{"id":"https://openalex.org/W2559650839","doi":"https://doi.org/10.1109/hpec.2016.7761613","title":"CUDA implementation of an optimal online Gaussian-Signal-in-Gaussian-Noise detector","display_name":"CUDA implementation of an optimal online Gaussian-Signal-in-Gaussian-Noise detector","publication_year":2016,"publication_date":"2016-09-01","ids":{"openalex":"https://openalex.org/W2559650839","doi":"https://doi.org/10.1109/hpec.2016.7761613","mag":"2559650839"},"language":"en","primary_location":{"id":"doi:10.1109/hpec.2016.7761613","is_oa":false,"landing_page_url":"https://doi.org/10.1109/hpec.2016.7761613","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE High Performance Extreme Computing Conference (HPEC)","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/A5001096003","display_name":"Nir Nossenson","orcid":null},"institutions":[{"id":"https://openalex.org/I4210140143","display_name":"Dynamic Systems (United States)","ror":"https://ror.org/043vm9914","country_code":"US","type":"company","lineage":["https://openalex.org/I4210140143"]},{"id":"https://openalex.org/I12912129","display_name":"Northeastern University","ror":"https://ror.org/04t5xt781","country_code":"US","type":"education","lineage":["https://openalex.org/I12912129"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Nir Nossenson","raw_affiliation_strings":["Complex Dynamic Systems and Control Laboratory, Northeastern University, Boston, MA, USA"],"affiliations":[{"raw_affiliation_string":"Complex Dynamic Systems and Control Laboratory, Northeastern University, Boston, MA, USA","institution_ids":["https://openalex.org/I12912129","https://openalex.org/I4210140143"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102737103","display_name":"Ariel Jaffe","orcid":"https://orcid.org/0000-0001-7637-7128"},"institutions":[{"id":"https://openalex.org/I53964585","display_name":"Weizmann Institute of Science","ror":"https://ror.org/0316ej306","country_code":"IL","type":"education","lineage":["https://openalex.org/I53964585"]}],"countries":["IL"],"is_corresponding":false,"raw_author_name":"Ariel J. Jaffe","raw_affiliation_strings":["Department of mathematics and computer science, Weizmann Institute of Science, Rehovot, Israel"],"affiliations":[{"raw_affiliation_string":"Department of mathematics and computer science, Weizmann Institute of Science, Rehovot, Israel","institution_ids":["https://openalex.org/I53964585"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5001096003"],"corresponding_institution_ids":["https://openalex.org/I12912129","https://openalex.org/I4210140143"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.08327517,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"10","issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9937999844551086,"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/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9937999844551086,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9929999709129333,"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/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9894000291824341,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/cuda","display_name":"CUDA","score":0.9153462648391724},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.820781946182251},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6916210651397705},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.5852543711662292},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5813198089599609},{"id":"https://openalex.org/keywords/throughput","display_name":"Throughput","score":0.5353899598121643},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.5226007103919983},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5194851160049438},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.4556898772716522},{"id":"https://openalex.org/keywords/signal-to-noise-ratio","display_name":"Signal-to-noise ratio (imaging)","score":0.43025606870651245},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.39899516105651855},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.31708914041519165},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.19651353359222412},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.12606292963027954},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.11403876543045044},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.09021583199501038}],"concepts":[{"id":"https://openalex.org/C2778119891","wikidata":"https://www.wikidata.org/wiki/Q477690","display_name":"CUDA","level":2,"score":0.9153462648391724},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.820781946182251},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6916210651397705},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.5852543711662292},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5813198089599609},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.5353899598121643},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.5226007103919983},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5194851160049438},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.4556898772716522},{"id":"https://openalex.org/C13944312","wikidata":"https://www.wikidata.org/wiki/Q7512748","display_name":"Signal-to-noise ratio (imaging)","level":2,"score":0.43025606870651245},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.39899516105651855},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.31708914041519165},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.19651353359222412},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.12606292963027954},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.11403876543045044},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.09021583199501038},{"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/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"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/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/hpec.2016.7761613","is_oa":false,"landing_page_url":"https://doi.org/10.1109/hpec.2016.7761613","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE High Performance Extreme Computing Conference (HPEC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5600000023841858,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1978990971","https://openalex.org/W2011422872","https://openalex.org/W2012219797","https://openalex.org/W2032555978","https://openalex.org/W2044535354","https://openalex.org/W2057210934","https://openalex.org/W2089499220","https://openalex.org/W2100819007","https://openalex.org/W2105934661","https://openalex.org/W2118203533","https://openalex.org/W2119362949","https://openalex.org/W2138924491","https://openalex.org/W2148752651","https://openalex.org/W2160261289","https://openalex.org/W2398362511","https://openalex.org/W3099729408"],"related_works":["https://openalex.org/W3062287","https://openalex.org/W2380390332","https://openalex.org/W2742145873","https://openalex.org/W2062253548","https://openalex.org/W4225414539","https://openalex.org/W4289522463","https://openalex.org/W1977763331","https://openalex.org/W1970319972","https://openalex.org/W4328011745","https://openalex.org/W2953254336"],"abstract_inverted_index":{"We":[0,47,76,89],"address":[1],"the":[2,79],"computationally":[3],"demanding":[4],"task":[5],"of":[6,11,21,34,53,68,84,102,112],"real":[7],"time":[8],"optimal":[9,56],"detection":[10,92],"a":[12,23,30,49,85,99,125,130],"Gaussian":[13,16],"Signal":[14],"in":[15,27],"Noise.":[17],"The":[18,115],"mathematical":[19],"principles":[20,36],"such":[22,54],"detector":[24,57,118],"were":[25],"formulated":[26],"1965,":[28],"but":[29],"full":[31],"real-time":[32],"implementation":[33,52,133],"these":[35],"was":[37,137],"not":[38],"possible":[39],"for":[40,98,109],"decades":[41],"mainly":[42],"due":[43],"to":[44,82,106,129],"technological":[45],"barriers.":[46],"present":[48],"CUDA":[50,116],"based":[51,87,117],"an":[55],"and":[58,72],"study":[59],"its":[60],"decision":[61],"making":[62],"speed":[63],"(or":[64],"throughput)":[65],"as":[66],"function":[67],"target":[69,100,110,135],"signal":[70,73],"duration":[71,101,111,136],"filter":[74],"length.":[75],"also":[77],"compare":[78],"throughput":[80,127],"results":[81],"those":[83],"CPU":[86,132],"design.":[88],"report":[90],"on":[91,120],"rates":[93],"ranging":[94],"from":[95],"3.5":[96],"KHz":[97,108],"10756":[103],"samples":[104],"up":[105],"15.6":[107],"92":[113],"samples.":[114,141],"running":[119],"384":[121],"parallel":[122],"cores":[123],"had":[124],"superior":[126],"comparing":[128],"pure":[131],"when":[134],"longer":[138],"than":[139],"600":[140]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
