Graduate Theses & Dissertations

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Historic Magnetogram Digitization
The conversion of historical analog images to time series data was performed by using deconvolution for pre-processing, followed by the use of custom built digitization algorithms. These algorithms have been developed to be user friendly with the objective of aiding in the creation of a data set from decades of mechanical observations collected from the Agincourt and Toronto geomagnetic observatories beginning in the 1840s. The created algorithms follow a structure which begins with pre-processing followed by tracing and pattern detection. Each digitized magnetogram was then visually inspected, and the algorithm performance verified to ensure accuracy, and to allow the data to later be connected to create a long-running time-series. Author Keywords: Magnetograms
Augmented Reality Sandbox (Aeolian Box)
The AeolianBox is an educational and presentation tool extended in this thesis to represent the atmospheric boundary layer (ABL) flow over a deformable surface in the sandbox. It is a hybrid hardware cum mathematical model which helps users to visually, interactively and spatially fathom the natural laws governing ABL airflow. The AeolianBox uses a Kinect V1 camera and a short focal length projector to capture the Digital Elevation Model (DEM) of the topography within the sandbox. The captured DEM is used to generate a Computational Fluid Dynamics (CFD) model and project the ABL flow back onto the surface topography within the sandbox. AeolianBox is designed to be used in a classroom setting. This requires a low time cost for the ABL flow simulation to keep the students engaged in the classroom. Thus, the process of DEM capture and CFD modelling were investigated to lower the time cost while maintaining key features of the ABL flow structure. A mesh-time sensitivity analysis was also conducted to investigate the tradeoff between the number of cells inside the mesh and time cost for both meshing process and CFD modelling. This allows the user to make an informed decision regarding the level of detail desired in the ABL flow structure by changing the number of cells in the mesh. There are infinite possible surface topographies which can be created by molding sand inside the sandbox. Therefore, in addition to keeping the time cost low while maintaining key features of the ABL flow structure, the meshing process and CFD modelling are required to be robust to variety of different surface topographies. To achieve these research objectives, in this thesis, parametrization is done for meshing process and CFD modelling. The accuracy of the CFD model for ABL flow used in the AeolianBox was qualitatively validated with airflow profiles captured in the Trent Environmental Wind Tunnel (TEWT) at Trent University using the Laser Doppler Anemometer (LDA). Three simple geometries namely a hemisphere, cube and a ridge were selected since they are well studied in academia. The CFD model was scaled to the dimensions of the grid where the airflow was captured in TEWT. The boundary conditions were also kept the same as the model used in the AeolianBox. The ABL flow is simulated by using software like OpenFoam and Paraview to build and visualize a CFD model. The AeolianBox is interactive and capable of detecting hands using the Kinect camera which allows a user to interact and change the topography of the sandbox in real time. The AeolianBox’s software built for this thesis uses only opensource tools and is accessible to anyone with an existing hardware model of its predecessors. Author Keywords: Augmented Reality, Computational Fluid Dynamics, Kinect Projector Calibration, OpenFoam, Paraview
Representation Learning with Restorative Autoencoders for Transfer Learning
Deep Neural Networks (DNNs) have reached human-level performance in numerous tasks in the domain of computer vision. DNNs are efficient for both classification and the more complex task of image segmentation. These networks are typically trained on thousands of images, which are often hand-labelled by domain experts. This bottleneck creates a promising research area: training accurate segmentation networks with fewer labelled samples. This thesis explores effective methods for learning deep representations from unlabelled images. We train a Restorative Autoencoder Network (RAN) to denoise synthetically corrupted images. The weights of the RAN are then fine-tuned on a labelled dataset from the same domain for image segmentation. We use three different segmentation datasets to evaluate our methods. In our experiments, we demonstrate that through our methods, only a fraction of data is required to achieve the same accuracy as a network trained with a large labelled dataset. Author Keywords: deep learning, image segmentation, representation learning, transfer learning
Support Vector Machines for Automated Galaxy Classification
Support Vector Machines (SVMs) are a deterministic, supervised machine learning algorithm that have been successfully applied to many areas of research. They are heavily grounded in mathematical theory and are effective at processing high-dimensional data. This thesis models a variety of galaxy classification tasks using SVMs and data from the Galaxy Zoo 2 project. SVM parameters were tuned in parallel using resources from Compute Canada, and a total of four experiments were completed to determine if invariance training and ensembles can be utilized to improve classification performance. It was found that SVMs performed well at many of the galaxy classification tasks examined, and the additional techniques explored did not provide a considerable improvement. Author Keywords: Compute Canada, Kernel, SDSS, SHARCNET, Support Vector Machine, SVM
Relationship Between Precarious Employment, Behaviour Addictions and Substance Use Among Canadian Young Adults
This thesis utilized a unique data-set, the Quinte Longitudinal Survey, to explore relationships among precarious employment and a range of mental health problems in a representative sample of Ontario young adults. Study 1 focused on various behavioural addictions (such as problem gambling, video gaming, internet use, exercise, compulsive shopping, and sex) and precarious employment. The results showed that precariously employed men were preoccupied with gambling and sex while their female counterparts preferred shopping. Gambling and excessive shopping diminished over time while excessive sexual practices increased. Study 2 focused on the association between precarious employment and substance abuse (such as tobacco, alcohol, cannabis, hallucinogens, stimulants, and other substances). The results showed that men used cannabis more than women, and the non-precarious employed group abused alcohol more than individuals in the precarious group. This research has implications for both health care professionals and intervention program developers when working with young adults in precarious jobs. Author Keywords: Behaviour Addictions, Precarious Employment, Substance Abuse, Young Adults
Exploring the Scalability of Deep Learning on GPU Clusters
In recent years, we have observed an unprecedented rise in popularity of AI-powered systems. They have become ubiquitous in modern life, being used by countless people every day. Many of these AI systems are powered, entirely or partially, by deep learning models. From language translation to image recognition, deep learning models are being used to build systems with unprecedented accuracy. The primary downside, is the significant time required to train the models. Fortunately, the time needed for training the models is reduced through the use of GPUs rather than CPUs. However, with model complexity ever increasing, training times even with GPUs are on the rise. One possible solution to ever-increasing training times is to use parallelization to enable the distributed training of models on GPU clusters. This thesis investigates how to utilise clusters of GPU-accelerated nodes to achieve the best scalability possible, thus minimising model training times. Author Keywords: Compute Canada, Deep Learning, Distributed Computing, Horovod, Parallel Computing, TensorFlow
Population-Level Ambient Pollution Exposure Proxies
The Air Health Trend Indicator (AHTI) is a joint Health Canada / Environment and Climate Change Canada initiative that seeks to model the Canadian national population health risk due to acute exposure to ambient air pollution. The common model in the field uses averages of local ambient air pollution monitors to produce a population-level exposure proxy variable. This method is applied to ozone, nitrogen dioxide, particulate matter, and other similar air pollutants. We examine the representative nature of these proxy averages on a large-scale Canadian data set, representing hundreds of monitors and dozens of city-level populations. The careful determination of temporal and spatial correlations between the disparate monitors allows for more precise estimation of population-level exposure, taking inspiration from the land-use regression models commonly used in geography. We conclude this work with an examination of the risk estimation differences between the original, simplistic population exposure metric and our new, revised metric. Author Keywords: Air Pollution, Population Health Risk, Spatial Process, Spatio-Temporal, Temporal Process, Time Series
Psychometric Properties of a Scale Developed from a Three-Factor Model of Social Competency
While existing models of emotional intelligence (EI) generally recognize the importance of social competencies (SC), there is a tendency in the literature to narrow the focus to competencies that pertain to the self. Given the experiential and perceptual differences between self- vs. other-oriented emotional abilities, this is an important limitation of existing EI models and assessment tools. This thesis explores the psychometric properties of a multidimensional model for SC. Chapter 1 describes the evolution of work on SCs in modern psychology and describes the multidimensional model of SC under review. Chapter 2 replicates this model across a variety of samples and explores the model’s construct validity via basic personality and EI constructs. Chapter 3 further explores the predictive validity of the SC measure within a group of project managers and several success and wellness variables. Chapter 4 examines potential applications for the model and suggestions for further research. Author Keywords: emotional intelligence, project management, social competency, work readiness
Cloud Versus Bare Metal
A comparison of two high performance computing clusters running on AWS and Sharcnet was done to determine which scenarios yield the best performance. Algorithm complexity ranged from O (n) to O (n3). Data sizes ranged from 195 KB to 2 GB. The Sharcnet hardware consisted of Intel E5-2683 and Intel E7-4850 processors with memory sizes ranging from 256 GB to 3072 GB. On AWS, C4.8xlarge instances were used, which run on Intel Xeon E5-2666 processors with 60 GB per instance. AWS was able to launch jobs immediately regardless of job size. The only limiting factors on AWS were algorithm complexity and memory usage, suggesting a memory bottleneck. Sharcnet had the best performance but could be hampered by the job scheduler. In conclusion, Sharcnet is best used when the algorithm is complex and has high memory usage. AWS is best used when immediate processing is required. Author Keywords: AWS, cloud, HPC, parallelism, Sharcnet
Compression Cone Method on Existence of Solutions for Semi-linear Equations
With wide applications in many fields such as engineering, physics, chemistry, biology and social sciences, semi-linear equations have attracted great interests of researchers from various areas. In the study of existence of solutions for such class of equations, a general and commonly applied method is the compression cone method for fixed-point index. The main idea is to construct a cone in an ordered Banach space based on the linear part so that the nonlinear part can be examined in a relatively smaller region. In this thesis, a new class of cone is proposed as a generalization to previous work. The construction of the cone is based on properties of both the linear and nonlinear part of the equation. As a result, the method is shown to be more adaptable in applications. We prove new results for both semi-linear integral equations and algebraic systems. Applications are illustrated by examples. Limitations of such new method are also discussed. Keywords: Algebraic systems; compression cone method; differential equations; existence of solutions; fixed point index; integral equations; semi-linear equations. Author Keywords: algebraic systems, differential equations, existence of solutions, fixed point index, integral equations, semi-linear equations
Predicting the Pursuit of Post-Secondary Education
Trait Emotional Intelligence (EI) includes competencies and dispositions related to identifying, understanding, using and managing emotions. Higher trait EI has been implicated in post-secondary success, and better career-related decision-making. However, there is no evidence for whether it predicts the pursuit of post-secondary education (PSE) in emerging adulthood. This study investigated the role of trait EI in PSE pursuit using a large, nationally-representative sample of Canadian young adults who participated in the National Longitudinal Survey for Children and Youth (NLSCY). Participants in this dataset reported on their PSE status at three biennial waves (age 20-21, 22-23, and 24-25), and completed a four-factor self-report scale for trait EI (Emotional Quotient Inventory: Mini) at ages 20-21 and 24-25. Higher trait EI subscale scores were significantly associated with greater likelihood of PSE participation both concurrently, and at 2- and 4-year follow-ups. Overall, these associations were larger for men than women. Trait EI scores also showed moderate levels of temporal stability over four years, including full configural and at least partial metric invariance between time points. This suggests that the measure stays conceptually consistent over the four years of emerging adulthood, and that trait EI is a relatively malleable attribute, susceptible to change with interventions during this age period. Author Keywords: Emerging Adulthood, Longitudinal, Post-Secondary Pursuit, Trait Emotional Intelligence
Positive Solutions for Boundary Value Problems of Second Order Ordinary Differential Equations
In this thesis, we study modelling with non-linear ordinary differential equations, and the existence of positive solutions for Boundary Value Problems (BVPs). These problems have wide applications in many areas. The focus is on the extensions of previous work done on non-linear second-order differential equations with boundary conditions involving first-order derivative. The contribution of this thesis has four folds. First, using a fixed point theorem on order intervals, the existence of a positive solution on an interval for a non-local boundary value problem is obtained. Second, considering a different boundary value problem that consists of the first-order derivative in the non-linear term, an increasing solution is obtained by applying the Krasnoselskii-Guo fixed point theorem. Third, the existence of two solutions, one solution and no solution for a BVP is proved by using fixed point index and iteration methods. Last, the results of Green's function unify some methods in studying the existence of positive solutions for BVPs of nonlinear differential equations. Examples are presented to illustrate the applications of our results. Author Keywords: Banach Space, Boundary Value Problems, Differential Equations, Fixed Point, Norm, Positive Solutions

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