{"id":"https://openalex.org/W4408354064","doi":"https://doi.org/10.1109/icassp49660.2025.10889295","title":"Neuromorphic Unlimited Sampling for High-Dynamic-Range Video Acquisition","display_name":"Neuromorphic Unlimited Sampling for High-Dynamic-Range Video Acquisition","publication_year":2025,"publication_date":"2025-03-12","ids":{"openalex":"https://openalex.org/W4408354064","doi":"https://doi.org/10.1109/icassp49660.2025.10889295"},"language":"en","primary_location":{"id":"doi:10.1109/icassp49660.2025.10889295","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49660.2025.10889295","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5066794666","display_name":"Abijith Jagannath Kamath","orcid":"https://orcid.org/0000-0002-1608-5550"},"institutions":[{"id":"https://openalex.org/I59270414","display_name":"Indian Institute of Science Bangalore","ror":"https://ror.org/04dese585","country_code":"IN","type":"education","lineage":["https://openalex.org/I59270414"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Abijith Jagannath Kamath","raw_affiliation_strings":["Indian Institute of Science,Department of Electrical Engineering,Bengaluru,India,560 012"],"affiliations":[{"raw_affiliation_string":"Indian Institute of Science,Department of Electrical Engineering,Bengaluru,India,560 012","institution_ids":["https://openalex.org/I59270414"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005652970","display_name":"Chandra Sekhar Seelamantula","orcid":"https://orcid.org/0000-0001-9049-1912"},"institutions":[{"id":"https://openalex.org/I59270414","display_name":"Indian Institute of Science Bangalore","ror":"https://ror.org/04dese585","country_code":"IN","type":"education","lineage":["https://openalex.org/I59270414"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Chandra Sekhar Seelamantula","raw_affiliation_strings":["Indian Institute of Science,Department of Electrical Engineering,Bengaluru,India,560 012"],"affiliations":[{"raw_affiliation_string":"Indian Institute of Science,Department of Electrical Engineering,Bengaluru,India,560 012","institution_ids":["https://openalex.org/I59270414"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5066794666"],"corresponding_institution_ids":["https://openalex.org/I59270414"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.06133502,"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":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T13114","display_name":"Image Processing Techniques and Applications","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9948999881744385,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.8479459881782532},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7019461393356323},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.6161622405052185},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.4967082142829895},{"id":"https://openalex.org/keywords/dynamic-range","display_name":"Dynamic range","score":0.48090314865112305},{"id":"https://openalex.org/keywords/high-dynamic-range","display_name":"High dynamic range","score":0.45460695028305054},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4343246817588806},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3026728332042694},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.19950240850448608},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09232917428016663}],"concepts":[{"id":"https://openalex.org/C151927369","wikidata":"https://www.wikidata.org/wiki/Q1981312","display_name":"Neuromorphic engineering","level":3,"score":0.8479459881782532},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7019461393356323},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.6161622405052185},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.4967082142829895},{"id":"https://openalex.org/C87133666","wikidata":"https://www.wikidata.org/wiki/Q1161699","display_name":"Dynamic range","level":2,"score":0.48090314865112305},{"id":"https://openalex.org/C2780056265","wikidata":"https://www.wikidata.org/wiki/Q106239881","display_name":"High dynamic range","level":3,"score":0.45460695028305054},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4343246817588806},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3026728332042694},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.19950240850448608},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09232917428016663},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp49660.2025.10889295","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49660.2025.10889295","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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":48,"referenced_works":["https://openalex.org/W206948248","https://openalex.org/W2003311156","https://openalex.org/W2003410902","https://openalex.org/W2011657506","https://openalex.org/W2016574277","https://openalex.org/W2067321816","https://openalex.org/W2157687163","https://openalex.org/W2158537680","https://openalex.org/W2166500461","https://openalex.org/W2222833901","https://openalex.org/W2503902107","https://openalex.org/W2567239141","https://openalex.org/W2737827235","https://openalex.org/W2798190016","https://openalex.org/W2802183119","https://openalex.org/W2940392246","https://openalex.org/W2944333051","https://openalex.org/W2963093735","https://openalex.org/W3089504710","https://openalex.org/W3091174658","https://openalex.org/W3092309493","https://openalex.org/W3114772133","https://openalex.org/W3158435804","https://openalex.org/W3199048522","https://openalex.org/W3217376978","https://openalex.org/W4214872825","https://openalex.org/W4221017392","https://openalex.org/W4224919088","https://openalex.org/W4225323109","https://openalex.org/W4225410762","https://openalex.org/W4289655206","https://openalex.org/W4304698033","https://openalex.org/W4308234049","https://openalex.org/W4372190820","https://openalex.org/W4372267398","https://openalex.org/W4372346848","https://openalex.org/W4372347437","https://openalex.org/W4375868808","https://openalex.org/W4377235326","https://openalex.org/W4382062669","https://openalex.org/W4385834398","https://openalex.org/W4388235536","https://openalex.org/W4392903490","https://openalex.org/W4394805000","https://openalex.org/W6852894234","https://openalex.org/W6854568951","https://openalex.org/W6857167688","https://openalex.org/W6925344326"],"related_works":["https://openalex.org/W2884377208","https://openalex.org/W2525745698","https://openalex.org/W2152030049","https://openalex.org/W2564543331","https://openalex.org/W2410869481","https://openalex.org/W2466475649","https://openalex.org/W1967888462","https://openalex.org/W2371190228","https://openalex.org/W4224781608","https://openalex.org/W4405363203"],"abstract_inverted_index":{"The":[0,28,207],"unlimited":[1,31,66,98,201],"sampling":[2,32,67,99,144,181],"framework":[3],"(USF)":[4],"is":[5,33,120,183,226],"a":[6,159,230],"computational":[7,57],"sensing":[8],"paradigm":[9],"that":[10,71,117,146,165],"addresses":[11],"the":[12,45,48,52,81,87,106,127,131,136,150,153,156,175,180,186,220,223],"practical":[13,41],"bottleneck":[14],"pertaining":[15],"to":[16,34,108],"finite":[17],"dynamic":[18,49],"range":[19,37,50],"and":[20,83,115,203],"quantization":[21],"resolution":[22],"of":[23,30,51,74,86,130,149,152,162,192,199,219],"standard":[24],"analog-to-digital":[25],"converters":[26],"(ADCs).":[27],"essence":[29],"capture":[35],"high-dynamic":[36],"(HDR)":[38],"signals":[39,76,110,164,195],"using":[40,68,90,196,233],"sensors":[42],"by":[43,55],"folding":[44,88],"signal":[46,82,133],"within":[47],"sensor,":[53],"followed":[54],"leveraging":[56],"techniques":[58],"for":[59,100,140,211],"reconstruction.":[60,206],"In":[61],"this":[62],"paper,":[63],"we":[64,95,104,189],"consider":[65,190],"neuromorphic/event-driven":[69],"encoders":[70],"enable":[72],"acquisition":[73,191,214],"HDR":[75,193,212,224],"as":[77,179],"they":[78],"simultaneously":[79],"fold":[80],"keep":[84],"track":[85],"instants":[89],"events.":[91,235],"At":[92],"ICASSP":[93],"2024,":[94],"proposed":[96,157,208],"neuromorphic":[97,200,209],"bandlimited":[101],"signals.":[102],"Herein,":[103],"extend":[105],"theory":[107],"include":[109],"in":[111,229],"principal":[112],"shift-invariant":[113,169],"spaces,":[114],"show":[116],"perfect":[118,141],"reconstruction":[119,142,176],"possible":[121],"without":[122],"an":[123,197],"oversampling":[124,218],"requirement":[125],"on":[126],"ADC.":[128,154],"Samples":[129],"folded":[132],"along":[134],"with":[135,143,174],"events":[137],"are":[138,147],"sufficient":[139],"rates":[145],"independent":[148],"dynamic-range":[151],"Within":[155],"technique,":[158],"large":[160],"class":[161],"smooth":[163],"lie":[166],"outside":[167],"any":[168],"space":[170],"can":[171],"be":[172],"accommodated":[173],"error":[177],"decreasing":[178],"interval":[182],"reduced.":[184],"On":[185],"experimental":[187],"front,":[188],"video":[194,213],"array":[198],"samplers":[202],"demonstrate":[204],"accurate":[205],"design":[210],"does":[215],"not":[216],"require":[217],"ADC":[221],"because":[222],"information":[225],"indirectly":[227],"captured":[228],"compressed":[231],"form":[232],"binary":[234]},"counts_by_year":[],"updated_date":"2025-12-28T23:10:05.387466","created_date":"2025-10-10T00:00:00"}
