{"id":"https://openalex.org/W4388271613","doi":"https://doi.org/10.1109/wisee58383.2023.10289283","title":"Comprehensive GPR Signal Analysis via Descriptive Statistics and Machine Learning","display_name":"Comprehensive GPR Signal Analysis via Descriptive Statistics and Machine Learning","publication_year":2023,"publication_date":"2023-09-06","ids":{"openalex":"https://openalex.org/W4388271613","doi":"https://doi.org/10.1109/wisee58383.2023.10289283"},"language":"en","primary_location":{"id":"doi:10.1109/wisee58383.2023.10289283","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wisee58383.2023.10289283","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Wireless for Space and Extreme Environments (WiSEE)","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/A5058064050","display_name":"Himan Namdari","orcid":"https://orcid.org/0000-0002-2379-2472"},"institutions":[{"id":"https://openalex.org/I107077323","display_name":"Worcester Polytechnic Institute","ror":"https://ror.org/05ejpqr48","country_code":"US","type":"education","lineage":["https://openalex.org/I107077323"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Himan Namdari","raw_affiliation_strings":["Worcester Polytechnic Institute (WPI),Worcester,MA,USA"],"affiliations":[{"raw_affiliation_string":"Worcester Polytechnic Institute (WPI),Worcester,MA,USA","institution_ids":["https://openalex.org/I107077323"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102945560","display_name":"Majid Moradikia","orcid":"https://orcid.org/0000-0002-5357-0328"},"institutions":[{"id":"https://openalex.org/I107077323","display_name":"Worcester Polytechnic Institute","ror":"https://ror.org/05ejpqr48","country_code":"US","type":"education","lineage":["https://openalex.org/I107077323"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Majid Moradikia","raw_affiliation_strings":["Worcester Polytechnic Institute (WPI),Worcester,MA,USA"],"affiliations":[{"raw_affiliation_string":"Worcester Polytechnic Institute (WPI),Worcester,MA,USA","institution_ids":["https://openalex.org/I107077323"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069493547","display_name":"Douglas T. Petkie","orcid":null},"institutions":[{"id":"https://openalex.org/I107077323","display_name":"Worcester Polytechnic Institute","ror":"https://ror.org/05ejpqr48","country_code":"US","type":"education","lineage":["https://openalex.org/I107077323"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Douglas Todd Petkie","raw_affiliation_strings":["Worcester Polytechnic Institute (WPI),Worcester,MA,USA"],"affiliations":[{"raw_affiliation_string":"Worcester Polytechnic Institute (WPI),Worcester,MA,USA","institution_ids":["https://openalex.org/I107077323"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111068105","display_name":"Radwin Askari","orcid":null},"institutions":[{"id":"https://openalex.org/I11957088","display_name":"Michigan Technological University","ror":"https://ror.org/0036rpn28","country_code":"US","type":"education","lineage":["https://openalex.org/I11957088"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Radwin Askari","raw_affiliation_strings":["Michigan Tech Research Institute (MTRI),Ann Arbor,Michigan,USA"],"affiliations":[{"raw_affiliation_string":"Michigan Tech Research Institute (MTRI),Ann Arbor,Michigan,USA","institution_ids":["https://openalex.org/I11957088"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056495275","display_name":"Seyed A. Zekavat","orcid":"https://orcid.org/0000-0002-1238-3147"},"institutions":[{"id":"https://openalex.org/I107077323","display_name":"Worcester Polytechnic Institute","ror":"https://ror.org/05ejpqr48","country_code":"US","type":"education","lineage":["https://openalex.org/I107077323"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Seyed Zekavat","raw_affiliation_strings":["Worcester Polytechnic Institute (WPI),Worcester,MA,USA"],"affiliations":[{"raw_affiliation_string":"Worcester Polytechnic Institute (WPI),Worcester,MA,USA","institution_ids":["https://openalex.org/I107077323"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5058064050"],"corresponding_institution_ids":["https://openalex.org/I107077323"],"apc_list":null,"apc_paid":null,"fwci":1.4207,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.8055771,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"127","last_page":"132"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11609","display_name":"Geophysical Methods and Applications","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/T11609","display_name":"Geophysical Methods and Applications","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/T10271","display_name":"Seismic Imaging and Inversion Techniques","score":0.9919999837875366,"subfield":{"id":"https://openalex.org/subfields/1908","display_name":"Geophysics"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11698","display_name":"Underwater Acoustics Research","score":0.9918000102043152,"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/descriptive-statistics","display_name":"Descriptive statistics","score":0.777458131313324},{"id":"https://openalex.org/keywords/ground-penetrating-radar","display_name":"Ground-penetrating radar","score":0.7328671216964722},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.647671103477478},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.4849846363067627},{"id":"https://openalex.org/keywords/signal-processing","display_name":"Signal processing","score":0.45474645495414734},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4515700042247772},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.44813308119773865},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3752303719520569},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.18166112899780273},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12954899668693542},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.11780008673667908}],"concepts":[{"id":"https://openalex.org/C39896193","wikidata":"https://www.wikidata.org/wiki/Q380344","display_name":"Descriptive statistics","level":2,"score":0.777458131313324},{"id":"https://openalex.org/C71813955","wikidata":"https://www.wikidata.org/wiki/Q503560","display_name":"Ground-penetrating radar","level":3,"score":0.7328671216964722},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.647671103477478},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.4849846363067627},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.45474645495414734},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4515700042247772},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.44813308119773865},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3752303719520569},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.18166112899780273},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12954899668693542},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.11780008673667908},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/wisee58383.2023.10289283","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wisee58383.2023.10289283","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Wireless for Space and Extreme Environments (WiSEE)","raw_type":"proceedings-article"},{"id":"pmh:oai:digitalcommons.mtu.edu:michigantech-p2-1807","is_oa":false,"landing_page_url":"https://digitalcommons.mtu.edu/michigantech-p2/810","pdf_url":null,"source":{"id":"https://openalex.org/S4377196391","display_name":"Digital Commons - Michigan Tech (Michigan Technological University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I11957088","host_organization_name":"Michigan Technological University","host_organization_lineage":["https://openalex.org/I11957088"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Michigan Tech Publications","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306114","display_name":"U.S. Department of Agriculture","ror":"https://ror.org/01na82s61"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W596928057","https://openalex.org/W1631122009","https://openalex.org/W1841379813","https://openalex.org/W1964718823","https://openalex.org/W1998742682","https://openalex.org/W2077691030","https://openalex.org/W2083638840","https://openalex.org/W2088219051","https://openalex.org/W2095649738","https://openalex.org/W2121479138","https://openalex.org/W2123617671","https://openalex.org/W2219187051","https://openalex.org/W2518909974","https://openalex.org/W2765285088","https://openalex.org/W2897109612","https://openalex.org/W2901222225","https://openalex.org/W2954995447","https://openalex.org/W3006929973","https://openalex.org/W3020890831","https://openalex.org/W3030068901","https://openalex.org/W3114741183","https://openalex.org/W3161144194","https://openalex.org/W3209631114","https://openalex.org/W4200071706","https://openalex.org/W6688552948"],"related_works":["https://openalex.org/W4315471419","https://openalex.org/W2946057701","https://openalex.org/W4386931161","https://openalex.org/W2374146176","https://openalex.org/W2065249286","https://openalex.org/W2366839571","https://openalex.org/W4223960160","https://openalex.org/W2027762722","https://openalex.org/W2356754952","https://openalex.org/W3090858966"],"abstract_inverted_index":{"This":[0,105],"paper":[1,36,75],"presents":[2],"a":[3,83,108],"comprehensive":[4],"analysis":[5,40],"of":[6,14,28,111,148],"how":[7,50],"various":[8],"soil":[9,45,68,172],"characteristics":[10,22],"impact":[11],"the":[12,54,64,98,103,114,132,136,146],"features":[13,62,101,112],"Ground":[15],"Penetrating":[16],"Radar":[17],"(GPR)":[18],"received":[19,65],"signals.":[20,56],"These":[21],"include":[23],"dielectric":[24],"properties,":[25],"thickness,":[26],"number":[27],"layers,":[29],"radar":[30],"configuration,":[31],"and":[32,88,153,174],"surface":[33],"roughness.":[34],"The":[35,57,74,125],"conducts":[37],"an":[38,119],"exhaustive":[39],"using":[41,121,158],"gprMax,":[42],"simulating":[43],"diverse":[44],"medium":[46],"scenarios":[47],"to":[48,96,169],"demonstrate":[49],"these":[51,159],"parameters":[52],"influence":[53],"GPR-received":[55],"proposed":[58,126],"methodology":[59],"extracts":[60,107],"critical":[61,133],"from":[63,113],"signal":[66],"for":[67],"characterization":[69],"through":[70],"descriptive":[71],"statistical":[72],"analysis.":[73,176],"then":[76],"deploys":[77],"Machine":[78],"Learning":[79,155],"(ML)":[80],"techniques,":[81],"specifically":[82],"Random":[84],"Forest":[85],"(RF)":[86],"model":[87],"Gini":[89],"Mean":[90],"Decrease":[91],"Impurity":[92],"(MDI)":[93],"as":[94],"measures,":[95],"identify":[97],"most":[99],"influential":[100],"in":[102,135],"dataset.":[104],"process":[106],"concise":[109],"set":[110],"time":[115],"domain,":[116],"followed":[117],"by":[118],"expansion":[120],"frequency":[122],"domain":[123],"features.":[124],"approach":[127],"not":[128],"only":[129],"effectively":[130],"captures":[131],"information":[134],"high-dimensional":[137],"GPR":[138],"data":[139],"but":[140],"also":[141],"reduces":[142],"its":[143],"dimensionality,":[144],"ensuring":[145],"preservation":[147],"essential":[149],"information.":[150],"Training":[151],"ML":[152],"Deep":[154],"(DL)":[156],"models":[157],"significant":[160],"features,":[161],"rather":[162],"than":[163],"complex":[164],"raw":[165],"A-scan":[166],"data,":[167],"leads":[168],"more":[170],"accurate":[171],"moisture":[173],"subsurface":[175]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":6}],"updated_date":"2026-03-10T16:38:18.471706","created_date":"2025-10-10T00:00:00"}