Exécution sur un appareil Android
Guides et tutoriels consacrés à la manière de convertir un modèle Simulink® en une application et de l'exécuter sur un appareil Android.
Rubriques
- Run Model on Android Devices
Create and run a Simulink model on an Android device.
- Simulink Scope Block and Mobile Apps
The features and properties of using the Simulink Scope block with mobile devices.
- DSP System Toolbox Array Plot Block and Mobile Apps
The features and properties of using the DSP System Toolbox™ Array Plot block with mobile devices.
- Tune Parameters and Monitor Data in Model Running on Android Devices
Use your host computer to monitor and control a model running on an Android device.
- Communicate with Hardware Using Connected IO
Get peripheral data from the hardware before deploying the Simulink model on the hardware.
- Import Project to Google Android Studio
Import project to Google® Android Studio.
- Model Multitask Execution and Real-Time Code Execution Profiling on Android Device
This example shows how to use multirate multitasking along with real-time code execution profiling for a Simulink® model that uses blocks from the Simulink Support Package for Android® Devices.
Sélection d՚exemples
Communicating with Android Devices
Use the Simulink® Support Package for Android® Devices to tune the parameters and monitor the signals of an algorithm running on an Android device.
Monitor Accelerometer Data Using Gauges on Android
Deploy gauges on an Android® device by using Simulink® Support Package for Android Devices. The Simulink model used in this example showcases the visualization capabilities of the Gauge and Half Gauge blocks. On deployment, the support package generates a standalone application with three gauges that monitor the accelerometer data along the three axes of the device in real-time.
Detect and Track Objects Using Deep Learning on Android Device
Use the Simulink® Support Package for Android® Devices to deploy a deep learning algorithm that detects and tracks an object on your Android device such as a phone or tablet. This algorithm uses the ResNet-18-based YOLOv2 neural network to identify the object captured by the camera. You can experiment with different objects around you to see how accurately the network detects images on your Android device.
Recognize and Display Spoken Commands on Android Device
Use the Simulink® Support Package for Android® Devices to deploy a deep learning algorithm that recognizes and displays commands spoken through your Android device such as a phone or tablet. This algorithm uses a deep learning convolutional neural network to recognize a given set of commands and displays them on the dashboard of the application. The algorithm used in this example is pretrained to recognize these commands: yes, no, up, down, left, right, on, off, stop, and go. The algorithm also recognizes silence and background noise.
Code Verification and Validation Using PIL Simulation on Android
Use the Simulink® Support Package for Android® Devices for code verification and validation using processor-in-the-loop (PIL) simulation on Android devices. It introduces the Simulink code generation and verification workflow by showing you how to configure a Simulink model to run PIL simulations on Android devices such as phones or tablets.
Classify Human Voice Using YAMNet on Android Device
Use the Simulink® Support Package for Android® Devices and a pretrained YAMNet network to classify human voices.
Model Code Profiling for Multiband Dynamic Range Compression System Using Android Device
Use multi-rate multitasking along with real-time code execution profiling for a digital audio multiband dynamic range compression system on an Android® device.
