Projets novateurs réalisés

Explorez des milliers de projets réussis issus de la collaboration entre organisations et talents postsecondaires.

31 620 projets complétés

2978
AB
5221
C.-B.
856
MB
696
NL
899
SK
9419
ON
9858
QC
98
PE
619
NB
1192
NS

Projets par catégorie

Développement d’un nouveau procédé écoresponsable pour traiter la Legionelle dans un système de refroidissement à l’échelle pilote

Le procédé de production de l’aluminium dans les usines de Rio Tinto (RT) nécessite l’utilisation de circuits de refroidissement à l’eau pour, entre autres, la coulée des lingots et le refroidissement des stations électriques. Certaines bactéries potentiellement dangereuses comme les légionelles Sont susceptibles de se développer dans les réseaux de refroidissement et puisque la bactérie représente un risque lorsqu’elle est projetée sous forme d’aérosols, ces procédés sont suivis rigoureusement. Les méthodes de contrôle actuellement utilisées nécessitent l’ajout de produits chimiques oxydants corrosifs (chlore, brome) qui peuvent être nocifs pour la santé humaine et l’environnement. Au cours des deux dernières années nos travaux ont permis d’identifier de nouveaux composés d’origine naturelle non toxique pour l’environnement qui ont démontré une forte activité contre Legionella pneumophila. Dans le cadre de ce projet de recherche, nous proposons de développer un nouveau procédé combinant plusieurs de ces composés pour traiter les légionelles dans un système de refroidissement à l’échelle pilote. Les produits anti-légionellose innovants développés dans le cadre de ce projet contribueront à créer une nouvelle expertise et pourront sans doute s’appliquer à différents réservoirs d’eau dans divers domaines. Le remplacement des produits oxydants toxiques par ces nouveaux produits naturels TOBECONT.

Voir la description complète du projet
Superviseur du corps professoral :

Jean Legault

Étudiant :

Partenaire :

Rio Tinto Alcan (Jonquière, QC)

Discipline :

Life Sciences

Secteur :

Manufacturing; Mining; Professional, scientific and technical services

Université :

Université du Québec à Chicoutimi

Programme :

Accelerate

Determining the physiological mechanisms behind the ObeEnd device on (1) hormones associated with weight loss and (2) recovery from exercise

In Canada, approximately 1 in 4 adults are living with obesity. Obesity is cause by a complex interplay of genetic, behavioural, and environmental factors that can increase the risk of developing non-communicable diseases such as Type 2 diabetes mellitus, coronary heart disease, asthma, several cancers, and disability in adulthood. Restricting caloric consumption and increasing physical activity levels are used in efforts to induce weight loss. However, using these methods alone may not be sufficient in sustaining long-term weight loss. The ObeEnd device manufactured by WAT Medical Enterprise, promises new and innovative wellness technology that uses electrical pulses to stimulate acupressure points to help control appetite and recovery from exercise. More research is needed to identify the physiological mechanisms of this device which will provide evidence for the company to make recommendations to optimize weight loss and help mitigate public health initiatives to reduce obesity among Canadians and associated co-morbidities.

Voir la description complète du projet
Superviseur du corps professoral :

Geoffrey Dover;Sylvia Santosa

Étudiant :

Partenaire :

WAT Medical

Discipline :

Life Sciences

Secteur :

Manufacturing

Université :

Concordia University

Programme :

Accelerate

Passive methane oxidation biosystems design and instrumentation

Landfill emissions can continue for decades after biogas extraction systems have been turned off. The possibility of land reclamation projects, such as turning an old landfill into a park, depends of passive, low-maintenance, technologies that can cope with these emissions.
The main goal of this research is to design, construct and monitor a pilot-scale passive methane oxidation biosystem (PMOB) at the old Montreal landfill, which is being turned into a park (Frédérik-Bach parc).
Our main hypothesis is that our design methodology will lead to an effective PMOB, i.e. one capable of abating the majority of the CH4 being collected from a lateral trench, where the biogas is poor in CH4. This design methodology is based mainly on the hydraulic properties of the PMOB and how they can affect gas migration.
The candidate will help documenting the effectiveness of this pilot-scale PMOB. She will bring back to Brazil the main elements of design to complete her doctoral program, which focuses on methane oxidation in landfill covers.

Voir la description complète du projet
Superviseur du corps professoral :

Alexandre Cabral

Étudiant :

Partenaire :

Universidade Federal de Campina Grande

Discipline :

Engineering

Secteur :

Environmental Science and Technology; Biotechnology; Sustainability & the Environment

Université :

Université de Sherbrooke

Programme :

Globalink Research Award

AI/ML classification of underwater contacts for defense applications

The objective of this project is to develop an automated sonar classifier leveraging modern machine learning approaches in order to decrease the human expertise and cognitive workload required in ships or submarines and enable new applications such as unmanned underwater vehicles.

Voir la description complète du projet
Superviseur du corps professoral :

Ioannis Mitliagkas

Étudiant :

Partenaire :

Ultra Intelligence & Communications

Discipline :

Computer science

Secteur :

Information and Communications Technology; Ocean Tech; Technology

Université :

Université de Montréal

Programme :

Accelerate

Adaptation des outils de la redirection écologique au contexte québécois

Le projet vise à adapter au contexte québécois, à l’intention d’une firme de consultation en gestion, le cadre théorique et les outils d’intervention de la « redirection écologique », un nouveau modèle de gestion développé en France qui vise à aider les entreprises à redéfinir leurs objectifs d’affaires pour faire face aux nouveaux défis écologiques qui se dessinent, notamment les changements climatiques et la consommation de ressources rares. Cette approche émergente va plus loin que la simple « économie verte » et invite les entreprises à renoncer aux activités et aux technologies dont l’empreinte écologique est peu soutenable. Son adoption permettra au Canada de réduire son empreinte et d’identifier les approches de gestion adaptées à un contexte où l’avenir est incertain, les investissements coûteux et les bénéfices des entreprises en déclin.

Voir la description complète du projet
Superviseur du corps professoral :

Yves-Marie Abraham;Alexandre Monnin

Étudiant :

Partenaire :

Coboom

Discipline :

Business

Secteur :

Professional, scientific and technical services

Université :

HEC Montréal

Programme :

Accelerate

Data analytics in asset management of erosion control structures

The project aims to study the use of advanced machine learning algorithms to enhance the ability of TRCA to detect shoreline erosion. Accurate detection of shoreline erosion will have significant contribution to optimizing TRCA asset management plans. This will enable TRCA to work with local communities to reduce the impacts of climate change on shoreline erosion, which in turn will help safe shoreline properties and community spaces from increased erosion. The accurate perdition of erosion will also enable TRCA to develop effective plans to protect natural habitat. It will also support building a long-term investment plan to protect shorelines.

Voir la description complète du projet
Superviseur du corps professoral :

Tamer El-Diraby

Étudiant :

Partenaire :

Toronto and Region Conservation Authority (Vaughan, ON)

Discipline :

Engineering

Secteur :

Professional, scientific and technical services; Public administration

Université :

University of Toronto

Programme :

Accelerate

Life Signs and Self-Harming Human Gestures, Actions and Behaviors Monitoring with Combined Physiological and Physical Indicators

Suicide is one of the most important causes of deaths in the prison environment, both in Canada and internationally. Rates of attempts of suicide and self-harm have been on the rise in recent years. To address this problem, there is a real and immediate need for an automated, private, and effective monitoring system that can detect attempts of suicide and self-harm in real-time. This project aims to develop monitoring solutions by studying common behavioral patterns and assessing vital signs changes with multi-modal sensing technologies and state-of-the-art artificial intelligence algorithms to detect gestures, actions or behaviors that can be classified as posing a threat to a prisoner’s health and safety. Innovative, reliable and trusted solutions are designed for triggering alarms when the conditions are met in order to assist prison environment’s personnel while ensuring the respect of prisoners’ privacy and confidentiality.

Voir la description complète du projet
Superviseur du corps professoral :

Pierre Payeur

Étudiant :

Partenaire :

Spectronix Inc

Discipline :

Engineering

Secteur :

Professional, scientific and technical services

Université :

University of Ottawa

Programme :

Accelerate

Machine Learning Techniques for Short-Term Electric Load Forecasting

Once produced, electricity is difficult to store in large quantities. Hence, accurate electric load forecasting is of critical importance to balance production and consumption for modern power grids integrating more and more intermittent renewable energy and variable loads such as electric vehicles. Short-term electric load forecasting for local areas is also of interest to efficiently respond to demand at the distribution level. Any significant forecasting error can result in reliability issues, loss of opportunity, or additional costs to the business, while improvements in forecasting performance could benefit both the consumers and utility companies by optimizing resources and costs. Most of the current short-term load forecasting algorithms assume that the load consumption and energy generation patterns are stationary, which is not the case in real world. In this project, we plan to use recent progress in machine learning to improve the performance and robustness of short-term electric load forecasting.

Voir la description complète du projet
Superviseur du corps professoral :

Benoit Boulet;Di Wu

Étudiant :

Partenaire :

Hydro-Quebec

Discipline :

Engineering

Secteur :

Professional, scientific and technical services; Utilities

Université :

McGill University

Programme :

Accelerate

Gameplay Test Automation with Reinforcement Learning

To ensure high performance for AMD’s graphics cards, the company performs extensive testing on computer game titles. Most gameplay testing is done manually, which results in significant effort and cost expenditure. Thus, this project’s objective is to develop a program capable of learning to automatically play a modern video game. Rather than aiming to optimize performance in the game, the goal is to automate basic actions to explore various graphical scenes the game offers, for the purpose of testing graphics cards. This program will run using reinforcement learning, a type of machine learning focused on enabling a program to learn how to navigate environments by offering it rewards for taking optimal actions. As such, the project’s success would also result in a deeper understanding of reinforcement learning algorithms in practice.

Voir la description complète du projet
Superviseur du corps professoral :

Amir-massoud Farahmand

Étudiant :

Partenaire :

AMD Canada

Discipline :

Computer science

Secteur :

Manufacturing; Professional, scientific and technical services

Université :

University of Toronto

Programme :

Accelerate

Developing MVP Driven Cloud Service Space

Smartphones are becoming a combination of a mobile phone, a digital wallet and mini personal computer all rolled into one place which adds to the need for such virtual space. Virtualization of space or what is generally known as Mobile Virtualization Platform (MVP) include servers, storage and applications into a single solution to eliminate complexity, lowers costs, and frees up management time. However, when assessing MVP solutions, user’s always point to solutions that are easier to use, set up, and administer. This research project is an attempt to provide mobile manageability, security, cost, compliance, application development and deployment over cloud space with a thin software layer (VMware MVP) that will be embedded on a mobile phone to decouple the applications and data from the underlying Smartphone hardware and enabling users to deploy applications and software stack on a wide variety of emulators without worrying about the underlying hardware differences.

Voir la description complète du projet
Superviseur du corps professoral :

Sabah Mohammed;Jinan Fiaidhi

Étudiant :

Partenaire :

VurBox

Discipline :

Computer science

Secteur :

Professional, scientific and technical services

Université :

Lakehead University

Programme :

Accelerate

Large-scale Inversion of geophysical data

When using geophysical methods to gain insight into the structure of earth, large geophysical data sets are collected. Since the earth is a 3D structure, the data must be interpreted and processed in 3D to be of the most value in the exploration process. This research will develop the capability to invert large gravity, magnetics, and airborne EM datasets accurately and in a reasonable timeframe. This requires the research and development of inversion software, data visualization and QC software, and inversion setup scripts. Currently much of the time spent on inversion projects is spent manipulating and formatting the data and additional inversion input files. By researching and developing efficient workflow procedures and robust and automated scripts to create the necessary inversion input files, this setup process can be streamlined and made much more efficient. The partner organization, Computational Geosciences Inc., provides modeling and data processing services for the resource sector.

Voir la description complète du projet
Superviseur du corps professoral :

Eldad Haber

Étudiant :

Partenaire :

Computational GeoSciences Inc

Discipline :

Mathematics

Secteur :

Mining

Université :

The University of British Columbia

Programme :

Accelerate

Automated Domain Specific Essay Scoring

Blees AI is an educational innovator who believe in transforming and improving the teaching experience and in turn positively impact students learning and maximize their potential. The goal of this project and the company is to provide a complete version of the software platform to the educators to assist their essay grading. Blees AI’s client a large certification body representing a Canadian profession experienced 84% scoring accuracy, 13x faster scoring results, and a 60% cost reduction opportunity by applying its technology. The company automates the process of grading students’ essays and instantly provides consistent and personalized feedback at scale. Also, the company is looking to build automated personalized feedback modules to the students using its data. The AI models do not evaluate sentence structures, or grammar, format, but evaluate the context of the student responses and look for the key concepts that educators are looking for.

Voir la description complète du projet
Superviseur du corps professoral :

Linbo Wang

Étudiant :

Partenaire :

Blees AI

Discipline :

Computer science

Secteur :

Information and cultural industries; Professional, scientific and technical services

Université :

University of Toronto

Programme :

Accelerate

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Mitacs est financé par le gouvernement du Canada, le gouvernement de l’Alberta, le gouvernement de la Colombie-Britannique, Research Manitoba, le gouvernement du Nouveau-Brunswick, le gouvernement de la Nouvelle-Écosse, le gouvernement de l’Ontario, Innovation PEI, le gouvernement du Québec, le gouvernement de la Saskatchewan, le gouvernement de Terre-Neuve-et-Labrador et le gouvernement du Yukon.