 Hi, my name is Raphael Eriva, and today we'll be talking about BluNerG-LP, the ST Microelectronics Bluetooth Low Energy 5.2 Certified SOC, and its capability to address up to 128 multiple concurrent connections. The demo that we have prepared uses one BluNerG-LP configured as central unit, enabled to maintain multiple concurrent connections with several BluNerG-LP power peripheral units such as light switches, light, pressure sensors, thermometer sensors, garage openers, vacuum cleaners, door sensors, doorbells, and fan. Welcome to this CS 2021, I'm here in the demo room, where I want to introduce you to BluNerG-LP, that is the latest introduction to our BluNerG product family. BluNerG-LP is a Bluetooth 5.2 compliant system on chip, and in particular today with this demonstration we want to highlight the capability of our device to provision, set up, and maintain a neutral power, very low latency, multi-link star network over a Bluetooth Low Energy radio link. Each node in this demonstration are based out of the ST-Eval-IDB-011MV1, that is this blue board, and it's the official evaluation kit that we recommend our customer for evaluating all the capabilities of our BluNerG-LP system on chip. In particular we have, here with me next to me, this blue hexagon is the master role that is right now managing 13 simultaneous Bluetooth Low Energy connections. For each node we have of course the ST-Evaluation Kit for BluNerG-LP, and we have connected to it a smart display. So let's see now a few details about this demonstration. So I'm here with the master, as you can see on the smart display there are a few functions, fun control, light control, barometer, thermometer, and a garage door opener. So we can go one by one, if we select the fun control, right now we have at a speed of zero, and we can boost it up at five, and on the display of the remote pier you will see the change of fun intensity. Let's get back, light control. We have here in this demo we have set up four smart light bulbs, we can control those bulbs from here, so we turn it on light number one, number two, number three, and number four. So as you can see on the demo panel, the four light bulbs now are on, and as you can see also we have a smart switch device that is basically getting the information from the master as well. So we can control also the light bulbs directly from the switch node, and I will show you later on in the demonstration how to do that. Let's get back, barometer and thermometer. So on the STV we have pressure and temperature sensor that is on board. So we make use of this device in order to set up a smart barometer. And here you see the read of the pressure, and the same is displayed on the remote barometer device. We get back, we check also what's the temperature in the room, and here we are. And finally we can activate some commands inside our smart home. So we turn it on, we open the garage door, or we can close it as well. Close it right now. Okay, so all those types of commands and actuations basically are done through right commands on specific Bluetooth low energy characteristics. We have again all those wireless link simultaneously managed by the master device. But now let's see also the other way of the communication, the other path. So from the smart connected peers back to the master how to report a synchronous notification on status or on event detection. Give me just one second, I move on the panel, on the demo panel. So I'm back here in front of my demo panel, so we have a few examples on how to report back to our smart device, smart master some events. As I was mentioning earlier we have this switch control that can control the status of the smart bulbs. So if I now I want to turn it off the lights from this device, I will go ahead and turn them off one by one like this. So now those light bulbs are off. We have, so those are basically notifications sent from this smart switch back to our master. A similar type of implementation can be for example a doorbell where when someone is ringing the bell on the master it will pop up a given notification. Here I'm closing now the... In addition to the blue energy dash LP system on chip on the STVAL we also have available one of our latest MEMS sensor that is a 6-axis accelerometer and gyro device. The commercial name is LSM6DSOX and the particularity of this device is that it embeds a machine learning core that can be programmed with a dedicated decision tree in order to detect a specific event or specific action performed on the device itself. So the idea here is that we put our main system on chip blue energy LP in deep stop mode. We have a dedicated inter-appline coming from the accelerometer. So once the decision tree will identify a specific pattern and a specific action it will interrupt and wake up to the inter-appline our system on chip that as a consequence will send a notification back to the master interrupting the application running in the master notifying the user that a specific event a specific action has been recognized in the remote sleep. This will greatly help in reducing the overall power consumption on the sleep device that can be therefore battery operated without any problem. So now let's see a few examples of this type of implementation. So the first example that we have implemented in the decision tree inside the LSM6DSOX is a decision tree that is detecting the status of a door or of a window. So we have here the sensor. We have three different actions that are or status that are detected when the door is opened when it's in a still position and when it's closing. So let's see now how the master will be interrupted asynchronously by the slave as soon as those events are detected inside the machine learning core. So here is our door. Now I open the door and you will see the door opening. I'm keeping it still and then let it close and now it's still again. So you will see in the master at the same time those events that are sent back as a Bluetooth low energy notification. Another implementation that makes use of this machine learning core efficiently is vibration monitoring. So we have implemented in one of the node inside this network this specific feature detection. So I'm here the node is of course connected to the master. As soon as I move it a little bit you will see low vibration that is reported back through an asynchronous notification to our master device. Vibration you can set different threshold. So we have here implemented no vibration when the object is still low vibration and high vibration. So now if I move it very a lot you will see high vibration reported back to the master. So the last implementation that makes use of the machine learning core inside the LSM-6 DSOX device is a smart appliance that makes use of the embedded feature for detecting movement. So here we have a smart vacuum cleaner that on top we have put there our ST-Eval and as soon as the device will start moving, please, an event is now reported back to the master. When we stop the event that the device now is stopped is sent to the master again making use of this asynchronous notification. One last remark about this demonstration. So far we have seen our blue energy being the master simultaneously managing multiple links with all those late devices. Of course if you need a slightly different network topology that is an additional master like for example a smartphone, being the master of the network master that means that this device will be a slave of the smartphone, this type of topology is of course possible. We have a dedicated example inside the device SDK and so with this last remark I would like to thank you for watching this video and bye.