Advanced Positioning and Location based services in 4G Mobile-IP Radio Access Networks

2004 IEEE 15TH IEEE International Symposium on personal, indoor and mobile radio communications (PIMRC 2004), Barcelona, Spain, 5-8 september 2004.This paper proposes an evolved architecture from 3G networks to provide basic and advanced positioning methods for location based services in mobile IPv6-based radio access networks. We start analyzing current status of location-based services (i.e. LBS or LCS) and architectures in 3G networks as well as state-of-the-art research on LBS and mobile Internet. Next we set the requirements the solution should fulfill. We continue proposing the evolved architecture for support of basic and advanced positioning methods, using MIPv6 and HMIPv6 as mobility scenario for the mobile IPv6 based RAN, describing element's functions and changes from current approaches as well as description of the dynamic behavior. We complete the proposal with a bandwidth analysis of the signaling, identifying issues when planning implementation of LCS services in the network

Is the predictability of emerging and developed stock markets really exploitable?.

A number of recent papers have analyzed the degree of predictability of stock markets. In this paper, we firstly study whether this predictability is really exploitable and secondly, if the economic significance of predictability is higher or lower in the emerging stock markets than in the developed ones. We use a variety of linear and nonlinear – Artificial Neural Networks – models and perform a computationally demanding forecasting experiment to assess the predictability of returns. Since we are interested in comparing the predictability in economic terms we also propose a modification in the nets’ loss function for market trading purposes. In addition, we consider both explicit and implicit trading costs for emerging and developed stock markets. Our conclusions suggest that, in contrast to some previous studies, if we consider total trading costs both the emerging as well as the developed stock returns are clearly nonpredictable. Finally, we find that Artificial Neural Networks do not provide superior performance than the linear models.Finance; Forecasting; Emerging stock markets; Artificial neural networks;

Signalling in voice over IP Networks

Voice signalling protocols have evolved, keeping with the prevalent move from circuit to packet switched networks. Standardization bodies have provided solutions for carrying voice traffic over packet networks while the main manufacturers are already providing products in workgroup, enterprise, or operator portfolio. This trend will accrue in next years due to the evolution of UMTS mobile networks to an “all-IP” environment. In this paper we present the various architectures that are proposed for signalling in VoIP, mainly: H.323, SIP and MGCP. We also include a brief summary about signalling in classical telephone networks and, at the end, we give some ideas about the proposed “all-IP” architectures in UMTS 3G mobile networks.Publicad

Microarrays as a functional approach to the transcriptome

Knowing a cell’s transcriptome is a fundamental requisite in order to analyze its response to the environment. Microarrays have supposed a revolution on this field as they are able to yield an overview of gene expression at any environmental condition on a genome-wide scale. This technique consists in the hybridisation of a nucleic acid sample, previously marked, with a probe (which might be made up of cDNA, oligonucleotides or PCR products) anchored to a solid surface (made of glass, plastic, silicon...) giving as a result a dot grid which reveals, after image analysis, which genes are being expressed. Nevertheless, this only can be achieved if information on the species genome has been generated. Different kinds of expression microarrays exist attending to the probe’s nature and the method used in its synthesis. In this poster two of these will be treated: Spotted Microarrays, for which the probe is synthesised prior to its fixation to the array and allow the analysis of two targets simultaneously. They can be easily customized, but lack high reproducibility and sensitivity. Oligonucleotide Microarrays, which are characterized by the direct printing of the probe on the array. In this case the probes consist on, invariably, oligonucleotides that are complementary to a small fraction of the gene it is representing at the microarray. Their application is somewhat restricted. This fact, however, makes them more reproducible. Currently, the approach towards the transcriptome studies from the Next Generation Sequencing technologies offers a large volume of information in a short amount of time needing less previous information on the target organism than that needed by microarrays, but their expensive price limits their use. The versatility of the latter, together with their reduced costs in comparison to other techniques, makes them an interesting resource in applications that may need less complexity

Self-organizing maps could improve the classification of Spanish mutual funds.

In this paper, we apply nonlinear techniques (Self-Organizing Maps, k-nearest neighbors and the k-means algorithm) to evaluate the official Spanish mutual funds classification. The methodology that we propose allows us to identify which mutual funds are misclassified in the sense that they have historical performances which do not conform to the investment objectives established in their official category. According to this, we conclude that, on average, over 40% of mutual funds could be misclassified. Then, we propose an alternative classification, based on a double-step methodology, and we find that it achieves a significantly lower rate of misclassifications. The portfolios obtained from this alternative classification also attain better performances in terms of return/risk and include a smaller number of assets.Finance; Mutual funds; Clustering; Self-organizing map (SOM); Investment analysis;

Risk forecasting models and optimal portfolio selection.

This study analyses, from an investor's perspective, the performance of several risk forecasting models in obtaining optimal portfolios. The plausibility of the homoscedastic hypothesis implied in the classical Markowitz model is dicussed and more general models which take into account assymetry and time varying risk are analysed. Specifically, it studies whether ARCH-type based models obtain portfolios whose risk-adjusted returns exceed those of the classical Markowitz model. The same analysis is performed with models based on the Lower Partial Moment (LPM) which take into account the assymetry in the distribution of returns. The results suggest that none of the models achieve a clearly superior average performance. It is also found that models based on semivariance perform as well as those based on the variance, but not better than, even if the evaluation criterion is based on the Reward-to-Semivariance ratio. When attention turns to the analysis of worst case performance, the results are clearly different. Models which employ LPM with a high degree of risk aversion (n>2) as the risk measure are consistently superior to those which employ a symmetric measure, either homoscedastic or heteroscedastic.

Pricing tranched credit products with generalized multifactor models

The market for tranched credit products (CDOs, Itraxx tranches) is one of the fastest growing segments in the credit derivatives industry. However, some assumptions underlying the standard Gaussian onefactor pricing model (homogeneity, single factor, Normality), which is the pricing standard widely used in the industry, are probably too restrictive. In this paper we generalize the standard model by means of a two by two model (two factors and two asset classes). We assume two driving factors (business cycle and industry) with independent tStudent distributions, respectively, and we allow the model to distinguish among portfolio assets classes. In order to illustrate the estimation of the parameters of the model, an empirical application with Moody's data is also included.

Experimental analysis of flapping wings

The development of this project is oriented towards the study of force coefficients and flow structures of a pitching and plunging airfoil at low Reynolds number. The experimental setup consisted of a NACA 0012 airfoil placed in a water channel. A motion system to control the airfoil allowed for a wide range of motion configurations. A waterproof Force/Torque sensor allowed capturing normal and axial forces, which were then be processed to obtain aerodynamic loads. The analysis of PIV results rendered interesting results, namely the fundamental role of the leading edge vortex in lift production, and the effects of LEV and TEV shedding in drag evolution.Ingeniería Aeroespacia