Innovative Projects Realized

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

30156 Completed Projects

2861
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5059
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812
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673
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842
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8957
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9368
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96
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579
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1120
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Projects by Category

Device-free indoor activity detection and localization using Wifi signals

Creating smart indoor environments that detect and understand human daily life activities is attracting significant amount of attention, due to their huge potential impact on context-aware technologies. Aerial Technologies, our partner organization, is developing products that provide device-free indoor localization and activity recognition Wi-Fi signals generated by off-the-shelf Wi-Fi enabled devices. This project will use state of the art machine learning and time-series analysis algorithms to improve the detection and classification capabilities of these systems.

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Faculty Supervisor:

Aditya Mahajan

Student:

Partner:

Aerial Technologies Inc

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

McGill University

Program:

Accelerate

Scale-up of a Green Plasticizer

Plasticizers are used to increase the flexibility and workability of relatively rigid polymers such as poly(vinyl chloride) (PVC). For decades, di-2-ethylhexyl phthalate (DEHP and sometimes termed as dioctyl phthalate (DOP)) was the most common plasticizer used in PVC formulations. However, DEHP has been shown to have negative health effects, such as being an endocrine disruptor. Recently, DEHP has been banned for use in children’s toys and phthalate alternatives are rapidly being offered. We have been developing succinate-based esters that biodegrade to non-toxic metabolites after being discarded while also performing as well or better than DEHP in blend formulations. The previous internship established a promising formulation based on diheptyl succinate (DHPS) with sustainable building blocks and a streamlined process that obeys almost all the 12 principles of green chemistry. TO BE CONT’D

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Faculty Supervisor:

Milan Maric

Student:

Partner:

CG Towers

Discipline:

Engineering

Sector:

Manufacturing

University:

McGill University

Program:

Accelerate

Cold plasma therapy as adjuvant to radiotherapy for breast cancer treatment

The goal of this research is to improve breast cancer local control and reduce treatment side effects by demonstrating that a new cold plasma technology, which generates locally specific reactive oxygen species, has an additive positive treatment impact when combined with conventional radiotherapy. This project has the potential to enable a Canadian technology, to become an adjuvant for radiotherapy. This project will perform the preclinical trials required to provide the fist evidences that this technology has the potential to become a clinically relevant tool when used in conjunction with radiotherapy.

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Faculty Supervisor:

Philip Wong;Sylvain Coulombe

Student:

Partner:

Nexplasmagen;Instadesign Dev

Discipline:

Life Sciences

Sector:

Manufacturing

University:

McGill University; Université de Montréal

Program:

Accelerate

Modeling of ASTM A27 carbon steel hydraulic turbine runner degradation by synergistic effects of erosion-cavitation and corrosion under dynamic turbulent flow

In hydropower plants like any other plants, part failures normally contribute to high maintenance and repair costs; revenue lost due to downtime and cost of replacement power; decreased operating efficiencies; and reduction of equipment service life. In working conditions, the flow of water over the part has a great effect on the rate of corrosion. This means that the shape and condition of the parts in use will have a significant impact on their lifetime, as even a small scratch could cause affect how solution moves over the part, and therefore how it will corrode. In this project we propose a combined experimental and simulation study of the corrosion of materials that are directly used in hydroelectric applications. By mimicking the working environment of these materials, we will develop predictive corrosion models that will support Hydro-Québec in their technology development.

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Faculty Supervisor:

Janine Mauzeroll

Student:

Partner:

Hydro-Quebec (Montreal, QC)

Discipline:

Engineering

Sector:

Construction and infrastructure; Professional, scientific and technical services; Utilities

University:

McGill University

Program:

Accelerate

Modélisation de la production de citrate par la levure Yarrowia lipolytica

L’acide citrique est un acide produit par les microorganismes, plantes et animaux. Il joue un rôle central dans le métabolisme de pratiquement tous les organismes vivants mais il est aussi utilisé comme additif alimentaire pour la diversité de ses propriétés fonctionnelles (régulateur d’acidité, chélatant, acidulant, et antioxydant). Yarrowia lipolytica est une levure oléagineuse GRAS (Generally Recognized As Safe) connue pour sa capacité à accumuler de grandes quantités de lipides mais elle est aussi utilisée pour la production d’acides organiques (principalement l’acide citrique). L’objectif est de proposer une stratégie de modélisation du métabolique de Yarrowia lipolytica capable de prédire la production de citrate et son optimisation. Le modèle devrait permettre l’identification des leviers d’optimisation de la production de citrate qui seront ensuite testés et validés expérimentalement.

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Faculty Supervisor:

Mario Jolicoeur

Student:

Partner:

Université Paris-Saclay

Discipline:

Life Sciences

Sector:

Education

University:

École Polytechnique de Montréal

Program:

Globalink Research Award

Elastographie par erreur en relation de comportement.

Ce projet est dans le domaine d’imagerie médicale appelé l’élastographie par résonance magnétique. Cette technique d’imagerie permet la cartographie des propriétés mécaniques des tissus mous comme le cerveau ou le foie à partir des données obtenues en utilisant un appareil d’imagerie par résonance magnétique. L’objectif du projet est d’accélérer le processus de génération de l’image tout en garantissant la qualité des résultats en couplant les algorithmes basés sur l’optimisation non-linéaire et les algorithmes de minimisation de l’erreur en relation de comportement. Ce projet aidera la communauté de recherche en élastographie par résonance magnétique à avancer sur une problématique fondamentale du domaine qui est d’obtenir des résultats qui soient à la fois significatifs et pertinents sur des applications de santé publique. Sur le plan sociétal, les applications de l’élastographie touchent une large gamme de situations médicales, des maladies du foie jusqu’à la sclérose en plaques, l’épilepsie et les commotions cérébrales.

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Faculty Supervisor:

Elijah Van Houten

Student:

Partner:

Université de Montpellier

Discipline:

Engineering

Sector:

Education

University:

Université de Sherbrooke

Program:

Globalink Research Award

Identification of RC connection behavior for seismic retrofitting

The project aims at defining a new modeling strategy of retrofitted structures by composite materials. This strategy should be able to take into account externally bonded (EB) fiber reinforced polymer (FRP) that strengthens structural components and to assess their interaction with the overall response of the structure subjected to seismic loading. This new strategy will be calibrated using experimental data coming from an original multi-scale campaign to assess material parameters and to identify the dissipative mechanisms to be included in the numerical strategy.

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Faculty Supervisor:

Nathalie Roy

Student:

Partner:

Université Claude Bernard Lyon 1

Discipline:

Engineering

Sector:

Education

University:

Université de Sherbrooke

Program:

Globalink Research Award

Validation d’un test navette adapté (5x5m) à des adolescents en situation d’obésité pédiatrique et de sa reproductibilité pour l’évaluation de la capacité aérobie dans cette population.

Le test de référence afin d’évaluer la capacité aérobie est un test d’effort maximal effectué sous surveillance médicale dans l’enceinte d’un service de médecine du sport. L’accès à ce test étant difficile pour les structures de soins de part son coût et sa complexité de mise en place, des tests de terrain existent afin de mesurer la capacité aérobie des sujets. Les tests de terrain aujourd’hui disponibles montrent une importante fiabilité auprès d’enfant normo-pondérés mais semblent ne pas être adaptés aux contraintes physiologiques et fonctionnelles induites par l’obésité infantile. C’est pourquoi notre projet tend à questionner la validité et reproductibilité d’un nouveau test de terrain (Test Navette adapté : 5 x 5m) pour l’évaluation des capacités aérobies d’enfants et adolescents obèses afin de fournir aux professionnels un test adapté à la population traitée et aux conditions de mise en situation.

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Faculty Supervisor:

Marie-Eve Mathieu

Student:

Partner:

Université Clermont Auvergne

Discipline:

Life Sciences

Sector:

Education

University:

Université de Montréal

Program:

Globalink Research Award

Improved pulse pressure approximation and pattern recognition algorithm for prediction of blood pressure-related health issues

Continuous blood pressure (BP) monitor is highly beneficial for detection and prevention of stroke and cardiovascular disease. The most common BP monitor technique still relies on using a cuff that slows the blood flow, which is both uncomfortable and makes continuous monitoring impossible. Furthermore, research has shown that due to the numerous artifacts, the existing cuff-less BP monitoring technologies such as pulse transit time (PTT) and tonometry are not effective. The purpose of this study is to introduce a non-invasive low-cost and continuous BP monitoring technique which is capable of estimating BP from dilation and retraction of the wrist radial artery. The mentioned approach utilizes two small displacement sensors which can be integrated to any wristband or smartwatch. After calibration, the proposed approach may be used for months to estimate blood pressure in an accurate, easy-to-use and low-cost fashion.

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Faculty Supervisor:

Hilmi Dajani

Student:

Partner:

VitalTracer Ltd (QC)

Discipline:

Engineering

Sector:

Biotechnology

University:

University of Ottawa

Program:

Accelerate

Potentiel d’utilisation du bois d’essences indigènes nordiques imprégné au méthacrylate de méthyle (MMA) pour les boiseries de canots

Les travaux de recherche appliquée visent à évaluer le potentiel du bois imprégné au polymère provenant d’essences indigènes nordiques (région de l’Abitibi) pour les boiseries de canots. L’application visée est les accessoires des canots et non la coque des canots. L’essence majoritairement utilisée par l’industrie du canot est le frêne blanc. Le partenaire désire utiliser une ressource locale, plus facile à obtenir et plus durable que le frêne. Les essences indigènes ne sont pas assez dures (peuplier faix-tremble, épinette-pin-sapin) ou pas assez durables (bouleau blanc) pour cette application. Dans le projet, des pièces de peuplier faux-tremble, de bouleau blanc et d’épinette seront imprégnées au polymère puis caractérisées en termes de propriétés mécaniques et de vieillissement accéléré. TO BE CONT’D

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Faculty Supervisor:

Sébastien Migneault;Ahmed Koubaa

Student:

Partner:

Abitibi&Co

Discipline:

Engineering

Sector:

Manufacturing

University:

Université du Québec en Abitibi-Témiscamingue

Program:

Accelerate

Classification of human activities and detection of behavioral anomalies using thermopile sensor and machine learning

Impending increase in senior population in developed countries is expected to overwhelm the health care system carry a significant social and economic cost. Number of solutions, that leverage ambient intelligence, have been proposed to help aging in place. HomeEXCEPT is working on a non-intrusive solution using a temperature sensor. The research will help in identification and classification of human activities, including fall detection. It will also help in uncovering patterns in daily activities of an individual and in identification of deviations from the usual behavior. This will be attained using machine learning and data mining techniques. The effort will improve sensor’s capability and its applicability in providing a comprehensive aide to aging in place.

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Faculty Supervisor:

Pawan Lingras

Student:

Partner:

Home Except Inc

Discipline:

Computer science

Sector:

Information and cultural industries

University:

Saint Mary's University

Program:

Accelerate

Efficient Machine Learning in a Collaborative Edge-cloud Architecture

With the growing application of learning-based technology in image recognition, speech translation, and other back-end systems like ad advertising, artificial intelligence (AI) has finally moved from research labs to real business. As AI moves closer to reality, it is expected to help us more in more personalized and even critical contexts, like smart home, autonomous car. Traditional learning approaches start to meet new challenges in data collection, resource assumption, latency, since these contexts usually request near real-time interaction and high accuracy to guarantee safety and quality of service, or demand data that may be deemed as private, mostly even in resource-constrained environments. The objective of the proposed research is to examine and eventually improve the performance of artificial intelligence, especially deep learning approaches, in the edge-cloud architecuture. We will investigate the trade-offs among resources, latency, accuracy during the process of applying learning approaches from a system’s perspective. The proposed research can greatly accelerate the adoption of artificial intelligence into real-world production.

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Faculty Supervisor:

Jiangchuan Liu

Student:

Partner:

The Hong Kong Polytechnic University

Discipline:

Computer science

Sector:

Information and Communications Technology; Technology; Transportation (excluding aerospace)

University:

Simon Fraser University

Program:

Globalink Research Award

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