Date of Award

Spring 1980

Document Type

Thesis

Department

Computational Modeling & Simulation Engineering

Program/Concentration

Modeling and Simulation

Committee Director

Muralidhara R. Varanasi

Committee Member

David Livingston

Committee Member

Fenton Wallace Harrison

Call Number for Print

Special Collections; LD4331.E53C43

Abstract

The Karhunen Loeve Transform has conclusively been shown to be the optimum data compression algorithm for signals belonging to the same second order stationary process. Consequently, researchers have been searching for a fast implementation for the transform. Fast imple­mentation methods similar to those developed for other orthogonal transforms are generally not applicable to the Karhunen Loeve Transform (KLT). Also, since the basis vectors composing the transformation matrix of the KLT are the eigenvectors of the covariance matrix of the input process, they must be computed first. The research re­ ported here is an attempt to reduce the dimensionality of the KLT thereby reducing the covariance matrix computation time, the eigenvector computation time, and the trans­formation time. A solution which satisfies these objectives described. The solution consists of a zero-order predictor preceding the KLT to reduce the dimension of the data before they are transformed.

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DOI

10.25777/4c6j-kq77

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