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
BC
812
MB
673
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842
SK
8957
ON
9368
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96
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579
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1120
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Projects by Category

Medical Device Utilizing Saliva for Concussion Screening

The project involves developing a medical device to detect the clinically validated concussion biomarker, S100B. The intern’s work focuses on creating a device that can identify a specific threshold of this biomarker, crucial for identifying concussion risk. This research offers HeadFirst a unique opportunity to understand the potential of S100B in saliva for concussion diagnosis, potentially revolutionizing the industry with groundbreaking insights.
Moreover, this project is a vital starting point for our ongoing concussion research. Experience gained here, along with data and knowledge, will drive future investigations into various concussion biomarkers, allowing us to refine diagnostic techniques and potentially uncover new markers. This commitment places HeadFirst at the forefront of concussion diagnosis advancements, strengthening our reputation as a field leader, and contributing to enhanced concussion management and care.

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Faculty Supervisor:

William Anderson

Student:

Partner:

HeadFirst

Discipline:

Engineering

Sector:

Manufacturing

University:

University of Waterloo

Program:

Accelerate

La combinaison de la cyclosporine 0.09% et de la thérapie IPL dans le traitement de la sécheresse oculaire chez les porteurs de lentilles cornéennes symptomatiques : essai clinique randomisé contrôlé par simulacre

La sécheresse oculaire est un problème très répandu qui peut causer des symptômes significatifs et nuire à la santé de la surface de l’oeil. Les porteurs de lentilles de contact sont particulièrement sujets à souffrir de
sécheresse oculaire. Ce projet va étudier l’impact de la combinaison de deux traitements différents sur les gens qui portent des lentilles de contact et qui souffrent de sécheresse. Le premier traitement est une goutte, la
cyclosporine. Le deuxième est un traitement avec de la lumière intense pulsée (IPL) fait en clinique pour stimuler les glandes qui produisent les larmes. Les participants seront recrutés et recevront de la cyclosporine pendant 4
mois. La moitié du groupe recevra ensuite un traitement IPL tandis que l’autre recevra un faux traitement IPL. Ce projet permettra de voir si le produit de cyclosporine produit par la compagnie Sun Pharma convient aux gens qui
portent des lentilles de contact.

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Faculty Supervisor:

Patrick Boissy;Langis Michaud

Student:

Partner:

SunPharma

Discipline:

Life Sciences

Sector:

Manufacturing

University:

Université de Sherbrooke

Program:

Accelerate

Optimisation de l’enroulement amortisseur et de l’épanouissement polaire des grands alternateurs hydrauliques

Ce projet a pour objectif d’améliorer les alternateurs que l’on retrouve dans les barrages hydroélectriques. Les travaux se concentrent sur la partie tournante de l’appareil et plus spécifiquement dans la région qu’on appelle l’épanouissement des pôles du rotor, soit la partie près de l’entrefer. Cette région de l’alternateur est le siège de phénomènes complexes qui ont une influence sur la performance et le rendement de l’alternateur. Dans ce travail, nous proposons de comparer différentes géométries de pôles et d’en optimiser la forme pour minimiser les pertes de l’alternateur. Des études spécifiques sur le profil du pôle et la distribution des amortisseurs seront menées en considérant des géométries d’alternateurs de grande puissance à nombre d’encoches par pôle et par phase entier. Un nouveau rotor avec des formes de pôles optimisées sera conçu pour un banc d’essai disponible au laboratoire et permettra de valider expérimentalement le concept le plus intéressant.

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Faculty Supervisor:

Jerome Cros

Student:

Partner:

GE Renewable Energy

Discipline:

Engineering

Sector:

Manufacturing; Other services (except public administration); Utilities

University:

Université Laval

Program:

Accelerate

Sequent AI – Vertical Strategy

Sequent AI is a consolidation of technology companies across Canada and the U.S. The Sequent AI team is looking to develop a verticalized strategy for IT solutions across the networking, infrastructure, cloud and managed services spaces. This project will allow us to develop key metrics and strategy to bolster our current initiatives

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Faculty Supervisor:

Sandy Staples

Student:

Partner:

Sequent AI

Discipline:

Business

Sector:

Management of companies and enterprises

University:

Queen's University

Program:

Business Strategy Internship

Automated Swimming Analytics

Swimming Canada is currently working to catch up to rival nations in the areas of data acquisition from race video. To gain a significant competitive intelligence advantage over other nations, Swimming Canada needs a mechanism to gather all necessary analytics quickly, accurately, and efficiently for all athletes in a pool. Recent advances in visual machine learning technology have made it possible to track objects in diverse environments. This project will focus on using these new advances to create a computer system capable of reproducing the current manually captured race analytics. When this is completed, such information can then be passed to the coaches/athletes and utilized to further the development of athletes at a given competition, and in the long term.

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Faculty Supervisor:

Ivan Bajic

Student:

Partner:

Own the Podium;Canadian Sport Institute Pacific

Discipline:

Engineering

Sector:

Arts, entertainment and recreation; Health and Related Sciences & Technology; Other services (except public administration); Professional, scientific and technical services; Retail trade

University:

Simon Fraser University

Program:

Accelerate

Optimization of new inhibitors of type 2 serine proteases as anti-influenza agents

The proposed project aims at optimizing new antivirals to fight influenza. Current antivirals, which target proteins of the virus, suffer from severe resistance owing to mutations in the virus. Our group has identified some enzymes in the human lung that are critical for the maturation of the influenza virus. By blocking these enzymes, we expect that the proposed treatment will be a lot less prone to the development of resistance since the target is the host and not the highly mutable virus. In order to achieve this, we plan to finance, with the help of Mitacs and our partner Neomed, three post-doctoral fellows and one PhD student to optimize compounds and test their activity on the specific enzymes expressed in the human lung. This project will allow our partner Neomed to advance these molecules toward clinical development and provide a much needed alternative to current anti-influenza agents.

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Faculty Supervisor:

Eric Marsault;Richard Leduc

Student:

Partner:

Neomed;IntelliSyn R&D;Amplia PharmaTek;Université de Sherbrooke

Discipline:

Life Sciences

Sector:

Manufacturing; Professional, scientific and technical services

University:

Université de Sherbrooke

Program:

Accelerate

Understanding and Predicting Daily Shifts in Emotion with Machine Learning

Individuals’ mood can change on a moment-to-moment basis. Understanding and predicating changes in mood, however, is difficult. A Canadian technology company, called UpBeing, has developed a mobile app to help collect data on mood (among other data) multiple times in a given day. The proposed study will use cutting-edge machine learning techniques and data provided by UpBeing to predict how individual’s moods will change over time. Additional statistical analyses will also be used to help carry out the goals of this study which include: (1) identifying patterns in how people’s mood change in a given day, (2) better understanding what contributes to changes in mood, and (3) determining the best approaches to data analyses to help address missing data.

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Faculty Supervisor:

Ga Wu

Student:

Partner:

UpBeing Inc.

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

Dalhousie University

Program:

Accelerate

Efficient Data Representation for Wildfire Predictions

Wildfires continue to pose severe threats to ecological systems, communities, and economies worldwide. Early and accurate prediction of wildfire occurrences is crucial for effective preparedness and response strategies. This study investigates the application of machine learning methods to predict global wildfire events, utilizing the SeasFire Cubes dataset — a scientific datacube designed explicitly for seasonal fire forecasting on a global scale. Data cubes, representing three-dimensional data (time, latitude, and longitude) with 54 features, present a holistic view of the Earth’s climate variables.
The outcomes of this research contribute valuable insights into the potential of machine learning for wildfire predictions. By evaluating three different approaches on the SeasFire Cubes dataset, we offer a thorough and meaningful comparison of their performance. This comparative analysis assists wildfire management and emergency response agencies in making informed decisions regarding the adoption of machine learning methodologies based on data availability and prediction requirements. The direct comparison of three diverse approaches using the same data facilitates the extraction of best practices from each method, potentially leading to the development of new machine learning models based on these insights.

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Faculty Supervisor:

Steve Easterbrook

Student:

Partner:

Lviv Polytechnic National University

Discipline:

Computer science

Sector:

Artificial Intelligence; Environmental Science and Technology; Forestry

University:

University of Toronto

Program:

Globalink Research Award

Détection automatique de semences de résineux pour l’évaluation en temps-réel de l’efficacité d’un semoir

Dans le but d’améliorer le taux d’ensemencement d’un semoir pneumatique de graines de résineux, un système automatisé de vision par ordinateur permettant la détection automatique des graines de semences de résineux permettant ainsi l’évaluation en temps-réel de l’efficacité d’ensemencement du semoir sera développé. Ce système automatisé sera constitué d’un système de capture d’images par caméra proche infrarouge et d’une interface logicielle permettant la mise en route du logiciel, l’analyse des images pour la détection des graines de semences et l’affichage des résultats découlant de la phase d’analyse de ces images. L’utilisation d’un système de capture d’images proche infrarouge découle du fait que la couleur du médium d’ensemencement est très similaire à celle des graines de semences de résineux, rendant impossible l’utilisation des systèmes de vision dans le visible. De par la nature continue des semoirs de graines de résineux, il est alors requis d’utiliser un système de vision par ordinateur fonctionnant en temps-réel ce qui permet alors l’ajustement rapide des défectuosités d’ensemencement du semoir.

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Faculty Supervisor:

François Meunier

Student:

Partner:

Jiffy Products of America;Quebec Ministère des Ressources naturelles

Discipline:

Computer science

Sector:

Advanced Manufacturing; Forestry

University:

Université du Québec à Trois-Rivières

Program:

Accelerate

Strategic planning for wildlife connectivity and habitat within current and future landscapes

The Toronto and Region Conservation Authority (TRCA) jurisdiction is one of the most densely populated areas in Canada, where urban land cover makes up over half of all land cover types (TRCA 2021). This has contributed to habitat loss and fragmentation for many species, which is a major contributor to biodiversity declines, hence the need for a conservation plan to better account for wildlife needs. Here we consider the movement and habitat needs of more sensitive species (e.g., amphibians, reptiles) to determine where priority wildlife crossings and habitat can currently be found. Secondly, using future landscape plans (2051) for urban development and natural cover we assess where priority crossings and habitat could be created to promote biodiversity and determine what type of potential enhancement action would be best (e.g., forest, wetland, meadow). This work addresses current and future planning that will aid in the reduction of biodiversity loss.

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Faculty Supervisor:

Marie-Josée Fortin

Student:

Partner:

Toronto and Region Conservation Authority

Discipline:

Life Sciences

Sector:

Arts, entertainment and recreation; Professional, scientific and technical services; Public administration

University:

University of Toronto

Program:

Accelerate

Capacity Planning and Optimization of WiMAX for Smart Grid, Part 2

Smart grid (SG) aims at modernizing the current power grid which can better manage the electricity through the grid and react to the system faults quicker. To realize this goal, many sensors are attached to different points of the power grid infrastructure. These sensors collect data and can be used for controlling, protecting, and monitoring the status of the grid by receiving comands from the utility control center. Hence, a two-way communication infrastructure seen to be required for smart grid realization.
One of the Hydro companies intends to modernize their power grid and our task is to design a wireless infrastructure given the number of end-user devices and the system specifications. It is one of the potential solutions considered for smart grid implementation. My task is to simulate the network architecture based on one of the wireless technologies and optimize the system performance in terms of delay, reliability and capacity using a network simulator before the implementation.

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Faculty Supervisor:

Lutz Lampe

Student:

Partner:

Powertech Labs Inc.

Discipline:

Engineering

Sector:

Energy and Utilities; Technology; Energy and Utilities; Technology

University:

The University of British Columbia

Program:

Accelerate

Crystallization-induced asymmetric transformation

Any new tool must match the sensitivity and ease of the best homogenous liquid sampling techniques. It must execute automated slurry capture, phase separation, and sample delivery without perturbing solute-solid equilibria. Our in-line slurry manipulation utilizes EasySampler™ slurry probe, capturing solids and liquids. The sample is delivered via a weak solvent to an inline filter for rapid solid-liquid phase segregation. This supernatant is analyzed to quantify dissolved components. Crystals are then rinsed, dried, dissolved and analyzed through high-performance liquid chromatography (HPLC). Proof of concept involves accessing enantiopure atropisomers, crucial for advanced materials. A recent example involving Bruton’s tyrosine kinase inhibitor highlights the challenge. We aim to employ real-time analysis of dynamic slurry systems to develop CIAT routes to access traditionally inaccessible atropisomers. Our methods could provide access to an entire category of asymmetric molecules via optimized CIAT. This breakthrough could potentially accelerate pharmaceutical development, countering significant R&D barriers.

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Faculty Supervisor:

Jason Hein

Student:

Partner:

Université de Rouen Normandie

Discipline:

Physics

Sector:

Education

University:

The University of British Columbia

Program:

Globalink Research Award

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