{"id":"https://openalex.org/W2809779881","doi":"https://doi.org/10.1109/fskd.2017.8393396","title":"A novel denoising algorithm for acceleration signal based on compressed sensing","display_name":"A novel denoising algorithm for acceleration signal based on compressed sensing","publication_year":2017,"publication_date":"2017-07-01","ids":{"openalex":"https://openalex.org/W2809779881","doi":"https://doi.org/10.1109/fskd.2017.8393396","mag":"2809779881"},"language":"en","primary_location":{"id":"doi:10.1109/fskd.2017.8393396","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fskd.2017.8393396","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","raw_type":"proceedings-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/A5101424094","display_name":"Jianning Wu","orcid":"https://orcid.org/0000-0002-7250-1184"},"institutions":[{"id":"https://openalex.org/I111753288","display_name":"Fujian Normal University","ror":"https://ror.org/020azk594","country_code":"CN","type":"education","lineage":["https://openalex.org/I111753288"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jianning Wu","raw_affiliation_strings":["School of Mathematics and Computer Science, Fujian Normal University, Fuzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Mathematics and Computer Science, Fujian Normal University, Fuzhou, China","institution_ids":["https://openalex.org/I111753288"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043627328","display_name":"Yun Ling","orcid":"https://orcid.org/0000-0001-7009-2818"},"institutions":[{"id":"https://openalex.org/I111753288","display_name":"Fujian Normal University","ror":"https://ror.org/020azk594","country_code":"CN","type":"education","lineage":["https://openalex.org/I111753288"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yun Ling","raw_affiliation_strings":["School of Mathematics and Computer Science, Fujian Normal University, Fuzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Mathematics and Computer Science, Fujian Normal University, Fuzhou, China","institution_ids":["https://openalex.org/I111753288"]}]},{"author_position":"last","author":{"id":null,"display_name":"Jiajing Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I111753288","display_name":"Fujian Normal University","ror":"https://ror.org/020azk594","country_code":"CN","type":"education","lineage":["https://openalex.org/I111753288"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiajing Wang","raw_affiliation_strings":["School of Mathematics and Computer Science, Fujian Normal University, Fuzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Mathematics and Computer Science, Fujian Normal University, Fuzhou, China","institution_ids":["https://openalex.org/I111753288"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101424094"],"corresponding_institution_ids":["https://openalex.org/I111753288"],"apc_list":null,"apc_paid":null,"fwci":0.4938,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.66572454,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"900","last_page":"904"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9997000098228455,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9983000159263611,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.8430083394050598},{"id":"https://openalex.org/keywords/acceleration","display_name":"Acceleration","score":0.8018230199813843},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.7689796686172485},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.7341185808181763},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.6344161033630371},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5540913343429565},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5389298796653748},{"id":"https://openalex.org/keywords/video-denoising","display_name":"Video denoising","score":0.5190271139144897},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.5184359550476074},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.517577052116394},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5105774998664856},{"id":"https://openalex.org/keywords/signal-reconstruction","display_name":"Signal reconstruction","score":0.45074138045310974},{"id":"https://openalex.org/keywords/signal-processing","display_name":"Signal processing","score":0.45020240545272827},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4483923017978668},{"id":"https://openalex.org/keywords/step-detection","display_name":"Step detection","score":0.41879767179489136},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.28949135541915894},{"id":"https://openalex.org/keywords/digital-signal-processing","display_name":"Digital signal processing","score":0.08986470103263855},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.06463021039962769}],"concepts":[{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.8430083394050598},{"id":"https://openalex.org/C117896860","wikidata":"https://www.wikidata.org/wiki/Q11376","display_name":"Acceleration","level":2,"score":0.8018230199813843},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.7689796686172485},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.7341185808181763},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.6344161033630371},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5540913343429565},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5389298796653748},{"id":"https://openalex.org/C30814859","wikidata":"https://www.wikidata.org/wiki/Q4119603","display_name":"Video denoising","level":5,"score":0.5190271139144897},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.5184359550476074},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.517577052116394},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5105774998664856},{"id":"https://openalex.org/C70958404","wikidata":"https://www.wikidata.org/wiki/Q7512728","display_name":"Signal reconstruction","level":4,"score":0.45074138045310974},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.45020240545272827},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4483923017978668},{"id":"https://openalex.org/C293773","wikidata":"https://www.wikidata.org/wiki/Q7608015","display_name":"Step detection","level":3,"score":0.41879767179489136},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.28949135541915894},{"id":"https://openalex.org/C84462506","wikidata":"https://www.wikidata.org/wiki/Q173142","display_name":"Digital signal processing","level":2,"score":0.08986470103263855},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.06463021039962769},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.0},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.0},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C23431618","wikidata":"https://www.wikidata.org/wiki/Q1404672","display_name":"Multiview Video Coding","level":4,"score":0.0},{"id":"https://openalex.org/C74650414","wikidata":"https://www.wikidata.org/wiki/Q11397","display_name":"Classical mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/fskd.2017.8393396","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fskd.2017.8393396","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5600000023841858,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1982285688","https://openalex.org/W1988561144","https://openalex.org/W2015418199","https://openalex.org/W2024417168","https://openalex.org/W2059599172","https://openalex.org/W2097323375","https://openalex.org/W2109449402","https://openalex.org/W2136017820","https://openalex.org/W2145343602","https://openalex.org/W2146842127","https://openalex.org/W2151227861","https://openalex.org/W2153663612","https://openalex.org/W2154304330","https://openalex.org/W2154991835","https://openalex.org/W2176737508","https://openalex.org/W2296616510","https://openalex.org/W4214540058","https://openalex.org/W4250955649","https://openalex.org/W6665303272"],"related_works":["https://openalex.org/W2387796150","https://openalex.org/W2139368882","https://openalex.org/W2536049644","https://openalex.org/W2903172302","https://openalex.org/W233850645","https://openalex.org/W2348643679","https://openalex.org/W4362581794","https://openalex.org/W2082304850","https://openalex.org/W2078677256","https://openalex.org/W2358922911"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3],"propose":[4],"a":[5,159],"novel":[6],"denoising":[7,96,113,136,153,163],"method":[8,100],"combining":[9],"wavelet":[10,27,45,53,59,94,131,150],"analysis":[11,151],"with":[12,29,47,91],"compressed":[13,37],"sensing":[14,38],"for":[15,62,133,152,162],"acceleration":[16,65,68,116,139,154],"signal.":[17,66,117,155],"The":[18,67,85],"basic":[19],"idea":[20],"is":[21,39,75],"that,":[22,89],"in":[23,149],"view":[24],"of":[25,36,73,81,115,127,130,138,146],"the":[26,32,79,92,103,112,128,135,144],"coefficients":[28,46,132],"no":[30,48],"noisy,":[31],"optimization":[33],"reconstruction":[34],"algorithm":[35],"adopted":[40],"to":[41,57,77],"recover":[42],"all":[43,51],"new":[44],"noisy":[49],"from":[50,70],"selected":[52,76],"coefficients,":[54],"which":[55,141],"contributes":[56],"further":[58],"inverse":[60],"transform":[61],"gaining":[63],"clean":[64],"signal":[69],"open":[71],"database":[72],"USC-HAD":[74],"validate":[78],"effectiveness":[80],"our":[82,98,122],"proposed":[83,99,123],"technique.":[84],"experimental":[86],"results":[87,119],"showed":[88],"compared":[90],"traditional":[93],"threshold":[95,147],"methods,":[97],"can":[101,142],"obtain":[102],"maximum":[104],"SNR":[105],"and":[106,109,171],"minimal":[107],"RMSE,":[108],"significantly":[110],"improve":[111],"performance":[114],"These":[118],"demonstrate":[120],"that":[121],"technique":[124],"take":[125],"advantage":[126],"sparsity":[129],"enhancing":[134],"ability":[137],"signal,":[140],"overcome":[143],"limitation":[145],"processing":[148],"Our":[156],"work":[157],"has":[158],"great":[160],"potential":[161],"other":[164],"biomedical":[165],"signals":[166],"such":[167],"as":[168],"ECG,":[169],"EEG":[170],"EMG.":[172]},"counts_by_year":[{"year":2020,"cited_by_count":2}],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-10-10T00:00:00"}
