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Optical Neural Networks start to learn...

Work is actively in progress at FEMTO-ST in order to design the photonic architectures dedicated to our future processors that will be computing through artificial intelligence concepts. This is currently done in the framework of the NeuroQNet Project which led recently to an important result in photonic brain-inspired computing: an experimental training methodology based on reinforcement learning was successfully demonstrated.The setup has involved an optical Read-Out of a photonic neural network, in the form of a matrix of micro-mirrors. It succeeded in training iteratively, and efficiently, the Read-Out for the prediction of a chaotic behavior, which was computed from a photonic network processor of 900 «light-neurons». Extrapolating this approach to more practical and highly complex problems, this could lead to ultra-fast and highly efficient weather forecast, without any numerical simulation of complicated equations modeling the atmosphere. This would simply need to learn the weather motions from the observation of past weather dynamics.

Similar groundbreaking brain-inspired processing concepts are also explored at FEMTO-ST in the framework of a very practical and concrete application: the diagnostic and prognostic of fuel-cell systems, in order to increase the lifetime of this clean electrical energy generator for the future (BiPhoProc project from the ANR call OH-RISQUE; a project jointly explored by the Energy Dept. at FEMTO-ST, as well as by the Mathematics laboratory of Besançon)

Research in this field at FEMTO-ST stands at the international forefront, as illustrated by a prestige conference to be held in Hanover, Germany, on 18-20 December 2018. The conference inviting some the best researchers worldwide (Standford, Berkeley, EPFL,...) on basic concepts for cognitive computing « Cognitive Computing: Merging Concepts with Hardware ») is co-organized by FEMTO-ST (Dr Daniel Brunner)

The publication on learning with photonic networks, published in  Optica, was moreover chosen for the issue cover by the editorial board, because of its high potential impact

Contact : Daniel Brunner,  OPTO Group, Optics Dept

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    Antoine Petit, CEO of the CNRS, was welcomed on February 11th at FEMTO-ST.

     

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  • Photo John Dudley

    John Dudley awarded by the international Society of Optics and Photonics (SPIE)

    He obtains  the "SPIE Harold E. Edgerton" Award 2019 for pioneering applications based on ultra-short light pulses in optical fibers.

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  • Award "innovation in optomechatronic research"

    Several members of different scientific departments of FEMTO-ST were rewarded for their joint paper "Photonic microsystem made by dynamic microassembly" at the ISOT conference "19th International Symposium on Optomechatronic Technology" which took place from November 5 to 8 in Cancun

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    Awarded for his « CellSelect project » he proposes a robot capable of improving the precision and speed of cell sorting in the field of innovative therapies.

    His work is the result of research carried out of Biomedical Microrobotics Team of FEMTO-ST Institute.

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  • Jean-Antoine Seon : awarded with the 2nd national PHD prize in robotics

    Jean Antoine was distinguished on November 22th at the CNRS headquarters for his pioneer international work on a micro robotic dextral hand.

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    For outstanding contributions to the open data initiative by conducting and posting experimental system fault and run-to-failure data sets with exceptional scientific value.

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    The American Institute of Physics (AIP) has featured our recent article in a special science highlight, known as a Scilight. Due to its novelty and close connection with ever-present phenomena, reported results are of wide interest to the general public.

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  • Entrepreneurs-PhD Award in Bourgogne-Franche-Comté: 3 laureates from FEMTO-ST institute

    Vladimir Gauthier (CellSelect project), Aliyasin El Ayouch (Metabsorber project), Romain Viala (MICAD project),   were rewarded during the regional final which took place on October 16 in Dijon

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    For one of the most dexterous miniature robots with 7 degrees of freedom, allowing micromanipulation and microassembly in extremely confined spaces

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