Projets novateurs réalisés

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

30156 projets achevés

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Projets par catégorie

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.

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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

Optimisation des contacts ohmiques et fabrication de lasers

Ce stage s’inscrit dans un projet qui a pour objectif d’optimiser les contraintes mécaniques dans les lasers. Afin d’optimiser les résistances dans les lasers, l’optimisation des contacts ohmiques est primordiale. Ainsi, l’objectif de ce stage est d’optimiser les contacts ohmiques pour la fabrication de lasers, visant une faible résistivité. Différents empilements métalliques et d’autres moyens de diminution de résistances séries seront évalués, notamment un aspect novateur et prometteur qui a déjà montré son efficacité sur des cellules solaires III-V : l’utilisation d’une couche de graphène pour diminuer les résistances séries des contacts.
Ce stage permettra de poser les premières briques d’optimisation de lasers, dans le but d’ouvrir une collaboration avec l’entreprise canadienne Laser Component Canada (fabricants de laser), l’Université de Sherbrooke, et l’Université de Rennes.

Voir la description complète du projet
Superviseur du corps professoral :

Gwenaelle Hamon

Étudiant :

Partenaire :

Université de Rennes 1

Discipline :

Engineering

Secteur :

Education

Université :

Université de Sherbrooke

Programme :

Globalink Research Award

Sustainability benefits of expanded gamut printing

Expanded gamut printing is a relatively new technology that allows print companies to move away from spot colours and needing an inventory of these colours on hand or ordering them from their ink supplier every time a print job requires a spot colour. With expanded gamut printing the same seven colours remain in the printing units and only the printing plates and the substrate get changed. This leads to less frequent print unit wash-ups and allows the so-called ganging of jobs resulting in less use of organic solvents and a lower consumption of paper. These savings make the operation of a print company more sustainable. The main goal of this project is to quantify these savings.

Voir la description complète du projet
Superviseur du corps professoral :

Martin Habekost;Krzysztof Krystosiak

Étudiant :

Partenaire :

Sina Printing

Discipline :

Business

Secteur :

Manufacturing

Université :

Toronto Metropolitan University

Programme :

Accelerate

Étude moléculaire de l’interaction entre les bactériophages de Clostridioides difficile et leur récepteur cellulaire, la protéine de surface SlpA

La bactérie Clostridioides difficile (Cd) est la cause principale de diarrhée infectieuse post-antibiotique dans les pays industrialisés (ICD). Bien que l’antibiothérapie soit le traitement standard contre les ICD, elle s’avère souvent inefficace pour empêcher la récurrence de l’infection. La phagothérapie est une alternative thérapeutique prometteuse, qui consiste à utiliser des bactériophages (phages), i.e. des virus infectants et tuant spécifiquement les bactéries. Cd possède à sa surface une enveloppe protéique majoritairement composée de la protéine SlpA. Dans un article que j’ai récemment publié (Royer et al, Microbiol Spectr 2023), j’ai démontré que les phages de Cd reconnaissent une ou plusieurs isoformes différentes de la protéine SlpA. De plus, j’ai démontré qu’une partie du domaine variable de SlpA est déterminante pour la reconnaissance et l’infection par certains phages. L’objectif de mon projet de doctorat est de caractériser en détail la spécificité d’interaction entre les phages et la protéine SlpA de Cd. Mes travaux permettront l’élaboration de cocktails de phages thérapeutiques ciblant différentes isoformes de SlpA et permettant de couvrir l’ensemble des souches de Cd responsables d’infections. Cette approche pourrait réduire les récidives et ainsi permettre un traitement efficace et durable des ICD.

Voir la description complète du projet
Superviseur du corps professoral :

Louis-Charles Fortier

Étudiant :

Partenaire :

Université Paris-Saclay

Discipline :

Life Sciences

Secteur :

Education

Université :

Université de Sherbrooke

Programme :

Globalink Research Award

Establishing the reliability of DTI and resting-state fMRI as markers of traumatic brain injury Year Two

The proposed research will investigate two brain imaging measures that hold potential as tools for the diagnosis and assessment of traumatic brain injury (TBI), including mild TBI (mTBI)/concussion. One type of image provides information about structural damage to the brain’s anatomical connections, and the other about the functional connectivity of networks of brain regions that underlie many mental abilities. The proposed research will investigate the relationship between these measures, their reliability an individual over time, and also the comparability of each measure between different scanners and software applications. The project will provide fundamental knowledge essential to the future development of accurate, objective, and individualized tools to diagnose and characterize mTBI, to track its progression over time, and to guide treatment and return-to-work/play decisions

Voir la description complète du projet
Superviseur du corps professoral :

Michael Noseworthy

Étudiant :

Partenaire :

Synaptive Medical Inc

Discipline :

Life Sciences

Secteur :

Health and Related Sciences & Technology; Manufacturing; Professional, scientific and technical services

Université :

McMaster University

Programme :

Elevate

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