分类
使用以数据为中心的 AI 工作流对信号进行分类。
App
函数
模块
| Wavelet Scattering | Simulink 中的小波散射网络建模 (自 R2022b 起) |
相关信息
主题
- Use Experiment Manager Templates for Signal Processing Workflows (Signal Processing Toolbox)
Set up and run deep learning experiments for signal segmentation, classification, and regression.
- Signal Segmentation by Sweeping Hyperparameters (Signal Processing Toolbox)
- Signal Classification by Sweeping Hyperparameters (Signal Processing Toolbox)
- Signal Classification Using Transfer Learning (Signal Processing Toolbox)
- Signal Regression by Sweeping Hyperparameters (Signal Processing Toolbox)
精选示例
Direction-of-Arrival Estimation Using Deep Learning
Estimate direction of arrival using deep learning by predicting angular directions directly from the sample covariance matrix.
Indoor Non-Line-Of-Sight Localization Using Deep Learning
To address the NLOS challenge, fingerprinting-based methods have gained popularity. Unlike traditional techniques that use low-dimensional range and angle features, fingerprinting can leverage high-dimensional signatures—such as channel state information (CSI) or range-angle heatmaps which encapsulate rich environmental information, including NLOS effects. Deep learning models excel at extracting meaningful patterns from these complex, high-dimensional inputs, enabling direct mapping from signal fingerprints to precise position estimates.
CBRS Band Radar Parameter Estimation Using YOLOX
Detect radar pulses in noise and estimates the pulse parameters using a combination of time-frequency maps and a deep-learning object detector.
使用长短期记忆网络对 ECG 信号进行分类
使用深度学习和信号处理对心跳心电图数据进行分类。
Spoken Digit Recognition with Custom Log Spectrogram Layer and Deep Learning
Classify spoken digits using a deep convolutional neural network and a custom spectrogram layer.
Hand Gesture Classification Using Radar Signals and Deep Learning
Classify ultra-wideband impulse radar signal data using a MISO convolutional neural network.
Musical Instrument Classification with Joint Time-Frequency Scattering
Classify musical instruments using joint time-frequency features paired with a 3-D convolutional network.
使用深度学习进行波形分割
使用时频分析和深度学习对人体心电图信号进行分割。
Classify Arm Motions Using EMG Signals and Deep Learning
Classify arm motions using labeled EMG signals and a long short-term memory network.
使用小波分析和深度学习对时间序列分类
使用深度学习和连续小波变换对心跳心电图数据进行分类。
Wavelet Time Scattering Classification of Phonocardiogram Data
Classify human phonocardiogram recordings using wavelet time scattering and a support vector machine classifier.
Export Labeled Data from Signal Labeler for Deep Learning Classification
Label signals and export data using Signal Labeler to train a deep learning classifier.
Machine Learning and Deep Learning Classification Using Signal Feature Extraction Objects
Use signal feature extraction objects and AI-based classification to identify faulty bearing signals in mechanical systems.
Acoustic Scene Classification with Wavelet Scattering
Use wavelet scattering and joint time-frequency scattering with a support vector machine to classify urban environments by sound.
Air Compressor Fault Detection Using Wavelet Scattering
Classify faults in acoustic recordings of air compressors using a wavelet scattering network and a support vector machine.
Pedestrian and Bicyclist Classification Using Deep Learning
Classify pedestrians and bicyclists based on their micro-Doppler characteristics using deep learning and time-frequency analysis.
基于小波散射和深度学习的口述数字识别
使用机器学习和深度学习方法对语音数字进行分类。
Time-Frequency Convolutional Network for EEG Data Classification
Classify electroencephalographic (EEG) time series from persons with and without epilepsy.
Signal Classification Using Wavelet-Based Features and Support Vector Machines
Classify electrocardiogram signals using features derived from wavelets and an autoregressive model.
Detect Air Compressor Sounds in Simulink Using Wavelet Scattering
Use the Wavelet Scattering block and a pretrained deep learning network to classify audio signals.
