Innovative Projects Realized

Explore thousands of successful projects resulting from collaboration between organizations and post-secondary talent.

31620 Completed Projects

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856
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696
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899
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9419
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98
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1192
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Projects by Category

Identifying Risk Factors for Hazardous Driving and Accident Propensity

Road safety affects everyone, Geotab has several years of driving and environmental data from over 2 million
connected vehicles providing the opportunity to make customers safer, as well as our communities and cities.
This project will leverage data and existing methods to build a model that can identify causal risk factors for
hazardous driving and accident propensity. The model will output a safety score representing the risk level of
fleets/drivers, which can facilitate high efficiency and safety management of light, medium and heavy duty
vehicles. With better understanding of environmental causality, safety concerns can be addressed proactively to
prevent accidents.

View Full Project Description
Faculty Supervisor:

Andrei Badescu;Sheldon Lin

Student:

Partner:

Geotab Inc

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services; Transportation and warehousing

University:

University of Toronto

Program:

Accelerate

Automated code fix suggestions based on source code syntax tree analysis

AMD manages a very large code base that supports multiple graphics products, operating systems, and customers with multiple releases per year. With a high rate of innovation and corresponding code changes it is a daunting task to ensure a given change works correctly on all applicable configurations for every release. It is therefore imperative to detect potential issues as soon as possible in development lifecycle, and ideally as soon as developers make code changes and where possible also propose a fix for detected issues.

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Faculty Supervisor:

Shurui Zhou

Student:

Partner:

AMD Canada

Discipline:

Computer science

Sector:

Manufacturing; Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Autonomous Navigation for Small UAV in Indoor GPS-denied Environments

With the evolution of unmanned aerial vehicles (UAVs) in recent years, more and more researchers are setting their sights on the application research of the indoor environment. Indoor applications include industrial facility inspection, warehouse inventory management, health sector, search, and rescue, among others. However, the use of UAVs in these applications requires continuous high-accuracy positioning and pose information and the consequent efficient obstacle avoidance algorithm. To address the above problem, we want to design an algorithm that can:
1. Perceive the surrounding environment.
2. Control attitude and position of UAVs.
3. Navigate UAV through obstacles.
4. Account for the changes in the dynamic environment and remain stable.

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Faculty Supervisor:

Igor Gilitschenski

Student:

Partner:

SOTI Inc

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Real-time Bidding Using Contextual Targeting

Ads keep the internet free. But, to keep them from becoming spam and degrading the user’s online experience, they need to be relevant to them. The traditional way the industry does this is by collecting a lot of information about every user and creating profiles that can be used to target users based on their online journey. However, users have different tolerances for how much information they want collected about them by any website/app. In general, the problem the industry needs to solve is to make sure the ads shown to users are relevant even in the absence of any user profile. This is the problem the intern is going to tackle. In this research, an End-to-End project will be built to create a contextual targeting proof of concept for mobile and desktop websites. Industry standard metrics such as click-through rate will be used to get an understanding of whether contextual targeting works.

View Full Project Description
Faculty Supervisor:

Annie Lee

Student:

Partner:

Pelmorex

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Modeling and Optimization of roof based BIPV/T with air source heat pump

Incorporating the Air Source Heat Pump (ASHP) into Building Integrated Photovoltaic/Thermal (BIPV/T) system has the potential to reduce building heating and cooling costs and dependence on non-renewable heating fuels. ASHPs could boost the quality and quantity of heat output of a BIPV/T system by delivering a seasonal Coefficient of Performance (COP) of between 2.0 and 4.0, which means 2-4 times more energy output than the amount of energy (electricity) consumed.
When used in Canada’s cold climates, however, ASHPs alone have been found to underperform at low temperatures due to the scarcity of heat that may be pumped out of the atmosphere. One solution to this is ASHP which can provide a higher COP at very low winter outdoor temperatures. The incorporation of PV/T + ASHP into building integrated sloped roof, solutions in existing residential and commercial buildings will furthermore have the potential to lower overall costs of such systems, significantly reduce GHG emissions and provide significant economic and other benefits for Canada in general and for southern Ontario and the Greater Toronto Area (GTA) in particular.

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Faculty Supervisor:

Alan Fung

Student:

Partner:

S2E Technologies Inc

Discipline:

Engineering

Sector:

Construction and infrastructure; Finance and Insurance; Professional, scientific and technical services

University:

Toronto Metropolitan University

Program:

Accelerate

Hypatia-Learn: State of the Art Mathematics Learning and Tutoring System

The project revolves around reading and understanding students solution to various mathematical problems. We wish to analyse the work done by students and the solution to these problems and provide math checking capabilities to various types of problems. Furthermore, this project looks to construct a virtual tutor that can analyse students work and provide feedback and hints to help the student arrive at the correct solution. We wish to achieve all of this while using a simple mathematical editor to allow students to type up mathematical solution cleanly and easily.

View Full Project Description
Faculty Supervisor:

Azadeh Farzan

Student:

Partner:

Hypatia Systems Inc

Discipline:

Computer science

Sector:

Education

University:

University of Toronto

Program:

Accelerate

Optimisation de l’aquaculture multitrophique en milieu tropical par l’intégration de la macroalgue Ulva lactuca dans la moulée du poisson marin Pompano de Floride (Trachinotus carolinus)

Les protéines dans les farines et huiles de poissons représentent l’élément le plus dispendieux des moulées commerciales. Son remplacement par une protéine végétale provenant de macroalgues produites dans les eaux de rejets piscicoles diminuerait les coûts associés à la production aquacole tout en réduisant les impacts environnementaux. Les Ulves, comme Ulva lactuca, ont été largement évaluées comme substitut alimentaire. Dans ce projet, nous remplacerons la farine de poissons par de l’Ulva (substitution de 25 %) et évaluerons les effets sur le rendement reproductif du Pompano de Floride, un poisson marin tropical commercialement prometteur. Les géniteurs seront nourris avec la moulée à base d’Ulva, produite dans le système AMTI (Aquaculture Multitrophique Intégrée), puis avec une moulée commerciale (Vitalis), pour comparer la qualité des œufs, à la base d’un bon développement larvaire, entre les 2 régimes. L’ajout d’Ulva dans la moulée du Pompano se refléterait par un rendement similaire ou supérieur à la moulée Vitalis.

View Full Project Description
Faculty Supervisor:

Réjean Tremblay

Student:

Partner:

Florida Atlantic University

Discipline:

Life Sciences

Sector:

Technology; Aquaculture and Fishing; Sustainability & the Environment

University:

Université du Québec à Rimouski

Program:

Globalink Research Award

Performance of Fiber-Reinforced Lightweight Self-Consolidating Concrete Columns Reinforced with Glass Fiber-Reinforced Polymer Bars and Spirals under Axial and Eccentric Loads

One of the main interests of the construction industry is the use of innovative materials to facilitate construction, extend service life and minimize maintenance and rehabilitation costs. Lightweight aggregate self-consolidating concrete (LWSCC) can be of great interest for reducing dead loads, section dimensions and project costs, especially for precast elements. Integrating GFRP reinforcement into LWSCC would effectively contribute to producing lighter and more durable concrete members for precast applications. Lightweight concrete is more brittle than normal-weight concrete (NWC). Furthermore, the brittleness of concrete may affect not only the failure mode but also the axial capacity of concrete columns. Adding fiber into LWSCC is an effective way to solve the brittleness of concrete and improve the tensile strength and crack resistance of concrete. This research project aims to develop fiber-reinforced lightweight aggregate self-consolidating concrete (FR-LWSCC) mixes for precast applications
and to provide an experimental work as well as extensive theoretical analysis and design recommendations of RC columns reinforced with FRP bars. The experimental results will be discussed in terms of moment–deflection behavior, flexural capacity, mode of failure, crack patterns, and crack widths.

View Full Project Description
Faculty Supervisor:

Brahim Benmokrane

Student:

Partner:

Sym-Tech Béton Préfabriqué Inc.

Discipline:

Engineering

Sector:

Manufacturing and Construction; Advanced Manufacturing; Sustainability & the Environment

University:

Université de Sherbrooke

Program:

Accelerate

Cloud platform of machine learning

Surgical Safety Technologies Inc. is expanding upon its existing OR Black Box® platform, which will allow users of the platform to build a personalized, user-created library of surgical videos in the cloud. There are many people and groups around the world who will use this video library to make sure that performance evaluations are fair and can be done quickly and easily. Among the methodologies used in this project are research on human-computer interaction, video relevance ranking, and business intelligence based on meta-data that comes from the cloud platform. The main goal of the project is to work with existing teams at SST to make prototypes and products that can be used on the cloud, and to make sure that the cloud solution can be used more widely.

View Full Project Description
Faculty Supervisor:

Igor Jurisica

Student:

Partner:

Surgical Safety Technologies Inc

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Neural Networks for Observable Market Data Validation

Observable Market Data is critical for effective valuation of trades for risk management purposes within the investment bank. The valuation process requires the existence of good quality data day by day, and dating back into the mid-2000’s. Not all assets have highly liquid data available either historically or at present, and there is significant interest within the industry in building models to both predict missing data and qualify available data. Historically this process has been highly manual, and due to the volume of data statistical methods are used to identify potentially suspect data. The use of these simplistic methods to gate incoming data results in known blind spots and false positives. This project seeks to use deep neural networks to tackle these two tasks : 1) flagging suspect data efficiently, 2) generating quality data that can be used to improve modeling where real data is unavailable.

View Full Project Description
Faculty Supervisor:

Kirill Serkh;Vardan Papyan

Student:

Partner:

CIBC

Discipline:

Computer science

Sector:

Finance and Insurance

University:

University of Toronto

Program:

Accelerate

Assessment and modelling of salmon streams in western Newfoundland

The approach to assessing Atlantic salmon in Newfoundland is based on patterns of salmon production and habitat that were established for populations in rivers much further south. The physical and biological characteristics of watersheds and rivers in Newfoundland are different from the southern rivers and the bases for conservation decisions are likely inappropriate. Establishing relationships between stream physical and biological characteristics that are unique to Newfoundland is prudent for better management of Newfoundland salmon populations.

View Full Project Description
Faculty Supervisor:

Robert Scott

Student:

Partner:

Atlantic Salmon Conservation Foundation

Discipline:

Life Sciences

Sector:

Other services (except public administration)

University:

Memorial University of Newfoundland

Program:

Accelerate

Advancing Gender Equality for Women in Venture Capital – Part 2

Women are vastly underrepresented in venture capital (VC) firms. There is also a lack of women entering the field of venture capital due to systemic barriers and a lack of momentum to shift existing barriers for women to enter the venture capital world. This research aims to understand the current status of women in VC firms, identify structural barriers facing them, and promote gender parity in VC firms and the investing community. The methods include a literature review, in-depth interviews with VC capitalists or senior leaders from VC firms, as well as the creation of an action plan for the partner organizations to drive changes in the investing community.

View Full Project Description
Faculty Supervisor:

Wendy Cukier

Student:

Partner:

Ethical Digital;National Angel Capital Organization

Discipline:

Sociology

Sector:

Other services (except public administration); Professional, scientific and technical services

University:

Toronto Metropolitan University

Program:

Accelerate

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Mitacs — Return on Innovation

Mitacs is funded by the Government of Canada, the Government of Alberta, the Government of British Columbia, Research Manitoba, the Government of New Brunswick, the Government of Newfoundland and Labrador, the Government of Nova Scotia, the Government of Ontario, Innovation PEI, the Government of Quebec, the Government of Saskatchewan, and the Government of Yukon.