A First Course in Wavelets with Fourier Analysis, 2nd Edition
A First Course in Wavelets with Fourier Analysis, 2nd Edition
ISBN: 9781118211151
Sep 2011
336 pages
$102.99
Description
A comprehensive, selfcontained treatment of Fourier analysis and wavelets—now in a new editionThrough expansive coverage and easytofollow explanations, A First Course in Wavelets with Fourier Analysis, Second Edition provides a selfcontained mathematical treatment of Fourier analysis and wavelets, while uniquely presenting signal analysis applications and problems. Essential and fundamental ideas are presented in an effort to make the book accessible to a broad audience, and, in addition, their applications to signal processing are kept at an elementary level.
The book begins with an introduction to vector spaces, inner product spaces, and other preliminary topics in analysis. Subsequent chapters feature:

The development of a Fourier series, Fourier transform, and discrete Fourier analysis

Improved sections devoted to continuous wavelets and twodimensional wavelets

The analysis of Haar, Shannon, and linear spline wavelets

The general theory of multiresolution analysis

Updated MATLAB code and expanded applications to signal processing

The construction, smoothness, and computation of Daubechies' wavelets

Advanced topics such as wavelets in higher dimensions, decomposition and reconstruction, and wavelet transform
Applications to signal processing are provided throughout the book, most involving the filtering and compression of signals from audio or video. Some of these applications are presented first in the context of Fourier analysis and are later explored in the chapters on wavelets. New exercises introduce additional applications, and complete proofs accompany the discussion of each presented theory. Extensive appendices outline more advanced proofs and partial solutions to exercises as well as updated MATLAB routines that supplement the presented examples.
A First Course in Wavelets with Fourier Analysis, Second Edition is an excellent book for courses in mathematics and engineering at the upperundergraduate and graduate levels. It is also a valuable resource for mathematicians, signal processing engineers, and scientists who wish to learn about wavelet theory and Fourier analysis on an elementary level.
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Preface and Overview ix
0 Inner Product Spaces 1
0.1 Motivation, 1
0.2 Definition of Inner Product, 2
0.3 The Spaces L2 and l2, 4
0.3.1 Definitions, 4
0.3.2 Convergence in L2 Versus Uniform Convergence, 8
0.4 Schwarz and Triangle Inequalities, 11
0.5 Orthogonality, 13
0.5.1 Definitions and Examples, 13
0.5.2 Orthogonal Projections, 15
0.5.3 Gram–Schmidt Orthogonalization, 20
0.6 Linear Operators and Their Adjoints, 21
0.6.1 Linear Operators, 21
0.6.2 Adjoints, 23
0.7 Least Squares and Linear Predictive Coding, 25
0.7.1 BestFit Line for Data, 25
0.7.2 General Least Squares Algorithm, 29
0.7.3 Linear Predictive Coding, 31
Exercises, 34
1 Fourier Series 38
1.1 Introduction, 38
1.1.1 Historical Perspective, 38
1.1.2 Signal Analysis, 39
1.1.3 Partial Differential Equations, 40
1.2 Computation of Fourier Series, 42
1.2.1 On the Interval −π ≤ x ≤ π, 42
1.2.2 Other Intervals, 44
1.2.3 Cosine and Sine Expansions, 47
1.2.4 Examples, 50
1.2.5 The Complex Form of Fourier Series, 58
1.3 Convergence Theorems for Fourier Series, 62
1.3.1 The Riemann–Lebesgue Lemma, 62
1.3.2 Convergence at a Point of Continuity, 64
1.3.3 Convergence at a Point of Discontinuity, 69
1.3.4 Uniform Convergence, 72
1.3.5 Convergence in the Mean, 76
Exercises, 83
2 The Fourier Transform 92
2.1 Informal Development of the Fourier Transform, 92
2.1.1 The Fourier Inversion Theorem, 92
2.1.2 Examples, 95
2.2 Properties of the Fourier Transform, 101
2.2.1 Basic Properties, 101
2.2.2 Fourier Transform of a Convolution, 107
2.2.3 Adjoint of the Fourier Transform, 109
2.2.4 Plancherel Theorem, 109
2.3 Linear Filters, 110
2.3.1 TimeInvariant Filters, 110
2.3.2 Causality and the Design of Filters, 115
2.4 The Sampling Theorem, 120
2.5 The Uncertainty Principle, 123
Exercises, 127
3 Discrete Fourier Analysis 132
3.1 The Discrete Fourier Transform, 132
3.1.1 Definition of Discrete Fourier Transform, 134
3.1.2 Properties of the Discrete Fourier Transform, 135
3.1.3 The Fast Fourier Transform, 138
3.1.4 The FFT Approximation to the Fourier Transform, 143
3.1.5 Application: Parameter Identification, 144
3.1.6 Application: Discretizations of Ordinary Differential Equations, 146
3.2 Discrete Signals, 147
3.2.1 TimeInvariant, Discrete Linear Filters, 147
3.2.2 ZTransform and Transfer Functions, 149
3.3 Discrete Signals & Matlab, 153
Exercises, 156
4 Haar Wavelet Analysis 160
4.1 Why Wavelets?, 160
4.2 Haar Wavelets, 161
4.2.1 The Haar Scaling Function, 161
4.2.2 Basic Properties of the Haar Scaling Function, 167
4.2.3 The Haar Wavelet, 168
4.3 Haar Decomposition and Reconstruction Algorithms, 172
4.3.1 Decomposition, 172
4.3.2 Reconstruction, 176
4.3.3 Filters and Diagrams, 182
4.4 Summary, 185
Exercises, 186
5 Multiresolution Analysis 190
5.1 The Multiresolution Framework, 190
5.1.1 Definition, 190
5.1.2 The Scaling Relation, 194
5.1.3 The Associated Wavelet and Wavelet Spaces, 197
5.1.4 Decomposition and Reconstruction Formulas: A Tale of Two Bases, 201
5.1.5 Summary, 203
5.2 Implementing Decomposition and Reconstruction, 204
5.2.1 The Decomposition Algorithm, 204
5.2.2 The Reconstruction Algorithm, 209
5.2.3 Processing a Signal, 213
5.3 Fourier Transform Criteria, 214
5.3.1 The Scaling Function, 215
5.3.2 Orthogonality via the Fourier Transform, 217
5.3.3 The Scaling Equation via the Fourier Transform, 221
5.3.4 Iterative Procedure for Constructing the Scaling Function, 225
Exercises, 228
6 The Daubechies Wavelets 234
6.1 Daubechies’ Construction, 234
6.2 Classification, Moments, and Smoothness, 238
6.3 Computational Issues, 242
6.4 The Scaling Function at Dyadic Points, 244
Exercises, 248
7 Other Wavelet Topics 250
7.1 Computational Complexity, 250
7.1.1 Wavelet Algorithm, 250
7.1.2 Wavelet Packets, 251
7.2 Wavelets in Higher Dimensions, 253
Exercises on 2D Wavelets, 258
7.3 Relating Decomposition and Reconstruction, 259
7.3.1 Transfer Function Interpretation, 263
7.4 Wavelet Transform, 266
7.4.1 Definition of the Wavelet Transform, 266
7.4.2 Inversion Formula for the Wavelet Transform, 268
Appendix A: Technical Matters 273
A.1 Proof of the Fourier Inversion Formula, 273
A.2 Technical Proofs from Chapter 5, 277
A.2.1 Rigorous Proof of Theorem 5.17, 277
A.2.2 Proof of Theorem 5.10, 281
A.2.3 Proof of the Convergence Part of Theorem 5.23, 283
Appendix B: Solutions to Selected Exercises 287
Appendix C: MATLAB® Routines 305
C.1 General Compression Routine, 305
C.2 Use of MATLAB’s FFT Routine for Filtering and Compression, 306
C.3 Sample Routines Using MATLAB’s Wavelet Toolbox, 307
C.4 MATLAB Code for the Algorithms in Section 5.2, 308
Bibliography 311
Index 313
"The discussions of applications avoid the deep jargon of signal processing … accessible to a wider audience." (Book News, December 2009)
 Offers selfcontained exposition of Fourier analysis and wavelets
 Contains expanded applications to signal processing and additional exercises throughout the book
 Provides complete proofs of the presented theory in addition to solutions to selected exercises in the back of the book
 Features improved sections devoted to continuous wavelets and twodimensional wavelets
 Presents updated Matlab code in the appendix and online via a related website