complex Gaussian random variable60B1562E10We prove the following theorem. Let α = a + i ? b {\\alpha=a+ib} be a nonzero complex number. Then the following statements hold: (i) Let either b ≠ 0 {beq 0} or b = 0 {b=0} and a > 0 {a>0} . Let ξ 1 {\\xi_{1}} ...
Complex Gaussian random variable 翻译结果2复制译文编辑译文朗读译文返回顶部 正在翻译,请等待... 翻译结果3复制译文编辑译文朗读译文返回顶部 Complex Gaussian random variables 翻译结果4复制译文编辑译文朗读译文返回顶部 Longer Gaussian random variables 翻译结果5复制译文编辑译文朗读译文返回顶部 ...
The normal or Gaussian distribution is often denoted byN(μ,σ2)N(μ,σ2). When a random variableXXis distributed normally with meanμμand varianceσ2σ2, we writeX∼N(μ,σ2)X∼N(μ,σ2). The formula for the distribution is ...
[23] introduced the use of adaptive wavelet thresholding for image denoising, by modeling the wavelet coefficients as a generalized Gaussian random variable, whose parameters are estimated locally (i.e., within a given neighborhood). Sanches et al. [113] conducted a Bayesian denoising algorithm to...
From this theorem, we can derive an equation for the characteristics of the jump process as follows: Using the relation for the Gaussian random variable ξ and the last relation in Eq. (5), with j = 4 and j = 6, we first estimate the jump amplitude and then the jump rate ...
where ξj=\unicodex03C32(j−12)2π2 and ψj(t)=2sin((j−12)πt) are, respectively, the eigenvalues and the eigenfunctions of the covariance operator Cζ(s,t), while Zj are independent Gaussian random variables N(0,1). In order to differentiate the groups, the mean functions...
the hidden vector in the model graph is a Gaussian distributed random variable \({Z}_{k}\), the fusion coding \({\varepsilon }_{tar}\) is connected with the context vector \({V}_{C}\), so that the decoder can generate different motion contours. so that the decoder can generate ...
Assuming additive Gaussian noise, the observed vector at time is of the form (11)where is the time dependent signal, is normalized such that is its direction, is a measure of the signal strength and the vector is a zero mean complex valued random noise, assumed to be independent of the ...
/2nn! ξ2 n for the Gaussian random variable ξ and the last relation in Eq. (5), with j = 4 and j = 6, we first estimate the jump amplitude σξ2 (x, t) and then the jump rate λ(x, t) as: σξ2 (x, t) = M (6)(x, 5M (4)(x, t) t) ...
(τmax). The C2Cτprobability distribution looks like a two-side-truncated Gaussian distribution in which the random variableτis bounded both above (τmax = 1089 ms) and below (τmin = 342 ms). It also looks likeτandI+are correlated, that is to say, the variation ofτ...