An efficient automatic fault detection system in an unmanned aerial vehicle using deep neural network

dc.contributor.guideNirmala,S
dc.coverage.spatialAn efficient automatic fault detection system in an unmanned aerial vehicle using deep neural network
dc.creator.researcherAyyasamy, T
dc.date.accessioned2024-02-21T04:41:42Z
dc.date.available2024-02-21T04:41:42Z
dc.date.awarded2024
dc.date.completed2023
dc.date.registered
dc.description.abstractnewline 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.
dc.description.note
dc.format.accompanyingmaterialDVD
dc.format.dimensions21cm.
dc.format.extentxviii,144p.
dc.identifier.urihttp://hdl.handle.net/10603/546309
dc.languageEnglish
dc.publisher.institutionFaculty of Information and Communication Engineering
dc.publisher.placeChennai
dc.publisher.universityAnna University
dc.relationp.133-143
dc.rightsuniversity
dc.source.universityUniversity
dc.subject.keywordComputer Science
dc.subject.keywordComputer Science Information Systems
dc.subject.keywordEngineering and Technology
dc.titleAn efficient automatic fault detection system in an unmanned aerial vehicle using deep neural network
dc.title.alternative
dc.type.degreePh.D.

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