Compressive Sensing for Wireless Communication : : Challenges and Opportunities.

Compressive Sensing for Wireless Communication : : Challenges and Opportunities.

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Bibliographic Details
:
Sankararajan, Radha.
Place / Publishing House:Aalborg : : River Publishers,, 2016.
©2016.
Year of Publication:2016
Edition:1st ed.
Language:English
Online Access:
Physical Description:1 online resource (494 pages)
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Table of Contents:
  • Intro
  • Front Cover
  • Half Title
  • RIVER PUBLISHERS SERIES IN COMMUNICATIONS
  • Title page - Compressive Sensingf or Wireless Communication: Challenges and Opportunities
  • Copyright Page
  • Content
  • Preface
  • Acknowledgement
  • List of Figures
  • List of Tables
  • List of Algorithms
  • List of Abbreviations
  • Chapter 1 - Introduction
  • 1.1 Overview
  • 1.2 Motivation
  • 1.3 Traditional Sampling
  • 1.4 Conventional Data Acquisition System
  • 1.4.1 Data Acquisition System
  • 1.4.2 Functional Components of DAQ
  • 1.4.3 Digital Image Acquisition
  • 1.5 Transform Coding
  • 1.5.1 Need for Transform Coding
  • 1.5.2 Drawbacks of Transform Coding
  • 1.6 Compressed Sensing
  • 1.6.1 Sparsity and Signal Recovery
  • 1.6.2 CS Recovery Algorithms
  • 1.6.3 Compressed Sensing for Audio
  • 1.6.4 Compressed Sensing for Image
  • 1.6.5 Compressed Sensing for Video
  • 1.6.6 Compressed Sensing for Computer Vision
  • 1.6.7 Compressed Sensing for Cognitive Radio Networks
  • 1.6.8 Compressed Sensing for Wireless Networks
  • 1.6.9 Compressed Sensing for Wireless Sensor Networks
  • 1.7 Book Outline
  • References
  • Chapter 2 - Compressed Sensing: Sparsity and Signal Recovery
  • 2.1 Introduction
  • 2.2 Compressed Sensing
  • 2.2.1 Compressed Sensing Process
  • 2.2.2 What Is the Need for Compressed Sensing?
  • 2.2.3 Adaptations of CS Theory
  • 2.2.4 Mathematical Background
  • 2.2.5 Sparse Filtering and Dynamic Compressed Sensing
  • 2.3 Signal Representation
  • 2.3.1 Sparsity
  • 2.4 Basis Vectors
  • 2.4.1 Fourier Transform
  • 2.4.2 Discrete Cosine Transform
  • 2.4.3 DiscreteWavelet Transform
  • 2.4.4 Curvelet Transform
  • 2.4.5 Contourlet Transform
  • 2.4.6 Surfacelet Transform
  • 2.4.7 Karhunen-Loève Theorem
  • 2.5 Restricted Isometry Property
  • 2.6 Coherence
  • 2.7 Stable Recovery
  • 2.8 Number of Measurements
  • 2.9 Sensing Matrix
  • 2.9.1 Null-Space Conditions.
  • 2.9.2 Restricted Isometry Property
  • 2.9.3 Gaussian Matrix
  • 2.9.4 Toeplitz and Circulant Matrix
  • 2.9.5 Binomial Sampling Matrix
  • 2.9.6 Structured Random Matrix
  • 2.9.7 Kronecker Product Matrix
  • 2.9.8 Combination Matrix
  • 2.9.9 Hybrid Matrix
  • 2.10 Sparse Recovery Algorithms
  • 2.10.1 Signal Recovery in Noise
  • 2.11 Applications of Compressed Sensing
  • 2.12 Summary
  • References
  • Chapter 3 - Recovery Algorithms
  • 3.1 Introduction
  • 3.2 Conditions for Perfect Recovery
  • 3.2.1 Sensing Matrices
  • 3.2.1.1 Null-space conditions
  • 3.2.1.2 The restricted isometry property
  • 3.2.2 Sensing Matrix Constructions
  • 3.3 L1 Minimization
  • 3.3.1 L1 Minimization Algorithms
  • 3.4 Greedy Algorithms
  • 3.4.1 Matching Pursuit (MP)
  • 3.4.1.1 Orthogonal matching pursuit (OMP)
  • 3.4.1.2 Directional pursuits
  • 3.4.1.3 Gradient pursuits
  • 3.4.1.4 StOMP
  • 3.4.1.5 ROMP
  • 3.4.1.6 CoSaMP
  • 3.4.1.7 Subspace pursuit (SP)
  • 3.5 Iterative Hard Thresholding
  • 3.5.1 Empirical Comparisons
  • 3.6 FOCUSS
  • 3.7 MUSIC
  • 3.8 Model-based Algorithms
  • 3.8.1 Model-based CoSaMP
  • 3.8.2 Model-based IHT
  • 3.9 Non-Iterative Algorithms for Image-Processing Applications
  • 3.9.1 Advantages of Non-Iterative Algorithms
  • 3.9.2 Non-Iterative Procedures for Recovery
  • 3.9.2.1 Procedure I
  • 3.9.2.2 Procedure II
  • 3.9.2.3 Procedure III
  • 3.9.3 NITRA
  • 3.9.4 R3A
  • 3.9.4.1 R3A-based StOMP
  • 3.9.5 SPMT
  • 3.9.5.1 SPMT for reconstruction of images and videos
  • 3.10 Summary
  • References
  • Chapter 4 - Compressive Sensing for Audio and Speech Signals
  • 4.1 Introduction
  • 4.1.1 Issues in Applying CS and Sparse Decompositions to Speech and Audio Signals
  • 4.2 Multiple Sensors Audio Model
  • 4.2.1 Reconstruction of Real, Non-Sparse Audio Signals
  • 4.2.2 Detection and Estimation of Truly Sparse Audio Signals.
  • 4.3 Compressive Sensing Framework for Speech Signal Synthesis
  • 4.3.1 DFT and LPC Transform Domain
  • 4.3.2 Hybrid Dictionary
  • 4.3.3 Level of Sparsity
  • 4.3.4 Remarks
  • 4.4 CS Reconstruction of the Speech and Musical Signals
  • 4.4.1 Recovery of Audio Signals with Compressed Sensing
  • 4.5 Noise Reduction in Speech and Audio Signals
  • 4.5.1 Data Sparsity of Speech Signals
  • 4.5.2 Formulation of the Optimization Problem for Speech Noise Reduction
  • 4.5.3 Solutions to the Optimization Problem
  • 4.6 DCT Compressive Sampling of Frequency-Sparse Audio Signals
  • 4.6.1 Performance of Compressive Sensing for Speech Signal with Combined Basis
  • 4.7 Single-Channel and Multi-Channel Sinusoidal Audio Coding Using CS
  • 4.7.1 Sinusoidal Model
  • 4.7.2 Single-Channel Sinusoidal Selection
  • 4.7.3 Multi-Channel Sinusoidal Selection
  • 4.8 Compressive Sensing for Speech Signal with Orthogonal Symmetric Toeplitz Matrix
  • 4.8.1 Orthogonal Symmetric Toeplitz Matrices (OSTM)
  • 4.9 Sparse Representations for Speech Recognition
  • 4.9.1 An EBW Compressed Sensing Algorithm
  • 4.9.2 Line Search A-Functions
  • 4.9.3 An Analysis of Sparseness and Regularization in Exemplar-based Methods for Speech Classification
  • 4.10 Speaker Identification Using Sparsely Excited Speech Signals and Compressed Sensing
  • 4.10.1 Sparsely Excited Speech
  • 4.10.2 GMM Speaker Identification
  • 4.10.3 Speaker Identification Using CS
  • 4.11 Joint Speech-Encoding Technology Based on Compressed Sensing
  • 4.11.1 Joint Speech-Encoding Scheme
  • 4.11.2 Wavelet Transform
  • 4.11.3 PCM
  • 4.12 Applications of Compressed Sensing to Speech Coding Based on Sparse Linear Prediction
  • 4.12.1 Compressed Sensing Formulation for Speech Coding
  • 4.13 Summary
  • References
  • Chapter 5 - Compressive Sensing for Images
  • 5.1 Introduction
  • 5.2 Compressive Sensing for Image Fusion.
  • 5.2.1 Multi-Resolution Image Fusion
  • 5.2.2 Multi-Focus Image Fusion
  • 5.3 Compressive Sensing for Image Compression
  • 5.4 Compressive Sensing for Image Denoising
  • 5.5 Compressive Sensing Image Reconstruction
  • 5.6 Compressive Sensing for Imaging Applications
  • 5.6.1 Compressive Magnetic Resonance Imaging
  • 5.6.2 Compressive Synthetic Aperture Radar Imaging
  • 5.6.3 Compressive Passive MillimeterWave Imaging
  • 5.6.4 Compressive Light Transport System
  • 5.7 Single-Pixel Camera
  • 5.8 Lensless Imaging by Compressive Sensing
  • 5.8.1 Lensless Imaging Architecture
  • 5.8.1.1 Compressive measurements
  • 5.8.1.2 Selection of aperture assembly
  • 5.8.2 Prototype for Lensless Imaging
  • 5.9 Case Study: Image Transmission in WMSN
  • 5.10 Summary
  • References
  • Chpater 6 - Compressive Sensing for Computer Vision
  • 6.1 Introduction
  • 6.2 Object Detection Techniques
  • 6.2.1 Optical Flow
  • 6.2.2 Temporal Difference
  • 6.2.3 Background Subtraction
  • 6.3 Object-Tracking Techniques
  • 6.3.1 Point Tracking
  • 6.3.2 Kernel Tracking
  • 6.3.3 Silhouette Tracking
  • 6.4 Compressive Video Processing
  • 6.4.1 CS Based on the DCT Approach
  • 6.4.2 CS Based on the DWT Approach
  • 6.4.3 CS Based on the Hybrid DWT-DCT Approach
  • 6.5 Compressive Sensing for Background Subtraction
  • 6.6 Compressive Sensing for Object Detection
  • 6.6.1 Sparsity of Background Subtracted Images
  • 6.6.2 The Background Constraint
  • 6.6.3 Object Detector Based on CS
  • 6.6.4 Foreground Reconstruction
  • 6.6.5 Adaptation of the Background Constraint
  • 6.7 Compressive Sensing for Object Recognition
  • 6.8 Compressive Sensing Target Tracking
  • 6.8.1 Kalman Filtered Compressive Sensing
  • 6.8.2 Joint Compressive Video Coding and Analysis
  • 6.8.3 Compressive Sensing for Multi-ViewTracking
  • 6.8.4 Compressive Particle Filtering.
  • 6.9 Surveillance Video Processing Using Compressive Sensing
  • 6.10 Performance Metrics
  • 6.11 Summary
  • References
  • Chapter 7 - Compressed Sensing for Wireless Networks
  • 7.1 Wireless Networks
  • 7.1.1 Categories of Wireless Networks
  • 7.1.1.1 3G cellular networks
  • 7.1.1.2 WiMAX network
  • 7.1.1.3 WiFi networks
  • 7.1.1.4 Wireless Ad hoc networks
  • 7.1.1.5 Wireless sensor networks
  • 7.1.2 Advanced Wireless Technologies
  • 7.1.2.1 OFDM technology
  • 7.1.2.2 Multiple antenna systems
  • 7.2 CS-based Wireless Communication
  • 7.2.1 Multi-Path Channel Estimation
  • 7.2.1.1 Channel model and training-based model
  • 7.2.1.2 Compressed channel sensing
  • 7.2.2 Random Field Estimation
  • 7.2.2.1 Random field model
  • 7.2.2.2 Matrix completion algorithm
  • 7.2.3 Other Channel Estimation Models
  • 7.2.3.1 Blind channel estimation
  • 7.2.3.2 Adaptive algorithm
  • 7.2.3.3 Group sparsity method
  • 7.3 Multiple Access
  • 7.3.1 Multiuser Detection
  • 7.3.1.1 Comparison between multiuser detection and compressive sensing
  • 7.3.1.2 Algorithm for multiuser detection
  • 7.3.2 Multiuser Access in Cellular Systems
  • 7.3.2.1 Uplink
  • 7.3.2.2 Downlink
  • 7.4 Summary
  • References
  • Chapter 8 - Compressive Spectrum Sensing for Cognitive Radio Networks
  • 8.1 Introduction
  • 8.2 Cognitive Radio and Dynamic Spectrum Access
  • 8.2.1 Dynamic Spectrum Access
  • 8.2.2 Cognitive Radio
  • 8.2.3 Cognitive Radio Architectures
  • 8.2.4 Physical Architecture of Cognitive Radio
  • 8.3 Spectrum Sensing for Cognitive Radio
  • 8.3.1 Spectrum Sensing Techniques
  • 8.3.2 Cooperative Spectrum Sensing
  • 8.4 Compressed Sensing in Cognitive Radio
  • 8.5 Collaborative Compressed Spectrum Sensing
  • 8.6 Distributed Compressed Spectrum Sensing
  • 8.7 Compressive Sensing for Wideband Cognitive Radios
  • 8.8 Research Challenges
  • 8.8.1 Sparse Basis Selection.
  • 8.8.2 Adaptive Wideband Sensing.

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