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Matching pursuits with timefrequency dictionaries
 IEEE Transactions on Signal Processing
, 1993
"... AbstractWe introduce an algorithm, called matching pursuit, that decomposes any signal into a linear expansion of waveforms that are selected from a redundant dictionary of functions. These waveforms are chosen in order to best match the signal structures. Matching pursuits are general procedures t ..."
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Cited by 1671 (13 self)
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AbstractWe introduce an algorithm, called matching pursuit, that decomposes any signal into a linear expansion of waveforms that are selected from a redundant dictionary of functions. These waveforms are chosen in order to best match the signal structures. Matching pursuits are general procedures
A new learning algorithm for blind signal separation

, 1996
"... A new online learning algorithm which minimizes a statistical dependency among outputs is derived for blind separation of mixed signals. The dependency is measured by the average mutual information (MI) of the outputs. The source signals and the mixing matrix are unknown except for the number of ..."
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Cited by 622 (80 self)
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A new online learning algorithm which minimizes a statistical dependency among outputs is derived for blind separation of mixed signals. The dependency is measured by the average mutual information (MI) of the outputs. The source signals and the mixing matrix are unknown except for the number
EntropyBased Algorithms For Best Basis Selection
 IEEE Transactions on Information Theory
, 1992
"... pretations (position, frequency, and scale), and we have experimented with featureextraction methods that use bestbasis compression for frontend complexity reduction. The method relies heavily on the remarkable orthogonality properties of the new libraries. It is obviously a nonlinear transformat ..."
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Cited by 675 (20 self)
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, we can use information cost functionals defined for signals with normalized energy, since all expansions in a given library will conserve energy. Since two expansions will have the same energy globally, it is not necessary to normalize expansions to compare their costs. This feature greatly enlarges
Complete discrete 2D Gabor transforms by neural networks for image analysis and compression
, 1988
"... A threelayered neural network is described for transforming twodimensional discrete signals into generalized nonorthogonal 2D “Gabor” representations for image analysis, segmentation, and compression. These transforms are conjoint spatial/spectral representations [lo], [15], which provide a comp ..."
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Cited by 478 (8 self)
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because t e elementary expansion functions are not orthogonal. One orthogonking approach developed for 1D signals by Bastiaans [8], based on biorthonormal expansions, is restricted by constraints on the conjoint sampling rates and invariance of the windowing function, as well as by the fact
Gabor’s signal expansion based on a nonorthogonal sampling geometry
"... Gabor’s signal expansion and the Gabor transform are formulated on a nonorthogonal timefrequency lattice instead of on the traditional rectangular lattice. The reason for doing so is that a nonorthogonal sampling geometry might be better adapted to the form of the window functions (in the timef ..."
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Gabor’s signal expansion and the Gabor transform are formulated on a nonorthogonal timefrequency lattice instead of on the traditional rectangular lattice. The reason for doing so is that a nonorthogonal sampling geometry might be better adapted to the form of the window functions (in the time
On the NonOrthogonal Sampling Scheme for Gabor’s Signal Expansion
"... Abstract—Gabor’s signal expansion and the Gabor transform are formulated on a nonorthogonal timefrequency lattice instead of on the traditional rectangular lattice. The reason for doing so is that a nonorthogonal sampling geometry might be better adapted to the form of the window functions (in ..."
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Abstract—Gabor’s signal expansion and the Gabor transform are formulated on a nonorthogonal timefrequency lattice instead of on the traditional rectangular lattice. The reason for doing so is that a nonorthogonal sampling geometry might be better adapted to the form of the window functions (in
UltraWideband AnalogtoDigital Conversion Via Signal Expansion
"... Abstract—We consider analog to digital (A/D) conversion, based on the quantization of coefficients obtained via the projection of a continuous time signal over a set of basis functions. The framework presented here for A/D conversion is motivated by the sampling of an input signal in domains which m ..."
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Cited by 10 (3 self)
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consider application to communications receivers, and provide a design example of a multicarrier UWB receiver. Index Terms—Analog to digital conversion (ADC), communications receiver, highspeed ADC, mixedsignal processing, quantization, signal expansion, ultrawideband. I.
Random models for sparse signals expansion on unions of bases with application to audio signals
 IEEE Trans. Signal Process
, 2008
"... ..."
Flexible Treestructured Signal Expansions Using Timevarying Wavelet Packets
, 1997
"... In this paper we address the problem of finding the best timevarying filter bank treestructured representation for a signal. The tree is allowed to vary at regular intervals, and the spacing of these changes can be arbitrarily short. The question of how to choose treestructured representations of ..."
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Cited by 23 (4 self)
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In this paper we address the problem of finding the best timevarying filter bank treestructured representation for a signal. The tree is allowed to vary at regular intervals, and the spacing of these changes can be arbitrarily short. The question of how to choose treestructured representations
Results 1  10
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