{"id":"https://openalex.org/W4412524713","doi":"https://doi.org/10.1145/3735014.3735884","title":"Signal Recognition Based on Neural Network with Dual-channel Time-frequency Characteristics","display_name":"Signal Recognition Based on Neural Network with Dual-channel Time-frequency Characteristics","publication_year":2024,"publication_date":"2024-12-13","ids":{"openalex":"https://openalex.org/W4412524713","doi":"https://doi.org/10.1145/3735014.3735884"},"language":"en","primary_location":{"id":"doi:10.1145/3735014.3735884","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3735014.3735884","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 International Conference on Big Data Mining and Information Processing","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/A5051457014","display_name":"J.G. Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiatong Liu","raw_affiliation_strings":["Shenyang City University, Shenyang, Liaoning, China"],"raw_orcid":"https://orcid.org/0009-0004-8509-1331","affiliations":[{"raw_affiliation_string":"Shenyang City University, Shenyang, Liaoning, China","institution_ids":[]}]},{"author_position":"last","author":{"id":null,"display_name":"Yuheng Peng","orcid":"https://orcid.org/0009-0006-9527-0223"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuheng Peng","raw_affiliation_strings":["Shenyang City University, Shenyang, Liaoning, China"],"raw_orcid":"https://orcid.org/0009-0006-9527-0223","affiliations":[{"raw_affiliation_string":"Shenyang City University, Shenyang, Liaoning, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"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":0.26527879,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"115","last_page":"119"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12131","display_name":"Wireless Signal Modulation Classification","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12131","display_name":"Wireless Signal Modulation Classification","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10891","display_name":"Radar Systems and Signal Processing","score":0.9732000231742859,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/T11447","display_name":"Blind Source Separation Techniques","score":0.9670000076293945,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/computer-science","display_name":"Computer science","score":0.6632863283157349},{"id":"https://openalex.org/keywords/time\u2013frequency-analysis","display_name":"Time\u2013frequency analysis","score":0.6021262407302856},{"id":"https://openalex.org/keywords/dual","display_name":"Dual (grammatical number)","score":0.5776205062866211},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5592260956764221},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.5324656963348389},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.4750652015209198},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.43865251541137695},{"id":"https://openalex.org/keywords/signal-processing","display_name":"Signal processing","score":0.42972755432128906},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.32204315066337585},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.29087090492248535},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.24920859932899475}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6632863283157349},{"id":"https://openalex.org/C142433447","wikidata":"https://www.wikidata.org/wiki/Q7806653","display_name":"Time\u2013frequency analysis","level":3,"score":0.6021262407302856},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.5776205062866211},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5592260956764221},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.5324656963348389},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.4750652015209198},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.43865251541137695},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.42972755432128906},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32204315066337585},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.29087090492248535},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.24920859932899475},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.0},{"id":"https://openalex.org/C124952713","wikidata":"https://www.wikidata.org/wiki/Q8242","display_name":"Literature","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3735014.3735884","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3735014.3735884","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 International Conference on Big Data Mining and Information Processing","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":13,"referenced_works":["https://openalex.org/W2168983878","https://openalex.org/W2612824601","https://openalex.org/W2734908518","https://openalex.org/W2759330029","https://openalex.org/W2775383661","https://openalex.org/W2884089434","https://openalex.org/W2945473916","https://openalex.org/W2999595914","https://openalex.org/W3088383832","https://openalex.org/W3099657079","https://openalex.org/W3101503122","https://openalex.org/W3123268700","https://openalex.org/W3148401945"],"related_works":["https://openalex.org/W2317351040","https://openalex.org/W2952466936","https://openalex.org/W1988622314","https://openalex.org/W2393949104","https://openalex.org/W4236344264","https://openalex.org/W3046201198","https://openalex.org/W4293061921","https://openalex.org/W2807441102","https://openalex.org/W2009424630","https://openalex.org/W2905220255"],"abstract_inverted_index":{"With":[0],"the":[1,6,9,21,30,49,66,80,92],"advancement":[2],"of":[3,8,25,48],"communication":[4],"technology,":[5],"utilization":[7],"electromagnetic":[10],"spectrum":[11],"has":[12],"become":[13],"increasingly":[14],"sophisticated.":[15],"However,":[16],"challenges":[17],"remain,":[18],"such":[19],"as":[20],"low":[22],"recognition":[23,86],"rate":[24],"signal":[26],"modulation":[27],"methods":[28,110],"and":[29,53,111],"tendency":[31],"for":[32],"confusion":[33],"when":[34],"extracting":[35],"single":[36],"time-frequency":[37,45,63,104,109],"features.":[38],"To":[39],"address":[40],"issues,":[41],"this":[42],"study":[43],"proposes":[44],"transformation":[46],"features":[47,76],"signal's":[50],"imaginary":[51],"part":[52],"designs":[54],"a":[55],"dual-channel":[56,94],"neural":[57],"network":[58,95],"Each":[59],"channel":[60,75],"extracts":[61],"distinct":[62],"features,":[64],"enhancing":[65],"network's":[67],"capacity":[68],"to":[69,84],"learn":[70],"complex":[71],"representations.":[72],"The":[73],"extracted":[74],"are":[77],"fused":[78],"in":[79],"fully":[81],"connected":[82],"layer":[83],"improve":[85],"accuracy.":[87],"Simulation":[88],"results":[89],"indicate":[90],"that":[91],"proposed":[93],"significantly":[96],"improves":[97],"classification":[98],"accuracy":[99],"by":[100],"effectively":[101],"integrating":[102],"multi-dimensional":[103],"information,":[105],"compared":[106],"with":[107],"single-channel":[108],"existing":[112],"image-based":[113],"approaches.":[114]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}