LanguageTerm (authority = ISO 639-3:2007); (type = text)
English
Abstract (type = abstract)
Data representation is an important information processing task which finds use in diverse engineering applications like signal processing, machine learning, medical imaging, and geophysical data analysis, to name a few. Once a good representation model is known for a given application then the next question is learning that model under practical constraints imposed by the application. Two such constraints are i) data is available at geographically distributed sites, and ii) streaming data. In such scenarios distributed, decentralized, and online algorithms can be deployed for solving data representation problems, which are the focus of this dissertation. Specifically, this thesis focuses on solving following three problems: i) Solution for PCA for high-rate streaming data, ii) collaborative dictionary learning for big, distributed data, and iii) through the wall radar imaging (TWRI) in distributed settings. This thesis proposes new methods to tackle challenges arising due to distributed and steaming nature of data, providing a theoretical analysis of the proposed methodologies (except TWRI), and finally using simulations to demonstrate the efficacy of the proposed methods
Subject (authority = RUETD)
Topic
Electrical and Computer Engineering
Subject (authority = local)
Topic
Consensus averaging
Subject (authority = LCSH)
Topic
Electronic data processing
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_10116
PhysicalDescription
Form (authority = gmd)
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (xi, 108 pages) : illustrations
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
RelatedItem (type = host)
TitleInfo
Title
School of Graduate Studies Electronic Theses and Dissertations
Identifier (type = local)
rucore10001600001
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.