Use the MATLAB® Coder™ Support Package for NVIDIA Jetson and NVIDIA DRIVE Platforms with embedded boards from NVIDIA®. The example uses a simple vector addition algorithm to illustrate: Connection to the embedded board from the MATLAB environment.

Capture and process images from a Raspberry Pi Camera Module V2 connected to the NVIDIA® Jetson Nano. The MATLAB® Coder™ Support Package for NVIDIA Jetson and NVIDIA DRIVE Platforms allows you to capture images from the Camera Module V2 and bring them into the MATLAB environment for processing. In this example you learn how to develop a Sobel edge detection algorithm by using this capability.

Generate and deploy a CUDA® executable for an image segmentation application that uses deep learning. It uses the MATLAB® Coder™ Support Package for NVIDIA Jetson and NVIDIA DRIVE Platforms to deploy the executable on the NVIDIA DRIVE™ platform. This example performs code generation on the host computer and builds the generated code on the target platform by using remote build capability of the support package. For more information, see Code Generation for Semantic Segmentation Network.

Deploy Image Processing Toolbox™ algorithms to NVIDIA® Jetson TX2 board using the GPU Coder™ Support Package for NVIDIA GPUs. The imtophat (Image Processing Toolbox) function that performs morphological top-hat filtering on a grayscale image is used as an example to demonstrate this concept. Top-hat filtering computes the morphological opening of the image (using imopen (Image Processing Toolbox)) and then subtracts the result from the original image. The generated CUDA® code uses shared memory to speed up the operations on the GPU.

Generate CUDA® code from a DAGNetwork object and deploy the generated code onto the NVIDIA® Jetson TX2 board using the GPU Coder™ Support Package for NVIDIA GPUs. This example uses the resnet50 deep learning network to classify images from a USB webcam video stream.

Deploy a Simulink® model on the NVIDIA® Jetson TX2 board for classifying webcam images. This example classifies images from a webcam in real-time by using the pretrained deep convolutional neural network, ResNet-50. The Simulink model in the example uses the camera and display blocks from the MATLAB® Coder™ Support Package for NVIDIA Jetson and NVIDIA DRIVE Platforms to capture the live video stream from a webcam and display the prediction results on a monitor connected to the Jetson platform.

Generate and deploy a CUDA® executable that classifies human electrocardiogram (ECG) signals using features extracted by the continuous wavelet transform (CWT) and a pretrained convolutional neural network (CNN).