This paper investigates the problem of blindly acquiring the channel gains for a synchronized multiuser system using the expectation maximization (EM) algorithm. The EM algorithm takes advantage of the finite alphabet property of the transmitted signal. It also provides MMSE estimates of the transmitted data that can be used by the receiver for decoding purposes. The algorithm has been applied ...

ÐA new method for the representation, recognition, and interpretation of parameterized gesture is presented. By parameterized gesture we mean gestures that exhibit a systematic spatial variation; one example is a point gesture where the relevant parameter is the two-dimensional direction. Our approach is to extend the standard hidden Markov model method of gesture recognition by including a glo...

For a direct-sequence spread-spectrum (DS-SS) system we pose and solve the problem of maximum-likelihood (ML) sequence estimation in the presence of narrowband interference, using the expectation-maximization (EM) algorithm. It is seen that the iterative EM algorithm obtains at each iteration an estimate of the interference which is then subtracted from the data before a new sequence estimate i...

Estimating parameters of mixture models is a typical application of the expectation maximization (EM) algorithm. For the family of multivariate exponential power (MEP) distributions, which is a generalization of the well known multivariate Gaussian distribution, we introduce an approximative EM algorithm, and a probabilistic variant called stochastic EM algorithm, which provides a significant s...

This paper attempts to overcome the local convergence problem of the Expectation Maximization (EM) based training of the Hidden Markov Model (HMM) in speech recognition. We propose a hybrid algorithm, Simulated Annealing Stochastic version of EM (SASEM), combining Simulated Annealing with EM that reformulates the HMM estimation process using a stochastic step between the EM steps and the SA. Th...

the spatial intensity inhomogeneities that are due to the equipment. This paper describes a statistical method that uses knowledge of tissue properties and intensity inhomogeneities to correct for these intensity inhomogeneities. Use of the Expectation-Maximization algorithm leads to a method (EM segmentation) for simultaneously estimating tissue class and the correcting gain eld. The algorithm...

In this paper iterative blind estimation of the complex amplitudes of the users is considered. A Gaussian mixture model formulation of the problem is introduced and Expectation Maximization (EM) algorithm for estimation of parameters for Gaussian mixture observation model is used. Simulation results compare the performance of the proposed algorithm with the Cramer-Rao bound.

The Expectation Maximization (EM) algorithm [1, 2] is one of the most widely used algorithms in statistics. Suppose we are given some observed data X and a model family parametrized by θ, and would like to find the θ which maximizes p(X |θ), i.e. the maximum likelihood estimator. The basic idea of EM is actually quite simple: when direct maximization of p(X |θ) is complicated we can augment the...

We present a MR image segmentation algorithm based on the conventional Expectation Maximization (EM) algorithm and the multiresolution analysis of images. Although the EM algorithm was used in MRI brain segmentation, as well as, image segmentation in general, it fails to utilize the strong spatial correlation between neighboring pixels. The multiresolution-based image segmentation techniques, w...