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Cramer-Rao Lower Bound and EM algorithm for Envelope-Based SNR Estimation of Nonconstant Modulus Constellations

IEEE TRANSACTIONS ON COMMUNICATIONS 2009年第6期57卷 1622-1627页

作者： Gappmair, Wilfried Lopez-Valcarce, Roberto Mosquera, Carlos Graz Univ Technol Inst Commun Networks & Satellite Commun A-8010 Graz Austria Univ Vigo Dept Signal Theory & Commun Vigo Spain

Signal-to-noise ratio (SNR) estimation for linearly modulated signals is addressed in this letter, focusing on envelope-based estimators, which are robust to carrier offsets and phase jitter, and on the challenging case of nonconstant modulus constellations. For comparison purposes, the true Cramer-Rao lower bound is numerically evaluated, obtaining an analytical expression in closed form for the asymptotic case of high SNR values, which quantifies the performance loss with respect to coherent estimation. As the maximum-likelihood algorithm is too complex for practical implementation, an expectation-maximization (EM) approach is proposed, achieving a good tradeoff between complexity and performance for medium-to-high SNRs. Finally, a hybrid scheme based on EM and moments-based estimates is suggested, which performs close to the theoretical limit over a wide SNR range.

关键词： Digital receivers nonconstant modulus signals envelope-based SNR estimation Cramer-Rao lower bound expectation-maximization algorithm

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On algorithms and Approximations for Progressively Type-I Censoring Schemes

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STATISTICAL ANALYSIS AND DATA MINING-AN ASA DATA SCIENCE JOURNAL 2024年第6期17卷

作者： El-Saeed, Ahmed R. Almetwally, Ehab M. Imam Mohammad Ibn Saud Islamic Univ IMSIU Fac Sci Dept Math & Stat Riyadh Saudi Arabia Obour High Inst Management & Informat Dept Basic Sci Al Qalyubia Egypt Delta Univ Sci & Technol Fac Business Adm Gamasa Egypt

This research aims to study the estimation of parameters for a Burr-XII distribution and to investigate optimal sampling plans under progressive Type-I censoring (PTIC). For point estimation, we employed the maximum-likelihood estimation (MLE) method using two numerical approaches: Newton-Raphson and the expectation-maximization algorithm. We also utilized Bayesian estimation with squared error loss and linear exponential loss functions. Specifically, two approximate Bayesian methods, the Lindley and Tierney-Kadane methods were examined. Additionally, Bayesian numerical estimation was performed using Markov Chain Monte Carlo with the Metropolis-Hastings (MH) algorithm. For interval estimation, we constructed asymptotic confidence intervals for MLE and the highest posterior density method within the Bayesian framework. The practical study involved Monte Carlo simulations to assess the efficiency and accuracy of the proposed estimation methods across different PTIC schemes. A real data analysis is also provided to illustrate the practical application of these methodologies in analyzing a clinical trial dataset. Data from a clinical trial using a PTIC scheme reveals patterns in pain relief, aiding in evaluating the antibiotic ointment's effectiveness. The study further investigates optimal sampling plans for the Burr-XII distribution under PTIC.

关键词： Bayesian estimation expectation-maximization algorithm Lindley approximation Markov chain Monte Carlo Newton-Raphson optimal sampling plans progressive Type-I censoring Tierney-Kadane approximation

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A CLUSTERING-BASED FUZZY WAVELET NEURAL NETWORK MODEL FOR SHORT-TERM LOAD FORECASTING

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INTERNATIONAL JOURNAL OF NEURAL SYSTEMS 2013年第5期23卷 1350024-1-1350024-19页

作者： Kodogiannis, Vassilis S. Amina, Mahdi Petrounias, Ilias Univ Westminster Sch Elect & Comp Sci London W1W 6UW England Univ Manchester Manchester Business Sch Manchester M15 6PB Lancs England

Load forecasting is a critical element of power system operation, involving prediction of the future level of demand to serve as the basis for supply and demand planning. This paper presents the development of a novel clustering-based fuzzy wavelet neural network (CB-FWNN) model and validates its prediction on the short-term electric load forecasting of the Power System of the Greek Island of Crete. The proposed model is obtained from the traditional Takagi-Sugeno-Kang fuzzy system by replacing the THEN part of fuzzy rules with a "multiplication" wavelet neural network (MWNN). Multidimensional Gaussian type of activation functions have been used in the IF part of the fuzzyrules. A Fuzzy Subtractive Clustering scheme is employed as a pre-processing technique to find out the initial set and adequate number of clusters and ultimately the number of multiplication nodes in MWNN, while Gaussian Mixture Models with the expectation maximization algorithm are utilized for the definition of the multidimensional Gaussians. The results corresponding to the minimum and maximum power load indicate that the proposed load forecasting model provides significantly accurate forecasts, compared to conventional neural networks models.

关键词： Short-term load forecasting fuzzy wavelet neural networks neural networks expectation-maximization algorithm fuzzy clustering

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Estimating metro passengers' path choices by combining self-reported revealed preference and smart card data

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2018年第Jul.期92卷 76-89页

作者： Zhang, Yongsheng Yao, Enjian Zhang, Junyi Zheng, Kangning Beijing Jiaotong Univ Sch Traff & Transportat Beijing 100044 Peoples R China Hiroshima Univ Grad Sch Int Dev & Cooperat 1-5-1 Kagamiyama Higashihiroshima 7398529 Japan

With the help of automated fare collection systems in the metro network, more and more smart card (SC) data has been widely accumulated, which includes abundant information (i.e., Big Data). However, its inability to record passengers' transfer information and factors affecting passengers' travel behaviors (e.g., socio-demographics) limits further potential applications. In contrast, self-reported Revealed Preference (RP) data can be collected via questionnaire surveys to include those factors;however, its sample size is usually very small in comparison to SC data. The purpose of this study is to propose a new set of approaches of estimating metro passengers' path choices by combining self-reported RP and SC data. These approaches have the following attractive features. The most important feature is to jointly estimate these two data sets based on a nested model structure with a balance parameter by accommodating different scales of the two data sets. The second feature is that a path choice model is built to incorporate stochastic travel time budget and latent individual risk-averse attitude toward travel time variations, where the former is derived from the latter and the latter is further represented based on a latent variable model with observed individual socio-demographics. The third feature is that an algorithm of combining the two types of data is developed by integrating an expectation-maximization algorithm and a nested logit model estimation method. The above-proposed approaches are examined based on data from Guangzhou Metro, China. The results show the superiority of combined data over single data source in terms of both estimation and forecasting performance.

关键词： Data fusion expectation-maximization algorithm Metro network Stochastic travel time budget Risk-averse attitude

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A tractable framework for estimating and combining spectral source models for audio source separation

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SIGNAL PROCESSING 2012年第8期92卷 1886-1901页

作者： Arberet, Simon Ozerov, Alexey Bimbot, Frederic Gribonval, Remi Ecole Polytech Fed Lausanne Inst Elect Engn CH-1015 Lausanne Switzerland Inst Natl Rech Informat & Automat F-35042 Rennes France Inst Rech Informat & Syst Aleatoires CNRS UMR 6074 F-35042 Rennes France

The underdetermined blind audio source separation (BSS) problem is often addressed in the time-frequency (TF) domain assuming that each TF point is modeled as an independent random variable with sparse distribution. On the other hand, methods based on structured spectral model, such as the Spectral Gaussian Scaled Mixture Models (Spectral-GSMMs) or Spectral Non-negative Matrix Factorization models, perform better because they exploit the statistical diversity of audio source spectrograms, thus allowing to go beyond the simple sparsity assumption. However, in the case of discrete state-based models, such as Spectral-GSMMs, learning the models from the mixture can be computationally very expensive. One of the main problems is that using a classical expectation-maximization procedure often leads to an exponential complexity with respect to the number of sources. In this paper, we propose a framework with a linear complexity to learn spectral source models (including discrete state-based models) from noisy source estimates. Moreover, this framework allows combining different probabilistic models that can be seen as a sort of probabilistic fusion. We illustrate that methods based on this framework can significantly improve the BSS performance compared to the state-of-the-art approaches. (c) 2012 Elsevier B.V. All rights reserved.

关键词： Blind source separation Multichannel audio Gaussian mixture model expectation-maximization algorithm Convolutive mixture

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Selective Transfer Based Evolutionary Multitasking Optimization for Change Detection

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2024年第3期8卷 2197-2212页

作者： Li, Hao Luo, Tianshi Liu, Liwen Gong, Maoguo Qiao, Wenyuan Xie, Fei Qin, A. K. Xidian Univ Sch Elect Engn Key Lab Collaborat Intelligence Syst Minist Educ Xian 710071 Peoples R China Xian Precis Machinery Res Inst Xian 710077 Peoples R China Xidian Univ Sch Acad Adv Interdisciplinary Res AOAIR Xian 710071 Peoples R China Swinburne Univ Technol Dept Comp Technol Hawthorn Vic 3122 Australia

Change detection in multitemporal remote sensing images aims to generate a difference image (DI) and then analyze it to identify the unchanged/changed areas. The current change detection techniques always investigate a single change detection task of two images from the image series one by one and may ignore the relevant information across the different tasks. Furthermore, theoretical results have demonstrated that the distribution of DI can be interpreted by a Rayleigh-Rice mixture model (RRMM). The parameters of RRMM are usually estimated by the expectation-maximization (EM) algorithm, which is easy to be trapped into local minima. In order to address these issues, a selective transfer based evolutionary multitasking change detection method is proposed to deal with multiple change detection tasks concurrently. For each change detection task, the log-likelihood function and centroid distance function are considered as two objectives to be optimized simultaneously. In the proposed method, a RRMM parameter estimation driven initialization method with random partition of the data is designed by maximum likelihood estimates of the parameters. Then the next population is generated by the intra-task and inter-task genetic transfer operators. A selective knowledge transfer based local search strategy is proposed to further improve the population by applying EM algorithm. In this strategy, the samples in the unchanged class of multiple tasks are utilized to estimate the parameters to acquire knowledge transferred from the other task. Experiments on three real remote sensing data sets demonstrate that the selective transfer based evolutionary multitasking change detection method is able to accelerate the convergence and achieve superior performance in terms of accuracy.

关键词： Evolutionary multitasking optimization expectation-maximization algorithm change detection multitemporal images

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Logistic model for pattern inference of subway passenger flows based on fare collection and vehicle location data

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APPLIED MATHEMATICAL MODELLING 2024年 130卷 472-495页

作者： Li, Chunya Xiong, Shifeng Xiong, Hui Sun, Xuan Qin, Yong Shanghai Univ Engn Sci Sch Math Phys & Stat Shanghai 201620 Peoples R China Chinese Acad Sci Acad Math & Syst Sci NCMIS KLSC Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China Hainan Univ Sch Mech & Elect Engn Haikou 570228 Peoples R China Beijing Jiaotong Univ Sch Traff & Transportat Beijing 100044 Peoples R China Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China

With large volume of passengers boarding and alighting through subway platforms, the stations are getting crowded, resulting in drops in the level of service and safety concerns, especially for subway systems operating at capacity during peak hours. Thus, it is crucial for subway agencies to sense changes in travel demand and adjust their management schemes accordingly. In this paper we propose a statistical approach to estimate dynamic passenger flows with automated data. First, we develop a dynamic logistic model for calculating passenger tap -out times, which can be employed to infer passenger flow characteristics at the aggregate level. In addition, a new passenger -to -train assignment model for any subway route is derived based on the dynamic model. Subsequently, we apply an expectation -maximization algorithm to estimate the model parameters with automated fare collection and automated vehicle location data. Finally, a crossvalidation method is employed to validate our approach with data obtained from several routes in Beijing subway system in China. Results of 95% prediction intervals indicate the effectiveness of the models and the proposed estimation methods.

关键词： Boarding probability Travel time prediction Congestion evaluation Available safe egress time expectation-maximization algorithm Dynamic logistics model

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Model-based capacitated clustering with posterior regularization

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2018年第2期271卷 594-605页

作者： Mai, Feng Fry, Michael J. Ohlmann, Jeffrey W. Stevens Inst Technol Sch Business Hoboken NJ 07030 USA Univ Cincinnati Dept Operat Business Analyt & Informat Syst Cincinnati OH 45221 USA Univ Iowa Tippie Coll Business Iowa City IA 52242 USA

We propose a heuristic approach to address the general class of optimization problems involving the capacitated clustering of observations consisting of variable values that are realizations from respective probability distributions. Based on the expectation-maximization algorithm, our approach unifies Gaussian mixture modeling for clustering analysis and cluster capacity constraints using a posterior regularization framework. To test our algorithm, we consider the capacitated p-median problem in which the observations consist of geographic locations of customers and the corresponding demand of these customers. Our heuristic has superior performance compared to classic geometrical clustering heuristics, with robust performance over a collection of instance types. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Heuristics Gaussian mixture models expectation-maximization algorithm Posterior regularization Capacitated p-median problem

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A robust multiplicative watermarking technique for digital images in curvelet domain using normal inverse Gaussian distribution

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第6期82卷 9223-9241页

作者： Shaik, Ayesha Masilamani, V Vellore Inst Technol Sch Comp Sci & Engn Chennai 600127 Tamilnadu India Indian Inst Informat Technol Design & Mfg Comp Sci & Engn Chennai 600127 Tamilnadu India

The illegal duplication of digital copies became easier because of increased communication speed and rapid growth of internetworked multimedia systems. So it is required to authenticate the legal owner of the digital data and protect the copyrights of the content owner. But the original image requirement during the verification of the owner becomes an overhead as it requires more storage. Instead of that, the watermark extraction without original data will be helpful which is also blind watermarking as the storage complexity is reduced. This article proposes a novel blind multiplicative watermarking (WM) system in the curvelet domain for copyright protection. The proper distribution of the curvelet coefficients has known by modeling the statistics of the curvelet coefficients of a digital image which is a heavy-tailed probability distribution function. It has shown that the normal inverse Gaussian (NIG) distribution suitably fits the empirical distribution. A secure watermark is used for watermarking, a combination of the pseudo-random sequence and the unique information of the owner in the proposed article. The design of the watermark extractor has been realized using NIG for the curvelet coefficients of digital images by using the above property. The watermark is decoded from NIG curvelet coefficients with expectation-maximization (EM) algorithm using maximum likelihood estimation (MLE). The watermark has been decoded using closed-form expressions in both the noise and noiseless environments. The experimental results on standard datasets (BOWS and SIPI) show that the proposed scheme provides approximately 20% improvement in PSNR and 50% reduction in bit error rate (BER) compared to the listed method in the article. The proposed technique shows high robustness against attacks such as cropping, rotation, median filtering, salt and pepper noise, Gaussian noise, average filtering, and gamma correction.

关键词： Watermarking Curvelet Normal inverse Gaussian distribution Maximum likelihood estimation expectation-maximization algorithm

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τ-Inflated Beta Regression Model for Estimating τ-Restricted Means and Event-Free Probabilities for Censored Time-to-Event Data

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BIOMETRICAL JOURNAL 2024年第8期66卷

作者： Wang, Yizhuo Murray, Susan Univ Michigan Dept Biostat Ann Arbor MI 48109 USA

In this research, we propose analysis of tau-restricted censored time-to-event data via a tau-inflated beta regression (tau-IBR) model. The outcome of interest is min(tau,T), where T and tau are the time-to-event and follow-up duration, respectively. Our analysis goals include estimation and inference related to tau-restricted mean survival time (tau-RMST) values and event-free probabilities at tau that address the censored nature of the data. In this setting, it is common to observe many individuals with min(tau,T)=tau, a point mass that is typically overlooked in tau-restricted event-time analyses. Our proposed tau-IBR model is based on a decomposition of min(tau,T) into tau[I(T >=tau)+(T/tau)I(Texpectation-maximization algorithm. An alternative multiple imputation (MI) algorithm for fitting the tau-IBR model has the additional advantage of producing uncensored datasets for analysis. Simulations indicate excellent performance of the tau-IBR model(s), and corresponding tau-RMST estimates, in independent and dependent censoring settings. We apply our method to the Azithromycin for Prevention of Chronic Obstructive Pulmonary Disease (COPD) Exacerbations Trial. In addition to tau-IBR model results providing a nuanced understanding of the treatment effect, visually appealing heatmaps of the tau-restricted event times based on our MI datasets are given, a visualization not typically available for censored time-to-event data.

关键词： expectation-maximization algorithm inflated beta regression multiple imputation of censored times-to-event restricted mean survival time

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