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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.
List of contents
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 Pr
About the author
Budati Anil Kumar, PhD, is an associate professor in the ECE Department, Gokaraju Rangaraju Institute of Engineering & Technology (Autonomous), Hyderabad, India. He has more than 12 years of experience in teaching and six years of experience in research 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 journal articles in his specialized research areas.
