Search results for "Gauss"

showing 10 items of 701 documents

Diffuse soil CO2 degassing from Linosa island

2014

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Volcano monitoringLineamentGeochemistrysoil degassingfluid geochemistryCO2lcsh:QC851-999Instruments and techniques; Soil degassing; Volcano monitoring; GeophysicsSequential Gaussian simulationSoil co2 fluxFlux (metallurgy)Soil degassingGeomorphologygeographygeography.geographical_feature_categoryRiftlcsh:QC801-809Instruments and techniquesSettore GEO/08 - Geochimica E VulcanologiaTectonicslcsh:Geophysics. Cosmic physicsGeophysicsVolcanoLinosaSoil CO_2 fluxlcsh:Meteorology. ClimatologyGeologyChannel (geography)
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Structure of longitudinal chromomagnetic fields in high energy collisions

2014

We compute expectation values of spatial Wilson loops in the forward light cone of high-energy collisions. We consider ensembles of gauge field configurations generated from a classical Gaussian effective action as well as solutions of high-energy renormalization group evolution with fixed and running coupling. The initial fields correspond to a color field condensate exhibiting domain-like structure over distance scales of order the saturation scale. At later times universal scaling emerges at large distances for all ensembles, with a nontrivial critical exponent. Finally, we compare the results for the Wilson loop to the two-point correlator of magnetic fields.

We compute expectation values of spatial Wilson loops in the forward light cone of high-energy collisions. We consider ensembles of gauge field configurations generated from a classical Gaussian effective action as well as solutions of high-energy renormalization group evolution with fixed and running coupling. The initial like structure over distance scales of oder the saturation scale. At later times universal scaling emerges at large distances for all ensembles with a nontrivial critical exponent. Finally we compare the resulats for the Wilson loop to the two-point correlator of magnetic fields. (C) 2014 The Authors. Published by Elsevier BV This is an open access article under the CC BY licenseNuclear and High Energy PhysicsWilson loopLARGE NUCLEINuclear TheoryField (physics)FOS: Physical sciences114 Physical sciences01 natural sciencesColor-glass condensateRENORMALIZATION-GROUPNuclear Theory (nucl-th)GLUON DISTRIBUTION-FUNCTIONSHigh Energy Physics - Phenomenology (hep-ph)Light cone0103 physical sciencesSCATTERINGGauge theory010306 general physicsSMALL-XEffective actionPhysicsCORRELATORSta114010308 nuclear & particles physicsCOLOR GLASS CONDENSATERenormalization groupEVOLUTIONJIMWLK EQUATIONHigh Energy Physics - PhenomenologySATURATIONQuantum electrodynamicsCritical exponentPhysics Letters B
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Gaussian quadrature rule for arbitrary weight function and interval

2005

Abstract A program for calculating abscissas and weights of Gaussian quadrature rules for arbitrary weight functions and intervals is reported. The program is written in Mathematica. The only requirement is that the moments of the weight function can be evaluated analytically in Mathematica. The result is a FORTRAN subroutine ready to be utilized for quadrature. Program summary Title of program: AWGQ Catalogue identifier:ADVB Program summary URL: http://cpc.cs.qub.ac.uk/summaries/ADVB Program obtained from: CPC Program Library, Queens University, Belfast, N. Ireland Computer for which the program is designed and others on which it has been tested: Computers: Pentium IV 1.7 GHz processor Ins…

Weight functionComputer scienceFortranMathematicsofComputing_NUMERICALANALYSISGeneral Physics and AstronomyGauss–Kronrod quadrature formulaTanh-sinh quadratureQuadrature (mathematics)symbols.namesakeHardware and ArchitecturesymbolsGaussian quadratureAlgorithmcomputerClenshaw–Curtis quadratureTest datacomputer.programming_languageComputer Physics Communications
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Modified Gaussian models applied to the description and deconvolution of peaks in chiral liquid chromatography.

2020

Abstract The description of the profiles of chromatographic peaks has been studied extensively, with a large number of proposed mathematical functions. Among them, the accuracy achieved with modified Gaussian models that describe the deviation of an ideal Gaussian peak as a change in the peak variance or standard deviation over time, has been highlighted. These models are, in fact, a family of functions of different complexity with great flexibility to adjust chromatographic peaks over a wide range of asymmetries and shapes. However, an uncontrolled behaviour of the signal may occur outside the region being fitted, forcing the use of different strategies to overcome this problem. In this wo…

Work (thermodynamics)ChromatographyChemistryGaussian010401 analytical chemistryOrganic ChemistryNormal DistributionOrder (ring theory)StereoisomerismGeneral MedicineModels Theoretical010402 general chemistry01 natural sciencesBiochemistryStandard deviation0104 chemical sciencesAnalytical ChemistryExponential functionsymbols.namesakesymbolsRange (statistics)Limit (mathematics)DeconvolutionStatistical physicsChromatography LiquidJournal of chromatography. A
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Estimation of peak capacity based on peak simulation.

2018

Peak capacity (PC) is a key concept in chromatographic analysis, nowadays of great importance for characterising complex separations as a criterion to find the most promising conditions. A theoretical expression for PC estimation can be easily deduced in isocratic elution, provided that the column plate count is assumed constant for all analytes. In gradient elution, the complex dependence of peak width with the gradient program implies that an integral equation has to be solved, which is only possible in a limited number of situations. In 2005, Uwe Neue developed a comprehensive theory for the calculation of PC in gradient elution, which is only valid for certain situations: single linear …

Work (thermodynamics)ChromatographyChromatographyChemistryElutionGaussian010401 analytical chemistryOrganic ChemistryMathematical analysisProbabilistic logicGeneral Medicine010402 general chemistry01 natural sciencesBiochemistryIntegral equationExpression (mathematics)0104 chemical sciencesAnalytical Chemistrysymbols.namesakeModels ChemicalsymbolsComputer SimulationAlgebraic expressionConstant (mathematics)Journal of chromatography. A
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Translocation time of periodically forced polymer chains.

2010

6 páginas, 11 figuras.-- PACS number(s): 36.20.-r, 05.40.-a, 87.15.A-, 87.10.-e

Work (thermodynamics)PeriodicityQuantitative Biology - Subcellular ProcessesTime FactorsPolymersGaussianThermal fluctuationsFOS: Physical sciencesChromosomal translocationCondensed Matter - Soft Condensed MatterNoise (electronics)SynchronizationQuantitative Biology::Subcellular Processessymbols.namesakeMotionNanotechnologyStatistical physicsPhysics - Biological PhysicsScalingSubcellular Processes (q-bio.SC)MathematicsPhysics::Biological PhysicsQuantitative Biology::BiomoleculesCondensed matter physicsTemperatureFunction (mathematics)Biological Physics (physics.bio-ph)FOS: Biological sciencessymbolsLinear ModelsSoft Condensed Matter (cond-mat.soft)Physical review. E, Statistical, nonlinear, and soft matter physics
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The role of age and emotional valence in word recognition: an ex-gaussian analysis

2015

[Otro] Cie¿om práce je posúdi¿ vplyv veku a emo¿nej valencie na znovupoznávanie slov v rámci ex-Gaussových distribu¿ných komponentov. Dvom vekovým skupinám sme administrovali test znovupoznávania slov, v ktorom sme manipulovali emo¿nou valenciou. U mladších respondentov sa prejavili štatisticky signifikantné rozdiely pri negatívnych slovách v experimentálnej podmienke a v podmienke s distrakciou. U starších respondentov sme v odpove¿ových ¿asoch nezistili jasnú tendenciu. Vzh¿adom na ex-Gaussovský parameter ¿, ktorý sa v literatúre ¿asto spája s nárokmi na pozornos¿, vekovo podmienené rozdiely v emo¿nej valencii nemali žiaden vplyv na negatívne slová. Ak sa zameriame na emo¿nú valenciu v ob…

YoungEmotional valencebehavioral disciplines and activitiesArousalAge groupsMemoryInformationTaskEmotional valencePicturesGeneral PsychologyExperienceProgramStatisticsEmotional wordsTEORIA E HISTORIA DE LA EDUCACIONEx-Gaussian componentsEx gaussianPsicologiaWord recognitionWord recognitionPsychologyArousalMATEMATICA APLICADAWord (group theory)Cognitive psychology
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Source separation on hyperspectral cube applied to dermatology

2010

International audience; This paper proposes a method of quantification of the components underlying the human skin that are supposed to be responsible for the effective reflectance spectrum of the skin over the visible wavelength. The method is based on independent component analysis assuming that the epidermal melanin and the dermal haemoglobin absorbance spectra are independent of each other. The method extracts the source spectra that correspond to the ideal absorbance spectra of melanin and haemoglobin. The noisy melanin spectrum is fixed using a polynomial fit and the quantifications associated with it are reestimated. The results produce feasible quantifications of each source compone…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingMaterials science[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingHuman skin[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing02 engineering and technology01 natural sciences010309 opticsAbsorbanceOptics[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing0103 physical sciences0202 electrical engineering electronic engineering information engineeringSource separationSource separation[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingPolynomial regressionIndependent Component Analysis.Spectral reflectanceKurtosisintegumentary systembusiness.industryNon-GaussianityHyperspectral imagingIndependent component analysisIndependent Component Analysis3. Good healthSkin patch020201 artificial intelligence & image processingbusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingVisible spectrum
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Noise removal using a nonlinear two-dimensional diffusion network

1998

Un reseau electrique non lineaire bidimensionnel, constitue de N×N cellules identiques, et modelisant l’equation de Nagumo discrete est presente. A l’aide d’une nouvelle description de la fonction non lineaire, on peut predire analytiquement l’evolution temporelle de la partie coherente du signal, ainsi que celle des perturbations de petites amplitudes qui lui sont superposees. Enfin, des applications a l’amelioration du rapport signal sur bruit, ou au traitement d’images sont suggerees.

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingNoise reductionDiffusion networkImage processing[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing01 natural sciences010305 fluids & plasmassymbols.namesakeSignal-to-noise ratio[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing[NLIN.NLIN-PS]Nonlinear Sciences [physics]/Pattern Formation and Solitons [nlin.PS][NLIN.NLIN-PS] Nonlinear Sciences [physics]/Pattern Formation and Solitons [nlin.PS]0103 physical sciencesElectronic engineering[ NLIN.NLIN-PS ] Nonlinear Sciences [physics]/Pattern Formation and Solitons [nlin.PS]Electrical and Electronic Engineering010306 general physics[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingMathematicsSignal processingMathematical analysisWhite noiseNonlinear systemGaussian noisesymbols[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingAnnales Des Télécommunications
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Noise estimation from digital step-model signal

2013

International audience; This paper addresses the noise estimation in the digital domain and proposes a noise estimator based on the step signal model. It is efficient for any distribution of noise because it does not rely only on the smallest amplitudes in the signal or image. The proposed approach uses polarized/directional derivatives and a nonlinear combination of these derivatives to estimate the noise distribution (e.g., Gaussian, Poisson, speckle, etc.). The moments of this measured distribution can be computed and are also calculated theoretically on the basis of noise distribution models. The 1D performances are detailed, and as our work is mostly dedicated to image processing, a 2D…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processingstep model02 engineering and technology[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingCCD sensornoise distributionsymbols.namesake[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processingdigital signalsalt and pepper noiseStatistics0202 electrical engineering electronic engineering information engineeringMedian filterImage noisePoisson noiseValue noiseNoise estimationMathematics[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingedge modelmultiplicative noiseNoise measurementNoise (signal processing)020206 networking & telecommunicationsComputer Graphics and Computer-Aided DesignNoise floorGaussian white noiseGradient noiseimpulse noiseGaussian noisenonlinear modelsymbols020201 artificial intelligence & image processingnoise estimatorAlgorithm[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingSoftware
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