Eingebettete KI-Systeme

Deep Learning auf ARM^®-basierten Geräten und eingebetteten NVIDIA^®-GPUs

Führen Sie Deep-Learning-Lösungen aus und stellen Sie diese auf Hardware bereit.

Funktionen

`cwtLayer`	Continuous wavelet transform layer (Seit R2022b)
`icwtLayer`	Inverse continuous wavelet transform layer (Seit R2024b)
`modwtLayer`	Maximal overlap discrete wavelet transform layer (Seit R2022b)
`stftLayer`	Short-time Fourier transform layer (Seit R2021b)
`istftLayer`	Inverse short-time Fourier transform layer (Seit R2024a)

`dlcwt`	Deep learning continuous wavelet transform (Seit R2022b)
`dlicwt`	Deep learning inverse continuous 1-D wavelet transform (Seit R2024b)
`dlmodwt`	Deep learning maximal overlap discrete wavelet transform and multiresolution analysis (Seit R2022a)
`dlstft`	Deep learning short-time Fourier transform (Seit R2021a)
`dlistft`	Deep learning inverse short-time Fourier transform (Seit R2024a)
`cwtfilterbank`	Continuous wavelet transform filter bank
`findchangepts`	Find abrupt changes in signal
`findpeaks`	Find local maxima
`modwt`	Maximal overlap discrete wavelet transform
`risetime`	Rise time of positive-going bilevel waveform transitions
`stft`	Short-time Fourier transform
`signalFrequencyFeatureExtractor`	Streamline signal frequency feature extraction (Seit R2021b)
`signalTimeFeatureExtractor`	Streamline signal time feature extraction (Seit R2021a)
`signalTimeFrequencyFeatureExtractor`	Streamline signal time-frequency feature extraction (Seit R2024a)
`waveletScattering`	Wavelet time scattering

Deep Learning Code Generation on ARM for Fault Detection Using Wavelet Scattering and Recurrent Neural Networks (Wavelet Toolbox)
Perform acoustic-based fault detection on a Raspberry Pi^® using wavelet scattering and recurrent neural networks. (Seit R2023a)

Use graph neural networks for power allocation in wireless networks.

Use wavelets to classify waveforms on a NVIDIA Jetson^®.

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

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

Generate and deploy a CUDA^® executable to classify electrocardiogram signals using wavelet-derived features.

Create and deploy a Simulink^® model for signal classification using wavelet-based features.

Classify human electrocardiogram signals on a Raspberry Pi using scalograms and a deep convolutional neural network.

Detect the presence of noise on a Raspberry Pi device.