Enhanced signatures for event classification: The projector approach

dc.citation.bibtexNameinproceedingsen_US
dc.citation.conferenceNameIEEE-SP International Symposium on Time-frequency and Time-scale Analysisen_US
dc.contributor.authorVenkatachalam, Vidyaen_US
dc.contributor.authorAravena, Jorge.L.en_US
dc.contributor.orgDigital Signal Processing (http://dsp.rice.edu/)en_US
dc.date.accessioned2007-10-31T01:08:00Z
dc.date.available2007-10-31T01:08:00Z
dc.date.issued1998-10-01en
dc.date.modified2004-11-04en_US
dc.date.note2004-01-09en_US
dc.date.submitted1998-10-01en_US
dc.descriptionConference Paperen_US
dc.description.abstractThe classification of nonstationary signals of unknown duration is of great importance in areas like oil exploration, moving target detection, and pattern recognition. In an earlier work, we provided a solution to this problem, based on the wavelet transform, by defining representations called <i>pseudo power signatures</i> for signal classes which were independent of signal length, and proposed a simple approach using the Singular Value Decomposition to generate these signatures. This paper offers a new approach resulting in more discriminating signatures. The enhanced signatures are obtained by solving a nonlinear minimization problem involving an inverse projection. The problem formulation, solution procedure, and computational algorithm are presented in this work. The efficacy of the projection signatures in separating highly correlated signal classes is demonstrated through a simulation example.en_US
dc.identifier.citationV. Venkatachalam and J. Aravena, "Enhanced signatures for event classification: The projector approach," 1998.
dc.identifier.doihttp://dx.doi.org/10.1109/TFSA.1998.721469en_US
dc.identifier.urihttps://hdl.handle.net/1911/20412
dc.language.isoeng
dc.subjectnonstationary signals*
dc.subjectnonlinear minimization problem*
dc.subject.keywordnonstationary signalsen_US
dc.subject.keywordnonlinear minimization problemen_US
dc.subject.otherTime Frequency and Spectral Analysisen_US
dc.titleEnhanced signatures for event classification: The projector approachen_US
dc.typeConference paper
dc.type.dcmiText
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