Quantum optical signals in telecommunication networks

Introducción a una red óptica metropolitana cuántica donde señales cuánticas y convencionales son multiplexadas en longitud de onda utilizando bandas separadas del espectro óptico. El enrutado se realiza con componentes pasivos para no perturbar los qubits. Se estudian dos redes: red de acceso WDM-PON y red metropolitana completa con backbone DWDM y redes de acceso WDM-PON.

Signals and Systems Students trainer.

Higher education students demand fast feedback about their assignments and the opportunity to repeat them in case they do in a wrong way. Here a computer based trainer for Signals and Systems students is presented. An application, that automatically generates and assesses thousands of numerically different versions of several Signals and Systems problems have been developed. This applet guides the students to find the solution and automatically assesses and grades the students proposed solution. The students can use the application to practice in solving several types of Signals and Systems basic problems. After selecting the problem type, the student introduces a seed and the application generates a numerical version of the selected problem. Then the application presents a sequence of questions that the students must solve and the application automatically assess their answers. After solving a given problem, the students can repeat the same numerical variation of the problem by introducing the same seed to the application. In this way, they can review their solution with the help of the hints given by the application for wrong solutions. This application can also be used as an automatic assessment tool by the instructor. When the assessment is made in a controlled environment (examination classroom or laboratory) the instructor can use the same seed for all students. Otherwise, different seeds can be assigned to different students and in this way they solve different numerical variation of the proposed problem, so cheating becomes an arduous task. Given a problem type, the mathematical or conceptual difficulty of the problem can vary depending on the numerical values of the parameters of the problem. The application permits to easily select groups of seeds that yield to numerical variations with similar mathematical or conceptual difficulty. This represents an advantage over a randomised task assignment where students are asked to solve tasks with different difficulty.

Bare-tether sheath and current: comparison of asymptotic theory and kinetic simulations in stationary plasma

Analytical expressions for current to a cylindrical Langmuir probe at rest in unmagnetized plasma are compared with results from both steady-state Vlasov and particle-in-cell simulations. Probe bias potentials that are much greater than plasma temperature (assumed equal for ions and electrons), as of interest for bare conductive tethers, are considered. At a very high bias, both the electric potential and the attracted-species density exhibit complex radial profiles; in particular, the density exhibits a minimum well within the plasma sheath and a maximum closer to the probe. Excellent agreement is found between analytical and numerical results for values of the probe radiusR close to the maximum radius Rmax for orbital-motion-limited (OML) collection at a particular bias in the following number of profile features: the values and positions of density minimum and maximum, position of sheath boundary, and value of a radius characterizing the no-space-charge behavior of a potential near the high-bias probe. Good agreement between the theory and simulations is also found for parametric laws jointly covering the following three characteristic R ranges: sheath radius versus probe radius and bias for Rmax; density minimum versus probe bias for Rmax; and (weakly bias-dependent) current drop below the OML value versus the probe radius for R > Rmax.

Multi-dimensional classification using Bayesian networks for stationary and evolving streaming data

Hoy en día, con la evolución continua y rápida de las tecnologías de la información y los dispositivos de computación, se recogen y almacenan continuamente grandes volúmenes de datos en distintos dominios y a través de diversas aplicaciones del mundo real. La extracción de conocimiento útil de una cantidad tan enorme de datos no se puede realizar habitualmente de forma manual, y requiere el uso de técnicas adecuadas de aprendizaje automático y de minería de datos. La clasificación es una de las técnicas más importantes que ha sido aplicada con éxito a varias áreas. En general, la clasificación se compone de dos pasos principales: en primer lugar, aprender un modelo de clasificación o clasificador a partir de un conjunto de datos de entrenamiento, y en segundo lugar, clasificar las nuevas instancias de datos utilizando el clasificador aprendido. La clasificación es supervisada cuando todas las etiquetas están presentes en los datos de entrenamiento (es decir, datos completamente etiquetados), semi-supervisada cuando sólo algunas etiquetas son conocidas (es decir, datos parcialmente etiquetados), y no supervisada cuando todas las etiquetas están ausentes en los datos de entrenamiento (es decir, datos no etiquetados). Además, aparte de esta taxonomía, el problema de clasificación se puede categorizar en unidimensional o multidimensional en función del número de variables clase, una o más, respectivamente; o también puede ser categorizado en estacionario o cambiante con el tiempo en función de las características de los datos y de la tasa de cambio subyacente. A lo largo de esta tesis, tratamos el problema de clasificación desde tres perspectivas diferentes, a saber, clasificación supervisada multidimensional estacionaria, clasificación semisupervisada unidimensional cambiante con el tiempo, y clasificación supervisada multidimensional cambiante con el tiempo. Para llevar a cabo esta tarea, hemos usado básicamente los clasificadores Bayesianos como modelos. La primera contribución, dirigiéndose al problema de clasificación supervisada multidimensional estacionaria, se compone de dos nuevos métodos de aprendizaje de clasificadores Bayesianos multidimensionales a partir de datos estacionarios. Los métodos se proponen desde dos puntos de vista diferentes. El primer método, denominado CB-MBC, se basa en una estrategia de envoltura de selección de variables que es voraz y hacia delante, mientras que el segundo, denominado MB-MBC, es una estrategia de filtrado de variables con una aproximación basada en restricciones y en el manto de Markov. Ambos métodos han sido aplicados a dos problemas reales importantes, a saber, la predicción de los inhibidores de la transcriptasa inversa y de la proteasa para el problema de infección por el virus de la inmunodeficiencia humana tipo 1 (HIV-1), y la predicción del European Quality of Life-5 Dimensions (EQ-5D) a partir de los cuestionarios de la enfermedad de Parkinson con 39 ítems (PDQ-39). El estudio experimental incluye comparaciones de CB-MBC y MB-MBC con los métodos del estado del arte de la clasificación multidimensional, así como con métodos comúnmente utilizados para resolver el problema de predicción de la enfermedad de Parkinson, a saber, la regresión logística multinomial, mínimos cuadrados ordinarios, y mínimas desviaciones absolutas censuradas. En ambas aplicaciones, los resultados han sido prometedores con respecto a la precisión de la clasificación, así como en relación al análisis de las estructuras gráficas que identifican interacciones conocidas y novedosas entre las variables. La segunda contribución, referida al problema de clasificación semi-supervisada unidimensional cambiante con el tiempo, consiste en un método nuevo (CPL-DS) para clasificar flujos de datos parcialmente etiquetados. Los flujos de datos difieren de los conjuntos de datos estacionarios en su proceso de generación muy rápido y en su aspecto de cambio de concepto. Es decir, los conceptos aprendidos y/o la distribución subyacente están probablemente cambiando y evolucionando en el tiempo, lo que hace que el modelo de clasificación actual sea obsoleto y deba ser actualizado. CPL-DS utiliza la divergencia de Kullback-Leibler y el método de bootstrapping para cuantificar y detectar tres tipos posibles de cambio: en las predictoras, en la a posteriori de la clase o en ambas. Después, si se detecta cualquier cambio, un nuevo modelo de clasificación se aprende usando el algoritmo EM; si no, el modelo de clasificación actual se mantiene sin modificaciones. CPL-DS es general, ya que puede ser aplicado a varios modelos de clasificación. Usando dos modelos diferentes, el clasificador naive Bayes y la regresión logística, CPL-DS se ha probado con flujos de datos sintéticos y también se ha aplicado al problema real de la detección de código malware, en el cual los nuevos ficheros recibidos deben ser continuamente clasificados en malware o goodware. Los resultados experimentales muestran que nuestro método es efectivo para la detección de diferentes tipos de cambio a partir de los flujos de datos parcialmente etiquetados y también tiene una buena precisión de la clasificación. Finalmente, la tercera contribución, sobre el problema de clasificación supervisada multidimensional cambiante con el tiempo, consiste en dos métodos adaptativos, a saber, Locally Adpative-MB-MBC (LA-MB-MBC) y Globally Adpative-MB-MBC (GA-MB-MBC). Ambos métodos monitorizan el cambio de concepto a lo largo del tiempo utilizando la log-verosimilitud media como métrica y el test de Page-Hinkley. Luego, si se detecta un cambio de concepto, LA-MB-MBC adapta el actual clasificador Bayesiano multidimensional localmente alrededor de cada nodo cambiado, mientras que GA-MB-MBC aprende un nuevo clasificador Bayesiano multidimensional. El estudio experimental realizado usando flujos de datos sintéticos multidimensionales indica los méritos de los métodos adaptativos propuestos. ABSTRACT Nowadays, with the ongoing and rapid evolution of information technology and computing devices, large volumes of data are continuously collected and stored in different domains and through various real-world applications. Extracting useful knowledge from such a huge amount of data usually cannot be performed manually, and requires the use of adequate machine learning and data mining techniques. Classification is one of the most important techniques that has been successfully applied to several areas. Roughly speaking, classification consists of two main steps: first, learn a classification model or classifier from an available training data, and secondly, classify the new incoming unseen data instances using the learned classifier. Classification is supervised when the whole class values are present in the training data (i.e., fully labeled data), semi-supervised when only some class values are known (i.e., partially labeled data), and unsupervised when the whole class values are missing in the training data (i.e., unlabeled data). In addition, besides this taxonomy, the classification problem can be categorized into uni-dimensional or multi-dimensional depending on the number of class variables, one or more, respectively; or can be also categorized into stationary or streaming depending on the characteristics of the data and the rate of change underlying it. Through this thesis, we deal with the classification problem under three different settings, namely, supervised multi-dimensional stationary classification, semi-supervised unidimensional streaming classification, and supervised multi-dimensional streaming classification. To accomplish this task, we basically used Bayesian network classifiers as models. The first contribution, addressing the supervised multi-dimensional stationary classification problem, consists of two new methods for learning multi-dimensional Bayesian network classifiers from stationary data. They are proposed from two different points of view. The first method, named CB-MBC, is based on a wrapper greedy forward selection approach, while the second one, named MB-MBC, is a filter constraint-based approach based on Markov blankets. Both methods are applied to two important real-world problems, namely, the prediction of the human immunodeficiency virus type 1 (HIV-1) reverse transcriptase and protease inhibitors, and the prediction of the European Quality of Life-5 Dimensions (EQ-5D) from 39-item Parkinson’s Disease Questionnaire (PDQ-39). The experimental study includes comparisons of CB-MBC and MB-MBC against state-of-the-art multi-dimensional classification methods, as well as against commonly used methods for solving the Parkinson’s disease prediction problem, namely, multinomial logistic regression, ordinary least squares, and censored least absolute deviations. For both considered case studies, results are promising in terms of classification accuracy as well as regarding the analysis of the learned MBC graphical structures identifying known and novel interactions among variables. The second contribution, addressing the semi-supervised uni-dimensional streaming classification problem, consists of a novel method (CPL-DS) for classifying partially labeled data streams. Data streams differ from the stationary data sets by their highly rapid generation process and their concept-drifting aspect. That is, the learned concepts and/or the underlying distribution are likely changing and evolving over time, which makes the current classification model out-of-date requiring to be updated. CPL-DS uses the Kullback-Leibler divergence and bootstrapping method to quantify and detect three possible kinds of drift: feature, conditional or dual. Then, if any occurs, a new classification model is learned using the expectation-maximization algorithm; otherwise, the current classification model is kept unchanged. CPL-DS is general as it can be applied to several classification models. Using two different models, namely, naive Bayes classifier and logistic regression, CPL-DS is tested with synthetic data streams and applied to the real-world problem of malware detection, where the new received files should be continuously classified into malware or goodware. Experimental results show that our approach is effective for detecting different kinds of drift from partially labeled data streams, as well as having a good classification performance. Finally, the third contribution, addressing the supervised multi-dimensional streaming classification problem, consists of two adaptive methods, namely, Locally Adaptive-MB-MBC (LA-MB-MBC) and Globally Adaptive-MB-MBC (GA-MB-MBC). Both methods monitor the concept drift over time using the average log-likelihood score and the Page-Hinkley test. Then, if a drift is detected, LA-MB-MBC adapts the current multi-dimensional Bayesian network classifier locally around each changed node, whereas GA-MB-MBC learns a new multi-dimensional Bayesian network classifier from scratch. Experimental study carried out using synthetic multi-dimensional data streams shows the merits of both proposed adaptive methods.

Secure transmission of information by digital chaotic signals

Protecting signals is one of the main tasks in information transmission. A large number of different methods have been employed since many centuries ago. Most of them have been based on the use of certain signal added to the original one. When the composed signal is received, if the added signal is known, the initial information may be obtained. The main problem is the type of masking signal employed. One possibility is the use of chaotic signals, but they have a first strong limitation: the need to synchronize emitter and receiver. Optical communications systems, based on chaotic signals, have been proposed in a large number of papers. Moreover, because most of the communication systems are digital and conventional chaos generators are analogue, a conversion analogue-digital is needed. In this paper we will report a new system where the digital chaos is obtained from an optically programmable logic structure. This structure has been employed by the authors in optical computing and some previous results in chaotic signals have been reported. The main advantage of this new system is that an analogue-digital conversion is not needed. Previous works by the authors employed Self-Electrooptical Effect Devices but in this case more conventional structures, as semiconductor laser amplifiers, have been employed. The way to analyze the characteristics of digital chaotic signals will be reported as well as the method to synchronize the chaos generators located in the emitter and in the receiver.

Analysis of digital chaotic optical signals

The main objective of this paper is to present some tools to analyze a digital chaotic signal. We have proposed some of them previously, as a new type of phase diagrams with binary signals converted to hexadecimal. Moreover, the main emphasis will be given in this paper to an analysis of the chaotic signal based on the Lempel and Ziv method. This technique has been employed partly by us to a very short stream of data. In this paper we will extend this method to long trains of data (larger than 2000 bit units). The main characteristics of the chaotic signal are obtained with this method being possible to present numerical values to indicate the properties of the chaos.

Transmission of digital chaotic and information-bearing signals in optical communication systems

A new proposal to have secure communications in a system is reported. The basis is the use of a synchronized digital chaotic systems, sending the information signal added to an initial chaos. The received signal is analyzed by another chaos generator located at the receiver and, by a logic boolean function of the chaotic and the received signals, the original information is recovered. One of the most important facts of this system is that the bandwidth needed by the system remain the same with and without chaos.

Optically programmable logic cells as basic units for transmission and synchronization of chaotic signals

We proposed an optical communications system, based on a digital chaotic signal where the synchronization of chaos was the main objective, in some previous papers. In this paper we will extend this work. A way to add the digital data signal to be transmitted onto the chaotic signal and its correct reception, is the main objective. We report some methods to study the main characteristics of the resulting signal. The main problem with any real system is the presence of some retard between the times than the signal is generated at the emitter at the time when this signal is received. Any system using chaotic signals as a method to encrypt need to have the same characteristics in emitter and receiver. It is because that, this control of time is needed. A method to control, in real time the chaotic signals, is reported.

Discrimination of AE Signals From Friction and Concrete Cracking in a Reinforced Concrete Frame Subjected to Seismic Trainings

This paper shows the preliminary results of the development and application of a procedure to filter the Acoustic Emission (AE) signals to distinguish between AE signals coming from friction and AE signals coming from concrete cracking. These signals were recorded during the trainings of an experiment carried out on a reinforced concrete frame subjected to dynamic loadings with the shaking table of the University of Granada (Spain). Discrimination between friction and cracking AE signals is the base to develop a successful procedure and damage index based on AE testing for health monitoring of RC structures subjected to earthquakes.

Logic cells as basic structures to add/drop WDM information signals

Nowadays, in order to take advantage of fiber optic bandwidth, any optical communications system tends to be WDM. The way to extract a channel, characterized by a wavelength, from the optical fiber is to filter the specific wavelength. This gives the systems a low degree of freedom due to the fact of the static character of most of the employed devices. In this paper we will present a different way to extract channels from an optical fiber with WDM transmission. The employed method is based on an Optically Programmable Logic Cells (OPLC) previously published by us, for other applications as a chaotic generator or as basic element for optical computing. In this paper we will describe the configuration of the OPLC to be employed as a dropping device. It acts as a filter because it will extract the data carried by a concrete wavelength. It does depend, internally, on the wavelength. We will show how the intensity of the signal is able to select the chosen information from the line. It will be also demonstrated that a new idea of redundant information it is the way of selecting the concrete wavelength. As a matter of fact this idea is apparently the only way to use the OPLC as a dropping device. Moreover, based on these concepts, a similar way to route signals to different routes is reported. The basis is the use of photonic switching configurations, namely Batcher or Bayan structures, where the unit switching cells are the above indicated OPLCs.

Laser diode bistability as sensor of optical signals pameters

Laser Diodes have been employed many times as light sources on different kinds of optical sensors. Their main function in these applications was the emission of an optical radiation impinging onto a certain object and, according to the characteristics of the reflected light, some information about this object was obtained. Laser diodes were acting, in a certain way, just as passive devices where their only function was to provide the adequate radiation to be later measured and analyzed. The objective of this paper is to report a new concept on the use of laser diodes taking into account their optical bistable properties. As it has been shown in several places, different laser diodes as, for example, DFB lasers and FP lasers, offer bistable characteristics being these characteristics a function of different parameters as wavelength, light polarization or temperature. Laser Bistability is strongly dependent on them and any small variation of above parameters gives rise to a strong change in the characteristics of its non-linear properties. These variations are analyzed and their application in sensing reported. The dependence on wavelength, spectral width, input power and phase variations, mainly for a Fabry-Perot Laser structure as basic configuration, is shown in this paper.

Aerodynamic Analysis of Cup Anemometers Performance: The Stationary Harmonic Response

The effect of cup anemometer shape parameters, such as the cups’ shape, their size, and their center rotation radius, was experimentally analyzed.This analysis was based on both the calibration constants of the transfer function and the most important harmonic termof the rotor’smovement,which due to the cup anemometer design is the third one.This harmonic analysis represents a new approach to study cup anemometer performances. The results clearly showed a good correlation between the average rotational speed of the anemometer’s rotor and the mentioned third harmonic term of its movement.

Processing of ultra-wideband low-frequency signals, for application in Foliage Penetration (FOPEN) Synthetic Aperture Radar (SAR) systems

Foliage Penetration (FOPEN) radar systems were introduced in 1960, and have been constantly improved by several organizations since that time. The use of Synthetic Aperture Radar (SAR) approaches for this application has important advantages, due to the need for high resolution in two dimensions. The design of this type of systems, however, includes some complications that are not present in standard SAR systems. FOPEN SAR systems need to operate with a low central frequency (VHF or UHF bands) in order to be able to penetrate the foliage. High bandwidth is also required to obtain high resolution. Due to the low central frequency, large integration angles are required during SAR image formation, and therefore the Range Migration Algorithm (RMA) is used. This project thesis identifies the three main complications that arise due to these requirements. First, a high fractional bandwidth makes narrowband propagation models no longer valid. Second, the VHF and UHF bands are used by many communications systems. The transmitted signal spectrum needs to be notched to avoid interfering them. Third, those communications systems cause Radio Frequency Interference (RFI) on the received signal. The thesis carries out a thorough analysis of the three problems, their degrading effects and possible solutions to compensate them. The UWB model is applied to the SAR signal, and the degradation induced by it is derived. The result is tested through simulation of both a single pulse stretch processor and the complete RMA image formation. Both methods show that the degradation is negligible, and therefore the UWB propagation effect does not need compensation. A technique is derived to design a notched transmitted signal. Then, its effect on the SAR image formation is evaluated analytically. It is shown that the stretch processor introduces a processing gain that reduces the degrading effects of the notches. The remaining degrading effect after processing gain is assessed through simulation, and an experimental graph of degradation as a function of percentage of nulled frequencies is obtained. The RFI is characterized and its effect on the SAR processor is derived. Once again, a processing gain is found to be introduced by the receiver. As the RFI power can be much higher than that of the desired signal, an algorithm is proposed to remove the RFI from the received signal before RMA processing. This algorithm is a modification of the Chirp Least Squares Algorithm (CLSA) explained in [4], which adapts it to deramped signals. The algorithm is derived analytically and then its performance is evaluated through simulation, showing that it is effective in removing the RFI and reducing the degradation caused by both RFI and notching. Finally, conclusions are drawn as to the importance of each one of the problems in SAR system design.

Speech Signals Feature Extraction Model for a Speaker’s Gender and Age Identification System

Durante el proceso de producción de voz, los factores anatómicos, fisiológicos o psicosociales del individuo modifican los órganos resonadores, imprimiendo en la voz características particulares. Los sistemas ASR tratan de encontrar los matices característicos de una voz y asociarlos a un individuo o grupo. La edad y sexo de un hablante son factores intrínsecos que están presentes en la voz. Este trabajo intenta diferenciar esas características, aislarlas y usarlas para detectar el género y la edad de un hablante. Para dicho fin, se ha realizado el estudio y análisis de las características basadas en el pulso glótico y el tracto vocal, evitando usar técnicas clásicas (como pitch y sus derivados) debido a las restricciones propias de dichas técnicas. Los resultados finales de nuestro estudio alcanzan casi un 100% en reconocimiento de género mientras en la tarea de reconocimiento de edad el reconocimiento se encuentra alrededor del 80%. Parece ser que la voz queda afectada por el género del hablante y las hormonas, aunque no se aprecie en la audición. ABSTRACT Particular elements of the voice are printed during the speech production process and are related to anatomical and physiological factors of the phonatory system or psychosocial factors acquired by the speaker. ASR systems attempt to find those peculiar nuances of a voice and associate them to an individual or a group. Age and gender are inherent factors to the speaker which may be represented in voice. This work attempts to differentiate those characteristics, isolate them and use them to detect speaker’s gender and age. Features based on glottal pulse and vocal tract are studied and analyzed in order to achieve good results in both tasks. Classical methodologies (such as pitch and derivates) are avoided since the requirements of those techniques may be too restrictive. The final scores achieve almost 100% in gender recognition whereas in age recognition those scores are around 80%. Factors related to the gender and hormones seem to affect the voice although they are not audible.

Transient behavior of a system composed of conductive thin wire structures excited by harmonic and lightning type signals

The transient response of a system of independent electrodes buried in a semi-infinite conducting medium is studied. Using a simple and versatile numerical scheme written by the authors and based on the Electric Field Integral Equation (EFIE), the effect caused by harmonic signals ranging on frequency from Hz to hundred of MHz, and also by lightning type driving signal striking at a remote point far from the conductors, is extensively studied. The value of the scalar potential appearing on the electrodes as a function of the frequency of the applied signal is one of the variables investigated. Other features such as the input impedance at the injection point of the signal and the Ground Potential Rise (GPR) over the electrode system are also discussed