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21st Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Meghdadi, Hamid Cances, Jean-Pierre Meghdadi, Vahid Univ Limoges ENSIL F-87068 Limoges France

ISBN: (纸本)9781424480166

This paper addresses the problem of transmitting data to multiple mobile stations using a decode-and-forward strategy. Precoding vectors are used in relays to cancel out multiple access interference at the mobile stations. Statistical distribution of signal to noise ratio (SNR) is approximated by an expectation maximization algorithm. Based on this distribution, system performance is evaluated for low and high SNR. Simulation results confirm the analytic calculations and show that the maximum diversity advantage can be obtained, which is the product of the number of antennas at each relay by the number of relays minus the total number of system constraints.

关键词： cooperative communication decode-and-forward diversity diversity reception expectation maximization algorithm expectation-maximisation algorithm mobile stations mobile stations multiuser detection precoding precoding multiuser cooperative scheme semi-analytic approach signal to noise ratio statistical distribution statistical distributions

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CLASSIFICATION OF COMPLEX PEDESTRIAN ACTIVITIES FROM TRAJECTORIES

CLASSIFICATION OF COMPLEX PEDESTRIAN ACTIVITIES FROM TRAJECT...

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IEEE International Conference on Image Processing

作者： Nascimento, Jacinto C. Marques, Jorge S. Figueiredo, Mario A. T. Inst Super Tecn Inst Sistemas & Robot P-1049001 Lisbon Portugal Inst Super Tecn Inst Telecommun P-1049001 Lisbon Portugal

ISBN: (纸本)9781424479948

We propose a method to classify human trajectories, modeled by a set of motion vector fields, each tailored to describe a specific motion regime. Trajectories are modeled as being composed of segments corresponding to different motion regimes, each generated by one of the underlying motion fields. Switching among the motion fields follows a probabilistic mechanism, described by a field of stochastic matrices. This yields a space-dependent motion model which can be estimated using an expectation-maximization (EM) algorithm. To address the model selection question (how many fields to use?), we adopt a discriminative criterion based on classification accuracy on a held out set. Experiments with real data (human trajectories in a shopping mall) illustrate the ability of the proposed approach to classify complex trajectories into high level classes (client versus non-client).

关键词： complex pedestrian activity expectation maximization algorithm expectation-maximisation algorithm human trajectory classification image classification motion estimation motion vector field probabilistic mechanism space dependent motion model stochastic matrices video surveillance

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An EM algorithm-Based Channel Estimation for OFDM Amplify-and-Forward Relaying Systems

An EM Algorithm-Based Channel Estimation for OFDM Amplify-an...

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2010 IEEE International Conference on Communications

作者： Sheu, Jeng-Shin Sheen, Wern-Ho Natl Yunlin Univ Sci & Technol Dept Comp Sci & Informat Engn Touliu Yunlin Taiwan Chaoyang Univ Technol Dept Informat & Commun Engn Taichung 41349 Taiwan

ISBN: (纸本)9781424464043

The issue of channel estimation is investigated for the orthogonal frequency-division multiplexing (OFDM) relaying system with amplify-and-forward (AF) operation mode. Channel estimation in the AF mode is unique in that both the source-to-relay and relay-to-destination channels are needed at the destination in order to perform optimum combining of the received signals from the direct (source-to-destination) and relay paths if a cooperative diversity transmission is employed. Until now, however, there have been only literatures to study the estimation of the composite channel of the relay path. In this work, an iterative expectation-maximization (EM) algorithm-based channel estimation is proposed for respective estimations of the source-to-relay and relay-to-destination links at the destination. Computer simulations show that the iterative channel estimator performs satisfactorily and converges rapidly.

关键词： Channel estimation amplify-and-forward OFDM relaying systems expectation maximization algorithm

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Joint Iterative Channel Estimation and Data Detection for MIMO-CDMA Systems over Frequency-Selective Fading Channels

Joint Iterative Channel Estimation and Data Detection for MI...

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IEEE Global Telecommunications Conference (GLOBECOM 2010)

作者： Assra, Ayman Hamouda, Walaa Youssef, Amr Concordia Univ Dept Elect & Comp Engn Montreal PQ H3G 1M8 Canada Concordia Univ Concordia Inst Informat Syst Engn Montreal PQ H3G 1M8 Canada

ISBN: (纸本)9781424456383

In this paper, we present an iterative joint channel estimation and data detection technique for multiple-input multiple-output (MIMO) code-division multiple-access (CDMA) systems over frequency-selective fading channels. Based on the expectation-maximization (EM) algorithm, the proposed iterative receiver achieves a performance close to the optimum maximum-likelihood (ML) receiver. In addition, the performance of the proposed receiver is optimized through weight coefficients using the minimum mean-square error (MMSE) criterion. Compared to the single-user bound, our results show that the proposed receiver can mitigate the multiple-access interference and attain the full system diversity. Furthermore, our simulation results confirm that the proposed receiver is near-far resistant and offers fast convergence in severe near-far scenarios.

关键词： MIMO communication MIMO-CDMA system channel estimation code division multiple access data detection technique diversity reception expectation maximization algorithm expectation-maximisation algorithm fading channels frequency selective fading channel full

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Genomic selection and complex trait prediction using a fast EM algorithm applied to genome-wide markers

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BMC BIOINFORMATICS 2010年第1期11卷 1-12页

作者： Shepherd, Ross K. Meuwissen, Theo H. E. Woolliams, John A. Cent Queensland Univ Sch Informat & Commun Technol Rockhampton Qld 4702 Australia Norwegian Univ Life Sci Inst Anim & Aquacultural Sci N-1432 As Norway Univ Edinburgh Royal Dick Sch Vet Studies Roslin EH25 9PS Midlothian Scotland Univ Edinburgh Roslin Inst Roslin EH25 9PS Midlothian Scotland

Background: The information provided by dense genome-wide markers using high throughput technology is of considerable potential in human disease studies and livestock breeding programs. Genome-wide association studies relate individual single nucleotide polymorphisms (SNP) from dense SNP panels to individual measurements of complex traits, with the underlying assumption being that any association is caused by linkage disequilibrium (LD) between SNP and quantitative trait loci (QTL) affecting the trait. Often SNP are in genomic regions of no trait variation. Whole genome Bayesian models are an effective way of incorporating this and other important prior information into modelling. However a full Bayesian analysis is often not feasible due to the large computational time involved. Results: This article proposes an expectation-maximization (EM) algorithm called emBayesB which allows only a proportion of SNP to be in LD with QTL and incorporates prior information about the distribution of SNP effects. The posterior probability of being in LD with at least one QTL is calculated for each SNP along with estimates of the hyperparameters for the mixture prior. A simulated example of genomic selection from an international workshop is used to demonstrate the features of the EM algorithm. The accuracy of prediction is comparable to a full Bayesian analysis but the EM algorithm is considerably faster. The EM algorithm was accurate in locating QTL which explained more than 1% of the total genetic variation. A computational algorithm for very large SNP panels is described. Conclusions: emBayesB is a fast and accurate EM algorithm for implementing genomic selection and predicting complex traits by mapping QTL in genome-wide dense SNP marker data. Its accuracy is similar to Bayesian methods but it takes only a fraction of the time.

关键词： Quantitative Trait Locus Single Nucleotide Polymorphism Lasso Genomic Selection expectation maximization algorithm

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Probabilistic principal component analysis for metabolomic data

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BMC BIOINFORMATICS 2010年第1期11卷 571-571页

作者： Nyamundanda, Gift Brennan, Lorraine Gormley, Isobel Claire Univ Coll Dublin Sch Math Sci Dublin Ireland Univ Coll Dublin Conway Inst Sch Agr Food Sci & Vet Med Dublin Ireland

Background: Data from metabolomic studies are typically complex and high-dimensional. Principal component analysis (PCA) is currently the most widely used statistical technique for analyzing metabolomic data. However, PCA is limited by the fact that it is not based on a statistical model. Results: Here, probabilistic principal component analysis (PPCA) which addresses some of the limitations of PCA, is reviewed and extended. A novel extension of PPCA, called probabilistic principal component and covariates analysis (PPCCA), is introduced which provides a flexible approach to jointly model metabolomic data and additional covariate information. The use of a mixture of PPCA models for discovering the number of inherent groups in metabolomic data is demonstrated. The jackknife technique is employed to construct confidence intervals for estimated model parameters throughout. The optimal number of principal components is determined through the use of the Bayesian Information Criterion model selection tool, which is modified to address the high dimensionality of the data. Conclusions: The methods presented are illustrated through an application to metabolomic data sets. Jointly modeling metabolomic data and covariates was successfully achieved and has the potential to provide deeper insight to the underlying data structure. Examination of confidence intervals for the model parameters, such as loadings, allows for principled and clear interpretation of the underlying data structure. A software package called MetabolAnalyze, freely available through the R statistical software, has been developed to facilitate implementation of the presented methods in the metabolomics field.

关键词： Principal Component Analysis Bayesian Information Criterion expectation maximization algorithm Metabolomic Data Loading Matrix

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FACTOR ANALYZED VOICE MODELS FOR HMM-BASED SPEECH SYNTHESIS

FACTOR ANALYZED VOICE MODELS FOR HMM-BASED SPEECH SYNTHESIS

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Kyosuke Kazumi Yoshihiko Nankaku Keiichi Tokuda Nagoya Institute of Technology Nagoya Japan

ISBN: (纸本)9781424442959

This paper describes factor analyzed voice models for realizing various voice characteristics in the HMM-based speech synthesis. The eigenvoice method can synthesize speech with arbitrary voice characteristics by interpolating representative HMM sets. However, the objective of PCA is to accurately reconstruct each speaker-dependent HMM set, and this is not equivalent to estimating models which represent training data accurately. To overcome this problem, we propose a general speech model which generates speech utterances with various voice characteristics directly. In the proposed method, the HMM states, factors representing voice characteristics and contextual decision trees are simultaneously optimized within a unified framework.

关键词： HMM-based speech synthesis eigenvoice factor analysis expectation maximization algorithm deterministic annealing EM algorithm expectation-maximisation algorithm Voice Factor analysis Hidden Markov models Speech synthesis Speech Decision Trees

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Research on Network Self-protection System Model and Its Security Event Correlation Engine

Research on Network Self-protection System Model and Its Sec...

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2010 International Conference on Services Science, Management and Engineering (SSME 2010)

作者： Hongyu Yang School of Computer Science and Technology Civil Aviation University of China Tianjin 300300,China Dongming Wang School of Computer Science and Technology Civil Aviation University of China Tianjin 300300,China Chuangxi Chen Dep.of ground maintenance China Southern Airlines Co.Ltd. Guangzhou 510406,China

Manual analysis of security events inevitably results in a large number of attacks can not being detected timely This paper presents a new network self-protection system model,gives its architecture and designs its information security event correlative analysis *** the correlation engine,the expectation maximization(EM) algorithm was designed to solve the problem by lack of security *** the bucket elimination algorithm was used to enhance the computational efficiency of Bayesian *** experimental results demonstrate that the reasoning speed of the correlation engine fulfills the need of network protection against unknown attacks.

关键词： self-protection correlation engine Bayesian network bucket elimination algorithm expectation maximization algorithm

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Image fusion based on an expectation maximization algorithm

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OPTICAL ENGINEERING 2005年第7期44卷 077001-1-077001页

作者： Liu, G Jing, ZL Sun, SY Shanghai Jiao Tong Univ Inst Aerosp Informat & Control Sch Elect Informat & Elect Engn Shanghai 200030 Peoples R China Shanghai Jiao Tong Univ Inst Aerosp Sci & Technol Shanghai 200030 Peoples R China

A novel image fusion method based on an expectation maximization (EM) algorithm and the discrete wavelet frame (DWF) transform is proposed. The registered images are first decomposed using the DWF transform, which is both aliasing-free and translation-invariant. The DWF decomposes the image signal into a multiresolution representation with both low-frequency coarse information and high-frequency detail information. The EM algorithm is used to fuse the low-frequency coarse information of the registered images. The informative importance measure is applied to fuse the high-frequency detail information of the registered images. The final fused image is obtained by taking the inverse transform of the composite multiresolution representations. Simulation results show that the proposed method outperforms the conventional image fusion methods. (c) 2005 Society of Photo-Optical Instrumentation Engineers.

关键词： information fusion image fusion discrete wavelet frame transform expectation maximization algorithm retinal process

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Theoretical and Numerical Study of MLEM and OSEM Reconstruction algorithms for Motion Correction in Emission Tomography

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IEEE TRANSACTIONS ON NUCLEAR SCIENCE 2009年第5期56卷 2739-2749页

作者： Dey, Joyoni King, Michael A. Univ Massachusetts Sch Med Div Nucl Med & Radiol Worcester MA 01655 USA

Patient body-motion and respiratory-motion impacts the image quality of cardiac SPECT and PET perfusion images. Several algorithms exist in the literature to correct for motion within the iterative maximum-likelihood reconstruction framework. In this work. three algorithms are derived starting with Poisson statistics to correct for patient motion. The first one is a motion compensated MLEM algorithm (MC-MLEM). The next two algorithms called MGEM-1 and MGEM-2 (short for Motion Gated OSEM, 1 and 2) use the motion states as subsets, in two different ways. Experiments were performed with NCAT phantoms (with exactly known motion) as the source and attenuation distributions. Experiments were also performed on in anthropomorphic phantom and a patient study. The SIMIND Monte Carlo simulation software was used to create SPECT projection images of the NCAT phantoms. The projection images were then modified to have Poisson noise levels equivalent to that of clinical acquisition. We investigated application of these algorithms to correction of (1) 1 large body-motion of 2 cm in Superior-Inferior (SI) and Anterior-Posterior (AP) directions each and (2) respiratory motion of 2 cm in SI and 0.6 cm in AP. We determined the bias with respect to the NCAT phantom activity for noiseless reconstructions as well as the bias-variance for noisy reconstructions. The MGEM-1 advanced along the bias-variance curve faster than the MC-MLEIM with iterations. The MCEM-1 also lowered the noiseless bias (with respect to NCAT truth) faster with iterations, compared to file MC-MLEM algorithms, as expected with subset algorithms. For the body motion correction with two motion states, after the 9th iteration the Was was close to that of MC-MLEM at iteration 17, reducing the number of iterations by a factor of 1.89. For the respiratory motion correction with 9 motion states, based on the noiseless bias, the iteration reduction factor was approximately 7. For the MGEM-2, however. bias-plot or the bias-var

关键词： expectation maximization algorithm image reconstruction

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