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

Automating Configuration Management and Deployment in Large-scale Data Centers Augmented with Edge Data Centers

Data centers are now growing and expanding massively. They are large scale and heterogeneous. In addition, they rely more and more on emerging technologies such as Software Defined Networking (SDN) and Network Functions Virtualization (NFV) with “network softwarization” as their key feature. Moreover, they are now being augmented with edge data centers rooted in concepts such as cloudlets, ETSI Mobile Edge Computing (MEC), and fog computing. Such data centers bring a host of new challenges when it comes to the automation of configuration management and deployment. For instance, edge data centers can be mobile. This mobility may cause unpredictable network topology changes and migration of the hosted applications to hardware with different configuration requirements. Accordingly, operators must implement increasingly sophisticated network policies that have to be translated into low-level configuration commands and adjusted to the changes in the network condition. In addition, some applications may have location constraints on some of their components for legislative reasons. Due to this mobility, the mapping of application components to the infrastructure might dissatisfy such constraints. Traditionally, these tasks are done manually. However, this process is tedious, costly, and does not scale. TO BE CONT’D

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Faculty Supervisor:

Roch Glitho

Student:

Carla Mouradian

Partner:

Ericsson Canada

Discipline:

Engineering - other

Sector:

Information and communications technologies

University:

Program:

Elevate

Pipeline Strain Demand and Capacity under Geotechnical Threats

Pipelines are often subjected to longitudinal stresses due to ground movements such as moving slopes and discontinuous permafrost areas. In these cases, numerical models are used to calculate strain demand which is then compared to tensile and compressive strain capacities (TSC and CSC) which are functions of the pipeline and girth weld material properties. In terms of strain demand, we have shown that current numerical models are inappropriate as they neglect the Bourdon effect–the tendency of pressurized pipes to straighten–at the location of bends and elbows. In terms of capacity, our previous work has evaluated the efficacy of XFEM to predict the TSC for pipelines. We have also produced CSC equations for pipelines under the combined effect of axial forces and bending moments. TO BE CONT’D

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Faculty Supervisor:

Samer Adeeb

Student:

Nahid Elyasi

Partner:

Enbridge Employee Services Canada Inc.

Discipline:

Engineering - civil

Sector:

Oil and gas

University:

Program:

Accelerate

Therapeutic Intervention of Cannabinoids in a Pre-Clinical Rat Model of PTSD

National Legalization of Cannabis has occurred in Canada. Very little is known about the different compounds in the Cannabis Plant. The two main compounds are THC and CBD. Both of them have shown some benefits for different conditions. We would like to determine the best dose and ratio of compounds within the Cannabis plant for PTSD (Post-Traumatic Stress Disorder) and Anxiety to build evidence for a large-scale clinical trial. More data will help support safe and effective use of Cannabis, which may provide an alternative to traditional pharmaceuticals. We would also like to determine how best to integrate medical Cannabis into current treatment options.

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Faculty Supervisor:

Yanbo Zhang

Student:

Jacob Cohen

Partner:

Aurora Cannabis

Discipline:

Pharmacy / Pharmacology

Sector:

Medical devices

University:

Program:

Accelerate

Optimization of group equivariant convolutional networks

The explosion of popularity of deep learning owes a lot to the success of convolutional neural networks, widely used in diverse fields including computer vision and natural language processing. Recently, the group equivariant convolutional neural network (G-CNN) was introduced, where equivariance of symmetries inherent in the data set is built in the architecture of the networks. While the G-CNNs has proven to exploit inherent symmetries more effectively than traditional CNNs, their architectural design and implementation require a deeper understanding of the mathematical concept of symmetries. We propose to develop better mathematical tools suitable for deep learning on G-CNNs, with two main goals: (1) improving the optimization methods of G-CNNs by exploiting the geometry underlying the inherent symmetry of the data set; (2) generalizing the architecture of G-CNNs to adapt to other practical learning problems, such as speech recognition and image processing.

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Faculty Supervisor:

Joel Kamnitzer

Student:

Chia-Cheng Liu

Partner:

Borealis AI

Discipline:

Mathematics

Sector:

Information and communications technologies

University:

Program:

Accelerate

A Machine Learning Approach for Digitalization of Engineering Specifications and Documents

Across industries, many engineering documents and drawings have accumulated over the past few decades. However, they are mostly archived in paper or rudimentary electronic form (typically in an image or PDF format), rendering information retrieval highly inconvenient. As such, a lot of valuable engineering data have been left unutilized or at very least, difficult to access. Unfortunately, the existing open source tools do not offer a simple remedy. With this in mind, the proposed project aims to develop a systematic approach based on machine learning techniques for the digitalization of engineering specifications and documents.

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Faculty Supervisor:

Jun Chen

Student:

Qiqi Wang

Partner:

Discipline:

Engineering - computer / electrical

Sector:

Information and communications technologies

University:

Program:

Accelerate

Fall and injury prevention in older adults to position Schlegel Villages as‘Safety Innovators’

As a result of the baby boomer generation, an increasing proportion of Canada’s population is now comprised of older adults / senior citizens. Increased numbers of these older adults are living in private Retirement Homes, and these consumers are emphasizing resident safety programs/policies when deciding between facilities. Towards providing state-of-the-science care for their residents, the industry partner (Schlegel Villages Inc.) wants to lead the marketplace as a ‘Safety Innovator’ through programs to prevent falls (and fall-related injuries). Accordingly, the goal of this research is to improve our understanding of falls (where and why they occur), and to use this information to development/implement approaches to reduce the risk of falls (and associated injuries) within Schlegel Villages’ Retirement Homes. The research will focus on three novel technologies including: i) safety flooring, ii) virtual reality, and ii) wireless balance monitoring systems.

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Faculty Supervisor:

Andrew Laing

Student:

Taylor Cleworth

Partner:

Schlegel Villages Inc

Discipline:

Kinesiology

Sector:

Medical devices

University:

Program:

Accelerate

Effectiveness of vegetation and habitat characteristics as predictors of insect parasitoid populations

Climate change, land development, invasive species, and other disturbances can alter the composition, structure, and functions of native vegetation across landscapes. These disturbances also impact insect parasitoids, which are a key, and often overlooked, component of biodiversity. By their ability to control other insect populations, they are integral for fostering resilient and functional forests. Understanding and monitoring vegetation structure and composition and how it relates to parasitoid populations will help to quickly detect, measure, and forecast negative changes to forest ecosystems. This research will explore the link between plant and parasitoid populations across forests of different successional stages and disturbance regimes to provide (1) a strong basis on which to create and improve ecological restoration and rehabilitation programs and (2) data on the relationship between vegetation and parasitoids to identify and detect the effects of future disturbances and cascade effects on animal species as part of regular, long-term management of forests. This is especially relevant in Ontario as invasive species, such as the emerald ash borer (EAB), Agrilus planipennis Fairmaire (Coleoptera: Buprestidae), continue to spread rapidly throughout the province having detrimental impacts on forests across the landscape.

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Faculty Supervisor:

Stephen Murphy

Student:

Justin Gaudon

Partner:

rare Charitable Research Reserve

Discipline:

Environmental sciences

Sector:

Forestry

University:

Program:

Elevate

Towards creating intelligent heat stress monitoring and management solutions to safeguard health and wellness

The scientific challenge for this research project is to advance our understanding of the impacts of heat stress in heat vulnerable workers and to use this information towards the creation of intelligent heat stress monitoring and management solutions to safeguard health and wellness. Currently, our understanding of the effects of heat exposure on vulnerable individuals remains incomplete, limiting our ability to implement protective measures to optimize performance/safety during work in hot environments. Our work employing the world’s only air calorimeter (device to measure precisely whole-body heat loss) shows that government-recommended heat exposure guidelines fail to protect workers, especially older adults, against dangerous increases in core temperature during work in the heat. This is because they do not consider factors like age, chronic disease and others that can affect heat dissipation. Moreover, they do not account for factors that modify a person’s day-to-day tolerance to heat (exposure time, hydration, others). Thus, this project aims to develop thresholds for ‘high-risk’ work conditions based on ambient temperature, work intensity and duration, and biometric data which will be integrated into a first-generation ‘heat’ app and lead to the creation of a physiological monitoring system for the assessment of heat strain in heat vulnerable workers.

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Faculty Supervisor:

Glen Kenny

Student:

Martin Poirier

Partner:

SmartCone Technologies

Discipline:

Kinesiology

Sector:

Medical devices

University:

Program:

Elevate

Zebrafish high throughput screens for development of drugs targeting sepsis, stroke and other chronic diseases

Currently there is a huge challenge in drug screens as the vast majority of the candidate drugs fail in clinical trials either due to no efficacy or drug toxicity. As an alternative to traditional animal models, zebrafish have recently emerged as a powerful vertebrate paradigm to study human disease and to use its developing embryos for drug screens. In contrast to traditional cell-based screening, the zebrafish provides a whole vertebrate system for drug screening. It combines the biological complexity with the ability for high throughput screening and quick assessment of potential toxicity. It is expected that the candidate drugs identified from the zebrafish screen will have a higher success rate than the cell-culture based screens. In this grant, we will employ zebrafish disease models of sepsis, stroke, diabetes, neurological and inflammatory diseases to screen for new chemical drugs, which will be validated in mouse models.

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Faculty Supervisor:

John Marshall

Student:

Patricia Leighton

Partner:

ZebraPeutics Inc

Discipline:

Medicine

Sector:

Medical devices

University:

Program:

Accelerate

Next-generation sequencing for the analysis of antibody development in vaccinated rabbits (Oryctolagus cuniculus) and in a humanized rat (Rattus norvegicus OmniRat) model.

ImmunoPrecise antibodies is a company that specialized in the production of custom antibodies, the Y-shaped molecules produced by living things to defend itself from infection. Their ability to recognize and bind to specific targets allows for their use in diverse scientific analyses where their specificity of binding is manipulated to allow for localization and/or isolation of specific targets. Using next-generation sequencing the sequence that produces these molecules can be determined and they can be made synthetically decreasing the cost and time required for their production as well as increasing the number of different antibodies that can be produced in the same timeframe.

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Faculty Supervisor:

Stephanie Willerth

Student:

Cuong Hieu Le

Partner:

Immuno-Precise Antibodies

Discipline:

Engineering - mechanical

Sector:

Life sciences

University:

Program:

Accelerate

Evaluating Sustainable Governance for Non-Profit Organizations in Rural Canada

Non-profit organizations play an increasingly important role in rural regions. Yet, these organizations are chronically lacking in capacity, including being both understaffed and underfinanced, as well as having high turnover rates and loss of institutional memory as a result. This research project aims to explore how non-profit organizations and charities in rural communities deliver their mandate in light of these challenges, with a particular emphasis on human resources. The objective is to identify and understand the critical factors that contribute to successful governance of their organizations and what strategies and practices have been successful in other places. This research will conduct 3-5 case studies on rural based non-profit organizations and/or charities to determine how they finance human resource supports, and what lessons learned may be transferable to other cases.

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Faculty Supervisor:

Ryan Gibson

Student:

Katie Allen

Partner:

Canadian Rural Revitalization Foundation

Discipline:

Environmental sciences

Sector:

Management of companies and enterprises

University:

Program:

Accelerate

Improved Yield of Poly-4-Hydroxybutyrate Bioplastic via Genetic Modification in R. eutropha

Plastics are the prime contributor to the global litter crisis. Every second, a quarter tonne of non-degradable plastics enter the world’s oceans. Despite this, petroleum-based plastic production continues to increase, with more than 300 billion kilograms of virgin plastic produced annually. Our team is slightly altering the metabolism of a strain of bacteria to efficiently mass produce biodegradable plastic from hemp substrate. Unlike other biodegradable plastics, ours has excellent strength and flex characteristics. Further, it readily biodegrades under natural conditions – both on land and in water. Others have demonstrated that this class of material (polyhydroxyalkanoate, PHA) may completely degrade in river water into H2O and CO2 in less than one month. Unlike petro-plastics, PHAs are biocompatible (benign to the human body). TO BE CONT’D

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Faculty Supervisor:

Martina Hausner

Student:

Spencer Crook

Partner:

Shepherdess Biotech

Discipline:

Biology

Sector:

Life sciences

University:

Program:

Accelerate

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