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E-raamat: Intelligent Multi-modal Data Processing [Wiley Online]

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Intelligent Multi-modal Data ProcessingSuurem pilt
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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.
List of contributors
xv
Series Preface xix
Preface xxi
About the Companion Website xxv
1 Introduction
1(4)
Soham Sarkar
Abhishek Basu
Siddhartha Bhattacharyya
1.1 Areas of Application for Multimodal Signal
1(1)
1.1.1 Implementation of the Copyright Protection Scheme
1(1)
1.1.2 Saliency Map Inspired Digital Video Watermarking
1(1)
1.1.3 Saliency Map Generation Using an Intelligent Algorithm
2(1)
1.1.4 Brain Tumor Detection Using Multi-Objective Optimization
2(1)
1.1.5 Hyperspectral Image Classification Using CNN
2(1)
1.1.6 Object Detection for Self-Driving Cars
2(1)
1.1.7 Cognitive Radio
2(1)
1.2 Recent Challenges
2(1)
References
3(2)
2 Progressive Performance Of Watermarking Using Spread Spectrum Modulation
5(36)
Arunothpol Debnath
Anirban Saha
Tirtha Sankar Das
Abhishek Basu
Avik Chattopadhyay
2.1 Introduction
5(4)
2.2 Types of Watermarking Schemes
9(1)
2.3 Performance Evaluation Parameters of a Digital Watermarking Scheme
10(1)
2.4 Strategies for Designing the Watermarking Algorithm
11(4)
2.4.1 Balance of Performance Evaluation Parameters and Choice of Mathematical Tool
11(2)
2.4.2 Importance of the Key in the Algorithm
13(1)
2.4.3 Spread Spectrum Watermarking
13(1)
2.4.4 Choice of Sub-band
14(1)
2.5 Embedding and Detection of a Watermark Using the Spread Spectrum Technique
15(3)
2.5.1 General Model of Spread Spectrum Watermarking
15(2)
2.5.2 Watermark Embedding
17(1)
2.5.3 Watermark Extraction
18(1)
2.6 Results and Discussion
18(13)
2.6.1 Imperceptibility Results for Standard Test Images
20(1)
2.6.2 Robustness Results for Standard Test Images
20(2)
2.6.3 Imperceptibility Results for Randomly Chosen Test Images
22(1)
2.6.4 Robustness Results for Randomly Chosen Test Images
22(2)
2.6.5 Discussion of Security and the key
24(7)
2.7 Conclusion
31(5)
References
36(5)
3 Secured Digital Watermarking Technique And Fpga Implementation
41(28)
Rank Karmakar
Zinia Haque
Tirtha Sankar Das
Rajeev Kamal
3.1 Introduction
41(9)
3.1.1 Steganography
41(1)
3.1.2 Cryptography
42(1)
3.1.3 Difference between Steganography and Cryptography
43(1)
3.1.4 Covert Channels
43(1)
3.1.5 Fingerprinting
43(1)
3.1.6 Digital Watermarking
43(1)
3.1.6.1 Categories of Digital Watermarking
44(1)
3.1.6.2 Watermarking Techniques
45(2)
3.1.6.3 Characteristics of Digital Watermarking
47(1)
3.1.6.4 Different Types of Watermarking Applications
48(1)
3.1.6.5 Types of Signal Processing Attacks
48(1)
3.1.6.6 Performance Evaluation Metrics
49(1)
3.2 Summary
50(1)
3.3 Literary Survey
50(1)
3.4 System Implementation
51(4)
3.4.1 Encoder
52(1)
3.4.2 Decoder
53(1)
3.4.3 Hardware Realization
53(2)
3.5 Results and Discussion
55(2)
3.6 Conclusion
57(7)
References
64(5)
4 Intelligent Image Watermarking For Copyright Protection
69(28)
Subhrajit Sinha Roy
Abhishek Basu
Avik Chattopadhyay
4.1 Introduction
69(3)
4.2 Literature Survey
72(3)
4.3 Intelligent Techniques for Image Watermarking
75(3)
4.3.1 Saliency Map Generation
75(2)
4.3.2 Image Clustering
77(1)
4.4 Proposed Methodology
78(4)
4.4.1 Watermark Insertion
78(3)
4.4.2 Watermark Detection
81(1)
4.5 Results and Discussion
82(8)
4.5.1 System Response for Watermark Insertion and Extraction
83(2)
4.5.2 Quantitative Analysis of the Proposed Watermarking Scheme
85(5)
4.6 Conclusion
90(2)
References
92(5)
5 Video Summarization Using A Dense Captioning (Densecap) Model
97(34)
Sourav Das
Anup Kumar Kolya
Arindam Kundu
5.1 Introduction
97(1)
5.2 Literature Review
98(3)
5.3 Our Approach
101(1)
5.4 Implementation
102(6)
5.5 Implementation Details
108(2)
5.6 Result
110(17)
5.7 Limitations
127(1)
5.8 Conclusions and Future Work
127(1)
References
127(4)
6 A Method Of Fully Autonomous Driving In Self-Driving Cars Based On Machine Learning And Deep Learning
131(26)
Harinandan Tunga
Rounak Saha
Samarjit Kar
6.1 Introduction
131(1)
6.2 Models of Self-Driving Cars
131(4)
6.2.1 Prior Models and Concepts
132(1)
6.2.2 Concept of the Self-Driving Car
133(1)
6.2.3 Structural Mechanism
134(1)
6.2.4 Algorithm for the Working Procedure
134(1)
6.3 Machine Learning Algorithms
135(7)
6.3.1 Decision Matrix Algorithms
135(1)
6.3.2 Regression Algorithms
135(1)
6.3.3 Pattern Recognition Algorithms
135(2)
6.3.4 Clustering Algorithms
137(1)
6.3.5 Support Vector Machines
137(1)
6.3.6 Adaptive Boosting
138(1)
6.3.7 TextonBoost
139(1)
6.3.8 Scale-Invariant Feature Transform
140(1)
6.3.9 Simultaneous Localization and Mapping
140(1)
6.3.10 Algorithmic Implementation Model
141(1)
6.4 Implementing a Neural Network in a Self-Driving Car
142(1)
6.5 Training and Testing
142(1)
6.6 Working Procedure and Corresponding Result Analysis
143(3)
6.6.1 Detection of Lanes
143(3)
6.7 Preparation-Level Decision Making
146(1)
6.8 Using the Convolutional Neural Network
147(1)
6.9 Reinforcement Learning Stage
147(1)
6.10 Hardware Used in Self-Driving Cars
148(3)
6.10.1 Lidar
148(1)
6.10.2 Vision-Based Cameras
149(1)
6.10.3 Radar
150(1)
6.10.4 Ultrasonic Sensors
150(1)
6.10.5 Multi-Domain Controller (MDC)
150(1)
6.10.6 Wheel-Speed Sensors
150(1)
6.10.7 Graphics Processing Unit (GPU)
151(1)
6.11 Problems and Solutions for SDC
151(2)
6.11.1 Sensor Disjoining
151(1)
6.11.2 Perception Call Failure
152(1)
6.11.3 Component and Sensor Failure
152(1)
6.11.4 Snow
152(1)
6.11.5 Solutions
152(1)
6.12 Future Developments in Self-Driving Cars
153(1)
6.12.1 Safer Transportation
153(1)
6.12.2 Safer Transportation Provided by the Car
153(1)
6.12.3 Eliminating Traffic Jams
153(1)
6.12.4 Fuel Efficiency and the Environment
154(1)
6.12.5 Economic Development
154(1)
6.13 Future Evolution of Autonomous Vehicles
154(1)
6.14 Conclusion
155(1)
References
155(2)
7 The Problem Of Interoperability Of Fusion Sensory Data From The Internet Of Things
157(26)
Doaa Mohey Eldin
About Ella Hassanien
Ehab E. Hassanein
7.1 Introduction
157(1)
7.2 Internet of Things
158(2)
7.2.1 Advantages of the IoT
159(1)
7.2.2 Challenges Facing Automated Adoption of Smart Sensors in the IoT
159(1)
7.3 Data Fusion for IoT Devices
160(1)
7.3.1 The Data Fusion Architecture
160(1)
7.3.2 Data Fusion Models
161(1)
7.3.3 Data Fusion Challenges
161(1)
7.4 Multi-Modal Data Fusion for IoT Devices
161(9)
7.4.1 Data Mining in Sensor Fusion
162(1)
7.4.2 Sensor Fusion Algorithms
163(1)
7.4.2.1 Central Limit Theorem
163(1)
7.4.2.2 Kalman Filter
163(1)
7.4.2.3 Bayesian Networks
164(1)
7.4.2.4 Dempster-Shafer
164(1)
7.4.2.5 Deep Learning Algorithms
165(3)
7.4.2.6 A Comparative Study of Sensor Fusion Algorithms
168(2)
7.5 A Comparative Study of Sensor Fusion Algorithms
170(5)
7.6 The Proposed Multimodal Architecture for Data Fusion
175(1)
7.7 Conclusion and Research Trends
176(1)
References
177(6)
8 Implementation Of Fast, Adaptive, Optimized Blind Channel Estimation For Multimodal Mimo-Ofdm Systems Using Mfpa
183(22)
Shovon Nandi
Narendra Nath Pathak
Arnab Nandi
8.1 Introduction
183(2)
8.2 Literature Survey
185(2)
8.3 STBC-MIMO-OFDM Systems for Fast Blind Channel Estimation
187(6)
8.3.1 Proposed Methodology
187(1)
8.3.2 OFDM-Based MIMO
188(1)
8.3.3 STBC-OFDM Coding
188(1)
8.3.4 Signal Detection
189(1)
8.3.5 Multicarrier Modulation (MCM)
189(1)
8.3.6 Cyclic Prefix (CP)
190(1)
8.3.7 Multiple Carrier-Code Division Multiple Access (MC-CDMA)
191(1)
8.3.8 Modified Flower Pollination Algorithm (MFPA)
192(1)
8.3.9 Steps in the Modified Flower Pollination Algorithm
192(1)
8.4 Characterization of Blind Channel Estimation
193(2)
8.5 Performance Metrics and Methods
195(1)
8.5.1 Normalized Mean Square Error (NMSE)
195(1)
8.5.2 Mean Square Error (MSE)
196(1)
8.6 Results and Discussion
196(2)
8.7 Relative Study of Performance Parameters
198(3)
8.8 Future Work
201(1)
References
201(4)
9 Spectrum Sensing For Cognitive Radio Using A Filter Bank Approach
205(26)
Srijibendu Bagchi
Jawad Yaseen Siddiqui
9.1 Introduction
205(2)
9.1.1 Dynamic Exclusive Use Model
206(1)
9.1.2 Open Sharing Model
206(1)
9.1.3 Hierarchical Access Model
206(1)
9.2 Cognitive Radio
207(1)
9.3 Some Applications of Cognitive Radio
208(1)
9.4 Cognitive Spectrum Access Models
209(1)
9.5 Functions of Cognitive Radio
210(1)
9.6 Cognitive Cycle
211(1)
9.7 Spectrum Sensing and Related Issues
211(2)
9.8 Spectrum Sensing Techniques
213(3)
9.9 Spectrum Sensing in Wireless Standards
216(2)
9.10 Proposed Detection Technique
218(3)
9.11 Numerical Results
221(1)
9.12 Discussion
222(1)
9.13 Conclusion
223(1)
References
223(8)
10 Singularity Expansion Method In Radar Multimodal Signal Processing And Antenna Characterization
231(18)
Nandan Bhattacharyya
Jawad Y. Siddiqui
10.1 Introduction
231(1)
10.2 Singularities in Radar Echo Signals
232(1)
10.3 Extraction of Natural Frequencies
233(1)
10.3.1 Cauchy Method
233(1)
10.3.2 Matrix Pencil Method
233(1)
10.4 SEM for Target Identification in Radar
234(2)
10.5 Case Studies
236(3)
10.5.1 Singularity Extraction from the Scattering Response of a Circular Loop
236(1)
10.5.2 Singularity Extraction from the Scattering Response of a Sphere
237(1)
10.5.3 Singularity Extraction from the Response of a Disc
238(1)
10.5.4 Result Comparison with Existing Work
239(1)
10.6 Singularity Expansion Method in Antennas
239(4)
10.6.1 Use of SEM in UWB Antenna Characterization
240(1)
10.6.2 SEM for Determining Printed Circuit Antenna Propagation Characteristics
241(1)
10.6.3 Method of Extracting the Physical Poles from Antenna Responses
241(1)
10.6.3.1 Optimal Time Window for Physical Pole Extraction
241(1)
10.6.3.2 Discarding Low-Energy Singularities
242(1)
10.6.3.3 Robustness to Signal-to-Noise Ratio (SNR)
243(1)
10.7 Other Applications
243(1)
10.8 Conclusion
243(1)
References
243(6)
11 Conclusion
249(4)
Soham Sarkar
Abhishek Basu
Siddhartha Bhattacharyya
References
250(3)
Index 253
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.
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