This book was written by the author through personal experience. Wavelet theory is the result of a dialogue between scientist and the author. This book emphasize about the following: 1. time-frequency weeding where it motivates decompositions over elementary “atoms” that are well concentrated in time and frequency. 2. Scale for zooming Wavelets - scaled waveforms that measure signal variations. By traveling through scales, zooming procedures provide powerful characterizations of signal structures such as singularities. 3. Sparse representations - An orthonormal basis is useful if it defines a representation where signals are well approximated with a few non-zero coefficients. Applications to signal estimation in noise and image compression are closely related to approximation theory. 4. Try it non-linear and diagonal - Linearity has long predominated because of its apparent simplicity. We are used to slogans that often hide the limitations of “optimal” linear procedures such as Wiener filtering or Karhunen-Lohe bases expansions. In sparse representations, simple non-linear diagonal operators can considerably outperform “optimal” linear procedures, and fast algorithms are available. See contents: Introduction to a transient world, fourier kingdom, discrete revolution, frames, wavelet zoom, wavelet bases, wavelet packet and local cosine bases, estimations are approximations, transform coding, mathematical complements, and software toolboxes. Copy this link and paste to download this book (http://rapidshare.de/files/21333361/MALLAT__S.__1999_._A_Wavelet_Tour_of_Signal_Processing__2nd_ed._.rar)Categories
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Posted: October 1st, 2008, 5:21am CEST by i-garlic
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This book was written by the author through personal experience. Wavelet theory is the result of a dialogue between scientist and the author. This book emphasize about the following: 1. time-frequency weeding where it motivates decompositions over elementary “atoms” that are well concentrated in time and frequency. 2. Scale for zooming Wavelets - scaled waveforms that measure signal variations. By traveling through scales, zooming procedures provide powerful characterizations of signal structures such as singularities. 3. Sparse representations - An orthonormal basis is useful if it defines a representation where signals are well approximated with a few non-zero coefficients. Applications to signal estimation in noise and image compression are closely related to approximation theory. 4. Try it non-linear and diagonal - Linearity has long predominated because of its apparent simplicity. We are used to slogans that often hide the limitations of “optimal” linear procedures such as Wiener filtering or Karhunen-Lohe bases expansions. In sparse representations, simple non-linear diagonal operators can considerably outperform “optimal” linear procedures, and fast algorithms are available. See contents: Introduction to a transient world, fourier kingdom, discrete revolution, frames, wavelet zoom, wavelet bases, wavelet packet and local cosine bases, estimations are approximations, transform coding, mathematical complements, and software toolboxes. Copy this link and paste to download this book (http://rapidshare.de/files/21333361/MALLAT__S.__1999_._A_Wavelet_Tour_of_Signal_Processing__2nd_ed._.rar)Full download