Through the increasing use of unmanned aerial vehicles as remote sensing tools, shadows become evident in aerial imaging; this fact, alongside the higher spatial resolution obtained by high-resolution mounted cameras, presents a challenging issue when performing different image processing tasks related to urban areas monitoring. Accordingly, the state-of-the-art reported works can correct the shadow regions, but the heterogeneity between the corrected shadow and non-shadow areas is still evident and especially noticeable in concrete and asphalt regions. The present work introduces a local color transfer methodology to shadow removal which is based on the CIE L*a*b (Lightness, a and b) color space that considers chromatic differences in urban regions, and it is followed by a color tuning using the HSV color space. The quantitative comparison was executed by using the shadow standard deviation index (SSDI), where the proposed work provided low values that improve up to 19 units regarding other tested methods. The qualitative comparison was visually realized and proved that the proposed method enhances the color correspondence without losing texture information. Quantitative and qualitative results validate the results of color correction and texture preservation accuracy of the proposed method against other published methodologies.

Shadows in remote sensing images often introduce challenges in accurate segmentation due to their variability in shape, size, and texture. To address these issues, this study proposes the Supervised Dynamic Kernel U-Net (SDKU-Net), a novel architecture designed to enhance shadow detection in complex remote sensing scenarios. SDKU-Net integrates dynamic kernel adjustment, a combined loss function incorporating Focal and Tversky Loss, and optimizer switching to effectively tackle class imbalance and improve segmentation quality. Using the AISD dataset, the proposed method achieved state-of-the-art performance with an Intersection over Union (IoU) of 0.8552, an F1-Score of 0.9219, an Overall Accuracy (OA) of 96.50%, and a Balanced Error Rate (BER) of 5.08%. Comparative analyses demonstrate SDKU-Net’s superior performance against established methods such as U-Net, U-Net++, MSASDNet, and CADDN. Additionally, the model’s efficient training process, requiring only 75 epochs, highlights its potential for resource-constrained applications. These results underscore the robustness and adaptability of SDKU-Net, paving the way for advancements in shadow detection and segmentation across diverse fields.

The reliability of electromechanical systems is a critical factor in modern Industry 4.0, as unexpected failures in induction motors or gearboxes can cause costly downtime, productivity losses, and increased maintenance demands. Infrared thermography offers a non-invasive and real-time means of monitoring thermal behavior, yet its effective use for fault diagnosis remains challenging due to sensitivity to noise, environmental variability, and the need for robust feature extraction. This work proposes a novel end-to-end convolutional neural network (CNN) methodology for detecting and classifying faults in electromechanical systems through the processing of infrared thermography images. The method integrates an automatic preprocessing stage that isolates the Relevant Heating Areas (RHAs), preserving their geometric and thermal descriptors while filtering irrelevant background information. A tailored data augmentation strategy, including controlled noise injection, was designed to improve robustness under realistic acquisition conditions. The CNN architecture combines 3 × 3 and 5 × 5 kernels to capture both fine-grained and global heating patterns. Experimental validation is carried out under nine different faulty conditions, achieving 99.7% accuracy and demonstrating strong resilience against Gaussian blur and additive Gaussian noise. The results suggest that the method provides a scalable, interpretable, and efficient approach for fault diagnosis in electromechanical systems within Industry 4.0 environments.

Global regulations on vehicle emissions have compelled the automotive industry to undergo rapid advancements over the past decades, prioritizing strategies for developing engines with greater efficiency and lower emissions. While the market for electric vehicles continues to expand, internal combustion (IC) engines remain indispensable and constitute the majority of the global vehicle fleet. Among the critical parameters for controlling engine performance, emissions, and fuel economy, the air–fuel ratio (AFR) is a key determinant. Inadequate control or fluctuations in AFR, whether in steady-state or transient conditions, can lead to higher levels of pollutant emissions, increased fuel consumption, and engine instability. For more than a century, AFR control has progressed from carburetor-based systems to electronically controlled port fuel injection (PFI) or gasoline direct injection (GDI) systems. In PFI or GDI systems, the precision of AFR regulation depends heavily on the performance of the controller governing the actuation of fuel injectors. This article reviews recent advancements in AFR control techniques, categorizing them into feedback, model-based, parameter estimation, robust, fuzzy, adaptive, neural network (NN), and machine learning (ML) approaches. At the end of each section, a comparative discussion outlines the most appropriate use cases for each technique, their relative performance against alternative approaches, implementation limitations, and studies that have demonstrated their applicability in real-world scenarios. Furthermore, graphical representations are provided to illustrate the annual volume of published research on IC engine control using each technique. These figures offer valuable insights into prevailing research trends and support the identification of promising directions for the development and implementation of future control strategies.