Estimation of the Bivariate Stable Spectral Representation by theProjection Method

A method of estimating the spectral representation of a generalized bivariatestable distribution is presented, based on a series of maximum likelihood (ML)estimates of the stable parameters of univariate projections of the data. Thecorresponding stable spectral density is obtained by solving a quadraticprogram. The proposed method avoids the often arduous task of computing themultivariate stable density, relying instead on the standard univariate stabledensity. The paper applies this projection procedure, under the simplifyingassumption of symmetry, to simulated data as well as to foreign exchangereturn data, with favorable results. Kanter projection coefficients governingconditional expectations are computed from the estimated spectral density. For the simulated data these compare well to their known true values.     

An Artificial Neural Network Classifier Design Based-on Variable Kernel and Non-Parametric Density Estimation

This paper proposes a probabilistic variant of the SOM-kMER (Self Organising Map-kernel-based Maximum Entropy learning Rule) model for data classification. The classifier, known as pSOM-kMER (probabilistic SOM-kMER), is able to operate in a probabilistic environment and to implement the principles of statistical decision theory in undertaking classification problems. A distinctive feature of pSOM-kMER is its ability in revealing the underlying structure of data. In addition, the Receptive Field (RF) regions generated can be used for variable kernel and non-parametric density estimation. Empirical evaluation using benchmark datasets shows that pSOM-kMER is able to achieve good performance as compared with those from a number of machine learning systems. The applicability of the proposed model as a useful data classifier is also demonstrated with a real-world medical data classification problem.     

Some Statistical-Estimation Methods for Stochastic Finite-State Transducers

Formal translations constitute a suitable framework for dealing with many problems in pattern recognition and computational linguistics. The application of formal transducers to these areas requires a stochastic extension for dealing with noisy, distorted patterns with high variability. In this paper, some estimation criteria are proposed and developed for the parameter estimation of regular syntax-directed translation schemata. These criteria are: maximum likelihood estimation, minimum conditional entropy estimation and conditional maximum likelihood estimation. The last two criteria were proposed in order to deal with situations when training data is sparse. These criteria take into account the possibility of ambiguity in the translations: i.e., there can be different output strings for a single input string. In this case, the final goal of the stochastic framework is to find the highest probability translation of a given input string. These criteria were tested on a translation task which has a high degree of ambiguity.     

ON THE PARTIAL SUMS OF RESIDUALS IN AUTOREGRESSIVE AND MOVING AVERAGE MODELS

Abstract. The limiting process of partial sums of residuals in stationary and invertible autoregressive moving-average models is studied. It is shown that the partial sums converge to a standard Brownian motion under the assumptions that estimators of unknown parameters are root- n consistent and that innovations are independent and identically distributed random variables with zero mean and finite variance or, more generally, are martingale differences with moment restrictions specified in Theorem 1. Applications for goodness-of-fit and change-point problems are considered. The use of residuals for constructing nonparametric density estimation is discussed.     

Accurate mirror-based camera pose estimation with explicit geometric meanings

An accurate closed form solution is proposed to estimate camera pose by several mirrored reference object images acquired via a planar mirror under different unknown poses. Compared with state-of-the-art methods, our method is more accurate when there are more than three images and has explicit geometric meanings. This method also properly handles cases in which some of the mirror poses are parallel. The central idea is to minimize an error metric based on all reflections of rotation, which enables the camera rotation to be estimated directly by SVD of sum of mirrored camera rotations. After that, the camera translation is computed by solving a large system of linear equations to minimize object space collinearity error. Both synthesized data and real data experiments show the advantages of our approach.     

Tracking of moving interfaces in sedimentation processes using electrical impedance tomography

Sedimentation monitoring is widely used to control and optimize industrial processes. In this paper we propose a novel computational method for sedimentation monitoring using electrical impedance tomography (EIT). EIT measurements consist of electric current and voltage measurements that are made on the surface of the sedimentation tank and therefore they do not interfere with the sedimentation process. The proposed computational method is based on shape estimation and state estimation formulation of the EIT problem. The sedimentation is parameterized by the locations of the phase interfaces and conductivities of the phase layers. Three different evolution models for the state parameters are considered and the state estimates are computed using the extended Kalman filter algorithm. The performance of the method and the models are evaluated using simulated data from a six electrode EIT measurement configuration. From the results a promising performance of the method can be seen.     

Horizons and Ergoregions in Superfluids

Ripplons—gravity-capillary waves on the free surface of a liquid or at the interfaces between two superfluids—are the most favorable excitations for simulation of the general-relativistic effects related to horizons and ergoregions. The white-hole horizon for the “relativistic” ripplons at the surface of the shallow liquid is easily simulated using the kitchen-bath hydraulic jump. The same white-hole horizon is observed in quantum liquid—superfluid ⁴He. The ergoregion for the “non-relativistic” ripplons is generated in the experiments with two sliding ³He superfluids. The common property experienced by all these ripplons is the Miles instability inside the ergoregion or horizon. Because of the universality of the Miles instability, one may expect that it could take place inside the horizon of the astrophysical black holes, if there is a preferred reference frame which comes from the trans-Planckian physics. If this is the case, the black hole would evapotate much faster than due to the Hawking radiation. Hawking radiation from the artificial black hole in terms of the quantum tunneling of phonons and ripplons is also discussed.      

Image edge detection based on multi-fractal spectrum analysis

In this paper, an image edge detection method based on multi-fractal spectrum analysis is presented. The coarse grain H?lder exponent of the image pixels is first computed. then, its multi-fractal spectrum is estimated by the kernel estimation method. Finally, the image edge detection is done by means of different multi-fractal spectrum values. Simulation results show that this method is efficient and has better locality compared with the traditional edge detection methods such as the Sobel method. __________ Translated from Journal of Hunan University (Natural Sciences), 2004, 31(4): 28–33     

DS-SS通信扩频序列的估计

该文给出一种在各通信参数未知的情况下估计出DS-SS通信中伪随机码的方法,并对这一算法进行了仿真及性能分析。     

矿业软件在矿产储量评价中的应用

矿产储量评价的基本内容是建立矿床地质模型进行品位估计和储量分类分级．地质控制条件限制是矿床建模的重要手段。地质统计学方法是矿块品位估计以及储量分级等的有效方法．国内的应用实例表明。国际先进矿业软件是矿产储量评价的先进工具，应用矿业软件进行矿产储量评价是市场经济的需要。