{"id":"https://openalex.org/W7135087958","doi":"https://doi.org/10.48550/arxiv.2603.10537","title":"An Event-Driven E-Skin System with Dynamic Binary Scanning and real time SNN Classification","display_name":"An Event-Driven E-Skin System with Dynamic Binary Scanning and real time SNN Classification","publication_year":2026,"publication_date":"2026-03-11","ids":{"openalex":"https://openalex.org/W7135087958","doi":"https://doi.org/10.48550/arxiv.2603.10537"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.10537","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.10537","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.10537","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5128811922","display_name":"Gaishan Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Gaishan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122166240","display_name":"Zhengnan Fu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fu, Zhengnan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015503995","display_name":"A. K. Tripathi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tripathi, Anubhab","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128896329","display_name":"Junyi Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Junyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5002380437","display_name":"Arindam Basu","orcid":"https://orcid.org/0000-0003-1035-8770"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Basu, Arindam","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9434000253677368,"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":0.9434000253677368,"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/T10338","display_name":"Advanced Sensor and Energy Harvesting Materials","score":0.028599999845027924,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.008799999952316284,"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/neuromorphic-engineering","display_name":"Neuromorphic engineering","score":0.7926999926567078},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6237000226974487},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.604200005531311},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.4968999922275543},{"id":"https://openalex.org/keywords/data-acquisition","display_name":"Data acquisition","score":0.4823000133037567},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4666999876499176},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.45590001344680786},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.4415999948978424},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.414900004863739}],"concepts":[{"id":"https://openalex.org/C151927369","wikidata":"https://www.wikidata.org/wiki/Q1981312","display_name":"Neuromorphic engineering","level":3,"score":0.7926999926567078},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7398999929428101},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6237000226974487},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.604200005531311},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5712000131607056},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.4968999922275543},{"id":"https://openalex.org/C163985040","wikidata":"https://www.wikidata.org/wiki/Q1172399","display_name":"Data acquisition","level":2,"score":0.4823000133037567},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4666999876499176},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.45590001344680786},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.4415999948978424},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.4318000078201294},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.414900004863739},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4090000092983246},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3626999855041504},{"id":"https://openalex.org/C116409475","wikidata":"https://www.wikidata.org/wiki/Q1385056","display_name":"External Data Representation","level":2,"score":0.35679998993873596},{"id":"https://openalex.org/C53016008","wikidata":"https://www.wikidata.org/wiki/Q620167","display_name":"Front and back ends","level":2,"score":0.3416000008583069},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.33629998564720154},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.3174999952316284},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.3109000027179718},{"id":"https://openalex.org/C194739806","wikidata":"https://www.wikidata.org/wiki/Q66221","display_name":"Computer data storage","level":2,"score":0.3061000108718872},{"id":"https://openalex.org/C2779190172","wikidata":"https://www.wikidata.org/wiki/Q4913888","display_name":"Binary data","level":3,"score":0.2937999963760376},{"id":"https://openalex.org/C1232282","wikidata":"https://www.wikidata.org/wiki/Q1783551","display_name":"Data conversion","level":2,"score":0.2822999954223633},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.2711000144481659},{"id":"https://openalex.org/C25797200","wikidata":"https://www.wikidata.org/wiki/Q828137","display_name":"Compression ratio","level":3,"score":0.267300009727478},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.2583000063896179},{"id":"https://openalex.org/C180016635","wikidata":"https://www.wikidata.org/wiki/Q2712821","display_name":"Compression (physics)","level":2,"score":0.25450000166893005},{"id":"https://openalex.org/C63435697","wikidata":"https://www.wikidata.org/wiki/Q864135","display_name":"Binary code","level":3,"score":0.2506999969482422}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.10537","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.10537","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.10537","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.10537","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"This":[0,44,143],"paper":[1],"presents":[2],"a":[3,24,34,48,54,60,65,83,109,119,131,146],"novel":[4],"hardware":[5],"system":[6,20,117],"for":[7,125,161],"high-speed,":[8],"event-sparse":[9],"sampling-based":[10],"electronic":[11],"skin":[12],"(e-skin)that":[13],"integrates":[14],"sensing":[15,153],"and":[16,32,59,101,164],"neuromorphic":[17,133,155],"computing.":[18],"The":[19,75],"is":[21,80,141],"built":[22],"around":[23],"16x16":[25],"piezoresistive":[26],"tactile":[27,134],"array":[28],"with":[29],"front":[30],"end":[31],"introduces":[33],"event-based":[35],"binary":[36],"scan":[37,52],"search":[38],"strategy":[39,46],"to":[40,108,154],"classify":[41],"the":[42,71,99,104,116],"digits.":[43],"event-driven":[45,149],"achieves":[47],"12.8x":[49],"reduction":[50],"in":[51],"counts,":[53],"38.2x":[55],"data":[56,67,72,78],"compression":[57],"rate":[58],"28.4x":[61],"equivalent":[62],"dynamic":[63],"range,":[64],"99%":[66],"sparsity,":[68],"drastically":[69],"reducing":[70],"acquisition":[73],"overhead.":[74],"resulting":[76],"sparse":[77],"stream":[79],"processed":[81],"by":[82],"multi-layer":[84],"convolutional":[85],"spiking":[86],"neural":[87],"network":[88],"(Conv-SNN)":[89],"implemented":[90],"on":[91],"an":[92,158],"FPGA,":[93],"which":[94],"requires":[95],"only":[96],"65%":[97],"of":[98,103,123],"computation":[100],"15.6%":[102],"weight":[105],"storage":[106],"relative":[107],"CNN.":[110],"Despite":[111],"these":[112],"significant":[113],"efficiency":[114],"gains,":[115],"maintains":[118],"high":[120],"classification":[121],"accuracy":[122],"92.11%":[124],"real-time":[126],"handwritten":[127],"digit":[128],"recognition.":[129],"Furthermore,":[130],"real":[132],"dataset":[135],"using":[136],"Address":[137],"Event":[138],"Representation":[139],"(AER)":[140],"constructed.":[142],"work":[144],"demonstrates":[145],"fully":[147],"integrated,":[148],"pipeline":[150],"from":[151],"analog":[152],"classification,":[156],"offering":[157],"efficient":[159],"solution":[160],"robotic":[162],"perception":[163],"human-computer":[165],"interaction.":[166]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-03-13T00:00:00"}