Innovations 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

Investigation and design of large supercapacitor banks for grid energy storage system

Supercapacitors provide high power density, extra-long cycle life, and wide operating temperature ranges and are poised to grow rapidly in the energy storage market. They offer good complementary features to the batteries and can be used to implement fast cycling speed grid energy storage systems given the cost being driven down. With its own charging and manufacuturing technologies developed, the industry partner is set to move forward to design and implement large supercapacitor banks for distribution grid use. This research proposal is intended to identify and design optimal solutions for the targeted large supercapacitor bank, including its internal cell arrangements, balancing schemes, and investigate the behavior and performance of the designed systems.

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Faculty Supervisor:

Jiacheng Jason Wang

Student:

Behnam Mohammad

Partner:

Atlas Power Generation

Discipline:

Engineering - mechanical

Sector:

Professional, scientific and technical services

University:

Simon Fraser University

Program:

Accelerate

Automated Identification, Classification, and Measurements of Pipe SurfaceDefects in Different Manufacturing Steps at Evraz

This research project focuses on Identification, Classification, and Measurement (ICM) of defects in different manufacturing steps at Evraz. Images/videos of the defects will be collected by a combination of the existing pipe inspection systems in use at Evraz and also the robotic system, equipped with a camera vision, to be completed in this project. Also, we will investigate about how economically beneficial the technology and its implementation would be. A literature review of previous efforts at other universities and/or industry will be first conducted to augment our pipe-defect data pool. Automated digital reporting on each pipe will be conducted in this project by using machine-learning-based techniques. The image/video databases will be utilized for training a Convolutional Neural Network (CNN). Field data will be divided into: (1) training, (2) validation, and (3) testing data sets. Training data will be utilized to train the network, validation data will be used to optimize the network architecture via hypothesis testing, and test data set will be used for testing the performance of the networks. This will lead to generating score cards for the pipes manufactured in Evraz.

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Faculty Supervisor:

Mehran Mehrandezh;Christine Chan

Student:

Aidin Vahidmohammadi;Marzieh Zamani

Partner:

Evraz

Discipline:

Engineering

Sector:

University:

University of Regina

Program:

Accelerate

Identifying Questions for Game-Based Learning through Deep Learning

Game-based learning tools often make use of questions to measure and encourage learning, but generating questions can be challenging, especially at the scale that companies like Axonify are required to do. In this project, the intern will design, implement, and evaluate a system that can apply machine-learning on a corpus of text (e.g., a textbook) to automatically generate questions that can be used in game-based learning tools. This system will allow Axonify to scale their products to larger corpora of source material, larger sets of questions, and ultimately have a much larger market as a result.

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Faculty Supervisor:

Mark Hancock;Stacey Scott

Student:

Marvin Pafla

Partner:

Axonify

Discipline:

Engineering

Sector:

University:

Program:

Accelerate

Attribute-Driven Automatic Generation of Realistic Face Textures

When creating a video game, every digital character must be created by professional artists. Their work is very labor intensive because the number of created characters are in the thousands, each of which has multiple visual components that must be created for each one. “Scanning” real actors to create a digital version of themselves can help speed up this process, but each scan must be altered to preserve the actor’s anonymity. Given all of the face data Ubisoft has from past projects, we plan to create a system that can generate faces of completely new digital characters automatically with just a few desirable attributes and with high quality and believable results. The faces generated should be good enough to use on player characters and minor characters with little to no modification by artists. This will give them more time to work on major characters in the game.

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Faculty Supervisor:

Sudhir Mudur

Student:

Christian Murphy

Partner:

Ubisoft Divertissement

Discipline:

Engineering - computer / electrical

Sector:

University:

Concordia University

Program:

Accelerate

Investigations on amino acids for optimal gut health and productivity in broiler chickens raised without antimicrobial growth promoters

The largest cost of raising chickens is feed, therefore maintaining chickens in an environment that supports proper nutrition is essential for productivity and profitability. However, nutrient absorption and gut adaptation to luminal inflammatory stress is challenging production efficiency as a consequence of the restriction on the use of antibiotic growth promoters (AGP) and anti-coccidial drugs. The metabolic changes induced by inflammation are homeostatic in nature and thus nutrients that would have been utilized for growth and skeletal muscle accretion are diverted to support host defense systems. The available scientific information on aspects of amino acids nutrition and gut health is fragmented. The proposed research will use meta-analysis approach to establish a bibliographic database on interactions among amino acids and other nutrients on metabolic and immune responses in broiler chickens and identify gaps in knowledge that require further experimentation.

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Faculty Supervisor:

Elijah G Kiarie;Marie-Pierre Létourneau Montminy

Student:

Emily Kim

Partner:

Ajinomoto Animal Nutrition Europe

Discipline:

Animal science

Sector:

Agriculture

University:

Program:

Accelerate

Phytotechnologies Monitoring Impacts and Resilience of Native Species and Northern Climates

The Phytoremediation Pilot Project is a collaborative effort between Aya Kitchens and Landscape Architect Pete North to create a buffer system that will stabilize soil contamination left by historic industrial activity at 1551 Catepillar Rd., Mississauga. The site borders the Little Etobicoke Creek, a tributary to the Etobicoke Creek and designated a Significant Natural Area, and prior to the installation of the Phytoremediation Pilot Project groundwater had been transporting contaminants from the soil to the creek. Through strategic tree planting, the Phytoremediation Project takes advantage of plant action, namely the extraction of water, to alter the flow of groundwater so that contaminants never reach the creek. The research proposed here within aims to monitor and analyze the environmental and social impacts of the Phytoremediation Project.

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Faculty Supervisor:

Pete North

Student:

Donald Morgan Quinn

Partner:

AyA Kitchens

Discipline:

Architecture and design

Sector:

Manufacturing

University:

University of Toronto

Program:

Accelerate

Unified Estimation of turbulence eddy dissipation rate of atmospheric Turbulence for effective flight plan management

The aim of the proposed project is to develop a machine learning classification that predicts energy from turbulent flow atmospheric systems. Being able to predict turbulent flows is of great importance since the atmosphere features strongly in the invisible infrastructure of aviation from established navigation waypoints to conduit airways – the highways in the sky. A primary consequence of the onset of turbulence in the atmosphere is the dramatic unpredictability and the challenge in forecasting the phenomenon. More than 100 years after Osborne Reynold’s seminal experiments on the transition of flow through a pipe from a laminar (smooth) to a turbulent state, the exact quantification that drives this phenomenon on a meta-scale level still vexes the aviation and meteorological community. In this project we aim use artificial intelligence and machine learning in order to predict energy from turbulent flow atmospheric systems thus allowing the development of a turbulence prediction model and design flight routes.

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Faculty Supervisor:

Reda Alhajj

Student:

Salim Afra

Partner:

Skyplan

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Calgary

Program:

Elevate

Canadian Oil and Gas Production, Supply Costs, Economic Impacts and Emissions Outlook

In this study, an integrated model will be developed to forecast oil and gas productions in the next 20 years for Canadian basins. Using the output of the model, supply costs estimation, economic impact analysis and GHG emissions calculation will be conducted over the forecast period from 2020 to 2040. The developed integrated model will provide industrial partners and policymakers with a better strategy to make good investment decisions. This project is defined as part of CERI’s 2019-2020 research plan. To perform the project in a more efficient way, a new researcher is required to join the team to help in developing the predictive model, meeting the specified project objectives and writing technical reports and journal paper.

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Faculty Supervisor:

Nader Mahinpey

Student:

Seyedtoufigh Bararpourhamzehkolaei

Partner:

Canadian Energy Research Institute

Discipline:

Engineering - chemical / biological

Sector:

Professional, scientific and technical services

University:

University of Calgary

Program:

Accelerate

The Great River Rapport

The Great River Rapport is an initiative that involves collaboration, consultation, and input from scientists, Indigenous partners, citizens and students. The goal is to provide a report on the health of the Upper St. Lawrence River ecosystem and inspire people to become engaged and aware of how ecosystem health is linked to all of us. Through public workshops, presentations, and online surveys, citizens and students will share their concerns and questions about the health of the river, and River Institute scientists and academic partners will use the public feedback to establish themes, compile scientific data, and identify ecological indicators that define the health of the ecosystem. This process will also reveal environmental trends, predict future impacts, and help identify needs for future research. Outcomes will include a Technical Report and an interactive online space with stories, Indigenous Knowledge, pictures, videos, and educational messages that reflect the concerns of the community.

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Faculty Supervisor:

Frances Pick

Student:

Mary Ann Christine Perron

Partner:

St. Lawrence River Institute of Environmental Sciences

Discipline:

Biology

Sector:

Professional, scientific and technical services

University:

University of Ottawa

Program:

Accelerate

Scaffolding immersive, non-fiction storytelling collaboration: experiments in live journalism

How do you build trust with an audience expecting a factual, reported story while adding elements of performance to it? This research project explores the potential of the live stage for non-fiction narrative. By experimenting with different models of audience-focused experiences, it aims to answer questions like: How can live journalism rebuild trust between the practitioners of journalism and the public (audience)?; How does one maintain the authenticity of the experience beyond the intimate performance spaces; how does one scale; and How can space be used more democratically in the staging of the show?”

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Faculty Supervisor:

Sonya Fatah;Louis-Etienne Dubois

Student:

Ashley Fraser

Partner:

Cirque du Soleil

Discipline:

Journalism / Media studies and communication

Sector:

Arts, entertainment and recreation

University:

Ryerson University

Program:

Accelerate

Statistical machine learning methods applied to ATB data for debt collection optimization, small business lending decision modelling, and open banking initiatives

The intern will research new modelling technology to determine if the new models can make a significant improvement in servicing customers for loan approvals, debt collections, and open banking. The intern will work closely with the partner to understand the banking process and opportunity. The partner organization will receive several benefits from working with the innovative and knowledgeable intern including cross-training of techniques through collaboration, enhanced model accuracy, and enabling the partner to test new techniques. The partner regularly hires former interns to open roles based on their experience on several projects and their proven performance in completing projects efficiently and innovatively.

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Faculty Supervisor:

Bei Jiang

Student:

Lisa Shulman

Partner:

ATB Financial

Discipline:

Statistics / Actuarial sciences

Sector:

Finance, insurance and business

University:

University of Alberta

Program:

Accelerate

Reinforcement Learning for Predictive Sports Analytics

Our project develops novel machine learning algorithms for interpreting complex, multi-agent scenarios in sports analytics. The collaboration with our industrial partner SPORTLOGiQ will tackle open problems in deep reinforcement learning to build novel capabilities in sports analytics for ice hockey. Deep reinforcement learning is a breakthrough technology with prominent successes in games such as Go (AlphaGo) and Chess (AlphaZero). We will develop fundamental algorithmic advances and apply them to tasks including: – player evaluation – event predictions (match outcomes, next action, expected scores) – recognizing types of players, teams, play sequences, and tactics – identifying characteristic strengths and weaknesses of players and teams Montreal-based SPORTLOGiQ uses advanced computer vision to extract information about events from video of sports matches. Their information is more detailed than that provided by any other company or organization. This project will build significant Canadian capacity in sports analytics, support academic research, advance commercialization in the sports industry, and train highly qualified personnel.

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Faculty Supervisor:

Oliver Schulte;Pascal Poupart

Student:

Guiliang Liu;Yu-dong Luo;Michael (Mike) Rudd;Xiangyu (Shawn) Sun;Amur Ghose;Michael John (Jack) Davis

Partner:

SPORTLOGiQ Inc.

Discipline:

Sector:

Professional, scientific and technical services

University:

Program:

Accelerate

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