Banco de medidas automático para linealización de amplificadores

This work is focused on building and configuring a measurement test bench for non linear High Power Amplifiers, more precisely those ones based on the Envelope Elimination and Restoration. At first sight the test bench is composed of several arbitrary waveform generators, an oscilloscope, a vector signal generator and a spectrum analyzer all of them controlled remotely. The test bench works automatically, that is why several software control programs have been developed in order to control all this equipment. The control programs have been developed in Matlab/Octave Scripting language and at last chance in a more low level language as C. The signal processing algorithms, taking into account that the time alignment one is the most important, have been developed in Matlab/Octave Scripting too. An improvement of 10dB in the ACPR(Adjacent Channel Power Ratio) has been obtained just by applying the time alignment algorithm developed in this work

Building a New Water Culture in Spain

For over 100 years, water policy and man­ agement in Spain have been instruments of economic and social transformation. Sig­ nificant public and private investments in water supply infrastructures have equipped Spain with over 1,200 major dams, 20 major desalination plants ? with more under construction ? and several inter­basin water transfers. The system has been apparently very successful, with an increase in overall water availability, strong associated eco­ nomic development and few urban water supply shortages. This success has been supported by a widespread consensus among a strong and largely closed water policy community made up of water manag­ ers, irrigators, electric (hydropower) utilities and developers.

Regularized model learning in EDAs for continuous and multi-objective optimization

Probabilistic modeling is the de�ning characteristic of estimation of distribution algorithms (EDAs) which determines their behavior and performance in optimization. Regularization is a well-known statistical technique used for obtaining an improved model by reducing the generalization error of estimation, especially in high-dimensional problems. `1-regularization is a type of this technique with the appealing variable selection property which results in sparse model estimations. In this thesis, we study the use of regularization techniques for model learning in EDAs. Several methods for regularized model estimation in continuous domains based on a Gaussian distribution assumption are presented, and analyzed from di�erent aspects when used for optimization in a high-dimensional setting, where the population size of EDA has a logarithmic scale with respect to the number of variables. The optimization results obtained for a number of continuous problems with an increasing number of variables show that the proposed EDA based on regularized model estimation performs a more robust optimization, and is able to achieve signi�cantly better results for larger dimensions than other Gaussian-based EDAs. We also propose a method for learning a marginally factorized Gaussian Markov random �eld model using regularization techniques and a clustering algorithm. The experimental results show notable optimization performance on continuous additively decomposable problems when using this model estimation method. Our study also covers multi-objective optimization and we propose joint probabilistic modeling of variables and objectives in EDAs based on Bayesian networks, speci�cally models inspired from multi-dimensional Bayesian network classi�ers. It is shown that with this approach to modeling, two new types of relationships are encoded in the estimated models in addition to the variable relationships captured in other EDAs: objectivevariable and objective-objective relationships. An extensive experimental study shows the e�ectiveness of this approach for multi- and many-objective optimization. With the proposed joint variable-objective modeling, in addition to the Pareto set approximation, the algorithm is also able to obtain an estimation of the multi-objective problem structure. Finally, the study of multi-objective optimization based on joint probabilistic modeling is extended to noisy domains, where the noise in objective values is represented by intervals. A new version of the Pareto dominance relation for ordering the solutions in these problems, namely �-degree Pareto dominance, is introduced and its properties are analyzed. We show that the ranking methods based on this dominance relation can result in competitive performance of EDAs with respect to the quality of the approximated Pareto sets. This dominance relation is then used together with a method for joint probabilistic modeling based on `1-regularization for multi-objective feature subset selection in classi�cation, where six di�erent measures of accuracy are considered as objectives with interval values. The individual assessment of the proposed joint probabilistic modeling and solution ranking methods on datasets with small-medium dimensionality, when using two di�erent Bayesian classi�ers, shows that comparable or better Pareto sets of feature subsets are approximated in comparison to standard methods.

Failure Detectors in Homonymous Distributed Systems (with an Application to Consensus).

This paper is on homonymous distributed systems where processes are prone to crash failures and have no initial knowledge of the system membership (?homonymous? means that several processes may have the same identi?er). New classes of failure detectors suited to these systems are ?rst de?ned. Among them, the classes H? and H? are introduced that are the homonymous counterparts of the classes ? and ?, respectively. (Recall that the pair h?,?i de?nes the weakest failure detector to solve consensus.) Then, the paper shows how H? and H? can be implemented in homonymous systems without membership knowledge (under different synchrony requirements). Finally, two algorithms are presented that use these failure detectors to solve consensus in homonymous asynchronous systems where there is no initial knowledge ofthe membership. One algorithm solves consensus with hH?, H?i, while the other uses only H?, but needs a majority of correct processes. Observe that the systems with unique identi?ers and anonymous systems are extreme cases of homonymous systems from which follows that all these results also apply to these systems. Interestingly, the new failure detector class H? can be implemented with partial synchrony, while the analogous class A? de?ned for anonymous systems can not be implemented (even in synchronous systems). Hence, the paper provides us with the ?rst proof showing that consensus can be solved in anonymous systems with only partial synchrony (and a majority of correct processes).

Influence of delay time on regularity estimation for voice pathology detection

The employment of nonlinear analysis techniques for automatic voice pathology detection systems has gained popularity due to the ability of such techniques for dealing with the underlying nonlinear phenomena. On this respect, characterization using nonlinear analysis typically employs the classical Correlation Dimension and the largest Lyapunov Exponent, as well as some regularity quantifiers computing the system predictability. Mostly, regularity features highly depend on a correct choosing of some parameters. One of those, the delay time �, is usually fixed to be 1. Nonetheless, it has been stated that a unity � can not avoid linear correlation of the time series and hence, may not correctly capture system nonlinearities. Therefore, present work studies the influence of the � parameter on the estimation of regularity features. Three � estimations are considered: the baseline value 1; a � based on the Average Automutual Information criterion; and � chosen from the embedding window. Testing results obtained for pathological voice suggest that an improved accuracy might be obtained by using a � value different from 1, as it accounts for the underlying nonlinearities of the voice signal.

An Agent Architecture to fulfill Real-Time Requirements

In this paper we present AMSIA, an agent architecture that combines the possibility of using di erent reasoning methods with a mechanism to control the resources needed by the agent to ful ll its high level objectives. The architecture is based on the blackboard paradigm which o ers the possibility of combining di erent reasoning techniques and opportunistic behavior. The AMSIA architecture adds a representation of plans of objectives allowing di erent reasoning activities to create plans to guide the future behavior of the agent. The opportunism is in the acquisition of high-level objectives and in the modi cation of the predicted activity when something doesn't happen as expected. A control mechanism is responsible for the translation of plans of objectives to concrete activities, considering resource-boundedness. To do so, all the activity in the agent (including control) is explicitly scheduled, but allowing the necessary exibility to make changes in the face of contingencies that are expected in dynamic environments. Experimental work is also presented.

How vague can your patent be? Vagueness strategies in U.S. patents

Patent claims defi ne the protection scope of the intellectual property sought by the patent applicant or patentee. Broad claims are valuable as they can describe more expansive rights to the invention. Therefore, if these claims are too broad a potential infringer will more easily argue against them. But if the claims are too narrow the scope of protection of the intellectual property is greatly reduced. Patent claims have to be, on the one hand, determinate and precise enough and, on the other hand, as inclusive as possible. Therefore patent applicants must fi nd a balance in the broadness of the scope defi ned by their claims. This balance can be achieved by the choice of words with a convenient degree of semantic indeterminacy, by the choice of modifi ers or other strategies. In fact, vagueness in patent claims is a desirable characteristic for such documents. A quantitative and qualitative analysis of a corpus of 350 U.S. patents provides a promising starting point to understand the linguistic instruments used to achieve the balance between property claim scope and precision of property description. To conclude, some issues relating vagueness and pragmatics are suggested as a line of further research.

Overview of the Spanish Fuel Cycle: Technical Tours Organized by Spanish young Generation in Nuclear.

Spanish Young Generation in Nuclear (Jóvenes Nucleares, JJNN) is a non-profrt organization that depends on the Spanish Nuclear Society (Sociedad Nuclear Española, SNE).Since one of rts main goals is to spread the knowledge about nuclear power,severa! technical tours to facilities wrth an importan!role in the nuclear fuel cycle have been organized for the purpose ofleaming about the different stages of the Spanish tuel cycle. Spanish Young Generation in Nuclear had the opportunity to visit ENUSA Fuel Assembly Factory in Juzbado (Salamanca, Spain), Where it could be understood the front-end cycle which involves the uranium supply and storage, design and manufacturing of fuel bundles for European nuclear power plants. Alterwards, due to the tour of Almaraz NPP (PWR) and Santa María de Garoña NPP (BWR), rt could be comprehended how to obtain energy from this fuel in two different types of reactors.Furthermore,in these two plants, the facilities related to the back-end cycle could be toured. lt was possible to watch the Spent FuelPools, where the fuel bundles are stored under water until their activity is reduced enough to transport them to an Individual Temporary Storage Facility orto the Centralized Temporary Storage. Finally, a technical tour to ENSA Heavy Components Factory (ENSA) was accomplished, Where it could be experienced at first hand how different Nuclear Steam Supply System (NSSS) components and other nuclear elements, such as racks or shipping and storage casks for spent nuclear fuel, are manulactured. All these perlonned technical tours were a complete success thanks to a generous care and know-how of the wor1<ers in charge of leading the technicaltours.The unanimous opinion of the participants was that taking part in this kind of activities is a worthWhile expeñence which has exceeded their expectations.

GMM-based classifiers for the automatic detection of obstructive sleep apnea

The aim of automatic pathological voice detection systems is to serve as tools, to medical specialists, for a more objective, less invasive and improved diagnosis of diseases. In this respect, the gold standard for those system include the usage of a optimized representation of the spectral envelope, either based on cepstral coefficients from the mel-scaled Fourier spectral envelope (Mel-Frequency Cepstral Coefficients) or from an all-pole estimation (Linear Prediction Coding Cepstral Coefficients) forcharacterization, and Gaussian Mixture Models for posterior classification. However, the study of recently proposed GMM-based classifiers as well as Nuisance mitigation techniques, such as those employed in speaker recognition, has not been widely considered inpathology detection labours. The present work aims at testing whether or not the employment of such speaker recognition tools might contribute to improve system performance in pathology detection systems, specifically in the automatic detection of Obstructive Sleep Apnea. The testing procedure employs an Obstructive Sleep Apnea database, in conjunction with GMM-based classifiers looking for a better performance. The results show that an improved performance might be obtained by using such approach.

Performance-based seismic design of a retaining wall

The design of containment walls suffering seismic loads traditionally has been realized with methods based on pseudoanalitic procedures such as Mononobe-Okabe's method, which it has led in certain occasions to insecure designs, that they have produced the ruin of many containment walls suffering the action of an earthquake. The recommendations gathered in Mononobe-Okabe's theory have been included in numerous Codes of Seismic Design. It is clear that a revision of these recommendations must be done. At present there is taking place an important review of the design methods of anti-seismic structures such as containment walls placed in an area of numerous earthquakes, by means of the introduction at the beginning of the decade of 1990 the Displacement Response Spectrum (DRS) and the Capacity Demand Diagram (CDD) that suppose an important change in the way of presenting the Elastic Response Spectrum (ERS). On the other hand in case of action of an earthquake, the dynamic characteristics of a soil have been referred traditionally to the speed of the shear waves that can be generated in a site, together with the characteristics of plasticity and damping of the soil. The Principle of the energy conservation explains why a shear upward propagating seismic wave can be amplified when travelling from a medium with high shear wave velocity (rock) to other medium with lower velocity (soil deposit), as it happened in the earthquake of Mexico of 1985. This amplification is a function of the speed gradient or of the contrast of impedances in the border of both types of mediums. A method is proposed in this paper for the design of containment walls in different soils, suffering to the action of an earthquake, based on the Performance-Based Seismic Design.

Understanding replication of experiments in software engineering: a classification

Context: Replication plays an important role in experimental disciplines. There are still many uncertain- ties about how to proceed with replications of SE experiments. Should replicators reuse the baseline experiment materials? How much liaison should there be among the original and replicating experiment- ers, if any? What elements of the experimental configuration can be changed for the experiment to be considered a replication rather than a new experiment? Objective: To improve our understanding of SE experiment replication, in this work we propose a classi- fication which is intend to provide experimenters with guidance about what types of replication they can perform. Method: The research approach followed is structured according to the following activities: (1) a litera- ture review of experiment replication in SE and in other disciplines, (2) identification of typical elements that compose an experimental configuration, (3) identification of different replications purposes and (4) development of a classification of experiment replications for SE. Results: We propose a classification of replications which provides experimenters in SE with guidance about what changes can they make in a replication and, based on these, what verification purposes such a replication can serve. The proposed classification helped to accommodate opposing views within a broader framework, it is capable of accounting for less similar replications to more similar ones regarding the baseline experiment. Conclusion: The aim of replication is to verify results, but different types of replication serve special ver- ification purposes and afford different degrees of change. Each replication type helps to discover partic- ular experimental conditions that might influence the results. The proposed classification can be used to identify changes in a replication and, based on these, understand the level of verification.