Proceedings Paper
One-dimensional signal classification by multiscale wavelet representation| Format | Member Price | Non-Member Price |
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Paper Abstract
This paper describes a modular method for classification of 1-D signals which utilizes the shift-invariant MultiScale WAvelet Representation (MSWAR). The classification employs three modules. Representation module that uses the generalization of the multiresolution wavelet representation. Measurement module that uses local and global measures to establish measures of similarity between the reference and observed signals. And finally, classification module that employs a set of decision rules. These rules are derived based on theoretical and experimental considerations, and under specified conditions, guarantee the correct classification of observed signals with five types of deformities.
Paper Details
Date Published: 20 August 1992
PDF: 11 pages
Proc. SPIE 1706, Adaptive and Learning Systems, (20 August 1992);
Published in SPIE Proceedings Vol. 1706:
Adaptive and Learning Systems
Firooz A. Sadjadi, Editor(s)
PDF: 11 pages
Proc. SPIE 1706, Adaptive and Learning Systems, (20 August 1992);
Show Author Affiliations
Hamed Sari-Sarraf, Univ. of Tennessee/Knoxville (United States)
Dragana P. Brzakovic, Univ. of Tennessee/Knoxville (United States)
Published in SPIE Proceedings Vol. 1706:
Adaptive and Learning Systems
Firooz A. Sadjadi, Editor(s)
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