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    Method and apparatus for authentication of a 3D structure
    3.
    发明授权
    Method and apparatus for authentication of a 3D structure 有权

    公开(公告)号：US10789463B2

    公开(公告)日：2020-09-29

    申请号：US15985091

    申请日：2018-05-21

    Applicant: ALPVISION S.A.

    Inventor： Martin Kutter ,  Fred Jordan

    IPC: G06K9/00 ,  G07D7/00 ,  G07D7/2033 ,  G07D7/202 ,  G06K7/14 ,  G06T7/00

    Abstract: New authentication features are proposed that are visible, can be authenticated with a mobile equipment and yet are challenging to counterfeit. In a possible embodiment, the surface of the authentication feature may have three-dimensional characteristics, which can be recognized by a handheld camera, such as a smartphone camera, while it cannot be easily reproduced by a simple scan and print procedure. In a further possible embodiment, at least two different viewpoints of the authentication feature may be acquired using a smartphone camera and the resulting images may be analyzed using the smartphone processor to identify the three-dimensional characteristics of the authentication feature. The manufacturing of the feature may be performed at a low cost by embossing the three dimensional structure on a surface. The authentication feature may be carried by a self-adhesive label or directly embedded on the product packaging.

    Cognitive computing methods and systems based on biological neural networks
    5.
    发明授权
    Cognitive computing methods and systems based on biological neural networks 有权

    公开(公告)号：US12210967B2

    公开(公告)日：2025-01-28

    申请号：US17274020

    申请日：2019-09-08

    Applicant: ALPVISION S.A.

    Inventor： Frederic Jordan ,  Martin Kutter ,  Yves Delacretaz

    IPC: G06F17/00 ,  G06N3/00 ,  G06N3/06 ,  G06N3/08

    Abstract: A Biological Neural Network (BNN) core unit comprising a neural cell culture, an input stimulation unit, an output readout unit may be controlled through its various life cycles to provide data processing functionality. An automation system comprising an environmental and chemical controller unit adapted to operate with the BNN stimulation and readout data interfaces facilitates the monitoring and adaptation of the BNN core unit parameters. Pre-processing and post-processing of the BNN interface signals may further facilitate the training and reinforcement learning by the BNN. Multiple BNN core units may also be assembled together as a stack. The proposed system provides a BNN Operating System as a core component for a wetware server to receive, process and transmit data for different client applications without exposing the BNN core unit components to the client user while requiring significantly less energy than conventional silicon-based hardware and software information processing for high-level cognitive computing tasks.

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