{"id":"https://openalex.org/W2009918297","doi":"https://doi.org/10.1109/globalsip.2013.6737078","title":"Computationally-efficient blind sub-Nyquist sampling for sparse spectra","display_name":"Computationally-efficient blind sub-Nyquist sampling for sparse spectra","publication_year":2013,"publication_date":"2013-12-01","ids":{"openalex":"https://openalex.org/W2009918297","doi":"https://doi.org/10.1109/globalsip.2013.6737078","mag":"2009918297"},"language":"en","primary_location":{"id":"doi:10.1109/globalsip.2013.6737078","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globalsip.2013.6737078","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE Global Conference on Signal and Information 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/A5030647153","display_name":"Sameer Pawar","orcid":"https://orcid.org/0000-0001-8954-7747"},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sameer Pawar","raw_affiliation_strings":["Department of EECS, U C Berkeley","Dept. of EECS, Univ. of California-Berkeley, Berkeley, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of EECS, U C Berkeley","institution_ids":["https://openalex.org/I95457486"]},{"raw_affiliation_string":"Dept. of EECS, Univ. of California-Berkeley, Berkeley, CA, USA","institution_ids":["https://openalex.org/I95457486"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003102864","display_name":"Venkatesan Ekambaram","orcid":null},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Venkatesan Ekambaram","raw_affiliation_strings":["Department of EECS, U C Berkeley","Dept. of EECS, Univ. of California-Berkeley, Berkeley, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of EECS, U C Berkeley","institution_ids":["https://openalex.org/I95457486"]},{"raw_affiliation_string":"Dept. of EECS, Univ. of California-Berkeley, Berkeley, CA, USA","institution_ids":["https://openalex.org/I95457486"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5030620564","display_name":"Kannan Ramchandran","orcid":"https://orcid.org/0000-0002-4567-328X"},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kannan Ramchandran","raw_affiliation_strings":["Department of EECS, U C Berkeley","Dept. of EECS, Univ. of California-Berkeley, Berkeley, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of EECS, U C Berkeley","institution_ids":["https://openalex.org/I95457486"]},{"raw_affiliation_string":"Dept. of EECS, Univ. of California-Berkeley, Berkeley, CA, USA","institution_ids":["https://openalex.org/I95457486"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.08296162,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"26","issue":null,"first_page":"1065","last_page":"1068"},"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":0.9991999864578247,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/nyquist-rate","display_name":"Nyquist rate","score":0.7948512434959412},{"id":"https://openalex.org/keywords/nyquist\u2013shannon-sampling-theorem","display_name":"Nyquist\u2013Shannon sampling theorem","score":0.696262538433075},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.6363429427146912},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.607574462890625},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6004437208175659},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5832879543304443},{"id":"https://openalex.org/keywords/nonuniform-sampling","display_name":"Nonuniform sampling","score":0.5831226706504822},{"id":"https://openalex.org/keywords/bandwidth","display_name":"Bandwidth (computing)","score":0.5283737182617188},{"id":"https://openalex.org/keywords/signal-reconstruction","display_name":"Signal reconstruction","score":0.495705246925354},{"id":"https://openalex.org/keywords/nyquist-frequency","display_name":"Nyquist frequency","score":0.4940791428089142},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.45749980211257935},{"id":"https://openalex.org/keywords/decimation","display_name":"Decimation","score":0.4118438959121704},{"id":"https://openalex.org/keywords/signal-processing","display_name":"Signal processing","score":0.2905140221118927},{"id":"https://openalex.org/keywords/digital-signal-processing","display_name":"Digital signal processing","score":0.16047829389572144},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.1477174460887909},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.12668627500534058},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.08021214604377747}],"concepts":[{"id":"https://openalex.org/C65914096","wikidata":"https://www.wikidata.org/wiki/Q6273772","display_name":"Nyquist rate","level":4,"score":0.7948512434959412},{"id":"https://openalex.org/C288623","wikidata":"https://www.wikidata.org/wiki/Q679800","display_name":"Nyquist\u2013Shannon sampling theorem","level":2,"score":0.696262538433075},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.6363429427146912},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.607574462890625},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6004437208175659},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5832879543304443},{"id":"https://openalex.org/C20326153","wikidata":"https://www.wikidata.org/wiki/Q7049638","display_name":"Nonuniform sampling","level":3,"score":0.5831226706504822},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.5283737182617188},{"id":"https://openalex.org/C70958404","wikidata":"https://www.wikidata.org/wiki/Q7512728","display_name":"Signal reconstruction","level":4,"score":0.495705246925354},{"id":"https://openalex.org/C98273374","wikidata":"https://www.wikidata.org/wiki/Q1501757","display_name":"Nyquist frequency","level":3,"score":0.4940791428089142},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.45749980211257935},{"id":"https://openalex.org/C173642442","wikidata":"https://www.wikidata.org/wiki/Q1253346","display_name":"Decimation","level":3,"score":0.4118438959121704},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.2905140221118927},{"id":"https://openalex.org/C84462506","wikidata":"https://www.wikidata.org/wiki/Q173142","display_name":"Digital signal processing","level":2,"score":0.16047829389572144},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.1477174460887909},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.12668627500534058},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.08021214604377747},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/globalsip.2013.6737078","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globalsip.2013.6737078","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE Global Conference on Signal and Information Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.46000000834465027,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1494749725","https://openalex.org/W1501755821","https://openalex.org/W2012365979","https://openalex.org/W2111855076","https://openalex.org/W2116962375","https://openalex.org/W2123629701","https://openalex.org/W2141116650","https://openalex.org/W2141341167","https://openalex.org/W2145096794","https://openalex.org/W2146509513","https://openalex.org/W2148909075","https://openalex.org/W2153002160","https://openalex.org/W2158537680","https://openalex.org/W2168156431","https://openalex.org/W2289917018","https://openalex.org/W2296616510","https://openalex.org/W4250955649"],"related_works":["https://openalex.org/W2054047433","https://openalex.org/W2124861257","https://openalex.org/W2093801379","https://openalex.org/W2123195949","https://openalex.org/W2545081564","https://openalex.org/W2149589543","https://openalex.org/W3190154454","https://openalex.org/W1547932452","https://openalex.org/W1973881903","https://openalex.org/W2119250743"],"abstract_inverted_index":{"The":[0],"sampling":[1,25,47,72],"of":[2,12,33,50,73,76,91,100,112,125,156,165,182],"spectrally":[3,51],"sparse":[4,52,78],"high-bandwidth":[5],"signals":[6,85],"below":[7],"the":[8,21,37,126,129,144,154,163,180,183],"Nyquist":[9],"rate":[10,26,106],"is":[11,57,118,133],"importance":[13],"in":[14,36,120,162],"many":[15],"applications.":[16],"In":[17,80],"order":[18],"to":[19,39,87,142,159],"keep":[20],"front-end":[22,61],"analog-to-digital":[23],"converter":[24],"low,":[27],"there":[28],"has":[29],"been":[30],"a":[31,58,64,74,105],"lot":[32],"recent":[34],"interest":[35],"literature":[38],"develop":[40],"efficient":[41,66],"and":[42,48,63,114,177],"practical":[43],"architectures":[44],"for":[45,69,109,122],"low-rate":[46],"recovery":[49],"signals.":[53,79],"Our":[54,147],"main":[55],"contribution":[56],"simple,":[59],"implementation-friendly":[60],"sampling-architecture":[62],"computationally":[65,134],"back-end":[67],"algorithm":[68,132,148],"blind":[70],"sub-Nyquist":[71],"class":[75],"band-limited":[77,86],"particular,":[81],"we":[82],"show":[83],"that":[84,172],"W":[88,121],"Hz":[89,108],"consisting":[90],"k":[92,113,117],"tones":[93],"can":[94],"be":[95],"reliably":[96],"recovered":[97],"from":[98],"sets":[99],"uniform":[101],"samples":[102,166],"obtained":[103],"at":[104],"4k":[107],"asymptotic":[110],"values":[111],"W,":[115],"when":[116],"sub-linear":[119],"noiseless":[123],"observations":[124],"signal.":[127],"Further,":[128],"proposed":[130],"reconstruction":[131],"efficient,":[135],"requiring":[136],"only":[137],"O(k":[138],"log":[139],"k)":[140],"computations":[141],"recover":[143],"sinusoidal":[145],"components.":[146],"succeeds":[149],"with":[150,153],"high":[151],"probability,":[152],"probability":[155],"failure":[157],"vanishing":[158],"zero":[160],"asymptotically":[161],"number":[164],"acquired.":[167],"We":[168],"provide":[169],"simulation":[170],"results":[171],"corroborate":[173],"our":[174],"theoretical":[175],"findings,":[176],"also":[178],"analyze":[179],"noise-robustness":[181],"system.":[184]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}