Fluctuations and pseudo long range dependence in network flows: A non-stationary Poisson process model

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.

Network resource allocation attack detection with long range dependence

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.

A NEW METHOD TO COMPENSATE CLUTTER RANGE DEPENDENCE FOR FORWARD LOOKING AIRBORNE RADARS

The clutter direction-Doppler curves are not aligned on the near range bins for forward looking airborne radar. As a result, the performance of clutter suppression by Space-Time Adaptive Processing (STAP) degrades greatly because of the clutter range dependence. To deal with this problem, a new compensated method is proposed in this paper. The method rebuilds the clutter covariance matrix based on spatial high resolution Minimum Variance Distortionless Response (MVDR) spectrum, and then finds a matrix to transform the covariance matrix of short-range gate to the referred far-range gate. The method can compensate the clutter range dependence well. The simulation results show validity of the method.

On use of the alpha stable self-similar stochastic process to model aggregated VBR video traffic

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.

Study of Self-Similarity in Variable Bit Rate Video Sources

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.

Identification of Stochastic Nature of Yarn Hairiness

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.

Modeling the MPEG-1 Video Traffic

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.