• 제목/요약/키워드: $L_{2,1}$ norm

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A maximum likelihood sequence detector in impulsive noise environment (충격성 잡음 환경에서의 최우 검출기)

  • 박철희;조용수
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.21 no.6
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    • pp.1522-1532
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    • 1996
  • In this paper, we compare the performance of channel estimators with the L$_{1}$-norm and L$_{2}$-norm criteria in impulaive noise environment, and show than the L$_{1}$-norm criterion is appropriate for that situation. Also, it is shown that the performance of the conventional maximum likelihood sequence detector(MLSD) can be improved by applying the same principle to mobile channels. That is, the performance of the conventional MLSD, which is known to be optimal under the Gaussian noise assumption, degrades in the impulsive noise of radio mobile communication channels. So, we proposed the MLSD which can reduce the effect of impulsive noise effectively by applying the results of channel estimators. Finally, it is confirmed by computer simulation that the performance of MLSD is significantly affected depending on the types of branch metrics, and that, in the impulsive noise environments, the proposed one with new branch metrics performs better thatn the conventional branch metric, l y(k)-s(k) l$^{[-992]}$ .

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HIGHER ORDER OF FULLY DISCREATE SOLUTION FOR PARABOLIC PROBLEM IN $L_{\infty}$

  • Lee, H.Y.;Lee, J.R.
    • Journal of applied mathematics & informatics
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    • v.4 no.1
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    • pp.17-30
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    • 1997
  • In this work we approximate the solution of initialboun-dary value problem using a Galerkin-finite element method for the spatial discretization and Implicit Runge-Kutta method for the spatial discretization and implicit Runge-Kutta methods for the time stepping. To deal with the nonlinear term f(x, t, u), we introduce the well-known extrapolation sheme which was used widely to prove the convergence in $L_2$-norm. We present computational results showing that the optimal order of convergence arising under $L_2$-norm will be preserved in $L_{\infty}$-norm.

$L^{\infty}$-CONVERGENCE OF MIXED FINITE ELEMENT METHOD FOR LAPLACIAN OPERATOR

  • Chen, Huan-Zhen;Jiang, Zi-Wen
    • Journal of applied mathematics & informatics
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    • v.7 no.1
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    • pp.61-82
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    • 2000
  • In this paper two so-called regularized Green's functions are introduced to derive the optimal maximum norm error estimates for the unknown function and the adjoint vector-valued function for mixed finite element methods of Laplacian operator. One contribution of the paper is a demonstration of how the boundedness of $L^1$-norm estimate for the second Green's function ${\lambda}_2$ and the optimal maximum norm error estimate for the adjoint vector-valued function are proved. These results are seemed to be to be new in the literature of the mixed finite element methods.

A New Block Matching Algorithm for Motion Estimation (움직임 추정을 위한 새로운 블록 정합 알고리즘)

  • Jung, Soo-Mok
    • Journal of Information Technology Services
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    • v.2 no.2
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    • pp.111-119
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    • 2003
  • In this paper, an efficient block matching algorithm which is based on the Block Sum Pyramid Algorithm (BSPA) is presented. The cost of BSPA[1] was reduced in the proposed algorithm by using l2 norm and partial distortion elimination technique. Motion estimation accuracy of the proposed algorithm is equal to that of BSPA. The efficiency of the proposed algorithm was verified by experimental results.

WEIGHTED NORM ESTIMATES FOR THE DYADIC PARAPRODUCT WITH VMO FUNCTION

  • Chung, Daewon
    • Bulletin of the Korean Mathematical Society
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    • v.58 no.1
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    • pp.205-215
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    • 2021
  • In [1], Beznosova proved that the bound on the norm of the dyadic paraproduct with b ∈ BMO in the weighted Lebesgue space L2(w) depends linearly on the Ad2 characteristic of the weight w and extrapolated the result to the Lp(w) case. In this paper, we provide the weighted norm estimates of the dyadic paraproduct πb with b ∈ VMO and reduce the dependence of the Ad2 characteristic to 1/2 by using the property that for b ∈ VMO its mean oscillations are vanishing in certain cases. Using this result we also reduce the quadratic bound for the commutators of the Calderón-Zygmund operator [b, T] to 3/2.

SOLUTIONS OF NONLINEAR FUNCTIONAL DIFFERENTIAL EQUATIONS IN $L^p$ SPACES

  • Ha, Ki-Sik;Shin, Ki-Yeon
    • Communications of the Korean Mathematical Society
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    • v.9 no.2
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    • pp.303-315
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    • 1994
  • Let X be a real Banach space with norm ∥ㆍ∥. Let T > 0, r ≥a be fixed constants. We denote by L/sup p/ the usual L/sup p/( -r, 0; X) with norm ∥ㆍ∥/sub p/ for 1 ≤p < ∞. Our object is to study the existence of solutions of nonlinear functional evolution equations of the type (FDE) x'(t) + A(t)x(t) = G(t, x/sub t/), 0 ≤t ≤T, x/sub 0/ = ø.(omitted)

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JACOBI SPECTRAL GALERKIN METHODS FOR VOLTERRA INTEGRAL EQUATIONS WITH WEAKLY SINGULAR KERNEL

  • Yang, Yin
    • Bulletin of the Korean Mathematical Society
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    • v.53 no.1
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    • pp.247-262
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    • 2016
  • We propose and analyze spectral and pseudo-spectral Jacobi-Galerkin approaches for weakly singular Volterra integral equations (VIEs). We provide a rigorous error analysis for spectral and pseudo-spectral Jacobi-Galerkin methods, which show that the errors of the approximate solution decay exponentially in $L^{\infty}$ norm and weighted $L^2$-norm. The numerical examples are given to illustrate the theoretical results.

Iterative Least-Squares Method for Velocity Stack Inversion - Part B: CGG Method (속도중합역산을 위한 반복적 최소자승법 - Part B: CGG 방법)

  • Ji Jun
    • Geophysics and Geophysical Exploration
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    • v.8 no.2
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    • pp.170-176
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    • 2005
  • Recently the velocity stack inversion is having many attentions as an useful way to perform various seismic data processing. In order to be used in various seismic data processing, the inversion method used should have properties such as robustness to noise and parsimony of the velocity stack result. The IRLS (Iteratively Reweighted Least-Squares) method that minimizes ${L_1}-norm$ is the one used mostly. This paper introduce another method, CGG (Conjugate Guided Gradient) method, which can be used to achieve the same goal as the IRLS method does. The CGG method is a modified CG (Conjugate Gradient) method that minimizes ${L_1}-norm$. This paper explains the CGG method and compares the result of it with the one of IRSL methods. Testing on synthetic and real data demonstrates that CGG method can be used as an inversion method f3r minimizing various residual/model norms like IRLS methods.