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.

Committee Chair

Jeremy Buhler

Committee Members

Jeremy Buhler Christopher Gill Jian Wang

Degree

Master of Science (MS)

Author's Department

Computer Science & Engineering

Author's School

McKelvey School of Engineering

Document Type

Thesis

Date of Award

12-2023

Language

English (en)

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