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检索条件"主题词=Expectation-maximisation algorithm"

共 493 条记录，以下是271-280 订阅

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Bayesian Filtering for Phase Noise Characterization and Carrier Synchronization of up to 192 Gb/s PDM 64-QAM

Bayesian Filtering for Phase Noise Characterization and Carr...

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European Conference on Optical Communication (ECOC)

作者： Zibar, D. Carvalho, L. Piels, M. Doberstein, A. Diniz, J. Nebendahl, B. Franciscangelis, C. Estaran, J. Haisch, H. Gonzalez, N. G. de Oliveira, J. R. F. Monroy, I. T. Tech Univ Denmark DTU Foton Build 343 DK-2800 Lyngby Denmark CPqD Ctr Pesquisa & Desenvolvimento Telecomunicac Campinas SP Brazil Agilent Technol D-71034 Boblingen Germany

We show that phase noise estimation based on Bayesian filtering outperforms conventional time-domain approaches in the presence of moderate measurement noise. Additionally, carrier synchronization based on Bayesian fi... 详细信息

ISBN: (纸本)9781479930661

关键词： Bayes methods expectation-maximisation algorithm filtering theory optical communication optical modulation phase noise quadrature amplitude modulation Bayesian filtering PDM 64-QAM bit rate 192 Gbit/s carrier synchronization coherent optical communication systems expectation maximization phase noise characterization phase noise estimation spectral efficiency Estimation Filtering Laser noise Optical noise Phase noise Signal to noise ratio

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Model choice for binned-EM algorithms of fourteen parsimonious Gaussian mixture models by BIC and ICL criteria

Model choice for binned-EM algorithms of fourteen parsimonio...

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IEEE International Conference on System Science and Engineering (ICSSE)

作者： Wu, Jingwen Hamdan, Hani SUPELEC Dept Signal Proc & Elect Syst Gif Sur Yvette France

ISBN: (纸本)9781479900077;9781479900091

Choosing the right model is an important step in model-based clustering approaches. In this framework, BIC and ICL criteria were proposed to choose a model for clustering of standard data. On the other hand, in order to accelerate the data processing when using EM algorithm, this algorithm was adapted to binned data (binned-EM algorithm). Then fourteen binned-EM algorithms of fourteen parsimonious Gaussian mixture models were developed to replace the binned-EM algorithm of the most general Gaussian mixture model when data have a simple structure. So this paper studies the application of BIC and ICL criteria to select a good model which better fits binned data, when clustering is based on these fourteen binned-EM algorithms. Numerical experiments on simulated and real data are performed, and the experimental results are analyzed.

关键词： Bayes methods Gaussian processes expectation-maximisation algorithm pattern clustering

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Interference Suppression Using EM algorithm in OFDM Transmissions

Interference Suppression Using EM Algorithm in OFDM Transmis...

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IEEE Global Communications Conference (GLOBECOM)

作者： Yoda, Naotoshi Ohtsuki, Tomoaki Mashino, Jun Sugiyama, Takatoshi Keio Univ Dept Informat & Comp Sci Kohoku Ku 3-14-1 Hiyoshi Yokohama Kanagawa 2238522 Japan NTT Access Network Serv Syst Labs Yokosuka Kanagawa 2390847 Japan

ISBN: (纸本)9781479935123

Interference detection and suppression schemes are proposed for coded OFDM systems in the presence of narrow-band interference. Previously proposed interference detection schemes perform with a recursive forward error correcting (FEC) decoding, thus its calculation is heavy. The proposed scheme is based on an expectation maximization (EM) algorithm to detect the interference before the FEC decoding process. The complexity is reduced because the FEC decoding is performed only once. Simulation results indicate that the proposed scheme achieves almost the same bit error rate performance as the conventional scheme, while decreasing the times of the FEC decoding.

关键词： OFDM modulation error statistics expectation-maximisation algorithm forward error correction interference suppression iterative decoding modulation coding radiocommunication recursive estimation signal detection EM algorithm bit error rate performance coded OFDM transmission system expectation maximization algorithm interference suppression scheme narrowband interference detection scheme recursive FEC iterative decoding wireless communication Bit error rate Decoding Forward error correction Interference Iterative decoding OFDM Receivers

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An EM-based phase-noise estimator for MIMO systems

An EM-based phase-noise estimator for MIMO systems

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IEEE International Conference on Communications (ICC)

作者： Tarable, Alberto Montorsi, Guido Benedetto, Sergio Chinnici, Stefano IEIIT CNR I-10129 Turin Italy Politecn Torino I-10129 Turin Italy Ericsson Telecomunicaz SpA I-20090 Milan Italy

ISBN: (纸本)9781467331227

In this paper, we derive a phase-noise estimator for MIMO systems affected by independent phase noise at each antenna. The proposed phase-noise estimator employs the expectation-maximization algorithm and can incorporate channel decoding. The main feature of the proposed estimator lies in the fact that it estimates separately phase-noise samples at the transmitter and at the receiver, instead of estimating, as it is typical in single-antenna case, the sum of the samples at the two sides. Simulation results show that the proposed estimator allows obtaining a very good performance at an affordable complexity.

关键词： MIMO communication antennas channel coding expectation-maximisation algorithm

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The Impact of Reconstruction algorithms on Semi-automatic Small Lesion Segmentation for PET: A Phantom Study

The Impact of Reconstruction Algorithms on Semi-automatic Sm...

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33rd Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBS)

作者： Ballangan, Cherry Chan, Chung Wang, Xiuying Feng, David Dagan Univ Sydney Biomed & Multimedia Informat Technol BMIT Res Grp Sch Informat Technol Sydney NSW 2006 Australia Univ Sydney Biomed & Multimedia Informat Technol BMIT Res Grp Brain & Mind Res Inst Fac Hlth Sci Sydney NSW 2006 Australia Yale Univ Dept Diagnost Radiol New Haven CT 06520 USA Univ Sydney BMIT Res Grp Sch IT Sydney NSW 2006 Australia Hong Kong Polytech Univ Ctr Multimedia Signal Proc Dept Elect & Informat Engn Hong Kong Peoples R China Shanghai Jiao Tong Univ Med X Res Inst Shanghai 200030 Peoples R China

ISBN: (纸本)9781424441228

A robust lesion segmentation method is critical for quantification of lesion activity in positron emission tomography (PET), especially for the cases where lesion boundary is not discernible in the corresponding computed tomography (CT). However, lesion delineation in PET is a challenging task, especially for small lesions, due to the low intrinsic resolution, image noise and partial volume effect. The combinations of different reconstruction methods and post-reconstruction smoothing on PET images also affect the segmentation result significantly which has always been overlooked. Therefore, the aim of this study was to investigate the impact of different reconstruction methods on semiautomated small lesion segmentation for PET images. Four conventional segmentation methods were evaluated including region growing technique based on maximum intensity (RGmax) and mean intensity (RGmean) thresholds, Fuzzy c-mean (FCM) and watershed (WS) technique. All these methods were evaluated on a physical phantom scan which was reconstructed with Ordered Subset expectation Maximization (OSEM) with Gaussian post-smoothing and Maximum a Posteriori (MAP) with quadratic prior respectively. The results demonstrate that: 1) the performance of all the segmentation methods subject to the smoothness constraint applied on the reconstructed images;2) FCM method applied on MAP reconstructed images yielded overall superior performance than other evaluated combinations.

关键词： Gaussian post-smoothing Image reconstruction Image segmentation Lesions OSEM PET Phantoms Positron emission tomography Reconstruction algorithms Smoothing methods diseases expectation-maximisation algorithm fuzzy c-mean technique fuzzy reasoning image noi

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Identification of dynamical biological systems based on random effects models 37

Identification of dynamical biological systems based on rand...

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37th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Batista, Levy Bastogne, Thierry Djermoune, El-Hadi CNRS UMR 7039 CRAN BP 70239 F-54506 Vandoeuvre Les Nancy France INRIA BIGS F-54506 Vandoeuvre Les Nancy France Telecom Nancy CYBERnano Villers Les Nancy 54602 France

ISBN: (纸本)9781424492701

System identification is a data-driven modeling approach more and more used in biology and biomedicine. In this application context, each assay is always repeated to estimate the response variability. The inference of the modeling conclusions to the whole population requires to account for the inter-individual variability within the modeling procedure. One solution consists in using random effects models but up to now no similar approach exists in the field of dynamical system identification. In this article, we propose a new solution based on an ARX (Auto Regressive model with eXternal inputs) structure using the EM (expectation-maximisation) algorithm for the estimation of the model parameters. Simulations show the relevance of this solution compared with a classical procedure of system identification repeated for each subject.

关键词： EM ARX dynamical biological systems based ARX gene random effect System identification Response Variability biomedicine biological organs expectation-maximisation algorithm Solutions

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expectation Maximization Based Parameter Estimation by Sigma-Point and Particle Smoothing 17

Expectation Maximization Based Parameter Estimation by Sigma...

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17th International Conference on Information Fusion (FUSION)

作者： Kokkala, Juho Solin, Arno Sarkka, Simo Aalto Univ Dept Biomed Engn & Computat Sci Espoo Finland

ISBN: (纸本)9781479916344

We consider parameter estimation in non-linear state space models by using expectation-maximization based numerical approximations to likelihood maximization. We present a unified view of approximative EM algorithms that use either sigma-point or particle smoothers to evaluate the integrals involved in the expectation step of the EM method, and compare these methods to direct likelihood maximization. For models that are linear in parameters and have additive noise, we show how the maximization step of the EM algorithm is available in closed form. We compare the methods using simulated data, and discuss the differences between the approximations.

关键词： Approximation algorithms Approximation methods Equations Mathematical model Numerical models Parameter estimation Smoothing methods Parameter estimation smoothing methods Approximation method Approximation algorithms numerical models Mathematical Model expectation-maximisation algorithm smoothing

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Bayesian Learning for Joint Sparse OFDM Channel Estimation and Data Detection

Bayesian Learning for Joint Sparse OFDM Channel Estimation a...

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IEEE Global Telecommunications Conference (GLOBECOM 2010)

作者： Prasad, Ranjitha Murthy, Chandra. R. Indian Inst Sci Dept Elect & Commun Engn Bangalore 560012 Karnataka India

ISBN: (纸本)9781424456383

The impulse response of a typical wireless multipath channel can be modeled as a tapped delay line filter whose non-zero components are sparse relative to the channel delay spread. In this paper, a novel method of estimating such sparse multipath fading channels for OFDM systems is explored. In particular, Sparse Bayesian Learning (SBL) techniques are applied to jointly estimate the sparse channel and its second order statistics, and a new Bayesian Cramer-Rao bound is derived for the SBL algorithm. Further, in the context of OFDM channel estimation, an enhancement to the SBL algorithm is proposed, which uses an expectation Maximization (EM) framework to jointly estimate the sparse channel, unknown data symbols and the second order statistics of the channel. The EM-SBL algorithm is able to recover the support as well as the channel taps more efficiently, and/or using fewer pilot symbols, than the SBL algorithm. To further improve the performance of the EM-SBL, a threshold-based pruning of the estimated second order statistics that are input to the algorithm is proposed, and its mean square error and symbol error rate performance is illustrated through Monte-Carlo simulations. Thus, the algorithms proposed in this paper are capable of obtaining efficient sparse channel estimates even in the presence of a small number of pilots.

关键词： Bayes methods Bayesian Cramer-Rao bound Monte Carlo methods Monte-Carlo simulations OFDM modulation channel delay spread channel estimation data detection data symbols error statistics expectation maximization framework expectation-maximisation algorithm

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Estimation of articulated motion using kinematically constrained mixture densities

Estimation of articulated motion using kinematically constra...

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IEEE Nonrigid and Articulated Motion Workshop

作者： Hunter, EA Kelly, PH Jain, RC Univ of California San Diego United States

ISBN: (纸本)0818680407

We address the problem of articulated posture estimation in it's general form. Namely, the recovery of full 3D articulated posture parameters from an uncontrolled scene. Stochastic modeling of low-level segmented image data is unified with models of object kinematic structure through a constrained mixture of observation processes. A modified expectation-Maximization algorithm is proposed for this purpose. Early experiments qualitatively demonstrate the efficacy of our approach, and provide a context for integration for more sophisticated image cues.

关键词： ARTICULATED image data LOW LEVEL expectation-maximisation algorithm

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Channel Estimation in Underwater Cooperative OFDM System with Amplify-and-Forward Relaying

Channel Estimation in Underwater Cooperative OFDM System wit...

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IEEE Global Communications Conference (GLOBECOM)

作者： Senol, Habib Panayirci, Erdal Erdogan, Mustafa Uysal, Murat Kadir Has Univ Dept Elect Elect Engn TR-34083 Istanbul Turkey Ozyegin Univ Dept Elect Elect Engn TR-34794 Istanbul Turkey

ISBN: (纸本)9781467309219;9781467309202

This paper is concerned with a challenging problem of channel estimation for amplify-and-forward cooperative relay based orthogonal frequency division multiplexing (OFDM) systems in the presence of sparse underwater acoustic channels and of the correlative non-Gaussian noise. We exploit the sparse structure of the channel impulse response to improve the performance of the channel estimation algorithm, due to the reduced number of taps to be estimated. The resulting novel algorithm initially estimates the overall sparse channel taps from the source to the destination as well as their locations using the matching pursuit (MP) approach. The correlated non-Gaussian effective noise is modeled as a Gaussian mixture. Based on the Gaussian mixture model, an efficient and low complexity algorithm is developed based on the combinations of the MP and the space-alternating generalized expectation-maximization (SAGE) technique, to improve the estimates of the channel taps and their location as well as the noise distribution parameters in an iterative way. The proposed SAGE algorithm is designed in such a way that, by choosing the admissible hidden data properly on which the SAGE algorithm relies, a subset of parameters is updated for analytical tractability and the remaining parameters for faster convergence Computer simulations show that underwater acoustic (UWA) channel is estimated very effectively and the proposed algorithm has excellent symbol error rate and channel estimation performance.

关键词： Gaussian processes OFDM modulation amplify and forward communication channel estimation cooperative communication error statistics expectation-maximisation algorithm transient response underwater acoustic communication

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