Innovative Projects Realized

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

30156 Completed Projects

2861
AB
5059
BC
812
MB
673
NL
842
SK
8957
ON
9368
QC
96
PE
579
NB
1120
NS

Projects by Category

Animal Recognition From Natural Scene Images

With the development of imaging technology and research progress on wildlife monitoring, camera trapping becomes one of the best ways to record the presence and activity of mammals in a given area. The approach to monitoring wildlife can assist people in the community with decision making about preserving biodiversity. Camera trapping generates a huge volume of image data. In the past, experts analyzed such image data manually, which required domain knowledge and took significant time. In this project, we aim to develop an animal recognition system that can help analyze wildlife images, which record the presence of large mammals such as deer, moose, wolves, bear, etc., and automatically identify species of those animals. Such a system can help experts save significant time and better understand wildlife images and activities of animals around the certain area in a reasonable time. A Windows environment application will be built. This work will support ecologists to make a better decision on protecting and preserving the biodiversity in the province of Alberta.

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Faculty Supervisor:

Osmar Zaiane

Student:

Partner:

Alberta Innovates - Technology Futures (Vegreville)

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Alberta

Program:

Accelerate

Design and Construction of a Prototype micro-Combined Heat and Power Unit operating on an organic Rankine cycle fueled with Hydrogen Enriched Natural Gas up to 100 % Hydrogen

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

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Faculty Supervisor:

Michael Pegg

Student:

Partner:

Net Zero Atlantic

Discipline:

Engineering

Sector:

Clean Technology; Energy and Utilities; Manufacturing and Construction

University:

Dalhousie University

Program:

Accelerate

Understanding the dependence on convenience: towards new patterns of food consumption

This project explores the post-pandemic impact of the rise of convenience on consumer eating habits. As convenience becomes a major driver of the food industry, our research examines how this emphasis can obscure essential elements such as ritual, culture, social connections, impacting the perception and pleasure of eating. Our research questions target the influence of convenience on food choices and the correlation with pleasure, while assessing its economic impact. The study will use a mixed-method approach, combining surveys, experiments, and interviews to explore the consumption habits of four generations. This research will inform companies, decision-makers and stakeholders on how to harmonize convenience and food pleasures, offering strategic recommendations in a changing culinary landscape.

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Faculty Supervisor:

Jordan LeBel

Student:

Partner:

Coopérative Radish

Discipline:

Sociology

Sector:

Professional, scientific and technical services

University:

Concordia University

Program:

Accelerate

Optimizing business attraction through enhanced incubator introduction

Business incubators have the proven ability to significantly improve the success rate of startup companies. A key factor in the potential for positive impact is ensuring alignment between the services and mission of the incubator and the needs and values of the startup company. An in-depth introduction program offers an innovative approach to assessing alignment by both parties. The objective of this project is to significantly improve existing program content and update the mode of delivery to ensure a comprehensive and positive orientation to the incubator services, ecosystem capabilities, and community stakeholder collaboration; ultimately attracting promising innovators to the local market. The project work plan includes researching the needs of target start-up companies, reviewing external program models, proposing improvements, engaging with ecosystem partners, coordinating delivery logistics and supporting evaluation.

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Faculty Supervisor:

Stuart Smyth

Student:

Partner:

Global Agri-food Advancement Partnership

Discipline:

Business

Sector:

Education

University:

University of Saskatchewan

Program:

Business Strategy Internship

Co Operation Student – Deep learning applications in multispectral retinal imagery

The internship opportunity involves active participation in an agile AIS AI team dedicated to advancing the DeepMSI AI product. The intern will engage in impactful research under the AI team lead, focusing on refining core AI algorithms with a special emphasis on multispectral retinal image processing and deep learning. The objectives include exploring advancements in domain adaptation, semi-supervised pre-training, multi-scale feature fusion, and context awareness in convolutional and transformer architectures. Additionally, the intern will contribute to the company’s AI software suite and cloud platform, optimizing processes and expanding infrastructure. The role extends to addressing software-related challenges, introducing new functionalities, and improving overall performance. Miscellaneous tasks, such as data acquisition workflow management, doctor support during biomarker labeling, and documentation writing, further enrich the intern’s experience in diverse aspects of AI workflows.

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Faculty Supervisor:

François Légaré

Student:

Partner:

AI-Spectral Technology Corp

Discipline:

Physics

Sector:

Artificial Intelligence; Biotechnology; Biomanufacturing

University:

Université du Québec : Institut national de la recherche scientifique

Program:

Business Strategy Internship

Study of extracellular vesicles in cardiometabolic disease

This project will focus on transforming scientific research excellence into improved healthspan via the development of
improved diagnostics to allow personalized therapeutic strategies, with novel treatment and rejuvenation approaches
targeting cardiometabolic disease. Exciting preliminary data has been generated by the York University research
team. The Konkuk University research team have the established expertise to capitalize on the early discoveries
made at YorkU. Hence, the project will be of great mutual benefit for both institutions. Success of this research also
has more far-reaching implications in new therapeutic approaches and health benefits for our population.

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Faculty Supervisor:

Gary Sweeney

Student:

Partner:

Konkuk University

Discipline:

Life Sciences

Sector:

Biotechnology; Nanotechnology; Health and Related Sciences & Technology

University:

York University

Program:

Globalink Research Award

Étude de l’impact environnemental et financier d’un modèle mutualisé en agriculture urbaine sur l’activité de ses membres dans une perspective de reproductibilité du modèle. Cas de la Centrale agricole.

L’objectif principal de l’étude est de documenter le modèle de la Centrale agricole en effectuant une collecte de données permettant de réaliser une évaluation environnementale et économique du modèle mutualisé sur l’activité de ses membres. Pour la coopérative, les présents travaux permettront de préciser et de générer de la donnée sur les impacts du modèle de la Centrale. Cette quantification permettra d’optimiser le modèle en place tout en justifiant le soutien à la Centrale lors de demandes de financements ou de partenariats. Aussi, ces nouvelles données permettront de légitimer la reproduction du modèle de la coopérative ailleurs au Québec pour répondre à des demandes acheminées par des municipalités, MRC et entreprises.

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Faculty Supervisor:

Frédéric Monette

Student:

Partner:

La Centrale Agricole

Discipline:

Engineering

Sector:

Agriculture

University:

École de technologie supérieure

Program:

Business Strategy Internship

Sustainable Wildfire Prevention Using RPAS and Computer Vision

This research project in Canada focuses on sustainable natural resource management, particularly in forest areas. By integrating Remotely Piloted Aircraft Systems (RPAS) and Computer Vision (CV), the project aims to improve forest fire prevention and management. Collaborating with industry partners like Spexi Geospatial, the team combines academic research with practical solutions to enhance AI-driven environmental strategies. The project develops novel approaches for forest fire detection, using orthogonal drone images labeled with a unique fire risk system. A key innovation is the use of raw (LOG) images for training CV models, considering varying natural light conditions, over conventional RGB images. The research also explores the use of oblique drone images for detailed post-wildfire assessments, offering high-resolution views of vegetation and terrain structures that are vital for thorough evaluations. This pioneering approach leverages drone-based oblique imagery for both forest fire prevention and post-event analysis.

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Faculty Supervisor:

Michal Aibin

Student:

Partner:

Spexi Geospatial

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

British Columbia Institute of Technology

Program:

Accelerate

Extracting Document Structure from Text-Intensive Images, A Multi-Modal Approach

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

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Faculty Supervisor:

Siamak Ravanbakhsh

Student:

Partner:

ServiceNow Canada

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

McGill University

Program:

Accelerate

Assessment of microplastics released by reusable face masks under different conditions

Face masks play an important role in preventing droplets and filtrating exhalations coming from infected subjects or against various threats in the wearer’s surroundings. Approximately 129 billion single-use masks were discarded per month worldwide during the pandemic. The inadequate handling and management of single-use face masks cause many environmental problems. In contrast to disposable masks, reusable masks can be washed several times and then reused after sufficient drying. Such masks can be a good alternative for reducing the negative environmental impact. This study will examine the release of microplastics from reusable etrëma masks under different conditions. The reusable etrëma masks will be compared with disposable masks to verify whether etrëma masks are effective in reducing the release of microplastics and reducing the burden on the environment. The results can be used to support the development of reusable face masks with less environmental impact.

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Faculty Supervisor:

Chunjiang An

Student:

Partner:

Frëtt Solutions

Discipline:

Engineering

Sector:

Manufacturing

University:

Concordia University

Program:

Accelerate

Investigating Cannflavins as potential antimicrobial agents against antibiotic-resistant pathogens

Antimicrobial resistance is a growing, global problem. Our arsenal of effective antibiotics against drug resistant
bacteria and fungi is quite limited and innovative approaches are needed. Focusing on cannflavins, this proposal
will evaluate if these hemp-derived molecules with anti-inflammatory and anti-bacterial effects (1) are candidates
for interacting with pathogen protein targets, (2) are effective to eliminate drug-resistant bacteria and fungi, and
(3) eliminate azole-resistant vaginal candidiasis in a mouse model and/or improve wound healing in a diabetic
ulcer mouse model. By enhancing the druggability of these compounds and investigating enzyme interaction
pharmacology, and the in vivo activity, the project aims to advance innovative solutions in the battle against
antimicrobial resistance.

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Faculty Supervisor:

Ellen Wasan

Student:

Partner:

Canurta Inc

Discipline:

Life Sciences

Sector:

Manufacturing

University:

University of Saskatchewan

Program:

Accelerate

The Evaluation of Stresses in Spinal Instrumentation Devices Using a Hybrid Modelling Approach

Numalogics is a company that uses finite element models of the spine to offer consulting services for other companies, assist in the design of new spinal implants and surgical tools, and simulate surgical procedures. Currently, they apply pre-established loads to their models when running simulations of basic spinal movements. They require to assess whether these loads are representative of physiological muscle forces acting on the spine when producing these movements. Therefore, the goal of this project is to (1) create a framework for applying muscle forces to Numalogic’s spine models, and (2) determine if the stresses and load distributions in spinal implants estimated from these simulations are significantly different when using the pre-established loads versus muscle forces. Numalogics will directly benefit from this research, as we will be able to setup a framework for them to apply muscle forces to their models in the future.

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Faculty Supervisor:

Ryan Graham

Student:

Partner:

Numalogics

Discipline:

Engineering

Sector:

Manufacturing; Professional, scientific and technical services

University:

University of Ottawa

Program:

Accelerate

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