Sistemi IA integrati
Eseguire e implementare soluzioni di Deep Learning su dispositivi basati su processori ARM® e GPU NVIDIA® integrate. Generare codice e utilizzare l'hardware per ottimizzare la segmentazione del segnale, la classificazione e il rilevamento delle anomalie.
Informazioni complementari
Esempi in primo piano
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.
