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

GaN Modelling for the EV Application

The use of new technologies, such as Gallium Nitride electronic switches, allows very efficient and compact power converters to be manufactured at reasonable cost. This is particularly interesting in applications such as electric vehicles. This project focuses on developing software simulation models and techniques for deploying these latest high voltage switches, in particular focussing on how they may best be controlled. The next step will be to design complete power control modules for specific applications.

Voir la description complète du projet
Superviseur du corps professoral :

Patrick Palmer;William Dunford

Étudiant :

Partenaire :

Crosslight Software Inc

Discipline :

Engineering

Secteur :

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

Université :

Simon Fraser University

Programme :

Accelerate

Identification of high-frequency periodic acoustic fish tags with deep learning

Innovasea produces fish tags and receivers to track the presence and motion of fish and marine mammals while underwater. Fish tracking (acoustic telemetry) is used by researchers worldwide to determine the abundance and habits of marine life, make decisions about fishing seasons and allowed catches, and help protect marine mammals. Innovasea has developed a novel high-frequency tag technology that is suitable for very small fish and generates more precise trajectories. However, the new smaller fish tags send no explicit identification information so signals from a specific fish tag are isolated from background noise and other fish tags based on the period and/or pattern of the signals. To obtain useful fish tracking trajectories, Innovasea currently applies manual processing which requires expert knowledge.
In this project we will apply advanced deep learning techniques to large manually processed training sets provided by Innovasea to eliminate the manual preprocessing steps. The project is scoped with an initial phase to test feasibility of the concept and subsequent phases for development with an eventual aim of transitioning the best performing prototype system to a fully realized system for filtering Innovasea data.

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Superviseur du corps professoral :

Stan Matwin

Étudiant :

Partenaire :

InnovaSea Marine Systems Canada Inc

Discipline :

Computer science

Secteur :

Information and cultural industries; Manufacturing

Université :

Dalhousie University

Programme :

Accelerate

Nature Based Outdoor Recreation ROI Framework

The project will develop a conceptual framework to quantify the value of nature based recreation. The tool will focus on improvements in physical health, improvements in mental health and health benefits associated with improved ecosystem protection. 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 investments in nature based outdoor recreation.

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Superviseur du corps professoral :

Jeffrey Wilson

Étudiant :

Partenaire :

Collegeway Consulting Ltd.

Discipline :

Sociology

Secteur :

Sustainability & the Environment; Health and Related Sciences & Technology; Information and Communications Technology

Université :

University of Waterloo

Programme :

Accelerate

Improving Financial Health in Canada

The goal of this partnership is to develop data-driven tools that will empower individual Canadians to improve their financial resilience and reduce their levels of financial stress. Individuals will complete a short questionnaire; based on their responses, will be assigned to one of three groups – financially stressed, financially coping or financially comfortable – giving them a clear picture of where they lie on the “financial wellness spectrum.” They will be informed of how changes to their response profile would lead to movement up or down the spectrum and will be pointed towards resources that can help them with their specific circumstances. The project will help the Canadian Payroll Association (CPA) cement its role as a thought leader with respect to financial well-being in Canada.

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Superviseur du corps professoral :

Matthew Davison;Adam Metzler

Étudiant :

Partenaire :

Canadian Payroll Association

Discipline :

Mathematics

Secteur :

Other services (except public administration)

Université :

The University of Western Ontario

Programme :

Accelerate

In-Kind Student Edition: Skills and Services Impact Study

To date, there is a gap in the research determining how in-kind giving can be used to motivate student engagement. In tandem with Algonquin College, this project seeks to understand how in-kind giving can help students get involved in achieving the United Nations Sustainable Development Goals. Through a mixed-methods approach involving interviews, focus groups, and surveys, the research will create a student handbook. This handbook will guide student engagement toward in-kind giving and illustrate how this engagement will deliver on the UN-SDGs. With the data gathered through the creation of the handbook, a report will be generated with practical considerations for campuses and students on how to integrate the SDGs.

Voir la description complète du projet
Superviseur du corps professoral :

Paloma Raggo

Étudiant :

Partenaire :

Project K(IN)D

Discipline :

Sociology

Secteur :

Other services (except public administration)

Université :

Carleton University

Programme :

Accelerate

Next Generation candidate screening and assessment platform featuring psychological profiling though gamification

This project is the first step to providing Thinking North’s Purple Squirrel recruitment platform to go beyond traditional matching with a novel, data-backed holistic candidate matching process. To provide a robust system, Thinking North is collaborating with Seneca’s School of Software Design and Data Science to use advanced artificial intelligence and gamification techniques to combine “psychology” and “gamification,” known as “psychification,” to enhance the screening aspect for a recruitment process. Psychification builds on the data at Thinking North to create a gamified, interactive way to assess how motivated candidates are for open positions within select technology industries. This psychification and screening platform will benefit companies that are challenged by the need for speed and accuracy in recruiting. Specifically, we are addressing the emerging gig economy where staffing for projects and shorter commitments calls for an even more effective process than we have seen before.

Voir la description complète du projet
Superviseur du corps professoral :

Mark Buchner

Étudiant :

Partenaire :

Thinking North

Discipline :

Computer science

Secteur :

Finance and Insurance

Université :

Seneca College of Applied Arts and Technology

Programme :

Accelerate

Development of Lightweight Thermally Conductive Products Reinforced with Graphene Nanoplatelets for Automotive Industry

The focus of this project is replacing some of the current automotive parts by a stronger and lighter thermally-conductive polymer nanocomposite. This project takes advantage of the exceptional mechanical and thermal properties of graphene as a commercially viable and environmentally friendly nanomaterial, through an industrial scale process, i.e. injection molding. Therefore, the outcome of this research would technologically benefit our industrial partner, i.e. Axiom Group Inc., to sustain itself and/or to grow in the competitive market. Bearing in mind the global scope of our industrial partner, as a leading developer and manufacturer in the plastic injection molding industry, the products and technologies developed as a result of this project’s findings are expected to be commercialized and introduced to both Canadian and world markets.

Voir la description complète du projet
Superviseur du corps professoral :

Patrick C Lee

Étudiant :

Partenaire :

Axiom Plastics Inc

Discipline :

Engineering

Secteur :

Professional, scientific and technical services

Université :

University of Toronto

Programme :

Accelerate

Evaluating Latency in Virtual Production Pipelines with Integrated Prediction Model for Motion Capture Data

Virtual Production (VP) is seeing a dramatic spike in interest and adaptation as the global film industry, particularly Hollywood, has been shutdown due to Covid-19. Virtual production is a broad term referring to a spectrum of computer-aided production and visualization filmmaking methods, and is also being used for broader applications from animation to industrial visualization. Of particular interest is the novel application of virtual production for live theatrical performance where machine learning is driving digital puppeteering from real-time motion capture for innovative and compelling storytelling. Machine learning for real-time motion mapping predictions is a key component of the live performance virtual production pipeline. However, serving predictions from trained machine learning models is emerging as a dominant challenge in production machine learning. These computationally intensive prediction pipelines must run continuously with a tight latency budget and in response to stochastic and often bursty query arrival processes. In this proposal research project, the intern(s) will review the latest applications and research related to latency issues in virtual production pipelines, with a focus on machine learning prediction.

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Superviseur du corps professoral :

Jiannan Wang

Étudiant :

Partenaire :

AMPD;Shocap Entertainment

Discipline :

Computer science

Secteur :

Information and cultural industries; Professional, scientific and technical services

Université :

Simon Fraser University

Programme :

Accelerate

Using Bayesian Learning paradigms to enhance the clinical utility of an AI decision support tool for mental illness.

The Machine Learning team at Aifred Health is aiming to improve the performance of their core decision support models and the intern will assist in the development of a system that will intelligently iterate across dozens of unique model configurations to find the best performing one by leveraging previous configurations and their performance outcomes. The intern will also work on building a prototype of a model that takes advantage of longitudinal data to assess patient response trajectories so that a doctor can have a higher resolution view into whether the patient is getting better or rapidly degrading and needs to take action, thereby increasing the chances of remission. This work will ultimately help accelerate us in our mission to bring better mental healthcare to all.

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Superviseur du corps professoral :

Jean-Jules Brault

Étudiant :

Partenaire :

Aifred Health Inc

Discipline :

Computer science

Secteur :

Health and Related Sciences & Technology; Information and cultural industries; Professional, scientific and technical services

Université :

Polytechnique Montréal

Programme :

Accelerate

Continuous Identity Verification using Blockchain Technology

With physical interactions greatly restricted due to the COVID-19 pandemic, the world is forced to rapidly adapt and embrace digital transformation. There is an urgent need for a digital identity infrastructure to authenticate users who rely on online services. The system must accurately validate physical identities, provide each user the ability to control access to its personal data, and support usage auditing. Furthermore, the infrastructure must operate at a large scale, maintain security and confidentiality while being practical and convenient to use for the average user. To address this important challenge, we propose a multi-disciplinary approach which combines biometrics, artificial intelligence, and blockchain technologies. Our objective is to explore relevant use cases, particularly those related to COVID-19, and develop a working prototype.

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Superviseur du corps professoral :

Chamseddine Talhi;Kaiwen Zhang;Jean-Philippe Roberge;Vincent Levesque

Étudiant :

Partenaire :

IPtoki Inc

Discipline :

Engineering

Secteur :

Professional, scientific and technical services

Université :

École de technologie supérieure

Programme :

Accelerate

Development of a flexible and breathable PLA-based film as an eco-friendly alternative to animal leather

Leather manufacturing is a by-product of the meat and dairy industries, and leather can come from any animal. More than a billion animals are slaughtered every year for their skins. But, the most popular type of skin used is coming from cow, calf and buffalo, and accounts for 64% of leather. Turning skin into leather requires many steps, chemicals and generate a significant amount of waste. It is not surprising that the leather industry is consider as a cruel, inhuman and polluting business. Fortunately, leather can be obtained using different animal-free sources of material (e.g. plastics, apple fibers, pineapple, tree bark and cork). Most of the time those animal-free leathers are referred to as artificial, synthetic, faux or vegan leather. Among plastic-based leather, polyurethane (PU) and Polyvinyl Chloride (PVC) are the most used ones. However, those plastics are still oil-based ones and, overall, the artificial leather still represents one-third of the footprint of whole animal leather industry. This project aims at developing new fully bio-composite derived from renewable resource with comparable flexibility, breathability and feeling to genuine leather.

Voir la description complète du projet
Superviseur du corps professoral :

Saïd Elkoun

Étudiant :

Partenaire :

Gestion Dryad Inc

Discipline :

Engineering

Secteur :

Manufacturing

Université :

Université de Sherbrooke

Programme :

Accelerate

Development of a data processing and machine learning pipeline using non-invasive diffuse reflectance spectroscopy eye imaging for Alzheimer disease screening

Alzheimer’s disease (AD) is a progressive neurodegenerative disease and is currently the most common cause of dementia, affecting approximately 50 million individuals worldwide. RetiSpec has developed a non-invasive, label-free retinal imager that can uniquely detect and quantify AD biomarkers – namely, signatures of A? aggregates. The combination of hyperspectral imaging and machine learning may be a quick, simple, and cost-effective method that can be used to identify AD retinal biomarkers years before the emergence of clinical symptoms. In this project novel tissue spectroscopy methods will be developed to detect the presence of amyloid plaque and soluble oligomers, and to quantify changes in microvasculature. To this end machine learning methods will be applied to data acquired in more than 100 patients to develop predictive models able to automatically screen patients for AD as part of regular eye exams.

Voir la description complète du projet
Superviseur du corps professoral :

Frédéric Leblond

Étudiant :

Partenaire :

Retispec

Discipline :

Engineering

Secteur :

Manufacturing

Université :

Polytechnique Montréal

Programme :

Accelerate

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