Innovative Projects Realized

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

29670 Completed Projects

2811
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4990
BC
801
MB
663
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825
SK
8841
ON
9197
QC
95
PE
568
NB
1088
NS

Projects by Category

Neutronics simulation on the McMaster Nuclear Reactor (MNR)

This research will develop and test new software tools in reactor physics (openMC) for neutronic calculations. It involves development/extension of the existing McMaster Nuclear Reactor (MNR) core physics models in the openMC code and benchmarking against existing results obtained with other tools. Once benchmarked the student will perform a serries of simulations to mimic the recent MNR Feedback Coefficient Tests where the moderator temperature was physically changed and the impact on core reactivity was measured by observing the impact on control rod position. The student will parametrically simulate the MNR core for a wide range of control rod positions and moderator temperatures and compare the results to the transients measured during the MNR experiments. The student will document the model and results in an end of term report.

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Faculty Supervisor:

David Novog

Student:

Partner:

Université Grenoble Alpes

Discipline:

Physics

Sector:

Education

University:

McMaster University

Program:

Globalink Research Award

Parameter Optimization for Additive Manufacturing using Machine Learning

Additive manufacturing (AM), also known as 3D printing, is a process of building products with the material layer by layer. It can produce the products with complex geometries in a simple setup. But it is a challenge in selecting right printing parameters to build a quality part. This project proposes using machine learning techniques for selection of process parameters to improve the AM efficiency and product quality and reduce costs. The expected solution of this project has significant potential for an efficient and sustainable AM processing tool used by manufacturing industries to improve the AM efficiency and product quality while simultaneously reducing printing time and cost.

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Faculty Supervisor:

Qingjin Peng

Student:

Partner:

North Forge

Discipline:

Engineering

Sector:

Education; Management of companies and enterprises; Professional, scientific and technical services

University:

University of Manitoba

Program:

Accelerate

Facteurs explicatifs de la diversité végétative sur les 36 iles du Lac Hébécourt, QC, Canada

Les écosystèmes boréaux englobent une multitude de lacs de morphologies variées, dont certains abritent des îles. Les îles boisées constituent des interfaces forêts-lacs, présentant des dynamiques végétales et de perturbation, distinctives du continent. Néanmoins, l’influence de la structure forestière et des dynamiques de perturbation sur la biodiversité des environnements insulaires a été insuffisamment explorée, ce qui pose des défis pour les efforts de conservation. Ce projet vise à déchiffrer les principaux moteurs de la diversité biologique des 36 îles du lac Hébécourt, au sud-ouest du Québec, en caractérisant la composition et la structure actuelles des arbres, et en évaluant la diversité et l’abondance des communautés d’insectes et de champignons. La richesse et l’abondance des espèces entre les îles seront mis en relation avec divers paramètres géographiques et les conditions du sol des horizons minéraux-organiques. En utilisant une approche multiproxy impliquant la détermination des dates d’établissement des arbres, l’identification des cicatrices de feu, et la datation radiocarbone du charbon du sol, nous établirons une chronologie du temps écoulé depuis le dernier feu pour chaque île. Les résultats visent à améliorer notre compréhension de la succession forestière post-perturbation dans les écosystèmes insulaires lacustres, et à mieux comprendre les peuplements forestiers anciens.

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Faculty Supervisor:

Yves Bergeron

Student:

Partner:

Université de Montpellier

Discipline:

Earth science

Sector:

Education

University:

Université du Québec en Abitibi-Témiscamingue

Program:

Globalink Research Award

Favorable propagation studies for massive MIMO systems

Favorable propagation studies for massive MIMO systems: mMIMO is a key technology for 5G and beyond, due to its ability to deliver high spectral efficiency/rate in multi-user environments using simplified (linear) processing. Its advantages can be fully exploited under certain conditions known compactly as “favorable propagation” (FP). Unlike the known methods, which rely on sophisticated beamforming algorithms (e.g. successive interference cancellation, maximum SNR or minimum mean square error) and thus are difficult to implement when the number of antennas is large, our approach targets reducing drastically implementation’s complexity and improving its robustness by exploiting the FP property. The known FP studies were carried out in simplified environments, and under a number of simplified assumptions. It remains unclear whether FP will hold in realistic 5G environments under practical constraints. This project will address these issues by studying the FP in realistic propagation environments (multipath propagation, correlated fading), for moderate to large number of antennas various user configurations and array geometries, with implementation errors included. The results of performance analysis will be used to optimize system design and performance and to develop processing algorithms under various practical constraints.

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Faculty Supervisor:

Sergey Loyka

Student:

Partner:

Ericsson Canada Inc (Ottawa, ON)

Discipline:

Engineering

Sector:

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

University:

University of Ottawa

Program:

Accelerate

Magnetic field assisted DED towards the development of functionally graded materials

Metal additive manufacturing (AM), or metal 3D printing, enables “unique structures printed for unique functions” and “the right materials printed in the right place”. Therefore, it is the preferred fabrication method for creating functionally graded materials (FGMs), whose material properties varies over its volume to meet industrial needs. In this project, I leverage external magnetic fields during the AM process to locally tailor the microstructures of the materials being printed, consequently creating controlled graded mechanical properties. Using in-situ high-speed synchrotron X-Ray imaging combined with analytical, simulation, and machine learning techniques, I hope to establish a quantitative relation between the magnetic field parameters and the resultant microstructure and mechanical properties and use it to guide the development of metallic FGMs. This research project aligns with the research interests for both the host (UCL) and home (UofT) universities. The successful completion of this project will add valuable data and experiences to UCL’s ongoing research in the metal additive manufacturing process. For UofT, success of this proposed project will enable the design and manufacturing of high performing metallic FGMs revolutionary to Canada’s aerospace, automotive, and energy sectors.

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Faculty Supervisor:

Yu Zou

Student:

Partner:

University College London

Discipline:

Engineering

Sector:

Education

University:

University of Toronto

Program:

Globalink Research Award

Entrepreneurial Reconciliation

This research utilizes the community as the learning environment and embeds the strengths and gifts of its members to foster entrepreneurial growth. The research aims to show that learning is everyone’s responsibility and when done from as asset-based approach can benefit entire communities. Students work with community members to bring ideas and business to communities through an 8-week internship paired with academic credit. The goal is to record success and growth on communities in profit and not for profit business.

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Faculty Supervisor:

Sacha DeWolfe

Student:

Partner:

Joint Economic Development Initiative

Discipline:

Business

Sector:

Education

University:

Mount Allison University

Program:

Accelerate

Intelligent cyber-threat awareness system for 5G networks

5G is the latest and fastest wireless internet technology that is not only being used by phone companies, but also in businesses, factories, and the military. As 5G is being used in more places and for different purposes, it gets more complicated and faces more security risks. Imagine many different gadgets, from phones to cars to tiny sensors, all connecting to this internet. The more gadgets, the more chances for bad actors to attack. Our research project wants to use a special method, called the “FiGHT framework”, to spot and understand these potential threats. We will gather data, like logs and security checks, to map out the risks. With the help of artificial intelligence, we want to create a smart system that can tell us where the dangers might come from and how to protect against them. This will be helpful for experts who try to find these threats and those who work to stop them, making 5G safer for everyone.

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Faculty Supervisor:

Abdelouahed Gherbi

Student:

Partner:

Ericsson Canada Inc (Quebec)

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

École de technologie supérieure

Program:

Accelerate

Wearable Tech for Lone Workers

Our project introduces advanced Wearables and smartphone-based software to enhance lone worker safety. By monitoring health, vital signs, and location, we enable early issue detection and rapid response in case of incapacitation or falls. This innovation leads to reduced insurance premiums, minimised downtime, lower legal costs, improved talent retention, and an enhanced corporate reputation. The solution addresses critical safety concerns, contributing to both financial savings and intangible benefits, making it a compelling investment for our organization.

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Faculty Supervisor:

Jennifer Caswell

Student:

Partner:

Colas Canada

Discipline:

Computer science

Sector:

Construction and infrastructure

University:

Southern Alberta Institute of Technology

Program:

Business Strategy Internship

Decoding Mental States using Contrastive Learning to Overcome Inter-individual Variability in Physiological Signals

OrbMedic, an Ottawa-based company, is developing the OrbMedic ADPT system, an advanced technological solution designed to detect and address early indicators of mental health challenges by analyzing physiological signals such as heart rate and skin conductance. Recognizing that individual physiological responses can vary widely, this research project seeks to refine the OrbMedic ADPT’s analytical capabilities to consistently interpret these signals across diverse individuals. By achieving this, OrbMedic anticipates enhancing the accuracy and applicability of their product.

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Faculty Supervisor:

Hussein Al-Osman

Student:

Partner:

ORBMEDIC

Discipline:

Computer science

Sector:

Information and cultural industries

University:

University of Ottawa

Program:

Accelerate

Practices and Techniques for Prototyping Big Data Applications

Big data analytics has emerged, in the past few years, as a subject of great fascination and intrigue. It has become a vital factor in the decision making process for leaders of various sectors, from government bodies to corporate executives to scientists and researchers. It has gained considerable attention recently due to the exponential growth of data generation by individuals and corporations alike. Numerous research groups around the world are attempting to envision innovative and efficient methods to manage, analyze and visualize big data. Their efforts are generally aimed at the development level or are specific to the use case in hand. However, the early system design procedure, such as low and high-fidelity prototyping, of big data applications has not been studied. In this project, I intend to apply agile methodologies to analyze current techniques and practices and, possibly, to define innovative methods for prototyping big data applications.

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Faculty Supervisor:

Frank Maurer

Student:

Partner:

Universidade Federal de São Paulo

Discipline:

Computer science

Sector:

University:

University of Calgary

Program:

Globalink Research Award

Towards an Operational Analytical Framework for Planning On-Demand Transit Services and a Case Study of MiWay

The proposed project aims to demonstrate the application of an analytical framework, developed by the team at the University of Toronto, to plan on-demand transit services in Mississauga. The analytical framework will be enhanced with an in-depth investigation of existing simulation tools with respect to their adequacy for modelling various ODT scenarios and operational designs. The selected simulation tool that best suits the modeling requirements will be used to carry out a quantitative analysis of on-demand transit service in Mississauga, Ontario. This will help showcase the analytical framework through a real-world case study. The case study will involve developing multiple ODT scenarios in consultation with MiWay, modelling the scenarios in the preferred simulation platform and analyzing the simulation output to make final recommendations.

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Faculty Supervisor:

Amer Shalaby

Student:

Partner:

The City of Mississauga;University of Toronto

Discipline:

Engineering

Sector:

Transportation and warehousing

University:

University of Toronto

Program:

Accelerate

Software development for remaining useful life prediction of bearings

Bearings are crucial components in various industries, such as power generation, aerospace, and oil and gas. Predicting a bearing’s Remaining Useful Life (RUL) is essential for Condition-Based Maintenance. To achieve accurate RUL predictions, an accurate Health Indicator (HI) that represents degradation patterns is necessary. However, existing HIs can be affected by time varying working conditions and external interference, leading to decreased prediction accuracy. This project aims to develop reliable RUL prediction software for bearings by using a novel signal processing-based HI and data-driven prediction. The new HI leverages advanced signal processing techniques to identify degradation patterns. The software’s effectiveness will be validated using two public run-to-failure bearing datasets and one lab dataset created by the project team.

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Faculty Supervisor:

Xihui Liang

Student:

Partner:

North Forge

Discipline:

Engineering

Sector:

Education; Management of companies and enterprises; Professional, scientific and technical services

University:

University of Manitoba

Program:

Accelerate

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