UWSpace

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Recent Submissions

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    Constructing call graphs of Scala programs
    (Unviersity of Waterloo, 2014-05-14) Ali, Karim; Rapoport, Marianna; Lhotak, Ondrej; Dolby, Julian; Tip, Frank
    As Scala gains popularity, there is growing interest in programming tools for it. Such tools often require call graphs. However, call graph construction algorithms in the literature do not handle Scala features, such as traits and abstract type members. Applying existing call graph construction algorithms to the JVM bytecodes generated by the Scala compiler produces very imprecise results due to type information being lost during compilation. We adapt existing call graph construction algorithms, Name-Based Resolution (RA) and Rapid Type Analysis (RTA), for Scala, and present a formalization based on Featherweight Scala. We evaluate our algorithms on a collection of Scala programs. Our results show that careful handling of complex Scala constructs greatly helps precision and that our most precise analysis generates call graphs with 1.1-3.7 times fewer nodes and .5-18.7 times fewer edges than a bytecode-based RTA analysis.
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    3-D reconstruction from few projections: Structural assumptions for graceful degradation perspectives on open data: Issues and opportunities
    (University of Waterloo, 2014-05-07) Cormier, Michael; Lizotte, Daniel J.; Mann, Richard
    We present a spatial-domain method for the reconstruction of a three-dimensional density distribution from one or more projections (images formed by integration of density along lines of sight) and using the three-dimensional reconstruction to explain features of the two-dimensional images. The advantages of our proposed method are that it degrades gracefully down to a single image, that is uses linear equations and constraints (allowing the use of convex optimization), that it is amenable to three-dimensional structural biases, and that ambiguity can be expressed precisely (it is possible to "know what we don't know"). Previously described methods have some, but not all, of these properties.
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    Rethinking Early-Stage Construction Planning Using Virtual Reality and Point Cloud Processing
    (University of Waterloo, 2026-08-25) Katsimpalis, Emmanouil
    Construction planning is inherently complex, requiring practitioners to define scope, determine feasible execution sequences, and evaluate cost and resource implications. These challenges are amplified in early-stage planning, where decisions made under high uncertainty can significantly impact project performance, particularly in complex industrial facilities such as nuclear power plants, where access is limited and legacy assets often lack reliable drawings or models. Despite this, existing approaches such as Building Information Modeling (BIM) and digital twins rely on time-consuming, high-fidelity modeling processes that are not well aligned with the need for rapid and flexible decision-making for early-stage planning. As a result, there is a need for fit-for-purpose planning approaches that prioritize speed, usability, and decision support over detailed model reconstruction. This thesis investigates two fit-for-purpose uses of digital technologies for early-stage construction planning: virtual reality (VR) as an interactive environment for exploring and generating construction sequences, and point cloud processing as a direct source of geometric information for quantity estimation. Compared to traditional planning tools, VR enhances spatial understanding through intuitive interaction, real-time physics-informed feedback, and realistic simulation enabled by advances in physics engines and game development platforms. This research extends VR beyond visualization by using it as a process generation tool, where user interactions are captured and structured to enable the automatic generation of construction schedules. While VR supports process generation, it does not inherently provide quantitative information about the physical scope of work. To address this, point clouds obtained from laser scanning are leveraged directly for quantity estimation, avoiding labor-intensive Scan-to-BIM workflows. By structuring point cloud data into meaningful objects and applying geometry-based analysis techniques, key quantities such as volumes, areas, and lengths can be computed efficiently. The results demonstrate that these approaches can support faster and more flexible planning processes by reducing reliance on detailed modeling and enabling more direct interaction with planning data. This thesis contributes a fit-for-purpose perspective to construction planning, highlighting how different digital representations can be strategically applied to improve early-stage decision-making.
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    A risk management approach for distributed event-based systems
    (University of Waterloo, 2014-04-21) Savinov, Sergey; Alencar, Paulo
    In this paper we present a Risk Management System. The goal of the RMS is to decrease risks related to information access. The goal is met by taking the context of operations into account; providing motivation to users through incentives (e.g., punishments or rewards); providing support to track changes and ability to reason about risk-related attributes; providing support to reason about tasks, plans and goals; providing process-based system guidance; decreasing the costs per operation for growing systems by making human intervention optional.
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    Scalable Single-Step Open-Air Combinatorial Growth of Multifunctional Metal Oxide Thin Films by Spatial Atomic Layer Deposition
    (American Chemical Society, 2026-08-25) Shahin, Ahmed; Saini, Agosh; Vidish, Denys; Azar, Mahdi Hasanzadeh; Kim, Na Young; Musselman, Kevin P.
    Compositionally graded metal oxide thin films enable rapid exploration of structure–property relationships, yet their fabrication typically relies on vacuum-based, multistep processes. Here, we present an atmospheric-pressure spatial atomic layer approach that enables the single-step, open-air synthesis of zinc–tin–oxide (ZTO) thin films with lateral compositional gradients over 3-in. wafer-scale substrates. A custom reactor head with spatially tailored precursor delivery generates continuous and reproducible composition gradients in under 1 h without vacuum processing or sequential depositions. Comprehensive structural, morphological, optical, and compositional characterization confirms smooth lateral variations in ZTO film composition and crystallinity, with Sn/Zn atomic ratios spanning approximately 0.4–0.8 across one of the films, forming unique material libraries in a single open-air deposition step. As a proof of concept, the graded ZTO films are integrated into chemiresistive sensor arrays comprising 20 compositionally distinct sensing columns on a single wafer, exhibiting composition- and temperature-dependent (at 200 °C) responses to volatile organic compounds within a single device platform. Additionally, electrical measurements performed across the gradient show unique current–voltage properties at each composition, alluding to multi-usage in electronic applications within the same chip. This work establishes a scalable and cost-effective strategy for rapid fabrication of oxide material libraries and wafer-scale collective device assemblies under ambient conditions.