 Hello welcome for the demonstration of integration of ST sensors with the industrial cloud for the applications related to the predictive maintenance of machine. So currently we are integrating it with the industrial cloud provided by the general electrics GE. The cloud is called the predicts cloud and what we are showing here is emulating the scenarios for the devices in the real life on the machines. So this is a board here on which we have all the ST sensors for example the accelerometer, gyroscope, pressure sensor, temperature, humidity. So this collects all the information related to the motion as well as the environment. Apart from that it also collects information related to certain key events. So we for example we have tried to emulate here the scenario that if there is a tilt in the machine or if there is a free fall of the machine so the sensor will detect that event will gather all the corresponding data around it which is the raw data package it and send it to the predicts cloud. Currently we are using SIGFox as a media to send the data to the predicts but it can be any media as the sensors are essentially agnostic of the media of transmission. So as you can see as I move the board you the data is collected and the event is sent to the predicts. So you can see a wake-up event happened here and then corresponding to that at that point what was the temperature what was the humidity and all the raw data which corresponding to the acceleration at that point of time. Once all the data is available on the cloud the user or the customer can run lot of analytics on this data which allows them to see whether the pattern of the data which is arriving currently from the machine matches with the desired pattern of the machine. So if there are deviation that means there is possibly a need of maintenance. So this is how it will allow us to predict the needs for the maintenance and allow the customers let's say to work on the machine's maintenance before it actually breaks. So this will allow for the saving of the time cost and the customers to eventually help even their customer better. So that essentially is the summary of the demonstration. Thank you.