This qualitative study examined the experiences of 20 African American men who graduated from predominately White institutions in one mid-Atlantic state between the years of 2001 and 2011 with doctoral degrees in Education or in a Humanities and Sciences field. Interviews were conducted to gather the lived experiences of the African American men in their own voices. The study addressed the following research questions: 1. Why do African American men persist to doctoral degree completion? 2. How do African American men perceive their doctoral student experience? A descriptive model that presents the internal and external factors revealed in the study is provided. Five main internal factors that contribute to the persistence of African American men in doctoral programs: personal refinement, academic refinement, professional refinement, motivation, and effective coping mechanisms were revealed. Three major external factors, support systems, positive relationships with the advisor/chair and committee, and financial support. In addition, the impact of the participants’ racial identity was explored and yielded both negative and positive effects on the doctoral student experience. Based upon the results, recommendations are offered for universities and departments, advisors and faculty, and future and current African American male doctoral students to aid them in persistence to degree completion.

Image completion is the process of filling missing regions of an image based on the known sections of the image. This technique is useful for repairing damaged images or removing unwanted objects from images. Research on this technique is plentiful. This thesis compares three different approaches to image completion. In addition, a new method is proposed which combines features from two of these algorithms to improve efficiency.

acase@tulane.edu / 1 / SamanthaHoyt

As the demand for energy worldwide increases, the oil and gas industry will need to increase recovery from unconventional gas reservoirs (UGR). UGRs include Tight Gas Sand (TGS), coalbed methane and gas shales. To economically produce UGRs, one must have adequate product price and one must use the most current technology. TGS reservoirs require stimulation as a part of the completion, so improvement of completion practices is very important. We did a thorough literature review to extract knowledge and experience about completion and stimulation technologies used in TGS reservoirs. We developed the principal design and two modules of a computer program called Tight Gas Sand Advisor (TGS Advisor), which can be used to assist engineers in making decisions while completing and stimulating TGS reservoirs. The modules include Perforation Selection and Proppant Selection. Based on input well/reservoir parameters these subroutines provide unambiguous recommendations concerning which perforation strategy(s) and what proppant(s) are applicable for a given well. The most crucial parameters from completion best-practices analyses and consultations with experts are built into TGS Advisor’s logic, which mimics human expert’s decision-making process. TGS Advisor’s recommended procedures for successful completions will facilitate TGS development and improve economical performance of TGS reservoirs.

As the demand for energy worldwide increases, the oil and gas industry will need to increase recovery from unconventional gas reservoirs (UGR). UGRs include Tight Gas Sand (TGS), coalbed methane and gas shales. To economically produce UGRs, one must have adequate product price and one must use the most current technology. TGS reservoirs require stimulation as a part of the completion, so improvement of completion practices is very important. We did a thorough literature review to extract knowledge and experience about completion and stimulation technologies used in TGS reservoirs. We developed the principal design and two modules of a computer program called Tight Gas Sand Advisor (TGS Advisor), which can be used to assist engineers in making decisions while completing and stimulating TGS reservoirs. The modules include Perforation Selection and Proppant Selection. Based on input well/reservoir parameters these subroutines provide unambiguous recommendations concerning which perforation strategy(s) and what proppant(s) are applicable for a given well. The most crucial parameters from completion best-practices analyses and consultations with experts are built into TGS Advisor's logic, which mimics human expert's decision-making process. TGS Advisor's recommended procedures for successful completions will facilitate TGS development and improve economical performance of TGS reservoirs.

Thesis (Ph. D.)--University of Texas at Austin, 2000. / Vita. Includes bibliographical references (leaves 299-304). Available also in a digital version from Dissertation Abstracts.

No description available.

Thesis (M.Ed.)--Cleveland State University, 2009. / Abstract. Title from PDF t.p. (viewed on June 11, 2009). Includes bibliographical references (p. 101-105). Available online via the OhioLINK ETD Center. Also available in print.

Often, data organized in matrix form contains missing entries. Further, such data has been observed to exhibit effective low-rank, and has led to interest in the particular problem of low-rank matrix-completion: Given a partially-observed matrix, estimate the missing entries such that the output completion is low-rank. The goal of this thesis is to improve matrix-completion algorithms by explicitly analyzing two sources of information in the observed entries: their locations and their values. First, we provide a categorization of a new approach to matrix-completion, which we call structural. Structural methods quantify the possibility of completion using tests applied only to the locations of known entries. By framing each test as the class of partially-observed matrices that pass the test, we provide the first organizing framework for analyzing the relationship among structural completion methods. Building on the structural approach, we then develop a new algorithm for active matrix-completion that is combinatorial in nature. The algorithm uses just the locations of known entries to suggest a small number of queries to be made on the missing entries that allow it to produce a full and accurate completion. If a budget is placed on the number of queries, the algorithm outputs a partial completion, indicating which entries it can and cannot accurately estimate given the observations at hand. Finally, we propose a local approach to matrix-completion that analyzes the values of the observed entries to discover a structure that is more fine-grained than the traditional low-rank assumption. Motivated by the Singular Value Decomposition, we develop an algorithm that finds low-rank submatrices using only the first few singular vectors of a matrix. By completing low-rank submatrices separately from the rest of the matrix, the local approach to matrix-completion produces more accurate reconstructions than traditional algorithms.

This thesis is concerned with problems encountered in image-based rendering (IBR) systems. The significance of such systems is increasing as virtual reality as well as augmented reality are finding their way into many applications, from entertainment to military. Particularly, I propose methods that are based on graph theory to address the open problems in the literature of image and video compositing, and scene completion. For a visually plausible compositing, it is first required to separate the object to be composed from the background it was initially captured against, a problem that is known as natural image matting. It aims, using some user interactions, to calculate a map that depicts how much a background color(s) contributes to the color of every other pixel in an image. My contributions to matting increase the accuracy of the map calculation as well as automate the whole process, by eliminating the need for user interactions. I propose several techniques for sampling user interactions which enhance the quality of the calculated maps. They rely on statistics of non-parametric color models as well as graph transduction and iterative graph cut techniques. The presented sampling strategies lead to state-of-the-art separation, and their efficiency was acknowledged by the standard benchmark in the literature. I have adopted the Gestalt laws of visual grouping to formulate a novel cost function to automate the generation of interactions that otherwise have to be provided manually. This frees the matting process from a critical limitation when used in rendering contexts. Scene completion is another task that is often required in IBR systems. This document presents a novel image completion method that overcomes a few drawbacks in the literature. It adopts a binary optimization technique to construct an image summary, which is then shifted according to a map, calculated with combinatorial optimization, to complete the image. I also present the formulation with which the proposed method can be extended to complete scenes, rather than images, in a stereoscopically and temporally-consistent manner.