Reconfigurable multiport solid-state transformer for DC fast charging stations

This thesis presents a Solid-State Transformer (SST)-based Electric Vehicle (EV) fast charging station that enhances reliability and optimizes power distribution. Traditional SST-based stations rely on a shared DC bus, susceptible to faults that disrupt the entire system. The proposed system introduces multiple independent DC buses, isolating faults and improving reliability while eliminating the need for additional DC-DC converters, reducing costs and space. Reconfigurable ports deliver 800V, 400V, and 200V, accommodating heavy, medium, and light EVs. A dynamic reconfiguration algorithm adjusts the ports based on vehicle requirements. MATLAB/Simulink simulations confirm stable charging currents and voltages under varying loads. A scaled-down prototype achieves an efficiency of 93.75%, with experimental results showing light EVs charged at 1.25A/50V and heavy EVs at 2.5A/100V, with an 8.2% voltage ripple. Grid current THD remains below 5%, meeting IEEE 519-2022 standards. The system enhances EV fast charging stations' reliability, efficiency, and cost-effectiveness

Progress in question: a qualitative thematic analysis of disciplinary power, and Black senior leadership in Canadian policing

The perception of “progress” for racialized officers within contemporary Canadian policing – particularly equity, diversity, and inclusion reforms – remains underexplored. Existing scholarship predominantly emphasizes increased representation at lower ranks, often neglecting the implications of tokenism within white, male-dominated workforces. This study examines the disciplinary proceedings of former Toronto Police Superintendent Stacy Clarke, analyzing tribunal discourse – court documents, promotional policies, exhibits, and testimonies – and related media coverage to interrogate how race, discipline, and compliance are constructed. Employing a case study and qualitative thematic approach, this research contextualizes the socio-legal framings of Clarke’s ruling as the first Black female superintendent in the service’s history, situating it within structural and cultural impediments to racialized officers’ professional mobility. Five key themes emerge, revealing identity-management narratives, over-surveillance, and a marked absence of substantive discourse on anti-Black racism and mentorship opportunities. Findings underscore the need for targeted policy interventions. Implications, limitations, and future directions are also discussed

Latent Dirichlet Variational Autoencoder: a novel approach for hyperspectral image analysis and pixel unmixing exploring deep learning architectures

This thesis investigates deep learning-based methods for hyperspectral image analysis, focusing on pixel unmixing and classification tasks. Recognizing the challenges of high data dimensionality and limited labeled data availability, this research proposes innovative techniques to improve both the accuracy and efficiency of hyperspectral image interpretation. Initially, the impact of spectral band normalization and outlier removal on image segmentation scale selection is explored, leading to a robust method for Object- Based Image Analysis (OBIA). Subsequently, the research delves into the application of autoencoders for spectral dimensionality reduction, culminating in a comparative analysis demonstrating their efficacy in preserving crucial information for classification while achieving significant data compression. Building upon these findings, this thesis introduces the Latent Dirichlet Variational Autoencoder (LDVAE), a novel architecture specifically designed for hyperspectral pixel unmixing. The LDVAE model introduces an approach to hyperspectral pixel unmixing by incorporating a Dirichlet distribution within its latent space. This design enables LDVAE to effectively model abundance vectors, satisfying the inherent sum-to-one and non-negativity constraints, while simultaneously learning a low-dimensional representation of endmember spectra. The generative nature of LDVAE further allows for the synthesis of new hyperspectral pixels by reconstructing spectra from the learned Dirichlet distributions. Evaluations on benchmark datasets demonstrate that LDVAE achieves state-of-the-art performance in both endmember extraction and abundance estimation tasks. This thesis also introduces additional contributions to hyperspectral unmixing, addressing the challenges posed by limited labeled data and the potential for exploiting spatial information. Specifically, we extend the Latent Dirichlet Variational Autoencoder (LDVAE) framework in two key directions. First, recognizing the scarcity of labeled data and the inherent spatial coherence within hyperspectral imagery, we develop an iterative analysis-synthesis approach using the LDVAE (iLDVAE). This novel framework facilitates automatic endmember extraction and refines the unmixing process iteratively. Second, acknowledging the importance of spatial context, we propose SpACNN-LDVAE, which integrates the LDVAE with Convolutional Neural Networks (CNNs) and spatial attention mechanisms. This architecture effectively captures local spatial relationships between pixels, yielding a more informative latent representation for improved unmixing performance. The SpACNN-LDVAE enhances both endmember extraction and abundance estimation accuracy, particularly in scenes exhibiting complex spatial structures. These contributions provide robust and efficient tools for hyperspectral image analysis, offering potential benefits across various application domains, including agriculture, forestry, mineralogy, and environmental monitoring

Beyond rules: how Large Language Models are redefining cryptographic misuse detection

The use of Large Language Models (LLMs) in software development is rapidly growing, with developers increasingly relying on these models for coding assistance, including security-critical tasks. Our work presents a comprehensive comparison between traditional static analysis tools for cryptographic API misuse detection—CryptoGuard, CogniCrypt, and Snyk Code—and the LLMs—GPT, Llama, Claude, and Gemini. Using benchmark datasets (OWASP, CryptoAPI, and MASC), we evaluate the effectiveness of each tool in identifying cryptographic misuses. Our findings show that GPT 4-o-mini surpasses current state-of-the-art static analysis tools on the CryptoAPI and MASC datasets, though it lags on the OWASP dataset. Additionally, we assess the quality of LLM responses to determine which models provide actionable and accurate advice, giving developers insights into their practical utility for secure coding. This study highlights the comparative strengths and limitations of static analysis versus LLM-driven approaches, offering valuable insights into the evolving role of AI in advancing software security practices

Development and evaluation of wind tunnel testing methodology for ADAS camera perception in rain

Advanced Driver Assistance System (ADAS) technologies are rapidly improving to enhance road safety and reduce accidents. However, adverse weather, particularly rain, continues to degrade sensor perception and effectiveness. Despite this, few studies address sensor degradation due to rain, with no established standards for benchmarking sensor performance loss. The objective of this thesis is to develop a methodology that surpasses conventional spray-based approaches in realism, allowing for controlled, repeatable, and quantifiable evaluation of sensor performance in rain. This thesis develops VeRSA, the most realistic indoor rain simulation system in open literature, now adopted commercially. Using VeRSA, camera image quality and object detection under dynamic rain are benchmarked, revealing key limitations in existing metrics. These findings enable the creation of rain-degraded datasets to enhance detection by retraining neural networks. Finally, a novel mathematical model is derived and validated to correlate rainfall with image degradation, establishing a foundation for predicting perception degradation

Language, power, and representation: developing a framework for digital best practices in autism discourse

The complex and evolving autism narrative is shaped by diverse actors, with digital platforms and autism organizations playing a critical role in advocacy and community engagement. Yet, autistic perspectives often remain underrepresented due to structural imbalances and top-down communication approaches, underscoring the need to assess three principles: inclusivity, accessibility, and credibility of online discourse. In this study, the websites of 14 Canadian autism advocacy organizations were critically analyzed to evaluate their alignment with these principles. Informed by Habermas’s Communicative Action Theory, a qualitative approach employing critical discourse analysis was used to examine website content and design. The organizations were selected through a multi-step process that used internet traffic ranking tools to ensure representation across provinces and territories. Each website was assessed for inclusivity, accessibility, and alignment with advocacy goals, drawing on key metrics such as the presence of autistic self-advocates, accessibility features, and transparent communication practices. The findings revealed considerable variability in website quality, with noticeable gaps in accessibility and the meaningful inclusion of autistic voices. To address these gaps, the CLEAR Framework (Clarity, Logic, Evidence, Accessibility, Representation) was developed and applied as an evidence-based tool rooted in universal design and neurodiversity principles. By emphasizing straightforward language, coherent messaging, credible evidence, accessible formats, and genuine autistic representation, the framework operationalized theoretical principles into actionable criteria, offering a structured tool for evaluating and refining how organizations communicate their missions, values, and practices. Ultimately, this research provides insights for guiding autism discourse, policy, and practice while also laying the groundwork for further investigations into equitable and inclusive digital advocacy across diverse contexts

Exploring crime, deviance and community among Sri Lankan Tamil youth in Canada

The Sri Lankan Tamil community remains significantly understudied within Western literature on youth crime and deviance, despite being one of the largest diaspora populations in Canada. Much of the existing research generalizes offending patterns under the broad label of "South Asian youth," often neglecting the distinct cultural, historical backgrounds and experiences of specific groups within this category. This thesis seeks to address this gap by examining community, crime and deviance among second- generation Sri Lankan Tamil youth in Canada. Through semi-structured interviews with members of the Tamil diaspora, this study uncovers key challenges faced by Tamil youth, including strained family dynamics and difficulties navigating community ties. The findings offer a novel contribution by applying an intersectional lens that bridges diaspora studies and criminology, offering insights into the specific experiences shaping crime and deviance among diasporic youth

Design and development of an LLM-based framework for synthetic data generation

The increasing demand for high-quality datasets in fields such as healthcare, finance, and cybersecurity is hindered by challenges such as data scarcity, privacy concerns, and regulatory restrictions. This thesis introduces a novel framework for generating synthetic data using fine-tuned Large Language Models (LLMs) and Generative AI techniques. The framework generates realistic, domain-specific datasets that preserve complex patterns while ensuring privacy through differential privacy methods. It can create synthetic data from scratch and augment existing datasets, thereby offering a scalable solution across industries. Prototype implementation and extensive testing demonstrate the framework’s effectiveness in balancing data utility and privacy, making it a valuable tool for overcoming data access challenges while complying with privacy and regulatory standards. A comprehensive evaluation comparing proprietary and open-source LLMs demonstrates the framework’s superiority in terms of data fidelity, statistical similarity, and computational efficiency

Advancements in resonant and current source gate driving techniques for fast switching Wide Band Gap (WBG) Metal Oxide Field Effect Transistors (MOSFETs)

Traditional methods for driving the Metal Oxide Field Effect Transistor, like the Voltage Source Gate Driver (VSGD), prove to be inefficient for high-frequency operation, especially in applications where Wide-Bandgap devices are used. This is because of their losses and poor control over the switching transitions. This thesis investigates various alternatives, specifically the Multi-Resonant Gate Driver (MRGD) and the Current Source Gate Driver (CSGD). A sweep-based optimization method is presented to increase the MRGD design accuracy and frequency response. Hardware prototypes of the MRGD demonstrate 34% efficiency improvement over VSGD. Furthermore, a new dual-channel Isolated CSGD is proposed, which provides two galvanically isolated gate-drive signals with switching time control. Comparative analysis demonstrates 20% efficiency improvement over VSGD. The design is validated using a hardware-in-the-loop (HIL) real-time simulator and the gate switch controller is successfully embedded on a DSP by Texas Instruments, with results that closely matched across simulation and HIL platforms

Automated goal model generation from user stories using Large Language Models

In agile software development, user stories capture stakeholder needs but often fail to represent complex requirement relationships. Goal modeling addresses this by linking high-level goals to specific requirements, but manually transforming user stories into goal models is challenging. This research explores using Large Language Models (LLMs) to automate goal model generation through multi-step prompt engineering. LLMs extract intentional elements—goals, tasks, actors, and resources—and generate Goal-oriented Requirements Language (GRL) models compatible with tools like jUCMNav. The study evaluates GPT-4, Llama, and Cohere, focusing on syntactic completeness and correctness. GPT-4 outperforms others, particularly in extracting implicit goals and soft goals, but struggles with intricate relationships like means-end and contribution links. Despite limitations, LLMs show promise in automating labor-intensive aspects of goal modeling, making the process more efficient. This research highlights their potential to support requirements engineers and integrate goal modeling into agile workflows