NDLI: An Intelligent Wireless Electronic Nose Node for Monitoring Gas Mixtures Using Neuro-Fuzzy Networks Implemented on a Microcontroller

Content Provider	IEEE Xplore Digital Library
Author	Young Wung Kim Jung Hwan Cho Gi Joon Jeon
Copyright Year	2007
Description	Author affiliation: Kyungpook Nat. Univ., Kyungpook (Young Wung Kim)
Abstract	This paper presents an intelligent wireless electronic nose node (WENN) that has been designed to classify and quantify binary gas mixtures, $NH_{3}$ and $H_{2}S,$ the main malodors in various environments. The proposed WENN is based on embedded PC technology and neuro-fuzzy network algorithms. The hardware part of the designed system consists of a microcontroller for processing the measured data set obtained from a micro-gas sensor array, and a Zigbee-ready RF transceiver for transmitting the processed data to a base node. The main program embedded on the designed hardware performs real-lime classification and concentration estimation of the binary gas mixtures using the fuzzy ART and ARTMAP neural networks. To verify performance of the designed intelligent WENN, the measured data from the experiments for the binary gas mixtures have been executed using the WENN. The results show the reproducibility of the measured data and the verification of real-time classification and concentration estimation for the target gas.
Starting Page	100
Ending Page	104
File Size	4914131
Page Count	5
File Format	PDF
ISBN	9781424408238
DOI	10.1109/CIMSA.2007.4362547
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-06-27
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Intelligent networks Electronic noses Monitoring Fuzzy neural networks Microcontrollers Sensor arrays Intelligent sensors Hardware Sensor systems Radio frequency embedded system Wireless electronic nose node gas mixture micro-gas sensor array fuzzy ART classification estimation of concentration
Content Type	Text
Resource Type	Article

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