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Description
Modern radio frequency (RF) sensors are digital systems characterized by wide band frequency range, and capable to perform multi-function tasks such as: radar, electronic warfare (EW), and communications simultaneously on different sub-arrays. This demands careful understanding of the behavior of each sub-system and how each sub-array interacts with the others. A way to estimate and measure the active reflection coefficient (ARC) to calculate the active voltage standing wave ratio (VSWR) of multiple input multiple output (MIMO) radar when elements (or sub-arrays) are driven with different waveforms has been developed. This technique will help to understand and incorporate bounds in the design of MIMO systems and its waveforms to avoid damages by large power reflections and to improve system performance. The methodology developed consists of evaluating the active VSWR at each individual antenna element or sub-array from (1) estimates of the ARC by using computational electromagnetic (CEM) tools or (2) by directly measuring the ARC at each antenna element or sub-array. The former methodology is important especially at the design phase where trade offs between element shapes and geometrical configurations are taking place. The former methodology is expanded by directly measuring ARC using an experimental radar testbed Baseband-digital at Every Element MIMO Experimental Radar (BEEMER) system to assess the active VSWR, side-lobe levels and antenna pattern effects when different waveforms are transmitted. An optimization technique is implemented to mitigate the effects of the ARC in co-located MIMO radars by waveform design.
ContributorsColonDiaz, Nivia (Author) / Aberle, James T. (Thesis advisor) / Bliss, Daniel W. (Thesis advisor) / Diaz, Rodolfo (Committee member) / Janning, Dan (Committee member) / Arizona State University (Publisher)
Created2021
Description
The use of spatial data has become very fundamental in today's world. Ranging from fitness trackers to food delivery services, almost all application records users' location information and require clean geospatial data to enhance various application features. As spatial data flows in from heterogeneous sources various problems arise. The study of entity matching has been a fervent step in the process of producing clean usable data. Entity matching is an amalgamation of various sub-processes including blocking and matching. At the end of an entity matching pipeline, we get deduplicated records of the same real-world entity. Identifying various mentions of the same real-world locations is known as spatial entity matching. While entity matching received significant interest in the field of relational entity matching, the same cannot be said about spatial entity matching. In this dissertation, I build an end-to-end Geospatial Entity Matching framework, GEM, exploring spatial entity matching from a novel perspective. In the current state-of-the-art systems spatial entity matching is only done on one type of geometrical data variant. Instead of confining to matching spatial entities of only point geometry type, I work on extending the boundaries of spatial entity matching to match the more generic polygon geometry entities as well. I propose a methodology to provide support for three entity matching scenarios across different geometrical data types: point X point, point X polygon, polygon X polygon. As mentioned above entity matching consists of various steps but blocking, feature vector creation, and classification are the core steps of the system. GEM comprises an efficient and lightweight blocking technique, GeoPrune, that uses the geohash encoding mechanism to prune away the obvious non-matching spatial entities. Geohashing is a technique to convert a point location coordinates to an alphanumeric code string. This technique proves to be very effective and swift for the blocking mechanism. I leverage the Apache Sedona engine to create the feature vectors. Apache Sedona is a spatial database management system that holds the capacity of processing spatial SQL queries with multiple geometry types without compromising on their original coordinate vector representation. In this step, I re-purpose the spatial proximity operators (SQL queries) in Apache Sedona to create spatial feature dimensions that capture the proximity between a geospatial entity pair. The last step of an entity matching process is matching or classification. The classification step in GEM is a pluggable component, which consumes the feature vector for a spatial entity pair and determines whether the geolocations match or not. The component provides 3 machine learning models that consume the same feature vector and provide a label for the test data based on the training. I conduct experiments with the three classifiers upon multiple large-scale geospatial datasets consisting of both spatial and relational attributes. Data considered for experiments arrives from heterogeneous sources and we pre-align its schema manually. GEM achieves an F-measure of 1.0 for a point X point dataset with 176k total pairs, which is 42% higher than a state-of-the-art spatial EM baseline. It achieves F-measures of 0.966 and 0.993 for the point X polygon dataset with 302M total pairs, and the polygon X polygon dataset with 16M total pairs respectively.
ContributorsShah, Setu Nilesh (Author) / Sarwat, Mohamed (Thesis advisor) / Pedrielli, Giulia (Committee member) / Boscovic, Dragan (Committee member) / Arizona State University (Publisher)
Created2021
Description
Globally, rivers are being heavily dammed and over-utilized to the point where water shortages are starting to occur. This problem is magnified in arid and semi-arid regions where climate change, growing populations, intensive agriculture and urbanization have created tremendous pressures on existing river systems. Regulatory incentives have been enacted in recent decades that have spurred river restoration programs in the United States. But what kind of governance does river restoration require that is different from allocative institutional set-ups? Are these recovery programs succeeding in restoring ecological health and resilience of the rivers? Do the programs contribute to social-ecological resilience of the river systems more broadly? This study aims to tackle these key questions for two Colorado River sub-basin recovery programs (one in the Upper Basin and one in the Lower Basin) through utilization of different frameworks and methodologies for each. Organizational resilience to institutional and biophysical disturbances varies, with the Upper Basin program being more resilient than the Lower Basin program. Ecological resilience as measured by beta diversity (for the Upper Basin) was a factor of the level of hydrological and technological interventions rather than an occurrence of the natural flow regime. This points to the fact that in a highly-dampened and managed system like the Colorado River, the dampened flow regime alone is not a significant factor in maintaining community diversity and ecological health. A broad-scale social-ecological analysis supports the finding that the natural feedback between social and ecological elements is broken and recovery efforts are more an attempt at resuscitating the river system to maintain a semblance of historic levels of fish populations and aquatic processes. Adaptive management pathways for the future need to address and build pathways to transformability into recovery planning to achieve resilience for the river system.
ContributorsSrinivasan, Jaishri (Author) / Schoon, Michael L (Thesis advisor) / Sabo, John L (Thesis advisor) / White, Dave D (Committee member) / Janssen, Marcus A (Committee member) / Arizona State University (Publisher)
Created2021
Description
The meteoric rise of Deep Neural Networks (DNN) has led to the development of various Machine Learning (ML) frameworks (e.g., Tensorflow, PyTorch). Every ML framework has a different way of handling DNN models, data types, operations involved, and the internal representations stored on disk or memory. There have been initiatives such as the Open Neural Network Exchange (ONNX) for a more standardized approach to machine learning for better interoperability between the various popular ML frameworks. Model Serving Platforms (MSP) (e.g., Tensorflow Serving, Clipper) are used for serving DNN models to applications and edge devices. These platforms have gained widespread use for their flexibility in serving DNN models created by various ML frameworks. They also have additional capabilities such as caching, automatic ensembling, and scheduling. However, few of these frameworks focus on optimizing the storage of these DNN models, some of which may take up to ∼130GB storage space(“Turing-NLG: A 17-billion-parameter language model by Microsoft” 2020). These MSPs leave it to the ML frameworks for optimizing the DNN model with various model compression techniques, such as quantization and pruning. This thesis investigates the viability of automatic cross-model compression using traditional deduplication techniques and storage optimizations. Scenarios are identified where different DNN models have shareable model weight parameters. “Chunking” a model into smaller pieces is explored as an approach for deduplication. This thesis also proposes a design for storage in a Relational Database Management System (RDBMS) that allows for automatic cross-model deduplication.
ContributorsDas, Amitabh (Author) / Zou, Jia (Thesis advisor) / Zhao, Ming (Thesis advisor) / Yang, Yingzhen (Committee member) / Arizona State University (Publisher)
Created2021
Description
The knee joint has essential functions to support the body weight and maintain normal walking. Neurological diseases like stroke and musculoskeletal disorders like osteoarthritis can affect the function of the knee. Besides physical therapy, robot-assisted therapy using wearable exoskeletons and exosuits has shown the potential as an efficient therapy that helps patients restore their limbs’ functions. Exoskeletons and exosuits are being developed for either human performance augmentation or medical purposes like rehabilitation. Although, the research on exoskeletons started early before exosuits, the research and development on exosuits have recently grown rapidly as exosuits have advantages that exoskeletons lack. The objective of this research is to develop a soft exosuit for knee flexion assistance and validate its ability to reduce the EMG activity of the knee flexor muscles. The exosuit has been developed with a novel soft fabric actuator and novel 3D printed adjustable braces to attach the actuator aligned with the knee. A torque analytical model has been derived and validate experimentally to characterize and predict the torque output of the actuator. In addition to that, the actuator’s deflation and inflation time has been experimentally characterized and a controller has been implemented and the exosuit has been tested on a healthy human subject. It is found that the analytical torque model succeeded to predict the torque output in flexion angle range from 0° to 60° more precisely than analytical models in the literature. Deviations existed beyond 60° might have happened because some factors like fabric extensibility and actuator’s bending behavior. After human testing, results showed that, for the human subject tested, the exosuit gave the best performance when the controller was tuned to inflate at 31.9 % of the gait cycle. At this inflation timing, the biceps femoris, the semitendinosus and the vastus lateralis muscles showed average electromyography (EMG) reduction of - 32.02 %, - 23.05 % and - 2.85 % respectively. Finally, it is concluded that the developed exosuit may assist the knee flexion of more diverse healthy human subjects and it may potentially be used in the future in human performance augmentation and rehabilitation of people with disabilities.
ContributorsHasan, Ibrahim Mohammed Ibrahim (Author) / Zhang, Wenlong (Thesis advisor) / Aukes, Daniel (Committee member) / McDaniel, Troy (Committee member) / Arizona State University (Publisher)
Created2021
Forward and Backward Machine Learning for Modeling Copper Diffusion in Cadmium Telluride Solar Cells
Description
To optimize solar cell performance, it is necessary to properly design the doping profile in the absorber layer of the solar cell. For CdTe solar cells, Cu is used for providing p-type doping. Hence, having an estimator that, given the diffusion parameter set (time and Temperature) and the doping concentration at the junction, gives the junction depth of the absorber layer, is essential in the design process of CdTe solar cells (and other cell technologies). In this work it is called a forward (direct) estimation process. The backward (inverse) problem then is the one in which, given the junction depth and the desired concentration of Cu doping at the CdTe/CdS heterointerface, the estimator gives the time and/or the Temperature needed to achieve the desired doping profiles. This is called a backward (inverse) estimation process. Such estimators, both forward and backward, do not exist in the literature for solar cell technology. To train the Machine Learning (ML) estimator, it is necessary to first generate a large set of data that are obtained by using the PVRD-FASP Solver, which has been validated via comparison with experimental values. Note that this big dataset needs to be generated only once. Next, one uses Machine Learning (ML), Deep Learning (DL) and Artificial Intelligence (AI) to extract the actual Cu doping profiles that result from the process of diffusion, annealing, and cool-down in the fabrication sequence of CdTe solar cells. Two deep learning neural network models are used: (1) Multilayer Perceptron Artificial Neural Network (MLPANN) model using a Keras Application Programmable Interface (API) with TensorFlow backend, and (2) Radial Basis Function Network (RBFN) model to predict the Cu doping profiles for different Temperatures and durations of the annealing process. Excellent agreement between the simulated results obtained with the PVRD-FASP Solver and the predicted values is obtained. It is important to mention here that it takes a significant amount of time to generate the Cu doping profiles given the initial conditions using the PVRD-FASP Solver, because solving the drift-diffusion-reaction model is mathematically a stiff problem and leads to numerical instabilities if the time steps are not small enough, which, in turn, affects the time needed for completion of one simulation run. The generation of the same with Machine Learning (ML) is almost instantaneous and can serve as an excellent simulation tool to guide future fabrication of optimal doping profiles in CdTe solar cells.
ContributorsSalman, Ghaith (Author) / Vasileska, Dragica (Thesis advisor) / Goodnick, Stephen M. (Thesis advisor) / Ringhofer, Christian (Committee member) / Banerjee, Ayan (Committee member) / Arizona State University (Publisher)
Created2021
Description
The relationship between ethnicity and police-related outcomes has garnered significant attention from researchers. Although prior research has advanced scholarship, important questions still remain. First, previous studies about perceptions of police legitimacy have been conducted without examining whether this measure functions the same for different ethnic groups. Second, only a few studies have examined the effect of ethnicity on willingness to call the police, and they have produced mixed findings. Third, little attention has been paid to the effect of ethnic context on willingness to call the police. Against this backdrop, this dissertation extends prior work by providing an empirical assessment of willingness to call the police in relation to item-, individual-, and contextual-levels of ethnic effect. Specifically, Chapter 2 examines whether the perceptions of police legitimacy measure is invariant between Whites and Hispanics. Chapter 3 applies the group position thesis and Tyler’s process-based model of policing to assess the relationship between ethnicity and willingness to call the police. Chapter 4 investigates the extent to which theoretical arguments drawn from the minority threat perspective and social disorganization theory can be applied to explain the relationship between ethnic context and willingness to call the police. Using data collected from the Arizona Crime Victimization Survey (AZCVS) and the US Census, this dissertation produces three main findings. First, Chapter 2 finds that the perceptions of police legitimacy measure functions consistently across White and Hispanic subsamples. Second, Chapter 3 finds that Hispanics tended to show a lower level of trust in police compared to Whites, which in turn resulted in their unwillingness to call the police. This finding partially supports the notion that the group position thesis and Tyler’s process-based model can be combined to explain the relationship between ethnicity and willingness to call the police. Third, Chapter 4 finds that ethnic context affects individual willingness to call the police, partially through perceived risk of property crime victimization, suggesting that the minority threat perspective may be better able to explain the relationship between ethnic context and willingness to call the police than social disorganization theory. Given these findings, their implications for theory, future research, and policy are discussed.
ContributorsCheon, Hyunjung (Author) / Wang, Xia (Thesis advisor) / Katz, Charles M (Committee member) / Decker, Scott H (Committee member) / Arizona State University (Publisher)
Created2021
Description
Local governments are creatures of their ecosystems. In this dissertation, institutional influences on local government finances are assessed both theoretically and empirically in this three-essay dissertation. Employing two tax and expenditure limitations (TELs) and local government form, this dissertation evaluates how these three components along with the local government ecology, influence financial outcomes around local fiscal condition, revenue capacity, and forecasting bias. First, this dissertation examines the effect of removing assessment restrictions on the solvency of local governments. As all TELs are not equivalent, expected impacts from assessment restrictions should be comparatively minimal. Using a multiple synthetic matching design, I match Minnesota municipalities against weighted counterfactuals before the lifting of assessment restrictions in 2011 and evaluate outcomes. Results indicate that municipal solvency was unaffected by a release from assessment restrictions.
The second essay evaluates the moderating effect of voter support of TELs on property taxes. I propose that municipalities in favor of restrictions would have limited tax growth, even without restrictions; and oppositional constituencies face the greatest shift. Using voter support for the Taxpayer Bill of Rights Amendment in Colorado as a moderator, constituent preferences differentiate the change in property tax trends from implementation of the amendment. Employing both a Hausman-Taylor model and a comparative matching design, a significant relationship is found between the impact of property tax restrictions and the preferences of local government voters.
In the last essay, I investigate an association between form of government and municipal revenue forecasting bias. Granting that a municipal governments form alters the nature of the governance that it provides; the essay presents that a reformed council-manager form of government would have lower revenue forecast bias via political pressure than a mayor-council form. Results from pooled ordinary least squares design indicate no statistically significant relationship between forecast bias and municipal form of government.
The dissertation serves to illuminate, and eliminate, some institutional predictors of local government finances, and intones an ecological dominance over local government finance. Further, the dissertation provide significant nuance in how additional research can provide definitive answers on the effects of structural changes on the finances of local governments.
ContributorsShumberger, Jason D (Author) / Singla, Akheil (Thesis advisor) / Bretschneider, Stuart (Thesis advisor) / Swindell, David (Committee member) / Arizona State University (Publisher)
Created2021
Description
Visual question answering (VQA) is a task that answers the questions by giving an image, and thus involves both language and vision methods to solve, which make the VQA tasks a frontier interdisciplinary field. In recent years, as the great progress made in simple question tasks (e.g. object recognition), researchers start to shift their interests to the questions that require knowledge and reasoning. Knowledge-based VQA requires answering questions with external knowledge in addition to the content of images. One dataset that is mostly used in evaluating knowledge-based VQA is OK-VQA, but it lacks a gold standard knowledge corpus for retrieval. Existing work leverages different knowledge bases (e.g., ConceptNet and Wikipedia) to obtain external knowledge. Because of varying knowledge bases, it is hard to fairly compare models' performance. To address this issue, this paper collects a natural language knowledge base that can be used for any question answering (QA) system. Moreover, a Visual Retriever-Reader pipeline is proposed to approach knowledge-based VQA, where the visual retriever aims to retrieve relevant knowledge, and the visual reader seeks to predict answers based on given knowledge. The retriever is constructed with two versions: term based retriever which uses best matching 25 (BM25), and neural based retriever where the latest dense passage retriever (DPR) is introduced. To encode the visual information, the image and caption are encoded separately in the two kinds of neural based retriever: Image-DPR and Caption-DPR. There are also two styles of readers, classification reader and extraction reader. Both the retriever and reader are trained with weak supervision. The experimental results show that a good retriever can significantly improve the reader's performance on the OK-VQA challenge.
ContributorsZeng, Yankai (Author) / Baral, Chitta (Thesis advisor) / Yang, Yezhou (Committee member) / Ghayekhloo, Samira (Committee member) / Arizona State University (Publisher)
Created2021
Description
The power-flow problem has been solved using the Newton-Raphson and Gauss-Seidel methods. Recently the holomorphic embedding method (HEM), a recursive (non-iterative) method applied to solving nonlinear algebraic systems, was applied to the power-flow problem. HEM has been claimed to have superior properties when compared to the Newton-Raphson and other iterative methods in the sense that if the power-flow solution exists, it is guaranteed that a properly configured HEM can find the high voltage solution and, if no solution exists, HEM will signal that unequivocally. Provided a solution exists, convergence of HEM in the extremal domain is claimed to be theoretically guaranteed by Stahl’s convergence-in-capacity theorem, another advantage over other iterative nonlinear solver.In this work it is shown that the poles and zeros of the rational function from fitting the local PMU measurements can be used theoretically to predict the voltage-collapse point. Different numerical methods were applied to improve prediction accuracy when measurement noise is present.
It is also shown in this work that the dc optimal power flow (DCOPF) problem can be formulated as a properly embedded set of algebraic equations. Consequently, HEM may also be used to advantage on the DCOPF problem. For the systems examined, the HEM-based interior-point approach can be used to solve the DCOPF problem.
While the ultimate goal of this line of research is to solve the ac OPF; tackled in this work, is a precursor and well-known problem with Padé approximants: spurious poles that are generated when calculating the Padé approximant may, at times, prevent convergence within the functions domain. A new method for calculating the Padé approximant, called the Padé Matrix Pencil Method was developed to solve the spurious pole problem. The Padé Matrix Pencil Method can achieve accuracy equal to that of the so-called direct method for calculating Padé approximants of the voltage-functions tested while both using a reduced order approximant and eliminating any spurious poles within the portion of the function’s domain of interest: the real axis of the complex plane up to the saddle-node bifurcation point.
ContributorsLi, Songyan (Author) / Tylavsky, Daniel (Thesis advisor) / Ayyanar, Raja (Committee member) / Weng, Yang (Committee member) / Wu, Meng (Committee member) / Arizona State University (Publisher)
Created2021