基于滑动窗口的LRU大流检测算法

由于高速链路中的流量测量缺乏可扩展性且高速链路中及时准确的识别大流对网络测量、控制和管理有重要的意义,且在高速链路中能有效、合理的利用有限的资源,是解决资源紧缺的有效方法,基于此提出一种基于滑动窗口的资源可控的LRU淘汰机制大流量测量算法。通过"滑动窗口"机制,实现流大小的无偏估计,然后采用LRU的思想过滤小流、检测大流。仿真实验证明该算法的可行性。     

分布式隐蔽流量异常的多尺度空间检测

分布式异常流量（如DDoS等）分布式地存在于网络多条链路中,且单条链路的流量异常特征不明显,检测具有很大的难度。丈中提出一种分布式隐蔽异常流量的多尺度空间检测方法,可在网络中的骨干结点上进行早期检测,该方法对骨干网络结点上直接可得的多条链路流量分别进行多尺度小波包分析,找到不同时段下的异常频段,获取该时段下的多个异常重构信号,再从空间上通过核密度估计评估这些信号构成的高维空间点在该时段下的异常程度,作为检测依据。美国教育骨干网实际流量数据和合成的分布式异常流量检测结果表明：文中方法能取得比现有方法更好的检测结果。     

基于抽样流长与完全抽样阈值的异常流自适应抽样算法

高速IP网络的流量测量与异常检测是网络测量领域研究的热点。针对目前网络流量测量算法对小流估计精度偏低,对异常流量筛选能力较差的缺陷,该文提出一种基于业务流已抽样长度与完全抽样阈值S的自适应流抽样算法(AFPT)。AFPT算法根据完全抽样阈值S筛选对异常流量敏感相关的小流,同时根据业务流已抽样长度自适应调整抽样概率。仿真和实验结果表明,AFPT算法的估计误差与理论上界相符,具有较强的异常流量筛选能力,能够有效提高异常检测算法的准确率。     

基于自相似的异常流量检测模型

现行网络中存在诸多影响网络安全和服务性能的异常流量,异常流量的存在不仅影响用户的正常使用,而且会造成网络拥塞和网络瘫痪,甚至会篡改和破坏用户及服务器的数据,造成不可估量的损失。为及时发现这些流量,设计了一个基于自相似特性的异常流量检测模型。根据现行网络流量大速度快等特点,该模型设计分为简单流分类模块、自适应抽样模块、实时估计Hurst参数模块以及异常流量判断模块四部分。设计的此检测模型能够在很大程度上保证网络流量检测的准确性和高效性。     

带宽测量实验研究及其算法改进

带宽是网络规划、管理和性能优化的重要指标.主动带宽测量技术可以跨越多个自治系统实现端到端的带宽测量,因此被广泛研究,目前已提出了大量的主动带宽测量算法.比较算法性能是设计精确、高效、健壮带宽测量算法的基础.本文定义算法的性能评价指标;通过大量带宽测量实验,从原理和实验结果分析指出误差累计和背景流量影响是影响现有测量算法性能的最主要原因.在此基础上,提出和实现一个任意链路带宽测量方法,该方法可以消除逐跳测量造成的误差累计和背景流量影响.     

基于链路特征的DDoS攻击检测方法

提出了一种基于链路特征的DDoS攻击检测方法,利用极大似然估计技术推出网络内部链路特征分布,应用自组织映射神经网络进行链路特征活动轮廓学习和异常链路检测。实验结果表明,该方法有效,具有一定的发展潜力,对于DDoS攻击的检测和预防具有重要现实意义。     

单源多径路由网络拥塞链路识别

针对多径路由带来的端到端测量路径不确定性以及布尔模型不能很好地解决多拥塞链路的问题,该文在识别端到端测量路径的基础上,提出一种基于扩展状态空间的网络拥塞链路识别算法。首先基于探测流时延相关性进行自适应聚类,进而得到各路径与探测流之间的映射关系。其次采用多门限的方式,将具有不同丢包程度的拥塞路径赋予不同的拥塞状态。最后将拥塞链路识别问题转化为一个约束最优化问题,并提出基于扩展状态空间的拥塞链路识别算法(ESSCLI)算法求解该问题。仿真结果表明,ESSCLI算法能够在多种不同网络场景下取得比当前算法更高的拥塞链路检测率。     

基于贝叶斯融合的时空流异常行为检测模型

针对直接利用卷积自编码网络未考虑视频时间信息的问题,该文提出基于贝叶斯融合的时空流异常行为检测模型。空间流模型采用卷积自编码网络对视频单帧进行重构,时间流模型采用卷积长短期记忆(LSTM)编码-解码网络对短期光流序列进行重构。接着,分别计算空间流模型和时间流模型下每帧的重构误差,设计自适应阈值对重构误差图进行二值化,并基于贝叶斯准则对空间流和时间流下的重构误差进行融合,得到融合重构误差图,并在此基础上进行异常行为判断。实验结果表明,该算法在UCSD和Avenue视频库上的检测效果优于现有异常检测算法。     

深圳监测站卫星链路校准方法及误差分析

卫星链路校准是卫星监测设施维护的重要环节,链路测量的准确度直接关系到测量星上信号EIRP值及PFD值的置信度.本文以深圳站卫星监测链路校准测量为例,详细分析了链路各部分误差来源及误差大小,然后针对性地提出了减小测量误差的方法.     

基于边缘流量测量的算法研究

随着互联网的发展,理解网络行为对于网络管理、规划和发展都有重要意义,网络流量测量是研究网络行为的基础。现有的网络流量测量方法大都需要在网络的内部核心节点处进行测量,需要的网络开销比较大。文中在网络边缘处进行流量测量,然后根据边缘测量结果估计网络各个链路上的流量,提出了网络流量估计算法,节省网络测量开销。最后经过仿真证明该算法可行。     

A fast lightweight approach to origin-destination IP traffic estimation using partial measurements

In this paper, a novel approach is proposed for estimating traffic matrices. Our method, called PamTram for PArtial Measurement of TRAffic Matrices, couples lightweight origin-destination (OD) flow measurements along with a computationally lightweight algorithm for producing OD estimates. The first key aspect of our method is to actively select a small number of informative OD flows to measure in each estimation interval. To avoid the heavy computation of optimal selection, we use intuition from game theory to develop randomized selection rules, with the goals of reducing errors and adapting to traffic changes. We show that it is sufficient to measure only one flow per measurement period to drastically reduce errors-thus rendering our method lightweight in terms of measurement overhead. The second key aspect is an explanation and proof that an Iterative Proportional Fitting algorithm approximates traffic matrix estimates when the goal is a minimum mean-squared error; this makes our method lightweight in terms of computation overhead. A one-step error bound is provided for PamTram that bounds the average error for the worst scenario. We validate our method using data from Sprint's European Tier-1 IP backbone network and demonstrate its consistent improvement over previous methods.     

Fast localization and segmentation of optic disk in retinal images using directional matched filtering and level sets

The optic disk (OD) center and margin are typically requisite landmarks in establishing a frame of reference for classifying retinal and optic nerve pathology. Reliable and efficient OD localization and segmentation are important tasks in automatic eye disease screening. This paper presents a new, fast, and fully automatic OD localization and segmentation algorithm developed for retinal disease screening. First, OD location candidates are identified using template matching. The template is designed to adapt to different image resolutions. Then, vessel characteristics (patterns) on the OD are used to determine OD location. Initialized by the detected OD center and estimated OD radius, a fast, hybrid level-set model, which combines region and local gradient information, is applied to the segmentation of the disk boundary. Morphological filtering is used to remove blood vessels and bright regions other than the OD that affect segmentation in the peripapillary region. Optimization of the model parameters and their effect on the model performance are considered. Evaluation was based on 1200 images from the publicly available MESSIDOR database. The OD location methodology succeeded in 1189 out of 1200 images (99% success). The average mean absolute distance between the segmented boundary and the reference standard is 10% of the estimated OD radius for all image sizes. Its efficiency, robustness, and accuracy make the OD localization and segmentation scheme described herein suitable for automatic retinal disease screening in a variety of clinical settings.     

Characteristics of origin-destination pair traffic in Funet

In this paper we analyze measurements gathered from a 2.5~Gbps link in the Finnish university network (Funet) in 2004. The traffic is broken down into origin-destination (OD) pair components based on source and destination IP address. We study the traffic characteristics of these components, and identify four typical representative OD pairs. For these pairs we investigate the validity of a moving IID Gaussian model. We find that the statistical properties of these OD pairs differ significantly from each other, with only some of them close to Gaussian. The OD pairs are also found to have some cross-correlation between each other, contradicting an often made assumption about OD pair independence. Furthermore, the existence of a mean-variance relation between the OD pairs is studied. We find that there is a relation between mean and variance, but for some periods of time it is rather weak.     

Choosing an OD Consultant Five Revealing Questions

Because a number of misconceptions exist concerning the nature of organization development (OD), prospective clients may have difficulty in choosing an OD consultant. This article identifies five questions as critical to the choice of an OD consultant. It identifies two distinguishable orientations-the systemic (SR) and human relations (HR)-among consultants in responding to these questions. Based on research interviews, the opinions of eleven established OD consultants to the issues raised by these questions are described using quotes. Finally, a check list of questions is provided to help a client better define his needs for consulting services and choose a consultant with an orientation appropriate to the task.     

Automatic optic disc detection from retinal images by a line operator

Under the framework of computer-aided eye disease diagnosis, this paper presents an automatic optic disc (OD) detection technique. The proposed technique makes use of the unique circular brightness structure associated with the OD, i.e., the OD usually has a circular shape and is brighter than the surrounding pixels whose intensity becomes darker gradually with their distances from the OD center. A line operator is designed to capture such circular brightness structure, which evaluates the image brightness variation along multiple line segments of specific orientations that pass through each retinal image pixel. The orientation of the line segment with the minimum/maximum variation has specific pattern that can be used to locate the OD accurately. The proposed technique has been tested over four public datasets that include 130, 89, 40, and 81 images of healthy and pathological retinas, respectively. Experiments show that the designed line operator is tolerant to different types of retinal lesion and imaging artifacts, and an average OD detection accuracy of 97.4% is obtained.     

Accurate and efficient optic disc detection and segmentation by a circular transformation

Under the framework of computer-aided diagnosis, this paper presents an accurate and efficient optic disc (OD) detection and segmentation technique. A circular transformation is designed to capture both the circular shape of the OD and the image variation across the OD boundary simultaneously. For each retinal image pixel, it evaluates the image variation along multiple evenly-oriented radial line segments of specific length. The pixels with the maximum variation along all radial line segments are determined, which can be further exploited to locate both the OD center and the OD boundary accurately. Experiments show that OD detection accuracies of 99.75%, 97.5%, and 98.77% are obtained for the STARE dataset, the ARIA dataset, and the MESSIDOR dataset, respectively, and the OD center error lies around six pixels for the STARE dataset and the ARIA dataset which is much smaller than that of state-of-the-art methods ranging 14-29 pixels. In addition, the OD segmentation accuracies of 93.4% and 91.7% are obtained for STARE dataset and ARIA dataset, respectively, that consists of many severely degraded images of pathological retinas that state-of-the-art methods cannot segment properly. Furthermore, the algorithm runs in 5 s, which is substantially faster than many of the state-of-the-art methods.     

陆用车载组合导航系统里程仪快速标定

为减小里程仪刻度因子误差对组合导航精度的影响,分析了安装误差以及轮胎内外温度与里程仪刻度因子的关系,提出组合导航系统中里程仪快速标定法,推导出组合导航系统卡尔曼滤波方程,进行系统仿真。结果表明,将里程仪刻度系数进行标定后,里程仪刻度系数误差明显提高两个量级,里程仪测量输出数据的准确度也明显提高,同时组合导航的定位精度也得到了提高。     

Detection of optic disc in retinal images by means of a geometrical model of vessel structure

We present here a new method to identify the position of the optic disc (OD) in retinal fundus images. The method is based on the preliminary detection of the main retinal vessels. All retinal vessels originate from the OD and their path follows a similar directional pattern (parabolic course) in all images. To describe the general direction of retinal vessels at any given position in the image, a geometrical parametric model was proposed, where two of the model parameters are the coordinates of the OD center. Using as experimental data samples of vessel centerline points and corresponding vessel directions, provided by any vessel identification procedure, model parameters were identified by means of a simulated annealing optimization technique. These estimated values provide the coordinates of the center of OD. A Matlab prototype implementing this method was developed. An evaluation of the proposed procedure was performed using the set of 81 images from the STARE project, containing images from both normal and pathological subjects. The OD position was correctly identified in 79 out of 81 images (98%), even in rather difficult pathological situations.     

Optic disk and cup segmentation from monocular color retinal images for glaucoma assessment

Automatic retinal image analysis is emerging as an important screening tool for early detection of eye diseases. Glaucoma is one of the most common causes of blindness. The manual examination of optic disk (OD) is a standard procedure used for detecting glaucoma. In this paper, we present an automatic OD parameterization technique based on segmented OD and cup regions obtained from monocular retinal images. A novel OD segmentation method is proposed which integrates the local image information around each point of interest in multidimensional feature space to provide robustness against variations found in and around the OD region. We also propose a novel cup segmentation method which is based on anatomical evidence such as vessel bends at the cup boundary, considered relevant by glaucoma experts. Bends in a vessel are robustly detected using a region of support concept, which automatically selects the right scale for analysis. A multi-stage strategy is employed to derive a reliable subset of vessel bends called r-bends followed by a local spline fitting to derive the desired cup boundary. The method has been evaluated on 138 images comprising 33 normal and 105 glaucomatous images against three glaucoma experts. The obtained segmentation results show consistency in handling various geometric and photometric variations found across the dataset. The estimation error of the method for vertical cup-to-disk diameter ratio is 0.09/0.08 (mean/standard deviation) while for cup-to-disk area ratio it is 0.12/0.10. Overall, the obtained qualitative and quantitative results show effectiveness in both segmentation and subsequent OD parameterization for glaucoma assessment.     

Orbit Determination Accuracy Improvement for Geostationary Satellite with Single Station Antenna Tracking Data

An operational orbit determination (OD) and prediction system for the geostationary Communication, Ocean, and Meteorological Satellite (COMS) mission requires accurate satellite positioning knowledge to accomplish image navigation registration on the ground. Ranging and tracking data from a single ground station is used for COMS OD in normal operation. However, the orbital longitude of the COMS is so close to that of satellite tracking sites that geometric singularity affects observability. A method to solve the azimuth bias of a single station in singularity is to periodically apply an estimated azimuth bias using the ranging and tracking data of two stations. Velocity increments of a wheel offloading maneuver which is performed twice a day are fixed by planned values without considering maneuver efficiency during OD. Using only single‐station data with the correction of the azimuth bias, OD can achieve three‐sigma position accuracy on the order of 1.5 km root‐sum‐square.