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Telecommunications Forum (TELFOR)

作者： Miroslav D. Lutovac Vladimir M. Mladenović Dragutin Lj. Debeljković Ovaj rad delimicno je finansiran sa projekta Nauke i tehnoloskog razvoja Republike Srbije

ISBN: (纸本)9781509000562

In this paper, a new approach of solving the continuous-time systems, with pure time-delay elements, using symbolic tools is presented. Although symbolic computer tools cannot find the closed form solution in frequency domain (using Laplace transform method), the final solution can be derived in time domain. Described symbolic solution is implemented by using software package Mathematica (the Wolfram language), and application package SchematicSolver.

关键词： Tungsten Computers Telecommunications Algebra MIMO expectation-maximization algorithms Electronic mail

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Effect of Residual Channel Estimation Errors in Random Access Methods for Satellite Communications

Effect of Residual Channel Estimation Errors in Random Acces...

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IEEE Vehicular Technology Conference

作者： Karine Zidane Jerome Lacan Marie-Laure Boucheret Charly Poulliat Mathieu Gineste Damien Roques Caroline Bes Arnaud Deramecourt University of Toulouse ISAE/DMIA & TeSA University of Toulouse IRIT/ENSEEIHT Thales Alenia Space CNES

ISBN: (纸本)9781479980895

In recent random access methods used for satellite communications, collisions between packets are not considered as destructive. In fact, to deal with the collision problem, successive interference cancellation is performed at the receiver. Generally, it is assumed that the receiver has perfect knowledge of the interference. In practice, the interference term is affected by the transmission channel parameters, i.e., channel attenuation, timing offsets, frequency offsets and phase shifts, and needs to be accurately estimated and canceled to avoid performance degradation. In this paper, we study the performance of an enhanced channel estimation technique combining estimation using an autocorrelation based method and the expectation-maximization algorithm integrated in a joint estimation and decoding scheme. We evaluate the effect of residual estimation errors after successive interference cancellation. To validate our experimental results, we compare them to the Cramer-Rao lower bounds for the estimation of channel parameters in case of superimposed signals.

关键词： Satellite communication Network coding Channel estimation expectation-maximization algorithms Cramer Rao Bounds

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Convergence and Stability of Iteratively Re-weighted Least Squares algorithms

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2014年第1期62卷 183-195页

作者： Ba, Demba Babadi, Behtash Purdon, Patrick L. Brown, Emery N. MIT Dept Brain & Cognit Sci Cambridge MA 02139 USA Massachusetts Gen Hosp Dept Anesthesia Crit Care & Pain Med Boston MA 02114 USA MIT Harvard MIT Div Hlth Sci & Technol Cambridge MA 02139 USA

In this paper, we study the theoretical properties of iteratively re-weighted least squares (IRLS) algorithms and their utility in sparse signal recovery in the presence of noise. We demonstrate a one-to-one correspondence between the IRLS algorithms and a class of expectation-maximization (EM) algorithms for constrained maximum likelihood estimation under a Gaussian scale mixture (GSM) distribution. The EM formalism, as well as the connection to GSMs, allow us to establish that the IRLS algorithms minimize smooth versions of the l(nu) 'norms', for 0 < nu <= 1. We leverage EM theory to show that the limit points of the sequence of IRLS iterates are stationary points of the smooth "norm" minimization problem on the constraint set. We employ techniques from Compressive Sampling (CS) theory to show that the IRLS algorithm is stable, if the limit point of the iterates coincides with the global minimizer. We further characterize the convergence rate of the IRLS algorithm, which implies global linear convergence for nu = 1 and local super-linear convergence for 0 < nu < 1. We demonstrate our results via simulation experiments. The simplicity of IRLS, along with the theoretical guarantees provided in this contribution, make a compelling case for its adoption as a standard tool for sparse signal recovery.

关键词： Compressive sampling constrained maximum likelihood estimation Gaussian scale mixtures expectation-maximization algorithms mathematical programming

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Multiple model approach to linear parameter varying time-delay system identification with EM algorithm

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JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS 2014年第12期351卷 5565-5581页

作者： Yang, Xianqiang Gao, Huijun Harbin Inst Technol Res Inst Intelligent Control & Syst Harbin 150080 Heilongjiang Peoples R China

This paper is concerned with the identification problem of linear parameter varying (LPV) time-delay systems. Due to inherent nonlinearity, the industrial processes are often approximately described by an LPV model constructed by synthesizing multiple local models. Time-delay is commonly experienced in industrial processes and it can be parameter varying or constant in the process model. The multiple model identification of LPV systems with parameter varying or constant time-delay is formulated in the scheme of the expectation-maximization (EM) algorithm and the parameter varying property and the time-delay property of the process are handled simultaneously. The irrigation channel example and high purity distillation column example are used to present the effectiveness of the proposed method. (C) 2014 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

关键词： expectation-maximization algorithms LINEAR systems TIME delay systems PARAMETERS (Statistics) IDENTIFICATION (Statistics) MANUFACTURING processes NONLINEAR theories

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The Path Inference Filter: Model-Based Low-Latency Map Matching of Probe Vehicle Data

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2014年第2期15卷 507-529页

作者： Hunter, Timothy Abbeel, Pieter Bayen, Alexandre Univ Calif Berkeley Coll Engn Dept Elect Engn & Comp Sci Berkeley CA 94720 USA

We consider the problem of reconstructing vehicle trajectories from sparse sequences of GPS points, for which the sampling interval is between 1 s and 2 min. We introduce a new class of algorithms, which are altogether called the path inference filter (PIF), that maps GPS data in real time, for a variety of tradeoffs and scenarios and with a high throughput. Numerous prior approaches in map matching can be shown to be special cases of the PIF presented in this paper. We present an efficient procedure for automatically training the filter on new data, with or without ground-truth observations. The framework is evaluated on a large San Francisco taxi data set and is shown to improve upon the current state of the art. This filter also provides insights about driving patterns of drivers. The PIF has been deployed at an industrial scale inside the Mobile Millennium traffic information system, and is used to map fleets of data in San Francisco and Sacramento, CA, USA;Stockholm, Sweden;and Porto, Portugal.

关键词： expectation-maximization algorithms filtering GPS data Markov random fields maximum likelihood estimation network-free data path observation generation probabilistic map matching route choice modeling

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Directionlet-based method using the Gaussian mixture prior to SAR image despeckling

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2014年第3期35卷 1143-1161页

作者： Lu, Yixiang Gao, Qingwei Sun, Dong Zhang, Dexiang Anhui Univ Sch Elect Engn & Automat Hefei 230601 Peoples R China

In this article, a new denoising algorithm is proposed based on the directionlet transform and the maximum a posteriori (MAP) estimation. The detailed directionlet coefficients of the logarithmically transformed noise-free image are considered to be Gaussian mixture probability density functions (PDFs) with zero means, and the speckle noise in the directionlet domain is modelled as additive noise with a Gaussian distribution. Then, we develop a Bayesian MAP estimator using these assumed prior distributions. Because the estimator that is the solution of the MAP equation is a function of the parameters of the assumed mixture PDF models, the expectation-maximization (EM) algorithm is also utilized to estimate the parameters, including weight factors and variances. Finally, the noise-free SAR image is restored from the estimated coefficients yielded by the MAP estimator. Experimental results show that the directionlet-based MAP method can be successfully applied to images and real synthetic aperture radar images to denoise speckle. View full text

关键词： GAUSSIAN mixture models (Statistics) PROBABILITY density function SPECKLE noise GAUSSIAN distribution expectation-maximization algorithms SYNTHETIC aperture radar

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Identification of LTI Time-Delay Systems with Missing Output Data Using GEM Algorithm

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MATHEMATICAL PROBLEMS IN ENGINEERING 2014年第1期2014卷 1-8页

作者： Yang, Xianqiang Karimi, Hamid Reza Harbin Inst Technol Res Inst Intelligent Control & Syst Harbin 150080 Heilongjiang Peoples R China Univ Agder Dept Engn Fac Sci & Engn N-4898 Grimstad Norway

This paper considers the parameter estimation for linear time-invariant (LTI) systems in an input-output setting with output error (OE) time-delay model structure. The problem of missing data is commonly experienced in industry due to irregular sampling, sensor failure, data deletion in data preprocessing, network transmission fault, and so forth;to deal with the identification of LTI systems with time-delay in incomplete-data problem, the generalized expectation-maximization (GEM) algorithm is adopted to estimate the model parameters and the time-delay simultaneously. Numerical examples are provided to demonstrate the effectiveness of the proposed method.

关键词： LINEAR time invariant systems TIME delay systems MISSING data (Statistics) expectation-maximization algorithms GENERALIZABILITY theory (Statistics)

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Estimating Parameters of a Probabilistic Heterogeneous Block Model via the EM Algorithm

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JOURNAL OF PROBABILITY AND STATISTICS 2015年第1期2015卷 1-14页

作者： Bolla, Marianna Elbanna, Ahmed Budapest Univ Technol & Econ Inst Math Muegyet Rakpart 3 H-1111 Budapest Hungary

We introduce a semiparametric block model for graphs, where the within- and between-cluster edge probabilities are not constants within the blocks but are described by logistic type models, reminiscent of the 50-year-old Rasch model and the newly introduced alpha-beta models. Our purpose is to give a partition of the vertices of an observed graph so that the induced subgraphs and bipartite graphs obey these models, where their strongly interlaced parameters give multiscale evaluation of the vertices at the same time. In this way, a profoundly heterogeneous version of the stochastic block model is built via mixtures of the above submodels, while the parameters are estimated with a special EM iteration.

关键词： RASCH models PARAMETER estimation HETEROGENEOUS catalysis expectation-maximization algorithms PHYSICAL constants STOCHASTIC models

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High-Dimensional Cluster Analysis with the Masked EM Algorithm

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NEURAL COMPUTATION 2014年第11期26卷 2379-2394页

作者： Kadir, Shabnam N. Goodman, Dan F. M. Harris, Kenneth D. UCL UCL Inst Neurol London WC1E 6DE England UCL UCL Dept Neurosci Physiol & Pharmacol London WC1E 6DE England Harvard Univ Sch Med Massachusetts Eye & Ear Infirm Eaton Peabody LabsDept Otol & Laryngol Boston MA 02114 USA

Cluster analysis faces two problems in high dimensions: the "curse of dimensionality" that can lead to overfitting and poor generalization performance and the sheer time taken for conventional algorithms to process large amounts of high-dimensional data. We describe a solution to these problems, designed for the application of spike sorting for next-generation, high-channel-count neural probes. In this problem, only a small subset of features provides information about the cluster membership of any one data vector, but this informative feature subset is not the same for all data points, rendering classical feature selection ineffective. We introduce a "masked EM" algorithm that allows accurate and time-efficient clustering of up to millions of points in thousands of dimensions. We demonstrate its applicability to synthetic data and to real-world high-channel-count spike sorting data.

关键词： expectation-maximization algorithms CLUSTER analysis (Statistics) DIMENSIONAL reduction algorithms MATHEMATICAL analysis COMPUTATIONAL neuroscience NEURAL computers

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Probabilistic partitioning methods to find significant patterns in ChIP-Seq data

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BIOINFORMATICS 2014年第17期30卷 2406-2413页

作者： Nair, Nishanth Ulhas Kumar, Sunil Moret, Bernard M. E. Bucher, Philipp Ecole Polytech Fed Lausanne Sch Comp & Commun Sci Lab Computat Biol & Bioinformat CH-1015 Lausanne Switzerland Ecole Polytech Fed Lausanne Sch Life Sci Swiss Inst Expt Canc Res ISREC CH-1015 Lausanne Switzerland Swiss Inst Bioinformat CH-1015 Lausanne Switzerland

Motivation: We have witnessed an enormous increase in ChIP-Seq data for histone modifications in the past few years. Discovering significant patterns in these data is an important problem for understanding biological mechanisms. Results: We propose probabilistic partitioning methods to discover significant patterns in ChIP-Seq data. Our methods take into account signal magnitude, shape, strand orientation and shifts. We compare our methods with some current methods and demonstrate significant improvements, especially with sparse data. Besides pattern discovery and classification, probabilistic partitioning can serve other purposes in ChIP-Seq data analysis. Specifically, we exemplify its merits in the context of peak finding and partitioning of nucleosome positioning patterns in human promoters.

关键词： SEQUENCE alignment (Bioinformatics) PROBABILITY theory PARALLEL algorithms IMMUNOPRECIPITATION HIGH throughput screening (Drug development) EUCLIDEAN distance expectation-maximization algorithms

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