{"id":"https://openalex.org/W3000247991","doi":"https://doi.org/10.1109/apccas47518.2019.8953103","title":"A Data-Efficient Training Model for Signal Integrity Analysis based on Transfer Learning","display_name":"A Data-Efficient Training Model for Signal Integrity Analysis based on Transfer Learning","publication_year":2019,"publication_date":"2019-11-01","ids":{"openalex":"https://openalex.org/W3000247991","doi":"https://doi.org/10.1109/apccas47518.2019.8953103","mag":"3000247991"},"language":"en","primary_location":{"id":"doi:10.1109/apccas47518.2019.8953103","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apccas47518.2019.8953103","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5009381590","display_name":"Tingrui Zhang","orcid":"https://orcid.org/0000-0001-5740-4960"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tingrui Zhang","raw_affiliation_strings":["University of Electronic Science and Technology of China, Chengdu"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100325186","display_name":"Siyu Chen","orcid":"https://orcid.org/0000-0002-8784-1341"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Siyu Chen","raw_affiliation_strings":["University of Electronic Science and Technology of China, Chengdu"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004166302","display_name":"Shuwu Wei","orcid":null},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuwu Wei","raw_affiliation_strings":["University of Electronic Science and Technology of China, Chengdu"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078286204","display_name":"Jienan Chen","orcid":"https://orcid.org/0000-0003-1265-0775"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jienan Chen","raw_affiliation_strings":["University of Electronic Science and Technology of China, Chengdu"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu","institution_ids":["https://openalex.org/I150229711"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I150229711"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"186","last_page":"189"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12131","display_name":"Wireless Signal Modulation Classification","score":0.9929999709129333,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T12131","display_name":"Wireless Signal Modulation Classification","score":0.9929999709129333,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T12153","display_name":"Advanced Optical Sensing Technologies","score":0.9801999926567078,"subfield":{"id":"https://openalex.org/subfields/3105","display_name":"Instrumentation"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T14117","display_name":"Integrated Circuits and Semiconductor Failure Analysis","score":0.9735000133514404,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7604333758354187},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.6693487763404846},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.627463161945343},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.6244901418685913},{"id":"https://openalex.org/keywords/signal-integrity","display_name":"Signal integrity","score":0.5969288349151611},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5956735610961914},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5125779509544373},{"id":"https://openalex.org/keywords/train","display_name":"Train","score":0.5052843689918518},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4458511471748352},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.43875443935394287},{"id":"https://openalex.org/keywords/diagram","display_name":"Diagram","score":0.42147719860076904},{"id":"https://openalex.org/keywords/signal-processing","display_name":"Signal processing","score":0.41954857110977173},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.41561001539230347},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.38071149587631226},{"id":"https://openalex.org/keywords/digital-signal-processing","display_name":"Digital signal processing","score":0.13930895924568176},{"id":"https://openalex.org/keywords/computer-hardware","display_name":"Computer hardware","score":0.12039196491241455},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.07854288816452026}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7604333758354187},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.6693487763404846},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.627463161945343},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.6244901418685913},{"id":"https://openalex.org/C44938667","wikidata":"https://www.wikidata.org/wiki/Q4503810","display_name":"Signal integrity","level":3,"score":0.5969288349151611},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5956735610961914},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5125779509544373},{"id":"https://openalex.org/C190839683","wikidata":"https://www.wikidata.org/wiki/Q2448197","display_name":"Train","level":2,"score":0.5052843689918518},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4458511471748352},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.43875443935394287},{"id":"https://openalex.org/C186399060","wikidata":"https://www.wikidata.org/wiki/Q959962","display_name":"Diagram","level":2,"score":0.42147719860076904},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.41954857110977173},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.41561001539230347},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38071149587631226},{"id":"https://openalex.org/C84462506","wikidata":"https://www.wikidata.org/wiki/Q173142","display_name":"Digital signal processing","level":2,"score":0.13930895924568176},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.12039196491241455},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.07854288816452026},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"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/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C123745756","wikidata":"https://www.wikidata.org/wiki/Q1665949","display_name":"Interconnection","level":2,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/apccas47518.2019.8953103","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apccas47518.2019.8953103","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS)","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":13,"referenced_works":["https://openalex.org/W1511623646","https://openalex.org/W1530536511","https://openalex.org/W1950302812","https://openalex.org/W2046758483","https://openalex.org/W2116712079","https://openalex.org/W2117766083","https://openalex.org/W2160347686","https://openalex.org/W2165698076","https://openalex.org/W2782731083","https://openalex.org/W2795534956","https://openalex.org/W2796332614","https://openalex.org/W2899434757","https://openalex.org/W6631793999"],"related_works":["https://openalex.org/W618248309","https://openalex.org/W2377336366","https://openalex.org/W1568097102","https://openalex.org/W1601203902","https://openalex.org/W2289987414","https://openalex.org/W2075798043","https://openalex.org/W4390419160","https://openalex.org/W4225671779","https://openalex.org/W2102464536","https://openalex.org/W2361332776"],"abstract_inverted_index":{"The":[0,81,113],"signal":[1,15,51,73,143],"integrity":[2,52,74,144],"analysis":[3,16,53,75],"is":[4,36],"an":[5],"essential":[6],"part":[7],"of":[8,60,86,115,125],"electronic":[9],"design,":[10],"while":[11],"direct":[12],"high":[13,25],"speed":[14],"becomes":[17],"a":[18,40,72,87],"time-consuming":[19],"work.":[20],"As":[21],"machine":[22],"learning":[23],"exhibits":[24],"performance":[26],"in":[27,30,117,140],"communication":[28],"fields":[29],"recent":[31],"years,":[32],"deep":[33],"neural":[34,119],"network(DNN)":[35],"thought":[37],"to":[38,43,111,137],"be":[39],"key":[41],"tool":[42],"predict":[44],"eye":[45,127],"diagram":[46,128],"metrics.":[47],"However,":[48],"DNN":[49],"based":[50,77],"faces":[54],"two":[55],"challenges:":[56],"demands":[57],"for":[58,93,108],"amounts":[59],"labelled":[61,104],"data":[62,105],"and":[63,90,130,134],"long":[64],"training":[65,109],"period.":[66],"In":[67],"this":[68],"paper,":[69],"we":[70],"propose":[71],"model":[76,82],"on":[78],"transfer":[79],"learning.":[80],"makes":[83],"full":[84],"use":[85],"trained":[88],"network":[89,120],"trains":[91],"networks":[92],"various":[94],"channel":[95],"environments.":[96],"To":[97],"achieve":[98],"the":[99,118,122,126],"same":[100],"predicting":[101],"accuracy,":[102],"64%":[103],"are":[106],"utilized":[107],"compared":[110,136],"DNN.":[112],"application":[114],"hyper-parameters":[116],"improves":[121],"prediction":[123],"accuracy":[124],"width":[129],"height":[131],"by":[132],"42.7%":[133],"49.24%":[135],"current":[138],"methods":[139],"few":[141],"shot":[142],"analysis.":[145]},"counts_by_year":[{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
