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Counting cells in a Neubauer chamber on microbiological culture plates is a laborious task that depends on technical experience. As a result, efforts have been made to advance computer vision-based approaches, increasing efficiency and reliability through quantitative analysis of microorganisms and calculation of their characteristics, biomass concentration, and biological activity. However, variability that still persists in these processes poses a challenge that is yet to be overcome. In this work, we propose a solution adopting a YOLOv5 network model for automatic cell recognition and counting in a case study for laboratory cell detection using images from a CytoSMART Exact FL microscope. In this context, a dataset of 21 expert-labeled cell images was created, along with an extra Sperm DetectionV dataset of 1024 images for transfer learning. The dataset was trained using the pretrained YOLOv5 algorithm with the Sperm DetectionV database. A laboratory test was also performed to confirm result’s viability. Compared to YOLOv4, the current YOLOv5 model had accuracy, precision, recall, and F1 scores of 92%, 84%, 91%, and 87%, respectively. The YOLOv5 algorithm was also used for cell counting and compared to the current segmentation-based U-Net and OpenCV model that has been implemented. In conclusion, the proposed model successfully recognizes and counts the different types of cells present in the laboratory.

Uno de los problemas más relevantes y activos en la comunidad científica es el reconocimiento de escenas. Los sistemas de visión por computador, aunque han tenido gran desarrollo en la última década, están enfocados en su mayoría en información de apariencia visual para comprender las escenas, sin tener en cuenta el potencial de información que puede aportar el análisis del contexto de los objetos relevantes en la representación de la escena. Dicha información permitirá que las máquinas perciban relaciones, coincidencias y diversos componentes de una escena de forma similar a una persona...

Este Articulo describe la implementación de un sistema de control difuso (FLC) y un controlador convencional proporcional-integral-derivativo (PID) para la velocidad y torque de un motor DC de potencia media, basado en la variación de corriente de armadura y en el control del ciclo de trabajo. El sistema de control ha sido aplicado a un motor DC de Potencia media, utilizando Python, y un sistema embebido de bajo costo - Raspberry Pi, dicho trabajo es fundamental dada la importancia de implementar controladores de velocidad y torque que permitirán la optimización de la energía para su correcto funcionamiento lográndose a través de la regulación del ciclo de trabajo aplicado a la potencia del motor DC., This article describes the implementation of a fuzzy and a PID control system for the velocity and torque of a DC power motor, based in the variation of the armor current and its duty cycle. The control system has been applied to a medium power DC motor, using Python, and a low-cost embedded system - Raspberry Pi, this work is fundamental given the importance of implementing velocity and torque controllers that yield energy optimization and correct operation being achieved through the regulation of the duty cycle applied to the power of the DC motor.