8 research outputs found

    Robust Andrew's sine estimate adaptive filtering

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    The Andrew's sine function is a robust estimator, which has been used in outlier rejection and robust statistics. However, the performance of such estimator does not receive attention in the field of adaptive filtering techniques. Two Andrew's sine estimator (ASE)-based robust adaptive filtering algorithms are proposed in this brief. Specifically, to achieve improved performance and reduced computational complexity, the iterative Wiener filter (IWF) is an attractive choice. A novel IWF based on ASE (IWF-ASE) is proposed for impulsive noises. To further reduce the computational complexity, the leading dichotomous coordinate descent (DCD) algorithm is combined with the ASE, developing DCD-ASE algorithm. Simulations on system identification demonstrate that the proposed algorithms can achieve smaller misalignment as compared to the conventional IWF, recursive maximum correntropy criterion (RMCC), and DCD-RMCC algorithms in impulsive noise. Furthermore, the proposed algorithms exhibit improved performance in partial discharge (PD) denoising.Comment: 5 pages, 5 figure

    Traffic State Prediction Using 1-Dimensional Convolution Neural Networks and Long Short-Term Memory

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    Traffic prediction is a vitally important keystone of an intelligent transportation system (ITS). It aims to improve travel route selection, reduce overall carbon emissions, mitigate congestion, and enhance safety. However, efficiently modelling traffic flow is challenging due to its dynamic and non-linear behaviour. With the availability of a vast number of data samples, deep neural network-based models are best suited to solve these challenges. However, conventional network-based models lack robustness and accuracy because of their incapability to capture traffic’s spatial and temporal correlations. Besides, they usually require data from adjacent roads to achieve accurate predictions. Hence, this article presents a one-dimensional (1D) convolution neural network (CNN) and long short-term memory (LSTM)-based traffic state prediction model, which was evaluated using the Zenodo and PeMS datasets. The model used three stacked layers of 1D CNN, and LSTM with a logarithmic hyperbolic cosine loss function. The 1D CNN layers extract the features from the data, and the goodness of the LSTM is used to remember the past events to leverage them for the learnt features for traffic state prediction. A comparative performance analysis of the proposed model against support vector regression, standard LSTM, gated recurrent units (GRUs), and CNN and GRU-based models under the same conditions is also presented. The results demonstrate very encouraging performance of the proposed model, improving the mean absolute error, root mean squared error, mean percentage absolute error, and coefficient of determination scores by a mean of 16.97%, 52.1%, 54.15%, and 7.87%, respectively, relative to the baselines under comparison

    Wavelet-based multi-carrier code division multiple access systems

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    EThOS - Electronic Theses Online ServiceGBUnited Kingdo

    Numerical and theoretical study of flapping airfoil aerodynamics using a parallelized immersed-boundary method

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    Flight has fascinated humans for centuries. Human inventions such as missiles, aircraft , unmanned aerial vehicles (UAV), and micro air vehicle (MAV) are inspired by natural flying expertise. As natural flyers usually operate in a vortex-dominated environment, interactions between their wings and the vortices have significant influences on force generation and flying efficiency. Some interesting phenomena induced from such vortex-body interactions have gotten a lot of attention in the past few decades. A good example is that birds and insects are credited with extracting energy from ambient vortices. In a simpler form, bio-inspired airfoils with either passive or active flapping motions are found to have the potential to harvest energy from incoming vortices generated from an upstream object, i.e. a cylinder. The current study identified the interaction modes of the leading edge vortex (LEV) and trailing edge vortex (TEV) between the active flapping airfoil and the incoming vortices. The relation between the interaction modes and the energy extraction capacity of an active harvester is investigated guided by a potential theory. The interaction modes induced by a passive energy harvester always benefit the energy extraction efficiency. However, the dynamic response of the passive harvester was found to vary corresponding to the properties of the incoming vortical wake. A profound appreciation of energy extracting mechanisms can provide a solution for the energy consumption issue of MAV and UAV. However, difficulties are encountered in practical applications of energy harvesting on how to detect the locations of generated vortices and what the trajectory of the vortex downstream of the moving body is. Some observations are realized and the fluid dynamics of the phenomena is beyond the fundamentals described in the textbook. One well-known instance is the asymmetric wake formed downstream of a symmetric sinusoidal heaving airfoil. In this study, factors that influence the formation of the asymmetric wakes on both the near wake and far wake regions are demonstrated. Novel vortex models are developed to explore the vortex dynamic mechanisms of the asymmetric wake and its development from the near wake region to the far wake region. In order to analyze the flow fields for the bio-inspired problems, Computational Fluid Dynamics (CFD) provides powerful and convenient tools. The shape of bio-inspired wings/airfoils and their maneuvers are usually very complicated. In CFD, the immersed-boundary (IB) method is an advantageous approach to simulate such problems. In this study, an immersed-boundary method is implemented in a parallel fashion in order to speed up the computational rate.. A variety of numerical schemes have been applied to the IB method, including different spatial schemes and temporal schemes; their performances are investigated. In addition, the IB method has been successfully implemented with the fluid-structure interaction models for studying passive mobile objectives, i.e. the energy harvester. The possibility of coupling other fluid dynamic models, i.e. species transport model and turbulence models, is also demonstrated

    Proceedings of the Third International Mobile Satellite Conference (IMSC 1993)

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    Satellite-based mobile communications systems provide voice and data communications to users over a vast geographic area. The users may communicate via mobile or hand-held terminals, which may also provide access to terrestrial cellular communications services. While the first and second International Mobile Satellite Conferences (IMSC) mostly concentrated on technical advances, this Third IMSC also focuses on the increasing worldwide commercial activities in Mobile Satellite Services. Because of the large service areas provided by such systems, it is important to consider political and regulatory issues in addition to technical and user requirements issues. Topics covered include: the direct broadcast of audio programming from satellites; spacecraft technology; regulatory and policy considerations; advanced system concepts and analysis; propagation; and user requirements and applications

    Multiresolution methods for materials modeling via coarse-graining

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    Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 2005.Includes bibliographical references (p. 209-222).(cont.) time, while obtaining useful information about the thermodynamic behavior of the system. We show how statistical mechanics can be formulated using the wavelet transform as a coarse-graining technique. For small systems in which exact enumerations of all states is possible, we illustrate how the method recovers reasonably good estimates for physical properties (errors no more than 10%) with several orders of magnitude fewer operations than are required for an exact enumeration. In addition, we illustrate that errors introduced by the wavelet transform vanish in the neighborhood of fixed points of systems as determined by RG theory. Using scaling results from simulations at different length scales, we estimate the thermodynamic behavior of the original system without performing simulations on the full original system. In addition, we make the method adaptive by using fluctuation properties of the system to set criteria under which further coarse graining or refinement of the system is required. We demonstrate our method for the Ising universality class of problems. We also examine the applicability of the WAMC framework to polymer chains. Polymers are quintessential examples of the need for simulations at multiple scales: at one end, we can study short chains using quantum chemistry methods; yet polymers can have relaxation times on the order of seconds or longer, and molecular weights of 10⁶ or more. Even with modern computational resources, simulating behavior at long times or for long chains is still prohibitively expensive ...Multiscale modeling of physical systems often requires the use of multiple types of simulations to bridge the various length scales that. need to be considered: for example, a density-functional theory at the electronic scale will be combined with a molecular-dynamics simulation at the atomistic level, and with a finite-element method at the macroscopic level. An improvement to this scheme would be a method which is capable of consistently simulating a system at multiple levels of resolution without passing from one simulation type to another, so that different simulations can be studied at a common length scale by appropriate coarse-graining or refinement of a given model. We introduce the wavelet transform as the basis for a new coarse-graining framework. A family of orthonormal basis, the wavelet transform separates data sets, such as spatial coordinates or signal strengths, into subsets representing local averages and local differences. The wavelet transform has several desirable properties for coarse-graining: it is hierarchical, compact, and has natural applications to approximating physical data sets. As a hierarchical method, it can be used to rescale a Hamiltonian to a desired length scale, and at the same time also rescales the particles of the system by creating "blocked" particles in the spirit of renor-malization group (RG) calculations. The wavelet-accelerated Monte Carlo (WAMC) framework performs a Monte Carlo simulations on a small system which will be transformed into a block particle to obtain the probability distribution of the blocked particle; a Monte Carlo simulation is then performed on the resulting system of blocked particles. This method, which can be repeated as needed, can achieve significant speed-ups in computationalby Ahmed E. Ismail.Ph.D

    Memorias del Congreso Argentino en Ciencias de la Computación - CACIC 2021

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    Trabajos presentados en el XXVII Congreso Argentino de Ciencias de la Computación (CACIC), celebrado en la ciudad de Salta los días 4 al 8 de octubre de 2021, organizado por la Red de Universidades con Carreras en Informática (RedUNCI) y la Universidad Nacional de Salta (UNSA).Red de Universidades con Carreras en Informátic
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