분류

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.

(Phased Array System Toolbox)

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.

장단기 기억 신경망을 사용하여 심전도 신호 분류하기

딥러닝과 신호 처리를 사용하여 심박 심전도 데이터를 분류합니다.

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.

(Signal Processing Toolbox)

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.

딥러닝을 사용한 파형 분할

시간-주파수 분석과 딥러닝을 사용하여 사람의 심전도 신호를 분할합니다.

Classify Arm Motions Using EMG Signals and Deep Learning

Classify arm motions using labeled EMG signals and a long short-term memory network.

(Signal Processing Toolbox)

웨이블릿 분석 및 딥러닝을 사용하여 시계열 분류하기

딥러닝과 연속 웨이블릿 변환을 사용하여 심박 심전도 데이터를 분류합니다.

Wavelet Time Scattering Classification of Phonocardiogram Data

Classify human phonocardiogram recordings using wavelet time scattering and a support vector machine classifier.

(Wavelet Toolbox)

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.

(Wavelet Toolbox)

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.

(Wavelet Toolbox)

Pedestrian and Bicyclist Classification Using Deep Learning

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

(Radar Toolbox)

Spoken Digit Recognition with Wavelet Scattering and Deep Learning

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

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.

(Wavelet Toolbox)

Detect Air Compressor Sounds in Simulink Using Wavelet Scattering

Use the Wavelet Scattering block and a pretrained deep learning network to classify audio signals.

(DSP System Toolbox)