Projets novateurs réalisés

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

30 508 projets complétés

2882
AB
5105
C.-B.
825
MB
681
NL
860
SK
9051
ON
9491
QC
97
PE
586
NB
1141
NS

Projets par catégorie

Machine learning and the COVID Black Box: Safe monitoring of COVID-19 ICU beds, assessment centres, and surgeries

The project aims to optimize healthcare provider and patient safety and monitor PPE use, to optimize resource utilization during the COVID-19 pandemic. Assessment of surgical data from an operating room is a complex process that may require significant resources such as expert input and advanced technology. Automation brings a considerable opportunity to greatly reducing these significant resource requirements – e.g., using computer vision software to detect clinically relevant actions during surgery. With the data collected from operating room black box, the main aim is to analyze 1) hand hygiene, 2) adherence to personal protective equipment (PPE) protocols, 3) breaches in safety, and 4) system vulnerability in Ontario.

Voir la description complète du projet
Superviseur du corps professoral :

Animesh Garg

Étudiant :

Partenaire :

Surgical Safety Technologies Inc

Discipline :

Computer science

Secteur :

Professional, scientific and technical services

Université :

University of Toronto

Programme :

Accelerate

Mitigating Snowy Owl Aircraft Collisions by Relocation

To aid in active management of Snowy Owls and other raptors at airports, it is essential to understand the spatial distribution and movement behaviour of birds both on and off the airfield. The impact of airfields on birds may be particularly pronounced because airfields provide open, undeveloped land similar to early successional habitats that are perceived as high quality by many species. Airport collisions are a significant threat to Snowy Owls and humans, and preventative measures cost over $500 million dollars each in North America alone. By using tracking technology and GIS software, our project will quantify movement data and environmental factors influencing relocations of Snowy Owls from airport facilities. This research will improve our understanding of Snowy Owl relocation behaviour and provide critical data to improve relocation efforts and to minimize collisions between airplanes and Snowy Owls.

Voir la description complète du projet
Superviseur du corps professoral :

Kyle Elliott

Étudiant :

Partenaire :

Falcon Environmental Services

Discipline :

Life Sciences

Secteur :

Professional, scientific and technical services

Université :

McGill University

Programme :

Accelerate

COVID-19 Monitor: a rolling public opinion study on the dynamics of the pandemic

This project will undertake a year-long rolling study of public opinion across eight countries—Canada, the United States, the United Kingdom, Australia, New Zealand, France, Germany and Brazil. It will measure the health, social, and economic impacts of COVID-19 throughout the duration of the pandemic and during its immediate aftermath. The study will be comprised of multiple survey waves in each country, mostly drawn from Vox Pop Labs’ proprietary online respondent panel, which is comprised of several million people worldwide. The proposed research team will be responsible for continuously adapting the survey design to reflect the changing dynamics around the pandemic, for analyzing the incoming data from the surveys, and for promptly and continuously engaging in knowledge mobilization via means such as government briefings, media contributions, and other public information resources.

Voir la description complète du projet
Superviseur du corps professoral :

Michelle Dion

Étudiant :

Partenaire :

Vox Pop Labs Inc

Discipline :

Sociology

Secteur :

Professional, scientific and technical services

Université :

McMaster University

Programme :

Accelerate

Generating a COVID-19 vaccine using ministring DNA and virus-like particles

The COVID-19 pandemic is a global health crisis on an unprecedented scale, with over 1 million confirmed cases, spread over 200 countries. With the world at a virtual standstill, and no existing treatments, there is an enormous need for novel therapeutics and vaccines to combat COVID-19. Our group is working on a DNA vaccine strategy that exploits our proprietary miniaturized DNA vector technology, called ministring DNA (msDNA), to encode and deliver specially engineered copies of COVID-19 viral proteins. Once delivered into human host cells, these proteins will be designed to self-assemble into viral-like particles (VLPs). VLPs are safe, generate a strong host immune response, and have a record of commercial success as vaccine products. We are confident that our strategy will lead to the development of an effective COVID-19 DNA vaccine.

Voir la description complète du projet
Superviseur du corps professoral :

Marc Aucoin;Shawn Wettig;Andrew Doxey;Roderick Slavcev;Mahla Poudineh

Étudiant :

Partenaire :

Mediphage Bioceuticals Inc

Discipline :

Life Sciences

Secteur :

Manufacturing; Professional, scientific and technical services

Université :

University of Waterloo

Programme :

Accelerate

Sous-réseau d’antenne pour Radar à Synthèse d’Ouverture destiné à la mesure de masse de neige

Le projet consistera à concevoir et analyser un sous-réseau d’antenne qui devra s’intégrer à une grande antenne réseau, formant le coeur d’un radar à synthèse d’ouverture (RSO). Ce RSO sera utilisé sur une satellite à basse orbite et sa mission sera d’obtenir des images de la couverture, et plus spécifiquement de la masse de neige dans les régions nordiques du Canada. Le suivi de l’évolution de la masse de neige devient nécessaire à cause du réchauffement global. L’étudiant stagiaire bénéficiera de l’expertise d’ingénieurs spécialisés en conception d’antennes de satellite de la compagnie MDA, chef de file mondial dans le domaine des charges utiles de satellites de communication et de télédétection.

Voir la description complète du projet
Superviseur du corps professoral :

Jean-Jacques Laurin

Étudiant :

Partenaire :

MacDonald, Dettwiler, and Associates Ltd (Sainte-Anne-de-Bellevue, QC)

Discipline :

Engineering

Secteur :

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

Université :

Polytechnique Montréal

Programme :

Accelerate

Pattern Recognition and Event Forecasting to Increase the Performance of Industrial Bioprocesses, in an Effort to Accelerate the Sustainable Development of the Transformation Industry

The first objective is to use ML to reduce the modeling error in predicting the end-of-growth of a batch, reducing the emission of CO2 and water consumption of synthesized products. The second objective is to formulate the algorithms to facilitate its integration into our analytics solution. The third objective is to validate shared learning when applied for 1) forecasting other events and 2) forecasting the same events using similar but different datasets from different users. Finally, the fourth objective is to use this algorithm as a starting point to develop a learning platform to help our customers to learn from the best practice of the industry. The success of the project will 1) enable a significant increase in the efficiency of our customers’ processes with significant environment and social impacts, 2) trigger a series of sales, 3) improve our toolset to create a better offer to our customers, 4) to set a baseline for the development of advanced AI-based tools and expertise.

Voir la description complète du projet
Superviseur du corps professoral :

Ioannis Mitliagkas

Étudiant :

Partenaire :

BioIntelligence Technologies inc

Discipline :

Computer science

Secteur :

Manufacturing; Professional, scientific and technical services

Université :

Université de Montréal

Programme :

Accelerate

Improvement of covid-19 treatment with machine learning

Currently, testing the population more at risk to be severely ill is a cumbersome process. There is a limit to the capacity and the resources of the healthcare system that show a need to be more efficient in discovering infected person. Being able to quickly detect infected person helps reduce the risk of infecting others and it is especially important in environments like nursing homes where there is a high density of person at risk. Using an earpiece that work as a connected object, it is possible to monitor some signals such as cardiac rhythm, level of CO2, and cough. Using machine learning, those data could be used to detect automatically if a person has symptoms that should be checked by a health professional. It would improve the efficiency of detecting infected persons and, therefore, help healthcare providers manage the workload caused by covid-19.

Voir la description complète du projet
Superviseur du corps professoral :

Ioannis Mitliagkas

Étudiant :

Partenaire :

JACOBB

Discipline :

Computer science

Secteur :

Professional, scientific and technical services

Université :

Université de Montréal

Programme :

Accelerate

Development of Optical Clearing Materials Towards Oral Lesion Pathology Screening with Optical Coherence Tomography

The early identification of oral cancers will help to reduce serious complications and death associated with these diseases. Currently, identification of cancerous tissues relies significantly on visual identification during oral exams before more accurate further testing is performed. The use of optical coherence tomography (OCT) has allowed for more accurate initial identification of cancerous tissues, but suffers from limitations in resolution and how far into tissues can be analyzed. We propose to develop methods and techniques to allow deeper and clearer imaging into oral tissues to allow for more accurate identification of early-stage cancerous tissues. This will involve the development of materials that can be applied to the gum tissues to allow for better imaging into the tissues, as well as modifications of the OCT system and image processing techniques to improve depth and clarity of images.

Voir la description complète du projet
Superviseur du corps professoral :

Ozzy Mermut

Étudiant :

Partenaire :

LiveVue Technologies

Discipline :

Life Sciences

Secteur :

Professional, scientific and technical services

Université :

York University

Programme :

Accelerate

Online economic tool to quantify the health benefits of municipal investments in urban greenspaces

7.2. Public Project Overview:
The project will develop an online tool to quantify the economic return on investment resulting from improvements in public health attributed to investments in urban greenspaces. The tool will focus on three pathways: improvements in physical health, improvements in mental health and health benefits associated with improved climate resiliency. The tool will provide provincial, municipal and community organizations a mechanism to support informed program, policy and planning decisions and will help users better understand and communicate the value of greenspace investments.

Voir la description complète du projet
Superviseur du corps professoral :

Jeffrey Wilson

Étudiant :

Partenaire :

Green Analytics

Discipline :

Sociology

Secteur :

Information and cultural industries; Professional, scientific and technical services; Wholesale trade

Université :

University of Waterloo

Programme :

Accelerate

Real-time quality monitoring tool development for remote laser welding ofsheet metal assemblies in automotive applications

Individual stamped sheet metal components need to be joined to create sheet metal assemblies
used in a car. Powerful laser light delivered by a robot is used to quickly weld these assemblies by
making dozens of individual welds within a few minutes. For economical production, it is essential
to minimize the total production cycle time. At the same time, high-quality welds must be
produced. Undetected defective weld joints would lead to severe penalties if delivered to the
company’s customer. Currently, all assemblies must be manually inspected. An automated realtime
laser weld monitoring system is being investigated. The system monitors the intensity of the
radiation emitted at the weld. It has been found that the system is not meeting the company’s
requirements. The proposed project intends to identify the defects that need to be detected, design
experiments where defects are reproduced artificially, and correlate these with the observations of
the monitoring system.

Voir la description complète du projet
Superviseur du corps professoral :

Gene Zak

Étudiant :

Partenaire :

Van-Rob Inc

Discipline :

Engineering

Secteur :

Manufacturing

Université :

Queen's University

Programme :

Accelerate

Intelligent Card Detection for Automatic Documentation & e-Commerce

The goal of this project is to develop a Card Detection for Automatic Documentation solution that will be able to detect all features of a collectible card from a scanned image. Given a large number of design variations in cards, simple OCR cannot provide all available information. We aim to deliver an AI-driven solution deploying learning-based approaches and computer vision algorithms to extract all possible variations of elements found on a card. New card entries will be added to the database for online trading. Web-crawler tools will be developed to search reference information from the Internet to put a price tag on the card. This full-stack framework includes a front-end to back-end routing and task execution pipeline. We anticipate benefits to not only the collectible card online trading businesses, but also Canada technologically and economically.

Voir la description complète du projet
Superviseur du corps professoral :

Irene Cheng

Étudiant :

Partenaire :

JACOBB;Kronozio

Discipline :

Computer science

Secteur :

Professional, scientific and technical services

Université :

University of Alberta

Programme :

Accelerate

SOTI SNAP Digital Signage

SOTI has developed a software product called SOTI SNAP that allows anyone to create an app with no programming or technical knowledge. The goal of this project is to develop media scheduler widgets so that users can create playlists consisting of mixed media (videos, images, documents) and then deploy them at specific hours on certain days. Since one user may have multiple mobile devices and one mobile device may be controlled by multiple users, we should ensure all the devices have up-to-date media playlists, display and play back the content smoothly once the playlists are changed in one device. Additionally, all the process should not consume too much energy. The media scheduler widgets will help SOTI attract more clients and make users work more efficiently.

Voir la description complète du projet
Superviseur du corps professoral :

Yashar Ganjali

Étudiant :

Partenaire :

SOTI Inc

Discipline :

Computer science

Secteur :

Information and cultural industries; Professional, scientific and technical services

Université :

University of Toronto

Programme :

Accelerate

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