Spatial Identification Methods and Systems for RFID Tags

Disertační práce je zaměřena na metody a systémy pro měření vzdálenosti a lokalizaci RFID tagů pracujících v pásmu UHF. Úvod je věnován popisu současného stavu vědeckého poznání v oblasti RFID prostorové identifikace a stručnému shrnutí problematiky modelování a návrhu prototypů těchto systémů. Po specifikaci cílů disertace pokračuje práce popisem teorie modelování degenerovaného kanálu pro RFID komunikaci. Detailně jsou rozebrány metody měření vzdálenosti a odhadu směru příchodu signálu založené na zpracování fázové informace. Pro účely lokalizace je navrženo několik scénářů rozmístění antén. Modely degenerovaného kanálu jsou simulovány v systému MATLAB. Významná část této práce je věnována konceptu softwarově definovaného rádia (SDR) a specifikům jeho adaptace na UHF RFID, která využití běžných SDR systémů značně omezují. Diskutována je zejména problematika průniku nosné vysílače do přijímací cesty a požadavky na signál lokálního oscilátoru používaný pro směšování. Prezentovány jsou tři vyvinuté prototypy: experimentální dotazovač EXIN-1, měřicí systém založený na platformě Ettus USRP a anténní přepínací matice pro emulaci SIMO systému. Závěrečná část je zaměřena na testování a zhodnocení popisovaných lokalizačních technik, založených na měření komplexní přenosové funkce RFID kanálu. Popisuje úzkopásmové/širokopásmové měření vzdálenosti a metody odhadu směru signálu. Oba navržené scénáře rozmístění antén jsou v závěru ověřeny lokalizačním měřením v reálných podmínkách.The doctoral thesis is focused on methods and systems for ranging and localization of RFID tags operating in the UHF band. It begins with a description of the state of the art in the field of RFID positioning with short extension to the area of modeling and prototyping of such systems. After a brief specification of dissertation objectives, the thesis overviews the theory of degenerate channel modeling for RFID communication. Details are given about phase-based ranging and direction of arrival finding methods. Several antenna placement scenarios are proposed for localization purposes. The degenerate channel models are simulated in MATLAB. A significant part of the thesis is devoted to software defined radio (SDR) concept and its adaptation for UHF RFID operation, as it has its specialties which make the usage of standard SDR test equipment very disputable. Transmit carrier leakage into receiver path and requirements on local oscillator signals for mixing are discussed. The development of three experimental prototypes is also presented there: experimental interrogator EXIN-1, measurement system based on Ettus USRP platform, and antenna switching matrix for an emulation of SIMO system. The final part is focused on testing and evaluation of described positioning techniques based on complex backscatter channel transfer function measurement. Both narrowband/wideband ranging and direction of arrival methods are validated. Finally, both proposed antenna placement scenarios are evaluated with real-world measurements.

Model Predictive Control Based on Deep Learning for Solar Parabolic-Trough Plants

En la actualidad, cada vez es mayor el interés por utilizar energías renovables, entre las que se encuentra la energía solar. Las plantas de colectores cilindro-parabólicos son un tipo de planta termosolar donde se hace incidir la radiación del Sol en unos tubos mediante el uso de unos espejos con forma de parábola. En el interior de estos tubos circula un fluido, generalmente aceite o agua, que se calienta para generar vapor y hacer girar una turbina, produciendo energía eléctrica. Uno de los métodos más utilizados para manejar estas plantas es el control predictivo basado en modelo (model predictive control, MPC), cuyo funcionamiento consiste en obtener las señales de control óptimas que se enviarán a la planta basándose en el uso de un modelo de la misma. Este método permite predecir el estado que adoptará el sistema según la estrategia de control escogida a lo largo de un horizonte de tiempo. El MPC tiene como desventaja un gran coste computacional asociado a la resolución de un problema de optimización en cada instante de muestreo. Esto dificulta su implementación en plantas comerciales y de gran tamaño, por lo que, actualmente, uno de los principales retos es la disminución de estos tiempos de cálculo, ya sea tecnológicamente o mediante el uso de técnicas subóptimas que simplifiquen el problema. En este proyecto, se propone el uso de redes neuronales que aprendan offline de la salida proporcionada por un controlador predictivo para luego poder aproximarla. Se han entrenado diferentes redes neuronales utilizando un conjunto de datos de 30 días de simulación y modificando el número de entradas. Los resultados muestran que las redes neuronales son capaces de proporcionar prácticamente la misma potencia que el MPC con variaciones más suaves de la salida y muy bajas violaciones de las restricciones, incluso disminuyendo el número de entradas. El trabajo desarrollado se ha publicado en Renewable Energy, una revista del primer cuartil en Green & sustainable science & technology y Energy and fuels.Nowadays, there is an increasing interest in using renewable energy sources, including solar energy. Parabolic trough plants are a type of solar thermal power plant in which solar radiation is reflected onto tubes with parabolic mirrors. Inside these tubes circulates a fluid, usually oil or water, which is heated to generate steam and turn a turbine to produce electricity. One of the most widely used methods to control these plants is model predictive control (MPC), which obtains the optimal control signals to send to the plant based on the use of a model. This method makes it possible to predict its future state according to the chosen control strategy over a time horizon. The MPC has the disadvantage of a significant computational cost associated with resolving an optimization problem at each sampling time. This makes it challenging to implement in commercial and large plants, so currently, one of the main challenges is to reduce these computational times, either technologically or by using suboptimal techniques that simplify the problem. This project proposes the use of neural networks that learn offline from the output provided by a predictive controller to then approximate it. Different neural networks have been trained using a 30-day simulation dataset and modifying the number of irradiance and temperature inputs. The results show that the neural networks can provide practically the same power as the MPC with smoother variations of the output and very low violations of the constraints, even when decreasing the number of inputs. The work has been published in Renewable Energy, a first quartile journal in Green & sustainable science & technology and Energy and fuels.Universidad de Sevilla. Máster en Ingeniería Industria

Book review: Input-to-State Stability for PDEs, Iasson Karafyllis, Miroslav Krstic. Communications and Control Engineering, Springer, (2018). ISBN: 978-3-319-91011-6

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Biophysical mechanisms of long-distance transport of liquids and signaling in high plants

Wave phenomena have been observed in numerous experiments with whole plants. One of possible mechanisms of the long-distance high-speed signaling in high plants is connected with concentration waves that can propagate through the conducting systems of plants. One-dimensional axisymmetrical stationary flow of a viscous liquid with osmotically active dissolved component through a long thin rigid cylindrical tube is considered as a model of the conducting vessel of the plant. Constant concentrations of the component at the inlet and outlet of the vessel are maintained by the live cells of the vegetative organs of the plant. Nonlinear concentration distribution along the tube and the parabolic velocity profiles are obtained. Propagation of small excitations of concentrations and velocities along the tube is considered. Expression for the wave velocity U is presented. The range U=20-60 m/s is obtained by numerical estimations at wide variations of the parameters within the physiological limits. The time delay in signal transmission in the system root-leaves corresponds to the experimental data. In that way the concentration waves can mediate high-speed transferring of information between the organs of plants

Combined State and Parameter Estimation for a Static Model of the Maypole (Hoop-Column) Antenna Suface

Parameter and state estimation techniques are discussed for an elliptic system arising in a developmental model for the antenna surface of the Maypole Hoop/Column antenna. A computational algorithm based on spline approximations for the state and elastic parameters is given and numerical results obtained using this algorithm are summarized