Electrostatic, Non-Fluorescent, Fluctuation Enhanced, Bacterium Spore Analyzer

Award Information
Agency:
Department of Defense
Amount:
$69,999.00
Program:
SBIR
Contract:
W911SR-09-C-0032
Solitcitation Year:
2009
Solicitation Number:
2009.1
Branch:
Office for Chemical and Biological Defense
Award Year:
2009
Phase:
Phase I
Agency Tracking Number:
C091-109-0053
Solicitation Topic Code:
CBD09-109
Small Business Information
SIGNAL PROCESSING, INC.
13619 Valley Oak Circle, ROCKVILLE, MD, 20850
Hubzone Owned:
N
Woman Owned:
Y
Socially and Economically Disadvantaged:
Y
Duns:
620282256
Principal Investigator
 Chiman Kwan
 Chief Technology Officer
 (240) 505-2641
 chiman.kwan@signalpro.net
Business Contact
 Chihwa Yung
Title: President
Phone: (301) 315-2322
Email: chihwa.yung@signalpro.net
Research Institution
N/A
Abstract
This SBIR project, by utilizing the principle of Fluctuation-Enhanced Sensing (FES), aims to explore the potential of enhancing the sensitivity and selectivity of electrostatic bacterium spore analyzers, specifically Ion Mobility Spectrometers (IMS) and Mass Spectrometers (MS). We propose a high performance framework that incorporates FES to enhance the detection and classification of bio-aerosols. There are several key components in our system. First, for IMS and MS, different theoretical noise analysis techniques will be applied to analyze noise behavior in different sensors. These theoretical analyses will provide critical information on the sensing limits of different sensors. Second, a library of signal processing/pattern recognition tools will be incorporated to further enhance the detection and classification capability of our framework. We will use a low noise amplifier to enlarge the small stochastic fluctuations in the sensor. Features such as mean-square fluctuations, skewness, kurtosis, power spectrum, zero-crossing patterns, bispectrum images of the fluctuations will be extracted. Various advanced and proven classification algorithms will be used for different features. Finally, we will feed the decisions from different classifiers into a fusion algorithm. A single decision will be drawn, which is robust and optimal, as all information has been taken into account.

* information listed above is at the time of submission.

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