Results 61 to 70 of about 24,857 (199)
ANALISIS PAJAK KENDARAAN BERMOTOR MENGGUNAKAN MODEL MULTISCALE AUTOREGRESSIVE DENGAN MAXIMAL OVERLAP DISCRETE WAVELET TRANSFORM (Studi Kasus di UP3AD Kab.Temanggung) [PDF]
, 2013 Time series analysis is applied in many fields, one of them is in the economic field. In this paper will consider analysis of the time series on data income taxes motor vehicles UP3AD Kab.Temanggung using Maximal Overlap Wavelet Transform Discrete (MODWT)
WAHYUNINGRUM, SRI
core
A Multiscale Guide to Brownian Motion
, 2015 We revise the Levy's construction of Brownian motion as a simple though still rigorous approach to operate with various Gaussian processes. A Brownian path is explicitly constructed as a linear combination of wavelet-based "geometrical features" at ...
Beliaev, Dmitry +2 more
core +1 more source
Efficient Kilometer‐Scale Precipitation Downscaling With Conditional Wavelet Diffusion
Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 2, April 2026.Abstract Precipitation products such as Integrated Multi‐satellitE Retrievals have coarse resolution (∼10 ${\sim} 10$ km), which limits their application in hydrological modeling and extreme weather analysis. We propose the Wavelet Diffusion Model (WDM), a fast generative framework for high‐quality precipitation downscaling trained on multi‐radar multi‐
Chugang Yi +4 more
wiley +1 more source
Pseudodifferential operators on ultrametric spaces and ultrametric wavelets
, 2004 A family of orthonormal bases, the ultrametric wavelet bases, is introduced in quadratically integrable complex valued functions spaces for a wide family of ultrametric spaces.
Khrennikov, A. Yu., Kozyrev, S. V.
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Haar–Fisz Estimation of Evolutionary Wavelet Spectra [PDF]
Journal of the Royal Statistical Society Series B: Statistical Methodology, 2006 SummaryWe propose a new ‘Haar–Fisz’ technique for estimating the time-varying, piecewise constant local variance of a locally stationary Gaussian time series. We apply our technique to the estimation of the spectral structure in the locally stationary wavelet model. Our method combines Haar wavelets and the variance stabilizing Fisz transform.
Fryzlewicz, Piotr, Nason, Guy P.
openaire +3 more sources
Function spaces for decoupling
Journal of the London Mathematical Society, Volume 113, Issue 4, April 2026.Abstract We introduce new function spaces LW,sq,p(Rn)$\mathcal {L}_{W,s}^{q,p}(\mathbb {R}^{n})$ that yield a natural reformulation of the ℓqLp$\ell ^{q}L^{p}$ decoupling inequalities for the sphere and the light cone. These spaces are invariant under the Euclidean half‐wave propagators, but not under all Fourier integral operators unless p=q$p=q$, in ...
Andrew Hassell +3 more
wiley +1 more source
Comparison of wavelet approximation order in different smoothness spaces
International Journal of Mathematics and Mathematical Sciences, 2006 In linear approximation by wavelet, we approximate a given function by a finite term from the wavelet series. The approximation order is improved if the order of smoothness of the given function is improved, discussed by Cohen (2003), DeVore (1998), and ...
M. R. Islam +2 more
doaj +1 more source
Reversible implementation of a disrete linear transformation [PDF]
, 2010 Discrete linear transformations form important steps in processing information. Many such transformations are injective and therefore are prime candidates for a physically reversible implementation into hardware. We present here the first steps towards a
Burignat, Stéphane +2 more
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Haar Wavelet Neural Network Model
2018 International Conference on Artificial Intelligence and Data Processing (IDAP), 2018 Convolutional neural networks, one of the most important methods of deep learning which is a popular and modern research topic. Nowadays, thismethod has been applied many problems in a short time and obtained successful results for science and the industry.
Pala, T. +4 more
openaire +2 more sources
Feature selection using Haar wavelet power spectrum [PDF]
BMC Bioinformatics, 2006 Feature selection is an approach to overcome the 'curse of dimensionality' in complex researches like disease classification using microarrays. Statistical methods are utilized more in this domain. Most of them do not fit for a wide range of datasets. The transform oriented signal processing domains are not probed much when other fields like image and ...
Sahu Rajendra +2 more
openaire +3 more sources