Measured Equation of Invariance(MEI) is a new concept in computational electromagnetics. It has been demonstrated that the MEI technique can be used to terminate the meshes very close to the object boundary and still strictly preserves the sparsity of the FD equations. Therefore, the nal system matrix encountered by MEI is a sparse matrix with size similar to that of integral equation methods. However, complicated Green's function and disagreeable Sommerfeld integrals make the traditional MEI very dicult, if not impossible, to be applied to analyze multilayer and multiconductor interconnects. In this paper, we propose the Geometry Independent MEI(GIMEI) which substantially improved the original MEI method. We use GIMEI for capacitance extraction of general three-dimension VLSI interconnect. Numerical results are i n g o o d agreement with published data and those obtained by using FASTCAP [1], while GIMEI is generally an order of magnitude faster than FASTCAP and uses signicant less memory than FAST-CAP.
a b s t r a c tMutual information can be used as a measure for the association of a genetic marker or a combination of markers with the phenotype. In this paper, we study the imputation of missing genotype data. We first utilize joint mutual information to compute the dependence between SNP sites, then construct a mathematical model in order to find the two SNP sites having maximal dependence with missing SNP sites, and further study the properties of this model. Finally, an extension method to haplotype-based imputation is proposed to impute the missing values in genotype data. To verify our method, extensive experiments have been performed, and numerical results show that our method is superior to haplotype-based imputation methods. At the same time, numerical results also prove joint mutual information can better measure the dependence between SNP sites. According to experimental results, we also conclude that the dependence between the adjacent SNP sites is not necessarily strongest.
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