575,323 research outputs found
Noise Variance Estimation In Signal Processing
We present a new method of estimating noise
variance. The method is applicable for 1D and 2D signal
processing. The essence of this method is estimation of the scatter
of normally distributed data with high level of outliers. The
method is applicable to data with the majority of the data points
having no signal present. The method is based on the shortest
half sample method. The mean of the shortest half sample
(shorth) and the location of the least median of squares are
among the most robust measures of the location of the mode. The
length of the shortest half sample has been used as the
measurement of the data scatter of uncontaminated data. We
show that computing the length of several sub samples of varying
sizes provides the necessary information to estimate both the
scatter and the number of uncontaminated data points in a
sample. We derive the system of equations to solve for the data
scatter and the number of uncontaminated data points for the
Gaussian distribution. The data scatter is the measure of the
noise variance. The method can be extended to other
distributions
Bias-Correcting the Realized Range-Based Variance in the Presence of Market Microstructure Noise
Market microstructure noise is a challenge to high-frequency based estimation of the integrated variance, because the noise accumulates with the sampling frequency. In this paper, we analyze the impact of microstructure noise on the realized range-based variance and propose a bias-correction to the rangestatistic. The new estimator is shown to be consistent for the integrated variance and asymptotically mixed Gaussian under simple forms of microstructure noise, and we can select an optimal partition of the high-frequency data in order to minimize its asymptotic conditional variance. The finite sample properties of our estimator are studied with Monte Carlo simulations and we implement it on high-frequency data from TAQ. We find that a bias-corrected range-statistic often has much smaller confidence intervals than the realized variance. --Bias-Correction,Integrated Variance,Market Microstructure Noise,Realized Range-Based Variance,Realized Variance
Disconnected Loop Noise Methods in Lattice QCD
A comparison of the noise variance between algorithms for calculating
disconnected loop signals in lattice QCD is carried out. The methods considered
are the Z(N) noise method and the Volume method. We find that the noise
variance is strongly influenced by the Dirac structure of the operator.Comment: espcrc.sty file needed. Talk presented at Lattice '97, Edinburgh,
Scotlan
Anomalous scaling law for noise variance and spatial resolution in differential phase contrast computed tomography
In conventional absorption based x-ray computed tomography (CT), the noise
variance in reconstructed CT images scales with spatial resolution following an
inverse cubic relationship. Without reconstruction, in x-ray absorption
radiography, the noise variance scales as an inverse square with spatial
resolution. In this letter we report that while the inverse square relationship
holds for differential phase contrast projection imaging, there exists an
anomalous scaling law in differential phase contrast CT, where the noise
variance scales with spatial resolution following an inverse linear
relationship. The anomalous scaling law is theoretically derived and
subsequently validated with phantom results from an experimental Talbot-Lau
interferometer system
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