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dc.contributor.advisorDoboli, Alexen_US
dc.contributor.authorRajagopal, Shreyas Kodasaraen_US
dc.contributor.otherDepartment of Computer Engineeringen_US
dc.date.accessioned2013-05-22T17:35:26Z
dc.date.available2013-05-22T17:35:26Z
dc.date.issued1-Dec-10en_US
dc.date.submitted10-Decen_US
dc.identifierRajagopal_grad.sunysb_0771M_10403en_US
dc.identifier.urihttp://hdl.handle.net/1951/59832
dc.description43 pg.en_US
dc.description.abstractThe thesis is about an embedded system application aimed at identifying the semantics of traffic based on acoustic data. Sound localization, classification and clustering are used for scene understanding. The report presents a set of experiments used to simulate different traffic scenarios. An alternative implementation for sound localization is also explored, where fixed point representation of rational numbers is used instead of floating point numbers. The results for both the implementations are compared in terms of execution speed and accuracy for a Programmable System-on-Chip (PSoC).en_US
dc.description.sponsorshipStony Brook University Libraries. SBU Graduate School in Department of Computer Engineering. Charles Taber (Dean of Graduate School).en_US
dc.formatElectronic Resourceen_US
dc.language.isoen_USen_US
dc.publisherThe Graduate School, Stony Brook University: Stony Brook, NY.en_US
dc.subject.lcshComputer engineeringen_US
dc.subject.otherclassification, clustering, ontology, scene understanding, sound localizationen_US
dc.titleTraffic Scene Understanding using Sound-based Localization, SVM Classification and Clusteringen_US
dc.typeThesisen_US
dc.description.advisorAdvisor(s): Doboli, Alex . Committee Member(s): Hong, Sangjin.en_US
dc.mimetypeApplication/PDFen_US


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