A NEW ADMISSION CONTROL APPROACH BASED ON PREDICTION

Admission control plays an important role in providing QoS to network users.Mo-tivated by the measurement-based admission control algorithm,this letter proposed a new ad-mission control approach for integrated service packet network based on traffic prediction .In the letter ,FARIMA(p,d,q,)models in the admission control algorithm is deployed.A method to simplify the FARIMA model fitting procedure and hence to reduce the time of traffic modeling and prediction is suggested.The feasibility-study experiments show that FARIMA models which have less number of parameters can be used to model and predict actual traffic on quite a large time scale.Simulation results validate the promising approach.     

Using labeled data to evaluate change detectors in a multivariate streaming environment

We consider the problem of detecting changes in a multivariate data stream. A change detector is defined by a detection algorithm and an alarm threshold. A detection algorithm maps the stream of input vectors into a univariate detection stream. The detector signals a change when the detection stream exceeds the chosen alarm threshold. We consider two aspects of the problem: (1) setting the alarm threshold and (2) measuring/comparing the performance of detection algorithms. We assume we are given a segment of the stream where changes of interest are marked. We present evidence that, without such marked training data, it might not be possible to accurately estimate the false alarm rate for a given alarm threshold. Commonly used approaches assume the data stream consists of independent observations, an implausible assumption given the time series nature of the data. Lack of independence can lead to estimates that are badly biased. Marked training data can also be used for realistic comparison of detection algorithms. We define a version of the receiver operating characteristic curve adapted to the change detection problem and propose a block bootstrap for comparing such curves. We illustrate the proposed methodology using multivariate data derived from an image stream.     

Joint Data Detection and Channel Tracking for OFDM Systems With Phase Noise

This paper addresses the problem of data detection in orthogonal frequency division multiplexing (OFDM) systems operating under a time-varying multipath fading channel. Optimal detection in such a scenario is infeasible, which makes the introduction of approximations necessary. The typical joint data-channel estimators are decision directed, that is, assume perfect past data decisions. However, their performance is subject to error propagation phenomena. The variational Bayes method is employed here, which approximates the joint data and channel distribution as a separable one, greatly simplifying the problem. The data detection part of the resulting algorithm provides soft data estimates that are used for channel tracking. The channel itself is modeled as an autoregressive process allowing for a Kalman-like tracking algorithm. According to the developed algorithm, both data and channel estimates are exchanged and updated in an iterative manner. The performance of the proposed algorithm is evaluated by simulations. Furthermore, since OFDM is extremely sensitive to the presence of phase noise, the algorithm is extended to operate under severe phase noise conditions, with moderate performance degradation.      

改进的基于小波变换和FARIMA模型的网络流量预测算法

提出了一种改进的基于小波变换和FARIMA模型的网络流量预测算法,先对经过预处理的流量进行小波分解,再进行Mallat算法单支重构,接着用FARIMA模型分别对重构后的单支进行预测,最后合成流量。该算法较之传统的首先用FARIMA模型对小波系数进行预测再进行小波重构的算法减小了预测误差。仿真实验也验证了改进算法的预测准确性。     

Optimal reception of digital data over the Gaussian channel with unknown delay and phase jitter

Asymptotically optimum (in the sense of minimum per-symbol error rate) receiver structures for data communication over the white Gaussian channel with unknown time delay and carrier phase jitter are developed. The receiver structures apply to the following suppressed-carrier modulation systems: double sideband (DSB), quadrature amplitude modulation (QAM) with an arbitrary constellation, vestigial sideband (VSB) and single sideband. The resulting minimum error probability receivers are asymptotically equivalent to maximum-likelihood digital {em sequence}-estimating receivers. The optimum structures implicitly derive joint maximum-likelihood estimates of the unknown parameters and of the sequence of data symbols. It is shown that the parameter estimates can be obtained from two data-directed stochastic approximation algorithms. Unlike traditional theoretical treatments of this communication situation, which have separated the highly important carrier phase and timing recovery problem from the detection problem, a unified theory is presented from which the complete ideal receiver structure can be deduced.     

A two-dimensional Prony's method for spectral estimation

The problem of estimating the parameters of a model for bidimensional data made up by a linear combination of damped two-dimensional sinusoids is considered. Frequencies, amplitudes, phases, and damping factors are estimated by applying a generalization of the monodimensional Prony's method to spatial data. This procedure finds the desired quantities after an autoregressive model fitting to the data, a polynomial rooting, and a least-squares problem solution. The autoregressive models involved have a particular nature that simplifies the analysis. In fact, their characteristic polynomial factors into two parts so that many of their properties can be easily determined. Quick estimates of the parameters computed are found by using standard one-dimensional autoregressive estimation methods. An iterative procedure for refining the autoregressive parameters estimates which gives rise to better frequency estimates is also discussed. Some simulation results are reported     

Fast-convergence trilinear decomposition algorithm for angle and range estimation in FDA-MIMO radar

A frequency diverse array (FDA) multiple-input multiple-output (MIMO) radar employs a small frequency increment across transmit elements to produce an angle-range-dependent beampattern for target angle and range detection. The joint angle and range estimation problem is a trilinear model. The traditional trilinear alternating least square (TALS) algorithm involves high computational load due to excessive iterations. We propose a fast-convergence trilinear decomposition (FC-TD) algorithm to jointly estimate FDA-MIMO radar target angle and range. We first use a propagator method to obtain coarse angle and range estimates in the data domain. Next, the coarse estimates are used as initialized parameters instead of the traditional TALS algorithm random initialization to reduce iterations and accelerate convergence. Finally, fine angle and range estimates are derived and automatically paired. Compared to the traditional TALS algorithm, the proposed FC-TD algorithm has lower computational complexity with no estimation performance degradation. Moreover, Cramér-Rao bounds are presented and simulation results are provided to validate the proposed FC-TD algorithm effectiveness.     

Novel multireceiver communication systems configurations based onoptimal estimation theory

A multireceiver configuration for the purpose of carrier arraying and/or signal arraying is presented. The configuration is arrived at by formulating the carrier and/or signal arraying problem as an optimal estimation problem and consists of two stages. The first stage optimally estimates various phase processes received at different receivers with coupled phase-locked loops, (PLL) wherein the individual PLLs acquire and track their respective receivers' phase processes but are aided by each other in an optimal manner via low-frequency error signals. The coupled PLL estimator is followed by a linear or nonlinear combining of the quadrature-phase components of the mixers from various receivers for the purpose of data detection. The proposed configuration results in the minimization of the effective radio loss as the combiner output and thus maximization of energy per bit to noise power spectral density ratio is achieved. An adaptive algorithm for the estimator of the signal model parameters when these are not known a priori is also presented     

改进的基于时变FARIMA模型和小波变换的网络流量预测算法

网络的流量特性是反映网络实时状态的一个重要特征,对于网络流量的分析、预测一直是该领域的研究热点。传统的基于时间序列模型的方法在计算效率和多尺度分析能力方面存在一定的局限性。本文提出了一种改进的基于小波变换和时变FARIMA模型的流量预测方法,利用小波变换的多尺度分析特性将原有的流量数据进行分解,在使用时变FARIMA模型进行预测,可大大提高算法的执行效率和预测的准确性。最后,本文选取了Bellcore提供的真实的网络流量进行了仿真实验,验证了提出的预测方法的准确性和有效性。     

基于FARIMA-GARCH模型的网络业务预测算法

网络流量的波动性与自相似特性为其精确预测提出了挑战。为此,提出了一种基于FARIMA-GARCH模型的预测算法。该算法首先利用分段双向CUSUM检测算法对流量序列的均值进行有效检测,并在此基础上将序列零均值化;然后采用限定搜索法对分数差分阶数进行精确估计;在获得模型参数后,使用FARIMA-GARCH模型对网络流量进行预测。仿真实验表明,限定搜索法能够获得比传统算法更高的估计精度。随后采用真实网络流量验证了预测算法的性能,在保持与FARIMA预测算法等价的时间复杂度下,其均方根和相对均方根误差与RBF神经网络预测算法相当,而高于FARIMA预测算法。同时,预测算法对突发流量的跟踪和预测性能明显优于对比算法,且有更好的区间估计性能。     

Joint Detection-Estimation of Brain Activity in Functional MRI: A Multichannel Deconvolution Solution

Analysis of functional magnetic resonance imaging (fMRI) data focuses essentially on two questions: first, a detection problem that studies which parts of the brain are activated by a given stimulus and, second, an estimation problem that investigates the temporal dynamic of the brain response during activations. Up to now, these questions have been addressed independently. However, the activated areas need to be known prior to the analysis of the temporal dynamic of the response. Similarly, a typical shape of the response has to be assumed a priori for detection purpose. This situation motivates the need for new methods in neuroimaging data analysis that are able to go beyond this unsatisfactory tradeoff. The present paper raises a novel detection-estimation approach to perform these two tasks simultaneously in region-based analysis. In the Bayesian framework, the detection of brain activity is achieved using a mixture of two Gaussian distributions as a prior model on the “neural” response levels, whereas the hemodynamic impulse response is constrained to be smooth enough in the time domain with a Gaussian prior. All parameters of interest, as well as hyperparameters, are estimated from the posterior distribution using Gibbs sampling and posterior mean estimates. Results obtained both on simulated and real fMRI data demonstrate first that our approach can segregate activated and nonactivated voxels in a given region of interest (ROI) and, second, that it can provide spatial activation maps without any assumption on the exact shape of the Hemodynamic Response Function (HRF), in contrast to standard model-based analysis.     

Analytical approach to changepoint detection in Laplacian noise

The paper presents an analytical method using the Bayesian inference framework for the identification of time-series discontinuities, i.e. changepoints, in impulsive Laplacian noise. Exact expressions for the posterior density of the changepoint positions and the associated Bayesian model evidence are given for DC step changes. The performance of the analytical approach is compared to that predicted by a Gaussian assumption to the noise statistics and Markov chain Monte Carlo methods     

Optimal simultaneous detection and estimation of filtered discretesemi-Markov chains

An optimal algorithm for the detection of noisy filtered discrete semi-Markov chains is presented. Estimation of the underlying model parameters is also considered. For a given path of the discrete semi-Markov chain the optimum estimates of the model parameters obtained by the maximum likelihood method are expressed as functions of the path. These functions are then used to derive a single maximum a posteriori criterion for the optimal detection of the unknown single path. The final optimization is carried out numerically by a combination of gradient, divide-and-conquer, and search techniques. This set of techniques is referred to as the integer most likely search detector. Experimental results, using synthetic data, demonstrate the potential of the algorithm     

互联网流量与两个相关模型

由于互联网具有多构性、突发连续性和自相似性等特征,使得用传统模型进行的排队分析、性能估计与实际网络有较大差距。本文介绍了两种互联网流量模型:AR IMA模型,FAR IMA模型,讨论了它们的适用范围、模型数学定义,描述了参数定阶推导等相关问题。     

Parameter estimation for autoregressive Gaussian-mixture processes:the EMAX algorithm

The problem of estimating parameters of discrete-time non-Gaussian autoregressive (AR) processes is addressed. The subclass of such processes considered is restricted to those whose driving noise samples are statistically independent and identically distributed according to a Gaussian-mixture probability density function (pdf). Because the likelihood function for this problem is typically unbounded in the vicinity of undesirable, degenerate parameter estimates, the maximum likelihood approach is not fruitful. Hence, an alternative approach is taken whereby a finite local maximum of the likelihood surface is sought. This approach, which is termed the quasimaximum likelihood (QML) approach, is used to obtain estimates of the AR parameters as well as the means, variances, and weighting coefficients that define the Gaussian-mixture pdf. A technique for generating solutions to the QML problem is derived using a generalized version of the expectation-maximization principle. This technique, which is referred to as the EMAX algorithm, is applied in four illustrative examples; its performance is compared directly with that of previously proposed algorithms based on the same data model and that of conventional least-squares techniques     

FARIMA with stable innovations model of Great Salt Lake elevation time series

Great Salt Lake (GSL) is the largest salt lake in the western hemisphere, the fourth-largest terminal lake in the world. The elevation of GSL has critical effect on the people who live nearby and their properties. It is crucial to build an exact model of GSL elevation time series in order to predict the GSL elevation precisely. Although some models, such as ARIMA or FARIMA (fractional auto-regressive integrated moving average), GARCH (generalized auto-regressive conditional heteroskedasticity) and FIGARCH (fractional integral generalized auto-regressive conditional heteroskedasticity) have been proposed to characterize the variation of GSL elevation, which have been unsatisfactory. Therefore, it became a key point to build a more appropriate model of GSL elevation time series. In this paper a new model based on FARIMA with stable innovations is applied to analyze the data and predict the future elevation levels. From the analysis we can see that the new model can characterize GSL elevation time series more accurately. The new model will be beneficial to predict GSL elevation more precisely.     

网络流量预测中的时间序列模型比较研究

网络流量预测在新一代网络协议设计、网络管理与诊断、设计高性能路由器等方面都具有重要意义.目前通常采用ARMA和FARIMA时序模型对网络流量序列进行拟合与预测,但没有对时间尺度的大小与模型选择的关系进行研究.本文对实际网络流量在不同时间尺度(毫秒、秒、分)下进行了流量预测建模并对预测性能进行比较,分析表明使用时序模型进行流量预测时,大时间尺度(分)流量预测较小时间尺度(毫秒、秒)具有更小的预测误差.并且,对于小时间尺度上的自相似流量序列,自相似模型FARIMA并没有较其他时序模型有更好的预测性能.     

Seizure detection of newborn EEG using a model-based approach

Seizures are often the first sign of neurological disease or dysfunction in the newborn. However, their clinical manifestation is often subtle, which tends to hinder their diagnosis at the earliest possible time. This represents an undesirable situation since the failure to quickly and accurately diagnose seizure can lead to longer-term brain injury or even death. Here, the authors consider the problem of automatic seizure detection in the neonate based on electroencephalogram (EEG) data. They propose a new approach based on a model for the generation of the EEG, which is derived from the histology and biophysics of a localized portion of the brain. They show that by using this approach, good detection performance of electrographic seizure is possible. The model for seizure is first presented along with an estimator for the model parameters. Then the authors present a seizure-detection scheme based on the model parameter estimates. This scheme is compared with the quadratic detection filter (QDF), and is shown to give superior performance over the latter. This is due to the ability of the model-based detector to account for the variability (nonstationarity) of the EEG by adjusting its parameters appropriately