Mathematical Tools in Signal Processing with C++ and Java Simulations使用 C++与JAVA模拟的信号处理中的数学工具
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作者： Willi-Hans Steeb 著
出 版 社：
出版时间： 2005-9-1字数：版次： 1页数： 283印刷时间： 2005/09/01开本： 16开印次： 1纸张： 胶版纸I S B N ： 9789812565006包装： 精装内容简介
In recent decades, the study of signal processing has become increasingly complex, with new techniques and applications constantly being developed for the processing, transformation, and interpretation of signals. This book provides a comprehensive introduction to the traditional and modern methods used in signal processing. It is designed to impart to the reader the mathematical techniques used in modelling signals and systems, encompassing standard mathematical tools as well as newer techniques such as wavelets and neural networks. C++ and Java implementations furnish these descriptions. The book offers an excellent balance of theory and application, beginning with a complete framework of discrete-time signal processing.
目录
Preface
1 Sampling Theory
1.1 Introduction
1.2 Nyquist Theorem
1.3 Lagrange Sampling Theorem
1.4 Application
1.5 Transmission Formats
2 Quantisation
2.1 Introduction
2.2 Scalar Quantisation
2.3 Mu-Law and A-Law
2.4 Application
2.5 Vector Quantisation
2.5.1 Introduction
2.5.2 Design Problem
2.5.3 LBG Design Algorithm
2.5.4 Example
3 Digital Linear Filters
3.1 Introduction
3.2 Finite Impulse Response Filters
3.3 Infinite Impulse Response Filters
3.4 Digital Filters from Analog Filters
3.5 Matlab Filter Implementation
3.6 Generating Functions
3.6.1 Introduction
3.6.2 Linear Difference Equations
3.6.3 Properties of Generating Functions
4 Convolution
4.1 Introduction
4.2 Circular Convolution
4.3 Noncircular Convolution
5 Discrete Fourier Transform
5.1 Introduction
5.2 Properties of the Discrete Fourier Transform
5.3 Windows
5.4 Fast Fourier Transform
5.5 Program
5.6 Discrete Two-Dimensional Fourier Transform
6 Discrete cosine-Transform
6.1 Introduction
6.2 cosine-Transform
6.2.1 One-Dimensional Case
6.2.2 Two-Dimensional Case
6.3 Program
7 Discrete Wavelets
7.1 Introduction
7.2 Multiresolution Analysis
7.3 Pyramid Algorithm
7.4 Biorthogonal Wavelets
7.5 Two-Dimensional Wavelets
8 z-Transform
8.1 Introduction
8.2 Simple Examples
8.3 Radius of Convergence
8.4 Properties of the z-Transform
8.5 Poles and Zeros
8.6 Inverse z-Transform
8.7 Linear Difference Equations
8.8 z-Transform and System Function
8.9 An Application
9 Discrete Hidden Markov Processes
9.1 Introduction
9.2 Markov Chains
9.3 Discrete Hidden Markov Processes
……
10 Linear prediction analysis
11 Neural networks
12 X-ray tomography
13 Data compression
14 Digital signal processors
Bibliography
Index
