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The development of mobile communication technologies beyond 5G tries to achieve strict requirements, such as ultra-high data rates, reduced latency, high reliability, and extensive coverage. A rising technology is Reconfigurable Intelligent Surfaces (RIS), which can ma- nipulate electromagnetic waves to improve signal quality without the need for additional Base Stations (BSs). This thesis studies the feedback control signaling required for op- timal RIS configuration, focusing on minimizing the signaling overhead while maximizing Signal-to-Noise Ratio (SNR). Two methods are explored: Phase-Shift Quantization and Codebook-Based methods. The Phase-Shift Quantization method studies the impact of discrete phase shifts on each of the elements of the RIS on SNR, demonstrating that with increased resolution, the SNR performance improves. On the other hand, the Codebook-Based method predefines a set of configurations of the RIS, known as the codebook, to reduce the feedback overhead. An optimized version of the Codebook method is also developed, which takes into account a typical position of BS and User Equipment (UE) in mobile networks, reducing the codebook size and improving efficiency. The Rician fading model is also studied in the thesis, to simulate more realistic urban com- munication environments, comparing its impact on SNR with the traditional Line-of-Sight (LoS) scenario. While the presence of multipath components in Rician fading scenarios increases the dispersion in the SNR values, the RIS still performs sufficiently well. The results show the importance of making control signaling as efficient as possible to achieve an optimal balance between maximizing SNR and minimizing resource usage, which is the trade-off studied in this thesis., Outgoing

The expressiveness of neural networks highly depends on the nature of the activation function, although these are usually assumed predefined and fixed during the training stage. Under a signal processing perspective, in this paper we present Expressive Neural Network (ENN), a novel model in which the non-linear activation functions are modeled using the Discrete Cosine Transform (DCT) and adapted using backpropagation during training. This parametrization keeps the number of trainable parameters low, is appropriate for gradient-based schemes, and adapts to different learning tasks. This is the first non-linear model for activation functions that relies on a signal processing perspective, providing high flexibility and expressiveness to the network. We contribute with insights in the explainability of the network at convergence by recovering the concept of bump, this is, the response of each activation function in the output space. Finally, through exhaustive experiments we show that the model can adapt to classification and regression tasks. The performance of ENN outperforms state of the art benchmarks, providing above a 40% gap in accuracy in some scenarios., This work is part of the project IRENE (PID2020-115323RB-C31), funded by MCIN/AEI/10.13039/501100011033., Peer Reviewed, Postprint (published version)

Electromagnetic interferences or EMIs are electromagnetic disturbance generated by an external or internal sources that affects radio communications or causes triggers malfunctioning of electronic systems. These interferences can seriously impact WLAN communications, so there are methods to evaluate these EMI and standards that define the emission limits to protect electronic devices. The amplitude probability distribution (APD) is a measuring function used for assessing electromagnetic disturbances, especially those with a stochastic and time-varying distribution. According to the CISPR 16-1-1, the APD has been defined as the cumulative distribution of the probability of time that the amplitude of disturbance exceeds a specified level. The APD is highly correlated to the bit error rate (BER) of digital communication systems and, therefore, a redefinition of radiated emission limits on the basis of the APD would be very meaningful in terms of protecting wireless systems from unintentional interferences. However, establishing emissions requirements based on the current standard APD method can be misleading and not completely traceable methodology-wise. This is because, when analyzing electromagnetic compatibility (EMC) standards, it becomes evident the current specifications for APD measurements are unclear and ill-posed. However, the possibilities offered by this interference measurement make it perfect to test the performance of software defined radios as measurement instruments for WLAN communications. In this project we will propose an improved and more general APD definition accompanied with a calculation algorithm. Moreover, we argue that the APD measurements shall move from a frequentist approach and adopt a bayesian one. Through numerical simulations, we deliver evidence that exemplifies and supports our revised APD definition. Finally, we will experimentally validate this new APD measurement from EMI tests, then we will compare the impact of these EMIs on an, Las interferencias electromagnéticas o EMI son perturbaciones electromagnéticas generadas por una fuente externa o interna que afectan a las comunicaciones por radio o provocan el mal funcionamiento de sistemas electrónicos. Estas interferencias pueden afectar gravemente a las comunicaciones WLAN, por lo que existen métodos para evaluar estas EMIs y normas que definen los límites de emisión para proteger los dispositivos electrónicos. La distribución de probabilidad de amplitud (APD) es una función de medida utilizada para evaluar las perturbaciones electromagnéticas, especialmente las que tienen una distribución estocàstica y variable en el tiempo. Según la norma CISPR 16-1-1, la APD se define como la distribución acumulativa de la probabilidad temporal de que la amplitud de la perturbación supere un nivel especificado. La APD esta altamente correlacionada con la tasa de errores de bit (BER) de los sistemas de comunicación digital y, por lo tanto, una redefinición de los límites de emisión radiada sobre la base de la APD sería muy significativa en términos de protección de los sistemas inalámbricas frente a interferencias involuntarias. Sin embargo, los requisitos de emisiones basados en el método APD estándar actual, puede inducir a error y no ser completamente trazable desde el punto de vista metodológico. Esto se debe a que, al analizar las normas de compatibilidad electromagnética (CEM), resulta evidente que las especificaciones actuales para las mediciones APD son poco claras y están mal planteadas. Sin embargo, las posibilidades que ofrece esta medida de interferencias la hacen perfecta para comprobar el desempeño de las radios definidas por software como instrumentos de medida de las comunicaciones WLAN. En este proyecto propondremos una definición de APD mejorada y más general, acompañada de un algoritmo para su cálculo. Además, defendemos que las mediciones de APD abandonen el enfoque frecuentista y adopten uno bayesiano. Mediante simulaciones numéricas, a

This thesis takes part in the LIRA project. This project is an ESA's contract, with the aim to implement a fiber optic temperature sensor which fulfils the requirements of the LISA project, which is based in the detection of gravitational waves. The purpose of this thesis is to implement some of the digital processing blocks to obtain the temperature values. It also compares the area usage of the author implementation in VHDL with a C++ code converted to Verilog with the SmartHLS program. As expected, the code development speeds up and it is easier to synthesize with SmartHLS. Since LiberoSoc tools are not known for their efficiency and can be challenging to synthesize. However writing directly the VHDL code is more efficient than using the SmartHLS tool., Esta tesis forma parte del proyecto LIRA, un contrato de la ESA, con el objetivo de implementar un sensor de temperatura de fibra óptica que cumpla con los requisitos del proyecto LISA. Un proyecto basado en la detección de ondas gravitacionales. El propósito de esta tesis es implementar algunos de los bloques de procesamiento digital para obtener los valores de temperatura. También, compara el uso del área de la implementación del código hecho por el autor en VHDL con un código C++ convertido a Verilog por el programa SmartHLS. Como era de esperar, el desarrollo del código es más rápido y es más fácil de sintetizar con SmartHLS. Dado que las herramientas de LiberoSoc no son conocidas por su eficiencia y pueden resultar desafiantes para sintetizar. Sin embargo, escribir directamente el código VHDL es más eficiente que usar la herramienta SmartHLS., Aquesta tesi forma part del projecte LIRA. Aquest projecte és un contracte de l'ESA, amb l'objectiu d'implementar un sensor de temperatura de fibra òptica que compleixi amb els requisits del projecte LISA, que es basa en la detecció d'ones gravitacionals. El propòsit d'aquesta tesi és implementar alguns dels blocs de processament digital per obtenir els valors de temperatura. També compara l'ús de l'àrea de la implementació del codi fet per l'autor en VHDL amb un codi C++ convertit a Verilog amb el programa SmartHLS. Com era d'esperar, el desenvolupament del codi és més ràpid i més fàcil de sintetitzar amb SmartHLS. Atès que les eines de LiberoSoc no són conegudes per la seva eficiència i poden resultar desafiants per sintetitzar. No obstant això, escriure directament el codi VHDL és més eficient que utilitzar l'eina SmartHLS.

This study presents a new signal-processing method to mitigate radio-frequency interference (RFI), based on the Karhunen–Loève transform (KLT). The main property of this method is that it is adaptive and RFI-agnostic, that is, it can potentially mitigate any type of RFI waveform. It is based on the estimation of the covariance matrix of the process, and the derivation of a basis that spans the RFI subspace. The methodology to find this basis is explained in detail. The theoretical performance obtained using KLT is explored by simulations, along with the evaluation of the influence of the system parameters. In addition, a couple of examples with real data are provided., This work was supported in part by the Project “GNSS Environmental and Societal Missions (GENESIS)—Subproject Universitat Politécnica de Catalunya (UPC)” under Grant PID2021-126436OB-C21, in part by the Spanish Ministerio de Ciencia e Innovación under Grant MCIN/AEI/10.13039/501100011033, and in part by the European Union “European Regional Development Fund (ERDF).”, Peer Reviewed, Postprint (author's final draft)

En entorns foscos i de difícil accés, l'aplicació del monitoratge acústic passiu (MAP) ha demostrat ser essencial per a una millor comprensió del comportament dels animals i la biodiversitat. La implementació de MAP ha posat en manifest la necessitat de mètodes de aprenentatge automàtic (machine learning, ML) per detectar i classificar senyals en paisatges sonors complexos. Aquesta tesi té com a objectiu trobar les aplicacions i les limitacions d'un conjunt d’algoritmes basats en ML, anomenats soundscape_IR, per intentar detectar i classificar una sèrie de crits d'ecolocalització de diferents espècies de ratpenats i unitats de cant de la balena de Groenlandia (Balaena mysticetus) etiquetades. Els resultats mostren que els models entrenats mitjançant la els algoritmes examinats només es poden utilitzar com a detectors, no com a classificadors, ja que no són específics de les dades en què s'han entrenat. Malgrat el rendiment pobre del model, els algoritmes encara es poden utilitzar per a la detecció de senyals en conjunts de dades extensos, accelerant així el procés d'anàlisi de dades. Paral·lelament, es realitzen una sèrie de proves a l'aigua per avaluar la sensibilitat de dos hidròfons., En entornos oscuros y de difícil acceso, la aplicación del monitoreo acústico pasivo (MAP) ha demostrado ser esencial para una mejor comprensión del comportamiento animal y la biodiversidad. La implementación de MAP ha puesto de relieve la necesidad de métodos de aprendizaje automático (machine learning, ML) para detectar y clasificar señales en paisajes sonoros complejos. Esta tesis tiene como objetivo probar las aplicaciones y limitaciones de una de un conjunto de algoritmos basados en ML, anomenado soundscape_IR, para intentar detectar y clasificar una variedad de gritos de ecolocalización de diferentes especies de murciélagos y unidades de canto etiquetadas de ballenas de Groenlandia (Balaena mysticetus). Los resultados muestran que los modelos entrenados con los algoritmos examinados solo pueden servir como detectores, no clasificadores, ya que no son específicos de los datos con los que fueron entrenados. A pesar del bajo rendimiento del modelo, los algoritmos aún se puede utilizar para la detección de señales en conjuntos de datos extensos, lo que acelera el proceso de análisis de datos. Al mismo tiempo, se realizan una serie de pruebas en el agua para evaluar la sensibilidad de dos hidrófonos., In hard-to-reach and dark environments, the application of passive acoustic monitoring (PAM) has proven to be essential for a better understanding of animal behaviour and biodiversity. The implementation of PAM has highlighted the need for Machine Learning (ML) methods to detect and classify signals in complex soundscapes. This thesis aims to test the applications and limitations of one such ML toolbox, soundscape_IR, by attempting to detect and classify a range of echolocation calls from different bat species and labelled bowhead whale (Balaena mysticetus) song units. Results show that the models trained using the examined toolbox can only serve as detectors, not classifiers, as they are not specific to the data that they were trained on. Despite poor model performances, the toolbox can still be utilised for signal detection in extensive datasets, thereby expediting the data analysis process. Concurrently, a series of in-water tests are performed to assess the sensitivity of two hydrophones.

In recent times, there has been a surge of interest in LiDAR imaging systems, particularly in outdoor terrestrial applications associated with computer vision. However, a significant hurdle preventing their widespread implementation lies in their limited tolerance for adverse weather conditions, such as fog. To address this challenge, researchers have explored the capability of polarization in improving detection capabilities in such media. This paper explores the potential of LiDAR technology to obtain polarized images through fog and investigates the impact of fog on object detection using digitized temporal signals and point clouds. The study utilizes a LiDAR-polarized imaging system using circular polarization, which has been shown to enhance image contrast in highly-dispersive media. The analysis of the polarimetric information of the backscattered light signal in fog reveals its influence on object detection and evaluates the range difference between orthogonal polarimetric channels: coplanar and cross-configuration. The results demonstrate that cross-configuration detection provides larger range and more detailed point clouds compared to coplanar configuration, particularly benefiting metallic objects, for the same foggy conditions. By utilizing circularly polarized incident light and cross-configuration detection, the LiDAR system can improve the signal-to-noise ratio by filtering out the co-polarized fog responses. However, the range of the system may be reduced compared to nonpolarized detection. Overall, our findings indicate that utilizing a cross-polarization detection setup can effectively reduce the impact of fog backscatter while preserving the return signal from objects of interest in the majority of cases., This work was supported by the Spanish Ministry of Science and Innovation (MICINN) under projects PID2020-119484RB-I00, TED2021-132338B-I00, and PDC2021-121038-I00. The first author gratefully acknowledges the Universitat Politècnica de Catalunya and Banco Santander for the financial support of her predoctoral research grant., Peer Reviewed, Postprint (author's final draft)

Spacecraft entering a planetary atmosphere are surrounded by a plasma layer containing high levels of ionization. The high electron number densities cause attenuation and rotation of the polarization of the emitted signal, leading to communication blackout. This work presents experimental measurements of radio signal attenuation and Faraday rotation due to an ionized plasma flow. These measurements are conducted at the VKI Plasmatron using circularly polarized directive horn lens antennas with a waveguide orthomode transducer. Clear attenuations are observed when the signal is propagating through the plasma, and Faraday rotation measurements show a good agreement with the theoretical estimation. Additionally, the jet temperature and electron number density distributions are experimentally measured with emission spectroscopy, and these results show a good agreement with numerical estimations. Overall, the signal propagation results comply with the temperature and electron density measurements., Diana Luís research is funded by a doctoral fellowship (2021.04930.BD) granted by Fundação para a Ciência e Tecnologia (FCT Portugal). The research of Andrea Fagnani is supported by a doctoral fellowship awarded from the Research Foundation Flanders. The MEESST project is funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 899298., Peer Reviewed, Postprint (published version)

This paper explores the problem of estimating a temporal series measured from multiple independent sensors with unequal and stationary measurement errors with unknown variances. By formulating the data fusion problem as a joint Maximum Likelihood estimation of sensor covariances and a fusion rule, a batch data driven method is derived involving a residual covariance determinant minimization of a diagonal matrix. It is shown that yielding useful learning from data with good generalization properties in the joint regression and fusion approach requires the assumption of some structure on the sensor noises and/or on the temporal series to be estimated. An efficient data driven algorithm is proposed to obtain the best linear sensor combiner, whose performance is numerically analyzed and compared with the Cramer-Rao Lower Bound of the estimated parameters., This work has been supported by the Spanish Ministry of Science and Innovation through project RODIN (PID2019-105717RB-C22 / AEI / 10.13039/501100011033) and by the Catalan Government (AGAUR) under grant 2017 SGR 578., Peer Reviewed, Postprint (author's final draft)

The interest in LiDAR imaging systems has recently increased in outdoor ground-based applications related to computer vision, in fields like autonomous vehicles. However, for the complete settling of the technology, there are still obstacles related to outdoor performance, being its use in adverse weather conditions one of the most challenging. When working in bad weather, data shown in point clouds is unreliable and its temporal behavior is unknown. We have designed, constructed, and tested a scanning-pulsed LiDAR imaging system with outstanding characteristics related to optoelectronic modifications, in particular including digitization capabilities of each of the pulses. The system performance was tested in a macro-scale fog chamber and, using the collected data, two relevant phenomena were identified: the backscattering signal of light that first interacts with the media and false-positive points that appear due to the scattering properties of the media. Digitization of the complete signal can be used to develop algorithms to identify and get rid of them. Our contribution is related to the digitization, analysis, and characterization of the acquired signal when steering to a target under foggy conditions, as well as the proposal of different strategies to improve point clouds generated in these conditions., This work was supported by the Spanish Ministry of Science and Innovation (MICINN) under the project PID2020-119484RB-I00. The first author gratefully acknowledges the Universitat Politècnica de Catalunya and Banco Santander for the financial support of her predoctoral research grant., Peer Reviewed, Postprint (author's final draft)

Els models climàtics estimen la dinàmica dels fluids a la Terra resolent equacions contínues en quadrícules finites. Els processos físics per sota de la resolució dels models climàtics tenen efectes estadístics a les escales resoltes. Per tenir-los en compte, els models incorporen parametritzacions. El desenvolupament de parametritzacions efectives és crucial per produir prediccions climàtiques precises a llarg termini. En aquest treball, abordem aquest problema utilitzant l'scattering transform per estudiar i modelar el problema de clausura en el domini d'scattering, i desenvolupem un model generatiu i un mètode de superresolució per a camps de turbulència., Los modelos climáticos estiman la dinámica de los fluidos en la Tierra resolviendo ecuaciones en cuadrículas finitas. Los procesos físicos por debajo de la resolución de los modelos climáticos tienen efectos estadísticos en las escalas resueltas. Para tenerlos en cuenta, los modelos incorporan parametrizaciones. El desarrollo de parametrizaciones efectivas es crucial para producir predicciones climáticas precisas a largo plazo. En este trabajo, abordamos este problema utilizando la scattering transform para estudiar y modelar el problema de clausura en el dominio de scattering, y desarrollamos un modelo generativo y un método de superresolución para campos de turbulencia., Climate models estimate the dynamics of the fluids on Earth by solving equations on finite grids. The physical processes below the resolution of climate models have statistical effects on the resolved scales. To account for them, models incorporate subgrid parametrizations. Devising effective parametrizations is crucial when producing accurate, long-term climate predictions. In this work, we address this challenge by using the scattering transform to study and model the closure problem in the scattering domain and we develop a generative model and a super-resolution method for turbulence fields., Outgoing

En este trabajo se realiza un proyecto que implica la programación de una interfaz web la cual da acceso a los controles de un procesador de señal de audio: DRC o compresor básico. A través de esta se da solución al control del DRC de manera remota para interactuar con hardware local, es decir el propio computador que aloja el programa, y también un esbozo de su uso con hardware en equipos remotos. Para conseguir estos objetivos se realizó una investigación tanto del hardware más adecuado para realizar y alojar el programa, como los principios que sigue un compresor de audio para diseñar uno básico que encaje con las cotas del trabajo. Se optó por alojarlo en una Raspberry Pi 4 junto a una PiSound. Se realizó la construcción, de manera virtual, de un DRC mediante Pure Data el cual cuenta con controles básicos como son el umbral, ratio, repuesta, ganancia y lookahead. Además de un selector que permite escoger entre una detección de valor eficaz o de pico en la señal de entrada para calcular la ganancia. A los controles que se construyeron en el compresor se les dotó de comunicación local para poder intercambiar datos con la interfaz web. Esta se hizo, principalmente, con Python (Framework Flask, para el lanzamiento de la aplicación web), HTML (con Bootstrap, para la organización estructural de etiquetas de la página y la responsividad), CSS (para manejar el aspecto visual) y JavaScript (para la gestión de la información de los diferentes apartados). La interfaz, finalmente, cuenta con tres apartados: el control de los parámetros del programa en Pure Data, una gráfica que representa la función de transferencia del compresor, y los actuadores que permiten aplicar el proceso a la señal

This project focused on the study and design of an interface for remote audio processing, with the objective of acquiring by filtering, biasing, and amplifying an analog signal before digitizing it by means of two MCP3208 ADCs to achieve a 24-bit resolution signal. The resulting digital signal was then transmitted to a Raspberry Pi using SPI protocol, where it was processed by a Flask server that could be accessed from both local and remote networks. The design of the PCB was a critical component of the project, as it had to accommodate various components and ensure accurate signal acquisition and transmission. The PCB design was created using KiCad software, which allowed for the precise placement and routing of all components. A major challenge in the design of the interface was to ensure that the analog signal was not distorted during acquisition and amplification. This was achieved through careful selection of amplifier components and using high-pass and low-pass filters to remove any unwanted noise. Once the analog signal was acquired and digitized, the resulting digital signal was transmitted to the Raspberry Pi using SPI protocol. The Raspberry Pi acted as the host for a Flask server, which could be accessed from local and remote networks using a web browser. The Flask server allowed for the processing of the digital signal and provided a user interface for controlling the gain and filtering parameters of the analog signal. This enabled the user to adjust the signal parameters to suit their specific requirements, making the interface highly flexible and adaptable to a variety of audio processing applications. The final interface was capable of remote audio processing, making it highly useful in scenarios where the audio signal needed to be acquired and processed in a location separate from the user. For example, it could be used in a recording studio, where the audio signal from the microphone could be remotely processed using the interface. The gain and filt

This letter evaluates the impact of subspace leakage in conventional (single-dimension) and dimensionally spread null-space precoding schemes. This phenomenon arises when the null-space inference procedure lacks precision and suffers subspace detection errors. The analysis relies on the signal-to-interference-per-dimension ratio (SIDR) metric, which jointly measures the transmitted power efficiency and the interference-mitigation robustness of the adopted transmission scheme. Based on theoretical and numerical analyses of the SIDR, this letter provides the explicit SIDR performance gain of dimension spreading-based null-space precoding schemes as an interference mitigation strategy in front of conventional approaches., This work has been supported by the Spanish Ministry of Science and Innovation through projects PID2019-105717RB-C22 / MCIN / AEI / 10.13039/501100011033 and PID2022-136512OB-C21, and fellowship FPI BES-2017-080071., Peer Reviewed, Postprint (author's final draft)

Higher-order link prediction (HOLP) seeks missing links capturing dependencies among three or more network nodes. Predicting high-order links (HOLs) can for instance reveal hyperlinks in the structure of drug substance and metabolic networks. Existing methods either make restrictive assumptions regarding the emergence of HOLs, or, they rely on reduced dimensionality models of limited expressiveness. To overcome these limitations, the HOLP approach developed here leverages distribution similarities across embeddings as captured by a learnable probability metric. The intuition underpinning the novel approach is that sets of nodes whose embeddings are less similar in distribution, are less likely to be connected by a HOL. Specifically, nonlinear dimensionality reduction is effected through a Gaussian process latent variable model that yields nodal embeddings, and also learns a data-driven similarity function (kernel). This kernel forms the core of a maximum mean discrepancy probability metric. Tests on benchmark datasets illustrate the potential of the proposed approach., This work was supported in part by NSF grants 1901134, 2126052, 2212318, 2220292. A. Pages-Zamora was supported by grants PID 2019-104958RB-C41 and RED2018-102668-T funded by MCIN/AEI/10.13039/501100011033; and by 2021 SGR 01033 funded by Dept. de Recerca i Univ. de la Generalitat de Catalunya 10.13039/501100002809., Peer Reviewed, Postprint (author's final draft)

The constant transformation of our surroundings often goes unnoticed, but it occurs nonetheless. Imperceptibly, our skin undergoes slight color changes as our hearts beat, and our heads subtly move with each breath. While these alterations may escape our eyes, they are captured by cameras. We can magnify the variation in those small signals to extract important information such as heartbeat rate, which in turn can be used in many real-life applications such as polygraph tests. The polygraph, or lie detector test, is a tool that measures physiological changes in an individual to detect deception. This thesis aimed to explore the feasibility of using video information to extract heartbeat rates, replacing the traditional sensors that are commonly used to extract this information. Through a series of experiments involving videos of people standing still, and a video of a real-life suspect that was lying, it was consistently possible to enhance the color changes that occur in a person's skin when their heartbeats. Though it was not possible to develop a fully-functioning lie detector test, this thesis proves that the motion and color magnification technique is reliable even when applied to videos of individuals engaged in speech and movement. It also proved that it is an interesting solution that has the potential of revolutionizing how interviews and interrogations are conducted.

Spacecraft entering a planetary atmosphere are surrounded by a plasma layer containing high levels of ionization due to the high temperatures on the shock layer. The high electron number densities cause attenuation and rotation of the emitted signal, leading to communication blackout. This work presents experimental measurements of radio signal attenuation and Faraday rotation due to an ionized plasma flow. These measurements are conducted at the VKI Plasmatron using circularly polarized directive horn lens antennas with a waveguide orthomode transducer. Clear attenuations are observed when the signal is propagating through the plasma, and Faraday rotation measurements show a good agreement with the theoretical estimation., Diana Luıs research is funded by a doctoral fellowship (2021.04930.BD) granted by Fundaçao para a Ciencia e Tecnologia (FCT Portugal). The MEESST project is funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 899298., Postprint (author's final draft)

Treball desenvolupat en el marc del programa "European Project Semester"., Acoustic Traffic Monitor for a Smart City Concept aims to create a high-performance sensor that can accurately detect and analyse sound waves. The sensor is designed to have a compact form factor, low cost, and energy-efficient operation, making it a suitable solution for a broad range of applications such as security surveillance, environmental monitoring, and industrial automation. The sensor uses advanced signal processing algorithms, including machine learning, to classify and identify different types of sounds, making it versatile in its applications. The project team will use a combination of experimental and parnumerical techniques to optimise the sensor's performance and explore different ways of integrating the sensor into existing systems. In addition to the technical aspects of the project, the team will also focus on creating a viable business and marketing plan. The project will involve market research to identify potential customers and competitors, as well as determining the best pricing strategy for the sensor. The team will also explore different distribution channels and work on creating a strong brand identity for the sensor. By creating a high-performance, cost-effective sensor and implementing an effective business and marketing plan, the project aims to bring a new product to the market that can have a significant impact on various industries. The successful development of this acoustic sensor concept has the potential to revolutionise sound wave detection and analysis, making it an exciting project with significant commercial potential., Incoming

This paper deals with scenario-aware, uncoordinated, and distributed signaling techniques in the context of feedforward opportunistic communications, that is, when the opportunistic transmitting node does not cooperate with any other node in a heterogeneous communication context. In this signaling technique, each network node individually follows a transmission strategy based on the locally sensed occupied and unused physical-layer network resources to minimize the induced interference onto other coexisting networks, taking into account the impact of the sensing errors and the locality of the sensing information. The paper identifies and characterizes critical invariance properties of the transmitted pulse shaping waveforms that guarantee the detectability of the feedforward transmitted signal by the uncoordinated receiving nodes, irrespective of the sensing signal space basis. The paper also shows that, under mild operating conditions, the proposed transmission scheme asymptotically defines efficient alternatives in the frequency domain, such as the circulant-shaping TDMA (CS-TDMA) modulation, and all of them admit a direct adaptation to frequency-selective channels. Numerical evaluation of the proposed schemes validates the provided theoretical models., This work has been supported by the Spanish Ministry of Science and Innovation through project RODIN (PID2019-105717RB-C22 / MCIN / AEI / 10.13039/501100011033) and fellowship FPI BES-2017-080071, Peer Reviewed, Postprint (author's final draft)

This paper proposes and evaluates the behavior of a new health indicator to estimate the capacity fade of lithium-ion batteries and their state of health (SOH). This health indicator is advantageous because it does not require the acquisition of data from full charge–discharge cycles, since it is calculated within a narrow SOC interval where the voltage vs. SOC relationship is very linear and that is within the usual transit range for most practical charge and discharge cycles. As a result, only a small fraction of the data points of a full charge–discharge cycle are required, reducing storage and computational resources while providing accurate results. Finally, by using the battery model defined by the Nernst equation, the behavior of future charge–discharge cycles can be accurately predicted, as shown by the results presented in this paper. The proposed approach requires the application of appropriate signal processing techniques, from discrete wavelet filtering to prediction methods based on linear fitting and autoregressive integrated moving average algorithms., This research was funded by Ministerio de Ciencia e Innovación de España, grant number TED2021-130007B-I00, and by the Generalitat de Catalunya, grant number 2021 SGR 00392., Peer Reviewed, Postprint (published version)

Els senyals electroencefalogràfics (EEG) son comunament utilitzats per investigar les funcions corticals. Una aplicació clínica rellevant és l'estudi de l'epilèpsia, una patologia del sistema nerviós central causada pel mal funcionament de neurones a l'escorça cerebral. Una senyal indicativa d'epilèpsia és la forma d’ona denominada com “spike-and-wave”. És un patró visible a l’EEG, l’anàlisi del qual permet obtenir una eina capaç de detectar convulsions de manera eficient, donant com a resultat un mètode de diagnòstic d’epilèpsia. Per tant, l'objectiu de l'estudi és obtenir un algorisme òptim i automatitzat per tal d'identificar els “spike-and-waves” a l'EEG de pacients epilèptics. S’estudia des d’una millora d'un model anterior implementat a Matlab, amb una selecció més precisa de prototips i nombroses proves basades en una àmplia base de dades. Els resultats s'han comparat amb un grup control i amb pacients amb trastorn per dèficit d'atenció i hiperactivitat (TDAH) per tal d'entendre com aquestes formes d’ona són característiques dels pacients epilèptics, però també poden aparèixer en subjectes amb TDAH, amb menys freqüència. A més, s'ha estudiat un mètode de prevenció; intentant trobar un llindar per alarmar el metge per tal de prevenir la crisi epilèptica, en funció del nombre i la freqüència de patrons detectats. L'estudi consta d'una breu introducció als conceptes bàsics, una descripció de la base de dades i del mètode utilitzat i dels resultats aconseguits, així com una discussió i conclusions posteriors de l'anàlisi realitzat., Una señal electroencefalográfica (EEG) se utiliza para investigar las funciones corticales. Una aplicación clínica relevante es el estudio de la epilepsia, una patología del sistema nervioso central causada por el mal funcionamiento de las neuronas de la corteza cerebral. Un evento patológico indicativo de epilepsia es el pico y onda, denominado como “spike-and-wave”. Se trata de patrones visibles en el EEG, que pueden ser analizados para obtener una herramienta capaz de detectarlos de manera eficiente, dando como resultado un método de diagnóstico de epilepsia y detección de convulsiones. Por tanto, el objetivo del estudio es obtener un algoritmo óptimo y automatizado para identificar picos y ondas en el EEG de pacientes epilépticos. El enfoque es a través de una mejora de un modelo anterior implementado en Matlab, con una selección más precisa de prototipos y numerosas pruebas basadas en una grande base de datos. Los resultados se han comparado con un grupo de control y con pacientes con trastorno por déficit de atención y hiperactividad (TDAH) para comprender cómo los picos y ondas son característicos de los pacientes epilépticos pero también pueden aparecer en sujetos con TDAH, con menos frecuencia. Además, se ha estudiado un método de prevención; tratando de encontrar un umbral para avisar al médico con el fin de prevenir una crisis epiléptica, en función del número y la frecuencia de los patrones detectados. El estudio consta de una breve introducción a los conceptos básicos, una descripción de la base de datos y el método utilizado y los resultados obtenidos, así como una discusión y conclusiones del análisis realizado., An electroencephalographic signal (EEG) is used in order to investigate cortical functions. A relevant clinical application is the study of epilepsy, a pathology in the central nervous system caused by the cerebral cortex neuron’s malfunction. A pathological event indicative of epilepsy is the spike-and-wave. These are visible patterns seen in the EEG which can be analyzed in order to obtain a tool capable of efficiently detecting them, resulting in a method of diagnosis of epilepsy and detection of seizures. Therefore, the objective of the study is to obtain an optimal and automated algorithm in order to identify spike-and-waves in the EEG of epileptic patients. The approach is through an improvement of a previous model implemented in Matlab, with a more precise selection of spike-and-wave prototypes and numerous tests based on a large dataset. The results have been compared to a control group and to recordings of attention deficit hyperactivity disorder (ADHD) patients in order to understand how spike-and-waves are characteristic of epileptic patients but can also appear in ADHD subjects, with less frequency. Moreover, a prevention method has been studied; attempting to find a threshold to alarm the doctor in order to prevent epileptic crisis, based on the number and frequency of firings. The study consists of a brief introduction to the basic concepts, a description of the dataset and the method used and the results achieved, as well as further discussion and conclusions to the analysis made throughout the study., Outgoing

Oceans are progressively gaining importance around the globe as nations turn to them as a source of energy and resources. In this context, the development of underwater communications becomes crucial for the proper development of these activities. These communications, however, come with challenges of their own that researchers need to overcome to create reliable and low latency systems. In this project, we focus on underwater acoustic communications and aim to find a solution for the bandwidth restrictions they present. Specifically, we will focus on proving the viability, using real-life data collected at the Suruga Bay in Japan, of MIMO-OFDM to solve the problem at hand. Using the collected results of the 10km underwater link SIMO experiment, we will apply reverse engineering to obtain real underwater acoustic channels. Once obtained, we will create a MIMO-OFDM system and test its configuration parameters to prove the viability of the system. Results confirm the spectral efficiency improvement when MIMO-OFDM is introduced, and encourages the development of such systems in the future. We also provide a deep analysis on the system parameters and conclude on the optimal configuration for future designs.

Un dels majors problemes en la creació de contingut audiovisual es el soroll, però existeixen noves tecnologies amb la capacitat d'aprendre i adaptar-se per eliminar-lo. Així doncs, en aquest projecte es valora l'ús d'una arquitectura U-Net (Deep Learning), dissenyada per la segmentació d'imatges, com a solució a un problema de neteja de soroll dins l'àmbit audiovisual, generant una xarxa capaç d'aprendre amb dades pròpies les característiques d'una font. Analitzant exemples preexistents, es detallen les funcions i característiques que fan que una xarxa neuronal sigui capaç de reconèixer una font i aïllar-la d'una mescla o senyal sorollosa. D'aquesta manera, es pot analitzar la seva funcionalitat i possibles aplicacions en produccions en viu., Uno de los problemas más grandes de la generación de contenido audiovisual es el ruido, pero existen nuevas tecnologías capaces de aprender y adaptarse para eliminarlo. Así pues, en este proyecto se valora el uso de una arquitectura U-Net (Deep Learning), diseñada para la segmentación de imágenes, como solución a un problema de limpieza de ruido en el ámbito audiovisual, generando una red capaz de aprender con datos propios las características de una fuente. Analizando ejemplos preexistentes, se detallan las funciones y características que hacen que una red neuronal sea capaz de reconocer una fuente y aislarla de una mezcla o señal ruidosa. De esta manera se puede analizar su funcionalidad y posibles aplicaciones en producciones en vivo., One of the greatest problems faced in audiovisual productions is the existence of noise, but there are new technologies able to learn and adapt to delete it. Therefore, this project evaluates the use of a U-Net architecture (Deep Learning), created as an image segmentation tool, as a solution to clean noise from a signal on audiovisual applications, creating a network able to learn from a user-created database the characteristics of a source. Analyzing previous examples, this project explains the functions and characteristics which make a neural network able to recognize a source and separate it from a mix or noisy signal. This way, being able to analyze its functionality and possible applications in live productions.

Passive microwave remote sensing encompasses techniques well suited for use in systems with limited power budget, due to the low consumption linked to reception-only operation. Some examples of these techniques include Microwave Radiometry (MWR), Global Navigation Satellite Systems Reflectometry (GNSS-R), and Radio-Frequency Interference (RFI) detection algorithms. Small satellite missions including these instruments, such as FSSCat, have proven the value of passive remote sensing techniques with high revisit times due to the low orbits, and the possibility to horizontally scale the mission with more satellites. The Flexible Microwave Payload (FMPL) used in the FSSCat mission has continued development within the RITA Payload project, and it is now being used in Drone-based platforms to provide in-situ data using space-proven acquisition algorithms. In this work, the development of a drone-based FMPL will be presented. © 2022 IEEE., This work was supported by the projects of Spanish Ministerio de Ciencia, Innovacion y Universidades and EU ´ ERDF project ref. RTI2018-099008-B-C21/AEI/10.13039/501100011033 ”SENSING WITH PIONEERING OPPORTUNISTIC TECHNIQUES”, and Adrian Perez-Portero (FISDUR2020/105) and Amadeu Gonga (FI2022/809) were supported by grants for recruitment of early-stage research staff of the Agencia de Gesti ` o d’Ajuts Universitaris i de Recerca (AGAUR) Generalitat de Catalunya, Spain, and the Grant RYC-2016-20918 financed by MCIN/AEI /10.13039/501100011033 and by ESF Investing in your future., Peer Reviewed, Postprint (published version)

In this paper, the time variation of signals from several SCADA systems of geographically closed turbines are analysed and compared. When operating correctly, they show a clear pattern of joint variation. However, the presence of a failure in one of the turbines causes the signals from the faulty turbine to decouple from the pattern. From this information, SCADA data is used to determine, firstly, how to derive reference signals describing this pattern and, secondly, to compare the evolution of different turbines with respect to this joint variation. This makes it possible to determine whether the behaviour of the assembly is correct, because they maintain the well-functioning patterns, or whether they are decoupled. The presented strategy is very effective and can provide important support for decision making in turbine maintenance and, in the near future, to improve the classification of signals for training supervised normality models. In addition to being a very effective system, it is a low computational cost strategy, which can add great value to the SCADA data systems present in wind farms., Peer Reviewed, Objectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant::7.a - Per a 2030, augmentar la cooperació internacional per tal de facilitar l’accés a la investigació i a les tecnolo­gies energètiques no contaminants, incloses les fonts d’energia renovables, l’eficiència energètica i les tecnologies de combustibles fòssils avançades i menys contaminants, i promoure la inversió en infraestructures energètiques i tecnologies d’energia no contaminant, Objectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant::7.b - Per a 2030, ampliar la infraestructura i millorar la tecnologia per tal d’oferir serveis d’energia moderns i sos­tenibles per a tots els països en desenvolupament, en particular els països menys avançats, els petits estats insulars en desenvolupament i els països en desenvolupament sense litoral, d’acord amb els programes de suport respectius, Objectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant, Postprint (published version)

En un món on l'automatització i la mecanització dels processos cada vegada està adquirint més importància, que els propis sistemes d'adquisició de dades les puguin analitzar i prendre decisions a l'instant són els que més predominen en l'actualitat. El desenvolupament de la intel·ligència artificial i els seus vessants ha estat clau per a aquesta automatització, sobretot en el camp de la visió artificial en, per exemple, el filtrat i tractament d'imatges. Un altre camp bastant desenvolupat incideix en el reconeixement de senyals d'àudio, i molts productes se centren en aquest reconeixement per a implementar-se, per exemple, en les llars intel·ligents. En aquest treball la idea general és molt semblant a la de les aplicacions que s'acaben d'esmentar, però en aquest cas s'aplica a un robot capaç de rebre ordres senzilles i executar-les, per a aconseguir aquest objectiu s'utilitzaran diferents processos de caracterització dels senyals d'àudio, juntament amb una xarxa neuronal encarregada de classificar-les. No obstant això, la implementació d'aquest procés no és l'única part essencial del treball, perquè ho és realitzar un estudi de com funcionen les xarxes neuronals en comparació amb altres algorismes bàsics aplicats de Machine Learning., En un mundo donde la automatización y la mecanización de los procesos cada vez está adquiriendo más importancia, que los propios sistemas de adquisición de datos las puedan analizar y tomar decisiones al instante son los que más predominan en la actualidad. El desarrollo de la inteligencia artificial y sus vertientes ha estado clave para esta automatización, sobre todo en el campo de la visión artificial en, por ejemplo, el filtrado y tratamiento de imágenes. Otro campo bastante desarrollado incide en el reconocimiento de señales de audio, y muchos productos se centran en este reconocimiento para implementarse, por ejemplo, en los hogares inteligentes. En este trabajo la idea general es muy parecida a la de las aplicaciones que se acaban de mencionar, pero en este caso se aplica a un robot capaz de recibir órdenes sencillas y ejecutarlas, para conseguir este objetivo se utilizarán diferentes procesos de caracterización de las señales de audio, junto con una red neuronal encargada de clasificarlas. Sin embargo, la implementación de este proceso no es la única parte esencial del trabajo, pues lo es realizar un estudio de cómo funcionan las redes neuronales en comparación con otros algoritmos básicos aplicados de Machine Learning., Nowadays, in a world where automation and mechanization of processes are increasingly important, the data acquisition systems that can analyze them and make decisions instantly are the ones that predominate. The development of artificial intelligence and its different aspects has been crucial for this automation, especially in the visual field, for instance in image filtering. Audio signal recognition is another fairly developed field, and many products focus on this recognition for, for example, smart homes. The general idea of this project is very similar to that of the product just named, but it applies to a robot capable of receiving simple commands and acting upon them, different characterizations of audio signals will be used, along with a neural network for classifying each order according to this principle. However, the implementation of this process is not the essential part, since it is to make a study of how neural networks work in comparison with other basic applied Machine Learning algorithms.

Sensor signals do not suit direct electronic communication and need further processing before being digitized. Therefore, the requirements of the digitizer add to those needed to fulfill the basic specifications for the measurement system, namely measurement range, resolution and accuracy, and general constraints such as power consumption and cost. This tutorial describes how to use the quotient between the measurement range and the resolution, termed dynamic range, as a guiding parameter to find out the signal processing functions required in the signal chain, and determines the basic parameters of each function for low-frequency signals., Peer Reviewed, Preprint

This paper proposes a practical way to solve the Uplink-Downlink Covariance Conversion (UDCC) problem in a Frequency Division Duplex (FDD) communication system. The UDCC problem consists in the estimation of the Downlink (DL) spatial covariance matrix from the prior knowledge of the Uplink (UL) spatial covariance matrix without the need of a feedback transmission from the User Equipment (UE) to the Base Station (BS). Estimating the DL sample spatial covariance matrix is unfeasible in current massive Multiple-Input Multiple-Output (MIMO) deployments in frequency selective or fast fading channels due to the required large training overhead. Our method is based on the application of sparse filtering ideas to the estimation of a quantized version of the so-called Angular Power Spectrum (APS), being the common factor between the UL and DL spatial channel covariance matrices., This work has been supported by the Spanish Ministry of Science and Innovation through project RODIN (PID2019-105717RB-C22 / AEI / 10.13039/501100011033) and by the Catalan Government (AGAUR) under grant 2017 SGR 578., Peer Reviewed, Postprint (author's final draft)

Gaussianity tests have been used for decades in statistics to determine if a dataset is well modeled by a normal distribution. More recently, they have also found their place in machine learning. The reason of this proliferation is that some parametric methods are only applicable to normal distributions. On the other hand, the number of applications of information theory to statistical signal processing has grown over the years. In this thesis we use information-theoretic metrics to test normality. Focusing on a moderate computational complexity, we define Rényi negentropy and propose a plug-in estimator of it based on Kernel Density Estimation (KDE). In order to evaluate the performance of the estimator, we feed it with normal, uniform and triangular data and we compute its Mean Squared Error (MSE). Finally, we establish the initial steps of two Gaussianity detection problems based on the proposed estimator and U-statistics, respectively., Los tests de gaussianidad se han utilizado durante décadas en estadística para determinar si un conjunto de datos está bien moldeado por una distribución normal. Más recientemente, también han encontrado su sitio en el aprendizaje automático. La razón de esa proliferación es que algunos métodos paramétricos sólo son aplicables a distribuciones normales. Por otra parte, el número de aplicaciones de la teoría de la información en el procesamiento de señal estadístico ha crecido a lo largo de los años. En esta tesis utilizamos métricas procedentes de la teoría de la información para elaborar tests de normalidad. Centrándonos en conseguir una complejidad computacional moderada, definimos la neguentropía de Rényi y proponemos un estimador basado en la kernel density estimation (KDE). Para evaluar el rendimiento del estimador, lo alimentamos con datos normales, uniformes y triangulares y calculamos su error cuadrático medio (MSE). Por último, establecemos los pasos iniciales de dos problemas de detección de gaussianidad basados ??en el estimador propuesto anteriormente y en U-statistics, respectivamente., Els tests de gaussianitat s'han utilitzat durant dècades en estadística per tal de determinar si un conjunt de dades està ben modelat per una distribució normal. Més recentment, també han trobat el seu lloc en l'aprenentatge automàtic. La raó d'aquesta proliferació és que alguns mètodes paramètrics només són aplicables a distribucions normals. D'altra banda, el nombre d'aplicacions de la teoria de la informació al processament de senyal estadístic ha crescut al llarg dels anys. En aquesta tesi fem servir mètriques procedents de la teoria de la informació per elaborar tests de normalitat. Centrant-nos en aconseguir una complexitat computacional moderada, definim la neguentropia de Rényi i en proposem un estimador basat en la kernel density estimation (KDE). Per avaluar el rendiment de l'estimador, l'alimentem amb dades normals, uniformes i triangulars i en calculem l'error quadràtic mig (MSE). Finalment, establim els passos inicials de dos problemes de detecció de gaussianitat basats en l'estimador proposat anteriorment i en U-statistics, respectivament.

Two popular Crest Factor Reduction techniques applicable to OFDM signals have been studied and implemented within this project. To provide adequate background, a brief description of OFDM communication systems is included in the first sections, and the problem of high Peak to Average Power Ratio in multi-carrier transmission schemes is presented. An overview of the most relevant Crest Factor Reduction techniques is performed, and the Clipping and Filtering and Peak Windowing algorithms are selected as implementation candidates due to their protocol agnostic nature. Several implementation architectures have been discussed, and the most computationally efficient one has been used to implement both algorithms within srsRAN, an open source, full software radio implementation of an LTE network with all of its components. In order to provide a comprehensive evaluation of the developed algorithms, an extensive measurement campaign has been designed and carried out, comprised of synthetic testing, as well as experimental physical signal measurements in a laboratory setting, and an outdoor measurement campaign. To speed up the measurement process, an automated measurement system has been designed in MATLAB, which remotely operates a signal analyser to perform spectrum and statistical power measurements, as well as IQ sample collection of the generated LTE signals. This measurement system has been used to carry out unwanted emissions tests following the LTE conformance testing procedures, which are also discussed within this document. For the outdoor measurements, an LTE single cell network has been deployed using a software radio transceiver and an LTE phone, with the aim of assessing the end to end quality of service improvements provided by the developed algorithms. The gathered results illustrate that the implemented Crest Factor Reduction techniques can provide significant network performance gains in situations with limited available transmission power. Additional gains, Objectius de Desenvolupament Sostenible::9 - Indústria, Innovació i Infraestructura

Air pollution monitoring platforms play a very important role in preventing and mitigating the effects of pollution. Recent advances in the field of graph signal processing have made it possible to describe and analyze air pollution monitoring networks using graphs. One of the main applications is the reconstruction of the measured signal in a graph using a subset of sensors. Reconstructing the signal using information from neighboring sensors is a key technique for maintaining network data quality, with examples including filling in missing data with correlated neighboring nodes, creating virtual sensors, or correcting a drifting sensor with neighboring sensors that are more accurate. This paper proposes a signal reconstruction framework for air pollution monitoring data where a graph signal reconstruction model is superimposed on a graph learned from the data. Different graph signal reconstruction methods are compared on actual air pollution data sets measuring O3, NO2, and PM10. The ability of the methods to reconstruct the signal of a pollutant is shown, as well as the computational cost of this reconstruction. The results indicate the superiority of methods based on kernel-based graph signal reconstruction, as well as the difficulties of the methods to scale in an air pollution monitoring network with a large number of low-cost sensors. However, we show that the scalability of the framework can be improved with simple methods, such as partitioning the network using a clustering algorithm., This work is supported by the National Spanish funding PID2019-107910RB-I00, by regional project 2017SGR-990, and with the support of Secretaria d’Universitats i Recerca de la Generalitat de Catalunya i del Fons Social Europeu., Peer Reviewed, Postprint (author's final draft)

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., In this paper, we consider the problem of estimating and tracking an R-dimensional subspace with relevant information embedded in an N-dimensional ambient space, given that N>>R. We focus on a formulation of the signal subspace that interprets the problem as a least squares optimization. The approach we present relies on the geometrical concepts behind the Affine Projection Algorithms (APA) family to obtain the Affine Projection Subspace Tracking (APST) algorithm. This on-line solution possesses various desirable tracking capabilities, in addition to a high degree of configurability, making it suitable for a large range of applications with different convergence speed and computational complexity requirements. The APST provides a unified framework that generalises other well-known techniques, such as Oja’s rule and stochastic gradient based methods for subspace tracking. This algorithm is finally tested in a few synthetic scenarios against other classical adaptive methods., This work has been supported by the Spanish Ministry of Science and Innovation through project RODIN (PID2019-105717RB-C22 / AEI / 10.13039/501100011033) and by the Catalan Government (AGAUR) under grant 2017 SGR 578., Peer Reviewed, Postprint (author's final draft)

L’objectiu d’aquest treball de fi de grau ha estat estudiar solucions per mitigar l’impacte de les vibracions generades pels rotors verticals en un prototip de drone VTOL encara en fase de disseny. Aquest model pertany a l’empresa catalana Catuav, amb la que s’ha acordat com s’abordava el disseny de les solucions. D’aquesta manera, en aquest memòria es recullen cronològicament explicats les diferents fases del projecte. En primer lloc, s’ha dut a terme una caracterització del comportament dinàmic de l’estructura afectada. Amb aquest objectiu, s’ha realitzat un estudi de les dades de la IMU de vols previs, un anàlisi modal experimental i un anàlisi modal operacional. És important remarcar que aquesta fase no ha estat fixe. S'ha allargat i modificat acord amb la informació que s'anava obtenint per tal de poder obtenir la informació necessària per poder dissenyar les solucions. A partir d’aquí, s’ha construït un model d’elements finits amb el mateix comportament dinàmic. L’objectiu d’aquest ha estat poder avaluar l’eficàcia de les solucions dissenyades sense necessitat d’implementar-les a l’estructura real. Seguint les pautes marcades per Catuav, el següent pas ha consistit en analitzar quina afectació al comportament dinàmic tenien diferents possibles modificacions estructurals. Per últim, s’ha estudiat els resultats obtinguts per tal de proposar diferents línies d’actuació per a mitigar l'efecte de les vibracions., The aim of this final degree project has been to study solutions to mitigate the impact of the vibrations generated by the vertical rotors on a prototype VTOL drone still in the design stage. This model belongs to the Catalan company Catuav, with which it has been agreed how the design of the solutions was approached. In this way, this report explains chronologically the different phases of the project. In first place, a characterization of the dynamic behavior of the affected structure has been carried out. To this end, an analysis of the IMU data from previous flights, an experimental modal analysis and an operational modal analysis have been performed. It is important to note that this phase of the project has not been fixed at any moment. It has been extended and modified according to the information that was being obtained in order to be able to get the necessary information to develop the possible solutions. From this, a finite element model with the same dynamic behavior has been built. The aim of this has been to be able to evaluate the effectiveness of the designed solutions without having to implement them in the real structure. Following the guidelines set by Catuav, the next step has been to analyze what impact on dynamic behavior had different possible structural modifications. Finally, the results obtained have been studied in order to propose different lines of action to mitigate the effect of vibrations.

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works, This article proposes a family of phase-locked loop schemes based on sliding modes. The use of sliding mode algorithms ensures fast response and global stability. In particular, two new algorithms are presented, both based on a complex framework for representing three-phase signals. This article compares the obtained algorithms with the traditional schemes, and a faster response is obtained when sliding modes are used. Additionally, as an application example, the algorithm is combined with a complex-coefficient filter that allows an easy identification of both positive and negative sequence harmonics. The proposed algorithms are illustrated by numerical simulations and experimentally validated using a digital signal processor., Peer Reviewed, Postprint (published version)