Sistemas de IA integrados

Deep learning en dispositivos basados en ARM^® y unidades GPU NVIDIA^® integradas

Ejecute y despliegue soluciones de deep learning en hardware.

Funciones

`cwtLayer`	Continuous wavelet transform (CWT) layer (desde R2022b)
`modwtLayer`	Maximal overlap discrete wavelet transform (MODWT) layer (desde R2022b)
`stftLayer`	Short-time Fourier transform layer (desde R2021b)

`dlcwt`	Deep learning continuous wavelet transform (desde R2022b)
`dlmodwt`	Deep learning maximal overlap discrete wavelet transform and multiresolution analysis (desde R2022a)
`dlstft`	Deep learning short-time Fourier transform (desde R2021a)
`cwtfilterbank`	Continuous wavelet transform filter bank
`findchangepts`	Find abrupt changes in signal
`findpeaks`	Encontrar los máximos locales
`modwt`	Maximal overlap discrete wavelet transform
`risetime`	Rise time of positive-going bilevel waveform transitions
`stft`	Transformada de Fourier de tiempo corto (desde R2019a)
`signalFrequencyFeatureExtractor`	Streamline signal frequency feature extraction (desde R2021b)
`signalTimeFeatureExtractor`	Streamline signal time feature extraction (desde R2021a)
`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.

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.