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.

In modern power systems, interharmonics have emerged as noninteger frequency components that appear in current or voltage signals. Double-stage AC-DC-AC converters have been identified as a primary source of interharmonics. Previous studies have highlighted modulation techniques, filters, passive components, unbalanced conditions and DC link effects as relevant factors. However, the interaction between these factors and the inertial properties of induction motors has not been explored. This study assesses various types of AC drives, including variable frequency drives (VFDs) and soft starters (AC choppers), to investigate their interaction with machine inertial properties in interharmonic generation. The employed technique for time-frequency spectral analysis is the wavelet synchrosqueezed transform combined with a clustering method. This paper demonstrates that the interaction of the inertial properties of low-power induction motors with the variations of the switching frequencies of the VFDs, the filters used in the DC link and a soft start indirectly contribute to harmonic generation and potentially to interharmonics in the electrical grid and the outputs of the inverter.

Background/Objectives: Accurate histopathological classification of lung and colon tissues remains difficult due to subtle morphological overlap between benign and malignant regions. Deep learning approaches have advanced diagnostic precision, yet models often lack interpretability or require complex multi-stage pipelines. This study aimed to develop an end-to-end dual-branch attention network capable of achieving high accuracy while preserving computational efficiency and transparency. Methods: The architecture integrates EfficientNetV2-B0 and MobileNetV3-Small backbones through a cross-gated fusion mechanism that adaptively balances global context and fine structural details. Efficient channel attention and generalized mean pooling enhance discriminative learning without external feature extraction or optimization stages. Results: The network achieved 99.84% accuracy, precision, recall, and F1-score, with an MCC of 0.998. Grad-CAM maps showed strong spatial correspondence with diagnostically relevant histological structures. Conclusions: The end-to-end framework enables the reliable, interpretable, and computationally efficient classification of lung and colon histopathology and has potential applicability to computer-assisted diagnostic workflows.

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.

A comparative study of one-stage-amp performance improvement based on simulations in 22 nm, 45 nm, 90 nm, and 180 nm in deep submicrometer CMOS technologies is discussed. Generic SPICE models were used to simulate the circuits. It is shown that in all cases a simple modification using resistive local common mode feedback increases open-loop gain and gain-bandwidth product, peak output currents, and slew rate by close to an order of magnitude. It is shown that this modification is especially appropriate for its utilization in current CMOS technologies since large factor improvements were not available in previous technologies. The OTAs with resistive local common mode feedback require simple phase lead compensation with a very small additional silicon area and keep supply requirements and static power dissipation unchanged.

A ridesharing system is a transport mode where two or more users share the same vehicle and divide the trip’s expenses based on similar routes and itineraries. Popular ridesharing systems, such as Uber, Flinc, and Lyft, define a matching among users based only on the coincidence of routes. However, these systems do not guarantee a stable matching (i.e., a matching in which no user prefers another different from the assigned one). In this work, a new ridesharing system model is proposed, including three types of trips: identical, inclusive, and partial. This model is used to introduce a new algorithm to address the stable matching problem for ridesharing systems. Finally, a set of experimental simulations of the proposed algorithm is conducted. Experimental results show that the proposed algorithm always produces a stable matching.