 At ST, we are offering a wide range of products to augment your application and improve the love of your customers. The S-Template 2 G4 and its rich set of advanced analog peripherals is the perfect candidate for motor control applications, like washing machine, for example. In order to go a step further, we have implemented machine learning on the microcontroller to enable advanced features such as predictive maintenance and smart usage optimization. Here, we have used an optimized AI algorithm generated and trained in a matter of minutes thanks to Nano-HGI Studio. We can accurately estimate the load of the laundry with current measurement data. And with this, we can, in fact, achieve an unparalleled level of energy and water efficiency. No additional sensors are needed. And moreover, we are increasing the accuracy of the prediction up to three times versus state of the art. The uncertainty of this regression algorithm is 100 grams versus almost 500 grams of uncertainty with standard method, which is a real game changer. In this way, the cycle can be optimized. The machine will be able to automatically use the right quantity of water and the right amount of energy. The quality of your clothes is preserved and the planet is happier. There are three key facts you should take away here. First, everything is done on a single cost and energy-effective microcontroller. Indeed, the motor control algorithm generated with the ST motor control SDK enable us to control the jump motion very precisely. This new sensorless algorithm enable energy saving during multiple start and stop and washing cycles. On top of this, the machine learning library are optimized enough to fit on the internal memory of the same microcontroller, enabling the best accuracy on the market. This way, we don't need any connectivity since the processing is happening on the very deep edge. And by that, I mean on the motor itself. So increasing security and safety. This means we keep saving a lot of energy and cost by processing the signal directly on the MCU and outputting only meaningful information. Then you can organize the rest of your system without any constraints. And finally, no additional hardware is needed. The current from the motor control algorithm is also used to extrapolate the weight of the claws. This demo is only one example of what you can achieve today using ST most advanced dedicated product and software tool, enabling state-of-the-art of artificial antigens and motor control solutions. For more information, please visit st.com slash stm32ai.