Classification and localization of electromagnetic and ultrasonic pulsed emitters

Mención Internacional en el título de doctorThe localization of radiative sources is very important in many fields of work such as: sonar, radar and underwater radar, indoor localization in wireless networks, earthquake epicenter localization, defective assets localization in electrical facilities and so forth. In the process of locating radiative sources exist many issues which can provoke errors in the localization. The signals acquired may belong to different sources or they can be mixed with environmental noise, then, their separation before using localization algorithms is of great interest to be efficient and accurate in the computational process. Furthermore, the geometry and radiation characteristics of the receivers, the nature of the signal or their measuring process may cause deviations in the signal onset calculus and therefore the source localization could be displaced from the actual position. In this thesis, there are three kinds of algorithms to undertake three steps in the emitter localization: signal separation, onset and time delay estimation of the signals and source localization. For each step, in order to reduce the error in the localization, several algorithms are analyzed and compared in each application, to choose the most reliable. As the first step, to separate different kinds of signals is of interest to facilitate further processing. In this thesis, different optimization techniques are presented over the power ratio (PR) maps method. The PR uses a selective spectral signal characterization to extract the features of the analyzed signals. The technique identifies automatically the most representative frequency bands which report a great separation of the different kinds of signals in the PR map. After separating and selecting the signals, it is of interest to compare the algorithms to calculate the onset and time delay of the pulsed signals to know their performance because the time variables are inputs to the most common triangulation algorithms to locate radiative and ultrasonic sources. An overview of the algorithms used to estimate the time of flight (ToF) and time differences of arrival (TDoA) of pulsed signals is done in this thesis. In the comparison, there is also a new algorithm based on statics of high order, which is proposed in this thesis. The survey of their performance is done applied to muscle deep estimation, localization in one dimension (1D), and for the localization of emitters in three dimensions (3D). The results show how the presented algorithm yields great results. As the last step in the radiative source localization, the formulation and principle of work of both iterative and non-iterative triangulation algorithms are presented. A new algorithm is presented as a combination of two already existing improving their performance when working alone. All the algorithms, the proposed and the previous which already exist, are compared in terms of accuracy and computational time. The proposed algorithm reports good results in terms of accuracy and it is one of the fastest in computational time. Once the localization is achieved, it is of great interest to understand how the errors in the determination of the onset of the signals are propagated in the emitter localization. The triangulation algorithms estimate the radiative source position using time variables as inputs: ToF, TDoA or pseudo time of flight (pToF) and the receiver positions. The propagation of the errors in the time variables to the radiative source localization is done in two dimensions (2D) and 3D. New spherical diagrams have been created to represent the directions where the localization is more or less sensible to the errors. This study and their sphere diagrams are presented for several antenna layouts. Finally, how the errors in the positioning of the receivers are propagated to the emitter localization is analyzed. In this study, the effect in the propagation of both the relative distance from the receivers to the emitter and the direction between them has been characterized. The propagation of the error considering the direction is also represented in spherical diagrams. For a preferred direction identified in the spheres, the propagated error in the source localization has been quantified regarding both the source distance and the magnitude of the errors in the receivers positioning.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Andrea Cavallini.- Secretario: José Antonio García Souto.- Vocal: Iliana Portugués Peter

An adaptive stochastic resonance method based on multi-agent cuckoo search algorithm for bearing fault detection

Bearing is widely used in the rotating machinery and prone to failure due to the harsh working environment. The bearing fault-induced impulses are weak because of poor background noise, long vibration transmission path, and slight fault degree. Therefore, the bearing fault detection is difficult. A novel adaptive stochastic resonance method based on multi-agent cuckoo search algorithm for bearing fault detection is proposed. Stochastic resonance (SR) is like a nonlinear filter, which can enhance the weak fault-induced impulses while suppressing the noise. However, the parameters of the nonlinear system exert an influence on the SR effect, and the optimal parameters are difficult to be found. Multi-agent cuckoo search (MACS) algorithm is an excellent heuristic optimization algorithm and can be used to search the parameters of nonlinear system adaptively. Two bearing fault signals are used to validate the effectiveness of our proposed method. Three other adaptive SR methods based on cuckoo search algorithm, particle swarm optimization or genetic algorithm are also used for comparison. The results show that MACS can find the optimal parameters more quickly and more accurately, and our proposed method can enhance the fault-induced impulses efficiently

Advanced constellation and demapper schemes for next generation digital terrestrial television broadcasting systems

206 p.Esta tesis presenta un nuevo tipo de constelaciones llamadas no uniformes. Estos esquemas presentan una eficacia de hasta 1,8 dB superior a las utilizadas en los últimos sistemas de comunicaciones de televisión digital terrestre y son extrapolables a cualquier otro sistema de comunicaciones (satélite, móvil, cable¿). Además, este trabajo contribuye al diseño de constelaciones con una nueva metodología que reduce el tiempo de optimización de días/horas (metodologías actuales) a horas/minutos con la misma eficiencia. Todas las constelaciones diseñadas se testean bajo una plataforma creada en esta tesis que simula el estándar de radiodifusión terrestre más avanzado hasta la fecha (ATSC 3.0) bajo condiciones reales de funcionamiento.Por otro lado, para disminuir la latencia de decodificación de estas constelaciones esta tesis propone dos técnicas de detección/demapeo. Una es para constelaciones no uniformes de dos dimensiones la cual disminuye hasta en un 99,7% la complejidad del demapeo sin empeorar el funcionamiento del sistema. La segunda técnica de detección se centra en las constelaciones no uniformes de una dimensión y presenta hasta un 87,5% de reducción de la complejidad del receptor sin pérdidas en el rendimiento.Por último, este trabajo expone un completo estado del arte sobre tipos de constelaciones, modelos de sistema, y diseño/demapeo de constelaciones. Este estudio es el primero realizado en este campo

Optimal FRP Jacket Placement in RC Frame Structures Towards a Resilient Seismic Design

This paper proposes an optimal plan for seismically retrofitting reinforced concrete (RC) frame structures. In this method, the columns are wrapped by fiber-reinforced polymer (FRP) layers along their plastic hinges. This technique enhances their ductility and increases the resiliency of the structure. Two meta-heuristic algorithms (i.e., genetic algorithm and particle swarm optimization) are adopted for this purpose. The number of FRP layers is assumed to be the design variable. The objective of the optimization procedure was to provide a uniform usage of plastic hinge rotation capacity for all the columns, while minimizing the consumption of the FRP materials. Toward this aim, a single objective function containing penalty terms is introduced. The seismic performance of the case study RC frame was assessed by means of nonlinear pushover analyses, and the capacity of the plastic hinge rotation for FRP-confined columns was evaluated at the life safety performance level. The proposed framework was then applied to a non-ductile low-rise RC frame structure. The optimal retrofit scheme for the frame was determined, and the capacity curve, inter-story drift ratios, and fragility functions were computed and compared with alternative retrofit schemes. The proposed algorithm offers a unique technique for the design of more resilient structures.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Power and Bandwidth allocation for High-Throughput Satellites using Particle Swarm Optimization

Resource allocation has always been a key issue in expensive multibeam satellite systems. In theory, dynamic allocation always gives better results than static allocation. In this work, the student will analyze different algorithms and their performance in the dynamic resource allocation of power and bandwidth in a multibeam satellite system. Within this context, the student will implement and compare state of the art optimization algorithms in order to obtain the best possible solution of the distribution problem.Outgoin

A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning

Reservoir computing (RC), first applied to temporal signal processing, is a recurrent neural network in which neurons are randomly connected. Once initialized, the connection strengths remain unchanged. Such a simple structure turns RC into a non-linear dynamical system that maps low-dimensional inputs into a high-dimensional space. The model's rich dynamics, linear separability, and memory capacity then enable a simple linear readout to generate adequate responses for various applications. RC spans areas far beyond machine learning, since it has been shown that the complex dynamics can be realized in various physical hardware implementations and biological devices. This yields greater flexibility and shorter computation time. Moreover, the neuronal responses triggered by the model's dynamics shed light on understanding brain mechanisms that also exploit similar dynamical processes. While the literature on RC is vast and fragmented, here we conduct a unified review of RC's recent developments from machine learning to physics, biology, and neuroscience. We first review the early RC models, and then survey the state-of-the-art models and their applications. We further introduce studies on modeling the brain's mechanisms by RC. Finally, we offer new perspectives on RC development, including reservoir design, coding frameworks unification, physical RC implementations, and interaction between RC, cognitive neuroscience and evolution.Comment: 51 pages, 19 figures, IEEE Acces