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Exact reduced-complexity maximum likelihood reconstruction of multiple 3-d objects from unlabeled unoriented 2-d projections and electron microscopy of viruses

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2007年第12期16卷 2865-2878页

作者： Lee, Junghoon Doerschuk, Peter C. Johnson, John E. Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA Scripps Res Inst Dept Mol Biol La Jolla CA 92037 USA

In cryo-electron microscopy, the data is comprised of noisy 2-D projection images of the 3-D electron scattering intensity of the object where the orientation of the projections is unknown. Often, the images show randomly selected objects from a mixture of different types of objects. Objects of different type may be unrelated, e.g., different species of virus, or related, e.g., different conformations of the same species of virus. Due to the low SNR and the 2-D nature of the data, it is challenging to determine the type of the object shown in an individual image. A statistical model and maximum likelihood estimator that computes simultaneous 3-D reconstruction and labels using an expectation maximization algorithm exists but requires extensive computation due to the numerical evaluation of 3-D or 5-D integrations of a square matrix of dimension equal to the number of degrees of freedom in the 3-D reconstruction. By exploiting the geometry of rotations in 3-D, the estimation problem can be transformed so that the inner-most numerical integral has a scalar rather than a matrix integrand. This leads to a dramatic reduction in computation, especially as the number of degrees of freedom in the 3-D reconstruction increases. Numerical examples of the 3-D reconstructions are provided based on synthetic and experimental images where the objects are small spherical viruses.

关键词： cryo-electron microscopy (cryo EM) expectation maximization algorithm maximum likelihood estimation structural biology 3-D signal reconstruction tomography virology virus

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Improved Parameter Estimation with Threshold Adaptation of Cognitive Local Sensors

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JOURNAL OF COMMUNICATIONS AND NETWORKS 2012年第5期14卷 471-480页

作者： Seol, Dae-Young Lim, Hyoung-Jin Song, Moon-Gun Im, Gi-Hong Pohang Univ Sci & Technol Dept Elect & Elect Engn Pohang 790784 South Korea

Reliable detection of primary user activity increases the opportunity to access temporarily unused bands and prevents harmful interference to the primary system. By extracting a global decision from local sensing results, cooperative sensing achieves high reliability against multipath fading. For the effective combining of sensing results, which is generalized by a likelihood ratio test, the fusion center should learn some parameters, such as the probabilities of primary transmission, false alarm, and detection at the local sensors. During the training period in supervised learning, the on/off log of primary transmission serves as the output label of decision statistics from the local sensor. In this paper, we extend unsupervised learning techniques with an expectation maximization algorithm for cooperative spectrum sensing, which does not require an external primary transmission log. Local sensors report binary hard decisions to the fusion center and adjust their operating points to enhance learning performance. Increasing the number of sensors, the joint-expectation step makes a confident classification on the primary transmission as in the supervised learning. Thereby, the proposed scheme provides accurate parameter estimates and a fast convergence rate even in low signal-to-noise ratio regimes, where the primary signal is dominated by the noise at the local sensors.

关键词： Cognitive radio cooperative spectrum sensing expectation maximization algorithm likelihood ratio test unsupervised learning

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Neural networks for predicting conditional probability densities: Improved training scheme combining EM and RVFL

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NEURAL NETWORKS 1998年第1期11卷 89-116页

作者： Husmeier, D Taylor, JG Univ London Kings Coll Dept Math London WC2R 2LS England

Predicting conditional probability densities with neural networks requires complex (at least two-hidden-layer) architectures, which normally leads to rather long training times. By adopting the RVFL concept and constraining a subset of the parameters to randomly chosen initial values (such that the EM-algorithm can be applied), the training process can be accelerated by about two orders of magnitude. This allows training of a whole ensemble of networks at the same computational costs as would be required otherwise for training a single model. The simulations performed suggest that in this way a significant improvement of the generalization performance can be achieved. (C) 1998 Elsevier Science Ltd. All rights reserved.

关键词： automatic relevance determination conditional probability density expectation maximization algorithm gaussian mixture model generalization performance maximum likelihood model complexity network committees random vector functional link net approach time series prediction

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Stochastic feature compensation methods for speaker verification in noisy environments

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APPLIED SOFT COMPUTING 2014年 19卷 198-214页

作者： Sarkar, Sourjya Rao, K. Sreenivasa Indian Inst Technol Sch Informat Technol Kharagpur 721302 W Bengal India

This paper explores the significance of stereo-based stochastic feature compensation (SFC) methods for robust speaker verification (SV) in mismatched training and test environments. Gaussian Mixture Model (GMM)-based SFC methods developed in past has been solely restricted for speech recognition tasks. Application of these algorithms in a SV framework for background noise compensation is proposed in this paper. A priori knowledge about the test environment and availability of stereo training data is assumed. During the training phase, Mel frequency cepstral coefficient (MFCC) features extracted from a speaker's noisy and clean speech utterance (stereo data) are used to build front end GMMs. During the evaluation phase, noisy test utterances are transformed on the basis of a minimum mean squared error (MMSE) or maximum likelihood (MLE) estimate, using the target speaker GMMs. Experiments conducted on the NIST-2003-SRE database with clean speech utterances artificially degraded with different types of additive noises reveal that the proposed SV systems strictly outperform baseline SV systems in mismatched conditions across all noisy background environments. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Speaker verification Noisy environment Minimum mean squared error Maximum likelihood estimate expectation maximization algorithm Gaussian Mixture Models

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An EM algorithm for dynamic estimation of interfacial boundary in stratified flow of immiscible liquids using EIT

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FLOW MEASUREMENT AND INSTRUMENTATION 2011年第6期22卷 517-528页

作者： Khambampati, Anil Kumar Kim, Sin Kim, Kyung Youn Jeju Natl Univ Dept Elect Engn Cheju 690756 South Korea Jeju Natl Univ Appl Radiol Sci Res Inst Cheju 690756 South Korea Jeju Natl Univ Dept Nucl & Energy Engn Cheju 690756 South Korea

Estimation of interfacial boundary between two immiscible liquids in two-phase flows through pipe line provides information about the flow characteristics and thus can aid in design and monitoring of the flow process. The interfacial boundary can be represented in several ways, one such method is the front point approach. Front points describe the location and the shape of the interfacial boundary separating the immiscible liquids. During the flow process, due to fluctuations the interfacial boundary and so the front points which describe the boundary changes with time. The time-varying interfacial boundary can be estimated using dynamic inverse algorithms based on Kalman filter. However, algorithms based on Kalman filter require complete knowledge of model parameters (initial states, state transition matrix, and noise covariance matrices) for implementation. In processes involving complex flow pattern such as two-phase flows, it is difficult to represent the model parameters in a prior form. This uncertainty in model parameters causes suboptimal performance of the Kalman type filters. In this paper, we employ expectation maximization algorithm (EM) to estimate model parameters along with the interfacial boundary using electrical impedance tomography (EIT). The estimation of model parameters reduces the modeling uncertainty and thus results in improving the tracking of interfacial boundary. Numerical and experimental studies are performed to validate the performance of the proposed method. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Electrical impedance tomography Immiscible liquids Dynamic estimation Kalman filter expectation maximization algorithm

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Combining generative and discriminative models in a framework for articulated pose estimation

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INTERNATIONAL JOURNAL OF COMPUTER VISION 2006年第3期67卷 251-276页

作者： Rosales, Romer Sclaroff, Stan MIT Comp Sci & Artificial Intelligence Lab Cambridge MA 02139 USA Boston Univ Dept Comp Sci Image & Video Comp Grp Boston MA 02215 USA

We develop a method for the estimation of articulated pose, such as that of the human body or the human hand, from a single (monocular) image. Pose estimation is formulated as a statistical inference problem, where the goal is to find a posterior probability distribution over poses as well as a maximum a posteriori (MAP) estimate. The method combines two modeling approaches, one discriminative and the other generative. The discriminative model consists of a set of mapping functions that are constructed automatically from a labeled training set of body poses and their respective image features. The discriminative formulation allows for modeling ambiguous, one-to-many mappings (through the use of multi-modal distributions) that may yield multiple valid articulated pose hypotheses from a single image. The generative model is defined in terms of a computer graphics rendering of poses. While the generative model offers an accurate way to relate observed (image features) and hidden (body pose) random variables, it is difficult to use it directly in pose estimation, since inference is computationally intractable. In contrast, inference with the discriminative model is tractable, but considerably less accurate for the problem of interest. A combined discriminative/generative formulation is derived that leverages the complimentary strengths of both models in a principled framework for articulated pose inference. Two efficient MAP pose estimation algorithms are derived from this formulation;the first is deterministic and the second non-deterministic. Performance of the framework is quantitatively evaluated in estimating articulated pose of both the human hand and human body.

关键词： human body pose hand pose nonrigid and articulated pose estimation statistical inference generative and discriminative models mixture models expectation maximization algorithm

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Estimation and diagnostic tools in reparameterized slashed Rayleigh regression model. An application to chemical data

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2020年 207卷 104189-104189页

作者： Gallardo, Diego I. Gomez-Deniz, Emilio Leao, Jeremias Gomez, Hector W. Univ Atacama Fac Ingn Dept Matemat Copiapo Chile Univ Las Palmas Gran Canaria Dept Quantitat Methods Econ Las Palmas Gran Canaria Spain Univ Las Palmas Gran Canaria TiDES Inst Las Palmas Gran Canaria Spain Univ Fed Amazonas Dept Estatist Manaus AM Brazil Univ Antofagasta Fac Ciencias Basicas Dept Matemat Antofagasta Chile

In this paper, we introduce a regression model where the response variable is reparameterized slashed Rayleigh (RSR) distributed and which is indexed by mean and precision parameters. The proposed regression model is useful for situations where the variable of interest is continuous and restricted to the positive real line and is related to other variables through the mean and precision parameters. In addition, the RSR model has properties that its competitor distributions of the exponential family do not have. Estimation is performed by expectation maximization (EM) and extensions. Furthermore, we discuss residuals and influence diagnostic tools. Finally, we also carry out two applications to real-world data that demonstrate the usefulness of the proposed model.

关键词： expectation maximization algorithm Linear regression Reparameterized Rayleigh distribution Monte Carlo simulation

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Cerebrovascular segmentation from TOF using stochastic models

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MEDICAL IMAGE ANALYSIS 2006年第1期10卷 2-18页

作者： Hassouna, MS Farag, AA Hushek, S Moriarty, T Univ Louisville Comp Vis & Image Proc Lab Louisville KY 40292 USA Norton Hosp iMRI Dept Louisville KY 40202 USA Univ Louisville Dept Neurol Surg Louisville KY 40292 USA

In this paper, we present an automatic statistical approach for extracting 3D blood vessels from time-of-flight (TOF) magnetic resonance angiography (MRA) data. The voxels of the dataset are classified as either blood vessels or background noise. The observed volume data is modeled by two stochastic processes. The low level process characterizes the intensity distribution of the data, while the high level process characterizes their statistical dependence among neighboring voxels. The low level process of the background signal is modeled by a finite mixture of one Rayleigh and two normal distributions, while the blood vessels are modeled by one normal distribution. The parameters of the low level process are estimated using the expectation maximization (EM) algorithm. Since the convergence of the EM is sensitive to the initial estimate of the model parameters, an automatic method for parameter initialization, based on histogram analysis, is provided. To improve the quality of segmentation achieved by the proposed low level model especially in the regions of significantly vascular signal loss, the high level process is modeled as a Markov random field (MRF). Since MRF is sensitive to edges and the intracranial vessels represent roughly 5% of the intracranial volume, 2D MRF will destroy most of the small and medium sized vessels. Therefore, to reduce this limitation, we employed 3D MRF, whose parameters are estimated using the maximum pseudo likelihood estimator (MPLE), which converges to the true likelihood under large lattice. Our proposed model exhibits a good fit to the clinical data and is extensively tested on different synthetic vessel phantoms and several 2D/3D TOF datasets acquired from two different MRI scanners. Experimental results showed that the proposed model provides good quality of segmentation and is capable of delineating vessels down to 3 voxel diameters. (c) 2005 Elsevier B.V. All rights reserved.

关键词： magnetic resonance angiography time-of-flight expectation maximization algorithm Markov random fields statistical segmentation

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Individualized survival predictions using state space model with longitudinal and survival data

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JOURNAL OF THE ROYAL SOCIETY INTERFACE 2024年第216期21卷 20230682-20230682页

作者： Cauchi, Mark Mills, Andrew R. Lawrie, Allan Kiely, David G. Kadirkamanathan, Visakan Univ Sheffield Dept Automatic Control & Syst Engn Mappin St Sheffield S1 3JD England Imperial Coll London Natl Heart & Lung Inst Dovehouse St London SW3 6LY England Univ Sheffield Royal Hallamshire Hosp Sheffield NIHR Biomed Res Ctr Sheffield Sheffield Pulm Vasc Dis Unit Beech Hill Rd Sheffield S10 2RX England Univ Sheffield Dept Clin Med Beech Hill Rd Sheffield S10 2RX England

Monitoring disease progression often involves tracking biomarker measurements over time. Joint models (JMs) for longitudinal and survival data provide a framework to explore the relationship between time-varying biomarkers and patients' event outcomes, offering the potential for personalized survival predictions. In this article, we introduce the linear state space dynamic survival model for handling longitudinal and survival data. This model enhances the traditional linear Gaussian state space model by including survival data. It differs from the conventional JMs by offering an alternative interpretation via differential or difference equations, eliminating the need for creating a design matrix. To showcase the model's effectiveness, we conduct a simulation case study, emphasizing its performance under conditions of limited observed measurements. We also apply the proposed model to a dataset of pulmonary arterial hypertension patients, demonstrating its potential for enhanced survival predictions when compared with conventional risk scores.

关键词： expectation maximization algorithm joint model longitudinal data pulmonary arterial hypertension state space model survival data

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Deep Unfolding-Based Joint Beamforming and Detection Design for Ambient Backscatter Communications With IRS

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IEEE COMMUNICATIONS LETTERS 2023年第4期27卷 1145-1149页

作者： Wang, Ze Xu, Hongbo Wang, Ji Liu, Wanxian He, Xiuli Zhou, Aizhi Cent China Normal Univ Dept Elect & Informat Engn Wuhan 430079 Peoples R China

In this letter, we investigate a novel ambient backscatter communication (AmBC) system in which an intelligent reflecting surface (IRS) acts as a passive transmitter to communicate with a reader. We are interested in the joint design of the IRS reflecting beamforming and symbol detection to minimize the bit error rate (BER). However, the problem is challenging to be solved optimally, since the BER is related to the clustering-based detector without a concrete close-form expression and the IRS reflecting unit modulus constraint is non-convex. To solve this issue, we propose a novel deep unfolding neural network (DUNN) combining data-driven and model-driven for passive reflecting beamforming design and symbol detection, which is learned to approximate the BER model from the training samples and the unit modulus constraint is satisfied by treating the optimization variables as network parameters. Numerical results demonstrate that the proposed scheme has superior performance of detection.

关键词： Symbols Radio frequency Backscatter Optimization Array signal processing Training Neural networks Ambient backscatter communications intelligent reconfigurable surface deep unfolding symbol detection expectation maximization algorithm

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