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

Catégorie

Paper Conference

Auteurs

Ambard, M, Guo, B, Martinez, D, Bermak, A

Année

2008

Titre

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

Journal / Livre / Conférence

DELTA, IEEE International Symposium on Electronic Design, Test & Applications

Résumé

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.

Mots-clés

Tin Oxide, Gas Sensor Array, Spike Timing Computation, Supervised Learning

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