Analysis of local time-frequency entropy features for nonstationary signal components time supports detection |
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| Affiliation: | 1. Faculty of Engineering, University of Rijeka, Croatia;2. College of Engineering, Qatar University, Doha, Qatar;3. Centre for Clinical Research, University of Queensland, Brisbane, Australia;1. Planetary Science Directorate, Southwest Research Institute, 1050 Walnut St. #300, Boulder, CO 80302, USA;2. Center for Wave Phenomena, Colorado School of Mines, 1500 Illinois Street, Golden, CO 80401, USA;1. Department of Physics, Hanyang University, 04763 Seoul, South Korea;2. HYU-HPSTAR-CIS High Pressure Research Center, Hanyang University, 04763 Seoul, South Korea;3. Spallation Neutron Source, Oak Ridge National Laboratory, 37831, TN, USA;4. Center for High Pressure Science and Technology Advanced Research, Shanghai 201203, PR China;1. Howard University College of Medicine, Washington, DC;2. Division of Dermatology, Department of Medicine, University of Washington, Seattle, Washington;3. Division of Hematology, Department of Medicine, University of Washington, Seattle, Washington;4. Division of Dermatopathology, Department of Pathology, University of Washington, Seattle, Washington;1. Department of Mathematics and Statistics, University of Ottawa, 585 King Edward Avenue, Ottawa ON K1N 6N5, Canada;2. Department of Mathematics and Statistics, University of Cyprus, P.O. Box 20537, CY 1678 Nicosia, Cyprus;3. Department of Mathematics and Statistics, University of New South Wales, Sydney, NSW 2052, Australia;1. Reservoir Labs, New York City, NY, United States;2. Division of Computer, Electrical and Mathematical Science and Engineering, King Abdullah University of Science and Technology, Thuwal, 23955-2900, Saudi Arabia;3. Department of Mathematics, Stanford University, United States;4. Laboratoire de Probabilités et Modèles Aléatoires, Université Paris Diderot, France;5. Institute for Computational and Mathematical Engineering, Stanford University, United States;6. Mechanical Engineering Department, Stanford University, CA, United States |
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| Abstract: | Identification of different specific signal components, produced by one or more sources, is a problem encountered in many signal processing applications. This can be done by applying the local time-frequency-based Rényi entropy for estimation of the instantaneous number of components in a signal. Using the spectrogram, one of the most simple quadratic time-frequency distributions, the paper proves the local applicability of the counting property of the Rényi entropy. The paper also studies the influence of the entropy order and spectrogram parameters on the estimation results. Numerical simulations are provided to quantify the observed behavior of the local entropy in the case of intersecting components. The causes of decrements in the local number of time supports in the time-frequency plane are also studied. Finally, results are provided to illustrate the findings of the study and its potential use as a key step in multicomponent instantaneous frequency estimation. |
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| Keywords: | Time-frequency Rényi entropy Spectrogram Component number |
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