Quantum random number Gaussian noise signal generator

2019 ◽  
Vol 27 (7) ◽  
pp. 1492-1499
Author(s):  
余恒炜 YU Heng-wei ◽  
孙晓娟 SUN Xiao-juan ◽  
王星辰 WANG Xing-chen ◽  
蒋 科 JIANG Ke ◽  
吴 忧 WU-You ◽  
...  
Keyword(s):  
Gaussian Noise ◽  
Random Number ◽  
Noise Signal ◽  
Signal Generator

The Radar Signal Detection Based on Higher Order Statistics

2013 ◽  
Vol 310 ◽  
pp. 421-423
Author(s):  
Chun Yu Wang ◽  
Xing Long Qi ◽  
Run Lan Tian ◽  
Lin Ren
Keyword(s):  
Signal Detection ◽  
Order Statistics ◽  
Gaussian Noise ◽  
Signal Detection Theory ◽  
Detection Method ◽  
Noise Signal ◽  
Higher Order ◽  
Radar Signal ◽  
Higher Order Statistics ◽  
Colored Gaussian Noise

Radar signal detection theory is significant for the radar signal detection, and there are many radar signal detection method at present. In this paper, higher order statistics was used to achieve the radar signal detection. It analyzed the basic theory of higher order statistics and higher order statistics in radar signal detection. And it achieved radar signal detection in the MATLAB software, colored Gaussian noise signal detection method based on dual-spectrum was used to detect the radar signal mixed with man-made noise.

SYNCHRONIZING CHAOS IN DRIVEN CHUA'S CIRCUIT

1993 ◽  
Vol 03 (04) ◽  
pp. 1057-1066 ◽  
Author(s):  
K. MURALI ◽  
M. LAKSHMANAN
Keyword(s):  
Adaptive Control ◽  
Gaussian Noise ◽  
Control Algorithm ◽  
Noise Signal ◽  
The Novel ◽  
Chua’S Circuit ◽  
Chua's Circuit ◽  
Adaptive Control Algorithm

In this paper, we report the numerical observations of synchronization in driven Chua's circuit using the criterion of Pecora and Carroll. Also, synchronization behavior of this system is investigated under the influence of Gaussian noise signal and we point out that it is possible to obtain synchronization even in this environment. More interestingly, we also point out the novel possibility of controlling unsynchronized motion using the adaptive control algorithm.

Differential Form of Bivariate MMSE Estimator Based on Gaussian Noise

2016 ◽  
Vol 26 (01) ◽  
pp. 1750008
Author(s):  
P. Kittisuwan ◽  
C. Chinrungrueng
Keyword(s):  
Differential Form ◽  
Gaussian Noise ◽  
Additive White Gaussian Noise ◽  
Minimum Mean Square Error ◽  
Integral Form ◽  
Noise Signal ◽  
Original Form ◽  
Reduction Methods ◽  
Mmse Estimator ◽  
Heavy Tailed

In fact, the noise signal is an important problem in signal, circuits and systems. The minimum mean square error (MMSE) estimation technique is useful in several additive white Gaussian noise (AWGN) reduction methods. Original form of MMSE estimator is the integral form. Unfortunately, integral form of MMSE estimator cannot be obtained in simple form for any interesting peaked, heavy-tailed densities (also known as super-Gaussian densities). In this work, we proposed a differential form of bivariate MMSE estimator. The development depends on bivariate Taylor series. The proposed estimator requires no integration. In fact, the derivation is an extension of the existing results for differential form of univariate MMSE estimator.

scholarly journals Method of synthesis of gaussian noise signal in dynamic chaos systems

2012 ◽  
Vol 2 (10(56)) ◽  
pp. 25-27
Author(s):  
Николай Васильевич Захарченко ◽  
Владимир Викторович Корчинский ◽  
Бронислав Казимирович Радзимовский
Keyword(s):  
Gaussian Noise ◽  
Noise Signal ◽  
Dynamic Chaos

scholarly journals Low Distortion of Noise Filter Realization with 6.34 V/μs Fast Slew Rate and 120 mVp-p Output Noise Signal

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 1008
Author(s):  
Fang-Ming Yu ◽  
Kun-Cheng Lee ◽  
Ko-Wen Jwo ◽  
Rong-Seng Chang ◽  
Jun-Yi Lin
Keyword(s):  
Input Signal ◽  
Gaussian Noise ◽  
Fast Response ◽  
Noise Signal ◽  
High Order ◽  
Phase Delay ◽  
Slew Rate ◽  
Noise Filter ◽  
Output Noise ◽  
Actual Signal

In order to reduce Gaussian noise, this paper proposes a method via taking the average of the upper and lower envelopes generated by capturing the high and low peaks of the input signal. The designed fast response filter has no cut-off frequency, so the high order harmonics of the actual signal remain unchanged. Therefore, it can immediately respond to the changes of input signal and retain the integrity of the actual signal. In addition, it has only a small phase delay. The slew rate, phase delay and frequency response can be confirmed from the simulation results of Multisim 13.0. The filter outlined in this article can retain the high order harmonics of the original signal, achieving a slew rate of 6.34 V/μs and an almost zero phase difference. When using our filter to physically test the input signal with a noise level of 3 Vp-p Gaussian noise, a reduced noise signal of 120 mVp-p is obtained. The noise can be suppressed by up to 4% of the raw signal.

Assessment of the Negentropy Based Stopping Time Criterion in the Diffusive Restoration of Digital Images

2007 ◽  
Vol 3 (1) ◽  
pp. 13-21
Author(s):  
F. Rodenas ◽  
P. Mayo ◽  
D. Ginestar ◽  
G. Verdú
Keyword(s):  
Gaussian Noise ◽  
Digital Images ◽  
Stopping Time ◽  
Noise Signal ◽  
Differential Entropy ◽  
Statistical Measure ◽  
Stopping Criterion ◽  
Time Criterion ◽  
Iterative Filtering ◽  
Robust Measures

One method successfully employed to denoise digital images is the diffusive iterative filtering. An important point of this technique is the estimation of the stopping time of the diffusion process. In this paper, we propose a stopping time criterion based on the evolution of the negentropy of the ’noise signal’ with the diffusion parameter. The nonlinear diffusive filter implemented with this stopping criterion is evaluated by using several noisy test images with different statistics. Assuming that images are corrupted by additive Gaussian noise, a statistical measure of the Gaussianity can be used to estimate the amount of noise removed from noisy images. In particular, the differential entropy function or, equivalently, the negentropy are robust measures of the Gaussianity. Because of computational complexity of the negentropy function, it is estimated by using an approximation of the negentropy introduced by Hyv¨arinen in the context of independent component analysis.

scholarly journals Bayesian estimation of the self-similarity exponent of the Nile River fluctuation

2011 ◽  
Vol 18 (3) ◽  
pp. 441-446 ◽  
Author(s):  
S. Benmehdi ◽  
N. Makarava ◽  
N. Benhamidouche ◽  
M. Holschneider
Keyword(s):  
Time Series ◽  
Gaussian Noise ◽  
Hurst Exponent ◽  
Noise Signal ◽  
The Self ◽  
Nile River ◽  
Fractional Gaussian Noise ◽  
Self Similarity ◽  
Error Bars ◽  
Integrated Time Series

Abstract. The aim of this paper is to estimate the Hurst parameter of Fractional Gaussian Noise (FGN) using Bayesian inference. We propose an estimation technique that takes into account the full correlation structure of this process. Instead of using the integrated time series and then applying an estimator for its Hurst exponent, we propose to use the noise signal directly. As an application we analyze the time series of the Nile River, where we find a posterior distribution which is compatible with previous findings. In addition, our technique provides natural error bars for the Hurst exponent.

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