Detection of burst noise using the chi-squared goodness of fit test

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dc.contributor.advisorHassibi, Arjangen
dc.contributor.committeeMemberSwanson, Ericen
dc.creatorMarwaha, Shubraen
dc.date.accessioned2010-06-04T14:48:46Zen
dc.date.accessioned2017-05-11T22:19:59Z
dc.date.available2010-06-04T14:48:46Zen
dc.date.available2017-05-11T22:19:59Z
dc.date.issued2009-08en
dc.date.submittedAugust 2009en
dc.descriptiontexten
dc.description.abstractStatistically more test samples obtained from a single chip would give a better picture of the various noise processes present. Increasing the number of samples while testing one chip would however lead to an increase in the testing time, decreasing the overall throughput. The aim of this report is to investigate the detection of non-Gaussian noise (burst noise) in a random set of data with a small number of samples. In order to determine whether a given set of noise samples has non-Gaussian noise processes present, a Chi-Squared ‘Goodness of Fit’ test on a modeled set of random data is presented. A discussion of test methodologies using a single test measurement pass as well as two passes is presented from the obtained simulation results.en
dc.description.departmentElectrical and Computer Engineeringen
dc.format.mimetypeapplication/pdfen
dc.identifier.urihttp://hdl.handle.net/2152/ETD-UT-2009-08-180en
dc.language.isoengen
dc.subjectBurst Noiseen
dc.subjectThermal Noiseen
dc.subjectChi-Squared Distributionen
dc.subjectGaussianen
dc.subjectPearson's Goodness-of-fiten
dc.titleDetection of burst noise using the chi-squared goodness of fit testen
dc.type.genrethesisen

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