Robotic applications, such as educational programs, are well-known. Nonetheless, there are challenges to be implemented in other settings, e.g., mine detection, agriculture support, and tasks for industry 4.0. The main challenge consists of robotic operations supported by autonomous decision using sensed-based features extraction. A prototype of a robot assembled using mechanical parts of a LEGO MINDSTORMS Robotic Kit EV3 and a Raspberry Pi controlled through servo algorithms of 2D and 2D1/2 vision approaches was implemented to tackle this challenge. This design is supported by simulations based on image, position, and a hybrid scheme for visual servo controllers. Practical implementation is operated using navigation guided by running up image-based visual servo control algorithms embedded in a Raspberry Pi that uses a control criterion based on error evolution to compute the difference between a target and sensed image. Images are collected by a camera installed on a mobile robotic platform manually and automatically operated and controlled using the Raspberry Pi. An Android application to watch the images by video streaming is shown here, using a smartphone and a video related to the implemented robot’s operation. This kind of robot might be used to complete field reactive tasks in the settings mentioned above, since the detection and control approaches allow self-contained guidance.

A prototype device based on a modified domestic microwave oven for liquid food products pasteurization is presented. This novel design has a coiling glass pipe adapted inside a microwave cavity to allow liquid continuous flow, in which several temperature optical sensors based on Fiber Bragg Grating were installed to measure, by means of contact, the circulating liquid temperature in the pipe, while the oven is on, to obtain the profile temperature as a function of time at different pipe points. The temperatures at liquid input and output were also measured with thermocouples. This device was tested to establish how well it may perform using different standardized liquids with well-known physicochemical and dielectric properties, such as water, water with dissolved sugars, water with dissolved salts, and water with dissolved sugars and salts. It could be observed that the maximum temperature reached was 90 °C for distilled water, 80 °C for water with dissolved salts, 60 °C for water with dissolved sugars and 80 °C for water with dissolved sugars and salts, showing that these data were in agreement with previous results in the literature. This type of device would be potentially useful to establish the device’s efficiency in terms of retention time, energy consumption, and volume of processed liquid, thus, broadening the use of this microwave heating technology with several types of liquid substances.

This research study compares the performance of different digital image processing algorithms based on computer vision segmentation methods to process satellite multispectral images of Normalized Difference Vegetation Index (NDVI) to estimate agricultural cropland yield as a proposal for supporting spatial planning decisions. NDVI multispectral images were collected from Sentinel-2 L2-A satellite with distinctive features to be processed through these algorithms implemented in an owned and friendly software interface developed in MATLAB App Designer. These are based on image color detection, using three techniques: rectangular thresholding method, simple thresholding method, and segmentation through Mahalanobis discriminant classifier. The segmented images were used to estimate cropland yields as a function of NDVI variations and the characteristics of each analyzed image, employing a linear model that assigned a yield to each segmented area as a function of a specific NDVI range. Algorithm accuracy was determined as a function of expected cropland yield. Results show that the rectangular thresholding method tends to average cropland yield value in slightly non-uniform images. In contrast, thresholding by pixel and Mahalanobis methods performed better on highly non-uniform NDVI images, with deviations less than 8% compared with the expected cropland yield. The rectangular thresholding method could be a more straightforward tool regarding computational cost since, e.g., the demarcation of rectangular areas is easier in any cultivated area, facilitating the implementation of spatial support plans for farmers. The proposal is to use the rectangular thresholding method as a planning tool, as the other methods may be used for more accurate estimations.

La detección de anomalías encefálicas es uno de los aspectos más importantes en el diagnóstico médico. En este sentido, el procesamiento de imágenes se ha convertido en una herramienta fundamental para la agilizar el proceso con alta confiabilidad y precisión. En este trabajo, se implementaron algoritmos de aprendizaje para la detección de anomalías encefálicas en imágenes, con propósito de diagnóstico médico. Se implementaron dos algoritmos de procesamiento de imágenes a imágenes de resonancia magnética cerebral. El primero, k-means, y el otro el de máquina de soporte vectorial. El primero es un algoritmo de agrupamiento de datos no supervisado, y el segundo se basa en el aprendizaje supervisado para la clasificación. Se aplicaron ambos algoritmos y se compararon los resultados, obteniéndose una tasa de predicción de 43.75% con algoritmo k-means y de 80% con algoritmo de máquina de soporte vectorial, luego del entrenamiento con 50 imágenes de pacientes sanos y 50 imágenes de pacientes con anomalía cerebral.