A spiking neural network for gas discrimination using a tin oxide sensor array

Maxime Ambard, Bin Guo, Dominique Martinez, Amine Bermak

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Citations (Scopus)

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 fromg gas sensor arrays.

Original languageEnglish
Title of host publicationProceedings - 4th IEEE International Symposium on Electronic Design, Test and Applications, DELTA 2008
Pages394-397
Number of pages4
DOIs
Publication statusPublished - 2008
Externally publishedYes
Event4th IEEE International Symposium on Electronic Design, Test and Applications, DELTA 2008 - Hong Kong, SAR, Hong Kong
Duration: 23 Jan 200825 Jan 2008

Other

Other4th IEEE International Symposium on Electronic Design, Test and Applications, DELTA 2008
CountryHong Kong
CityHong Kong, SAR
Period23/1/0825/1/08

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Keywords

  • Gas sensor array
  • Spike timing computation
  • Supervised learning
  • Tin oxide

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this

Ambard, M., Guo, B., Martinez, D., & Bermak, A. (2008). A spiking neural network for gas discrimination using a tin oxide sensor array. In Proceedings - 4th IEEE International Symposium on Electronic Design, Test and Applications, DELTA 2008 (pp. 394-397). [4459579] https://doi.org/10.1109/DELTA.2008.116