Innovative Projects Realized

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

13270 Completed Projects

1072
AB
2795
BC
430
MB
106
NF
348
SK
4184
ON
2671
QC
43
PE
209
NB
474
NS

Projects by Category

10%
Computer science
9%
Engineering
1%
Engineering - biomedical
4%
Engineering - chemical / biological

Next-Generation Coating Materials for Architectural Lighting

Architectural and decorative lighting have significant impacts on our daily lives, especially our moods and mental health. We propose to combine supramolecular chemistry and cellulose nanocrystal gels, two intriguing research field in chemistry and material sciences, in order to develop the next-generation coating materials for architectural lighting. Such gel materials have the ability to change color and pattern spontaneously over time, which can be programmed or customized. Through our proposed innovations, our partner organization Bocci could potentially enhance their business with growing market share of the architectural lighting industry. It will generate more revenues and create more job opportunities.

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Faculty Supervisor:

Mark MacLachlan

Student:

Zhen Xu

Partner:

Bocci

Discipline:

Chemistry

Sector:

Other

University:

University of British Columbia

Program:

Solar Simulation for Real-World Conditions and Dye-Sensitized Solar Cells Efficiency Characterization

Currently, the scientific community is aware of the potential of dye sensitized solar cells – they are translucent, conduct 100% renewable energy using the Sun’s energy, and are inexpensive to manufac-ture. They possess the potential to revolutionize Canada’s energy system for the better. This research project will show, using a unique solar simulator, how dye sensitized solar cells can work efficiently under more conditions than have currently been tested: such as air pollution, position of the Sun rela-tive to the Earth, and elevation. This project seeks to prove that these cells really can work any where, under any conditions, making them Canada’s ubiquitous form of energy production.

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Faculty Supervisor:

Bryan Koivisto

Student:

Anna Leckman

Partner:

Arevi Consulting Inc

Discipline:

Biology

Sector:

Professional, scientific and technical services

University:

Ryerson University

Program:

Deep learning-based illuminant estimation for mobile devices

Humans possess the ability to see objects as having the same color even when viewed under different illuminations. Cameras inherently lack this capability. A process called auto white balance (AWB) has to be applied by the camera to mimic this behavior of the human visual system. AWB is one of the first steps in a series of operations performed on-board the camera as the raw image recorded by the sensor is processed. It plays a crucial role in ensuring that the colors in the final image that is output to the user are correctly represented. In recent years, AI algorithms for AWB have demonstrated superior performance over conventional methods. However, existing AI solutions are too computationally expensive for use on smartphones and mobile cameras. The goal of this project is to devise an AI algorithm that is light-weight and capable of running in real time on-device. This project will help Samsung Electronics Canada develop an improved and more practical auto white balance AI algorithm applicable to modern smartphone camera images.

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Faculty Supervisor:

Michael S. Brown

Student:

Abdullah Abuolaim

Partner:

Samsung Electronics Canada

Discipline:

Engineering - computer / electrical

Sector:

Manufacturing

University:

York University

Program:

Accelerate

Requirement Engineering Support for Machine Learning Development – A Workforce Management Application

The proposed research project will conduct requirements engineering to analyze the design of an ML initiative at Jombone. By taking a systematic approach, this project aims to help Jombone identify and design an effective solution strategy to develop their system in accordance to the objectives of the organization. This project will pay special attention to human-centered issues, such as model transparency and unconscious bias, which are of particular concern in HR applications. The expected results for this project will include requirement definition for the target ML system and its ongoing support, as well as publications of research findings at academic research venues.

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Faculty Supervisor:

Eric Yu

Student:

Rohith Sothilingam

Partner:

Jombone

Discipline:

Other

Sector:

Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Assessing Sport Performance Using Mobile EEG

Perhaps one of the most tantalizing goals of a coach or trainer is to determine how best to maximize athlete performance. In recent years there has been an explosion in the wealth of data related to athlete training and performance – such as a better understanding of athlete mental health and the impact it has on performance. However, there are still gaps in how coaches and trainers use this data and there is a disconnect between the theories springing from academia and the applied nature of this data. Our hope with this project is to bridge this gap by applying our knowledge of neuroscience to better understand how baseball players perform with an industrial partner that is using cutting edge technology to understand sport performance. Specifically, we hope to use sport performance data from baseball players combined with our understanding of neural-imaging data and mental health information to see if we can better understand what makes an athlete perform well.

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Faculty Supervisor:

Olave Krigolson

Student:

Thomas Ferguson

Partner:

Mental Stats Technology

Discipline:

Psychology

Sector:

Health care and social assistance

University:

University of Victoria

Program:

Accelerate

“Healthy Bodies, Healthy Minds”- Gender Transformative Menstrual Hygiene Management (MHM) Program Implementation in Low and Middle-Income Countries (LMICs)

The Canadian Red Cross (CRC) has historically been at the forefront supporting local level programming in international humanitarian contexts. The GHU provides both technical and operational support to CRC field experts who are responsible for on-the-ground programming, and advocacy on behalf of local communities, bringing their voices to the CRC when setting its global health agenda. The “Health Bodies, Healthy Minds” project is an active 3-year CRC initiative to increase equal opportunity for girls to attend school in South Sudan. One way it will do this is to improve safe hygiene and sanitation practices, specifically tackling menstrual hygiene management (MHM). This proposed research aims to synthesize relevant scholarly literature in support of the program implemented by CRC and to understand the challenges implementers face when they set priorities and make resource allocation decisions for MHM in a humanitarian context.

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Faculty Supervisor:

Lydia Kapiriri

Student:

Donya (Shaghayegh) Razavi

Partner:

Canadian Red Cross

Discipline:

Other

Sector:

University:

McMaster University

Program:

Structural Geology and Controls on Gold Mineralization, MaginoDeposit, Wawa Subprovince, Northern Ontario

The intern will determine which factors determine the distribtuion of gold at the Magino gold project, near Wawa Ontario. This will be accomplished by geologic mapping and examination of drill core at the Magino site during two summer field seasons. Samples and data collected in the filed will be returned to Laureitan University and examined using petrographic microscope and scanning electron microprobes as well as preparation of a geologic map and cross section focused on the structural geologic framework of the deposit. The partner Argonaut Gold will benefit from this research through an improved understanding o nthe distribtuion of gold within the Magino deposit and the geologic events which resulted in this distribution. This will help the partner in exploration at the deposit and surrounding areas.

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Faculty Supervisor:

Bruno Lafrance;Ross Sherlock

Student:

Ian C Campos

Partner:

Argonaut Gold Inc.

Discipline:

Geography / Geology / Earth science

Sector:

University:

Laurentian University

Program:

Accelerate

Developing a watershed approach to manage anthropogenic and environmental stressors in an eastern Lake Ontario watershed

Water quality in the watersheds of the Great Lakes are under ever-increasing pressures from population growth, urban expansion, economic development, nutrient enrichment, and climate change. We aim to develop a statistical model to understand the relative influence of anthropogenic stressors on water quality for the central Lake Ontario watershed surrounding the cities of Oshawa, Whitby, and Ajax. The project will: review potential ecological, water quality, climate, population, land cover, social, and economic data sources from global re-analysis data, open-access databases, government, industry, environmental networks, and scientific literature; and develop a basic prototype of a machine-learning data intensive watershed analytical approach to understand how anthropogenic stressors impact water quality. These models will be used to forecast water quality conditions under different scenarios of population growth and climate change. The results of this research will be useful for non-profit government organizations, conservation agencies, and urban planners to manage water quality in the Great Lakes watershed.

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Faculty Supervisor:

Sapna Sharma;Usman Khan

Student:

Luke Moslenko

Partner:

Pollution Probe

Discipline:

Biology

Sector:

Professional, scientific and technical services

University:

York University

Program:

Accelerate

Novel Corrective and Training Procedures for Neural Network Compliance

In AI safety, compliance ensures that a model adheres to operational specifications at runtime to avoid adverse events for the end user. This proposal looks at obtaining model compliance in two ways: (i) applying corrective measures to a non-compliant Machine Learning (ML) model and (ii) ensuring compliance throughout the model’s training process. We aim to achieve the first via removal of gradient information related to features involved in biasing the model. For the second, we look at incorporating constraints from the world of satisfiability to enforce desired specifications into gradient based methods used for training. Real-world applications for both approaches include bias and robustness compliance in the world of finance via, for example, ensuring that loans are handed out to clients in fair ways.

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Faculty Supervisor:

Vijay Ganesh

Student:

Vineel Nagisetty;Laura Graves

Partner:

Borealis AI

Discipline:

Engineering - computer / electrical

Sector:

Finance, insurance and business

University:

University of Waterloo

Program:

Accelerate

Autobot: Data-driven metadata tagging of building automation systems

As Building Automation Systems (BAS) are becoming a standard in commercial buildings, and additional 3rd party applications can help buildings owners gain insights from their BAS, structured metadata management becomes the key to success. However, as converting traditional sensors naming convention to structured tagging systems is an expensive and time-consuming process, this project aims at automating the process. By leveraging modern Machine Learning techniques combined with Rule-based systems, Autobot is developed to automate the process. This project builds on previous research at Brainbox AI (Mishra, et al., 2020) to apply the software to additional building types and improve the process’s overall performance.

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Faculty Supervisor:

Adam Rysanek

Student:

Claude Demers-Belanger

Partner:

BrainBox AI

Discipline:

Engineering - mechanical

Sector:

Professional, scientific and technical services

University:

University of British Columbia

Program:

Accelerate

Evaluating the role of summer forage quality and quantity on cow moose condition, fecundity, and survival in WMU 3-18

Moose populations in the Thompson-Okanagan region have declined at an alarming rate (up to 90%) in the past 25 years. The moose population decline corresponds with a dramatic increase in forestry cut blocks for salvage logging after mountain pine beetle infestation. Plants in cut blocks grow in full sunlight and have more energy to create compounds that protect them from being eaten by herbivores and reduce their nutritional quality. We will compare the nutritional quality of plants growing in and out of cut blocks to see if cut blocks provide less nutrition for moose. We will also determine whether use of cut blocks by female moose affects their health, pregnancy rates, and survival. We anticipate our work will help to understand the moose population decline and inform management activities to curb it.

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Faculty Supervisor:

Karl Larsen

Student:

Camille Roberge

Partner:

Teck Highland Valley Copper Partnership

Discipline:

Resources and environmental management

Sector:

University:

Thompson Rivers University

Program:

Geothermal Optimization Software – Part 1

In the last decade optimization is expanded in many applications from food production to sophisticated applications such as engine fuel efficiency. In the proposed package, it is tried to apply optimization techniques along with physics based analytical and semi-analytical methodologies to create a compelling framework which can help thermal-process based oil industry to reduce their GHG and also better evaluate their CAPEX. Many SAGD projects are overspent on their facilities due to under prediction or overprediction of their oil production expectations. this package will help operators to predict their expectations and improve their predictions as more inputs are provided such 4D seismic, temperature and pressure observation wells, production data, and geological characterization.

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Faculty Supervisor:

Apostolos Kantzas

Student:

Farzad Bashtani

Partner:

Ashaw Energy

Discipline:

Engineering - chemical / biological

Sector:

Professional, scientific and technical services

University:

University of Calgary

Program:

Accelerate

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