Projets novateurs réalisés

Explorez des milliers de projets réussis issus de la collaboration entre organisations et talents postsecondaires.

30 508 projets complétés

2882
AB
5105
C.-B.
825
MB
681
NL
860
SK
9051
ON
9491
QC
97
PE
586
NB
1141
NS

Projets par catégorie

Developing and evaluating an AI engine for branching narratives in serious medicalgames

People often find it difficult to motivate themselves to perform activities that are beneficial, such as exercising,
adhering to a diet, or educating themselves about their health. In contrast, digital games motivate players for hours
at a time. As such, there is increasing interest in serious games that motivate behaviour and retain attention in
serious contexts. Game-based narratives have been effective at educating and persuading players in serious games
for health; however, they are time-consuming to create as each game scenario must be scripted manually.
Furthermore, game narratives tend to unfold in a linear fashion regardless of player input, which reduces their
potential efficacy as they do not respond to the player, their state, or in-game decisions.
In this project, we will explore the use of artificial intelligence (AI) approaches to create branching narratives in
serious game design. We will create an AI engine that responds to player and system inputs and evaluate the
persuasiveness, engagement, and efficacy of the procedurally-generated narratives in the context of serious games
for health.

Voir la description complète du projet
Superviseur du corps professoral :

Regan Mandryk

Étudiant :

Partenaire :

MindLab Interactive AI Inc

Discipline :

Computer science

Secteur :

Professional, scientific and technical services

Université :

University of Saskatchewan

Programme :

Accelerate

Probiotic modulation of mast cells

Probiotics are bacteria that provide health benefits when eaten. However it is difficult to identify which bacteria may have beneficial properties and therefore identification of new probiotics is a time consuming and expensive process. In this project the intern will examine the effect of different potential probiotic bacteria on a specific cell type, the mast cell, which plays an important role in food allergy. The project will determine if screening for the ability of bacteria to prevent mast cell activation will identify probiotics that could reduce allergic responses. The project will also identify how certain bacteria interact with mast cells, knowledge that would allow for the development of more effective probiotics. The successful completion of this project will provide Lallemand Health Solutions with a way to accelerate identification and development of new probiotics and identify new potential health benefits of existing probiotics.

Voir la description complète du projet
Superviseur du corps professoral :

Paul Forsythe

Étudiant :

Partenaire :

Lallemand Bio Ingredients

Discipline :

Life Sciences

Secteur :

Agriculture; Manufacturing; Professional, scientific and technical services

Université :

McMaster University

Programme :

Accelerate

The Guelph Civic Accelerator

This project will evaluate the Civic Accelerator program in Guelph and research key features of the program. The Civic Accelerator is an innovative approach to economic development, using public sector procurement and challenge competitions to support “civic tech” entrepreneurs, startups, students and companies. This project will support the on-going development of the Civic Accelerator program in Guelph as well as adoption of the model more broadly. Specifically, this project will look at how topics for the challenges are identified, how the participating businesses are supported, how ideas are evaluated in the “challenge” and alternative ways in which the program could be delivered. Answers to these questions will directly inform future iterations of the Accelerator in Guelph as well as CODX’s efforts to seed “Open Data Innovation Challenges” in municipalities across Canada.

Voir la description complète du projet
Superviseur du corps professoral :

Harry Cummings

Étudiant :

Partenaire :

Innovation Guelph;Canadian Open Data Exchange;University of Guelph

Discipline :

Business

Secteur :

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

Université :

University of Guelph

Programme :

Accelerate

Biofuelnet Project 4I: Pyrolysis Of Residues From Well-Established Biochemical Processes For Biomass Conversion Into Liquid Fuel

This project will complete a life cycle analysis of the pyrolysis of sewage sludge as an alternative to incineration. Pyrolysis is the thermal decomposition of organic material in the absence of oxygen into a solid called biochar, a liquid called, bio-oil, and uncondensable gases. The uncondensable gases and bio-oil product can be used as a fuel to power the process or as a feedstock for valuable chemicals. The char is a carbon rich solid matrix that can capture and stabilise heavy metals contained in the sewage sludge and sequester carbon that would normally be released during incineration. The life cycle assessment with be performed using the cradle to the grave approach including any capital and ongoing costs as well as accounting for any environmental releases. This is the first study where the pyrolysis experiments and life cycle analysis will be completed by the same person.

Voir la description complète du projet
Superviseur du corps professoral :

Cedric Briens;Franco Berruti

Étudiant :

Partenaire :

Trojan Technologies;Western University

Discipline :

Engineering

Secteur :

Construction and infrastructure; Manufacturing

Université :

Western University

Programme :

Accelerate

Investigation and development of an air-core dry-type reactor noise prediction model

Air-core dry-type electrical reactors are integrated into power system infrastructures to limit current and regulate voltage in transmission lines. These reactors, are designed and built to facilitate customer specific requirements using an elementary noise prediction model, which was developed almost 30 years ago. With increasingly stricter noise emission guidelines set by the environmental regulatory bodies, the need to better predict and meet specific noise requirements has become more important to the design and manufacturing of the reactors. The objective of the research is to identify the fundamental structural and electrical mechanisms of noise generation for the reactors and to use this information to develop a more advanced noise prediction model. Having the ability to accurately predict noise emissions at the early design stage will not only allow Trench to meet the specific noise requirements for their customers, but also give them an important competitive advantage over other manufacturers.

Voir la description complète du projet
Superviseur du corps professoral :

Colin Novak

Étudiant :

Partenaire :

Trench Canada;University of Windsor

Discipline :

Engineering

Secteur :

Manufacturing

Université :

University of Windsor

Programme :

Accelerate

Net ecosystem exchange of carbon dioxide over agricultural fields near Lacombe, Alberta: data management and processing

Agriculture/ Agri-Food Canada (AAFC) and Campbell Scientific Canada (CSC) have been operating an “eddy covariance” (EC) meteorological tower near Lacombe, Alberta that measures the flux of carbon dioxide (CO2) between agricultural fields and the atmosphere. This tower provides data that is used to assess plant growth and decomposition across fields which is critical for understanding crop viability and the role of Canadian agriculture in the global carbon cycle. Several years of EC data have accumulated from the Lacombe tower, however AAFC/CSC do not have resources to verify these data. Using several software packages, I will correct several years of CO2 flux data from the AAFC/CSC tower. I will statistically compare flux calculations between each program and choose the most suitable software for AAFC/CSC to use for future data processing. I will then develop a software use manual to guide data processors to consistently calculate EC flux data for this station.

Voir la description complète du projet
Superviseur du corps professoral :

Vincent St Louis

Étudiant :

Partenaire :

Discipline :

Sociology

Secteur :

Information and cultural industries

Université :

University of Alberta

Programme :

Accelerate

Advanced cluster and predictive analysis tool development for corporate real estate energy usage

The objective of this project is to develop an analytics tool for REALPAC to use to better classify buildings using the “20 x ‘15” dataset collected by REALPAC since 2009. Preliminary analysis has been conducted of this data in past years, but this has been limited to a simple retrospective analysis. The tool that will be developed will incorporate “big data” techniques such as machine learning, which will allow the classification of buildings as “likely strong performers”, “likely poor performers”, “high probability for significant energy conservation”, and “low probability for significant energy conservation”. The intern will undertake data cleaning and classification tasks, as well as the development and testing of the predictive models and associated algorithms that will make up this tool. This tool, in turn, will provide REALPAC with a depth of insight previously unavailable to inform both public policy as well as corporate sustainability strategies of its member organizations. TO BE CONT.

Voir la description complète du projet
Superviseur du corps professoral :

Jenn McArthur

Étudiant :

Partenaire :

Real Property Association of Canada

Discipline :

Engineering

Secteur :

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

Université :

Toronto Metropolitan University

Programme :

Accelerate

Information Quality (IQ) assurance and control for the BIM lifecycle data

Managing complex, fragmented, and high volume portfolios of data that are generated during the lifecycle of buildings poses major challenges for the Architecture, Engineering, Construction, and Operations (AECO) industry. Required information during the operation and maintenance phase of a building’s lifecycle is usually lost (or not transferred) at information handover stages, and extensive rework should be done to revive them. This projects aims to identify lifecycle information requirements for the operation and maintenance of buildings. Additionally, an information quality management solution will be proposed to ensure the availability and the accuracy of the required data items throughout the lifecycle. The project outcomes will be used by the industry to improve processes related to asset management and the operation and maintenance of buildings. Additionally, newly devised processes help practitioners to ensure the quality of lifecycle data, to reduce the operation costs, and improve the functionality of buildings.

Voir la description complète du projet
Superviseur du corps professoral :

Daniel Forgues

Étudiant :

Partenaire :

Pomerleau

Discipline :

Engineering

Secteur :

Construction and infrastructure

Université :

École de technologie supérieure

Programme :

Accelerate

10 Channel Prototype to 16 Channel Medical Grade EEG Headset

Epilepsy affects an estimated 50 million people worldwide. These people can experience unexpected seizures that makes it risky for them to engage in everyday activities like driving and walking. A portable wireless neuromonitoring headset prototype that is worn on the head has been developed by Avertus Inc. to address this issue. The headset is designed to read brain waves, and, through a wireless connection to a cell phone, warn the wearer that the device has measured brain activity characteristic with an oncoming seizure. Improvements to the robustness and comfort of the headset are required to make it easier for epilepsy patients to wear and to help improve its accuracy of seizure prediction. TO BE CONT’D

Voir la description complète du projet
Superviseur du corps professoral :

Julie Audet;Martín Del Campo

Étudiant :

Partenaire :

Avertus Inc;University of Toronto

Discipline :

Engineering

Secteur :

Manufacturing; Professional, scientific and technical services

Université :

University of Toronto

Programme :

Accelerate

Indigenous Rights and Environmental Assessment in Canada

Environmental assessment (EA) – the federal, provincial and territorial policy planning tool for deciding on industrial developments – is contentious and under review. Despite increased consideration of Indigenous rights and title as part of the EA process, articulation of concerns with decision-making and significance determination is not evident. In the absence of alternatives, EA is coming to better reflect Indigenous governance and decision-making. Our proposal builds on Firelight’s expertise working for First Nations to assert rights and title in EA processes. An examination of Indigenous based EA is timely given an ongoing federal review of the Canadian Environmental Assessment Act. Under Firelight’s direction, Dr. Hoogeveen will examine innovative EA managed and implemented by Indigenous Governments. She will do this through an examination of Indigenous controlled models of EA through the analysis of four case studies. TO BE CONT’D

Voir la description complète du projet
Superviseur du corps professoral :

Terre Satterfield

Étudiant :

Partenaire :

The Firelight Group

Discipline :

Sociology

Secteur :

Professional, scientific and technical services

Université :

The University of British Columbia

Programme :

Elevate

Balancing costs and benefits of invasive species management for endangered wetland reptiles

Invasive species can have major effects on the landscape, but sometimes their effects are assumed to be negative before they are scientifically tested. The common reed is an extremely tall and robust grass that is moving rapidly into wetlands across Canada. Common reed is believed to threaten some reptiles by reducing their access to suitable habitats, but this has not been tested. In this project, we use state-of-the-art tracking equipment to directly test whether endangered turtles and snakes are forced to change their habitat use in areas impacted by the common reed. We also test the impact of current control measures for common reed (application of the herbicide glyphosate) by assessing chemical loads in our study wetland. Our research fills critical knowledge gaps that will allow managers to make informed decisions, balancing the benefits of controlling this invasive plant against the potential costs of chemical control.

Voir la description complète du projet
Superviseur du corps professoral :

Christina Davy;Joanna Freeland

Étudiant :

Partenaire :

Wildlife Preservation Canada

Discipline :

Life Sciences

Secteur :

Professional, scientific and technical services

Université :

Trent University

Programme :

Accelerate

Vision-Based Welding Control and Quality Assurance – Year two

Manual welding is a highly demanding task which requires extensive expertise for certain applications such as pipe welding. To facilitate the welding process, increase productivity, and decrease welder’s required level of skill, Novarc Technologies has designed and manufactured a collaborative Spool Welding Robot (SWR) equipped with a laser assistant weld path tracking. Our proposed research takes into account the fact that currently, welders rely on their eyes and the limited view of the weld pool through the helmet to control the welding process. This is a very arduous task which requires training and experience. Therefore, machine vision and AI can effectively take over the task of visual inspection and detection. Our goals in this industrial research are to use weld image for weld path tracking and torch distance control. We also aim to extract information about weld quality using computer vision and machine learning techniques.

Voir la description complète du projet
Superviseur du corps professoral :

Farrokh Sassani

Étudiant :

Partenaire :

Novarc Technologies

Discipline :

Engineering

Secteur :

Advanced Manufacturing; Technology; Automotive

Université :

The University of British Columbia

Programme :

Elevate

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