Design, validation and reliability analysis of microelectromechanical-based novel disdrometer for advanced driver assistance system applications

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master_Jadoon_Nabeel_Ahmad_Khan_2025.pdf (24.15 MB)

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School of Electrical Engineering | Master's thesis
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2025-07-18

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Smart Systems Integrated Solutions

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Joint International Master in Smart Systems Integrated Solutions

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en

Pages

83

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Abstract

This thesis studies a compact, MEMS-based optical disdrometer developed to provide real-time measurements of droplet size, spatial position, and velocity, serving as a reference instrument for advanced driver assistance systems (ADAS) applications such as road slipperiness detection and automated wiper control. The system integrates a resonant 1D MEMS mirror and 25\,mm optics to scan a laser-generated rectangular light sheet across a small detection volume, with signal acquisition performed by a Redpitaya FPGA (STEMlab 125-14) operating at 125\,MSa/s. By synchronizing MEMS micro-mirror motion with analog signal capture through a physics-informed time-to-position calibration, robust droplet edge detection is achieved using Savitzky–Golay filtering. The disdrometer accurately estimates droplet diameters from 0.5\,mm to 6\,mm and velocities between 0.5\,m/s and 10\,m/s within a 21\,mm × 35\,mm detection area. The acquisition pipeline is optimized for real-time operation, achieving a total measurement latency below 3.5\,ms and an FPGA trigger-to-buffer delay of less than 30\,$\mu$s, ensuring deterministic, high-speed capture. Three experimental campaigns validated the system’s performance: controlled droplet generation with calibrated nozzles for spatial accuracy benchmarking, steel sphere with known diameter for size and temporal validation, and natural rainfall measurements cross-verified against meteorological data, achieving over 98\% detection efficiency across all scenarios. Mechanical and electrical robustness were further enhanced through compact board stacking and custom electronics design, a ventilated enclosure for thermal management, and impedance-matched 50\,$\Omega$ terminations. This work establishes a versatile platform for future droplet and particle sensing applications, extending beyond ADAS to include fuel cell diagnostics, electrolyzer monitoring, and fluid purity analysis.

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Supervisor

Vuorinen, Vesa

Keywords

MEMS-based disdrometer, rain droplet measurements, autonomous driving systems, signal processing optimization, droplet trajectory analysis, advanced driver assistance system

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https://urn.fi/URN:NBN:fi:aalto-202508196318

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[dipl] Sähkötekniikan korkeakoulu / ELEC