设X_n^(1)≤X_2^(2)≤…≤X_n^(n)是n个具有公共分布函数共场所F(X)的独立随机变量的顺序统计量,而Y_n^(1)≤Y_n^(2)≤…≤Y_n^(n)是n个具有公共分布函数G(x)的独立随机变量的顺序统计量,0≤r≤1,integral from (-∞) to (+∞)|x|~r(dF(...设X_n^(1)≤X_2^(2)≤…≤X_n^(n)是n个具有公共分布函数共场所F(X)的独立随机变量的顺序统计量,而Y_n^(1)≤Y_n^(2)≤…≤Y_n^(n)是n个具有公共分布函数G(x)的独立随机变量的顺序统计量,0≤r≤1,integral from (-∞) to (+∞)|x|~r(dF(x))<+∞,integral from (-∞) to (+∞) |x|~r(dG(x))<+∞, 在0<r<1时,本文首次给出了对任一n≥1,都有E(|x_n^(n)|~r)=E(|Y_n^(n)|~r)的充分条件,并证明了条件“对任一n≥1,有E(|X_n^(n)|~r)=E(|Y_n^(n)|~r)”与条件“对任一n≥1,存在k_n:1≤k_n≤n,使E(|X_n^(k_n)|~r)=E(|Y_n^(k_n)|~r)”的等价性.文中证明“对任一n≥1,有 E(|X_n^(n)|~r)=E(|Y_n^(n)|~r)的必要条件是F(x)≡G(x)的方法弥补[1]中的不足,应用本文结论还可改进有关文献的结果.展开更多
This paper develops a powerful technique called threshold decomposition which is introduced for the analysis and implementation of median filter. This technique called generalized decomposition(GTD) is better than the...This paper develops a powerful technique called threshold decomposition which is introduced for the analysis and implementation of median filter. This technique called generalized decomposition(GTD) is better than the original method in the theoretical analysis and VLSI realization.展开更多
文摘设X_n^(1)≤X_2^(2)≤…≤X_n^(n)是n个具有公共分布函数共场所F(X)的独立随机变量的顺序统计量,而Y_n^(1)≤Y_n^(2)≤…≤Y_n^(n)是n个具有公共分布函数G(x)的独立随机变量的顺序统计量,0≤r≤1,integral from (-∞) to (+∞)|x|~r(dF(x))<+∞,integral from (-∞) to (+∞) |x|~r(dG(x))<+∞, 在0<r<1时,本文首次给出了对任一n≥1,都有E(|x_n^(n)|~r)=E(|Y_n^(n)|~r)的充分条件,并证明了条件“对任一n≥1,有E(|X_n^(n)|~r)=E(|Y_n^(n)|~r)”与条件“对任一n≥1,存在k_n:1≤k_n≤n,使E(|X_n^(k_n)|~r)=E(|Y_n^(k_n)|~r)”的等价性.文中证明“对任一n≥1,有 E(|X_n^(n)|~r)=E(|Y_n^(n)|~r)的必要条件是F(x)≡G(x)的方法弥补[1]中的不足,应用本文结论还可改进有关文献的结果.
基金Supported by the National Natural Science Foundation of China
文摘This paper develops a powerful technique called threshold decomposition which is introduced for the analysis and implementation of median filter. This technique called generalized decomposition(GTD) is better than the original method in the theoretical analysis and VLSI realization.
