List of contributors xv
Series Preface xix
Preface xxi
About the Companion Website xxv
1 Introduction 1
Soham Sarkar, Abhishek Basu, and Siddhartha Bhattacharyya
1.1 Areas of Application for Multimodal Signal 1
1.1.1 Implementation of the Copyright Protection Scheme 1
1.1.2 Saliency Map Inspired Digital Video Watermarking 1
1.1.3 Saliency Map Generation Using an Intelligent Algorithm 2
1.1.4 Brain Tumor Detection Using Multi-Objective Optimization 2
1.1.5 Hyperspectral Image Classification Using CNN 2
1.1.6 Object Detection for Self-Driving Cars 2
1.1.7 Cognitive Radio 2
1.2 Recent Challenges 2
References 3
2 Progressive Performance of Watermarking Using Spread Spectrum Modulation 5
Arunothpol Debnath, Anirban Saha, Tirtha Sankar Das, Abhishek Basu, and Avik Chattopadhyay
2.1 Introduction 5
2.2 Types of Watermarking Schemes 9
2.3 Performance Evaluation Parameters of a Digital Watermarking Scheme 10
2.4 Strategies for Designing the Watermarking Algorithm 11
2.4.1 Balance of Performance Evaluation Parameters and Choice of Mathematical Tool 11
2.4.2 Importance of the Key in the Algorithm 13
2.4.3 Spread Spectrum Watermarking 13
2.4.4 Choice of Sub-band 14
2.5 Embedding and Detection of a Watermark Using the Spread Spectrum Technique 15
2.5.1 General Model of Spread Spectrum Watermarking 15
2.5.2 Watermark Embedding 17
2.5.3 Watermark Extraction 18
2.6 Results and Discussion 18
2.6.1 Imperceptibility Results for Standard Test Images 20
2.6.2 Robustness Results for Standard Test Images 20
2.6.3 Imperceptibility Results for Randomly Chosen Test Images 22
2.6.4 Robustness Results for Randomly Chosen Test Images 22
2.6.5 Discussion of Security and the key 24
2.7 Conclusion 31
References 36
3 Secured Digital Watermarking Technique and FPGA Implementation 41
Ranit Karmakar, Zinia Haque, Tirtha Sankar Das, and Rajeev Kamal
3.1 Introduction 41
3.1.1 Steganography 41
3.1.2 Cryptography 42
3.1.3 Difference between Steganography and Cryptography 43
3.1.4 Covert Channels 43
3.1.5 Fingerprinting 43
3.1.6 Digital Watermarking 43
3.1.6.1 Categories of Digital Watermarking 44
3.1.6.2 Watermarking Techniques 45
3.1.6.3 Characteristics of Digital Watermarking 47
3.1.6.4 Different Types of Watermarking Applications 48
3.1.6.5 Types of Signal Processing Attacks 48
3.1.6.6 Performance Evaluation Metrics 49
3.2 Summary 50
3.3 Literary Survey 50
3.4 System Implementation 51
3.4.1 Encoder 52
3.4.2 Decoder 53
3.4.3 Hardware Realization 53
3.5 Results and Discussion 55
3.6 Conclusion 57
References 64
4 Intelligent Image Watermarking for Copyright Protection 69
Subhrajit Sinha Roy, Abhishek Basu, and Avik Chattopadhyay
4.1 Introduction 69
4.2 Literature Survey 72
4.3 Intelligent Techniques for Image Watermarking 75
4.3.1 Saliency Map Generation 75
4.3.2 Image Clustering 77
4.4 Proposed Methodology 78
4.4.1 Watermark Insertion 78
4.4.2 Watermark Detection 81
4.5 Results and Discussion 82
4.5.1 System Response for Watermark Insertion and Extraction 83
4.5.2 Quantitative Analysis of the Proposed Watermarking Scheme 85
4.6 Conclusion 90
References 92
5 Video Summarization Using a Dense Captioning (DenseCap) Model 97
Sourav Das, Anup Kumar Kolya, and Arindam Kundu
5.1 Introduction 97
5.2 Literature Review 98
5.3 Our Approach 101
5.4 Implementation 102
5.5 Implementation Details 108
5.6 Result 110
5.7 Limitations 127
5.8 Conclusions and Future Work 127
References 127
6 A Method of Fully Autonomous Driving in Self-Driving Cars Based on Machine Learning and Deep Learning 131
Harinandan Tunga, Rounak Saha, and Samarjit Kar
6.1 Introduction 131
6.2 Models of Self-Driving Cars 131
6.2.1 Prior Models and Concepts 132
6.2.2 Concept of the Self-Driving Car 133
6.2.3 Structural Mechanism 134
6.2.4 Algorithm for theWorking Procedure 134
6.3 Machine Learning Algorithms 135
6.3.1 Decision Matrix Algorithms 135
6.3.2 Regression Algorithms 135
6.3.3 Pattern Recognition Algorithms 135
6.3.4 Clustering Algorithms 137
6.3.5 Support Vector Machines 137
6.3.6 Adaptive Boosting 138
6.3.7 TextonBoost 139
6.3.8 Scale-Invariant Feature Transform 140
6.3.9 Simultaneous Localization and Mapping 140
6.3.10 Algorithmic Implementation Model 141
6.4 Implementing a Neural Network in a Self-Driving Car 142
6.5 Training and Testing 142
6.6 Working Procedure and Corresponding Result Analysis 143
6.6.1 Detection of Lanes 143
6.7 Preparation-Level Decision Making 146
6.8 Using the Convolutional Neural Network 147
6.9 Reinforcement Learning Stage 147
6.10 Hardware Used in Self-Driving Cars 148
6.10.1 LIDAR 148
6.10.2 Vision-Based Cameras 149
6.10.3 Radar 150
6.10.4 Ultrasonic Sensors 150
6.10.5 Multi-Domain Controller (MDC) 150
6.10.6 Wheel-Speed Sensors 150
6.10.7 Graphics Processing Unit (GPU) 151
6.11 Problems and Solutions for SDC 151
6.11.1 Sensor Disjoining 151
6.11.2 Perception Call Failure 152
6.11.3 Component and Sensor Failure 152
6.11.4 Snow 152
6.11.5 Solutions 152
6.12 Future Developments in Self-Driving Cars 153
6.12.1 Safer Transportation 153
6.12.2 Safer Transportation Provided by the Car 153
6.12.3 Eliminating Traffic Jams 153
6.12.4 Fuel Efficiency and the Environment 154
6.12.5 Economic Development 154
6.13 Future Evolution of Autonomous Vehicles 154
6.14 Conclusion 155
References 155
7 The Problem of Interoperability of Fusion Sensory Data from the Internet of Things 157
Doaa Mohey Eldin, Aboul Ella Hassanien, and Ehab E. Hassanein
7.1 Introduction 157
7.2 Internet of Things 158
7.2.1 Advantages of the IoT 159
7.2.2 Challenges Facing Automated Adoption of Smart Sensors in the IoT 159
7.3 Data Fusion for IoT Devices 160
7.3.1 The Data Fusion Architecture 160
7.3.2 Data Fusion Models 161
7.3.3 Data Fusion Challenges 161
7.4 Multi-Modal Data Fusion for IoT Devices 161
7.4.1 Data Mining in Sensor Fusion 162
7.4.2 Sensor Fusion Algorithms 163
7.4.2.1 Central Limit Theorem 163
7.4.2.2 Kalman Filter 163
7.4.2.3 Bayesian Networks 164
7.4.2.4 Dempster-Shafer 164
7.4.2.5 Deep Learning Algorithms 165
7.4.2.6 A Comparative Study of Sensor Fusion Algorithms 168
7.5 A Comparative Study of Sensor Fusion Algorithms 170
7.6 The Proposed Multimodal Architecture for Data Fusion 175
7.7 Conclusion and Research Trends 176
References 177
8 Implementation of Fast, Adaptive, Optimized Blind Channel Estimation for Multimodal MIMO-OFDM Systems Using MFPA 183
Shovon Nandi, Narendra Nath Pathak, and Arnab Nandi
8.1 Introduction 183
8.2 Literature Survey 185
8.3 STBC-MIMO-OFDM Systems for Fast Blind Channel Estimation 187
8.3.1 Proposed Methodology 187
8.3.2 OFDM-Based MIMO 188
8.3.3 STBC-OFDM Coding 188
8.3.4 Signal Detection 189
8.3.5 Multicarrier Modulation (MCM) 189
8.3.6 Cyclic Prefix (CP) 190
8.3.7 Multiple Carrier-Code Division Multiple Access (MC-CDMA) 191
8.3.8 Modified Flower Pollination Algorithm (MFPA) 192
8.3.9 Steps in the Modified Flower Pollination Algorithm 192
8.4 Characterization of Blind Channel Estimation 193
8.5 Performance Metrics and Methods 195
8.5.1 Normalized Mean Square Error (NMSE) 195
8.5.2 Mean Square Error (MSE) 196
8.6 Results and Discussion 196
8.7 Relative Study of Performance Parameters 198
8.8 Future Work 201
References 201
9 Spectrum Sensing for Cognitive Radio Using a Filter Bank Approach 205
Srijibendu Bagchi and Jawad Yaseen Siddiqui
9.1 Introduction 205
9.1.1 Dynamic Exclusive Use Model 206
9.1.2 Open Sharing Model 206
9.1.3 Hierarchical Access Model 206
9.2 Cognitive Radio 207
9.3 Some Applications of Cognitive Radio 208
9.4 Cognitive Spectrum Access Models 209
9.5 Functions of Cognitive Radio 210
9.6 Cognitive Cycle 211
9.7 Spectrum Sensing and Related Issues 211
9.8 Spectrum Sensing Techniques 213
9.9 Spectrum Sensing in Wireless Standards 216
9.10 Proposed Detection Technique 218
9.11 Numerical Results 221
9.12 Discussion 222
9.13 Conclusion 223
References 223
10 Singularity Expansion Method in Radar Multimodal Signal Processing and Antenna Characterization 231
Nandan Bhattacharyya and Jawad Y. Siddiqui
10.1 Introduction 231
10.2 Singularities in Radar Echo Signals 232
10.3 Extraction of Natural Frequencies 233
10.3.1 Cauchy Method 233
10.3.2 Matrix Pencil Method 233
10.4 SEM for Target Identification in Radar 234
10.5 Case Studies 236
10.5.1 Singularity Extraction from the Scattering Response of a Circular Loop 236
10.5.2 Singularity Extraction from the Scattering Response of a Sphere 237
10.5.3 Singularity Extraction from the Response of a Disc 238
10.5.4 Result Comparison with Existing Work 239
10.6 Singularity Expansion Method in Antennas 239
10.6.1 Use of SEM in UWB Antenna Characterization 240
10.6.2 SEM for Determining Printed Circuit Antenna Propagation Characteristics 241
10.6.3 Method of Extracting the Physical Poles from Antenna Responses 241
10.6.3.1 Optimal Time Window for Physical Pole Extraction 241
10.6.3.2 Discarding Low-Energy Singularities 242
10.6.3.3 Robustness to Signal-to-Noise Ratio (SNR) 243
10.7 Other Applications 243
10.8 Conclusion 243
References 243
11 Conclusion 249
Soham Sarkar, Abhishek Basu, and Siddhartha Bhattacharyya
References 250
Index 253
