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IEEE Asia Pacific Conference on Circuits and Systems (APCCAS)

作者： Yamada, Keita Ichige, Koichi Yokohama Natl Univ Dept Elect & Comp Engn Hodogaya Ku Yokohama Kanagawa 2408501 Japan

ISBN: (纸本)9781479952304

This paper proposes a high-resolution DOA (Direction Of Arrival) estimation method using GROUSE and EM algorithms for low-cost array antenna system. The low-cost array antenna system, which uses less receiver modules than array elements, often leads worse DOA estimation accuracy when comparing with the full-cost array antenna system. The proposed method first employs the modified GROUSE algorithm to pre-estimate DOAs, and then the EM algorithm is applied to further improve the DOA estimation accuracy. Performance of the proposed approach is evaluated through computer simulation.

关键词： antenna arrays direction-of-arrival estimation expectation-maximisation algorithm DOA estimation method EM algorithms array antenna system direction of arrival modified GROUSE algorithm Accuracy Antenna arrays Arrays Direction-of-arrival estimation Estimation Receivers Signal to noise ratio Direction of arrival estimation expectation-maximisation algorithm Antenna arrays signal to noise ratios Receivers Grouse Angle of arrival ANTENNA SYSTEM

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Semi-analytic Approach To Evaluate Performance of a Precoded Multiuser Cooperative Scheme

Semi-analytic Approach To Evaluate Performance of a Precoded...

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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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Efficient Clinical Decision Making by Learning from Missing Clinical Data

Efficient Clinical Decision Making by Learning from Missing ...

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IEEE Symposium on Computational Intelligence in Healthcare and e-Health (CICARE)

作者： Farooq, Kamran Yang, Peipei Hussain, Amir Huang, Kaizhu MacRae, Calum Eckl, Chris Slack, Warner Univ Stirling Comp Sci & Math Div Stirling FK9 4LA Scotland Chinese Acad Sci Natl Lab Pattern Recognit Beijing 100864 Peoples R China Brigham & Womens Hosp Harvard Med Sch Boston MA USA Sitekit Solut Ltd Skye Scotland Harvard Med Sch Beth Israel Clin Informat Div Boston MA USA

ISBN: (纸本)9781467358835

Clinical decision making frequently involves making decisions under uncertainty because of missing key patient data (e.g. demographics, episodic and clinical diagnosis details) - this information is essential for modern clinical decision support systems to perform learning, inference and prediction operations. Machine learning and clinical informatics experts aim to reduce this clinical uncertainty by learning from the missing clinical attributes with a view to improve the overall decision making. These high-dimensional clinical datasets are often complex and carry multifaceted patterns of key missing clinical attributes. In this paper we highlight the problem of learning from incomplete real patient data acquired from Raigmore Hospital in Scotland, UK) from a statistical perspective-the likelihood-based approach to deal with this challenging issue. There are multiple benefits of our approach: to complement existing SVM (Support Vector Machine) techniques to deal with missing data within a statistical framework, and to illustrate a set of challenging statistical machine learning algorithms, derived from the likelihood-based framework that handles clustering, classification, and function approximation from missing/incomplete data in an intelligent and resourceful manner. Our work concentrates on the implementation of mixture modelling algorithms as well as utilising expectation-Maximization techniques for the estimation of mixture components and for dealing with the missing clinical data of chest pain patients.

关键词： data handling decision making decision support systems expectation-maximisation algorithm health care learning (artificial intelligence) medical information systems patient diagnosis support vector machines

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Process Monitoring with Global Probability Boundary-Based on Gaussian Mixture Model

Process Monitoring with Global Probability Boundary-Based on...

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10th IEEE International Conference on Control and Automation (IEEE ICCA)

作者： Wu, Qun Du, Wenli Qian, Feng Ma, Qingsong East China Univ Sci & Technol Minist Educ Key Lab Adv Control & Optimizat Chem Proc Shanghai 200237 Peoples R China China Huanqiu Contracting & Engn Corp Elect & Instrument Dept Shanghai Peoples R China

ISBN: (纸本)9781467347082;9781467347075

Considering that the operation data hardly follow a uniform Gaussian distribution in complex industrial process, the Gaussian mixture model (GMM) is utilized as the tool of process monitoring in this article. The classic expectation maximization (EM) algorithm is adopted to estimate the model parameters, which often results in the model structural redundancy. Thus the consolidation operator is proposed and introduced to Figueiredo-Jain algorithm that is an advanced method of EM. The new approach can automatically optimize the number of Gaussian components on one hand, and solve the poor convergence problem of F-J method when Gaussian components overlapping too much during initialization on the other. With the obtained model, a criterion based on global probability is exploited for the real-time process monitoring. The validity and effectiveness of the proposed approach are illustrated through the coal-water slurry gasification control system.

关键词： Gaussian distribution chemical industry expectation-maximisation algorithm probability process monitoring

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REM-Seg: A Robust EM algorithm for Parallel Segmentation and Registration of Point Clouds

REM-Seg: A Robust EM Algorithm for Parallel Segmentation and...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Eckart, Benjamin Kelly, Alonzo Carnegie Mellon Univ Inst Robot Pittsburgh PA 15213 USA

ISBN: (纸本)9781467363587

For purposes of real-time 3D sensing, it is important to be able to quickly register together incoming point cloud data. In this paper, we devise a method to quickly and robustly decompose large point clouds into a relatively small number of meaningful surface patches from which we register new data points. The surface patch representation sidesteps the costly problem of matching points to points since incoming data only need to be compared with the patches. The chosen parametrization of the patches (as Gaussians) leads to a smooth data likelihood function with a well-defined gradient. This representation thus forms the basis for a robust and efficient registration algorithm using a parallelized gradient descent implemented on a GPU using CUDA. We use a modified Gaussian Mixture Model (GMM) formulation solved by expectation Maximization (EM) to segment the point cloud and an annealing gradient descent method to find the 6-DOF rigid transformation between the incoming point cloud and the segmented set of surface patches. We test our algorithm, Robust EM Segmentation (REM-Seg), against other GPU-accelerated registration algorithms on simulated and real data and show that our method scales well to large numbers of points, has a wide range of convergence, and is suitably accurate for 3D registration.

关键词： Gaussian processes expectation-maximisation algorithm gradient methods image colour analysis image registration image representation image segmentation solid modelling

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Bayesian restoration of noisy images with the EM algorithm

Bayesian restoration of noisy images with the EM algorithm

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International Conference on Image Processing

作者： Saeed, M Rabiee, HR Karl, WC Nguyen, TQ Massachusetts Inst of Technology Cambridge United States

ISBN: (纸本)0818681837

In this paper, we demonstrate that a window-based Gaussian mixture model can be applied in the development of a robust nonlinear filter for image restoration. Via the EM algorithm, we utilize ML estimation of the spatially-varying model parameters to achieve the desired noise suppression and detail preservation. We demonstrate that this approach is a powerful tool which gives us information about the local statistics of noisy images, We demonstrate that the estimated local statistics can be efficiently utilized for outlier detection and edge detection. The advantage of our algorithm is that it can simultaneously suppress additive Gaussian and impulsive noise, while preserving fine details and edges.

关键词： expectation-maximisation algorithm Noisy Image image restoration technique Noise suppression Impulse noise Edge detection Bayesian inference Nonlinear filters restoration Demonstrate

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Statistical inference for nonignorable missing-data problems:a selective review

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Statistical Theory and Related Fields 2018年第2期2卷 105-133页

作者： Niansheng Tang Yuanyuan Ju Key Lab of Statistical Modelling and Data Analysis of Yunnan Province Yunnan UniversityKunming650091People’s Republic of China

Nonignorable missing data are frequently encountered in various settings, such as economics,sociology and biomedicine. We review statistical inference for nonignorable missing-data problems, including estimation, influence analysis and model selection. For estimation of meanfunctionals, we review semiparametric method and empirical likelihood (EL) approach. For estimation of parameters in exponential family nonlinear structural equation models, we introduceexpectation-maximisation algorithm, Bayesian approach, and Bayesian EL method. For influenceanalysis, we investigate the case-deletion method and local influence analysis method fromthe frequentist and Bayesian viewpoints. For model selection, we present the modified Akaikeinformation criterion and penalised method.

关键词： Bayesian method empirical likelihood method expectation-maximisation algorithm local influence analysis missing data model selection

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A First-Order Primal-Dual Reconstruction algorithm for Few-View SPECT

A First-Order Primal-Dual Reconstruction Algorithm for Few-V...

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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： Wolf, Paul Jorgensen, Jakob H. Schmidt, Taly Gilat Sidky, Emil Y. Marquette Univ Dept Biomed Engn Milwaukee WI 53201 USA Tech Univ Lyngby Dept Informat & Math Modeling Lyngby Denmark Univ Chicago Dept Radiol Chicago IL 60637 USA

ISBN: (纸本)9781467320306;9781467320283

A sparsity-exploiting algorithm intended for few-view Single Photon Emission Computed Tomography (SPECT) reconstruction is proposed and characterized. The algorithm models the object as piecewise constant subject to a blurring operation. Monte Carlo simulations were performed to provide more projection data of a phantom with varying smoothness across the field of view. For all simulations, reconstructions were performed across a sweep of the two primary design parameters: the blurring parameter and the weighting of the total variation (TV) minimization term. Maximum-Likelihood expectation Maximization (MLEM) reconstructions were performed to provide reference images. Spatial resolution, accuracy, and signal-to-noise ratio was calculated and compared for all reconstructions. In general, increased values of the blurring parameter and TV weighting parameters reduced noise and streaking artifacts, while decreasing spatial resolution. The reconstructed images demonstrate that the algorithm introduces low-frequency artifacts in some cases, but eliminates streak artifacts due to angular undersampling. Further, as the number of views was decreased from 60 to 9 the accuracy of images reconstructed using the proposed algorithm varied by less than 3%. Overall, the results demonstrate preliminary feasibility of a sparsity-exploiting reconstruction algorithm which may be beneficial for few-view SPECT.

关键词： Monte Carlo methods expectation-maximisation algorithm image denoising image reconstruction image resolution medical image processing minimisation phantoms single photon emission computed tomography

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Cerebrospinal Fluid Image Segmentation using Spatial Fuzzy Clustering Method with Improved Evolutionary expectation Maximization

Cerebrospinal Fluid Image Segmentation using Spatial Fuzzy C...

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35th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Abdullah, Afnizanfaizal Hirayama, Akihiro Yatsushiro, Satoshi Matsumae, Mitsunori Kuroda, Kagayaki Univ Teknol Malaysia Fac Comp Artificial Intelligence & Bioinformat Grp Johor Baharu Malaysia Tokai Univ Sch Med Dept Neurosurg Hiratsuka Kanagawa 25912 Japan Tokai Univ Sch Informat Sci & Technol Dept Human & Informat Sci Hiratsuka Kanagawa 25912 Japan

ISBN: (纸本)9781457702167

Visualization of cerebrospinal fluid (CSF), that flow in the brain and spinal cord, plays an important role to detect neurodegenerative diseases such as Alzheimer's disease. This is performed by measuring the substantial changes in the CSF flow dynamics, volume and/or pressure gradient. Magnetic resonance imaging (MRI) technique has become a prominent tool to quantitatively measure these changes and image segmentation method has been widely used to distinguish the CSF flows from the brain tissues. However, this is often hampered by the presence of partial volume effect in the images. In this paper, a new hybrid evolutionary spatial fuzzy clustering method is introduced to overcome the partial volume effect in the MRI images. The proposed method incorporates expectation Maximization (EM) method, which is improved by the evolutionary operations of the Genetic algorithm (GA) to differentiate the CSF from the brain tissues. The proposed improvement is incorporated into a spatial-based fuzzy clustering (SFCM) method to improve segmentation of the boundary curve of the CSF and the brain tissues. The proposed method was validated using MRI images of Alzheimer's disease patient. The results presented that the proposed method is capable to filter the CSF regions from the brain tissues more effectively compared to the standard EM, FCM, and SFCM methods.

关键词： biological fluid dynamics biomedical MRI diseases expectation-maximisation algorithm fuzzy logic genetic algorithms image segmentation medical image processing neurophysiology pattern clustering

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Traffic Speed Forecasting by Mixture of Experts

Traffic Speed Forecasting by Mixture of Experts

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14th International IEEE Conference on Intelligent Transportation Systems (ITSC)

作者： Coric, Vladimir Wang, Zhuang Vucetic, Slobodan Temple Univ Philadelphia PA 19122 USA Siemens Res Corp Princeton NJ 08540 USA

ISBN: (纸本)9781457721977

Traffic speed is one of the most important quantities for travel information systems. Accurate speed forecasting can help in trip planning by allowing travelers to avoid the congested routes, either by choosing the alternative routes or by changing the departure time. It is also helpful for traffic monitoring, control, and planning. An important feature of traffic is that it consists of free flow and congested regimes, which have significantly different properties. Training a single traffic speed predictor for both regimes typically results in suboptimal accuracy. To address this problem, a mixture of experts algorithm which consists of two regime-specific linear predictors and a decision tree gating function was developed. A generalized expectation maximization algorithm was used to train the linear predictors and the decision tree. The proposed algorithm was evaluated on a 5-mile stretch of I35 highway in Minneapolis containing 10 single loop detector stations, with prediction horizons ranging from 5 minutes to one hour ahead. Experimental results showed that mixture of experts approach outperforms several popular benchmark approaches.

关键词： Forecasting Linear regression Markov processes Prediction algorithms Regression tree analysis Road transportation decision tree decision tree gating function decision trees expectation maximization algorithm expectation-maximisation algorithm experts algo

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