A Novel Approach for Spectral Unmixing and Classification of Chemical and Biological Agents

Award Information
Agency: Department of Defense
Branch: Office for Chemical and Biological Defense
Contract: FA8650-04-M-1606
Agency Tracking Number: C031-0166
Amount: $100,000.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: N/A
Solicitation Number: N/A
Timeline
Solicitation Year: N/A
Award Year: 2003
Award Start Date (Proposal Award Date): N/A
Award End Date (Contract End Date): N/A
Small Business Information
7519 Standish Place, Suite 200, Rockville, MD, 20855
DUNS: 161911532
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Chiman Kwan
 Director of Research and
 (301) 294-5238
 ckwan@i-a-i.com
Business Contact
 Marc Toplin
Title: Director of Contracts
Phone: (301) 294-5215
Email: mtoplin@i-a-i.com
Research Institution
N/A
Abstract
Besides performing a thorough statistical analysis of the government furnished data sets to determine the features and metrics for spectral unmixing, Intelligent Automation, Inc. and Prof. C. Chang of University of Maryland at Baltimore County also proposean innovative approach to detect and classify chemical and biological agents. There are two major steps. First, we propose to apply a recently developed linear spectral random mixture analysis (LSRMA) method to perform the spectral unmixing operation. Themethod extends commonly used linear spectral unmixing to random spectral unmixing in the sense that the abundance fractions considered in the latter are now modeled by random parameters rather than the unknown constants treated in the former. Thistreatment is more close to reality as agents have deformable shapes. Most importantly, the proposed LSRMA does not require any a priori information and can be fully automated in computers. Second, once the spectral information has been unmixed, we proposeto apply a state-of-the-art technique in pattern recognition to perform automatic detection and classification of agents. The technique is called Support Vector Machine (SVM), which has several remarkable advantages such as global optimal solution, noover-training issue, and better performance than most existing classification schemes. The proposed algorithm will be useful for chemical and biological agent detection and classification. The market for military applications is quite large. Otherpotential applications include law enforcement agency, homeland security, border and coast patrol. The potential market is both domestic and international in scope.

* Information listed above is at the time of submission. *

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