Search results for "online"

showing 10 items of 4526 documents

Prediction of Hidden Oscillations Existence in Nonlinear Dynamical Systems: Analytics and Simulation

2013

From a computational point of view, in nonlinear dynamical systems, attractors can be regarded as self-excited and hidden attractors. Self-excited attractors can be localized numerically by a standard computational procedure, in which after a transient process a trajectory, starting from a point of unstable manifold in a neighborhood of equilibrium, reaches a state of oscillation, therefore one can easily identify it. In contrast, for a hidden attractor, a basin of attraction does not intersect neighborhoods of equilibria. While classical attractors are self-excited, attractors can therefore be obtained numerically by the standard computational procedure, for localization of hidden attracto…

Computer scienceOscillationbusiness.industryProcess (computing)State (functional analysis)Machine learningcomputer.software_genreManifoldNonlinear Sciences::Chaotic DynamicsAttractorTrajectoryPoint (geometry)Transient (oscillation)Artificial intelligenceStatistical physicsbusinesscomputer
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Path Integral approach via Laplace’s method of integration for nonstationary response of nonlinear systems

2019

In this paper the nonstationary response of a class of nonlinear systems subject to broad-band stochastic excitations is examined. A version of the Path Integral (PI) approach is developed for determining the evolution of the response probability density function (PDF). Specifically, the PI approach, utilized for evaluating the response PDF in short time steps based on the Chapman–Kolmogorov equation, is here employed in conjunction with the Laplace’s method of integration. In this manner, an approximate analytical solution of the integral involved in this equation is obtained, thus circumventing the repetitive integrations generally required in the conventional numerical implementation of …

Computer sciencePath IntegralMonte Carlo methodMarkov processProbability density function02 engineering and technologyNonstationary response01 natural sciencessymbols.namesake0203 mechanical engineering0103 physical sciencesProbability density functionApplied mathematics010301 acousticsVan der Pol oscillatorLaplace transformMechanical EngineeringEvolutionary excitationLaplace’s methodCondensed Matter PhysicsNonlinear system020303 mechanical engineering & transportsMechanics of MaterialsLaplace's methodPath integral formulationsymbolsSettore ICAR/08 - Scienza Delle Costruzioni
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Multi-level optimization of a fiber transmission system via nonlinearity management

2006

Nonlinearity management is explored as a complete tool to obtain maximum transmission reach in a WDM fiber transmission system, making it possible to optimize multiple system parameters, including optimal dispersion pre-compensation, with fast simulations based on the continuous-wave approximation. © 2006 Optical Society of America.

Computer sciencePhysics::OpticsPolarization-maintaining optical fiber02 engineering and technology01 natural sciencesGraded-index fiber[PHYS.PHYS.PHYS-AO-PH] Physics [physics]/Physics [physics]/Atmospheric and Oceanic Physics [physics.ao-ph]010309 optics020210 optoelectronics & photonicsOpticsWavelength-division multiplexing0103 physical sciencesDispersion (optics)0202 electrical engineering electronic engineering information engineeringFiber optic splitterDispersion-shifted fiberSpontaneous emissionPlastic optical fiber[PHYS.PHYS.PHYS-AO-PH]Physics [physics]/Physics [physics]/Atmospheric and Oceanic Physics [physics.ao-ph]Mode volumecomputer simulation; nonlinear control systems; optimizationbusiness.industryComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKSSingle-mode optical fiberNonlinear opticsTransmission systemAtomic and Molecular Physics and OpticsFiber-optic communication[ PHYS.PHYS.PHYS-AO-PH ] Physics [physics]/Physics [physics]/Atmospheric and Oceanic Physics [physics.ao-ph]Raman amplifiersTransmission (telecommunications)Fiber optic sensorbusiness
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Texture Classification with Generalized Fourier Descriptors in Dimensionality Reduction Context: An Overview Exploration

2008

In the context of texture classification, this article explores the capacity and the performance of some combinations of feature extraction, linear and nonlinear dimensionality reduction techniques and several kinds of classification methods. The performances are evaluated and compared in term of classification error. In order to test our texture classification protocol, the experiment carried out images from two different sources, the well known Brodatz database and our leaf texture images database.

Computer sciencebusiness.industryDimensionality reductionFeature extractionComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONNonlinear dimensionality reductionPattern recognitionContext (language use)Texture (geology)Term (time)symbols.namesakeFourier transformsymbolsArtificial intelligencebusiness
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Reduction of the number of spectral bands in Landsat images: a comparison of linear and nonlinear methods

2006

We describe some applications of linear and nonlinear pro- jection methods in order to reduce the number of spectral bands in Land- sat multispectral images. The nonlinear method is curvilinear component analysis CCA, and we propose an adapted optimization of it for image processing, based on the use of principal-component analysis PCA, a linear method. The principle of CCA consists in reproducing the topol- ogy of the original space projection points in a reduced subspace, keep- ing the maximum of information. Our conclusions are: CCA is an im- provement for dimension reduction of multispectral images; CCA is really a nonlinear extension of PCA; CCA optimization through PCA called CCAinitP…

Computer sciencebusiness.industryDimensionality reductionQuantization (signal processing)Multispectral imageGeneral EngineeringImage processingPattern recognitionImage segmentationSpectral bandsNonlinear Sciences::Cellular Automata and Lattice GasesAtomic and Molecular Physics and OpticsStatistics::Machine LearningComputer Science::Computer Vision and Pattern RecognitionPrincipal component analysisComputer visionArtificial intelligenceProjection (set theory)businessSubspace topologyOptical Engineering
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Non-linear Invertible Representation for Joint Statistical and Perceptual Feature Decorrelation

2000

The aim of many image mappings is representing the signal in a basis of decorrelated features. Two fundamental aspects must be taken into account in the basis selection problem: data distribution and the qualitative meaning of the underlying space. The classical PCA techniques reduce the statistical correlation using the data distribution. However, in applications where human vision has to be taken into account, there are perceptual factors that make the feature space uneven, and additional interaction among the dimensions may arise. In this work a common framework is presented to analyse the perceptual and statistical interactions among the coefficients of any representation. Using a recen…

Computer sciencebusiness.industryFeature vectormedia_common.quotation_subjectComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONPattern recognitionlaw.inventionLinear mapNonlinear systemInvertible matrixlawPerceptionHuman visual system modelArtificial intelligencebusinessDecorrelationmedia_common
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Multilevel system optimisation via nonlinearity management

2006

Nonlinearity management is explored as a multilevel tool to obtain maximum transmission reach in a WDM system. A technique for the fast calculation of the optimal dispersion pre-compensation in systems with distributed amplification is proposed.

Computer sciencebusiness.industryNoise (signal processing)Distributed amplifierNonlinear optics02 engineering and technology01 natural sciences010309 opticsNonlinear system020210 optoelectronics & photonicsOpticsSignal-to-noise ratioTransmission (telecommunications)Wavelength-division multiplexing0103 physical sciencesDispersion (optics)0202 electrical engineering electronic engineering information engineeringElectronic engineeringbusinessComputingMilieux_MISCELLANEOUS
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Manifold Learning with High Dimensional Model Representations

2020

Manifold learning methods are very efficient methods for hyperspectral image (HSI) analysis but, unless specifically designed, they cannot provide an explicit embedding map readily applicable to out-of-sample data. A common assumption to deal with the problem is that the transformation between the high input dimensional space and the (typically low) latent space is linear. This is a particularly strong assumption, especially when dealing with hyperspectral images due to the well-known nonlinear nature of the data. To address this problem, a manifold learning method based on High Dimensional Model Representation (HDMR) is proposed, which enables to present a nonlinear embedding function to p…

Computer sciencebusiness.industryNonlinear dimensionality reductionHyperspectral imaging020206 networking & telecommunicationsPattern recognition02 engineering and technologyFunction (mathematics)ManifoldNonlinear systemKernel (linear algebra)Transformation (function)0202 electrical engineering electronic engineering information engineeringEmbedding020201 artificial intelligence & image processingArtificial intelligencebusinessIGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium
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Reconfigurable photonic routers based on multimode interference couplers

2015

We present a design approach for compact reconfigurable light routers with N access waveguides (WGs) based on multimode interference (MMI) couplers. The proposed devices comprise two MMI couplers, which are employed as power splitters and combiners, respectively, linked by an array of N single-mode WGs. When the effective refractive index of the WGs is modulated with the proper relative optical phase difference, the light can switch paths between the preset output channel and the remaining output WGs. Taking advantage of the transfer phases between the access ports of the MMI couplers, we derive very simple phase relations between the modulated WGs that enable the reconfiguration of the out…

Computer sciencebusiness.industryPhase (waves)Control reconfigurationStatistical and Nonlinear Physics02 engineering and technology021001 nanoscience & nanotechnology01 natural sciencesTransfer matrixAtomic and Molecular Physics and Optics010309 opticsOpticsSurface wave0103 physical sciencesElectronic engineeringPower dividers and directional couplersMultimode interferencePhotonics0210 nano-technologybusinessEffective refractive indexJournal of the Optical Society of America B
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Hidden attractors on one path : Glukhovsky-Dolzhansky, Lorenz, and Rabinovich systems

2017

In this report, by the numerical continuation method we visualize and connect hidden chaotic sets in the Glukhovsky-Dolzhansky, Lorenz and Rabinovich systems using a certain path in the parameter space of a Lorenz-like system.

Computer sciencechaosChaoticFOS: Physical sciencesPhysics::Data Analysis; Statistics and ProbabilityParameter space01 natural sciences010305 fluids & plasmasRabinovich systemLorenz system0103 physical sciencesAttractorGlukhovsky–Dolzhansky systemApplied mathematics010301 acousticsEngineering (miscellaneous)kaaosteoriaApplied Mathematicsta111Lorenz-like systemNonlinear Sciences - Chaotic DynamicsNonlinear Sciences::Chaotic DynamicsNumerical continuationModeling and SimulationPath (graph theory)numeerinen analyysiChaotic Dynamics (nlin.CD)hidden attractorInternational Journal of Bifurcation and Chaos
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