盲信號(hào)處理：理論與實(shí)踐（英文版）
定 價(jià)：150 元
- 作者：史習(xí)智 著
- 出版時(shí)間：2011/1/1
- ISBN：9787313058201
- 出 版 社：上海交通大學(xué)出版社
- 中圖法分類：TN911.7
- 頁(yè)碼：368
- 紙張：膠版紙
- 版次：1
- 開本：16開
Blind Signal Processing Theory and Practice not only introduces related fundamental mathematics, but also reflects the numerous advances in the field, such as probability density estimation-based processing algorithms,underdetermined models, complex value methods, uncertainty of order in the separation of convolutive mixtures in frequency domains, and feature extraction using Independent Component Analysis (ICA). At the end of the book, results from a study conducted at Shanghai Jiao Tong University in the areas of speech signal processing, underwater signals, image feature extraction, data compression, and the like are discussed.
This book will be of particular interest to advanced undergraduate students,graduate students, university instructors and research scientists in related disciplines. Xizhi Shi is a Professor at Shanghai Jiao Tong University.
Chapter 1 Introduction
1.1 Introduction
1.2 Blind Source Separation
1.3 Independent Component Analysis (ICA)
1.4 The Historical Development and Research Prospect of Blind Signal Processing
References
Chapter 2 Mathematical Description of Blind Signal Processing
2.1 Random Process and Probability Distribution
2.2 Estimation Theory
2.3 Information Theory
2.4 Higher-Order Statistics
2.5 Preprocessing of Signal
2.6 Complex Nonlinear Function
2.7 Evaluation Index
References
Chapter 3 Independent Component Analysis
3.1 Problem Statement and Assumptions
3.2 Contrast Functions
3.3 Information Maximization Method of ICA
3.4 Maximum Likelihood Method and Common Learning Rule
3.5 FastICA Algorithm
3.6 Natural Gradient Method
3.7 Hidden Markov Independent Component Analysis
References
Chapter 4 Nonlinear PCA & Feature Extraction
4.1 Principal Component Analysis & Infinitesimal Analysis
4.2 Nonlinear PCA and Blind Source Separation
4.3 Kernel PCA
4.4 Neural Networks Method of Nonlinear PCA and Nonlinear Complex PCA
References
Chapter 5 Nonlinear ICA
5.1 Nonlinear Model and Source Separation
5.2 Learning Algorithm
5.3 Extended Gaussianization Method of Post Nonlinear Blind Separation
5.4 Neural Network Method for Nonlinear ICA
5.5 Genetic Algorithm of Nonlinear ICA Solution
5.6 Application Examples of Nonlinear ICA
References
Chapter 6 Convolutive Mixtures and Blind Deconvolution
6.1 Description of Issues
6.2 Convolutive Mixtures in Time-Domain
6.3 Convolutive Mixtures Algorithms in Frequency-Domain
6.4 Frequency-Domain Blind Separation of Speech Convolutive Mixtures
6.5 Bussgang Method
6.6 Multi-channel Blind Deconvolution
References
Chapter 7 Blind Processing Algorithm Based on Probability Density Estimation
7.1 Advancing the Problem
7.2 Nonparametric Estimation of Probability Density Function
7.3 Estimation of Evaluation Function
7.4 Blind Separation Algorithm Based on Probability Density Estimation
7.5 Probability Density Estimation of Gaussian Mixtures Model
7.6 Blind Deconvolution Algorithm Based on Probability Density Function Estimation
7.7 On-line Algorithm of Nonparametric Density Estimation
References
Chapter 8 Joint Approximate Diagonalization Method
8.1 Introduction
8.2 JAD Algorithm of Frequency-Domain Feature
8.3 JAD Algorithm of Time-Frequency Feature
8.4 Joint Approximate Block Diagonalization Algorithm of Convolutive Mixtures
8.5 JAD Method Based on Cayley Transformation
8.6 Joint Diagonalization and Joint Non-Diagonalization Method
8.7 Nonparametric Density Estimating Separating Method Based on Time-Frequency Analysis
References
Chapter 9 Extension of Blind Signal Processing
9.1 Blind Signal Extraction
9.2 From Projection Pursuit Technology to Nonparametric Density Estimation-Based ICA
9.3 Second-Order Statistics Based Convolutive Mixtures Separation Algorithm
9.4 Blind Separation for Fewer Sensors than Sources--Underdetermined Model
9.5 FastlCA Separation Algorithm of Complex Numbers in Convolutive Mixtures
9.6 On-line Complex ICA Algorithm Based on Uncorrelated Characteristics of Complex Vectors
9.7 ICA-Based Wigner-Ville Distribution
9.8 ICA Feature Extraction
9.9 Constrained ICA
9.10 Particle Filtering Based Nonlinear and Noisy ICA
References
Chapter 10 Data Analysis and Application Study
10.1 Target Enhancement in Active Sonar Detection
10.2 ECG Artifacts Rejection in EEG with ICA
10.3 Experiment on Underdetermined Blind Separation of A Speech Signal
10.4 ICA in Human Face Recognition
10.5 ICAin Data Compression
10.6 Independent Component Analysis for Functional MRI Data Analysis
10.7 Speech Separation for Automatic Speech Recognition System
10.8 Independent Component Analysis of Microarray Gene Expression Data in the Study of Alzheimer's Disease (AD)
References
Index
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