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dc.contributor.advisorKozat, Süleyman Serdar
dc.contributor.authorUtlu, İhsan
dc.date.accessioned2017-03-01T13:04:51Z
dc.date.available2017-03-01T13:04:51Z
dc.date.copyright2017-01
dc.date.issued2017-02
dc.date.submitted2017-02-23
dc.identifier.urihttp://hdl.handle.net/11693/32812
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (M.S.): Bilkent University, Department of Electrical and Electronics Engineering, İhsan Doğramacı Bilkent University, 2017.en_US
dc.descriptionIncludes bibliographical references (leaves 46-52).en_US
dc.description.abstractWe propose a novel algorithm for distributed processing applications constrained by the available communication resources using diffusion strategies that achieves up to a 103 fold reduction in the communication load over the network, while delivering a comparable performance with respect to the state of the art. After the computation of the local estimates, the information is diffused among the processing elements (or nodes) non-uniformly in time by conditioning the information transfer on level-crossings of the diffused parameter, resulting in a greatly reduced communication requirement. We provide the mean and meansquare stability analyses of the proposed algorithm, and illustrate the gain in communication efficiency compared to other reduced-communication distributed estimation schemes.en_US
dc.description.statementofresponsibilityby İhsan Utlu.en_US
dc.format.extentix, 52 leaves : charts (some color) ; 29 cm.en_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectDistributed estimationen_US
dc.subjectAdaptive networksen_US
dc.subjectEvent-triggered communicationen_US
dc.subjectLevel-crossing quantizationen_US
dc.titleEvent-triggered distributed estimation with reduced communication loaden_US
dc.title.alternativeDüşük iletişim yüklü olay-tetikli dağıtık kestirimen_US
dc.typeThesisen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US
dc.identifier.itemidB155244


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