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Improving of the interpretation of linear filtering preprocessing-based multiscale permutation entropy., , and . SSP, page 190-194. IEEE, (2023)On the segmentation of plantar foot thermal images with Deep Learning., , , , and . EUSIPCO, page 1-5. IEEE, (2019)Multiscale Permutation Entropy: Statistical Characterization on Autoregressive and Moving Average Processes., , , and . EUSIPCO, page 1-5. IEEE, (2019)Do the generalized correlation methods improve time delay estimation of the muscle fiber conduction velocity?, , , and . ISABEL, page 181:1-181:5. ACM, (2011)Empirical mode decomposition revisited using ordinal pattern concepts.. EUSIPCO, page 2186-2190. IEEE, (2022)Local Legendre Polynomial Fitting-Based Preprocessing for Improving the Interpretation of Permutation Entropy in Stationary Time Series.. EUSIPCO, page 1998-2002. IEEE, (2023)Detection of sEMG muscle activation intervals using Gaussian Mixture Model and Ant Colony Classifier., , , and . EUSIPCO, page 1713-1717. IEEE, (2016)Legendre polynomial modeling of time-varying delay applied to surface EMG signals - Derivation of the appropriate time-dependent CRBs., , , and . Signal Process., (2015)Improvement of Statistical Performance of Ordinal Multiscale Entropy Techniques Using Refined Composite Downsampling Permutation Entropy., , , and . Entropy, 23 (1): 30 (2021)Are Deep Learning Models Practically Good as Promised? A Strategic Comparison of Deep Learning Models for Time Series Forecasting., , and . EUSIPCO, page 1477-1481. IEEE, (2022)