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 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; in revised form: 31 January 2013 / Accepted: 21 February 2013 / Published: 1 March 2013
(This article belongs to the Special Issue Gas Sensors - 2013)
PDF Full-text Download PDF Full-Text [1281 KB, Updated Version, uploaded 4 March 2013 16:03 CET]
The original version is still available [1753 KB, uploaded 1 March 2013 15:29 CET]
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

Article Statistics

Load and display the download statistics.

Citations to this Article

Cite This Article

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.

AMA 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(3):2967-2985.

Chicago/Turabian Style

Benrekia, Fayçal; Attari, Mokhtar; Bouhedda, Mounir. 2013. "Gas Sensors Characterization and Multilayer Perceptron (MLP) Hardware Implementation for Gas Identification Using a Field Programmable Gate Array (FPGA)." Sensors 13, no. 3: 2967-2985.

Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert