Search results for "computational geometry"

showing 10 items of 139 documents

Error bounds for a convexity-preserving interpolation and its limit function

2008

AbstractError bounds between a nonlinear interpolation and the limit function of its associated subdivision scheme are estimated. The bounds can be evaluated without recursive subdivision. We show that this interpolation is convexity preserving, as its associated subdivision scheme. Finally, some numerical experiments are presented.

Mathematical optimizationNonlinear subdivision schemesbusiness.industryApplied MathematicsNumerical analysisMathematicsofComputing_NUMERICALANALYSISStairstep interpolationComputer Science::Computational GeometryConvexityMultivariate interpolationComputational MathematicsError boundsComputer Science::GraphicsNearest-neighbor interpolationTheoryofComputation_ANALYSISOFALGORITHMSANDPROBLEMCOMPLEXITYComputingMethodologies_SYMBOLICANDALGEBRAICMANIPULATIONApplied mathematicsComputer Science::Symbolic ComputationConvexity preservingbusinessSpline interpolationSubdivisionInterpolationMathematicsComputingMethodologies_COMPUTERGRAPHICSJournal of Computational and Applied Mathematics
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Solving the pentahedron problem

2015

Nowadays, all geometric modelers provide some tools for specifying geometric constraints. The 3D pentahedron problem is an example of a 3D Geometric Constraint Solving Problem (GCSP), composed of six vertices, nine edges, five faces (two triangles and three quadrilaterals), and defined by the lengths of its edges and the planarity of its quadrilateral faces. This problem seems to be the simplest non-trivial problem, as the methods used to solve the Stewart platform or octahedron problem fail to solve it. The naive algebraic formulation of the pentahedron yields an under-constrained system of twelve equations in eighteen unknowns. Even if the use of placement rules transforms the pentahedron…

Mathematical optimization[ INFO ] Computer Science [cs]Interval (mathematics)[INFO.INFO-CG]Computer Science [cs]/Computational Geometry [cs.CG]Industrial and Manufacturing EngineeringDesargues’ theoremPolyhedronAl-Kashi theorem[INFO]Computer Science [cs]Algebraic numberFinite setMathematicsGeometric constraint solving problemsQuadrilateralGeometric modeling with constraintsSolution set[ MATH.MATH-NA ] Mathematics [math]/Numerical Analysis [math.NA]SolverComputer Graphics and Computer-Aided DesignPentahedronPentahedronComputer Science ApplicationsAlgebraInterval solver[ INFO.INFO-CG ] Computer Science [cs]/Computational Geometry [cs.CG][MATH.MATH-NA]Mathematics [math]/Numerical Analysis [math.NA]
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The Reconstruction of Polyominoes from Approximately Orthogonal Projections

2001

The reconstruction of discrete two-dimensional pictures from their projection is one of the central problems in the areas of medical diagnostics, computer-aided tomography, pattern recognition, image processing, and data compression. In this note, we determine the computational complexity of the problem of reconstruction of polyominoes from their approximately orthogonal projections. We will prove that it is NP-complete if we reconstruct polyominoes, horizontal convex polyominoes and vertical convex polyominoes. Moreover we will give the polynomial algorithm for the reconstruction of hv-convex polyominoes that has time complexity O(m3n3).

Mathematics::CombinatoricsPolyominoComputational complexity theoryComputer scienceOrthographic projectionRegular polygonVector projectionComputer Science::Computational GeometryCombinatoricsProjection (mathematics)Computer Science::Discrete MathematicsTomographyAlgorithmTime complexityComputer Science::Formal Languages and Automata TheoryImage compression
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Free Minor Closed Classes and the Kuratowski theorem

2009

Free-minor closed classes [2] and free-planar graphs [3] are considered. Versions of Kuratowski-like theorem for free-planar graphs and Kuratowski theorem for planar graphs are considered.

Mathematics::LogicACM Graph Theory: G.2.2FOS: MathematicsMathematics - CombinatoricsMathematics::Metric GeometryMathematics::General TopologyCombinatorics (math.CO)Computer Science::Computational Geometry
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"Table 10" of "Measurement of double-differential muon neutrino charged-current interactions on C$_8$H$_8$ without pions in the final state using the…

2017

Covariance matrix for flux normalization error (fully correlated) in Analysis II.

NUMU C --> MU- XAstrophysics::High Energy Astrophysical PhenomenaD2SIG/DP/DCOSTHETAComputer Science::Computational Geometry
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"Table 8" of "Measurement of double-differential muon neutrino charged-current interactions on C$_8$H$_8$ without pions in the final state using the …

2017

Covariance matrix for shape systematics error in Analysis II.

NUMU C --> MU- XStatistics::MethodologyD2SIG/DP/DCOSTHETAComputer Science::Computational Geometry
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"Table 9" of "Measurement of double-differential muon neutrino charged-current interactions on C$_8$H$_8$ without pions in the final state using the …

2017

Covariance matrix for statistical errors in Analysis II.

NUMU C --> MU- XStatistics::MethodologyD2SIG/DP/DCOSTHETAComputer Science::Computational Geometry
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A nonlinear Chaikin-based binary subdivision scheme

2019

Abstract In this work we introduce and analyze a new nonlinear subdivision scheme based on a nonlinear blending between Chaikin’s subdivision rules and the linear 3-cell subdivision scheme. Our scheme seeks to improve the lack of convergence in the uniform metric of the nonlinear scheme proposed in Amat et al. (2012), where the authors define a cell-average version of the PPH subdivision scheme (Amat et al., 2006). The properties of the new scheme are analyzed and its performance is illustrated through numerical examples.

Nonlinear subdivisionScheme (programming language)business.industryApplied MathematicsMathematicsofComputing_NUMERICALANALYSISBinary numberComputer Science::Computational GeometryComputational MathematicsNonlinear systemMetric (mathematics)Convergence (routing)Applied mathematicsbusinesscomputerComputingMethodologies_COMPUTERGRAPHICSMathematicsSubdivisioncomputer.programming_languageJournal of Computational and Applied Mathematics
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Time integral and time differential Mössbauer measurements on [57Co/Mn(bipy)3](PF6)2

1994

The Mossbauer emission spectra of nucleogenic iron(II) complexes with a low spin (LS) ground state show two metastable iron(II) high spin (HS) states at low temperatures. In order to identify these metastable HS states, the compound [57Co/Mn(bipyridine)3](PF6)2 has been studied by time differential Mossbauer emission spectroscopy (TDMES) and optical lifetime measurements of excited electronic states in the corresponding Fe-doped Mn compound. The lifetime of one of the HS states of the nucleogenic iron(II) determined by TDMES has been measured to be the same as the lifetime of the laser-excited iron(II) electronic state.

Nuclear and High Energy PhysicsAstrophysics::High Energy Astrophysical PhenomenaAnalytical chemistryComputer Science::Computational GeometryCondensed Matter PhysicsAtomic and Molecular Physics and OpticsBipyridinechemistry.chemical_compoundchemistryNucleogenicMetastabilityMössbauer spectroscopyCondensed Matter::Strongly Correlated ElectronsEmission spectrumPhysical and Theoretical ChemistryGround stateSpectroscopySpin (physics)Hyperfine Interactions
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The Belle II vertex detector integration

2019

Belle II DEPFET, PXD, and SVD Collaborations: et al.

Nuclear and High Energy PhysicsPhysics::Instrumentation and DetectorsSilicon sensorPhase (waves)Computer Science::Computational Geometry7. Clean energy01 natural scienceslaw.inventionNuclear physicsBelle II; Data acquisition; Pixel detector; Silicon sensor; Strip detector; Vertex detector; Nuclear and High Energy Physics; InstrumentationData acquisitionlaw0103 physical sciencesVertex detectorBelle IIStrip detectorColliderInstrumentationNuclear and High Energy PhysicPhysicsInterconnectionPixel010308 nuclear & particles physicsDetectorBelle II; data acquisition; pixel detector; silicon sensor; strip detector; vertex detectorData acquisitionPixel detectorUpgradeHigh Energy Physics::ExperimentFocus (optics)Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
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