{"id":"https://openalex.org/W2400214132","doi":"https://doi.org/10.1109/icassp.2016.7472907","title":"A method to reconstruct coverage loss maps based on matrix completion and adaptive sampling","display_name":"A method to reconstruct coverage loss maps based on matrix completion and adaptive sampling","publication_year":2016,"publication_date":"2016-03-01","ids":{"openalex":"https://openalex.org/W2400214132","doi":"https://doi.org/10.1109/icassp.2016.7472907","mag":"2400214132"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2016.7472907","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2016.7472907","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5041128881","display_name":"Symeon Chouvardas","orcid":null},"institutions":[{"id":"https://openalex.org/I4210123571","display_name":"Huawei Technologies (France)","ror":"https://ror.org/02rbzf697","country_code":"FR","type":"company","lineage":["https://openalex.org/I2250955327","https://openalex.org/I4210123571"]}],"countries":["FR"],"is_corresponding":true,"raw_author_name":"Symeon Chouvardas","raw_affiliation_strings":["Huawei France R&D, Mathematical and Algorithmic Sciences Lab, Paris, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Huawei France R&D, Mathematical and Algorithmic Sciences Lab, Paris, France","institution_ids":["https://openalex.org/I4210123571"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5105877821","display_name":"Stefan Valentin","orcid":"https://orcid.org/0000-0003-4181-402X"},"institutions":[{"id":"https://openalex.org/I4210123571","display_name":"Huawei Technologies (France)","ror":"https://ror.org/02rbzf697","country_code":"FR","type":"company","lineage":["https://openalex.org/I2250955327","https://openalex.org/I4210123571"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Stefan Valentin","raw_affiliation_strings":["Huawei France R&D, Mathematical and Algorithmic Sciences Lab, Paris, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Huawei France R&D, Mathematical and Algorithmic Sciences Lab, Paris, France","institution_ids":["https://openalex.org/I4210123571"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083971005","display_name":"Moez Draief","orcid":null},"institutions":[{"id":"https://openalex.org/I4210123571","display_name":"Huawei Technologies (France)","ror":"https://ror.org/02rbzf697","country_code":"FR","type":"company","lineage":["https://openalex.org/I2250955327","https://openalex.org/I4210123571"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Moez Draief","raw_affiliation_strings":["Huawei France R&D, Mathematical and Algorithmic Sciences Lab, Paris, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Huawei France R&D, Mathematical and Algorithmic Sciences Lab, Paris, France","institution_ids":["https://openalex.org/I4210123571"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015149788","display_name":"Mathieu Leconte","orcid":null},"institutions":[{"id":"https://openalex.org/I4210123571","display_name":"Huawei Technologies (France)","ror":"https://ror.org/02rbzf697","country_code":"FR","type":"company","lineage":["https://openalex.org/I2250955327","https://openalex.org/I4210123571"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Mathieu Leconte","raw_affiliation_strings":["Huawei France R&D, Mathematical and Algorithmic Sciences Lab, Paris, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Huawei France R&D, Mathematical and Algorithmic Sciences Lab, Paris, France","institution_ids":["https://openalex.org/I4210123571"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5041128881"],"corresponding_institution_ids":["https://openalex.org/I4210123571"],"apc_list":null,"apc_paid":null,"fwci":3.0865,"has_fulltext":false,"cited_by_count":28,"citation_normalized_percentile":{"value":0.9036614,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"6390","last_page":"6394"},"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":0.9847999811172485,"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/T12153","display_name":"Advanced Optical Sensing Technologies","score":0.9779000282287598,"subfield":{"id":"https://openalex.org/subfields/3105","display_name":"Instrumentation"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6956278681755066},{"id":"https://openalex.org/keywords/thresholding","display_name":"Thresholding","score":0.6815123558044434},{"id":"https://openalex.org/keywords/matrix-completion","display_name":"Matrix completion","score":0.6336712837219238},{"id":"https://openalex.org/keywords/plan","display_name":"Plan (archaeology)","score":0.5284469127655029},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.5249093770980835},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5231505632400513},{"id":"https://openalex.org/keywords/adaptive-sampling","display_name":"Adaptive sampling","score":0.44384145736694336},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.441921591758728},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4365249276161194},{"id":"https://openalex.org/keywords/sparse-matrix","display_name":"Sparse matrix","score":0.429180771112442},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.37792330980300903},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3516421616077423},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.19062912464141846},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18694084882736206},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.14801570773124695},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.07356023788452148}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6956278681755066},{"id":"https://openalex.org/C191178318","wikidata":"https://www.wikidata.org/wiki/Q2256906","display_name":"Thresholding","level":3,"score":0.6815123558044434},{"id":"https://openalex.org/C2778459887","wikidata":"https://www.wikidata.org/wiki/Q6787865","display_name":"Matrix completion","level":3,"score":0.6336712837219238},{"id":"https://openalex.org/C2776505523","wikidata":"https://www.wikidata.org/wiki/Q4785468","display_name":"Plan (archaeology)","level":2,"score":0.5284469127655029},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.5249093770980835},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5231505632400513},{"id":"https://openalex.org/C2781395549","wikidata":"https://www.wikidata.org/wiki/Q4680762","display_name":"Adaptive sampling","level":3,"score":0.44384145736694336},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.441921591758728},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4365249276161194},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.429180771112442},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37792330980300903},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3516421616077423},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.19062912464141846},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18694084882736206},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.14801570773124695},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.07356023788452148},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","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/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"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/C95457728","wikidata":"https://www.wikidata.org/wiki/Q309","display_name":"History","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.2016.7472907","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2016.7472907","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.41999998688697815,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W263545290","https://openalex.org/W368469426","https://openalex.org/W1506796937","https://openalex.org/W1559695736","https://openalex.org/W1777890474","https://openalex.org/W1926369758","https://openalex.org/W1979690255","https://openalex.org/W2000157792","https://openalex.org/W2016723762","https://openalex.org/W2070283784","https://openalex.org/W2093540532","https://openalex.org/W2099969214","https://openalex.org/W2103972604","https://openalex.org/W2114961911","https://openalex.org/W2119281476","https://openalex.org/W2134332047","https://openalex.org/W2144730813","https://openalex.org/W2169030369","https://openalex.org/W2611328865","https://openalex.org/W3106096591","https://openalex.org/W4212863985","https://openalex.org/W6609866339","https://openalex.org/W6630260749","https://openalex.org/W6640363541"],"related_works":["https://openalex.org/W2038693912","https://openalex.org/W1991602789","https://openalex.org/W1582396021","https://openalex.org/W1988359706","https://openalex.org/W312558119","https://openalex.org/W4210985407","https://openalex.org/W138014004","https://openalex.org/W2075598034","https://openalex.org/W1829869244","https://openalex.org/W2335441444"],"abstract_inverted_index":{"Accurate":[0],"coverage":[1,43,68],"maps":[2,20],"are":[3],"an":[4],"important":[5],"tool":[6],"for":[7,39],"network":[8],"planning":[9],"and":[10,128],"operation":[11],"but":[12],"it":[13],"is":[14,108],"often":[15],"impossible":[16],"to":[17,35,58,79,98],"obtain":[18],"these":[19],"completely":[21],"from":[22,63],"measurements.":[23],"In":[24],"this":[25],"paper":[26],"we":[27],"describe":[28],"two":[29],"new":[30],"methods":[31],"that":[32,104,117,129],"enable":[33],"operators":[34,97],"minimize":[36],"the":[37,52,73,81,90,126],"cost":[38],"obtaining":[40],"a":[41,60,64,105],"complete":[42,61],"map":[44,62],"at":[45,110],"high":[46],"accuracy.":[47,134],"Our":[48,113],"first":[49],"method":[50,95],"applies":[51],"Singular":[53],"Value":[54],"Thresholding":[55],"(SVT)":[56],"algorithm":[57],"reconstruct":[59],"sparse":[65],"matrix":[66],"of":[67,89,125],"data.":[69],"We":[70],"then":[71],"use":[72],"Query":[74],"by":[75],"Committee":[76],"(QbC)":[77],"rationale":[78],"identify":[80],"areas":[82],"where":[83],"further":[84,131],"measurements":[85],"would":[86],"maximize":[87],"accuracy":[88],"completed":[91],"map.":[92],"This":[93],"second":[94],"allows":[96],"plan":[99],"their":[100],"drive":[101],"tests":[102],"such":[103],"given":[106],"budget":[107],"spent":[109],"highest":[111],"efficiency.":[112],"numerical":[114],"examples":[115],"illustrate":[116],"our":[118],"proposed":[119],"completion":[120],"technique":[121],"outperforms":[122],"relevant":[123],"state":[124],"art":[127],"QbC":[130],"enhances":[132],"reconstruction":[133]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":4},{"year":2016,"cited_by_count":1}],"updated_date":"2026-04-29T09:16:38.111599","created_date":"2025-10-10T00:00:00"}
