Long-term memory dynamics of continental and oceanic monthly temperatures in the recent 125 years

For both Northern and Southern hemispheres, the long-term memory dynamics for continent and ocean temperature records in the recent 125 years is studied in this paper. It is found that the records exhibit long-range memory and multifractality characteristics where large temperature anomalies display a more random behavior than the overall time series. A 256-month moving window was used to compute the time evolution of the fractal scaling exponent, giving the following results: (i) Ocean temperatures are more persistent than land temperatures, a result already reported in recent publications, (ii) All records show multifractality features, reflecting the nonlinear behavior of the temperature dynamics. Continent temperatures present sharper multifractal spectra than ocean temperatures, (iii) The persistency, as revealed by the scaling exponent, for ocean temperatures displays a cyclic behavior around a nearly constant average value of 22 years, (iv) The persistency for the Northern Hemisphere land temperature is also cyclical but with an increasing trend, and (v) The time at which the Northern Hemisphere continent temperature persistency will converge into the Northern Hemisphere ocean behavior was estimated with linear and exponential extrapolation functions, showing hitting dates around 2050±20 A.D. Potential implications of these results concerning the nature of climate change are also discussed.     

基于有限状态自动机的复合事件监测模型

通过对复合事件的自动机检测模型的研究，给出了构造事件表达式的自动机模型的完整过程。其中的关键步骤：从复合事件到NFA，从NFA到DFA都进行了详细的说明。在NFA向DFA转换过程中给出了子集构造算法、状态最小化算法。     

Breaks or long memory behavior: An empirical investigation

Are structural break models true switching models or long memory processes? The answer to this question remains ambiguous. In recent years, many papers have dealt with this problem. Some studies have shown that, under specific conditions, switching models and long memory processes can easily be confused. In this paper, using several generating models (the mean-plus-noise model, the stochastic permanent break model, the Markov switching model, the threshold autoregressive (TAR) model, the sign model, and the structural change model) and several estimation techniques (the Geweke–Porter–Hudak (GPH) technique, detrended fluctuation analysis (DFA), the exact local Whittle (ELW) method, and wavelet methods) we show that, even if the answer is quite simple in some cases, it can be mitigated in other cases. Using French and American inflation rates, we found that the most appropriate process that takes into account the important features of these series is a model that simultaneously combines changes in regimes and long memory behavior. The main result of this study indicates that estimating a long memory parameter without taking into account the presence of breaks in the data sets may lead to misspecification and hence to overestimating the true parameter.     

基于DFA的未知S盒逆向分析方法研究

To evaluate the security of cipher algorithms with secret operations, we built a new reverse engineering analysis based on Differential Fault Analysis (DFA) to recover the secret S-boxes in Secret Private Network (SPN) and Feistel structures, which are two of the most typical structures in block ciphers. This paper gives the general definitions of these two structures and proposes the reverse engineering analysis of each structure. Furthermore, we evaluate the complexity of the proposed reverse analyses and theoretically prove the effectiveness of the reverse method. For the Twofish-like and AES-like algorithms, the experimental results verify the correctness and efficiency of the reverse analysis. The proposed reverse analysis can efficiently recover the secret S-boxes in the encryption algorithms with SPN and Feistel structures. It can successfully recover the Twofish-like algorithm in 2.3 s with 256 faults and the AES-like algorithm in 0.33 s with 23 faults.     

DCCA cross-correlation coefficient: Quantifying level of cross-correlation

In this paper, a new coefficient is proposed with the objective of quantifying the level of cross-correlation between nonstationary time series. This cross-correlation coefficient is defined in terms of the DFA method and the DCCA method. The implementation of this cross-correlation coefficient will be illustrated with selected time series.     

一种基于确定型有穷自动机的动物识别系统的设计方法

提出了一种新的动物识别系统的设计方法.通过对有穷自动机理论与动物识别系统进行分析,建立了一个基于动物特征的确定型有穷自动机的模型.该模型描述了当输入一个动物特征值后,自动机的状态将发生转移,当输入的动物特征值充足时,即可识别出动物的种类.最后,根据这种模型设计出了动物识别系统,并上机进行了实现.     

基于层次管理结构的移动IP切换

在介绍了移动IP及其切换原理的基础上．针对移动环境下移动节点的频繁切换造成的切换延迟大的情况,提出了基于层次管理结构的移动IP切换方案,能够有效地实现快速切换。     

Scaling analysis of the effects of load on hand tremor movements in essential tremor

In this paper we have used the Wavelet Transform (WT) and the Detrended Fluctuation Analysis (DFA) methods to analyze hand tremor movements in essential tremor (ET), in two different recording conditions (before and after the addition of wrist-cuff load). We have analyzed the time series comprised of peak-to-peak (PtP) intervals, extracted from regions around the first three main frequency components of the power spectra (PwS) of the recorded tremors, in order to substantiate results related to the effects of load on ET, to distinguish between multiple sources of ET, and to separate the influence of peripheral factors on ET.Our results show that, in ET, the dynamical characteristics, that is, values of respective scaling exponents, of the main frequency component of recorded tremors change after the addition of load. Our results also show that in all the observed cases the scaling behavior of the calculated functions changes as well—the calculated WT scalegrams and DFA functions display a shift in the position of the crossover when the load is added. We conclude that the difference in behavior of the WT and DFA functions between different conditions in ET could be associated with the expected pathology in ET, or with some additional mechanism that controls movements in ET patients, and causes the observed changes in scaling behavior.     

基于正则表达式的通用应用层协议解析技术

面对当前网络的复杂性和异构性,设计了一种基于正则表达式的通用应用层协议解析方案。为满足应用层协议解析的实时性,采用基于DFA的正则表达式匹配引擎,并利用DFA分组算法,避免了DFA状态数的指数级增长。实验表明此方案具有较高的实时性和正确率。