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

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AN EFFICIENT STOCHASTIC APPROXIMATION EM algorithm USING CONDITIONAL PARTICLE FILTERS

AN EFFICIENT STOCHASTIC APPROXIMATION EM ALGORITHM USING CON...

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Lindsten, Fredrik Linkoping Univ Div Automat Control Linkoping Sweden

ISBN: (纸本)9781479903566

I present a novel method for maximum likelihood parameter estimation in nonlinear/non-Gaussian state-space models. It is an expectation maximization (EM) like method, which uses sequential Monte Carlo (SMC) for the intermediate state inference problem. Contrary to existing SMC-based EM algorithms, however, it makes efficient use of the simulated particles through the use of particle Markov chain Monte Carlo (PMCMC) theory. More precisely, the proposed method combines the efficient conditional particle filter with ancestor sampling (CPF-AS) with the stochastic approximation EM (SAEM) algorithm. This results in a procedure which does not rely on asymptotics in the number of particles for convergence, meaning that the method is very computationally competitive. Indeed, the method is evaluated in a simulation study, using a small number of particles, with promising results.

关键词： Gaussian processes Markov processes Monte Carlo methods approximation theory convergence of numerical methods expectation-maximisation algorithm maximum likelihood estimation particle filtering (numerical methods) stochastic processes

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Real-Time Prediction of Power System Frequency in FNET: A State Space Approach

Real-Time Prediction of Power System Frequency in FNET: A St...

引用

IEEE International Conference on Smart Grid Communications (SmartGridComm)

作者： Dong, Jin Ma, Xiao Djouadi, Seddik M. Li, Husheng Kuruganti, Teja Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN 37996 USA Oak Ridge Natl Lab Oak Ridge TN 37831 USA

ISBN: (纸本)9781479915262

This paper proposes a novel approach to predict power frequency by applying a state-space model to describe the time-varying nature of power systems. It introduces the expectation maximization (EM) and prediction error minimization (PEM) algorithms to dynamically estimate the parameters of the model. In this paper, we discuss how the proposed models can be used to ensure the efficiency and reliability of power systems in Frequency Monitoring Network (FNET), if serious frequency fluctuation or measurement failure occur at some nodes;this is achieved without requiring the exact model of complex power systems. Our approach leads to an easy online implementation with high precision and short response time that are key to effective frequency control. We randomly pick a set of frequency data for one power station in FNET and use it to estimate and predict the power frequency based on past measurements. Several computer simulations are provided to evaluate the method. Numerical results showed that the proposed technique could achieve good performance regarding the frequency monitoring with very limited measurement input information.

关键词： expectation-maximisation algorithm frequency control power system control power system measurement power system reliability power system simulation

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An innovative multimodal/multispectral image registration method for medical images based on the expectation-maximization algorithm 37

An innovative multimodal/multispectral image registration me...

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

作者： Arce-Santana, Edgar Campos-Delgado, Daniel U. Mejia-Rodriguez, Aldo Reducindo, Isnardo Univ Autonoma San Luis Potosi Fac Ciencias San Luis Potosi Slp Mexico

ISBN: (纸本)9781424492701

In this paper, we present a methodology for multimodal/multispectral image registration of medical images. This approach is formulated by using the expectation-Maximization (EM) methodology, such that we estimate the parameters of a geometric transformation that aligns multimodal/multispectral images. In this framework, the hidden random variables are associated to the intensity relations between the studied images, which allow to compare multispectral intensity values between images of different modalities. The methodology is basically composed by an iterative two-step procedure, where at each step, a new estimation of the joint conditional multispectral intensity distribution and the geometric transformation is computed. The proposed algorithm was tested with different kinds of medical images, and the obtained results show that the proposed methodology can be used to efficiently align multimodal/multispectral medical images.

关键词： expectation-maximisation algorithm image registration medical image processing expectation-maximization algorithm geometric transformation hidden random variables iterative two-step procedure joint conditional multispectral intensity distribution multimodal image registration method multimodal medical images multispectral image registration method multispectral medical images Biomedical imaging Image registration Magnetic resonance imaging Positron emission tomography Single photon emission computed tomography expectation-maximisation algorithm image registration Geometric transformation single photon emission computed tomography Medical imaging Biomedical imaging Medical image processing PET Scan images Magnetic Resonance Imaging

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Video Enhancement of People Wearing Polarized Glasses: Darkening Reversal and Reflection Reduction

Video Enhancement of People Wearing Polarized Glasses: Darke...

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26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Ye, Mao Zhang, Cha Yang, Ruigang Univ Kentucky Lexington KY 40506 USA Microsoft Res Redmond WA USA

ISBN: (纸本)9780769549897

With the wide-spread of consumer 3D-TV technology, stereoscopic videoconferencing systems are emerging. However, the special glasses participants wear to see 3D can create distracting images. This paper presents a computational framework to reduce undesirable artifacts in the eye regions caused by these 3D glasses. More specifically, we add polarized filters to the stereo camera so that partial images of reflection can be captured. A novel Bayesian model is then developed to describe the imaging process of the eye regions including darkening and reflection, and infer the eye regions based on Classification expectation-Maximization (EM). The recovered eye regions under the glasses are brighter and with little reflections, leading to a more nature videoconferencing experience. Qualitative evaluations and user studies are conducted to demonstrate the substantial improvement our approach can achieve.

关键词： Bayes methods cameras expectation-maximisation algorithm eye glass image classification stereo image processing video communication

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Phase Noise and Carrier Frequency Offset in OFDM systems: Joint Estimation and Hybrid Cramer-Rao Lower Bound

Phase Noise and Carrier Frequency Offset in OFDM systems: Jo...

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IEEE 14th Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Salim, Omar Hazim Nasir, Ali A. Mehrpouyan, Hani Xiang, Wei Univ So Queensland Fac Engn & Surveying Toowoomba Qld 4350 Australia Australian Natl Univ Res Sch Informat Sci & Engn Canberra ACT 0200 Australia Calif State Univ Dept Elect & Comp Engn Bakersfield CA USA

ISBN: (纸本)9781467355773

In this paper, a new iterative pilot-aided algorithm based on expectation conditional maximization (ECM) for joint estimation of Wiener phase noise (PHN) and carrier frequency offset (CFO) in orthogonal frequency division multiplexing (OFDM) systems is proposed. Next, a new expression for the hybrid Cramer-Rao lower bound (HCRB) for joint estimation of PHN and CFO in OFDM systems is derived. Numerical results show that the proposed estimator outperforms existing algorithms in terms of mean square error while performing close to the derived HCRB at moderate PHN variances. Moreover, the proposed estimator is found to be computationally more efficient than existing algorithms since it jointly estimates PHN and CFO in a few iterations.

关键词： OFDM modulation expectation-maximisation algorithm frequency estimation mean square error methods phase noise

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List-mode Reconstruction for the Biograph mCT with Probabilistic Line-of-Response Positioning and Event-by-Event Motion Correction

List-mode Reconstruction for the Biograph mCT with Probabili...

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IEEE Nuclear Science Symposium / Medical Imaging Conference Record (NSS/MIC) / 19th Room-Temperature Semiconductor X-ray and Gamma-ray Detector Workshop

作者： Jin, Xiao Chan, Chung Mulnix, Tim Liu, Chi Carson, Richard E. Yale Univ PET Ctr New Haven CT 06520 USA

ISBN: (纸本)9781467320306;9781467320283

In this study, we address two issues that degrade spatial resolution in PET. First, conventionally, the Siemens Biograph mCT scanner collects data in 32-bit list mode, so each LOR is defined as a sinogram bin (instead of detector pair) with axial span and transaxial mashing of adjacent angles reducing the number of LORs, thus degrading resolution. In this work, we proposed a probabilistic assignment of LOR positions (pLOR) that addresses axial and transaxial LOR grouping in 32-bit data. Secondly, respiratory motion results in image artifacts in whole-body scans. Gated reconstruction does not correct for intra-gate motion. We have previously demonstrated for brain imaging that event-by-event correction can eliminate intra-frame motion. Therefore, we developed a list-mode reconstruction algorithm for the Siemens Biograph mCT with pLOR positioning and event-by-event motion correction, based on MOLAR (Motion-compensation OSEM List-mode algorithm for Resolution-Recovery Reconstruction). This algorihtm was compared to the manufacturer's PSF+TOF algorithm. Static phantom and animal studies demonstrated that MOLAR with pLOR gives faster contrast recovery than the PSF+TOF algorithm that uses the average LOR positioning for all sub-LORs within each sinogram bin. Moving phantom and a whole-body human study suggested that event-by-event motion correction is able to reduce image blurring introduced by the intra-gate motion. We conclude that list-mode reconstruction with pLOR positioning and event-by-event motion correction can generate high quality images for the meT, and has great potential to optimally recover fine structures in whole-body PET scans with motion.

关键词： expectation-maximisation algorithm image resolution image restoration medical image processing motion compensation phantoms positron emission tomography probability

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Region-based image querying

Region-based image querying

引用

IEEE Workshop on Content-Based Access of Image and Video Libraries

作者： Carson, C Belongie, S Greenspan, H Malik, J Computer Science Division University of California at Berkeley Berkeley 94720 CA United States

ISBN: (纸本)0818679816

Retrieving images from large and varied collections using image content as a key is a challenging and important problem. In this paper we present a new image representation which provides a transformation from the raw pixel data to a small set of localized coherent regions in color and texture space. This so-called ''blobworld'' representation is based on segmentation using the expectation - Maximization algorithm on combined color and texture features. The texture features we use for the segmentation arise from a new approach to texture description and scale selection. We describe a system that uses the blobworld representation to retrieve images. An important and unique aspect of the system is that, in the context of similarity-based querying, the user is allowed to view the internal representation of the submitted image and the query results. Similar systems do not offer the user this view into the workings of the system;consequently, the outcome of many queries on these systems can be quite inexplicable, despite the availability of knobs for adjusting the similarity metric.

关键词： image representation texture Internal representations expectation-maximisation algorithm Color

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Development of GPU Based Image Reconstruction Method for Clinical SPECT

Development of GPU Based Image Reconstruction Method for Cli...

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IEEE Nuclear Science Symposium / Medical Imaging Conference Record (NSS/MIC) / 19th Room-Temperature Semiconductor X-ray and Gamma-ray Detector Workshop

作者： Liu, Hui Ma, Tianyu Chen, Si Liu, Yaqiang Wang, Shi Jin, Yongjie Tsinghua Univ Dept Engn Phys Minist Educ Key Lab Particle & Radiat Imaging Beijing 100084 Peoples R China

ISBN: (纸本)9781467320306;9781467320283

The aim of this work is to develop, validate and preliminarily evaluate two graphic processing units (GPU) implementations of the OS-EM iterative image reconstruction algorithms for a clinical SPECT system. The Monte Carlo software SIMSET was used to simulate the data acquisition of a typical SPECT camera with a parallel-hole collimator for the Shepp-Logan phantom. CUD A was adopted for the GPU programming of the OS-EM algorithm with high complexity. For the first implementation, the system matrix used for reconstruction was pre-computed by Monte Carlo simulation. A major task in the GPU programing in this case was to efficiently access every element of the pre-stored system matrix and compute the projection and back-projection for each image update. In the second implementation, the system matrix was calculated on-the-fly. A rotation-based projector and back-projector was employed, which reduced the complexity for the GPU programming and can achieve higher efficiency theoretically than other methods of computing projector and back-projectors. We modeled the depth-dependent collimator detector response in the matrix computation. The results showed that, for both GPU implementations, there are seldom differences of images reconstructed by the GPU and CPU implementation of the OS-EM algorithm. In the first matrix computation strategy, an acceleration ratio of 31.9 was achieved by using the GPU implementation compared with the CPU implementation of the same algorithm. In the second strategy, the acceleration ratio was 33.9. The images reconstructed with the first strategy demonstrated higher quantitative accuracy due to the more accurate modeling of the system matrix. But the corresponding reconstructed time was more than the second strategy. We also applied the second GPU implementation of the OS-EM method to clinical SPECT patient cardiac data and achieved the reconstruction of a 64x64x64 image within 8 seconds. Compared with the FBP reconstruction image, the OS-EM r

关键词： Monte Carlo methods biomedical equipment cameras cardiology collimators data acquisition expectation-maximisation algorithm graphics processing units image reconstruction medical image processing parallel architectures phantoms single photon emission computed tomography

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Model-based clustering with Hidden Markov Model regression for time series with regime changes

Model-based clustering with Hidden Markov Model regression f...

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International Joint Conference on Neural Networks (IJCNN)

作者： Chamroukhi, Faicel Same, Allou Aknin, Patrice Govaert, Gerard Univ Paris 13 Comp Sci Lab CNRS LIPNUMR 7030 F-93430 Villetaneuse France IFSTTAR Research Unit UPE GRETTIA Boulogne France CNRS UMR 6599 Heudiasyc Lab Paris France

ISBN: (纸本)9781424496365

This paper introduces a novel model-based clustering approach for clustering time series which present changes in regime. It consists of a mixture of polynomial regressions governed by hidden Markov chains. The underlying hidden process for each cluster activates successively several polynomial regimes during time. The parameter estimation is performed by the maximum likelihood method through a dedicated expectation-Maximization (EM) algorithm. The proposed approach is evaluated using simulated time series and real-world time series issued from a railway diagnosis application. Comparisons with existing approaches for time series clustering, including the stand EM for Gaussian mixtures, K-means clustering, the standard mixture of regression models and mixture of Hidden Markov Models, demonstrate the effectiveness of the proposed approach.

关键词： Clustering algorithms Data models Gaussian mixtures Hidden Markov models K-means clustering Markov processes Polynomials Spline Time series analysis expectation-maximisation algorithm expectation-maximization algorithm hidden Markov model regression hidden Markov models maximum likelihood method model-based clustering parameter estimation pattern clustering polynomial regressions railway diagnosis railway engineering regression analysis time series time series clustering

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De-Novo Motif Finding using Genetic algorithm 28

De-Novo Motif Finding using Genetic Algorithm

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IEEE 28th Convention of Electrical and Electronics Engineers in Israel (IEEEI)

作者： Manevitz, Miriam Samson, Moshe Hebrew Univ Jerusalem Sch Comp Sci & Engn IL-91905 Jerusalem Israel

ISBN: (纸本)9781479959877

One of the central problems in bioinformatics is de-novo finding of recurring motifs in the DNA. Since these motifs are preserved throughout evolution they probably have a significant biological role. One of the most widely used existing tools uses expectation Maximization (EM) algorithm in order to learn the parameters of a statistical model based on partial data. One such method is based on assuming the Motif-data is generated by a Hidden Markov Model (HMM). This method is called the MEME algorithm. Despite its success, this method is in its essence a hill-climbing method, and as such, is known to be subject to being caught in local optima. In this work, we tackled the problem by using, instead, a genetic algorithm, and to search for the optimal probabilities of the HMM model. In certain occasions we succeeded in achieving better results using GA.

关键词： DNA biochemistry bioinformatics data mining evolution (biological) expectation-maximisation algorithm genetic algorithms genetics hidden Markov models learning (artificial intelligence) molecular biophysics molecular configurations probability DNA motif preservation EM algorithm HMM model MEME algorithm de novo motif finding evolution expectation maximization algorithm genetic algorithm hidden Markov model hill-climbing method local optima motif data generation optimal probability search parameter learning partial data recurring DNA motif finding statistical model Equations Genetic algorithms Hidden Markov models Mathematical model Sociology Statistics expectation-maximisation algorithm Hidden Markov models Genetic algorithm (GA) Bioinformatics molecular biology Parameter Learning statistical models (nuclear) Sociology Molecular Conformation DNA data mining biochemistry Mathematical Model Genetics evolution

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