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dc.contributor.authorSayeed, Akbar M.
dc.creatorSayeed, Akbar M.
dc.date.accessioned 2007-10-31T01:04:20Z
dc.date.available 2007-10-31T01:04:20Z
dc.date.issued 1997-04-20
dc.date.submitted 1997-04-20
dc.identifier.urihttps://hdl.handle.net/1911/20333
dc.description Conference Paper
dc.description.abstract Generalizing the concept of time-frequency representations, Cohen has recently proposed a general method, based on operator correspondence rules, for generating joint distributions of arbitrary variables. As an alternative to considering all such rules, which is a practical impossibility in general, Cohen has proposed the kernel method in which different distributions are generated from a fixed rule via an arbitrary kernel. In this paper, we derive a simple but rather stringent necessary condition, on the underlying operators, for the kernel method (with the kernel functionally independent of the variables) to generate all bilinear distributions. Of the specific pairs of variables that have been studied, essentially only time and frequency satisfy the condition; in particular, the important variables of time and scale do not. The results warrant further study for a systematic characterization of bilinear distributions in Cohen's method.
dc.language.iso eng
dc.subjectTemporary
dc.subject.otherTime Frequency and Spectral Analysis
dc.title On the Equivalence of the Operator and Kernel Methods for Joint Distributions of Arbitrary Variables
dc.type Conference paper
dc.date.note 2004-01-09
dc.citation.bibtexName inproceedings
dc.date.modified 2004-01-22
dc.contributor.orgDigital Signal Processing (http://dsp.rice.edu/)
dc.subject.keywordTemporary
dc.citation.conferenceName IEEE Transactions on Signal Processing
dc.type.dcmi Text
dc.type.dcmi Text
dc.identifier.citation A. M. Sayeed, "On the Equivalence of the Operator and Kernel Methods for Joint Distributions of Arbitrary Variables," 1997.


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    Publications by Rice University Electrical and Computer Engineering faculty and graduate students

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