{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/115570"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/115570","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Algorithm optimization for real-time volumetric particle tracking velocimetry under non-uniform illumination for airflow studies","abstract":"The student, Yu Zhao, submitted this Dissertation for approval on 2022-04-18 at 14:09.","abstract_html":"The student, Yu Zhao, submitted this Dissertation for approval on 2022-04-18 at 14:09.","abstract_has_math":false,"creators":["Zhao, Yu"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Agricultural & Biological Engr","degree_department":null,"school":null,"contributors":["Zhang, Yuanhui","Grift, Tony","Wang, Xinlei","Sun, Yigang"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-05","date_published":"2022-05","updated_at":"2026-07-22T22:24:54Z","subjects":["Building Environment","Particle Tracking","Real-time Visualization","Flow Pattern"],"languages":["en","eng"],"rights":["Copyright 2022 Yu Zhao"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/115570","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Zhang, Yuanhui","Grift, Tony","Wang, Xinlei","Sun, Yigang"]},{"key":"dc:creator","label":"Author","values":["Zhao, Yu"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-05","2022-04-22"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Agricultural & Biological Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Building Environment","Particle Tracking","Real-time Visualization","Flow Pattern"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2022 Yu Zhao"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/115570"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The student, Yu Zhao, submitted this Dissertation for approval on 2022-04-18 at 14:09.","This Dissertation was approved for publication on 2022-04-22 at 08:57.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-05-01","The student, Yu Zhao, accepted the attached license on 2022-04-18 at 14:02.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17757 on 2022-11-11 at 12:06:04","Volumetric Particle Tracking Velocimetry (VPTV) is a scalable real-time method for measuring 3D velocity fields. Based on the previous work in our group, grayscale cameras equipped with Field Programmable Gate Arrays (FPGA) were used to reduce the data flow rate and achieve real-time 3D measurement. However, the centroids of particle segments were used in FPGA cameras for particle detection, which could be hindered when particles were streak-shaped due to non-uniform illumination and long exposure time. To improve particle detection, a hybrid VPTV system was proposed containing both regular grayscale cameras and FPGA grayscale cameras. In this hybrid system, the exposure time was extended, and the “good features to track (GFTT)” approach was used to find the corners of particle streaks. The Graphics Processing Units (GPU) were used to process the grayscale images and perform the GFTT. This approach resulted in a 65% enhancement of particle detection ratio compared to the previous algorithm. With GPU acceleration, the processing time per frame was accelerated 15 times compared to the CPU version. In addition, a data-analysis sequence correspondence (DSC) algorithm was developed to improve the correspondence task in this hybrid system. The DSC constructed datasets to train a logistic regression classifier to identify the correct correspondence using only two cameras in actual measurements. The results showed that this approach could achieve an 82% correct prediction ratio and thus can improve the correspondence task in VPTV."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Algorithm optimization for real-time volumetric particle tracking velocimetry under non-uniform illumination for airflow studies"]}]}],"canonical_facts":{"dc:contributor":["Zhang, Yuanhui","Grift, Tony","Wang, Xinlei","Sun, Yigang"],"dc:creator":["Zhao, Yu"],"dc:date":["2022-05","2022-04-22"],"dc:description":["The student, Yu Zhao, submitted this Dissertation for approval on 2022-04-18 at 14:09.","This Dissertation was approved for publication on 2022-04-22 at 08:57.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-05-01","The student, Yu Zhao, accepted the attached license on 2022-04-18 at 14:02.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17757 on 2022-11-11 at 12:06:04","Volumetric Particle Tracking Velocimetry (VPTV) is a scalable real-time method for measuring 3D velocity fields. Based on the previous work in our group, grayscale cameras equipped with Field Programmable Gate Arrays (FPGA) were used to reduce the data flow rate and achieve real-time 3D measurement. However, the centroids of particle segments were used in FPGA cameras for particle detection, which could be hindered when particles were streak-shaped due to non-uniform illumination and long exposure time. To improve particle detection, a hybrid VPTV system was proposed containing both regular grayscale cameras and FPGA grayscale cameras. In this hybrid system, the exposure time was extended, and the “good features to track (GFTT)” approach was used to find the corners of particle streaks. The Graphics Processing Units (GPU) were used to process the grayscale images and perform the GFTT. This approach resulted in a 65% enhancement of particle detection ratio compared to the previous algorithm. With GPU acceleration, the processing time per frame was accelerated 15 times compared to the CPU version. In addition, a data-analysis sequence correspondence (DSC) algorithm was developed to improve the correspondence task in this hybrid system. The DSC constructed datasets to train a logistic regression classifier to identify the correct correspondence using only two cameras in actual measurements. The results showed that this approach could achieve an 82% correct prediction ratio and thus can improve the correspondence task in VPTV."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/115570"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Yu Zhao"],"dc:subject":["Building Environment","Particle Tracking","Real-time Visualization","Flow Pattern"],"dc:title":["Algorithm optimization for real-time volumetric particle tracking velocimetry under non-uniform illumination for airflow studies"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Agricultural & Biological Engr"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:54Z"}