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Blind Modulation Classification over Fading Channels Using expectation-maximization

IEEE COMMUNICATIONS LETTERS 2013年第9期17卷 1692-1695页

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

We propose a blind modulation classification algorithm when the channel coefficient, the noise power and the energy of the transmitted signal are unknown at the receiver. First, under each candidate modulation scheme, we evaluate the unknown parameters using the iterative expectation maximization algorithm. Modulation classification is then accomplished by minimizing the distance between the log-likelihood of the received data and the expected log-likelihood under each candidate modulation scheme. Results are presented from simulations in terms of detection probability vs. SNR for the class of BPSK, QPSK, 16QAM and 64QAM modulation schemes. The results show a significant improvement over QHLRT and are very close to the upper bound ALRT-UB [1].

关键词： Blind modulation classification parameter estimation expectation-maximization algorithm

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expectation-maximization enables Phylogenetic Dating under a Categorical Rate Model

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SYSTEMATIC BIOLOGY 2024年第5期73卷 823-838页

作者： Mai, Uyen Charvel, Eduardo Mirarab, Siavash Univ Calif San Diego Dept Comp Sci & Engn La Jolla CA 92093 USA Univ Calif San Diego Bioinformat & Syst Biol Grad Program La Jolla CA 92093 USA Univ Calif San Diego Dept Elect & Comp Engn La Jolla CA 92093 USA

Dating phylogenetic trees to obtain branch lengths in time units is essential for many downstream applications but has remained challenging. Dating requires inferring substitution rates that can change across the tree. While we can assume to have information about a small subset of nodes from the fossil record or sampling times (for fast-evolving organisms), inferring the ages of the other nodes essentially requires extrapolation and interpolation. Assuming a distribution of branch rates, we can formulate dating as a constrained maximum likelihood (ML) estimation problem. While ML dating methods exist, their accuracy degrades in the face of model misspecification, where the assumed parametric statistical distribution of branch rates vastly differs from the true distribution. Notably, most existing methods assume rigid, often unimodal, branch rate distributions. A second challenge is that the likelihood function involves an integral over the continuous domain of the rates, often leading to difficult non-convex optimization problems. To tackle both challenges, we propose a new method called Molecular Dating using Categorical-models (MD-Cat). MD-Cat uses a categorical model of rates inspired by non-parametric statistics and can approximate a large family of models by discretizing the rate distribution into k categories. Under this model, we can use the expectation-maximization algorithm to co-estimate rate categories and branch lengths in time units. Our model has fewer assumptions about the true distribution of branch rates than parametric models such as Gamma or LogNormal distribution. Our results on two simulated and real datasets of Angiosperms and HIV and a wide selection of rate distributions show that MD-Cat is often more accurate than the alternatives, especially on datasets with exponential or multimodal rate distributions.

关键词： Categorical model expectation-maximization algorithm molecular dating phylogenetic dating time tree

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Markov chain Monte Carlo and expectation maximization approaches for estimation of haplotype frequencies for multiply infected human blood samples

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MALARIA JOURNAL 2016年第1期15卷 1-15页

作者： Ken-Dror, Gie Hastings, Ian M. Univ Liverpool Liverpool Sch Trop Med Pembroke Pl Liverpool L5 3QA Merseyside England

Background: Haplotypes are important in anti-malarial drug resistance because genes encoding drug resistance may accumulate mutations at several codons in the same gene, each mutation increasing the level of drug resistance and, possibly, reducing the metabolic costs of previous mutation. Patients often have two or more haplotypes in their blood sample which may make it impossible to identify exactly which haplotypes they carry, and hence to measure the type and frequency of resistant haplotypes in the malaria population. Results: This study presents two novel statistical methods expectation-maximization (EM) and Markov chain Monte Carlo (MCMC) algorithms to investigate this issue. The performance of the algorithms is evaluated on simulated datasets consisting of patient blood characterized by their multiplicity of infection (MOI) and malaria genotype. The datasets are generated using different resistance allele frequencies (RAF) at each single nucleotide polymorphisms (SNPs) and different limit of detection (LoD) of the SNPs and the MOI. The EM and the MCMC algorithm are validated and appear more accurate, faster and slightly less affected by LoD of the SNPs and the MOI compared to previous related statistical approaches. Conclusions: The EM and the MCMC algorithms perform well when analysing malaria genetic data obtained from infected human blood samples. The results are robust to genotyping errors caused by LoDs and function well even in the absence of MOI data on individual patients.

关键词： Haplotype reconstruction Multiplicity of infection Single nucleotide polymorphisms expectation-maximization algorithm Markov chain Monte Carlo

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Applying expectation-maximization Evaluation on Approximate Optimal Control 12

Applying Expectation-Maximization Evaluation on Approximate ...

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12th Annual IEEE International Systems Conference (SYSCON)

作者： Zhang, Songtao Dubay, Rickey Univ New Brunswick Dept Mech Engn Fredericton NB Canada

ISBN: (纸本)9781538636640

In this paper we proposed an approach of approximating optimal tracking via expectation-maximization (EM) evaluation. From the discussion of applying reinforcement learning (RL) for a system with unknown internal dynamics, we present the challenge of using a classical frame of Q-learning on a tracking task. Further we explained the idea of redefining the cost function (i.e. criterion) of Q-learning to satisfy the requirement for the system dynamic knowledge for the tracking task. We explained the advantages of dividing the original trajectory tracking task into two machine learning subtasks (i.e. learning the quadratic regulator and learning the baseline command generator) on-line. Details are given on the integration of the Q-learning frame and EM algorithm as well as the convergence to the optimum control via iterative estimation of an optimal regulator and a baseline generator. Initial simulation results of this approach using a second order system showed the ability of the Q-learning frame integrated with the EM algorithm approximates to the optimal tracking task.

关键词： Q-learning quadratic control expectation-maximization algorithm adaptive dynamic programming

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An expectation-maximization-based Approach to the Relative Grid-Locking Problem

An Expectation-Maximization-based Approach to the Relative G...

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IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)

作者： Fortunati, Stefano Gini, Fulvio Greco, Maria S. Farina, Alfonso Graziano, Antonio Giompapa, Sofia Univ Pisa Dept Ingn Informaz Pisa Italy SELEX Sistemi Integrati Rome Italy

ISBN: (纸本)9781467307536

An important prerequisite for successful multisensory integration is that the data from the reporting sensors are transformed to a common reference frame free of systematic or registration bias errors. If not properly corrected, the registration errors can seriously degrade the global surveillance system performance. The relative sensor registration (or grid-locking) process aligns remote data to local data under the assumption that the local data are bias free and that all biases reside with the remote sensor. In this paper, we take into account all registration errors involved in the grid-locking problem. An EM-based estimator of these bias terms is derived and its statistical performance compared to the hybrid Cramer-Rao lower bound (HCRLB).

关键词： Multi-sensor system sensor registration bias errors expectation-maximization algorithm HCRLB

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Variational Bayesian Inference for Multichannel Dereverberation and Noise Reduction

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2014年第8期22卷 1320-1335页

作者： Schmid, Dominic Enzner, Gerald Malik, Sarmad Kolossa, Dorothea Martin, Rainer Ruhr Univ Bochum Inst Commun Acoust D-44780 Bochum Germany

Room reverberation and background noise severely degrade the quality of hands-free speech communication systems. In this work, we address the problem of combined speech dereverberation and noise reduction using a variational Bayesian (VB) inference approach. Our method relies on a multichannel state-space model for the acoustic channels that combines frame-based observation equations in the frequency domain with a first-order Markov model to describe the time-varying nature of the room impulse responses. By modeling the channels and the source signal as latent random variables, we formulate a lower bound on the log-likelihood function of the model parameters given the observed microphone signals and iteratively maximize it using an online expectation-maximization approach. Our derivation yields update equations to jointly estimate the channel and source posterior distributions and the remaining model parameters. An inspection of the resulting VB algorithm for blind equalization and channel identification (VB-BENCH) reveals that the presented framework includes previously proposed methods as special cases. Finally, we evaluate the performance of our approach in terms of speech quality, adaptation times, and speech recognition results to demonstrate its effectiveness for a wide range of reverberation and noise conditions.

关键词： Dereverberation expectation-maximization algorithm noise reduction state-space model variational Bayes

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Variational learning for Gaussian mixture models

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 2006年第4期36卷 849-862页

作者： Nasios, Nikolaos Bors, Adrian G. Univ York Dept Comp Sci York YO10 5DD N Yorkshire England

This paper proposes a joint maximum likelihood and Bayesian methodology for estimating Gaussian mixture models. In Bayesian inference, the distributions of parameters are modeled, characterized by hyperparameters. In the case of Gaussian mixtures, the distributions of parameters are considered as Gaussian for the mean, Wishart for the covariance, and Dirichlet for the mixing probability. The learning task consists of estimating the hyperparameters characterizing these distributions. The integration in the parameter space is decoupled using an unsupervised variational methodology entitled variational expectation-maximization (VEM). This paper introduces a hyperparameter initialization procedure for the training algorithm. In the first stage, distributions of parameters resulting from successive runs of the expectation-maximization algorithm are formed. Afterward, maximum-likelihood estimators are applied to find appropriate initial values for the hyperparameters. The proposed initialization provides faster convergence, more accurate hyperparameter estimates, and better generalization for the VEM training algorithm. The proposed methodology is applied in blind signal detection and in color image segmentation.

关键词： Bayesian inference expectation-maximization algorithm Gaussian mixtures maximum log-likelihood estimation variational training

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An exact algorithm for time-dependent variational inference for the dynamic stochastic block model

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PATTERN RECOGNITION LETTERS 2020年 138卷 362-369页

作者： Bartolucci, Francesco Pandolfi, Silvia Univ Perugia Dept Econ Perugia Italy

An exact algorithm for estimating the dynamic stochastic block model is proposed. This model assumes a hidden Markov chain for the evolution of the social behavior of a group of individuals at repeated time occasions and may be used to assign these individuals to the latent blocks in a dynamic fashion. For the estimation of this model, the proposed exact algorithm maximizes the target function introduced by Matias and Miele [7]. This function is derived from a variational approximation of the model log-likelihood, based on the assumption that the latent variables identifying the blocks are a posteriori independent across individuals, but not across time occasions. A simulation study is performed to compare the exact algorithm with the approximate maximization algorithm proposed by Matias and Miele [7]. Results show that there is a certain advantage of the first in terms of dynamic assignment of individuals to the latent blocks in comparison to the true blocking structure, as measured by the adjusted Rand index. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Adjusted rand index expectation-maximization algorithm Longitudinal data

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Hierarchical adaptive Kalman filtering for interplanetary orbit determination

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 1998年第3期34卷 883-896页

作者： Chaer, WS Bishop, RH Ghosh, J Univ Texas Dept Aerosp Engn & Engn Mech Austin TX 78712 USA Univ Texas Dept Elect & Comp Engn Austin TX 78712 USA

A modular and flexible approach to adaptive Kalman filtering has recently been introduced using the framework of a mixture-of-experts regulated by a gating network. Each expert is a Kalman biter modeled with a different realization of the unknown system parameters. The unknown or uncertain parameters can include elements of the state transition matrix, observation mapping matrix, process noise covariance matrix, and measurement noise covariance matrix. The gating network performs on-line adaptation of the weights given to individual filters based on performance. The mixture-of-experts approach Is extended here to a hierarchical architecture which involves multiple levels of gating. The proposed architecture provides a multilevel hypothesis testing capability, The utility of the hierarchical architecture is illustrated via the problem of interplanetary navigation (Mars Pathfinder) using simulated radiometric data, It serves as a useful tool for assisting navigation teams in the process of selecting the parameters of the navigational biter over various operating regimes. it is shown that the scheme has the capability of detecting changes in the system parameters and switching filters appropriately for optimal performance. Furthermore, the expectation-maximization (EM) algorithm is shown to be applicable in the proposed framework.

关键词： adaptive Kalman filters iterative methods learning (artificial intelligence) maximum likelihood estimation navigation neural net architecture realisation theory state estimation tracking filters Mars Pathfinder expectation-maximization algorithm gating n expectation-maximisation algorithm system parameter maximum likelihood estimation iterative methods adaptive Kalman filters navigation tracking filters neural net architecture Mars Pathfinder State estimation realisation theory

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An Iteratively Optimized Patch Label Inference Network for Automatic Pavement Distress Detection

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第7期23卷 8652-8661页

作者： Tang, Wenhao Huang, Sheng Zhao, Qiming Li, Ren Huangfu, Luwen Chongqing Univ Sch Big Data & Software Engn Chongqing 400044 Peoples R China Minist Educ Key Lab Dependable Serv Comp Cyber Phys Soc Chongqing 400044 Peoples R China Chongqing Jiaotong Univ Sch Informat Sci & Engn Chongqing 400074 Peoples R China San Diego State Univ Fowler Coll Business San Diego CA 92182 USA

We present a novel deep learning framework named the Iteratively Optimized Patch Label Inference Network (IOPLIN) for automatically detecting various pavement distresses that are not solely limited to specific ones, such as cracks and potholes. IOPLIN can be iteratively trained with only the image label via the expectation-maximization Inspired Patch Label Distillation (EMIPLD) strategy, and accomplish this task well by inferring the labels of patches from the pavement images. IOPLIN enjoys many desirable properties over the state-of-the-art single branch CNN models such as GoogLeNet and EfficientNet. It is able to handle images in different resolutions, and sufficiently utilize image information particularly for the high-resolution ones, since IOPLIN extracts the visual features from unrevised image patches instead of the resized entire image. Moreover, it can roughly localize the pavement distress without using any prior localization information in the training phase. In order to better evaluate the effectiveness of our method in practice, we construct a large-scale Bituminous Pavement Disease Detection dataset named CQU-BPDD consisting of 60,059 high-resolution pavement images, which are acquired from different areas at different times. Extensive results on this dataset demonstrate the superiority of IOPLIN over the stateof-the-art image classification approaches in automatic pavement distress detection. The source codes of IOPLIN are released on https://***/DearCaat/ioplin, and the CQU-BPDD dataset is able to be accessed on https://***/CQU-BPDD/.

关键词： Pavement distress detection Convolutional Neural Networks expectation-maximization algorithm image classification object localization

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