Projets novateurs réalisés

Explorez des milliers de projets réussis issus de la collaboration entre organisations et talents postsecondaires.

31 620 projets complétés

2978
AB
5221
C.-B.
856
MB
696
NL
899
SK
9419
ON
9858
QC
98
PE
619
NB
1192
NS

Projets par catégorie

Automated CT Data Analysis for Nuclear Reactor Maintenance

This project is aiming at the development of integrated computed tomography (CT) for automated integrity control of tools used in nuclear reactors during maintenance and inspection activities. Due to large number of complex tools, and the fact that the current process of tool integrity check is performed manually, the time required to finish the integrity check associated with each reactor maintenance and inspection task is extremely high. The current process is prone to human errors, where human inspectors might falsely assumes remaining parts are in the vault, with extended outage time that increases the costs of nuclear reactor repair and maintenance work in the order of millions of dollars. The proposed project is employing X-ray Computed Tomography (CT) technology to scan every tool before and after every vault work. Using advanced image processing, the pre-use scan before the vault work can be used to check every critical part of the tool. The automated pre-use scan process will verify if the tool is ready for vault use or not which will reduce time and cost.

Voir la description complète du projet
Superviseur du corps professoral :

Hossam Gaber;Jing Ren

Étudiant :

Partenaire :

New Vision Systems

Discipline :

Engineering

Secteur :

Professional, scientific and technical services

Université :

University of Ontario Institute of Technology

Programme :

Accelerate

Non-invasive automated assessment of tonic attention (vigilance) of commercial airline pilots during simulated flights – Part 2

With the goal of increasing the safety of civilian air flight, the detection of a decrease in pilot attention is becoming an important need in civilian aeronautics. Multiple models used for the detection of hypovigilant states have been developed over the years in experimental conditions, but barriers still exist limiting current use. First, some of these models require the execution of behavioral tasks that can disrupt pilot workflow. Second, other models relying on the use of physiological monitoring devices are still too cumbersome. The main objective of this project is to review all of the scientific publications about hypovigilance detection models so that future functional models can integrate the best evidence available. Although the main domain of application is aeronautics, other domains would also benefit from such technologies such as ground transportation and medical care.

Voir la description complète du projet
Superviseur du corps professoral :

Patrick Archambault

Étudiant :

Partenaire :

Thales Canada Inc

Discipline :

Life Sciences

Secteur :

Aerospace; Life Sciences (not health); Technology

Université :

Université Laval

Programme :

Accelerate

Real-time FPGA-based Architecture for Image andVideo Upscaling from Local Self-Examples

This project is about image upscaling which refers to the task of constructing a high
resolution image of a given lower resolution image, while preserving features of the original
image, such as sharpness and texture. One of the utmost difficulties that limit practical
application of state-of4he-art upscaling algorithms is their inherent computation demand, and
thus, conventional software implementations of upscaling algorithms are often unable to
deliver the required performance in many commercial applications such as mobile devices,
digital TVs, media players, and satellite imaging. In this project, we aim at a commercially
viable hardware (FPGA-based) implementation of an upscaling method that exploits local
self-similarities in the input image. We propose to desig a resources and power optimized
FPGA-based reconfigurable IP core that shall upscale HD1 D8Dp video streams in real-time
(30 frames/s). Our proposal outlines how we plan to achieve such a commerciallP core for a
robust image upscaling.

Voir la description complète du projet
Superviseur du corps professoral :

Aishy Amer

Étudiant :

Partenaire :

TandemLaunch Inc

Discipline :

Engineering

Secteur :

Finance and Insurance; Professional, scientific and technical services

Université :

Concordia University

Programme :

Accelerate

Development and Evaluation of Interventions for Farm Machinery Operators to Improve Musculoskeletal and Cognitive Health

Producers and farm workers are exposed to whole-body vibration (WBV) on a regular basis when operating farm machinery. The short-term effect of WBV include cognitive impairment and musculoskeletal disorders, such as low back pain. To aid in promoting worker cognitive and musculoskeletal health, rest and/or activity breaks may provide relief from these hazards and can be implemented immediately without cash investment. For these breaks to be appealing, they must be effective in reducing adverse health effects from WBV exposure, and must be feasible for field implementation. This multidisciplinary project will develop, test, and evaluate effective activity break interventions for agricultural equipment operators in order to mitigate adverse cognitive and musculoskeletal health effects during prolonged WBV. Worker participation and engagement will be incorporated throughout this project, to advise in developing best practices for field testing implementation. Outcomes of this work will include a set of guidelines and recommendations for field-test implementation.

Voir la description complète du projet
Superviseur du corps professoral :

Stephan Milosavljevic

Étudiant :

Partenaire :

Agrivita Canada Inc.

Discipline :

Life Sciences

Secteur :

Other services (except public administration)

Université :

University of Saskatchewan

Programme :

Accelerate

Waterloo Affordable Housing Living Lab (WAHLL)

Waterloo Region is facing an acute housing crisis across the housing continuum. Waterloo Affordable Housing Living Lab (WAHLL) is an applied research collaborative aimed at understanding the local housing system to better enable investment in sustainable solutions. WAHLL is comprised of the Kitchener Waterloo Community Foundation (KWCF), Union: Sustainable Development Co-operative (Union Co-operative), and University of Waterloo’s ‘Waterloo Institution for Social Innovation and Resilience’ (WISIR). KWCF has convened a Housing Innovation Roundtable (HIR) which operates across sectors and will serve as the core stakeholder group for this collaborative. Union Co-operative seeks to leverage the growing social finance market to acquire properties in Waterloo Region, and the Co-operative is an HIR participant that will provide the perspective of a prototypical organization-level intervention. WISIR will undertake system mapping and modelling built on their Social Innovation Lab process and their work in community-based social finance.

Voir la description complète du projet
Superviseur du corps professoral :

Michele Mastroeni;Anthony Piscitelli;Sean Geobey;Sean Geobey;Anthony Piscitelli

Étudiant :

Partenaire :

Union: Sustainable Development Co-operative;Kitchener Waterloo Community Foundation

Discipline :

Sociology

Secteur :

Health and Related Sciences & Technology; Real estate and rental and leasing

Université :

Conestoga College Institute of Technology and Advanced Learning; Ontario College of Art & Design University; University of Waterloo

Programme :

Accelerate

Energ-AI: Artificial Intelligence in Electrical Power Engineering

Classical engineering, referring to the three fields of civil, mechanical and electrical engineering, is currently based on traditional working methods. For example, the validation of plans is often done in paper version and the engineer must interpret photos and drawings manually, which introduces a risk of error due to the human factor. In addition, the shortage of labor in this area means that the economic potential of this industry in Canada cannot be exploited to its full value. In order to improve the efficiency, reliability, profitability and safety of the public and workers, the use of new digital technologies would allow engineering to migrate to the modern era.
To do this, the proposed project is to use artificial intelligence models to automatically interpret drawings and photos of electrical installations. This will allow the consulting engineering firm CIMA+ to stand out and offer these new services to its customers.

Voir la description complète du projet
Superviseur du corps professoral :

Martin Vallières;François Bouffard

Étudiant :

Partenaire :

CIMA+ (Sherbrooke, QC)

Discipline :

Engineering

Secteur :

Professional, scientific and technical services

Université :

Université de Sherbrooke

Programme :

Accelerate

Optimisation de la puissance et de l’énergie spécifiques des cellules au lithium-ion par métaheuristique combinée à la modélisation.

Le présent projet vise l’amélioration des performances des cellules au lithium-ion servant au stockage d’énergie électrique. L’énergie et la puissance des cellules au lithium-ion font parties de leurs propriétés les plus importantes. Une optimisation sera donc effectuée afin de maximiser l’énergie ainsi que la puissance de ces dispositifs. Le travail sera réalisé pour les trois formats de cellules les plus communs : les formats cylindrique, prismatique et sachet. De plus, le projet étudiera les deux matériaux, ou composés chimiques les plus utilisés actuellement pour le stockage d’énergie (NCA et NMC). Les résultats de l’étude conduiront à diverses configurations de cellules optimisées offrant des performances maximales. Ces cellules pourront être utilisées pour équiper des appareils électroniques ou des véhicules électriques, entre autres. Les configurations obtenues seront donc comparées afin de déterminer lesquelles répondent le mieux à chaque catégorie d’application.

Voir la description complète du projet
Superviseur du corps professoral :

Raynald Guilbault

Étudiant :

Partenaire :

Institut de Recherche Hydro-Québec

Discipline :

Engineering

Secteur :

Professional, scientific and technical services; Utilities

Université :

École de technologie supérieure

Programme :

Accelerate

Granular Jamming-based Mechanism for Robot Fish Actuation

With the advantages of swimming with quiet, compliant, and continuous strokes, soft robot fish have significant potentials to be used in the study of marine life and ocean environment quietly without disturbing marine animals. Our project is to design and build a robot fish that is self-contained and capable of mimicking body kinematics of biological fish in nature. A new actuation mechanism is proposed in this project. It will efficiently transmit the motor power to the continuous oscillation of soft tail to allow the robot fish to maneuver in the water. The actuation’s overall design is like a car engine, but instead, actuated by the motor and filled with granular elastic balls.

Voir la description complète du projet
Superviseur du corps professoral :

Ting Zou

Étudiant :

Partenaire :

Springboard Atlantic Inc.

Discipline :

Engineering

Secteur :

Ocean Tech; Technology

Université :

Memorial University of Newfoundland

Programme :

Accelerate

Development of an aligned rolling footgear to reduce seabed impacts of bottom trawl fisheries

Northern Shrimp (Pandalus borealis) and Greenland Halibut (Reinhardtius hippoglossoides) located off the east coast of Nunavut (Arctic Canada) are currently harvested by factory freezer vessels using bottom trawls. This fishery is a major contribution to the territory’s economy. However, bottom trawling is not without its ecological impact. Bottom trawl’s footgear, located below the fishing line and in contact with the seabed to protect the netting and to guide animals into the trawl net, can cause an impact to the seabed. This project will aid in the development of a bottom trawl net with reduced impact by developing an innovative footgear that is aligned with the towing direction and capable of rolling.

Voir la description complète du projet
Superviseur du corps professoral :

Paul Winger

Étudiant :

Partenaire :

Springboard Atlantic Inc.

Discipline :

Life Sciences

Secteur :

Aquaculture and Fishing

Université :

Memorial University of Newfoundland

Programme :

Accelerate

A deep learning system to extract and structure key information from academic written texts

To further the speed and efficiency of knowledge acquisition from academic works, it would be useful to know, at quick glance, key information from the text. In academic works, key information would include named entities and their relationships, arguments, event descriptions, and topics contained in the text.
We propose an automated system to extract the above key information from PDF documents, to then be stored and structured. The stored and structured information would then be able to provide information to a user based on their requests. By building from our previous project, we focus now on the information in the text, rather than the structure. This interdisciplinary project combines natural language processing (NLP), computer vision, human-computer interaction (HCI), computational linguistics, text tokenization and preprocessing, entity extraction, text summarization, hierarchical multi-label classification, and textual logic relationships analysis.

Voir la description complète du projet
Superviseur du corps professoral :

Seok-bum Ko;Roy Ka-Wei Lee

Étudiant :

Partenaire :

Living Sky Technologies

Discipline :

Engineering

Secteur :

Professional, scientific and technical services

Université :

University of Saskatchewan

Programme :

Accelerate

Vehicle Dynamics Modelling and Simulation for Use in the Development of a Self-Healing Auto Cyber Security System (SHACS) Proof-of-Concept

Vehicles rely on small computers located in various places. The electronic signals sent between these computers must be dependable. However, currently these signals can easily be hacked which threatens the vehicle and the people in and around it. A project is underway, involving Akimbo Technologies Inc., Solana Networks Inc., and the Carleton University Applied Dynamics Laboratory to develop methods for protecting vehicles from this threat. To support this work, the current project is developing computer programs that can be used instead of real vehicles and drivers to help develop and evaluate these methods. This project will help the industrial partners by allowing them to test their work without the safety risk and expense of doing the tests in real vehicles with real drivers. Expertise in this area will improve safety for Canadians and lead to economic growth as this work turns into commercial products and opportunities for the industrial partners.

Voir la description complète du projet
Superviseur du corps professoral :

Robert Langlois;Fidel Khouli

Étudiant :

Partenaire :

Akimbo Technologies Inc;Solana Networks

Discipline :

Engineering

Secteur :

Transportation and warehousing

Université :

Carleton University

Programme :

Accelerate

Critères de conception d’une unité mobile detraitement des lampes au mercure

Considérant leur vie utile, les lampes au mercure offre, comparativement aux lampes à incandescence, un
bénéfice environnemental. Cependant, il devient nécessaire de récupérer et traiter les lampes en fin de vie afin
d’une part, minimiser les rejets de mercure dans l’environnement et, d’autre part, favoriser le recyclage des sousproduits.
Dans ce contexte, le projet vise à concevoir une unité mobile de traitement. Cette conception repose
sur l’établissement de scénarios types de service de traitement (volumes et catégories de lampes; distance; flux
journaliers; etc.) afin d’évaluer les flux massiques et volumiques des matériaux/matières en regard des filières de
recyclage/valorisation potentielles. Les retombées du projet vise à répondre aux attentes de la nouvelle
règlementation associée à la responsabilité élargie des producteurs tout en favorisant la mise en oeuvre d’une
solution économiquement viable en minimisant le transport de grands volumes de lampes au mercure et propice
à améliorer l’offre de service du partenaire.

Voir la description complète du projet
Superviseur du corps professoral :

Mathias Glaus

Étudiant :

Partenaire :

MultiRecycle

Discipline :

Engineering

Secteur :

Université :

École de technologie supérieure

Programme :

Accelerate

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Mitacs — Retour sur innovation

Mitacs est financé par le gouvernement du Canada, le gouvernement de l’Alberta, le gouvernement de la Colombie-Britannique, Research Manitoba, le gouvernement du Nouveau-Brunswick, le gouvernement de la Nouvelle-Écosse, le gouvernement de l’Ontario, Innovation PEI, le gouvernement du Québec, le gouvernement de la Saskatchewan, le gouvernement de Terre-Neuve-et-Labrador et le gouvernement du Yukon.