Innovative Projects Realized

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

29670 Completed Projects

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801
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663
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8841
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568
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1088
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Projects by Category

Recherche et développement d’un modèles de prédiction de niveau de nappe phréatique pour une plateforme d’aide à la décision de type SaaS pour une application d’optimisation environnementale

THIS IS A GENERIC TEXT PUT IN PLACE AS THERE WAS NO PROJECT OVERVIEW

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Faculty Supervisor:

Ioannis Mitliagkas

Student:

Partner:

NORDIKeau Inc

Discipline:

Computer science

Sector:

Transportation and warehousing

University:

Université de Montréal

Program:

Accelerate

AI-Based Content Adaptive Video Compression

The rapid evolution of video resolution has significantly increased the video bitrate requirement, making data transfer a challenging task for data-intensive applications like video conferencing, cloud gaming and game streaming. With the rise of machine learning, studies have shown the potential of embedding conventional video compression algorithms with AI-based methods to enhance their performance. This research project aims to explore the potential features from the input video that can be leveraged by machine learning algorithms to predict the optimal parameters used in the video compression process, with the goal of maximizing the quality of the decompressed video under fixed bitrate constrain.

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Faculty Supervisor:

Qiang Sun

Student:

Partner:

AMD Canada

Discipline:

Computer science

Sector:

Manufacturing; Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Multi-task Reinforcement Learning for Video Games

An important component of modern video games is the non-player character (NPC), moving entities in-game that are not controlled by a human, which may cooperate with, oppose, or otherwise interact with the player. For an NPC to interact with the game word it must often perform complex tasks that are difficult to program explicitly. Research has explored using artificial intelligence, particularly reinforcement learning, to train NPCs to achieve the desired behavior, but prior work has often focused on training NPCs only within one game. Our goal is to investigate using multi-task reinforcement learning to train NPCs that are more robust and can easily transfer from one gaming task to another without needing to be retrained. Success could, in the long term, lead to downstream development of artificial intelligence that can transfer reliably between different use cases.

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Faculty Supervisor:

Florian Shkurti

Student:

Partner:

AMD Canada

Discipline:

Computer science

Sector:

Manufacturing; Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Multimodal Game Event Detection via Machine Learning

The partner company (AMD) is a major innovator in the field of computer graphics and visualization, they manufacture Graphical Processing Unit (GPU) which are used by many gamers around the world. While playing video games, gamers tend to perform out-of-band actions such as saving the last few minutes of gameplay after a challenging fight in a first-person shooter game. Gamers usually search for walkthroughs or FAQs when failing to complete a difficult level or scene in a video game. This project aims to use Computer Vision and Machine learning to detect such events in near real-time during a gameplay. Detection of such events can then trigger actions via AMD’s software to improve the gameplay experience of gamers who are using AMD hardware, thus providing benefit to their customers.

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Faculty Supervisor:

Chris Mcintosh

Student:

Partner:

AMD Canada

Discipline:

Computer science

Sector:

Manufacturing; Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Understanding the concept of ehealth literacy

eHealth literacy is an evolving concept. It influences the development of the content of digital interventions, how individuals interact with the information they receive, and how programmers and information scientists adapt their designs. However, the challenge is that it has no universal definition nor measuring tool. This challenge makes it difficult to compare and communicate the outcomes and results of studies because researchers often choose the concept and approach that fits their research. Findings show that current concepts seem inadequate as they were developed before the recent emergence of Web 3.0 and 4.0. As such, there is a need to create a standardised definition of eHealth literacy, identify or develop a robust measurement tool, and evaluate the association between eHealth literacy and adherence to behavioural changes in response to eHealth interventions. For the 12 weeks internship, we hope to identify relevant definitions from literature and synthesise them to develop a standardised definition.

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Faculty Supervisor:

Simon Bacon

Student:

Partner:

University of Birmingham

Discipline:

Sociology

Sector:

Health and Related Sciences & Technology; Information and Communications Technology

University:

Concordia University

Program:

Globalink Research Award

Real-time Control Software, Calibration, and Assessment of a Redundant Robotic System Operating in a Supersonic Wind-Tunnel

MAE Robotics Inc. is working on a unique robot system called Captive Trajectory System (CTS) for supersonic wind tunnels. The robot will move aerodynamic models and prototypes inside the tunnel to measure and simulate their motion trajectories. This research project will focus on developing and implementing the appropriate robot control architecture, various redundancy resolution schemes, motion capture system, and calibration strategies to make sure the robot operates accurately in the harsh environment of the wind tunnel. The intern will help with robot calibration using the motion capture system and implementing visual servoing to increase accuracy. This research will help MAE Robotics create a high-quality and accurate robot system that can be used for other niche applications and attract clients to their research facility in Canada.

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Faculty Supervisor:

Mojtaba Ahmadi;Nafiseh Kahani

Student:

Partner:

MAE Robotics

Discipline:

Engineering

Sector:

Manufacturing; Professional, scientific and technical services

University:

Carleton University

Program:

Elevate

Visual comfort in a hedonic mobile multitasking context

The research problem of interest to EssilorLuxottica and to be addressed in this Mitacs Accelerate project with two M.Sc interns is the user experience of people who wear glasses in dynamic mobile contexts. Its significance for Canadians who wear glasses is due to the reality that smartphones have become an essential tool for interacting with service providers in the entertainment, relaxation and tourism sectors. The objectives are as follows: to better understand the challenges of these users in a natural context of dynamic vision, and identify the most and least comfortable moments as well as user behavior during these moments. The proposed methodology is to measure neurophysiological data with high temporal precision first in a controlled lab environment at Tech3Lab, HEC Montréal, and then in a semi-controlled experiment at an interactive Augmented reality exhibit at the Insectarium in Montreal, using an application developed by EssilorLuxottica for iPhone and mobile UX measurement tools from Tech3Lab

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Faculty Supervisor:

Pierre-Majorique Léger;Constantinos Coursaris

Student:

Partner:

Ville de Montréal (Espace pour la vie);Essilor

Discipline:

Engineering

Sector:

Manufacturing

University:

HEC Montréal

Program:

Accelerate

Analyzing Winter Wheat Growth Pattern using Remote Sensing.

The proposed research project focuses on improving winter wheat production in Northwestern Ontario, a region with a short growing season and unpredictable climate. Farmers must select optimal planting dates for winter wheat for maximum production and it is challenging due to the number of growing days and planting depth before winter. Hence, the project aims to analyze the growth pattern using remote sensing to develop standard procedure for the region. A multispectral camera attached to a drone will be used to capture images of the test area with a very high spatial resolution, which will then be processed using specialized image processing software and machine learning algorithm to obtain accurate information on crop growth dynamics. It is expected that this project will provide valuable information for improving production and efficiency in the region’s agriculture.

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Faculty Supervisor:

Muditha Heenkenda

Student:

Partner:

Instituto Tecnológico de Costa Rica

Discipline:

Engineering

Sector:

Agriculture and Food; Technology; Environmental Science and Technology

University:

Lakehead University

Program:

Globalink Research Award

Mapping and Modelling Tolerant Hardwood Species Regime Preferences across New Brunswick

Hardwood tree species provide many ecological and economic benefits to New Brunswick and across Canada. These benefits relate to biodiversity and habitat, high-quality timber products, and of course maple syrup. Understanding where these species thrive provincially will be necessary to help New Brunswick forests to withstand the challenges posed by climate change. Using relevant and advanced software such as ArcGIS pro (a mapping and spatial analysis software) in conjunction with provincially available and in-house created layers relating to environmental gradients, will be the keystone to this analysis. This allows for rapid application of provincial data to illustrate the locations across the province where hardwood trees (specifically sugar maple) will thrive. Having such a tool will allow the Northern Hardwood Research Institute to implement the most recent research in their management plans for clients to help maintain and create a robust hardwood tree community.

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Faculty Supervisor:

Paul A. Arp

Student:

Partner:

Northern Hardwoods Research Institute

Discipline:

Earth science

Sector:

Agriculture; Professional, scientific and technical services

University:

University of New Brunswick

Program:

Accelerate

Vers la personnalisation de masse par une stratégie de tarification dynamique en contexte de PME

Les PME manufacturières du Québec sont en constante évolution et doivent faire preuve d’agilité pour se
démarquer de la concurrence. La personnalisation des biens, une technique de production couramment utilisée
au Québec, est un des moyens empruntés pour faire sa place dans un marché compétitif. En revanche, la mise
en production de produits hautement personnalisée peut amener des coûts supplémentaires considérables.
Comment faire pour optimiser les revenus d’une entreprise offrant une multitude de combinaison d’un produit et
faisant face à des besoins variés ? Comme dans l’industrie touristique, les prix dynamiques tendent à suggérer
un prix de vente optimale en fonction de paramètres en temps réel. Mais quelles sont les variables à considérer
dans une entreprise manufacturière ? Quels sont les préalables à implanter avant l’utilisation d’une telle stratégie
? Les impacts de l’utilisation de prix dynamiques en entreprise seront mesurés afin de déterminer quels sont les
bénéfices sur l’augmentation de la rentabilité.

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Faculty Supervisor:

Sébastien Gamache

Student:

Partner:

Robovic

Discipline:

Engineering

Sector:

Manufacturing

University:

Université du Québec à Trois-Rivières

Program:

Accelerate

Estimating Sugarcane Maturity Using Remote Sensing Techniques

Sugarcane is native to the warm temperate, tropical regions of the world and is highly weather dependent. Recent climate change has hampered its growth, resulting in reduced yields, forcing farmer to adapt different practices in Costa Rica. There are four main phenological and development stages in Sugarcane. This project is focused on the maturation process of the crop, which is influenced by several factors, including climate. Currently, sugarcane maturity is measured by ground sampling, which is a tedious, cost effective and not entirely practical methodology. Hence, it is necessary to develop time and cost effective method for identifying sugarcane maturity levels. The research aims to develop a method for estimating sugarcane maturity using remote sensing data. The expected outcomes will help to increase sugarcane management and competitiveness in order to mitigate the detrimental effects of climate change on sugarcane growth.

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Faculty Supervisor:

Muditha Heenkenda

Student:

Partner:

Instituto Tecnológico de Costa Rica

Discipline:

Engineering

Sector:

Agriculture and Food; Environmental Science and Technology; Technology

University:

Lakehead University

Program:

Globalink Research Award

Personalization with Integration of Sensor Signals in Cloud Architecture

This research project focuses on the integration anonymized signals from sensors. The capture of this data and application provides enhancement and personalization and additional clarity of information to optimize the value of benefits and features for FutureCite membership services.

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Faculty Supervisor:

Russell Greiner

Student:

Partner:

FutureCite Inc.

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

University of Alberta

Program:

Accelerate

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