{"id":"https://openalex.org/W4402159493","doi":"https://doi.org/10.1109/icc51166.2024.10622840","title":"DRNet: Efficient Few-Shot Learning Model for Regenerating Restricted-Access Area Maps","display_name":"DRNet: Efficient Few-Shot Learning Model for Regenerating Restricted-Access Area Maps","publication_year":2024,"publication_date":"2024-06-09","ids":{"openalex":"https://openalex.org/W4402159493","doi":"https://doi.org/10.1109/icc51166.2024.10622840"},"language":"en","primary_location":{"id":"doi:10.1109/icc51166.2024.10622840","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icc51166.2024.10622840","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICC 2024 - IEEE International Conference on Communications","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/A5034201659","display_name":"Van-Linh Nguyen","orcid":"https://orcid.org/0000-0002-3472-0108"},"institutions":[{"id":"https://openalex.org/I148099254","display_name":"National Chung Cheng University","ror":"https://ror.org/0028v3876","country_code":"TW","type":"education","lineage":["https://openalex.org/I148099254"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Van-Linh Nguyen","raw_affiliation_strings":["National Chung Cheng University,Dept. of Computer Science and Information Engineering,Chiayi,Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Chung Cheng University,Dept. of Computer Science and Information Engineering,Chiayi,Taiwan","institution_ids":["https://openalex.org/I148099254"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052254001","display_name":"Lan-Huong Nguyen","orcid":"https://orcid.org/0000-0002-2364-1295"},"institutions":[{"id":"https://openalex.org/I148099254","display_name":"National Chung Cheng University","ror":"https://ror.org/0028v3876","country_code":"TW","type":"education","lineage":["https://openalex.org/I148099254"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Lan-Huong Nguyen","raw_affiliation_strings":["National Chung Cheng University,Dept. of Computer Science and Information Engineering,Chiayi,Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Chung Cheng University,Dept. of Computer Science and Information Engineering,Chiayi,Taiwan","institution_ids":["https://openalex.org/I148099254"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101957678","display_name":"Yuhao Liu","orcid":"https://orcid.org/0000-0003-3098-957X"},"institutions":[{"id":"https://openalex.org/I148366613","display_name":"National Yang Ming Chiao Tung University","ror":"https://ror.org/00se2k293","country_code":"TW","type":"education","lineage":["https://openalex.org/I148366613"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Yu-Hao Liu","raw_affiliation_strings":["College of Artificial Intelligence, National Yang Ming Chiao Tung University (NYCU),Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Artificial Intelligence, National Yang Ming Chiao Tung University (NYCU),Taiwan","institution_ids":["https://openalex.org/I148366613"]}]}],"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":0.11196982,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4626","last_page":"4631"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9958999752998352,"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":0.9958999752998352,"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/T11739","display_name":"Microwave Imaging and Scattering Analysis","score":0.9926000237464905,"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"}},{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9846000075340271,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7545846700668335},{"id":"https://openalex.org/keywords/shot","display_name":"Shot (pellet)","score":0.5873674154281616},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.41354259848594666},{"id":"https://openalex.org/keywords/computer-graphics","display_name":"Computer graphics (images)","score":0.3250303566455841},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.09236595034599304}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7545846700668335},{"id":"https://openalex.org/C2778344882","wikidata":"https://www.wikidata.org/wiki/Q278938","display_name":"Shot (pellet)","level":2,"score":0.5873674154281616},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41354259848594666},{"id":"https://openalex.org/C121684516","wikidata":"https://www.wikidata.org/wiki/Q7600677","display_name":"Computer graphics (images)","level":1,"score":0.3250303566455841},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.09236595034599304},{"id":"https://openalex.org/C191897082","wikidata":"https://www.wikidata.org/wiki/Q11467","display_name":"Metallurgy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icc51166.2024.10622840","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icc51166.2024.10622840","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICC 2024 - IEEE International Conference on Communications","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":17,"referenced_works":["https://openalex.org/W2800469297","https://openalex.org/W2895040926","https://openalex.org/W2909204560","https://openalex.org/W2938514801","https://openalex.org/W2962944637","https://openalex.org/W2971911836","https://openalex.org/W2980334917","https://openalex.org/W2983582925","https://openalex.org/W3023686478","https://openalex.org/W3106393161","https://openalex.org/W3153570500","https://openalex.org/W4205989892","https://openalex.org/W4226083587","https://openalex.org/W4294769604","https://openalex.org/W4387869514","https://openalex.org/W4389633647","https://openalex.org/W4392152672"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2074502265","https://openalex.org/W4214877189","https://openalex.org/W2773965352","https://openalex.org/W2381179799","https://openalex.org/W2980279061","https://openalex.org/W2334685461","https://openalex.org/W2366718574","https://openalex.org/W2359774528"],"abstract_inverted_index":{"WiFi":[0],"and":[1,25,32],"cellular":[2],"networks":[3],"have":[4],"become":[5],"essential":[6],"components":[7],"of":[8,62,121,131,139,156,189],"our":[9,85],"modern":[10],"lives.":[11],"On":[12],"the":[13,80,100,113,119,122,129,165,170,187,197],"other":[14],"hand,":[15],"these":[16],"connectivity":[17],"technologies":[18],"also":[19],"enable":[20],"many":[21],"radio":[22,64],"localization":[23,65],"techniques":[24],"even":[26],"user":[27],"monitoring":[28,115],"that":[29,59,128],"offer":[30],"convenience":[31],"valuables,":[33],"such":[34],"as":[35],"object":[36],"tracking":[37,43],"in":[38,44,74,144,149],"augmented":[39],"reality":[40],"or":[41,173,195],"patient":[42],"emergency":[45],"calls.":[46],"This":[47],"paper":[48],"describes":[49],"a":[50,55,92,137,153,180,204],"novel":[51],"approach":[52],"for":[53,98,176],"recreating":[54],"restricted-access":[56,205],"architectural":[57],"drawing":[58,78,94],"takes":[60],"advantage":[61],"passive":[63],"on":[66,104],"multiple":[67],"users.":[68],"Initially,":[69],"we":[70,90,111],"produce":[71],"data":[72,81,106,116],"points":[73,82,107],"an":[75],"empty":[76],"building":[77],"from":[79,108],"provided":[83],"by":[84,193,200],"built-in":[86],"signal-to-image-based":[87],"localization.":[88,109],"Next,":[89],"propose":[91],"few-shot":[93],"reconstruction":[95],"model,":[96],"DRNet,":[97],"regenerating":[99],"raw":[101],"structure":[102],"based":[103],"collected":[105],"Finally,":[110],"combine":[112],"time-series":[114],"to":[117,152,167],"predict":[118],"functionality":[120],"rooms.":[123],"The":[124,141],"assessment":[125],"findings":[126],"indicate":[127],"precision":[130],"accessible":[132,174],"area":[133],"regeneration":[134],"can":[135],"reach":[136],"maximum":[138],"83.4%.":[140],"methodology":[142],"used":[143],"this":[145],"study":[146],"exhibits":[147],"versatility":[148],"its":[150],"applicability":[151],"wide":[154],"range":[155],"prospective":[157],"applications.":[158],"For":[159],"instance,":[160],"law":[161],"enforcement":[162,182],"authorities":[163],"possess":[164],"capacity":[166],"effectively":[168],"ascertain":[169],"ingress/egress":[171],"pathways":[172],"locations":[175,192],"potential":[177],"intrusion":[178],"during":[179],"narcotics":[181],"operation.":[183],"Another":[184],"scenario":[185],"involves":[186],"disclosure":[188],"frequently":[190],"visited":[191],"criminals":[194],"elucidating":[196],"purposes":[198],"served":[199],"certain":[201],"areas":[202],"inside":[203],"facility.":[206]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
