{"id":"https://openalex.org/W3206730932","doi":"https://doi.org/10.1109/igsc54211.2021.9651610","title":"An Adaptive Sampling and Edge Detection Approach for Encoding Static Images for Spiking Neural Networks","display_name":"An Adaptive Sampling and Edge Detection Approach for Encoding Static Images for Spiking Neural Networks","publication_year":2021,"publication_date":"2021-10-18","ids":{"openalex":"https://openalex.org/W3206730932","doi":"https://doi.org/10.1109/igsc54211.2021.9651610","mag":"3206730932"},"language":"en","primary_location":{"id":"doi:10.1109/igsc54211.2021.9651610","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igsc54211.2021.9651610","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 12th International Green and Sustainable Computing Conference (IGSC)","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/A5088525102","display_name":"Peyton Chandarana","orcid":"https://orcid.org/0000-0002-8945-8653"},"institutions":[{"id":"https://openalex.org/I155781252","display_name":"University of South Carolina","ror":"https://ror.org/02b6qw903","country_code":"US","type":"education","lineage":["https://openalex.org/I155781252"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Peyton Chandarana","raw_affiliation_strings":["University of South Carolina, Columbia, SC"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of South Carolina, Columbia, SC","institution_ids":["https://openalex.org/I155781252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041919496","display_name":"Junlin Ou","orcid":"https://orcid.org/0000-0003-2735-5840"},"institutions":[{"id":"https://openalex.org/I155781252","display_name":"University of South Carolina","ror":"https://ror.org/02b6qw903","country_code":"US","type":"education","lineage":["https://openalex.org/I155781252"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Junlin Ou","raw_affiliation_strings":["University of South Carolina, Columbia, SC"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of South Carolina, Columbia, SC","institution_ids":["https://openalex.org/I155781252"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5115597960","display_name":"Ramtin Zand","orcid":"https://orcid.org/0000-0002-1786-1152"},"institutions":[{"id":"https://openalex.org/I155781252","display_name":"University of South Carolina","ror":"https://ror.org/02b6qw903","country_code":"US","type":"education","lineage":["https://openalex.org/I155781252"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ramtin Zand","raw_affiliation_strings":["University of South Carolina, Columbia, SC"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of South Carolina, Columbia, SC","institution_ids":["https://openalex.org/I155781252"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I155781252"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T10502","display_name":"Advanced Memory and Neural Computing","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T10581","display_name":"Neural dynamics and brain function","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.9972000122070312,"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/computer-science","display_name":"Computer science","score":0.8022210597991943},{"id":"https://openalex.org/keywords/spiking-neural-network","display_name":"Spiking neural network","score":0.6676186919212341},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6586081385612488},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.6166044473648071},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5600761771202087},{"id":"https://openalex.org/keywords/spike","display_name":"Spike (software development)","score":0.5011749267578125},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.49136748909950256},{"id":"https://openalex.org/keywords/edge-detection","display_name":"Edge detection","score":0.480910062789917},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.44135648012161255},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4395908713340759},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.43902474641799927},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4288651645183563},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.4158291518688202},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.32643306255340576},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.24669688940048218},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.09449902176856995}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8022210597991943},{"id":"https://openalex.org/C11731999","wikidata":"https://www.wikidata.org/wiki/Q9067355","display_name":"Spiking neural network","level":3,"score":0.6676186919212341},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6586081385612488},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.6166044473648071},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5600761771202087},{"id":"https://openalex.org/C2781390188","wikidata":"https://www.wikidata.org/wiki/Q25203449","display_name":"Spike (software development)","level":2,"score":0.5011749267578125},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.49136748909950256},{"id":"https://openalex.org/C193536780","wikidata":"https://www.wikidata.org/wiki/Q1513153","display_name":"Edge detection","level":4,"score":0.480910062789917},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.44135648012161255},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4395908713340759},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.43902474641799927},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4288651645183563},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.4158291518688202},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.32643306255340576},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.24669688940048218},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.09449902176856995},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igsc54211.2021.9651610","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igsc54211.2021.9651610","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 12th International Green and Sustainable Computing Conference (IGSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.800000011920929}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1570411240","https://openalex.org/W1593079125","https://openalex.org/W2078028152","https://openalex.org/W2112796928","https://openalex.org/W2775079417","https://openalex.org/W2783525259","https://openalex.org/W2885060605","https://openalex.org/W2892077605","https://openalex.org/W2898323475","https://openalex.org/W2931400272","https://openalex.org/W2941300701","https://openalex.org/W2963760575","https://openalex.org/W2964338223","https://openalex.org/W3210232381","https://openalex.org/W4288403646","https://openalex.org/W6761044538","https://openalex.org/W6803376173"],"related_works":["https://openalex.org/W2542565870","https://openalex.org/W4306175885","https://openalex.org/W4312604567","https://openalex.org/W2165312143","https://openalex.org/W2977464668","https://openalex.org/W4297619707","https://openalex.org/W4251092571","https://openalex.org/W4386617144","https://openalex.org/W2583316550","https://openalex.org/W3110622310"],"abstract_inverted_index":{"Current":[0],"state-of-the-art":[1],"methods":[2],"of":[3,46,118,152,212],"image":[4,215],"classification":[5],"using":[6,100,142,230],"convolutional":[7],"neural":[8,36,48],"networks":[9,37,49],"are":[10,39,164,183],"often":[11],"constrained":[12],"by":[13,59],"both":[14],"latency":[15,55],"and":[16,56,103,129,139,163,180,201,206,210,223,232,239,254],"power":[17,57],"consumption.":[18],"This":[19],"places":[20],"a":[21,90],"limit":[22],"on":[23,124],"the":[24,43,125,132,154,158,170,191,208,213,221,251],"devices,":[25,29],"particularly":[26],"low-power":[27],"edge":[28,101,114,122,133],"that":[30,157],"can":[31,72],"employ":[32],"these":[33,54],"methods.":[34],"Spiking":[35],"(SNNs)":[38],"considered":[40],"to":[41,52,166,186,189,204,250],"be":[42,73,81,187],"third":[44],"generation":[45],"artificial":[47],"which":[50],"aim":[51],"address":[53],"constraints":[58],"taking":[60],"inspiration":[61],"from":[62,226],"biological":[63],"neuronal":[64],"communication":[65],"processes.":[66],"Before":[67],"data":[68],"such":[69,156,175],"as":[70,176],"images":[71,95,128,135],"input":[74],"into":[75,84,96,136,194],"an":[76,104,143],"SNN,":[77],"however,":[78],"they":[79],"must":[80],"first":[82,119],"encoded":[83],"spike":[85,98,195,227],"trains.":[86,196],"Herein,":[87],"we":[88],"propose":[89],"method":[91,108],"for":[92,109,260],"encoding":[93,173,216,235,261],"static":[94,127],"temporal":[97,234],"trains":[99,228],"detection":[102,115,123],"adaptive":[105,148,233],"signal":[106],"sampling":[107,153],"use":[110,198],"in":[111,169,242],"SNNs.":[112],"The":[113,147],"process":[116],"consists":[117,151],"performing":[120],"Canny":[121],"2D":[126],"then":[130,184],"converting":[131],"detected":[134],"two":[137],"X":[138],"Y":[140],"signals":[141,155,159,193,225],"image-to-signal":[144],"conversion":[145],"method.":[146],"signaling":[149],"approach":[150],"maintain":[160],"enough":[161],"detail":[162],"sensitive":[165],"abrupt":[167],"changes":[168],"signal.":[171],"Temporal":[172],"mechanisms":[174],"threshold-based":[177],"representation":[178],"(TBR)":[179],"step-forward":[181],"(SF)":[182],"able":[185],"used":[188,259],"convert":[190],"sampled":[192],"We":[197],"various":[199],"error":[200,247],"indicator":[202],"metrics":[203],"optimize":[205],"evaluate":[207],"efficiency":[209],"precision":[211],"proposed":[214],"approach.":[217],"Comparison":[218],"results":[219],"between":[220],"original":[222],"reconstructed":[224],"generated":[229],"edge-detection":[231],"mechanism":[236],"exhibit":[237],"<tex>$18\\times$</tex>":[238],"<tex>$7\\times$</tex>":[240],"reduction":[241],"average":[243],"root":[244],"mean":[245],"square":[246],"(RMSE)":[248],"compared":[249],"conventional":[252],"SF":[253],"TBR":[255],"encoding,":[256],"respectively,":[257],"while":[258],"MNIST":[262],"dataset.":[263]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
