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dc.contributor.authorHadjicostis, Christoforos N.en
dc.creatorHadjicostis, Christoforos N.en
dc.date.accessioned2019-04-08T07:46:03Z
dc.date.available2019-04-08T07:46:03Z
dc.date.issued2003
dc.identifier.urihttp://gnosis.library.ucy.ac.cy/handle/7/43544
dc.description.abstractThis paper discusses a systematic methodology for calculating the aliasing probability when an arbitrary finite-state machine is used to compact the response of a combinational circuit to a sequence of randomly generated test input vectors. The proposed approach is general and is based on simultaneously tracking the states of two (fictitious) compactors, one driven by the response of the fault-free combinational circuit and the other one driven by the response of the faulty combinational circuit. By deriving the overall Markov chain that describes the combined behavior of these two compactors, we are able to calculate the exact aliasing probability based on its stationary distribution and to demonstrate regimes over which nonlinear compactors may be preferable over linear compactors.en
dc.sourceProceedings - IEEE International Symposium on Circuits and Systemsen
dc.sourceProceedings - IEEE International Symposium on Circuits and Systemsen
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-0038758027&partnerID=40&md5=092f9f057f6c6eae65de32711bb32cfb
dc.subjectMathematical modelsen
dc.subjectProbabilityen
dc.subjectMarkov processesen
dc.subjectMatrix algebraen
dc.subjectFinite automataen
dc.subjectCombinatorial circuitsen
dc.subjectFinite-state machinesen
dc.titleAliasing probability calculations in nonlinear compactorsen
dc.typeinfo:eu-repo/semantics/conferenceObject
dc.description.volume5
dc.description.startingpageV529
dc.description.endingpageV532
dc.author.facultyΠολυτεχνική Σχολή / Faculty of Engineering
dc.author.departmentΤμήμα Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών / Department of Electrical and Computer Engineering
dc.type.uhtypeConference Objecten
dc.contributor.orcidHadjicostis, Christoforos N. [0000-0002-1706-708X]
dc.gnosis.orcid0000-0002-1706-708X


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