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

Helping Ontario Meet its Environmental Goals- Hydrogen Adoption report for Ontario

Hydrogen technology in Ontario has incredible potential as an alternative energy source that can eliminate the use of harmful fuels and greatly reduce greenhouse gas emissions. Canada is currently developing a nation-wide strategy to increase the adoption of hydrogen technology; however, Ontario is underrepresented in this discussion. Science Concepts International will work with its interns to create a report that outlines the current state of hydrogen technology, discusses several possible projects that would benefit Ontario, and then analyse the impacts these projects may have on the environment and the economy. The report will help formalize the current state and future of hydrogen technology in Ontario and inform on how this initiative will help Ontario meet its sustainability goals. Science Concepts International is a consulting company that seeks to develop marketable expertise in helping Ontario companies become part of solutions as outlined in the report. SCI will hold a conference at which it will be able to use this report as a basis for presenting to key individuals the feasibility and benefits of hydrogen technology adoption in Ontario.

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Faculty Supervisor:

Michael Fowler

Student:

Partner:

Science Concepts International

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

University of Waterloo

Program:

Accelerate

Investigation of microwave imaging techniques for monitoring grain bins

Microwave Tomography (MWT) is an emerging modality where the goal is to estimate the electrical
properties of an object-of-interest. This is done by transmitting a microwave signal into the OI and
collecting measurements outside the OI. The measurements are inputs to an optimization algorithm
that solves for the unknown electrical properties. As this modality has been successfully adapted in
biomedical imaging and geophysical surveying, the proposed project looks into the feasibility of using
this technology to monitor moisture content and insect infestations inside grain bins for early detection
of the grains spoilage. The project is a two-part study. The first part consists of running simulations to
study the electromagnetic signal behavior inside grain bins with varying electrical properties due to the
presence of excess moisture or insects. The second part consists of performing a paper design and
simulation of an experimental measurement system. This work is done by the Electromagnetic
Imaging Laboratory at the University of Manitoba in collaboration with…TOBECONT’D

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Faculty Supervisor:

Joe LoVetri

Student:

Partner:

151 Research Inc

Discipline:

Engineering

Sector:

Manufacturing

University:

University of Manitoba

Program:

Accelerate

Automatic detection and classification of abnormal human blood cells using computer vision and deep learning

This research proposal aims to enhance the performance and add more features to the automated microscope system that is being developed by Smart Labs ltd. This research goal is to increase the overall accuracy of the system while running in real-time. The current prototype has an accuracy of 91% and can process 17 frames per second. Moreover, it can only classify 2 types of cell abnormalities using traditional image processing techniques. This proposal aims to: 1) increase the accuracy from 91% to at least 95%, 2) increase the frame rate to be able to run at 40 frames per second, and 3) be able to classify a minimum of 10 abnormal cell types. The output of this research will enable Smart Labs to process the slides in real-time as they continue moving under the microscope’s objective lens, as well as being able to classify various blood cell abnormalities. This is crucial in having a commercial product that can be presented to labs in Canada and the international medical sector.

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Faculty Supervisor:

Mohamed Shehata

Student:

Partner:

Smart Labs Ltd

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

The University of British Columbia - Okanagan

Program:

Accelerate

Automatic Annotation of Vertebral Heart Score and Tibial Plateau Angle in X-ray Images

The proposed work combines artificial intelligence and diagnostic medicine. Using X-ray images, radiologists and veterinarians can perform an array of measurements to assess patient health. In a veterinary setting, standard measurements and annotations are performed on X-ray images to assess heart and knee health in canines, namely vertebral heart score (VHS) and tibial plateau angle (TPA). Provided a database of related radiographic images, the chosen intern can develop a method to automatically place annotations and perform these measurements through applications of machine learning. The methods developed in this research will be immediately applicable to the partner organization; these tools will be integrated within iMi’s x-ray imaging system for use in veterinary clinics. Additionally, the methods developed will allow iMi to quickly implement new auto-annotation modalities in human and veterinary clinic settings.

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Faculty Supervisor:

Youmin Zhang

Student:

Partner:

Innotech Medical Industries Corp

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Concordia University

Program:

Accelerate

Application of time-frequency based techniques to assess Auditory Brainstem Responses in newborn hearing assessment

Automatic detection and classification of the Auditory Brainstem Responses (ABR) is used in newborn hearing screening. Improved detection algorithms will reduce test time, prevent infants with hearing loss from being missed while reducing the number of normal hearing babies referred to diagnostic testing. We have already improved the objectivity of ABR classification in neurological assessments by using Continuous Wavelet Transform (CWT) and Machine Learning (ML). In the proposed project, we seek to validate our findings further to improve the objectivity in the newborn hearing assessment. We intend to implement the algorithm in real-time for faster and accurate diagnosis of hearing impairment. The project will be carried in partnership with Vivosonic Inc.; a reputable company focused on auditory screening and diagnostics. It is expected that the outcome of this work will be beneficial to the partner to improve their system to supply clinicians with valuable clinical tools.

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Faculty Supervisor:

Prudence Allen

Student:

Partner:

Vivosonic Inc.

Discipline:

Engineering

Sector:

Artificial Intelligence; Health and Related Sciences & Technology; Commercial Services

University:

The University of Western Ontario

Program:

Accelerate

Développement de solutions industrielles pour le traitement laser de surfaces métalliques

Le traitement des surfaces est une pratique courante dans l’industrie manufacturière afin d’améliorer les propriétés mécaniques des pièces. Cette technique est particulièrement exploitée dans l’industrie automobile, où les surfaces de pièces risquent d’être endommagées. Les traitements couramment utilisés sont souvent complexes, aléatoires et dommageables pour la surface traitée. Le traitement par laser est ainsi une technique de grand intérêt puisque la technique est rapide, versatile, peu dommageable et automatisable. La recherche proposée porte sur le développement de solutions industrielles pour le traitement laser de surfaces métalliques. Le projet porte sur le développement de nouveaux produits et procédés de traitements de surface, dont le marquage, la texturation, le nettoyage et la trempe martensitique de l’acier au carbone.

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Faculty Supervisor:

Martin Bernier;Denis Laurendeau

Student:

Partner:

Laserax

Discipline:

Engineering

Sector:

Manufacturing; Professional, scientific and technical services

University:

Université Laval

Program:

Accelerate

Battery pack thermal prediction and fault detection using machine learning

This project aims to use machine learning to determine the thermal state of a battery pack used in electrical vehicles. In fact, battery pack are made of numerous individual cells, and it is essential to monitor their respective state to ensure the battery pack operates in ideal conditions. By doing so, the autonomy of the electrical vehicle can be increased, and the duration of the battery pack useful life can be extended. With this project, we would like to infer the thermal distribution in the whole battery pack by monitoring as few cells as possible to reduce hardware cost.

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Faculty Supervisor:

François Grondin

Student:

Partner:

Calogy Solutions

Discipline:

Engineering

Sector:

Manufacturing

University:

Université de Sherbrooke

Program:

Accelerate

Evaluation des couts socio-economiques relies aux bris des infrastructures souterraines au Quebec

Au Quebec, en 2011 , on ne recense pas moins de 5 bris d’infrastructure souterraine par jour. Ces
accidents mettent en danger la sante et la securite des travailleurs et sont tres couteux. Cependant, si
les couts de reparation d’un reseau (couts directs) sont relativement faciles a identifier et a imputer a
un evenement, les couts sociaux et environnementaux (couts indirects) sont quant a eux difficilement
quantifiables et de surcroit rarement pris en compte dans les decisions en matiere de travaux ou de
prevention. Les bris peuvent en effet occasionner des couts relies au deploiement des services
d’urgence, aux perturbations de la circulation, aux interruptions de service, a I’evacuation des
residents etc …
L’evaluation des couts socio-economiques relies aux bris des infrastructures souterraines que nous
nous proposons de realiser en collaboration avec l’Aliiance pour la Protection des Infrastructures
Souterraines du Quebec, (dont les partenaires sont Gaz Metro, Bell et Hydro-Quebec entre autre)
estune etape incontournable qui va permettre, en fournissant…TOBECONT’D

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Faculty Supervisor:

Nathalie de Marcellis-Warin

Student:

Partner:

Alliance pour la protection des infrastructures souterraines duQuebec

Discipline:

Business

Sector:

University:

École Polytechnique de Montréal

Program:

Accelerate

Virtual Energy Analysts & Transitioning Commercial Buildings to Net Zero

Owners of large commercial and institutional buildings are looking for ways to cut operating costs, reduce emissions, and green their operations. To make this process quicker, more responsive, and lower-cost, Edge Energy Technologies is developing a Virtual Energy Analyst Service. The goal of the service is to use real-time data to inform analysis of building energy consumption patterns by a remote analyst, to support building owners in reaching a net zero energy position for their buildings. The research question we are addressing in this project is the question of virtual service delivery: How can we provide real-time, valuable and actionable advice to building owners and managers about their energy consumption in an entirely virtual model, without requiring a staff member or consultant to visit the properties? Moving services to a virtual mode was already a goal for Edge Energy prior to the 2020 COVID-19 pandemic, which has served to emphasize the value of offering a virtual service. To accomplish this, Edge Energy and NSCC will engage Mitacs interns to research, develop, and test virtual energy services with the guidance of an experienced energy manager and in collaboration with real building managers and owners who will test the service in operation.

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Faculty Supervisor:

Wayne Groszko

Student:

Partner:

Edge Energy

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

Nova Scotia Community College

Program:

Accelerate

Identifiability of latent factors through multiple self-supervision

Human perception has developed the ability to decompose scenes into fine grained elements. This lays the foundation for strong generalization to new situations where the base concepts can be recomposed to interpret objects never seen before. While it has been shown that, in the general case, proper decomposition is not possible, new paradigms provide provable decomposition in constrained environments. We hypothesize that the multiple sensory systems of human perception offer a strong signal for decomposing scenes in a proper way. While the 2-view approach has been explored in various forms in the machine learning community, we believe that the granularity of the decomposition depends on the number of views integrated.

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Faculty Supervisor:

Simon Lacoste-Julien

Student:

Partner:

ServiceNow Canada

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Université de Montréal

Program:

Accelerate

An electrochemical microfluidic sensor for cannabinoid detection

Cannabis legalization creates a pressing need to improve existing screening methods. Currently, the two devices approved by the office other the attorney general of Canada (i.e. the Drager 5000 and SoToxa) have not been embraced by the vast majority of police forces who deemed both options unaffordable, difficult to use and inaccurate. The present project aims to create a next generation cannabis detection device capable of accurately assessing the blood concentration of THC. This will be accomplished by harnessing recent advances in the fields of analytical chemistry and micro engineering to create a portable, accurate, affordable and easy-to-use detection kit.

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Faculty Supervisor:

Steve Shih

Student:

Partner:

Strem Biotechnologie

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

Concordia University

Program:

Accelerate

Architectural and Design Modelling for Blockchain-Intensive Systems

Blockchain technologies are increasingly becoming integral parts of information systems in domains that exhibit an increased need for resilience and can make no assumption of trust between parties. However, properly adopting blockchain in an information system design remains difficult, unsystematic and requires thorough understanding of the technology. In this project we explore ways by which traditional model-driven architecting and design techniques can be augmented to support incorporation of blockchain components. We do so by iteratively devising designs for a real world case in the crop supply chain domain and focus on the aspects of the process and the artifacts that are most affected by the inclusion of a blockchain component. This way we hope to acquire knowledge that can form the basis for the development of design patterns, language extensions and architectural decision support tools useful for systematic incorporation of blockchain technologies to information systems.

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Faculty Supervisor:

Sotirios Liaskos

Student:

Partner:

Grain Discovery

Discipline:

Computer science

Sector:

Finance and Insurance

University:

York University

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.