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E-raamat: Wavelet Transforms and Their Applications

  • Formaat: PDF+DRM
  • Ilmumisaeg: 25-Nov-2014
  • Kirjastus: Birkhauser Boston Inc
  • Keel: eng
  • ISBN-13: 9780817684181
  • Formaat - PDF+DRM
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  • Formaat: PDF+DRM
  • Ilmumisaeg: 25-Nov-2014
  • Kirjastus: Birkhauser Boston Inc
  • Keel: eng
  • ISBN-13: 9780817684181

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This textbook is an introduction to wavelet transforms and accessible to a larger audience with diverse backgrounds and interests in mathematics, science, and engineering. Emphasis is placed on the logical development of fundamental ideas and systematic treatment of wavelet analysis and its applications to a wide variety of problems as encountered in various interdisciplinary areas. Topics and Features: * This second edition heavily reworks the chapters on Extensions of Multiresolution Analysis and Newlandss Harmonic Wavelets and introduces a new chapter containing new applications of wavelet transforms * Uses knowledge of Fourier transforms, some elementary ideas of Hilbert spaces, and orthonormal systems to develop the theory and applications of wavelet analysis * Offers detailed and clear explanations of every concept and method, accompanied by carefully selected worked examples, with special emphasis given to those topics in which students typically experience difficulty * Includes carefully chosen end-of-chapter exercises directly associated with applications or formulated in terms of the mathematical, physical, and engineering context and provides answers to selected exercises for additional help Mathematicians, physicists, computer engineers, and electrical and mechanical engineers will find Wavelet Transforms and Their Applications an exceptionally complete and accessible text and reference. It is also suitable as a self-study or reference guide for practitioners and professionals.

Arvustused

It can be seen as a reference text or as a study book, complete with definitions, theorems, proofs and exercises. The book is an up to date reference work on univariate Fourier and wavelet analysis including recent developments in multiresolution, wavelet analysis, and applications in turbulence. The systematic construction of the chapters with extensive lists of exercises make it also very suitable for teaching. (Adhemar Bultheel, euro-math-soc.eu, February, 2015)

The book is primarily aimed at advanced undergraduates and graduate students across all of applied mathematics. It is a good source of information for all professionals interested in wavelet transforms and their applications. (Yuri A. Farkov, zbMATH 1308.42030, 2015)

1 Brief Historical Introduction
1(28)
1.1 Fourier Series and Fourier Transforms
1(3)
1.2 Gabor Transforms
4(2)
1.3 The Wigner-Ville Distribution and Time-Frequency Signal Analysis
6(4)
1.4 Wavelet Transforms
10(9)
1.5 Wavelet Bases and Multiresolution Analysis
19(7)
1.6 Applications of Wavelet Transforms
26(3)
2 Hilbert Spaces and Orthonormal Systems
29(100)
2.1 Introduction
29(1)
2.2 Normed Spaces
30(3)
2.3 The Lp Spaces
33(5)
2.4 Generalized Functions with Examples
38(10)
2.5 Definition and Examples of an Inner Product Space
48(3)
2.6 Norm in an Inner Product Space
51(3)
2.7 Definition and Examples of Hilbert Spaces
54(5)
2.8 Strong and Weak Convergences
59(2)
2.9 Orthogonal and Orthonormal Systems
61(5)
2.10 Properties of Orthonormal Systems
66(9)
2.11 Trigonometric Fourier Series
75(4)
2.12 Orthogonal Complements and the Projection Theorem
79(5)
2.13 Linear Functionals and the Riesz Representation Theorem
84(2)
2.14 Separable Hilbert Spaces
86(2)
2.15 Linear Operators on Hilbert Spaces
88(18)
2.16 Eigenvalues and Eigenvectors of an Operator
106(10)
2.17 Exercises
116(13)
3 Fourier Transforms and Their Applications
129(114)
3.1 Introduction
129(1)
3.2 Fourier Transforms in L1 (R)
130(5)
3.3 Basic Properties of Fourier Transforms
135(14)
3.4 Fourier Transforms in L2(R)
149(15)
3.5 Discrete Fourier Transforms
164(5)
3.6 Fast Fourier Transforms
169(4)
3.7 Poisson's Summation Formula
173(6)
3.8 The Shannon Sampling Theorem and Gibbs' Phenomenon
179(11)
3.9 Heisenberg's Uncertainty Principle
190(2)
3.10 Applications of Fourier Transforms in Mathematical Statistics
192(7)
3.11 Applications of Fourier Transforms to Ordinary Differential Equations
199(4)
3.12 Solutions of Integral Equations
203(3)
3.13 Solutions of Partial Differential Equations
206(12)
3.14 Applications of Multiple Fourier Transforms to Partial Differential Equations
218(5)
3.15 Construction of Green's Functions by the Fourier Transform Method
223(13)
3.16 Exercises
236(7)
4 The Gabor Transform and Time-Frequency Signal Analysis
243(44)
4.1 Introduction
243(1)
4.2 Classification of Signals and the Joint Time-Frequency Analysis of Signals
244(4)
4.3 Definition and Examples of the Gabor Transform
248(4)
4.4 Basic Properties of Gabor Transforms
252(5)
4.5 Frames and Frame Operators
257(8)
4.6 Discrete Gabor Transforms and the Gabor Representation Problem
265(3)
4.7 The Zak Transform and Time-Frequency Signal Analysis
268(3)
4.8 Basic Properties of Zak Transforms
271(6)
4.9 Applications of Zak Transforms and the Balian-Low Theorem
277(7)
4.10 Exercises
284(3)
5 The Wigner-Ville Distribution and Time-Frequency Signal Analysis
287(50)
5.1 Introduction
287(1)
5.2 Definition and Examples of the WVD
288(9)
5.3 Basic Properties of the WVD
297(8)
5.4 The WVD of Analytic Signals and Band-Limited Signals
305(4)
5.5 Definitions and Examples of the Woodward Ambiguity Functions
309(7)
5.6 Basic Properties of Ambiguity Functions
316(6)
5.7 The Ambiguity Transformation and Its Properties
322(4)
5.8 Discrete WVDs
326(4)
5.9 Cohen's Class of Time-Frequency Distributions
330(3)
5.10 Exercises
333(4)
6 The Wavelet Transforms and Their Basic Properties
337(38)
6.1 Introduction
337(3)
6.2 Continuous Wavelet Transforms and Examples
340(11)
6.3 Basic Properties of Wavelet Transforms
351(3)
6.4 The Discrete Wavelet Transforms
354(10)
6.5 Orthonormal Wavelets
364(6)
6.6 Exercises
370(5)
7 Multiresolution Analysis and Construction of Wavelets
375(66)
7.1 Introduction
375(1)
7.2 Definition of MRA and Examples
376(7)
7.3 Properties of Scaling Functions and Orthonormal Wavelet Bases
383(18)
7.4 Construction of Orthonormal Wavelets
401(15)
7.5 Daubechies' Wavelets and Algorithms
416(17)
7.6 Discrete Wavelet Transforms and Mallat's Pyramid Algorithm
433(4)
7.7 Exercises
437(4)
8 Extensions of Multiresolution Analysis
441(34)
8.1 Introduction
441(1)
8.2 p-MRA on a Half-Line R+
442(21)
8.3 Nonuniform MRA
463(12)
9 Newland's Harmonic Wavelets
475(14)
9.1 Introduction
475(1)
9.2 Harmonic Wavelets
475(6)
9.3 Properties of Harmonic Scaling Functions
481(3)
9.4 Wavelet Expansions and Parseval's Formula
484(1)
9.5 Concluding Remarks
485(1)
9.6 Exercises
486(3)
10 Wavelet Transform Analysis of Turbulence
489(28)
10.1 Introduction
489(3)
10.2 Fourier Transforms in Turbulence and the Navier-Stokes Equations
492(8)
10.3 Fractals, Multifractals, and Singularities in Turbulence
500(6)
10.4 Farge's Wavelet Transform Analysis of Turbulence
506(3)
10.5 Adaptive Wavelet Method for Analysis of Turbulent Flows
509(3)
10.6 Meneveau's Wavelet Analysis of Turbulence
512(5)
Answers and Hints for Selected Exercises 517(14)
Bibliography 531(14)
Index 545
Lokenath Debnath, Ph.D., is a Professor of Mathematics at The University of Texas-Pan American. He received his Ph.D. in Applied Mathematics from the University of London, and a Ph.D. in Pure Mathematics from the University of Calcutta.  His areas of interest are applied mathematics, applied partial differential equations, integral transforms, fluid dynamics, and continuum mechanics. Firdous Ahmad Shah, Ph.D., is an Assistant Professor in the Post Graduate Department of Mathematics at the University of Kashmir.  His areas of specialization are: wavelets, wavelet packets, applications of wavelets in financial time series, and wavelet neural networks.