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

Multi-Channel User Linkage through Probabilistic Matching

People utilize multiple devices to complete various tasks, making their online identities fragmented. Advertising is as much about knowing when not to promote a product as it is about when to do it. For example, before being sent alcohol and cannabis ads, the user must be identifiable as being over 19. Age information may only be available on a channel different than the one through which the user is connecting. This makes it difficult to gain a holistic understanding of users and develop a single marketing strategy across devices. Pelmorex Audience, mobile advertising division of Pelmorex Corp., would like to explore advanced large-scale probabilistic matching techniques to link different representations of the same user across channels, in order to create a master set of user profiles. This will enable them to enhance the user experience by limiting ad repetition on different environments, while also customizing ads to their interests.

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Faculty Supervisor:

Marsha Chechik

Student:

Partner:

Pelmorex

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Embedded sensor fusion network

Highly accurate 3D object detectors require significant computational resources, and reducing computation and memory load while maintaining the same level of performance is a critical task for any safe and reliable autonomous vehicle. This research project investigates the deployment of an accurate 3D object detection model to a resource constrained architecture by changing the model structure, its parameters as well as its activity during operation. Through a multi-level optimization, both the amount of computation as well as the memory load will be reduced while maintaining 3D object detection performance. The knowledge gained from this experiment will help the industry partner to develop new architectures for ever more complicated computational tasks.

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Faculty Supervisor:

Robert Laganiere

Student:

Partner:

Synopsys Inc

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Ottawa

Program:

Accelerate

Creating a Predictive Vegetation Model to Guide Wetland Restoration in the South Arm Tidal Marshes

I will be focusing my study on a small tidal marsh called Frenchies Island within the South Arm Marshes of the Fraser River, which has become overrun with an invasive species of cattail. Frenchies, like many tidal marsh islands has had a dike constructed around its perimeter and has therefore been cut off from the natural incoming of water from the tidal cycle, as well as from high flows of the Fraser river. The goal of this project is to create a predictive model to forecast the type of plant cover that is likely to grow on the site, once the invasive cattail has been weakened or eradicated on the site. The model will be created by first characterizing both Frenchies and areas that are not dominated by cattail (undisturbed sites) through ground surveys in terms of the distribution of elevation, soil salinity and plant cover. The results from my mapping of the non-disturbed sites will be applied to Frenchies Island to create the predictive model. TO BE CON’T

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Faculty Supervisor:

Anayansi Cohen-Fernandez

Student:

Partner:

Ducks Unlimited Canada (BC)

Discipline:

Earth science

Sector:

Finance and Insurance; Other services (except public administration); Professional, scientific and technical services

University:

British Columbia Institute of Technology

Program:

Accelerate

Technical Research on Unconventional Resources Development in Canada

Within this project, interns will review the peer-review literature on the four different topics: (1) Liquefied Natural Gas (LNG), (2) Hydraulic Fracturing, (3) Anomalous Induced Seismicity, (4) Surface and Ground Water access, transport, flowback chemistry, conservation, recycling, treatment and disposal after use in hydraulic fracturing operations.
The aim of the project is to perform the qualitative analysis of the listed subjects in order to compile scientific-based, unbiased information that will be used to update the partner website, presented at conferences as well as released to public in a form of bulletins. Moreover, student will be meeting regularly with the industry champions, to exchange the knowledge and discuss the most important aspects of the researched topics. The emphasis will be put on distinguishing true, scientific information from falsehoods and current misconceptions.

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Faculty Supervisor:

David Eaton;Mirko Van Der Baan

Student:

Partner:

Canadian Society for Unconventional Resources

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

University of Alberta; University of Calgary

Program:

Accelerate

Molecular effects of novel high-CBD Cannabis cultivars: from mechanisms to novel applications and therapeutics

With the legalization of cannabis, a serious issue facing Canadians is: how will consumers know their products are safe and created with their health in mind? Many Cannabis products claim diverse health benefits, ranging from treating pain to reducing inflammation and affecting tumor growth. While this is incredibly exciting, have all of these products been researched to ensure their claims are rooted in scientific proof? Presently, from a wide variety of strains that are being grown by licensed producers of medical cannabis in Canada, none have documented research-proven medicinal properties. What medicinal properties do these strains have? How do they affect tumor growth? Can they cause any potential adverse effects? How do they affect normal somatic tissues and organs as well as the central nervous system (CNS)? These questions remain to be answered. TO BE CONT’D

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Faculty Supervisor:

Igor Kovalchuk;Olga Kovalchuk;Robbin Gibb;Igor Kovalchuk

Student:

Partner:

Sundial Growers Inc

Discipline:

Life Sciences

Sector:

Agriculture

University:

University of Lethbridge

Program:

Accelerate

Linear Stability Analysis and Coherent Structure Identification for a Stratified Bickley Jet

This project will involve the application of the methods of linear stability analysis to a type of canonical stratified shear flow known as a Bickley jet in order to identify dynamically relevant processes (manifested as “coherent structures”) and gain insight into turbulent mixing in the oceans and other geophysical flow regimes. The objectives of the project will be to develop a linear stability analysis code which builds on and broadens the scope of previous work, determine unstable regions of the flow where a transition to turbulence might take place, physically interpret the structures identified through linear stability analysis, and investigate non-linear evolution using the DIABLO Direct Numerical Simulation code. The hoped-for outcome is insight into turbulent mixing, especially as it occurs in the oceans, which will help to improve, for example, climate-change modelling.

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Faculty Supervisor:

Qi Zhou

Student:

Partner:

University of Cambridge

Discipline:

Engineering

Sector:

Education

University:

University of Calgary

Program:

Globalink Research Award

Intelligence artificielle appliquée dans l’industrie du bois

La forêt est une ressource importante pour le Québec et le Canada. Des entreprises d’ici produisent les machines et outils nécessaires à la transformation du bois. Les moulins à scie modernes sont déjà hautement informatisés afin d’optimiser la valeur de la ressource sylvestre, mais il y a place à l’amélioration. Ce projet de recherche développe des outils d’apprentissage supervisé, une forme d’intelligence artificielle, afin de permettre aux machines d’être plus efficaces en devenant plus intelligente. Des modèles apprenant des erreurs passées pourront ainsi être intégrés aux machines afin d’ajuster leurs opérations. Certains défis doivent toutefois être relevés, par exemple, de bien gérer les erreurs de mesures présentes dans les données historique qui servent à l’apprentissage des modèles.

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Faculty Supervisor:

Jean-François Plante;Robert Platt

Student:

Partner:

FPInnovations (Québec, QC)

Discipline:

Computer science

Sector:

Manufacturing; Professional, scientific and technical services

University:

HEC Montréal; McGill University Health Center

Program:

Accelerate

Coalition and Spin-off for Frequent-flyer program

I intend to study the two trends in the industry of airlines: coalition and spin-off; I want to find out how the implementation of one affects the practice of the other as well as how they inform the airlines’ strategic decisions to maximize profit for the long term. To map out the relationship and interaction between the two trends, I plan to develop a conceptual model to simulate how the two trends can evolve for an indefinite period of time with the help of dynamic programming. After the development of the model, I would like to validate the model with the dataset that can be obtained from the labs at the Miami Business School. The trends of coalition and spin-off are expected to facilitate the realization of the operation goals of the airlines, depending on their internal resources and their interaction with the commercial partners.

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Faculty Supervisor:

Changmin Jiang

Student:

Partner:

University of Miami

Discipline:

Business

Sector:

Education

University:

University of Manitoba

Program:

Globalink Research Award

Designing Student Success: Building a Mobile Application to Improve Student Retention and Persistence

Ipse offers self-help to students transitioning to college or university to achieve their goals in a way that suits their personality. It uses machine-learning and crowdsourcing to recommend action plans to the students. The proposed research in collaboration with Ipse is aimed at furthering our understanding of personality traits and identification of suitable action plans based on those traits. Specifically we will survey a target population to identify common student traits and the associated action plans. We will propose advanced machine learning techniques to recommend an action plan based on a student’s personality. We will also explore various visualisation approaches to improve student participation. The research will allow Ipse to further develop/improve their product that would ultimately result in an engaged student population.

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Faculty Supervisor:

Yasushi Akiyama;Steven Smith;Pawan Lingras

Student:

Partner:

Ipse Media

Discipline:

Computer science

Sector:

Education; Information and cultural industries

University:

Saint Mary's University

Program:

Accelerate

Collaborative task completed by two small humanoid robots in a cluttered environment using real time stabilisation and 3D motion planning

In general, humanoid robots are more apt than other mobile robots to move in environments made for humans, their models. As such, they are better suited to be used in emergency situations caused by natural disasters happening in urban environments.
Developing collaborative systems would reduce the cost of robotic solutions, as less expensive robots could be used without reducing the overall performance.
The project aims to develop control algorithms allowing two humanoid robots to move objects in a cluttered environment. A previous project showed that it is possible for two humanoid robots using 2D motion planning to travel in a cluttered environment. Another previous project showed it is possible to move objects in a stable way by using a controller based on simplified physics models. The goal is to continue these projects and develop a real time stabilizer to improve the individual stability of the robots. It is also desired to improve the motion planning by achieving it in three dimensions instead of two. These improvements should allow motion otherwise impossible when planned in two dimensions, as done previously, as well as improve the overall performance of the system.

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Faculty Supervisor:

Wael Suleiman

Student:

Partner:

University of Tsukuba

Discipline:

Engineering

Sector:

Education

University:

Université de Sherbrooke

Program:

Globalink Research Award

Predicting Risk of Aggressive Responsive Behaviours among People Suffering from Dementia using Natural Language Processing (NLP) and Machine Learning (ML).

Patients with dementia will eventually experience significant loss of cognitive function. Many will have difficulty properly communicating life’s challenges and instead become agitated, resulting in verbal or physical aggression. Monitoring the risk of a resident harming themselves or others due to aggressive behavior is a priority within a long-term care facility where dementia is present. Caregivers at Shannex regularly record resident health and behaviour using computing systems. Each of these systems digitally record information either as structured data or unstructured text, providing an on-going log of each resident’s patient history. The objective of this project is to use natural language processing (NLP) and machine learning (ML) techniques to develop models that can predict the probability of a resident exhibiting aggressive behaviours that may harm themselves or others within the next week.

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Faculty Supervisor:

Daniel Silver

Student:

Partner:

Shannex Inc

Discipline:

Computer science

Sector:

Information and Communications Technology; Health and Related Sciences & Technology; Other

University:

Acadia University

Program:

Accelerate

Conception des infrastructures de transport sur pergélisol instable

Les forces armees canadiennes sont appelees a consolider leur presence eta intensifier leurs operations dans le Nord canadien. La plupart de ces operations requierent des infrastructures (batiments, chemins et pistes d’atterrissage) et une grande capacite de mobilite terrestre. Les environnements pergelisoles qui caracterisent une grande proportion du territoire nordique Canadien sont potentiellement tres instables et constituent une grande source d’incertitude pour le deploiement d’infrastructures et pour Ia realisation d’operations terrestres. Le projet propose porte sur le developpement de principes et de methodes pour Ia conception, Ia construction et l’entretien d’infrastructures de transport en milieux pergelisoles dans un contexte de changements climatiques. Le present projet pour sur les problematiques de mobilite sur terrain nature! et de deploiement d’infrastructures temporaires ou permanentes pour les operations de Ia defense nationale sur pergelisol sensible dans le Nord canadien.

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Faculty Supervisor:

Guy Dore

Student:

Partner:

SNC-Lavalin Group Inc (Montreal, QC)

Discipline:

Engineering

Sector:

Construction and infrastructure; Professional, scientific and technical services

University:

Université Laval

Program:

Accelerate

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