Publications

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A major goal of next-generation physical protection systems is to extend defenses far beyond the usual outer-perimeter-fence boundaries surrounding protected facilities. Mitigation of nuisance alarms is among the highest priorities. A solution to this problem is to create a robust capability to Automatically Recognize Malicious Indicators of intruders. In extended defense applications, it is not enough to distinguish humans from all other potential alarm sources as human activity can be a common occurrence outside perimeter boundaries. Our approach is unique in that it employs a stimulus to determine a malicious intent indicator for the intruder. The intruder's response to the stimulus can be used in an automatic reasoning system to decide the intruder's intent.

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During the past several years, there has been a growing recognition of the threats posed by the use of shallow tunnels against both international border security and the integrity of critical facilities. This has led to the development and testing of a variety of geophysical and surveillance techniques for the detection of these clandestine tunnels. The challenges of detection of these tunnels arising from the complexity of the near surface environment, the subtlety of the tunnel signatures themselves, and the frequent siting of these tunnels in urban environments with a high level of cultural noise, have time and again shown that any single technique is not robust enough to solve the tunnel detection problem in all cases. The question then arises as to how to best combine the multiple techniques currently available to create an integrated system that results in the best chance of detecting these tunnels in a variety of clutter environments and geologies. This study utilizes Taguchi analysis with simulated sensor detection performance to address this question. The analysis results show that ambient noise has the most effect on detection performance over the effects of tunnel characteristics and geological factors.

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A Fourier Transform hyperspectral imager uses optical intereferometry to obtain hyperspectral data. Taking a Fourier Transform of the interferogram yields the frequency spectrum of the incident light. An optical system using a standard frame rate camera can generate such interferograms at a rate of 30 frames per second. Rather than store all of the raw interferogram data and process it afterwards, it is useful to have the ability to process the raw data in real time, generating and storing the hyperspectral data itself rather than the original interferograms. This real-time processing would result in a significant reduction in the data bandwidth and storage requirements, which are of particular interest in typical airborne environments with limited computing resources on board. This report details the digital signal processing algorithm and code developed for a processing subsystem based on the Texas Instruments TMS320C6201 fixed point processor. The function of this subsystem is to compute the magnitude Fourier Transform of the interferogram data at a rate of 30 frames per second.

Alignment of DNA sequences is a necessary step prior to comparison of sequence data. High-speed alignment is needed due to the large size of DNA databases. Correlation, a standard pattern recognition technique, can be used to perform alignment. Correlation can be performed rapidly using optical techniques. Thus, optical correlation offers the potential for high-speed processing of DNA sequence data. This report describes research efforts to apply one-dimensional acousto-optical correlation methods to the problem of DNA sequence alignment. Experimental results are presented.