A Computational Model for C. elegans Locomotory Behavior: Application to Multiworm Tracking

A computational approach is presented for modeling and quantifying the structure and dynamics of the nematode C. elegans observed by time-lapse microscopy. Worm shape and conformations are expressed in a decoupled manner. Complex worm movements are expressed in terms of three primitive patterns-peristaltic progression, deformation, and translation. The model has been incorporated into algorithms for segmentation and simultaneous tracking of multiple worms in a field, some of which may be interacting in complex ways. A recursive Bayesian filter is used for tracking. Unpredictable behaviors associated with interactions are resolved by multiple-hypothesis tracking. Our algorithm can track worms of diverse sizes and conformations (coiled/uncoiled) in the presence of imaging artifacts and clutter, even when worms are overlapping with others. A two-observer performance assessment was conducted over 16 image sequences representing wild-type and uncoordinated mutants as a function of worm size, conformation, presence of clutter, and worm entanglement. Overall detected tracking failures were 1.41%, undetected tracking failures were 0.41%, and segmentation errors were 1.11% of worm length. When worms overlap, our method reduced undetected failures from 12% to 1.75%, and segmentation error from 11% to 5%. Our method provides the basis for reliable morphometric and locomotory analysis of freely behaving worm populations.     

Using Articulated Models for Tracking Multiple C. elegans in Physical Contact

We present a method for tracking and distinguishing multiple C. elegans in a video sequence, including when they are in physical contact with one another. The worms are modeled with an articulated model composed of rectangular blocks, arranged in a deformable configuration represented by a spring-like connection between adjacent parts. Dynamic programming is applied to reduce the computational complexity of the matching process. Our method makes it possible to identify two worms correctly before and after they touch each other, and to find the body poses for further feature extraction. All joint points in our model can be also considered to be the pseudo skeleton points of the worm body. It solves the problem that a previously presented morphological skeleton-based reversal detection algorithm fails when two worms touch each other. The algorithm has many applications in the study of physical interactions between C. elegans.     

Segmentation for robust tracking in the presence of severe occlusion

Tracking an object in a sequence of images can fail due to partial occlusion or clutter. Robustness to occlusion can be increased by tracking the object as a set of "parts" such that not all of these are occluded at the same time. However, successful implementation of this idea hinges upon finding a suitable set of parts. In this paper we propose a novel segmentation, specifically designed to improve robustness against occlusion in the context of tracking. The main result shows that tracking the parts resulting from this segmentation outperforms both tracking parts obtained through traditional segmentations, and tracking the entire target. Additional results include a statistical analysis of the correlation between features of a part and tracking error, and identifying a cost function that exhibits a high degree of correlation with the tracking error.     

Modeling Propagation Dynamics of Bluetooth Worms (Extended Version)

In the last few years, the growing popularity of mobile devices has made them attractive to virus and worm writers. One communication channel often exploited by mobile malware is the Bluetooth interface. In this paper, we present a detailed analytical model that characterizes the propagation dynamics of Bluetooth worms. Our model captures not only the behavior of the Bluetooth protocol but also the impact of mobility patterns on the Bluetooth worm propagation. Validation experiments against a detailed discrete-event Bluetooth worm simulator reveal that our model predicts the propagation dynamics of Bluetooth worms with high accuracy. We further use our model to efficiently predict the propagation curve of Bluetooth worms in big cities such as Los Angeles. Our model not only sheds light on the propagation dynamics of Bluetooth worms, but also allows to predict spreading curves of Bluetooth worm propagation in large areas without the high computational cost of discrete-event simulation.     

蠕虫正则表达式特征自动提取技术研究

提出一种实用的蠕虫正则表达式特征自动提取方法,该方法由蠕虫传播网络流样本获取、特征树生成、高假阳性特征剔除、特征融合这4步组成。该方法的优点是可输出具有强描述能力的包含“.*”、“.{k}”、“|”、“(c){k}”等元字符的正则表达式特征。基于蜜罐系统Honeybow实现了该方法,并针对互联网上数种真实蠕虫进行了实验。实验结果表明,该方法可以准确地提取真实蠕虫的正则表达式特征,可以在蜜罐、蠕虫及恶意代码分析等系统中应用。     

Medical image segmentation using minimal path deformable models with implicit shape priors.

This paper presents a new method for segmentation of medical images by extracting organ contours, using minimal path deformable models incorporated with statistical shape priors. In our approach, boundaries of structures are considered as minimal paths, i.e., paths associated with the minimal energy, on weighted graphs. Starting from the theory of minimal path deformable models, an intelligent "worm" algorithm is proposed for segmentation, which is used to evaluate the paths and finally find the minimal path. Prior shape knowledge is incorporated into the segmentation process to achieve more robust segmentation. The shape priors are implicitly represented and the estimated shapes of the structures can be conveniently obtained. The worm evolves under the joint influence of the image features, its internal energy, and the shape priors. The contour of the structure is then extracted as the worm trail. The proposed segmentation framework overcomes the short-comings of existing deformable models and has been successfully applied to segmenting various medical images.     

跟踪制导雷达中杂波图的设计与实现

常规的跟踪制导雷达由于工作在距离波门模式,基本没有杂波干扰问题。现代战争环境要求跟踪制导雷达具有自主目标搜索能力,因此跟踪制导雷达也将面临杂波抑制问题。杂波图是地面情报雷达常用的一种杂波抑制方法,其实现方法不适用于跟踪制导雷达这种波束扫描变化十分灵活的雷达体制。文中根据制导雷达在不同工作模式下波束扫描的变化规律,通过对工作波束进行统一编号来构建三维杂波图,根据编号可以方便地实现杂波图的建立及扩展。该方法适用于具有搜索模式的跟踪制导雷达,简单易行,杂波抑制效果明显。     

基于随机实验的蠕虫传播预测研究

蠕虫传播预测是蠕虫防御的基础之一，但随着蠕虫扫描策略日趋多样和互联网结构逐步复杂，在蠕虫爆发初期及时建立精确的蠕虫传播模型变得越来越困难。利用随机仿真实验来模拟蠕虫在网络中的传播行为，通过统计分析仿真实验结果，发现蠕虫传播实验结果是一个随机过程，而实验结果间存在很高的线性相关性。由此提出一种基于仿真实验统计结果的蠕虫传播趋势预测方法，该方法可以利用0．1％存在漏洞主机的感染信息精确的预测蠕虫传播趋势。     

Multi-agent system for Worm Detection and Containment in Metropolitan Area Networks

Active worms can cause widespread damages at so high a speed that effectively precludes humandirected reaction, and patches for the worms are always available after the damages have been caused, which has elevated them self to a first-class security threat to Metropolitan Area Networks （MAN）. Multi-agent system for Worm Detection and Containment in MAN （MWDCM） is presented to provide a first-class automatic reaction mechanism that automatically applies containment strategies to block the propagation of the worms and to protect MAN against worm scan that wastes a lot of network bandwidth and crashes the routers. Its user agent is used to detect the known worms. Worm detection agent and worm detection correlation agent use two-stage based decision method to detect unknown worms. They adaptively study the accessing in the whole network and dynamically change the working parameters to detect the unknown worms. MWDCM confines worm infection within a macro-cell or a micro-cell of the metropolitan area networks, the rest of the accesses and hosts continue functioning without disruption. MWDCM integrates Worm Detection System （WDS） and network management system. Reaction measures can be taken by using Simple Network Management Protocol （SNMP） interface to control broadband access server as soon as the WDS detect the active worm. MWDCM is very effective in blocking random scanning worms. Simulation results indicate that high worm infection rate of epidemics can be avoided to a degree by MWDCM blocking the propagation of the worms.     

基于DEM的机载气象雷达地杂波剔除方法

针对机载气象雷达气象模式地杂波抑制问题,提出利用回波幅度信息的基于数字高程模型（DEM,Digital Elevation Model）的时域地杂波剔除方法。首先利用回波幅度信息对存在雷达回波信号的数据区域进行分割,然后对区域的边界距离单元利用基于DEM的可视性算法分析其触地情况。根据分析结果可知,存在回波信号的区域可分成仅含气象的区域、仅含杂波的区域以及二者相连的区域,其中相连区域还需进行逐距离单元判断确定分界距离单元。将杂波存在的距离单元予以剔除,即可去除地杂波。经实测数据验证,该方法能够根据雷达参数和DEM数据准确地判断杂波所在的距离单元,有效地剔除地杂波。提出的方法仅处理存在回波信号的区域,且不需要对所有的距离单元进行分析,可以大大减小运算量。      

Real-time vessel segmentation and tracking for ultrasound imaging applications

A method for vessel segmentation and tracking in ultrasound images using Kalman filters is presented. A modified Star-Kalman algorithm is used to determine vessel contours and ellipse parameters using an extended Kalman filter with an elliptical model. The parameters can be used to easily calculate the transverse vessel area which is of clinical use. A temporal Kalman filter is used for tracking the vessel center over several frames, using location measurements from a handheld sensorized ultrasound probe. The segmentation and tracking have been implemented in real-time and validated using simulated ultrasound data with known features and real data, for which expert segmentation was performed. Results indicate that mean errors between segmented contours and expert tracings are on the order of 1%-2% of the maximum feature dimension, and that the transverse cross-sectional vessel area as computed from estimated ellipse parameters a, b as determined by our algorithm is within 10% of that determined by experts. The location of the vessel center was tracked accurately for a range of speeds from 1.4 to 11.2 mm/s.     

基于表征转换机的SAR图像目标分割方法

针对SAR(Synthetic Aperture Radar)图像中的目标分割问题,由于目标与杂波空间模式(像素强度和分布)不同,通过分析图像空间模式的方式可达到分辨目标和杂波并分割目标的目的。该文基于表征转换机理论提出一种有效的SAR图像目标分割方法,该算法分析SAR图像中的空间模式,计算其与参考杂波图像的相似程度,最后将与参考杂波相似程度较高的部分消除以达到分割目标的目的,并在衡量相似度部分使用基于累积直方图的自动阈值选取办法。仿真和实测数据的实验验证了此算法的有效性。      

Automatic tracking, feature extraction and classification of C elegans phenotypes

This paper presents a method for automatic tracking of the head, tail, and entire body movement of the nematode Caenorhabditis elegans (C. elegans) using computer vision and digital image analysis techniques. The characteristics of the worm's movement, posture and texture information were extracted from a 5-min image sequence. A Random Forests classifier was then used to identify the worm type, and the features that best describe the data. A total of 1597 individual worm video sequences, representing wild type and 15 different mutant types, were analyzed. The average correct classification ratio, measured by out-of-bag (OOB) error rate, was 90.9%. The features that have most discrimination ability were also studied. The algorithm developed will be an essential part of a completely automated C. elegans tracking and identification system.     

Data fusion in ubiquitous networked robot systems for urban services

There is a clear trend in the use of robots to accomplish services that can help humans. In this paper, robots acting in urban environments are considered for the task of person guiding. Nowadays, it is common to have ubiquitous sensors integrated within the buildings, such as camera networks, and wireless communications like 3G or WiFi. Such infrastructure can be directly used by robotic platforms. The paper shows how combining the information from the robots and the sensors allows tracking failures to be overcome, by being more robust under occlusion, clutter, and lighting changes. The paper describes the algorithms for tracking with a set of fixed surveillance cameras and the algorithms for position tracking using the signal strength received by a wireless sensor network (WSN). Moreover, an algorithm to obtain estimations on the positions of people from cameras on board robots is described. The estimate from all these sources are then combined using a decentralized data fusion algorithm to provide an increase in performance. This scheme is scalable and can handle communication latencies and failures. We present results of the system operating in real time on a large outdoor environment, including 22 nonoverlapping cameras, WSN, and several robots.     

Target tracking in infrared imagery using weighted composite reference function-based decision fusion.

In this paper, we propose a novel decision fusion algorithm for target tracking in forward-looking infrared image sequences recorded from an airborne platform. An important part of this study is identifying the failure modes in this type of imagery. Our strategy is to prevent these failure modes from developing into tracking failures. The results furnished by competing ego-motion compensation and tracking algorithms are evaluated based on their similarity to a target model constructed using the weighted composite reference function.     

A stochastic worm model

Internet worm infection continues to be one of top security threats and has been widely used by botnets to recruit newbots. In order to defend against future worms, it is important to understand how worms propagate and how different scanning strategies affect worm propagation dynamics. In our study, we present a (stochastic) continuous-time Markov chain model for characterizing the propagation of Internet worms. The model is developed for uniform scanning worms, and further for local preference scanning worms and flash worms. Specifically, for uniform and local preference scanning worms, we are able to (1) provide a precise condition that determines whether the worm spread would eventually stop and (2) obtain the distribution of the total number of infected hosts. By using the same modeling approach, we reveal the underlying similarity and relationship between uniform scanning and local preference scanning worms. Finally, we validate the model by simulating the propagation of worms.     

企业内网蠕虫检测和控制研究

目前已有一些全球化的网络蠕虫监测方法,但这些方法并不能很好地适用于局域网.为此,文中提出一种使用本地网协同检测蠕虫的方法,该方法注重分析扫描蠕虫在本地网的行为,通过这些方法给出预警信息,以揭示蠕虫在本地网络中的活动情况。并针对不同的行为特性使用不同的处理方法.结果表明,该方法可以准确、快速地检测出入侵本地网络的扫描蠕虫。     

Adaptive online performance evaluation of video trackers

We propose an adaptive framework to estimate the quality of video tracking algorithms without ground-truth data. The framework is divided into two main stages, namely, the estimation of the tracker condition to identify temporal segments during which a target is lost and the measurement of the quality of the estimated track when the tracker is successful. A key novelty of the proposed framework is the capability of evaluating video trackers with multiple failures and recoveries over long sequences. Successful tracking is identified by analyzing the uncertainty of the tracker, whereas track recovery from errors is determined based on the time-reversibility constraint. The proposed approach is demonstrated on a particle filter tracker over a heterogeneous data set. Experimental results show the effectiveness and robustness of the proposed framework that improves state-of-the-art approaches in the presence of tracking challenges such as occlusions, illumination changes, and clutter and on sequences containing multiple tracking errors and recoveries.     

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。     

An Airborne Radar Clutter Tracking Algorithm Based on Multifractal and Fuzzy C-Mean Cluster

For an airborne Iookdown radar, clutter power often changes dynamically about 80 dB with wide distributions as the platform moves. Therefore, clutter tracking techniques are required to guide the selection of const false alarm rate （CFAR） schemes. In this work, clutter tracking is done in image domain and an algorithm combining multifractal and fuzzy C-mean （FCM） cluster is proposed. The clutter with large dynamic distributions in power density is converted to steady distributions of multifractal exponents by the multifractal transformation with the optimum moment. Then, later, the main lobe and side lobe are tracked from the multifractal exponents by FCM clustering method.