This paper presents an experimental platform for regulating the DC motor angular speed powered by photovoltaic cells. The experimental platform comprises an Eco Green Energy EGE-260P-60 solar panel, DC/DC SEPIC converter, DC bus, DC/DC buck converter, DC motor and Nexys 4 board with an Artix-7 100T FPGA. The DC/DC SEPIC converter is used for harvesting the maximum amount of energy from the PV cells using the perturb and observe algorithm to track the maximum power point. The DC/DC buck converter is used as the motor drive using the active disturbance rejection control to regulate the angular speed of the DC motor. In addition, the FPGA architecture design is presented using a hierarchical top-down methodology with the VHDL hardware description language and Xilinx System Generator tool. The software takes advantage of the FPGA’s concurrency to simultaneously evaluate the different processes, which is the main reason for choosing this digital device. Several tests were performed on the platform such as irradiance changes, DC bus variations, DC motor connection and load torque variations applied in the motor shaft. The results indicate that the maximum power is obtained from the photovoltaic cells, establishing the minimum operating conditions. In addition, the control approach estimates and cancels the effects of disturbances caused by variations in the environmental conditions, photovoltaic system, DC bus, and load changes in order to regulate DC motor speed.

The development of improved power semiconductor devices has allowed different methods of energy conversion to significantly improve their efficiency and be able to operate with higher power levels. These advances have helped to satisfy the growing needs of industrial applications, such as motor drives, power supplies for personal computers, office equipment, spacecraft power systems, laptop computers, and telecommunications equipment. As a result, power electronics is considered a basic subject in engineering careers with a focus on electronics, which leads to an imperative need to count on specialized tools in this area. Furthermore, it is essential to include methodologies to efficiently incorporate these tools into courses. This paper presents an educational web-based tool focused on teaching design of DC motors control systems, with emphasis on design of DC–DC converters and FPGA-based control, under a project-based learning approach. A qualitative research method was utilized in a performed experiment to evaluate this proposed tool.

This paper presents our experience implementing Collaborative Working Spheres (CWS) in the context of an international undergraduate course on Global Software Development (GSD) during the COVID-19 pandemic. Many universities around the world increased their efforts in creating educational alternatives for adequately addressing the educational challenges that this pandemic has introduced. A particular case is the training of software skilled graduates to work in globally distributed environments because learning this topic requires a lot of practical work when student motivation could have been negatively affected by the COVID-19 pandemic. However, providing highly practical GSD courses during a pandemic is a challenging task for many of these universities. It is against this backdrop that we have developed an educational tool to provide CWS, enabling undergraduate students to acquire practical experience in GSD and improve their communication and teamworking skills, even during the COVID-19 pandemic. An international empirical evaluation was conducted involving students and teachers from seven universities in different countries around the world. The obtained results showed that our approach can make a significant contribution to the development of practical projects on undergraduate GSD courses with students developing their knowledge and social skills associated with this topic. The data collected on the teachers’ perceptions suggested that our approach could also be useful in introducing the GSD approach at undergraduate level when social distancing is in place.

In the field of object recognition, feature descriptors have proven to be able to provide accurate representations of objects facilitating the recognition task. In this sense, Histograms of Oriented Gradients (HOG), a descriptor that uses this approach, together with Support Vector Machines (SVM) have proven to be successful human detection methods. In this paper, we propose a scheme consisting of improved HOG and a classifier with a neural approach to producing a robust system for object recognition. The main contributions of this work are: First, we propose an improved gradient calculation that allows for better discrimination for the classifier system, which consists of performing a threshold over both the magnitude and direction of the gradients. This improvement reduces the rate of false positives. Second, although HOG is particularly suited for human detection, we demonstrate that it can be used to represent different objects accurately, and even perform well in multi-class applications. Third, we show that a classifier that uses a neuronal approach is an excellent complement to a HOG-based feature extractor. Finally, experimental results on the well-known Caltech 101 dataset illustrate the benefits of the proposed scheme.