Search results for "White noise"

showing 10 items of 132 documents

Filter approach to the stochastic analysis of MDOF wind-excited structures

1999

Abstract In this paper, an approach useful for stochastic analysis of the Gaussian and non-Gaussian behavior of the response of multi-degree-of-freedom (MDOF) wind-excited structures is presented. This approach is based on a particular model of the multivariate stochastic wind field based upon a particular diagonalization of the power spectral density (PSD) matrix of the fluctuating part of wind velocity. This diagonalization is performed in the space of eigenvectors and eigenvalues that are called here wind-eigenvalues and wind-eigenvectors, respectively. From the examination of these quantities it can be recognized that the wind-eigenvectors change slowly with frequency while the first wi…

Astrophysics::High Energy Astrophysical PhenomenaGaussianAerospace EngineeringGeometryOcean EngineeringCondensed Matter PhysicWind speedMatrix (mathematics)symbols.namesakePhysics::Atmospheric and Oceanic PhysicsEigenvalues and eigenvectorsMathematicsCivil and Structural EngineeringStochastic processMechanical EngineeringMathematical analysisSpectral densityStatistical and Nonlinear PhysicsFilter (signal processing)White noiseCondensed Matter PhysicsMultivariate stochastic analysis; Filter equations; Wind-excited structuresNuclear Energy and EngineeringPhysics::Space PhysicssymbolsStatistical and Nonlinear Physic
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Artifacts and Errors in Cross-Spectrum Phase Noise Measurements : Invited lecture

2021

Inserting an attenuator between the oscillator under test and the phase noise analyzer, one expects that the white phase noise increases monotonically with the attenuation. By contrast, we observe that with some oscillators the white noise has sharp minimum for a given value of the attenuation, which clearly indicates problem. With other oscillators, it increases monotonically with the attenuation, but the values are not consistent with the thermal energy introduced by the attenuator. In both cases artifacts are present, which takes the form of a sharp notch in the spectrum, occurring where the white FM noise crosses the white PM noise. Such anomalous behavior is the tip of the iceberg, and…

Attenuator (electronics)PhysicsSpectrum analyzerOpticsbusiness.industryAttenuationPhase noiseWhite noisebusinessCross-spectrumNoise (radio)Time–frequency analysis2021 Joint Conference of the European Frequency and Time Forum and IEEE International Frequency Control Symposium (EFTF/IFCS)
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Path integral solution for non-linear system enforced by Poisson White Noise

2008

Abstract In this paper the response in terms of probability density function of non-linear systems under Poisson White Noise is considered. The problem is handled via path integral (PI) solution that may be considered as a step-by-step solution technique in terms of probability density function. First the extension of the PI to the case of Poisson White Noise is derived, then it is shown that at the limit when the time step becomes an infinitesimal quantity the Kolmogorov–Feller (K–F) equation is fully restored enforcing the validity of the approximations made in obtaining the conditional probability appearing in the Chapman Kolmogorov equation (starting point of the PI). Spectral counterpa…

Characteristic function (probability theory)Mechanical EngineeringMathematical analysisFokker-Planck equationAerospace EngineeringConditional probabilityKolmogorov-Feller eqautionOcean EngineeringStatistical and Nonlinear PhysicsProbability density functionWhite noiseCondensed Matter PhysicsPoisson distributionPath Integral Solutionsymbols.namesakeNuclear Energy and EngineeringPath integral formulationsymbolsFokker–Planck equationSettore ICAR/08 - Scienza Delle CostruzioniChapman–Kolmogorov equationCivil and Structural EngineeringMathematicsProbabilistic Engineering Mechanics
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Probabilistic response of nonlinear systems under combined normal and Poisson white noise via path integral method

2011

In this paper the response in terms of probability density function of nonlinear systems under combined normal and Poisson white noise is considered. The problem is handled via a Path Integral Solution (PIS) that may be considered as a step-by-step solution technique in terms of probability density function. A nonlinear system under normal white noise, Poissonian white noise and under the superposition of normal and Poisson white noise is performed through PIS. The spectral counterpart of the PIS, ruling the evolution of the characteristic functions is also derived. It is shown that at the limit when the time step becomes an infinitesimal quantity an equation ruling the evolution of the pro…

Characteristic function (probability theory)Stochastic resonanceMechanical EngineeringMathematical analysisShot noiseAerospace EngineeringOcean EngineeringStatistical and Nonlinear PhysicsProbability density functionWhite noiseCondensed Matter PhysicsPoisson distributionsymbols.namesakeNormal white noise Poisonian white noise combined white noisesAdditive white Gaussian noiseNuclear Energy and EngineeringGaussian noisesymbolsSettore ICAR/08 - Scienza Delle CostruzioniCivil and Structural EngineeringMathematics
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Identification of stiffness, dissipation and input parameters of multi degree of freedom civil systems under unmeasured base excitations

2009

A time domain dynamic identification technique based on a statistical moment approach has been formulated for civil systems under base random excitations in the linear state. This technique is based on the use of classically damped models characterized by a mass proportional damping. By applying the Itô stochastic calculus, special algebraic equations that depend on the statistical moments of the response can be obtained. These equations can be used for the dynamic identification of the mechanical parameters that define the structural model, in the case of unmeasured input as well, and the identification of the input itself. Furthermore, the above equations demonstrate the possibility of id…

Civil structureLinear modelMechanical EngineeringStochastic calculusSystem identificationLinear modelAerospace EngineeringOcean EngineeringStatistical and Nonlinear PhysicsWhite noiseCondensed Matter PhysicsParameter identification problemMoment (mathematics)Settore ICAR/09 - Tecnica Delle CostruzioniAlgebraic equationMass proportional dampingNuclear Energy and EngineeringControl theoryApplied mathematicsRandom vibrationTime domainSystem identificationSettore ICAR/08 - Scienza Delle CostruzioniCivil and Structural EngineeringMathematicsProbabilistic Engineering Mechanics
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An output-only stochastic parametric approach for the identification of linear and nonlinear structures under random base excitations: Advances and c…

2014

In this paper a time domain output-only Dynamic Identification approach for Civil Structures (DICS) first formulated some years ago is reviewed and presented in a more generalized form. The approach in question, suitable for multi- and single-degrees-of-freedom systems, is based on the statistical moments and on the correlation functions of the response to base random excitations. The solving equations are obtained by applying the Itô differential stochastic calculus to some functions of the response. In the previous version ([21] Cavaleri, 2006; [22] Benfratello et al., 2009), the DICS method was based on the use of two classes of models (Restricted Potential Models and Linear Mass Proport…

Civil structureMathematical optimizationBase excitationGeneralizationMechanical EngineeringSystem identificationStochastic calculusAerospace EngineeringOcean EngineeringStatistical and Nonlinear PhysicsWhite noiseWhite noiseCondensed Matter PhysicsNonlinear systemSettore ICAR/09 - Tecnica Delle CostruzioniNuclear Energy and EngineeringNonlinear stiffneApplied mathematicsNonlinear dampingTime domainSystem identificationCivil and Structural EngineeringMathematicsParametric statisticsEquation solving
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A nonlinear electronic circuit mimicking the neuronal activity in presence of noise

2013

We propose a nonlinear electronic circuit simulating the neuronal activity in a noisy environment. This electronic circuit is ruled by the set of Bonhaeffer-Van der Pol equations and is excited with a white gaussian noise, that is without external deterministic stimuli. Under these conditions, our circuits reveals the Coherence Resonance signature, that is an optimum of regularity in the system response for a given noise intensity.

Coherence ResonanceStochastic resonanceneural network[PHYS.PHYS.PHYS-BIO-PH]Physics [physics]/Physics [physics]/Biological Physics [physics.bio-ph]02 engineering and technologyTopology01 natural sciencesNoise (electronics)symbols.namesakeComputer Science::Emerging TechnologiesNoise generator[NLIN.NLIN-PS]Nonlinear Sciences [physics]/Pattern Formation and Solitons [nlin.PS]Control theory[ PHYS.PHYS.PHYS-BIO-PH ] Physics [physics]/Physics [physics]/Biological Physics [physics.bio-ph]0103 physical sciences[NLIN.NLIN-PS] Nonlinear Sciences [physics]/Pattern Formation and Solitons [nlin.PS]0202 electrical engineering electronic engineering information engineering[ NLIN.NLIN-PS ] Nonlinear Sciences [physics]/Pattern Formation and Solitons [nlin.PS]Value noisestochastic resonance010306 general physicsComputingMilieux_MISCELLANEOUSPhysics[PHYS.PHYS.PHYS-BIO-PH] Physics [physics]/Physics [physics]/Biological Physics [physics.bio-ph]020208 electrical & electronic engineeringShot noiseWhite noiseNoise floor[SPI.TRON] Engineering Sciences [physics]/Electronics[SPI.TRON]Engineering Sciences [physics]/Electronics[ SPI.TRON ] Engineering Sciences [physics]/ElectronicsGaussian noisesymbolsnonlinear circuit
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Digital information receiver based on stochastic resonance

2003

International audience; An electronic receiver based on stochastic resonance is presented to rescue subthreshold modulated digital data. In real experiment, it is shown that a complete data restoration is achieved for both uniform and Gaussian white noise.

Complete data[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingComputer scienceStochastic resonance[ PHYS.COND.CM-DS-NN ] Physics [physics]/Condensed Matter [cond-mat]/Disordered Systems and Neural Networks [cond-mat.dis-nn]Digital dataNonlinear signal processing[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing01 natural sciences010305 fluids & plasmas[NLIN.NLIN-PS]Nonlinear Sciences [physics]/Pattern Formation and Solitons [nlin.PS][INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing0103 physical sciencesElectronic engineering[ NLIN.NLIN-PS ] Nonlinear Sciences [physics]/Pattern Formation and Solitons [nlin.PS][PHYS.COND.CM-DS-NN]Physics [physics]/Condensed Matter [cond-mat]/Disordered Systems and Neural Networks [cond-mat.dis-nn]stochastic resonance010306 general physicsEngineering (miscellaneous)Subthreshold conductionbusiness.industryApplied MathematicsWhite noise[SPI.TRON]Engineering Sciences [physics]/Electronics[ SPI.TRON ] Engineering Sciences [physics]/ElectronicsNonlinear systemModeling and SimulationNonlinear dynamicsTelecommunicationsbusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
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Extension of The Stochastic Differential Calculus To Complex Processes

1996

In structural engineering complex processes arise to predict the first excursion failure, fatigue failure, etc. Indeed to solve these problems the envelope function, which is the modulus of a complex process, is usually introduced. In this paper the statistics of the complex response process related to the envelope statistics of linear systems subjected to parametric stationary normal white noise input are evaluated by using extensively the properties of stochastic differential calculus.

Complex responseProcess (engineering)Multivariable calculusExcursionLinear systemMathematical analysisApplied mathematicsDifferential calculusWhite noiseMathematicsParametric statistics
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Noise assisted image processing by ensembles of R-SETs

2017

AbstractWe study how noise can assist the processing of an image in a resistance-single electron transistor (R-SET) model. The image is an 8-bit black and white picture. Every grey level is codified linearly into a sub-threshold input potential applied for a prescribed time window to an ensemble of R-SETs that transforms it into a spiking frequency. The addition of a background white noise potential of high amplitude permits the ensemble to process the image by means of the stochastic resonance phenomenon. Aside from the positive aspects, we analyse the negative impact of using noise and how we can minimize it using redundancy and a longer measuring time. The results are compared with the c…

Computer Networks and CommunicationsComputer scienceStochastic resonancebusiness.industryImage processing02 engineering and technologyWhite noise021001 nanoscience & nanotechnologyMachine learningcomputer.software_genre03 medical and health sciencesNoise0302 clinical medicineRedundancy (information theory)Dark-frame subtractionImage noiseMedian filterArtificial intelligence0210 nano-technologybusinesscomputerAlgorithm030217 neurology & neurosurgerySoftwareInternational Journal of Parallel, Emergent and Distributed Systems
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