{"id":"https://openalex.org/W2129690331","doi":"https://doi.org/10.1109/icassp.2009.4960229","title":"Detection from a multi-channel sensor using a hierarchical Bayesian model","display_name":"Detection from a multi-channel sensor using a hierarchical Bayesian model","publication_year":2009,"publication_date":"2009-04-01","ids":{"openalex":"https://openalex.org/W2129690331","doi":"https://doi.org/10.1109/icassp.2009.4960229","mag":"2129690331"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2009.4960229","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2009.4960229","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 IEEE International Conference on Acoustics, Speech and Signal 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/A5048027459","display_name":"I. Smith","orcid":null},"institutions":[{"id":"https://openalex.org/I4210126779","display_name":"Observatoire de la C\u00f4te d\u2019Azur","ror":"https://ror.org/039fj2469","country_code":"FR","type":"facility","lineage":["https://openalex.org/I201841394","https://openalex.org/I4210126779"]},{"id":"https://openalex.org/I201841394","display_name":"Universit\u00e9 C\u00f4te d'Azur","ror":"https://ror.org/019tgvf94","country_code":"FR","type":"education","lineage":["https://openalex.org/I201841394"]},{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"funder","lineage":["https://openalex.org/I1294671590"]}],"countries":["FR"],"is_corresponding":true,"raw_author_name":"I. Smith","raw_affiliation_strings":["UMR 6525 H. Fizeau, CNRS, Observatoire de la C\u00f4te dAzur, Universit\u00e9 de Nice-Sophia Antipolis, Nice, France","UMR 6525 H. Fizeau, Universit\u00e9 de Nice-Sophia Antipolis, CNRS, Observatoire de la C\u00f4te d'Azur, Campus Valrose, F-06108 cedex, France#TAB#"],"affiliations":[{"raw_affiliation_string":"UMR 6525 H. Fizeau, CNRS, Observatoire de la C\u00f4te dAzur, Universit\u00e9 de Nice-Sophia Antipolis, Nice, France","institution_ids":["https://openalex.org/I201841394","https://openalex.org/I4210126779","https://openalex.org/I1294671590"]},{"raw_affiliation_string":"UMR 6525 H. Fizeau, Universit\u00e9 de Nice-Sophia Antipolis, CNRS, Observatoire de la C\u00f4te d'Azur, Campus Valrose, F-06108 cedex, France#TAB#","institution_ids":["https://openalex.org/I201841394","https://openalex.org/I1294671590"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5113993307","display_name":"A. Ferrari","orcid":null},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"funder","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I4210126779","display_name":"Observatoire de la C\u00f4te d\u2019Azur","ror":"https://ror.org/039fj2469","country_code":"FR","type":"facility","lineage":["https://openalex.org/I201841394","https://openalex.org/I4210126779"]},{"id":"https://openalex.org/I201841394","display_name":"Universit\u00e9 C\u00f4te d'Azur","ror":"https://ror.org/019tgvf94","country_code":"FR","type":"education","lineage":["https://openalex.org/I201841394"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"A. Ferrari","raw_affiliation_strings":["UMR 6525 H. Fizeau, CNRS, Observatoire de la C\u00f4te dAzur, Universit\u00e9 de Nice-Sophia Antipolis, Nice, France","UMR 6525 H. Fizeau, Universit\u00e9 de Nice-Sophia Antipolis, CNRS, Observatoire de la C\u00f4te d'Azur, Campus Valrose, F-06108 cedex, France#TAB#"],"affiliations":[{"raw_affiliation_string":"UMR 6525 H. Fizeau, CNRS, Observatoire de la C\u00f4te dAzur, Universit\u00e9 de Nice-Sophia Antipolis, Nice, France","institution_ids":["https://openalex.org/I201841394","https://openalex.org/I4210126779","https://openalex.org/I1294671590"]},{"raw_affiliation_string":"UMR 6525 H. Fizeau, Universit\u00e9 de Nice-Sophia Antipolis, CNRS, Observatoire de la C\u00f4te d'Azur, Campus Valrose, F-06108 cedex, France#TAB#","institution_ids":["https://openalex.org/I201841394","https://openalex.org/I1294671590"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5048027459"],"corresponding_institution_ids":["https://openalex.org/I1294671590","https://openalex.org/I201841394","https://openalex.org/I4210126779"],"apc_list":null,"apc_paid":null,"fwci":0.848,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.76485088,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"195","issue":null,"first_page":"2897","last_page":"2900"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9983000159263611,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9983000159263611,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11443","display_name":"Advanced Statistical Process Monitoring","score":0.9914000034332275,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11890","display_name":"Scientific Measurement and Uncertainty Evaluation","score":0.9865000247955322,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6899224519729614},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5929168462753296},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.5775935649871826},{"id":"https://openalex.org/keywords/signal-to-noise-ratio","display_name":"Signal-to-noise ratio (imaging)","score":0.5476192831993103},{"id":"https://openalex.org/keywords/randomness","display_name":"Randomness","score":0.5214269161224365},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5063191652297974},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4345616102218628},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.4308663308620453},{"id":"https://openalex.org/keywords/posterior-probability","display_name":"Posterior probability","score":0.4296590983867645},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.4216499626636505},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.42082318663597107},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.41058871150016785},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3724766969680786},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.21732327342033386},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2045496106147766},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.11008965969085693}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6899224519729614},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5929168462753296},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.5775935649871826},{"id":"https://openalex.org/C13944312","wikidata":"https://www.wikidata.org/wiki/Q7512748","display_name":"Signal-to-noise ratio (imaging)","level":2,"score":0.5476192831993103},{"id":"https://openalex.org/C125112378","wikidata":"https://www.wikidata.org/wiki/Q176640","display_name":"Randomness","level":2,"score":0.5214269161224365},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5063191652297974},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4345616102218628},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.4308663308620453},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.4296590983867645},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.4216499626636505},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.42082318663597107},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.41058871150016785},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3724766969680786},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.21732327342033386},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2045496106147766},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.11008965969085693},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.2009.4960229","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2009.4960229","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 IEEE International Conference on Acoustics, Speech and Signal Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.4000000059604645}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1532177360","https://openalex.org/W1569625389","https://openalex.org/W1965173775","https://openalex.org/W1968336920","https://openalex.org/W1985093013","https://openalex.org/W1996284193","https://openalex.org/W2053125503","https://openalex.org/W2057571146","https://openalex.org/W2087443225","https://openalex.org/W2099900778","https://openalex.org/W2115611540","https://openalex.org/W2124707817","https://openalex.org/W2139399617","https://openalex.org/W2141736115","https://openalex.org/W2153086947","https://openalex.org/W2277729655","https://openalex.org/W2487122920","https://openalex.org/W3095037216","https://openalex.org/W3099714932","https://openalex.org/W3100341618","https://openalex.org/W4255582690","https://openalex.org/W4292403327"],"related_works":["https://openalex.org/W71678127","https://openalex.org/W2157655363","https://openalex.org/W4205763938","https://openalex.org/W2292189132","https://openalex.org/W4288092343","https://openalex.org/W2134332527","https://openalex.org/W4386114318","https://openalex.org/W2888496681","https://openalex.org/W2790979771","https://openalex.org/W2963009826"],"abstract_inverted_index":{"Direct":[0],"imaging":[1],"of":[2,24,102,126],"exoplanets":[3],"involves":[4],"very":[5],"low":[6,85],"signal-to-noise":[7],"ratio":[8,89],"data,":[9],"that":[10],"need":[11],"to":[12,39,69,87],"be":[13,32],"carefully":[14],"acquired":[15],"and":[16,60,74,77],"processed.":[17],"Multi-band":[18],"devices":[19],"enable":[20],"the":[21,52,78,99,103],"simultaneous":[22],"record":[23],"images":[25],"in":[26,83],"different":[27],"spectral":[28],"bands.":[29],"They":[30],"can":[31],"used":[33],"either":[34],"for":[35],"spectroscopy":[36],"purposes":[37],"or":[38],"improve":[40],"detection":[41],"capabilities.":[42],"This":[43,118],"work":[44],"aims":[45],"at":[46],"detecting":[47],"a":[48,56,111,127],"potential":[49],"source,":[50],"when":[51],"source":[53],"moves":[54],"on":[55],"random":[57],"background":[58],"spatially":[59],"inter-spectrally":[61],"correlated.":[62],"A":[63],"hierarchic":[64],"Bayesian":[65],"model":[66],"is":[67,96,120],"derived":[68],"take":[70],"into":[71],"account":[72],"correlations":[73],"their":[75],"randomness,":[76],"high":[79],"dynamic":[80],"range":[81],"involved":[82],"potentially":[84],"signal":[86],"noise":[88],"data.":[90],"The":[91],"point":[92],"null":[93],"hypothesis":[94],"test":[95],"addressed":[97],"using":[98,110,122],"posterior":[100],"distribution":[101],"likelihood":[104],"ratio.":[105],"Its":[106],"percentiles":[107],"are":[108],"computed":[109],"simple":[112],"Markov":[113],"Chain":[114],"Monte":[115],"Carlo":[116],"method.":[117],"algorithm":[119],"illustrated":[121],"ID":[123],"simulated":[124],"data":[125],"dual":[128],"band":[129],"signal.":[130]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}