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
Fundamentals of Wavelets offer a practical, up-to-date overview of wavelet theory from an engineering point of view.
This volume is designed as a textbook for an introductory course on wavelet analysis and time-frequency analysis aimed at graduate students or advanced undergraduates in science and engineering.
In addition, this volume also: •Provides a historical overview of the evolution of signal processing techniques, from the Fourier transform to wavelet transform •Introduces the fundamental mathematics for understanding what wavelet ...
This book presents connections between the different aspects of wavelet and subband theory.
The first part of the book is devoted to the fundamentals of wavelet analysis. The construction of wavelet bases and the fast computation of the wavelet transform in both continuous and discrete settings is covered.
A. Cohen, I. Daubechies, and J.-C. Feauveau, Biorthogonal bases of compactly supported wavelets, Comm. ... J. Carrier, L. Greengard, and V. Rokhlin, A fast adaptive multipole algorithm for particle simulations, SIAM J. Sci. Comput.
This book contains information on how to tackle many important problems using a multiscale statistical approach.
A comprehensive treatment of wavelets for both engineers and mathematicians.
The book is ideally suited as a text for undergraduate and graduate students of mathematics and engineering. This book provides comprehensive information on the conceptual basis of wavelet theory and it applications.
Students are introduced to the powerful foundations of modern signal processing, including the basic geometry of Hilbert space, the mathematics of Fourier transforms, and essentials of sampling, interpolation, approximation and compression ...