분류

신호 특성 분류, sequence-to-sequence 분류를 통해 신호 분할 수행

시간-주파수 기법을 사용하여 신호 분류를 위한 특징을 도출합니다.

함수

`cwtLayer`	Continuous wavelet transform (CWT) layer (R2022b 이후)
`modwtLayer`	Maximal overlap discrete wavelet transform (MODWT) layer (R2022b 이후)
`stftLayer`	Short-time Fourier transform layer (R2021b 이후)
`istftLayer`	Inverse short-time Fourier transform layer (R2024a 이후)

`dlcwt`	Deep learning continuous wavelet transform (R2022b 이후)
`dlmodwt`	Deep learning maximal overlap discrete wavelet transform and multiresolution analysis (R2022a 이후)
`dlstft`	Deep learning short-time Fourier transform (R2021a 이후)
`dlistft`	Deep learning inverse short-time Fourier transform (R2024a 이후)
`cwtfilterbank`	Continuous wavelet transform filter bank
`findchangepts`	신호 내 급격한 변화 찾기
`findpeaks`	국소 최댓값 구하기
`modwt`	Maximal overlap discrete wavelet transform
`risetime`	Rise time of positive-going bilevel waveform transitions
`stft`	단시간 푸리에 변환
`signalFrequencyFeatureExtractor`	Streamline signal frequency feature extraction (R2021b 이후)
`signalTimeFeatureExtractor`	Streamline signal time feature extraction (R2021a 이후)
`waveletScattering`	Wavelet time scattering

`edfheader`	Create header structure for EDF or EDF+ file (R2021a 이후)
`edfinfo`	Get information about EDF/EDF+ file (R2020b 이후)
`edfread`	Read data from EDF/EDF+ file (R2020b 이후)
`edfwrite`	Create or modify EDF or EDF+ file (R2021a 이후)
`paddata`	Pad data by adding elements (R2023b 이후)
`resize`	Resize data by adding or removing elements (R2023b 이후)
`trimdata`	Trim data by removing elements (R2023b 이후)
`signalDatastore`	Datastore for collection of signals (R2020a 이후)

Model wavelet scattering network in Simulink (R2022b 이후)

Classify Arm Motions Using EMG Signals and Deep Learning (Signal Processing Toolbox)
Classify arm motions using labeled EMG signals and a long short-term memory network.
Detect Air Compressor Sounds in Simulink Using Wavelet Scattering (DSP System Toolbox)
Use the Wavelet Scattering block and a pretrained deep learning network to classify audio signals.
Detect Anomalies in ECG Data Using Wavelet Scattering and LSTM Autoencoder in Simulink (DSP System Toolbox)
Use wavelet scattering and deep learning network to detect anomalies in ECG signals.
Manage Data Sets for Machine Learning and Deep Learning Workflows (Signal Processing Toolbox)
Organize, access, and manage data sets for different AI applications.

Use signal feature extraction objects and AI-based classification to identify faulty bearing signals in mechanical systems.

Use the locally interpretable model-agnostic explanation technique to interpret decision-making processes of a deep learning network.

Label signals and export data using Signal Labeler to train a deep learning classifier.

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

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

딥러닝과 신호 처리를 사용하여 심박 심전도 데이터를 분류합니다.

딥러닝과 연속 웨이블릿 변환을 사용하여 심박 심전도 데이터를 분류합니다.

Classify spoken digits using both machine learning and deep learning techniques.

Classify ultra-wideband impulse radar signal data using a MISO convolutional neural network.