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AI for Signals and Images

Wavelet-based techniques for machine learning and deep learning, GPU acceleration, hardware deployment, signal labeling

Wavelet techniques are effective for obtaining sparse, compressive data representations or features, which you can use in machine learning and deep learning workflows. Wavelet Toolbox™ supports deployment of multiscale feature extraction algorithms through MATLAB^® Coder™ and GPU Coder™ for a number of targets. To take advantage of the performance benefits offered by a modern graphics processing unit (GPU), certain Wavelet Toolbox functions can perform operations on a GPU. These functions provide GPU acceleration for your workflows. Wavelet Toolbox also provides functionality to perform signal labeling.

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Featured Examples

Anomaly Detection Using Autoencoder and Wavelets

Learn how wavelet features can be used to detect arc faults in a DC system.

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Classify Time Series Using Wavelet Analysis and Deep Learning

Classify electrocardiogram data using deep learning and the continuous wavelet transform.

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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.

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Detect Anomalies Using Wavelet Scattering with Autoencoders

Learn how to develop an alert system for predictive maintenance using wavelet scattering and deep learning.

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Time-Frequency Convolutional Network for EEG Data Classification

Classify electroencephalographic (EEG) time series from persons with and without epilepsy.

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Fault Detection Using Wavelet Scattering and Recurrent Deep Networks

Classify faults in acoustic recordings of air compressors using a wavelet scattering network paired with a recurrent neural network.

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Human Health Monitoring Using Continuous Wave Radar and Deep Learning

Reconstruct electrocardiogram signals using a bidirectional long short-term memory network and wavelet multiresolution analysis.

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Parasite Classification Using Wavelet Scattering and Deep Learning

Classify parasitic infections using wavelet image scattering and deep learning.

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Signal Recovery with Differentiable Scalograms and Spectrograms

Use differentiable time-frequency transforms and gradient descent to recover a time-domain signal without the need for phase information.

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Time-Frequency Feature Embedding with Deep Metric Learning

Construct wavelet-based feature embeddings to classify EEG signals from persons with and without epilepsy.

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AI for Signals and Images

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Related Information

Featured Examples

Anomaly Detection Using Autoencoder and Wavelets

Classify Time Series Using Wavelet Analysis and Deep Learning

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

Detect Anomalies Using Wavelet Scattering with Autoencoders

Time-Frequency Convolutional Network for EEG Data Classification

Fault Detection Using Wavelet Scattering and Recurrent Deep Networks

Human Health Monitoring Using Continuous Wave Radar and Deep Learning

Parasite Classification Using Wavelet Scattering and Deep Learning

Signal Recovery with Differentiable Scalograms and Spectrograms

Time-Frequency Feature Embedding with Deep Metric Learning