基于深度相机的小型无人机室内三维地图构建

为了解决小型无人机在室内光线不足情况下的避障以及路径规划问题,设计了一种基于深度相机的无人机室内地图构建系统。文中使用Pixhawk控制板和低成本嵌入式结构光深度相机硬件平台,为避障以及路径规划目标提供室内环境信息。采用反传感器模型算法,利用深度相机和位姿传感器提供的信息来筛选处理出有效的障碍物信息,并构建室内的三维地图,其中深度相机通过激光扫描的方式来获取障碍物点云的描述信息,利用位姿传感器获取无人机的高度信息。实验结果表明,使用该系统能够快速获取室内地图,对障碍物的判断准确率比较高,且不受光线影响,可以广泛应用于无人机的室内导航,实现不依赖外部光源的室内无人机地图构建系统。     

基于模糊规划的无线传感器网络节点选择方法

针对无线传感器网络区域重构问题,提出了一种新的基于模糊规划算法的传感器选择方法。算法利用反距离加权插值法对插值点的数据进行预测,并且使用容斥原理计算每个节点的正常工作概率。以传感器正常工作概率,误差精度为约束条件,以传感器数量最少化为目标函数,求解0-1整数规划。进一步,考虑误差阈值和工作概率模糊的情况,将节点选择问题公式化为模糊规划求解。利用传感器温度数据对0-1整数规划和模糊规划算法进行分析评估,结果证明模糊规划算法在相同约束情况下,相较于0-1整数规划约能减少35%的传感器节点数量。     

结合Riss的基于Lazy框架的SMT求解技术

SMT涉及很多相关的理论,并且可以描述很多SAT不能描述的问题。因此,对SMT求解器的研究有非常重要的意义。文章提出了另一种求解SMT问题的方法。首先,编译SMT公式并转换为命题变量的合取范式;然后借鉴了在2014SAT竞赛中的Riss求解算法,得到一个的可满足的一组赋值;最后把得当前解与理论求解器进行交互并且验证与在特定理论背景下的可满足性。由于在SMT的求解的过程中结合了先进的Riss求解算法,因此在求解某些SMT问题的时候效果比较好。     

文摘选登

把红外传感器组装入系统时,要研究考虑的问题包括正确安装和防护,以及与系统的其余部件如何接合等问题。这里讨论的某些问题适用于任何种类的传感器,而某些问题是红外传感器所独有的。包装问题包括防止大气污染和高温环境的影响。视场和背景必须得到控制。将红外测辐射热计接入系统的方法有多种,其中包括电流回路,电压输出,数字转换等。传感器可以同各种部件连接,譬如同显示器、数据获取系统、控制器等连接。接合传感器要考虑的问题之一是电噪声问题。还必须避免存在可能降低精度的接地回路。各部件     

基于压力传感器的汽车侧面安全气囊的改进

由于近年来交通事故不断增多,汽车侧面碰撞造成的人员伤亡数量不断增加,而且安全气囊的使用情况并不能使人满意,某些情况下甚至会出现气囊引爆时的冲击伤害到人体。鉴于这种情况,提出了基于压力传感器对现有汽车侧面安全气囊的改进,通过压力传感器可以减少从碰撞到引爆所需时间,以及通过座椅的传感器获取乘坐人员信息对安全气囊进行调整,以达到对人员保护的最佳效果。     

基于蚂蚁算法的无线传感器网络远程信息获取方法

为解决无线传感器网络中节点数据传输实时性与使用寿命之间的矛盾,并实现无线传感器网络的Internet远程信息获取,本文建立了适合于远程信息获取的传感器网络通信模型;在该模型基础上,借鉴蚂蚁算法在无线ad hoc网络中的应用,考虑到无线传感器网络要求能量消耗小、易损坏和移动性的特点,对蚂蚁算法进行改进,提出蚁后算法,分别从本地信息获取和远程信息获取两个角度对信息获取的可行性进行分析,使得Internet的信息获取成为可能,并提出建立新的无线传感器休眠模式,以解决无线传感器数据传输实时性和使用时间的矛盾;最后,利用网络仿真器对该算法进行了仿真,并且与SPIN协议所得的结果相对比,根据仿真结果对比分析其在能源利用和远程信息获取的可行性.     

一种基于Euclidean的无线传感器网络三维定位算法

 针对传感器网络在三维空间的应用,基于Euclidean定位算法,提出了对无线传感器节点进行三维定位的算法.将计算未知节点与锚节点间距离问题抽象为求解六面体顶点间的距离.根据问题的抽象,本文使用所提出的坐标法进行求解,并采用循环迭代的方式来提高节点的定位比例.仿真结果表明,三维空间的Euclidean定位算法各项指标均为良好,能有效地实现三维环境中的传感器节点定位.     

多源观测逆问题的多尺度分布式分层求解算法

针对多源观测逆问题求解时所需的计算量过大这问题,该文给出了多源观测逆问题的一种多尺度分布式分层求解算法。其基本思想是:首先,对各传感器上采集到的观测数据分别进行多尺度分解;其次,基于每个传感器的观测信息,得到目标信号的小波变换系数的局部最优估计值;然后,基于相对误差协方差矩阵提供的信息,在每个尺度上将目标信号的小波系数或最粗尺度系数的局部估计值进行融合;最后,做小波逆变换,得到目标信号基于全局信息的融合估计值。采用该算法求解多源观测逆问题既能得到与采用集中式求解算法相当的估计效果,又能有效地降低求解所需的计算量,进一步增强算法的可实施性。     

基于已知两个方向角的相对位姿标定

提出一种求解摄像机外部参数的方法。传统上一般是至少要用3组2D=3D的对应点,利用PnP问题求解的方法,求解摄像机外部参数,而本文中是根据给定的已标定摄像机的两组2D=3D的对应点,和一个给定的垂直方向（垂直方向可以通过一些物理设备求得,例如陀螺仪,IMUs）[4],利用PnP问题求解的方法,求解摄像机的绝对位置。这个问题最终转化为一个求解含有4个变量,且变量的最高次数为2的多项式方程组,在此,本文中利用Groebner基法求解多项式方程组。     

叠加训练序列的判决指导信道估计方法

针对实际应用中信道先验信息未知时无法求解信道相关矩阵的问题,提出了一种改 进的叠加训练序列的判决指导信道估计方法。该方法利用每次迭代的信道估计值求解信道相 关矩阵。推导了该方法中信道相关矩阵的求解公式。仿真结果表明,在未知信道先验信息的 情况下,与利用信道先验信息的叠加训练序列的信道估计方法相比,改进方法取得了与之几 乎一样的性能,因此改进方法更适用于实际应用。     

Counting Objects by Diffused Index: Geometry-free and training-free approach

Counting objects is a fundamental but challenging problem. In this paper, we propose diffusion-based, geometry-free, and learning-free methodologies to count the number of objects in images. The main idea is to represent each object by a unique index value regardless of its intensity or size, and to simply count the number of index values. First, we place different vectors, refer to as seed vectors, uniformly throughout the mask image. The mask image has boundary information of the objects to be counted. Secondly, the seeds are diffused using an edge-weighted harmonic variational optimization model within each object. We propose an efficient algorithm based on an operator splitting approach and alternating direction minimization method, and theoretical analysis of this algorithm is given. An optimal solution of the model is obtained when the distributed seeds are completely diffused such that there is a unique intensity within each object, which we refer to as an index. For computational efficiency, we stop the diffusion process before a full convergence, and propose to cluster these diffused index values. We refer to this approach as Counting Objects by Diffused Index (CODI). We explore scalar and multi-dimensional seed vectors. For Scalar seeds, we use Gaussian fitting in histogram to count, while for vector seeds, we exploit a high-dimensional clustering method for the final step of counting via clustering. The proposed method is flexible even if the boundary of the object is not clear nor fully enclosed. We present counting results in various applications such as biological cells, agriculture, concert crowd, and transportation. Some comparisons with existing methods are presented.     

Barrier coverage with wireless sensors

When a sensor network is deployed to detect objects penetrating a protected region, it is not necessary to have every point in the deployment region covered by a sensor. It is enough if the penetrating objects are detected at some point in their trajectory. If a sensor network guarantees that every penetrating object will be detected by at least k distinct sensors before it crosses the barrier of wireless sensors, we say the network provides k-barrier coverage. In this paper, we develop theoretical foundations for k-barrier coverage. We propose efficient algorithms using which one can quickly determine, after deploying the sensors, whether the deployment region is k-barrier covered. Next, we establish the optimal deployment pattern to achieve k-barrier coverage when deploying sensors deterministically. Finally, we consider barrier coverage with high probability when sensors are deployed randomly. The major challenge, when dealing with probabilistic barrier coverage, is to derive critical conditions using which one can compute the minimum number of sensors needed to ensure barrier coverage with high probability. Deriving critical conditions for k-barrier coverage is, however, still an open problem. We derive critical conditions for a weaker notion of barrier coverage, called weak k-barrier coverage.     

When users become sensors: can we trust their readings?

Smart sensor network arises as a new generation of sensor networks, where a crowd of possibly anonymous volunteers are involved in the tasks of collecting data from the surrounding environment and providing it to the community. In this paper, we analyze one of the main questions, often forgotten, in these scenarios: whether or not we can trust the sensor readings provided by the individuals engaged in the sensor network. We introduce a data reliability architecture capable of filtering and validating sensor readings in an open environment, where not all users are equally honest. Simulations based on a real world smart sensor application allow us to make a first analysis of the proposed architecture's performance and the variables that have a significant impact on this performance. Copyright © 2013 John Wiley & Sons, Ltd.     

Managed Ecosystems of Networked Objects

Small embedded devices such as sensors and actuators will become the cornerstone of the Future Internet. To this end, generic, open and secure communication and service platforms are needed in order to be able to exploit the new business opportunities these devices bring. In this paper, we evaluate the current efforts to integrate sensors and actuators into the Internet and identify the limitations at the level of cooperation of these Internet-connected objects and the possible intelligence at the end points. As a solution, we propose the concept of Managed Ecosystem of Networked Objects, which aims to create a smart network architecture for groups of Internet-connected objects by combining network virtualization and clean-slate end-to-end protocol design. The concept maps to many real-life scenarios and should empower application developers to use sensor data in an easy and natural way. At the same time, the concept introduces many new challenging research problems, but their realization could offer a meaningful contribution to the realization of the Internet of Things.     

非高斯噪声下的机动目标跟踪

本文讨论了被动传感器在随机干扰条件下进行机动目标跟踪的方法，其观测量包含非高斯噪声，也可能包含影响观测值的随机干扰。与基于Ｋａｌｍａｎ滤波的常见方法不同，采用动态规划算法进行多假设检验，从而估计目标的状态。仿真试验表明本文方法能有效地处理非高斯噪声情况下的目标跟踪问题，而基于Ｋａｌｍａｎ滤波的跟踪方法，比如ＥＫＦ则效果较差。     

A nonparametric statistical method for image segmentation using information theory and curve evolution.

In this paper, we present a new information-theoretic approach to image segmentation. We cast the segmentation problem as the maximization of the mutual information between the region labels and the image pixel intensities, subject to a constraint on the total length of the region boundaries. We assume that the probability densities associated with the image pixel intensities within each region are completely unknown a priori, and we formulate the problem based on nonparametric density estimates. Due to the nonparametric structure, our method does not require the image regions to have a particular type of probability distribution and does not require the extraction and use of a particular statistic. We solve the information-theoretic optimization problem by deriving the associated gradient flows and applying curve evolution techniques. We use level-set methods to implement the resulting evolution. The experimental results based on both synthetic and real images demonstrate that the proposed technique can solve a variety of challenging image segmentation problems. Futhermore, our method, which does not require any training, performs as good as methods based on training.     

Nonparametric hypothesis tests for statistical dependency

Determining the structure of dependencies among a set of variables is a common task in many signal and image processing applications, including multitarget tracking and computer vision. In this paper, we present an information-theoretic, machine learning approach to problems of this type. We cast this problem as a hypothesis test between factorizations of variables into mutually independent subsets. We show that the likelihood ratio can be written as sums of two sets of Kullback-Leibler (KL) divergence terms. The first set captures the structure of the statistical dependencies within each hypothesis, whereas the second set measures the details of model differences between hypotheses. We then consider the case when the signal prior models are unknown, so that the distributions of interest must be estimated directly from data, showing that the second set of terms is (asymptotically) negligible and quantifying the loss in hypothesis separability when the models are completely unknown. We demonstrate the utility of nonparametric estimation methods for such problems, providing a general framework for determining and distinguishing between dependency structures in highly uncertain environments. Additionally, we develop a machine learning approach for estimating lower bounds on KL divergence and mutual information from samples of high-dimensional random variables for which direct density estimation is infeasible. We present empirical results in the context of three prototypical applications: association of signals generated by sources possessing harmonic behavior, scene correspondence using video imagery, and detection of coherent behavior among sets of moving objects.     

An Information-Based Approach to Sensor Management in Large Dynamic Networks

This paper addresses the problem of sensor management for a large network of agile sensors. Sensor management, as defined here, is the process of dynamically retasking agile sensors in response to an evolving environment. Sensors may be agile in a variety of ways, e.g., the ability to reposition, point an antenna, choose sensing mode, or waveform. The goal of sensor management in a large network is to choose actions for individual sensors dynamically so as to maximize overall network utility. Sensor management in the multiplatform setting is a challenging problem for several reasons. First, the state space required to characterize an environment is typically of very high dimension and poorly represented by a parametric form. Second, the network must simultaneously address a number of competing goals. Third, the number of potential taskings grows exponentially with the number of sensors. Finally, in low-communication environments, decentralized methods are required. The approach we present in this paper addresses these challenges through a novel combination of particle filtering for nonparametric density estimation, information theory for comparing actions, and physicomimetics for computational tractability. The efficacy of the method is illustrated in a realistic surveillance application by simulation, where an unknown number of ground targets are detected and tracked by a network of mobile sensors.     

Managing Complexity in Object-Oriented Analysis

The current approaches in Object-Oriented Analysis have limitations on modeling complex real world systems because they require priori knowledge about objects and their interactions before applying them. This may be practical in small systems and systems with clear domain knowledge, but not in large real world systems with unclear domain knowledge. Our approach uses a stepwise refinement technique in a top-down manner to the Object-Oriented Analysis stage with the application of use cases. This approach is especially good for new areas where we do not know all the information in advance. We present the approach with an example of its application to the B-ISDN service modeling and distributed systems.