Please use this identifier to cite or link to this item: https://hdl.handle.net/11499/7587
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dc.contributor.authorÜkte, Adem-
dc.contributor.authorKızılkaya, Aydın-
dc.contributor.authorElbi, Mehmet Doğan-
dc.date.accessioned2019-08-16T12:30:36Z
dc.date.available2019-08-16T12:30:36Z
dc.date.issued2014-
dc.identifier.issn1051-2004-
dc.identifier.urihttps://hdl.handle.net/11499/7587-
dc.identifier.urihttps://doi.org/10.1016/j.dsp.2013.11.014-
dc.description.abstractThe problem of reconstructing a known high-resolution signal from a set of its low-resolution parts exposed to additive white Gaussian noise is addressed in this paper from the perspective of statistical multirate signal processing. To enhance the performance of the existing high-resolution signal reconstruction procedure that is based on using a set of linear periodically time-varying (LPTV) Wiener filter structures, we propose two empirical methods combining empirical mode decomposition- and least squares support vector machine regression-based noise reduction schemes with these filter structures. The methods originate from the idea of reducing the effects of white Gaussian noise present in the low-resolution observations before applying them directly to the LPTV Wiener filters. Performances of the proposed methods are evaluated over one-dimensional simulated signals and two-dimensional images. Simulation results show that, under certain conditions, considerable improvements have been achieved by the proposed methods when compared with the previous study that only uses a set of LPTV Wiener filter structures for the signal reconstruction process. © 2013 Elsevier Inc.en_US
dc.language.isoenen_US
dc.publisherElsevier Inc.en_US
dc.relation.ispartofDigital Signal Processing: A Review Journalen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectEmpirical mode decompositionen_US
dc.subjectHigh-resolution signal reconstructionen_US
dc.subjectLeast squares support vector machineen_US
dc.subjectMultirate signal processingen_US
dc.subjectStatistical signal processingen_US
dc.subjectLeast squares approximationsen_US
dc.subjectSignal reconstructionen_US
dc.subjectSupport vector machinesen_US
dc.subjectTime varying control systemsen_US
dc.subjectWhite noiseen_US
dc.subjectEmpirical Mode Decompositionen_US
dc.subjectHigh resolutionen_US
dc.subjectLeast squares support vector machinesen_US
dc.subjectGaussian noise (electronic)en_US
dc.titleTwo empirical methods for improving the performance of statistical multirate high-resolution signal reconstructionen_US
dc.typeArticleen_US
dc.identifier.volume26en_US
dc.identifier.issue1en_US
dc.identifier.startpage36
dc.identifier.startpage36en_US
dc.identifier.endpage49en_US
dc.authorid0000-0001-7126-0289-
dc.authorid0000-0001-8361-9738-
dc.authorid0000-0003-2521-5115-
dc.identifier.doi10.1016/j.dsp.2013.11.014-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopus2-s2.0-84893963414en_US
dc.identifier.wosWOS:000331921400003en_US
dc.identifier.scopusqualityQ2-
dc.ownerPamukkale University-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextnone-
item.languageiso639-1en-
item.openairetypeArticle-
item.fulltextNo Fulltext-
item.cerifentitytypePublications-
crisitem.author.dept10.04. Electrical-Electronics Engineering-
crisitem.author.dept10.04. Electrical-Electronics Engineering-
crisitem.author.dept10.04. Electrical-Electronics Engineering-
Appears in Collections:Mühendislik Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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