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 |