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Systèmes d’IA embarqués

Deep Learning sur des dispositifs basés sur ARM® et des GPU NVIDIA® embarqués

Exécutez et déployez des solutions de Deep Learning sur du hardware.

Fonctions

développer tout

cwtLayerContinuous wavelet transform layer (depuis R2022b)
icwtLayerInverse continuous wavelet transform layer (depuis R2024b)
modwtLayerMaximal overlap discrete wavelet transform layer (depuis R2022b)
stftLayerShort-time Fourier transform layer (depuis R2021b)
istftLayerInverse short-time Fourier transform layer (depuis R2024a)
dlcwtDeep learning continuous wavelet transform (depuis R2022b)
dlicwtDeep learning inverse continuous 1-D wavelet transform (depuis R2024b)
dlmodwtDeep learning maximal overlap discrete wavelet transform and multiresolution analysis (depuis R2022a)
dlstftDeep learning short-time Fourier transform (depuis R2021a)
dlistftDeep learning inverse short-time Fourier transform (depuis R2024a)
cwtfilterbankContinuous wavelet transform filter bank
findchangeptsFind abrupt changes in signal
findpeaksTrouver les maxima locaux
modwtMaximal overlap discrete wavelet transform
risetime Rise time of positive-going bilevel waveform transitions
stftShort-time Fourier transform
signalFrequencyFeatureExtractorStreamline signal frequency feature extraction (depuis R2021b)
signalTimeFeatureExtractorStreamline signal time feature extraction (depuis R2021a)
signalTimeFrequencyFeatureExtractorStreamline signal time-frequency feature extraction (depuis R2024a)
waveletScatteringWavelet time scattering

Rubriques

Sélection d՚exemples

Wireless Resource Allocation Using Graph Neural Network

Wireless Resource Allocation Using Graph Neural Network

Use graph neural networks for power allocation in wireless networks.

Modulation Classification Using Wavelet Analysis on NVIDIA Jetson

Modulation Classification Using Wavelet Analysis on NVIDIA Jetson

Use wavelets to classify waveforms on a NVIDIA Jetson®.

Deploy Signal Segmentation Deep Network on Raspberry Pi

Deploy Signal Segmentation Deep Network on Raspberry Pi

Generate a MEX function and a standalone executable to perform waveform segmentation on a Raspberry Pi.

Classify ECG Signals Using DAG Network Deployed to FPGA

Classify ECG Signals Using DAG Network Deployed to FPGA

Classify human electrocardiogram (ECG) signals by deploying a trained directed acyclic graph (DAG) network.

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

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.

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

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.

Deploy Signal Classifier Using Wavelets and Deep Learning on Raspberry Pi

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.

Real-Time Noise Detection on Raspberry Pi Using Deep Signal Anomaly Detector

Real-Time Noise Detection on Raspberry Pi Using Deep Signal Anomaly Detector

Detect the presence of noise on a Raspberry Pi device.