About MacSphere

MacSphere is McMaster University's Institutional Repository (IR). The purpose of an IR is to bring together all of a University's research under one umbrella, with an aim to preserve and provide access to that research. The research and scholarly output included in MacSphere has been selected and deposited by the individual university departments and centres on campus.

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Communities in MacSphere

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Recent Submissions

Decentralized and Intelligent Estimation: Theory and Applications
(2026) Alsadi, Naseem; Gadsden, S. Andrew; Mechanical Engineering
Contemporary technological development has had a profound impact on the architecture and operation of modern systems. In particular, smart systems, defined by their capacity for adaptation, have emerged as a dominant paradigm across various sectors. This dissertation presents two complementary surveys that establish the conceptual foundation for the technical contributions that follow. The first is a comprehensive examination of smart system architectures, framed through the lens of cognitive dynamic systems, which decomposes smart systems into five core components: control, perception, knowledge, communication, and security. The second is a focused survey on Intelligent Estimation, which explores the intersection of estimation theory and learning-based systems. Motivated by the increasing reliance on secure and distributed inference, the first technical contribution introduces Decentralized Estimation (DeEst), a novel data-driven decentralized estimation framework that integrates data-driven local inference with blockchain-based federated consensus. In DeEst, each node maintains a local estimator informed by historical observations and contributes parameter updates to a shared global model via a blockchain-federated learning protocol. This architecture eliminates the need for a central aggregator while ensuring robustness to communication failures, malicious nodes, and node data heterogeneity. The second contribution focuses on estimator robustness at the node level through the development of the Sliding Sigmoid Filter (SSF), an extension of the Sliding Innovation Filter (SIF). By incorporating a nonlinear sigmoid-based saturation function, the SSF enables smoother transitions across innovation magnitudes, improving estimation stability in the presence of abrupt deviations or measurement outliers. The SSF’s capacity to modulate updates adaptively makes it particularly well-suited for decentralized implementations, where maintaining local estimator reliability in the face of system faults is essential to ensuring system-wide accuracy. The final contribution presents a novel learning paradigm, referred to as Intelligent Estimation, which reinterprets neural network training as a probabilistic filtering problem. In contrast to conventional gradient-based optimizers such as SGD or Adam, which often suffer from poor convergence in noisy settings, Intelligent Estimation employs estimation methods, such as the SSF, to adaptively scale weight updates based on innovation magnitudes, enabling context-aware and noise-resilient learning. The approach is empirically validated across diverse benchmark datasets, demonstrating improvements in both convergence behavior and generalization performance.
Horizon-scanning brief: Health-system innovations for British Columbia
(2022-08) Wilson MG; Bhuiya AR; DeMaio P; Lavis JN
Insights from key-informant interviews, evidence documents and the experiences of jurisdictions in Canada and internationally that help explain why a particular topic related to the issue of health-system innovations for British Columbia may warrant attention. This brief was designed to inform horizon-scanning panel deliberations.
Prior-Informed Visual Enhancement and 3D Reconstruction under Challenging Illumination
(2025) Zhou, Han
Low-light image enhancement (LLIE) and illumination-robust 3D scene reconstruction remain fundamental challenges in computer vision. Images captured under adverse illumination suffer from detail loss, amplified noise, and color shifts, degrading visual quality and hindering recognition, navigation, and scene understanding. Despite rapid progress, many LLIE pipelines still depend on image-to-image mappings trained on limited or synthetic datasets, and neglect auxiliary priors available from external data or physics. As a result, they overfit to seen degradations and break under heavy noise, motion, or extreme dynamic range. Meanwhile, modern 3D scene representation techniques, such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), rely on high-quality multi-view images and deteriorate severely with dark inputs. This thesis investigates how external priors (learned prior from normal-light images), perceptual knowledge extracted from vision–language models, and illumination-invariant physical cues, can be integrated to improve deep learning approaches in both 2D visual content enhancement and 3D scene reconstruction. For visual content enhancement, two novel approaches, named GLARE and GPP-LLIE, are proposed to explore generative priors for low-light image enhancement. In \textbf{GLARE}, to mitigate the ambiguity of the enhancement process, the VQGAN with a discrete coodebook is employed to learn latent priors from normal-light images. Then, by aligning low-light features to this small proxy space via latent normalizing flow and adaptively transforming input features into the decoding process, GLARE restores missing structures and improves perceptual quality under extreme degradations. In \textbf{GPP-LLIE}, we first develop a pipeline to extract perceptual priors from large vision-language models. Then, these priors are integrated into the diffusion process as perceptual guidance, achieving results with high perceptual quality and demonstrating good generalization across real-world scenarios. For 3D scene reconstruction under adverse illumination conditions, we present \textbf{LITA-GS}, an illumination-agnostic 3D Gaussian Splatting framework. By incorporating \textit{physical illumination-agnostic priors} with progressive denoising, LITA-GS achieves reference-free novel view synthesis that remains consistent and stable even under adverse lighting. Together, these three contributions demonstrate the value of \textbf{codebook-based latent priors, perceptual quality priors, and physical illumination-agnostic priors} in addressing challenges across 2D and 3D vision. Extensive experiments confirm improvements in fidelity, perceptual realism, and robustness, establishing a foundation for future research on illumination-robust visual systems.
USING REPRODUCIBLE PERFORMANCE VARIATION AND GROWTH AS THE BIOMARKERS TO PREDICT TOLERANCE TO COPPER IN JUVENILE RANIBOW TROUT (ONCORHYNCHUS MYKISS)
(2001-11) Sheryl Emma Edwards
This study investigated whether or not growth and reproducible performance variation (i.e: biomarkers) in juvenile rainbow trout could be used to predict Cu sensitivity in individuals. Therefore there were two main objectives. The first was to identify and describe reproducible performance variability in individual fish. This was accomplished by evaluating individual performance in a series oftests designed to challenge the resting physiology ofthe fish (ie: challenge test). With the exception of growth, the performance measures had to show both individual variability and reproducibility from trial to trial. The second objective involved examining the relationship(s) that the biomarkers had on an individual’s tolerance to copper. This was accomplished through the use of both univariate and multivariate analytical techniques to determine how each biomarker affected tolerance individually, as well as how each ofthe biomarkers interacted with one another to affect tolerance. In addressing these objectives, this study has produced four important results. The first is that the reproducible performance variation identified in each ofthe challenge tests is real, and the tests can therefore be used as biomarkers ofsensitivity. Second, the performance of an individual in any one challenge test is unrelated to its performance in the others. Third, individual performance in the challenge tests did not significantly predict growth. Fourth, sensitivity to copper can be predicted, although a significant prediction is contingent upon two factors. The first is that multiple biomarkers be measured in each individual, and multivariate analyses be conducted to examine how the biomarkers interact with one another to effect tolerance. This is in contrast to examining how each biomarker effects tolerance individually. Second, the appropriate ‘suite’ of biomarkers must be evaluated. Not all combinations of biomarkers will convey tolerance, so it is important to ensure that all possible combinations ofbiomarkers are evaluated for their effects on tolerance. Based on the results ofthis study, the following conclusions are apparent: (1) individual variation amplified through the use of challenge tests is a useful tool for predicting individual tolerance; (2) tolerance cannot be reliably predicted using individual biomarkers; (3) multivariate analysis is a valuable tool for improving both interpretation and analysis of data with multiple biomarkers; and (4) since individual tolerance can be predicted, mortality due to copper exposure cannot be completely random.
Modeling and Experimental Characterization of Core Loss of A Switched Reluctance Machine
(2026) Vithana Pathirannahalage, Sudesh Champika, Priyadarshana
Core loss significantly impacts the efficiency and thermal performance of Switched Reluctance Machines (SRMs), especially under high speeds. Conventional measurement methods are often inadequate for capturing losses in assembled stator cores, where flux paths are complex, and excitation waveforms are non-sinusoidal. This thesis addresses these limitations by developing and experimentally validating a practical core loss characterization method tailored for SRM stators. The study begins with a comprehensive review of core loss mechanisms and measurement challenges, highlighting the effects of manufacturing processes such as punching and welding on material properties. A core loss measurement method is introduced based on transformer induction theory, enabling time-domain reconstruction of magnetic field quantities from voltage and current waveforms. The method is validated using a ring core and SRM stator geometry with finite element analysis (FEA), replicating practical excitation conditions. An experimental setup is built with a fractional SRM stator core and custom magnetizing yokes. The experimental setup captures induced voltage and current waveforms from the stator. The proposed method is experimentally validated over a wide frequency range from 100 Hz to 10,000 Hz and under varying flux density levels, confirming the applicability for practical core loss characterization of SRM stators.

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