Quadratic modified fermat transforms for fast convolution and adaptive filtering

C. Radhakrishnan, W. K. Jenkins

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

7 Scopus citations

Abstract

Previously it was shown that the Modified Fermat Number Transform (MFNT) reduces computational requirements for an adaptive filter architecture based on convolution achieved by FNT block processing (FNTBP). The work of this paper extends the MFNT to a Quadratic MFNT (QMFNT) by introducing Left-angle circular convolution and interpreting the result as a quadratic representation of the convolution output obtained by combining the Left-angle and Right-angle circular convolutions. The resulting computational complexity is similar to the MFNT but provides a significant reduction of the dynamic range constraints on the input sequences. This results in an adaptive filter architecture that has increased computational efficiency and potentially lower power requirements when used to realize nano-scale adaptive filters.

Original languageEnglish (US)
Title of host publication2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3806-3809
Number of pages4
ISBN (Print)9781424442966
DOIs
StatePublished - 2010
Event2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Dallas, TX, United States
Duration: Mar 14 2010Mar 19 2010

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010
Country/TerritoryUnited States
CityDallas, TX
Period3/14/103/19/10

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Quadratic modified fermat transforms for fast convolution and adaptive filtering'. Together they form a unique fingerprint.

Cite this