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Proceedings Paper

Computational intelligence in biological sensing
Author(s): Jerome J. Braun; Yan Glina; Jonathan K. Su; Timothy J. Dasey
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Paper Abstract

This paper presents an alternative, computational intelligence based paradigm for biological attack detection. Conventional approaches to this difficult problem include sensor technologies and analytical modeling approaches. However, the processes that constitute the environmental background as well as those which occur as the result of an attack are extremely complex. This phenomenological complexity, in terms of both physics and biology aspects, is a challenge difficult to overcome by conventional approaches. In contrast to such approaches, the proposed approach is centered on automatic learning to discriminate between sensor signals that are in a normal range from those that are likely to represent a biological attack. It is argued that constructing machine learning methods robust enough to perform such a task is often more feasible than constructing an adequate model that could form a basis for bioattack detection. The paper discusses machine learning and multisensor information fusion methods in the context of biological attack detection in a subway environment, including recognition architecture and its components. However, the applicability of the proposed approach is much broader than the subway bioattack protection case, extending to a wide range of CBR defense applications.

Paper Details

Date Published: 13 August 2004
PDF: 12 pages
Proc. SPIE 5416, Chemical and Biological Sensing V, (13 August 2004); doi: 10.1117/12.541046
Show Author Affiliations
Jerome J. Braun, MIT Lincoln Lab. (United States)
Yan Glina, MIT Lincoln Lab. (United States)
Jonathan K. Su, MIT Lincoln Lab. (United States)
Timothy J. Dasey, MIT Lincoln Lab. (United States)

Published in SPIE Proceedings Vol. 5416:
Chemical and Biological Sensing V
Patrick J. Gardner, Editor(s)

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