15,925 research outputs found
Robust statistics for deterministic and stochastic gravitational waves in non-Gaussian noise I: Frequentist analyses
Gravitational wave detectors will need optimal signal-processing algorithms
to extract weak signals from the detector noise. Most algorithms designed to
date are based on the unrealistic assumption that the detector noise may be
modeled as a stationary Gaussian process. However most experiments exhibit a
non-Gaussian ``tail'' in the probability distribution. This ``excess'' of large
signals can be a troublesome source of false alarms. This article derives an
optimal (in the Neyman-Pearson sense, for weak signals) signal processing
strategy when the detector noise is non-Gaussian and exhibits tail terms. This
strategy is robust, meaning that it is close to optimal for Gaussian noise but
far less sensitive than conventional methods to the excess large events that
form the tail of the distribution. The method is analyzed for two different
signal analysis problems: (i) a known waveform (e.g., a binary inspiral chirp)
and (ii) a stochastic background, which requires a multi-detector signal
processing algorithm. The methods should be easy to implement: they amount to
truncation or clipping of sample values which lie in the outlier part of the
probability distribution.Comment: RevTeX 4, 17 pages, 8 figures, typos corrected from first version
Outlier detection techniques for wireless sensor networks: A survey
In the field of wireless sensor networks, those measurements that significantly deviate from the normal pattern of sensed data are considered as outliers. The potential sources of outliers include noise and errors, events, and malicious attacks on the network. Traditional outlier detection techniques are not directly applicable to wireless sensor networks due to the nature of sensor data and specific requirements and limitations of the wireless sensor networks. This survey provides a comprehensive overview of existing outlier detection techniques specifically developed for the wireless sensor networks. Additionally, it presents a technique-based taxonomy and a comparative table to be used as a guideline to select a technique suitable for the application at hand based on characteristics such as data type, outlier type, outlier identity, and outlier degree
Outlier Detection Techniques For Wireless Sensor Networks: A Survey
In the field of wireless sensor networks, measurements that
significantly deviate from the normal pattern of sensed data are
considered as outliers. The potential sources of outliers include
noise and errors, events, and malicious attacks on the network.
Traditional outlier detection techniques are not directly
applicable to wireless sensor networks due to the multivariate
nature of sensor data and specific requirements and limitations of
the wireless sensor networks. This survey provides a comprehensive
overview of existing outlier detection techniques specifically
developed for the wireless sensor networks. Additionally, it
presents a technique-based taxonomy and a decision tree to be used
as a guideline to select a technique suitable for the application
at hand based on characteristics such as data type, outlier type,
outlier degree
A new Edge Detector Based on Parametric Surface Model: Regression Surface Descriptor
In this paper we present a new methodology for edge detection in digital
images. The first originality of the proposed method is to consider image
content as a parametric surface. Then, an original parametric local model of
this surface representing image content is proposed. The few parameters
involved in the proposed model are shown to be very sensitive to
discontinuities in surface which correspond to edges in image content. This
naturally leads to the design of an efficient edge detector. Moreover, a
thorough analysis of the proposed model also allows us to explain how these
parameters can be used to obtain edge descriptors such as orientations and
curvatures.
In practice, the proposed methodology offers two main advantages. First, it
has high customization possibilities in order to be adjusted to a wide range of
different problems, from coarse to fine scale edge detection. Second, it is
very robust to blurring process and additive noise. Numerical results are
presented to emphasis these properties and to confirm efficiency of the
proposed method through a comparative study with other edge detectors.Comment: 21 pages, 13 figures and 2 table
Detecting a stochastic background of gravitational waves in the presence of non-Gaussian noise: A performance of generalized cross-correlation statistic
We discuss a robust data analysis method to detect a stochastic background of
gravitational waves in the presence of non-Gaussian noise. In contrast to the
standard cross-correlation (SCC) statistic frequently used in the stochastic
background searches, we consider a {\it generalized cross-correlation} (GCC)
statistic, which is nearly optimal even in the presence of non-Gaussian noise.
The detection efficiency of the GCC statistic is investigated analytically,
particularly focusing on the statistical relation between the false-alarm and
the false-dismissal probabilities, and the minimum detectable amplitude of
gravitational-wave signals. We derive simple analytic formulae for these
statistical quantities. The robustness of the GCC statistic is clarified based
on these formulae, and one finds that the detection efficiency of the GCC
statistic roughly corresponds to the one of the SCC statistic neglecting the
contribution of non-Gaussian tails. This remarkable property is checked by
performing the Monte Carlo simulations and successful agreement between
analytic and simulation results was found.Comment: 15 pages, 8 figures, presentation and some figures modified, final
version to be published in PR
A constructive and unifying framework for zero-bit watermarking
In the watermark detection scenario, also known as zero-bit watermarking, a
watermark, carrying no hidden message, is inserted in content. The watermark
detector checks for the presence of this particular weak signal in content. The
article looks at this problem from a classical detection theory point of view,
but with side information enabled at the embedding side. This means that the
watermark signal is a function of the host content. Our study is twofold. The
first step is to design the best embedding function for a given detection
function, and the best detection function for a given embedding function. This
yields two conditions, which are mixed into one `fundamental' partial
differential equation. It appears that many famous watermarking schemes are
indeed solution to this `fundamental' equation. This study thus gives birth to
a constructive framework unifying solutions, so far perceived as very
different.Comment: submitted to IEEE Trans. on Information Forensics and Securit
Directional Bilateral Filters
We propose a bilateral filter with a locally controlled domain kernel for
directional edge-preserving smoothing. Traditional bilateral filters use a
range kernel, which is responsible for edge preservation, and a fixed domain
kernel that performs smoothing. Our intuition is that orientation and
anisotropy of image structures should be incorporated into the domain kernel
while smoothing. For this purpose, we employ an oriented Gaussian domain kernel
locally controlled by a structure tensor. The oriented domain kernel combined
with a range kernel forms the directional bilateral filter. The two kernels
assist each other in effectively suppressing the influence of the outliers
while smoothing. To find the optimal parameters of the directional bilateral
filter, we propose the use of Stein's unbiased risk estimate (SURE). We test
the capabilities of the kernels separately as well as together, first on
synthetic images, and then on real endoscopic images. The directional bilateral
filter has better denoising performance than the Gaussian bilateral filter at
various noise levels in terms of peak signal-to-noise ratio (PSNR)
Detection of multiplicative noise in stationary random processes using second- and higher order statistics
This paper addresses the problem of detecting the presence of colored multiplicative noise, when the information process can be modeled as a parametric ARMA process. For the case of zero-mean multiplicative noise, a cumulant based suboptimal detector is studied. This detector tests the nullity of a specific cumulant slice. A second detector is developed when the multiplicative noise is nonzero mean. This detector consists of filtering the data by an estimated AR filter. Cumulants of the residual data are then shown to be well suited to the detection problem. Theoretical expressions for the asymptotic probability of
detection are given. Simulation-derived finite-sample ROC curves are shown for different sets of model parameters
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