Wiliot chip produces low-level sensory data, the actual sensing is performed in the cloud. The chip has a digital I/O that can detect a change of state. It can be built into a sticker that is torn when opening an envelope to detect such an event. The raw data measured by the physical sensors are then combined with cloud-based machine learning algorithms, which translates these raw values onto the corresponding physical domains through algorithms that are self-learning and improving over time.
What sensors do you have on chip?
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Hi self learning Algorithms, exactly what are the learning over time ?
Efficiency, optimal data gathering, error rate reduction, operational resilience, data organisation?
It would be good to understand the basis of the self learning algorithms?
Thanks
Hans
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