Innovative Projects Realized

L2M – Augmented Reality-Enhanced Precision Robotic Manipulator Simulation and Training System

The global manufacturing sector is shifting towards high-mix, low-volume (HMLV) production. While large corporations can invest in advanced automation, Canada’s small and medium-sized enterprises (SMEs) face a significant “adoption chasm.” This gap is driven by prohibitive upfront capital costs for robotic systems, a critical lack of in-house programming and maintenance expertise, and the financial risk of investing in technology without a clear return on investment (ROI).

To bridge this chasm, this project proposes the development of an Augmented Reality-Enhanced Precision Robotic Manipulator Simulation and Training System. This innovative platform will function as a low-cost, low-risk “strategic sandbox” for SMEs to embrace Industry 4.0. Leveraging the Unity 3D engine and Microsoft HoloLens 2, the system will provide a high-fidelity digital twin environment where companies can virtually design, prototype, and validate robotic automation processes before committing significant capital.

The system directly dismantles key adoption barriers by enabling clear ROI analysis and mitigating investment risk. It addresses the skills gap with an intuitive, simulation-based training module to rapidly upskill the existing workforce. Furthermore, it enhances operational agility by allowing for the rapid virtual reprogramming of robotic tasks for custom orders and replacing inefficient paper-based workflows with real-time, in-situ digital work instructions.

L2M – Harnessing Adiponectin and Extracellular Vesicle (EV) Biology to Enhance Healthspan

This project explores the commercial potential of a new therapy using adiponectin-enriched extracellular vesicles (Adipo-EVs) to treat age-related cardiometabolic diseases, focusing on stakeholder discovery, market fit, and commercialization planning.

L2M – Road Ally

This project aims to strategically grow Road Ally, a new mobile app that connects drivers facing roadside emergencies with nearby helpers, offering a faster and community-based alternative to traditional services. The intern will conduct market research and develop data-driven strategies to attract more users and helpers, and explore new ways for the app to generate revenue. This work will directly benefit Road Ally by providing a clear roadmap for expanding its reach, increasing its user base, and ensuring its long-term financial stability as it continues to make roads safer and empower local communities across Canada.

L2M – Cross-Modality Translation in Medical Imaging: Bridging Diagnostic Domains

Medical imaging is vital to modern healthcare, yet high costs and limited access slow patients’ paths to
diagnosis. Radiologists often correlate imaging findings across multiple imaging modalities and sequences
to arrive at a specific diagnosis, but each extra scan or sequence increases scanner time, healthcare costs,
wait-list pressure, radiation for ionizing exams, and energy use. An AI image-to-image translator can begin
with the quickest, least-burdensome scan or sequence and digitally create the other needed images, so
patients avoid additional exams or sequences. This saves scanner hours, contrast agents, and energy,
lowers healthcare costs and radiation exposure, and still gives clinicians information required for
diagnosis. The project will survey existing image-to-image translation technologies, gather stakeholder
priorities, and deliver a roadmap that moves synthetic imaging from research into routine care, offering
multiple potential advantages for health systems and patients.

IA Conversationelle pour l’apprentissage du français

Francoflex offre une IA conversationelle qui aide à l’apprentissage du français au travail. Elle aide à l’intégration des travailleurs dont la première langue n’est pas le français.
Pour assurer de l’efficacité du modèle de reconnaissance audio et un bon apprentissage, il faut entraîner un modèle capable de reconnaître des accents forts dans différentes langues natives d’apprenants en français.

L2M – AI-Guided Haptic Robotics for Scalable Surgical Skill Training

Surgical trainees, our primary users in medical schools and teaching hospitals, face a critical barrier: limited access to consistent, expert-guided, hands-on practice. This limitation originates from the constraints of the conventional surgical training model, which depends heavily on the physical presence of expert surgeons. To address this limitation, we are constructing a robot-assisted surgical training platform that enables surgical residents to receive expert-level guidance and correction without constant presence of supervising surgeons. This product addresses core challenges in surgical education, including limited expert availability, duty hour restrictions for surgical residents, reduced hands-on exposure, and the lack of real-time, high-fidelity feedback. The system combines haptic robots with AI, to emulate the motor behavior of expert surgeons. Unlike conventional surgical simulators, which either lack meaningful feedback or depend on surgeons for assessment, our platform provides adaptive, context-aware, real-time guidance through force feedback and motion correction, effectively replicating an expert mentor’s role during hands-on training.

L2M – Selective PSRV for Methane Emission Reduction from Storage Tanks

This project aims to develop an innovative, low-cost technology to prevent methane leaks from oil storage tanks, addressing a significant source of greenhouse gas emissions. Traditional tank valves either release harmful gases into the air or are costly to operate. Our proposed solution is a specialized membrane valve that allows harmless gases, such as nitrogen and oxygen, to escape while capturing methane. This system is passive, energy-efficient, and can be easily retrofitted to existing tanks. In collaboration with Edmonton Unlimited, this research will produce critical first-time data on the technology’s effectiveness, helping the oil and gas industry meet strict emission standards, reduce environmental impact, and save costs.

Vers un environnement engageant sur l’investissement socialement responsable au sein d’un dispositif de sensibilisation sur le Web

L’objectif de ce projet de recherche est d’identifier l’ensemble des éléments permettant de développer un environnement engageant sur l’investissement socialement responsable (ISR) (c’est-à-dire menant au changement comportemental) au sein d’un dispositif de sensibilisation sur le Web. Il répond aux deux freins majeurs du développement actuel du marché du détail de l’ISR : un manque d’information et une complexité des produits ISR. Ce projet répond à la fois à la mission, aux objetctis et à l’engagement en termes de développement durable de Fondaction. En outre, il offrira une valeur ajoutée à Fondaction dans sa propre stratégie de dispositif de communication médiatisée par le Web. Le présent projet de recherche servira de point d’ancrage au développement de la Plateforme Ethiquette – investissement responsable : plateforme de sensibilisation à l’ISR.

L2M – Commercialization of Fluroine-free Ultra Water-repellent Coating Technology

The main applicant is leading a research-based venture spun out of Simon Fraser University that has developed a patented, PFAS-free ultra water-repellent (superhydrophobic) nanocoating technology. Our technology offers a more environmentally-friendly alternative to incumbent water-repellent coatings. The current goal of the venture is to explore the best technology-market match to commercialize the technology. Key operation activities include lab-scale R&D (material synthesis & characterization) and pilot trials with collaborators from the construction and textile industry.

L2M – ThermaSwitch: Passive Films for Adaptive Heat Management

This project aims to develop and validate a low-cost, energy-efficient film made from common or recycled plastics that can passively manage heat without using electricity or fuel. By microstructuring the surface of materials like polyethylene and polymethylpentene (TPX), we create thin, flexible films that can either retain or release heat depending on their arrangement. When two films are pressed together, they allow thermal radiation to pass through; when separated, they scatter it, creating a switchable system that mimics smart windows—but without electronics or coatings.

The research will focus on refining the production process using scalable mold-based manufacturing techniques and testing the films in real-world environments such as buildings or greenhouses. The system can reduce heating and cooling needs by passively managing thermal energy, helping reduce operational costs and carbon emissions. It offers a competitive advantage over existing insulation and reflective materials by being adaptable, affordable, and easy to integrate into current construction or agricultural practices.

The partner organization will benefit by gaining early access to a versatile and sustainable technology that aligns with energy efficiency goals and carbon reduction targets. This innovation supports their role in advancing clean technologies and can lead to new product lines, pilot projects, and potential commercialization. By participating in the development phase, the partner helps shape a product with real market relevance, strengthening their position in Alberta’s and Canada’s growing clean-tech economy.

Reliable, Continuous, and Safe Updates of Digital Twin Services

The intern Mr. Andersen will travel from Aarhus University, Denmark to Polytechnique Montréal, Canada, to research the engineering of Digital Twins (DTs). DTs are virtual representations of physical systems, like the concrete mixing machine which is the focus of this project. Mr. Andersen will work with Prof. Bentley Oakes on the research problem of ensuring that DTs and their services can be reliably deployed and continually updated in operation. The aim is to develop techniques such that DTs are trustworthy, up-to-date throughout their lifecycle, tested during development, and deployed afterwards without downtime. The project will create a technical architecture which creates mathematical models of the DT and system behaviour, and integrates these models with other formalized knowledge of DT and the service creation process. The approach will be validated on an industrial case study of a concrete mixing machine, developed at a Danish company. This research is part of Mr. Andersen’s PhD dissertation. Thus, he brings his expertise of DT engineering to the lab of Prof. Oakes to transfer this knowledge with the members of the lab. Likewise, Mr. Andersen will be trained in the research process of Prof. Oakes, bringing these new perspectives and experiences back to Aarhus University.

L2M – AI-Based Alarm Management System

Over the four-month Lab2Market internship, discussions with plant operators, maintenance teams, and technology vendors will determine how the existing AI alarm-diagnosis system should be fine-tuned for real-world deployment. Targeted interviews, and rapid feedback loops will uncover the highest-value use cases, reveal functionality gaps, and establish clear upgrade priorities. These market-discovery insights will provide us with a data-driven roadmap for refining features, ensuring seamless integration with control systems, setting pricing strategies, and identifying early adopters. Consequently, costly guesswork will be avoided, product-market fit accelerated, and a pipeline of potential pilot sites secured.