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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Kennedy, Robert F. Nahavandi, Saeid |
| Copyright Year | 2008 |
| Abstract | 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. |
| Starting Page | 3 |
| Ending Page | 8 |
| File Size | 610633 |
| Page Count | 6 |
| File Format | |
| ISBN | 9780769531199 |
| DOI | 10.1109/CISP.2008.749 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-05-27 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | ANN Microcontrollers Microcontroller Device Artificial neural networks Mobile robots Low-power Intelligent sensors Sensor arrays Neural Network Intelligent Gas Electronic noses Silicon Power system reliability Robot Gas detectors Testing Military |
| Content Type | Text |
| Resource Type | Article |
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