Innovative Projects Realized

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

29670 Completed Projects

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801
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663
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825
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8841
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9197
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95
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568
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1088
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Projects by Category

An Integrated Software Suite for Rail Condition Analysis using Machine Learning

Rail transit and freight rail properties apply rail grinding to maintain rail condition and ensure satisfactory performance of rail infrastructure systems. The proposed research investigates and applies a variety of computationally intelligent algorithms to establish useful relationships between rail corrugation, noise generation, and vibration. These relationships will support more timely and effective rail grinding interventions. The algorithms will process real-world rail corrugation, noise, and vibration data collected from three rail transit properties in North America. The long-term research goal is the development of a generic and transferrable rail corrugation index, which will help rail maintenance practitioners determine when rail corrugation is likely to generate unacceptable noise and vibration. Consequently, the research directly supports rail and vehicle asset management programs, helps reduce noise irritation for passengers and citizens in the vicinity of rail transit lines, and improves ride quality.

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Faculty Supervisor:

Ian Jeffrey;Jonathan Regehr

Student:

Partner:

Advanced Rail Management

Discipline:

Engineering

Sector:

Professional, scientific and technical services; Transportation and warehousing

University:

University of Manitoba

Program:

Accelerate

Exploring the effectiveness of a pilot parasport coach mentorship program.

Informal learning involves acquiring knowledge outside of a structured setting in which
learning is self-directed and developed from experience, exposure, and interactions with their
environments (Nelson et al., 2006). Examples of informal coach learning includes experience
as an athlete, coach observation, self-reflection, reading books, exploring the internet, and
learning from experts or mentors in the field (Fairhurst et al., 2017; Taylor et al., 2014).
Mentorship has been considered and utilized as an informal learning opportunity where coaches
seek out more experienced professionals in their field to shadow and learn from (Bloom, 2013;
Kram, 1985; Ragins & Kram, 2007). One of the first studies on coach mentorship in parasport
was conducted by Fairhurst and colleagues (2017) who interviewed six Canadian Paralympic
coaches on their experiences with formal and informal learning opportunities. The results
revealed that four out of six coaches had a mentor, three of which were informal relationships
and one from a formal mentorship program, and all coaches acted as mentors throughout their
careers. Coaches described learning highly-specialized parasport-specific skills from their
mentors, such as information pertaining to the physiology of their athletes’ disability and
developing a parasport training program, and considered this relationship to be their most
significant learning experience.

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Faculty Supervisor:

Gordon Bloom

Student:

Partner:

Coaches Association of Ontario

Discipline:

Life Sciences

Sector:

Arts, entertainment and recreation

University:

McGill University

Program:

Accelerate

Virtual Hamilton

This research project entails the development and user study assessment of a prototype participatory urban planning tool for the City of Hamilton (Virtual Hamilton), which integrates GIS data with a 3D virtual city model to facilitate stakeholder visualization, collaboration, content contribution, and communication. In consultation with City of Hamilton ward councilors and urban renewal, planning and economic development officials, Barton Street and Kenilworth Ave, areas of the downtown, slated for significant redevelopment activity, were selected as appropriate sites to be included in the development of a prototype virtual city model in partnership with McMaster University. The feasibility of developing the virtual city prototype has been considered and ensured by its limitation to a small subsection of the City of Hamilton and its utilization of a prepared virtual world platform and access to GIS expertise and data.

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Faculty Supervisor:

David Harris Smith

Student:

Partner:

City of Hamilton

Discipline:

Computer science

Sector:

Public administration

University:

McMaster University

Program:

Accelerate

Effects of sedation with dexmedetomidine-butorphanol and general anesthesia (propofol-isoflurane) on the Feline Grimace Scale scores

The Feline Grimace Scale is a validated tool that uses changes in facial expressions for acute pain assessment in cats. The tool is a valid, reliable, sensitive and specific for feline acute pain recognition for various types of painful conditions (www.felinegrimacescale.com). The aim of this research project is to assess the effects of sedation and general anesthesia on the Feline Grimace Scale scores in cats. Based on our clinical experience, we hypothesize that sedation and general anesthesia will significantly increase the FGS scores when scores are compared with saline (no sedation) or before general anesthesia (controls). This investigation could represent a significant advance in feline pain management, considering the residual effects of sedatives and anesthetics could be a major bias/confounder in pain assessment affecting clinical decisions in feline practice. Indeed, veterinarians do not always know if changes in facial expressions after surgery, for example, are due to pain or the administration of sedatives and anesthetics. This study may provide important insights in the field of pain relief contributing to feline health and welfare.

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Faculty Supervisor:

Paulo Steagall

Student:

Partner:

Federal University of Parana

Discipline:

Life Sciences

Sector:

Health and Related Sciences & Technology; Education

University:

Université de Montréal

Program:

Globalink Research Award

Detecting noise and artifact in CW ultrasound signal processing using machine learning and cloud-based tools

Continuous wave (CW) ultrasound systems are extremely sensitive to movement, noise, and artifacts of reflective tissues within the body that return doppler ultrasound signals to the receiver. In the application of CW ultrasound to clinical applications, classifying and handling noise/artifacts is essential for broad clinical adoption. A machine learning (ML) algorithm is commonly used for pattern recognition of large sets of data, such as physiological signals, and it has been used recently for biomedical applications. Moreover, cloud computing allows the execution of large and complex calculations without the need for expensive or dedicated hardware. The aim of this project is to develop cloud-based computing tools in the Python programming language that will annotate, classify, and label physiological signals. These annotations and labeled signals will then inform and feed digital and ML signal processing methods. Furthermore, process automation for new incoming data and quality assurance checks for the remotely acquired signals will also be developed as cloud-based tool.

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Faculty Supervisor:

Jeremy Brown

Student:

Partner:

Flosonics Medical

Discipline:

Engineering

Sector:

Manufacturing

University:

Dalhousie University

Program:

Accelerate

Enabling Transactive Energy Markets using Blockchain-Based Distributed Ledger Technologies

In this project, we propose the use of blockchains to provide reliability, security, and transparency for collecting and querying IoT data in the energy sector. In particular, we will implement and evaluate our blockchain solution for enabling transactions in local energy markets, where homeowners have the capacity to generate energy (through renewables), stock energy (through electrical vehicle batteries), and consume energy. We will investigate various energy-related use cases and assess their relevance for Hydro-Quebec, as well as determine the added value of using a blockchain solution. We will then build a prototype, using open source blockchain software, to realize a studied use case using smart contracts. Finally, we will evaluate our prototype using a combination of public and cloud networks, in order to assess the challenge gaps surrounding the current systems, and further identify further research opportunities to improve the applicability and reliability of blockchain technology.

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Faculty Supervisor:

Kaiwen Zhang

Student:

Partner:

Hydro-Quebec (Shawinigan, QC)

Discipline:

Computer science

Sector:

Professional, scientific and technical services; Utilities

University:

École de technologie supérieure

Program:

Accelerate

Highly active N-heterocyclic carbene-functionalized metal nanoparticles for CO2 fixation

Climate change has become a major concern over the past decades. In this context, one of the biggest issues is the amount of carbon dioxide (CO2) produced every year around the globe. In response, researchers have discovered creative solutions to recycle CO2 via a process commonly known as CO2 fixation. More recently, the use of CO2 as a C1 feedstock for the N-methylation of amine groups using a hydrogen source has emerged as an attractive avenue. Typically, methylation chemistry requires the use of toxic methylating reagents such as methyl iodide, formaldehyde, formic acid derivatives or dimethyl carbonate. Besides, the abundance of CO2 as C1 feedstock makes great advances toward a greener and safer methylation chemistry. However, for this reaction to be efficient and profitable, the use of a catalyst is critical. Metallic nanoparticles, more specifically gold- and silver nanomaterials, have attracted significant interest in the past decade as active materials for catalyzing numerous well-known reactions such as Suzuki, Heck or Sonogashira coupling reactions. Herein, we aim to develop normal and mesoionic NHCs-stabilized AgNPs and AuNPs and investigate their catalytic activities toward N-methylation of amines using CO2 and a hydride source. We expect to get TOBECONT’.

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Faculty Supervisor:

Ali Nazemi

Student:

Partner:

University of Central Florida

Discipline:

Physics

Sector:

Nanotechnology; Environmental Science and Technology; Sustainability & the Environment

University:

Université du Québec à Montréal

Program:

Globalink Research Award

Code F (stage 2) FSA UL

Dans un contexte de pandémie causée par la Covid-19, l’entreprise Code F. se doit d’opérer un pivot de son modèle d’affaires afin d’adapter ses solutions aux besoins de la clientèle et de sa communauté. Ce pivot de modèle d’affaires se traduisant par un virage technologique et numérique d’une entreprise d’économie sociale, ainsi que par une adaptation de l’offre de service de l’organisation, le stagiaire devra réaliser une analyse stratégique afin d’aider l’organisation à adapter ses activités et solutions dans le contexte actuel de pandémie (et pour le futur) et pour effectuer son virage numérique et technologique.

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Faculty Supervisor:

Margaret Schomaker

Student:

Partner:

Code F

Discipline:

Business

Sector:

Finance and Insurance; Professional, scientific and technical services

University:

Université Laval

Program:

Business Strategy Internship

Venture Analyst Intern

The Venture Analyst Intern researches markets to understand evolving customer needs, market size, trends, and competition. They work closely with Product Management and Business Development Managers to understand how our individual customer’s needs relate to broader market needs to help us build products that will achieve broad market success.

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Faculty Supervisor:

Michael Maier

Student:

Partner:

AltaML

Discipline:

Business

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

University of Alberta

Program:

Business Strategy Internship

5G-ELITE: AI-driven 5G Network Slice Operations & Management

5G mobile networks will support a wide range of services beyond traditional voice and data services, such as augmented/virtual reality, autonomous vehicle control, and smart cities, which will open new revenue streams for the mobile network operators. Network slicing is an enabling technology for partitioning the physical network into multiple network slices, each tailored to accommodate the requirements of these diverse services on the same physical network. As 5G networks are being deployed, effective performance and fault management for network slices is becoming increasingly important to the network operators. However, for large network operators like Rogers, traditional reactive human-in-the-loop management approaches cannot cope with the diverse, and stringent 5G service requirements. We propose an automated and proactive approach to network slice management, building on the advances in AI, ML, and large-scale data processing. The proposed research has great potential to materialize in Rogers Communications’ 5G networks, increasing their industrial competitiveness.

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Faculty Supervisor:

Raouf Boutaba;Nashid Shahriar;Raouf Boutaba

Student:

Partner:

Rogers Communications Inc.;University of Waterloo

Discipline:

Computer science

Sector:

Information and cultural industries

University:

University of Regina; University of Waterloo

Program:

Accelerate

Comparative Genomics Study to Identify Genes and Pathways Controlling Reproductive Longevity in Mammals

Reproductive Longevity (RL) is a complex biological trait under the control of many genes. In livestock, RL of female breeders is a key productivity factor, especially for dairy cows. The genes that determine RL in humans are potential sources of diagnostic and therapeutic innovations for fertility and reproductive health. The proposed research aims to identify RL-controlling genes in mammals using a unique model system, selectively-bred mouse lines that reproduce twice as long and have twice as many litters as matched control lines. The project Partner, Performance Genomics Inc. (PGI), is applying whole genome mapping and sequencing technologies sourced through service laboratories to generate complete genomic datasets for the select and control mice. The internship project will apply bioinformatics tools and approaches to “mine” the RL Mouse genomics data with the goal of selecting and ranking candidates for genes and mutations responsible for doubling of RL in the mouse.

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Faculty Supervisor:

Robert Beiko

Student:

Partner:

Performance Genomics Inc

Discipline:

Computer science

Sector:

Agriculture

University:

Dalhousie University

Program:

Accelerate

Advancing sustainable management of construction, renovation and demolition (CRD) waste

The waste generated from construction, renovation and demolition (CRD) activities accounts for around 27% of all waste generated in Canada. The large amount of CRD waste which could be prevented or recycled is sent to landfill without utilization, resulting in environmental problems and waste of resources. The aim of project is to provide the best management practices and policy recommendations for CRD waste management in Montreal West. The outcome of this study is expected to be used for supporting the strategic planning of CRD waste management to achieve sustainable development in Montreal West. The city of Montreal West will be able to make an informed decision when updating its residual materials management strategy, taking into account the life cycle of CRD residues.

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Faculty Supervisor:

Chunjiang An

Student:

Partner:

Ville de Montréal-Ouest

Discipline:

Engineering

Sector:

Public administration

University:

Concordia University

Program:

Accelerate

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