专题:Blind Source Separation Techniques

This cluster of papers focuses on blind source separation and independent component analysis, with applications in signal decomposition, biomedical signals, and processing of convolutive mixtures. The methods often utilize sparse representation and exploit the temporal structure of the signals, particularly in the frequency domain.
最新文献
Blind source separation of event-related potentials using recurrent neural network

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FMRFT: Fusion Mamba and DETR for Query Time Sequence Intersection Fish Tracking

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Underwater acoustic signal recognition system with multi-scale hybrid cepstral feature strategy and joint deep network

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High-Resolution Time-Frequency Feature Selection and EEG Augmented Deep Learning for Motor Imagery Recognition

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Emotion recognition in EEG Signals: Deep and machine learning approaches, challenges, and future directions

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Frequency adaptive maximum second-order cyclostationarity blind deconvolution for bearing fault diagnosis

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Enhanced spectrum sensing for 5G and LTE signals using advanced deep learning models and hyperparameter tuning

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Applications of Fast Iterative Filtering in NMR Spectroscopy: Baseline Correction

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Noise Integral‐Based Sparse Bayesian Learning for DOA Estimation Using Grid Pruning and Adaptation

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SlimSeiz: Efficient Channel-Adaptive Seizure Prediction Using a Mamba-Enhanced Network

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近5年高被引文献
An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG

article Full Text OpenAlex 424 FWCI40.136

Advances in Sine Cosine Algorithm: A comprehensive survey

article Full Text OpenAlex 316 FWCI32.851

Low-Rank Matrix Recovery via Efficient Schatten p-Norm Minimization

article Full Text OpenAlex 288 FWCI11.007

A Review of Wavelet Analysis and Its Applications: Challenges and Opportunities

review Full Text OpenAlex 278 FWCI9.635

A high-performance neuroprosthesis for speech decoding and avatar control

article Full Text OpenAlex 249 FWCI73.294

Semi-Supervised DAS VSP Data Denoising Using Signal and Noise Distribution Difference

article Full Text OpenAlex 214 FWCI115.728

Self-Supervised Learning for Electroencephalography

article Full Text OpenAlex 214 FWCI26.714

Bearing fault diagnosis method based on adaptive maximum cyclostationarity blind deconvolution

article Full Text OpenAlex 191 FWCI21.074

A review on the application of blind deconvolution in machinery fault diagnosis

review Full Text OpenAlex 184 FWCI9.247

Iterative quantum amplitude estimation

article Full Text OpenAlex 178 FWCI16.425