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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 than 98% of correct recognition. The robustness of the proposed method is tested by recognizing gas measurements with simulated drift. Less than 1% of performance degradation is noted at the worst case scenario which represents a significant improvement when compared to the current state-of-the-art.

This paper presents a study of temperature modulation for tin-oxide gas sensor. The main objective of this work is to perform temperature modulation experimental setup for tin oxide gas sensors in order to improve the selectivity of the sensor array and to virtually increase the number of sensors. Typically, tin oxide sensors operate by heating at a relatively high temperature (around 300 o C a microhotplate structure). A convex microhotplate is proposed in order to improve the thermal properties of the structure and enable efficient temperature modulation process to be carried-out. Temperature modulation is shown to increase the number of our sensors from 16 physical sensors (integrated on-chip) up to 12 000 virtual sensors. This will enable the emulation of a very large number of sensors typically found in biological systems.Index Terms-Temperature Modulation, tin oxide gas sensor array, olfactory systems.

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