Projets novateurs réalisés

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

29 670 projets achevés

2811
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4990
C.-B.
801
MB
663
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825
SK
8841
ON
9197
QC
95
PE
568
NB
1088
NS

Projets par catégorie

Homeporter GPT

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Superviseur du corps professoral :

Dhanya Sridhar

Étudiant :

Partenaire :

HomePorter Inc.

Discipline :

Computer science

Secteur :

Real estate and rental and leasing

Université :

Université de Montréal

Programme :

Accelerate

Coalescence Free Surfaces

Water injection in the industrial gas turbines is frequently used to improve the turbine performance during hot days. Injected water evaporates in the compressor section providing effective cooling leading to increased gas density and improved performance. Water injection technology poses several technological challenges. The injection system needs to be optimized to provide a uniform droplet size distribution across the inlet cross section. Water droplet interaction with static components of a compressor, such as casings and vanes may create accumulation of a water film dynamically shedding off. Such behavior leads to a non-optimum water distribution decreasing the overall efficiency of the water injection system. The purpose of the project is to understand the dynamic interaction between the wet air stream and compressor components. The planned work will be realized through computer modeling and experimental validation.

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Superviseur du corps professoral :

Ali Dolatabadi

Étudiant :

Partenaire :

Rolls-Royce (Dorval, QC)

Discipline :

Engineering

Secteur :

Manufacturing

Université :

Concordia University

Programme :

Accelerate

Manufacturing and supply chain optimization of a mobile plastics recycling plant for deployment in remote Indigenous communities

Plastics recycling remains a major challenge to global environmental sustainability, particularly for vulnerable populations such as remote Indigenous communities in which plastic waste is not viably captured by existing recycling networks. This research project aims to support the on-going work by NetZero Enterprises Inc., in the technical and business R&D for scaling-up the company’s mobile plastics processing plant, for deployment in processing waste PET in such remote communities. The processed PET will re-enter the plastics supply chain and provide income for the community leasing the mobile plant, such as re-bar used in construction, which will be upcycled and functionalized in targeted applications.

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Superviseur du corps professoral :

Babak Mohamadpour Tosarkani;Mohammad Arjmand;Abbas Sadeghzadeh Milani

Étudiant :

Partenaire :

NetZero Enterprises Inc.

Discipline :

Engineering

Secteur :

Manufacturing

Université :

The University of British Columbia - Okanagan

Programme :

Accelerate

Correction automatique de textes français avec Electra

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Superviseur du corps professoral :

Yoshua Bengio

Étudiant :

Partenaire :

Druide Informatique

Discipline :

Computer science

Secteur :

Information and cultural industries

Université :

Université de Montréal

Programme :

Accelerate

Detecting Intrusions Stemming from MSFT Co-Pilot in Windows 11

Living-off-the-land binaries (LOLBins) refer to legitimate executables pre-installed with the operating system, like powershell.exe and certutil.exe, exploited by attackers for sophisticated fileless attacks. These attacks, leveraging LOLBins, are often undetectable and pose challenges for detection, incident response, and threat hunting. Microsoft Copilot’s integration as a default tool in Windows 11 adds complexity to the threat landscape. This project aims to extract novel atomic indicators from incidents involving attacks utilizing Microsoft Copilot, contributing to threat intelligence. The extracted IOCs play a crucial role in enhancing security awareness without increasing the complexity of threat detection. This project can be divided into five steps: Data Collection, build a model for automated IOC extraction, testing and evaluation, fine-tuning and deployment and reporting and presentation. The initial stages focus on collection of data and identifying actionable intelligence by coordinating with the threat intelligence team. This data can be used to train, test, and deploy the developed automated IOC extraction model. On successful deployment of the model, it can be integrated with the Threat Intelligence feed to use the actionable intelligence in threat detection and incident response.

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Superviseur du corps professoral :

Ali Dehghantanha

Étudiant :

Partenaire :

eSentire

Discipline :

Computer science

Secteur :

Cyber Security; Information and Communications Technology; Technology

Université :

University of Guelph

Programme :

Accelerate

Developing domain specific deep learning models for phishing detection

Leverage machine learning to develop accurate and sophisticated malicious website detection tools

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Superviseur du corps professoral :

Ali Dehghantanha

Étudiant :

Partenaire :

Arctic Wolf Networks

Discipline :

Computer science

Secteur :

Information and cultural industries; Professional, scientific and technical services

Université :

University of Guelph

Programme :

Accelerate

User Traces and Adaptive Application Management, within the implementation of a new application for DreamIT

We will research and explore ways of tracking user behavior on a mobile application, as well as being able to remotely update the application without having to update program code. Thus we aim to provide developers with tools and options when it comes to customizing their application depending on user behavior and environments. Meanwhile the partner organization DreamIT will benefit primarily by receiving a new mobile application allowing them to meet their contract. They will also gain insight into the mobile application field, along with tasting some of the fruits from the latest research endeavors. They have stated that this will allow them to expand as a company, and grow into new possible areas of work.

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Superviseur du corps professoral :

Ralph Deters

Étudiant :

Partenaire :

DreamIT

Discipline :

Computer science

Secteur :

Health and Related Sciences & Technology; Information and Communications Technology; New and Digital Media; Other

Université :

University of Saskatchewan

Programme :

Accelerate

Anomaly and Defect Detection in Orchards

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Superviseur du corps professoral :

Aaron Courville

Étudiant :

Partenaire :

Vivid Machines

Discipline :

Computer science

Secteur :

Professional, scientific and technical services

Université :

Université de Montréal

Programme :

Accelerate

Vision-Language Models adapted to the blind community

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Superviseur du corps professoral :

Aishwarya Agrawal

Étudiant :

Partenaire :

Technologies HumanWare Inc.

Discipline :

Computer science

Secteur :

Manufacturing

Université :

Université de Montréal

Programme :

Accelerate

Super Resolution for Electronic Magnifiers for Low Vision People

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Superviseur du corps professoral :

Aishwarya Agrawal

Étudiant :

Partenaire :

Technologies HumanWare Inc.

Discipline :

Computer science

Secteur :

Manufacturing

Université :

Université de Montréal

Programme :

Accelerate

Monitoring of training and performances in Quebec biathletes

To ensure optimal physical development and competitive performances, specific training parameters such as intensity and volume (i.e., the training load) should be appropriately manipulated during and across training phases. Athletes in biathlon perform large amount of physical training that can lead to excessive fatigue and ultimately hinder physiological adaptations. Monitoring fatigue is therefore paramount to ensure proper development and reduce risks of injury and illness to a minimum. One key objective of our partner, Biathlon Quebec, is to unify monitoring and training practices of athletes across Quebec to facilitate data sharing and encourage communication between athletes and support/coaching staff to build a stronger provincial biathlon competition program. The aims of this project are 1) to implement systematic monitoring of performances and fatigue in the context of this sport federation, and 2) to assess potential relationships between the autonomic nervous system that regulates varied physiological functions and skiing and riffle shooting performances in both males and females of varied biological ages. This will contribute to enhance training periodisation to specific biological needs and promote health and well-being.

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Superviseur du corps professoral :

François Billaut

Étudiant :

Partenaire :

INS Québec;Fédération québécoise de biathlon

Discipline :

Life Sciences

Secteur :

Professional, scientific and technical services

Université :

Université Laval

Programme :

Accelerate

Habitat features affecting the success of the Endangered Ord’s Kangaroo Rat

This project focuses on evaluating habitat quality at two spatial scales: local and landscape. At a local scale, habitat quality will be assessed using body conditions (mass) and demographics (the number of females to juveniles) and vegetation characteristics important to Ord’s kangaroo rats. Assessment of habitat quality at a landscape scale involves using genetic material obtained from fur to examine how the entire Ord’s kangaroo rat population in Canada can be broken down into units to understand potential barriers to movement in the landscape. The Royal Saskatchewan Museum will benefit from this research by advancing understanding of wildlife and conservation in Saskatchewan and the larger prairie landscape while also promoting the Royal Saskatchewan Museum as an institution of advanced research.

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Superviseur du corps professoral :

Christopher Somers;Ryan Fisher

Étudiant :

Partenaire :

Friends of the Royal Saskatchewan Museum

Discipline :

Earth science

Secteur :

Arts, entertainment and recreation

Université :

University of Regina

Programme :

Accelerate