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 language | English (US) |
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| Title of host publication | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
| Editors | Anon |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 2385-2388 |
| Number of pages | 4 |
| Volume | 3 |
| State | Published - 1997 |
| Event | Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP. Part 1 (of 5) - Munich, Ger Duration: Apr 21 1997 → Apr 24 1997 |
Other
| Other | Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP. Part 1 (of 5) |
|---|---|
| City | Munich, Ger |
| Period | 4/21/97 → 4/24/97 |
ASJC Scopus subject areas
- Signal Processing
- Electrical and Electronic Engineering
- Acoustics and Ultrasonics