Thesis (Ph. D.)--Hong Kong University of Science and Technology, 2002. / Includes bibliographical references (leaves 105-110). Also available in electronic version. Access restricted to campus users.

Thesis (M.S.)--Mississippi State University. Department of Mechanical Engineering. / Title from title screen. Includes bibliographical references.

Thesis (M.S.)--Mississippi State University. Department of Computer Science. / Title from title screen. Includes bibliographical references.

No description available.

Computational imaging (CI) systems are hybrid imagers in which the optical and post-processing sub-systems are jointly optimized to maximize the task-specific performance. In this dissertation we consider a form of CI system that measures the linear projections (i.e., features) of the scene optically, and it is commonly referred to as feature-specific imaging (FSI). Most of the previous work on FSI has been concerned with image reconstruction. Previous FSI techniques have also been non-adaptive and restricted to the use of ambient illumination.We consider two novel extensions of the FSI system in this work. We first present an adaptive feature-specific imaging (AFSI) system and consider its application to a face-recognition task. The proposed system makes use of previous measurements to adapt the projection basis at each step. We present both statistical and information-theoretic adaptation mechanisms for the AFSI system. The sequential hypothesis testing framework is used to determine the number of measurements required for achieving a specified misclassification probability. We demonstrate that AFSI system requires significantly fewer measurements than static-FSI (SFSI) and conventional imaging at low signal-to-noise ratio (SNR). We also show a trade-off, in terms of average detection time, between measurement SNR and adaptation advantage. Experimental results validating the AFSI system are presented.Next we present a FSI system based on the use of structured light. Feature measurements are obtained by projecting spatially structured illumination onto an object and collecting all of the reflected light onto a single photodetector. We refer to this system as feature-specific structured imaging (FSSI). Principal component features are used to define the illumination patterns. The optimal LMMSE operator is used to generate object estimates from the measurements. We demonstrate that this new imaging approach reduces imager complexity and provides improved image quality in high noise environments. We then generalize the FSSI system by making use of random projections (i.e., using no object prior) to define the illumination patterns. Object estimates are generated using L1-norm minimization and gradient-projection sparse reconstruction algorithms. Experimental results validating the FSSI system are presented.

A robust scholarly discussion had been conducted for more than 30 years on the professional impact of theory on communication practice, including Vocate‘s important observation in 1997 that a rigorous education in theory creates ―learnable intelligence‖ in professional communicators. However, the question of the actual professional impact of studying communication theory has not been asked of those best positioned to judge—communication practitioners who have rigorously studied communication theory. This research attempts to determine whether learning theory actually has affected practice among communication professionals who have explicitly studied theory in pursuit of a master‘s degree in professional communication. As well, this research examines a linked issue in Canada—whether theory is being taught in practical post-secondary public relations programs. Following the latter question this research examines a pedagogical approach at the community college level towards teaching theory relevant to a professional setting. Finally, it examines the link between communication theory and the evolution of the critical communication professional.

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The rapid adoption of the internet has occurred despite the lack of a ubiquitous identity meta-system. The status quo is a patchwork of proprietary security systems. A number of security issues have arisen as a result which threaten to lead to a loss of trust in the internet, and may limit the scope of applications built on it; effectively constraining the potential of the internet as a platform for business and services. Current initiatives by a broad consortium of industry leaders promise a vastly improved landscape with a set of interoperable protocols and systems, built on open specifications, and guided by a set of core identity principles, enabling a more secure online experience. Simultaneously there have arisen a large number of location aware web application and services which detect and use a user’s location to enhance their application experience. These advances, although useful, present new security and privacy issues. This paper investigates the operation of one of the new identity technologies, information cards, and proposes extensions to the existing supported schemas to incorporate recent advances in geo-location technology. The proposal is supported by reference to existing o pen source implementations.

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No description available.