By Topic

Data representation and feature selection for colorimetric sensor arrays used as explosives detectors

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Tommy S. Alstrøm ; Dept. of Informatics and Mathematical Modeling, Technical University of Denmark, Richard Petersens Plads 321, 2800 Kgs. Lyngby, Denmark ; Jan Larsen ; Natalie V. Kostesha ; Mogens H. Jakobsen
more authors

Within the framework of the strategic research project Xsense at the Technical University of Denmark, we are developing a colorimetric sensor array which can be useful for detection of explosives like DNT, TNT, HMX, RDX and TATP and identification of volatile organic compounds in the presence of water vapor in air. In order to analyze colorimetric sensors with statistical methods, the sensory output must be put into numerical form suitable for analysis. We present new ways of extracting features from a colorimetric sensor and determine the quality and robustness of these features using machine learning classifiers. Sensors, and in particular explosive sensors, must not only be able to classify explosives, they must also be able to measure the certainty of the classifier regarding the decision it has made. This means there is a need for classifiers that not only give a decision, but also give a posterior probability about the decision. We will compare K-nearest neighbor, artificial neural networks and sparse logistic regression for colorimetric sensor data analysis. Using the sparse solutions we perform feature selection and feature ranking and compare to Gram-Schmidt orthogonalization.

Published in:

2011 IEEE International Workshop on Machine Learning for Signal Processing

Date of Conference:

18-21 Sept. 2011