In disaster scenarios involving airborne contaminants, where the dispersal of toxic agents can impact human lives, first responders require fast and accurate dispersal trajectory information. Existing methods that detect the local presence of an agent do not provide insight towards dispersal trajectory, and long range spread is either simulated with sparse reference data or measured long after the dispersion is complete. The lightweight and porous form of the milkweed seed offers natural inspiration for a novel sensor platform.
In this research project, we will partner with the Financial Services Regulatory Authority of Ontario (FSRA) to enhance its default prediction model for private companies administering pension plans in Ontario. Our goal is to enhance the current model’s timeliness in predicting default of private companies by addressing the lack of publicly accessible information from these private entities.
This project targets development of applied methods and practical solutions to risk management problems where only partial observation of a system is possible. Such settings are commonplace in financial and other context but can be challenging to address due to a limited number of production-grade ready-to-use solutions. The scientific component of the project employs approaches from a quickly developing and active area in machine learning. More extensive use of these approaches by Canadian banking institutions will lead to a more robust financial system and better service.
We live in a world where prisons are seen as a necessary condition for public safety and accountability. But there were no prisons on Turtle Island prior to colonization! The abolitionist dr eam of a world without prisons is grounded in a concrete historical context. This project both recovers and invents decolonial abolitionist methods for dismantling colonial institutions like prisons and jails, and for building freer, healthier, and more just communities.
This research effort will measure the impacts on Ontario's electricity sector of an increased level of imports from Quebec and its reliability to balance the renewable electricity generation facilities in Ontario. For this analysis, the historical operating information from both Quebec and Ontario's electricity systems will be used. A comprehensive analysis will be carried out to identify the critical parameters that determine the economic feasibility of this option. A model will be developed for the appraisal of electricity trade between Quebec and Ontario.
To provide an economic solution to Canada’s ageing rail infrastructure, this project will investigate the use of helical piles in reinforcing railway embankments. This research will consist of the in-field monitoring of both test piles and a helical pile reinforced embankment. Prior to the embankment field investigation, test piles will be analyzed to provide a more accurate prediction of strength and failure mode for foundations using helical piles.
The current project will focus on understanding the behavior of one of the most important CANDU reactor components when it is subjected to the reactor environment. This study will develop a fundamental understanding of the X-750 material’s behavior resulting in innovative technologies that benefit the nuclear industry in Canada.
High-altitude balloons are a promising technology for providing high-speed wireless internet in remote regions of Canada. In this project, we propose a simple, cheap and effective distributed control mechanism for the design of air-based wireless internet systems for deployment in remote regions of Canada. This technology could facilitate the availability of fast and reliable internet connection in remote regions of Canada. The main motivation is to improve the efficiency of the response to crisis situations such as the recent Covid-19 pandemic.
Surface enhanced Raman scattering (SERS) is emerging as a promising technique for rapid, ultrasensitive and highly specific (bio)chemical detection. SERS has been successfully used for the detection of minute quantities of illicit drugs, food contaminants, environmental pollutants, even bacteria and viruses. Unfortunately, like most other surface-based detection methods, SERS also suffers from the same bottleneck, namely the slow transport of the target analyte from the bulk of the sample to the detection surface (also known as sampling).
The current non-destructive and fast method of hyperspectral imaging technology are used for different application from remote sensing to medical imaging and food processing. Due to the nature of acquired data which are massive as well as physical consideration depend on the type of application, the careful, fast and accurate data analysis is mandatory to accelerate the usage of HSI technology. To cope with the type of HSI data in which the sparsity assumption is applicable, this project aims to address the HSI data representation though the sparse problem under deep learning approach.