Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/458510
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dc.date.accessioned2023-02-16T06:10:43Z-
dc.date.available2023-02-16T06:10:43Z-
dc.identifier.urihttp://hdl.handle.net/10603/458510-
dc.description.abstractOceans cover a significant expanse of our planet than it is covered by the landmass. In all five newlinedominions where human endeavours take place, land, sea, underwater, atmosphere and space, newlinethe underwater activities are the most hidden and perhaps the most difficult. Obviously newlinenaval forces take advantage of the covertness offered by the sea to carry out operations that newlineare otherwise difficult to execute in open territories. The element of surprise and stealth newlinemake underwater warfare of paramount interest among the world s leading navies. Since the newlineearly efforts in probing the oceans, acoustics has remained as the predominant mode. The newlinesingle most ubiquitous equipment referred to as SOund NAvigation and Ranging (SONAR), newlinethe underwater equivalent of RAdio Detection And Ranging (RADAR), in its many forms newlinehelped and is continuing to help in exploring the depths of the oceans. newlinePassive acoustic target recognition stood at the vanguard of underwater acoustic research newlinefor several decades in the past while considering naval defence scenario and might continue newlineso for the coming decades. Passive acoustics plays a crucial role in Naval Non Co-operative newlineTarget Recognition (NCTR) systems, especially in Anti-Submarine Warfare (ASW) by virtue newlineof its tactical advantages. The target classification processes were historically performed by newlinetrained sonar operators all the way from the passive listening tubes to the modern digital newlinesonar console. In a modern strategic scenario, the human factors are the major limiting aspect newlinethat compromises the endurance and performance of any system. Unmanned systems are newlineincreasingly being preferred in all defence verticals as well, due to their low operational cost newlineand reduced risks of collateral loss.
dc.format.extentxxiv,279
dc.languageEnglish
dc.relation
dc.rightsuniversity
dc.titlePassive Sonar Automated Target Classification using Deep Hierarchical Feature Learning Approaches
dc.title.alternative
dc.creator.researcherKamal, Surej
dc.subject.keywordDeep Convolutional Neural Networks
dc.subject.keywordElectronics Engineering
dc.subject.keywordEngineering and Technology
dc.subject.keywordSpectro-Temporal Feature Learning
dc.subject.keywordUnder Water Target Recognition
dc.description.note
dc.contributor.guideSupriya, M H
dc.publisher.placeCochin
dc.publisher.universityCochin University of Science and Technology
dc.publisher.institutionDepartment of Electronics
dc.date.registered2016
dc.date.completed2021
dc.date.awarded2022
dc.format.dimensions
dc.format.accompanyingmaterialDVD
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Department of Electronics

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01_title.pdfAttached File58.05 kBAdobe PDFView/Open
02_preliminary pages.pdf264.57 kBAdobe PDFView/Open
03_content.pdf103.88 kBAdobe PDFView/Open
04_abstract.pdf100.45 kBAdobe PDFView/Open
05_chapter1.pdf2.06 MBAdobe PDFView/Open
06_chapter2.pdf339.06 kBAdobe PDFView/Open
07_chapter3.pdf431.88 kBAdobe PDFView/Open
08_chapter4.pdf6.16 MBAdobe PDFView/Open
09_chapter5.pdf6.31 MBAdobe PDFView/Open
10_chapter6.pdf3.18 MBAdobe PDFView/Open
11_chapter7.pdf9.75 MBAdobe PDFView/Open
12_chapter8.pdf120.21 kBAdobe PDFView/Open
14_annexures.pdf22.41 MBAdobe PDFView/Open
80_recommendation.pdf157.8 kBAdobe PDFView/Open


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