Theses and Dissertations from UMD
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New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
More information is available at Theses and Dissertations at University of Maryland Libraries.
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Item Next-generation Mass Spectrometry With Multi-omics For Discoveries In Cell And Neurodevelopmental Biology(2022) Li, Jie; Nemes, Peter; Chemistry; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Understanding tissue formation advances our understanding of the causes of disease and the obtained knowledge can be potentially applied to develop personalized interventions. However, to explore the underlying mechanisms that govern tissue formation, there is a high and unmet need to develop new technologies to characterize different types of biomolecules from early-stage embryonic precursor cells and their descendent cells during development. This dissertation discusses new technological advancements to facilitate multi-omic (proteomic and metabolomic) analysis to explore cell-to-cell differences and uncover mechanisms underlying tissue formation. The work presented herein illustrates the development of in vivo microsampling and single-cell mass spectrometry (MS) to uncover cell heterogeneity among embryonic cells. Additionally, this dissertation work studies the biological role of metabolites in cell fate determination by exploring the mechanisms underlying metabolite-induced cell fate change. Moreover, this work introduces a novel technique called MagCar developed to track and isolate tissue-specific cells at later stages, which enables studying temporal molecular changes to gain new information about tissue formation.Item Identification of Operators on Elementary Locally Compact Abelian Groups(2015) Civan, Gokhan; Benedetto, John J; Mathematics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Measurement of time-variant linear channels is an important problem in communications theory with applications in mobile communications and radar detection. Kailath addressed this problem about half a century ago and developed a spreading criterion for the identifiability of time-variant channels analogous to the band limitation criterion in the classical sampling theory of signals. Roughly speaking, underspread channels are identifiable and overspread channels are not identifiable, where the critical spreading area equals one. Kailath's analysis was later generalized by Bello from rectangular to arbitrary spreading supports. Modern developments in time-frequency analysis provide a natural and powerful framework in which to study the channel measurement problem from a rigorous mathematical standpoint. Pfander and Walnut, building on earlier work by Kozek and Pfander, have developed a sophisticated theory of "operator sampling" or "operator identification" which not only places the work of Kailath and Bello on rigorous footing, but also takes the subject in new directions, revealing connections with other important problems in time-frequency analysis. We expand upon the existing work on operator identification, which is restricted to the real line, and investigate the subject on elementary locally compact abelian groups, which are groups built from the real line, the circle, the integers, and finite abelian groups. Our approach is to axiomatize, as it were, the main ideas which have been developed over the real line, working with lattice subgroups. We are thus able to prove the various identifiability results for operators involving both underspread and overspread conditions in both general and specific cases. For example, we provide a finite dimensional example illustrating a necessary and sufficient condition for identifiability of operators, owing to the insight gleaned from the general theory. In working up to our main results, we set up the quite considerable technical background, bringing some new perspectives to existing ideas and generally filling what we consider to be gaps in the literature.Item SPARSE ACQUISITION AND RECONSTRUCTION FOR SOME COMPUTER VISION PROBLEMS(2011) Reddy, Nagilla Dikpal; Chellappa, Ramalingam; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Sparse representation, acquisition and reconstruction of signals guided by theory of Compressive Sensing (CS) has become an active research research topic over the last few years. Sparse representations effectively capture the idea of parsimony enabling novel acquisition schemes including sub-Nyquist sampling. Ideas from CS have had significant impact on well established fields such as signal acquisition, machine learning and statistics and have also inspired new areas of research such as low rank matrix completion. In this dissertation we apply CS ideas to low-level computer vision problems. The contribution of this dissertation is to show that CS theory is an important addition to the existing computational toolbox in computer vision and pattern recognition, particularly in data representation and processing. Additionally, in each of the problems we show how sparse representation helps in improved modeling of the underlying data leading to novel applications and better understanding of existing problems. In our work, the impact of CS is most felt in the acquisition of videos with novel camera designs. We build prototype cameras with slow sensors capable of capturing at an order of magnitude higher temporal resolution. First, we propose sub-Nyquist acquisition of periodic events and then generalize the idea to capturing regular events. Both the cameras operate by first acquiring the video at a slower rate and then computationally recovering the desired higher temporal resolution frames. In our camera, we sense the light with a slow sensor after modulating it with a fluttering shutter and then reconstruct the high speed video by enforcing its sparsity. Our cameras offer a significant advantage in light efficiency and cost by obviating the need to sense, transfer and store data at a higher frame rate. Next, we explore the applicability of compressive cameras for computer vision applications in bandwidth constrained scenarios. We design a compressive camera capable of capturing video using fewer measurements and also separate the foreground from the background. We model surveillance type videos with two processes, a slower background and a faster but spatially sparse foreground such that we can recover both of them separately and accurately. By formulating the problem in a distributed CS framework we achieve state-of-the-art video reconstruction and background subtraction. Subsequently we show that if the camera geometry is provided in a multi-camera setting, the background subtracted CS images can be used for localizing the object and tracking it by formulating its occupancy in a grid as a sparse reconstruction problem. Finally, we apply CS to robust estimation of gradients obtained through photometric stereo and other gradient-based techniques. Since gradient fields are often not integrable, the errors in them need to be estimated and removed. By assuming the errors, particularly the outliers, as sparse in number we accurately estimate and remove them. Using conditions on sparse recovery in CS we characterize the distribution of errors which can be corrected completely and those that can be only partially corrected. We show that our approach has the important property of localizing the effect of error during integration where other parts of the surface are not affected by errors in gradients at a particular location. This dissertation is one of the earliest to investigate the implications of compressive sensing theory to some computer vision problems. We hope that this effort will spur more interest in researchers drawn from computer vision, computer graphics, computational photography, statistics and mathematics.Item Environmental Performance and Sustainability of Bioretention Cells(2009) Jones, Philip Sumner; Davis, Allen P; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Bioretention cells use vegetation and soil media for source control of urban stormwater runoff, alleviating waterway impairment. Environmental performance of two cells was investigated. First, a cell capturing road runoff was monitored for one year. At a second cell, media were sampled to measure lifetime metal accumulation and evaluate the environmental, health, and maintenance implications of metal sequestration. Monitoring found high metal and suspended solids removal, generally poor nutrient performance, and chloride export. Runoff volume and peak flow rate reduction occurred for small storm events. For larger events, outflow volume consistently exceeded inflow because of unique site conditions. Lead, copper, and zinc media concentrations in the second cell were elevated but well below cleanup thresholds. Metals were strongly bound to bioretention media and largely immobile; lead bioavailability was comparable to generic soil estimates. Most metal accumulation was near the inflow point in the top 3 to 12 cm of media.