In this paper the authors derive a new class of finite-dimensional recursive filters for linear dynamical systems. The Kalman filter is a special case of their general filter. Apart from being of mathematical interest, these new finite-dimensional filters can be used with the expectation maximization (EM) algorithm to yield maximum likelihood estimates of the parameters of a linear dynamical sy...

We propose an EM-based algorithm to efficiently detect transmitted data in an OFDM system as well as estimating the channel impulse response (CIR). The maximum likelihood estimate of CIR is obtained by using channel statistics (their means and covariances) via the expectation-maximization (EM) algorithm. This algorithm can improve signal detection and the channel estimation accuracy by making u...

the geometric distribution of states duration is one of the main performance limiting assumptions of hidden markov modeling of speech signals. stochastic segment models, generally, and segmental hmm, specifically, overcome this deficiency partly at the cost of more complexity in both training and recognition phases. in this paper, a new duration modeling approach is presented. the main idea of ...

Successive user detection algorithm is used to observe the multi user ranging signals and calculate there corresponding parameters. Using IEEE 802.16 specification in Orthogonal Frequency Division Multiple Access (OFDMA), initial ranging method designed an algorithm called Moment Maximum Likelihood Detection (MMLD) to detect the codes assigned and predicting offset timing. The objective functio...

The HMM (Hidden Markov Model) is a probabilistic model of the joint probability of a collection of random variables with both observations and states. The GMM (Gaussian Mixture Model) is a finite mixture probability distribution model. Although the two models have a close relationship, they are always discussed independently and separately. The EM (Expectation-Maximum) algorithm is a general me...

y In this paper, a space-alternating generalized expectation-maximization (SAGE) algorithm is presented for the numerical computation of maximum-likelihood (ML) and penalized maximum-likelihood (PML) estimates of the parameters of covariance matrices with linear structure for complex Gaussian processes. By using a less informative hidden-data space and a sequential parameter-update scheme, a SA...

The concept of submodularity plays a vital role in combinatorial optimization. In particular, many important optimization problems can be cast as submodular maximization problems, including maximum coverage, maximum facility location and max cut in directed/undirected graphs. In this paper we present the first known approximation algorithms for the problem of maximizing a nondecreasing submodul...

Efficient and accurate extraction of the prostate, in particular its clinically meaningful sub-regions from 3D MR images, is of great interest in image-guided prostate interventions and diagnosis of prostate cancer. In this work, we propose a novel multi-region segmentation approach to simultaneously locating the boundaries of the prostate and its two major sub-regions: the central gland and th...