5 research outputs found

    Metamateriales magnéticos de anillos resonantes para aplicaciones en imagen médica por resonancia magnética

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    La presente memoria recoge los resultados de una investigación multidisciplinar que combina la reciente disciplina de los metamateriales electromagnéticos con la técnica de obtención de imágenes médicas mediante resonancia magnética. La resonancia magnética (RM) [1] es una de las principales técnicas de obtención de imágenes médicas junto con la tomografía axial computerizada (TAC), la ecografía por ultrasonidos y la tomografía por emisión de positrones (PET, por sus siglas en inglés Positron Emission Tomography). De entre todas estas técnicas, la RM y el TAC son las que ofrecen mejor resolución de imagen, pero mientras que el TAC hace uso de Rayos X, en la RM se emplean radiaciones no ionizantes, lo que hace que la RM carezca de los riesgos asociados a exposiciones prolongadas que sí presenta el TAC. No obstante, frente al TAC, la principal desventaja de la RM es que requiere un intervalo de tiempo mayor para adquirir las imágenes, lo que puede resultar incómodo para el paciente además de hacer muy difícil la obtención de imágenes en tiempo real que muestren, por ejemplo, la actividad cardíaca de forma instantánea. La RM se basa en la aplicación de campos magnéticos estáticos muy intensos (desde 0.2 a 7 Teslas) y la detección de ondas electromagnéticas muy débiles en el rango de la radiofrecuencia (RF). Los avances actuales en cuanto a resolución y rapidez de adquisición de la imagen se basan en el empleo de campos magnéticos cada vez más intensos. Así, los equipos de RM comercializados abarcan desde 0.2 a 3 Teslas, y aún cuando existen prototipos que alcanzan los 7 Teslas, cuyo desarrollo se haya impulsado principalmente por investigaciones en neurociencia, las actuales condiciones regulatorias referidas a la exposición a campos electromagnéticos no facilitan la comercialización de estos prototipos. La alternativa al empleo de campos magnéticos más intensos es la optimización en la detección de la RF, que de manera convencional se realiza mediante bobinas detectoras. En relación con esto último resulta de interés la reciente disciplina de los metamateriales electromagnéticos surgida en el campo del Electromagnetismo aplicado durante la pasada década. Los metamateriales son estructuras periódicas artificiales fabricadas a partir de elementos conductores y aislantes convencionales

    1-D broadside-radiating leaky-wave antenna based on a numerically synthesized impedance surface

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    A newly-developed deterministic numerical technique for the automated design of metasurface antennas is applied here for the first time to the design of a 1-D printed Leaky-Wave Antenna (LWA) for broadside radiation. The surface impedance synthesis process does not require any a priori knowledge on the impedance pattern, and starts from a mask constraint on the desired far-field and practical bounds on the unit cell impedance values. The designed reactance surface for broadside radiation exhibits a non conventional patterning; this highlights the merit of using an automated design process for a design well known to be challenging for analytical methods. The antenna is physically implemented with an array of metal strips with varying gap widths and simulation results show very good agreement with the predicted performance

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    Antennas and Electromagnetics Research via Natural Language Processing.

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    Advanced techniques for performing natural language processing (NLP) are being utilised to devise a pioneering methodology for collecting and analysing data derived from scientific literature. Despite significant advancements in automated database generation and analysis within the domains of material chemistry and physics, the implementation of NLP techniques in the realms of metamaterial discovery, antenna design, and wireless communications remains at its early stages. This thesis proposes several novel approaches to advance research in material science. Firstly, an NLP method has been developed to automatically extract keywords from large-scale unstructured texts in the area of metamaterial research. This enables the uncovering of trends and relationships between keywords, facilitating the establishment of future research directions. Additionally, a trained neural network model based on the encoder-decoder Long Short-Term Memory (LSTM) architecture has been developed to predict future research directions and provide insights into the influence of metamaterials research. This model lays the groundwork for developing a research roadmap of metamaterials. Furthermore, a novel weighting system has been designed to evaluate article attributes in antenna and propagation research, enabling more accurate assessments of impact of each scientific publication. This approach goes beyond conventional numeric metrics to produce more meaningful predictions. Secondly, a framework has been proposed to leverage text summarisation, one of the primary NLP tasks, to enhance the quality of scientific reviews. It has been applied to review recent development of antennas and propagation for body-centric wireless communications, and the validation has been made available for comparison with well-referenced datasets for text summarisation. Lastly, the effectiveness of automated database building in the domain of tunable materials and their properties has been presented. The collected database will use as an input for training a surrogate machine learning model in an iterative active learning cycle. This model will be utilised to facilitate high-throughput material processing, with the ultimate goal of discovering novel materials exhibiting high tunability. The approaches proposed in this thesis will help to accelerate the discovery of new materials and enhance their applications in antennas, which has the potential to transform electromagnetic material research
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