The expectation-maximization (EM) algorithm applied to image identification and restoration

Received signal strength (RSS) changes of static wireless nodes can be used for device-free localization and tracking (DFLT). Most RSS-based DFLT systems require access to calibration data, either RSS measurements from a time period when the area was not occupied by people, or measurements while a person stands in known locations. Such calibration periods can be very expensive in terms of time and effort, making system deployment and maintenance challenging. This paper develops an Expectation-Maximization (EM) algorithm based on Gaussian smoothing for estimating the unknown RSS model parameters, liberating the system from supervised training and calibration periods. To fully use the EM algorithm’s potential, a novel localization-and-tracking system is presented to estimate a target’s arbitrary trajectory. To demonstrate the effectiveness of the proposed approach, it is shown that: (i) the system requires no calibration period; (ii) the EM algorithm improves the accuracy of existing DFLT methods; (iii) it is computationally very efficient; and (iv) the system outperforms a state-of-the-art adaptive DFLT system in terms of tracking accuracy.

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Models and Algorithms for Tracking Target with Coordinated Turn Motion

Mathematical Problems in Engineering ◽

10.1155/2014/649276 ◽

2014 ◽

Vol 2014 ◽

pp. 1-10 ◽

Cited By ~ 8

Author(s):

Xianghui Yuan ◽

Feng Lian ◽

Chongzhao Han

Keyword(s):

Em Algorithm ◽

Expectation Maximization ◽

Multiple Models ◽

Interacting Multiple Model ◽

Multiple Model ◽

Motion Model ◽

Single Model ◽

Kinematic Constraint ◽

The Em Algorithm ◽

Turn Rate

Tracking target with coordinated turn (CT) motion is highly dependent on the models and algorithms. First, the widely used models are compared in this paper—coordinated turn (CT) model with known turn rate, augmented coordinated turn (ACT) model with Cartesian velocity, ACT model with polar velocity, CT model using a kinematic constraint, and maneuver centered circular motion model. Then, in the single model tracking framework, the tracking algorithms for the last four models are compared and the suggestions on the choice of models for different practical target tracking problems are given. Finally, in the multiple models (MM) framework, the algorithm based on expectation maximization (EM) algorithm is derived, including both the batch form and the recursive form. Compared with the widely used interacting multiple model (IMM) algorithm, the EM algorithm shows its effectiveness.

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EM algorithm for longitudinal data with non-ignorable missing values: An application to health data

Bangladesh Journal of Scientific Research ◽

10.3329/bjsr.v27i2.26231 ◽

2016 ◽

Vol 27 (2) ◽

pp. 133-142

Author(s):

Radia Taisir ◽

M Ataharul Islam

Keyword(s):

Longitudinal Data ◽

Em Algorithm ◽

Expectation Maximization ◽

Missing Values ◽

Likelihood Method ◽

Estimation Methods ◽

Binary Response ◽

Complex Method ◽

Missing Responses ◽

Regression Parameters

Longitudinal studies involves repeated observations over time on the same experimental units and missingness may occur in non-ignorable fashion. For such longitudinal missing data, a Markov model may be used to model the binary response along with a suitable non-response model for the missing portion of the data. It is of the primary interest to estimate the effects of covariates on the binary response. Similar model for such incomplete longitudinal data exists where estimation of the regression parameters are obtained using likelihood method by summing over all possible values of the missing responses. In this paper, we propose an expectation-maximization (EM) algorithm technique for the estimation of the regression parameters which is computationally simple and produces similar efficient estimates as compared to the existing complex method of estimation. A comparison of the existing and the proposed estimation methods has been made by analyzing the Health and Retirement Survey (HRS) data of United States.Bangladesh J. Sci. Res. 27(2): 133-142, December-2014

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Improved Initialization of the EM Algorithm for Mixture Model Parameter Estimation

Mathematics ◽

10.3390/math8030373 ◽

2020 ◽

Vol 8 (3) ◽

pp. 373

Author(s):

Branislav Panić ◽

Jernej Klemenc ◽

Marko Nagode

Keyword(s):

Em Algorithm ◽

Density Estimation ◽

Mixture Model ◽

Expectation Maximization ◽

State Of The Art ◽

R Package ◽

Likelihood Estimator ◽

Local Optima ◽

The Em Algorithm ◽

Initialization Algorithm

A commonly used tool for estimating the parameters of a mixture model is the Expectation–Maximization (EM) algorithm, which is an iterative procedure that can serve as a maximum-likelihood estimator. The EM algorithm has well-documented drawbacks, such as the need for good initial values and the possibility of being trapped in local optima. Nevertheless, because of its appealing properties, EM plays an important role in estimating the parameters of mixture models. To overcome these initialization problems with EM, in this paper, we propose the Rough-Enhanced-Bayes mixture estimation (REBMIX) algorithm as a more effective initialization algorithm. Three different strategies are derived for dealing with the unknown number of components in the mixture model. These strategies are thoroughly tested on artificial datasets, density–estimation datasets and image–segmentation problems and compared with state-of-the-art initialization methods for the EM. Our proposal shows promising results in terms of clustering and density-estimation performance as well as in terms of computational efficiency. All the improvements are implemented in the rebmix R package.

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Interest of the ordered subsets expectation maximization (OS-EM) algorithm in pinhole single-photon emission tomography reconstruction: a phantom study

European Journal of Nuclear Medicine and Molecular Imaging ◽

10.1007/s002590050019 ◽

2000 ◽

Vol 27 (2) ◽

pp. 140-146 ◽

Cited By ~ 53

Author(s):

Christian Vanhove ◽

Michel Defrise ◽

Philippe R. Franken ◽

Hendrik Everaert ◽

Frank Deconinck ◽

...

Keyword(s):

Em Algorithm ◽

Expectation Maximization ◽

Single Photon ◽

Photon Emission ◽

Phantom Study ◽

Single Photon Emission Tomography ◽

Emission Tomography ◽

Single Photon Emission ◽

Tomography Reconstruction ◽

Photon Emission Tomography

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Doppler Velocity Estimation of Overlapping Linear-Period-Modulated Ultrasonic Waves Based on an Expectation-Maximization Algorithm

Advances in Acoustics and Vibration ◽

10.1155/2014/921876 ◽

2014 ◽

Vol 2014 ◽

pp. 1-7 ◽

Cited By ~ 1

Author(s):

Natee Thong-un ◽

Minoru K. Kurosawa

Keyword(s):

Em Algorithm ◽

Expectation Maximization ◽

Pulse Compression ◽

Moving Objects ◽

Expectation Maximization Algorithm ◽

Ultrasonic Waves ◽

Velocity Estimation ◽

Doppler Velocity ◽

The Em Algorithm ◽

Linear Period

The occurrence of an overlapping signal is a significant problem in performing multiple objects localization. Doppler velocity is sensitive to the echo shape and is also able to be connected to the physical properties of moving objects, especially for a pulse compression ultrasonic signal. The expectation-maximization (EM) algorithm has the ability to achieve signal separation. Thus, applying the EM algorithm to the overlapping pulse compression signals is of interest. This paper describes a proposed method, based on the EM algorithm, of Doppler velocity estimation for overlapping linear-period-modulated (LPM) ultrasonic signals. Simulations are used to validate the proposed method.

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