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Battery-Free IoT Water Sensor Nodes: Design and Evaluation Across Different Radio Architectures
No full textWater-induced structural damage represents a significant challenge across residential, commercial, and industrial environments. Traditional water monitoring systems often rely on battery-powered nodes or wired infrastructure, posing recurring maintenance burdens and limiting large-scale or long-term deployments. This thesis presents a series of battery-free water sensor node architectures, each powered by water-activated electrochemical cells that use the presence of a leak as both the trigger and energy source for wireless communication.
The investigation spans multiple wireless protocols—Bluetooth Low Energy (BLE), RF-assisted BLE, LoRa, and LTE-M—each selected to address specific trade-offs in range, energy demand, and infrastructure dependency. The BLE-based system demonstrates the feasibility of low-power communication using only a brief energy burst generated upon leak detection. An RF-augmented variant integrates ambient RF harvesting to support periodic heartbeat pings, improving observability between leak events. To extend communication beyond localized zones, a LoRa-based design leverages a step-up converter and supercapacitor to meet higher transmission power demands. Finally, a gateway-independent LTE-M implementation is introduced, using a two-stage electrode configuration and comparator-controlled capacitor discharge to enable cellular uplinks directly to the cloud.
This thesis also presents the design and simulation of dual-mode helical antenna systems for hybrid terrestrial and satellite communication, ensuring global connectivity even in areas with limited cellular coverage. Two distinct antenna architectures are proposed: a PCB-based antenna that supports both broadside and end-fire radiation modes, and a 3D-printed geometry that combines quadrifilar and bifilar helices into a vertical structure. These antenna designs offer hardware-level compatibility for seamless integration into both LTE and satellite IoT networks.
Together, the contributions offer a modular and scalable approach to zero-maintenance leak detection. Experimental results validate the practical viability of BLE, LoRa, and LTE-M systems under energy-constrained conditions. While satellite integration remains simulation-based, the antenna designs provide an important step toward globally connected, infrastructure-independent sensing. This work charts a pathway for robust and sustainable water monitoring at scale
How can information systems assist vulnerable communities in their transition toward viability?
No full textInformation Systems (IS) have the potential to play a crucial role in addressing complex global challenges, from climate change and resource management to social inequality and food insecurity. Despite their widespread applications in numerous sectors, the use of IS to support the sustainability of small-scale fisheries (SSF) remains underexplored. SSF are critical to global food and nutrition security, particularly on islands, where they are essential for local economies and cultural identity. These communities face significant vulnerabilities due to their geographic position, environmental uncertainties, and socio-economic constraints. Existing systems for fisheries are often generic, insufficiently localized, or not tailored to the specific needs of these communities. These gaps limit the ability of the fishers to adapt to changing conditions, optimize resource use, and ensure long-term sustainability. There is a growing need to design and implement IS that are not only accessible but also relevant for SSF. Such systems are crucial for empowering fishing communities, helping them navigate their challenges and fostering a transition toward sustainability.
To address the existing gaps, the main objective of this research is to identify the key principles for designing IS tailored to vulnerable small-scale fishers, supporting their transition toward viability and sustainability. The first part of the study examines SSFs’ food systems in island communities, identifying critical factors that influence their vulnerability and viability with regard to food and nutrition security. The second and third parts of the study focus on IS, specifically identifying the requirements of users within these communities and evaluating whether existing systems adequately address these requirements. Finally, the study proposes key design principles for developing IS that can effectively support the sustainability of SSF.
The first part of this thesis focuses on fishers in Small Island Developing States (SIDS), identifying their vulnerabilities and potential viabilities concerning food and nutrition security. The second and third parts of the research focus on fishers in Kumirmari Island, India. Data for the latter sections are collected through surveys and interviews. Interviews are conducted with the members of the information system development team behind the Fisher Friend app. The study introduces the User-Requirements Hierarchy (URH), which is developed based on the Contextual Participatory Design approach. Then, it suggests PUCT Dimensions—including Polycentricity, User-Centricity, Contextuality, and Technicality— as key elements for Information System design for vulnerable communities.
This study contributes to the information system field, particularly in its role in supporting vulnerable communities. It advances the discourse on context-driven design by highlighting the importance of considering both vulnerabilities and viabilities. The study underscores that vulnerability and viability exist on a continuum, and to help communities overcome their vulnerabilities, their long-term decisions as well as viabilities must be recognized and invested in. While community empowerment and agency have emerged in the literature as core principles of technologies for vulnerable populations, their practical application remains unclear. A gap exists between the theoretical understanding and the practical guidance needed to implement it effectively in real-world situations. This research aims to address this gap by introducing the URH and PUCT Dimensions. The study presents design principles that ensure IS align with fisheries' realities and requirements. Drawing from field-based evidence from Kumirmari, the research provides novel insights into designing digital tools, uncovering unrecognized challenges and barriers, and emphasizing the need for polycentric approaches in system development. Practically, this study informs the design of new technologies, ensuring that they are not imposed on communities but are instead developed collaboratively.
The findings of this thesis aim to assist in developing IS that support vulnerable communities in moving toward viability, with a specific focus on food and nutrition security. The findings support creating systems that align with the complex contexts of these communities. The suggested approaches can set the stage for future data collection, knowledge creation, and information system design efforts; helping ensure that the delivered systems are equitable, sustainable, and contextually relevant for vulnerable communities. Ultimately, these systems transform information into actionable knowledge, empowering communities to make informed decisions that drive more sustainable practices
Protection of Multi-Terminal HVDC Grids
No full textThis thesis introduces four novel protection schemes for multi-terminal HVDC grids: primary protection, fault location identification, and breaker/relay failure backup protection schemes. The proposed single-ended primary protection scheme employs Hilbert-Huang Transform (HHT) to extract two instantaneous features from local voltage measurements, namely the instantaneous frequency and energy. Abrupt changes in these features during internal faults are detected as outliers. Simultaneous outliers in the extracted features correspond to internal faults, which offers a setting-less fault detection criterion; thus, eliminating the need for simulation-based and grid-specific thresholds. The proposed double-ended fault location identification scheme employs the setting-less outlier-based criterion to capture the arrival instants of the initial fault-induced backward traveling waves at both line terminals, based on which the fault location is identified with high precision
Design Ionically Crosslinked CNT Hydrogels that Mimic Cardiac Tissue Mechanics and Conductivity
No full textMyocardial infarction remains a leading cause of mortality worldwide due to the heart's limited regenerative ability, resulting in disrupted electrical signaling and compromised contraction. Injectable hydrogels have emerged as a promising, minimally invasive approach to support myocardial healing and restore function post-MI, especially when designed to mimic key characteristics of native cardiac tissue, such as filamentous nanostructure, conductivity, and essential mechanical properties. Such biomimetic materials could potentially prevent pathological progression and enhance cardiac repair. However, the development of an injectable hydrogel that combines both conductivity and a fibrillar structure to effectively mimic cardiac tissue, promote cell growth, and support organ repair has yet to be achieved.The objective of this thesis is to develop an injectable hydrogel with conductivity and a filamentous architecture that mimics cardiac tissue. The primary hypothesis is that carbon nanotubes (CNTs) can serve as the main building blocks for creating nanocolloidal hydrogels with biomimetic filamentous structures. This structural arrangement is designed to mimic the extracellular matrix (ECM), providing a biomimetic environment that supports mechanical resilience. CNTs have attracted significant interest due to their exceptional conductivity and tunable mechanical properties, making them ideal candidates for reinforcing hydrogels in tissue engineering applications, particularly as artificial ECM scaffolds for myocardial regeneration.
To incorporate CNTs as the primary building blocks of the hydrogel, they first stabilized in water, addressing one of the major concerns in their biomedical applications—toxicity. This was achieved through non-covalent functionalization by wrapping CNTs with the anionic polymer polystyrene sulfonate (PSS), which introduces electrostatic repulsion, preventing aggregation and ensuring a stable dispersion. Upon introducing salt (CaCl2), the stabilized CNT-PSS suspension undergoes gelation through electrostatic interactions, forming a hydrogel network. Both SWCNTs and MWCNTs have been explored as building blocks hydrogel development. This study incorporates both to compare their structural, mechanical, and electrical properties, identifying the most suitable option for myocardial tissue engineering. By tuning the ratio of Ca2+ cations to CNT-PSS, the rheological and structural properties of the hydrogel were systematically varied.
The resulting hydrogels, formulated with both MWCNTs and SWCNTs, exhibited injectability at low salt concentration and strain-stiffening behavior at 1.9M. Additionally, both hydrogel systems demonstrated electrical conductivity, highlighting their potential for myocardial tissue engineering applications. With the SWCNT based hydrogel showing higher strain stiffening behavior than MWCNT based hydrogel
Broadcast is all you need: Robust Multiplayer Tracking in Ice Hockey using Monocular Videos
No full textMOT in ice hockey pursues the combined task of detecting and associating players across a given sequence to maintain their identities. Tracking players in sports using monocular broadcast videos is an important computer vision problem that enables several downstream analytics and enhances viewership experience. However, existing tracking approaches encounter significant challenges in dealing with occlusions, blurs, camera pan-tilt-zoom effects, and dynamic player movements prevalent in telecast feeds. These challenges are further exacerbated in fast-paced sports such as ice hockey, where existing trackers struggle to maintain identity consistency due to players' sudden, non-linear motion patterns. In this thesis, acknowledging the fundamental role of quality datasets, we first present two hockey tracking datasets: our previously developed HTD-1 and a newly curated, open-source dataset called HTD-2, annotated from broadcast NHL games. Based on this new dataset, we establish a reference benchmark by evaluating six SOTA tracking methods to enable performance comparisons in hockey MOT. A detailed study is conducted for each algorithm to understand their merits and drawbacks on tracking players. Next, to address the present limitations, we propose a novel tracking model formulating MOT as a bipartite graph matching problem cued with homography inputs. Specifically, we disambiguate the positional representation of occluded players as viewed through broadcast footage, by warping them onto a view-invariant overhead rink template and encode their transformations into the graph message passing network. This ensures reliable spatial context for identity-preserved track prediction. Experimental results demonstrate that our model achieves a 10 times reduction in IDsw and a 32.45% improvement in IDF1 score compared to the existing baseline on HTD-1, establishing a new SOTA. The proposed model also exhibits strong generalization capabilities, achieving 92.8% IDF1 and only 60 IDsw during cross-validation on HTD-2. Finally, ablation studies are presented to validate our performance and substantiate our approach
Advancing Causal Representation Learning: Enhancing Robustness and Transferability in Real-World Applications
No full textConventional supervised learning methods heavily depend on statistical inference, often assuming that data is identically and independently distributed (i.i.d). However, this assumption rarely holds in real-world scenarios, where environments or domains frequently shift, posing significant challenges to model robustness and generalization. Moreover, statistical models are typically treated as black boxes, with their learned representations remaining opaque and challenging to interpret. My research addresses these issues through a causal learning perspective, aiming to enhance the interpretability and adaptability of machine learning models in dynamic and uncertain environments.
I have developed innovative methods for learning causal models that are applicable to a wide range of machine learning tasks, including transfer learning, out-of-distribution generalization, reinforcement learning, and action classification. The first method introduces a generative model tailored to learn causal variables in scenarios where the causal graph is known, such as Human Trajectory Prediction. By incorporating domain knowledge, this approach models the underlying causal mechanisms, leading to improved performance on both synthetic and real-world datasets. The results demonstrate that this generative model outperforms traditional statistical models, particularly in out-of-distribution contexts.
The second method targets the more challenging scenario where the causal structure is unknown. I have explored various conditions and assumptions that facilitate the discovery of causal relationships without prior knowledge of the causal graph. This method combines advanced techniques in causal inference and machine learning to uncover the underlying causal graph and variables from observed data. Evaluations on both real-world and synthetic datasets show that this method not only surpasses existing approaches in causal representation learning but also brings AI systems closer to practical, real-world applications by enhancing reliability and interpretability.
Overall, my research contributes significant advancements to the field of causal learning, providing novel solutions that improve model interpretability and robustness. These methods lay a strong foundation for developing AI systems capable of adapting to diverse and evolving real-world conditions, thereby broadening the scope and impact of machine learning across various domains
From disaster recovery to whole-of-society resilience: The impact of the 2021 British Columbia atmospheric rivers event on flood risk management policy and governance
No full textFlooding poses significant risks to the safety, well-being, and long-term security of many Canadian communities. In recent years, extreme weather events, as a result of a changing climate, have cost Canadians billions in insured and uninsured losses annually, and such losses do not encapsulate the various social, ecological, and health impacts that are difficult to quantify. In addition to climate change, misaligned land-use planning, intensified development in floodprone areas, fragmented risk governance, gaps in funding and policy, and an over-reliance on protective structures continue to place many Canadians in harm’s way, while creating barriers for proactive adaptations at the watershed scale. Major disasters, like the 2021 atmospheric rivers floods in British Columbia, underscore the need for transformative flood risk management [FRM] policy and governance by highlighting the systemic drivers of flood risk, namely a FRM system that was never designed to withstand the dynamic realities of the present day. Such focusing events—relatively rare, sudden, and impactful events like disasters—are often critical in generating significant public interest around a focal issue, garnering political will to advance policy agendas, and enabling governance actors to advocate for policy reform. In the post-disaster landscape, coalitions of policy actors can seek to leverage these emergent ‘windows of opportunity’ to advance a paradigm shift in how various public issues, like disaster risk reduction and climate change adaptation, are understood and managed, who is involved in decision-making processes, and what solutions are considered socially-acceptable and politically-feasible. Actors are most likely to be successful in advancing agenda items if enabled by the institutional environments that policy processes are embedded within, and if there is an existing foundation of collaboration among others within the broader policy community. This research, utilizing a case study of a major Canadian flood disaster, evaluates the ways in which policy champions, advocacy coalitions, and institutional actors have sought to leverage existing relationships, prior learnings, and post-disaster momentum to advance shifts in FRM policy and governance at the local, regional, and provincial scales. Semi-structured key informant interviews provide insights into how the disaster manifested as a focusing event, what enabling conditions contributed to the creation of a window of opportunity for policy change, and how recent shifts in British Columbia’s flood governance and policy regimes have been shaped by longer-term institutional developments and interjurisdictional partnerships. This research illustrates the transformational nature of adaptive learning and multi-scalar governance, and is intended to assist FRM decision-makers, policymakers, and practitioners in advancing resilience
Operating Systems are a Service
No full textOS containers have set the standard for the deployment of applications in modern
systems. OS containers are combined sandboxes/manifests of applications that isolate
the running applications and its dependencies from other applications running on top of
the same kernel. Containers make it easy to provide multi-tenancy and control over the
application, making it ideal for use within cloud architectures such as serverless.
This thesis explores and develops novel systems to address three problems faced by
containers and the services that use them. First, OS containers currently lack a fast
checkpoint-restore mechanism. Second, container security is still inadequate due to its
underlying security mechanisms, which provide coarse-grained policies that are abused.
Third, the lack of a benchmark for serverless clouds, one of the largest consumers of
containers, and specifically checkpoint-restore.
This thesis outlines solutions to these problems. First, ObjSnap, a storage system
designed and built for two modern single-level store systems, Aurora and MemSnap, which
enable checkpoint restore for container systems. ObjSnap is a transactional copy-on-write
object store that can outperform other storage systems by up to 4×. Second, we introduce
SlimSys, a framework that tackles security issues found within containers by binding a
policy to kernel resources. Lastly, we introduce Orcbench, the first benchmark used to
evaluate serverless orchestrators
Widespread yet Unreliable: A Systematic Analysis of the Use of Presence Questionnaires
No full textPresence, as a psychological state, is typically assessed using questionnaires. While many researchers in this field assume that these self-report instruments are standardized, the reliability of such questionnaires remains uncertain. This knowledge gap challenges the accuracy and validity of data derived from studies assessing presence. Ensuring reliable and precise data collection and reporting is essential for the credibility of findings in presence research, because inaccuracies may cause errors in conclusions, which affects theoretical understandings, methodological approaches and practical applications. To address this issue, we conducted a systematic analysis of 397 empirical quantitative studies on presence. We investigated the use of presence scales, including applications, modifications, a variety of measures and reporting practices. We found that the majority of the presence studies modify questionnaires, do not re-validate them and improperly report their methods. Based on these findings, we propose solutions to enhance transparency and validation of the presence measurements.SSHRC INSIGHT Grant (grant number: 435-2022-0476), NSERC Discovery Grant (grant number: RGPIN-2023-03705), CFI John R. Evans Leaders Fund (grant number: 41844), Meta Research Award, and the Lupina Foundatio
A Compressive-Sensing-Capable CMOS Electrochemical Capacitance Image Sensor with Two-Dimensional Code-Division-Multiplexed Readout
No full textElectrochemical capacitance imaging is a technique used to observe biological analyte or processes at the surface of an electrode, immersed in an electrolyte, via small changes in capacitance. This technique has various applications in biosensing such as biomedical diagnostics, neural interfaces and DNA sensors. Complimentary metal-oxide-semiconductor (CMOS) technology is well suited for implementing electrochemical capacitance image sensors since high spatial resolution electrode arrays and readout circuitry can be integrated on the same chip.
This thesis presents the design and simulation of a 256 × 256 pixel electrochemical capacitance image sensor fabricated in a 180-nm analog/mixed-signal CMOS process. Our image sensor features a novel two-dimensional code-division-multiplexed (2D CDM) readout architecture that directly outputs analog coefficients of the 2D Walsh transform of the image. To the best of our knowledge, we are the first to implement true 2D CDM readout in the capacitive image sensor space. For passive-pixel sensors, CDM readout yields a signal-to-noise ratio (SNR) increase over traditional time-division-multiplexed (TDM) readout through integrating orthogonal combinations of all pixels for the entire frame time.
Use of the 2D Walsh transform enables compressive sensing at the time of array readout, which is achieved by exploiting the energy compaction property of the Walsh domain. Compressive sensing provides analog lossy image compression that can enable a frame rate increase or power consumption decrease. In addition, our transform domain readout architecture removes the layout requirement for pitch-matched column amplifiers, requiring only one larger column circuit for the full array. Some potential advantages introduced by this include reductions to both amplifier flicker noise and fixed-pattern noise from transistor mismatch.
Our sensor uses two-transistor switched-capacitor pixels with a 3.2 × 3.2 μm² working electrode and 3.88 μm grid pitch to enable charge-based capacitance measurement. On-chip 256-bit parallel Walsh code generators enable power efficient orthogonal code generation. Full-chip post-layout analog simulation with a biological capacitance image demonstrates that we can achieve a structural similarity index (SSIM) of 0.875 versus a reference image. SSIM values range from 0 to 1, where 1 indicates complete image similarity