Thesis (Ph. D.)--University of Washington, 1984. / Vita. Bibliography: leaves [335]-348.

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Thesis (M.S.)--California Polytechnic State University, 2009. / Mode of access: Internet. Title from PDF title page; viewed on Jan. 19, 2010. Major professor: John Saghri, Associate Professor. "Presented to the faculty of California Polytechnic State University, San Luis Obispo." "In partial fulfillment of the requirements for the degree [of] Master of Science in Electrical Engineering." "December 2009." Includes bibliographical references (p. 67-68).

Thesis (M.S. in E.C.E)--School of Electrical and Computer Engineering, Georgia Institute of Technology, 2004. Directed by Aaron Lanterman. / Includes bibliographical references (leaves 48-49).

Micro-Doppler is defined as scattering produced by non-rigid-body motion. This dissertation involves the design of a multiple frequency continuous wave (MFCW) radar for micro-Doppler research and detection and classification algorithm design. First, sensor hardware is developed and tested. Various design tradeoffs are considered, with the application of micro-Doppler based detection and classification in mind. A diverse database of MFCW radar micro-Doppler signatures was collected for this dissertation. The micro-Doppler signature database includes experimental data from human, vehicle, and animal targets. Signatures are acquired from targets with varying ranges, velocities, approach angles, and postures. The database is analyzed for micro-Doppler content with a focus on its application to target classification. Joint time-frequency detection algorithms are developed to improve detection performance by exploiting noise-spreading and the micro-Doppler phenomenon. Following detection algorithm development, this dissertation covers the design of micro- Doppler feature extraction, feature selection, and classification algorithms. Feature selection is performed automatically via a Fisher score initialized sequential backward selection algorithm. Classification is performed using two distinct approaches: a generative statistical classification algorithm based on Gaussian mixture models (GMMs) and a discriminative statistical classification algorithm based on support vector machines (SVMs). Classifier performance is analyzed in detail on a micro-Doppler signature database acquired over a three-year period. Both the SVM and GMM classifiers perform well on the radar target classification task (high accuracy, low nuisance alarm probability, high F-measure, etc.). The performance of both classifiers is remarkably similar, and neither algorithm dominates the other in any performance metric when using the chosen feature set. (However, the difference between SVM and GMM classification accuracy becomes statistically significant when many redundant features are present in the feature set.) The accuracy of both classifiers is shown to vary as a function of approach angle, which physically corresponds to the angular dependence of micro-Doppler. The results suggest that overall classifier performance is more sensitive to feature selection than classifier selection (with GMM being more sensitive to redundant features than SVM). Both classifiers are robust enough to handle human targets attempting to evade detection by either army crawling or hands-and-knees crawling. / text

Electronically displayed images are becoming increasingly important as an interface between man and information systems. Lengthy periods of intense observation are no longer unusual. There is a growing awareness that specific demands should be made on displayed images in order to achieve an optimum match with the perceptual properties of the human visual system. These demands may vary greatly, depending on the task for which the displayed image is to be used and the ambient conditions. Optimal image specifications are clearly not the same for a home TV, a radar signal monitor or an infrared targeting image display. There is, therefore, a growing need for means of objective measurement of image quality, where "image quality" is used in a very broad sense and is defined in the thesis, but includes any impact of image properties on human performance in relation to specified visual tasks. The aim of this thesis is to consolidate and comment on the image measure literatures, and to find through experiment the salient properties of electronically displayed real world complex imagery that impacts on human performance. These experiments were carried out for well specified visual tasks (of real relevance), and the appropriate application of image measures to this imagery, to predict human performance, was considered. An introduction to certain aspects of image quality measures is given, and clutter metrics are integrated into this concept. A very brief and basic introduction to the human visual system (HVS) is given, with some basic models. The literature on image measures is analysed, with a resulting classification of image measures, according to which features they were attempting to quantify. A series of experiments were performed to evaluate the effects of image properties on human performance, using appropriate measures of performance. The concept of image similarity was explored, by objectively measuring the subjective perception of imagery of the same scene, as obtained through different sensors, and which underwent different luminance transformations. Controlled degradations were introduced, by using image compression. Both still and video compression were used to investigate both spatial and temporal aspects of HVS processing. The effects of various compression schemes on human target acquisition performance were quantified. A study was carried out to determine the "local" extent, to which the clutter around a target, affects its detectability. It was found in this case, that the excepted wisdom, of setting the local domain (support of the metric) to twice the expected target size, was incorrect. The local extent of clutter was found to be much greater, with this having implications for the application of clutter metrics. An image quality metric called the gradient energy measure (GEM), for quantifying the affect of filtering on Nuclear Medicine derived images, was developed and evaluated. This proved to be a reliable measure of image smoothing and noise level, which in preliminary studies agreed with human perception. The final study discussed in this thesis determined the performance of human image analysts, in terms of their receiver-operating characteristic, when using Synthetic Aperture Radar (SAR) derived images in the surveillance context. In particular, the effects of target contrast and background clutter on human analyst target detection performance were quantified. In the final chapter, suggestions to extend the work of this thesis are made, and in this context a system to predict human visual performance, based on input imagery, is proposed. This system intelligently uses image metrics based on the particular visual task and human expectations and human visual system performance parameters. / Thesis (Ph.D.)--Medical School; School of Computer Science, 1999.

Thesis (Ph. D.)--University of Washington, 2007. / Vita. Includes bibliographical references (p. 111-114).

Thesis (M. S.)--Aerospace Engineering, Georgia Institute of Technology, 2008. / Committee Chair: Eric Feron; Committee Member: Eric Johnson; Committee Member: Jerry Seitzman.

Thesis (M.S.)--California Polytechnic State University, 2009. / Mode of access: Internet. Title from PDF title page; viewed on February 18, 2009. Major professor: John Saghri, Ph.D. "Presented to the Electrical Engineering Department faculty of California Polytechnic State University, San Luis Obispo, California." "In partial fulfillment of the requirements for the degree [of] Master of Science in Electrical Engineering." "January 2009." Includes bibliographical references (p. 134-136). Also available on microfiche.

Thesis (M.S.)--California Polytechnic State University, 2009. / Title from PDF title page; viewed on June 11, 2009. "June 2009." "In partial fulfillment of the requirements for the degree [of] Master of Science in Electrical Engineering." "Presented to the Electrical Engineering faculty of California Polytechnic State University, San Luis Obispo." Major professor: John Saghri, Ph.D. Includes bibliographical references (p. 72-74). Also available on microfiche.