 I guess you know how time consuming can be searching for a motor fault when you don't have visibility of your system. Now, you don't have to deal with this problem anymore. Let me introduce you our latest cutting edge reference design. It combines the world of motor control and the world of sensing. This will add advanced motor control algorithm and a wide range of sensors like accelerometers, gyroscopes, environmental sensors and even more, audio and ultrasound microphones. Thanks to all this data, you can track and analyze any aspect of your motor performance, optimize its efficiency and prevent downtime cost. Now let me explain you how this reference design works. Here we have these two boards, the motor control and the S-twin box. The motor control board runs control algorithm while acquiring all motor data. It's based on the small and compact ST-SPIN32G4 and it can drive a brushless motor up to 250W. And here we have the S-twin box, the IoT industrial node that brings sensing, connectivity and ultra low power computation into the system. Now these two boards can be easily connected together, thanks to this flat cable. And you can start immediately to monitor all sensors and motor information while running sophisticated motor control algorithms. But most importantly, you can label and collect all these synchronized and heterogeneous data into reusable datasets. Now that's really useful, isn't it? The availability of all this data allows you to design and develop your own solution, as in this particular case, where we use them to feed the digital representation of our physical motor, like in a digital twin. All of this it's possible thanks to our user-friendly application, that is an extension of the ST-HGI Datalog, dedicated for motor control. Now let me show you how this reference design enables us to create an example of a normally detection application using our Nano Edge AI Studio, a toolkit for design machine learning model on ST-N32 microcontroller. Here we are combining the motor current with the MEMS Vibrameter information to detect and identify three different kinds of system anomalies, motor-based vibration, asymmetric load and belt fault. When the ST-M32 Nano Edge AI model running on the ST-WIN box detects an anomaly, drives the motor control board to bring the system in safe condition and so avoiding permanent deterioration. So now we can really say that this system closed the gap between three words, motor control, IoT and artificial intelligence, enabling you to create your next level smart motor control solution. For more information check out ST.com.