{"id":"https://openalex.org/W3049699979","doi":"https://doi.org/10.1109/globecom42002.2020.9322456","title":"Semi-Supervised Learning with GANs for Device-Free Fingerprinting Indoor Localization","display_name":"Semi-Supervised Learning with GANs for Device-Free Fingerprinting Indoor Localization","publication_year":2020,"publication_date":"2020-12-01","ids":{"openalex":"https://openalex.org/W3049699979","doi":"https://doi.org/10.1109/globecom42002.2020.9322456","mag":"3049699979"},"language":"en","primary_location":{"id":"doi:10.1109/globecom42002.2020.9322456","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom42002.2020.9322456","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2020 - 2020 IEEE Global Communications Conference","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2008.07111","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100350902","display_name":"Kevin Chen","orcid":"https://orcid.org/0000-0003-3035-1891"},"institutions":[{"id":"https://openalex.org/I4210086894","display_name":"Research Center for Information Technology Innovation, Academia Sinica","ror":"https://ror.org/000zgvm20","country_code":"TW","type":"facility","lineage":["https://openalex.org/I4210086894","https://openalex.org/I84653119"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Kevin M. Chen","raw_affiliation_strings":["Research Center for Information Technology Innovation, Academia Sinica, Taipei, Taiwan","Research Center for Information Technology Innovation Academia Sinica, Taipei, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Research Center for Information Technology Innovation, Academia Sinica, Taipei, Taiwan","institution_ids":["https://openalex.org/I4210086894"]},{"raw_affiliation_string":"Research Center for Information Technology Innovation Academia Sinica, Taipei, Taiwan","institution_ids":["https://openalex.org/I4210086894"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5052576795","display_name":"Ronald Y. Chang","orcid":"https://orcid.org/0000-0003-4620-6824"},"institutions":[{"id":"https://openalex.org/I4210086894","display_name":"Research Center for Information Technology Innovation, Academia Sinica","ror":"https://ror.org/000zgvm20","country_code":"TW","type":"facility","lineage":["https://openalex.org/I4210086894","https://openalex.org/I84653119"]},{"id":"https://openalex.org/I84653119","display_name":"Academia Sinica","ror":"https://ror.org/05bxb3784","country_code":"TW","type":"facility","lineage":["https://openalex.org/I84653119"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Ronald Y. Chang","raw_affiliation_strings":["Research Center for Information Technology Innovation, Academia Sinica, Taipei, Taiwan","Academia Sinica"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Research Center for Information Technology Innovation, Academia Sinica, Taipei, Taiwan","institution_ids":["https://openalex.org/I4210086894"]},{"raw_affiliation_string":"Academia Sinica","institution_ids":["https://openalex.org/I84653119"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1041,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.43000296,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":1.0,"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/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":1.0,"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/T10860","display_name":"Speech and Audio Processing","score":0.9991000294685364,"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"}},{"id":"https://openalex.org/T11698","display_name":"Underwater Acoustics Research","score":0.9940999746322632,"subfield":{"id":"https://openalex.org/subfields/1910","display_name":"Oceanography"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/discriminator","display_name":"Discriminator","score":0.8852827548980713},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7724094390869141},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6791834831237793},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-supervised learning","score":0.5989663600921631},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5497211217880249},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.5100730061531067},{"id":"https://openalex.org/keywords/fingerprint","display_name":"Fingerprint (computing)","score":0.48381108045578003},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47080671787261963},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.45778360962867737},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3794448673725128},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.362186998128891},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.15075117349624634},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.08970621228218079}],"concepts":[{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.8852827548980713},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7724094390869141},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6791834831237793},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.5989663600921631},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5497211217880249},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.5100730061531067},{"id":"https://openalex.org/C2777826928","wikidata":"https://www.wikidata.org/wiki/Q3745713","display_name":"Fingerprint (computing)","level":2,"score":0.48381108045578003},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47080671787261963},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.45778360962867737},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3794448673725128},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.362186998128891},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.15075117349624634},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.08970621228218079},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/globecom42002.2020.9322456","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom42002.2020.9322456","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2020 - 2020 IEEE Global Communications Conference","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2008.07111","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2008.07111","pdf_url":"https://arxiv.org/pdf/2008.07111","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"mag:3049699979","is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/2008.07111","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.2008.07111","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2008.07111","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2008.07111","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2008.07111","pdf_url":"https://arxiv.org/pdf/2008.07111","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"score":0.6700000166893005,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G3182433109","display_name":null,"funder_award_id":"MOST 106-2628-E-001-001-MY3,MOST 109-2221-E-001-013-MY3","funder_id":"https://openalex.org/F4320309618","funder_display_name":"Ministry of Science and Technology"}],"funders":[{"id":"https://openalex.org/F4320309618","display_name":"Ministry of Science and Technology","ror":"https://ror.org/02b207r52"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3049699979.pdf","grobid_xml":"https://content.openalex.org/works/W3049699979.grobid-xml"},"referenced_works_count":25,"referenced_works":["https://openalex.org/W1991807971","https://openalex.org/W2025241138","https://openalex.org/W2089695767","https://openalex.org/W2099471712","https://openalex.org/W2134671728","https://openalex.org/W2143228105","https://openalex.org/W2173520492","https://openalex.org/W2309512289","https://openalex.org/W2412510955","https://openalex.org/W2604490975","https://openalex.org/W2770281069","https://openalex.org/W2890784233","https://openalex.org/W2917207600","https://openalex.org/W2922174720","https://openalex.org/W2952979195","https://openalex.org/W2963373786","https://openalex.org/W2964029185","https://openalex.org/W2964121744","https://openalex.org/W2984128863","https://openalex.org/W6631190155","https://openalex.org/W6685352114","https://openalex.org/W6715189028","https://openalex.org/W6718379498","https://openalex.org/W6735839387","https://openalex.org/W6754715930"],"related_works":["https://openalex.org/W3121381145","https://openalex.org/W2978716864","https://openalex.org/W3213439509","https://openalex.org/W3119933956","https://openalex.org/W3047027700","https://openalex.org/W2345578832","https://openalex.org/W2155051889","https://openalex.org/W2953868242","https://openalex.org/W2890651561","https://openalex.org/W3206054395","https://openalex.org/W2911593136","https://openalex.org/W3153968517","https://openalex.org/W2966341653","https://openalex.org/W2968718498","https://openalex.org/W2789969611","https://openalex.org/W2963587475","https://openalex.org/W2185207014","https://openalex.org/W2930988055","https://openalex.org/W2542037019","https://openalex.org/W2760017128"],"abstract_inverted_index":{"Device-free":[0],"wireless":[1],"indoor":[2,16,35],"localization":[3,17,36],"is":[4,146],"a":[5,20,27,42,49,111],"key":[6],"enabling":[7],"technology":[8],"for":[9],"the":[10,60,72,76,103,115,123,130,143],"Internet":[11],"of":[12,45,52,87,99,114,117,129,142],"Things":[13],"(IoT).":[14],"Fingerprint-based":[15],"techniques":[18],"are":[19,134],"commonly":[21],"used":[22],"solution.":[23],"This":[24],"paper":[25],"proposes":[26],"semi-supervised,":[28],"generative":[29],"adversarial":[30],"network":[31],"(GAN)-based":[32],"device-free":[33],"fingerprinting":[34],"system.":[37],"The":[38,120],"proposed":[39,77,104,131,144],"system":[40,79,106,133,145],"uses":[41],"small":[43],"amount":[44,51,86,98,116],"labeled":[46,88,100,118],"data":[47,54,62],"and":[48,90,126,137],"large":[50],"unlabeled":[53],"(i.e.,":[55],"semi-supervised),":[56],"thus":[57],"considerably":[58],"reducing":[59],"expensive":[61],"labeling":[63],"effort.":[64],"Experimental":[65],"results":[66],"show":[67],"that,":[68],"as":[69],"compared":[70],"to":[71],"state-of-the-art":[73],"supervised":[74],"scheme,":[75],"semi-supervised":[78,105],"achieves":[80],"comparable":[81],"performance":[82,93,109],"with":[83,94],"equal,":[84,95],"sufficient":[85],"data,":[89],"significantly":[91],"superior":[92],"highly":[96],"limited":[97],"data.":[101,119],"Besides,":[102],"retains":[107],"its":[108],"over":[110],"broad":[112],"range":[113],"interactions":[121],"between":[122],"generator,":[124],"discriminator,":[125],"classifier":[127],"models":[128],"GAN-based":[132],"visually":[135],"examined":[136],"discussed.":[138],"A":[139],"mathematical":[140],"description":[141],"also":[147],"presented.":[148]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}