Modeling

Prepare Simulink^® model for hardware connection, add blocks to support NVIDIA^® hardware

Prepare Simulink model for hardware connection, add blocks to support NVIDIA hardware

Blocks

Communication

Serial Read	Read data from serial port
Serial Write	Write data to serial port

Network

TCP/IP Receive	Receive data over TCP/IP network from remote host
TCP/IP Send	Send data over TCP/IP network to another remote host
UDP Receive	Receive UDP packets from UDP host
UDP Send	Send UDP packets to UDP host

Audio and Video

ALSA Audio Capture	Capture audio from sound card using ALSA
ALSA Audio Playback	Send audio to sound card for playback using ALSA
Audio File Read	Read audio frames from an audio file
Camera	Capture video from a USB or CSI camera connected to the NVIDIA target
SDL Video Display	Display video on a monitor connected to the NVIDIA target

Topics

Model Configuration Parameters for NVIDIA Hardware Board

Parameter and configuration options for creating and running applications on an NVIDIA hardware board.

Open Block Library for NVIDIA Hardware

Locate Simulink block library for NVIDIA hardware.

Simulation Acceleration by Using GPU Coder (GPU Coder)

Improve simulation speed by using NVIDIA GPUs.

Code Generation from Simulink Models with GPU Coder (GPU Coder)

Generate CUDA code from Simulink models by using GPU Coder.

Deep Learning in Simulink by Using MATLAB Function Block (GPU Coder)

Simulate and generate code for deep learning models in Simulink using MATLAB function blocks.

Deep Learning in Simulink by Using Deep Neural Networks Library (GPU Coder)

Simulate and generate code for deep learning models in Simulink using library blocks.

Serial Port Mapping for NVIDIA Jetson Boards

Identify the port names for the serial ports on NVIDIA Jetson boards.

Featured Examples

Deploy and Classify Webcam Images on NVIDIA Jetson TX2 Platform from Simulink

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.

Open Script

GPU Code Generation for Lane Detection in Simulink

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 (GPU Coder).

GPU Code Generation for a Fog Rectification Simulink Model

A typical implementation of fog rectification algorithm. The example uses conv2, im2gray, and imhist (Image Processing Toolbox) functions. This example closely follows Fog Rectification (GPU Coder) example. This example illustrates the following concepts: Verification of GPU Environment.

Code Generation for a Deep Learning Simulink Model to Classify ECG Signals

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).

Code Generation for a Deep Learning Simulink Model that Performs Lane and Vehicle Detection

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 (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).

Deploy Signal Classifier on NVIDIA Jetson Using Wavelet Analysis and Deep Learning

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).

Keyword Spotting in Audio using MFCC and LSTM Networks on NVIDIA Embedded Hardware from Simulink

Deploy a Simulink® model on the NVIDIA® Jetson board for keyword spotting in audio data. This example identifies the keyword(YES) in the input audio data using Mel Frequency Cepstral Coefficients (MFCC) and a pretrained Bidirectional Long Short-Term Memory (BiLSTM) network.

MATLAB Coder Support Package for NVIDIA Jetson and NVIDIA DRIVE Platforms Documentation

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