Innovative Projects Realized

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

29670 Completed Projects

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

Projects by Category

Using semi-supervised learning for classification of sport images

Artificial intelligence (AI) is rapidly becoming one of the most critical aspects in both business and science, and an increasing number of leading technology companies in Canada are at the forefront of AI development and innovation. The proposed research project aims at developing AI algorithms that have the ability to accurately classify the sport practiced or the sporting equipment present in an image. This project is broadly in line with the AI Strategy by the federal government to make Canada a global AI research powerhouse, and seeks to develop automated image classification models that will help increase the visibility and competitiveness of Décathlon Canada, a worldwide leading company in the sports market.

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Faculty Supervisor:

Abdessamad Ben Hamza

Student:

Partner:

Décathlon Canada

Discipline:

Computer science

Sector:

Retail trade

University:

Concordia University

Program:

Accelerate

An ultra-small vital sign monitoring multi-sensing platform

This collaborative research project between iMD research and prof. Benoit Gosselin aims to design and test an tiny, inexpensive and easy-to-use wearable multi-sensing platform to continuously monitor patients remotely, and help greatly to manage COVID-19. The envisioned platform will use CMOS custom integrated circuits and advanced packaging technology to achieve an extreme level of miniaturization. This design will combine ultra-small electronic components available off the shelf (COTS) and custom integrated modules and continuously measure temperature, heart rate (HR), blood pressure (BP),
oxygen saturation (SPO2) and respiratory rate (RR) remotely, within a very compact device of extended battery life. ASICs and ultra-small COTS will be combined and interconnected together using an advanced heterogeneous chip integration strategy based on silicon interposers. The power consumption of the whole multi-technology microsystem will be very low, within a few tens of microwatts, in order to extend the battery life.

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Faculty Supervisor:

Benoit Gosselin

Student:

Partner:

iMD Research

Discipline:

Engineering

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

Université Laval

Program:

Accelerate

Scene Graph Image Interpretation Tools

In this project we will look at tools to represent the content of an image and the relationships between its salient objects. The purpose of these tools is not only to enumerate the object represented in an image and identify their surroundings but also to describe how these entities are interacting with each other. We will do so in too ways; first we will look at methods to detect these entities in the image and then parse them into triplets. A triplet is formed of two objects (nodes in the space) and their relationship (a labeled connection in the space). We will make sure that the method that we propose permits to establish the explainability of the results given the inputs. Second, we will look into a method that parse these triplets into sentences that represent the corresponding images captions.

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Faculty Supervisor:

Philippe Langlais

Student:

Partner:

Thales Recherche et Technologie

Discipline:

Computer science

Sector:

Management of companies and enterprises; Manufacturing; Professional, scientific and technical services

University:

Université de Montréal

Program:

Accelerate

Creating Safer Cities for Salmon: A case study of how Indigenous, Federal, Provincial, and Municipal policies align with standards and objectives of the Salmon-Safe eco-certification in Vancouver, BC

This proposed research project will undertake a review of Indigenous, federal, provincial and local government policy in order to identify areas of alignment with Salmon-Safe BC’s Urban Standards and overall program goals. The Salmon-Safe Standards are designed to help mitigate the impacts of urban development to watersheds through the application of five core standards and two context-specific standards at the site level. Currently, little is known how an eco-certification specific to salmon habitat and watershed protection aligns with governmental policy. Gaining a deeper understanding of alignment between Salmon-Safe eco-certification standards for urban site development and the standards and regulations set by government policy, could allow for more efficient use of resources and expertise to promote better building compliance and protect salmon in BC.

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Faculty Supervisor:

Zafar Adeel

Student:

Partner:

Fraser Basin Council

Discipline:

Earth science

Sector:

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

University:

Simon Fraser University

Program:

Accelerate

AAV Vectored Immunoprophylaxis for Prevention of SARS-CoV-2 in the Elderly and Immunocompromised

As of May 1st, 2020, over 3.2 million cases of COVID-19 have been confirmed, resulting in over 233,000 deaths globally. This project aims to provide an alternative vaccine for the prevention and treatment COVID-19 in high risk individuals, mainly the elderly and immunocompromised, who do not respond well to tranditional vaccination. Using a single viral vector platform, we will deliver broadly protective monoclonal antibody genes isolated from human survivors of COVID-19 to provide sustained levels of protection against SARS-CoV-2 infection for all individuals, particularly the elderly and immunocompromised. This project will help Avamab Pharma Inc. bring its first AAV VIP therapy to human clinical trials, with the ultimate goal of providing high risk patient populations with an urgently needed prophylactic against COVID-19.

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Faculty Supervisor:

Sarah Wootton

Student:

Partner:

Avamab Pharma Inc

Discipline:

Life Sciences

Sector:

Professional, scientific and technical services

University:

University of Guelph

Program:

Accelerate

Measurement-based Optimal Dispatch of Distributed Energy Resources in the Power Distribution System

The integration of significant capacities of distributed energy resources (DERs) such as renewable wind and solar generation for a more sustainable energy future creates several challenges to the reliable and efficient operation of power distribution systems. These include: (i) Uncertain and intermittent nature of renewable generation compromises power quality for end-customers. (ii) Up-to-date distribution-system network topologies are not well known and their real-time monitoring is limited. As a result, effective management of DERs is challenging. (iii) Accurate DER control may require solving complex optimization problems.
To this end, the goal of this research project is to study distributed measurement-based methods to design DER management systems by developing equivalent network-sensitivity models of distribution systems from real-time measurements.

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Faculty Supervisor:

Yu (Christine) Chen;Liwei Wang

Student:

Partner:

Enbala Power Networks Inc

Discipline:

Engineering

Sector:

Professional, scientific and technical services; Utilities

University:

The University of British Columbia

Program:

Accelerate

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

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