Innovative Projects Realized

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

30508 Completed Projects

2882
AB
5105
BC
825
MB
681
NL
860
SK
9051
ON
9491
QC
97
PE
586
NB
1141
NS

Projects by Category

Diffusion Based Generative Modeling for Robotic Grasping

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Faculty Supervisor:

Igor Gilitschenski

Student:

Partner:

Ocado Technology

Discipline:

Computer science

Sector:

Artificial Intelligence; Technology; Advanced Manufacturing

University:

University of Toronto

Program:

Accelerate

Conception, fabrication et programmation d’un drone autonome capable d’atterrir sur la canopée pour l’acquisition de données en forêt tropicale.

L’objectif du projet est de produire un drone capable d’atterrir de façon autonome dans la canopée pour y transporter des charges utiles permettant de récupérer des données sur l’environnement. Des technologies novatrices, comportant une partie mécanique et informatique, sont nécessaires pour y arriver. Premièrement, un train d’atterrissage doit être développé pour protéger les hélices des branches sans affecter les caractéristiques de vol du drone. Il doit aussi permettre au drone d’être stable au repos dans la canopée. Ensuite, un algorithme de vision basé sur une caméra apte à percevoir la profondeur doit être développé pour détecter une zone d’atterrissage sécuritaire dans le feuillage. Ces deux éléments sont finalement combinés à des technologies de contrôle existantes pour effectuer des manoeuvres d’atterrissage autonome sur la canopée des forêts tropicales.

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Faculty Supervisor:

Alexis Lussier Desbiens

Student:

Partner:

Outreach Robotics

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

Université de Sherbrooke

Program:

Accelerate

Effectiveness of Sophia Recovery Centre’s Lotus Pond Program

The present study will examine the effectiveness of the Sophia Recovery Centre’s Lotus Pond program in terms of outcomes for Sophia Guests on measures of well-being, quality of life, and substance use behaviour goals, the quality of peer recovery support experienced by Sophia Guests, and the efficiency of Sophia Peers at providing peer recovery support. The study will also compare characteristics of Sophia Guests who remain in the Lotus Pond program to those who withdraw. Findings from this study will be used to improve Sophia’s peer recovery support process.

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Faculty Supervisor:

David Speed

Student:

Partner:

Sophia Recovery Centre

Discipline:

Sociology

Sector:

Health and Related Sciences & Technology

University:

University of New Brunswick

Program:

Accelerate

Concept d’opération pour améliorer le processus d’optimisation de l’efficacité énergétique pour l’ensemble du cycle de vie du bâtiment

L’objectif principal du projet de recherche est de développer une approche d’optimisation de l’efficacité énergétique d’un bâtiment à chaque étape du processus de conception à l’aide de la simulation énergétique. Ceci dans le but d’assurer une meilleure conception des bâtiments futurs à l’aide de données recueillies dans des bâtiments existants similaires en formulant des recommandations pour la conception. L’approche proposée vise à améliorer les pratiques de l’entreprise partenaire en offrant un support au niveau de l’approche de simulation. Les retombées pour l’entreprise partenaire sont les suivantes :
? Amélioration de l’utilisation des outils de simulation énergétique.
? Aide de prise à la décision par rapport au processus d’optimisation à préconiser pendant la conception.
? Aide à la prise de décision et l’élaboration de solutions concertées, concrètes.

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Faculty Supervisor:

Danielle Monfet

Student:

Partner:

Cimaise

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

École de technologie supérieure

Program:

Accelerate

Machine learning model for corrosion detection and assessment for pad mounted equipment

Corrosion of electrical utility infrastructure represents a major operational challenge that currently relies on labor-intensive manual inspections. This project aims to develop an automated machine learning system to detect and assess corrosion using publicly available street-level imagery, enabling frequent, low-cost monitoring across the distribution network. The proposed methodology will leverage recent advances in object detection and semantic segmentation. Upon successful development, the corrosion detection system will be integrated into EPCOR’s asset management workflow. Automated analysis of public imagery can provide frequent, low-cost monitoring to prioritize field inspections and maintenance activities. Reducing operational costs while extending asset life represents major potential cost savings for the utility industry.

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Faculty Supervisor:

Zhigang (Will) Tian

Student:

Partner:

EPCOR Utilities Inc.

Discipline:

Engineering

Sector:

Utilities

University:

University of Alberta

Program:

Accelerate

Optimal motion planning under kino-dynamic constraints

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Faculty Supervisor:

Igor Gilitschenski

Student:

Partner:

Ocado Technology

Discipline:

Computer science

Sector:

Artificial Intelligence; Advanced Manufacturing; Technology

University:

University of Toronto

Program:

Accelerate

Recommending Investment Opportunities using Reinforcement Learning

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Faculty Supervisor:

Silvana Pesenti;Scott Sanner

Student:

Partner:

Balyasny Asset Management (Canada) ULC

Discipline:

Computer science

Sector:

Finance and Insurance

University:

University of Toronto

Program:

Accelerate

Exploring and Applying Streaming Data Analytics in IoT Big Data Environments to Enhance Fleet Safety and Smart City Infrastructure.

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Faculty Supervisor:

Nick Koudas

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

Increasing emissions reporting accuracy and providing actionable reduction strategies with AI

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Faculty Supervisor:

Andrei Badescu

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

Identifying Causal Risk Factors for Hazardous Driving and Accident Propensity for Safer Fleets and Smart Cities

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Faculty Supervisor:

Andrei Badescu

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

Use of Natural Language Models to Support Assessment of Cognitive and Socio- Emotional Dynamics Among Learners in Online Active Learning Environments

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Faculty Supervisor:

Steve Engels

Student:

Partner:

University of Toronto Schools

Discipline:

Computer science

Sector:

Education

University:

University of Toronto

Program:

Accelerate

Explanations with Meaningful Predictive Properties via Clustered Shapley Values

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Faculty Supervisor:

Sheila McIlraith

Student:

Partner:

Signal 1 AI

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Toronto

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.