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Proceedings Paper

Symbolic And Numeric Real-Time Signal Processing
Author(s): John S. Baras
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Paper Abstract

We consider real-time sequential detection and estima-tion problems for non-gaussian signal and noise models. We develop optimal algorithms and several architectures for real-time implementation based on numerical algorithms, including asynchronous implementations of multigrid algorithms. These implementations are of high complexity, costly and cannot easily accomodate model variability. We then propose and analyze a different class of algorithms, which are symbolic, of the neural network type. The preliminary results presented here demonstrate that these algorithms have remarkably lower complexity and cost, work well under model variability and their performance is nearly optimal. We also discuss how these type of algorithms are incorporated in the DELPHI system for integrated design of signal processing systems.

Paper Details

Date Published: 16 December 1989
PDF: 10 pages
Proc. SPIE 0977, Real-Time Signal Processing XI, (16 December 1989); doi: 10.1117/12.948562
Show Author Affiliations
John S. Baras, University of Maryland (United States)

Published in SPIE Proceedings Vol. 0977:
Real-Time Signal Processing XI
J. P. Letellier, Editor(s)

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