Innovative Projects Realized

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

29670 Completed Projects

2811
AB
4990
BC
801
MB
663
NL
825
SK
8841
ON
9197
QC
95
PE
568
NB
1088
NS

Projects by Category

Manganese Salen Systems for C-H Functionalization

Selective incorporation of valuable building blocks into feedstock chemicals is a very important, albeit often times difficult challenge. We plan to develop catalysts; materials to efficiently convert feedstock chemicals into ones with valuable functionality. The catalysts developed by the
current project will incorporate two traditional reactive sites as a means to enhance activity and selectivity of our selected transformations. Through various techniques we plan to interrogate the properties of our developed systems in order to inform decisions about second generation
materials with enhanced activity over their predecessors. This project will result in a selection of new materials for the efficient conversion of feedstock chemicals into value added products.

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Faculty Supervisor:

Tim Storr

Student:

Partner:

Université Grenoble Alpes

Discipline:

Physics

Sector:

Education

University:

Simon Fraser University

Program:

Globalink Research Award

Computed Tomography Images Enhancement and Denoising

In Computed Tomography (CT) Imaging, X-ray radiation is used to penetrate through the internal structure of the patient body in order to produce digital images. Therefore patient could be exposed to certain level of X-ray radiation dose. Accumulation of these exposures beyond certain threshold could increase risk of fatal cancer. Thus it is of paramount importance to lower the amount of radiation exposure during CT images acquisition. However, a low radiation dose in CT images would result to lower image quality. To maintain lower dose usage and still produce images with a high quality therefore one needs to develop advanced image processing tools in order to reduce noise and enhance CT images. This internship therefore will attempt to bring advanced methods of image processing tools that are specifically tailored to reduce noise and enhance CT images to the partner organization.

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Faculty Supervisor:

M. Stella Atkins

Student:

Partner:

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Simon Fraser University

Program:

Accelerate

Modélisation numérique du comportement de la connexion à la base des pylônes tubulaires

Face à la demande grandissante en électricité, les gestionnaires de réseaux électriques doivent implanter de nouvelles lignes de transport d’énergie dans les grands centres de distribution. Ces endroits très peuplés imposent de nombreuses contraintes au niveau de la conception de ces lignes. Parce qu’ils sont esthétiques et compacts, les pylônes tubulaires sont souvent sélectionnés dans ces zones urbaines. Actuellement, les méthodes de dimensionnement de la connexion à la base d’un pylône tubulaire sont fondées sur des hypothèses simplificatrices ou sur des formules empiriques. L’objectif principal de ce projet est donc d’élaborer une approche numérique permettant d’étudier l’influence des propriétés géométriques des plaques d’ancrage sur le comportement de la connexion à la base des pylônes tubulaires. Ce projet mènera à des prédictions plus réalistes de la répartition des contraintes mécaniques dans ces connexions et pourrait permettre de développer des méthodes de dimensionnement pour concevoir des pylônes plus fiables et plus économiques.

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Faculty Supervisor:

Sébastien Langlois

Student:

Partner:

Hydro-Québec Équipement et services partagés

Discipline:

Engineering

Sector:

Utilities

University:

Université de Sherbrooke

Program:

Accelerate

Analysis of bike route choice behaviour using the link-based recursive logit model

Concerned by the nuisances of motorized travel on urban life, policy makers are faced with the challenge of making cycling a more attractive alternative for everyday transportation. Despite the clear advantage of being healthy, cheap and sustainable, cycling yet fails to grow into more than a marginal mode of transport. One of the barriers lies in the design of road networks, which are often unsafe and unpractical for bike use. Route choice models can help achieve this objective by gaining insight into the trade-offs cyclists make when choosing their routes, hence providing helpful guidance for improving network infrastructure. The issue associated with such models is choice set generation, a time-consuming and theoretically problematic imperative. The contribution of this study consists in estimating a link-based bike route choice model which does not require to sample any choice set of paths. Moreover, we will provide validation results as well as comparing predicted bike traffic flows with traffic counts in a real network. These results are important to the partner organization in order to assess the possible impact of integrating the model in their traffic simulation software.

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Faculty Supervisor:

Emma Frejinger

Student:

Partner:

INRO Consultants Inc

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Université de Montréal

Program:

Accelerate

Amélioration de l’interactivité en réalité augmentée pour applications en jeux vidéo

La réalité augmentée, soit la superposition en temps réel d’éléments virtuels à la réalité, est une technologie ayant vu le jour il y a déjà presque vingt ans. Avec l’arrivée des plateformes mobiles et des capteurs RGBD à faible cout, cette technologie reprend en popularité et devient un sujet de recherche prisé de plusieurs institutions. L’applicabilité de la réalité augmentée dans le domaine du divertissement numérique est évidente : utiliser l’environnement réel de l’utilisateur et de le faire interagir avec des objets ou personnages virtuels amène une panoplie d’innovations dans l’industrie. Les recherches effectuées dans le cadre de ce projet vont faciliter l’accessibilité des différentes technologies de réalités augmentées aux non-initiés de la vision artificielle. En effet, nous proposons de traiter les informations brutes des caméras 3D, et de construire, en temps réel, une représentation simplifiée du monde. Par exemple, un algorithme permettant d’extraire les surfaces planaires au fil du temps sera développé. Cette information permettra la conception rapide et intuitive de nouvelles applications mobiles de réalité augmentée.

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Faculty Supervisor:

Jean-Francois Lalonde

Student:

Partner:

Frima Studio

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

Université Laval

Program:

Accelerate

Computer vision system for ice detection on power cables

In northern countries, ice storms can cause major power disruptions such as the one that occurred in the Toronto area on December 2013 that left more than 300,000 customers with no electricity immediately after the storm. Prediction of ice formation on power cables can help on taking actions for removing the ice before a major problem occurs. Currently Manitoba Hydro HVDC Research Centre has a vision based ice detection system that uses digital images taken from the overhead line conductors. The system, as it is currently implemented, yields a number of misclassification such as an alarm indicating ice formation in a cable that does not present an ice formation problem, no alarm when there is ice, etc. With state of the art digital image processing techniques the project aims to improve the current performance of this system.

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Faculty Supervisor:

Gabriel Thomas

Student:

Partner:

Manitoba Hydro International Ltd

Discipline:

Engineering

Sector:

Professional, scientific and technical services; Utilities

University:

University of Manitoba

Program:

Accelerate

Automatic Image Filtering Using Deep Learning

Two Hat Security is a company that develops next generation moderation tools for social networking apps. Since images are of the most important data shared by social networking apps, an important problem for the company is to identify images that are unsafe or inappropriate. In particular, images containing certain objects (e.g. knife, gun, bikini, etc.) are considered unsafe. It is obviously not practical to manually sift through all the images to find the unsafe ones. The objective of this project is to develop algorithms to automatically filter images and identify those that are considered unsafe. We will develop algorithms using deep learning (in particular, convolutional neural network) to identify whether an image contains any of the objects that are considered to be unsafe.

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Faculty Supervisor:

Yang Wang

Student:

Partner:

Two Hat Security Research Corp

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

University of Manitoba

Program:

Accelerate

Analyse du processus de passage à l’échelle d’initiatives innovantes développées dans le cadre des reformes du système de santé en Tunisie

Comme la plupart des pays en transition, la Tunisie est un pays qui est confronté à une restructuration constante de son système de santé pour faire face à différents défis. Dans la cadre de la nouvelle restructuration de son système, le gouvernement tunisien a décidé de mettre à l’échelle des projets les plus prometteurs et efficaces au niveau local. Bien que les projets locaux aient un impact positif sur la santé des populations cibles, leur déploiement à plus grande échelle présentent encore d’importantes difficultés. Pourtant, de tels projets sont essentiels pour documenter de la faisabilité et de l’efficacité, et permettent d’identifier les conditions de succès lors de la mise à l’échelle. Cette étude vise examiner la mise en oeuvre de quelques projets locaux déjà initiés en intégrant l’analyse des conditions de pérennisation dans le processus de la mise à l’échelle. Nos activités de recherche sur le terrain nous permettront de mettre en évidence la nature et le niveau d’influence de plusieurs facteurs qui sont associés à ces processus. Les publications et les communications de cette étude contribueront à la diffusion de ses conclusions dans la communauté scientifique. TO BE CONT’D

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Faculty Supervisor:

François Champagne

Student:

Partner:

Université de Tunis

Discipline:

Life Sciences

Sector:

Education

University:

Université de Montréal

Program:

Globalink Research Award

Re-thinking hydro dam management: A conservation tool for preserving of Nova Scotia’s important freshwater habitats.

The implementation of hydroelectric power over the past 100 years in Nova Scotia has resulted in changes to the habitats of many important species. In the watersheds where hydro dams have been constructed, plant, fish and animal habitat have all been impacted to some extent. Although some of these impacts have been positive (the creation or reservoirs have made good fish habitat for example), degradation in the habitat of many species has also been documented. However it may be possible to manage hydro dam infrastructure in such a way that these important habitats are preserved or even recovered. The objective of this project is to research and develop management strategies that could be used by Nova Scotia Power to manage their hydro dams in a way that is more conducive to species and their habitat needs, while also meeting their electricity production targets.

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Faculty Supervisor:

Michelle Adams

Student:

Partner:

Discipline:

Life Sciences

Sector:

Utilities

University:

Dalhousie University

Program:

Accelerate

Monitoring and Assessment of Net Zero Energy Home and Net Zero Energy Ready Home

The project involves monitoring and evaluating the energy performance of two houses built by a mainstream builder – one Net Zero Energy (NZE) and one Net Zero Ready (NZR). Both have advanced zoned variable-capacity air source heat pump systems (ASHPs), heat pump water heaters (HPWHs) and drain water heat recovery. The NZE house has a Photovoltaic (PV) grid-tied/battery system. Objectives are to assess energy and comfort performance and to identify options for optimizing performance. A key issue is how to optimize the way ASHP, HPWH and PV systems work together, in order to minimize peak electricity demand, maximize efficiency and environmental benefits, and minimize costs to builders, homeowners and utilities. Performance will be modelled in TRNSYS and a user interface refined to enable researchers and industry to simulate different NZE approaches and identify optimum strategies. The project is related to the intern’s field of study and will provide valuable experience monitoring and modelling real-world high-performance housing. It builds on research of the partner organization and its industry partners.

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Faculty Supervisor:

Alan Fung

Student:

Partner:

Toronto and Region Conservation Authority (Vaughan, ON)

Discipline:

Engineering

Sector:

Professional, scientific and technical services; Public administration

University:

Toronto Metropolitan University

Program:

Accelerate

An enhanced visual robot navigation using a laser range sensor

Accurate robot positioning and navigation is an ongoing research problem, especially in environments that are unknown or hostile. Nowadays, there is an increase in the number of applications robots can be deployed in, these applications range from robots providing concierge services in hotels, guiding museum visitors to the desired booth, and assisting firefighters to locate trapped individuals. All of these applications rely on accurate robot positioning to successfully achieve their task. Most robots come mounted with a camera that can perform face/object recognition, and can be used for navigation as well. However, accurate positioning via camera suffers due the typical nature of the unknown environment it is deployed in. To solve this problem, this research investigates augmenting a camera with a laser range sensor to improve the overall accuracy of the positioning algorithm.

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Faculty Supervisor:

Edward Park

Student:

Partner:

Recon Instruments Inc

Discipline:

Engineering

Sector:

Manufacturing

University:

Simon Fraser University

Program:

Accelerate

Étude différentielle et prédictive du succès universitaires chez les francophones au Nouveau-Brunswick

L’Université de Moncton, seule université francophone du Nouveau-Brunswick, est située dans un milieu à forte majorité anglophone. L’état des connaissances sur les déterminants de la réussite universitaire chez cette population n’est que parcellaire et peu discriminant. Comme le souligne Landry (2014), les différents groupes populationnels du Nouveau-Brunswick constituent un microcosme du pays et sont en ce sens une population attrayante pour la recherche. L’objectif ici est de rendre compte, selon la provenance géographique, des différences susceptibles d’influencer la réussite universitaire des étudiants vivant en milieu minoritaire, plus particulièrement la réussite aux différents cours de français. L’ICRML a comme mandat de promouvoir une plus grande connaissance de la situation des minorités de langue officielle du Canada et une meilleure compréhension des enjeux prioritaires qui les concernent. Ce projet de recherche s’inscrit tout à fait dans cette mission et, comme il est question d’étudier des variables psychologiques d’une minorité linguistique, ce projet est d’autant plus pertinent pour le stagiaire. En outre, ce projet lui permettra d’approfondir ses connaissances concernant de nouvelles avenues de recherches, en collaborant avec des experts dans le domaine.

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Faculty Supervisor:

François Vigneau

Student:

Partner:

Institut canadien de recherche sur les minorités linguistiques

Discipline:

Sociology

Sector:

Professional, scientific and technical services

University:

Université de Moncton

Program:

Accelerate

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