Please use this identifier to cite or link to this item:
https://hdl.handle.net/10356/13300
Title: | Neural network model for differential GPS | Authors: | Low, Kwong Hwee. | Keywords: | DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence DRNTU::Engineering::Electrical and electronic engineering::Wireless communication systems |
Issue Date: | 1998 | Abstract: | The Global Positioning System (GPS) is a worldwide satellite-based system consisting of a constellation of 24 satellites and their ground stations. GPS uses the satellites as reference points to calculate positions accurate to a matter of meters. In fact, with advanced forms of GPS, measurements to better than a centimeter can be made. GPS receivers have been miniaturized to just a few integrated circuits and so are becoming very economical, which makes the technology accessible to virtually everyone. In fact, GPS is finding its way into cars, boats, planes, construction equipment, movie making gear, farm machinery, and even laptop computers. | URI: | http://hdl.handle.net/10356/13300 | Schools: | School of Electrical and Electronic Engineering | Fulltext Permission: | restricted | Fulltext Availability: | With Fulltext |
Appears in Collections: | EEE Theses |
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LOW_KWONG_HWEE_1998.pdf Restricted Access | 8.85 MB | Adobe PDF | View/Open |
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