{"id":"https://openalex.org/W2920502342","doi":"https://doi.org/10.1109/acssc.2018.8645178","title":"Frequency-Warped Cepstral Heatmaps for Deep Learning of Human Motion Signatures","display_name":"Frequency-Warped Cepstral Heatmaps for Deep Learning of Human Motion Signatures","publication_year":2018,"publication_date":"2018-10-01","ids":{"openalex":"https://openalex.org/W2920502342","doi":"https://doi.org/10.1109/acssc.2018.8645178","mag":"2920502342"},"language":"en","primary_location":{"id":"doi:10.1109/acssc.2018.8645178","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acssc.2018.8645178","pdf_url":null,"source":{"id":"https://openalex.org/S4363608623","display_name":"2018 52nd Asilomar Conference on Signals, Systems, and Computers","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"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":"2018 52nd Asilomar Conference on Signals, Systems, and Computers","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/A5028575081","display_name":"Bar\u0131\u015f Erol","orcid":"https://orcid.org/0000-0002-6977-8801"},"institutions":[{"id":"https://openalex.org/I7863295","display_name":"Villanova University","ror":"https://ror.org/02g7kd627","country_code":"US","type":"education","lineage":["https://openalex.org/I7863295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Baris Erol","raw_affiliation_strings":["Center for Advanced Communications, Villanova University, Villanova, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center for Advanced Communications, Villanova University, Villanova, USA","institution_ids":["https://openalex.org/I7863295"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025392795","display_name":"Sevgi Z\u00fcbeyde G\u00fcrb\u00fcz","orcid":"https://orcid.org/0000-0001-7487-9087"},"institutions":[{"id":"https://openalex.org/I17301866","display_name":"University of Alabama","ror":"https://ror.org/03xrrjk67","country_code":"US","type":"education","lineage":["https://openalex.org/I17301866"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sevgi Z. Gurbuz","raw_affiliation_strings":["Dept. Electrical and Computer Engineering, University of Alabama, Tuscaloosa, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. Electrical and Computer Engineering, University of Alabama, Tuscaloosa, USA","institution_ids":["https://openalex.org/I17301866"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007661434","display_name":"Moeness G. Amin","orcid":"https://orcid.org/0000-0002-0926-4120"},"institutions":[{"id":"https://openalex.org/I7863295","display_name":"Villanova University","ror":"https://ror.org/02g7kd627","country_code":"US","type":"education","lineage":["https://openalex.org/I7863295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Moeness G. Amin","raw_affiliation_strings":["Center for Advanced Communications, Villanova University, Villanova, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center for Advanced Communications, Villanova University, Villanova, USA","institution_ids":["https://openalex.org/I7863295"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.6938,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.88138091,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"6210","issue":null,"first_page":"1234","last_page":"1238"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11038","display_name":"Advanced SAR Imaging Techniques","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"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/T11038","display_name":"Advanced SAR Imaging Techniques","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/T12740","display_name":"Gait Recognition and Analysis","score":0.9926000237464905,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T11698","display_name":"Underwater Acoustics Research","score":0.9900000095367432,"subfield":{"id":"https://openalex.org/subfields/1910","display_name":"Oceanography"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/spectrogram","display_name":"Spectrogram","score":0.9195936322212219},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7699846029281616},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6510987877845764},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5992252230644226},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5568719506263733},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.5299623608589172},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.5172898769378662},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5044771432876587},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5002052783966064},{"id":"https://openalex.org/keywords/mel-frequency-cepstrum","display_name":"Mel-frequency cepstrum","score":0.48008376359939575},{"id":"https://openalex.org/keywords/filter-bank","display_name":"Filter bank","score":0.4257567822933197},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.417473703622818},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.36242562532424927},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.09409749507904053}],"concepts":[{"id":"https://openalex.org/C45273575","wikidata":"https://www.wikidata.org/wiki/Q578970","display_name":"Spectrogram","level":2,"score":0.9195936322212219},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7699846029281616},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6510987877845764},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5992252230644226},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5568719506263733},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.5299623608589172},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.5172898769378662},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5044771432876587},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5002052783966064},{"id":"https://openalex.org/C151989614","wikidata":"https://www.wikidata.org/wiki/Q440370","display_name":"Mel-frequency cepstrum","level":3,"score":0.48008376359939575},{"id":"https://openalex.org/C100515483","wikidata":"https://www.wikidata.org/wiki/Q3268235","display_name":"Filter bank","level":3,"score":0.4257567822933197},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.417473703622818},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.36242562532424927},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.09409749507904053}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/acssc.2018.8645178","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acssc.2018.8645178","pdf_url":null,"source":{"id":"https://openalex.org/S4363608623","display_name":"2018 52nd Asilomar Conference on Signals, Systems, and Computers","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"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":"2018 52nd Asilomar Conference on Signals, Systems, and Computers","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.6299999952316284}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W2014801104","https://openalex.org/W2093505553","https://openalex.org/W2136655611","https://openalex.org/W2143941456","https://openalex.org/W2157770256","https://openalex.org/W2242223225","https://openalex.org/W2298692413","https://openalex.org/W2577021971","https://openalex.org/W2746870488","https://openalex.org/W2789243953","https://openalex.org/W2802472557","https://openalex.org/W2807946084","https://openalex.org/W2978589234","https://openalex.org/W4285719527","https://openalex.org/W4293860566","https://openalex.org/W6674342456","https://openalex.org/W6731853769"],"related_works":["https://openalex.org/W2327143552","https://openalex.org/W2766680336","https://openalex.org/W2133320490","https://openalex.org/W4289830142","https://openalex.org/W1497065097","https://openalex.org/W2125446021","https://openalex.org/W3150393935","https://openalex.org/W2980055100","https://openalex.org/W2186790562","https://openalex.org/W2548564146"],"abstract_inverted_index":{"Automatic":[0],"target":[1],"recognition":[2,38,47,73],"(ATR)":[3],"using":[4],"micro-Doppler":[5],"analysis":[6,111],"is":[7,49,60,196],"a":[8,13,61,84,113,129,169,190],"technique":[9],"that":[10,176,186],"has":[11],"been":[12],"topic":[14],"of":[15,55,76,128,163,187],"great":[16],"research":[17],"over":[18],"the":[19,56,66,74,96,124,145,151,156,161,164,177],"past":[20],"decade,":[21],"with":[22],"key":[23],"applications":[24],"to":[25,95,155,185],"border":[26],"control":[27],"and":[28,39,117,158],"security,":[29],"perimeter":[30],"defense,":[31],"as":[32,34,92],"well":[33],"indoor":[35],"human":[36,40,45],"activity":[37],"gait":[41],"analysis.":[42],"Typically,":[43],"radar-based":[44],"motion":[46,72,173],"task":[48],"based":[50,108],"on":[51,109],"time-frequency":[52],"(TF)":[53],"representation":[54,107],"radar":[57,147],"signal":[58,68],"which":[59,80],"natural":[62],"tool":[63],"for":[64],"revealing":[65],"local":[67],"behavior.":[69],"However,":[70],"in":[71,83],"number":[75],"similar":[77],"classes":[78],"increases":[79],"might":[81],"result":[82],"degraded":[85],"classification":[86,183],"performance":[87],"when":[88,189],"spectrograms":[89,157,188],"are":[90,142],"given":[91],"an":[93,118],"input":[94],"deep":[97],"neural":[98,193],"networks":[99],"(DNNs).":[100],"This":[101],"paper":[102],"proposes":[103],"another":[104],"two-dimensional":[105],"(2D)":[106],"cepstral":[110,140,179],"from":[112,144,168],"diversified":[114,130],"simulation":[115,131],"database":[116,132,174],"optimization":[119],"procedure.":[120],"We":[121],"first":[122],"find":[123],"optimum":[125],"filter":[126,153],"bank":[127,154],"by":[133,149],"employing":[134],"performance-based":[135],"genetic":[136],"algorithm":[137],"(GA).":[138],"Then,":[139],"heatmaps":[141,180],"computed":[143],"real":[146],"data":[148],"applying":[150],"optimized":[152],"then":[159],"taking":[160],"logarithm":[162],"output.":[165],"Results":[166],"achieved":[167],"6-class":[170],"multi-aspect":[171],"angle":[172],"show":[175],"proposed":[178],"provide":[181],"superior":[182],"accuracy":[184],"4-layer":[191],"convolutional":[192],"network":[194],"(CNN)":[195],"employed.":[197]},"counts_by_year":[{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
