東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Intelligent Energy-Efficient Drones:...

Sarkar, Sayani.

Linked to FindBook

Google Book

Amazon

博客來

Intelligent Energy-Efficient Drones: Path Planning, Real-Time Monitoring and Decision-Making.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Intelligent Energy-Efficient Drones: Path Planning, Real-Time Monitoring and Decision-Making./
Author:	Sarkar, Sayani.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	165 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-01, Section: A.
Contained By:	Dissertations Abstracts International83-01A.
Subject:	Computer engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28496600
ISBN:	9798519185318

Intelligent Energy-Efficient Drones: Path Planning, Real-Time Monitoring and Decision-Making.
Sarkar, Sayani.

Intelligent Energy-Efficient Drones: Path Planning, Real-Time Monitoring and Decision-Making. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 165 p.

Source: Dissertations Abstracts International, Volume: 83-01, Section: A.

Thesis (Ph.D.)--University of Louisiana at Lafayette, 2021.

This item must not be sold to any third party vendors.

Intelligent drones are the new frontier of science due to their mobility and significant advances in sensors, IoT modules, and artificial intelligence. They are designed to carry an optional payload, the camera system, and onboard electronics for remotely controlling the system and have been used for aerial photography, hobbyist drone racing, artificial aerial swarming, and different monitoring, surveying, and mapping applications. But UAV has a shorter flight time due to limited battery life. To address this issue, we study three research areas (a) Given limited battery power, planning intelligent routes for UAVs that would facilitate optimized flight time for real-time monitoring, (b) UAV's flight time predictions when payloads are attached to it, and (c) Conserve drone energy and expedite onboard decision making with secured Internet of Drone architecture. The motivation behind those studies are optimized multirotor UAVs short flight time due to its limited battery life and expedite real-time onboard decision-making power. The research contributions are towards sustainable social well-being and an intelligent future, with aerial imaging with AI, computer vision, IoT and remote sensing sensors, machine learning tools, and cloud computing research.

ISBN: 9798519185318Subjects--Topical Terms:

621879
Computer engineering.
Subjects--Index Terms:

Deep learning

Intelligent Energy-Efficient Drones: Path Planning, Real-Time Monitoring and Decision-Making.
LDR:02551nmm a2200409 4500 001 2284926
005 20211124093255.5
008 220723s2021 ||||||||||||||||| ||eng d
020 $a 9798519185318
035 $a (MiAaPQ)AAI28496600
035 $a AAI28496600
040 $a MiAaPQ $c MiAaPQ
100 1 $a Sarkar, Sayani. $3 3564144
245 1 0 $a Intelligent Energy-Efficient Drones: Path Planning, Real-Time Monitoring and Decision-Making.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 165 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-01, Section: A.
500 $a Advisor: Totaro, Michael W.;Kumar, Ashok.
502 $a Thesis (Ph.D.)--University of Louisiana at Lafayette, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Intelligent drones are the new frontier of science due to their mobility and significant advances in sensors, IoT modules, and artificial intelligence. They are designed to carry an optional payload, the camera system, and onboard electronics for remotely controlling the system and have been used for aerial photography, hobbyist drone racing, artificial aerial swarming, and different monitoring, surveying, and mapping applications. But UAV has a shorter flight time due to limited battery life. To address this issue, we study three research areas (a) Given limited battery power, planning intelligent routes for UAVs that would facilitate optimized flight time for real-time monitoring, (b) UAV's flight time predictions when payloads are attached to it, and (c) Conserve drone energy and expedite onboard decision making with secured Internet of Drone architecture. The motivation behind those studies are optimized multirotor UAVs short flight time due to its limited battery life and expedite real-time onboard decision-making power. The research contributions are towards sustainable social well-being and an intelligent future, with aerial imaging with AI, computer vision, IoT and remote sensing sensors, machine learning tools, and cloud computing research.
590 $a School code: 1363.
650 4 $a Computer engineering. $3 621879
650 4 $a Computer science. $3 523869
650 4 $a Robotics. $3 519753
650 4 $a Cryptography. $3 532586
650 4 $a Cameras. $3 524039
650 4 $a Computers. $3 544777
650 4 $a Remote sensing. $3 535394
650 4 $a Sensors. $3 3549539
650 4 $a Neural networks. $3 677449
650 4 $a Robots. $3 529507
650 4 $a Internet of Things. $3 3538511
650 4 $a Unmanned aerial vehicles. $3 3560267
650 4 $a Design. $3 518875
650 4 $a Drones. $3 3549538
650 4 $a Algorithms. $3 536374
650 4 $a Surveillance. $3 3559358
650 4 $a Cloud computing. $3 1016782
650 4 $a Heart rate. $3 3563099
650 4 $a Vehicles. $3 2145288
653 $a Deep learning
653 $a Drones
653 $a Energy conservation
653 $a Unmanned aerial vehicle
653 $a Flight time predictions
653 $a Payloads
690 $a 0464
690 $a 0984
690 $a 0771
690 $a 0389
690 $a 0799
710 2 $a University of Louisiana at Lafayette. $b Computer Engineering. $3 3348685
773 0 $t Dissertations Abstracts International $g 83-01A.
790 $a 1363
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28496600