Mathematical Tools In Signal Processing With C++ And Java Simulations - Willi-hans Steeb

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Mathematical Tools In Signal Processing With C++ And Java Simulations

By: Willi-hans Steeb

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Hardcover | 9 September 2005

At a Glance

Hardcover
300 Pages

Dimensions(cm)
23.3 x 15.4 x 2.1

Hardcover

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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.

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You Can Find This Book In

Non-Fiction Computing & I.T.Computer Science Digital Signal Processing (DSP)Computer Programming & Software Development Programming & Scripting Languages Web Programming Software Engineering Systems Analysis & Design Science Physics Booktopia Publisher Services

World Scientific Publishing Engineering & Technology Electronics & Communications Engineering Electronics Engineering

Preface	p. v
Sampling Theory	p. 1
Introduction	p. 1
Nyquist Theorem	p. 5
Lagrange Sampling Theorem	p. 7
Application	p. 8
Transmission Formats	p. 10
Quantisation	p. 13
Introduction	p. 13
Scalar Quantisation	p. 15
Mu-Law and A-Law	p. 18
Application	p. 24
Vector Quantisation	p. 26
Introduction	p. 26
Design Problem	p. 27
LBG Design Algorithm	p. 29
Example	p. 31
Digital Linear Filters	p. 36
Introduction	p. 36
Finite Impulse Response Filters	p. 41
Infinite Impulse Response Filters	p. 42
Digital Filters from Analog Filters	p. 46
Matlab Filter Implementation	p. 49
Generating Functions	p. 50
Introduction	p. 50
Linear Difference Equations	p. 51
Properties of Generating Functions	p. 52
Convolution	p. 54
Introduction	p. 54
Circular Convolution	p. 55
Noncircular Convolution	p. 57
Discrete Fourier Transform	p. 62
Introduction	p. 62
Properties of the Discrete Fourier Transform	p. 65
Windows	p. 67
Fast Fourier Transform	p. 68
Program	p. 69
Discrete Two-Dimensional Fourier Transform	p. 72
Discrete cosine-Transform	p. 74
Introduction	p. 74
cosine-Transform	p. 75
One-Dimensional Case	p. 75
Two-Dimensional Case	p. 76
Program	p. 78
Discrete Wavelets	p. 81
Introduction	p. 81
Multiresolution Analysis	p. 84
Pyramid Algorithm	p. 86
Biorthogonal Wavelets	p. 92
Two-Dimensional Wavelets	p. 98
z-Transform	p. 101
Introduction	p. 101
Simple Examples	p. 103
Radius of Convergence	p. 105
Properties of the z-Transform	p. 107
Poles and Zeros	p. 110
Inverse z-Transform	p. 111
Linear Difference Equations	p. 112
z-Transform and System Function	p. 113
An Application	p. 115
Discrete Hidden Markov Processes	p. 117
Introduction	p. 117
Markov Chains	p. 119
Discrete Hidden Markov Processes	p. 123
Forward-Backward Algorithm	p. 127
Viterbi Algorithm	p. 131
Baum-Welch Algorithm	p. 133
Distances Between HMMs	p. 133
Application of HMMs	p. 134
Program	p. 138
Linear Prediction Analysis	p. 149
Introduction	p. 149
Human Speech Production	p. 150
LPC Modelling	p. 152
Restrictions of LPC	p. 155
Mathematical Model	p. 157
LPC Analysis	p. 159
LPC Synthesis	p. 165
LPC Applications	p. 166
Bank-of-Filters Model	p. 167
Mel-Cepstrum	p. 169
CELP	p. 171
Neural Networks	p. 172
Introduction	p. 172
Competitive Learning and Quantisation	p. 176
Multilayer Perceptrons	p. 180
Introduction	p. 180
Cybenko's Theorem	p. 181
Back-Propagation Algorithm	p. 182
X-ray Tomography	p. 198
Introduction	p. 198
Radon Transform	p. 199
Discretisation	p. 204
Data Compression	p. 206
Introduction	p. 206
LZW Compression	p. 208
Introduction	p. 208
Compression Procedure	p. 209
Examples	p. 210
Decompression Procedure	p. 216
Entropy Encoding	p. 217
Huffman Code	p. 220
Arithmetic Coding	p. 227
Burrows-Wheeler Transform	p. 244
Wavelet Data Compression	p. 248
Fractal Data Compression	p. 250
Digital Signal Processors	p. 256
Introduction	p. 256
Instruction Sets	p. 258
IEEE Floating Point Format	p. 261
Data Representation	p. 262
Addressing Modes	p. 262
Applications: Sum-of-Product	p. 265
Survey of Microprocessors	p. 271
Bibliography	p. 275
Index	p. 281
Table of Contents provided by Ingram. All Rights Reserved.

Mathematical Tools In Signal Processing With C++ And Java Simulations

At a Glance

Hardcover

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