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Ashutosh Kumar Dubey received his PhD degree in Computer Science and Engineering from JK Lakshmipat University, Jaipur, Rajasthan, India. He is currently in the Department of Computer Science and Engineering, Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India. His research areas are data mining, optimization, machine learning, cloud computing, artificial intelligence, big data, IoT and object-oriented programming.
Abhishek Kumar is a Doctorate in computer science from the University of Madras and more than 50 publications in reputed peer reviewed national and international journals, books & conferences. His research interests include artificial intelligence, image processing, computer vision, data mining, machine learning.
S. Rakesh Kumar received his M.E. degree in Computer Science and Engineering from Anna University Chennai in 2016. His main research areas are big data analytics, network security and cloud computing.
N. Gayathri received her B. Tech as well as M. Tech. degree in Computer Science and Engineering from Thiagarajar College of Engineering, Madurai, India. Her research interests include cloud computing, big data analytics and network security.
Pasenjit Das PhD is an associate professor at Chitkara University, Himachal Pradesh, India. He has 15 + years' experience in industry and academia and his research areas are data mining, machine learning and image processing.
Preface xvii
1 Introduction to Robotics 1Srinivas Kumar Palvadi, Pooja Dixit and Vishal Dutt
1.1 Introduction 1
1.2 History and Evolution of Robots 3
1.3 Applications 6
1.4 Components Needed for a Robot 7
1.5 Robot Interaction and Navigation 10
1.5.1 Humanoid Robot 11
1.5.2 Control 11
1.5.3 Autonomy Levels 12
1.6 Conclusion 12
References 13
2 Techniques in Robotics for Automation Using AI and IoT 15Sandeep Kr. Sharma, N. Gayathri, S. Rakesh Kumar and Rajiv Kumar Modanval
2.1 Introduction 16
2.2 Brief History of Robotics 16
2.3 Some General Terms 17
2.4 Requirements of AI and IoT for Robotic Automation 20
2.5 Role of AI and IoT in Robotics 21
2.6 Diagrammatic Representations of Some Robotic Systems 23
2.7 Algorithms Used in Robotics 25
2.8 Application of Robotics 27
2.9 Case Studies 30
2.9.1 Sophia 30
2.9.2 ASIMO 30
2.9.3 Cheetah Robot 30
2.9.4 IBM Watson 31
2.10 Conclusion 31
References 31
3 Robotics, AI and IoT in the Defense Sector 35Rajiv Kumar Modanval, S. Rakesh Kumar, N. Gayathri and Sandeep Kr. Sharma
3.1 Introduction 36
3.2 How Robotics Plays an Important Role in the Defense Sector 36
3.3 Review of the World's Current Robotics Capabilities in the Defense Sector 38
3.3.1 China 38
3.3.2 United State of America 39
3.3.3 Russia 40
3.3.4 India 41
3.4 Application Areas of Robotics in Warfare 43
3.4.1 Autonomous Drones 43
3.4.2 Autonomous Tanks and Vehicles 44
3.4.3 Autonomous Ships and Submarines 45
3.4.4 Humanoid Robot Soldiers 47
3.4.5 Armed Soldier Exoskeletons 48
3.5 Conclusion 50
3.6 Future Work 50
References 50
4 Robotics, AI and IoT in Medical and Healthcare Applications 53Pooja Dixit, Manju Payal, Nidhi Goyal and Vishal Dutt
4.1 Introduction 53
4.1.1 Basics of AI 53
4.1.1.1 AI in Healthcare 54
4.1.1.2 Current Trends of AI in Healthcare 55
4.1.1.3 Limits of AI in Healthcare 56
4.1.2 Basics of Robotics 57
4.1.2.1 Robotics for Healthcare 57
4.1.3 Basics of IoT 59
4.1.3.1 IoT Scenarios in Healthcare 60
4.1.3.2 Requirements of Security 61
4.2 AI, Robotics and IoT: A Logical Combination 62
4.2.1 Artificial Intelligence and IoT in Healthcare 62
4.2.2 AI and Robotics 63
4.2.2.1 Limitation of Robotics in Medical Healthcare 66
4.2.3 IoT with Robotics 66
4.2.3.1 Overview of IoMRT 67
4.2.3.2 Challenges of IoT Deployment 69
4.3 Essence of AI, IoT, and Robotics in Healthcare 70
4.4 Future Applications of Robotics, AI, and IoT 71
4.5 Conclusion 72
References 72
5 Towards Analyzing Skill Transfer to Robots Based on Semantically Represented Activities of Humans 75Devi.T, N. Deepa, S. Rakesh Kumar, R. Ganesan and N. Gayathri
5.1 Introduction 76
5.2 Related Work 77
5.3 Overview of Proposed System 78
5.3.1 Visual Data Retrieval 79
5.3.2 Data Processing to Attain User Objective 80
5.3.3 Knowledge Base 82
5.3.4 Robot Attaining User Goal 83
5.4 Results and Discussion 83
5.5 Conclusion 85
References 85
6 Healthcare Robots Enabled with IoT and Artificial Intelligence for Elderly Patients 87S. Porkodi and D. Kesavaraja
6.1 Introduction 88
6.1.1 Past, Present, and Future 88
6.1.2 Internet of Things 88
6.1.3 Artificial Intelligence 89
6.1.4 Using Robotics to Enhance Healthcare Services 89
6.2 Existing Robots in Healthcare 90
6.3 Challenges in Implementation and Providing Potential Solutions 90
6.4 Robotic Solutions for Problems Facing the Elderly in Society 98
6.4.1 Solutions for Physical and Functional Challenges 98
6.4.2 Solutions for Cognitive Challenges 98
6.5 Healthcare Management 99
6.5.1 Internet of Things for Data Acquisition 99
6.5.2 Robotics for Healthcare Assistance and Medication Management 102
6.5.3 Robotics for Psychological Issues 103
6.6 Conclusion and Future Directions 103
References 104
7 Robotics, AI, and the IoT in Defense Systems 109Manju Payal, Pooja Dixit, T.V.M. Sairam and Nidhi Goyal
7.1 AI in Defense 110
7.1.1 AI Terminology and Background 110
7.1.2 Systematic Sensing Applications 111
7.1.3 Overview of AI in Defense Systems 112
7.2 Overview of IoT in Defense Systems 114
7.2.1 Role of IoT in Defense 116
7.2.2 Ministry of Defense Initiatives 117
7.2.3 IoT Defense Policy Challenges 117
7.3 Robotics in Defense 118
7.3.1 Technical Challenges of Defense Robots 120
7.4 AI, Robotics, and IoT in Defense: A Logical Mix in Context 123
7.4.1 Combination of Robotics and IoT in Defense 123
7.4.2 Combination of Robotics and AI in Defense 124
7.5 Conclusion 126
References 127
8 Techniques of Robotics for Automation Using AI and the IoT 129Kapil Chauhan and Vishal Dutt
8.1 Introduction 130
8.2 Internet of Robotic Things Concept 131
8.3 Definitions of Commonly Used Terms 132
8.4 Procedures Used in Making a Robot 133
8.4.1 Analyzing Tasks 133
8.4.2 Designing Robots 134
8.4.3 Computerized Reasoning 134
8.4.4 Combining Ideas to Make a Robot 134
8.4.5 Making a Robot 134
8.4.6 Designing Interfaces with Different Frameworks or Robots 134
8.5 IoRT Technologies 135
8.6 Sensors and Actuators 137
8.7 Component Selection and Designing Parts 138
8.7.1 Robot and Controller Structure 140
8.8 Process Automation 141
8.8.1 Benefits of Process Automation 141
8.8.2 Incorporating AI in Process Automation 141
8.9 Robots and Robotic Automation 142
8.10 Architecture of the Internet of Robotic Things 142
8.10.1 Concepts of Open Architecture Platforms 143
8.11 Basic Abilities 143
8.11.1 Discernment Capacity 143
8.11.2 Motion Capacity 144
8.11.3 Manipulation Capacity 144
8.12 More Elevated Level Capacities 145
8.12.1 Decisional Self-Sufficiency 145
8.12.2 Interaction Capacity 145
8.12.3 Cognitive Capacity 146
8.13 Conclusion 146
References 146
9 An Artificial Intelligence-Based Smart Task Responder: Android Robot for Human Instruction Using LSTM Technique 149T. Devi, N. Deepa, SP. Chokkalingam, N. Gayathri and S. Rakesh Kumar
9.1 Introduction 150
9.2 Literature Review 152
9.3 Proposed System 152
9.4 Results and Discussion 157
9.5 Conclusion 161
References 162
10 AI, IoT and Robotics in the Medical and Healthcare Field 165V. Kavidha, N. Gayathri and S. Rakesh Kumar
10.1 Introduction 165
10.2 A Survey of Robots and AI Used in the Health Sector 167
10.2.1 Surgical Robots 167
10.2.2 Exoskeletons 168
10.2.3 Prosthetics 170
10.2.4 Artificial Organs 171
10.2.5 Pharmacy and Hospital Automation Robots 172
10.2.6 Social Robots 173
10.2.7 Big Data Analytics 175
10.3 Sociotechnical Considerations 176
10.3.1 Sociotechnical Influence 176
10.3.2 Social Valence 177
10.3.3 The Paradox of Evidence-Based Reasoning 178
10.4 Legal Considerations 180
10.4.1 Liability for Robotics, AI and IoT 180
10.4.2 Liability for Physicians Using Robotics, AI and IoT 181
10.4.3 Liability for Institutions Using Robotics, AI and IoT 182
10.5 Regulating Robotics, AI and IoT as Medical Devices 183
10.6 Conclusion 185
References 185
11 Real-Time Mild and Moderate COVID-19 Human Body Temperature Detection Using Artificial Intelligence 189K. Logu, T. Devi, N. Deepa, S. Rakesh Kumar and N. Gayathri
11.1 Introduction 190
11.2 Contactless Temperature 191
11.2.1 Bolometers (IR-Based) 192
11.2.2 Thermopile Radiation Sensors (IR-Based) 193
11.2.3 Fiber-Optic Pyrometers 193
11.2.4 RGB Photocell 194
11.2.5 3D Sensor 195
11.3 Fever Detection Camera 196
11.3.1 Facial Recognition 197
11.3.2 Geometric Approach 198
11.3.3 Holistic Approach 198
11.3.4 Model-Based 198
11.3.5 Vascular Network 199
11.4 Simulation and Analysis 200
11.5 Conclusion 203
References 203
12 Drones in Smart Cities 205Manju Payal, Pooja Dixit and Vishal Dutt
12.1 Introduction 206
12.1.1 Overview of the Literature 206
12.2 Utilization of UAVs for Wireless Network 209
12.2.1 Use Cases for WN Using UAVs 209
12.2.2 Classifications and Types of UAVs 210
12.2.3 Deployment of UAVS Using IoT Networks 213
12.2.4 IoT and 5G Sensor Technologies for UAVs 214
12.3 Introduced Framework 217
12.3.1 Architecture of UAV IoT 217
12.3.2 Ground Control Station 218
12.3.3 Data Links 218
12.4 UAV IoT Applications 223
12.4.1 UAV Traffic Management 223
12.4.2 Situation Awareness 223
12.4.3 Public Safety/Saving Lives 225
12.5 Conclusion 227
References 227
13 UAVs in Agriculture 229DeepanshuSrivastava, S. RakeshKumar and N. Gayathri
13.1 Introduction 230
13.2 UAVs in Smart Farming and Take-Off Panel 230
13.2.1 Overview of Systems 230
13.3 Introduction to UGV Systems and Planning 234
13.4 UAV-Hyperspectral for Agriculture 236
13.5 UAV-Based Multisensors for Precision Agriculture 239
13.6 Automation in Agriculture 242
13.7 Conclusion 245
References 245
14 Semi-Automated Parking System Using DSDV and RFID 247Mayank Agrawal, Abhishek Kumar Rawat, Archana, SandhyaKatiyar and Sanjay Kumar
14.1 Introduction 247
14.2 Ad Hoc Network 248
14.2.1 Destination-Sequenced Distance Vector (DSDV) Routing Protocol 248
14.3 Radio Frequency Identification (RFID) 249
14.4 Problem Identification 250
14.5 Survey of the Literature 250
14.6 PANet Architecture 251
14.6.1 Approach for Semi-Automated System Using DSDV 252
14.6.2 Tables for Parking Available/Occupied 253
14.6.3 Algorithm for Detecting the Empty Slots 255
14.6.4 Pseudo Code 255
14.7 Conclusion 256
References 256
15 Survey of Various Technologies Involved in Vehicle-to-Vehicle Communication 259Lisha Kamala K., Sini Anna Alex and Anita Kanavalli
15.1 Introduction 259
15.2 Survey of the Literature 260
15.3 Brief Description of the Techniques 262
15.3.1 ARM and Zigbee Technology 262
15.3.2 VANET-Based Prototype 262
15.3.2.1 Calculating Distance by Considering Parameters 263
15.3.2.2 Calculating Speed by Considering Parameters 263
15.3.3 Wi-Fi-Based Technology 263
15.3.4 Li-Fi-Based Technique 264
15.3.5 Real-Time Wireless System 266
15.4 Various Technologies Involved in V2V Communication 267
15.5 Results and Analysis 267
15.6 Conclusion 268
References 268
16 Smart Wheelchair 271Mekala Ajay, Pusapally Srinivas and Lupthavisha Netam
16.1 Background 271
16.2 System Overview 275
16.3 Health-Monitoring System Using IoT 275
16.4 Driver Circuit of Wheelchair Interfaced with Amazon Alexa 276
16.5 MATLAB Simulations 277
16.5.1 Obstacle Detection 277
16.5.2 Implementing Path Planning Algorithms 278
16.5.3 Differential Drive Robot for Path Following 280
16.6 Conclusion 282
16.7 Future Work 282
Acknowledgment 283
References 283
17 Defaulter List Using Facial Recognition 285Kavitha Esther, Akilindin S.H., Aswin S. and Anand P.
17.1 Introduction 286
17.2 System Analysis 287
17.2.1 Problem Description 287
17.2.2 Existing System 287
17.2.3 Proposed System 287
17.3 Implementation 289
17.3.1 Image Pre-Processing 289
17.3.2 Polygon Shape Family Pre-Processing 289
17.3.3 Image Segmentation 289
17.3.4 Threshold 289
17.3.5 Edge Detection 291
17.3.6 Region Growing Technique 291
17.3.7 Background Subtraction 291
17.3.8 Morphological Operations 291
17.3.9 Object Detection 292
17.4 Inputs and Outputs 292
17.5 Conclusion 292
References 293
18 Visitor/Intruder Monitoring System Using Machine Learning 295G. Jenifa, S. Indu, C. Jeevitha and V. Kiruthika
18.1 Introduction 296
18.2 Machine Learning 296
18.2.1 Machine Learning in Home Security 297
18.3 System Design 297
18.4 Haar-Cascade Classifier Algorithm 298
18.4.1 Creating the Dataset 298
18.4.2 Training the Model 299
18.4.3 Recognizing the Face 299
18.5 Components 299
18.5.1 Raspberry Pi 299
18.5.2 Web Camera 300
18.6 Experimental Results 300
18.7 Conclusion 302
Acknowledgment 302
References 303
19 Comparison of Machine Learning Algorithms for Air Pollution Monitoring System 305Tushr Sethi and R. C. Thakur
19.1 Introduction 305
19.2 System Design 306
19.3 Model Description and Architecture 307
19.4 Dataset 308
19.5 Models 310
19.6 Line of Best Fit for the Dataset 312
19.7 Feature Importance 313
19.8 Comparisons 315
19.9 Results 318
19.10 Conclusion 318
References 321
20 A Novel Approach Towards Audio Watermarking Using FFT and CORDIC-Based QR Decomposition 323Ankit Kumar, Astha Singh, Shiv Prakash and Vrijendra Singh
20.1 Introduction and Related Work 324
20.2 Proposed Methodology 326
20.2.1 Fast Fourier Transform 328
20.2.2 CORDIC-Based QR Decomposition 329
20.2.3 Concept of Cyclic Codes 331
20.2.4 Concept of Arnold's Cat Map 331
20.3 Algorithm Design 331
20.4 Experiment Results 334
20.5 Conclusion 337
References 338
21 Performance of DC-Biased Optical Orthogonal Frequency Division Multiplexing in Visible Light Communication 339S. Ponmalar and Shiny J.J.
21.1 Introduction 340
21.2 System Model 341
21.2.1 Transmitter Block 341
21.2.2 Receiver Block 342
21.3 Proposed Method 342
21.3.1 Simulation Parameters for OptSim 343
21.3.2 Block Diagram of DCO-OFDM in OptSim 343
21.4 Results and Discussion 344
21.5 Conclusion 352
References 353
22 Microcontroller-Based Variable Rate Syringe Pump for Microfluidic Application 355G. B. Tejashree, S. Swarnalatha, S. Pavithra, M. C. Jobin Christ and N. Ashwin Kumar
22.1 Introduction 356
22.2 Related Work 357
22.3 Methodology 358
22.3.1 Hardware Design 359
22.3.2 Hardware Interface with Software 360
22.3.3 Programming and Debugging 361
22.4 Result 362
22.5 Inference 363
22.5.1 Viscosity (¿) 365
22.5.2 Time Taken 365
22.5.3 Syringe Diameter 366
22.5.4 Deviation 366
22.6 Conclusion and Future Works 366
References 368
23 Analysis of Emotion in Speech Signal Processing and Rejection of Noise Using HMM 371S. Balasubramanian
23.1 Introduction 372
23.2 Existing Method 373
23.3 Proposed Method 374
23.3.1 Proposed Module Description 375
23.3.2 MFCC 376
23.3.3 Hidden Markov Models 379
23.4 Conclusion 382
References 383
24 Securing Cloud Data by Using Blend Cryptography with AWS Services 385Vanchhana Srivastava, Rohit Kumar Pathak and Arun Kumar
24.1 Introduction 385
24.1.1 AWS 387
24.1.2 Quantum Cryptography 388
24.1.3 ECDSA 389
24.2 Background 389
24.3 Proposed Technique 392
24.3.1 How the System Works 393
24.4 Results 394
24.5 Conclusion 396
References 396
Index 399
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