Deep Learning, Machine Learning und das Internet der Dinge
R2026aVerwenden Sie das Simulink® Support Package für Arduino®-Hardware zusammen mit DSP System Toolbox™ und Statistics and Machine Learning Toolbox™, um Deep Learning, Machine Learning und IoT-Anwendungen zu implementieren.
Enthaltene Beispiele
Identify Punch and Flex Hand Gestures Using Machine Learning Algorithm on Arduino Hardware
Use the Simulink® Support Package for Arduino® Hardware to identify punch and flex hand gestures using a machine learning algorithm. The example is deployed on an Arduino Nano 33 IoT hardware board that uses an onboard LSM6DS3 6DOF IMU sensor to identify the hand gestures. The output from the machine learning algorithm, after identifying whether a hand gesture is a punch or a flex, is transmitted to the serial port where 0 represents a punch and 1 represents a flex.
Identify Shapes Using Machine Learning on Arduino Hardware
Use the Simulink® Support Package for Arduino® Hardware to identify shapes such as a triangle and circle using a machine learning algorithm. The model in this example is deployed on an Arduino Nano 33 IoT hardware board with an onboard LSM6DS3 IMU Sensor.
Perform Predictive Maintenance for Rotating Device Using Machine Learning Algorithm on Arduino
Use Simulink® Support Package for Arduino® Hardware to predict and monitor the health of a rotating device using a machine learning algorithm. Using the support package, you can collect data from rotating device connected to an internet of things (IoT) sensor to monitor its condition and performance.
