Biologically inspired feature rank codes for hardware friendly gas identification with the array of gas sensors

In this paper, we propose a biologically inspired rank-order-based classifier to facilitate the development of a smart electronic olfaction system, because the state-of-the-art pattern recognition algorithms are not suitable for the said objective due to their computationally intensive nature. In order to mimic biological olfactory rank codes, the features of gas sensors in an electronic olfaction system are ranked to form rank codes in our classifier instead of treating them as multidimensional data points. This classifier relies on probability rank tables, which are built for all target gases, because one-to-one mapping between the rank codes and the target gases is not practically found with the existing gas sensor technology. The table for each target gas records the probability of each sensor ID at each rank, and hence also serves as a visual interpretation tool. Due to the limited diversity in electronic olfaction system rank codes compared with biological olfactory codes, gas pairs are considered in this classifier by transforming the original multi-gas identification problem into pairwise classification problems in order to evaluate the discriminatory power between the rank codes for each pair of target gases. This discriminatory power at each rank not only helps to determine the applicability of the classifier but also serves as a weight for that rank in order to improve the classification performance. In addition, insightful quantitative feedback is integrated into the classifier in order to avoid misclassifications, and as a result, we achieve a 100% classification performance at the cost of a 3.79% rejection of uncertain predictions with a data set of an array of FIS inc. and Figaro inc. gas sensors exposed to five target gases.