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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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Patent
27 Feb 2001
TL;DR: In this paper, a multi-bit sigma-delta converter for converting an N-bit digital input to an n-bit output representing an over-sampled, lower resolution n-bits version of the N-bits digital input is presented.
Abstract: An audio path is constructed to include a multi-bit sigma-delta converter for converting an N-bit digital input to an n-bit output representing an over-sampled, lower resolution n-bit version of the N-bit digital input; a formatter for converting the n-bit output to an m signal output (e.g., as a thermometer code, a SDM format or a PWM format); an m-by-m switching matric for receiving the m output signals and for reordering the m output signals, m class-D drivers individual ones of which are driven by one of the reordered m output signals for driving one of m speakers; and a dynamic element matching (DEM) block coupled to the switching matric for controlling the reordering of the m output signals driving the m class-D drivers for spreading the distortion due at least to driver-speaker pair mismatch to wide band noise. The DEM may operate to generate white noise, or it may generate shaped (colored) noise.

84 citations

Journal ArticleDOI
TL;DR: The methodology can be used to estimate for any station how much the accuracy of the linear trend will improve when one tries to subtract the annual signal from the GPS time- series by using a physical model and it is demonstrated that for short time-series the trend error is more influenced by the fact that the noise properties also need to be estimated from the data.

84 citations

Journal ArticleDOI
TL;DR: It is shown that the algorithms converge to the unknown characteristic in a pointwise manner and that the mean integrated square error converges to zero as the number of observations tends to infinity.
Abstract: The non-linearity in a discrete system governed by the Hammerstein functional is identified. The system is driven by a random while input signal and the output is disturbed by a random white noise. No parametric a priori information concerning the non-linearity is available and non-parametric algorithms are proposed. The algorithms are derived from the trigonometric as well as Hermite orthogonal series. It is shown that the algorithms converge to the unknown characteristic in a pointwise manner and that the mean integrated square error converges to zero as the number of observations tends to infinity. The rate of convergence is examined. A numerical example is also given.

84 citations

Journal ArticleDOI
TL;DR: In this article, a weak solution to the stochastic partial differential equation driven by a one sided, α-stable noise without negative jumps is presented. But it is not a Lipschitz function, and is not Gaussian noise.
Abstract: We construct a weak solution to the stochastic partial differential equation driven by a one sided, α-stable noise without negative jumps. We prove the weak existence of the solution for parameters . The facts that, for is not a Lipschitz function, and is not a Gaussian noise require the development of new methods, which we believe are of independent interest. We also show that when the above equation gives an alternative description of super-Brownian motion with stable branching mechanism.

84 citations

Journal ArticleDOI
TL;DR: General performance analysis of the shift covariant class of quadratic time-frequency distributions as instantaneous frequency (IF) estimators, for an arbitrary frequency-modulated (FM) signal, is presented and the variance expression for the estimation bias and variance is derived.
Abstract: General performance analysis of the shift covariant class of quadratic time-frequency distributions (TFDs) as instantaneous frequency (IF) estimators, for an arbitrary frequency-modulated (FM) signal, is presented. Expressions for the estimation bias and variance are derived. This class of distributions behaves as an unbiased estimator in the case of monocomponent signals with a linear IF. However, when the IF is not a linear function of time, then the estimate is biased. Cases of white stationary and white nonstationary additive noises are considered. The well-known results for the Wigner distribution (WD) and linear FM signal, and the spectrogram of signals whose IF may be considered as a constant within the lag window, are presented as special cases. In addition, we have derived the variance expression for the spectrogram of a linear FM signal that is quite simple but highly signal dependent. This signal is considered in the cases of other commonly used distributions, such as the Born-Jordan and the Choi-Williams distributions. It has been shown that the reduced interference distributions outperform the WD but only in the case when the IF is constant or its variations are small. Analysis is extended to the IF estimation of signal components in the case of multicomponent signals. All theoretical results are statistically confirmed.

84 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023238
2022535
2021488
2020541
2019558
2018537