{"id":"https://openalex.org/W3000413046","doi":"https://doi.org/10.1109/sensors43011.2019.8956540","title":"Multimodal Data Fusion of Spatial Fields in Sensor Networks","display_name":"Multimodal Data Fusion of Spatial Fields in Sensor Networks","publication_year":2019,"publication_date":"2019-10-01","ids":{"openalex":"https://openalex.org/W3000413046","doi":"https://doi.org/10.1109/sensors43011.2019.8956540","mag":"3000413046"},"language":"en","primary_location":{"id":"doi:10.1109/sensors43011.2019.8956540","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sensors43011.2019.8956540","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE SENSORS","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/A5100354124","display_name":"Pengfei Zhang","orcid":"https://orcid.org/0000-0002-1017-929X"},"institutions":[{"id":"https://openalex.org/I3005327000","display_name":"Institute for Infocomm Research","ror":"https://ror.org/053rfa017","country_code":"SG","type":"facility","lineage":["https://openalex.org/I115228651","https://openalex.org/I3005327000","https://openalex.org/I91275662"]}],"countries":["SG"],"is_corresponding":true,"raw_author_name":"Pengfei Zhang","raw_affiliation_strings":["Institute for Infocomm Research (I2R),Singapore","Institute for Infocomm Research (I2R), Singapore"],"affiliations":[{"raw_affiliation_string":"Institute for Infocomm Research (I2R),Singapore","institution_ids":["https://openalex.org/I3005327000"]},{"raw_affiliation_string":"Institute for Infocomm Research (I2R), Singapore","institution_ids":["https://openalex.org/I3005327000"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087616647","display_name":"Gareth W. Peters","orcid":"https://orcid.org/0000-0003-2768-8979"},"institutions":[{"id":"https://openalex.org/I32062511","display_name":"Heriot-Watt University","ror":"https://ror.org/04mghma93","country_code":"GB","type":"education","lineage":["https://openalex.org/I32062511"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Gareth W. Peters","raw_affiliation_strings":["Heriot-Watt University,Department of Actuarial Mathematics and Statistics,Edinburgh,UK","Department of Actuarial Mathematics and Statistics, Heriot-Watt University, Edinburgh, UK"],"affiliations":[{"raw_affiliation_string":"Heriot-Watt University,Department of Actuarial Mathematics and Statistics,Edinburgh,UK","institution_ids":["https://openalex.org/I32062511"]},{"raw_affiliation_string":"Department of Actuarial Mathematics and Statistics, Heriot-Watt University, Edinburgh, UK","institution_ids":["https://openalex.org/I32062511"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051994726","display_name":"Ido Nevat","orcid":"https://orcid.org/0000-0003-1567-3446"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ido Nevat","raw_affiliation_strings":["TUMCREATE,Singapore","TUMCREATE, Singapore"],"affiliations":[{"raw_affiliation_string":"TUMCREATE,Singapore","institution_ids":[]},{"raw_affiliation_string":"TUMCREATE, Singapore","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003977347","display_name":"Keng Boon Teo","orcid":null},"institutions":[{"id":"https://openalex.org/I3005327000","display_name":"Institute for Infocomm Research","ror":"https://ror.org/053rfa017","country_code":"SG","type":"facility","lineage":["https://openalex.org/I115228651","https://openalex.org/I3005327000","https://openalex.org/I91275662"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Keng Boon Teo","raw_affiliation_strings":["Institute for Infocomm Research (I2R),Singapore","Institute for Infocomm Research (I2R), Singapore"],"affiliations":[{"raw_affiliation_string":"Institute for Infocomm Research (I2R),Singapore","institution_ids":["https://openalex.org/I3005327000"]},{"raw_affiliation_string":"Institute for Infocomm Research (I2R), Singapore","institution_ids":["https://openalex.org/I3005327000"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100328534","display_name":"Yixin Wang","orcid":"https://orcid.org/0000-0002-1515-6003"},"institutions":[{"id":"https://openalex.org/I3005327000","display_name":"Institute for Infocomm Research","ror":"https://ror.org/053rfa017","country_code":"SG","type":"facility","lineage":["https://openalex.org/I115228651","https://openalex.org/I3005327000","https://openalex.org/I91275662"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Yixin Wang","raw_affiliation_strings":["Institute for Infocomm Research (I2R),Singapore","Institute for Infocomm Research (I2R), Singapore"],"affiliations":[{"raw_affiliation_string":"Institute for Infocomm Research (I2R),Singapore","institution_ids":["https://openalex.org/I3005327000"]},{"raw_affiliation_string":"Institute for Infocomm Research (I2R), Singapore","institution_ids":["https://openalex.org/I3005327000"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100354124"],"corresponding_institution_ids":["https://openalex.org/I3005327000"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.21672785,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9943000078201294,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9943000078201294,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.9939000010490417,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9919000267982483,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/sensor-fusion","display_name":"Sensor fusion","score":0.6484924554824829},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.6187435388565063},{"id":"https://openalex.org/keywords/pearson-product-moment-correlation-coefficient","display_name":"Pearson product-moment correlation coefficient","score":0.6015706658363342},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.5627249479293823},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5449965000152588},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.5312200784683228},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.5126432776451111},{"id":"https://openalex.org/keywords/spatial-correlation","display_name":"Spatial correlation","score":0.48233553767204285},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.479798823595047},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.47775712609291077},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.37725070118904114},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3467039465904236},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33709633350372314},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.33348944783210754},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.21232962608337402}],"concepts":[{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.6484924554824829},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6187435388565063},{"id":"https://openalex.org/C55078378","wikidata":"https://www.wikidata.org/wiki/Q1136628","display_name":"Pearson product-moment correlation coefficient","level":2,"score":0.6015706658363342},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.5627249479293823},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5449965000152588},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.5312200784683228},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.5126432776451111},{"id":"https://openalex.org/C150060386","wikidata":"https://www.wikidata.org/wiki/Q7574054","display_name":"Spatial correlation","level":2,"score":0.48233553767204285},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.479798823595047},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.47775712609291077},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37725070118904114},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3467039465904236},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33709633350372314},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33348944783210754},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.21232962608337402},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/sensors43011.2019.8956540","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sensors43011.2019.8956540","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE SENSORS","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W164706946","https://openalex.org/W1651792978","https://openalex.org/W1981182625","https://openalex.org/W1982012380","https://openalex.org/W2111619626","https://openalex.org/W2190345096","https://openalex.org/W2612795230","https://openalex.org/W2783021959","https://openalex.org/W2943666166","https://openalex.org/W4211049957","https://openalex.org/W4288334121","https://openalex.org/W6793868555"],"related_works":["https://openalex.org/W2102148524","https://openalex.org/W1036938216","https://openalex.org/W357196361","https://openalex.org/W53228292","https://openalex.org/W2132659060","https://openalex.org/W2031992971","https://openalex.org/W3214791684","https://openalex.org/W2353265673","https://openalex.org/W2152662039","https://openalex.org/W2070814628"],"abstract_inverted_index":{"We":[0,96],"develop":[1,43],"a":[2,88],"robust":[3,47],"data":[4,129],"fusion":[5],"algorithm":[6,50],"for":[7,91,123,127],"field":[8],"reconstruction":[9],"of":[10,16,56,64,75,114],"multiple":[11],"physical":[12,58],"phenomena.":[13],"The":[14],"contribution":[15],"this":[17],"paper":[18],"is":[19],"twofold:":[20],"First,":[21],"we":[22,42],"demonstrate":[23],"how":[24],"multi-spatial":[25],"fields":[26],"which":[27,104],"can":[28,38],"have":[29],"any":[30],"marginal":[31],"distributions":[32],"and":[33,46,85],"exhibit":[34],"complex":[35],"dependence":[36],"structures":[37],"be":[39],"constructed.":[40],"Second,":[41],"an":[44],"efficient":[45],"linear":[48,67,108],"estimation":[49],"to":[51,79],"predict":[52],"the":[53,57,65,73,77,92,102,106,115,121],"mean":[54],"behavior":[55],"phenomena":[59],"using":[60,124],"rank":[61,93],"correlation":[62,94,110],"instead":[63],"conventional":[66,107],"Pearson":[68,109],"correlation.":[69],"Our":[70],"approach":[71],"has":[72,87],"advantage":[74],"avoiding":[76],"need":[78],"derive":[80],"intractable":[81],"predictive":[82],"posterior":[83],"distribution":[84],"also":[86],"tractable":[89],"solution":[90],"values.":[95],"show":[97],"that":[98],"our":[99,125],"model":[100,103],"outperforms":[101],"uses":[105],"metric":[111],"in":[112],"terms":[113],"prediction":[116],"mean-squared-errors":[117],"(MSE).":[118],"This":[119],"provides":[120],"motivation":[122],"models":[126],"multimodal":[128],"fusion.":[130]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}