Search results for "Gauss–Seidel method"

showing 3 items of 3 documents

Path integral solution handled by Fast Gauss Transform

2009

Abstract The path integral solution method is an effective tool for evaluating the response of non-linear systems under Normal White Noise, in terms of probability density function (PDF). In this paper it has been observed that, using short-time Gaussian approximation, the PDF at a given time instant is the Gauss Transform of the PDF at an earlier close time instant. Taking full advantage of the so-called Fast Gauss Transform a new integration method is proposed. In order to overcome some unsatisfactory trends of the classical Fast Gauss Transform, a new version termed as Symmetric Fast Gauss Transform is also proposed. Moreover, extensions to the two Fast Gauss Transform to MDOF systems ar…

Mechanical EngineeringMathematical analysisMathematicsofComputing_NUMERICALANALYSISAerospace EngineeringOcean EngineeringStatistical and Nonlinear PhysicsProbability density functionWhite noiseCondensed Matter Physicssymbols.namesakeNuclear Energy and EngineeringKronecker deltaComputingMethodologies_SYMBOLICANDALGEBRAICMANIPULATIONPath integral formulationsymbolsTwo-sided Laplace transformApplied mathematicsGauss–Seidel methodSettore ICAR/08 - Scienza Delle CostruzioniPath integral solution Fast Gauss Transform Symmetric Fast Gauss Transform Fokker-Planck equation Ito calculusS transformGaussian processCivil and Structural EngineeringMathematicsProbabilistic Engineering Mechanics
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Denoising of MR spectroscopy signals using total variation and iterative Gauss-Seidel gradient updates

2015

We present a fast variational approach for denoising signals from magnetic resonance spectroscopy (MRS). Differently from the TV approaches applied to denoising of images, this is the first time to our knowledge that it has been used for the processing of free induction decay signals from single-voxel spectroscopy (SVS) acquisitions. Another novelty in this study is the direct use of the Euler Lagrange formulation coupled with Gauss Seidel gradient updates to improve the speed of iteration and reduce ringing. Results from brain MRS signals show improvement in signal to noise ratio as well as reduction in estimation error in the quantification of metabolites.

Free induction decayReduction (complexity)Mathematical optimizationSignal-to-noise ratioNoise reductionGauss–Seidel methodRingingTotal variation denoisingSpectroscopyAlgorithmMathematics2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI)
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Post‐processing of Gauss–Seidel iterations

1999

Algebra and Number TheoryApplied MathematicsMathematical analysisApplied mathematicsGauss–Seidel methodFinite element methodMathematicsNumerical Linear Algebra with Applications
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