{"id":"https://openalex.org/W2903483152","doi":"https://doi.org/10.23919/eusipco.2018.8553290","title":"Compressive Sensing of Temporally Correlated Sources Using Isotropic Multivariate Stable Laws","display_name":"Compressive Sensing of Temporally Correlated Sources Using Isotropic Multivariate Stable Laws","publication_year":2018,"publication_date":"2018-09-01","ids":{"openalex":"https://openalex.org/W2903483152","doi":"https://doi.org/10.23919/eusipco.2018.8553290","mag":"2903483152"},"language":"en","primary_location":{"id":"doi:10.23919/eusipco.2018.8553290","is_oa":false,"landing_page_url":"https://doi.org/10.23919/eusipco.2018.8553290","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 26th European Signal Processing Conference (EUSIPCO)","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/A5064685227","display_name":"George Tzagkarakis","orcid":"https://orcid.org/0000-0002-4099-691X"},"institutions":[{"id":"https://openalex.org/I8901234","display_name":"Foundation for Research and Technology Hellas","ror":"https://ror.org/052rphn09","country_code":"GR","type":"facility","lineage":["https://openalex.org/I8901234"]}],"countries":["GR"],"is_corresponding":true,"raw_author_name":"George Tzagkarakis","raw_affiliation_strings":["EONOS Investment Technologies, Institute of Computer Science Foundation for Research and Technology-Hellas, Heraklion, Paris, France, Greece"],"affiliations":[{"raw_affiliation_string":"EONOS Investment Technologies, Institute of Computer Science Foundation for Research and Technology-Hellas, Heraklion, Paris, France, Greece","institution_ids":["https://openalex.org/I8901234"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012674762","display_name":"John P. Nolan","orcid":"https://orcid.org/0000-0002-9669-382X"},"institutions":[{"id":"https://openalex.org/I181401687","display_name":"American University","ror":"https://ror.org/052w4zt36","country_code":"US","type":"education","lineage":["https://openalex.org/I181401687"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"John P. Nolan","raw_affiliation_strings":["Math/Stat Department, American University, Washington, DC"],"affiliations":[{"raw_affiliation_string":"Math/Stat Department, American University, Washington, DC","institution_ids":["https://openalex.org/I181401687"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060388738","display_name":"Panagiotis Tsakalides","orcid":"https://orcid.org/0000-0003-4918-603X"},"institutions":[{"id":"https://openalex.org/I142617266","display_name":"University of Crete","ror":"https://ror.org/00dr28g20","country_code":"GR","type":"education","lineage":["https://openalex.org/I142617266"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Panagiotis Tsakalides","raw_affiliation_strings":["Computer Science Department, University of Crete, Heraklion, Greece"],"affiliations":[{"raw_affiliation_string":"Computer Science Department, University of Crete, Heraklion, Greece","institution_ids":["https://openalex.org/I142617266"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5064685227"],"corresponding_institution_ids":["https://openalex.org/I8901234"],"apc_list":null,"apc_paid":null,"fwci":0.4054,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.60992452,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1710","last_page":"1714"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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":0.9997000098228455,"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"}},{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9993000030517578,"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/restricted-isometry-property","display_name":"Restricted isometry property","score":0.7878411412239075},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.7793331146240234},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5714914798736572},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5487180948257446},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5433765649795532},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.49508848786354065},{"id":"https://openalex.org/keywords/isotropy","display_name":"Isotropy","score":0.4813964366912842},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.48066580295562744},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.43202877044677734},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.418995201587677},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.4121934473514557},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40594518184661865},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.35399961471557617},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.19690704345703125},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.16061687469482422},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.12603133916854858}],"concepts":[{"id":"https://openalex.org/C17902559","wikidata":"https://www.wikidata.org/wiki/Q17099734","display_name":"Restricted isometry property","level":3,"score":0.7878411412239075},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.7793331146240234},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5714914798736572},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5487180948257446},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5433765649795532},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.49508848786354065},{"id":"https://openalex.org/C184050105","wikidata":"https://www.wikidata.org/wiki/Q273163","display_name":"Isotropy","level":2,"score":0.4813964366912842},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.48066580295562744},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.43202877044677734},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.418995201587677},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4121934473514557},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40594518184661865},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.35399961471557617},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.19690704345703125},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.16061687469482422},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.12603133916854858},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/eusipco.2018.8553290","is_oa":false,"landing_page_url":"https://doi.org/10.23919/eusipco.2018.8553290","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 26th European Signal Processing Conference (EUSIPCO)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7900000214576721,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W15797847","https://openalex.org/W1978189606","https://openalex.org/W1983481547","https://openalex.org/W2022894179","https://openalex.org/W2125680629","https://openalex.org/W2129131372","https://openalex.org/W2137198385","https://openalex.org/W2146000945","https://openalex.org/W2154332973","https://openalex.org/W2162409952","https://openalex.org/W2270529078","https://openalex.org/W2471288921","https://openalex.org/W2962957039","https://openalex.org/W3125735862","https://openalex.org/W4246756383","https://openalex.org/W6600662163"],"related_works":["https://openalex.org/W1595179898","https://openalex.org/W2949399405","https://openalex.org/W4301619540","https://openalex.org/W4297570801","https://openalex.org/W2952050240","https://openalex.org/W2568623927","https://openalex.org/W2085436476","https://openalex.org/W2025666718","https://openalex.org/W1523595302","https://openalex.org/W1959156040"],"abstract_inverted_index":{"This":[0],"paper":[1],"addresses":[2],"the":[3,41,49,64,80,93,100,129],"problem":[4],"of":[5,10,57,74,105,121,131],"compressively":[6],"sensing":[7,33,134,142],"a":[8,31,71,106],"set":[9,73],"temporally":[11],"correlated":[12,94],"sources,":[13,122],"in":[14],"order":[15],"to":[16,46,59],"achieve":[17],"faithful":[18],"sparse":[19,95,109],"signal":[20],"reconstruction":[21],"from":[22,87,99],"noisy":[23],"multiple":[24],"measurement":[25],"vectors":[26,85],"(MMV).":[27],"To":[28],"this":[29],"end,":[30],"simple":[32],"mechanism":[34],"is":[35,76],"proposed,":[36],"which":[37],"does":[38],"not":[39],"require":[40],"restricted":[42],"isometry":[43],"property":[44],"(RIP)":[45],"hold":[47],"near":[48],"sparsity":[50],"level,":[51],"whilst":[52],"it":[53],"provides":[54],"additional":[55],"degrees":[56],"freedom":[58],"better":[60],"capture":[61],"and":[62,125],"suppress":[63],"inherent":[65],"sampling":[66],"noise":[67,126],"effects.":[68],"In":[69],"particular,":[70],"reduced":[72],"MMVs":[75,102],"generated":[77],"by":[78,103],"projecting":[79],"source":[81],"signals":[82,96],"onto":[83],"random":[84,101],"drawn":[86],"isotropic":[88],"multivariate":[89],"stable":[90],"laws.":[91],"Then,":[92],"are":[97],"recovered":[98],"means":[104],"recently":[107],"introduced":[108],"Bayesian":[110],"learning":[111],"algorithm.":[112],"Experimental":[113],"evaluations":[114],"on":[115],"synthetic":[116],"data":[117],"with":[118],"varying":[119],"number":[120],"correlation":[123],"values,":[124],"strengths,":[127],"reveal":[128],"superiority":[130],"our":[132],"proposed":[133],"mechanism,":[135],"when":[136],"compared":[137],"against":[138],"well-established":[139],"RIP-based":[140],"compressive":[141],"schemes.":[143]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}