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

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VARIATIONAL BAYESIAN EM algorithm FOR MODELING MIXTURES OF NON-STATIONARY SIGNALS IN THE TIME-FREQUENCY DOMAIN (HR-NMF)

VARIATIONAL BAYESIAN EM ALGORITHM FOR MODELING MIXTURES OF N...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Roland Badeau Angelique Dremeau Institut Mines-Telecom Telecom ParisTech CNRS LTCI

ISBN: (纸本)9781479903573

We recently introduced the high-resolution nonnegative matrix factorization (HR-NMF) model for analyzing mixtures of non-stationary signals in the time-frequency domain, and highlighted its capability to both reach high spectral resolution and reconstruct high quality audio signals. In order to estimate the model parameters and the latent components, we proposed to resort to an expectation-maximization (EM) algorithm based on a Kalman filter/smoother. The approach proved to be appropriate for modeling audio signals in applications such as source separation and audio inpainting. However, its computational cost is high, dominated by the Kalman filter/smoother, and may be prohibitive when dealing with high-dimensional signals. In this paper, we consider two different alternatives, using the variational Bayesian EM algorithm and two mean-field approximations. We show that, while significantly reducing the complexity of the estimation, these novel approaches do not alter its quality.

关键词： Nonnegative Matrix Factorization High Resolution methods expectation-Maximization algorithm Variational inference expectation-maximisation algorithm Variational Inference matrix decomposition audio signals signals mixtures Signal timing Modeling Source separation Spectral resolution model parameters Audio nonstationary random signal

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Bayesian Inference for Neighborhood Filters with Application in Denoising

Bayesian Inference for Neighborhood Filters with Application...

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IEEE Conference on Computer Vision and Pattern Recognition

作者： Chao-Tsung Huang National Tsing Hua University

ISBN: (纸本)9781467369657

Range-weighted neighborhood filters are useful and popular for their edge-preserving property and simplicity, but they are originally proposed as intuitive tools. Previous works needed to connect them to other tools or models for indirect property reasoning or parameter estimation. In this paper, we introduce a unified empirical Bayesian framework to do both directly. A neighborhood noise model is proposed to reason and infer the Yaroslavsky, bilateral, and modified non-local means filters. An EM+ algorithm is devised to estimate the essential parameter, range variance, via the model fitting to empirical distributions. Finally, we apply this framework to color-image denoising. Experimental results show that the proposed model fits noisy images well and the range variance is estimated successfully. The image quality can also be improved by a proposed recursive fitting and filtering scheme.

关键词： Bayesian inference Noise reduction Neighborhood expectation-maximisation algorithm mode matching Filters Noise models Image quality Parameter estimation Frameworks

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Off-grid DOA estimation for passive radar

Off-grid DOA estimation for passive radar

引用

IET International Radar Conference (IET IRC 2020)

作者： J. Wen J. Yi X. Wan Wuhan University School of Electronic Information Wuhan People's Republic of China

To solve the problem that the true target direction of arrival (DOA) is not on the discrete grid points, we adopt a new off-grid sparse strategy to estimate the target DOA of passive radar. This strategy is the root sparse Bayesian learning method, which utilizes a coarse grid and treats the sampling position in the coarse grid as an adaptive parameter. Then, the coarse mesh is iteratively refined using the expectation maximization algorithm, and the estimated angle is the root of a certain polynomial. Simulation results show that the method has higher estimation accuracy and lower computational complexity in DOA estimation of passive radar. In addition, this method can not only estimate the DOA of multi-target signals in a single snapshot, but also provide new ideas for DOA estimation of coherent signals.

关键词： Bayes methods learning (artificial intelligence) iterative methods passive radar direction-of-arrival estimation expectation-maximisation algorithm computational complexity radar signal processing polynomials radar computing

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Impulse Noise Suppression for Single-Carrier Coded Systems by Monte Carlo expectation Maximization

Impulse Noise Suppression for Single-Carrier Coded Systems b...

引用

IEEE Vehicular Technology Conference

作者： Der-Feng Tseng Shi-Shun Lin National Taiwan University of Science and Technology Department of Electrical Engineering Taipei 106 Taiwan

ISBN: (纸本)9781538663592;9781538663585

An efficient approach to alleviating performance loss caused by unknown (non-stationary) impulse noise, which is typically sparse and whose statistics are difficult to model accurately, was made possible in power line communications systems when the clipping operation is properly implemented. As reported in numerous papers, the clipping threshold, which impacts system performance profoundly, is closely related with the probability of impulse occurrence and the strength of the background noise. This paper first highlights that the least-absolute-shrinkage-and-selection-operator (LASSO) algorithm, aimed at estimating the regularization parameter λ and sparse vector of regression coefficients, can be interpreted as an effective clipping operation. Subsequently, a hybrid of the Bayesian LASSO and the maximum a posteriori algorithm using the Monte Carlo expectation maximization (MCEM) is proposed to simultaneously estimate the regularization parameter and detect the convolutional codeword in single-carrier coded systems subject to the impulse noise. Moreover, by exploiting information exchangeable between those two aforementioned entities, a novel termination rule on the EM iteration stage is devised. Numerical results attest the efficacy of the proposed scheme, despite a lack of statistical knowledge on impulse noise models.

关键词： Bayes methods carrier transmission on power lines convolutional codes expectation-maximisation algorithm impulse noise interference suppression Monte Carlo methods regression analysis Impulse noise carrier transmission on power lines expectation-maximisation algorithm convolutional coding Bayes methods suppression of interference Monte Carlo technique regression coefficients regression analysis

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MIMO RADAR DETECTION UNDER PHASE SYNCHRONIZATION ERRORS

MIMO RADAR DETECTION UNDER PHASE SYNCHRONIZATION ERRORS

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Murat Akcakaya Arye Nehorai Department of Electrical and Systems Engineering Washington University in St.louis St.louis MO. 63130 USA

ISBN: (纸本)9781424442959

We consider the problem of target detection for coherent multi-input multi-output (MIMO) radar with widely separated antennas in the presence of phase synchronization mismatch between the transmitter and receiver pairs. First, we introduce a data model using von-Mises distribution to represent the phase error terms. Then we employ expectation-maximization algorithm to estimate the error distribution parameter and target returns as well as the noise variance. We develop a generalized likelihood ratio test (GLRT) target detector using these estimates and demonstrate the effect of phase uncertainties on the detection performance using Monte Carlo simulations.

关键词： MIMO Radar Signal Processing Signal Detection Phase error phase error signal acquisition Object detection radar detection data models expectation-maximisation algorithm

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Study and Implementation of an Iterative Decoder for SFBC-OFDM Systems with Imperfect Channel Knowledge

Study and Implementation of an Iterative Decoder for SFBC-OF...

引用

IEEE Mediterranean Electrotechnical Conference

作者： Ayman MASSAOUDI Noura SELLAMI Nouri MASMOUDI Mohamed SIALA SUP'COM Cite Technologique des Communications ENIS Route de Soukra

ISBN: (纸本)9781424457939

In this paper, we consider the problem of the Space Frequency Block Codes (SFBC) decoding, according to the Maximum A Posteriori (MAP) criterion when the channel is not perfectly known at the receiver. We propose to use the expectation Maximization (EM) algorithm to solve this problem. Simulation results show that the performance of our iterative decoder using the EM algorithm is very close to the performance obtained when the channel is perfectly known. We also propose an efficient implementation of the iterative decoder (based on the EM algorithm) on FPGA.

关键词： Field programmable gate arrays expectation-maximisation algorithm Iterative spatial frequency Imperfect decoders space-frequency block coding

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A study of color image segmentation based on stochastic expectation maximization algorithm in HSV model

A study of color image segmentation based on stochastic expe...

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Systems and Control in Aerospace and Astronautics, 2006. ISSCAA 2006. 1st International Symposium on

作者： Yudong Guan Qi Zhang Xutao Zhang Youhua Jia Shen Wang Harbin Institute of Technology Harbin China

ISBN: (纸本)0780393953

This paper addresses a study of target segmentation on two color images based on SEM algorithm and region growing algorithm. Background image and target-existing image are converted from RGB space to HSV space. The Euclid distance between these two transformed images in HSV space is computed and compared with that in RGB space. To segment the target region from the background, SEM algorithm is applied. Then the MAP criterion is used for further segmentation. With certain prior knowledge about the size of the target, final segmentation result is got by region growing algorithm. The result of simulation shows that these segmentation methods are very efficient when used together.

关键词： expectation-maximisation algorithm image colour analysis image segmentation stochastic processes Euclid distance HSV model HSV space MAP criterion RGB space SEM algorithm color image segmentation hue saturation value region growing algorithm stochastic e

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Periodic Finite State Controllers for Efficient POMDP and DEC-POMDP Planning 11

Periodic Finite State Controllers for Efficient POMDP and DE...

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Annual Conference on Neural Information Processing Systems

作者： Joni Pajarinen Jaakko Peltonen Aalto University Department of Information and Computer Science P.O. Box 15400 FI-00076 Aalto Finland Aalto University Department of Information and Computer Science Helsinki Institute for Information Technology HUT P.O. Box 15400 FI-00076 Aalto Finland

ISBN: (纸本)9781618395993

Applications such as robot control and wireless communication require planning under uncertainty. Partially observable Markov decision processes (POMDPs) plan policies for single agents under uncertainty and their decentralized versions (DEC-POMDPs) find a policy for multiple agents. The policy in infinite-horizon POMDP and DEC-POMDP problems has been represented as finite state controllers (FSCs). We introduce a novel class of periodic FSCs, composed of layers connected only to the previous and next layer. Our periodic FSC method finds a deterministic finite-horizon policy and converts it to an initial periodic infinite-horizon policy. This policy is optimized by a new infinite-horizon algorithm to yield deterministic periodic policies, and by a new expectation maximization algorithm to yield stochastic periodic policies. Our method yields better results than earlier planning methods and can compute larger solutions than with regular FSCs.

关键词： Policies Periodic infinite horizon expectation-maximisation algorithm Robot control CARRIER SYSTEM Controllers

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Fast approximate i-vector estimation using PCA

Fast approximate i-vector estimation using PCA

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： M. K. Omar IBM T. J. Watson Res. Center Yorktown Heights NY USA

ISBN: (纸本)9781467369985

The i-vector representation has become increasingly popular in speaker and language recognition systems. The estimation of the projection matrix of the i-vector model is usually performed using the iterative expectation maximization (EM) algorithm. This work presents a novel approach to estimate the projection matrix of the i-vector representation and to estimate the i-vector representation for each utterance. In this approach, we formulate the estimation of the projection matrix as a principal component analysis (PCA) problem. Using the relation between PCA and a linear Gaussian model trained using the EM algorithm, we show that an approximate solution of the i-vector estimation can be obtained as the solution of a PCA problem. We evaluate the performance of our approximate i-vector estimation on the language recognition task of the robust automatic transcription of speech (RATS) project. The proposed approach reduces by 50% relative the computational time required to estimate the i-vector projection matrix and by 42% relative the computational time to estimate the i-vector representation compared to the standard EM-based approach to i-vector estimation. In addition, our experiments show improvements up to 29% relative in language recognition performance in terms of equal error rate compared to the standard EM-based i-vector estimation.

关键词： EM algorithm PCA i-vector estimation language recognition language recognition principal components analysis Passive Cutaneous Anaphylaxis expectation-maximisation algorithm vector representation

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Design of a Cosmic-ray Muon Tomography System for Dry Storage Cask Imaging

Design of a Cosmic-ray Muon Tomography System for Dry Storag...

引用

IEEE Nuclear Science Symposium

作者： Can Liao Haori Yang Department of Nuclear Engineering and Radiation Health Physics Oregon State University

ISBN: (纸本)9781479960989

The overarching goal of this project is to develop an imaging system to monitor the content of a dry storage cask (DSC) with cosmic ray muons. The imaging technique utilizing multiple Coulomb scattering was demonstrated to be a good non-destructive assay method for high-Z materials, such as used nuclear fuel assemblies in DSCs. In this paper, we report our simulation work on the 2D and 3D muon tomography of a DSC. The Point of Closest Approach (POCA) and Maximum-Likelihood expectation-Maximization (ML-EM) algorithms are used for image reconstruction. The reconstructed image showed good spatial resolution and good material discrimination ability, even though the detector has only 1cm by 1cm spatial resolution. The prototype design uses EJ-200 plastic scintillator as the detector for muon detection. Saint-Gobain BCF-92 wavelength shifting (WLS) optical fiber is used for the light transfer providing multi-channel position information. The optical fibers are coupled to two Hamamatsu H8500C MAPMTs. The PMT signals could then be measured with multi-channel QDCs. SiPM arrays are also being evaluated for readout. This prototype system is currently under construction. Preliminary tests results are shown in this paper. Full experimental results will be presented and discussed in the future.

关键词： FUEL ASSEMBLIES Imaging cosmic rays Casks detector COULOMB SCATTERING Differential scanning calorimetry MUONS Imaging systems Spatial resolution image reconstruction Optical Fibers expectation-maximisation algorithm

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