{"id":"https://openalex.org/W6963684827","doi":"https://doi.org/10.21227/me2h-t256","title":"S11 parameters for 27 tags at 3 tag-t0-reader positions (left-middle-right)","display_name":"S11 parameters for 27 tags at 3 tag-t0-reader positions (left-middle-right)","publication_year":2021,"publication_date":"2021-07-25","ids":{"openalex":"https://openalex.org/W6963684827","doi":"https://doi.org/10.21227/me2h-t256"},"language":"en","primary_location":{"id":"doi:10.21227/me2h-t256","is_oa":true,"landing_page_url":"https://doi.org/10.21227/me2h-t256","pdf_url":null,"source":{"id":"https://openalex.org/S7407051695","display_name":"IEEE DataPort","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Dataset"},"type":"dataset","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.21227/me2h-t256","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Arjomandi, Larry","orcid":"https://orcid.org/0000-0002-7597-2109"},"institutions":[{"id":"https://openalex.org/I2801239119","display_name":"Australian Regenerative Medicine Institute","ror":"https://ror.org/02qa5kg76","country_code":"AU","type":"facility","lineage":["https://openalex.org/I2801037857","https://openalex.org/I2801239119","https://openalex.org/I56590836"]},{"id":"https://openalex.org/I56590836","display_name":"Monash University","ror":"https://ror.org/02bfwt286","country_code":"AU","type":"education","lineage":["https://openalex.org/I56590836"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Arjomandi, Larry","raw_affiliation_strings":["Monash University"],"raw_orcid":"https://orcid.org/0000-0002-7597-2109","affiliations":[{"raw_affiliation_string":"Monash University","institution_ids":["https://openalex.org/I2801239119","https://openalex.org/I56590836"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I2801239119","https://openalex.org/I56590836"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T12713","display_name":"Forest Ecology and Biodiversity Studies","score":0.13079999387264252,"subfield":{"id":"https://openalex.org/subfields/1109","display_name":"Insect Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T12713","display_name":"Forest Ecology and Biodiversity Studies","score":0.13079999387264252,"subfield":{"id":"https://openalex.org/subfields/1109","display_name":"Insect Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11594","display_name":"Tree-ring climate responses","score":0.12219999730587006,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11880","display_name":"Forest ecology and management","score":0.0640999972820282,"subfield":{"id":"https://openalex.org/subfields/2309","display_name":"Nature and Landscape Conservation"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.8373000025749207},{"id":"https://openalex.org/keywords/chipless-rfid","display_name":"Chipless RFID","score":0.680899977684021},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5152000188827515},{"id":"https://openalex.org/keywords/ambiguity","display_name":"Ambiguity","score":0.47209998965263367},{"id":"https://openalex.org/keywords/inverse-synthetic-aperture-radar","display_name":"Inverse synthetic aperture radar","score":0.43059998750686646},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3919000029563904},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.37959998846054077}],"concepts":[{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.8373000025749207},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6855999827384949},{"id":"https://openalex.org/C2775974325","wikidata":"https://www.wikidata.org/wiki/Q16154688","display_name":"Chipless RFID","level":3,"score":0.680899977684021},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5491999983787537},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5152000188827515},{"id":"https://openalex.org/C2780522230","wikidata":"https://www.wikidata.org/wiki/Q1140419","display_name":"Ambiguity","level":2,"score":0.47209998965263367},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4505999982357025},{"id":"https://openalex.org/C109094680","wikidata":"https://www.wikidata.org/wiki/Q6060432","display_name":"Inverse synthetic aperture radar","level":4,"score":0.43059998750686646},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3919000029563904},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.37959998846054077},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.3779999911785126},{"id":"https://openalex.org/C78336883","wikidata":"https://www.wikidata.org/wiki/Q4779385","display_name":"Aperture (computer memory)","level":2,"score":0.3447999954223633},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.31690001487731934},{"id":"https://openalex.org/C2779464207","wikidata":"https://www.wikidata.org/wiki/Q4226196","display_name":"Coded aperture","level":3,"score":0.3156999945640564},{"id":"https://openalex.org/C157899210","wikidata":"https://www.wikidata.org/wiki/Q1395022","display_name":"Convolutional code","level":3,"score":0.2847000062465668},{"id":"https://openalex.org/C2778000800","wikidata":"https://www.wikidata.org/wiki/Q830043","display_name":"Handshake","level":3,"score":0.27239999175071716},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.26269999146461487},{"id":"https://openalex.org/C2780385302","wikidata":"https://www.wikidata.org/wiki/Q367158","display_name":"Protocol (science)","level":3,"score":0.25369998812675476}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.21227/me2h-t256","is_oa":true,"landing_page_url":"https://doi.org/10.21227/me2h-t256","pdf_url":null,"source":{"id":"https://openalex.org/S7407051695","display_name":"IEEE DataPort","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Dataset"}],"best_oa_location":{"id":"doi:10.21227/me2h-t256","is_oa":true,"landing_page_url":"https://doi.org/10.21227/me2h-t256","pdf_url":null,"source":{"id":"https://openalex.org/S7407051695","display_name":"IEEE DataPort","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Dataset"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Abstract\u2014Chipless":[0],"RFID":[1,176],"tag":[2,120],"decoding":[3,57,107,132,146],"has":[4,134],"some":[5,34],"inherent":[6],"degrees":[7],"of":[8,27,67,70],"uncertainty":[9],"because":[10],"there":[11],"is":[12,79,95,109,127,149,156],"no":[13],"handshake":[14],"protocol":[15],"between":[16],"chipless":[17,72,164,175,178],"tags":[18,36,73],"and":[19,91,122,138,144],"readers.":[20],"This":[21,148],"paper":[22],"initially":[23],"compares":[24],"the":[25,38,71,92,124,150],"outcome":[26],"different":[28],"pattern":[29,47,180],"recognition":[30,48],"methods":[31,49,66,160],"to":[32,54,117,161],"decode":[33,118,162],"frequency-based":[35,163],"in":[37],"mm-wave":[39],"spectrum.":[40],"It":[41,126],"will":[42],"be":[43],"shown":[44,128],"that":[45,129],"these":[46],"suffer":[50],"from":[51,100],"almost":[52,140],"2":[53],"5%":[55],"false":[56,145],"rate.":[58],"To":[59],"overcome":[60],"this":[61,130],"mis-decoding":[62],"problem,":[63],"two":[64],"novel":[65],"making":[68,96],"images":[69,99,121],"are":[74],"presented.":[75],"The":[76],"first":[77,151],"method":[78,133,155],"using":[80],"continuous":[81],"wave":[82],"imaging":[83],"based":[84,111],"on":[85,112],"side":[86],"looking":[87],"aperture":[88],"radar":[89],"concepts,":[90],"second":[93],"one":[94],"virtual":[97],"2-D":[98,106],"1-D":[101],"backscattering":[102],"signals.":[103],"Then":[104],"a":[105,113,153],"algorithm":[108],"suggested":[110],"convolutional":[114,182],"neural":[115,183],"network":[116],"those":[119],"compare":[123],"results.":[125],"combined":[131],"very":[135],"high":[136],"accuracy,":[137],"it":[139],"removes":[141],"any":[142],"ambiguity":[143],"problems.":[147],"time":[152],"deep-learning":[154],"used":[157],"with":[158],"image-construction":[159],"tags.":[165],"Index":[166],"Terms\u2014RFID,":[167],"Side":[168],"Looking":[169],"Aperture":[170,173],"Radar,":[171,174],"Synthetic":[172],"tags,":[177],"sensors,":[179],"recognition,":[181],"network,":[184],"Deep-learning,":[185],"mmwave":[186],"band.":[187]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2025-10-10T00:00:00"}
