Innovative Projects Realized

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29670 Completed Projects

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Projects by Category

Spatial flood risk mapping and forecasting using GIS and remote sensing in Central Vietnam

Floods have been considered the most common and leading cause of natural disasters worldwide. In Vietnam as well as in Canada, more frequent and severe floods have been documented to growing number of negative health outcomes. Alongside concurrent mitigation efforts, developing accurate methods to identify the health impacts of flooding to adapt will be crucial. This postdoctoral project is an extension of the fellow’s PhD research with other aspects of the impacts of floods on two different cultural and different living-way countries/provinces focusing on human health. In this project, the NASA’s MODIS Near Real-Time Global Flood Water (MFW) will be extracted with the support of Geographic Information System (GIS) and Google Earth tools to examine the model showing the relationship between floods and human health, using case studies of Thua-Thien-Hue in Vietnam and New Brunswick in Canada. The approach is implemented using a novel external dataset comprising satellite images, thereby allowing a highly precise and objective geographical measure of flood data. This project will open up a promising research direction that would effectively help address the existing and future challenges of flooding impacts in Canada and Vietnam.

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Faculty Supervisor:

Tri Nguyen-Quang

Student:

Partner:

Hoa Sen University ;Hue University of Sciences

Discipline:

Earth science

Sector:

Sustainability & the Environment; Environmental Science and Technology

University:

Dalhousie University

Program:

Globalink Research Award

Leveraging SSL 2 Generate High Quality 3D Face Avatar from Portrait Image

High-fidelity 3D face reconstruction from monocular images aims to obtain a 3D representation of the subject from a single or multiple input image. Recently, self-supervised deep-based methods have demonstrated impressive performance in 3D face reconstruction. These methods are efficient and produce plausible face reconstruction. However, for AAA production (games and movies), they do not yet meet the production-level requirements. For instance, the estimated geometry does not fully recover the likeness of the subject and the estimated texture maps used for rendering typically have low resolution. However, at least 4K texture maps are required in AAA productions. In this work, we aim to push the quality of the reconstruction provided by self-supervised methods to reach production-level needs.

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Faculty Supervisor:

Steve Engels

Student:

Partner:

Ubisoft Toronto

Discipline:

Computer science

Sector:

Information and cultural industries

University:

University of Toronto

Program:

Accelerate

Facial Landmark Detection with Synthetic data

Facial landmark detection is a computer vision problem where the goal is to predict the location of specific points on a face, like the eyes, nose, and mouth. This is useful for things like facial recognition and 3D modeling. To train a model to do this, we need a lot of images with those points already marked, which can be expensive and time-consuming. So instead, we can create synthetic images using scans of real people’s faces. Models trained on synthetic data have shown promising outcomes and have been successful. However, these models don’t work well on images taken with helmet-mounted cameras, which are commonly used in film and video games. This research aims to create synthetic data that looks like it was taken with these cameras and train models on it to improve performance.

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Faculty Supervisor:

Steve Engels

Student:

Partner:

Ubisoft Toronto

Discipline:

Computer science

Sector:

Information and cultural industries

University:

University of Toronto

Program:

Accelerate

Security enhancement of free-space quantum key distribution system

Satellites have become a crucial part of everyday life. We use satellites for watching television, navigation, weather
predication, national defense, and everything in between. Therefore, the safety and security of our satellites is
very important.
However, there are two problems that put satellites at risk of cyberattack:
1. As computers become more advanced, old cryptographic protocols become obsolete.
2. Encryption keys are lost or compromised.
QEYnet is working to address both issues and secure satellite communications using quantum key distribution
(QKD). QKD uses the properties of quantum mechanics to ensure secure communication, now and into the future.
Satellite QKD requires two main components: a quantum transmitter on the ground, and a quantum receiver on
the satellite. In this research project, we test the complete end-to-end QKD system for security vulnerabilities and
ensure both components are safe from cyberattack.

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Faculty Supervisor:

Li Qian

Student:

Partner:

QEYnet Inc.

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Pre-contact Indigenous agricultural systems in southwestern Manitoba

We aim to identify the different agricultural crops grown by Indigenous farmers in the Pierson Wildlife Management area in southwestern Manitoba prior to the arrival of European settlers. Agriculture was an important aspect of life for many Indigenous peoples living in southwestern Manitoba. Identifying plant remains associated with different crops will tell us about daily meals people ate. Identifying past farming practices through the same plant remains can inform us of the ways people grew domesticated and non domesticated plants, and how producing food may have organized their lives. Indigenous peoples had sophisticated food-getting and cultivation practices. Identifying these through archaeology helps challenge stereotypes of Indigenous peoples as foragers and decolonizes the history of agriculture in Canada.

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Faculty Supervisor:

Mary Malainey

Student:

Partner:

Manitoba Archaeological Society

Discipline:

Sociology

Sector:

Arts, entertainment and recreation

University:

Brandon University

Program:

Accelerate

Design and develop a speech to intent system for numerical data

THIS IS A GENERIC TEXT PUT IN PLACE AS THERE WAS NO PROJECT OVERVIEW

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Faculty Supervisor:

Ioannis Mitliagkas

Student:

Partner:

BusPas Inc.

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

Université de Montréal

Program:

Accelerate

Investigation of the impact of global supply chain disruptions on international trade

This research project aims to study how natural disasters, political events, and other factors can disrupt global trade and supply chains, and how this affects the economy. The study will focus on the impact of the war in Ukraine and natural disasters in Japan. By identifying the products and industries most affected, the intern hopes to help policymakers and businesses develop strategies to mitigate the negative effects of supply chain disruptions on international trade. Intern will also analyze how the disruptions affected trade flows between Ukraine and neighboring countries, as well as global trade flows, and evaluate the impact on the global economy, including GDP growth and employment. The intern will use advanced research methods including statistical analysis to provide new insights into the complex dynamics of global trade and supply chain disruptions, ultimately contributing to more effective risk management strategies and more resilient global trade networks.

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Faculty Supervisor:

Anindya Sen

Student:

Partner:

Precarpathian National University

Discipline:

Sociology

Sector:

Public Service, Policy, and Governance; Commercial Services; Finance and Insurance

University:

University of Waterloo

Program:

Globalink Research Award

Frost removal in the presence of an electric field on a fin and tube evaporator

High voltage electrodes will be used to remove the frost from the front part of the fin and tube evaporator inside the case of the refrigeration system provided by the company. Over the period of a few hours the frost buildup can become quite substantial on the fin leading edge. It is expected that the electrostatic forces will affect the way that frost forms on the surface and the efficiency of the evaporator can be increased. This defrosting system leads to better performance of the refrigeration system and increasing the time between the traditional defrosting cycles. The anti-frosting performance of this new defrosting system will be tested in a commercial refrigerator.

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Faculty Supervisor:

Kazimierz Adamiak

Student:

Partner:

Cayuga Displays Inc

Discipline:

Engineering

Sector:

Manufacturing

University:

Western University

Program:

Accelerate

Développement d’une méthode non-supervisée d’apprentissage profond pour la détection de défaillance à partir de signaux acoustiques et vibratoires

THIS IS A GENERIC TEXT PUT IN PLACE AS THERE WAS NO PROJECT OVERVIEW

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Faculty Supervisor:

Ioannis Mitliagkas

Student:

Partner:

Institut de Recherche Hydro-Québec

Discipline:

Computer science

Sector:

Professional, scientific and technical services; Utilities

University:

Université de Montréal

Program:

Accelerate

Wicking and reinforcing behaviour of a novel wicking nonwoven geotextile – geogrid composite

Excess water in the road base can lead to damage to roads from several mechanisms including decreasing stiffness of road base, freeze-thaw cycles, and swelling/shrinking of subgrades in expansive soils. Reducing the time that a pavement system is saturated is known to increase the lifespan of roads. Recent innovative geosynthetic products can further remove water from base materials due to suction or “wicking”. A novel geosynthetic product has been developed consisting of a wicking nonwoven geotextile bonded with a geogrid. The objective of this research is to test and quantify its wicking abilities and to determine its ability to improve performance of pavements with expansive subgrades. It is anticipated that this novel wicking geotextile – geogrid composite will improve drainage in unsaturated soil and limit differential subgrade swelling in expansive soil. The outcomes can increase adoption of wicking geosynthetics in Canada and increase lifespan of roads.

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Faculty Supervisor:

Jamie Bartz;Marolo Alfaro

Student:

Partner:

Titan Environmental Containment

Discipline:

Engineering

Sector:

Construction and infrastructure; Manufacturing; Professional, scientific and technical services

University:

University of Manitoba

Program:

Accelerate

DNS tunneling detection method based on ML & DL models

Domain Name System (DNS) tunnels as a covert communication channel between a controlled host and a master
server can be utilized by malicious attackers disguising the master server as an authoritative domain name server.
DNS tunneling can cause significant harm due to its ability to easily evade network security mechanisms by using
DNS traffic, so it is crucial to detect the malicious domain in advance. In this research, the performance of the
machine learning and deep learning models with existing detection methods are compared to determine their
effectiveness in detecting DNS tunneling activity, and optimize the models by tuning hyperparameters, adjusting
the architecture of the models, or combining multiple models to achieve better performance in the real-time
prediction.

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Faculty Supervisor:

Murat Erdogdu

Student:

Partner:

BlueCat Networks

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Real-time DNS tunneling detection using Machine Learning & Deep Learning techniques

Domain Name System (DNS) tunneling is a malicious technique that enables attackers to bypass network security
measures and steal sensitive information. Traditional detection methods that rely on signature-based approaches
are often ineffective against advanced attacks. In light of this, recent years have seen a growing interest in the
use of deep learning techniques for network intrusion detection. This research aims to explore the feasibility of
using deep learning algorithms for the detection of DNS tunneling in real-time network traffic. The study will involve
the analysis of a large dataset of DNS traffic to develop and evaluate a deep learning-based model. The model’s
performance will be compared against existing detection methods, and the outcomes of this research will
contribute to improving the effectiveness of DNS tunneling detection, enhancing network security overall.

View Full Project Description
Faculty Supervisor:

Murat Erdogdu

Student:

Partner:

BlueCat Networks

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

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