CHATGPT FOR POINT CLOUD 3D OBJECT PROCESSING

Large-scale pretrained language models have been a revolution in human-machine communication. Recently, such language models also generate code for required tasks. The objective of this work is to evaluate the functionality of the codes generated by ChatGPT (version 15-Dec-2022) for point cloud processing. The programming language selected for the test was MATLAB due to the extensive use in prototyping and toolboxes for Computer Vision and LiDAR. Using the Question-Answer system, the ChatGPT was asked for codes to calculate surface normals, curvature, eigenvalues, and eigenfeatures, with specific parameters and outputs. The provided codes were compiled and executed. The results show that ChatGPT can generate functional code for very specific and short applications, however, it is not capable of generating large code involving the correct use of loops, indexes, or equations

A DEEP LEARNING APPROACH FOR THE RECOGNITION OF URBAN GROUND PAVEMENTS IN HISTORICAL SITES

Urban management is a topic of great interest for local administrators, particularly because it is strongly connected to smart city issues and can have a great impact on making cities more sustainable. In particular, thinking about the management of the physical accessibility of cities, the possibility of automating data collection in urban areas is of great interest. Focusing then on historical centres and urban areas of cities and historical sites, it can be noted that their ground surfaces are generally characterised by the use of a multitude of different pavements. To strengthen the management of such urban areas, a comprehensive mapping of the different pavements can be very useful. In this paper, the survey of a historical city (Sabbioneta, in northern Italy) carried out with a Mobile Mapping System (MMS) was used as a starting point. The approach here presented exploit Deep Learning (DL) to classify the different pavings. Firstly, the points belonging to the ground surfaces of the point cloud were selected and the point cloud was rasterised. Then the raster images were used to perform a material classification using the Deep Learning approach, implementing U-Net coupled with ResNet 18. Five different classes of materials were identified, namely sampietrini, bricks, cobblestone, stone, asphalt. The average accuracy of the result is 94%

ROBUST TECHNIQUES FOR BUILDING FOOTPRINT EXTRACTION IN AERIAL LASER SCANNING 3D POINT CLOUDS

The building footprint is crucial for a volumetric 3D representation of a building that is applied in urban planning, 3D city modeling, cadastral and topographic map generation. Aerial laser scanning (ALS) has been recognized as the most suitable means of large-scale 3D point cloud data (PCD) acquisition. PCD can produce geometric detail of a scanned surface. However, it is almost impossible to get point clouds without noise and outliers. Besides, data incompleteness and occlusions are two common phenomena for PCD. Most of the existing methods for building footprint extraction employ classification, segmentation, voting techniques (e.g., Hough-Transform or RANSAC), or Principal Component Analysis (PCA) based methods. It is known that classical PCA is highly sensitive to outliers, even RANSAC which is known as a robust technique for shape detection is not free from outlier effects. This paper presents a novel algorithm that employs MCMD (maximum consistency within minimum distance), MSAC (a robust variant of RANSAC) and a robust regression to extract reliable building footprints in the presence of outliers, missing points and irregular data distributions. The algorithm is successfully demonstrated through two sets of ALS PCD

Test engineering education in Europe: the EuNICE-Test project

The paper deals with a European experience of education in industrial test of ICs and SoCs using remote testing facilities. The project addresses the problem of the shortage in microelectronics engineers aware with the new challenge of testing mixed-signal SoCs far multimedia/telecom market. It aims at providing test training facilities at a European scale in both initial and continuing education contexts. This is done by allowing the academic and industrial partners of the consortium to train engineers using the common test resources center (CRTC) hosted by LIRMM (Laboratoire d'Informatique, de Robotique et de Microelectronique de Montpellier, France). CRTC test tools include up-to-date/high-tech testers that are fully representative of real industrial testers as used on production testfloors. At the end of the project, it is aimed at reaching a cruising speed of about 16 trainees per year per center. Each trainee will have attend at least one one-week training using the remote test facilities of CRTC

PEDESTRIAN PATHFINDING in URBAN ENVIRONMENTS: PRELIMINARY RESULTS

With the rise of urban population, many initiatives are focused upon the smart city concept, in which mobility of citizens arises as one of the main components. Updated and detailed spatial information of outdoor environments is needed to accurate path planning for pedestrians, especially for people with reduced mobility, in which physical barriers should be considered. This work presents a methodology to use point clouds to direct path planning. The starting point is a classified point cloud in which ground elements have been previously classified as roads, sidewalks, crosswalks, curbs and stairs. The remaining points compose the obstacle class. The methodology starts by individualizing ground elements and simplifying them into representative points, which are used as nodes in the graph creation. The region of influence of obstacles is used to refine the graph. Edges of the graph are weighted according to distance between nodes and according to their accessibility for wheelchairs. As a result, we obtain a very accurate graph representing the as-built environment. The methodology has been tested in a couple of real case studies and Dijkstra algorithm was used to pathfinding. The resulting paths represent the optimal according to motor skills and safety

Influencia del cruce industrial en los rendimientos productivos y la calidad de la canal de corderos de la raza ovina Segureña

Productive traits (birth weight, slaughter weight, average daily gain), carcass quality traits (conformation, fatness score, carcass and leg blockness and tissue composition) in lambs from either pure Segureña breed or crossbred with Texel were studied. Improvements in the crossbred animals were observed, both in productive traits: higher birth and slaughter weights and average daily gain, and in carcass quality: bigger conformation, lower fatness score and a higher muscle composition in contrast with a lower fat percentage. The results indicate that the crossbreeding between these two breeds could be a recommended tool to develop and use in certain farms and production conditions. Se han estudiado los rendimientos productivos (peso al nacimiento, peso al sacrificio, y ganancia media diaria), así como los parámetros de calidad de la canal (conformación, engrasamiento, índices de compacidad de la canal y de la pierna, y composición tisular) de corderos provenientes de la raza Segureña y del cruce industrial de ésta con la raza Texel. Se han observado mejoras en los animales cruzados, tanto en la productividad: mayor peso al nacimiento y al sacrificio y una mayor ganancia media diaria de peso, como en la calidad de la canal: mejor conformación, menor engrasamiento, y una mayor composición muscular en contraposición a un menor porcentaje de grasa. Los resultados obtenidos indican que el cruce entre estas dos razas podría ser una herramienta recomendable a desarrollar y utilizar en determinadas explotaciones y especificaciones de producción

Drug residues in goat s milk after to the prophylactic use of antibiotics in intravaginal sponges for estrus synchronization

[EN] The aim of this study was to determine whether the prophylactic use of antibiotics in intravaginal sponges used for estrus synchronization in goats may result in the presence of inhibitors in milk and, therefore, of positive results by microbial screening tests. Ninetyeight Murciano-Granadina goats were used, divided into 7 groups of 14 animals. Intravaginal sponges were placed in 6 groups using 2 concentrations of 3 different antibiotics: doxycycline, oxytetracycline, and sulfathiazole- framycetin. The sponges of the control group were placed without antibiotics. Milk samples were collected daily until 7 d posttreatment and analyzed using 3 microbial tests. Positive samples were retested by specific receptor-binding assays to confirm the positive results. Vaginal status was evaluated by visual assessment of the external aspect of the sponges after removal. The microbial test response was not affected by either day posttreatment or dose of antibiotic used, except for oxytetracycline at the higher concentration. Moreover, no positive results were obtained using receptor-binding assays, suggesting that residues, if present in milk, did not exceed the regulatory (safety) levels established for these drugs. The occurrence of soiled sponges was higher in the control group. With respect to the dose of antibiotics used, no significant differences were found for the lower dose administered. However, a significant increase in the percentage of clean sponges was observed for the higher dose of doxycycline. We conclude that the prophylactic use of low doses of doxycycline, oxytetracycline, or sulfathiazole in intravaginal sponges used for synchronization of estrus helps to reduce clinical vaginitis in dairy goats and does not seem to be the cause of positive results in microbial inhibitor tests used to detect antibiotics in goat milk.This work is part of the AGL-2009-11524 Project funded by the Spanish Ministry of Science and Innovation (Madrid, Spain). The authors are grateful to the Diputacion de Castellon and UNISENSOR S.A (Angleur, Belgium) for their support.Romero Rueda, T.; Balado, J.; Althaus, RL.; Beltrán Martínez, MC.; Molina Pons, MP. (2016). Drug residues in goat s milk after to the prophylactic use of antibiotics in intravaginal sponges for estrus synchronization. Journal of Dairy Science. 99(1):141-145. https://doi.org/10.3168/jds.2015-10200S14114599

TREE DIGITISATION FROM POINT CLOUDS WITH UNREAL ENGINE

Trees are fundamental parts of urban areas and green urbanism. Although much effort is being put into the digitisation of urban areas, trees present great complexity and are usually replaced by predefined models. On the one hand, trees are elements composed of trunk, branches, and leaves, each with a completely different structure and geometry. On the other hand, the tree parts are closely related to each species. Therefore, in order to obtain a realistic digital urban environment, in 3D models such as CityGML or Metaverse, it is necessary that the trees correspond faithfully to reality. The aim of this work is to propose a method to digitise trees from Mobile Laser Scanning and Terrestrial Laser Scanning data. The process takes advantage of the differentiation between trunks and leaves for their segmentation by point cloud geometric features. Unreal Engine is then used to digitise each part. Trunk and branches are geometrically preserved. For dense canopy trees, predefined leaves according to the species are imported and the alpha shape of the crown is filled. For non-dense canopy trees, the canopy is imported and modified to fit the branches. The method was tested on four real case studies. The results show realistic trees, with correct trunk and foliage segmentation, but highly dependent on the life/canopy repositories. Unreal Engine was a very complete and useful tool for the digitisation of trees generating realistic textures and lighting options

Importancia de la acreditación de laboratorios de organismos de regulación enológicos

A medida que aumenta la globalización de los suministros de alimentos, resulta cada vez más evidente la necesidad de reforzar los sistemas que velan por la inocuidad de los alimentos en todos los países. La acreditación es el reconocimiento formal, que hace una tercera parte, de que un organismo cumple con los requisitos especificados y es competente para desarrollar tareas específicas de evaluación de la conformidad. INAVI como organismo regulador de la vitivinicultura de la República Oriental del Uruguay cuenta desde el año 2011 con un Laboratorio Acreditado por el OUA (Organismo Uruguayo de Acreditación) en la Norma ISO/IEC 17025:2005. En la actualidad cuenta con 16 alcances en dos matrices diferentes vinos y sidras asegurando así su competencia técnica. Los Organismos Reguladores en Enología con frecuencia deben tomar decisiones en referencia a la salud y bienestar de los consumidores, protección del medio ambiente, control del cumplimiento de requisitos existentes reglamentarios y legales, generación de nuevos requisitos y regulaciones, así como asignación de recursos técnicos y financieros. Para poder tomar estas decisiones los Organismos de Regulación deben tener confianza en los resultados generados, la acreditación de sus laboratorios de ensayo genera la confianza necesaria para el desarrollo de dichas actividades