A multiplication-free framework for signal processing and applications in biomedical image analysis

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dc.citation.epage1127en_US
dc.citation.spage1123en_US
dc.contributor.authorSuhre, A.en_US
dc.contributor.authorKeskin F.en_US
dc.contributor.authorErsahin, T.en_US
dc.contributor.authorCetin-Atalay, R.en_US
dc.contributor.authorAnsari, R.en_US
dc.contributor.authorCetin, A.E.en_US
dc.coverage.spatialVancouver, BC, Canadaen_US
dc.date.accessioned2016-02-08T12:06:57Z
dc.date.available2016-02-08T12:06:57Z
dc.date.issued2013en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.departmentDepartment of Molecular Biology and Geneticsen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.descriptionDate of Conference: 26-31 May 2013en_US
dc.description.abstractA new framework for signal processing is introduced based on a novel vector product definition that permits a multiplier-free implementation. First a new product of two real numbers is defined as the sum of their absolute values, with the sign determined by product of the hard-limited numbers. This new product of real numbers is used to define a similar product of vectors in RN. The new vector product of two identical vectors reduces to a scaled version of the l1 norm of the vector. The main advantage of this framework is that it yields multiplication-free computationally efficient algorithms for performing some important tasks in signal processing. An application to the problem of cancer cell line image classification is presented that uses the notion of a co-difference matrix that is analogous to a covariance matrix except that the vector products are based on our new proposed framework. Results show the effectiveness of this approach when the proposed co-difference matrix is compared with a covariance matrix. © 2013 IEEE.en_US
dc.identifier.doi10.1109/ICASSP.2013.6637825en_US
dc.identifier.urihttp://hdl.handle.net/11693/27967
dc.language.isoEnglishen_US
dc.publisherIEEEen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/ICASSP.2013.6637825en_US
dc.source.title2013 IEEE International Conference on Acoustics, Speech and Signal Processingen_US
dc.subjectAbsolute valuesen_US
dc.subjectBiomedical image analysisen_US
dc.subjectCancer cell linesen_US
dc.subjectComputationally efficienten_US
dc.subjectInner producten_US
dc.subjectProduct definitionen_US
dc.subjectRegion covarianceen_US
dc.subjectAlgorithmsen_US
dc.subjectCell cultureen_US
dc.subjectCovariance matrixen_US
dc.subjectImage classificationen_US
dc.subjectSignal processingen_US
dc.subjectVectorsen_US
dc.titleA multiplication-free framework for signal processing and applications in biomedical image analysisen_US
dc.typeConference Paperen_US
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