Prediction of Satellite Shadowing in Smart Cities with Application to IoT

The combination of satellite direct reception and terrestrial 5G infrastructure is essential to guarantee coverage in satellite based-Internet of Things, mainly in smart cities where buildings can cause high power losses. In this paper, we propose an accurate and fast graphical method for predicting the satellite coverage in urban areas and SatCom on-the-move scenarios. The aim is to provide information that could be useful in the IoT network planning process, e.g., in the decision of how many terrestrial repeaters are really needed and where they should be placed. Experiments show that the shadowed areas predicted by the method correspond almost perfectly with experimental data measured from an Eutelsat satellite in the urban area of Barcelona.Ministerio de Industria, Turismo y Comercio de España TSI-020301-2009-3

L1-norm unsupervised Fukunaga-Koontz transform

Article number 107942The Fukunaga-Koontz transform (FKT) is a powerful supervised feature extraction method used in twoclass recognition problems, particularly when the classes have equal mean vectors but different covariance matrices. The present work proves that it is also possible to perform the FKT in an unsupervised manner, sparing the need for labeled data, by using a variant of L1-norm Principal Component Analysis (L1-PCA) that minimizes the L1-norm in the feature space. Rigorous proof is given in the case of data drawn from a mixture of Gaussians. A working iterative algorithm based on gradient-descent in the Stiefel manifold is put forward to perform L1-norm minimization with orthogonal constraints. A number of numerical experiments on synthetic and real data confirm the theoretical findings and the good convergence characteristics of the proposed algorithm

Sobre el análisis en componentes independientes de imágenes naturales

En esta Tesis se estudian matemática y experimentalmente, los resultados obtenidos al realizar el análisis en componentes independientes (abreviadamente, ICA, del inglés Independent Component Analysis) de imágenes naturales. El trabajo publicado en 1995 por Bell y Sejnowski [BellSej95], estableciendo una conexión entre los resultados obtenidos al aplicar ICA a imágenes naturales y el comportamiento de ciertas neuronas de la corteza visual primaria, suscitó un gran interés y motivó la aparición de numerosos artículos en los que, mediante diversos experimentos, se ofrecían distintos matices de esta conexión (por citar algunos ejemplos, [CaywWT04, HyvHH03, vanHat98a]). En esta Tesis se aporta, por primera vez, una prueba matemática que explica por qué se observa este interesante comportamiento cuando ICA es aplicado a imágenes naturales.Premio Extraordinario de Doctorado U

Revista de enseñanza universitaria

Resumen tomado del autorDescribe la experiencia de profesores noveles en el 'Curso de Profesores Noveles' que desde hace algún tiempo, el Instituto de Ciencias de la Educación (ICE) de la Universidad de Sevilla viene organizando anualmente con el fin de ayudar a los profesores principiantes en su formación como docentes. Entre las actividades llevadas a cabo destacar la grabación de las clases en video, para después analizarlas, la realización de encuestas a los alumnos para conocer la labor docente.AndalucíaUniversidad de León. Facultad de Educación. Servicio de Biblioteca; Campus de Vegazana, s. n.; 24071 León; Tel. +34987291146; Fax +34987291145; [email protected]; [email protected]

Neural Net with Two Hidden Layers for Non-Linear Blind Source Separation

Abstract. In this paper, we present an algorithm that minimizes the mutual information between the outputs of a perceptron with two hidden layers. The neural network is then used as separating system in the NonLinear Blind Source Separation problem. 1 Introduction. Various signal processing applications involve estimating signals of interest from distorted observations. The so-called Blind Source Separation (BSS) is one that consists of retrieving unobserved source signals s1(t),..., sN (t), assumed to be mutually statistically independent, from only N observed signals x1(t),...