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15th IEEE International Symposium on High-Assurance Systems Engineering (HASE)

作者： Okamura, Hiroyuki Dohi, Tadashi Hiroshima Univ Dept Informat Engn Grad Sch Engn Higashihiroshima 7398527 Japan

ISBN: (纸本)9781479934652

This paper proposes a novel framework of software reliability growth models with software metrics. Our approach is to integrate a classical Poisson-regression-based fault prediction with non-homogeneous Poisson process based software reliability growth models. The remarkable feature of this approach is to handle time series data of fault detections and software metrics for a number of modules at the same time. In the paper, we present the modeling framework that combines Poissonregression-based fault prediction and software reliability growth models, and also develop an efficient algorithm to estimate model parameters based on EM (expectation-maximization) algorithm. In numerical experiments, by comparing the proposed model with both Poisson-regression-based fault prediction and nonhomogeneous Poisson process based software reliability growth models, we discuss the effectiveness of using time series data of fault detections and software metrics from both viewpoints of reliability estimation and fault prediction.

关键词： expectation-maximisation algorithm fault diagnosis parameter estimation regression analysis software metrics software reliability stochastic processes time series EM algorithm Poisson-regression-based fault prediction expectation-maximization algorithm fault detections model parameter estimation nonhomogeneous Poisson process reliability estimation software metrics software reliability evaluation software reliability growth models time series data Computational modeling Data models Fault detection Predictive models Software Software metrics Software reliability EM algorithm Poisson regression software metrics software reliability growth model

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Conditional Markov regime switching model applied to economic modelling

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ECONOMIC MODELLING 2014年 38卷 258-269页

作者： Goutte, Stephane Univ Paris 8 LED F-93526 St Denis France ESG Management School F-75011 Paris France

In this paper we discuss the calibration issues of regime switching models built on mean-reverting and local volatility processes combined with two Markov regime switching processes. In fact, the volatility structure of these models depends on a first exogenous Markov chain whereas the drift structure depends on a conditional Markov chain with respect to the first one. The structure is also assumed to be Markovian and both structure and regime are unobserved. Regarding this construction, we extend the classical expectation-maximization (EM) algorithm to be applied to our regime switching model. We apply it to economic data (Euro/Dollar (USD) foreign exchange rate and Brent oil price) to show that such modelling clearly identifies both mean reverting and volatility regime switches. Moreover, it allows us to make economic interpretations of this regime classification as in some financial crises or some economic policies. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Markov regime switching expectation-maximization algorithm Mean-reverting Local volatility Economic data

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Iterative Joint Detection, Decoding, and Synchronization with a Focus on Frame Timing

Iterative Joint Detection, Decoding, and Synchronization wit...

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IEEE Wireless Communications and Networking Conference (WCNC)

作者： Jakubisin, Daniel Phelps, Christopher Ian Buehrer, R. Michael Virginia Tech Wireless VT Mobile & Portable Radio Res Grp Blacksburg VA 24061 USA

ISBN: (纸本)9781479930838

The concept of an iterative receiver has gained attention as a means of performing reliable synchronization, especially at the low signal-to-noise ratios enabled by error correction codes. In this paper, we consider joint detection of the information bits and estimation of the channel gain, carrier phase, symbol timing, frame timing, and noise power. Our particular focus is on the frame timing where we evaluate the complexity of the iterative receiver by characterizing the frame offset distribution. A method for dynamically choosing the set of frame offsets processed by the iterative receiver is presented. The receiver utilizes the expectation-maximization algorithm to perform estimation and the sum-product algorithm to perform soft demodulation and decoding. Numerical results are presented to characterize the frame offset distribution and to demonstrate the receiver's performance.

关键词： demodulation error correction codes expectation-maximisation algorithm iterative decoding synchronisation wireless channels carrier phase channel gain estimation expectation-maximization algorithm frame offset distribution frame timing information bits iterative joint detection iterative receiver noise power signal-to-noise ratios soft decoding soft demodulation sum-product algorithm symbol timing synchronization Electronic countermeasures Estimation Iterative decoding Receivers Synchronization Testing

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The Optimal Hyperparameter for Bayesian Clustering and its Application to the Evaluation of Clustering Results 7

The Optimal Hyperparameter for Bayesian Clustering and its A...

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Joint 7th International Conference on Soft Computing and Intelligent Systems (SCIS) and 15th International Symposium on Advanced Intelligent Systems (ISIS)

作者： Yamazaki, Keisuke Tokyo Inst Technol Dept Computat Intelligence & Syst Sci Midori Ku G5-194259 Nagatsuta Yokohama Kanagawa Japan

ISBN: (纸本)9781479959556

In a probabilistic approach to cluster analysis, parametric models, such as a mixture of Gaussian distributions, are often used. Since the parameter is unknown, it is necessary to estimate both the parameter and the labels of the clusters. Recently, the statistical properties of Bayesian clustering have been studied. The theoretical accuracy has been analyzed, and it has been found to be better than the maximum-likelihood method, which is based on the expectation-maximization algorithm. However, the effect of a prior distribution on the clustering result remains unknown. In the present paper, we theoretically and experimentally investigate the behavior of the optimal hyperparameter, which is the parameter of the prior distribution, and we propose an evaluation method for the clustering result, based on the prior optimization.

关键词： Bayes methods Gaussian distribution expectation-maximisation algorithm optimisation pattern clustering Bayesian clustering cluster analysis expectation-maximization algorithm maximum-likelihood method optimal hyperparameter parameter estimation parametric model prior distribution prior optimization probabilistic approach statistical property Accuracy Maximum likelihood estimation Optimization Probabilistic logic Shape

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EM-based ML Channel Estimation in OFDM Systems with Phase Distortion Using RB-EKF

EM-based ML Channel Estimation in OFDM Systems with Phase Di...

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International Symposium on Wireless Personal Multimedia Communications (WPMC)

作者： Carvajal, Rodrigo Godoy, Boris I. Aguero, Juan C. Yuz, Juan I. Creixell, Werner Univ Tecn Federico Santa Maria Elect Engn Dept Valparaiso Chile Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW Australia Univ Newcastle Sch Elect Engn & Comp Sci Callaghan NSW Australia

ISBN: (纸本)9789860334074

In this paper we address the joint estimation of the channel impulse response in orthogonal frequency division multiplexing systems with phase distortion, namely phase noise and carrier frequency offset, phase noise bandwidth and the additive noise variance. The estimation algorithm is based on an implementation of the Extended Kalman Filter within the general framework of the expectation-maximization algorithm. We focus on the partial training case, where the transmitted signal is not fully known. To tackle this problem, we utilize a Rao-Blackwellized Extended Kalman Filter. We also compare our results with another nonlinear filtering technique, namely Rao-Blackwellized Particle Filtering, applied to this joint estimation problem. The performance of the two filtering techniques considered in this paper is evaluated in terms of the mean square error of the channel estimates and the numerical complexity introduced by each of these techniques.

关键词： Kalman filters OFDM modulation channel estimation computational complexity expectation-maximisation algorithm mean square error methods optimisation particle filtering (numerical methods) phase noise transient response EM-based ML channel estimation OFDM systems RB-EKF method Rao-Blackwellized extended Kalman filter carrier frequency offset channel impulse response expectation-maximization algorithm maximum likelihood estimation mean square error nonlinear filtering technique orthogonal frequency division multiplexing systems phase distortion phase noise bandwidth Channel estimation Complexity theory Estimation OFDM Training Vectors Mean square error Orthogonal frequency division multiplexing expectation-maximisation algorithm Phase distortion channel impulse response channel estimation Kalman filters Phase noise carrier frequency offset Complexity theory maximum likelihood estimation TRANSIENT RESPONSE complexity classes Functional training

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Fluorescence lifetime discrimination using expectation-maximization algorithm with joint deconvolution

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JOURNAL OF BIOMEDICAL OPTICS 2009年第6期14卷 064009-064009页

作者： Fu, Chit Yaw Ng, Beng Koon Razul, Sirajudeen Gulam Nanyang Technol Univ Sch Elect & Elect Engn Photon Res Ctr Singapore 639798 Singapore Nanyang Technol Univ Sch Elect & Elect Engn Div Informat Engn Singapore 639798 Singapore

The fluorescence lifetime technique offers an effective way to resolve fluorescent components with overlapping emission spectra. The presence of multiple fluorescent components in biological compounds can hamper their discrimination. The conventional method based on the nonlinear least-squares technique is unable to consistently determine the correct number of fluorescent components in a fluorescence decay profile. This can limit the applications of the fluorescence lifetime technique in biological assays and diagnoses where more than one fluorescent component is typically encountered. We describe the use of an expectation-maximization (EM) method with joint deconvolution to estimate the fluorescence decay parameters, and the Bayesian information criterion (BIC) to accurately determine the number of fluorescent components. A comprehensive simulation and experimental study is carried out to compare the performance and accuracy of the proposed method. The results show that the EMBIC method is able to accurately identify the correct number of fluorescent components in samples with weakly fluorescing components. (C) 2009 Society of Photo-Optical Instrumentation Engineers. [DOI: 10.1117/1.3258835]

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expectation-maximization-based joint data detection and channel estimation for a cellular multi-carrier code division multiple access network using single-hop relaying

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2013年第4期26卷 449-464页

作者： Kabaoglu, Nihat Maltepe Univ Dept Elect & Elect Engn Istanbul Turkey

This paper is concerned with channel estimation and data detection for a cellular multi-carrier code division multiple access network using single-hop relaying in the presence of frequency selective fading channels. The proposed expectationmaximization (EM) algorithm was used to jointly estimate both the coefficients of the channel between a relay and a base station and the data. EM algorithm is particularly suited to multi-carrier code division multiple access systems because they have multi-carrier signal format. The considered network uses single-hop relaying technique to provide a higher quality transmission to the users with low quality channels. The base station (managing mechanism) gives them an opportunity to send their messages via the users with high quality channels in a time sharing mode. The performance of the proposed EM algorithm, with and without hopping and with cooperative communication technique, was analyzed by a computer simulation, and the results are presented. Copyright (c) 2011 John Wiley & Sons, Ltd.

关键词： opportunistic communication MC-CDMA expectation-maximization algorithm data detection and channel estimation

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Soft Sensor for CO x Content in Tail Gas of PX Oxidation Side Reactions based on Particle Filters and EM algorithm

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IERI Procedia 2014年 6卷 63-71页

作者： Ranran Liu Zhengming Li School of Electrical and Information Engineering Jiangsu UniversityZhenjiang 212013China Changzhou Liu Guojun Vocational Technology College Changzhou 213004China

Based on expectation-maximization algorithm, parameter estimation was proposed for data-driven nonlinear models in this work. On this basis, particle filters were used to approximately calculate integrals, deriving EM algorithm based on particle filter. And the effectiveness of using the proposed algorithm for the soft sensor of CO x content in tail gas of PX oxidation side reactions was verified through simulation results.

关键词： expectation-maximization algorithm particle filtering data-driven soft sensor

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Blind Modulation Classification over Fading Channels Using expectation-maximization

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IEEE COMMUNICATIONS LETTERS 2013年第9期17卷 1692-1695页

作者： Soltanmohammadi, Erfan Naraghi-Pour, Mort Louisiana State Univ Div Elect & Comp Engn Sch Elect Engn & Comp Sci Baton Rouge LA 70803 USA

We propose a blind modulation classification algorithm when the channel coefficient, the noise power and the energy of the transmitted signal are unknown at the receiver. First, under each candidate modulation scheme, we evaluate the unknown parameters using the iterative expectation maximization algorithm. Modulation classification is then accomplished by minimizing the distance between the log-likelihood of the received data and the expected log-likelihood under each candidate modulation scheme. Results are presented from simulations in terms of detection probability vs. SNR for the class of BPSK, QPSK, 16QAM and 64QAM modulation schemes. The results show a significant improvement over QHLRT and are very close to the upper bound ALRT-UB [1].

关键词： Blind modulation classification parameter estimation expectation-maximization algorithm

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The dynamic expectation-conditional maximization either' algorithm

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JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY 2012年第2期74卷 313-336页

作者： He, Yunxiao Liu, Chuanhai Purdue Univ Dept Stat W Lafayette IN 47907 USA Yale Univ New Haven CT USA

. The expectationconditional maximization either (ECME) algorithm has proven to be an effective way of accelerating the expectationmaximization algorithm for many problems. Recognizing the limitation of using prefixed acceleration subspaces in the ECME algorithm, we propose a dynamic ECME (DECME) algorithm which allows the acceleration subspaces to be chosen dynamically. The simplest DECME implementation is what we call DECME-1, which uses the line that is determined by the two most recent estimates as the acceleration subspace. The investigation of DECME-1 leads to an efficient, simple, stable and widely applicable DECME implementation, which uses two-dimensional acceleration subspaces and is referred to as DECME-2. The fast convergence of DECME-2 is established by the theoretical result that, in a small neighbourhood of the maximum likelihood estimate, it is equivalent to a conjugate direction method. The remarkable accelerating effect of DECME-2 and its variant is also demonstrated with several numerical examples.

关键词： Conjugate direction expectation-conditional maximization algorithm expectation-conditional maximization either algorithm expectation-maximization algorithm Square iterative methods Successive over-relaxation

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