Wavelets are spatially localized functions whose amplitude drops off exponentially outside a small "window". They are used to magnify experimental or numerical data and have become powerful tools in signal processing and other computational sciences. This book gives scientists and engineers a practical understanding of wavelets--their origins, their purpose, their use, and their prospects. It covers the applications of wavelets as a diagnostic tool and the use of wavelet basis functions to solve differential equations. Each chapter was written by one of five lecturers of a course sponsored by the Institute of Computer Applications in Science and Engineering (ICASE) and the NASA Langley Research Center. Not only does this book treat the latest advances on the subject, but it also attempts to impart practical knowledge to allow scientists and engineers to evaluate objectively where these tools stand in relation to their needs.
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Gordon Erlebacher, M. Yousuff Hussaini, and Leland M. Jameson are all at the Institute for Computer Applications to Science and Engineering (ICASE), NASA/Langley Research Center, Hampton, VA.
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Inside This Book
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fast numerical algorithms, generalized devil staircase, inverse fractal problem, turbulent velocity signal, satisfy standard estimates, wavelet transform modulus maxima method, singularity spectrum, maxima lines, weak boundedness property, analyzing wavelet, wavelet plane, multifractal formalism, generalized fractal dimensions, dilation equation, asymptotic signal, wavelet equation, structure function method, local scaling properties, tree matching algorithm, singularity spectra, turbulence analysis, local scaling exponents, scaling covariance, snowflake fractals, wavelet algorithms Key Phrases - Capitalized Phrases (CAPs): (learn more)
Wavelet Analysis of Fractals, Filter Banks, New York, Fluid Mech, Arneodo Figure, Van Atta, Academic Press, World Scientific, Pure Appl, Ten Lectures, Lecture Notes, Ben Amar, Van Ness, Tree Algorithms Related, London Math, Beylkin Figure, Mathematische Annalen, Springer Verlag, Yale University, Cambridge Univ, Intermittency Function, Supported Wavelets, Cantor Set Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Surprise Me!
fast numerical algorithms, generalized devil staircase, inverse fractal problem, turbulent velocity signal, satisfy standard estimates, wavelet transform modulus maxima method, singularity spectrum, maxima lines, weak boundedness property, analyzing wavelet, wavelet plane, multifractal formalism, generalized fractal dimensions, dilation equation, asymptotic signal, wavelet equation, structure function method, local scaling properties, tree matching algorithm, singularity spectra, turbulence analysis, local scaling exponents, scaling covariance, snowflake fractals, wavelet algorithms Key Phrases - Capitalized Phrases (CAPs): (learn more)
Wavelet Analysis of Fractals, Filter Banks, New York, Fluid Mech, Arneodo Figure, Van Atta, Academic Press, World Scientific, Pure Appl, Ten Lectures, Lecture Notes, Ben Amar, Van Ness, Tree Algorithms Related, London Math, Beylkin Figure, Mathematische Annalen, Springer Verlag, Yale University, Cambridge Univ, Intermittency Function, Supported Wavelets, Cantor Set Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Surprise Me!
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8
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- Beyond Wavelets, Volume 10 (Studies in Computational Mathematics) by Grant Welland on page 131, and page 133
- Computational Earthquake Science, Part II (Pageoph Topical Volumes) (Pt. 2) by Andrea Donnellan on page 28, and page 42
- The Nonlinear Workbook: Chaos, Fractals, Cellular Automata, Neural Networks, Genetic Algorithms, Gene Expression Programming, Support Vector M by Willi-Hans Steeb in Back Matter
- Advances in Imaging and Electron Physics, Volume 126 by Peter W. Hawkes on page 87
- Applied Mathematics by Gerald D. Mahan on page 214
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