{"id":"https://openalex.org/W4200004457","doi":"https://doi.org/10.1109/vtc2021-fall52928.2021.9625536","title":"Artificial Neural Networks-based Ambient RF Energy Harvesting with Environment Detection","display_name":"Artificial Neural Networks-based Ambient RF Energy Harvesting with Environment Detection","publication_year":2021,"publication_date":"2021-09-01","ids":{"openalex":"https://openalex.org/W4200004457","doi":"https://doi.org/10.1109/vtc2021-fall52928.2021.9625536"},"language":"en","primary_location":{"id":"doi:10.1109/vtc2021-fall52928.2021.9625536","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vtc2021-fall52928.2021.9625536","pdf_url":null,"source":{"id":"https://openalex.org/S4363607774","display_name":"2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall)","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/A5018037374","display_name":"Jonathan C. Kwan","orcid":"https://orcid.org/0000-0002-4212-2319"},"institutions":[{"id":"https://openalex.org/I168635309","display_name":"University of Calgary","ror":"https://ror.org/03yjb2x39","country_code":"CA","type":"education","lineage":["https://openalex.org/I168635309"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Jonathan C. Kwan","raw_affiliation_strings":["University of Calgary,Department of Electrical and Software Engineering,Calgary,Alberta,Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Calgary,Department of Electrical and Software Engineering,Calgary,Alberta,Canada","institution_ids":["https://openalex.org/I168635309"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008263449","display_name":"Jesse M. Chaulk","orcid":null},"institutions":[{"id":"https://openalex.org/I26916880","display_name":"Alberta University of the Arts","ror":"https://ror.org/00mn36586","country_code":"CA","type":"education","lineage":["https://openalex.org/I26916880"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Jesse M. Chaulk","raw_affiliation_strings":["Acuspire,Calgary,Alberta,Canada","Acuspire, Calgary, Alberta, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Acuspire,Calgary,Alberta,Canada","institution_ids":["https://openalex.org/I26916880"]},{"raw_affiliation_string":"Acuspire, Calgary, Alberta, Canada","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011776428","display_name":"Abraham O. Fapojuwo","orcid":"https://orcid.org/0000-0003-2577-3416"},"institutions":[{"id":"https://openalex.org/I168635309","display_name":"University of Calgary","ror":"https://ror.org/03yjb2x39","country_code":"CA","type":"education","lineage":["https://openalex.org/I168635309"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Abraham O. Fapojuwo","raw_affiliation_strings":["University of Calgary,Department of Electrical and Software Engineering,Calgary,Alberta,Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Calgary,Department of Electrical and Software Engineering,Calgary,Alberta,Canada","institution_ids":["https://openalex.org/I168635309"]}]}],"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":0.16185917,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"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/T11392","display_name":"Energy Harvesting in Wireless Networks","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/T11392","display_name":"Energy Harvesting in Wireless Networks","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/T10148","display_name":"Advanced MIMO Systems Optimization","score":0.996999979019165,"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/T11230","display_name":"Innovative Energy Harvesting Technologies","score":0.9915000200271606,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical 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/artificial-neural-network","display_name":"Artificial neural network","score":0.6796423196792603},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6707504391670227},{"id":"https://openalex.org/keywords/radio-frequency","display_name":"Radio frequency","score":0.6471976637840271},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.552678108215332},{"id":"https://openalex.org/keywords/wireless-sensor-network","display_name":"Wireless sensor network","score":0.4705536663532257},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.3848927319049835},{"id":"https://openalex.org/keywords/electronic-engineering","display_name":"Electronic engineering","score":0.38378089666366577},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3709717094898224},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.2754365801811218},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.19066956639289856},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.11658519506454468},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10400497913360596},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.07512331008911133}],"concepts":[{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6796423196792603},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6707504391670227},{"id":"https://openalex.org/C74064498","wikidata":"https://www.wikidata.org/wiki/Q3396184","display_name":"Radio frequency","level":2,"score":0.6471976637840271},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.552678108215332},{"id":"https://openalex.org/C24590314","wikidata":"https://www.wikidata.org/wiki/Q336038","display_name":"Wireless sensor network","level":2,"score":0.4705536663532257},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3848927319049835},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.38378089666366577},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3709717094898224},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.2754365801811218},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.19066956639289856},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.11658519506454468},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10400497913360596},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.07512331008911133}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vtc2021-fall52928.2021.9625536","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vtc2021-fall52928.2021.9625536","pdf_url":null,"source":{"id":"https://openalex.org/S4363607774","display_name":"2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5400000214576721,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W592963477","https://openalex.org/W2094000142","https://openalex.org/W2137293732","https://openalex.org/W2160265402","https://openalex.org/W2215113479","https://openalex.org/W2537001988","https://openalex.org/W2597963303","https://openalex.org/W2624023996","https://openalex.org/W2884005267","https://openalex.org/W2906153113","https://openalex.org/W2912074258","https://openalex.org/W2963121780","https://openalex.org/W2966186994","https://openalex.org/W3035897868","https://openalex.org/W6683768324"],"related_works":["https://openalex.org/W1975451135","https://openalex.org/W2890570089","https://openalex.org/W2989915292","https://openalex.org/W2295628284","https://openalex.org/W3148968234","https://openalex.org/W145760256","https://openalex.org/W2028086369","https://openalex.org/W2123841989","https://openalex.org/W2129477171","https://openalex.org/W2073793005"],"abstract_inverted_index":{"This":[0,29],"paper":[1],"proposes":[2],"a":[3,84,114],"cascading":[4],"artificial":[5],"neural":[6],"networks":[7],"(ANNs)":[8],"algorithm":[9,31,68,103],"with":[10,21],"performance-enhancing":[11],"filters":[12],"for":[13],"ambient":[14,138],"radio":[15],"frequency":[16],"(RF)":[17],"energy":[18],"harvesting":[19],"(EH)":[20],"environment":[22],"detection":[23],"(ED),":[24],"referred":[25],"to":[26,73,79,88,110],"as":[27,112,130,132],"ANN-ED.":[28],"ANN-ED":[30,67,102],"can":[32,69,128],"reliably":[33],"operate":[34],"in":[35,142],"both":[36],"urban":[37],"and":[38,49,56],"rural":[39],"environments":[40,144],"where":[41],"there":[42],"is":[43],"unpredictable":[44],"availability":[45],"of":[46,75,108,119,137,148,152],"unintended":[47,58],"sources":[48],"dynamic":[50],"channel":[51],"conditions":[52],"between":[53],"the":[54,57,66,76,89,101,146],"sensors":[55,64,141],"sources.":[59],"Numerical":[60],"results":[61],"show":[62],"that":[63,118],"using":[65,100],"successfully":[70],"sense":[71],"up":[72,109],"98.7%":[74],"data":[77],"compared":[78,87],"an":[80,93,105,120],"ideal":[81],"sensor,":[82],"offering":[83],"significant":[85,115],"improvement":[86,116],"0.3%":[90],"achieved":[91],"by":[92],"ANNs-based":[94,121],"RF":[95,122,139],"EH":[96,123,140],"without":[97,124],"ED.":[98],"Sensors":[99],"have":[104],"accuracy":[106,127],"rate":[107],"100%":[111],"well;":[113],"over":[117],"ED":[125],"whose":[126],"be":[129],"low":[131],"0%.":[133],"The":[134],"reliable":[135],"operation":[136],"all":[143],"enhances":[145],"practicality":[147],"its":[149],"usage":[150],"regardless":[151],"location.":[153]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
