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001 ocn936213523;

003 OCoLC;

005 20160329120808.0;

006 m o d ;

007 cr unu||||||||;

008 160129s2015 ncuad ob 000 0 eng d;

035 a| (Sirsi) o936213523;

035 a| (OCoLC)936213523;

049 a| EREE;

090 a| QA76.87;

100 1 a| Hedayati, Hooman,e| author.?| UNAUTHORIZED;

245 10 a| MRSL :b| autonomous neural network-based self-stabilizing system /c| by Hooman Hedayati.;

246 3 a| MRSL autonomous neural network-based self-stabilizing system;

264 1 a| [Greenville, N.C.] :b| [East Carolina University],c| 2015.;

300 a| 44 pages :b| illustrations, charts;

336 a| textb| txt2| rdacontent;

337 a| computerb| c2| rdamedia;

338 a| online resourceb| cr2| rdacarrier;

347 a| text fileb| PDFc| 813.1 KB2| rda;

538 a| System requirements: Adobe Reader.;

538 a| Mode of access: World Wide Web.;

502 b| M.S.c| East Carolina Universityd| 2015.;

500 a| Presented to the faculty of the Department of Computer Science.;

500 a| Advisor: Nasseh Tabrizi.;

500 a| Title from PDF t.p. (viewed March 8, 2016).;

504 a| Includes bibliographical references.;

520 3 a| Stabilizing and localizing the positioning systems autonomously in the areas without GPS accessibility is a difficult task. In this thesis we describe a methodology called Most Reliable Straight Line (MRSL) for stabilizing and positioning camera-based objects in 3-D space. The camera-captured images are used to identify easy-to-track points "interesting points" and track them on two consecutive images. The distance between each of interesting points on the two consecutive images are compared and one with the maximum length is assigned to MRSL, which is used to indicate the deviation from the original position. To correct this our trained algorithm is deployed to reduce the deviation by issuing relevant commands, this action is repeated until MRSL converges to zero. To test the accuracy and robustness, the algorithm was deployed to control positioning of a Quadcopter. It was demonstrated that the Quadcopter (a) was highly robust to any external forces, (b) can fly even if the Quadcopter experiences loss of engine, (c) can fly smoothly and positions itself on a desired location.;

650 0 a| Neural networks (Computer science)=| ^A263002;

650 0 a| Global Positioning System.=| ^A312695;

700 1 a| Tabrizi, M. H. N.,e| degree supervisor.?| UNAUTHORIZED;

710 2 a| East Carolina University.b| Department of Computer Science.?| UNAUTHORIZED;

856 40 z| Access via ScholarShipu| http://hdl.handle.net/10342/5142;

949 a| Click on web addressw| ASISh| JOYNER101;

949 a| Click on web addressw| ASISh| HSL111;

949 o| wjh;

994 a| C0b| ERE;

596 a| 1 4;

998 a| 4356329;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 4356329-1001l| JNETm| JOYNERr| Ys| Yt| JNE3ETDu| 1/29/2016x| ETDz| JERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 4356329-2001l| HSLELECm| HSLr| Ys| Yt| HEETDu| 1/29/2016x| ETDz| HERESOURCE;

MRSL : autonomous neural network-based self-stabilizing system / by Hooman Hedayati.

Author/creator	Hedayati, Hooman author.
Other author	Tabrizi, M. H. N., degree supervisor.
Other author	East Carolina University. Department of Computer Science.
Format	Theses and dissertations
Publication	[Greenville, N.C.] : [East Carolina University], 2015.
Description	44 pages : illustrations, charts
Supplemental Content	Access via ScholarShip
Subjects	Neural networks (Computer science) Global Positioning System.

Variant title	MRSL autonomous neural network-based self-stabilizing system
Summary	Stabilizing and localizing the positioning systems autonomously in the areas without GPS accessibility is a difficult task. In this thesis we describe a methodology called Most Reliable Straight Line (MRSL) for stabilizing and positioning camera-based objects in 3-D space. The camera-captured images are used to identify easy-to-track points "interesting points" and track them on two consecutive images. The distance between each of interesting points on the two consecutive images are compared and one with the maximum length is assigned to MRSL, which is used to indicate the deviation from the original position. To correct this our trained algorithm is deployed to reduce the deviation by issuing relevant commands, this action is repeated until MRSL converges to zero. To test the accuracy and robustness, the algorithm was deployed to control positioning of a Quadcopter. It was demonstrated that the Quadcopter (a) was highly robust to any external forces, (b) can fly even if the Quadcopter experiences loss of engine, (c) can fly smoothly and positions itself on a desired location.
General note	Presented to the faculty of the Department of Computer Science.
General note	Advisor: Nasseh Tabrizi.
General note	Title from PDF t.p. (viewed March 8, 2016).
Dissertation note	M.S. East Carolina University 2015.
Bibliography note	Includes bibliographical references.
Technical details	System requirements: Adobe Reader.
Technical details	Mode of access: World Wide Web.

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MRSL : autonomous neural network-based self-stabilizing system / by Hooman Hedayati.

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