Examination of scale-invariant characteristics of epileptic electroencephalograms using wavelet-based analysis

There is evidence that biological and physiological systems including the brain exhibit can exhibit fractal characteristics that can be used to identify the state of the system. In this study, wavelet-based fractal analysis is used to examine self-similar or scale-invariant characteristics of intracranial EEG data in terms of the spectral exponent. The intracranial EEG data were recorded from subjects with epilepsy during non-seizure period and during epileptic seizure activity. From the computational results, it is observed that the self-similar or scale-invariant characteristics of the intracranial EEG data obtained during these two periods are significantly different. The actual value of the estimated spectral exponent depends on the wavelet bases used for the computations.     

片上网络业务量的自相似性分析及模型研究

进行片上网络的架构、映射、流控与服务质量(Quality of Service,QoS)等研究时,迫切需要一个准确的业务量模型用于延时分析与测试验证,以保证设计的性能。而现有的基于马尔科夫模型和回归模型的短程相关模型无法准确地描述业务量的突发性和分形特性,不适用于基于流水的通信信号处理片上系统(System on Chip,SoC)芯片。为了解决这个问题,通过理论与实验相结合的方法,研究了网络拓扑、任务流图、映射对业务量自相似性的影响,根据通信系统的信号处理特点建立了多处理器片上系统(Multi-core Processing System on Chip,MPSoC)数据关联模型,利用典型DSP系统进行建模实验,用实测的业务量Hurst参数拟合数据关联模型参数与Hurst参数的经验函数关系式,建立了用MPSoC数据关联模型预测和估计业务量Hurst参数的方法。实验表明,采用该业务量模型估计的Hurst参数与其真实值误差较小,能较准确地描述业务量的自相似性。     

Self-similarity of parallel machines

Self-similarity is a property of physical systems that describes how to scale parameters such that dissimilar systems appear to be similar. Computer systems are self-similar if certain ratios of computational forces, also known as computational intensities, are equal. Two machines with different computational power, different network bandwidth and different inter-processor latency behave the same way if they have the same ratios of forces. For the parallel conjugate gradient algorithm studied in this paper, two machines are self-similar if and only if the ratio of one force describing latency effects to another force describing bandwidth effects is the same for both machines. For the two machines studied in this paper, this ratio, which we call the mixing coefficient, is invariant as problem size and processor count change. The two machines have the same mixing coefficient and belong to the same equivalence class.     

低通平滑滤波对网络流量自相似性质的影响

从Hurst参数的小波估计方法出发,介绍了平滑滤波后的网络基础(低频)流量和原始流量在自相似性质的上表现规律,探讨了低通平滑滤波对流量自相似性质的影响。     

煤粉碎粒度分布的分形模型

从自相似分形几何原理出发,建立了煤粉碎粒度分布的分形模型,得到了煤粉碎的分形粒度分布公式。结果表明:煤粉碎的粒度分布维数取决于煤粒的粉碎概率和粉碎相似比,影响煤粉碎粒度分布的因素有煤粉碎的粒度分布维数和煤粉碎后煤粒的最大粒径,煤粉碎的粒度分布维数反映了煤粉碎后煤粉颗粒群粒级的粗细程度。通过分形粒度分布与实际粒度分布的比较,验证了本模型的正确性,可用于指导工程实际。     

一种新型的基于网络流量自相似性的DDoS入侵诊测方法

This paper presents a new method of DDoS Intrude Detection Based on Self-Similarity of Network Traffics based on analysis of parameter of self-similar, which includes Hurst parameter, Holder parameter (Time variable function H(t)), we do research on the affect of H parameter change brought by DDoS attack. And we discover the DDoS attack can be detected in some extent by measure the change of H parameter, as it showed by the research result this network traffic based method can detected DDoS attack and is more reliable on the recognition of all kinds of DDoS attack than any other method based on character recognition.     

基于小波域LS方法的图象超分辨率重构算法

为了能够有效地重构出高分辨率图象，提出了一种基于小波域最小二乘法(LS)的图象超分辨率重构算法．该算法是利用多尺度边缘的自相似性，由低分辨率图象通过预测来得到高分辨率图象小波变换的3个高频通道，以实现图象超分辨率重构．由于该算法保持了图象边缘附近的几何正则性，因而能够重构出较高质量的图象．同时，由于小波系数的预测只在边缘处进行，因此该算法具有较小的计算复杂度．实验表明，该算法较好地实现了图象超分辨率重构．     

Univariate imputation method for recovering missing data in wastewater treatment process

High-quality data play a paramount role in monitoring, control, and prediction of wastewater treatment process (WWTP) and can effectively ensure the efficient and stable operation of system. Missing values seriously degrade the accuracy, reliability and completeness of the data quality due to network collapses, connection errors and data transformation failures. In these cases, it is infeasible to recover missing data depending on the correlation with other variables. To tackle this issue, a univariate imputation method (UIM) is proposed for WWTP integrating decomposition method and imputation algorithms. First, the seasonal-trend decomposition based on loess method is utilized to decompose the original time series into the seasonal, trend and remainder components to deal with the nonstationary characteristics of WWTP data. Second, the support vector regression is used to approximate the nonlinearity of the trend and remainder components respectively to provide estimates of its missing values. A self-similarity decomposition is conducted to fill the seasonal component based on its periodic pattern. Third, all the imputed results are merged to obtain the imputation result. Finally, six time series of WWTP are used to evaluate the imputation performance of the proposed UIM by comparing with existing seven methods based on two indicators. The experimental results illustrate that the proposed UIM is effective for WWTP time series under different missing ratios. Therefore, the proposed UIM is a promising method to impute WWTP time series.     

背景流量仿真及其在卫星网络性能分析中的应用

研究表明网络流量具有自相似特性,而自相似网络流量的生成是网络性能分析的一个关键环节,为此设计了一种生成网络背景流量的方法。在VC++环境中对该方法进行了仿真,验证了其有效性。进一步将该方法应用于卫星网络仿真,对卫星网络端到端的延迟进行了初步分析,结果表明网络流量的突发特性影响着卫星网络的性能,这是在进行卫星网络路由优化与流量分配时需要考虑的因素。