DySpect-Net: Neural Network Predicting Spectrum From Non-Uniformly Sampled Data

Accurate spectral estimation from irregularly sampled signals is a long-standing challenge in signal processing, particularly in biomedical applications. Traditional methods, such as the Fast Fourier Transform (FFT), rely on resampling, which introduces artefacts and computational inefficiencies. To...

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Ngā kaituhi matua:	Jana Sturekova, Patrik Kamencay, Peter Sykora, Silvia Suchalova, Daniel Matthes, Mirco Fuchs
Hōputu:	Artigo
Reo:	Inglês
I whakaputaina:	IEEE 2025-01-01
Rangatū:	IEEE Access
Ngā marau:	Spectral estimation non-uniform sampling deep learning neural networks frequency analysis DySpect-Net
Urunga tuihono:	https://ieeexplore.ieee.org/document/11153421/
Ngā Tūtohu:	Tāpirihia he Tūtohu Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!

DySpect-Net: Neural Network Predicting Spectrum From Non-Uniformly Sampled Data

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