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) suffe...
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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.
In positron emission tomography (PET), a radioactive compound is injected into the body to promote a tissue-dependent emission rate. expectationmaximization (EM) reconstruction algorithms are iterative techniques whi...
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In positron emission tomography (PET), a radioactive compound is injected into the body to promote a tissue-dependent emission rate. expectationmaximization (EM) reconstruction algorithms are iterative techniques which estimate the concentration coefficients that provide the best fitted solution, for example, a maximum likelihood estimate. In this paper, we combine the EM algorithm with a level set approach. The level set method is used to capture the coarse scale information and the discontinuities of the concentration coefficients. An intrinsic advantage of the level set formulation is that anatomical information can be efficiently incorporated and used in an easy and natural way. We utilize a multiple level set formulation to represent the geometry of the objects in the scene. The proposed algorithm can be applied to any PET configuration, without major modifications. Copyright (C) 2007 Tony F. Chan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Outliers due to occlusions and contrast and offset signal deviations notably hinder recognition and retrieval of facial images. We propose a new maximum likelihood matching score with "soft masking" of outli...
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Outliers due to occlusions and contrast and offset signal deviations notably hinder recognition and retrieval of facial images. We propose a new maximum likelihood matching score with "soft masking" of outliers which is robust in these conditions. Differences between two images are modelled by unknown contrast and offset deviations from an unknown template and by independent pixel-wise errors. The error distribution is a mixture of a zero-centred Gaussian noise with an unknown variance and uniformly distributed outliers. The matching score combines the maximum likelihood estimates of model parameters and the soft masks being produced by a simple iterative expectation-maximisation algorithm. Experiments with facial images from the MIT face database show the robustness of this technique in the presence of large occlusions.
Based on the observation that an attack applied on a watermarked image, from a decoding point of view, modifies the distribution of the detection values away from the ideal distribution (without attack) for correspond...
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Based on the observation that an attack applied on a watermarked image, from a decoding point of view, modifies the distribution of the detection values away from the ideal distribution (without attack) for corresponding watermarking scheme, we propose a generic maximum likelihood decoding scheme by approximating the distribution with a finite Gaussian mixture model. The parameters of the model are estimated using expectation-maximization algorithm. The scheme allows the decoding to be automatically adapted to attacks that the watermarked images have undergone and, in consequence, to improve the decoding accuracy. Experiments on a QIM based watermarking system have clearly verified the significant improvement of the decoding accuracy achieved by the proposed maximum likelihood decoding in comparison to conventional threshold decoding.
Harmonic sinusoidal representations of speech have proven to be useful in many speech processing tasks. This work focuses on the phase spectra of the harmonics and provides a methodology to analyze and subsequently to...
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Harmonic sinusoidal representations of speech have proven to be useful in many speech processing tasks. This work focuses on the phase spectra of the harmonics and provides a methodology to analyze and subsequently to model the statistics of the harmonic phases. To do so, we propose the use of a wrapped Gaussian mixture model (WGMM), a model suitable for random variables that belong to circular spaces, and provide an expectation-maximization algorithm for training. The WGMM is then used to construct a phase quantizer. The quantizer is employed in a prototype variable rate narrow-band VoIP sinusoidal codec that is equivalent to iLBC in terms of PESQ-MOS, at ~13 kbps.
Shifted excitation Raman spectroscopy results in multiple observations of the sum of a material's fluorescent and Raman spectra. The fluorescent spectrum is typically stationary with respect to the excitation freq...
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Shifted excitation Raman spectroscopy results in multiple observations of the sum of a material's fluorescent and Raman spectra. The fluorescent spectrum is typically stationary with respect to the excitation frequency induced by the instrument, while the Raman spectrum is subject to a nonlinear shift which depends explicitly and in a known manner upon the excitation frequency. This phenomenon has been exploited to reconstruct Raman spectra indirectly by subtracting spectra observed at two closely spaced excitation frequencies. The technique, known as shifted excitation Raman difference spectroscopy (SERDS), is of limited utility, however, in that observations with low photon counts are difficult to process accurately, and that one must still reconstruct the spectrum from the estimate of the derivative. This paper presents an innovative alternative approach to Raman spectrum reconstruction based on an expectation-maximization algorithm and multiresolution photon-limited signal analysis. Using this method, it is shown that using multiple excitation frequencies (while keeping the total excitation laser power and total expected photon counts constant) can result in dramatic improvements in reconstruction accuracy.
We propose a linear dynamical system response (LDSR) model to describe amplitude variability across trials in event-related magnetoencephalographic/electroencephalographic (MEG/EEG) data. Variability across trials may...
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We propose a linear dynamical system response (LDSR) model to describe amplitude variability across trials in event-related magnetoencephalographic/electroencephalographic (MEG/EEG) data. Variability across trials may reflect habituation, fatigue, or changes in cognitive states of the brain. A wide range of trial-to-trial variability can be represented using the LDSR model, including the constant response (CR) model as a limiting case. The spatiotemporal signal waveform is assumed constant but unknown, and is represented using a linear combination of spatial and temporal basis functions. The background noise is assumed to be spatially correlated with unknown covariance matrix. We obtain the maximum-likelihood estimates of the amplitude variability and signal waveform via a generalized expectation-maximization algorithm. The expectation step involves a Kalman fixed-interval smoother which tracks the trial-to-trial amplitude variability while the maximization step estimates the signal waveform, spatial noise covariance, and LDSR model parameters. We demonstrate the effectiveness of the proposed model using both real and simulated evoked response data. The performance of the algorithm is analyzed in terms of the mean squared error of the amplitude estimates.
This paper presents a new expectation-Maximisation algorithm for classifying a data set originated from an unknown number of sources. The proposed algorithm is based on the Kullback-Leibler divergence and uses a minim...
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This paper presents a new expectation-Maximisation algorithm for classifying a data set originated from an unknown number of sources. The proposed algorithm is based on the Kullback-Leibler divergence and uses a minimum message length criteria to penalise adding extra data sources. It is able to estimate the parameters of the model for each data source and to determine the total number of sources producing the data. We apply our algorithm to the classification of spikes originated from multiple neurons but recorded by a single microelectrode. The obtained experimental results show the effectiveness of the proposed algorithm.
This paper introduces a method to model the rapid attenuation fluctuations (the scintillation) on satellite links with generating the time series of attenuation levels. The applied model is a hidden Markov model which...
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This paper introduces a method to model the rapid attenuation fluctuations (the scintillation) on satellite links with generating the time series of attenuation levels. The applied model is a hidden Markov model which is parameterized from an appropriate filtered Gaussian white noise signal. For the parameterization of the Markov chain the Baum-Welch expectation-maximization algorithm has been used. The resulting Markov model is applicable to generate scintillation time series for any desired duration with the time and amplitude resolution of the original training data. From the synthesized time series the statistical properties like the cumulative distribution and the dynamics of the scintillation can be also determined. To prove the accuracy of the model at first the cumulative distribution function of the original and synthesized time series are compared. A further test of the model validity is the comparison with real scintillation measurement on a land mobile satellite link. Finally, the spectral test of the filtered Gaussian model, the hidden Markov model and the satellite link measurement shows that the constant and decreasing parts in the periodograms are showing similarities what confirms the good quality of the model.
In this paper we present new detectors for additive watermarks when the power of the watermark is unknown. These detectors are based on modeling the image using student-t statistics. As a result, due to the generative...
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ISBN:
(纸本)9781424412730
In this paper we present new detectors for additive watermarks when the power of the watermark is unknown. These detectors are based on modeling the image using student-t statistics. As a result, due to the generative properties of the student-t density function, such models are spatially adaptive and the expectation-maximization algorithm can be used to obtain maximum likelihood estimates of their parameters. Using these image models detectors based on the generalized likelihood ratio and Rao tests are derived for this problem. Numerical experiments are presented that demonstrate the properties of these detectors and compared them with previously proposed detectors.
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