A Bayesian strategy to enhance the performance of indoor localization systems

This work describes the probabilistic modelling af a Bayesian-based mechanism to improve location estimates of an already deployed location system by fusing its outputs with low-cost binary sensors. This mechanism takes advantege of the localization captabilities of different technologies usually present in smart environments deployments. The performance of the proposed algorithm over a real sensor deployment is evaluated using simulated and real experimental data.

Prediction of PM10 concentrations using Fuzzy c-Means and ANN

Salamanca has been considered among the most polluted cities in Mexico. The vehicular park, the industry and the emissions produced by agriculture, as well as orography and climatic characteristics have propitiated the increment in pollutant concentration of Particulate Matter less than 10 μg/m3 in diameter (PM10). In this work, a Multilayer Perceptron Neural Network has been used to make the prediction of an hour ahead of pollutant concentration. A database used to train the Neural Network corresponds to historical time series of meteorological variables (wind speed, wind direction, temperature and relative humidity) and air pollutant concentrations of PM10. Before the prediction, Fuzzy c-Means clustering algorithm have been implemented in order to find relationship among pollutant and meteorological variables. These relationship help us to get additional information that will be used for predicting. Our experiments with the proposed system show the importance of this set of meteorological variables on the prediction of PM10 pollutant concentrations and the neural network efficiency. The performance estimation is determined using the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The results shown that the information obtained in the clustering step allows a prediction of an hour ahead, with data from past 2 hours

Stress Detection by means of Stress Physiological Template

This paper describes a stress detection system based on fuzzy logic and two physiological signals: Galvanic Skin Response and Heart Rate. Instead of providing a global stress classification, this approach creates an individual stress templates, gathering the behaviour of individuals under situations with different degrees of stress. The proposed method is able to detect stress properly with a rate of 99.5%, being evaluated with a database of 80 individuals. This result improves former approaches in the literature and well-known machine learning techniques like SVM, k-NN, GMM and Linear Discriminant Analysis. Finally, the proposed method is highly suitable for real-time applications

Caracterizacion de la sequia en pastos anuales en Dehesas / Characterization of pasture drought in dehesas of Spain

La sequía es un término meteorológico que significa un periodo seco prolongado. El objetivo de este trabajo es caracterizar el fenómeno estacional de la sequía en pastos anuales de dehesa. Durante 2010 y 2011 se realizó un seguimiento del pasto herbáceo en El Cubo de Don Sancho (Salamanca), Trujillo (Cáceres) y Pozoblanco (Córdoba). Se midió la producción herbácea y se caracterizó botánicamente cada zona, además se midió mensualmente la variación del contenido de agua en el suelo mediante un TDR y la precipitación. Los datos de campo de precipitación, humedad del suelo y cantidad de pasto en pie, y los datos estimados de evaporación se han comparado con la evolución del índice de vegetación para seguros de sequía por teledetección determinado por Agroseguro. Los resultados mostraron un retardo entre la acumulación de agua en el suelo y el crecimiento del pasto, que se transfiere a las medidas del índice de vegetación por teledetección. En los dos años de estudio los periodos de sequía sucedieron al inicio de crecimiento, justo después de la sequía estacional típica del verano.

Unsupervised system to classify SO2 pollutant concentrations in Salamanca, Mexico

Salamanca is cataloged as one of the most polluted cities in Mexico. In order to observe the behavior and clarify the influence of wind parameters on the Sulphur Dioxide (SO2) concentrations a Self-Organizing Maps (SOM) Neural Network have been implemented at three monitoring locations for the period from January 1 to December 31, 2006. The maximum and minimum daily values of SO2 concentrations measured during the year of 2006 were correlated with the wind parameters of the same period. The main advantages of the SOM Neural Network is that it allows to integrate data from different sensors and provide readily interpretation results. Especially, it is powerful mapping and classification tool, which others information in an easier way and facilitates the task of establishing an order of priority between the distinguished groups of concentrations depending on their need for further research or remediation actions in subsequent management steps. For each monitoring location, SOM classifications were evaluated with respect to pollution levels established by Health Authorities. The classification system can help to establish a better air quality monitoring methodology that is essential for assessing the effectiveness of imposed pollution controls, strategies, and facilitate the pollutants reduction.

Decentralized multi-tasks distribution in heterogeneous robot teams by means of ant colony optimization and learning automata

This paper focuses on the general problem of coordinating multiple robots. More specifically, it addresses the self-election of heterogeneous specialized tasks by autonomous robots. In this paper we focus on a specifically distributed or decentralized approach as we are particularly interested on decentralized solution where the robots themselves autonomously and in an individual manner, are responsible of selecting a particular task so that all the existing tasks are optimally distributed and executed. In this regard, we have established an experimental scenario to solve the corresponding multi-tasks distribution problem and we propose a solution using two different approaches by applying Ant Colony Optimization-based deterministic algorithms as well as Learning Automata-based probabilistic algorithms. We have evaluated the robustness of the algorithm, perturbing the number of pending loads to simulate the robot’s error in estimating the real number of pending tasks and also the dynamic generation of loads through time. The paper ends with a critical discussion of experimental results.

Comparison of NDVI fields obtained from different remote sensors

Satellite image data have become an important source of information for monitoring vegetation and mapping land cover at several scales. Beside this, the distribution and phenology of vegetation is largely associated with climate, terrain characteristics and human activity. Various vegetation indices have been developed for qualitative and quantitative assessment of vegetation using remote spectral measurements. In particular, sensors with spectral bands in the red (RED) and near-infrared (NIR) lend themselves well to vegetation monitoring and based on them [(NIR - RED) / (NIR + RED)] Normalized Difference Vegetation Index (NDVI) has been widespread used. Given that the characteristics of spectral bands in RED and NIR vary distinctly from sensor to sensor, NDVI values based on data from different instruments will not be directly comparable. The spatial resolution also varies significantly between sensors, as well as within a given scene in the case of wide-angle and oblique sensors. As a result, NDVI values will vary according to combinations of the heterogeneity and scale of terrestrial surfaces and pixel footprint sizes. Therefore, the question arises as to the impact of differences in spectral and spatial resolutions on vegetation indices like the NDVI and their interpretation as a drought index. During 2012 three locations (at Salamanca, Granada and Córdoba) were selected and a periodic pasture monitoring and botanic composition were achieved. Daily precipitation, temperature and monthly soil water content were measurement as well as fresh and dry pasture weight. At the same time, remote sensing images were capture by DEIMOS-1 and MODIS of the chosen places. DEIMOS-1 is based on the concept Microsat-100 from Surrey. It is conceived for obtaining Earth images with a good enough resolution to study the terrestrial vegetation cover (20x20 m), although with a great range of visual field (600 km) in order to obtain those images with high temporal resolution and at a reduced cost. By contranst, MODIS images present a much lower spatial resolution (500x500 m). The aim of this study is to establish a comparison between two different sensors in their NDVI values at different spatial resolutions. Acknowledgements. This work was partially supported by ENESA under project P10 0220C-823. Funding provided by Spanish Ministerio de Ciencia e Innovación (MICINN) through project no. MTM2009-14621 and i-MATH No. CSD2006-00032 is greatly appreciated.

Overview of the Spanish Fuel Cycle: Technical Tours Organized by Spanish young Generation in Nuclear.

Spanish Young Generation in Nuclear (Jóvenes Nucleares, JJNN) is a non-profrt organization that depends on the Spanish Nuclear Society (Sociedad Nuclear Española, SNE).Since one of rts main goals is to spread the knowledge about nuclear power,severa! technical tours to facilities wrth an importan!role in the nuclear fuel cycle have been organized for the purpose ofleaming about the different stages of the Spanish tuel cycle. Spanish Young Generation in Nuclear had the opportunity to visit ENUSA Fuel Assembly Factory in Juzbado (Salamanca, Spain), Where it could be understood the front-end cycle which involves the uranium supply and storage, design and manufacturing of fuel bundles for European nuclear power plants. Alterwards, due to the tour of Almaraz NPP (PWR) and Santa María de Garoña NPP (BWR), rt could be comprehended how to obtain energy from this fuel in two different types of reactors.Furthermore,in these two plants, the facilities related to the back-end cycle could be toured. lt was possible to watch the Spent FuelPools, where the fuel bundles are stored under water until their activity is reduced enough to transport them to an Individual Temporary Storage Facility orto the Centralized Temporary Storage. Finally, a technical tour to ENSA Heavy Components Factory (ENSA) was accomplished, Where it could be experienced at first hand how different Nuclear Steam Supply System (NSSS) components and other nuclear elements, such as racks or shipping and storage casks for spent nuclear fuel, are manulactured. All these perlonned technical tours were a complete success thanks to a generous care and know-how of the wor1of leading the technicaltours.The unanimous opinion of the participants was that taking part in this kind of activities is a worthWhile expeñence which has exceeded their expectations.