@article{phaisangittisagul_nagle_areekul_2010, title={Intelligent method for sensor subset selection for machine olfaction}, volume={145}, ISSN={["0925-4005"]}, DOI={10.1016/j.snb.2009.12.063}, abstractNote={A fundamental design concept for an array of sensors used in machine olfaction devices, electronic noses (e-noses), is that each sensor should maximize the overall sensitivity and provides different selectivity profiles over the range of target odor application. Ideally, each sensor should produce a different response to a given odor so that a unique odor pattern is created. Since this is rarely the case, sensor selection or reduction is needed when classification performance, cost, and technology limitations are issues of concern. The first step in the selection/reduction process is to generate features from each sensor's output waveform. In practice, some of the features obtained from an array of sensors are redundant and irrelevant due to cross-sensitivity and odor characteristics. As a result, inappropriate features or a poor configuration of features can lead to a deterioration of classification performance, or a more complex classification algorithm may be required. Hence, sensor selection for e-nose systems is of great important. In this study, a novel computationally efficient method is introduced by selecting the first few critical sensors based on a maximum margin criterion among different odor classes. Then, a stochastic search algorithm, a genetic algorithm (GA), uses those features as an initial step to optimize our sensor selection problem. The advantages of the proposed method are not only to avoid any initial misstep starting the search, but also to reduce the searching space for the optimal sensor array. From the experimental results on coffee and soda data sets, the number of selected sensors is significantly reduced (up to 90%) and classification performance is near 100%.}, number={1}, journal={SENSORS AND ACTUATORS B-CHEMICAL}, author={Phaisangittisagul, Ekachai and Nagle, H. Troy and Areekul, Vutipong}, year={2010}, month={Mar}, pages={507–515} }
 @article{phaisangittisagul_nagle_2008, title={Sensor selection for machine olfaction based on transient feature extraction}, volume={57}, ISSN={["1557-9662"]}, DOI={10.1109/TIM.2007.910117}, abstractNote={Machine olfaction devices, which are often called electronic noses (e-noses), are gaining favor for odor assessment applications in several industrial sectors, such as beverage, perfumery, and food. From a design point of view, the number of sensors in these devices for a particular odor application should be minimized without degrading classification accuracy. This paper deals with selecting sensors for e-noses to make small portable devices with fast response times and reduced cost possible. Prior research efforts have been reported in the open literature and have shown that many advantages can be gained by properly selecting the input features before forwarding to a pattern classification algorithm. This selection process can reduce the dimensionality of the feature space, remove redundant and irrelevant features, speed up classification, and improve classification performance. In this paper, the transient features of an array of sensors obtained by applying a multiresolutional approximation technique from the discrete wavelet transform (DWT) are investigated to search for an optimal sensor array to be implemented in the e-nose system. A genetic algorithm is adapted to tailor a gas sensor array for two different odor data sets (coffee and soda). From the experimental results, the input features obtained by applying the DWT to the transient sensor responses not only provide a significant reduction in the number of sensors when compared to traditional features but also improve the classification rate to near 100%.}, number={2}, journal={IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT}, author={Phaisangittisagul, Ekachai and Nagle, H. Troy}, year={2008}, month={Feb}, pages={369–378} }