Innovative Projects Realized

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

29670 Completed Projects

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95
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568
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Projects by Category

Automated fault detection in commercial refrigeration systems

A refrigerant leak is one of the major contributors of commercial refrigeration unexpected breakdowns. Conventional methods of leak detection using physical sensors are expensive, have limited capability of square footage coverage, and incapable of detecting slow and progressive refrigerant leaks. Accordingly, the development of a smart leak detection system without the need to include additional physical sensors using AI models based on actual operating conditions could significantly reduce the overhead costs associated with system shutdowns and refrigerant fill-ups in grocery stores. The goal for this project is to analyze data collected from various systems, driving meaningful insights, and developing AI models for detecting refrigerant leaks in the form of anomalies. The outcome of achieving the project objectives would have a significant environmental impact, substantial cost-saving, and most importantly, reduce human efforts and erroneous leak maintenance and monitoring processes.

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Faculty Supervisor:

Ayan Sadhu

Student:

Partner:

Kalder at Neelands

Discipline:

Engineering

Sector:

Construction and infrastructure

University:

The University of Western Ontario

Program:

Accelerate

Connector theory for entropy inequalities

Quantum entropy inequalities fundamentally limit how information can be distributed in a quantum system. However, it is an open problem to list and show all the entropy inequalities for the quantum setting beyond 3-party systems, which has proven extremely challenging both numerically and analytically. As the number of qubits we can experimentally initiate and control grows, it is becoming increasingly important to understand a quantum system’s ability to store information about correlations between subsystems. This project aims to combine methods from quantum information theory and tensor networks in order to study the entropy inequalities for system sizes well beyond the reach of current numerical tools. This approach is based on the recently introduced connector theory, which provides a novel tool to coarse-graining many-body quantum systems through convex optimization while preserving certain properties of interest.

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Faculty Supervisor:

Graeme Smith

Student:

Partner:

Institute for Quantum Optics and Quantum Information – Vienna

Discipline:

Mathematics

Sector:

Quantum Science

University:

University of Waterloo

Program:

Globalink Research Award

Photosynthesis, stomatal conductance, and transpiration of strawberry plants.

The spectral composition of light-emitting diodes (LEDs) reportedly results in higher crop yield and reduced thermal damage to plants. Given that the stomata response represents a link between the plant and the outside environment, exploring the relationship between photosynthesis, stomatal conductance, and transpiration is paramount. This study will investigate the photosynthetic efficiency and stomatal conductance of strawberry plants to understand their plant development responses under specific environmental conditions. Using a combination of gas exchange measurements and leaf area analysis, photosynthesis rates (measured as net CO2 assimilation) and stomata conductance in strawberry plants exposed to established light intensities, humidity levels, and temperatures will be analysed. These findings will provide insights into the adaptability of plants to varying climates (strawberry, cannabis, etc) and can inform agricultural practices aimed at optimizing crop yield and quality.

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Faculty Supervisor:

Mark Lefsrud

Student:

Partner:

Ferme d’Hiver

Discipline:

Life Sciences

Sector:

Cannabis; Agriculture and Food

University:

McGill University

Program:

Accelerate

Energy Disaggregation over Large-Scale Appliances

Energy Disaggregation is to find the energy consumption of individual appliances from only a single measure of household electricity consumption. Accurate energy disaggregation helps identify major energy guzzlers in the house and motivates users to take proper actions for energy saving. To pursue aneasy-to-use and scalable solution to energy disaggregation for contemporary large-scale appliances, we have proposed a solution of semi-intrusive appliance load monitoring (SfALM). Nevertheless, it is proved to be NP-hard to solve the optimization problem and achieve high-precision energy disaggregation in SIALM. Thus, it may be not feasible to find the optimal solution in our case where the appliance number is large. Therefore, we are motivated to design efficient algorithms and validate our solution via highperformance computers and servers. After this project, we are expected to provide efficient algorithms to solve the optimization problem in our case and achieve high-precision energy disaggregation over contemporary large-scale appliances.

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Faculty Supervisor:

Kui Wu

Student:

Partner:

Huazhong University of Science and Technology

Discipline:

Computer science

Sector:

Education

University:

University of Victoria

Program:

Globalink Research Award

Seed funding: Feasibility study of MacDon datasets for machine learning development

This project aims to determine if MacDon’s existing data can be used to develop machine learning models for image segmentation. Image segmentation means identifying different objects in an image by assigning each pixel to a specific category. MacDon has lots of unlabelled video and image data collected from their farming equipment. Traditionally, large amounts of labelled data are needed to create effective machine learning models. MacDon tried to create synthetic (artificial) data similar to their real field data, but the models trained with this synthetic data did not perform well on actual field data. In this project, we’ll analyze both MacDon’s real and synthetic data, as well as their current models, to find out what improvements are needed. We will look at the quality, relevance, size, variability, and noise in the datasets. We’ll also investigate the consistency of the labels in the data. For the models, we will examine why they did not meet performance expectations and suggest improvements. The goal is to provide a detailed report with recommendations on how MacDon can improve their data and models for better image segmentation, laying the groundwork for future collaboration and development.

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Faculty Supervisor:

Christopher Henry;Shaowei Wang

Student:

Partner:

MacDon Industries Ltd.

Discipline:

Computer science

Sector:

Manufacturing

University:

University of Manitoba

Program:

Accelerate

Portrait de satisfaction, de motivation, de contraintes et de besoins en termes de loisir chez les Ahuntsicois et Ahuntsicoises.

L’arrondissement d’Ahuntsic-Cartierville et les organismes de loisir qui le compose aimerait produire un portrait des participants.es aux activités de loisirs offertes par le milieu associatif afin d’optimiser les services offerts par le milieu. Des questions relatives aux besoins, à la motivation, à la satisfaction, à la pratique et au processus d’inscription, pour ne nommer que celle-là, ont été identifiées par les acteurs locaux. Les objectifs de ce stage seraient de:
· Comprendre les croyances qui motivent les comportements de loisir, les contraintes qui encouragent ou dissuadent les comportements de loisir, et les comportements de loisir des habitants de l’Arrondissement Ahuntsic-Cartierville;
· Comprendre les significations du loisir et de loisirs variés dans diverses communautés culturelles présentes sur le territoire de l’arrondissement;
· Porter une attention particulière à certaines personnes appartenant à des groupes considérés comme minoritaires ou marginaux dans le contexte québécois, comme les personnes issues de l’immigration internationale, les personnes appartenant à des communautés culturelles minoritaires en territoire québécois, ou de la diversité de genre et sexuelle.

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Faculty Supervisor:

Jean-Marc Adjizian

Student:

Partner:

Ville de Montréal (Arrondissement d’Ahuntsic- Cartierville)

Discipline:

Sociology

Sector:

Public administration

University:

Université du Québec à Trois-Rivières

Program:

Accelerate

The contributions of plasma composition to neurovascular dysfunction in Parkinson’s disease

Parkinson’s disease is mostly known for the extensive loss of dopaminergic neurons in the brain that results in motor impairments in patients living with the disease. The causes for this neuronal death are under active investigation, and recent studies suggest that disease onset/progression could originate from or be aggravated by peripheral factors. Therefore, molecules located both inside and outside the brain could contributes to the pathology. In this project, we propose to investigate how plasma composition affects changes to the blood-brain barrier, neuroinflammation and neurodegeneration in a novel model of Parkinson’s disease. We will perform these experiments in a human brain microfluidic chip that reproduces the complexity of the blood-brain barrier in vitro. Successful completion of this project will shed new light onto the potentially detrimental effects of plasma molecules that accumulate in the blood of PD patients, and could lead to the identification of endothelial targets to develop new therapeutic strategies for people with PD.

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Faculty Supervisor:

Aurelie de Rus Jacquet

Student:

Partner:

Université de Lille

Discipline:

Life Sciences

Sector:

Health and Related Sciences & Technology

University:

Université Laval

Program:

Globalink Research Award

Multi-tiered Threat Intelligence Service Development for a Managed Security Services Provider

With the increasing complexity and frequency of cyber threats, Managed Security Service Providers (MSSPs) like ISA Cybersecurity must continuously evolve their threat intelligence capabilities. This project proposes the development of a Multi- Tiered Threat Intelligence Service (MTIS) to enhance the detection, classification, and response to diverse cybersecurity threats. This project aims to fortify ISA Cybersecurity’s ability to protect its clients’ critical infrastructure and data by integrating advanced analytics, threat intelligence, and real-time monitoring.

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Faculty Supervisor:

Ali Dehghantanha

Student:

Partner:

ISA Cybersecurity

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Guelph

Program:

Business Strategy Internship

Projets de mise en valeur du patrimoine québécois

Le présent stage a pour objet la mise en valeur du patrimoine québécois grâce au déploiement d’un ensemble de projets culturels au courant d’une année. En tant qu’entreprise se spécialisant dans la mise en valeur patrimoniale, Artéfact urbain met en place de nombreux projets tels que des expositions, des événements, des festivals annuels et des dispositifs numériques. Le stage organisé avec l’étudiante Amélie Nadeau a pour objectif d’intégrer les connaissances conceptuelles et académiques de l’étudiante en lien avec la conservation et la valorisation du patrimoine, tout en lui permettant d’étudier la démarche d’une entreprise spécialisée de manière concrète sur le terrain. Pour ce faire, l’étudiante participera à toutes les étapes des projets de l’entreprise pour une année complète. De cette manière, il lui sera possible de côtoyer les travailleur·euse·s culturel·le·s qui font partie de l’entreprise (et qui proviennent de disciplines et spécialisations variées), de rencontrer de’important·e·s acteur·rice·s participant à la sauvegarde du patrimoine québécois (issus des milieux politiques, économiques, municipaux, communautaires, etc.), de même que plusieurs membres des communautés qui participent de près ou de loin aux projets concernés.

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Faculty Supervisor:

Marc Grignon

Student:

Partner:

Artéfact urbain

Discipline:

Sociology

Sector:

Arts, entertainment and recreation

University:

Université Laval

Program:

Business Strategy Internship

Cross modality image processing in cardiac and spinal images

With the advances of medical imaging, accurate diagnosis has been significantly enhanced, especially when utilizing cross-modality imaging for complicated diagnoses such as the spine and cardiovascular system. However, cross modality image processing poses a challenge due to large amount of data generated. Computer Tomography (CT) and Magnetic Resonance Imaging (MRI) images, for example, have great capacity for screening, diagnosis, treatment and prevention of cardiac and spinal diseases. Gated cardiac MRI (Magnetic Resonance Imaging) or CT (Computed Tomography) sequences, for example, recorded from a complete cardiac cycle, contain 1500-5000 two dimensional images. The tools to handle these images are insufficient to best use the time of specialists such as radiologists and surgeons. . Development of an intelligent system to facilitate cross modality diagnosis and clinical monitoring will greatly increase specialist productivity and increase the information learned from each set of patient scans.

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Faculty Supervisor:

Manas Sharma

Student:

Partner:

Victoria Hospital Imaging Associates

Discipline:

Life Sciences

Sector:

Health and Related Sciences & Technology

University:

Western University

Program:

Accelerate

Machine Learning developer intern working within cross-functional teams to develop and commercialize AI-powered solutions in the Public Services Healthcare sector (1)

AltaML builds artificial intelligence (AI)-enabled solutions to business problems. We work with organisations, bringing together their data and domain expertise with our AI expertise, to develop AI solutions that are deployed in their operations. We also commercialize AI-enabled products business via industry-specific ventures, yielding scalability from our investment in the first solution. Competition for tech talent is fierce, and our talent strategy includes a talent accelerator program, designed to rapidly equip highly qualified individuals with hands-on work experience in applied AI while providing partners with continuous and cost-effective development of AI solutions. AltaML’s AI Lab for Government, also known as GovLab, is a talent accelerator for public service professionals, post-secondary students and recent graduates. GovLab.ai’s mission is to set a global example of how to transform the public sector through applied AI, and is designed to encourage the growth of technical and business AI skill sets that are in high demand across Alberta and around the world. The project comprises internships in a variety of technical and business roles within our organization and within our GovLab program. Within the organization, roles include associate machine learning developer, business development associate, communications associate and finance associate. Within GovLab specifically, roles include associate machine learning developer, associate business solutions consultant, and project delivery associate. The difference is mainly that in GovLab, there is a focus on public sector problems, whereas in AltaML overall, we work across sectors.

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Faculty Supervisor:

Scott Yam

Student:

Partner:

AltaML

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

Queen's University

Program:

Business Strategy Internship

Analyse des politiques de substitution aux biosimilaires des payeurs publics canadiens. Mise en oeuvre de ces politiques et estimation de l’impact financier

Le projet consistera à revoir les politiques de substitution aux biosimilaires dans l’ensemble des provinces canadiennes et de relever les différences et les similitudes entre elles. De même, les politiques canadiennes seront comparées avec celles disponibles ailleurs dans le monde (par exemple : Royaume-Uni et France). Par la suite, le projet visera à estimer les économies réalisées ou non attribuables à la mise en place et à l’instauration des politiques de substitution aux biosimilaires dans l’ensemble des provinces canadiennes. À notre connaissance, ces informations ont été publiées uniquement pour la province de la Colombie-Britannique pour une période allant de 2020 à 2022 (1). Finalement, le projet permettra à Teva Canada Innovation d’évaluer l’impact financier associé à la mise en oeuvre des politiques de substitution aux biosimilaires pertinents pour l’entreprise et de le comparer entre les provinces et les autres pays susmentionnés.
Référence (1): Patented Medicine Prices Review Board. Biosimilars in Canada: Policies to Promote Switching and What It Means for Payers [Internet]. [cité le 27 juin 2024]. Disponible: https://www.canada.ca/content/dam/pmprb-cepmb/documents/npduis/analyticalstudies/posters/2023/biosimilars-policies-promote-switching.pdf

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Faculty Supervisor:

Alice Dragomir

Student:

Partner:

Teva Canada

Discipline:

Life Sciences

Sector:

Professional, scientific and technical services

University:

Université de Montréal

Program:

Accelerate

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