our Head of the Department, Dr. S. Pal for their constant inspiration and encouragement. Without their support, this research article would not have been possible.
References 1. L.K. Bandyopadhyay, S.K Chaulya, P.K Mishra, Wireless communication in underground mines. RFID Based Sens. Netw. (Springer Publications, New York, 2010) 2. A. Kumar, T.M.G. Kingson, R.P. Verma, A. Kumar, R. Mandal, S. Dutta, S.K. Chaulya, G.M. Prasad, Application of gas monitoring sensors in underground coal mines and hazardous area. Int. J. Comput. Technol. Electron. Eng. 23–34 (2017) 3. P. Misra, S. Kanhere, D. Ostry, S. Jha, Safety assurance and rescue communication systems in high-stress environments: a mining case study, in Topics in Design& Implementation, IEEE Communications Magazine
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4. J. Durkin, Apparent earth conductivity over coal mines as estimated from through-the-earth electromagnetic transmission tests (1984) 5. A.E. Forooshani, S. Bashir, D.G. Michelson, A survey of wireless communications and propagation modeling in underground mines. IEEE Commun. Surv. Tutor. 1–22 (2013) 6. Y.S. Dohare, T. Maity, P.S. Das, P.S. Paul, Wireless communication and environment monitoring in underground coal mines—review 7. X. Niu, X. Haung, Z. Zhaol, Y. Zhang, C. Haung, L. Cui, The design and evaluation of a wireless sensor network for mine safety monitoring. IEEE Global Telecommun. 8. A.K. Gogoi, R. Raghuram, Analysis of VLF loop antennas on the earth surface for underground mine communication, in Antennas and Propagation Society International Symposium 1996. AP-S. Digest, vol. 2, pp. 962–965 (1996) 9. J.R. Wait, Antennas in the geophysical environment-some examples. Proc. IEEE 80(1), 200– 203 (1992) 10. L.G. Stolarczyk, Emergency and operational low and medium frequency band radio communications system for underground mines. IEEE Trans. Ind. Appl. 27(4), 780–790 (1991) 11. V.M. Machado, J.A. Brandão Faria, Accurate electromagnetic analysis of MF TL backup communications in cylindrical tunnels. IEEE Trans. Antenn. Propag 63(5), 2032–2040 (2015) 12. M. Cypriani, G. Delisle, N. Hakem, Wi-Fi-based positioning in a complex underground environment. J. Netw. 10 (2015) 13. R.L. Lagace, Detection of trapped miner electromagnetic signals above coal mines (1980) 14. W.H. Schiffbauer, J.F. Brune, Coal mine communications, American Longwall Mag. (2006) 15. Hongzhi Guo, Zhi Sun, Chi Zhou, Practical design and implementation of metamaterialenhanced magnetic induction communication. Access IEEE 5, 17213–17229 (2017)
Challenges of WSN in the Fossil Fuel Industry with Recommended Sensor Node Architecture Devesh Mishra, K. K. Agrawal, R. S. Yadav, Naresh Agrawal and J. P. Mishra
Abstract In this paper, a wide-ranging analysis have been made for ad hoc network of wireless sensor nodes in oil pipeline surveillance environment containing challenges in conditional health monitoring of pipelines for detection of all time threats like leakage corrosion, erosion, blockage, and obstruction in cross country pipelines and for pipelines in refineries, petrochemical plants where determination of threat to pipeline is hazardous for maintenance engineers. The detection of threat in fossils fuel industry is divided into upstream, midstream, and downstream zones with the scope of surveillance of pipelines from wireless sensor nodes. The responsibilities of WSN are not only limited to threat detection but it has also its role in proficient, effective running of crude—refined oil in pipelines. A comparative analysis of prevalent communication technologies has been done with a correlation between them based on their size, weight, and cost of different globally available sensor motes. Finally, a recommended sensor mote architecture has been discussed. The structure discussed in the end provides subsequent provisions for cost-effective, conscious of design contemplations for ad hoc network based wireless sensor motes in pipeline threat detection. Keywords Wireless sensor network · Pipeline · Corrosion · Motes · Oil industry · IoT
D. Mishra (B) · R. S. Yadav Department of Electronics & Communication Engineering, University of Allahabad, Allahabad, India e-mail: [email protected] K. K. Agrawal Department of CSE, ABESIT, Ghaziabad, India e-mail: [email protected] N. Agrawal · J. P. Mishra IETE Allahabad Centre, Allahabad, India © Springer Nature Singapore Pte Ltd. 2020 V. Nath and J. K. Mandal (eds.), Nanoelectronics, Circuits and Communication Systems, Lecture Notes in Electrical Engineering 642, https://doi.org/10.1007/978-981-15-2854-5_13
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1 Introduction The industries involved in the crude oil extraction, survey, filtering, cleansing, and decontaminating to obtain petroleum with products involves lots of crucial steps of assessment of infrastructure involved in all these steps from initial drawing out to marketing phase. It is the primary objective of oil industries to preserve, secure and retain the chattels. WSN provides a favorable way out for harms related to health monitoring and corrosion assessment in pipeline scenario. An ad hoc network of sensor nodes based tools was formerly developed for defense applications in disaster management, rescue operation, battlefield, and in counterinsurgency operations. Ad hoc network of sensor nodes has very wide applications due to its foregone conclusion, efficacy, and competence. These days ad hoc network of sensor nodes make their presence in civil and trade applications like leakage detection, corrosion measurement and health monitoring of pipelines in process industries. Wireless Ad hoc sensor network is a customary, ubiquitous, and predominant tool for detection of damage and corrosion finding in fossil fuel industries. These ad hoc sensor networks have ample applications in pipeline reconnaissance with the detection of different threats to pipelines from corrosion, erosion, hostile operating conditions, and aging infrastructure. For effective operation of a wireless sensor node in pipeline environment it could have the following properties (i) It must have a suitable number of wireless sensor motes available, (ii) topology for sensor positioning (iii) collecting data from sensors. The designing of the sensor node depends upon the parameter detection, however, a lot of features are common for various specific tasks [1]. The field of research for pipeline health monitoring has its value due to economic, environmental, and social health benefits. For health monitoring of pipelines covering large geographical areas ad hoc sensor network is the most cost-effective tool. Moreover, applications of WSN does not only include pipeline reconnaissance. Merely, it also has a solicitation for leakage detection and level monitoring of storage tanks, condition-based monitoring in the refinery, pressure monitoring in pipeline and refinery. The modus-operand of oil industries is distributed into three measures that are (i) Upstream, (ii) Midstream, and (iii) downstream as shown in Fig. 1. Survey, investigation, searching, and rowing of crude oil to the surface through state of the art equipment, the raw material for oil industries comes in upstream class [2]. While midstream covers storage and transportation of crude oil. The collected or imported crude oil is stored in storage tanks and transported to refinery or petrochemical plants through pipelines, an oil tanker in the road, rail or barge for finally filtration and purification initiation the last class of operation. The downstream category consists of decontaminating rudimentary lubricant, at different column heights and produces lots of petroleum by-products like gasoline, waxes, and other petrochemical by-products including petrol and diesel. Lastly, at this stage, all the petroleum products are produced to market for distribution and retail [3].
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Fig. 1 Distribution of operation in the oil industry [4]
2 Oil Industry Scenario 2.1 Leakage Detection with Traditional Monitoring Leakage is a tactic of runny to seepage from the tanker, pipeline or container. This system generates an economic loss, material loss, and environmental loss. Leakage may cause massive catastrophe to society in the case of fossil fuels. Leaks are mainly caused by aging infrastructure, poor corrosion protection techniques, and in the absence of periodic health monitoring. With the increase in the pipeline network, the occurrence rate of leakage also rises, which proportionally increases the need for leakage discovery scheme [5]. • Traditional networks have higher installation and maintenance cost. • Localization of fault, obstruction or leakage is very difficult in the traditional infrastructure-based wired network and it became even more complex at inaccessible heights on column overhead piping or in case of fragmented network operation of sensor nodes go shut down. • Networks based on fixed infrastructure creates more hitches in pipeline scenario operating in hostile environment carrying fuel running at a specific temperature— Pressure sometimes below the surface of earth along with slug and wax. • The wired infrastructure-based network is incompatible for vibration monitoring of pumps and compressors placed in refinery and petrochemical plants [6]. • In an infrastructure-based wired network, the channel (wires) can be smashed down due to high temperature, life-threatening survival, a revelation to inorganic– organic compounds, corrosive environment and hostile surroundings around the wires. • Traditional old detection methods for periodic inspection of pipelines in refinery during annual plant shut have been conducted by maintenance team could be life compromising for human workers doing the job at the terrific height of 50–60 m above the ground level to measure the thickness of overhead piping [7].
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• For corrosion and thickness measurement of overhead, column requires big scaffolding which is costly and time-consuming. And in case of damage generates a huge economic loss to the oil industry. • Environmental pollution cannot be overlooked in the case of the traditional method of maintenance. This is very time-consuming and infeasible causing suffering, triggering more and more social as well as environmental damages [8].
2.2 Obligation of WSN There are numerous benefits of using an ad hoc network of sensor nodes as a tool in the petroleum industry few of them are listed below. • It eradicates connection and operative charges due to the lack of permanent cables. Extra supplementary saving comes from repair-free asset. • The motes are a collection of thermal, acoustic, seismic, magnetic, visual, infrared with various other sensors which have the quality of measuring lots of changes in a parameter like Pressure, Temperature, weather conditions, humidity, stress, soil makeup, presence and absence of obstacles, the velocity of objects [9]. • These tiny motes decrease faulty network because of its multi-hop design features which maintain the reliability, security, connectivity and information forwarding even in case of node failure. Due to these WS nodes have a high level of reliability and guarantees elevated production. In the oil industry, the applications of WSN are divided into two groups. The first one is health monitoring of unalike, diverse considerations and second is the detection of irregular, unusual, and uncharacteristic factors which compromise the environmental, economic, and safety of employees [10]. The possible uses including latent usages are tank level, storage level, vibration monitoring, veracity monitoring of pipes, as well as leakage detection and localization, obstruction detection and many more applications as shown in Fig. 2. The sensed information about factors, constraints and anomalous behavior captured is transmitted within the ad hoc network for processing, thereby taking decisions for the safety of industrial paraphernalia.
3 Concerning Issues for WSN The main objective of sensor nodes network in wireless ad hoc mode is a transfer of information or a sensed parameter from the foundation nodule to an endpoint node in a multi-hop manner [3].
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Fig. 2 Uses of WSN in the oil industry [4]
3.1 Wireless Ad Hoc Sensor Network Dependencies The wireless ad hoc sensor node functionality is dependent upon many factors few main are listed below. • The power efficiency of a sensor node is the backbone of the pipeline surveillance operation. Sensor motes with their small compact size operation wirelessly with an onboard power supply unit. Since sensor motes run over batter source very few of them use renewable energy for their operation due to their compact size, it is a challenging issue to use solar power in sensor motes. For the detection of any change of parameter to be sensed and for transfer of information, sensor motes need power. Because of this reason motes use power in a very optimal manner to last their batteries for years. Energy harvesting techniques should be adopted by sensor motes. During idle conditions, they could use 100 mW power while during operation 500 mw power could be used to sustain energy. • Presently, most of the sensors are used for the detection of temperature, pressure, and humidity. In case of the oil industry, for efficient and effective monitoring of pipes sensor motes are required for detection of corrosion and erosion in ferromagnetic pipelines, vibration from compressors, leakage detection, blockage and obstruction detection, pressure and temperature monitoring of running refined or crude oil in the pipeline [11]. • The transfer of information in the wireless network is managed by (MAC) media access control. In a cluster of sensor nodes at most of the instances, there arose two
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problems (i) Exposed terminal and (ii) Hidden terminal. Different MAC protocols have been used for optimal data transmission within the network. • Routing in wireless sensor network takes care of determining paths between source node behaving as transmitter and destination node working as a receiver. Routing in a sensor network is selecting an optimal path for the packet available for transmission from the initial node to the sink node. Routing and forwarding are closely related to each other but are a bit different. Routing always takes care of optimal path considering constraints about the security of the network and the transmission should be energy efficient for motes while forwarding the packet between intended source-destination node using routes that are determined through routing [12]. • Communication technologies and bandwidth are critical issues in a wireless ad hoc sensor network. Currently, for sensor networking the commonly used technologies are Bluetooth, Ultra-wideband (UWB), Wi-Fi, and Zigbee. The comparative chart on the basis of IEEE specification, signal rate, transmission range, transmission power, and a maximum number of nodes are shown in Table 1. The extract of this table shows that WSN with Zigbee has capability of connection with more than 65,000 nodes having transmission bandwidth of 250 Kbps suitable mainly for physical sensed parameter in comparison to the three other prevalent techniques mainly useful for multimedia transmission having bandwidth of 1 Mbps, 110 Mbps, and 54 Mbps for Bluetooth, UWB, and Wi-Fi, respectively [13]. • The motes of the wireless sensor network must maintain a low cost to sustain competitiveness with traditional infrastructure-based network. Table 2 shows a Table 1 Contrastive analysis of sensor network tools [14] Wireless standard attributes
Bluetooth
UWB
Wi-Fi
ZigBee
IEEE Specification
802.15.1
802.15.3
802.11a/b/g
802.15.4
Signal rate (Max.)
1 Mbps
110 Mbps
54 Mbps
250 Kbps
Transmission range
10 m
10 m
500 m
10–100 m
Nominal transmission power
0–10.0 dBm
−41.30 dBm
15–20.0 dBm
−25 to 0.0 dBm
Maximum number of nodes
8.0
8.0
2007.0
> 65,000.0
Table 2 Correlation between features and cost of motes [14]
Name
Dimension in (mm)
Weigh (g)
Price
MicaZ
58 * 32 * 7
18
US$99
TelosB
65 * 31 * 6
23
US$99
IRIS
24.23 * 24.23 * 7.5
3
US$115
SHIMMER
44.5 * 20 * 13
10
US$262
TinyNode
30 * 40
–
US$180
Sun SPOT
41 * 23 * 70
54
US$750
Cricket
~58 * 32 * 7
~18
US$225
LOTUS
76 * 34 * 7
18
US$300
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comparative analysis on the basis of their size, weight, and cost of different globally recognized motes. Wireless sensor motes are in the primitive stage of their technological development due to this fact more enhance research is needed for cheaper sensor motes [15].
3.2 Contemplation Aspects of WSN in the Oil Industry Amalgamation and distribution of facts and data is the primary objective of the link of sensors. Each sensor mote is unique in its operation no sensor can be capable to detect all the physical parameters in a single unit. Therefore, a network is composed of the integration of all the parameters sensed from different sensors to perform data mining on it. Efficient, effective, and effortless operation of motes, the ad hoc sensor network should be capable enough to detect corrosion, erosion, temperature, pressure, leakage in pipelines distributed among large geographical areas, and finally summing up all the pipeline surveillance data for data mining to locate menace, peril to the pipeline network in the oil industry [16]. Sensors are devices that can detect the change in physical parameters and provide output in the form of voltage or current. Each sensor operation is unique in nature with matchless physical parameter detection. In maintenance and conditional health monitoring of pipelines frequently used sensors are temperature, pressure, acoustic, magnetic, optical, piezoelectric, and ultrasonic sensors. Still, it is very difficult for a single sensor unit to have sensing capabilities of all these parameters and sensor with multi-modal sensing capability is still thought-provoking research pitch [17]. Detection of events related to threats to pipeline distributed into the large geographical area is an important part of surveillance. Various localization tools such as GPS are used within sensor motes to determine the appropriate site of leakage or corrosion-erosion. Deployment of motes in a pipeline network to cover the overall length of the pipeline is subjected to the topology of sensor motes distribution. Frequently used wireless sensor topologies are mesh, tree, ring, and star. While selecting an optimal topology for oil pipeline surveillance power efficiency, transmission range [18].
4 Recommended Sensor Node Architecture for Oil Industry Figure 3 shows the block diagram of sensor node architecture useful in oil pipeline surveillance. It encompasses a power supply with a renewable source of energy which is highly recommended for futuristic sensor nodes, transceiver unit that is responsible for reception and transmission of sensed data and information [19]. In a multi-hop culture of operation, each sensor node has to receive and forward the sensed data based on the routing algorithm. The communication unit of the mote may use Zigbee
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Fig. 3 The architecture of recommended WSN node [14]
or Wi-Fi as a communication tool both of them possess transceiver properties. Wi-Fi provides a greater opportunity if huge bandwidth is needed but it requires greater power as well while Zigbee has an efficient operation with limited power but its major compromises include lack of transmission bandwidth and interoperability with other existing technologies like Bluetooth [20]. The microcontroller unit embedded in the sensor mote has an inbuilt ADC and DAC, a memory unit with the translatorencryption segment. The microcontroller unit has standard interfacing protocols such as UART, SPI, USB, I2C, and GPIO. Out of all these USB has the greatest simplicity, popularity due to its favorable and similar operations. For greater operation speed and compactness in size, the microcontroller is used in motes instead of a microprocessor in it. The sensor connected with the microcontroller is a kind of transducer that can detect any change in the physical characteristics of his surroundings and provide mostly analog output in the form of voltage or current [21].
5 Conclusion and Future Work Research and development in the field of infrastructure-less ad hoc network based wireless sensor nodes have been very swift in the past few epochs. With the cumulative number of accidents in oil pipeline environments around the globe, there is a burning need of early threat warning system based on wireless sensor network which is used to measure, sense and transmit actionable data of physical parameters of the pipeline which allows maintenance person take preventive measures on time. This article explains the reliability and truthfulness of wireless sensor nodes tool commonly known as motes for fossil fuel oil pipeline inspection. Challenges of pipeline surveillance with the duties of WSN have been presented here. In this article, some inadequacies, limitations, and weakness of available WSN motes for pipeline health monitoring have been described with few procedures to remove them. Therefore, the WSN mote structural design offered will have upgraded, better quality power proficiency as well as enriched data trans-reception quality [22].
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Amalgamation of dissimilar multi-agent network of wireless measuring device commonly known motes, having multi-modal sensing capabilities and application of perceptive on the various motes output, will guarantee for no untruthful early threat detection can be experienced.
References 1. D. Mishra, K.K. Agrawal, R.S. Yadav, R. Srivastava, Design of out pipe crawler for oil refinery based on analysis & classification of locomotion and adhesion techniques. IEEE UPCON, India, 2018, pp. 1–7. https://doi.org/10.1109/upcon.2018.8596988 2. I. Jawhar, N. Mohamed, D.P. Agrawal, A hierarchical wireless sensor network design for pipeline network infrastructure, in Industrial Wireless Sensor Networks. http://dx.doi.org/10. 1016/B978-1-78242-230-3.00010-6 Copyright © 2016 Elsevier Ltd. All rights reserved 3. M. Meribout, A wireless sensor network-based infrastructure for real-time and online pipeline inspection. IEEE Sens. J. 11(11) (2011) 4. M.Y. Aalsalem, W.Z. Khan, W. Gharib, M.K. Khan, Q. Arshad, Wireless sensor networks in oil and gas industry: recent advances, taxonomy, requirements, and open challenges. J. Netw. Comput. Appl. (2018) 5. B. Rashid, M.H. Rehmani, Applications of wireless sensor networks for urban areas: a survey. J. Netw. Comput. Appl. (2015), http://dx.doi.org/10.1016/j.jnca.2015.09.008i 6. J. Eze, Robot. Comput. Integr. Manuf. (2016). https://doi.org/10.1016/j.rcim.2016.12.007 7. F. Karray, M.W. Jmal, A. Garcia-Ortiz, M. Abid, A.M. Obeid, A comprehensive survey on wireless sensor node hardware platforms. Comput. Netw. (2018). https://doi.org/10.1016/j. comnet.2018.05.010 8. S. Datta, S. Sarka, A review on different pipeline fault detection methods. http://dx.doi.org/10. 1016/j.jlp.2016.03.010-4230/ © 2016 Elsevier Ltd. All rights reserved 9. D. Mishra, K.K. Agrawal, R.S. Yadav, Contemporary status of machine prognostics in process industries based on oil and gas: A critical analysis. RICE, 2018. https://doi.org/10.1109/RICE. 2018.8627898 10. D. Mishra, K.K. Agrawal, A. Abbas, R. Srivastava, R.S. Yadav, PIG [Pipe Inspection Gauge]: An artificial dustman for cross country pipelines. Procedia Comput. Sci. 152, 333–340 (2019) 11. D. Mishra, K.K. Agrawal, R.S. Yadav, T. Pande, N.K. Shukla, in Fruition of CPS & IoT in context to Industry 4.0. Intelligent Communication Control and Devices (ICICCD 2018), vol 989, Chapter 39 (2018). https://doi.org/10.1007/978-981-13-8618-3_39. ISBN: 978-981-138618-3, ISSN: 2194-5357 12. D. Mishra, K.K. Agrawal, R.S. Yadav, T. Pande, N.K. Shukla, Mathematical Analysis & Simulation for Designing Two-Dimensional Out Pipe Crawler for Oil Industry. IEEE EDKON, India, 2018. https://doi.org/10.1109/EDKCON.2018.8770498. e-ISBN: 9781538664155 13. A. Kesarwani, D. Mishra, A. Srivastva, K.K. Agrawal, Design and development of automatic soil moisture monitoring with irrigation system for sustainable growth in agriculture. SSRN Electron. J. 14. A.K. Tariq, A.T. Ziyad, A.O. Abdullah, Wireless sensor networks for leakage detection in underground pipelines: a survey paper, © 2013 The Authors. Published by Elsevier B.V 15. D. Mishra, R.S. Yadav, T. Pande, N.K. Shukla, K.K. Agrawal, R. Srivastava, Study of robotic technique for in-pipe inspection with taxonomy and exploration of out oil pipe crawler, CRC Press. https://doi.org/10.1201/9780429444272. ISBN 9780429444272 16. D. Mishra, R.S. Yadav, K.K. Agrawal, A. Abbas, Study of Application Layer Protocol for Real-Time Monitoring and Maneuvering, vol 1059, Chap 38 (AISC-Springer). https://doi.org/ 10.1007/978-981-15-0324-5. ISBN: 978-981-15-0324-5, ISSN: 2194-5357
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17. D. Mishra, R.S. Yadav, K.K. Agrawal, A. Abbas, R.K. Singh, Study of Application Layer MQTT Protocol with Assessment & Comparison of Hardware to Enable IoT, (August 28, 2019). Available at SSRN: https://ssrn.com/abstract=3444099 18. D. Mishra, R.S. Yadav, K.K. Agrawal, in Kinematic modelling and emulation of robot for traversing over the pipeline in the refinery. Microsystem Technologies 19. D. Mishra, R.S. Yadav, K.K. Agrawal, in Renewable energy-based soil moisture metering system using a smartphone with IoT. Recent Advances in Computer Science and Communications (2020). https://doi.org/10.2174/2213275912666190801114258 20. P. Tiwari, D. Mishra, R.K. Singh, A. Rizvi, V. Singh, A. Kushwaha, K.K. Agrawal, Livestock harvesting through electric reaping-hook for sustainable growth in agriculture. SSRN Electron. J. 21. K.K. Agrawal, D. Mishra, A. Pandey, A. Rizvi, R.K. Singh, Aquatic Surveillance Robot Based on IoT. SSRN Electron. J. 22. D. Mishra, T. Pande, K.K. Agrawal, A. Abbas, A.K. Pandey, R. S. Yadav, in Smart Agriculture System Using IoT, Proceedings of ICAICR 2019, Article 39, Association for Computing Machinery (2019). https://doi.org/10.1145/3339311.3339350. ACM ISBN 978-1-4503-66526/19/06
FPGA-Based Smart Irrigation System Sanjay Kumar Surshetty, Alisha Oraon, Renuka Kumari, Shradha Shreya and Vijay Nath
Abstract In this paper, the main focus is on a smart irrigation system which is a vital need in today’s world. The twenty-first century deals with technology and automation, making our lives easier, adaptable, and cost-effective. In our country, India is divided into two groups of people, the agro-based farmers whose livelihood depends on food production and on the other side the consumers who depend solely on the consumption of food produced by the farmers. Both have an interdependent link. India an agriculture-based country, it is very important that the methods used for agriculture are efficient to satisfy the increasing demand. This led to the origination of smart irrigation system which is one of the smartest methods used in agriculture. It provides an automatic irrigation of crop fields and does not require human involvement. It keeps the track of the water level as well as soil moisture content. This ensures the proper and healthy growth of plants. The major factor involving agriculture is the depletion of water which keeps on increasing day by day. The smart irrigation system also resolves this issue as the water is conserved by the automated water system consisting of a sensor that senses the climatic data such as humidity, soil moisture, and water level. The agriculture field is irrigated based on this data and water flow.
S. K. Surshetty (B) · A. Oraon · R. Kumari · S. Shreya · V. Nath VLSI Design Group, Department of Electronics & Communication Engineering, Birla Institute of Technology, Mesra, Ranchi 835215, Jharkhand, India e-mail: [email protected] A. Oraon e-mail: [email protected] R. Kumari e-mail: [email protected] S. Shreya e-mail: [email protected] V. Nath e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 V. Nath and J. K. Mandal (eds.), Nanoelectronics, Circuits and Communication Systems, Lecture Notes in Electrical Engineering 642, https://doi.org/10.1007/978-981-15-2854-5_14
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1 Introduction As per the literature survey, it is found that maximum farmers are doing agriculture on classical methods only due to lack of knowledge. With a lot of efforts, they are getting very less return in comparison to applied their efforts and applied their value in farming. They did not know which type of seeds; fertilizers are useful for their soils, and how much water is required. Somewhere scarcities of waters also created big problems. To see all such problems of farmers, a new technique has to be introduced to giving the perfect solution to enhance their productivity, market value, knowledge of seeds, fertilizers, soils testing facility, types crops to be planted, landmarking on the basis of availability of waters, and so many. This system is known as smart irrigation systems which details are discussed below with suitable examples. These techniques are not spread all over the farmers but it is tried by each country and pursuing Government to facilitate farmers such as useful techniques. Water scarcity is one of the biggest problems that is faced in today’s world. The increase in population has increased the demand for food production to feed the billions of people especially for the country like India which is still a developing one. Therefore, water should be used judiciously which can only be achieved by a smarter system like smart irrigation system. Smart irrigation is an intelligent and feasible system for operating an irrigation system for proper measurement and estimation of moisture content. In conventional system, the farmers have to handle the process of irrigation. Farmer has to keep a check of soil moisture level and based on this estimation, they turn on the pump to irrigate the respective agriculture field. Farmers keep waiting for the water to flow sufficiently to the agriculture field. After a certain period of time, farmers switch off the motor. The conventional system of irrigation consumes time as well as effort from the farmer’s side especially when it is required to irrigate multiple crop fields in different areas. By doing this, we can increase the yield and quality of the crop. For this, a new system was proposed in this paper to improve the irrigation system. By using this system the farmer can monitor soil moisture, water level, temperature, humidity, and dew point sensors for the better cultivation of the crop. The required control system is designed in XILINX 8.1-series FPGA using Verilog@HDL. The sensors are integrated into the FPGA through inbuilt A/D converter (ADC). The communication between various devices takes place through RS232. Simulation and synthesis are performed to the remote monitoring system by using suitable tools and analyzing the performance of the system by comparing speed constraints. This system will monitor all these parameters through different sensors. Soil moisture sensor will measure the water content in the soil, i.e., it will check whether the soil is dry or wet. Water level sensor senses the water in the water source. Temperature sensor and humidity sensor are used for forecasting the weather conditions. Dew point sensor will convert the water vapor into liquid state.
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Fig. 1 Block diagram of proposed system
The major disadvantage of this type of system is that the plants are prone to many types of diseases. There should be a proper watch over the water level and requirement needed for plants depending on different terrain which may be sloppy or hilly. Hence there is an automated system that can fulfill the purpose. As the system also employs memory in which all the sensor values are stored and accordingly switches pumps on or off by using relays [1]. A sensor is used to sense the changes in the parameters like temperature, humidity, water level soil moisture, etc. The sensor is followed by FPGA which reads the data based on climatic parameters [2]. The automated irrigation system is very advantageous for the farmers in terms of time consumption and it minimizes their need for interference and tracking in the agricultural fields. It ensures proper irrigation and uniform distribution of water for the crops. Hence, the work is done with higher efficiency once an automatic irrigation system is installed in the agriculture field irrespective of types of crop planted in that particular field (Figs. 1 and 2).
2 Significance of Irrigation In country like India, the farmers entirely depend on monsoon for agriculture. Rainfall is an important factor that controls agriculture, as monsoon rainfall is not very predictable and is uncertain and irregular and thus the lives of the farmers (i.e. producers) and the consumers are co-dependent on monsoon. As irrigation is the essential factor that affects the productivity and quality of crops, so uneven and unequal rainfall affects the livelihood of farmers normally, the total rainfall occurs in the month of mid of June to mid of October. Thus, it becomes very essential to keep checking the water level of the agriculture field in rest of the months of the year and irrigating the field as per the requirement.
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Fig. 2 Figure representing the block diagram for smart irrigation system
3 Irrigation Procedure Various different types of methods have been adopted for irrigating the agriculture field for different types of crop fields. The basic methods used by farmers are channel system, sprinkler system, drip system. Among the above-mentioned methods channel system of irrigation is being considered as the traditional method. The smart irrigation system evolved as a new technology for irrigation of the farm field which automatically irrigates the field according to the requirement [3].
3.1 Channel System The channel system is the traditional and largely used method irrigation which is being adopted from the very beginning. One of the advantages of this method is that the total capital investment required is not very high and can be easily implemented. The channel system consists of pipes which are connected with a water pump and while pumping starts it allows water to flow from the lake, river or bore well to the farming field. The farmers, on the other hand, are fully engaged for irrigating the crop field with the help of number of workers. This system is a fail as it requires a large amount of workers for watering and also huge amount of water gets wasted (Fig. 3).
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Fig. 3 Figure of channel system procedure
3.2 Sprinkler System The sprinkler system method of irrigation proves to be more useful as it provides better irrigation even when the quantity of water available is very less. The pump consists of main pipes and perpendicular pipes. When the pump starts, the water starts flowing through the main pipe and then it starts flowing through the perpendicular pipes connected with the main pipe. A nozzle is present on the top of the perpendicular pipe which rotates automatically at regular intervals and thus helps in irrigating the field. Sprinkler system is also useful on the sandy soil. This system requires less number of workers and wastage of water is less as compared to traditional channel irrigation (Fig. 4). Fig. 4 Figure of sprinkler system procedure
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Fig. 5 Drip system
3.3 Drip System In the drip system method, waterfalls drop by drop at the position of the roots. It is one of the best methods of watering fruit plants, Garden and trees. The pump consists of main pipes and sub pipes. First, water flows through the main pipe and then flow gets divided into sub pipe11s. Nozzles are attached to the top surface of these sub pipes. This drip system method is highly efficient than the channel and drip irrigation method. Wastage of water is very less in this method of irrigation and no workers are needed for irrigating. Farmers keep track of the status of the farm field then the motor is started accordingly and the direction of nozzles is chosen as per the requirement. Then automatically the agriculture fields are watered and at particular intervals, the farmer keep checking the status of the field, while the whole crop is irrigating to turn off the motor when there is no need (Fig. 5).
3.4 Smart Irrigation System The three methods of irrigation system [4–6] mentioned above generally operates by a user but a smart irrigation is a system which is controlled by autonomous means. Smart irrigation represents a system automatically controlling the total irrigation system irrespective of the presence of farmers in the field and send messages to the farmer about the status of the farm field and also about the changes in operation of the farm field as the system is fully automated no manual involvement is required. This system proves to be the most efficient technique in a modern day era for farmers among all the outdated methodology as there are minimal chances of wastage of water. The operation fully depends on the condition that is being analyzed by the sensors attached in the full system. One of the biggest advantages is also that since there is no involvement of human, the precious time of farmers is saved.
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Fig. 6 Soil moisture sensor
4 System Components 4.1 Soil Moisture Sensor In any irrigation system, one of the most important steps is to first measure the amount of water present in the soil which further helps in determining how much irrigation is needed in the field. For increasing the productivity, yields and quality of the crops, management of soil moisture content is of great necessity. To know the amount of water level in the soil, the soil moisture sensor is used to get the exact numerical value of the water content. Thus accordingly at the final stage, decision can be made whether to run the motor or not or for how much motor should be run (Fig. 6).
4.2 Temperature Sensor A temperature sensor [7–9] is used to measure the temperature of the fields. This is most effective field parameter which has a greater influence on other parameters.
4.3 Humidity Sensor Humidity refers to the presence of water content in the atmosphere. In agriculture, measurement of humidity is important for plantation protection and soil moisture monitoring. The humidity sensor is also referred to as hygrometer. It continuously detects the humidity level and gives the relative content of humidity in the atmosphere which then helps in finally predicting the occurrence of rainfall. Humidity sensor gives back the result of measurement in terms of relative humidity present in the air. Also, it helps in the measurement of both temperature and humidity of the air.
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Relative humidity is expressed as a percentage. Basically, relative humidity refers to the ratio of actual moisture content in the air to the highest amount of moisture content that air can hold at that particular temperature. The warmer the air is, the more moisture it can hold, so relative humidity changes with fluctuations in temperature.
4.4 Field Module Field module is a sensor-based system used in irrigation system which finally performs the operation on the data obtained from the sensors in previous stages. To get the input data, moisture sensor, humidity sensor and temperature sensor are being interfaced with the FPGA board to get all the required information of irrigation field. Operation in FPGA board is performed in two formats, i.e., digital and analog. While measuring in digital mode, it is being checked whether the output value is higher or lower with respect to the reference voltage being considered. For the case of the absence of moisture, the value of output obtained is higher than the reference value. While measuring in analog mode, the numerical value of moisture content obtained is more accurate and appropriate one. So in many cases analog mode is being considered for the measurement for more accurate output.
4.5 Control Module Control module [10–17] plays the most vital and final stage of this irrigation system [18]. Control module is the one that has the control on the working of the motor. It basically determines when to run the motor and for how much time motor should be run to provide the proper irrigation to the agriculture field as per the requirement. In this, the FPGA board is interfaced with the motor and voltage supply is provided to the motor according to the analog output obtained from the FPGA board. The board has inbuilt digital to analog converter which gives an 8-bit analog voltage output which is then fed to the motor. The flow diagram representing the working of the motor is as shown below in Fig. 7.
5 Conclusion The main motive of doing this project is to introduce an enhanced system of irrigation which can provide an autonomous control on the irrigation of different crops. FPGA implementation of an irrigation control system leads to the improvement in yield and overall productivity of the crops. The system will monitor all the field parameters thus providing the necessary information to the farmers. It becomes very efficient to use in rural areas. The use of FPGA board in the field module and control module
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Fig. 7 Block diagram representing control module
helps in processing and interfacing the real-time values of physical parameters which involve the environmental conditions and thus enhances the irrigation system by increasing the efficiency of motor. The usage of this FPGA enhances the application area of sensor networks in all agricultural environments by improving the speed of operation. This irrigation system has high processing speed and has lower complexity as compared to the previous ones developed in other papers. Thus, provides efficient irrigation system for the better production of crops. Acknowledgements We are thankful to the Department of Electronics and Communication Engineering, Birla Institute of Technology Mesra, Ranchi for providing the laboratory facilities. We are also thankful to our Vice-Chancellor, Prof. M. K. Mishra and our Head of the Department, Dr. S. Pal for their constant inspiration and encouragement. Without their support, this research article would not have been possible.
References 1. K. Sindhu, Y. Sri chakrapani, M. Kamaraju, FPGA implementation of irrigation control system. Int. J. Sci. Eng. Res. 5(12) (2014) 2. P. Ranade, S.B. Takale, Smart irrigation system using FPGA based wireless sensor network. Int. Res. J. Eng. Technol. (IRJET) 03(05) (2016) 3. A. Salam Al-Ammri, S. Ridah (Department of Mechatronics Engineering Baghdad University, Iraq), Smart irrigation system using wireless sensor network. Int. J. Eng. Res. Technol. (IJERT) 3(1) (2014) 4. P. Behera, C. Kumar Sahu Sambalpur University Institute of Information,” A low cost smart irrigation control system” IEEE Sponsored 2nd International Conference On Electronics And Communication System (ICECS 2015) Conference Paper, February 2015
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5. M.I. Husni, M.K. Hussein, M.S.B. Zainal, S.A.B. Hamzah, D. Bin Md Nor, H.B.M. Poad (Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia (UTHM) Batu Pahat, Malaysia), Soil moisture monitoring using field programmable gate array. Indonesian J. Electr. Eng. Comput. Sci. 6. K.K. Namala, A.V. Krishna Kanth Prabhu, A. Math, A. Kumari, S. Kulkarni (School of Engineering, Central University of Karnataka, Kalaburagi, India), Smart irrigation with embedded system, in 2016 IEEE Bombay Section Symposium (IBSS) 7. A. Pandey, V. Nath, A CMOS temperature sensor and auto-zeroing circuit with inaccuracy of −1/+0.7 °C between −30 °C to 150 °C. Microsyst. Technol. 23(3), 4211–4219 (Springer, Berlin, Heidelberg, 2016) 8. S. Chakraborty, A. Pandey, V. Nath (Microsyst Technol), Ultra high gain CMOS op-amp design using self-cascoding and positive feedback. Microsyst. Technol. 23(3), 541–552 (Springer, Berlin, Heidelberg, 2017) 9. D. Prasad, V. Nath, An ultra-low power high-performance CMOS temperature sensor with an inaccuracy of −0.3 °C/+0.1 °C for aerospace applications. Microsyst. Technol. 24(3), 1553– 1563 (Springer, Berlin, Heidelberg, 2018) 10. N. Nidhi, D. Prasad, V. Nath, A high-performance energy-efficient 75.17 dB two-stage operational amplifier, in Nanoelectronics, Circuits and Communication Systems. Lecture Notes in Electrical Engineering, ed. by V. Nath, J. Mandal, vol. 511 (Springer, Singapore, 2019), pp. 469–474 11. U. Raj, N. Nidhi, V. Nath, Automated toll plaza using barcode-laser scanning technology, in Nanoelectronics, Circuits and Communication Systems. Lecture Notes in Electrical Engineering, ed. by V. Nath, J. Mandal, vol. 511 (Springer, Singapore, 2019), pp. 475–481 12. S. Chaudhary, A. Prava, N. Nidhi, V. Nath, Design of all-terrain rover quadcopter for military engineering services, in Nanoelectronics, Circuits and Communication Systems. Lecture Notes in Electrical Engineering, ed. by V. Nath, J. Mandal, vol. 511 (Springer, Singapore, 2019), pp. 507–513 13. R. Mohan, A.K. Suraj, S. Agarawal, S. Majumdar, V. Nath, Design of robot monitoring system for aviation, in Nanoelectronics, Circuits and Communication Systems. Lecture Notes in Electrical Engineering, ed. by V. Nath, J. Mandal, vol. 511 (Springer, Singapore, 2019), pp. 535–547 14. E.V.V. Hari Charan, I. Pal, A. Sinha, R.K.R. Baro, V. Nath, Electronic toll collection system using barcode technology, in Nanoelectronics, Circuits and Communication Systems. Lecture Notes in Electrical Engineering, ed. by V. Nath, J. Mandal, vol. 511 (Springer, Singapore, 2019), pp. 549–556 15. V. Goel, P. Kumari, P. Shikha, D. Prasad, V. Nath, Design of smartphone controlled robot using bluetooth, in Nanoelectronics, Circuits and Communication Systems. Lecture Notes in Electrical Engineering, ed. by V. Nath, J. Mandal, vol. 511 (Springer, Singapore, 2019), pp. 557–563 16. P.K. Sinha, S. Saraiyan, M. Ghosh, V. Nath, Design of earthquake indicator system using ATmega328p and ADXL335 for disaster management, in Nanoelectronics, Circuits and Communication Systems. Lecture Notes in Electrical Engineering, ed. by V. Nath, J. Mandal, vol. 511 (Springer, Singapore, 2019), pp. 565–572 17. D. Raju, L. Eleswarapu, R. Saiv, V. Nath, Study and design of smart embedded system for aviation system: a review, in Nanoelectronics, Circuits and Communication Systems. Lecture Notes in Electrical Engineering, ed. by V. Nath, J. Mandal, vol. 511 (Springer, Singapore, 2019), pp. 573–590 18. S. Darshna, T. Sangavi, S. Mohan, A. Soundharya, S. Desikan, Smart irrigation system. IOSR J. Electron. Commun. Eng. (IOSR-JECE) 10(3), Ver. II, 32–36 (May–Jun 2015). e-ISSN 22782834, p-ISSN 2278-8735
Smart Healthcare System Using IoT Rashi Patel, Nishu Sinha, Kuhu Raj, Deepak Prasad and Vijay Nath
Abstract Healthcare is an important aspect of any developing economy. In a country like India, poverty and lack of resources in rural areas prevent the inhabitants from getting appropriate medical facilities. In this paper, we have proposed a system that helps find the necessary medical help in rural areas whenever required using IoT. This is accomplished by creating a database that contains information of all the doctors and can be used to find which doctor is free. The medical records of patients can be sent to that doctor and he/she can be given immediate attention. This will make the healthcare system more time-efficient, safe and cost-efficient. We can also add alarms to make sure that the different medical conditions of the patient are above par. An alarm should be generated if his condition deteriorates. This will make the response time faster. Keywords Database · Immediate response · IoT · Smart healthcare · Automated alarms
R. Patel · N. Sinha · K. Raj · D. Prasad (B) · V. Nath Department of Electronics and Communication Engineering, Birla Institute of Technology Mesra, Ranchi, Jharkhand, India e-mail: [email protected] R. Patel e-mail: [email protected] N. Sinha e-mail: [email protected] K. Raj e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 V. Nath and J. K. Mandal (eds.), Nanoelectronics, Circuits and Communication Systems, Lecture Notes in Electrical Engineering 642, https://doi.org/10.1007/978-981-15-2854-5_15
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1 Introduction A system by which a number of objects can sense, communicate and share information over private or public networks. These interlinked objects collect and share the data/information at regular intervals, analyse and initiate required action—providing an intelligent network for analysing, planning and decision making is known as Internet of Things. The aim of a smart healthcare system is to add an IoT framework that gives secure health awareness checking. Bhoomika B. K. et al. has focussed on the patients who stay at home during their post-operational days where checking is usually done via overseers or medical caretaker but an efficient and ceaseless observation may not be accomplished by this system. So, an attempt at creating a keen health awareness framework has been made by her, where regular checking of the patient is accomplished. The main aim of our system is to provide immediate care to patients. This paper proposes to achieve this objective by sensing the health parameters of a patient by a wireless sensor network (interacting with smart phone through Bluetooth module in the microcontroller nRF52832). Wireless sensor network is used to refer to a group of dedicated sensors dispersed spatially for monitoring and recording the physical conditions of the environment and organising the collected data at a central location. In this paper, the central location will send the data to the database of hospitals via the internet to provide hospitals with immediate awareness. The sensors then are used for transmitting data to a smart phone, which will be stored in the cloud server via the internet using CoAP (Constrained Application Protocol). Doctor and nursing staff will use CoAP as well as SOAP (Simple Object Access Protocol) to view the details of the patients as well as to feed the information regarding availability of ambulances, doctors and empty beds on the database to ensure the patients are provided with immediate care, by comparing the data in the database.
2 Related Work The Internet of things [1–6] has been driving the innovation in technology since its name was coined in 1985. Many IoT-based systems have been proposed in the past for a smart healthcare system or smart hospital management. There was the use of an RFID-based system for mobile object positioning in hospitals, this system was limited only to monitoring of medical devices in hospitals and nursing homes [7]. A survey was undertaken to understand the evolution of WSN towards IoT in which devices were connected to each other through WSN. The evolution of WSN networks and their roles in today’s smart healthcare system is shown in this survey [8]. Various wearable tags were combined together to form a night-care system for monitoring disabled and elderly people [9]. This system relied on the use of UHF
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RFID which estimated sleep parameters, human activity and identifies emergencies during night time. A smart healthcare system was proposed in [1]. The authors created an RFID and WSN integrated SHS which used 6LoWPANdevices. The system, here, makes use of RFID tags to tag patients and devices which needs to be carried over patient’s body that can cause discomfort in some situations, as well as RFID tags that cannot be programmed, thus limiting their use in large numbers if cost efficiency of the system is considered. An augmented module was proposed for smart environmental sensing [1]. Ambient light and temperature sensing was the main motivation behind this system. In our proposed system we have assured immediate care by employing sensors in hospitals and a GPS tracking device in the ambulance, as well as creating a database and monitoring it to make sure that the current information reaches the needful in a financially economical manner, thus, providing a cost-efficient and time-efficient solution.
3 System Overview The architecture proposes an alert, time-efficient and cost-efficient structure with the help of the components mentioned below connected in a network reflected in the form of block diagram (see Fig. 1). Wireless sensor networks are employed at both the patient’s end as well as doctor’s end and are updated on the database on the main server. The vacancy of hospitals and ambulances is known by the wireless sensors connected to the remote server, updating it on the main server (in the block diagram
Fig. 1 Block diagram representing the system architecture
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the pc server). The sensors giving analog data, it will then be signal conditioned and fed to the appropriate microcontroller. At the patient’s end, the Nordic microcontroller is employed ensuring low cost and significant Bluetooth range. While at the Hospitals end, any microcontroller which suits the Bluetooth range required by the hospitals and cost supported by them, can be employed. The microcontroller will be connected to an alarm system which will start buzzing in case the patients’ data through sensors go beyond the alarming condition state, set previously as references to compare with, in microcontrollers. The need of immediate care will be indicated and resources can then be employed. The data from microcontrollers are then sent to the PC server from where it goes to the individual user servers at every hospital’s end. The patient and doctors can both be aware of the necessary data at the same time connecting the two ends using an App having patients as well as doctors interface. The Doctors can send their availability at Hospital through PC user or put a constant check on patients while at Home through the Doctors App. Thus, the patients are informed about the nearby hospitals where they can receive appropriate and immediate care. The next section discusses the architecture and explains how the objective of this project is achieved.
4 Architectural Details 4.1 Nordic nRF52832 Microcontroller Nordic nRF52832 microcontroller is the powerful low-cost microcontroller available with built-in Bluetooth module having hardware support for Bluetooth supported by a 2.4 GHz transceiver. It consists of 512 kB flash and 64 KB RAM, two data rates of 1 and 2 Mbps, 12 bit ADC and 3 Master/Slave SPI. Its low cost and built-in Bluetooth module helps to cut down the cost supported by the patients.
4.2 Sensor Network 4.2.1
At Patient End
Sensing network (example shown in Fig. 2) consists of three sensors namely temperature sensor [10–13], heartbeat sensor, and level sensor. This sensing point of these sensors is attached to the patient’s body and the end is connected with the microcontroller. The various sensors, i.e., temperature sensor, heartbeat sensor and level sensor sense the temperature, heartbeat and saline level of the patient respectively. All these sensors work in the real-time system and send real-time parameters. Then the microcontroller sends these values to the patient’s app via Bluetooth. Heartbeat
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Fig. 2 Wireless sensor network [18]
values are displayed in the form of a graph. Saline Level is detected by the level sensor. Threshold values are already assigned to the microcontroller.
4.2.2
At Hospital End
The hospitals will also consist of wireless sensors in the operation theatre as well as patient’s beds and seats inside doctor’s cabin. The WiFi Weight sensor (as shown in Fig. 3) is a connected weighing scale that can measure weights of various load Fig. 3 WiFi smart pallet—WiFi weight sensor
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capacities and upload the measured weight periodically to the remote server whenever the weight changes by set threshold. That will indicate whether the doctor or operation theatre is occupied with patients or not. It can be operated from mains or with batteries.
4.3 Piezoelectric Buzzer For temperature sensor, heartbeat sensor and level sensor, the microcontroller compares all these real-time parameters with the threshold values and if some serious condition occurs then the alarm is generated through buzzer which is also connected to the microcontroller an “alert” message is sent to the doctor through WiFi. Otherwise, SMS is sent through Android SMS chat.
4.4 User Interface 4.4.1
Doctor’s App
Doctor will login to the Android App. He can check the patient’s health parameters through this app. Also, he can upload his timings and days during which he will be available in the hospital.
4.4.2
Patient’s App
Patient’s App will contain all of the patient’s parameters. Login Id will be created for each patient. The values sensed by the sensors will be sent to the patient’s app and would alert the urgency when present in the cloud server [14–17].
4.4.3
Admin Application
The Admin Application consists of a major operation: monitoring and updating the database. The Desktop Admin Application manages the server and the database. It registers the patients, updates information and manipulates the data.
4.5 Ambulance Tracking Device The ambulance’s availability position can also be tracked by a GPS tracker [18] as example of one given (in Fig. 4) and uploaded by the remote server (hospitals server)
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Fig. 4 Flash track HW (Hardwired GPS vehicle tracker)
to the main server for the patient’s information who can also use the ambulance in case his location is close to that of the ambulance’s location. Input range is from 8–30 V DC. Thus, it can be supported using normal batteries as well as since cost is low it can be supported by the belonging to the hospital of rural areas too.
5 Conclusion With the help of above architecture, an alert, time-efficient and cost-efficient structure can be developed available to each and every patient in need. Any lag between the patients to be operated will be thus eliminated except the travel time. Travel time lag can also be eliminated in the case where lag cannot be tolerated, i.e., in the case of urgent requirements, in which direct communication can occur between the required doctor and patient through the patient id suggesting the small relaxations or relief possible at that time. Acknowledgements We are thankful to the Department of Electronics and Communication Engineering BIT Mesra, Ranchi for providing the laboratory facilities. We are also thankful to our Vice-Chancellor, Prof. S. Konar and our Head of the Department, Dr. S. Pal for their constant inspiration and encouragement. Without their support, this research article would not have been possible.
References 1. S. Tyagi, A. Agrawal, P. Maheswari, A conceptual framework for IoT-based health care system using cloud computing, in Sixth International Conference Cloud System and Big Data Engineering, July (2016), pp. 503–507
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2. A. Archip, N. Botezatu, E. Serban, P.C. Herghelegiu, A. Zal, An IoT based system for remote patient monitoring, in Seventeenth International Carpathian Control Conference, June (2016), pp. 1–6 3. K.P. Nitha, K.U. Sreekanth, A study on health care in Internet of Things. Int. J. Recent. Innov. Trends Comput. Commun. 4, 44–47 (2016) 4. M. Hassanalieragh, A. Page, T. Soyata, G. Sharma, M. Aktas, G. Mateos, B. Kantarci, S. Andreescu, Health monitoring and management using Internet-of-Things (IoT) sensing with cloud-based processing: opportunities and challenges, in IEEE International Conference on Services Computing, August (2015), pp. 285–292 5. S.M. Raizul Islam, D. Kwak, M.D. Humaun Kabir, M. Hossain, K.-S. Kwag, The Internet of Things for health care: a comprehensive survey. IEEE Access 3, 678–708 (2015) 6. D. de donno, L. Palano, An Iot-aware architecture for smart health care system. IEEE Internet Things J. 2(6), 515–526 (2015) 7. A.A.N. Shirehjini, A. Yassine, S. Shirmohammadi, Equipment location in hospitals using RFID-based positioning system. IEEE Trans. Inf Technol. Biomed. 16(6), 1058–1069 (2012) 8. C. Occhiuzzi, C. Vallese, S. Amendola, S. Manzari, G. Marrocco, NIGHT-care: a passive RFID system for remote monitoring and control of overnight living environment. Procedia Comput. Sci. 32, 190–197 (2014) 9. D. De Donno, L. Catarinucci, L. Tarricone, RAMSES: RFID augmented module for smart environmental sensing. IEEE Trans. Instrum. Meas. 63(7), 1701–1708 (2014) 10. R. Paradiso, G. Loriga, N. Taccini, A wearable health care system based on knitted integrated sensors. IEEE Trans. Inf. Technol. Biomed. 9(3), 337344 (2005) 11. A. Pandey, V. Nath, A CMOS temperature sensor and auto-zeroing circuit with inaccuracy of -1/+ 0.7 °C between -30 °C to 150 °C. Microsyst. Technol. 23(3), 4211–4219 (2016). Springer, Berlin, Heidelberg 12. S. Chakraborty, A. Pandey, V. Nath, Ultra high gain CMOS op-amp design using self-cascoding and positive feedback. Microsyst. Technol. 23(3), 541–552 (2017). Springer, Berlin, Heidelberg 13. D. Prasad, V. Nath, An ultra-low power high-performance CMOS temperature sensor with an inaccuracy of -0.3oC/ +0.1oC for aerospace applications. Microsyst. Technol. 24(3), 1553– 1563 (2018). Springer, Berlin, Heidelberg 14. C. Occhiuzzi, C. Valleseb, S. Amendolab, S. Manzarib, G. Marroccob, NIGHT-care: a passive RFID system for remote monitoring and control of overnight living environment, in Fifth International Conference on Ambient Systems, Networks and Technologies, June, vol. 32 (2014), pp. 190–197 15. A. Benharref, M.A. Serhani, Novel cloud and SOA-based framework for E-health monitoring using wireless biosensors. IEEE J. Biomed. Health Inform. 18(1), 46–55 (2014) 16. M. Li, S. Yu, Y. Zheng, K. Ren, W. Lou, Scalable and secure sharing of personal health records in on cloud computing using attribute based encryption. IEEE Trans. Parallel Distrib. Syst. 24(1), 131–143 (2013) 17. Y. Mao, Y. Chen, G. Hackmann, M. Chen, C. Lu, M. Kollef, T.C. Bailey, Medical data mining for early deterioration warning in general hospital wards, in IEEE 11th International Conference on Data Mining Workshops (ICDMW), December (2011), pp. 1042–1049 18. Y. Chen, Q. Zhao, On the lifetime of wireless sensor networks. IEEE Commun. Lett. 9(11), 976–978 (2005)
An Area-Optimized Architecture for LiCi Cipher Nigar Ayesha, Pulkit Singh and Bibhudendra Acharya
Abstract With the rise in ubiquitous computing in resource-constrained devices, there is the huge emphasis being laid on minimal area consumption, power utilization, and optimum cost without compromising on the security objectives. Selecting lower area overhead as the target, two architectures for LiCi cipher are proposed, which primarily reduce the LUTs, slices utilized. S-boxes are one of the chief reasons for the increase in area and cost due to the memory requirements. In this chapter, we reduce the number of S-boxes, thereby reducing the overall area footprint. Based on the proposed architecture, power consumption is minimized. Keywords LiCi cipher · Lightweight cryptography · Low-area · Low-power
1 Introduction The advancement of pervasive computing has immensely enhanced the requirements for end-to-end security. With the technical advancements in IoT (Internet of Things), the potential threats due to data leakage have intensified. In recent times, IoT has become the ultimate bridge between the physical world and the virtual world in many occupations. Such massive networks accompany the problems of data storage and processing, which in turn necessitates the need to strengthen the network from security breaches. Any loss of confidential data can have devastating effects along with newer challenges to deal with the outcomes [1]. With the increasing device interaction, vulnerability due to security loopholes is unavoidable in many cases unless preventive measures are taken. As a result, with evolving technologies, equivalent N. Ayesha · P. Singh · B. Acharya (B) Department of Electronics and Communication Engineering, National Institute of Technology Raipur, Raipur, India e-mail: [email protected] N. Ayesha e-mail: [email protected] P. Singh e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 V. Nath and J. K. Mandal (eds.), Nanoelectronics, Circuits and Communication Systems, Lecture Notes in Electrical Engineering 642, https://doi.org/10.1007/978-981-15-2854-5_16
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security has become essential for an application to be effective in terms of both application and security. The essential cryptographic attributes expected are authenticity, integrity, confidentiality and non-repudiation [2]. However, the traditional cryptographic services, which are ideal for desktop or server/client environments, are not realistic solutions when other constraints are to be considered. Although excellent security is provided by block ciphers like AES [3], it is not the best option when moderate security is adequate. When in area, power utilization with moderate security is the selection criteria for an application, selection of AES for encryption might enhance the production costs. These have formed the basis for an increased demand for a sub-branch of cryptography, namely lightweight cryptography which is tailored to meet the security requirements with an optimum level of security [4]. This domain is especially significant when the devices in question are resourceconstrained like RFID tags, sensor nodes in IoT, health care services, applicationspecific embedded systems, distributed control systems, etc. [5]. Due to the limitations, suitable tradeoffs are to be chosen from area utilization, power consumption, processing speed, operating costs, and security strength. The standards for an application meeting government services, where the focus is on high security with no bars on cost, a contrast to an application dealing with faster response like airbrakes with moderate security requirements [6]. These criteria ultimately influence the hardware and software implementations in terms of the number of rounds, parallel or serial architectures, key size, RAM and ROM size, register size, latency, encryption standards, and complexity [7]. Among the popular block cipher, cryptographic structures are SubstitutionPermutation Networks and Feistel networks. SPN network primarily aims to impart confusion as well as diffusion. Confusion results in complicating the connection between the plaintext and the key, while diffusion targets achieving a great variation in the ciphertext when a minor modification is made in the plaintext. For confusion, S-boxes are utilized, and diffusion is attained through permuting the data using Pbox. Among the popular SPN networks are AES [3], PRESENT [8], KLEIN [9], LED [10], mCrypton [11] etc. The other variant consists of Feistel networks, which operate on half of the data per round and typically require a considerable number of rounds to impart adequate security. As a result, the number of rounds increases compared to the SPN networks. However, due to the repetitive structure, the implementation costs are greatly reduced when decryption is a segment of the application. Among the widely used Feistel networks are CLEFIA [12], SIMON [13], PICCOLO [14], TWINE [15], SPECK [13], XTEA [16], etc. When the optimization in area is the aim of the desired application, round-based structures are ideal for hardware optimization. However, tradeoffs exist in area utilization, power with an enhanced latency, reduced throughput. S-boxes are pivotal in determining the strength of the cipher [17]. They have additional properties of introducing non-linearity, algebraic complexity, and the advanced avalanche effect. LUT-based S-boxes are crucial factors in determining the overall area footprint as their number is proportionate to the LUTs occupied [18].
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FPGAs have gained interest in recent times due to their reconfigurable nature and a short time to market, which facilitates modifications in design. On the other hand, ASICs are suitable only for established applications, where the user fully customizes the specifications. Both ASICs and FPGAs have similar logic functions and hardware description languages. FPGA provides the opportunity for mass production to suit various applications. In an FPGA structure, the conventional metrics in evaluating area utilization are the slice count, flip-flops and the LUTs utilized [19]. Keeping the area constraints as the chief objective of the design, we attempt to compare the improvements in LUTs, slices for modifications in the number of S-boxes utilized. The popular area optimization techniques were studied from the FPGA implementations of the PRESENT cipher, which were effective in enhancing the area performance of the previous designs [20].
1.1 Contributions i. Reducing the number of S-boxes used for encrypting the plaintext from eight to one. Total, three S-boxes (two S-boxes for the key). ii. Using two S-boxes, which are common to the plaintext as well as key scheduling, thereby requiring only two on the whole.
1.2 Framework of This Chapter This chapter is organized as follows. Section 2 deals with the details of the LiCi cipher [21], Sect. 3 elaborates on the proposed architecture and the data flow. Section 4 provides details of the hardware specifications. Section 5 presents the performance evaluation and Sect. 6 is the conclusion of the work.
2 Algorithm Overview LiCi [21] is a recently introduced balanced Feistel structure, which focuses on both hardware and software implementations with minimal memory and power requirements. It is composed of 64-bits plaintext, 128-bits key and a ciphertext of 64-bits. From the 64-bits of plaintext, the 32-bits from the MSB are chosen to be passed through the S-box, which is the same as the one used in PRESENT cipher [5]. The S-box of PRESENT is effective against attacks and being nibble based, it has reduced memory constraints compared to the byte-oriented inputs in the earlier designs [22]. The substituted output is XORed with the 32-bits from LSB of the plaintext and the 32 LSB bits of the round key.
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The output is circularly shifted to the left by three and then XORed with the substituted bits of the plaintext and the 32 MSB bits of the round key. This output is circularly shifted to the right by seven. The corresponding operations are followed for 31 rounds to obtain the resultant ciphertext. S-box for LiCi cipher X
0123456789ABCDEF
S [X]
3FE10A58C4B2976D
Key Scheduling The key scheduling is inspired from PRESENT cipher, which is quite robust and resistant to various attacks. From the 128-bits of the key, the 64-bits from the LSB are utilized in the design. For a given key, K = k127 k126 k125 . . . k2 k1 k0 K1 = k31 k30 k29 . . . k2 k1 k0 K2 = k63 k62 k61 . . . k34 k33 k32 The key is updated as 1. K ≪ 13 (Key is circularly shifted to the left by 13-bits) 2. S[k3 k2 k1 k0 ] → [k3 k2 k1 k0 ] 3. S[k7 k6 k5 k4 ] → [k3 k2 k1 k0 ] 4. [k63 k62 k61 k60 k59 ]⊕RCi → [k63 k62 k61 k60 k59 ] Here RCi is the value of the round counter, which is XORed with the selected 5 bits from the Key Register K (k59 –k63 ).
3 Proposed Architectures 3.1 Round-Based Architecture This architecture is inspired from the base architecture of PRESENT [22] which is among the earliest lightweight ciphers. As repetitive operations are performed in each of the 31 rounds, the output of the previous stage is fed as the input to the next stage for the remaining rounds. Figure 1 demonstrates the dataflow of the proposed architecture utilizing lower resources.
An Area-Optimized Architecture for LiCi Cipher Fig. 1 Proposed round-based architecture for LiCi cipher
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ct(msb)||ct(lsb 64
pt
64 0
1
0
1
128 128 bit Reg
load
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128
64 bit Reg
Shf 35 kW)
C-0
81
3
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C-2
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C-0, C-1, C-2, C-3, C-4 shows the five classes in which the load is classified. These five groups represent very low load, low load, normal load, high load, and very high load groups of loads respectively. This means all the loads in the different nodes of NIT Patna campus for 24 h. between 0 and 5 kW will be grouped under C-0. Similarly, the other load groups are formed and their corresponding range of load values is shown in the table. For random forest method similar result is presented in Table 4. With a single data sample to train from, this method forms 2000 number of estimators while bootstrapping, gini index is used for tracking and minimizing the error rate at each step for tree formation. De-correlation further brings down the variance in the system. Table 2
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Table 4 Confusion matrix for Random forest method of classification Classes
C-0
C-1
C-2
C-3
C-0
83
0
0
0
C-4 0
C-1
4
4
1
0
0
C-2
0
1
0
1
0
C-3
0
3
11
28
0
C-4
0
0
0
3
0
presents a confusion matrix which gives the classification details for random forest model. This method gives 83% precise prediction of classes and the misplaced classes are also grouped in the immediate adjoining class to the actual class of prediction. The outcome of SVC classification can be constructed as per our requirement. The correction prediction of model depends on the value of C and the value of ‘gamma’ as shown in Table 5. Two corresponding confusion matrices in presented in Tables 5 and 6 for two distinct value of C and ‘gamma’. It can be seen from the table that for large value of C (refer Table 5), which allows for more number of observation to be in soft margin, the prediction is spread over all columns but for smaller value of C (refer Table 6), which do not allow more number of observations to be in soft margin, the prediction is concentrated. We even have two such classification classes where nothing could be classified due the strict margin as compared to classification represented in Table 5. Therefore, it depends Table 5 SVC confusion matrix for C = 1000 and gamma = 0.001 Confusion matrix for C = 1000 and gamma = 0.001 Classes
C-0
C-1
C-2
C-0
83
0
0
C-3 0
C-4 0
C-1
4
4
1
0
0
C-2
0
1
0
1
0
C-3
0
2
8
32
0
C-4
0
0
0
3
0
Table 6 SVC confusion matrix for C = 0.1 and gamma = 0.01 Confusion matrix for C = 0.1 and gamma = 0.01 Classes
C-0
C-1
C-2
C-0
83
0
0
C-3 0
C-4 0
C-1
7
0
0
2
0
C-2
1
0
0
1
0
C-3
1
0
0
41
0
C-4
0
0
0
3
0
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Table 7 Comparison of the proposed method with other established methods Classifier method
Accuracy (%) Proposed method
Ref [18]
Ref [17]
K =1
84
70.18
–
K =5
86
73.17
–
SVM
89.21
73.34
87.65
Random forest
83
71.74
87.99
KNN
on the requirement that which kind of classification we need, to set the value of C and ‘gamma’ for the SVC model. Hence, we see from the above discussion that the load forecasting or load prediction using load classification has high value of accuracy, more than 83% for all the cases and even more the 89% for some cases using KNN and SVC method. For the remaining 11–17% loads the mismatch in predication was different by a single class or group, this means, a load is grouped to its immediate higher or lower group than the actual group. Therefore, it can be concluded that the proposed load classification-based load forecasting technique results into good accuracy.
6 Comparison with Other Methods There are similar works in the area of load forecasting 18] as well as other area like medical applications [18]. The following table shows the comparison of accuracy of the proposed method with these above-mentioned methods (Table 7). It can be seen from the above table that the proposed method shows better accuracy compared to the other two established methods.
7 Conclusion This paper presented a load forecasting methodology using load classification. The proposed method uses the large amount of smart meter data to classify the loads first then these load classes are used to predict next day load pattern. The proposed methods work with good accuracy even sin case of missing data due to communication failure which is a very common phenomenon in a smart meter-based power system environment. Three most efficient and commonly used classification algorithms were investigated and results for the same are evaluated for NIT Patna smart grid system. The evaluation results show that the proposed method has high degree of accuracy in predicting day-ahead load profile.
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Acknowledgements This work is supported by the Science and Engineering Research Board, Dept of Science and Technology (DST), Government of India, grants number ECR/2017/001027.
References 1. C. Chen, S. Phan, Data analytics: from smart meters to smart decisions. Electr. Eng. Comput. Sci. Newsl., Expon. 7 (2018) 2. P.D. Diamantoulakis, V.M. Kapinas, G.K. Karagiannidis, Big data analytics for dynamic energy management in smart grids. Big Data Res. 2(3), 94–101 (2015) 3. G. Grigoras, O. Ivanov, M. Gavrilas, Customer classification and load profiling using data from Smart Meters, in 12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL) (Belgrade, 2014), pp. 73–78 4. P. Carrollpaula, T. Murphy, M. Hanley, D. Dempsey, J. Dunne, Household classification using smart meter data. J. Off. Stat. 34(1), 1–25 (2018) 5. J.L. Viegas, S.M. Vieira, R. Melício, V.M.F. Mendes, J.M.C. Sousa, Classification of new electricity customers based on surveys and smart metering data. Energy, pp. 804–817 107 (2016) 6. K. Hopf, M. Sodenkamp, I. Kozlovkiy et al., Feature extraction and filtering for household classification based on smart electricity meter data. Comput. Sci. Res. Dev. 31(3), 141–148 (2016) 7. J. Jacques, C. Preda, Functional data clustering: a survey. Adv. Data Anal. Classif. 8, 231–255 (2014) 8. Y. Wang, Q. Chen, C. Kang, M. Zhang, K. Wang, Y. Zhao, Load profiling and its application to demand response: a review. Tsinghua Sci. Technol. 20, 117–129 (2015) 9. G. Chicco, Customer behaviour and data analytics, in 2016 International Conference and Exposition on Electrical and Power Engineering (EPE) (Iasi, 2016), pp. 771–779 10. M. Chaouch, Clustering-based improvement of nonparametric functional time series forecasting: application to intra-day household-level load curves. IEEE Trans. Smart Grid 5, 411–419 (2014) 11. F.L. Quilumba, W.J. Lee, H. Huang, D.Y. Wang, R.L. Szabados, Using smart meter data to improve the accuracy of intraday load forecasting considering customer behavior similarities. IEEE Trans. Smart Grid 6, 911–918 (2015) 12. Y. Wang, Q. Chen, T. Hong, C. Kang, Review of smart meter data analytics: applications, methodologies, and challenges. IEEE Trans. Smart Grid, https://doi.org/10.1109/tsg.2018. 2818167 13. B. Auder, J. Cugliari, Y. Goude, J.M. Poggi, Scalable Clustering of Individual Electrical Curves for Profiling and Bottom-Up Forecasting. Energies 11(7), 1–22 (2018) 14. Y. Yan, Y. Qian, H. Sharif, D. Tipper, A survey on smart grid communication infrastructures: motivations, requirements and challenges. IEEE Commun. Surv. Tutor. 15, 5–20 (2013) 15. M. Azaza, F. Wallin, Evaluation of classification methodologies and features selection from smart meter data. Energy Proceedia 142, 2250–2256 (2017) 16. G. James, D. Witten, T. Hastie, R. Tibshirani, An Introduction to Statistical Learning, Springer Texts in Statistics (2013) 17. M.A. Hambali, M.A. Oladunjoye, Electric Power Load Forecast Using Decision Tree Algorithms. Comput., Inf. Syst., Dev. Inform. Allied Res. J. 7(4), 29–42 (2016) 18. J.P. Kandhasamy, S. Balamurali, Performance analysis of classifier models to predict diabetes mellitus. Procedia Comput. Sci. 47, 45–51 (2015)
Internet of Things (IoT) Based Green Logistics Operations for Sustainable Development in the Indian Context Shailendra Kumar Singh and Supriyo Roy
Abstract The article explores how the implementation of IoT can help the logistics sector make it more sustainable and efficient. Logistics involves the transportation, storage, and handling of products during their journey from raw material source, through the production process to the final point sale or consumption and the associated reverse logistics. Since logistics is ubiquitous, involves multiple players, machinery and is primarily propelled by fossil fuels, it provides a huge scope for the implementation of IoT-enabled sustainability measures. The article begins with the literature review and concludes with suggestions and future research directions. The critical analysis of the India logistics sector illustrates that it is characterized by numerous inefficiencies and causes huge economic, environmental, and social burdens. The deployment of IoT in the logistics sector would not only contribute to efficiency but also sustainability in logistics operations. The article could provide a different perspective to the policy-makers, managers, and academicians engaged in the logistics sector. Further, the analysis of the complex interplay between sustainability dimensions and logistics, and deployment of IoT provides a fertile area for future research. Keywords Internet of things (IoT) · Green logistic (GL) · Sustainable development (SD) · Logistics and supply chain · RFID
1 Introduction Initially, there was little or no concern about environmental issues. It was thought that nature has unlimited capacity to assimilate and replenish resources. Over the past two decades, against the backdrop of increasing customer expectations, stakeholder pressure, NGOs, (social) media, stringent environmental and social regulations, logistics sector has come under tremendous pressure to minimize the environmental and social impact of logistics operations [1, 2]. S. K. Singh (B) · S. Roy Department of Management, BIT Mesra, Ranchi, Jharkhand, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 V. Nath and J. K. Mandal (eds.), Nanoelectronics, Circuits and Communication Systems, Lecture Notes in Electrical Engineering 642, https://doi.org/10.1007/978-981-15-2854-5_27
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Logistics is a subset of the supply chain that is responsible for the movement from raw material sources into the facility, through the production process, and then out to downstream and finally the reverse flow [3]. Logistics functions are very critical and relevant across agriculture, manufacturing and service sectors. On the one hand, logistics has immensely contributed to growth and development, but on the other hand, its key functions are a major source of environmental and social problems [4]. Logistics operations are largely dependent upon fossil fuels resulting into emissions and ultimately pose a threat to the environment and society and the economy itself. The growth of logistics functions has generated unwanted environmental and social impacts such as energy and resource depletion, air pollution and waste generation, health, accidents, etc. [5]. Environmental concern is considered to be one of the greatest challenges that the global community faces today. Having reviewed numerous scientific studies, the Intergovernmental Panel on Climate Change (IPCC, 2013) concluded that human activities are the dominant cause of global warming. Since industrialization, there has been a considerable increase in the levels of GHG emission generated by human activities (known as “anthropogenic” GHGs), and as a result, their concentration in the atmosphere has also increased resulting global warming. A 2–3 °C increase in temperature is often cited as a threshold limit, beyond which it may be impossible to avoid dangerous consequences of the global climate system [6]. In the given scenario, sustainable development (SD) will help to ensure the lasting existence of people, planet, and profit (3Ps). The concept of SD was launched by the Commission on Environment and Development (WCED) as a ‘global objective’ to guide policies for balanced growth taking into account the 3Ps. ‘Our common future’, the report published in 1987 by the WCED defines SD as the development that “meets the needs of the present without compromising the ability of future generations to meet their own needs” [7]. Thus, SD focuses on the intergenerational equity and rational use of natural resources for the lasting existence of human being and the planet earth [8]. As a result, scholars studied logistics as an environmental and social issue, from the development of environmental logistics strategies [9] to the improvement of fuel efficiency [10–12] and emission reduction [13] including research about vehicular safety, rail, and airline industries [14]. Logistics operations generate numerous externalities i.e. pollution, traffic congestion, accidents, greenhouse gas (GHG) emissions and energy consumption that threaten environmental and social health. Traditionally, the key objective of logistics has been to organize it in a way that maximizes monetary benefits. The calculation of profit, however, has included only the economic costs that the business directly incurs. The larger environmental and social costs are traditionally excluded from the balance sheet [3]. But now firms are under constant observation for business operations thanks to ever-increasing pressure from customers, governments, civil society (social) media and technologies. For the last two decades, companies have come under immense pressure to make their operations environmentally friendly. Consequently, organizations have begun to respond and reconfigure their traditional business model in view of the growing need for sustainable practices. Incorporating environmental and social dimensions
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into logistics is termed as ‘Green Logistics’ (GL), and is perceived to be the solution to achieve SD. GL deals with procurement, production, and distribution of goods and services in a sustainable way, with equitable consideration of economic, environmental and social aspects [15].
2 Fundamentals 2.1 Green Logistics The Council of Supply Chain Management Professionals (www.cscmp.org, 2009) defines logistics management as “the part of supply chain management that plans, creates and monitors the efficient, cost-effective flow and storage of goods, semifinished items and manufactured products as well as related information between the point of origin and the point of consumption in order to meet customers’ requirements.” Logistics involves the management of transportation, storage, and movement of products as they move from different sources, through the production process to their final point of sale or consumption. Since logistics is propelled by fossil fuel, it has a direct impact on the economy, environment, and society [16]. The changing business environment has forced the logistics sector to shift from the traditional business model and adopt a model that takes economic, social and environmental dimensions of the business operations into account in an equitable manner. This transition in the logistics sector resulted in a new concept that is termed as Green Logistics (GL). Simply put, GL deals with procuring, producing and distributing goods in a sustainable manner, taking economic, environmental and social factors into account [15, 17]. Basically, the concept of GL is aligned with the concept of SD. SD requires a completely different approach towards nature that has been seen, since long, as an evil force to be conquered for generating material prosperity. Secondly, it advocates the concept of intergenerational equity (Fig. 1).
2.2 Internet of Things IoT has emerged as a boon to business organizations. It is one of the burning topics in both industry and academia. However, it is still at a nascent stage and lacks a standard definition [19]. Identifying, sensing, networking, and computation (Fig. 2) are key processes of IoT. It helps the integration of innovations and services that provide personalized users’ interaction with various ‘things’. Simply put, IoT is a giant network of connected ‘things’ including people [20]. The relationship could be between people–people, people–things, and things–things. This interconnectedness has potential not only transform the existing business models for economic benefit but also could contribute enormously to sustainability [21].
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Fig. 1 Three dimensions of GL [18]
Fig. 2 Elements of IoT [22]
The applications of IoT in various domains has transformed the existing ways of doing things and yielded positive results in almost all respect. LSPs using IoT enjoy greater visibility into operations and unlock new business value. This visibility, in turn, helps transform the way LPSs make decisions with regard to inventory, storage, route planning, last mile delivery, operational health, and safety, etc. IoT has the potential to dramatically change the way logistics and supply chain activities are performed [23]. In this article, the focus is on how IoT could help the logistics sector to make it sustainable and more efficient, with special reference to Indian. In the subsequent sections, we present a review of related studies and the review of Indian logistics scenario. Then, the following section deliberates upon the application of IoT in the Indian logistics sector. Thereafter, we demonstrate how IoT-enabled technologies could be used in logistics to make it greener. Then the final section suggests future research and concludes the paper.
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3 Related Studies A number of studies have been conducted recently relating to IoT [24]. The application of IoT no more confined to business. Now it is extensively used in areas such as health, traffic, and transport, security, exploration, weather prediction, etc. These applications of IoT are not only economically viable but also contribute extensively to effectiveness, efficiency and more importantly SD. Govindan et al. [25] highlighted importance of big data analytics and its applications in logistics and supply chain management. In the same line, Kaur and Singh [26] provided a model for a sustainable supply chain using big data. The study of Lui et al. [27] proposed a model for sustainable logistics that is based on a bottom-up approach and is driven by real-time data. Katal et al. [28] discussed various issues relating to big data such as standardization, tools, features, sources, and best practices. Several studies have been conducted with regard to IoT, its challenges, elements, domains of application. Barreto et al. [29] discussed some key issues that organizations face in the context of logistics 4.0. Thibaud et al. [30] conducted a comprehensive review of IoT applications in high-risk industries such as healthcare, supply chain, mining, and energy sector, smart transportation, and related challenges. Luthra et al. [31] studied major challenges in implementing IoT systems in India. Prasad and Kumar [32] discussed the potential energy efficiency reliability barriers with examples and some suggested remedies for the deployment of IoT. Bibri [21] reviewed and synthesized the relevant literature on the status quo of IoT and its application for sustainability. Abdel-Basset et al. [33] proposed a system that can handle and manage issues of traditional supply chain and highlighted some open research issues. The study of Reyna [34] deliberated upon challenges in blockchain, its integration with IoT and surveyed the most relevant work to analyze how blockchain could enhance IoT application. The study of Gregor et al. [35] focused on the Smart Logistic System based on latest technologies and various IoT concepts, expounded the term Smart product. Yasanur [36] studied highlighted the benefits of the logistics digitization and its impact on the sustainability performance of the logistics system. Nowicka [37] proposed smart city logistics using Cloud Computing Model to handle continuously changing requirements with regard to traffic, congestion, smooth transportation. His proposed solution is interoperable IT system usage based on the cloud computing model. IoT is a budding field of study and concept is still evolving. Therefore, the literature on its application in relation to sustainability is still limited [21].
4 Indian Logistics Scenario India is the seventh largest country in the world in terms of area and the second in terms of population with about 1.3 billion people. It has shown consistent economic growth of 7% growth in the medium [38]. As per various estimates, the country’s
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economic growth would be 7–10% per annum over the next 20 years [39]. Since the demand for logistics and economic growth are positively correlated, efficient logistics will not only ensure this sustained economic growth but also be decisive to make this growth sustainable. Against this backdrop, the demand for logistics is expected to increase manifold in the future. Indian logistics sector is not efficient when compared with others countries. As per various estimates, logistic cost accounts for 14% of GDP in the country, the highest among comparable developing countries [39]. Efficient logistics infrastructure and balanced modal share are critical to support the economic growth of India. Nonetheless, to sustain this growth, the government and private players would have to make decisions that ensure the social and environmental and economic justification. Logistics operations in India are very complex and infrastructure is still evolving. In India, road is the main mode of transportation accounting for about 61% of total freight movement, railways, on the other hand, shares only 30% and remaining by air, pipelines, and waterways [40]. The Indian logistics sector is characterized by a large number of small, fragmented and unorganized players with low entry barrier. Organized players account for only 10%, whereas 74% of LSPs are unorganized with limited resources [40]. Even organized logistics players do not provide multimodal services and other value-added services. The long duration of ownership of vehicles and overloading is a common phenomenon on Indian roads, resulting in road damage, traffic, emission, accidents, etc. The poor condition of roads and their connectivity result into lower returns and increased operating costs. The national highways account for 2% of the total road network in the country, service serving 40% of the total traffic. Of the total road network, only 10% are motorable. Further, it is estimated that transportation costs account for about 40% of the total logistics cost in India [41]. The total share of railways declines year on year. Rail freight lacks reliability and trackability. As far as air freight is concerned, it is characterized by the absence of integrated cargo infrastructure and inadequate rail and road connectivity. Indian ports are overloaded and characterized by high turnaround time, lack of coordination between ports and the Customs authorities, poor infrastructure, and outdated equipments. As per various estimates, warehousing cost in the country is around 20–25%. The warehousing sector in India faces many challenges at various fronts. Warehousing constitutes around 20–25% of total logistics costs. More than 80% warehouses are small in size and are not occupied with facilities to accommodate the varying nature of goods [42]. These warehouses are in the hand of small entrepreneurs who lack investment capacity for maintenance and expansion. Mostly warehouses are located at the outskirt of cities. Large warehousing is under control of government agencies with the focus on food grain storage. In India, the structure of warehousing subsector of logistics is not sustainable. Despite these challenges, the country’s logistics sector has been growing for last many years. However, inadequate logistics infrastructure costs approximately 4.3% of GDP per year [43]. Further, the overview of the Indian logistics sector indicates that there is a huge scope for improvement. The current state of logistics not only costs in monetary terms but is a big challenge to SD. A number of green initiatives
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are already underway. However, the application of IoT can play a very decisive role by addressing many challenges faced by the sector. The following section focuses on the application of IoT in logistics.
5 IoT in INDIA Having discussed the key challenges of the logistics sector in India, now the question arises whether the Indian logistics sector is ready for the IoT revolution? A survey conducted by KPMG (2015) in India found that Indian enterprises and the suppliers of IoT solutions are still evolving and will take time to be mature. The study observed that Indian businesses are not yet ready to adopt IoT in their business processes. As far as consumer IoT adoption is concerned, it would be slow in India due to challenges, namely internet availability, bandwidth, reliability, data security, cost of cost of IoT-enabled systems and device, lack of vendor activity, and overall infrastructure. Internet connectivity is still a major issue in the country. Further, global suppliers still think that the Indian consumer market is not are not ready for sophisticated and advanced products. This trend is very much visible in the IoT space that lacks vendor activity. Other than the internet, the supporting infrastructure is still not mature for IoT. To deal with these challenges, the government and industry leaders are gradually gearing up for digitization and smartization. The government of India recently announced its plan to invest $15 billion in the Indian IoT market in the next five years. The Department of Electronics and Information Technology has further planned to create IoT industry with USD 15 billion by 2020 [31]. Further, it has planned to augment capacity development for IoT specific skills, and support Research and Development in IoT domains. Industry leaders are well aware that they are operating in the globalized world and do not want to left behind to their peers at the global level. Some of the areas like logistics, fleet telematics, smart cities and utility services, industrial automation, etc., can grow in India in the near future. However, IoT application in the country is still at the budding stage.
6 Application Areas of IoT Smart cities: Now IoT is an integral part of ICT and plays a critical role in addressing sustainability issues in urban areas [21]. IoT technologies are helpful in advanced traffic control systems. IoT plays a key role in monitoring the vehicular movement in metros and on highways and ensures traffic management. Additionally, the smart parking device system may help monitor parking spaces and provides drivers with automated parking advice. Sensors could monitor particulate matter in the air, collect smog information, and transmit such information to different agencies.
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Healthcare: IoT technologies are being deployed in the healthcare sector. For instance, IoT may help in clinical and home care, health logistics. The clinical and home care gap can be bridged by IoT application. Physical parameters of patients like body temperature, blood pressure, breathing, and patient activities can be monitored remotely using fitted sensors. In this way, information can be locally aggregated and sent to remote medical facilities for monitoring and further diagnosis. Another relevant application is personalized healthcare and well-being solutions. Sensors help people monitor their daily activities and adapt to a healthy lifestyle. Mining and energy industry: The mining and energy industry deploys a significant number of professionals in different locations. Safety issues, processes integration, adverse working conditions, accurate data collection are some areas where IoT are being deployed. Environmental monitoring: IoT in environmental monitoring is being used intensively such as endangered species protection, extreme weather monitoring, water safety, commercial farming, and environmental protection. Installed sensors monitor and detect every change in environment and transmit real-time data to the monitoring center for analysis and prompt action. Further, it allows access to areas, where human access might be difficult such as tsunami, volcanic areas, extreme weather conditions, etc. The applications of IoT can help communicate from such locations. Security and surveillance: Security and surveillance is key to homes, office premises, shopping centers, factory floors, parking places, and many other public places. IoT-enabled technologies are helping make safer cities, homes, and organizations. Businesses and security agencies are using IoT-based technology to monitor various aspects of security. They make smarter decisions based on real-time input data. Ambient sensors cameras are used to monitor the suspicious movement of men and machines and allow a quick response. Further, IoT solutions for security system are able to prevent loss of valuable assets as they provide greater visibility over entry and exit of a facility in real-time. Considering the versatility of IoT, it can be virtually deployed in almost any area. This provides real-time communication and better economic, social and environmental performance.
7 IoT Application in Logistics for SD IoT can work on two fronts to make logistics green. The first one involves designing energy-efficient IoT elements such as identification, sensing, communication protocols, computation, networking architectures for connecting the physical objects [22, 32]. The second is to deploy IoT-based technologies to minimize the negative impacts of logistics operations. With the advancement technologies, IoT has a great potential to enhance the logistics operations towards SD. It can help the logistics sector by developing a new business model and improving operational effectiveness and efficiency (Fig. 3).
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Fig. 3 IoT-enabled capabilities [44]
Following are some key areas of IoT application in logistics sector that can contribute to and augment economic, social and environmental efficiency. Warehouses are at the heart of logistics operations and play a crucial role in ensuring the flow of goods and provide a competitive advantage for LSPs. However, it is one of the key sources of CO2 emission [43]. A major portion of warehouse emissions is caused by heating, cooling, air conditioning, lighting and other types of equipments, and these are mainly related to warehouse size [45]. Warehousing, a subsector of logistics, in India is in a poor state, mostly owned by small players. The major bottlenecks which are found from the literature are lack of warehousing facilities [46], inadequate capacities to serve the needs, irregular supply of power, lack of technology [47], and poor transportation infrastructure. These inefficiencies lead to economic, social and environmental problems. In the warehouse, the widespread adoption of tagging will facilitate the IoT-driven smart-inventory management. Cameras attached to the gateways could detect damage by scanning pallets for anomalies. Once pallets reach the desired location, attached tags send signals to the warehouse management system (WMS) that, in turn, provides real-time visibility into inventory levels. This process checks the costly out-of-stock situation and prevents partial or frequent deliveries. During outbound delivery, an outbound gateway scans pallets to ensure right items, in the right order for delivery are going out. This way stock level is automatically updated in the WMS for accurate inventory control [48].
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IoT has a unique capability to interconnect physical world. It can help optimal assets utilization by interconnecting machines and vehicles. Further, IoT can facilitate predictive asset lifecycle management, preventive measures such as maintenance and repairs even before the damages take place. The fitted sensors allow keeping a track on the material damage which happens with time and allows to automatically decide what is the ideal time for a maintenance break. The deployment of IoT-enabled technologies can help prevent fatalities and worker health through connected devices and machines. Smart energy management devices are another domain to optimize and minimize energy consumption in the warehouse. These devices reduce energy consumption cuts overhead costs along with the carbon footprint of the warehouse. Another area of IoT is fleet and asset management. Sensors can monitor idle and use status of vehicles and then send this data for analysis. Health and safety is another key area for IoT application preventing potential collisions, accident and alerting drivers and the central system. Smart cameras installed in the vehicle can keep an eye on driver fatigue by tracking eye movement, pupil size, and blink frequency. For instance, Caterpillar, a construction and mining equipment manufacturer, is using this technology successfully to monitor sleepy truck drivers and for preventing potential accidents. Satisfactory last mile delivery has been a key challenge for LSPs. Timely and costeffective delivery of packets is valued by the customer. LSPs have to find innovative solutions at this important stage in the supply chain. IoT is one of the primary technology solutions which are employed to ensure visibility, making the last mile delivery efficient, delivering better monitoring practices for in-transit cargo. IoT can connect the LSP to the end customer and provides a greater control over in the last mile delivery. Cloud-based GPS & RFID systems could provide logisticians key location statistics which were otherwise difficult to gather. This makes the whole process of supply chain agile, deliver a greater control to the supply chain managers to predict the time of delivery and schedule accordingly. The accurate information about the location and tentative time of delivery also helps in rerouting that can help better utilization of assets. Extensive coverage of the internet, the proliferation of smart devices and home products can help flexible delivery, which in turn, increases customer delight. Tagged packets offer greater visibility during their journey. With IoT-enabled solutions, the customer can trace the exact location of his parcel, expected arrival time and can change the delivery address as required. IoT can offer solutions to automatic replenishment and anticipatory shipping. For instance, installed sensors monitor inventory levels. When a retailer has low stock, an order is automatically placed to the nearest distribution center. This reduces lead time and avoids stock-outs. Such an algorithm has been patented by Amazon. It predicts customer’s purchase before he actually executes it. This is a good example of anticipatory shipping.
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8 Conclusion and Future Scope India’s economy has seen unprecedented growth during the last decade. This necessitates huge demand for logistics services. It is evident from the discussion that the logistics sector needs to adjust and embrace innovations of the information era. Especially, the logistics sector has to adapt and effectively deploy latest technologies as the sector is strongly dependent on information sharing. The business models that have worked well in the past may no longer work tomorrow. In the Indian context, the scope and role of LSPs have changed remarkably over the years. However, the Indian logistics sector is characterized by a number of inefficiencies. These inefficiencies in logistics operations pose a big challenge to SD. Increased competition, changing regulations, customers’ expectation and pressure for sustainable behavior require an immediate response and complete change in business model. Such change will result in new forms of value creation for the customer and reduces significant economic and environmental burdens. In this context, IoT has the potential to provide economic value to the firm along with social and environmental benefits. Further, empirical and qualitative research may be conducted on the application of IoT and big data analytics into a specific logistics function that could drive the business towards data transparency and sustainable solutions. To conclude, the application of IoT in logistics provides plenty of opportunities to march on the path of SD. Increased digitization, increasing smartphone user-base and related infrastructural supports by the government would provide opportunities to the firms and users to embrace IoT for SD.
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A Scientific Exploration of the Time Theory in Hindustani Ragas Saurabh Sarkar, Sandeep Singh Solanki and Soubhik Chakraborty
Abstract Indian classical music is quite a diverse area having two forms: Hindustani and Carnatic where raga is the nucleus. Time of rendition of a raga is given due importance in Hindustani classical music. This work is an attempt for investigating time domain as well as frequency domain features of different ragas which are rendered during day and night. As there are views both for and against the time theory of ragas, we seek a scientific exploration in order to reveal some truths. Keywords Hindustani raga · Rendition time · Audio features · Audio signal processing
1 Introduction Indian Classical music has unique feature for each and every raga and several musicians and musicologists do follow the time theory. The idea of rendering a raga at a particular time is credited to Pt. Vishnu Narayan Bhatkhande and it is quite interesting to know that the time concept is almost a century old. However, there are also views against which demands a scientific investigation as ours. The present work employs feature extraction of Hindustani ragas where in both time domain as well as frequency domain audio features have been considered for analysis. Feature extraction is considered as an important task in music information retrieval (MIR). It is an essential processing step performed in several popular areas of speech/audio processing such as speech recognition, speaker identification, audio event detection, multimodal analysis of the movie content, etc. Relevant information S. Sarkar (B) · S. S. Solanki Department of ECE, Birla Institute of Technology, Mesra, Ranchi, India e-mail: [email protected] S. S. Solanki e-mail: [email protected] S. Chakraborty Department of Mathematics, Birla Institute of Technology, Mesra, Ranchi, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 V. Nath and J. K. Mandal (eds.), Nanoelectronics, Circuits and Communication Systems, Lecture Notes in Electrical Engineering 642, https://doi.org/10.1007/978-981-15-2854-5_28
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is extracted in the form of feature set from the raw signal and thereafter machine learning tasks are performed. An extensive study was carried out on Hindustani ragas with special focus on day and night ragas. All features of these ragas can be found in [1]. There are three views behind time theory in Hindustani ragas. First view says that the time theory must be maintained strictly. The second view says that the time theory is illogical and need not be followed strictly. There is a third theory which says that every raga generates a particular emotion which can be connected to some particular time of the day, e.g. raga bhairav generates a feeling of waking up from sleep which may be related to morning, however it is not forcing the artist to perform raga bhairav in the morning only [2]. Thus it is not fully supporting the first theory neither it is opposing it. Therefore, due to all these controversial views which are subjective in nature, we are motivated to pursue a scientific study where objective viewpoint is maintained, which is a novelty.
2 Related Work Raga, is the nucleus in both the forms of classical music and time of rendition is given due importance only in Hindustani unlike in Carnatic, which is more ornamental. Our work, here, is confined to North Indian ragas. Recent research initiatives in music computation along with music performance, tonal system, melodic and metric concepts, improvisation and notations are discussed in [3]. Reference [4] presents a comparative study on ragas rendered at two different time instants. A comparative study on recognition, classification, retrieval, etc., is presented in [5]. A survey of music information retrieval tasks has been done in [6]. References [7, 8] has applied different MIR tasks for recognition. References [9–13] discusses various other tasks related to audio signal classification as well as MIR. In our present work, we have concentrated upon day and night ragas (Fig. 1).
3 Feature Extraction Feature extraction in an integral step in any MIR task. Here, we have extracted both the time-domain as well as frequency-domain audio features. These features are as follows: A. Spectral Centroid and Spread: The spectral centroid and the spectral spread are two simple measures of spectral position and shape.
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Fig. 1 Rendition time of different Hindustani ragas [15]
B. Spectral Entropy: Spectral entropy is a measure of abrupt changes in the energy level of an audio signal, in frequency domain. C. Flatness: The flatness indicates whether the distribution is smooth or spiky, and results from the simple ratio between the geometric mean and the arithmetic mean. D. Energy: It can be defined as the quadratic mean of all the amplitude values of the audio signal. E. Zero-Crossing Rate (ZCR):
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It is an indication of the number of times the given audio signal changes its value, from negative to positive and vice versa, divided by the length of the frame. F. Spectral Skewness: It is a measure of the asymmetry of the distribution around the mean value. G. Spectral Kurtosis: It is an indication of the flatness of the energy distribution. The above mentioned audio features are elaborated in [14].
4 Methodology Recording was done at 44100 Hz sampling rate, 16 kHz mono channel with 16 bits/sample. Only the voice of the singer was taken for recording and the sound of the accompanying instrument was provided to the vocalist through an earphone. The approximate duration of each raga clip was of 3–4 min duration. Four number of ragas (both day and night) were selected for experimentation. After calculation of audio features, correlation coefficients were calculated for different feature pairs and t-test was performed so as to derive for a conclusion. The block diagram representation of the various steps involved in the work is shown in Fig. 2. The correlation coefficient ‘r’ for two different variables, say ‘x’ and ‘y’ is (x − x)(y ¯ − y¯ ) r = 2 (x − x) ¯ (y − y¯ )2 To test whether the association is merely apparent, and might have arisen by chance we use the ‘t’ test as per the following formula: √ r n−2 t= 1 − r2 Pre-processing
Framing & Windowing
Raga Input
T-test Analysis
Fig. 2 Block diagram representation
Feature Extraction
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In the above formula, the statistic ‘t’ follows Student’s t-distribution with (n−2) degrees of freedom.
5 Results Features so calculated are presented in the following tables (Tables 1 and 2) for day ragas as well as night ragas respectively. Table 3 gives the correlation coefficient between feature pair of interest. Table 1 Various features of day ragas Feature
Raga Deshkar
Todi
Vrindawani sarang
Multani
Centroid
1696.941
1896.664
1746.628
1831.997
Entropy
0.86607
0.883
0.87611
0.8762
Flatness
0.050298
0.085913
0.058811
0.062568
Kurtosis
13.5243
17.1807
14.6461
14.7012
Energy
0.020523
0.14558
0.065432
0.060644
Skewness
2.6658
3.0862
2.8348
2.8091
ZCR
2126.97
8714.909
1319.537
7015.405
Spread
432.7147
2346.951
2194.073
2236.899
Table 2 Various features of night ragas Feature
Raga Yaman
Jayjavanti
Bihag
Bageshri
Centroid
1956.485
1682.391
1716.178
1785.699
Entropy
0.88646
0.86819
0.85865
0.87405
Flatness
0.075743
0.063484
0.08287
0.070744
Kurtosis
16.96
17.7437
20.7071
20.2739
Energy
0.029
0.044159
0.025811
0.029631
Skewness
3.0333
3.1733
3.5241
3.3654
ZCR
1535.32
3602.7081
910.8012
840.9328
Spread
2293.938
2184.8934
2476.018
2214.0743
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Table 3 Correlation coefficient ‘r’ between various feature pairs (for morning raga) Feature pair
Correlation coefficient (r)
t (calculated)
Table t at 5% level
Table t at 10% level
Result
Spectral centroid and ZCR
+0.942
3.97
4.30
2.92
Significant at 10% level
Note ‘t’ values of all other pairs were found to be insignificant for day ragas and ‘t’ values for all pairs including Spectral Centroid and ZCR were found to be insignificant for night ragas
6 Conclusion Spectral centroid and ZCR are the two distinguishing statistical pairs whose correlation coefficient can be used to differentiate a morning raga and a night raga. This is because among all the feature pairs that we have considered for a morning raga; the correlation coefficient was found to be significant for the above two feature pairs only whereas when pairs of other features were taken, the correlation coefficient was found to be insignificant. For night ragas, all possible pairs are uncorrelated. Hence, it is concluded that only spectral centroid and zero-crossing rate, which depict the shape of the distribution with respect to spectral position and the rate of sign change respectively, and not other features can be used to distinguish between a day raga and a night raga. Acknowledgements The first author whole heartedly thanks Mr. Gopesh Gaur for his recordings used in this research. Mr. Gaur represents the Dagarvani tradition of Dhrupad. Dhrupad is one of the oldest traditions of the Hindustani Classical Music. In this research article only recording are used.
References 1. N.A. Jairajbhoy, The rags of North India: their structure and evolution (Faber and Faber, London, 1971) 2. O.C. Ganguly, Ragas & Raginis. Nalanda Publications (1948) 3. S. Rao, P. Rao, An overview of Hindustani music in the context of computational musicology. J. New Music Res. 43(1), 21–33 (2014). https://doi.org/10.1080/09298215.2013.831109 4. S. Chakraborty et al., An interesting comparison between a morning raga with an evening one using graphical statistics, (Philomusica on-line, SAGGI, 2008) 5. T.C. Nagavi, N.U. Bhajantri, Overview of automatic Indian music information recognition, classification and retrieval system, in Proceedings of IEEE International Conference on Recent Trends in Information Systems (2011) 6. S. Gulati, P. Rao, A survey of raga recognition techniques and improvement to the state of the art. Sound Music Comput (2011) 7. H. Sharma, R.S. Bali, Raga identification of hindustani music using soft computing techniques, in Proceedings of 2014 RAECS UIET Punjab University Chandigarh (2014), pp. 06–08
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8. T. Katte, B.S. Tiple, Techniques for Indian classical raga identification-a survey, in Proceedings of the Annual IEEE India Conference (INDICON) (2014) 9. P. Dhanalakshmi, S. Palanivel, V. Ramalingam, Classification of audio signals using AANN and GMM. Appl. Soft Comput. 11, 716–723 (2011) 10. Y.-P. Huanga, S.-L. Laib, F.E. Sandnes, A repeating pattern based Query-by-Humming fuzzy system for polyphonic melody retrieval. Appl. Soft Comput. 33, 197–206 (2015) 11. V. Arora, L. Behera, Multiple FO estimation and source clustering of polyphonic music audio using PLCA and HM-RFs. IEEEIACM Trans. Audio Speech Lang. Process. 23(2), 278–287 (2015) 12. S. Ghisingh, S. Sharma, V.K. Mittal, Acoustic analysis of Indian classical music using signal processing methods, in IEEE Conference on TENCON, Malaysia. https://doi.org/10.1109/ tencon.2017.8228104 13. Y.M.G. Costaa, L.S. Oliveirab, C.N. Silla, An evaluation of convolutional neural networks for music classification using spectrograms, Appl. Soft Comput. 52(C), 28–38 (2017) 14. S. Sarkar, S.S. Solanki, S. Chakraborty, Time domain analysis of Indian classical raga rendition, in 3rd International Conference on Computational Intelligence and Communication Technology (CICT) (IEEE Xplore, Ghaziabad, India, 2017), pp. 1–5. https://doi.org/10.1109/ ciact.2017.7977385 15. S. Raga. www.itcsra.org/SamayRaga.aspx. Accessed 04 Jan 2019 16. S. Chakraborty, G. Mazzola, S. Tewari, M. Patra, A statistical comparison of Bhairav (a Morning Raga) and Bihag (a Night Raga), in Computational Musicology in Hindustani Music. Computational Music Science (Springer, Cham, 2014)
Frequency-Based Passive Chipless RFID Tag Using Microstrip Openstub Resonators V. Karthikeyan, U. Saravanakumar and P. Suresh
Abstract A novel chipless RFID (Radio Frequency Identification) tag is presented to make low cost tags, reduce the size of RFID tag and make more ability to secure than available RFID tags. It consists of multiresonator circuit using open stub resonators, transmitter and receiver disc monopole antennas. The multiresonator circuit which is having ten-bit data encoding capacity and ultra-wideband disc monopole tag antenna are simulated in Ansys HFSS (High-Frequency Structural Simulator) software. This Chipless RFID tag is simulated on a FR4 substrate, 4.3 dielectric constant, and 0.0018 loss tangent and 50 characteristics impedance. In this work, the backscatter signal of multiresonator circuit is processed to realize the unique identification number from the RFID tag by using the presence or absence coding technique at the receiver of RFID reader circuit and return loss characteristics of ultra-wide band (UWB) disc monopole antenna is observed −10 dB from 1.39 to 15.25 GHz. Keywords Chipless RFID · Multiresonator circuit · Resonance coding techniques · UWB disc monopole antenna
1 Introduction Nowadays, chipless RFID (Radio Frequency Identification) is very interesting research area. As its name itself mentioned, chipless RFID is a wireless data capturing technique, which uses RF waves for wide range of application. For example, few major applications are Chain Management, health care and traffic signal monitoring, access control automatic identification of objects, tracking, security surveillance, etc. V. Karthikeyan (B) · U. Saravanakumar · P. Suresh Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology, Chennai, India e-mail: [email protected] U. Saravanakumar e-mail: [email protected] P. Suresh e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 V. Nath and J. K. Mandal (eds.), Nanoelectronics, Circuits and Communication Systems, Lecture Notes in Electrical Engineering 642, https://doi.org/10.1007/978-981-15-2854-5_29
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Traditionally, the RFID technology was used for the identification friend aircrafts from enemy aircraft. This method is utilized first in second world war by assigning a unique identification code to aircraft transponder [1]. In 1945, the first RFID tag was developed by Leon Theremin known as “the Thing” or “spy device” because it captures human voice sound waves. The Schematic diagram of thing is shown in Fig. 1. The function of the thing, it is membrane of the cavity vibrates in accordance with the sound conversation in the room. This will alter the impedance of the cavity and hence changes the antenna impedance. Therefore, incident currents on the antenna are modulated with sound waves. The backscattered signal is demodulated at the RFID reader [1]. The first backscatter research and development were done by Stockman to solve the reflected power [2] problem of wireless identification. Nowadays instead of barcode, RFID Tags are not used because the tag price is still much higher while comparing with the barcode. Hence, a high amount of investments has contributed and more number of surveys are taken to reduce the tag price. Even though the conventional RFID has advantages such as information, long read range and mass storage of data, its growth is not attractive because of the price factor. Because of the merits of RFID technology, it is now becoming a bigwig in the commercial market. Due to the rapid research, the cost of RFID tag is becoming lower on every year [3–5]. RFID system contains three main modules such as RFID reader, RFID tag and middle software. The whole RFID system is displayed in Fig. 2. In the RFID system, RFID tag stores a unique identifier number in the chip, like the bar codes or a magnetic strip on cards. To retrieve information stored in the RFID tag, the device must be
Fig. 1 Block diagram of the thing developed by L. Theremin
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Fig. 2 Complete block diagram of the RFID systems
scanned with its scanning device and every tag have common information like model number, location of assembly and serial number. According to the application read range requirements, RFID divided into three categories. Those are as follows: • LF (Low Frequency) Tags—up to 1 m • HF (High Frequency) Tags—up to 2 m • VHF (Very High Frequency) Tags—up to 10–12 m [6]. The frequencies commonly used for RFID technologies at microwave band are 2.45 GHz and 5.8 GHz. And the question is, why do designers prefer chipless RFID Tag than chip RFID Tag? The reasons are • Chipped RFID tag is printed on Silicon wafer and it is a fixed cost (around US $1,000). • Design the Chip RFID Tag on Silicon wafer and testing of chip design with tag antenna is a costly manufacturing process. • Some space is needed to place the chip in RFID tag. This paper is ordered as follows, the basics of chipless RFID system describes in Sect. 2, the design of multiresonator circuit and UWB disc monopole antenna illustrates in Sect. 3. Section 4 discuss about results of chipless RFID system followed by a conclusion in Sect. 5.
2 Chipless RFID System The chipless RFID tag provides the unique identification number without the silicon chip. In these types of tag, unique number is store in time or frequency domain of the interrogation signal. It is based on properties of substrate or properties of designing various conductor. The chipless RFID tag implementation divided into two categories such as reflectometry and frequency signature tags. Further, time domain reflectometry is classified into two types [7] and they are • Surface Acoustic Wave (SAW) tags • Delay line-based tags.
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Frequency signature-based tag is also having two types and they are listed below • Multiresonator Based tag • Multiscaterrer based tag. Spectral signature-based tag is fully printable but time-based tag is not fully printable. Time-based tag creates train pulses which is useful to encode data. Spectral signature-based chipless tags use a specific frequency band to encode data using resonant structures and Spectral-based tag provides better encoding capacity and more advantages compare to time domain reflectometry-based tag. Those advantages of Spectral tag are greater data storage capability, fully printable, low cost, robust and more flexible [8, 9]. So Spectral signature-based tags are very compatible than timedomain-based tag [10]. In spectral-based tags, multiresonator-based tags are better for tag design because of multiscaterrer-based chipless tags have some drawbacks. Drawback in the multiscatterer-based tags is due to the presence of higher harmonics in the integral multiples of the fundamental mode with these resonators. In the spectral signature-based tags, each data bit is related with resonant peak or Tip at a predetermined frequency in the desired spectrum [11–13]. The concept of a multiresonator based tag with input and output characteristics is exposed in Fig. 3. It contains of a multiresonating circuit, a horizontally polarized UWB disc monopole retransmitting tag antenna and a vertically polarized UWB disc monopole receiving tag antenna. The tag is interrogated by the reader sending continuous wave signal a certain distance with constant amplitude and phase. When the tag is present in read range, the receiver UWB disc monopole antenna receives the interrogation signal which is transmitted by reader, and passing to the multiresonating circuit. The multiresonator section encodes unique number bits using open
Fig. 3 Block diagram of multiresonator based chipless RFID tag
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stub resonator by altering phase and amplitude at predetermined frequencies of the desired spectrum in the interrogating signal. Now the signal contains the unique spectral signature, it is called the encoded signal. Then encoded signal is retransmitted back to the reader using the tag retransmitting UWB disc monopole antenna [14, 15]. The receiving and retransmitting tag antennas are placed in vertically to reduce interference between the interrogation signal and the retransmitted backscatter (encoded) signal which containing the unique number [16, 17].
3 Design of Multiresonator Circuit and Disc Monopole Antenna A. Mulresonator Circuit Design The multiresonator circuit is designed to attenuate on predetermined frequency in the desired band for encode the unique identification code. Size of the multiresonating circuit is determined by the parameters such as predetermined frequency bands, space separation between the resonators and transmission line and dielectric properties of the substrate [18]. The multiresonator circuit is designed by using microstrip open circuit resonator. Among the various types of the multiresonating techniques, the size of the proposed multiresonator circuit is very small compared with other tags working on the same frequency. The structure of the multiresonator section is displayed in Fig. 4. The main advantage of resonator is direct connection of open stub resonator and main transmission line (Table 1). All the resonators have to be placed close to a transmission line with equal space. All resonators are resonating separately due to its quarter wavelength (λg/4) transmission line. Space between the two resonators are kept 1 mm to minimize the mutual coupling and cross polarization [19] ‘L’ shape open stub resonator is placed on the transmission line and it is presented in Fig. 5. Alternate method of SRRs (Split Ring
Fig. 4 Structure of eight-bit multiresonator circuit using openstub resonator
328 Table 1 Multiresonator circuit transmission lines width and length value
V. Karthikeyan et al. Transmission line width and length
Value (mm)
W
7
W1
3
W2
1
W3
0.5
Lf
17.59
Ltx
16.84
Lp
12
L1
21
L2
19.5
L3
19
L4
17.5
L5
15.25
L6
13.5
L7
11.5
L8
10.5
Fig. 5 a Design of the eight-bit multiresonator circuit using openstub resonator. b Multiresonating circuit design with disconnected open stub resonators
Resonators) loaded circuit are not suitable resonator for encode bit in received interrogation signal which is received by receiving UWB disc tag antenna [20, 21]. The overall geometry dimension of the proposed 8-bit multiresonator circuit is 30 mm× 25 mm × 1.6 mm. The specification of the proposed multiresonator circuit is listed below. • • • •
Substrate = FR4 Dielectric permittivity = 4.3 Substrate height = 1.6 mm Tangent loss = 0.0018
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• Conductor thickness = 30 μm • Character impedance = 50 . An impedance convertor section (tapering section) is introduced between feed line (50 ) and main transmission line (28 ) to avoid the impedance mismatch. This tapering section length (LT) is equivalent to 0.25 λd, where λd is the substrate wavelength corresponding to the operations of low frequency. Final model of the eight-bit open stub resonator based mutiresonator circuit is depicted in Fig. 5a. The exciting circuit resonant frequencies of the circuit are found at 2.08, 2.23, 2.36, 2.56, 2.81, 3.21, 3.61 and 4.03 GHz. The Absence or Presence coding technique is used in the multiresonator circuit of RFID tag, hence one resonator can represent 1 bit of information. The presence of the resonance at a predefined frequency indicates bit 1 and absence is indicated as bit 0. The 8-bit multiresonator circuit design is generated the unique identification code 11111111. Traditionally the method of generating different bit combination is achieved by removing entire resonator from the multiresonator circuit. Instead of traditional method to generate different bit combination from the mutiresonator circuit, the connection between transmission line and resonator is removed and this method is minimizing the frequency shift of the encoded signal. Connection removed multiresonator circuit design is displayed in Fig. 5b, which is generated 10111111 unique identification code. This 8-bit multiresonator circuit design can create 256 unique identification number using presence or absence of resonance coding techniques. The 10-bit encoding capacity of multiresonator circuit has designed and it has shown in Fig. 6a. The digit of unique identification code can be increased by adding a greater number of the microstrip open stub resonator. We can create various combination of unique identification number using connection remove method. Figure 6b shows that 10-bit multiresonator design with connection remove method. The spectral based RFID tag is working with frequency signature and its concept is depends upon encoding an n-bit digital information with n number of resonance frequencies. Every bit is identified by a single resonance frequency with the amplitude variation.
Fig. 6 a Design of the 10-bit multiresonator circuit using openstub resonator. b 10-bit multiresonating circuit design with disconnected open stub resonators
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B. Wideband Disc Monopole Antenna Design The design of RFID tag based on multiresonator circuit requires two UWB antennas, those are using to receive the reader interrogation signal and retransmit the backscattered signal from multiresonating circuits to reader. The overall antenna size depends on substrate permittivity, geometry and minor resonance frequency of tag. In this tag, microstrip antenna is opted because of its compact structure and wide band operation. This antenna provides a good impedance match and radiation characteristics at the preferred frequency band. The estimated power of the back scattered signals can be calculate using the Friis transmission equation. The signal power density reaches the chipless RFID tag in free space is given by, S=
Pt G r 2 G /H z 4πr 2
where, Pt —Transmitted signal power, Gr —Gain of the reader transmitting antenna, R—Distance between the reader antenna and tag. The received signal power by the tag antenna is defined as Pa = s Ae =
Sλ2 G t (watts) 4π
(2)
Ae —Effective area of tag antenna, Gt —Gain of the tag antenna, λ—Wavelength of the signal. In this calculation of received power, both antennas used by the RFID reader for transmission of interrogation signal and reception of backscattered signal are considered identical. Similarly, receiving and retransmitting UWB disc monopole antenna is also considered as identical in the RFID tag. Hence, the collected power signal by the RFID reader after encoded bit by multiresonator circuit is Pr x =
Pt G 2t G r2 λ4 L( f ) (watts) (4πr )2
L(f )—Insertion loss circuit function frequency of multiresonator circuit. The received backscattered signal strength Prx must be above the noise floor for the successful identification. In the RFID system, the noise floor of back scattered signal depends on the polarization isolation between the reader antennas and environmental conditions. The specification of the UWB disc monopole antenna is specified as follows (Table 2). • Substrate = FR4 • Dielectric permittivity = 4.3 • Substrate height = 1.6 mm
Frequency-Based Passive Chipless RFID Tag … Table 2 UWB disc monopole antenna dimension values
331
Patch, feed transmission line and ground width and length
Value (mm)
W3
3
Lg
0.6
Lg1
40
Lg2
20
R
15
W3
3
• Tangent loss = 0.0018 • Conductor thickness = 30 μm • Character impedance = 50 . For successful reception and retransmission of interrogation signal, the radiation pattern of the antenna should be uniform [14]. Figure 7 shows the geometry of the UWB disc monopole antenna with physical parameters. The antenna shows very good impedance match over operating frequency range of multiresonator circuits. The transmitting and receiving tag antennas is placed as cross polarized to reduce the nosiness between the interrogation signal and the back scattered signal and also is cancel the noise interference at the reader’s receiver because nature of the receiver is expecting polarized signal. Figure 8 shows the different view of UWB disc monopole antenna design. Fig. 7 Structure of the UWB disc monopole antenna
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Fig. 8 Top and bottom view of UWB disc monopole antenna
The return loss S (1, 1) of the UWB disc monopole antenna must be less than − 10 dB, then only maximum power passes through the conductor. Over the predefined frequency range of RFID tag, UWB antenna confirms omnidirectional radiation pattern. To analysis the radiation pattern of the UWB disc monopole antenna, it is kept inside the closed area that is presented in Fig. 9.
Fig. 9 Design of UWB disc monopole antenna is placed in the closed surface area
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4 Result and Discussion A. Result of Multiresonator Circuit The resonant frequencies of the circuit are found to be at 1.97, 2.22, 2.39, 2.49, 2.81, 3.1, 3.59, and 3.88 GHz. Absence or Presence Coding technique is used in this presented tag. The presence of the resonance at a predefined frequency indicates bit 1 and absence of resonance indicates bit 0. The eight-bit multiresonator circuit design using microstrip open circuit resonator is generated unique identification code 11111111 and it is presented in Fig. 10. The method of generating different bit combination from the mutiresonator circuit, instead of removing entire resonator from the tag, the connection between transmission line and resonator is removed to minimise the frequency shift in the multiresonating circuit. In eight-bit data capacity, the resonance of multiresonator circuit designs is achieved in the range of −26 to − 48 dB (Tables 3 and 4). The eight-bit multiresonator circuit with disconnected open stub resonator design resonance has shown in Fig. 11. Every resonance is indicating one bit of the
Fig. 10 Resonance of 8-bit multiresonator circuit with non-disconnected open stub resonators
Table 3 Insertion loss characteristics of 8-bit multiresonator circuit with non-disconnected open stub resonators
S. no.
Frequency (GHz)
S21 (dB)
1
1.9
−26.002
2
2.2
−45.808
3
2.3
−33.7038
4
2.45
−37.47
5
2.8
−48.8132
6
3.1
−44.7490
7
3.5
−36.3245
8
3.9
−41.6735
334 Table 4 Insertion loss characteristics of 8-bit multiresonator circuit with disconnected open stub resonators
V. Karthikeyan et al. S. no.
Frequency (GHz)
S21 (dB)
1
1.9
−27.0433
2
2.3
−29.9256
3
2.45
−35.8795
4
2.8
−48.5414
5
3.1
−45.0252
6
3.5
−32.9018
7
3.9
−34.6928
Fig. 11 Resonance of 8-bit multiresonator circuit with disconnected open stub resonators
unique identification code and Fig. 11 is indicating the unique identification code of 10111111. This 8-bit multiresonator circuit design can create 256 unique identification codes using presence or absence of resonance coding techniques. The current flow in all the ports of multiresonator circuit is shown in Fig. 12 and Table 5.
Fig. 12 Current flow in the multiresonator circuit port 1 and 2 transmission lines
Frequency-Based Passive Chipless RFID Tag … Table 5 Insertion loss characteristics of 10-bit multiresonator circuit with non-disconnected open stub resonators
335
S. no.
Frequency (GHz)
S21 (dB)
1
1.9
−26.8341
2
2.2
−45.8630
3
2.3
−29.4049
4
2.45
−36.9661
5
2.8
−47.4669
6
3.1
−41.9751
7
3.5
−37.6116
8
3.9
−41.6735
9
4.1
−32.7978
10
4.7
−25.0703
The resonance of 10-bit data capacity of frequency based tag is presented in Fig. 13. The ten-bit data capacity structure of multiresonator circuit design can generate 1024 unique identification codes using presence or absence of resonance coding techniques. The digits of unique identification code can be increased by adding the number of the microstrip open stub resonator (Table 6). The resonance of 10-bit multiresonator circuit with disconnected openstub resonator has shown in Fig. 14 and it has the unique identification code of 1110111111. The current distribution over the multiresonator circuit is shown in Fig. 15. The current distributions over the multiresonator circuits have less emission because of the uniform surface and it indicates in blue colour on the other hand red colour which indicates high emission value.
Fig. 13 Resonance of ten-bit multiresonator circuit using openstub resonator
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Table 6 Insertion loss characteristics of 10-bit multiresonator circuit with disconnected open stub resonators S. no.
Frequency (GHz)
S21 (dB)
1
1.9
−26.8341
2
2.2
−45.8630
3
2.3
−29.4049
4
2.8
−47.4669
5
3.1
−41.9751
6
3.5
−37.6116
7
3.9
−41.6735
8
4.1
−32.7978
9
4.7
−25.0703
Fig. 14 Resonance of 10-bit multiresonating circuit with disconnected open stub resonators
Fig. 15 Current distribution of the modified multiresonator circuit design
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Fig. 16 Reflection characteristics of UWB disc monopole antenna
B. Resullt of UWB Disc Monopole Antenna In this work, the UWB disc monopole antenna is designed with bandwidth of 13.86 GHz and its return loss below. −10 dB from the frequency range 1.39– 15.25 GHz which means that 0.1 percentage of the input signal reflected back remains 99.9% transferred to disc patch. The return loss characteristic of antenna is displayed in Fig. 16. The UWB disc monopole antenna will transmit the resonance generated by the multiresonator circuit in the frequency range between 1.39–15.25 GHz. Especially the return loss for our antenna at 5 GHz ISM frequency is 0.01%. The maximum radiated signal power of our UWB disc monopole antenna is reached at 0 and 180°, shown in Fig. 17. The current distribution of the antenna is given in Fig. 18a. The maximum radiated signal power is 5.48 eˆ3 V/m. As like multiresonator circuits, the current distribution of UWB disc monopole antenna also has less emission because of the uniform surface. The uniform surface is needed for successful reception and retransmission signal. Figure 18b describes the current flow in the port of UWB disc monopole antenna (Table 7).
5 Conclusion The 10-bit microstrip open stub multiresonator circuit and UWB disc monopole antenna are simulated and analysed in this work. The 10-bit multiresonator-based chipless RFID tag able to create 1024 unique identification numbers using connection remove method. The main advantage of chipless tag is, it enables to encode data in two parameters of signal. Other advantages of chipless RFID tag are more flexible,
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Fig. 17 Radiation pattern of UWB disc monopole antenna
Fig. 18 The current metrics of UWB disc monopole antenna: a Current distribution in the patch transmission line b current flow in wave port
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Table 7 Comparison of different types of the resonators used for chipless RFID S. no.
Resonator type
Frequency range (GHz)
No. of bits
Coverage range (cm)
Overall size of tag (W ×L mm2 )
No. of resonators state
Reference no.
1
Spur line
2.38–4.04
8
5
60 × 30
2
18
2
Spiral resonator
5–11
23
35
–
2
19
CPW
3.3–5.5
8
30
55 × 154
2
20
Spiral
2–2.5
6
40
–
2
2
3
E-Shaped
3.3–4.3
8
40
100 × 160
2
7
4
Slotted spiral
3.1–9
8
–
–
2
21
5
C-Section
2.3–10.6
12
40
55 × 100
2
22
6
Dual band capacitive loaded stepped Impedence
2.5–5.5
8
50
–
3
23
7
Spur line
1.9–4.04
12
–
40 × 120
2
24
8
Microstrip coupled spiral
5–10.7
6
10
65 × 107
2
25
9
U resonator
1.85–2.25
6
50
75 × 114
2
26
10
Spiral
4–6
−9
1
–
2
27
11
Complementary split ring
6.8–11.2
8
10
80 × 140
2
5
12
Open stub
1.9–4.7
10
50
60 × 99
4
Proposed
fully printable, robust, greater data storage capability and low cost. Therefore, the multiresonator-based chipless RFID tag replaces the existing chip RFID tag due to its merits compare to other chipless RFID tag techniques. Those merits are coverage range, overall size of the tag, more secure than barcode and chip RFID tag and tag cost. The entire openstub multiresonator based tag will be fabricated and tested in the anechoic chamber. The required test measurement setup components are Agilent PNA E8362B, transmitting & receiving horn antenna and fabricated chipless RFID tag. The complete hardware test setup is presented in Fig. 19. The insertion loss of multiresonator circuit is captured by receiver horn antenna through transmitting antenna of RFID tag, then insertion loss or backscatter signal forward to network analyser and digit of the unique number is identified using tip of the backscatter signal.
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Fig. 19 Hardware test measurement setup of chipless RFID in the anechoic chamber
References 1. A.A.C. Alves, D.H. Spadoti, L.L. Bravo-Roger, Optically controlled multiresonator for passive chipless RFID tag. IEEE Microw. Wirel. Comp. Lett. 28, 467–469 (2018) 2. Y.F. Weng, S.W. Cheung, T.I. Yuk, L. Liu, Design of chipless UWB RFID system using a CPW multi-resonator. IEEE Antennas Propag. Mag. 55(1) (2013) 3. D. Girbau, J. Lorenzo, A. Lázarom, C. Ferrater, R. Villarino, Frequency—Coded chipless RFID tag based on dual—band resonators. IEEE Antennas Wirel. Propag. Lett. 11 (2012) 4. S. Preradovic, N. Karmarkar A novel chipless RFID system based on planar multiresonators for barcode replacement. IEEE Int. Conf. 16–17 (2011) 5. C.M. NIJAS, Design and development of compact chipless RFID tags with high data encoding capacity. Ph.D. dissertation, Cochin University of Science and Technology, 2015 6. S. Preradovic, I. Balbin, N.C. Karmakar, G.Swiegers, Chipless frequency signature based RFID transponders, in Proceedings of the 38th European Microwave Conference, (Monash University Bldg 72, Clayton Campus, Melbourne, 2008) 7. J.J. Martínez, F.J. Herraiz, G. Galindo, Design and characterization of a passive temperature sensor based on a printed MIW delay line. IEEE Sens. J. 16(22), 7884–7891 (2016) 8. H. Huang, Flexible wireless antenna sensor: a review. IEEE Sens. J. 13(10), 3865–3872 9. U. Fatima, T. Hayat, Cost effective fully printable chip less RFID tag antenna, in 19th International Multi-Topic Conference (INMIC) (2016) 10. S. Preradovic, I. Balbin, N.C. Karmakar, G.F. Swiegers, Multiresonator-based chipless RFID system for low-cost item tracking. IEEE Trans. Microw. Theory Techn. 57(5) (2009) 11. L. Reindl, G. Scholl, T. Ostertag, H. Scherr, U. Wolff, F. Schmidt, Theory and application of passive SAW radio transponders as sensors. IEEE Trans. Ultrasonics Ferroelect. Freq. Control 45(5), 1281–1292 12. U. Kraiser, W. Steinhagen, A low-power transponder IC for highperformance identification systems. IEEE J. Solid State Circ. 30(3), 306–310 (1995) 13. I. Balbin, N.C. Karmakar, Phase-encoded chipless rfid transponder for large scale low cost applications. IEEE Microw. Wirel. Comp. Lett. 19(8) (2009) 14. K.V.S. Rao, P.V. Nikitin, S.M. Lam, Antenna design for UHF RFID tags: a review and a practical application. IEEE Trans. Antennas 53(12), 3870–3876 (2005) 15. U. Karthaus, M. Fischer, Fully integrated passive UHF RFID transponder IC with minimum RF input power. IEEE J. Solid State Circ. 38(10), 1602–1608 (2003)
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16. S. Preradovic, N. Karmakar, Design of fully printable planar chipless RFID transponder with 35-bit data capacity, in Proceedings of the 39th European Microwave Week (Rome, Italy, 2009), pp. 13–16 17. A. Vena, E. Perret, D. Kaddour, T. Baron, Toward a reliable chipless RFID humidity sensor tag based on silicon nanowires. IEEE Trans. Microw. Theory Tech. 64(9), 2977–2985 (2016) 18. N.M.N. Haase, G. Fuge, H.K. Trieu, A.P. Zeng, A.F. Jacob, Miniaturized transmission line sensor for broadband dielectric characterization of biological liquids and cell suspensions. IEEE Trans. Microw. Theory Tech. 63(10), 3026–3033 (2015) 19. M. Khaliel, A. El-Awamry, A. Fawky, T. Kaiser, A Novel Design Approach for Co/CrossPolarizing Chipless RFID Tags of High Coding Capacity. IEEE J. Radio Freq. Identif. 2469– 7281 (2016) 20. J. Naqui, F. Martín, Application of broadside-coupled split ring resonator (BC-SRR) loaded transmission lines to the design of rotary encoders for space applications, in IEEE MTT-S International Microwave Symposium (IMS’16) (San Francisco, 2016) 21. J. Mata-Contreras, C. Herrojo, F. Martín, Application of split ring resonator (SRR) loaded transmission lines to the design of angular displacement and velocity sensors for space applications. IEEE Trans. Microw. Theory Techn. (2014)
Automatic Bird Species Recognition Based on Spiking Neural Network Ricky Mohanty, Bandi Kumar Mallik and Sandeep Singh Solanki
Abstract The main focus of this paper is to analyze sound waves produced by birds of many different kind using spectral features, which is present in an audio recording. This data helps in to give program examination and remote checking of the entire populace and species of birds. This can help for natural preservation of these species. Also survival plans and actions can be studied. The sounds produced by these birds have special acoustic features which could help us in detecting the type of birds. In this paper, we study about a technique which will help in finding the type of birds using the Mel Frequency Cepstral Coefficient as well as wavelet features and classification performance is compared using two different models. The classification of these features is done using Spiking Neural Network (SNN) and Gaussian Mixture Model (GMM). Spiking neural network has edge over GMM in some aspects such as less computation time and more accuracy. Keywords Mel frequency cepstral coefficient · Discrete wavelet transform · Spiking neural network (SNN) and gaussian mixture model (GMM) · Bird species recognition
1 Introduction Sound perceived in our day-to-day life is from various organisms as found in speech utter by human, avian sound, amphibian calls, mammalian calls, etc. These make sounds to do meet their daily chore or any sign of danger. These sound differ from R. Mohanty (B) · S. S. Solanki Department of Electronics & Communication, Birla Institute of Technology (BIT), Mesra, Ranchi, India e-mail: [email protected] S. S. Solanki e-mail: [email protected] B. K. Mallik Central Poultry Development Organisation, Eastern Region, Bhubaneswar, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 V. Nath and J. K. Mandal (eds.), Nanoelectronics, Circuits and Communication Systems, Lecture Notes in Electrical Engineering 642, https://doi.org/10.1007/978-981-15-2854-5_30
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one type to other is by unique marked sound and are of great significance for monitoring ecosystem and preserving wildlife by getting hold of many species which are countable. Many researchers have come up with this basic idea that bird’s sound are of two types calls and song. Bird sounds are made up series of small sounds from a bird sound archive. Those small sounds are so called as elements. Birds utter sounds of a major artefact in nature. Also some bird sounds have a tonal sound, which are basically pure fundamental frequency, a number of harmonics of the fundamental frequency, or a number of non-harmonic frequencies. Bird call specific species classification is a cognitive task of classification problem that initiates from pre-processing along with feature extraction from continuous recordings. After pre-processing of the small framed sounds, a set of features are extricated from it that are used to classify small frames into different class labels of bird species. Recognition of bird species depending on their audio signal is a cognitive computing problem, and as such, it consists of a stage that focuses to extricate appropriate attributes from the signal and a modelling part that focus to spread of the appropriate attributes in space. Decades ago great endeavour were worked out for bird species automatic recognition were dependent on matching of template of signal time-frequency domain attributes along with dynamic time warping (DTW), for example, see [1]. The work in [1] was based on two different bird species and there was manual separation of small frames of the templates of respective elements. Researchers have mentioned many methods of representing avian sounds that are used for classification of bird individuals or flock of birds of different species in their work. Frequency domain methods includes Mel frequency cepstral coefficients (MFCC) used by many researchers in pattern recognition [2–5] and linear predictive coding [6]. In this work, time-frequency domain feature extraction method is used wavelets as well described in [7]. The aim of this paper is to point out pattern recognition using third-generation neural network as shown in this Fig. 1. In this paper, illustration of a bird sound recognition system using (GMM or SNN model) which uses Mel Frequency cepstral coefficient (MFCC) as well as wavelet features of the sound to classify them into class label. The next section of the paper illustrates the proposed system following with the simulation and the results recorded. Finally, the last section is conclusion in the paper.
Fig. 1 A block diagram of the intelligent bird call recognition system
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2 Proposed System In this portion of the paper, description of the proposed model as in given in Fig. 2. This model starts with extricating the attributes using MFCC and Wavelet. Classify them to class label using GMM followed by SNN. The following subsections will explain in brief the various techniques used to make the proposed system. A. DataAcquisition and Segmentation Bird sound is composed of both songs and calls. But in poultry birds, calls are prominently used. Calls are basically isolated sounds which are used for specific purpose, i.e., Predator approaching near calls, breeding calls, feeding calls, environmental change calls, etc. The data acquisition was carried out in wav format with 44.1 kHz sampling rate. The bird sound is divided into phrases, syllable and element. Each pulse denotes the syllable which is used for classification. The bird sounds database was made collecting data from Central poultry development organization(CPDO) Eastern Region, Bhubaneswar, Odisha, India as shown in Table 1. Before further process of the bird sound in the paper, silence portion frames are removed from 1 min recording of birds. After data acquisition of field recording bird data, pre-processing is done by segmenting bird wav file into pulses (syllables) as proposed in [8]. The Bird call decomposition into M syllables is done in the following steps. Step 1—Spectrogram of a bird call is obtained using STFT. In STFT, the size of the hamming window is 256, FFT size with zero padding was 1024 and a spectrum vector was computed with 75% overlap (64 samples step). Spectrogram is represented in form of matrices S(f, t). Step 2—For m = 0, 1, 2, …, M−1, the step from 3 to 7 is repeated. Step 3—f m and t m is computed in a way that lS(f m , t m ) l is maximum value in the spectrogram f m and t m represent maximum amplitude places of mth sinusoidal syllable.
Fig. 2 The structure of proposed system
346 Table 1 Birds considered in the current work
R. Mohanty et al. Sl. no.
Common name
Individuals
Calls
1
Barred plymouth rock
9
50
2
White plymouth rock
11
36
3
Red codish
8
40
4
Black rock
7
28
5
Naked neck
8
50
6
Rhode island red
9
46
7
Kalinga brown
14
35
8
Japanese quail
8
43
9
Guinea fowl
10
55
10
Whiteleg horn
9
48
11
AW cross
5
42
12
Aseel brown
15
60
13
Colour cross
12
43
14
Vanaraja
8
65
133
641
Total
Step 4—ωm (0) = f m is computed as frequency parameter and amplitude db. Step 5—Beginning from lS(f m , t m ) l, find the maximum peak of S(f, t) from t < t m and for t > t m until Am (t m −t) = Am (0)−μ where μ is stopping criteria which is 30 db here. It will measure how the syllables begin and end at t s and t e respectively around maximum time t. Step 6—The obtained frequency and amplitude trajectories corresponding to the mth syllable is saved in the function ωm (τ ) and Am (τ ) where τ = t−t s , …, t + t e . Step 7—S(f, lt s , t s + 1, t e l) = 0 is set to erase the area of mth syllable. A set of 7 frequency and amplitude trajectories extracted from Aseel Brown call is shown in Fig. 3. B. Feature Extraction Features are extracted from each syllable for bird call classification. The feature extraction method used in this work is Discrete Wavelet Transform (DWT) and Mel frequency cepstral coefficient (MFCC). 1. Wavelet Features The technique which is specially used for analysing acoustic signals such as speech signal is wavelet transform. To overcome the critical issue of short-time Fourier transform (STFT) related to the frequency and time resolution of STFT, this technique was developed. DWT offers for high frequencies high time resolution and for low frequencies high frequency resolution whereas STFT gives steady time resolution for all the frequencies. This attribute makes it similar to human ear which also works on the same characteristic of time-frequency resolution.
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Fig. 3 Seven syllable from a Aseel brown call
The DWT is of one of a kind or significant case of the Wavelet Transform. It provides an exact and precise depiction of the signal in frequency and time which can be evaluated very easily. The following equation below can be utilized to define DWT: −j x(k)2 2 ϕ(2− j n − k) (1) W ( j, k) = j
k
where, ϕ (k) in the Eq. (1) is called the mother decay and it can be defined as time domain oriented with limited energy and rapid decay. In order to perform analysis of DWT, a quick and tomb shaped algorithm associated to multi-rate filter banks can be used. DWT being the multi-ratefilterbank that can be taken as a continual Q filter bank having octave spacing in the of the filters center. Half of the sample of the sub-band higher frequency is present in each neighbouring sub-band. The signal is decomposed into coarse estimation and detail information by the pyramidal algorithm so that the signal can be examined at different frequency bands with various resolutions. Then, using the same decomposition step, the coarse estimation is further decomposed. In order to get proper signal resolution, one after other filtering (high pass and low pass) of the time function data is carried out. The equation given below can be utilize to define it
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yhigh [k] =
x[n]g[2k − n]
(2)
x[n]h[2k − n]
(3)
n
ylow [k] =
n
where yhigh [k], ylow [k] in Eqs. (2) and (3) represent after sub-sampling the output of filter both high pass and low pass respectively. The count of resulting wavelets is nearly the synonymous as the input points due to downsampling. 2. Mel Frequency Cepstral Coefficient This is one of the universally used methods of feature speech extraction’s system usually use up to 20 MFCC coefficients (10–12 are normal) [6, 7, 9, 10]. This depiction can be considered to be as static MFCC feature vector though it is calculated from one time frame only. The series of static feature vectors calculated from the successive time frames depicts the progression of the features in time (Fig. 4). As discussed in the earlier section, each bird sound consists of a series of frames possessing unique attributes. Any two frames is portion out from same type bird sound might get distinguished significantly. Therefore, the feature vectors extricated Fig. 4 Block diagram of MFCC
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from the frame of identical bird species will disclose many values in the feature space. So subsequently, system involving the sound of each bird species with a single feature vector is bound to fail. A plan of action can be done to manage this problem is to systemize the sounds of each species with a series of depictive prototype vectors. These prototype vectors can be acquire by labelling all frames procure from identical bird species into some subcategories such that frames with same type of feature vectors are congregate together. So for congregating and classifying these feature vectors a traditional method of GMM and compared with performance of spiking neural network is used in this work. C. Classification 1. Spiking Neural Networks To distinguishing the specific data, the process of pattern recognition is done which is depends on the befitting knowledge or stochastic information extricated from the framework. By the collection of data, usually, the pattern is classified. To implement pattern recognition, several types of model are used but those models are hardly biological inspired. Spiking neurons have ability of processing action potentials which are important feature of information transmission in neural systems [10]. Because it has good biological evidence, patterns are better classified in spiking neural network. In the pattern recognition, brain-inspired neural network renders the better computational power. The spiking neural network reflects high accuracy and optimum performance for identifying the scrums of pattern. Different spiking models are mentioned in the works of researchers as in [11–13] such as Izhikevich Model (IM), Hodgkin-Huxley Model (HH) and integrate and fire (IF) model. When compared with another model it has been noticed that IF model is simple and has higher performance. When supervised learning mechanism is used in neural network to examine the pattern, model was chosen for this work is leaky integrate fire models. The whole system includes the encoding, readout, learning process. In the spiking neural networks, using temporal encoding and learning can be beneficial for recognizing patterns effectiveness and efficiency. The weight has to be updated during the learning and to filter out the particular patterns, the readout process is used. Spikes are generated from the input stimuli by using the process of encoding. Temporal encoding is tool to create the patterns of spikes whereas rate code cannot do. Latency code in temporal coding is utilized here. In which, coding the information that is time response relative to the encoded window. Each neuron has to be fired after the information encoding is done. In the encoding process, each small unit has to be chosen and convert the input point into the latency information. Conversion of binary information into the temporal pattern of spikes is discrete in nature. On using the spiking neural network, the particular information is analyzed. In process of the training, a traditional rule is used for neurons have to fire and learn, i.e., Hebbian-based training rule. Neural Network training is used to speed up the fast neural information processing. By using integrated fire neurons, the evident patterns are recognized. In a hierarchical
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Fig. 5 Flow of the proposed technique
manner, each neuron is set for arrangement and the information is processed each layer which is taken care of frame by frame. By Hebbian-based training, each frame has to change its structure as in [14–16]. Simulation In this part of the paper, the description of how the proposed technique is simulated. Figure 5 shows the flow of the proposed technique. As taken account from the above figure the sound of birds is taken as the input which is the first module of the system which is then given to feature extraction module. Feature Extraction: In the proposed system, feature extraction techniques used is the Mel frequency cepstral coefficients, Wavelet. Transform like DWT and various other features which are calculated faster. For classification purpose the Spiking Neural network and Gaussian Mixture Model are used to classify the system. The system is divided into two main models first is the training model where the bird’s sounds are input and wavelet features are calculated and stored in the database which can be used later. Once the system is trained it is then tested by implementing the same techniques. The system begins by taking the birds sound as input and Wavelet Transform features are calculated which are classified the by using SNN and GMM with the features available in the database.
3 Result In this section of the paper, we start with illustration of the test results found while testing the proposed system. The testing starts with GMM along with Wavelet. All bird species mentioned in Table 1 is used (Table 2).
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Table 2 Comparison of GMM and SNN classification along with MFCC and wavelet feature extraction in term of accuracy % and delay in time (seconds) No of inputs (epochs)
GMM with MFCC
GMM with wavelet
SNN with wavelet
Delay in time in Sec
Accuracy (%)
Delay in time in Sec
Accuracy (%)
Delay in time in Sec
Accuracy (%)
10
0.82
75
0.62
78
0.52
82
20
0.82
75
0.65
78
0.52
82
50
0.85
75
0.68
75
0.54
80
100
0.88
72
0.73
75
0.60
80
200
0.89
71
0.75
75
0.61
80
500
0.91
70
0.76
73
0.63
78
1000
0.91
70
0.76
73
0.67
78
As observed from the above table GMM along with MFCC worked at mean delay of 0.8–0.9. Whereas giving accuracy of 70–75%. As observed from the above table GMM along with Wavelet worked at mean delay of 0.6–0.8 where as giving accuracy of 70–80%. As observed from the above table SNN along with Wavelet worked at mean delay of 0.5–0.7 where as giving accuracy of 80–85%. This is reflected in graphical representation in Figs. 6 and 7.
Fig. 6 Graphical representation of GMM and SNN on basis of accuracy percentage versus no of inputs
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Fig. 7 Graphical representation of GMM and SNN on basis of computation time versus no of inputs
4 Conclusion In this system we are proposing three different techniques from bird sound recognition namely where the first technique uses Gaussian Mixture Model with Mel Frequency Cepstral Coefficients. The second technique used is Gaussian Mixture Model with Wavelet and finally Spiking Neural Network with Wavelet is the third technique. We have implemented and tested all the three techniques and the results are show in the above section. As observed the SNN with Wavelet turns up to be the best technique with high accuracy and low delay. In future, this type of brain-inspired third-generation neural network will make great revolution in various area of science.
References 1. S. Anderson, A. Dave, D. Margoliash, Template based automatic recognition of birdsong syllables from continuous recordings. J. Acoust. Soc. Am. 100, 1209–1219 (1996) 2. J. Kogan, D. Margoliash, Automated recognition of bird song elements from continuous recordings using dynamic time warping and hidden Markov models: a comparative study. J. Acoust. Soc. Am. 103(4), 2185–2196 (1998) 3. S. Fagerlund, A. Harma, Parameterization of inharmonic bird sounds for automatic recognition, in 13th European Signal Processing Conference (EUSIPCO 2005) (Antalya, Turkey, 2005)
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4. C. Kwan et al., An automated acoustic system to monitor and classify birds. EURASIP J. Appl. Signal Process. 19 (2006) 5. C. Lee, C. Han, C. Chuang, Automatic classification of bird species from their sounds using two-dimensional cepstral coefficients, in IEEE Transactions on Audio, Speech and Language Processing, vol. 16(8) (2008), pp. 1541–1550 6. V. Xavier et al., Gammatone wavelet features for sound classification in surveillance applications, in 20th European Signal Processing Conference (EUSIPCO 2012) (Bucharest, Romania, 2012), pp. 27–31 7. A. Selin, J. Turunen, J.T. Tanttu, Wavelets in automatic recognition of bird sounds, in EURASIP Journal on Signal Processing Special Issue on Multirate Systems and Applications, 2007 (2007) 8. A. Harma, Automatic identification of bird species based on sinusoidal modeling of syllables, in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 5 (Hong Kong, 2003), pp. 545–548 9. C.F. Juang, T.M. Chen, Birdsong recognition using prediction-based recurrent neural fuzzy networks. Neurocomputing, 71(1–3), 121–130 (2007) 10. E.R. Kandel, J.H. Schwartz, T.M. Jessell, Principles of Neural Science, vol. 4 (McGraw-Hill, New York, NY, USA, 2000 11. S.M. Bohte, E.M. Bohte, H.L. Poutr, J.N. Kok, Unsupervised clustering with spiking neurons by sparse temporal coding and multilayer RBF networks. IEEE Trans. Neural Netw. 13, 426–435 (2002) 12. P. Somervuo, A. Härma, S. Fagerlund, Parametric representations of bird sounds for automatic species recognition. IEEE Trans. Audio Speech Lang. Process 14(6), 2252–2263 (2006) 13. S. Mitra, S Fusi, G. Indiveri, Real-time classification of complex patterns using spike based learning in neuromorphic VLSI. IEEE Trans. Biomed. Circ. Syst. 3(1), 32–42 (2009) 14. R. Gütig, H. Sompolinsky, The tempotron: a neuron that learns spike timing-based decisions. Nat. Neurosci. 9(3), 420–428 (2006) 15. Y. Linde, A. Buzo, R. Gray, An algorithm for vector quantizer design. IEEE Trans. Commun. 28(1), 84–95 (1980) 16. J. Dennis, Q. Yu, H. Tang, H.D. Tran, H. Li, Temporal coding of local spectrogram features for robust sound recognition, in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (2013), pp. 803–807
Learning Discriminative Classifier Parameter for Visual Object Tracking by Detection Vijay K. Sharma, Bibhudendra Acharya, K. K. Mahapatra and Vijay Nath
Abstract In this paper, we propose an algorithm to learn the discriminative parameter vector in successive frames of a video. The learned parameter vector is used for visual object tracking. The learning method selects some positive and negative examples from example sets. Using iterative steps, one set of positive and negative example is selected in a single iteration. The weight update strategy is then used to estimate the proportionate weight of selected example set. The examples are selected based on the criterion that when the parameter vector is updated using these examples, the classification score between parameter vector and some examples will be higher. The proportionate weights are estimated to each selected example pair based on exploring different weights; using assumption that when the parameter vector is updated, it results in higher classification score between some positive examples and the updated parameter vector. The update strategy further estimates the scalar multiplier value of selected example pairs, except the first pair, considering the target appearance change and the target drift. The tracking results of the proposed discriminative parameter learning method is comparable to the state-of-the-art video trackers. Keywords Visual object tracking · Online learning · Discriminative classifier · Parameter update
V. K. Sharma · B. Acharya (B) Department of Electronics and Communication Engineering, National Institute of Technology Raipur, Raipur, India e-mail: [email protected] K. K. Mahapatra Department of Electronics and Communication Engineering, National Institute of Technology Rourkela, Rourkela, India e-mail: [email protected] V. Nath Department of Electronics and Communication Engineering, Birla Institute of Technology, Mesra, Ranchi, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 V. Nath and J. K. Mandal (eds.), Nanoelectronics, Circuits and Communication Systems, Lecture Notes in Electrical Engineering 642, https://doi.org/10.1007/978-981-15-2854-5_31
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1 Introduction Visual object tracking is used in different computer vision applications. Some of them are automatic surveillance for finding suspected activities in important sites, human–computer interaction using hand gesture, real-time traffic monitoring, automatic analysis of sports video, etc. Visual object tracking is also used in defence for the estimation of object trajectory. In visual object tracking, the task is to predict the location and size of an object in each frame of a video, given the object location and size in the first video frame only. The prediction of object location becomes complex and challenging because of several different factors. For example, in successive video frames, target appearance may undergo one or more of the following changes: partial or complete occlusion, scale change, motion blur, deformation, and pose change. The brightness variation of the scene and the target clutter (presence of similar object near the target) also pose serious challenge to the visual tracking. Appearance model and motion model are the two basic steps in visual object tracking. The target appearance can be represented using highly complex features. For visual object tracking, scale invariant feature transform (SIFT) based feature is explored in [1, 2]. Speeded-up robust features (SURF) has been used in [3, 4]. In [5], local binary pattern (LBP) feature is used. Statistically learned mathematical model of the target is an effective appearance model. Based on the examples used for construction and learning of mathematical model, there are two different categories of object tracking: generative and discriminative. The generative object tracker constructs the appearance model using positive examples (tracked samples in successive video frames) only. Incremental visual tracker (IVT) in [6], kernel-based tracker in [7] and L1 based trackers in [8–11] use generative appearance model for visual tracking. The tracking performance can be increased by using the background information, around the target, in the model construction. The background information is used in discriminative object tracking which considers object tracking as a binary classification task. Therefore, the purpose of discriminative object tracking is to learn an effective discriminative classifier to separate the target from the background. The discriminative classifier is based on ensemble learning [12], correlation filter [13, 14], ratio of probabilities [15–17] and support vector machine (SVM) framework [18–21]. Ma et al. in [14] used features learned from convolution neural network (CNN) for correlation computations. Although, the tracking performance of CNNbased tracker is better, tracking speed in terms of frames per second (FPS) is very low. Ning et al. in [21] used discriminative parameter learning called dual linear structured SVM (DLSSVM) using high-dimensional features. The parameter vector learned using high-dimensional feature in linear space is an effective discriminative parameter. The important thing about parameter learning in linear space is that the classification task becomes computationally more efficient
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[21]. It is because, for kernel-based classifier, the classification of a large number of target candidates can be done using non-linear kernels only [19]. Object trackers are also based on hybrid generative discriminative method, wherein both generative and discriminative techniques can be exploited for a better tracking performance [22, 23]. The motion model is used to get the most probable target candidates out of many target candidates. Sliding window and particle filter are the two important approaches of the motion model. The sliding window approach simply considers a high number of target patches around the object in the previous frame as the target candidates [12, 15, 21, 17]. Particle filter-based motion model significantly reduces the number of target candidates [6, 8–10, 24]. However, the tracking accuracy in particle filter-based motion model is degraded because of inadequate target candidates. Correlation filter and spatio-temporal context-based trackers use all samples within a region [13, 25]. In this paper, we use discriminative approach for visual object tracking, wherein the appearance model is learned as discriminative parameter vector. The parameter vector in the first video frame is learned using SVM approach. In the successive video frames, positive and negative samples around the target in the current frame has been selected for parameter update based on classification score computed between parameter vector and some positive example sets. The weight or multiplier value of a pair of positive and negative example set that forms a discriminative vector has been estimated, also based on classification score in some positive example sets. In the proposed parameter learning method, a step is also present to consider the target drift in order to improve the tracking performance. The remaining part of this paper is organized as follows. The proposed parameter learning method is described in Sect. 2. Experimental results and comparisons are given in Sect. 3. Section 4 concludes the paper.
2 Proposed Parameter Learning Method Let wt ∈ Rd (d is the feature dimension) be the discriminative parameter vector in the t-th (current) video frame. The proposed method seeks to learn the parameter vector using a set of positive and negative examples of current frame in order to find the highest classification score from the target sample in next, i.e., (t + 1)-th, video frame. The proposed learning steps are given in Algorithm 1. The entire steps can be explained using four basic subparts as follows.
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A. Construction of Positive and Negative Example Set + + The principal positive example set, X t+ = {xt,i }i=1 with each xt,i ∈ Rd is constructed using tracked samples in the successive video frames. The total number of samples is limited to np. If the number of samples in the set exceeds beyond np, one sample from the middle is removed from successive frames. The other two types of positive + nc + no + d example sets are X Ot+ = {xo+ t,i }i=1 and XC t = {xct,i }i=1 with each xot,i ∈ R + d + and xct,i ∈ R . Set X Ot is formed by samples in the current video frame whose loss (computed using loss = 1 − OR, where OR is the overlapping ratio between the tracked target and the samples) is less than 0.25. The positive sample set XCt+ is ˆ tT xˆ i constructed by taking a few samples whose classification score computed using w d is higher among all target candidates, where xi ∈ R represents a target candidate. The hat symbol on a vector (ˆx) represents the vector of unit. np
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− nn L2 norm. The negative sample set X t− = {xt,i }i=1 is composed of samples (in the current frame) whose loss is more than 0.25. Among all negative samples, some ˆ tT xˆ i− are highest, are top samples (40 in this paper), whose scores computed using w selected for the set X t− .
B. Construction of Discriminative Vector in the Current Frame The first constructed discriminative vector is v ∈ Rd obtained in step 11 of Algorithm 1. It is the difference between vectors x p and xn . Vector x p is simply the tracked sample in the current (t-th) video frame while vector xn is the negative sample from X t− which gives the highest score after the update step. The update step is given in step 5 while the score calculation is given in step 6 of Algorithm 1. After construction of the first discriminative vector, two samples (x p and xn ) are removed from the example set for next iteration (i.e., for Iter value more than 1). The function Remv E L E M E N T (x1:k , xr ) takes a set of k vectors and remove a vector xr (where 1 ≤ r ≤ k) from the set. The number of samples in the output set decreases by 1. In the successive iterations, x p and xn are obtained using step 18 and step 8, respectively. The discriminative vector in successive iterative step Iter is constructed by u I ter ← α1 (x p − xn ), where α1 is the initial multiplier value (α1 = α0 ).
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C. Estimation of Multiplier Value The relative multiplier value (weight) of each discriminative vector is estimated by computing score as give in step 29 of Algorithm 1. The weight is changed by α0 in each step. Using updated weight, the vector w1 is updated as given in step 28. The score for checking the best update (or weight) is computed using positive examples obtained by sets X Ot+ and XCt+ . For each vector u I ter , the maximum weight is limited to th1 while th2 is the maximum weight (represented by αT ) for the entire iterative steps (which is Nit). D. Final Update After updating the individual vectors (i.e., v and u I ter ), the update of discriminative weight from current frame to the next frame wt+1 is given by Eqs. (1) and (2) as, wt+1 ← wt + c0 v
(1)
wt+1 ← wt+1 + sc × M E AN (u I ter )
(2)
where, c0 in Eq. (1) is regularizer that controls the amount of update using best vectors, while sc in Eq. (2) is the weight obtained by Eq. (3) as, + T + T xˆ t,np xˆ t,np−1 − wt+1 | sc = |wt+1
(3)
+ + where, xt,np is the tracked instance in current video frame while xt,np−1 is the tracked instance in previous video frame. If there is large difference between tracked examples in current and previous frames, it might be because of non-accurate tracking or target appearance variation due to several different factors including pose change. The weight computed by Eq. (3) ensures that these factors are taken into account while updating. For very low difference, the tracking is accurate and the update requirement from samples other than best sample (v) is negligible. The MEAN function in Eq. (2) computes the average of all samples. The final update of wt+1 is done + 1 ˆ t+1 )T xˆ t,np−1 . This update is given by Eq. (4) as, by comparing the score (w 1 1 wt+1 ← wt+1 + s f × vˆ 1
(4)
where, sf is a multiplier and vector v1 is the difference between target sample in first video frame and best negative sample. When a target starts drifting, the sample tracked in the previous frame is more accurate than the sample tracked in the current frame. Using Eq. (4), we try to stop the drifting by updating the parameter with the most accurate sample (which is the target in the initial frame). At the same time, it ensures that the update is proper which gives higher score in the previous sample.
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E. Target Tracking After parameter update, the tracking is performed by computing the score (wt+1 )T xt+1,i where,wt+1 is the updated discriminative parameter vector and xt+1,i is the target candidate. The target candidates are cropped using sliding window approach as in [21]. The tracked instance is the one whose score (wt+1 )T xt+1,i is highest among all.
3 Experimental Results All simulations are performed on a Notebook system with Intel i7 processor core, 8 GB of RAM, Windows 10 OS and MATLAB 2016a. The values of various parameters used in this paper are as follows. The number of vectors in set X t+ is np = 24 while in set XCt+ , it is nc = 10. The total number of iterations is set to Nit = 5, while for initial 4 frames, it is equal to frame number. We set other values as th1 = 0.1, th2 = 0.5, sf = 0.001 and c0 = 0.12. The feature set (feature extraction) of each sample used in this paper is the high dimensional feature, also used in [21]. In the initial video frame, the discriminative parameter vector w1 is learned using LIBSVM software [26]. The positive examples for training SVM using LIBSVM are the samples whose loss is less than 0.05. These samples are repeated 100 times. The negative examples are all those samples whose loss is higher than 0.25. The dataset containing challenging video sequences are taken from CVPR 2013 paper [27]. The benchmark dataset is available online at Web. 1 There are two standard metrics for tracking performance evaluation. The first one is centre error (CE) while the second one is overlap rate (OR) [27]. The centre error is calculated by finding Euclidean distance between the ground truth locations of target and the tracked locations. The overlap rate measures the overlap ratio of the bounding boxes between tracked samples and the ground truth from each frame. The OR is given by O R = area(BT ∩BG ) , where BT is the bounding box of the tracked object and BG is the bounding area(BT ∪BG ) box of the ground truth. For comparison of tracking performances, we have simulated some state-of-theart trackers: e.g., DLSSVM in [21], KCF (HOG) in [13], CNN in [14] and STC in [25]. The STC tracker runs very fast but its accuracy is very poor in many different video sequences. For fair evaluation, all the parameters in each tracker have been kept unique. The average centre error, where the average is taken for entire frames of a video, is shown in Table 1. The average OR is shown in Table 2. The NR values in some of the videos for STC tracker is used to indicate that no results could be obtained. From Tables 1 and 2, it can be concluded that the proposed update strategy achieves performance equivalent to state-of-the-art video trackers in terms of CE and OR. The visual tracked results in some of the video frames are shown in Figs. 1, 1 https://sites.google.com/site/wuyi2018/benchmark.
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Table 1 Average centre error Video
STC (in [25])
DLSSVM (in [21])
KCF (HOG) (in [13])
CNN (in [14])
Proposed
Basketball
75.2
18.6
7.8
3.5
7.5
Bolt
139.8
4.1
6.3
6.8
3.8
Boy
25.9
2.8
2.8
2.9
2.8
Cardark
2.8
1.4
6.0
5.8
1.9
Carscale
89.3
13.2
16.1
29.2
13.0
Couple
NR
6.8
47.5
7.3
6.6
Crossing
34.0
2.0
2.2
2.7
2.3
David
12.1
7.9
8.0
7.7
9.9
David 2
5.5
2.0
2.0
3.5
2.2
David 3
6.3
8.2
4.3
3.6
7.5
Deer
400.0
4.3
21.1
5.1
4.3
Dog 1
21.1
5.1
4.2
4.6
3.3
Doll
NR
5.7
8.3
5.2
5.2
Dudek
25.5
12.6
12.0
10.7
14.0
Faceocc
25.0
16.0
13.9
19.4
15.3
Faceocc 2
10.1
7.5
7.6
7.0
13.1
Freeman 1
NR
85.0
94.8
8.0
14.0
Freeman 3
39.4
6.0
19.2
19.9
6.3
Fish
3.9
4.6
4.0
4.3
4.8
Fleetface
85.0
23.4
25.5
27.6
24.4
Football
10.0
5.2
5.8
4.6
6.4
Football 1
72.5
5.1
5.4
2.6
2.4
Girl
21.8
3.6
11.9
3.9
14.6
Jogging (1)
149.9
3.1
88.2
3.8
3.2
Jogging (2)
160.1
6.1
144.4
3.1
7.9
Jumping
67.2
4.6
26.1
4.3
4.5
Lemming
96.2
152.0
77.8
153.4
81.6
Mhyang
4.5
2.7
3.9
2.5
3.3
Mountainbike
7.0
7.4
7.6
7.7
8.1
Shaking
9.6
7.5
112.5
11.1
32.8
Singer 1
5.7
15.4
10.7
9.2
17.7
Soccer
245.4
42.9
15.3
13.3
29.7
Subway
NR
3.1
2.9
2.8
3.7
Suv
51.4
16.0
3.4
5.1
7.1
Sylv
9.4
5.8
12.9
12.6
5.4 (continued)
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Table 1 (continued) Video
STC (in [25])
DLSSVM (in [21])
KCF (HOG) (in [13])
CNN (in [14])
Proposed
Tiger 1
63.5
10.7
8.0
11.7
10.3
Tiger 2
57.4
16.6
47.4
19.2
12.4
Trellis
33.7
7.7
7.7
6.4
9.1
Walking
7.1
6.1
3.9
3.9
6.8
Woman
NR
2.9
10.0
8.4
2.9
Note Italic values represent the best result while bold values represent the second best result Table 2 Average overlap rate Video
STC (in [25])
DLSSVM (in [21])
KCF (HOG) (in [13])
CNN (in [14])
Proposed
Basketball
0.287
0.668
0.674
0.844
0.660
Bolt
0.036
0.763
0.678
0.702
0.769
Boy
0.543
0.779
0.777
0.774
0.782
Cardark
0.750
0.858
0.614
0.639
0.825
Carscale
0.446
0.423
0.419
0.417
0.426
Couple
NR
0.602
0.200
0.586
0.585
Crossing
0.248
0.731
0.710
0.708
0.729
David
0.522
0.544
0.538
0.541
0.530
David 2
0.588
0.835
0.827
0.753
0.825
David 3
0.432
0.717
0.772
0.787
0.700
Deer
0.040
0.752
0.623
0.746
0.752
Dog1
0.506
0.550
0.551
0.542
0.555
Doll
NR
0.568
0.534
0.566
0.567
Dudek
0.587
0.728
0.727
0.734
0.720
Faceocc
0.186
0.756
0.774
0.715
0.771
Faceocc 2
0.689
0.761
0.751
0.759
0.669
Freeman 1
NR
0.217
0.214
0.422
0.344
Freeman 3
0.251
0.346
0.324
0.313
0.340
Fish
0.510
0.824
0.839
0.833
0.820
Fleetface
0.419
0.609
0.589
0.580
0.598
Football
0.593
0.783
0.762
0.801
0.739
Football1
0.354
0.702
0.710
0.808
0.822
Girl
0.334
0.728
0.545
0.702
0.532
Jogging (1)
0.168
0.749
0.185
0.724
0.749
Jogging (2)
0.143
0.724
0.124
0.776
0.744
Jumping
0.069
0.702
0.274
0.711
0.705 (continued)
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Table 2 (continued) Video
STC (in [25])
DLSSVM (in [21])
KCF (HOG) (in [13])
CNN (in [14])
Proposed
Lemming
0.231
0.227
0.384
0.230
0.397
Mhyang
0.688
0.815
0.796
0.812
0.795
Mountainbike
0.583
0.727
0.711
0.704
0.713
Shaking
0.623
0.721
0.039
0.659
0.509
Singer 1
0.531
0.350
0.355
0.355
0.350
Soccer
0.102
0.265
0.422
0.474
0.359
Subway
NR
0.713
0.754
0.781
0.665
Suv
0.512
0.734
0.883
0.816
0.831
Sylv
0.525
0.734
0.643
0.659
0.740
Tiger 1
0.261
0.726
0.785
0.707
0.745
Tiger 2
0.110
0.599
0.354
0.569
0.675
Trellis
0.470
0.621
0.631
0.612
0.612
Walking
0.598
0.543
0.530
0.543
0.550
Woman
NR
0.773
0.705
0.717
0.793
Note Italic values represent the best result while bold values represent the second best result
2 and 3. The Basketball, Bolt and Football videos contain heavy target clutters and also there is appearance variations in the form of pose change and deformation. Our proposed tracker is able to track the targets despite all these challenges.
4 Conclusion In this paper, we propose a discriminative parameter learning method for visual object tracking. The learning method is based on positive and negative example selection and weight assignment to the selected examples. In the proposed method, we try to update the parameter vector while considering the classification (score) of selected positive example set. The update method also considers the target drifting problem. The tracking results of the proposed method are comparable to the state-of-the-art visual object trackers.
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366 Fig. 2 Visual tracked results in (from top to bottom) Football, Jogging (1), and Suv video sequences
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References 1. L. Sun, G. Liu, Visual object tracking based on combination of local description and global representation. IEEE Trans. Circ. Syst. Video Technol. 21(4), 408–420 (2011) 2. H. Zhou, Y. Yuan, C. Shi, Object tracking using SIFT features and mean shift. Comput. Vis. Image Underst. 113(3), 345–352 (2009) 3. J. Li, Y. Wang, Y. Wang, Visual tracking and learning using speeded up robust features. Pattern Recogn. Lett. 33(16), 2094–2101 (2012) 4. W. He, T. Yamashita, H. Lu, S. Lao, Surf tracking, in IEEE 12th International Conference on Computer Vision. (IEEE, 2009), pp. 1586–1592 5. W. Chuan-xu, L. Zuo-yong, A new face tracking algorithm based on local binary pattern and skin color information, in International Symposium on Computer Science and Computational Technology, vol. 2. (IEEE, 2008), 657–660 6. D.A. Ross, J. Lim, R.-S. Lin, M.-H. Yang, Incremental learning for robust visual tracking. Int. J. Comput. Vision 77(1–3), 125–141 (2008) 7. D. Comaniciu, V. Ramesh, P. Meer, Kernel-based object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25(5), 564–577 (2003) 8. D. Wang, H. Lu, M.-H. Yang, Online object tracking with sparse prototypes. IEEE Trans. Image Process. 22(1), 314–325 (2013) 9. T. Bai, Y.F. Li, Robust visual tracking with structured sparse representation appearance model. Pattern Recogn. 45(6), 2390–2404 (2012) 10. X. Mei, H. Ling, Robust visual tracking using l1 minimization, in 2009 IEEE 12th International Conference on Computer Vision. (IEEE, 2009), pp. 1436–1443 11. S. Zhang, H. Yao, H. Zhou, X. Sun, S. Liu, Robust visual tracking based on online learning sparse representation. Neurocomputing 100, 31–40 (2013) 12. Z. Kalal, K. Mikolajczyk, J. Matas, Tracking-learning-detection. IEEE Trans. Pattern Anal. Mach. Intell. 34(7), 1409–1422 (2012) 13. J. Henriques, R. Caseiro, P. Martins, J. Batista, High-speed tracking with kernelized correlation filters. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 583–596 (2015) 14. C. Ma, J.B. Huang, X. Yang, M.H. Yang, Hierarchical convolutional features for visual tracking, in Proceedings of the IEEE international conference on computer vision. (2015), pp. 3074–3082 15. B. Babenko, M.H. Yang, S. Belongie, Robust object tracking with online multiple instance learning, in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 8. (2011). pp. 1619–1632 16. K. Zhang, L. Zhang, M.-H. Yang, Real-time object tracking via online discriminative feature selection. IEEE Trans. Image Process. 22(12), 4664–4677 (2013) 17. K. Zhang, H. Song, Real-time visual tracking via online weighted multiple instance learning. Pattern Recogn. 46(1), 397–411 (2013) 18. S. Avidan, Support vector tracking. IEEE Trans. Pattern Anal. Mach. Intell. 26(8), 1064–1072 (2004) 19. S. Hare, A. Saffari, P.H. Torr, Struck: structured output tracking with kernels, in 2011 IEEE International Conference on Computer Vision (ICCV). (IEEE, 2011), pp. 263–270 20. Y. Bai, M. Tang, Robust tracking via weakly supervised ranking svm, in 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). (IEEE, 2012), pp. 1854–1861 21. J. Ning, J. Yang, S. Jiang, L. Zhang, M.H. Yang, Object tracking via dual linear structured svm and explicit feature map, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. (2016), pp. 4266–4274 22. Y. Xie, W. Zhang, C. Li, S. Lin, Y. Qu, Y. Zhang, Discriminative object tracking via sparse representation and online dictionary learning. IEEE Trans. Cybern. 44(4), 539–553 (2014) 23. Y. Yin, D. Xu, X. Wang, M. Bai, Online state-based structub SVM combined with incremental PCA for robust visual tracking. IEEE Trans. Cybern. 45(9), 1988–2000 (2015) 24. J. Cruz-Mota, M. Bierlaire, J. Thiran, Sample and pixel weighting strategies for robust incremental visual tracking. IEEE Trans. Circ. Syst. Video Technol. 23(5), 898–911 (2013)
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A 46.8 µW/1.12 GHz 7th Stage New Ring Voltage Controlled Oscillator Madhusudan Maiti, Suraj Kumar Saw, Vijay Nath, Swarnendu Kumar Chakraborty and Alak Majumder
Abstract This work unveils an ultra-low power design of single-ended ring voltage controlled oscillator (VCO), with wide tuning capability. The circuit reads an average power and power-delay-product (PDP) of 45.18 µW in pre-layout, 46.8 µW in postlayout and 1.31 fJ in pre-layout respectively when simulated for 90 nm CMOS using CADENCE Virtuoso platform at a control voltage of 1 V and supply voltage of 1.2 V. It also offers a phase noise of −80.75 dBc/Hz in pre-layout, −84.12 dBc/Hz in post-layout at 1 MHz offset and an output oscillation frequency of 1.78 GHz in pre layout and 1.12 GHz post layout. The final physical layout of the proposed circuit estimated an area of 73.49 µm2 . The performance metrics also include a wide tuning range as high as 95.78%, which gets reduced by a small 1.66% during post-layout computation. Corner analyses were performed through 500 runs of Monte Carlo to prove the robustness of the proposed circuit. Keywords Voltage controlled oscillator · Ring oscillator · Clock data recovery · Phase noise · Output noise
M. Maiti (B) · S. K. Saw · S. K. Chakraborty · A. Majumder Department of Electronics and Communication Engineering, National Institute of Technology, Yupia 791112, Arunachal Pradesh, India e-mail: [email protected] S. K. Saw e-mail: [email protected] A. Majumder e-mail: [email protected] V. Nath Department of Electronics & Communication Engineering, Birla Institute of Technology Mesra, Ranchi 835215, Jharkhand, India e-mail: [email protected] S. K. Chakraborty Department of Computer Science and Engineering, National Institute of Technology, Yupia 791112, Arunachal Pradesh, India © Springer Nature Singapore Pte Ltd. 2020 V. Nath and J. K. Mandal (eds.), Nanoelectronics, Circuits and Communication Systems, Lecture Notes in Electrical Engineering 642, https://doi.org/10.1007/978-981-15-2854-5_32
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1 Introduction The voltage control oscillator (VCO) is an essential block in transceiver for phase looked loop and clock and data recovery circuits that are used both in analog and digital communication. It is of two types; viz. LC resonant based approach and ring oscillator based design. The LC resonant based oscillator, made by active devices and coupled with LC resonant, is complex for on chip fabrication, as it takes large surface area due to inductor, consume more energy and its performance is very poor in terms of tuning range, which potentially reduces the data rate [1, 2]. Although LC oscillator has better quality phase noise, ring oscillator has few copious advantages. For this aspect, the ring oscillator based VCO is used frequently for clock generator subsystem circuits in wire line transceivers and digital application because of its simpler integration process, low die area and flexibility for a wide range of tuning [3]. Despite its extensive usage, there are certain areas where it can be improved such as new hybrid design, modeling and analysis of it. Several key areas viz. oscillation frequency, power, area, tuning range, phase noise and application can be looked upon for a better modified VCO design. This has motivated us to delineate a novel approach of hybrid ring VCO design in 90 nm CMOS technology to get the better of the existing works in this study. The ring oscillator consists of cascaded delay cells and is of two types: single ended ring oscillator and differential ring oscillator. For both the types, delay of each stage is controlled by control voltage (Vcont) which is shown in Fig. 1 [1–3]. The oscillation frequency ( f 0 ) depends on number of stages which given by [3, 4] fo =
1 2N td
(1)
where, N is number of stage and td is delay of each stage. Also, the phase noise of the single ended ring VCO [3–6] is expressed as L{ f } =
Fig. 1 Basic block of ring oscillator: a differential b single ended [3]
8 kbt T Vdd f 02 3η P Vchar f 2
(2)
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where, Vchar = V γ V = Gate over drive voltage, kbt = Boltzmann constant, T = Absolute temperature, γ = a coefficient which depend on the device condition. η = characteristics constant and f = Offset frequency. P = Paveg + Psc
(3)
where,P, Psc and Paveg are the total power dissipation, short circuit power dissipation and average power dissipation respectively. Further, the tuning range of the oscillator is expressed by Eq. (4). FTR =
f h − fl × 100 fh
(4)
where, FTR is frequency tuning range, f h is higher frequency component and fl is lower frequency components.
2 Prior Research Despite the fact that voltage control oscillators are used in modern communication systems, it suffers from certain loopholes. One of most popular CMOS LC VCO have been demonstrated previously in different techniques such as inductor optimization, LC tank based oscillator and wideband Bi-CMOS VCO as stated in [7–9]. There was more complexity in fabrication and poor performance in tuning range which adversely affects the data rate. In order to overcome the same, in 2001 Yan et al. represented a CMOS ring oscillator [10], with more penalties in oscillation frequency. In 2003, a novel design approach of inductor and varactor based VCO was offered by Berny et al. [11] only to achieve a large frequency. But it had taken large area and low tuning range of 28%, which was not enough for modern communication circuits. In 2010, Jovanovic et al. [12], proposed some techniques to remove the oscillation frequency instability; but it failed to mitigate the issues pertaining to phase noise. Chen et al. in 2011 [13] came up with a novel approach of dual operation mode ring oscillator where they used differential delay stage and common mode delay stage and it offered better stability in oscillation frequency with some penalties in phase noise and tuning rage. In this regard, Hsu et al. in 2013 [14], presented an ultra low power LC VCO using body-biased technique that improved the phase noise issues and oscillation frequency; but it did not improve tuning range. In order to improve the frequency tuning range, Rout et al. in 2014 [15], recommended the use of current starved ring VCO with more number of repeated transistor delay cells. The design approach was comparatively easier; but oscillation frequency did not match up to the expected level. A new ring VCO topology with inductor peaking was proposed by Li et al. in 2017 [16], which achieved a higher oscillation frequency with a penalty in area overhead and power dissipation. Finally, we can conclude that LC-based oscillators
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offer poor tuning range and area penalty, which has been somewhat addressed by several approaches of ring VCO, which still fails to maintain smaller phase noise. Keeping in mind the above mentioned aspects, in our current work we have proposed a lesser phase noise, low die area hybrid ring VCO with large tuning range compared to LC VCO.
3 Working Principle of Proposed VCO The proposed VCO is a single-ended ring oscillator to attain lower power, lesser phase noise and thermal noise and it consists of two types of delay cell: one is basic inverter and the other is current starved with power switching placed alternatively. The whole circuit arrangement is shown in Fig. 2. The bias circuit consisting of PMOS (P1) and NMOS (N1 and N2) is controlling the current for frequency tuning by control voltage (Vcont). The PMOS (P2) and NMOS (N3) work as a basic delay cell, whereas the other delay cell is made of PMOS (P3 and P4) and NMOS (N4 and N5) to work as current starved with power switching where delay gets changed based on the gate voltage of P4 and N4 transistors. The demonstrated seven stage ring oscillator consists of four basic inverters and three current starved power switching to generate very much stable output frequency with low power dissipation and wide frequency tuning. The output frequency of seven stage ring VCO is inversely proportional to total delay time of the circuit.
Fig. 2 Proposed circuit of VCO
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Fig. 3 Transient analysis of proposed VCO at 1.2 V supply and controlled voltage varying from 0 to 1 V
4 Performance Metrics of Proposed VCO The proposed VCO is designed and simulated on Cadence virtuoso platform in 90 nm CMOS technology with a power supply voltage of 1.2 V. The performance analyses of our proposed VCO are discussed below.
4.1 Transient Analysis Figure 3 displays the transient of the proposed VCO circuit when control voltage is swept from low (0 V) to high (1 V). We have observed that the oscillation frequency increases with the rise in control signal. It reads a stable output frequency of 1.78 GHz when computed at a control signal and supply voltage of 1 V and 1.2 V respectively.
4.2 Effect of Control Voltage Sweep on Output Frequency The control signal is varied to see how the oscillation frequency gets affected and the same is plotted in Fig. 4 at different supply voltage. It is perceived that the output frequency is proportional to control voltage up to a certain value, after that it gets saturated. Again, we observed that at same control voltage and different supply voltages, the proposed VCO schematic is found to read an output frequency of 1.36 GHz, 1.78 GHz, 2.07 GHz, 2.24 GHz, 2.35 GHz, and 2.43 GHz respectively when supply voltage is kept 1 V, 1.2 V, 1.4 V, 1.6 V, 1.8 V, and 2 V respectively. Furthermore, we found that the tuning range of this circuit is noted to be 95.78% at a supply voltage at 1.2 V.
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Fig. 4 Oscillation frequency varies with control voltage
Fig. 5 Output frequency versus temperature
4.3 Temperature Variation Versus Oscillation Frequency The sensitivity of output oscillation frequency of the proposed VCO is tested with temperature variation pre-layout case at different supply voltages 1 V, 1.2 V, 1.4 V, 1.6 V, 1.8 V, and 2 V respectively and is shown in Fig. 5. It is detected that the output frequency gets decreased slowly with the increase in temperature which prove that circuit’s stability.
4.4 Noise Analysis The noise analysis is one of the most important concerns in VCO, as small noise interference could drive to dramatic change in its timing properties and frequency spectrum. Figure 6 presents the phase noise and output noise of proposed ring VCO in pre-layout. When the phase noise of schematic in ‘no skew’ gets changed by tiny
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Fig. 6 Phase noise and output noise analysis at pre layout in nominal state
Table 1 Phase noise and output noise @ 1 MHz offset frequency Pro-cess corners
No skew Phase noise (dBc/Hz)
Output noise (dB)
Five percent process skew Phase noise (dBc/Hz) Mean
Std. dev (m)
Mean
Std. dev (m)
TT
−80.75
−144.5
−80.74
76.78
−144.5
237.4
FF
−79.14
−144.0
−79.14
77.48
−144.0
251.9
FS
−79.87
−145.4
−79.87
68.89
−145.4
208.4
SF
−80.29
−144.2
−80.30
90.88
−144.2
251.9
SS
−80.64
−145.0
−82.63
81.32
−145.0
217.9
Output noise (dB)
0.012% in ‘5% skew’ counterpart, the output noise reads a deviation of zero percent only at nominal corner. Table 1 is summarized the proposed circuits in phase noise and output noise at five process corner in No skew and five percent process skew situation.
4.5 Power and Delay Analysis The performance metrics like power, delay and PDP of the proposed circuit are analyzed at different process corners through five hundred sample runs of MonteCarlo in ‘no skew’ and five percent skew’ condition to check its reliability at 1.2 V supply voltage and 1 V control signal in 10 ns transient time. The histogram of all the three parameters is shown in Fig. 7, which detects very small standard deviation. Figure 8a shows the transient of power which reads an average value of 45.18 µW for pre layout simulation. We have also observed from Fig. 8b that the power dissipation variation with respect to variation of supply voltage. Table 2 summarizes the values of the performance metrics at all corners, e.g., nominal (NN), fast fast (FF), fast slow
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Fig. 7 Histogram plot of a average power, b delay and c PDP
Fig. 8 a Transient response of power; b average power as a function of VDD
(FS), slow fast (SF) and slow slow (SS). While, the average power dissipation, delay and power delay product (PDP) computation in nominal (NN) corner values changed in five percent process skew by 0.044%, 0.034% and 1.2% respectively from no skew condition.
5 Physical Layout The proposed VCO physical layout is shown in Fig. 9 which reads an effective area of as small as 73.49 µm2 (15.72 µm × 4.675 µm) only to massively outsmart the prior arts in terms of area overhead and more compact structure.
45.18
63.53
42.38
39.69
28.89
FS
SF
SS
43.94
95.45
32.73
22.05
29.47
1.27
3.78
1.38
1.40
1.31
28.87
39.68
42.38
63.53
45.16
223.1
649.8
74.4
310.1
266.4
43.98
95.53
32.74
22.06
29.48
Mean (ps)
FF
Delay
Mean (µW)
Std. dev (nW)
Average power
PDP (fJ)
Average power (µW)
Delay (ps)
Five percent process skew
No skew
NN
Process corners
Table 2 Performance metrics of power and delay analysis
661.2
2310.0
277.0
254.7
391.0
Std. dev (fs)
PDP
1.27
3.78
1.38
1.40
1.33
Mean (fJ)
9.28
29.74
9.30
9.35
9.80
Std. Dev (aJ)
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Fig. 9 Physical layout of proposed VCO
6 Performance Comparison The proposed ring VCO circuit is compared with different existing circuits as displayed in Table 3, where it has outplayed the prior works in terms of performance parameters. The proposed circuit does not only enhance the tuning range, but also improves the frequency stability thereby offering very low power consumption and compact physical layout. Table 3 Comparison with prior art Reference
Technology (nm)
TCAS-II, 2013 [17]
EESCO-IEEE, 2015, [18]
JSSC, 2012 [19]
JSSC, 2013, [20]
Automatika, 2015, [21]
IEEE Access, 2017, [22]
This work Pre layout
Post layout
65
90
90
90
180
180
90
90
Supply (V)
1
1
0.7
0.5
5
1
1.2
1.2
Power consumption (mW)
10
0.309
37
0.078
15
2.5
0.0451
0.0468
Oscillation frequency (GHz)
1.01
0.450
1
0.480
0.21
1.03
1.78
1.12
Phase noise at 1 MHz (dBc/Hz)
−119.8
−105.7
−96
−94.0
–
−105
−80.75
−84.12
Tuning range (%)
70
–
12
–
0.06
0.53
95.78
94.19
Layout (mm2 )
22540
946
0.13
1900
1
0.032
–
0.000074
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7 Conclusion The prior works on ring VCO, a basic building block of complex IC; differ in terms of architecture, realization of delay cells and number of stages available. In this article, we have attempted to unearth a new ring oscillator based VCO consisting of basic inverter and current starved with power switching. The proposed design does not only provide low power and lesser area, but also is found suitable to work smoothly at a power supply as small as 0.6 V and better tuning range which improve the data rate in communication circuits. It is tested at five different process corners through five hundreds statistical runs of Monte Carlo to define its effectiveness and robustness to be employed in low-power applications. Acknowledgements We wish to thanks MEITY, Government of India for providing supporting CAD tool environments for carrying out this research work.
References 1. S. Jafar et al., A 10-Gb/s CMOS clock and data recovery circuit with a half-rate linear phase detector. IEEE J. Solid State Circ. 36(5), 761–768 (2001) 2. C.K.K. Yang, R. Farjad-Rad, M.A. Horowitz, A 0.5-/spl mu/m CMOS 4.0-Gbit/s serial link transceiver with data recovery using oversampling. IEEE J. Solid State Circ. 33(5), 713–722 (1998) 3. J. Jalil, M.B.I. Reaz, M.A.M. Ali, CMOS differential ring oscillators: review of the performance of CMOS ROs in communication systems. IEEE Microw. Mag. 14(5), 97–109 (2013) 4. G.S. Jovanovi´c, M.A. Stojˇcev, A method for improvement stability of a CMOS voltage controlled ring oscillators. IEEE (2007) 5. P.K. Rout, D.P. Acharya, G. Panda, A multiobjective optimization based fast and robust design methodology for low power and low phase noise current starved VCO. IEEE Trans. Semicond. Manuf. 27(1), 43–50 (2014) 6. L. Bisdounis, S. Nikolaidis, O. Loufopavlou, Propagation delay and short-circuit power dissipation modeling of the CMOS inverter. IEEE Trans. Circ. Syst. I Fundam. Theory Appl. 45(3), 259–270 (1998) 7. S. Francesco, S. Deantoni, R. Castello, A 1.3 GHz low-phase noise fully tunable CMOS LC VCO. IEEE J. Solid State Circ. 35(3), 356–361 (2000) 8. J.J. Kucera, Wideband BiCMOS VCO for GSM/UMTS direct conversion receivers. Digest of Technical Papers (2001 IEEE International Solid-State Circuits Conference, 2001 ISSCC, IEEE, 2001) 9. D. Ham, A. Hajimiri, Concepts and methods in optimization of integrated LC VCOs. IEEE J. Solid State Circ. 36(6), 896–909 (2001) 10. W.S. Yan et al., A 900 MHz CMOS low phase noise voltage control ring oscillators. IEEE Trans. Circ. Syst. II 48(2) (2001) 11. A.D. Berny, A.M. Niknejad, R.G. Meyer, A wideband low-phase-noise CMOS VCO, in Proceedings of the IEEE 2003 Conference on Custom Integrated Circuits 2003, (IEEE, 2003) 12. G. Jovanovic, M. Stojcev, Z. Stamenkovic, A CMOS voltage controlled ring oscillator with improved frequency stability. Sci. Publ. State Univ. Novi Pazar Ser. A Appl. Math. Inf. Mech. 2(1), 1–9 (2010) 13. Z.Z. Chen, T.C. Lee, The design and analysis of dual-delay-path ring oscillators. IEEE Trans. Circ. Syst. I Regul. Pap. 58(3), 470–478 (2011)
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14. M.T. Hsu, T.H. Han, P.Y. Lee, Design of sub-1mW Q-enhancement CMOS LC VCO with body-biased technique, in Proceedings of the World Congress on Engineering, vol. 2 (2013) 15. P.K. Rout, D.P. Acharya, G. Panda, A multiobjective optimization based fast and robust design methodology for low power and low phase noise current starvedVCO. IEEE Trans. Semicond. Manuf. 27(1), 43–50 (2014) 16. K. Li, F. Meng, Dave J. Thomson, Peter Wilson, G.T. Reed, Analysis and implementation of an ultra-wide tuning range CMOS ring-VCO with inductor peaking. IEEE Microwave Wirel. Compon. Lett. 27(1), 49–51 (2017) 17. J.-M. Kim et al., A low-noise four-stage voltage-controlled ring oscillator in deepsubmicrometer CMOS technology. IEEE Trans. Circ. Syst. II Express Briefs 60(2), 71–75 (2013) 18. B.S. Choudhury, S. Maity, A low phase noise CMOS ring VCO for short range device application, in Proceedings of the Conference on Electrical Electronics Signals Communication and Optimisation, EESCO-IEEE (2015) 19. M.M. Abdul-Latif, E. Sanchez-Sinencio, Low phase noise wide tuning range N-push cycliccoupled ring oscillators. IEEE J. Solid State Circ. 47(6), 12781294 (2012) 20. Y. Ho, Y.S. Yang, C. Chang, C. Su, A near-threshold 480 MHz 78 µW all-digital PLL with a bootstrapped DCO. IEEE J. Solid State Circ. 48(11), 2805–2814 (2013) 21. J. Jin, C. Wang, J. Sun, Novel four phase third-order quadrature oscillators with grounded capacitors. Automatika 56(2), 207–216 (2015) 22. J. Jim, K. Zhou, L.V. Zahoo, Designing RF ring oscillator using current-mode technology. IEEE Access 5, 217
A Current Mode VCO Design Approach for Higher Oscillation Frequency Suraj Kumar Saw, Madhusudan Maiti, Vijay Nath and Alak Majumder
Abstract A new structure of a voltage controlled oscillator (VCO) is proposed to have minimal phase noise. The proposed VCO interpolates five common source stages in ring structure with additional transistors as bias element using control voltage, Vctrl , to steer the current and frequency of the proposed oscillator. Each of the inverters is made up of a driver and a load transistor. The load transistors are biased using a bias voltage, Vbias in order to pump current constantly, while the drivers are enabled depending on the next stage output. The proposed inverter logic with additional transistor improves the gain of the VCO by increasing the slope of the output frequency, fVCO versus Vctrl . The frequency can be tuned between 1.44 GHz and 10.6 GHz by varying Vctrl from 0.0 V to 1.2 V. An average current of 1.77 mA (2.126 mW) was consumed by these five MCML inverters to generate signals with a frequency of 10.6 GHz from a 1.2 V supply while having a phase noise of −138.1 dBc/Hz and output noise, −168.4 dB. Keywords Voltage controlled oscillator · Phase locked loop · Current mode logic · Phase noise · Output noise
S. K. Saw (B) · M. Maiti · A. Majumder Integrated Circuits and System Laboratory, National Institute of Technology, Arunachal Pradesh 79112, India e-mail: [email protected] M. Maiti e-mail: [email protected] A. Majumder e-mail: [email protected] V. Nath Department of Electronics and Communication Engineering, Birla Institute of Technology Mesra, Ranchi 835215, Jharkhand, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 V. Nath and J. K. Mandal (eds.), Nanoelectronics, Circuits and Communication Systems, Lecture Notes in Electrical Engineering 642, https://doi.org/10.1007/978-981-15-2854-5_33
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1 Introduction To operate the circuit in a higher frequency is a key challenge of any integrated analog circuits. Voltage controlled oscillator helps to perform in a high frequency with significant low-power consumption which enable to operate the function of Phase locked loop. A ring oscillator design is more preferable rather than LC tank VCO because LC tank VCO exist large phase noise with huge power supply and complex design. Nonlinearity in aspect of frequency gain (Kvco ) and tuning range is also the vital problem of LCVCO which deteriorates the operational efficiency when it used in PLL as a block, to remove this nonlinearity and tuning range ring oscillator based VCO is preferred. In contemporary days many VCO architecture is proposed to get rid of frequency and tuning range. In [1], new technique is design a voltage-to-current converter which lowers the phase noise and consumes large power with broad chip area to conquer such demerits. Feedback-based transformer Voltage controlled oscillator is design and achieved low-power consumptions [2]. Another approach used by optimizing the cell ratio which results in reduced phase noise and lower flicker noise is found with lower tuning range [3]. To reduce bias current a bias source, the biasing circuit is made with the help of PMOS for getting reduced thermal noise and flicker noise [4]. The performance of the VCO transceiver is depends on power supply noise and phase noise by implementing a bias current source is designed in [5, 6]. To increase the frequency tuning range a new technique has been implemented by using CML front end of the line (FOEL) which helps to enhance the frequency tuning range and observe that full swing is obtained [7]. A new technique portrayed to remunerate the energy drop of the resonator the cross-connected current-reuse pair is utilized as a negative conductance generator [8]. Despite, the operation of conventional MCML circuits is found to be depending upon by current and power supply noise. It is noticed that with the increase in switching frequencies the current increases almost linearly. High-speed signaling technique used a MCML is adopted to generate a wide tuning range with a high-frequency application [9]. The paper is structured as follows: Sect. 1 demonstrates the introduction section followed by deep literature survey, Sect. 2 describes Description of the proposed voltage controlled oscillator. In Sect. 3 illustrate the simulation results and analysis followed by Sect. 4 performance comparison with prior art. The concluding remarks are given in Sect. 5.
2 Description of the Proposed VCO The VCO circuit described in Fig. 1 is a modification of the ring oscillator circuit, where the p-channel gates are controlled to obtain the required operating frequency. It is observed that five common source (CS) stages are cascaded with the output of the last stage fed back into the input of the first stage. Each of the CS stages is
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Fig. 1 Proposed five stage VCO circuits
made up of an n-channel driver and a p-channel load, whose gates are biased, Vbias to generate the desired frequency. Since the load is a PMOS gate, a logic-0 at its gate will enable it, thereby generating voltage levels at the corresponding drain to be used at the subsequent stages. For a gpdk 90 nm process file and supply voltage, Vdd, of 1.2 V the Vbias so required is generated using the gates, NM1, NM2 and NM3. A pulse having a swing of 0.1–1.1 V and switching at 1.2 GHz is applied at the gate of NM3. Thereafter, adjusting the dimensions of the gates, NM1 and NM2 an appropriate voltage level is generated at the source of NM2 such that the PMOS gates, PM1…PM5 are on. While simulating a PMOS gate it is noted that a voltage level close to 0.2 V turns it on. Subsequently, keeping the NM1 and NM2 dimensions fixed at 120 nm results in 0.08 V at the corresponding drain. For a Vdd fixed at 1.2 V the fosc of the VCO can be tuned from 1.44 GHz to 10.6 GHz while varying Vctrl from 0 to 1.2 V.
3 Simulation Results and Analysis The schematic of the VCO circuit is simulated and designed using gpdk 90 nm technology CMOS process file. The oscillation frequency ( f osc ) is given by Eq. (1). f osc =
1 2N tdelay
(1)
where, N is number of stages and tdelay is the delay of individual inverter. Further the frequency tuning range (FTR) is expressed in (2) FTR =
f h − fl × 100 fh
(2)
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Fig. 2 Transient response of proposed five stage VCO circuit
where, f h is the higher frequency component and fl is the lower frequency one. Figure 2 displays the transient of the proposed five stage VCO circuit while Vctrl is swept from 0 V to high 1.2 V. It is worthy to mention that fosc increases with an increase in Vctrl . The fosc is found to be equal to 7.09 GHz when Vctrl is 0.3 V. The slope of the VCO is found to be constant for Vctrl between 0.65 and 1.2 V. Subsequently the FTR is noted to be 7.09–10.606 GHz while Vctrl is swept from 0.3 to 1.2 V for a supply voltage of 1.2 V. Figure 3a portrays the FTR over a wide range of supply voltage. Figure 3b depicts the variation in fosc as a function of temperature and Vctrl . The fosc drops at a rate of 8.87% while Vctrl is between 0.6 and 1.2 V. For Vctrl between 0.6 and 0.3 V there is a drastic drop of 44.3%. This change in drop is identical while temperature varies from −27 to 90 °C. Figure 3c conforms to Eq. (1), where an increase in number of stages lowers the corresponding fosc is shown in Table 1. The Monte Carlo study of a NN file is plotted in Fig. 3d. The value of fosc was about 10.6 GHz, but figure shows that the actual value shift little bit with standard deviation close to 232.537 M.
3.1 Power Delay and PDP Analysis To investigate the reliability and performance of the proposed VCO a 500 run Monte Carlo simulation is performed with no skew and 5% skew. Table 2 depicts the values of average power, delay and power delay product (PDP) while varying the parameters, rise time, fall time, load capacitance and Vctrl . The average power, delay and PDP are estimated and found to have least variation at different corner as shown in Fig. 4a.
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Fig. 3 Plot of frequency versus a Vctrl at different Vdd, b Vctrl at different temperature, c number of stages and d Monte carlo study of fosc for a NN process file
The post-layout variations for the given parameters are also shown in Fig. 4b. For a given NN process file the average power, delay and PDP in the pre- and post-layout are observed to have 0.094%, 0.98%, 0.95%, and 0.047%, 0.98%, 0.95% with respect to 5% skew respectively.
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Table 1 fosc Variation with number of stages
No. of stages
Frequency (GHz) Pre-layout
Post-layout
3
18.277
1.5556
5
10.606
9.211
7
7.813
6.766
9
6.225
5.342
11
5.178
4.43
13
4.433
3.75
Table 2 Power delay and PDP analysis at distinct corner with skew and no skew condition of both pre-layout and post-layout Pre-layout
Process
Corners
No skew
Power (µW)
TT 2.126
Delay (ps) PDP (fJ) 5% process skew
Post-layout
No skew
51.88 139.5
70.38 110.7
σ
90.43
111.6
69.29
Delay (ps)
X
72.38
55.3
77.78
PDP (fJ)
X σ
Power (µW) PDP (fJ)
1.623 153.9
148.7
1.432 122.4
2.577 67.63 174.3 2.58 108.7 73.73 2.084 190.0
SS 1.524 92.07 140.3 1.525 67.34 104 2.187 158.4
2.707
3.227
2.864
3.790
2.092
2.642
1.561
2.52
1.501
74.5 155.9
X σ
89.11
Delay (ps)
X
74.55
PDP (fJ)
X σ
1.34
1.575
SF
3.225
Power (µW)
σ
2.69
1.573
X
σ
2.128
2.69
FS
Power (µW)
Delay (ps) 5% process skew
65.91 140.1
FF
2.093
1.725 155.9 3.07
58.83 155.4 2.641 109.8 58.86 1.34 155.3 2.969
80.69 125.9 1.560 68.79 80.72 1.621 125.8 3.06
75.28 189.7 2.520 106.4 75.36 2.11 189.7 2.754
104.0 156.1 1.502 66.92 104.0 2.45 156.0 3.314
σ = standard deviation and X = mean
3.2 Phase Noise Analysis The noise analysis is one of the important parameter of any VCO design. Small phase noise interference could drive to a dramatic change in timing properties and frequency spectrum while having a wide tuning range. Figure 5 presents the phase
Fig. 4 Monte carlo analysis of average power, delay and PDP a pre-layout, b post-layout
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Fig. 4 (continued)
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Fig. 5 Phase noise both pre-layout and post-layout at 1 MHz offset
Table 3 Phase noise analysis at different corners Corner
TT
FF
FS
SF
SS
Phase noise (dBc/Hz) (Pre-layout)
No skew
−139.3
−140.8
−140.2
−136.6
−136.8
5% process skew
−139.28
−140.6
−140.1
−136.5
−136.6
Phase noise (dBc/Hz) (Post-layout)
No skew
−138.16
−139.8
−139.1
−135.1
−135.3
5% process skew
−139.2
−139.8
−138.1
−135.1
−135.3
noise in pre-layout and post-layout stages. In no skew, the change in phase noise 0.81% while with a 5% skew the error is 0.023% for the pre-layout and post-layout stages respectively. Table 3 depicts the phase noise values at distinct corner for both pre and post-layout cases.
3.3 Post-layout Analysis The layout of the transistor use to build the VCO of Fig. 1 is shown in Fig. 6. The area is found to be equal to 0.117 * 0.09 = 0.105 mm2 . In post-layout interconnect the internal capacitance and resistance are 267 and 129 respectively, which further cause shift in corresponding parameters value (Fig. 7). For NN file the Monte Carlo study of oscillation frequency (fosc ) in post-layout is shown in Fig. 7. In comparison to the pre-layout data the fosc in post-layout is
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Fig. 6 Layout design of proposed VCO circuits
Fig. 7 Oscillation frequency with fixed Vdd and fixed Vctrl post-layout
found to be further shifted by amount 9.1875 GHz, with standard deviation close to 187.210 M.
4 Performance Comparison The proposed circuit is compared with the existing circuits in terms of different performance analysis which portrays in Table 4. The proposed VCO which improved tuning range with enhance performance efficiency in terms of compact layout, less
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Table 4 Comparision tables with different architecture Parameters
[10]
[11]
[2]
Proposed work Pre-layout
Post-layout 90
Technology (nm)
90
90
90
90
Supply (V)
1.2
1.8
0.6
1.2
1.2
Avg. Power (mW)
12.0
3.0
3.0
2.126
2.092
Phase noise (dBc/Hz) @1 MHz offset
−109.6
−116.4
−114.1
−139.3
−138.1
Output noise (dB) @1 MHz offset
–
–
–
−168.3
−168.4
Area (mm2 )
0.16
0.36
0.09
–
0.105
Oscillation Freq. (GHz)
15
20.8
19
10.606
9.21
Tuning range (%)
14
4.8
8.2
1.44–10.6 (86.4%)
1.27–9.21 (86.2%)
delay, power and PDP and also increases the frequency of oscillation Further, a very less phase noise is obtained in both pre and post-layout simulation.
5 Conclusion This paper describes the working of a low power VCO. Eliminating the need of any reference current circuits the proposed VCO attains a wide tuning range, 1.44– 10.606 GHz while varying the Vctrl from 0 to 1.2 V. The post-layout data is 1.27– 9.2 GHz. In addition to tuning range the power in both post-layout and pre-layout stages are 2.091 mW and 2.126 mW respectively. Acknowledgements This work is financially supported by TEQUIP phase III, and technically sponsored to SMDP-C2SD project department of Meity Government of India.
References 1. J. Ko, C. An, C. Kim et al., V-I converter-based voltage-controlled oscillator with improved linear gain characteristic. Electron. Lett. 51(15), 1211–1212 (2015) 2. S.L. Liu, X.C. Tian, Y. Hao et al., A bias-varied low-power Kb and VCO in 90 nm CMOS technology. IEEE Microw. Wirel. Compon. Lett. 22(6), 321 (2012) 3. A. Jerng, C.G. Sodini, The impact of device type and sizing on phase noise mechanisms. IEEE J. Solid-State Circuits 40(2), 360 (2005) 4. J. Yin, H.C. Luong, A 57.5–90.1 GHz magnetically tuned multimode CMOS VCO. IEEE J. Solid-State Circuits 48(8) (2013)
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5. R. Mrakami, T. Ito, K. Okada et al., An ultra-compact LC-VCO using a stacked-spiral inductor. IEICE Electron. Express 8(7), 512 (2011) 6. E. Hegazi, E. Sjöland, A.A. Abidi, A filtering technique to lower LC oscillator phase noise. IEEE J. Solid-State Circuits 36(12), 1921 (2001) 7. D.D. Kim, C. Cho, J. Kim, 5 GHz 11-stage CML VCO with 40% frequency tuning in 0.13 µm SOI CMOS, in IEEE Topical Meeting on Silicon Monolithic Integrated Circuits in RF Systems, 2009. SiRF’09 (IEEE, 2009) 8. L. Zhu et al., IC design of low power, wide tuning range VCO in 90 nm CMOS technology. J. Semicond. 35(12), 125013 (2014) 9. A.J. Mondal, A. Majudmer, B.K. Bhattacharyya, A design methodology for MOS current mode logic VCO, in 2017 IEEE International Symposium on Nanoelectronic and Information Systems (iNIS) (IEEE, 2017) 10. C. Zhang, Z. Wang, Y. Zhao, S.M. Park, A 15 GHz, −182 dBc/Hz/mW FOM, rotary traveling wave VCO in 90 nm CMOS. IEEE Microw. Wirel. Compon. Lett. 22(4), 206–208 (2012) 11. H. Chang, Y. Chiu, K-band CMOS differential and quadrature voltage-controlled oscillators for low-phase-noise and low-power applications. IEEE Trans. Microw. Theory Techn. 60(1), 46–59 (2012)
Internet of Things Based Smart Lighting System Using ESP8266 and Zigbee Animesh Sharma, Ashish Deopa, Anushka Ahuja, Vidushi Goel and Vijay Nath
Abstract We have incorporated smart embedded system that control street light based on detection of sunlight and detection of vehicle and any other obstacle on the street. With this a lot of power can be which is wasted when lights are kept ON even during daytime and glow at night unnecessarily. During the daytime, the lights will be automatically OFF and during night time, whenever a vehicle will approach the street, the light will automatically switch ON. The proposed system provides a solution for energy conservation and makes efficient use of energy by utilizing it for other purposes like providing more electricity to homes, public transportation. We have used embedded system with Internet of Things capability so that all the information, such as real-time information of the street light (ON/OFF status, faults) can be accessed through the internet anywhere and anytime. Keywords IoT · Zigbee · ESP8266 · Adaptive street lights · LED · LDR · IR sensor
A. Sharma · A. Deopa · A. Ahuja · V. Goel (B) · V. Nath Embedded Lab Group, Department of ECE, B. I. T. Mesra, Ranchi 835215, JH, India e-mail: [email protected] A. Sharma e-mail: [email protected] A. Deopa e-mail: [email protected] A. Ahuja e-mail: [email protected] V. Nath e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 V. Nath and J. K. Mandal (eds.), Nanoelectronics, Circuits and Communication Systems, Lecture Notes in Electrical Engineering 642, https://doi.org/10.1007/978-981-15-2854-5_34
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1 Introduction IoT is a network of several devices connected together with enhanced power to talk to each other, human beings, and their surroundings to perform different tasks. IoT can be used to easily monitor and control a large number of devices such as, domestic appliances, automobiles, etc., in an efficient and secure manner. In our work, we have integrated IoT with public street lights and aim to create a Smart Street Lighting System which saves a lot of energy than using conventional street lights. Our main aim is to keep the architecture simple and robust. Power saving can be done by making the lights to be ON only in the absence of sunlight. For more efficiency regarding lighting the bulbs when a vehicle approaches, we use a network of sensors which communicate with each other in a centralized fashion so that a street light knows when a vehicle is approaching it and takes action accordingly. Requirement for a management system is to analyze provided data and take intelligent decisions to ensure efficient usage of power. Home automation using IoT using RFID technology for controlling and monitoring various domestic appliances is discussed in [1]. A review of Zigbee wireless protocol has been discussed in [2]. A detailed description of Zigbee and its topology, usage, etc., has been discussed in [3]. Performance Analysis of Zigbee in Wireless Body Area Network has been discussed in [4]. A detailed definition of IoT, its components and applications have been laid down in [5]. The use of internet of things in security and its detailed architecture has been discussed in [6]. A detailed description of smart cities and their application can be seen in [7]. A framework for creating a smart city has been discussed in [8]. The use of IoT in Energy consumption and power systems have been laid down in [9]. A design of such a smart home automation system complete with hardware modules and software modules are proposed in [10]. A cloud based model of using IoT and Wireless Sensor Networks for continuous sensing is proposed in [11]. This cloud model is based on Aneka on private and public clouds interactions. A cloud-based home automation model, the various interconnection of appliances, its features and architecture is discussed in [12]. Smart agriculture and environment protection techniques using IoT, Smart Grid and Wireless Sensor Network are discussed in [13]. A description of %g technology in self organization has been laid down in [14]. A graphical user interface designed with LabVIEW to monitor various local street lights using a main server where the street light and local controller are connected via Zigbee protocol and IoT is used to connect to the main server [15]. In this project, we use the following components so as to design a smart and adaptive internet based street light system. LED (Light Emitting Diode) is used as it uses very less energy. LDR (Light Dependent Resistor) is used to detect the presence of light to switch ON/OFF during Night/Day respectively. We can use IR sensor as well as piezoelectric sensor to detect incoming objects or vehicles to control street lights in the vicinity. Microcontroller is used to detect the data from the sensor and accordingly control the light.
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Disadvantages of Existing System 1. 2. 3. 4. 5.
Manual Switching is done. More energy consumption Expensive, since light is unnecessarily is let ON Periodic check is must. More man power is required.
Advantages of Existing System 1. 2. 3. 4.
Man power is entirely eliminated. The cost is significantly reduced as lamps are replaced by LEDs The communication is made entirely wireless. The CO2 emission is reduced.
2 Components 2.1 ESP8266 Chip The ESP8266 is a low-cost Wireless-enabled microchip with TCP/IP along with a microcontroller. This chip helps the microcontrollers to connect to a Wi-Fi network in order to make simple TCP/IP connections using Hayes-style commands. ESP8266 comes with the following features: 2.4 GHz Wi-Fi, general-purpose input/output (16 GPIO), Inter-Integrated Circuit (I2 C) serial communication protocol, analogy-todigital conversion (10-bit ADC), Serial Peripheral Interface (SPI), serial communication protocol, I2 S (Inter-IC Sound) interfaces with DMA(Direct Memory Access), UART(on decided pins only), pulse-width modulation (PWM) and PCB trace antenna which has a great coverage. Arduino Uno differs from an ESP8266 board such that the FTDI USB-to-serial driver chip is not used in it. Instead, it has the Atmega16U2 programmed as a USB-to-serial converter.
2.2 Zigbee IoT-enabled wireless protocols follow the IEEE 802.15.4 standard. It controls the Physical and MAC layer of the OSI model. ZigBee is one of the most widely used wireless protocol for street lighting systems whose standard is laid down by ZigBee Alliance and is most efficient for short-range communication. It uses 802.15.4 as MAC layer. Layer 3 and above are defined by some additional services including encryption, authentication, association (only valid nodes can be added to topology), and routing protocol (AODV, a reactive ad hoc protocol). ZigBee has three types of nodes which include coordinator, routers, and end devices. A street lighting system
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has one to one correspondence, and therefore can be implemented easily by using ZigBee as the communication protocol.
2.3 Monitoring Station The monitoring station located in each first lamp at a periodic gap, consists of several components: a sensor to detect presence, light and failure, and also an emergency switch. These devices work together to collect and transfer all of the data to a microcontroller which processes the data and sends it to the local controller using Zigbee and thus automatically sets the appropriate course of action. Each sensor has a priority assigned to it for sending data for transmission, for example, higher priority is assigned to emergency switch.
2.4 Presence Detector This sensor detects the presence of a vehicle or pedestrian which would require the lamps to be switched on. This particular function is dependent on the pattern of the street; if there are no crossroads, one sensor is adequate while for a street which needs more precise control, several presence detectors are essential. This sensor facilitates the turning ON of the lamps only when needed, hence resulting in the low wastage of precious electricity. Careful consideration needs to be given to the placing of the sensor. It can neither be placed too high for risk of not being able to perform detection (e.g., failing to sense the presence of children), nor too low to sense inconsequential entities (e.g., detection of small animals). All objects with a temperature above absolute zero release heat energy in the form of radiation. Although this radiation is emitted at infrared wavelengths and is invisible to the human eye, it can be detected easily by devices designed for such use. An individual PIR sensor detects the difference in amount of infrared radiation striking it. The difference varies in accordance with the temperature as well as other characteristics of the object below the sensor. Whenever there occurs a difference in between the normal temperature and the temperature of the object below, a change in output voltage is caused, thus triggering the detection. The motion sensor will be placed at a particular lamp (first lamp in a series) which will be coordinating the working of the next few lamps in series. This pattern will be repeated throughout the stretch of the road. We came to the conclusion that in industrial applications, the PIR Motion Sensor provides good performance and is cost efficient which is shown in Fig. 1.
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Fig. 1 PIR motion sensor
2.5 Light Sensor A light sensor detects the amount of sunlight and delivers information about the atmosphere and the time of the day. The purpose of this sensor is to ensure that a minimum amount of light is always available on the street for a passerby. An LDR is a component which has variable resistance that changes with the intensity of light falling on it. This makes them worthy of being used in light sensing circuits. LightDependent Resistor (LDR) is composed of a piece of exposed semiconductor material such as CdS whose electrical resistance changes from several thousand ohms in the absence of light to a few hundred ohms in the presence of light by the creation of electron-hole pairs in the material. When it gets dark, the resistance will increase by a significant amount. Owing to Ohm’s Law, current in the branch falls significantly. As a result of low current in the base, PNP transistor switches on. The transistor then amplifies the current which flows from collector to emitter. There is now a potential difference across the street light and current flows through it, lighting it up. The sensor being used must essentially have high sensitivity in the spectrum of visibility, to provide a photocurrent which is big enough for low levels of illumination. On the basis of this luminance, the microcontroller activates the lamp in order to sustain a persistent level of light. This is only required during dawn, dusk and at night since sunlight provides enough illumination during the day. Also, during dawn and dusk, it is not mandatory to operate the lamps at full power since there are still traces of sunlight in the sky. Resistance vs Illumination graph of LDR is shown in Fig. 2.
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Fig. 2 Resistance versus illumination graph of LDR
3 System Description Our proposed system consists mainly of three components 1. LED Street lights section 2. Local controllers 3. Main control server. The LED Street lights section consists of LED Street lights, with LDR and IR sensor attached to it along with an ESP8266 microcontroller to log the sensor data and take actions based on incoming sensor data. Let us call the street lights with these components as intelligent lights (IL) and the street lights without them as conventional lights (CL). The ILs will be placed at a distance of approx. 500 m or 1 km. The CLs between these ILs will only have a Zigbee module attached to it so that all the street lights are connected to each other as Zigbee’s range is 10–100 m. These ILs are in turn connected to Local controllers where we can monitor the street lights falling in the range of the local controller. These Local Controllers are connected to the Internet and send the data to the main control server. The main server is also connected to the Internet which completes our whole IoT application. Our sensor is logged on to the main server so as to visualize and monitor the functioning of street lights in various areas. Block diagram for street lights and Local controller connection is shown in Fig. 3. Local Control server and main server circuit diagram shown in Fig. 4.
4 Methodology This system saves energy consumption in a twofold manner. First of all, we use the LDR sensor to control the light and make it ON only if the surrounding light gets low, i.e., during evening or at night. Also, the lights are not kept ON all night. We
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Fig. 3 Block diagram for street lights and Local controller connection
have used a motion sensor, namely IR sensor to sense if a moving object has crossed the IL (Intelligent Lights). If the IR sensor gives a reading, this data is sent to the next IL so that it can turn ON the CLs (conventional lights) in its proximity. In this way as a moving object moves on a road, the ILs keep on transmitting the message to the next IL and that IL controls the switching of the CLs assigned to it. We can use a timer in our logic so that the lights go OFF after certain duration of time if no object is detected for a certain amount of time. If an object is detected again before the timer expires, we will restart the timer and the same process keeps on repeating. When the timer dies out, it then waits for another deflection in the IR sensor to start its count again. The ILs transfer data continuously between them and to the Local controllers where the functioning of these lights is monitored. These can be then sent to a Main control server which finally stores all this data and provides a GUI to monitor and visualize the proper functioning of the lights.
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Fig. 4 Block diagram for local controller and main control server connection
5 Conclusion In this paper, we have described an intelligent public street lighting module which brings together new technologies, which are on the rise today, to improve the energy efficiency of the existing street lighting system. By making use of LED lights, we are already conserving a lot of electrical energy. With integration of IoT [16–21] based, microcontroller [18] managed lighting functions, the LEDs can acquire an extremely intelligent and sustainable position. By connecting the entire system through the Internet, it becomes very easy to detect errors and also to regularly check up on the maintenance of the lights and the intelligent components. This system is suitable for both urban and rural settings. It is always flexible, extendable and completely open to adaptation in accordance with the demands of the user. The dependability of the sensors, the simplicity of the IoT module, the high speed of processing, the diminished costs, and the ease of use and installation are the various factors which lend this project commercial viability and practical soundness. Acknowledgements We would like to thank the professors of our department, Dr. S. Pal, Head of Department of Electronics and Communication Engineering and Dr. M. K. Mishra, Vice Chancellor. Without their constant support, it would not have been possible to complete this research article.
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References 1. M. Darianian, M. Peter Michael, Smart home mobile RFID-based Internet-of-Things systems and services, in 2008 International Conference on Advanced Computer Theory and Engineering (IEEE, 2008), pp. 116–120 2. C.M. Ramya, M. Shanmugaraj, R. Prabakaran, Study on ZigBee technology, in 2011 3rd International Conference on Electronics Computer Technology (IEEE, Kanyakumari, 2011), pp. 297–301. https://doi.org/10.1109/icectech.2011.5942102 3. P. Dhillon, H. Sadawarti, A review paper on Zigbee (IEEE 802.15.4) standard. Int. J. Eng. Res. Technol. 3(4) (2014) 4. A.S. Appadurai, K.R. Deepak, Performance analysis of Zigbee and OWC in wireless body area network. Int. J. Adv. Res. Electron. Commun. Eng. (IJARECE) 5(3) (2016) 5. K.A.M. Zeinab, S.A.A. Elmustafa, Internet of Things applications, challenges and related future technologies. World Scientific News (2017) 6. J. Lin, W. Yu, N. Zhang, X. Yang, H. Zhang, W. Zhao, A survey on Internet of Things: architecture, enabling technologies, security and privacy, and applications. IEEE Internet of Things J. 4(5) (2017) 7. J. Jin, J. Gubbi, S. Marusic, M. Palaniswami, An information framework for creating a smart city through Internet of Things. IEEE Internet of Things J. 1(2), 112–121 (2014) 8. Anita Gehlot, Rajesh Singh, IoT and Zigbee based street light monitoring system with LabVIEW. Int. J. Sensor Appl. Control Syst. 4(2), 1–8 (2016) 9. G. Bedi, G.K. Venayagamoorthy, R. Singh, R.R. Brooks, K.-C. Wang, Review of Internet of Things (IoT) in electric power and energy systems. IEEE Internet of Things J. 5(2), 847–870 (2018) 10. B. Li, J. Yu, Research and application on the smart home based on component technologies and Internet of Things. Proc. Eng. 15, 2087–2092 (2011) 11. J. Gubbi, Internet of Things (IoT): a vision, architectural elements, and future directions. Future Gen. Comput. Syst. 29(7), 1645–1660 (2013) 12. X. Ye, J. Huang, A framework for cloud-based smart home, in 2011 International Conference on Computer Science and Network Technology (ICCSNT), vol. 2 (IEEE, 2011) 13. L. Li, The applications of wifi-based wireless sensor network in internet of things and smart grid, n 2011 6th IEEE Conference on Industrial Electronics and Applications (IEEE, 2011) 14. M. Dalla Cia, F. Mason, D. Peron, F. Chiariotti, M. Polese, T. Mahmoodi, A. Zanella, Using smart city data in 5G self-organizing networks. IEEE Internet of Things J. 5(2), 645–654 (2018) 15. A. Zanella, N. Bui, A. Castellani, L. Vangelista, M. Zorzi, Internet of Things for smart cities. IEEE Internet of Things J. 1(1) (2014) 16. V. Bohra, D. Prasad, N. Nidhi, A. Tiwari, V. Nath, Design strategy for smart toll gate billing system, in Proceeding of the Second International Conference on Microelectronics, Computing & Communication Systems (MCCS 2017), ed. by V. Nath, J. Mandal. Lecture Notes in Electrical Engineering, vol. 476 (Springer, Singapore, 2019), pp. 615–621 17. Q. Razi, V. Nath, Design of smart embedded system for agricultural update using Internet of Things, in Nanoelectronics, Circuits and Communication Systems, ed. by V. Nath, J. Mandal. Lecture Notes in Electrical Engineering, vol. 511 (Springer, Singapore, 2019), pp. 372–382 18. S. Fatma, V. Nath, Study and design of smart embedded system for train track monitoring using IoTs, in Nanoelectronics, Circuits and Communication Systems, ed. by V. Nath, J. Mandal. Lecture Notes in Electrical Engineering, vol. 511 (Springer, Singapore, 2019), pp. 385–395 19. A. Kumar, V. Nath, Study and design of smart embedded system for smart city using Internet of Things, in Nanoelectronics, Circuits and Communication Systems, ed. by V. Nath, J. Mandal. Lecture Notes in Electrical Engineering, vol. 511 (Springer, Singapore, 2019), pp. 397–408 20. S. Anand, V. Nath Study and design of smart embedded system for remote health monitoring using Internet of Things, in V. Nath, J. Mandal. Nanoelectronics, Circuits and Communication Systems. Lecture Notes in Electrical Engineering, vol. 511 (Springer, Singapore, 2019), pp. 409–414
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Simulating and Designing of High-Order LPF with DFI Suman Nehra, P. K. Ghosh and Meena Singh
Abstract The advance and scalable CMOS technology based higher order lowpass filter has been proposed in this paper. The third-, fifth-, seventh- and ninth-order LPF, LC prototype filters using ladder structure are designed. For the implementation of structure load resistor as active element and floating inductor are used. Current sources and CMOS are used in the implementation of DFI. In the circuit we have varying external bias voltage and current, we can tune floating inductor by this. The simulation is done on Tanner EDA tool 13.0 using 0.5 µm CMOS technology. Keywords CMOS · LPF · Active resistor · PDP
1 Introduction Nowadays, integrated circuit technology is mostly used for the integration of any electronic circuits. As there is development in VLSI technologies, there is demand to fabricate integrated signal processing all in one chip, which is very much good for designing an analog circuits [1]. Basically the circuits of VLSI for analog contain filters, amplifiers, oscillators, digital-to-analog converters and analog-to-digital converter [2]. For key driving factor for any system is are low power dissipation, high packing density, high gain, easy in designing and so on. In analog circuit integration is done using analog filters [3]. CMOS technologies provide low cost, high integration and good reliability which form digital and analog circuit in a single chip [4]. S. Nehra Department of ECE, Bhartiya IET, Sikar, Rajasthan, India e-mail: [email protected] P. K. Ghosh ECE Department, NSHM Knowledge Campus, Durgapur, West Bengal, India e-mail: [email protected] M. Singh (B) EEE Department, University Polytechnic, BIT Mesra, Ranchi, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 V. Nath and J. K. Mandal (eds.), Nanoelectronics, Circuits and Communication Systems, Lecture Notes in Electrical Engineering 642, https://doi.org/10.1007/978-981-15-2854-5_35
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Analog filters are used in different areas such as in communication system designing, medical instruments, industrial electronics, control system design and radar, military application. [5, 6]. Depending upon their application filters are electrical signal as an input and in output, the signal has specified features which may be in terms of time domain or frequency domain. The fundamental nature of a filter can be described by evaluating the frequency dependent behavior of the impedance of inductor and capacitor [7, 8]. Passive inductor is large in size, unable to work at moderate range and the exact values are not very close to each other [9, 10]. So active inductor is used for the designing of filters. In signal processing, LPF are mostly used as a basic element, the main function of LPF is to pass a band of frequency below a cutoff frequency and reject the band of frequencies above than the cutoff frequency. In this paper third-, fifth-, seventh-, and ninth-order LPF are designed by using proposed differential floating inductor [11, 12]. Paper is organized as: In second part, we introduce proposed differential floating inductor. Third part presents the higher order filters which include third-, fifth-, seventh-, and ninth-order low-pass filters. Fourth part provides simulation results and the conclusion of paper is in fifth part of paper.
2 Higher Order Filters In most of the applications of the filters they require higher order filters. As the order of filter increases, the number of components required for the formation of the circuit also increases and the cutoff frequency decreases. In stop band the roll off rate will changes at the rate of 20 db/decade for the first-order filter, 40 db/decade for the second-order filter, 60 db/decade for third-order filter and so on. First-order filters and second-order filter is cascade to design third-order filter. In similar manner fourthorder filter is designed by cascading the two second-order filters [13, 14]. Similarly all other higher order filters were designed. Depending upon the applications of filters and where it is used they are designed for any limit. As the order of the system increases the number of components used in filter will also increases due to which size of the filter is increased. Therefore due to their complex design higher order filter are very expensive. The schematic diagram for third-order RLC LPF is shown in Fig. 1a, topology for fifth-order LPF was shown in Fig. 1b similarly Fig. 1c and d shows the topology for seventh- and ninth-order LPF. Proposed active DFI was designed with the help of CMOS and current sources [15]. Seven CMOS are used in the designing of proposed active DFI [4]. M1 and M2 form the cross-coupled pair, common drain configuration is used in M3 and M4 from dc point of view [16, 17]. For describing the electronically tunable feature of inductor, passive inductors used in the circuit of third-, fifth-, seventh-, and ninth shown in Fig. 1 are replaced by active inductor, with capacitance C of 50 f and current
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Fig. 1 a Third-order LPF topology, b fifth-order LPF topology, c seventh-order LPF topology, d ninth-order LPF topology
sources of 10 µA. Bias currents IB1 and IB2 are varied for different values such as 15 and 5 µA. Its bias voltage Vb is also varied from 1.95 to 2.05 V with the difference of 0.01 V to varying the transconductance of the CMOS. Fifth-order, seventh-order and ninth-order low-pass filter has been designed to show the stretch ability of the circuit. Active inductor is designed by using CMOS implementation. Active filters possess several advantages over passive filters such as high cutoff frequency, lesspower consumption, etc., to achieve these we have replaced the passive components such as inductors used in different topology of filters and load resistance with active components using CMOS.
3 Simulation Results The proposed floating inductor and higher order active low pass filters was analyzed by the use TSPICE. 0.5 µm technology is used for experimental result for the circuit implemented with CMOS. Figure 2a gives the response of third-order active LPF, frequency response for fifth-order LPF as given in Fig. 2b, similarly Fig. 2c and d shows the frequency response for seventh- and ninth-order LPF. The variations in 3 dB frequencies of third-order LPF w.r.t. the bias current (IB ) and bias voltage (Vb ) shown in Fig. 3a and b. These figures reveal the designed filter with the proposed floating inductor gives the appropriate result as compare to conventional active inductor. It is clear from the graph as shown in Fig. 3c and d that designing the filter with suggested active inductor has less power delay product and power consuming than the conventional one. The Fig. 4a shows the variation of cutoff frequency with bias voltage, Fig. 4b shows the variation with bias current and Fig. 4c and d shows the change in power
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Fig. 2 a Response of third-order LPF, b Response of fifth-order LPF, c Response of seventh-order LPF, d Response of ninth-order LPF
consumption and PDP w.r.t. voltage, from these graphs, the conclusion we draw is that the fifth-order LPF with proposed active inductor has more cutoff frequency, less power consuming and less PDP than the conventional filter. For seventh-order LPF variation in cutoff frequency with bias voltage was given in Fig. 5a and with bias current it was shown in Fig. 5b, proposed active DFI in filters shows higher cutoff frequency than the conventional one. In active filter we can easily change the gain and frequency, they are easy to design and analyze, they have low power dissipation than the passive filter. Changes in power delay product and power consumption for seventh-order LPF was shown in Fig. 5c and d, suggested active filter gives the appropriate result as compare to conventional filter. Similarly in Fig. 6a shows the variation of bias voltage with cutoff frequency and Fig. 6b shows variation with bias current ninth-order active filter that was designed with the suggested floating inductor given higher cutoff frequency than the conventional filter. Ninth-order active filter that was designed with suggested floating inductor shows less power delay product and less power consumption than the convention filter, i.e., shown in Fig. 6c and d as order of filter increases the pass band
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Fig. 3 a Plot of change in 3 dB Frequency and bias voltage, b plot of change in 3 dB Frequency and bias current, c plot of change in power consumption and bias voltage, d plot of change in PDP and bias voltage for third-order LPF
Fig. 4 a Plot of change in 3 dB frequency and bias voltage, b plot of change in 3 dB Frequency and bias current LPF, c plot of change in power consumption, d plot of change in PDP and bias voltage for fifth-order LPF
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Fig. 5 a Plot of change in 3 dB frequency and bias voltage, b plot of change in 3 dB frequency and bias current, c plot of change in power consumption and bias voltage, d plot of change in PDP and bias voltage for seventh-order LPF
Fig. 6 a Plot of change in 3 dB frequency and bias voltage, b plot of change in 3 dB Frequency and bias current for ninth-order LPF, c plot of change in power consumption, d plot of change in PDP with bias voltage for ninth order LPF
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Fig. 7 a Frequency curve in terms of order of filter, b plot of change in 3 dB Frequency and bias in terms of filter order, c plot of change in 3 dB Frequency and bias current in terms of filter order, d plot of change in power consumption and bias voltage w.r.t the order of filter
frequency decreases. As we increase the order it follows the ideal curve of LPF. Figure 7a shows the response of LPF in which order is taken as function. Changes in cutoff frequency with bias current and voltage as a function of filter order was given in Fig. 7b and c. The simulation shows that there is decrease in pass band frequency when filter order is increased. Figure 7d shows that the filter with lowest order, i.e., third-order has least power consumption and the ninth-order lowpass filter shows the highest power consumption. There is a increase in the number of components used in the circuit as order of the filter is increased due to this power consumption of the circuit is also increase. Table 1 provides the simulation results and design parameters used for third-, fifth-, seventh-, and ninth-order low-pass filter.
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Table 1 Design parameters and values for third, fifth, seventh, and ninth order low pass filter S. No.
Design parameters
Values
1.
Supply voltage (VDD )
2.5
2.
Bias voltage (Vb )
2
3.
Supply voltage (Vbb )
2
4.
Supply voltage (VDD )
2.5
5.
C1 = C2 = C3 = C4 = C5 = C6
50f
6.
Current sources
10 µA
7.
Resistance R1
1 K
8.
Cutoff frequency for third-order proposed LPF
53.44 MHz
9.
Magnitude for third-order proposed LPF
−1.85 dB
10.
Cutoff frequency for fifth-order proposed LPF
10.06 MHz
11.
Magnitude for 5rd order proposed LPF
−0.02 dB
12.
Cutoff frequency for seventh-order proposed LPF
9.39 MHz
13.
Magnitude for seventh-order proposed LPF
−0.03 dB
11.
Cutoff frequency for ninth-order proposed LPF
6.72 MHz
12.
Magnitude for ninth-order proposed LPF
−0.5 dB
4 Conclusion The key factor for the study and analysis of filter is its many application in different areas such as signal processing, medical instruments, control system, communication system, industrial electronics, etc. Depending upon the application and given conditions there are various methods for the designing of a filter. A floating inductor using 7-CMOSs with two current sources has been proposed. In this paper 0.5 µm CMOS technology is used.
References 1. B. Razavi, Design considerations for direct-conversion receivers. IEEE Trans. Circuits Syst. II 44(6), 428–435 (1997) 2. P.R. Gray, P.J. Hurst, S.H. Lewis, R.G. Meyer, Analysis and Design of Analog Integrated Circuits, 4th edn. (Wiley & Sons Pvt. Ltd., 2009) 3. J, Yoon, H. Kim, C. Park, J. Yang, H. Song, S. Lee, B. Kim, A new RF CMOS Gilbert Mixer with improved noise figure and linearity. IEEE Trans. Microw. Theor. Techn. 56(3) (2008) 4. L. Lu, H. Hsieh, Y. Liao, A wide tuning-range CMOS VCO with a differential tunable active inductor. IEEE Trans. Microw. Theor. Techn. 54(9) (2006) 5. S. Heydarzadeh, P. Torkzadeh, 1 GHz CMOS band-pass filter design using an active inductor and capacitor. Am. J. Electr. Electron. Eng. 1(3), 37–41 (2013) 6. J. Manjula, S. Malarvizh, Performance analysis of active inductor based tunable band pass filter for multiband RF front end. Int. J. Eng. Technol. (IJET) 5(3) (2013), ISSN: 0975-4024
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Spelling Correction of OCR-Generated Hindi Text Using Word Embedding and Levenshtein Distance Shashank Srigiri and Sujan Kumar Saha
Abstract Optical Character Recognition (OCR) systems for Indian languages including Hindi often suffer from poor accuracy due to the wide character variety, compound characters, vowel signs, and other issues. Spelling errors play the key role in the low accuracy. Output level post processing can be an effective technique to detect and correct the spelling errors. In this paper we propose a system for spelling error correction from OCR-generated text in Hindi. The proposed technique is based on neural word embedding and Levenshtein distance. The Continuous Bag-of-Word (CBOW) model is used to learn the word embeddings using large corpora. Dictionary, named entity recognition and context information are used to detect the spelling errors. Once the errors are detected, then the word embeddings are used to find the top most likely words in that context that can replace the erroneous word. These words form the candidate list of words. Then Levenshtein distance is computed between the wrong word and these candidates list of words. The word having the least distance is chosen as the corrected word. The system is tested on ‘The Gita’ where we achieve a reasonable accuracy. Keywords OCR text · OCR postprocessing · Spelling error correction · Word embedding
1 Introduction The necessity of digitalization is rapidly increasing in the modern era. Due to the growth of information and communication technologies (ICT) and the wide availability of handheld devices, people often prefer digitized content over the print materials including books and newspaper. Also, it is easier to organize digitized data and analyze them for various purposes. So, a huge amount of data is being produced and S. Srigiri (B) · S. K. Saha Department of Computer Science & Engineering, BIT Mesra, Ranchi 835215, JH, India e-mail: [email protected] S. K. Saha e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 V. Nath and J. K. Mandal (eds.), Nanoelectronics, Circuits and Communication Systems, Lecture Notes in Electrical Engineering 642, https://doi.org/10.1007/978-981-15-2854-5_36
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transformed into a digital form including various books, magazines, and newspapers daily. The easiest approach is to convert a print text media to image formation. However, the images of texts are not machine-readable. Again, manual conversion of a book to a digital format is highly time-consuming and costly. So, we need Optical Character Recognition (OCR) to perform the digitalization. When we focus on the task of converting old literature and books into digital form for preservation, first we use an OCR system to perform the conversion task. We found that a considerable amount of effort has been devoted for OCR development for various Indian languages the last few years. Examples of OCR systems are Google’s Vision API, Tesseract, etc. Many of the existing OCR systems, especially for Hindi, generate some amount of errors during the conversion process. However, an error-free text is expected as the output of the system to make the text usable for further applications. An output level post processing is one of the possible approaches for correction of the OCRgenerated text. Post-processing is a crucial task in improving the quality of OCRgenerated output, which is important to the success of any text analyzing system. An OCR Post-processing model attempts to detect and correct various mistakes in the noisy OCR output. The spelling mistake is one of the most critical issues in performing digitalization. Especially, the majority of the Indian languages including Hindi are morphologically rich. The major difficulties include a large number of symbols in the vocabulary, compound characters including compound consonants (two or more consonants are compounded to make a new representation having a valid symbol), and vowel signs (called ‘matra’, where a consonant is compounded with a vowel). These also make the digitization task even difficult. Identification of all these correctly through an OCR system is difficult. So, to remove the errors in the OCR-generated text, we propose an output level rectification process as a post-processing step. In this paper, we propose an OCR post-processing error correction model. The major components of the proposed approach include the neural word embedding and edit distance. The Continuous Bag of Word (CBOW) model is trained to capture the semantic similarities of the words. After identification of the incorrect words, we apply the CBOW model to get the most possible words that occur in similar context. These words create the candidate list. The list is ranked based on the probability. Then we compute the edit distance between the incorrect word and candidate list word. Now the word having the least distance is chosen as the replacement. When multiple words having the same least distance value are found, then the word having the higher probability is chosen. Our system works with an accuracy of 65% considering the top most prediction and an accuracy of 71.67% if top three words are taken into account. To sum up, our contributions are as follows. We propose an OCR post-processing model which integrates OCR-specific features and performs the correction. The proposed technique makes use of neural word embedding. The evaluation result shows that the proposed model is capable of providing high-quality candidates in the top suggested list.
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2 Related Works The research of OCR post-processing already has a large variety of models for correcting OCR-generated errors. The post-processing model can be viewed as an integrated system with features which detect and correct misspellings of both nonword and real-word errors in the OCR-generated text. There are two ways of spelling correction. They are interactive spelling correction and non-interactive or automated spelling correction. Our work majorly focuses on non-interactive spelling correction, working on the text that has been produced by the OCR. In our model the correction is performed automatically by taking into account the context as well as the string distance. An automated spelling correction system can be applied in many diverse fields such as integrating them with the databases and are a very powerful resource in restoring ancient books and documents. Recently, several contributions have been made in the area of OCR spelling correction taking into account the Chinese Medical Records [1]. Morphological Analyzer and a Named Entity Recognizer have been the fundamental components of their system. Wordnet has been used in several papers. An n-gram model, A* search, and trie has been applied by paper [2] for word similarity weights, correction candidate proposal and manual sentence alignment. Estimation of probability of occurrence of an edit operation is done from the training corpus. Later A* search is used. A* search gives a weighted list of correction candidates. Reordering of correction candidates is done to determine the best correction. The paper focuses that the context plays much important role than the edit operation on classification. Paper [3] uses two types of knowledge implicit knowledge and explicit knowledge. It also uses Hidden Markov Models and Viterbi search to dynamically search the pattern. The best sequence of output states (which are corrections or correct words) can be found using the Viterbi algorithm with respect to any particular hidden Markov model. Papers [4–7] use recurrent neural networks and other techniques for better text recognition. Papers [8, 9] use Deep Learning and Neural Networks.
3 Proposed Technique In this section, we discuss in detail the various steps used for processing, in our proposed model. In our proposed model, we use lexicons which are basically a list of unique words. We use several resources during the process of detection of errors including lexicons and a Named Entity Recognizer. The tools used in our proposed model are Tesseract, Gensim, etc.
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3.1 Error Detection Error detection is the first step in the OCR post-processing model. Error detection step identifies the wrong words or errors. Since the correct words will not proceed through the further post-processing steps we only mark those words as correct which we are highly confident of. We have used lexicons and n-grams extracted from a large corpus for spelling error detection. Spelling Errors can be divided into two types, (1) Invalid words indicate words that are not present in the vocabulary, and (2) Real-world errors where the word is present in the vocabulary but it was not actually meant. We have used a named entity recognizer, morphological analyzer and a set of rules to detect the errors. We also maintain list of named entities that could occur in our context.
3.2 Error Correction For error correction we use a combination of neural word embedding and edit distance. The generic workflow of the proposed system is presented in Fig. 1. The individual components are discussed below.
Fig. 1 High-level workflow of the system
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3.3 Levenshtein Distance For the process of correction, it is required to determine the similarity between two strings. By using the similarity one can try to solve the spelling correction. The most accepted concepts of string similarity are Levenshtein Distance and Longest Common Subsequence. Levenshtein Distance gives the minimum number of Insertions or deletions or replacements that are required to transform one string into another. According to Levenshtein Algorithm, the possible operations are 1. Replacement of one letter with another letter. 2. Deletion of a character. 3. Insertion of a character. Let s1 be a string of length n and another string s2 be a string of length m. The Levenshtein Distance is calculated recursively as follows: Let a, b, c be integers. a = Edit_Distance (s1, s2, n − 1, m − 1) + (s1 [n − 1]! = s2 [m − 1]); b = Edit_Distance (s1, s2, n − 1, m) + 1; c = Edit_Distance (s1, s2, n, m − 1) + 1; levenstein_distance = min (a, b, c); The minimum of a, b, c is the minimum number of edits required to transform string s1 into s2 or vice versa. To perform the above operation for Hindi Language we use a transliteration scheme called as WX Converter.
3.4 WX Converter To perform the aforementioned task, we use a transliteration scheme to represent Hindi in ASCII.1 This scheme was developed at IIT Kanpur. Features of this transliteration scheme are: Each consonant and vowel has a unique mapping into ASCII. This is advantageous in computational view. The usage of small case letters is for un-aspirated consonants and short vowels whereas to denote the aspirated consonants and long vowels we use capital case letters.
1 https://github.com/ltrc/indic-wx-converter.
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3.5 Word Embeddings A word embedding is a method that provides dense vector representation of the words, capturing some information about their meaning. They (word embeddings) basically use an algorithm for training a set of continuous valued and fixed-length dense vectors which are based on a large corpus of text. Every word is represented by some point in the embedding space. These points are studied and then moved around based on the surroundings of target word. Word2Vec is an algorithm used for learning word embeddings from any available text corpus. The two main training algorithms which can be used to learn the embedding from corpus: they are continuous bag of words (CBOW) and skip grams. Gensim provides Word2Vec class to work with a Word2Vec model. We use the above class and its methods to predict the most probable words that could occur in the context.
3.6 Continuous Bag of Word Model One approach to get the words that could occur in a context is by Continuous Bag of Words model (CBOW). Continuous Bag of words is basically predicting a center word from its surrounding context. The known parameter in our model is a sentence and it is represented by one-hot word vectors. One-hot vectors which is our input is represented by x(c) . And the only output obtained from the CBOW model is represented by y(c) (Fig. 2). Notation for CBOW model2 : 1. wi : Word i from vocabulary V 2. v ∈ R n·|V | : Input word matrix 3. u ∈ R |V |·n : Output word matrix vi , u i : i-th column of v,the input vector representation of word wi and the i-th row of u, the output vector representation of word wi respectively. Steps involved in the CBOW 1. Consider a window of size m and generate one hot vectors (x (c−m) , . . . , x (c−1) , x (c+1) , . . . , x (c+m) ). 2. Get the embedded word vectors for the above one hot vectors. (λc−m = vx (c−m) , .., λc+m = vx (c+m) ). 3. Calculate the average of these vectors vˆ = (λc−m +λc−m+1 +· · ·+λc+m )÷(2 ∗ m). 4. Generation of a score vector z = u vˆ . 5. Turning those scores into probabilities using softmax yˆ = so f tmax(z). Formulating our objective we get minimi ze J = − log P(wc |wc−m , .., wc−1 , .., wc+m ). 2 http://cs224d.stanford.edu/lecture_notes/notes1.pdf.
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Fig. 2 Demonstration of CBOW model (see Footnote 1)
= −lop P(u c |ˆv ) |V | = −u cT vˆ + log
j=1
exp(u cT vˆ )
3.7 Error Correction With the Proposed Model After identifying the wrong words present in the sentence with the help of aforementioned error detection tools, the incorrectness can be resolved by using (CBOW) Continuous Bag Of Words. Each sentence of the text file is passed through the Error Detection step. With the help of lexicons, named entity recognizer we can know if each word of the sentence fits well or not. If the word does not fit well then we know that, there, a conversion error has taken place and now we need to rectify the mistake. Now after applying CBOW at the context of wrong word, we have a group of words which could possibly replace the wrong word in the sentence, in the decreasing order of the probabilities of their occurrence. Now then, we can apply Levenshtein distance
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algorithm and the first occurrence of the minimum edit distance is the word we have been searching for. If the Levenshtein Distances are same for two words, then the word with higher probability is taken into account.
4 Result and Discussion Here we present the results we achieved in our experiments.
4.1 Working with an Example First, we explain the working of the system with the help of an example. Let us ”. consider the example, “ is the wrongly converted word. Applying the Continuous Bag of Words Now w.r.t the context we get the following words and their respective probabilities that could possibly occur in place of . As of now we consider the results of the top 5 words with highest probabilities. The results are shown in Table 1. Now we apply the Levenshtein Distance algorithm to each of the words, the word with the least Levenshtein distance is predicted as the most possible output. If two or more words have same Levenshtein distances then the word with the highest probability is considered as the answer. (huA) because it has Therefore, the predicted output word in the above case is the least Levenshtein Distance in the list of words considered for the Error Correction that has been obtained from the CBOW Model. It correctly predicted the output. If two or more words have the same Levenshtein Distance w.r.t the wrong word, then the word having the highest probability among these two or more words is selected. Table 1 Words, Probabilities and their similarity w.r.t. wrong word Word
Probability
WX notation
Levenshtein Distance w.r.t humA ( )
0.00690721
rahawA
4
0.00686402
lewA
3
0.00274093
xewA
3
0.00184459
huA
1
0.00074632
kEsA
3
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Table 2 Accuracy of the system with Word2Vec parameters S. no.
Min. count
Vector size
Accuracy (topmost word only) (%)
Accuracy (considering top 3 words) (%)
1
10
200
60
66.67
2
30
200
53.33
60
3
10
300
65
71.67
4
30
300
48.3
56.67
5
10
400
43.3
51.67
4.2 Experimental Setting We considered ‘The Gita’ to perform the OCR translation and post-processing steps. The name of the OCR used is Tesseract. We used a text corpus of size 3.93 gb to train our model. The training corpus belongs to IIT Bombay. The test data has around 220 sentences (including right and wrong sentences) out of which 129 sentences have not been correctly translated by the OCR. The proposed postprocessing module is applied on the OCR-generated text to correct the errors. The results are presented below.
4.3 Experimental Results First, we perform experiments to finalize the Word2Vec parameters. We had conducted a set of experiments by varying the different parameters. Table 2 summarizes our experiments through Word2Vec parameters. Here vector_size represents dimensionality of word vectors. MinCount is used to ignore all words with total frequency less than the MinCount. The highest accuracy is achieved by considering the word vectors of size 300 and minimum count of 10. With these parameters, the accuracy of the system becomes 65% if only the top most word is considered. The accuracy has been computed manually by verifying the automatically generated word with the actual word. To check the ranking capability of the system, we perform another set of experiments where the system predicts three words and actual word is one of these. With this setting the accuracy increases to 71.67%. However, only the most probable word is required to be predicted by the system. Therefore, the final accuracy of the system is 65%. This value implies 65% of the errors generated by the OCR can be corrected by the proposed system. So, the value is impressive, and the proposed system can be combined with the existing OCR system as a postprocessing unit to improve the overall accuracy of the OCR. We provide no comparative study because, there exists no standard data set based on the Gita domain to compare our model, to the best of our knowledge.
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5 Conclusion and Future Scope To summarize, the proposed system helps in correcting the OCR-generated output. We use Lexicons, morphological analyser, CBOW model, Levenshtein distance and a large text corpus for the process of correction. Our model works with an accuracy of 65%. The work can be further extended by using a sophisticated machine learning algorithm and string similarity computation algorithm. Semantic analysis can also be performed for better prediction of the candidate list. We can also use word sense disambiguation techniques to further improve its accuracy. The proposed technique can be extended to handwritten text recognition through OCR.
References 1. Y.Y. Lv, Y.I. Deng, M.L. Liu, Q.Y. Lu, Automatic error checking and correction of electronic medical records. Front. Artif. Intell. Appl. 281, 32–40 (2016). https://doi.org/10.3233/978-161499-619-4-32 2. J. Evershed, K. Fitch, Correcting noisy OCR: context beats confusion, in Proceedings of the First International Conference on Digital Access to Textual Cultural Heritage, DATeCH’14 (ACM, New York, NY, USA, 2014), pp. 45–51. https://doi.org/10.1145/2595188.2595200 3. D. Hladek, J. Sta, S. Onda, J. Juhar, L. Kovács, Learning string distance with smoothing for OCR spelling correction. Multimedia Tools Appl. 76(22), 24549–24567 (2017) 4. Wu Hao, Ke Zhang, Guohui Tian, Simultaneous face detection and pose estimation using convolutional neural network cascade. Access IEEE 6, 49563–49575 (2018) 5. C. Yao, X. Bai, W. Liu, Y. Ma, Z. Tu, Detecting texts of arbitrary orientations in natural images, in Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition (2012), pp. 1083–1090 6. A. Mishra, K. Alahari, C.V. Jawahar, Scene text recognition using higher order language priors, in Proceedings of the 23rd British Machine Vision Conference (2012) 7. T. Novikova, O. Barinova, P. Kohli, V. Lempitsky, Large-lexicon attribute-consistent text recognition in natural images, in Proceedings of 12th European Conference on Computer Vision (2012), pp. 752–765 8. S. Rawls, H. Cao, E. Sabir, P. Natarajan, Combining deep learning and language modeling for segmentation-free OCR from raw pixels, in 1st International Workshop on Arabic Script Analysis and Recognition, ASAR 2017 (Nancy, France, 2017) 9. C.M. Bishop, Neural Networks for Pattern Recognition (Oxford University Press, Inc., New York, NY, USA, 1995)
NLIIRS: A Question and Answering System for Unstructured Information Using Annotated Text Segment Comparison Banerjee Partha Sarathy, Chakraborty Baisakhi, Tripathi Deepak, Gupta Hardik and Sourabh S. Kumar Abstract In the current scenario, handling of the unstructured data and extracting information is one of the most crucial aspects. This paper proposes a novel system, Natural Language Information Interpretation and Representation System (NLIIRS) which can accept information in natural language text and can answer to the queries without storing or converting the data into natural language text. Nowadays the presence of available information is large in number in the light of unstructured data. The unstructured information that we get is in the shape of natural languages texts. Protection is needed for the government operative information or any delicate data so that it might be best used when the data can be extricated effectively and effortlessly. Natural Language Information Interpretation and Representation System (NLIIRS) acknowledges the data as characteristic natural language text, process the data and enables the client to recover data by rendering question in natural language. The inquiries subsequently asked are reacted by NLIIRS as expression based answers. The entire frameworks have been composed on Natural Language Tool Kit (NLTK) of Stanford University which helped us to create POS tag, tokenize the information, and shaping the tree structure. The Noval content handling calculation uses the lemmatizer, stemmer and ne chunker to set up the content for data recovery through Q&A. The upside of this framework is that it need not bother with preparing or training. This framework will empower the client to recover any data of his/her decision from the accessible unstructured data. Keywords Lemmatizer · NLP · NLIIRS · NLTK · ne chunker · Question and answering · Stemmer
B. P. Sarathy · C. Baisakhi National Institute of Technology, Durgapur, WB, India T. Deepak · G. Hardik (B) · S. S. Kumar Jaypee University of Engineering and Technology, Noida, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 V. Nath and J. K. Mandal (eds.), Nanoelectronics, Circuits and Communication Systems, Lecture Notes in Electrical Engineering 642, https://doi.org/10.1007/978-981-15-2854-5_37
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1 Introduction The information accessible on the internet and additionally of over different electronic correspondence direct is generally as unstructured information. This unstructured information is as NTL. The best trial of the process is that, regardless of whether the framework has grasped the information content or not is to fire an inquiry and replying over the data that has been given to it. Data recovery utilizing inquiry and noting can be performed in three unique circumstances. The primary condition is when organized data as the table is accessible. The alternative for data recovery for these situations is by and large as an organized query language. The second condition is the point at which no organized data is accessible yet the inquiry and noting framework (Q&A framework) are prepared with a specific dataset. The third circumstance is the point at which the framework does not utilize any preparation information, and the made inquiry is terminated straightforwardly on the information message by utilization of the content handling calculation. These sorts of frameworks are relatively quick. At the point when the access data in the everyday life is expanding it is basic to have a framework that can acknowledge information in the unstructured shape and enables that client to recover data effortlessly. In organized information, the data can be recovered effectively by the utilization of an organized question dialect. While recovering the data from an organized information the client must be sufficiently gifted to take in the question dialect. The proposed framework gives the flexibility to the client to make an inquiry in common dialect without taking in the unpredictable grammar of the organized question dialect. The NLIIRS makes the procedure of data recovery less demanding by enabling the client to make the inquiry in common dialect. This NLIIRS acknowledges the information data as any common dialect content. This acknowledged content is prepared by the proposed content handling calculation and takes the client inquiries as a question asked in normal dialect. The NLIIRS gives the appropriate response in tidbit frame. It answers the questions of “who”, “where” and “when” class. The framework answers questions of “what” class with lesser efficiency.
2 Utility This framework discovers its utility where the strong structure is required to process the accessible data which is in natural language. It discovers use in missionbasic circumstances like continuous preparing of data and furthermore recovering the required data in the event that it is accessible in the information content. For instance on account of protection utility when a client experiences any data in the unstructured frame he or she would then be able to remove any applicable data by just putting an inquiry in common dialect. At the point when touchy data is to be removed continuously, from unstructured information then this framework discovers its utility. The information gets prepared and the data is extricated with no altering
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consequently upgrading the security of the framework. All in all, it can likewise be used for removing the tidbit data from given unstructured information, for instance, to separate the origination of Lord Rama frame the given book of Ramayan. This sort of utilization makes it a correct contender for the instructive business. It can likewise be utilized as a program expansion to seek and recover just the significant data from the agave website page or web blog. The utility of the proposed work is more wide spread as it is better than the factoid answer search techniques described in [1]. The application of transfer learning on reading comprehension may prove to be more beneficial for application in a wider range of applications as in [2].
3 Advantages The primary points of interest of the proposed NLIIRS is that the framework takes less memory then Structured Query Language (SQL) based framework as the proposed show scans for significant hubs as opposed to looking through the whole segment while if there should be an occurrence of a SQL framework. The framework can be refreshed effortlessly as more inquiry structures or cases can be included effectively. On the off chance that a superior Parts Of Speech (POS) tagger or Named Entity Recognizer (NER) is accessible in future, the framework could refresh its NER bundle. Client preparing of the framework is not required. The proposed work keeps away from noting the inquiry when the pertinent data is not accessible in the information content. For the most part, different models give wrong answers as yield, when the information is not accessible in the information content. A few frameworks utilize a prepared framework for Q&A. This preparation procedure includes preparing the Q&A framework on a specific space on which the Q&A is to be performed. Numerous past proposed modules needed to store the info message yet in this proposed show the information is not put away thus making the framework considerably quicker. The application of neural network as in [3] may also prove to be efficient. One of the interesting highlights of this framework is that it underpins content to discourse transformation henceforth the client can simply enter the content on which the Q&A. After this, the client needs to make an inquiry and the framework will naturally stand up the tidbit reply. It can likewise respond in due order regarding the aloof type of characteristic dialect content like if the info content is: “The apple was eaten by John” and the regular dialect inquiry is who ate an apple then the NLIIRS framework will give the right answer. Another most imperative element of the proposed display is that it can give the right response to an inquiry whose outcome has in excess of one formal person, place or thing. In some work that has already been done the method of text sequence matching has been mentioned as advantageous as in [4].
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4 Related Work The basic Named Entity Recognition (NER) based Question and Answering (Q&A) uses the concepts of machine learning as stated in [5]. These systems are generally not so much reliable in expressing the semantics of the natural language text. The method of skip gram is applied to solve the problem. The mapping of NER model of Q&A to the skip gram model has been done so as to achieve better results. The author has shown in [5] that the precision, recall and accuracy have considerably improved by adopting the current technique. The concept of NER and Q&A has always played a key role in designing systems based on natural language processing. NER is important in the process of information extraction as illustrated in [6]. The work said above uses a fuzzy support vector machine based NER for the model. The above technique is used to filter the Q&A model for factoid answering. The purpose of the Q&A system is to extract to the point answers for the questions asked in natural language. The work described in [7] proposes a web scale Q&A system which has an edge over the conventional open domain system. In this work there is no need to specify the rules for the questions. The model stated in [7] categorizes the questions in basic five categories. The chances that an answer will be generated for a Q&A system depend on the legal availability of the fact that whether there exists a legitimate answer. After the system verifies that yes there is a valid answer for the available text. This initiates the system for fetching the answer by the system. The above said system mimics the cognitive abilities of the human beings to choose answer among the various available options as in [8, 9]. The factoid Q&A system is mostly supported by the knowledge bases. Most of the words that form the query in the form of natural language text have petty importance. To detect the entity mention in the query has been achieved by using the sequence tagging model as described in [10]. One of the works has proposed the deep learning method for Q&A which is of non-factoid class as shown in [11]. In the work described in [12], answer recapitulation is uprooted from the text with numerous features which is then sent to CNN with question to acquire a brief, and systematic semantic representation. Dissimilar to the foregoing deep models, unrelated information is separated, and superior representations are produced for question and answer (Q&A), which is compulsory for question answering of non-factoid ones.
5 Proposed Model The proposed model takes the input to the shape common dialect content and stores it in a rundown which is a vast length of the exhibit, as a string. It is the description of Fig. 1. At the point when the client puts a question identified with a content, which it appeared as “Query” in the chart, the framework likewise makes an extra duplicate of that query, i.e., Query duplicate. In the Query duplicate segment, we isolate out the scrutinizing words like “who”, “when”, “where” and “what”. Presently
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Fig. 1 System architecture
the left behind content in the Query duplicate is handled by “word. splitter”. These outstanding words are then put away in a rundown and are consecutively looked in the natural language content info. Suppose after the hunting procedure of the primary word from the rundown in the information content, in the event that it shows up in “N” sentences then these sentences are isolated out. The following word from the rundown is then looked in those “N” sentences and now the frameworks will isolate out “N-M” sentences, where “M” is the number of sentences in which the second expression of the rundown does not exist. Presently for the following word seek, the estimation of “N” will be refreshed as “N-M”. This procedure of looking through the ensuing words will proceed until the point that no more words are left in the words list. The rest of the sentences will appear as a surmised reply. Presently the likelihood of finding the exact tidbit solution to the question will be accessible in these sentences itself. These sentences will be put away in “result0” as a string. Presently the “query” with the scrutinizing words like “who”, “when” and “where” and “what” is passed to the “tokenizer” for Tokenization. The result of this tokenization is passed to the pos tagger fro pos labelling reason. The result of pos labelling as a rundown is subdivided under the under the heads: “inquiry adj” for
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descriptors, “quest_prpl” for relational words, “quest_nn1” for regular thing, “journey adj” for modifiers, “quest_prpl” for relational words, “quest_nn1” for normal thing, “quest_verb1” for verbs and finally “mission” for question words like “who”, “when”, “where” and “what” records, for grouping. While order “quest_verb1” for verbs the framework clubs up all the verb compose grouped by natural Language Tool Kit (NLTK), under one head “quest_verb1”. A similar rundown, which is the yield of pos labelling is likewise passed to “ne_chunker”. The “ne_chunker” chunks/characterize the rundown into “PERSON”, “GPE” and “ORGANISATION” as yield appeared as P1, G1 and O1. Presently, result0 is passed to a split_text work that parts the content of result0 into passages and the sections into sentences and stores it in a rundown. The framework would now be able to pass the sentences one by one for tokenizing. The result of this tokenizing is passed to the pos tagger for pos labelling reason. The result of pos labelling as a rundown is subdivided under the heads: “text_adj” for descriptive words, “text_nn” for a typical thing, “text_verb0” for verbs. A similar rundown, which is the yield of pos labelling is additionally passed to “ne_chunker”.The “ne_chunker” chunks/order the rundown into “PERSON”, “GPE” and”ORGANISATION” as yield appeared as P, G and o, as on account of question preparing portrayed previously. While ordering “text_verb0” for verbs the framework clubs up the entire verb compose arranged by Natural Language Tool Kit (NLTK), under one head “text_verb0”. Presently the “quest_verb1”, and in addition the “text_verb0”, are passed to “lemma” for lemmatizing and stemming. This progression discovers the root verb from the different verbs accessible like, from “go”, “going”, “gone” of a similar root verb “go”. It will follow out the root verb “go” at the point when the root verb of both inquiry, and in additional content, is same the framework looks at the thing in “comparosion_noun” and a descriptive word in “comparison_adj” of both, i.e., for content and question. Presently the yield of “comparison_noun”, “comparison_adj” and additionally “lemma” is passed to “Type Quest”. The “Type Quest” will give the yield as tidbit reply, if adequate information is accessible, based on the rationale portrayed in Algorithm 5. The tidbit answer will be put away in “result1” for showing the tidbit result and the same “result1” fragment will now be passed to content to discourse for replicating the machine talked yield.
6 Prerequisites The pseudo code for the proposed model has been provided in the algorithm 1. The pseudo code illustrates the steps involved in the process of accepting the unstructured text and how the question and answering can be done.
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Pseudocode– 1
The system constructs a func on fun1(text) that can call another func on SPIS (paragraph), and store the output in a list (an array of infinite length).
2
The system constructs another func on to split query into words and store it in a word list. In this func on split_line, the func on extracts word[0] from word list and append the remaining item in empty string.
3
The
system
calls
function
fun1(text),
split_line(query) and stores its result in a list named as list1.The system takes the first word of string and searches it in the list of sentences. Result of this search passes as an argument for gh(x) and do it recursively for all words in list. An empty string is taken and it concatenates its items in a string s. 4
The system tokenizes, POS tags the query and each sentence of list is returned by splitter function using NLTK Stanford. Tagged output is passed to be further classified and categorized into following category: NNP, NN, VBG, VBZ, PRP, VBN, VBP, VBD, WRB, WP & then stores it in a separate list.
5
The system applies Ne_chunk for chunking to the previously
POS
Tagged
list
into
PERSON,
ORGANIZATION, GPE. “Extend” all the type of verb in a list i.e. verb1. Extend the wp and wrb10 tagged words in a list quest. 6
The system lemmatizes the verbs used in query and sentence of the text, followed by comparison of noun and adjective.
7
The system invokes the logic function to get the factoid answer of user’s query, which works on grammatical strategies of English.
8
Logic function first check question type: Who, Where. If the question type is “WHERE” - The system checks for ORGANIZATION or GPE in query and match it to text given. If match found system will compare root of verb of query and matched sentences, else “No data about this person”.
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If the ques on type is “WHO”, system will find the data about PERSON by comparing PERSON with Person in text, ask for in query, else print error. If match found, system will compare the root of verb used in query and text. Similarly, compare adjec ve and noun of query and text. If all of comparison are passed, print the PERSON list. If the user ask for a specific GPE or ORGANIZATION, the system then compares GPE, ORGANIZATION in query and text, if match found system print GPE and ORGANZIATION list. If the list matches in both the cases, The system then prints the en ty present in the list PERSON else “NO DATA”.
10 The system provides factoid output for user’s query if sufficient data is available.
7 Results and Conclusions TEXT in Natural Language → John is playing football in America. Tom is living in America. Tom cruise is a good person. Paris is the capital of France. Paris is good place. Tata is a well known person. The comparison of the proposed model NLIIRS has been compared with that of [13]. The provided Graph 1 is a Bar Graph in which we have placed “Total no. of que.” on the Y axis and some details of the answers, i.e., whether the questions are: (1) answered, (2) not answered, (3) answered but not correct, and (4) Correct answer. On the X axis. The Graph 2 above is the diagrammatic representation of Confusion Matrix in which we have performed some queries on a given text and observed the response. Based on that observation, we have shown some calculated values with percentages in bracket. The above-mentioned Table 1 is the statistical representation of some questions that were asked, and which were answered as TP, TN, FP, and FN. We have also calculated the accuracy for this work.
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Graph 1 Question and Answer Response Graph
8 Prerequisites The client must have some essential thought of the given unstructured information with the goal that the client can make applicable inquiries. For performing question and answer the natural language query must be gone before by a “Wh” word like “Who”, “Where” and “When”. It is normal that the clients should utilize the best possible English language for better proficiency of the framework. The proposed framework does not bolster a question mark and this attribute has no effect on the working of the consistent piece of the framework.
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Graph 2 Confusion Matrix Graph
Table 1 Confusion Matrix Table Summation of question been asked
True positive
True negative
False positive
False negative
Acc.
68
28
14
12
14
61.70%
9 Limitations The proposed framework is not so efficient as client input POS NEchunker framework, as the POS tagger of Stanford University is once in a while unfit to clarify the provincial things: for instance, the Indian name and Indian city names. The framework cannot pass judgment on the strained of the verb accurately, for instance, if the information content is: “John was eating apple last night”. The question that is asked is “what is John eating?” then the framework will give reply as “apple”, despite the fact that in the information content: “John was not eating the apple” at the present minute. The proposed framework cannot work appropriately on compound sentences which include “and”.
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10 Conclusions This NLIIRS system accepts any natural language text on a particular topic according to the choice of the user and processes it. This text is then used for answering the questions asked by the user in natural language. The query asked by the user may be in the form of “Wh” category like “Who”, “Where”, “What”. The “What” class of user queries is not efficient in the present system. This system provides the advantage that the queries asked in the form of natural language is not converted back to SQL statements for retrieving the information. Acknowledgements This publication is an outcome of the R&D work undertaken project under the Visvesvaraya Ph.D. Scheme of Ministry of Electronics and Information Technology, MeitY, Government of India, being implemented by Digital India Corporation. This research work has been done at Research Project Lab of National Institute of Technology (NIT), Durgapur, India. Financial support was received from Visvesvaraya Ph.D. Scheme, Deity, Govt. of India (Order Number: PHD-MLA/4 (29)/2014_2015 Dated-27/4/2015) to carry out this research work. The authors would like to thank the Department of Computer Science and Engineering, NIT, Durgapur, for academically supporting this research work. The authors would also like to thank the Department of Computer Science and Engineering, Jaypee University of Engineering & Technology, Guna MP.
References 1. D. Lagun, C.-H. Hsieh, D. Webster, V. Navalpakkam, Towards better measurement of attention and satisfaction in mobile search, in Proceedings of the 37th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (2014), pp. 113–122 2. R. Kadlec, O. Bajgar, J. Kleindienst, From particular to general: a preliminary case study of transfer learning in reading comprehension. Machine Intelligence Workshop (NIPS, 2016) 3. M. Iyyer, J.L. Boyd-Graber, L.M.B. Claudino, R. Socher, H. Daum´e III. A neural network for factoid question answering over paragraphs. In Empirical Methods in Natural Language Processing (EMNLP). pages 633–644 4. S. Wang, J. Jiang, A compare-aggregate model for matching text sequences (2016) 5. F. Yan-hong, Y. Hong, S. Geng, Y. Xun-ran, Domain named entity recognition method based on skip-gram model, in IEEE First International Conference on Electronics Instrumentation & Information Systems (EIIS), China, 22 February 2018 (2018) 6. A. Mansouri, L.S. Affendey, A. Mamat, R.A. Kadir, Semantically factoid question answering using fuzzy SVM named entity recognition, in IEEE International Symposium on Information Technology, Kuala Lumpur, Malaysia (2008) 7. P. Ranjan, R.C. Balabantaray, Question answering system for factoid based question, in IEEE 2nd International Conference on Contemporary Computing and Informatics (IC3I), Noida, India (2016) 8. K.N. Acheampong, Z.-H. Pan, E.-Q. Zhou, X.-Y. Li, Answer triggering of factoid questions: a cognitive approach, in IEEE 13th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), China (2017) 9. P.S. Banerjee, B. Chakraborty, D. Tripathi et al., A Information Retrieval Based on Question and Answering and NER for Unstructured Information Without Using SQL, Wireless Personal Communications (Springer, US, 2019). https://doi.org/10.1007/s11277-019-06501-z 10. K. Lei, Y. Deng, B. Zhang, Y. Shen, Open domain question answering with character-level deep learning models, in IEEE 10th International Symposium on Computational Intelligence and Design, China (2017)
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11. M. Tan, B. Xiang, B. Zhou, LSTM-based deep learning models for non-factoid answer selection. Comput. Sci. (2015) 12. R. Ma, J. Zhang, M. Li, L. Chen, J. Gao, Hybrid answer selection model for non-factoid question answering, in 2017 International Conference on Asian Language Processing (IALP) (2018) 13. S. Cucerzan, E. Agichtein, Factoid question answering over unstructured and structured web content. Microsoft Research, One Microsoft Way, Redmond, WA 98052 (2011)
A French to English Language Translator Using Recurrent Neural Network with Attention Mechanism Akshay Sharma, Partha Sarathy Banerjee, Akshit Sharma and Akshansh Yadav
Abstract In today’s world there are many people who are facing the problem of language translator for ex: talking to a person who only knows a language which you do not understand or you have some information in a language like French and you only Know English, etc. This type of problem can be overcome by a technology called machine translation. This paper proposed machine translation using the recurrent neural network with attention mechanism, where the recurrent neural network (RNN) are types of neural networks designed for capturing information from sequence and time series data. RNN is useful to learn pattern in a given set of data as the human language is one big complex pattern or a complicated pattern. In machine translation, two recurrent neural networks work together for the transformation of one sequence to the other sequence. An encoder network will change an input sequence in a vector and on the other hand the decoder network will change the vector in a new sequence. To improve the above-stated RNN model we will use the attention mechanism which helps to focus on the specific range of the input sequences. Keywords RNN · NLP · Language translator · Machine translation
A. Sharma (B) · P. S. Banerjee · A. Sharma · A. Yadav Department of Computer Science and Engineering, Jaypee University of Engineering and Technology, Guna, India e-mail: [email protected] P. S. Banerjee e-mail: [email protected] A. Sharma e-mail: [email protected] A. Yadav e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 V. Nath and J. K. Mandal (eds.), Nanoelectronics, Circuits and Communication Systems, Lecture Notes in Electrical Engineering 642, https://doi.org/10.1007/978-981-15-2854-5_38
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1 Introduction Natural Language Processing (NLP) is a field of computer science which is concerned with the interaction between human languages. Machine Translation is a system which translates between two different languages in the form of text or voice. It has five basic approaches one of which is Neural Machine Translation. Neural Machine Translation uses a neural network which can predict the sequence of words [1]. This system uses a recurrent neural network which is a method used in machine translation. It is widely used and better than the statistical model because RNN can handle large datasets and has higher accuracy than the statistical model [2]. Recurrent Neural Network (RNN) uses Sequence to Sequence approach and uses its own output as input. An RNN is composed of Long short-term memory (LSTM) units. LSTM networks are used for processing and making predictions on the basis of data [3]. This system uses two RNN’s encoder and decoder. In this system, encoder reads a sequence (input) and converts it into a single vector and then decoder reads that vector and produces an output according to it. In this system, we used attentionbased neural machine translation which has significantly increased the accuracy of the model. Here we used the attention mechanism just before the decoder RNN due to which decoder can focus on different parts of the encoder’s output and the accuracy of the model is increased [4]. In this system, we used a simple dataset which we preprocessed for our model to work correctly. We used Tokenization, normalization, trimming [5].
2 Utility The model which is proposed in the paper is being utilized in several areas of the world in a different manner. It is divided into two major categories namely Machine translation which is used in the industry for the business purpose and the online/app MT for consumer use. The services of MT which have been used all over the world presently are divided into Multi-domain MT services and the Specialized MT services. Examples, where this model has been used, are 1-Kantan MT, 2-SYSTRAN [6], 3-Canopy Innovations–Healthcare. These are the areas where this model is applied presently. Lingua Custodia-Finance is another area where this model is utilized.
3 Related Work Natural Language Processing (NLP) is a field of artificial intelligence where the focus is on the representation of the knowledge and also logical reasoning. It includes certain approaches namely statistical approach, the sequence to sequence approach, etc. It has led to very large-scale applications namely machine learning and data
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mining, as stated in [7]. By the utilization of NLP, the developer can perform a certain task namely machine translation. Machine translation is a method that translates one natural language into the other natural language. The main motive behind this is that to fill the gap between the two different languages spoken by the people, communities, and countries. The use or you can say that the need for machine translation is immense for the present scenario, which has been clearly illustrated in [8]. There are some types machine translation, namely Rule-Based Translation, the main motive of this particular research is the development of machine translation system which converts one particular language into another using the rule-based machine translation and through this, full analyses of the source text are done for further conversion. This machine translation type can translate a wide expanse of texts, as elaborated in [9]. Another type is Statistical Machine Translation. It is performed by training the translation model with an excess of bilingual and monolingual corpora, i.e., source texts and their translations. It also helps in the description of word alignment and language modeling problems and there is an overview of these two problems in this paper, as mentioned in [10]. Another technology which has made machine translation model more accurate is neural machine translation which is based on the exemplar of machine translation and it is one of the latest or you can say that the newest approach to the machine translation. This neural machine translation uses the neural networks which consist of nodes which are concertedly modeled after the human brain. There is some comparison regarding SMT and NMT and which one is better that is incorporated in [11]. In machine translation, Recurrent Neural Networks comes under sequence to sequence learning or neural machine translation which shows properties like repeated connection. RNN is an effective approach for machine translation as it is more accurate than statistical method as stated in [12]. RNN has a network of encoder and decoder, the encoder converts the input data into vector and decoder takes the output of the encoder and gives output in a particular pattern to for translation of language as illustrated in [13]. RNN with long short-term memory (LSTM) is a technology which has achieved better results with more accuracy in machine translation. Using LSTM long-term dependencies can be avoided as illustrated in [14, 15]. Recurrent Neural Network (RNN) and Attention mechanism are the two techniques which are being used to improve Neural Machine Translation (NMT) for bilingual parallel corpus as stated in [16]. Attention mechanism is a process which is used in Neural Machine Translation (NMT) for increasing the accuracy of a machine learning model as stated in [17]. It uses the current decoder hidden state for the vector calculation which is produced by the encoder. As mentioned in [18] attention mechanism focuses on the raw data and increased the accuracy of the machine learning model. The above table, i.e., Table 1 is comparing the proposed paper with the research paper “Recurrent Neural Networks with External Memory for Spoken Language Understanding” [19].
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Table 1 Comparison table A French to English language translator using recurrent neural network with attention mechanism
Recurrent neural networks with external memory for spoken language understanding
1. The model proposed in the paper uses RNN’s Encoder and Decoder with Attention Mechanism Approach
1. The model proposed in the paper uses simply RNN’s Encoder and Decoder
2. This paper is based on Recurrent Neural Network
2. This paper also uses a Recurrent Neural Network approach
3. The model proposed in this paper is relatively accurate than traditional sequence to sequence approach i.e. RNN’s Encoder-Decoder model
3. The model proposed in this paper is relatively accurate than a statistical approach which is based on probability
4 Proposed Model The given Fig. 1 is the architecture of the proposed model which we have trained through a dataset having approximately 155,000 words and we have downloaded it from manythings.org. Firstly, the dataset was preprocessed by removing non-letter characters, normalizing the data, Tokenizing, and converting word to index, etc. After this the input data will reach to the encoder, the encoder will output some values for each and every word from the input data. For every input data or word, the encoder
Fig. 1 System architecture
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will output a value or a vector and a hidden state which will be used for the next set of input. After that, the input data reach to decoder here the decoder takes the output of the encoder as input and then gives output words which are arranged in sequence to create a translation. But before this, Attention mechanism which is inserted above the decoder plays an important role as it helps the decoder to give attention to various parts of the output given by encoder for every stage of the decoder’s output. After this, the decoder predicts the translated final sequence of words.
5 Algorithms
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In Algorithm 1 the sentence will be tokenized then each word will be given an index and it also counts the words. In algorithm 2 the Unicode data will be converted into ASCII values and then preprocessing of the data will take place in which nonletter character will be removed and then the data will be normalized. And then in output, it gives a filtered pair. In algorithm 3 we are importing the languages and then calling an R_L function to prepare the data. In Algorithm 4 encoder and decoder are defined, the encoder gives some values for each word from the input and the decoder take the output of the encoder and attention mechanism and predicts the result. In Algorithm 5 we are defining attention mechanism, attention mechanism is helping the decoder to focus on every part of the sentence. In algorithm 6 we are giving training to our model and with the preprocessed data. In algorithm 7 the user will give input in one language and get output in another language.
6 Result In this system, we used a Unicode French-English dataset for machine translation. After the translation, this system is capable of translating between these two languages with an average loss of 0.6413. In this model as the number of pairs per batch is increasing the average loss is decreasing per batch. This system compared 75000word pairs. In the graph Fig. 2, the x-axis shows the number of pairs and the y-axis shows an average loss. In Figs. 3 and 4, the input sentence is in French language and the output sentence is in the English Language. The second line of output is the actual translation of the French language and the last line is the prediction of that French language based upon this system.
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Fig. 2 Average data loss graph
Fig. 3 Translation result
Fig. 4 Final translation output
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7 Conclusion The proposed work successfully converts any text provided as input in French to its English version. The accuracy of the proposed system is dependent on the size of the dataset that will be used to train the system. The authors do not claim that the language translation will be of very high efficiency because the conversion is solely dependent on the accuracy of the dataset.
8 Prerequisites The model needs training with the dataset for a considerable amount of time. The proposed model used integrated development environment: Anaconda and Spyder.
9 Limitations The training time is considerably high and depends on the size of the dataset. At present, the utility of the system is limited to French to English conversion.
10 Abbreviation Table
S. No.
Abbreviation
Description
1
SOS
Starting of string
2
EOS
Ending of string
3
wti
Word to index
4
wtc
Word to count
5
itw
Index to word
6
In_L
Input language
7
Out_L
Output language
8
h_size
Hidden size
9
Out_size
Output size
10
In_size
Input size
11
Atten_com
Attention combine
12
Att_weight
Attention weight
13
Att_app
Attention applied (continued)
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(continued) S. No.
Abbreviation
14
tfp
Description Tensor from pair
15
In_ten
Input tensor
16
Tar_ten
Target tensor
17
tfr
Teacher forcing ratio
18
eval
Evaluate
19
Ot_word
Output word
20
Ot_sent
Output sentence
21
S
Self
22
U_A
Unicode to ASCII
23
N_S
Normalize string
24
R_L
Reading languages
25
FP
Filter pair
26
FDP
Filtered pair
27
gru
Gated recurrent units
28
ifs
Indexes from sentences
Acknowledgements This publication is an outcome of the R&D work undertaken project under the Visvesvaraya Ph.D. Scheme of Ministry of Electronics & Information Technology, MeitY, Government of India, being implemented by Digital India Corporation. Financial support was received from Visvesvaraya Ph.D. Scheme, Deity, Govt. of India (Order Number: PHD-MLA/4 (29)/2014_2015 Dated–27/4/2015) to carry out this research work. The authors would like to thank the Department of Computer Science and Engineering, NIT, Durgapur, for academically supporting this research work. The authors would also like to thank the Department of Computer Science and Engineering, Jaypee University of Engineering & Technology, Guna (MP) for its support and encouragement and rendering its labs for the research work.
References 1. K. Chen, T. Zhao, M. Yang, L. Liu, A. Tamura, R. Wang, M. Utiyama, E. Sumita, A neural approach to source dependence based context model for statistical machine translation, in IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 26, no. 2 (2018) (China, 2017), pp. 266–280 2. A. Hermanto, T.B. Adji, N.A. Setiawan, Recurrent neural network language model for EnglishIndonesian machine translation: experimental study, in International Conference on Science in Information Technology (ICSITech) (Indonesia, 2016) 3. Q. Yang, Z. He, F. Ge, Y. Zhang, Sequence-to-sequence prediction of personal computer software by recurrent neural network, in International Joint Conference on Neural Networks (IJCNN) (Anchorage, AK, USA, 2017) 4. S. Ye, W. Guo, Recursive annotations for attention-based neural machine translation, in International Conference on Asian Language Processing (IALP) (Singapore, 2018)
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5. R. Sunil, V. Jayan, V.K. Bhadran, Preprocessors in NLP applications: in the context of English to Malayalam machine translation, in Annual IEEE India Conference (INDICON) (India, 2013) 6. E. Michon, M.Q. Pham, J. Crego, J. Senellart, Proceedings of the fifth workshop on NLP for similar languages, varieties and dialects, in Association for Computational Linguistics (New Mexico, USA, 2018), pp. 128–136 7. A. Gelbukh, Natural language processing, in Fifth International Conference on Hybrid Intelligent Systems (HIS’05) (IEEE, Rio de Janeiro, Brazil, 2006) 8. S. Saini, V. Sahula, A survey of machine translation techniques and systems for Indian languages, in 2015 IEEE International Conference on Computational Intelligence and Communication Technology (IEEE, Ghaziabad, India, 2015) 9. M. Kasthuri, S. Britto Ramesh Kumar, Rule-based machine translation system, in 2014 World Congress on Computing and Communication Technologies (IEEE, Trichirappalli, India, 2014) 10. A.R. Babhulgaonkar, S.V. Bharad, Statistical machine translation, in 2017 1st International Conference on Intelligent Systems and Information Management (ICISIM), IEEE, Aurangabad, India Department of Info. Tech., Dr. B.A. Tech. Uni., Raigad, Maharashtra, India, and Department of Info. Tech., Dr. B.A. Tech. Uni., Raigad, Maharashtra, India (2017) 11. W. He, Z. He, H. Wu, H. Wang, in Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), Improved Neural Machine Translation with SMT Features, China (2016) 12. R. Agrawal, D. Misra Sharma, Building an effective MT system for English-Hindi using RNN’S. Int. J. Artif. Intell. Appl. (IJAIA) (2017) 13. L. Lu, X. Zhang, S. Renais, On Training the Recurrent Neural Network Encoder-Decoder for Large Vocabulary End-to-end Speech Recognition (Shanghai, China, 2016) 14. H. Palangi, L. Deng, Y. Shen, J. Gao, X. He, J. Chen, X. Song, R. Ward, Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval (2016) 15. H. Ming, Y. Lu, Z. Zhang, M. Dong, A Light-Weight Method of Building an LSTM-RNN-based Bilingual TTS System (Singapore, 2018) 16. X.-X. Wang, C.-H. Zhu, S. Li, T.-J. Zhao, D.-Q. Zheng, Neural machine translation research based on the semantic vector of the tri-lingual parallel corpus, in International Conference on Machine Learning and Cybernetics (ICMLC) (Jeju, South Korea, 2017) 17. Y. Yu, Y. Huang, W. Chao, P. Zhang, History attention for source-target alignment in neural machine translation, in Tenth International Conference on Advanced Computational Intelligence (ICACI) (Xiamen, China, 2018) 18. Y. Tang, J. Xu, K. Matsumoto, C. Ono, Sequence-to-sequence model with attention for time series classification, in IEEE 16th International Conference on Data Mining Workshops (ICDMW) (Spain, 2017) 19. B. Peng, K. Yao, L. Jing, K.-F. Wong, Recurrent neural networks with external memory for spoken language understanding, in Natural Language Processing and Chinese Computing, Lecture Notes in Computer Science, vol. 9362 (Springer, Cham, 2015)
Energy-Aware Clustering in Wireless Sensor Networks Randheer, Surender Kumar Soni, Sanjeev Kumar and Rahul Priyadarshi
Abstract The demand of the WSN in the various areas in increasing very rapidly. The nodes in the WSN are provided with the limited supply of energy. So, the utilization of the energy in the network must be performed efficiently. The protocol proposed in this paper uses the clustering strategy for increasing the lifespan of the system. In this paper a Cluster head selection procedure has been proposed for the proper utilization of the energy. The simulation is performed in the MATLAB simulator. The simulation results show that the proposed approach has enhanced the system lifespan as compared to the existing protocols in terms of various performance factors of the network. Keywords Cluster · Routing · WSN · Clusterhead · Energy · Nodes
1 Introduction Wireless Sensor Networks are constituted with tiny electro mechanical devices, i.e., sensors nodes. Sensor nodes communicate via RF signals with one or more powerful sinks called base stations (BSs). Communication can be called single hop if they can contact directly to base station [1–3]; or can be Multi-hop communication in which intermediate nodes takes part for communication from sensor node to base station. Randheer (B) · S. K. Soni · S. Kumar Department of Electronics & Communication Engineering, National Institute of Technology, Hamirpur 177005, H.P, India e-mail: [email protected] S. K. Soni e-mail: [email protected] S. Kumar e-mail: [email protected] R. Priyadarshi Department of Electronics & Communication Engineering, National Institute of Technology, Patna 800005, Bihar, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 V. Nath and J. K. Mandal (eds.), Nanoelectronics, Circuits and Communication Systems, Lecture Notes in Electrical Engineering 642, https://doi.org/10.1007/978-981-15-2854-5_39
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Formally sensor networks on basis of their functionality are classified into two types one is proactive and the other is reactive networks. Proactive networks are passive in nature and well suited for data aggregation type applications. In this type of networks nodes sense and send data on periodical basis, i.e., on regular intervals. In Reactive networks, opposite to passive networks sensor nodes respond immediately and only to changes in the significant considerations of interest. Reactive networks are more suitable for time critical applications. To prolong lifetime of sensor networks it will be more proficient if sensor nodes can cooperate with each other. That is the reason why sensor networks need management planes. As per requirement three management planes will work which are named as mobility, power, and task management plane. These planes solve objectives like; first to make sensor nodes work collectively in a power efficient way, second to perform routing of data in wireless sensor network, and to stake resources among them. Without these three planes, each sensor node will act and work as an individual. Power management plane ensures to manage how should a sensor node uses its power like when to turn off receiver; when to broadcast low power message and to quit from routing so that sensor nodes can save energy for sensing. The mobility management plane is in use when sensor nodes are not stationary in networks. Mobility plane helps to maintain a path back to the user, and also sensor nodes could keep track of their neighbor sensors. The task management plane divides and schedules the tasks among sensors which are distributed in monitoring region. It is not always required that all sensor nodes keep sensing simultaneously in a specific region. It is additionally conceivable that some sensor nodes play out the errand more than the others relying upon their energies. Figure 1 shows the routing in WSN. Another important point of discussion in WSN is lifetime of sensor networks [4, 5]. The requisite lifespan of a sensor network may be some hours or several years, as per application requirements. The requirement is actually not only lifetime also degree of robustness and energy efficiency of the nodes is required [6, 7]. When there are large number of sensors associated to network, it is practical impossible to
Fig. 1 Clustering in WSN
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recharge batteries of all individual sensor nodes and also it is very tough to ensure performance of wireless sensor networks [8]. It will not be easy to support of time bound omission critical data in challenge of limited energy [9, 10]. Now a day’s sensor network has also taken a turn towards ubiquitous sensing. Some of the major characteristics of WSN are Environment driven: Wireless senor networks are driven by the surrounding environment. Sensor networks generate new values whenever some changes occur in the environment. In sensor networks, pattern of traffic changes considerably on periodical basis. Their main objective is gathering of information from the surroundings whereas ad hoc network focuses on distributed computing. Data redundancy: In sensor networks, nodes are deployed with high density. So, nodes deployed in nearby locations measure approximately same values about the environment. Removal of redundant information helps in increasing energy efficiency and network lifetime [11]. Further, changes in environment occur very slowly so successive readings contain related values about the parameters [12]. Self-organization: Sensor nodes must operate autonomously so that sensors can self-organize and self-heal under different circumstances. Communication range: Sensor nodes have small communication range only a few meters whereas range of ad hoc networks is in hundreds of meters.
2 Related Work First Clustering protocol was proposed was LEACH [13]. Authors targeted at issue of communication energy reduction, which give advantages of flexibility and energy efficiency, which give maximize network lifetime. Authors have also integrated the concept of “energy aware” such systems. In LEACH the CH collects the information from all the nodes and convey that to the BS [14]. In LEACH protocol, the sensor nodes which are cluster members use to transmit their data to a local cluster head. It works in two phases which are Setup Phase and Steady phase. Author has presented PEGASIS protocol which improved network life by factor in contrast to LEACH protocol. PEGASIS [15] has mainly focused on data aggregation and routing of data. It works on the basis of Travelling Salesman Problem (TSP). The working criteria of this protocol are chain based. Chain is formed among sensor nodes and each sensor node will communicate with only two nodes one for receiving from and one for transmitting to neighbors who are close. Collected data will be transmitted from node to next node of chain, processed to fuse, and finally a selected sensor node will send it to the BS. This methodology will disperse the load of energy uniformly among the sensor in the system. Here one more point that this protocol said is that the sensor nodes take turns conveying to the BS so that the regular energy expended by every node per round is abridged. In [16] the author proposed a protocol which works on the heterogeneous environment and the nodes in the system have different energy levels.
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LEACH-C protocol is centralized in nature so works under high-powered BS [2]. It ensures that equal distribution will be for energy load. This protocol starts in manner that sensor nodes will send their current location and current residual energy to base station. Base station will calculate average energy of network on the basis of received energy information of individual sensor nodes. Those sensor nodes that are having residual energy less than the average network energy will not be considered for cluster heads role. Remaining sensors will be considered for clusters and base station will try to find k clusters among them using annealing algorithm [17]. Annealing algorithm will try to select cluster members under cluster head by considering minimum energy required to communicate over distance between cluster heads and their cluster members. Once BS is done with decision about cluster heads and their cluster members, BS will broadcast the list of selected Cluster heads.
3 WSN Clustering Clustering works for reduction in transmission cost and to save battery life in WSNs. Clustering is a grouping method in which, instead of a set of nodes only the (cluster head) will transmit the aggregated information. It is an implementation of hierarchical network. Hierarchical network in contrast of flat network having some more valued nodes or it can also be said at higher level nodes are having some extra duties. Same hierarchical approach is being used in clustering. Cluster Heads are at higher level and they collect sensed attributes from cluster members which are at lower level in contrast of cluster heads [18, 19]. Cluster heads will aggregate all sensory attributes and forward this aggregated information to the higher leveled cluster heads or base station (which maybe sometime sink node) [20]. Figure 2 shows the cluster formation in WSN. The primary aim of clustering is to minimize the energy needed for communication to lessen the energy usage of network nodes beyond which it is also having more advantages. Clustering protocols can be compared on various features like Less Load, Reduce Transmission Cost, Load balancing, More Robust System, Reduced Latency, Data Aggregation, Improved Network Life, More Scalable Network, centralized, distributive. Some clustering protocols are power based, some may some under location aware protocols and some protocols have factors feature of multilevel clustering or some may support and multi-hop inter-cluster communication.
4 Proposed Scheme The protocols used in the WSN always aim to maximize the lifespan of the system. For doing so the utilization of the energy should be done in a competent way. Several methodologies have been proposed previously to enhance the system lifespan. The proposed technique is based on the methodology of clustering. The proposed scheme
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Fig. 2 Clustering formation in WSN
uses an efficient technique to select the CH within a cluster to utilize the system energy in such a way that the network lifespan can be maximized. The CH election procedure in proposed approach is totally energy dependent. The proposed technique uses the node energy for the assortment of CH which makes the network energy efficient. The CH selection procedure starts with the generation of random number. The random value is then matched with the threshold function. If that number comes smaller than threshold value then that node become CH for that round. The selection of an efficient threshold function is very crucial part of any clustering protocol. The overall system performance is totally dependent on the threshold function used for CH assortment. An efficient threshold can enhance the network lifespan by selecting the CH as per the energy level of the system. In the proposed technique the threshold function uses the initial energy and aggregate energy of the network for the CH selection which make it efficient than other protocols. The proposed protocol is based on the heterogeneous environment in which the nodes in the network have the different energies. The nodes are categorized on the basis of their energy levels. The nodes in the proposed scheme are categorized as simple nodes and super nodes. The super nodes are provided with some extra energy than the simple nodes. The thresholds for both the nodes are different as per their energy levels. The simple nodes threshold function is given as T (simple) =
⎧ ⎨ ⎩
1−Pspl
P spl r mod
1 Pspl
∗ (1 − E remain )−1 ∗ E init , if n ∈ G 0,
otherwise
(1)
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The super nodes threshold function is given as: T (super) =
⎧ ⎨ ⎩
1−Psupel
P supe r mod
1 Psupe
∗ (1 − E remain )−1 ∗ E init , if n ∈ G 0,
(2)
otherwise
where Pspl and Psupe are the CH selection probability of the simple and super nodes and E remain and E init are residual and initial energy.
5 Simulation and Results The proposed procedure is simulated in the MATLAB simulator. To measure the efficiency of the proposed technique the proposed protocol is compared with various other techniques. The comparison is done using the various factors of performance. The parameters used during the simulation of the proposed technique are shown in Table 1. Figure 3 shows the graph among the dead nodes versus the rounds in the network. The performance of the network in proposed protocol is better than the existing techniques. The figure clearly portrays that the proposed approach has improved the network lifespan and has enhanced the system performance. Figure 4 shows the total packets to BS in the various round of the network. The packet count in the proposed protocol is much better than the other existing protocols. The throughput of the proposed scheme shows that it has enhanced the network performance and can be considered as a good agreement. Figure 5 shows the live nodes in various round of the network. The lifespan of the system in proposed scheme is greater than the existing approaches. The techniques used in the proposed scheme for CH selection has enhanced the lifespan of the system. Table 1 Simulation parameters
Parameters
Value
εefs
8 pJ/bit/m2
Size of network
100 m * 100 m
E DA
5 nJ/bit
εamp
0.0012 pJ/bit/m4
Initial energy
0.5 J
Rounds
4000
E elec
40 nJ/bit
k
4000
Total nodes
100
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Fig. 3 Dead nodes
Fig. 4 Throughput of network
6 Conclusion In this paper a clustering protocol is proposed which uses the nodes energy for the selection of the CH in the network. The simulation results of the proposed approach showed that the proposed scheme is efficient than the prevailing protocol in terms
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Fig. 5 Network lifespan
of throughput and lifespan of the system. The CH selection technique castoff in the proposed approach is energy efficient and has enriched the system performance. The proposed approach can be further extended by applying some other factor for the CH selection and by performing comparison with other approaches.
References 1. P. Saini, A.K. Sharma, E-DEEC-enhanced distributed energy efficient clustering scheme for heterogeneous WSN, in 2010 First International Conference on Parallel, Distributed and Grid Computing (PDGC 2010) (2010), pp. 205–210 2. H. Chen, C. Zhang, X. Zong, C. Wang, LEACH-G: an optimal cluster-heads selection algorithm based on LEACH 3. T.K. Jain, D.S. Saini, S.V. Bhooshan, Cluster head selection in a homogeneous wireless sensor network ensuring full connectivity with minimum isolated nodes. J. Sensors 2014, 1–8 (2014) 4. O. Zytoune, Y. Fakhri, D. Aboutajdine, Lifetime maximisation algorithm in wireless sensor network. Int. J. Ad Hoc Ubiquitous Comput. 6(3), 140 (2010) 5. N.T. Van, T.-T. Huynh, B. An, An energy efficient protocol based on fuzzy logic to extend network lifetime and increase transmission efficiency in wireless sensor networks. J. Intell. Fuzzy Syst. 1–8 (2018) 6. R. Priyadarshi, S.K. Soni, P. Sharma, An Enhanced GEAR Protocol for Wireless Sensor Networks (Springer, Singapore, 2019), pp. 289–297 7. R. Priyadarshi, L. Singh, S. Kumar, I. Sharma, A hexagonal network division approach for reducing energy hole issue in WSN, Eur. J. Pure Appl. Math. 118 (2018) 8. R. Priyadarshi, P. Rawat, V. Nath, Energy dependent cluster formation in heterogeneous wireless sensor network. Microsyst. Technol. 1–9 (2018) 9. R. Priyadarshi, A. Bhardwaj, Node Non-uniformity for Energy Effectual Coordination in WSN
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10. R. Priyadarshi, S.K. Soni, V. Nath, Energy efficient cluster head formation in wireless sensor network. Microsyst. Technol. (2018) 11. R. Priyadarshi, L. Singh, I. Sharma, S. Kumar, Energy efficient leach routing in wireless sensor network (2018) 12. R. Priyadarshi, H. Tripathi, A. Bhardwaj, A. Thakur, A. Thakur, Performance metric analysis of modified LEACH routing protocol in wireless sensor network. Int. J. Eng. Technol. 7(1–5), 196 (2017) 13. W.B. Heinzelman, A.P. Chandrakasan, H. Balakrishnan, An application-specific protocol architecture for wireless microsensor networks. IEEE Trans. Wirel. Commun. 1(4), 660–670 (2002) 14. R.M. Bani Hani, A.A. Ijjeh, A Survey on LEACH-Based Energy Aware Protocols for Wireless Sensor Networks 15. S. Lindsey, C.S. Raghavendra, PEGASIS: power-efficient gathering in sensor information systems, in Proceedings, IEEE Aerospace Conference, vol. 3, pp. 3-1125–3-1130 16. G. Smaragdakis, I. Matta, A. Bestavros, SEP: a stable election protocol for clustered heterogeneous wireless sensor networks 17. M.S. Ali, T. Dey, R. Biswas, ALEACH: Advanced LEACH routing protocol for wireless microsensor networks, in 2008 International Conference on Electrical and Computer Engineering (2008), pp. 909–914 18. V. Kumar et al., Multi-hop communication based optimal clustering in Hexagon and Voronoi cell structured WSNs, AEU—Int. J. Electron. Commun. (2018) 19. R. Priyadarshi, L. Singh, A. Singh, A. Thakur, SEEN: stable energy efficient network for wireless sensor network, in 2018 5th International Conference on Signal Processing and Integrated Networks (SPIN) (2018), pp. 338–342 20. R. Priyadarshi, L. Singh, A. Singh, others, A novel HEED protocol for wireless sensor networks, in 2018 5th International Conference on Signal Processing and Integrated Networks (SPIN) (2018), pp. 296–300
Glucometer: A Survey Pragati Prerna and Sanjay Shankar Tripathy
Abstract Glucometers are the device that measures level of sugar in blood. They are used for the diagnostic purpose in the physician’s office and emergency room for verifying hyperglycemia (when the level of glucose is too high in blood) and hyperglycemia (when the level glucose is too low in blood). It is also helpful for managing the tight glycemic control in rigorous care units and SMBG at home. Stress can also be one of the reasons for hyperglycemia and therefore it can result in various relatable critical conditions like stroke, trauma, and others which generally demands rigorous care management. In different circumstances, hypoglycemia can be due to several critical conditions. Every year around 400.0 million people are affected by this severe disease. Glucometers play a vital role in managing diabetes. If levels of glucose are well controlled, some of the significant complications of diabetes, such as blindness, amputations, and kidney failure, can be addressed. Regulation of blood glucose levels is important because hypoglycemia and hyperglycemia can lead to possible coma, brain damage, or death. In addition to its benefits, it reduces severe complications for the longer duration plus shorter duration and serious issues of hypoglycemia. Keywords Hypoglycemia · Hyperglycemia · Glycemic · SMBG · Trauma
1 Introduction Till 1962, people who were suffering from diabetes did not have any facility of home glucose monitoring. The first home glucometers were introduced in 1962 by Clark and Ann Lyons. He based his biosensor on a thin layers of glucose oxidize (GOx) present on an oxygen electrode. The amount that GOx consumes oxygen with the substrate glucose during the enzymatic reaction was the final readout. This idea became P. Prerna (B) · S. S. Tripathy ECE Department, BIT Mesra, Ranchi, India e-mail: [email protected] S. S. Tripathy e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 V. Nath and J. K. Mandal (eds.), Nanoelectronics, Circuits and Communication Systems, Lecture Notes in Electrical Engineering 642, https://doi.org/10.1007/978-981-15-2854-5_40
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very famous and praised by many scientist. The efforts by him, made him the “father of biosensors,” particularly regarding the glucose-sensing for diabetic patients. Ames Reflectance Meter created by Sir. Anton H. Clemens was another glucometer. During 1970 Ames Reflectance Meter was used for treatment in the hospitals of America. But because of the complications in the machine use of multistep sequences was necessary to get a reading which was also not of a very approximate range. First of all the user of “Ames” meter, had to match a strip to a color chart, which was followed after applying a droplet of blood, the user has to wait for at least 50 s for it to be ready and then wash the blood droplet using warm water. Also before using droplets of blood on the strip, there was a procedure for it. Later a color chart with a needle model was also introduced which more accuracy but still not used by many people. Until 1981 the first easily used home glucose meters were not introduced. The working of the meters was based on the same basic principle which is used by us still now. In this process, the blood is exposed to an enzyme (its two main types are glucose oxidizes and glucose dehydrogenizes). The result by it was more accurate as compared to other meters but it is also more susceptible to reacting with other substances. The blood oxidizes (it loses electrons) once it is exposed to this enzyme followed by passing it across the electrode; that measures current. The current flow is proportional to the quantity of oxidized glucose, i.e., more the glucose present in the sample of blood, higher will be the oxidation resulting in the higher current, and hence a higher indicating number. There were two types of diabetes patients (i.e., type 1 and type 2) type 1 diabetes who adopted home glucose testing more as compared to type 2 diabetes just because of type 1 need to check their sugar level 4–5 times.
2 Type of Glucometer Depending upon its accuracy the glucometers are available in different shapes, sizes, and cost. (a) Hospital glucometers These are special types of glucometers that are used in hospital for multi-patient as shown in Fig. 1a. The records provided by these types of glucometers are of more accurate quality. These types of meters have good capability of handling the data and are proposed in such a way that it can transfer the glucose results which are recorded in computer for the purpose of billing. (b) Meter that uses test strip Some properties of glucometers are following. These can vary according to the model. • Size: The sizes of blood glucometers are nearly the size of our hands, while the size of hospital glucometers can be of remote control size and also they are battery powered.
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Fig. 1 a Hospital; b house hold glucose; c continuous glucose monitor; d test strips without meter; and e non-invasive glucometer
• Test strips: test strips have chemicals contained in it which gets reacted with the glucose that is present in the blood drop. Some models has a plastic strips in it which contain a small amount of impregnated with glucose oxidase along with some other components. After every measurement, the strips are discarded, i.e., one-time usable. • Coding: Coding on the device is different in different batches; some devices need an user to manually put the code that is shown by the vial of test strips. By entering the coding into the glucometer, the meter will be measured to the batch of test strips. However, in some test strips, information of codes are present; but many others strips contain a microchip in its vial and it is to be inserted into the meter. These two methods help in reducing user error.
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• Blood sample’s volume: Sample of the blood drop may vary from 0.2 to 1.0 µL depending on the various models. • Alternate site testing: we can use other less sensitive areas of our body for testing the sugar level like forearms instead of using fingertips. This testing can be done when the level of blood glucose is firm in the body, like before taking sleep, pre-meal or on a fasting day. • Testing times: The time can vary from 2 to 50 s with respect to different models. • Display: It shows a glucose unit in mg/dL or mmol/L. • Cost: Cost of monitoring of this glucometers can be considerable because of the cost of the test strips. Meters are often provided at a very low cost by producers so that they can get benefit from test strips. Diabetics of type 1 may tests more frequently compared to type 2. Therefore it comes at an affordable cost. (c) Continuous glucose monitor This device is common for the people suffering from type 1 diabetes as it is used for monitoring blood glucose on a regular basis. It takes a reading onset of the intervening period with a small electrode which is placed under the skin adhesively. The data is sent to a separate receiver by a transmitter attached to the electrode as shown in Fig. 1c, d. (d) Test strips without meter Since the 1980s the test strips have been widely used. In this only test strips that change color are used so that it can be read visually without using the meter. One of the advantages of using this is that we can cut the test strip longitudinally into some pieces to save money. But test strips reading are not as exact as or appropriate as readings of the meter. (e) Noninvasive glucose monitoring This method came into existence since 1975 and still continuing. In this method measurement of blood glucose levels is done without drawing blood, or cutting or pricking the skin, causing pain or trauma as in Fig. 1e. This product does not require any clinically or commercially viable product. After 1999, FDA approved the sale of only one such product, based on its technique of invasive electrically exerting glucose from skin, but soon it was removed because of its poor performance and sometimes it also damages the skin of its users. (f) Accuracy The accuracy of Blood glucometers should be equal to the accuracy standards set by the ISO, [1] i.e. International Organization for Standardization. The ISO has decided that Blood glucometers must give the results within ±15.0% of a laboratory standard for concentrations above 100.0 mg/dL or within ±15.0 mg/dL for concentrations below 100.0 mg/dL at least 95.0% of the time. Although, there are several characteristics that directly affect the accuracy’s [2–4] level of a test like pressure use to clean the strip, normal temperature, calibration of the meter, dirt on meter, quality
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and size of blood sample, level of some substances in blood etc. There are several models which may vary depending upon these factors if the results are inaccurate then the meters show the error message.
3 Working Principle of Glucometer The glucose concentration in the blood can be determined with the help of glucometers [5, 6] by taking blood samples. These glucometers are depending upon the technology of electrochemical. The electrochemical uses test strips to carry out the measurements. In a disposable test strip a small drop of the blood solution is placed for the glucose measurement using glucometer.
3.1 Test Strip of Glucometer The glucose from blood can be monitored using test strip and every test strips have an enzyme know as glucoxidase. The Gluconic acid is formed when reaction with enzymes takes place in the blood sample with glucose. This acid reacts again with another chemical present in the test strip known as ferricyanide. This Ferrocyanide is formed when ferricyanide and the gluconic acid combined together. After creating the ferrocyanide, a current runs on the sample of blood in test strip. By reading the ferrocyanide by current it will able to determine the glucose in the sample of blood, on the strip. Then the reading is visible on the display screen of the meter. There are two most important methods which is used for measuring glucose in the blood are Amperometric and Colorimetric method. Test strip of glucometer is shown in Fig. 2.
Fig. 2 Test strip of glucometer
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3.2 Digital Glucometer In this section, we will discuss about the software development and the hardware design of the glucometer using the Amperometric method with PIC16LF178X device as shown in Fig. 3. These are some of the following peripherals of device PIC16LF178X: • • • • • • •
12-bit (SAR) ADC, up to eleven channels 2 × 8-bit DAC Two Op Amps Extreme low-power (XLP) operation Internal EEPROM Inter-Integrated Circuit (I2C™) 16-bit Timer.
If the blood sample will be placed onto the test strip then the glucose faces a chemical change, and electrons are formed. By these electrons, the current flowing through the circuit can be measured. According to the concentration of glucose, the current will change and the measurement of current can be done by current-to-voltage conversion using the internal Op Amps of the PIC16LF178X device and the filtering of high-frequency signals.
Fig. 3 Digital glucometer [7]
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Therefore, the signal which is filtered is fed to the device of the 12-bit ADC module. The voltage at each instant can be obtained by the PIC16LF178X device at the Analog to digital converter channel for about 1.50 s after the solution sample is placed. On an average of about 2048.0 Analog to digital converter readings were taken. Using the regression equation the average values of glucose concentration are determined and displayed on the LCD in units of mmol/L or mg/dL. The EEPROM has the facility to store up to 32.0 glucose readings and can be checked later on the LCD. The power to the glucometer Demo Board is equipped from the onboard metal battery (3V 225mAHCR2032).
4 Glucometer’s Features (a) (b) (c) (d) (e) (f) (g)
A smaller design with rubber side grips. 3 µL blood sample. 26 s results. 480 test memory. Unique Accu-Chek Comfort Curve test strips for comfortable testing. Computer data download for better diabetes management. Regular cleaning is not required.
5 Advantages of Glucometer (a) Glucometer help uses to make the correct decision to feel good and avoiding complexities. (b) Using glucometer on a daily basis is helpful for controlling the glucose level and also keeping ourselves in a proper diet. (c) It gives an origin that helps us to tell if levels of glucose are gradually increasing or decreasing. (d) It gives a sudden response and makes us aware of any gradual or significant changes in glucose levels in the blood. (e) It helps to upgrade ourselves giving information about any changes in glucose level.
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6 Result and Conclusion Diabetes care and management can be self-monitored by using testing strips as it is simple as well as an integral component. Though identifying the accuracy of glucometer is challenging part but it has shown improvement up till now in its accuracy and precision. Analyzation of these glucometers is with respect to ISO criteria because of the reason that it is more precise and practical as compared to other guidelines. As glucose in whole blood is unstable, and the process to make glucose reading stable through glycolysis inhibitors can impede with some glucometers. By differentiating between the result of meter and clinical laboratory methods we can obtain technical accuracy for the glucometer. The meter must need to be assessed before using. A precise meter model is to be selected checking its clinical as well as technical conduct on its predetermined sample of patient. Accuracy in the result of the meter can be influence by several factors, these includes operators, technique, patient physiologic, environmental vulnerability, and medication effects. It seems that the glucometers available for out-patient care are far better in terms of accuracy and precision as compared to glucometers available for in-patient care at least in research settings. Validation of glucometers makes the choice of medical personnel and patients more clear and ultimately results in an improvement in the quality of health care. In recent years introduction of continuous glucose monitors (CGM) was the biggest achievement. As name itself indicates that this device gives the continuous reading of glucose levels. However, measurement of glucose is done in different (less accurate) way. Instead of taking blood from the finger the measurement of glucose is done by measuring interstitial fluid which is between the cells, present under the skin. But, using CGM contain certain limitation, among them the main disadvantage is that the interstitial fluid will by about 11–16 min trail behind blood and therefore leading in less accuracy. Due to this reason this device is less chosen by people and they shift their preference to conventional meter.
References 1. G. Van den Berghe, A. Wilmer, G. Hermans, W. Meersseman, P.J. Wouters, I. Milants, E. Van Wijngaerden, H. Bobbaers, R. Bouillon, Department of Intensive Care Medicine, Insulin therapy in the medical ICU. 354(5), 449–461 (2006). [email protected] (Catholic University of Leuven, Leuven, Belgium) 2. American Diabetes Association, Standards of medical care in diabetes. Diabetes Care 31(Suppl. 1), S12–S54 (2008). https://doi.org/10.2337/dc08-S012 3. L. Dufaitre-Patouraux, P. Vague, V. Lassmann-Vague. History, accuracy and precision of SMBG device. Diabetes Metab 29 (2003) 4. International Organization for Standardization. ISO/IEC Guide 99. Geneva: International Organization for Standardization. International Diabetes Federation. “Global Guideline for Type 2 Diabetes 2013”
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5. Namrata Dalvi Microchip Technology Inc. © 2013. Glucometer Reference Design 6. M.D. Ksenia Tonyushkina, J.H. Nichols, Glucose meters: a review of technical challenges to obtaining accurate results. J.Diabetes Sci. Technol. 3(4), 971–980 (2009). Published online 2009 Jul, https://doi.org/10.1177/193229680900300446 7. The National Institute of Diabetes and Digestive and Kidney Diseases. Continuous Glucose Monitoring (December 2008). Retrieved 21 February 2016.
Performance and Comparison Analysis of Image Processing Based Forest Fire Detection Lucky Singh, Ashok Kumar and Rahul Priyadarshi
Abstract A wildland fire is an unrestrained fire that happens mostly in forest regions, though it can additionally occupy agricultural or urban regions. Some of the foremost reasons of wildfires: human elements, both premeditated and unintended, are the most typical ones. The quantity and effect of forest fires are anticipated to rise due to the global warming. So as to combat against those catastrophes, it is vital to espouse an inclusive, convoluted approach that allows a continuous situational cognizance and immediate awareness. This paper describes the analysis of existing forest fire detection method and further simulations of all existing method have been done using MATLAB Tool. It has been also noticed that if the optimal algorithms can be implemented for each and every part of detecting motion zone and take out the fire characteristics, the performance of system may be further enhanced. Keywords RGB color model · YCbCr color model · Motion detection · Color detection · Edge detection
1 Introduction Forest fires are a recurrent phenomenon, natural or manmade, in many parts of the world [1]. Vulnerable areas are mainly located in temperate climates where pluviometry is high enough to enable a significant level of vegetation, but summers are very hot and dry, creating a dangerous fuel load. Global warming will contribute to L. Singh (B) · A. Kumar Department of Electronics and Communication Engineering, National Institute of Technology, Hamirpur, HP 177005, India e-mail: [email protected] A. Kumar e-mail: [email protected] R. Priyadarshi Department of Electronics and Communication Engineering, National Institute of Technology, Patna, Bihar 800005, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 V. Nath and J. K. Mandal (eds.), Nanoelectronics, Circuits and Communication Systems, Lecture Notes in Electrical Engineering 642, https://doi.org/10.1007/978-981-15-2854-5_41
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increase the number and importance of these disasters. Every season, not only are thousands of forest hectares destroyed by wildland fires, but also assets, properties, and public resources and facilities are destroyed [2]. Moreover, firefighter and civilians are at risk, with a terrible toll in human lives each year. Early fire detection in the event of an outbreak is pivotal to prevent loss of life and properties. Traditional smoke detection systems are sensor-based [3] and detect the presence of the by-products of combustion, such as smoke, heat, and radiation. As a result, they are not reliable in open space and windy conditions. Moreover, their response speed depends on how quickly the combustion products get close enough to the sensors to activate them [4]. Also, sensors cannot deliver adequate information for appropriate appraisal of the fire’s size, position and dynamics. An alternate solution is a computer vision-based detection system [5], which deals the following advantages. • Low-cost early and fast detection. • Visual feedback information about the position and state of fire. Researchers in image processing have made several attempts to determine most appropriate color space for fire detection. Authors in [6] proposed three rules based on the RGB components. However, the downside of this algorithm is that fire representation in the RGB color space is not robust against illumination change. Therefore, it is not possible to separate a color between chrominance and intensity. To tackle this limitation, Authors in [7] transformed the RGB color space into YCbCr color space where the split-up among luminance and chrominance is possible. Some authors converted the RGB color space to a new flame-based color space in which fire pixels are emphasized, and the non-fire pixels are darkened, making the fire-parts in the converted image to be extracted efficiently with the Otsu thresholding algorithm [8]. While this method performs well in a forest fire situation, it is not very efficient in an environment with a higher color similarity in the background. Figure 1
Fig. 1 Fire in the forest
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Fig. 2 Detected fire in the forest
shows original image of fire in the forest region and Fig. 2 shows the fire detected in the forest by one of the simple techniques of image processing. Fire flame detection using motion estimation [9] and color information can also be implemented. Wireless sensors node can also be haphazardly spread in forest and to build a self-organized and energy efficient network between sensors [10–12] to cover huge regions in forests that might be disposed or in threat of fire injury at any time [13–16].
2 Fire Detection Method A. RGB Color Model Authors in [6, 17] presents straightforward indication of the suggested fire detection is to accept an RGB (red, green, blue) model established chromatic and disorder measurement for removing fire and smoke pixels. In point of the segmentation of fire types, color processing will have a reduced amount of false alarms from the function of fire pixel is mostly realized by saturation and intensity of R element. Removed fire pixels will be confirmed if it is an actual fire by both dynamics of progress and ailment, and additional smoke. Based on iterative inspection on rising proportion of fires, a fire-alarm is specified when the alarm-raising situation is happened. The flames typically show rosy colors; besides, the color of the flame will change with the growing heat. B. RGB Color Model with Statistic Parameters Authors in [18] examines about image processing Fire Detection System (FDS). To sense fire, simply by means of color information might yield false alarm, thus temporal and color disparity information would be used to obtain a decent performance
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of a FDS. Most of the researches used the stationary Threshold Values (TVs) in their FDS. But the experimental atmosphere was so restricted. This paper substitutes the empirical fixed TV with statistic TVs so that automatic computation without observed stationary TVs is conceivable. The TVs of red color may be substituted with statistic value using the Statistic Color Model (SCM), they swapped the red color TVs with the SCM value, the average value, but their method still used six TVs to eradicate the luminance element of the RGB color space by the color channel ratio. HSI transform is done for getting the binary background mask through the frame difference. C. YCbCr Color Model To sense fire at Class A and class C objects which is specified in the National Fire Protection Association Standard. Paper, wood, rubber, cloth or plastic are classified as Class A objects where electric motors and power boards are classified as Class C objects. Class A and Class C which is almost having same fire flame features. This method attains video input over the camera, for example a web camera. System will activate a clear alarm and compromise graphic images in the fire. This process generally uses one of the algorithms determined by color situations and fire progress inspection. Input from system might be actual video on the camera for instance a web camera. Predictable fire and background are parted from established color separation and obtained fire position frames. Afterward, both the successive fire position frames are in comparison to recognize fire progress. Once the fire progress over 5 frames, alarm be initiated and also monitor will show it using red box superimposed detected fire. HSV color model is employed to sense statistics related to brightness and color. YCbCr color model [19] can be used to sense statistics related to brightness. As a result, we have considered 13% of data difference that have taken from our tests. Furthermore, to validate that it is authentic fire, five-time fire progress between successive frames has been tested. If these situations are accurate, system triggers alarm sound and delivers image warning by place over red box at fire flame in video sequence. Overall correctness from the tests is over and above 90%, signifying its efficiency and practicality. D. YCbCr Color Model with Statistical Parameters Authors in [20] suggest YCbCr color model for flame pixel arrangement which uses numerical characteristic of fire image which include mean as well as standard deviation since in YCbCr color space; relation between pixels is comparatively large related to other color models. The center of flame is white in color similar to cloud. Related to the formerly familiarized flame pixel cataloging approaches, the planned technique senses fire which having true Detection Rate (DR) and very less false DR. Planned system delivers almost 99.4% true DR. It uses YCbCr color space. Since YCbCr color space split-up luminance information from chrominance information than other color spaces. In this technique there is a requirement for altering RGB color space into one of the color spaces where chrominance and intensity separation is more distinguish. Based on the above idea we select YCbCr color space for cataloging of fire pixels.
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E. K-Mean Clustering Color Model Authors in [21] uses algorithm for segmentation process. The clustering methods can be used to segment any image into various clusters based on the similarity criteria like color or texture. In this research we have developed a method to segment color images using K-means clustering algorithm. K-means clustering algorithm divides image into K clusters based on similarity between pixels in that cluster. In this research we have used Euclidean distance formula to define clusters in K-mean clustering. The proposed method has been applied to a variety of images and conclusions have been drawn. Segmentation implies the division of an image into different objects or connected regions that do not overlap. The two basic properties in image segmentation are discontinuity and similarity. In discontinuity, method is to partition an image based on sudden variations in intensity [22], for example isolated points, lines and edges in an image. The principle methods in similarity are based on separating an image into sections which are alike according to a set of predefined standards. The present researches on image segmentation using clustering algorithms reveals that K-means clustering algorithm so far produces best results but some improvements can be made to improve the results.
3 Simulation and Results Performance of the existing color model is carried out using more than hundred images with the help of MATLAB TOOL. True DR and False DR have been compared of all color model specified above. Figure 3 shows the True DR and False DR of different color space model and also it has been seen clearly that K-mean clustering color model having best results compared to all color space model proposed earlier (Table 1).
4 Conclusion In this paper an image-based FDS is studied and further performance of existing color model has been analyzed in terms of false DR and true DR. For this we have considered more than 100 images which consist of fire region as well as non-fire region. Further comparison of all existing color space model have been carried out, that is totally on the basis of computer vision-based methods. Texture or shape statistics except area can be used to advance system’s fire detection performance. Performance of fire pixel may be also enhanced by applying smoke detection in initial stage of fire, together with fire detection method.
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Fig. 3 True DR and False DR of existing color model
Table 1 True DR and False DR of different color model Color model
True detection rate
False detection rate
RGB color model (A)
0.87
0.59
RGB color model with statistic parameters (B)
0.89
0.51
YCbCr color model (C)
0.91
0.45
YCbCr color model with statistical parameters (D)
0.94
0.32
K-mean clustering color model (E)
0.997
0.10
References 1. C. Yuan, Y. Zhang, and Z. Liu, A survey on technologies for automatic forest fire monitoring, detection, and fighting using unmanned aerial vehicles and remote sensing techniques. Can. J. For. Res. (2015) 2. M. Bonazountas, D. Kallidromitou, P. Kassomenos, N. Passas, A decision support system for managing forest fire casualties. J. Environ. Manage. (2007) 3. B.C. Ko, K.H. Cheong, J.Y. Nam, Fire detection based on vision sensor and support vector machines. Fire Saf. J. (2009) 4. S.J. Chen, D.C. Hovde, K.A. Peterson, A.W. Marshall, Fire detection using smoke and gas sensors. Fire Saf. J. (2007) 5. C.-B. Liu, N. Ahuja, Vision based fire detection, in Proceedings of the 17th International Conference on Pattern Recognition, ICPR (2004) 6. T.H. Chen, P.H. Wu, Y.C. Chiou, An early fire-detection method based on image processing, in Proceedings of International Conference on Image Processing, ICIP (2004) 7. T. Celik, Fast and efficient method for fire detection using image processing. ETRI J. (2010) 8. H.J. Vala, A. Baxi, A review on Otsu image segmentation algorithm. Int. J. Adv. Res. Comput. Eng. Technol. (2013)
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9. R. Priyadarshi, S.K. Soni, R. Bhadu, V. Nath, Performance analysis of diamond search algorithm over full search algorithm. Microsyst. Technol. 24(6), 2529–2537 (2018) 10. R. Priyadarshi, L. Singh, A. Singh, et al., A novel HEED protocol for wireless sensor networks, in 2018 5th International Conference on Signal Processing and Integrated Networks (SPIN), (2018), pp. 296–300 11. R. Priyadarshi, L. Singh, A. Singh, A. Thakur, SEEN: Stable energy efficient network for wireless sensor network, in 2018 5th International Conference on Signal Processing and Integrated Networks (SPIN) (2018), pp. 338–342 12. R. Priyadarshi, A. Bhardwaj, Node non-uniformity for energy effectual coordination in WSN (2017) 13. R. Priyadarshi, P. Rawat, V. Nath, Energy dependent cluster formation in heterogeneous wireless sensor network. Microsyst. Technol 14. R. Priyadarshi, S.K. Soni, V. Nath, Energy efficient cluster head formation in wireless sensor network. Microsyst. Technol. 7, 1–10 (2018) 15. R. Priyadarshi, H. Tripathi, A. Bhardwaj, A. Thakur, A. Thakur, Performance metric analysis of modified LEACH routing protocol in wireless sensor network. Int. J. Eng. Technol. 7(1–5), 196 (2017) 16. R. Priyadarshi, S. K. Soni, and P. Sharma, An enhanced GEAR protocol for wireless sensor networks, in Lecture Notes in Electrical Engineering, vol. 511 (2019), pp. 289–297 17. G. Marbach, M. Loepfe, T. Brupbacher, An image processing technique for fire detection in video images. Fire Saf. J. (2006) 18. B.H. Cho, J.W. Bae, S.H. Jung, Image processing-based fire detection system using statistic color model, in Proceedings—ALPIT 2008, 7th International Conference on Advanced Language Processing and Web Information Technology (2008) 19. J. Seebamrungsat, S. Praising, P. Riyamongkol, Fire detection in the buildings using image processing, in Proceedings of the 2014 3rd ICT International Senior Project Conference, ICTISPC 2014 (2014) 20. C.E. Premal, S.S. Vinsley, Image processing based forest fire detection. Int. Conf. Circuit, Power Comput. Technol. (2014) 21. A.Z. Chitade, D.S.K. Katiyar, Colour based image segmentation using K-means clustering. Int. J. Eng. Sci. Technol. (2010) 22. R. Priyadarshi, L. Singh, I. Sharma, S. Kumar, Energy efficient leach routing in wireless sensor network. Int. J. Pure Appl. Math. 118(20), 135–142 (2018)
A Novel Hybrid Precoding Technique for Millimeter Wave Tarun Gupta, Ashok Kumar and Rahul Priyadarshi
Abstract Millimeter wave (mmWave) signals undergo high order of path loss than the microwave signals and all cellular systems currently used in most wireless systems. We face many problems regarding high cost, high power consumption and low SNR of signal transmission due to various losses in mmWave communication. Hence to improve the quality of signal one should use effective and efficient techniques. As for mmWave, frequency spectrum availability is too high which is completely unoccupied. Hence mmWave systems should use large antenna arrays so that low wavelength can be obtained. It decreases path loss with improvement in beam forming gain. To use multiple data stream with beamforming is called precoding. Analog precoding is very useful in RF domain. But due to hardware complexity it is not used for MIMO system. Thus precoders and combiners are used at transmitter and receiver respectively. Zero forcing algorithms can be used to reduce the interference in system. Hybrid precoding technique is essential solution for improving spectral efficiency in mmWave. This paper describes a novel hybrid precoding technique using to improve spectral efficiency of mmWave system. Further for simulation of novel hybrid precoding, we will plot the graph using MATLAB tool and will observe improvement in spectral efficiency compared to analog precoding and ZF hybrid precoding. Keywords Millimeter wave · Antenna array · Beamforming · Precoding · Novel hybrid precoder · Zero forcing algorithm · Spectral efficiency
T. Gupta (B) · A. Kumar Department of Electronics and Communication Engineering, National Institute of Technology, Hamirpur, HP 177005, India e-mail: [email protected] A. Kumar e-mail: [email protected] R. Priyadarshi Department of Electronics and Communication Engineering, National Institute of Technology, Patna, Bihar 800005, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 V. Nath and J. K. Mandal (eds.), Nanoelectronics, Circuits and Communication Systems, Lecture Notes in Electrical Engineering 642, https://doi.org/10.1007/978-981-15-2854-5_42
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1 Introduction Current systems live with limited microwave spectrum. Nowadays there is rapid increment in users thus we require high bandwidth. Hence for future cellular system, millimeter wave (mmWave) communication is very promising technology. mmWave we can utilize large bandwidth. It can enable multi-gigabit data rates [1, 2]. Smaller wavelength of mmWave means small captured energy at antenna. mmWave systems need high beamforming gain. Thus use of directional precoding with large antenna array is helpful for longer links and it provide efficient received signal power too [3, 4]. High-frequency signals result in severe path losses because large bandwidth means more effect of noise over it. Analog precoding is used with phase shifters in which constant weighted constraints are placed with RF precoder. In analog beamforming analog switches can be used in replacement of phase shifters [5–7]. But this provide limited amount of gain and performance in correlated channels is also very weak [8]. Hence to limit the effect of this noise and losses at mmWave, small form factor can be used as compressed form of large antenna arrays. In digital beamforming high-power consumption due to hardware constraints is main problem. To design precoding matrices we require channel estimation method. Obtaining these matrices is very difficult in mmWave systems because in mmWave systems a large number of antennas with low signal-to-noise ratio (SNR) are used before beamforming process. In mmWave beamforming mixed signal components causes high-cost- and highpower consumption thus we have to limit RF chains [3, 9]. For digital processing we require mainly RF hardware and exclusively allocated baseband for each antenna element. The use of mmWave in MIMO system can face two main challenges • A very large number of RF (Radio Frequency) chains which is required by each antenna element. It increases energy consumption and cost of system. • It is very difficult to estimate the channel state information between each transmitter and receiver antenna. Hence to overcome from above problems a very effective solution of hybrid precoding was used. Hybrid precoding uses a mixed structure of analog beamforming in the radio frequency domain and digital precoding in baseband. In this paper we will discuss about hybrid precoding technique for improvement in spectral efficiency with the help of novel hybrid precoder. These will resultant with joint consideration of following factors: • Large antenna arrays • Novel hybrid precoding with RF constraints • Limited feedback We consider single-user and multi-user novel hybrid precoding in which large antenna arrays are used by limited number of transmitter and receiver. We estimated the channel and then defined weight constraints to precoders and combiners. Comparison between lower bound theorem, analog beamforming, and fully digital beamforming will be observed in reference to spectral efficiency with SNR.
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2 Related Work Hybrid beamforming technique was introduced first of all in the 2000s by some authors in [10, 11]. It is considered that transmitted number of data stream is responsible for lower limited number of up-down conversion chains. MmWave has very less value of wavelength and hence high-frequency capability. Because of increment in number of user available spectrum is not enough. Thus mmWave systems are invented where spectrum can be used efficiently. Digital beamforming was used to overcome from this problem. But due to limited, capacity is was not so much useful. After digital beamforming technique analog beamforming was used this was based on controlled operation of the phase of transmitted signal by each and every antenna with the use of networks of analog phase shifters. This technique was also limited for single user MIMO systems. Now it was required a proper solution in which both the properties of above two techniques can be used. Hence hybrid beamforming came into existence. Some of them are as below: In hybrid beamforming based on instantaneous CSI, to find beamforming matrices for analog and digital precoders with assumption of instantaneous CSI at transmitter side is very difficult process. Hence two main methodologies were used • Approximating the beamformer • Decoupling the design of analog and digital beamformer. Hence in narrow band massive MIMO systems analog beamforming can provide similar functionality as obtained in digital precoding technique. This beamforming design with instantaneous CSI can achieve the similar level of performance as fully digital beamforming if Ns ≤ NRF [10]. In hybrid precoding based on average CSI to obtain the optimal solution, for each snapshot of channel first we need to design digital beamformer and after this analog beamformer is designed which is based on their time average. In hybrid beamforming with selection, a selection stage was involved. It fulfills the requirement of the antenna selection and excludes analog beamforming. With the help of spatial MPCs beamforming gain can be improved by performing analog beamforming before process of selection. With reference to the entries of codebook as accomplishment from switch positions, this design can be considered as selective hybrid beamforming technique. The codebook design is elaborated, for example, in [12]. Since complexity for best port was increasing due to increment in RF chains. Eigen-diversity beamforming is an effective solution which does not contain the information of the instantaneous CSI [13]. It constructs the selection matrix for realization of each channel with the use of an optimized probability distribution (OPD), thereby use the maximum advantages of temporal diversity. Another hybrid precoding with channel sparsity concentrates on array gain to fixed number of multi path in RF domain, while allocating powers and multiplexing data stream in baseband. This system is optimised solution in the fixed number of large antenna arrays only. Another technique for hybrid beamforming was used with codebooks. It performs downlink training using pre-defined beam IDs to the transmitter. To minimize the mean square error (MSE) with the pre-defined
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beam pattern each codeword is constructed with the help of Orthogonal Matching Pursuit technology (OMP). Further hybrid precoding with spatially sparse precoding approximates the fully digital beamformers at mmWave bands with finite number of path and large antenna array [14]. The multipath sparsity puts a limit on the analog precoders to a finite set of vectors. Hence optimization of baseband precoder can be converted into a reconstruction of matrix using cardinality constraints on the group of RF chains. There is still a huge gap in Spectral Efficiency between a fully digital with phase shifters and the hybrid combiner with switches. Further continuous aperture based (CAP)-MIMO transceivers are also used for hybrid beamforming structure. This technique uses large lens antenna in replacement of large antenna array [15–17]. Feed antenna is used to excite lens antenna by an array. The feed array is named as beam selector and high beam gain can be produced by lens antenna at different angles. With the help of above techniques, Hybrid precoding technique with partial channel knowledge was proposed. In this method precoders were designed in three stages. First of all BS makes full use of reciprocity of channel and information of its angle of arrival to impart precoded symbols. These symbols permit the MS to evaluate the gain of each and every propagation path precisely. Now with the help of received symbols a conditional covariance matrix of channel is constructed by the MS by taking average over the undetermined base station (BS) scattering. Now MS uses matching algorithm to design hybrid precoders that approximate the dominant subspace of channel. The spectral efficiency achieved by this proposed algorithm approach toward the same rates which is achieved by optimal unconstrained digital precoding methodology with complete information about channel [18]. SYSTEM MODEL: Consider the multi user mmWave system as shown in Fig. 1. In this system N B is defined as number of antennas at base station and N RF is defined as RF chains which are assumed to make communication with U number of user at mobile station. Each mobile station is equipped with NM number of antennas as shown in Fig. 2. Now we assume that BS can serve maximum number of user simultaneously which is equal to BS base station RF chains, i.e., U ≤ N RF .
Fig. 1 Hybrid beamforming architecture
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Fig. 2 When #users = 4, #Tx = 64, #Rx = 4, #channel path = 1
For the downlink scenario, the BS uses U × U baseband precoders matrix FBB = BB f 1 , f 2BB , f 3BB , . . . , fUBB which is preceded by an NBS × U RF precoders, FRF = [ f 1RF , f 2RF , f 3RF , . . . fURF ]. Hence transmitted signal can be given as x = F RF F BB s,
(1)
where s = [s1 , s2 , s3 , s4 . . . , sU ]T is the U × 1 vector for transmitted signal, such that E[ss∗] = UP IU , and P is total transmitted average power. Now we consider that equal power is allocated among users. FRF Entries have constant modulus as these are implemented with the help of analog phase shifters. These entries are normalized 2 to satisfy [FRF ]m,n = NB−1 . Except this angle of analog phase shifters are also quantized which consist limited set of possible values. For quantized value we made an assumption, 1 [FRF ]m,n = √ e j∅m,n , NB where ∅m,n is a quantized angle. For simplification we considered a narrow block-fading channel model as [14, 19, 20] in which uth MS receives signal as ru = Hu
U
FRF f nBB sn + n u ,
(2)
n=1
where Hu is the NMS × NBS matrix which is used for mmWave channel between uth MS and BS, and nu ∼η 0, σ 2 I is Gaussian noise which is corrupting the obtained signal at receiver.
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At the uth MS, the RF combiner wu is used for processing the received signal ru yu =
wu∗ Hu
U
FRF f nBB sn + wn∗ n u ,
(3)
n=1
where wu has a same constraint as RF precoder. In this work we have used novel hybrid precoder.
3 Proposed Work Efficient designing of analog and digital combiners at the MS and precoders at the BS to maximize the spectral efficiency. The matrix which is representing channel Hu can be given as Hu =
Lu NB NM αu,l aM θu,l aB∗ ϕu,l , L u l=1
(4)
where αu,l is the complex gain of lth path. The variables ϕu,l and θu,l [0, 2π] are the lth path’s angles of departure and angel of arrival respectively. aB ϕu,l and aM θu,l are antenna response vector of BS and MS respectively. The value for aBS can be given as
T 1 2π 2π 1, e j λ d sin(ϕ), . . . . . . .., e j(NBS −1) λ d sin(ϕ) , aB (ϕ) = √ NB
(5)
Similarly aM also can be formed. When the received signal at uth MS obtained in Eq. (2) is processed with RF combiner wu , the rate can be given as Ru = log2 1 +
2 P ∗ wu Hu FRF f uBB U P BB 2 ∗ n=u wu Hu FRF f u U
Thus sum rate of the system can be defined as Rsum =
+ σ2
U
(6)
Ru .
u=1
For RF phase shifters quantized angles are available because of this reason the analog beamforming can consider limited values. There are many codebooks which are used to select vectors. But here we will use beamsteering codebook because in this codebook the beamforming vectors can be considered as spatial matched filters. Hence we get same form of array response vectors. Let F represents beamsteering 2πk codebook, with cardinality |F| = NQ . Hence F consists of vectors as aB = NQQ ,
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where kQ takes values 0, 1, 2 and (NQ − 1). Similarly RF combiner codebook formation is to be done. Beamforming design can be classified in two types. First is iterative coordinated beamforming in which global channel information at the transmitter is required. In mmWave, matrix designed for large channel is fed back to BS which needs a huge feedback overhead that is very difficult to obtain. Except this it depends on the use of machine vectors at Mobile station side (MS) which cannot be produced with hybrid architecture due to hardware limitations [21]. Second is non-iterative design with CSI at transmitter and receiver. These are used to avoid design complexity using some algorithm like block diagonalization. Although block diagonalization technique requires global channel information at transmitter [22], our proposed method is designed to obtain high performance while requiring small feedback and low training overhead. The propose hybrid precoders to minimized the mean is designed 2 . square error (MSE) of all data streams E = s − sˆ Dividing precoding operation into two domains is main challenge and each domain should consist different constraints. Hence according to proposed idea calculation of precoders should be done in two parts. The first stage consists join designing of BS RF precoders and MS RF precoders to maximize desired signal power for each user. In this stage, RF beamforming, combining vectors f uRF and wu are designed by the BS and each MS. With the help of these vectors the power for user u can be maximized. In the second stage to manage multi-user interference designing of BS digital precoders is performed. In this stage BS trains channels, h u = wu∗ Hu FRF , u = 1, 2, . . . , U, with the MS. Each MS u quantizes its effective channel values with the help of designed codebook H .
4 Simulation and Results First three graph (Figs. 2, 3, and 4) shows the variation of spectral efficiency when number of channel paths were changed. After this Figs. 5, 6, and 7 show the variations of spectral efficiency when number of transmitters were varied. And at the end Figs. 8, 9, and 10 shows the variation when number of receivers were changed.
5 Conclusion In this paper we considered limited feedback precoding with novel hybrid precoder for mmWave. For the improvement of spectral efficiency we used novel hybrid precoder. Different methods and designs of beamforming were discussed. We considered Gaussian noise in multi-user MIMO system and simulated the result with the change
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Fig. 3 When #users = 4, #Tx = 64, #Rx = 4, #channel path = 10
Fig. 4 When #users = 4, #Tx = 64, #Rx = 4, #channel path = 20
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Fig. 5 When #users = 4, #Tx = 4, #Rx = 4, #channel path = 5
Fig. 6 When #users = 4, #Tx = 9, #Rx = 4, #channel path = 5
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Fig. 7 When #users = 4, #Tx = 16, #Rx = 4, #channel path = 5
Fig. 8 When #users = 4, #Tx = 64, #Rx = 4, #channel path = 5
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Fig. 9 When #users = 4, #Tx = 64, #Rx = 9, #channel path = 5
Fig. 10 When #users = 4, #Tx = 64, #Rx = 16, #channel path = 5
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of sum rate v/s SNR. We improved spectral efficiency in comparison to analog beamforming and ZF hybrid precoding. For different number of paths we observed that novel hybrid precoding has better impact than any other.
References 1. S. Yong, C. Chong, An overview of multigigabit wireless through millimeter wave technology: potentials and technical challenges. EURASIP J. Wireless Commun. Netw. 2007(1), 50 (2007) 2. R. Daniels, R.W. Heath Jr., 60 GHz wireless communications: emerging requirements and design recommendations. IEEE Veh. Technol. Mag. 2(3), 41–50 (2007) 3. Z. Pi, F. Khan, An introduction to millimeter-wave mobile broadband systems. IEEE Commun. Mag. 49(6), 101–107 (2011) 4. G. Hendrantoro, R. Bultitude, D. Falconer, Use of cell-site diversity in millimeter-wave fixed cellular systems to combat the effects of rain attenuation. IEEE J. Sel. Areas Commun. 20(3), 602–614 (2002) 5. A.F. Molisch, M.Z. Win, Y.-S. Choi, J.H. Winters, Capacity of MIMO systems with antenna selection. IEEE Trans. Wireless Commun. 4(4), 1759–1772 (2005) 6. A. Gorokhov, D.A. Gore, A.J. Paulraj, Receive antenna selection for MIMO spatial multiplexing: theory and algorithms. IEEE Trans. Signal Process. 51(11), 2796–2807 (2003) 7. A.F. Molisch, M.Z. Win, MIMO systems with antenna selection. IEEE Microwave Mag. 5(1), 46–56 (2004). https://doi.org/10.1109/MMW.2004.1284943 8. Z. Xu, S. Sfar, R.S. Blum, Analysis of MIMO systems with receive antenna selection in spatially correlated Rayleigh fading channels. IEEE Trans. Veh. Technol. 58(1), 251–262 (2009) 9. S. Sanayei, A. Nosratinia, Antenna selection in MIMO systems. IEEE Commun. Mag. 42(10), 68–73 (2004) 10. X. Zhang, A. Molisch, S.-Y. Kung, Variable-Phase-Shift- Based RF-Baseband Codesign for MIMO Antenna Selection. IEEE Trans. Signal Processing 53(11), 4091–4103 (2005) 11. P. Sudarshan et al., Channel Statistics-Based RF Pre-Processing with Antenna Selection. IEEE Trans. Wireless Commun. 5(12), 3501–3511 (2006) 12. S. Hur et al., Millimeter Wave Beamforming for Wireless Backhaul and Access in Small Cell Networks. IEEE Trans. Commun. 61(10), 4391–4403 (2013) 13. J. Choi, Diversity Eigenbeamforming for Coded Signals. IEEE Trans. Commun. 56(6), 1013– 1021 (2008) 14. O. El Ayach et al., Spatially Sparse Precoding in Millimeter Wave MIMO Systems. IEEE Trans. Wireless Commun. 13(3), 1499–1513 (2014) 15. R. Priyadarshi, M.P. Singh, A. Bhardwaj, P. Sharma, Amount of fading analysis for composite fading channel using holtzman approximation, in 2017 Fourth International Conference on Image Information Processing (ICIIP), Shimla (2017), pp. 1–5. https://doi.org/10.1109/iciip. 2017.8313759 16. R. Priyadarshi, M. P. Singh, H. Tripathi, P. Sharma, Design and performance analysis of vivaldi antenna at very high frequency, in 2017 Fourth International Conference on Image Information Processing (ICIIP), Shimla (2017), pp. 1–4. https://doi.org/10.1109/iciip.2017.8313758 17. M.P. Singh, R. Priyadarshi, P. Sharma, A. Thakur, Small size rectangular microstrip patch antenna with a cross slot using SIW, in 2017 Fourth International Conference on Image Information Processing (ICIIP), Shimla (2017), pp. 1–4. https://doi.org/10.1109/iciip.2017. 8313757 18. A. Alkhateeb, G. Leus, R. Heath, Limited feedback hybrid precoding for multi-user millimeter wave systems. IEEE Trans. Wireless Commun. 14(11), 6481–6494 (2015) 19. A. Alkhateeb, O. El Ayach, G. Leus, R. Heath, Channel estimation and hybrid precoding for millimeter wave cellular systems. IEEE J. Select. Topics Sig. Proc. 8(5), 831–846 (2014)
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20. P. Xia, S.-K. Yong, J. Oh, C. Ngo, A practical SDMA protocol for 60 GHz millimeter wave communications, in Proceedings of the Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA (October 2008), pp. 2019–2023 21. C.-B. Chae, D. Mazzarese, T. Inoue, R. Heath, Coordinated beamforming for the multiuser MIMO broadcast channel with limited feedforward. IEEE Trans. Sig. Proc. 56(12), 6044–6056 (2008) 22. F. Boccardi, H. Huang, A near-optimum technique using linear precoding for the MIMO broadcast channel, in International Conference on Acoustics, Speech, and Signal Processing, vol. 3 (April 2007), pp. III–17–III–20
K-NN Classification for Large Dataset in Hadoop Framework Ritesh Jha, V. Bhattacharya, Vinit Singh and I. Mukherjee
Abstract In the big data domain, the traditional algorithms approach fails in the classification for the large data set as they are memory dependent and suitable for small and medium size data sets and our proposed model attempts to apply the existing classification for the large data set. The proposed model shows percentage of reduction of time for optimal k-number of nearest neighbors against the varying number of Datanodes in Hadoop framework. Keywords Big data · K-NN · Hadoop · MapReduce
1 Introduction In today’s world, data is rapidly being generated from every aspect of our lives. Our buying habits, social media interactions, daily schedules, etc., which add to the already huge pile of data that we generate from stock markets, medicine, traffic and almost every other industry. This is Big Data [1], and it impacts our lives a lot more than we can imagine. Therefore, it is vital that we have reliable and efficient means for analyzing this data. Such challenges are solved by using frameworks like Apache Hadoop [2]. R. Jha (B) · V. Bhattacharya · I. Mukherjee Department of Computer Science and Engineering, Birla Institute of Technology, Mesra Ranchi 835215, JH, India e-mail: [email protected] V. Bhattacharya e-mail: [email protected] I. Mukherjee e-mail: [email protected] V. Singh Department of Chemical Engineering, Birla Institute of Technology, Mesra Ranchi 835215, JH, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 V. Nath and J. K. Mandal (eds.), Nanoelectronics, Circuits and Communication Systems, Lecture Notes in Electrical Engineering 642, https://doi.org/10.1007/978-981-15-2854-5_43
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Any data mining or Machine Learning algorithm has its own limitations and used for small- to medium-sized data [3, 4, 12, 13]. They support only that kind of data that fit into physical memory. Big data is complex, the biggest challenge when handling huge amounts of data is how we apply supervised or unsupervised techniques. Classification, which is supervised learning, is one of the important tasks in a number of fields like biomedicine, social media, etc. [15]. In this paper, we applied the k-Nearest Neighbors (k-NN) algorithm [5] which is used mostly in clustering [6, 7, 16–20]. The k-NN algorithm was implemented in the Hadoop framework for Poker Data set [8]. The data set includes One million instances used for the purpose of training the algorithm, and this was used to test the accuracy of the model. The experiment also deals with the test accuracy for varying values of “k” and the impact of changing the number of data nodes on the execution time. The organization of paper as follows: Part-II provides background related to the kNN algorithm. Part-III explains the methodology including the Hadoop and MapReduce Frameworks. Part-IV describes the Experimental setup. Part-V discusses the results. Part-VI delivers with the concluding notes for the paper.
2 Background k-NN is a non-parametric, lazy learning algorithm. In k-NN, the training part is very fast due to the lack of any generalization. The working of K-NN consists of two phases: 1. Training Phase(s). 2. Testing Phase(s). In the training phase, it stores the value of . In the testing phase, the class label is predicted for the testing data. The sequential procedure for the K-NN is as follows: 1. 2. 3. 4.
Input the value “k”. Load the Normalized(0-1 scale) training data. Load the Normalized (0-1 scale) testing data. Calculate the Euclidian Distance for each instance of the testing data from the training data 5. Perform majority vote among the “k” closest neighbors 6. Perform majority function for the class label from the testing data. 7. Procedure repeat until all testing data is classified.
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3 Methodology This section describes the frameworks and the methodologies that were used in performing this experiment. A. Hadoop Framework For distributed computing dealing with large or big data, Apache developed open source software Hadoop and it has own filesystem called HDFS [9, 10].the functionality of hadoop to process the large dataset (Fig. 1). In HDFS, one special node/cluster is known as Namenode and the job of Namenode is to manage the namespace of file system. The other nodes or clusters are known as slaves or Datanodes. HDFS breaks large files into a number of chunks, called blocks, and replicates them across the different nodes. HDFS does not support the dynamic updating of data. HDFS very easily maintains a fault-tolerant system and if any node fails, the Namenode detects it and transfers the job to other nearby Datanodes. This is carried out by the JobTracker. The sole responsibility of JobTracker is to assign a task to the Tasktracker in the DataNodes where the data is locally present. Tasktracker periodically communicates with the Jobtracker for updating the task execution information. B. MapReduce Framework MapReduce [9] has two main steps: 1. Map. 2. Reduce (Fig. 2). Each step has only (key, value) pairs as input and output. The Mapper job is to take input in the form of (key, value) pairs and generate a set of intermediate (key, value)
Fig. 1 Hadoop Framework [11]
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Fig. 2 MapReduce framework
pairs. Suffle/sort does the sorting on the list which is supplied by the Mapper. Finally the list is passed to the reduce function and reduce function outputs the final result which is stored in the HDFS block. MapReduce is the best approach for dealing with large data sets even of sizes of Petabytes, etc. C. Mapreduce Algorithm for k-NN 1. Mapper for k-NN: Mapper role is to find a set of the closest “k” (key, value) pairs, containing the distance and the class of the K-NN for the given test case. These key-value pairs are then forwarded to the Reducer [14]. TrS is the Training Dataset who’s each entry is normalized on 0-1 scale so that the results are not weighted. TC is the test case for which we need to find the class, and it is also normalized on 0-1 scale to avoid weighted results. The map() method is run by MapReduce once for each row supplied as the training data. It then calculates the distance of each instance of the training data from the testing data. The distance and respective class is then stored. The new pair is pushed into K-NN Map and then the largest entry is removed (if, length of K-NN Map > k). The key-value pairs, i.e., Distance and Class of k-nearest neighbors, are then emitted.
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2. Reducer for k-NN The reduce() method accepts the key-value pairs emitted by the Mapper. It then creates a List of Classes. The occurrence of each class is counted and the class with the most number of instances is emitted. This majority vote winner Class is then the class of the Test Case.
4 Results Poker Data Set was used for the purpose of this experiment. The data set contained 1,000,000 instances in the Training Data set (size: 23 MB) and 15,000 (size: 400 KB) instances in the testing Data set (Table 1). The following table shows the runtime needed and the accuracy obtained when running 2 Datanodes and with 4 Datanodes. The experiment was conducted for different values of K, and the best value was obtained as K = 8 as repeated in table. It can be seen that time reduction is up to 88% of time to run the same tasks when running on more number of DataNodes. The performance of the setups with greater number of DataNodes increases as the data size increases. With increase in data size, the number of blocks created in HDFS increases resulting in the generation of more maps per DataNode. The performance of K-NN has been significantly increased in Hadoop framework with respect to sequential behavior of K-NN algorithm. Therefore speedup has been compared by running the tasks on different number of Datanodes. The result shows the good speed up for four Datanodes as compared to two Datanodes. Table 1 Experimental results K
Number of datanodes
Run time
Accuracy (%)
Percentage reduction of time 2-datanodes to 4-datanodes
8
2
772
47.9334
–
8
4
684
47.9334
88%
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5 Experimental Setup The experimental setup used for this paper consisted of 5 Personal Computers: 1 Namenode (or Master Node) and 4 Datanodes (or Slave Nodes). Each system was identical with the following specifications: 8 GB DDR3 RAM, Intel Core i7 5th Gen Processors and a 1 Terabyte Hard disk drive.
The Operating System used was Linux Ubuntu-18.04 with Apache Hadoop-2.7.7.
6 Concluding Remarks The k-NN algorithm becomes limited to small or medium size data, hence the proposed method uses the distributed framework for the K-NN model for large dataset in order to improve efficiency with respect to sequential processing. From the experiment, result shows as the number of Datanodes increases, speedup also increases.
References 1. S. Sagiroglu, D. Sinanc, Big data: a review, in International Conference on Collaboration Technologies and Systems (CTS) (2013), pp. 42–47 2. Apache, Apache hadoop. https://hadoop.apache.org 3. Chunye Wang, Xiaofeng Zhou, The MapReduce parallel study of KNN algorithm. Adv. Mater. Res. 989–994, 2123–2127 (2014) 4. C. Yadav, S. Wang, M. Kumar, Algorithm and approaches to handle large data—a survey. Int. J. Comput. Sci. Netw. 2(3), 37–41 (2013) 5. W. Kim, Y. Kim, K. Shim, Parallel computation of k-nearest neighbor joins using MapReduce, in IEEE International Conference on Big Data (Big Data) (2016) 6. J. Dean, S. Ghemawat, MapReduce: simplified data processing on large clusters. Commun. ACM 51(1) (2008), 107–113 7. M. Muja, D.G. Lowe, Scalable nearest neighbor algorithms for high dimensional data. IEEE Trans. Pattern Anal. Mach. Intell. 36(11), 2227–2240 (2014) 8. UCI Machine Learning Repository: Poker Hand Data Set. https://archive.ics.uci.edu/ml/ datasets/Poker+Hand
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9. S. Pakize, A. Gandomi, Comparative study of classification algorithms based on MapReduce model. Int. J. Innov. Res. Adv. Eng. 1(7), 215–254 (2014) 10. G. Song, Z. Meng, F. Huet, F. Magoules, L. Yu, et al., A hadoop MapReduce performance prediction method, in HPCC 2013, Nov 2013, Zhangjiajie, China (2013), pp. 820–825 11. P.P. Anchalia, K. Roy, The k-nearest neighbor algorithm using MapReduce paradigm, in Fifth International Conference on Intelligent Systems, Modelling and Simulation, IEEE. https://doi. org/10.1109/isms.2014.94 (2014) 12. A. Zaslavsky, C. Perera, D. Georgakopoulos, Sensing as a service and big data, in Proceedings of the International Conference on Advances in Cloud Computing (ACC) (2012), pp. 21–29 13. A. Nanda Kumar, N. Yambem, A survey on data mining algorithms on Apache Hadoop platform. Int. J. Emerg. Technol. Adv. Eng. 4(1), 563–565 (2014) 14. S. Santini, R. Jain, Similarity measures. IEEE Trans. Pattern Anal. Mach. Intell. 21(9), 871–883 (1999) 15. M. Mineli, M, Chambers, A. Dhiraj, Big data: Big Analytics Emerging Business Intelligence and Analytics Trends for Today’s Business (Wiley, 2013) 16. T.M. Cover, P.E. Hart, Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21–27 (1967) 17. G. Chatzigeorgakidis et al., FML-kNN: scalable machine learning on Big Data using k-nearest neighbor joins. J. Big Data 5, 4 (2018) 18. G. Chatzigeorgakidis, S. Karagiorgou, S. Athanasiou, S. Skiadopoulos, A MapReduce based k-NN joins probabilistic classifier, in Proceedings of IEEE BigData (2015), pp. 952–957 19. G. Song, J. Rochas, F. Huet, F. Magoulès, Solutions for processing k nearest neighbor joins for massive data on MapReduce, in Proceedings of PDP (2015), pp. 279–87 20. T. Tulgar, A. Haydar, ˙I. Er¸san, A distributed K nearest neighbor classifier for big data. Balk. J. Electr. Comput. Eng. 6(2) (2018)
Performance Analysis of Novel Energy Aware Routing in Wireless Sensor Network Sanjeev Kumar, Surender Kumar Soni, Randheer and Rahul Priyadarshi
Abstract Large numbers of sensors are being deployed in unattended regions, e.g., in forests, seas and also in remote areas. The motivation behind it is to reduce the human assistance and improve lifetime of network. Since these sensor nodes are battery constraint and excessive use of any node causes early depletion in residual energy level of that node in the network and node can die early. So in this paper by using GEAR (Geographical and Energy Aware Routing) Protocol, it is emphasis that commination among the nodes should be energy efficient by an intelligent neighbor selection to forward the packet to the sink. GEAR is recursive data dissemination scheme inside the region. Group-Based Deployment Model and applying energy aware routing makes GEAR more effective than the previous. Keywords PAGASIS · GEAR · Beacon · Cluster · Routing · WSN · Energy · Nodes
1 Introduction The key concern in Wireless Sensor Network (WSN) communication is to reduce the power dissipation while communication takes place in the network. Nodes are deployed in those regions where human assistance is difficult or almost not possible S. Kumar (B) · S. K. Soni · Randheer Department of Electronics and Communication Engineering, National Institute of Technology Hamirpur, Hamirpur, HP 177005, India e-mail: [email protected] S. K. Soni e-mail: [email protected] Randheer e-mail: [email protected] R. Priyadarshi Department of Electronics and Communication Engineering, National Institute of Technology Patna, Patna, Bihar 800005, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 V. Nath and J. K. Mandal (eds.), Nanoelectronics, Circuits and Communication Systems, Lecture Notes in Electrical Engineering 642, https://doi.org/10.1007/978-981-15-2854-5_44
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Fig. 1 Hardware structural design of WSN
like in flood forest-fire and earthquake like situation. So these nodes are deployed to reduce both human efforts and assistance [1]. But issue start with the battery life of the deployed nodes. Since, nodes are constrained with the battery then there is a need to utilize the nodes properly like wake (working time in a day) and sleep (energy conservation time) and also routing so that all nodes can effectively take part in the communication and make it energy efficient [2]. In WSN, the motive of this work is reduction in energy consumed by sensor node and increase the network lifetime for longer time period. Sensor nodes are used to monitor different type of physical phenomenon, i.e., earthquake, battlefield, defense, environment monitoring, agricultural, activity, atmospheric change and home automation. Basically sensor node sense the physical changes in environment, after this the sensed physical value changes into digital mode through analog to digital converter, after that the computational work is done by processing unit and gather the information in order to transmit it to Base Station(BS) via wireless link. The hardware structural design of WSN is shown in Fig. 1 which mainly contains of four important components • • • •
Power Unit Transceiver Unit Processing Unit Sensing Unit. The basic component of sensor nodes are
• • • • •
Microcontroller Power unit External memory Transceiver One or more sensor nodes Challenges in WSN
• Sensor node lifetime • Localization
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Fig. 2 Residual energy
• Related to scalability • Cost of node Application of WSN • • • •
Military Health purpose Environmental Commercial related
The above applications of WSN are mainly used in now a days. Military applications [3] are used for monitoring the enemy forces of their activity and surveillance of battlefield or many more. The health purpose application of WSN is to monitor the patient within a hospital. Sensor nodes are also used in the industrial and commercial purpose. In these modern days, deployment of numbers of sensor node could be possible, due to development in micro-electro system Technology. Due to increase in the Number of deployment of sensor node, it is difficult to recharge the battery of sensor node [4]. An important key for WSN is, to boost the power of sensor node for prolong time period. There are different kinds of clustering algorithm proposed, for which energy consumption by network is well managed by reduction in the transmission range of the sensor. In this, group communication with the BS is being managed by the Cluster Head (CH) [5]. CH receives all group messages, aggregates and forward to the BS, no longer transmit data directly to the BS. The entire sensor node in cluster forwarded its data to their corresponding CH. To avoid collision, it uses TDMA (Time Division Multiple Access) schedule. In predefined time slot all sensor node transmit their data to allotted CH, otherwise sensor node turn off their transceiver for save energy of each sensor node through TDMA scheduling. The motive behind this work is to trim down the energy
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consumption of sensor node in term of throughput and network lifetime by logically divide the sensor node in four different region [6]. In this region we use different kinds of communication hierarchy. Nodes in one region use direct communication to BS if threshold distance is less than distance between BS and node, another region communicate direct to gateway node if threshold distance is greater than BS and gateway node. Since all the nodes in WSN are battery constrained, we can say that they have a certain lifetime. In any particular scenario if nodes are deployed then they should work till their lifetime or near to it but if any node dies quite early due to only energy issue, i.e., depletion of energy of the battery can cause a serious issue to the communication system of that particular region [7]. It may occur due to the frequent use of that node in the region. So, motivation is that to make the communication among the nodes effective in terms of nearest.
2 Related Work Work towards the GEAR [6] started after the shortcomings of Hierarchical Routing, since they are including all the nodes in chain formation from source to destination in PEGASIS [11] or if we see the working of GPSR then it includes the nodes near the perimeter of the region and nearer to destination and use it frequently, in this manner if a node is using quite frequently, its energy level will go down and this node will get down and next time to establish the communication we have select some other node which might be away from the destination [8]. In this way energy to transmit data towards the sink will more than the previous node. In contrast GEAR provides new approach in which all nodes are not involved in spite of this it include only those nodes which is geographically aware and have information about the residual energy [9]. Here is description about PEGASIS methodology that is been done before we start GEAR. Greedy approach is used to make the chain from source to destination, e.g., DFS. Main objective of PEGASIS are Uniform energy across the network and by this increase the network lifetime also Reduce data delay incur on their way to source to sink. It assumes network is homogenous [10]. Nodes have global knowledge about them. In this devices contain CDMA equipped transceivers. Nodes have responsibility to deliver data to the sink. It uses the chain structure, nodes communicate with their closest neighbor to the end node. Greedy means to add the nearest neighbor only on the basis of RSSI. Chain leader is selected on the basis of highest residual energy and it shifts its position after each round [12]. Issues with PEGASIS • Since greedy approach is used in chain formation and if nodes are involved in chain formation are of low residual the communication may drain down without completion. • It is assumed that in PEGASIS methodology that all nodes can directly communicate with the sink, so in long range communication high amount of energy will be dissipated.
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• Bottleneck issues in order to form chain to the neighbor. Geographical Routing provides the mechanism to deliver the packet in a destination location based on the location information only [13]. There is concept of regions to which data has to be delivered for this purpose whole geographic area is divided to number of sub-regions and the target packet is delivered to that region and then it is delivered to that specific node to which is targeted to [14]. Properties of geographical routing • Scalability • Statelessness • Low maintenance overhead. Since as discussed previously all nodes in WSN are constrained with the energy so there is a need to provide an energy aware metrics for the purpose of making communication effective at geographical point of view. GEAR uses this energy aware metrics [15] to compute the neighbor selection in order to balance the energy consumption among the nodes. Main area of concern in this metric is that as much we will take time to make the geographical partition regions will be more specified and packets will be delivered more effectively in that specified region. After the region definition is tries to provide an assurance of high throughput with less delay [16]. It is also gives the idea to save the node energy, e.g., if we have a geographical region and we need to send the data to a particular node in a specified region then it will send the data to only that targeted region and cut other nodes to involve in this. When a WSN is created it contains large number of Nodes and each node has equal importance viz. if one Node will be used again-n-again for the communication in order to use the shorter path its battery will be depleted and consequently node will die. Then issue will arise that which node will be taken as middle path for shortest path from source to sink [17]. In order to this the load should be share among all the nodes in order to make the communication effective and improve the network lifetime. It uses a load sharing approach similar to load balancing in distributed network. When a node sends data to its closest neighbor with minimum amount of data to be transmitted. GEAR is a location-based routing protocol which takes energy conservation as a major area of concern in routing the data from source to sink [18]. It uses an energy-aware neighbor selection and forwards the packet in recursive manner. It has better network lifetime time other non-geographical routing. GEAR Protocol has two phases for packet forwarding Packets towards the target region and disseminating packet inside the target region. For forwarding packets towards the target region there are two scenarios as follows [19]: • When a neighbor close to the sink exists near the source, then it uses next hop packet forwarding to disseminate the packet towards the sink. • When neighbor to sink exists far away from the source then it uses cost computation functions and computation of HOLES to deliver the packet to the sink. Assumptions for GEAR protocol before simulation • Nodes are destined to a specified target region.
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• Each know about its own energy level and location information as well as neighbor location information using low cost GPS or some beacon and beacon less services. • Link between two nodes are symmetric, i.e., communication between each node is bidirectional. As it is discussed previously that GEAR protocol [6] has two phases. But the packet dissemination is always dependent on the neighbor selection. In GEAR we talk about energy-aware neighbor selection. In contrast [20] to other routing methodologies GEAR can support a larger number of nodes. This advantage of GEAR adds complexity in neighbor selection because there could be a number of neighbors then how to select a better one is an issue with energy awareness [21].
3 Proposed Model There are issues for Energy–Aware- and Location-based routing due to which transmission become inefficient and continuously looped causes the node to die earlier. Here two solutions are proposed these are as following • Beaconless and GPS-less Location Discovery • Energy-Aware Routing Beacons, sometime calls as Anchor, are a node which has information about its location either by Manual or GPS configuration in this. When a beacon broadcast its information to nearby nodes other nodes arrange themselves according to that information. It is also high energy node which is used for this purpose. At the same time if we see GPS is also used for this purpose it provide correct location but it has so certain and unavoidable inherent issues. So we can say in using GPS and Beacons there is a strong chance that network lifetime will reduce. These are as following issues concern with using it: • GPS is a costly device • GPS connects with satellite which is an energy-consuming process. • Large amount of energy dissipated in using beacons. Solution: Group-based Deployment Scheme • First of all here is a concept of deployment point, sensor nodes are grouped into n groups and each group is deployed according to that reference point. • It is possible that groups may not fall in a uniform manner so those locations usually follows a probability distribution function called priori, with this it is possible to discover the location of the nodes using observing the group membership of the node. In this for discovering the location likelihood function is used. For this kind of localization we have two components. • Reference Point: Whose coordinates are known?
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• Spatial Relationship between sensor nodes and reference point. When sensor nodes are deployed corresponding to a deployment point then the coordinates of that point is usually known. So using spatial relationship and reference point we can find the location of sensor nodes. At each deployment point a group of nodes are deployed.
4 Methodology In order to develop the GEAR protocol following methodology has been used but before doing this there are few prerequisites those are performed to setup the network like: • Nodes Selection: It deals with the node for a particular scenario like selection of node for temperature sensing, humidity or for sea-level monitoring is done. • Node Setting: Nodes are selected and their settings are made or changed from default to require. • Node Deployment: Nodes will be deployed as they are discussed above.
5 Simulation and Results Performance of the proposed protocol is carried out using MATLAB TOOL. In network implementation and simulation there are some steps that have to be performed before the simulation process starts. Table 1 shows the simulation parameters. There are some predefined expectations from GEAR to improve the result. It is not like those protocols which uses DFS for data dissemination. Figure 2 displays how GEAR shows stability as transmission starts while Flooding leaks in stability. Table 1 Simulation parameters
Simulation variable
Set up values
Sensor area
100 m × 100 m
No. of sensor nodes
1000 nodes
Initial energy of nodes
0.5 J
Base station location
(50 m, 50 m)
Number of rounds
1500
E elec
40 nJ/bit
εfs
8 pJ/bit/m2
εamp
0.001 pJ/bit/m4
E DA
5.5 nJ/bit
k
4000 bits
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It dissipates large amount of data as compare to GEAR and also proves that network lifetime in this is also quite low. Graph shows the nominal drop in GEAR after each time interval and number of rounds which FLOODING gives a sharp drop in its residual energy level. It proves GEAR’s better performance in improving network lifetime. Hence, a basic issue for making communication energy efficient is done in this dissertation. It is done using all proposed methodology and solution. Figure 3 shows the Histogram view of Residual Energy. It can easily visualize from Fig. 2 that GEAR has better performance and network lifetime. Table 2 shows a comparison from flooding with parameters like their approach, use of energy and network size. Theoretically, some comparison are made on different performance parameters, it can say that GEAR has better performance in comparison of data centric and hierarchical routing if we see it is not like Flooding of SPIN which unnecessarily broadcast data and consumes node energy, Unlike PEGASIS and LEACH it does not waste power in election of CH. It is different by using data-centric approach and location-based routing to make it accurate and energy-efficient.
Fig. 3 Histogram view of residual energy
Table 2 Simulation comparison of flooding and GEAR
Parameter
Flooding
GEAR
Approach
Flat
Location based
Traffic type
Uniform
Uniform
Energy dissipation
Discrete
Linear
Communication
Broadcasting
Recursive forwarding
Network lifetime
Imbalanced
Balanced
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References 1. P. Saini, A.K. Sharma, E-DEEC–enhanced distributed energy efficient clustering scheme for heterogeneous WSN, in 2010 First International Conference On Parallel, Distributed and Grid Computing (PDGC 2010) (2010), pp. 205–210 2. H. Chen, C. Zhang, X. Zong, C. Wang, LEACH-G: an optimal cluster-heads selection algorithm based on LEACH. 3. T.K. Jain, D.S. Saini, S.V. Bhooshan, Cluster head selection in a homogeneous wireless sensor network ensuring full connectivity with minimum isolated nodes. J. Sensors 2014, 1–8 (2014) 4. O. Zytoune, Y. Fakhri, D. Aboutajdine, Lifetime maximisation algorithm in wireless sensor network. Int. J. Ad Hoc Ubiquitous Comput. 6(3), 140 (2010) 5. N. T. Van, T.-T. Huynh, and B. An, “An energy efficient protocol based on fuzzy logic to extend network lifetime and increase transmission efficiency in wireless sensor networks,” J. Intell. Fuzzy Syst., pp. 1–8, Aug. 2018 6. R. Priyadarshi, S.K. Soni, P. Sharma, An Enhanced GEAR Protocol for Wireless Sensor Networks (Springer, Singapore, 2019), pp. 289–297 7. R. Priyadarshi, L. Singh, S. Kumar, I. Sharma, A hexagonal network division approach for reducing energy hole issue in WSN. Eur. J. Pure Appl. Math. 118 (2018) 8. R. Priyadarshi, P. Rawat, V. Nath, Energy dependent cluster formation in heterogeneous wireless sensor network. Microsyst. Technol., 1–9 (2018) 9. R. Priyadarshi, A. Bhardwaj, Node non-uniformity for energy effectual coordination in WSN 10. R. Priyadarshi, S.K. Soni, V. Nath, Energy efficient cluster head formation in wireless sensor network. Microsyst. Technol. (2018) 11. W. Guo, W. Zhang and G. Lu, PEGASIS protocol in wireless sensor network based on an improved ant colony algorithm, in 2010 Second International Workshop on Education Technology and Computer Science, Wuhan (2010), pp. 64–67. https://doi.org/10.1109/etcs. 2010.28 12. R. Priyadarshi, L. Singh, I. Sharma, S. Kumar, Energy efficient leach routing in wireless sensor network (2018) 13. R. Priyadarshi, H. Tripathi, A. Bhardwaj, A. Thakur, A. Thakur, Performance metric analysis of modified LEACH routing protocol in wireless sensor network. Int. J. Eng. Technol. 7(1–5), 196 (2017) 14. W.B. Heinzelman, A.P. Chandrakasan, H. Balakrishnan, An application-specific protocol architecture for wireless microsensor networks. IEEE Trans. Wirel. Commun. 1(4), 660–670 (2002) 15. R.M. Bani Hani, A.A. Ijjeh, A survey on LEACH-based energy aware protocols for wireless sensor networks. 16. S. Lindsey, C.S. Raghavendra, PEGASIS: power-efficient gathering in sensor information systems,” in Proceedings, IEEE Aerospace Conference, vol. 3, pp. 3-1125–3-1130 17. G. Smaragdakis, I. Matta, A. Bestavros, SEP: a stable election protocol for clustered heterogeneous wireless sensor networks* 18. M.S. Ali, T. Dey, R. Biswas, ALEACH: advanced LEACH routing protocol for wireless microsensor networks, in 2008 International Conference on Electrical and Computer Engineering (2008), pp. 909–914 19. V. Kumar et al., Multi-hop communication based optimal clustering in hexagon and voronoi cell structured WSNs. AEU—Int. J. Electron. Commun. (2018) 20. R. Priyadarshi, L. Singh, A. Singh, A. Thakur, SEEN: stable energy efficient network for wireless sensor network, in 2018 5th International Conference on Signal Processing and Integrated Networks (SPIN) (2018), pp. 338–342 21. R. Priyadarshi, L. Singh, A. Singh, and others, A Novel HEED Protocol for Wireless Sensor Networks, in 2018 5th International Conference on Signal Processing and Integrated Networks (SPIN) (2018), pp. 296–300
IoT-Based Moisture Sensor in Cement Industry Raghav Charan, Aditya Kumar, Bhavya Harika, Vidushi Goel and Vijay Nath
Abstract This paper demonstrates an IoT-based moisture sensor. The novelty of the given sensor is that there would be no need of physically or chemically testing of the cement in cement production industry. This job is programmed in a microcontroller applicable for the amount of workload. It gives results quickly which would be a boon to this industry. Here, moisture sensor, pH sensor are regulated by means of IOT. It would fasten the process of production as most of the processes will be automated and no human intervention of the work will be needed for most cases. Significantly the quality of the cement will be increased as machines can do the work assigned accurately and precisely. Minute errors which could not be detected by humans can be detected by this automaton. It helps in automation of the complete work which is once done by the humans and provides better results with minimized errors and increased accuracy and precision. Suitable automation methodology should be used to minimize the power consumption. Use of Internet of Things in the cement industry will definitely increase the flexibility of the industry. Keywords IoT · Moisture sensor · pH sensor
1 Introduction Manufacture of cement includes many raw materials. Limestone, clay, sand, iron ore and during combination of these, there is high probability of getting error. So, proper regulating and error detecting processes must be used. Moisture, ph, texture and other parameters should be tested as shown in Fig. 1 [1]. Maintaining the right threshold values guarantees the correct and required chemical reactions occur and the desired R. Charan · A. Kumar · B. Harika · V. Goel (B) · V. Nath VLSI Design Group, Department of ECE, B.I.T. Mesra, Ranchi, JH 835215, India e-mail: [email protected] B. Harika e-mail: [email protected] V. Nath e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 V. Nath and J. K. Mandal (eds.), Nanoelectronics, Circuits and Communication Systems, Lecture Notes in Electrical Engineering 642, https://doi.org/10.1007/978-981-15-2854-5_45
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Fig. 1 Connected devices
consistency is obtained. To produce best-quality cement, an accurate and precise moisture testing mechanism is key points. Uniformity is the most concerned parameter for the quality of concrete mixing, which gives essential information regarding the quality of the concrete. Concrete from different portions of batch which are produced would have essentially the same properties such as moisture content. Many methods including moisture content measurement moisture-to-cement ratio determination and aggregate settlement of the result had been done to estimate the regularity of concrete mixture, but no effective methods can accurately reflect the mixing efficiency as much as this procedure does. Different moisture condition of concrete can be observed by microwave reflection. This is very effective technique to measure the moisture conditions of mixers. Generally, cement industry is slower to adopt new techniques frequently. Due to affect in production and wastage of old setup but this technique applied to old machine with some essential setup and make automatic in operation and also ready to replacement of new setup which enhance the monitoring, production, quality, security and quantity of the product. In real world embedded sensors are operational with machine learning and IoT that reduce the human interface and increase the scope of production and transportation and marketing. Now this approach is applied in cement industry which block diagram shown in Fig. 1. For the operations of IoT microcontroller setup is required which has explained in reference [2–9]. From the literature survey it is found that cement industry adopting new technology very slow. They wish to utilize existing infrastructure only. But who are ready to change or adopt the new technology they enhance the production of their industry and very fast they become leader in the market. Using new technology safety, security, and productions are increased very big way.
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2 Networking IoT Networks allow us to communicate the required information and collect desired data from sensors—so let’s look at how messages are sent within a network [10]. Though this network is simple, it has several key components 1. Devices—this consists of computers, printers, routers and servers. 2. Media—the cabling or wireless connections. 3. Services—software that support operations, such as e-mail hosting as shown in Fig. 2. Communication in the network [11] is carried through a phase—currently which means through either a cable (for example, metallic wires in copper cables, or else glass or plastic fibres in fibre optic cables) the wireless transmission. The different types of media have different varieties of characteristics, which designs each better suited to various circumstances, taking these into consideration factors such as 1. The distance a signal should travel 2. The environment it would travel 3. The amount and speed of the data to be processed 4. The revenue of the media for its installation. The IoT adds more circumstances and considerations for connectivity … making the ‘world wide web’ look more like a web than ever before [12].
Fig. 2 Diagram of IoT network
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End devices are those which give a way for users to connect with the network real life Examples include computers, smart phones and sensors. End devices would either be the source or destination of data going through the network. Every device will have a unique Internet Protocol (IP) address, so it would be distinguishable from all other devices. While sending a message, the IP address of the destination is mentioned to specify where the message is being sent, similar to the address printed on the envelope of a letter to reach its destination. The addresses used in process of networks are called Internet Protocol (IP) addresses [13]. Ipv4 (version 4) is in use currently. Ipv4 addresses have four sets of numbers separated by periods, and every number is between 0-255. Ipv6 is being developed using 6 sets of numbers, colons and hexadecimal numbering to allow many more destination addresses. These are used to facilitate IoT, also to add features for improved routing, security and data flow. It matches a name (like facebook.com) to a number (like 157.240.8.35)—because humans tends to deal with names and computers uses numbers.
3 Programming IoT The consideration for programming for IoT [14] is that the small devices we use have: 1. Limited access to power supply 2. Limited amount of computational power 3. Limited memory to store Digital data is data that is represented using binary system. Big data is that have become so large and so complex that advanced programming and processing is necessary to capture the data, and then to store, analyse, search, share, and visualize it [1]. IBM gives information regarding the four V’s of Big Data as 1. Volume (quantity—this observes and tracks what happens without sampling) 2. Velocity (speed—this is generally available in real time) 3. Variety (this extracts from multiple sources such as text, images, audio, video) 4. Veracity (data quality—describes whether it stays true, or some is lost or damaged).
4 Securing IoT We have an idea of the Internet of Things [2] that involves: 1. Extra devices which are connected; 2. Extra networking to connect the devices; 3. Extra programming to direct them to desired devices and networking; 4. A large volume of extra data pouring into the internet; and
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5. More machine-to-machine interactions and autonomous decision making. Each of these layers adds additional security issues. Securing the Internet of Things is a highly required and it is not a simple task that sits across the top of its devices, networks and applications, it must be considered and factored into the required design and planning phase. Many IoT devices are purposely made very small and low powered, so that this increases the difficulty of securing them. Connecting all these things up which further makes private data about us, our lives, and our businesses accessible. The Internet of Things challenges the ethics of privacy, factors which should be designed for. Apart from these security risks, we really have a need to know how to keep these IoT devices physically secure from the environment. These IoT devices are growing fast and making their place stable in our lives, and they must be protected for our safety also. Moisture sensor with IoT devices is shown in Fig. 3. It has the following applications: 1. Authentication—IoT devices are linking to the network create a trust relationship, based on validated identity through steps such as: passwords, tokens, biometrics, RFID, X.509 digital certificate, shared secret, or endpoint MAC address. 2. Authorisation—a trustworthy relationship is established which is based on authentication and authorisation of the desired device that determines what information could be accessed and shared.
Fig. 3 Moisture sensor
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3. Network Enforced Policy—this is what which controls all elements that enroute and transport endpoint traffic securely over the network through established security protocols. 4. Secure Analytics: Visibility and Control—gives us the required reconnaissance, threat detection, and threat mitigation for all elements that correlate information.
5 Equipment Monitoring Zoy Global is an international fame underground mining equipment manufacturing company. They demonstrate one an example of the cost-saving benefits associated with remote monitoring. Installed at a one of his client’s site, one of Zoy Global’s mining machines started overheating. All ideas the power adjustment control on one of the motors must have failed. But after reviewing the machine’s sensor data log book, it was illustrated that a heat exchange device required replacement—a smaller repair job to be needed [13]. This approach is highly useful in applying in the cement industry. Remote monitoring could be used for oversee operations of huge vehicles in the quarries and to report on metrics such as pollution generation and fuel consumption per ton. It could also be used in crane-to-crane communication to avoid unnecessary collisions or to safely move the containers. The circuit diagram of the moisture sensor with a microcontroller is shown in Fig. 4.
6 Predictive Maintenance and Quality When equipment/machines are running with load the fitted sensors collect the data and when data analyse and find some malfunctions then immediately stop the machine and process to repair by which we secure and save from breakdown of the machine or systems. Predictive maintenance and quality is applied in all automated industry. Predictive quality consists of reviewing all types of process data such as pressure and electricity and integrating it to quality parameters. Particularly sensors track essential characteristics of the product as it is being manufactured and allowing for all time adjustments to control the resulting quality. The ability to determine the quality is very useful in cement industry. It has largely minimum cost in the oil and gas industry. As an example when piece of machinery on a remote oil rig, such as blowout preventers, mud pumps, or ship regulators, fails as expected, drilling halts until the necessary repair machinery can be given. This same procedure can be followed to the operations of cement plants by improving on essential cement equipment such as kilns and grinding mills [10, 14–16].
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Fig. 4 Circuit connections of sensor
Through the Internet of Things setup we can remotely monitor the any cement or other industries data such as pressure, temperature, quality of product, electricity, water, status of fuel, and status of raw materials. Through the predictive setup we can also monitor the transportation cost, and driving behaviour [16], and other integrated information. Determination of quality of cement is very important and essential segment of this system. High-quality of cement requires that unusual homogeneous meal goes into the kiln. The limestone must be crushed thoroughly, which requires monitoring the input materials as well as adjusting the speed and power of the vertical roller mill to increase the best output. Most applications include tracking and measuring the durability of the concrete mixture from which cement to be prepared.
7 Connected Logistics As we are aware that logistic is also very important component in cement industries. There are several material needs to transport from different places and mixed in perfect amount and restrict with collision of any transport vehicles is the prime job of IoT with inbuilt smart sensors. Through this process increase the production yields. Streamlining logistics is one of the best examples of it. Similar to the way the Internet of Things which links smart devices with farm suppliers and service providers for
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Fig. 5 Block diagram of different logistic
more precise farming; connected logistics can also improve quarry production. Innovation integrates key production parameters such as weather, workforce status, and soil conditions to manage operations. Block diagram of different logistics is shown in Fig. 5. Block diagram of different logistics is shown in Fig. 5. In this network different communication devices are attached with different transport facility which restrict the collision, and assure the timely delivery. It also shows the perfection of technology and product. Improving the yield per quarry on a large scale could have a significant, positive impact to profitability. Fleet management is golden opportunity to leverage data insights for increased efficiency. Connected fleets combine vehicle telemetric information (tire pressure, engine speed, etc.); driving behaviour data (speed, accelerating, braking, time spent loading and unloading); and business data to lower transportation costs. Driving behaviour [16] can be determined in a similar way by looking at fuelconsuming patterns and integrating it to routes or other work information. Figure 6 shows the prototype which we implemented to demonstrate the use of moisture sensor in cement industry.
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Fig. 6 Prototype model of moisture sensor used in cement industry
8 Conclusion Internet of Things can be used in a variety of ways. In the next few years almost all of the objects around us will be connected to the internet. If there is a proper networking made and we select a suitable microcontroller IoT will be very useful. If we have proper security then, IoT will impact life to a great extent. Cement industry will be benefitted as almost all processes will become automatic with human. Acknowledgements We would like to express our special thanks of gratitude to our ViceChancellor Prof. S. Konar, HOD ECE Prof. Srikanta Pal who gave us the golden opportunity to do this wonderful research work on the topic IoT Based Moisture Sensor in Cement Industry which also helped us in doing a loT of research and we came to know about so many new things. We are really thankful to them.
References 1. 2. 3. 4.
https://www.edx.org/ https://www.cisco.com/c/dam/en_us/solutions/trends/iot/docs/computing-overview.pdf www.cotmac.io/automation-cement-plant/ https://www.digitalistmag.com/digital-economy/2015/12/01/iot-digitization-reinforcecement-industry-03814141 5. S. Sahay, N. Sharma, S. Raj, K. Neelam, D. Prasad, V. Nath, Development of wireless power transfer system with internet of things, in Proceedings of the Third International Conference on Microelectronics, Computing and Communication Systems, Lecture Notes in Electrical Engineering, vol. 556, ed. by V. Nath, J. Mandal (Springer, Singapore, 2019), pp. 613–622 6. Mohan R., Suraj A.K., Agarawal S., Majumdar S., Nath V, Design of Robot Monitoring System for Aviation, in Nanoelectronics, Circuits and Communication Systems. Lecture Notes in Electrical Engineering, vol. 511, ed. by V. Nath , J. Mandal (Springer, Singapore, 2019), pp. 535–547
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7. A. Kumar, V. Nath, Study and design of smart embedded system for smart city using internet of things, in Nanoelectronics, Circuits and Communication Systems. Lecture Notes in Electrical Engineering, vol. 511, ed. by V. Nath, J. Mandal (Springer, Singapore, 2019), pp. 397–408 8. S. Anand, V. Nath, Study and design of smart embedded system for remote health monitoring using internet of things, in Nanoelectronics, Circuits and Communication Systems. Lecture Notes in Electrical Engineering, vol. 511, ed. by V. Nath, J. Mandal (Springer, Singapore), pp. 409–414 9. P. Rajiv, R. Raj, R. Singh, R. Nagarkar, A.K. Chaurasia, S. Agarwal, V. Nath, An ultra-lowpower internet-controlled home automation system, in Proceedings of the International Conference on Microelectronics, Computing & Communication Systems. Lecture Notes in Electrical Engineering, vol. 453, ed. by V. Nath (Springer, Singapore, 2018), pp. 271–280 10. https://www.rs-online.com/designspark/eleven-internet-of-things-iot-protocols-you-need-toknow-about 11. https://www.digitalistmag.com/digital-economy/2015/12/01/iot-digitization-reinforcecement-industry 12. https://www.ibm.com/blogs/internet-of-things/what-is-the-iot/ 13. https://ieeexplore.ieee.org/document/795186 14. http://iot.intersog.com/blog/12-popular-programming-languages-for-iot-development-in2017/ 15. http://www.moisttech.com/cement-moisture/ 16. ntrans.iastate.edu/mtc/documents/studentPapers/2003/jio ng.pdf
Internet of Things for Smart Class Rooms: A Review Dev Gupta, Prashant Kumar Nayak, Yashwi Parashar, Vidushi Goel and Vijay Nath
Abstract A lot of time is wasted in the entering and settling of the students at their respective desk and in addition to that the process of attendance, calling the name of each and every student. All this makes it difficult for teachers to handle a large number of students all alone without any proper planning and technology. Even if he tries to handle them, then it would be a lot of waste of time both for the students and the teacher which is not good for the studies’ point of view. The wastage of time does not just get finished here, it still continues as the lecture progress starts from the attendance to the doubts, etc. Installing smart devices and emerging trending technologies will help teachers to focus on student’s learning process in place of wasting time in taking attendance for a large group of students. By which teachers can give enough time for developing the student’s minds, giving it the initial root for the tree which he has to grow himself in order to succeed in life. Connection of the devices working on the platform of Internet having unique IP address would for sure change the classroom experience. This paper consists of some practical scenarios of about how Internet of Things (IoT) can be used for a better classroom experience and how teachers can focus on student’s skills and which will help to save the crucial time needed for completing the teaching.
1 Introduction The Internet of Things is a novel communication platform that provides the facility to connect the devices, objects and handle its operation such as door, chair, etc., and also the day to day regular activities which are till now performed by the humans. Now with the help of IoT human interface will be reduced and it will be operated by microcontroller, smart sensors, microprocessor, embedded systems with the support D. Gupta · P. K. Nayak · Y. Parashar · V. Goel (B) · V. Nath VLSI Design Group, Department of ECE, B.I.T. Mesra, Ranchi, JH 835215, India V. Nath e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 V. Nath and J. K. Mandal (eds.), Nanoelectronics, Circuits and Communication Systems, Lecture Notes in Electrical Engineering 642, https://doi.org/10.1007/978-981-15-2854-5_46
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of Internet. Thus, the IoT concept, aims at making the Internet used for much more than just for social contacts and search engine [1]. In Today’s world of Automation, IOT is playing a handy role in making various things, place, and work more and more smarter and efficient not only in terms of time but also in quality and quantity. This is the technology which should be known by more and more people and should be implemented at various places to reduce the man power at place of repetitive works which could be done by the machines without inclusion of any external source [2]. It can be seen that various cities use it in making the city smart like for bridges (traffic controlling system), garbage management system, smart irrigation system, etc. There are many devices which can provide an ample amount of data regarding any process much more than a man would be able to tell in a specified amount of time. The thing in the Internet of Things is the entity or the quantity that has a unique system having the ability to transfer the data over the network [1–5]. This application has made all the works highly compatible and efficient in terms of time and complexity. It also helps in improvising the preciseness and the economic outcome other than the well-known point of human intervention. This internet has taken a giant jump from the “Internet of communication” to “Internet of things.” This technology is bringing more and more opportunities but still an issue of security and privacy (encryption) is there which still needs to be developed. The data obtained from the devices (sensors, actuators, etc.) installed in accordance with the requirement at the position of workplace is highly useful for yielding services to the public administration, people, etc. [2]. In this paper, we intend to use this fast-growing technology in making the class smarter, i.e., a classroom that records the day to day activities of the students and enables in providing real-time feedback on the lecture quality given by the lecturer. The satisfaction levels of the students regarding the lecture. This feedback would help the lecturer in improving the quality of teaching so that it would increase the interest of students in the subject [2].
2 Smart Classroom Concept This smart class topic came into consideration after analyzing the difficulties being faced both by the students and children. Smart classroom and distance education effectively will be a success with the support of current technology of IoT. In the day to day teaching the feedback plays a crucial role for the lecturer or professors, whether the students are really satisfied with the teaching or not and also what part is interesting and what is not. This would help the lecturer in making the presentation methods more attractive than others. This technology will also send the material of the topic taught in class directly to the student ID’s [3]. Usually the interest of the student’s decreases as the time passes, so this step needs to be taken to avoid that problem. All this helps in making the learning process shorter
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and smarter. The IoT technology can be integrated with the social and behavioral analysis, smart classroom takes the place of standard classroom that actively listens and analyzes the voices, conversations, movements, behavior, etc., so that we get a conclusion about the lecturers’ presentation and listeners’ teaching method [2].
3 Previous Work Previous work does not have a correlation with this subject, as its focus is learning in a different manner. Existing work demonstrate the ambient digitization, preparation of written materials into electronic form, tele-education, web-based distance learning, human to computer interaction, interaction in a classroom or a conference, etc. This is one of the rarest attempts to address the issues of live feedback on the lecture’s quality and its requirements. In this, automated capture of audio, video, slides, and handwritten notations during a live lecture has been proposed. A system for the location of the lecturer and tracking a lecturer in the room using acoustic and visual cues are also described. Some platforms developed at MIT can be used to measure various aspects of interaction, including nonlinguistic social signals by analyzing the person’s voice tone, facial movements, or gestures utilizing wearable device. Some researchers conducted at MIT Media Laboratory indicated that wearable sensors create social index of interest. The devices should be worn all the time to provide a measurement parameter. This is not the most perfect of the solutions as under observation people behave differently and are not as natural as they are in real life. Nonetheless, the previous work can be a good starting point for future purposes. This paper focuses and addresses the real-time feedback on the quality of lecture through the observation of audience parameters and the time scale digitalization [5].
4 Requirements Before the analysis of parameters of system desired parameters should be existing. System must be able to extract the required information from the source or ambient. Sound recording, motion detection, scene capturing, and interpretation of extracted parameters are in real time. The picture images that are captured are processed for fidgeting—small differences between frames; for avoiding the third party influence (e.g., someone has changed their seating position), also the non-fidgeting movements [2]. Sound sensor achieves the sound level. Real-time feedback is described as a chart in real time; where there is time correlation and data fusion of multimodal data (such as visual, audio, and environmental). The existing and timely dispersed patterns must be correlated and synchronized on a time scale. Real versus laboratory world: Experimental research is connected to get a more real-life feedback. For accuracy of measurement the conditions are kept as close to as it can be in the real world. It is
526 Table 1 Sensors and their paramters
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Parameters
GSM module (global system for mobile)
Attendance
PIR sensor
Camera fidgeting
Fidgeting motion existence
Motion level
Microphone noise
Sound level
Noise existence
Sound level
still unsuitable to extract cues from a “live” environment, due to the limitations of the technology. Social interaction integrity [6]. For sociological purpose the awareness of the sensors is advised that the sensors should be located anywhere in the classrooms but the students should not wear it. If the students know that they are under observation, they might not behave naturally thus affecting social integrity. Thus an approach with less invasive sensors should be done. Open architecture observed parameters may contain sensitive information that can be used by different platforms, or for later researches in social and computer sciences. It needs openness of the system architecture which is based on cloud computing technology and IoT that will enable information technology (IT) services over the Internet protocol (IP) and will also include dynamic virtual and some scalable sources [6].
5 Primary Analysis Our initial target was to test the satisfaction level of the audience or learner with ongoing presentation. For this purpose, we test on two university students of about 300 and analyzed them as per given Table 1[5].
6 Practical Implementation Attendance of the students can be monitored using Nyman, a smart band. It uses ECG patterns to identify a particular student. Beacon can be used to push warm up exercises on students. All of these help a great deal as the focus of the teachers can now be completely on the students. Neurosensory can provide insights to the brain of students. It can be used to know about their brain activity and measure their pulse [4]. This can be used by the teachers as they can figure out the students who are particularly energetic. “Haptic” vibrations can be passed by the teachers if they want to pass some information to a particular student. There are some pattern recognition software’s or data analytics which can be used for contextual understanding. They can be used to record the behavioral activities of the students. The classroom temperature can be also measured using this concept [4].
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IoT can be effectively implemented using sensor gloves. It can be worn by the students who want to learn the particular language. The computer machine can convert it into written language which provides feedback in return to the user [4]. Smart board is a very important tool for effective teaching today. First of all it displays the images or texts from the computer screen to the board. Now these images can be controlled or moved using a pen provided for the board. So the teachers can move as well as scroll the texts or images on the board using the pen. So it effectively runs the application [4]. So why have the smart boards become so popular? Innovation in the style of learning is the main outbreak. Learning has become easier using the touching board. The visual learners can easily grab any concept due to the visual display. This style of teaching is much cleaner and maintaining the smart boards is easier. It is a very effective tool for taking down notes. A particular student can be assigned to write down the points which others can also see and follow at the same time [5]. The real-time data plotting setup dataset is shown in Fig. 1. Many images and types of videos or some other media files can also be displayed on the smart board. Diagrams can also be drawn on the smart boards. Many types of classroom games can be played on the smart board for interactive learning. [4] In various lands IoT smart classrooms are already in implementation which is shown in Fig. 2. It has been done quantifiable. The sensors have been installed in the classrooms which can monitor the C3 + 2 levels, the pressure in the rooms, humidity level as well as the room temperature. The change in odor can also be easily detected using these sensors. The students can also study the change in environment [1].
Fig. 1 Real-time data plotting
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Fig. 2 Smart board
Smart board flow chart is shown in Fig. 3. Social media also has a major role in this system. The teachers can use social media to get to know the younger generation and their reality. Participation of students can also be increased using social media. The bell is not the time limit now as online participation can also be done. Cameras can also be installed in the classrooms. They can help in recording the lectures of
Fig. 3 Smart board flow
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the teachers. This lecture can be used in the future. Also the behavior of the students can be monitored and they can be properly disciplined [5]. Flowchart
7 Limitation of IoT It was divided into four sectors connectivity, business works and medical facilities, farming fields. It uses the ipv6 protocols for the data transfer using scalability. This technology is still in progress and once it comes into play then it would help in making the availability of IP addresses easier and quicker. Horizontal markets and vertical markets. It even includes form and designing of products and many tools which are used for analysis [6–12]. Connectivity: M2M communications have proven to be more of use and includes wireless communication, It includes smart wearable’s, smart homes. Business Model: This has made the communication mode more accurate and has improved the aesthetic part to a much higher level [13–18]. Data Acquisition system collects the data from devices and analyze it for the improvisation in the overall system not only in real time but also in the further future [9–21].
8 Conclusion In our research paper, we have taken a new outlook on the Smart classroom system. We have gained a real-time feedback using IoT. We have tried to make an innovation on the smart classroom environment. We need to understand the underlying problem of the present-day structure so as to create a new system. In our paper, the focus has been the monitoring as well as the sensing technology. Both of these have been used to analyze the behavior of the listener who are the students over here in a particular environment. These have been used to collect information which can be used to know the activities taking place in the classrooms. The correlation of the sound,
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movement’s existence and also how intense it is can be used to get an insight of the activities. This type of environment can be used by lecturers to know the attention of the students. So they can also improve themselves on a personal level and get to know the response of the students. There are many new ways and system which are yet to come in the smart classroom system and we have just tried our bit.
9 Result and Discussion The sensor present on every student’s desk will be taking the input from the physical world like the motion, noise, movement, etc., which prototype model is shown in Fig. 4. This real-time data will be sent directly to the predefined Arduino board (microcontroller) through the normal connections made between them. This data will then be processed to the mobile phone using the ethernet shield of Arduino which is providing the Internet bridge between the WiFi from the mobile phone and the ethernet of the laptop, specific IP addresses are allotted to the different sensors and the analyzed data is sent through the cloud server using the ethernet shield to the place of outcome so that it can be seen at any time or at any place. In addition to the normal data, a graph will also be plotted on the same. Fig. 4 Model prototype
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Components Required (1) (2) (3) (4) (5)
Arduino board. Arduino Shield. Sensors (potentiometer, PIR sensor). Internet Connection. Think speak website.
Acknowledgements I am highly indebted to Prof. S. Pal, HOD, Electronics and Communication Engineering, for his assistance and constant source of encouragement. I wish to express my profound and deep sense of gratitude to Prof. S. Konar, Vice Chancellor, Birla Institute of Technology, Mesra for sparing his valuable time to extend help in every step of my project work. I would also like to thank the staff of ECE Department for his generous guidance. Last but not the list we would like to thank our friends and family for their help and in every way for the success of this project report.
References 1. Arkessa “The future of learning-IOT and connected of classroom” 2. www.edx.com, “Tutorials on IOT”, www.linkedin.com, “IOT in the modern world” 3. U. Anliker, et al., AMON: a wearable multiparameter medical monitoring and alert system. IEEE Trans. Inf. Technol. Biomed. 8(4), 415–427 (2004) 4. L. Biel, et al., ECG analysis: a new approach in human identification. IEEE Trans. Instrum. Meas. 50(3), 808–812 (2001) 5. G.D. Clifford, F. Azuaje, P.E. McSharry, Advanced Methods and Tools for ECG Data Analysis (Artech House, Norwood, MA, USA, 2006) 6. R.K. Ganti, et al., Satire: a software architecture for smart attire, in International Conference on Mobile Systems, Applications and Services (Uppsala, Sweden, 2006), pp. 110–123 7. A.L. Goldberger, et al., PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation 101(23), e215–e220 (2000) 8. B. Gyselinckx, et al., Human++: emerging technology for body area networks, in IFIP International Conference on Very Large Scale Integration & System-on-Chip (Nice, France, 2006), pp. 175–180 9. S.A. Israel, et al., ECG to identify individuals. Pattern Recognit. 38(1), 133–142 (2004) 10. S. Kumar, et al., Using continuous biometric verification to protect interactive login sessions, in Computer Security Applications Conference (Tucson, AZ, USA, 2005), pp. 441–450 11. K. Lorincz, et al., Sensor networks for emergency response: challenges and opportunities. IEEE Pervasive Comput. 3(4), 16–23 (2004) 12. D. Lucani, et al., A portable ECG monitoring device with Bluetooth and Holter capabilities for telemedicine applications, in International Conference of the IEEE Engineering in Medicine and Biology Society (New York City, NY, USA, 2006), pp. 5244–5247 13. T. Martin, E. Jovanov, D. Raskovic, Issues in wearable computing for medical monitoring applications: a case study of a wearable ECG monitoring device, in International Symposium on Wearable Computers (Atlanta, GA, USA, 2000), pp. 43–49 14. S. Myagmar, A.J. Lee, W. Yurcik, Threat modeling as a basis for security requirements, in Symposium on Requirements Engineering for Information Security (Paris, France, 2005) 15. J. Pan, W.J. Tompkins, A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. BME 32(3), 230–236 (1985) 16. J.A. Paradiso, T. Starner, Energy scavenging for mobile and wireless electronics. IEEE Pervasive Comput. 4(1), 18–27 (2005)
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17. C. Park, et al., An ultra-wearable, wireless, low power ECG monitoring system, in IEEE Biomedical Circuits and Systems Conference (London, UK, 2006) 18. C.C. Poon, Y.-T. Zhang, S.-D. Bao, A novel biometrics method to secure wireless body area sensor networks for telemedicine and m-health. IEEE Commun. Mag. 44(4), 73–81 (2006) 19. B. Shneiderman. The eyes have it: a task by data type taxonomy for information visualizations, in IEEE Symposium on Visual Languages (Boulder, CO, USA, 1996), pp. 336–343 20. M. Steffen, A. Aleksandrowicz, S. Leonhardt, Mobile noncontact monitoring of heart and lungactivity. IEEE Trans. Biomed. Circuits Syst. 1(4), 250–257 (2007) 21. G. Wübbeler, et al., Verification of humans using the electro-cardiogram. Pattern Recognit. Lett. 28(10), 1172–1175 (2007)
Study and Design of Smart City with Internet of Things: A Review Gaurav Chaudhary, Harsh Agrawal, Sneha Tirkey and Vijay Nath
Abstract The basic concept of any smart City is about the upbringing or development of any area in a city that is aiming in the integration of the features of that city in the concept of smart houses and parking, efficient energy being managed, the power and the waste management of the area along with traffic management and some other services for the community. Most of the smart cities are well equipped with different types of devices that are connected through IoT. This following paper will provide you with the basic concept and the general ideas that are recently being implemented in some smart cities throughout the world. Most of the concepts that are being used for this kind of development are based on the idea of IoT. IoT helps in quick and smart management of the sources and the equipment that are used for this type of management and development in the areas. Recently, there have been a number of challenges all around the places in the basic implementation of IoT in the smart city processes. The processes and methods for the making of smart city is being demonstrated in this paper using IoT. Keywords Internet of Things (IoT) · Cloud computing
G. Chaudhary · H. Agrawal · S. Tirkey · V. Nath (B) Department of ECE, B.I.T. Mesra, Ranchi 835215, JH, India e-mail: [email protected] G. Chaudhary e-mail: [email protected] H. Agrawal e-mail: [email protected] S. Tirkey e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 V. Nath and J. K. Mandal (eds.), Nanoelectronics, Circuits and Communication Systems, Lecture Notes in Electrical Engineering 642, https://doi.org/10.1007/978-981-15-2854-5_47
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1 Introduction The basic question that comes up in one’s mind is as to what actually is a smart city. There are many definitions for it but one of the most useful and accurate answer or definition of smart City is, it is a well planned and modified or enhanced city that is brought up using technical ways. These cities use up some protocols to up bring or better the standards of the city or the area by giving and providing the people in it the basic requirements and fulfilling the basic necessities of the people in a quick and better way. The level of development depends on the techniques and ways that are used to make the city work more efficiently and quickly without much labor or manual work. There are many advantages that are offered in the making of the smart city. The governance and the basic systems can be improved in a city to bring up the development rate of the city. Features like the proper rain harvesting, education system, smart houses with parking, and security systems use simple but safe IoT systems and management. The ways that are used in the making should be quick, easy, and efficient and should carry a unique address for its operating system. This kind of growth improves the condition of the remote regions of any city on a good level. The smart homes that are a major part of any smart city have advanced facilities for the government as well as for the people living in it. The basic necessity of a smart city is to provide quick and better access to the functions and to improve the living standards of the place by providing some of the basic requirements to all the people in a much better, efficient, and user-friendly way. The concepts of the smart city are now eventually helping us in getting the solutions for the making of these cities more and more efficiently each day and much more sustainable as well. It also helps in the organization and management of the society in a bigger scale. IoT helps in these kinds of management for quick, real time solutions. It helps the government to keep an updated record of all the events as they happen around the city. It reduces the manual work and does everything with much better security of the information and helps in giving solutions to the people in a small time period itself. The smart city also integrates the information and communication tech and many other physical devices that are connected to the networking system to make the best use of the systems. A city can be much more prepared to react to any of the challenges between the government and the public of the place. Good governance [1–4] is an aspect of any of the smart city administrations. It even uses the techniques of e-governance and e-government. The small and most basic step for this is telecommunication network that has some wireless networks for the operating of the structures.
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2 Methodology The basic idea of any of the smart city has the following things involved in the development, like a better governance system, better and much more efficient power and energy systems, the responsible citizenship, new techs, latest infrastructure, proper transport system, new and advanced mobility or security purpose, smartautomated homes, education system betterment, new ways for parking and some advanced health care techs. An architecture view of smart city is shown in Fig. 1. Better governance four basically has a goal of getting good efficiency and steady improvement by the ideas by using of new technology to better up the living standards, this kind of governance also changes the way of giving out a good output to the public in the section of public services by the help of e-governance. The energy efficiency
Fig. 1 Concept of smart city
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Fig. 2 Block diagram of the smart energy grid
management05 gives us a very better and a much cost-effective and reliable energy generation and the usage of the services that take in energy as a source. It also has generation of power by smart grids that store and use the power way more efficiently. Block diagram of energy gird is shown in Fig. 2. It consists of major four unit’s like smart generation, smart consumption, smart storage, and smart grid. One of the many applications of any smart city has- the building of a wire-less city that has smart-home systems and smart-transportation ways. It should also have the public service-center and the development of social management with management of urban system in a smarter and better way. The medical system, treatment, construction should be uplifted using IoT and other smart, quick, and efficient ways. The ways of urban planning include—protection of the environment by good, sustainable, and residential growth through timeline that are viable. Latest technology six includes many issues that cover better and efficient energy [5, 6] usage and water usage management, correct lighting on the roads and the safety of the people in the streets. It also has the capability of learning by any past previews or accidents to better the output by anticipating and giving out reasons and answers about the ways that are useful for the next time. Therefore, it can generate and sustain itself without manual labor. The infrastructure nowadays means to a smart system that has sensing techs embedded in those infrastructures and equipment that are in it. These sensors and devices are connected with any communication network that permits real time info acceptance and generation and to analyze them in quick time span. In today’s sensing days, it has the ability to respond in real time as the user may require. Better mobility [8] can be said to be ICT-based systems that gives the inhabitants of the city with good control and balance to the access and much better efficient usage of the time. It betters the efficiency by getting the traffic info in a
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Fig. 3 Smart mobility layer
quick and reasonable timing, and good time management. This as a result gives a good constructed network or any system that joins and gives out the info from company and the institutions in the public sector. The smart mobility layer consists of five layers such as user experience layer, transport service layer, data gathering layer, coordinates among different transportation enterprises and data management layer which is shown in Fig. 3. Smart homes nine works out with some additive functions that are needed in any house like some multi-level security, providing them with some new and modern [7] techs and devices, comforts and some much better automated systems that take time and access into consideration for the betterment of the systems and to give good facilities to the public by the government. The facility given by the government to the people should for sure be nature friendly and should be good for the real world in various ways. The compatibility of the devices and the nature should be good to get a good efficiency and better ways for the people. The use of IT is made to take in and give out the info by joining some subsystems and optimizing the performance. The smart home defines with some important accessories such as energy management, water facilities, smart parking, surveillance and security, 24 × 7 Internet connectivity, medical facilities, pollution free and waste management which is shown in Fig. 4. The smart systems are made in such a way so that they can efficiently deal with some of the difficult and complicated situations by assuming and being able to take decisions with interacting and exchanging information with the surrounding. Energy autonomous devices and networks are used for better output. Better healthcare is also given to the people in any smart city with the advantage of the treating of any disease round the corner and even in any urban or remote area when any medication cannot be provided personally by a nurse or any medical assistance. IoT helps in implementing these functions online for the people all around for real time management and better service. Better waste management 11 gives an observation and controls that are showed as based on the pollution caused. It needs a quick reaction before the situation worsens. Application of IoT [5–10] in smart city is shown in Fig. 5.
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Fig. 4 Feature of smart home
Fig. 5 Applications of IoT in smart-city
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In this review, there is a good and comprehensive report of the smart-housing systems and good management tasks for waste and water or power. Parking systems are renovated for better facilities to the people along with security systems for quick actions when needed. All these works and factors are important for the building of any smart city and associating it with Internet makes the work in real-time domain and much more efficient and better for the government as well as for the people who use them. The smart-city facility helps the connection of the government to the people in a much better and larger scale for the upliftment of the facilities and the management issues. A role of IoT connectivity in block level is shown in Fig. 6. As per the need quality of comfortable living peoples are moving normal home to smart home which provide excellent facility to survive modern life. For the creation of smart home several components are required such as energy, waste management, water supply, Internet services, smart mobiles, etc. The flow diagram for developing a smart home is shown in Fig. 7. It consists of user, Internet, and IoT servers where user refers to a human being that uses all available facility and infrastructure present
Fig. 6 IoT architecture
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Fig. 7 Smart homes connection with user and IoT server
in real world which needs to arrange them properly and handle and observe them wisely. Smart home connection with user and IoT server is shown in Fig. 7. Internet of Things (IoT) is an interface/connectivity between wide input signal from different home-based applications, and by the support of any platform of IoT-based wireless communication such as Raspberry, Zigbee, IoT (ESp8266), and WSN (wireless sensor network) it generates the suitable output which under observation and control of user via Internet. The complete IoT functional setup is shown in Fig. 8. In smart home various smart devices are fitted to detect, monitor, and control the abnormal conditions like fire, smoke, intensity of light, speed of fan, active alarm in an emergency, etc. It is possible with the help of several smart sensors such as, gas, IR, PIR, current, RFID, and LDR, etc. First, connect the PIC controller to WiFi model ESP8266 via Internet which uses specific commands. With the help of wireless protocol IoT server [11–15] is communicated to all devices. Process description of ESP8266 connection with different relay and relay driver is shown in Fig. 9. When establishing the secure connection it initializes the all reading parameters with various sensors where all sensors have specific values. The sensor data is treating as input which is communicated to web server along with temporary storing in cloud by cloud computing. These data can be accessed any time anywhere around the clock. During the working condition of setup if any sensors threshold value exceed their threshold value then the actuation come in picture to control many parameters such as motion detection, gas leakage, and notifying on the web server which tend to control remotely via Internet. Similarly, all other applications can be monitored, observed,
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Fig. 8 Architecture of IoT server
Fig. 9 Process description of ESP8266
and controlled automatically. Electrical appliances such as fan, bulb, alarm, and surveillance system should be prepared in such a way that it works both in automated, control, and monitor them with the help of an IoT server. Since each sensor has defined a unique IP address; they can be controlled with the help of Internet on remote server which are more reliable, flexible, and easily accessible. Development of smart home required software design skill and asset, output analysis, Smart city challenges and concern, IoT challenges. The generated documents makes special tags or codes to
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write plain text file, which further utilizes web browser to interpret and display the generated information on the screen which is known as .htm or .html file. A. Software Design: For the web page development HTML (Hypertext Mark-up Language) is used to change the display. It is a standard language which is used to create web pages and web pages applications. B. Output Analysis: A web page has to develop to continuously monitor the home appliances through the Internet and it is also enabled by the ESP8266Wi-Fi module. Gas, PIR, LDR, and current sense data sensor display on webpage and the user can control these data as per needs. Web page for smart home is shown in Fig. 10. C. Smart City Challenges and Concerns: There are some smart city challenges which are shown in Fig. 11. D. IoT Challenges: The Internet of Things (IoT) applications enables several applications in different sectors such as the smart city, smart grid, smart education, and smart home . It provides the ability to manage, control, monitor, observe the working unit remotely. Any smart city includes very high degree on information technology addition/integration and also provides the comprehensive applications of the various information resources. Major challenges of IoT are shown in Fig. 12.
Fig. 10 Webpage of smart home
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Fig. 11 Block diagram of smart city challenges
The main components of any urban development for any smart city should include some smart technology with the smart industry and quick services with smart management and intelligent life. IR, RFID, GPS, laser scanners and many more sensors are connected with IoT through Internet. Some specific protocols exchange the information and communicate in order to achieve the intelligent recognition and monitoring, tracking the location and managing it. There are three feature that are important for being instrumented, interconnected, and intelligent. Smart city can be created by integrating all these intelligent features and its new/advance stage for further IoT development. Internet of things is basic facility to create a smart city with entire connection with smart device. It is a result of both industry and academia efforts together and today are hot topic of research in the range of legacy and security because all the financial and medical assets are also playing in the same network of IoT.
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Fig. 12 Major challenges of an IoT
3 Conclusion In this research article IoT has been reviewed, designed, and implemented. In our country like India numbers of citizens are not aware about this technology but tomorrow this technology will reach up to villages and enhance the life of farmers. As the cities are modernized and so peoples are enjoying this technology. In this paper IoT features and its implementation techniques are thoroughly discussed. The smart city requires equal participation of both government and citizens of the country. Creating a smart city first commences at our home with the help of smart electronic gadgets and also by proper waste management system which is included in various household activities. The IoT is running in the various architectures via Internet. It controlled, observed, and managed from anywhere and anytime. Application of IoTs in smart cities provides an accurate, better result to improve standards of living and makes our country like India better for tomorrow. Acknowledgements We would like to express our special thanks of gratitude to our ViceChancellor Prof. S. Konar, HOD ECE Prof. Srikanta Pal who gave us the golden opportunity to do this wonderful research work on the topic IoT Based Moisture Sensor in Cement Industry which also helped us in doing a lot of research and we came to know about so many new things. We are really thankful to them.
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References 1. 2. 3. 4. 5. 6.
7. 8. 9. 10. 11.
12.
13.
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http://www.itu.int/en/ITU-T/focusgroups/ssc/pages/default What is a smart city?—Definition from www.WhatIs.com http://smartcities.ieee.org/ C. Zhu, V.C.M. Leung, L. Shu, E.C.H. Ngai, Green Internet of Things for smart world. IEEE Access 3, 2151–2162 (2015) A. Zanella, N. Bui, A. Castellani, L. Vangelista, M. Zorzi, Internet of Things for smart cities. IEEE Internet Things J. 1, 22–32 (2014) D. Kyriazis, T. Varvarigou, D. White, A. Rossi, J. Cooper, Sustainable smart city IoT applications: heat and electricity management amp; Eco-conscious cruise control for public transportation, in Proceedings of the 2013 IEEE 14th International Symposium on “A World of Wireless, Mobile and Multimedia Networks” (Wow Mom) (Madrid, Spain, 4–7 June 2013), pp. 1–5 Smart mobility diagnosis of the present situation in Mexico pdf B.M.M. Ei-Basioni, S.M. Abdul-Kader, M. Abdelmonium Fakhreldin, Smart home design using wireless sensor network and biometric technologies. 2(3) (2016) Z.-L. Wiu, N. Saito, The smart home. Proceedings IEEE 101(11) (2013) D.-M. Han, J.-H. Lim, Design and Implementation of Smart home energy management system based on Zigbee. IEEE Trans. Consum. Electron. 56(3) (2013) S. Sahay, N. Sharma, S. Raj, K. Neelam, D. Prasad, V. Nath, Development of wireless power transfer system with Internet of Things, in Proceedings of the Third International Conference on Microelectronics, Computing and Communication Systems, Lecture Notes in Electrical Engineering, ed. by V. Nath, J. Mandal, vol. 556 (Springer, Singapore, 2019), pp. 613–622 R. Mohan, A.K. Suraj, S. Agarawal, S. Majumdar, V. Nath, Design of robot monitoring system for aviation, in Nanoelectronics, Circuits and Communication Systems. Lecture Notes in Electrical Engineering, ed. by V. Nath, J. Mandal, vol. 511 (Springer, Singapore, 2019), pp. 535–547 A. Kumar, V. Nath, Study and design of smart embedded system for smart city using Internet of Things, in Nanoelectronics, Circuits and Communication Systems. Lecture Notes in Electrical Engineering, ed. by V. Nath, J. Mandal, vol. 511 (Springer, Singapore, 2019), pp. 397–408 S. Anand, V. Nath, Study and design of smart embedded system for remote health monitoring using Internet of Things, in Nanoelectronics, Circuits and Communication Systems. Lecture Notes in Electrical Engineering, ed. by V. Nath, J. Mandal, vol. 511 (Springer, Singapore, 2019), pp. 409–414 P. Rajiv, R. Raj, R. Singh, R. Nagarkar, A.K. Chaurasia, S. Agarwal, V. Nath, An ultralow-power internet-controlled home automation system, in Proceedings of the International Conference on Microelectronics, Computing & Communication Systems. Lecture Notes in Electrical Engineering, ed. by V. Nath, vol. 453 (Springer, Singapore, 2018), pp. 271–280
Development of Heart Rate Monitoring Controller Using Fingertip Sensors Sai Namith Garapati, K. Venkatesh, Kushwanth Reddy, Sahithi Unkili, Deepak Prasad and Vijay Nath
Abstract This research article is the illustration of design and verification of a fingertip sensor-based heart rate monitoring system. This device observed the flow of blood through the finger via optical sensor. It also shows the advantage of the portability over a tape-based recording system. Here, ECG signal is used to measure the heart rate with the help of Discrete Fourier Transformation (DFT), this is a novel feature of this setup. In this analysis we find the better accuracy in real signal even under intense physical phenomenon. In this research work signal of HRM device and ECG have been compared on oscilloscope and find the excellent results/output response. HRM device is very economical than the ECG and a friendly user interface.
1 Introduction The rate of heart beat is known as heart rate or beat per minute (bpm). A device which samples heartbeat signal and calculates the beat per minute is popularly called as heart rate monitor (HRM) device. Mainly electrical and optical methods are deployed for sampling of heart signals. A normal human’s heart rate is defined to be 72 bpm [1–3]. For removing the order of harmonics and noise from signal HRM devices are used. It also uses zero-crossing algorithms to count the number of beats in a fixed S. N. Garapati · K. Venkatesh · K. Reddy · S. Unkili · D. Prasad · V. Nath (B) Department of ECE, VLSI Design Group, BIT Mesra, Ranchi 835215, JH, India e-mail: [email protected] S. N. Garapati e-mail: [email protected] K. Venkatesh e-mail: [email protected] K. Reddy e-mail: [email protected] S. Unkili e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 V. Nath and J. K. Mandal (eds.), Nanoelectronics, Circuits and Communication Systems, Lecture Notes in Electrical Engineering 642, https://doi.org/10.1007/978-981-15-2854-5_48
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time interval. This zero crossing algorithm may lead to false reading caused by a local noise that may result in number of local zero crossing. In the proposed design, the zero crossing problems are vanished by adopting the Fourier transform technique for the digitized signal. By using this technique, any transient noise in the signal will be canceled out. Here, optical technology has been adopted to use infra-red Light Emitter Diode (LED) and photo-sensor to get exact heart rate using index finger. The next module is microcontroller which reads the signal from analog to digital converter (ADC) and displays on LCD. Using this HRN device, continuous monitoring of patient’s health is possible. In case HRM device is in a continuous monitoring mode, the device would alert the medical professional or the person accompanying the patient, if the heart rate falls out a given range [4–7]. The Block diagram of HRM System is shown in Fig. 1. Fingerprint Sensor is shown in Fig. 2a and pulse detection circuit is shown in Fig. 2b.
Fig. 1 Block diagram of HRM system [4]
Fig. 2 a Fingerprint sensor, b pulse detection circuit
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2 System Hardware The proposed HRM device has several functions such as 1. The system gives an optical mechanism detecting modulations caused by electrical changes or physical changes in heart movement. 2. The system allows the user to enter information like name, age, and telephone number. 3. The system provides an LCD screen to show output of measured heartbeat rate and this display is used when entering user and configuration data. 4. The device provides an audible warning signal. HRM system is shown in Fig. 1, it consists of an infra red (IR) LED which is used as a transmitter and IR phototransistor used as a receiver that facilitate as a fingertip sensor. The details are as follows.
2.1 Fingertip Sensor In this setup IR light emitting diode is used as transmitter and IR photo detector is used as a receiver. The amount of infra red light passes through the tissue indicates the output [8–10]. The volume of blood decides the amount of light passes from it. Figure 2 demonstrates an experimental setup where finger is placed between transmitter and receiver. In second experiment Infra red transmitter and receiver placed on same plane and finger will play the role of reflector of the incident light. The main frequency 50 Hz noise will be reduced by IR filter of Phototransistor. Figure 2b demonstrates the pulse detection circuit, in which IR LED is illustrated as forward biased resistors to generate a flow of current. For production of maximum light a special type of registers are to be selected. For reducing the current in system photo register should connect in series.
2.2 Amplification and Finger Stage The filter or amplifier circuit is a standard design and is documented in many sources (e.g., 0) as analyzed by the circuit. A very weak signal is received from IR sensors, its voltage is approximately 50 αV which also contains the significant noise. The signal is affected by the interference caused resulting from movement of artifacts like rings and main 50 Hz. It is known that the standard ECG signal has frequency components in the range −0.05 to 200 Hz. If is filtered to the range 0–50 Hz, the signal does not suffer any significant loss of quality or information within the signal. The filtering process is required to block the higher frequency noise components from the signal. A value of 1 μF capacitor is used to reduce/ block the dc components available in the signal (Fig. 3).
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Fig. 3 Fingertip sensor’s output signal
2.3 Physical Properties Used devices are operational at 9 V battery and its life is one year for normal use. Its packaging is small, light weight, and portable. The selling price of products is put minimum as product is available in the market. The current estimates of the cost of the components are SDG150. The microcontroller is the main component in our device. It acquires the ECG signal via the ADC, computes the heart rate, controls the LCD, keypad, and GSM modem. ATMEGA32 is the microcontroller which is used in applications. The software calculation algorithm facility is available to measure the heart rate. The overall flow of the software application is depicted in Fig. 4.
Fig. 4 Demonstration of Fourier Transform, G(f ) as a function of frequency (bpm)
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Table 1 Heartbeat rate measurement using developed device and an oscilloscope Gender
Age
Male (M)
22
HR on display 97
HR on scope 96
Error (%) 1.03
M
22
83
81
2.41
M
20
78
78
0
M
22
90
87
3.33
M
20
80
79
1.25
Female (F)
22
77
77
0
F
22
104
103
F
19
75
75
0
F
20
69
71
2.81
F
22
83
85
2.35
0.96
Table 2 Measuring value of heartbeat rate before and after exercise with newly introduced device together with wrist measurements. Here the condition before exercise is represented as BE and after exercise represented as AE Age
Condition
24
BE
65
64
AE
90
88
BE
91
88
AE
110
100
15
HR
HR, manual
3 Results and Discussion Figure 4 shows an analog signal received from the sensor and feed it to ADC port of the microcontroller [10–12]. The signal is consistent with the standard ECG signals which are used to measure heart rate and is also used in other types of diagnosis. The device used to measure the heartbeat rate of a number of male and female volunteers; results as well as the bps measured simultaneously using the heartbeat pattern of the same volunteers are displayed on the oscilloscope are shown in Table 1. These results give us an agreement. with the analog measurements, errors of around 1.4% (Table 2).
4 Conclusions In this article low cost and portable HRM devices are developed and tested which are working properly as per need. The proposed design is handy to implement, and the results of the tests have closed to the normal heart beat rate. This device has several advantages which can directly adopt in clinical and nonclinical environments.
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Acknowledgements We would like to thank the staff of the Electronics Laboratory BIT Mesra, for their valuable assistance. We are grateful to the academic staffs of the Department of Electronics and Communication Engineering for the continuous help and support. We thank Prof. S. Konar, Vice-Chancellor BIT Mesra and Prof. S. Pal HOD Department of ECE for providing the required facility. In this research article authors utilized the biometric attendance system HRM of BIT Mesra for validation of the result. Authors team thanks to BIT Mesra for providing infrastructure facility to carry out this research work.
References 1. M.M.A. Hashem, R. Shams, M.A. Kader, M.A. Sayed, Design and development of a heart rate measuring device using fingertip. Department of Computer Science and Engineering, Khulna University of Engineering and Technology (KUET), Khulna 9203, Bangladesh (2010) 2. S. Allender, V. Peto, P. Scarborough, A. Boxer, M. Rayner, Coronary heart disease statistics. British Health Promotion Research Group, Heart Foundation Department of public health, University of Oxford (2007) 3. M. Fezari, M. Bousbia-Salah, M. Bedda, Microcontroller based heart rate monitor. Int. Arab. J. Inf. Technol. 5(4) (2008) 4. D. Ibrahim, K. Buruncuk, Heart rate measurement from the finger using a low-cost microcontroller (2002), http://www.emo.org.tr/ekler/a568a2aa8c19a31_ek.pdf 5. B. Boashash, Time-Frequency signal analysis and processing: a comprehensive reference (Oxford, Elsevier Science, 2003) 6. R. Fensli, A wireless ECG system for continuous event recording and communication to a clinical alarm station, in Proceedings of the 26th Annual International Conference of the IEEE EMBS (2004) 7. U. Anchalia, K.P. Reddy, A. Modi, N. Kumari, D. Prasad, V. Nath, Study and design of biometric security systems: fingerprint and speech technology, in Proceedings of the Third International Conference on Microelectronics, Computing and Communication Systems, Lecture Notes in Electrical Engineering, ed. by V. Nath, J. Mandal, vol. 556 (Springer, Singapore, 2019), pp. 577–584 8. S. Sahay, N. Sharma, S. Raj, N. Kumari, D. Prasad, V. Nath, Development of Wireless power transfer system with Internet of Things, in Proceedings of the Third International Conference on Microelectronics, Computing and Communication Systems, Lecture Notes in Electrical Engineering, ed. by V. Nath, J. Mandal, vol. 556 (Springer, Singapore, 2019), pp. 613–622 9. Y. Goyal, S. Agarwal, S. Barman, A. Pranav, D. Prasad, V. Nath, Evolution of pacemaker: a review, in Proceedings of the Third International Conference on Microelectronics, Computing and Communication Systems, Lecture Notes in Electrical Engineering, ed. by V. Nath, J. Mandal, vol. 556 (Springer, Singapore, 2019), pp. 649–654 10. P. Kumar, A. Kalra, K.J. Prakash, D. Prasad, V. Nath, Study and design of obstacle detection mechanism, in Proceedings of the Third International Conference on Microelectronics, Computing and Communication Systems, Lecture Notes in Electrical Engineering, ed. by V. Nath, J. Mandal, vol. 556 (Springer, Singapore, 2019), pp. 661–666 11. V. Goel, H. Raj, K. Muthigi, S.S. Kumar, D. Prasad, V. Nath, Development of human detection system for security and military applications, in Proceedings of the Third International Conference on Microelectronics, Computing and Communication Systems, Lecture Notes in Electrical Engineering, ed. by V. Nath, J. Mandal, vol. 556 (Springer, Singapore, 2019), pp 195–200 12. A. Swaroop, A. Kumar, S. Tirkey, N. Kumari, D. Prasad, V. Nath, Design of light-sensing automatic headlamps and taillamps for automobiles, in Proceedings of the Third International Conference on Microelectronics, Computing and Communication Systems, Lecture Notes in Electrical Engineering, ed. by V. Nath, J. Mandal, vol. 556 (Springer, Singapore, 2019), pp 301–308
Bluetooth-Controlled Electronics Home Appliances Prashant, Ritesh, Hrithik, Vishal, Abhishek Pandey and Vijay Nath
Abstract In this modern era of twenty-first century, automation always plays an important role in human life. We can control or automate the electric-related home appliances like light, door, fan, AC and many more. Through this process we can secure and save the systems from lightening, theft, etc. We can reduce the human efforts and reduce the time which is most important in current environment. This system also helps physically challenged people. For this complete setup we require microcontroller, bluetooth, and communication devices such as phones and tablets.
1 Introduction In modern systems to seeing the busy life, home automation is the best solution for easy lifestyle which provides desirable foods, comfortable lives, and secure systems in hand [1, 2]. All the activities are operational by handheld mobile apps only. For arranging home automation, some common setups are required such as microcontroller, Bluetooth devices, regular power supply, Arduino board, some specific Prashant · Ritesh · Hrithik · Vishal · A. Pandey (B) · V. Nath Department of Electronics and Communication Engineering, Birla Institute of Technology Mesra, Ranchi 835215, Jharkhand, India e-mail: [email protected] Prashant e-mail: [email protected] Ritesh e-mail: [email protected] Hrithik e-mail: [email protected] Vishal e-mail: [email protected] V. Nath e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 V. Nath and J. K. Mandal (eds.), Nanoelectronics, Circuits and Communication Systems, Lecture Notes in Electrical Engineering 642, https://doi.org/10.1007/978-981-15-2854-5_49
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gadgets, smart mobiles, and Internet facility [3, 4]. In today’s scenario, majority of peoples are very close to mobiles. To seeing the closeness with mobiles, HAS (Home Automation System) software has been designed, developed, and launched in the market that is more helpful to accelerate the home activities in simple manner [5, 6]. Through this system you can do not only personal activities but also you can perform external activities in the near by market which are available in your affordable cost [7]. They can order without any hesitation and receive it on your feasible time and place [8, 9]. HAS software is more flexible for Android users in a step toward the ease of the duty by controlling a one to twenty-four different appliances in a home environment [10, 11].
2 Methodology In current communication setup bluetooth are used which receives or transmits the data serially or parallel. In serial communication small packet is send continuously one by one while in parallel communication bulk data can be processed in particular time. Bluetooth setups communicate with Arduino via serial data transmission through the two specific pins (RXD and TXD pins) without finding any difficulty [12]. Programming of serial transmission through Bluetooth is quite easier with Arduino rather than any other microcontroller. Serial data transmission is easier, cheaper, and faster than parallel transmission of data. Normally RF (radio frequency) depends upon the parallel communication [13]. The numbers of important components are required to make the home automation systems which are as follows.
2.1 Android Phone In the world of technology and progressing electronics appliances, the android mobile phone has become very useful to mankind. They have an inbuilt Bluetooth application for transmission of different files and data [14]. Apart from the original application an additional applications, namely, the Bluetooth controller can be developed whose main purpose shall be to transmit serial data wirelessly in the form of UHF radio waves IJSE.
2.2 Bluetooth Module This is an important module to receive the data sent by android phone serial communication. HC5.0 and HC6.0 two versions are available in the present market. These can be used for receiving and sending the data by phone. HC5.0 is cheaper than HC6.0. Bluetooth have some special quality/beauty such as small form factor, low-cost radio
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Fig. 1 Bluetooth module
solutions providing the links between mobile-computer, mobile phones, and other hand held portable devices and connectivity to Internet [15, 16]. This is an easily available device which enables the users to connect a wide range of computing and telecommunication devices without the need of purchase, carry or connect cables. It is a fully wireless technology that operates on an unlicensed radio spectrum [17, 18]. There is no extra charge between two bluetooth devices. Bluetooth is a problem surrounded by infrared and cable synchronizing systems. The world leading hardware vendors such as Siemens, Intel, Toshiba, Motorola, and Ericsson, have developed a specification for a very small radio module to be built into computer, telephone, and entertainment equipment [19]. From the user’s angle, there are three important features to Bluetooth: The used module is shown in Fig. 1.
2.3 Arduino Uno This is the cheapest microprocessor among all other Arduino boards. It is preferable to use this board not only because of cost but also due to its compact size and greater memory and speed than the common board Arduino Uno R3. It is better than other Arduino boards of its range and capability. The basic module of arduino Uno is depicted in Fig. 2.
2.4 IC L293DNE This IC consists of four transistors that convert the digital power supply (Vcc) to direct current of approximately 12 V. It is used in building a motor driver circuits especially for the purpose of making robotic equipments and devices.
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Fig. 2 Arduino Uno
2.5 Relay Switch The relay switch of 8-channel module and interfacing of relay switch with microcontroller is shown in Figs. 3 and 4 repectively.
Fig. 3 An 8-channel relay module
Fig. 4 Complete setup of Arduino Uno
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3 Working of the Process The android phone possesses an application the Bluetooth controller, which is shown in Fig. 5. This application could be found in the app store or else it can be built by app developers. The purpose of this application is to transmit serial data wirelessly via Bluetooth. You just need to define a specific character that must be sent which will serve the purpose. For example, to turn a light bulb ON a character let say “B” is sent. Within the electric circuit, the Bluetooth module is present whose purpose is to act as the receiver. It receives the serial data transmitted by android phone and decodes them. For the character “B” (for bulb) the Bluetooth module decodes the character into its binary form (i.e., in terms of 1 and 0). The control unit of the electric circuit is the Arduino Nano V3.0 Processor. The Arduino board and the Bluetooth module are connected with each other through the transmit pin and receive pin. The transmit pin and receive pin are termed as TXD and RXD pins, respectively. These two pins are necessary for serial data transmission. Any device working over serial data must possess these two pins. When one pin sends the data the later receives and the process continues. The entire process works under the mechanism of transmission of serial data and converting them into digital data. This purpose is to be served by the Arduino board where it acts as the converter of serial data to digital data. For example, the Arduino can be so programmed that when it receives the character “B” via serial communication it has to provide a positive voltage to one of the pins (let say pin 12) which shall give the output. The Arduino board sends commands that are perceived by a relay switch based on the instruction given by the android phone (either ON or OFF). The relay acts as a switch used for providing AC supply to the electrical appliance. The output obtained from the Arduino is converted to 12 V by using L293DNE motor driver IC and that is fed to the relay switch as input thus switching the AC supply as output. Thus, we are able to switch ON/OFF any electric device or home appliance by a single tap on the android phone.
Fig. 5 Android phone possesses an application the bluetooth controller
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4 Results and Conclusion Home automation has become a necessity these days for the common man. The craze of using android phones is also increasing since it provides the user a variety of applications in solving various problems. With the rapid increase in technology human beings have always developed a craze for becoming lazier than before. Hence the operation of home appliances using an android phone from a distance can be a great achievement in serving the society. This is also a cheap mode of operation as a result it will also be effective in serving the common men and their families. Unlike radio waves Bluetooth have a higher frequency and no interference occurs once connected to a device which is advantageous for the purpose. The use of Bluetooth technology in home automation will serve the best using the abovementioned method to the society. Acknowledgements An endeavor over long period can be successful only with advice and guidance of many well-wishers. I wish to express my profound and deep sense of gratitude to, Dr. M. K. Mishra, Vice Chancellor, Birla Institute of Technology, Mesra for sparing his valuable time to extend help in every step of my project work. My sincere thanks to the management and Dr. Mahesh Chandra, Dean Student Welfare, Birla Institute of Technology, Mesra for providing me the opportunity to conduct my project work. I am highly indebted to Dr. S. Pal, H.O.D, Electronics and Communication Department, for his assistance and constant source of encouragement. I would also like to thank the staff of E&C Department for their generous guidance. Last but not the least we would like to thank our friends and family for their help in every way for the success of this project report.
References 1. N. Sriskanthan, F. Tan, A. Karand, Bluetooth based home automation system. J. Microprocess. Microsyst. 26, 281–289 (2002) 2. M.I. Ramli, M. Helmy, A. Wahab, A. Nabihah, Towards smart home: control electrical devices online, in Nornabihah Ahmad International Conference on Science and Technology: Application in Industry and Education (2006) 3. A.R. Al-Ali, M. AL-Rousan, Java-based home automation system R. IEEE Trans. Consum. Electron. 50(2) (2004) 4. G. Pradeep, B. Santhi Chandra, M. Venkateswarao, Ad-hoc low powered 802.15.1 protocol based automation system for residence using mobile devices. Department of ECE, K L University, Vijayawada, Andhra Pradesh, India IJCST, Vol. 2, SP. 1, December 2011 5. E. Yavuz, B. Hasan, I. Serkan, K. Duygu, Safe and secure PIC based remote control application for intelligent home. Int. J. Comput. 6. N. Nidhi, D. Prasad, V. Nath, Different aspects of smart grid: an overview, in Nanoelectronics, Circuits and Communication Systems. Lecture Notes in Electrical Engineering, ed. by V. Nath, J. Mandal, vol. 511 (2019), pp. 451–456 7. Q. Razi, V. Nath, Design of smart embedded system for agricultural update using Internet of Things, in Nanoelectronics, Circuits and Communication Systems. Lecture Notes in Electrical Engineering, ed. by V. Nath, J. Mandal, vol. 511 (Springer, Singapore, 2019), pp. 372–382 8. S. Fatma, V. Nath, Study and design of smart embedded system for train track monitoring using IoTs, in Nanoelectronics, Circuits and Communication Systems. Lecture Notes in Electrical Engineering, ed. by V. Nath, J. Mandal, vol. 511 (Springer, Singapore, 2019), pp. 385–395
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9. A. Kumar, V. Nath, Study and design of smart embedded system for smart city using Internet of Things, in Nanoelectronics, Circuits and Communication Systems. Lecture Notes in Electrical Engineering, ed. by V. Nath, J. Mandal, vol. 511 (Springer, Singapore, 2019), pp. 397–408 10. S. Anand, V. Nath, Study and design of smart embedded system for remote health monitoring using Internet of Things, in Nanoelectronics, Circuits and Communication Systems. Lecture Notes in Electrical Engineering, ed. by V. Nath, J. Mandal, vol. 511 (Springer, Singapore, 2019), pp. 409–414 11. C. Kumar, V. Nath, Design of smart embedded system for auto toll billing system using IoTs, in Nanoelectronics, Circuits and Communication Systems. Lecture Notes in Electrical Engineering, ed. by V. Nath, J. Mandal, vol. 511 (Springer, Singapore, 2019), pp. 415–424 12. N. Nidhi, D. Prasad, V. Nath, Different aspects of smart grid: an overview, in Nanoelectronics, Circuits and Communication Systems. Lecture Notes in Electrical Engineering, ed. by V. Nath, J. Mandal, vol. 511 (Springer, Singapore, 2019), pp. 451–456 13. N. Nidhi, D. Prasad, V. Nath, A high-performance energy-efficient 75.17 dB two-stage operational amplifier, in Nanoelectronics, Circuits and Communication Systems. Lecture Notes in Electrical Engineering, ed. by V. Nath, J. Mandal, vol. 511 (Springer, Singapore, 2019), pp. 469–474 14. U. Raj, N. Nidhi, V. Nath, Automated toll plaza using barcode-laser scanning technology, in Nanoelectronics, Circuits and Communication Systems. Lecture Notes in Electrical Engineering, ed. by V. Nath, J. Mandal, vol. 511 (Springer, Singapore, 2019), pp. 475–481 15. S. Chaudhary, A. Prava, N. Nidhi, V. Nath, Design of all-terrain rover Quadcopter for Military engineering services, in Nanoelectronics, Circuits and Communication Systems. Lecture Notes in Electrical Engineering, ed. by V. Nath, J. Mandal, vol. 511 (Springer, Singapore, 2019), pp. 507–513 16. R. Mohan, A.K. Suraj, S. Agarawal, S. Majumdar, V. Nath, Design of robot monitoring system for aviation, in Nanoelectronics, Circuits and Communication Systems. Lecture Notes in Electrical Engineering, ed. by V. Nath, J. Mandal, vol. 511 (Springer, Singapore, 2019), pp. 535–547 17. E.V.V. Hari Charan, I. Pal, A. Sinha, R.K.R. Baro, V. Nath, Electronic toll collection system using barcode technology, in Nanoelectronics, Circuits and Communication Systems. Lecture Notes in Electrical Engineering, ed. by V. Nath, J. Mandal, vol. 511 (Springer, Singapore, 2019), pp. 549–556 18. V. Goel, K.P. Riya, P. Shikha, P.D. Tanushree, V. Nath, Design of smartphone controlled robot using bluetooth, in Nanoelectronics, Circuits and Communication Systems. Lecture Notes in Electrical Engineering, ed. by V. Nath, J. Mandal, vol. 511 (Springer, Singapore, 2019), pp. 557–563 19. P.K. Sinha, S. Saraiyan, M. Ghosh, V. Nath, Design of earthquake indicator system using ATmega328p and ADXL335 for disaster management, in Nanoelectronics, Circuits and Communication Systems. Lecture Notes in Electrical Engineering, ed. by V. Nath, J. Mandal, vol. 511 (Springer, Singapore, 2019), pp. 565–572
RFID (MF-RC522) and Arduino Nano-Based Access Control System Saurav Kumar Agarwal, Abhishek Singh, Vatsalya Aniraj, Abhishek Pandey, Deepak Prasad and Vijay Nath
Abstract This research paper demonstrates the design of RFID-based access control system using Arduino Nano. It is a modern identification device used in various places such as offices and universities. It comprises a RFID (MF-RC522) reader and a tag, if a valid tag is put in front of the reader the access is allowed otherwise the access is denied. This setup is consisting of Arduino-Nano, RFID Reader, LCD Display, LED, etc. For compiling the complete setup we used Arduino-IDE. Keywords RFID (Radio-Frequency Identification Device) · LCD (Liquid Crystal Display) · Arduino Nano
1 Introduction Radio-frequency identification device (RFID) is one of the most popular and promising technology that can be used in wireless system. It has a very wide application range from industrial field to home control. Many researchers have utilized price tag management system. RFID system is constructed primarily by using three components: an antenna or coil, a transceiver (with decoder), and a transponder (RF tag) electronically programmed with distinctive data [1–3]. The transponder tags contain unique identifying information in every RFID system. This data is often as tiny as one binary bit or perhaps an oversized array of bits representing such things as associate degree identity code, personal medical data, or virtually any sort of data that may be S. K. Agarwal · A. Singh · V. Aniraj · A. Pandey (B) · D. Prasad · V. Nath Department of ECE, VLSI Design Group, Birla Institute of Technology, Mesra, Ranchi 835215, JH, India e-mail: [email protected] S. K. Agarwal e-mail: [email protected] A. Singh e-mail: [email protected] V. Nath e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 V. Nath and J. K. Mandal (eds.), Nanoelectronics, Circuits and Communication Systems, Lecture Notes in Electrical Engineering 642, https://doi.org/10.1007/978-981-15-2854-5_50
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Fig. 1 Access control system block diagram [4]
kept within the type of digital binary. On the other hand, RFID transceiver makes communication with passive tag. The passive tag is powered by the incident electromagnetic radiations [4, 5]. A microchip is fitted inside the tag. The power generated by the electromagnetic radiation is well enough to make the internal memory and transmission work. This paper description is divided into four sections: 1. Methodology. 2. Working principle. 3. Advantages of using RFID. 4. The block diagram of Applications of Access Control System is shown in Fig. 1.
2 Methodology The components used in the designing of RFID-based access control system are as follows.
2.1 RFID RC522 RFID module used in the system is shown in Fig. 2. It consists of a RFID reader and a tag as a verification of the valid person. It has a major benefit over others that the tag need not be aligned to the reader to read the information, because of this reason it finds its application in many areas of the new technologies. A RFID card is shown in the figure. It consists of the following pins—SS, SCK, MOSI, MISO, IRQ, GND, RST, and VCC [6].
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Fig. 2 Circuit diagram of RFID-RC522 [2]
Fig. 3 Arduino Nano [3]
2.2 Arduino Nano It is an ATMega328P-based small, breadboard-friendly board. Its functions are almost similar to Arduino Duemilanove, but in a different package. It differs in the only way that it lacks only a DC\ Power jack and works with a Mini-B USB cable instead of a standard one. It has a total of 30 pins out of which 14 are digital pins and 8 are analog pins [7]. An arduino Nano is as shown in Fig. 3.
2.3 16 * 2 LCD Screen A 16 * 2 LCD screen is used to display the information to the user. LCD stands for Liquid Crystal Display. It has 16 rows and 2 columns and hence the name 16 * 2. LCD displays are used in many areas such as monitors, televisions, calculators, digital, and watches.. The 16 × 2 LCD display is the most commonly used display device
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Fig. 4 16 * 2 LCD screen
in DIYs and circuits. This LCD displays 16 characters in each line in 2 such lines. In a 16 * 2, LCD each character is displayed in a 5 × 7-pixel matrix. It can be used in 2 modes—4 bit mode and 8 bit mode. It consists of 16 pins. A 16 * 2 LCD screen is shown in Fig. 4 [8].
3 Working Principle Any RFID (Radio-Frequency Identifier Device) system mainly consists of three key elements: 1. Transponder which is also known as RFID tag. It keeps unique ID code that carries object-identifying data. 2. A reader that reads RFID tag. 3. A database that stores all the records. The process starts with the generation and emission of low-level radio frequency in magnetic field which is used in the activation of tag. The tag transmits a unique identification code (UID). The data is reached to reader and then forwarded to the local application systems. Finally, the backend system identifies and stores the information. A RFID function through SPI communication method, where Serial Peripheral Interface (SPI) is a synchronous serial communication method which is used for a short distance communication [9, 10]. The information is displayed on an LCD. The LCD is used in the 4-bit mode which means that information is sent 4 bit at a time. This method is useful as it reduces the number of pins to be used in the Arduino board and the saved pins can be used for other tasks to be done. A 10 k- potentiometer is used to adjust the contrast of the LCD display so that the information can be clearly displayed [11]. This simple principle is used to allow access to the authorized person inside any area. In this RFID is set as a slave and the Arduino board is set as the master. First, a master tag is selected and other tags are added to or removed from the database only by using this master tag. If a tag is read and found not to be in the database then the access to the person is denied and “Access Denied” is displayed on the screen.
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Fig. 5 Flowchart of RFID systems
When the tag is valid, the access is given to the person and door opens and “Access Allowed” is displayed. In all this system working, Arduino plays a major part in synchronizing the tasks of all the other components. Here, Harvard architecture-based Arduino processor is used where separate memory is available for the program code and program data. A memory where program codes are stored is known as program memory (flash memory) and where data are stored is known as data memory. Arduino has advanced features where program can burn directly in the specified device without any external burner. Arduino has 0.5 KB of Bootloader that permits the program to be burned/loaded directly into the circuits. Then, solely we have got to try and do is to transfer the Arduino package and write the code. This all makes using Arduino a very simple task for us. This model can be made using another Arduino as well such as Arduino Uno, Arduino Mega, etc., but here we are using Arduino Nano due to its small size and can be fitted easily anywhere. Also, it is cheaper than Arduino such as Arduino Uno and Arduino Mega. The working flow chart of the project is shown in Fig. 5. The system performs according to the input conditions and the inputs applied, giving access to only the authorized people and not to the unauthorized ones. Even a buzzer can be attached to the system to alert the security if an invalid person tries to enter the area. And, a speaker can be attached to welcome the valid person in the room or building.
4 Advantages of Using RFID One of the main reasons for using RFID as the identification device is that it has various advantages than the other various identification devices. Some of the advantages of RFID over others are as follows [12, 13]:
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(1) An RFID reader can scan a tag within their limited frequency range. It does not have any line-of-sight limitations. For barcode reading reader should be very close to barcode. (2) RFID systems can scan multiple objects at a time [14]. (3) RFID readers can perform scanning function automatically. It has capacities to scan in milliseconds [15].
5 Applications of Access Control System The RFID-based access control system has various applications in various fields due to its simple working and high security [16]. Some of the applications of RFID-based access control system are as follows: 1. It can be used in offices, colleges, and hostels as an attendance and security system. It will allow only a valid member to enter the college or office. 2. It can even be used in libraries so that a valid library member can take the book from the library. It will prevent stealing of books. Acknowledgements We would like to thank the staff of the Electronics Laboratory BIT Mesra, for their valuable assistance. We are grateful to the academic staffs of the Department of Electrical and Electronic Engineering for continuous help and support. We thank Prof. S. Konar, Vice-Chancellor BIT Mesra and Prof. S. Pal HOD Department of ECE for providing the infrastructure facility to carry out this research work.
References 1. U. Raj, N. Nidhi, V. Nath, Automated toll plaza using barcode-laser scanning technology, in Nanoelectronics, Circuits and Communication Systems, ed. by V. Nath, J. Mandal. Lecture Notes in Electrical Engineering, vol. 511 (Springer, Singapore, 2019), pp. 475–481 2. S. Chaudhary, A. Prava, N. Nidhi, V. Nath, Design of all-terrain rover quadcopter for military engineering services, in Nanoelectronics, Circuits and Communication Systems, ed. by V. Nath, J. Mandal. Lecture Notes in Electrical Engineering, vol. 511 (Springer, Singapore, 2019), pp. 507–513 3. R. Mohan, A.K. Suraj, S. Agarawal, S. Majumdar, V. Nath, Design of robot monitoring system for aviation, in Nanoelectronics, Circuits and Communication Systems, ed. by V. Nath, J. Mandal. Lecture Notes in Electrical Engineering, vol. 511 (Springer, Singapore, 2019), pp. 535– 547 4. E.V.V. Hari Charan, I. Pal, A. Sinha, R.K.R. Baro, V. Nath, Electronic toll collection system using barcode technology, in Nanoelectronics, Circuits and Communication Systems, ed. by V. Nath, J. Mandal. Lecture Notes in Electrical Engineering, vol. 511 (Springer, Singapore, 2019), pp. 549–556
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5. V. Goel, Riya, P. Kumari, P. Shikha, Tanushree, D. Prasad, V. Nath, Design of smartphone controlled robot using bluetooth, in Nanoelectronics, Circuits and Communication Systems, ed. by V. Nath, J. Mandal. Lecture Notes in Electrical Engineering, vol. 511 (Springer, Singapore, 2019), pp. 557–563 6. P.K. Sinha, S. Saraiyan, M. Ghosh, V. Nath, Design of earthquake indicator system using ATmega328p and ADXL335 for disaster management, in Nanoelectronics, Circuits and Communication Systems, ed. by V. Nath, J. Mandal. Lecture Notes in Electrical Engineering, vol. 511 (Springer, Singapore, 2019), pp. 565–572 7. D. Raju, L. Eleswarapu, R. Saiv, V. Nath, Study and design of smart embedded system for aviation system: a review, in Nanoelectronics, Circuits and Communication Systems, ed. by V. Nath, J. Mandal. Lecture Notes in Electrical Engineering, vol. 511 (Springer, Singapore, 2019), pp. 573–590 8. D.K. Maurya, A. Kumar, S. Kaunoujiya, D. Prasad, V. Nath, Study and design of smart industry: a review, in Nanoelectronics, Circuits and Communication Systems, ed. by V. Nath, J. Mandal. Lecture Notes in Electrical Engineering, vol. 511 (Springer, Singapore, 2019), pp. 591–598 9. S. Sanjay Kumar, A. Khalkho, S. Agarwal, S. Prakash, D. Prasad, V. Nath, Design of smart security systems for home automation, in Nanoelectronics, Circuits and Communication Systems, ed. by V. Nath, J. Mandal. Lecture Notes in Electrical Engineering, vol. 511 (Springer, Singapore, 2019), pp. 599–604 10. V. Goel, S. Kumar, A. Muralidharan, N. Markham, D. Prasad, V. Nath, Auto-train track fault detection system, in Nanoelectronics, Circuits and Communication Systems, ed. by V. Nath, J. Mandal. Lecture Notes in Electrical Engineering, vol. 511 (Springer, Singapore, 2019), pp. 605– 610 11. N. Nidhi, D. Prasad, V. Nath, Internet of Things/ everything: an overview. Int. J. Pure Appl. Math. 119(12) (2018) 12. P. Roopchandka, S. Khanam, R. Dhan, K. Neelam, D. Prasad, V. Nath, Design of Password based door locking system, in Proceedings of the Third International Conference on Microelectronics, Computing and Communication Systems, ed. by V. Nath, J. Mandal. Lecture Notes in Electrical Engineering, vol. 556 (Springer, Singapore, 2019), pp. 605–612 13. R. Jain, A. Ashu, S. Lal, K. Neelam, D. Prasad, V. Nath, Application of Burglary alarm system to avoid railway accidents, in Proceedings of the Third International Conference on Microelectronics, Computing and Communication Systems. Lecture Notes in Electrical Engineering, vol. 556 (Springer, Singapore, 2019), pp. 595–604 14. C. Tyagi, S. Verma, A. Pandey, D. Prasad, V. Nath, Study and design of electro-pneumatic shifting system, in Proceedings of the Third International Conference on Microelectronics, Computing and Communication Systems, ed. by V. Nath, J. Mandal. Lecture Notes in Electrical Engineering, vol. 556 (Springer, Singapore, 2019), pp. 481–488 15. A. Jha, A. Singh, R. Kerketta, D. Prasad, K. Neelam, V. Nath, Development of autonomous garbage collector robot, in Proceedings of the Third International Conference on Microelectronics Computing and Communication Systems, ed. by V. Nath, J. Mandal. Lecture Notes in Electrical Engineering, vol. 556 (Springer, Singapore, 2019), pp. 567–576 16. G. Singh, K. Nivedita, S. Minz, K. Neelam, D. Prasad, V. Nath, Design of water overflow indicator alarm and controller, in Proceedings of the Third International Conference on Microelectronics, Computing and Communication Systems, V. Nath, J. Mandal. Lecture Notes in Electrical Engineering, vol. 556 (Springer, Singapore, 2019), pp. 623–630
Smart Irrigation System: A Review Shivani Bitla, Sai Santhan, Shreya Bhagat, Abhishek Pandey and Vijay Nath
Abstract This paper demonstrates the importance of smart irrigation and its application, and how different methods can be applied in order to achieve optimal growth and cost-effectiveness and design of smart irrigation systems using Arduino. Present work has become more and more interested in minimal labor force and increased cost-effectiveness. Agriculture is a sector that should escalate for humans to sustain and reign. Major factors that affects agriculture are irrigation and climatic conditions; since, climatic conditions cannot be controlled and irrigation systems can be inculcated for better functionality and consistency, a particular type of smart system can be incorporated in order to attain maximum quality and quantity of crop. Keyword Smart irrigation system (SIS)
1 Introduction Smart Irrigation [1] can be simply defined as the application of controlled amount of watering for plants at required intervals. Irrigation systems can be broadly classified on the basis of the type of water flow and climatic conditions. Based on water flow we have low flow and high flow. We can incorporate both of them into the same space if required. Low flow irrigation systems consist of micro spray, drip emitters, S. Bitla · S. Santhan · S. Bhagat · A. Pandey (B) · V. Nath Department of ECE, VLSI Design Group, B.I.T. Mesra, Ranchi, Jharkhand 835215, India e-mail: [email protected] S. Bitla e-mail: [email protected] S. Santhan e-mail: [email protected] S. Bhagat e-mail: [email protected] V. Nath e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 V. Nath and J. K. Mandal (eds.), Nanoelectronics, Circuits and Communication Systems, Lecture Notes in Electrical Engineering 642, https://doi.org/10.1007/978-981-15-2854-5_51
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or drip lines. High flow systems consist of fixed spray, rotor, impact, bubbler, and soaker [2]. Depending upon the type of crop, moisture required to grow, climatic conditions required, minerals present and required in soil, we can broadly classify smart irrigation system into three types. They are 1. Dry soil and/or low monsoon supply that requires more amount of water to be supplied. 2. Typical conditions that require moderate supply of water. 3. Self-sufficient moisture soil and excess of monsoon water supply that not only requires water to be provided when not present and draining out excess water when required and storing it for further use. For the first type of smart irrigation system we can use utilize real-time climatic conditions provided by the web or by real-time weather insights module of Arduino. A system that is used for automated irrigation is referred as smart irrigation system. These days farmers struggle very hard in the agricultural fields and irrigation of the fields has become more and more difficult for them due to irregularity in the work and negligence. For example, sometimes it so happens that they switch the motor ON and then they forget to switch it OFF as they get stuck in some other work. This kind of negligence leads to wastage of a huge amount of water, electricity, and other resources as well, which could be used for other purposes or could be saved for use in future. Therefore, an automated system is required to optimize the use the water for agricultural crops and to reduce human interference [3]. An automated irrigation system will help in improving the quality and in increasing the quantity of the crops cultivated. The proposed mechanism automatically turns the motor ON or OFF by detecting the moisture content in the soil using a soil moisture sensor. The benefits of using this technique of automated irrigation are • • • •
Decrement of human interference. Irrigation process becomes more feasible and affordable. To reduce unnecessary wastage of water resource. Improvement in the quality and increment in the quantity of crops produced.
This system can be developed using an Arduino microcontroller, which is programmed in such a way that it collects the input signal according to the moisture content of the soil (using soil moisture sensor) and gives the output to the OP-AMP which operates the pump (motor) [4].
2 Proposed System In this system, a soil moisture sensor is used to sense the moisture content in the soil, i.e., to detect if the soil contains enough water or not. The soil moisture sensor is interfaced with the Arduino microcontroller which works using the procedure of simulating on PROTEUS software. Based on the above it activates the DC motor
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using OP-AMP that compares the moisture content level of the soil with a predefined reference value (reference value is already set according to the crop requirement) that operates the pump through relay. Our aim is to develop a smart irrigation system which detects the moisture content in the soil and accordingly turns the water supply system ON or OFF automatically. Smart irrigation system is based on Arduino microcontroller. This design is based on Arduino microcontroller. This prototype monitors the amount of water in the soil. A predefined value of soil moisture (i.e., amount of water) is set and can be changed with the type of crops according to the requirements of the crops. In case there is a deviation in soil moisture from the specified range, the irrigating system is signaled to be turned ON or OFF. In the case when the soil is dry, it activates the irrigation system, i.e., pumping water for watering the crops in the agricultural field. The project is mainly based on C programming language. During simulation, pin 3 and pin 2 are used as input pins for switch and DC motor, respectively. This system gives us an opportunity to implement automated irrigation system on a large scale for various farming and agricultural purposes. This irrigation system might prove to be advantageous. Considering the prevailing conditions and shortage of water, the optimum irrigation schedules should be determined and implemented not only in houses but also in farms to conserve water as due to primitive agricultural techniques water is wasted at a huge scale in farms.
3 Working The proposed model is implemented, using a PROTEUS-based Arduino microcontroller that is programmed in C language. In the simulation process, four components are used. They are • • • •
Arduino microcontroller Switch Simple DC motor Standard resistance of 10 k.
The components, switch, DC motor and standard 10 k resistance are interfaced with Arduino microcontroller in a way that the switch and the resister are connected to pin 3 and the DC motor is connected to pin 2. All the abovementioned components are connected as inputs. The OP-AMP is used as the comparator which interfaces the microcontroller and the sensing arrangement [5]. As the microcontroller receives the signal, it produces an output. The produced output drives a relay and operates the pump. An LCD is also used to display the detected moisture content in the soil and the status of the water pump. This LCD is interfaced with the microcontroller. Sensor circuit is used for sensing the condition of the soil and for comparing the voltage signal thus produced with the predefined reference voltage, i.e., 5 V. If the produced voltage signal is less than the reference value, it indicates that the soil is dry and so a high signal goes to the microcontroller [logic 1], that turns the motor ON and
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Fig. 1 Various components interfaced with microcontroller
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power supply
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thus makes the motor to pump water in the agricultural field. As the microcontroller receives the data from the sensing system, it compares the data received with a predefined value. It is programmed in a way that it generates the output signal and activates the relays for operating the water pump based on the received and compared data. The sensing arrangement is set up, by using two stiff metallic rods. These rods are placed in the agricultural field at a particular distance. The required connections are made from these metallic rods. These connections are then interfaced with the control unit for controlling the operations of the pump according to the moisture content of the soil. When the soil gets wet, the voltage signal produced rises above the reference value of the voltage and so a low signal goes to the microcontroller which turns the motor OFF and stops the pumping of water in the field. The LCD screen is used to display soil moisture content and the status of the motor. Figure 1 shows the various components like Soil moisture sensor, LCD display, Relay, DC motor have interfaced with microcontroller.
4 Automatic Sensing Options Most of the farmers or field owners who wish to cultivate their land, cannot deal with different systems or equipment since they might find it hard to regulate and utilize smart modules effectively and further making it inadaptable. Hence providing the system with intelligence to vary between different modes depending on the weather and water required by the plant that requires irrigation is necessary. This increases the number of functionality of the system in order to work. The most important control required is water supply management system [6]. Depending on the crop and type of soil, this system works on different in-built libraries from which the system takes decisions of the amount of water required by the crop in various conditions. This is a default condition that takes into consideration of optimal time to water the plants, frequency at which the plants require watering,
Smart Irrigation System: A Review Fig. 2 The above hierarchy describes the different functions and their subdivisions in it to maximize reliability
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climate vary control
phase of the crops for optimization and controls the functioning of the motors and related components (Fig. 2). Climate vary control has to occur on a regular basis in order to validate continuous functioning of the water supply system for optimal use. This makes sure excess water is not provided and hence reduces the water consumption. This is designed to analyze the climatic changes on regular basis and give the input to the water supply control. The frequency at which the analysis should occur will vary upon various factors such as time of the year and variation of climate at that particular area in order to reduce constant monitoring. The frequency can be determined by history of climate at the particular area. To make the system more reliable for those who cannot access mechanical help instantaneously fault backup systems are required and should be properly designed. The irregularities in different sensors or equipments used leads to irregularity in power and current consumptions this leads to an extra control that can be names as system default control. This works in frequent monitoring of different components so as to reduce the risk of failure of the entire system. There is a backup configuration given in order to keep the system working even when a few components stop working. The backup configurations are to be designed so as to work even when there are no sufficient information inputs and rely on previously stored data and keep the system running using the remaining sensors and components. The failure of any system may occur or be detected by lack or consumption of surplus power. The entire configurations have different divisions making sure all the work is distributed abruptly. Plant phase control is an optional control that can be added to further minimize the water consumption. Water requirement varies depending on the growth phase of plant or crop.
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Fig. 3 This figure defines the daily functioning of the SIS weather report
soil moisture sensors system default watering system
5 Type of Soil and System Controls Depending upon the moisture levels and surrounding climates we need to vary working of different components so as to reduce power consumption and effective stimulations of SIS. A particular hierarchy is followed in order to manage the effectiveness of SIS to define the functions of SIS at any defined time which is given in Fig. 3. Input of the plant growing is definitely required that makes the necessity of definite database listing plants and their sustainable climatic conditions [7]. The above diagram defines the process that the system undergoes on a regular basis that can be combined with machine learning or big data in order to increase higher dependency by determining how regular this function should occur. This can lead to a systematic alert in the SIS to detect irregularities in the atmosphere to change its modes to make sure all the work is dependent on the SIS instead. This leaves minimal input by the users and maximum free-hand irrigation.
6 Components Required In the developed irrigation system, Arduino Mega 2560 as microcontroller, SIM900 as GSM module, YL69 as soil humidity sensor, LM35 as temperature sensor, L293D as motor driving chip, and SMS-TORK as solenoid valve were utilized. Arduino Mega 2560 is shown in Fig. 4. SIM900 GSM module operated at frequencies of 850, 900, 1800, and 1900 MHz is shown in Fig. 5. This GSM module keeps the irrigation system always connected with the network by using AT commands.
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Fig. 4 Arduino Mega 2560
Fig. 5 Simcom Sim900
Soil humidity sensor shown in Fig. 6 is used probing into the soil to detect the moisture content of the soil. This sensor has two probes that are used to pass current after being inserted in the soil and the current flowing through the soil determines the moisture content present in the soil [8]. This works on the principle that determines the difference of resistivity of the soil under moist and dry conditions which varies the current flowing through it. Temperature sensor (LM35) [9] that is to be within 0.5° of the actual temperature guaranteed for proper working is shown in Fig. 7. DC motors or step motors, motor driving circuits used cannot always be used by outputs of microcontrollers directly. Motors are controlled by raising the signal obtained from microcontroller via motor driving circuits. L293D has the ability to control two way-two motors independently whose connection diagram is shown in Fig. 8.
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Fig. 6 Soil humidity sensor
Fig. 7 Temperature sensor (LM35)
7 Conclusion The design of SIS must contain temperature sensor, humidity sensor, GSM module, and microcontroller. Further functionalities can be added for different configuration in order to increase efficiency and reliability by the user. The additional functions addable are illustrated in terms of their working and further research and implementation can be done on the basis of equipment required and available. This method can be used by farmers who do not have strength for agriculture having large agricultural
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Fig. 8 Connection diagram of L293D
area, minimum water availability and do not have huge labor availability. This can also be used by small-scale belts and home plants. The project “Smart Irrigation System” is used for optimum use of water in agricultural fields and to avoid unnecessary wastage of water. This system helps to minimize the human intervention in agricultural process. Since farming is a time-consuming process, this technique is useful in saving the time of the farmer which otherwise is required in inspection of the field for checking the condition of the soil of the entire field and then switching ON and OFF the motor for pumping water. On the other hand, this system uses soil moisture sensor for sensing the soil moisture and uses a microcontroller for switching ON and OFF the motor automatically according to the water requirement of the field and hence this technique helps in saving both water and time. In this system, the reference value can be set according to the requirement of the crop and can be varied for different varieties of crops. This system is quite feasible and affordable and is also helpful in the regions where there is a scarcity of water and thus it helps to improve the sustainability. Acknowledgements We would like to thank the professors of our department, Dr. S. Pal, Head of Department of Electronics and Communication Engineering and Dr. M. K. Mishra, Vice-Chancellor. Without their constant support, it would not have been possible to complete this research article.
References 1. Q. Razi, V. Nath, Design of smart embedded system for agricultural update using internet of things, in Nanoelectronics, Circuits and Communication Systems, edited by V. Nath, J. Mandal. Lecture Notes in Electrical Engineering, vol. 511 (Springer, Singapore, 2019), pp. 372–382 2. www.ashlandsaveswater.org
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3. T. Li-fang, Application of autocontrol technology in water-saving garden irrigation, in 2012 International Conference on Computer Science and Information Processing (CSIP) (2012) 4. www.elprocus.com 5. www.nevonrojects.com 6. Y. Zhao, J. Zhang, J. Guan, W. Yin, Study on precision water-saving irrigation automatic control system by plant physiology, in 4th IEEE Conference on Industrial Electronics and Applications, ICIEA 2009 (IEEE, 2009), pp. 1296–1300 7. M. Allani, M. Jabloun, A. Sahli, V. Hennings, J. Massmann, H. Muller, Enhancing on farm and regional irrigation management using MABIA-region tool, in 2012 IEEE Fourth International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications (PMA) (IEEE, 2012), pp. 18–21 8. A.K. Tripathy, A. Vichare, R.R. Pereira, V.D. Pereira, J.A. Rodrigues, Open source hardware based automated gardening system using low-cost soil moisture sensor, in 2015 ICTSD (2015) 9. http://www.datasheetcafe.com/sen0114-datasheet-moisture-sensor/
Enactment of Advanced Traffic Control System Using Verilog HDL Rohan Kumar, Master Abz, Abhishek Sinha, Abhishek Pandey and Vijay Nath
Abstract Traffic has been one of the most prominent services in each and every country in the world. In this research article an advanced traffic control system has been discussed which incorporates the use of Infrared sensors which helps in the proper regulation of traffic signals. Four roads system has been discussed among which two are part of so-called main road while other two are the parts of side roads. An Infrared system has been incorporated in the two side roads which define certain threshold for the number of vehicles on the side roads, which further helps in proper regulation of the traffic signals. This system has been designed using Verilog hardware description language. This system makes use of well-defined state diagram which forms the base for the design of the system using the Verilog hardware description language. The design has been simulated in Xilinx ISE. This article also discusses the design of the infrared sensor which has been incorporated in the design of the traffic management system. Keywords Traffic management system · Four way road system · Infrared sensor · State diagram · Verilog HDL
R. Kumar · M. Abz · A. Sinha · A. Pandey (B) · V. Nath Department of Electronics and Communication Engineering, VLSI Design Group, Birla Institute of Technology Mesra, Ranchi 835215, India e-mail: [email protected] R. Kumar e-mail: [email protected] M. Abz e-mail: [email protected] A. Sinha e-mail: [email protected] V. Nath e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 V. Nath and J. K. Mandal (eds.), Nanoelectronics, Circuits and Communication Systems, Lecture Notes in Electrical Engineering 642, https://doi.org/10.1007/978-981-15-2854-5_52
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1 Introduction Traffic has been the concern for the management of each and every country in the world. It is a major concern especially in developing countries like India where the population is very high, as a result of which the number of vehicles on the road has increased by significant amount in the past few years. A proper traffic management system is required in places like these for the smooth conduction of daily life. Traffic system is a well-known system. Traditional traffic system consists of three lights namely Red, Yellow and Green. Red light indicates the vehicles to stop, Yellow light indicates the vehicles to wait; and Green light indicates the vehicles that the passage is clear and they can move in the desired direction [1]. There were several drawbacks to these traditional traffic system such as these traffic systems were unable to work in smart manner because they remain green for certain period of time, they remain red and yellow for certain period of time. This results in inefficient management of traffic because there may arise some cases in which there are no vehicles or very less number of vehicles on certain road and the traffic light for that road indicates green color for certain fixed period of time and there may be some road consisting of dense population of vehicles. Thus this is certainly waste of time and a representation of inefficient management system. In today’s era, time is the most important asset which everyone wants to save. Thus it cannot be afforded to waste certain amount of time unnecessarily on the roads. Thus, there is need of some smart traffic management system which can manage the traffic signals based on the density of the vehicles on certain road. Some kind of sensors [2–7] can be incorporated in order to make smart traffic management system. These sensors can be utilized to sense the number of vehicles on a particular road and depending on the number of vehicles on the road, the sensor can send certain required output to the system to indicate the required signal on the traffic light [8]. Thus, in this way unnecessarily wastage of time can be avoided and efficiently the traffic can be managed. There are number of sensors which can be utilized for this purpose. One such sensor is the infrared sensors. The infrared sensors make use of the principle that whenever any object comes in front of the sensor it will indicate the presence of that object. Thus this principle of the infrared sensor can be used in the traffic management system by placing such sensors on certain points on the road. These sensors will ultimately mark the threshold value for the number of the vehicles above which there will be the need for the clearance of the road and hence the sensor will indicate the system to make the required light ON [9]. Thus, in this way a smart traffic management system can be designed which incorporates the use of infrared sensor and it removes the major drawbacks of the traditional traffic control system. Thus, this system can be utilized to save the time which is the main parameter of interest in today’s world. The main reason behind using such a system is its efficiency and the motive, which this system is fulfilling which is the save of the time, one of the important asset in today’s world which everyone in this world wants to save.
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2 Infrared Sensors Infrared sensors are used for the detection of objects that come in front of the sensor. Infrared sensors have been employed in the construction of the smart traffic management system. In the advanced and smart traffic management system discussed in this research article, there are mainly two parts of the roads, namely, main road and side road [1, 10]. It has been assumed that generally the traffic in the main road is densely populated as compared to that on the side road. Thus, infrared sensors have been placed on the side roads at a certain point, at which if the vehicles approaches, the sensor will immediately give the traffic system to indicate the required color. The basic principle of working of the infrared sensor is the emission of heat by the objects. Every object in this universe emits some amount of heat energy regardless of their size [11]. This concept is utilized in the working of the infrared sensor. Infrared sensors continuously radiates infrared radiations, when these infrared radiations strike any object in front of it, then that radiation is reflected back to the sensor and it indicates the traffic light to switch ON the necessary light. Thus, from Fig. 1, it is evident that there are two basic units of the infrared sensors namely the IR Transmitter and the IR Receiver. The IR Transmitter is responsible for the continuous emission of the infrared radiations in the free space. Whenever any object momentarily comes in front of the sensor, the heat which is generated by that object makes the infrared radiations to get reflected back toward the sensor. This reflected radiation is then received by the IR receiver. Upon the reception of this reflected radiation, the receiver module sends certain signals to the traffic light to indicate about the activation of the required color of light [12]. This sensor has been incorporated in the traffic system which has been discussed in this research article. Fig. 1 Infrared sensor working principle
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3 System’s State Diagram The smart traffic control system which has been discussed in this article utilizes the state diagram which is shown in Fig. 2. There are basically five states namely P0, P1, P2, P3, P4. State P0 signifies that the lights of main road are Green and that of side road are Red. State P1 signifies that the lights of main road are Yellow and that of side road are Red. State P2 signifies that the lights of main road are Red and that of side road are also Red. State P3 signifies that the lights of main road are Red and that of side road are Green. State P4 signifies that the lights of main road are Red and that of side road are Yellow [13]. The switching of states takes place among themselves by the application of the input signal which is nothing but the output of the infrared sensor. When the input is 0 then state P0 remains at P0 otherwise it switches to P1. When the input signal is either 0 or 1, the state P1 switches to P2. Similarly, when the input signal is either 0 or 1, the state P2 switches to P3. When the input signal is 1, the state P3 remains at P3 otherwise it switches to P4. When the input signal is either 0 or 1, the state P4 switches to P0 again. Thus, this state diagram has been utilized for the designing of the traffic control system [14]. The input signal which has been the main aspect for the switching of the states are the output signals generated by the infrared sensors. The four lane crossing which has been analyzed in this research article is shown in Fig. 3. The four lane crossing consists of two roads namely main road and side road. It has been assumed that the number of vehicles is generally very high in the main road as compared to that on the side roads [15, 16]. Thus, it is unnecessary to keep the green lights on the side roads in ON position for equal number of time duration as that of the green lights of the main road. Thus, to ensure proper maintenance Fig. 2 State diagram of smart traffic control system
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Fig. 3 Four lane crossing diagram
of the traffic system, infrared sensors have been utilized on the side roads. Infrared sensors are indicated by the dotted lines in Fig. 3. These infrared sensors define a certain threshold value. If the waiting vehicles on the side roads have not reached the threshold value, the input to the system will be 0 and the switching between the states will takes place accordingly [13]. On the other hand, if the number of waiting vehicles has reached the threshold value, the input to the system will be 1 and the switching between the states will take place accordingly.
4 Conclusions and Results It is very clear that what exactly are the drawbacks of the traditional traffic control system were and how these limitations were overcome by the smart traffic management system. This system makes use of the infrared sensors in order to check the number of vehicles on the usually densely populated road. This infrared sensors sets the threshold value for the number of vehicles and which finally decides whether the input to the system or the output of the sensors will be 0 or 1. Thus, this efficient system ensures better performance as compared to that of the traditional traffic control system [17]. This system also ensures time saving, which is very important aspect in today’s era. All these state diagrams are employed in the designing using language Verilog HDL. These simulations were performed in the Xilinx ISE software. The results for the simulation of the abovementioned state diagram is shown Fig. 4. Thus, it can be concluded that the smart traffic management system is much more efficient in terms of performance as compared to that of the traditional traffic lights. And also this can be concluded that, why the traditional system of traffic light is losing its demand in today’s time, just because of their number of drawbacks.
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Fig. 4 Simulations results
These drawbacks have been taken care of by introducing a smart and efficient traffic control system in this research article. The simulation results in Fig. 4 clearly suggest the working principle of the smart traffic control system proposed in this research article [1]. Whenever the infrared sensor installed in the side roads produces positive signals, the state switches from the state transition diagram and thus, activating the corresponding colors of each state. Thus by incorporating the infrared sensor in the smart traffic control system, the entire traffic system works in an efficient manner and it also eradicates those drawbacks as posed by the traditional traffic management system [10, 8]. Thus, this traffic management system which has been proposed in this research article lays the foundation for the tremendously bright future in the development of the country, as the traffic management of any country is the reflection of its development status. Having a nice and better and efficient traffic management system makes any country much more proud, developed, and ahead of the other countries in the world who are still lagging in these fields. Acknowledgements We are thankful to the Department of Electronics and Communication Engineering BIT Mesra, Ranchi for providing the laboratory facilities. We are also thankful to our
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Vice-Chancellor, Dr. M. K. Mishra and our Head of the Department, Dr. S. Pal for their constant inspiration and encouragement. Without their support, this research article would not have been possible.
References 1. P.P. Chu, FPGA Prototyping by VHDL Examples-Xilinx Spartan-3 Version (Wiley, 2008) 2. U. Raj, N. Nidhi, V. Nath, Automated toll plaza using barcode-laser scanning technology, in Nanoelectronics, Circuits and Communication Systems, edited by V. Nath, J. Mandal. Lecture Notes in Electrical Engineering, vol. 511 (Springer, Singapore, 2019), pp. 475–481 3. S. Chaudhary, A. Prava, N. Nidhi, V. Nath, Design of all-terrain rover quadcopter for military engineering services, in Nanoelectronics, Circuits and Communication Systems, edited by V. Nath, J. Mandal. Lecture Notes in Electrical Engineering, vol. 511 (Springer, Singapore, 2019), pp. 507–513 4. R. Mohan, A.K. Suraj, S. Agarawal, S. Majumdar, V. Nath, Design of robot monitoring system for aviation, in Nanoelectronics, Circuits and Communication Systems, edited by V. Nath, J. Mandal. Lecture Notes in Electrical Engineering, vol. 511 (Springer, Singapore, 2019), pp. 535–547 5. E.V.V. Hari Charan, I. Pal, A. Sinha, R.K.R. Baro, V. Nath, Electronic toll collection system using barcode technology, in Nanoelectronics, Circuits and Communication Systems, edited by V. Nath, J. Mandal. Lecture Notes in Electrical Engineering, vol. 511 (Springer, Singapore, 2019), pp. 549–556 6. V. Goel, Riya, P. Kumari, P. Shikha, Tanushree, D. Prasad, V. Nath, Design of smartphone controlled robot using bluetooth, in Nanoelectronics, Circuits and Communication Systems, edited by V. Nath, J. Mandal. Lecture Notes in Electrical Engineering, vol. 511 (Springer, Singapore, 2019), pp. 557–563 7. P.K. Sinha, S. Saraiyan, M. Ghosh, V. Nath, Design of earthquake indicator system using ATmega328p and ADXL335 for disaster management, in Nanoelectronics, Circuits and Communication Systems, edited by V. Nath, J. Mandal. Lecture Notes in Electrical Engineering, vol. 511 (Springer, Singapore, 2019), pp. 565–572 8. M.-K. Yoon, S. Mohan, J. Choi, L. Sha, Memory heat map: anomaly detection in realtime embedded systems using memory behavior, in 2015 52nd ACM/EDAC/IEEE Design Automation Conference (DAC) (2015), pp. 1–6 9. R.K.V. Maeda, Q. Cai, J. Xu, Z. Wang, Z. Tian, Fast and accurate exploration of multi-level caches using hierarchical reuse distance, in 2017 IEEE International Symposium on High Performance Computer Architecture (HPCA) (2017), pp. 145–156 10. N.P. Kakirde, A. Davari, J. Wang, Trajectory tracking of unmanned aerial vehicle using servomechanism strategy, in Proceedings of the Thirty-Seventh Southeastern Symposium on System Theory SSST’05 (2005), pp. 163–166 11. F. Mehboob, M. Abbas, R. Jiang, Traffic event detection from road surveillance videos based on fuzzy logic, in SAI Computing Conference (SAI) (2016), pp. 188–194 12. H. Sasaki, F.-H. Su, T. Tanimoto, S. Sethumadhavan, Why do programs have heavy tails?, in 2017 IEEE International Symposium on Workload Characterization (IISWC) (2017), pp. 135– 145 13. Z. Yuye, Y. Weisheng, Research of traffic signal light intelligent control system based on microcontroller, in 2009 First International Workshop on Education Technology and Computer Science 14. S.V. Lahade, S.R. Hirekhan, Intelligent and adaptive traffic light controller (IA-TLC) using FPGA, in 2015 International Conference on Industrial Instrumentation and Control (ICIC) (2015), pp. 618–623
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15. H.S. Kia, C. Ababei, Improving fault tolerance of network-on-chip links via minimal redundancy and reconfiguration, in 2011 International Conference on Reconfigurable Computing and FPGAs (ReConFig) (2011), pp. 363–368 16. Y.S. Huang, T.H. Chung, T. Lin, Design and analysis urban traffic lights using timed colour petri nets, in Proceedings of IEEE International Conference on Networking, Sensing and Control (2006), pp. 248–253 17. H.T. Hong, Q.B. Xuan, D.D. Van, N.P. Ngoc, T.N. Huu, Hardware-efficient implementation of WFQ algorithm on Net FPGA-based open flow switch, in 2016 International Conference on Advanced Technologies for Communications (ATC) (2016), pp. 431–436