Computational aspects of electromagnetic NDE phenomena

The development of theoretical models that characterize various physical phenomena is extremely crucial in all engineering disciplines. In nondestructive evaluation (NDE), theoretical models are used extensively to understand the physics of material/energy interaction, optimize experimental design parameters and solve the inverse problem of defect characterization. This dissertation describes methods for developing computational models for electromagnetic NDE applications. Two broad classes of issues that are addressed in this dissertation are related to (i) problem formulation and (ii) implementation of computers;The two main approaches for solving physical problems in NDE are the differential and integral equations. The relative advantages and disadvantages of the two approaches are illustrated and models are developed to simulate electromagnetic scattering from objects or inhomogeneities embedded in multilayered media which is applicable in many NDE problems. The low storage advantage of the differential approach and the finite solution domain feature of the integral approach are exploited. Hybrid techniques and other efficient modeling techniques are presented to minimize the storage requirements for both approaches;The second issue of computational models is the computational resources required for implementation. Implementations on conventional sequential computers, parallel architecture machines and more recent neural computers are presented. An example which requires the use of massive parallel computing is given where a probability of detection model is built for eddy current testing of 3D objects. The POD model based on the finite element formulation is implemented on an NCUBE parallel computer. The linear system of equations is solved using direct and iterative methods. The implementations are designed to minimize the interprocessor communication and optimize the number of simultaneous model runs to obtain a maximum effective speedup;Another form of parallel computing is the more recent neurocomputer which depends on building an artificial neural network composed of numerous simple neurons. Two classes of neural networks have been used to solve electromagnetic NDE inverse problems. The first approach depends on a direct solution of the governing integral equation and is done using a Hopfield type neural network. Design of the network structure and parameters is presented. The second approach depends on developing a mathematical transform between the input and output space of the problem. A multilayered perceptron type neural network is invoked for this implementation. The network is augmented to build an incremental learning network which is motivated by the dynamic and modular features of the human brain

A Machine Learning Scheme for Estimating the Diameter of Reinforcing Bars Using Ground Penetrating Radar

Ground Penetrating Radar (GPR) is a well- established tool for detecting and locating reinforcing bars (rebars) in concrete structures. However, using GPR to quantify the diameter of rebars is a challenging problem that current processing approaches fail to tackle. To that extent, we have developed a novel machine learning framework that can estimate the diameter of the investigated rebar within the resolution range of the employed antenna. The suggested approach combines neural networks and a random forest regression, and has been trained entirely using synthetic data. Although the training process relied only on numerical training sets, nonetheless, the suggested scheme is successfully evaluated in real data indicating the generalization capabilities of the resulting regression. The only required input of the proposed technique is a single A-scan, avoiding laborious measurement configurations and multi-sensor approaches. Additionally, the results are provided in real-time making this method practical and commercially appealing

An Invitation to the Study of Brain Networks, with Some Statistical Analysis of Thresholding Techniques

We provide a brief introduction to the nascent application of network theory to mesoscale networks in the human brain. Following an overview of the typical data-gathering, processing, and analysis methods employed in this field, we describe the process for inferring a graph from neural time series. A crucial step in the construction of a graph from time series is the thresholding of graph edges to ensure that the graphs represent physiological relationships rather than artifactual noise. We discuss the most popular currently employed methodologies and then introduce one of our own, based on the theory of random matrices. Finally, we provide a comparison of our random-matrix-theory thresholding approach with two dominant approaches on a data set of 1,000 real resting-state functional magnetic resonance imaging scans

Network-based brain computer interfaces: principles and applications

Brain-computer interfaces (BCIs) make possible to interact with the external environment by decoding the mental intention of individuals. BCIs can therefore be used to address basic neuroscience questions but also to unlock a variety of applications from exoskeleton control to neurofeedback (NFB) rehabilitation. In general, BCI usability critically depends on the ability to comprehensively characterize brain functioning and correctly identify the user s mental state. To this end, much of the efforts have focused on improving the classification algorithms taking into account localized brain activities as input features. Despite considerable improvement BCI performance is still unstable and, as a matter of fact, current features represent oversimplified descriptors of brain functioning. In the last decade, growing evidence has shown that the brain works as a networked system composed of multiple specialized and spatially distributed areas that dynamically integrate information. While more complex, looking at how remote brain regions functionally interact represents a grounded alternative to better describe brain functioning. Thanks to recent advances in network science, i.e. a modern field that draws on graph theory, statistical mechanics, data mining and inferential modelling, scientists have now powerful means to characterize complex brain networks derived from neuroimaging data. Notably, summary features can be extracted from these networks to quantitatively measure specific organizational properties across a variety of topological scales. In this topical review, we aim to provide the state-of-the-art supporting the development of a network theoretic approach as a promising tool for understanding BCIs and improve usability

Human Body Scattering Effects at Millimeter Waves Frequencies for Future 5G Systems and Beyond

[ES] Se espera que las futuras comunicaciones móviles experimenten una revolución técnica que vaya más allá de las velocidades de datos de Gbps y reduzca las latencias de las velocidades de datos a niveles muy cercanos al milisegundo. Se han investigado nuevas tecnologías habilitadoras para lograr estas exigentes especificaciones. Y la utilización de las bandas de ondas milimétricas, donde hay mucho espectro disponible, es una de ellas. Debido a las numerosas dificultades técnicas asociadas a la utilización de esta banda de frecuencias, se necesitan complicados modelos de canal para anticipar las características del canal de radio y evaluar con precisión el rendimiento de los sistemas celulares en milimétricas. En concreto, los modelos de propagación más precisos son los basados en técnicas de trazado de rayos deterministas. Pero estas técnicas tienen el estigma de ser computacionalmente exigentes, y esto dificulta su uso para caracterizar el canal de radio en escenarios interiores complejos y dinámicos. La complejidad de la caracterización de estos escenarios depende en gran medida de la interacción del cuerpo humano con el entorno radioeléctrico, que en las ondas milimétricas suele ser destructiva y muy impredecible. Por otro lado, en los últimos años, la industria de los videojuegos ha desarrollado potentes herramientas para entornos hiperrealistas, donde la mayor parte de los avances en esta emulación de la realidad tienen que ver con el manejo de la luz. Así, los motores gráficos de estas plataformas se han vuelto cada vez más eficientes para manejar grandes volúmenes de información, por lo que son ideales para emular el comportamiento de la propagación de las ondas de radio, así como para reconstruir un escenario interior complejo. Por ello, en esta Tesis se ha aprovechado la capacidad computacional de este tipo de herramientas para evaluar el canal radioeléctrico milimétricas de la forma más eficiente posible. Esta Tesis ofrece unas pautas para optimizar la propagación de la señal en milimétricas en un entorno interior dinámico y complejo, para lo cual se proponen tres objetivos principales. El primer objetivo es evaluar los efectos de dispersión del cuerpo humano cuando interactúa con el canal de propagación. Una vez evaluado, se propuso un modelo matemático y geométrico simplificado para calcular este efecto de forma fiable y rápida. Otro objetivo fue el diseño de un reflector pasivo modular en milimétricas, que optimiza la cobertura en entornos de interior, evitando la interferencia del ser humano en la propagación. Y, por último, se diseñó un sistema de apuntamiento del haz predictivo en tiempo real, para que opere con el sistema de radiación en milimétricas, cuyo objetivo es evitar las pérdidas de propagación causadas por el cuerpo humano en entornos interiores dinámicos y complejos.[CA] S'espera que les futures comunicacions mòbils experimenten una revolució tècnica que vaja més enllà de les velocitats de dades de Gbps i reduïsca les latències de les velocitats de dades a nivells molt pròxims al milisegundo. S'han investigat noves tecnologies habilitadoras per a aconseguir estes exigents especificacions. I la utilització de les bandes d'ones millimètriques, on hi ha molt espectre disponible, és una d'elles. A causa de les nombroses dificultats tècniques associades a la utilització d'esta banda de freqüències, es necessiten complicats models de canal per a anticipar les característiques del canal de ràdio i avaluar amb precisió el rendiment dels sistemes cellulars en millimètriques. En concret, els models de propagació més precisos són els basats en tècniques de traçat de rajos deterministes. Però estes tècniques tenen l'estigma de ser computacionalment exigents, i açò dificulta el seu ús per a caracteritzar el canal de ràdio en escenaris interiors complexos i dinàmics. La complexitat de la caracterització d'estos escenaris depén en gran manera de la interacció del cos humà amb l'entorn radioelèctric, que en les ones millimètriques sol ser destructiva i molt impredicible. D'altra banda, en els últims anys, la indústria dels videojocs ha desenrotllat potents ferramentes per a entorns hiperrealistes, on la major part dels avanços en esta emulació de la realitat tenen a veure amb el maneig de la llum. Així, els motors gràfics d'estes plataformes s'han tornat cada vegada més eficients per a manejar grans volums d'informació, per la qual cosa són ideals per a emular el comportament de la propagació de les ones de ràdio, així com per a reconstruir un escenari interior complex. Per això, en esta Tesi s'ha aprofitat la capacitat computacional d'este tipus de ferramentes per a avaluar el canal radioelèctric millimètriques de la manera més eficient possible. Esta Tesi oferix unes pautes per a optimitzar la propagació del senyal en millimètriques en un entorn interior dinàmic i complex, per a la qual cosa es proposen tres objectius principals. El primer objectiu és avaluar els efectes de dispersió del cos humà quan interactua amb el canal de propagació. Una vegada avaluat, es va proposar un model matemàtic i geomètric simplificat per a calcular este efecte de forma fiable i ràpida. Un altre objectiu va ser el disseny d'un reflector passiu modular en millimètriques, que optimitza la cobertura en entorns d'interior, evitant la interferència del ser humà en la propagació, per a així evitar pèrdues de propagació addicionals. I, finalment, es va dissenyar un sistema d'apuntament del feix predictiu en temps real, perquè opere amb el sistema de radiació en millimètriques, l'objectiu del qual és evitar les pèrdues de propagació causades pel cos humà en entorns interiors dinàmics i complexos.[EN] Future mobile communications are expected to experience a technical revolution that goes beyond Gbps data rates and reduces data rate latencies to levels very close to a millisecond. New enabling technologies have been researched to achieve these demanding specifications. The utilization of mmWave bands, where a lot of spectrum is available, is one of them. Due to the numerous technical difficulties associated with using this frequency band, complicated channel models are necessary to anticipate the radio channel characteristics and to accurately evaluate the performance of cellular systems in mmWave. In particular, the most accurate propagation models are those based on deterministic ray tracing techniques. But these techniques have the stigma of being computationally intensive, and this makes it difficult to use them to characterize the radio channel in complex and dynamic indoor scenarios. The complexity of characterizing these scenarios depends largely on the interaction of the human body with the radio environment, which at mmWaves is often destructive and highly unpredictable. On the other hand, in recent years, the video game industry has developed powerful tools for hyper-realistic environments, where most of the progress in this reality emulation has to do with the handling of light. Therefore, the graphic engines of these platforms have become more and more efficient to handle large volumes of information, becoming ideal to emulate the radio wave propagation behavior, as well as to reconstruct a complex interior scenario. Therefore, in this Thesis one has taken advantage of the computational capacity of this type of tools to evaluate the mmWave radio channel in the most efficient way possible. This Thesis offers some guidelines to optimize the signal propagation in mmWaves in a dynamic and complex indoor environment, for which three main objectives are proposed. The first objective has been to evaluate the scattering effects of the human body when it interacts with the propagation channel. Once evaluated, a simplified mathematical and geometrical model has been proposed to calculate this effect in a reliable and fast way. Another objective has been the design of a modular passive reflector in mmWaves, which optimizes the coverage in indoor environments, avoiding human interference in the propagation, in order to avoid its harmful scattering effects. And finally, a real-time predictive beam steering system has been designed for the mmWaves radiation system, in order to avoid propagation losses caused by the human body in dynamic and complex indoor environments.Romero Peña, JS. (2022). Human Body Scattering Effects at Millimeter Waves Frequencies for Future 5G Systems and Beyond [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/19132

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

Graph analysis of functional brain networks: practical issues in translational neuroscience

The brain can be regarded as a network: a connected system where nodes, or units, represent different specialized regions and links, or connections, represent communication pathways. From a functional perspective communication is coded by temporal dependence between the activities of different brain areas. In the last decade, the abstract representation of the brain as a graph has allowed to visualize functional brain networks and describe their non-trivial topological properties in a compact and objective way. Nowadays, the use of graph analysis in translational neuroscience has become essential to quantify brain dysfunctions in terms of aberrant reconfiguration of functional brain networks. Despite its evident impact, graph analysis of functional brain networks is not a simple toolbox that can be blindly applied to brain signals. On the one hand, it requires a know-how of all the methodological steps of the processing pipeline that manipulates the input brain signals and extract the functional network properties. On the other hand, a knowledge of the neural phenomenon under study is required to perform physiological-relevant analysis. The aim of this review is to provide practical indications to make sense of brain network analysis and contrast counterproductive attitudes