The development of diagnostic systems for rotating machines such as induction motors (IMs) is a task of utmost importance for the industrial sector. Reliable diagnostic systems allow for the accurate detection of different faults. Different methods based on the acquisition of thermal images (TIs) have emerged as diagnosis systems for the detection of IM faults to prevent the further generation of faults. However, these methods are based on artisanal feature selection, so obtaining high accuracy rates is usually challenging. For this reason, in this work, a new system for fault detection in IMs based on convolutional neural networks (CNNs) and thermal images (TIs) is presented. The system is based on the training of a CNN using TIs to select and extract the most salient features of each fault present in the IM. Subsequently, a classifier based on a decision tree (DT) algorithm is trained using the features learned by the CNN to infer the motor conditions. The results of this methodology show an improvement in the accuracy, precision, recall, and F1-score metrics for 11 different conditions.

The detection of faults in induction motors has been one of the main challenges to the industry in recent years. An effective fault detection method is fundamental to ensure operational security and productivity. Different models for intelligent fault diagnosis have been recently proposed. However, not all of them are accessible for some manufacturing processes because of the black-box approach, the complexity of hyperparameter tuning, high-dimensionality feature vectors, and the need for sophisticated computational resources. In this paper, a method for the detection of an unbalance fault in induction motors based on a low-dimensional feature vector and a low-complexity classification approach is proposed. The feature vector presented in this manuscript is based on texture features, which are a basic tool for image processing and image understanding. Nevertheless, texture features have not been explored as a powerful instrument for induction motor fault analysis. In this approach, texture features are used to analyze a set of vibration signals belonging to two different classes: an unbalanced motor and a healthy motor. Training-validation and testing stages are developed to build and evaluate the performance of the classifier, respectively. The results show higher accuracy and lower training time in comparison with different state-of-the-art approaches.

Research has shown that longitudinal chromatic aberration (LCA) of the human eye is generated across all of the eye’s optical surfaces. However, it may not be necessary to measure the LCA from the first surface of the cornea to the retina, as it is known that most of the changes that can modify the path of light occur from the first surface of the cornea to the last surface of the crystalline lens. This investigation presents the study of an objective technique that allows the measurement of longitudinal chromatic aberration (LCA) on the last crystalline lens surface by developing a pulse width wavefront system using a Hartmann test, Purkinje image, and Zernike polynomial. A blue pulse (440–480 nm) and a red pulse (580–640 nm) were used to generate a pattern of spots in the human eye. This pattern generated on the posterior surface of the crystalline lens of the human eye allows the reconstruction of the wavefront via a modal method with Zernike polynomials. Once the wavefront is reconstructed, Zernike coefficients can be used to quantify the LCA. The methodology and objective measurements of the magnitude of the longitudinal chromatic aberration of five test subjects are explained in this article.

Nearest-neighbour classifiers are simple and effective, but their performance and inference cost depend strongly on the size and quality of the reference (design) set. This work studies an evolutionary prototype selection strategy for k-nearest neighbour (k-NN) classification, where a genetic algorithm (GA) evolves compact, class-balanced prototype banks from the design partition and the selected prototypes are then used by a 1-NN classifier. Individuals encode fixed numbers of prototype indices per class, and fitness is defined as the number of correctly classified test samples. We evaluate the approach across five scenarios: two synthetic Gaussian benchmarks (2D, with different overlap levels), a synthetic 3D “three moons” dataset, and three real datasets (Breast Cancer Wisconsin, Wine, and a reduced MNIST setting using 8 × 8 digit images). For each scenario, results are aggregated over repeated runs with different random seeds and compared against standard 1-NN and 3-NN baselines that use all design samples as neighbours. The experimental evidence shows that GA-selected prototype banks can match ceiling performance in highly separable cases and provide consistent gains in noisier or more redundant settings, while reducing the neighbour set size by orders of magnitude. These results support the hypothesis that evolutionary, class-balanced prototype selection improves k-NN generalization and efficiency without requiring changes to the underlying distance metric or classifier structure. The results show that the proposed method is well aligned with application scenarios in which memory or latency budgets specify a hard upper bound on the number of prototypes that can be stored or consulted. In such cases, a simple single-objective algorithm like the proposed approach is a natural choice, and the results reported here provide a baseline against which more complex methods can be fairly compared.

Lung diseases are among the leading causes of death worldwide. Nowadays, to detect lung diseases, a specialist uses auscultation to make a diagnosis. Newer auscultation devices based on stethoscopes allow these sounds to be recorded for later analysis. However, the diagnosis process is time-consuming and relies on medical expertise to generate an accurate diagnosis. For these reasons, automated and objective diagnostic systems are crucial for the early detection of lung diseases and preventing them from worsening. In this study, a computer-aided diagnostic system that integrates the Radial Short-Time Fourier Transform (RSTFT) with a Convolutional Neural Network (CNN) enhanced by attention mechanisms is presented. The RSTFT is employed to convert lung sound recordings from a public dataset into angular frequency representations, which are used as input to the CNN. The network automatically extracts discriminative features and classifies the recordings into five categories: Chronic Obstructive Pulmonary Disease (COPD), bronchiectasis, pneumonia, asthma, and healthy lungs. Experimental results demonstrate that the proposed method outperforms several state-of-the-art approaches in terms of accuracy, precision, recall, and F1-score. These findings indicate that the proposed RSTFT–CNN framework provides an effective and reliable solution for the automated diagnosis of lung diseases, offering valuable support for clinical decision-making and early intervention.

Nearest-neighbor classifiers are accurate and easy to deploy, but their memory footprint and inference time grow with the size of the reference set. This paper studies an evolutionary prototype selection strategy for k-nearest neighbor (K-NN) classification aimed at extreme, class-balancedreduction. A compact genetic algorithm (GA) evolves a fixed number of prototype indices per class drawn from a disjoint design partition; the selected prototypes are then used by a 1-NN classifier, with fitness defined as the number of correctly classified test instances. To address concerns about generality and baseline strength, we evaluate an experimental suite including synthetic 2D Gaussians ($\sigma =0.5$ and $\sigma =1.0$) and a 3D three-moons geometry, as well as public benchmarks spanning binary and multi-class settings and higher-dimensional data (Breast Cancer Wisconsin, Wine, Reduced MNIST/Digits 8 × 8, Forest CoverType with seven classes, and a 10D five-class spiral benchmark). We compare against K-NN baselines with $k\in \{1,3,5,7\}$ using all design samples, and include GA operator ablations (GA1/GA2/GA3). Each scenario is repeated over 30 independent runs, reporting mean ± std, min/max, per-run distributions, win/tie/loss counts, and non-parametric significance tests (paired Wilcoxon with Holm correction; Friedman where applicable). Across datasets, the GA-selected prototype banks—often orders of magnitude smaller than the full design set—match or improve accuracy, with frequent statistically supported wins against strong K-NN baselines, and in the hardest cases provide substantial compression with no loss relative to the best baseline. These results establish a reproducible baseline for extreme, class-balanced prototype reduction suitable for memory- and latency-constrained deployments and for fair comparison against more elaborate prototype selection methods.