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
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825
SK
8841
ON
9197
QC
95
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568
NB
1088
NS

Projects by Category

Modeling Commodity Marketplace for Proof of Work Networks

Recent market instability, volatility, and Bitcion (BTC) halving event combined to create significant challenges for this sector, resulting in many hashing power producers being forced into bankruptcy. The sector has grown very rapidly and has been plagued with boom-bust cycles that have been difficult for producers to weather due to the lack of hedging tools/financial instruments at their disposal.
Pow.re Corporation offers clearinghouse-type services providing hashing power producers the ability to sell their risk to speculators. We are building an unprecedented clearing and settlement system for new financial instruments based off of the underlying commodity to proof of work networks. Concurrently, providing speculators with an ample arbitrage opportunity and the ability to hedge against unique market dynamics.
Digital asset issuance relies on proof of work consensus. Production capacity and value of goods produced of any one network participant is a multi-variable problem with continuous fluctuations.
The goal of this research is to model the market dynamics impacting these new instruments and designing levers to mitigate adverse effects.

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Faculty Supervisor:

Michèle Breton

Student:

Partner:

Pow.re Corporation

Discipline:

Mathematics

Sector:

Finance and Insurance

University:

HEC Montréal

Program:

Accelerate

Validating RSK as a molecular target for the treatment of cancer

Relapses in cancer usually result in the tumour becoming resistant to chemotherapy. This leaves a treatment gap that needs to be filled for these patients. One such cancer is triple negative breast cancer, this type of breast cancer is very aggressive with a high death rate. Dr. Sandra Dunn’s research group want to find new drug targets which we can then find drug inhibitors to use in cancer treatment. One such drug target is the protein RSK, this protein has been implicated in causing drug resistance and giving patients expressing this protein in their tumours a poor prognosis. We currently want to test the concept that inhibiting RSK2 in triple negative breast cancer cells will reduce growth and make previously drug resistant cells sensitive to chemotherapy again.

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Faculty Supervisor:

Sandra Dunn

Student:

Partner:

Center for Drug Research and Development

Discipline:

Life Sciences

Sector:

Manufacturing; Professional, scientific and technical services

University:

The University of British Columbia

Program:

Accelerate

Integrity Monitoring of Motion Estimation and Hazard Detection Algorithms in Environmentally-Impacted Scenarios

Imagine some of the difficult driving conditions experienced by vehicle operators. In these conditions, the sun might be blindingly bright, or the snow might obfuscate what is going on around the vehicle. Surprisingly, the sensors used by autonomous vehicles to understand the environment they are in suffer from similar effects. As a field, robotics has yet to tackle integrity monitoring of the sensors used in autonomous applications. In order to transport safety critical loads, such as people or train freight, it is imperative to know when the data the autonomous vehicle receives is corrupted by environmental effects and adjust the behaviour of the autonomous vehicle accordingly. In this project, we aim to develop an algorithm that uses a sensor’s datastream and the outputs of the algorithms that rely on that sensor’s datastream to monitor the integrity of the data collected by the sensor.

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Faculty Supervisor:

Jonathan Kelly

Student:

Partner:

Thales Canada Inc (North York, ON)

Discipline:

Engineering

Sector:

Professional, scientific and technical services; Transportation and warehousing

University:

University of Toronto

Program:

Accelerate

Awechigewin: Developing a Virtual Approach to Community-Based Planning with Michipicoten First Nation

The proposed research project will use a combination of Participatory Action Research and Indigenous Research Methods to create an online engagement tool to gather Michipicoten First Nation (MFN) member’s perspectives on draft planning strategies and policies regarding six priority areas. Engagement is a challenge for MFN as a displaced and widely dispersed community, challenges which are heightened by the COVID-19 pandemic. Online engagement is an important tool for reaching Michipicoten citizens on- and off-reserve, particularly during the pandemic. Findings from the intern’s research will be used by MFN to support their ongoing Community Land Use Planning Process. The research could also be use by other Indigenous communities to inform their planning and engagement efforts during the pandemic and beyond.

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Faculty Supervisor:

Janice Barry

Student:

Partner:

The First Nation of Michipicoten Cultural Association

Discipline:

Sociology

Sector:

Other services (except public administration)

University:

University of Waterloo

Program:

Accelerate

Dimension reduction for microbiome data

Vast amounts of new data, including sequencing data from microbiome, generated by researchers and industry present challenging problems for extracting meaningful information. The main goal of this project is to develop a convenient software package to reduce the complexity of microbiome data, and more generally any kind of datasets showing similar properties. As this tool will help to efficiently study imperfect and complex datasets which are ubiquitous in particular in biology and health, we will consider other potential applications, e.g. studying health records from intensive care units. A utilization in data collected from sensors present in connected objects could also be valuable. In a nutshell, this project will help analyse data for which there is no real alternative currently, and this applies notably to some of the newest kinds of data, which have important potential in biology, health and industry.

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Faculty Supervisor:

Khanh Dao Duc

Student:

Partner:

École Polytechnique

Discipline:

Mathematics

Sector:

Education

University:

The University of British Columbia

Program:

Globalink Research Award

Infrastructure Sensor-based Automated Driving: Development and Demonstration

In this proposal, we intend to answer how infrastructure sensors can be used for autonomous driving. Using infrastructure sensors make automated driving safer, more simplified, and cost effective especially for multiple autonomous vehicles operating in known environments such as large residential/commercial complexes and resorts. Infrastructure sensors replace the main onboard vehicle perception sensors with infrastructure sensors mounted on the side of the road, for example on light posts. The information sent by all the sensor nodes to the cloud is used to localize all the autonomous and non-autonomous vehicles, pedestrians, and all other objects on the road for path planning and path tracking of any number of autonomous vehicles on the road. To enable infrastructure sensor-based automated driving, we will extend our navigation, object detection/classification, and motion planning/control modules from vehicle-installed sensors to infrastructure-installed sensors.

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Faculty Supervisor:

Amir Khajepour;Ehsan Hashemi

Student:

Partner:

S2E Technologies Developments Inc;Canadian Urban Transit Research and Innovation Consortium (ON)

Discipline:

Engineering

Sector:

Agriculture; Professional, scientific and technical services

University:

University of Waterloo

Program:

Accelerate

Deuil et covid-19: besoins et interventions possibles

Le contexte des mortalités survenues pendant la pandémie actuelle semble constituer un facteur de risque majeur de complication du deuil, d’autant que les pratiques rituelles funéraires ont été empêchées ou réduites. Pour évaluer le deuil de ces personnes et élaborer de meilleures mesures de soutien à celles-ci, l’organisme Deuil-02 du Saguenay réalisera une étude quantitative et qualitative. Ainsi, une enquête en ligne assortie d’entretiens semi-dirigés seront menés par une étudiante en travail social. Avec les résultats de ce cette étude, l’organisme partenaire sera mieux en mesure d’accompagner les personnes endeuillées dans le contexte de la pandémie. Et l’étudiante pourra utiliser ces données probantes pour ses études supérieures; elle pourra également mieux connaître la réalité de l’intervention communautaire pour soutenir les endeuillés, notamment en temps de pandémie.

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Faculty Supervisor:

Jacques Cherblanc

Student:

Partner:

Deuil 02

Discipline:

Sociology

Sector:

Health and Related Sciences & Technology

University:

Université du Québec à Chicoutimi

Program:

Accelerate

Identifying and Modeling Families of Serial Tests

Ciena is a leading corporation supplying IT products. Before delivering any product, the company makes sure all products go through a set of tests which are recorded into log files. Operating in a crowded and highly competitive market, Ciena is continuously running after innovation for remaining highly competitive. Therefore, the company wants to increase their data analytical solutions capabilities by exploring the huge amount of log data that are continuously gathered. The diversity and the time evolving aspect of the generated log information yield complex and massive data which are difficult to handle. Digging into such data pose new challenges in elaborating effective mining algorithms. The aim of this project is to devise analytical tools to fully grab the massive heterogeneous generated data. To this end, we intend developing novel datamining / machine learning models that extract useful patterns that offer insight to track the status of products tests over different time stamps. The goal is to infer the hidden inter-dependencies between test cases and thus build a dynamic multidimensional heterogeneous network structures for mimicking the logs ecosystem environment to first identify families of tests. Then, model these families to finally predict early symptoms of failure products.

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Faculty Supervisor:

Mohamed Bouguessa

Student:

Partner:

Ciena Canada (Saint-Laurent, QC)

Discipline:

Computer science

Sector:

Information and cultural industries; Manufacturing

University:

Université du Québec à Montréal

Program:

Accelerate

The Economic Transitions of Refugees Resettling in Rural Nova Scotia Since 2015: Learning from Refugee Newcomers, Sponsorship Groups, and Employers

The project explores the facilitators and barriers to the successful economic transitions of privately sponsored refugees resettled in rural areas of Nova Scotia since 2015. While acknowledging differences in pre-migration experiences, we seek to better understand 1) the post-migration factors shaping their economic transitions, such as gender, parental status, race, age and health; 2) how refugees’ transitions are informed by cultural, intercultural, economic, and social variables; and 3) how resettlement by private sponsors in rural settings influences refugees’ economic transitions. A qualitative research design will be used to learn from refugees resettled in rural and remote areas of Nova Scotia, the volunteers who provide/d resettlement support, and the employers who hire/d newcomers. This study will help explain why privately sponsored refugees tend to integrate into the economy more rapidly and securely compared to government-sponsored refugees. This project is done in partnership with Syria Antigonish Families Embrace, SAFE, a community-based refugee sponsorship group established in Antigonish Nova Scotia in 2015.

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Faculty Supervisor:

Norine Verberg

Student:

Partner:

Syria Antigonish Families Embrace

Discipline:

Sociology

Sector:

Health and Related Sciences & Technology; Other services (except public administration)

University:

St. Francis Xavier University

Program:

Accelerate

Advanced Signal Processing and Machine Learning for Autonomous Screening in Unconstrained Environments

The COVID-19 pandemic has abruptly and undoubtedly changed the world as we knew at the end of the second decade of the 21st century. Of particular interest to this research program is development of advanced autonomous screening (thermographic and cognitive) systems via integration of signal processing and deep learning models to address the following three requirements and challenges of the project’s industrial partner: (i) Development of an AI-empowered avatar that autonomously performs their Cognistat assessments. The objective is to convert Cognistat technician administered solution to an autonomous AI-based solution; (ii) Addressing the industrial partner’s challenges for development of thermographic screening solutions in unconstrained environments for real-life environments such as clinics and hospitals, and; (iii) Validation of duplicated medical documents, which is a general challenge faced across the scope of the project. One specific requirement is development of an autonomous and AI-driven duplicated medical record matching engine.

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Faculty Supervisor:

Arash Mohammadi

Student:

Partner:

Novatek International

Discipline:

Engineering

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

Concordia University

Program:

Accelerate

Closing the Gap: A Study of Implementation and Effectiveness of the Youth in Transition (YouIT) Intensive Case Management Services

The proposed research will consist of an evaluation of both the implementation and effectiveness of the YouIT program for youth and young adults with mental health and addiction problems. An implementation evaluation will be conducted to document whether the program has been implemented as intended (based on best practices) and how the program is actually implemented. The outcome evaluation will examine the outcomes of clients over the course of their participation in the YouIT program. This evaluation will provide CMHA ways to improve program delivery and will identify changes to effectively provide ICM services to youth. An outcome evaluation will help the organization understand the effectiveness of the YouIT program. Through this evaluation, the organization will have a better understanding of the demographics and needs of their clients in the program. Deliverables for the project will include two written reports and a community forum.

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Faculty Supervisor:

Tim Aubry;John Sylvestre

Student:

Partner:

Canadian Mental Health Association (Ottawa)

Discipline:

Sociology

Sector:

Health and Related Sciences & Technology

University:

University of Ottawa

Program:

Accelerate

A Study of Sgr A*’s X-ray variability

This project will investigate the duty cycle of the Milky Way’s supermassive black hole Sagittarius A* (Sgr A*), by studying the Xray
features present in the inner regions of our Galaxy. The X-ray light emitted by Sgr A* over the last several centuries is still
propagating in the central molecular zone, generating X-ray echoes. The variability and the spectral properties of this reflected
emission reveals the existence of several short outbursts from Sgr A* over the corresponding period. Despite an increasing
precision in the description of these past events, their exact nature is still unknown. Our work will connect present-day monitoring
of Sgr A*’s flaring activity to reconstructed evidence of its past activity. This effort will help constrain the energy budget required
for each variability epoch, and its impact on the black hole’s environment.

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Faculty Supervisor:

Daryl Haggard

Student:

Partner:

Université Grenoble Alpes

Discipline:

Physics

Sector:

Education

University:

McGill University

Program:

Globalink Research Award

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