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| 編輯推薦： |
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肓信号处理是现代数学信号处理、计算智能学近年来迅速发展的重要方向，在电子信息、通信、生物医学、图像增强、雷达、地球物理信号处理等众多领域有广泛的应用前景。史习智编著的这本《盲信号处理理论与实践》较系统地介绍了盲信号处理的基本理论、数学描述、独立分量分析、非线性PCA、非线性ICA、卷积混合和盲解卷积、盲信号处理的扩展、数据分析和应用研究等。本书可作为作为高年级本科生、研究生的教材，也可作为电子信息、通信、图像处理、遥感、雷达、生物医学信号处理、地震、语言信号处理等相关领域科技人员的参考书。
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| 內容簡介： |
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.
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| 目錄：
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Chapter 1Introduction
1.1Introduction
1.2Blind Source Separation
1.3Independent Component Analysis ICA
1.4The Historical Development and Research Prospect of Blind Signal Processing
References
Chapter 2Mathematical Description of Blind Signal Processing
2.1Random Process and Probability Distribution
2.2Estimation Theory
2.3Information Theory
2.4Higher-Order Statistics
2.5Preprocessing of Signal
2.6Complex Nonlinear Function
2.7Evaluation Index
References
Chapter 3Independent Component Analysis
3.1Problem Statement and Assumptions
3.2Contrast Functions
3.3Information Maximization Method of ICA
3.4Maximum Likelihood Method and Common Learning Rule
3.5FastICA Algorithm
3.6Natural Gradient Method
3.7Hidden Markov Independent Component Analysis
References
Chapter 4Nonlinear PCA Feature Extraction
4.1Principal Component Analysis Infinitesimal Analysis
4.2Nonlinear PCA and Blind Source Separation
4.3Kernel PCA
4.4Neural Networks Method of Nonlinear PCA and Nonlinear Complex PCA
References
Chapter 5Nonlinear ICA
5.1Nonlinear Model and Source Separation
5.2Learning Algorithm
5.3Extended Gaussianization Method of Post Nonlinear Blind Separation
5.4Neural Network Method for Nonlinear ICA
5.5Genetic Algorithm of Nonlinear ICA Solution
5.6Application Examples of Nonlinear ICA
References
Chapter 6Convolutive Mixtures and Blind Deconvolution
6.1Description of Issues
6.2Convolutive Mixtures in Time-Domain
6.3Convolutive Mixtures Algorithms in Frequency-Domain
6.4Frequency-Domain Blind Separation of Speech Convolutive Mixtures
6.5Bussgang Method
6.6Multi-channel Blind Deconvolution
References
Chapter 7Blind Processing Algorithm Based on Probability Density Estimation
7.1Advancing the Problem
7.2Nonparametric Estimation of Probability Density Function
7.3Estimation of Evaluation Function
7.4Blind Separation Algorithm Based on Probability Density Estimation
7.5Probability Density Estimation of Gaussian Mixtures Model
7.6Blind Deconvolution Algorithm Based on Probability Density Function Estimation
7.7On-line Algorithm of Nonparametric Density Estimation
References
Chapter 8Joint Approximate Diagonalization Method
8.1Introduction
8.2JAD Algorithm of Frequency-Domain Feature
8.3JAD Algorithm of Time-Frequency Feature
8.4Joint Approximate Block Diagonalization Algorithm of Convolutive Mixtures
8.5JAD Method Based on Cayley Transformation
8.6Joint Diagonalization and Joint Non-Diagonalization Method
8.7Nonparametric Density Estimating Separating Method Based on Time-Frequency Analysis
References
Chapter 9Extension of Blind Signal Processing
9.1Blind Signal Extraction
9.2From Projection Pursuit Technology to Nonparametric Density Estimation-Based ICA
9.3Second-Order Statistics Based Convolutive Mixtures Separation Algorithm
9.4Blind Separation for Fewer Sensors than Sources--Underdetermined Model
9.5FastlCA Separation Algorithm of Complex Numbers in Convolutive Mixtures
9.6On-line Complex ICA Algorithm Based on Uncorrelated Characteristics of Complex Vectors
9.7ICA-Based Wigner-Ville Distribution
9.8ICA Feature Extraction
9.9Constrained ICA
9.10 Particle Filtering Based Nonlinear and Noisy ICA
References
Chapter 10Data Analysis and Application Study
10.1Target Enhancement in Active Sonar Detection
10.2ECG Artifacts Rejection in EEG with ICA
10.3Experiment on Underdetermined Blind Separation of A Speech Signal
10.4ICA in Human Face Recognition
10.5ICAin Data Compression
10.6Independent Component Analysis for Functional MRI Data Analysis
10.7Speech Separation for Automatic Speech Recognition System
10.8Independent Component Analysis of Microarray Gene Expression Data in the Study of Alzheimer''s Disease AD
References
Index
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