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 GPU Coder™ Support Package for NVIDIA GPUs to capture the live video stream from a webcam and display the prediction results on a monitor connected to the Jetson platform.

Generate CUDA® code for a Simulink® model that can detect and output lane marker boundaries on an image. This example takes RGB image as an input and uses the imresize (Image Processing Toolbox), rgb2gray, ordfilt2 (Image Processing Toolbox), hough (Image Processing Toolbox), houghpeaks (Image Processing Toolbox), and houghlines (Image Processing Toolbox) functions that are part of Image Processing Toolbox™ to detect lane markings. This example closely follows Lane Detection on the GPU by Using the houghlines Function.

A typical implementation of fog rectification algorithm. The example uses conv2, rgb2gray, and imhist (Image Processing Toolbox) functions. This example closely follows Fog Rectification example. This example illustrates the following concepts: Verification of GPU Environment.

Demonstrates how you can use powerful signal processing techniques and Convolutional Neural Networks together to classify ECG signals. We will also showcase how CUDA® code can be generated from the Simulink® model. This example uses the pretrained CNN network from the Classify Time Series Using Wavelet Analysis and Deep Learning example of the Wavelet Toolbox™ to classify ECG signals based on images from the CWT of the time series data. For information on training, see Classify Time Series Using Wavelet Analysis and Deep Learning (Wavelet Toolbox).

Develop a CUDA® application from a Simulink® model that performs lane and vehicle detection using convolutional neural networks (CNN). This example takes the frames of a traffic video as an input, outputs two lane boundaries that correspond to the left and right lanes of the ego vehicle, and detects vehicles in the frame. This example uses the pretrained lane detection network from the Lane Detection Optimized with GPU Coder example of the GPU Coder Toolbox™. For more information, see Lane Detection Optimized with GPU Coder. This example also uses the pretrained vehicle detection network from the Object Detection Using YOLO v2 Deep Learning example of the Computer Vision toolbox™. For more information, see Object Detection Using YOLO v2 Deep Learning (Computer Vision Toolbox).

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).