Multi-Scale Adaptive Sampling with Mobile Agents for Mapping of Forest Fires

The use of robotics in distributed monitoring applications requires wireless sensors that are deployed efficiently. A very important aspect of sensor deployment includes positioning them for sampling at locations most likely to yield information about the spatio-temporal field of interest, for instance, the spread of a forest fire. In this paper, we use mobile robots (agents) that estimate the time-varying spread of wildfires using a distributed multi-scale adaptive sampling strategy. The proposed parametric sampling algorithm, “EKF-NN-GAS” is based on neural networks, the extended Kalman filter (EKF), and greedy heuristics. It combines measurements arriving at different times, taken at different scale lengths, such as from ground, airborne, and spaceborne observation platforms. One of the advantages of our algorithm is the ability to incorporate robot localization uncertainty in addition to sensor measurement and field parameter uncertainty into the same EKF model. We employ potential fields, generated naturally from the estimated fire field distribution, in order to generate fire-safe trajectories that could be used to rescue vehicles and personnel. The covariance of the EKF is used as a quantitative information measure for sampling locations most likely to yield optimal information about the sampled field distribution. Neural net training is used infrequently to generate initial low resolution estimates of the fire spread parameters. We present simulation and experimental results for reconstructing complex spatio-temporal forest fire fields “truth models”, approximated by radial basis function (RBF) parameterizations. When compared to a conventional raster scan approach, our algorithm shows a significant reduction in the time necessary to map the fire field.     

Linear Image Reconstruction by Sobolev Norms on the Bounded Domain

The reconstruction problem is usually formulated as a variational problem in which one searches for that image that minimizes a so called prior (image model) while insisting on certain image features to be preserved. When the prior can be described by a norm induced by some inner product on a Hilbert space, the exact solution to the variational problem can be found by orthogonal projection. In previous work we considered the image as compactly supported in and we used Sobolev norms on the unbounded domain including a smoothing parameter γ>0 to tune the smoothness of the reconstructed image. Due to the assumption of compact support of the original image, components of the reconstructed image near the image boundary are too much penalized. Therefore, in this work we minimize Sobolev norms only on the actual image domain, yielding much better reconstructions (especially for γ≫0). As an example we apply our method to the reconstruction of singular points that are present in the scale space representation of an image.     

小数据集贝叶斯网络多父节点参数的修复

具有已知结构的小数据集贝叶斯网络多父节点参数学习是一个重要而困难的研究课题,由于信息不充分,使得无法直接对多父节点参数进行有效的估计,如何修复这些参数便是问题的核心.针对问题提出了一种有效的小数据集多父节点参数修复方法,该方法首先使用Bootstrap抽样扩展小数据集,然后分别将Gibbs抽样与最大似然树和贝叶斯网络相结合,通过依次对扩展数据按一定比例的迭代修正来实现对多父节点参数的修复.实验结果表明,这种方法能够有效地使大部分多父节点参数得到修复.     

一致性测试的静态需求测试研究

针对一致性测试中的静态需求测试通常采用手工方法而产生的效率低问题,提出一种基于统计学习的方法,包括静态需求的关联规则表示,并对采样的协议数据进行关联规则挖掘,如果得到的规则与需求规则一致,静态需求就可以得到统计上的测试检验。该方法在OSPFv3协议测试中取得了较好的效果。     

高速ADC频域特性测试系统的设计

针对传统专用高速模数转换器频域特性测试系统构建难度大、成本高的问题,利用基于加窗和插值FFr算法的测试方法,提出一种低成本高速ADC频域特性测试系统,给出ADC采样时钟驱动电路、ADC输入信号驱动电路、FIFO缓存电路及USB接口电路的关键设计技术。     

一种基于不放回抽样的图像混沌加密算法

针对现有基于混沌序列的图像加密算法的缺陷,提出一种基于不放回抽样的图像混沌加密算法。该算法采用可变模运算和间接地址映射技术,将混沌序列一对一、高度随机地转换为图像像素地址,实现图像的位置置乱和灰度扰乱加密。对混沌序列的分布无严格要求,使混沌序列的参数和初值可以作为密钥被使用。实验结果表明,与现有同类算法相比,该算法性能优越、运算速度快且易于实现。     

基于感知网格的无线传感器网络动态采样策略

在分析无线传感器网络时空相关性模型的基础上,提出一种基于感知网格的无线传感器网络动态采样策略.将监测区域划分为多个感知网格,感知网格内只有簇头节点保持活跃状态,当出现异常数据后再激活感知网格内其他节点来获得更详细的信息.该策略通过减少无线传感器节点之间相同的或相近的采样数据上传来降低冗余信息的传输.仿真结果表明:该策略显著提高了无线传感器网络能量效率.     

交换式以太网控制系统传感器变采样调度研究

针对基于交换式以太网的网络化控制系统的数据传输和控制性能优化问题,提出了传感器根据给定值与实测值差额的绝对值对照静态调度表变采样周期的调度优化方法.使用TrueTime 2.0工具箱搭建了交换式以太网控制系统仿真平台进行仿真研究,并与定采样周期方法进行了对比,仿真结果验证了其可行性.此方法在实际的工程应用中具有一定的参考价值.     

Co-design of H∞ jump observers for event-based measurements over networks

This work presents a strategy to minimise the network usage and the energy consumption of wireless battery-powered sensors in the observer problem over networks. The sensor nodes implement a periodic send-on-delta approach, sending new measurements when a measure deviates considerably from the previous sent one. The estimator node implements a jump observer whose gains are computed offline and depend on the combination of available new measurements. We bound the estimator performance as a function of the sending policies and then state the design procedure of the observer under fixed sending thresholds as a semidefinite programming problem. We address this problem first in a deterministic way and, to reduce conservativeness, in a stochastic one after obtaining bounds on the probabilities of having new measurements and applying robust optimisation problem over the possible probabilities using sum of squares decomposition. We relate the network usage with the sending thresholds and propose an iterative procedure for the design of those thresholds, minimising the network usage while guaranteeing a prescribed estimation performance. Simulation results and experimental analysis show the validity of the proposal and the reduction of network resources that can be achieved with the stochastic approach.     

Observer-based consensus in networks of discrete-time switched linear dynamics under a fixed topology

This paper considers the consensus problem of discrete-time switched linear multi-agent systems under a fixed communication topology. An observer-based protocol with sampled data is proposed for solving such a problem. Assume that the digraph has a directed spanning tree and that all switched subsystem of each agent are stabilisable and detectable. It is shown that the proposed protocol solves the consensus problem when the sampling period is sufficiently small and the average dwell time of the switching signal is sufficiently large. Moreover, to reduce the conservatism, a sufficient condition for consensus is obtained to design the feedback gain matrices and the observer gain matrices by linear matrix inequalities, which guarantee that consensus can be achieved when the sampling period and the average dwell time are in the general case. Finally, the effectiveness of the theoretical results is demonstrated through an example.