Abstract
Gas recognition is a new emerging research area with many civil, military, and industrial applications. The success of any gas recognition system depends on its computational complexity and its robustness. In this work, we propose a new low-complexity recognition method which is tested and successfully validated for tin-oxide gas sensor array chip. The recognition system is based on a vector angle similarity measure between the query gas and the representatives of the different gas classes. The latter are obtained using a clustering algorithm based on the same measure within the training data set. Experimented results on our in-house gas sensors array show more than98%of correct recognition. The robustness of the proposed method is tested by recognizing gas measurements with simulated drift. Less than1%of performance degradation is noted at the worst case scenario which represents a significant improvement when compared to the current state-of-the-art.
Highlights
The detection and discrimination of gases using microelectronic gas sensor array are required in various industry and domestic applications, such as automobiles, safety, indoor air quality, medicine and food industry [1, 2]
Input signals generated by the data acquisition board and used to control the mass flow controllers (MFCs) are pulse signals corresponding to different concentrations
All these techniques are preceded by a dimensionality reduction step using Principal component analysis (PCA), while in our case dimensionality reduction is inherently embedded into the classifier, which constitutes a significant advantage
Summary
The detection and discrimination of gases using microelectronic gas sensor array are required in various industry and domestic applications, such as automobiles, safety, indoor air quality, medicine and food industry [1, 2]. SnO2-based gas sensing film shows high response to a large variety of target gases but low level of selectivity to a given target gas. This phenomenon is widely exhibited in the animal and human biological olfactory systems [4]. The proposed recognition system uses the vector angle similarity measure between the investigated gas and the different gas class representatives obtained using a clustering algorithm with the same similarity measure. This modelling allowed us to reach high recognition rate and has shown high robustness against the drift phenomenon.
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