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
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8841
ON
9197
QC
95
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568
NB
1088
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Projets par catégorie

Free radical polymerization of lignin and PCL for 3D printing materials

The main goal of her study is to make sustainable polymers from lignin that can be used in three dimensional (3D) printing material production. The student will work on making 3D printing materials from polymerized lignin and polycaprolactone (PCL). At Abo Akademi, the student will use different monomers to generate composites of lignin and PCL. Then, she will characterize the properties and performance of the produced lignin-PCL as a 3 D printing material using the advanced tools available at Abo Akademi. She will optimize the properties of the polymers to achieve the best performance. It is expected that she will need 6 months (starting June 2023) to complete this project in Finland. It is also expected that, in addition to scientific outcomes that benefits both institutes, this student mobility will strengthen the collaboration between Lakehead and Abo Akademi universities. It is also expected that this collaboration will create an opportunity for Canada to strengthen itself in knowledge and material development for sustainable product fabrications.

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Superviseur du corps professoral :

Pedram Fatehi

Étudiant :

Partenaire :

Åbo Akademi University

Discipline :

Engineering

Secteur :

Education

Université :

Lakehead University

Programme :

Globalink Research Award

3D computer vision

Monocular depth estimation aims to infer the distance information of objects in a 2D image. It is an integral part of many computer vision tasks and has applications to autonomous driving, robotics, and virtual reality, among others. This project focuses on developing a new deep-learning-based monocular depth estimation method with high efficiency, competitive performance, and low generalization error. To this end, various approaches will be explored, including the exploitation of 3D prior and geometric information as well as the design of new loss functions. The project will lead to publications in top computer vision conferences /journals, new datasets, and patents.

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Superviseur du corps professoral :

Jun Chen

Étudiant :

Partenaire :

New York University

Discipline :

Engineering

Secteur :

Education

Université :

McMaster University

Programme :

Globalink Research Award

Photovoltaic window coatings

This project aims to conduct research towards the development and implementation of photovoltaic energy generating windows. Through the use of window coatings, the windows of an everyday home will be converted to a source of generating energy to power the home. This research will take us a step closer to our goals of net-zero by developing windows that can generate energy to power the home as well as tests on their use in charging applications. The benefits to the partner organization will be the data then allowing the company to develop and implement solar windows.

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Superviseur du corps professoral :

Wayne Groszko

Étudiant :

Partenaire :

Kohltech Windows And Entrance Systems

Discipline :

Engineering

Secteur :

Manufacturing

Université :

Nova Scotia Community College

Programme :

Accelerate

Canadian Faces of Learning Disabilities (CFOLD) – Obtaining and Disseminating Knowledge

The objective of this project is to provide a research report synthesizing Canadian research that directly relates to Learning Disabilities (LD). This project will benefit Canadians as it will provide stakeholders who support individuals with LD with easy access to information. Easy access to recent research will help to educate Canadians on a variety of aspects pertaining to LD such as causes, diagnostic criteria, assessment procedures, underlying cognitive processing difficulties, learning strategies, assistive technology, and help individuals to develop an understanding of their diagnosis. The objectives of this project are to: i) collect research; ii) provide an analysis of recently published Canadian research; and iii) support knowledge mobilization pertaining to the last decade of Canadian research on LD, and identify topics in need of further exploration.

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Superviseur du corps professoral :

Lauren Goegan;Gabrielle Young

Étudiant :

Partenaire :

Learning Disabilities Association of Canada (ON)

Discipline :

Sociology

Secteur :

Professional, scientific and technical services

Université :

University of Manitoba

Programme :

Business Strategy Internship

Investigating Smart Wearable Systems for Workplace Wellness Management

Japan is a leading example of a nation with a rapidly ageing society and currently consists of the highest proportion of elderly adults worldwide. Among others, this has led to a series of downstream concerns, including labour shortage issues and reduced ability of working individuals to finance those who are retired. As one of the strategies to address these concerns, combined government and society-wide efforts over recent years have encouraged greater employment opportunities for elderly. However, given that elders are often more prone to age-related difficulties, such as decreased physical and cognitive function, this higher proportion of working elders gives rise to a pressing need for an effective workplace wellness management system. Hence, this project aims to investigate the physiological differences between a “healthy” and “diseased” worker as well as to refine the tolerance that is used when differentiating between the two states. By exploring various ways of defining the health-disease boundary, the project will reduce the likelihood of false positives when identifying “disease”, thus improving confidence when separating the two states for use in real-world conditions.

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Superviseur du corps professoral :

Arthur Chan

Étudiant :

Partenaire :

Osaka University

Discipline :

Engineering

Secteur :

Education

Université :

University of Toronto

Programme :

Globalink Research Award

NEGF based Cryogenic MOSFET simulation including inelastic scattering

The project aims to develop a state-of-the-art numerical simulator to compute transistor’s physical behaviors at deep cryogenic temperatures. First of its kind, the simulator will incorporate physical effects critical for transistor’s operations at cryogenic temperature such as inelastic scattering, while maintaining computational efficiency and robustness. The successful outcome will provide the research community a widely desired tool for understanding and predicting how realistic MOSFETs behave under deep cryogenic temperatures. It is expected that the simulator will provide critical enhancement to the current product line of the partner organization, Nanoacademic Technologies, a leading company in cryogenic temperature numerical simulation for semiconductor devices.

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Superviseur du corps professoral :

Lan Wei

Étudiant :

Partenaire :

Nanoacademic Technologies Inc.

Discipline :

Engineering

Secteur :

Professional, scientific and technical services

Université :

University of Waterloo

Programme :

Accelerate

Preparation of Quantum Machine Learning Datasets with Quantum Advantage and Challenges using State-of-the-art Classical Machine Learning

Machine Learning (ML) approaches generally consist of training an algorithm on a given dataset containing data which has to be analyzed or otherwise understood. For an ML application to be successful, careful thought must be given to ensuring that the architecture of the algorithm chosen is fit for the task at hand: some architectures are tailored for sequential data (stock market data, audio data, etc.) while others are tailored for image data. One subset of ML algorithms is Quantum Machine Learning, which seeks to utilize quantum computing techniques. This research project aims to select a set of quantum datasets and evaluate the performance of both quantum and traditional ML algorithms on them, in order to demonstrate that quantum machine learning can outperform classical machine learning methods on certain tasks of interest, such as classifying quantum circuits. The expected outcomes of this research are to advance the field of quantum machine learning and to lay the groundwork for future work in this area.

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Superviseur du corps professoral :

Arthur Chan

Étudiant :

Partenaire :

Osaka University

Discipline :

Computer science

Secteur :

Education

Université :

University of Toronto

Programme :

Globalink Research Award

Natural Health Products to Manage Cancers of Dogs: A Pre-clinical Investigation

More than half of Canadian households have companion animals such as dogs or cats. However, cancer has become the leading cause of death in dogs. Currently, available treatments have limitations and compromise the quality of life of dogs. Dragonfly Research Inc (Adored Beast Apothecary) wishes to develop unique natural health products (NHP) to prevent and treat the cancers of dogs. The overall objective of the proposed research project is to assess the anti-oxidative, anti-inflammatory, and tumor suppression ability of the patent-pending natural product formula derived from Chaga mushroom and microalgae using a pre-clinical experimental model of mice. The intern (a postdoctoral fellow) will conduct the animal study to examine cancer preventive and treatment properties of the new natural health product in comparison to two major components. The expected result will become useful for the industry partner to design and perform a clinical study using dogs, and progress with business development and commercialization.

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Superviseur du corps professoral :

Vasantha Rupasinghe

Étudiant :

Partenaire :

Dragonfly Research Inc.

Discipline :

Life Sciences

Secteur :

Health and Related Sciences & Technology; Advanced Manufacturing; Agriculture and Food

Université :

Dalhousie University

Programme :

Accelerate

Generative 3D Modelling for Game Asset Creation using Deep Learning Techniques

The 3D entertainment industry has expanded quickly in recent years, largely driven by animated content, streaming services, video game development, AR/VR/XR. The new trend enabled by the ubiquitous graphics processing power is that users are becoming creators. Surprisingly, the fundamentals of 3D creation have not changed in 45 years. This puts the creation of 3D out of reach for 99.8% of consumers and, for professionals, state-of-the-art methods are still too costly and labor intensive to practically meet growing 3D demand. Generative Adversarial Networks (GANs) and other AI technologies offer new possibilities in the AI generation of 3D and 2D art assets. This project investigates the potential of using deep learning techniques for generative 3D modelling for game development and has significant implications for the industry revenue stream.

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Superviseur du corps professoral :

Ali Mahdavi-Amiri

Étudiant :

Partenaire :

Tori Technologies

Discipline :

Computer science

Secteur :

Professional, scientific and technical services

Université :

Simon Fraser University

Programme :

Accelerate

Two-step personalized federated learning algorithm in reality

Machine learning attempts to model high-level abstractions in data using multiple processing layers with complex structures or non-linear transformations. Federated learning is a distributed machine learning approach that allows multiple parties to collaborate on training while preserving user data privacy. However, the data from each party is typically non-independent and identically distributed (Non-IID), which can negatively impact the training effectiveness of the model. This study proposes a contrastive learning method to mitigate the impact of Non-IID data distribution on model training. Additionally, this study researches the feasibility of deploying this method on edge devices, for example, the Internet of Things (IoT). The primary objective of this research project is to demonstrate combining contrastive learning with clustering methods. It can solve the impact caused by Non-IID distribution in federated learning and produce models that balance both generality and personalization. The study aims to validate the research methods through more diverse datasets and data distributions closer to reality.

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Superviseur du corps professoral :

Patrick Hung

Étudiant :

Partenaire :

National Cheng Kung University

Discipline :

Computer science

Secteur :

Artificial Intelligence; Technology

Université :

University of Ontario Institute of Technology

Programme :

Globalink Research Award

A Study on the Effectiveness of Computer Vision Models for Addressing Environmental Problems Using UAVs and USVs

The research project, guided by Professor Stephen Smith, focuses on addressing environmental challenges related to water pollution and debris detection in the water areas, with a specific emphasis on garbage and waste detection on the water surface. The project entails a systematic literature review and analysis of various computer vision models to detect and classify garbage, which involves processing raw data from sensors on board aerial vehicles. Additionally, the project investigates how to plan the motion of the aerial vehicles over a body of water to detect and monitor garbage’s subsequent motion. The research will attempt to propose a new algorithm or modify an existing algorithm for more accurate waste detection in the water using computer vision techniques. While the project does not involve the direct use of unmanned aerial vehicles or unmanned surface vehicles, the intern will conduct a thorough investigation of their potential implementation in environmental monitoring. The expected outcomes of the project are generating insights into the effectiveness of computer vision models for environmental monitoring and management, and identifying potential applications for future research.

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Superviseur du corps professoral :

Stephen Smith

Étudiant :

Partenaire :

Kharkiv National University of Economics

Discipline :

Computer science

Secteur :

Artificial Intelligence; Sustainability & the Environment; Environmental Science and Technology

Université :

University of Waterloo

Programme :

Globalink Research Award

Establishing Travel Needs of Older Adults

Many older adults in Canada are becoming increasingly isolated from their communities. This is largely due to
the fact that Canadian cities are built with cars in mind, and many older adults rely heavily on driving to get
around. However, as they age and lose their ability to drive, many older adults often are unable to travel as
much as they used to, which greatly limits their activities and social interactions.
Unfortunately, there aren’t many good alternatives for older adults who can no longer drive. Public transportation
is often not very reliable or convenient, and walking or biking long distances can be difficult or unsafe. Taxis are
often too expensive to use on a regular basis, so many older adults end up relying on family and friends to give
them rides. This can be inconvenient, and it may make them feel like a burden on others.
To help combat this problem, it’s important to ensure that older adults can easily access important services and
amenities in their communities. However, this requires better transportation options that are specifically tailored
to the needs of older adults. To achieve this, we need to better understand what these needs are. This research
will be the first to study the transportation needs of older adults

Voir la description complète du projet
Superviseur du corps professoral :

Ajay Agarwal

Étudiant :

Partenaire :

City of Kingston

Discipline :

Sociology

Secteur :

Public administration

Université :

Queen's University

Programme :

Accelerate

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