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Hardwarebereitstellung

C/C++ Codegenerierung, GPU Codegenerierung, Raspberry Pi^®, NVIDIA^® Jetson^®

Stellen Sie Deep-Learning- und Machine-Learning-Workflows auf Hardware bereit.

Enthaltene Beispiele

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.

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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.

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Deep Learning Code Generation on ARM for Fault Detection Using Wavelet Scattering and Recurrent Neural Networks

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.

Seit R2023a
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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.

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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.

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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^®.

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