高脉冲重复频率雷达混合滤波解距离模糊方法

针对高脉冲重复雷达距离模糊问题,提出一种新的混合滤波解距离模糊方法.通过把脉冲间隔数和脉冲间隔变化量作为目标待估计状态,对离散的脉冲间隔数、间隔变化量和连续的目标状态(位置和速度)进行混合滤波,从而将解距离模糊问题转换为对脉冲间隔数的估计问题.初始时刻,由于距离量测模糊,利用多个高脉冲重复频率(HPRF)采用欧氏距离对混合状态进行初始化.在滤波过程中,为了避免门限选择,首先根据脉冲间隔变化量的有限个离散取值,将六维向量的混合滤波模型等效为有限个五维向量的混合滤波模型;然后对量测分别进行混合滤波;最后通过对各模型得到的新息的2范数进行比较获得混合状态估计,从而实现解距离模糊.混合滤波解距离模糊方法为实现高脉冲重复频率雷达实时解距离模糊提供了一种新的研究思路,仿真结果表明,本文提出的混合滤波方法在只有一个脉冲重复频率获得目标量测时较现有多假设方法收敛速度快,并且可以克服多假设方法在脉冲间隔数变化时发生的解模糊错误.     

Coordination of discrete‐time multi‐agent systems via relative output feedback

This paper investigates the joint effects of agent dynamic and network topology on the consensusability of linear discrete‐time multi‐agent systems via relative output feedback. An observer‐based distributed control protocol is proposed. A necessary and sufficient condition for consensusability under this control protocol is given, which explicitly reveals how the intrinsic entropy rate of the agent dynamic and the eigenratio of the undirected communication graph affect consensusability. As a special case, multi‐agent systems with discrete‐time double integrator dynamics are discussed where a simple control protocol directly using two‐step relative position feedback is provided to reach a consensus. Finally, the result is extended to solve the formation and formation‐based tracking problems. The theoretical results are illustrated by simulations. Copyright © 2010 John Wiley & Sons, Ltd.     

Energy-aware weighted graph based dynamic topology control algorithm

In this paper a new energy-aware weighted dynamic topology control (WDTC) algorithm is proposed to extend the lifetime of wireless network and balance the nodes’ energy consumption. The idea is that each node builds its local minimum spanning tree (MST) based on the energy-aware weighted graph and the network topology is adjusted accordingly. It was proved theoretically that the topology under WDTC algorithm could preserve the network connectivity and a sufficient condition for the degree of no more than 6 was also given. Simulation shows that WDTC algorithm can effectively prolong the network lifetime and has good topological features.     

PKI网络信任模型的分析与研究

信任模型是整个网络安全体系的基础,决定了在网络上采用信任的形式与采用该形式带来的信任风险,并提供了建立和管理信任关系的框架。可见,对于信任模型的研究在网络安全中非常重要。文章着重分析了基于PKI的各类信任模型的信任机制、认证过程及其存在优缺点,指出了各类信任模型对网络信息安全产生影响的主要原因。通过总结与对比,指出了PKI信任模型的设计策略与发展趋势。     

STBC系统在非同分布Nakagami信道下的一种快速性能评估算法

针对非同分布的Nakagami信道,基于矩生成函数MGF(Moment Generation Function)的分析方法,提出正交空时分组码系统STBC(Space-Time Block Coding)的一种快速性能评估算法,不需要涉及超几何函数积分运算,可在中高信噪比时,快速准确地估计STBC系统的符号错误概率性能。在平坦瑞利衰落信道下的计算机仿真表明,该算法与已有的STBC系统的近似估计算法相比,具有较优的性能。     

Maximum entropy membership functions for discrete fuzzy variables

Due to the deficiency of information, the membership function of a fuzzy variable cannot be obtained explicitly. It is a challenging work to find an appropriate membership function when certain partial information about a fuzzy variable is given, such as expected value or moments. This paper solves such problems for discrete fuzzy variables via maximum entropy principle and proves some maximum entropy theorems with certain constraints. A genetic algorithm is designed to solve the general maximum entropy model for discrete fuzzy variables, which is illustrated by some numerical experiments.     

JPEG隐密分析中的重压缩检测

重压缩检测是多级隐密分析中关键的预处理部分,高准确率的重压缩检测是隐密分析获得更高性能的重要前提条件.深入研究了重压缩对于JPEG图像各种特征的影响,基于此提出了一种融合直方图分布特征、Benford特征、DFT特征的重压缩检测算法.仿真实验表明,该算法具有更高的检测率,能够适用于JPEG多级隐密分析中的重压缩检测.     

Comparison of individual,ensemble and integrated ensemble machine learning methods to predict China’s SME credit risk in supply chain finance

Supply chain finance (SCF) becomes more important for small- and medium-sized enterprises (SMEs) due to global credit crunch, supply chain financing woes and tightening credit criteria for corporate lending. Currently, predicting SME credit risk is significant for guaranteeing SCF in smooth operation. In this paper, we apply six methods, i.e., one individual machine learning (IML, i.e., decision tree) method, three ensemble machine learning methods [EML, i.e., bagging, boosting, and random subspace (RS)], and two integrated ensemble machine learning methods (IEML, i.e., RS–boosting and multi-boosting), to predict SMEs credit risk in SCF and compare the effectiveness and feasibility of six methods. In the experiment, we choose the quarterly financial and non-financial data of 48 listed SMEs from Small and Medium Enterprise Board of Shenzhen Stock Exchange, six listed core enterprises (CEs) from Shanghai Stock Exchange and three listed CEs from Shenzhen Stock Exchange during the period of 2012–2013 as the empirical samples. Experimental results reveal that the IEML methods acquire better performance than IML and EML method. In particular, RS–boosting is the best method to predict SMEs credit risk among six methods.

     

A divide-and-conquer hole-filling method for handling disocclusion in single-view rendering

Large holes are unavoidably generated in depth image based rendering (DIBR) using a single color image and its associated depth map. Such holes are mainly caused by disocclusion, which occurs around the sharp depth discontinuities in the depth map. We propose a divide-and-conquer hole-filling method which refines the background depth pixels around the sharp depth discontinuities to address the disocclusion problem. Firstly, the disocclusion region is detected according to the degree of depth discontinuity, and the target area is marked as a binary mask. Then, the depth pixels located in the target area are modified by a linear interpolation process, whose pixel values decrease from the foreground depth value to the background depth value. Finally, in order to remove the isolated depth pixels, median filtering is adopted to refine the depth map. In these ways, disocclusion regions in the synthesized view are divided into several small holes after DIBR, and are easily filled by image inpainting. Experimental results demonstrate that the proposed method can effectively improve the quality of the synthesized view subjectively and objectively.