• Title/Summary/Keyword: the Dirac delta distribution

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ON THE PARAMETIC INTEREST OF THE BLACK-SCHOLES EQUATION

  • Kananthai, Amnuay
    • Journal of applied mathematics & informatics
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    • v.28 no.3_4
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    • pp.923-929
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    • 2010
  • We have discovered some parametics $\lambda$ in the Black-Scholes equation which depend on the interest rate $\gamma$ and the Volatility $\sigma$ and later is named the parametic interest. On studying the parametic interest $\lambda$, we found that such $\lambda$ gives the sufficient condition for the existence of solutions of the Black-Scholes equation which is either weak or strong solutions.

Two-Dimensional Probability Functions of Morphological Dilation and Erosion of a Memoryless Source

  • Sangsin Na;Park, Tae-Young
    • Journal of Electrical Engineering and information Science
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    • v.1 no.1
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    • pp.151-155
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    • 1996
  • This paper derives the two-dimensional probability distribution and density functions of morphological dilation and erosion of a one-dimensional memoryless source and reports numerical results for a uniform source, thus providing methodology for joint distributions for other morphological operations. The joint density functions expressed in closed forms contain the Dirac delta functions due to the joint discontinuity within the dilation and erosion. They also exhibit symmetry between these two morphological density functions of dilated and/or eroded sources, in the computation of other higher moments thereof, and in multidimensional quantization.

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Performance Enhancement of Algorithms based on Error Distributions under Impulsive Noise (충격성 잡음하에서 오차 분포에 기반한 알고리듬의 성능향상)

  • Kim, Namyong;Lee, Gyoo-yeong
    • Journal of Internet Computing and Services
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    • v.19 no.3
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    • pp.49-56
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    • 2018
  • Euclidean distance (ED) between error distribution and Dirac delta function has been used as an efficient performance criterion in impulsive noise environmentsdue to the outlier-cutting effect of Gaussian kernel for error signal. The gradient of ED for its minimization has two components; $A_k$ for kernel function of error pairs and the other $B_k$ for kernel function of errors. In this paper, it is analyzed that the first component is to govern gathering close together error samples, and the other one $B_k$ is to conduct error-sample concentration on zero. Based upon this analysis, it is proposed to normalize $A_k$ and $B_k$ with power of inputs which are modified by kernelled error pairs or errors for the purpose of reinforcing their roles of narrowing error-gap and drawing error samples to zero. Through comparison of fluctuation of steady state MSE and value of minimum MSE in the results of simulation of multipath equalization under impulsive noise, their roles and efficiency of the proposed normalization method are verified.