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

Low Frequency Electric Field Imaging.

Chung, Hugh E.

Linked to FindBook

Google Book

Amazon

博客來

Low Frequency Electric Field Imaging.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Low Frequency Electric Field Imaging./
Author:	Chung, Hugh E.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
Description:	163 p.
Notes:	Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.
Contained By:	Dissertation Abstracts International79-01B(E).
Subject:	Electrical engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10617075
ISBN:	9780355155464

Low Frequency Electric Field Imaging.
Chung, Hugh E.

Low Frequency Electric Field Imaging. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 163 p.

Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.

Thesis (Ph.D.)--Arizona State University, 2017.

Electric field imaging allows for a low cost, compact, non-invasive, non-ionizing alternative to other methods of imaging. It has many promising industrial applications including security, safely imaging power lines at construction sites, finding sources of electromagnetic interference, geo-prospecting, and medical imaging. The work presented in this dissertation concerns low frequency electric field imaging: the physics, hardware, and various methods of achieving it.

ISBN: 9780355155464Subjects--Topical Terms:

649834
Electrical engineering.

Low Frequency Electric Field Imaging.
LDR:03014nmm a2200349 4500 001 2154244
005 20180330130622.5
008 190424s2017 ||||||||||||||||| ||eng d
020 $a 9780355155464
035 $a (MiAaPQ)AAI10617075
035 $a (MiAaPQ)asu:17308
035 $a AAI10617075
040 $a MiAaPQ $c MiAaPQ
100 1 $a Chung, Hugh E. $3 3341966
245 1 0 $a Low Frequency Electric Field Imaging.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2017
300 $a 163 p.
500 $a Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.
500 $a Adviser: David R. Allee.
502 $a Thesis (Ph.D.)--Arizona State University, 2017.
520 $a Electric field imaging allows for a low cost, compact, non-invasive, non-ionizing alternative to other methods of imaging. It has many promising industrial applications including security, safely imaging power lines at construction sites, finding sources of electromagnetic interference, geo-prospecting, and medical imaging. The work presented in this dissertation concerns low frequency electric field imaging: the physics, hardware, and various methods of achieving it.
520 $a Electric fields have historically been notoriously difficult to work with due to how intrinsically noisy the data is in electric field sensors. As a first contribution, an in-depth study demonstrates just how prevalent electric field noise is. In field tests, various cables were placed underneath power lines. Despite being shielded, the 60 Hz power line signal readily penetrated several types of cables.
520 $a The challenges of high noise levels were largely addressed by connecting the output of an electric field sensor to a lock-in amplifier. Using the more accurate means of collecting electric field data, D-dot sensors were arrayed in a compact grid to resolve electric field images as a second contribution. This imager has successfully captured electric field images of live concealed wires and electromagnetic interference.
520 $a An active method was developed as a third contribution. In this method, distortions created by objects when placed in a known electric field are read. This expands the domain of what can be imaged because the object does not need to be a time-varying electric field source. Images of dielectrics (e.g. bodies of water) and DC wires were captured using this new method.
520 $a The final contribution uses a collection of one-dimensional electric field images, i.e. projections, to reconstruct a two-dimensional image. This was achieved using algorithms based in computed tomography such as filtered backprojection. An algebraic approach was also used to enforce sparsity regularization with the L1 norm, further improving the quality of some images.
590 $a School code: 0010.
650 4 $a Electrical engineering. $3 649834
650 4 $a Electromagnetics. $3 3173223
690 $a 0544
690 $a 0607
710 2 $a Arizona State University. $b Electrical Engineering. $3 1671741
773 0 $t Dissertation Abstracts International $g 79-01B(E).
790 $a 0010
791 $a Ph.D.
792 $a 2017
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10617075