@inproceedings{8fcae8771cfd41f589ac318e042a1af0,
title = "FPGA implementation of a neural network classifier for gas sensor array applications",
abstract = "A primitive gas recognition system which can discriminate limited species of industrial gas was designed and simulated. The 'electronic nose' consists of an array of 8 microhotplate based SnO2thin film gas sensors with different selectivity patterns, signal collecting unit and a signal pattern recognition and decision part in programmable logic device chip. BP (Back Propagation) neural networks with Multilayer Perceptron structure was designed and implemented on FPGA (Field Programmable Gate Array), of twenty thousand gate level chip by VHDL language for processing the input signals from 8 kinds of gas sensors. The network contained eight input units, one hidden layer with 4 neurons and output with 5 regular neurons. The 'electronic nose' system successfully discriminated 5 kinds of industrial gases in computer simulation. A small application has been tested on the APS X208 FPGA test board.",
keywords = "E-nose, FPGA-implementation, Gas sensor, Neural network classifier, VHDL",
author = "Faycal Benrekia and Mokhtar Attari and Amine Bermak and Khaled Belhout",
year = "2009",
doi = "10.1109/SSD.2009.4956804",
language = "English",
isbn = "9781424443468",
series = "2009 6th International Multi-Conference on Systems, Signals and Devices, SSD 2009",
publisher = "IEEE Computer Society",
booktitle = "2009 6th International Multi-Conference on Systems, Signals and Devices, SSD 2009",
address = "United States",
note = "2009 6th International Multi-Conference on Systems, Signals and Devices, SSD 2009 ; Conference date: 23-03-2009 Through 26-03-2009",
}