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

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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Superviseur du corps professoral :

Sergey Loyka

Étudiant :

Partenaire :

Ericsson Canada Inc (Ottawa, ON)

Discipline :

Engineering

Secteur :

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

Université :

University of Ottawa

Programme :

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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Superviseur du corps professoral :

Yu Zou

Étudiant :

Partenaire :

University College London

Discipline :

Engineering

Secteur :

Education

Université :

University of Toronto

Programme :

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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Superviseur du corps professoral :

Sacha DeWolfe

Étudiant :

Partenaire :

Joint Economic Development Initiative

Discipline :

Business

Secteur :

Education

Université :

Mount Allison University

Programme :

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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Superviseur du corps professoral :

Abdelouahed Gherbi

Étudiant :

Partenaire :

Ericsson Canada Inc (Quebec)

Discipline :

Computer science

Secteur :

Professional, scientific and technical services

Université :

École de technologie supérieure

Programme :

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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Superviseur du corps professoral :

Jennifer Caswell

Étudiant :

Partenaire :

Colas Canada

Discipline :

Computer science

Secteur :

Construction and infrastructure

Université :

Southern Alberta Institute of Technology

Programme :

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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Superviseur du corps professoral :

Hussein Al-Osman

Étudiant :

Partenaire :

ORBMEDIC

Discipline :

Computer science

Secteur :

Information and cultural industries

Université :

University of Ottawa

Programme :

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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Superviseur du corps professoral :

Frank Maurer

Étudiant :

Partenaire :

Universidade Federal de São Paulo

Discipline :

Computer science

Secteur :

Université :

University of Calgary

Programme :

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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Superviseur du corps professoral :

Amer Shalaby

Étudiant :

Partenaire :

The City of Mississauga;University of Toronto

Discipline :

Engineering

Secteur :

Transportation and warehousing

Université :

University of Toronto

Programme :

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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Superviseur du corps professoral :

Xihui Liang

Étudiant :

Partenaire :

North Forge

Discipline :

Engineering

Secteur :

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

Université :

University of Manitoba

Programme :

Accelerate

Cyber SOC Enhancement: Elevating Recurring Ticket Investigations and Client-Centric KPIs

In the modern era of increased digital and online operations, Security Operation Centers (SOCs) serve as frontline warriors, diligently monitoring and responding to a myriad of cyber security incidents. The SOC team at the project partner’s organization routinely investigates and responds to similar security incidents faced by clients. This project focuses on improving the quality and efficiency of SOC ticket investigation capabilities by developing a machine learning approach to automatically identify and manage repetitive security alerts reported to their incident ticketing system. A secondary goal of the project is to establish a process for identifying and evolving key performance indicators (KPIs) or metrics for various SOC clients. The ability to implement tailored KPIs for each client will help provide the partner with a competitive edge for better defending clients against cyber threats. Through this research, the project team seeks to improve the effectiveness of the partner company’s SOC in defending its clients against malicious cyberattacks.

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Superviseur du corps professoral :

Rozita Dara

Étudiant :

Partenaire :

GlassHouse Systems

Discipline :

Computer science

Secteur :

Manufacturing; Professional, scientific and technical services

Université :

University of Guelph

Programme :

Accelerate

5G-TSN/DetNet Integration for Industrial Automation

The 4th industrial revolution, Industry 4.0, aims for flexible production via automated robots. Central to this is the integration of the 5G System (5GS) with deterministic, low latency, wireline data communication. Foundational technologies include IEEE 802.1 Time Sensitive Networking (TSN) and IETF Deterministic Networking (DetNet), which, when combined with 5G, enhance industrial automation. The synergy permits wireless deployment of untethered I/O devices, like mobile robots and drones, elevating manufacturing flexibility, adaptability, and scalability. This wireless approach circumvents complex wireline network changes, but also mandates stringent timing and reliability for industrial operations. Disruptions from hardware/software issues or unexpected delays could pose safety and productivity risks. Although TSN and DetNet offer reliability and deterministic standards for Ethernet and IP networks, 5G’s standardization has evolved to synergize with TSN/DetNet for industrial contexts. This research aims to enhance the reliability of this integration, accounting for both network hardware/software.

Voir la description complète du projet
Superviseur du corps professoral :

Marc St-Hilaire

Étudiant :

Partenaire :

Ericsson Canada Inc (Ottawa, ON)

Discipline :

Computer science

Secteur :

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

Université :

Carleton University

Programme :

Accelerate

Integrated analyses of transcriptome for the hepatic and gut mucosa tissues and gut microbiome of broiler chickens under heat stress conditions

The world’s rising environmental temperatures due to climate change have a severe impact on poultry, which is highly sensitive to heat due to lacking sweat glands and being covered with feathers. Researchers are focused on developing strategies to relieve heat stress in poultry, with dietary interventions being extensively studied. Heat stress affects appetite and animal welfare, and early detection through specific genes like HSP-related and oxidative-related genes can help. The gut microbiome, a complex colony of microorganisms in the gastrointestinal tract, plays a crucial role in the health and immunity of poultry. Transcriptomic analysis has revealed physiological changes caused by heat stress in poultry, but the link between microbiota changes and host immunity and nutrition remains unclear.
The study aims to understand the mechanisms of heat stress and develop interventions for the poultry industry by investigating the relationship between gut, liver, and microbiome through tissue-specific transcriptomic analysis in the liver and Peyer’s patches. This research will contribute to improving poultry production and animal welfare.

Voir la description complète du projet
Superviseur du corps professoral :

Xin Zhao

Étudiant :

Partenaire :

National Pingtung University of Science and Technology

Discipline :

Life Sciences

Secteur :

Life Sciences (not health); Agriculture and Food; Sustainability & the Environment

Université :

McGill University

Programme :

Globalink Research Award

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Mitacs est financé par le gouvernement du Canada, le gouvernement de l’Alberta, le gouvernement de la Colombie-Britannique, Research Manitoba, le gouvernement du Nouveau-Brunswick, le gouvernement de la Nouvelle-Écosse, le gouvernement de l’Ontario, Innovation PEI, le gouvernement du Québec, le gouvernement de la Saskatchewan, le gouvernement de Terre-Neuve-et-Labrador et le gouvernement du Yukon.