Planificación de una red 4G

La intensa evolución tecnológica que está experimentando nuestra sociedad en las últimas décadas hace que se estén desarrollando continuamente nuevas tecnologías que proporcionan mejoras tanto en la calidad como en la seguridad del servicio, este es el caso del 4G. A día de hoy, en España, la cuarta generación de comunicaciones móviles se ve encabezada por LTE, mientras que LTE-Advanced sólo se está implantando en las principales ciudades de nuestro país durante los últimos meses. Por este motivo, se ha creído interesante realizar una planificación sobre una zona que, hasta el momento, no está cubierta por cobertura LTE-Advanced. Además hay que tener en cuenta la naturaleza del terreno en el que trabajaremos, ya que se aleja del suelo urbano que encontramos en las principales ciudades con LTE-Advanced, como Madrid, Barcelona o Valencia. El estudio de esta zona semirural es de gran interés ya que uno de los objetivos de la cuarta generación es hacer llegar conexión a internet de calidad a lugares en los que no puede llegar la fibra óptica, como por ejemplo estas zonas semirurales. Para añadir aún más interés en el estudio, se ha decidido utilizar la banda de 800 MHz para el despliegue de la red. Esta banda que anteriormente era utilizada para la transmisión TDT, recientemente ha quedado liberada, en el conocido como Dividendo Digital para su uso en comunicaciones móviles. La tecnología LTE-Advanced se está empezando a desplegar en esta banda aunque realmente hasta Noviembre del año 2015 no tendremos un uso real de la misma, por lo que en estos momentos las redes 4G están utilizando la banda de 2.6 GHz. La utilización de la banda de 800 MHz conllevará mejoras tanto al usuario como a las operadoras, las cuales iremos viendo a lo largo del desarrollo del proyecto. La planificación pasará por distintas fases de optimización y expansión en las que se analizaran tanto la parte radioeléctrica como su capacidad. Se analizaran señales del tipo RSRP, RSSI o RSRQ y para el análisis de capacidad se definirá un conjunto de usuarios, distribuidos adecuadamente por toda la zona, que permitirá estudiar en detalle la capacidad de nuestra red. Para finalizar, se realizarán varias pruebas que demostrarán lo importante que es la tecnología MIMO tanto en LTE como en LTE-Advanced. ABSTRACT. Nowadays, our society is experiencing an intense pace of technological evolution which causes the constant development of new technologies. In the network planning area, these new technologies are focused on improving both quality and safety of service, with the recent deployment of 4G technologies in our networks. This project focuses on Spain, where the fourth generation of mobile communications is led by LTE, because LTE-Advanced has only been deployed in the largest cities, so far. The goal of this project is to plan, deploy and simulate LTE-Advanced network, of an area that hasn´t yet been covered. Furthermore, it will be taken into account the nature of the terrain where the network will be developed, as it moves away from urban areas in the major cities with LTE-Advanced, including Madrid, Barcelona and Valencia. The study of these semi-rural areas is extremely important because one of the main objectives of the fourth generation technologies is to get high-speed internet access to places that can be reached through other technologies, such as optical fiber. In order to adjust to the actual needs, the project was developed for the 800 MHz band. Those frequencies used to be assigned for digital terrestrial TV, but they have recently been released through the Digital Dividend in 2015 to use with mobile communications. That is the reason why, the LTE-Advanced technology in Spain is starting to be deployed in those frequencies. Despite the freeing of the 800 MHz band, it is not allowed to use it until November 2015, so 4G networks are currently using the 2.6 GHz band. The use of the 800 MHz band will led to advantages and improvements to users and operators, which will be detailed over the project. Each step of the planning of the 4G network is detailed. It is analyzed the optimization and expansion of the network, based on the radio and capacity premises. RSRP, RSSI or RSRQ signals were analyzed and an analysis of the network capacity was carried out. Finally, several tests are developed to show the importance of MIMO in LTE and LTE-Advanced.

Enriquecimiento de fuentes de datos con descripciones lingüísticas mediante bases de datos geográficas externas

El presente trabajo se ha centrado en la investigación de soluciones para automatizar la tarea del enriquecimiento de fuentes de datos sobre redes de sensores con descripciones lingüísticas, con el fin de facilitar la posterior generación de textos en lenguaje natural. El uso de descripciones en lenguaje natural facilita el acceso a los datos a una mayor diversidad de usuarios y, como consecuencia, permite aprovechar mejor las inversiones en redes de sensores. En el trabajo se ha considerado el uso de bases de datos abiertas para abordar la necesidad de disponer de un gran volumen y diversidad de conocimiento geográfico. Se ha analizado también el enriquecimiento de datos dentro de enfoques metodológicos de curación de datos y métodos de generación de lenguaje natural. Como resultado del trabajo, se ha planteado un método general basado en una estrategia de generación y prueba que incluye una forma de representación y uso del conocimiento heurístico con varias etapas de razonamiento para la construcción de descripciones lingüísticas de enriquecimiento de datos. En la evaluación de la propuesta general se han manejado tres escenarios, dos de ellos para generación de referencias geográficas sobre redes de sensores complejas de dimensión real y otro para la generación de referencias temporales. Los resultados de la evaluación han mostrado la validez práctica de la propuesta general exhibiendo mejoras de rendimiento respecto a otros enfoques. Además, el análisis de los resultados ha permitido identificar y cuantificar el impacto previsible de diversas líneas de mejora en bases de datos abiertas. ABSTRACT This work has focused on the search for solutions to automate the task of enrichment sensor-network-based data sources with textual descriptions, so as to facilitate the generation of natural language texts. Using natural language descriptions facilitates data access to a wider range of users and, therefore, allows better leveraging investments in sensor networks. In this work we have considered the use of open databases to address the need for a large volume and diversity of geographical knowledge. We have also analyzed data enrichment in methodological approaches and data curation methods of natural language generation. As a result, it has raised a general method based on a strategy of generating and testing that includes a representation using heuristic knowledge with several stages of reasoning for the construction of linguistic descriptions of data enrichment. In assessing the overall proposal three scenarios have been addressed, two of them in the environmental domain with complex sensor networks and another real dimension in the time domain. The evaluation results have shown the validity and practicality of our proposal, showing performance improvements over other approaches. Furthermore, the analysis of the results has allowed identifying and quantifying the expected impact of various lines of improvement in open databases.

Estudo sistêmico da geração de conhecimento no IPEN

Com o escopo de fornecer subsídios para compreender como o processo de colaboração científica ocorre e se desenvolve em uma instituição de pesquisas, particularmente o IPEN, o trabalho utilizou duas abordagens metodológicas. A primeira utilizou a técnica de análise de redes sociais (ARS) para mapear as redes de colaboração científica em P&D do IPEN. Os dados utilizados na ARS foram extraídos da base de dados digitais de publicações técnico-científicas do IPEN, com o auxílio de um programa computacional, e basearam-se em coautoria compreendendo o período de 2001 a 2010. Esses dados foram agrupados em intervalos consecutivos de dois anos gerando cinco redes bienais. Essa primeira abordagem revelou várias características estruturais relacionadas às redes de colaboração, destacando-se os autores mais proeminentes, distribuição dos componentes, densidade, boundary spanners e aspectos relacionados à distância e agrupamento para definir um estado de redes mundo pequeno (small world). A segunda utilizou o método dos mínimos quadrados parciais, uma variante da técnica de modelagem por equações estruturais, para avaliar e testar um modelo conceitual, apoiado em fatores pessoais, sociais, culturais e circunstanciais, para identificar aqueles que melhor explicam a propensão de um autor do IPEN em estabelecer vínculos de colaboração em ambientes de P&D. A partir do modelo consolidado, avaliou-se o quanto ele explica a posição estrutural que um autor ocupa na rede com base em indicadores de ARS. Nesta segunda parte, os dados foram coletados por meio de uma pesquisa de levantamento com a utilização de um questionário. Os resultados mostraram que o modelo explica aproximadamente 41% da propensão de um autor do IPEN em colaborar com outros autores e em relação à posição estrutural de um autor na rede o poder de explicação variou entre 3% e 3,6%. Outros resultados mostraram que a colaboração entre autores do IPEN tem uma correlação positiva com intensidade moderada com a produtividade, da mesma forma que, os autores mais centrais na rede tendem a ampliar a sua visibilidade. Por fim, vários outros indicadores estatísticos bibliométricos referentes à rede de colaboração em P&D do IPEN foram determinados e revelados, como, a média de autores por publicação, média de publicações por autores do IPEN, total de publicações, total de autores e não autores do IPEN, entre outros. Com isso, esse trabalho fornece uma contribuição teórica e empírica aos estudos relacionados à colaboração científica e ao processo de transferência e preservação de conhecimento, assim como, vários subsídios que contribuem para o contexto de tomada de decisão em ambientes de P&D.

Real time motion estimation using a neural architecture implemented on GPUs

This work describes a neural network based architecture that represents and estimates object motion in videos. This architecture addresses multiple computer vision tasks such as image segmentation, object representation or characterization, motion analysis and tracking. The use of a neural network architecture allows for the simultaneous estimation of global and local motion and the representation of deformable objects. This architecture also avoids the problem of finding corresponding features while tracking moving objects. Due to the parallel nature of neural networks, the architecture has been implemented on GPUs that allows the system to meet a set of requirements such as: time constraints management, robustness, high processing speed and re-configurability. Experiments are presented that demonstrate the validity of our architecture to solve problems of mobile agents tracking and motion analysis.

Electricity market price spike forecast with data mining techniques

Electricity market price forecast is a changeling yet very important task for electricity market managers and participants. Due to the complexity and uncertainties in the power grid, electricity prices are highly volatile and normally carry with spikes. which may be (ens or even hundreds of times higher than the normal price. Such electricity spikes are very difficult to be predicted. So far. most of the research on electricity price forecast is based on the normal range electricity prices. This paper proposes a data mining based electricity price forecast framework, which can predict the normal price as well as the price spikes. The normal price can be, predicted by a previously proposed wavelet and neural network based forecast model, while the spikes are forecasted based on a data mining approach. This paper focuses on the spike prediction and explores the reasons for price spikes based on the measurement of a proposed composite supply-demand balance index (SDI) and relative demand index (RDI). These indices are able to reflect the relationship among electricity demand, electricity supply and electricity reserve capacity. The proposed model is based on a mining database including market clearing price, trading hour. electricity), demand, electricity supply and reserve. Bayesian classification and similarity searching techniques are used to mine the database to find out the internal relationships between electricity price spikes and these proposed. The mining results are used to form the price spike forecast model. This proposed model is able to generate forecasted price spike, level of spike and associated forecast confidence level. The model is tested with the Queensland electricity market data with promising results. Crown Copyright (C) 2004 Published by Elsevier B.V. All rights reserved.

Bayesian retrieval of scatterometer wind fields

The retrieval of wind fields from scatterometer observations has traditionally been separated into two phases; local wind vector retrieval and ambiguity removal. Operationally, a forward model relating wind vector to backscatter is inverted, typically using look up tables, to retrieve up to four local wind vector solutions. A heuristic procedure, using numerical weather prediction forecast wind vectors and, often, some neighbourhood comparison is then used to select the correct solution. In this paper we develop a Bayesian method for wind field retrieval, and show how a direct local inverse model, relating backscatter to wind vector, improves the wind vector retrieval accuracy. We compare these results with the operational U.K. Meteorological Office retrievals, our own CMOD4 retrievals and a neural network based local forward model retrieval. We suggest that the neural network based inverse model, which is extremely fast to use, improves upon current forward models when used in a variational data assimilation scheme.

Simultaneous clock recovery and data regeneration using a nonlinear optical loop mirror as an all-optical mixer

High-speed optical clock recovery, demultiplexing and data regeneration will be integral parts of any future photonic network based on high bit-rate OTDM. Much research has been conducted on devices that perform these functions, however to date each process has been demonstrated independently. A very promising method of all-optical switching is that of a semiconductor optical amplifier-based nonlinear optical loop mirror (SOA-NOLM). This has various advantages compared with the standard fiber NOLM, most notably low switching power, compact size and stability. We use the SOA-NOLM as an all-optical mixer in a classical phase-locked loop arrangement to achieve optical clock recovery, while at the same time achieving data regeneration in a single compact device

Research activities arising from the University of Kent

In this paper I describe research activities in the field of optical fiber sensing undertaken by me after leaving the Applied Optics Group at the University of Kent. The main topics covered are long period gratings, neural network based signal processing, plasmonic sensors, and polymer fiber gratings. I also give a summary of my two periods of research at the University of Kent, covering 1985–1988 and 1991–2001.

Forecasting and risk analysis applied to management planning and control

The aim of this research was to improve the quantitative support to project planning and control principally through the use of more accurate forecasting for which new techniques were developed. This study arose from the observation that in most cases construction project forecasts were based on a methodology (c.1980) which relied on the DHSS cumulative cubic cost model and network based risk analysis (PERT). The former of these, in particular, imposes severe limitations which this study overcomes. Three areas of study were identified, namely growth curve forecasting, risk analysis and the interface of these quantitative techniques with project management. These fields have been used as a basis for the research programme. In order to give a sound basis for the research, industrial support was sought. This resulted in both the acquisition of cost profiles for a large number of projects and the opportunity to validate practical implementation. The outcome of this research project was deemed successful both in theory and practice. The new forecasting theory was shown to give major reductions in projection errors. The integration of the new predictive and risk analysis technologies with management principles, allowed the development of a viable software management aid which fills an acknowledged gap in current technology.

Exploiting uncertainty in nonlinear stochastic control problem

This work introduces a novel inversion-based neurocontroller for solving control problems involving uncertain nonlinear systems which could also compensate for multi-valued systems. The approach uses recent developments in neural networks, especially in the context of modelling statistical distributions, which are applied to forward and inverse plant models. Provided that certain conditions are met, an estimate of the intrinsic uncertainty for the outputs of neural networks can be obtained using the statistical properties of networks. More generally, multicomponent distributions can be modelled by the mixture density network. Based on importance sampling from these distributions a novel robust inverse control approach is obtained. This importance sampling provides a structured and principled approach to constrain the complexity of the search space for the ideal control law. The developed methodology circumvents the dynamic programming problem by using the predicted neural network uncertainty to localise the possible control solutions to consider. Convergence of the output error for the proposed control method is verified by using a Lyapunov function. Several simulation examples are provided to demonstrate the efficiency of the developed control method. The manner in which such a method is extended to nonlinear multi-variable systems with different delays between the input-output pairs is considered and demonstrated through simulation examples.

Investigation of uncoordinated coexisting IEEE 802.15.4 networks with sleep mode for machine-to-machine communications

The low-energy consumption of IEEE 802.15.4 networks makes it a strong candidate for machine-to-machine (M2M) communications. As multiple M2M applications with 802.15.4 networks may be deployed closely and independently in residential or enterprise areas, supporting reliable and timely M2M communications can be a big challenge especially when potential hidden terminals appear. In this paper, we investigate two scenarios of 802.15.4 network-based M2M communication. An analytic model is proposed to understand the performance of uncoordinated coexisting 802.15.4 networks. Sleep mode operations of the networks are taken into account. Simulations verified the analytic model. It is observed that reducing sleep time and overlap ratio can increase the performance of M2M communications. When the networks are uncoordinated, reducing the overlap ratio can effectively improve the network performance. © 2012 Chao Ma et al.

Simultaneous clock recovery and data regeneration using a nonlinear optical loop mirror as an all-optical mixer

High-speed optical clock recovery, demultiplexing and data regeneration will be integral parts of any future photonic network based on high bit-rate OTDM. Much research has been conducted on devices that perform these functions, however to date each process has been demonstrated independently. A very promising method of all-optical switching is that of a semiconductor optical amplifier-based nonlinear optical loop mirror (SOA-NOLM). This has various advantages compared with the standard fiber NOLM, most notably low switching power, compact size and stability. We use the SOA-NOLM as an all-optical mixer in a classical phase-locked loop arrangement to achieve optical clock recovery, while at the same time achieving data regeneration in a single compact device

Neural Control of Chaos and Aplications

Signal processing is an important topic in technological research today. In the areas of nonlinear dynamics search, the endeavor to control or order chaos is an issue that has received increasing attention over the last few years. Increasing interest in neural networks composed of simple processing elements (neurons) has led to widespread use of such networks to control dynamic systems learning. This paper presents backpropagation-based neural network architecture that can be used as a controller to stabilize unsteady periodic orbits. It also presents a neural network-based method for transferring the dynamics among attractors, leading to more efficient system control. The procedure can be applied to every point of the basin, no matter how far away from the attractor they are. Finally, this paper shows how two mixed chaotic signals can be controlled using a backpropagation neural network as a filter to separate and control both signals at the same time. The neural network provides more effective control, overcoming the problems that arise with control feedback methods. Control is more effective because it can be applied to the system at any point, even if it is moving away from the target state, which prevents waiting times. Also control can be applied even if there is little information about the system and remains stable longer even in the presence of random dynamic noise.

Neural Networks: A Diagnostic Tool for Gastric Electrical Uncoupling?

Neural Networks have been successfully employed in different biomedical settings. They have been useful for feature extractions from images and biomedical data in a variety of diagnostic applications. In this paper, they are applied as a diagnostic tool for classifying different levels of gastric electrical uncoupling in controlled acute experiments on dogs. Data was collected from 16 dogs using six bipolar electrodes inserted into the serosa of the antral wall. Each dog underwent three recordings under different conditions: (1) basal state, (2) mild surgically-induced uncoupling, and (3) severe surgically-induced uncoupling. For each condition half-hour recordings were made. The neural network was implemented according to the Learning Vector Quantization model. This is a supervised learning model of the Kohonen Self-Organizing Maps. Majority of the recordings collected from the dogs were used for network training. Remaining recordings served as a testing tool to examine the validity of the training procedure. Approximately 90% of the dogs from the neural network training set were classified properly. However, only 31% of the dogs not included in the training process were accurately diagnosed. The poor neural-network based diagnosis of recordings that did not participate in the training process might have been caused by inappropriate representation of input data. Previous research has suggested characterizing signals according to certain features of the recorded data. This method, if employed, would reduce the noise and possibly improve the diagnostic abilities of the neural network.

An OTDR and gratings assisted multifunctional fiber sensing system

We report a distributed multifunctional fiber sensing network based on weak-fiber Bragg gratings (WFBGs) and long period fiber grating (LPG) assisted OTDR system. The WFBGs are applied for temperature, strain, and vibration monitoring at key position, and the LPG is used as a linear filter in the system to convert the wavelength shift of WFBGs caused by environmental change into the power change. The simulation results show that it is possible to integrate more than 4472 WFBGs in the system when the reflectivity of WFBGs is less than {10}^{-5}. Besides, the back-Rayleigh scattering along the whole fiber can also be detected which makes distributed bend sensing possible. As an experimental demonstration, we have used three WFBGs UV-inscribed with 50-m interval at the end of a 2.6-km long fiber, which part was subjected for temperature, strain, and vibration sensing, respectively. The ratio of the intensity of output and input light is used for temperature and strain sensing, and the results show strain and temperature sensitivities are 4.2 \times {10}^{-4}{/\mu \varepsilon } and 5.9 \times {10}^{-3}{{/ {^{\circ }}\textrm {C}}} , respectively. Detection of multiple vibrations and single vibration with the broad frequency band up to 500 Hz are also achieved. In addition, distributed bend sensing which could be simultaneously realized in this system has been proposed.