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Electrical and Computer Engineering

Electrical Engineering and Computer Science Faculty Publications

Series

1993

Articles 1 - 7 of 7

Full-Text Articles in Engineering

Obscured Object Detection Via Bayesian Target Modeling Techniques, Rufus H. Cofer Nov 1993

Obscured Object Detection Via Bayesian Target Modeling Techniques, Rufus H. Cofer

Electrical Engineering and Computer Science Faculty Publications

Underground objects are by nature often severely obscured although the general character of the intervening random media may be reasonably understood. The task of detecting these underground objects also implies that their exact location and or orientation is not known. To partially counter these difficulties, one may; however, be given a model of the target of interest, e.g. a particular tank type, a water pipe, etc. To set up a quality framework for solution of the above problem, this paper utilizes the paradigm of Bayesian decision theory that promises minimum error detection given that certain probability density functions can be …

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Robust Linear Quadratic Regulation Using Neural Network, Kisuck Yoo, Michael Thursby Jul 1993

Robust Linear Quadratic Regulation Using Neural Network, Kisuck Yoo, Michael Thursby

Electrical Engineering and Computer Science Faculty Publications

Using an Artificial Neural Network (ANN) trained with the Least Mean Square (LMS) algorithm we have designed a robust linear quadratic regulator for a range of plant uncertainty. Since there is a trade-off between performance and robustness in the conventional design techniques, we propose a design technique to provide the best mix of robustness and performance. Our approach is to provide different control strategies for different levels of uncertainty. We describe how to measure these uncertainties. We will compare our multiple strategies results with those of conventional techniques e.g. H∞ control theory. A Lyapunov equation is used to define stability …

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Detection And Location Of Pipe Damage By Artificial-Neural-Netprocessed Moire Error Maps, Barry G. Grossman, Frank S. Gonzalez, Joel H. Blatt, Scott Christian Cahall May 1993

Detection And Location Of Pipe Damage By Artificial-Neural-Netprocessed Moire Error Maps, Barry G. Grossman, Frank S. Gonzalez, Joel H. Blatt, Scott Christian Cahall

Electrical Engineering and Computer Science Faculty Publications

A novel automated inspection technique to recognize, locate, and quantify damage is developed. This technique is based on two already existing technologies: video moire metrology and artificial neural networks. Contour maps generated by video moire techniques provide an accurate description of surface structure that can then be automated by means of neutral networks. Artificial neural networks offer an attractive solution to the automated interpretation problem because they can generalize from the learned samples and provide an intelligent response for similar patterns having missing or noisy data. Two dimensional video moire images of pipes with dents of different depths, at several …

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Fiber Optic Sensor For The Simultaneous Detection Of Strain And Temperature, Barry G. Grossman, Walid Emil Costandi Mar 1993

Fiber Optic Sensor For The Simultaneous Detection Of Strain And Temperature, Barry G. Grossman, Walid Emil Costandi

Electrical Engineering and Computer Science Faculty Publications

For simple interferometric fiberoptic sensors, the effects of strain and temperature are indistinguishable. This paper addresses that problem by demonstrating a single wavelength, two-mode polarimetric fiberoptic sensor capable of simultaneously measuring temperature and strain. The sensor consists of two interferometers, a polarimetric and a two-mode, formed in a single elliptical core fiber. The interferometers respond with different sensitivities to strain and temperature like simultaneous functions of the two variables. A technique is developed by which the outputs of the interferometers are used simultaneously to measure strain and temperature with rms errors less than 30 με and 1°C. Finally, measurement results …

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Optical Image Analysis Using Fractal Techniques, Samuel Peter Kozaitis, Harold Gregory Andrews, Wesley E. Foor Feb 1993

Optical Image Analysis Using Fractal Techniques, Samuel Peter Kozaitis, Harold Gregory Andrews, Wesley E. Foor

Electrical Engineering and Computer Science Faculty Publications

Using an optical technique, we classified images of natural terrain based on their fractal dimension. We calculated the fractal dimension from an optically generated power spectrum obtained with a magneto-optic spatial light modulator (SLM). By using the fractal dimension to classify images of natural terrain, our post processing was simpler that when a ring-wedge detector was used.

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Feature-Based Correlation Filters For Object Recognition, Samuel Peter Kozaitis, Wesley E. Foor Feb 1993

Feature-Based Correlation Filters For Object Recognition, Samuel Peter Kozaitis, Wesley E. Foor

Electrical Engineering and Computer Science Faculty Publications

Using an optical correlator, we experimentally evaluated a binary phase-only filter (BPOF) designed to recognize objects not in the training set used to design the filter. Such a filter is essential for recognizing objects from actual sensors. We used an approach that is as descriptive as a BPOF yet robust to object and background variations of an unknown or nonrepeatable type. We generated our filter by comparing the values of spatial frequencies of a training set. Our filter was easily calculated and offered potentially superior performance to other correlation filters.

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Design Of Distortion-Invariant Correlation Filters Using Supervised Learning, Samuel Peter Kozaitis, Rufus H. Cofer, Wesley E. Foor Jan 1993

Design Of Distortion-Invariant Correlation Filters Using Supervised Learning, Samuel Peter Kozaitis, Rufus H. Cofer, Wesley E. Foor

Electrical Engineering and Computer Science Faculty Publications

We designed binary phase-only filters from a training set of images using a statistical approach. We forced images into clusters and designed filters to recognize objects from that cluster. We report on results obtained by computer simulation comparing the performance of filters to recognize objects from clusters of one and two classes.

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