Segmentation of brain tissues from MR images is medically valuable for helping to assess many diseases. In this paper, we propose a three-layer Gaussian mixture model framework (3L-GMM) for fully automatic tissue segm...
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Segmentation of brain tissues from MR images is medically valuable for helping to assess many diseases. In this paper, we propose a three-layer Gaussian mixture model framework (3L-GMM) for fully automatic tissue segmentation of three-dimensional brain MR images by using spatial structure information. It uses separate GMMs to model the intensity information, the spatial structure information, and the intensity-spatial feature vector, respectively. We implement the brain tissues segmentation task by maximizing the a posteriori probability of the 3L-GMM model. Experiments are conducted on the threedimensional, T1-weighted, simulated and in vivo MR images of the BrainWeb and IBSR data sets. The qualitative and quantitative comparisons with the gold standard demonstrate that the proposed model can achieve performance improvement over the state-of-the-art methods in the literature.
This paper is concerned with identification of nonlinear systems with a noisy scheduling variable, and the measurement of the system has an unknown time delay. Auto regressive exogenous (ARX) models are selected as th...
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This paper is concerned with identification of nonlinear systems with a noisy scheduling variable, and the measurement of the system has an unknown time delay. Auto regressive exogenous (ARX) models are selected as the local models, and multiple local models are identified along the process operating points. The dynamics of a nonlinear system are represented by associating a normalized exponential function with each of the ARX models; therein, the normalized exponential function is acted as the probability density function. The parameters of the ARX models and the exponential functions as well as the unknown time delay are estimated simultaneously under the expectationmaximization (EM) algorithm using the retarded input-output data. A CSTR example is given to verify the proposed identification approach.
Unlike case-control studies, family-based tests for association are protected against population stratification. Complex genetic traits are often governed by quantitative precursors and it has been argued that it may ...
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Unlike case-control studies, family-based tests for association are protected against population stratification. Complex genetic traits are often governed by quantitative precursors and it has been argued that it may be a more powerful strategy to analyze these quantitative precursors instead of the clinical end point trait. Although methods have been developed for family-based association tests for single quantitative traits, it is of interest to develop such methods for multivariate phenotypes. We propose a novel transmission-based approach based on a trio design using a simple logistic regression to test for association with a multivariate phenotype. We use our proposed method to analyze data on systolic and diastolic blood pressure levels provided in Genetic Analysis Workshop 18. However, we find that the bivariate analysis of the two phenotypes did not provide more promising results compared to univariate analyses, suggesting a possibility of a different set of major genetic variants modulating the two phenotypes.
In this paper,we present a prediction and compensation method for Micro-Electro-Mechanical System (MEMS) gyroscope random drift,which is based on relevance vector *** relevance vector machine (RVM) model is establishe...
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ISBN:
(纸本)9781479946983
In this paper,we present a prediction and compensation method for Micro-Electro-Mechanical System (MEMS) gyroscope random drift,which is based on relevance vector *** relevance vector machine (RVM) model is established based on the feature of MEMS gyroscope random drift and the parameters are trained by the expectationmaximization (EM) *** phase space reconstruction,the time sequence of random drift is accessed in the *** final experimental results indicate that our proposed methodology can achieve both the least complexity of structure and goodness of fit to data,and also can predict the gyroscope random drift ***,by compensating random drift using the predicting result,the precision of gyroscopes application could be improved well.
We propose a method to obtain the maximum likelihood (ML) parameter estimation of the Gamma-Gamma (Gamma - Gamma) distribution representing the free space optical (FSO) channel irradiance fluctuations. The proposed me...
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We propose a method to obtain the maximum likelihood (ML) parameter estimation of the Gamma-Gamma (Gamma - Gamma) distribution representing the free space optical (FSO) channel irradiance fluctuations. The proposed method is based on the expectationmaximization (EM) algorithm and the generalized Newton method using a non-quadratic approximation. The numerical results show that, for all turbulence conditions, the proposed ML method is more accurate than the fractional moments (FMOM) method and the numerical ML method (two dimensional numerical maximization of log-likelihood function using the Nelder-Mead algorithm). Moreover, the proposed ML is a fast and stable iterative method, because the iterations always converge to the global optimum with high convergence rate.
Background: Many problems in computational biology require alignment-free sequence comparisons. One of the common tasks involving sequence comparison is sequence clustering. Here we apply methods of alignment-free com...
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Background: Many problems in computational biology require alignment-free sequence comparisons. One of the common tasks involving sequence comparison is sequence clustering. Here we apply methods of alignment-free comparison (in particular, comparison using sequence composition) to the challenge of sequence clustering. Results: We study several centroid based algorithms for clustering sequences based on word counts. Study of their performance shows that using k-means algorithm with or without the data whitening is efficient from the computational point of view. A higher clustering accuracy can be achieved using the soft expectationmaximization method, whereby each sequence is attributed to each cluster with a specific probability. We implement an open source tool for alignment-free clustering. It is publicly available from github: https://***/luscinius/afcluster. Conclusions: We show the utility of alignment-free sequence clustering for high throughput sequencing analysis despite its limitations. In particular, it allows one to perform assembly with reduced resources and a minimal loss of quality. The major factor affecting performance of alignment-free read clustering is the length of the read.
A method to estimate the parameters of radar reflectivity distribution functions of convective storm systems is presented. To carry out this estimation, the probability density distribution of the radar reflectivity, ...
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A method to estimate the parameters of radar reflectivity distribution functions of convective storm systems is presented. To carry out this estimation, the probability density distribution of the radar reflectivity, P(Z), is computed using data collected on continental convective storm systems with the radar of Little Rock in central Arkansas. We show that P(Z) can be modeled as a mixture of Gaussian components, each of them corresponding to a type of precipitation. The EM (expectationmaximization) algorithm is used to decompose P(Z) in these merged components. In the precipitation associated with intense continental convective storms, four main populations are considered: shallow precipitation, stratiform precipitation, convective precipitation, and hail. Each component is described by the fraction of area occupied inside P(Z) and by the Gaussian parameters, mean and variance. The retrieval of the mixed distribution by a linear combination of the Gaussian components gives a very satisfactory P(Z) fitting. It is shown that this method enables to follow the evolution with time of the various precipitation components of a convective system crossing the radar observed area. (C) 2013 Elsevier B.V. All rights reserved.
The gamma distribution is used as a lifetime distribution widely in reliability analysis. Lifetime data are often left truncated, and right censored. The EM algorithm is developed here for the estimation of the scale ...
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The gamma distribution is used as a lifetime distribution widely in reliability analysis. Lifetime data are often left truncated, and right censored. The EM algorithm is developed here for the estimation of the scale and shape parameters of the gamma distribution based on left truncated and right censored data. The Newton-Raphson method is also used for the same purpose, and then these two methods of estimation are compared through an extensive Monte Carlo simulation study. The asymptotic variance-covariance matrix of the MLEs under the EM framework is obtained by using the missing information principle (Louis, 1982). Then, the asymptotic confidence intervals for the parameters are constructed. The confidence intervals based on the EM algorithm and the Newton-Raphson method are then compared empirically in terms of coverage probabilities. Finally, all the methods of inference discussed here are illustrated through a numerical example.
This paper deals with system identification of general nonlinear dynamical systems with an uncertain scheduling variable. A multi model approach is developed;wherein, a set of local auto regressive exogenous (ARX) mod...
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This paper deals with system identification of general nonlinear dynamical systems with an uncertain scheduling variable. A multi model approach is developed;wherein, a set of local auto regressive exogenous (ARX) models are first identified at different process operating points, and are then combined to describe the complete dynamics of a nonlinear system. An expectation-maximization (EM) algorithm is used for simultaneous identification of local ARX models, and for computing the probability associated with each of the local ARX models taking effect. A smoothing algorithm is used to estimate the distribution of the hidden scheduling variables in the EM algorithm. If the dynamics of the scheduling variables are linear, Kalman smoother is used;whereas, if the dynamics are nonlinear, sequential Monte-Carlo (SMC) method is used. Several simulation examples, including a continuous stirred tank reactor (CSTR) and a distillation column, are considered to illustrate the efficacy of the proposed method. Furthermore, to highlight the practical utility of the developed identification method, an experimental study on a pilot-scale hybrid tank system is also provided. (C) 2013 Elsevier Ltd. All rights reserved.
This paper develops and illustrates a new maximum-likelihood based method for the identification of Hammerstein-Wiener model structures. A central aspect is that a very general situation is considered wherein multivar...
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This paper develops and illustrates a new maximum-likelihood based method for the identification of Hammerstein-Wiener model structures. A central aspect is that a very general situation is considered wherein multivariable data, non-invertible Hammerstein and Wiener nonlinearities, and colored stochastic disturbances both before and after the Wiener nonlinearity are all catered for. The method developed here addresses the blind Wiener estimation problem as a special case. (C) 2012 Elsevier Ltd. All rights reserved.
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