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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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Principais autores: Jana Sturekova, Patrik Kamencay, Peter Sykora, Silvia Suchalova, Daniel Matthes, Mirco Fuchs
Format: Artigo
Sprog:Inglês
Udgivet: IEEE 2025-01-01
Serier:IEEE Access
Fag:
Spectral estimation
non-uniform sampling
deep learning
neural networks
frequency analysis
DySpect-Net
Online adgang:https://ieeexplore.ieee.org/document/11153421/
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