Multidimensional mapping techniques for convolution

I. W. Selesnick, C. S. Burrus

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

It is shown that the permutation employed in the Agarwal-Cooley algorithm and the algorithm of Zalcstein is the most basic of a larger class of similarity transforms useful in the efficient computation of convolution. Using the notion of similarity and employing companion matrices, one-dimensional to multidimensional mappings are discussed for convolution, in particular, the noncircular convolutions that arise in the Winograd circular convolution algorithm. The use of such mappings in multidimensional interpolation algorithms is also discussed.

Original languageEnglish (US)
Title of host publicationDigital Speech Processing
PublisherPubl by IEEE
Pages111.288-291
ISBN (Print)0780309464
StatePublished - 1993
Event1993 IEEE International Conference on Acoustics, Speech and Signal Processing - Minneapolis, MN, USA
Duration: Apr 27 1993Apr 30 1993

Publication series

NameProceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing
Volume3
ISSN (Print)0736-7791

Other

Other1993 IEEE International Conference on Acoustics, Speech and Signal Processing
CityMinneapolis, MN, USA
Period4/27/934/30/93

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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