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The increasing threats posed by climate change and urbanization have elevated the importance of addressing Urban Heat Island (UHI) phenomenon, a critical concern impacting cities across the United States. This dissertation comprises three articles that collectively investigate the effectiveness of trees and greenspaces in managing UHI, creating a Heat Health Score (HHS) to identify areas experiencing UHI effects and investigate the perceived effectiveness of policies and programs aimed at reducing UHI in cities, while providing recommendations for Charlotte, NC in particular. Article 1 shows that urban greenspaces consisting of trees can help reduce the UHI effect by creating shade and cooling spaces, potentially reducing energy costs, improving human living conditions, providing food and habitat to wildlife, and improving aesthetics and land values. In Article 2, measures to mitigate the effect of UHI are evaluated from select cities and a ratio of daily average high temperature between locations and the corresponding difference in land cover of tree and shrub areas, create the Heat Health Score (HHS) (a unique metric) which allows municipalities and community groups to gauge the heat health between locations. These results show that most urban locations remain hotter and with lower vegetative cover than their suburban or rural counterparts, however, changes in tree and shrub cover can impact these results in a positive way. Results from Article 3 elucidate the perceived successes and challenges of current policies through a qualitative survey. These responses offer practical recommendations for policymakers across the US but for Charlotte, NC in particular.
These results draw from the real-world experiences and lessons uncovered in the three articles, in aggregate, these provide a valuable resource for city leaders and policymakers striving to create a more sustainable and climate-resilient city. It stresses the importance of urban greenspaces and urban trees in particular, provides community leadership with an easily accessible, not previously defined tool to discern urban heat health through the use of free, open-source data to score heat health, and provides insight into the perceived effectiveness of policies and programs used to mitigate UHI in cities in the United States.
Renewable energy resources advancement and offerings are steadily increasing, a major factor leading to its global fast adoption. The connection of these resources to the electric grid, however, needs to be studied to ensure efficiency both from an operational and regulatory standpoint. The IEEE 1547 has been used to establish standards for grid interconnection of some renewable energy resources (RERs). In this dissertation, the operations of RERs connected to the grid with respect to their control, management, and optimization are studied. It is of note that RERs are intermittent in nature and this can have effects on the power quality metrics or utility objectives on either network separately or collectively. For instance, the stability of the grid can be affected due to the low inertia of these resources, which can impact the voltage or the grid frequency. A novel adaptive controller was developed to damp the oscillations caused by these RERs, the controller was initially tested with RERs in one network architecture, and it offers advantages such as dynamically responsive support to the grid to control the frequency, a frequency spectrum was used to determine the amount of support required in an adaptive manner. The architecture was then expanded to a network model that has both transmission and distribution networks integrated together with the interconnection of multiple RERs connected to the grid, the capabilities of the proposed architecture were evaluated with different test cases with different grid events. The architecture had the capability to control multiple generators as well as damp the oscillations observed during the test cases and simulations performed, by adaptively updating the gain of the power system stabilizers (PSS).
On the management side, A new technique was developed with grid-connected RERs that provide real-time visibility of two integrated networks during operation. Presently, the operations don't offer such capabilities as the transmission system operator (TSO) is often times blind to the distribution system operator (DSO). Our technique makes it possible for the transmission network to adjust itself in real-time in case of sudden changes in the distribution network with RERs connected, A stochastic linear optimization technique; Linear decision rule (LDR) that establishes the relationship between the generators in the transmission network and the RERs in the distribution network was implemented, the technique addresses one of the major issues with integrated T&D networks which is boundary mismatch caused by the reverse power flow from the distribution network, in addition to offering the operational advantages required by most utilities like minimization of voltage deviation, and minimization of cost of operation, as it eliminates the need for curtailing RERs which is the current implementation used by most utilities, the technique theorem proof was also discussed. Furthermore, Grid Connected RERs are multiperiod in nature, it is therefore imperative to study their behavior at each time interval, the optimization framework was extended to such studies to handle the reverse power flow operation due to the irradiance daily curve, and the optimal power flow formulation was transformed to multiperiod optimal power flow MPOPF. The effectiveness of the proposed architecture was tested with an irradiance curve, and a typical residential load curve, it demonstrated the capability to reduce the boundary mismatch while ensuring the grid objectives for each network were achieved. Finally, the impact of electric vehicle charging was studied and a management approach was developed, Electric vehicles (EVs) adoption is also increasing impacts of the distribution network on the transmission network with respect to grid penetration, we developed a two-stage stochastic linear optimization in the integrated T&D to handle the uncertainty with electric vehicle charging and compared with effective EV charging management technique that was developed.
As the entry point into higher education for over half of the bachelor’s degree earners, (Trapani & Hale, 2019), community colleges are positioned to have a positive impact of bringing a more diverse student group into science, technology, engineering, and mathematics (STEM) majors. However, the vertical transfer function from community colleges into a four-year university is often not clear resulting in a gap between those with transfer aspirations and bachelor degree attainment. There are unique barriers that women of color transfer students encounter that can threaten persistence in STEM. This study contributed to the few studies (Allen et al., 2022, Jackson, 2013, & Reyes, 2011) that have focused on the experiences of women of color in STEM and transfer. The purpose of this qualitative phenomenological transfer student study was to understand how pre- and post-transfer women of color in STEM majors experience the transition into university from community college in North Carolina. This longitudinal study used interview data from 14 women who participated in a larger transfer study. Six of the women provided three interviews. Guided by the reconceptualized model of multiple dimensions of identity (Abes et al., 2007), the role of social identities and the impact on educational decisions was explored. Five major themes were identified: (1) the internalization of community college stigma, (2) blindsided: post-transfer rigor, (3) the loss of personal connection post-transfer, (4) feeling behind and other perceived roadblocks for STEM transfer students, and (5) can’t do it alone: leaning on support networks for success. The findings from this study led to recommendations to the current articulation agreement structure in North Carolina, and recommendations for post-transfer institutions to better support women of color in STEM.
The rapid proliferation and widespread adoption of microgrids (MG) necessitate the
development of new methodologies to holistically model all the active components
within MGs. It’s crucial to focus on specific islanding requirements, especially when
the primary grid power is unavailable. In order to ensure a high level of reliability
in an interconnected MG network, this dissertation presents an optimal scheduling
model designed to minimize the day-ahead costs of the MGs while taking into account
the existing operational constraints.
This problem is thoughtfully decomposed using Bender’s Decomposition method
into two key operating conditions: grid-connected and resilient operations. The ultimate
goal is to ensure that each MG within the network maintains sufficient online
capacity in the event of an emergency islanding situation, such as during extreme
weather events. These events often come with uncertainties regarding their timing
and duration, necessitating the consideration of multiple potential islanding scenarios
for each event.
The primary objective of this thesis is to establish optimal scheduling that guarantees
the feasibility of islanding for all conceivable scenarios of such events, with
load shedding as a last resort. The optimization model has been put into practice
across different layouts of the modified IEEE 123-bus test system, encompassing various
events over a 24-hour period. In addition to proposing a day-ahead scheduling
approach oriented towards resiliency for multiple MGs, a comprehensive cost analysis
and comparisons among all the test cases are also offered. The results convincingly
demonstrate the utility of the proposed day-ahead scheduling algorithm, particularly
for MG owners looking to foster collaborations with neighboring MGs. Lastly, after
iv
comparing with the traditional Single Stage MILP approach, the proposed method
has proven to be computationally faster for practical usage. It has been shown that
decomposing the problem using the proposed model makes it possible to combat real
life events with thousand scenarios, where the single stage approach may fail.
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.
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.
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.
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.
The AI-digital era is characterized by an unprecedented surge in data usage, spanning from data centers to IoT devices. This growth has driven the evolution of AI-optimized networks, designed to fuse AI capabilities with advanced network solutions seamlessly. However, these networks grapple with challenges such as the complexity of network layer protocols, discrepancies between simulated AI models and their real-world implementations, and the need for decentralized AI training due to network distribution.
To address these challenges, we propose the AI-oriented Network Operating System (AINOS). At the core of AINOS are two foundational sub-platforms: the "Network Gym," tailored for AI-driven network training, and "Federated Computing," designed for decentralized training methodologies. AINOS provides a comprehensive toolkit for rapid prototyping, deployment, and validation of AI-optimized networks, bridging the gap from simulation to real-world deployment.
Harnessing the powerful features of AINOS, we prototyped AI-optimized networking solutions using a safe Reinforcement Learning (RL) strategy for Traffic Engineering (TE) at both the link and network layers. At the link layer, we implemented a scalable RL-based traffic splitting mechanism that learns optimal traffic split ratios across Wi-Fi and LTE through guided exploration. For the network layer, we devised an online Multi-agent Reinforcement Learning (MA-RL) approach with domain-specific refinements to determine optimal paths in real-time for wireless multi-hop networks. In our exploration of Network Assisted AI optimization, we reduced Federated Learning training time with our MA-RL multi-routing approach and proposed a robust Decentralized Federated Learning solution that leverages single-hop connections for enhanced network performance. Our results demonstrate the strengths of AI-enhanced networks in proficiently managing heterogeneity and latency.
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.
