A new incident source with different angles was constructed for dealing with wide-angle scattering problems. Considering the impendence matrix in method of moments (MOM) is independent from incident angles, the equiv...
A new incident source with different angles was constructed for dealing with wide-angle scattering problems. Considering the impendence matrix in method of moments (MOM) is independent from incident angles, the equivalent relationship between induced current and the measured CS-current was build, while the CS-current can be computed directly under the new incident source. Finally, we can reconstruct the induce current by utilizing the theory of compressive sensing (CS). Compared with traditional MOM, the computational complexity can be greatly reduced.
Although many methods of refining initialization have appeared, the sensitivity of K-Means to initial centers is still an obstacle in applications. In this paper, we investigate a new class of clustering algorithm, K-...
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The PMCHWT equation of the double negative media (DNG) is obtained based on its constitutive relationship. And the surface currents and radar cross section (RCS) at a single frequency point is computed by Method of mo...
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Ana\ysis of electromagnetic scattering characteristics from wire antennas using method of moments has been performed over the past decades. In this paper, an efficient technique is adopted to evaluate the potential an...
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Ana\ysis of electromagnetic scattering characteristics from wire antennas using method of moments has been performed over the past decades. In this paper, an efficient technique is adopted to evaluate the potential and reaction integrals arising in radar cross section (RCS) computation of cylindrical antenna by method of moments. The singularity of potential and reaction integrals is extracted through the semi-analytical method. The induced current on the surface and RCS of cylindrical antenna are calculated. Numerical examples are presented to illustrate the computational accuracy and efficiency of the proposed technique.
A new incident source with different angles was constructed for dealing with wide-angle scattering problems. Considering the impendence matrix in method of moments (MOM) is independent from incident angles, the equiva...
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In this paper we review the major approaches to non-rigid object reconstruction based on multi-view images. It tries to reflect the profile of this area by focusing more on those subjects that have been given more imp...
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In this paper, we propose a distributed group signature scheme with traceable signers for mobile Ad hoc networks. In such scheme, there isn't a trusted center, and all members of Ad hoc group cooperate to generate...
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ISBN:
(纸本)9781424472352
In this paper, we propose a distributed group signature scheme with traceable signers for mobile Ad hoc networks. In such scheme, there isn't a trusted center, and all members of Ad hoc group cooperate to generate all system parameters and all private/public keys. Any ' members in Ad hoc group can collaboratively generate the group signature with the help of a designated clerk, where the signer respectively generates the partial signature by using his private key and the clerk can check the correctness of the partial signature. Furthermore, anyone can verify the validity of the group signature by the group public key and trace back to find the identities of signers from the warrant created by the clerk. What's more, it can dynamically increase the parameter values of ~t and ~n, according to the actual security needs of mobile Ad hoc networks, but the private and public keys of the whole Ad hoc group still are not changed for relatively long-term stability.
Particle swarm optimization (PSO) is a recently proposed population-based random search algorithm, which performs well in some optimization problems. In this paper, we proposed an improved PSO algorithm to solve portf...
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ISBN:
(纸本)9781424476534
Particle swarm optimization (PSO) is a recently proposed population-based random search algorithm, which performs well in some optimization problems. In this paper, we proposed an improved PSO algorithm to solve portfolio selection problems. The proposed approach IPSO employs an opposite mutation operator to enhance the performance of the standard PSO. In order to verify the performance of IPSO, we test it on five well-known benchmark function optimization problems. At last, we use IPSO to solve a classical portfolio selection problem. The results show that the proposed approach is effective and achieves better results than standard PSO.
The high-order finite-difference time-domain (HO-FDTD) technique is used in the simulation of ground-penetrating radar modeling in three dimensions (3-D), which can improve accuracy and reduce the error caused by nume...
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The high-order finite-difference time-domain (HO-FDTD) technique is used in the simulation of ground-penetrating radar modeling in three dimensions (3-D), which can improve accuracy and reduce the error caused by numerical dispersion effectively. To absorb waves reflected from edges we implement convolutional perfectly matched layer (CPML) absorbing boundaries. It can efficiently absorb the reflections and greatly increase the computation efficiency. The surface-based reflection and cross-hole GPR modeling are simulated, and numerical results show the efficiency of the method.
Inferring protein functions from different data sources is a challenging task in the post-genomic era, as a large number of crude protein structures from structural genomics project are now solved without their bioche...
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Inferring protein functions from different data sources is a challenging task in the post-genomic era, as a large number of crude protein structures from structural genomics project are now solved without their biochemical functions characterized. Recently, many different methods have been used to predict protein functions including those based on Protein-Protein Interaction (PPI), structure, sequence relationship, gene expression data, etc. Among these approaches, methods based on protein interaction data are very promising. In this paper, we studied a network-based method using locally linear embedding (LLE). LLE is a robust learning algorithm that manipulates dimensionality reduction, neighborhood-preserving embedding for high-dimensional data. We first embed both annotated and unannotated proteins in a low dimensional Euclidean space;then, we apply semi-supervised learning techniques to classify unannotated proteins into different functional groups. Finally, we made predictions to the unknown functional proteins in yeast. 5-fold cross validation is then applied to the GO terms to compare the performance of different approaches, and the proposed method performs significantly better than the others.
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