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Wavelet Decomposition (wavelet + decomposition)
Selected AbstractsShort-Term Traffic Volume Forecasting Using Kalman Filter with Discrete Wavelet DecompositionCOMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 5 2007Yuanchang Xie Short-term traffic volume data are often corrupted by local noises, which may significantly affect the prediction accuracy of short-term traffic volumes. Discrete wavelet decomposition analysis is used to divide the original data into several approximate and detailed data such that the Kalman filter model can then be applied to the denoised data and the prediction accuracy can be improved. Two types of wavelet Kalman filter models based on Daubechies 4 and Haar mother wavelets are investigated. Traffic volume data collected from four different locations are used for comparison in this study. The test results show that both proposed wavelet Kalman filter models outperform the direct Kalman filter model in terms of mean absolute percentage error and root mean square error. [source] Nonparametric Identification of a Building Structure from Experimental Data Using Wavelet Neural NetworkCOMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 5 2003Shih-Lin Hung By combining wavelet decomposition and artificial neural networks (ANN), wavelet neural networks (WNN) are used for solving chaotic signal processing. The basic operations and training method of wavelet neural networks are briefly introduced, since these networks can approximate universal functions. The feasibility of structural behavior modeling and the possibility of structural health monitoring using wavelet neural networks are investigated. The practical application of a wavelet neural network to the structural dynamic modeling of a building frame in shaking tests is considered in an example. Structural acceleration responses under various levels of the strength of the Kobe earthquake were used to train and then test the WNNs. The results reveal that the WNNs not only identify the structural dynamic model, but also can be applied to monitor the health condition of a building structure under strong external excitation. [source] Restoration of degraded moving image for predicting a moving objectELECTRONICS & COMMUNICATIONS IN JAPAN, Issue 2 2009Kei Akiyama Abstract Iterative optimal calculation methods have been proposed for degraded static image restoration based on the multiresolution wavelet decomposition. However, it is quite difficult to apply these methods to process moving images due to the high computational cost. In this paper, we propose an effective restoration method for degraded moving images by modeling the motion of moving object and predicting the future object position. We verified our method by computer simulations and experiments to show that our method can achieve favorable results. © 2009 Wiley Periodicals, Inc. Electron Comm Jpn, 92(2): 38,48, 2009; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecj.10013 [source] Combined neural network model to compute wavelet coefficientsEXPERT SYSTEMS, Issue 3 2006nan Güler Abstract: In recent years a novel model based on artificial neural networks technology has been introduced in the signal processing community for modelling the signals under study. The wavelet coefficients characterize the behaviour of the signal and computation of the wavelet coefficients is particularly important for recognition and diagnostic purposes. Therefore, we dealt with wavelet decomposition of time-varying biomedical signals. In the present study, we propose a new approach that takes advantage of combined neural network (CNN) models to compute the wavelet coefficients. The computation was provided and expressed by applying the CNNs to ophthalmic arterial and internal carotid arterial Doppler signals. The results were consistent with theoretical analysis and showed good promise for discrete wavelet transform of the time-varying biomedical signals. Since the proposed CNNs have high performance and require no complicated mathematical functions of the discrete wavelet transform, they were found to be effective for the computation of wavelet coefficients. [source] Estimating diurnal to annual ecosystem parameters by synthesis of a carbon flux model with eddy covariance net ecosystem exchange observationsGLOBAL CHANGE BIOLOGY, Issue 2 2005Bobby H. Braswell Abstract We performed a synthetic analysis of Harvard Forest net ecosystem exchange of CO2 (NEE) time series and a simple ecosystem carbon flux model, the simplified Photosynthesis and Evapo-Transpiration model (SIPNET). SIPNET runs at a half-daily time step, and has two vegetation carbon pools, a single aggregated soil carbon pool, and a simple soil moisture sub-model. We used a stochastic Bayesian parameter estimation technique that provided posterior distributions of the model parameters, conditioned on the observed fluxes and the model equations. In this analysis, we estimated the values of all quantities that govern model behavior, including both rate constants and initial conditions for carbon pools. The purpose of this analysis was not to calibrate the model to make predictions about future fluxes but rather to understand how much information about process controls can be derived directly from the NEE observations. A wavelet decomposition enabled us to assess model performance at multiple time scales from diurnal to decadal. The model parameters are most highly constrained by eddy flux data at daily to seasonal time scales, suggesting that this approach is not useful for calculating annual integrals. However, the ability of the model to fit both the diurnal and seasonal variability patterns in the data simultaneously, using the same parameter set, indicates the effectiveness of this parameter estimation method. Our results quantify the extent to which the eddy covariance data contain information about the ecosystem process parameters represented in the model, and suggest several next steps in model development and observations for improved synthesis of models with flux observations. [source] Optimized damage detection of steel plates from noisy impact testINTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING, Issue 7 2006G. Rus Abstract Model-based non-destructive evaluation proceeds measuring the response after an excitation on an accessible area of the structure. The basis for processing this information has been established in recent years as an iterative scheme that minimizes the discrepancy between this experimental measurement and sequence of measurement trials predicted by a numerical model. The unknown damage that minimizes this discrepancy by means of a cost functional is to be found. The damage location and size is quantified and sought by means of a well-conditioned parametrization. The design of the magnitude to measure, its filtering for reducing noise effects and calibration, as well as the design of the cost functional and parametrization, determines the robustness of the search to combat noise and other uncertainty factors. These are key open issues to improve the sensitivity and identifiability during the information processing. Among them, a filter for the cost functional is proposed in this study for maximal sensitivity to the damage detection of steel plate under the impact loading. This filter is designed by means of a wavelet decomposition together with a selection of the measuring points, and the optimization criterion is built on an estimate of the probability of detection, using genetic algorithms. Numerical examples show that the use of the optimal filter allows to find damage of a magnitude several times smaller. Copyright © 2006 John Wiley & Sons, Ltd. [source] Monitoring roughness and edge shape on semiconductors through multiresolution and multivariate image analysisAICHE JOURNAL, Issue 5 2009Pierantonio Facco Abstract Photolithography is one of the most important processes in the production of integrated circuits. Usually, attentive inspections are required after this process, but are limited to the measurement of some physical parameters such as the critical dimension and the line edge roughness. In this paper, a novel multiresolution multivariate technique is presented to identify the abnormalities on the surface of a photolithographed device and the location of defects in a sensitive fashion by comparing it to a reference optimum, and generating fast, meaningful and reliable information. After analyzing the semiconductor surface image in different levels of resolutions via wavelet decomposition, the application of multivariate statistical monitoring tools allows the in-depth examination of the imprinted features of the product. A two level nested PCA model is used for surface roughness monitoring, while a new strategy based on "spatial moving window" PCA is proposed to analyze the shape of the patterned surface. The effectiveness of the proposed approach is tested in the case of semiconductor surface SEM images after the photolithography process. The approach is general and can be applied also to inspect a product through different types of images, different phases of the same production systems, or different processes. © 2009 American Institute of Chemical Engineers AIChE J, 2009 [source] An unsupervised classification method of uterine electromyography signals: Classification for detection of preterm deliveriesJOURNAL OF OBSTETRICS AND GYNAECOLOGY RESEARCH (ELECTRONIC), Issue 1 2009M. O. Diab Abstract Aim:, This article proposes an unsupervised classification method that can be applied to the electromyography signal of uterine contractions for the detection of preterm birth. Methods:, The frequency content of the electromyography changes from one woman to another, and during pregnancy, so wavelet decomposition is first used to extract the parameters of each contraction, and an unsupervised statistical classification method based on Fisher's test is used to classify the events. A principal component analysis projection is then used as evidence of the groups resulting from this classification. Another method of classification based on a competitive neural network is also applied on the same signals. Both methods are compared. Results:, Results show that uterine contractions may be classified into independent groups according to their frequency content and according to term (either at recording or at delivery). [source] Reduction of errors in ASL cerebral perfusion and arterial transit time maps using image de-noisingMAGNETIC RESONANCE IN MEDICINE, Issue 3 2010Jack A. Wells Abstract In this work, the performance of image de-noising techniques for reducing errors in arterial spin labeling cerebral blood flow and arterial transit time estimates is investigated. Simulations were used to show that the established arterial spin labeling cerebral blood flow quantification method exhibits the bias behavior common to nonlinear model estimates, and as a result, the reduction of random errors using image de-noising can improve accuracy. To assess the effect on precision, multiple arterial spin labeling data sets acquired from the rat brain were processed using a variety of common de-noising methods (Wiener filter, anisotropic diffusion filter, gaussian filter, wavelet decomposition, and independent component analyses). The various de-noising schemes were also applied to human arterial spin labeling data to assess the possible extent of structure degradation due to excessive spatial smoothing. The animal experiments and simulated data show that noise reduction methods can suppress both random and systematic errors, improving both the precision and accuracy of cerebral blood flow measurements and the precision of transit time maps. A number of these methods (and particularly independent component analysis) were shown to achieve this aim without compromising image contrast. Magn Reson Med, 2010. © 2010 Wiley-Liss, Inc. [source] Stochastic perturbation approach to the wavelet-based analysisNUMERICAL LINEAR ALGEBRA WITH APPLICATIONS, Issue 4 2004M. Kami Abstract The wavelet-based decomposition of random variables and fields is proposed here in the context of application of the stochastic second order perturbation technique. A general methodology is employed for the first two probabilistic moments of a linear algebraic equations system solution, which are obtained instead of a single solution projection in the deterministic case. The perturbation approach application allows determination of the closed formulas for a wavelet decomposition of random fields. Next, these formulas are tested by symbolic projection of some elementary random field. Copyright © 2004 John Wiley & Sons, Ltd. [source] | |||||||||||||