Theses and Dissertations - Department of Educational Leadership, Policy & Technology Studies

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    Methodology for Optimizing Phase-Shifted Full-Bridge Converters Employing Wide Band-Gap Semiconductors
    (University of Alabama Libraries, 2020) Shahabi, Ali; Lemmon, Andrew; University of Alabama Tuscaloosa
    Switching loss is a major factor in determining the performance of modern power electronics converters. Soft-switching-based converters are, consequently, developed to mitigate this loss mechanism. The phase-shifted full-bridge (PSFB) converter is such a converter that is appealing in many high-power applications, such as datacenters. Understanding the underlying principles of the zero-voltage switching (ZVS) mechanism within this converter and fine-tuning the corresponding system parameters are necessary to achieve higher efficiency and power density. Despite the importance of this subject, there is a lack of broad studies that investigate the interdependence effects of system parameters on ZVS realization and modeling the ZVS transitions accordingly. This dissertation identifies the switching deadtime values as parameters of particular sensitivity for this topology. Subtle changes to the switching deadtime values can result in dramatic changes to the overall system efficiency, especially for certain combinations of other system parameters. This dissertation provides a set of empirically validated analytical tools that provide new insight into the interdependence of these parameters and offer useful guidance to practitioners seeking to maximize the performance of this topology, especially for implementations that utilize Wide Band-Gap (WBG) semiconductors in their structure. A set of practical guidelines is also provided to assist with fine-tuning this topology for maximum performance. Moreover, these sets of analytical tools are employed in this dissertation to design and implement a 10-kW, SiC-based, synchronous-rectified PSFB converter, which is optimized for efficiency and power density.
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    Application of Advanced Analytical Tools to Predict Preterm Birth and Travel for Prenatal Care
    (University of Alabama Libraries, 2020) Kunwor, Sujit; McManus, Denise J.; Parton, Jason M; University of Alabama Tuscaloosa
    Preterm birth is one of the many widely studied pregnancy related issues. It is often associated with individual health status, but it has also been studied with respect to neighborhood characteristics. It has been shown that preterm birth rates are particularly higher in groups with a lower socioeconomic status such as Medicaid enrolled populations. High preterm birth rate is one of the contributing factors of infant mortality rate, which in turn brings tragedy to the family along with potential socioeconomic impact to the society. In this dissertation, individual and neighborhood level information of a sample population of Medicaid enrolled pregnant women from the state of Alabama were studied. The neighborhood level characteristics were used in addition to individual level health information to compare the predictive potential of traditional and machine learning analytical methods on classifying preterm births. In the first chapter, advanced analytical methods such as gradient boosting, random forest and logistic regression were used to predict the pregnancy outcome. In the second chapter, network analysis was used to construct a network based on travel information of Medicaid enrolled pregnant women from Alabama. The network features were studied to shed light upon the most central nodes in the travel network using various centrality measures such as betweenness and closeness centralities. The travels to receive healthcare by pregnant women were compared to the benchmark physical degree of separation from the origin to the destination locations. In the third chapter, weighted networks constructed using the travel frequencies among counties were used to compare link weight prediction models. The frequency of travel between any two counties in the state were predicted using two supervised learning models and a deep learning model. Overall, this dissertation showed use cases of various advanced analytical tools on the healthcare sector. The chapters in the dissertation cover descriptive analysis (Chapter II), predictive analysis (Chapter I & Chapter III) and prescriptive analysis (Chapter I).
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    Upward Bound: A Qualitative Study of Bridging the Opportunity Gap
    (University of Alabama Libraries, 2020) Lowe, Elizabeth; Bray, Nathaniel; University of Alabama Tuscaloosa
    The purpose of this study was to investigate how former Upward Bound students’ experiences may have influenced their high school completion and college readiness to bridge the opportunity gap. This study describes the personal experiences of former Upward Bound students and current staff to understand how a specific Upward Bound Program assists its students with being college ready. The results gathered provided indications about how the Upward Bound Program influenced the students’ transition from high school to college. The main data collection method was the use of interviews. Qualitative research methods were used to explain the data gathered from the interviews. The results of the study suggested that the Upward Bound Program positively influenced its students through the activities, awareness, and support provided by the staff to impact the opportunity gap.
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    High Probability of Hiding (HPH) and Quality of Autonomy (QoA) Orientated 3D Transformative Intelligent Routing for Airborne Networks
    (University of Alabama Libraries, 2020) Zhang, Lin; Hu, Fei; Kumar, Sunil; University of Alabama Tuscaloosa
    Airborne networks (ANs), such as networks of aircraft or flying drones, are finding use in various applications. Research on ANs is performed in this article from a communication protocol design perspective. Multiple flying objects with same or similar tasks generally form a flocking network where each object is considered as an airborne node. High probability of hiding (HPH)-oriented communication, which aims to avoid easy signal detection by adversaries, is thus very important for a robust AN. To protect the data of wireless communications, HPH requires to hide the very existence of radio signals [7]. Chapter 2 focuses on the robust routing protocols in ANs. Particularly, an AN equipped with the latest antenna technology, called multi-beam directional antenna (MBDA), is mainly considered. MBDA allows the simultaneous packet delivery in multiple directions without RF interference among the antenna beams. We have found that MBDAs can actually bring new opportunities to enhance the popularly used AN routing scheme, i.e., optimized link state routing protocol (OLSR). In particular, MBDAs enable OLSR to better achieve HPH, a critical requirement in many applications with adversary nodes nearby which try to eavesdrop the signals. Chapter 3 aims to solve a challenging issue in the ANs: in a three-dimensional (3D) airborne network which performs flocking operations (i.e., changing formations from time to time), how do we efficiently establish/maintain one or multiple paths between region regions (i.e., groups of airborne nodes with location proximity and task similarity), during the dynamic airborne nodes flocking process? A distributed routing scheme is desired that only uses localized message exchange (among 1-hop neighbors) without GPS position information. In Chapter 4, a task-adaptive, quality of autonomy (QoA)-based band routing scheme is analyzed. Adaptive Batch Coding (ABC)-based transport and routing layer co-design realizes smart inter-region routing for high priority task-command traffic. Scored time-delay embedding (STDE) technique provides a way for finding out and representing the time series’ periodicity quantitatively, which is suitable for airborne node stability estimation. Multi-time-granularity prediction (MTGP)-based band routing scheme is utilized to make the airborne communication support QoA metrics. Early backup (detour) node/path setup is achieved by the prediction results.
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    Queuing Model Based Approaches for Extremely High Throughput Wireless Networks with Big Data Transmission
    (University of Alabama Libraries, 2020) Manohar, Immanuel David Rajan; Hu, Fei; University of Alabama Tuscaloosa
    In this work we look at Big Data transmission using wireless networks. The first goal is to model the modern communication node equipped with multibeam antenna working on IEEE 802.11ac/ax (5G/6G) standard. We first model the flow of packets through the node using Queuing theory that reflects the more modern communication standard. To make the model practically applicable we also make it computationally simple that it can be executed in real time in smaller processors. This single node model is also extended to a adhoc wireless mesh network scenario and a complete model for the network is derived. We then test the model and use it to optimize a simulated network. The results show that using this accurate model to optimize networks vastly outperforms the traditional models popularly used today. With the queuing model developed, we analyze and develop both Routing and Transport layer protocols for the challenging problem of aerial drone flocks. Drone flocks pose unique challenge in networking where the mobility is really high as the aerial drones can move at speeds of about 70mph. This results in a network topology that is constantly changing. We use prediction, clustering based methods to establish a hierarchical routing scene. The routes developed are designed to use multiple paths to have redundancies built in for robustness. This routing protocol outperforms the popular OLSR protocol for mesh routing by a significant margin. It exchanges much less control packets to maintain the routes and also has better throughput and lesser packet loss because of multiple paths. With this routing protocol established, we also develop a TCP protocol that avoids congestion by choosing different paths for the packets based on congestion in each path. This TCP protocol uses a queuing model similar to the one used in modeling IEEE 802.11ac. To simulate and verify these protocols, a MATLAB based Network Simulator is built which enables the use of complex mobility patterns. This simulator functions similar to the popular Network Simulator 3 (NS3) with the added advantage of being able to use both time and event based simulations. The final contribution of this work is to use the resulting robust communication grid and queuing model to develop a mobile grid computing scene that can function using drones. This mobile grid we establish exploits the mobility and varying computational capability to optimally distribute computations among the various nodes in the network. The simulations demonstrate the performance of this technique in comparison to work stealing algorithm popularly used in grid computing scenarios.
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    The Experiences of Low-Income African American College Students at an Affluent and Predominantly White Institution
    (University of Alabama Libraries, 2020) Deneen, Tina; Holley, Karri A.; University of Alabama Tuscaloosa
    ABSTRACT The purpose of this study was to explore the lived experiences of low-income African Americans who attended a wealthy and predominantly white institution. Using a basic interpretive research design, 21 low-income African American students attending a university in the southeastern United States participated in individual interviews. The study included students who identify as African American and who are considered low-income based on Pell grant status or their qualification for free or reduced-cost lunch in high school. Results from this qualitative study indicated that low-income African American students encountered many challenges during their college experience. Students expressed feelings of insignificance and alienation related to being a valued member of the campus community. Most study participants expected to feel welcomed to their new surroundings, but discovered that assimilation was not as effortless as expected. In the midst of feelings of isolation and rejection, these students used their social, navigational, and linguistic capital to take control of and effectively manage their lived experiences.
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    Bioimpedance Applied on Skeletal Muscle Assessing to Resistance Training Effects
    (University of Alabama Libraries, 2020) Fu, Bo; Freeborn, Todd J.; University of Alabama Tuscaloosa
    Electrical impedance measurements of biological tissues, termed as bioimpedance, quantify the passive electrical properties of biological tissues including skeletal muscles. Resistance training is important to skeletal muscle in terms of strength gaining, but could introduce fatigue and damage with improper training protocols. For this dissertation, the effect of bicep curl exercise is studied using bioimpedance measurement on localized bicep tissue. Several research tasks are set for this dissertation. Firstly, the accuracy and limitations of impedance measurements with Keysight E4990A impedance analyzer under high residual impedance were studied, with OSL compensation, tetrapolar configuration and 10 kHz to 100 kHz recommended for bioimpedance measurement for maximum accuracy. Secondly, localized bioimpedance was used to assess the changes of bicep tissue of healthy young adults participating in isotonic exercise designed to induce skeletal muscle fatigue with significant changes of pre- and post-exercise impedances observed, caused by the change of muscle property. Next, bioimpedance experiment was further performed on bicep tissue of healthy young adults participating and recovering from eccentric bicep curls exercise with significant change of impedance observed 72 and 96 hours post-exercise, different from isotonic experiment result in terms of recovery time potentially due to the delayed-onset feature of eccentric exercise. Lastly, the use of an equivalent electrical circuit, the Cole-impedance model, to represent bicep tissue bioimpedance was evaluated by fitting the data collected from isotonic and eccentric exercises. Significant changes in Cole impedance parameters, Rinf, R1 and C were observed in isotonic eccentric exercise, proposed to be related to muscle swelling and architecture alteration, respectively. Conclusions and contributions of the current study were shown at the end, as well as limitations and future improvements.
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    An Examination of State-Level Professional Development Policy: A Policy Analysis
    (University of Alabama Libraries, 2020) Burchfield, Gene Heaton; Johnson, Bob L.; University of Alabama Tuscaloosa
    Professional Development is one of the most common methods for school systems to provide additional training and disseminate new information to faculty members. As such, the professional development process carries a large burden in terms of time, money, and other valuable school resources. Declining state test scores and public pressure to raise student achievement levels places even more importance on the professional development process. Many public and private education research groups have identified features that improve the effectiveness of professional development. State Departments of Education also routinely provide professional development guidance to schools and school systems through professional development policies. This study will examine professional development policies provided by the Alabama State Department of Education, as well as the professional development policies of a purposeful sample of other States from across the United States. By employing qualitative research methods, this study will seek to determine if the professional development policies of academically successful states contain commonalities that could be included in a list of recommendations for state-level professional development policymakers in the State of Alabama.
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    American Sign Language Recognition Using Adversarial Learning in a Multi-Frequency RF Sensor Network
    (University of Alabama Libraries, 2020) Macks, Trevor F; Gurbuz, Sevgi Z; University of Alabama Tuscaloosa
    Human computer interaction (HCI) based technologies, such as Alexa or Siri, have become increasingly prevalent in the daily lives of American citizens. However, access to these technologies is gated by the need to communicate with spoken commands, which subsequently precludes the Deaf community from benefitting from the quality of life improvements they provide. Current approaches to providing HCI technologies or ASL conversion largely rely on video and image processing techniques, haptic gloves, and wifi based systems. However, wearables restrict users from engaging in their normal daily activities, while video raises privacy concerns. To help ASL compatible HCI technology, we propose a multi-frequency RF sensing network for the recognition of a basic lexicon of signs. When validating the network on a daily activities dataset, we had good performance, with classification accuracy's around 90% or higher. For ASL data, we have 2 datasets: native and imitation. The native dataset is small, and was collected from Deaf individuals. Our imitation dataset is larger, and was collected from hearing individuals prompted by copysigning videos. We show that imitation data cannot be used in lieu of native ASL data for training and benchmarking classifiers because the two datasets possess disparate distributions in feature space. Alternatively, we investigate adversarial learning as a means for mitigating the challenge of insufficient training data. Cross frequency training is one option for augmenting the training dataset which suffers from severe performance degradation when data from one frequency is used to pre-train a network for classification of data at another frequency. We show that data synthesized using Generative Adversarial Networks (GANs) can be used to reduce but not completely eliminate cross-frequency training degradation. An auxiliary conditional generative adversarial network (ACGAN) with kinematic sifting is used to augment and classify human activity data and recognize ASL signs. While the proposed network performed well with daily activities, its performance could not be adequately validated on ASL data due to sparsity of native ASL data and statistical inconsistencies of imitation signing data. Future directions for overcoming these challenges and extending the proposed techniques to ASL recognition are discussed.
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    Quantification of HVAC Energy Savings for Occupancy Sensing in Buildings: Hardware and Software
    (University of Alabama Libraries, 2021) Ye, Zhijing; Hu, Fei; University of Alabama Tuscaloosa
    Heating, ventilation and cooling (HVAC) is the largest source of residential energy consumption. Occupancy sensors’ data can be used for HVAC control since they indicate the number of people in the building. HVAC/sensor interactions show the essential features of a typical cyber-physical system (CPS). However, there are communication protocol incompatibility issues in the CPS interface between the sensors and the building HVAC server. Through either wired or wireless communication links, the server always needs to understand the communication schedule to receive occupant values from sensors. We will illustrate the background of building energy consumption optimization problem. In this paper, we first propose two hardware-based emulators to investigate the use of wired/wireless communication interfaces for occupancy sensor-based building CPS control. The synchronization scheme between sensors and HVAC server requirements will be discussed. We have built two hardware/software emulation platforms to investigate the sensor/HVAC integration strategies. The first emulator demonstrates the residential building’s energy control by using sensors and Raspberry pi boards to emulate the functions/responses of a static thermostat. In this case, room HVAC temperature settings could be changed in real-time with a high resolution based on the collected sensor data. The second emulator is built to show the energy control in commercial building by transmitting the sensor data and control signals via BACnet in HVAC system. Both emulators discussed above are portable (i.e., all hardware units can be easily taken to a new place) and have extremely low cost. We test the whole system with YABE (Yet Another BACnet Explorer) and WebCTRL. Secondly, power system is facing a rapid transition to a highly interpretable, interactable and intelligent system. Effective simulations based on fast energy data processing algorithms have attracted many attentions due to the massive amount of data generated by the edge sensing devices in the smart grid systems. Machine learning (ML) and deep learning (DL) can be used to improve the performance of energy consumption forecasting. However, substantial computational resources are required for the training and inference of deep neural network. Instead of simply adopting the DL model for the offline processing of aggregated residential load, our platform can perform the online analysis of the load of building energy system with dynamic and stochastic characteristics. Today, edge computing platform that consists of a fine mesh of compute nodes and end devices, has become a promising system to reduce the computation complexity. In this paper, we propose an online, distributed, edge-computing-oriented simulation methodology to analyze the building energy data. A long short-term memory (LSTM) based framework is used for real-time forecasting of the building energy load. A public dataset is used to prove the effectiveness of the simulation model. The results show that the proposed simulation model provides satisfactory online load forecasting performance and has good scalability. At last, In order to reduce the negative influence on the environment and improve sustainability, it is very important to efficiently manage energy consumption. There are some energy prediction methods using sensors, such as collecting data utilizing Internet of Things (IoT) based on widely used smart meters. Some machine learning models including support vector regression are used to predict the energy consumption. However, they are not able to figure out the relationships between time dependency input signals. Therefore, a sequence to sequence convolutional bi-directional long short-term memory (Seq2Seq CNN bi-LSTM) with self-attention model is proposed in this paper in order to predict the building energy consumption. In the framework, the important time series energy information can be extracted from several input variables using CNN model. Because of the long-range temporal dependencies are offered, the Seq2Seq models could provide better accuracy by using two bi-LSTM architectures including encoder and decoder. Meanwhile, bi-LSTMs are also used to capture the pattern of time series data. Specifically, the above information and the trends of time series in two directions including the forward and backward states are used by bi-LSTM layers to make better predictions. Self-attention model can highlight the most relevant input information in the energy prediction by allocating the attention weights. The connection burden can also be alleviated by the attention mechanism. In this case, the self-attention can be used to ignore the irrelevant information and amplify the needed information. We may also apply deep reinforcement learning to the energy optimization problem.
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    Four Eras of Student Discourse At Athens State University
    (University of Alabama Libraries, 2021) Wade, George Michael; McKnight, Douglas; University of Alabama Tuscaloosa
    This study examines 523 student newspapers from Athens State University from 1901 through 2000. The result is the discovery of unique student voices as expressed through perennial themes such as race and women’s roles. These voices are periodized into four eras in which dominant values, beliefs, behaviors, and norms vary over time. These four eras are labelled the eras of conformity, collegiality, emancipation, and vocationalism to capture their central feature. This study adds to the history of higher education by focusing on the expressions of students as reflected in the topics they wrote about in the student newspapers. The use of student newspapers as the primary source demonstrates how this type of evidence can be used to create themes which help understand the past. It also shows the development and changes in the perennial issues of race and women’s roles in small southern colleges.
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    Artificial Intelligence for Building Energy Management in the Electricity Market and Transmission Power Flow Planning
    (University of Alabama Libraries, 2021) Gao, Yixiang; Li, Shuhui; University of Alabama Tuscaloosa
    Due to the high uncertainty of building loads and customer comfort demands and extremely nonlinear building thermal characteristics, developing an effective building energy management (BEM) technology is facing great challenges. This dissertation focuses on building energy management from the day-ahead and real-time planning perspectives in the electricity market. In the day-ahead planning, this dissertation presents price-sensitive demand response strategies for smart buildings by regulating their controllable loads to minimize building electricity costs and flatten the net buildings' loads. The learning-based HVAC model and the detailed physics-based non-HVAC model are then applied to an optimization problem to determine the optimal management scheduling of building loads based on day-ahead electricity price. In addition, this dissertation proposes an hourly decoupled AC/DC power flow approach for the day-ahead planning of multi-terminal HVDC systems. The proposed method simplifies the power flow computation of multi-terminal HVDC systems while accurately reflecting the operation and control characteristics of VSC (voltage source converter) stations in an HVDC network. In real-time planning, an optimization problem in a 5-minute time frame is proposed to manage real-time building energy consumption uncertainties based on the real-time clearing price and balance the real-time deviations from the building energy consumption negotiated in the day-ahead market. To help regulate power system real-time frequency fluctuation, an economic and hierarchical control approach for multi-thermal-zone buildings is developed to participate in the ancillary service market of the electric power systems. The energy consumption of variable speed drive (VSD) fans in the HVAC system is controlled up and down to follow dynamic auto-generation control (AGC) signals from the power system operators or control centers. A decoupling method is developed to balance the power system frequency regulation requirement in 5 seconds and real-time building energy planning developed above in 5 minutes. In this dissertation, computer simulation models for building and grid integration from different power systems planning perspectives are developed. All the proposed methods are studied theoretically and evaluated via computer simulations, based on which results and conclusions for each is obtained and reported in the dissertation.
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    In-Band Full-Duplex Underwater Acoustic Communications
    (University of Alabama Libraries, 2021) Guo, Zheng; Song, Aijun; University of Alabama Tuscaloosa
    In-band full-duplex (IBFD) communications is a promising technique for increased spectral efficiency. This dissertation investigates IBFD acoustic communications in the underwater environment. Four research thrusts are investigated. Due to the absence of near-field acoustic propagation models, these efforts are supported by extensive field tests in local rivers and lakes. Firstly, the near-field SI characteristics were analyzed at multiple acoustic frequencies. The SI cancellation performance deteriorated with the increased signal carrier frequency. Fast channel fluctuations were identified as the reason for the deterioration. The channel variation ratio (CVR) was proposed to quantify the intensity of channel fluctuations. Experimental and simulated results showed that CVRs were larger at higher acoustic frequencies, and large CVRs led to the deteriorated SI suppression. Secondly, the impact of channel fluctuations on the performance of the least-squares channel estimator was quantified. The channel estimation performance, measured by the channel estimation mean squared error (MSE) and the signal prediction error (SPE), was linked with the CVR by analytic expressions. Both the MSE and SPE had an error floor for time-varying impulse responses. It was confirmed that an optimum estimated channel length, achieving the minimum estimation error, existed for time-varying impulse responses. Next, multiple SI suppression methods, including physical separation, digital SI cancellation, directional transducers, and acoustic baffles, were investigated through field experiments. It was found that a 7-m physical separation provided a maximum SI reduction of 32.6 dB. The achieved digital SI cancellation decreased with the increased physical separation ranges. Marginal SI suppression was observed with directional transducers and acoustic baffles. Lastly, an iterative IBFD receiver was developed for IBFD acoustic communications. A control mechanism was used to regulate the digital SI cancellation. The SI cancellation control mechanism and the receiver performance were evaluated by the experimental measurements. A bit error rate (BER) in the order of $10^{-2}$ was achieved at the range of 80 m for IBFD communications with the 28-kHz carrier frequency in a lake environment. With a lower carrier frequency of 10 kHz, a similar BER was demonstrated at the extended range of 100 m with a smaller receiving array.
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    Leveraging Temporal Information for Fast Object Detection in High-Resolution Videos
    (University of Alabama Libraries, 2021) Adreon, Colin; Gan, Yu; University of Alabama Tuscaloosa
    Detecting objects in high-resolution videos in real-time has proven extremelydifficult. The large size of high-resolution images makes traditional object- detection methods impractical within the short time period between video frames. Previous approaches to this problem have relied on techniques which select re- gions to analyze within a frame through pyramid pooling and attention pipelin- ing. We propose a novel approach which uses historical location information from earlier frames to inform decisions relating to specific regions in later frames. When run on a dataset of 4k videos, this approach has shown significant improve- ments in temporal efficiency without reducing accuracy over both attention- based methods and more naı̈ve approaches. At lower frame rates, this algorithm is able to process high-resolution video data in real time and can be used to monitor video camera footage without human intervention.
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    Ultrawide-Band Polarimetric Multi-Channel Radar for Measurements of Polar Ice Sheets
    (University of Alabama Libraries, 2021) Li, Linfeng; Yan, Jie-Bang; University of Alabama Tuscaloosa
    Accelerated loss of polar ice sheets from retreating glaciers and fast-flowing ice streams is the major contributor to the increasing sea level rise in the past decades. Current models are generally predicting the continued worsening situation in the short-term future. However, these predictions still have an appreciable uncertainty due to the incomplete understanding of the ice sheet processes and boundary conditions. To assist the study of the ice flow dynamics to improve the prediction of future sea level rise, a high-resolution full-polarimetric radar system is developed in this dissertation to measure and characterize various glacial ice properties. Polarimetric measurement of polar ice sheets is a useful tool to understand ice flow dynamics associated with crystal orientation fabric (COF). To provide high-quality data of anisotropic COF over a large area, two generations of ultra-wideband (UWB) multi-channel radars were developed and deployed to the East Greenland Ice Core Project (EGRIP) site to assist the study of the Northeast Greenland Ice Stream (NEGIS). The first-generation radar, supporting dual co-polarized measurements (VV and HH) with a bandwidth of 160 MHz was deployed to Greenland in 2019 as a concept validation. The second upgraded generation, which will be deployed to Greenland in Summer 2022, supports single-pass quad-polarized measurements (VV, VH, HH, and HV) will enable the characterization of COF as a function of depth. With an improved bandwidth of 300 MHz, the new radar is capable of mapping ice sheet stratigraphy with less than 0.3 m vertical resolution.This dissertation focuses on the system level design, hardware development, system integration and performance characterization of two-generation UWB polarimetric radars. To enable the quad-polarized measurement with more than 3:1 bandwidth, I designed a UWB, co-planar tightly coupled array (TCA) with a novel balun-less feed structure. This antenna array is designed for ground-based and airborne radars which require low-profile and deployable structure. For the upgraded radar system, I also developed a high-power Transmit/Receive—Polarization (T/R-Pol) switch to enable single-pass full polarimetric measurements. With these enabling technologies, the UWB polarimetric multi-channel radar presented in this dissertation can characterize multiple ice sheet properties, including spatial anisotropic COF, high-resolution ice sheet stratigraphy, and basal condition. This information provides us the ice flow history from different aspects and helps us predict the future sea level rise.
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    Nanostructure Characterization, Fabrication and Devices of 2D Mos2 and Mos2/WS2 Hetrostructures
    (University of Alabama Libraries, 2021) Garg, Sourav; Kung, Patrick; University of Alabama Tuscaloosa
    Sparked by the 2D graphene, advanced 2D transition metal dichalcogenides have captured enough attention due to their extraordinary properties and are promising enough for future high speed flexible electronic and optoelectronic devices. Among all the transition metal dichalcogenides, molybdenum disulphide (MoS2) and tungsten disulphide (WS2) are explored most extensively since the last few years because of their complementary nature to metallic graphene. These thin 2D materials are semiconducting in nature, and moreover, bandgap also changes from indirect to direct as these materials are thinned down from bulk to monolayer form. In this study, a stabilized and large area growth of MoS2 monolayers has been established on oxide and semiconducting substrates such as (0001) sapphire, (100) p-type SiO2/Si, GaN and Ga2O3 using low pressure chemical vapor deposition. The quality and crystalline nature of grown MoS2 is deeply investigated optically by micro-photoluminescence and micro-Raman spectroscopy. Topography and morphology are characterized by scanning electron and atomic force microscopy. The applications of as grown MoS2 monolayers have been studied by the fabrication of large area photodetector. Also, the gas sensing ability of MoS2 has been explored by using CO2 gas, and the minimum detection limit found is 200ppm. In-addition one step growth of ternary alloys Mo1-xWxS2 has been achieved by LPCVD. Different compositions of W in MoS2 have been investigated by micro-photoluminescence and micro-Raman spectroscopy. In-plane heterojunctions of atomic-thick (2D) semiconductors (MoS2/WS2) are novel structures that can potentially pave the way for efficient ultrathin and flexible optoelectronics, such as light sources and photovoltaics. Such heterostructures are very rare and not much is known about their characteristics. They can only be achieved through a synthetic growth process such as chemical vapor deposition (CVD). This is unlike vertical heterostructures, for which the materials can be mechanically stacked one layer on top of the other. Here, we report a one-step CVD growth of monolayer thick MoS2/WS2 in-plane heterostructures. We have characterized their morphological and optical properties using micro-Raman and photoluminescence spectroscopy. Kelvin probe force microscope was used to extract the contact potential difference profile across the MoS2/WS2 heterojunction boundary. The junction region of these heterostructures are observed to be a ternary alloy Mo1-xWxS2. Moreover, through the tip enhanced Raman spectroscopy (TERS), the minimum junction width is extracted out to be pixel limited 25nm. Also, some novel Raman modes are detected through TERS in MoS2, and WS2 monolayers, which were not elaborated before.
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    Exploring Substance Use, Mental Health, and Safety Among College Students At Four-Year Public Institutions
    (University of Alabama Libraries, 2021) Young, Akeisha; Laanan, Frankie; University of Alabama Tuscaloosa
    Substance use and misuse among college students has presented unique challenges for institutions of higher education. As such, this three-article dissertation sought to examine and explore antecedent factors, such as student demographics, protective behavior strategies, mental health, academic performance, and how they interact with alcohol consumption, including heavy episodic drinking. Using the 2015 – 2019 American College Health Association – National College Health Assessment II (ACHA-NCHA II) data cohorts, the first article examined the factor structure of the items assessing protective behavioral strategies and mental health through exploratory factor analysis using principle component analysis. Building upon the first article, the second article used the factor solution found in the previous article to include in structural equation model analyses to assess the indirect and direct effects of the predictor variables of interest on academic performance. Moving from the empirical articles, the third article focused on a higher education practitioner’s perspective of the current status of collegiate recovery centers and how results from analyses in the previous two articles inform how higher education professionals and administrators should combat the increasingly alarming issues with substance use prevalent among college students. This three-article dissertation concluded with discussions on future directions for research as well as implications and recommendations for practitioners and policy in institutions of higher education.
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    On the Triggering Mechanism for Self-Sustained Oscillation in Wide Band-Gap Semiconductors
    (University of Alabama Libraries, 2021) Jimenez, Sergio Julian; Lemmon, Andrew N.; University of Alabama Tuscaloosa
    Wide bandgap (WBG) semiconductors are very attractive due to the outstanding characteristics that enable high-efficiency power electronics converters. However, due to the achievable fast transitions, these devices can suffer from unintended behavior such as under-damped ringing, voltage and current overshoot, half-bridge shoot-through, increased electromagnetic interference (EMI), and self-sustained oscillation (SSO).This thesis provides an analytical treatment of the triggering mechanism leading to SSO, which is an undesired phenomenon that may occur during turn-off transitions of WBG transistors due to their fast switching performance. For this analysis, a large signal model has been developed in state-space form to determine the likelihood of forced cycles in a simplified application circuit. Forced cycles are known to be a necessary but not sufficient condition for SSO to occur. In this sense, preventing the occurrence of forced cycles eliminates any possibility of destabilizing the circuit. Forced cycles occur when the gate-source voltage of the active switch rings back above threshold and causes channel conduction. The model presented in this thesis is capable of predicting the maximum gate-source voltage ring-back for any level of intrinsic parasitics and operating conditions. The model presented in this thesis is empirically validated with an application circuit utilizing GaN high electron mobility transistors (HEMTs). GaN HEMTs are known for very high switching speed, which also introduces susceptibility to SSO. The modeling framework introduced in this thesis is expected to be useful to application designers in creating application circuits that take maximum advantage of the attractive properties of WBG devices while ensuring immunity to the SSO phenomenon by some intentionally selected design margin.
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    Integration and Testing of an Uwb Airborne Radar System for Polar Ice Sounding
    (University of Alabama Libraries, 2021) Sutphin, Jared; Yan, Stephen; University of Alabama Tuscaloosa
    An ultra-wideband, airborne radar system is developed to further advance polar region research. The polar regions are the best place to detect and predict future climate due to their sensitivity and responsiveness to climate change. Ice sounding radars can detect ice thickness, basal topography, and englacial layers to help aid in these future predictions. Very-high frequency and ultra-high frequency radar systems have proven capable of providing deep internal layering and bedrock topography measurements. This thesis includes the work done on the design, integration, and testing of such a radar system. The radar is an 8-channel, modular system operating from 170-470 MHz. It is designed to operate from a DHC-6 Twin Otter aircraft platform and map near bed layers at 3-3.5 km thick ice. These measurements will provide data for 3D ice-bed topography and basal conditions, long-term accumulation rates, flow dynamics, and ice-shelf melt rates. This data is used to help develop ice models which can reveal more about how the polar regions affect the Earth.