{"id":"https://openalex.org/W2002747720","doi":"https://doi.org/10.1109/vetecs.2012.6240318","title":"Neural Networks Model of an UWB Channel Path Loss in a Mine Environment","display_name":"Neural Networks Model of an UWB Channel Path Loss in a Mine Environment","publication_year":2012,"publication_date":"2012-05-01","ids":{"openalex":"https://openalex.org/W2002747720","doi":"https://doi.org/10.1109/vetecs.2012.6240318","mag":"2002747720"},"language":"en","primary_location":{"id":"doi:10.1109/vetecs.2012.6240318","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vetecs.2012.6240318","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE 75th Vehicular Technology Conference (VTC Spring)","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/A5078043405","display_name":"M. Kalakh","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"M. Kalakh","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056849748","display_name":"N. Kandil","orcid":"https://orcid.org/0000-0003-4757-5103"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"N. Kandil","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5018712158","display_name":"Nadir Hakem","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"N. Hakem","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5078043405"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2493,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":{"value":0.5826226,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12024","display_name":"Ultra-Wideband Communications Technology","score":0.9998000264167786,"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/T12024","display_name":"Ultra-Wideband Communications Technology","score":0.9998000264167786,"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/T10901","display_name":"Advanced Data Compression Techniques","score":0.996399998664856,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10936","display_name":"Millimeter-Wave Propagation and Modeling","score":0.9930999875068665,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7553910613059998},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.7435520887374878},{"id":"https://openalex.org/keywords/path-loss","display_name":"Path loss","score":0.6991804242134094},{"id":"https://openalex.org/keywords/attenuation","display_name":"Attenuation","score":0.5950199365615845},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.5727019906044006},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.5661576986312866},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.5610437989234924},{"id":"https://openalex.org/keywords/backpropagation","display_name":"Backpropagation","score":0.4445798397064209},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.368541955947876},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36263853311538696},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.33627593517303467},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.18567347526550293},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1737229824066162},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.1125326156616211},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.08920237421989441}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7553910613059998},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.7435520887374878},{"id":"https://openalex.org/C194273485","wikidata":"https://www.wikidata.org/wiki/Q1478845","display_name":"Path loss","level":3,"score":0.6991804242134094},{"id":"https://openalex.org/C184652730","wikidata":"https://www.wikidata.org/wiki/Q2357982","display_name":"Attenuation","level":2,"score":0.5950199365615845},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.5727019906044006},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.5661576986312866},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.5610437989234924},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.4445798397064209},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.368541955947876},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36263853311538696},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.33627593517303467},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.18567347526550293},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1737229824066162},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.1125326156616211},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.08920237421989441},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vetecs.2012.6240318","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vetecs.2012.6240318","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE 75th Vehicular Technology Conference (VTC Spring)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1497501540","https://openalex.org/W2017795724","https://openalex.org/W2114691387","https://openalex.org/W2116616164","https://openalex.org/W2122190385","https://openalex.org/W2137299697","https://openalex.org/W2144938690","https://openalex.org/W2157833113","https://openalex.org/W2162418533","https://openalex.org/W2165159988"],"related_works":["https://openalex.org/W2894173309","https://openalex.org/W4206400463","https://openalex.org/W2961085424","https://openalex.org/W2157746493","https://openalex.org/W2792697259","https://openalex.org/W1495379181","https://openalex.org/W637433680","https://openalex.org/W2391384657","https://openalex.org/W2035645164","https://openalex.org/W2373685396"],"abstract_inverted_index":{"This":[0],"paper":[1],"aims":[2],"to":[3,48,60,76,83,89],"present":[4],"an":[5],"UWB":[6],"propagation":[7],"channel":[8],"modeling":[9,68,91],"with":[10],"neural":[11,35],"networks":[12,55],"in":[13,21],"a":[14,27],"mine":[15],"environment,":[16],"focusing":[17],"on":[18],"the":[19,22,49],"change":[20],"path":[23],"loss":[24],"attenuation":[25],"as":[26,64],"function":[28],"of":[29],"distance":[30],"and":[31,45,66,95],"frequency.":[32],"A":[33],"trained":[34],"network":[36],"can":[37],"be":[38,72,81,98],"used":[39],"for":[40,85],"high-level":[41],"design,":[42],"providing":[43],"fast":[44],"accurate":[46],"answers":[47],"task":[50],"it":[51],"has":[52],"learned.":[53],"Neural":[54],"are":[56],"effective":[57],"alternatives":[58],"methods":[59,62,78],"conventional":[61],"such":[63],"statistical":[65],"stochastic":[67],"methods,":[69],"which":[70,79,93],"could":[71,80],"computationally":[73],"expensive,":[74],"or":[75,88],"analytical":[77],"difficult":[82],"obtain":[84],"new":[86],"environments,":[87],"empirical":[90],"solutions":[92],"range":[94],"accuracy":[96],"may":[97],"limited.":[99]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2026-03-25T13:04:00.132906","created_date":"2025-10-10T00:00:00"}