Projets novateurs réalisés

Manual 3D reconstruction using multiple RGB-D cameras and thermal cameras

The proposed project aims to develop a method for three-dimensional reconstruction of manual work using multiple RGB-D cameras and a thermal camera. This innovative approach will enable the digital archiving of intangible cultural heritage, such as traditional craft techniques and dance performances, which are currently predominantly documented in 2D formats. By integrating rich information about hand-object interactions and object shapes, the project will contribute to preserving and sharing cultural knowledge. Additionally, it will enhance the research capabilities of participating institutions by advancing three-dimensional archival techniques.

Mathematical Modelling of Counteracting Gait Asymmetries in Human Locomotion

This proposed project aims to develop a control model that can counteract gait asymmetries in human walking. Gait asymmetries can arise in two different scenarios. It can be introduced from the environment, such as uneven terrain or strong winds, that would cause the body to need to adjust its motion to stay balanced. It can also be introduced via an imbalance in muscle strength or weight. The first scenario is applicable to robotics, as more advanced robots strive to be able to manoeuvre in various environments. The second scenario is more applicable to healthcare, as it involves our internal body composition that can be affected by a wide range of diseases/disorders. Having a model that can counteract gait asymmetries will be beneficial for future research in the field as well and build on the current research that has already been done. The findings of this project can contribute to the design of walking assistance devices and prosthetics to improve fall prevention in elderly walking strategies as well as more sophisticated humanoid robots.

Interdisciplinary perspectives on participatory practices in visualizing older adults’ health information.

This Mitacs Globalink Research Award will support a Ph.D. student (Freeman) on a 24-week research trip to Sweden from 30 June – 15 December. Freeman will be hosted at Linnaeus University in Kalmar, Sweden, and will travel within the region to conduct interviews that will form a major part of Freeman’s thesis research, in collaboration with her research host. Freeman’s thesis explores how collaborative design approaches can lead to the development of effective health information-sharing technologies that improve the care of older adults. The primary contribution of Freeman’s research will be recommendations for preparing diverse collaborators to be effective co-design partners. Understanding what different stakeholder groups seek from a co-design partnership is foundational to Freeman’s research. In Sweden, Freeman will interview academics and professionals with experience in age care, information visualization and co-design at Linnaeus University and other institutions in the region. Co-design has conceptual roots in the Participatory Design movements of Northern Europe and Scandinavia. This region also hosts leading information visualization researchers and designers. Connecting with researchers and practitioners in this region will enrich Freeman’s thesis research and academic career. Freeman’s thesis research will guide future productive co-design partnerships, generating novel and functional designs for health information-sharing tools.

Valorisation des poussières de four à arc électrique (EAF) issus de la fabrication de l’acier

Ce projet de recherche vise la valorisation des métaux contenus dans les poussières d’aciérie du procédé EAF de l’usine de Arcelor Mittal à Contrecoeur. Ce produit pourrait éventuellement être exploitée comme un gisement de ressources secondaires afin d’en valoriser le contenu en métaux surtout au niveau du zinc. La stagiaire a jusqu’à présent procédé à la caractérisation de ces poussières et à fait de nombreux essais de mise en solution, de purification et de précipitation afin de produire un concentré de zinc et un concentré de plomb. Il reste néanmoins du travail à effectuer au laboratoire avant de procéder à l’écriture du mémoire en collaboration étroite avec le responsable du stage. Pour Arcelor Mittal ce stage sera l’occasion de connaître en profondeur le procédé qui vise la valorisation de ces poussières. L’entreprise pourra ainsi simultanément procéder à une évaluation du potentiel réel d’application de la technologie en développement.

Designing Machine Learning Models for Predictive Analysis of Pressure Drop and Temperature in Free-Breathing Polymer Electrolyte Membrane Fuel Cell Stacks to find Optimal Fabrication Parameters

Climate change has emerged as one of the most critical problems of the 21st century, driven by the rise in greenhouse gas emissions. The polymer electrolyte membrane fuel cell (PEMFC) is a device that converts chemical energy into clean electricity with zero emissions. The proposed research aims to utilize machine learning algorithms to predict the pressure drop and temperature in PEMFCs by using data sets related to Singapore’s state-of-the-art PEMFCs. By applying different data pre-processing methods, the predictive algorithms will identify optimal fabrication parameters and operating conditions such that these fuel cells operate at their highest efficiency, while reducing cost. As a result, this research will lead to the improvement of PEMFCs such that they can be a competitive alternative to fossil fuel-based energy systems.

The revolving door of reintegration: Understanding the experiences and challenges of previously incarcerated people in Ontario

The transition from incarceration to the community (i.e., reintegration) is often characterized by uncertainty, unease, and instability. While there is a strong understanding reintegrative barriers like a lack of safe and stable housing, employment, and social support, reintegration is often characterized as a series of distinct challenges that determine its success. This overlooks how previously incarcerated people’s interactions with services and supports, or lack thereof, are woven into how they understand and navigate reintegration. Using interviews and secondary documentation, this study aims to (1) identify previously incarcerated people’s needs during reintegration, and the barriers they face in fulfilling these needs; (2) understand the role of reintegrative services and supports, or lack thereof, in shaping their experiences post-release; and (3) explore reintegration as a relational process. Doing so will produce evidence-based research that can advise programs/services in the community and/or correctional facilities and contribute to policy and public education on reintegration.

Geovisual Analytics for Smart Cities: Designing and Developing a User Interface for an Immersive Environment

This proposal represents part of a doctoral thesis aimed at developing an interface for immersive environments, allowing for geovisual analyses targeted at smart cities, using digital twins to investigate flood-related data. The proper application of interface design in this environment enables an improved understanding and perception of emerging risks, which are crucial elements in flood risk management. For the 13 weeks of research, we propose planning, conducting, and analyzing interviews with Canadian users to identify the user profile in developed countries, aiming for a future comparison with the needs of users in middle-income countries. As a result, we expect to finalize the proposed research project with detailed documentation on the needs, functionalities, and specifications of users in this environment.

Stretchable Electronics for 6G Anomalous Reflectors

6G is the next generation of wireless network technologies. 6G signals are millimeter-waves, which are highly prone to blockage from obstacles such as trees or buildings. Due to this, behavior of the wireless environment is highly probabilistic and coverage holes can appear. This project aims to develop a reconfigurable intelligent surface (RIS) which has the potential to mitigate coverage holes and allow for the wireless environment to be more programmable by steering signals. The RIS being explored in this project is an elastic anomalous reflector which can be mechanically stretched to change the direction of beams reflected off the surface. This approach shows promise as it works for millimeter waves, has a fairly large steering range, and is low cost. This project explores optimizations to previous designs of the device in order to increase its beam steering range as well as its reflection efficiency.

This will benefit the Sanada Lab at Osaka University by furthering their progress in research on this RIS. This project will benefit the University of Toronto by bringing unique perspectives and knowledge on 6G technologies, RIS, and metamaterials that may help advance research in related areas.

Post-reform China’s Evolving Compensation and Resettlement Mechanism for Land Expropriation: A Case Study in Shanghai’s Urban Fringe Region

The research will portray the institutional evolution of post-reform China’s compensation and resettlement mechanism for land expropriation through a case study in Shanghai’s urban fringe region. The project will incorporate archive studies (Phase I), interviews with various stakeholders (Phase II), and ground surveys with affected rural households (Phase II). The study will not only probe the effectiveness of policy implementation, but will also contribute to the achievement of a more sustainable and just planning and development approach for cities in the world.

Shifting perceptions of substance use in northwestern Ontario

The Northwestern Health Unit (NWHU) aims to address substance use issues prevalent in its catchment area by integrating two projects. Firstly, targeting high alcohol consumption rates, the initiative will require behavior change strategies to combat the normalization of alcohol use. By tailoring interventions to diverse subgroups, NWHU endeavors to shift cultural attitudes, thus reducing associated health risks. Secondly, addressing misconceptions surrounding harm reduction, particularly in the context of drug use prevalent in the region, the project seeks to educate staff, stakeholders, and the public. By promoting evidence-based interventions, NWHU aims to mitigate stigma and enhance support for harm reduction programs. This holistic approach aims to foster healthier behaviors and attitudes, ultimately improving community well-being.

Innovation dans l’entraînement sportif: optimisation de l’interface d’un simulateur de boxe en réalité virtuelle

Ce projet de recherche a pour but d’améliorer un simulateur de boxe en réalité virtuelle afin d’aider les athlètes de boxe canadiens à s’entraîner de manière plus performante tout en réduisant les risques de blessures, telles que les commotions cérébrales. Le stagiaire sera responsable de l’évaluation de l’interface utilisateur du simulateur, en établissant une étude scientifique visant à comprendre l’expérience d’utilisation par des athlètes de haut niveau, des entraîneurs et des non-athlètes. En se fondant sur les retours des utilisateurs, le stagiaire proposera des suggestions d’améliorations pour rendre l’interface plus conviviale et mieux adaptée aux besoins de chacun.
L’organisme partenaire, l’Institut national du sport du Québec (INS Québec), bénéficiera de ces améliorations puisqu’il aura accès à un nouvel outil d’entraînement novateur qui répondra aux besoins des athlètes professionnels de boxe. Ainsi, il sera possible d’offrir une méthode d’entraînement plus sécuritaire, plus performante et plus efficace que les méthodes de boxe traditionnelles. En développant une solution qui améliore la performance des athlètes de boxe canadienne, l’INS Québec contribuera à accroître la visibilité et la réputation de la boxe canadienne sur la scène mondiale. Cela pourrait mener à davantage de succès sportifs et à plus de médailles.

Explainable time-series machine learning using the tsfresh module

The central idea of this project is the observation that publications using systematic time series feature extraction from the Open Source machine learning library tsfresh, always need to visualize and explain the extracted features. Due to the fact that tsfresh uses 168 algorithms to compute up to 800 time series features, this process can be highly automated using an object oriented approach. This project will contribute towards the development of a Python module for explainable time-series machine learning, which will assists researchers and engineers in the interpretation of their time-series based machine learning models.