Una metodología de posicionamiento cooperativo diferencial para el posicionamiento de dispositivos múltiples

Introduction: This publication is the product of research developed within the research lines of the Advanced and Large-scale Computing (Cage) research group throughout 2018, which supports the work of a master’s degree in Systems Engineering at the Industrial University of Santander. Objetive: An approach to a cooperative positioning algorithm is described in this paper, where a set of devices exchange GPS satellite observables and distance estimations with nearby devices in order to increase their positioning accuracy. Methodology: Different scenarios are established where GPS receivers exchange satellite information, using different ionospheric correction models, with the purpose of evaluating which conditions potentially improve the position accuracy. Conclusions: The results show our approach yields increased accuracy when all receivers use the same ionospheric correction model. Moreover, it was observed that the noise levels and uncertainty usually due to factors related to distance from remote devices to the main receiver did not influence positioning improvement when the separation between receiver pairs was large. Originality: The proposed algorithm allows for exploitation of the nature of the problem without increasing complexity at the hardware and software level, and to offer a low-cost cooperative positioning solution alternative. Restrictions: The results presented in the document are based on the execution of the cooperative algorithm using Rinex files of gnss reference stations. So, for scenarios in which the separation distances between reference stations are very high, the error levels in cooperative positioning can be very large.Introducción: esta publicación es el producto de una investigación del grupo de investigación de computación avanzada y en gran escala (Cage) de la Universidad Industrial de Santander, a lo largo de 2018. Objetivo: Se propone un algoritmo de posicionamiento cooperativo en el que un conjunto de dispositivos intercambia observables satelitales, y estimaciones de distancia entre dispositivos GPS cercanos, con el objetivo de aumentar su precisión de posicionamiento. Metodología: se establecen escenarios donde los receptores de GPS intercambian información satelital, y utilizan diferentes modelos de corrección ionosférica con el fin de evaluar las condiciones en que es posible mejorar la precisión en posicionamiento. Conclusiones: El algoritmo propuesto produce una mayor precisión cuando todos los receptores emplean el mismo modelo de corrección ionosférica. Además, el nivel de incertidumbre en la medida de distancia entre dispositivos no presenta mayor influencia sobre la mejora de la precisión, cuando la separación entre receptores es muy grande. Originalidad: el algoritmo propuesto permite explotar la naturaleza del problema sin aumentar la complejidad a nivel de hardware y software, y se ofrece como una alternativa de solución de posicionamiento cooperativo de bajo costo. Limitación: Los resultados exponen la ejecución del algoritmo cooperativo utilizando archivos Rinex de estaciones de referencia gnss. Por lo tanto, para los escenarios en que la distancia de separación entre estaciones es muy alta, los niveles de error en posicionamiento pueden ser elevados

On the representativeness of convolutional neural networks layers

Convolutional Neural Networks (CNN) are the most popular of deep network models due to their applicability and success in image processing. Although plenty of effort has been made in designing and training better discriminative CNNs, little is yet known about the internal features these models learn. Questions like, what specific knowledge is coded within CNN layers, and how can it be used for other purposes besides discrimination, remain to be answered. To advance in the resolution of these questions, in this work we extract features from CNN layers, building vector representations from CNN activations. The resultant vector embedding is used to represent first images and then known image classes. On those representations we perform an unsupervised clustering process, with the goal of studying the hidden semantics captured in the embedding space. Several abstract entities untaught to the network emerge in this process, effectively defining a taxonomy of knowledge as perceived by the CNN. We evaluate and interpret these sets using WordNet, while studying the different behaviours exhibited by the layers of a CNN model according to their depth. Our results indicate that, while top (i.e., deeper) layers provide the most representative space, low layers also define descriptive dimensions.This work was partially supported by the IBM/BSC Technology Center for Supercomputing (Joint Study Agreement, No. W156463), by the Spanish Government through Programa Severo Ochoa (SEV-2015-0493), by the Spanish Ministry of Science and Technology through TIN2015-65316-P project and by the Generalitat de Catalunya (contracts 2014-SGR-1051).Peer ReviewedPostprint (author's final draft

A Latin American Perspective to Agricultural Ethics

The mixture of political, social, cultural and economic environments in Latin America, together with the enormous diversity in climates, natural habitats and biological resources the continent offers, make the ethical assessment of agricultural policies extremely difficult. Yet the experience gained while addressing the contemporary challenges the region faces, such as rapid urbanization, loss of culinary and crop diversity, extreme inequality, disappearing farming styles, water and land grabs, malnutrition and the restoration of the rule of law and social peace, can be of great value to other regions in similar latitudes, development processes and social problems. This chapter will provide a brief overview of these challenges from the perspective of a continent that is exposed to the consequences of extreme inequality in multiple dimensions and conclude by arguing for the need to have a continuous South-South dialogue on the challenges of establishing socially and environmentally sustainable food systems

Multiple Scenario Generation of Subsurface Models:Consistent Integration of Information from Geophysical and Geological Data throuh Combination of Probabilistic Inverse Problem Theory and Geostatistics

Neutrinos with energies above 1017 eV are detectable with the Surface Detector Array of the Pierre Auger Observatory. The identification is efficiently performed for neutrinos of all flavors interacting in the atmosphere at large zenith angles, as well as for Earth-skimming \u3c4 neutrinos with nearly tangential trajectories relative to the Earth. No neutrino candidates were found in 3c 14.7 years of data taken up to 31 August 2018. This leads to restrictive upper bounds on their flux. The 90% C.L. single-flavor limit to the diffuse flux of ultra-high-energy neutrinos with an E\u3bd-2 spectrum in the energy range 1.0 7 1017 eV -2.5 7 1019 eV is E2 dN\u3bd/dE\u3bd < 4.4 7 10-9 GeV cm-2 s-1 sr-1, placing strong constraints on several models of neutrino production at EeV energies and on the properties of the sources of ultra-high-energy cosmic rays

Big Data: Retos y Oportunidades

Qué es Big Data, los retos a los que se enfrenta y las oportunidades que brinda. Universidad Industrial de Santander, Colombia (UIS). México. Video/flv. Video completo.Integración de diferentes fuentes de datos. Modelos de predicción continuos de datos históricos en tiempo real.(Semi) descubrimiento automático del conocimiento, ahora es posible. Infraestructura informática. Los equipos multidisciplinarios (en casa, w / academy).VTS_01_2.VOB/C

On the representativeness of convolutional neural networks layers

Convolutional Neural Networks (CNN) are the most popular of deep network models due to their applicability and success in image processing. Although plenty of effort has been made in designing and training better discriminative CNNs, little is yet known about the internal features these models learn. Questions like, what specific knowledge is coded within CNN layers, and how can it be used for other purposes besides discrimination, remain to be answered. To advance in the resolution of these questions, in this work we extract features from CNN layers, building vector representations from CNN activations. The resultant vector embedding is used to represent first images and then known image classes. On those representations we perform an unsupervised clustering process, with the goal of studying the hidden semantics captured in the embedding space. Several abstract entities untaught to the network emerge in this process, effectively defining a taxonomy of knowledge as perceived by the CNN. We evaluate and interpret these sets using WordNet, while studying the different behaviours exhibited by the layers of a CNN model according to their depth. Our results indicate that, while top (i.e., deeper) layers provide the most representative space, low layers also define descriptive dimensions.This work was partially supported by the IBM/BSC Technology Center for Supercomputing (Joint Study Agreement, No. W156463), by the Spanish Government through Programa Severo Ochoa (SEV-2015-0493), by the Spanish Ministry of Science and Technology through TIN2015-65316-P project and by the Generalitat de Catalunya (contracts 2014-SGR-1051).Peer Reviewe

Erratum: Combined fit of spectrum and composition data as measured by the Pierre Auger Observatory

We present a combined fit of a simple astrophysical model of UHECR sources to both the energy spectrum and mass composition data measured by the Pierre Auger Observatory. The fit has been performed for energies above 5 ⋅ 10(18) eV, i.e. the region of the all-particle spectrum above the so-called ankle feature. The astrophysical model we adopted consists of identical sources uniformly distributed in a comoving volume, where nuclei are accelerated through a rigidity-dependent mechanism. The fit results suggest sources characterized by relatively low maximum injection energies, hard spectra and heavy chemical composition. We also show that uncertainties about physical quantities relevant to UHECR propagation and shower development have a non-negligible impact on the fit results