• Title/Summary/Keyword: biased Gaussian

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Recursive Estimation of Biased Zero-Error Probability for Adaptive Systems under Non-Gaussian Noise (비-가우시안 잡음하의 적응 시스템을 위한 바이어스된 영-오차확률의 반복적 추정법)

  • Kim, Namyong
    • Journal of Internet Computing and Services
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    • v.17 no.1
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    • pp.1-6
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    • 2016
  • The biased zero-error probability and its related algorithms require heavy computational burden related with some summation operations at each iteration time. In this paper, a recursive approach to the biased zero-error probability and related algorithms are proposed, and compared in the simulation environment of shallow water communication channels with ambient noise of biased Gaussian and impulsive noise. The proposed recursive method has significantly reduced computational burden regardless of sample size, contrast to the original MBZEP algorithm with computational complexity proportional to sample size. With this computational efficiency the proposed algorithm, compared with the block-processing method, shows the equivalent robustness to multipath fading, biased Gaussian and impulsive noise.

Distance Measure for Biased Probability Density Functions and Related Equalizer Algorithms for Non-Gaussian Noise (편이 확률밀도함수 사이의 거리측정 기준과 비 가우시안 잡음 환경을 위한 등화 알고리듬)

  • Kim, Namyong
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.37A no.12
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    • pp.1038-1042
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    • 2012
  • In this paper, a new distance measure for biased PDFs is proposed and a related equalizer algorithm is also derived for supervised adaptive equalization for multipath channels with impulsive and time-varying DC bias noise. From the simulation results in the non-Gaussian noise environments, the proposed algorithm has proven not only robust to impulsive noise but also to have the capability of cancelling time-varying DC bias noise effectively.

Euclidean Distance of Biased Error Probability for Communication in Non-Gaussian Noise (비-가우시안 잡음하의 통신을 위한 바이어스된 오차 분포의 유클리드 거리)

  • Kim, Namyong
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.14 no.3
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    • pp.1416-1421
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    • 2013
  • In this paper, the Euclidean distance between the probability density functions (PDFs) for biased errors and a Dirac-delta function located at zero on the error axis is proposed as a new performance criterion for adaptive systems in non-Gaussian noise environments. Also, based on the proposed performance criterion, a supervised adaptive algorithm is derived and applied to adaptive equalization in the shallow-water communication channel distorted by severe multipath fading, impulsive and DC-bias noise. The simulation results compared with the performance of the existing MEDE algorithm show that the proposed algorithm yields over 5 dB of MSE enhancement and the capability of relocating the mean of the error PDF to zero on the error axis.

Note on Stochastic Orders through Length Biased Distributions

  • Choi, Jeen-Kap;Lee, Jin-Woo
    • Journal of the Korean Data and Information Science Society
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    • v.10 no.1
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    • pp.243-250
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    • 1999
  • We consider $Y=X{\lambda}Z,\;{\lambda}>0$, where X and Z are independent random variables, and Y is the length biased distribution or the equilibrium distribution of X. The purpose of this paper is to consider the distribution of X or Y when the distribution of Z is given and the distribution of Z when the distribution of X or Y is given, In particular, we obtain that the necessary and sufficient conditions for X to be $X^{2}({\upsilon})\;is\;Z{\sim}X^{2}(2)\;and\;for\;Z\;to\;be\;X^{2}(1)\;is\;X{\sim}IG({\mu},\;{\mu}^{2}/{\lambda})$, where $IG({\mu},\;{\mu}^{2}/{\lambda})$ is two-parameter inverse Gaussian distribution. Also we show that X is smaller than Y in the reverse Laplace transform ratio order if and only if $X_{e}$ is smaller than $Y_{e}$ in the Laplace transform ratio order. Finally, we can get the results that if X is smaller than Y in the Laplace transform ratio order, then $Y_{L}$ is smaller than $X_{L}$ in the Laplace transform order, and that if X is smaller than Y in the reverse Laplace transform ratio order, then $_{\mu}X_{L}$ is smaller than $_{\nu}Y_{L}$ in the Laplace transform order.

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지자기 전달함수의 로버스트 추정

  • Yang, Jun-Mo;O, Seok-Hun;Lee, Deok-Gi;Yun, Yong-Hun
    • Journal of the Korean Geophysical Society
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    • v.5 no.2
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    • pp.131-142
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    • 2002
  • Geomagnetic transfer function is generally estimated by choosing transfer to minimize the square sum of differences between observed values. If the error structure sccords to the Gaussian distribution, standard least square(LS) can be the estimation. However, for non-Gaussian error distribution, the LS estimation can be severely biased and distorted. In this paper, the Gaussian error assumption was tested by Q-Q(Quantile-Quantile) plot which provided information of real error structure. Therefore, robust estimation such as regression M-estimate that does not allow a few bad points to dominate the estimate was applied for error structure with non-Gaussian distribution. The results indicate that the performance of robust estimation is similar to the one of LS estimation for Gaussian error distribution, whereas the robust estimation yields more reliable and smooth transfer function estimates than standard LS for non-Gaussian error distribution.

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Power spectrum estimation of EEG signal using robust method (로보스트 방법을 이용한 EEG 신호의 전력밀도 추정)

  • 김택수;허재만;김종순;유선국;박상희
    • 제어로봇시스템학회:학술대회논문집
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    • 1991.10a
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    • pp.736-740
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    • 1991
  • EEG(Electroencephalogram) background signals can be represented as the sun of a conventional AR(Autoregressive) process and an innovation process, or a prediction error process. We have seen that conventional estimation techniques. such as least square estimates(LSE) or Gaussian maximum likelihood estimates(MLE-G) are optimal when the innovation process satisfies the Gaussian or presumed distribution. But when the data are contaminated by outliers, or artifacts, these assumptions are not met and conventional estimation techniques can badly fall and be strongly biased. It is known that EEG can be easily affected by artifacts. So we suggest a robust estimation technique which considerably performs well against those artifacts.

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Power Spectrum Estimation of EEG Signal Using Robust Filter (로버스트 필터를 이용한 EEG 신호의 스펙트럼 추정)

  • 김택수;허재만
    • Journal of Biomedical Engineering Research
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    • v.13 no.2
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    • pp.125-132
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    • 1992
  • Background EEG signals can be represented as the sum of a conventional AR process and an innovation process. It Is know that conventional estimation techniques, such as least square estimates (LSE) or Gaussian maximum likelihood estimates (MLE-G ) are optimal when the innovation process satisfies the Gaussian or presumed distribution. When the data are contaminated by outliers, however, these assumptions are not met and the power spectrum estimated by conventional estimation techniques may be fatally biased. EEG signal may be affected by artifacts, which are outliers in the statistical term. So the robust filtering estimation technique is used against those artifacts and it performs well for the contaminated EEG signal.

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Biased Zero-Error Probability for Adaptive Systems under Non-Gaussian Noise (비-가우시안 잡음하의 적응 시스템을 위한 바이어스된 영-오차확률)

  • Kim, Namyong
    • Journal of Internet Computing and Services
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    • v.14 no.1
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    • pp.9-14
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    • 2013
  • The criterion of zero-error probability provides a limitation on error probability functions being used for adaptive systems when the error samples are shifted by the influence of DC-bias noise. In this paper, we employ a bias term in the error distribution and propose a new criterion of the biased zero-error probability with error being zero. Also, by maximizing the proposed criterion on expanded filter structures, a supervised adaptive algorithm has been derived. From the simulation results of supervised equalization, the algorithm based on the proposed criterion yielded zero-centered and highly concentrated error samples without disturbance in the environments of strong impulsive and DC-bias noise.

Performance Analysis according to Filter Window Size in Random Number Generator Using Filter Algorithm (실난수생성기에서 필터 윈도우크기에 관한 연구)

  • Hong, Jin-Keun
    • Proceedings of the Korea Contents Association Conference
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    • 2004.11a
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    • pp.344-347
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    • 2004
  • Critical cryptography applications require the production of an unpredictable and unbiased stream of binary data derived from a fundamental noise mechanism. In this paper, we proposed a RNG with Gaussian noise using filter algorithm. The proposed scheme is designed to reduce the statistical property of the biased bit stream in the output of a RNG. Experimental show that we analysis the loss rate according to window size and propose optimum window size.

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Left Ventricular Image Processing and Displays of Cardiac Function

  • Kuwahara, Michiyoshi
    • Journal of Biomedical Engineering Research
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    • v.6 no.1
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    • pp.1-4
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    • 1985
  • Background EEG signals can be represented as the sum of a conventional AR process and an innovation process. It is know that conventional estimation techniques, such as least square estimates (LSE) or Gauasian maximum likelihood estimates (MLE-G) are optimal when the innovation process satisfies the Gaussian or presumed distribution. When the data are contaminated by outliers, however, these assumptions are not met and the power spectrum estimated by conventional estimation techniques may be fatally biased. EEG signal may be affected by artifacts, which are outliers in the statistical term. So the robust filtering estimation technique is used against those artifacts and it performs well for the contaminated EEG signal.

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