Dissertation Defense Announcements

Candidate Name: Liana Kreamer
Title: Participation in Virtual Meetings: Using the “Chat” as a Mechanism to Elevate Attendee Voices
 November 09, 2023  12:30 PM
Location: Cone 208
Abstract:

A key component of team performance is participation among group members. One widespread organizational function that provides a stage for participation is the workplace meeting. With the shift to remote work, roughly half of all meetings are now conducted virtually (Cisco, 2022). In this new context, meeting participation is mediated through technology – which presents new challenges and opportunities for meeting leaders and attendees. One encouraging opportunity that can elevate meeting participation is the use of written chat during virtual meetings. Text-based chat offers a second avenue of participation during a meeting, where attendees can synchronously contribute to the conversation through writing, in a manner that typically does not disrupt the verbal discussion. The current study leverages research and theory on (a) individual differences (i.e., status characteristics theory), (b) employee perceptions of psychological safety and (c) work group participation in a virtual context to explore potential antecedents of engaging in chat during virtual meetings. Results suggest women and those high in job level participate in the meeting chat more frequently than their counterparts. We find perceptions of psychological safety moderate the relationship between job level and chat participation. Employees low in job level who have high perceptions of psychological safety participate in chat more frequently compared to their counterparts who report low perceptions of psychological safety. Results contribute to our understanding of written communication in virtual meetings, unpacking the individual differences in chat participation in a technology-mediated space. Further, our findings enhance our understanding of psychological safety; and how creating a psychologically safe environment can influence one’s method of participation in virtual meetings.



Candidate Name: Vinit Amrutlal Katariya
Title: Advancing Highway Safety: Embedded-edge AI for Real-time Applications
 October 30, 2023  9:30 AM
Location: EPIC 3344
Abstract:

In the rapidly evolving landscape of intelligent transportation, the pressing need for real-time Artificial intelligence-based trajectory prediction and anomaly detection in highway scenarios is paramount. Ensuring the safety of highway workers, optimizing traffic flow, and enhancing surveillance mechanisms necessitate advancements tailored for embedded-edge platforms. This dissertation responds to these imperatives by developing a lightweight deep learning model that transitions from traditional LSTMs to leverage the efficiency of Agile Temporal Convolutional Networks, achieving streamlined computational requirements without sacrificing accuracy. An extensive vehicle trajectory dataset is presented, capturing a diverse range of driving scenes and road configurations from 1.6 million frames. To further the field, an innovative vehicle trajectory prediction model is introduced, employing attention-based mechanisms and outperforming existing benchmarks. The research culminates in an integrated AI pipeline optimized for real-time anomaly detection on highways. This system, synergized with a pioneering anomaly-specific dataset, sets new benchmarks in highway safety and surveillance, showcasing the potential of AI-driven solutions in addressing contemporary transportation challenges.



Candidate Name: Christopher Neff
Title: Human-Centric Computer Vision for the Artificial Intelligence of Things
 October 27, 2023  10:00 AM
Location: EPIC 3344
Abstract:

This dissertation presents a comprehensive exploration of innovative approaches and systems at the intersection of edge computing, deep learning, and real-time video analytics, with a focus on real-world computer vision for the Artificial Intelligence of Things (AIoT). The research comprises four distinct articles, each contributing to the advancement of AIoT systems, intelligent surveillance, lightweight human pose estimation, and real-world domain adaptation for person re-identification.

The first article, REVAMP2T: Real-time Edge Video Analytics for Multicamera Privacy-aware Pedestrian Tracking, introduces REVAMP2T, an integrated end-to-end IoT system for privacy-built-in decentralized situational awareness. REVAMP2T presents novel algorithmic and system constructs to push deep learning and video analytics next to IoT devices (i.e. video cameras). On the algorithm side, REVAMP2T proposes a unified integrated computer vision pipeline for detection, reidentification, and racking across multiple cameras without the need for storing the streaming data. At the same time, it avoids facial recognition, and tracks and reidentifies pedestrians based on their key features at runtime. On the IoT system side, REVAMP2T provides infrastructure to maximize hardware utilization on the edge, orchestrates global communications, and provides system-wide re-identification, without the use of personally identifiable information, for a distributed IoT network. For the results and evaluation, this article also proposes a new metric, Accuracy•Efficiency (Æ), for holistic evaluation of AIoT systems for real-time video analytics based on accuracy, performance, and power efficiency. REVAMP2T outperforms current state-of-the-art by as much as thirteen-fold Æ improvement.

The second article, Ancilia: Scalable Intelligent Video Surveillance for the
Artificial Intelligence of Things, presents an end-to-end scalable intelligent video
surveillance system tailored for the Artificial Intelligence of Things. Ancilia brings
state-of-the-art artificial intelligence to real-world surveillance applications while respecting ethical concerns and performing high-level cognitive tasks in real-time. Ancilia aims to revolutionize the surveillance landscape, to bring more effective, intelligent, and equitable security to the field, resulting in safer and more secure communities without requiring people to compromise their right to privacy.

The third article, EfficientHRNet: Efficient and Scalable High-Resolution Networks for Real-Time Multi-Person 2D Human Pose Estimation, focuses on the increasing demand for lightweight multi-person pose estimation, a vital component of emerging smart IoT applications. Existing algorithms tend to have large model sizes and intense computational requirements, making them ill-suited for real-time applications and deployment on resource-constrained hardware. Lightweight and real-time approaches are exceedingly rare and come at the cost of inferior accuracy. This article presents EfficientHRNet, a family of lightweight multi-person human pose estimators that are able to perform in real-time on resource-constrained devices. By unifying recent advances in model scaling with high-resolution feature representations, EfficientHRNet creates highly accurate models while reducing computation enough to achieve real-time performance. The largest model is able to come within 4.4% accuracy of the current state-of-the-art, while having 1/3 the model size and 1/6 the computation, achieving 23 FPS on Nvidia Jetson Xavier. Compared to the top real-time approach, EfficientHRNet increases accuracy by 22% while achieving similar FPS with 1 the power. At every level, EfficientHRNet proves to be more computationally efficient than other bottom-up 2D human pose estimation approaches, while achieving highly competitive accuracy.

The final article introduces the concept of R2OUDA: Real-world Real-time Online Unsupervised Domain Adaptation for Person Re-identification. Following the popularity of Unsupervised Domain Adaptation (UDA) in person reidentification, the recently proposed setting of Online Unsupervised Domain Adaptation (OUDA) attempts to bridge the gap towards practical applications by introducing a consideration of streaming data. However, this still falls short of truly representing real-world applications. The R2OUDA setting sets the stage for true real-world real-time OUDA, bringing to light four major limitations found in real-world applications that are often neglected in current research: system generated person images, subset distribution selection, time-based data stream segmentation, and a segment-based time constraint. To address all aspects of this new R2OUDA setting, this paper further proposes Real-World Real-Time Online Streaming Mutual Mean-Teaching (R2MMT), a novel multi-camera system for real-world person re-identification. Taking a popular person re-identification dataset, R2MMT was used to construct over 100 data subsets
and train more than 3000 models, exploring the breadth of the R2OUDA setting to understand the training time and accuracy trade-offs and limitations for real-world
applications. R2MMT, a real-world system able to respect the strict constraints of the proposed R2OUDA setting, achieves accuracies within 0.1% of comparable OUDA methods that cannot be applied directly to real-world applications.

Collectively, this dissertation contributes to the evolution of intelligent surveillance, lightweight human pose estimation, edge-based video analytics, and real-time unsupervised domain adaptation, advancing the capabilities of real-world computer vision in AIoT applications.



Candidate Name: Shreyashi Shukla
Title: Short Term Peak Timing Forecasting
 November 03, 2023  10:00 AM
Location: EPIC 1229
Abstract:

Peak load forecasting is crucial for reliable and effective grid operation. The day-to-day operation of the power grid requires load scheduling and dispatches of different energy resources including Energy Storage System and Demand Side Management programs. An effective implementation of these peak-shaving strategies relies heavily on when the peak demand occurs. Hence, forecasting the timing of peak load is as important as forecasting its magnitude.
A review of relevant literature indicates that there is no inclusive study on the topic of peak timing forecasting. This research aims to bridge the gap between industry requirements and academic research by addressing some key questions. First, the study defines the different forms of peak timing problems that arise in grid operation. Next, we investigate the problem of how we measure the peak timing forecast errors. The research critically reviews error measures used in the literature for peak timing forecasting. Based on the findings five new application-specific error measures are proposed. The research then focuses on one of the manifestations of the peak timing problem, that is, forecasting daily peak hours.
We analyzed the accuracy of peak hour forecasts from a state-of-the-art hourly load forecasting model and set it as the benchmark. The model selection process using different peak timing errors and load shape errors is investigated. Furthermore, two different frameworks for peak hour forecasting have been developed. The effectiveness of the proposed frameworks is empirically demonstrated in two case studies. The first case study is from a medium-sized Utility in the U.S. and the second one is from ISO New England. The proposed models demonstrate improved forecast results on the benchmark model by 12-16% in the test years of the two case studies. Additionally, when the models are only evaluated on the critical days with very high demands, they outperform the benchmark by 25-53%. Findings from this study emphasize the importance of developing explicit models for peak hour forecasting by analyzing the key determinants that vary with geographical location and regional factors.



Candidate Name: Hannah Luce
Title: Establishing Time-Continuous Normative Scores for Teaching Strategies GOLD Using a Multilevel Growth Curve Modeling Approach
 October 20, 2023  10:30 AM
Location: https://charlotte-edu.zoom.us/j/9802966419?pwd=b1AwVG9Ra3FMUzJpZy9USnZXZW1SZz09.
Abstract:

Young children are assessed to meet federal mandates and inform policy decisions, provide teachers with useful information to make instructional decisions and set reasonable learning goals, and facilitate communication with families. While young children are frequently assessed using whole-child assessments which often yield criterion-referenced score interpretations, norm-referenced score interpretations can help teachers understand relative performance and set reasonable goals for growth. Although researchers have provided validity evidence for both criterion- and norm-referenced score interpretations for one widely used early childhood assessment, GOLD®, current national normative scores lack precision for several reasons, including the use of two-time-point and cross-sectional data. To improve estimates, a nationally representative sample of assessment records from 18,000 children ages birth through kindergarten was fitted to a series of hierarchical linear models (HLMs) to establish normative estimates conditional on months of age or instruction. Secondary study purposes included making inferences about the nature of growth from birth through kindergarten, providing evidence of the most effective time metric for modeling developmental growth, and examining the relationship between child-level characteristics and normative scores. Results indicated that a) HLMs provide reasonably valid normative ability and growth estimates, b) developmental growth, as measured by GOLD®, from birth through kindergarten is non-linear, c) the most effective time metric depends on the age band and domain of development, and d) child-level characteristics, including, race/ethnicity, gender, and primary language are associated with significantly different patterns of preliminary performance and growth for children who are one- or two-years of age or older.



Candidate Name: Faith Butta
Title: A Spatial Evaluation of Housing and Supportive Service Locations for the Formerly Homeless: The Case of Charlotte, North Carolina
 October 25, 2023  12:00 PM
Location: Zoom - Please contact Faith Butta at fbutta@charlotte.edu for the link
Abstract:

The Housing Act of 1949 set its goals to revitalize American cities and provide adequate housing and suitable living environments for families. Although this goal has been achieved for some Americans, the lack of affordable housing and homelessness continues to be a serious public policy issue. Chronic homelessness, after declining for years, is on the rise. As a remedy, many cities have adopted the Housing First model, as part of their Continuum of Care, to place people who are homeless into housing. The purpose of this study was to learn more about the locations of Housing First placements and assess their proximity to supportive services in Charlotte, North Carolina. Using geospatial analysis, the findings revealed that housing placements were quite concentrated, with the majority being located in just six zip codes, where median rents were well below the city’s average and poverty rates were higher. Residents were also disproportionately Black or Hispanic. Although most housing placements were close to bus stops, they were not close to other services (e.g., grocery stores, pharmacies, hospitals, schools, or recreation areas). Moreover, nonprofit service providers responding to an online survey acknowledged that transportation, staffing, and funding for supportive services could be better. By adopting Housing First and implementing other efforts to increase affordable housing, Charlotte has demonstrated a clear interest in preventing and ending homelessness. Yet, there are still opportunities to do things differently by learning from other communities, which have adopted a range of creative and innovative policy solutions.



Candidate Name: Michelle Pazzula Jimenez
Title: English Language Development Teachers’ Experiences with Multilingual Student Advocacy During Mandated School Closures
 October 30, 2023  10:00 AM
Location: Virtual: Zoom - Please contact Michelle Pazzula Jimenez at mpazzula@Charlotte.edu for the link
Abstract:

The effects of the COVID-19 pandemic changed the way education transpired for teachers and learners worldwide. Widespread virtual learning brought deeper academic and social inequities among K-12 diverse learners to light. Multilingual learners and their teachers were no exception. Research has yet to deeply explore the topic of ELD teachers’ experiences with advocating for their multilingual students during this unique time in educational history, as well as the lessons they learned during the pandemic that inform their advocacy work today. This phenomenological study used in-depth, semi-structured interviews to investigate these experiences. Potential implications for this study include teacher preparation, professional development, as well as policy-making decisions surrounding advocacy needs for multilingual learners.



Candidate Name: Corinne Kingsbery
Title: The Impact of a Multilevel Coaching Intervention on Preservice Teachers’ Fidelity of Implementation of a Reading Intervention
 October 18, 2023  9:30 AM
Location: COED 110
Abstract:

Teacher preparation programs (TPPs) can equip preservice teachers (PSTs) with skills to implement evidence-based interventions in reading with fidelity by engaging PSTs in carefully designed clinical experience opportunities. Providing PSTs with extensive feedback through coaching is one method to strengthen support for PSTs’ implementation of evidence-based interventions, improve PSTs’ fidelity of implementation, and increase the likelihood of positively impacting students’ reading outcomes. This study contributed to gaps in the literature on preparing elementary education PSTs to implement evidence-based practices (EBPs) in reading with fidelity and the impact of sustained and responsive feedback during an authentic reading tutoring clinical experience. To individualize coaching support and facilitate a responsive approach to coaching centered on PSTs’ levels of fidelity, first, this study examined the impact of a multilevel coaching intervention on PSTs’ fidelity of implementation of an evidence-based reading intervention during a tutoring clinical experience. Second, this study examined PSTs’ perceptions of the feasibility, effectiveness, and future impact of the multilevel coaching intervention.

Results of this single-case, multiple baseline across participants study indicated a functional relation between the multilevel coaching intervention and PSTs’ fidelity of implementation, inclusive of both structural and process dimensions of fidelity. Furthermore, PSTs found the multilevel coaching intervention to be socially valid, indicating the intervention was feasible, effective, and impactful on their future teaching experiences. The findings of this study provide relevant implications regarding teacher preparation and coaching support. Major implications include (a) providing PSTs as novice learners with authentic clinical experiences, inclusive of coaching support, when implementing EBPs; (b) viewing fidelity as a multidimensional construct that can inform coaching support and teacher practices; and (c) enhancing TPPs with experiences that impact PSTs’ beliefs and perceptions about teaching reading and their own ability to do so. A few suggestions for future research include (a) investigating the efficiency of various coaching models at supporting PSTs to implement EBPs with fidelity, (b) examining the role of instructional pacing and other factors that may impact the extent to which EBPs are implemented with fidelity, (c) determining the effects of multiple dimensions of fidelity (i.e., structure and process) and the interaction on student outcomes, and (d) extending research findings on coaching supports that impact PSTs’ knowledge and the subsequent impact on student outcomes in reading.



Candidate Name: Hannah L Stokes
Title: ANALYSIS OF BASEBALL PITCHING: AN INVESTIGATION OF SHOULDER EXAM, BIOMECHANICS, AND INJURY
 October 16, 2023  10:00 AM
Location: Duke Centennial Hall, Room 308
Abstract:

Throwing arm injuries are common because of the demand on the shoulder. Shoulder exams and pitching mechanics are regularly monitored by team physicians. Excessive instability and joint loading in baseball pitching are risk factors for throwing arm injuries. Altering baseball pitching mechanics affects both performance and the risk of injury. The purpose of this study is to investigate the relationship among injuries, shoulder exam variables, and pitching biomechanics in collegiate baseball pitchers. Pitching biomechanics, shoulder exam tests, and self-reported injury questionnaires were used to study 177 collegiate baseball pitchers. Pitching biomechanics where high-speed cameras record the athlete pitching. This allows us to capture both the athletes body position and calculate joint loadings. Shoulder exam tests where the athletes lay on their backs and their shoulder range of motion, flexibility, and stiffness is measured. Injury questionnaires is where the athletes report if they have had any injuries or surgeries. Our findings show that the shoulder exam, pitching biomechanics, and injury questionnaire variables are related. The ability to understand the relationship between shoulder exam variables, baseball pitching mechanics, and injuries helps further our knowledge and pushes forward the underlying goal of this study which is to improve performance and reduce injuries.



Candidate Name: David Aaron Gonzalez Jr
Title: Surface scatter of antireflective nanostructured dielectric interfaces
 September 26, 2023  10:00 AM
Location: Grigg Hall, Rm 238, 9320 Robert D. Snyder Rd, Charlotte, NC 28262, USA
Abstract:

Random anti-reflective nanostructured surfaces (rARSS) enhance optical transmission through suppression of Fresnel reflection at layered-media boundaries. Windows with rARSS treatment are characterized (transmittance, reflectance, and scatter) using spectrophotometry and scatterometry to assess transmissive scatter performance over various spectral bands. Using measured spectral data, partial-integrated scatter values were obtained, allowing the comparison of random anti-reflective surface performance to optically flat surfaces.

Using a transfer function approach, an approximation of far-field light scatter can be modeled based on surface statistics. rARSS feature topology was determined using optical profilometry to obtain statistical surface roughness parameters, to assess the structured-surface feature scales. Random rough surfaces are well-modeled by Gaussian statistics, making them ideal candidates for a surface transfer function approach of surface scatter analysis.
The Generalized Harvey-Shack surface scatter theory was used to calculate surface feature diffractive effects. Scatter distributions predicted using a Gaussian two-parameter model of a random surface and structured surface metrology data were compared to measured scatter data for assessment of the transfer function model validity within the bandlimit of interest. Results show that prediction of wide angle rARSS optical scatter is viable using the transfer function approach, but the theory fails to predict transmission enhancement due to the inclusion of roughness.




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