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深度学习在信号处理领域的应用

利用信号处理和通信应用扩展深度学习工作流

通过将 Deep Learning Toolbox™ 与 Signal Processing Toolbox™、Wavelet Toolbox™ 和 Communications Toolbox™ 结合使用,将深度学习应用于信号处理和通信应用。有关音频和语音处理领域的应用,请参阅深度学习在音频处理领域的应用

App

信号标注器Label signal attributes, regions, and points of interest

主题

使用长短期记忆网络对 ECG 信号进行分类

此示例说明如何使用深度学习和信号处理对来自 PhysioNet 2017 Challenge 的心电图 (ECG) 数据进行分类。具体而言,该示例使用长短期记忆网络和时频分析。

使用小波分析和深度学习对时序分类

此示例说明如何使用连续小波变换 (CWT) 和深度卷积神经网络 (CNN) 对人体心电图 (ECG) 信号进行分类。

使用深度学习进行调制分类

此示例说明如何使用卷积神经网络 (CNN) 进行调制分类。

使用深度学习进行波形分割

此示例说明如何使用递归深度学习网络和时频分析来分割人体心电图 (ECG) 信号。

Label QRS Complexes and R Peaks of ECG Signals Using Deep Learning Network

This example shows how to use custom autolabeling functions in Signal Labeler to label QRS complexes and R peaks of electrocardiogram (ECG) signals.

Pedestrian and Bicyclist Classification Using Deep Learning

Classify pedestrians and bicyclists based on their micro-Doppler characteristics using time-frequency analysis and a deep learning network.

Radar and Communications Waveform Classification Using Deep Learning

This example shows how to classify radar and communications waveforms using the Wigner-Ville distribution (WVD) and a deep convolutional neural network (CNN).

Generate Synthetic Signals Using Conditional Generative Adversarial Network

Use a conditional generative adversarial network to produce synthetic data for model training.

Crack Identification From Accelerometer Data

This example shows how to use wavelet and deep learning techniques to detect transverse pavement cracks and localize their position.

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

This example shows how to generate and deploy a CUDA® executable that classifies human electrocardiogram (ECG) signals using features extracted by the continuous wavelet transform (CWT) and a pretrained convolutional neural network (CNN).

Deploy Signal Classifier Using Wavelets and Deep Learning on Raspberry Pi

This example shows the workflow to classify human electrocardiogram (ECG) signals using the Continuous Wavelet Transform (CWT) and a deep convolutional neural network (CNN).

特色示例