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   -> Volume 2, Issue 17

Two papers available
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P. S. Krishnaprasad, University of Maryland

PostPosted: Fri Nov 29, 2002 3:39 pm    
Subject: Two papers available
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Two papers available

Abstracts of two Papers in the 27th Annual Asilomar Conference on Signals, Systems and
computers, Nov 1 - Nov 3, 1993, Asilomar, CA.

If interested in a copy, send e-mail to Include your

P. S. Krishnaprasad Tel: (301)-405-6843 (work)
Department of Electrical Engineering &
Institute for Systems Research Fax: (301)-405-6707
A.V. Williams Building - Rm 2233 Internet:
University of Maryland, College Park, MD 20742.

Orthogonal Matching Pursuit: Recursive Function Approximation
with Applications to Wavelet Decomposition

Y. C. Pati, R. Rezaiifar, and P. S. Krishnaprasad

In this paper we describe a recursive algorithm to
compute representations of functions with respect to nonorthogonal
and possibly overcomplete {em dictionaries} of elementary
building blocks {em e.g. /} affine (wavelet) frames.
We propose a modification to the Matching Pursuit algorithm
of Mallat and Zhang (1992) that maintains full backward orthogonality
of the residual (error) at every step and thereby leads to improved
We refer to this modified algorithm as Orthogonal Matching
Pursuit (OMP). It is shown that all additional computation required
for the OMP algorithm may be performed recursively.

A Fast Recursive Algorithm for System Identification and Model
Reduction Using Rational Wavelets

Y. C. Pati, R. Rezaiifar, P.S. Krishnaprasad, and W. P. Dayawansa

In earlier work [Pati and Krishnaprasad 1992]
it was shown that rational wavelet frame decompositions
of the Hardy space H2 may be used to efficiently capture time-frequency
localized behavior of stable linear systems, for purposes of system
identification and model-reduction. In this paper we examine the
problem of efficient computation of low-order rational wavelet
approximations of stable linear systems.
We describe a variant of the Matching Pursuit algorithm
[Mallat and Zhang 1992] that utilizes successive projections onto
two-dimensional subspaces to construct rational wavelet approximants.
The methods described here are illustrated by means of
both simulations and experimental results.
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