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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825
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8841
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95
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568
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Projects by Category

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

Développement d’un amortisseur de motoneige semi-actif ajustable à commande électromécanique

La nouvelle suspension des motoneiges Ski-Doo de l’entreprise Bombardier Produits Récréatifs
(BRP), le R-motion, présente un amortisseur passif dimensionné pour un type spécifique de motoneige.
L’objectif est maintenant de concevoir un amortisseur semi-actif couvrant la vaste plage de
fonctionnement de trois amortisseurs KYB présents sur les modèles de motoneiges GSX, MXZ X et
MXZ X-RS et, ainsi, de permettre à BRP d’avoir un seul amortisseur pour la suspension R-motion.
Les caractéristiques d’amortissement devront être modifiables au niveau du volant en fonction du type
de terrain et du mode de conduite choisi par le pilote. Le dynamomètre pour amortisseurs (“Dyno-Shock”)
est une machine hydraulique d’essais du centre de
technologies avancées (CTA) qui a permis de caractériser les trois amortisseurs. La plage d’opération
du nouvel amortisseur a ainsi pu être établie.
Une étude menée sur des technologies d’amortisseurs semi-actifs oriente, pour ce projet, les recherches
vers les technologies de valve proportionnelle.
Les résultats attendus concernent le contrôle en détente et en compression d’une….TBC

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Faculty Supervisor:

Alain Desrochers

Student:

Partner:

Bombardier Produits Recreatifs

Discipline:

Engineering

Sector:

University:

Université de Sherbrooke

Program:

Accelerate

Advancing Strong-Scaling CFD Simulations on Manycore HPC Hardware

Science and engineering companies rely on powerful computers to simulate, in virtual space, the performance of new processes and products. The computer architectures of these systems are evolving to provide even faster virtual solutions with less energy requirements. This hardware evolution impacts software design and implementation of simulation tools such as Computational Fluid Dynamics (CFD). The intern’s project involves software design and implementation related to novel CPU/GPU parallel computers and the CFD software used by the partner organization. The CFD software is essential to product development activities requiring efficient use of powerful supercomputers.

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Faculty Supervisor:

Andrew Gerber

Student:

Partner:

Envenio Inc

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

University of New Brunswick

Program:

Accelerate

Hand Pose Reconstruction with Advanced Sensor And Deep Learning

This project explores 3D pose hand reconstruction using machine learning models with sensor data from innovative input devices. The goal is to propose an approach that provides real-time high fidelity hand reconstruction and understand how users perceive its quality to improve user experience and social interaction in AR/VR. The proposed methodology is to train a deep learning network that learns the mapping from sensor signals to hand pose joint locations. User studies will be performed to assess the quality of predicted hand motions. New evaluation metrics will be explored with respect to user perception ratings in different application scenarios. The training data will be collected using both the Tactual wearable device and motion capture devices. This project allows the Tactual to tailor and develop their Prism sensing technology for new products in AR/VR and prepare new sensing applications in valuable markets such as Automotive and Assisting Living.

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Faculty Supervisor:

Andrei Badescu;Fanny Chevalier;Arvind Gupta

Student:

Partner:

Tactual Labs Co

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Le trouble orofacial myofonctionnel

Le trouble orofacial myofontionel (TOM) réfère à l’ensemble des comportements oromusculaires inadaptés. Il s’agit d’un trouble moteur impliquant la musculature de la bouche et du visage, ce qui interfère avec une maturation normale des structures orofaciale. En orthophonie, les enfants référés pour un TOM présentent généralement une déglutition atypique et un problème d’articulation de la parole marqué par un sigmatisme antérieur et/ou latéral. La mise sur pied d’un protocole recourant à un outil d’imagerie clinique (échographe) aurait un fort potentiel d’amélioration quant à l’évaluation des progrès réalisés, à la réadaptation possible et à la qualité des services dispensés à cette clientèle. La stagiaire participera à la prise de mesures avec l’échographe et sera aussi en charge de l’analyse des données.

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Faculty Supervisor:

Franco Leporé;Lucie Ménard

Student:

Partner:

Clinique MultiSens

Discipline:

Life Sciences

Sector:

Health and Related Sciences & Technology

University:

Université de Montréal

Program:

Accelerate

Développement d’un exosquelette pour les travailleurs de la construction afin de réduire les efforts au tronc et aux épaules

Les blessures ou lésions au travail constituent la 2e cause la plus importante d’invalidité au monde. Afin d’aider les travailleurs dans leurs tâches et réduire l’exposition aux blessures, l’utilisation d’exosquelettes est une avenue prometteuse. Il existe déjà plusieurs exosquelettes d’assistance pour la production automobile et les industries manufacturières, mais ceux-ci ne sont pas adaptés aux travailleurs de la construction. Il devient donc nécessaire de développer un exosquelette spécifique aux travailleurs de la construction sachant que juste au Québec, plus de 138 000 salariés exerçant un métier de la construction pourraient bénéficier d’un tel exosquelette. L’objectif principal de la recherche proposée est donc de développer une preuve de concept d’exosquelette d’assistance du haut du corps pour l’industrie de la construction. Le partenaire Biolift pourrait ensuite commercialiser à l’international l’exosquelette issu de cette recherche.

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Faculty Supervisor:

Maxime Raison;Sofiane Achiche

Student:

Partner:

Biolift

Discipline:

Engineering

Sector:

Manufacturing; Professional, scientific and technical services

University:

Polytechnique Montréal

Program:

Accelerate

The design and implementation of a gamified eHealth movement and mindfulness solution for school-aged children

The online delivery of primary school curriculums may work well for subjects like math and science, but not so well for physical education. Now more than ever, it is crucial that we ensure that children continue to benefit from the countless positive mental and physical health outcomes associated with regular involvement in physical activity. To help keep children moving during this stressful time (i.e., COVID-19), Mitacs is partnering with X Movement to develop an ExerGame Smartphone App that children can use to compete in physical activity and mindfulness challenges against their school friends and family members. We will assess their weekly physical activity habits using FitBits. We are also going to examine if children who use our ExerGame see improvements to their mood and emotional control, resiliency, and life satisfaction. This partnership will help to validate X Movement physical activity and mindfulness programs, while also helping to ignite a child’s love for mindful-movement.

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Faculty Supervisor:

Sidney Kennedy

Student:

Partner:

X Movement

Discipline:

Life Sciences

Sector:

Education

University:

University of Toronto

Program:

Accelerate

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