Innovative Projects Realized

Explore thousands of successful projects resulting from collaboration between organizations and post-secondary talent.

31133 Completed Projects

2940
AB
5159
BC
837
MB
685
NL
882
SK
9292
ON
9695
QC
97
PE
601
NB
1161
NS

Projects by Category

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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Faculty Supervisor:

Lan Wei

Student:

Partner:

Nanoacademic Technologies Inc.

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

University of Waterloo

Program:

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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Faculty Supervisor:

Arthur Chan

Student:

Partner:

Osaka University

Discipline:

Computer science

Sector:

Education

University:

University of Toronto

Program:

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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Faculty Supervisor:

Vasantha Rupasinghe

Student:

Partner:

Dragonfly Research Inc.

Discipline:

Life Sciences

Sector:

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

University:

Dalhousie University

Program:

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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Faculty Supervisor:

Ali Mahdavi-Amiri

Student:

Partner:

Tori Technologies

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Simon Fraser University

Program:

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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Faculty Supervisor:

Patrick Hung

Student:

Partner:

National Cheng Kung University

Discipline:

Computer science

Sector:

Artificial Intelligence; Technology

University:

University of Ontario Institute of Technology

Program:

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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Faculty Supervisor:

Stephen Smith

Student:

Partner:

Kharkiv National University of Economics

Discipline:

Computer science

Sector:

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

University:

University of Waterloo

Program:

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

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Faculty Supervisor:

Ajay Agarwal

Student:

Partner:

City of Kingston

Discipline:

Sociology

Sector:

Public administration

University:

Queen's University

Program:

Accelerate

Ai-based musical intervention to improve emotion through a personalized speaker

Mental disorders have a significant influence on the daily activities of Canadians. Musical intervention can provide a non-invasive treatment through changing emotional state and creating positive mood. The main objective of this project is to provide a long-term solution for musical intervention through an optimized machine learning framework for an intelligent real-time emotion recognition and musical intervention system integrated in an empathetic speaker. During music play, the emotional influence will be detected and according to the mood change measured from the brain signals, the music database will be customized. This project in partnership with Pi-Electronics, a leading company in acoustic technology with an extensive portfolio of advanced audio-visual electronic initiatives, is expected to achieve remarkable industrial benefits for musical intervention using smart speakers. Ultimately, it will provide a low-cost and low-risk solution to improve the subjective well-being and consequently quality of life for Canadians and worldwide users.

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Faculty Supervisor:

Soodeh Nikan;Abdallah Shami

Student:

Partner:

Pi Electronics Technology Inc

Discipline:

Engineering

Sector:

Manufacturing; Professional, scientific and technical services

University:

The University of Western Ontario

Program:

Accelerate

Using analytic hierarchy processes to resolve multi-criteria decision making

Many real life decision consider a multitude of criteria, one such example is in healthcare where the patient’s condition, available resources, chance of recovery, cost etc all need to be consider when administrating care. An analytic hierarchy process makes the multi-criteria decisions by first converting the problem into a set of mathematical constraints by pair-wise comparing the importances of the constraints. This project aims to optimize this approach as well as uncover its convergence properties. The expected outcomes are for a more efficient algorithm.

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Faculty Supervisor:

Arthur Chan

Student:

Partner:

Osaka University

Discipline:

Engineering

Sector:

Education

University:

University of Toronto

Program:

Globalink Research Award

Forecasting Vehicle Maintenance Needs and Breakdowns using Predictive Maintenance

Improving road safety has a direct impact on the lives of drivers as well as the costs incurred by companies operating commercial vehicles. One important aspect of road safety is timely and effective vehicle maintenance. By forecasting vehicle maintenance needs and predicting breakdowns before they occur, valuable insights can be provided to drivers and fleet managers ahead of time. This information allows them to make informed decisions on when to perform vehicle maintenance and avoid accidents arising from unexpected vehicle breakdowns while on the road. The goal of this project is to take advantage of Geotab’s data collected from more than 2 million connected vehicles to develop and evaluate models for forecasting vehicle maintenance needs and predicting malfunctions. The output of this project will be of value to Geotab’s customers, as well as to the wider community in understanding patterns for vehicle malfunctions and reducing accidents arising from them.

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Faculty Supervisor:

Andrei Badescu

Student:

Partner:

Geotab Inc

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services; Transportation and warehousing

University:

University of Toronto

Program:

Accelerate

Contact-Rich Visuotactile Manipulation

Robotic manipulation involving contact-rich tasks continues to be a challenging, yet critically important, research problem with many potential applications, including domestic assistance, automated agriculture, and advanced manufacturing. Many of these tasks involve both unstructured environments and complicated dexterous manipulation. Existing approaches that rely on purely visual sensors and predefined models are brittle and prone to failure. This research project will investigate how tactile sensing can provide a sense of touch to robot manipulators so that they can handle more difficult tasks.

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Faculty Supervisor:

Jonathan Kelly

Student:

Partner:

Samsung Electronics Canada

Discipline:

Computer science

Sector:

Manufacturing

University:

University of Toronto

Program:

Accelerate

A Machine Learning Framework for Exploring Mortality in Developing Countries with Verbal Autopsies

This research project, backed by Unity Health Toronto and the Centre for Global Health Research (CGHR), aims to explore the use of machine learning in predicting causes of death using verbal autopsy data from low-to-middle-income countries. Verbal autopsy is a cost-effective and efficient method for documenting deaths in regions with limited resources. By employing advanced analytics and artificial intelligence, this project seeks to improve the accuracy of determining causes of death, quickly identifying disease patterns and trends, which can ultimately improve public health policies, strategies, and preparedness. The success of this research is expected to strengthen Unity Health Toronto’s reputation as an innovator in global health research, enrich epidemiological understanding, and foster collaborations with global experts. Ultimately, the findings will benefit healthcare professionals, patient care, and contribute to the resilience of Canada’s healthcare system.

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Faculty Supervisor:

Frank Rudzicz

Student:

Partner:

Unity Health Toronto

Discipline:

Computer science

Sector:

Health and Related Sciences & Technology

University:

University of Toronto

Program:

Accelerate

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Mitacs — Return on Innovation

Mitacs is funded by the Government of Canada, the Government of Alberta, the Government of British Columbia, Research Manitoba, the Government of New Brunswick, the Government of Newfoundland and Labrador, the Government of Nova Scotia, the Government of Ontario, Innovation PEI, the Government of Quebec, the Government of Saskatchewan, and the Government of Yukon.