DocumentCode :
3091117
Title :
A Spiking Neural Network for Gas Discrimination Using a Tin Oxide Sensor Array
Author :
Ambard, Maxime ; Guo, Bin ; Martinez, Dominique ; Bermak, Amine
Author_Institution :
LORIA-INRIA, Nancy
fYear :
2008
fDate :
23-25 Jan. 2008
Firstpage :
394
Lastpage :
397
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 4times4 tin oxide gas sensor array implemented in our in-house 5 mum process. This scheme has been successfully 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 :
array signal processing; computerised instrumentation; electronic noses; learning (artificial intelligence); neural nets; tin compounds; SnO2; bioinspired signal processing method; carbon monoxide; common statistical classifier; ethanol; gas discrimination; hardware processing data; hydrogen; methane; monolithic tin oxide gas sensor array implementation; odor discrimination; size 5 mum; spiking neural network; supervised learning rule; Array signal processing; Biomedical signal processing; Biosensors; Gas detectors; Gases; Neural networks; Sensor arrays; Supervised learning; Testing; Tin; Gas Sensor Array; Spike TimingComputation; Supervised Learning; Tin Oxide;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electronic Design, Test and Applications, 2008. DELTA 2008. 4th IEEE International Symposium on
Conference_Location :
Hong Kong
Print_ISBN :
978-0-7695-3110-6
Type :
conf
DOI :
10.1109/DELTA.2008.116
Filename :
4459579
Link To Document :
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