Wavelet-based transformations for nonlinear signal processing

Robert D. Nowak, Richard G. Baraniuk

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

Abstract

Nonlinearities are often encountered in the analysis and processing of real-world signals. This paper develops new transformations for nonlinear signal processing. The theory of tensor norms is employed to show that wavelets provide an optimal basis for the new transformations. The results are applied to Volterra kernel identification.

Original languageEnglish (US)
Title of host publicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Editors Anon
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2385-2388
Number of pages4
Volume3
StatePublished - 1997
EventProceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP. Part 1 (of 5) - Munich, Ger
Duration: Apr 21 1997Apr 24 1997

Other

OtherProceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP. Part 1 (of 5)
CityMunich, Ger
Period4/21/974/24/97

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Acoustics and Ultrasonics

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