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Full-Text Articles in Engineering
Circular Optimal Trade-Off And Distance-Classifier Correlation Filters, Samuel Peter Kozaitis, Sila Thangwaritorn
Circular Optimal Trade-Off And Distance-Classifier Correlation Filters, Samuel Peter Kozaitis, Sila Thangwaritorn
Electrical Engineering and Computer Science Faculty Publications
We use circular versions of advanced distortion-invariant filters such as optimal trade-off synthetic discriminant function and distance classifier correlation filters to obtain rotation invariance with an optical correlator. The filter noise performance is compared using a common measure of probability of error because the filters have different characteristics. The filters are real-valued so they can be implemented on a variety of SLMs. The circular symmetry of the filters significantly decreases their computational requirement.
Comparison Of Neural Network Applications For Channel Assignment In Cellular Tdma Networks And Dynamically Sectored Pcs Networks, William S. Hortos
Comparison Of Neural Network Applications For Channel Assignment In Cellular Tdma Networks And Dynamically Sectored Pcs Networks, William S. Hortos
Electrical Engineering and Computer Science Faculty Publications
The use of artificial neural networks (NNs) to address the channel assignment problem (CAP) for cellular time-division multiple access and code-division multiple access networks has previously been investigated by this author and many others. The investigations to date have been based on a hexagonal cell structure established by omnidirectional antennas at the base stations. No account was taken of the use of spatial isolation enabled by directional antennas to reduce interference between mobiles. Any reduction in interference translates into increased capacity and consequently alters the performance of the NNs. Previous studies have sought to improve the performance of Hopfield- Tank …
Obscured Object Detection Via Bayesian Target Modeling Techniques, Rufus H. Cofer
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 …
Explanation Mode For Bayesian Automatic Object Recognition, Thomas L. Hazlett, Rufus H. Cofer, Harold K. Brown
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 …