Revision of EBG metamaterials and active antennas

This review of Electromagnetic Band Gap (EGB) metamaterials and steering integrated antennas was carried out in IMST GmbH under a short collaboration stay. This activity is in line with Coordinating the Antenna Research in Europe (CARE). The aim is to identify the newest trends, and suggest novel solutions and design methodologies for various applications.

Novel Gravity Distortion Evaluation on Large Antenna Array for Deep Space Communication

This paper presents a simple gravity evaluation model for large reflector antennas and the experimental example for a case study of one uplink array of 4x35-m antennas at X and Ka band. This model can be used to evaluate the gain reduction as a function of the maximum gravity distortion, and also to specify this at system designer level. The case study consists of one array of 35-m antennas for deep space missions. Main issues due to the gravity effect have been explored with Monte Carlo based simulation analysis.

Rectangular room dimensions estimation using narrowband signal and sectorized antennas

A system for estimation of unknown rectangular room dimensions based on two radio transceivers, both capable of full duplex operations, is presented. The approach is based on CIR measurements taken at the same place where the signal is transmitted (generated), commonly known as self- to-self CIR. Another novelty is the receiver antenna design which consists of eight sectorized antennas with 45° aperture in the horizontal plane, whose total coverage corresponds to the isotropic one. The dimensions of a rectangular room are reconstructed directly from radio impulse responses by extracting the information regarding features like round trip time, received signal strength and reverberation time. Using radar approach the estimation of walls and corners positions are derived. Additionally, the analysis of the absorption coefficient of the test environment is conducted and a typical coefficient for office room with furniture is proposed. Its accuracy is confirmed through the results of volume estimation. Tests using measured data were performed, and the simulation results confirm the feasibility of the approach.

Research activities of Spanish antenna groups

When we look at the history of electricity and electromagnetism in Spain we discover that the most important Spanish researchers are generally out of the official institutions or stable research groups until the 20th century [1] [2]. In the 20th century most of the scientific research is done in stable research institutions and universities and the most important electromagnetism research centres in Spain are located in the Faculty of Physics of the most important universities, the National Scientific Research Council (CSIC) and the School for Telecommunication Engineering created in 1923. But the greatest impulse of research in the antenna and radiowave propagation field is done after 1960 reaching the first national URSI conference in 1980. After that year, the relation between groups and the number of research groups is continuously growing and the relation to industry is also increasing. When Spain joins the European research organizations (COST, ERC...) and the European Union in 1985 the research support experience a fast growing and the participation in the European research structures. In the antenna design field, there exist some specializations although most of the groups have dome specific projects in almost all the antenna analysis and design fields. Here, we have selected the most important and characteristic area related to each of the research groups and institutions. The easiest way to classify the research work in antennas is the selection between antenna analysis, design and measurement. After that the selected frequency bands technology, the type of antennas and the related circuits can be a good criterion to describe the variety of research work and specialization between groups.

Application of postprocessing techniques methods for noise reduction in cylindrical near field antenna measurements

Three different methods to reduce the noise power in the far-field pattern of an antenna when it is measured in a cylindrical near field system are presented and compared. The first one is based on a modal filtering while the other two are based on spatial filtering, either on an antenna plane or either on a cylinder of smaller radius. Simulated and measured results will be presented.

Procesos de dualización en la sociedad de la información: algunas polémicas públicas de índole tecnológica

La tecnología ha cambiado el mundo, pero las consecuencias de estos cambios en la sociedad no siempre se han pronosticado bien. Las Tecnologías de la Información transformaron el método de producción industrial. La nueva industria produce ideas y conceptos, no objetos. Este cambio ha dado como resultado una sociedad dualizada, ha desaparecido gran parte de la clase media y han aumentado las diferencias entre la clase alta y la baja. Las exigencias educativas de los nuevos puestos de trabajo innovadores son superiores a los de la industria tradicional, pero inferiores en los puestos de trabajo de producción. Además, el número de puestos de trabajo disponibles de este tipo es menor que en la industria tradicional, se necesita menos mano de obra, los procesos se pueden automatizar, las tareas mecánicas se aprenden en poco tiempo y son trabajos temporales, cuyo número dependerá de la demanda global. Para que el proceso de innovación funcione, las empresas se reúnen en las zonas financieras de grandes ciudades, como Nueva York o Londres, que fueron las primeras con acceso a las redes de telecomunicación. De esta manera se producen sinergias que contribuyen a mejorar el proceso innovador global. Estas ideas y conceptos que cambian el mundo necesitan de este entorno de producción, que no puede ser replicado, y son tan importantes que su acceso está restringido para la mayor parte del mundo por distintos mecanismos de control. El despliegue de las redes de telecomunicaciones inalámbricas ha sido enorme en los últimos años. El cliente busca llamar desde cualquier lugar y llevar un acceso a Internet en teléfono móvil. Para conseguirlo, las operadoras de telefonía móvil necesitan poner antenas de telefonía móvil en las ciudades, pero la instalación cerca de edificios no está siendo fácil. Pocos quieren tener una antena cerca por los problemas de salud de las personas que padecen los que ya viven o trabajan cerca de una. Los efectos del electromagnetismo en los seres humanos no están claros y provocan desconfianza hacia las antenas. La digitalización de los contenidos, que ha sido necesaria para transmitir contenido en Internet, permite que cualquier persona con un ordenador y una conexión a Internet pueda publicar un disco, una película o un libro. Pero esa persona también puede copiar los originales y enviarlos a cualquier lugar del mundo sin el permiso del autor. Con el fin de controlar la copia no autorizada, los derechos de autor se están usando para cambiar leyes e incluir sistemas de censura en Internet. Estos sistemas permiten a los autores eliminar el contenido ilegal, pero también pueden ser usados para censurar cualquier tipo de información. El control de la información es poder y usarlo de una manera o de otra afecta a todo el planeta. El problema no es la tecnología, que es solo una herramienta, es la forma que tienen los gobiernos y las grandes empresas de usarlo. Technology has changed the world, but the consequences of these changes in society have not always been well predicted. The Information Technology transformed the industrial production method. The new industry produces ideas and concepts, not objects. This change has resulted in a society dualized, most of the middle class has disappeared and the differences between high and low class have increased. The educational requirements of new innovative jobs are higher than the ones of the traditional industry, but lower in production jobs. Moreover, the number of available jobs of this type is lower than in the traditional industry, it takes less manpower, processes can be automated, mechanical tasks are learned in a short time and jobs are temporary, whose number depends on global demand. For the innovation process works, companies meet in the business districts of large cities, like New York or London, which were the first with access to telecommunications networks. This will produce synergies that improve the overall innovation process. These ideas and concepts that change the world need this production environment, which cannot be replicated, and are so important that their access is restricted to most of the world by different control mechanisms. The deploy of wireless telecommunications networks has been enormous in recent years. The client seeks to call from anywhere and to bring Internet access in his mobile phone. To achieve this, mobile operators need to put cell towers in cities, but the installation near buildings is not being easy. Just a few want to have an antenna closely because of the health problems suffered by people who already live or work near one. The effects of electromagnetism in humans are unclear and cause distrust of antennas. The digitization of content, which has been necessary to transmit Internet content, allows anyone with a computer and an Internet connection to be able to publish an album, a movie or a book. But that person can also copy the originals and send them anywhere in the world without the author's permission. In order to control the unauthorized copying, copyright is being used to change laws and include Internet censorship systems. These systems allow authors to eliminate illegal content, but may also be used to censor any information. The control of knowledge is power and using it in one way or another affects the whole planet. The problem is not technology, which is just a tool, but the way that governments and large corporations use it.

Bifocal reflector antenna system for radar imaging at 300 GHz

A 300 GHz radar imaging system is presented, including descriptions of the radar sensor and antenna subsystems. The antenna consists of a Bifocal Ellipsoidal Gregorian Reflector whose beam is scanned by a combination of the rotation and vertical tilting of a flat small secondary mirror. A prototype is being mounted and its characterization will be presented.

Collective Organization of Complex Networks: Emergent Scales, Vulnerability, and Targeting of Desired Dynamics

Cuando una colectividad de sistemas dinámicos acoplados mediante una estructura irregular de interacciones evoluciona, se observan dinámicas de gran complejidad y fenómenos emergentes imposibles de predecir a partir de las propiedades de los sistemas individuales. El objetivo principal de esta tesis es precisamente avanzar en nuestra comprensión de la relación existente entre la topología de interacciones y las dinámicas colectivas que una red compleja es capaz de mantener. Siendo este un tema amplio que se puede abordar desde distintos puntos de vista, en esta tesis se han estudiado tres problemas importantes dentro del mismo que están relacionados entre sí. Por un lado, en numerosos sistemas naturales y artificiales que se pueden describir mediante una red compleja la topología no es estática, sino que depende de la dinámica que se desarrolla en la red: un ejemplo son las redes de neuronas del cerebro. En estas redes adaptativas la propia topología emerge como consecuencia de una autoorganización del sistema. Para conocer mejor cómo pueden emerger espontáneamente las propiedades comúnmente observadas en redes reales, hemos estudiado el comportamiento de sistemas que evolucionan según reglas adaptativas locales con base empírica. Nuestros resultados numéricos y analíticos muestran que la autoorganización del sistema da lugar a dos de las propiedades más universales de las redes complejas: a escala mesoscópica, la aparición de una estructura de comunidades, y, a escala macroscópica, la existencia de una ley de potencias en la distribución de las interacciones en la red. El hecho de que estas propiedades aparecen en dos modelos con leyes de evolución cuantitativamente distintas que siguen unos mismos principios adaptativos sugiere que estamos ante un fenómeno que puede ser muy general, y estar en el origen de estas propiedades en sistemas reales. En segundo lugar, proponemos una medida que permite clasificar los elementos de una red compleja en función de su relevancia para el mantenimiento de dinámicas colectivas. En concreto, estudiamos la vulnerabilidad de los distintos elementos de una red frente a perturbaciones o grandes fluctuaciones, entendida como una medida del impacto que estos acontecimientos externos tienen en la interrupción de una dinámica colectiva. Los resultados que se obtienen indican que la vulnerabilidad dinámica es sobre todo dependiente de propiedades locales, por tanto nuestras conclusiones abarcan diferentes topologías, y muestran la existencia de una dependencia no trivial entre la vulnerabilidad y la conectividad de los elementos de una red. Finalmente, proponemos una estrategia de imposición de una dinámica objetivo genérica en una red dada e investigamos su validez en redes con diversas topologías que mantienen regímenes dinámicos turbulentos. Se obtiene como resultado que las redes heterogéneas (y la amplia mayora de las redes reales estudiadas lo son) son las más adecuadas para nuestra estrategia de targeting de dinámicas deseadas, siendo la estrategia muy efectiva incluso en caso de disponer de un conocimiento muy imperfecto de la topología de la red. Aparte de la relevancia teórica para la comprensión de fenómenos colectivos en sistemas complejos, los métodos y resultados propuestos podrán dar lugar a aplicaciones en sistemas experimentales y tecnológicos, como por ejemplo los sistemas neuronales in vitro, el sistema nervioso central (en el estudio de actividades síncronas de carácter patológico), las redes eléctricas o los sistemas de comunicaciones. ABSTRACT The time evolution of an ensemble of dynamical systems coupled through an irregular interaction scheme gives rise to dynamics of great of complexity and emergent phenomena that cannot be predicted from the properties of the individual systems. The main objective of this thesis is precisely to increase our understanding of the interplay between the interaction topology and the collective dynamics that a complex network can support. This is a very broad subject, so in this thesis we will limit ourselves to the study of three relevant problems that have strong connections among them. First, it is a well-known fact that in many natural and manmade systems that can be represented as complex networks the topology is not static; rather, it depends on the dynamics taking place on the network (as it happens, for instance, in the neuronal networks in the brain). In these adaptive networks the topology itself emerges from the self-organization in the system. To better understand how the properties that are commonly observed in real networks spontaneously emerge, we have studied the behavior of systems that evolve according to local adaptive rules that are empirically motivated. Our numerical and analytical results show that self-organization brings about two of the most universally found properties in complex networks: at the mesoscopic scale, the appearance of a community structure, and, at the macroscopic scale, the existence of a power law in the weight distribution of the network interactions. The fact that these properties show up in two models with quantitatively different mechanisms that follow the same general adaptive principles suggests that our results may be generalized to other systems as well, and they may be behind the origin of these properties in some real systems. We also propose a new measure that provides a ranking of the elements in a network in terms of their relevance for the maintenance of collective dynamics. Specifically, we study the vulnerability of the elements under perturbations or large fluctuations, interpreted as a measure of the impact these external events have on the disruption of collective motion. Our results suggest that the dynamic vulnerability measure depends largely on local properties (our conclusions thus being valid for different topologies) and they show a non-trivial dependence of the vulnerability on the connectivity of the network elements. Finally, we propose a strategy for the imposition of generic goal dynamics on a given network, and we explore its performance in networks with different topologies that support turbulent dynamical regimes. It turns out that heterogeneous networks (and most real networks that have been studied belong in this category) are the most suitable for our strategy for the targeting of desired dynamics, the strategy being very effective even when the knowledge on the network topology is far from accurate. Aside from their theoretical relevance for the understanding of collective phenomena in complex systems, the methods and results here discussed might lead to applications in experimental and technological systems, such as in vitro neuronal systems, the central nervous system (where pathological synchronous activity sometimes occurs), communication systems or power grids.

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.

Man-pack antenna at X-band

In this paper a low cost man-pack antenna for satellite communications at X band is presented. The antenna has dual circular polarization in Tx and Rx.

On the design of a planar phased array radar antenna architecture for space debris situational awareness

The Space Situational Awareness (SSA) program from the European Space Agency (ESA) protects Europe's citizens and their satellite-based services by detecting space hazards. ESA Ground Systems (GS) division is currently designing a phased array radar composed of thousands of radiating elements for future stages of the SSA program [1]. The radar shall guarantee the detection of most of the Low Earth Orbit (LEO) space debris, providing a general map of space junk. While range accuracy is mainly dictated by the radar waveform, the detection and tracking of small objects in LEO regimes is highly dependent on the angular accuracy achieved by the smart phased array antenna, demonstrating the important of the performance of this architecture.

Dynamics of brain networks in aesthetic appreciation

Neuroimage experiments have been essential for identifying active brain networks. During cognitive tasks as in, e.g., aesthetic appreciation, such networks include regions that belong to the default mode network (DMN). Theoretically, DMN activity should be interrupted during cognitive tasks demanding attention, as is the case for aesthetic appreciation. Analyzing the functional connectivity dynamics along three temporal windows and two conditions, beautiful and not beautiful stimuli, here we report experimental support for the hypothesis that aesthetic appreciation relies on the activation of two different networks, an initial aesthetic network and a delayed aesthetic network, engaged within distinct time frames. Activation of the DMN might correspond mainly to the delayed aesthetic network. We discuss adaptive and evolutionary explanations for the relationships existing between the DMN and aesthetic networks and offer unique inputs to debates on the mind/brain interaction.

AALUMO: a user model ontology for ambient assisted living services supported in next-generation networks

Ambient Assisted Living (AAL) services are emerging as context-awareness solutions to support elderly people?s autonomy. The context-aware paradigm makes applications more user-adaptive. In this way, context and user models expressed in ontologies are employed by applications to describe user and environment characteristics. The rapid advance of technology allows creating context server to relieve applications of context reasoning techniques. Specifically, the Next Generation Networks (NGN) provides by means of the presence service a framework to manage the current user's state as well as the user's profile information extracted from Internet and mobile context. This paper propose a user modeling ontology for AAL services which can be deployed in a NGN environment with the aim at adapting their functionalities to the elderly's context information and state.

A reflection suppression technique for far field antenna measurements

This paper presents a reflection suppression technique for far field antenna measurements. The technique is based on a source reconstruction over a surface greater than the antenna itself. To be able to perform the reflection construction the next steps are required: the complete far field antenna pattern is obtained through interpolation of the acquired cuts, the currents are obtained through a holographic technique, the field out of the antenna area is filtered, and the pattern is reconstructed. The algorithm is used with measurements in the LEHA-UPM antenna measurement facilities and in the outdoor far field facility of LIT INPE in Brazil.

ASYTRAIN: a new methodology for teaching and learning antennas

This paper presents ASYTRAIN, a new tool to teach and learn antennas, based on the use of a modular building kit and a low cost portable antenna measurement system that lets the students design and build different types of antennas and observe their characteristics while learning the insights of the subjects. This tool has a methodology guide for try-and-test project development and, makes the students be active antenna engineers instead of passive learners. This experimental learning method arises their motivation during the antenna courses.