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A low-cost intelligent gas sensing device for military applications

Kennedy, Robert F. and Nahavandi, Saeid 2008, A low-cost intelligent gas sensing device for military applications, in CISP 2008 : Proceedings of the First International Congress on Image and Signal Processing, IEEE Computer Society, Piscataway, N.J., pp. 3-8, doi: 10.1109/CISP.2008.749.

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Title A low-cost intelligent gas sensing device for military applications
Author(s) Kennedy, Robert F.
Nahavandi, Saeid
Conference name IEEE International Congress on Image and Signal Processing (1st : 2008 : Hainan, China)
Conference location Hainan, China
Conference dates 27-30 May 2008
Title of proceedings CISP 2008 : Proceedings of the First International Congress on Image and Signal Processing
Editor(s) Li, Dongguang
Deng, Guang
Publication date 2008
Conference series International Congress on Image and Signal Processing
Start page 3
End page 8
Total pages 6
Publisher IEEE Computer Society
Place of publication Piscataway, N.J.
Summary The field of electronic noses and gas sensing has been developing rapidly since the introduction of the silicon based sensors. There are numerous systems that can detect and indicate the level of a specific gas. We introduce here a system that is low power, small and cheap enough to be used in mobile robotic platforms while still being accurate and reliable enough for confident use. The design is based around a small circuit board mounted in a plastic case with holes to allow the sensors to protrude through the top and allow the natural flow of gas evenly across them. The main control board consists of a microcontroller PCB with surface mount components for low cost and power consumption. The firmware of the device is based on an algorithm that uses an Artificial Neural Network (ANN) which receives input from an array of gas sensors. The various sensors feeding the ANN allow the microcontroller to determine the gas type and quantity. The Testing of the device involves the training of the ANN with a number of different target gases to determine the weightings for the ANN. Accuracy and reliability of the ANN is validated through testing in a specific gas filled environment.
Notes ©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
ISBN 9780769531199
Language eng
DOI 10.1109/CISP.2008.749
Field of Research 080101 Adaptive Agents and Intelligent Robotics
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2008, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30018331

Document type: Conference Paper
Collections: School of Engineering and Information Technology
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.