Browsing by Autor "Carmelina Ierardi"

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    Modelado de micro-central hidráulica para el diseño de controladores con aplicación en regiones aisladas de Honduras
    (2020) Alejandro Tapia; Pablo Millán; Fabio Gómez-Estern; Carmelina Ierardi; Álvaro Rodríguez del Nozal
    [Resumen] Este artículo trata el modelado de una planta de micro-generación destinada al abastecimiento eléctrico de regiones aisladas en países en vías de desarrollo. El objetivo del modelo es caracterizar fielmente el comportamiento de estas microcentrales ante acciones externas como la actuación sobre la válvula de admisión y la conexión o desconexión repentina de cargas. Este modelo permitirá el desarrollo de estrategias de control eficientes, robustas y sencillas, adaptadas al contexto de precariedad de este tipo de instalaciones.
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    State-of-the-art in multispectral remote sensing for water body identification in agriculture
    (2025) D. Merchán; José María Manzano; Carmelina Ierardi
    Monitoring water in agricultural landscapes is essential for sustainable resource management, irrigation planning, and early flood detection. Recent advances in remote sensing, particularly the availability of high-resolution multispectral imagery from satellites and Unmanned Aerial Vehicles (UAVs), have greatly improved the capacity to detect and analyze water with higher spatial and temporal precision. This paper reviews 48 peer-reviewed studies published in the last six years, selected based on their focus on agricultural or non-urban areas, use of multispectral imagery, methodological detail, real-world validation, and scientific relevance. The analysis examines four main detection methodologies-spectral indices, decision trees, machine learning techniques, and neural networks-highlighting their respective strengths, limitations, and applicability to agricultural water monitoring. It also synthesizes information on platforms, sensors, and spectral bands most commonly used, as well as issues of data accessibility (free vs. commercial). The review consolidates the current state of the art in multispectral remote sensing for agricultural water detection and identifies key challenges and research opportunities to advance both methodological development and practical applications.