Projets novateurs réalisés

L2M – Enabling knowledge transfer between science education and coastal communities by leveraging generative AI and climate science publications

There are over 250 million scientific publications and reports with an increasing rate published each year, yet many are not accessible to the public due to their technical language and content hidden behind paywalls. This project aims to leverage AI (Artificial Intelligence) and a curated database of ocean-climate literature to enable educators and students to think critically and ask challenging questions about how climate change will affect their communities, as well as empower them to engage with and apply scientific knowledge towards climate solutions. But before we start designing a system, we need a real-world understanding of who educators are and what barriers exist with finding evidence. With secondary and post-secondary educators in Atlantic Canada as an initial population, a study focusing on the context of actors and the socio-cultural-organizations is needed to understand information needs, work demands, perceptions, and how they currently search for information.

Fast Scenario Identification and Classification

Self-driving cars represent a transformative innovation in transportation, promising safer and more efficient travel. However, their development faces significant challenges, including accurate prediction, path planning, and safe maneuver execution, especially under varying driving conditions. Ensuring safety across all potential scenarios within the operational design domain is paramount. To effectively address this, we propose developing algorithms that are both cost-effective and low-latency. This would enable faster processing and identification of specific scenarios that are rare or of particular interest to autonomous vehicle developers.

Optimisation et contrôle des flux d’air dans les bâtiments du CIMEC

In recent years, factors like thermal comfort, indoor air quality, and energy consumption in school buildings have received increasing attention. Lecture theatres and chemistry labs pose significant challenges for HVAC design due to thermal stratification in lecture theatres and ineffective ventilation in chemistry labs, caused by large spaces and pollutant generation, respectively. Despite many studies on these topics, there’s a lack of research on thermal stratification in cold climates like Quebec, where the issue is severe in winter, and on the effectiveness of local ventilation methods in chemistry labs.
To address these challenges, this project will conduct field tests and numerical simulations to analyze air distribution in an existing lecture theatre and a planned new chemistry lab in a Quebec college. The goal is to optimize indoor environment conditions and energy efficiency for both spaces. The outcomes will enhance thermal comfort, energy efficiency, and air quality, providing guidance for the design and control of air distribution, aiding renovations and new developments. This will help schools in Quebec and across Canada achieve healthier indoor environments, better thermal comfort, and greater energy efficiency.

L2M QC 2024 – “Brave Technologies : A new breast cancer diagnostic device”

Breast cancer remains a major global health concern, with high prevalence rates in new case diagnoses across the world. The primary challenge in breast cancer detection centers on achieving early and precise diagnosis as actual methods have limitations. The primary challenge in breast cancer detection lies in achieving early and accurate diagnosis. Current methods face significant limitations, including difficulties in accommodating the variability in tumor characteristics such as size, shape, and location. Our system offers substantial opportunities to address these issues effectively by providing a non-invasive, patient-friendly, and precise detection method, particularly advantageous for women with dense breast tissue. By introducing our device to the market and building trust with customers, we will be able to detect breast cancer at early stages, providing patients with a better chance to fight and overcome this disease, thereby contributing to reducing mortality rates caused by breast cancer. The main objective of this project is for the intern to conduct an in-depth market study in the Final Users, private sector, and non-profit entities segments for the potential applications of the BRAVE System, an innovative, accessible, and affordable breast cancer detection method specifically designed for women with dense breast tissue

Lossless Ethernet

Telecom operators are demanding more flexible, scalable and energy efficient products. As a system integrator managing networks for operators, Ericsson realize that the benefits of using modern and improved systems is becoming even more critical, as they offer a reduced Operational and Capital Expenditure (i.e. OPEX and CAPEX) of Information and Communication Technology (ICT) systems. The goal of this project is to develop a mechanism to prevent congestion in the network, prevent packets from being dropped and to guarantee delivery of packets between collaborating end-hosts. For such mechanism to be used in a high traffic network, a number of characteristics must be evaluated against a number of constraints and in different network deployment scenarios

Visual Regression Testing of Video Games Using Foundation Models

During visual regression testing of video games, automated techniques are employed to identify visual bugs. Visual bugs can be detected by just looking at them, such as those related to texture and lighting, but also more complex ones like those related to the physics engine (such as a flying horse) that require common-sense reasoning. The impact of visual bugs varies from minor irritations to rendering a game unplayable. Although human experts can often easily spot these bugs, automated detection is challenging. As a result, visual regression testing usually requires significant resources for manual testing.

This project proposes leveraging foundation models to address the visual regression testing problem. Foundation models are large-scale machine learning models that are pre-trained on very large amounts of data from different domains. This project investigates how foundation models can be leveraged and fine-tuned for visual regression testing of games that were published by Electronic Arts (EA), a world-leading game publisher with over 8,000 game makers. Automated techniques for detecting visual bugs in video games would help reduce the cost of testing for EA. The project’s goal is not to replace testers but to help them find more bugs in less time.

L2M-Enhancing Fertility Treatment: A Mobile Application Utilizing Behavioral Change Strategies

A fertility app is proposed to help couples assess their risk of infertility, create personalized behavior change programs, and track fertility-related biomarkers. Personal, health, and lifestyle information will be collected and synced with smart devices to monitor menstrual cycles, ovulation, stress levels, heart rate, sleep patterns, and physical activity. Data will be continuously analyzed by AI algorithms to predict how lifestyle changes can improve fertility markers. Advanced features will include the integration of blood test results and tracking of basal body temperature and cervical mucus to enhance predictive accuracy. Personalized diet, stress management, and exercise plans will be offered, incorporating exercise psychology principles to improve adherence and fertility outcomes. Additionally, educational content and community support will be provided, empowering users with knowledge and fostering a supportive community. Healthcare appointments can be tracked, and prompts for questions to ask healthcare providers will be given. The partner organization will benefit from this comprehensive tool by supporting individuals in their fertility journey, improving reproductive health outcomes, and gaining valuable insights through data collection and analysis.

Bourse postdoctrale RSMC-Mitacs-iA en santé mentale au travail automne 2024

Le travail agricole comporte certaines contraintes contextuelles relatives, entre autres, à l’économie, aux conditions climatiques et à l’environnement. Ces contraintes de travail engendrent un stress intense chez les propriétaires d’entreprises agricoles. En effet, selon une étude canadienne publiée en 2016, près de 60 % d’entre eux était à risque de vivre de la détresse psychologique. Cette étude n’a toutefois interrogé que 10 agriculteurs québécois (moins de 1% des participants). L’étude la plus récente portant sur la santé mentale des agriculteurs québécois remonte à près de 20 ans et montrait que 49,5 % des participants avaient un niveau de détresse psychologique élevé. Une recherche quantitative sera donc menée pour mettre à jour ces connaissances puisqu’elles sont nécessaires pour mettre en place des interventions psychosociales appropriées aux besoins de ce groupe de population. Cette recherche s’intéresse ainsi au risque de vivre de la détresse psychologique chez les propriétaires québécois d’entreprises agricoles, à leur attitude envers le recours aux services d’aide en santé mentale, ainsi qu’à leur capacité de résilience. Des analyses comparatives seront effectuées entre divers sous-groupes établis selon les caractéristiques des personnes participantes (p. ex. selon leur âge, leur sexe, le type d’entreprise agricole). Les résultats de cette recherche pourraient ainsi contribuer à bonifier les interventions psychosociales à l’égard de ce groupe de population.

Seasonality and ecology of the northern house mosquito in Calgary

Climate change is changing the distribution of mosquitoes, allowing them to become established in new places, potentially resulting in increased risk of mosquito-borne disease. The recent arrival of the invasive northern house mosquito, Culex pipiens, to Alberta is one such example. This mosquito, a major pest species found natively in Europe, is considered among the most important mosquitoes for the transmission of West Nile virus in North America. This species is highly urban and thrives in human-altered environments where it utilizes artificial containers to breed and warm structures to survive cold temperatures. To understand the risk the introduction of this species poses in a new environment, it is important to investigate its ecology within an Albertan context. The objective of this project is to investigate the seasonality of this mosquito’s activity, what larval habitats it uses, and which traps are most effective for surveillance of this species. The results of this project will help guide future monitoring and control efforts by the City of Calgary, aiming to provide a better understanding of the biology of the northern house mosquito in Calgary, and ultimately building resilience against invasive species in the future by informing improved monitoring.

Digital Twin and Generative AI Applied to Rapid Interior Design

Digital twin is a virtual representation of an object or system designed to accurately reflect the physical object. In the interior design field, this physical object can be a room of a building, or the entire building itself. Creating a digital twin for interior design involves capturing a virtual representation of a physical space, then manipulating it according to criteria, allowing for visual exploration and experimenting with colors, textures, styles, and environmental characteristics that impact those who reside inside. The digital twin can be used to analyze various materials and finishes, to determine the optimal combination of components and their interaction with each other in support of efficiencies in energy, cost of maintenance, and long-term use. These recommendations can then be adopted and constructed into the final product.
The Digital Integration Centre of Excellence Technology Access Centre (DICE-TAC) at Saskatchewan Polytechnic (Sask Polytech) and Metric Design Inc. (Metric) will collaborate on the research and development of a prototype digital twin for interior design. This prototype system will define the parameters best suited for a digital twin for interior design by identifying the data that can then be used to help manipulate and experiment with the digital twin in the interior design platform. Metric offers Residential & Commercial Interior Design and Renovations services. The purpose of this project is to develop a prototype digital twin system to explore the process of utilizing various design tools, such as generative AI in interior design. Additionally, the twin will provide a platform for experimenting with various components and provide predictive results to be expected from the actual artifact, should those components be adopted. The digital twin approach to interior design will speed up design efforts, more easily communicate themes and concepts to the client, and reduce or eliminate possible errors in the final product, before any real development of the product is undertaken. This approach will provide Metric Design a competitive advantage in the design industry while providing high quality services at lower cost, to their clients.

L2M QC 2024 – «CO2 to e-fuel»

Carbon dioxide (CO2) atmospheric concentration has increased by 50% (from 280 ppm to 420 ppm) in the last century. Despite efforts to reduce emissions, industries vital to our economy, such as mining, marine transportation, and aviation, remain dependent on fossil fuels for their operations. These sectors are considered hard-to-decarbonize because they lack viable alternatives to fossil fuels. In turn, they release CO2 and are forced to capture it to avoid paying a carbon tax. Once CO2 is captured, it is typically sequestered underground, but this is not a viable solution as there is no guarantee the CO2 will remain there, and it provides no economic advantage. Instead, transforming CO2 into commodity products such as fuels, chemicals, and carbon nanofibers creates a new market opportunity to replace fossil fuel-derived products by recycling CO2. In terms of alternative fuels, this project aims to decarbonize hard-to-decarbonize operations by replacing their fossil fuel with an electro-fuel, or e-fuel (derived from renewable electricity). Our e-fuel is carbon-neutral, free of toxic chemicals and can be used in the engines that the hard-to-decarbonize sector already own. Market research will assess the demand for recycled carbon-neutral products such as fuel, carbon nanofibers, and carbon-based chemicals. This research will validate the type of process we will build, which depends on CO2 capture capability, renewable electricity availability, and end-product needs. Furthermore, it will aid in discovering partnerships with early adopters, potential clients, and investors. This comprehensive market validation is crucial for guiding the project’s next steps towards pilot testing.

L2M QC 2024 – «Surveillance respiratoire et cardiaque en temps réel avec technologie portable non invasive.»

Le projet proposé vise à développer un appareil portable, non invasif, sans fil et rentable pour la surveillance continue en temps réel de l’activité respiratoire et cardiaque. Cet appareil aidera à diagnostiquer et à surveiller les maladies en fournissant des mesures continues de la fréquence respiratoire avec une précision comparable aux normes médicales, ce qui est crucial pour des conditions telles que l’apnée du sommeil, les infections respiratoires et les maladies chroniques telles que l’asthme et la MPOC. En remédiant aux limites et au non-fiabilité des méthodes de surveillance respiratoire portables existantes, cette technologie en instance de brevet peut améliorer de manière significative les soins aux patients dans les hôpitaux, en particulier dans les unités de soins intensifs, et améliorer la qualité de vie des personnes souffrant de maladies chroniques. Le projet devrait bénéficier au système de santé en réduisant les coûts, en augmentant l’efficacité, en améliorant l’allocation des ressources de santé et en fournissant de meilleurs résultats pour les patients grâce aux capacités de surveillance à distance.