
Intelligent Multi-Modal Data Processing
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Intelligent Multi-Modal Data Processing 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.
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:
* Includes an in-depth analysis of the state-of-the-art applications of signal and data processing
* Contains contributions from noted experts in the field
* Offers information on hybrid differential evolution for optimal multilevel image thresholding
* Presents a fuzzy decision based multi-objective evolutionary method for video summarisation
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.
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Persons
Soham Sarkar, PhD, is an Assistant Professor in the Department of Electronics and Communication Engineering of RCC Institute of Information Technology, Kolkata, India.
Abhishek Basu, PhD, is an Assistant Professor and former Head of the Department of Electronics and Communication Engineering department of RCC Institute of Information Technology, Kolkata, India.
Siddhartha Bhattacharyya, PhD, is a Professor of Computer Science and Engineering at CHRIST (Deemed to be University), Bangalore, India.
Content
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
Preface
The advancement of digital media brings with it opportunities. The internet boom of this millennium allows digital data to travel around the world in real time. Through progress in technology and digital devices, the volume of digital data is increasing exponentially: it is predicted that by 2025, the world will have 163 ZB (zettabytes) of data - almost 10 times more than exists today. With this explosion in the volume of digitized volume, appropriate techniques for data processing and analysis have become a challenging proposition for scientists and researchers. The goal of multidimensional data processing is either to extract information or patterns from data in large databases or to understand the nature of the process that produced the data. As a result, such data processing is an open field for research. Scientists and researchers are investing significant effort in discovering novel and efficient methods for storing and archiving data. Newer, higher-dimensional data structures have been created for this purpose. In addition, a huge amount of bandwidth is required for accurate transmission of such voluminous data. So, efforts are also underway to evolve suitable transmission mechanisms as well as security for data.
Recent applications like hyperspectral remote sensing of images and working with medical images deal with huge amounts of data. Hyperspectral images are particularly useful for object classification due to their rich content information. This large dimensionality also creates issues in practice (the curse of dimensionality), such as the Hough phenomena. Efficient segmentation, recognition, and analysis of multidimensional data, such as hyperspectral images, medical images, social media, and audio signals, remain a challenge. Image segmentation is a fundamental process in many image, video, and computer vision applications. It is a critical step in content analysis and image understanding. In the literature, several image segmentation techniques, such as gray-level thresholding, interactive pixel classification, neural network-based approaches, edge detection, and fuzzy rule-based segmentation, have been reported. In addition to increasing storage capacity, researchers are also applying intelligent data-processing techniques to reduce the computational complexity of algorithms while increasing their efficiency. Computational intelligence techniques like evolutionary algorithms, fuzzy sets, rough sets, classification tools, and deep learning tools are used extensively to successfully achieve these goals.
Digital data produced through data-processing algorithms has fundamental advantages of transportability, proficiency, and accuracy; but on the other hand, the data thus produced brings in several redundancies. To solve this challenging problem with data transmission in network surroundings, research on information security and forensics provides efficient solutions that can shield the privacy, reliability, and accessibility of digital information from malicious intentions.
This volume comprises 11 chapters on the various facets of multimodal data processing, ranging from cryptography to sensors and communication data analysis.
Chapter 1 introduces the concepts of multimodal data processing, with an emphasis on issues and challenges in this domain. The chapter also elucidates the different application areas of multimodal data processing.
Digital information science has emerged to seek a copyright protection solution for digital content disseminated through communication channels. A review of the related literature suggests that most of the domain methods have poor capacity control and are vulnerable to attacks. Chapter 2 presents a casting analogy and performance investigation of the proposed transform domain representative data-hiding system using a digital modulation technique. A watermark is constructed using a Boolean operation on the author signature data with an adaptive classifier that approximates the frequency masking characteristics of the visual system.
In Chapter 3, the authors present a digital image watermarking technique based on biometrics and implement it in hardware using a field-programmable gate array (FPGA). This scheme is focused on the covariance saliency method. For extreme security and individual authentication, biometrics such as the iris are introduced. This technique hides biometric information in a cover image so efficiently that the robustness and imperceptibility of the cover image are less likely to be affected and the image is not distorted (as proven during several attacks). A hardware implementation of this algorithm is also provided for the sake of self-sufficiency.
In Chapter 4, an invisible, spatial domain-based image watermarking scheme is demonstrated. One of the most traditional spatial techniques is simple least significant bits (LSB) replacement, which offers high data transparency for embedded information. However, only a small amount of data can be hidden in the case of single-bit (preferably LSB) replacement; consequently, the payload capacity is much less. Additionally, the data sustainability or robustness of the watermark is decreased, as most attacks affect the LSB plane. Thus, the proposed logic follows an adaptive LSB replacement technique where multiple bits are replaced from each pixel to implant the watermark. This adaptive bit replacement is performed in such a way that both the payload and the signal-to-noise ratio can be increased up to a certain level. Intelligent image clustering is utilized to obtain an optimized result.
Video content summarization is a popular research area. Everyday storage of video data is becoming increasingly important and popular. In the process, it is growing into big data. Summarization is an effective technique to obtain video content from large video data. In addition, indexing and browsing are required for large video data. One of the effective techniques for video summarization is based on keyframes: important video frames. In Chapter 5, the authors propose a keyframe-based video summarization technique using a dense captioning model. Initially, video data is taken as input to the model. The model generates region captioning as output, which is converted into a chunk of sentences after applying the clustering technique. This chunk of sentences is summarized to obtain video summary output.
In the modern era, self-driving cars are the most attention-grabbing development in the autonomous vehicle industry. Until now, Google and Tesla have been the most the encouraging participants in this industry. However, no one has yet achieved fully autonomous driving. Chapter 6 is based on autonomous driving in self-driving cars and focuses on fully autonomous driving in any situation. This driving achievement is possible due to the use of reinforcement learning and modern algorithms created for autonomous driving.
The Internet of Things (IoT) is an important means of connecting smart devices called sensors through a physical or cloud network. It amasses a large amount of data from these devices. However, an interoperability problem occurs when integrating data from different sensors or devices because the sensors' data sets are not compatible with each other. The process of data fusion in the IoT network has to be homogeneous and consistent, so the control of data is an important feature in this field. The IoT provides opportunities for data fusion in computer-based systems to improve operational performance, increase common dimensionality, and reduce ambiguity. Chapter 7 introduces an evolutionary study of multimodal data fusion in the smart environment. It examines data fusion motivations for the IoT with a specific focus on using algorithms (such as probabilistic techniques, artificial intelligence algorithms, and theory of belief methods) and particular IoT environments (centralized, distributed, hybrid, or blockchain systems).
Chapter 8 illustrates new, fast, adaptive, optimized blind channel estimation for a cyclic prefix-aided, space-time block-coded multiple input-multiple output orthogonal frequency division multiplexing (STBC-MIMO-OFDM) system. The bottleneck of earlier blind channel estimation techniques was due to high complexity and low convergence. Also, accurate transmission of multimodal data such as hyperspectral images, medical images in the healthcare sector, massive data in social media, and audiovisual signals is still under research. To overcome this problem, a modified flower pollination algorithm (MFPA) has been implemented to optimize data. The optimized MFPA provides good bit error rate (BER) and symbol error rate (SER) performance compared to the traditional flower pollination algorithm (FPA).
In recent times, the radio spectrum has been revealed as a limited resource due to the advent of various state-of-the-art wireless applications. Spectrum regulators initially adopted a static spectrum allocation (SSA) strategy to serve wireless applications on a non-interfering basis. However, although this strategy ensures the least interference between wireless applications, it is a bottleneck to serving huge numbers of spectrum users. The SSA strategy allocates frequency bands to licensed users. In Chapter 9, spectrum sensing is performed using a filter bank approach, which is a specific type of periodogram obtained from received data. The power spectral density of the received signal is estimated from a finite number of observations. In the present research, the Capon method is applied, which uses one bandpass filter to calculate an estimated spectrum value. This filter is designed to be selective based on the received data. Finally, binary...
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