{"id":"https://openalex.org/W7225827006","doi":"https://doi.org/10.71204/6bsh1667","title":"Ultrasonic Signal Denoising Based on One-Dimensional Convolution and SWT-MRA Multi-Scale Representation","display_name":"Ultrasonic Signal Denoising Based on One-Dimensional Convolution and SWT-MRA Multi-Scale Representation","publication_year":2026,"publication_date":"2026-11-11","ids":{"openalex":"https://openalex.org/W7225827006","doi":"https://doi.org/10.71204/6bsh1667"},"language":"en","primary_location":{"id":"doi:10.71204/6bsh1667","doi":"https://doi.org/10.71204/6bsh1667","is_oa":true,"landing_page_url":"https://doi.org/10.71204/6bsh1667","pdf_url":null,"source":{"id":"https://openalex.org/S5407048976","display_name":"Journal of Computer Science and Digital Technology","issn_l":"3068-1448","issn":["3068-1448"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computer Science and Digital Technology","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.71204/6bsh1667","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137567486","display_name":"Weijiang Qiao","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I174104030","display_name":"Taiyuan Normal University","ror":"https://ror.org/051k00p03","country_code":"CN","type":"education","lineage":["https://openalex.org/I174104030"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weijiang Qiao","raw_affiliation_strings":["Taiyuan Normal University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Taiyuan Normal University","institution_ids":["https://openalex.org/I174104030"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5161435901","display_name":"Yuhan Wang","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I174104030","display_name":"Taiyuan Normal University","ror":"https://ror.org/051k00p03","country_code":"CN","type":"education","lineage":["https://openalex.org/I174104030"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuhan Wang","raw_affiliation_strings":["Taiyuan Normal University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Taiyuan Normal University","institution_ids":["https://openalex.org/I174104030"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5161420099","display_name":"Guanghan Ding","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I174104030","display_name":"Taiyuan Normal University","ror":"https://ror.org/051k00p03","country_code":"CN","type":"education","lineage":["https://openalex.org/I174104030"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guanghan Ding","raw_affiliation_strings":["Taiyuan Normal University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Taiyuan Normal University","institution_ids":["https://openalex.org/I174104030"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5067612188","display_name":"Yanlong Wei","orcid":"https://orcid.org/0009-0007-8068-3233","observed_orcids":["https://orcid.org/0009-0007-8068-3233","https://orcid.org/0009-0001-8749-3631"]},"institutions":[{"id":"https://openalex.org/I174104030","display_name":"Taiyuan Normal University","ror":"https://ror.org/051k00p03","country_code":"CN","type":"education","lineage":["https://openalex.org/I174104030"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanlong Wei","raw_affiliation_strings":["Taiyuan Normal University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Taiyuan Normal University","institution_ids":["https://openalex.org/I174104030"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I174104030"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"2","issue":"2","first_page":"30","last_page":"50"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9961000084877014,"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"}},"topics":[{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9961000084877014,"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/T10662","display_name":"Ultrasonics and Acoustic Wave Propagation","score":0.002199999988079071,"subfield":{"id":"https://openalex.org/subfields/2211","display_name":"Mechanics of Materials"},"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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.00039999998989515007,"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":[],"concepts":[{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.6392999887466431},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.5993000268936157},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.5885000228881836},{"id":"https://openalex.org/C191178318","wikidata":"https://www.wikidata.org/wiki/Q2256906","display_name":"Thresholding","level":3,"score":0.5515000224113464},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5393999814987183},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5048999786376953},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5012999773025513},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.49559998512268066},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.48649999499320984},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.47999998927116394},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.47769999504089355},{"id":"https://openalex.org/C197424946","wikidata":"https://www.wikidata.org/wiki/Q1165717","display_name":"Waveform","level":3,"score":0.47369998693466187},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.4571000039577484},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4140999913215637},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.39969998598098755},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3910999894142151},{"id":"https://openalex.org/C112633086","wikidata":"https://www.wikidata.org/wiki/Q381287","display_name":"White noise","level":2,"score":0.3862000107765198},{"id":"https://openalex.org/C81288441","wikidata":"https://www.wikidata.org/wiki/Q20736125","display_name":"Ultrasonic sensor","level":2,"score":0.3749000132083893},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3474000096321106},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.34630000591278076},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.34450000524520874},{"id":"https://openalex.org/C122383733","wikidata":"https://www.wikidata.org/wiki/Q865920","display_name":"Approximation error","level":2,"score":0.29429998993873596},{"id":"https://openalex.org/C13944312","wikidata":"https://www.wikidata.org/wiki/Q7512748","display_name":"Signal-to-noise ratio (imaging)","level":2,"score":0.2874000072479248},{"id":"https://openalex.org/C73339587","wikidata":"https://www.wikidata.org/wiki/Q1375942","display_name":"Stationary wavelet transform","level":5,"score":0.28459998965263367}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.71204/6bsh1667","doi":"https://doi.org/10.71204/6bsh1667","is_oa":true,"landing_page_url":"https://doi.org/10.71204/6bsh1667","pdf_url":null,"source":{"id":"https://openalex.org/S5407048976","display_name":"Journal of Computer Science and Digital Technology","issn_l":"3068-1448","issn":["3068-1448"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computer Science and Digital Technology","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.71204/6bsh1667","doi":"https://doi.org/10.71204/6bsh1667","is_oa":true,"landing_page_url":"https://doi.org/10.71204/6bsh1667","pdf_url":null,"source":{"id":"https://openalex.org/S5407048976","display_name":"Journal of Computer Science and Digital Technology","issn_l":"3068-1448","issn":["3068-1448"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computer Science and Digital Technology","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1653130573","https://openalex.org/W1901129140","https://openalex.org/W1996129715","https://openalex.org/W2030503915","https://openalex.org/W2074701306","https://openalex.org/W2132984323","https://openalex.org/W2146842127","https://openalex.org/W2153448827","https://openalex.org/W2167323806","https://openalex.org/W4200252892","https://openalex.org/W4226213816","https://openalex.org/W4294775232","https://openalex.org/W4376286482","https://openalex.org/W4387142558","https://openalex.org/W4404360573","https://openalex.org/W4413031706","https://openalex.org/W7135403679"],"related_works":[],"abstract_inverted_index":{"Ultrasonic":[0],"measurement":[1],"signals":[2],"are":[3],"susceptible":[4],"to":[5,189],"random":[6,108],"noise,":[7],"impulsive":[8],"artifacts,":[9],"and":[10,29,67,87,99,134,151,155,170,176],"high-frequency":[11],"interference,":[12],"while":[13,158],"conventional":[14],"wavelet":[15,43,62],"thresholding":[16],"may":[17],"attenuate":[18],"useful":[19],"signal":[20,37],"components.":[21,71],"To":[22],"address":[23],"the":[24,60,100,111,115,145,183,190],"trade-off":[25],"between":[26],"noise":[27],"suppression":[28],"waveform":[30,213],"preservation,":[31],"this":[32],"study":[33],"proposes":[34],"an":[35,135],"ultrasonic":[36,212],"denoising":[38,80],"method":[39,91,117],"that":[40,182,206],"integrates":[41],"stationary":[42],"transform-based":[44],"multiresolution":[45],"analysis":[46],"(SWT-MRA)":[47],"with":[48,144,166,215],"a":[49,78,119,126,217],"one-dimensional":[50],"convolutional":[51],"neural":[52],"network":[53,221],"(CNN).":[54],"An":[55],"eight-level":[56],"SWT-MRA":[57,192],"decomposition":[58],"using":[59,106],"sym8":[61],"produces":[63],"one":[64],"approximation":[65],"component":[66],"eight":[68],"equal-length":[69],"detail":[70],"A":[72],"dilated":[73],"residual":[74],"CNN":[75],"then":[76],"learns":[77],"component-domain":[79],"mapping":[81],"under":[82],"joint":[83],"waveform-,":[84],"frequency-,":[85],"gradient-,":[86],"component-level":[88,196],"constraints.":[89],"The":[90],"was":[92,187],"evaluated":[93],"on":[94],"three":[95],"independent":[96,112],"experimental":[97],"batches,":[98],"learning-based":[101],"models":[102],"were":[103,172],"repeatedly":[104],"trained":[105],"five":[107],"seeds.":[109],"On":[110],"test":[113],"set,":[114],"proposed":[116],"achieved":[118],"mean":[120,128],"absolute":[121],"error":[122,130],"(MAE)":[123],"of":[124,132,140],"0.003685,":[125],"root":[127],"square":[129],"(RMSE)":[131],"0.005024,":[133],"output":[136,160],"signal-to-noise":[137],"ratio":[138],"(SNR)":[139],"22.198":[141],"dB.":[142,164],"Compared":[143,165],"time-domain":[146],"CNN,":[147],"it":[148],"reduced":[149,173],"MAE":[150,169],"RMSE":[152,171],"by":[153,162,174],"3.10%":[154],"3.39%,":[156],"respectively,":[157],"increasing":[159],"SNR":[161],"0.297":[163],"1D":[167],"U-Net,":[168],"9.22%":[175],"10.19%,":[177],"respectively.":[178],"Ablation":[179],"experiments":[180],"showed":[181],"main":[184],"performance":[185],"gain":[186],"attributable":[188],"explicit":[191,207],"multiscale":[193,208],"representation,":[194],"whereas":[195],"auxiliary":[197],"supervision":[198],"provided":[199],"limited":[200],"additional":[201],"benefit.":[202],"These":[203],"results":[204],"demonstrate":[205],"representation":[209],"can":[210],"improve":[211],"recovery":[214],"only":[216],"modest":[218],"increase":[219],"in":[220],"size.":[222]},"counts_by_year":[],"updated_date":"2026-11-12T12:28:57.059296","created_date":"2026-11-12T00:00:00"}