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dc.identifier.urihttp://hdl.handle.net/1951/59832
dc.identifier.urihttp://hdl.handle.net/11401/71384
dc.description.sponsorshipThis work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree.en_US
dc.formatMonograph
dc.format.mediumElectronic Resourceen_US
dc.language.isoen_US
dc.publisherThe Graduate School, Stony Brook University: Stony Brook, NY.
dc.typeThesis
dcterms.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).
dcterms.available2013-05-22T17:35:26Z
dcterms.available2015-04-24T14:47:16Z
dcterms.contributorHong, Sangjin.en_US
dcterms.contributorDoboli, Alexen_US
dcterms.creatorRajagopal, Shreyas Kodasara
dcterms.dateAccepted2013-05-22T17:35:26Z
dcterms.dateAccepted2015-04-24T14:47:16Z
dcterms.dateSubmitted2013-05-22T17:35:26Z
dcterms.dateSubmitted2015-04-24T14:47:16Z
dcterms.descriptionDepartment of Computer Engineeringen_US
dcterms.extent43 pg.en_US
dcterms.formatMonograph
dcterms.formatApplication/PDFen_US
dcterms.identifierhttp://hdl.handle.net/1951/59832
dcterms.identifierRajagopal_grad.sunysb_0771M_10403en_US
dcterms.identifierhttp://hdl.handle.net/11401/71384
dcterms.issued2010-12-01
dcterms.languageen_US
dcterms.provenanceMade available in DSpace on 2013-05-22T17:35:26Z (GMT). No. of bitstreams: 1 Rajagopal_grad.sunysb_0771M_10403.pdf: 276510 bytes, checksum: 37330c90de2e5c2fbcb31310fbb30c2d (MD5) Previous issue date: 1en
dcterms.provenanceMade available in DSpace on 2015-04-24T14:47:16Z (GMT). No. of bitstreams: 3 Rajagopal_grad.sunysb_0771M_10403.pdf.jpg: 1894 bytes, checksum: a6009c46e6ec8251b348085684cba80d (MD5) Rajagopal_grad.sunysb_0771M_10403.pdf.txt: 40404 bytes, checksum: 9c2a98b686996676630dc7e4951565ce (MD5) Rajagopal_grad.sunysb_0771M_10403.pdf: 276510 bytes, checksum: 37330c90de2e5c2fbcb31310fbb30c2d (MD5) Previous issue date: 1en
dcterms.publisherThe Graduate School, Stony Brook University: Stony Brook, NY.
dcterms.subjectclassification, clustering, ontology, scene understanding, sound localization
dcterms.subjectComputer engineering
dcterms.titleTraffic Scene Understanding using Sound-based Localization, SVM Classification and Clustering
dcterms.typeThesis


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