Masters Theses
Abstract
"Landmine detection using hand-held units is a difficult problem due to varying type and composition of metals in landmines. This research explores spatially distributed features to discriminate landmines from other harmless objects. The feature calculation involves the wavelet decomposition of Metal Detector (MD) energy sequences to obtain the approximate and detailed coefficients, and correlation of these coefficients with Weighted Density Distribution (WDD) functions. The features calculated are evaluated on a standard back propagation neural network on real data sets with more than 1500 mine encounters of varying shape, size and metal content. The effectiveness of wavelet decomposition and WDD functions is investigated"--Abstract, page iii.
Advisor(s)
Stanley, R. Joe
Committee Member(s)
Agarwal, Sanjeev, 1971-
Moss, Randy Hays, 1953-
Department(s)
Electrical and Computer Engineering
Degree Name
M.S. in Electrical Engineering
Publisher
University of Missouri--Rolla
Publication Date
Summer 2004
Pagination
viii, 50 pages
Note about bibliography
Includes bibliographical references (pages 48-49).
Rights
© 2004 Kalyan Ram Achanta, All rights reserved.
Document Type
Thesis - Restricted Access
File Type
text
Language
English
Subject Headings
Land mines -- DetectionMines (Military explosives) -- DetectionWavelets (Mathematics)
Thesis Number
T 8623
Print OCLC #
62231101
Recommended Citation
Achanta, Kalyan Ram, "Landmine discrimination using wavelet decomposition and weighted density distribution functions" (2004). Masters Theses. 3621.
https://scholarsmine.mst.edu/masters_theses/3621
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