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Proceedings Paper

Adaptive self-defining basis functions for wavelet transforms specified with data modeling
Author(s): Holger M. Jaenisch; James W. Handley; Claude G. Songy; Carl T. Case
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Paper Abstract

It has been shown in the extensive literature that wavelets are applicable to data processing. However, two shortcomings exist in using this mathematical technique for real-time in-line data processing. These must be addressed before any robust wavelet processing architectures can be created and applied to data in a non man-in-the-loop fashion. The first is autonomous selection of the basis function, and the other knowing when to stop acquiring data and invoke the wavelet transformation. Once these two ambiguities are resolved, autonomous feature selection algorithms can be created and the utility and performance of the resulting wavelet features evaluated. In January 2002, the authors began an IRAD study to examine varioius proposed methods for resolving these issues. This paper presents the results to date for generating wavelet transform basis functions from given 1-D time series data.

Paper Details

Date Published: 27 November 2002
PDF: 11 pages
Proc. SPIE 4789, Algorithms and Systems for Optical Information Processing VI, (27 November 2002); doi: 10.1117/12.450845
Show Author Affiliations
Holger M. Jaenisch, SPARTA, Inc. (United States)
James W. Handley, SPARTA, Inc. (United States)
Claude G. Songy, SPARTA, Inc. (United States)
Carl T. Case, SPARTA, Inc. (United States)


Published in SPIE Proceedings Vol. 4789:
Algorithms and Systems for Optical Information Processing VI
Bahram Javidi; Demetri Psaltis, Editor(s)

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