{"id":"https://openalex.org/W7155384524","doi":"https://doi.org/10.1504/ijsnet.2026.153116","title":"A lightweight depthwise separable convolution network for event detection in phase-sensitive optical time domain reflectometry","display_name":"A lightweight depthwise separable convolution network for event detection in phase-sensitive optical time domain reflectometry","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7155384524","doi":"https://doi.org/10.1504/ijsnet.2026.153116"},"language":"en","primary_location":{"id":"doi:10.1504/ijsnet.2026.153116","is_oa":false,"landing_page_url":"https://doi.org/10.1504/ijsnet.2026.153116","pdf_url":null,"source":{"id":"https://openalex.org/S31905469","display_name":"International Journal of Sensor Networks","issn_l":"1748-1279","issn":["1748-1279","1748-1287"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310317825","host_organization_name":"Inderscience Publishers","host_organization_lineage":["https://openalex.org/P4310317825"],"host_organization_lineage_names":["Inderscience Publishers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Sensor Networks","raw_type":"journal-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/A5134459444","display_name":"Jing Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I184843921","display_name":"Hebei University of Technology","ror":"https://ror.org/018hded08","country_code":"CN","type":"education","lineage":["https://openalex.org/I184843921"]},{"id":"https://openalex.org/I4403386774","display_name":"Hebei Petroleum University of Technology","ror":"https://ror.org/02t67p115","country_code":null,"type":"education","lineage":["https://openalex.org/I4403386774"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jing Wang","raw_affiliation_strings":["Hebei Petroleum University of Technology, Hebei, 067000, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hebei Petroleum University of Technology, Hebei, 067000, China","institution_ids":["https://openalex.org/I184843921","https://openalex.org/I4403386774"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5134459444"],"corresponding_institution_ids":["https://openalex.org/I184843921","https://openalex.org/I4403386774"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.42309034,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"50","issue":"4","first_page":"285","last_page":"295"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10205","display_name":"Advanced Fiber Optic Sensors","score":0.935699999332428,"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/T10205","display_name":"Advanced Fiber Optic Sensors","score":0.935699999332428,"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/T10767","display_name":"Advanced Photonic Communication Systems","score":0.009499999694526196,"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/T12153","display_name":"Advanced Optical Sensing Technologies","score":0.006500000134110451,"subfield":{"id":"https://openalex.org/subfields/3105","display_name":"Instrumentation"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.7336999773979187},{"id":"https://openalex.org/keywords/reflectometry","display_name":"Reflectometry","score":0.7235000133514404},{"id":"https://openalex.org/keywords/time-domain","display_name":"Time domain","score":0.6258000135421753},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.5680999755859375},{"id":"https://openalex.org/keywords/separable-space","display_name":"Separable space","score":0.5651000142097473},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5004000067710876},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.4634999930858612},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.4431999921798706},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4275999963283539}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7631000280380249},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.7336999773979187},{"id":"https://openalex.org/C2778925768","wikidata":"https://www.wikidata.org/wiki/Q3454718","display_name":"Reflectometry","level":3,"score":0.7235000133514404},{"id":"https://openalex.org/C103824480","wikidata":"https://www.wikidata.org/wiki/Q185889","display_name":"Time domain","level":2,"score":0.6258000135421753},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.5680999755859375},{"id":"https://openalex.org/C70710897","wikidata":"https://www.wikidata.org/wiki/Q680081","display_name":"Separable space","level":2,"score":0.5651000142097473},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5004000067710876},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.47769999504089355},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.4634999930858612},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.4431999921798706},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4275999963283539},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.4049000144004822},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37369999289512634},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.3718999922275543},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.3598000109195709},{"id":"https://openalex.org/C46244369","wikidata":"https://www.wikidata.org/wiki/Q193709","display_name":"Rayleigh scattering","level":2,"score":0.3522999882698059},{"id":"https://openalex.org/C137270730","wikidata":"https://www.wikidata.org/wiki/Q120811","display_name":"Detection theory","level":3,"score":0.3310999870300293},{"id":"https://openalex.org/C187327482","wikidata":"https://www.wikidata.org/wiki/Q580729","display_name":"Optical time-domain reflectometer","level":5,"score":0.31949999928474426},{"id":"https://openalex.org/C191486275","wikidata":"https://www.wikidata.org/wiki/Q210028","display_name":"Scattering","level":2,"score":0.3109999895095825},{"id":"https://openalex.org/C72279823","wikidata":"https://www.wikidata.org/wiki/Q1139726","display_name":"Impulse response","level":2,"score":0.31029999256134033},{"id":"https://openalex.org/C13944312","wikidata":"https://www.wikidata.org/wiki/Q7512748","display_name":"Signal-to-noise ratio (imaging)","level":2,"score":0.30790001153945923},{"id":"https://openalex.org/C194232370","wikidata":"https://www.wikidata.org/wiki/Q162","display_name":"Optical fiber","level":2,"score":0.2989000082015991},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.2809999883174896},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.2687000036239624},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2678000032901764},{"id":"https://openalex.org/C24590314","wikidata":"https://www.wikidata.org/wiki/Q336038","display_name":"Wireless sensor network","level":2,"score":0.2621000111103058}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1504/ijsnet.2026.153116","is_oa":false,"landing_page_url":"https://doi.org/10.1504/ijsnet.2026.153116","pdf_url":null,"source":{"id":"https://openalex.org/S31905469","display_name":"International Journal of Sensor Networks","issn_l":"1748-1279","issn":["1748-1279","1748-1287"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310317825","host_organization_name":"Inderscience Publishers","host_organization_lineage":["https://openalex.org/P4310317825"],"host_organization_lineage_names":["Inderscience Publishers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Sensor Networks","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Phase-sensitive":[0],"optical":[1],"time":[2],"domain":[3],"reflectometer":[4],"(\u03a6-OTDR)":[5],"leverages":[6],"Rayleigh":[7],"scattering":[8],"for":[9,49],"long-distance,":[10],"real-time":[11],"monitoring,":[12],"continuously":[13],"sensing":[14],"along":[15],"the":[16,119],"fibre":[17],"to":[18,38,97],"detect":[19],"events":[20],"like":[21],"environmental":[22],"disturbances.":[23],"However,":[24],"existing":[25],"detection":[26,47,131],"methods":[27],"are":[28],"often":[29],"compromised":[30],"by":[31],"dynamic":[32],"noise":[33],"and":[34,54,84,107,115,126,133],"signal":[35,79],"drift,":[36],"leading":[37],"reduced":[39],"accuracy.":[40],"This":[41],"study":[42],"introduces":[43],"Lite-PhiOTDR,":[44],"a":[45,52,68,86,91,116],"streamlined":[46],"network":[48,71],"\u03a6-OTDR.":[50],"Initially,":[51],"trend-separation":[53],"denoising":[55],"module":[56],"using":[57],"one-dimensional":[58],"convolution":[59,63,114],"is":[60,81,95],"developed.":[61],"Large-kernel":[62],"removes":[64],"low-frequency":[65],"trends,":[66],"while":[67],"small":[69],"convolutional":[70],"suppresses":[72],"high-frequency":[73],"noise,":[74],"resulting":[75],"in":[76],"an":[77],"input":[78],"that":[80],"zero-mean,":[82],"smooth,":[83],"possesses":[85],"high":[87],"signal-to-noise":[88],"ratio.":[89],"Additionally,":[90],"lightweight":[92,117],"feature-extraction":[93],"structure":[94],"implemented":[96],"characterise":[98],"various":[99],"time-series":[100],"patterns,":[101],"including":[102],"short-term":[103],"mutations,":[104],"periodic":[105],"disturbances,":[106],"slow":[108],"drifts.":[109],"By":[110],"employing":[111],"depthwise":[112],"separable":[113],"design,":[118],"proposed":[120],"method":[121],"significantly":[122],"reduces":[123],"parameter":[124],"count":[125],"computational":[127],"complexity,":[128],"thereby":[129],"enhancing":[130],"accuracy":[132],"enabling":[134],"feasible":[135],"embedded":[136],"deployment.":[137]},"counts_by_year":[],"updated_date":"2026-04-25T06:01:18.069262","created_date":"2026-04-24T00:00:00"}