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Digital maps within cars are not only the basis for navigation but also for advanced driver assistance systems. Therefore more and more up-to-date details about the environment of the vehicle are required which means that they have to be enriched with further attributes such as detailed representations of intersections. In the future we will be able to extract details of the environment out of the...
Speckle hinders information in synthetic aperture radar (SAR) images and makes automatic information extraction very difficult. The Bayesian approach allows us to perform the despeckling of an image while preserving its texture and structures. This model-based approach relies on a prior model of the scene. This paper presents an evaluation of two despeckling and texture extraction model-based methods...
This paper discusses the development of a recursive estimator which systematically arrives at sparse parameter estimates. Prior work achieved this by utilizing a Gaussian sum filter. This paper shows the relationship between the implementation using a Gaussian sum filter, where the mean and covariance of each component is propagated, and the equivalent representation using an information filter. We...
In a distributed estimation system, the fusion center receives the local estimates from sensors and fuses them to be an optimal estimation in terms of some criterion. Recently, the best linear unbiased estimation (BLUE) fusion was proposed to minimize the mean square error of the fused estimate, in which the weights to optimally combine the local estimates are determined by the covariance matrix of...
Estimation of distribution algorithm is a new class of evolutionary algorithms. It builds a probability model of promising solutions and samples new individuals from the model. In this paper, we propose a new EDA in which the copula theory is applied to constitute the probabilistic model in the conventional multivariate EDAs. The proposed algorithm employs firstly kernel estimation method to estimate...
In this paper, we study the statistical features of Chinese foreign exchange market data. Furthermore, we mainly fit the sample tail data employed generalized pareto distribution (GPD), when building VaR model based on fat-tail distribution of extreme value theory (EVT), we show that the model can be appropriate to be applied to Chinese foreign exchange market data. We have also proposed a procedure...
Sequences that can be assumed to have been generated by a number of Markov models, whose outputs are randomly interleaved but where the actual sources are hidden, occur in a number of practical situations where data is captured as an unlabeled stream of events. We present a practical method for estimating model parameters on large data sets under the assumption that all sources are identical. Results...
Given the current financial crisis, there is renewed interest in modelling how the price of commodities change in the market. Traditionally, such models have assumed constant parameters. However, large and sudden changes in the parameters can also be anticipated due to market shocks. This paper is aimed at addressing this issue. We first describe a bias-variance trade-off in parameter estimation when...
A number of problems in computer vision require the estimation of a set of matrices, each of which is defined only up to an individual scale factor and represents the parameters of a separate model, under the assumption that the models are intrinsically interconnected. One example of such a set is a family of fundamental matrices sharing an infinite homography. Here an approach is presented to estimating...
In order to reduce the computational load of the recursive least squares (RLS) algorithm, a decomposition based least squares algorithm is developed for non-uniformly sampled multirate systems. The main ideal is to decompose the identification model of the non-uniformly sampled systems into several submodels with smaller dimensions and fewer parameters based on the hierarchical identification principle...
Traditional statistical data processing approach needs to know the distribution regularity of samples. But in the antiradiation missiles (ARM), when aerial defense radar uses active-decoying, the sample distribution regularity usually can't be known or has many likelihoods. To improve the precision and the stabilization of the angle-measure in the active-decoying environment, a grey processing approach...
In order to solve error between the system output values and the model estimation values in non-linear system. A new method, the particle swarm optimization & sequential quadratic programming (PSO-SQP), is proposed to realize the on-line optimization to nonlinear complex system. Firstly, the PSO-SQP algorithm is proposed to solve the slow search speed of the PSO and easy convergence to the local...
In this paper, a new decision tree construction algorithm (MIDT) is proposed. MIDT (Multiple Informative Decision Tree) uses principal component analysis to integrate information gain, samples distribution information and correlation coefficient as the basis of the selection of splitting attributes. This method can overcome the disadvantage of ID3 decision tree construction method that uses information...
This paper presents an estimation method of rotational direction and speed for free running AC machines driven by an inverter without speed and voltage sensor. The method has four estimation modes, and the method utilized only the measured phase current of machines. The amplitude of current during the estimation is suppressed to lower levels such as the exciting current, which is smaller than the...
In order to synthetically utilize the multi-steganalysis algorithms, and ulteriorly enhance the detection accuracy, the united-judgment methods based on parameter-estimation were proposed for image steganalysis. According to two types of universal blind detection and specific steganalysis, a united-judgment method based on weight and threshold, and also a method based on segment were proposed in this...
In modern driver assistance systems the environment perception plays a decisive role in order to evaluate the current traffic scene. The reliable recognition of the drivable area provides essential information for lane departure warning systems which in turn contribute to active road safety. Most systems on lane recognition do reliable work on well marked roads and under good weather and lighting...
Estimating network parameters from noisy data is a hard problem that can be made even more difficult by the presence of a malicious adversary who may corrupt the measurement process by capturing a trusted node or perturbing data externally. The adversary may have complete knowledge of the networking protocols that rely on the parameter estimates and may adjust its effect on the system to push protocols...
There are many instances where it is desirable or even essential to rapidly build a functional radio receiver to recover symbols from an unknown modulated source. The term ??rapid radio?? refers to a demonstrable analysis environment and receiver implementation methodology for rapid deployment. An automated tool for signal analysis requires several stages for the estimation and classification of the...
We consider the problem of parameter estimation of Markovian models where the exact computation of the partition function is not possible or computationally too expensive with MCMC methods. The main idea is then to approximate the expression of the likelihood by a simpler one where we can either have an analytical expression or compute it more efficiently. We consider two approaches: Variational Bayes...
Distributed localization algorithm continues to be an important and challenging topic in today's wireless sensor networks (WSNs). The accurate estimations of distances among nodes are premises for the accurate estimations of node positions. In this paper, we propose some schemes towards the DV-Hop algorithm to improve the distance estimations. These improvements are based on accurate analysis of hop...
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