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Francesco Carlo Morabito

Researcher at Mediterranea University of Reggio Calabria

Publications -  413
Citations -  6934

Francesco Carlo Morabito is an academic researcher from Mediterranea University of Reggio Calabria. The author has contributed to research in topics: Artificial neural network & Electroencephalography. The author has an hindex of 39, co-authored 399 publications receiving 5676 citations. Previous affiliations of Francesco Carlo Morabito include Telecom Italia & Mediterranean University.

Papers
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Image fusion techniques for remote sensing applications

TL;DR: Three typical applications of data fusion in remote sensing are described and the results achieved by the proposedtechniques applied to real-time remote sensing situations are presented.
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Multivariate Multi-Scale Permutation Entropy for Complexity Analysis of Alzheimer’s Disease EEG

TL;DR: The possibility of distinguish among the brain states related to Alzheimer’s disease patients and Mild Cognitive Impaired subjects from normal healthy elderly is checked on a real, although quite limited, experimental database.
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Automatic Artifact Rejection From Multichannel Scalp EEG by Wavelet ICA

TL;DR: The method here proposed is shown to yield improved success in terms of suppression of artifact components while reducing the loss of residual informative data, since the components related to relevant EEG activity are mostly preserved.
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Prevalence and concomitants of glucose intolerance in European obese children and adolescents.

TL;DR: In these grossly obese children, both insulin resistance and impaired insulin secretion contribute to the elevation of glycemia, and the degree of obesity is related to cardiovascular risk factors independently of insulin resistance.
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Empirical Mode Decomposition vs. Wavelet Decomposition for the Extraction of Respiratory Signal From Single-Channel ECG: A Comparison

TL;DR: Two techniques of decomposition of the ECG signal into suitable bases of functions are proposed, such as the empirical mode decomposition (EMD) and the wavelet analysis, and performance achieved by applying these algorithms to extract the respiratory waveform shape from single-channel ECG is presented.