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In this paper we present an application that utilizes a novel two-level fusion architecture to detect and track disease outbreaks across public health system databases. In the first fusion level, collected data is used to detect and track indicative bio-events using latent semantic analysis and unsupervised clustering. In the second fusion level, clusters produced via the first are used to feed dynamic...
Uncertainty makes the analysis of even simple situations difficult. It forces situation analysts to formulate and manage hypotheses during the construction of the explicit representations of the real world situations. Because of human cognitive limitations, this may quickly become overwhelming, even for the most experienced and capable analysts. In an attempt to provide better support systems, this...
The paper addresses the problem of target-tracking in tactical military surveillance operations. More specifically, a closed-loop approach to adapt the sensing and tracking operations is proposed and compared to the conventional open-loop and static approach. The objective is to control and maintain, over a certain volume of interest and by way of clustering and scheduling strategies, the level of...
In this paper, a sequential track association algorithm for multiple electronic support measures (ESM) sensors is proposed based on the application of clustering techniques. Association metrics are developed based on the use of the information theoretic criteria, in particular, the Akaike Information Criterion (AIC). The proposed association algorithm is able to handle track components of differing...
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