Institutional-Repository, University of Moratuwa.  

Vision-based performance analysis of an active microfluidic droplet generation system using droplet images

Show simple item record

dc.contributor.author Mudugamuwa, A
dc.contributor.author Hettiarachchi, S
dc.contributor.author Melroy, G
dc.contributor.author Dodampegama, S
dc.contributor.author Konara, M
dc.contributor.author Roshan, U
dc.contributor.author Amarasinghe, R
dc.contributor.author Jayathilaka, D
dc.contributor.author Wang, P
dc.date.accessioned 2023-06-23T09:12:26Z
dc.date.available 2023-06-23T09:12:26Z
dc.date.issued 2022
dc.identifier.citation Mudugamuwa, A., Hettiarachchi, S., Melroy, G., Dodampegama, S., Konara, M., Roshan, U., Amarasinghe, R., Jayathilaka, D., & Wang, P. (2022). Vision-Based Performance Analysis of an Active Microfluidic Droplet Generation System Using Droplet Images. Sensors, 22(18), Article 18. https://doi.org/10.3390/s22186900 en_US
dc.identifier.issn 1424-8220 en_US
dc.identifier.uri http://dl.lib.uom.lk/handle/123/21154
dc.description.abstract This paper discusses an active droplet generation system, and the presented droplet generator successfully performs droplet generation using two fluid phases: continuous phase fluid and dispersed phase fluid. The performance of an active droplet generation system is analysed based on the droplet morphology using vision sensing and digital image processing. The proposed system in the study includes a droplet generator, camera module with image pre-processing and identification algorithm, and controller and control algorithm with a workstation computer. The overall system is able to control, sense, and analyse the generation of droplets. The main controller consists of a microcontroller, motor controller, voltage regulator, and power supply. Among the morphological features of droplets, the diameter is extracted from the images to observe the system performance. The MATLAB-based image processing algorithm consists of image acquisition, image enhancement, droplet identification, feature extraction, and analysis. RGB band filtering, thresholding, and opening are used in image pre-processing. After the image enhancement, droplet identification is performed by tracing the boundary of the droplets. The average droplet diameter varied from ~3.05 mm to ~4.04 mm in the experiments, and the average droplet diameter decrement presented a relationship of a second-order polynomial with the droplet generation time. en_US
dc.language.iso en_US en_US
dc.publisher MDPI en_US
dc.subject active droplet generation en_US
dc.subject droplet microfluidics en_US
dc.subject performance analysis en_US
dc.subject computer vision en_US
dc.subject image processing en_US
dc.subject lab on a chip en_US
dc.title Vision-based performance analysis of an active microfluidic droplet generation system using droplet images en_US
dc.identifier.year 2022 en_US
dc.identifier.journal Sensors en_US
dc.identifier.issue 18 en_US
dc.identifier.volume 22 en_US
dc.identifier.pgnos 6900[16p.] en_US
dc.identifier.doi https://doi.org/10.3390/s22186900 en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record