{"id":"https://openalex.org/W2551369898","doi":"https://doi.org/10.1109/icspcc.2016.7753732","title":"Fractional Fourier transform estimation of simple randomly sampled signals","display_name":"Fractional Fourier transform estimation of simple randomly sampled signals","publication_year":2016,"publication_date":"2016-08-01","ids":{"openalex":"https://openalex.org/W2551369898","doi":"https://doi.org/10.1109/icspcc.2016.7753732","mag":"2551369898"},"language":"en","primary_location":{"id":"doi:10.1109/icspcc.2016.7753732","doi":"https://doi.org/10.1109/icspcc.2016.7753732","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icspcc.2016.7753732","pdf_url":null,"source":{"id":"https://openalex.org/S4363608295","display_name":"IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5023751589","display_name":"Liyun Xu","orcid":"https://orcid.org/0000-0001-7655-6660","observed_orcids":["https://orcid.org/0000-0001-7655-6660"]},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liyun Xu","raw_affiliation_strings":["School of Mathematics and Statistics, Beijing Institute of Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mathematics and Statistics, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101859838","display_name":"Feng Zhang","orcid":"https://orcid.org/0000-0002-4566-6119","observed_orcids":["https://orcid.org/0000-0002-4566-6119"]},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feng Zhang","raw_affiliation_strings":["Department of Electronic Engineering, Beijing Institute of Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I125839683"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":93},"biblio":{"volume":"15","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11210","display_name":"Mathematical Analysis and Transform Methods","score":0.9965999722480774,"subfield":{"id":"https://openalex.org/subfields/2604","display_name":"Applied Mathematics"},"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/T11210","display_name":"Mathematical Analysis and Transform Methods","score":0.9965999722480774,"subfield":{"id":"https://openalex.org/subfields/2604","display_name":"Applied Mathematics"},"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/T12300","display_name":"Advanced Electrical Measurement Techniques","score":0.0017000000225380063,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.0007999999797903001,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/fractional-fourier-transform","display_name":"fractional Fourier transform","score":0.9829999804496765},{"id":"https://openalex.org/keywords/random-sampling","display_name":"random sampling","score":0.8759999871253967},{"id":"https://openalex.org/keywords/non-uniform-sampling","display_name":"non-uniform sampling","score":0.9950000047683716},{"id":"https://openalex.org/keywords/non-stationary-signals","display_name":"non-stationary signals","score":0.8809999823570251},{"id":"https://openalex.org/keywords/spectral-estimation","display_name":"spectral estimation","score":0.8840000033378601},{"id":"https://openalex.org/keywords/unbiased-estimation","display_name":"unbiased estimation","score":0.9100000262260437},{"id":"https://openalex.org/keywords/estimation-variance","display_name":"estimation variance","score":0.765999972820282},{"id":"https://openalex.org/keywords/sampling-jitter","display_name":"sampling jitter","score":0.9819999933242798},{"id":"https://openalex.org/keywords/observation-error","display_name":"observation error","score":0.800000011920929}],"concepts":[{"id":"https://openalex.org/C30049272","wikidata":"https://www.wikidata.org/wiki/Q6555326","display_name":"Spectral density estimation","level":3,"score":0.7152225971221924},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.6842718720436096},{"id":"https://openalex.org/C46681722","wikidata":"https://www.wikidata.org/wiki/Q4725589","display_name":"Alias","level":2,"score":0.6170138120651245},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.5472303628921509},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5408926010131836},{"id":"https://openalex.org/C57733114","wikidata":"https://www.wikidata.org/wiki/Q1006032","display_name":"Discrete Fourier transform (general)","level":5,"score":0.5330983996391296},{"id":"https://openalex.org/C20353970","wikidata":"https://www.wikidata.org/wiki/Q1056998","display_name":"Simple random sample","level":3,"score":0.5031768679618835},{"id":"https://openalex.org/C31942786","wikidata":"https://www.wikidata.org/wiki/Q679800","display_name":"Coherent sampling","level":4,"score":0.4855062961578369},{"id":"https://openalex.org/C76563020","wikidata":"https://www.wikidata.org/wiki/Q4817582","display_name":"Fractional Fourier transform","level":4,"score":0.4851475656032562},{"id":"https://openalex.org/C62058723","wikidata":"https://www.wikidata.org/wiki/Q7049066","display_name":"Non-uniform discrete Fourier transform","level":5,"score":0.4793435037136078},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.4586496353149414},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4581564664840698},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4480205774307251},{"id":"https://openalex.org/C501101116","wikidata":"https://www.wikidata.org/wiki/Q18378116","display_name":"Multidimensional signal processing","level":4,"score":0.42827263474464417},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.4254334270954132},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.4232531487941742},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.39227569103240967},{"id":"https://openalex.org/C203024314","wikidata":"https://www.wikidata.org/wiki/Q1365258","display_name":"Fourier analysis","level":3,"score":0.28842347860336304},{"id":"https://openalex.org/C84462506","wikidata":"https://www.wikidata.org/wiki/Q173142","display_name":"Digital signal processing","level":2,"score":0.2299252152442932},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.1401606798171997},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.11557969450950623},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icspcc.2016.7753732","doi":"https://doi.org/10.1109/icspcc.2016.7753732","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icspcc.2016.7753732","pdf_url":null,"source":{"id":"https://openalex.org/S4363608295","display_name":"IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W606473257","https://openalex.org/W1503019004","https://openalex.org/W1965936301","https://openalex.org/W2000076756","https://openalex.org/W2048881887","https://openalex.org/W2049472408","https://openalex.org/W2052660741","https://openalex.org/W2085533345","https://openalex.org/W2107164950","https://openalex.org/W2107207107","https://openalex.org/W2133001976","https://openalex.org/W2133278518","https://openalex.org/W2137722695","https://openalex.org/W2141028418","https://openalex.org/W2146262488","https://openalex.org/W2149736621","https://openalex.org/W2166760964","https://openalex.org/W2170411504"],"related_works":["https://openalex.org/W2741373247","https://openalex.org/W3040794270","https://openalex.org/W4412444062","https://openalex.org/W3030221356","https://openalex.org/W2045205194","https://openalex.org/W1847929618","https://openalex.org/W2115168542","https://openalex.org/W2011703510","https://openalex.org/W2038044778","https://openalex.org/W1503019004"],"abstract_inverted_index":{"Random":[0],"sampling":[1,8,61,78],"as":[2,12],"a":[3,13],"special":[4],"kind":[5],"of":[6,30,49,60,68,98],"nonuniform":[7],"has":[9],"been":[10],"used":[11],"digital":[14],"alias-free":[15],"signal":[16],"processing":[17],"method":[18],"in":[19,106],"analog-to-digital":[20],"conversion.":[21],"This":[22],"paper":[23],"presents":[24],"the":[25,35,55,69,77,83,91,99,107],"fractional":[26,70,94],"Fourier":[27],"transform":[28],"estimation":[29,41,56,72],"nonstationary":[31],"signals":[32],"based":[33],"on":[34,66],"simple":[36],"random":[37],"sampling.":[38],"The":[39,47,58,85],"spectrum":[40,71],"is":[42,51],"proved":[43],"to":[44,53,82],"be":[45,88],"unbiased.":[46],"variance":[48],"it":[50],"calculated":[52],"show":[54],"effect.":[57],"effects":[59],"jitters":[62,79],"and":[63,104],"observation":[64],"errors":[65],"performance":[67],"are":[73,102],"analyzed.":[74],"Among":[75],"them,":[76],"introduce":[80],"bias":[81,86],"estimation.":[84],"can":[87],"compensated":[89],"by":[90],"new":[92],"defined":[93],"characteristic":[95],"function.":[96],"All":[97],"analysis":[100],"results":[101],"simulated":[103],"verified":[105],"last":[108],"numerical":[109],"experiments.":[110]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2016-11-30T00:00:00"}
