Deep Neural Networks for Estimating Regularization Parameter in Sparse Time–Frequency Reconstruction
Time–frequency distributions (TFDs) are crucial for analyzing non-stationary signals. Compressive sensing (CS) in the ambiguity domain offers an approach for TFD reconstruction with high performance, but selecting the optimal regularization parameter for various signals remains challenging. Traditio...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Technologies |
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| Online Access: | https://www.mdpi.com/2227-7080/12/12/251 |
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