KI-Anwendungen
Audio, Biomedizin, Predictive Maitenance, Radar und drahtlose Kommunikation
Integration von Signalverarbeitungs- und Deep-Learning-Techniken in reale Anwendungen wie Spracherkennung, Elektrokardiogramm-Klassifizierung und Elektroenzephalogramm-Entrauschung.
Themen
Audio
- Anomaly Detection Using Convolutional Autoencoder with Wavelet Scattering Sequences
Detect anomalies in acoustic data using wavelet scattering and thedeepSignalAnomalyDetectorobject. (Seit R2024a) - 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. (Seit R2021a) - 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. - Denoise Speech Using Deep Learning Networks
Denoise speech signals using fully connected and convolutional neural networks. - 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. (Seit R2024b) - 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. (Seit R2024b) - 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.
Biomedizinisch
- Human Health Monitoring Using Continuous Wave Radar and Deep Learning
Use a deep learning network to reconstruct electrocardiograms from continuous-wave radar signals. (Seit R2022b) - Human Activity Recognition Using Signal Feature Extraction and Machine Learning
Extract features from smartphone sensor signals and use them to classify human activity. (Seit R2021b) - Hand Gesture Classification Using Radar Signals and Deep Learning
Classify ultra-wideband impulse radar signal data using a MISO convolutional neural network. (Seit R2021b) - Classify ECG Signals Using Long Short-Term Memory Networks
Classify heartbeat electrocardiogram data using deep learning and signal processing. - Detect Anomalies in Signals Using deepSignalAnomalyDetector
Use autoencoders to detect abnormal points or segments in time-series data. (Seit R2023a) - Waveform Segmentation Using Deep Learning
Segment human electrocardiogram signals using time-frequency analysis and deep learning. - Classify Arm Motions Using EMG Signals and Deep Learning
Classify arm motions using labeled EMG signals and a long short-term memory network. (Seit R2022a) - Denoise EEG Signals Using Differentiable Signal Processing Layers
Remove EOG noise from EEG signals using deep learning regression. (Seit R2021b) - Classify Time Series Using Wavelet Analysis and Deep Learning
Classify ECG signals using the continuous wavelet transform and a deep convolutional neural network. - Signal Source Separation Using W-Net Architecture
Use a deep learning network to separate two mixed signal sources. (Seit R2022b) - 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. (Seit R2023a)
Lärm, Vibrationen und Rauheit
- 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. (Seit R2024a) - Anomaly Detection Using Convolutional Autoencoder with Wavelet Scattering Sequences
Detect anomalies in acoustic data using wavelet scattering and thedeepSignalAnomalyDetectorobject. (Seit R2024a) - Detect Anomalies in Machinery Using LSTM Autoencoder
Use a long short-term memory autoencoder to detect anomalies in data from an industrial machine. (Seit R2023a) - 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.
Radar und Funk
- 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. (Seit R2024b) - 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. (Seit R2021b) - 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. (Seit R2021a) - 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. (Seit R2024a) - Radar Target Classification Using Machine Learning and Deep Learning (Radar Toolbox)
Classify radar returns using machine and deep learning approaches. (Seit R2021a) - 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).
Verwandte Informationen
- AI for Audio (Audio Toolbox)
- AI for Radar (Radar Toolbox)
- KI für drahtlose Kommunikation (Communications Toolbox)
