Systèmes d’IA embarqués
Exécutez et déployez des solutions de Deep Learning sur des équipements basés sur des processeurs ARM® et des GPU NVIDIA® embarqués. Générez du code et utilisez le hardware pour rationaliser la segmentation des signaux, la classification et la détection d’anomalies.
Informations connexes
Sélection d՚exemples
Deploy Signal Segmentation Deep Network on Raspberry Pi
Generate a MEX function and a standalone executable to perform waveform segmentation on a Raspberry Pi®.
Modulation Classification Using Wavelet Analysis on NVIDIA Jetson
Use wavelets to classify waveforms on a NVIDIA Jetson®.
Classify ECG Signals Using DAG Network Deployed to FPGA
Classify human electrocardiogram (ECG) signals by deploying a trained directed acyclic graph (DAG) network.
Deploy Wavelet Scattering and TensorFlow Lite Network on a Raspberry Pi Using Simulink
Generate and deploy Raspberry Pi code to detect air compressor sounds using the Wavelet Scattering Simulink® block and a pretrained deep learning network.
Deep Learning Code Generation on ARM for Fault Detection Using Wavelet Scattering and Recurrent Neural Networks
Perform acoustic-based fault detection on a Raspberry Pi using wavelet scattering and recurrent neural networks.
Deploy Signal Classifier on NVIDIA Jetson Using Wavelet Analysis and Deep Learning
Generate and deploy a CUDA® executable to classify electrocardiogram signals using wavelet-derived features.
Deploy Signal Classifier Using Wavelets and Deep Learning on Raspberry Pi
Classify human electrocardiogram signals on a Raspberry Pi using scalograms and a deep convolutional neural network.
Code Generation for a Deep Learning Simulink Model to Classify ECG Signals
Create and deploy a Simulink model for signal classification using wavelet-based features.
Real-Time Noise Detection on Raspberry Pi Using Deep Signal Anomaly Detector
Detect the presence of noise on a Raspberry Pi device.
