Innovative Projects Realized

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

31620 Completed Projects

2978
AB
5221
BC
856
MB
696
NL
899
SK
9419
ON
9858
QC
98
PE
619
NB
1192
NS

Projects by Category

Investigation of Sustainable Community-Based Strategies for the Mitigationof the Environmental Effects of Oil Sands Tailings Ponds

The development of the Athabasca Oil Sands in Alberta has been a source of national and
global controversy in recent years. This is in part due to the impact it is having on the health
and way of life of locals and the creation of large toxic waste ponds. These ponds are known
to leak into the Athabasca River which carries the pollutants downstream. People living
downstream from the oil sands are then affected by the contaminated water and the wildlife in
the area becomes unsafe for consumption. This project seeks to find a way to mitigate the
harmful effects of oil sands operations on the downstream communities by targeting the
chemicals within the tailings ponds, preventing groundwater seepage and proper reclamation
of the tailings ponds.

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Faculty Supervisor:

Robert Fleisig

Student:

Partner:

Hatch Ltd

Discipline:

Engineering

Sector:

University:

McMaster University

Program:

Accelerate

Let’s Do This Together! Developing a Knowledge-based Documentary Media, Community of Practice and Institute at the University of New Brunswick

Academic institutions and funding agencies are increasingly asking their researchers to conduct more outreach activities, including knowledge-mobilization endeavours. However, researchers often lack training and/or resources to effectively communicate with non-academic audiences. Using the DOCTalks Guide: Cross-sector Collaborative Practices for Knowledge-based Documentary Media, we propose a DOCTalks Institute for Knowledge-based Documentary Media and an associated Community of Practice at the University of New Brunswick. This project will employ one PhD intern for 12 months to conduct primary research using face-to-face interviews and online surveys in order to a) Identify UNB??s policies and procedures to establish a DOCTalks Centre; b) Prepare and execute a strategic plan to operate and fund the DOCTalks Centre at UNB; and c) Promote the Centre to other UNB faculties and research services. To test the efficacy of the Centre, the intern will study the documentary media project referred to as “APPLIED CANNABIS” providing an ethnographic account of the social practices that emerge as participants engage with each other on a documentary film. This investigation will identify skills, resources, and funding opportunities that will help encourage other researchers to produce knowledge-based documentary media as part of a their knowledgemobilization activities.

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Faculty Supervisor:

Rob Moir;Paul De Decker

Student:

Partner:

DOCTalks Festival & Symposium Inc

Discipline:

Business

Sector:

Information and cultural industries

University:

University of New Brunswick

Program:

Accelerate

Development and Characterization of COVID-19 vaccine candidate

The goal of this study will be to characterize a SARS-CoV2 antigen and the formulated drug product that will contain SARS-CoV2 antigen and a squalene-based adjuvant under tight timeline to release the material for COVID-19 vaccine clinical trials.

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Faculty Supervisor:

Yi Sheng;Paola Battiston

Student:

Partner:

Sanofi

Discipline:

Life Sciences

Sector:

Pharmaceuticals; Biotechnology; Other; COVID-19 related Research and Solutions

University:

Seneca College of Applied Arts and Technology; York University

Program:

Accelerate

Deep learning approaches for semantic textual similarity on low-resource languages and specialized domains

The aim of this research is to investigate from traditional methods to deep learning methods, how to measure the meaning relationship between two sentences, by combining the local context, at word-level, and the global context at the sentence-level, and their ability to model informativeness and diversity of meanings expressed in natural language, i.e. in English or in French.
Moreover, as we are interested in Information Extraction of entities, concepts, triplet and semantic relation in unstructured text, we will adapt the BERT model for low resource domains and languages. Evaluations on the proposed model will be conducted by experimenting several specific in-domain versus out-of-domain open source datasets and comparing with the state-of-the-art approaches.

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Faculty Supervisor:

Fatiha Sadat

Student:

Partner:

Thales Canada Inc

Discipline:

Computer science

Sector:

Information and Communications Technology

University:

Université du Québec à Montréal

Program:

Accelerate

Insurance fraud detection in automobile insurance

We will focus on creating a series of time dependent models for detecting fraudulent claims depending on the level of dynamic information available, and fine tuning these models before testing them with live data and putting the retained models into production. Our objective is to better filter our actual label-claims to form a better control group on which we can train robust classifiers that will detect fraud. During the next months we are going to filter our data by 1) identifying business rules that trigger an automatic classification, and 2) delete variables that cannot be used to detect fraud. We will then apply robust classifiers to our new filtered data. During the last months of the project we will 1) identify relevant events threshold in a claim’s life that will trigger a prediction from the system, and 2) proceed to algorithm selection, fine-tuning, live tests, and putting the retained models into production

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Faculty Supervisor:

Georges Dionne;François Bellavance

Student:

Partner:

Intact

Discipline:

Business

Sector:

Finance and Insurance; Technology; Other

University:

HEC Montréal

Program:

Accelerate

Infection and Immunity Screening

The COVID-19 crisis in Canada has transformed from one of containment to one of mitigation, as the disease has begun to spread through the community, slowed by “social distancing.” An ideal mitigation strategy requires extensive testing to determine (i) who has COVID-19, (ii) who has recovered from it (presumably with immunity), and (iii) who has yet to contract it. The proposed research aims to use mass manufacturing methods to produce a diagnostic test cartridge for COVID-19 that can be produced in large enough numbers and for a low-cost to fill the enormous testing capacity required to stretch far beyond the limits of the current (centralized laboratory-based) system. This proposal forms an important part of a larger project to develop a rapid test for COVID-19 infection and immunity that can be operated in hospitals, doctor’s offices, businesses, airports, schools, homes, and beyond. Sci-Bots will be able to use the manufacturing methods developed as part of this project to mass produce test cartridges for sale to new and existing customer

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Faculty Supervisor:

Aaron Wheeler

Student:

Partner:

Sci-Bots

Discipline:

Physics

Sector:

Manufacturing

University:

University of Toronto

Program:

Accelerate

Exploratory pharmacokinetic and preliminary efficacy modelling of select orally administered antiviral compounds following DehydraTECH formulation enhancement

Researchers around the world are racing to find treatment solutions to combat COVID-19, the disease cause by infection of the novel coronavirus (SARS-CoV-2). The use of antiretroviral therapy has recently shown preliminary promise. However, a barrier relates to bioavailability challenges, i.e., poor uptake, of these drugs. Poor bioavailability limits drug utility which could be paramount in combating rapid health declines in COVID-19. DehydraTECH is a patented formulation processing technology developed by Lexaria Bioscience Corp that has been shown to enhance the body??s uptake of these drugs. In turn, the purpose of this study is to determine the plasma uptake of the lipophilic antiviral compounds darunavir and efavirenz with and without the DehydraTECH formulation. Through collaboration of Lexaria Bioscience Corp with the University of Windsor, two randomized placebo-controlled studies will be performed. Given positive results from this research, the Company will make its technology available to researchers throughout the world looking to maximize the effectiveness of their own drug investigations.

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Faculty Supervisor:

Anthony Bain

Student:

Partner:

Lexaria CanPharm ULC

Discipline:

Life Sciences

Sector:

Professional, scientific and technical services

University:

University of Windsor

Program:

Accelerate

Personnaliser l’accompagnement de consommateurs dans une plateforme numérique de coaching virtuel

Les Éditions Protégez-Vous souhaitent innover dans la diffusion de leur contenu afin d’accompagner les consommateurs dans leurs choix, en particulier avec une plateforme numérique de coaching virtuel. Une première phase consiste à proposer une plateforme avec le contenu du guide pratique « 100 gestes pour la planète » pour accompagner les consommateurs à faire des choix sensés et écoresponsables. Cependant, ce contenu doit être présenté de manière précise afin que le processus d’accompagnement se révèle efficace. Pour cela, des techniques d’intelligence artificielle seront explorées pour analyser les traces laissées par les utilisateurs dans la plateforme, et les transformer en actions en vue de sensibiliser ces utilisateurs à leur propre cheminement. Le stage a pour but de proposer un premier modèle d’analyse, qui pourra être testé plus tard avec un échantillon de consommateurs.

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Faculty Supervisor:

Laurence Capus

Student:

Partner:

Les Éditions Protégez-Vous

Discipline:

Computer science

Sector:

Information and cultural industries

University:

Université Laval

Program:

Accelerate

Mobile Data Usage & Signal Strength – Manage, Analyze and predict estimated data usage and signal strength to conduct automatic cause analysis using deep neural network and unsupervised learning techniques

Enterprise mobility management enables to collect various metrics from million of devices. This industrial research project focuses on identifying the key performance indicators and formulas to identify and predict coverage issues and identify data usage problems within a device. Using the key performance indicators, the intern will explore all feasible machine learning approaches. Final goal is to design an AI smart diagnostic system to detect the issue related to data usage and signal strength and conduct automatic cause analysis. This would allow the clients to act proactively by getting deeper insights in their mobile applications. The solutions aim to provide a data usage and signal strength diagnostic solution to help client avoid their business disruption due to issues in mobile devices. This will help the Canadian organization to minimize the maintenance cost while reducing downtime and minimal business disruption.

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Faculty Supervisor:

Murat Erdogdu

Student:

Partner:

SOTI Inc

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Transfert de données anonymes en mobilité à travers la « preuve à divulgation nulle de connaissance »

A-Malgam Technologies Inc. est une entreprise québécoise spécialisée dans le développement Blockchain et IoT pour la mobilité des données massives. Dans un souci d’offrir des solutions plus conviviales et sécurisées à ses membres-clients, A-Malgam souhaite explorer les capacités des technologies émergentes et ce qu’elles peuvent apporter lorsqu’il est sujet de transfert de données anonymes en mobilité. Le secteur des transports, et plus particulièrement celui de la mobilité des personnes, est un domaine où la diffusion d’équipements connec-tés, collectant constamment des données, est probablement la plus évidente. Avec cette démocratisation des systèmes connectés en mobilité, il devient de plus en plus important pour les intervenants de protéger la vie privée des citoyens. C’est afin de remédier à cette problématique que l’anonymisation des transferts de don-nées prend tout son sens. Malheureusement, les processus actuels de transfert de données n’offrent présen-tement aucun moyen viable pour lutter contre la problématique de traçabilité de transactions et sont le plus souvent vulnérables à plusieurs failles notamment la possibilité d’ajout d’émetteurs fautifs. Ce projet vise à créer un tout nouvel algorithme qui assure et certifie l’anonymat des données collectées.

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Faculty Supervisor:

Mohamed Mejri

Student:

Partner:

A-malgam Technologies Inc

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Université Laval

Program:

Accelerate

Methods for the Estimation of Traffic Matrices

The accurate knowledge of origin/destination traffic matrices allows network operators to efficiently
perform network management operations to maximize network performance (minimize network
congestion, average delay, jitter, energy consumption, etc.) and increase network reliability in case
of device failures and other exceptional events. However, traffic matrices cannot be directly
measured, and network operators, such Videotron, can only rely on estimations based on the
elaboration of a limited amount of information, such as partial peak flow, link utilization, SNMP link
counts, multicast group composition and multicast trees. The aim of this project consists in
developing an efficient method for the optimal estimation of traffic matrices, when both multicast
and unicast traffic is considered. In this way Videotron will improve the performance of network
management operations that are crucial for the efficient functioning of the network.

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Faculty Supervisor:

Brunilde Sansò

Student:

Partner:

Videotron (Montreal, QC)

Discipline:

Engineering

Sector:

Information and cultural industries

University:

École Polytechnique de Montréal

Program:

Accelerate

Development of a solution to assess the quality and to optimize AI-based video codecs

Current video codecs consider algorithms to analyze video imagery in order to find out which bits can be removed for file size reduction without subjective video frame degradation. Integrating AI with encoding process improves the quality of encoding and decoding. AI permits the software to proactively assess the quality of the encoded video before transmission. This allows the compressing system to detect and remedy any possible artifacts in the video frames. The main objectives of the company regarding this project can be summarized as 1) Quality assessment regarding the AI-based video codecs from the industry perspective. 2) Determination of codec capability of running on off-the-shelf hardware. 3) Evaluation of AI-based codecs optimization techniques such as vectorization, SIMD, GPU, etc. The enhancement in such technologies could improve the entertainment industry in the country and also potentially assist smart city projects in Canada.

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Faculty Supervisor:

Nizar Bouguila

Student:

Partner:

Avid Technologies

Discipline:

Computer science

Sector:

Information and cultural industries; Manufacturing

University:

Concordia University

Program:

Accelerate

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Mitacs — Return on Innovation

Mitacs is funded by the Government of Canada, the Government of Alberta, the Government of British Columbia, Research Manitoba, the Government of New Brunswick, the Government of Newfoundland and Labrador, the Government of Nova Scotia, the Government of Ontario, Innovation PEI, the Government of Quebec, the Government of Saskatchewan, and the Government of Yukon.