A novel stochastic millimeter-wave indoor radio channel model

The paper introduces a model for the stochastic millimeter-wave indoor radio channel. This model relates the stochastic properties of the radio channel to the underlying geometry of the investigated environment. The geometric simplicity of the millimeter-wave channel allows examining fundamental deterministic properties of the wave propagation behavior in environments of predefined randomness, i.e., environments whose dimensions and properties are described by various probability distributions. The influence of the randomness on the radio channel is studied for the down-link of a wireless local area network at 60 GHz. Joint amplitudes of path lengths, angles of departure, and amplitudes, as well as spatial power densities, average power of the direct paths, and k factors are investigated.     

A multiscale stochastic image model for automated inspection

We develop a novel multiscale stochastic image model to describe the appearance of a complex three-dimensional object in a two-dimensional monochrome image. This formal image model is used in conjunction with Bayesian estimation techniques to perform automated inspection. The model is based on a stochastic tree structure in which each node is an important subassembly of the three-dimensional object. The data associated with each node or subassembly is modeled in a wavelet domain. We use a fast multiscale search technique to compute the sequential MAP (SMAP) estimate of the unknown position, scale factor, and 2-D rotation for each subassembly. The search is carried out in a manner similar to a sequential likelihood ratio test, where the process advances in scale rather than time. The results of this search determine whether or not the object passes inspection. A similar search is used in conjunction with the EM algorithm to estimate the model parameters for a given object from a set of training images. The performance of the algorithm is demonstrated on two different real assemblies.     

基于马尔可夫链的网络蠕虫传播模型

提出了网络蠕虫的随机传播模型。首先,基于马尔可夫链对于网络蠕虫进行了建模,并且讨论了模型的极限分布以及平稳分布的存在性。然后,讨论了网络蠕虫在传播初期灭绝的充要条件以及在传播后期灭绝的必要条件。最后,讨论了网络蠕虫的传播规模。仿真实验对于模型进行了验证,讨论了模型中传播参数,时间参数以及漏洞主机数等相关参数对于网络蠕虫传播的影响,并且与G-W模型进行了数据对比,说明了本模型的优势。     

Commwarrior worm propagation model for smart phone networks

Commwarrior worm is capable of spreading through both Bluetooth and multimedia messaging service （MMS） in smart phone networks. According to the propagation characteristics of Bluetooth and MMS, we built the susceptibleexposed-infected-recovered-dormancy （SEIRD） model for the Bluetooth and MMS hybrid spread mode and performed the stability analysis. The simulation results show good correlation with our theoretical analysis and demonstrate the effectiveness of this dynamic propagation model. On the basis of the SEIRD model, we further discuss at length the influence of the propagation parameters such as user gather density in groups, moving velocity of smart phone, the time for worm to replicate itself, and other interrelated parameters on the propagation of the virus. On the basis of these analytical and simulation results, some feasible control strategies will be proposed to restrain the spread of mobile worm such as commwarrior on smart phone network.     

A stochastic model for the interconnection topology of digitalcircuits

Rent's rule has been successfully applied to a priori estimation of wire length distributions. However, this approach is very restrictive: the circuits are assumed to be homogeneous. In this paper, recursive clustering is described as a more advanced model for the partitioning behavior of digital circuits. It is applied to predict the variance of the terminal count distribution. First, the impact of the block degree distribution is analyzed with a simple model. A more refined model incorporates the effect of stochastic self similarity. Finally, the model is further extended to describe the effects of heterogeneity. This model is a promising candidate for more accurate a priori estimation tools     

A stochastic MIMO radio channel model with experimental validation

Theoretical and experimental studies of multiple-input/multiple-output (MIMO) radio channels are presented. A simple stochastic MIMO model channel has been developed. This model uses the correlation matrices at the mobile station (MS) and base station (BS) so that results of the numerous single-input/multiple-output studies that have been published in the literature can be used as input parameters. The model is simplified to the narrowband channels. The validation of the model is based upon data collected in both picocell and microcell environments. The stochastic model has also been used to investigate the capacity of MIMO radio channels, considering two different power allocation strategies, water filling and uniform and two different antenna topologies, 4/spl times/4 and 2/spl times/4. Space diversity used at both ends of the MIMO radio link is shown to be an efficient technique in picocell environments, achieving capacities within 14 b/s/Hz and 16 b/s/Hz in 80% of the cases for a 4/spl times/4 antenna configuration implementing water filling at a SNR of 20 dB.     

面向异构网络环境的蠕虫传播模型Enhanced-AAWP

合理地建立蠕虫传播模型将有助于更准确地分析蠕虫在网络中的传播过程。首先通过对分层的异构网络环境进行抽象,在感染时间将影响到蠕虫传播速度的前提下使用时间离散的确定性建模分析方法,推导出面向异构网络环境的蠕虫传播模型Enhanced-AAWP。进而基于Enhanced-AAWP模型分别对本地优先扫描蠕虫和随机扫描蠕虫进行深入分析。模拟结果表明,NAT子网的数量、脆弱性主机在NAT子网内的密度以及本地优先扫描概率等因素都将对蠕虫在异构网络环境中的传播过程产生重要的影响。     

IPv6网络环境下的蠕虫传播模型研究

研究了IPv6网络中,蠕虫在子网间和子网内传播的多种扫描策略;讨论了基于P2P的去重复和可控机制;研究了一种能够在IPv6网络中形成大规模传播的新型混合式蠕虫——NHIW。NHIW具有随机扫描蠕虫的特点,同时能够迅速获取子网内的易感染活跃主机IPv6地址,并能解决重复感染的问题。通过研究NHIW不同传播阶段的时延,理论分析其传播率,建立了NHIW的3层传播模型TLWPM。实验表明,NHIW能够在IPv6网络中形成大规模传播。最后,针对NHIW的特点,讨论了相关防御策略。     

A stochastic vehicle mobility model with environmental condition adaptation capability

In mobile communication systems, mobility modeling is involved in several aspects such as signaling and traffic load analysis. In particular, accurate mobility models are essential for the evaluation of the system design alternatives and network implementation cost issues. In this paper, we propose a mobility model that is appropriate for practical analysis of the mobile systems' design issues. Our model provides different levels of details for the user mobility behavior and can be adapted to any environmental topology. This model considers direction and velocity of the user mobility. The motion direction is modeled based on real street patterns and is described by discrete state Gauss–Markov technique whose parameters are calculated by combining the information derived by the topological map and wireless network performance report (WNPR) of the simulation area. In the proposed method, the user velocity is assumed to be the average velocity that is found based on traffic condition and type of the street. The computational complexity of our model is to the extent that can be used not only in mobility management test procedures but also in order to calculate some useful parameters of these procedures. In this paper, we apply real data to validate the proposed model by simulation which is used to confirm the accuracy of the proposed model and its analytical analysis. Copyright © 2008 John Wiley & Sons, Ltd.     

A stochastic model of fault introduction and removal duringsoftware development

Two broad categories of human error occur during software development: (1) development errors made during requirements analysis, design, and coding activities; (2) debugging errors made during attempts to remove faults identified during software inspections and dynamic testing. This paper describes a stochastic model that relates the software failure intensity function to development and debugging error occurrence throughout all software life-cycle phases. Software failure intensity is related to development and debugging errors because data on development and debugging errors are available early in the software life-cycle and can be used to create early predictions of software reliability. Software reliability then becomes a variable which can be controlled up front, viz, as early as possible in the software development life-cycle. The model parameters were derived based on data reported in the open literature. A procedure to account for the impact of influencing factors (e.g., experience, schedule pressure) on the parameters of this stochastic model is suggested. This procedure is based on the success likelihood methodology (SLIM). The stochastic model is then used to study the introduction and removal of faults and to calculate the consequent failure intensity value of a small-software developed using a waterfall software development     

A stochastic model for burst-correcting convolutional decoders (Corresp.)

A stochastic model is described for the decoder of an optimal burst-correcting convolutional code. From this model, an upper bound is obtained forbar{p}, the error probability per word after decoding.     

高速实时的一种邮件蠕虫异常检测模型

提出了一种基于带泄漏的积分触发测量方法的电子邮件蠕虫异常检测方法,用来检测邮件蠕虫在传播过程中的流量异常。根据邮件流量所表现出的明显的日周期特性和周周期特性,首先计算出当前邮件流量和历史邮件流量的最小Hellinger距离,通过带泄漏的积分触发方法把邮件流量的Hellinger积累起来,从而把邮件蠕虫在传播过程中没有明显流量特征的慢速酝酿阶段的异常特征进行积累,达到在其进入快速传播期之前检测出异常的目的。检测过程只需要检查邮件的流量信息,因而适合大规模高速网络的异常检测。     

A stochastic model of TCP/IP with stationary random losses

In this paper, we present a model for TCP/IP congestion control mechanism. The rate at which data is transmitted increases linearly in time until a packet loss is detected. At this point, the transmission rate is divided by a constant factor. Losses are generated by some exogenous random process which is assumed to be stationary ergodic. This allows us to account for any correlation and any distribution of inter-loss times. We obtain an explicit expression for the throughput of a TCP connection and bounds on the throughput when there is a limit on the window size. In addition, we study the effect of the Timeout mechanism on the throughput. A set of experiments is conducted over the real Internet and a comparison is provided with other models that make simple assumptions on the inter-loss time process. The comparison shows that our model approximates well the throughput of TCP for many distributions of inter-loss times.     

A stochastic process associated with the EAR(l) model

A stochastic bivariate process{(T_{n}, X_{n}): n=0,1,2, cdots }is considered. TheT_{n}are the occurrence times of a random event generated by a Poisson stochastic point process. EachX_{n}is the amplitude associated with thenth event at random timeT_{n}and is constructed fromX_{n}-1and the interarrival timeT_{n}-T_{n-1}, according to the first-order autoregressive exponential time series model (EAR(l)). Moments and joint distributions for the bivariate process are obtained, as well as the distribution of some extreme values related to the bivariate process.     

模型检验下蠕虫病毒检测器的设计与实现

现在蠕虫病毒检测技术主要是基于病毒特征库,通过特征码的匹配来确定。这种方法的主要缺点是病毒特征库的更新总是滞后于病毒的发布,实时性效果较差。这里提出了一种新的方法,采用模型检验技术的方法,结合蠕虫病毒的入侵原理,改进计算逻辑树的规范设计,从汇编代码层面对蠕虫病毒行为进行特征提取,有效建模,实验结果显示这种方法能够有效地检测蠕虫病毒及其变种。     

A stochastic geometrical vector model of macro- and megacellular communication channels

This paper describes a physically based stochastic approach that allows to predict the narrowband fading statistics for macro- and megacellular networks operating in urban areas. On one hand, macrocellular channels have been mainly covered in the literature by means of empirical models. On the other hand, megacells are typical of upcoming communication systems, such as broadband wireless access and high-altitude platform-based networks. Generally, the path gain of a wireless fading channel can be statistically characterized by a Ricean distribution, which includes Rayleigh fading as a particular case. The key parameter of the distribution is the K-factor K, defined as the ratio of the dominant component to the scatter contribution. The K-factor is estimated by means of a physical model, conditionally to a set of geometrical parameters, such as the building height, the street width and the base station-to-terminal distance. These parameters are statistically described by analytical distributions. The model is successfully compared with experimental data at 1.6 GHz and typical applications are presented. Finally, because of its physical background, the model performs very well in analyzing polarization (MIMO) multiplexing schemes.     

A stochastic multipath channel model including path directions for indoor environments

A novel stochastic channel model for the indoor propagation channel is presented. It is especially for, but not limited to future communication systems with multiple antennas like space division multiple access (SDMA), spatial filtering for interference reduction (SFIR), or multiple-input/multiple-output (MIMO). The model is designed for indoor scenarios, straight forward extendable to urban environments. It is based on physical wave propagation. The new approach describes the channel by multipath components, each characterized by its transfer matrix (including loss), delay, direction of arrival, and departure. The appearance and disappearance of multipath components over time is modeled as a birth and death process, a marked Poisson process. This enables first-time the correct modeling of spatial and temporal correlations. In each modeling step, path properties change according to the motion of transmitter and receiver. The changing delay times of propagation paths yield a realistic Doppler behavior of the channel. Deterministic ray tracing results are used to produce the huge data sets required for the statistical evaluation of the parameters of the proposed model. This method enables an automated parameter extraction for new environments or frequencies. The ray tracing tool has been verified by narrowband, wideband, and directional channel measurements. The novel stochastic spatial channel model allows the simulation of third-generation broadband radio systems including arbitrary antenna configurations and patterns. System simulations for the bit-error rate of radio links can be performed including intelligent antenna configurations like SDMA, SFIR, or MIMO. Furthermore, the capacity of complete systems can be investigated.     

HPBR：用于蠕虫检测的主机报文行为评级模型

在定义用户网络访问行为习惯模型的基础上,提出了一种主机数据包行为的评价模型--HPBR(hostpacket behavior ranking),用于对主机的网络通信数据包行为进行综合评级.介绍了HPBR中层次式累计评级过程以及性能分析和优化过程,最后,给出了HPBR模型的应用.     

A stochastic network model of the interaction between cardiacrhythm and artificial pacemaker

In order to study heart-pacemaker interaction (HPI), a computer model of the cardiac conduction system has been developed which includes the effects of artificial pacemaker function and failure. The stochastic network model of cardiac conduction consists of five vertices, each representing a functional electrophysiologic element. Electrophysiologic multidimensional conditional probability functions determine the depolarization status of each vertex. The atrioventricular (AV) node is emulated using a mathematical model which includes the influence of past cycle lengths on AV nodal conduction time. Twenty-three classes of arrhythmias may be simulated and, for pacing simulation, one of 12 antibradycardia pacing modes may be chosen. Random effects of pacemaker malfunction including oversensing, undersensing, or failure-to-capture may be simulated through the use of probability distribution functions. This model should prove useful in the development of pacemaker algorithms, determining patient-specific pacemaker therapy, and predicting causes for apparent pacemaker malfunction. The model has been used in the development of an expert system to analyze paced ECGs for pacemaker function and malfunction