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@inbook{992722, author = {Karas, Pavel and Svoboda, David}, address = {Rijeka (CRO)}, booktitle = {Design and Architectures for Digital Signal Processing}, doi = {http://dx.doi.org/10.5772/3456}, edition = {1st ed.}, keywords = {convolution; algorithms; FFT; separable convolution; recursive filters; parallelization; decomposition}, howpublished = {tištěná verze "print"}, language = {eng}, location = {Rijeka (CRO)}, isbn = {978-953-51-0874-0}, pages = {179-208}, publisher = {InTech}, title = {Algorithms for Efficient Computation of Convolution}, url = {http://www.intechopen.com/books/design-and-architectures-for-digital-signal-processing/algorithms-for-efficient-computation-of-convolution}, year = {2013} }
TY - CHAP ID - 992722 AU - Karas, Pavel - Svoboda, David PY - 2013 TI - Algorithms for Efficient Computation of Convolution VL - Neuveden PB - InTech CY - Rijeka (CRO) SN - 9789535108740 KW - convolution KW - algorithms KW - FFT KW - separable convolution KW - recursive filters KW - parallelization KW - decomposition UR - http://www.intechopen.com/books/design-and-architectures-for-digital-signal-processing/algorithms-for-efficient-computation-of-convolution L2 - http://www.intechopen.com/books/design-and-architectures-for-digital-signal-processing/algorithms-for-efficient-computation-of-convolution N2 - Convolution is an important mathematical tool in both fields of signal and image processing. It is em-ployed in filtering, denoising, edge detection, correlation, compression, deconvolution, simulation, and in many other applications. Although the concept of convolution is not new, the efficient computation of convolution is still an open topic. As the amount of processed data is constantly increasing, there is considerable request for fast manipulation with huge data. Moreover, there is demand for fast algorithms which can exploit computational power of modern parallel architectures. The aim of this chapter is to review the algorithms and approaches for computation of convolution with regards to various properties such as signal and kernel size or kernel separability (when pro-cessing n-dimensional signals). Target architectures include superscalar and parallel processing units (namely CPU, DSP, and GPU), programmable architectures (e.g. FPGA), and distributed systems (such as grids). The structure of the chapter is designed to cover various applications with respect to the signal size, from small to large scales. ER -
KARAS, Pavel and David SVOBODA. Algorithms for Efficient Computation of Convolution. In \textit{Design and Architectures for Digital Signal Processing}. 1st ed. Rijeka (CRO): InTech, 2013, p.~179-208. ISBN~978-953-51-0874-0. Available from: https://dx.doi.org/10.5772/3456.
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