Projets novateurs réalisés

Surveillance permanente de l’état des machines opérant àvitesse et charge variables

Le changement de comportement dynamique des machines tournantes (ponts roulants, éoliennes, robots) en fonction de la vitesse et de la charge se confond facilement avec un changement d’état de l’équipement, ce qui déclenche souvent de fausses alarmes sur des machines saines. De plus, la plupart de ces machines opèrent à très basses vitesses, ce qui cause des problèmes sur la sélection de capteurs efficaces et sur les normes inexistantes dans ce genre d’application. Le projet proposé a pour objectif le développement d’une méthode de diagnostic d’endommagement de machines opérant en régime variable et la conception d’une chaîne de mesure permettant la surveillance en ligne de systèmes rotatifs. Une première phase sera allouée à l’étude de faisabilité, le choix de l’architecture, et la mise en place de la chaine d’acquisition. Une deuxième phase consistera à développer un système d’autodiagnostic en mettant en œuvre les techniques d’intelligence artificielles pour définir des seuils de sévérité à ne pas dépasser en tenant compte de la vitesse et la charge appliquées. Les travaux proposés permettront de détecter l’état de dégradation de la machine, d’obtenir un gain de productivité en diminuant les temps d’arrêt et d’avoir une diminution du risque d’accident lors de l’opération des machines.

Applied AI Solution Development

This project aims to help Rogers Communications bring real-world AI and Generative AI solutions to life across Business units like Customer Care, Network, HR, Rogers Business. A team of eight interns from the University of Waterloo will work with Roger’s Data & AI Strategy team to turn identified promising use cases into working solutions. Their focus will be on building small-scale models (called MVPs), testing them with users, tracking their performance, and preparing them for wider use. The project will speed up Roger’s ability to adopt AI in a responsible and scalable way, while also giving students hands-on experience in a real business setting. Ultimately, it helps Rogers improve service, efficiency and innovation, while training the next generation of AI talent.

TRLUP – Stretcher Aid

Our Stretcher Aid is a new approach to increasing ergonomics and overall care for both caregiver and patient. Our product is designed to create more usable working space for Nurses and Caregivers, allowing for more ergonomic choices during emergency situations. With the support of New Ventures BC, we will be able to utilize the resources available to advance project development while gaining the knowledge to secure IP and structure a successful business around it.

L2M – Neuro-integrated carbene coatings

Brain-Computer Interfaces (BCIs) are a revolutionary technology with the potential to significantly improve the lives of individuals with neurological disorders, but their long-term use is severely limited because the implants often fail within a short period. This project, a collaboration with Queen’s University, aims to solve this critical problem by developing and commercializing “Neuro-Integrated Carbene Coatings” (NICC), a groundbreaking new material designed to create an exceptionally stable and long-lasting bond between the BCI electrode and the brain tissue. By pioneering the use of N-heterocyclic carbene (NHC) chemistry, the project will create a coating that is both biocompatible and durable, minimizing the body’s immune response and preventing the material degradation that causes current devices to fail. The expected benefit to the partner organization is the validation and development of a clear commercialization strategy for this transformative coating technology, positioning it as a vital solution for the rapidly growing BCI market and ultimately enabling the creation of a new Canadian deep-tech venture.

L2M- AI Lab Assistant

This project initially targets drug discovery labs, with long-term plans to expand into broader biomedical research. As AI becomes integral to scientific work, we aim to build a custom AI assistant that goes beyond basic chat support, capable of running code, executing ML/DL pipelines, and assisting with lab-specific tasks.

L2M – Artificial Intelligence Integrated Medical Media Suite

The Artificial Intelligence Integrated Medical Media Suite (AIIMMS) is a smart, AI-powered crash cart designed to support emergency response teams during coding events. By integrating live video, audio, patient biometrics, and patient electronic health record data into a centralized interface, AIIMMS can help reduce the cognitive load and enhance clinical decision-making in real time. The system is powered by an AI model trained to interpret real-time video and audio data, provide guidance in determining a differential diagnosis, and assist in adhering to ACLS protocols. By streamlining the coding workflow with data-driven systems, AIIMMS aims to achieve a faster Return of Spontaeneous Circulation (ROSC) time to improve patient outcomes in hospitals such as SRI.

Revitalisation socio-territoriale en contexte nordique

Le projet de recherche se déroulera dans la municipalité de Baie-Johan-Beetz, une communauté nordique d’environ 80 habitant·e·s située sur la Côte-Nord du Québec. Cette localité fait face à plusieurs défis communs aux régions éloignées : vieillissement de la population, centralisation des services, rareté des logements et difficultés à attirer de nouveaux résident·e·s. Malgré des efforts importants pour revitaliser le territoire (marketing, développement résidentiel, projets communautaires), la municipalité est limitée par un manque de ressources humaines et une surcharge administrative. Le stage permettra d’appuyer la municipalité de façon concrète, en participant à ses activités quotidiennes tout en menant une analyse des initiatives de revitalisation mises en place. Cela inclut un travail sur les archives, les outils de communication, la sensibilisation citoyenne et l’évaluation des freins rencontrés dans les projets. Le stage offrira un temps de recul et une expertise externe difficile à mobiliser en temps normal. Il visera à renforcer la capacité d’action municipale et à produire des recommandations utiles, tout en valorisant les forces locales. Pour l’organisme partenaire, les bénéfices sont à la fois pratiques (soutien direct) et stratégiques (recul, analyse, transmission des savoirs). Pour la personne stagiaire, c’est une occasion d’approfondir ses compétences en aménagement du territoire, en revitalisation des milieux nordiques et en recherche ancrée dans le terrain.

L2M – Healthcare Systems Performance Optimization using Reinforcement Learning-Enhanced Functional Resonance Analysis Method

Healthcare systems operate as complex socio-technical environments, involving unpredictable interactions among patients, clinicians, technologies, administrators, and institutional policies. These systems are often under significant pressure to improve performance, reduce costs, and enhance patient safety—yet they frequently lack the tools to simulate the consequences of operational decisions before implementation. Traditional approaches like retrospective analysis or static models fail to capture dynamic system behavior and interdependencies, especially when working with limited or sensitive data.

This project addresses that gap by developing a simulation-based decision-support tool that integrates the Functional Resonance Analysis Method (FRAM) with Reinforcement Learning (RL). FRAM helps map functional relationships within healthcare processes, capturing variability in everyday work. RL, in turn, enables the system to learn from simulated experiences by rewarding effective decisions and avoiding poor ones—thereby identifying optimal strategies even in complex and low-data environments.

The aim is to provide hospital administrators, system planners, and policymakers with a user-friendly platform for exploring “what-if” scenarios without disrupting real-world operations. By simulating changes in patient flow, staffing, or resource allocation, the tool can uncover unintended consequences and support more resilient planning.

Through the Lab2Market Validate program, we will conduct structured customer discovery interviews, engage with healthcare stakeholders, and incorporate their feedback to refine the tool’s usability, interface design, and integration requirements. The ultimate goal is to bridge the gap between academic research and real-world application by building a product that is technically robust, practically usable, and commercially viable.

This innovation not only supports more informed healthcare decision-making but also contributes to Canada’s broader priorities around improving public service delivery, fostering applied AI innovation, and enhancing productivity in a resource-constrained health system.

L2M – AR-CAD: An Augmented Reality-based Computed Aided Design Tool

This project will advance the development of AR-CAD, an innovative augmented reality (AR) design tool that allows engineers to create and manipulate 3D models in real-world scale using a headset. Unlike traditional CAD tools that rely on 2D screens and complex interfaces, AR-CAD enables intuitive, hands-on design, helping users visualize and validate parts more quickly. The software exports watertight STL files that are ready for use in 3D printing workflows, significantly reducing design errors, time-to-prototype, and material waste. The partner organization will benefit from early access to a disruptive CAD technology with the potential to improve productivity, reduce costs, and create new market opportunities in rapid prototyping and digital manufacturing.

L2M – Artificial Intelligence Enabled LED Lighting

Cities and lighting companies currently spend weeks and tens of thousands of dollars to create new plastic lenses every time a different streetlight beam is needed or when light pollution must be reduced. Our design is a simple film that sticks to the front of an LED and updates the light patterns quickly—no new lens required, and no delays. We have developed software powered by artificial intelligence that converts any desired beam shape into a printable pattern on this thin film. Once printed, the film is stick to the LED to produce the exact light distribution required. The film weighs less than a paperclip, and can be manufactured in just a few hours. The global exterior LED lighting market is valued at approximately CAD$19? billion. Capturing even 1% of that market represents a CAD?$190? million opportunity. Through Lab2Market, we will identify our initial customer segment—municipalities, stadium operators, or signage companies.

Host-pathogen interaction between a three-dimensional tissue model with a binary cell system and dual species biofilm Under Normoglycemic and Diabetic Conditions

Apical periodontitis is a disease caused by the persistence of microorganisms and their virulence factors in the root canal system. This infection leads to inflammation of the soft tissues and changes in the periapical bone structure. Bacteria such as Fusobacterium nucleatum and Parvimonas micra form biofilms that are resistant to conventional cleaning, making treatment more challenging. Additionally, systemic conditions like diabetes mellitus further aggravate this scenario by compromising the immune response and intensifying inflammation, influencing both the progression and healing of apical periodontitis.
This project will use an in vitro three-dimensional model composed of periodontal ligament fibroblasts and macrophages to simulate the periapical environment. These models will be exposed to bacterial biofilms grown on dentin discs and analyzed under normal and hyperglycemic conditions in terms of cell viability, morphological changes, and interactions with microorganisms.
The aim is to gain a better understanding of the interactions between endodontic infection, periapical inflammation, and systemic conditions such as diabetes mellitus, thereby contributing to the development of more effective and personalized therapies. The use of in vitro three-dimensional tissue models represents an advance in endodontic research, as it enables the analysis of complex cellular interactions that more accurately reflect the real clinical environment.

A novel scaffold for tympanic membrane repair

The ability to hear, and the quality of our hearing, depends on the health of the eardrum. Eardrum perforations due to diseases and accidents can be treated using grafts, such as autologous grafts, allografts and xenografts. These replacements suffer from various limitations such as donor site morbidity, long operation time and healing time, and risk of infection transmission, and more importantly, none of these grafts are able to replicate the complex microanatomy for sound quality reproduced by the native eardrum. In this project, we propose to develop a novel graft with oriented collagen core-shell fibers and growth factors in the core. The oriented fibers mimic the local structure of the native eardrum, producing better sound quality. Furthermore, the delivery of growth factors would
improve healing and promote closure of the eardrum perforation. The successfully developed graft will be commercialized by our industrial partner.