Authors:
Lucas Massa
1
;
Tiago Vieira
1
;
Allan Martins
2
and
Bruno G. Ferreira
3
Affiliations:
1
Institute of Computing, Federal University of Alagoas, Lourival Melo Mota Av., Maceió, Brazil
;
2
Department of Electrical Engineering, Federal University of Rio Grande do Norte, Natal, Brazil
;
3
Edge Innovation Center, Federal University of Alagoas, Maceió, Brazil
Keyword(s):
Particle Density Estimation, Convolutional Autoencoder, Particle Size Estimation, Acoustofluidics.
Abstract:
Micro-particle density is important for understanding different cell types, their growth stages, and how they respond to external stimuli. In previous work, a Gaussian curve fitting method was used to estimate the size of particles, in order to later calculate their density. This approach required a long processing time, making the development of a Point of Care (PoC) device difficult. Current work proposes the application of a convolutional autoencoder (AE) to estimate single particle density, aiming to develop a PoC device that overcomes the limitations presented in the previous study. Thus, we used the AE to bottleneck a set of particle images into a single latent variable to evaluate its ability to represent the particle’s diameter. We employed an identical physical apparatus involving a microscope to take pictures of particles in a liquid submitted to ultrasonic waves before the settling process. The AE was initially trained with a set of images for calibration. The acquired par
ameters were applied to the test set to estimate the velocity at which the particle falls within the ultrasonic chamber. This velocity was later used to infer the particle density. Our results demonstrated that the AE model performed much better, notably exhibiting significantly enhanced computational speed while concurrently achieving comparable error in density estimation.
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