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The winning teams in the category Cyber-Physical Systems

The Idea Challenge finalists in Cyber-Physical Systems

The Stockholm-based start-up Gleechi AB received the first prize for their high-tech software solution which aims to revolutionize the way people interact with computers. Based on machine learning algorithms, Gleechi has developed the first software to generate natural hand motion. Examples include gaming, animation, virtual reality, digital showrooms, robotics and serious gaming.

Silver went to UAVIA Drones from France who offer one-click aerial inspections and surveillance. The technology enables total autonomous drone operation in the field without any need for human intervention.

The Italian start-up Cohaerentia received the third prize. The team from Milan developed a multipurpose fibre optic sensor exploiting a universal hardware core and software customized for predictive maintenance manifold demands.

The winning teams will not only receive a cash prize but also get access to EIT Digital’s pan-European innovation network and will benefit international growth support through the EIT Digital Accelerator.

The Idea Challenge pitches were included in the Paris Node Innovation Day showcasing the recent activities in France and Europe along the area of Cyber-Physical Systems. The keynote was held by Julien Mascolo, Senior Researcher in Factory Innovation Department at FIAT Research Centre, who presented the future of manufacturing for the automotive industry.

The three winners of the Idea Challenge in the category Cyber-Physical Systems are:

First place 40,000 €: Gleechi

Gleechi have developed the first software solution that makes it easy for hands to be animated so they can move and interact freely and realistically.

Second Place 25,000 €: UAVIA

UAVIA enables total autonomous drone operation in the field without any need for human intervention. – IoT and Drones finally united.

Third Place 15,000 €: Cohaerentia

Multipurpose fibre optic sensor exploiting a universal hardware core and software customized for predictive maintenance manifold demands.

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