Thesis (Ph.D.)--University of Delaware, 2007. / Principal faculty advisor: Kenneth E. Barner, Dept. of Electrical and Computer Engineering. Includes bibliographical references.

Thesis (Ph. D.)--University of Hong Kong, 2006.

Progressive developments in computing and sensor technologies during the past decades have enabled the formulation of increasingly advanced problems in statistical inference and signal processing. The thesis is concerned with statistical estimation methods, and is divided into three parts with focus on two different areas: sensor fusion and sparse signal processing. The first part introduces the well-established Bayesian, Fisherian and least-squares estimation frameworks, and derives new estimators. Specifically, the Bayesian framework is applied in two different classes of estimation problems: scenarios in which (i) the signal covariances themselves are subject to uncertainties, and (ii) distance bounds are used as side information. Applications include localization, tracking and channel estimation. The second part is concerned with the extraction of useful information from multiple sensors by exploiting their joint properties. Two sensor configurations are considered here: (i) a monocular camera and an inertial measurement unit, and (ii) an array of passive receivers. New estimators are developed with applications that include inertial navigation, source localization and multiple waveform estimation. The third part is concerned with signals that have sparse representations. Two problems are considered: (i) spectral estimation of signals with power concentrated to a small number of frequencies,and (ii) estimation of sparse signals that are observed by few samples, including scenarios in which they are linearly underdetermined. New estimators are developed with applications that include spectral analysis, magnetic resonance imaging and array processing. / <p>QC 20130426</p>

<p>In this thesis, two different topics are treated related to wireless communication. Part I presents three different reconfigurable MEMS integrated antennas for MIMO applications. Simulation and measurement results are presented along with brief discussions on the topic of antenna selection with reconfigurable antenna elements. Part II presents an estimator for the coupling matrix of an antenna array with two slightly different approaches. CRB is derived and discussed in terms of parameter cost.</p>

Thesis (M.S.)--University of Alabama at Birmingham, 2009. / Title from PDF title page (viewed Jan. 21, 2010). Additional advisors: Yulia Karpeshina, Ian Knowles, Rudi Weikard. Includes bibliographical references (p. 19).

Thesis (Ph. D.)--University of Texas at Austin, 1998. / Includes bibliographical references (leaves 103-104). Available also in a digital version from Dissertation Abstracts.

Akademisk avhandling--Lund. / Extra t.p. with thesis statement inserted. Bibliography: p. 119-120.

Thesis (Ph. D.)--University of Missouri-Columbia, 2003. / Typescript. Vita. Includes bibliographical references (leaves 107-111). Also available on the Internet.

Thesis (Ph. D.)--University of Chicago, Department of Statistics, August 2001. / Includes bibliographical references. Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 2003. / Typescript. Vita. Includes bibliographical references (leaves 107-111). Also available on the Internet.