{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/104911"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/104911","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Multidimensional and multivariate empirical mode decomposition","abstract":"Over the last decade, Empirical Mode Decomposition (EMD) has developed into a versatile tool for adaptive, scale-based modal decomposition. EMD has proven to be capable of decomposing multivariate signals with cross-channel mode alignment. However, the algorithms for envelope identification in multivariate EMD come with a computational burden rendering it unsuitable for the large computational demands of multidimensional signal processing. The current work introduces an alternative approach to multivariate EMD, and by combining it with existing fast and adaptive algorithms, paves the way for performing multivariate EMD on multidimensional signals. The application of the algorithm developed through the current study, when applied to the Direct Numerical Simulation (DNS) of a flat-plate boundary layer (a large dataset), revealed the desired scale separation behaviour across multiple data channels. This proves that the algorithm could be useful for a broad range of future problems.","abstract_html":"Over the last decade, Empirical Mode Decomposition (EMD) has developed into a versatile tool for adaptive, scale-based modal decomposition. EMD has proven to be capable of decomposing multivariate signals with cross-channel mode alignment. However, the algorithms for envelope identification in multivariate EMD come with a computational burden rendering it unsuitable for the large computational demands of multidimensional signal processing. The current work introduces an alternative approach to multivariate EMD, and by combining it with existing fast and adaptive algorithms, paves the way for performing multivariate EMD on multidimensional signals. The application of the algorithm developed through the current study, when applied to the Direct Numerical Simulation (DNS) of a flat-plate boundary layer (a large dataset), revealed the desired scale separation behaviour across multiple data channels. This proves that the algorithm could be useful for a broad range of future problems.","abstract_has_math":false,"creators":["Thirumalaisamy, Mruthun R."],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Aerospace Engineering","degree_department":null,"school":null,"contributors":["Ansell, Phillip J."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-08-23T20:01:15Z","date_published":"2019-08-23T20:01:15Z","updated_at":"2026-07-22T22:24:42Z","subjects":["empirical mode decomposition","modal analysis"],"languages":["en"],"rights":["Copyright 2019 Mruthun Thirumalaisamy"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/104911","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Ansell, Phillip J."]},{"key":"dc:creator","label":"Author","values":["Thirumalaisamy, Mruthun R."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-08-23T20:01:15Z","2019-04-26","2019-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Aerospace Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["empirical mode decomposition","modal analysis"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2019 Mruthun Thirumalaisamy"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/104911"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Over the last decade, Empirical Mode Decomposition (EMD) has developed into a versatile tool for adaptive, scale-based modal decomposition. EMD has proven to be capable of decomposing multivariate signals with cross-channel mode alignment. However, the algorithms for envelope identification in multivariate EMD come with a computational burden rendering it unsuitable for the large computational demands of multidimensional signal processing. The current work introduces an alternative approach to multivariate EMD, and by combining it with existing fast and adaptive algorithms, paves the way for performing multivariate EMD on multidimensional signals. The application of the algorithm developed through the current study, when applied to the Direct Numerical Simulation (DNS) of a flat-plate boundary layer (a large dataset), revealed the desired scale separation behaviour across multiple data channels. This proves that the algorithm could be useful for a broad range of future problems.","Submission original under an indefinite embargo labeled 'Open Access'. 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However, the algorithms for envelope identification in multivariate EMD come with a computational burden rendering it unsuitable for the large computational demands of multidimensional signal processing. The current work introduces an alternative approach to multivariate EMD, and by combining it with existing fast and adaptive algorithms, paves the way for performing multivariate EMD on multidimensional signals. The application of the algorithm developed through the current study, when applied to the Direct Numerical Simulation (DNS) of a flat-plate boundary layer (a large dataset), revealed the desired scale separation behaviour across multiple data channels. This proves that the algorithm could be useful for a broad range of future problems.","Submission original under an indefinite embargo labeled 'Open Access'. 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