A General Method for Sensor Planning in Multi-Sensor Systems: Extension to Random Occlusion

Systems utilizing multiple sensors are required in many domains. In this paper, we specifically concern ourselves with applications where dynamic objects appear randomly and the system is employed to obtain some user-specified characteristics of such objects. For such systems, we deal with the tasks of determining measures for evaluating their performance and of determining good sensor configurations that would maximize such measures for better system performance. We introduce a constraint in sensor planning that has not been addressed earlier: visibility in the presence of random occluding objects. occlusion causes random loss of object capture from certain necessitates the use of other sensors that have visibility of this object. Two techniques are developed to analyze such visibility constraints: a probabilistic approach to determine “average” visibility rates and a deterministic approach to address worst-case scenarios. Apart from this constraint, other important constraints to be considered include image resolution, field of view, capture orientation, and algorithmic constraints such as stereo matching and background appearance. Integration of such constraints is performed via the development of a probabilistic framework that allows one to reason about different occlusion events and integrates different multi-view capture and visibility constraints in a natural way. Integration of the thus obtained capture quality measure across the region of interest yields a measure for the effectiveness of a sensor configuration and maximization of such measure yields sensor configurations that are best suited for a given scenario. The approach can be customized for use in many multi-sensor applications and our contribution is especially significant for those that involve randomly occurring objects capable of occluding each other. These include security systems for surveillance in public places, industrial automation and traffic monitoring. Several examples illustrate such versatility by application of our approach to a diverse set of different and sometimes multiple system objectives. Most of this work was done while A. Mittal was with Real-Time Vision and Modeling Department, Siemens Corporate Research, Princeton, NJ 08540.     

Optimal IDS Sensor Placement and Alert Prioritization Using Attack Graphs

We optimally place intrusion detection system (IDS) sensors and prioritize IDS alerts using attack graph analysis. We begin by predicting all possible ways of penetrating a network to reach critical assets. The set of all such paths through the network constitutes an attack graph, which we aggregate according to underlying network regularities, reducing the complexity of analysis. We then place IDS sensors to cover the attack graph, using the fewest number of sensors. This minimizes the cost of sensors, including effort of deploying, configuring, and maintaining them, while maintaining complete coverage of potential attack paths. The sensor-placement problem we pose is an instance of the NP-hard minimum set cover problem. We solve this problem through an efficient greedy algorithm, which works well in practice. Once sensors are deployed and alerts are raised, our predictive attack graph allows us to prioritize alerts based on attack graph distance to critical assets.

可重构系统中的实时任务在线调度与放置算法

有效的任务调度与放置是发挥可重构计算性能优势的重要因素.针对实时任务在二维可重构器件上的在线调度问题,定义了调度算法完全识别的概念,即算法不会拒绝能够成功调度的任务.提出了新的实时在线调度与放置算法,充分利用了任务的时间信息,实现了完全识别的调度.实验表明,与已有的算法相比,新算法显著地改善了调度效果,而运行开销没有明显增加.     

Camera cooperation for achieving visual attention

In this paper we address the problem of establishing a computational model for visual attention using cooperation between two cameras. More specifically we wish to maintain a visual event within the field of view of a rotating and zooming camera through the understanding and modeling of the geometric and kinematic coupling between a static camera and an active camera. The static camera has a wide field of view thus allowing panoramic surveillance at low resolution. High-resolution details may be captured by a second camera, provided that it looks in the right direction. We derive an algebraic formulation for the coupling between the two cameras and we specify the practical conditions yielding a unique solution. We describe a method for separating a foreground event (such as a moving object) from its background while the camera rotates. A set of outdoor experiments shows the two-camera system in operation.     

Real-Time Camera Tracking for Mobile Devices: The VisiTrack System

This paper presents VisiTrack, a novel approach for video based incremental tracking in real-time. The major objectives in the development of VisiTrack was to design or select algorithms that are well suited for embedded real-time computation. We had a special focus on latency reduction and storage minimization since the algorithms should run on mobile devices like PDAs with the appropriate extension, i.e. mainly a camera, in real-time. The image analysis, camera localization and feature position approximation of VisiTrack are explained in detail. The CV-SDF model, an extension of Synchronous Dataflow graphs (SDF), supporting the principles of linear processing and fine-grained pipelining was defined and applied for the design of all VisiTrack modules in order to fulfill real-time constraints and reduce system latency. Furthermore the camera localization and position approximation include mechanisms for minimization of errors that may arise for instance due to measurement inaccuracies. Current applications of VisiTrack in the augmented reality domain and robotic self localization show its good performance. However VisiTrack is not limited to these application domains.     

Decentralized Delayed-State Information Filter (DDSIF): A new approach for cooperative decentralized tracking

This paper presents a decentralized data fusion approach to perform cooperative perception with data gathered from heterogeneous sensors, which can be static or carried by robots. In particular, a decentralized delayed-state information filter (DDSIF) is described, in which full-state trajectories (that is, delayed states) are considered to fuse the information. This approach allows obtaining an estimation equal to that provided by a centralized system and reduces the impact of communication delays and latency in the estimation. The sparseness of the information matrix maintains the communication overhead at a reasonable level. The method is applied to cooperative tracking, and some results in disaster management scenarios are shown. In this kind of scenario, the target might move in both open-field and indoor areas, so the fusion of data provided by heterogeneous sensors is beneficial. The paper also shows experimental results with real data and integrating several sources of information.     

WWW中缓存模型的优化设计

ＷＷＷ提供了方便的手段来访问远程信息资源，对于Ｗｅｂ用户而言，衡量Ｗｅｂ服务质量的一个重要指标就是检索信息所花费的时间。缩短检索时间的途径很多，此文主要介绍通过设置缓存机制，降低用户对资源访问请求次数，达到缩短用户直观感觉上的检索时间的方法。并讨论一种基于Ｃ／Ｓ结构的缓存模型，提出了新的缓存替换算法，综合考虑了文档长度，网络负载等参数，实现了缓存模型的优化。经过实验验证，该算法优于目前的缓存替换算法。     

传感网络在水电边坡变形优化检测中应用仿真

在水电边坡变形的检测过程中,需要根据坡变幅度,布置传感节点,传递相关数据,由于坡变幅度一般都伴随不规则起伏,造成传感器节点传递的信号无法形成线性特征,传统的检测方法为了克服非线性变化,需要在大起伏区域布置多个节点,以保证检测的准确性,一旦水电边坡规模大,地质条件较为复杂,将会导致检测节点过多,降低了检测效率。为解决上述问题,提出采用坡变估计模型的水电边坡变形优化传感器检测方法。构建传感网络水电边坡变形检测模型,分别对大坡变区域进行模型的坡变参数预估、参数精估、检验模型适应性、模型修正和模型预报,根据上述模型进行模型最优化评测,实现传感网络在水电边坡变形优化检测。实验结果表明,利用改进算法进行水电边坡变形优化检测,可以减小检测误差、提高检测的准确性,提高检测效率。     

Sensor Placement for Sensing Coverage and Data Precision in Wireless Sensor Networks

We present a novel paradigm of sensor placement concerning data precision and estimation. Multiple abstract sensors are used to measure a quantity of a moving target in the scenario of a wireless sensor network. These sensors can cooperate with each other to obtain a precise estimate of the quantity in a real-time manner. We consider a problem on planning a minimum-cost scheme of sensor placement with desired data precision and resource consumption. Measured data is modeled as a Gaussian random variable with a changeable variance. A gird model is used to approximate the problem. We solve the problem with a heuristic algorithm using branch-and-bound method and tabu search. Our experiments demonstrate that the algorithm is correct in a certain tolerance, and it is also efficient and scalable.     

A dominance-based stepwise approach for sensor placement optimization

A Wireless Sensor Network (WSN) usually consists of numerous wireless devices deployed in a region of interest, each of which is capable of collecting and processing environmental information and communicating with neighboring devices. The problem of sensor placement becomes non trivial when we consider environmental factors such as terrain elevations. In this paper, we differentiate a stepwise optimization approach from a generic optimization approach, and show that the former is better suited for sensor placement optimization. Following a stepwise optimization approach, we propose a Crowd-Out Dominance Search (CODS), which makes use of terrain information and intersensor relationship information to facilitate the optimization. Finally, we investigate the effect of terrain irregularity on optimization algorithm performances, and show that the proposed method demonstrates better resistance to terrain complexity than other optimization methods.     

Zero-Degree algorithm for Internet GateWay placement in backbone wireless mesh networks

Internet GateWays (IGWs) are responsible for connecting the backbone wireless mesh networks (BWMNs) to the Internet/wired backbone. An IGW has more capabilities than a simple wireless mesh router (WMR) but is more expensive. Strategically placing the IGWs in a BWMN is critical to the Wireless Mesh Network (WMN) architecture. In order to solve the problem of IGWs placement in BWMNs, a novel algorithm is proposed in this paper. The new algorithm is involved in placing a minimum number of IGWs so that the Quality of Service (QoS) requirements are satisfied. Different from existing algorithms, this new algorithm incrementally identifies IGWs and prioritively assigns wireless mesh routers (WMRs) based on the computed degree of WMRs to identified IGWs. Performance evaluation results show that proposed algorithm outperforms other alternative algorithms by comparing the number of gateways placed in different scenarios. Furthermore, having control of the distribution of IGWs in order to locate them closest to available Internet/wired network connection points is an added advantage of this algorithm.     

Optimizing I/O server placement for parallel I/O on switch-based irregular networks

In this paper, we study I/O server placement for optimizing parallel I/O performance on switch-based clusters, which typically adopt irregular network topologies to allow construction of scalable systems with incremental expansion capability. Finding optimal solution to this problem is computationally intractable. We quantified the number of messages travelling through each network link by a workload function, and developed three heuristic algorithms to find good solutions based on the values of the workload function. The maximum-workload-based heuristic chooses the locations for I/O nodes in order to minimize the maximum value of the workload function. The distance-based heuristic aims to minimize the average distance between the compute nodes and I/O nodes, which is equivalent to minimizing average workload on the network links. The load-balance-based heuristic balances the workload on the links based on a recursive traversal of the routing tree for the network. Our simulation results demonstrate performance advantage of our algorithms over a number of algorithms commonly used in existing parallel systems. In particular, the load-balance-based algorithm is superior to the other algorithms in most cases, with improvement ratio of 10 to 95% in terms of parallel I/O throughput.     

Perception-based learning for motion in contact in task planning

This paper presents a new approach to error detection during motion in contact under uncertainty for robotic manufacturing tasks. In this approach, artificial neural networks are used for perception-based learning. The six force-and-torque signals from the wrist sensor of a robot arm are fed into the network. A self-organizing map is what learns the different contact states in an unsupervised way. The method is intended to work properly in complex real-world manufacturing environments, for which existent approaches based on geometric analytical models may not be feasible, or may be too difficult. It is used for different tasks involving motion in contact, particularly the peg-in-hole insertion task, and complex insertion or extraction operations in a flexible manufacturing system. Several real examples for these cases are presented.Category: (8) AI in Robotics and Manufacturing/FMS.     

Optimal modalities for radiative transfer-neural network estimation of canopy biophysical characteristics: Evaluation over an agricultural area with CHRIS/PROBA observations

Neural networks trained over radiative transfer simulations constitute the basis of several operational algorithms to estimate canopy biophysical variables from satellite reflectance measurements. However, only little attention was paid to the training process which has a major impact on retrieval performances. This study focused on the several modalities of the training process within neural network estimation of LAI, FCOVER and FAPAR biophysical variables. Performances were evaluated over both actual experimental observations and model simulations. The SAIL and PROSPECT radiative transfer models were used here to simulate the training and the synthetic test datasets. Measurements of LAI, FCOVER and FAPAR were achieved over the Barrax (Spain) agricultural site for a range of crop types concurrently to CHRIS/PROBA satellite image acquisition. Results showed that the spectral band selection was specific to LAI, FCOVER and FAPAR variables. The optimal band set provided significantly improved performances for LAI, while only small differences were observed for the other variables. Gaussian distributions of the radiative transfer model input variables performed better than uniform distributions for which no prior information was exploited. Including moderate uncertainties in the reflectance simulations used in the training process improved the flexibility of the neural network in cases where simulations departed slightly from observations. Simple neural network architecture with a single hidden layer of five tangent sigmoid transfer functions was performing as good as more complex architectures if the training dataset was larger than ten times the number of coefficients to tune. Small sensitivity of performances was observed depending on the way the solution was selected when several networks were trained in parallel. Finally, comparison with a NDVI based approach showed the generally better retrieval accuracy of neural networks.