Innovative Projects Realized

Effect of mixture proportions and constituents on the material and structural performance of ECC link slabs

Concrete bridge decks have joints, which are created because of the method used to build a bridge structure. These joints allow water and other deleterious materials to pass through the bridge deck corroding the deck and the supporting girders. Link slabs are provided to conceal these joints. A link slab is a thin slab made of special concrete called ECC. The main ingredient of ECC is fly ash which comes from coal-fired thermal plants. These plants emit a large amount of pollutants. Hence, Canada and many other countries have decided to decommission all coal-fired plants. Ontario has already decommissioned its last coal-fired thermal plant in 2014. This has created an acute shortage of fly ash. In the near future, fly ash will not be available. Hence, this Mitacs research project will develop ECC using alternative materials such as slag and GCC which are and will be readily available.

Teacher Assessment Using New Technologies

Educational reform will require changing the way we assess for student success. Personalized
learning will only become a reality as we transform the way we make meaning of teaching and
learning through assessment This industry partner FreshGrade.com is an educational assessment
application for elementary education (Kindergarten – Grade 6) addressing the need for a simple,
cloud-based solution to help teachers, students, and parents make sense of learning. Tailored for
classrooms of young learners, FreshGrade.com facilitates the meaningful and varied collection of rich
evidence of learning, creating a digital platform where teachers, students and parents can define and
assess success. The overarching question guiding this research is how might new technologies help
teachers, students, and parents with the visualization of student data and how this visualization can
inform assessment practices.

Geometric Deep Learning of Volatility Surfaces

Options are financial instruments that are used to manage risk, hedge investments, and speculate. The value of these options depends on the price of the underlying asset and a multitude of different variables. As a result, pricing models can become complex, requiring infeasibly expensive routines or simulations to be run to price a single option. One reason this procedure can be slow is that the model’s parameters need to be tuned to the market’s current conditions, reflected by an implied volatility surface (IVS), which gives the value of options with different parameters. While the IVS has been researched extensively, it is still not understood well. We propose the use of deep learning to better understand the IVS, and plan to release our models to the public for future research. Riskfuel creates options pricing tools using deep learning, and will be using the model for accelerating pricing.

Efficient Screening Method for Novel Fluorescent Emitters

Canadian public and private researchers have taken an interest in a new class of light-emitting materials which can produce better colour in OLED smartphone and television screens. Currently, the only way to fully confirm that a specific material in this class has the desired properties is to build it into a prototype OLED pixel. This slows discovery, because researchers need to figure out how to make larger batches of a specific material just to confirm whether that material is useful. This project will create a testing method that only needs small quantities, which is based on a method that was proven to work for a similar kind of material. When complete, researchers will be able to test materials more quickly, which will speed up the process of creating materials for specific tasks.

Multi-sensor long-range object detection & classification under challenging perceptual conditions

Autonomous vehicles must be constantly aware of all aspects of the driving environment, and so are typically designed with both omni-directional and long-range forward sensor footprints. The ability to accurately detect, track and predict the motion of distant vehicles and pedestrians along the driving route remains a significant challenge, for today’s state of the art perception methods, however, despite ever-more complex network designs and ever-better sensor configurations. The inherent need in long-range detection for high resolution appearance and depth is difficult to achieve with affordable hardware, leads to real-time performance challenges, and to significant drops in detection accuracy with range to objects due to heavy occlusion and viewpoint limitations.
In this project, we will attempt to make a major breakthrough in long-range 3D object detection and tracking for autonomous driving. Our strategy will involve three components, two led by University of Toronto researchers, and one by Gatik. First, we will develop novel 3D object detection methods operating over depth ranges of over 200 m, that combine vision and lidar data to produce accurate bounding box positions. Second.

Scaling up CO2 electrocatalysis

CERT has developed a technology to convert carbon dioxide into chemicals using water and renewable electricity in a system called a CO2 electrolyzer. They are scaling up this technology as part of the Carbon XPRIZE competition, a global race to find new technologies to make valuable products from CO2. CERT will take the CO2 from a natural gas power plant in Alberta and convert it into ethylene, a chemical building block used to make everything from plastics to textiles and cosmetics.
Interns from the University of Toronto will join the team and work on improving the carbon conversion technology. Through this work, the team will demonstrate the worlds largest CO2 electrolyzer. This research provides a path towards preventing climate change by converting CO2 into useful products.

A Feasibility Study on the Development of Oshawa Hydrogen Economy Hub

This project aims to investigate the establishment of a hydrogen hub in the Pickering and Oshawa region to reduce emissions from fossil fuels. Transportation sector is one of the major air pollution sources in Canada. Produced hydrogen will be used in vehicles operated with fuel cell that will be produced by GM and Hyundai. Fuel cells do not emit NOx, SO2, CO2 compared to conventional fossil fuels. Within the scope of this study, the proposed system will be investigated thermodynamically to optimize hydrogen production. In conclusion, this project aims to reduce air pollutants from fossil fuels, as well as to create new business opportunities, and to establish an economic hub based on hydrogen in Oshawa and its surrounding area.

Machine Learning Engineering and Optimization for Improving Seafood Production

In this project, machine learning and optimization will be applied to a 20 GB dataset on raw fish quality and process control parameters collected by the Tally software over a three-year period in a large industrial tuna cannery processor. The goal of the research is to design predictive machine learning and optimization algorithms maximizing the production yields and reducing waste, which could save hundreds of thousands or even millions of dollars a year depending on the seafood processor scale. The partner organization, ThisFish Inc. will embed the AI solutions obtained in this research project in the existing Tally software and offer to its customers, seafood processing companies in Canada and abroad. In addition, the project has an obvious sustainability aspect as it increases traceability of a product in the supply chain ensuring that seafood comes from environmentally and socially responsible harvesters and processors.

Ice coverage prediction for the St-Lawrence River

This project aims at creating a model for forecasting ice formation in the St. Lawrence Seaway between the Welland Canal and Quebec City. This will improve drastically the planning of all maritime operations during the winter transition period, before the freeze-up.

Explore Remote Virtual Technologies Using Real-time Very Long Baseline Interferometry (VLBI)

This program will cement Canada’s leadership in global Very Long Baseline Interferometry (VLBI), a key technology in radio astronomy and geodesy. In partnership with Thoth Technology Inc., our team of leading radio astronomers will (i) develop new capabilities to compress, transport, and process large amounts of data between geographically distinct locations to enable real-time VLBI, and (ii) use this capability to make precise astrometric measurements of Fast Radio Bursts (FRBs) and pulsars, and through their scintillation properties, study their local environments.

The program will have several far-reaching benefits. Scientifically, advances to VLBI will improve the precision of pulsar localization by orders of magnitude and allow for a systematic spatial localization of FRBs. The project will also position Canada’s Algonquin Radio Observatory as the nexus of a new, low-frequency, global VLBI network and Thoth Technology as one of the world’s premier VLBI providers to large-scale, international telescopes. More far-reaching still, next generation VLBI will underpin the high-precision geodetic reference frame upon which autonomous vehicles will rely. As such, advances to VLBI could profoundly impact nearly every aspect of our economy, from engineering and construction to transportation and precision agriculture.

Co-designing the Governance of Natural Climate Solutions in Canada’s Agriculture Sector

The project will focus on setting out the enabling conditions for nature-based climate solutions. Within this objective, the team will develop a portfolio of opportunities for — and a discussion of the implications of — (i) new policy tools and modifications to existing laws and regulations and (ii) market-based approaches, all to enable and accelerate adoption of practices that show promise to reduce greenhouse gas emissions and sequester carbon in Canada’s agriculture sector. This is an important step to take in advancing the agriculture sector’s role as a solutions provider in climate change mitigation. This research will contribute to a wider initiative in Canada to identify, enhance, and accelerate natural environments such as croplands, grasslands, forests, wetlands, and peatlands capacity to contribute to cost-effective solutions to climate change mitigation, playing a key role in meeting the Paris Climate Agreement and keeping global warming below 2°C.

Development of ultrasonic based oil-water interface level monitoring device

The project aims at developing prototype of a new ultrasonic based device to monitor and
maintain oil-water interface position and oil layer height. Need for such a device has been
identified for applications in oil-water separators commonly used in a number of processes.
The ability to monitor interface position as well as liquid-layer thickness is essential to ensure
continuous operation of separators and avoid complications in downstream processing and/or
accidental discharge to sanitary or storm sewer systems. This is important to meet
environmental regulations for discharge waters into sewer system and avoid penalties.
Improved monitoring and control of these operations will enable them to become more
competitive. The new device would have several desirable features such as self-calibration,
low power consumption, resistance to fouling etc. These features were identified after a
review of potential applications with our industry partner. Further a review of alternative
technologies identified ultrasonic based device to hold the most potential to meet the
requirements.