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White noise
About: White noise is a research topic. Over the lifetime, 16496 publications have been published within this topic receiving 318633 citations.
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TL;DR: A novel intelligent fault diagnosis method for Lithium-ion batteries based on the support vector machine, which can identify the fault state and degree timely and efficiently and provides the theoretical basis for future fault hierarchical management strategy of the battery system.
127 citations
TL;DR: In this paper, a blind approach to the sampled Hammerstein-Wiener model identification is proposed, where all internal variables can be recovered solely based on the output measurements and identification of linear and nonlinear parts can be carried out No a priori structural knowledge about the input nonlinearity is assumed and no white noise assumption is imposed on the input.
127 citations
TL;DR: A model for CMOS FPN is presented as the sum of two components: a column and a pixel component, modeled by a first order isotropic autoregressive random process, and each component is assumed to be uncorrelated with the other.
Abstract: Fixed pattern noise (FPN) for a CCD sensor is modeled as a sample of a spatial white noise process. This model is, however, not adequate for characterizing FPN in CMOS sensors, since the redout circuitry of CMOS sensors and CCDs are very different. The paper presents a model for CMOS FPN as the sum of two components: a column and a pixel component. Each component is modeled by a first order isotropic autoregressive random process, and each component. Each component is modeled by a first order isotropic autoregressive random process, and each component is assumed to be uncorrelated with the other. The parameters of the processes characterize each component of the FPN and the correlations between neighboring pixels and neighboring columns for a batch of sensor. We show how to estimate the model parameters from a set of measurements, and report estimates for 64 X 64 passive pixel sensor (PPS) and active pixel sensor (APS) test structures implemented in a 0.35 micron CMOS process. High spatial correlations between pixel components were measured for the PPS structures, and between the column components in both PPS and APS. The APS pixel components were uncorrelated.
126 citations
TL;DR: A detector of a spatially distributed target in white Gaussian noise using a simple detector form, whose detection performance is robust over different scattering densities.
Abstract: A detector of a spatially distributed target in white Gaussian noise is developed. A reasonable distribution for the a priori target scatterer density is assumed, and a detector that incorporates this a priori knowledge is given. A simple detector form results, whose detection performance is robust over different scattering densities.
126 citations
TL;DR: In this paper, a simple, but realistic, estimate of the frequency uncertainty in time-series analyses is presented, which is based on a simple formula for the upper limit of the amplitude, frequency, and phase uncertainties.
Abstract: Context. Several approaches to estimating frequency, phase, and amplitude errors in time-series analyse have been reported in the literature, but they are either time-consuming to compute, grossly overestimating the error, or are based on empirically determined criteria.Aims. A simple, but realistic estimate of the frequency uncertainty in time-series analyses is our goal here.Methods. Synthetic data sets with mono- and multi-periodic harmonic signals and with randomly distributed amplitude, frequency, and phase were generated and white noise added. We tried to recover the input parameters with classical Fourier techniques and investigated the error as a function of the relative level of noise, signal, and frequency difference.Results. We present simple formulas for the upper limit of the amplitude, frequency, and phase uncertainties in time-serie analyses. We also demonstrate the possibility of detecting frequencies that are separated by less than the classical frequency resolution and of finding that the realistic frequency error is at least 4 times smaller than the classical frequency resolution.
125 citations