{"id":"https://openalex.org/W3028451766","doi":"https://doi.org/10.3390/e22050585","title":"Spectral-Based SPD Matrix Representation for Signal Detection Using a Deep Neutral Network","display_name":"Spectral-Based SPD Matrix Representation for Signal Detection Using a Deep Neutral Network","publication_year":2020,"publication_date":"2020-05-22","ids":{"openalex":"https://openalex.org/W3028451766","doi":"https://doi.org/10.3390/e22050585","mag":"3028451766","pmid":"https://pubmed.ncbi.nlm.nih.gov/33286357"},"language":"en","primary_location":{"id":"doi:10.3390/e22050585","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e22050585","pdf_url":"https://www.mdpi.com/1099-4300/22/5/585/pdf?version=1590136701","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1099-4300/22/5/585/pdf?version=1590136701","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5050820675","display_name":"Jiangyi Wang","orcid":"https://orcid.org/0009-0007-3452-6034"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiangyi Wang","raw_affiliation_strings":["School of Meteorology and Oceanography, National University of Defence Technology, Changsha 410073, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Meteorology and Oceanography, National University of Defence Technology, Changsha 410073, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058024618","display_name":"Xiaoqiang Hua","orcid":"https://orcid.org/0000-0001-7687-7720"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiaoqiang Hua","raw_affiliation_strings":["School of Meteorology and Oceanography, National University of Defence Technology, Changsha 410073, China"],"raw_orcid":"https://orcid.org/0000-0001-7687-7720","affiliations":[{"raw_affiliation_string":"School of Meteorology and Oceanography, National University of Defence Technology, Changsha 410073, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109302523","display_name":"Xinwu Zeng","orcid":null},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinwu Zeng","raw_affiliation_strings":["School of Meteorology and Oceanography, National University of Defence Technology, Changsha 410073, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Meteorology and Oceanography, National University of Defence Technology, Changsha 410073, China","institution_ids":["https://openalex.org/I170215575"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5058024618"],"corresponding_institution_ids":["https://openalex.org/I170215575"],"apc_list":{"value":2000,"currency":"CHF","value_usd":2165},"apc_paid":{"value":2000,"currency":"CHF","value_usd":2165},"fwci":0.816,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.78764563,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"22","issue":"5","first_page":"585","last_page":"585"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12131","display_name":"Wireless Signal Modulation Classification","score":0.9998000264167786,"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":0.9998000264167786,"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/T10057","display_name":"Face and Expression Recognition","score":0.9979000091552734,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9979000091552734,"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/representation","display_name":"Representation (politics)","score":0.5921623110771179},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.49321454763412476},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.4755629897117615},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4715549945831299},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4661966860294342},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.43101999163627625},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4200507402420044},{"id":"https://openalex.org/keywords/statistical-physics","display_name":"Statistical physics","score":0.35077422857284546},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.34360581636428833},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.2515069246292114},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.1781790852546692}],"concepts":[{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5921623110771179},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.49321454763412476},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.4755629897117615},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4715549945831299},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4661966860294342},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43101999163627625},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4200507402420044},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.35077422857284546},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.34360581636428833},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.2515069246292114},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.1781790852546692},{"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/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.3390/e22050585","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e22050585","pdf_url":"https://www.mdpi.com/1099-4300/22/5/585/pdf?version=1590136701","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},{"id":"pmid:33286357","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/33286357","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:doaj.org/article:1bf0aaa3fb0e4275b1ced0c3751aedc8","is_oa":true,"landing_page_url":"https://doaj.org/article/1bf0aaa3fb0e4275b1ced0c3751aedc8","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy, Vol 22, Iss 5, p 585 (2020)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/1099-4300/22/5/585/","is_oa":true,"landing_page_url":"http://dx.doi.org/10.3390/e22050585","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy","raw_type":"Text"},{"id":"pmh:oai:pubmedcentral.nih.gov:7517104","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7517104","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy (Basel)","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/e22050585","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e22050585","pdf_url":"https://www.mdpi.com/1099-4300/22/5/585/pdf?version=1590136701","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4534416616","display_name":null,"funder_award_id":"No.61901479","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3028451766.pdf","grobid_xml":"https://content.openalex.org/works/W3028451766.grobid-xml"},"referenced_works_count":31,"referenced_works":["https://openalex.org/W286877588","https://openalex.org/W1983496390","https://openalex.org/W2034470681","https://openalex.org/W2035328597","https://openalex.org/W2097117768","https://openalex.org/W2114267371","https://openalex.org/W2130014182","https://openalex.org/W2144093206","https://openalex.org/W2194775991","https://openalex.org/W2204257188","https://openalex.org/W2515363767","https://openalex.org/W2517500902","https://openalex.org/W2520696524","https://openalex.org/W2544817929","https://openalex.org/W2558385255","https://openalex.org/W2558748708","https://openalex.org/W2575262780","https://openalex.org/W2596126413","https://openalex.org/W2604865066","https://openalex.org/W2618530766","https://openalex.org/W2736806241","https://openalex.org/W2790122812","https://openalex.org/W2795696415","https://openalex.org/W2889630487","https://openalex.org/W2936761377","https://openalex.org/W2962772276","https://openalex.org/W2963728031","https://openalex.org/W2964163713","https://openalex.org/W3000240650","https://openalex.org/W6659522680","https://openalex.org/W6687483927"],"related_works":["https://openalex.org/W2062195135","https://openalex.org/W2795079307","https://openalex.org/W2793058541","https://openalex.org/W1983629434","https://openalex.org/W2055929693","https://openalex.org/W4324271173","https://openalex.org/W2352227742","https://openalex.org/W4390679071","https://openalex.org/W1967645776","https://openalex.org/W3006966347"],"abstract_inverted_index":{"The":[0,177],"symmetric":[1],"positive":[2],"definite":[3],"(SPD)":[4],"matrix":[5,67,88,125,132,140,161],"has":[6,119,168],"attracted":[7],"much":[8],"attention":[9],"in":[10],"classification":[11,108],"problems":[12],"because":[13],"of":[14,25,37,171],"its":[15,189],"remarkable":[16],"performance,":[17],"which":[18,41],"is":[19,103],"due":[20],"to":[21,46,55,78,91,113,155],"the":[22,26,35,56,93,99,116,158],"underlying":[23],"structure":[24],"Riemannian":[27],"manifold":[28,112],"with":[29,71,165],"non-negative":[30],"curvature":[31],"as":[32,34],"well":[33],"use":[36,194],"non-linear":[38],"geometric":[39],"metrics,":[40],"have":[42],"a":[43,64,85,106,111,149,169],"stronger":[44],"ability":[45],"distinguish":[47],"SPD":[48,66,80,87,131,139,160],"matrices":[49,81],"and":[50,82,137,148],"reduce":[51],"information":[52],"loss":[53],"compared":[54],"Euclidean":[57],"metric.":[58],"In":[59],"this":[60,97],"paper,":[61],"we":[62],"propose":[63],"spectral-based":[65,159],"signal":[68,100,121,162],"detection":[69,101,163,186],"method":[70,164,183],"deep":[72,86,166],"learning":[73,89,167],"that":[74,157,180,193],"uses":[75],"time-frequency":[76],"spectra":[77],"construct":[79],"then":[83],"exploits":[84],"network":[90],"detect":[92],"target":[94,120],"signal.":[95],"Using":[96],"approach,":[98],"problem":[102,109],"transformed":[104],"into":[105],"binary":[107],"on":[110,134,142],"judge":[114],"whether":[115],"input":[117],"sample":[118],"or":[122],"not.":[123],"Two":[124],"models":[126],"are":[127,153],"applied,":[128],"namely,":[129],"an":[130,138],"based":[133,141],"spectral":[135,143,191],"covariance":[136],"transformation.":[144],"A":[145],"simulated-signal":[146,151],"dataset":[147,152],"semi-physical":[150],"used":[154],"demonstrate":[156],"gain":[170],"1.7-3.3":[172],"dB":[173],"under":[174],"appropriate":[175],"conditions.":[176],"results":[178],"show":[179],"our":[181],"proposed":[182],"achieves":[184],"better":[185],"performances":[187],"than":[188],"state-of-the-art":[190],"counterparts":[192],"convolutional":[195],"neural":[196],"networks.":[197]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":3}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}
