Las comunicaciones móviles en los edificios inteligentes

Este trabajo presenta una plataforma de red móvil aplicada a los entornos de edificios inteligentes. En primer lugar, los edificios inteligentes han sido definidos como “aquellos que utilizan la tecnología computacional para gestionar de manera autónoma todos los servicios que ofrece un entorno de un edificio tales como optimizar el confort del usuario, el consumo de energía y la seguridad” [Callaghan 00].Desde el punto de vista de las telecomunicaciones específicamente desde el punto de vista de las comunicaciones móviles es muy importante para los edificios inteligentes considerar la comunicación entre sistemas autónomos inteligentes, dispositivos personales inteligentes, gestión de sistemas de información, dispositivos móviles, etc.Postprint (published version

Vulnerable road users and connected autonomous vehicles interaction: a survey

There is a group of users within the vehicular traffic ecosystem known as Vulnerable Road Users (VRUs). VRUs include pedestrians, cyclists, motorcyclists, among others. On the other hand, connected autonomous vehicles (CAVs) are a set of technologies that combines, on the one hand, communication technologies to stay always ubiquitous connected, and on the other hand, automated technologies to assist or replace the human driver during the driving process. Autonomous vehicles are being visualized as a viable alternative to solve road accidents providing a general safe environment for all the users on the road specifically to the most vulnerable. One of the problems facing autonomous vehicles is to generate mechanisms that facilitate their integration not only within the mobility environment, but also into the road society in a safe and efficient way. In this paper, we analyze and discuss how this integration can take place, reviewing the work that has been developed in recent years in each of the stages of the vehicle-human interaction, analyzing the challenges of vulnerable users and proposing solutions that contribute to solving these challenges.This work was partially funded by the Ministry of Economy, Industry, and Competitiveness of Spain under Grant: Supervision of drone fleet and optimization of commercial operations flight plans, PID2020-116377RB-C21.Peer ReviewedPostprint (published version

Mdi

The story that we present tries to take readers on a journey of technological marvel, and attempts to show a little view of what the future can be like. After all, the sky's the ceiling, and we are far from our limits. Our story develops the action in the Earth and mention Kepler 4b and Kepler 7B planets to show that distances there are not a problem anymore and everybody can travel from one planet to other like we do using the subway in a city. We do not get into the problems of analyse the specific physical characteristics of each planet neither to explain how humans achieve to live there. We only want to show that in a world of disruptive technologies and innovation, the imagination is and will always be a wonderful tool.Preprin

Monitoring tomato leaf disease through convolutional neural networks

Agriculture plays an essential role in Mexico’s economy. The agricultural sector has a 2.5% share of Mexico’s gross domestic product. Specifically, tomatoes have become the country’s most exported agricultural product. That is why there is an increasing need to improve crop yields. One of the elements that can considerably affect crop productivity is diseases caused by agents such as bacteria, fungi, and viruses. However, the process of disease identification can be costly and, in many cases, time-consuming. Deep learning techniques have begun to be applied in the process of plant disease identification with promising results. In this paper, we propose a model based on convolutional neural networks to identify and classify tomato leaf diseases using a public dataset and complementing it with other photographs taken in the fields of the country. To avoid overfitting, generative adversarial networks were used to generate samples with the same characteristics as the training data. The results show that the proposed model achieves a high performance in the process of detection and classification of diseases in tomato leaves: the accuracy achieved is greater than 99% in both the training dataset and the test dataset.This work was partially funded by the State Research Agency of Spain under grant number PID2020-116377RB-C21.Peer ReviewedPostprint (published version

The Fifth International Conference on Intelligent Environments (IE 09): a report

The development of intelligent environments is considered an important step towards the realization of the ambient intelligence vision. Intelligent environments are technologically augmented everyday spaces, which intuitively support human activity. The IE conferences traditionally provide a leading edge forum for researchers and engineers to present their latest research and to discuss future directions in the area of intelligent environments. This article briefly presents the content of the Fifth International Conference on Intelligent Environments (IE09), which was held July 20–21 at the Castelldefels campus, of the Technical University of Catalonia, near Barcelona, Spain.Postprint (published version

Remotely piloted aircraft systems and a wireless sensors network for radiological accidents

In critical radiological situations, the real time information that we could get from the disaster area becomes of great importance. However, communication systems could be affected after a radiological accident. The proposed network in this research consists of distributed sensors in charge of collecting radiological data and ground vehicles that are sent to the nuclear plant at the moment of the accident to sense environmental and radiological information. Afterwards, data would be analyzed in the control center. Collected data by sensors and ground vehicles would be delivered to a control center using Remotely Piloted Aircraft Systems (RPAS) as a message carrier. We analyze the pairwise contacts, as well as visiting times, data collection, capacity of the links, size of the transmission window of the sensors, and so forth. All this calculus was made analytically and compared via network simulations.Peer ReviewedPostprint (published version

Communication technologies to design vehicle-to-vehicle and vehile-to-infrastructures applications

Intelligent Transport Systems use communication technologies to offer real-time traffic information services to road users and government managers. Vehicular Ad Hoc Networks is an important component of ITS where vehicles communicate with other vehicles and road-side infrastructures, analyze and process received information, and make decisions according to that. However, features like high vehicle speeds, constant mobility, varying topology, traffic density, etc. induce challenges that make conventional wireless technologies unsuitable for vehicular networks. This paper focuses on the process of designing efficient vehicle-to-vehicle and vehicle-to road-side infrastructure applications.Peer ReviewedPostprint (published version

Feature selection model based on EEG signals for assessing the cognitive workload in drivers

In recent years, research has focused on generating mechanisms to assess the levels of subjects’ cognitive workload when performing various activities that demand high concentration levels, such as driving a vehicle. These mechanisms have implemented several tools for analyzing the cognitive workload, and electroencephalographic (EEG) signals have been most frequently used due to their high precision. However, one of the main challenges in implementing the EEG signals is finding appropriate information for identifying cognitive states. Here, we present a new feature selection model for pattern recognition using information from EEG signals based on machine learning techniques called GALoRIS. GALoRIS combines Genetic Algorithms and Logistic Regression to create a new fitness function that identifies and selects the critical EEG features that contribute to recognizing high and low cognitive workloads and structures a new dataset capable of optimizing the model’s predictive process. We found that GALoRIS identifies data related to high and low cognitive workloads of subjects while driving a vehicle using information extracted from multiple EEG signals, reducing the original dataset by more than 50% and maximizing the model’s predictive capacity, achieving a precision rate greater than 90%.This work has been funded by the Ministry of Science, Innovation and Universities of Spain under grant number TRA2016-77012-RPeer ReviewedPostprint (published version

Vehicle density in VANET Applications

This paper analyzes how street-level traffic data affects routing in VANETs applications. First, we offer a general review about which protocols and techniques would fit best for VANET applications. We selected five main technical aspects (Transmission, Routing, Quality of Service, Security and Location) that we consider are differential aspects of VANETs from current Ad-Hoc Networks. Second, the paper analyzes how to configure each technical aspect according to the goal of a wide range of VANET applications. Third, we look at the routing aspect in depth, specifically focusing on how vehicle density affects routing, which protocols are the best option when there is a high/low density, etc. Finally, this research implements a sensor technology, based on an acoustics sensor that has been deployed around the city of Xalapa in México, to obtain reliable information on the real-time density of vehicles. The levels of density were discretized and the obtained data samples were used to feed a traffic simulator, which allowed us to obtain a global picture of the density of the central area of the city. According to the specific levels of vehicle density at a specific moment and place, VANET applications may adapt the routing protocol in a real-time wayPeer ReviewedPostprint (published version

Elaboración del plan de mejora de una asignatura de programación

En este trabajo se expone el proceso que se ha llevado a cabo para la elaboración del plan de mejora en la asignatura de Introducción a los Ordenadores (grado en Ingeniería de Sistemas de Telecomunicación y grado en Telemática, EPSC). La elaboración del plan de mejora se realiza una vez ha finalizado el periodo de impartición de la asignatura, parte del conjunto de datos y evidencias que los profesores de la asignatura han ido recogiendo a lo largo del curso así como de la relación de acciones no previstas (correcciones) llevadas a cabo durante el mismo (medida y seguimiento del proceso de enseñanza-aprendizaje) y consta de tres etapas: análisis de los datos, análisis de la información y establecimiento de acciones de mejora. El análisis de datos tiene como objetivo transformarlos en información. Cada uno de los datos recogidos es una afirmación aislada de la realidad pero la información es un conjunto de datos a los que se ha asociado un significado. Este análisis requiere seleccionar, organizar y presentar el conjunto de datos y evidencias disponibles de forma que facilite la posterior extracción de conclusiones útiles. La segunda etapa transforma la información en conocimiento. En esta fase se determinan las debilidades, amenazas, fortalezas y oportunidades (análisis DAFO) del proceso de enseñanza-aprendizaje de la asignatura, comparando los resultados obtenidos con los objetivos previstos (tanto objetivos de aprendizaje como objetivos relativos a rendimiento académico, tiempo de dedicación del estudiante o satisfacción del mismo). En esta etapa también se determinan las causas de las debilidades y amenazas. En la tercera y última etapa, se determina qué debilidades se quieren corregir, que amenazas se quieren prevenir y que oportunidades se quieren aprovechar (priorización), y se determinan las acciones a implementar (correctoras, preventivas o de mejora).Peer Reviewe