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Articles 331 - 360 of 370

Full-Text Articles in Electrical and Computer Engineering

Novel High-Speed Architecture For Machine Vision Applications, Bassam S. Farroha, Raghvendra G. Deshmukh Oct 1996

Novel High-Speed Architecture For Machine Vision Applications, Bassam S. Farroha, Raghvendra G. Deshmukh

Electrical Engineering and Computer Science Faculty Publications

This paper focuses on producing a state-of-the-art technique for designing an image recognition system for machine vision applications. The motivation behind the new system design is to provide a unique methodology, using strategic design techniques, to implement a system that addresses real-world image recognition applications. The introduction of application-specific, massively parallel array of processors, where low-level processing is accomplished on reconfigurable hardware structures, highlights the scheme. The system was built and simulated on a VLSI chip and results were verified using Electric Rules Check and Harris Timing Analysis examination tools. The system is composed of there functional layers and a …

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Real-Time Optically Processed Face Recognition System Based On Arbitrary Moiré Contours, Rafael A. Andrade, Bernard R. Gilbert, Donald W. Dawson, Chris L. Hart, Samuel Peter Kozaitis, Joel H. Blatt Sep 1996

Real-Time Optically Processed Face Recognition System Based On Arbitrary Moiré Contours, Rafael A. Andrade, Bernard R. Gilbert, Donald W. Dawson, Chris L. Hart, Samuel Peter Kozaitis, Joel H. Blatt

Electrical Engineering and Computer Science Faculty Publications

We demonstrate a hybrid electronic-optical real-time system that performs 3-D face recognition. We constructed custom reference gratings that formed a desired moire pattern when mixed with images of structurally illuminated faces. The moiré patterns could be in any form such as, equal depth contours, error maps, or any arbitrary pattern. We demonstrate video methods to generate such error maps in real time, thus developing a real-time automated face recognition system based on the optical processing of arbitrary moiré contours. We chose the moiré pattern to be in the form of a Fresnel zone plate, which is displayed on a liquid …

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Multiresolution Wavelet Processing For Binary Phase-Only Filters, Samuel Peter Kozaitis Jul 1996

Multiresolution Wavelet Processing For Binary Phase-Only Filters, Samuel Peter Kozaitis

Electrical Engineering and Computer Science Faculty Publications

We used the discrete wavelet transform to approximate an image at a lower resolution in preparation for object recognition using correlation techniques. We cross- correlated the low-resolution image with a similarly processed image containing an object of interest. Then, we synthesized the cross-correlation result to the resolution of original image. Using this approach, we avoided cross- correlating an image multiple times and passing information between levels of an image representation such as an image pyramid. We satisfactorily identified objects at 1/4 resolution using the wavelet representation and binary phase-only filters. We chose wavelets based on their impulse response and found …

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Real-Time Optically Processed Face-Recognition System Based On Arbitrary Moire Contours, Rafael A. Andrade, Bernard R. Gilbert, Donald W. Dawson, Chris L. Hart, Samuel Peter Kozaitis, Joel H. Blatt Jul 1996

Real-Time Optically Processed Face-Recognition System Based On Arbitrary Moire Contours, Rafael A. Andrade, Bernard R. Gilbert, Donald W. Dawson, Chris L. Hart, Samuel Peter Kozaitis, Joel H. Blatt

Electrical Engineering and Computer Science Faculty Publications

A real-time diffraction based optical processed 3-D shape recognition system has been built and demonstrated. The system uses an Ar-ion laser interferometer to project variable spatial frequency structural illumination on 3 dimensional targets which are viewed by a camera. The video data is mixed with a computer generated mask (converted to RS-170 video) and the resulting output video signal is sent to a liquid crystal television (modified to function as a spatial light modulator) which is illuminated by a He-Ne laser. The video mixing process, based on a commercial Chroma-Key circuit, generates an arbitrary moire pattern which is a function …

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Determination Of Adaptively Adjusted Coefficients For Hopfield Neural Networks Utilizing The Energy Function, Chiyeon Park, Donald W. Fausett Mar 1996

Determination Of Adaptively Adjusted Coefficients For Hopfield Neural Networks Utilizing The Energy Function, Chiyeon Park, Donald W. Fausett

Electrical Engineering and Computer Science Faculty Publications

With its potential for parallel computation and general applicability, the Hopfield neural network has been investigated and improved by many researchers in order to extend its usefulness to various combinatorial problems. In spite of its success in several applications within different energy function formulations, determination of the energy coefficients has been based primarily on trial and error methods since no practical and systematic way of finding good values has been available preciously, although some theoretical analyses have been presented. In this paper, we present a methodical procedure which adaptively determines the energy coefficients leading to a valid solution as the …

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Neural Network Architecture For Solving The Algebraic Matrix Riccati Equation, Fredric M. Ham, Emmanuel G. Collins Mar 1996

Neural Network Architecture For Solving The Algebraic Matrix Riccati Equation, Fredric M. Ham, Emmanuel G. Collins

Electrical Engineering and Computer Science Faculty Publications

This paper presents a neurocomputing approach for solving the algebraic matrix Riccati equation. This approach is able to utilize a good initial condition to reduce the computation time in comparison to standard methods for solving the Riccati equation. The repeated solutions of closely related Riccati equations appears in homotopy algorithms to solve certain problems in fixed-architecture control. Hence, the new approach has the potential to significantly speed-up these algorithms. It also has potential applications in adaptive control. The structured neural network architecture is trained using error backpropagation based on a steepest-descent learning rule. An example is given which illustrates the …

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Similarity-Based Learning For Pattern Classification, Laurene V. Fausett Mar 1996

Similarity-Based Learning For Pattern Classification, Laurene V. Fausett

Electrical Engineering and Computer Science Faculty Publications

Several standard neural networks, including counterpropagation networks, predictive ART networks, and radial basis function networks, are based on a combination of clustering (unsupervised learning) and mapping (supervised learning). A comparison of the characteristics of these networks for pattern classification problems is presented.

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Partial Least-Squares Regression Neural Network (Plsnet) With Supervised Adaptive Modular Learning, Fredric M. Ham, Ivica Kostanic Mar 1996

Partial Least-Squares Regression Neural Network (Plsnet) With Supervised Adaptive Modular Learning, Fredric M. Ham, Ivica Kostanic

Electrical Engineering and Computer Science Faculty Publications

We present in this paper an adaptive linear neural network architecture called PLSNET. This network is based on partial least-squares (PLS) regression. The architecture is a modular network with stages that are associated with the desired number of PLS factors that are to be retained. PLSNET actually consists of two separate but coupled architectures, PLSNET-C for PLS calibration, and PLSNET-P for prediction (or estimation). We show that PLSNET-C can be trained by supervised learning with three standard Hebbian learning rules that extracts the PLS weight loading vectors, the regression coefficients, and the loading vectors for the univariate output component case …

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Comparison Of Function Approximation With Sigmoid And Radial Basis Function Networks, Gary Russell, Laurene V. Fausett Mar 1996

Comparison Of Function Approximation With Sigmoid And Radial Basis Function Networks, Gary Russell, Laurene V. Fausett

Electrical Engineering and Computer Science Faculty Publications

Theoretical and computational results have demonstrated that several types of neural networks have the universal approximation property, i.e., the ability to represent any continuous function to an arbitrary degree of accuracy, given enough hidden units. However, practical considerations, such as the relative advantages of different networks for function approximation using a small to moderate number of hidden units, are not as well understood. This paper presents preliminary results of investigations into the comparison of networks using sigmoidal activation functions and networks using radial basis functions. In particular, we consider the ability of several such networks to learn mappings from the …

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Application Of Neural Networks To Channel Assignment For Cellular Cdma Networks With Multiple Services And Mobile Base Stations, William S. Hortos Mar 1996

Application Of Neural Networks To Channel Assignment For Cellular Cdma Networks With Multiple Services And Mobile Base Stations, William S. Hortos

Electrical Engineering and Computer Science Faculty Publications

The use of artificial neural networks to the channel assignment problem for cellular code- division multiple access (CDMA) telecommunications systems is considered. CDMA takes advantage of voice activity and spatial isolation because its capacity is only interference limited, unlike time-division multiple access (TDMA) and frequency-division multiple access (FDMA) where capacities are bandwidth limited. Any reduction in interference in CDMA translates linearly into increased capacity. FDMA and TDMA use a frequency reuse pattern as a method to increase capacity, while CDMA reuses the same frequency for all cells and gains a reuse efficiency by means of orthogonal codes. The latter method …

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Discrete Riccati Equation Solutions: Distributed Algorithms, Demetrios G. Lainiotis, Konstantinos N. Kostas Plataniotis, Paraskevas Papaparaskeva Jan 1996

Discrete Riccati Equation Solutions: Distributed Algorithms, Demetrios G. Lainiotis, Konstantinos N. Kostas Plataniotis, Paraskevas Papaparaskeva

Electrical Engineering and Computer Science Faculty Publications

In this paper new distributed algorithms for the solution of the discrete Riccati equation are introduced. The algorithms are used to provide robust and computational efficient solutions to the discrete Riccati equation. The proposed distributed algorithms are theoretically interesting and computationally attractive.

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Pixel-Registered Image Fusion, Rufus H. Cofer, Samuel Peter Kozaitis Jul 1995

Pixel-Registered Image Fusion, Rufus H. Cofer, Samuel Peter Kozaitis

Electrical Engineering and Computer Science Faculty Publications

One of the highest potential uses of image fusion is that of recognition of critical targets. The continuing image fusion question then is how to make optimal use of the often disparate forms of encountered image detail during fusion. Toward this end, many techniques have been advanced for fusion to a single viewable image. Fewer techniques have been suggested toward fusion with the goal of directly improving target detection or recognition. Based upon emerging trends in pixel accurate registration of images, we show the theoretical foundations required to optimally fuse target imagery for recognition. Results obtained can be applied to …

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Multiresolution Binary Optical Correlator Using The Wavelet Transform, Samuel Peter Kozaitis Jun 1995

Multiresolution Binary Optical Correlator Using The Wavelet Transform, Samuel Peter Kozaitis

Electrical Engineering and Computer Science Faculty Publications

We used morphological filters to approximate wavelet scaling functions for multiresolution processing of an image. Because some spatial light modulators (SLMs) can only display binary data, wavelet processing of binary images is inhibited. Therefore, we considered an alternative way - morphological processing - to generate a wavelet representation that consists entirely of binary elements. The effects of these filters are dependent on the input signal and cannot be generalized. Therefore, we used a statistical approach to approximate the scaling functions or various wavelets using morphological filters. ©2005 Copyright SPIE - The International Society for Optical Engineering.

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Nondispersive Triple-Correlation Spectrometer For Trace Gas Analysis, Samuel Peter Kozaitis, Bruce J. Bradshaw Jun 1995

Nondispersive Triple-Correlation Spectrometer For Trace Gas Analysis, Samuel Peter Kozaitis, Bruce J. Bradshaw

Electrical Engineering and Computer Science Faculty Publications

We showed that the output of an autocorrelation with a gas spectrum could be triple-correlated to reduce the effects of Gaussian noise. Such processing is directly applicable to a correlation spectrometer but could be applied to other designs. In spite of the added processing, triple-correlating the output of a spectrometer allowed us to generally increase the SNR by over a factor of ten. Even with as few as two measurements good results were obtained.

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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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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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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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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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Bayesian Defeat Of Camouflage, Rufus H. Cofer Sep 1992

Bayesian Defeat Of Camouflage, Rufus H. Cofer

Electrical Engineering and Computer Science Faculty Publications

A new technique is shown for refining and reducing incoming camouflage data based upon the Bayesian paradigm. Innovation is displayed in use of a statistical conditioning sequence that avoids the need to form target features from the data. The result is a simplified and more accurate probabilistic indication of actual target presence. This probabilistic indication can then be incorporated into a variety of target detection scenarios or, alternately, to form the basis of a theoretically optimal Bayesian target detector. Numeric simulation is presented to show the effectiveness of the technique against simulated camouflage

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Supercomputer-Based Spherical Scene Projector, Harold K. Brown, John G. Madry, Rufus H. Cofer, Samuel Peter Kozaitis Sep 1992

Supercomputer-Based Spherical Scene Projector, Harold K. Brown, John G. Madry, Rufus H. Cofer, Samuel Peter Kozaitis

Electrical Engineering and Computer Science Faculty Publications

A multi-technology high performance computing system based on the Open Parallel Architecture Design Specification (OPADS) platform is being evaluated for use as a graphics engine for spherical scene projection. This system is designed to make available the massive quantities of real-time processing power needed to support complete real time scene generation and projection of complex dynamical maneuvers for applications such as scientific visualization and three dimensional database creation and interaction. A comparison is also provided between head mounted projection systems and walk-in spherical scene projection systems.

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Explanation Mode For Bayesian Automatic Object Recognition, Thomas L. Hazlett, Rufus H. Cofer, Harold K. Brown Sep 1992

Explanation Mode For Bayesian Automatic Object Recognition, Thomas L. Hazlett, Rufus H. Cofer, Harold K. Brown

Electrical Engineering and Computer Science Faculty Publications

One of the more useful techniques to emerge from AI is the provision of an explanation modality used by the researcher to understand and subsequently tune the reasoning of an expert system. Such a capability, missing in the arena of statistical object recognition, is not that difficult to provide. Long standing results show that the paradigm of Bayesian object recognition is truly optimal in a minimum probability of error sense. To a large degree, the Bayesian paradigm achieves optimality through adroit fusion of a wide range of lower informational data sources to give a higher quality decision - a very …

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Practical Constraints Pertinent To The Design Of Neural Networks, Said Sadek Abdallah, Rufus H. Cofer Aug 1992

Practical Constraints Pertinent To The Design Of Neural Networks, Said Sadek Abdallah, Rufus H. Cofer

Electrical Engineering and Computer Science Faculty Publications

in designing a feedforward neural network for numerical computation using the backpropagation algorithm it is essential to know that the resulting network has a practical global minimum, meaning that convergence to a stationary solution can be achieved in reasonable time and using a network of reasonable size. This is in contrast to theoretical results indicating that any square-integrable (L2) function can be computed assuming that an unlimited number of neurons are available. A class of problems is discussed that does not fit into this category. Although these problems are conceptually simple, it is shown that in practice convergence to a …

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Feature-Based Correlation Filters For Distortion Invariance, Samuel Peter Kozaitis, Robert Petrilak, Wesley E. Foor Jul 1992

Feature-Based Correlation Filters For Distortion Invariance, Samuel Peter Kozaitis, Robert Petrilak, Wesley E. Foor

Electrical Engineering and Computer Science Faculty Publications

In an optical correlator, binary phase-only filters (BPOFs) that recognize objects that vary in a nonrepeatable way are essential for recognizing objects from actual sensors. An approach is required that is as descriptive as a BPOF yet robust to object and background variations of an unknown or nonrepeatable type. We developed a BPOF that was more robust than a synthetic discriminant function (SDF) filter. This was done by creating a filter that retained the invariant features of a training set. By simulation, our feature-based filter offered a range of performance by setting a parameter to different values. As the value …

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Optical Estimation Of Fractal Dimension For Image Assessment, Samuel Peter Kozaitis, Rufus H. Cofer Jul 1992

Optical Estimation Of Fractal Dimension For Image Assessment, Samuel Peter Kozaitis, Rufus H. Cofer

Electrical Engineering and Computer Science Faculty Publications

We modeled an optical system for estimation of the fractal dimension to provide a measure of surface roughness for an entire image and for image segmentation. Although the simulated optical result was similar to that calculated by digital techniques, both suffered from problems known to occur with estimating fractal dimension. Furthermore, the optical estimation did not have as good a resolution as that obtained with digital estimates due primarily to the limited dynamic range of the detector.

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Multiresolution Template Matching Using An Optical Correlator, Samuel Peter Kozaitis, Zia Saquib, Rufus H. Cofer, Wesley E. Foor Jul 1992

Multiresolution Template Matching Using An Optical Correlator, Samuel Peter Kozaitis, Zia Saquib, Rufus H. Cofer, Wesley E. Foor

Electrical Engineering and Computer Science Faculty Publications

Infrared imagery of 512 × 512 pixels was processed with 128 × 128 arrays by computer simulation of an optical correlator using various correlation filters. Pyramidal processing using binary phase-only filters (BPOFs), synthetic discriminant function (SDF) filters, and feature-based filters was used to process an entire image in parallel at different resolutions. Results showed that both SDF and feature-based filters were more robust to the effects of thresholding input imagery than BPOFs. The feature-based filters offered a range of performance by setting a parameter to different values. As the value of the parameter was changed, correlation peaks within the training …

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Detection, Location, And Quantification Of Structural Damage By Neural-Netprocessed Moire Profilometry, Barry G. Grossman, Frank S. Gonzalez, Joel H. Blatt, Jeffery A. Hooker Mar 1992

Detection, Location, And Quantification Of Structural Damage By Neural-Netprocessed Moire Profilometry, Barry G. Grossman, Frank S. Gonzalez, Joel H. Blatt, Jeffery A. Hooker

Electrical Engineering and Computer Science Faculty Publications

The development of efficient high speed techniques to recognize, locate, and quantify damage is vitally important for successful automated inspection systems such as ones used for the inspection of undersea pipelines. Two critical problems must be solved to achieve these goals: the reduction of nonuseful information present in the video image and automatic recognition and quantification of extent and location of damage. Artificial neural network processed moire profilometry appears to be a promising technique to accomplish this. Real time video moire techniques have been developed which clearly distinguish damaged and undamaged areas on structures, thus reducing the amount of extraneous …

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Composite Damage Assessment Employing An Optical Neural Network Processor And An Embedded Fiberoptic Sensor Array, Barry G. Grossman, Xing Gao, Michael H. Thursby Dec 1991

Composite Damage Assessment Employing An Optical Neural Network Processor And An Embedded Fiberoptic Sensor Array, Barry G. Grossman, Xing Gao, Michael H. Thursby

Electrical Engineering and Computer Science Faculty Publications

This paper discusses a novel approach for composite damage assessment with potential for DoD, NASA, and commercial applications. We have analyzed and modeled a two dimensional composite damage assessment system for real-time monitoring and determination of damage location in a composite structure. The system combines two techniques: a fiberoptic strain sensor array and an optical neural network processor. A two dimensional fiberoptic sensor array embedded in the composite structure during the manufacturing process can be used to detect changes in the mechanical strain distribution caused by subsequent damage to the structure. The optical processor, a pre-trained Kohonen neural network, has …

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