John Wiley & Sons Human Communication Technology Cover In den 16 Kapiteln dieses Buches werden neue Architekturen, Vernetzungsparadigmen, vertrauenswürdige.. Product #: 978-1-119-75059-8 Regular price: $195.33 $195.33 Auf Lager

Human Communication Technology

Internet-of-Robotic-Things and Ubiquitous Computing

Anandan, R. / Suseendran, G. / Balamurugan, S. / Mishra, Ashish / Balaganesh, D. (Herausgeber)

Artificial Intelligence and Soft Computing for Industrial Transformation

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1. Auflage Dezember 2021
496 Seiten, Hardcover
Wiley & Sons Ltd

ISBN: 978-1-119-75059-8
John Wiley & Sons

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In den 16 Kapiteln dieses Buches werden neue Architekturen, Vernetzungsparadigmen, vertrauenswürdige Strukturen und Plattformen für die Integration von Anwendungen in verschiedenen Geschäftsbereichen und Branchen erörtert, die für die Einführung (stationärer oder mobiler) ?intelligenter Dinge? in kollaborativen autonomen Flotten erforderlich sind. Die neuen Anwendungen beschleunigen den Fortschritt der Paradigmen beim Design autonomer Systeme und die Verbreitung des Internets der robotischen Dinge (IoRT). Kollaborative robotische Dinge können im IoRT mit anderen Dingen kommunizieren, selbständig lernen, sicher mit der Welt, Menschen und anderen Dingen interagieren und sich Eigenschaften wie Selbstwartung, Selbstwahrnehmung, Selbstheilung und Ausfallsicherheit aneignen. Durch die Allgegenwärtigkeit der robotischen Dinge gewinnt das ?Internet der robotischen Dinge?, das die Sensoren und die Objekte robotischer Dinge miteinander verbindet, zunehmend an Popularität.

Preface xix

1 Internet of Robotic Things: A New Architecture and Platform 1
V. Vijayalakshmi, S. Vimal and M. Saravanan

1.1 Introduction 2

1.1.1 Architecture 3

1.1.1.1 Achievability of the Proposed Architecture 6

1.1.1.2 Qualities of IoRT Architecture 6

1.1.1.3 Reasonable Existing Robots for IoRT Architecture 8

1.2 Platforms 9

1.2.1 Cloud Robotics Platforms 9

1.2.2 IoRT Platform 10

1.2.3 Design a Platform 11

1.2.4 The Main Components of the Proposed Approach 11

1.2.5 IoRT Platform Design 12

1.2.6 Interconnection Design 15

1.2.7 Research Methodology 17

1.2.8 Advancement Process--Systems Thinking 17

1.2.8.1 Development Process 17

1.2.9 Trial Setup-to Confirm the Functionalities 18

1.3 Conclusion 20

1.4 Future Work 21

References 21

2 Brain-Computer Interface Using Electroencephalographic Signals for the Internet of Robotic Things 27
R. Raja Sudharsan and J. Deny

2.1 Introduction 28

2.2 Electroencephalography Signal Acquisition Methods 30

2.2.1 Invasive Method 31

2.2.2 Non-Invasive Method 32

2.3 Electroencephalography Signal-Based BCI 32

2.3.1 Prefrontal Cortex in Controlling Concentration Strength 33

2.3.2 Neurosky Mind-Wave Mobile 34

2.3.2.1 Electroencephalography Signal Processing Devices 34

2.3.3 Electromyography Signal Extraction of Features and Its Signal Classifications 37

2.4 IoRT-Based Hardware for BCI 40

2.5 Software Setup for IoRT 40

2.6 Results and Discussions 42

2.7 Conclusion 47

References 48

3 Automated Verification and Validation of IoRT Systems 55
S.V. Gayetri Devi and C. Nalini

3.1 Introduction 56

3.1.1 Automating V&V--An Important Key to Success 58

3.2 Program Analysis of IoRT Applications 59

3.2.1 Need for Program Analysis 59

3.2.2 Aspects to Consider in Program Analysis of IoRT Systems 59

3.3 Formal Verification of IoRT Systems 61

3.3.1 Automated Model Checking 61

3.3.2 The Model Checking Process 62

3.3.2.1 PRISM 65

3.3.2.2 UPPAAL 66

3.3.2.3 SPIN Model Checker 67

3.3.3 Automated Theorem Prover 69

3.3.3.1 ALT-ERGO 70

3.3.4 Static Analysis 71

3.3.4.1 CODESONAR 72

3.4 Validation of IoRT Systems 73

3.4.1 IoRT Testing Methods 79

3.4.2 Design of IoRT Test 80

3.5 Automated Validation 80

3.5.1 Use of Service Visualization 82

3.5.2 Steps for Automated Validation of IoRT Systems 82

3.5.3 Choice of Appropriate Tool for Automated Validation 84

3.5.4 IoRT Systems Open Source Automated Validation Tools 85

3.5.5 Some of Significant Open Source Test Automation Frameworks 86

3.5.6 Finally IoRT Security Testing 86

3.5.7 Prevalent Approaches for Security Validation 87

3.5.8 IoRT Security Tools 87

References 88

4 Light Fidelity (Li-Fi) Technology: The Future Man-Machine-Machine Interaction Medium 91
J.M. Gnanasekar and T. Veeramakali

4.1 Introduction 92

4.1.1 Need for Li-Fi 94

4.2 Literature Survey 94

4.2.1 An Overview on Man-to-Machine Interaction System 95

4.2.2 Review on Machine to Machine (M2M) Interaction 96

4.2.2.1 System Model 97

4.3 Light Fidelity Technology 98

4.3.1 Modulation Techniques Supporting Li-Fi 99

4.3.1.1 Single Carrier Modulation (SCM) 100

4.3.1.2 Multi Carrier Modulation 100

4.3.1.3 Li-Fi Specific Modulation 101

4.3.2 Components of Li-Fi 102

4.3.2.1 Light Emitting Diode (LED) 102

4.3.2.2 Photodiode 103

4.3.2.3 Transmitter Block 103

4.3.2.4 Receiver Block 104

4.4 Li-Fi Applications in Real Word Scenario 105

4.4.1 Indoor Navigation System for Blind People 105

4.4.2 Vehicle to Vehicle Communication 106

4.4.3 Li-Fi in Hospital 107

4.4.4 Li-Fi Applications for Pharmacies and the Pharmaceutical Industry 109

4.4.5 Li-Fi in Workplace 110

4.5 Conclusion 111

References 111

5 Healthcare Management-Predictive Analysis (IoRT) 113
L. Mary Gladence, V. Maria Anu and Y. Bevish Jinila

5.1 Introduction 114

5.1.1 Naive Bayes Classifier Prediction for SPAM 115

5.1.2 Internet of Robotic Things (IoRT) 115

5.2 Related Work 116

5.3 Fuzzy Time Interval Sequential Pattern (FTISPAM) 117

5.3.1 FTI SPAM Using GA Algorithm 118

5.3.1.1 Chromosome Generation 119

5.3.1.2 Fitness Function 120

5.3.1.3 Crossover 120

5.3.1.4 Mutation 121

5.3.1.5 Termination 121

5.3.2 Patterns Matching Using SCI 121

5.3.3 Pattern Classification Based on SCI Value 122

5.3.4 Significant Pattern Evaluation 123

5.4 Detection of Congestive Heart Failure Using Automatic Classifier 124

5.4.1 Analyzing the Dataset 125

5.4.2 Data Collection 126

5.4.2.1 Long-Term HRV Measures 127

5.4.2.2 Attribute Selection 128

5.4.3 Automatic Classifier--Belief Network 128

5.5 Experimental Analysis 130

5.6 Conclusion 132

References 134

6 Multimodal Context-Sensitive Human Communication Interaction System Using Artificial Intelligence-Based Human-Centered Computing 137
S. Murugan, R. Manikandan and Ambeshwar Kumar

6.1 Introduction 138

6.2 Literature Survey 141

6.3 Proposed Model 145

6.3.1 Multimodal Data 145

6.3.2 Dimensionality Reduction 146

6.3.3 Principal Component Analysis 147

6.3.4 Reduce the Number of Dimensions 148

6.3.5 CNN 148

6.3.6 CNN Layers 149

6.3.6.1 Convolution Layers 149

6.3.6.2 Padding Layer 150

6.3.6.3 Pooling/Subsampling Layers 150

6.3.6.4 Nonlinear Layers 151

6.3.7 ReLU 151

6.3.7.1 Fully Connected Layers 152

6.3.7.2 Activation Layer 152

6.3.8 LSTM 152

6.3.9 Weighted Combination of Networks 153

6.4 Experimental Results 155

6.4.1 Accuracy 155

6.4.2 Sensibility 156

6.4.3 Specificity 156

6.4.4 A Predictive Positive Value (PPV) 156

6.4.5 Negative Predictive Value (NPV) 156

6.5 Conclusion 159

6.6 Future Scope 159

References 160

7 AI, Planning and Control Algorithms for IoRT Systems 163
T.R. Thamizhvani, R.J. Hemalatha, R. Chandrasekaran and A. Josephin Arockia Dhivya

7.1 Introduction 164

7.2 General Architecture of IoRT 167

7.2.1 Hardware Layer 168

7.2.2 Network Layer 168

7.2.3 Internet Layer 168

7.2.4 Infrastructure Layer 168

7.2.5 Application Layer 169

7.3 Artificial Intelligence in IoRT Systems 170

7.3.1 Technologies of Robotic Things 170

7.3.2 Artificial Intelligence in IoRT 172

7.4 Control Algorithms and Procedures for IoRT Systems 180

7.4.1 Adaptation of IoRT Technologies 183

7.4.2 Multi-Robotic Technologies 186

7.5 Application of IoRT in Different Fields 187

References 190

8 Enhancements in Communication Protocols That Powered IoRT 193
T. Anusha and M. Pushpalatha

8.1 Introduction 194

8.2 IoRT Communication Architecture 194

8.2.1 Robots and Things 196

8.2.2 Wireless Link Layer 197

8.2.3 Networking Layer 197

8.2.4 Communication Layer 198

­­8.2.5 Application Layer 198

8.3 Bridging Robotics and IoT 198

8.4 Robot as a Node in IoT 200

8.4.1 Enhancements in Low Power WPANs 200

8.4.1.1 Enhancements in IEEE 802.15.4 200

8.4.1.2 Enhancements in Bluetooth 201

8.4.1.3 Network Layer Protocols 202

8.4.2 Enhancements in Low Power WLANs 203

8.4.2.1 Enhancements in IEEE 802.11 203

8.4.3 Enhancements in Low Power WWANs 204

8.4.3.1 LoRaWAN 205

8.4.3.2 5G 205

8.5 Robots as Edge Device in IoT 206

8.5.1 Constrained RESTful Environments (CoRE) 206

8.5.2 The Constrained Application Protocol (CoAP) 207

8.5.2.1 Latest in CoAP 207

8.5.3 The MQTT-SN Protocol 207

8.5.4 The Data Distribution Service (DDS) 208

8.5.5 Data Formats 209

8.6 Challenges and Research Solutions 209

8.7 Open Platforms for IoRT Applications 210

8.8 Industrial Drive for Interoperability 212

8.8.1 The Zigbee Alliance 212

8.8.2 The Thread Group 213

8.8.3 The WiFi Alliance 213

8.8.4 The LoRa Alliance 214

8.9 Conclusion 214

References 215

9 Real Time Hazardous Gas Classification and Management System Using Artificial Neural Networks 219
R. Anitha, S. Anusooya, V. Jean Shilpa and Mohamed Hishaam

9.1 Introduction 220

9.2 Existing Methodology 220

9.3 Proposed Methodology 221

9.4 Hardware & Software Requirements 223

9.4.1 Hardware Requirements 223

9.4.1.1 Gas Sensors Employed in Hazardous Detection 223

9.4.1.2 NI Wireless Sensor Node 3202 226

9.4.1.3 NI WSN gateway (NI 9795) 228

9.4.1.4 COMPACT RIO (NI-9082) 229

9.5 Experimental Setup 232

9.5.1 Data Set Preparation 233

9.5.2 Artificial Neural Network Model Creation 236

9.6 Results and Discussion 240

9.7 Conclusion and Future Work 243

References 244

10 Hierarchical Elitism GSO Algorithm For Pattern Recognition 245
Ilavazhagi Bala S. and Latha Parthiban

10.1 Introduction 246

10.2 Related Works 247

10.3 Methodology 248

10.3.1 Additive Kuan Speckle Noise Filtering Model 249

10.3.2 Hierarchical Elitism Gene GSO of MNN in Pattern Recognition 251

10.4 Experimental Setup 255

10.5 Discussion 255

10.5.1 Scenario 1: Computational Time 256

10.5.2 Scenario 2: Computational Complexity 257

10.5.3 Scenario 3: Pattern Recognition Accuracy 258

10.6 Conclusion 260

References 260

11 Multidimensional Survey of Machine Learning Application in IoT (Internet of Things) 263
Anurag Sinha and Pooja Jha

11.1 Machine Learning--An Introduction 264

11.1.1 Classification of Machine Learning 265

11.2 Internet of Things 267

11.3 ML in IoT 268

11.3.1 Overview 268

11.4 Literature Review 270

11.5 Different Machine Learning Algorithm 271

11.5.1 Bayesian Measurements 271

11.5.2 K-Nearest Neighbors (k-NN) 272

11.5.3 Neural Network 272

11.5.4 Decision Tree (DT) 272

11.5.5 Principal Component Analysis (PCA) t 273

11.5.6 K-Mean Calculations 273

11.5.7 Strength Teaching 273

11.6 Internet of Things in Different Frameworks 273

11.6.1 Computing Framework 274

11.6.1.1 Fog Calculation 274

11.6.1.2 Estimation Edge 275

11.6.1.3 Distributed Computing 275

11.6.1.4 Circulated Figuring 276

11.7 Smart Cities 276

11.7.1 Use Case 277

11.7.1.1 Insightful Vitality 277

11.7.1.2 Brilliant Portability 277

11.7.1.3 Urban Arranging 278

11.7.2 Attributes of the Smart City 278

11.8 Smart Transportation 279

11.8.1 Machine Learning and IoT in Smart Transportation 280

11.8.2 Markov Model 283

11.8.3 Decision Structures 284

11.9 Application of Research 285

11.9.1 In Energy 285

11.9.2 In Routing 285

11.9.3 In Living 286

11.9.4 Application in Industry 287

11.10 Machine Learning for IoT Security 290

11.10.1 Used Machine Learning Algorithms 291

11.10.2 Intrusion Detection 293

11.10.3 Authentication 294

11.11 Conclusion 294

References 295

12 IoT-Based Bias Analysis in Acoustic Feedback Using Time-Variant Adaptive Algorithm in Hearing Aids 301
G. Jayanthi and Latha Parthiban

12.1 Introduction 302

12.2 Existence of Acoustic Feedback 303

12.2.1 Causes of Acoustic Feedback 303

12.2.2 Amplification of Feedback Process 304

12.3 Analysis of Acoustic Feedback 304

12.3.1 Frequency Analysis Using Impulse Response 305

12.3.2 Feedback Analysis Using Phase Difference 306

12.4 Filtering of Signals 310

12.4.1 Digital Filters 310

12.4.2 Adaptive Filters 311

12.4.2.1 Order of Adaptive Filters 311

12.4.2.2 Filter Coefficients in Adaptive Filters 311

12.4.3 Adaptive Feedback Cancellation 312

12.4.3.1 Non-Continuous Adaptation 312

12.4.3.2 Continuous Adaptation 314

12.4.4 Estimation of Acoustic Feedback 315

12.4.5 Analysis of Acoustic Feedback Signal 317

12.4.5.1 Forward Path of the Signal 317

12.4.5.2 Feedback Path of the Signal 317

12.4.5.3 Bias Identification 319

12.5 Adaptive Algorithms 320

12.5.1 Step-Size Algorithms 321

12.5.1.1 Fixed Step-Size 322

12.5.1.2 Variable Step-Size 323

12.6 Simulation 325

12.6.1 Training of Adaptive Filter for Removal of Acoustic Feedback 325

12.6.2 Testing of Adaptive Filter 326

12.6.2.1 Subjective and Objective Evaluation Using KEMAR 326

12.6.2.2 Experimental Setup Using Manikin Channel 327

12.7 Performance Evaluation 328

12.8 Conclusions 333

References 334

13 Internet of Things Platform for Smart Farming 337
R. Anandan, Deepak B.S., G. Suseendran and Noor Zaman Jhanjhi

13.1 Introduction 337

13.2 History 338

13.3 Electronic Terminologies 339

13.3.1 Input and Output Devices 339

13.3.2 GPIO 340

13.3.3 ADC 340

13.3.4 Communication Protocols 340

13.3.4.1 UART 340

13.3.4.2 I2C 340

13.3.4.3 SPI 341

13.4 IoT Cloud Architecture 341

13.4.1 Communication From User to Cloud Platform 342

13.4.2 Communication From Cloud Platform To IoT Device 342

13.5 Components of IoT 343

13.5.1 Real-Time Analytics 343

13.5.1.1 Understanding Driving Styles 343

13.5.1.2 Creating Driver Segmentation 344

13.5.1.3 Identifying Risky Neighbors 344

13.5.1.4 Creating Risk Profiles 344

13.5.1.5 Comparing Microsegments 344

13.5.2 Machine Learning 344

13.5.2.1 Understanding the Farm 345

13.5.2.2 Creating Farm Segmentation 345

13.5.2.3 Identifying Risky Factors 346

13.5.2.4 Creating Risk Profiles 346

13.5.2.5 Comparing Microsegments 346

13.5.3 Sensors 346

13.5.3.1 Temperature Sensor 347

13.5.3.2 Water Quality Sensor 347

13.5.3.3 Humidity Sensor 347

13.5.3.4 Light Dependent Resistor 347

13.5.4 Embedded Systems 349

13.6 IoT-Based Crop Management System 350

13.6.1 Temperature and Humidity Management System 350

13.6.1.1 Project Circuit 351

13.6.1.2 Connections 353

13.6.1.3 Program 356

13.6.2 Water Quality Monitoring System 361

13.6.2.1 Dissolved Oxygen Monitoring System 361

13.6.2.2 pH Monitoring System 363

13.6.3 Light Intensity Monitoring System 364

13.6.3.1 Project Circuit 365

13.6.3.2 Connections 365

13.6.3.3 Program Code 366

13.7 Future Prospects 367

13.8 Conclusion 368

References 369

14 Scrutinizing the Level of Awareness on Green Computing Practices in Combating Covid-19 at Institute of Health Science-Gaborone 371
Ishmael Gala and Srinath Doss

14.1 Introduction 372

14.1.1 Institute of Health Science-Gaborone 373

14.1.2 Research Objectives 374

14.1.3 Green Computing 374

14.1.4 Covid-19 375

14.1.5 The Necessity of Green Computing in Combating Covid-19 376

14.1.6 Green Computing Awareness 379

14.1.7 Knowledge 380

14.1.8 Attitude 381

14.1.9 Behavior 381

14.2 Research Methodology 381

14.2.1 Target Population 382

14.2.2 Sample Frame 382

14.2.3 Questionnaire as a Data Collection Instrument 383

14.2.4 Validity and Reliability 383

14.3 Analysis of Data and Presentation 383

14.3.1 Demographics: Gender and Age 384

14.3.2 How Effective is Green Computing Policies in Combating Covid-19 at Institute of Health Science-Gaborone? 386

14.3.3 What are Green Computing Practices Among Users at Gaborone Institute of Health Science? 388

14.3.4 What is the Role of Green Computing Training in Combating Covid-19 at Institute of Health

Science-Gaborone? 388

14.3.5 What is the Likelihood of Threats Associated With a Lack of Awareness on Green Computing

Practices While Combating Covid-19? 390

14.3.6 What is the Level of User Conduct, Awareness and Attitude With Regard to Awareness on Green Computing Practices at Institute of Health Science-Gaborone? 391

14.4 Recommendations 393

14.4.1 Green Computing Policy 393

14.4.2 Risk Assessment 394

14.4.3 Green Computing Awareness Training 394

14.4.4 Compliance 394

14.5 Conclusion 394

References 395

15 Detailed Analysis of Medical IoT Using Wireless Body Sensor Network and Application of IoT in Healthcare 401
Anurag Sinha and Shubham Singh

15.1 Introduction 402

15.2 History of IoT 403

15.3 Internet of Objects 405

15.3.1 Definitions 405

15.3.2 Internet of Things (IoT): Data Flow 406

15.3.3 Structure of IoT--Enabling Technologies 406

15.4 Applications of IoT 407

15.5 IoT in Healthcare of Human Beings 407

15.5.1 Remote Healthcare--Telemedicine 408

15.5.2 Telemedicine System--Overview 408

15.6 Telemedicine Through a Speech-Based Query System 409

15.6.1 Outpatient Monitoring 410

15.6.2 Telemedicine Umbrella Service 410

15.6.3 Advantages of the Telemedicine Service 411

15.6.4 Some Examples of IoT in the Health Sector 411

15.7 Conclusion 412

15.8 Sensors 412

15.8.1 Classification of Sensors 413

15.8.2 Commonly Used Sensors in BSNs 415

15.8.2.1 Accelerometer 417

15.8.2.2 ECG Sensors 418

15.8.2.3 Pressure Sensors 419

15.8.2.4 Respiration Sensors 420

15.9 Design of Sensor Nodes 420

15.9.1 Energy Control 421

15.9.2 Fault Diagnosis 422

15.9.3 Reduction of Sensor Nodes 422

15.10 Applications of BSNs 423

15.11 Conclusions 423

15.12 Introduction 424

15.12.1 From WBANs to BBNs 425

15.12.2 Overview of WBAN 425

15.12.3 Architecture 426

15.12.4 Standards 427

15.12.5 Applications 427

15.13 Body-to-Body Network Concept 428

15.14 Conclusions 429

References 430

16 DCMM: A Data Capture and Risk Management for Wireless Sensing Using IoT Platform 435
Siripuri Kiran, Bandi Krishna, Janga Vijaykumar and Sridhar manda

16.1 Introduction 436

16.2 Background 438

16.2.1 Internet of Things 438

16.2.2 Middleware Data Acquisition 438

16.2.3 Context Acquisition 439

16.3 Architecture 439

16.3.1 Proposed Architecture 439

16.3.1.1 Protocol Adaption 441

16.3.1.2 Device Management 443

16.3.1.3 Data Handler 445

16.4 Implementation 446

16.4.1 Requirement and Functionality 446

16.4.1.1 Requirement 446

16.4.1.2 Functionalities 447

16.4.2 Adopted Technologies 448

16.4.2.1 Middleware Software 448

16.4.2.2 Usability Dependency 449

16.4.2.3 Sensor Node Software 449

16.4.2.4 Hardware Technology 450

16.4.2.5 Sensors 451

16.4.3 Details of IoT Hub 452

16.4.3.1 Data Poster 452

16.4.3.2 Data Management 452

16.4.3.3 Data Listener 453

16.4.3.4 Models 454

16.5 Results and Discussions 454

16.6 Conclusion 460

References 461

Index 463
R. Anandan PhD, completed his PhD in Computer Science and Engineering, is an IBMS/390 Mainframe professional, and is recognized as a Chartered Engineer from the Institution of Engineers in India and received a fellowship from Bose Science Society, India. He is a Professor in the Department of Computer Science and Engineering, School of Engineering, Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai, Tamil Nadu, India. He has published more than 110 research papers in various international journals, authored 9 books in the computer science and engineering disciplines, and has received 13 awards.

G. Suseendran PhD, received his PhD in Information Technology-Mathematics from Presidency College, University of Madras, Tamil Nadu, India. He passed away during the production of this book.

S. Balamurugan PhD, SMIEEE, ACM Distinguished Speaker, received his PhD from Anna University, India. He has published 57 books, 300+ international journals/conferences, and 100 patents. He is the Director of the Albert Einstein Engineering and Research Labs. He is also the Vice-Chairman of the Renewable Energy Society of India (RESI). He is serving as a research consultant to many companies, startups, SMEs, and MSMEs. He has received numerous awards for research at national and international levels.

Ashish Mishra PhD, is a professor in the Department of Computer Science and Engineering, Gyan Ganga Institute of Technology and Sciences, Jabalpur [M.P]. He received his PhD from AISECT University, Bhopal, India. He has published many research papers in reputed journals and conferences, been granted 1 patent, and has authored/edited 4 books in the areas of data mining, image processing, and artificial intelligence.

D. Balaganesh PhD, is a Dean of Faculty Computer Science and Multimedia, Lincoln University College, Malaysia. He has developed software applications "Timetable Automation", "Online Exam" as well as published the book Computer Applications in Business.

R. Anandan, Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai, Tamil Nadu, India; G. Suseendran, University of Madras, Tamil Nadu, India; S. Balamurugan, Anna University, India; A. Mishra, Gyan Ganga Institute of Technology and Sciences, Jabalpur MP, India; D. Balaganesh, Lincoln University College, Malaysia