Adapted Wavelet Analysis: From Theory to Software

by Mladen Victor Wickerhauser

Overview

This detail-oriented text is intended for engineers and applied mathematicians who must write computer programs to perform wavelet and related analysis on real data. It contains an overview of mathematical prerequisites and proceeds to describe hands-on programming techniques to implement special programs for signal analysis and other applications. From the table of contents: - Mathematical Preliminaries - Programming Techniques - The Discrete Fourier Transform - Local Trigonometric Transforms - Quadrature Filters - The Discrete Wavelet Transform - Wavelet Packets - The Best Basis Algorithm - Multidimensional Library Trees - Time-Frequency Analysis - Some Applications - Solutions to Some of the Exercises - List of Symbols - Quadrature Filter Coefficients

Product Details

ISBN-13:	9781568810416
Publisher:	Taylor & Francis
Publication date:	04/17/1996
Edition description:	New Edition
Pages:	498
Product dimensions:	6.00(w) x 9.00(h) x (d)

About the Author

Mladen Victor Wickerhauser is professor of mathematics and statistics at Washington University, St. Louis. He holds a PhD from Yale University. Professor Wickerhauser’s research interests include harmonic analysis, wavelets, and numerical algorithms for data compression. He has six US patents and 118 publications, one of which led to an algorithm used by the FBI to encode fingerprint images.

Table of Contents

1. Mathematical Preliminaries 2. Programming Techniques 3. The Discrete Fourier Transform 4. Local Trigonometric Transforms 5. Quadrature Filters 6. The Discrete Wavelet Transform 7. Wavelet Packets 8. The Best Basis Algorithm 9. Multidimensional Library Trees 10. Time-Frequency Analysis 11. Some Applications

Editorial Reviews

A detail-oriented text that begins with an overview of mathematical prerequisites, followed by chapters that examine the properties of waveforms used in adapted wavelet analysis: discrete "fast" Fourier transforms, orthogonal and biorthogonal wavelets, wavelet packets, and localized trigonometric or lapped orthogonal functions. Other chapters discuss the "best-basis" method, time-frequency analysis, and combinations of these algorithms useful for signal analysis, denoising, and data compression. Each chapter discusses the technical aspects of implementation giving examples in pseudocode backed up with a Standard C source code (on optional diskette) and closes with a list of worked exercises. Annotation c. Book News, Inc., Portland, OR (booknews.com)

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