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

dc.contributor.authorMudugamuwa, A
dc.contributor.authorHettiarachchi, S
dc.contributor.authorMelroy, G
dc.contributor.authorDodampegama, S
dc.contributor.authorKonara, M
dc.contributor.authorRoshan, U
dc.contributor.authorAmarasinghe, R
dc.contributor.authorJayathilaka, D
dc.contributor.authorWang, P
dc.date.accessioned2023-06-23T09:12:26Z
dc.date.available2023-06-23T09:12:26Z
dc.date.issued2022
dc.description.abstractThis 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.identifier.citationMudugamuwa, 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/s22186900en_US
dc.identifier.doihttps://doi.org/10.3390/s22186900en_US
dc.identifier.issn1424-8220en_US
dc.identifier.issue18en_US
dc.identifier.journalSensorsen_US
dc.identifier.pgnos6900[16p.]en_US
dc.identifier.urihttp://dl.lib.uom.lk/handle/123/21154
dc.identifier.volume22en_US
dc.identifier.year2022en_US
dc.language.isoen_USen_US
dc.publisherMDPIen_US
dc.subjectactive droplet generationen_US
dc.subjectdroplet microfluidicsen_US
dc.subjectperformance analysisen_US
dc.subjectcomputer visionen_US
dc.subjectimage processingen_US
dc.subjectlab on a chipen_US
dc.titleVision-based performance analysis of an active microfluidic droplet generation system using droplet imagesen_US

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