{"id":"https://openalex.org/W2793443222","doi":"https://doi.org/10.1109/camsap.2017.8313153","title":"Efficient recovery from noisy quantized compressed sensing using generalized approximate message passing","display_name":"Efficient recovery from noisy quantized compressed sensing using generalized approximate message passing","publication_year":2017,"publication_date":"2017-12-01","ids":{"openalex":"https://openalex.org/W2793443222","doi":"https://doi.org/10.1109/camsap.2017.8313153","mag":"2793443222"},"language":"en","primary_location":{"id":"doi:10.1109/camsap.2017.8313153","is_oa":false,"landing_page_url":"https://doi.org/10.1109/camsap.2017.8313153","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","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/A5082395835","display_name":"Osman Musa","orcid":"https://orcid.org/0000-0003-4642-1006"},"institutions":[{"id":"https://openalex.org/I145847075","display_name":"TU Wien","ror":"https://ror.org/04d836q62","country_code":"AT","type":"education","lineage":["https://openalex.org/I145847075"]}],"countries":["AT"],"is_corresponding":true,"raw_author_name":"Osman Musa","raw_affiliation_strings":["Institute of Telecommunications, Technische Universit\u00e4t Wien, Austria"],"affiliations":[{"raw_affiliation_string":"Institute of Telecommunications, Technische Universit\u00e4t Wien, Austria","institution_ids":["https://openalex.org/I145847075"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070718076","display_name":"G\u00e1bor Hann\u00e1k","orcid":"https://orcid.org/0000-0003-3586-8072"},"institutions":[{"id":"https://openalex.org/I145847075","display_name":"TU Wien","ror":"https://ror.org/04d836q62","country_code":"AT","type":"education","lineage":["https://openalex.org/I145847075"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Gabor Hannak","raw_affiliation_strings":["Institute of Telecommunications, Technische Universit\u00e4t Wien, Austria"],"affiliations":[{"raw_affiliation_string":"Institute of Telecommunications, Technische Universit\u00e4t Wien, Austria","institution_ids":["https://openalex.org/I145847075"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5077998995","display_name":"Norbert Goertz","orcid":null},"institutions":[{"id":"https://openalex.org/I145847075","display_name":"TU Wien","ror":"https://ror.org/04d836q62","country_code":"AT","type":"education","lineage":["https://openalex.org/I145847075"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Norbert Goertz","raw_affiliation_strings":["Institute of Telecommunications, Technische Universit\u00e4t Wien, Austria"],"affiliations":[{"raw_affiliation_string":"Institute of Telecommunications, Technische Universit\u00e4t Wien, Austria","institution_ids":["https://openalex.org/I145847075"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5082395835"],"corresponding_institution_ids":["https://openalex.org/I145847075"],"apc_list":null,"apc_paid":null,"fwci":0.7203,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.70816128,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"abs 1305 1786","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":1.0,"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":1.0,"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/T11778","display_name":"Electrical and Bioimpedance Tomography","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"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/T10378","display_name":"Advanced MRI Techniques and Applications","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/additive-white-gaussian-noise","display_name":"Additive white Gaussian noise","score":0.8348608613014221},{"id":"https://openalex.org/keywords/message-passing","display_name":"Message passing","score":0.7370445132255554},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.7257428169250488},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.6857447624206543},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6095314025878906},{"id":"https://openalex.org/keywords/scalar","display_name":"Scalar (mathematics)","score":0.6072531342506409},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.493838369846344},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4834216833114624},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.44753339886665344},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.41166695952415466},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.28491902351379395},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.1672801673412323},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.14000695943832397},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.0796712338924408}],"concepts":[{"id":"https://openalex.org/C169334058","wikidata":"https://www.wikidata.org/wiki/Q353292","display_name":"Additive white Gaussian noise","level":3,"score":0.8348608613014221},{"id":"https://openalex.org/C854659","wikidata":"https://www.wikidata.org/wiki/Q1859284","display_name":"Message passing","level":2,"score":0.7370445132255554},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.7257428169250488},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.6857447624206543},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6095314025878906},{"id":"https://openalex.org/C57691317","wikidata":"https://www.wikidata.org/wiki/Q1289248","display_name":"Scalar (mathematics)","level":2,"score":0.6072531342506409},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.493838369846344},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4834216833114624},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.44753339886665344},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.41166695952415466},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.28491902351379395},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.1672801673412323},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.14000695943832397},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.0796712338924408},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","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:10.1109/camsap.2017.8313153","is_oa":false,"landing_page_url":"https://doi.org/10.1109/camsap.2017.8313153","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"No poverty","score":0.6100000143051147,"id":"https://metadata.un.org/sdg/1"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1512231839","https://openalex.org/W2029719587","https://openalex.org/W2107949992","https://openalex.org/W2166670884","https://openalex.org/W2296616510","https://openalex.org/W2486545612","https://openalex.org/W2594506622","https://openalex.org/W2963753848","https://openalex.org/W3145937384","https://openalex.org/W4250955649"],"related_works":["https://openalex.org/W1974175953","https://openalex.org/W2594506622","https://openalex.org/W1911817635","https://openalex.org/W2353656796","https://openalex.org/W2011915004","https://openalex.org/W2074565401","https://openalex.org/W2242778450","https://openalex.org/W2380803194","https://openalex.org/W2186922242","https://openalex.org/W1607924090"],"abstract_inverted_index":{"Compressed":[0],"sensing":[1],"(CS)":[2],"is":[3,23],"a":[4],"novel":[5],"technique":[6],"that":[7],"allows":[8],"for":[9,73,85,89],"stable":[10],"reconstruction":[11,119],"with":[12,78],"sampling":[13],"rate":[14,18],"lower":[15],"than":[16],"Nyquist":[17],"if":[19],"the":[20,61,82,86,94,101,113],"unknown":[21],"vector":[22],"sparse.":[24],"In":[25],"many":[26],"practical":[27],"applications":[28],"CS":[29,75],"measurements":[30,49,76],"are":[31],"first":[32],"scalar":[33],"quantized":[34,74],"and":[35,70,100,124],"later":[36],"corrupted":[37,77],"in":[38,52,121],"different":[39,90],"ways.":[40],"Reconstruction":[41],"by":[42],"conventional":[43,118],"techniques":[44],"on":[45],"such":[46],"highly":[47],"distorted":[48],"will":[50],"result":[51],"poor":[53],"accuracy.":[54],"To":[55],"address":[56],"this":[57],"problem,":[58],"we":[59],"use":[60],"well":[62],"established":[63],"generalized":[64],"approximate":[65],"message":[66],"passing":[67],"(GAMP)":[68],"algorithm":[69,115],"tailor":[71],"it":[72],"noise.":[79],"We":[80],"provide":[81],"necessary":[83],"expressions":[84],"nonlinear":[87],"updates":[88],"noise":[91,105],"models,":[92],"namely":[93],"symmetric":[95],"discrete":[96],"memoryless":[97],"channel":[98],"(SDMC)":[99],"additive":[102],"white":[103],"Gaussian":[104],"(AWGN)":[106],"channel.":[107],"Numerical":[108],"results":[109],"show":[110],"superiority":[111],"of":[112],"GAMP":[114],"compared":[116],"to":[117],"algorithms":[120],"both":[122],"SDMC":[123],"AWGN":[125],"channels.":[126]},"counts_by_year":[{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}