FPGA Based Run Time Reconfigurable Gas Discrimination System

In this paper a gas discrimination system based on five classification algorithms including K nearest neighbors (KNN), multi-layer perceptron (MLP), radial basis function (RBF), Gaussian mixture model (GMM) and probabilistic principal component analysis (PPCA) has been presented. A Committee machine (CM) is used in which the results from each classifier are first transformed to confidences and then a weighted combination rule is used to generate the final decision result. In order to overcome the problem of very high computational complexity of the CM requiring large amount of hardware resources, we propose a novel time multiplexing hardware implementation using a dynamically reconfigurable field programmable gate array (FPGA) platform. The system is successfully tested for combustible gas identification application using our in-house tin-oxide gas sensors.