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Most existing books on wavelets are either too mathematical or they focus on too narrow a specialty. This book provides a thorough treatment of the subject from an engineering point of view. It is a one-stop source of theory, algorithms, applications, and computer codes related to wavelets. This second edition has been updated by the addition of:
a section on "Other Wavelets" that describes curvelets, ridgelets, lifting wavelets, etc
a section on lifting algorithms
Sections on Edge Detection and Geophysical Applications
Section on Multiresolution Time Domain Method (MRTD) and on Inverse problems
Auteur
Jaideva C. Goswami, PhD, is an Engineering Advisor at
Schlumberger in Sugarland, Texas. He is also a former professor of
Electronics and Communication Engineering at the Indian Institute
of Technology, Kharagpur. Dr. Goswami has taught several short
courses on wavelets and contributed to the Wiley Encyclopedia of
Electrical and Electronics Engineering as well as Wiley
Encyclopedia of RF and Microwave Engineering. He has many research
papers and patents to his credit, and is a Fellow of IEEE.
Andrew K. Chan, PhD, is on the faculty of Texas A&M
University and is the coauthor of Wavelets in a Box and Wavelet
Toolware. He is a Life Fellow of IEEE.
Contenu
1.What is this book all about?
2.1 Linear Spaces.
2.2 Vectors and Vector Spaces.
2.3 Basis Functions, Orthogonality and Biothogonality.
2.4 Local Basis and Riesz Basis.
2.5 Discrete Linear Normed Space.
2.6 Approximation by Orthogonal Projection.
2.7 Matrix Algebra and Linear Transformation.
2.8 Digital Signals.
2.9 Exercises.
2.10 References.
3.1 Fourier Series.
3.2 Rectified Sine Wave.
3.3 Fourier Transform.
3.4 Properties of Fourier Transform.
3.5 Examples of Fourier Transform.
3.6 Poisson's Sum and Partition of ZUnity.
3.7 Sampling Theorem.
3.8 Partial Sum and Gibb's Phenomenon.
3.9 Fourier Analysis of Discrete-Time Signals.
3.10 Discrete Fourier Transform (DFT).
3.11 Exercise.
3.12 References.
4.1 Window Function.
4.2 Short-Time Fourier Transform.
4.3 Discrete Short-Time Fourier Transform.
4.4 Discrete Gabor Representation.
4.5 Continuous Wavelet Transform.
4.6 Discrete Wavelet Transform.
4.7 Wavelet Series.
4.8 Interpretations of the Time-Frequency Plot.
4.9 Wigner-Ville Distribution.
4.10 Properties of Wigner-Ville Distribution.
4.11 Quadratic Superposition Principle.
4.12 Ambiguity Function.
4.13 Exercise.
4.14 Computer Programs.
4.15 References.
5.1 Multiresolution Spaces.
5.2 Orthogonal, Biothogonal, and Semiorthogonal
Decomposition.
5.3 Two-Scale Relations.
5.4 Decomposition Relation.
5.5 Spline Functions and Properties.
5.6 Mapping a Function into MRA Space.
5.7 Exercise.
5.8 Computer Programs.
5.9 References.
6.1 Necessary Ingredients for Wavelet Construction.
6.2 Construction of Semiorthogonal Spline Wavelets.
6.3 Construction of Orthonormal Wavelets.
6.4 Orthonormal Scaling Functions.
6.5 Construction of Biothogonal Wavelets.
6.6 Graphical Display of Wavelet.
6.7 Exercise.
6.8 Computer Programs.
6.9 References.
7.1 Decimation and Interpolation.
7.2 Signal Representation in the Approximation Subspace.
7.3 Wavelet Decomposition Algorithm.
7.4 Reconstruction Algorithm.
7.5 Change of Bases.
7.6 Signal Reconstruction in Semiorthogonal Subspaces.
7.7 Examples.
7.8 Two-Channel Perfect Reconstruction Filter Bank.
7.9 Polyphase Representation for Filter Banks.
7.10 Comments on DWT and PR Filter Banks.
7.11 Exercise.
7.12 Computer Program.
7.13 References.
8.1 Fast Integral Wavelet Transform.
8.2 Ridgelet Transform.
8.3 Curvelet Transform.
8.4 Complex Wavelets.
8.5 Lifting Wavelet transform.
8.6 References.
9.1 Wavelet Packet.
9.2 Wavelet-Packet Algorithms.
9.3 Thresholding.
9.4 Interference Suppression.
9.5 Faulty Bearing Signature Identification.
9.6 Two-Dimensional Wavelets and Wavelet Packets.
9.7 Edge Detection.
9.8 Image Compression.
9.9 Microcalcification Cluster Detection.