EDICS Number SP--2.3.1 Time-Frequency Signal Analysis Unitary similarity transformations furnish a powerful vehicle for generating infinite generic classes of signal analysis and processing tools based on concepts different from time, frequency, and scale. Implementation of these new tools involves simply preprocessing the signal by a unitary transformation, performing standard processing techniques on the transformed signal, and then (in some cases) transforming the resulting output. The resulting unitarily equivalent systems focus on the critical signal characteristics in large classes of signals and, hence, prove useful for representing and processing signals that are not well matched by current techniques. As specific examples of this procedure, we generalize linear time-invariant systems, orthonormal basis and frame decompositions, and joint time-frequency and time-scale distributions, illustrating the utility of the unitary equivalence concept for uniting seemingly disparate approaches proposed in the literature.
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438
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77
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29
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22
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22
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22
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18
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16
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16
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14
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Beyond time-frequency analysis: Energy densities in one and many dimensions
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14
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The interference structure of the Wigner distribution and related time-frequency signal representations
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12
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The use of orthogonal transforms for improving performance of adaptive filters
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12
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Warped wavelet bases: Unitary equivalence and signal processing
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11
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11
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10
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10
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7
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7
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New dimensions in wavelet analysis
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6
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Time-frequency perspectives: The chirplet transform
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6
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6
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A signal transform covariant to scale changes
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6
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6
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FAMlet, To Be or Not To Be a Wavelet
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5
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The Fourier-Mellin transform and mammalian hearing
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5
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Time-frequency design and processing of signals via smoothed Wigner distributions
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4
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A simple Wilson basis with exponential decay
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4
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Scale-invariant Wigner distribution
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4
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4
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3
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3
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Signal transforms covariant and invariant to operators
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3
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3
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2
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2
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2
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