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On a mixed and multiscale domain decomposition method [PDF]

Computer Methods in Applied Mechanics and Engineering, 2007
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Pierre Gosselet, Pierre Ladevèze, David Neron   +2 more
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Multiscale Decomposition in Low-Rank Approximation

IEEE Signal Processing Letters, 2017
In low-rank approximation methods, it is often assumed that the data matrix is composed of two globally low-rank and sparse matrices. Moreover, real data matrices often consist of local patterns in multiple scales. The conventional low-rank approximation techniques do not reveal the local patterns from the data matrices.
Abdolali, Maryam, Rahmati, Mohammad
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A multiscale multiplicative decomposition for elastoplasticity of polycrystals

International Journal of Plasticity, 2003
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Clayton, J. D., McDowell, D. L.
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Generalized Variational Mode Decomposition: A Multiscale and Fixed-Frequency Decomposition Algorithm

IEEE Transactions on Instrumentation and Measurement, 2021
To overcome the limitations of variational mode decomposition (VMD) algorithm that its frequency scales and spectrum positions cannot be flexibly adjusted to decompose signals as required, a generalized VMD (GVMD) was proposed. This article addresses the fundamental theory of GVMD. In order to highlight the local characteristics of the signal much more
Yanfei Guo, Zhousuo Zhang
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A Multiscale Treatment of Angeli’s Salt Decomposition

Journal of Chemical Theory and Computation, 2008
Sodium trioxodinitrate's (Na2N2O3, Angeli's salt) unique cardiovascular effects have been associated with its ability to yield HNO upon dissociation under physiological conditions. Due to its potential applications in new therapies for heart failure, the dissociation of Angeli's salt has recently received increased attention.
Juan, Torras, Gustavo de M, Seabra, Adrian E, Roitberg   +2 more
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Adaptive Multiscale Decomposition of Graph Signals

IEEE Signal Processing Letters, 2016
This paper proposes an adaptive multiscale decomposition algorithm for graph signals. We develop two types of graph signal cost functions: $\alpha$ -sparsity functional and graph signal entropies, to capture the energy compaction of the signal components. The adaptive decomposition can then be constructed by applying a minimum cost constraint during
Xianwei Zheng, Yuan Yan Tang, Jianjia Pan, Jiantao Zhou 0001   +3 more
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Nonlinear multiscale decompositions: The approach of A. Harten

Numerical Algorithms, 2000
Data-dependent interpolatory techniques can be used in the reconstruction step of a multiresolution scheme designed \textit{``à la Harten''}. In this paper the authors analyze the class of Essentially Non-Oscillatory (ENO) interpolatory techniques described by \textit{A. Harten, B. Engquist, S. Osher} and \textit{S. Chakravarthy} [J. Comput. Phys.
Francesc Aràndiga, Rosa Donat
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The Multiscale Morphology Decomposition Theorem

1994
Sieves decompose one dimensional bounded functions, e.g. f to a set of increasing scale granule functions {d r } r=1 R , that that represent the information in a manner that is analogous to the pyramid of wavelets obtained by linear decomposition.
J. Andrew Bangham, Pierre Chardaire, Paul D. Ling   +2 more
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Hierarchical decomposition of multiscale skeletons

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001
This paper presents a new procedure to hierarchically decompose a multi-scale discrete skeleton. The skeleton is a linear pattern representation that is generally recognized as a good shape descriptor. For discrete images, the discrete skeleton is often preferable. Multi-resolution representations are convenient for many image analysis tasks.
Borgefors G, Ramella G, Sanniti di Baja G   +2 more
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Signal Decomposition using Multiscale Admixture Models

2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07, 2007
Admixture models are "mixtures of mixtures" that decompose an object into multiple latent components, with the component proportions varying stochastically across objects. Recent work in machine learning has successfully developed admixture models for text, and work in population genetics has developed such models to analyze complex groups of ...
Matus Telgarsky, John D. Lafferty
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