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Proceedings Paper

Self-similar set identification in the time-scale domain
Author(s): Sam Heidari; George A. Tsihrintzis; Chrysostomos L. Nikias; Edmond A. Jonckheere
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Paper Abstract

This paper presents an extensive study of the wavelet transform of self-similar signals and its properties. In particular, theorems are derived for the wavelet transform of deterministic self- similar signals and are used to identify and characterize them. Applications of interest, such as characterization and analysis of real chaotic signals in the presence of additive noise, are included.

Paper Details

Date Published: 11 October 1994
PDF: 11 pages
Proc. SPIE 2303, Wavelet Applications in Signal and Image Processing II, (11 October 1994); doi: 10.1117/12.188781
Show Author Affiliations
Sam Heidari, Univ. of Southern California (United States)
George A. Tsihrintzis, Univ. of Southern California (United States)
Chrysostomos L. Nikias, Univ. of Southern California (United States)
Edmond A. Jonckheere, Univ. of Southern California (United States)


Published in SPIE Proceedings Vol. 2303:
Wavelet Applications in Signal and Image Processing II
Andrew F. Laine; Michael A. Unser, Editor(s)

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