Functional data often exhibit both amplitude and phase variation around a common base shape, with phase variation represented by a so called warping function. The process of removing phase variation by curve alignment...
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Functional data often exhibit both amplitude and phase variation around a common base shape, with phase variation represented by a so called warping function. The process of removing phase variation by curve alignment and inference of the warping functions is referred to as curve registration. When functional data are observed with substantial noise, model-based methods can be employed for simultaneous smoothing and curve registration. However, the nonlinearity of the model often renders the inference computationally challenging. An alternative method for model-based curve registration is proposed which is computationally more stable and efficient than existing approaches in the literature. The proposed method is applied to the analysis of elephant seal dive profiles. The result shows that more intuitive groupings can be obtained by clustering on phase variations via the predicted warping functions. (C) 2018 Elsevier B.V. All rights reserved.
In this paper, we introduce a novel method for joint channel estimation and symbol detection in a MIMO wireless communication system. The algorithm uses the expectation-maximization (em) algorithm for channel estimati...
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ISBN:
(纸本)0780390199
In this paper, we introduce a novel method for joint channel estimation and symbol detection in a MIMO wireless communication system. The algorithm uses the expectation-maximization (em) algorithm for channel estimation, together with a new complex-domain sphere decoding (SD) algorithm for near-optimal detection of data symbols. The proposed algorithm is tested on a MIMO communication system. Simulation results show that, even with a short training sequence, the algorithm provides a near optimal performance with a reasonable computational complexity. We also compare the computational complexity of the proposed sphere decoding approach with other techniques and show that it has a lower computational complexity without any performance penalty.
Object-based video segmentation is an important issue for many multimedia applications. A video segmentation method based on em algorithm is proposed. We consider video segmentation as an unsupervised classification p...
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Object-based video segmentation is an important issue for many multimedia applications. A video segmentation method based on em algorithm is proposed. We consider video segmentation as an unsupervised classification problem and apply em algorithm to obtain the maximum-likelihood estimation of the Gaussian model parameters for model-based segmentation. We simultaneously combine multiple features (motion, color) within a maximum likelihood framework to obtain accurate segment results. We also use the temporal consistency among video frames to improve the speed of em algorithm. Experimental results on typical MPEG-4 sequences and real scene sequences show that our method has an attractive accuracy and robustness.
A method of computing the observed information for the hidden Markov model using the em algorithm and the results of Louis (1982) is described. Generating the ''exact'' information may be computational...
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A method of computing the observed information for the hidden Markov model using the em algorithm and the results of Louis (1982) is described. Generating the ''exact'' information may be computationally intensive for large datasets but an approximation is given which significantly reduces the computational effort in most cases.
This paper proposes a new approach for the joint processing of signal detection and channel estimation based on the expectation-maximization (em) algorithm in orthogonal frequency division multiplexing (OFDM) mobile c...
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This paper proposes a new approach for the joint processing of signal detection and channel estimation based on the expectation-maximization (em) algorithm in orthogonal frequency division multiplexing (OFDM) mobile communications. Conventional schemes based on the em algorithm estimate a channel impulse response using Kalman filter, and employ the random walk model or the first-order autoregressive (AR) model to derive the process equation for the filter. Since these models assume that the time-variation of the impulse response is white noise without considering any autocorrelation property, the accuracy of the channel estimation deteriorates under fast-fading conditions, resulting in an increased packet error rate (PER). To improve the accuracy of the estimation of fast-fading channels, the proposed scheme employs a differential model that allows the correlated time-variation to be considered by introducing the first-and higher-order time differentials of the channel impulse response. In addition, this paper derives a forward recursive form of the channel estimation along both the frequency and time axes in order to reduce the computational complexity. Computer simulations of channels under fast multipath fading conditions demonstrate that the proposed method is superior in PER to the conventional schemes that employ the random walk model.
An iterative method to recover perfectly focused images from a set of light microscopic images is proposed. The method is based on the em algorithm, and it assumes a prior knowledge about the Point Spread Function of ...
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An iterative method to recover perfectly focused images from a set of light microscopic images is proposed. The method is based on the em algorithm, and it assumes a prior knowledge about the Point Spread Function of the optical system, as well as about the optical parameter settings of the acquisition system. The method is applied to the visualization of integrated circuit samples through an optical microscope and to the recovery of their depth information.
Using the expression for the unnormalized nonlinear filter for a hidden Markov model, we develop a dynamic-programming-like backward recursion for the filter. This is combined with some ideas from reinforcement learni...
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Using the expression for the unnormalized nonlinear filter for a hidden Markov model, we develop a dynamic-programming-like backward recursion for the filter. This is combined with some ideas from reinforcement learning and a conditional version of importance sampling in order to develop a scheme based on stochastic approximation for estimating the desired conditional expectation. This is then extended to a smoothing problem. Applying these ideas to the em algorithm, a reinforcement learning scheme is developed for estimating the partially observed log-likelihood function. A stochastic approximation scheme maximizes this function over the unknown parameter. The two procedures are performed on two different time scales, emulating the alternating 'expectation' and 'maximization' operations of the em algorithm. We also extend this to a continuous state space problem. Numerical results are presented in support of our schemes.
Consider a system which is made up of multiple components connected in a series. In this case, the failure of the whole system is caused by the earliest failure of any of the components, which is commonly referred to ...
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Consider a system which is made up of multiple components connected in a series. In this case, the failure of the whole system is caused by the earliest failure of any of the components, which is commonly referred to as competing risks. In certain situations, it is observed that the determination of the cause of failure may be expensive, or may be very difficult to observe due to the lack of appropriate diagnostics. Therefore, it might be the case that the failure time is observed, but its corresponding cause of failure is not fully investigated. This is known as masking. Moreover, this competing risks problem is further complicated due to possible censoring. In practice, censoring is very common because of time and cost considerations on experiments. In this paper, we deal with parameter estimation of the incomplete lifetime data in competing risks using the em algorithm, where incompleteness arises due to censoring and masking. Several studies have been carried out, but parameter estimation for incomplete data has mainly focused on exponential models. We provide the general likelihood method, and the parameter estimation of a variety of models including exponential, s-normal, and lognormal models. This method can be easily implemented to find the MLE of other models. Exponential and lognormal examples are illustrated with parameter estimation, and a graphical technique for checking model validity.
The servo turret is a complex electromechanical hydraulic component that is the most likely to fail in a numerical control lathe. Reliability evaluation is used to make statistical inferences about the reliability cha...
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The servo turret is a complex electromechanical hydraulic component that is the most likely to fail in a numerical control lathe. Reliability evaluation is used to make statistical inferences about the reliability characteristics of products according to all the information related to product reliability. Failure data is the basis of reliability evaluation;however, it is very difficult to collect many accurate failure data for reliability evaluation. In this paper, the reliability of servo turret is evaluated based on failure data that contains accurate failure data and interval censored data. First, a mixture Weibull distribution is chosen for fitting the reliability model. Then, expectation-maximization algorithm is used for estimating the parameters of the distribution which contains hidden variable, and the confidence interval of parameters is constructed using the delta method. In the simulation, different percentages of accurate data and interval data are used and compared with data containing only accurate data. The accuracy of this method is evaluated by mean square error. Finally, the method is applied to the failure data of servo turret and the parameters of mixture Weibull distribution are determined. For possibly simplifying the mixed Weibull distribution, the hypothesis of shape or scale parameters being equal is tested. The hazard property and mean time between failure are then estimated and associated 95 % confidence intervals are obtained.
Gene classification problem is studied considering the ratio of gene expression levels, X, in two-channel microarrays and a non-observed categorical variable indicating how differentially expressed the gene is: non di...
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Gene classification problem is studied considering the ratio of gene expression levels, X, in two-channel microarrays and a non-observed categorical variable indicating how differentially expressed the gene is: non differentially expressed, down-regulated or up-regulated. Supposing X from a mixture of Gamma distributions, two methods are proposed and results are compared. The first method is based on an hierarchical Bayesian model. The conditional predictive probability of a gene to belong to each group is calculated and the gene is assigned to the group for which this conditional probability is higher. The second method uses em algorithm to estimate the most likely group label for each gene, that is, to assign the gene to the group which contains it with the higher estimated probability.
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