A note on a common misconception in estimation

An example illustrating a common misconception in minimum mean squared error estimation is presented.     

一种基于DFT的次优高精度频率估计算法与实现

在快速傅里叶变换(FFT)粗估计的基础上,通过曲线拟合,得到一种实现简单的次优高精度频率估计算法.现有的精确估计算法多采用FFT输出的幅度信息,或是FFT的复数输出进行精确估计.本文提出了利用幅度平方信息做精确估计的算法,有效地简化了运算复杂度,实现结构简单.通过仿真验证了本算法在低信噪比下也具有较高的估计精度.     

一种基于局部特征的三维模型检索算法

在基于内容的三维模型检索系统中,特征提取技术是三维模型检索的关键。为此,提出基于局部特征的三维模型检索算法。定义一种新的局部特征描述符：曲度,将其作为三维模型检索时的特征。曲度作为对平均曲率与高斯曲率的校正,在不增加额外计算量的前提下,可同时克服平均曲率对平滑模型的不敏感性和高斯曲率分布较均匀的缺点,更真实地反映三维模型的局部弯曲程度。实验结果表明,以曲度作为特征进行检索,可明显提高检索的查准率,配合全局特征检索时则可在保证查全率的基础上,大幅提高检索的准确性。     

A grand spread estimator using a graphics processing unit

The spread of a source is defined as the number of distinct destinations to which the source has sent packets during a measurement period. Spread estimation is essential in traffic monitoring, measurement, intrusion detection, to mention a few. To support high speed networking, recent research suggests implementing a spread estimator in fast but small on-chip memory such as SRAM. A state-of-the-art estimator can hold succinct information about 10 million distinct packets using 1 MB SRAM. This implies that a measurement period should restart whenever every 10 million distinct packets fill up the SRAM. Spread estimation is a challenging problem because two spread values from different measurement periods cannot be aggregated to derive the total value. Therefore, current spread estimators have a serious limitation concerning the length of the measurement period because SRAM is available a few megabytes at most. In this paper, we propose a spread estimator that utilizes a large memory space of a graphics processing unit on a commodity PC. The proposed estimator utilizes a 1 GB memory, a hundred times larger than those of current spread estimators, and its throughput is still around 160 Gbps. According to our experiments, the proposed scheme can cover a measurement period of a few dozen hours while the current state-of-the-art can cover only one hour. To the best of our knowledge, this has not been achieved by any spread estimators thus far.     

Adaptive reversible data hiding based on a local smoothness estimator

A novel reversible watermarking (RW) scheme based on a local smoothness estimator and multi-step embedding strategy is proposed in this paper. All the pixels are divided into four equal parts. Correspondingly, the watermark embedding process is separated into four independent steps. Thus each step is performed to embed watermark information into its corresponding image part. In each step, for each to-be-embedded pixel, a local smoothness estimator defined as the variance of its total neighbors is presented to estimate its local smoothness. An obvious advantage of introducing this estimator is that it can determine those pixels in smooth regions accurately. In fact, accurate determination means the decrease in embedding distortion. At the low embedding rate (ER), modifications induced by difference expansion (DE) are done only to those pixels located in smooth regions. Hence, the proposed method can obtain high embedding capacity while maintaining good visual quality. With ER gradually increased, adaptive embedding is employed. In adaptive embedding, for one to-be-embedded pixel, 1 or 2 bits are adaptively embedded according to the strength of relationship among all the pixels surrounding it. The experimental results demonstrate that the proposed method is effective.     

A mobile robot iconic position estimator using a radial laser scanner

Position determination for a mobile robot is an important part of autonomous navigation. In many cases, dead reckoning is insufficient because it leads to large inaccuracies over time. Beacon- and landmark-based estimators require the emplacement of beacons and the presence of natural or man-made structure respectively in the environment. In this paper, we present a new algorithm for efficiently computing accurate position estimates based on a radially-scanning laser rangefinder that does not require structure in the environment. The algorithm employs a connected set of short line segments to approximate the shape of any environment and can easily be constructed by the rangefinder itself. We describe techniques for efficiently managing the environment map, matching the sensor data to the map, and computing the robot's position. We present accuracy and runtime results for our implementation.A portion of this paper was presented at the IEEE Int. Conference on Robotics and Automation, Nice, France, May 1992.This work was carried out during the stay of the first and third authors in the Field Robotics Center, The Robotics Institute, Carnegie Mellon University, under grant of the Spanish Government. The research was sponsored in part by the United States Bureau of Mines, contract No. 358021.     

一种大尺度Gauss模糊的快速采样算法

针对大模板Gauss模糊的传统算法计算复杂度较高的缺点,提出了一种基于快速采样的新算法,将计算复杂度从O(N2)降到O(N)级别.利用极大似然估计理论,对传统Gauss模板和采样模板进行分析,给出了采样算法和传统算法的联系与区别.实验表明,新算法在极大的提高算法的速度的同时,可以将两者之间的能量误差控制在1%左右,当采样点等于10N时,人眼基本无法察觉两种算法的差异.     

A two-level estimator for time varying parameters

A crucial part in an adaptive control system is the estimation of the unknown parameters of the process. The estimation is often done using a Kalman filter or an Extended Kalman filter. These estimators give good results if the parameters are not varying too fast. When the parameters are varying fast there are difficulties for the estimator to follow the variations.This paper outlines a new approach to the estimation problem. The new estimator consists of two parts. One conventional Kalman filter for fine estimation and one estimator for coarse estimation. The coarse estimator consists of a finite number of fixed a priori models and a decision mechanism which points out the model which best fits the data.The paper describes the two-level estimator and discusses its properties. Some numerical examples illustrate the behavior of the estimator.     

A quantile estimator under two-phase sampling for stratification

Recently, the estimation of a population quantile has received quite attention. Existing quantile estimators generally assume that values of an auxiliary variable are known for the entire population, and most of them are defined under simple random sampling without replacement. Assuming two-phase sampling for stratification with arbitrary sampling designs in each of the two phases, a new quantile estimator and its variance estimator are defined. The proposed estimators can be used when the population auxiliary information is not available, which is a common situation in practice. Desirable properties such as the unbiasedness are derived. Suggested estimators are compared numerically with an alternative stratification estimator and its variance estimator, and desirable results are observed. Confidence intervals based upon the proposed estimators are also defined, and they are compared via simulation studies with the confidence intervals based upon the stratification estimator. The proposed confidence intervals give desirable coverage probabilities with the smallest interval lengths.     

基于曲率特征的点云快速简化算法

为了提高实体反求的效率,提出一种点云快速简化算法.该算法依据特征点群曲率变化的特点在点云邻域拟合曲面上搜寻特征点并进行储存,依据搜寻结果对点云进行特征点分布评估,并根据评估结果设定相应的简化距离对点云进行简化.算法充分保留了特征区域点云,使得简化后的点云能够较好地表达形状,整个搜寻过程只针对高斯曲率极值点的附近点,相对于需要在全局上进行曲率计算的传统简化算法,该算法在运行速度上具有明显优势.     

三角网格曲面上离散曲率估算方法的比较与分析

对国际上近几年提出的三角网格曲面上估算平均曲率的7种方法和估算高斯曲率的4种方法,进行了系统的总结与大量的实验,并给出误差统计和分析比较,给出了对高斯曲率和平均曲率的估算效果最优的方法,以及较稳定和误差较小的几个新公式．     

On asymptotic behaviors of a sensitivity penalization based robust state estimator

Asymptotic properties are investigated in this paper for the robust state estimator derived by Zhou (2008) [11]. A new formula is derived for the update of the pseudo-covariance matrix of estimation errors. In the case where plant nominal parameters are time invariant, it is shown that, in order to guarantee that this pseudo-covariance matrix converges to a constant positive definite matrix, it is necessary and sufficient that some stabilizability and detectability conditions are satisfied. It is also proved that when these conditions are satisfied, the robust estimator converges to a stable time-invariant system. Moreover, when the system is exponentially stable, this estimate is asymptotically unbiased and its estimation errors are upper bounded.     

A non-parametric estimator for setting reservation prices in procurement auctions

Electronic auction markets collect large amounts of auction field data. This enables a structural estimation of the bid distributions and the possibility to derive optimal reservation prices. In this paper we propose a new approach to setting reservation prices. In contrast to traditional auction theory we use the buyer’s risk statement for getting a winning bid as a key criterion to set an optimal reservation price. The reservation price for a given probability can then be derived from the distribution function of the observed drop-out bids. In order to get an accurate model of this function, we propose a nonparametric technique based on kernel distribution function estimators and the use of order statistics. We improve our estimator by additional information, which can be observed about bidders and qualitative differences of goods in past auctions rounds (e.g. different delivery times). This makes the technique applicable to RFQs and multi-attribute auctions, with qualitatively differentiated offers.     

A robust error estimator and a residual-free error indicator for reduced basis methods

The Reduced Basis Method (RBM) is a rigorous model reduction approach for solving parameterizedpartial differential equations. It identifies a low-dimensional subspace for approximation of the parametric solution manifold that is embedded in high-dimensional space. A reduced order model is subsequently constructed in this subspace. RBM relies on residual-based error indicators or a posteriori error bounds to guide construction of the reduced solution subspace, to serve as a stopping criteria, and to certify the resulting surrogate solutions. Unfortunately, it is well-known that the standard algorithm for residual norm computation suffers from premature stagnation at the level of the square root of machine precision.In this paper, we develop two alternatives to the standard offline phase of reduced basis algorithms. First, we design a robust strategy for computation of residual error indicators that allows RBM algorithms to enrich the solution subspace with accuracy beyond root machine precision. Secondly, we propose a new error indicator based on the Lebesgue function in interpolation theory. This error indicator does not require computation of residual norms, and instead only requires the ability to compute the RBM solution. This residual-free indicator is rigorous in that it bounds the error committed by the RBM approximation, but up to an uncomputable multiplicative constant. Because of this, the residual-free indicator is effective in choosing snapshots during the offline RBM phase, but cannot currently be used to certify error that the approximation commits. However, it circumvents the need for a posteriori analysis of numerical methods, and therefore can be effective on problems where such a rigorous estimate is hard to derive.     

A colorization algorithm based on local MAP estimation

This paper presents a colorization algorithm which adds color to monochrome images. In this paper, the colorization problem is formulated as the maximum a posteriori (MAP) estimation of a color image given a monochrome image. Markov random field (MRF) is used for modeling a color image which is utilized as a prior for the MAP estimation. The MAP estimation problem for a whole image is decomposed into local MAP estimation problems for each pixel. Using 0.6% of whole pixels as references, the proposed method produced pretty high quality color images with 25.7-32.6 dB PSNR values for eight images.     

Adaptive optimisation-offline cyber attack on remote state estimator

Security issues of cyber-physical systems have received increasing attentions in recent years. In this paper, deception attacks on the remote state estimator equipped with the chi-squared failure detector are considered, and it is assumed that the attacker can monitor and modify all the sensor data. A novel adaptive optimisation-offline cyber attack strategy is proposed, where using the current and previous sensor data, the attack can yield the largest estimation error covariance while ensuring to be undetected by the chi-squared monitor. From the attacker's perspective, the attack is better than the existing linear deception attacks to degrade the system performance. Finally, some numerical examples are provided to demonstrate theoretical results.     

A novel robust position estimator for self-sensing magnetic levitation systems based on least squares identification

In this work a novel method is introduced for the estimation of the position of a self-sensing magnetic levitation system, based on a least squares identification strategy. In the first step, a detailed mathematical model of the magnetic levitation system is derived and the properties of this system are analyzed for the case of a pulse-width modulated control. Based on this model, an estimation algorithm for the inductance of the magnetic levitation system is introduced. In classical position estimation schemes known form the literature large estimation errors are typically induced by a deviation of the electric resistance from its nominal value or by a fast motion of the levitated object. In this work it is shown that these errors can be exactly compensated by means of a suitable estimation strategy. Furthermore, it is outlined that the chosen structure of the estimation scheme allows for a very efficient implementation in real-time hardware. Afterwards, the design of a cascaded position controller for the magnetic levitation system is briefly summarized. Finally, the excellent quality and the high robustness of the proposed position estimator is demonstrated by means of simulation studies and measurement results on a test bench.     

A practical approximation algorithm for the LMS line estimator

The problem of fitting a straight line to a finite collection of points in the plane is an important problem in statistical estimation. Robust estimators are widely used because of their lack of sensitivity to outlying data points. The least median-of-squares (LMS) regression line estimator is among the best known robust estimators. Given a set of n points in the plane, it is defined to be the line that minimizes the median squared residual or, more generally, the line that minimizes the residual of any given quantile q, where 0<q?1. This problem is equivalent to finding the strip defined by two parallel lines of minimum vertical separation that encloses at least half of the points.The best known exact algorithm for this problem runs in O(n²) time. We consider two types of approximations, a residual approximation, which approximates the vertical height of the strip to within a given error bound ε_r?0, and a quantile approximation, which approximates the fraction of points that lie within the strip to within a given error bound ε_q?0. We present two randomized approximation algorithms for the LMS line estimator. The first is a conceptually simple quantile approximation algorithm, which given fixed q and ε_q>0 runs in O(nlogn) time. The second is a practical algorithm, which can solve both types of approximation problems or be used as an exact algorithm. We prove that when used as a quantile approximation, this algorithm's expected running time is . We present empirical evidence that the latter algorithm is quite efficient for a wide variety of input distributions, even when used as an exact algorithm.     

高斯曲率约束的MRG骨架提取优化算法

三维模型的骨架保持了模型的拓扑特性,并被广泛应用于模型相似性比较、计算机动画及压缩等领域.根据多分辨率Reeb图的原理,提出了一种基于离散高斯曲率约束的骨架提取优化算法.通过计算网格顶点的离散高斯曲牢判断曲面局部凸凹特性,以获取模型表面的双曲极值点作为约束点;并依据约束点及其邻域的μ函数值产生的分裂线进行区域细分,获得子连通区域、确定关节点、形成优化的骨架结构.实验结果表明,该算法有效地突出了模型的拓扑分支特征以及模型表面的细节,提高了骨架提取的精度和效率.