Modular solution of dynamic multi-phase systems

Binary Decision Diagram (BDD)-based solution approaches and Markov chain based approaches are commonly used for the reliability analysis of multi-phase systems. These approaches either assume that every phase is static, and thus can be solved with combinatorial methods, or assume that every phase must be modeled via Markov methods. If every phase is indeed static, then the combinatorial approach is much more efficient than the Markov chain approach. But in a multi-phased system, using currently available techniques, if the failure criteria in even one phase is dynamic, then a Markov approach must be used for every phase. The problem with Markov chain based approaches is that the size of the Markov model can expand exponentially with an increase in the size of the system, and therefore becomes computationally intensive to solve. Two new concepts, phase module and module joint probability, are introduced in this paper to deal with the s-dependency among phases. We also present a new modular solution to nonrepairable dynamic multi-phase systems, which provides a combination of BDD solution techniques for static modules, and Markov chain solution techniques for dynamic modules. Our modular approach divides the multi-phase system into its static and dynamic subsystems, and solves them independently; and then combines the results for the solution of the entire system using the module joint probability method. A hypothetical example multi-phase system is given to demonstrate the modular approach.     

Reconstruction of the amplitude-phase distribution of the field of the received signal in the aperture of a phased antenna array from the measured power

A method for solution of a complex scientific and engineering problem, which is known as the phase problem, is proposed. The phase problem is the problem of reconstruction of the amplitude-phase distribution of the signal field over the receiving aperture from the measured power. The solution is presented for a phased antenna array.     

基于最大熵的隐马尔可夫模型文本信息抽取

文本信息抽取是处理海量文本的重要手段之一.最大熵模型提供了一种自然语言处理的方法.提出了一种基于最大熵的隐马尔可夫模型文本信息抽取算法.该算法结合最大熵模型在处理规则知识上的优势,以及隐马尔可夫模型在序列处理和统计学习上的技术基础,将每个观察文本单元所有特征的加权之和用来调整隐马尔可夫模型中的转移概率参数,实现文本信息抽取.实验结果表明,新的算法在精确度和召回率指标上比简单隐马尔可夫模型具有更好的性能.     

Phase retrieval algorithm using Bayesian approach

The proposed method consists in image reconstruction from noisy spectral magnitude. Most of classical methods are deterministic, and when the available measured spectral magnitude does not fill uniformly the Fourier domain, the results obtained by such methods are not satisfactory. We consider here the case where the considered image is assumed to be composed of a finite number of homogenous regions. We propose then an appropriate Gauss–Markov with a hidden Potts–Markov Random Field to model such images, the whole prior model is then a hierarchical Markov model. We then use a Bayesian framework and an MCMC algorithm to compute a satisfactory solution to the inverse problem.     

Quantitative Reliability Evaluation of Repairable Phased-Mission Systems Using Markov Approach

A quantitative reliability model for a phased mission system is developed using a Markov process. Two cases for the mission-phase change times are assumed: 1) to be known in advance and 2) to be random variables. A method of solution is presented and illustrated by examples. The solution of phased-mission systems is equivalent to solving a sequence of uni-phase systems with appropriate initial conditions.     

A Bayesian filtering technique for SAR interferometric phase fields

SAR interferograms are affected by a strong noise component which often prevents correct phase unwrapping and always impairs the phase reconstruction accuracy. To obtain satisfactory performance, most filtering techniques exploit prior information by means of ad hoc, empirical strategies. In this paper, we recast phase filtering as a Bayesian estimation problem in which the image prior is modeled as a suitable Markov random field, and the filtered phase field is the configuration with maximum a posteriori probability. Assuming the image to be residue free and generally smooth, a two-component MRF model is adopted, where the first component penalizes residues, while the second one penalizes discontinuities. Constrained aimulated annealing is then used to find the optimal solution. The experimental analysis shows that, by gradually adjusting the MRF parameters, the algorithm filters out most of the high-frequency noise and, in the limit, eliminates all residues, allowing for a trivial phase unwrapping. Given a limited processing time, the algorithm is still able to eliminate most residues, paving the way for the successful use of any subsequent phase unwrapping technique.     

A non-homogeneous Markov model for phased-mission reliabilityanalysis

Three assumptions of Markov modeling for reliability of phased-mission systems that limit flexibility of representation are identified. The proposed generalization has the ability to represent state-dependent behavior, handle phases of random duration using globally time-dependent distributions of phase change time, and model globally time-dependent failure and repair rates. The approach is based on a single nonhomogeneous Markov model in which the concept of state transition is extended to include globally time-dependent phase changes. Phase change times are specified using nonoverlapping distributions with probability distribution functions that are zero outside assigned time intervals; the time intervals are ordered according to the phases. A comparison between a numerical solution of the model and simulation demonstrates that the numerical solution can be several times faster than simulation     

Phase unwrapping via graph cuts.

Phase unwrapping is the inference of absolute phase from modulo-2pi phase. This paper introduces a new energy minimization framework for phase unwrapping. The considered objective functions are first-order Markov random fields. We provide an exact energy minimization algorithm, whenever the corresponding clique potentials are convex, namely for the phase unwrapping classical Lp norm, with p > or = 1. Its complexity is KT (n, 3n), where K is the length of the absolute phase domain measured in 2pi units and T (n, m) is the complexity of a max-flow computation in a graph with n nodes and m edges. For nonconvex clique potentials, often used owing to their discontinuity preserving ability, we face an NP-hard problem for which we devise an approximate solution. Both algorithms solve integer optimization problems by computing a sequence of binary optimizations, each one solved by graph cut techniques. Accordingly, we name the two algorithms PUMA, for phase unwrappping max-flow/min-cut. A set of experimental results illustrates the effectiveness of the proposed approach and its competitiveness in comparison with state-of-the-art phase unwrapping algorithms.     

相控阵体制SAR系统的误差建模与补偿方法

在基于相控阵天线体制的合成孔径雷达(SAR)系统中,中央电子设备和相控阵天线的非理想特性,会引入幅相误差,造成SAR信号幅相特性畸变,影响SAR图像等数据产品的质量。本文建立了相控阵体制SAR系统误差的模型,并设计了误差校正方法。研究结果表明：雷达中央电子设备的非理想特性会引入固定的幅频、相频误差;相控阵天线的非理想特性所引入的幅频、相频误差会随着波束指向的变化而变化,该误差主要根源于有源器件在不同频点处的性能差异,并会受到T/R模块移相衰减量的调制;可通过测量或分析计算对相控阵SAR的系统误差进行提取,并在SAR成像处理阶段实施误差补偿。     

Optimal steady-state temperature distribution for a phased arrayhyperthermia system

A method is presented for the evaluation of optimal amplitude and phase excitations for the radiating elements of a phased array hyperthermia system, in order to achieve desired steady-state temperature distributions inside and outside of malignant tissues. Use is made of a detailed electromagnetic and thermal model of the heated tissue in order to predict the steady-state temperature at any point in tissue. Optimal excitations are obtained by minimizing the squared error between desired and model predicted temperatures inside the tumor volume, subject to the constraint that temperatures do not exceed an upper bound outside the tumor. The penalty function technique is used to solve the constrained optimization problem. Sequential unconstrained minima are obtained by a modified Newton method. Numerical results for a four element phased array hyperthermia system are presented     

相控阵天线的设计与FDTD分析

基于工作频率在1.79 GHz的微带天线,首先研究了移相器的设计方法,然后利用两种不同方法实现的移相器,分别设计出了相控阵天线。通过FDTD进行建模和仿真实验,计算了相控阵天线在不同扫描角的远场辐射方向图,分析了实验误差,得出了结论,所设计的相控阵天线结构简约,主瓣尖锐,最大扫描角大于45°。     

Hidden Markov models for wavelet-based blind source separation.

In this paper, we consider the problem of blind source separation in the wavelet domain. We propose a Bayesian estimation framework for the problem where different models of the wavelet coefficients are considered: the independent Gaussian mixture model, the hidden Markov tree model, and the contextual hidden Markov field model. For each of the three models, we give expressions of the posterior laws and propose appropriate Markov chain Monte Carlo algorithms in order to perform unsupervised joint blind separation of the sources and estimation of the mixing matrix and hyper parameters of the problem. Indeed, in order to achieve an efficient joint separation and denoising procedures in the case of high noise level in the data, a slight modification of the exposed models is presented: the Bernoulli-Gaussian mixture model, which is equivalent to a hard thresholding rule in denoising problems. A number of simulations are presented in order to highlight the performances of the aforementioned approach: 1) in both high and low signal-to-noise ratios and 2) comparing the results with respect to the choice of the wavelet basis decomposition.     

Hidden Markov models with first-order equalization for noisy speechrecognition

A particularly effective distortion measure that takes into account the norm shrinkage bias in the noisy cepstrum is considered. A first-order equalization mechanism, specifically aiming at avoiding the norm shrinkage problem, is incorporated in a hidden Markov model (HMM) framework to model the speech cepstral sequence. Such a modeling technique requires special care, as the formulation inevitably involves parameter estimation from a set of data with singular dispersion. Solutions to this HMM stochastic modeling problem are provided, and algorithms for estimating the necessary model parameters are given. It is experimentally shown that incorporation of the first-order mean equalization model makes the HMM-based speech recognizer robust to noise. With respect to a conventional HMM recognizer, this leads to an improvement in recognition performance which is equivalent to a gain of about 15-20 dB in signal-to-noise ratio     

Separation of Non-Negative Mixture of Non-Negative Sources Using a Bayesian Approach and MCMC Sampling

This paper addresses blind-source separation in the case where both the source signals and the mixing coefficients are non-negative. The problem is referred to as non-negative source separation and the main application concerns the analysis of spectrometric data sets. The separation is performed in a Bayesian framework by encoding non-negativity through the assignment of Gamma priors on the distributions of both the source signals and the mixing coefficients. A Markov chain Monte Carlo (MCMC) sampling procedure is proposed to simulate the resulting joint posterior density from which marginal posterior mean estimates of the source signals and mixing coefficients are obtained. Results obtained with synthetic and experimental spectra are used to discuss the problem of non-negative source separation and to illustrate the effectiveness of the proposed method.     

Reliability Modeling Using SHARPE

Combinatorial models such as fault trees and reliability block diagrams are efficient for model specification and often efficient in their evaluation. But it is difficult, if not impossible, to allow for dependencies (such as repair dependency and near-coincident-fault type dependency), transient and intermittent faults, standby systems with warm spares, and so on. Markov models can capture such important system behavior, but the size of a Markov model can grow exponentially with the number of components in this system. This paper presents an approach for avoiding the large state space problem. The approach uses a hierarchical modeling technique for analyzing complex reliability models. It allows the flexibility of Markov models where necessary and retains the efficiency of combinatorial solution where possible. Based on this approach a computer program called SHARPE (Symbolic Hierarchical Automated Reliability and Performance Evaluator) has been written. The hierarchical modeling technique provides a very flexible mechanism for using decomposition and aggregation to model large systems; it allows for both combinatorial and Markov or semi-Markov submodels, and can analyze each model to produce a distribution function. The choice of the number of levels of models and the model types at each level is left up to the modeler. Component distribution functions can be any exponential polynomial whose range is between zero and one. Examples show how combinations of models can be used to evaluate the reliability and availability of large systems using SHARPE.     

A probabilistic framework based on hidden markov model for fiducial identification in image-guided radiation treatments

Fiducial tracking is a common target tracking method widely used in image-guided procedures such as radiotherapy and radiosurgery. In this paper, we present a multifiducial identification method that incorporates context information in the process. We first convert the problem into a state sequence problem by establishing a probabilistic framework based on a hidden Markov model (HMM), where prior probability represents an individual candidate's resemblance to a fiducial; transition probability quantifies the similarity of a candidate set to the fiducials' geometrical configuration; and the Viterbi algorithm provides an efficient solution. We then discuss the problem of identifying fiducials using stereo projections, and propose a special, higher order HMM, which consists of two parallel HMMs, connected by an association measure that captures the inherent correlation between the two projections. A novel algorithm, the concurrent viterbi with association (CVA) algorithm, is introduced to efficiently identify fiducials in the two projections simultaneously. This probabilistic framework is highly flexible and provides a buffer to accommodate deformations. A simple implementation of the CVA algorithm is presented to evaluate the efficacy of the framework. Experiments were carried out using clinical images acquired during patient treatments, and several examples are presented to illustrate a variety of clinical situations. In the experiments, the algorithm demonstrated a large tracking range, computational efficiency, ease of use, and robustness that meet the requirements for clinical use.      

Optimal unsupervised learning multicategory dependent hypotheses pattern recognition

A Bayesian decision theory approach is applied to the solution of the problem of unsupervised parametric pattern recognition. The parametric model for this investigation includes the cases where both constant and time-varying unknown parameters are present, and, most significantly, the unknown hypotheses do not constitute a statistically independent sequence. They are restricted only to be from a source with finite-order Markov dependence. The resulting optimal learning system is found and shown to grow initially in size and memory until theNth observation (whereNis the highest Markov order), and subsequently to remain of fixed size and memory. It can, therefore, operate indefinitely and continue to improve its ability to recognize patterns utilizing only a fixed size memory. In summary, the main contributions of this paper are the following: begin{enumerate} item the extension of previous investigations of the unsupervised parametric pattern recognition problem to include cases where both constant and time-varying unknown parameter vectors are simultaneously present; item that the a priori probabilities of the hypotheses, the time-varying parameters, and their transition laws may, if constant, be expressed as functions of the constant unknown parameter and, thus, also be learned; and item the removal of the assumption of statistical independence between hypotheses for the sequence of observations. end{enumerate}     

Biomagnetic source detection by maximum entropy and graphical models

This article presents a new approach for detecting active sources in the cortex from magnetic field measurements on the scalp in magnetoencephalography (MEG). The solution of this ill-posed inverse problem is addressed within the framework of maximum entropy on the mean (MEM) principle introduced by Clarke and Janday. The main ingredient of this regularization technique is a reference probability measure on the random variables of interest. These variables are the intensity of current sources distributed on the cortical surface for which this measure encompasses all available prior information that could help to regularize the inverse problem. This measure introduces hidden Markov random variables associated with the activation state of predefined cortical regions. MEM approach is applied within this particular probabilistic framework and simulations show that the present methodology leads to a practical detection of cerebral activity from MEG data.     

Phased array imaging of moving targets with randomized beamsteering and area spotlighting

This paper presents a system model and inversion for imaging moving targets using phased arrays. The system model provides a mathematical framework to represent the motion of a moving target in the beam-steering domain which is identified as the slow-time domain. The inversion provides a reconstruction of the moving targets in the spatial and velocity domains. It is shown that a randomized beam steering strategy in the slow-time domain can improve the resolution in the velocity domain. The imaging problem is also formulated for a phased array system that spotlights a target area with its transmitted beam to improve the target to clutter power ratio, and obtains beam-steered data in the receive mode for high-resolution imaging. We cite a diagnostic medical ultrasound problem due to the practical difficulties and challenges that are associated with it.     

Markov Approach to Percolation Theory Based Propagation in Random Media

For line-of-sight links in random media or urban areas, propagation may be approximated through sequential reflections of an optical ray in a two-dimensional medium of disordered lossless scatterers. Franceschetti approximated such percolation-based optical-ray propagation by a Markov process with two absorbing barriers, provided numerical solutions for the probability of a ray passing through the percolation lattice and solved-both approximately and exactly-a corresponding problem based on the theory of martingales. In this paper we solve exactly the Markov-theoretical formulation of the problem and prove that both the Markov and martingale approaches are equivalent. Our proof is an application of the Perron-Frobenius theory which provides an elegant framework for the study of the asymptotic behavior of stochastic processes. We demonstrate that for a wide range of vacancies and incident angles the exact solution of the Markov-theoretical formulation performs significantly better than the commonly used Wald approximation in the martingale approach. This has a number of implications on the accuracy of the model, especially for low density propagation media.