Innovative Projects Realized

Adaptive EMG-Driven Control for Multi-DOF Robotic Exoskeletons

This project aims to develop intelligent control systems for robotic exoskeletons that can better assist human movement. Using artificial intelligence, specifically deep reinforcement learning, the project will train controllers in advanced computer simulations that model both the human body and the robotic device. Muscle activity signals (EMG) will be incorporated so that the exoskeleton can adapt its assistance based on the user’s physical effort. The trained controllers will then be tested and refined on a real multi-joint exoskeleton. The project will benefit the participating institutions by strengthening their collaboration in AI and assistive robotics, and enhancing expertise in adaptive robotic systems for healthcare and rehabilitation applications.

APEGA Science Olympics Scoring System

The APEGA Science Olympics is an annual competition that relies on a large volunteer base to evaluate student teams. Currently, the events use a paper-based judging process, distributing clipboards and scoring sheets to more than 100 judges to assess approximately 300 teams. After the event, all paper forms must be collected and manually entered and processed, resulting in significant administrative overhead and delays in producing results and providing feedback to students.
The manual review process introduces several challenges beyond inefficiency. Judges have limited ability to review or correct scores once submitted, making error correction difficult and time consuming, at the limited points where it’s available at all. Additionally, last-minute changes (such as a volunteer no-showing on the day) require manual reassignment of judges to teams, creating confusion and further increasing administrative burden. These issues all together lead to delayed feedback, increased risk of data errors (including missed teams), and overall, a process that does not scale well as the competition grows.

Analysis and experimental demonstration of fast optical switching

The rapid growth of artificial intelligence workloads, cloud computing, and resource-disaggregated services is significantly increasing traffic inside data centers, pushing conventional electrical switching architectures toward limits in bandwidth density, power consumption, and I/O scalability [1]. Electrical switching fabrics require multiple tiers and repeated opticalelectrical-optical conversions, which increase latency, energy consumption, and operational complexity as network scale grows. Optical switching has therefore emerged as a promising approach to improve scalability by routing traffic directly in the optical domain while remaining largely independent of modulation format and data rate [1-4].
The partner organization develops and evaluates high-speed interconnect technologies and network architectures for data-center environments. Its main activities include integration of optical subsystems, evaluation of emerging interconnect technologies, and identification of practical deployment paths that improve performance without increasing infrastructure complexity or power consumption. A key challenge faced by the partner is determining whether fast optical switching techniques can be implemented using commercially viable components while maintaining error-free performance and practical link budgets compatible with existing transceiver ecosystems. Broader benefits include reduced data-center energy consumption and improved efficiency of digital infrastructure supporting cloud and AI services.
This project proposes the analysis and experimental demonstration of a nanosecond-scale wavelength-routed optical switching node using passive wavelength multiplexing and routing elements such as arrayed waveguide gratings (AWGs) [1]. The work will investigate whether fast optical gating or amplification is required to support high-speed operation and how such elements affect performance and power efficiency [5]. A proof-of-concept 4×4 demonstration will be used to validate switching behavior and transmission performance at PAM4 100 GBd. The anticipated outcomes include validated
performance data, architecture guidelines for scaling toward larger systems.

Automatisation multi-niveaux par l’IA

À L’Original, notre promesse est simple : faire entrer l’art dans la vie de tout le monde à Montréal. Vous n’êtes pas artistes? Aucun problème. Notre projet Automatisation multiniveaux met la technologie au service de l’humain pour que chacune et chacun découvre, crée et s’approprie l’art en galerie, en ligne et à la maison. L’Original existe pour démocratiser l’art. La technologie est notre véhicule pour y arriver, avec une approche plus personnelle, plus fluide et plus inclusive.

RAG signifie Recherche Augmentée par Génération. Imaginez une bibliothécaire ultra rapide qui connaît nos œuvres et vos goûts, associée à une plume claire qui sait formuler des recommandations. Ensemble, elles vont chercher l’information dans nos collections, la vérifient, la résument et vous proposent des actions concrètes : quelles œuvres voir, comment les essayer virtuellement chez vous, comment rencontrer une artiste ou comment obtenir un devis pour une murale.

Notre automatisation multiniveaux orchestre plusieurs modules. Un module RAG pour le catalogue suggère des œuvres adaptées à votre budget et à votre style. Un module RAG pour le soutien répond vite et bien aux questions courantes. Un module RAG pour le marketing adapte nos contenus aux tendances locales. Un module RAG pour la relation avec le public prépare, après votre visite, un message chaleureux et personnalisé. Résultat : une expérience simple, humaine et sur mesure, dans nos espaces et sur nos plateformes numériques.

L’IA guide, l’humain crée. Nos stagiaires vous le disent : « Vous êtes les artistes d’aujourd’hui. »

Behaviour mechanisms of catch rates

Recreational fishing is seeing increasing numbers of fish caught and released, either because individual fishers choose not to harvest for personal or conservation reasons, or because fishing regulations allow few numbers or sizes of fish to be harvested. However, if released fish become less catchable for future fishers, this may lead to decreasing catch rate, and dissatisfied fishers, which runs counter to fisheries goals. We will investigate whether one of a number of mechanisms causing ‘catchability’ of individual fish to decline by tagging a large number of fish in a small ‘learn to fish pond’ and monitor their catches over time. This will help better set regulations with an aim to promote conservation and high catch rates.

Immobilized bio-based aerogel photocatalysts for degradation of organic pollutants in water

This project will develop new bio-based materials to support the removal of persistent organic pollutants from water using light-driven processes. The research will focus on creating porous, plant-protein-based aerogels that can act as stable platforms for immobilizing photocatalytic materials, making them easier to handle, recover, and reuse in water treatment applications. By combining these bio-derived supports with light-active and conductive components, the project aims to improve the efficiency and durability of pollutant degradation under laboratory conditions. The work will contribute to the development of more sustainable and reusable water treatment materials and will facilitate the transfer of advanced materials fabrication and characterization techniques back to the Canadian home institution. This knowledge exchange will strengthen research capacity, support future collaborative projects, and provide advanced training that benefits both the host institution and the Mitacs Globalink program.

Photochemical Supramolecular Assemblies for Solar Energy Conversion to Hydrogen

As part of this project, the intern will work in the laboratories of Paola Ceroni and Garry Hanan with the goal of producing hydrogen from solar energy. To achieve this, a ruthenium metal complex will be synthesized and characterized in order to obtain a catalyst. Experiments and research will be conducted in both laboratories to strengthen the collaboration between these two renowned researchers. This internship will lay the groundwork for future research and studies that will contribute to the advancement of knowledge in the field of green hydrogen. The first phase of the project will consist of designing and identifying new ligands that enable the creation of a stable, durable complex capable of absorbing light in the optimal frequency range. The second phase will involve the synthesis of the complex and the study of its physical properties, such as absorption, emission, and quantum yield. The project will then be completed with the optimization and further characterization of the synthesized complex. With a promising future within Canada’s energy transition, the advances achieved through this project will contribute to future work in this rapidly growing field and help generate investment in the Canadian energy sector.

Metallic Hydrides Nanostructures for the Storage of Hydrogen

Solid-state hydrogen storage in MgH2 has strong potential due to its high energy density, but it is limited by technical constraints. Dehydrogenation requires high temperatures (300–400?°C), which are unsuitable for many applications, and the hydrogen release kinetics remain slow because of the low diffusion within the crystalline structure.
To address these challenges, our project adopts an integrated approach combining theoretical modeling and experimental synthesis. On the theoretical side, DFT, Monte Carlo, and molecular dynamics simulations will be performed on nanostructured hydrides. The effects of single doping and co-doping with transition metals (Co, Ti, V, Fe, Ni) will be studied to improve reactivity and reduce the dehydrogenation temperature. Experimentally, MgH2 nanostructures will be synthesized via solution-based chemistry in the presence of various catalysts, including oxides, halides, and metals. The resulting powders and pellets will be characterized to analyze the mechanisms of hydrogen absorption and desorption.The overall objective is to better understand and optimize the thermodynamic and kinetic properties of nanostructured hydrides, with the goal of developing more efficient, fast, and safe solid-state hydrogen storage systems for long-term applications.

Développement de composants et lasers fibrés

Fondée en 2000 et basée à Québec, Photonique indie Canada inc. (ancien nom : TeraXion inc.) est reconnu comme l’un des principaux fabricants mondiaux de technologies photoniques de pointe, notamment dans les domaines des réseaux de Bragg à fibre optique (FBG), des lasers à largeur de raie étroite et de l’optique intégrée, son équipe dédiée est composée de plus de 55 chercheurs, ingénieurs et technologues qui fournissent des produits et des solutions personnalisées à des entreprises de premier plan dans les secteurs des lasers industriels, des communications optiques, des dispositifs médicaux, de l’aérospatiale et de la défense. Depuis très récemment, indie/Teraxion développe des sources lasers à fibre ultrarapides en collaboration avec l’Université Laval [1-3]. Ces produits lasers ultrarapides sont présentement en introduction dans le marché et le présent projet vise à poursuivre l’innovation dans ce domaine pour obtenir une place dominante dans ce marché en forte croissance et ce, avec des technologies de ruptures. Obtenir des impulsions brèves de qualité dans des bandes spectrales moins communes mais avec beaucoup de potentiel commercial requiert le développement de composants spécifiquement adaptés à des designs novateurs de cavités laser ultrarapides. Le présent projet permettra spécifiquement de contribuer au développement de lasers à fibre au néodyme opérant autour de 920nm.

Bridging the EV Capital Gap via a Green Fintech Operating Model.

This project will help Avonhead Electric build a repeatable, data-driven way to finance EV charging infrastructure. The intern will turn Avonhead’s current manual, one-off deal work into a standardized underwriting and reporting system by creating (1) a risk scorecard that combines traditional credit metrics with real-world charger performance data (like uptime and utilization), (2) a set of reusable financing templates and term sheets, and (3) an investor-ready dashboard that tracks portfolio performance. The benefit to Avonhead is faster, more consistent credit decisions, lower legal and transaction friction, and a clearer path to raising larger, lower-cost capital (like warehouse facilities and, eventually, securitization) because the business can prove risk and performance in a structured, institutional-grade way.

Biomechanical Risk Factors of Non-Specific Low Back pain in cyclists

This project investigates biomechanical risk factors associated with non-specific low back pain in road cyclists. Using motion capture and isokinetic core strength testing, spinal movement patterns and muscular deficits are analyzed during cycling under controlled laboratory conditions and highly realistic treadmill cycling with the athlete’s own bicycle. The goal is to better understand how segmental spinal motion and movement variability relate to low back pain, and to generate evidence that can inform injury prevention, performance optimization, and rehabilitation strategies in endurance cycling.

ESROP – Project A4: Medical image processing/healthcare analytics with deep learning/explainable AI/federated learning

This project focuses on using deep learning techniques and AI for medical image processing, optimizing these techniques to maximize efficiency and accuracy in processing large sets medical data. This project specifically aims to improve generalization and explainability of deep learning models used in medical imaging, focusing on medical image segmentation to explore how current medical image processing techniques can be optimized. The project is a collaboration between the University of Toronto and King Mongkut’s University of Technology Thonburi, and will bring novel approaches to deep learning in medical image processing to both institutions, a rapidly growing field within healthcare which is crucial in improving the accuracy and efficiency of diagnosis.