Projets novateurs réalisés

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

29 670 projets achevés

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801
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663
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825
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Projets par catégorie

Development of a system to transform audio and video feeds of medical consultations into structured notes and summaries

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Superviseur du corps professoral :

Dhanya Sridhar

Étudiant :

Partenaire :

Dialogue Technologies Inc.

Discipline :

Computer science

Secteur :

Artificial Intelligence; Health and Related Sciences & Technology

Université :

Université de Montréal

Programme :

Accelerate

Edge-Cloud Video Streaming Pipeline for Video Action Recognition

Streaming Cameras have become ubiquitous in the urban and industrial landscape. This research project aims to improve the AI-based action recognition capability of consumer-class home camera streams, which often have limited bandwidth and degraded video quality. The project proposes to develop a network-aware, video-action recognition AI pipeline that pushes key operations of traditional action recognition pipelines to the edge and uses this in concert with a cloud-based infrastructure to provide high-precision recognition capability. The benefit to the partner organization, SAIC-Toronto and Samsung Electronics Canada, is advancing the state-of-the-art in action recognition in resource impoverished and dynamic environments, and sharing any newly gained knowledge, patents, and publications resulting from the research.

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Superviseur du corps professoral :

Nandita Vijaykumar

Étudiant :

Partenaire :

Samsung Electronics Canada

Discipline :

Computer science

Secteur :

Manufacturing

Université :

University of Toronto

Programme :

Accelerate

ML for Action Detection in Movies for Haptic Effects Generation

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Superviseur du corps professoral :

Aaron Courville

Étudiant :

Partenaire :

D-BOX Technologies Inc.

Discipline :

Computer science

Secteur :

Artificial Intelligence

Université :

Université de Montréal

Programme :

Accelerate

Deep Learning for drug molecule and target representations

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Superviseur du corps professoral :

Ioannis Mitliagkas

Étudiant :

Partenaire :

Valence Discovery Inc

Discipline :

Computer science

Secteur :

Professional, scientific and technical services

Université :

Université de Montréal

Programme :

Accelerate

Emerging Event Classification System

The goal is to develop a system that can rapidly detect and report emerging disease outbreaks worldwide by analyzing clusters of news articles using Large Language Models. The objective is to create an efficient and effective way of identifying “disease
events” that can alert public health officials to take prompt action.

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Superviseur du corps professoral :

Annie Lee

Étudiant :

Partenaire :

BlueDot Inc

Discipline :

Computer science

Secteur :

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

Université :

University of Toronto

Programme :

Accelerate

Smart Battery Research

The proposed project seeks to develop a Machine Learning-based software solution that accurately measures the capacity, State of Health (SoH), State of Charge (SoC), and cycle count of non-smart batteries utilized in mobile fleets. The project’s primary objective is to bridge the gap between smart and non-smart batteries by monitoring non-smart battery capacity and other pertinent parameters. It includes conducting experiments on Lithium-Ion batteries to obtain valuable data and parameters, which will be utilized to develop mathematical models and Machine Learning algorithms for predicting those parameters for non-smart batteries. The aim is to integrate non-smart batteries into SOTI’s XSight dashboard, providing customers with precise information for a broader range of battery models. This project is expected to benefit SOTI and its customers significantly, while also contributing to sustainable technology development.

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Superviseur du corps professoral :

Arvind Gupta;Huaxiong Huang

Étudiant :

Partenaire :

SOTI Inc

Discipline :

Computer science

Secteur :

Information and cultural industries; Professional, scientific and technical services

Université :

University of Toronto

Programme :

Accelerate

Simulation of Remote Control on a Mobile Device

Mobile devices have become a crucial tool for businesses, and SOTI MobiControl is a leading mobile device management solution that provides remote control capabilities. However, to ensure proper product functionality and scalability of SOTI MobiControl, the company is looking to research the simulation of remote controlling a mobile device for automation testing. By testing the remote-control feature under various scenarios and conditions, SOTI can identify and address any issues that may arise, resulting in a better-performing product and improved user experience. This research will enable SOTI to maintain worker productivity by ensuring that the remote-control functionality is optimized and scalable. The simulation of remote controlling a mobile device for automation testing is a critical aspect of product development for SOTI MobiControl, contributing to the productivity of workers who rely on these devices to perform their job.

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Superviseur du corps professoral :

Eyal de Lara

Étudiant :

Partenaire :

SOTI Inc

Discipline :

Computer science

Secteur :

Information and cultural industries; Professional, scientific and technical services

Université :

University of Toronto

Programme :

Accelerate

Mining Event Tracing for Windows (ETW)

As cyber adversaries are becoming more creative, analysts are required to figure out more innovative ways to detect them to be able to respond before it’s too late. To detect any underlying threat inside a system, data logs are collected showing events and activities occurring inside the system. Adversaries nowadays are capable of evading detection and doing activities that do not always get recorded. Event Tracing for Windows (ETW) offers new data sources to collect logs from that can be of great benefit in detecting adversaries and their movement inside computer systems. ETW is quite flexible and spans many different log providers that can cover a huge deal of logs. This project will work on mining data obtained from ETW logs to create a tool that detects malicious patterns that indicate that a system is compromised or if it’s under attack.

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Superviseur du corps professoral :

Charlie Obimbo

Étudiant :

Partenaire :

eSentire

Discipline :

Computer science

Secteur :

Cyber Security; Information and Communications Technology; Technology

Université :

University of Guelph

Programme :

Accelerate

Cloud Hosting Cost Optimization

The proposed research project will focus on analyzing and optimizing the cloud infrastructure used by SOTI to manage mobile devices globally. The intern will analyze the current cloud architecture and hosting costs, identify areas for improvement, and propose and implement optimizations to reduce system requirements and minimize costs. The expected benefit to SOTI is a more cost-effective and efficient cloud infrastructure that maintains the performance and quality of their technology solutions. This project will also benefit the Canadian community by promoting cost-effective and sustainable technology solutions for mobile device management.

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Superviseur du corps professoral :

Baochun Li

Étudiant :

Partenaire :

SOTI Inc

Discipline :

Computer science

Secteur :

Information and cultural industries; Professional, scientific and technical services

Université :

University of Toronto

Programme :

Accelerate

Application of Machine Learning and Data Science for classification of BDD (Behavior Driven Development) Test Development and Execution

Continuous integration (CI) and continuous delivery (CD) are practices that help software development teams deliver code changes more often and with fewer issues. To ensure that code changes are working as they should, developers use Behavior Driven Development (BDD) tests. But running all these tests against every code change can be time-consuming and costly. This project aims at classifying and categorizing the BDD tests into smaller categories and creating a recommendation system that assigns the right tests to each code change. Instead of running all the tests, the proposed solution would recommend running only the tests that are relevant to the specific code change, resulting in faster and more efficient software development for the partner organization.

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Superviseur du corps professoral :

Shurui Zhou

Étudiant :

Partenaire :

SOTI Inc

Discipline :

Computer science

Secteur :

Information and cultural industries; Professional, scientific and technical services

Université :

University of Toronto

Programme :

Accelerate

Command and Control Automation and Reporting

A red team is a group of cybersecurity experts who are tasked with simulating real-world attacks on an organization’s systems and networks. They do this by using a variety of tools and techniques to identify vulnerabilities and weaknesses in an organization’s defenses. This project implements command-and-control infrastructure, which is critical for the red team or simulated attackers to remotely control systems compromised by them and receive stolen data from the compromised systems.

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Superviseur du corps professoral :

Xiaodong Lin

Étudiant :

Partenaire :

Lares LLC

Discipline :

Computer science

Secteur :

Professional, scientific and technical services

Université :

University of Guelph

Programme :

Accelerate

Automating Insider Threat monitoring and detection

Insider threat involves individuals who have access to company resources and causes harm to the institution. These insiders can be employees, consultants, contractors, and third-party companies. Different types of insiders include people who intentionally harm the company, those to masquerade as a trusted entity, and those who unintentionally cause harm. Insider threats can lead to the disclosure, alteration, or destruction of sensitive information. To defend against such threats, companies need a system to detect and reduce insider threat risk. This project focuses on developing a system that automates the identification of high-risk groups for insider threats and generating remediation strategies for each risk a group may pose. The project will benefit EQ Bank’s Insider risk team in categorizing and identifying key hallmarks of insiders. This will provide more context to the team in making strategies to mitigate the risk emerging from potential insiders.

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Superviseur du corps professoral :

Ali Dehghantanha

Étudiant :

Partenaire :

Equitable Bank

Discipline :

Computer science

Secteur :

Finance and Insurance

Université :

University of Guelph

Programme :

Accelerate

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