A new image fusion performance metric based on visual information fidelity

Because subjective evaluation is not adequate for assessing work in an automatic system, using an objective image fusion performance metric is a common approach to evaluate the quality of different fusion schemes. In this paper, a multi-resolution image fusion metric using visual information fidelity (VIF) is presented to assess fusion performance objectively. This method has four stages: (1) Source and fused images are filtered and divided into blocks. (2) Visual information is evaluated with and without distortion information in each block. (3) The visual information fidelity for fusion (VIFF) of each sub-band is calculated. (4) The overall quality measure is determined by weighting the VIFF of each sub-band. In our experiment, the proposed fusion assessment method is compared with several existing fusion metrics using the subjective test dataset provided by Petrovic. We found that VIFF performs better in terms of both human perception matching and computational complexity.     

Efficient bidirectional quantum secure communication with two-photon entanglement

An efficient bidirectional quantum secure communication protocol is proposed with two-photon entanglement. Compared with the previous protocol proposed by Shi et al., our protocol can achieve higher efficiency. Meanwhile, for the same length secret messages, only half of entangled photon pairs need to be prepared in our protocol. And the number of classical bits in public classical communication is also a half of that in the previous protocol. Moreover, the information leakage does not exist in our scheme.     

Small sample improvements in the threshold cointegration test using residual-based moving block bootstrap

A residual-based moving block bootstrap procedure for testing the null hypothesis of linear cointegration versus cointegration with threshold effects is proposed. When the regressors and errors of the models are serially and contemporaneously correlated, our test compares favourably with the Sup LM test proposed by Gonzalo and Pitarakis. Indeed, shortcomings of the former motivated the development of our test. The small sample performance of the bootstrap test is investigated by Monte Carlo simulations, and the results show that the test performs better than the Sup LM test.     

使用HTTP Tunneling技术实现穿透网络防火墙的通信

近年来,防火墙在网络中得到了广泛的应用.它能够比较有效地防范病毒与黑客的攻击,保护网络的安全.但是也带来了一些不利影响,使一些需要进行网络通信的应用软件系统不能正常工作.介绍一个解决方案,并且重点介绍通过HTTP Tunneling技术使用基于文本的HTTP协议传送二进制数据的方法.     

Web数据挖掘在个性化推荐服务的应用

随着Internet的迅速发展和普及,人们在网络上开展的活动也越来越多,尤其是电子商务的兴起,使得网上购物逐渐成为一种时尚.然而由于互联网上的信息量日益增长,人们面对这些庞大的信息感到无所适从,花费大量时间在搜寻信息上,因此如何为用户提供更好的服务变得非常迫切.本文以数据挖掘理论为基础,分析和探讨了关联规则挖掘算法并应用该方法实现web个性化推荐服务.     

浅议各类数据的备份与归档管理

本文从各类数据在计算机中存在的状态以及在存储过程中的安全问题,对数据的安全性和保护性进行了论证。     

A piecewise analysis method to stability analysis of linear continuous/discrete systems with time‐varying delay

The delay‐dependent stability problem of linear continuous/discrete systems with time‐varying delay is investigated based on a piecewise analysis method (PAM). In the method, the variation interval of the time delay is firstly divided into several subintervals. By checking the variation of the Lyapunov functional in every subinterval, some new delay‐dependent stability criteria are derived. Several numerical examples show that our method can lead to much less conservative results than those in the existing references. Moreover, when the number of the divided subintervals increases, the corresponding criteria can provide an improvement on the results. Copyright © 2008 John Wiley & Sons, Ltd.     

Shift-invariant interpattern association neural network

A shift-invariant neural network that uses the translation-invariant property of the modulus Fourier spectra with the heteroassociation interpattern association memory is proposed. A binary encoding of a spectral sampling of the training set is used to preserve the main features. Computer simulations and experimental demonstrations are provided that show the shift-invariant property of the proposed optical neural network.