Run on Target Hardware

Getting Started with Deploying a MATLAB Function on the Raspberry Pi Hardware

Deploy a MATLAB® function as a standalone executable on the Raspberry Pi® Hardware using MATLAB Support Package for Raspberry Pi Hardware.

Deploy an Edge Detection Algorithm on the Raspberry Pi Hardware

Deploy an Edge detection function on the Raspberry Pi® hardware as a standalone executable using MATLAB® Support Package for Raspberry Pi Hardware. In this example, you will learn how to acquire live image from a webcam connected to the Raspberry Pi hardware, run the edge detection function on the acquired image, and display the result on the monitor that is connected to the same Raspberry Pi hardware.

Identify Objects Within Live Video Using ResNet-50 on Raspberry Pi Hardware

Predict the objects in a live video stream on Raspberry Pi® by deploying an image classification algorithm using MATLAB® Support Package for Raspberry Pi Hardware. The algorithm uses ResNet-50 neural network to identify the objects captured by the webcam that is connected to the Raspberry Pi hardware.

Send Inputs to MATLAB Function from Command Line of Raspberry Pi

Deploy a MATLAB® function that accepts inputs from command line of Raspberry Pi® using MATLAB Support Package for Raspberry Pi Hardware. In this example, a MATLAB function for adding two numbers is used for deployment.

Send Inputs to MATLAB Function from Command Line of Raspberry Pi in MATLAB Online

Deploy a MATLAB® function that accepts inputs from command line of Raspberry Pi® using MATLAB Support Package for Raspberry Pi Hardware in MATLAB Online. In this example, a MATLAB function for adding two numbers is used for deployment.

Classify Static Image Using Deep Learning on Raspberry Pi

Generate and deploy code for an image classification algorithm using MATLAB® Support Package for Raspberry Pi® Hardware. The algorithm uses the ResNet-50 neural network to identify the image that is passed as an input using the command line of Raspberry Pi.

Speech Command Recognition Code Generation on Raspberry Pi

Generate code and deploy feature extraction and speech command recognition network on Raspberry Pi hardware.

(Audio Toolbox)

Keyword Spotting in Noise Code Generation on Raspberry Pi

Demonstrates code generation for keyword spotting using a Bidirectional Long Short-Term Memory (BiLSTM) network and mel frequency cepstral coefficient (MFCC) feature extraction on Raspberry Pi™. MATLAB® Coder™ with Deep Learning Support enables the generation of a standalone executable (.elf) file on Raspberry Pi. Communication between MATLAB® (.mlx) file and the generated executable file occurs over asynchronous User Datagram Protocol (UDP). The incoming speech signal is displayed using a timescope. A mask is shown as a blue rectangle surrounding spotted instances of the keyword, YES. For more details on MFCC feature extraction and deep learning network training, visit Keyword Spotting in Noise Using MFCC and LSTM Networks.

(Audio Toolbox)

Acoustics-Based Machine Fault Recognition Code Generation on Raspberry Pi

Demonstrates code generation for acoustics-based machine fault recognition using a long short-term memory (LSTM) network and spectral descriptors. This example uses MATLAB® Coder™, MATLAB Coder Interface for Deep Learning, MATLAB Support Package for Raspberry Pi™ Hardware to generate a standalone executable (.elf) file on a Raspberry Pi that leverages performance of the ARM® Compute Library. The input data consists of acoustics time-series recordings from faulty or healthy air compressors and the output is the state of the mechanical machine predicted by the LSTM network. This standalone executable on Raspberry Pi runs the streaming classifier on the input data received from MATLAB and sends the computed scores for each label to MATLAB. Interaction between MATLAB script and the executable on your Raspberry Pi is handled using the user datagram protocol (UDP). For more details on audio preprocessing and network training, see Acoustics-Based Machine Fault Recognition.