Now updated—the authoritative treatment of wavelets from an engineering point of view

Wavelet theory originated from research activities in many areas of science and engineering. As a result, it finds applications in a wide range of practical problems. Wavelet techniques are specifically suited for nonstationary signals for which classic Fourier methods are ineffective. Developments over the last decade have led to many new wavelet applications such as image compression, turbulence, human vision, radar, and earthquake prediction.

Based on courses taught by the authors at Texas A&M University as well as related conferences, Fundamentals of Wavelets is a textbook offering an up-to-date engineering approach to wavelet theory. It balances a discussion of wavelet theory and algorithms with its far-ranging practical applications in signal processing, image processing, geophysical applications, electromagnetic wave scattering, and boundary value problems.

In a clear, progressive format, the book describes:

• Basic concepts of linear algebra, Fourier analysis, and discrete signal analysis

• Theoretical aspects of time-frequency analysis and multiresolution analysis

• Construction and properties of various real and complex wavelets and curvelets

• Algorithms for computing wavelet transformations

• Applications to signal processing, geophysical applications, and boundary value problems

This Second Edition features new sections on curvelets, ridgelets, and lifting wavelet transforms; complex wavelets; edge detection and geophysical applications; and multiresolution time domain method. It covers time-frequency analysis techniques such as short-time Fourier transform and Wigner-Ville distribution. Concluding chapters present interesting applications of wavelets to signal processing and boundary value problems. Numerous examples and figures are also included along with simple matlab® programs.

Fundamentals of Wavelets is an essential introduction to wavelet theory for students and professionals alike in a practical, real-world engineering context. It is ideally suited for senior and graduate students in electrical engineering, physics, and mathematics; research engineers and physicists; and design and software engineers in the telecommunications and signal processing industries.