Innovative Projects Realized

Participation et bien-être des utilisateurs du bras JACO

Le bras robotisé JACO de Kinova Robotics® fait l’objet de recherche en continu afin de couvrir toute l’étendue des connaissances sur ses impacts sur les utilisateurs au Québec. Ce projet se donne comme objectif de réaliser une analyse secondaire d’entrevues précédemment menées dans la province de Québec auprès de 21 utilisateurs du bras robotisé JACO afin de décrire leur niveau de participation à toutes sortes d’activités et d’identifier les impacts de l’utilisation de cette technologie sur leur bien-être. Les résultats de cette analyse permettront d’élargir nos connaissances concernant les impacts de cette technologie en matière de participation et de bien-être des utilisateurs de cette technologie.

Investigation of state-of-the-art tools and methods for analysis of PTSD using neurovisualization

This project investigates existing and potentially new approaches to analyze functional brain structures in individuals with post-traumatic stress disorder (PTSD), using advanced neuroimaging such as fMRI, and machine learning. The goal is to identifying patterns built on dependent features within the images representing the connectivity matrices of brain regions. The project will investigate whether causal AI approaches such as Granger causality, as well as probabilistic graph models such as Bayesian networks can be used to analyze probabilistic dependencies between brain regions.

Making the Invisible Visible – Translating Groundwater Science and Engineering to Ensure Canada’s Water Security

One third of Canadians drink groundwater, yet the threats to groundwater remain ‘out of sight, out of mind’ for most Canadians and decision makers. Small, rural communities and First Nations disproportionately rely on groundwater as a drinking water resources but often lack the resources to advocate for effective policy that ensures groundwater security. Although groundwater is Canada’s largest freshwater resource, it is significantly underrepresented in national policy and management frameworks.

The MITACS intern will work between Dalhousie University and the International Association of Hydrogeologists – Canadian National Chapter (IAH-CNC) to conduct applied research on effective means of translating relevant groundwater science into educational and policy material. Key knowledge translation and science mobilization priority initiatives of the IAH-CNC will be advanced through the associated policy and management products produced. The intern will work closely with senior hydrogeology consultants, government professionals, and academics within IAH-CNC membership to develop and refine groundwater management or policy recommendations, conduct stakeholder meetings, develop groundwater communications material, prepare an opinion editorial or review paper, and attend/lead key water-related conferences events to strength networks.

The search for novel enzymes capable of biodegrading polystyrene, polyethylene, and polyvinyl chloride

Petroleum-based plastics are recalcitrant and pervasive environmental contaminants. Recycling only recovers 9% of post-consumer plastic, the rest is landfilled, incinerated, or discharged into the environment. While most plastic recycling methods convert plastic into lower value products, biocatalytic plastic depolymerization to component monomers then available for resynthesis of valuable products is an attractive alternative. Biocatalyst-based recycling is already used to recycle polyethylene terephthalate (PET). However, we do not have promising enzyme candidates for recycling for the most abundant plastics: polyethylene, polystyrene, and polyvinyl chloride which collectively account for ~45% of plastic waste produced annually. The homoatomic C-C backbone of these polymers renders them more resistant to degradation than the heteroatomic backbone of plastics like PET. Enzymes capable of oxidatively cleaving C-C bonds are of particular interest in development of biocatalyst-based recycling of these plastics. Laccases are a key enzyme family that cleave C-C bonds and that have the potential to impact C-C backbone plastics. The overarching goal of the Mitacs Globalink research project is to identify novel laccases by synthesizing and screening functional metagenomic libraries from diverse environments, and preparing these novel laccases identified for expression and purification to assess their suitability as biocatalysts for industrial recycling of C-C backbone plastics.

L2M – PORT-EM: Smart Port Energy & EV Infrastructure Management

The transition to low-carbon ocean economies requires innovative energy solutions for ports and coastal communities, where electrification of ships, ferries, and vehicles is accelerating. Building on prior research in probabilistic EV charging demand forecasting, energy consumption modeling, and emissions analysis, this project proposes a Smart Port Energy and EV Infrastructure Management Platform. The system integrates machine learning algorithms (LSTM, Random Forest, SVR etc.) to forecast highly variable port energy demand while incorporating tidal, offshore wind, and wave energy generation models. Coupled with embedded controllers for real-time load management, the platform enables demand balancing, peak reduction, and emissions minimization. Unlike existing generic energy management tools, this solution is purpose-built for the multi-source, high-variability environment of maritime ports, directly linking EV adoption with the ocean economy. The project will validate both the technical feasibility and market potential of ocean-focused smart grid solutions, advancing sustainable port operations in Atlantic Canada and beyond.

L2M – Medication Adherence Device

Older adults have difficulty keeping track of the frequency of their dosage as well as opening the medication packaging. Nonadherence to prescribed medication can lead to hospitalization which increases the burden of care both for the patients and the medication system. Our solution, Pillsette, is a smart medication adherence device that offers a reusable pill storage unit that can be operated manually or automated, and a companion app or SMS agent that alerts the patients (or their caregivers) at the time for the medication.

Jumeau Numérique: Profil d’échange thermique de la cuisson du sucre à la crème

NASUCO CANADA est une entreprise québécoise qui se spécialise dans la production, la transformation et la commercialisation de petits fruits, notamment des bleuets sauvages. L’entreprise souhaite optimiser son procédé de fabrication afin de soutenir sa croissance et améliorer la qualité de ses produits. Un des besoins essentiels de l’entreprise est d’optimiser le temps de cuisson du sucre à crème.
Le système de contrôle thermique automatisé (PID) actuellement en place repose exclusivement sur la mesure de la température. Cette approche, limitée à une plage restreinte de températures, ne tient pas compte de l’apport calorifique spécifique de la vapeur sous pression dans le processus de cuisson. Face à cette contrainte, plusieurs entreprises du secteur contournent le dispositif de régulation en utilisant l’interrupteur de pression de sécurité comme mécanisme de contrôle. Bien que cette pratique permette une amélioration ponctuelle de l’efficacité, elle engendre une grande variabilité du procédé et pose d’importants enjeux en matière de santé et sécurité.

Nos observations sur le terrain démontrent par ailleurs l’existence d’un profil de température particulier lors de la cuisson du sucre à la crème. En effet, les phases d‘homogénéisation des ingrédients, d‘ébullition de l‘eau et de cuisson du sucre vont chacune influencer, le taux de gras, de sucre et d‘eau dans le mélange, ce qui impact directement la chaleur spécifique du sucre à la crème. Lorsque le profil de chaleur spécifique change, la sensibilité de la pression à l‘intérieur de la double parois de la marmite passe d‘un état stable à un état instable en quelques secondes, rendant les paramètres de commande standard inadéquat dans une situation ou dans l‘autre, ce qui explique les limites d’un système de régulation basé uniquement sur la température pour assurer une gestion optimale et stable du procédé.

L2M – AI-optimized RF energy harvesting and Simultaneous Wireless Information and Power Transfer (SWIPT) platform

Ocean technology companies and research organizations rely on autonomous sensors, buoys, and communication nodes to monitor marine environments and support offshore operations. While these devices provide essential data, their dependence on batteries creates high servicing costs, logistical challenges, and limited deployment lifetimes in harsh marine conditions. Overcoming these limitations is critical for enabling sustainable, long-term ocean monitoring.
This project will develop a wireless RF energy harvesting and Simultaneous Wireless Information and Power Transfer (SWIPT) system tailored for marine applications. By delivering both power and data wirelessly, the approach aims to eliminate battery dependence, reduce costs, and enable continuous, scalable sensor operation. Unlike incremental improvements in battery technology, this solution offers a transformative approach to powering remote ocean platforms.
The research objectives are fourfold: (1) model and simulate RF energy harvesting and SWIPT systems; (2) design machine learning algorithms for adaptive impedance matching, beamforming, and scheduling; (3) integrate RF and ML simulations to test system performance under dynamic marine conditions; and (4) validate the framework using representative marine sensor case studies. In parallel, the project will explore biodegradable housing materials to reduce environmental impact and align with sustainability goals.
By combining RF engineering, AI-driven optimization, and application-focused validation, the project will deliver a proof-of-concept system that advances ocean monitoring capabilities. The outcomes are expected to reduce operational costs, extend device lifetimes, and strengthen Canada’s leadership in ocean innovation, supporting applications in fisheries, offshore energy, and climate research.

L2M –Next-Generation Marine and Offshore Components via Canadian High-Entropy Alloys and Laser Powder Bed Fusion (LPBF)

This project focuses on developing next-generation marine and offshore components using high-entropy alloys (HEAs) derived from Canadian resources and manufactured through Laser Powder Bed Fusion (LPBF), an advanced metal additive manufacturing technology. The marine and offshore oil and gas industries operate in some of the world’s most corrosive and mechanically demanding environments, where corrosion, fatigue, and biofouling severely limit component lifetimes and drive up maintenance costs. Conventional materials, including stainless steels, bronzes, and nickel alloys, offer only partial solutions and cannot meet the complex design and durability requirements of modern marine systems.

To address these challenges, this project introduces a two-stage innovation framework. The first stage focuses on designing HEAs using strategic Canadian elements, niobium (Nb), aluminum (Al), copper (Cu), and iron (Fe), to achieve outstanding resistance to corrosion, fatigue, and biofouling. These alloys are engineered at the microstructural level to withstand harsh seawater exposure, reduce maintenance intervals, and improve energy efficiency. The second stage leverages LPBF additive manufacturing to fabricate intricate geometries such as pump blades, valve housings, impellers, and fluid-handling components that are impossible to produce by traditional methods.

The integration of HEA alloy design with LPBF manufacturing will establish a Canadian-led innovation framework that links domestic materials resources with advanced manufacturing expertise. This project not only addresses critical needs in marine and offshore industries but also strengthens Canada’s position in the global blue economy by developing sustainable, high-performance solutions from homegrown materials. The outcomes, optimized alloy compositions, validated manufacturing parameters, and demonstrated prototypes, will provide a foundation for future industrial adoption, supporting Canada’s economic growth, technological leadership, and environmental resilience.

Analyse et traitement de nuages de points massifs sur GPU

Ce projet de recherche vise à améliorer le traitement des données 3D produites par des capteurs comme le Lidar, utilisés pour cartographier des environnements en haute précision. Ces capteurs génèrent des milliards de points, appelés nuages de points, qui sont aujourd’hui difficiles à analyser rapidement et efficacement.
L’objectif est de développer des outils capables de traiter ces données en temps quasi réel, directement pendant leur acquisition, grâce à la puissance des cartes graphiques (GPU). Cela permettrait d’accélérer les processus industriels, de réduire les coûts et d’améliorer la qualité des résultats, même avec des données incomplètes ou bruitées. Le projet est centré autour du recrutement de Léo Arnal–Anger, doctorant à l’Université de Toulouse, qui effectuera un stage à l’Université de Sherbrooke. Il sera encadré par deux professeurs, experts en géométrie 3D et en calcul haute performance. Ensemble, ils développeront des algorithmes innovants et publieront leurs résultats dans des journaux scientifiques de premier plan. Ce projet renforce la collaboration entre les laboratoires STORM (France) et SHERGRAPH (Canada), tout en formant un jeune chercheur dans un domaine technologique en pleine expansion. Il ouvre la voie à des applications concrètes dans les villes intelligentes, la préservation du patrimoine et l’ingénierie des infrastructures.

Building a comprehensive online library of various state-of-the-art algorithms in network science

The study of complex systems using networks has become a central theme across disciplines, from social sciences and economics to biology and information systems. Graph theory, which models entities as nodes and their pairwise relations as edges, has provided a powerful framework for analyzing such systems. Graph-based methods have enabled advances in tasks such as community detection, link prediction, and anomaly detection.

The main objective of the internship is to develop and expand a comprehensive online library that consolidates algorithms for mining graphs and hypergraphs, with the focus on synthetic graphs and hypergraphs. This platform will function as a central resource for researchers and practitioners, improving usability, reproducibility, and collaboration across the community.

Financializing Justice? The Politics of Climate Finance in Colombia’s Energy Transition

This project explores how countries are managing the shift from fossil fuels to clean energy, with a focus on just energy transitions that aim to provide an equitable transition for workers and local communities. The research focuses on Colombia’s national state-led just transition plan. By conducting on-the-ground fieldwork, the project will examine whether the financial models used in the just transition plan lead to fair outcomes for local communities, particularly those in areas reliant on coal. The insights gained from this study will help inform better policies that promote equitable and democratic energy systems. This research will also establish a new, long-term partnership between Queen’s University and the Universidad Nacional de Colombia, enabling future collaborations on critical global climate challenges.