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dc.contributor.authorSoto-Quiros, Pablo
dc.contributor.authorTorokhti, Anatoli
dc.date.accessioned2018-07-19T21:11:21Z
dc.date.available2018-07-19T21:11:21Z
dc.date.issued2016
dc.identifierhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84969919967&doi=10.1109%2fLSP.2016.2556714&partnerID=40&md5=2c25e482ea7b1bdac66f09e397d10f2fes
dc.identifier.citationSoto-Quiros, P., & Torokhti, A. (2016). Generalized brillinger-like transforms. IEEE Signal Processing Letters, 23(6), 843-847.es
dc.identifier.issn10709908
dc.identifier.urihttps://hdl.handle.net/2238/9826
dc.descriptionArtículo científicoes
dc.description.abstractWe propose novel transforms of stochastic vectors, called the generalized Brillinger transforms (GBT1 and GBT2), which are generalizations of the Brillinger transform (BT). The GBT1 extends the BT to the cases when the covariance matrix and the weighting matrix are singular, and moreover, the weighting matrix is not necessarily symmetric. We show that the GBT1 may computationally be preferable over another related optimal technique, the generic Karhunen–Loève transform (GKLT). The GBT2 generalizes the GBT1 to provide, under the condition we impose, better associated accuracy than that of the GBT1. It is achieved because of the increase in a number of parameters to optimize compared to that in the GBT1.es
dc.language.isoenges
dc.publisherIEEE Signal Processing Letterses
dc.relation.hasversion10.1109/LSP.2016.2556714es
dc.sourceIEEE Signal Processing Letterses
dc.subjectCompresión de datoses
dc.subjectFiltraciónes
dc.subjectTransformacioneses
dc.subjectVectoreses
dc.subjectResearch Subject Categories::MATHEMATICSes
dc.titleGeneralized brillinger-like transformses
dc.typeartículo originales


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