Adaptive self-defining basis functions for wavelet transforms specified with Data Modeling
Holger M. Jaenisch, James W. Handley, Claude G. Songy, Carl T. Case · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2002
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.