Tutoriales multimedia para sistemas de telecomunicación

Este Proyecto Fin de Carrera está destinado a la ilustración y aprendizaje del uso de varios dispositivos de los laboratorios del Departamento de Ingeniería Audiovisual y Comunicaciones, de la Escuela Universitaria de Ingeniería Técnica de Telecomunicación, de la Universidad Politécnica de Madrid, en forma de vídeos tutoriales basados en mediciones y prácticas habituales de las asignaturas del departamento para que puedan ser utilizados por los alumnos de la escuela como apoyo a las explicaciones del profesor en ocasiones puntuales. En concreto se han realizado hasta seis vídeos tutoriales en los que se explica: el diseño de un circuito impreso y la creación y fabricación de éste. Por otro lado, también se ha explicado el fenómeno del ruido de fase y cómo es el proceso de su medida, como una de las muchas características de un analizador de espectros. A modo de análisis, se ha realizado otro tutorial acerca de la modulación en FM, sus características y sus aplicaciones. Por último se ha hecho un estudio del comportamiento de un analizador de redes, exponiendo su funcionamiento y explicando su proceso de calibración. Para la realización de estos tutoriales se han utilizado diferentes aplicaciones sobre creación de vídeos multimedia, animación, producción de audio y narración. En especial se han usado: Sprint-Layout 5.0, Adobe Flash Professional CS5.5, Camtasia studio 7, Corel VideoStudio Pro X4, Loquendo TTS7 y WinPlot. Para el apartado de las grabaciones de las diferentes escenas se ha necesitado el uso de distintos instrumentos de medida del laboratorio tales como: analizador de espectros, analizador de redes, generador de señal, generador de funciones, osciloscopio y otros equipos adicionales como: cámara de vídeo y trípode del departamento. Para la composición de los diferentes tutoriales se ha comenzado creando un guion, para cada uno de ellos, estableciendo la aparición de las imágenes, vídeos, y locución. A continuación se exponen los diferentes temas en los que se han basado estos tutoriales de laboratorio, uno a uno. ABSTRACT. This Project is destined to learn the use of several devices at the laboratory of “Ingeniería Audiovisual y Comunicaciones” Department at “Escuela Universitaria de Ingeniería técnica de Telecomunicaciones” of “Universidad Politécnica de Madrid”, on the way as tutorial videos base on the subjects from this department to be used by the college students as help of the teacher’s explanations. In this project you will find up to six tutorial videos, showing: printed circuit design, printed circuit board manufacture. You can also find an explanation about the phenomenon of phase noise and how it’s its measurement process, as one of the many features of a spectrum analyzer. Another tutorial video is based on FM modulation, its features and applications. The last tutorial explains the networks analyzer functionalities and its calibration process. To carry out these tutorials different applications have been used to create multimedia videos, animation, audio production and storytelling. Such as Sprint Layout 5.0, Camtasia 7.0, Corel VideoStudio Pro X4, Adobe Flash Professional CS5.5, Loquendo TTS7 y WinPlot. About the recording side of the different scenes, several equipment have been required at the laboratory, such as spectrums analyzer, signal generator, oscilloscope, function generator, network analyzer and other additional devices, such as: a video camera with its tripod. The composition of the different tutorials has begun creating a script, for each of them, setting the times of appearance of images, video, speech and music. After this abstract, the different topics of the tutorials are showed, one by one.

Projeto de uma antena para controlo wireless de um robot

A comunicação e transmissão de informação sem fios tornou - se uma realidade cada vez mais utilizada pelas sociedades contemporâneas. A nível profissional, as forças armadas de cada país acharam conveniente modernizar os seus meios, por forma a aumentar a eficiência e a segurança em determinadas tarefas. Nesse sentido, o Exército português adquiriu um robot (ROVIM) cuja função é desempenhar ações de reconhecimento e vigilância de modo a obter informações de forma segura. O objetivo desta dissertação é dimensionar e construir uma antena para controlo wireless do robot (ROVIM). As especificações técnicas desta antena requerem dois modos de operação, um com uma largura de feixe larga e outro com uma largura de feixe estreita. Para alcançar esses objetivos dimensionou-se e construiu-se duas antenas. Na dissertação são construídas duas antenas, a primeira é uma antena Yagi – Uda convencional e a segunda é uma antena com uma estrutura nova que permite a regulação do ganho e da largura de feixe a -3 dB. A primeira antena será o modelo base da segunda antena, que apresenta a inovação do controlo das caraterísticas de radiação. Esse controlo é possível através da introdução de díodos e do respetivo circuito de polarização na estrutura da antena. Inicialmente, as antenas foram dimensionadas e simuladas recorrendo ao programa de simulação CST MWS, de modo a operarem na banda dos 2,4 GHz. Após a construção das antenas, as caraterísticas de radiação foram medidas recorrendo à câmara anecoica e ao network analyzer, permitindo assim a comparação dos resultados medidos com os simulados.

Desenvolvimento teórico e experimental de FSS com elementos fractais de Gosper em estruturas de multicamadas

The fractal self-similarity property is studied to develop frequency selective surfaces (FSS) with several rejection bands. Particularly, Gosper fractal curves are used to define the shapes of the FSS elements. Due to the difficulty of making the FSS element details, the analysis is developed for elements with up to three fractal levels. The simulation was carried out using Ansoft Designer software. For results validation, several FSS prototypes with fractal elements were fabricated. In the fabrication process, fractals elements were designed using computer aided design (CAD) tools. The prototypes were measured using a network analyzer (N3250A model, Agilent Technologies). Matlab software was used to generate compare measured and simulated results. The use of fractal elements in the FSS structures showed that the use of high fractal levels can reduce the size of the elements, at the same time as decreases the bandwidth. We also investigated the effect produced by cascading FSS structures. The considered cascaded structures are composed of two FSSs separated by a dielectric layer, which distance is varied to determine the effect produced on the bandwidth of the coupled geometry. Particularly, two FSS structures were coupled through dielectric layers of air and fiberglass. For comparison of results, we designed, fabricated and measured several prototypes of FSS on isolated and coupled structures. Agreement was observed between simulated and measured results. It was also observed that the use of cascaded FSS structures increases the FSSs bandwidths and, in particular cases, the number of resonant frequencies, in the considered frequency range. In future works, we will investigate the effects of using different types of fractal elements, in isolated, multilayer and coupled FSS structures for applications on planar filters, high-gain microstrip antennas and microwave absorbers

High-precision force sensing using a single trapped ion

We introduce quantum sensing schemes for measuring very weak forces with a single trapped ion. They use the spin-motional coupling induced by the laser-ion interaction to transfer the relevant force information to the spin-degree of freedom. Therefore, the force estimation is carried out simply by observing the Ramsey-type oscillations of the ion spin states. Three quantum probes are considered, which are represented by systems obeying the Jaynes-Cummings, quantum Rabi (in 1D) and Jahn-Teller (in 2D) models. By using dynamical decoupling schemes in the Jaynes-Cummings and Jahn-Teller models, our force sensing protocols can be made robust to the spin dephasing caused by the thermal and magnetic field fluctuations. In the quantum-Rabi probe, the residual spin-phonon coupling vanishes, which makes this sensing protocol naturally robust to thermally-induced spin dephasing. We show that the proposed techniques can be used to sense the axial and transverse components of the force with a sensitivity beyond the yN/\wurzel{Hz}range, i.e. in the xN/\wurzel{Hz}(xennonewton, 10^−27). The Jahn-Teller protocol, in particular, can be used to implement a two-channel vector spectrum analyzer for measuring ultra-low voltages.

Monitoring network optimisation for spatial data classification using support vector machines

The paper presents a novel method for monitoring network optimisation, based on a recent machine learning technique known as support vector machine. It is problem-oriented in the sense that it directly answers the question of whether the advised spatial location is important for the classification model. The method can be used to increase the accuracy of classification models by taking a small number of additional measurements. Traditionally, network optimisation is performed by means of the analysis of the kriging variances. The comparison of the method with the traditional approach is presented on a real case study with climate data.

Using Neural Network Classifier Support Vector Machine Regression for the prediction of Melting Point of Drug – like compounds

In our study we use a kernel based classification technique, Support Vector Machine Regression for predicting the Melting Point of Drug – like compounds in terms of Topological Descriptors, Topological Charge Indices, Connectivity Indices and 2D Auto Correlations. The Machine Learning model was designed, trained and tested using a dataset of 100 compounds and it was found that an SVMReg model with RBF Kernel could predict the Melting Point with a mean absolute error 15.5854 and Root Mean Squared Error 19.7576

Predicting body and carcass characteristics of 2 broiler chicken strains using support vector regression and neural network models

As a new modeling method, support vector regression (SVR) has been regarded as the state-of-the-art technique for regression and approximation. In this study, the SVR models had been introduced and developed to predict body and carcass-related characteristics of 2 strains of broiler chicken. To evaluate the prediction ability of SVR models, we compared their performance with that of neural network (NN) models. Evaluation of the prediction accuracy of models was based on the R-2, MS error, and bias. The variables of interest as model output were BW, empty BW, carcass, breast, drumstick, thigh, and wing weight in 2 strains of Ross and Cobb chickens based on intake dietary nutrients, including ME (kcal/bird per week), CP, TSAA, and Lys, all as grams per bird per week. A data set composed of 64 measurements taken from each strain were used for this analysis, where 44 data lines were used for model training, whereas the remaining 20 lines were used to test the created models. The results of this study revealed that it is possible to satisfactorily estimate the BW and carcass parts of the broiler chickens via their dietary nutrient intake. Through statistical criteria used to evaluate the performance of the SVR and NN models, the overall results demonstrate that the discussed models can be effective for accurate prediction of the body and carcass-related characteristics investigated here. However, the SVR method achieved better accuracy and generalization than the NN method. This indicates that the new data mining technique (SVR model) can be used as an alternative modeling tool for NN models. However, further reevaluation of this algorithm in the future is suggested.

Neural network-based wind vector retrieval from satellite scatterometer data

Obtaining wind vectors over the ocean is important for weather forecasting and ocean modelling. Several satellite systems used operationally by meteorological agencies utilise scatterometers to infer wind vectors over the oceans. In this paper we present the results of using novel neural network based techniques to estimate wind vectors from such data. The problem is partitioned into estimating wind speed and wind direction. Wind speed is modelled using a multi-layer perceptron (MLP) and a sum of squares error function. Wind direction is a periodic variable and a multi-valued function for a given set of inputs; a conventional MLP fails at this task, and so we model the full periodic probability density of direction conditioned on the satellite derived inputs using a Mixture Density Network (MDN) with periodic kernel functions. A committee of the resulting MDNs is shown to improve the results.

Neural network-based wind vector retrieval from satellite scatterometer data

Obtaining wind vectors over the ocean is important for weather forecasting and ocean modelling. Several satellite systems used operationally by meteorological agencies utilise scatterometers to infer wind vectors over the oceans. In this paper we present the results of using novel neural network based techniques to estimate wind vectors from such data. The problem is partitioned into estimating wind speed and wind direction. Wind speed is modelled using a multi-layer perceptron (MLP) and a sum of squares error function. Wind direction is a periodic variable and a multi-valued function for a given set of inputs; a conventional MLP fails at this task, and so we model the full periodic probability density of direction conditioned on the satellite derived inputs using a Mixture Density Network (MDN) with periodic kernel functions. A committee of the resulting MDNs is shown to improve the results.

Lineage divergence detected in the malaria vector Anopheles marajoara (Diptera: Culicidae) in Amazonian Brazil

Background: Cryptic species complexes are common among anophelines. Previous phylogenetic analysis based on the complete mtDNA COI gene sequences detected paraphyly in the Neotropical malaria vector Anopheles marajoara. The ""Folmer region"" detects a single taxon using a 3% divergence threshold. Methods: To test the paraphyletic hypothesis and examine the utility of the Folmer region, genealogical trees based on a concatenated (white + 3' COI sequences) dataset and pairwise differentiation of COI fragments were examined. The population structure and demographic history were based on partial COI sequences for 294 individuals from 14 localities in Amazonian Brazil. 109 individuals from 12 localities were sequenced for the nDNA white gene, and 57 individuals from 11 localities were sequenced for the ribosomal DNA (rDNA) internal transcribed spacer 2 (ITS2). Results: Distinct A. marajoara lineages were detected by combined genealogical analysis and were also supported among COI haplotypes using a median joining network and AMOVA, with time since divergence during the Pleistocene (< 100,000 ya). COI sequences at the 3' end were more variable, demonstrating significant pairwise differentiation (3.82%) compared to the more moderate 2.92% detected by the Folmer region. Lineage 1 was present in all localities, whereas lineage 2 was restricted mainly to the west. Mismatch distributions for both lineages were bimodal, likely due to multiple colonization events and spatial expansion (similar to 798 - 81,045 ya). There appears to be gene flow within, not between lineages, and a partial barrier was detected near Rio Jari in Amapa state, separating western and eastern populations. In contrast, both nDNA data sets (white gene sequences with or without the retention of the 4th intron, and ITS2 sequences and length) detected a single A. marajoara lineage. Conclusions: Strong support for combined data with significant differentiation detected in the COI and absent in the nDNA suggest that the divergence is recent, and detectable only by the faster evolving mtDNA. A within subgenus threshold of >2% may be more appropriate among sister taxa in cryptic anopheline complexes than the standard 3%. Differences in demographic history and climatic changes may have contributed to mtDNA lineage divergence in A. marajoara.

A hypothesis for explaining single outbreaks (like the Black Death in European cities) of vector-borne infections

We propose a mechanism by which single outbreaks of vector-borne infections can happen even when the value of the basic reproduction number, R(o), of the infection is below one. With this hypothesis we have shown that dynamical models simulations demonstrate that the arrival of a relatively small (with respect to the host population) number of infected vectors can trigger a short-lived epidemic but with a huge number of cases. These episodes are characterized by a sudden outbreak in a previously virgin area that last from weeks to a few months, and then disappear without leaving vestiges. The hypothesis proposed in this paper to explain those single outbreaks of vector-borne infections, even when total basic reproduction number, Ro, is less than one (which explain the fact that those infections fail to establish themselves at endemic levels), is that the vector-to-host component of Ro is greater than one and that a sufficient amount of infected vectors are imported to the vulnerable area, triggering the outbreak. We tested the hypothesis by performing numerical simulations that reproduce the observed outbreaks of chikungunya in Italy in 2007 and the plague in Florence in 1348. The theory proposed provides an explanation for isolated outbreaks of vector-borne infections, ways to calculate the size of those outbreaks from the number of infected vectors arriving in the affected areas. Given the ever-increasing worldwide transportation network, providing a high degree of mobility from endemic to virgin areas, the proposed mechanism may have important implications for public health planning. (C) 2009 Elsevier Ltd. All rights reserved.

Strategic Bidding for Electricity Markets Negotiation Using Support Vector Machines

Energy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors’ research group has developed a multi-agent system: MASCEM (Multi- Agent System for Competitive Electricity Markets), which simulates the electricity markets environment. MASCEM is integrated with ALBidS (Adaptive Learning Strategic Bidding System) that works as a decision support system for market players. The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from the market context. This paper presents the application of a Support Vector Machines (SVM) based approach to provide decision support to electricity market players. This strategy is tested and validated by being included in ALBidS and then compared with the application of an Artificial Neural Network, originating promising results. The proposed approach is tested and validated using real electricity markets data from MIBEL - Iberian market operator.

Short-term Wind Speed Forecasting using Support Vector Machines

Wind speed forecasting has been becoming an important field of research to support the electricity industry mainly due to the increasing use of distributed energy sources, largely based on renewable sources. This type of electricity generation is highly dependent on the weather conditions variability, particularly the variability of the wind speed. Therefore, accurate wind power forecasting models are required to the operation and planning of wind plants and power systems. A Support Vector Machines (SVM) model for short-term wind speed is proposed and its performance is evaluated and compared with several artificial neural network (ANN) based approaches. A case study based on a real database regarding 3 years for predicting wind speed at 5 minutes intervals is presented.

Individual Classification of Mild Cognitive Impairment Subtypes by Support Vector Machine Analysis of White Matter DTI.

BACKGROUND AND PURPOSE: MCI was recently subdivided into sd-aMCI, sd-fMCI, and md-aMCI. The current investigation aimed to discriminate between MCI subtypes by using DTI. MATERIALS AND METHODS: Sixty-six prospective participants were included: 18 with sd-aMCI, 13 with sd-fMCI, and 35 with md-aMCI. Statistics included group comparisons using TBSS and individual classification using SVMs. RESULTS: The group-level analysis revealed a decrease in FA in md-aMCI versus sd-aMCI in an extensive bilateral, right-dominant network, and a more pronounced reduction of FA in md-aMCI compared with sd-fMCI in right inferior fronto-occipital fasciculus and inferior longitudinal fasciculus. The comparison between sd-fMCI and sd-aMCI, as well as the analysis of the other diffusion parameters, yielded no significant group differences. The individual-level SVM analysis provided discrimination between the MCI subtypes with accuracies around 97%. The major limitation is the relatively small number of cases of MCI. CONCLUSIONS: Our data show that, at the group level, the md-aMCI subgroup has the most pronounced damage in white matter integrity. Individually, SVM analysis of white matter FA provided highly accurate classification of MCI subtypes.