多用户MIMO系统低复杂度收发联合波束成形方案

基于多用户多输入多输出(MIMO)有限反馈系统提出了一种收发联合波束成形方法。该方法在发送端采用每个用户酉速率控制(PU2RC)波束成形技术,在接收端采用基于信干噪比(SINR)最大化的天线合并算法。天线合并算法利用用户SINR的广义Rayleigh熵特性寻找最佳的天线合并矢量。仿真结果表明,该方法能充分利用信道信息,不仅改善了传统PU2RC系统的和速率性能,相对于已有的迫零(ZF)联合波束成形方法,对信道状态信息的量化误差的敏感性更低,用户选择算法的复杂度更低,且低速率反馈条件下和速率性能更优。     

Multi-verse optimization algorithm- and salp swarm optimization algorithm-based optimization of multilevel inverters

Neural Computing and Applications - Renewable energy sources are installed into both distribution and transmission grids more and more with the introduction of smart grid concept. Hence, efficient...     

CFAR processing with switching exponential smoothers for nonhomogeneous environments

Conventional constant false alarm rate (CFAR) methods use a fixed number of cells to estimate the background variance. For homogeneous environments, it is desirable to increase the number of cells, at the cost of increased computation and memory requirements, in order to improve the estimation performance. For nonhomogeneous environments, it is desirable to use less number of cells in order to reduce the number of false alarms around the clutter edges. In this work, we present a solution with two exponential smoothers (first order IIR filters) having different time-constants to leverage the conflicting requirements of homogeneous and nonhomogeneous environments. The system is designed to use the filter having the large time-constant in homogeneous environments and to promptly switch to the filter having the small time constant once a clutter edge is encountered. The main advantages of proposed Switching IIR CFAR method are computational simplicity, small memory requirement (in comparison to windowing based methods) and its good performance in homogeneous environments (due to the large time-constant smoother) and rapid adaptation to clutter edges (due to the small time-constant smoother).     

Exploring brain connectivity with two-dimensional neural maps

We introduce two-dimensional neural maps for exploring connectivity in the brain. For this, we create standard streamtube models from diffusion-weighted brain imaging data sets along with neural paths hierarchically projected into the plane. These planar neural maps combine desirable properties of low-dimensional representations, such as visual clarity and ease of tract-of-interest selection, with the anatomical familiarity of 3D brain models and planar sectional views. We distribute this type of visualization both in a traditional stand-alone interactive application and as a novel, lightweight web-accessible system. The web interface integrates precomputed neural-path representations into a geographical digital-maps framework with associated labels, metrics, statistics, and linkouts. Anecdotal and quantitative comparisons of the present method with a recently proposed 2D point representation suggest that our representation is more intuitive and easier to use and learn. Similarly, users are faster and more accurate in selecting bundles using the 2D path representation than the 2D point representation. Finally, expert feedback on the web interface suggests that it can be useful for collaboration as well as quick exploration of data.     

A note on double lacunary statistical $$\sigma$$-convergence of fuzzy numbers

Quite recently, Sava? (Appl Math Lett 21:134–141, 2008), defined the lacunary statistical analogue for double sequence \(X=\{X_{k,l}\}\) of fuzzy numbers as follows: a double sequence \(X=\{X_{k,l}\}\) is said to be lacunary P-statistically convergent to \(X_{0}\) provided that for each \(\epsilon >0\)

$ P-\lim_{r,s}\frac{1}{h_{r,s}}\left | \{(k,l)\in I_{r,s}: d(X_{k,l },X_0)\geq \epsilon\}\right|= 0. $

In this paper we introduce and study double lacunary \(\sigma\)-statistical convergence for sequence of fuzzy numbers and also we get some inclusion theorems.

     

A new ECG beat clustering method based on kernelized fuzzy c-means and hybrid ant colony optimization for continuous domains

The kernelized fuzzy c-means algorithm uses kernel methods to improve the clustering performance of the well known fuzzy c-means algorithm by mapping a given dataset into a higher dimensional space non-linearly. Thus, the newly obtained dataset is more likely to be linearly seprable. However, to further improve the clustering performance, an optimization method is required to overcome the drawbacks of the traditional algorithms such as, sensitivity to initialization, trapping into local minima and lack of prior knowledge for optimum paramaters of the kernel functions. In this paper, to overcome these drawbacks, a new clustering method based on kernelized fuzzy c-means algorithm and a recently proposed ant based optimization algorithm, hybrid ant colony optimization for continuous domains, is proposed. The proposed method is applied to a dataset which is obtained from MIT–BIH arrhythmia database. The dataset consists of six types of ECG beats including, Normal Beat (N), Premature Ventricular Contraction (PVC), Fusion of Ventricular and Normal Beat (F), Artrial Premature Beat (A), Right Bundle Branch Block Beat (R) and Fusion of Paced and Normal Beat (f). Four time domain features are extracted for each beat type and training and test sets are formed. After several experiments it is observed that the proposed method outperforms the traditional fuzzy c-means and kernelized fuzzy c-means algorithms.     

Electrical properties of monoazacrown ether-substituted phthalocyanines

D.c. conductivities of polycrystalline monoazacrown ether-substituted phthalocyanines (M=2H, Ni, Zn, Pb, Cu) and diphthalocyanine (M=Lu) are measured as Au-MPc-Au sandwiches to be of the order 10^–10–10^–12 S m^–1. Chemical doping with oxidants (e.g. NOBF₄) and enhancing the stacking of planar phthalocyanine moieties through the formation of alkali metal adducts with sodium and potassium ions leads to increase in conductivity of the order 10¹–10². The low conductivity and the diamagneticity of the bis(phthalocyaninato)-lutetium can be ascribed to the lack of radical nature in LuH(Pc)₂. For the a.c. conductivities, lead and lutetium complexes form a group with higher conductivities and the rest show lower conductivity. The conduction activation energies calculated from Arrhenius plots exhibit the lowest value (0.40 eV) for the lutetium compound.Part of this work was presented at NATO-ASI on Semiconductor Materials and Processing Technologies, Erice, Sicily, 1–13 July 1991.     

印刷线路板电镀废水综合治理

简述线路板电镀废水中氰、铜氨、油墨废水治理方法和混合废水综合治理工艺流程及原理。     

多传感器数据融合技术的现状及展望

阐述了多传感器数据融合的原理，对数据融合算法的研究动态进行了评述，指出了数据融合技术存在的问题，对今后的发展趋势进行了展望。