The Fast Integrated Mobility Spectrometer (FIMS) has emerged as an innovative instrument in the aerosol science domain. It employs a spatially varying electric field to separate charged aerosol particles by their electrical mobilities. These separated particles are then enlarged through vapor condensation and imaged in real time by a high-speed CCD camera. FIMS achieves near 100% detection efficiency for particles ranging from 10 nm to 600 nm with a temporal resolution of one second. However, FIMS’ real-time capabilities are limited by an offline data analysis process. Deferring analysis until hours or days after measurement makes FIMS' capabilities less valuable for probing dynamic, rapidly changing environments. Our research aims to address this limitation by developing a real-time data analysis pipeline for FIMS, allowing for adaptive aerosol measuring, eliminating lengthy delays between data collection and analysis, and boosting FIMS' potential for aerosol research. The pipeline is written in C++, making it suitable for deployment even in low-power embedded systems. The design also allows for easy future upgrades like new data types or machine learning integrations. Benchmarks confirm its efficiency. All real-time components operate within established limits, yielding results that are consistent with traditional offline methods. The real-time capabilities of this pipeline significantly extend FIMS's utility in dynamic, rapidly changing environments.
","abstract_html":"<p>The Fast Integrated Mobility Spectrometer (FIMS) has emerged as an innovative instrument in the aerosol science domain. It employs a spatially varying electric field to separate charged aerosol particles by their electrical mobilities. These separated particles are then enlarged through vapor condensation and imaged in real time by a high-speed CCD camera. FIMS achieves near 100% detection efficiency for particles ranging from 10 nm to 600 nm with a temporal resolution of one second. However, FIMS’ real-time capabilities are limited by an offline data analysis process. Deferring analysis until hours or days after measurement makes FIMS' capabilities less valuable for probing dynamic, rapidly changing environments. Our research aims to address this limitation by developing a real-time data analysis pipeline for FIMS, allowing for adaptive aerosol measuring, eliminating lengthy delays between data collection and analysis, and boosting FIMS' potential for aerosol research. The pipeline is written in C++, making it suitable for deployment even in low-power embedded systems. The design also allows for easy future upgrades like new data types or machine learning integrations. Benchmarks confirm its efficiency. All real-time components operate within established limits, yielding results that are consistent with traditional offline methods. The real-time capabilities of this pipeline significantly extend FIMS's utility in dynamic, rapidly changing environments.</p>","abstract_has_math":false,"creators":["Wang, Daisy"],"institution":null,"degree_name":"Master of Science (MS)","degree_level":"Thesis","degree_discipline":"Computer Science & Engineering","degree_department":null,"school":null,"contributors":["Jeremy Buhler","Jeremy Buhler Christopher Gill Jian Wang"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-12-01T08:00:00Z","date_published":"2023-12-01T08:00:00Z","updated_at":"2026-07-24T06:13:14Z","subjects":["real time system","aerosol size distribution","Fast Integrated Mobility Spectrometer","mobile real-time atmospheric sensing","Environmental Engineering","Other Computer Engineering"],"languages":["English (en)"],"rights":["I have not registered my thesis with the U.S. Copyright Office, and do not intend to."],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["https://openscholarship.wustl.edu/eng_etds/985"],"render_values":[{"text":"https://openscholarship.wustl.edu/eng_etds/985","href":"https://openscholarship.wustl.edu/eng_etds/985","code":true}]}]},"links":{"outbound_url":"https://doi.org/10.7936/cdb5-j514","outbound_label":"DOI","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Jeremy Buhler","Jeremy Buhler Christopher Gill Jian Wang"]},{"key":"dc:creator","label":"Author","values":["Wang, Daisy"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2023-12-19T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science & Engineering","McKelvey School of Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MS)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["real time system","aerosol size distribution","Fast Integrated Mobility Spectrometer","mobile real-time atmospheric sensing","Environmental Engineering","Other Computer Engineering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English (en)"]},{"key":"dc:rights","label":"Dc Rights","values":["I have not registered my thesis with the U.S. Copyright Office, and do not intend to."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://doi.org/10.7936/cdb5-j514","https://openscholarship.wustl.edu/eng_etds/985"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The Fast Integrated Mobility Spectrometer (FIMS) has emerged as an innovative instrument in the aerosol science domain. It employs a spatially varying electric field to separate charged aerosol particles by their electrical mobilities. These separated particles are then enlarged through vapor condensation and imaged in real time by a high-speed CCD camera. FIMS achieves near 100% detection efficiency for particles ranging from 10 nm to 600 nm with a temporal resolution of one second. However, FIMS’ real-time capabilities are limited by an offline data analysis process. Deferring analysis until hours or days after measurement makes FIMS' capabilities less valuable for probing dynamic, rapidly changing environments. Our research aims to address this limitation by developing a real-time data analysis pipeline for FIMS, allowing for adaptive aerosol measuring, eliminating lengthy delays between data collection and analysis, and boosting FIMS' potential for aerosol research. The pipeline is written in C++, making it suitable for deployment even in low-power embedded systems. The design also allows for easy future upgrades like new data types or machine learning integrations. Benchmarks confirm its efficiency. All real-time components operate within established limits, yielding results that are consistent with traditional offline methods. The real-time capabilities of this pipeline significantly extend FIMS's utility in dynamic, rapidly changing environments.
"]},{"key":"dc:title","label":"Title","values":["Real-time Analysis of Aerosol Size Distributions with the Fast Integrated Mobility Spectrometer (FIMS)"]}]}],"canonical_facts":{"dc:contributor":["Jeremy Buhler","Jeremy Buhler Christopher Gill Jian Wang"],"dc:creator":["Wang, Daisy"],"dc:date.available":["2023-12-19T08:00:00Z"],"dc:description.abstract":["The Fast Integrated Mobility Spectrometer (FIMS) has emerged as an innovative instrument in the aerosol science domain. It employs a spatially varying electric field to separate charged aerosol particles by their electrical mobilities. These separated particles are then enlarged through vapor condensation and imaged in real time by a high-speed CCD camera. FIMS achieves near 100% detection efficiency for particles ranging from 10 nm to 600 nm with a temporal resolution of one second. However, FIMS’ real-time capabilities are limited by an offline data analysis process. Deferring analysis until hours or days after measurement makes FIMS' capabilities less valuable for probing dynamic, rapidly changing environments. Our research aims to address this limitation by developing a real-time data analysis pipeline for FIMS, allowing for adaptive aerosol measuring, eliminating lengthy delays between data collection and analysis, and boosting FIMS' potential for aerosol research. The pipeline is written in C++, making it suitable for deployment even in low-power embedded systems. The design also allows for easy future upgrades like new data types or machine learning integrations. Benchmarks confirm its efficiency. All real-time components operate within established limits, yielding results that are consistent with traditional offline methods. The real-time capabilities of this pipeline significantly extend FIMS's utility in dynamic, rapidly changing environments.
"],"dc:identifier":["https://doi.org/10.7936/cdb5-j514","https://openscholarship.wustl.edu/eng_etds/985"],"dc:language":["English (en)"],"dc:rights":["I have not registered my thesis with the U.S. Copyright Office, and do not intend to."],"dc:subject":["real time system","aerosol size distribution","Fast Integrated Mobility Spectrometer","mobile real-time atmospheric sensing","Environmental Engineering","Other Computer Engineering"],"dc:title":["Real-time Analysis of Aerosol Size Distributions with the Fast Integrated Mobility Spectrometer (FIMS)"],"thesis:degree_discipline":["Computer Science & Engineering","McKelvey School of Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science (MS)"]},"updated_at":"2026-07-24T06:13:14Z"}