AI 응용 사례
오디오, 생체의학, 예측 정비, 레이다 및 무선 통신
신호 처리 기법과 딥러닝 기법을 음성 인식, 심전도 분류 및 뇌전도 잡음 제거 같은 실제 사례에 통합합니다.
도움말 항목
오디오
- Anomaly Detection Using Convolutional Autoencoder with Wavelet Scattering Sequences
Detect anomalies in acoustic data using wavelet scattering and thedeepSignalAnomalyDetectorobject. (R2024a 이후) - 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. (R2021a 이후) - Train Spoken Digit Recognition Network Using Out-of-Memory Features
Train a spoken digit recognition network on out-of-memory auditory spectrograms using a transformed datastore. - 딥러닝 신경망을 사용하여 음성 잡음 제거하기
완전 연결 신경망과 컨벌루션 신경망을 사용하여 음성 신호의 잡음을 제거합니다. - Acoustic Scene Classification with Wavelet Scattering (Wavelet Toolbox)
Use wavelet scattering and joint time-frequency scattering with a support vector machine to classify urban environments by sound. (R2024b 이후) - Musical Instrument Classification with Joint Time-Frequency Scattering (Wavelet Toolbox)
Classify musical instruments using joint time-frequency features paired with a 3-D convolutional network. (R2024b 이후) - Acoustic Scene Recognition Using Late Fusion (Wavelet Toolbox)
Create a multi-model late fusion system for acoustic scene recognition. - Wavelet Time Scattering Classification of Phonocardiogram Data (Wavelet Toolbox)
Classify human phonocardiogram recordings using wavelet time scattering and a support vector machine classifier.
생체의학
- Human Health Monitoring Using Continuous Wave Radar and Deep Learning
Use a deep learning network to reconstruct electrocardiograms from continuous-wave radar signals. (R2022b 이후) - Human Activity Recognition Using Signal Feature Extraction and Machine Learning
Extract features from smartphone sensor signals and use them to classify human activity. (R2021b 이후) - Hand Gesture Classification Using Radar Signals and Deep Learning
Classify ultra-wideband impulse radar signal data using a MISO convolutional neural network. (R2021b 이후) - 장단기 기억 신경망을 사용하여 심전도 신호 분류하기
딥러닝과 신호 처리를 사용하여 심박 심전도 데이터를 분류합니다. - Detect Anomalies in Signals Using deepSignalAnomalyDetector
Use autoencoders to detect abnormal points or segments in time-series data. (R2023a 이후) - 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. (R2022a 이후) - Denoise EEG Signals Using Differentiable Signal Processing Layers
Remove EOG noise from EEG signals using deep learning regression. (R2021b 이후) - 웨이블릿 분석 및 딥러닝을 사용하여 시계열 분류하기
연속 웨이블릿 변환과 심층 컨벌루션 신경망을 사용하여 심전도 신호를 분류합니다. - Signal Source Separation Using W-Net Architecture
Use a deep learning network to separate two mixed signal sources. (R2022b 이후) - Wavelet Time Scattering Classification of Phonocardiogram Data (Wavelet Toolbox)
Classify human phonocardiogram recordings using wavelet time scattering and a support vector machine classifier. - Time-Frequency Convolutional Network for EEG Data Classification (Wavelet Toolbox)
Classify electroencephalographic (EEG) time series from persons with and without epilepsy. (R2023a 이후)
소음, 진동, 가혹성(Noise, Vibration, Hashness)
- 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. (R2024a 이후) - Anomaly Detection Using Convolutional Autoencoder with Wavelet Scattering Sequences
Detect anomalies in acoustic data using wavelet scattering and thedeepSignalAnomalyDetectorobject. (R2024a 이후) - Detect Anomalies in Machinery Using LSTM Autoencoder
Use a long short-term memory autoencoder to detect anomalies in data from an industrial machine. (R2023a 이후) - Crack Identification from Accelerometer Data (Deep Learning Toolbox)
Use wavelet and deep learning techniques to detect and localize transverse pavement cracks. - Detect Anomalies Using Wavelet Scattering with Autoencoders (Deep Learning Toolbox)
Learn how to develop an alert system for predictive maintenance using wavelet scattering and deep learning. - Fault Detection Using Wavelet Scattering and Recurrent Deep Networks (Deep Learning Toolbox)
Classify faults in acoustic recordings of air compressors using a wavelet scattering network paired with a recurrent neural network.
레이다 및 무선 통신
- Automated Labeling of Time-Frequency Regions for AI-Based Spectrum Sensing Applications
Use rule-based methods or unsupervised learning techniques to help automate time-frequency data labeling. - Export Labeled Data from Signal Labeler for AI-Based Spectrum Sensing Applications
Use deep learning networks and the Signal Labeler app to identify frames from the Bluetooth® and Wi-Fi® wireless standards. - Wireless Resource Allocation Using Graph Neural Network
Use graph neural networks for power allocation in wireless networks. (R2024b 이후) - 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. - Direction-of-Arrival Estimation Using Deep Learning
Estimate direction of arrival using deep learning by predicting angular directions directly from the sample covariance matrix. - Hand Gesture Classification Using Radar Signals and Deep Learning
Classify ultra-wideband impulse radar signal data using a MISO convolutional neural network. (R2021b 이후) - Pedestrian and Bicyclist Classification Using Deep Learning (Radar Toolbox)
Classify pedestrians and bicyclists based on their micro-Doppler characteristics using deep learning and time-frequency analysis. (R2021a 이후) - LPI Radar Waveform Classification Using Time-Frequency CNN (Radar Toolbox)
Train a time-frequency convolutional neural network (CNN) to classify received radar waveforms based on modulation scheme. (R2024a 이후) - Radar Target Classification Using Machine Learning and Deep Learning (Radar Toolbox)
Classify radar returns using machine and deep learning approaches. (R2021a 이후) - Radar and Communications Waveform Classification Using Deep Learning (Phased Array System Toolbox)
Classify radar and communications waveforms using the Wigner-Ville distribution (WVD) and a deep convolutional neural network (CNN).
관련 정보
- AI를 사용한 오디오 처리 (Audio Toolbox)
- AI를 사용한 레이다 (Radar Toolbox)
- AI를 사용한 무선 통신 (Communications Toolbox)
