A Spiking Neural Network for Gas Discrimination Using a Tin Oxide Sensor Array
Category
Paper Conference
Authors
Ambard, M, Guo, B, Martinez, D, Bermak, A
Year
2008
Title
A Spiking Neural Network for Gas Discrimination Using a Tin Oxide Sensor Array
Journal / book / conference
DELTA, IEEE International Symposium on Electronic Design, Test & 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.
Keywords
Tin Oxide, Gas Sensor Array, Spike Timing Computation, Supervised Learning