A Wavelet Tour of Signal ProcessingThis book is intended to serve as an invaluable reference for anyone concerned with the application of wavelets to signal processing. It has evolved from material used to teach "wavelet signal processing" courses in electrical engineering departments at Massachusetts Institute of Technology and Tel Aviv University, as well as applied mathematics departments at the Courant Institute of New York University and École Polytechnique in Paris.
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It is a mathematically wellorganized book on wavelet theories. Also, it well explains the most important theories such as wavelet zooming with beautiful language. I am perfectly satisfied with it.
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User Review  Galal  GoodreadsVery good book for the basics and suitable for the beginners . Read full review
Contents
1  
20  
42  
CHAPTER IV TIME MEETS FREQUENCY  67 
CHAPTER V FRAMES  125 
CHAPTER VI WAVELET ZOOM  163 
CHAPTER VII WAVELET BASES  220 
CHAPTER VIII WAVELET PACKET AND LOCAL COSINE BASES  321 
CHAPTER IX AN APPROXIMATION TOUR  376 
CHAPTER X ESTIMATIONS ARE APPROXIMATIONS  434 
CHAPTER XI TRANSFORM CODING  525 
APPENDIX A MATHEMATICAL COMPLEMENTS  591 
APPENDIX B SOFTWARE TOOLBOXES  603 
612  
629  
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Common terms and phrases
algorithm amplitude approximation error best basis biorthogonal wavelet bounded variation calculated circular convolution compact support computed conjugate mirror filters constructed converges cosine bases covariance Daubechies decay decomposed decomposition defined derive diagonal dictionary discrete Fourier discrete Fourier transform discrete signal distortion rate dyadic wavelet transform energy Figure filter bank filter h finite following theorem frame Gaussian implies inner products instantaneous frequency interpolation intervals inverse KarhunenLoève basis linear log2 matching pursuit minimax minimax risk minimizes modulus maxima multiresolution noise nonlinear approximation nonzero obtained operator optimal orthogonal basis orthonormal basis piecewise polynomial Proof Proposition proves quantization reconstruction sampling satisfies scaling functions Section shows signal f singularities ſº space spline stationary process thresholding estimator timefrequency transform code translation invariant tree vanishing moments vectors verify WAVELAB wavelet basis wavelet coefficients wavelet packet basis WignerVille distribution windowed Fourier transform zero