{"id":"https://openalex.org/W2545458225","doi":"https://doi.org/10.1109/acssc.2011.6190014","title":"High-resolution non-parametric spectral estimation using the Hirschman uncertainty and filter banks","display_name":"High-resolution non-parametric spectral estimation using the Hirschman uncertainty and filter banks","publication_year":2011,"publication_date":"2011-11-01","ids":{"openalex":"https://openalex.org/W2545458225","doi":"https://doi.org/10.1109/acssc.2011.6190014","mag":"2545458225"},"language":"en","primary_location":{"id":"doi:10.1109/acssc.2011.6190014","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acssc.2011.6190014","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR)","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/A5100765215","display_name":"Guifeng Liu","orcid":"https://orcid.org/0000-0002-0261-5100"},"institutions":[{"id":"https://openalex.org/I103163165","display_name":"Florida State University","ror":"https://ror.org/05g3dte14","country_code":"US","type":"education","lineage":["https://openalex.org/I103163165"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Guifeng Liu","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Florida State University, Tallahassee, FL, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Florida State University, Tallahassee, FL, USA","institution_ids":["https://openalex.org/I103163165"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035366862","display_name":"Victor DeBrunner","orcid":"https://orcid.org/0000-0003-2198-2552"},"institutions":[{"id":"https://openalex.org/I103163165","display_name":"Florida State University","ror":"https://ror.org/05g3dte14","country_code":"US","type":"education","lineage":["https://openalex.org/I103163165"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Victor DeBrunner","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Florida State University, Tallahassee, FL, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Florida State University, Tallahassee, FL, USA","institution_ids":["https://openalex.org/I103163165"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100765215"],"corresponding_institution_ids":["https://openalex.org/I103163165"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.34072763,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"336","last_page":"340"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9995999932289124,"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"}},"topics":[{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9995999932289124,"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"}},{"id":"https://openalex.org/T12300","display_name":"Advanced Electrical Measurement Techniques","score":0.9965999722480774,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9958999752998352,"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/mathematics","display_name":"Mathematics","score":0.649387776851654},{"id":"https://openalex.org/keywords/discrete-fourier-transform","display_name":"Discrete Fourier transform (general)","score":0.6355158090591431},{"id":"https://openalex.org/keywords/additive-white-gaussian-noise","display_name":"Additive white Gaussian noise","score":0.6030924916267395},{"id":"https://openalex.org/keywords/discrete-cosine-transform","display_name":"Discrete cosine transform","score":0.579608142375946},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5362178087234497},{"id":"https://openalex.org/keywords/spectral-density-estimation","display_name":"Spectral density estimation","score":0.5232438445091248},{"id":"https://openalex.org/keywords/filter-bank","display_name":"Filter bank","score":0.49883437156677246},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.46392473578453064},{"id":"https://openalex.org/keywords/white-noise","display_name":"White noise","score":0.4575693905353546},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.44824159145355225},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.42398664355278015},{"id":"https://openalex.org/keywords/fourier-transform","display_name":"Fourier transform","score":0.3696136772632599},{"id":"https://openalex.org/keywords/fractional-fourier-transform","display_name":"Fractional Fourier transform","score":0.30156129598617554},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.2609747052192688},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.22961044311523438},{"id":"https://openalex.org/keywords/mathematical-analysis","display_name":"Mathematical analysis","score":0.16903555393218994},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.13231569528579712}],"concepts":[{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.649387776851654},{"id":"https://openalex.org/C57733114","wikidata":"https://www.wikidata.org/wiki/Q1006032","display_name":"Discrete Fourier transform (general)","level":5,"score":0.6355158090591431},{"id":"https://openalex.org/C169334058","wikidata":"https://www.wikidata.org/wiki/Q353292","display_name":"Additive white Gaussian noise","level":3,"score":0.6030924916267395},{"id":"https://openalex.org/C2221639","wikidata":"https://www.wikidata.org/wiki/Q2877","display_name":"Discrete cosine transform","level":3,"score":0.579608142375946},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5362178087234497},{"id":"https://openalex.org/C30049272","wikidata":"https://www.wikidata.org/wiki/Q6555326","display_name":"Spectral density estimation","level":3,"score":0.5232438445091248},{"id":"https://openalex.org/C100515483","wikidata":"https://www.wikidata.org/wiki/Q3268235","display_name":"Filter bank","level":3,"score":0.49883437156677246},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.46392473578453064},{"id":"https://openalex.org/C112633086","wikidata":"https://www.wikidata.org/wiki/Q381287","display_name":"White noise","level":2,"score":0.4575693905353546},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.44824159145355225},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.42398664355278015},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.3696136772632599},{"id":"https://openalex.org/C76563020","wikidata":"https://www.wikidata.org/wiki/Q4817582","display_name":"Fractional Fourier transform","level":4,"score":0.30156129598617554},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.2609747052192688},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.22961044311523438},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.16903555393218994},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.13231569528579712},{"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/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C203024314","wikidata":"https://www.wikidata.org/wiki/Q1365258","display_name":"Fourier analysis","level":3,"score":0.0},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/acssc.2011.6190014","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acssc.2011.6190014","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.6800000071525574,"display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":4,"referenced_works":["https://openalex.org/W1987138902","https://openalex.org/W1996348247","https://openalex.org/W2096121910","https://openalex.org/W2124092400"],"related_works":["https://openalex.org/W2118155316","https://openalex.org/W2382218334","https://openalex.org/W2090697566","https://openalex.org/W2116983948","https://openalex.org/W2155549970","https://openalex.org/W3141389362","https://openalex.org/W2057875231","https://openalex.org/W2140256262","https://openalex.org/W2183119314","https://openalex.org/W2343504300"],"abstract_inverted_index":{"The":[0],"traditional":[1],"Heisenberg-Weyl":[2,35],"measure":[3],"quantifies":[4],"the":[5,15,34,37,46,50,58,66,76,84,134,137,145,161,164,185,188,193,209,215,222,225],"joint":[6,41],"localization,":[7],"uncertainty,":[8],"or":[9,101],"concentration":[10],"of":[11,22,40,60,68,96,106,174,187,192],"a":[12,20,122],"signal":[13,26],"in":[14,28,31,94,108,195,201,217],"phase":[16],"plane":[17],"based":[18,44],"on":[19,45],"product":[21],"energies":[23],"expressed":[24],"as":[25],"variances":[27],"time":[29],"and":[30,71,89,113,136,220],"frequency.":[32],"Unlike":[33],"measure,":[36],"Hirschman":[38,77],"notion":[39],"uncertainty":[42],"is":[43,81],"entropy":[47],"rather":[48],"than":[49],"energy.":[51],"Furthermore,":[52],"its":[53,97,170],"definition":[54],"extends":[55],"naturally":[56],"from":[57],"case":[59],"infinitely":[61,72],"supported":[62,73],"continuous-time":[63],"signals":[64],"to":[65,83,99,153,177,211,214,224,228],"cases":[67,105],"both":[69],"finitely":[70],"discrete-time":[74],"signals,":[75],"optimal":[78],"transform":[79,87,92],"(HOT)":[80],"superior":[82,213],"discrete":[85,90],"Fourier":[86],"(DFT)":[88],"cosine":[91],"(DCT)":[93],"terms":[95,173],"ability":[98],"separate":[100],"resolve":[102],"two":[103,197],"limiting":[104],"localization":[107],"frequency,":[109],"viz":[110],"pure":[111],"tones":[112],"additive":[114,202],"white":[115,203],"noise.":[116],"In":[117,181],"this":[118],"paper,":[119],"we":[120,183],"implement":[121],"stationary":[123],"spectral":[124],"estimation":[125,157],"method":[126],"using":[127,133],"filter":[128,142],"banks,":[129],"which":[130],"are":[131],"constructed":[132],"HOT":[135],"DFT.":[138],"We":[139,159,168,207],"combine":[140],"these":[141],"banks":[143],"with":[144,190],"classic":[146],"interpolating":[147],"procedure":[148],"developed":[149],"by":[150],"Barry":[151],"Quinn":[152],"develop":[154],"our":[155],"line":[156],"algorithm.":[158],"call":[160],"resulting":[162],"algorithm":[163],"smoothed":[165,179],"HOT-DFT":[166,189,210],"periodogram.":[167,180],"compare":[169,184],"performance":[171,186],"(in":[172],"frequency":[175,199,218],"resolution)":[176],"Quinn's":[178],"particular,":[182],"that":[191],"DFT":[194,216],"resolving":[196],"close":[198],"components":[200],"Gaussian":[204],"noise":[205],"(AWGN).":[206],"find":[208],"be":[212],"estimation,":[219],"ascribe":[221],"difference":[223],"HOT's":[226],"relationship":[227],"entropy.":[229]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
