 Okay, so let's move on. After some of the sensors, we do have as well here power dedicated to automotive solutions. And let's come back a little bit to the AI but in the automotive world, which is a bit different from industrial. And I'm here together with Alessandro and Max, and they will show us a few words about AI in automotive, the solution from SD. So guys, the stage is yours. Yes, thank you. So thank you very much and welcome. We are very pleased to present you this new solution for AI at the edge in the automotive market. And we are able to put a complete neural network inside a normal microcontroller. So no specific IC. But we come close to the microphone. But with a very standard microcontroller, this is our course for Mac, but we can also use our course for one Mac. We're able to embed an LSTM neural network, which is able to analyze the status of the car while driving. Analyzing status like bumpy roads or normal drive or parking or anything else. And obviously this can serve as a base to build other neural network applications on the edge without having to use any other power inside the car. What do we see here? What we see here is just a simple system which is analyzing data coming from a sensor. We're using one of our accelerometer six axis. This data coming into the microcontroller is analyzed in time series of six seconds and every six seconds a response has been produced. So that's it. All right. Road state monitoring is just one of the many automotive segments you are in. Road state monitoring is one of the new trends because obviously we all want to increase safety for the driver. So for one side we want to increase the safety by monitoring the streets, but also by monitoring the status of health of the driver. So this data collection that here is simply done with an accelerometer can be expanded with several types of sensors and combined together to give a better picture of the status of health of the driver. So this is for sure a trend in the automotive today. Thank you guys. Thank you. Thank you. Thank you.