Information from an image occurs over multiple and distinct spatial scales. Image pyramid multiresolution representations are a useful data structure for image analysis and manipulation over a spectrum of spatial scales. This paper employs the Gaussian–Laplacian pyramid to separately treat different spatial frequency bands of a texture. First, we generate three images corresponding to three levels of the Gaussian–Laplacian pyramid for an input image to capture intrinsic details. Then, we aggregate features extracted from gray and color texture images using bioinspired texture descriptors, information-theoretic measures, gray-level co-occurrence matrix feature descriptors, and Haralick statistical feature descriptors into a single feature vector. Such an aggregation aims at producing features that characterize textures to their maximum extent, unlike employing each descriptor separately, which may lose some relevant textural information and reduce the classification performance. The experimental results on texture and histopathologic image datasets have shown the advantages of the proposed method compared to state-of-the-art approaches. Such findings emphasize the importance of multiscale image analysis and corroborate that the descriptors mentioned above are complementary.

Remote Sensing is a technique to obtain data from objects without physical contact using sensors. If it is performed by hyperspectral sensors, then is possible to have a large number of bands that allows many applications. In this case, a large amount of data needs to be processed and analyzed, and therefore the practical use of these images have challenges with storage, transport and processing time. Another issue is that many of these bands contain redundant information due to high correlation between them that can cause instability in the convergence of computing processes. Because of these problems there is a need to select important bands to be processed. In many monitoring applications through remote sensing prior knowledge of areas can be used to improve the processing. Algorithms that use this information are known as supervised methods. This study introduces a methodology for band selection based on Genetic Algorithms and Gaussian Maximum Likelihood Classifier. The results were significant and demonstrate that the proposed method can reduce about 26,56% of the number of bands in hyperspectral images maintaining good quality for interpretation. The result of the similarity of the segmentation of images generated with fewer bands and more bands in the proposed methodology and the other algorithm were also compared. With the results expected to apply the proposed methodology in practical work and further study.

Position-based visual servoing is a technique for vision-based robot control, which operates in the 3D workspace and uses a known model of the object to perform tasks of feature extraction and positioning. Among the many applications, position-based visual servoing is used in industrial environments, for object manipulation operations on conveyor belts. Mathematical morphology, a well-known computer vision technique, can be used for features extraction, representation of objects and to perform actions such as recognition by images. The representation of the object occurs with the assistance of a structuring element and the morphological operations are used to locate the object center. This work explores the binary mathematical morphology whose operators are trained by a reconfigurable system developed in a FPGA with the representation of the 3D model of the object to be applied in visual servoing in controlled applications such as tasks in industrial environments.

The COVID-19 pandemic has intensified the demand for effective asynchronous educational videos, prompting renewed attention to howemotional tone influences learning outcomes. Beyond sustaining attention and motivation, the affective cues conveyed by educators directly shape students’ emotional responses and engagement. This study investigates how educators’ sentiment and emotional cues, extracted from video subtitles, correlate with students’ emotional responses and engagement metrics in asynchronous learning environments. Educator affect was inferred from subtitle analysis, while student reactions were characterized through comments, likes, and viewing patterns. Sentiment assessments using VADER and RoBERTa revealed moderately strong and consistent correlations: subtitles with negative sentiment were frequently associated with anger, sadness, fear, disgust, and disapproval in student comments, whereas positive emotional cues aligned with more favorable and constructive responses. These results highlight the significant role of emotional dynamics in asynchronous learning and indicate that strategic modulation of affective expression in educational content may optimize student engagement and knowledge retention.

O desperdício de alimentos é um problema global agravado por fatores como pobreza e má gestão, impactando a segurança alimentar e a sustentabilidade. Este estudo propõe o uso de algoritmos de Aprendizado de Máquina (ML) para reduzir o desperdício no Restaurante Estudantil (RE) do IFSULDEMINAS - Campus Muzambinho. Foram coletados dados de produção, descarte, frequência de usuários e meteorológicos, testando três algoritmos: Deep Learning (DL), Random Forest (RF) e Support Vector Regressor (SVR). O RF obteve o melhor desempenho (R²-Score de 0,98 e MAE de 6,97), seguido pelo SVR, enquanto o DL teve os piores resultados. O protótipo do sistema, integrado ao RF, foi validado por um especialista, destacando sua precisão e usabilidade. O estudo sugere a viabilidade do ML para prever demanda e reduzir desperdício. Futuras melhorias incluem a integração automática de dados meteorológicos, visando aprimorar a gestão do RE e promover práticas sustentáveis.