Innovative Projects Realized

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

30156 Completed Projects

2861
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5059
BC
812
MB
673
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842
SK
8957
ON
9368
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96
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579
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1120
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Projects by Category

Investigation of the Susceptibility of High Voltage Hydroelectricity Transmission Lines to Wildfire Smoke

The generation of clean hydroelectric power occurs often in locations far from consumers. High voltage transmission lines that traverse vast terrains are employed for the transmission of the generated clean electric energy. They are exposed to various environmental conditions, including wildfire smoke. Wildfire smoke has the potential to decrease the breakdown strength of air insulation, which serves as the primary insulation system for overhead power lines, leading to outages. Investigating the effect of smoke on the insulation strength of air is crucial to increase the reliability of the power transmission network and reducing wildfires ignited by electrical discharges. The proposed research studies the discharge characteristics and insulating properties of air under different smoke conditions. Laboratory experiments will be designed and conducted to determine the dielectric characteristics of air under various smoke conditions. In addition, guidelines for safe live line maintenance and mitigation techniques will also be established.

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Faculty Supervisor:

Behzad Kordi

Student:

Partner:

Manitoba Hydro

Discipline:

Engineering

Sector:

Professional, scientific and technical services; Utilities

University:

University of Manitoba

Program:

Accelerate

Optimizing Credibility Complements in Property and Casualty Insurance: A Data-Driven Approach for Enhanced Premium Estimation

Within the field of property and casualty insurance (P&C), one of the most pertinent dilemmas is the estimation of the insurance premiums required to accommodate potential losses. Due to the inherently random nature of losses, one of the tools utilized within P&C insurance is credibility theory, which utilizes a weighted average between past portfolio data and the portfolio’s general risk class. This allows insurance companies to give greater weight to more reliable data and theoretically increase the accuracy of their premiums.
However, the numerous options for a portfolio’s risk class, also denoted as the complement of credibility, create a subjective pricing model that may not be optimal for a given portfolio. Currently, the complements of credibility that are employed are finite and limited to traditional criteria. Therefore, generating a model capable of optimizing the complement of credibility may be beneficial by using more data from separate risk classes. Gathering data from various business units and portfolios could generate a prediction model that calculates a custom complement for each portfolio. This would increase the general relevancy of the complement of credibility and the associated profitability while ensuring that insurance premiums are more accurate and reliable.

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Faculty Supervisor:

Anas Abdallah

Student:

Partner:

Co-operators (General Insurance)

Discipline:

Mathematics

Sector:

Finance and Insurance

University:

McMaster University

Program:

Accelerate

Innovative energy efficiency in residential buildings: main blower system for interroom air transfer

Residential buildings contribute approximately 17% to Canada’s overall primary energy consumption, with the majority allocated to space heating, particularly space heating. The proposed research will focus on optimizing
energy efficiency through inter-room air transfer, utilizing a central blower system. SmartCocoon’s product includes fans at each room diffuser (AKA vent/register) such that heat can be strategically reallocated to occupied
spaces. While past work focused on modelling potential energy savings and comfort improvement, this project involves detailed field testing of various control strategies in an occupied home. The outcomes will guide
recommendations to our partner, Smart Cocoon, for implementing energy-efficient control strategies in their interroom air movement system. By considering factors such as room occupancy, solar heat gain, and existing HVAC
configurations, the system will achieve significant reductions in operational costs while optimizing comfort and energy conservation, aligning with the broader goal of sustainable residential energy consumption.

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Faculty Supervisor:

William O'Brien

Student:

Partner:

Smart Cocoon Inc.

Discipline:

Engineering

Sector:

Manufacturing

University:

Carleton University

Program:

Accelerate

Visual SLAM for Navigation & Mapping

Autonomous vehicles (AVs) promise to enhance safety, reduce emissions, and improve transportation system efficiency and reliability. The growing demand for AVs is shaping the future of the automotive industry by transforming the in-vehicle experience and paving the way for large-scale implementation of autonomous driving. AV technology requires onboard intelligence relying on sensors and systems such as global navigation satellite systems (GNSS), including GPS, vehicle motion sensors and remote sensing systems, including cameras, light detection and ranging (LiDAR) and radar. AVs capable of sensing the environment and navigating without human input require robust, high-precision positioning at the decimeter level of accuracy under all operational environments. During this internship, we will devise a vision-based navigation (VBN) module enabling decimeter level of positioning accuracy relying on centralized visual-inertial odometry supported by HD-Maps aiding and a deep Learning-based outlier rejection to mitigate the effect of dynamic objects. This project will advance visual self-localization and mapping for AVs and will contribute to the autonomous systems research at Queen’s. It will also elevate the intern’s expertise in areas of growing demand.

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Faculty Supervisor:

Aboelmagd Noureldin

Student:

Partner:

Indian Institute of Technology Roorkee

Discipline:

Computer science

Sector:

Artificial Intelligence; Automotive; Information and Communications Technology

University:

Queen's University

Program:

Globalink Research Award

Investigating the Effects of Emotion on Visuo-Somatosensory Integration by Using an AI-Powered Markerless Motion Capture System

We use all forms of sensory information when directing our movements to ensure that we reach our target. Conversely, when we have conflicting tactile and visual streams of information, like shaving in the mirror, the brain tends to inhibit the tactile information in order to favour our visual information. However the extent to which this is observed, based on our emotional response to the tactile sensation, is not understood. We plan to build an AI-powered markerless motion capture model (MMC) to track a goal-directed movement across pleasant and unpleasant surfaces, while analyzing electroencephalography (EEG) to investigate how the brain responds. By bringing our AI experience to the host institution, we hope to leave behind accessibility to motion capture technology through a pipeline and AI-powered MMC model that they are able to use for further research. The host institution has also offered a course as well as training with utilizing EEG technology, which will be utilized in conjunction with the results of this project, for further research projects and an undergraduate thesis at the home institution.

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Faculty Supervisor:

Gerome Mansion

Student:

Partner:

Aix-Marseille Université

Discipline:

Physics

Sector:

Education

University:

Queen's University

Program:

Globalink Research Award

Neural Pathways to Health: Deep Learning Applications in Medical Science

Machine Learning algorithms proved to be very helpful in medical science in the presence of enormous amounts of data. Particularly, it analyzes medical data, such as images, genetic information, and patient records, aiding healthcare professionals in faster and more accurate diagnoses. Deep learning has gained significance in automated report generation, expediting the diagnostic process and improving overall accuracy for more effective treatment plans. Specifically, applications like interpreting chest x-ray images highlight the need for precision in the absence of practitioners. To ensure trustworthiness and reliability, incorporating explainability concepts is crucial.
Machine Learning customizes medical interventions based on individual patient characteristics, considering factors like genetics, lifestyle, and health history. This personalized approach enhances treatment effectiveness, minimizes side effects, and boosts patient satisfaction. In the realm of dermatopathology research, interpretable deep learning techniques are applied to analyze histological images of common skin cancers such as intraepidermal carcinoma (IEC), squamous cell carcinoma (SCC), and basal cell carcinoma (BCC). The study involves classifying skin tissue into 12 dermatological classes, including structures like sweat glands and hair follicles, showcasing the potential of automatic machine analysis in this field.

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Faculty Supervisor:

Sabah Mohammed;Saad Ahmed

Student:

Partner:

National University of Sciences and Technology

Discipline:

Computer science

Sector:

Artificial Intelligence; Health and Related Sciences & Technology

University:

Lakehead University

Program:

Globalink Research Award

Investigation of Consumers’ Perception Toward Sustainable Product Attributes of Consumer Electronics: A Canadian Case Study

The proposed research attempts to understand Canadian consumers’ perception toward sustainable product attributes of consumer electronics products and factors affecting the consumers’ choices concerning sustainable consumer electronics during product purchase. The study uses the Theory of Planned Behavior (TPB) as its theoretical framework and further attempts to extend the TPB by incorporating additional constructs such as environmental concern and environmental knowledge in it. Data will be collected using a mixed method consisting of a self-administered questionnaire survey and focus groups. The data collected from the survey and focus groups will be analyzed with the help of structural equation modeling (SEM) and the content analysis technique, respectively.

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Faculty Supervisor:

Jay Park

Student:

Partner:

Cheil Canada

Discipline:

Sociology

Sector:

Professional, scientific and technical services

University:

Toronto Metropolitan University

Program:

Accelerate

Using Machine Learning Techniques to ImproveAutomatic Keyword Extraction from Textual Web Content

This internship aims to implement an advanced system for automatic keyword extraction from textual web contents. Keyword extraction not only provides a concise and salient representation of a document, but also can be used in web-based applications such as efficient indexing, intelligent tag recommendation, and contextual advertising. In this research project, a hybrid keyword extraction approach is proposed which combines the knowledge learned from an annotated dataset of manually extracted keywords with the knowledge
acquired by processing a huge unannotated corpus of data, using machine learning techniques.

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Faculty Supervisor:

Greg Mori

Student:

Partner:

BroadbandTV

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

Simon Fraser University

Program:

Accelerate

Improving Handover Management in 5G New Radio

The demand for higher bandwidth and ultra-lower latency has increased due to the rise of wireless devices. To meet these challenges, fifth generation (5G) new radio (NR) offers enhanced bandwidths with higher data rates and ultra-lower latency for real-time applications. The ultra-dense networks in 5G NR can improve the coverage. However, it can cause problem of frequent user handovers in inter- or intra-network, leading to high delays and degraded quality of service (QoS). The handover in mobility is the primary key performance indicator (KPI), which requires reliability and lower latency. Inefficient network deployment can lead to issues in the user handover process, especially in high mobility. Therefore, optimizing the handover in 5G NR for high-mobility users is crucial. The research project will set the ground for long-term partnerships between Lakehead University and MUET.

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Faculty Supervisor:

Waleed Ejaz

Student:

Partner:

Mehran University of Engineering and Technology

Discipline:

Engineering

Sector:

Information and Communications Technology; Technology; Artificial Intelligence

University:

Lakehead University

Program:

Globalink Research Award

Exploring the effects of iTBS delivered by the cTMS vs traditional rTMS

Canada currently employs patterned non-invasive brain stimulation techniques in the treatment of conditions such as depression and anxiety. The goal of this study will aim to determine whether there is a more effective way to deliver brain stimulation compared to traditional methods. We will compare a new controlled TMS compared to the traditional TMS hardware. The outcomes of this research allow us to undercover whether the use of the controlled TMS is more effective and can cause longer lasting effects in the brain compared to the traditional methods. This will be important for treatments using brain stimulation as it will provide potentially more impactful therapeutic interventions that use non-invasive brain stimulation.

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Faculty Supervisor:

Aimee Nelson

Student:

Partner:

Rogue Research Inc

Discipline:

Life Sciences

Sector:

Manufacturing; Professional, scientific and technical services

University:

McMaster University

Program:

Accelerate

Monitoring and Interpretation of Anaerobic Digester Sensor Data

Anessa is developing software that assesses the feasibility of an anaerobic digestion facility at the earliest stages when trying to determine if a facility should be constructed. The challenge is that little information is fully known and there are a lot of factors that can affect the outcome to be positive or negative in the investment to construct and operate a facility.
Some of these factors are based on the organic waste materials quantity and quality that are available, but some of it is also based on the legislative policies and incentives for clean energy. This research proposes 3 aspects that are vital to advance our research into this domain. First, to improve and refine our algorithms for predicting the quantity and quality of biogas that can be attained from waste via anaerobic digestion. Second, to complete market research into the various policies and incentives in place worldwide and the effects of these policies for anaerobic digestion uptake in constructing facilities.
Third, to identify and implement a rigorous and automated quality assurance engineering practice on our software solution to ensure correctness.

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Faculty Supervisor:

David Bremner

Student:

Partner:

Anessa

Discipline:

Computer science

Sector:

Green/Alternative Energy; Artificial Intelligence; Information and Communications Technology

University:

University of New Brunswick

Program:

Accelerate

Prototyping an app to monitor and support patients’ recovery after receiving CAR-CD19 T-cell-therapy, in partnership with Curetrax

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

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Faculty Supervisor:

Joseph Ferenbok;Edyta Marcon

Student:

Partner:

Curetrax Inc.

Discipline:

Life Sciences

Sector:

Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

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