Innovative Projects Realized

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

31620 Completed Projects

2978
AB
5221
BC
856
MB
696
NL
899
SK
9419
ON
9858
QC
98
PE
619
NB
1192
NS

Projects by Category

The effect of bioceramic textiles on skin temperature

The following application is to support a collaboration between the University of Alberta and Mark’s Work Wearhouse (MWW) – a Canadian clothing and footwear retailer. We aim is to evaluate the effects of bioceramic textiles on skin temperature. The research for the proposed project will be conducted in Dr Steinback’s laboratory at the University of Alberta (Edmonton, AB). There, post-doctoral fellow, Dr Michael Tymko, will be responsible for measuring skin temperature (chest and forearm), heart rate, respiration, and sweat rate in young and healthy participants (n=20). Each participant will be required to wear separate bioceramic textiles constructed by MWW. The results from the proposed experiment will improve our fundamental understanding on how bioceramic textiles impact body temperature regulation. This research collaboration will directly benefit MWW by evaluating the thermal impact of their bioceramic clothing in health humans.

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Faculty Supervisor:

Craig Steinback

Student:

Partner:

Mark's

Discipline:

Life Sciences

Sector:

Retail trade

University:

University of Alberta

Program:

Accelerate

Large Scale Graph Representation Learning

One assumption that is commonly used in machine learning is that samples are statistically independent. In effect, each sample of data doesn’t tell you anything about any other sample in the dataset. This is not true for all types of data; there are some types of datasets where relationships between samples can be modeled as a graph. One example is the yelp dataset, where users and businesses can be represented by nodes, and user reviews can be represented by edges. Graph neural networks(GNNs) are a type of machine learning architecture that take advantage of the extra information given by graphs. However, they are much slower than traditional neural networks and they are currently designed for static, unchanging graphs. The objective of this research is to address both issues: to develop methods to increase the training speed of GNNs on large graphs and to adapt the architecture for time-varying information. Improvements the GNN methods will also yield improvements to downstream tasks such as recommending you a new restaurant based on your previous reviews.

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Faculty Supervisor:

Sushant Sachdeva

Student:

Partner:

Microsoft Canada

Discipline:

Computer science

Sector:

Information and cultural industries

University:

University of Toronto

Program:

Accelerate

Speech enhancement and recognition with generative adversarial network

While taking foreign language tests, people may record responses with different background noises. The contaminated audios can lead to unusual results in speech recognition and scoring by the scoring systems. Pearson would like to develop a more robust system for the automated speech recognition machine to work with clean and noisy records. Audio files are typically from 5 to 90 seconds long. There are popular softwares which are built to address these problems, but their results need to be tested with the particular kinds of inputs that is obtained as test responses. These may have varies types of noise, distortions and various other complicating factors. Improving these systems would greatly benefit Pearson’s competitiveness in the market and would also contribute towards expanding the boundaries of knowledge in speech enhancement.

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Faculty Supervisor:

Gerald Penn

Student:

Partner:

Pearson Inc

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Understanding Public Perceptions about Roles and Responsibilities for Community Recycling

The proposed research aims to provide a comprehensive explanation of the residents’ perceptions of roles and responsibilities of their local municipalities involved in community recycling in North America. It also will investigate the effect of the modern digital communication solution to the residents’ recycling engagement and education of proper household waste sorting, and examine whether likely residents would accept alternate funding arrangements for digital communication and education technology. An electronic questionnaire-based survey with residents in North America will be conducted to acquire and analyze required data to achieve the objectives. The research findings will enable the partner organization to understand the residents’ perception of the roles and responsibilities of community recycling. They will help the partner organization develop new markets and business models across North America. Also, the partner organization will obtain in-depth feedback from its users regarding its web and mobile applications to improve the product and service quality.

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Faculty Supervisor:

Jonghun Jay Park

Student:

Partner:

Recycle Coach

Discipline:

Sociology

Sector:

Administrative and support, waste management and remediation services

University:

Toronto Metropolitan University

Program:

Accelerate

Conserving Wildlife through Computer Vision and Aerostats

Elephant and rhino population in southern African countries has been drastically decreasing due to poaching. This project aims to use tethered aerostats (blimps) equipped with robust cameras for constant detection and monitoring of these animals to protect them from external threats. The chosen location for implementation in Nyika National Park in northern Zambia, mainly due to favorable weather conditions anywhere in the world at this location to suspend aerostats all year round. In addition, this project also aims to apply the same technology in St. Lewis, Laborador, where polar bears encroaching into human habitats has remained an issue. This technology will detect polar bears in the vicinity and transfer this information automatically to the relevant authorities and safeguard the human/animal population.

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Faculty Supervisor:

Jonathan Kelly

Student:

Partner:

Solar Ship Inc

Discipline:

Engineering

Sector:

Manufacturing; Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Mapping & Modelling the dead zones of the Bras d’Or Lake estuary in the context of Aquaculture development

The people of Canada are transitioning from hunting and gathering in the ocean to cultivating and nurturing the ocean. This research and the outcomes of the academic–industry partnerships it engenders will contribute significantly to the economic, ecological and social sustainability of that transition. There is a particular concern on the part of the growing Aquaculture industry, their clients and the people of Canada that aquaculture be developed in a manner the is both economically and ecologically sustainable. This project will provide science-based knowledge, enhanced technology and predictive models that address one of the most complex and daunting challenges to these anticipated outcomes of aquaculture development during a period of accelerating climate change: the causes and effects of reduced oxygen concentrations in natural water bodies where aquaculture activities are undertaken. The team of academic and industry partners includes oceanographers from eastern Canada’s leading universities, the nation’s largest food and agriculture science corporation, and a Mi’kmaq First Nation that operates the only finfish aquaculture business in the Bras d’Or Lake UNESCO Biosphere Reserve.

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Faculty Supervisor:

Bruce Hatcher;Jinyu Sheng;Douglas Wallace

Student:

Partner:

Perennia Food and Agriculture Inc;Waycobah Fisheries Ltd;Pro-Oceanus Systems Inc

Discipline:

Physics

Sector:

Agriculture; Information and cultural industries

University:

Cape Breton University

Program:

Accelerate

Prédire et diagnostiquer la performance des boulettes de minerai de fer dans les procédés industriels de réduction directe

La performance des boulettes de minerai de fer dans les procédés industriels de réduction directe est normalement évaluée par des tests de laboratoire normalisés ou simplifiés. Toutefois, les performances réelles ne sont pas nécessairement liées aux résultats obtenus puisqu’ils ne simulent pas les conditions industrielles. Des tests de réduction plus avancés ont toutefois été développés afin de simuler plus adéquatement les conditions réelles d’opération des modules de réduction directe. Néanmoins, ces tests de laboratoires ne simulent pas toutes les zones des modules industriels, ni tous les phénomènes réactionnels qui ont lieu pendant la réduction.
Le projet de recherche a donc pour but de développer une méthodologie permettant de simuler adéquatement, à l’échelle laboratoire, la progression de la réduction des boulettes et d’évaluer simultanément tous les paramètres d’intérêt (réductibilité, métallisation, résistance à la dégradation et à la déformation de même que carburation).

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Faculty Supervisor:

Josée Duchesne

Student:

Partner:

COREM

Discipline:

Earth science

Sector:

Mining

University:

Université Laval

Program:

Accelerate

Economic benefits of local purchasing

This research project will investigate local economic impacts resulting from municipal government procurement through locally-owned suppliers in comparison to procurement through national or multinational suppliers with local operations. The objective of the project is to determine a BC analysis to quanitify the economic impact of local purchasing decisions. Using the LM3 method, this research will involve the collection and analysis of records from a locally owned business (Mills Basics) and public documents from a national chain (Office Max) and multinational chains (Office Depot and Staples) in the same sector(Office Supplies). The specific measuring process will look at the: 1. source of income (total income into the office supply retailers) and will then look at how it is 2. circulated back into the local economy in the forms of wages and benefits, locally retained profit, local procurement of goods and services and local charitable.

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Faculty Supervisor:

James Tansey

Student:

Partner:

Columbia Institute;LOCO BC

Discipline:

Business

Sector:

Other

University:

The University of British Columbia

Program:

Accelerate

AI-enabled Performance Enhancement for the Reconfigurable Multi-Player RAN

In 5G and beyond networks, softwarization of network functions, as well as disaggregation of software and hardware, are the recent moves pushing Radio Access Networks (RAN) to be ultra-agile, reconfigurable and flexible. This flexibility comes along with complexity that goes beyond traditional algorithms’ capabilities to optimize the RAN. In addition, in future RANs, multiple-players interacting within the same RAN environment will increase the burden on proper decision making. Many researchers in academia and the telecom industry have turned to AI/ML to handle the rising complexity of wireless networks. Dr. Erol-Kantarci, one of the leading researchers in the area, will join forces with Ericsson to address this bleeding-edge challenge and develop advanced machine learning tools for future RANs. With this project, her team will develop hierarchical and planned distributed learning techniques under partial observability to optimize the multi-player RAN based on policies. These techniques will provide Ericsson an edge over the rapidly changing technology scene.

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Faculty Supervisor:

Melike Erol-Kantarci

Student:

Partner:

Ericsson Canada Inc (Quebec);Ericsson Canada Inc (Montreal, QC)

Discipline:

Engineering

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

University of Ottawa

Program:

Accelerate

Developing Indicators of Ecosystem Stress Using Long-term Data From Pristine and Physically-altered Experimental Lakes

Climate change and expansion of industrial development into the boreal zone are two, prominent stressors that Canadian boreal lakes face. This puts the quantity and quality of freshwater fish habitat at risk, and the subsequent productivity of fish populations in a given system. The objective if this work is to define the components of fish habitat (e.g. temperature, oxygen, nutrients, prey availability, structural features) that are most strongly linked to fish productivity. We will use a combination of (1) long-term fisheries and limnological monitoring data (~40 – 50 years) from pristine lakes at the IISD-Experimental Lakes Area (IISD-ELA) in northwestern Ontario, (2) fisheries and limnological data collected from lakes before and after they were manipulated to mimic impacts from hydroelectric developments (i.e. large reductions in water balance), and (3) multi-year, high-resolution data tracking the movement and habitat use of lake trout in several systems, in order to define the best ecosystem indicators of the productivity of fish populations. This work will help to identify climate and industrial development impacts on fish populations, and less-costly and more efficient ways to monitor fish productivity in stressed ecosystems.

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Faculty Supervisor:

Paul Blanchfield

Student:

Partner:

IISD Experimental Lakes Area Inc

Discipline:

Life Sciences

Sector:

Professional, scientific and technical services

University:

Queen's University

Program:

Accelerate

Detecting, Extracting and Merging Receipts from Uploaded Smartphone Images

Sensibill provides financial tools like digital receipt data that help banks and credit unions better know and serve their customers. Users can upload digital images through tools and the company would do image processing first and then use processed images to analyze. However, the previous image processing algorithm is time-consuming for users and doesn’t satisfy the use case of long receipts that don’t fit on a single image. The project is aimed to build a method that can efficiently generate one clear final image from multiple uploaded images.

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Faculty Supervisor:

Lueder Kahrs;Michael Guerzhoy

Student:

Partner:

Sensibill Inc

Discipline:

Computer science

Sector:

Information and cultural industries

University:

University of Toronto

Program:

Accelerate

An Improved Approach to Watershed Management and Adaptive Decision Making in the Great Lakes

With collaboration between the Council of the Great Lakes Region, Pollution Probe and Lambton College, the proposed project is focused on continuing the development of an artificial intelligence visualization tool to enable users to select growth constraints and visualize resulting changes to watershed health, predict how watersheds will evolve over time and prescribe actions to protect them. This AI tool’s purpose is to analyze historical watershed data and to predict changes to a watershed over time. Looking into water quality and the minimum and maximum of water quality thresholds.

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Faculty Supervisor:

Pedram Faghihi

Student:

Partner:

Council of the Great Lakes Region;Pollution Probe

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Lambton College

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.