eScholar

eScholar stores, preserves and disseminates digital copies of the research and scholarly output of eScholar faculty, researchers and students. These can include the following items:

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  • Pre- and post-prints of academic journal articles
  • Theses and dissertations
  • Major projects and papers
  • Reports/working papers and conference proceedings

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Now showing 1 - 5 of 9

Recent Submissions

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    Divergent Perspectives Race, Trust, and Human Rights Concerns in Public Perceptions Towards AI Policing Technologies in Canada
    (2026-05-05) Samuels-Wortley, Kanika; Wortley, Scot; Blair, Sara
    This study presents findings from the first nationally representative examination of public perceptions of artificial intelligence (AI) in Canadian law enforcement. Drawing on a sample of 2,014 Canadians , with deliberate oversampling of Black and Indigenous respondents, this quantitative study investigates whether awareness of AI in policing varies by race; how Black and Indigenous peoples evaluate specific AI applications; what ethical and governance concerns racialized communities express; and what conditions communities believe must be in place for AI-assisted policing to be considered legitimate. Findings reveal that nearly 70% of respondents had minimal awareness of AI in policing yet held strong governance expectations once informed. Notably, Black and South Asian respondents expressed higher levels of support than White respondents, while Indigenous respondents reported the lowest confidence in AI's public safety benefits, police transparency, and ethical governance. These divergent patterns challenge monolithic framings of racialized opposition and suggest that the relationship between lived experiences of discriminatory policing and technology acceptance is considerably more complex than existing discourse acknowledges. Across all groups, over 70% identified transparency, independent oversight, and federal regulation as foundational conditions for legitimacy, underscoring that AI governance is necessary.
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    Experimental investigation of a biofuel cell
    (2026-04-01) Seela, Mohamed; Dincer, Ibrahim
    This thesis presents an investigation of bioenergy generation through a soil-based microbial fuel cell (MFC). The MFC utilized soil samples of 100 g and various environmental and operational conditions to assess the generation of voltage. The parameters of interest included temperature, pH, glucose concentration, sodium chloride, and volume of water. The temperature experiment showed that moderate temperatures of 35 °C and 45 °C yielded stable voltage outputs. The maximum voltage obtained was approximately 55 mV at a temperature of 45 °C and 50 mV at a temperature of 35 °C. On the other hand, higher temperatures of 50 °C decreased the voltage from approximately 30 mV to as low as 16 mV. The experiment on glucose concentration showed the optimal concentration of glucose, as a voltage of approximately 66 mV was obtained at a glucose concentration of 1.0 g. The experiment on sodium chloride showed improved performance at moderate salinity. The addition of 1.5 g of sodium chloride at a temperature of 25 °C yielded a peak voltage of approximately 70 mV. On the other hand, the addition of 1.0 g of sodium chloride at a temperature of 40 °C yielded a peak voltage of approximately 100 mV. The experiment on pH and temperature showed exceptional performance at a pH of 7 and a temperature of 40 °C. The voltage was as high as approximately 950 mV. Soil moisture was also seen to greatly influence performance, as the addition of 300 mL of water yielded an initial voltage of approximately 427 mV. The voltage gradually decreased over time to approximately 230 mV due to mass transfer limitations. The results show that bioenergy generation through soil-based MFC improves at optimized environmental conditions. The scientific literature consistently shows that soil based microbial fuel cells generate more bioenergy when key environmental conditions are optimized.
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    A distributed multi-vehicle architecture for autonomous driving simulation with an application to autonomous valet parking
    (2026-04-01) Islam, Zubair; El-Darieby, Mohamed
    Simulation platforms are essential for developing and validating autonomous driving systems before real-world deployment. However, most existing simulators are designed for single-vehicle scenarios and provide limited support for distributed multi-vehicle experimentation across multiple computing hosts. This thesis presents the Distributed Multi- Autonomous Vehicle Architecture (DMAVA), which enables multiple AVs to operate concurrently in a shared simulation environment while maintaining independent autonomy stacks. The architecture integrates Autoware, AWSIM Labs, ROS 2 namespaces, and Zenoh-based cross-host communication to support distributed experimentation. Building on this framework, the Distributed Multi-Vehicle Autonomous Valet Parking System (DMV-AVP) is introduced as an application layer that integrates infrastructure-assisted perception through a YOLOv5-based overhead camera module and a multi-vehicle AVP coordination framework for managing shared parking resources. The system enables coordinated vehicle sequencing, exclusive parking spot allocation, and distributed vehicle state synchronization. Experimental validation using two-host and three-host deployments demonstrates stable localization, reliable inter-host communication, and coordinated multi-vehicle AVP behavior.
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    ClinicalTrACE: a self-correcting agent with interpretable uncertainty for clinical question answering
    (2026-04-01) Wadie, Peter; Elgazzar, Khalid; Alwidian, Sanaa
    Answering precise questions about patient records requires retrieving events that satisfy type, temporal, and content constraints simultaneously, a multi-constraint satisfaction problem that embedding-based systems cannot solve. We introduce ClinicalTrACE, a self-correcting agent that retrieves through explicit structured queries with no fine-tuning and no task-specific training data, achieving 94.1% accuracy versus 76.5% for RAG and 55.3% for a fine-tuned baseline trained on 400K examples. Ablation shows that retrieval design drives this gain: categorical constraints alone contribute +12.6%, while scaling from 3B to 14B parameters adds only +5.1%. We also develop TrACE+, an uncertainty framework that predicts errors from ClinicalTrACE’s observable execution trace. A domain-agnostic variant achieves 0.83 AUROC with stable calibration (ECE = 0.060) and transfers across hospital systems; a domain-aware variant reaches 0.86 AUROC and 97.9% accuracy at 75% coverage at the cost of portability, revealing a clear generalization-discrimination tradeoff.
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    A secure multi-layer embedded framework for real-time fault detection in Industrial IoT power supply systems
    (2026-04-01) Tran, Ba Vu; Youssef, Mohamed
    Industrial power supplies are traditionally designed as passive components with limited diagnostic visibility and secure remote supervision. This thesis presents the design and experimental validation of a secure, multi-layer Industrial Internet of Things (IIoT)-enabled power-supply platform that integrates deterministic protection, embedded diagnostics, and authenticated remote monitoring within a unified architecture. The platform combines real-time hard-fault protection with quantized neural network (QNN)-based soft-fault diagnostics executed on a microcontroller-class device, enabling early detection of incipient degradation with bounded latency. Industrial interoperability is maintained through Modbus RTU communication, while secure access is provided via TLS-encrypted HTTPS and a web-based dashboard. Experimental results demonstrate stable electrical operation under sustained thermal stress, consistent communication timing with an average latency of 5.76 ms, and diagnostic performance exceeding 96%, with sub-millisecond inference latency. These results demonstrate that intelligence, connectivity, and cybersecurity can be co-designed while preserving bounded real-time behavior and operational reliability.