In recent decades, advances in the development of solar tracking systems (STSs) have led to concentrating solar technologies to increase their energy conversion efficiency. These systems, however, still have areas of opportunity or improving their performance and reducing their manufacturing costs. This paper presents the design, construction and evaluation of a high-precision dual-axis solar tracking system with a technology readiness level of 7–8. The system is controlled by a low-cost Arduino board in a closed-loop control using a micro-electromechanical solar sensor. Real-time tracking experiments were performed under a clear sky as well as during partly and mostly cloudy days. Solar tracking accuracy was evaluated in an operational environment using test procedures adapted from the International Electrotechnical Commission (IEC) 62817 standard. The total mean instantaneous solar tracking error on a clear day measured with a calibrated digital solar sensor was 0.37${}^{°}$ and 0.52${}^{°}$ with a developed pinhole projection system. Similarly, the total mean reported solar tracking accuracy achieved was 0.390${}^{°}$ on a sunny day and 0.536${}^{°}$ on a partially cloudy day. An annual power generation analysis considering a conventional photovoltaic (PV) panel system and a typical concentrator photovoltaic (CPV) module as payloads was also presented. Simulations showed an increase in the generation of up to 37.5% for a flat panel with dual-axis tracking versus a fixed panel. In the case of the CPV system, first, a ray tracing study was implemented to determine the misalignment coefficient, then the annual power generation was estimated. The developed STS allowed the CPV modules to reach at least 90% of their nominal energy conversion efficiency.

This paper proposes a deep learning model based on an artificial neural network with a single hidden layer for predicting the diagnosis of multiple sclerosis. The hidden layer includes a regularization term that prevents overfitting and reduces the model complexity. The purposed learning model achieved higher prediction accuracy and lower loss than four conventional machine learning techniques. A dimensionality reduction method was used to select the most relevant features from 74 gene expression profiles for training the learning models. The analysis of variance test was performed to identify the statistical difference between the mean of the proposed model and the compared classifiers. The experimental results show the effectiveness of the proposed artificial neural network.

This article presents two procedures involving a maximal hyperconnected function and a hyperconnected lower leveling to segment the brain in a magnetic resonance imaging T1 weighted using new openings on a max-tree structure. The openings are hyperconnected and are viscous transformations. The first procedure considers finding the higher hyperconnected maximum by using an increasing criterion that plays a central role during segmentation. The second procedure utilizes hyperconnected lower leveling, which acts as a marker, controlling the reconstruction process into the mask. As a result, the proposal allows an efficient segmentation of the brain to be obtained. In total, 38 magnetic resonance T1-weighted images obtained from the Internet Brain Segmentation Repository are segmented. The Jaccard and Dice indices are computed, compared, and validated with the efficiency of the Brain Extraction Tool software and other algorithms provided in the literature.

Virtual remote laboratories have already been successfully implemented in educational centers for practical learning of mechatronics and robotic systems. This article presents the development of a virtual articulated UR-type robot, designed as an educational tool that is suitable for programming and evaluating both the inverse kinematics control of the robot and the independent control of the robot joints. The 3D model of the virtual robot was developed in the Blender V2.79 software and uses the Modbus TCP industrial communication protocol for the communication to an external controller implemented in CoDeSys V3.5 software. The developed system allows the students to generate and test their own control algorithm for the robot joints with the visualization of the achieved performance in 3D and real time. Tailored control systems can be compared on the virtual robot. In this study, a novel technique for the joint position control based on an FSM is proposed and verified with the virtual UR5 robots to prove that the developed system is a suitable platform to teach and learn the inverse kinematics control and independent joint control of the UR5 robotic arm.

A simple and robust methodology for plant disease diagnosis using images in the visible spectrum of plants, even in uncontrolled environments, is presented for possible use in mobile applications. This strategy is divided into two main parts: on the one hand, the segmentation of the plant, and on the other hand, the identification of color associated with diseases. Gaussian mixture models and probabilistic saliency segmentation are used to accurately segment the plant from the background of an image, and HSV thresholds are used in order to achieve the identification and quantification of the colors associated with the diseases. Proper identification of the colors associated with diseases of interest combined with adequate segmentation of the plant and the background produces a robust diagnosis in a wide range of scenarios.

The automobile lateral-view mirrors are the most important visual support for driver safety; therefore, it is important they have robust quality control. Typically, the distortion of a lateral-view mirror is measured using the JIS-D-5705 standard; however, this methodology requires an expert person to perform the measurements and calculations manually, which can induce measurement errors. In this work, a semi-automatic distortion calculation method based on image processing is presented. Distortion calculations of five commercial mirrors from different manufacturers were performed, and a comparative study was carried out between the JIS-D-5705 standard and the proposed method. Experimental results performed according to the JIS-D-5705 standard showed that all mirrors have a distortion lower than 5%, indicating that all meet the standard. On the other hand, the proposed method was able to detect that one of the mirrors presented an important distortion, which was not detected by the methodology proposed in the standard; therefore, that mirror should not meet the standard. Then, it was possible to conclude that the proposed distortion calculation method, based on image processing, has higher robustness and precision than the standard. In addition, an appropriate and effective behavior against changes in scale, resolution, and, unlike the standard, against changes in image rotation was also shown.