Steady-state analysis of a complex adaptive notch filter using modified gradient algorithm

Based on power spectral density (PSD) analytical technique, mean square error (MSE) (or variance) of the frequency estimate of a first-order complex adaptive IIR notch filter (ANF) using modified complex plain gradient (MCPG) algorithm is investigated in this paper. The steady-state expression for MSE is derived in closed form. A quantitative analysis for the estimation MSE has been carried out. It has been revealed that the MSE of frequency estimate is independent of an input frequency of a complex sinusoid. In addition, computer simulations are treated to corroborate the theoretical analysis and the relationships between MSE and system parameters are shown.     

Research on online review-trust model and synthetical value model based on power-law and density clustering algorithm

The existing online shopping researches on consumer reviews are mostly based on the attitude change model (ACM). Although the ACM is valuable, it is not easy to judge the trustworthiness of the reviews and measure the values of the reviews. Based on the online data acquisition technology, we have got the data of 360buy, a domestic large-scale business to customer (B2C) commerce website in China. With application of data-mining and the density clustering algorithm (DBSCAN), we focus on the intervals distribution and the synthetic value of consumer reviews. The distribution of review interval can be depicted by the power-law function which presents a monotonically increasing relationship between the power-exponent and the customers’ concerns with the commodity: the higher the exponent is, the more attention will be drawn. We also find that the value of online reviews can be measured by the expertise value, which is the attraction and the quality of the reviews. Based on the above results, we have constructed the online review-trust model and the synthetic value model. The relationship between the power-exponent and the consumer attention has played a vital role in the consumer attention to online-shopping, and then the synthetic value model will help people find out useful reviews more effectively.     

Implicit QR algorithms for palindromic and even eigenvalue problems

In the spirit of the Hamiltonian QR algorithm and other bidirectional chasing algorithms, a structure-preserving variant of the implicit QR algorithm for palindromic eigenvalue problems is proposed. This new palindromic QR algorithm is strongly backward stable and requires less operations than the standard QZ algorithm, but is restricted to matrix classes where a preliminary reduction to structured Hessenberg form can be performed. By an extension of the implicit Q theorem, the palindromic QR algorithm is shown to be equivalent to a previously developed explicit version. Also, the classical convergence theory for the QR algorithm can be extended to prove local quadratic convergence. We briefly demonstrate how even eigenvalue problems can be addressed by similar techniques. D. S. Watkins partly supported by Deutsche Forschungsgemeinschaft through Matheon, the DFG Research Center Mathematics for key technologies in Berlin.     

From C58 to C62 and back: Stability,structural similarity,and ring current

An increasing number of observations show that non‐classical isomers may play an important role in the formation of fullerenes and their exo‐ and endo‐derivatives. A quantum‐mechanical study of all classical isomers of C₅₈, C₆₀, and C₆₂, and all non‐classical isomers with at most one square or heptagonal face, was carried out. Calculations at the B3LYP/6‐31G* level show that the favored isomers of C₅₈, C₆₀, and C₆₂ have closely related structures and suggest plausible inter‐conversion and growth pathways among low‐energy isomers. Similarity of the favored structures is reinforced by comparison of calculated ring currents induced on faces of these polyhedral cages by radial external magnetic fields, implying patterns of magnetic response similar to those of the stable, isolated‐pentagon C₆₀ molecule. © 2016 Wiley Periodicals, Inc.     

Analysis of User＇s Weight in Microblog Network Based on User Influence and Active Degree

Based on user’s in-degree distribution, traditional ranking algorithms of user’s weight usually neglect the considerations of the differences among user’s followers and the features of user’s tweets. In order to analyze the factors which impact on user’s weight, under the analysis of the data collected from SINA Microblog network, this paper discovers that user influence and active degrees are the dominant factors for this issue. The proposed algorithm evaluates user influence by user’s follower number, the influence of user’s followers and the reciprocity between users. User’s active degree is modeled by user’s participation and the quality of user’s tweets. The models are tested by different data groups to confirm the parameters for the final calculation. Eventually, this paper compares the computational results with the user’s ranking order given by the SINA official application. The performance of this algorithm presents a stronger stability on the fluctuant range of the value of user’s weight.     

Performance comparisons between PCA-EA-LBG and PCA-LBG-EA approaches in VQ codebook generation for image compression

The aim of this study is to generate vector quantisation (VQ) codebooks by integrating principle component analysis (PCA) algorithm, Linde-Buzo-Gray (LBG) algorithm, and evolutionary algorithms (EAs). The EAs include genetic algorithm (GA), particle swarm optimisation (PSO), honey bee mating optimisation (HBMO), and firefly algorithm (FF). The study is to provide performance comparisons between PCA-EA-LBG and PCA-LBG-EA approaches. The PCA-EA-LBG approaches contain PCA-GA-LBG, PCA-PSO-LBG, PCA-HBMO-LBG, and PCA-FF-LBG, while the PCA-LBG-EA approaches contain PCA-LBG, PCA-LBG-GA, PCA-LBG-PSO, PCA-LBG-HBMO, and PCA-LBG-FF. All training vectors of test images are grouped according to PCA. The PCA-EA-LBG used the vectors grouped by PCA as initial individuals, and the best solution gained by the EAs was given for LBG to discover a codebook. The PCA-LBG approach is to use the PCA to select vectors as initial individuals for LBG to find a codebook. The PCA-LBG-EA used the final result of PCA-LBG as an initial individual for EAs to find a codebook. The search schemes in PCA-EA-LBG first used global search and then applied local search skill, while in PCA-LBG-EA first used local search and then employed global search skill. The results verify that the PCA-EA-LBG indeed gain superior results compared to the PCA-LBG-EA, because the PCA-EA-LBG explores a global area to find a solution, and then exploits a better one from the local area of the solution. Furthermore the proposed PCA-EA-LBG approaches in designing VQ codebooks outperform existing approaches shown in the literature.     

利用目标气压高度的多点定位算法

多点定位系统应用于广域目标定位时,由于精度几何弱化因子的影响,采用三维定位算法直接进行目标定位的精度比较差,甚至有发散的可能。由于用于定位的民航机载二次应答机所发送的部分应答信号中含有目标的气压高度信息,利用该气压高度信息,对三维定位算法进行修正,降低定位方程的维度,保证算法的收敛性,提高定位精度。该算法应用于实际系统的测试结果证明,采用该算法的目标定位精度有了很大提高。     

Path-following interior point algorithms for the Cartesian <Emphasis Type="Italic">P</Emphasis><Subscript>*</Subscript>(κ)-LCP over symmetric cones

In this paper, we establish a theoretical framework of path-following interior point algorithms for the linear complementarity problems over symmetric cones (SCLCP) with the Cartesian P _*(κ)-property, a weaker condition than the monotonicity. Based on the Nesterov-Todd, xy and yx directions employed as commutative search directions for semidefinite programming, we extend the variants of the short-, semilong-, and long-step path-following algorithms for symmetric conic linear programming proposed by Schmieta and Alizadeh to the Cartesian P _*(κ)-SCLCP, and particularly show the global convergence and the iteration complexities of the proposed algorithms. This work was supported by National Natural Science Foundation of China (Grant Nos. 10671010, 70841008)