Projets novateurs réalisés

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

30156 projets achevés

2861
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5059
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812
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673
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842
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8957
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9368
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96
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579
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1120
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Projets par catégorie

Development of operational control system for multi-robot coordination in a fleet based environment

Operating a fleet of autonomous robots introduces challenges such as reduced safety for humans working around the robots, reliability of intern communication of the robots, coordination behaviour of the robots, etc. At Fourien we have developed autonomous robots which work very well as stand alone units but there is a need of development of a fleet of the robots so that multiple robots can be deployed to perform complex tasks of material handling on a large factory floor. Through this project, we are targeting to perform research and development to fund solutions for coordinated task management among multiple robots when they work in a team.

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Superviseur du corps professoral :

Li Cheng

Étudiant :

Partenaire :

Fourien

Discipline :

Engineering

Secteur :

Manufacturing; Professional, scientific and technical services

Université :

University of Alberta

Programme :

Accelerate

The Automation of System AssemblyRecognition

Traditional 3D reverse-engineering and prototyping involves 3D modeling and manual parts recognition. With the advent of 3D scanning technologies, it becomes possible to automate this workflow. This research internship aims at creating a novel mechanism to automate the process of assembly recognition, as part of a new workflow of 3D interactive prototyping. The research will be focused on technologies and algorithms suitable for volume graphics model. Research topic includes 3D shape matching, alignment and recognition. Prototypes will be built to verify and evaluate the research results.

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Superviseur du corps professoral :

Karan Singh

Étudiant :

Partenaire :

NGRAIN

Discipline :

Computer science

Secteur :

Professional, scientific and technical services

Université :

University of Toronto

Programme :

Accelerate

Development of Inherently Antimicrobial Fabrics

Pleasant aromas fill our homes and enrich our lives, whether it’s by helping us relax after a long day’s work or evoking some of our fondest memories. This project will test scented fabrics prepared by Aroma Textiles Lab for their ability to inhibit the growth of the bacteria that are commonly found on our skin and on sweat-soaked fabrics. The same essential oils that give these fabrics their pleasant aroma also have significant antimicrobial activity, and so if successful this work will identify fabrics that are not only pleasing to the nose, but are free from microbes and thus stay fresher longer.

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Superviseur du corps professoral :

Brandon Findlay

Étudiant :

Partenaire :

Aroma Textiles Lab

Discipline :

Life Sciences

Secteur :

Pharmaceuticals; Life Sciences (not health)

Université :

Concordia University

Programme :

Accelerate

Valorisation de sous-produits de la luzerne dans le secteur des cosméceutiques

Les produits cosmétiques et de santé naturels représentent le segment dont la croissance est la plus rapide au cours des
dernières années dans le marché des produits de soin. Ces produits ont des applications variées dont l’efficacité repose sur le
choix des ingrédients actifs. La luzerne produite au Québec par le partenaire, propose un ingrédient particulièrement
intéressant pour l’industrie par sa composition chimique, mais également leurs cultures locales. Les études sur l’activité
biologique des chloroplaste de luzerne ont démontré un potentiel antioxydant important et d’autres activités biologiques
restent à découvrir. Dans le cadre de ce projet, les partenaires souhaitent donc valoriser la luzerne pour le secteur des
cosmétiques.

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Superviseur du corps professoral :

Lionel Ripoll;André Pichette;Jean Legault

Étudiant :

Partenaire :

Innovation Virentia Inc.

Discipline :

Life Sciences

Secteur :

Manufacturing

Université :

Université du Québec à Chicoutimi

Programme :

Accelerate

Market 360 AI: AI-enabled Discovery of Customer and Market Insights

Organizations across various industries struggle to deliver personalized offerings and expanded access for target populations. Developing sub-population profiles will help identify priority sub-populations with the greatest potential to benefit from personalized support and intervention, and inform the development, targeting and delivery of personalized offerings. The objective of this research project is to enhance Deloitte’s Market 360 AI tool across 3 dimensions. Firstly, the project aims to improve the tool’s time-series clustering methodology with the goal of delivering meaningful and interpretable results, as well as allowing for the ingestion of high-dimensional data. Secondly, the project will study different application of this tool across different markets, including but not limited to the drug discovery industry. Finally, the project aims to develop a methodology that can accurately determine the latest market size for higher-level geographic areas.

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Superviseur du corps professoral :

Murat Erdogdu

Étudiant :

Partenaire :

Deloitte Canada

Discipline :

Computer science

Secteur :

Professional, scientific and technical services

Université :

University of Toronto

Programme :

Accelerate

Multimodal Geolocation and Traffic Management in Healthcare Centers

Throughout the COVID-19 pandemic, there has been an emerging need of using new advances in information and communication technologies (ICT) to improve our healthcare system to manage a massive influx of patients. Indoor geolocation techniques and optimized traffic management algorithms are substantial for serving a large number of patients in healthcare centers while respecting procedures and protocols implemented during the pandemic. In this project, we will develop a framework which combines the signal from multiple communications techniques to precisely locate the position of users in a center. We will also apply multiple artificial intelligence (AI) and machine learning (ML) techniques to improve the geolocation and filter interferences. Then, we will assess the risk of COVID-19 infection of people living in a building or a healthcare facility. The framework will be implemented on Nano Data Center (NDC) edge computing platform of Humanitas’s to perform real-time assessment. The outcome of this project will contribute significantly to reopening our society and economy in post-pandemic era.

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Superviseur du corps professoral :

Kim Khoa Nguyen

Étudiant :

Partenaire :

Humanitas Solutions

Discipline :

Computer science

Secteur :

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

Université :

École de technologie supérieure

Programme :

Accelerate

Ubiquity: Intelligent Supply Chain Management

The intern will first determine which one of the two modules in the Ubiquity product is more likely to be improved using the machine learning and deep learning methods, and then implement the proposed method or research on other methods. To improve the sensitivity of the pricing and promotion module towards the small price changes, the intern will use deep neural network to improve the existing forecasting model. For the assortment planning module, the intern will evaluate and choose various machine learning algorithms to learn the interaction between items’ inventory and forecast the demand of all assortments in the future. The benefits of both modules including the improvement of the performance of the existing demand forecasting models in the Ubiquity product.

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Superviseur du corps professoral :

Murat Erdogdu

Étudiant :

Partenaire :

Deloitte Canada

Discipline :

Computer science

Secteur :

Professional, scientific and technical services

Université :

University of Toronto

Programme :

Accelerate

Classical and Quantum Metaheuristic Optimization Tools to Improve the Constrained Vehicle Routing Problem Solutions

The constrained vehicle routing problem is a typical optimization problem with many real-life applications, such as last-mile route planning for delivery services. The goal is to find the optimal routes for a set of vehicles to deliver all the packages, such that the time and cost of delivery is minimized, sometimes with additional constraints such as a loading capacity for each vehicle. In recent years, quantum computing has started to show great potential in providing a speedup to optimization solutions. This project aims to understand the current state-of-the-art classical solutions to the vehicle routing problem and explore the application of quantum computing in this field, with the hope of developing a quantum hybrid algorithm that a customer will eventually use to improve their logistics planning.

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Superviseur du corps professoral :

Christopher Beck

Étudiant :

Partenaire :

ForeQast Technologies Limited

Discipline :

Computer science

Secteur :

Professional, scientific and technical services

Université :

University of Toronto

Programme :

Accelerate

Forecasting Patient Flow Pressures

At St. Michael’s Hospital (SMH), having insight into patient flow throughout the hospital is essential to resource planning and operational efficiency. When patients are admitted, discharged, or transferred in the hospital, several actions need to be taken to ensure patients receive timely care and resources do not become backlogged. Improving patient flow reduces wait times, improves operational efficiency, and ultimately improves care. However, due to reasons like the epidemic outbreak, traditional methods of controlling the flow of patients are no longer effective. In this project, Unity Health Toronto is seeking to find better way to control the patient flow, efficient by utilizing state-of-the-art machine learning models.

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Superviseur du corps professoral :

Igor Jurisica

Étudiant :

Partenaire :

Unity Health Toronto

Discipline :

Computer science

Secteur :

Health and Related Sciences & Technology

Université :

University of Toronto

Programme :

Accelerate

Fabrication of Deformable Micro Mirrors

Histology studies the microscopic anatomy of biological tissues, which is critical to clinical disease management and fundamental to biological understanding. However, current approaches that rely on bright-field microscopy require extensive issue preparation prior to imaging and may thus take days. Photoacoustic remote sensing, or PARS, can dramatically reduce the histology bio-imaging time from days to minutes. PARS microscopy must be capable of 3D imaging or optical sectioning, which enables visualizing multiple layers of disease tissue without the need for lengthy physical sectioning. This in turn calls for auto-focusing or adaptive optics, to focus on different depths of the tissue (i.e. optical sectioning). In this project, we will fabricate resonant deformable micro-mirrors that can shift the focus by ~10cm as needed for high throughput PARS microscopy, with mirror actuation voltage down to 20V. Besides auto-focusing, the mirror can also correct aberrations of the optical system. Moreover, the resonant micro-mirror can avoid the high electrode count (on the order of hundreds), complex drive electronics and control software, as needed by the conventional DC/static actuation of deformable micro-mirrors.

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Superviseur du corps professoral :

Bo Cui

Étudiant :

Partenaire :

illumiSonics Inc

Discipline :

Engineering

Secteur :

Manufacturing

Université :

University of Waterloo

Programme :

Accelerate

Modeling Application Performance under Multi-Instance (Multi-Stream) Execution Scenarios

Multi-stream execution is a technique in GPUs that allows multiple operations/kernels from the same program to effectively use GPUs without explicitly stating the affinity of threads to the cores. Several recent optimizations in Machine Learning (ML) algorithms leverage multi-stream execution. While performance modeling of ML applications is well studied under single-stream execution, performance models of novel ML applications under multi-stream execution is lacking. There is a pressing need to develop performance models for multi-stream execution – that would be the primary area of exploration under this co-op/internship. Specifically, we are expecting to study the state of the art (SOTA) in performance modeling for multi-stream execution and develop first principles performance models, conduct validation with silicon performance, and integrate the performance models in an internal simulator developed at AMD.

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Superviseur du corps professoral :

Gennady Pekhimenko

Étudiant :

Partenaire :

AMD Canada

Discipline :

Computer science

Secteur :

Manufacturing; Professional, scientific and technical services

Université :

University of Toronto

Programme :

Accelerate

Optimized operational strategy planning model for Smart Net Zero Community

Due to some complex technical problems, both power plant’s generators and local grid transmission lines do not have the potential to generate and transmit enough electricity to cover and meet all load demand during peak hours. Based on this technical difficulty, there is no solution for customers to use costly energy during peak hours and accordingly pay more money for their consumed electricity(Refer to electrical pricing scheme).In this case, in order to remedy above mentioned problem, smart solar communities as grid-friendly consumers, would be established with the purpose of supporting the local grids. This feature would be more important when local grids are in peak period hours. To this end, solar communities should shift their internal electric loads from peak load period to off-peak hours. Regardless of shifting the loads, they should either decrease overall amount of energy consumption or used dynamic load shedding/shaving methods. In this case, for managing load demand during peak hours, a community-scale optimal strategy planning model is necessary. Base on community-scale dynamic complexity and uncertainty, this model should be constructed based on inexact optimization techniques such as Interval Parameter linear Programming, (Mixed) Integer Linear Programming, Fuzzy Linear Programming or integration of above methods.

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Superviseur du corps professoral :

Alan Fung

Étudiant :

Partenaire :

S2E Technologies Inc

Discipline :

Engineering

Secteur :

Construction and infrastructure; Finance and Insurance; Professional, scientific and technical services

Université :

Toronto Metropolitan University

Programme :

Accelerate

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