Publications

Bikhazi, Nicolas B.; Nguyen, Hung D.

Abstract not provided.

Proceedings - International Carnahan Conference on Security Technology

Koch, Mark W.; Fogler, Robert J.; Nguyen, Hung D.; Giron, Casey; Yee, Mark L.

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. ©2010 IEEE.

Koch, Mark W.; Nguyen, Hung D.; Giron, Casey; Yee, Mark L.; Drescher, Steven M.

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.

Koch, Mark W.; Nguyen, Hung D.

Abstract not provided.

Nguyen, Hung D.

Abstract not provided.

Koch, Mark W.; Nguyen, Hung D.

We have developed algorithms to automatically learn a detection map of a deployed sensor field for a virtual presence and extended defense (VPED) system without apriori knowledge of the local terrain. The VPED system is an unattended network of sensor pods, with each pod containing acoustic and seismic sensors. Each pod has the ability to detect and classify moving targets at a limited range. By using a network of pods we can form a virtual perimeter with each pod responsible for a certain section of the perimeter. The site's geography and soil conditions can affect the detection performance of the pods. Thus, a network in the field may not have the same performance as a network designed in the lab. To solve this problem we automatically estimate a network's detection performance as it is being installed at a site by a mobile deployment unit (MDU). The MDU will wear a GPS unit, so the system not only knows when it can detect the MDU, but also the MDU's location. In this paper, we demonstrate how to handle anisotropic sensor-configurations, geography, and soil conditions.

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009

Koch, Mark W.; Giron, Casey; Nguyen, Hung D.

We have developed algorithms for a virtual presence and extended defense (VPED) system that automatically learns the detection map of a deployed sensor field without a-priori knowledge of the local terrain. The VPED system is a network of sensor pods, with each pod containing acoustic and seismic sensors. Each pod has a limited detection range, but a network of pods can form a virtual perimeter. The site's geography and soil conditions can affect the detection performance of the pods. Thus a network in the field may not have the same performance as a network designed in the lab. To solve this problem we automatically estimate a network's detection performance as it is being constructed. We demonstrate results using simulated and real data. © 2009 IEEE.

Koch, Mark W.; Fogler, Robert J.; Nguyen, Hung D.

Abstract not provided.

Symons, Neill P.; Elbring, Gregory J.; Nguyen, Hung D.; O'Rourke, William T.; Stalker, K.T.

Abstract not provided.

Nguyen, Hung D.; Yee, Mark L.; Stalker, K.T.

Abstract not provided.

Yee, Mark L.; Nguyen, Hung D.; Craft, David C.; O'Rourke, William T.; Slinkard, Megan E.

Abstract not provided.

Proceedings of SPIE - The International Society for Optical Engineering

Ladd, Mark D.; Alam, M.K.; Sleefe, Gerard E.; Nguyen, Hung D.

This paper will describe an algorithm for detecting and classifying seismic and acoustic signals for unattended ground sensors. The algorithm must be computationally efficient and continuously process a data stream in order to establish whether or not a desired signal has changed state (turned-on or off). The paper will focus on describing a Fourier-based technique that compares the running power spectral density estimate of the data to a predetermined signature in order to determine if the desired signal has changed state. How to establish the signature and the detection thresholds will be discussed as well as the theoretical statistics of the algorithm for the Gaussian noise case with results from simulated data. Actual seismic data results will also be discussed along with techniques used to reduce false alarms due to the inherent nonstationary noise environments found with actual data.