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

Network Traffic Classification for Cyber Threat and Malware Detection

Bell’s Cyber Threat Intelligence (CTI) team is collaborating with academic institutions in order to further research and develop cyber security analytics for the protection of critical infrastructure and data. The focus of this research is to create and leverage a traffic classification project specifically for network security purposes. This research to design distributed algorithms fast enough for analyzing massive high-dimensional
data generated by network traffic to detect cyber threats/ attacks and anomaly in the network.

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Faculty Supervisor:

Bijan Raahemi

Student:

Miguel Garzon

Partner:

BCE Inc

Discipline:

Engineering - computer / electrical

Sector:

Information and communications technologies

University:

Program:

Accelerate

Synthesis of diamond and diamond-like films

This project is geared towards the development of a cost-effective method to fabricate thin films of carbon materials, such as diamond. The idea is to use solution-based methods coupled to electrochemistry to produce the films. Avenues for the deposition of the film on surfaces of arbitrary shapes will also be explored. Carbon based thin films, such as diamond-like films and conductive diamond films, have extraordinary mechanical and electrical properties that can be explored in several applications, including batteries, chemical sensors, protective coating in microelectronic industry and others. The company intends to commercialize this potential new technology by generating a new line of products and services.

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Faculty Supervisor:

Alexandre Brolo

Student:

Sapanbir Singh Thind

Partner:

Epic Ventures

Discipline:

Chemistry

Sector:

Nanotechnologies

University:

Program:

Accelerate

Increasing Value: How Best to Recognize and Reward Fish Harvesters and Communities in Newfoundland and Labrador for Sustainable Fisheries Practices

In 2015, Canada exported $6 billion in fish and seafood. Fishing is important not only economically, but also socially, and environmentally. In order to have seafood now and into the future, sustainable development of the fisheries is important. One way to make sure that fishing is being done in this way, is to recognize and reward harvesters for sustainable fishing practices using recommendation lists, eco-labels, and traceability systems. This research will study local management and governance decisions that led to changes for sustainable development in Newfoundland fisheries. This study will then determine which awareness recommendation list, eco-label or traceability system will recognize and reward fish harvesters the most for their sustainable and other valued fishing practices, such as economic returns to harvesters and communities, increased safety, and best handling practices

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Faculty Supervisor:

Paul Foley

Student:

Courtenay Parlee

Partner:

Fisheries Science Stewardship and Sustainability Board

Discipline:

Environmental sciences

Sector:

Fisheries and wildlife

University:

Program:

Accelerate

Application of Neural Speech Synthesis Techniques to Improve Lyrical Audio Recordings

2012 marked a pivotal milestone in the field of neural networks. The intersection of general purpose computing using Graphics Processing Units (GPUs), labelled big datasets, and very large neural networks (called deep neural networks) enabled a break-through in machine learning that has led to impressive results in many fields and applications, such as self-driving vehicles and real-time language translation. Recently, the advances offered by these techniques have been applied to the areas of music and speech synthesis, which have opened up exciting new areas of applications. The work proposed in this application is one such example, namely to create an application to modify and improve audio recordings of amateur singers using the latest developments in artificial recurrent neural networks.

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Faculty Supervisor:

Christopher Henry

Student:

Reid Lowdon

Partner:

Bigshig Music Inc

Discipline:

Computer science

Sector:

Media and communications

University:

Program:

Accelerate

Development of an infrastructure for AAL technology data exchange to inform policy and governance guidelines

The UbiLab and the CSA Group are collaborating on the development of a roadmap for a data integration infrastructure that will enable Ambient Assisted Living technology to share data at a wider scale. The current technology landscape has resulted in manufacturers of Internet of Things and Ambient Assisted Living technologies generating siloed data that provide limited benefits and insights to the final users. The infrastructure being proposed in this project will enable data to flow seamlessly between Internet of Things, mobile health, and Ambient Assisted Living technologies, empowering innovators to leverage larger amounts of data for their product development. We will explore the technical infrastructure, as well as the necessary policy and governance guidelines to enable innovators to integrate the data generated by their new technology with the platform being proposed. This project will enable the UbiLab and the CSA group to become leaders in Ambient Assisted Living data integration.

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Faculty Supervisor:

Plinio Morita

Student:

Dia Rahman

Partner:

Canadian Standards Association

Discipline:

Epidemiology / Public health and policy

Sector:

Medical devices

University:

Program:

Accelerate

Faster on the bobsleigh race track

The intern will support the Canadian Bobsleigh Team in its efforts to reduce ice friction between a bobsleigh and the race track. Therefore, a numerical heat transfer model will be developed, and novel procedures to attain the smoothest possible runner surfaces will be researched. Furthermore, we will apply concepts underlying lubrication theory to runner surfaces bycombining hard and soft coatings through micromachining. All these efforts will result in a better understanding for the variables that are most relevant to ice friction in the context of international bobsleigh competitions. The novel developments coming out of this project will help the Canadian athletes to also compete in the race for the best sporting material and ultimately result in a higher medal count for Canada.

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Faculty Supervisor:

Anne-Marie Kietzig

Student:

Damon Aboud

Partner:

Bobsleigh Canada Skeleton

Discipline:

Engineering - chemical / biological

Sector:

Automotive and transportation

University:

Program:

Accelerate

Cumulative Environmental Effects from Unconventional Oil and Gas Activity in the Liard River Watershed: Greenhouse Gas Emissions, Freshwater Extraction, and Risk of Cross-Contamination

The Liard River Watershed covers 275 000 square kilometres in Northeastern British Columbia. This vast area is increasingly being developed for its underlying shale gas resources using hydraulic fracturing (“fracking”). However, there are few studies investigating the environmental impacts of such activity in this vast area. Fossil fuels and freshwater are two of Canada’s most important natural resources, and therefore an understanding of the water-energy nexus is paramount. This study will investigate effects of oil and gas activity on water resources in the Liard River Watershed it terms of both quantity and quality. These results will be incorporated into a larger report on the cumulative impacts of human activity within the Liard River Watershed that the partner organization is compiling for one of its clients. A GIS interface will be developed in order to facilitate research goals and to provide a platform for future consultation projects.

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Faculty Supervisor:

Romain Chesnaux

Student:

Joshua Wisen

Partner:

David Suzuki Foundation

Discipline:

Engineering

Sector:

Environmental industry

University:

Program:

Accelerate

Removal of THMs by Aeration in a Conventional Lime/Soda Softening Plant With High DOC Water

Surface waters, such as lakes and rivers, often have high amounts of natural organic matter formed from decaying plants and animals. Drinking water treatment plants that use these water sources often face with high levels of carcinogenic chlorine disinfection by-products (DBPs) which are result of reaction between natural organic matters and chlorine that is added to water for disinfection. Canadian water quality guidelines set maximum acceptable levels for these harmful DBPs and hence water treatment plants are obliged to come up with appropriate solutions to meet the guidelines. There are different strategies to reduce DBPs including: enhance removal of organic matters, application disinfectants alternative to chlorine, or removal of DBPs after they have been formed. This project aims to study aeration of potable water as a strategy for removal of DBPs specially trihalomethanes (THMs). TO BE CONT’D

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Faculty Supervisor:

Beata Gorczyca

Student:

Saeideh Mirzaei

Partner:

Associated Engineering

Discipline:

Engineering - civil

Sector:

Natural resources

University:

Program:

Accelerate

The Effective Knowledge Transfer of Novel Approaches to Understanding and Addressing the Risk Factors for Cognitive Fatigue in Wildland Firefighting Settings

Given the increased risk to fatigue in fire zone dispatchers working in wildland fire settings, and the lack of scientific literature about this topic, the purpose of this study is to create evidence-based knowledge translation tools to help address and reduce the risks for cognitive fatigue and poor decision making in wildland firefighting settings. These materials will be originated based on an onsite investigation to further understand the complex relationship and mechanisms of fatigue in coordinator staff during a typical fire season. As a final result, we will create effective knowledge translations tools to assist future strategies to successfully manage occupational cognitive impairment.

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Faculty Supervisor:

Darren Warburton

Student:

Juliano Schwartz

Partner:

Health and Fitness Society of British Columbia

Discipline:

Medicine

Sector:

Medical devices

University:

Program:

Accelerate

Automated transaction classification using machine learning algorithm

The procurement process of an organization is key to understand company costs. Organizations gather large amounts of data coming from different sources (e.g. income statement, balance sheet, general ledger lines). This information is heterogeneous in nature as it is a mix of unstructured and structured data. Moreover, it needs to be cleaned and consolidated in a taxonomy to enable category management. The objective is to group like-to-like items and/or services into categories from Supply Market Analysis point of view and consider category management for the holistic spend. Supervised and unsupervised machine learning algorithm seemed to be natural choices for this kind of problem because of the nature of the available data. PwC has already a first iteration of a classification product, dubbed SAM (Spend Analysis Machine) and it is based on supervised learning for text classification on general ledger accounts and supplier characteristics. 

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Faculty Supervisor:

Maciej Augustyniak, Manuel Morales

Student:

Charles Ashby-Léporé, Jean Hounkpe

Partner:

PricewaterhouseCoopers

Discipline:

Mathematics

Sector:

Finance, insurance and business

University:

Program:

Accelerate

Control Forecasting Feature

Control is a leader in mobile payment analytics and alerts for SaaS, subscription, and eCommerce businesses, enabling instant intelligence anywhere via its Android, iOS, and web-based products. We collect our customers’ payment data and provide them with their key business metrics that helps them monitor their performance. In order to improve our service to customers we are moving towards providing customers with predictive analytics, that is we want to enable our users to forecast the future of their business based on the models that will learn from their historic payment data and yield forecasts for short-term future. This will help business owners to make informed business decisions and set their strategies on insights that are driven from their data. This requires studying the data and testing forecasting techniques to measure the performance of each method in predicting the future data, which is the main objective of this project.

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Faculty Supervisor:

Mary-Catherine Kropinski

Student:

Seyedsaeed Mirazimi

Partner:

Control Mobile Inc

Discipline:

Mathematics

Sector:

Information and communications technologies

University:

Program:

Accelerate

Evaluation of the accuracy of the in-situ individual particle sizing technology

The research objectives are to better understand the limitations of a new particle sizing system in terms of accuracy on particle size, of accuracy on size distribution, of accuracy on particle concentration and finally of size dynamic range. During the research project, the sources of the limitations will be identified and improvements to the technology will be proposed. State-of-the-art equipment for the generation of particles with known sizes and concentrations available at the University of Alberta will be used for the characterization of the technology. Improvements to the mathematical model and on the way to resolve the inverse problem to retrieve particle size will be done in collaboration with researchers at INO. The outcome of the project will be a well characterized particle sizing technology ready to be transferred to the industry for commercialization.

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Faculty Supervisor:

Jason Olfert

Student:

Nafiseh Sang-Nourpour

Partner:

Institut national d'optique

Discipline:

Engineering - mechanical

Sector:

Environmental industry

University:

Program:

Accelerate

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