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Energy consumption measurements and optimization in mobile networks – ECOMOME
The energy consumption of mobile networks has been the source of animated debates in the recent period, with the deployment of 5G technologies. However, the energy consumption estimations put forward by the different parties in the debate showed significant differences, up to two orders of magnitude. This is a result of a lack of accurate models and meaningful metrics in this field. ECOMOME addresses this problem of accurately modelling and optimising the energy consumption of a mobile network.
Energetics of neural computation – ENACT
growth of energy consumption by Artificial Intelligence models and the recognition that metabolic c
End-to-End Neural Approaches for Speech Translation – ON-TRAC
The ON-TRAC project proposes to radically change the architectures currently used in speech translation by exploring end-to-end neural approaches.<br />By performing this task with a single deep neural network, it is possible to better optimize its performance compared to a cascade system which requires first to transcribe automatically and then translate this transcription.<br />With ON-TRAC, it becomes possible to translate without transcribing the source language.
Enabling dynamic and Intelligent workflows in the future EuroHPCecosystem – eFlows4HPC
A workflow platform for HPC with big data analytics demonstrated in manufacturing, climate computing, and urgent computing for natural hazards.
Enabling Learnability in Embodied Movement Interaction – ELEMENT
While so-called Natural User Interfaces are becoming widespread, the use of expressive body movements remains limited in most human-computer applications.<br /><br />With «adaptable« and «learnable« systems, we want to facilitate the appropriation of movements or gestural interactions. We develop systems that could adapt to different motor skills, towards complex and expressive interactions.
Emergent communication through curiosity-driven multi-agent reinforcement learning – ECOCURL
The ECOCURL project proposes to leverage recent contributions in Multi-Agent Reinforcement Learning to study how compositional communication systems can emerge in artificial agent populations and support the open-ended discovery of increasingly complex cooperative strategies. The results of the project will be eventually demonstrated in a rich 3D environment and disseminated in the scientific community and high-visibility outreach events.
Embedded multi-sourCE analySis for the exploration of the underwAteR environment (CESAR) – CESAR
is to fit out underwater drones with embedded intelligence for observation and analysis of the underwater
Embedded Artificial Intelligence for Quality Assurance in Additive Manufacturing and Advanced 3D Material Characterization – AIQuAM3D
will open up application fields of artificial intelligence (AI) and improve the use of AI in cutting-edge
Embedded Artificial Intelligence and Endoscopic Capsule – ICI-Lab
pathology markers. These will be based on artificial intelligence algorithms, including deep learning, vector [...] trees. The study, design and validation of intelligent endoscopic video capsules are the purpose of
