{"id":"https://openalex.org/W3049596131","doi":"https://doi.org/10.1109/tsp49548.2020.9163583","title":"FPGA Based Accelerator for Buried Objects Identification","display_name":"FPGA Based Accelerator for Buried Objects Identification","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3049596131","doi":"https://doi.org/10.1109/tsp49548.2020.9163583","mag":"3049596131"},"language":"en","primary_location":{"id":"doi:10.1109/tsp49548.2020.9163583","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp49548.2020.9163583","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 43rd International Conference on Telecommunications and Signal Processing (TSP)","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/A5064888822","display_name":"Mostafa Elsaadouny","orcid":"https://orcid.org/0000-0001-9788-0072"},"institutions":[{"id":"https://openalex.org/I904495901","display_name":"Ruhr University Bochum","ror":"https://ror.org/04tsk2644","country_code":"DE","type":"education","lineage":["https://openalex.org/I904495901"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Mostafa Elsaadouny","raw_affiliation_strings":["Institute of Microwave Systems Ruhr University Bochum, Bochum, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Microwave Systems Ruhr University Bochum, Bochum, Germany","institution_ids":["https://openalex.org/I904495901"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068415634","display_name":"Jan Barowski","orcid":"https://orcid.org/0000-0002-0962-179X"},"institutions":[{"id":"https://openalex.org/I904495901","display_name":"Ruhr University Bochum","ror":"https://ror.org/04tsk2644","country_code":"DE","type":"education","lineage":["https://openalex.org/I904495901"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Jan Barowski","raw_affiliation_strings":["Institute of Microwave Systems Ruhr University Bochum, Bochum, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Microwave Systems Ruhr University Bochum, Bochum, Germany","institution_ids":["https://openalex.org/I904495901"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5046336938","display_name":"Ilona Rolfes","orcid":"https://orcid.org/0000-0001-8195-4786"},"institutions":[{"id":"https://openalex.org/I904495901","display_name":"Ruhr University Bochum","ror":"https://ror.org/04tsk2644","country_code":"DE","type":"education","lineage":["https://openalex.org/I904495901"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Ilona Rolfes","raw_affiliation_strings":["Institute of Microwave Systems Ruhr University Bochum, Bochum, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Microwave Systems Ruhr University Bochum, Bochum, Germany","institution_ids":["https://openalex.org/I904495901"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I904495901"],"apc_list":null,"apc_paid":null,"fwci":1.0955,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.71409043,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":"26","issue":null,"first_page":"559","last_page":"562"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11609","display_name":"Geophysical Methods and Applications","score":0.9969000220298767,"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":0.9969000220298767,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9966999888420105,"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/T12549","display_name":"Image and Object Detection Techniques","score":0.980400025844574,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/field-programmable-gate-array","display_name":"Field-programmable gate array","score":0.8202881813049316},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7764102220535278},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.635075032711029},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.596728503704071},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.553745448589325},{"id":"https://openalex.org/keywords/computer-architecture","display_name":"Computer architecture","score":0.5406098365783691},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48020991683006287},{"id":"https://openalex.org/keywords/coprocessor","display_name":"Coprocessor","score":0.46085092425346375},{"id":"https://openalex.org/keywords/hardware-acceleration","display_name":"Hardware acceleration","score":0.4222300946712494},{"id":"https://openalex.org/keywords/application-specific-integrated-circuit","display_name":"Application-specific integrated circuit","score":0.41873103380203247},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.41077911853790283},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.32338660955429077}],"concepts":[{"id":"https://openalex.org/C42935608","wikidata":"https://www.wikidata.org/wiki/Q190411","display_name":"Field-programmable gate array","level":2,"score":0.8202881813049316},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7764102220535278},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.635075032711029},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.596728503704071},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.553745448589325},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.5406098365783691},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48020991683006287},{"id":"https://openalex.org/C86111242","wikidata":"https://www.wikidata.org/wiki/Q859595","display_name":"Coprocessor","level":2,"score":0.46085092425346375},{"id":"https://openalex.org/C13164978","wikidata":"https://www.wikidata.org/wiki/Q600158","display_name":"Hardware acceleration","level":3,"score":0.4222300946712494},{"id":"https://openalex.org/C77390884","wikidata":"https://www.wikidata.org/wiki/Q217302","display_name":"Application-specific integrated circuit","level":2,"score":0.41873103380203247},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.41077911853790283},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.32338660955429077},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tsp49548.2020.9163583","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp49548.2020.9163583","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 43rd International Conference on Telecommunications and Signal Processing (TSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.44999998807907104}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W594283781","https://openalex.org/W2028166238","https://openalex.org/W2076794619","https://openalex.org/W2112796928","https://openalex.org/W2130306094","https://openalex.org/W2163605009","https://openalex.org/W2285660444","https://openalex.org/W2553754377","https://openalex.org/W2613168994","https://openalex.org/W2615700708","https://openalex.org/W2730434298","https://openalex.org/W2762246393","https://openalex.org/W2789876780","https://openalex.org/W2807156154","https://openalex.org/W2913669962","https://openalex.org/W2950656546","https://openalex.org/W3099952554","https://openalex.org/W6679349572","https://openalex.org/W6684191040"],"related_works":["https://openalex.org/W1485756991","https://openalex.org/W2376218453","https://openalex.org/W2984236338","https://openalex.org/W2609406488","https://openalex.org/W2057084000","https://openalex.org/W2518118925","https://openalex.org/W3159273459","https://openalex.org/W4394999","https://openalex.org/W2106011773","https://openalex.org/W4319952061"],"abstract_inverted_index":{"The":[0,136],"convolutional":[1],"neural":[2],"networks(ConvNets)":[3],"have":[4,22,46,59,115],"emerged":[5],"during":[6],"the":[7,16,61,64,75,79,84,96,108,111,120,124,147],"past":[8],"few":[9],"years":[10],"due":[11],"to":[12,49,106,118,132],"various":[13,51],"advancements":[14],"in":[15,24,34],"field":[17],"of":[18,63,78,110],"artificial":[19],"intelligence.":[20],"They":[21],"participated":[23],"different":[25,44],"applications":[26],"and":[27,30,68,123],"complex":[28],"problems":[29],"gained":[31],"significant":[32],"success":[33],"images":[35,92],"classification":[36,93,148],"tasks.":[37],"As":[38],"these":[39,55],"algorithms":[40],"require":[41],"intensive":[42],"operations,":[43],"researches":[45],"been":[47,128],"conducted":[48],"provide":[50],"hardware":[52],"accelerators":[53],"for":[54,73,90],"algorithms.":[56],"Different":[57],"studies":[58],"investigated":[60],"implementation":[62],"graphic":[65],"processing":[66,76,143],"units(GPUs)":[67],"application-specific":[69],"integrated":[70,129],"circuits":[71],"(ASICs)":[72],"decreasing":[74],"time":[77,142],"ConvNets.":[80],"In":[81],"this":[82],"research,":[83],"authors":[85],"present":[86],"a":[87],"fast":[88],"framework":[89,101],"GPR":[91,121],"based":[94],"on":[95,130],"Xilinx":[97],"PYNQ":[98,131],"platform.":[99],"This":[100],"offers":[102],"high-level":[103],"language":[104],"integration":[105],"facilitate":[107],"design":[109],"projects.":[112],"Various":[113],"experiments":[114],"taken":[116],"place":[117],"prepare":[119],"dataset":[122],"designed":[125],"ConvNet":[126],"has":[127],"perform":[133],"real-time":[134],"calculations.":[135],"obtained":[137],"results":[138],"indicate":[139],"an":[140],"outstanding":[141],"improvement":[144],"while":[145],"preserving":[146],"accuracy.":[149]},"counts_by_year":[{"year":2020,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
