Cognitive Computing Models in Communication Systems

A concise book on the latest research focusing on problems and challenges in the areas of data transmission technology, computer algorithms, AI-based devices, computer technology, and their solutions.

The book provides a comprehensive overview of state-of-the-art research work on cognitive models in communication systems and computing techniques. It also bridges the gap between various communication systems and solutions by providing the current models and computing techniques, their applications, the strengths and limitations of the existing methods, and the future directions in this area.

The contributors showcase their latest research work focusing on the issues, challenges, and solutions in the field of data transmission techniques, computational algorithms, artificial intelligence (AI)-based devices, and computing techniques.

Readers will find in this succinctly written and unique book:

• Topics covering the applications of advanced cognitive devices, models, architecture, and techniques.
• A range of case studies and applications that will provide readers with the tools to apply cutting-edge models and algorithms.
• In-depth information about new cognitive computing models and conceptual frameworks and their implementation.

Audience

The book is designed for researchers and electronics engineers, computer science engineers, industrial engineers, and mechanical engineers (both in academia and industry) working in the fields of machine learning, cognitive computing, mobile communication, and wireless network system.

Budati Anil Kumar, PhD, is an associate professor in ECE Department, Gokaraju Rangaraju Institute of Engineering & Technology (Autonomous), Hyderabad- 500090, Telangana State, India. He has more than 12 years of experience in teaching and six years of experience in research and published and has published more than 50 research articles in journals and conferences.  His current research interests include Cognitive Radio Networks, Software Defined Radio Networks, Artificial Intelligence, 6G emerging Technologies, Mulsemedia Computing and UAVs in 5G and 6G.

S. B. Goyal, PhD, is Director, Faculty of Information Technology, City University, Malaysia. He has more than 20 experience and has published 100+ papers in journals and conferences.

Sardar M.N. Islam, PhD, is Director of Decision Sciences and Modelling Program at Victoria University, Australia.  He has authored 31scholarly academic books in different disciplines.  As well as more than 250 journalarticles in his specialized research areas.

Preface xi

Acknowledgement xiii

1 Design of a Low-Voltage LDO of CMOS Voltage Regulator for Wireless Communications 1
S. Pothalaiah, Dayadi Lakshmaiah, B. Prabakar Rao, D. Nageshwar Rao, Mohammad Illiyas and G. Chandra Sekhar

1.1 Introduction 2

1.2 LDO Controller Arrangement and Diagram Drawing 2

1.2.1 Design of the LDO Regulator 4

1.2.1.1 Design of the Fault Amplifier 4

1.2.1.2 Design of the MPT Phase 8

1.3 Conclusion 14

References 14

2 Performance Analysis of Machine Learning and Deep Learning Algorithms for Smart Cities: The Present State and Future Directions 15
Pradeep Bedi, S. B. Goyal, Sardar MN Islam, Jia Liu and Anil Kumar Budati

2.1 Introduction 16

2.2 Smart City: The Concept 16

2.3 Application Layer 18

2.3.1 Smart Homes and Buildings 18

2.3.1.1 Smart Surveillance 18

2.3.2 Smart Transportation and Driving 19

2.3.3 Smart Healthcare 19

2.3.4 Smart Parking 19

2.3.5 Smart Grid 19

2.3.6 Smart Farming 19

2.3.7 Sensing Layer 20

2.3.8 Communication Layer 20

2.3.9 Data Layer 20

2.3.10 Security Layer 21

2.4 Issues and Challenges in Smart Cities: An Overview 21

2.5 Machine Learning: An Overview 22

2.5.1 Supervised Learning 22

2.5.2 Support Vector Machines (SVMs) 22

2.5.3 Artificial Neural Networks 23

2.5.4 Random Forest 24

2.5.5 Naïve Bayes 25

2.6 Unsupervised Learning 26

2.7 Deep Learning: An Overview 26

2.7.1 Autoencoder 27

2.7.2 Convolution Neural Networks (CNNs) 27

2.7.3 Recurrent Neural Networks (RNNs) 28

2.8 Deep Learning vs Machine Learning 29

2.9 Smart Healthcare 30

2.9.1 Evolution Toward a Smart Healthcare Framework 30

2.9.2 Application of ML/DL in Smart Healthcare 31

2.10 Smart Transport System 33

2.10.1 Evolution Toward a Smart Transport System 33

2.10.2 Application of ML/DL in a Smart Transportation System 34

2.11 Smart Grids 36

2.11.1 Evolution Toward Smart Grids 36

2.11.2 Application of ML/DL in Smart Grids 38

2.12 Challenges and Future Directions 40

2.13 Conclusion 41

References 41

3 Application of Machine Learning Algorithms and Models in 3D Printing 47
Chetanpal Singh

3.1 Introduction 48

3.2 Literature Review 50

3.3 Methods and Materials 65

3.4 Results and Discussion 69

3.5 Conclusion 70

References 72

4 A Novel Model for Optimal Reliable Routing Path Prediction in MANET 75
S.R.M. Krishna, S. Pothalaiah and R. Santosh

4.1 Introduction 76

4.2 Analytical Hierarchical Process Technique 77

4.3 Mathematical Models and Protocols 78

4.3.1 Rough Sets 78

4.3.1.1 Pawlak Rough Set Theory Definitions 78

4.3.2 Fuzzy TOPSIS 79

4.4 Routing Protocols 80

4.4.1 Classification of Routing Paths 80

4.5 RTF-AHP Model 81

4.5.1 Rough TOPSIS Fuzzy Set Analytical Hierarchical Process Algorithm 81

4.6 Models for Optimal Routing Performance 83

4.6.1 Genetic Algorithm Technique 84

4.6.2 Ant Colony Optimization Technique 84

4.6.3 RTF-AHP Model Architecture Flow 84

4.7 Results and Discussion 85

4.8 Conclusion 88

References 88

5 IoT-Based Smart Traffic Light Control 91
Sreenivasa Rao Ijjada and K. Shashidhar

5.1 Introduction 92

5.2 Scope of the Proposed Work 93

5.3 Proposed System Implementation 94

5.4 Testing and Results 99

5.5 Test Results 100

5.6 Conclusion 104

References 105

6 Differential Query Execution on Privacy Preserving Data Distributed Over Hybrid Cloud 107
Sridhar Reddy Vulapula, P. V. S. Srinivas and Jyothi Mandala

6.1 Introduction 107

6.2 Related Work 108

6.3 Proposed Solution 110

6.3.1 Data Transformation 110

6.3.2 Data Distribution 113

6.3.3 Query Execution 114

6.4 Novelty in the Proposed Solution 115

6.5 Results 115

6.6 Conclusion 119

References 120

7 Design of CMOS Base Band Analog 123
S. Pothalaiah, Dayadi Lakshmaiah, Bandi Doss, Nookala Sairam and K. Srikanth

7.1 Introduction 124

7.2 Proposed Technique of the BBA Chain for Reducing Energy Consumption 125

7.3 Channel Preference Filter 130

7.4 Programmable Amplifier Gain 132

7.5 Executed Outcomes 133

7.6 Conclusion 135

References 135

8 Review on Detection of Neuromuscular Disorders Using Electromyography 137
G. L. N. Murthy, Rajesh Babu Nemani, M. Sambasiva Reddy and M. K. Linga Murthy

8.1 Introduction 138

8.2 Materials 139

8.3 Methods 140

8.4 Conclusion 142

References 142

9 Design of Complementary Metal–Oxide Semiconductor Ring Modulator by Built-In Thermal Tuning 145
P. Bala Murali Krishna, Satish A., R. Yadgiri Rao, Mohammad Illiyas and I. Satya Narayana

9.1 Introduction 146

9.2 Device Structure 147

9.3 dc Performance 149

9.4 Small-Signal Radiofrequency Assessments 149

9.5 Data Modulation Operation (High Speed) 150

9.6 Conclusions and Acknowledgments 152

References 153

10 Low-Power CMOS VCO Used in RF Transmitter 155
D. Subbarao, Dayadi Lakshmaiah, Farha Anjum, G. Madhu Sudhan Rao and G. Chandra Sekhar

10.1 Introduction 156

10.2 Transmitter Architecture 157

10.3 Voltage-Controlled Ring Oscillator Design 158

10.4 CMOS Combiner 161

10.5 Conclusion 163

References 163

11 A Novel Low-Power Frequency-Modulated Continuous Wave Radar Based on Low-Noise Mixer 165
Dayadi Lakshmaiah, Bandi Doss, J.V.B. Subrmanyam, M.K. Chaitanya, Suresh Ballala, R. Yadagirir Rao and I. Satya Narayana

11.1 Introduction 166

11.2 FMCW Principle 168

11.3 Results 174

11.4 Conclusion 178

References 179

12 a Highly Integrated Cmos Rf T X

Used for IEEE 802.15.4 181
Dayadi Lakshmaiah, Subbarao, C.H. Sunitha, Nookala Sairam and S. Naresh

12.1 Introduction 182

12.2 Related Work 182

12.3 Simulation Results and Discussion 185

12.4 Conclusion 186

References 187

13 A Novel Feedforward Offset Cancellation Limiting Amplifier in Radio Frequencies 189
Dayadi Lakshmaiah, L. Koteswara Rao, I. Satya Narayana, B. Rajeshwari and I. Venu

13.1 Introduction 190

13.2 Hardware Design 190

13.2.1 Limiting Amplifier 190

13.2.2 Offset Extractor 192

13.2.3 Architecture and Gain 192

13.2.4 Quadrature Detector 192

13.2.5 Sensitivity 194

13.3 Experimental Results 195

13.4 Conclusion 195

References 196

14 A Secured Node Authentication and Access Control Model for IoT Smart Home Using Double-Hashed Unique Labeled Key-Based Validation 199
Sulaima Lebbe Abdul Haleem

14.1 Introduction 200

14.2 Challenges in IoT Security and Privacy 203

14.2.1 Heterogeneous Communication and Devices 203

14.2.2 Physical Equipment Integration 204

14.2.3 Resource Handling Limitations 204

14.2.4 Wide Scale 204

14.2.5 Database 204

14.3 Background 209

14.4 Proposed Model 210

14.4.1 Communication Flow 214

14.4.1.1 IoT Node and Registration Authority 214

14.4.1.2 User and Local Authorization Authority 215

14.5 Results 215

14.6 Conclusion 218

14.7 Claims 218

References 219

Index 221

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