Fusion of a Variable Baseline System and a Range Finder

One of the greatest difficulties in stereo vision is the appearance of ambiguities when matching similar points from different images. In this article we analyze the effectiveness of using a fusion of multiple baselines and a range finder from a theoretical point of view, focusing on the results of using both prismatic and rotational articulations for baseline generation, and offer a practical case to prove its efficiency on an autonomous vehicle

Asphalted Road Temperature Variations Due to Wind Turbine Cast Shadows

The contribution of this paper is a technique that in certain circumstances allows one to avoid the removal of dynamic shadows in the visible spectrum making use of images in the infrared spectrum. This technique emerged from a real problem concerning the autonomous navigation of a vehicle in a wind farm. In this environment, the dynamic shadows cast by the wind turbines' blades make it necessary to include a shadows removal stage in the preprocessing of the visible spectrum images in order to avoid the shadows being misclassified as obstacles. In the thermal images, dynamic shadows completely disappear, something that does not always occur in the visible spectrum, even when the preprocessing is executed. Thus, a fusion on thermal and visible bands is performed

Simulador de Robótica Educativa para la promoción del Pensamiento Computacional | Educational Robotics simulator for fostering Computational Thinking

Este trabajo presenta una herramienta Web libre y gratuita que facilita a cualquier centro educativo la enseñanza de conceptos básicos sobre robótica y programación y que, al mismo tiempo, permite desarrollar habilidades relacionadas con el pensamiento computacional: descomposición, abstracción, reconocimiento de patrones y pensamiento algorítmico. Dicha herramienta permite diseñar y personalizar un robot a través del uso de distintos tipos de sensores. Tras su creación, dicho robot se podrá poner a prueba en un entorno de simulación mediante distintos retos. En dicho entorno podremos definir el comportamiento del robot por medio de un lenguaje de programación visual basado en bloques. Dichos bloques permiten definir las acciones a llevar a cabo por el robot en función de la información recogida por los sensores con el objetivo de superar los desafíos propuestos.This work presents a free software tool that facilitates the teaching of basic robotics and programming concepts at any educational institution. At the same time, it allows the development of computational thinking skills to be carried out: decomposition, abstraction, pattern recognition and algorithmic thinking. This tool allows the design and configuration of a robot through the specification of different types of sensors. After designing the robot, its behaviour can be simulated by means of different challenges proposed to the user. This behaviour is defined through a block-based visual programming language. Blocks allow actions that the robot has to perform based on the information gathered by the different sensors to be defined in order to pass a challenge

Curriculum de Ciencias de la Computación en Canarias

En 2016, se puso en marcha el Aula Cultural de Pensamiento Computacional de la Universidad de La Laguna para dar respuesta social a la confusión que surge al integrar las Tecnologías de la Información y las Comunicaciones en la educación. Es importante distinguir entre los términos “Alfabetización Digital”, “Pensamiento Computacional” y “Ciencias de la Computación”. Las habilidades digitales se centran en el uso de la tecnología, mientras que el Pensamiento Computacional implica comprender los fundamentos de las Ciencias de la Computación. Esta materia disuade especialmente a las mujeres de perseguir carreras tecnológicas. En 2021 se puso en marcha el “Proyecto C**4: Curriculum de Ciencias de la Computación en Canarias” para promover las Ciencias de la Computación en los estudios preuniversitarios a través del Pensamiento Computacional. El proyecto, en desarrollo hasta 2024, cuenta con dos acciones: un diagnóstico del sistema educativo en Canarias sobre recursos, formación docente y percepción de los estudiantes; y una hoja de ruta que analice cómo la recién aprobada reforma educativa se adapta a esta realidad. También busca impulsar el conocimiento y la participación de la minoría, abordando las barreras existentes y mejorando la educación en Ciencias de la Computación

Soil micromorphological image classification using deep learning: The porosity parameter

Identifying components and microstructures in soil and sediment thin sections is one of the many subjects of analysis in archeological research, as these features can provide information regarding the deposit from which they were extracted, such as its origin and nature, clues about their associated human contexts or alteration processes they might have undergone over time. This article presents a Deep Learning system based on Convolutional Neural Networks (CNN) to classify different porosity types of structures in photomicrographs from archeological soils and sediment thin sections, as a first step to build and expand a database that will boost research in this field of archeological research. The results obtained are encouraging and show that the presented models can be successfully applied to this classification task. The trained models have been used to estimate the quantity of the different microstructures in test images, obtaining a median error of around 2%. (C) 2021 Elsevier B.V. All rights reserved.University of La Laguna, Spain; Spanish Ministry of Science, Innovation and Universities under the EIRA project [1207_2020

Educational robotics simulator for fostering computational thinking

Este trabajo presenta una herramienta Web libre y gratuita que facilita a cualquier centro educativo la enseñanza de conceptos básicos sobre robótica y programación y que, al mismo tiempo, permite desarrollar habilidades relacionadas con el pensamiento computacional: descomposición, abstracción, reconocimiento de patrones y pensamiento algorítmico. Esta herramienta permite diseñar y personalizar un robot móvil a través del uso de distintos tipos de sensores. Tras su creación, el robot se podrá poner a prueba en un entorno de simulación mediante distintos retos. El comportamiento del robot se puede definir a través de un lenguaje de programación visual basado en bloques que permite mover el robot a partir de la información del entorno recogida por los sensores.This work presents a free software tool that facilitates the teaching of basic robotics and programming concepts at any educational institution. At the same time, it allows the development of computational thinking skills to be carried out: decomposition, abstraction, pattern recognition and algorithmic thinking. This tool allows the design and configuration of a robot through the specification of different types of sensors. After designing the robot, its behaviour can be simulated by means of different challenges proposed to the user. This behaviour is defined through a block-based visual programming language. Blocks allow actions that the robot has to perform based on the information gathered by the different sensors to be defined in order to pass a challeng

Improving Odometric Accuracy for an Autonomous Electric Cart

In this paper, a study of the odometric system for the autonomous cart Verdino, which is an electric vehicle based on a golf cart, is presented. A mathematical model of the odometric system is derived from cart movement equations, and is used to compute the vehicle position and orientation. The inputs of the system are the odometry encoders, and the model uses the wheels diameter and distance between wheels as parameters. With this model, a least square minimization is made in order to get the nominal best parameters. This model is updated, including a real time wheel diameter measurement improving the accuracy of the results. A neural network model is used in order to learn the odometric model from data. Tests are made using this neural network in several configurations and the results are compared to the mathematical model, showing that the neural network can outperform the first proposed model

An approach based on the ifcOWL ontology to support indoor navigation

This paper presents an indoor navigation support system based on the Building Information Models (BIM) paradigm. Although BIM is initially defined for the Architecture, Engineering and Construction/Facility Management (AEC/FM) industry, the authors believe that it can provide added value in this context. To this end, the authors will focus on the Industry Foundation Classes (IFC) standard for the formal representation of BIM. The approach followed in this paper will be based on the ifcOWL ontology, which translates the IFC schemas into Ontology Web Language (OWL). Several modifications of this ontology have been proposed, consisting of the inclusion of new items, SWRL rules and SQWRL searches. This way of expressing the elements of a building can be used to code information that is very useful for navigation, such as the location of elements related to the actions desired by the user. It is important to note that this design is intended to be used as a complement to other well-known tools and techniques for indoor navigation. The proposed modifications have been successfully tested in a variety of simulated and real scenarios. The main limitation of the proposal is the immense amount of information contained in the ifcOWL ontology, which causes difficulties involving its processing and the time necessary to perform operations on it. Those elements that are considered important have been selected, removing those that seem secondary to navigation. This procedure will result in a significant reduction in the storage and semantic processing of the information. Thus, for a system with 1000 individuals (in the ontological sense), the processing time is about 90 s. The authors regard this time as acceptable, since in most cases the tasks involved can be considered part of the system initialization, meaning they will only be executed once at the beginning of the process

Interpretable surrogate models to approximate the predictions of convolutional neural networks in glaucoma diagnosis

Deep learning systems, especially in critical fields like medicine, suffer from a significant drawback, their black box nature, which lacks mechanisms for explaining or interpreting their decisions. In this regard, our research aims to evaluate the use of surrogate models for interpreting convolutional neural network (CNN) decisions in glaucoma diagnosis. Our approach is novel in that we approximate the original model with an interpretable one and also change the input features, replacing pixels with tabular geometric features of the optic disc, cup, and neuroretinal rim. We trained CNNs with two types of images: original images of the optic nerve head and simplified images showing only the disc and cup contours on a uniform background. Decision trees were used as surrogate models due to their simplicity and visualization properties, while saliency maps were calculated for some images for comparison. The experiments carried out with 1271 images of healthy subjects and 721 images of glaucomatous eyes demonstrate that decision trees can closely approximate the predictions of neural networks trained on simplified contour images, with R-squared values near 0.9 for VGG19, Resnet50, InceptionV3 and Xception architectures. Saliency maps proved difficult to interpret and showed inconsistent results across architectures, in contrast to the decision trees. Additionally, some decision trees trained as surrogate models outperformed a decision tree trained on the actual outcomes without surrogation. Decision trees may be a more interpretable alternative to saliency methods. Moreover, the fact that we matched the performance of a decision tree without surrogation to that obtained by decision trees using knowledge distillation from neural networks is a great advantage since decision trees are inherently interpretable. Therefore, based on our findings, we think this approach would be the most recommendable choice for specialists as a diagnostic tool