{"id":"https://openalex.org/W2771342141","doi":"https://doi.org/10.1109/atnac.2017.8215428","title":"A deep learning approach to fingerprinting indoor localization solutions","display_name":"A deep learning approach to fingerprinting indoor localization solutions","publication_year":2017,"publication_date":"2017-11-01","ids":{"openalex":"https://openalex.org/W2771342141","doi":"https://doi.org/10.1109/atnac.2017.8215428","mag":"2771342141"},"language":"en","primary_location":{"id":"doi:10.1109/atnac.2017.8215428","is_oa":false,"landing_page_url":"https://doi.org/10.1109/atnac.2017.8215428","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 27th International Telecommunication Networks and Applications Conference (ITNAC)","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/A5088838600","display_name":"Linchen Xiao","orcid":null},"institutions":[{"id":"https://openalex.org/I887968799","display_name":"RWTH Aachen University","ror":"https://ror.org/04xfq0f34","country_code":"DE","type":"education","lineage":["https://openalex.org/I887968799"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Linchen Xiao","raw_affiliation_strings":["RWTH Aachen University, Aachen, Germany"],"affiliations":[{"raw_affiliation_string":"RWTH Aachen University, Aachen, Germany","institution_ids":["https://openalex.org/I887968799"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083706779","display_name":"Arash Behboodi","orcid":"https://orcid.org/0000-0001-8229-2809"},"institutions":[{"id":"https://openalex.org/I887968799","display_name":"RWTH Aachen University","ror":"https://ror.org/04xfq0f34","country_code":"DE","type":"education","lineage":["https://openalex.org/I887968799"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Arash Behboodi","raw_affiliation_strings":["RWTH Aachen University, Aachen, Germany"],"affiliations":[{"raw_affiliation_string":"RWTH Aachen University, Aachen, Germany","institution_ids":["https://openalex.org/I887968799"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066277118","display_name":"Rudolf Mathar","orcid":"https://orcid.org/0000-0002-9585-605X"},"institutions":[{"id":"https://openalex.org/I887968799","display_name":"RWTH Aachen University","ror":"https://ror.org/04xfq0f34","country_code":"DE","type":"education","lineage":["https://openalex.org/I887968799"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Rudolf Mathar","raw_affiliation_strings":["RWTH Aachen University, Aachen, Germany"],"affiliations":[{"raw_affiliation_string":"RWTH Aachen University, Aachen, Germany","institution_ids":["https://openalex.org/I887968799"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5088838600"],"corresponding_institution_ids":["https://openalex.org/I887968799"],"apc_list":null,"apc_paid":null,"fwci":1.7523,"has_fulltext":false,"cited_by_count":37,"citation_normalized_percentile":{"value":0.86149236,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"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":0.9961000084877014,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9897000193595886,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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":0.7979569435119629},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.6585600972175598},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6314895153045654},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6287035346031189},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5573312640190125},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5573279857635498},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.514928936958313},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.47623899579048157},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.432748019695282},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4135090708732605},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.10109603404998779}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7979569435119629},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.6585600972175598},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6314895153045654},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6287035346031189},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5573312640190125},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5573279857635498},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.514928936958313},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.47623899579048157},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.432748019695282},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4135090708732605},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.10109603404998779}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/atnac.2017.8215428","is_oa":false,"landing_page_url":"https://doi.org/10.1109/atnac.2017.8215428","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 27th International Telecommunication Networks and Applications Conference (ITNAC)","raw_type":"proceedings-article"},{"id":"pmh:oai:publications.rwth-aachen.de:711732","is_oa":false,"landing_page_url":"https://publications.rwth-aachen.de/search?p=id:%22RWTH-CONV-220340%22","pdf_url":null,"source":{"id":"https://openalex.org/S4306401033","display_name":"RWTH Publications (RWTH Aachen)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I887968799","host_organization_name":"RWTH Aachen University","host_organization_lineage":["https://openalex.org/I887968799"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Piscataway, NJ : IEEE 7 pp. (2017). doi:10.1109/ATNAC.2017.8215428","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W317456669","https://openalex.org/W1522301498","https://openalex.org/W1609591105","https://openalex.org/W1630879738","https://openalex.org/W1677182931","https://openalex.org/W1698155719","https://openalex.org/W1806891645","https://openalex.org/W1981373725","https://openalex.org/W2095705004","https://openalex.org/W2136922672","https://openalex.org/W2146502635","https://openalex.org/W2163605009","https://openalex.org/W2309512289","https://openalex.org/W2541811185","https://openalex.org/W2552911271","https://openalex.org/W2769423763","https://openalex.org/W4235243453","https://openalex.org/W6674330103","https://openalex.org/W6681435938","https://openalex.org/W6684191040","https://openalex.org/W6792841710"],"related_works":["https://openalex.org/W4390421286","https://openalex.org/W4280563792","https://openalex.org/W2140186469","https://openalex.org/W4389724018","https://openalex.org/W4318719684","https://openalex.org/W2775233965","https://openalex.org/W4318559728","https://openalex.org/W4360995913","https://openalex.org/W4312193868","https://openalex.org/W4308165509"],"abstract_inverted_index":{"Fingerprinting":[0],"Localization":[1],"Solutions":[2],"(FPSs)":[3],"enjoy":[4],"huge":[5],"popularity":[6],"due":[7],"to":[8,29,55,93,104,139],"their":[9],"good":[10],"performance":[11,142],"and":[12,48,80],"minimal":[13],"environment":[14],"information":[15],"requirement.":[16],"Considered":[17],"as":[18],"a":[19,42,112,144],"data-driven":[20],"approach,":[21],"many":[22],"modern":[23],"data":[24,87,107],"analytics":[25],"can":[26,90,101],"be":[27,91,102],"used":[28,103],"improve":[30],"its":[31],"performance.":[32],"In":[33],"this":[34],"paper,":[35],"we":[36],"propose":[37],"tow":[38],"learning":[39,44,99],"algorithms,":[40],"namely":[41],"deep":[43,98],"architecture":[45],"for":[46,53],"regression":[47],"Support":[49],"Vector":[50],"Machine":[51],"(SVM)":[52],"classification,":[54],"output":[56],"the":[57,62,69,76,95,106,116],"estimated":[58],"location":[59],"directly":[60],"from":[61],"measured":[63],"fingerprints.":[64],"The":[65,97,122],"design":[66],"issues":[67],"of":[68,133],"proposed":[70],"neural":[71],"network":[72],"is":[73,84,119,137],"discussed":[74,85],"including":[75],"training":[77,135],"algorithm,":[78],"regularization":[79],"hyperparameter":[81],"selection.":[82],"It":[83],"how":[86],"augmentation":[88],"methods":[89],"utilized":[92],"extend":[94],"measurements.":[96],"approach":[100],"save":[105],"collection":[108],"time":[109],"significantly":[110,120],"using":[111],"pre-trained":[113,145],"model.":[114,146],"Moreover":[115],"run-time":[117],"complexity":[118],"reduced.":[121],"numerical":[123],"analysis":[124],"show":[125],"that":[126],"in":[127],"some":[128],"case,":[129],"only":[130],"10":[131],"percent":[132],"original":[134],"database":[136],"enough":[138],"get":[140],"acceptable":[141],"on":[143]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":11},{"year":2020,"cited_by_count":8},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
