Details

Intelligent Multi-Modal Data Processing


Intelligent Multi-Modal Data Processing


The Wiley Series in Intelligent Signal and Data Processing 1. Aufl.

von: Soham Sarkar, Abhishek Basu, Siddhartha Bhattacharyya

119,99 €

Verlag: Wiley
Format: PDF
Veröffentl.: 06.04.2021
ISBN/EAN: 9781119571421
Sprache: englisch
Anzahl Seiten: 288

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Beschreibungen

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

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