This study aims to develop and evaluate an Augmented Reality (AR) application to teach power electronics to beginners. For this purpose, two topics were presented: The first was the design of a series-connected Resistance–Inductor–Capacitor (RLC) circuit in AR, the space-state equations of which were analyzed in an interactive way, and its assembly in a virtual protoboard to analyze the voltage and currents as measured by an oscilloscope. The second presented topic in AR was about Bidirectional Direct Current (DC)–DC converters, known as Buck–Boost; the aim was to study their behavior when energy is exchanged between two systems, usually photovoltaic panels, electric vehicles, and storage systems. The attitudes of the students towards the AR application was significantly better than those towards traditional teaching. The measurements of the developed skills indicated better cognitive performance when using AR technology. The designed AR tool was used in an industry course to explore the students’ opinions, who provided valuable feedback.

This work proposes a real-time electricity bill for quantifying the energy used in domestic facilities in Mexico. This bill is a low-cost tool that takes advantage of the IoT technology for generating an easy reading real-time bill allowing the customers to constantly review and administrate their energy consumption. Using low-cost sensors and the electronic board Particle^® Photon, an energy meter is proposed. The presented prototype is extremely compact and satisfies safety measures to be used by anyone in a domestic installation. The measurement data is displayed and processed in real-time, and an appropriate algorithm determines the accumulated kWh. The energy consumed is displayed using an Html interface of easy interpretation for the customers, given recommendations about their consumption habits and some alarms in case of abnormal or high consumption. As a reinforcement measure for avoiding large consumption bills, the system is programmed to send messages to the user, remembering if the estimated consumption is large.

In this study, a low-cost proposed platform for training dynamics (PPTD) is proposed based on operational amplifiers to understand the dynamics and variables of the agricultural tractor John Deere tractor model 4430 to gain autonomy and analyze the behavior of control algorithms proposed in real time by state feedback. The proposed platform uses commercial sensors and interacts with the Arduino Uno and/or Daq-6009 board from National Instruments. A mobile application (APP) was also developed for real-time monitoring of autonomous control signals, the local reference system, and physical and dynamic variables in the tractor; this platform can be used as a mobile alternative applied to a tractor in physically installed form. In the presented case, the PPTD was mounted on a John Deere tractor to test its behavior; moreover, it may be used on other tractor models similarly as established here. The established results of this platform were compared with models established in MATLAB, validating the proposal. All simulations and developments are shared through a web-link as open-source files so that anyone with basic knowledge of electronics and modeling of vehicles can reproduce the proposed platform.

This paper presents a new filter to remove impulse noise in digital color images. The filter is adaptive in the sense that it uses a detection stage to only correct noisy pixels. Detecting noisy pixels is performed by a binary classification model generated via genetic programming, a paradigm of evolutionary computing based on natural biological selection. The classification model training considers three impulse noise models in color images: salt and pepper, uniform, and correlated. This is the first filter generated by genetic programming exploiting the correlation among the color image channels. The correction stage consists of a vector median filter version that modifies color channel values if some are noisy. An experimental study was performed to compare the proposed filter with others in the state-of-the-art related to color image denoising. Their performance was measured objectively through the image quality metrics PSNR, MAE, SSIM, and FSIM. Experimental findings reveal substantial variability among filters based on noise model and image characteristics. The findings also indicate that, on average, the proposed filter consistently exhibited top-tier performance values for the three impulse noise models, surpassed only by a filter employing a deep learning-based approach. Unlike deep learning filters, which are black boxes with internal workings invisible to the user, the proposed filter has a high interpretability with a performance close to an equilibrium point for all images and noise models used in the experiment.

This research centers on the implementation of photovoltaic systems in residential applications, coupled with battery-based energy storage, and evaluates their efficiency in generating energy, specifically for lighting in buildings. The methodology hinges on detecting interharmonic signals to characterize potentially disruptive frequencies and identify the origins of various failures. Multiple case studies are presented to validate the method’s efficacy, including one involving fluorescent lamp circuits and another examining variations in solar radiation during the summer season. Real-world experiments are conducted in a residential setting, and the results are thoroughly analyzed. Various types of interharmonic generation behaviors are demonstrated, which are influenced by fluctuations in solar radiation and the appropriate installation of solar panels. The findings reveal that the absence of solar radiation below 300 W/m² in a photovoltaic system relying on energy storage adversely affects interharmonics in luminaires installed within a residential space.