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Conference papers
A Spiking Neural Network for Gas Discrimination using a Tin Oxide Sensor Array
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.
Cited literature [12 references]
https://hal.inria.fr/inria-00401777
Contributor : Maxime Ambard <>
Submitted on : Monday, July 6, 2009 - 10:09:02 AM
Last modification on : Friday, February 26, 2021 - 3:28:03 PM
Long-term archiving on: : Tuesday, June 15, 2010 - 7:27:38 PM
Contributor : Maxime Ambard <>
Submitted on : Monday, July 6, 2009 - 10:09:02 AM
Last modification on : Friday, February 26, 2021 - 3:28:03 PM
Long-term archiving on: : Tuesday, June 15, 2010 - 7:27:38 PM
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DELTA_2008.pdf
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