{"id":"https://openalex.org/W2035266290","doi":"https://doi.org/10.12720/jcm.9.6.506-514","title":"Application of EMD Denoising Approach in Noisy Blind Source Separation","display_name":"Application of EMD Denoising Approach in Noisy Blind Source Separation","publication_year":2014,"publication_date":"2014-01-01","ids":{"openalex":"https://openalex.org/W2035266290","doi":"https://doi.org/10.12720/jcm.9.6.506-514","mag":"2035266290"},"language":"en","primary_location":{"id":"doi:10.12720/jcm.9.6.506-514","is_oa":false,"landing_page_url":"https://doi.org/10.12720/jcm.9.6.506-514","pdf_url":null,"source":{"id":"https://openalex.org/S184821498","display_name":"Journal of Communications","issn_l":"1796-2021","issn":["1796-2021","2374-4367"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4323867253","host_organization_name":"International Communication Association","host_organization_lineage":["https://openalex.org/P4323867253"],"host_organization_lineage_names":["International Communication Association"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Communications","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101622926","display_name":"Wei Wu","orcid":"https://orcid.org/0000-0002-3137-4086"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wei Wu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5009162241","display_name":"Hua Peng","orcid":"https://orcid.org/0000-0001-5761-3535"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hua Peng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.1586,"has_fulltext":false,"cited_by_count":21,"citation_normalized_percentile":{"value":0.78621011,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"9","issue":"6","first_page":"506","last_page":"514"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9562000036239624,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9546999931335449,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.789135217666626},{"id":"https://openalex.org/keywords/blind-signal-separation","display_name":"Blind signal separation","score":0.7232229709625244},{"id":"https://openalex.org/keywords/hilbert\u2013huang-transform","display_name":"Hilbert\u2013Huang transform","score":0.6395813226699829},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6213993430137634},{"id":"https://openalex.org/keywords/additive-white-gaussian-noise","display_name":"Additive white Gaussian noise","score":0.5993500351905823},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5844075679779053},{"id":"https://openalex.org/keywords/thresholding","display_name":"Thresholding","score":0.5749446749687195},{"id":"https://openalex.org/keywords/piecewise","display_name":"Piecewise","score":0.5639694929122925},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5286833643913269},{"id":"https://openalex.org/keywords/white-noise","display_name":"White noise","score":0.5235133767127991},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5004293918609619},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.48631319403648376},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.48033252358436584},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47755393385887146},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.4202398657798767},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.32778891921043396},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.06983354687690735},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.066905677318573},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.06420117616653442}],"concepts":[{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.789135217666626},{"id":"https://openalex.org/C120317606","wikidata":"https://www.wikidata.org/wiki/Q17105967","display_name":"Blind signal separation","level":3,"score":0.7232229709625244},{"id":"https://openalex.org/C25570617","wikidata":"https://www.wikidata.org/wiki/Q1006462","display_name":"Hilbert\u2013Huang transform","level":3,"score":0.6395813226699829},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6213993430137634},{"id":"https://openalex.org/C169334058","wikidata":"https://www.wikidata.org/wiki/Q353292","display_name":"Additive white Gaussian noise","level":3,"score":0.5993500351905823},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5844075679779053},{"id":"https://openalex.org/C191178318","wikidata":"https://www.wikidata.org/wiki/Q2256906","display_name":"Thresholding","level":3,"score":0.5749446749687195},{"id":"https://openalex.org/C164660894","wikidata":"https://www.wikidata.org/wiki/Q2037833","display_name":"Piecewise","level":2,"score":0.5639694929122925},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5286833643913269},{"id":"https://openalex.org/C112633086","wikidata":"https://www.wikidata.org/wiki/Q381287","display_name":"White noise","level":2,"score":0.5235133767127991},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5004293918609619},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.48631319403648376},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.48033252358436584},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47755393385887146},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.4202398657798767},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.32778891921043396},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.06983354687690735},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.066905677318573},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.06420117616653442},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.12720/jcm.9.6.506-514","is_oa":false,"landing_page_url":"https://doi.org/10.12720/jcm.9.6.506-514","pdf_url":null,"source":{"id":"https://openalex.org/S184821498","display_name":"Journal of Communications","issn_l":"1796-2021","issn":["1796-2021","2374-4367"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4323867253","host_organization_name":"International Communication Association","host_organization_lineage":["https://openalex.org/P4323867253"],"host_organization_lineage_names":["International Communication Association"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Communications","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1489793438","https://openalex.org/W1514159701","https://openalex.org/W1548802052","https://openalex.org/W2007221293","https://openalex.org/W2079724595","https://openalex.org/W2109896502","https://openalex.org/W2136424517","https://openalex.org/W2138402108","https://openalex.org/W2155686237","https://openalex.org/W2156855760","https://openalex.org/W2158940042","https://openalex.org/W2160097539","https://openalex.org/W2164061460","https://openalex.org/W2537559174","https://openalex.org/W2737101842","https://openalex.org/W3103391113","https://openalex.org/W3216905214","https://openalex.org/W6741692844","https://openalex.org/W6804521139"],"related_works":["https://openalex.org/W1974392290","https://openalex.org/W1911817635","https://openalex.org/W2810224748","https://openalex.org/W2353656796","https://openalex.org/W2011915004","https://openalex.org/W2130974430","https://openalex.org/W3103566292","https://openalex.org/W2074565401","https://openalex.org/W1967187784","https://openalex.org/W1607924090"],"abstract_inverted_index":{"Blind":[0],"Source":[1],"Separation":[2],"(BSS)":[3],"algorithms":[4],"based":[5],"on":[6],"the":[7,14,31,65,84,87,99,107],"noise-free":[8],"model":[9],"are":[10],"not":[11],"applicable":[12],"when":[13,106],"Signal":[15],"Noise":[16],"Ratio":[17],"(SNR)":[18],"is":[19,28,110],"low.":[20,111],"In":[21],"view":[22],"of":[23,102],"this":[24],"situation,":[25],"our":[26],"solution":[27],"to":[29,55],"denoise":[30,56],"mixtures":[32,57],"with":[33,58,83],"additive":[34],"white":[35],"Gaussian":[36],"noise":[37],"firstly,":[38],"and":[39,68,71,95],"then":[40,72],"use":[41],"BSS":[42,103],"algorithms.":[43],"This":[44,61],"paper":[45],"proposes":[46],"a":[47,91],"piecewise":[48],"Empirical":[49],"Mode":[50],"Decomposition":[51],"(EMD)":[52],"thresholding":[53],"approach":[54,62,89],"strong":[59],"noise.":[60],"can":[63,96],"distinguish":[64],"noise-dominated":[66],"IMFs":[67],"signal-dominated":[69],"IMFs,":[70],"respectively":[73],"apply":[74],"different":[75],"thresholdings":[76],"methods.":[77],"Simulation":[78],"results":[79],"show":[80],"that":[81],"compared":[82],"Wavelet":[85],"denoising,":[86],"proposed":[88],"has":[90],"better":[92],"denoising":[93],"performance,":[94],"remarkably":[97],"enhance":[98],"separation":[100],"performance":[101],"algorithms,":[104],"especially":[105],"signal":[108],"SNR":[109]},"counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":6},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":4},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}