{"id":"https://openalex.org/W3203282878","doi":"https://doi.org/10.1109/ispa52656.2021.9552119","title":"An Accurate and Efficient Zero-Crossing Line Classifier for Multiscale Parameter Estimation of Gaussian Signals Subject to Noise","display_name":"An Accurate and Efficient Zero-Crossing Line Classifier for Multiscale Parameter Estimation of Gaussian Signals Subject to Noise","publication_year":2021,"publication_date":"2021-09-13","ids":{"openalex":"https://openalex.org/W3203282878","doi":"https://doi.org/10.1109/ispa52656.2021.9552119","mag":"3203282878"},"language":"en","primary_location":{"id":"doi:10.1109/ispa52656.2021.9552119","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ispa52656.2021.9552119","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA)","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/A5068636699","display_name":"Robin Leeker","orcid":null},"institutions":[{"id":"https://openalex.org/I159743108","display_name":"Dortmund University of Applied Sciences and Arts","ror":"https://ror.org/03dv91853","country_code":"DE","type":"education","lineage":["https://openalex.org/I159743108"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Robin Leeker","raw_affiliation_strings":["University of Applied Sciences and Arts Dortmund, Dortmund, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Applied Sciences and Arts Dortmund, Dortmund, Germany","institution_ids":["https://openalex.org/I159743108"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049149990","display_name":"Nicolai Spicher","orcid":"https://orcid.org/0000-0002-2879-9948"},"institutions":[{"id":"https://openalex.org/I34809795","display_name":"Medizinische Hochschule Hannover","ror":"https://ror.org/00f2yqf98","country_code":"DE","type":"education","lineage":["https://openalex.org/I34809795"]},{"id":"https://openalex.org/I94509681","display_name":"Technische Universit\u00e4t Braunschweig","ror":"https://ror.org/010nsgg66","country_code":"DE","type":"education","lineage":["https://openalex.org/I94509681"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Nicolai Spicher","raw_affiliation_strings":["Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Braunschweig, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Braunschweig, Germany","institution_ids":["https://openalex.org/I94509681","https://openalex.org/I34809795"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5013175411","display_name":"Markus Kukuk","orcid":"https://orcid.org/0000-0001-6358-1824"},"institutions":[{"id":"https://openalex.org/I159743108","display_name":"Dortmund University of Applied Sciences and Arts","ror":"https://ror.org/03dv91853","country_code":"DE","type":"education","lineage":["https://openalex.org/I159743108"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Markus Kukuk","raw_affiliation_strings":["University of Applied Sciences and Arts Dortmund, Dortmund, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Applied Sciences and Arts Dortmund, Dortmund, Germany","institution_ids":["https://openalex.org/I159743108"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.13598145,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"36","last_page":"40"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9980999827384949,"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"}},"topics":[{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9980999827384949,"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"}},{"id":"https://openalex.org/T11021","display_name":"ECG Monitoring and Analysis","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.9866999983787537,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/zero-crossing","display_name":"Zero crossing","score":0.6653774380683899},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.613845705986023},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5784286260604858},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5497966408729553},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5384924411773682},{"id":"https://openalex.org/keywords/estimation-theory","display_name":"Estimation theory","score":0.46891555190086365},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.43244022130966187},{"id":"https://openalex.org/keywords/noise-measurement","display_name":"Noise measurement","score":0.42754656076431274},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40252307057380676},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3958948254585266},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3803221583366394},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.35378724336624146},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.17318958044052124},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.1532566249370575},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.13756614923477173},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.06482067704200745}],"concepts":[{"id":"https://openalex.org/C120415902","wikidata":"https://www.wikidata.org/wiki/Q462383","display_name":"Zero crossing","level":3,"score":0.6653774380683899},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.613845705986023},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5784286260604858},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5497966408729553},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5384924411773682},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.46891555190086365},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.43244022130966187},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.42754656076431274},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40252307057380676},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3958948254585266},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3803221583366394},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.35378724336624146},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.17318958044052124},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.1532566249370575},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.13756614923477173},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.06482067704200745},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ispa52656.2021.9552119","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ispa52656.2021.9552119","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1983657072","https://openalex.org/W2002312729","https://openalex.org/W2004217976","https://openalex.org/W2022812046","https://openalex.org/W2024023249","https://openalex.org/W2038856295","https://openalex.org/W2076087677","https://openalex.org/W2099619765","https://openalex.org/W2904950082","https://openalex.org/W3003372106"],"related_works":["https://openalex.org/W2547595264","https://openalex.org/W2986378528","https://openalex.org/W1568409219","https://openalex.org/W2361550794","https://openalex.org/W3178345791","https://openalex.org/W1550854977","https://openalex.org/W2949211747","https://openalex.org/W2092661960","https://openalex.org/W4231055618","https://openalex.org/W2069850525"],"abstract_inverted_index":{"The":[0,18,244],"multiscale":[1,256],"parameter":[2,95,257],"estimation":[3,258],"framework":[4],"is":[5,20],"a":[6,108,117,151,229,260],"method":[7,19,221],"for":[8,82,94,103,222,228,250],"estimating":[9],"the":[10,28,43,53,58,76,130,136,140,143,164,176,204,215,219,237,248],"true":[11],"parameters":[12],"of":[13,25,60,80,132,142,167,170,178,232],"signals":[14,185,254],"subject":[15,186],"to":[16,57,64,68,112,139,187,198,242,259],"noise.":[17,69],"based":[21],"on":[22,51],"detecting":[23],"lines":[24,134,169],"zero-crossings":[26],"within":[27],"Continuous":[29],"Wavelet":[30],"Transform":[31],"and":[32,62,154,208,227],"substituting":[33],"their":[34],"locations":[35],"in":[36,121],"time":[37],"into":[38],"analytical":[39],"equations":[40],"directly":[41],"expressing":[42],"unknown":[44],"signal":[45,59],"parameters.":[46,145],"Evidently,":[47],"this":[48,127,147,179],"approach":[49,129],"depends":[50],"selecting":[52],"correct":[54,109],"lines,":[55],"corresponding":[56],"interest":[61],"not":[63,89],"other":[65],"phenomena":[66],"related":[67],"This":[70],"task":[71],"can":[72],"be":[73,92],"posed":[74],"as":[75],"binary":[77],"classification":[78,110,119,131],"problem":[79],"determining":[81],"each":[83],"zero-crossing":[84,133,168],"line":[85],"found":[86],"whether":[87],"or":[88],"it":[90],"should":[91],"used":[93],"estimation.":[96],"It":[97],"has":[98],"been":[99],"shown":[100],"that":[101,214],"even":[102],"very":[104,113],"high":[105],"noise":[106,190,224,230],"levels,":[107],"leads":[111],"accurate":[114],"estimates,":[115],"while":[116],"wrong":[118],"results":[120],"highly":[122],"inaccurate":[123],"estimates.":[124],"Therefore,":[125],"with":[126,191],"particular":[128],"poses":[135],"limiting":[137],"factor":[138],"accuracy":[141],"estimated":[144,207],"In":[146],"work,":[148],"we":[149,212],"propose":[150],"novel,":[152],"efficient":[153],"more":[155],"robust":[156],"classifier":[157,181,217,246],"called":[158],"\u201cstencil":[159],"operator\u201d":[160],"which":[161],"accurately":[162],"detects":[163],"best":[165],"combination":[166],"Gaussian":[171,184],"input":[172],"signals.":[173],"We":[174],"evaluate":[175],"performance":[177],"new":[180,216,261],"using":[182,255],"synthetic":[183],"white":[188],"(Gaussian)":[189],"signal-to-noise":[192],"ratios":[193],"ranging":[194],"from":[195,240],"50":[196],"dB":[197,235],"-":[199],"20":[200],"dB.":[201],"By":[202],"studying":[203],"error":[205,239],"between":[206],"ground":[209],"truth":[210],"parameters,":[211],"show":[213],"outperforms":[218],"current":[220],"all":[223],"levels":[225],"considered":[226],"level":[231],"e.g.":[233],"-12":[234],"improves":[236],"median":[238],"132%":[241],"28%.":[243],"proposed":[245],"pushes":[247],"boundary":[249],"analyzing":[251],"heavily":[252],"disturbed":[253],"level.":[262]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}