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A novel characterization of signal space distortion and its application to the quantification of localization algorithm error

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TitleInfo
Title
A novel characterization of signal space distortion and its application to the quantification of localization algorithm error
Name (type = personal)
NamePart (type = family)
Francisco
NamePart (type = given)
John-Austen
DisplayForm
John-Austen Francisco
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Martin
NamePart (type = given)
Richard P
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Richard P Martin
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Advisory Committee
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chair
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Trappe
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Wade
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Wade Trappe
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Advisory Committee
Role
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internal member
Name (type = personal)
NamePart (type = family)
Nguyen
NamePart (type = given)
Thu D
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Thu D Nguyen
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Schiz
NamePart (type = given)
Alan
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Alan Schiz
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Advisory Committee
Role
RoleTerm (authority = RULIB)
outside member
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
Graduate School - New Brunswick
Role
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school
TypeOfResource
Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (encoding = w3cdtf); (qualifier = exact)
2015
DateOther (qualifier = exact); (type = degree)
2015-10
CopyrightDate (encoding = w3cdtf); (qualifier = exact)
2015
Place
PlaceTerm (type = code)
xx
Language
LanguageTerm (authority = ISO639-2b); (type = code)
eng
Abstract (type = abstract)
The constant reduction in cost and increase in the power of computing machinery has resulted in an ever-increasing interest and deployment of Internet-enabled sensing systems. Such systems have the distinctly difficult task of making use of noisy data sampled from dynamic environments. While some natural processes may have very exact theoretical models, real actual data rarely holds to the model, making the comparison, improvement and iterative development of sensing applications extremely difficult. The inability to determine whether a sensing system’s error is the result of noisy data or algorithmic miscomputation, or the prevalence and significance of signal errors in a particular environment make the causes of the error inscrutable. In such cases strongly amortized or very general probabilistic analysis is often used as a last resort resulting in conclusions that are overly generic, heuristic, or strongly underdetermined. We present a systematic method that can be used to construct a holistic synthetic error model for sensed data, the algorithms that process it and the environment in which it is sampled. We demonstrate how this method can be applied to the problem of laterative localization to construct deductive, analytic and evaluative mechanisms that allow model misperception, algorithmic error and environmental character to be understood.
Subject (authority = RUETD)
Topic
Computer Science
Subject (authority = ETD-LCSH)
Topic
Algorithms
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
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ETD_6657
PhysicalDescription
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electronic resource
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application/pdf
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text/xml
Extent
1 online resource (xvii, 197 p. : ill.)
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by John-Austen Francisco
RelatedItem (type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier (type = local)
rucore19991600001
Location
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NjNbRU
Identifier (type = doi)
doi:10.7282/T35T3NG6
Genre (authority = ExL-Esploro)
ETD doctoral
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Rights

RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Francisco
GivenName
John-Austen
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2015-08-18 14:28:32
AssociatedEntity
Name
John-Austen Francisco
Role
Copyright holder
Affiliation
Rutgers University. Graduate School - New Brunswick
AssociatedObject
Type
License
Name
Author Agreement License
Detail
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.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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Technical

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ETD
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windows xp
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