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IEEE International Conference on Communications (ICC)

作者： S. Kalyani K. Giridhar Telecommunication and Computer Networks (TeNeT) Group Department of Electrical Engineering Indian Institute of Technology Madras Chennai India

Decision directed channel tracking (DDCT) at high fade rates in OFDM based systems is addressed in this paper. Existing DDCT algorithms like the expectation-maximization (EM) algorithm (Al-Naffouri et al., 2002) suffer from error propagation and exhibit poor performance when applied to large frames at high fade rates. We propose a robust EM algorithm which mitigates the effect of error propagation and is able to track the channel in the decision directed mode even over frame durations experiencing 2-3 fade cycles. This EM algorithm uses the Huber's cost function in the maximization step instead of the non-robust least squares or Kalman cost function. Further, the noise variance is estimated using the robust median absolute deviation estimator instead of the standard maximum likelihood estimator. The proposed robust EM based DDCT scheme has a better error rate and MSE performance when compared to Kalman filter based pilot assisted channel tracking scheme with a 6.25% pilot overhead, even at a normalized Doppler of 0.04.

关键词： Statistics expectation-maximization algorithms OFDM Noise robustness Maximum likelihood estimation Working environment noise Cost function Channel estimation Parameter estimation Communications Society

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FastLOF: An expectation-maximization based Local Outlier detection algorithm

FastLOF: An Expectation-Maximization based Local Outlier det...

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International Conference on Pattern Recognition

作者： Markus Goldstein German Research Center for Artificial Intelligence Germany

ISBN: (纸本)9781467322164

Unsupervised anomaly detection techniques are becoming more and more important in a variety of application domains such as network intrusion detection, fraud detection and misuse detection. Today, unsupervised anomaly detection techniques are mainly based on quadratic complexity making it almost impossible to apply them on very large data sets. In this paper, an expectation-maximization algorithm is proposed which computes the Local Outlier Factor (LOF) incrementally and up to 80% faster than the standard method. Another advantage of FastLOF is that intermediate results can be used by a system already during computation. Evaluation on real world data sets reveal that FastLOF performs comparable to the best outlier detection algorithms although being significantly faster.

关键词： Intrusion detection Complexity theory expectation-maximization algorithms Context Feature extraction Training Clustering algorithms

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Image fusion using the expectation-maximization algorithm and a hidden Markov model

Image fusion using the expectation-maximization algorithm an...

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IEEE Conference on Vehicular Technology (VTC)

作者： J. Yang R.S. Blum ECE Department Lehigh University Bethlehem PA USA

A statistical signal processing approach to multisensor image fusion is presented. This approach is based on an image formation model in which the sensor images are described as the true scene corrupted by additive non-Gaussian distortion. A hidden Markov model (HMM) is fitted to the wavelet transforms of the sensor images to describe the correlations between the coefficients across wavelet decomposition scales. A set of iterative equations was developed using the expectation-maximization (EM) algorithm to estimate the model parameters and produce the fused images. We demonstrated the efficiency of this approach by applying this method to visual and radiometric images in concealed weapon detection (CWD) cases and night vision applications.

关键词： Image fusion expectation-maximization algorithms Hidden Markov models Signal processing algorithms Image sensors Sensor phenomena and characterization Layout Wavelet transforms Equations Iterative algorithms

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A Unified Approach for Representing Wireless Channels using EM-Based Finite Mixture of Gamma Distributions

A Unified Approach for Representing Wireless Channels using ...

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IEEE Global Communications Conference (GLOBECOM)

作者： Alhussein, Omar Muhaidat, Sami Liang, Jie Yoo, Paul D. Simon Fraser Univ Sch Engn Sci Burnaby BC V5A 1S6 Canada Khalifa Univ Sci Technol & Res Abu Dhabi U Arab Emirates Univ Surrey CCSR Guildford Surrey England

ISBN: (纸本)9781479974702

We present a unified framework to evaluate the error rate performance of wireless networks over generalized fading channels. In particular, we propose a new approach to represent different fading distributions by mixture of Gamma distributions. The new approach relies on the expectation-maximization (EM) algorithm in conjunction with the so-called Newton-Raphson maximization algorithm. We show that our model provides similar performance to other existing state-of-art models in both accuracy and simplicity, where accuracy is analyzed by means of mean square error (MSE). In addition, we demonstrate that this algorithm may potentially approximate any fading channel, and thus we utilize it to model both composite and non-composite fading models. We derive novel closed form expression of the raw moments of a dual-hop fixed-gain cooperative network. We also study the effective capacity of the end-to-end SNR in such networks. Numerical simulation results are provided to corroborate the analytical findings.

关键词： Mixture of gamma distributions expectation-maximization algorithms dual-hop Amplify-and-Forward (AF) semi-blind relaying

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Multi-frame image fusion using the expectation-maximization algorithm

Multi-frame image fusion using the expectation-maximization ...

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International Conference on Information Fusion

作者： J. Yang R.S. Blum ECE Department Lehigh University Bethlehem PA USA

A multi-frame image fusion scheme is proposed to fuse visual and thermal images for night vision applications. While many previous image fusion approaches perform the fusion on a frame-by-frame basis, this method considers optimum use of neighboring frames to incorporate temporal as well as sensor fusion. This fusion scheme is based on a statistical image formation model. The multiple sensor image frames are described as the true scene corrupted by additive non-Gaussian distortion. The expectation-maximization (EM) algorithm is used to estimate the parameters in the model and to produce the final fused result. The experimental results showed that the EM-based multi-frame image fusion scheme has significant advantage in terms of sensor noise reduction.

关键词： Image fusion expectation-maximization algorithms Sensor fusion Image sensors Fuses Night vision Sensor phenomena and characterization Layout Parameter estimation Noise reduction

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Areal Interpolation and Dasymetric Modeling

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GEOGRAPHICAL ANALYSIS 2013年第3期45卷 213-215页

作者： Qiu, Fang Cromley, Robert Univ Texas Dallas Richardson TX 75083 USA Univ Connecticut Dept Geog Storrs CT USA

An introduction is presented in which the editor discusses various reports within the issue on topics including areal interpolation methods, expectation-maximization algorithms and dasymetric interpolation algorithm.

关键词： INTERPOLATION expectation-maximization algorithms

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Improved Channel Estimation for Interference Cancellation in Random Access Methods for Satellite Communications 7

Improved Channel Estimation for Interference Cancellation in...

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7th Advanced Satellite Multimedia Systems Conference / 13th Signal Processing for Space Communications Workshop (ASMS/SPSC)

作者： Zidane, Karine Lacan, Jerome Boucheret, Marie-Laure Poulliat, Charly Univ Toulouse ISAE DMIA & TeSA Toulouse France Univ Toulouse IRIT ENSEEIHT Toulouse France

ISBN: (纸本)9781479958931

In the context of satellite communications, random access methods can significantly increase throughput and reduce latency over the network. The recent random access methods are based on multi-user multiple access transmission at the same time and frequency followed by iterative interference cancellation and decoding at the receiver. Generally, it is assumed that perfect knowledge of the interference is available at the receiver. In practice, the interference term has to be accurately estimated to avoid performance degradation. Several estimation techniques have been proposed lately in the case of superimposed signals. In this paper, we present an overview on existing channel estimation methods and we propose an improved channel estimation technique that combines estimation using an autocorrelation based method and the expectation-maximization algorithm, and uses pilot symbol assisted modulation to further improve the performance and achieve optimal interference cancellation.

关键词： Satellite communication Network coding Channel estimation expectation-maximization algorithms

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Using the expectation-maximization algorithm for depth estimation and segmentation of multi-view images

Using the expectation-maximization algorithm for depth estim...

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International Symposium on 3D Data Processing Visualization and Transmission

作者： N. Grammalidis L. Bleris M.G. Strintzis Department of Electrical and Computer Engineering University of Thessaloniki Thessaloniki Greece

An algorithm for joint depth estimation and segmentation from multi-view images is presented. The distribution of the luminance of each image pixel is modeled as a random variable, which is approximated by a "mixture of Gaussians model". After recovering 3D motion, a reference image is segmented into a fixed number of regions, each characterized by a distinct affine depth model with three parameters. The estimated depth parameters and segmentation masks are iteratively estimated using an expectation-maximization algorithm, similar to that proposed in Sawhney et al. (1996). In addition, the proposed algorithm is extended for cases where more than two images are available.

关键词： expectation-maximization algorithms Image segmentation Motion estimation Parameter estimation Layout Least squares approximation Application software Robustness Karhunen-Loeve transforms Pixel

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Density estimation using modified expectation-maximization algorithm for a linear combination of Gaussians

Density estimation using modified expectation-maximization a...

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IEEE International Conference on Image Processing

作者： A.A. Farag A. El-Baz G. Gimel'farb CVIP University of Louisville Louisville KY USA CVIE University of Louisville Louisville KY USA CITR University of Auckland Auckland New Zealand

In this paper we present a new approach for density estimation. The proposed approach is based on modifying expectation-maximization (EM) algorithm to approximate an empirical probability density function of scalar data with a linear combination of Gaussians (LCG). We also propose a novel EM-based sequential technique to get a close initial LCG approximation the modified EM algorithm should start with. Due to both positive and negative components, the LCG approximates inter-class transitions more accurately than a conventional mixture of only positive Gaussians. Experiments on simulated images demonstrate the accuracy of our approach.

关键词： expectation-maximization algorithms Gaussian processes Probability distribution Probability density function Gaussian approximation Frequency Approximation algorithms Image recognition Image segmentation Density measurement

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Impulse restoration-based template-matching using the expectation-maximization algorithm

Impulse restoration-based template-matching using the expect...

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IEEE International Conference on Image Processing

作者： A. Abu-Naser N.P. Galatsanos M.N. Wernick Department of Electrical and Computer Engineering Illinois Institute of Technology Chicago IL USA

It has been demonstrated that object recognition can be formulated as an image-restoration problem. In this approach, which we term impulse restoration, the objective is to restore a delta function indicating the object's location. We develop solutions based on impulse restoration for the Gaussian noise case. We propose a new iterative approach, based on the expectation-maximization (EM) algorithm, that simultaneously estimates the background statistics and restores a delta function at the location of the template. We use localization-receiver-operating characteristics (LROC) curves to evaluate quantitatively the performance of this approach and compare it with existing methods. We present experimental results that demonstrate that impulse restoration is a powerful approach for detecting known objects in images severely degraded by noise. Our experiments demonstrate that accurate modeling and estimation of the background and noise statistics are essential to successful object detection.

关键词： expectation-maximization algorithms Image restoration Discrete Fourier transforms Statistics Object detection Background noise Matched filters Signal to noise ratio Signal restoration Covariance matrix

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