Penalized minimum matching distance-guided EM algorithm

A novel and efficient mixture model fitting technique, called penalized minimum matching distance-guided expectation–maximization (EM) algorithm, is proposed. Penalized minimum matching distance is used to find the number of mixture components very accurately. We illustrate the excellent performance of the penalized minimum matching distance-guided EM algorithm with experiments involving Gaussian mixtures.     

Adaptive MLSDE using the EM algorithm

The theory of adaptive sequence detection incorporating estimation of channel and related parameters is studied in the context of maximum-likelihood (ML) principles in a general framework based on the expectation and maximization (EM) algorithm. A generalized ML sequence detection and estimation (GMLSDE) criterion is derived based on the EM approach, and it is shown how the per-survivor processing and per-branch processing methods emerge naturally from GMLSDE. GMLSDE is developed into a real time detection/estimation algorithm using the online EM algorithm with coupling between estimation and detection. By utilizing Titterington's (1984) stochastic approximation approach, different adaptive ML sequence detection and estimation (MLSDE) algorithms are formulated in a unified manner for different channel models and for different amounts of channel knowledge available at the receiver. Computer simulation results are presented for differentially encoded quadrature phase-shift keying in frequency flat and selective fading channels, and comparisons are made among the performances of the various adaptive MLSDE algorithms derived earlier     

An EM algorithm for dynamic SPECT

In this paper we present two variants of the EM algorithm for dynamic SPECT imaging. A version based on compartmental modeling which fits a sum of exponentials and a more general approach allowing for arbitrary decaying activities. The underlying probabilistic models are discussed and the incomplete and complete data spaces are shown to be physically meaningful. We indicate that the second method, leading to a convex program in the M step, is easier to treat numerically and we present a possible numerical approach. Some preliminary numerical tests indicating the feasibility of the method are included.     

Adaptive snakes using the EM algorithm.

Deformable models (e.g., snakes) perform poorly in many image analysis problems. The contour model is attracted by edge points detected in the image. However, many edge points do not belong to the object contour, preventing the active contour from converging toward the object boundary. A new algorithm is proposed in this paper to overcome this difficulty. The algorithm is based on two key ideas. First, edge points are associated in strokes. Second, each stroke is classified as valid (inlier) or invalid (outlier) and a confidence degree is associated to each stroke. The expectation maximization algorithm is used to update the confidence degrees and to estimate the object contour. It is shown that this is equivalent to the use of an adaptive potential function which varies during the optimization process. Valid strokes receive high confidence degrees while confidence degrees of invalid strokes tend to zero during the optimization process. Experimental results are presented to illustrate the performance of the proposed algorithm in the presence of clutter, showing a remarkable robustness.     

An EM algorithm for wavelet-based image restoration

This paper introduces an expectation-maximization (EM) algorithm for image restoration (deconvolution) based on a penalized likelihood formulated in the wavelet domain. Regularization is achieved by promoting a reconstruction with low-complexity, expressed in the wavelet coefficients, taking advantage of the well known sparsity of wavelet representations. Previous works have investigated wavelet-based restoration but, except for certain special cases, the resulting criteria are solved approximately or require demanding optimization methods. The EM algorithm herein proposed combines the efficient image representation offered by the discrete wavelet transform (DWT) with the diagonalization of the convolution operator obtained in the Fourier domain. Thus, it is a general-purpose approach to wavelet-based image restoration with computational complexity comparable to that of standard wavelet denoising schemes or of frequency domain deconvolution methods. The algorithm alternates between an E-step based on the fast Fourier transform (FFT) and a DWT-based M-step, resulting in an efficient iterative process requiring O(NlogN) operations per iteration. The convergence behavior of the algorithm is investigated, and it is shown that under mild conditions the algorithm converges to a globally optimal restoration. Moreover, our new approach performs competitively with, in some cases better than, the best existing methods in benchmark tests.     

Bar code recovery via the EM algorithm

The ubiquitous supermarket checkout scanner is indeed a well engineered and effective device. There is, nevertheless, demand for better devices. Existing scanners rely on simple and indeed low-cost signal processing to interpret bar code signals. These methods, nevertheless, fundamentally limit label reading and cannot be extended. A new method based on the deterministic EM algorithm is described. First results show a substantial improvement in label reading depth of field, which is an important performance parameter for bar code readers     

Multispectral image segmentation using the rough-set-initialized EM algorithm

The problem of segmentation of multispectral satellite images is addressed. An integration of rough-set-theoretic knowledge extraction, the Expectation Maximization (EM) algorithm, and minimal spanning tree (MST) clustering is described. EM provides the statistical model of the data and handles the associated measurement and representation uncertainties. Rough-set theory helps in faster convergence and in avoiding the local minima problem, thereby enhancing the performance of EM. For rough-set-theoretic rule generation, each band is discretized using fuzzy-correlation-based gray-level thresholding. MST enables determination of nonconvex clusters. Since this is applied on Gaussians, determined by granules, rather than on the original data points, time required is very low. These features are demonstrated on two IRS-1A four-band images. Comparison with related methods is made in terms of computation time and a cluster quality measure.     

FDTD algorithm in curvilinear coordinates [EM scattering]

The finite-difference-time-domain (FDTD) algorithm for the solution of electromagnetic scattering problems is formulated in generalized coordinates in two dimensions and implemented in a code with the lowest-order Bayliss-Turkel radiation boundary condition expressed in cylindrical coordinates. It is shown that, for a perfect conductor, such a formulation leads to a stable, well-posed algorithm and that, in regions where the curvature of the coordinate lines is not great, the dispersion and anisotropy effects are negligible. Such effects become more pronounced in regions of high curvature, leading to unphysical phase shifts. The magnitude of such shifts and the amount of wavefront distortion is studied via numerical experiments using a cylindrical mesh. Near-field results are given for two canonical shapes for each polarization: the circular cylinder and cylinders of square and rectangular cross sections. These results are compared with those obtained by exact eigenfunction expansion techniques, with method-of-moments (MM) solutions, and with solutions obtained from an alternate FDTD approach. In each case, agreement is excellent. The propagation of a plane wave through a polar space in the absence of a scatterer is also examined, and it is shown that the FDTD algorithm is capable of tracking the incident wave closely     

Maximum-likelihood localization of narrow-band autoregressivesources via the EM algorithm

The authors derive an expectation-maximization algorithm for the maximum-likelihood estimation of the directions of arrival of multiple narrowband autoregressive (AR) signals embedded in noise. The proposed algorithm simultaneously estimates the location parameters, the AR coefficients, and the signals. The additional structural information that the sources are of the AR type enables one to resolve the case in which the number of sources is equal to or possibly larger than the number of sensors. The authors provide examples demonstrating that the proposed algorithm can resolve two spectrally and spatially closely spaced sources by using a two-sensor array     

改进的高效MIMO-OFDM系统EM信道估计算法

针对MIMO-OFDM系统中期望最大化(EM)信道估计算法在高信噪比(SNR)下带来的误差地板(EF)现象,且OFDM符号的数据传输效率随发射天线数的增加而明显降低,提出一种改进的高效EM信道估计算法。该算法首先引入一种准确的等效信号模型并推导出一种修正的EM算法,改善了EM算法在高SNR下的性能;在多个OFDM间利用相位正交导频序列来提高数据传输效率,同时进行联合信道估计以提高估计性能。仿真实验验证了所提算法具有更高的信道估计性能和更高的数据传输效率。     

EM algorithm for image segmentation initialized by a tree structurescheme

In this correspondence, the objective is to segment vector images, which are modeled as multivariate finite mixtures. The underlying images are characterized by Markov random fields (MRFs), and the applied segmentation procedure is based on the expectation-maximization (EM) technique. We propose an initialization procedure that does not require any prior information and yet provides excellent initial estimates for the EM method. The performance of the overall segmentation is demonstrated by segmentation of simulated one-dimensional (1D) and multidimensional magnetic resonance (MR) brain images.     

The EM/MPM algorithm for segmentation of textured images: analysisand further experimental results

In this paper we present new results relative to the "expectation-maximization/maximization of the posterior marginals" (EM/MPM) algorithm for simultaneous parameter estimation and segmentation of textured images. The EM/MPM algorithm uses a Markov random field model for the pixel class labels and alternately approximates the MPM estimate of the pixel class labels and estimates parameters of the observed image model. The goal of the EM/MPM algorithm is to minimize the expected value of the number of misclassified pixels. We present new theoretical results in this paper which show that the algorithm can be expected to achieve this goal, to the extent that the EM estimates of the model parameters are close to the true values of the model parameters. We also present new experimental results demonstrating the performance of the EM/MPM algorithm.     

MIMO中继系统中预编码的级联算法

在两跳MIMO中继通信系统的预编码相关研究中,提出了一种级联预编码算法,该算法把两跳系统的预编码分解成两个独立的部分,从而把预编码问题转化成为求源节点到中继节点的预编码过程以及中继节点到目的节点的过程。本文使用MMSE准则,在简化迭代算法复杂度的同时,与一种只在中继节点进行联合优化的算法进行比较,由仿真可以看出,本文算法有一定的性能提升。     

Parallelization of the EM algorithm for 3-D PET image reconstruction

The EM algorithm for PET image reconstruction has two major drawbacks that have impeded the routine use of the EM algorithm: the long computation time due to slow convergence and a large memory required for the image, projection, and probability matrix. An attempt is made to solve these two problems by parallelizing the EM algorithm on multiprocessor systems. An efficient data and task partitioning scheme, called partition-by-box, based on the message passing model is proposed. The partition-by-box scheme and its modified version have been implemented on a message passing system, Intel iPSC/2, and a shared memory system, BBN Butterfly GP1000. The implementation results show that, for the partition-by-box scheme, a message passing system of complete binary tree interconnection with fixed connectivity of three at each node can have similar performance to that with the hypercube topology, which has a connectivity of log(2) N for N PEs. It is shown that the EM algorithm can be efficiently parallelized using the (modified) partition-by-box scheme with the message passing model.     

Bayesian reconstructions from emission tomography data using a modified EM algorithm

A novel method of reconstruction from single-photon emission computerized tomography data is proposed. This method builds on the expectation-maximization (EM) approach to maximum likelihood reconstruction from emission tomography data, but aims instead at maximum posterior probability estimation, which takes account of prior belief about smoothness in the isotope concentration. A novel modification to the EM algorithm yields a practical method. The method is illustrated by an application to data from brain scans.     

EM image segmentation algorithm based on an inhomogeneous hidden MRF model

This paper introduces a Bayesian image segmentation algorithm that considers the label scale variability of images. An inhomogeneous hidden Markov random field is adopted in this algorithm to model the label scale variability as prior probabilities. An EM algorithm is developed to estimate parameters of the prior probabilities and likelihood probabilities. The image segmentation is established by using a MAP estimator. Different images are tested to verify the algorithm and comparisons with other segmentation algorithms are carried out. The segmentation results show the proposed algorithm has better performance than others.     

Application of the EM algorithm for the multitarget/multisensortracking problem

An important problem in surveillance and reconnaissance systems is the tracking of multiple moving targets in cluttered noise environments using outputs from a number of sensors possessing wide variations in individual characteristics and accuracies. A number of approaches have been proposed for this multitarget/multisensor tracking problem ranging from reasonably simple, though ad hoc, schemes to fairly complex, but theoretically optimum, approaches. In this paper, we describe an iterative procedure for time-recursive multitarget/multisensor tracking based on use of the expectation-maximization (EM) algorithm. More specifically, we pose the multitarget/multisensor tracking problem as an incomplete data problem with the observable sensor outputs representing the incomplete data, whereas the target-associated sensor outputs constitute the complete data. Target updates at each time use an EM-based approach that calculates the maximum a posteriori (MAP) estimate of the target states, under the assumption of appropriate motion models, based on the outputs of disparate sensors. The approach uses a Markov random field (MRF) model of the associations between observations and targets and allows for estimation of joint association probabilities without explicit enumeration. The advantage of this EM-based approach is that it provides a computationally efficient means for approaching the performance offered by theoretically optimum approaches that use explicit enumeration of the joint association probabilities. We provide selected results illustrating the performance/complexity characteristics of this EM-based approach compared with competing schemes     

The Numeric Python EM project

The Numeric Python EM project started out as a conversion of ToyFDTD to Numeric Python (a fast vector oriented module for the Python programming language). Surprisingly, the simulation ran almost as fast as the original C-language version. Since Python is an interpreted language, the simulation conditions can easily be altered and run immediately. This makes Numeric Python an ideal prototyping language for EM simulators.     

Anomaly detection model based on multi-grained cascade isolation forest algorithm

The isolation-based anomaly detector,isolation forest has two weaknesses,its inability to detect anomalies that were masked by axis-parallel clusters,and anomalies in high-dimensional data.An isolation mechanism based on random hyperplane and a multi-grained scanning was proposed to overcome these weaknesses.The random hyperplane generated by a linear combination of multiple dimensions was used to simplify the isolation boundary of the data model which was a random linear classifier that can detect more complex data patterns,so that the isolation mechanism was more consistent with data distribution characteristics.The multi-grained scanning was used to perform dimensional sub-sampling which trained multiple forests to generate a hierarchical ensemble anomaly detection model.Experiments show that the improved isolation forest has better robustness to different data patterns and improves the efficiency of anomaly points in high-dimensional data.     

一种高效量子密钥级联纠错算法的实现与分析

误码纠错是量子密钥分配后续数据处理过程的关键 环节。基于奇偶监督矩阵码检错原理与级联纠错原理,提出了一种高效的级联纠错协议,并对 协议的相关参数进行了理论优化分析。根据分析结果,利用仿真数 据分别对单次级联纠错的纠错效率和纠错能力进行实验研究,得到不同初始误码率条件下的 最优参数选 择依据。利用优化的参数,对级联纠错协议的最终密钥生成效率进行了数据仿真分析。结果 表明,当初始误码率 分别为3、6和10%时,最终的密钥实际生成效率分别达到了77.67、63.21和46.82%。与理论值进行比较显示,本文 协议的密钥生成效率接近理论效率的上限值。