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Relevant bibliographies by topics / Wavelet approximation

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Academic literature on the topic 'Wavelet approximation'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Wavelet approximation"

1

Polanowski, Stanisław. "Application of movable approximation and wavelet decomposition to smoothing-out procedure of ship engine indicator diagrams." Polish Maritime Research 14, no. 2 (April 1, 2007): 12–17. http://dx.doi.org/10.2478/v10012-007-0008-y.

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Application of movable approximation and wavelet decomposition to smoothing-out procedure of ship engine indicator diagrams In this paper - on the basis of indicator diagram processing taken as an example - were shown possibilities of the smoothing-out and decomposing of run disturbances with the use of the movable multiple approximation based on the least squares criterion. The notion was defined of movable approximating object and constraints used to form approximation features. It was demonstrated that the multiple approximation can be used to decompose disturbances out of an analyzed run. The obtained smoothing-out results were compared with those obtained from full-interval approximation of runs by means of splines as well as wavelet decomposition with using various wavelets, Wavelet Explorer and Mathematica software. Smoothing-out quality was assessed by comparing runs of first derivatives which play crucial role in the advanced processing of indicator diagrams.

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BANAKAR, AHMAD, MOHAMMAD FAZLE AZEEM, and VINOD KUMAR. "COMPARATIVE STUDY OF WAVELET BASED NEURAL NETWORK AND NEURO-FUZZY SYSTEMS." International Journal of Wavelets, Multiresolution and Information Processing 05, no. 06 (November 2007): 879–906. http://dx.doi.org/10.1142/s0219691307002099.

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Based on the wavelet transform theory and its well emerging properties of universal approximation and multiresolution analysis, the new notion of the wavelet network is proposed as an alternative to feed forward neural networks and neuro-fuzzy for approximating arbitrary nonlinear functions. Earlier, two types of neuron models, namely, Wavelet Synapse (WS) neuron and Wavelet Activation (WA) functions neuron have been introduced. Derived from these two neuron models with different non-orthogonal wavelet functions, neural network and neuro-fuzzy systems are presented. Comparative study of wavelets with NN and NF are also presented in this paper.

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Romanchak, V. M. "Local transformations with a singular wavelet." Informatics 17, no. 1 (March 29, 2020): 39–46. http://dx.doi.org/10.37661/1816-0301-2020-17-1-39-46.

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The paper considers a local wavelet transform with a singular basis wavelet. The problem of nonparametric approximation of a function is solved by the use of the sequence of local wavelet transforms. Traditionally believed that the wavelet should have an average equal to zero. Earlier, the author considered singular wavelets when the average value is not equal to zero. As an example, the delta-shaped functions, participated in the estimates of Parzen – Rosenblatt and Nadara – Watson, were used as a wavelet. Previously, a sequence of wavelet transforms for the entire numerical axis and finite interval was constructed for singular wavelets. The paper proposes a sequence of local wavelet transforms, a local wavelet transform is defined, the theorems that formulate the properties of a local wavelet transform are proved. To confirm the effectiveness of the algorithm an example of approximating the function by use of the sum of discrete local wavelet transforms is given.

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Romanchak, V. M. "APPROXIMATELY SINGULAR WAVELET." «System analysis and applied information science», no. 2 (August 7, 2018): 23–28. http://dx.doi.org/10.21122/2309-4923-2018-2-23-28.

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The problem of approximation is relevant for most engineering applications. In this connection, the universal methods of approximation are of interest. The method of nonparametric approximation is developing in the paper – the method of singular wavelets. The method includes an effective numerical algorithm based on the summation of a recursive sequence of functions. The universal algorithm of approximation makes it possible to apply it to approximate one-dimensional and multidimensional functions, in decision support systems, in the processing of stochastic information, pattern recognition, and solution of boundary-value problems.The introduction explain the idea of the method of singular wavelets – to combine the theory of wavelets with the Nadaraya-Watson kernel regression estimator. Usually, Nadaraya-Watson kernel regression are considered as an example of non- parametric estimation. However, one parameter, the smoothing parameter, is still present in the traditional kernel regression algorithm. The choice of the optimal value of this parameter is a complex mathematical problem, and numerous studies have been devoted to this question. In the approximation by the method of singular wavelets, summation of Nadaraya-Watson kernel regression estimates with the smoothing parameter takes place, which solves the problem of the optimal choice of this parameter.In the main part of the paper theorems are formulated that determine the properties of the regularized wavelet transform. Sufficient conditions for uniform convergence of the wavelet series are obtained for the first time. To illustrate the effectiveness of the numerical approximation algorithm, we consider an example of the quasi-interpolation of the Runge function by wavelets with a uniform distribution of interpolation nodes.

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Tahami, M., A. Askari Hemmat, and S. A. Yousefi. "Numerical solution of two-dimensional first kind Fredholm integral equations by using linear Legendre wavelet." International Journal of Wavelets, Multiresolution and Information Processing 14, no. 01 (January 2016): 1650004. http://dx.doi.org/10.1142/s0219691316500041.

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In one-dimensional problems, the Legendre wavelets are good candidates for approximation. In this paper, we present a numerical method for solving two-dimensional first kind Fredholm integral equation. The method is based upon two-dimensional linear Legendre wavelet basis approximation. By applying tensor product of one-dimensional linear Legendre wavelet we construct a two-dimensional wavelet. Finally, we give some numerical examples.

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Khanna, Nikhil, Varinder Kumar, and S. K. Kaushik. "Wavelet packet approximation." Integral Transforms and Special Functions 27, no. 9 (June 6, 2016): 698–714. http://dx.doi.org/10.1080/10652469.2016.1189912.

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Guo, Shun Sheng, and A. S. Cavaretta. "LINEAR WAVELET APPROXIMATION." Analysis 13, no. 4 (December 1993): 351–62. http://dx.doi.org/10.1524/anly.1993.13.4.351.

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DeVore, R. A., S. V. Konyagin, and V. N. Temlyakov. "Hyperbolic Wavelet Approximation." Constructive Approximation 14, no. 1 (January 1, 1997): 1–26. http://dx.doi.org/10.1007/s003659900060.

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Pourakbari, Fatemeh, and Ali Tavakoli. "Modification of Multiple Knot B-Spline Wavelet for Solving (Partially) Dirichlet Boundary Value Problem." Advances in Applied Mathematics and Mechanics 4, no. 06 (December 2012): 799–820. http://dx.doi.org/10.4208/aamm.12-12s10.

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AbstractA construction of multiple knotB-spline wavelets has been given in [C. K. Chui and E. Quak, Wavelet on a bounded interval, In: D. Braess and L. L. Schumaker, editors. Numerical methods of approximation theory. Basel: Birkhauser Verlag; (1992), pp. 57-76]. In this work, we first modify these wavelets to solve the elliptic (partially) Dirichlet boundary value problems by Galerkin and Petrov Galerkin methods. We generalize this construction to two dimensional case by Tensor product space. In addition, the solution of the system discretized by Galerkin method with modified multiple knotB-spline wavelets is discussed. We also consider a nonlinear partial differential equation for unsteady flows in an open channel called Saint-Venant. Since the solving of this problem by some methods such as finite difference and finite element produce unsuitable approximations specially in the ends of channel, it is solved by multiple knotB-spline wavelet method that yields a very well approximation. Finally, some numerical examples are given to support our theoretical results.

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Cattani, Carlo, and Aleksey Kudreyko. "Application of Periodized Harmonic Wavelets towards Solution of Eigenvalue Problems for Integral Equations." Mathematical Problems in Engineering 2010 (2010): 1–8. http://dx.doi.org/10.1155/2010/570136.

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This article deals with the application of the periodized harmonic wavelets for solution of integral equations and eigenvalue problems. The solution is searched as a series of products of wavelet coefficients and wavelets. The absolute error for a general case of the wavelet approximation was analytically estimated.

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Dissertations / Theses on the topic "Wavelet approximation"

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Li, Zheng. "Approximation to random process by wavelet basis." View abstract/electronic edition; access limited to Brown University users, 2008. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3318378.

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Martin, Richard Luis. "Wavelet approximation of GRID fields for virtual screening." Thesis, University of Sheffield, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.531509.

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Schreiner, Michael [Verfasser]. "Wavelet Approximation by Spherical Up Functions / Michael Schreiner." Aachen : Shaker, 2004. http://d-nb.info/1170537413/34.

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Lee, Sang-Mook. "Wavelet-Based Multiresolution Surface Approximation from Height Fields." Diss., Virginia Tech, 2002. http://hdl.handle.net/10919/26203.

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A height field is a set of height distance values sampled at a finite set of sample points in a two-dimensional parameter domain. A height field usually contains a lot of redundant information, much of which can be removed without a substantial degradation of its quality. A common approach to reducing the size of a height field representation is to use a piecewise polygonal surface approximation. This consists of a mesh of polygons that approximates the surfaces of the original data at a desired level of accuracy. Polygonal surface approximation of height fields has numerous applications in the fields of computer graphics and computer vision. Triangular mesh approximations are a popular means of representing three-dimensional surfaces, and multiresolution analysis (MRA) is often used to obtain compact representations of dense input data, as well as to allow surface approximations at varying spatial resolution. Multiresolution approaches, particularly those moving from coarse to fine resolutions, can often improve the computational efficiency of mesh generation as well as can provide easy control of level of details for approximations. This dissertation concerns the use of wavelet-based MRA methods to produce a triangular-mesh surface approximation from a single height field dataset. The goal of this study is to obtain a fast surface approximation for a set of height data, using a small number of approximating elements to satisfy a given error criterion. Typically, surface approximation techniques attempt to balance error of fit, number of approximating elements, and speed of computation. A novel aspect of this approach is the direct evaluation of wavelet coefficients to assess surface shape characteristics within each triangular element at a given scale. Our approach hierarchically subdivides and refines triangles as the resolution level increases.
Ph. D.

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Grip, Niklas. "Wavelet and gabor frames and bases : approximation, sampling and applications." Doctoral thesis, Luleå, 2002. http://epubl.luth.se/1402-1544/2002/49.

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Zhanlav, Tugal. "Some choices of moments of refinable function and applications." Universitätsbibliothek Chemnitz, 2006. http://nbn-resolving.de/urn:nbn:de:swb:ch1-200601316.

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We propose a recursive formula for moments of scaling function and sum rule. It is shown that some quadrature formulae has a higher degree of accuracy under proposed moment condition. On this basis we obtain higher accuracy formula for wavelet expansion coefficients which are needed to start the fast wavelet transform and estimate convergence rate of wavelet approximation and sampling of smooth functions. We also present a direct algorithm for solving refinement equation.

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Trisiripisal, Phichet. "Image Approximation using Triangulation." Thesis, Virginia Tech, 2003. http://hdl.handle.net/10919/33337.

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An image is a set of quantized intensity values that are sampled at a finite set of sample points on a two-dimensional plane. Images are crucial to many application areas, such as computer graphics and pattern recognition, because they discretely represent the information that the human eyes interpret. This thesis considers the use of triangular meshes for approximating intensity images. With the help of the wavelet-based analysis, triangular meshes can be efficiently constructed to approximate the image data. In this thesis, this study will focus on local image enhancement and mesh simplification operations, which try to minimize the total error of the reconstructed image as well as the number of triangles used to represent the image. The study will also present an optimal procedure for selecting triangle types used to represent the intensity image. Besides its applications to image and video compression, this triangular representation is potentially very useful for data storage and retrieval, and for processing such as image segmentation and object recognition.
Master of Science

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Hartmann, Christoph [Verfasser], and Stephan [Akademischer Betreuer] Dahlke. "The p-Poisson Equation: Regularity Analysis and Adaptive Wavelet Frame Approximation / Christoph Hartmann ; Betreuer: Stephan Dahlke." Marburg : Philipps-Universität Marburg, 2018. http://d-nb.info/1168380103/34.

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Poungponsri, Suranai. "An Approach Based On Wavelet Decomposition And Neural Network For ECG Noise Reduction." DigitalCommons@CalPoly, 2009. https://digitalcommons.calpoly.edu/theses/101.

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Electrocardiogram (ECG) signal processing has been the subject of intense research in the past years, due to its strategic place in the detection of several cardiac pathologies. However, ECG signal is frequently corrupted with different types of noises such as 60Hz power line interference, baseline drift, electrode movement and motion artifact, etc. In this thesis, a hybrid two-stage model based on the combination of wavelet decomposition and artificial neural network is proposed for ECG noise reduction based on excellent localization features: wavelet transform and the adaptive learning ability of neural network. Results from the simulations validate the effectiveness of this proposed method. Simulation results on actual ECG signals from MIT-BIH arrhythmia database [30] show this approach yields improvement over the un-filtered signal in terms of signal-to-noise ratio (SNR).

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Silveira, Tiago da. "DETECÇÃO DO ESTADO DE SONOLÊNCIA VIA UM ÚNICO CANAL DE ELETROENCEFALOGRAFIA ATRAVÉS DA TRANSFORMADA WAVELET DISCRETA." Universidade Federal de Santa Maria, 2012. http://repositorio.ufsm.br/handle/1/5407.

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Conselho Nacional de Desenvolvimento Científico e Tecnológico
Many fatal traffic accidents are caused by fatigued and drowsy drivers. In this context, automatic drowsiness detection devices are an alternative to minimize this issue. In this work, two new methodologies to drowsiness detection are presented, considering a signal obtained from a single electroencephalography channel: (i) drowsiness detection through best m-term approximation, applied to the wavelet expansion of the analysed signal; (ii) drowsiness detection through Mahalanobis distance with wavelet coefficients. The results of both methodologies are compared with a method which uses Mahalanobis distance and Fourier coefficients to drowsiness detection. All methodologies consider the medical evaluation of the brain signal, given by the hypnogram, as a reference.
A sonolência diurna em motoristas, principal consequência da privação de sono, tem sido a causa de diversos acidentes graves de trânsito. Neste contexto, a utilização de dispositivos que alertem o condutor ao detectar automaticamente o estado de sonolência é uma alternativa para a minimização deste problema. Neste trabalho, duas novas metodologias para a detecção automática da sonolência são apresentadas, utilizando um único canal de eletroencefalografia para a obtenção do sinal: (i) detecção da sonolência via melhor aproximação por m-termos, aplicada aos coeficientes wavelets da expansão em série do sinal; e (ii) detecção da sonolência via distância de Mahalanobis e coeficientes wavelets. Os resultados de ambas as metodologias são comparados a uma implementação utilizando distância de Mahalanobis e coeficientes de Fourier. Para todas as metodologias, utiliza-se como referência a avaliação médica do sinal cerebral, dada pelo hipnograma.

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Books on the topic "Wavelet approximation"

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Liandrat, J. Resolution of the 1D regularized Burgers equation using a spatial wavelet approximation. Hampton, Va: Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 1990.

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Michel, Volker. Lectures on Constructive Approximation: Fourier, Spline, and Wavelet Methods on the Real Line, the Sphere, and the Ball. Boston: Birkhäuser Boston, 2013.

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Singh, S. P., ed. Approximation Theory, Wavelets and Applications. Dordrecht: Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-015-8577-4.

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Härdle, Wolfgang, Gerard Kerkyacharian, Dominique Picard, and Alexander Tsybakov. Wavelets, Approximation, and Statistical Applications. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4612-2222-4.

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1963-, Christensen Khadija Laghrida, ed. Approximation theory: From wavelets to polynomials. Boston: Birkhäuser, 2004.

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Christensen, Ole. Approximation Theory: From Taylor Polynomials to Wavelets. Boston, MA: Birkhäuser, 2005.

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Oyet, Alwell J. Robust designs for wavelet approximations of regression models. Toronto: University of Toronto, Dept. of Statistics, 1997.

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Jerri, Abdul J. The Gibbs Phenomenon in Fourier Analysis, Splines and Wavelet Approximations. Boston, MA: Springer US, 1998.

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Jerri, Abdul J. The Gibbs Phenomenon in Fourier Analysis, Splines and Wavelet Approximations. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4757-2847-7.

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Jerri, Abdul J. The Gibbs phenomenon in Fourier analysis, splines, and wavelet approximations. Dordrecht: Kluwer Academic Publishers, 1998.

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Book chapters on the topic "Wavelet approximation"

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Resnikoff, Howard L., and Raymond O. Wells. "Wavelet Approximation." In Wavelet Analysis, 202–35. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4612-0593-7_9.

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Lin, E. B. "Wavelet Transforms and Wavelet Approximations." In Approximation, Probability, and Related Fields, 357–65. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-2494-6_27.

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Michel, Volker. "Spherical Wavelet Analysis." In Lectures on Constructive Approximation, 183–238. Boston: Birkhäuser Boston, 2012. http://dx.doi.org/10.1007/978-0-8176-8403-7_7.

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Anastassiou, G. A., S. T. Rachev, and X. M. Yu. "Multivariate Probabilistic Wavelet Approximation." In Approximation, Probability, and Related Fields, 65–73. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-2494-6_4.

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Kunoth, Angela. "Optimized wavelet preconditioning." In Multiscale, Nonlinear and Adaptive Approximation, 325–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03413-8_10.

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Anastassiou, George A. "FUZZY WAVELET LIKE OPERATORS." In Fuzzy Mathematics: Approximation Theory, 191–207. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11220-1_12.

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Härdle, Wolfgang, Gerard Kerkyacharian, Dominique Picard, and Alexander Tsybakov. "Wavelet thresholding and adaptation." In Wavelets, Approximation, and Statistical Applications, 193–213. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4612-2222-4_11.

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Härdle, Wolfgang, Gerard Kerkyacharian, Dominique Picard, and Alexander Tsybakov. "Construction of wavelet bases." In Wavelets, Approximation, and Statistical Applications, 47–58. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4612-2222-4_6.

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Maass, Peter. "Wideband Approximation and Wavelet Transform." In Radar and Sonar, 83–88. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4684-7832-7_8.

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Anastassiou, George A. "Convex Probabilistic Wavelet Like Approximation." In Intelligent Mathematics: Computational Analysis, 13–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17098-0_2.

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Conference papers on the topic "Wavelet approximation"

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Wolnik, Barbara. "The wavelet type systems." In Approximation and Probability. Warsaw: Institute of Mathematics Polish Academy of Sciences, 2006. http://dx.doi.org/10.4064/bc72-0-26.

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Zhang, Q., and A. Benveniste. "Approximation by nonlinear wavelet networks." In [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing. IEEE, 1991. http://dx.doi.org/10.1109/icassp.1991.150188.

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Svenson, T. D., Jo A. Ward, and K. J. Harrison. "Uniform approximation of wavelet coefficients." In SPIE's 1996 International Symposium on Optical Science, Engineering, and Instrumentation, edited by Michael A. Unser, Akram Aldroubi, and Andrew F. Laine. SPIE, 1996. http://dx.doi.org/10.1117/12.255231.

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Wale, Sachin S., and Vinayak G. Asutkar. "Evaluation of wavelet connection coefficients by wavelet-Galerkin approximation." In 2014 Annual IEEE India Conference (INDICON). IEEE, 2014. http://dx.doi.org/10.1109/indicon.2014.7030425.

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Kida, Takuro, and Yuichi Kida. "Optimum interpolatory approximation in wavelet subspace." In SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, edited by Michael A. Unser, Akram Aldroubi, and Andrew F. Laine. SPIE, 1999. http://dx.doi.org/10.1117/12.366808.

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Bernard, Christophe P., Stephane G. Mallat, and Jean-Jeacques E. Slotine. "Wavelet interpolation networks for hierarchical approximation." In SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, edited by Michael A. Unser, Akram Aldroubi, and Andrew F. Laine. SPIE, 1999. http://dx.doi.org/10.1117/12.366782.

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Wirasaet, Damrongsak, and Samuel Paolucci. "Application of an Adaptive Wavelet Method to Natural-Convection Flow in a Differentially Heated Cavity." In ASME 2005 Summer Heat Transfer Conference collocated with the ASME 2005 Pacific Rim Technical Conference and Exhibition on Integration and Packaging of MEMS, NEMS, and Electronic Systems. ASMEDC, 2005. http://dx.doi.org/10.1115/ht2005-72864.

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We describe an adaptive wavelet method based on interpolating wavelets applied to the solution of a natural-convection flow. The adaptive wavelet method, owing to the approximation capabilities and spatial localization of wavelet functions, enables the solution of problems with local grid resolution consistent with the local demand of the physical problem. The adaptive method is applied to simulate the flow in a differentially heated square cavity at large Rayleigh numbers. Numerical results, whenever possible, are compared with those previously published.

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Hui Cheng, Chi Lu, Hai Han, and Jin-Wen Tian. "Multiscale wavelet support vector machine for image approximation." In International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR '07. IEEE, 2007. http://dx.doi.org/10.1109/icwapr.2007.4421656.

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Oltean, Gabriel, Laura-Nicoleta Ivanciu, and Botond Kirei. "Signal approximation using GA guided wavelet decomposition." In 2015 International Symposium on Signals, Circuits and Systems (ISSCS). IEEE, 2015. http://dx.doi.org/10.1109/isscs.2015.7203996.

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Cisar, Petar, and Sanja Maravic Cisar. "Approximation of Internet traffic in wavelet domain." In 2014 IEEE 12th International Symposium on Intelligent Systems and Informatics (SISY 2014). IEEE, 2014. http://dx.doi.org/10.1109/sisy.2014.6923563.

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Reports on the topic "Wavelet approximation"

1

Madych, Wolodymyr, and K. Grochenig. Multivariate Wavelet Representations and Approximations. Fort Belvoir, VA: Defense Technical Information Center, October 1994. http://dx.doi.org/10.21236/ada290147.

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Madych, Wolodymyr. Multivariate Wavelet Representations and Approximations. Fort Belvoir, VA: Defense Technical Information Center, May 1993. http://dx.doi.org/10.21236/ada269350.

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