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This paper examines the problem of target detection by a wireless sensor network. Sensors acquire measurements emitted from the target that are corrupted by noise, and initially make individual decisions about the presence/absence of the target. We propose the local vote decision fusion algorithm, in which sensors first correct their decisions using(More)
A reasonable definition of intrusion is: entering a community to which one does not belong. This suggests that in a network, intrusion attempts may be detected by looking for communication that does not respect community boundaries. In this paper, we examine the utility of this concept for identifying malicious network sources. In particular, our goal is to(More)
Our work is motivated by and illustrated with application of association networks in computational biology, specifically in the context of gene/protein regulatory networks. Association networks represent systems of interacting elements, where a link between two different elements indicates a sufficient level of similarity between element attributes. While(More)
Wireless sensor networks (WSN) are becoming an important tool in a variety of tasks, including monitoring and tracking of spatially occurring phenomena. These networks offer the capability of densely covering a large area, but at the same time are constrained by the limiting sensing, processing and power capabilities of their sensors. In order to complete(More)
This work introduces a novel framework for tracking multiple targets over time using binary decisions collected by a Wireless Sensor Network (WSN), and applies the methodology to two case studies: an experiment involving tracking people and a project tracking zebras. Unlike most existing methods, proposed tracking approach is based on a penalized maximum(More)
This study presents a general flexible approach for the design of wireless sensor network under the random deployment mechanism. The cost of sensing and communications is incorporated into the design of the network, while in addition allowing for unreliable sensors. In the proposed approach, cost is treated generically and can correspond to either a fixed(More)
In recent years, association networks and their applications have received increasing interest. The relationships in a network should ideally be ascertained without any preconceptions about the existence of a connection a priori. This would allow interpretations to be based on the underlying structure rather than on assumptions. Furthermore, a method that(More)
Data analysis of complex behaviors, intrusion attacks and system failures inherent in the Information Technology systems became one of the key strategies for ensuring the security of cyber assets. Data-driven anomaly detection methods can offer an appealing alternative to existing signature-based intrusion detection systems by capturing known and previously(More)
The use of electroencephalogram (EEG) for predictive purposes of seizures in epileptic patients has grown steadily with the access to greater computing power. Methods of seizure analysis to date have focused on modeling and computer aided machine learning to help increase sensitivity and specificity of seizure detection. Brain synchronization between(More)
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