English as a Medium of Instruction in Learning Professional Skills for Engineers

In this article, we reflect on the variables to be considered when teaching in English a subject of the bachelor’s degree of Computer Engineering: “Learning Professional Skills for Engineers”. In order to make this study, we start from an analysis of the recent history of teaching in English at university level and the institutional context in which it happens. Three research questions are posed, with the intent to check what minimum conditions must be met to be able to teach this subject in English. The results lead us to conclude that the option of English as a Medium of Instruction (EMI) is not the appropriate one, taking into account both the linguistic and didactic training of the teaching staff and the language accreditation of the students. However, it is feasible to opt for the Integrating Content and Language in Higher Education (ICLHE) option.En este artículo hacemos una reflexión sobre las variables a tener en cuenta a la hora de impartir en inglés una asignatura del grado de Ingeniería Informática: Habilidades Profesionales para Ingenieros. Para hacer este estudio partimos de un análisis de la trayectoria histórica de la docencia en inglés a nivel universitario y el contexto institucional en el que sucede. Tres preguntas de investigación son propuestas en este trabajo, mediante las cuales se pretende comprobar qué condiciones mínimas exigibles se deben dar para poder impartir docencia en inglés en la asignatura que nos ocupa. Los resultados nos llevan a concluir que la opción de inglés como Medio de Instrucción (EMI), no es la adecuada teniendo en cuenta la formación tanto lingüística como didáctica del profesorado y la acreditación en idiomas del alumnado. Sin embargo, sí resulta viable optar por la opción de Aprendizaje Integrado de Contenido y Lengua Extranjera en la Educación Superior (ICLHE).En aquest arEn este article fem una reflexió sobre les variables a tenir en compte a l'hora d'impartir una assignatura del grau d'Enginyeria Informàtica en anglès: Habilitats Professionals per a Enginyers. Per a realitzar este estudi partim d’una anàlisi de la trajectòria històrica de la docència en anglès en l’àmbit universitari i el context institucional en què ocorre. En este treball es plantegen tres preguntes de recerca, mitjançant les quals es pretén comprovar quines condicions mínimes exigibles s’han de donar per a poder impartir docència en anglès a l'assignatura que ens ocupa. Els resultats conclouen que l'opció d'anglès com a Mitjà d'Instrucció (EMI), no és l'adequada tenint en compte la formació tant lingüística com didàctica del professorat i l'acreditació en idiomes de l’alumnat. No obstant això, sí que resulta viable optar per l'opció d'Aprenentatge Integrat de Contingut i Llengua Estrangera en l'Educació Superior

Learning to See the Wood for the Trees: Deep Laser Localization in Urban and Natural Environments on a CPU

Localization in challenging, natural environments such as forests or woodlands is an important capability for many applications from guiding a robot navigating along a forest trail to monitoring vegetation growth with handheld sensors. In this work we explore laser-based localization in both urban and natural environments, which is suitable for online applications. We propose a deep learning approach capable of learning meaningful descriptors directly from 3D point clouds by comparing triplets (anchor, positive and negative examples). The approach learns a feature space representation for a set of segmented point clouds that are matched between a current and previous observations. Our learning method is tailored towards loop closure detection resulting in a small model which can be deployed using only a CPU. The proposed learning method would allow the full pipeline to run on robots with limited computational payload such as drones, quadrupeds or UGVs.Comment: Accepted for publication at RA-L/ICRA 2019. More info: https://ori.ox.ac.uk/esm-localizatio

Developing a re-configurable architecture for the remote operation of marine autonomous systems

In this experience report, we explain how we take advantage of microservices’ inherent modular nature to accomplish a highly adaptable software architecture that can deal with the trials and tribulations often occurring in marine research environments. We will show the National Oceanography Centre’s journey to develop a web system to remotely operate marine autonomous vehicles from anywhere in the world with an internet connection and how, due to new unforeseen requirements, we took the microservice pattern into a new direction to allow for standalone offline operations of Autonomous Underwater Vehicles (AUV) from research ships in some of the most challenging environments in the world

The problem of the failure of dynamically supported craft A systems approach

In 2 volsSIGLEAvailable from British Library Document Supply Centre- DSC:D44588/83 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

The left Figure shows the binary tree generated from agglomerative clustering of the point-set in the right Figure.

<p>Leafs (in the bottom of the tree) correspond to points in the image, each one being assigned a color (horizontal axis in the left Figure). Edges of the tree (blue lines) represent the cluster formations, where the higher clusters in the vertical axis are formed at later stages and convey higher-scale information (we refer the reader to the online version of the paper to appreciate the details).</p

Penalty incurred by selecting smaller clusters than the ground-truth structures.

<p>Agglomerative trees (on top of each cube) have followed distinct aggregation paths due to the particular imaging conditions of each image. In the middle we show the 7 topologically consistent matching configurations (i.e., matching non-overlapping point-sets) along with the sum of inliers found by RANSAC in each case. We consider that sets <i>P</i>₁ and <i>P</i>₂ in the left image share approximately half of the ground-truth corresponding points with sets <i>Q</i>₁ and <i>Q</i>₂ in the right image (i.e., ≃ 25 points), respectively, because they only overlap partially. Among the 7 possible matching configurations, the best score is obtained by selecting the clusters at the correct scales.</p

A toy example showing the process of finding the multiple structures (i.e., planar surfaces) in the two scenes of the cube shown in the left-hand side.

<p>The trees on top of each cube denote the structures generated by agglomerative clustering, where the leaf nodes are represented as circles on the cubes. On the right hand-side, we show the number of inliers found by RANSAC between each pair-wise cluster correspondence. The ground-truth solution that correctly matches the two surfaces is depicted with red dashed lines.</p

Penalty incurred when selecting clusters at a larger scale than the ground-truth structures.

<p>Each cube is a different image and each planar surface is a different structure. Ground-truth corresponding clusters are represented in the same color (along with their number of points). In the middle we show the topologically consistent configurations (i.e., matching non-overlapping clusters) along with the sum of inliers found by RANSAC for each configuration. The best configuration is the one matching the two structures at the correct scales (i.e., {(<i>P</i>₁,<i>Q</i>₁), (<i>P</i>₂,<i>Q</i>₂)}). Note that any configuration having into account the larger clusters (i.e., green and cyan circles) totally loses one of the structures (typically the smallest one) because, by definition, RANSAC is unable to simultaneously detect inliers in both structures.</p