Please use this identifier to cite or link to this item:
http://hdl.handle.net/10603/546309
Title: | An efficient automatic fault detection system in an unmanned aerial vehicle using deep neural network |
Researcher: | Ayyasamy, T |
Guide(s): | Nirmala,S |
Keywords: | Computer Science Computer Science Information Systems Engineering and Technology |
University: | Anna University |
Completed Date: | 2023 |
Abstract: | newline Unmanned aerial vehicles (UAVs) are planes that fly without a newlinepilot or other occupants. UAVs, often known as quotdrones,quot can occasionally be newlinefully or partially self-sufficient but are more usually flown by a human pilot newlinefrom a distance. Drones are now more widely used, easily accessible, and newlinetechnologically more advanced. Particularly capable of flying at various newlineheights and distances are drones. The drones normally can travel from very newlineclose range to 5000m and are more often than not used by hobbyists. newlineClose-range UAVs are flying around 50000m. Short-range drones newlinetravel up to ninety miles and are often used for espionage and territory newlinesurveillance. Middle-range UAVs can fly up to 65,000m distance and are newlineused for intelligence gathering, clinical research, and meteorological studies. newlineThe long-range drones can fly beyond four hundred-mile up to 3,000 in the newlineair. The packages of UAV are Precision agriculture, Ocean and coastal newlinestudies, Contaminant Spills and pollutants, Landfill Mapping and monitoring, newlinecorridor Mapping, Mining web page mapping, Crop and aquaculture farm newlinemonitoring, Mineral exploration, Spectral and thermal analysis, traffic newlinemonitoring, different environmental manage and track. |
Pagination: | xviii,144p. |
URI: | http://hdl.handle.net/10603/546309 |
Appears in Departments: | Faculty of Information and Communication Engineering |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
01_title.pdf | Attached File | 26.23 kB | Adobe PDF | View/Open |
02_prelimpages.pdf | 2.31 MB | Adobe PDF | View/Open | |
03_content.pdf | 68.32 kB | Adobe PDF | View/Open | |
04_abstract.pdf | 11.5 kB | Adobe PDF | View/Open | |
05_chapter1.pdf | 429.43 kB | Adobe PDF | View/Open | |
06_chapter2.pdf | 351.29 kB | Adobe PDF | View/Open | |
07_chapter3.pdf | 727.7 kB | Adobe PDF | View/Open | |
08_chapter4.pdf | 443.64 kB | Adobe PDF | View/Open | |
09_chapter5.pdf | 857.09 kB | Adobe PDF | View/Open | |
10_chapter6.pdf | 1.33 MB | Adobe PDF | View/Open | |
11_annexures.pdf | 122.04 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 58.21 kB | Adobe PDF | View/Open |
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