This paper presents the MultiWave Med system, which is a new software allowing to index and retrieve medical images through the comparison of their texture features. The features are extracted by Wavelet transforms, and are organized in feature vectors. The system extracts the image texture features, computes the distance between the query image to all images in the database, through the comparison of their features, and retrieve de n most similar images regarding this kind of feature. The proposed system has implemented both Daubechies and Gabor wavelets. The feature vectors extracted from the images are used to organize the images through access methods, which are the basis to perform the query-by-content operations over the images. The focus of this paper is to show the utility of the wavelet transforms on medical image characterization and their suitability for image indexing and retrieval.

(IA) ha tomado un rol protagónico importante en todo el mundo, ya que a través de diferentes algoritmos se realizan tareas automatizadas que facilitan la vida diaria y mejoran procesos, especialmente, en las organizaciones. Este crecimiento de la IA se ve reflejado también en la educación, puesto que ha generado una disrupción en los procesos de aprendizaje en los distintos niveles de enseñanza (Flores-Vivar & García-Peñalvo, 2023) y permite la personalización del aprendizaje, a partir de plataformas que ajustan su contenido y la dificultad de las tareas en función del progreso y aprendizaje de cada estudiante. Igualmente, se están utilizando otras herramientas que facilitan la creación de videos, textos e imágenes, que ayudan con las tareas diarias de los estudiantes. Esto también implicó replantear las estrategias de enseñanza y evaluación que los docentes realizan en su quehacer diario, principalmente en la educación superior, ya que queda en evidencia, la necesidad de lograr una mayor reflexión y un pensamiento crítico que vaya más allá de lo que una IA nos puede proporcionar con sus prompts.

At the beginning of a project or research that involves the issue of autonomous navigation of mobile robots, a decision must be made about working with traditional control algorithms or algorithms based on artificial intelligence. This decision is not usually easy, as the computational capacity of the robot, the availability of information through its sensory systems and the characteristics of the environment must be taken into consideration. For this reason, this work focuses on a review of different autonomous-navigation algorithms applied to mobile robots, from which the most suitable ones have been identified for the cases in which the robot must navigate in dynamic environments. Based on the identified algorithms, a comparison of these traditional and DRL-based algorithms was made, using a robotic platform to evaluate their performance, identify their advantages and disadvantages and provide a recommendation for their use, according to the development requirements of the robot. The algorithms selected were DWA, TEB, CADRL and SAC, and the results show that—according to the application and the robot’s characteristics—it is recommended to use each of them, based on different conditions.

In this work, we introduce a novel, distributed version of the N-FINDR endmember extraction algorithm, which is able to exploit computer cluster resources in order to efficiently process large volumes of hyperspectral data. The implementation of the distributed algorithm was done by extending the InterCloud Data Mining Package, originally adopted for land cover classification, through the HyperCloud-RS framework, here adapted for endmember extraction, which can be executed on cloud computing environments, allowing users to elastically administer processing power and storage space for adequately handling very large datasets. The framework supports distributed execution, network communication, and fault tolerance, transparently and efficiently to the user. The experimental analysis addresses the performance issues, evaluating both accuracy and execution time, over the processing of different synthetic versions of the AVIRIS Cuprite hyperspectral dataset, with 3.1 Gb, 6.2 Gb, and 15.1Gb respectively, thus addressing the issue of dealing with large-scale hyperspectral data. As a further contribution of this work, we describe in detail how to extend the HyperCloud-RS framework by integrating other endmember extraction algorithms, thus enabling researchers to implement algorithms specifically designed for their own assessment.

Evaluating the physical defects of green coffee beans are an important process in defining their quality. This evaluation is normally carried out by visual inspection or using traditional instruments which have some limitations. This work is focused on the implementation of a computer vision system combining a hardware prototype and a software module. The hardware was developed to capture the images of coffee beans, the software uses a White-Patch algorithm as a image enhancement procedure, color histograms as feature extractor and SVM for the classification task, a database of 1930 images was collected, we used 13 categories of defects described in the SCAA standard of evaluation. Results of classification achieved a 98.8% of overall detection accuracy, therefore the proposed system proved to be effective in classifying physical defects of green coffee beans. Finally a set of conclusions and future works are presented.