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12th IEEE International Workshop on Signal Processing Advances in Wireless Communications

作者： Bhatti, Jabran Noels, Nele Moeneclaey, Marc Univ Ghent Dept Telecommun & Informat Proc Ghent Belgium

ISBN: (纸本)9781424493326

We present two phase noise estimation algorithms for single-carrier burst communications. The first and second estimation techniques (denoted as PEA1 and PEA2 respectively) are based on a truncated discrete cosine transform (DCT) basis expansion of the phase noise and of the corresponding phasor, respectively and do not require any knowledge of the phase noise statistics. An initial pilot-based estimate is iteratively improved by means of the expectation-maximization algorithm, yielding a soft-decision-directed phase noise estimate. The performances of PEA1 and PEA2 are compared in terms of the mean-square phase error and bit-error rate. Numerical results show that PEA2 out performs PEA1 for practical values of the signal-to-noise ratio.

关键词： algorithm design and analysis Bit error rate Discrete cosine transforms Estimation Phase estimation Phase noise Signal processing algorithms bit-error rate channel estimation discrete cosine transforms error statistics expectation-maximisation algorithm expectation-maximization algorithm iterative phase noise estimation algorithm phase noise signal-to-noise ratio single-carrier burst communications telecommunication networks truncated DCT expansion truncated discrete cosine transform

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A simple channel estimator for OFDM with transmit diversity in rapid fading channels

A simple channel estimator for OFDM with transmit diversity ...

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14th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications

作者： Shan, SW Luo, HW Song, WT Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200030 Peoples R China

ISBN: (纸本)7505350668

This paper presents a simple channel estimator for orthogonal frequency division multiplexing (OFDM) systems with transmit diversity in rapid fading channels. The channels at the training bauds are estimated using the EM (expectation-maximization) algorithm. while the channels at the data bauds are estimated based on the method that the time-varying channel can be modelled as the linear combination of several time-invariant "Doppler channels". Computer simulations show that this estimator outperforms the decision-directed tracking in rapid fading channels and the performance of this method call be improved by iteration.

关键词： channel estimation OFDM modulation fading channels time-varying channels Doppler effect channel estimator OFDM transmit diversity fading channel orthogonal frequency division multiplexing training baud channel expectation-maximization algorithm data baud channel time-varying channel linear combination time-invariant Doppler channel decision-directed tracking iteration method

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Graph Representation Learning with Adaptive Mixtures 21

Graph Representation Learning with Adaptive Mixtures

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21st IEEE International Conference on Data Mining (IEEE ICDM)

作者： Tam, Da Sun Handason Xie, Siyue Lau, Wing Cheong Chinese Univ Hong Kong Dept Informat Engn Hong Kong Peoples R China

ISBN: (纸本)9781665424271

Graph Neural Networks (GNNs) are the current state-of-the-art models in learning node representations for many predictive tasks on graphs. Typically, GNNs reuses the same set of model parameters across all nodes in the graph to improve the training efficiency and exploit the translationally-invariant properties in many datasets. However, the parameter sharing scheme prevents GNNs from distinguishing two nodes that are isomorphic and that the translation invariance property may not exhibit in real-world graphs. In this paper, we present Graph Representation Learning with Adaptive Mixtures (GRAM), a novel approach for learning node representations in a graph by introducing multiple independent GNN models and a trainable mixture distribution for each node. GRAM is a general framework that can be readily embedded into existing models for enhancing representation learning. It allows for adaptive node clustering with mixtures and joint optimization of all GNN models simultaneously. To achieve this, we develop an expectation-maximization algorithm with stochastic gradient descent in a scalable manner. Specifically, in the E-Step, we approximate the posterior probability distribution of the latent cluster membership based on the k-th independent GNN models in the last iteration. In the M-step, we update all GNN models and the mixture parameters based on the refined latent cluster membership for downstream tasks, e.g., node classification. To fully exploit the graph structural information, we further implement a regularization term on unlabeled nodes, which encourages adjacent nodes to have the same mixture distribution. By jointly optimizing the model in an end-to-end manner, GRAM learns to capture different abstract patterns on graphs for different node clusters, which yields discriminative representation for each node. We evaluate GRAM on five benchmark datasets with extensive experiments. GRAM is demonstrated to consistently boost state-of-the-art GNN variants in node cl

关键词： graph neural networks expectation-maximization algorithm clustering mixture models

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VARIATIONAL BAYESIAN EM algorithm FOR MODELING MIXTURES OF NON-STATIONARY SIGNALS IN THE TIME-FREQUENCY DOMAIN (HR-NMF)

VARIATIONAL BAYESIAN EM ALGORITHM FOR MODELING MIXTURES OF N...

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

作者： Badeau, Roland Dremeau, Angelique CNRS LTCI Telecom ParisTech Inst Mines Telecom Paris France

ISBN: (纸本)9781479903566

We recently introduced the high-resolution nonnegative matrix factorization (HR-NMF) model for analyzing mixtures of non-stationary signals in the time-frequency domain, and highlighted its capability to both reach high spectral resolution and reconstruct high quality audio signals. In order to estimate the model parameters and the latent components, we proposed to resort to an expectation-maximization (EM) algorithm based on a Kalman filter/smoother. The approach proved to be appropriate for modeling audio signals in applications such as source separation and audio inpainting. However, its computational cost is high, dominated by the Kalman filter/smoother, and may be prohibitive when dealing with high-dimensional signals. In this paper, we consider two different alternatives, using the variational Bayesian EM algorithm and two mean-field approximations. We show that, while significantly reducing the complexity of the estimation, these novel approaches do not alter its quality.

关键词： Nonnegative Matrix Factorization High Resolution methods expectation-maximization algorithm Variational inference

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A Compensated EM-based Channel Estimation for Orthogonal Wavelet Packet Modulation System

A Compensated EM-based Channel Estimation for Orthogonal Wav...

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4th International Conference on Wireless Communications, Networking and Mobile Computing

作者： Yang, Min Liu, Zheng Dai, Jufeng Wang, Qiuhong Tianjin Univ Sch Elect Informat Engn Tianjin 300072 Peoples R China

ISBN: (纸本)9781424421077

This paper proposes a novel iterative channel estimator for orthogonal wavelet packet modulation (OWPM) systems over frequency-selective fading channels. The channel estimation algorithm herein designed Introduces linear Interpolation to compensate the original value offered by expextation-maximization (EM) algorithm for iterative step. With the utilization of the coherence among both subcarriers and symbols, the performance is improved substantially. We conduct particular simulations with comparing the bit-error-rate (BER) obtained from traditional EM algorithm and our algorithm for QPSK/16QAM. Moreover, the convergence behavior of the two algorithms Is also investigated through the simulations under frequency-selective multi-path conditions. The results show that our method performs better than the conventional EM algorithm both In BER and convergence performances.

关键词： channel estimation Orthogonal wavelet packet modulation iteration estimation expectation-maximization algorithm linear interpolation

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Identification of ARX Model with Multi-Gaussian Noises 19

Identification of ARX Model with Multi-Gaussian Noises

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19th International Conference on Control, Automation and Systems (ICCAS)

作者： Bai, Wentao Guo, Fan Chen, Lei Hao, Kuangrong Huang, Biao Donghua Univ Coll Informat Sci & Technol Shanghai 201620 Peoples R China Minist Educ Engn Res Ctr Digitized Text & Fash Technol Shanghai 201620 Peoples R China Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 2V4 Canada

ISBN: (纸本)9788993215182

In most industrial environments, the real process data points are usually subject to contamination from a variety of noises. Most of the traditional methods assume that the process noise is Gaussian white noise, which can lead to poor robustness of the model to abnormal data. In this paper, we consider an ARX model with multi-Gaussian white noises, assuming that the collected data is affected by different noise models and switching of noise model follows Markov chain probability. The parameters of the model are estimated by expectation-maximization (EM) algorithm. A numerical example and a continuous stirred tank reactor are employed to verify the effectiveness of the proposed algorithm.

关键词： ARX Markov chain expectation-maximization algorithm continuous stirred tank reactor

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UNSUPERVISED SEGMENTATION OF FOCUSED REGIONS IN IMAGES WITH LOW DEPTH OF FIELD

UNSUPERVISED SEGMENTATION OF FOCUSED REGIONS IN IMAGES WITH ...

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IEEE International Conference on Multimedia and Expo Workshops (ICMEW)

作者： Rafiee, G. Dlay, S. S. Woo, W. L. Newcastle Univ Dept Elect & Elect Engn Newcastle Upon Tyne NE1 7RU Tyne & Wear England

ISBN: (纸本)9781479900152

Unsupervised extraction of focused regions from images with low depth-of-field (DOF) is a problem without an efficient solution yet. In this paper, we propose an efficient unsupervised segmentation solution for this problem. The proposed approach which is based on ensemble clustering and graph-cut modeling aims to extract meaningful focused regions from a given image at two stages. In the first stage, a novel two-level based ensemble clustering technique is developed to classify image blocks into three constituent classes. As a result, object and background blocks are extracted. By considering certain pixels of object and background blocks as seeds, a constraint is provided for the next stage of the approach. In stage two, a minimal graph cuts is constructed by utilizing the max-flow method and using object and background seeds. Experimental results demonstrate that the proposed approach achieves an average F-measure of 91.7% and is computationally up to 2 times faster than existing unsupervised approaches.

关键词： Ensemble clustering expectation-maximization algorithm interest regions segmentation graph-cut optimization low depth-of-field

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Multi-Target Delay and Doppler Estimation in Bistatic Passive Radar Systems

Multi-Target Delay and Doppler Estimation in Bistatic Passiv...

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IEEE Radar Conference (RadarConf)

作者： Rashid, Mohammed Naraghi-Pour, Mort Louisiana State Univ Sch Elect Engn & Comp Sci Baton Rouge LA 70803 USA

ISBN: (纸本)9781728176093

We propose a computationally efficient algorithm for localizing multiple targets on a delay-Doppler plane in a bistatic passive radar system. Our algorithm aims to jointly compute the maximum likelihood (ML) estimate of the multiple targets' (MT) delays and Doppler shifts by developing the expectation maximization (EM) algorithm. The proposed method, referred to as EM-MT, decomposes the multi-dimensional estimation for delays and Doppler shifts into several independent, per-target optimization problems. At the output of EM-MT, we obtain the ML estimate of the delays and Doppler shifts of the multiple targets, and also the estimate of each target's component signal in the surveillance channel (SC). The latter signal can be used to subtract stronger targets from the SC in order to accurately estimate the weaker targets. Simulation results show that EM-MT outperforms the cross-correlation estimator, the modified CC (MCC) estimator and a previously proposed EM-based algorithm. Furthermore, unlike these algorithms, our proposed EM-MT algorithm achieves the Cramer-Rao lower bound.

关键词： Passive radar multiple targets delays and Doppler shifts estimation expectation-maximization algorithm

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PHOTON-LIMITED IMAGE DENOISING BY INFERENCE ON MULTISCALE MODELS

PHOTON-LIMITED IMAGE DENOISING BY INFERENCE ON MULTISCALE MO...

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15th IEEE International Conference on Image Processing (ICIP 2008)

作者： Lefkimmiatis, Stamatios Papandreou, George Maragos, Petros Natl Tech Univ Athens Sch ECE GR-15773 Athens Greece

ISBN: (纸本)9781424417650

We present an improved statistical model of Poisson processes, with applications to photon-limited imaging. We build on previous work, adopting a multiscale representation of the Poisson process in which the ratios of the underlying Poisson intensities (rates) in adjacent scales are modeled as mixtures of conjugate parametric distributions. Our main novel contributions are (1) a rigorous and robust regularized expectation-maximization (EM) algorithm for maximum-likelihood estimation of the rate-ratio density parameters directly from the observed Poisson data (counts);(2) extension of the method to work under a scale-recursive Hidden Markov Tree model (HMT) which couples the mixture label assignments in consecutive scales, thus modeling inter-scale coefficient dependencies in the vicinity of edges;and (3) exploration of a fully 2-D quad-tree image partitioning, involving Dirichlet-mixture rate-ratio densities, instead of the conventional separable binary image partitioning involving Beta-mixture rate-ratio densities. Experimental intensity estimation results on standard images with artificially simulated Poisson noise and photon-limited images with real shot noise demonstrate the effectiveness of the proposed approach.

关键词： Photon-Limited Imaging Poisson Bayesian inference Hidden Markov tree expectation-maximization algorithm

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Predicting the Loss Given Default Distribution with the Zero-Inflated Censored Beta-Mixture Regression that Allows Probability Masses and Bimodality

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JOURNAL OF FINANCIAL SERVICES RESEARCH 2021年第3期59卷 143-172页

作者： Hwang, Ruey-Ching Chu, Chih-Kang Yu, Kaizhi Natl Dong Hwa Univ Dept Finance Hualien Taiwan Natl Dong Hwa Univ Dept Appl Math Hualien Taiwan Southwestern Univ Finance & Econ Sch Stat Chengdu Peoples R China

We propose a new procedure to predict the loss given default (LGD) distribution. Studies find empirical evidence that LGD values have a high concentration at the endpoint 0. Thus, we first use a logistic regression to determine the probability that the LGD value of a defaulted debt equals zero. Further, studies find empirical evidence that positive LGD values have a low concentration at the endpoint 1 and a bimodal distribution on the interval (0,1). Therefore, we use a right-tailed censored beta-mixture regression to model the distribution of positive LGD data. To implement the proposed procedure, we collect 5554 defaulted debts from Moody's Default and Recovery Database and apply an expectation-maximization algorithm to estimate the LGD distribution. Using each of the k-fold cross-validation technique and the expanding rolling window approach, our empirical results confirm that the new procedure has better and more robust out-of-sample performance than its alternatives because it yields more accurate predictions of the LGD distribution.

关键词： Conditional distribution expectation-maximization algorithm Logistic regression Loss given default Right-tailed censored beta-mixture regression Zero-inflated model

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