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

Développement de technologies d’assemblage avancées en aluminium pour la fabrication ferroviaire

Bombardier Transport est un chef de file mondial dans l’industrie ferroviaire comprenant des unités de production partout dans le monde. La compagnie souhaite développer des technologies d’assemblage avancées adaptées à l’aluminium pour la fabrication ferroviaire. Celle-ci a identifié quatre procédés de soudage comme étant les technologies d’assemblage avancées les plus susceptibles de percer en production pour leurs nouvelles unités de fabrication. L’objectif principal du projet de recherche est de développer une approche quantitative pour l’évaluation des performances des assemblages soudés par l’étude des mécanismes fondamentaux régissant la formation des microstructures et le comportement mécanique statique et en fatigue des assemblages soudés. L’intérêt de cette coopération pour l’entreprise est d’augmenter la compréhension des phénomènes physiques sur une problématique précise de fabrication des composantes de caisses de trains en aluminium. Ceci permettra à la compagnie Bombardier Transport d’identifier la méthode d’assemblage la plus performante pour ses développements futurs.

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Faculty Supervisor:

Vincent Demers

Student:

Partner:

Bombardier Transportation Canada Inc

Discipline:

Engineering

Sector:

Manufacturing; Transportation and warehousing

University:

École de technologie supérieure

Program:

Accelerate

Error-correcting codes for adiabatic quantum computation

There are many difficult problems in diverse areas such as bioinformatics, transportation or finance, which are very hard to solve on a classical computer. A new type of computer was conceived, called a quantum computer, which operates on the laws of quantum physics, as opposed to the laws of classical mechanics. Such a quantum computer has not yet been engineered at a large scale; however there are currently several competing models. Of these, the D-Wave machine is the most advanced prototype under development, and several machines have been sold to academic and industrial customers. The greatest obstacle to engineering quantum computers is quantum noise – interaction with the environment which causes the computer to essentially act as a classical computer (or distorts results entirely). The solution to this problem is quantum error correction. Many quantum error correction schemes (quantum codes) exist, however the D-Wave device is only compatible with some of them. We will develop new quantum codes for the D-Wave device in order to lessen the gap between what is practically and theoretically possible of quantum computers.

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Faculty Supervisor:

Petr Lisonek

Student:

Partner:

D-Wave Systems Inc.

Discipline:

Mathematics

Sector:

Information and Communications Technology; Advanced Manufacturing

University:

Simon Fraser University

Program:

Accelerate

Enhancing the abiotic and biotic oxidation of manganese for cold groundwater biofiltration

Manganese (Mn) affects the flavour and colour of water, and causes scaling in pipes, even at low concentrations. Groundwater is a crucial water resource in Saskatchewan and is often naturally rich in Mn. Strong demands for securing clean water have arisen in a variety of public and industrial sectors. The study’s objective is to accelerate Mn removal from cold groundwater by taking advantage of the potential synergetic effects of combining abiotic and biotic Mn oxidation at low temperatures using Mn-oxide-coated anthracite and cold-adapted, Mn-oxidizing microbial consortia. This innovative approach aims at accelerating the often delayed Mn oxidation in the early stages of operation of a groundwater treatment unit. This chemical-free and energy-efficient technology has the potential to change the field of water treatment. This research is on the frontline of Canada’s innovation for water security and environmental protection, and the results will help Canada meet its present and future needs.

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Faculty Supervisor:

Won Jae Chang

Student:

Partner:

Delco Automation Inc

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

University of Saskatchewan

Program:

Accelerate

Solving large–scale non–smooth stochastic optimization problems

Machine learning focuses on writing computer programs that can ‘learn’ from data. This is becoming increasingly important, as we try to understand the huge amounts of complicated data we are collecting (both in academia and in industry). While machine learning is one of the key
tools we use analyze large datasets, it often has trouble dealing with the enormous sizes of modern datasets (for example, learning something about every webpage on the internet or about all users of Facebook). In a breakthrough paper in 2012, Francis Bach and Mark Schmidt
(along with another co–author) showed the surprising result that we can ‘learn’ from huge datasets at a similar speed to how we ‘learn’ from smaller datasets. However, there result assumed that our model was ‘differentiable’ and that our dataset had a finite size. In this internship, we will try to relax these assumptions. This will let us apply a much larger variety of machine learning techniques to huge datasets, and will further allow us to build systems that keep learning efficiently over time.

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Faculty Supervisor:

Mark Schmidt

Student:

Partner:

École normale supérieure

Discipline:

Computer science

Sector:

University:

The University of British Columbia

Program:

Globalink Research Award

Cross population study of white matter connectivity using dictionary learning and sparse coding

Understanding the structure and connections in the brain is an outstanding research problem. Many diseases impact the structure and hence connections between them, analyzing which may help in detection and diagnosis of these diseases. In this project, we try to learn an atlas of major connecting fibers (tube like connections) for a group of people, using dictionary learning based framework. This atlas is then used to
observe the similarities and differences in fibers across the group, and to segment fibers from new person. We will use twin dataset of the Human Connectome Project to study heritability of white matter clusters. The method will also be used to understand the differences in structural connectivity between healthy subjects, mild cognitive impairment (MCI) subjects, and Alzheimer’s disease (AD) subjects.

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Faculty Supervisor:

Christian Desrosiers

Student:

Partner:

Inria Paris - Rocquencourt Research Centre

Discipline:

Computer science

Sector:

University:

École de technologie supérieure

Program:

Globalink Research Award

Reproducibility of diffusion MRI white matter structural connectome

The ensemble of the human brain wiring configuration is known as the connectome. Using diffusion magnetic resonance imaging, we can approximate the connectome and use it to increase our understanding of normal function and of neuro-diseases. However, the complex interactions between each steps from brain images to the final wires give rise to many potential sources of error. Using an array of modern simulations of these steps, we will study and disentangle the effect of their interactions to increase the accuracy of the connectome estimation in humans.

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Faculty Supervisor:

Maxime Descoteaux

Student:

Partner:

Inria Rennes - Bretagne Atlantique Research Centre

Discipline:

Computer science

Sector:

University:

Université de Sherbrooke

Program:

Globalink Research Award

Automatic Resizing Framework for Time-Varying Non-Space Filling Visualizations

We propose the development of an automatic resizing framework for time-varying visualizations. This framework would support easy deployment of visualizations on a multitude of devices with varying display sizes. The second objective of the project is to address issues of data is interpretation that could arise as a result of visualization resizing through the use of visual cues as well as interaction techniques. The developed resizing algorithms will be instantiated in the form of prototype applications of visualizations such as, dynamic networks and scatter plots. These prototype applications will be evaluated based on not only the memory and time requirements of the resizing operation but also the efficiency of
the retargeted visualizations at supporting the user in accomplishing analytical tasks. The resizing framework developed during the course of this project will be available as open source software to prompt further refinement by the research community.

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Faculty Supervisor:

Christopher Collins

Student:

Partner:

Inria Lille - Nord Europe Research Centre

Discipline:

Computer science

Sector:

University:

University of Ontario Institute of Technology

Program:

Globalink Research Award

Technology Strategy Analysis for Resilience Software

A frequent prescription for managers leading a company’s technology assets is that the firm’s business strategy should lead the firm’s technology strategy. However, many technology firms find themselves in the opposite position: having a technology that can alter their business strategy. In such a case, questions arise as to the appropriate business response. Resilience Inc. – a software company located in Vancouver, BC faces the same issue. This firm offers two software applications and has a mature presence in the medical education market. Resilience now owns an integrated police personnel management application which would allow it to serve an entirely new and different market from that of medical education. This research will investigate whether such a market penetration should proceed and, if so, how. It will focus on the market potential, synergies, and how firms can adapt their business strategy to the ever changing capabilities of technology.

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Faculty Supervisor:

Ron Cenfetelli

Student:

Partner:

Discipline:

Business

Sector:

University:

The University of British Columbia

Program:

Accelerate

Multidimensional Tactile Displays and their applications for Wearable Devices

Engaging with visual displays is not always feasible or desired. They could be absent in certain contexts due to their fragility, cost or high battery consumption. Visual attention could be engaged elsewhere in an activity or be impaired due to disabilities. With the rise of wearables that remain in constant, sturdy contact with the skin, and the advances in tactile display capabilities, it is imperative to advance tactile interactions that are as independent and as advanced as visual interactions. This work proposes a novel tactile display that brings the concept of direct manipulation, which is traditionally applicable to visual displays, into the tactile space. The project proposes that two dimensional tactile display can lead to interactions that are analogous to ones that are enabled by visual displays. We expect that the users will be able to perceptually feel thedisplay with ease and perform fast, repatable interactions.

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Faculty Supervisor:

Ravin Balakrishnan

Student:

Partner:

Université de Lille

Discipline:

Computer science

Sector:

University:

University of Toronto

Program:

Globalink Research Award

Model selection for advanced statistical analysis of multiparametric MRI

Advanced quantitative multiparametric MRI techniques allow the imaging of tumor heterogeneity that was before only accessible by histology and the mapping of functional features not available to pathologists. This wealth of information is however difficult to interpret, even by experts in the field, because adequate analysis tools are missing. The combination of quantitative multiparametric MRI, which can assess the tumor heterogeneity, and advanced statistical analysis, is a new concept to improve the diagnostic and the therapeutic orientation of brain tumors. Each pixel can be associated with a “spectrum” which integrates information of each quantitative image. These spectra can then be clustered using statistical methods into a number of clusters. The choice of the number of clusters is an important issue in that the clusters can be matched with histological information and used to define a tumor signature and thus allow diagnosis. In this work we will focus on determining the optimal number of clusters in a data-driven and parsimonious way using advanced Bayesian models and inference methods.

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Faculty Supervisor:

Russell Steele

Student:

Partner:

Inria Grenoble - Rhône-Alpes Research Centre

Discipline:

Mathematics

Sector:

University:

McGill University

Program:

Globalink Research Award

Secure Outsourcing of Storage and Computation

The rise of cloud technologies and the proliferation of mobile devices have revolutionized data storage and their processing. Amongst numerous benefits, cloud technologies offer a flexible way to outsource storage and computation to the cloud vendors. As a result, sensitive data often end up being managed on remote servers maintained by third party outsourcing vendors. Whence, despite being envisioned as a promising service for the future, security and privacy issues remain the major inhibiting factors towards a wide scale acceptance of cloud technologies in practice. The goal of this project is to improve upon existing cloud technologies and design scalable and practical solutions for new and emerging problems.

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Faculty Supervisor:

Rei Safavi-Naini

Student:

Partner:

Inria Grenoble - Rhône-Alpes Research Centre

Discipline:

Computer science

Sector:

University:

University of Calgary

Program:

Globalink Research Award

Santé mentale globale: Accroître la capacité d’intégrer la santé mentale dans les soins primaires en Tunisie

En Tunisie, la majorité des personnes vivant avec des maladies mentales ne reçoivent pas de traitement. Le Ministère de la santé tunisien et le bureau de l’Organisation mondiale de la santé (OMS) à Tunis aimeraient offrir la formation mhGAP (développé par l’OMS) à des médecins
généralistes afin d’accroître leurs compétences en santé mentale et créer des services de proximité. Une étude randomisée en grappe sera menée pour évaluer l’efficacité de la formation implantée. Cent médecins généralistes travaillant dans quatre délégations seront recrutés. Le
bras d’intervention (deux délégations choisies au hasard) recevra la formation. Les principaux résultats incluent les connaissances et attitudes en santé mentale, ainsi que l’auto-efficacité des médecins généralistes dans la gestion des maladies mentales. Un volet qualitatif explorera
comment des facteurs contextuels peuvent influencer le succès de l’implantation de la formation et les résultats attendus. Cette étude est la première de son genre en Tunisie. Plusieurs contributions sont attendues: contribuer à l’avancement des données sur la formation mhGAP;
accroître la capacité de recherche en Tunisie; et complimenter les résultats d’une étude d’efficacité avec une évaluation d’implantation.

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Faculty Supervisor:

François Champagne

Student:

Partner:

Université de Tunis El Manar

Discipline:

Life Sciences

Sector:

Education

University:

Université de Montréal

Program:

Globalink Research Award

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