Esecuzione sull'hardware di destinazione

Use Simulink® Support Package for Raspberry Pi® Hardware to read from and write to an SPI EEPROM.

Illustrates how to use Simulink® Support Package for Raspberry Pi® Hardware to configure and read temperature from a TMP102 sensor.

Develop a Simulink® model to implement an algorithm to read the Accelerometer On-board Sense HAT and control the rotation of the image displayed on the LED matrix.

Use the Simulink® Support Package for Raspberry Pi® Hardware to control the color of an 8x8 LED matrix on Raspberry Pi Sense HAT from a web page over WebSockets.

Stream images captured from a webcam on a Raspberry Pi® board to the host computer using a ROS communication interface. In this example, you stream images from your Raspberry Pi board to your host computer using the ROS Publish blocks. You use the ROS MATLAB® command line interface to display the images on your host computer.

Identify the boundaries of objects in a live video stream on Raspberry Pi® hardware by using a MATLAB Function block with the Simulink® Support Package for Raspberry Pi Hardware. The process of identifying boundaries of objects is known as edge detection. This example implements the Sobel edge detection algorithm to identify the boundaries of the objects.

Use the V4L2 Video Capture and the SDL Video Display blocks from the Raspberry Pi® block library to implement an image inversion algorithm with a Simulink® model, and to run the model on Raspberry Pi hardware.

Use ALSA Audio Playback block from the Raspberry Pi® block library to implement a parametric audio equalizer algorithm with a Simulink® model and to run the model on Raspberry Pi hardware.

Shift the pitch of an audio signal on Raspberry Pi® hardware by using a MATLAB Function block with the Simulink® Support Package for Raspberry Pi Hardware.

Use the Simulink® Support Package for Raspberry Pi® Hardware to estimate the Direction of Arrival (DOA) of a sound source using multiple microphone pairs within a linear array using the Raspberry Pi hardware board. A servo motor is used to point towards the sound source based on the estimated DOA.

Use Raspberry Pi® hardware and an Android® device to build a surveillance camera.

Use Simulink® Support Package for Raspberry Pi® Hardware to monitor vehicle engine RPM and read the data on a web browser.

Use the Simulink® Support Package for Raspberry Pi® Hardware to implement MODBUS® TCP/IP communication between MODBUS client and server devices. It also shows how to communicate between the two devices in four modes of operation, Client Read, Client Write, Server Read, and Server Write.

Use the Simulink® Support Package for Raspberry Pi® Hardware to publish a message to a topic from Raspberry Pi in the ThingSpeak™ MQTT broker. This example also shows how to subscribe to a topic and receive a message from the ThingSpeak MQTT broker to Raspberry Pi. For more information on MQTT protocol, see Nozioni di base su MQTT (ThingSpeak) and Pubblicazione di messaggi MQTT e sottoscrizione di argomenti di messaggi. In this example, ThingSpeak is the MQTT broker and Raspberry Pi board is the MQTT client (publisher and subscriber).

Use the Simulink® Support Package for Raspberry Pi® Hardware to fetch and execute commands from a ThingSpeak™ TalkBack queue and use them to change the status of an LED onboard a Raspberry Pi hardware board.

Use the Simulink® Support Package for Raspberry Pi® Hardware to deploy a deep learning algorithm that classifies objects using the ResNet-50 convolutional neural network. This pretrained network is 50 layers deep and can classify images into 1000 object categories, such as keyboard, mouse, pencil, and many more. You can experiment with different objects in your surroundings to see how accurately the network classifies images on the Raspberry Pi hardware.

Use the Simulink® Support Package for Raspberry Pi® Hardware to recognize images of handwritten digits from zero to nine. In this example, a web camera interfaced with a Raspberry Pi hardware board is used to capture images of the handwritten numbers. The algorithm recognizes the digits and then outputs a label for the digit along with its prediction probability.

Use the Simulink® Support Package for Raspberry Pi® Hardware to predict and monitor the health of a rotating device using a machine learning algorithm. You can use this example for predictive maintenance of any rotating device or piece of equipment so that you can fix them before they fail.