Search results for "Wiener"

showing 10 items of 44 documents

Optical calibration of a multispectral imaging system based on interference filters

2005

We present a new approach to optically calibrate a multispectral imaging system based on interference filters. Such a system typically suffers from some blurring of its channel images. Because the effectiveness of spectrum reconstruction depends heavily on the quality of the acquired channel images, and because this blurring negatively affects them, a method for deblurring and denoising them is required. The blur is modeled as a uniform intensity distribution within a circular disk. It allows us to characterize, quantitatively, the degradation for each channel image. In terms of global reduction of the blur, it consists of the choice of the best channel for the focus adjustment according to…

DeblurringComputer sciencebusiness.industryNoise reductionWiener filterMultispectral imageComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONGeneral EngineeringImage processingReal imageAtomic and Molecular Physics and OpticsMultispectral pattern recognitionsymbols.namesakeComputer Science::GraphicsInterference (communication)Computer Science::Computer Vision and Pattern RecognitionsymbolsComputer visionArtificial intelligenceOptical filterFocus (optics)businessImage restorationOptical Engineering
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An integral for a banach valued function

2009

Abstract Using partitions of the unity ((PU)-partition), a new definition of an integral is given for a function f : [a, b] → X, where X is a Banach space, and it is proved that this integral is equivalent to the Bochner integral.

Discrete mathematicsBanach valued function (PU)-partition (PU)*-integral Bochner-integralGeneral MathematicsInfinite-dimensional vector functionBochner integralRiemann–Stieltjes integralRiemann integralBochner spaceExponential integralsymbols.namesakeSettore MAT/05 - Analisi MatematicasymbolsPaley–Wiener integralDaniell integralMathematicsTatra Mountains Mathematical Publications
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Interpolation and approximation in L2(γ)

2007

Assume a standard Brownian motion W=(W"t)"t"@?"["0","1"], a Borel function f:R->R such that f(W"1)@?L"2, and the standard Gaussian measure @c on the real line. We characterize that f belongs to the Besov space B"2","q^@q(@c)@?(L"2(@c),D"1","2(@c))"@q","q, obtained via the real interpolation method, by the behavior of a"X(f(X"1);@t)@[email protected]?f(W"1)-P"X^@tf(W"1)@?"L"""2, where @t=(t"i)"i"="0^n is a deterministic time net and P"X^@t:L"2->L"2 the orthogonal projection onto a subspace of 'discrete' stochastic integrals x"[email protected]?"i"="1^nv"i"-"1(X"t"""i-X"t"""i"""-"""1) with X being the Brownian motion or the geometric Brownian motion. By using Hermite polynomial expansions the…

Discrete mathematicsNumerical AnalysisHermite polynomialsGeneric propertyApplied MathematicsGeneral MathematicsLinear equation over a ringGaussian measuresymbols.namesakeWiener processsymbolsBesov spaceMartingale (probability theory)Real lineAnalysisMathematicsJournal of Approximation Theory
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ESCAPE TIMES IN STOCK MARKETS

2005

We study the statistical properties of escape times for stock price returns in the Wall Street market. In particular we get the escape time distribution for real data from daily transactions and for three models: (i) the Wiener process with drift and a constant market volatility, (ii) Heston and (iii) GARCH models, where the volatility is a stochastic process. We find that the first model is unable to catch all the features of the escape time distribution of real data. Moreover, the Heston model describes the probability density function for both return and escape times better than the GARCH model.

EconophysicsStochastic processGeneral MathematicsAutoregressive conditional heteroskedasticityGeneral Physics and AstronomyProbability density functionHeston modelsymbols.namesakeWiener processsymbolsEconometricsEscape TimesVolatility (finance)Mathematical economicsStock (geology)MathematicsFluctuation and Noise Letters
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Stochastic response determination of nonlinear oscillators with fractional derivatives elements via the Wiener path integral

2014

A novel approximate analytical technique for determining the non-stationary response probability density function (PDF) of randomly excited linear and nonlinear oscillators endowed with fractional derivatives elements is developed. Specifically, the concept of the Wiener path integral in conjunction with a variational formulation is utilized to derive an approximate closed form solution for the system response non-stationary PDF. Notably, the determination of the non-stationary response PDF is accomplished without the need to advance the solution in short time steps as it is required by the existing alternative numerical path integral solution schemes which rely on a discrete version of the…

Euler-Lagrange equationMechanical EngineeringMonte Carlo methodMathematical analysisAerospace EngineeringOcean EngineeringStatistical and Nonlinear PhysicsProbability density functionFractional derivativeCondensed Matter PhysicsFractional calculusEuler–Lagrange equationNonlinear systemNuclear Energy and EngineeringPath integral formulationNonlinear systemWiener Path IntegralStochastic dynamicFunctional integrationFractional variational problemFractional quantum mechanicsCivil and Structural EngineeringMathematicsProbabilistic Engineering Mechanics
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A Wiener Path Integral Technique for Non-Stationary Response Determination of Nonlinear Oscillators with Fractional Derivative Elements

2014

In this paper a novel approximate analytical technique for determining the non-stationary response probability density function (PDF) of randomly excited linear and nonlinear oscillators with fractional derivative elements is developed. Specifically, the concept of the Wiener path integral in conjunction with a variational formulation is utilized to derive an approximate closed form solution for the system response non-stationary PDF. Notably, the determination of the non-stationary response PDF is accomplished without the need to advance the solution in short time steps as it is required by the existing alternative numerical path integral solution schemes. In this manner, the analytical Wi…

Hybrid Monte CarloMathematical analysisMonte Carlo methodAnalytical techniquePath integral formulationfractional derivativeProbability density functionFunctional integrationstochastic responseClosed-form expressionWiener path integralMathematicsFractional calculusVulnerability, Uncertainty, and Risk
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Model Identification of a Network as Compressing Sensing

2013

In many applications, it is important to derive information about the topology and the internal connections of dynamical systems interacting together. Examples can be found in fields as diverse as Economics, Neuroscience and Biochemistry. The paper deals with the problem of deriving a descriptive model of a network, collecting the node outputs as time series with no use of a priori insight on the topology, and unveiling an unknown structure as the estimate of a "sparse Wiener filter". A geometric interpretation of the problem in a pre-Hilbert space for wide-sense stochastic processes is provided. We cast the problem as the optimization of a cost function where a set of parameters are used t…

IdentificationReduced modelTheoretical computer scienceGeneral Computer ScienceDynamical systems theoryComputer scienceNetworkTopology (electrical circuits)Dynamical Systems (math.DS)Systems and Control (eess.SY)Set (abstract data type)symbols.namesakeFOS: MathematicsFOS: Electrical engineering electronic engineering information engineeringElectrical and Electronic EngineeringMathematics - Dynamical SystemsMathematics - Optimization and ControlMathematics - General TopologySparsificationMechanical EngineeringWiener filterSystem identificationGeneral Topology (math.GN)Function (mathematics)Compressive sensingIdentification (information)Compressed sensingControl and Systems EngineeringOptimization and Control (math.OC)symbolsIdentification; Sparsification; Reduced models; Networks; Compressive sensingComputer Science - Systems and Control
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Diffusion equations with negentropy applied to denoise mammographic images.

2006

Mammography is a radiographic technique used for the detection of breast lesions. The analysis of the digital image normally requires a previous application of filters as a preprocessing step to reduce the noise level of the image, while preserving important details to carry out a suitable diagnostic. In the literature, there are a large amount of denoising techniques applied to different medical images. In this work we have studied the performance of a diffusive filter with a stopping condition based on the statistical concept of negentropy, applied to denoise mammographic images. The negentropy has been succesfully prove with other denoising methods as independent component analysis by th…

Image qualityNoise reductionEntropyPhysics::Medical PhysicsNormal DistributionBreast NeoplasmsDiffusionDigital imagesymbols.namesakeBreast DiseasesHumansComputer visionImage restorationMathematicsModels Statisticalbusiness.industryWiener filterReproducibility of ResultsFilter (signal processing)Models TheoreticalNon-local meansRadiographic Image EnhancementComputer Science::Computer Vision and Pattern RecognitionSubtraction TechniquesymbolsRadiographic Image Interpretation Computer-AssistedNegentropyArtificial intelligencebusinessArtifactsAlgorithmsMammography
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VOCES LITERARIAS DEL PERU: JULIO RAMON RIBEYRO, JORGE EDUARDO EIELSON, GABRIELA WINER

2016

ES UN CONJUNTO DE ENSAYOS SOBRE LA OBRA DE JULIO RAMON RIBEYRO, EN OCASION DE LOS VIENTE ANOS DE SU MUERTE, Y LA OBRA DE JORGE EDUARDO EIELSON Y GABRIELA WIENER, QUE, CADA UNA DE ELLOS CON SUS PROPIAS MODALIDADES, HA SEGUIDO EL CAMINO TRAZADO POR RIBEYRO.

JORGE EDUARDO EIELSONSettore L-LIN/06 - Lingua E Letterature Ispano-AmericaneLITERATURA PERUANA SIGLO XXJULIO RAMON RIBEYROGABRIELA WIENER
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Non Linear Image Restoration in Spatial Domain

2011

International audience; In the present work, a novel image restoration method from noisy data samples is presented. The restoration was per-formed by using some heuristic approach utilizing data samples and smoothness criteria in spatial domain. Unlike most existing techniques, this approach does not require prior modelling of either the image or noise statistics. The proposed method works in an interactive mode to find the best compromise between the data (mean square error) and the smoothing criteria. The method has been compared with the shrinkage approach, Wiener filter and Non Local Means algorithm as well. Experimental results showed that the proposed method gives better signal to noi…

Mathematical optimization[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingNoise reductionWiener filter020206 networking & telecommunications02 engineering and technology[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingNon-local meansMultiplicative noisesymbols.namesakeMean Square ErrorSignal-to-noise ratio[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingGaussian noiseSignal SmoothnessRestoration0202 electrical engineering electronic engineering information engineeringsymbols020201 artificial intelligence & image processing[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingAlgorithmSmoothingImage restorationNonlinear FilteringMathematics
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