{"id":"https://openalex.org/W2059209265","doi":"https://doi.org/10.1137/s1064827500369803","title":"The Bi-Gaussian S-Transform","display_name":"The Bi-Gaussian S-Transform","publication_year":2003,"publication_date":"2003-01-01","ids":{"openalex":"https://openalex.org/W2059209265","doi":"https://doi.org/10.1137/s1064827500369803","mag":"2059209265"},"language":"en","primary_location":{"id":"doi:10.1137/s1064827500369803","is_oa":false,"landing_page_url":"https://doi.org/10.1137/s1064827500369803","pdf_url":null,"source":{"id":"https://openalex.org/S165512578","display_name":"SIAM Journal on Scientific Computing","issn_l":"1064-8275","issn":["1064-8275","1095-7197"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320508","host_organization_name":"Society for Industrial and Applied Mathematics","host_organization_lineage":["https://openalex.org/P4310320508"],"host_organization_lineage_names":["Society for Industrial and Applied Mathematics"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SIAM Journal on Scientific Computing","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/A5073170545","display_name":"C. Robert Pinnegar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"C. R. Pinnegar","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5067022794","display_name":"L. Mansinha","orcid":"https://orcid.org/0000-0001-7288-8019"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"L. Mansinha","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.4799,"has_fulltext":false,"cited_by_count":94,"citation_normalized_percentile":{"value":0.89039276,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"24","issue":"5","first_page":"1678","last_page":"1692"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12300","display_name":"Advanced Electrical Measurement Techniques","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T12300","display_name":"Advanced Electrical Measurement Techniques","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9943000078201294,"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/T10534","display_name":"Structural Health Monitoring Techniques","score":0.9855999946594238,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/s-transform","display_name":"S transform","score":0.6966091394424438},{"id":"https://openalex.org/keywords/constant-q-transform","display_name":"Constant Q transform","score":0.6749361753463745},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.6699644327163696},{"id":"https://openalex.org/keywords/short-time-fourier-transform","display_name":"Short-time Fourier transform","score":0.6051274538040161},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5639420747756958},{"id":"https://openalex.org/keywords/window-function","display_name":"Window function","score":0.5539862513542175},{"id":"https://openalex.org/keywords/fourier-transform","display_name":"Fourier transform","score":0.5215611457824707},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.5016171932220459},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4971607029438019},{"id":"https://openalex.org/keywords/harmonic-wavelet-transform","display_name":"Harmonic wavelet transform","score":0.4743296504020691},{"id":"https://openalex.org/keywords/gaussian-function","display_name":"Gaussian function","score":0.46769312024116516},{"id":"https://openalex.org/keywords/fractional-fourier-transform","display_name":"Fractional Fourier transform","score":0.456886351108551},{"id":"https://openalex.org/keywords/hilbert\u2013huang-transform","display_name":"Hilbert\u2013Huang transform","score":0.41424939036369324},{"id":"https://openalex.org/keywords/mathematical-analysis","display_name":"Mathematical analysis","score":0.3729558289051056},{"id":"https://openalex.org/keywords/discrete-wavelet-transform","display_name":"Discrete wavelet transform","score":0.35294848680496216},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.33584803342819214},{"id":"https://openalex.org/keywords/spectral-density","display_name":"Spectral density","score":0.32059401273727417},{"id":"https://openalex.org/keywords/fourier-analysis","display_name":"Fourier analysis","score":0.2370932698249817},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.19709330797195435},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.1322830617427826},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.12351739406585693},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.1198013424873352},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.08537313342094421}],"concepts":[{"id":"https://openalex.org/C99234102","wikidata":"https://www.wikidata.org/wiki/Q7395403","display_name":"S transform","level":5,"score":0.6966091394424438},{"id":"https://openalex.org/C153705960","wikidata":"https://www.wikidata.org/wiki/Q5163634","display_name":"Constant Q transform","level":5,"score":0.6749361753463745},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.6699644327163696},{"id":"https://openalex.org/C166386157","wikidata":"https://www.wikidata.org/wiki/Q1477735","display_name":"Short-time Fourier transform","level":4,"score":0.6051274538040161},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5639420747756958},{"id":"https://openalex.org/C140101238","wikidata":"https://www.wikidata.org/wiki/Q1404885","display_name":"Window function","level":3,"score":0.5539862513542175},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.5215611457824707},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.5016171932220459},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4971607029438019},{"id":"https://openalex.org/C1109138","wikidata":"https://www.wikidata.org/wiki/Q3280930","display_name":"Harmonic wavelet transform","level":5,"score":0.4743296504020691},{"id":"https://openalex.org/C7218915","wikidata":"https://www.wikidata.org/wiki/Q1054475","display_name":"Gaussian function","level":3,"score":0.46769312024116516},{"id":"https://openalex.org/C76563020","wikidata":"https://www.wikidata.org/wiki/Q4817582","display_name":"Fractional Fourier transform","level":4,"score":0.456886351108551},{"id":"https://openalex.org/C25570617","wikidata":"https://www.wikidata.org/wiki/Q1006462","display_name":"Hilbert\u2013Huang transform","level":3,"score":0.41424939036369324},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.3729558289051056},{"id":"https://openalex.org/C46286280","wikidata":"https://www.wikidata.org/wiki/Q2414958","display_name":"Discrete wavelet transform","level":4,"score":0.35294848680496216},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.33584803342819214},{"id":"https://openalex.org/C168110828","wikidata":"https://www.wikidata.org/wiki/Q1331626","display_name":"Spectral density","level":2,"score":0.32059401273727417},{"id":"https://openalex.org/C203024314","wikidata":"https://www.wikidata.org/wiki/Q1365258","display_name":"Fourier analysis","level":3,"score":0.2370932698249817},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.19709330797195435},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.1322830617427826},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.12351739406585693},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.1198013424873352},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.08537313342094421},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1137/s1064827500369803","is_oa":false,"landing_page_url":"https://doi.org/10.1137/s1064827500369803","pdf_url":null,"source":{"id":"https://openalex.org/S165512578","display_name":"SIAM Journal on Scientific Computing","issn_l":"1064-8275","issn":["1064-8275","1095-7197"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320508","host_organization_name":"Society for Industrial and Applied Mathematics","host_organization_lineage":["https://openalex.org/P4310320508"],"host_organization_lineage_names":["Society for Industrial and Applied Mathematics"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SIAM Journal on Scientific Computing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4300000071525574,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W250380996","https://openalex.org/W1986754283","https://openalex.org/W1989491465","https://openalex.org/W2058165543","https://openalex.org/W2066063645","https://openalex.org/W2086275479","https://openalex.org/W2090048597","https://openalex.org/W2096684483","https://openalex.org/W2098914003","https://openalex.org/W2115755118","https://openalex.org/W3008609509","https://openalex.org/W4229868531"],"related_works":["https://openalex.org/W2805281296","https://openalex.org/W2349536044","https://openalex.org/W2357128470","https://openalex.org/W2037705918","https://openalex.org/W2351810542","https://openalex.org/W2138473335","https://openalex.org/W2390879839","https://openalex.org/W2433457936","https://openalex.org/W2546321306","https://openalex.org/W772094678"],"abstract_inverted_index":{"The":[0,20,89,162,188],"S-transform":[1,34,64,100,190],"kernel":[2,7],"is":[3,25,35,65,102,130,151,191],"derived":[4],"from":[5],"the":[6,9,13,23,33,49,55,63,70,76,82,96,103,124,136,141,155,165,170,179,185,195],"of":[8,15,22,28,38,51,57,62,98,126,132,157,164,198],"Fourier":[10,71],"transform":[11,72],"through":[12,154],"introduction":[14],"a":[16,26,36,86,127,148,201],"scalable,":[17],"translating":[18],"window.":[19],"width":[21],"window":[24,83,90,106,150],"function":[27],"inverse":[29],"frequency.":[30],"In":[31,145],"effect":[32],"method":[37],"spectral":[39],"localization,":[40],"with":[41,123,174,184],"some":[42],"similarities":[43],"to":[44,84,140],"wavelet":[45],"transforms":[46],"but":[47],"using":[48],"concept":[50,56],"frequency":[52],"rather":[53],"than":[54],"scale.":[58],"An":[59],"important":[60],"property":[61,80],"that":[66],"it":[67],"collapses":[68],"into":[69],"when":[73],"integrated":[74],"over":[75],"time":[77,133,175,202],"axis;":[78],"this":[79,146],"requires":[81],"satisfy":[85],"normalizing":[87],"condition.":[88],"which":[91],"has":[92],"been":[93],"used":[94],"in":[95,135,169,178,200],"majority":[97],"previous":[99],"research":[101],"symmetric":[104],"Gaussian":[105,160],"introduced":[107],"by":[108],"Stockwell,":[109],"Mansinha,":[110],"and":[111],"Lowe":[112],"[IEEE":[113],"Trans.":[114],"Signal":[115],"Process.,":[116],"44":[117],"(1996),":[118],"pp.":[119],"998--1001].":[120],"One":[121],"problem":[122],"use":[125],"Gaussian,":[128],"however,":[129],"degradation":[131],"resolution":[134,176],"time-frequency":[137,172],"spectrum":[138],"due":[139],"long":[142],"front":[143],"taper.":[144],"paper,":[147],"bi-Gaussian":[149,166,189],"introduced,":[152],"constructed":[153],"welding":[156],"two":[158],"half":[159],"windows.":[161],"asymmetry":[163,168],"introduces":[167],"resultant":[171],"spectrum,":[173],"better":[177,192],"\"front\"":[180],"direction,":[181],"as":[182],"compared":[183],"\"back\"":[186],"direction.":[187],"at":[193],"resolving":[194],"sharp":[196],"onset":[197],"events":[199],"series.":[203]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":7},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":8},{"year":2016,"cited_by_count":8},{"year":2015,"cited_by_count":5},{"year":2014,"cited_by_count":6},{"year":2013,"cited_by_count":5},{"year":2012,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
