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The method of apparent density mapping in frequency domain is very quick.

视密度反演的频率域方法具有速度快的特点,但由于其关键运算向下延拓算子的不稳定,造成该方法精度不能达到实际应用的要求。

Secondly,we give a class of invariant differential operators forCartan domain R of dimension n:If the Bergman metric of R is?

其次,我们对维数为n 的Cartan 域R,给出了一类不变微分算子:若R 的Bergman 度量为ds~2=?

Thirdly, on the base of the existent error estimate of elliptical Galerkin projection operator, obtain semi-discrete and complete-discrete error estimates betwent EFG solution and exact solution of parabolic partial differential equation. Semi-discrete error estimate show that the rank of r is accordance with the approximation rank of subspace S_h. Complete-discrete error estimates show that it not only have relation with radius r of domain of influence, but also have relation with the step length of time variable and the way of discrete.Lastly, give typical examples and draw up procedures of MATLAB.

再次,在"椭圆Galerkin投影"算子的误差估计的基础上,对用EFG法解抛物型偏微分方程的EFG解与精确解之间作了半离散和全离散的误差估计,半离散的误差估计表明所给出的误差界限关于r的阶是与子空间S_h的逼近阶相一致的,全离散的误差估计表明所产生的误差不但与影响域半径r有关,而且与离散时间变量的步长τ及其离散方式有关。

This paper is the continuance of that of Lewalle (2001). Lewalle has made use of Gaussian wavelet to obtain the N-S equations with flexion as fundamental quantity in the infinite domain. In that work, Lapalace operator has been degraded to one-order operator. Moreover the resulted N-S equations take the form of diffusion equations with local bilinear mutual interaction kernel.

本文是Lewalle工作的继续,Lewalle利用高斯子波,得到了以弯曲度为基本量的无穷域中N-S方程,此时Lapalace算子降低为一阶导数,并且N-S方程变形为具有局部化双线性相互作用的扩散方程的形式。

Using Lagrangian submanifold in symplectic space ,we give a complete discription and classification of the self-adjoint domains of the 2-nd order and high order singular differential operators on infinite interval .

给出了与微分算子自共轭域相对应的Lagrangian子流形的描述和分类情况。

First, the proposed scheme uses contourlet transform to extract multidirectional and multiscale texture information, and then obtains the feature points invariant to the affine transformation from the low and middle directional subbands by Harris-Affine detector. Second, the feature regions are adaptively computed by the feature scale of the local structure and normalized by U transform. The watermarking is embedded adaptively in those characteristic regions which are normalized. Finally, by vector quantization, several copies of the watermark are embedded into the no overlapped local feature regions in different directional subbands.

首先,用Contourlet变换提取出多尺度、多方向的纹理信息;再用Harris-Affine检测算子从变换域的中、低频方向子带中提取出仿射不变特征点,结合自适应局部结构的特征尺度确定特征区域,并用U变换对其归一化处理,水印就自适应地嵌入到归一化后的区域中;水印嵌入采纳矢量量化的策略,将水印信息重复嵌入到不同方向子带、多个不相交的局部仿射不变特征区域。

The FDTD formulations of dispersive medium are deduced. Using shift operator method, the recursive relation for 〓 and 〓 available for FDTD computation of plasma coating is obtained.

推导了色散介质FDTD的递推公式,给出了时域计算中电位移矢量〓和电场强度〓之间关系的移位算子法,并用该方法得到了本构关系在离散时域的递推关系。

We synthesize the beamlet source and beamlet records in the wavelet domain using both rectangle and Gaussian windows and then extrapolate the synthesized data with a Fourier finite-difference operator.We test the method using the standard Marmousi model.

我们在小波域中,分别应用矩形窗和Gaussian窗合成了小波束源和小波束记录,并基于傅立叶有限差分算子,对marmousi模型进行了叠前深度偏移试算。

In this algorithm, Harris corner detector is used to extract feature points, which gains sub pixel precision for features extraction. Then, a pseudo matching set is obtained by comparing local neighborhoods of features through intensity cross correlation method and these pseudo matches are divided into inliers and outliers using robust RANSAC algorithm.

此算法采用 Harris角检测算子进行特征点提取,使提取的精度达到了亚像素级,然后以特征点邻域灰度互相关法进行特征点匹配得到了初步的伪匹配集合,并运用稳健的RANSAC算法将伪匹配点集合划分为内点和外点,在内点域上运用 L M优化算法精确地估计出了图像间的点变换关系,最后采用颜色插值对交接处进行颜色过渡。

In this paper, the image intensity surface for the neighborhood of every pixel is well-fitted by the Least Squares Support Vector Machine, and the gradient and the zero-crossing operators are deduced from the LSSVM with the Radial Basis Function kernel function, as an example.

摘要该文利用最小二乘向量机对原始图像每一像素的邻域作灰度曲面的最佳拟合,并以径向基核函数为例导出了图像的梯度算子和零交叉算子。

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