Innovative Projects Realized

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

31620 Completed Projects

2978
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5221
BC
856
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696
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899
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9419
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9858
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98
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619
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1192
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Projects by Category

State Estimation and Active Equalization of Lithium-ion Batteries for Application in Battery Management System (BMS) for Electric Vehicles

Electric vehicles (EVs) industry is a promising solution to address the oil crisis and environmental pollution. There are some challenges that limited the widespread adoption of EVs such as limited driving range, long charging time, and safety consideration. To tackle these challenges, the battery management system (BMS) in electric vehicles requires substantial improvement. For instance, accurate battery on-line state estimation in BMS, such as state of charge (SOC), state of health (SOH), and state of power (SOP) can enhance the reliability of EVs. On the other hand, internal and external conditions of cells could cause inter-cell inconsistency of variables such as operating voltage, SOC, or capacity which accelerate the aging mechanism and decrease the battery life cycle. In this research, a physics-based model will be developed to accurately estimate the battery states and efficiently and rapidly equalize the inter-cell inconsistencies to enhance the battery life cycle.

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Faculty Supervisor:

Michael Fowler

Student:

Partner:

Granano Tech

Discipline:

Engineering

Sector:

Manufacturing

University:

University of Waterloo

Program:

Accelerate

Implementing Industry 4.0: Agent-based simulation as a supporting tool for companies’ digital transformation

Industry 4.0 is the main strategy to strengthen the competitiveness of the manufacturing sector over the next years. Simulation is a key enabling technology of Industry 4.0, supporting the development of planning and exploratory models to optimize decision making, the design and operations of complex and dynamic production systems. It may also support companies to evaluate the risks, costs, implementation barriers, impact on operational performance and a roadmap toward the 4th Industrial Revolution. This project aims to investigate how to model and simulate Industry 4.0 implementation scenarios to support companies’ digital transformation using agent technology. First, a general framework will be proposed to guide researchers and practitioners to modeling and simulating Industry 4.0 scenarios. Then, proofs-of-concept will be developed in collaboration with the industrial partner to validate the framework.

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Faculty Supervisor:

Fabiano Armellini;Luis Antonio de Santa-Eulalia

Student:

Partner:

Productique Québec Inc.

Discipline:

Engineering

Sector:

Advanced Manufacturing; Information and Communications Technology; Artificial Intelligence

University:

Polytechnique Montréal

Program:

Accelerate

Gate Driver Development for E-mode GaN Power HEMT

Crosslight Inc. is a leading provider of technology CAD tools for the design and simulation of semiconductor devices. They are also developing innovative gallium nitride (GaN) HEMT which is considered as one of the next generation materials for power electronics. Because of the material properties of GaN are different from the traditional silicon semiconductor devices, GaN offers superior advantages as power transistors, including low conduction loss, low switching loss, and high switching frequency. These benefits can both increase the system efficiency of power converters and decrease the passive component cost and the PCB size. However, there are some challenges existing in GaN power HEMTs as their gate structures are more prone to breakdown when compared to silicon transistors. Therefore, it is necessary to design a gate driver for GaN HEMT with the appropriate output voltage. This work could assist the market expansion of GaN power HEMTs by offering simple gate driving solutions.

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Faculty Supervisor:

Wai Tung Ng

Student:

Partner:

Crosslight Software Inc

Discipline:

Engineering

Sector:

Information and cultural industries; Manufacturing; Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Causes and Methods of Reducing Dilution at the Seabee Mine

Open stope mining is one of the most commonly used mining methods in Canada because of the strong
rock types of the Canadian shield, high production rates and the high extraction ratios associated with
this mining method. However, dilution associated with this mining method can add significant costs to
mining. This research looks at factors that may especially influence the dilution associated with
narrow veined orebodies. These factors include:
? induced stress changes,
? blast damage
? undercutting
Induced stress changes may weaken the rock mass prior to mining and blast damage as well as
undercutting could play a factor in stope dilution. It is difficult to obtain good blast fragmentation in
narrow vein mining, without using a high explosive concentration. These factors will be coupled with
established empirical design methods for assessing stope performance.
Through quantifying the above design parameters, this study aims to improve the prediction of open
stope dilution for narrow veined orebodies.

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Faculty Supervisor:

Doug Milne

Student:

Partner:

Claude Resources Inc

Discipline:

Engineering

Sector:

University:

University of Saskatchewan

Program:

Accelerate

Politiques de rétention du personnel en milieu rural

En milieu rural, plusieurs PME ne peuvent se développer à leur plein potentiel car elles peinent à embaucher une main-d’œuvre qualifiée et engagée. Compte du contexte actuel de pandémie qui invite l’ensemble des travailleurs à revoir et à se recentrer sur leurs besoins personnels et professionnels, les PME doivent plus que jamais être à l’écoute de leurs employés pour favoriser leur satisfaction et engagement au gré du temps. La présente recherche propose d’examiner les déterminants organisationnels du fonctionnement au travail et intention de demeurer en emploi des employés d’entreprises manufacturières en milieu rural. Plus spécifiquement, les pratiques de leadership, les mesures organisationnelles et les pratiques de gestion seront examinées en relation avec l’image organisationnelle (indicateur d’attraction), de même que la motivation, l’engagement, la satisfaction (indicateurs de rétention du personnel) et l’intention de quitter des employés (indicateur de roulement du personnel).

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Faculty Supervisor:

Stéphanie Austin

Student:

Partner:

Maxi-Drain;Excavation Alain Lemay;L4 Construction

Discipline:

Business

Sector:

Construction and infrastructure; Manufacturing

University:

Université du Québec à Trois-Rivières

Program:

Accelerate

DREAMLAND; Mobile

Le projet DREAMLAND; Mobile a pour objectif d’améliorer considérablement la qualité des soins hospitaliers promulgués aux grands brûlés lors des séances d’hydrothérapie et transformer une expérience hospitalière à priori négative, voir traumatisante, en une expérience ludique et positive, tant pour l’enfant que pour la famille qui l’accompagne.
Dans le cadre des MITACS, les stagiaires travaillerons à l’optimisation du jeu vidéo en réalité virtuelle DREAMLAND pour les séances de traitement d’hydrothérapie destinées aux enfants grands brulés. Cette expérience en réalité virtuelle est adaptée aux jeunes de 5 à 17 ans et est utilisée comme outil de distraction. Il permet de remplacer ou cohabiter avec la médicamentation analgésique aux effets fréquemment délétères, ceci afin de la réduire, voire même dans certains contextes l’éliminer complètement. Le game design de DREAMLAND a été précisément conçu afin d’optimiser le niveau d’engagement et d’immersion du jeune patient.

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Faculty Supervisor:

Cathy Vézina;Sylvie Le May

Student:

Partner:

Paperplane Therapeutics inc.

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Université de Montréal; Université du Québec en Abitibi-Témiscamingue

Program:

Accelerate

Interpretable dimensionality reduction of multivariate time series data using LSTM based autoencoders

Data collection over time is a common practice in many large organizations- including financial institutions and health care providers- often with the goal of using this data to predict future challenges and opportunities. While this data may contain valuable information, it is often unstructured, coming from different sources and recorded at different times. This lack of structure makes extracting useful information difficult, as most standard statistical and machine learning tools are designed to work with data in a fixed structure. This project will develop a framework for automatically learning a fixed length representation composed of interpretable features from unstructured data collected over time, which requires minimal intervention by human experts. The efficacy of the framework will be evaluated by learning representations for electronic health records, created by the Synthea simulator.

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Faculty Supervisor:

Ting Hu;Yuanzhu Chen

Student:

Partner:

NASDAQ Canada Inc

Discipline:

Computer science

Sector:

Finance and Insurance; Health and Related Sciences & Technology; Information and Communications Technology

University:

Memorial University of Newfoundland

Program:

Accelerate

Short Text Similarity Calculation and Related Question Recommendation in Customer Service Chatbots

We build up chatbots for commercial companies to serve their needs, such as customer services. Within the whole chatbot building platform, there is one core component which is the short text similarity calculation component. We would like to improve our calculation capability for matching similar questions, as well as recommend related questions for the customers while they are chatting with the customer service agents.

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Faculty Supervisor:

Animesh Garg

Student:

Partner:

RSVP Technologies Inc

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Développement d’un algorithme de reconnaissance d’images de papillons tropicaux

Le nombre d’espèces qui nous entourent est si important qu’il peut être ardu, même pour les spécialistes, de toutes les identifier. Cela est particulièrement vrai pour les insectes. De plus, avec l’avènement des technologies mobiles de l’information, la quantité et la qualité des images disponibles n’a jamais été aussi importante. Grâce aux appareils photos numérique et aux téléphones intelligents, virtuellement n’importe qui peut générer des données d’observations qui peuvent être utilisées pour le suivi de la biodiversité. L’intelligence artificielle s’impose comme une solution pour traiter toutes ces informations et faciliter l’identification des espèces sur les photos, pour les experts comme pour le public en général. Ce projet vise à développer un outil permettant d’identifier automatiquement des papillons à partir d’images de spécimens vivant prises avec une application mobile dans un musée. Grâce à cet outil, les visiteurs seront en mesure de trouver plus facilement de l’information sur les papillons qu’ils observent tout en réalisant qu’ils peuvent eux-mêmes contribuer à document la biodiversité après leur visite au musée.

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Faculty Supervisor:

Michael Brudno;Yoshua Bengio

Student:

Partner:

Institut de recherche en biologie végétale

Discipline:

Computer science

Sector:

Manufacturing; Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Youth Employment & Education and the COVID-19 Impact

The proposed project consists of a literature review and jurisdictional scan. We are seeking to understand the past and current literature on youth economic engagement, labour market engagement, post-secondary and training strategies and research, social wellbeing, and specific vulnerabilities for youth when engaging in the labour market. CFY will use this research to shape strategic recommendations to meet today’s challenges and barriers in the COVID pandemic and “new normal” so that Choices for Youth and other partners can act now and be ready to meet the needs of youth in the weeks and months ahead.

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Faculty Supervisor:

Natalie Slawinski

Student:

Partner:

Choices for Youth

Discipline:

Business

Sector:

Health and Related Sciences & Technology; Other services (except public administration)

University:

Memorial University of Newfoundland

Program:

Accelerate

Rural Response to COVID-19: A case study of Perth Huron, Ontario

The consequences of the COVID-19 pandemic are far-reaching and extend beyond the spread of the disease and efforts to quarantine it. With emergency management efforts underway, opportunities exist to develop more effective and efficient response measures to increase the resiliency of our communities amidst this and future public health crises. Developing impactful resilience strategies requires a regional- and community-scale focus. While most Canadians live in urban centres, nearly 20% of the national population resides in small and/or rural centres. Across Canada’s rural landscape are communities facing unique realities, complex challenges, and numerous opportunities. In partnership with the Social Research and Planning Council and the Huron Arts and Heritage Network, this project will examine Huron and Perth Counties as case studies to explore what planning activities are required in small and rural communities to best support ongoing recovery efforts and to increase resiliency and well-being over the long-term. Outcomes from this project will support rural communities to develop effective local policies and planning strategies to respond to the coronavirus pandemic and future disruptive events.

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Faculty Supervisor:

Leith Deacon;Wayne Caldwell;Silvia Sarapura;Sara Epp

Student:

Partner:

United Way Perth-Huron Social Research Planning Council;Huron Arts & Heritage Network

Discipline:

Sociology

Sector:

Health and Related Sciences & Technology; Other services (except public administration)

University:

University of Guelph

Program:

Accelerate

To develop an AI algorithm for continuous monitoring of mental health status using publicly available datasets

Poor mental health and stress are an expected outcome of the COVID-19 pandemic. Social distancing is taking another toll on the mental health of individuals. With most of the medical consultations being held online there is an urgent need to enable continuous monitoring of mental health by identifying risk factors for high stress and poor mental health and to provide individuals with information to improve their health and well-being. Wearable and mobile devices are an efficient and effective mean to achieve this goal in a very cost-effective manner. We would like to develop a new AI algorithm that will help assess mental health status of individuals in a real time fashion by using the continuous data feed from wearable devices. The aim of this project is to examine how accurately these measures could identify conditions of stress and poor mental health. We plan to apply novel algorithms on the already available datasets that are available in public domain to identify correlation between the various physiological markers and the poor mental health.

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Faculty Supervisor:

Steven Wang

Student:

Partner:

C2C Healthcare Inc

Discipline:

Mathematics

Sector:

Professional, scientific and technical services

University:

York University

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.