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Conventional electronic noses need complicated data preprocessing and tedious feature reduction steps for different sensors and applications. To overcome the drawbacks, a bio-inspired data processing method using a spike-based olfactory model is proposed in this paper, which consists of spike encoding by virtual olfactory receptor neurons (VORNs) and subsequent processing in a bionic olfactory bulb...
Traditional data processing methods for electronic noses (e-noses) need to use the whole response curves (including rise, steady and recovery phases) of sensor array, which leads to a long sampling time. The traditional methods also perform many steps such as signal pre-processing, feature generation/reduction, and classification, which increase the difficulty of selecting a suitable method for each...
We proposed a novel bio-inspired olfactory neural network to perform data processing in our designed electronic nose (e-nose). This olfactory neural network mimics the main structures of the mammal animals' olfactory system. It contains olfactory sensing neurons, mitral cells and granule cells. The proposed method can directly use the raw data collected from sensors in the e-nose without any data...
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