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http://hdl.handle.net/11375/14034
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DC Field | Value | Language |
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dc.contributor.advisor | Bakr, Mohamed H. | en_US |
dc.contributor.advisor | Deen, Jamal | en_US |
dc.contributor.author | Wang, Kai | en_US |
dc.date.accessioned | 2014-06-18T17:06:05Z | - |
dc.date.available | 2014-06-18T17:06:05Z | - |
dc.date.created | 2014-02-28 | en_US |
dc.date.issued | 2008-09 | en_US |
dc.identifier.other | opendissertations/8863 | en_US |
dc.identifier.other | 9892 | en_US |
dc.identifier.other | 5238817 | en_US |
dc.identifier.uri | http://hdl.handle.net/11375/14034 | - |
dc.description.abstract | <p>The traditional approach of Magnetic Tracking Systems (MTS) utilizes approximate models and Parameter Extraction (PE) for Position and Orientation (P&O) determination. The approximate models give inaccurate P&O information outside the "constrained region". PE is an iterative, intensive process for P&O calculations, which limits the speed of the tracking process.</p> <p>Our MTS approach aims at accurate real-time P&O tracking. We utilize Artificial Neural Networks (ANN) with PE functionality to carry out the computational task for real-time P&O tracking. We apply Space Mapping (SM) modeling afterwards for system calibration to improve the accuracy of P&0 determination</p> <p>This thesis addresses a different approach for P&O determination. The main motivation of this work is to determine the P&O in a fast and accurate manner. It this work, we mathematically develop and experimentally implement our MTS for both 2-D and 3-D examples. The results show good match between our extracted P&O based on our MTS approach and the actual P&0 measured values.</p> | en_US |
dc.subject | Electrical and Computer Engineering | en_US |
dc.subject | Electrical and Computer Engineering | en_US |
dc.title | Portable Magnetic Tracking Systems Exploiting Neural Networks and Space Mapping Modeling | en_US |
dc.type | thesis | en_US |
dc.contributor.department | Electrical and Computer Engineering | en_US |
dc.description.degree | Master of Applied Science (MASc) | en_US |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Size | Format | |
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fulltext.pdf | 44.4 MB | Adobe PDF | View/Open |
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