Intelligent Multi-Modal Data Processing (The Wiley Series in Intelligent Signal and Data Processing)

✍ Scribed by Soham Sarkar

• Publisher: Wiley
• Year: 2021
• Tongue: English
• Leaves: 291
• Edition: 1
• Category: Library

✦ Synopsis

A comprehensive review of the most recent applications of intelligent multi-modal data processing

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.

✦ Table of Contents

Cover
Title Page
Copyright
Contents
List of contributors
Series Preface
Preface
About the Companion Website
Chapter 1 Introduction
1.1 Areas of Application for Multimodal Signal
1.1.1 Implementation of the Copyright Protection Scheme
1.1.2 Saliency Map Inspired Digital Video Watermarking
1.1.3 Saliency Map Generation Using an Intelligent Algorithm
1.1.4 Brain Tumor Detection Using Multi‐Objective Optimization
1.1.5 Hyperspectral Image Classification Using CNN
1.1.6 Object Detection for Self‐Driving Cars
1.1.7 Cognitive Radio
1.2 Recent Challenges
References
Chapter 2 Progressive Performance of Watermarking Using Spread Spectrum Modulation
2.1 Introduction
2.2 Types of Watermarking Schemes
2.3 Performance Evaluation Parameters of a Digital Watermarking Scheme
2.4 Strategies for Designing the Watermarking Algorithm
2.4.1 Balance of Performance Evaluation Parameters and Choice of Mathematical Tool
2.4.2 Importance of the Key in the Algorithm
2.4.3 Spread Spectrum Watermarking
2.4.4 Choice of Sub‐band
2.5 Embedding and Detection of a Watermark Using the Spread Spectrum Technique
2.5.1 General Model of Spread Spectrum Watermarking
2.5.2 Watermark Embedding
2.5.3 Watermark Extraction
2.6 Results and Discussion
2.6.1 Imperceptibility Results for Standard Test Images
2.6.2 Robustness Results for Standard Test Images
2.6.3 Imperceptibility Results for Randomly Chosen Test Images
2.6.4 Robustness Results for Randomly Chosen Test Images
2.6.5 Discussion of Security and the key
2.7 Conclusion
References
Chapter 3 Secured Digital Watermarking Technique and FPGA Implementation
3.1 Introduction
3.1.1 Steganography
3.1.2 Cryptography
3.1.3 Difference between Steganography and Cryptography
3.1.4 Covert Channels
3.1.5 Fingerprinting
3.1.6 Digital Watermarking
3.1.6.1 Categories of Digital Watermarking
3.1.6.2 Watermarking Techniques
3.1.6.3 Characteristics of Digital Watermarking
3.1.6.4 Different Types of Watermarking Applications
3.1.6.5 Types of Signal Processing Attacks
3.1.6.6 Performance Evaluation Metrics
3.2 Summary
3.3 Literary Survey
3.4 System Implementation
3.4.1 Encoder
3.4.2 Decoder
3.4.3 Hardware Realization
3.5 Results and Discussion
3.6 Conclusion
References
Chapter 4 Intelligent Image Watermarking for Copyright Protection
4.1 Introduction
4.2 Literature Survey
4.3 Intelligent Techniques for Image Watermarking
4.3.1 Saliency Map Generation
4.3.2 Image Clustering
4.4 Proposed Methodology
4.4.1 Watermark Insertion
4.4.2 Watermark Detection
4.5 Results and Discussion
4.5.1 System Response for Watermark Insertion and Extraction
4.5.2 Quantitative Analysis of the Proposed Watermarking Scheme
4.6 Conclusion
References
Chapter 5 Video Summarization Using a Dense Captioning (DenseCap) Model
5.1 Introduction
5.2 Literature Review
5.3 Our Approach
5.4 Implementation
5.5 Implementation Details
5.6 Result
5.7 Limitations
5.8 Conclusions and Future Work
References
Chapter 6 A Method of Fully Autonomous Driving in Self‐Driving Cars Based on Machine Learning and Deep Learning
6.1 Introduction
6.2 Models of Self‐Driving Cars
6.2.1 Prior Models and Concepts
6.2.2 Concept of the Self‐Driving Car
6.2.3 Structural Mechanism
6.2.4 Algorithm for the Working Procedure
6.3 Machine Learning Algorithms
6.3.1 Decision Matrix Algorithms
6.3.2 Regression Algorithms
6.3.3 Pattern Recognition Algorithms
6.3.4 Clustering Algorithms
6.3.5 Support Vector Machines
6.3.6 Adaptive Boosting
6.3.7 TextonBoost
6.3.8 Scale‐Invariant Feature Transform
6.3.9 Simultaneous Localization and Mapping
6.3.10 Algorithmic Implementation Model
6.4 Implementing a Neural Network in a Self‐Driving Car
6.5 Training and Testing
6.6 Working Procedure and Corresponding Result Analysis
6.6.1 Detection of Lanes
6.7 Preparation‐Level Decision Making
6.8 Using the Convolutional Neural Network
6.9 Reinforcement Learning Stage
6.10 Hardware Used in Self‐Driving Cars
6.10.1 LIDAR
6.10.2 Vision‐Based Cameras
6.10.3 Radar
6.10.4 Ultrasonic Sensors
6.10.5 Multi‐Domain Controller (MDC)
6.10.6 Wheel‐Speed Sensors
6.10.7 Graphics Processing Unit (GPU)
6.11 Problems and Solutions for SDC
6.11.1 Sensor Disjoining
6.11.2 Perception Call Failure
6.11.3 Component and Sensor Failure
6.11.4 Snow
6.11.5 Solutions
6.12 Future Developments in Self‐Driving Cars
6.12.1 Safer Transportation
6.12.2 Safer Transportation Provided by the Car
6.12.3 Eliminating Traffic Jams
6.12.4 Fuel Efficiency and the Environment
6.12.5 Economic Development
6.13 Future Evolution of Autonomous Vehicles
6.14 Conclusion
References
Chapter 7 The Problem of Interoperability of Fusion Sensory Data from the Internet of Things
7.1 Introduction
7.2 Internet of Things
7.2.1 Advantages of the IoT
7.2.2 Challenges Facing Automated Adoption of Smart Sensors in the IoT
7.3 Data Fusion for IoT Devices
7.3.1 The Data Fusion Architecture
7.3.2 Data Fusion Models
7.3.3 Data Fusion Challenges
7.4 Multi‐Modal Data Fusion for IoT Devices
7.4.1 Data Mining in Sensor Fusion
7.4.2 Sensor Fusion Algorithms
7.4.2.1 Central Limit Theorem
7.4.2.2 Kalman Filter
7.4.2.3 Bayesian Networks
7.4.2.4 Dempster‐Shafer
7.4.2.5 Deep Learning Algorithms
7.4.2.6 A Comparative Study of Sensor Fusion Algorithms
7.5 A Comparative Study of Sensor Fusion Algorithms
7.6 The Proposed Multimodal Architecture for Data Fusion
7.7 Conclusion and Research Trends
References
Chapter 8 Implementation of Fast, Adaptive, Optimized Blind Channel Estimation for Multimodal MIMO‐OFDM Systems Using MFPA
8.1 Introduction
8.2 Literature Survey
8.3 STBC‐MIMO‐OFDM Systems for Fast Blind Channel Estimation
8.3.1 Proposed Methodology
8.3.2 OFDM‐Based MIMO
8.3.3 STBC‐OFDM Coding
8.3.4 Signal Detection
8.3.5 Multicarrier Modulation (MCM)
8.3.6 Cyclic Prefix (CP)
8.3.7 Multiple Carrier‐Code Division Multiple Access (MC‐CDMA)
8.3.8 Modified Flower Pollination Algorithm (MFPA)
8.3.9 Steps in the Modified Flower Pollination Algorithm
8.4 Characterization of Blind Channel Estimation
8.5 Performance Metrics and Methods
8.5.1 Normalized Mean Square Error (NMSE)
8.5.2 Mean Square Error (MSE)
8.6 Results and Discussion
8.7 Relative Study of Performance Parameters
8.8 Future Work
References
Chapter 9 Spectrum Sensing for Cognitive Radio Using a Filter Bank Approach
9.1 Introduction
9.1.1 Dynamic Exclusive Use Model
9.1.2 Open Sharing Model
9.1.3 Hierarchical Access Model
9.2 Cognitive Radio
9.3 Some Applications of Cognitive Radio
9.4 Cognitive Spectrum Access Models
9.5 Functions of Cognitive Radio
9.6 Cognitive Cycle
9.7 Spectrum Sensing and Related Issues
9.8 Spectrum Sensing Techniques
9.9 Spectrum Sensing in Wireless Standards
9.10 Proposed Detection Technique
9.11 Numerical Results
9.12 Discussion
9.13 Conclusion
References
Chapter 10 Singularity Expansion Method in Radar Multimodal Signal Processing and Antenna Characterization
10.1 Introduction
10.2 Singularities in Radar Echo Signals
10.3 Extraction of Natural Frequencies
10.3.1 Cauchy Method
10.3.2 Matrix Pencil Method
10.4 SEM for Target Identification in Radar
10.5 Case Studies
10.5.1 Singularity Extraction from the Scattering Response of a Circular Loop
10.5.2 Singularity Extraction from the Scattering Response of a Sphere
10.5.3 Singularity Extraction from the Response of a Disc
10.5.4 Result Comparison with Existing Work
10.6 Singularity Expansion Method in Antennas
10.6.1 Use of SEM in UWB Antenna Characterization
10.6.2 SEM for Determining Printed Circuit Antenna Propagation Characteristics
10.6.3 Method of Extracting the Physical Poles from Antenna Responses
10.6.3.1 Optimal Time Window for Physical Pole Extraction
10.6.3.2 Discarding Low‐Energy Singularities
10.6.3.3 Robustness to Signal‐to‐Noise Ratio (SNR)
10.7 Other Applications
10.8 Conclusion
References
Chapter 11 Conclusion
References
Index
EULA