Supervised Classification and Estimation of Hydrometeors From C-Band Dual-Polarized Radars: A Bayesian Approach

In this paper, a Bayesian statistical approach for supervised classification and estimation of hydrometeors, using a C-band polarimetric radar, is presented and discussed. The Bayesian Radar Algorithm for Hydrometeor Classification at C-band (BRAHCC) is supervised by a backscattering microphysical model, aimed at representing ten different hydrometeor classes in water, ice, and mixed phase. The expected error budget is evaluated by means of contingency tables on the basis of C-band radar noisy and attenuated synthetic data. Its accuracy is better than that obtained from a previously developed fuzzy logic C-band classification algorithm. As a second step of the overall retrieval algorithm, a multivariate regression is adopted to derive water content statistical estimators, exploiting simulated polarimetric radar data for each hydrometeor class. The BRAHCC methodology is then applied to a convective hail event, observed by two C-band dual-polarized radars in a network configuration. The hydrometeor classification along the line of sight, connecting the two C-band radars, is performed using the BRAHCC applied to path-attenuation-corrected data. Qualitative results are consistent with those derived from the fuzzy logic algorithm. Hydrometeor water content temporal evolution is tracked along the radar line of sight. Hail vertical occurrence is derived and compared with an empirical hail detection index applied along the radar connection line during the whole event.     

State and Disturbance Estimator for Time-Delay Systems With Application to Fault Estimation and Signal Compensation

For a state-space time-delay system with linearly coupled input and output disturbances, a simultaneous state and disturbance estimation technique is developed. For a nonlinear state-space time-delay system with dependent input and output disturbances, a nonlinear estimator is also proposed to estimate system state and disturbance at the same time. The proposed estimator techniques are applied next to estimate system state and fault signal. Via actuator and/or sensor signal compensation, a simple and efficient fault-tolerant operation can be realized. In the developed design, no limitations and prior knowledge are required on the considered faults. Moreover, identical actuator and/or sensor switches and control gain reconstruction are not necessary. Therefore, the proposed estimation and fault-tolerant scheme is economical and convenient in practical applications. After that, the design techniques are extended to the case of systems with a class of uncoupled input and output faults. Examples and simulations given show excellent signal estimation and fault-tolerant performance.     

A Bayesian Approach to Adaptive Detection in Nonhomogeneous Environments

We consider the adaptive detection of a signal of interest embedded in colored noise, when the environment is nonhomogeneous, i.e., when the training samples used for adaptation do not share the same covariance matrix as the vector under test. A Bayesian framework is proposed where the covariance matrices of the primary and the secondary data are assumed to be random, with some appropriate joint distribution. The prior distributions of these matrices require a rough knowledge about the environment. This provides a flexible, yet simple, knowledge-aided model where the degree of nonhomogeneity can be tuned through some scalar variables. Within this framework, an approximate generalized likelihood ratio test is formulated. Accordingly, two Bayesian versions of the adaptive matched filter are presented, where the conventional maximum likelihood estimate of the primary data covariance matrix is replaced either by its minimum mean-square error estimate or by its maximum a posteriori estimate. Two detectors require generating samples distributed according to the joint posterior distribution of primary and secondary data covariance matrices. This is achieved through the use of a Gibbs sampling strategy. Numerical simulations illustrate the performances of these detectors, and compare them with those of the conventional adaptive matched filter.     

A Bayesian Approach to Parameter and Reliability Estimation in the Poisson Distribution

For life testing procedures, a Bayesian analysis is developed with respect to a random intensity parameter in the Poisson distribution. Bayes estimators are derived for the Poisson parameter and the reliability function based on uniform and gamma prior distributions of that parameter. A Monte Carlo procedure is implemented to make possible an empirical mean-squared error comparison between Bayes and existing minimum variance unbiased, as well as maximum likelihood, estimators. As expected, the Bayes estimators have mean-squared errors that are appreciably smaller than those of the other two.     

A Bayesian Approach to Reliability Estimation Using a Loss Function

A Bayesian estimate of reliability for the exponential case is developed which utilizes the basic notion of loss in estimation theory. Since the loss associated with overestimation is usually greater than the loss associated with underestimation of reliability, the loss function can be a useful tool. The loss function and prior distribution of reliability presented are sufficiently flexible to be compatible with many situations in which reliability estimates are require. When no prior information is at hand and a symmetric loss is used, the resulting estimate is seen to be the minimum variance unbiased estimate of reliability. This agreement gives some credibility to the precision of the estimation approach.     

A New Approach to Motion Pattern Recognition and Its Application to Optical Flow Estimation

In this paper, a new methodology for extracting motion patterns is applied to optical flow estimation in the presence of multiple motions. The proposed approximation deals with the problem in two stages. In the first one, the most important motions are segmented; in the second one, the optical flow is estimated on the basis of the motions detected in the previous stage. To extract relevant motions, a new approach based on a spatio-temporal filtering is presented. The approach groups together parts of a moving object that have been separated into various filter responses because of the object's spatial structure, thereby avoiding the spatial dependency problem associated with a representation based on spatio-temporal filters. The proposed model, therefore, generates one "motion pattern" for each motion detected in the sequence. To obtain an optical flow estimation, which is able to represent multiple velocities, the gradient constraint is applied to the output of each filter so that multiple estimations of the velocity at the same location may be obtained. For each "motion pattern" detected in the previous stage, the velocities at a given point corresponding to the same motion are then combined using a probabilistic approach. In the application to optical flow estimation, the use of "motion patterns" allows multiple velocities to be represented, while the combination of estimations from different filters helps reduce the aperture problem. This technique is illustrated on real and simulated data sets, including sequences with occlusion and transparencies     

A General Harmonic Rule Controller for Run-to-Run Process Control

The existence of initial bias in parameter estimation is an important issue in controlling short-run processes in semiconductor manufacturing. Harmonic rule has been widely used in machine setup adjustment problems. This paper generalizes the harmonic rule to a new controller called general harmonic rule (GHR) controller in run-to-run process control. The stability and optimality of the GHR controller is discussed for a wide range of stochastic disturbances. A numerical study is performed to compare the sensitivity of the GHR controller, the exponentially weighted moving average (EWMA) controller and the variable EWMA controller. It is shown that the GHR controller is more robust than the EWMA controller when the process parameters are estimated with uncertainty.      

基于视觉的车道状态估计

车道状态估计是车辆辅助驾驶系统的关键功能。本文提出了一种基于教育处机视觉的车道状态估计新方法。提出了车道标线在图像平面中的一种描述,讨论了其性质,并应用于车道的检测。利用真实世界中车辆在二维图像平面中的透视特征,提出了基于二值有序变换（BROT）的障碍物检测新方法。由于采用单目视觉方法检测前方车辆以控制车辆的横向偏离和纵向间距,降低了系统的复杂度,实验结果显示了新方法的有效性。     

A Scaling Parameter Approach to Delay-Dependent State Estimation of Delayed Neural Networks

This brief is concerned with studying the delay-dependent state estimation problem of recurrent neural networks with time-varying delay. The neuron activation function is more general than the sigmoid functions, and the time-varying delay is allowed to vary fast with time. A scaling parameter based approach is proposed, and a delay-dependent criterion is derived under which the resulting error system is globally asymptotically stable. It is shown that the design of a proper state estimator is directly accomplished by means of the feasibility of a linear matrix inequality. Thanks to the introduction of a scaling parameter, the developed result can efficiently be applied to chaotic delayed neural networks.      

A Novel Approach for Target Detection and Classification Using Canonical Correlation Analysis

We present a novel detection approach, detection with canonical correlation (DCC), for target detection without prior information on the interference. We use the maximum canonical correlations between the target set and the observation data set as the detection statistic, and the coefficients of the canonical vector are used to determine the indices of components from a given target library, thus enabling both detection and classification of the target components that might be present in the mixture. We derive an approximate distribution of the maximum canonical correlation when targets are present. For applications where the contributions of components are non-negative, non-negativity constraints are incorporated into the canonical correlation analysis framework and a recursive algorithm is derived to obtain the solution. We demonstrate the effectiveness of DCC and its nonnegative variant by applying them on detection of surface-deposited chemical agents in Raman spectroscopy.     

一种改进的局部线性回归估计器及其在雷达高度计海况偏差估计中的应用

在建立雷达高度计海况偏差(Sea State Bias, SSB)非参数模型时,通常会用到局部线性回归(Local Linear Regression, LLR)估计器,而传统的局部线性回归估计器涉及高维矩阵运算,当建模的数据量较大时,估计海况偏差需要大量的时间,从而使得非参数估计方法很难用于高维海况偏差模型。该文提出一种改进的局部线性回归(Improved Local Linear Regression, ILLR)估计器,可以避免传统的LLR估计器所需的高维矩阵运算,在不影响海况偏差估计结果的条件下,将局部线性回归估计器获取加权函数的时间复杂度由O(N2)降低为O(N),从而大幅地降低估计海况偏差所需的时间,为实现高维非参数海况偏差模型的实时运算奠定了基础。     

短距离无线测控系统及其应用

文章介绍了一种以单片机为基础的短距离无线测控系统,采用射频电子技术来传送被测试物体与计算机间的数据和控制指令,系统主要由计算机、单片机和无线收发数传MODEM构成,介绍了系统的结构特点、工作原理,采取的抗干扰、降低系统功耗等措施,及其在停车场收费系统中的应用。     

系统入侵的分类与检测

本文讨论了计算机系统的安全问题,重点放在分布式系统和网络的安全方面。首先,我们研究了如何对计算机网络系统的非法入侵进行分类的问题,给出了有效的分类方法。然后,概括地介绍了系统的入侵检测技术,其中包括神经网络、专家系统等人工智能技术在计算机系统入侵检测中的应用。     

基于数据驱动的信息融合及其在车辆声辨识中的应用

传统多源信息融合方法大都依赖于事先建立的理论机理模型,一般会引入一定的简化操作。然而实际中的应用往往会较为复杂,建立的理论模型一般存在一定的偏差。在某些情况下,满足性能要求的理论模型甚至无法给出。针对这样的缺陷,该文根据数据驱动的思想,提出了两种基于数据驱动的信息融合实现方法。通过联合利用基于数据的特征集与基于模型的特征集,有效弥补了模型中缺失的信息,从而提高信息融合的性能。将其运用在一个基于声音信息融合的地面车辆辨识实例中,获得了良好的识别性能,展现出将数据驱动处理思路引入信息融合的可行性和优点。     

A New Discretization Approach With Application in Reliability Estimation

This paper introduces a new discretization technique that retains the basic structure of the failure rate function of the original life distribution. Earlier approaches for discretizing a continuous random variable, reported in the literature so far, are moment equalization, and discrete concentration techniques. This proposed approach has been used for approximating the reliability of complex systems where exact determination of survival probability is analytically intractable. These applications show a reasonable degree of closeness between this new method and the simulated results. Admissibility of the proposed method over the methods of discrete concentration and numerical integration has also been established.     

一种基于PN序列的OFDMA上行链路参数估计和信号检测方法

该文研究了OFDMA上行链路的频偏信道参数估计和信号检测问题,设计了一种OFDMA上行链路发射信号帧结构,信号帧的帧头由PN序列构成;提出了基于PN序列的频偏信道参数估计算法;并根据频偏信道参数采用时域最小均方误差检测算法进行信号检测。仿真表明：该文提出的频偏信道估计算法充分利用PN序列的相关特性,具有良好的抗噪特性、衰落信道适应性和高的估计精度;而时域最小均方误差信号检测算法也具有较好的误比特率性能。     

一种新颖的DOA估计算法

子空间类波达方向(Direction Of Arrival, DOA)估计算法的关键在于得到高质量的信号子空间估计。该文利用矩阵伪逆的双正交性,针对源信号不相关而其本身是色信号的情况,给出了一种新颖的DOA估计算法,它不需要知道噪声统计特性。该算法利用一组空时相关矩阵的结构化信息,能稳健而精确地估计出信号子空间,从而得到DOA的精确估计。仿真实验证实了所给算法的有效性。     

Semisupervised Classification Based Clustering Approach in WSN for Forest Fire Detection

Forest fire is a very considerable problem of ecological system. This paper depicts a novel technique which detects the high active(HA) zone (nearer to the epicenter of fire) in the forest and transmits all sensed data to the base station through wireless communication as early as possible. Fire office takes necessary action to prevent the spreading of fire. For this purpose sensors are deployed in forest zone to sense different data which are necessary for detecting forest fire and divides it into different clusters. A semisupervised rule-based classification model is proposed in this paper to detect whether its zone is high active, medium active (MA) or low active (LA) cluster in the forest. We train our proposed integrated model in such a way when only one parameter of sensed data is transmitted by the sensor nodes due to energy constraint to the initiator of that zone, initiator can be able to predict the state of (HA,MA,LA) zone with 96% accuracy. All the sensor nodes in HA cluster transmit their packet through cluster head to the base station continuously applying greedy forwarding technique. Authors consider energy saving strategy during cluster head selection and data transmission in HA zone. On the other hand, sensors in MA zone transmit packet periodically and LA zone avoids to transmit the sensed data. This way proposed technique transmits the sensed data from HA zone efficiently and quickly to forest office for forest fire prevention and saves the energy of all sensor nodes in the forest.

     

一种新颖的数据估计技术--dEWMA及其在RED中的应用

该文首先提出了一种新颖的估计技术——dEWMA(doubleExponentialWeightedMovingAverage)。该技术采用两个EWMA估计器进行数据测量,克服了传统EWMA估计技术不能同时获得良好的时效性和平稳性的缺点,能够为相关应用提供更加准确、灵活的数据估计。其次通过在RED网关中应用dEWMA技术,对其有效性进行验证。仿真结果表明采用dEWMA的RED性能较之未采用dEWMA的RED有明显的改善。     

一种鲁棒的摄像机运动分类算法

摄像机运动分类是基于内容的视频分析和理解的重要问题.本文通过对运动矢量场的分析,提出了一种基于统计学习的、分层次的摄像机运动分类算法.该算法利用支持向量机(SVM)在有限样本条件下的学习能力,实现摄像机运动类型的初步分类;然后,充分考虑运动矢量场的方向和位置信息,进一步区分缩放和旋转操作,并识别摄像机平移操作的方向.算法在运动矢量的预处理过程中引入摄影规则,有效地降低了前景运动噪声的影响.