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

Award Information
Agency:
Department of Defense
Branch
Office for Chemical and Biological Defense
Amount:
$69,999.00
Award Year:
2009
Program:
SBIR
Phase:
Phase I
Contract:
W911SR-09-C-0032
Award Id:
91453
Agency Tracking Number:
C091-109-0053
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
13619 Valley Oak Circle, ROCKVILLE, MD, 20850
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
620282256
Principal Investigator:
Chiman Kwan
Chief Technology Officer
(240) 505-2641
chiman.kwan@signalpro.net
Business Contact:
Chihwa Yung
President
(301) 315-2322
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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