38,663 research outputs found
Systematic approach to nonlinear filtering associated with aggregation operators. Part 1. SISO-filters
There are various methods to help restore an image from noisy distortions. Each technique has its advantages and disadvantages. Selecting the appropriate method plays a major role in getting the desired image. Noise removal or noise reduction can be done on an image by linear or nonlinear filtering. The more popular linear technique is based on average (on mean) linear operators. Denoising via linear filters normally does not perform satisfactorily since both noise and edges contain high frequencies. Therefore, any practical denoising model has to be nonlinear. In this work, we introduce and analyze a new class of nonlinear SISO-filters that have their roots in aggregation operator theory. We show that a large body of non-linear filters proposed to date constitute a proper subset of aggregation filters. (C) 2017 The Authors. Published by Elsevier Ltd.This work was supported by grants the RFBR No. 17-07-00886 and by Ural State Forest Engineering's Center of Excellence in "Quantum and Classical Information Technologies for Remote Sensing Systems"
Sparsity-Based Kalman Filters for Data Assimilation
Several variations of the Kalman filter algorithm, such as the extended
Kalman filter (EKF) and the unscented Kalman filter (UKF), are widely used in
science and engineering applications. In this paper, we introduce two
algorithms of sparsity-based Kalman filters, namely the sparse UKF and the
progressive EKF. The filters are designed specifically for problems with very
high dimensions. Different from various types of ensemble Kalman filters
(EnKFs) in which the error covariance is approximated using a set of dense
ensemble vectors, the algorithms developed in this paper are based on sparse
matrix approximations of error covariance. The new algorithms enjoy several
advantages. The error covariance has full rank without being limited by a set
of ensembles. In addition to the estimated states, the algorithms provide
updated error covariance for the next assimilation cycle. The sparsity of error
covariance significantly reduces the required memory size for the numerical
computation. In addition, the granularity of the sparse error covariance can be
adjusted to optimize the parallelization of the algorithms
Chebyshev and Conjugate Gradient Filters for Graph Image Denoising
In 3D image/video acquisition, different views are often captured with
varying noise levels across the views. In this paper, we propose a graph-based
image enhancement technique that uses a higher quality view to enhance a
degraded view. A depth map is utilized as auxiliary information to match the
perspectives of the two views. Our method performs graph-based filtering of the
noisy image by directly computing a projection of the image to be filtered onto
a lower dimensional Krylov subspace of the graph Laplacian. We discuss two
graph spectral denoising methods: first using Chebyshev polynomials, and second
using iterations of the conjugate gradient algorithm. Our framework generalizes
previously known polynomial graph filters, and we demonstrate through numerical
simulations that our proposed technique produces subjectively cleaner images
with about 1-3 dB improvement in PSNR over existing polynomial graph filters.Comment: 6 pages, 6 figures, accepted to 2014 IEEE International Conference on
Multimedia and Expo Workshops (ICMEW
Do Regional Integration Agreements Increase Business-Cycle Convergence? Evidence From APEC and NAFTA
Using monthly industrial sector data from January 1971 to March 2004, we test for business cycles convergence among the major APEC members: Japan, South Korea, Malaysia, Mexico, USA, and Canada. In addition, we examine the synchronization of business cycles among Australia, Japan, and South Korea, based on the quarterly data for the 1957-2003 period, as well as among the different economic sectors of the NAFTA countries from January 1970 through March 2004. We apply different techniques to identify business cycles. In particular, we propose a new trend-cycle decomposition method based on wavelet analysis. The results show that convergence of business cycles of Asia-Pacific countries is far from complete, but joining the APEC has increased the mean correlation of industrial production cycles of the member economies. On the other hand, although some economic sectors of the NAFTA countries already exhibited some degree of business cycle co-movement even during pre-NAFTA period, the volatility of pair-wise correlation of business cycles declined during NAFTA. In addition, we conclude that, in general, the transmission of business cycles is relatively slow, and, consequently, business cycles appear to be asynchronous.http://deepblue.lib.umich.edu/bitstream/2027.42/40151/3/wp765.pd
Entropy Balance and Dispersive Oscillations in Lattice Boltzmann Models
We conduct an investigation into the dispersive post-shock oscillations in
the entropic lattice-Boltzmann method (ELBM). To this end we use a root finding
algorithm to implement the ELBM which displays fast cubic convergence and
guaranties the proper sign of dissipation. The resulting simulation on the
one-dimensional shock tube shows no benefit in terms of regularization from
using the ELBM over the standard LBGK method. We also conduct an experiment
investigating of the LBGK method using median filtering at a single point per
time step. Here we observe that significant regularization can be achieved.Comment: 18 pages, 4 figures; 13/07/2009 Matlab code added to appendi
Effects of Applying Linear and Nonlinear Filters on Tests for Unit Roots with Additive Outliers
Conventional univariate Dickey-Fuller tests tend to produce spurious stationarity when there exist additive outlying observations in the time series. Correct critical values are usually obtained by adding dummy variables to the Dickey-Fuller regression. This is a nice theoretical result but not attractive from the empirical point of view since almost any result can be obtained just by a convenient selection of dummy variables. In this paper we suggest a robust procedure based on running Dickey-Fuller tests on the trend component instead of the original series. We provide both finite-sample and large-sample justifications. Practical implementation is illustrated through an empirical example based on the US/Finland real exchange rate series.Additive outliers, Dickey-Fuller test, Linear and nonlinear filtering, Bootstrap
Mathematical Modeling of Trending Topics on Twitter
Created in 2006, Twitter is an online social networking service in which users share and read 140-character messages called Tweets. The site has approximately 288 million monthly active users who produce about 500 million Tweets per day. This study applies dynamical and statistical modeling strategies to quantify the spread of information on Twitter. Parameter estimates for the rates of infection and recovery are obtained using Bayesian Markov Chain Monte Carlo (MCMC) methods. The methodological strategy employed is an extension of techniques traditionally used in an epidemiological and biomedical context (particularly in the spread of infectious disease). This study, which addresses information spread, presents case studies pertaining to the prevalence of several ātrendingā topics on Twitter over time. The study introduces a framework to compare information dynamics on Twitter based on the topical area as well as a framework for the prediction of topic prevalence. Additionally, methodological and results-based comparisons are drawn between the spread of information and the spread of infectious disease
Modeling electricity prices: international evidence
This paper analyses the evolution of electricity prices in deregulated markets. We present a general model that simultaneously takes into account the possibility of several factors: seasonality, mean reversion, GARCH behaviour and time-dependent jumps. The model is applied to equilibrium spot prices of electricity markets from Argentina, Australia (Victoria), New Zealand (Hayward), NordPool (Scandinavia), Spain and U.S. (PJM) using daily data. Six different nested models were estimated to compare the relative importance of each factor and their interactions. We obtained that electricity prices are mean-reverting with strong volatility (GARCH) and jumps of time-dependent intensity even after adjusting for seasonality. We also provide a detailed unit root analysis of electricity prices against mean reversion, in the presence of jumps and GARCH errors, and propose a new powerful procedure based on bootstrap techniques
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