Thesis (M.S.)--U.S. Naval Postgraduate School. / "AD-769 745." Includes bibliographical references. Also issued in print.

Thesis (M.S. in Management)--Naval Postgraduate School, June 1990. / Thesis Advisor(s): Gorman, Linda ; Crawford, Alice M. "June 1990." Description based on title screen as viewed on 20 October 2009. DTIC Identifier(s): Cost benefit, individualized instruction, opportunity costs. Author(s) subject terms: Instructional systems development, cost-benefit, military training, individualized instruction, cost-effectiveness, opportunity costs. Includes bibliographical references (p. 70-71). Also available in print.

Thesis (Ph. D.)--University of Texas at Austin, 2005. / Supervisor: Anthony P. Ambler. Vita. Includes bibliographical references.

Thesis (M.S. in Systems Engineering and Analysis)--Naval Postgraduate School, December 2009. / Thesis Advisor(s): Paulo, Eugene. Second Reader: Rhoades, Mark. "December 2009." Description based on title screen as viewed on January 27, 2010. Author(s) subject terms: Systems integration, System safety, System-of-Systems safety. Includes bibliographical references (p. 51-52). Also available in print.

No description available.

Space debris is a rising problem in today's world. Because there is so much in space that is unknown, it is critical to eventually catalog every piece. Since there are many attributes and properties attached to space objects, it is preferable to use an ontological classification method. The information presented in the ontology can then be used to answer questions about space debris. A Bayesian network would accomplish that because of its quantitative nature. The similarities between ontologies and Bayesian networks, such as their architectures and their flexibility, make it possible to integrate an ontology into a Bayesian network. Image determination and object collision assessment were used as applications to check the viability of integrating ontologies and Bayesian networks. It was determined that ontologies and Bayesian networks are tools that when combined can result in new useful quantitative information.

In Transforming Health Care Through Information Technology the PITAC offers six key recommendations that could significantly expand access to health care, improve its quality, reduce its costs, and transform the conduct of biomedical research. The PITAC sees these recommendations as critical steps toward addressing the challenges that exist to improving Americans' health and health care: *Establish pilot projects and Enabling Technology Centers to extend the practical uses of information technology to health care systems and biomedical research; *NIH, in close collaboration with NSF, DARPA, and DOE, should design and deploy a scalable national computing and information infrastructure to support the biomedical research community; *Congress should enhance existing privacy rules by enacting legislation that assures sound practices for managing personally identifiable health information; Establish programs to increase the pool of biomedical research and health care professionals with training at the intersection of health and information technology; *DHHS should outline its vision for using IT to improve health care and subsequently devote the resources to conduct the IT research critical to accomplishing these goals in the long term; and *DHHS should appoint a senior information technology leader to provide strategic leadership across DHHS and focus on the importance of information technology in addressing pressing problems in health care

Digital image restoration requires some knowledge of the degradation phenomena in order to attempt an inversion of that degradation. Typically, degradations which are included in the restoration process are those resulting from the optics and electronics of the imaging device. Occasionally, blurring caused by an intervening atmosphere, uniform motion or defocused optics is also included. Recently it has been shown that sampling, the conversion of the continuous output of an imaging system to a discrete array, further degrades or blurs the image. Thus, incorporating sampling effects into the restoration should improve the quality of the restored image. The system transfer function (the Fourier transform of the point spread function), was derived for the Landset Multi-Spectral Scanner and Thematic Mapper systems. Sampling effects were included, along with the relevant optical, instantaneous field of view and electronic filter data, in the system analysis. Using the system transfer function, a least squares (Wiener) filter was then derived. A Wiener filter requires the ratio of the power spectra of the scene and noise, which is often, for simplicity, assumed to be a constant over frequency. The restoration method used here includes models for the power spectra which are based on the study of several different types of Landsat scenes. The Wiener filter is then inverse Fourier transformed to find a restoration filter which is spatially windowed to suppress ringing. Qualitative and quantitative evaluations are made of the restored imagery. Comparisons are made to the approaches taken by other investigators, in particular, to one who has had success restoring the same type of imagery. It is found that the restoration method used here compares favorably with this previous work.

Statistical models for the joint statistics of image pixels are of central importance in many image processing applications. However the high dimensionality stemming from large problem size and the long-range spatial interactions make statistical image modeling particularly challenging. Commonly this modeling is simplified by a change of basis, mostly using a wavelet transform. Indeed, the wavelet transform has widely been used as an approximate whitener of statistical time series. It has, however, long been recognized that the wavelet coefficients are neither Gaussian, in terms of the marginal statistics, nor white, in terms of the joint statistics. The question of wavelet joint models is complicated and admits for possibilities, with statistical structures within subbands, across orientations, and scales. Although a variety of joint models have been proposed and tested, few models appear to be directly based on empirical studies of wavelet coefficient cross-statistics. Rather, they are based on intuitive or heuristic notions of wavelet neighborhood structures. Without an examination of the underlying statistics, such heuristic approaches necessarily leave unanswered questions of neighborhood sufficiency and necessity. This thesis presents an empirical study of joint wavelet statistics for textures and other imagery including dependencies across scale, space, and orientation. There is a growing realization that modeling wavelet coefficients as independent, or at best correlated only across scales, may be a poor assumption. While recent developments in wavelet-domain Hidden Markov Models (notably HMT-3S) account for within-scale dependencies, we find that wavelet spatial statistics are strongly orientation dependent, structures which are surprisingly not considered by state-of-the-art wavelet modeling techniques. To demonstrate the effectiveness of the studied wavelet correlation models a novel non-linear correlated empirical Bayesian shrinkage algorithm based on the wavelet joint statistics is proposed. In comparison with popular nonlinear shrinkage algorithms, it improves the denoising results.

<p> Many industries, such as healthcare, transportation, education, and other fields that involve large corporations and institutions, are complex systems composed of many diverse interacting components. Frequently, to improve performance within these industries, to move into new markets, or to expand capability or capacity, decision-makers face opportunities or mandates to implement innovations (new technology, processes, and services). Successful implementation of these innovations involves seamless integration with the policy, economic, social, and technological dynamics associated with the complex system. These dynamics are frequently difficult for decision-makers to observe and understand. Consequently, they take on risk from lack of insight into how best to implement the innovation and how their system-of-interest will ultimately perform. This research defines a framework for an integrated, multi-scale modeling and simulation systems approach that provides decision-makers with prospective insight into the likely performance to expect once an innovation of change is implemented in a complex system. The need for such a framework when modeling complex systems is described, and suitable simulation paradigms and the challenges related to implementing these simulations are discussed. A healthcare case study is used to demonstrate the framework&rsquo;s application and utility in understanding how an innovation, once fielded, will actually affect the larger complex system to which it belongs.</p>