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Sensors 2013, 13(3), 2967-2985; doi:10.3390/s130302967
Article

Gas Sensors Characterization and Multilayer Perceptron (MLP) Hardware Implementation for Gas Identification Using a Field Programmable Gate Array (FPGA)

1,2,* , 1
 and 2
1 Laboratory of Instrumentation (LINS), Faculty of Electronics and Computers, USTHB PO Box 32, Bab Ezzouar 16111, Algiers, Algeria 2 Department of Electrical Engineering and Computers, Faculty of Science and Technology, UYFM 26000, Medea, Algeria
* Author to whom correspondence should be addressed.
Received: 7 January 2013 / Revised: 31 January 2013 / Accepted: 21 February 2013 / Published: 1 March 2013
(This article belongs to the Special Issue Gas Sensors - 2013)
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Abstract

This paper develops a primitive gas recognition system for discriminating between industrial gas species. The system under investigation consists of an array of eight micro-hotplate-based SnO2 thin film gas sensors with different selectivity patterns. The output signals are processed through a signal conditioning and analyzing system. These signals feed a decision-making classifier, which is obtained via a Field Programmable Gate Array (FPGA) with Very High-Speed Integrated Circuit Hardware Description Language. The classifier relies on a multilayer neural network based on a back propagation algorithm with one hidden layer of four neurons and eight neurons at the input and five neurons at the output. The neural network designed after implementation consists of twenty thousand gates. The achieved experimental results seem to show the effectiveness of the proposed classifier, which can discriminate between five industrial gases.
Keywords: e-nose; gas sensor array; pattern recognition; neural network classifier; pic-microcontroller; FPGA-implementation e-nose; gas sensor array; pattern recognition; neural network classifier; pic-microcontroller; FPGA-implementation
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Benrekia, F.; Attari, M.; Bouhedda, M. Gas Sensors Characterization and Multilayer Perceptron (MLP) Hardware Implementation for Gas Identification Using a Field Programmable Gate Array (FPGA). Sensors 2013, 13, 2967-2985.

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