Innovative Projects Realized

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

13270 Completed Projects

1072
AB
2795
BC
430
MB
106
NF
348
SK
4184
ON
2671
QC
43
PE
209
NB
474
NS

Projects by Category

10%
Computer science
9%
Engineering
1%
Engineering - biomedical
4%
Engineering - chemical / biological

Exploring The Effects Of Functional Connectivity To Depict Key Differences Between Stages Of Sleep To Determine Why REM Protects Against Seizures

The purpose of analyzing the different stages of sleep by means of functional connectivity metrics is due to the overwhelming unlikelihood of seizures occurring during REM sleep. The functional connectivity metrics are computational algorithms run on a computer that determine if the signal from one node is similar to another in any way. These nodes record the electrical potentials from the brain and are arranged around the head in a certain pattern making up an electroencephalogram (EEG) recording. Determining the proper steps of manipulating the signals to reduce any anomalies from the signal is ideal for obtaining a truly reputable result. Through this work these algorithms can be adopted by the Health Sciences Centre to improve research in the field.

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Faculty Supervisor:

Marcus Ng;Zahra Kazem-Moussavi

Student:

Darion Toutant

Partner:

Health Sciences Centre Foundation

Discipline:

Engineering

Sector:

University:

University of Manitoba

Program:

Accelerate

A Serious Approach to Sickness Prevention in Motion Base Simulators

Training personnel to operate machinery in the construction workplace requires a major devotion of time, resources, and safeguards. Recent methods involving virtual reality and motion base simulators have drastically enhanced the training process, but some users of these new methods report sickness and discomfort. This research aims to remedy this issue with a theoretical and data-driven approach to (1) identifying what makes a simulator nauseogenic, and (2) reducing or avoiding these patterns of motion. The expected benefits include a greater adoption of simulator technology across the market, due to a quantifiable improvement in the experience by means of a motion sickness reduction approach.

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Faculty Supervisor:

Anouk Lamontagne

Student:

Séamas Weech

Partner:

Serious Labs

Discipline:

Physics / Astronomy

Sector:

Professional, scientific and technical services

University:

McGill University

Program:

Accelerate

Study of the Field Plate Design and the Packaging Parasitic Effects on GaN HEMTs

In the field of power electronics, one important goal is to make low power loss and super fast switching speed. In making this goal, GaN transistors are better choices than the conventional Si devices. In this project, we investigate the GaN technology design and details with simulation tools by Crosslight Software and testing data in order to upgrade the simulation software for better predictability in the device design, fabrication and packaging.

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Faculty Supervisor:

Guangrui Xia

Student:

Zeyu Wan

Partner:

Crosslight Software Inc

Discipline:

Engineering

Sector:

University:

University of British Columbia

Program:

Accelerate

A scenario-based modelling framework for projecting COVID-19 infections and deaths

The speed and extent of the COVID-19 pandemic has challenged our abilities, as forecasters, like never before. Early data on the disease’s epidemiology is limited, records of cases and infections are incomplete, and the dynamics and scientific understanding of the disease are changing daily. Scientists from around the world have been quick to respond by developing a plethora of mathematical models to predict future COVID-19 infections and deaths. Delivering this science to decision makers in an actionable form, however, remains a challenge. Our solution to this challenge has been to develop a general software framework for providing real-time forecasts of COVID-19 infections and deaths that can be rapidly deployed for use anywhere in the world. Our framework allows end users to generate forecasts that are specific to their jurisdiction and questions. The result is a tool that generates locally responsive, meaningful, and ultimately actionable forecasts.

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Faculty Supervisor:

Sarah (Sally) Otto

Student:

Tom Booker

Partner:

Apex Resource Management Solutions

Discipline:

Zoology

Sector:

Professional, scientific and technical services

University:

University of British Columbia

Program:

Accelerate

A conversational assistant for accessing Covid-related benefits

We will develop a conversational assistant that can answer Canadian employee and employer questions about Covid benefits. The assistant will ask the user a minimal, easy-to-understand set of questions to help them figure out whatever benefits they are eligible for and will direct them to the relevant sites for applying for these benefits. The partner organization will thus have yet another tool with which to support their clients through this crisis. Data collected from interactions with the tool will also be leveraged to provide real-time information to stakeholders about the benefits being sought, and hence of the extent of the economic challenge facing Canadian workers and businesses in the midst of the Covid pandemic.

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Faculty Supervisor:

Raj Singh

Student:

Enver Deniz Askin

Partner:

PaymentEvolution

Discipline:

Other

Sector:

Professional, scientific and technical services

University:

Carleton University

Program:

Accelerate

Combination of multi-horizon models for demand forecasting

This project aims to develop a retail demand forecasting model that can both handle the long-term and short-term forecasting, and adjust its parameters as more data come in. General long-term prediction models are relatively precise because the context often remains same over time, but can not quickly adapt to unforeseen events, like the global pandemics. It is then necessary to develop model with multi-horizon perspectives. With the understanding and results achieved by this project, accurate and real-time improvement solutions could be proposed and implemented. It therefore makes economic sense to delve into understanding the travel behaviors of customers and then adjusting the retail practices if unforeseen events occur. This project is expected to produce practical results benefiting the public in the form of improved customer experience, increased incomes, and analysis of COVID-19 propagation containment, etc.

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Faculty Supervisor:

Lijun Sun

Student:

Dingyi Zhuang

Partner:

ExPretio Technologies Inc

Discipline:

Engineering - civil

Sector:

Professional, scientific and technical services

University:

McGill University

Program:

Accelerate

How can hormone monitoring can improve women’s health?

Eli Health is developing a novel biosensing device to monitor sex hormones in saliva and a mobile application that leverages machine learning to interpret them. The mission of the organization is to enable women to take control of their health across their lives. It will first commercialize the device for fertility and contraception applications. In collaboration with Seasy Huang and academic supervisor Dr. Charlotte Usselman, this research explores other use cases where of hormone monitoring can have a positive impact on women’s health.

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Faculty Supervisor:

Charlotte Usselman

Student:

Tingyu (Seasy) Huang

Partner:

Eli Science Inc

Discipline:

Kinesiology

Sector:

Professional, scientific and technical services

University:

McGill University

Program:

Accelerate

Machine Learning for Modeling Soil-Tool Interactions

When construction equipment digs, this generates a complex set of physical interactions between the machine and the soil. Being able to accurately simulate such interactions in real-time opens the door to improved operator training and even adaptively-tuned digging operation that optimize the energy-efficiency of construction equipment. With recent advanced in artificial intelligence (AI), this is now within reach. Montreal-based CM Labs is already a global leader in simulations for construction vehicles, and this research will expand the features and types of vehicle simulations it offers and thus increase market share. Improving the realism of simulations involving soil also enables entering new engineering design and simulation markets, initially through our current construction equipment OEMs (Original Equipment Manufacturers), generating profits and new hires.

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Faculty Supervisor:

Krzysztof Skonieczny

Student:

Amin Haeri

Partner:

CM Labs Simulations

Discipline:

Engineering - computer / electrical

Sector:

Other

University:

Concordia University

Program:

Accelerate

A new design approach for mine haul road pavements with stabilized layers

Currently, the design method of haul road pavements cannot address the rolling resistance under the effects of stabilizers in pavement design. To address this, the overall objective of this research is to develop a new design approach using finite element modeling for mine haul road pavements with stabilized layers. In particular, the prediction of rolling resistance will be addressed in this new design approach. The results derived from the proposed investigation will strongly benefit the performance of truck haulage (both trucks and haul roads) in Canada’s mining sites.

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Faculty Supervisor:

Wei Victor Liu

Student:

Linping Wu

Partner:

Cypher Environmental Ltd

Discipline:

Engineering - civil

Sector:

University:

University of Alberta

Program:

Accelerate

AI-Based Fault Detection system for Mine Dewatering Systems

The removal and movement of water is an essential activity for the safe and productive operation of mines. In the mining industry pumps are often required to operate several km underground. If a pump were to fail in such a location, the results can be costly and potentially unsafe. It can be exceedingly difficult to apply conventional on-line condition monitoring systems to warn personnel of an imminent pump failure. Technosub, the industrial partner in this work, has recognized that there is presently no off-the-shelf, commercially available solution for remotely monitoring the majority of the pumps that they sell for operation in extreme locations. To address this challenge, Technosub is endeavoring to develop a small, self-contained, hardware-based fault detection device that is mounted directly to individual pumps. If a pump is about to fail, the system will then communicate this vital information to maintenance personnel. The proposed project involves the development of the computational framework for the intelligent system that will reside on the monitoring device to determine the pump’s condition. The intern on this project will gain valuable experience developing and applying advanced technologies to an industrial application.

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Faculty Supervisor:

Markus Timusk

Student:

Danyk Levesque

Partner:

Technosub

Discipline:

Engineering

Sector:

University:

Laurentian University

Program:

Accelerate

Autonomous Mission Planning and Simulation Software Design for Unmanned Surface Vehicles

Ocean industries and researchers need the on-site oceanographic and environmental data, but collecting that data is costly, challenging, and time-consuming. Unmanned surface vehicles (USVs) offer a promising solution for marine data collection. Equipped with oceanographic sensors, cameras and communication devices, the USV Data Xplorer developed by Open Ocean Robotics can voyage for extended periods thanks to the additionally generated solar energy from the USV’s solar panels. Mission range and the travel path for fulfilling the specified task will be dependent on the geographical location of the task area, energy management of the USV, and environmental conditions. Therefore, research on autonomous mission planning based on various practical factors is desirable and will play an instrumental role in the USV product development. An offline mission planner will be designed to predict and schedule a feasible task considering the following factors: the energy generated by the solar panels, power consumption of the USV, and impact of mission-specific environmental conditions including prevailing wind speeds, average currents, sunlight level, etc. A simulation software will be developed by integrating the above-mentioned algorithms and modules.

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Faculty Supervisor:

Yang Shi

Student:

Qi Sun

Partner:

Open Ocean Robotics

Discipline:

Engineering - mechanical

Sector:

Manufacturing

University:

University of Victoria

Program:

Accelerate

Development and Validation of a Protein-Protein Interactions Modeling Platform for Rapid Affinity Predictions and Pharmaceutical Applications

Developing a drug for new diseases cannot only be challenging but also time consuming. From the identification of a druggable target to a compound which can improve a condition it usually takes more than 12 years. Since there is basically an infinite number of possible compounds which can be turned into a drug it is literally the problem of finding a needle in a haystack. The trial and error method of making molecules in the laboratory and testing their efficiency has been proven successful for over a century. However, with ever growing numbers of druggable molecules, diseases and classes of drugs, this traditional workflow has become too time consuming. Efficient computer models can help rationally pre-select a much smaller number of potential drug candidate compounds which can then selectively been tested. This internship aims at testing and enhancing the predictability of a computational tool capable of guiding the development of a new emerging class of drugs, stimulating the human immune system. These drugs, called antibodies, can cure the condition by finding and interacting with their target.

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Faculty Supervisor:

Gilles Peslherbe

Student:

Philippe Archambault

Partner:

Chemical Computing Group

Discipline:

Biochemistry / Molecular biology

Sector:

Information and cultural industries

University:

Concordia University

Program:

Accelerate

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