Local Tomography of Large Networks under the Low-Observability Regime

This article studies the problem of reconstructing the topology of a network of interacting agents via observations of the state-evolution of the agents. We focus on the large-scale network setting with the additional constraint of $partial$ observations, where only a small fraction of the agents can be feasibly observed. The goal is to infer the underlying subnetwork of interactions and we refer to this problem as $local$ $tomography$. In order to study the large-scale setting, we adopt a proper stochastic formulation where the unobserved part of the network is modeled as an Erd\"{o}s-R\'enyi random graph, while the observable subnetwork is left arbitrary. The main result of this work is establishing that, under this setting, local tomography is actually possible with high probability, provided that certain conditions on the network model are met (such as stability and symmetry of the network combination matrix). Remarkably, such conclusion is established under the $low$-$observability$ $regime$, where the cardinality of the observable subnetwork is fixed, while the size of the overall network scales to infinity.Comment: To appear in IEEE Transactions on Information Theor

Stochastic methods for measurement-based network control

The main task of network administrators is to ensure that their network functions properly. Whether they manage a telecommunication or a road network, they generally base their decisions on the analysis of measurement data. Inspired by such network control applications, this dissertation investigates several stochastic modelling techniques for data analysis. The focus is on two areas within the field of stochastic processes: change point detection and queueing theory. Part I deals with statistical methods for the automatic detection of change points, being changes in the probability distribution underlying a data sequence. This part starts with a review of existing change point detection methods for data sequences consisting of independent observations. The main contribution of this part is the generalisation of the classic cusum method to account for dependence within data sequences. We analyse the false alarm probability of the resulting methods using a large deviations approach. The part also discusses numerical tests of the new methods and a cyber attack detection application, in which we investigate how to detect dns tunnels. The main contribution of Part II is the application of queueing models (probabilistic models for waiting lines) to situations in which the system to be controlled can only be observed partially. We consider two types of partial information. Firstly, we develop a procedure to get insight into the performance of queueing systems between consecutive system-state measurements and apply it in a numerical study, which was motivated by capacity management in cable access networks. Secondly, inspired by dynamic road control applications, we study routing policies in a queueing system for which just part of the jobs are observable and controllable

Reactive synthesis of Ti-Al intermetallics during microwave heating in an E-field maximum

The time-resolved X-ray diffraction synchrotron radiation technique was used in combination with E-field microwave heating to study in situ the kinetics of intermetallic phase formation in the Ti-Al system. The reaction of Ti with Al is triggered by the melting and spreading of Al onto the surface of Ti particles. The tetragonal TiAl 3 phase is the primary reaction product, formed by instantaneous nucleation at the interface between the unreacted Ti cores and the Al melt. The growth of TiAl 3 layers is diffusion-controlled. These preliminary results demonstrate that microwave heating can be used to rapidly synthesise intermetallic phases from high-purity elemental powders. © 2010 Elsevier B.V. All rights reserved.This work has been supported by the Swiss National Science Foundation (Grant 20PA21E-129193).Vaucher, S.; Stir, M.; Ishizaki, K.; Catalá Civera, JM.; Nicula, R. (2011). Reactive synthesis of Ti-Al intermetallics during microwave heating in an E-field maximum. Thermochimica Acta. 522(1):151-154. doi:10.1016/j.tca.2010.11.026S151154522

Novel Applications of Optical Diffraction Tomography: On-chip Microscopy and Detection of Invisibility Cloaks

[ES] La tomografía por difracción surge para mejorar las técnicas de imagen al considerar la naturaleza ondulatoria de la luz. Mientras que los primeros sistemas de imagen médica se basaban únicamente en fuentes sin difracción, este enfoque consigue mejorar la reconstrucción del índice de refracción de los objetos, lo que permite, por ejemplo, el estudio de estructuras subcelulares. Del mismo modo, la demanda de redes de telecomunicaciones cada vez más rápidas y seguras ha propiciado la aparición de la fotónica. Hace dos décadas, la combinación de estos dos campos dio lugar a los primeros sistemas de tomografía por difracción óptica (ODT), los cuáles han evolucionado rápidamente durante este siglo. En esta tesis, presentamos dos nuevas aplicaciones de la ODT. La primera está relacionada con el concepto del microscopio tomográfico de fase (TPM), una versión de la ODT que permite el estudio de células aisladas, con muchas aplicaciones biomédicas, como el diagnóstico y la prognosis del cáncer. Sin embargo, los sistemas TPM actuales son caros, pesados y complejos. Para resolver estos problemas, proponemos el concepto de TPM en chip. Con este fin, diseñamos una hoja de ruta hacia el primer dispositivo tomográfico integrado en el marco de la tecnología lab-on-a-chip (LoC), y desarrollamos los primeros pasos para ello: 1) Hasta ahora, sólo se han utilizado detectores planos para obtener los mapas de índice de refracción de los objetos estudiados en TPM, basados en la detección del campo difractado hacia delante. Sin embargo, los principios físicos fundamentales indican que medir también el campo difractado hacia detrás debería mejorar la resolución de las imágenes. Además, un detector plano no es la configuración óptima para el TPM en chip. En esta línea, hemos explorado la posibilidad de usar detectores circulares en este escenario, como una técnica más adecuada para las configuraciones en chip, demostrando al mismo tiempo que este enfoque proporciona una mejor resolución que el lineal. 2) Proponemos un esquema de TPM en chip basado en el uso de nanoantenas dieléctricas como fuente de luz y píxeles detectores ODT, y caracterizamos experimentalmente su comportamiento mediante microscopía óptica de campo cercano. En cuanto a la segunda aplicación, estudiamos el potencial de la ODT como nuevo paradigma en la detección de capas de invisibilidad realistas, una de las aplicaciones más importantes de los metamateriales. Hasta ahora, el scattering cross section (SCS) se ha utilizado como modelo de referencia para diseñar y observar la eficacia de estos dispositivos para ocultar objetos. En nuestro estudio, demostramos que la ODT puede detectar las capas de invisibilidad prácticas con una sensibilidad superior a la que ofrece el SCS, incluso a las frecuencias de trabajo óptimas. Además, es posible obtener una imagen representativa del tamaño y la forma de la capa, revelando claramente su existencia. Finalmente, se discuten las conclusiones extraídas de los resultados obtenidos. Además, se detallan las futuras líneas de trabajo para abordar los retos que no se han completado en esta tesis doctoral.[CA] La tomografia per difracció sorgeix per millorar les tècniques d'imatge anteriors en considerar la naturalesa ondulatòria de la llum. Mentre que els primers sistemes d'imatge mèdica es basaven únicament en fonts sense difracció, aquest enfocament aconsegueix millorar la reconstrucció de l'índex de refracció dels objectes, la qual cosa permet, per exemple, l'estudi d'estructures subcelulars. De la mateixa manera, la demanda de xarxes de telecomunicacions cada vegada més ràpides i segures ha propiciat l'aparició de la fotònica. Fa dues dècades, la combinació d'aquests dos camps va portar als primers sistemes de tomografia per difracció òptica (ODT), els quals han evolucionat ràpidament durant aquest segle. En aquesta tesi, presentem dues noves aplicacions de la ODT. La primera està relacionada amb el concepte del microscopi tomogràfic de fase (TPM), una versió de la ODT que permet l'estudi de cèl·lules aïllades, amb moltes aplicacions en biomedicina, com el diagnòstic i prognosi del càncer. No obstant això, els sistemes TPM actuals són cars, pesats i complexos. Per resoldre aquests problemes, proposem el concepte de TPM en xip. Per fer-ho, dissenyem un full de ruta cap al primer dispositiu tomogràfic integrat en el marc de la tecnologia lab-on-a-chip (LoC), i desenvolupem els primers passos a aquest efecte: 1) Fins ara, només s'han utilitzat detectors plans per a obtindre els mapes d'índex de refracció dels objectes estudiats en TPM, basats en la detecció del camp difractat cap avant. No obstant això, els principis físics fonamentals indiquen que mesurar també el camp difractat cap endarrere hauria de millorar la resolució de les imatges. A més, un detector pla no és la configuració òptima per al TPM en xip. En aquesta línia, hem explorat la possibilitat d'usar detectors circulars en aquest escenari, com una tècnica més adequada per a les configuracions en xip, demostrant al mateix temps que aquest enfocament proporciona una millor resolució que el lineal. 2) Proposem un esquema de TPM en xip basat en l'ús de nanoantenes dielèctriques com a font de llum i píxels detectors ODT, i caracteritzem experimentalment el seu comportament en camp pròxim mitjançant microscòpia òptica de camp pròxim. Pel que fa a la segona aplicació, estudiem el potencial de la ODT com a nou paradigma en la detecció de capes d'invisibilitat realistes, una de les aplicacions més importants dels metamaterials. Fins ara, el scattering cross section (SCS) s'ha utilitzat com a model de referència per a dissenyar i observar l'eficàcia d'aquests dispositius per a ocultar objectes. En el nostre estudi, vam demostrar que la ODT pot detectar les capes d'invisibilitat pràctiques amb una sensibilitat superior a la que ofereix el SCS, fins i tot a les freqüències de treball òptimes. A més, és possible obtindre una imatge representativa de la grandària i la forma de la capa, revelant clarament la seua existència. Finalment, es discuteixen les conclusions extretes dels resultats obtinguts i es detallen les futures línies de treball per a abordar els reptes que no s'han completat en aquesta tesi doctoral.[EN] Diffraction Tomography arises to improve previous imaging techniques by considering the wave nature of light. Whereas the first medical imaging systems relied only on non-diffracting sources, this approach results in an enhanced reconstruction of the object's refractive index distribution, allowing, for example, the study of subcellular structures. Likewise, the demand for increasingly faster and secure telecommunication networks led to the advent of photonics. Two decades ago, the combination of these two fields gave rise to the first optical diffraction tomography (ODT) systems, which have rapidly evolved during this century. In this thesis, we present two novel applications of ODT. The first one is related to the concept of tomographic phase microscopy (TPM), a version of ODT that enables the study of isolated cells, with many applications in biomedicine, such as the diagnosis and prognosis of cancer. Nevertheless, current TPM systems are expensive, heavy, and cumbersome. To solve these issues we propose the concept of on-chip TPM. For this purpose, we design a roadmap towards the first integrated tomographic device in the frame of lab-on-a-chip (LoC) technology and develop the first steps to this end: 1) Until now, only flat detectors have been used to obtain the refractive index maps of the objects studied in TPM, based on the detection of the forward scattering. However, fundamental physical principles indicate that measuring also the backscattered field should improve the resolution of the images. Moreover, a flat detector is not the optimal configuration for on-chip TPM. In this vein, we have explored the possibility of using circular detectors in this scenario as a more suitable technique for on-chip configurations, demonstrating at the same time that this approach provides a better resolution than the linear one. 2) We propose a TPM on-chip scheme based on the use of dielectric nanoantennas as the ODT light source and detector pixels, and experimentally characterize their near-field behavior via scanning near-field optical microscopy. As for the second application, we study the potential of ODT as a new paradigm in the detection of realistic invisibility cloaks, one of the most important applications of metamaterials. Up to now, the scattering cross section (SCS) has been used as the gold standard to design and observe the effectiveness of these devices in hiding objects. In our study, we show that ODT can detect practical invisibility cloaks with a higher sensitivity than that offered by the SCS, even at the optimal working frequencies. Moreover, it is possible to obtain an image depicting the size and shape of the cloak, clearly revealing their existence. Finally, the conclusions drawn from the obtained results are discussed. In addition, future lines of action to address the challenges that have not been completed in this doctoral thesis are detailed.Díaz Fernández, FJ. (2021). Novel Applications of Optical Diffraction Tomography: On-chip Microscopy and Detection of Invisibility Cloaks [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/180125TESI

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig