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dc.contributor.advisorKragh, Frank E.
dc.contributor.authorGarvey, Jennie Hill
dc.date.accessioned2012-03-14T17:38:20Z
dc.date.available2012-03-14T17:38:20Z
dc.date.issued2007-06
dc.identifier.urihttp://hdl.handle.net/10945/3421
dc.description.abstractThis thesis explores the "Infomax" method of Independent Component Analysis (ICA) to accomplish blind source separation (BSS). The Infomax method separates unknown source signals from a number of signal mixtures by maximizing the entropy of a transformed set of signal mixtures and is accomplished by performing gradient ascent in MATLAB. This work specifically focuses on small numbers of two types of signals: audio signals and simple communications signals (polar non-return to zero signals). The Infomax method is found to be successful and efficient only for small numbers of signals, and improvements to the gradient ascent algorithm should be made for the Infomax algorithm to succeed for more than three signal mixtures. MATLAB implementation code is included as appendices.en_US
dc.description.urihttp://archive.org/details/independentcompo109453421
dc.format.extentxviii, 105 p. : ill. (some col.) ;en_US
dc.publisherMonterey, California. Naval Postgraduate Schoolen_US
dc.subject.lcshEntropyen_US
dc.subject.lcshBlind source separationen_US
dc.titleIndependent component analysis by entropy maximization (infomax)en_US
dc.typeThesisen_US
dc.contributor.secondreaderRoberton, R. Clark
dc.contributor.corporateNaval Postgraduate School (U.S.)
dc.description.serviceUS Navy (USN) author.en_US
dc.identifier.oclc160105313
etd.thesisdegree.nameM.S.en_US
etd.thesisdegree.levelMastersen_US
etd.thesisdegree.disciplineElectrical Engineeringen_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US
etd.verifiednoen_US
dc.description.distributionstatementApproved for public release; distribution is unlimited.


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