The IACOB spectroscopic database of Galactic OB stars

We present the IACOB spectroscopic database, the largest homogeneous database of high-resolution, high signal-to-noise ratio spectra of Northern Galactic OB-type stars compiled up to date. The spectra were obtained with the FIES spectrograph attached to the Nordic Optical Telescope. We briefly summarize the main characeristics and present status of the IACOB, first scientific results, and some future plans for its extension and scientific exploitation.Comment: 2 pages. Poster contribution to the proceedings of the IAU272 "Active OB stars: structure, evolution, mass loss and critical limits

Modeling for investment on scenarios TIIE 28 and TIIE 91 days

This article presents through mathematical modeling two hypothetical scenarios of investment with monthly savings in the format of early annuity. In both cases, the term is referenced to one year, with deposits in different periods and changing effective rates. The development of modeling used information got from official sources about interest rates. The result seeks to determine the most profitable scenario based on the interest rate and its capitalization

UnrealROX+: An Improved Tool for Acquiring Synthetic Data from Virtual 3D Environments

Synthetic data generation has become essential in last years for feeding data-driven algorithms, which surpassed traditional techniques performance in almost every computer vision problem. Gathering and labelling the amount of data needed for these data-hungry models in the real world may become unfeasible and error-prone, while synthetic data give us the possibility of generating huge amounts of data with pixel-perfect annotations. However, most synthetic datasets lack from enough realism in their rendered images. In that context UnrealROX generation tool was presented in 2019, allowing to generate highly realistic data, at high resolutions and framerates, with an efficient pipeline based on Unreal Engine, a cutting-edge videogame engine. UnrealROX enabled robotic vision researchers to generate realistic and visually plausible data with full ground truth for a wide variety of problems such as class and instance semantic segmentation, object detection, depth estimation, visual grasping, and navigation. Nevertheless, its workflow was very tied to generate image sequences from a robotic on-board camera, making hard to generate data for other purposes. In this work, we present UnrealROX+, an improved version of UnrealROX where its decoupled and easy-to-use data acquisition system allows to quickly design and generate data in a much more flexible and customizable way. Moreover, it is packaged as an Unreal plug-in, which makes it more comfortable to use with already existing Unreal projects, and it also includes new features such as generating albedo or a Python API for interacting with the virtual environment from Deep Learning frameworks.Spanish Government PID2019-104818RB-I00 grant for the MoDeaAS project, supported with Feder funds. This work has also been supported by Spanish national grants for PhD studies FPU17/00166,ACIF/2018/197 and UAFPU2019-13. Experiments were made possible by a generous hardware donation from NVIDIA

A Review on Deep Learning Techniques for Video Prediction

The ability to predict, anticipate and reason about future outcomes is a key component of intelligent decision-making systems. In light of the success of deep learning in computer vision, deep-learning-based video prediction emerged as a promising research direction. Defined as a self-supervised learning task, video prediction represents a suitable framework for representation learning, as it demonstrated potential capabilities for extracting meaningful representations of the underlying patterns in natural videos. Motivated by the increasing interest in this task, we provide a review on the deep learning methods for prediction in video sequences. We firstly define the video prediction fundamentals, as well as mandatory background concepts and the most used datasets. Next, we carefully analyze existing video prediction models organized according to a proposed taxonomy, highlighting their contributions and their significance in the field. The summary of the datasets and methods is accompanied with experimental results that facilitate the assessment of the state of the art on a quantitative basis. The paper is summarized by drawing some general conclusions, identifying open research challenges and by pointing out future research directions.This work has been funded by the Spanish Government PID2019-104818RB-I00 grant for the MoDeaAS project, supported with Feder funds. This work has also been supported by two Spanish national grants for PhD studies, FPU17/00166, and ACIF/2018/197 respectively

Desarrollo experimental de controladores Fuzzy para procesos térmicos y neumáticos

In this project, a Fuzzy control system is proposed in an industrial process training module with two independent systems between them, one thermal and the other pneumatic. The control algorithm is developed in Python language v3.6 executed by a Raspberry Pi B+, both controllers depend on the error and change in error that are updated in times of 2 s and 1 s, for temperature and pressure respectively, communication with the plants uses A/D and D/A converters, the thermal Fuzzy was analyzed with three temperature references [50,100 and 150]°C, with a rise time of 191 s, 360 s and 505 s; steady state error of 5.5%, 0.7% y 0.7%, in the pneumatic system the speed of change between references is evaluated from 10 psi to 15 psi varying the activation of the compressor at the beginning of the experiments, the settling times obtained are 111 s and 106 s, with the compressor off the result is 116 s and 88 s, besides a maximum excess of 13% with inherent oscillations to the type system that are in an acceptable range. En este proyecto, se propone un sistema de control Fuzzy en un módulo de entrenamiento de procesos industriales con dos sistemas independientes entre sí, uno térmico y otro neumático, el algoritmo de control se desarrolla en lenguaje Python v3.6 ejecutado por una Raspberry Pi B+, ambos controladores dependen del error y cambio en el error que se actualizan en tiempos de 2 s y 1 s, para temperatura y presión respectivamente, la comunicación con las plantas emplea conversores A/D y D/A, el Fuzzy térmico se analizo con tres referencias de temperatura [50,100 y 150]°C, con un tiempo de subida de 191 s, 360 s y 505 s; error de estado estacionario de 5.5 %, 0.7% y 0.7 %, en el sistema neumático se evalúo la velocidad de cambio entre referencias de 10 psi a 15 psi variando la activación del compresor al inicio de los experimentos, los tiempos de asentamiento que se obtienen son 111 s y 106 s, con el compresor apagado el resultado es de 116 s y 88 s, además de un sobrepaso máximo de 13% con oscilaciones inherentes al tipo sistema que se encuentran en un rango aceptable.&nbsp

Experimentación de controladores digitales clásicos en un sistema embebido aplicado en un proceso térmico

This research project shows the implementation of classic digital controllers in an embedded system to control the temperature of a thermal system. The embedded system selected was in the computer board Raspberry Pi 2 B+ together with an ONE Arduino board. In the measurement stage the hardware was developed for to transmit the temperature data from the PT-100 thermometer resistance towards the Raspberry PI. For the implementation of the computational stage was developed an algorithm for the different controllers and based on this the controllers were programmed in Python 2.7. For the actuator a power stage was designed that converts the ac voltage into a voltage dc, from a PWM signal. This dc voltage is used to feed a thermal resistance that is responsible for executing the control action of the system. With the implementation of the types of controllers, a comparative analysis was realized highlighting the most relevant characteristics for each type of controller and allowing to also evaluate the performance of the Raspberry Pi. Based on the results obtained in the execution of the project, it concluded the feasibility to control thermal systems under different types of controllers embedded in the Raspberry Pi and with the possibility of developing intelligent control strategies for futures projects, through the use of software tools and free hardware.Este proyecto de investigación muestra la implementación de controladores digitales clásicos dentro de un sistema embebido para controlar la temperatura de un sistema térmico. El sistema embebido seleccionado fue el computador de placa reducida Raspberry Pi 2 B con una tarjeta Arduino UNO. En la etapa de medición se desarrolló el hardware para transmitir los datos de la temperatura desde el transductor PT-100 hacia la Raspberry Pi. Para la etapa de computacional se desarrollaron diferentes algoritmos de los controladores en la plataforma Python 2.7. Para el actuador se diseñó una etapa de potencia que convierte el voltaje ac en un voltaje dc, de acuerdo a una señal PWM. Este voltaje dc se utiliza para alimentar una resistencia térmica encargada de ejecutar la acción de control del sistema. Con el desarrollo de este tipo de controladores se realizó un análisis comparativo destacando las características más relevantes del sistema para cada tipo de controlador y permitiendo evaluar también el rendimiento de la Raspberry Pi. Con base en los resultados obtenidos en la ejecución del proyecto, se concluye que es viable controlar sistemas térmicos bajo diferentes tipos de controladores embebidos en la Raspberry Pi y con la posibilidad de desarrollar estrategias de control inteligente para futuros proyectos, con la ventaja de usar herramientas de software y hardware libre

Experimentación de controladores digitales clásicos en un sistema embebido aplicado en un proceso térmico

This research project shows the implementation of classic digital controllers in an embedded system to control the temperature of a thermal system. The embedded system selected was in the computer board Raspberry Pi 2 B+ together with an ONE Arduino board. In the measurement stage the hardware was developed for to transmit the temperature data from the PT-100 thermometer resistance towards the Raspberry PI. For the implementation of the computational stage was developed an algorithm for the different controllers and based on this the controllers were programmed in Python 2.7. For the actuator a power stage was designed that converts the ac voltage into a voltage dc, from a PWM signal. This dc voltage is used to feed a thermal resistance that is responsible for executing the control action of the system. With the implementation of the types of controllers, a comparative analysis was realized highlighting the most relevant characteristics for each type of controller and allowing to also evaluate the performance of the Raspberry Pi. Based on the results obtained in the execution of the project, it concluded the feasibility to control thermal systems under different types of controllers embedded in the Raspberry Pi and with the possibility of developing intelligent control strategies for futures projects, through the use of software tools and free hardware.Este proyecto de investigación muestra la implementación de controladores digitales clásicos dentro de un sistema embebido para controlar la temperatura de un sistema térmico. El sistema embebido seleccionado fue el computador de placa reducida Raspberry Pi 2 B con una tarjeta Arduino UNO. En la etapa de medición se desarrolló el hardware para transmitir los datos de la temperatura desde el transductor PT-100 hacia la Raspberry Pi. Para la etapa de computacional se desarrollaron diferentes algoritmos de los controladores en la plataforma Python 2.7. Para el actuador se diseñó una etapa de potencia que convierte el voltaje ac en un voltaje dc, de acuerdo a una señal PWM. Este voltaje dc se utiliza para alimentar una resistencia térmica encargada de ejecutar la acción de control del sistema. Con el desarrollo de este tipo de controladores se realizó un análisis comparativo destacando las características más relevantes del sistema para cada tipo de controlador y permitiendo evaluar también el rendimiento de la Raspberry Pi. Con base en los resultados obtenidos en la ejecución del proyecto, se concluye que es viable controlar sistemas térmicos bajo diferentes tipos de controladores embebidos en la Raspberry Pi y con la posibilidad de desarrollar estrategias de control inteligente para futuros proyectos, con la ventaja de usar herramientas de software y hardware libre

Retinal nerve fiber layer thickness analysis in normal and glaucomatous dogs using laser polarimetry diagnosis

Once the glaucomatous damage is progressive and irreversible, studies on the glaucoma onset as well as its development have been discussed. It is known that the early diagnostisis is extremely important to the stabilization and treatment. The retinal nerve fibers layer thickness analysis ''in vivo'' was proposed in human ophthalmology in order to establish the thickness changing, due to glaucoma, and have shown that such findings can be even detected six years priors to the first clinical signs. However, in Veterinary Medicine, such data need to be investigated and discussed. This study used two groups of dogs, a glaucomatous group and a normal group, that had been submitted to the retinal nerve fibers layer analysis through the GDx Nerve Fiber Analyzer. Statistical data showed that the nerve fiber layer of the glaucomatous group was thinner (p < 0.05), sustaining ganglion cells axons loss in glaucomatous eyes, compared to normal eyes.As lesões glaucomatosas são progressivas e irreversíveis. Estudos sobre o aparecimento e comportamento desta enfermidade vêm sendo discutidos na oftalmologia em geral. O diagnóstico precoce é de extrema importância para o tratamento e estabilização da mesma. A análise da espessura da camada de fibras nervosas retinianas ''in vivo'' foi proposta na oftalmologia humana, para estabelecer as alterações produzidas pelo glaucoma, e tem demonstrado que tais alterações podem ser detectadas até seis anos antes de alterações de campo visual e aumento de pressão intraocular. Entretanto, na Medicina Veterinária, estes dados carecem de estudo e discussão. Neste experimento foram utilizados dois grupos de cães, com olhos normais e olhos glaucomatosos, que foram submetidos à análise das fibras nervosas retinianas através do Analisador de Fibras Nervosas GDx. Os resultados estatísticos demonstraram que a camada de fibras nervosas dos olhos dos cães glaucomatosos estava mais delgada (p < 0,05) quando comparados aos olhos de cães normais, confirmando a perda de axônios das células ganglionares em olhos de cães com glaucoma

Laboratorio remoto eLab3D: un entorno innovador para el aprendizaje de competencias prácticas en electrónica

Este artículo muestra la utilización de las nuevas tecnologías al servicio del aprendizaje de competencias prácticas en electrónica, siendo un ejemplo de adaptación de los recursos educativos a diferentes contextos y necesidades. Se trata de un laboratorio remoto en el que la adecuada complementación de un hardware configurable y un software de última generación permite al estudiante realizar prácticas de electrónica y diseño de circuitos en un mundo virtual 3D. El usuario dispone de un avatar e interacciona con réplicas virtuales de instrumentos, placas de circuitos, componentes o cables de forma muy similar a como se opera en un laboratorio presencial. Pero lo realmente destacable es que el usuario manipula instrumentación y circuitos que están ubicados en un laboratorio real. Todo ello se ha conseguido con un sistema escalable y de bajo coste. Finalizado el diseño y desarrollo de la plataforma se han realizado las primeras pruebas con estudiantes, profesores y profesionales para valorar su percepción respecto al uso de eLab3D, obteniéndose unos resultados muy positivos