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Recent Submissions
Automated Tuning and Optimal Control of Spin Qubits in Quantum Dot Devices
(University of Waterloo, 2024-09-17) Paurevic, Andrija
Silicon quantum dots present a promising foundation for realizing scalable quantum processors, leveraging the advantages of a mature semiconductor industry. Two significant challenges hinder their development: the laborious tuning of these devices and the coherent control of their spin qubits. This thesis presents contributions towards addressing these challenges by harnessing physics-informed machine learning.
Tuning these devices involves navigating complex parameter spaces, plagued with variability and fabrication imperfections, to identify optimal operating conditions. This process demands extensive time and resources by a researcher to perform large amounts of data collection and analysis. My work takes steps towards on achieving fully autonomous tuning of these devices, with the automated formation of a single quantum dot. This work involves the application of data analysis and computer vision techniques to extract relevant features from data, guiding the tuning process in real-time. This tool allows single quantum dots to be formed autonomously, freeing researchers to focus on investigating the physics of the device. Progress in multi-dot systems was also made by developing a data segmentation model that successfully identifies and segments charge and dot configurations in charge stability diagram data. This enables rapid data analysis to determine optimal voltage settings for achieving the desired device state.
Optimal control is crucial for guiding quantum systems through unitary operations while minimizing decoherence. Using a simulated open quantum system Hamiltonian for spin qubits, I developed a protocol to optimize experimental control signals, allowing for the implementation of unitary gate operations with arbitrary fidelity. The protocol designed experimental pulses for single-qubit rotations and entangling gates in a two-qubit system, achieving fidelities above the error correction threshold. Additionally, it utilizes modern machine learning frameworks, making it scalable to multi-qubit systems.
The work presented in this thesis serves as an important foundation for future advancements in our research group.
Synthesis and Study of a Lithium-Selective Chelator
(University of Waterloo, 2024-09-17) Brutto, Mark
Lithium, the lightest metal on the periodic table, serves as a very valuable resource due to its many applications in things such as glass and ceramics, greases, and most importantly, batteries. The battery industry consumes the majority of our collected lithium, and this trend is expected to continue with increased electric vehicle usage. An increased awareness for our carbon footprint and greenhouse gas emissions, along with governmental legislation has led to an exponential increase in our lithium demand. Unfortunately, current lithium collection processes are unable to keep up with this increased demand, thus creating a need for new or improved lithium collection processes. The majority of lithium is collected from two major sources, lithium-rich brines in the ABC (Argentina, Bolivia, Chile) region and China, as well as minerals and ores typically found in China and Australia. Current techniques include expensive processes such as roasting and leaching from minerals and ores, or lengthy precipitation processes from pre-evaporated brines, both of which have proven to be unfit for future industrial demands.
This research aims to develop and study a lithium-selective ligand that will eliminate lengthy evaporation processes typically associated with lithium collection from brines. Chapter 1 begins with a literature review on lithium and its societal and economic importance. It will explore current lithium isolation processes and their drawbacks preventing more expansive and efficient collection. Chapter 2 will include the inspiration behind our ligand design, starting with a preliminary direction and a complete adjustment upon computational calculations. Chapter 3 will include the synthesis and study of our proposed motif, illustrating a cheap and efficient synthesis, and promising preliminary lithium selectivity when compared with other 1st group cations.
Dynamic Decision-Making Framework for Autonomous Vehicles in Urban Environments with Strong Interactions
(University of Waterloo, 2024-09-17) Shu, Keqi
Autonomous techniques are becoming increasingly integrated into our daily lives. Many advanced driver assistance systems (ADAS) functions, such as lane-keeping assist and car-following, are already implemented in manufactured vehicles. However, achieving true autonomy still poses many challenges. For instance, in urban areas with diverse types and numbers of traffic participants, the interactions are highly complex. Considering these strong interactions are time consuming and challenging. Additionally, the fast-changing nature of urban driving scenarios requires the decision-making of self-driving vehicles to be performed in real-time. The various behaviors of different traffic participants also make the corresponding decision-making challenging. Finally, in urban traffic scenarios, following traffic rules is the premise of any decision-making. However, the extensive and often difficult interpretation of traffic rules adds another layer of complexity.
This thesis aims to bring the decision-making process of autonomous driving techniques closer to real life by proposing a motion planning and decision-making framework for autonomous vehicle urban driving that addresses the aforementioned challenges. The framework utilizes game theory to formulate and consider strong interactions.
The behaviors of surrounding traffic participants are estimated more accurately by extracting realistic behavioral characteristics from real-world driving datasets. This helps establish more realistic modeling and estimation of various kinds of traffic participants, including aggressive, neutral, and conservative types. Accurate modeling of traffic participants improves the quality of interaction formulation, leading to sounder decision-making.
To ensure adherence to traffic regulations, the proposed framework extracts right-of-way information from traffic rules to generate behavioral parameters. This acts as a bridge integrating traffic rules into the decision-making process. The traffic rules not only help the ego vehicle estimate the future behaviors of surrounding traffic participants by extracting precedence but also generate rule-adhering behaviors for the ego vehicle.
Additionally, to improve the real-time performance of the framework in very crowded urban scenarios, the framework is equipped with a human-like attention-based traffic actor filter. This enables the autonomous vehicle to focus on critical traffic participants with a higher risk of collision, simplifying the decision-making and planning process, reducing computational effort, and ensuring real-time performance.
To implement the proposed framework in the real world, a full-size vehicle platform was developed, equipped with appropriate hardware sensors and onboard computers. A corresponding hierarchical software system was also developed to ensure the vehicle's operation.
The proposed framework was tested in both simulation and real-world scenarios. The results demonstrate that the autonomous vehicle can properly estimate the types of traffic participants by observing their behavior using the proposed technique. The vehicle then behaves according to these types, enabling interactive and human-like planning and decision-making at intersections. Furthermore, the autonomous vehicle is able to consider and adhere to traffic rules in very complicated urban traffic scenarios. These results demonstrate that the algorithm can make safe and efficient decisions in various urban traffic scenarios involving multiple types of traffic participants in real-time.
The simulation results show that the autonomous vehicle is able to properly estimate the types of traffic participants by observing their behavior using the proposed technique. Then the autonomous vehicle behave according to the types of those traffic participants to enable interactive and human-like planning and decision making at intersections.
Using Latent Class Analysis to Create Allostatic Load Profiles to Investigate the Effects of Occupational and Perceived Stress in Firefighters
(University of Waterloo, 2024-09-17) Elliott, Madelyn
Background: Allostatic load is a construct used to assess the sum of the effects of physiological stress across multiple body systems over time. Allostatic load is typically measured using the allostatic load index (ALI); however, this measure does not fully capture the multivariate nature of allostatic load. Using allostatic load profiles as opposed to the commonly used ALI, we hope to explore the multivariate nature of allostatic load and understand its association with perceived stress, since the existing literature exploring the association between perceived and physiological stress is inconclusive.
Objectives: The objectives of this thesis are to 1) develop allostatic load profiles using latent class analysis of both study-created and clinical-based thresholds for biomarkers of stress; 2) examine the association between allostatic load profiles and perceived stress in firefighters; 3) assess whether study-based or clinical-based thresholds are more suitable when measuring stress in the firefighters in question.
Methods: Using available biomarker data from a sample of 57 male firefighters in Waterloo Fire Rescue, we developed allostatic load profiles using latent class analysis. Biomarkers included systolic blood pressure (SBP), diastolic blood pressure (DBP), hemoglobin A1c (HbA1c), low-density lipoprotein (LDL), high-density lipoprotein (HDL), heart rate variability (HRV), cortisol, waist to hip ratio (WHR) and body mass index (BMI). We then employed logistic regression to assess the association between allostatic load profiles and perceived stress in these firefighters.
Results: Our results demonstrated that the use of allostatic load profiles (ALPs), created with study-based thresholds, showed how different biomarkers contribute to elevated or non-elevated physiological stress profiles. In regression models of ALP on Perceived Stress Scale (PSS-10) scores, we saw a consistent positive association such that an increase of 1 unit in PSS-10 score increased the odds of being in the elevated ALP group anywhere between 13.6% (OR = 1.136, 95% C.I. = 1.012, 1.299) in a model with PSS-10 only to 19.2% (OR = 1.192, 95% C.I. = 1.039, 1.400) in a model with PSS-10, length of service (LOS), and the following behavioural confounders: smoking, sleep hours, exercise and alcohol intake frequency.
Conclusions: Allostatic load profiles captured the multivariate nature of allostatic load and demonstrated a significant association with the PSS-10, whereas ALI was unable to significantly demonstrate a relationship with perceived stress. The results of this thesis also demonstrated the need for using study-based or study-specific thresholds when examining unique populations with different fitness levels than the general population. Further research can benefit from the use of allostatic load profiles in conjunction with study-based thresholds to accurately and completely address the needs of persons working in high-stress, dangerous occupations.
Two-Dimensional Separation via Hybrid Liquid Chromatography and Differential Ion Mobility Spectrometry for PFAS Characterization
(University of Waterloo, 2024-09-17) Ryan, Christopher
This thesis details the development and implementation of differential mobility spectrometry (DMS) methods for the separation of per- and polyfluoroalkyl substances (PFAS). PFAS have become ubiquitous environmental pollutants, posing significant risks to ecosystems and human health. The complexity of PFAS matrices in environmental samples necessitates separation prior to mass spectrometric analysis because co-elution of compounds can cause ion suppression and compromise analyte identification and quantification accuracy. Although liquid chromatography (LC) is commonly used in PFAS analyses, some PFAS species co-elute and could benefit from an additional orthogonal dimension of separation.
In Chapter 3 I explore the effects of solvent modifier on DMS behaviour for 224 compounds in negative mode electrospray ionization (ESI) mass spectrometry (MS). The data procured from these measurements will be used for machine learning (ML) purposes to predict the DMS behaviour of emerging environmental pollutants. Prior to this study, our library of DMS data was composed entirely of compounds that were measured in positive mode ESI MS and the distribution of observed dispersion behaviour was heavily skewed towards one behaviour type. Incorporation of the negative mode ESI data not only provided a better overall distribution of dispersion behaviour, but also allows for future ML models to be applicable for anions and cations alike. The results of this chapter also provide insight into the ion-neutral interactions that occur as analytes transit the DMS cell. From this it can be determined how different classes of compounds interact with various solvent modifiers, and how their analytical separation is influenced by the choice of modifier. This allowed us to determine the instrument conditions that lead to the optimal separation of the studied PFAS.
In Chapter 4, I utilize the optimal separation conditions determined in Chapter 3 in a hybrid LC×DMS-MS2 method. Here, I employ DMS following LC separation to analyse 34 PFAS species. Upon incorporating DMS in a 2D separation scheme, I observed baseline resolution of 29 compounds in the 2D space, with only two and three compounds co-eluting, respectively. In comparison, only 5 compounds were baseline resolved in 1-dimensional LC experiments. Because DMS measurements are acquired within seconds, targeted 2D LC×DMS-MS2 analyses operate on the same timescale as 1D LC-MS2 analysis. Additionally, limits of quantitation approach those observed in state-of-the-art LC-MS2 methods. Moreover, distinct trends observed in the 2D separation space for the various PFAS subclasses could enable analyte identification in future non-targeted analyses.