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💡 Active projects and challenges as of 04.08.2026 21:36.
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HE-Arc
Edge Device Training Testbed
Large Language Models (LLMs) have demonstrated remarkable performance across various natural language processing tasks. However, deploying them on embedded platforms remains a significant challenge due to hardware constraints and limited energy availability. This project addresses the growing demand for secure, autonomous AI capabilities in edge computing environments—where reliance on cloud infrastructure is often impractical due to privacy concerns, latency requirements, or unreliable connectivity. Our objective is to evaluate, adapt, and implement a Small Language Model (SLM) optimized for resource-constrained embedded systems, with a strong emphasis on offline functionality, ultra-low power consumption, very low computational resources, and maintaining a responsive user experience. We specifically aim to implement this Small Language Model (SLM) within a smart ashtray application, designed to assist and support smokers in their smoking cessation journey by providing personalized advice and autonomous monitoring directly on the device.
InnovCare Monitor - Surveillance nocturne EMS 2026
Chaque nuit, dans les EMS et à domicile, des chutes et incidents passent inaperçus faute de surveillance. InnovCare Monitor change ça : des capteurs sous-matelas et un radar à ondes millimétriques détectent les chutes et suivent les signes vitaux en temps réel — sans caméra, sans intrusion. Développé par un soignant avec 10 ans de terrain, le MVP est déjà fonctionnel. On cherche des partenaires tech et data pour aller plus loin.