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Wavelet and wavelet Stieltjes transforms
Proceedings of 32nd IEEE Conference on Decision and Control, 2002Some fundamental and useful properties of wavelet transforms are presented. A unified approach for both discrete and continuous time-frequency localization is introduced. >
T. Bielecki, J. Chen, S. Yau, E.B. Lin
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Lossless integer wavelet transform
IEEE Signal Processing Letters, 1997Signal compression can be obtained by wavelet transformation of integer input data followed by quantification and coding. As the quantification is usually lossy, the whole compression/decompression scheme is lossy too. We define a critical wavelet coefficient quantification, i.e., the coarsest quantification that allows perfect reconstruction.
Steven Dewitte, Jan Cornelis 0001
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Discrete Lattice Wavelet Transform
IEEE Transactions on Circuits and Systems II: Express Briefs, 2007The discrete wavelet transform (DWT) has gained a wide acceptance in denoising and compression coding of images and signals. In this work we introduce a discrete lattice wavelet transform (DLWT). In the analysis part, the lattice structure contains two parallel transmission channels, which exchange information via two crossed lattice filters.
Olkkonen, H., Olkkonen, Juuso
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International Journal of Geometric Methods in Modern Physics, 2006
We introduce the notion of extended wavelet transform for locally compact topological groups that are semidirect products with abelian normal factor, and we study its main properties. In particular, we show that this notion allows to define a weak wavelet transform — enjoying 'essentially' the same properties as a standard wavelet transform ...
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We introduce the notion of extended wavelet transform for locally compact topological groups that are semidirect products with abelian normal factor, and we study its main properties. In particular, we show that this notion allows to define a weak wavelet transform — enjoying 'essentially' the same properties as a standard wavelet transform ...
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1995 International Conference on Acoustics, Speech, and Signal Processing, 2002
Most machine speech analysis and processing is based on a warped spectral representation. The intent of the paper is to present a method by which proper warped representations can be computed efficiently. In the case of log-warping functions, the methods of the paper produce a wavelet-like transform as a linear convolution of a single log-warped ...
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Most machine speech analysis and processing is based on a warped spectral representation. The intent of the paper is to present a method by which proper warped representations can be computed efficiently. In the case of log-warping functions, the methods of the paper produce a wavelet-like transform as a linear convolution of a single log-warped ...
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Wavelets, Wavelet Filters, and Wavelet Transforms
2013Spectral characteristics of speech are known to be particularly useful in describing a speech signal such that it can be efficiently reconstructed after coding or identified for recognition. The wavelets are considered one of such efficient methods for representing the spectrum of speech signals.
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1998
Wavelets have generated a tremendous interest in both theoretical and applied mathematics over the past few years, and the fast wavelet transform (FWT) in particular has proven to be effective tool for e.g. numerical analysis and image processing.
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Wavelets have generated a tremendous interest in both theoretical and applied mathematics over the past few years, and the fast wavelet transform (FWT) in particular has proven to be effective tool for e.g. numerical analysis and image processing.
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CONVOLUTION FOR THE DISCRETE WAVELET TRANSFORM
International Journal of Wavelets, Multiresolution and Information Processing, 2011Translation and convolution associated with the discrete wavelet transform are investigated using properties of Calderón–Zygmund operator and Riesz fractional integral operator. Dual convolution is also studied. The wavelet convolution is applied to approximate functions belonging to certain Lp-spaces.
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Overcomplete Wavelet Transforms
1998This chapter deals with discrete wavelet transforms that are formed from the general samples of a continuous wavelet transform. Conceptually, there are few constraints on the spacing between sample points throughout the time—scale plane; however, computational consideration is restricted here to an interesting subclass of sampling sets that allow for ...
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Wavelet Transforms and Wavelet Approximations
1994We summarize properties of classical wavelet transforms and Wavelet Stieltjes transforms. Wavelet approximation problems are also considered for Wavelet Stieltjes transforms. This will give rise to some characterizations of general signals.
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