Ranking queries on uncertain data

Uncertain data is inherent in a few important applications. It is far from trivial to extend ranking queries (also known as top-k queries), a popular type of queries on certain data, to uncertain data. In this paper, we cast ranking queries on uncertain data using three parameters: rank threshold k, probability threshold p, and answer set size threshold l. Systematically, we identify four types of ranking queries on uncertain data. First, a probability threshold top-k query computes the uncertain records taking a probability of at least p to be in the top-k list. Second, a top-(k, l) query returns the top-l uncertain records whose probabilities of being ranked among top-k are the largest. Third, the p-rank of an uncertain record is the smallest number k such that the record takes a probability of at least p to be ranked in the top-k list. A rank threshold top-k query retrieves the records whose p-ranks are at most k. Last, a top-(p, l) query returns the top-l uncertain records with the smallest p-ranks. To answer such ranking queries, we present an efficient exact algorithm, a fast sampling algorithm, and a Poisson approximation-based algorithm. To answer top-(k, l) queries and top-(p, l) queries, we propose PRist+, a compact index. An efficient index construction algorithm and efficacious query answering methods are developed for PRist+. An empirical study using real and synthetic data sets verifies the effectiveness of the probabilistic ranking queries and the efficiency of our methods.     

模糊化的无线传感节点自适应定位算法

节点定位是无线传感网络的研究基础,具有重要的研究价值和研究意义.现有定位算法主要分为测距相关的算法(range-based)与测距无关的算法(range-free)两类.前者通常使用未经处理的、包含环境噪声的测距信号,导致其定位稳定性较差;后者常使用节点间的连通性信息,导致其定位精度相对较低.针对该现状,本文提出一种基于模糊处理的节点自适应定位算法(fuzzy localization, FL).FL算法首先收集有关测距信息,然后对测距信息进行模糊化处理,最后采用自适应算法计算节点位置.FL算法对测距信息的模糊化处理,减少了环境噪声对定位的影响,提高了节点定位的稳定性;同时细化了通讯半径内的距离估计,提高了节点的定位精度;且自适应算法能对模糊化过程进行自适应控制,取得有效模糊化参数.实验结果表明,与DV-Hop算法和Spring算法相比,FL算法减少了约31%和6%的定位误差,且定位稳定性较好.     

采用任意播方式的机会数据聚集算法

数据收集是无线传感网络研究的关键问题,是诸多无线传感网络应用的基础.降低数据聚集的延迟是数据聚集研究中的重点问题.现有的面向延迟的数据聚集算法,多是通过在树型网络结构上设计无冲突的节点调度算法,来降低数据聚集的延迟,没有考虑到无线网络数据易丢失的特性,不能达到期望的延迟效果.本文针对上述问题,提出一种采用任意播(anycast)方式的机会数据收集算法(OA算法).该算法利用机会传输(opportunistic transmission)的思想,用任意播方式传输数据,通过减少数据聚集中重传数据包的数目,来降低数据聚集的延迟.实验表明,与SPT(Shortest path tree)上的数据聚集延迟相比,该方法的发包数目减少了15%,延迟降低了10%.     

Real-time removal of ocular artifacts from EEG based on independent component analysis and manifold learning

Frequent occurrence of ocular artifacts leads to serious problems in interpreting and analyzing the electroencephalogram (EEG). In the present paper, a novel and robust technique is proposed to eliminate ocular artifacts from EEG signals in real time. Independent Component Analysis (ICA) is used to decompose EEG signals. The features of topography and power spectral density of those components are extracted. Moreover, we introduce manifold learning algorithm, a recently popular dimensionality reduction technique, to reduce the dimensionality of initial features, and then those new features are fed to a classifier to identify ocular artifacts components. A k-nearest neighbor classifier is adopted to classify components because classification results show that manifold learning with the nearest neighbor algorithm works best. Finally, the artifact removal method proposed here is evaluated by the comparisons of EEG data before and after artifact removal. The results indicate that the method proposed could remove ocular artifacts effectively from EEG signals with little distortion of the underlying brain signals and be satisfied the real-time application.     

A gradient-based adaptive error diffusion method with edge enhancement

This paper presents a gradient-based adaptive error diffusion method with edge enhancement effect while preserving the smoothness effect. The proposed method not only can modulate the threshold in halftoning dynamically but also can determine the weights of the error diffusion filter adaptively to diffuse the quantization error to neighboring pixels properly. Under six testing images, experimental results demonstrate that our proposed new error diffusion method has a good compromise between the edge enhancement effect in the halftone image and the image quality effect in the corresponding inverse halftone image when compared to the methods by Floyd–Steinberg, Eschbach and Knox, Hwang et al., Li, and Feng et al., but it has some execution-time degradation.     

Neural network-based sliding mode adaptive control for robot manipulators

This paper addresses the robust trajectory tracking problem for a robot manipulator in the presence of uncertainties and disturbances. First, a neural network-based sliding mode adaptive control (NNSMAC), which is a combination of sliding mode technique, neural network (NN) approximation and adaptive technique, is designed to ensure trajectory tracking by the robot manipulator. It is shown using the Lyapunov theory that the tracking error asymptotically converge to zero. However, the assumption on the availability of the robot manipulator dynamics is not always practical. So, an NN-based adaptive observer is designed to estimate the velocities of the links. Next, based on the observer, a neural network-based sliding mode adaptive output feedback control (NNSMAOFC) is designed. Then it is shown by the Lyapunov theory that the trajectory tracking errors, the observer estimation errors asymptotically converge to zero. The effectiveness of the designed NNSMAC, the NN-based adaptive observer and the NNSMAOFC is illustrated by simulations.     

随机非线性动态系统数值模拟与性态分析

针对具有随机参数非线性系统和随机非线性系统的数值模拟与性态分析问题,提出将蒙特卡罗模拟办法与数值积分法相结合,建立了迭代积分算法求取随机非线性系统在不副随机序列作用下的数值解集,分析了计算误差．其次,利用数值解集的样本值进行解过程性态分析,建立了系统状态的概率分布、均方和矩函数的数值汁箅公式,实现了该类系统数值模拟分析的完整算法和程序流程设计．最后,通过两类典型示例系统的数值仿真结粜验证本文算法与流程的正确性和有效性．     

STBC系统在非同分布Nakagami信道下的一种快速性能评估算法

针对非同分布的Nakagami信道,基于矩生成函数MGF(Moment Generation Function)的分析方法,提出正交空时分组码系统STBC(Space-Time Block Coding)的一种快速性能评估算法,不需要涉及超几何函数积分运算,可在中高信噪比时,快速准确地估计STBC系统的符号错误概率性能。在平坦瑞利衰落信道下的计算机仿真表明,该算法与已有的STBC系统的近似估计算法相比,具有较优的性能。