Results 191 to 200 of about 844,799 (232)
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DETECTING SINUSOIDS IN NON‐GAUSSIAN NOISE
Journal of Time Series Analysis, 1992Abstract.Spectral analysis is a well‐established procedure for detecting harmonic signals in a noisy environment. Much research has been done on methods that use second‐order statistics (i.e. the autocovariance function and power spectrum) such as Whittle's test, Bartlett's test, Hannan's test and the PriestleyP(Λ) test. When the noise is non‐Gaussian,
Lii, K.-S., Tsou, T.-H.
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IEEE Transactions on Acoustics, Speech, and Signal Processing, 1989
Algorithm description, block diagram, and complete C source code for a fast Gaussian noise generator which uses only integer operations and table lookups are presented. The algorithm achieves its speed of computation by using table lookups to eliminate the need for evaluating transcendental functions.
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Algorithm description, block diagram, and complete C source code for a fast Gaussian noise generator which uses only integer operations and table lookups are presented. The algorithm achieves its speed of computation by using table lookups to eliminate the need for evaluating transcendental functions.
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Joint detection—Estimation of Gaussian signals in white Gaussian noise
Information Sciences, 1970Recent results on joint detection-estimation for discrete data are extended to the continuous data case for Gaussian signals in White Gaussian noise. Stochastic differential equations are obtained describing the temporal evolution of the sufficient statistic for minimum Bayes-risk detection and the optimal mean-square error estimate of the signal state
Park, S. K., Lainiotis, D. G.
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Identifying Optimal Gaussian Filter for Gaussian Noise Removal
2011 Third National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics, 2011In this paper we show that the knowledge of noise statistics contaminating a signal can be effectively used to choose an optimal Gaussian filter to eliminate noise. Very specifically, we show that the additive white Gaussian noise (AWGN) contaminating a signal can be filtered best by using a Gaussian filter with specific characteristics.
Sunil Kumar Kopparapu, M. Satish
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Gaussian State Estimation with Non-Gaussian Measurement Noise
2018 Sensor Data Fusion: Trends, Solutions, Applications (SDF), 2018Many sensor data fusion approaches are based on physically motivated models. Some of them include non-Gaussian noise like radar clutter or GNSS multipath. Classical fusion algorithms like Kalman filters assume Gaussian noise processes. Nevertheless, they are widely applied to state estimation problems under non-Gaussian noise conditions. We investigate
Andreas Tollkuhn +2 more
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Gaussian noise and quantum-optical communication
Physical Review A, 1994The description of Gaussian noise for single-mode quantum fields is briefly reviewed and applied to calculate the maximal information properties of three quantum communication channels degraded by Gaussian noise. These channels are based on (i) heterodyne detection of coherent states, (ii) homodyne detection of squeezed states, and (iii) photodetection
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Journal of Applied Physics, 1958
For many years statisticians and research workers in communication theory have been interested in the problem of the ``zeros'' i.e., the statistics of the times a random function crosses through its average value. At present very few of the desired results are amenable to analytic or numerical solution; therefore, an experiment was designed for ...
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For many years statisticians and research workers in communication theory have been interested in the problem of the ``zeros'' i.e., the statistics of the times a random function crosses through its average value. At present very few of the desired results are amenable to analytic or numerical solution; therefore, an experiment was designed for ...
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High density shot noise and gaussianity
Journal of Applied Probability, 1971The distance from Gaussianity of the shot noise processis considered, wheretiare the random times of a Poisson process with average densityλ(t).WithF(x) the distribution function ofx(t) andG(x) that of a normal process with the same mean and variance asx(t) it is shown thatwhereIf the processx(t) is stationary with λ(t) =λandh(t, τ) =h(t – τ) and the ...
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Some Remarks on Generalized Gaussian Noise
2013The aim of this paper is to obtain sharp estimates about the behavior (local and at infinity) of the convolution of n copies of Generalized Gaussian Densities both in the symmetric and asymmetric case. Moreover, we obtain some improvement of the estimate of the parameters of these densities from data samples.
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Histogram-Based Gaussian Noise Removal
International Journal of Applied Research on Information Technology and Computing, 2014In modern science and technology, as the digital image processing gets more and more importance, the process of image quality enhancement and image restoration becomes a matter of concern for the researchers. In an image, denoising complexity increases from salt and pepper impulse noise to random-valued impulse noise, through to Gaussian noise. As salt
Manohar Koli, S. Balaji
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