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In this paper we apply a novel meta-heuristic approach, the Coral Reefs Optimization (CRO) algorithm, to solve a Mobile Network Deployment Problem (MNDP), in which the control of the electromagnetic pollution plays an important role. The CRO is a new bio-inspired meta-heuristic algorithm based on the growing and evolution of coral reefs. The aim of this paper is therefore twofold: first of all, we study the performance of the CRO approach in a real hard optimization problem, and second, we solve an important problem in the field of telecommunications, including the minimization of electromagnetic pollution as a key concept in the problem. We show that the CRO is able to obtain excellent solutions to the MNDP in a real instance in Alcalá de Henares (Madrid, Spain), improving the results obtained by alternative algorithms such as Evolutionary, Particle Swarm Optimization or Harmony Search algorithms. [ABSTRACT FROM AUTHOR]

A compact hybrid antenna consisting of a coplanar waveguide-fed disk antenna with a parasitic inverted-L element is proposed for mobile and wireless applications (GSM1800, PCS1900, UMTS2100, WLAN2450/5200 and 5800 MHz). The effect of the substrate loss tangent is analysed and two prototypes of the proposed antenna have been manufactured and experimentally studied. A good impedance match, gain and stable radiation patterns are obtained with an overall size of 56 mm×24 mm×6 mm. For a 6 dB return loss, the operating bandwidths are 76% at 2 GHz and 57% at 5 GHz. The measured peak gain at the lower operating band varies from 2 to 3.2 dBi, and at the higher operating band it varies from 3.8 to 5.8 dBi. Losses due to the FR4 substrate were found to be less than 0.4 dB at 5.8 GHz. [ABSTRACT FROM AUTHOR]

Abstract: This paper discusses the performance of Radial Basis Function networks (RBF) in a problem of spatial regression of pollutants in Madrid. Specifically, the spatial regression of NO x and O3 is considered, in such a way that, starting from a set of measuring points provided by the air quality monitoring network of Madrid, the complete surface of the pollutants in the city is obtained. This pollutant surface can be used as an initial step for modeling intra-urban pollution using land-use regression techniques for example. Also, different works has used a pollutant surface to study the patterns of pollution in different cities in the world and also to establish their air monitoring networks under mathematical criteria. The paper is focussed in analyzing the performance of RBF networks to obtain this first pollutant surface, so different RBF training algorithms are tested in this paper. Specifically, evolutionary-based RBF training algorithms are described, and compared with classical training algorithms for RBF networks with Gaussian kernels. The inclusion of meteorological variables in the RBF networks are also discussed in the paper. The experimental part of the article studies real results of the application of RBF networks to obtain a first pollutant surface of NO x and O3, using the data of the air pollution monitoring network of Madrid and the meteorological network of the city. [Copyright &y& Elsevier]

Nowadays there is a global concern with the growing frequency and magnitude of natural disasters, many of them associated with climate change at a global scale. When tackled during a stringent economic era, the allocation of resources to efficiently deal with such disaster situations (e.g., brigades, vehicles and other support equipment for fire events) undergoes severe budgetary limitations which, in several proven cases, have lead to personal casualties due to a reduced support equipment. As such, the lack of enough communication resources to cover the disaster area at hand may cause a risky radio isolation of the deployed teams and ultimately fatal implications, as occurred in different recent episodes in Spain and USA during the last decade. This issue becomes even more dramatic when understood jointly with the strong budget cuts lately imposed by national authorities. In this context, this article postulates cost-efficient multi-hop communications as a technological solution to provide extended radio coverage to the deployed teams over disaster areas. Specifically, a Harmony Search (HS) based scheme is proposed to determine the optimal number, position and model of a set of wireless relays that must be deployed over a large-scale disaster area. The approach presented in this paper operates under a Pareto-optimal strategy, so a number of different deployments is then produced by balancing between redundant coverage and economical cost of the deployment. This information can assist authorities in their resource provisioning and/or operation duties. The performance of different heuristic operators to enhance the proposed HS algorithm are assessed and discussed by means of extensive simulations over synthetically generated scenarios, as well as over a more realistic, orography-aware setup constructed with LIDAR (Laser Imaging Detection and Ranging) data captured in the city center of Bilbao (Spain). [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes the application of a novel meta-heuristic algorithm to the metropolitan wireless local area network deployment problem. In this problem, the coverage level of the deployed network must be maximized while meeting an assigned maximum budget, set beforehand. Specifically, we propose an approach based on the Harmony Search (HS) algorithm, with three main technical contributions: (1)the adaptation of the HS algorithm to a grouping scheme; (2)the adaptation of the improvisation operators driving the algorithm to the specific characteristics of the optimization problem to be tackled; and (3)its performance assessment via a simulated experiment inspired by real statistics in the city of Bilbao (Basque Country, northern Spain). Moreover, a comparison study of the proposed algorithm with a previous published grouping genetic algorithm is carried out, to further validate its performance. In light of the simulation results obtained from extensive experiments and several complexity considerations, we conclude that the proposed algorithm outperforms its genetically inspired counterpart, not only in terms of computation time, but also in the coverage level of the solution obtained. [Copyright &y& Elsevier]

Abstract: In this paper we present a novel system for addressing problems of local very short term (up to a time prediction horizon of 6h) temperature prediction based on Support Vector Regression algorithms (SVMr). Specifically, we construct SVMr banks based on the synoptic situation for each prediction period, incorporated by means of the well-known Hess–Brezowsky classification (HBC). We show how this SVMr bank structure obtains very good results in a real problem of short-term temperature prediction at Barcelona-El Prat International Airport (Spain), obtaining an average RMSE of 1.34°C in 6hour horizon prediction. Comparison with alternative neural techniques have been carried out in order to show the effectiveness of the proposed technique, and how the inclusion of the HBC classification is also able to improve the performance of these alternative neural algorithms in the problem. [Copyright &y& Elsevier]

Abstract: In this paper we present an application of the Support Vector Regression algorithm (SVMr) to the prediction of hourly ozone values in Madrid urban area. In order to improve the training capacity of SVMrs, we have used a recently proposed approach, based on reductions of the SVMr hyper-parameters search space. Using the modified SVMr, we study different influences which may modify the ozone prediction, such as previous ozone measurements in a given station, measurements in neighbors stations, and the influence of meteorologic variables. We use statistical tests to verify the significance of incorporating different variables into the SVMr. A comparison with the results obtained using a neural network (multi-layer perceptron) is also carried out. This study has been carried out in 5 different stations of the air pollution monitoring network of Madrid, so the conclusions raised are backed by real data. The final result of the work is a robust and powerful software for tropospheric ozone prediction in Madrid. Also, the prediction tool based on SVMr is flexible enough to incorporate any other prediction variable, such as city models, or traffic patters, which may improve the prediction obtained with the SVMr. [ABSTRACT FROM AUTHOR]