Dissertation Defense Announcements

Candidate Name: Brynton Lett
Title: UNDERSTANDING THE CAREER DECISION-MAKING PROCESS OF LGBTQ+ COLLEGE STUDENTS OF COLOR AT PREDOMINANTLY WHITE INSTITUTIONS IN THE SOUTH
 July 27, 2022  11:00 AM
Location: Virtual / Zoom
Abstract:

Career indecision is common among many college students. College students that
identify as LGBTQ+ and students of color may experience greater difficulty in the career
decision-making process. Students belonging to minoritized racial and sexual or gender identities
often deal with the additional stress of managing their multiple marginalized identities. This
additional stress can have an impact on their career development and career choice. A total
sample of seven participants was selected for the study. Participants completed a semi-
structured interview detailing their experiences navigating identity negotiation as racial and
sexual minorities influence the career decision-making process as LGBTQ+ students of color,
within the context of a predominantly white institution in the south. Given the unique personal
and contextual factors, Social Cognitive Career Theory was used to better understand the
experiences of these selected participants. The findings should support existing themes that have
emerged when looking at the experiences of other marginalized groups and should provide
additional insight to inform more multiculturally competent career counseling.



Candidate Name: Ehsan Aghaei
Title: Automated Classification and Mitigation of Cybersecurity Vulnerabilities
 July 26, 2022  1:00 PM
Location: CCI - Room 338
Abstract:

With the widespread use of computers and networks, cybersecurity has emerged as a crucial concern for many businesses as they fight off growing cyber threats by vulnerability exploitation. To identify and mitigate zero-day or unpatched vulnerabilities, intensive defensive measures are required, which calls for a thorough understanding of vulnerability characteristics and threat behavior from several angles. This compels enterprises to spend a considerable amount of money to safeguard their infrastructure from cyberattacks, relying on the costly, ineffective, error-prone, and slow process of experts' input. Therefore, security automation has been a solution for many business owners in the battle against the growing number of cyber threats by vulnerability exploitation.

The modern text analytics architectures have been built in novel ways for a variety of applications, assisting cybersecurity professionals in developing resilient mechanisms against threats. Utilizing such technologies can therefore be a viable approach for processing, understanding, and predicting vulnerabilities that are typically reported through unstructured text.

This dissertation utilizes deep learning, natural language processing, and Information Retrieval to build a series of models that are able to effectively and efficiently parse, assess, analyze, and mitigate the vulnerabilities based on their textual descriptions reported in Common Vulnerabilities and Exposures (CVE) format.
This research offers a cybersecurity language model, as the core component, which is then utilized for characterizing the vulnerabilities as well as retrieving the corresponding course of defense actions. As a result of this work, enterprises and cybersecurity researchers will be able to automatically process domain-specific texts, classify vulnerabilities to cybersecurity standards to obtain high-level knowledge, and retrieve the course of defense actions for the underlying threats.



Candidate Name: Raunak Mishra
Title: Modeling and Evaluating the Safety Effectiveness of Mini-Roundabouts
 July 26, 2022  11:00 AM
Location: EPIC 3344 Zoom Meeting Link https://charlotte-edu.zoom.us/j/92945670928?pwd=Kzh2S1F1bzQyVFZPcDlaU2JCcTJuQT09
Abstract:

Mini-roundabouts are a type of roundabout characterized by a small diameter, and fully traversable central island and splitter islands. They are an alternative intersection design option in areas with constraints requiring additional land acquisition. They may be retrofitted within the existing intersection boundaries. Also, they are better suited for traffic calming and reducing delay, thereby, reducing emissions. They are suited to environments where speeds are relatively low and environmental constraints preclude the use of larger roundabouts with raised central islands. The standard-size roundabouts are safer than traditional minor road stop-controlled or signalized intersections, better suited for traffic calming, and reduce delay as well as emissions. However, the safety benefits associated with mini-roundabouts are not well documented and must be evaluated for planners and engineers to consider more mini-roundabout installations in the United States.

The focus of this research is on evaluating the safety effectiveness of converting a stop-controlled intersection with a speed limit ≥ 35 mph (56.3 kmph) to a mini-roundabout and examining the role of influencing factors on their safety effectiveness in the United States. The methodology includes : 1) identification of mini-roundabout installations in the United States, 2) before and after crash data and traffic volume data collection at selected mini-roundabout locations, 3) before and after analysis for determining safety benefits of mini-roundabouts, 4) safety effectiveness and crash modification factors (CMFs) computation for mini-roundabouts based on before and after crash data, and, 5) examining the effect of traffic characteristics, geometric characteristics, and on-network and off-network characteristics on mini-roundabout safety effectiveness and after period crashes. Crash, traffic volume, and geometry data for 25 mini-roundabouts in eight states was collected to conduct before-after analysis using the naive and Empirical Bayes (EB) method. Additionally, crash and traffic volume data for 723 reference intersections were gathered and used for computing the calibration factors and developing jurisdiction-specific safety performance functions (SPFs).

Results indicated a decrease in total crashes and FI crashes when TWSC/OWSC intersections were converted to mini-roundabouts. However, an increase in PDO crashes was observed. Likewise, an increase in total number of crashes, FI crashes, and PDO when AWSC intersections were converted to mini-roundabouts. Converting a TWSC/OWSC intersection to a mini-roundabout has better safety benefits than converting an AWSC intersection to a mini-roundabout. The number of crashes in the before period, cross-street traffic volume, speed limit at major street and cross-street, and intersection skewness have a statistically significant influence on the safety effectiveness of mini-roundabouts at a 90% confidence level.

These findings are useful to researchers and practitioners for conducting safety benefit analysis and making informed decisions pertaining to converting a stop-controlled intersection to a mini-roundabout.



Candidate Name: Md Munir Hasan
Title: Ultra Low Power Techniques For Machine Learning on The Edge
 July 25, 2022  10:00 AM
Location: EPIC building.


Candidate Name: Haichen Liu
Title: “Si IGBT and SiC MOSFET” Hybrid Switch for Voltage Source Converters
 July 22, 2022  1:00 PM
Location: EPIC building 1332
Abstract:

The SiC devices have been a strong competitor than the conventional Si devices due to the superior characteristics of high operating voltage, low forward voltage, fast switching speed, and high operating temperature. However, the maturity of SiC technology is still in the progress of catching up with the Si devices, the device cost for SiC MOSFET is still much higher than the Si devices. In addition, the maximum current rating of the available SiC devices are still lower than the Si devices, this also limits the utilization of SiC device in high-power applications. In order to combine the Si IGBT’s advantages of low cost and high overload capability and the SiC MOSFET’s advantages of low switching loss. The Si IGBT and SiC MOSFET are connected in parallel as a new switching unit. In this dissertation, the Si IGBT and SiC MOSFET hybrid switch (Si/SiC HyS) in the application of voltage source converters is investigated.



Candidate Name: Xiwen Xu
Title: WIRELESS POWER TRANSFER FOR RAILWAY APPLICATIONS
 July 22, 2022  10:30 AM
Location: EPIC 1332
Abstract:

The United States trains have the highest energy demands in rail transport in the world. More than 90% of the trains are powered by diesel, which aggressively impacts climate change. In addition, the current procedure of charging an electric locomotive is more complicated compared with charging an electric vehicle. Thus, Inductive power transfer (IPT) technology has a huge potential for charging locomotives wirelessly. IPT technology has been extensively studied for EV application in the past decades. However, it has not drawn much attention to railway applications. Due to the unique requirements of the railway system, most of the EV coupler designs are not directly compatible with wireless charging applications for a train. To fill this technical gap, this dissertation discusses the design considerations for railway application and introduces a design of a modular 5-kW IPT system for rail locomotives. A novel W-I coupler is proposed for the 5-kW IPT system, and the system is optimized via ANSYS Maxwell, to achieve high power transfer capability and lower cost. The optimized LCL-S compensated IPT system is also proposed for the railway IPT system to improve the system efficiency. Besides, the factory manufacturing tolerance effect on the power transfer capability was also investigated. A 10% coil tolerance can lead to a power reduction of up to 61.3%. The dissertation proposed a frequency modulated maximum power point tracking method to adjust the inverter frequency to achieve its maximum power point. The simulation and experimental results are demonstrated and analyzed to validate the feasibility of the design.



Candidate Name: Quinton Krueger
Title: CHARACTERIZATION OF MICROBIOLOGICAL METHODS AND ASSESSMENT OF THE NEMATOSTELLA VECTENSIS MICROBIOME
 July 21, 2022  10:00 AM
Location: Woodward 155/ Zoom
Abstract:

Cnidarians are part of a complex clade of marine invertebrates that inhabit a variety of extreme environments. The conditions of these habitats are becoming more extreme with the progression of time. These organisms associate with bacteria, which are composed of a larger community, known as the microbiome. To better understand the interactions between individual bacterial isolates and the model cnidarian Nematostella vectensis, it is imperative to investigate and develop methodologies. Here, the impacts of antibiotics were quantified throughout the life stages with a variety of methods. Antibiotic treatment effectively eliminates the resident bacteria of N. vectensis, though the anemone experiences transcriptional changes, even after removal of the stressor. Additionally, two methods to vector bacteria to the terminal host were quantitatively compared: Prey Feeding Method (PFM), and Solution Uptake Method (SUM). The PFM resulted in higher sustained concentrations through two weeks, indicating its potential as a viable method to vector bacteria. Lastly, part of the culturable microbiome was assessed for viability through thermal and saline stressors. Investigation of these methods is imperative to quantifying the interactions between bacteria and the host organism. Together, the assessment of common methodologies in a common cnidarian model contributes directly to understanding individual bacteria from the microbiome of N. vectensis.



Candidate Name: Destini N. Petitt
Title: Efficacy of whole-watershed stream restoration for hydrologic retention, nitrogen, and suspended solids in a Piedmont urban forest watershed
 July 19, 2022  10:00 AM
Location: McEniry 201
Abstract:

The quality and quantity of water in degraded watersheds (i.e.: watersheds undergoing changing land-use, urbanization, agricultural use) may be impaired compared to undisturbed forested watersheds. To enhance the ecosystem function of impaired watersheds, restoration projects have been implemented on a variety of land-use types and at a variety of spatial scales. The 6km2 Reedy Creek urban forested watershed located in Charlotte, North Carolina, has undergone extensive, whole-watershed stream restoration to offset ecosystem degradation caused by historical agricultural use and urban development within the watershed. Streams within the Reedy Creek watershed exhibited channel straightening, disconnection with surrounding floodplains, channel widening, channel incision, and reduced groundwater retention.

This dissertation explores three research topics with the aim of understanding the efficacy of using restoration to reestablish ecological and hydrological functioning within the watershed. I investigated: 1) if whole-watershed stream restoration was successful at increasing recharge to the thick unsaturated zone and ultimately raising groundwater levels by increasing the elevation of stream channels; 2) if the whole-watershed stream restoration approach used in the Reedy Creek watershed in which stream channel morphology and characteristics were altered has changed N retention and seasonal flux; and 3) whether stream restoration has influenced TSS flux within the watershed at both baseflow and stormflow. Findings indicate that 1) restoration was successful at increasing groundwater levels and groundwater retention throughout the watershed; 2) that restoration was successful at decreasing growing season N flux but lead to an increase in N flux during the dormant season; and 3) that restoration was successful at reducing TSS flux at both baseflow and stormflow.



Candidate Name: A B M Mohaimenur Rahman
Title: Photoplethysmographic Sensor-based Non-intrusive and Secure Smart Sensing and Applications
 July 15, 2022  10:00 AM
Location: https://uncc.zoom.us/j/93443026272?pwd=N2ZnQVdNNHRaNGVBR25pUVVmdkdZZz09
Abstract:

The new era of next-generation intelligent systems is leveraging the usage of smart sensing technology to perform intelligent sensing tasks and collect useful information for different applications. This dissertation discusses secure smart sensing and applications based on the non-intrusive Photoplethysmographic (PPG) sensor, which is commonly available in current wearable devices.
In this dissertation, we first study how to authenticate a user's offline/online signature with data from the PPG sensor. We propose a novel method for offline and online signature authentication, leveraging the widely deployed PPG sensors in wrist-worn wearable devices. The unique blood flow changes in the supplicant's hand movement are being exploited in this system to validate the signature. Our experiments with real-life data sets verify the feasibility and efficiency of the proposed solutions.
In our final work, we focus on a system that can classify a user's lifted weighted object into its corresponding weight label. It leverages the change in the blood volume in the wrist region that occurred due to the strain caused by the different weights being lifted to classify the labels. We believe the importance of PPG sensing in secure smart sensing and applications during this technology era is immense.