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Proceedings Paper

Physio-associative temporal sensor integration
Author(s): Erik P. Blasch; James C. Gainey
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Paper Abstract

The paper describes the physio-associative temporal sensors integration algorithm which is motivated by the observed function of the thalamus and utilizes signals theory mathematics to model how a human efficiently perceives information in the environment. The algorithm is consistent with that of an aircraft pilot; namely, to estimate, filter, and predict sensed afferent signals and produce efferent controls under dynamic flight conditions. Dynamic sensor integration under uncertainty requires feature selection which can be formulated as an associative-learning problem in which sensed states are represented as current situational beliefs, and the information either excites or inhibits long-term memory associations. The objective of the learner/observer is to (1) abstract salient signals from the environment, (2) integrate the signal for real-time beliefs, and (3) compare beliefs to learned associations. Biologically, the paper models these processes from the biological systems of the eye, thalamus, and association-cortex; respectively. By selecting the optimal set of mutually non-exclusive sensors and comparing the integrated signal to learned associations, the physio-associative temporal algorithm maximizes the identification of targets in a simulated dynamic flight situation.

Paper Details

Date Published: 25 March 1998
PDF: 11 pages
Proc. SPIE 3390, Applications and Science of Computational Intelligence, (25 March 1998); doi: 10.1117/12.304834
Show Author Affiliations
Erik P. Blasch, Air Force Research Lab. (United States)
James C. Gainey, Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 3390:
Applications and Science of Computational Intelligence
Steven K. Rogers; David B. Fogel; James C. Bezdek; Bruno Bosacchi, Editor(s)

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