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Journal articles on the topic 'Basis decomposition'

Author: Grafiati

Published: 4 June 2021

Last updated: 13 February 2022

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1

Chen, Scott Shaobing, David L. Donoho, and Michael A. Saunders. "Atomic Decomposition by Basis Pursuit." SIAM Review 43, no. 1 (2001): 129–59. http://dx.doi.org/10.1137/s003614450037906x.

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2

Chen, Scott Shaobing, David L. Donoho, and Michael A. Saunders. "Atomic Decomposition by Basis Pursuit." SIAM Journal on Scientific Computing 20, no. 1 (1998): 33–61. http://dx.doi.org/10.1137/s1064827596304010.

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3

Gadirova, E. M. "ECOLOGICAL ASPECTS OF PHENOL DECOMPOSITION IN THE BASIS OF PHOTOLYSIS REACTIONS." Azerbaijan Chemical Journal, no. 1 (March 12, 2020): 71–76. http://dx.doi.org/10.32737/0005-2531-2020-1-71-76.

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4

FOULIS, DAVID J., SYLVIA PULMANNOVÁ, and ELENA VINCEKOVÁ. "TYPE DECOMPOSITION OF A PSEUDOEFFECT ALGEBRA." Journal of the Australian Mathematical Society 89, no. 3 (2010): 335–58. http://dx.doi.org/10.1017/s1446788711001042.

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AbstractEffect algebras, which generalize the lattice of projections in a von Neumann algebra, serve as a basis for the study of unsharp observables in quantum mechanics. The direct decomposition of a von Neumann algebra into types I, II, and III is reflected by a corresponding decomposition of its lattice of projections, and vice versa. More generally, in a centrally orthocomplete effect algebra, the so-called type-determining sets induce direct decompositions into various types. In this paper, we extend the theory of type decomposition to a (possibly) noncommutative version of an effect alge

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5

Abdulle, Assyr, and Patrick Henning. "A reduced basis localized orthogonal decomposition." Journal of Computational Physics 295 (August 2015): 379–401. http://dx.doi.org/10.1016/j.jcp.2015.04.016.

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6

Hoşten, Serkan, and Jay Shapiro. "Primary Decomposition of Lattice Basis Ideals." Journal of Symbolic Computation 29, no. 4-5 (2000): 625–39. http://dx.doi.org/10.1006/jsco.1999.0397.

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7

Lin, Jianzhi, Yongqiang Guo, Weixing Li, Yue Zhang, and Zengping Chen. "Polarimetric Calibration Based on Lexicographic-Basis Decomposition." IEEE Geoscience and Remote Sensing Letters 13, no. 8 (2016): 1149–52. http://dx.doi.org/10.1109/lgrs.2016.2574749.

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8

Li, Jichun, and Y. C. Hon. "Domain decomposition for radial basis meshless methods." Numerical Methods for Partial Differential Equations 20, no. 3 (2004): 450–62. http://dx.doi.org/10.1002/num.10096.

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9

Silvennoinen, Ari, and Peter‐Pike Sloan. "Moving Basis Decomposition for Precomputed Light Transport." Computer Graphics Forum 40, no. 4 (2021): 127–37. http://dx.doi.org/10.1111/cgf.14346.

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10

Li, Haitao, Jie Xiong, Jianhui Xie, Zhongbao Zhou, and Jinlong Zhang. "A Unified Approach to Efficiency Decomposition for a Two-Stage Network DEA Model with Application of Performance Evaluation in Banks and Sustainable Product Design." Sustainability 11, no. 16 (2019): 4401. http://dx.doi.org/10.3390/su11164401.

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Data envelopment analysis (DEA) is a data-driven tool for performance evaluation, benchmarking and multiple-criteria decision-making. This article investigates efficiency decomposition in a two-stage network DEA model. Three major methods for efficiency decomposition have been proposed: uniform efficiency decomposition, Nash bargaining game decomposition, and priority decomposition. These models were developed on the basis of different assumptions that led to different efficiency decompositions and thus confusion among researchers. The current paper attempts to reconcile these differences by r

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11

Abrahamsen, Trond A., Vladimir P. Fonf, Richard J. Smith, and Stanimir Troyanski. "Polyhedrality and Decomposition." Quarterly Journal of Mathematics 70, no. 2 (2018): 409–27. http://dx.doi.org/10.1093/qmath/hay050.

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Abstract The aim of this note is to present two results that make the task of finding equivalent polyhedral norms on certain Banach spaces, having either a Schauder basis or an uncountable unconditional basis, easier and more transparent. The hypotheses of both results are based on decomposing the unit sphere of a Banach space into countably many pieces, such that each one satisfies certain properties. Some examples of spaces having equivalent polyhedral norms are given.

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12

Cui, Lingli, Na Wu, Daiyi Mo, Huaqing Wang, and Peng Chen. "CQFB and PBP in Diagnosis of Local Gear Fault." Advances in Mechanical Engineering 6 (January 1, 2014): 670725. http://dx.doi.org/10.1155/2014/670725.

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The vibration signal of local gear fault is mainly composed of two components. One is the resonant signal and noise signal and the other one is the transient impulse signal including fault information. The quality factors corresponding to the two components are different. Hence, a method to diagnose local gear fault based on composite quality factor basis and parallel basis pursuit is proposed. First, two different quality factors bases are established using wavelet transform of variable quality factors to obtain the decomposition coefficient. Next, the parallel basis pursuit is adopted for th

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13

Pincin, Antonio. "Bases for finite fields and a canonical decomposition for a normal basis generator." Communications in Algebra 17, no. 6 (1989): 1337–52. http://dx.doi.org/10.1080/00927878908823792.

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14

Imam, A., and G. C. Johnson. "Decomposition of the Deformation Gradient in Thermoelasticity." Journal of Applied Mechanics 65, no. 2 (1998): 362–66. http://dx.doi.org/10.1115/1.2789063.

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The deformation gradient of a thermoelastic solid undergoing large deformations is decomposed into elastic and thermal components, corresponding to an intermediate configuration which is assumed to be stress-free. This decomposition is shown to be unique only to within a rigid-body motion of the intermediate configuration. An alternate decomposition is proposed in which this arbitrariness is removed. The thermoelastic theory developed on the basis of these decompositions is linearized, resulting in familiar expressions of linear thermoelasticity. The stress response function is further special

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15

Smith, Barry F., and Olof B. Widlund. "A Domain Decomposition Algorithm Using a Hierarchical Basis." SIAM Journal on Scientific and Statistical Computing 11, no. 6 (1990): 1212–20. http://dx.doi.org/10.1137/0911069.

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16

Cancelli, G., and M. Barni. "MPSteg-Color: Data Hiding Through Redundant Basis Decomposition." IEEE Transactions on Information Forensics and Security 4, no. 3 (2009): 346–58. http://dx.doi.org/10.1109/tifs.2009.2024028.

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17

Zhou, X., Y. C. Hon, and Jichun Li. "Overlapping domain decomposition method by radial basis functions." Applied Numerical Mathematics 44, no. 1-2 (2003): 241–55. http://dx.doi.org/10.1016/s0168-9274(02)00107-1.

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18

Liu, Feng Qin, and Peng Miao. "Analogical Basis Decomposition for Randomized Sampling Signal Reconstruction." Applied Mechanics and Materials 58-60 (June 2011): 1517–22. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.1517.

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Signal restoration from randomly sampling is needed in many different application environments, like time efficiency and low-power device or hardware failure. In this paper, we use the Analogical Basis Decomposition (ABD) theory to restore the signal by randomized sampling data in frequency domain. Based on the ABD theory, once standard basis are defined in the signal domain, the corresponding analogical basis can be obtained by randomized sampling each base in the frequency domain. Randomly sampled signal can be represented as sum of weighted analogical basis. We developed a fast matching pur

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19

Dodwell, Peter C., and MARIA LUCIA DE B. Simas. "Angular frequency filtering: A basis for pattern decomposition." Spatial Vision 5, no. 1 (1990): 59–74. http://dx.doi.org/10.1163/156856890x00093.

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20

Zhang, Rui, Fan Zhong, Lili Lin, Guanyu Xing, Qunsheng Peng, and Xueying Qin. "Basis image decomposition of outdoor time-lapse videos." Visual Computer 29, no. 11 (2013): 1197–210. http://dx.doi.org/10.1007/s00371-013-0776-6.

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21

Abadi, Agus Maman, Dhoriva Urwatul Wustqa, and Nurhayadi Nurhayadi. "Diagnosis of Brain Cancer Using Radial Basis Function Neural Network with Singular Value Decomposition Method." International Journal of Machine Learning and Computing 9, no. 4 (2019): 527–32. http://dx.doi.org/10.18178/ijmlc.2019.9.4.836.

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22

SUZUKI, M. "MATHEMATICAL BASIS OF COMPUTATIONAL PHYSICS." International Journal of Modern Physics C 07, no. 03 (1996): 355–59. http://dx.doi.org/10.1142/s0129183196000296.

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The present paper explains some general basic formulas concerning quantum Monte Carlo simulations, symplectic integration and other numerical calculations. A generalization of the BCH formula is given with an application to the decomposition of exponential operators in the presence of small parameters.

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23

Zhang Yulong, 张玉龙, 李亮 Li Liang, and 陈怀璧 Chen Huaibi. "Basis material selection principle for high energy X-ray basis material decomposition method." High Power Laser and Particle Beams 26, no. 2 (2014): 25101. http://dx.doi.org/10.3788/hplpb20142602.25101.

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24

Mendez, M. A., M. Balabane, and J. M. Buchlin. "Multi-scale proper orthogonal decomposition of complex fluid flows." Journal of Fluid Mechanics 870 (May 15, 2019): 988–1036. http://dx.doi.org/10.1017/jfm.2019.212.

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Data-driven decompositions are becoming essential tools in fluid dynamics, allowing for tracking the evolution of coherent patterns in large datasets, and for constructing low-order models of complex phenomena. In this work, we analyse the main limits of two popular decompositions, namely the proper orthogonal decomposition (POD) and the dynamic mode decomposition (DMD), and we propose a novel decomposition which allows for enhanced feature detection capabilities. This novel decomposition is referred to as multi-scale proper orthogonal decomposition (mPOD) and combines multi-resolution analysi

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25

Akhtarshenas, S. J., and M. A. Jafarizadeh. "L-S decomposition for 2x2 density matrix by using Wootters's basis." Quantum Information and Computation 3, no. 3 (2003): 229–48. http://dx.doi.org/10.26421/qic3.3-5.

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An analytical expression for optimal Lewenstein-Sanpera (L-S) decomposition of a generic two qubit density matrix is given. By evaluating the L-S decomposition of Bell decomposable states, the optimal decomposition for arbitrary full rank state of two qubit system is obtained via local quantum operations and classical communications (LQCC). In Bell decomposable case the separable state optimizing L-S decomposition, minimize the von Neumann relative entropy as a measure of entanglement. The L-S decomposition for a generic two-qubit density matrix is only obtained by using Wootters's basis. It i

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26

Cui, Xiaopeng, та Yu Shi. "Trotter errors in digital adiabatic quantum simulation of quantum ℤ2 lattice gauge theory". International Journal of Modern Physics B 34, № 30 (2020): 2050292. http://dx.doi.org/10.1142/s0217979220502926.

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Trotter decomposition is the basis of the digital quantum simulation. Asymmetric and symmetric decompositions are used in our GPU demonstration of the digital adiabatic quantum simulations of (2[Formula: see text]+[Formula: see text]1)-dimensional quantum [Formula: see text] lattice gauge theory. The actual errors in Trotter decompositions are investigated as functions of the coupling parameter and the number of Trotter substeps in each step of the variation of coupling parameter. The relative error of energy is shown to be equal to the Trotter error usually defined in terms of the evolution o

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27

QU, Shenning. "The Decomposition Analysis of Carbon Emissions: Theoretical Basis, Methods and Their Evaluations." Chinese Journal of Urban and Environmental Studies 08, no. 04 (2020): 2050020. http://dx.doi.org/10.1142/s2345748120500207.

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As an analytical framework for studying the characteristics of changes in things and their action mechanisms, the decomposition analysis of greenhouse gas emissions has been increasingly used in environmental economics research. The author introduces several decomposition methods commonly used at present and compares them. The index decomposition analysis (IDA) of carbon emissions usually uses energy identities to express carbon emissions as the product of several factor indexes, and decomposes them according to different weight-determining methods to clarify the incremental share of each inde

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28

Barkalov, Alexander, Larysa Titarenko, and Kazimierz Krzywicki. "Structural Decomposition in FSM Design: Roots, Evolution, Current State—A Review." Electronics 10, no. 10 (2021): 1174. http://dx.doi.org/10.3390/electronics10101174.

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The review is devoted to methods of structural decomposition that are used for optimizing characteristics of circuits of finite state machines (FSMs). These methods are connected with the increasing the number of logic levels in resulting FSM circuits. They can be viewed as an alternative to methods of functional decompositions. The roots of these methods are analysed. It is shown that the first methods of structural decomposition have appeared in 1950s together with microprogram control units. The basic methods of structural decomposition are analysed. They are such methods as the replacement

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29

Wang, Ji Lin. "Wavelet Digital Watermarking Algorithm on the Basis of SVD Decomposition." Applied Mechanics and Materials 333-335 (July 2013): 1056–59. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.1056.

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Based on SVD decomposition, an digital watermarking algorithm with the method of wavelet transform is proposed. By means of singular value decomposition to realize blind extracting, the watermarking image is embedded into intermediate frequency sub bands of wavelet composition. Finally, applying normalized cross-correlation function (NC) and peak signal to noise ratio (PSNC), it has proved that this algorithm has better invisibility and robustness.

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30

AGOSHKOV, V. I., and E. OVTCHINNIKOV. "PROJECTION DOMAIN DECOMPOSITION METHOD." Mathematical Models and Methods in Applied Sciences 04, no. 06 (1994): 773–94. http://dx.doi.org/10.1142/s0218202594000431.

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A domain decomposition method using the projection approach is considered. The original elliptic problem is transformed to a set of analogous problems in subdomains and an abstract equation on the interface between subdomains, the latter being solved using Galerkin projection method with some special basis functions defined on the interface. The condition number of the matrix of the resulting discrete problem is shown to be independent of both the number of basis functions and the coefficients of the original problem. Problems in subdomains can be solved independently with different solvers.

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31

Ling, Leevan, and E. J. Kansa. "Preconditioning for radial basis functions with domain decomposition methods." Mathematical and Computer Modelling 40, no. 13 (2004): 1413–27. http://dx.doi.org/10.1016/j.mcm.2005.01.002.

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32

LI, HONG, LUOQING LI, and YUAN Y. TANG. "MONO-COMPONENT DECOMPOSITION OF SIGNALS BASED ON BLASCHKE BASIS." International Journal of Wavelets, Multiresolution and Information Processing 05, no. 06 (2007): 941–56. http://dx.doi.org/10.1142/s0219691307002130.

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This paper mainly focuses on decomposition of signals in terms of mono-component signals which are analytic with strictly increasing nonlinear phase. The properties of Blaschke basis and the approximation behavior of Blaschke basis expansions are studied. Each Blaschke product is analytic and mono-component. An explicit expression of the phase function of Blaschke product is given. The convergence results for Blaschke basis expansions show that it is suitable to approximate a signal by a linear combination of Blaschke products. Experiments are presented to illustrate the general theory.

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33

Moro, Federico, and Massimo Guarnieri. "Efficient 3-D Domain Decomposition With Dual Basis Functions." IEEE Transactions on Magnetics 51, no. 3 (2015): 1–4. http://dx.doi.org/10.1109/tmag.2014.2352034.

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34

González-Casanova, Pedro, José Antonio Muñoz-Gómez, and Gustavo Rodríguez-Gómez. "Node adaptive domain decomposition method by radial basis functions." Numerical Methods for Partial Differential Equations 25, no. 6 (2009): 1482–501. http://dx.doi.org/10.1002/num.20410.

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35

Harlim, John, and Haizhao Yang. "Diffusion Forecasting Model with Basis Functions from QR-Decomposition." Journal of Nonlinear Science 28, no. 3 (2017): 847–72. http://dx.doi.org/10.1007/s00332-017-9430-1.

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36

Kizilkale, Can, Omer Egecioglu, and Cetin Kaya Koc. "A Matrix Decomposition Method for Optimal Normal Basis Multiplication." IEEE Transactions on Computers 65, no. 11 (2016): 3239–50. http://dx.doi.org/10.1109/tc.2016.2543228.

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37

Zhang, Rui, and John Castagna. "Seismic sparse-layer reflectivity inversion using basis pursuit decomposition." GEOPHYSICS 76, no. 6 (2011): R147—R158. http://dx.doi.org/10.1190/geo2011-0103.1.

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A basis pursuit inversion of seismic reflection data for reflection coefficients is introduced as an alternative method of incorporating a priori information in the seismic inversion process. The inversion is accomplished by building a dictionary of functions representing reflectivity patterns and constituting the seismic trace as a superposition of these patterns. Basis pursuit decomposition finds a sparse number of reflection responses that sum to form the seismic trace. When the dictionary of functions is chosen to be a wedge-model of reflection coefficient pairs convolved with the seismic

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38

Lewis, Gwyneth, Olla Solomyak, and Alec Marantz. "The neural basis of obligatory decomposition of suffixed words." Brain and Language 118, no. 3 (2011): 118–27. http://dx.doi.org/10.1016/j.bandl.2011.04.004.

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39

Kaneko, I. "A basis decomposition linear programming approach to limit analysis." Engineering Analysis with Boundary Elements 3, no. 1 (1986): 16–24. http://dx.doi.org/10.1016/0955-7997(86)90039-1.

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40

Kaneko, Ikuyo, and Alfonso Nappi. "A basis decomposition linear programming approach to limit analysis." Engineering Analysis 3, no. 1 (1986): 16–24. http://dx.doi.org/10.1016/0264-682x(86)90187-5.

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41

Chu, Peter C., Robin T. Tokmakian, Chenwu Fan, and L. Charles Sun. "Optimal Spectral Decomposition (OSD) for Ocean Data Assimilation." Journal of Atmospheric and Oceanic Technology 32, no. 4 (2015): 828–41. http://dx.doi.org/10.1175/jtech-d-14-00079.1.

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AbstractOptimal spectral decomposition (OSD) is applied to ocean data assimilation with variable (temperature, salinity, or velocity) anomalies (relative to background or modeled values) decomposed into generalized Fourier series, such that any anomaly is represented by a linear combination of products of basis functions and corresponding spectral coefficients. It has three steps: 1) determination of the basis functions, 2) optimal mode truncation, and 3) update of the spectral coefficients from innovation (observational increment). The basis functions, depending only on the topography of the

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42

Saini, Manish Kumar, Rajiv Kapoor, Ajai Kumar Singh, and Manisha. "Performance Comparison between Orthogonal, Bi-Orthogonal and Semi- Orthogonal Wavelets." Advanced Materials Research 433-440 (January 2012): 6521–26. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.6521.

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The main work in the wavelet analysis is to find a good wavelet basis to perform an optimal decomposition. The goal of the proposed study is to obtain a basis function that can give optimal information from PQ signal. The study presents the wavelet basis to obtain the reconstruction and decomposition filter coefficients for orthogonal, bi-orthogonal and semi-orthogonal wavelet basis. In this study, the task is to choose better wavelet basis which has been used for PQ signal compression or decomposition among orthogonal, bi-orthogonal and semi-orthogonal wavelet basis. Certain criterion have be

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43

CARRE, CHRISTOPHE, ALAIN LASCOUX, and BERNARD LECLERC. "TURBO-STRAIGHTENING FOR DECOMPOSITION INTO STANDARD BASES." International Journal of Algebra and Computation 02, no. 03 (1992): 275–90. http://dx.doi.org/10.1142/s0218196792000165.

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Specht and Hodge have shown that the space generated by products of minors of a matrix admits a linear basis in bijection with Young tableaux. The decomposition of any element into this basis is called straightening and corresponds to the iterative use of Plücker relations. Thanks to a well-known isomorphism between the space of harmonic polynomials and the space of polynomials modulo the ideal generated by symmetric polynomials, we can now use as a main technical tool the canonical scalar product on this later space. This leads to a different, and possibly better, algorithm for straightening.

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44

Ikonomopoulos, A., and A. Endou. "Wavelet decomposition and radial basis function networks for system monitoring." IEEE Transactions on Nuclear Science 45, no. 5 (1998): 2293–301. http://dx.doi.org/10.1109/23.725267.

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45

Astasheva, E. D. "GOALS DECOMPOSITION AS THE BASIS OF THE CITY DEVELOPMENT STRATEGY." Territory Development, no. 2 (2018): 66–72. http://dx.doi.org/10.32324/2412-8945-2018-2-66-72.

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46

Mayer, István, and Imre Bakó. "Many-Body Energy Decomposition with Basis Set Superposition Error Corrections." Journal of Chemical Theory and Computation 13, no. 5 (2017): 1883–86. http://dx.doi.org/10.1021/acs.jctc.7b00303.

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47

Mirzoev, D. Kh, Sh D. Otaev, S. G. Muhamedova, Zh A. Misratov, and U. M. Mirsaidov. "Physicochemical basis of sulfuric acid decomposition of Tajikistan aluminosilicate ores." Applied solid state chemistry 4 (December 31, 2019): 20–24. http://dx.doi.org/10.18572/2619-0141-2019-4-9-20-24.

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48

Winkler, Joab R. "Polynomial basis conversion made stable by truncated singular value decomposition." Applied Mathematical Modelling 21, no. 9 (1997): 557–68. http://dx.doi.org/10.1016/s0307-904x(97)00052-8.

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49

Sukkar, R. A., J. L. LoCicero, and J. W. Picone. "Decomposition of the LPC excitation using the zinc basis functions." IEEE Transactions on Acoustics, Speech, and Signal Processing 37, no. 9 (1989): 1329–41. http://dx.doi.org/10.1109/29.31288.

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50

Zeng, Ke, Guray Erus, Aristeidis Sotiras, Russell T. Shinohara, and Christos Davatzikos. "Abnormality Detection via Iterative Deformable Registration and Basis-Pursuit Decomposition." IEEE Transactions on Medical Imaging 35, no. 8 (2016): 1937–51. http://dx.doi.org/10.1109/tmi.2016.2538998.

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