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 ca...
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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.
We will discuss the reliability analysis of a series system under accelerated life tests when interval data are observed, while the components are assumed to have statistically independent exponential lifetime distrib...
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We will discuss the reliability analysis of a series system under accelerated life tests when interval data are observed, while the components are assumed to have statistically independent exponential lifetime distributions. In a series system, the system fails if any of the components fails. It is common to include masked data in which the component that causes failure of the system is not observed. First, we apply the maximum likelihood approach via the expectation-maximization algorithm, and use the parametric bootstrap method for the standard error estimation. When the proportion of the masking data is high, the maximum likelihood approach fails due to lack of information. A Bayesian approach is an appropriate alternative in such a case. Hence, we also study the Bayesian approach incorporated with a subjective prior distribution with the aid of the Markov chain Monte Carlo method. We derive statistical inference on the model parameters, as well as the mean lifetimes, and the reliability functions of the system and components. The proposed method is illustrated through a numerical example simulated from the underlying model under various masking levels.
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...
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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 expectationmaximizationalgorithm 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.
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-l...
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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.
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 ...
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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.
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. O...
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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.
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 operatin...
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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 -maximizationalgorithm 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.
ObjectiveWe review two alternative ways of modeling stability and change of longitudinal data by using time-fixed and time-varying covariates for the observed individuals. Both the methods build on the foundation of f...
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ObjectiveWe review two alternative ways of modeling stability and change of longitudinal data by using time-fixed and time-varying covariates for the observed individuals. Both the methods build on the foundation of finite mixture models, and are commonly applied in many fields but they look at the data from different perspectives. Our attempt is to make comparisons when the ordinal nature of the response variable is of interest. MethodsThe latent Markov model is based on time-varying latent variables to explain the observable behavior of the individuals. It is proposed in a semiparametric formulation as the latent process has a discrete distribution and is characterized by a Markov structure. The growth mixture model is based on a latent categorical variable that accounts for the unobserved heterogeneity in the observed trajectories and on a mixture of Gaussian random variables to account for the variability in the growth factors. We refer to a real data example on self-reported health status to illustrate their peculiarities and differences.
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 probabilit...
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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.
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 ...
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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.
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