Projets novateurs réalisés

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

30 508 projets complétés

2882
AB
5105
C.-B.
825
MB
681
NL
860
SK
9051
ON
9491
QC
97
PE
586
NB
1141
NS

Projets par catégorie

Visual Semantic SLAM using Bottlenose Camera System

Simultaneous Localization and Mapping (SLAM) is useful in multiple applications such as autonomous driving, augmented reality, and surveillance. Visual SLAM is a popular and widely used SLAM technique due to simplicity of the sensor network. Semantics and machine learning are used widely in recent research to achieve better robust real time SLAM performance. Hardware acceleration can be used to perform repetitive and compute intensive parts of the image processing in SLAM. In this research we plan to use the Bottlenose camera system from Labforge Inc which has these features of semantic segmentation, deep learning-based hardware accelerated object detection to achieve improved SLAM performance. The additional processing power from Bottlenose
camera processing will help to offload some work from the central processor and result in better real time performance. The research will take existing SLAM solutions and analyze how the Bottlenose camera system can be integrated with them to provide better and improved SLAM solutions

Voir la description complète du projet
Superviseur du corps professoral :

Mohamed Atia

Étudiant :

Partenaire :

Labforge Inc.

Discipline :

Engineering

Secteur :

Manufacturing

Université :

Carleton University

Programme :

Accelerate

Conception intégrée et fabrication d’une électrode composite flexible sèche d’électroencéphalographie et d’un système de support à pression variable pour utilisation mobile de longue durée in-situ

Durant les dernières années, de nouvelles méthodes d’analyse par ordinateur impliquant l’intelligence artificielle ont permis de grandement améliorer notre capacité à interpréter les signaux obtenus par électroencéphalographie pour des applications allant de la santé à la défense en passant par les sports et l’amélioration du bien-être mentale. Toutefois, les avancées dans ce domaine sont aujourd’hui limitées par les systèmes d’acquisition des signaux d’électroencéphalographie. Les électrodes et casques employés ne peuvent être portés pour de longues durées dû à l’asséchement graduels des électrodes humides ou l’inconfort provoqué par des méthodes sèches métalliques. Ce projet propose de développer un système casque/électrode à polymère composite flexible qui permet de contrôler la pression appliquée tout en étant confortable pour l’utilisateur pour de longues durées.

Voir la description complète du projet
Superviseur du corps professoral :

Ilyass Tabiai;Sofiane Achiche;Fabio Cicoira;Eric David;Gelareh Momen;Martine Dube

Étudiant :

Partenaire :

BMU Augmented Intelligence

Discipline :

Engineering

Secteur :

Professional, scientific and technical services

Université :

École de technologie supérieure; Polytechnique Montréal

Programme :

Accelerate

Knowledge Modeling and Product Development for Canadian Indoor Hydroponic Farming

Urban population is facing unprecedented growth resulting in the need for additional agricultural resources. Traditional farming practices seem vulnerable to cater to urbanities’ needs, placing an additional burden on the food and agriculture system. Furthermore, the unsettling environmental impact of traditional farming practices has aroused the metropolitan population to search for alternatives, such as indoor urban agriculture and vertical plant systems. This study aims to address the key issues faced by the Canadian urban population to generate knowledge for indoor hydroponic systems. Using existing and inferred knowledge from literature and through expert interviews, this research tries to provide a knowledge base for developing indoor hydroponic farming in a Canadian context. Emphasis will be laid on seed selection, growing inputs, and parameters to make indoor hydroponic farming accessible to common individuals and encourage more urbanites to successfully grow their favourite plants for fun and well-being all year round.

Voir la description complète du projet
Superviseur du corps professoral :

Rafiq Ahmad

Étudiant :

Partenaire :

HUMUZ FARM INC.

Discipline :

Engineering

Secteur :

Manufacturing

Université :

University of Alberta

Programme :

Accelerate

Statistical and Ethical Implications of Pimavanserin Drug Trials Published with Unexplained or Missing Datasets on Parkinson’s Disease

This project looks to examine the safety of the medication Pimavanserin, marketed under the brand name Nuplazid. Pimavanserin is prescribed to manage Parkinson’s Disease Psychosis, a common side effect of Parkinson Disease medications. This medication has already received regulatory approval in the United Stated by the Food and Drug Administration under a special program called the “Breakthrough therapy program”. Although it initially showed promising results, concerns have been raised regarding its safety and efficacy. As such, this study will look to examine the original clinical trials that served as an evidence source for its regulatory approval, with particular attention paid on the statistical methods used to compensate for missing data. The ultimate goal is to provide Health Canada and the Canadian Agency for Drugs and Technologies in Health with more evidence to determine if this medication is safe and effective for patients with Parkinson’s Disease here in Canada.

Voir la description complète du projet
Superviseur du corps professoral :

Andrew Johnson;Maxwell Smith

Étudiant :

Partenaire :

Parkinson Society Southwestern Ontario

Discipline :

Life Sciences

Secteur :

Other services (except public administration)

Université :

The University of Western Ontario

Programme :

Accelerate

Identifying Risk Factors for Hazardous Driving and Accident Propensity

Road safety affects everyone, Geotab has several years of driving and environmental data from over 2 million
connected vehicles providing the opportunity to make customers safer, as well as our communities and cities.
This project will leverage data and existing methods to build a model that can identify causal risk factors for
hazardous driving and accident propensity. The model will output a safety score representing the risk level of
fleets/drivers, which can facilitate high efficiency and safety management of light, medium and heavy duty
vehicles. With better understanding of environmental causality, safety concerns can be addressed proactively to
prevent accidents.

Voir la description complète du projet
Superviseur du corps professoral :

Andrei Badescu;Sheldon Lin

Étudiant :

Partenaire :

Geotab Inc

Discipline :

Computer science

Secteur :

Information and cultural industries; Professional, scientific and technical services; Transportation and warehousing

Université :

University of Toronto

Programme :

Accelerate

Automated code fix suggestions based on source code syntax tree analysis

AMD manages a very large code base that supports multiple graphics products, operating systems, and customers with multiple releases per year. With a high rate of innovation and corresponding code changes it is a daunting task to ensure a given change works correctly on all applicable configurations for every release. It is therefore imperative to detect potential issues as soon as possible in development lifecycle, and ideally as soon as developers make code changes and where possible also propose a fix for detected issues.

Voir la description complète du projet
Superviseur du corps professoral :

Shurui Zhou

Étudiant :

Partenaire :

AMD Canada

Discipline :

Computer science

Secteur :

Manufacturing; Professional, scientific and technical services

Université :

University of Toronto

Programme :

Accelerate

Autonomous Navigation for Small UAV in Indoor GPS-denied Environments

With the evolution of unmanned aerial vehicles (UAVs) in recent years, more and more researchers are setting their sights on the application research of the indoor environment. Indoor applications include industrial facility inspection, warehouse inventory management, health sector, search, and rescue, among others. However, the use of UAVs in these applications requires continuous high-accuracy positioning and pose information and the consequent efficient obstacle avoidance algorithm. To address the above problem, we want to design an algorithm that can:
1. Perceive the surrounding environment.
2. Control attitude and position of UAVs.
3. Navigate UAV through obstacles.
4. Account for the changes in the dynamic environment and remain stable.

Voir la description complète du projet
Superviseur du corps professoral :

Igor Gilitschenski

Étudiant :

Partenaire :

SOTI Inc

Discipline :

Computer science

Secteur :

Information and cultural industries; Professional, scientific and technical services

Université :

University of Toronto

Programme :

Accelerate

Real-time Bidding Using Contextual Targeting

Ads keep the internet free. But, to keep them from becoming spam and degrading the user’s online experience, they need to be relevant to them. The traditional way the industry does this is by collecting a lot of information about every user and creating profiles that can be used to target users based on their online journey. However, users have different tolerances for how much information they want collected about them by any website/app. In general, the problem the industry needs to solve is to make sure the ads shown to users are relevant even in the absence of any user profile. This is the problem the intern is going to tackle. In this research, an End-to-End project will be built to create a contextual targeting proof of concept for mobile and desktop websites. Industry standard metrics such as click-through rate will be used to get an understanding of whether contextual targeting works.

Voir la description complète du projet
Superviseur du corps professoral :

Annie Lee

Étudiant :

Partenaire :

Pelmorex

Discipline :

Computer science

Secteur :

Information and cultural industries; Professional, scientific and technical services

Université :

University of Toronto

Programme :

Accelerate

Modeling and Optimization of roof based BIPV/T with air source heat pump

Incorporating the Air Source Heat Pump (ASHP) into Building Integrated Photovoltaic/Thermal (BIPV/T) system has the potential to reduce building heating and cooling costs and dependence on non-renewable heating fuels. ASHPs could boost the quality and quantity of heat output of a BIPV/T system by delivering a seasonal Coefficient of Performance (COP) of between 2.0 and 4.0, which means 2-4 times more energy output than the amount of energy (electricity) consumed.
When used in Canada’s cold climates, however, ASHPs alone have been found to underperform at low temperatures due to the scarcity of heat that may be pumped out of the atmosphere. One solution to this is ASHP which can provide a higher COP at very low winter outdoor temperatures. The incorporation of PV/T + ASHP into building integrated sloped roof, solutions in existing residential and commercial buildings will furthermore have the potential to lower overall costs of such systems, significantly reduce GHG emissions and provide significant economic and other benefits for Canada in general and for southern Ontario and the Greater Toronto Area (GTA) in particular.

Voir la description complète du projet
Superviseur du corps professoral :

Alan Fung

Étudiant :

Partenaire :

S2E Technologies Inc

Discipline :

Engineering

Secteur :

Construction and infrastructure; Finance and Insurance; Professional, scientific and technical services

Université :

Toronto Metropolitan University

Programme :

Accelerate

Hypatia-Learn: State of the Art Mathematics Learning and Tutoring System

The project revolves around reading and understanding students solution to various mathematical problems. We wish to analyse the work done by students and the solution to these problems and provide math checking capabilities to various types of problems. Furthermore, this project looks to construct a virtual tutor that can analyse students work and provide feedback and hints to help the student arrive at the correct solution. We wish to achieve all of this while using a simple mathematical editor to allow students to type up mathematical solution cleanly and easily.

Voir la description complète du projet
Superviseur du corps professoral :

Azadeh Farzan

Étudiant :

Partenaire :

Hypatia Systems Inc

Discipline :

Computer science

Secteur :

Education

Université :

University of Toronto

Programme :

Accelerate

Optimisation de l’aquaculture multitrophique en milieu tropical par l’intégration de la macroalgue Ulva lactuca dans la moulée du poisson marin Pompano de Floride (Trachinotus carolinus)

Les protéines dans les farines et huiles de poissons représentent l’élément le plus dispendieux des moulées commerciales. Son remplacement par une protéine végétale provenant de macroalgues produites dans les eaux de rejets piscicoles diminuerait les coûts associés à la production aquacole tout en réduisant les impacts environnementaux. Les Ulves, comme Ulva lactuca, ont été largement évaluées comme substitut alimentaire. Dans ce projet, nous remplacerons la farine de poissons par de l’Ulva (substitution de 25 %) et évaluerons les effets sur le rendement reproductif du Pompano de Floride, un poisson marin tropical commercialement prometteur. Les géniteurs seront nourris avec la moulée à base d’Ulva, produite dans le système AMTI (Aquaculture Multitrophique Intégrée), puis avec une moulée commerciale (Vitalis), pour comparer la qualité des œufs, à la base d’un bon développement larvaire, entre les 2 régimes. L’ajout d’Ulva dans la moulée du Pompano se refléterait par un rendement similaire ou supérieur à la moulée Vitalis.

Voir la description complète du projet
Superviseur du corps professoral :

Réjean Tremblay

Étudiant :

Partenaire :

Florida Atlantic University

Discipline :

Life Sciences

Secteur :

Technology; Aquaculture and Fishing; Sustainability & the Environment

Université :

Université du Québec à Rimouski

Programme :

Globalink Research Award

Performance of Fiber-Reinforced Lightweight Self-Consolidating Concrete Columns Reinforced with Glass Fiber-Reinforced Polymer Bars and Spirals under Axial and Eccentric Loads

One of the main interests of the construction industry is the use of innovative materials to facilitate construction, extend service life and minimize maintenance and rehabilitation costs. Lightweight aggregate self-consolidating concrete (LWSCC) can be of great interest for reducing dead loads, section dimensions and project costs, especially for precast elements. Integrating GFRP reinforcement into LWSCC would effectively contribute to producing lighter and more durable concrete members for precast applications. Lightweight concrete is more brittle than normal-weight concrete (NWC). Furthermore, the brittleness of concrete may affect not only the failure mode but also the axial capacity of concrete columns. Adding fiber into LWSCC is an effective way to solve the brittleness of concrete and improve the tensile strength and crack resistance of concrete. This research project aims to develop fiber-reinforced lightweight aggregate self-consolidating concrete (FR-LWSCC) mixes for precast applications
and to provide an experimental work as well as extensive theoretical analysis and design recommendations of RC columns reinforced with FRP bars. The experimental results will be discussed in terms of moment–deflection behavior, flexural capacity, mode of failure, crack patterns, and crack widths.

Voir la description complète du projet
Superviseur du corps professoral :

Brahim Benmokrane

Étudiant :

Partenaire :

Sym-Tech Béton Préfabriqué Inc.

Discipline :

Engineering

Secteur :

Manufacturing and Construction; Advanced Manufacturing; Sustainability & the Environment

Université :

Université de Sherbrooke

Programme :

Accelerate

Précédent
1...1,2751,2761,2771,2781,2791,2801,281...2,543
Suivant

Joignez-vous à un écosystème d’innovation florissant.

Abonnez-vous à notre infolettre dès maintenant!

S’inscrire