Innovative Projects Realized

The relationship between low back pain, pain related fear, and the quality of movement in low back pain patients

This study proposes to evaluate the quality of movement in patients with low back pain using a novel device. We are going to measure and compare the movement of people with back pain to that of healthy people. In addition, we plan on correlating the psychological factors associated with low back pain to tissue pathology. Many previous studies have used self-report measures of movement, and now we will determine whether our method of measuring movement correlates with these self-reports. If this new device developed by backtrack is able to measure back specific movement that other movement devices cannot pick up, it would become a vital tool in designing effective treatment protocols. If clinicians have a clearer idea of what is happening, where, and the perception the individual has of his pain, then treatments would become more effective

Industrial Applications of Frequency Comb Lidar

The goal of the proposed research project is to explore applications of frequency combs, for which laboratory applications have already been demonstrated, in an industrial setting. The research will be mainly focused on chemical sensing of stack emissions for monitoring purposes. As a secondary objective, the use of frequency combs in vibrometric measurements for structural health monitoring applications will also be explored. For the most promising applications, the project will be concluded with work on a development roadmap for a commercial platform. The proposed work will be beneficial for the partner organizations, as it will provide INO an opportunity to develop an expertise in frequency combs, a new and promising technology with a multitude of potential applications in remote sensing and frequency metrology. TCC will also benefit from the work in the form of new options and improved performance of their emission and structural health monitoring techniques.

Integrating Quantum Key Distribution (QKD) for Secure Communication in Distributed Systems

This project aims to explore the potential of Quantum Key Distribution (QKD) for enhancing secure communication in distributed systems. A QKD environment will be simulated and quantum-generated keys will be integrated into a secure communication protocol. The project will involve building a simulated QKD layer and applying it to encrypt and authenticate messages in a distributed application.

Risk Prediction Modelling of Adverse COPD and CVD Outcomes in Patients with COPD

Patients with chronic obstructive pulmonary disease (COPD) often experience adverse health outcomes including COPD exacerbations and cardiovascular events. These events impose a significant economic burden on the healthcare system and lead to impairment of patients’ wellbeing. This project aims to develop a clinical prediction model (CPM) to predict the risk of COPD exacerbations and cardiovascular events in the COPD population using a large, multi-country observational cohort dataset. This project is part of the NOVELTY (a NOVEL observational longiTudinal study) investigation involving patients with obstructive lung disease which aims to understand patient characteristics, treatment patterns and disease burden over time and to identify phenotypes and endotypes. This project provides opportunities for early treatment which can reduce the severity and impact of acute COPD or CVD events.

Efficient large scale quantum error characterization

Quantum computing holds great promise in solving some problems that are intractable to its classical counterpart; however, current quantum devices are prone to errors, especially as they scale up. Our project focuses on developing efficient tools to detect and analyze these errors; we will construct an error map that characterizes where the different errors occur in the system and how the errors propagate. These insights will help error mitigation, thus improving the reliability of quantum computers. Through this collaboration, the Center for Quantum Software and Information at the University of Technology Sydney and the Institute for Quantum Computing (IQC) at the University of Waterloo will make progress on quantum characterization and develop and improve methods to boost quantum computing’s practical applications, benefiting both institutions and the broader quantum community. In addition, the intern and her supervisor are well connected in the research community in Australia, so this cooperation would also strengthen the relationship between the research communities at IQC and Australia.

Enhanced Grid Stability and Power Management in Nova Scotia through AI-Driven Autonomous Voltage Control

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Validation of Water-Based, pH Neutral, Organic Redox Flow Batteries for Large Scale Energy Storage

THIS IS A GENERIC TEXT PUT IN PLACE AS THERE WAS NO PROJECT OVERVIEW

Better wind turbine forecasting; wind speed and wind turbine power output at single turbine spatial scale

Wind turbine generator power output and consumer electricity demand vary independently from one another. This presents a difficult situation for electricity grid managers as they attempt to exactly match demand using wind turbines and conventional generators (e.g. hydro, fossil fuels). Accurate forecasting of wind turbine generator power enhances management of the electricity grid, allowing for more wind turbine generating capacity while maintaining grid stability. This research will perform a sensitivity analysis on, calibrate and validate the newest high resolution wind power forecasting model for Atlantic Canada. Resolution of space and time have increased from 12 km and 60 minutes, representing an entire wind farm with one forecast point, to 0.1 km and 5 minutes, representing a single wind turbine in a specific topographic and surface roughness environment. Sensitivity analysis will compare the incremental gains in forecast accuracy due to increasing resolution. Calibration will be completed by forecasting a variety of wind turbine types and farms and comparing with actual performance data. Validation will insure accurate forecasting for a variety of conditions, topographies, and wind farm layout.

Modélisation et validation d’un échangeur de chaleur pour le stockage thermique de l’énergie solaire

L’énergie solaire est devenue très compétitive, mais l’inadéquation entre l’offre et la demande limite son utilisation dans certaines applications en raison du coût élevé du stockage. Pour résoudre ce problème, l’entreprise Énergie Solaire Innovatrice ISP travaille sur le stockage de l’énergie solaire sous forme de chaleur, afin de l’utiliser plus tard, soit directement sous forme de chauffage, soit indirectement pour produire de l’électricité. Pour générer de l’électricité, la chaleur est convertie en vapeur qui alimente une turbine à vapeur, permettant ainsi de produire de l’électricité.

Le sable représente un excellent candidat pour le stockage de chaleur en raison de ses propriétés thermiques et environnementales. Cependant, bien qu’il ait une bonne capacité de stockage thermique, le transfert de chaleur dans le sable reste relativement lent. Il est donc nécessaire d’utiliser un échangeur de chaleur pour faciliter la propagation de la chaleur dans le sable. Pour choisir le design optimal, un outil de simulation est utilisé pour prédire le comportement du système en étudiant le transport et le stockage de chaleur dans le sable. Cela permet d’optimiser la conception et de tester virtuellement la performance avant de passer à la fabrication.

Estimation de la pose de la caméra physique en production virtuelle

Cette recherche explore l’intégration des espaces physique et virtuel en production cinématographique. L’espace physique regroupe acteurs, décors, éclairages et caméras, tandis que l’espace virtuel crée des environnements numériques immersifs. Pour un rendu visuel réaliste, les mouvements de la caméra physique doivent être précisément suivis et reproduits dans l’espace virtuel. Actuellement, ce suivi repose sur des marqueurs réfléchissants détectés par des caméras infrarouges, une solution complexe et coûteuse, sujette à des problèmes comme l’occlusion et des recalibrations fréquentes.

L’objectif de cette recherche est de concevoir un système novateur basé sur l’analyse en temps réel des indices de profondeur et de mouvement, capable de déterminer la pose de la caméra avec une précision millimétrique et une latence inférieure à 8 millisecondes. Cette approche éliminera le besoin d’infrastructures lourdes, rendant la production virtuelle plus accessible. En améliorant l’interaction entre les espaces physique et virtuel, ce projet ouvre la voie à des outils créatifs pour le cinéma, favorisant une synchronisation fluide et en temps réel entre les caméras physiques et les environnements numériques.

Evaluating Greenhouse Gas Emissions in Sustainable Cropping Systems

Greenhouse gas (GHG) emissions, particularly nitrous oxide (N2O), pose significant challenges to sustainable agriculture and climate change mitigation. This project investigates the impact of crop management practices on N2O emissions and productivity in contrasting agroecosystems in Canada and Argentina. Using data from the Elora Research Station in Ontario and field trials in Balcarce, Argentina, the research evaluates the effects of crop rotations, tillage, nitrogen fertilization, and cover cropping. The project aims to identify sustainable nitrogen management practices that minimize emissions and enhance crop yields.

This collaboration between the University of Guelph (Canada) and Universidad Nacional de Mar del Plata (Argentina) bridges expertise in sustainable cropping systems and nitrogen management. By integrating datasets from diverse agroecosystems, the research generates actionable insights into reducing GHG emissions, improving nitrogen use efficiency, and fostering climate-resilient farming practices. The findings will benefit policymakers, researchers, and farmers globally, supporting the transition to sustainable agriculture.

Assessment of surgical tool inpainting techniques in epilepsy neurosurgery

Inpainting refers to the process of removing a foreground object from an image and seamlessly replacing it with consistent and contextually relevant background pixels. In the context of epilepsy neurosurgery, training videos are recorded using a microscope-mounted camera to document surgical procedures. However, surgical instruments frequently obstruct the camera’s line of sight, partially occluding brain tissue that is critical for assessing surgical techniques. This project aims to develop advanced inpainting techniques to remove surgical instruments from video recordings while preserving the anatomical integrity of the underlying brain structures. We will investigate state-of-the-art methods to ensure that the reconstructed regions maintain high visual fidelity without introducing distortions or artifacts. By integrating these techniques into our neurosurgery simulation platform, we aim to enhance the training and evaluation of surgical procedures. Additionally, this study will provide valuable insights into the feasibility of inpainting as a viable alternative within our current workflow, potentially improving the clarity and usability of surgical training videos.