Centralized and decentralized detection with cost-constrained measurements

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buir.advisorGezici, Sinan
dc.contributor.authorLaz, Eray
dc.date.accessioned2016-05-31T08:45:30Z
dc.date.available2016-05-31T08:45:30Z
dc.date.copyright2016-05
dc.date.issued2016-05
dc.date.submitted2016-05-30
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionIncludes bibliographical references (leaves 39-42).en_US
dc.descriptionThesis (M.S.): Bilkent University, Department of Electrical and Electronics Engineering, İhsan Doğramacı Bilkent University, 2016.en_US
dc.description.abstractIn this thesis, optimal detection performance of centralized and decentralized detection systems is investigated in the presence of cost constrained measurements. For the evaluation of detection performance, Bayesian, Neyman-Pearson and J- divergence criteria are considered. The main goal for the Bayesian criterion is to minimize the probability of error (more generally, the Bayes risk) under a constraint on the total cost of the measurement devices. In the Neyman-Pearson framework, the probability of detection is to be maximized under a given cost constraint. In the distance based criterion, the J-divergence between the distributions of the decision statistics under di erent hypotheses is maximized subject to a total cost constraint. The probability of error expressions are obtained for both centralized and decentralized detection systems, and the optimization problems are proposed for the Bayesian criterion. The probability of detection and probability of false alarm expressions are obtained for the Neyman-Pearson strategy and the optimization problems are presented. In addition, J-divergences for both centralized and decentralized detection systems are calculated and the corresponding optimization problems are formulated. The solutions of these problems indicate how to allocate the cost budget among the measurement devices in order to achieve the optimum performance. Numerical examples are presented to discuss the results.en_US
dc.description.degreeM.S.en_US
dc.description.provenanceSubmitted by Betül Özen (ozen@bilkent.edu.tr) on 2016-05-31T08:45:30Z No. of bitstreams: 1 thesis.pdf: 383736 bytes, checksum: 8c83ae601a5788d844400cb34dcf6d2d (MD5)en
dc.description.provenanceMade available in DSpace on 2016-05-31T08:45:30Z (GMT). No. of bitstreams: 1 thesis.pdf: 383736 bytes, checksum: 8c83ae601a5788d844400cb34dcf6d2d (MD5) Previous issue date: 2016-05en
dc.description.statementofresponsibilityby Eray Laz.en_US
dc.format.extentviii, 42 leaves : illustrations, charts.en_US
dc.identifier.itemidB153346
dc.identifier.urihttp://hdl.handle.net/11693/29117
dc.language.isoEnglishen_US
dc.publisherBilkent Universityen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectHypothesis testingen_US
dc.subjectMeasurement costen_US
dc.subjectDecentralized detectionen_US
dc.subjectCentralized detectionen_US
dc.subjectSensor networksen_US
dc.titleCentralized and decentralized detection with cost-constrained measurementsen_US
dc.title.alternativeMaliyet kısıtlı ölçümlerle merkezi ve dağıtılmış sezimen_US
dc.typeThesisen_US

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Dept. of Electrical and Electronics Engineering - Master's degree