{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T20:03:59Z","timestamp":1789761839313,"version":"4.0.1"},"reference-count":17,"publisher":"Optica Publishing Group","issue":"19","license":[{"start":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T00:00:00Z","timestamp":1789689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/doi.org\/10.1364\/OA_License_v2#VOR"},{"start":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T00:00:00Z","timestamp":1789689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/opg.optica.org\/policies\/opg-tdm-policy.json"}],"funder":[{"name":"National Natural Science Foundation of China","award":["12521002"],"award-info":[{"award-number":["12521002"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"name":"China Postdoctoral Science Foundation","award":["25Z020703769"],"award-info":[{"award-number":["25Z020703769"]}],"id":[{"id":"https:\/\/ror.org\/0426zh255","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":["opg.optica.org"],"crossmark-restriction":false},"short-container-title":["Opt. Lett."],"published-print":{"date-parts":[[2026,10,1]]},"abstract":"<jats:p>\n                    Event-based particle image velocimetry (PIV) offers a promising approach for high-speed flow diagnostics. However, conventional processing pipelines, constrained by fixed event accumulation times and uniform inter-frame intervals, do not fully leverage the asynchronous nature of event data, compromising the fidelity of reconstructed velocity fields. This Letter presents a time-adaptive event-based PIV framework (TA-EB-PIV) whose core mechanism dynamically adjusts event accumulation time and sampling interval based on local velocity estimates. This approach overcomes the constraints of fixed temporal parameters, effectively suppresses near-wall reflection interference, and improves particle image density. Experiments on flow past a NACA0012 airfoil at a 12\u00b0 angle of attack (Re \u2248 6800) demonstrate the effectiveness of the proposed method. Compared to conventional fixed-interval PIV, TA-EB-PIV reduces the RMS divergence by 26.1%, standard deviation by 26.3%, and mean absolute divergence by 31.0%; outlier vectors by 24.8%; random error \u03b4(U)\/U\n                    <jats:sub>0<\/jats:sub>\n                    from 1.10% to 0.46%; and Reynolds shear stress uncertainty by nearly 30%; free-stream fluctuating velocity overestimation is reduced by 30.4% with near-wall noise markedly suppressed. POD analysis further reveals a more prominent spectral peak and an energy decay consistent with the \u22125\/3 Kolmogorov scaling law. By decoupling the correlation window from fixed time steps, this method achieves self-adaptive temporal parameters, establishing a novel, to the best of our knowledge, framework for high-fidelity velocity reconstruction in complex flows.\n                  <\/jats:p>","DOI":"10.1364\/ol.608317","type":"journal-article","created":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T13:00:07Z","timestamp":1787576407000},"page":"5329","update-policy":"https:\/\/doi.org\/10.1364\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Time-adaptive particle image velocimetry framework via an event camera under continuous illumination"],"prefix":"10.1364","volume":"51","author":[{"given":"Xuyi","family":"Liu","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/0220qvk04","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Zeng","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/0220qvk04","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-0327-8871","authenticated-orcid":true,"given":"Jiajun","family":"Cao","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/0220qvk04","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sen","family":"Li","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/0220qvk04","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Cai","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/0220qvk04","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1480-921X","authenticated-orcid":true,"given":"Yingzheng","family":"Liu","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/0220qvk04","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"285","published-online":{"date-parts":[[2026,9,18]]},"reference":[{"key":"ol-51-19-5329-R1","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1146\/annurev-fluid-120710-101204","type":"journal-article","volume":"45","author":"Westerweel","year":"2013","journal-title":"Annu. 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All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.","name":"copyright","label":"Copyright"}]}}