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Neural net device for IED gas identification

Kennedy, Robert and Nahavandi, Saeid 2010, Neural net device for IED gas identification, in ISIEA 2010 : 2010 IEEE Symposium on Industrial Electronics and Applications, IEEE, Piscataway, N.J., pp. 727-732.

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Title Neural net device for IED gas identification
Author(s) Kennedy, Robert
Nahavandi, Saeid
Conference name IEEE Symposium on Industrial Electronics and Applications (3rd : 2010 : Penang, Malaysia)
Conference location Penang, Malaysia
Conference dates 3-5 Oct. 2010
Title of proceedings ISIEA 2010 : 2010 IEEE Symposium on Industrial Electronics and Applications
Editor(s) [Seroji, Mohd Nawawi ]
Publication date 2010
Conference series IEEE Symposium on Industrial Electronics and Applications
Start page 727
End page 732
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) gas sensing
artificial
neural net
embedded
Summary Most of the embedded systems that detect gases today are for specific types and indicate the levels of the gas present with their standard sensors. We introduce here an adaptable system that can detect and distinguish the type of gas in a volatile environment such as searching for Improvised Explosive Devices (IEDs). This is achieved with a small device mounted on a mobile robot through the use of an algorithm that is an Artificial Neural Network (ANN). The input layer to the ANN is an array of environmental and gas sensors. The small device, comprising of a multilayer circuit board with sensors in a rugged lightweight case, mounts on the mobile robot and communicates the gaseous data to the robot.

The ANN is implemented in the hardware of a FPGA with the control of the ANN being achieved through the configurable processor and memory. Calibration and testing of the device involves the training of device and the ANN with specific target gases. The Accuracy of the device is validated through lab testing against high-end gas test instruments with known concentrations of gases.
Notes This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder
ISBN 9781424476473
Language eng
Field of Research 090609 Signal Processing
Socio Economic Objective 810104 Emerging Defence Technologies
HERDC Research category E1 Full written paper - refereed
HERDC collection year 2010
Copyright notice ©2010, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30034533

Document type: Conference Paper
Collections: Centre for Intelligent Systems Research
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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.