TY - GEN

T1 - Feature extraction using distribution representation for colorimetric sensor arrays used as explosives detectors

A1 - Alstrøm,Tommy Sonne

A1 - Raich,Raviv

A1 - Kostesha,Natalie

A1 - Larsen,Jan

AU - Alstrøm,Tommy Sonne

AU - Raich,Raviv

AU - Kostesha,Natalie

AU - Larsen,Jan

PB - IEEE

PY - 2012

Y1 - 2012

N2 - We present a colorimetric sensor array which is able to detect explosives such as DNT, TNT, HMX, RDX and TATP and identifying volatile organic compounds in the presence of water vapor in air. To analyze colorimetric sensors with statistical methods, a suitable representation of sensory readings is required. We present a new approach of extracting features from a colorimetric sensor array based on a color distribution representation. For each sensor in the array, we construct a K-nearest neighbor classifier based on the Hellinger distances between color distribution of a test compound and the color distribution of all the training compounds. The performance of this set of classifiers are benchmarked against a set of K-nearest neighbor classifiers that is based on traditional feature representation (e.g., mean or global mode). The suggested approach of using the entire distribution outperforms the traditional approaches which use a single feature.

AB - We present a colorimetric sensor array which is able to detect explosives such as DNT, TNT, HMX, RDX and TATP and identifying volatile organic compounds in the presence of water vapor in air. To analyze colorimetric sensors with statistical methods, a suitable representation of sensory readings is required. We present a new approach of extracting features from a colorimetric sensor array based on a color distribution representation. For each sensor in the array, we construct a K-nearest neighbor classifier based on the Hellinger distances between color distribution of a test compound and the color distribution of all the training compounds. The performance of this set of classifiers are benchmarked against a set of K-nearest neighbor classifiers that is based on traditional feature representation (e.g., mean or global mode). The suggested approach of using the entire distribution outperforms the traditional approaches which use a single feature.

KW - Hellinger distance

KW - Chemo–selective compounds

KW - Explosives detection

KW - Feature extraction

KW - K– nearest neighbor classification

U2 - 10.1109/ICASSP.2012.6288331

DO - 10.1109/ICASSP.2012.6288331

SN - 978-1-4673-0045-2

BT - 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

T2 - 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

T3 - I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings

T3 - en_GB

SP - 2125

EP - 2128

ER -