{"id":"https://openalex.org/W3034192317","doi":"https://doi.org/10.1109/isplc48789.2020.9115394","title":"Learning to Synthesize Noise: The Multiple Conductor Power Line Case","display_name":"Learning to Synthesize Noise: The Multiple Conductor Power Line Case","publication_year":2020,"publication_date":"2020-05-01","ids":{"openalex":"https://openalex.org/W3034192317","doi":"https://doi.org/10.1109/isplc48789.2020.9115394","mag":"3034192317"},"language":"en","primary_location":{"id":"doi:10.1109/isplc48789.2020.9115394","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isplc48789.2020.9115394","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Symposium on Power Line Communications and its Applications (ISPLC)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5052977700","display_name":"Nunzio A. Letizia","orcid":"https://orcid.org/0000-0003-1495-4449"},"institutions":[{"id":"https://openalex.org/I4210166741","display_name":"University of Klagenfurt","ror":"https://ror.org/05q9m0937","country_code":"AT","type":"education","lineage":["https://openalex.org/I4210166741"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Nunzio A. Letizia","raw_affiliation_strings":["Chair of Embedded Communication Systems, University of Klagenfurt, Austria"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chair of Embedded Communication Systems, University of Klagenfurt, Austria","institution_ids":["https://openalex.org/I4210166741"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046459259","display_name":"Andrea M. Tonello","orcid":"https://orcid.org/0000-0002-9873-2407"},"institutions":[{"id":"https://openalex.org/I4210166741","display_name":"University of Klagenfurt","ror":"https://ror.org/05q9m0937","country_code":"AT","type":"education","lineage":["https://openalex.org/I4210166741"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Andrea M. Tonello","raw_affiliation_strings":["Chair of Embedded Communication Systems, University of Klagenfurt, Austria"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chair of Embedded Communication Systems, University of Klagenfurt, Austria","institution_ids":["https://openalex.org/I4210166741"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5024069694","display_name":"Davide Righini","orcid":"https://orcid.org/0000-0002-2786-1092"},"institutions":[{"id":"https://openalex.org/I4210166741","display_name":"University of Klagenfurt","ror":"https://ror.org/05q9m0937","country_code":"AT","type":"education","lineage":["https://openalex.org/I4210166741"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Davide Righini","raw_affiliation_strings":["Chair of Embedded Communication Systems, University of Klagenfurt, Austria"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chair of Embedded Communication Systems, University of Klagenfurt, Austria","institution_ids":["https://openalex.org/I4210166741"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210166741"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":null,"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/T12146","display_name":"Power Line Communications and Noise","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/T12146","display_name":"Power Line Communications and Noise","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/T11444","display_name":"Electromagnetic Compatibility and Noise Suppression","score":0.9987000226974487,"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/T12357","display_name":"Digital Media Forensic Detection","score":0.9818000197410583,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/spectrogram","display_name":"Spectrogram","score":0.8553434610366821},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.7700550556182861},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6999162435531616},{"id":"https://openalex.org/keywords/noise-measurement","display_name":"Noise measurement","score":0.5479344129562378},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5035743117332458},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4502260088920593},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4460351765155792},{"id":"https://openalex.org/keywords/line","display_name":"Line (geometry)","score":0.4385482668876648},{"id":"https://openalex.org/keywords/noise-power","display_name":"Noise power","score":0.4333813786506653},{"id":"https://openalex.org/keywords/replicate","display_name":"Replicate","score":0.41132423281669617},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.4044286608695984},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3916415572166443},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3430691659450531},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3224599361419678},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.31522154808044434},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15476006269454956},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.1374950408935547}],"concepts":[{"id":"https://openalex.org/C45273575","wikidata":"https://www.wikidata.org/wiki/Q578970","display_name":"Spectrogram","level":2,"score":0.8553434610366821},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.7700550556182861},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6999162435531616},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.5479344129562378},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5035743117332458},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4502260088920593},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4460351765155792},{"id":"https://openalex.org/C198352243","wikidata":"https://www.wikidata.org/wiki/Q37105","display_name":"Line (geometry)","level":2,"score":0.4385482668876648},{"id":"https://openalex.org/C203234222","wikidata":"https://www.wikidata.org/wiki/Q2133519","display_name":"Noise power","level":3,"score":0.4333813786506653},{"id":"https://openalex.org/C2781162219","wikidata":"https://www.wikidata.org/wiki/Q26250693","display_name":"Replicate","level":2,"score":0.41132423281669617},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.4044286608695984},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3916415572166443},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3430691659450531},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3224599361419678},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.31522154808044434},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15476006269454956},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.1374950408935547},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isplc48789.2020.9115394","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isplc48789.2020.9115394","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Symposium on Power Line Communications and its Applications (ISPLC)","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":32,"referenced_works":["https://openalex.org/W1491464075","https://openalex.org/W1542618289","https://openalex.org/W1985203195","https://openalex.org/W2008705091","https://openalex.org/W2031239413","https://openalex.org/W2079217205","https://openalex.org/W2099471712","https://openalex.org/W2105435073","https://openalex.org/W2109243041","https://openalex.org/W2117886229","https://openalex.org/W2120847449","https://openalex.org/W2133871228","https://openalex.org/W2165682645","https://openalex.org/W2492457451","https://openalex.org/W2593054164","https://openalex.org/W2600284783","https://openalex.org/W2776471944","https://openalex.org/W2783345504","https://openalex.org/W2804646185","https://openalex.org/W2894295011","https://openalex.org/W2940090679","https://openalex.org/W2953372026","https://openalex.org/W2962760235","https://openalex.org/W2963684088","https://openalex.org/W2991588572","https://openalex.org/W4294643831","https://openalex.org/W4297817572","https://openalex.org/W4320013936","https://openalex.org/W6685352114","https://openalex.org/W6735906348","https://openalex.org/W6745560452","https://openalex.org/W6755257315"],"related_works":["https://openalex.org/W3108403339","https://openalex.org/W2162712524","https://openalex.org/W4287867034","https://openalex.org/W2175872006","https://openalex.org/W3005783148","https://openalex.org/W3021143552","https://openalex.org/W2004821155","https://openalex.org/W2131193330","https://openalex.org/W2097897855","https://openalex.org/W2620676623"],"abstract_inverted_index":{"The":[0,47,97,116],"performance":[1],"of":[2,22,56,99,109],"communication":[3,24],"systems":[4],"is":[5,74,119],"strongly":[6],"dependent":[7],"on":[8],"noise.":[9],"Modeling":[10],"and":[11,42,59,113,123,137],"reproducing":[12],"noise":[13,40,57,95,111,128],"patterns":[14],"play":[15],"an":[16],"important":[17],"role":[18],"in":[19],"the":[20,35,81,86,90,100,106,126,134,139],"development":[21],"enhanced":[23],"algorithms.":[25],"This":[26],"article":[27],"exploits":[28],"Machine":[29],"Learning":[30],"(ML)":[31],"techniques":[32],"to":[33,76,104],"analyze":[34],"Power":[36],"Line":[37],"Communication":[38],"(PLC)":[39],"distribution":[41],"synthetically":[43],"reproduce":[44],"unseen":[45],"traces.":[46,96],"generation":[48],"method":[49,118],"takes":[50],"as":[51,65,147],"input":[52],"a":[53],"dataset":[54],"consisting":[55],"measurements":[58],"processes":[60],"them":[61],"into":[62,93],"spectrograms,":[63],"represented":[64],"images.":[66],"A":[67],"Deep":[68],"Convolutional":[69],"Generative":[70],"Adversarial":[71],"Network":[72],"(DCGAN)":[73],"trained":[75],"generate":[77],"new":[78,94],"spectrograms":[79,92],"with":[80],"same":[82,140],"statistical":[83,143],"distribution.":[84],"Lastly,":[85],"Griffin-Lim":[87],"algorithm":[88],"converts":[89],"synthesized":[91],"scalability":[98],"proposed":[101],"approach":[102],"allows":[103],"incorporate":[105],"mutual":[107],"dependence":[108],"multi-conductor":[110],"traces":[112,129],"replicate":[114],"them.":[115],"presented":[117],"evaluated":[120],"through":[121],"qualitative":[122],"quantitative":[124],"metrics:":[125],"generated":[127],"are":[130,145],"perceived":[131],"indistinguishable":[132],"from":[133],"measured":[135],"ones,":[136],"at":[138],"time,":[141],"their":[142],"properties":[144],"preserved":[146],"proven":[148],"by":[149],"numerical":[150],"results.":[151]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
