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Proceedings Paper

Distributed multisensor processing, decision making, and control under constrained resources for remote health and environmental monitoring
Author(s): Ashit Talukder; Tanwir Sheikh; Lavanya Chandramouli
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Paper Abstract

Previous field-deployable distributed sensing systems for health/biomedical applications and environmental sensing have been designed for data collection and data transmission at pre-set intervals, rather than for on-board processing These previous sensing systems lack autonomous capabilities, and have limited lifespans. We propose the use of an integrated machine learning architecture, with automated planning-scheduling and resource management capabilities that can be used for a variety of autonomous sensing applications with very limited computing, power, and bandwidth resources. We lay out general solutions for efficient processing in a multi-tiered (three-tier) machine learning framework that is suited for remote, mobile sensing systems. Novel dimensionality reduction techniques that are designed for classification are used to compress each individual sensor data and pass only relevant information to the mobile multisensor fusion module (second-tier). Statistical classifiers that are capable of handling missing/partial sensory data due to sensor failure or power loss are used to detect critical events and pass the information to the third tier (central server) for manual analysis and/or analysis by advanced pattern recognition techniques. Genetic optimisation algorithms are used to control the system in the presence of dynamic events, and also ensure that system requirements (i.e. minimum life of the system) are met. This tight integration of control optimisation and machine learning algorithms results in a highly efficient sensor network with intelligent decision making capabilities. The applicability of our technology in remote health monitoring and environmental monitoring is shown. Other uses of our solution are also discussed.

Paper Details

Date Published: 12 April 2004
PDF: 12 pages
Proc. SPIE 5437, Optical Pattern Recognition XV, (12 April 2004); doi: 10.1117/12.548080
Show Author Affiliations
Ashit Talukder, Univ. of Southern California (United States)
Jet Propulsion Lab. (United States)
Tanwir Sheikh, Univ. of Southern California (United States)
Lavanya Chandramouli, Univ. of Southern California (United States)


Published in SPIE Proceedings Vol. 5437:
Optical Pattern Recognition XV
David P. Casasent; Tien-Hsin Chao, Editor(s)

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