In the study of complex networks （systems）, the scaling phenomenon of flow fluctuations refers to a certain powerlaw between the mean flux （activity） （Fi） of the i-th node and its variance σi as σi α （Fi）α...In the study of complex networks （systems）, the scaling phenomenon of flow fluctuations refers to a certain powerlaw between the mean flux （activity） （Fi） of the i-th node and its variance σi as σi α （Fi）α Such scaling laws are found to be prevalent both in natural and man-made network systems, but the understanding of their origins still remains limited. This paper proposes a non-stationary Poisson process model to give an analytical explanation of the non-universal scaling phenomenon： the exponent α varies between 1/2 and 1 depending on the size of sampling time window and the relative strength of the external/internal driven forces of the systems. The crossover behaviour and the relation of fluctuation scaling with pseudo long range dependence are also accounted for by the model. Numerical experiments show that the proposed model can recover the multi-scaiing phenomenon.展开更多
The approach of traffic abnormality detection of network resource allocation attack did not have reliable signatures to depict abnormality and identify them. However, it is crucial for us to detect attacks accurately....The approach of traffic abnormality detection of network resource allocation attack did not have reliable signatures to depict abnormality and identify them. However, it is crucial for us to detect attacks accurately. The technique that we adopted is inspired by long range dependence ideas. We use the number of packet arrivals of a flow in fixed-length time intervals as the signal and attempt to extend traffic invariant “self-similarity”. We validate the effectiveness of the approach with simulation and trace analysis.展开更多
The alpha stable self-similar stochastic process has been proved an effective model for high variable data traffic. A deep insight into some special issues and considerations on use of the process to model aggregated ...The alpha stable self-similar stochastic process has been proved an effective model for high variable data traffic. A deep insight into some special issues and considerations on use of the process to model aggregated VBR video traffic is made. Different methods to estimate stability parameter a and self-similar parameter H are compared. Processes to generate the linear fractional stable noise （LFSN） and the alpha stable random variables are provided. Model construction and the quantitative comparisons with fractional Brown motion （FBM） and real traffic are also examined. Open problems and future directions are also given with thoughtful discussions.展开更多
Variable bit rate(VBR)video will be the predominant application in the future's multimedia communication networks.Understanding the characteristics of VBR video is important for us to utilize network resources ef...Variable bit rate(VBR)video will be the predominant application in the future's multimedia communication networks.Understanding the characteristics of VBR video is important for us to utilize network resources effectively.A brief description of MPEG 1 video sources is given.The essentials of self similarity and long range dependence are also presented.Furthermore,some methods of estimating Hurst parameter are described in details.We analyze an hour long MPEG 1 video trace,and the details of statistical results are discussed.The results highlight that the trace generated by MPEG 1 code has both the short range dependence and long range dependence structures.展开更多
Yam hairiness is a complex concept, which generally cannot be completely defined by a single figure. Hairiness can be considered as the fiber ends and loops standing out from the main compact yarn body. Uster hairines...Yam hairiness is a complex concept, which generally cannot be completely defined by a single figure. Hairiness can be considered as the fiber ends and loops standing out from the main compact yarn body. Uster hairiness system characterizes the hairiness by H value, i.e. the mean value of the total length of all hairs within one centimeter of yarn. The raw data HI are in fact realization of spatial process （hairiness spatial process -- HSP） and can be used for more complex evaluation of hairiness characteristics in the space and frequency domain. The main aim of this contribution is description of some tools for spatial characterization of yarn hairiness. The simple methods for complex characterization of lISP statistical behavior （stationarity, independence, linearity etc. ） are presented. The techniques based on the embedding dimension and correlation integral or long-range dependences evaluation are discussed. The selected methods are core of HYARN program in MATLAB. Application of this program for deeper characterization of artificial data and cotton type yam are shown.展开更多
Now, the problem of modeling MPEG 1 video traffic still needs studying further. Based on the analysis of statistical characteristics of this kind of traffic, this paper presents a new traffic model. Simulation result...Now, the problem of modeling MPEG 1 video traffic still needs studying further. Based on the analysis of statistical characteristics of this kind of traffic, this paper presents a new traffic model. Simulation results show that the proposed model can reflect the statistical characteristics of the real MPEG 1 video traffic well.展开更多
基金Project supported in part by National Basic Research Program of China (973 Project) (Grant No 2006CB705506)Hi-Tech Research and Development Program of China (863 Project) (Grant No 2007AA11Z222)National Natural Science Foundation of China (Grant Nos 60721003 and 60774034)
文摘In the study of complex networks （systems）, the scaling phenomenon of flow fluctuations refers to a certain powerlaw between the mean flux （activity） （Fi） of the i-th node and its variance σi as σi α （Fi）α Such scaling laws are found to be prevalent both in natural and man-made network systems, but the understanding of their origins still remains limited. This paper proposes a non-stationary Poisson process model to give an analytical explanation of the non-universal scaling phenomenon： the exponent α varies between 1/2 and 1 depending on the size of sampling time window and the relative strength of the external/internal driven forces of the systems. The crossover behaviour and the relation of fluctuation scaling with pseudo long range dependence are also accounted for by the model. Numerical experiments show that the proposed model can recover the multi-scaiing phenomenon.
文摘The approach of traffic abnormality detection of network resource allocation attack did not have reliable signatures to depict abnormality and identify them. However, it is crucial for us to detect attacks accurately. The technique that we adopted is inspired by long range dependence ideas. We use the number of packet arrivals of a flow in fixed-length time intervals as the signal and attempt to extend traffic invariant “self-similarity”. We validate the effectiveness of the approach with simulation and trace analysis.
文摘The alpha stable self-similar stochastic process has been proved an effective model for high variable data traffic. A deep insight into some special issues and considerations on use of the process to model aggregated VBR video traffic is made. Different methods to estimate stability parameter a and self-similar parameter H are compared. Processes to generate the linear fractional stable noise （LFSN） and the alpha stable random variables are provided. Model construction and the quantitative comparisons with fractional Brown motion （FBM） and real traffic are also examined. Open problems and future directions are also given with thoughtful discussions.
文摘Variable bit rate(VBR)video will be the predominant application in the future's multimedia communication networks.Understanding the characteristics of VBR video is important for us to utilize network resources effectively.A brief description of MPEG 1 video sources is given.The essentials of self similarity and long range dependence are also presented.Furthermore,some methods of estimating Hurst parameter are described in details.We analyze an hour long MPEG 1 video trace,and the details of statistical results are discussed.The results highlight that the trace generated by MPEG 1 code has both the short range dependence and long range dependence structures.
基金Supported by the research project"Textile center"of Czech Ministry of Education1M4674788501
文摘Yam hairiness is a complex concept, which generally cannot be completely defined by a single figure. Hairiness can be considered as the fiber ends and loops standing out from the main compact yarn body. Uster hairiness system characterizes the hairiness by H value, i.e. the mean value of the total length of all hairs within one centimeter of yarn. The raw data HI are in fact realization of spatial process （hairiness spatial process -- HSP） and can be used for more complex evaluation of hairiness characteristics in the space and frequency domain. The main aim of this contribution is description of some tools for spatial characterization of yarn hairiness. The simple methods for complex characterization of lISP statistical behavior （stationarity, independence, linearity etc. ） are presented. The techniques based on the embedding dimension and correlation integral or long-range dependences evaluation are discussed. The selected methods are core of HYARN program in MATLAB. Application of this program for deeper characterization of artificial data and cotton type yam are shown.
文摘Now, the problem of modeling MPEG 1 video traffic still needs studying further. Based on the analysis of statistical characteristics of this kind of traffic, this paper presents a new traffic model. Simulation results show that the proposed model can reflect the statistical characteristics of the real MPEG 1 video traffic well.
