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 Efficient Models and Learning Strategies for Resource-Constrained Systems(2024) Rabbani, Tahseen Wahed Karim; Huang, Furong; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The last decade has seen sharp improvements in the performance of machine learning (ML) models but at the cost of vastly increased complexity and size of their underlying architectures. Advances in high-performance computing have enabled researchers to train and deploy models composed of hundreds of billions of parameters. However, harvesting the full utility of large models on smaller clients, such as Internet of Things (IoT) devices, without resorting to external hosting will require a significant reduction of parameters and faster, cheaper inference. In addition to augmenting IoT, efficient models and learning paradigms can reduce energy consumption, encourage technological equity, and are well-suited for deployment in real-world applications that require fast response in low-resource settings. To address these challenges, we introduce multiple, novel strategies for (1) reducing the scale of deep neural networks and (2) faster learning. For the size problem (1), we leverage tools such as tensorization, randomized projections, and locality-sensitive hashing to train on reduced representations of large models without sacrificing performance. For learning efficiency (2), we develop algorithms for cheaper forward passes, accelerated PCA, and asynchronous gradient descent. Several of these methods are tailored for federated learning (FL), a private, distributed learning paradigm where data is decentralized among resource-constrained edge clients. We are exclusively concerned with improving efficiency during training -- our techniques do not process pre-trained models or require a device to train over an architecture in its full entirety.Item Compression and Multi-Spectral Sensing for Video Based Physiological Monitoring(2022) Steinhauser, Carl Frederick; Wu, Min; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Remote physiological monitoring is an active area of research that extends monitoring capabilities traditionally found in a clinical setting towards the home, telehealth, and beyond. In particular, there is interest in leveraging consumer electronic devices for sensing physiological characteristics such as heart rate, heart rate variability, and blood oxygen saturation. This thesis focuses on enhancing the understanding and usage of the sensing component for these applications to improve the performance and quality of cardio-physiological monitoring. First, a close relationship between the color spaces used for video compression and the color projection planes commonly used for heart rate estimation is identified. % that results in higher compression of the physiological signal. The study demonstrates the impact of this observation on real and synthetic data to provide a foundation to guide future video coding to optimize its configurations to better preserve the heart rate signal for health related applications. Second, an investigation with a commercial-off-the-shelf (COTS) multi-spectral sensor is presented with key observations related to the sampling rate, exposure settings, and multi-channel processing. These observations will enable better usage of the sensor for future studies and data collections that leverage the more precise spectral measurements from the multi-spectral sensor compared to standard RGB cameras.Item Enabling Graph Analysis Over Relational Databases(2019) Xirogiannopoulos, Konstantinos; Deshpande, Amol; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Complex interactions and systems can be modeled by analyzing the connections between underlying entities or objects described by a dataset. These relationships form networks (graphs), the analysis of which has been shown to provide tremendous value in areas ranging from retail to many scientific domains. This value is obtained by using various methodologies from network science-- a field which focuses on studying network representations in the real world. In particular "graph algorithms", which iteratively traverse a graph's connections, are often leveraged to gain insights. To take advantage of the opportunity presented by graph algorithms, there have been a variety of specialized graph data management systems, and analysis frameworks, proposed in recent years, which have made significant advances in efficiently storing and analyzing graph-structured data. Most datasets however currently do not reside in these specialized systems but rather in general-purpose relational database management systems (RDBMS). A relational or similarly structured system is typically governed by a schema of varying strictness that implements constraints and is meticulously designed for the specific enterprise. Such structured datasets contain many relationships between the entities therein, that can be seen as latent or "hidden" graphs that exist inherently inside the datasets. However, these relationships can only typically be traversed via conducting expensive JOINs using SQL or similar languages. Thus, in order for users to efficiently traverse these latent graphs to conduct analysis, data needs to be transformed and migrated to specialized systems. This creates barriers that hinder and discourage graph analysis; our vision is to break these barriers. In this dissertation we investigate the opportunities and challenges involved in efficiently leveraging relationships within data stored in structured databases. First, we present GraphGen, a lightweight software layer that is independent from the underlying database, and provides interfaces for graph analysis of data in RDBMSs. GraphGen is the first such system that introduces an intuitive high-level language for specifying graphs of interest, and utilizes in-memory graph representations to tackle the problems associated with analyzing graphs that are hidden inside structured datasets. We show GraphGen can analyze such graphs in orders of magnitude less memory, and often computation time, while eliminating manual Extract-Transform-Load (ETL) effort. Second, we examine how in-memory graph representations of RDBMS data can be used to enhance relational query processing. We present a novel, general framework for executing GROUP BY aggregation over conjunctive queries which avoids materialization of intermediate JOIN results, and wrap this framework inside a multi-way relational operator called Join-Agg. We show that Join-Agg can compute aggregates over a class of relational and graph queries using orders of magnitude less memory and computation time.Item ELECTROCHEMICAL COMPRESSION WITH ION EXCHANGE MEMBRANES FOR AIR CONDITIONING, REFRIGERATION AND OTHER RELATED APPLICATIONS(2017) Tao, Ye; Wang, Chunsheng; Chemical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The refrigeration industry in the US are facing two main challenges. First of all, the phase down of HFCs in the future would require industries to seek alternative refrigerants which do not contribute to global warming. Secondly, the mechanical compressor in the small scale cooling system with a large energy impact is reaching its limitation due to heat transfer and manufacturing tolerances. Therefore there is an urgent need to develop a highly efficient compression process that works with environmentally friendly refrigerants. And the electrochemical compressor is developed to meet these requirement based on the following reasons. First of all, the electrochemical compressor can achieve an isothermal compression efficiency of greater than 90%. It also operates without moving parts, lubrication and noise. Most importantly, the compressor works with environmentally friendly refrigerants. In this thesis, three distinct electrochemical compression processes were studied. The first study is focused on modeling a metal hydride heat pump driven by electrochemical hydrogen compressor. The performance of the cooling-generating desorption reactor, the heating-generating absorption reactor, as well as the whole system were demonstrated. The results showed the superior performance of electrochemical hydrogen compressor over mechanical compressor in the system with optimized operating condition and COP. The second study demonstrated the feasibility of electrochemical ammonia compression with hydrogen as a carrier gas. The reaction mechanisms and the compression principle were verified and the compression efficiency was measured to be greater than 90%. The technology can be applied to ammonia vapor compression refrigeration cycle and ammonia storage. The third study is about developing and studying the electrochemical CO2 compression process with oxygen as a carrier gas. The reaction mechanism was verified and compared for both Pt and CaRuO3 electro-catalysts. And the latter was selected due to better CO2 and O2 absorption. The technology can potentially be applied in carbon dioxide transcritical refrigeration cycle and carbon capture. In conclusion, the electrochemical compression is a promising technology with higher compression efficiency and would bring a revolutionary change to the compressor engineering industry and global refrigeration and air conditioning market. It can also be used in fuel storage and separation based on the selective properties of the ion exchange membrane.Item COMPRESSIVE QUANTIZATION FOR SCALABLE CLOUD RADIO ACCESS NETWORKS(2016) Ma, Hang; Liu, K. J. Ray; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)With the proliferation of new mobile devices and applications, the demand for ubiquitous wireless services has increased dramatically in recent years. The explosive growth in the wireless traffic requires the wireless networks to be scalable so that they can be efficiently extended to meet the wireless communication demands. In a wireless network, the interference power typically grows with the number of devices without necessary coordination among them. On the other hand, large scale coordination is always difficult due to the low-bandwidth and high-latency interfaces between access points (APs) in traditional wireless networks. To address this challenge, cloud radio access network (C-RAN) has been proposed, where a pool of base band units (BBUs) are connected to the distributed remote radio heads (RRHs) via high bandwidth and low latency links (i.e., the front-haul) and are responsible for all the baseband processing. But the insufficient front-haul link capacity may limit the scale of C-RAN and prevent it from fully utilizing the benefits made possible by the centralized baseband processing. As a result, the front-haul link capacity becomes a bottleneck in the scalability of C-RAN. In this dissertation, we explore the scalable C-RAN in the effort of tackling this challenge. In the first aspect of this dissertation, we investigate the scalability issues in the existing wireless networks and propose a novel time-reversal (TR) based scalable wireless network in which the interference power is naturally mitigated by the focusing effects of TR communications without coordination among APs or terminal devices (TDs). Due to this nice feature, it is shown that the system can be easily extended to serve more TDs. Motivated by the nice properties of TR communications in providing scalable wireless networking solutions, in the second aspect of this dissertation, we apply the TR based communications to the C-RAN and discover the TR tunneling effects which alleviate the traffic load in the front-haul links caused by the increment of TDs. We further design waveforming schemes to optimize the downlink and uplink transmissions in the TR based C-RAN, which are shown to improve the downlink and uplink transmission accuracies. Consequently, the traffic load in the front-haul links is further alleviated by the reducing re-transmissions caused by transmission errors. Moreover, inspired by the TR-based C-RAN, we propose the compressive quantization scheme which applies to the uplink of multi-antenna C-RAN so that more antennas can be utilized with the limited front-haul capacity, which provide rich spatial diversity such that the massive TDs can be served more efficiently.Item Using a Discriminator to Improve Compressive Sensing Efficiency(2012) Hencke, Kevin; Benedetto, John; Applied Mathematics and Scientific Computation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Our work defines, implements, and evaluates a modification to a spectrum-based compression scheme for data streams coming from jet aircraft health-monitoring sensors. The modification consists of the addition of a discriminator which separates data streams into similar classes. We create and justify a simulation of a jet sensor network as a source for data streams. The data streams are compressed and decompressed under the new compression scheme and also under two old ones, and the reconstructions are evaluated for quality. The discriminator-based modification to the existing compression algorithm is found to yield better quality than the other two compression algorithms, at the cost of increased runtime.Item Biomechanics of the Intervertebral Disc: The Effects of Load History on Mechanical Behavior(2007-06-20) Gabai, Adam Shabtai; Hsieh, Adam; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Degenerative disc disease is associated with back pain, and can be a debilitating disorder. In addition to the biological contributions of genetics and aging, mechanical factors have been implicated in accelerating the progression of disc degeneration. Two studies were performed in order to explore the effects of various loading conditions on disc biomechanics. The first study explores the effects of compressive historical loads and disc hydration on subsequent creep loading and recovery. The second study investigates the restorative powers of creep distraction between compressive loading periods. In both cases three commonly applied mathematical models were employed to characterize disc behavior and the effectiveness of each model was validated. The studies confirm that hydration level has a significant impact on disc stiffness and time dependent behavior. Distraction and conditioning phases are shown to have a significant impact on hydration level and thus subsequent mechanical behavior.