Suhre, A.Keskin F.Ersahin, T.Cetin-Atalay, R.Ansari, R.Cetin, A.E.2016-02-082016-02-082013http://hdl.handle.net/11693/27967Date of Conference: 26-31 May 2013A 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.EnglishAbsolute valuesBiomedical image analysisCancer cell linesComputationally efficientInner productProduct definitionRegion covarianceAlgorithmsCell cultureCovariance matrixImage classificationSignal processingVectorsConference Paper10.1109/ICASSP.2013.6637825