Traditional modelling and simulation involves a human expert to manually design and create simulation models. However, these models quickly become obsolete for systems that continually change, such as smart manufacturing systems. This creates a need to create new simulation models which is difficult and expensive. Data-driven simulation extracts simulation models from data without explicit modelling […]

L’équipe de Gwenaëlle Hamon développe des procédés de micro-fabrication pour les dispositifs III-V, incluant des convertisseurs de puissance laser qui sont des dispositifs composés d’hétérostructures à base de GaAs (structure VEHSA (1) (2)). Ces hétérostructures convertissent la lumière d’un laser incident en électricité. La structure VEHSA (Vertical Epitaxial Heterostructure Architecture) consiste en un empilement de […]

Les parcs relais, ou « park-and-ride », jouent un rôle crucial dans la gestion de la mobilité urbaine, en offrant une alternative à l’utilisation de la voiture individuelle en centre-ville. Cependant, leur impact environnemental et leur efficacité opérationnelle peuvent varier considérablement en fonction de leur conception et de leur gestion. Ce projet propose une approche […]

Supersonic fluidic oscillators are known to provide pressure pulsations which improve the metal superplastic blow forming manufacturing process. The symmetrical nature of a previously developed oscillator design, however, restricts their application to cases where two identical parts are formed simultaneously. Pressure fluctuation amplitudes are not sufficient when used for single part forming. This research aims […]

Le projet est l’utilisation de diverses méthodes pour déterminer la capacité thermique des matériaux. Les applications directes sont : ? La détermination de la capacité du sol à fournir une énergie thermique. L’information obtenue permettra un meilleur dimensionnement de forage géothermique et ainsi optimiser les coûts de projets en géothermie résidentiel. ? La détermination des […]

Predicting Complete IV Curves for Perovskite Solar Cells with AI. Perovskite solar cells are a groundbreaking technology, offering high efficiency and low manufacturing costs, making them a promising candidate for the future of renewable energy. Their tunable properties and rapid development have captured significant interest in both academic and industrial sectors. This project focuses on […]

Perovskite solar cells stand out due to their rapid advancements and high efficiency, combined with the promise of cost-effective production. These attributes have placed them at the forefront of next-generation renewable energy solutions. This project integrates physics-based principles into AI models to reconstruct IV curves, reducing reliance on extensive input parameters. By incorporating optoelectronic governing […]

The purpose of the project is to design a paper-based device for the co-detection of nitric oxide (NO) and glucose in a tumor angiogenesis model. By combining paper technologies to electrochemical sensing, we envision that an easy-to-use, smart tumor model can be built. The devices will be tested on co-cultured endothelial and tumor cells, to […]

The proposed project aims to develop a new, eco-friendly construction material by combining fly ash and Sidoarjo mud to create a self-compacting geopolymer mix. This innovative material will replace traditional aggregates, making it more sustainable and reducing environmental impact. The project leverages the international collaboration to address waste management issues and promote sustainable construction practices. […]

Measurement of magnetic field vector by a compact diamond quantum sensor Measuring magnetic field is important for material and device characterization. Although various methods have been developed, challenges persist in accurately measuring local magnetic field vectors. Our lab has been developing a compact diamond quantum sensor to overcome the challenge. The diamond sensor uses nitrogen […]

The proposed MITACS GRA project is part of the Human Surgeome Project at the German Cancer Center, which uses advanced machine learning and deep learning technologies to improve surgical practices. By analyzing large amounts of surgical video data, the project aims to enhance surgical training, skill assessment, and workflow. It will develop systems that provide […]

This research explores the integration of quantum computing with statistical methods to enhance public transit origin–destination (OD) matrix estimation. Traditional OD estimation relies on automated fare collection (AFC) and automatic passenger counting (APC) data, which often present challenges due to incomplete coverage. Scaling techniques like iterative proportional fitting (IPF) help address these gaps, but their […]