Projets novateurs réalisés

Affordable Housing Analysis

This project is a historical analysis of Federal/Provincial affordable housing agreements signed from 2000 to 2011 and whether the housing stock continued to be rented at affordable rates after the agreements expired. Federal/provincial housing agreements during the 2000-2011 period primarily consisted of capital grants to the private sector (for profit and non-profit) to develop affordable rental housing. Affordable was broadly defined as “median market or below”. Grant recipients signed agreements with the Province which required that rents remained affordable for 20 years. Many of these agreements have now expired.

For this project the priority for West Central Women’s Resource Centre (WCWRC) and Right to Housing (RTH) is to ascertain whether any of the housing units previously funded through Federal/Provincial affordable housing agreements between 2000 and 2011 continue to rent at affordable rates after the agreements expired. This could further make the case for the importance of ongoing investment in social housing. Manitoba Housing and Renewal Corporation (MHRC) has agreed to work with RTH to provide data to determine the status of units funded through these agreements. The housing researcher will collect and analyze data and prepare a report.

L2M-Autonomous Wildfire Aerial Control Solution

We address the limitations and hazards of current wildfire detection and suppression methods, focusing initially on Alberta’s boreal zone. Alberta Wildfire uses aircraft, helicopters, specialized equipment, and trained personnel, but these methods have critical drawbacks. Crewed aerial systems cannot operate 24/7, leaving gaps when fires can go undetected. Additionally, deploying personnel is expensive and not scalable for vast high-risk areas. Refueling aircraft and replenishing fire suppressants are also time-consuming, hindering rapid response. The core issue is the inefficiency of current systems in timely, cost-effective, and comprehensive wildfire detection and suppression. This problem is exacerbated by the increasing frequency and intensity of wildfires due to climate change, which disrupt ecosystems, endanger lives, and incur significant economic costs. Our solution transforms lightweight crewed helicopters into autonomous firefighting UAVs. By integrating commercial autopilot systems and a custom hardware-software interface with water supply equipment, we enable efficient and cost-effective autonomous firefighting operations. Eliminating the need for human pilots enhances safety and allows continuous coverage, even at night and in poor visibility. Our system enables swift early fire control by autonomously responding to wildfires, initiating firefighting measures before other resources arrive. Stationing these autonomous helicopters near potential wildfire areas ensures prompt response, reducing the risk of wildfire spread and enhancing overall wildfire management.

Image Processing Production Monitoring AI Camera

Working with AI, an image processing model will be developed to be utilized on site in sawmills to increase productivity and efficiency of the mill by using a camera connected to an AI model to detect irregularities in the production line. The project will begin with image collection and labelling then move into model creation and training. If time permits, a user interface will also be developed for operators to view and compare results. ** Need to note how this project will benefit Iron Code

MRI-microscopy co-registration and 3D reconstruction of human cortex and amygdala for multi-modal assessment of epileptogenicity in drug-resistant epilepsy

Through combining information from MRI and microscopy, this research aims to propose advanced computational methods for the registration, reconstruction, and analysis of brain images. To improve the accuracy and resolution of the combined brain scans, a key component of this effort is the application of statistical techniques to create correlations between the various imaging modalities. Our approach focuses on developing sophisticated reconstruction procedures to generate high-definition composite images, reliable image registration algorithms to precisely superimpose MRI and microscopy images, and analytical tools that take advantage of these correlations to extract more profound insights from intricate neurological data. By providing researchers and clinicians with better tools to study brain anatomy and function, this improved imaging and analysis capabilities will greatly advance the diagnosis and management of neurological disorders.

Integrating High-Resolution UAV Data and Machine Learning for Monitoring and Analyzing Soil Erosion in an Agricultural Landscapes

This project aims to improve the monitoring and management of soil erosion in agricultural landscapes using advanced technologies like combinations of optical and LiDAR data from remotely piloted aircraft with machine learning. By creating detailed, high-resolution models of soil erosion and deposition, the project will help quantify erosion processes more efficiently than traditional methods. The expected benefits to participating institutions include enhancing their research capabilities through collaboration, providing valuable data for conservation efforts, and helping them develop more effective strategies for soil and water management. This approach will improve the precision of erosion assessments based on remote sensing data, assist in identifying critical areas for intervention, and contribute to more sustainable land management practices

Energy efficient iGrow air circulation system for raspberry cultivation (2)

Despite being the world’s third-largest raspberry importer, Canada faces challenges like high local wholesale prices, erratic climate, pest issues, and labor costs. This project offers a breakthrough solution—an innovative, scalable indoor farm—to address these issues. The goal is to create a cutting-edge platform that overcomes horticultural challenges, empowers future growers, and transforms Canadian indoor farming using advanced technology and sustainable methods.

The primary focus is to optimize the microclimate of the existing iGrow platform by managing airflow and vapor pressure deficit, vital factors impacting crop yield and plant health. To achieve this, a skilled postdoctoral researcher experienced in designing airflow for engineering applications using open-source libraries will be engaged.

The current iGrow system utilizes a closed-loop design to efficiently remove moisture from plants and recover energy from light fixtures. This captured heat serves to warm plant roots and condition incoming air, boosting energy efficiency. The appointed postdoctoral researcher will redesign airflow components—supply plenum, nozzles, and return ducts—to ensure controlled vapor pressure deficit and optimal airflow distribution within the closed-loop system, aiming for maximum benefits.

By merging technology and sustainability, this project aims to revolutionize indoor farming in Canada, leading to improved yields, plant health, and knowledge sharing among future growers.

Projets exploratoires en géomatique municipale intelligente

Le mandat de l’UdeS dans cette collaboration avec la Ville de Sherbrooke visera à répondre aux trois objectifs spécifiques suivants :
? Identifier, appliquer et évaluer des réseaux de neurones profonds de segmentation sémantique pour classifier des images aériennes selon une taxonomie définie par la Ville, en fonction de ses besoins et du potentiel des images et de la méthode.
? Collecter les images par Edge Computing, définir une architecture rapide de détection d’objets par apprentissage profond, entraîner le modèle à identifier les places de stationnement (libres et occupées) et évaluer les résultats.
? Définir des critères de bonnes pratiques en matière de développement urbain, collecter et préparer les couches géospatiales nécessaires et appliquer l’analyse multicritère pour identifier les zones propices pour de nouveaux développements résidentiels.

Meningioma Quality of Life (QOL) Validation Study

Meningiomas represent the most common type of brain tumours arising from the brain itself, accounting for about 30% of cases. Effective treatments like surgery and radiation exist, yet patients may continue to suffer from symptoms that lower their quality of life (QOL). Currently used tools for measuring QOL are not specific to the unique needs of meningioma patients and may not accurately reflect the QOL issues most important to them. This project aims to improve the care of patients with meningioma by shortening and validating an existing patient-centered, meningioma-specific questionnaire that comprehensively evaluates QOL in meningioma patients to ensure it is an accurate and reliable tool for use in routine care.

Developing a Social Media and Digital Strategy for the RBC Patient and Family Learning Space

The RBC Patient and Family Learning Space (PFLS) at CAMH provides free mental health education, workshops, and support to patients, families, and the community. As our programming grows, we need a stronger digital presence to reach more people. This project, funded by Mitacs, will involve two graduate public health interns researching and developing a digital communications and social media strategy to expand our reach and improve accessibility. Their work will help PFLS engage more effectively with diverse audiences, ensuring more people can access our valuable mental health resources and support.

Familiarization with modeling of dust dispersion in air supported by relative dust concentration mapping with a LiDAR

The intern will familiarize himself with the official dust dispersion model (AERMOD) and the innovative dust mapping technology from the partner organization to combine them in a future project. This combination is expected to provide new capabilities to the industry by providing the visualization of its dust emissions in 3D over large areas. This will translate in a better understanding and control of the industrial dust emission sources in real-time. This in turn will allow the industry to significantly reduce its fugitive dust emissions that can be harmful to the health of citizen and the environment. The industries covered include mines, ports, bulk materials handling, foundries, wood, agriculture, and more. Finally, following the development of these new capabilities, the partner organization will commercialize the resulting solutions to different industries in Canada and around the world to significantly reduce industrial impacts of dust emissions.

Renforcer l’adoption des technologies de sûreté des personnes vulnérables de Sentiom par des stratégies de marketing innovantes

Ce projet vise à soutenir la mission de Sentiom en développant des stratégies de communication innovantes pour promouvoir ses solutions technologiques de sûreté et durables dans les résidences pour personnes vulnérables. En mettant l’accent sur l’innovation, le stagiaire contribuera à :
– La création de campagnes numériques ciblées utilisant des données analytiques pour maximiser l’engagement.
– La mise en œuvre d’outils interactifs sur le site web, tels que des études de cas dynamiques, pour mieux démontrer l’impact des solutions Sentiom.
– Le développement de contenu éducatif, tel que des guides sur les bénéfices environnementaux et sociaux des technologies durables, visant à sensibiliser les gestionnaires d’immeubles et les communautés locales.
Ces efforts permettront d’accroître l’adoption des solutions de Sentiom, réduisant ainsi les coûts énergétiques, améliorant la qualité de vie des résidents vulnérables et générant des bénéfices mesurables. En travaillant avec une équipe multidisciplinaire, le stagiaire acquerra des compétences en marketing numérique dans un contexte d’innovation technologique et contribuera directement à l’expansion de Sentiom dans de nouveaux marchés. Ce projet répondra aux besoins des résidences pour personnes âgées en offrant des solutions intelligentes et durables, renforçant ainsi les bénéfices économiques, sociaux et de santé pour les communautés canadiennes.

Augmented Reality Second Language Acquisition Support

This project aims to develop and evaluate an advanced augmented reality (AR) and artificial intelligence (AI)-based system designed to enhance conversational fluency for intermediate-to-advanced language learners. By integrating language practice into everyday environments, the system will offer immersive and interactive learning experiences without reliance on traditional classroom instruction or physical conversation partners. Existing literature indicates that AR-based language learning tools have been effective in increasing engagement, motivation, and retention for beginner-level learners. However, there remains a lack of research on AR applications for learners who possess strong foundational knowledge of a second language but struggle with practical conversational skills. This project will explore whether AR-driven interactive experiences can provide meaningful and situated learning and practice opportunities, particularly for individuals who lack regular access to native speakers or formal conversational practice. The research will contribute to the advancement of AI-driven educational tools, contribute to the space of AR interaction design, and support the creation of accessible and cost-effective language learning solutions. The project aligns with the research expertise and interest of the participating faculty members, who specialize in AR, AI, and Human-Computer Interaction. The project will promote international collaboration and add to the expanding field of technology-assisted language acquisition.