Theses

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  • Synthetic aperture imaging in acoustic microscopy
    Synthetic aperture imaging in acoustic microscopy
    Acoustic microscopy (AM) provides micro-meter resolution using a highly focused single-element transducer. A drawback in AM is a relatively small depth of field, resulting in poor resolution outside the focus. Synthetic aperature focusing technique (SAFT) can be used to improve the image resolution throughout the field of view. SAFT mathematically synthesizes the effect of an array transducer and produces dynamic focusing and depth-independent resolution. SAFT in time domain with and without apodization, TD-SAFT and ATD-SAFT, respectively, and in frequency domain (FD-SAFT) were implemented and tested using simulated and experimental radio-frequency data from an acoustic microscope at 400 MHz. Lateral resolution of all the SAFT reconstructed images were better than those of the conventional B-mode images. While TD-SAFT and FD-SAFT permformed better than ATD-SAFT in improving the lateral resolution, ATD-SAFT provided lower side lobes. In conclusion, SAFT improves resolution in AM outside the focal region.
    Synthetic futurism:  exploring the possibilities of limitless architectural change and growth
    Synthetic futurism: exploring the possibilities of limitless architectural change and growth
    Buildings do not have to burst out of the ground with a predetermined identity. They have an inherent need to grow, change and reinvent themselves to reflect the changes among the people and context. In the ever-changing condition of the context, the design of a building must be conscious of and attuned to the growing needs of society. It cannot assume it is destined for a singular purpose, as instead it is defined by a continuity of growth and reinvention. With the onset of contextual changes, the fleeting moment of a design’s conception becomes less significant. In turn, what rises in import is the integration of contingency to allow a design to metabolize the effects of the contextual change and synthesize new solutions within a flexible, absorptive system. Each added component through its relationship with subcomponents and previously existing elements can serve to create diversity, continuity and flexible internal hierarchies between continuous servant and served space. The summation of the Group Form that results from the melding of many parts can allow the buildings identity to shift as the individual parts reform and change to form new cohesive identities. By manufacturing the base and set of core components, a radically diverse system can grow beyond the limits of the originating elements, adding malleability to the many comprising identities.
    System-Wide Enhancement Of Distribution System Voltage Stability Operations
    System-Wide Enhancement Of Distribution System Voltage Stability Operations
    To take on the challenge for improving the distribution system voltage stability, this thesis research carried out an extensive study of the stability issues and available technology dealing with the stability problems. Load shedding, Load reduction through transformer tap changing, reactive power compensation, and DG control are investigated in detail.The new strategies proposed, and formulations presented, in this thesis research, are designed for carrying out the corrective actions against voltage instability with a great degree of feasability to achieve optimal operations. A new concept of composite power was developed in this thesis research, for prediction of the trend of voltage stability, and a novel prediction of voltage stability and consequently determination of corrective actions was formulated.A detailed hardware/software based development of the algorithm and strategy for voltage stability enhancement was presented. A detailed set of case studies for verification of the voltage stability enhancement was developed.
    TDD Dynamics: Understanding the Impact of Test-Driven Development on Software Quality and Productivity
    TDD Dynamics: Understanding the Impact of Test-Driven Development on Software Quality and Productivity
    Test-Driven Development (TDD) is one of the cornerstone practices of the Extreme Programming agile methodology. Today, despite the large scale adoption of TDD in industry, including large software firms such as Microsoft and IBM, its usefulness with regard to the quality and productivity constructs it still under question. Empirical Research has failed to produce conclusive results; all possible results have been reported for both constructs. This research adopts non-empirical measures to gain a deeper understanding of TDD. A two-phased approach has been undertaken towards the goal. The first phase involves conducting a meta-analysis of past empirical research. The meta-analysis quantitatively combines the results of individual empirical studies and identifies moderator variables that could potentially govern the performance of TDD. The second phase of the approach involves the construction of a simulation model of a TDD-based development process. The presented model further analyzes the impact of changes in moderator variables.
    THE JEWISH IDENTITIES OF TORONTO An Examination of Toronto Jewish Identity Construction Through an Analysis of UJA’s Annual Campaign 2012 ‘Jewish Toronto Lives Here’
    THE JEWISH IDENTITIES OF TORONTO An Examination of Toronto Jewish Identity Construction Through an Analysis of UJA’s Annual Campaign 2012 ‘Jewish Toronto Lives Here’
    The Jewish identities of the Toronto Jewish community are complex, overlapping and multifaceted. United Jewish Appeal of Greater Toronto (UJA) is the main international Jewish philanthropy and the main Jewish philanthropic organization in Toronto. Through an examination of UJA’s 2012 annual campaign, ‘Jewish Toronto Lives Here,’ it is possible to explore how UJA has engaged in a dialogue with its community stakeholders and supporters to determine what Jewish identities exist in Toronto and construct what Jewish Toronto means. Using grounded theory to derive the identity concepts from UJA’s 2012 annual campaign, it is apparent that there are at least eight Jewish identities in Toronto in 2012. These identities relate to religion, ethnicity, history, education, preparing for the future, advocacy, repairing the world and Israel and are depicted in the campaign through references to UJA-supported institutions and initiatives. While UJA speaks to each of these identities in the campaign, the identities are never directly defined. This lack of definition enables UJA to oversee an inclusive and diverse Jewish community in Toronto. Through an application of Judith Butler’s gender theory and Martin Buber’s phenomenological approach to the data one can better comprehend how Jewish identity is potentially performed, while also being an intrinsic aspect of community members, in Jewish Toronto. UJA’s approach of non-definition and openness to different approaches to identity is a possible method that other organizations can look to when deciding how to communicate to a diverse group of stakeholders.
    Tailoring hydrogel microstructure via phase separation and kinetic trapping
    Tailoring hydrogel microstructure via phase separation and kinetic trapping
    Control of the microstructure of a biopolymeric phase-separated system is presented as part of an effort to develop a novel platform for controlled drug release. Under certain conditions, aqueous mixtures of biopolymers exhibit thermodynamic incompatibility and separate into distinct phases, each concentrated in one component and poor in the other. Upon initiation of phase separation (PS), droplets of one phase, the included phase, appear and ripen over time such that shared surface area with the continuous phase is minimized. Gelation is a means of halting droplet growth prior to bulk PS (BPS). The purpose of this research is to establish the means to dictate the microstructure of a PS system by: (i) understanding the effects of biopolymer concentration on PS temperature, TPS; (ii) modeling the growth of droplets within the included-phase; (iii) examining the efficacy of gelation as a means of trapping microstructure and (iv) investigating the characteristic microstructures of biopolymer systems undergoing a two-step temperature quench.
    Take the plea: the factors that influence innocent individuals to accept plea bargains
    Take the plea: the factors that influence innocent individuals to accept plea bargains
    Recently, plea bargaining has emerged as a factor that contributes to wrongful convictions. When a Crown offers a reduced sentence or lesser charge to a defendant in exchange for a guilty plea, there is the potential for innocent defendants to plead guilty. However, little is known about the factors that are influencing innocent defendants to accept plea bargains. The current study aimed to investigate the role of false evidence, risk, and modality on an innocent participant’s likelihood of accepting or rejecting a plea bargain. In a laboratory, innocent participants (N = 174) were accused of collaborating with another participant (confederate) on a problem solving task, and offered a plea bargain. Results showed that when participants were told there was an 80% chance of sanctions if they rejected the plea, they were more likely to admit guilt, and accept the plea. Additionally, participants who were high in compliance, high in fantasy proneness, or were younger, were more likely to accept the plea bargain. Implications of these findings for innocent defendants are discussed.
    Taking Youth Engagement To The Next Generation: Lessons From Best Youth Engagement Practices Toward Food Sustainability
    Taking Youth Engagement To The Next Generation: Lessons From Best Youth Engagement Practices Toward Food Sustainability
    Food is one of life’s most basic necessities. Yet the problems of our food system are becoming increasingly worse: global food security is in jeopardy, health related diseases are epidemic, and generations are increasingly disconnected from their food. The youth population, in particular, is largely missing from the food engagement and decision--making process. Yet it is this group that will inherit the problems of the food system, and constitute the next generation of eaters, policy--makers, and planners. This paper aims to fill this gap by examining ways to improve youth engagement in food sustainability by making it more widespread, meaningful and effective. Using a scan and analysis of best practice research, this paper offers recommendations –including cases, tools, principles and techniques – for stakeholders (such as NGOs, local governments and municipal planners) to improve their youth engagement strategies in food sustainability.
    Taking control of diabetes: child and adolescent perspectives on the evolution of self-care
    Taking control of diabetes: child and adolescent perspectives on the evolution of self-care
    This qualitative study employed an ethnographic approach to explore perspectives of children and adolescents on diabetes self-care. Their knowledge of diabetes and feelings about having the disease was also addressed. Rooted in the new sociological approach that acknowledges children’s right to participate in issues that concern them, forty eight paediatric patients between the ages of five and eighteen years participated in individual interviews. Participants were recruited from a diabetes outpatient clinic within the largest paediatric hospital in Canada. Data were coded using McCracken’s (1988) method of analysis. This paper presents a focused analysis of three major themes: self-care, knowledge and feelings. In-depth analyses of these integrated themes provided a rich understanding of how children and adolescents with diabetes come to accept their disease and how the process of self-care evolves over time. Despite the emotional challenges and complexity of managing diabetes, children and adolescents spoke of a resolve and readiness to obtain more knowledge about their disease. This paper describes the process of diabetes self-care from the perspectives of children and adolescents and offers suggestions for clinical practice and future research.
    Tangible visual analytics:  the integration of tangible interactions and computational techniques for biological data visualization and modelling with experts in the loop
    Tangible visual analytics: the integration of tangible interactions and computational techniques for biological data visualization and modelling with experts in the loop
    Understanding and interpreting the inherently uncertain nature of complex biological systems, as well as the time to an event in these systems, are notable challenges in the field of bioinformatics. Overcoming these challenges could potentially lead to scientific discoveries, for example paving the path for the design of new drugs to target specific diseases such as cancer, or helping to apply more effective treatment for these diseases. In general, reverse engineering of these types of biological systems using online datasets is difficult. In particular, finding a unique solution to these systems is hard due to their complexity and the small sample size of datasets. This remains an unsolved problem due to such uncertainty, and the often intractable solution space of these systems. The term"uncertainty" describes the application-based margin of significance, validity, and efficiency of inferred or predictive models in their ability to extract characteristic properties and features describing the observed state of a given biological system. In this work, uncertainties within two specific bioinformatics domains are considered, namely "gene regulatory network reconstruction" (in which gene interactions/relationships within a biological entity are inferred from gene expression data); and "cancer survivorship prediction" (in which patient survival rates are predicted based on clinical factors and treatment outcomes). One approach to reduce uncertainty is to apply different constraints that have particular relevance to each application domain. In gene network reconstruction for instance, the consideration of constraints such as sparsity, stability and modularity, can inform and reduce uncertainty in the inferred reconstructions. While in cancer survival prediction, there is uncertainty in determining which clinical features (or feature aggregates) can improve associated prediction models. The inherent lack of understanding of how, why and when such constraints should be applied, however, prompts the need for a radically new approach. In this dissertation, a new approach is thus considered to aid human expert users in understanding and exploring inherent uncertainties associated with these two bioinformatics domains. Specifically, a novel set of tools is introduced and developed to assist in evidence gathering, constraint definition, and refinement of models toward the discovery of better solutions. This dissertation employs computational approaches, including convex optimization and feature selection/aggregation, in order to increase the chances of finding a unique solution. These approaches are realized through three novel interactive tools that employ tangible interaction in combination with graphical visualization to enable experts to query and manipulate the data. Tangible interaction provides physical embodiments of data and computational functions in support of learning and collaboration. Using these approaches, the dissertation demonstrates: (1) a modified stability constraint for reconstructing gene regulatory network that shows improvement in accuracy of predicted networks, (2) a novel modularity constraint (neighbor norm) for extracting available structures in the data which is validated with Laplacian eigenvalue spectrum, and (3) a hybrid method for estimating overall survival and inferring effective prognosis factors for patients with advanced prostate cancer that improves the accuracy of survival analysis.
    Tangible visual analytics:  the integration of tangible interactions and computational techniques for biological data visualization and modelling with experts in the loop
    Tangible visual analytics: the integration of tangible interactions and computational techniques for biological data visualization and modelling with experts in the loop
    Understanding and interpreting the inherently uncertain nature of complex biological systems, as well as the time to an event in these systems, are notable challenges in the field of bioinformatics. Overcoming these challenges could potentially lead to scientific discoveries, for example paving the path for the design of new drugs to target specific diseases such as cancer, or helping to apply more effective treatment for these diseases. In general, reverse engineering of these types of biological systems using online datasets is difficult. In particular, finding a unique solution to these systems is hard due to their complexity and the small sample size of datasets. This remains an unsolved problem due to such uncertainty, and the often intractable solution space of these systems. The term"uncertainty" describes the application-based margin of significance, validity, and efficiency of inferred or predictive models in their ability to extract characteristic properties and features describing the observed state of a given biological system. In this work, uncertainties within two specific bioinformatics domains are considered, namely "gene regulatory network reconstruction" (in which gene interactions/relationships within a biological entity are inferred from gene expression data); and "cancer survivorship prediction" (in which patient survival rates are predicted based on clinical factors and treatment outcomes). One approach to reduce uncertainty is to apply different constraints that have particular relevance to each application domain. In gene network reconstruction for instance, the consideration of constraints such as sparsity, stability and modularity, can informand reduce uncertainty in the inferred reconstructions. While in cancer survival prediction, there is uncertainty in determining which clinical features (or feature aggregates) can improve associated prediction models. The inherent lack of understanding of how, why and when such constraints should be applied, however, prompts the need for a radically new approach. In this dissertation, a new approach is thus considered to aid human expert users in understanding and exploring inherent uncertainties associated with these two bioinformatics domains. Specifically, a novel set of tools is introduced and developed to assist in evidence gathering, constraint definition, and refinement of models toward the discovery of better solutions. This dissertation employs computational approaches, including convex optimization and feature selection/aggregation, in order to increase the chances of finding a unique solution. These approaches are realized through three novel interactive tools that employ tangible interaction in combination with graphical visualization to enable experts to query and manipulate the data. Tangible interaction provides physical embodiments of data and computational functions in support of learning and collaboration. Using these approaches, the dissertation demonstrates: (1) a modified stability constraint for reconstructing gene regulatory network that shows improvement in accuracy of predicted networks, (2) a novel modularity constraint (neighbor norm) for extracting available structures in the data which is validated with Laplacian eigenvalue spectrum, and (3) a hybrid method for estimating overall survival and inferring effective prognosis factors for patients with advanced prostate cancer that improves the accuracy of survival analysis.
    Target Design For Lida-Based ICP Pose Estimation For Space Vision Tasks
    Target Design For Lida-Based ICP Pose Estimation For Space Vision Tasks
    The goal of this thesis is to develop a methodology for designing 3D target shapes for accurate LIDAR pose estimation. Scanned from a range of views, this shape can be attached to the surface of a spacecraft and deliver accurate pose scanned. It would act as an LIDAR- based analogue to fiducial markers placed on the surface and viewed by CCD camera(s). Continuum Shape Constraint Analysis (CSCA) which assesses shapes for pose estimation and measures the performance of the Iterative Closest Point (ICP) Algorithm is used as a shape design tool. CSCA directly assesses the sensitivity of pose error to variation in viewing direction. Three of the CSCA measures, Noise Amplification Index, Minimal Eigen-value and Expectivity Index, were compared, and Expectivity Index was shown to be the best index to use as shape design tool. Using CSCA and numerical simulations, a Cuboctahedron was shown to be an optimal shape which delivers an accurate pose when viewed from all angles and the nitial pose guess is close to the true poses. Separate from Constraint Analysis, the problem of shape ambiguity was addressed using numerical tools. The Cuboctahedron was modified in order to resolve shape ambiguity - the tendency of the ICP algorithm to converge with low registration error on a pose configuration geometrically identical, but actually different from a “true pose”. The numerical characteristics of geometrical ambiguity were studied, and a heuristic design methodology to reduce shape ambiguity was developed and is presented in this thesis. A Reduced Ambiguity Cuboctahedron is the resultant shape that delivers an accurate pose from all views and does not suffer from shape ambiguity. The shapes were subjected to simulation and experimental validation. They were manufactured using 3D Rapid Prototyper, and a NEPTEC Design Group TriDAR Scanner was used to obtain experimental data for three shapes: the Tetrahedron, Cuboctahedron, and reduced Ambiguity Cuboctahedron. The Tetrahedron, which has poorly constrained views, was included in the testing process as a comparison shape. The simulation and experimental results were congruent, and validated the design methodology and the designed shapes.