{"id":"https://openalex.org/W3085056114","doi":"https://doi.org/10.1109/coins49042.2020.9191412","title":"Multi-sensor data augmentation for robust sensing","display_name":"Multi-sensor data augmentation for robust sensing","publication_year":2020,"publication_date":"2020-08-01","ids":{"openalex":"https://openalex.org/W3085056114","doi":"https://doi.org/10.1109/coins49042.2020.9191412","mag":"3085056114"},"language":"en","primary_location":{"id":"doi:10.1109/coins49042.2020.9191412","is_oa":false,"landing_page_url":"https://doi.org/10.1109/coins49042.2020.9191412","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Conference on Omni-layer Intelligent Systems (COINS)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5011960578","display_name":"Aaqib Saeed","orcid":"https://orcid.org/0000-0003-1473-0322"},"institutions":[{"id":"https://openalex.org/I83019370","display_name":"Eindhoven University of Technology","ror":"https://ror.org/02c2kyt77","country_code":"NL","type":"education","lineage":["https://openalex.org/I83019370"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Aaqib Saeed","raw_affiliation_strings":["Eindhoven University of Technology, Eindhoven, The Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Eindhoven University of Technology, Eindhoven, The Netherlands","institution_ids":["https://openalex.org/I83019370"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100339214","display_name":"Ye Li","orcid":"https://orcid.org/0000-0002-2699-6630"},"institutions":[{"id":"https://openalex.org/I83019370","display_name":"Eindhoven University of Technology","ror":"https://ror.org/02c2kyt77","country_code":"NL","type":"education","lineage":["https://openalex.org/I83019370"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Ye Li","raw_affiliation_strings":["Eindhoven University of Technology, Eindhoven, The Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Eindhoven University of Technology, Eindhoven, The Netherlands","institution_ids":["https://openalex.org/I83019370"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081886883","display_name":"Tan\u0131r \u00d6z\u00e7elebi","orcid":"https://orcid.org/0000-0001-7092-5998"},"institutions":[{"id":"https://openalex.org/I83019370","display_name":"Eindhoven University of Technology","ror":"https://ror.org/02c2kyt77","country_code":"NL","type":"education","lineage":["https://openalex.org/I83019370"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Tanir Ozcelebi","raw_affiliation_strings":["Eindhoven University of Technology, Eindhoven, The Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Eindhoven University of Technology, Eindhoven, The Netherlands","institution_ids":["https://openalex.org/I83019370"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111437816","display_name":"Johan J. Lukkien","orcid":null},"institutions":[{"id":"https://openalex.org/I83019370","display_name":"Eindhoven University of Technology","ror":"https://ror.org/02c2kyt77","country_code":"NL","type":"education","lineage":["https://openalex.org/I83019370"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Johan Lukkien","raw_affiliation_strings":["Eindhoven University of Technology, Eindhoven, The Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Eindhoven University of Technology, Eindhoven, The Netherlands","institution_ids":["https://openalex.org/I83019370"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I83019370"],"apc_list":null,"apc_paid":null,"fwci":0.0854,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.33227458,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.996399998664856,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.996399998664856,"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/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9950000047683716,"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/T12697","display_name":"Water Quality Monitoring Technologies","score":0.9944000244140625,"subfield":{"id":"https://openalex.org/subfields/2312","display_name":"Water Science and Technology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8191819190979004},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.6671857833862305},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5618487596511841},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5532630085945129},{"id":"https://openalex.org/keywords/black-box","display_name":"Black box","score":0.5065361261367798},{"id":"https://openalex.org/keywords/ranging","display_name":"Ranging","score":0.5009455680847168},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.47250616550445557},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4554528594017029},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.4529227316379547},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.44828853011131287},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.41756972670555115},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.41046154499053955},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.27681559324264526}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8191819190979004},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6671857833862305},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5618487596511841},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5532630085945129},{"id":"https://openalex.org/C94966114","wikidata":"https://www.wikidata.org/wiki/Q29256","display_name":"Black box","level":2,"score":0.5065361261367798},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.5009455680847168},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.47250616550445557},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4554528594017029},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.4529227316379547},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.44828853011131287},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.41756972670555115},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.41046154499053955},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.27681559324264526},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/coins49042.2020.9191412","is_oa":false,"landing_page_url":"https://doi.org/10.1109/coins49042.2020.9191412","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Conference on Omni-layer Intelligent Systems (COINS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.4000000059604645,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W4919037","https://openalex.org/W267862395","https://openalex.org/W1576660662","https://openalex.org/W2057907879","https://openalex.org/W2090465764","https://openalex.org/W2144691514","https://openalex.org/W2219995598","https://openalex.org/W2336687883","https://openalex.org/W2387306914","https://openalex.org/W2402846924","https://openalex.org/W2515503816","https://openalex.org/W2604096629","https://openalex.org/W2620664872","https://openalex.org/W2622068151","https://openalex.org/W2768582154","https://openalex.org/W2770173563","https://openalex.org/W2773662615","https://openalex.org/W2789136377","https://openalex.org/W2890264806","https://openalex.org/W2946822591","https://openalex.org/W2949736877","https://openalex.org/W2952217990","https://openalex.org/W2954428350","https://openalex.org/W2962182608","https://openalex.org/W2963352069","https://openalex.org/W3101667008","https://openalex.org/W3102326534","https://openalex.org/W3106753828","https://openalex.org/W4293154685","https://openalex.org/W4294583247","https://openalex.org/W4300106150","https://openalex.org/W4324106947","https://openalex.org/W6600213771","https://openalex.org/W6609785129","https://openalex.org/W6682642761","https://openalex.org/W6702989015","https://openalex.org/W6738960736","https://openalex.org/W6745609121","https://openalex.org/W6746638498","https://openalex.org/W6762619590","https://openalex.org/W6764945797"],"related_works":["https://openalex.org/W4384112194","https://openalex.org/W2783354812","https://openalex.org/W2103009189","https://openalex.org/W4312958259","https://openalex.org/W4390813131","https://openalex.org/W2349383066","https://openalex.org/W1969901537","https://openalex.org/W4328132048","https://openalex.org/W4308259661","https://openalex.org/W2376202349"],"abstract_inverted_index":{"Data":[0],"augmentation":[1,76,117,142],"is":[2,41,54],"a":[3,69,108,157],"crucial":[4],"technique":[5],"for":[6,12,35,72,107,148],"effectively":[7],"learning":[8],"deep":[9,105],"models":[10],"and":[11,31,57,127,183],"improving":[13],"their":[14],"generalization.":[15],"It":[16],"has":[17],"shown":[18],"remarkable":[19],"performance":[20,159],"gains":[21],"on":[22,144],"complex":[23,150],"sets":[24],"of":[25,49,87,96,111,140],"problems,":[26],"such":[27],"as":[28],"object":[29],"detection":[30],"image":[32],"classification.":[33],"However,":[34],"sensor":[36,52],"(time-series)":[37],"data,":[38],"its":[39],"potential":[40],"not":[42],"thoroughly":[43],"explored":[44],"even":[45],"though":[46],"the":[47,88,97,129,138,169,175],"acquisition":[48],"large":[50],"annotated":[51],"datasets":[53,147],"prohibitively":[55],"expensive":[56],"challenging":[58],"in":[59],"real-life.":[60],"In":[61,152],"this":[62],"work,":[63],"we":[64,114,155],"propose":[65,115],"Sensor":[66],"Augment":[67],"-":[68],"generalized":[70],"framework":[71],"automatically":[73],"discovering":[74],"data-specific":[75],"strategies":[77,143,176],"with":[78],"black-box":[79],"optimization":[80],"search":[81,130],"algorithms.":[82],"Our":[83],"approach":[84],"makes":[85],"use":[86],"user-defined":[89],"transformations":[90],"to":[91,103,123,164],"discover":[92],"an":[93],"optimal":[94],"combination":[95],"operations":[98,118],"that":[99,119,174],"can":[100,120,177,185],"be":[101,121,178],"used":[102,122],"train":[104],"networks":[106],"wide":[109],"variety":[110],"tasks.":[112,151],"Besides,":[113],"several":[116],"generate":[124],"synthetic":[125],"data":[126],"enrich":[128],"space":[131],"while":[132],"harnessing":[133],"existing":[134],"functions.":[135],"We":[136,171],"show":[137,173],"efficacy":[139],"learned":[141,179],"7":[145],"multi-sensor":[146],"4":[149],"our":[153],"experiments,":[154],"see":[156],"substantial":[158],"gain":[160],"ranging":[161],"from":[162,180],"1.5":[163],"10":[165],"F-score":[166],"points":[167],"over":[168],"baseline.":[170],"also":[172],"smaller":[181],"subsets,":[182],"they":[184],"transfer":[186],"well":[187],"between":[188],"related":[189],"datasets.":[190]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
