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Cell counting and velocity algorithms for hydrodynamic study of unsteady biological flows in micro-channels

算法 粒子图像测速 探测器 过程(计算) 计算机科学 测速 频道(广播) 粒子(生态学) 计算机视觉 模拟 物理 光学 机械 海洋学 湍流 地质学 操作系统 电信 计算机网络
作者
Federica Torrisi,Giovanna Stella,Francesca Guarino,Maide Bucolo
出处
期刊:Biomicrofluidics [American Institute of Physics]
卷期号:17 (1) 被引量:2
标识
DOI:10.1063/5.0138587
摘要

In this paper, the combination of two algorithms, a cell counting algorithm and a velocity algorithm based on a Digital Particle Image Velocimetry (DPIV) method, is presented to study the collective behavior of micro-particles in response to hydrodynamic stimuli. A wide experimental campaign was conducted using micro-particles of different natures and diameters (from 5 to 16μm ), such as living cells and silica beads. The biological fluids were injected at the inlet of a micro-channel with an external oscillating flow, and the process was monitored in an investigated area, simultaneously, through a CCD camera and a photo-detector. The proposed data analysis procedure is based on the DPIV-based algorithm to extrapolate the micro-particles velocities and a custom counting algorithm to obtain the instantaneous micro-particles number. The counting algorithm was easily integrated with the DPIV-based algorithm, to automatically run the analysis to different videos and to post-process the results in time and frequency domain. The performed experiments highlight the difference in the micro-particles hydrodynamic responses to external stimuli and the possibility to associate them with the micro-particles physical properties. Furthermore, in order to overcome the hardware and software requirements for the development of a real-time approach, it was also investigated the possibility to detect the flows by photo-detector signals as an alternative to camera acquisition. The photo-detector signals were compared with the velocity trends as a proof of concept for further simplification and speed-up of the data acquisition and analysis. The algorithm flexibility underlines the potential of the proposed methodology to be suitable for real-time detection in embedded systems.
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