Innovative Projects Realized

Pricing and Resource Allocation in Edge Computing

The emerging edge computing (EC) paradigm promises to deliver superior user experience and enable a wide range of Internet of Things (IoT) applications by bringing storage and computing facilities closer to the end users. Virtualization technologies such as Software-Defined Networking (SDN) and Network Function Virtualization (NFV) will allow sellers and buyers to access the open EC ecosystem. We envision the emergence of a novel EC marketplace where the telecom operator, such as Rogers Communications Canada, can act as a platform to facilitate the resource exchange among the sellers and buyers. This project aims to understand the tremendous potential of this new marketplace and propose efficient pricing and resource allocation mechanisms for the EC platform. We will investigate various design objectives ranging from fairness, privacy, truthfulness, social welfare maximization, to revenue maximization. The project will take both static and online settings into account to propose a set of novel algorithms tailored to provide win-win solutions for all stakeholders involved in the edge computing ecosystem.

Artificial Intelligence and Deterioration of Ocean Ecosystem

In the context of ocean sustainability of west coast of Canada, some questions that need to be considered are: what is the significance of environmental indicators related to the impact on marine aquatic species? How can changes in environment be predicted by patterns of bioindicators, for example as a result of hypoxia, affecting farmed and wild salmon? A starting point to answer these questions is the development of a centralized, common, accessible database that documents the shift in marine observation metadata collected in the area.

The objective of the study is to explore the spatial-temporal correlation between environment parameters and biological measurement of aquatic species in BC. We will develop a deep learning platform to integrate the information from environment conditions and the biological information of marine aquatic speices as follows: mortality of farmed Atlantic salmon (Salmon salar), abundance of wild Pacific salmon, and abundance of amphibian egg abundance in upstream habitat of wild salmon. The integration modeling of different sources of data, as the major output of the project will provide the analytic tool for ocean ecosystem service in the country.

Simeio: Anomaly Detection for Building Automation System – Year two

Buildings are an important energy consumer and are equipped with hundreds of sensors and control systems. The analysis of such massive data can reveal insights for building owners to optimize the building infrastructure. Currently, usage of such data is limited to traditional control systems, energy commissioning, and maintenance on a regular basis. Real-time monitoring and analysis of data can reveal insights about the performance of the building helping to reduce operating costs, lower utility bills, increase equipment life, improve tenant comfort, retention, and leasing rates; all while lowering carbon emissions. Simeio (A Cloud based software application developed by UCtriX team) is leveraging powerful artificial intelligence (AI) analytics to automatically detect anomalies and faults in the HVAC system and pinpoint any abnormalities or failures in a building. Simeio is a platform used by building owners, building managers, or higher authorities to ensure the sustainability and efficient operation of buildings.

Simeio: Anomaly Detection for Building Automation System

Buildings are an important energy consumer and are equipped with hundreds of sensors and control systems. The analysis of such massive data can reveal insights for building owners to optimize the building infrastructure. Currently, usage of such data is limited to traditional control systems, energy commissioning, and maintenance on a regular basis. Real-time monitoring and analysis of data can reveal insights about the performance of the building helping to reduce operating costs, lower utility bills, increase equipment life, improve tenant comfort, retention, and leasing rates; all while lowering carbon emissions. Simeio (A Cloud based software application developed by UCtriX team) is leveraging powerful artificial intelligence (AI) analytics to automatically detect anomalies and faults in the HVAC system and pinpoint any abnormalities or failures in a building. Simeio is a platform used by building owners, building managers, or higher authorities to ensure the sustainability and efficient operation of buildings.

Development and Applications of Cement Composites Made of Various Forms of Basalt Fibre – Year two

The structural health and performance of existing infrastructure in Canada has a large impact on the Canadian economy and hence, it is imperative that this infrastructure is kept in good operational conditions. A significant portion of this infrastructure was built during the post world war period, which suggests much of this infrastructure has surpassed their service life. Additionally, Canada’s extreme cold weather conditions give rise to adverse loading conditions such as freeze and thaw cycles, which further leads to damage and making this infrastructure more susceptible to failure. This proposed Mitacs fellowship project will develop various cement composite materials to facilitate a quick and straightforward rehabilitation process of existing damaged concrete structures. Various types of basalt fibre products such as basalt bundle dispersion fibres, basalt filament dispersion fibres, and basalt minibars will be used in various cement mixes to improve better bonding, mechanical, and durability properties. This work will be accomplished using experimental methods, which will be undertaken in the Structural Engineering Laboratory at the University of Windsor. The conclusions made from the laboratory tests will form the basis of rehabilitation techniques, which will then be applied in the field to rehabilitate concrete pavements, industrial floors, buildings, and bridges.

Development and Applications of Cement Composites Made of Various Forms of Basalt Fibre

The structural health and performance of existing infrastructure in Canada has a large impact on the Canadian economy and hence, it is imperative that this infrastructure is kept in good operational conditions. A significant portion of this infrastructure was built during the post world war period, which suggests much of this infrastructure has surpassed their service life. Additionally, Canada’s extreme cold weather conditions give rise to adverse loading conditions such as freeze and thaw cycles, which further leads to damage and making this infrastructure more susceptible to failure. This proposed Mitacs fellowship project will develop various cement composite materials to facilitate a quick and straightforward rehabilitation process of existing damaged concrete structures. Various types of basalt fibre products such as basalt bundle dispersion fibres, basalt filament dispersion fibres, and basalt minibars will be used in various cement mixes to improve better bonding, mechanical, and durability properties. This work will be accomplished using experimental methods, which will be undertaken in the Structural Engineering Laboratory at the University of Windsor. The conclusions made from the laboratory tests will form the basis of rehabilitation techniques, which will then be applied in the field to rehabilitate concrete pavements, industrial floors, buildings, and bridges.

Smart Work Zone Management – Year two

Construction zones are one of the leading contributors to Toronto’s ever-growing congestion. The aim of this study is to develop an integrated construction zone traffic management framework to minimize disruption of the traffic and reduce the effect in terms of congestion. This study leverages historical and real data collected from on-board construction trucks provided by the partner organization to find an insight as to how far upstream and downstream of the work zone congestion propagates. Using such information, it is then possible to develop novel prediction models determining the impact zone for future construction zones and selecting optimal work zone size and staging of vehicles and equipment. In addition to the prediction model as part of this collaboration, an innovative anticipatory vehicle routing algorithm will be developed that not only help motorist to avoid construction zones but also guides them to their destination while minimizing travel time and utilizing road network more efficiently.

Smart Work Zone Management

Construction zones are one of the leading contributors to Toronto’s ever-growing congestion. The aim of this study is to develop an integrated construction zone traffic management framework to minimize disruption of the traffic and reduce the effect in terms of congestion. This study leverages historical and real data collected from on-board construction trucks provided by the partner organization to find an insight as to how far upstream and downstream of the work zone congestion propagates. Using such information, it is then possible to develop novel prediction models determining the impact zone for future construction zones and selecting optimal work zone size and staging of vehicles and equipment. In addition to the prediction model as part of this collaboration, an innovative anticipatory vehicle routing algorithm will be developed that not only help motorist to avoid construction zones but also guides them to their destination while minimizing travel time and utilizing road network more efficiently.

Advanced Characterization Techniques for Doped Iridium-based Oxygen Evolution Electrocatalysts

Of late, the leaching of doped cations present in oxygen evolution electrocatalysts is reported to enhance oxygen evolution reaction performances. It is suggested that the enhancement is attributed to the increased roughness and modified composition of electrocatalyst surfaces during the dissolution of the cations. The detailed understanding of the enhancement has yet to be disclosed. In this project, iridium oxide-based electrocatalyst partially doped with monovalent cations will be prepared using aqua chemistry, and its electrochemical and physicochemical behavior during the oxygen evolution reaction will be analyzed with the aid of advanced in situ and ex situ characterization technqiues. This combinatorial research is expected to shed light on understanding and elucidation of the dissolution and reduction-oxidation phenomena of the doped cations present in the iridium oxide based electrocatalyst. Thereby, the influence towards oxygen evolution reaction caused by the aforementioned phenomena will be demonstrated. In addition, the findings will eventually help in designing more efficient electrocatalysts in favor of oxygen evolution reaction.

Physiologically based pharmacokinetic modeling to predict drug metabolism in the rat brain

Drug metabolism is a fundamental step of drugs to act and move in the body. Cytochorome P450 (CYP) enzymes, the most important metabolizing enzymes, are superfamily that metabolize a vast array of compounds, especially drugs. While drug metabolism occurs predominantly in the liver so that CYP enzymes in other organs has been understudied, brain CYP-mediated metabolism of drugs can also impact their local brain concentration. As reaching at effective concentration is a direct parameter to success or failure of drug treatment, brain metabolism could be significantly influence on therapeutic effects of drugs. The objective of this study is to describe brain metabolism via CYP enzymes of drugs in rats in vivo by using physiologically-based pharmacokinetic (PBPK) modeling which is a valuable mathematical approach to predict the temporal profiles of drugs and their metabolite in consideration of physiology of the species. This study is expected to suggest more effective approaches to treat and prevent brain diseases.

Development of next generation vision sensor with coded multi-exposure pixel and compressive sensing based readout

Vision sensor is essential components in sensor applications of industry 4.0 such as autonomous vehicle, Internet of Things (IoT), Neural Network. The main goal of this research is to study, design, and develop a new concept of computational vision sensors called “transport-aware”. Unlike conventional vision sensors which record all incident light, transport-aware vision sensor can be programmed to block some of that light, based on the actual 3D paths it followed through a scene. By using coded exposure pixel (CEP) image sensor, we can control the exposure of the vision sensor at the individual pixel level. And the compressive sensing(CS) based readout will enable very high-speed imaging. In this research, I will do research on designing the novel vision sensor which can see high-speed imaging by utilizing CEP and CS-based readout circuit. This novel image sensor can be used to applications such as self-driving cars, biomedical imaging, drones, robots, and machine vision.

Development of electrochemical carbon dioxide reduction technology using the metal / metal oxide low-dimensional nanostructures

Using my oxide nanostructure-metal nanoparticle structures synthetic skill and ultraviolet-light assist metal nanoparticle loading technology, will synthesize metal oxide nanostructure/metal nanoparticle heterojunction for achieving reducing of the overpotential and effective reduction of carbon dioxide. The host supervisor is excellent in techniques for increasing the selectivity of byproducts of carbon dioxide reduction, including combinations of copper and silver nanomaterials, and analyzing mechanisms [8]. Combining the research of my nanostructure synthesis and host supervisor’s selectivity control, high-priced alcohol and liquid carbonyl can be made with high selective. The material selection will be made based on my previous researches which show high catalytic efficiency and, TiO2 nanorod, ZnO nanorod, Cu2O nanowire, and WO3 nanoplate will be first considered. In addition, the combination of copper and silver, as well as copper will be considered as the metal nanoparticles to be used at the same time which showed high selectivity and actively studied by host supervisor.