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A Spiking Neural Network for Gas Discrimination using a Tin Oxide Sensor Array

Maxime Ambard 1 Bin Guo 2 Dominique Martinez 1 Amine Bermak 2 
1 CORTEX - Neuromimetic intelligence
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : We propose a bio-inspired signal processing method for odor discrimination. A spiking neural network is trained with a supervised learning rule so as to classify the analog outputs from a monolithic 4×4 tin oxide gas sensor array implemented in our in-house 5 µm process. This scheme has been sucessfully tested on a discrimination task between 4 gases (hydrogen, ethanol, carbon monoxide, methane). Performance compares favorably to the one obtained with a common statistical classifier. Moreover, the simplicity of our method makes it well suited for building dedicated hardware for processing data from gas sensor arrays.
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Submitted on : Monday, July 6, 2009 - 10:09:02 AM
Last modification on : Friday, February 4, 2022 - 3:30:54 AM
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Maxime Ambard, Bin Guo, Dominique Martinez, Amine Bermak. A Spiking Neural Network for Gas Discrimination using a Tin Oxide Sensor Array. 4th IEEE International Symposium on Electronic Design, Test & Applications - DELTA 2008, Jan 2008, Hong-Kong, Hong Kong SAR China. ⟨inria-00401777⟩



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