First- and second-order cyclostationary signal separation using morphological component analysis |
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| Affiliation: | 1. Université de Lyon, F-42023 Saint Etienne, France;2. Université de Saint Etienne, Jean Monnet, F-42000 Saint-Etienne, France;3. LASPI, IUT de Roanne, F-42334, France;4. Lebanese University, EDST, AZM Center for Research in Biotechnology, Tripoli, Lebanon;1. School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430081, China;2. Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan University of Science and Technology, Wuhan 430081, China;1. Univ Lyon, INSA Lyon, LVA, EA677, 69621, Villeurbanne, France;2. Institut de Recherche d’Hydro-Québec (IREQ), J3X 1S1, Varennes, QC, Canada;3. École de Technologie Supérieure (ÉTS), H3C 1K3, Montréal, QC, Canada;4. Andritz Hydro Canada Inc, H9R 1B9, Point-Claire, QC, Canada;1. Department of Astronautical Science and Mechanics, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China;2. Ministry of Education Key Laboratory of High-efficiency and Clean Mechanical Manufacture, School of Mechanical Engineering, Shandong University, Jinan, Shandong 250061, China;1. Vrije Universiteit Brussel, Department of Mechanical Engineering, Pleinlaan 2, Elsene, Belgium;2. Univ Lyon, INSA-Lyon, Laboratoire Vibrations, F-69621 Villeurbanne, France;3. MIT, Research Laboratory of Electronics, 77 Massachusetts Ave, Cambridge, MA 02139, USA;4. United States Naval Academy, Weapons & Systems Engineering Department, 105 Maryland Avenue, Annapolis, MD 21401, USA;1. Pusan National University, Pusan 609-735, Republic of Korea;2. Institute of Sound and Vibration Research, University of Southampton, Southampton SO17 1BJ, United Kingdom |
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| Abstract: | Cyclostationarity (CS) has proven to be effective in the treatment and identification of signal components for diagnostic and prognosis purposes. CS research has focused on algorithms, in terms of simplicity and computational efficiency. The performance of algorithms largely depends on the signals being analyzed.The objective of this research paper is to exploit the CS characteristics of signals in the context of morphological component analysis (MCA) method. It proposes a novel methodology used for separating between the periodic (First-Order Cyclostationarity: CS1) and random (Second-Order Cyclostationarity: CS2) sources by means of one sensor measurement. This MCACS2 methodology is based on MCA, where each of the two sources is sparsely represented by a special dictionary: i) the CS1 periodic structure is sparsely represented by means of the Discrete Cosine Transform dictionary, and ii) the CS2 random component is sparsely represented by a new proposed dictionary derived from Envelope Spectrum Analysis. Subsequently, a simulation study is performed in order to validate the proposed new MCACS2 method followed by tests on real GRF biomechanical signals. The result concludes by stating that such a novel algorithm provides an additional way for the exploitation of cyclostationarity and may be useful in other domain applications. |
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| Keywords: | Cyclostationarity Morphological component analysis Deterministic/random signal separation Source separation Biomechanical signal analysis |
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