Scientific Research
Most of my scientific work revolves around the concept of Information Entropy, which can be described as a measure of unpredictability, or the “complexity” of a system. The truth is, nobody knows exactly what Entropy actually is (xkcd), but there is no doubt it is a useful tool.
More specifically, I work with Permutation Entropy, which is a measure of the information contained in the order of data structures. The applications include, but are not limited to, bioelectrical signals, fault detection in rotatory machines, and stock market time series.
If you find this work interesting, feel free to contact me directly. I will be glad to discuss these topics in greater detail.
Scientific Research
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On the Statistical Properties of Multiscale Permutation Entropy and its Refinements, with Applications on Surface Electromyographic Signals
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The Refined Composite Downsampling Permutation Entropy Is a Relevant Tool in the Muscle Fatigue Study Using sEMG Signals
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The Impact of Linear Filter Preprocessing in the Interpretation of Permutation Entropy
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On the Implementation of Downsampling Permutation Entropy variants in the detection of Bearing Faults in Rotatory Machines
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Improvement of Statistical Performance of Ordinal Multiscale Entropy Techniques Using Refined Composite Downsampling Permutation Entropy
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On the Deterministic Estimaton of Multiscale Permutation Entropy of High-Order Autoregressive-Moving-Average Processes as a Function of ARMA Parameters
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Multiscale Permutation Entropy: Statistical Characterization on Autoregressive and Moving Average Processes
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On the Statistical Properties of Multiscale Permutation Entropy: Characterization of the Estimator’s Variance
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Theoretical Study of Multiscale Permutation Entropy on Finite-Length Fractional Gaussian Noise
