nils-herrmann/clean_scirepeval huggingface dataset:
Parameter estimation for sums of correlated gamma random variables. Application to anomaly detection in internet traffic

A new family of distributions, constructed by summing two correlated gamma random variables, is studied. First, a simple closed form expression for their density is derived. Second, the three parameters characterizing such a density are estimated by using the maximum likelihood (ML) principle. Numerical simulations are conducted to compare the performance of the ML estimator against those of the conventional estimator of moments. Finally, a multiresolution multivariate gamma based modeling of Internet traffic illustrates the potential interest of the proposed distributions for the detection of anomalies. Aggregated times series of IP packet counts are split into adjacent non overlapping time blocks. The distribution of the resulting time series are modeled by the proposed multivariate gamma based distributions, over a collection of different aggregation levels. The anomaly detection strategy is based on tracking changes along time of the corresponding multiresolution parameters.
Fast Fusion of Multi-Band Images Based on Solving a Sylvester Equation

This paper proposes a fast multi-band image fusion algorithm, which combines a high-spatial low-spectral resolution image and a low-spatial high-spectral resolution image. The well admitted forward model is explored to form the likelihoods of the observations. Maximizing the likelihoods leads to solving a Sylvester equation. By exploiting the properties of the circulant and downsampling matrices associated with the fusion problem, a closed-form solution for the corresponding Sylvester equation is obtained explicitly, getting rid of any iterative update step. Coupled with the alternating direction method of multipliers and the block coordinate descent method, the proposed algorithm can be easily generalized to incorporate prior information for the fusion problem, allowing a Bayesian estimator. Simulation results show that the proposed algorithm achieves the same performance as the existing algorithms with the advantage of significantly decreasing the computational complexity of these algorithms.
Prediction of rain attenuation series based on discretized spectral model

Spectral model is simple and efficient for modeling the rain attenuation which occurs in satellite communication channels. The prediction of this attenuation series is a vital step for adaptive coding or adaptive power control, which can improve the efficiency of a communication system. In simulation tasks, the discretized spectral model is usually used for generating the attenuation sequence. Due to this reason, in this paper we derive the conditional probability distribution of the predicted attenuation based on the discretized spectral model. This predictor can be used as a bound for others linear or nonlinear predictor of this model.
A hierarchical sparsity-smoothness Bayesian model for ℓ0 + ℓ1 + ℓ2 regularization

Sparse signal/image recovery is a challenging topic that has captured a great interest during the last decades. To address the ill-posedness of the related inverse problem, regularization is often essential by using appropriate priors that promote the sparsity of the target signal/image. In this context, ℓ<sub>0</sub> + ℓ<sub>1</sub> regularization has been widely investigated. In this paper, we introduce a new prior accounting simultaneously for both sparsity and smoothness of restored signals. We use a Bernoulli-generalized Gauss-Laplace distribution to perform ℓ<sub>0</sub> + ℓ<sub>1</sub> + ℓ<sub>2</sub> regularization in a Bayesian framework. Our results show the potential of the proposed approach especially in restoring the non-zero coefficients of the signal/image of interest.
Deep fusion of vector tracking GNSS receivers and a 3D city model for robust positioning in urban canyons with NLOS signals

In urban canyons, the GNSS satellite signals may travel an additional distance due to reflection and diffraction before reaching the receiver antenna. Where no direct path is available, this is called a non-line of- sight (NLOS) propagation and adds a positive bias to the geometric measured pseudorange. In this paper, we address the issue of GNSS positioning in harsh environments using constructively the NLOS signals. To exploit these biased signals, we compensate for the NLOS bias using a 3D GNSS simulation model of the environment. We use the 3D model as a priori information to characterize the additional NLOS bias. In this work, we propose a deep integration of the 3D model-based signal characterization into an advanced GNSS receiver architecture called vector tracking loops. The proposed architecture is a combination of a vector tracking receiver and a co-processor of external local information to correct the GNSS measurements. Obtained results with real GPS signals illustrates the effectiveness of this approach to improve the accuracy and reliability of the navigation solution in urban canyons.
Online Unmixing of Multitemporal Hyperspectral Images Accounting for Spectral Variability

Hyperspectral unmixing is aimed at identifying the reference spectral signatures composing a hyperspectral image and their relative abundance fractions in each pixel. In practice, the identified signatures may vary spectrally from an image to another due to varying acquisition conditions, thus inducing possibly significant estimation errors. Against this background, the hyperspectral unmixing of several images acquired over the same area is of considerable interest. Indeed, such an analysis enables the endmembers of the scene to be tracked and the corresponding endmember variability to be characterized. Sequential endmember estimation from a set of hyperspectral images is expected to provide improved performance when compared with methods analyzing the images independently. However, the significant size of the hyperspectral data precludes the use of batch procedures to jointly estimate the mixture parameters of a sequence of hyperspectral images. Provided that each elementary component is present in at least one image of the sequence, we propose to perform an online hyperspectral unmixing accounting for temporal endmember variability. The online hyperspectral unmixing is formulated as a two-stage stochastic program, which can be solved using a stochastic approximation. The performance of the proposed method is evaluated on synthetic and real data. Finally, a comparison with independent unmixing algorithms illustrates the interest of the proposed strategy.
Tissue motion estimation using dictionary learning: Application to cardiac amyloidosis

Cardiac strain estimation from ultrasound images is an efficient tool for the diagnosis of cardiac diseases. This study focuses on cardiac amyloidosis, a pathology characterized by non-specific early symptoms such as the increased wall thickness. Recent clinical studies have demonstrated that patients with cardiac amyloidosis present an apex-to-base gradient longitudinal strain pattern, i.e., a normal strain in apex and abnormally lower values for base segments. Existing cardiac motion estimation methods belong to three categories based on optical flow, speckle tracking and elastic registration. To overcome the ill-posedness of motion estimation, they use local parametric models (e.g., affine) or global regularizations (e.g., B-splines). The objective of this study is to evaluate a recently proposed cardiac motion estimation method based on dictionary learning on patients subjected to cardiac amyloidosis.
Echo Cancellation - A Likelihood Ratio Test for Double-Talk Versus Channel Change

Echo cancellers (ECs) are in wide use in both electrical (four-wire to two-wire mismatch) and acoustic (speaker-microphone coupling) applications. One of the main design problems is the control logic for adaptation. Basically, the algorithm weights should be frozen in the presence of double-talk and adapt quickly in the absence of double-talk. The control logic can be quite complicated since it is often not easy to discriminate between the echo signal and the near-end speaker. This paper derives a log-likelihood ratio test (LRT) for deciding between double-talk (freeze weights) and a channel change (adapt quickly) using a stationary Gaussian stochastic input signal model. The probability density function (pdf) of a sufficient statistic under each hypothesis is obtained, and the performance of the test is evaluated as a function of the system parameters. The receiver operating characteristics (ROCs) indicate that it is difficult to correctly decide between double-talk and a channel change based upon a single look. However, postdetection integration of approximately 100 sufficient statistic samples yields a detection probability close to unity (0.99) with a small false-alarm probability (0.01)
Sparse EEG Source Localization Using Bernoulli Laplacian Priors

Source localization in electroencephalography has received an increasing amount of interest in the last decade. Solving the underlying ill-posed inverse problem usually requires choosing an appropriate regularization. The usual ℓ<sub>2</sub> norm has been considered and provides solutions with low computational complexity. However, in several situations, realistic brain activity is believed to be focused in a few focal areas. In these cases, the ℓ<sub>2</sub> norm is known to overestimate the activated spatial areas. One solution to this problem is to promote sparse solutions for instance based on the ℓ<sub>1</sub> norm that are easy to handle with optimization techniques. In this paper, we consider the use of an ℓ<sub>0</sub> + ℓ<sub>1</sub> norm to enforce sparse source activity (by ensuring the solution has few nonzero elements) while regularizing the nonzero amplitudes of the solution. More precisely, the ℓ<sub>0</sub> pseudonorm handles the position of the nonzero elements while the ℓ<sub>1</sub> norm constrains the values of their amplitudes. We use a Bernoulli-Laplace prior to introduce this combined ℓ<sub>0</sub> + ℓ<sub>1</sub> norm in a Bayesian framework. The proposed Bayesian model is shown to favor sparsity while jointly estimating the model hyperparameters using a Markov chain Monte Carlo sampling technique. We apply the model to both simulated and real EEG data, showing that the proposed method provides better results than the ℓ<sub>2</sub> and ℓ<sub>1</sub> norms regularizations in the presence of pointwise sources. A comparison with a recent method based on multiple sparse priors is also conducted.
Joint Particle Filter and UKF Position Tracking Under Strong NLOS Situation

The location performance within a wireless communication system is severely impaired by biases introduced in the range measures because of the Non-Line-Of-Sight (NLOS) condition, caused by obstacles in the transmitted signal path. Usually not all the base stations (BS) are in NLOS, and the knowledge of the LOS-NLOS situation for each measure can improve the final positioning accuracy . This paper studies the use of a Particle Filter (PF) to track the LOS-NLOS situation for each BS. A Rao-Blackwellized approach is used to estimate both the position, by an appropriate Unscented Kalman Filter (UKF), and the situation, by a PF. The approach shows a remarkable reduction in positioning error and a high degree of scalability in terms of performance versus complexity.
An unsupervised Bayesian approach for the joint reconstruction and classification of cutaneous reflectance confocal microscopy images

This paper studies a new Bayesian algorithm for the joint reconstruction and classification of reflectance confocal microscopy (RCM) images, with application to the identification of human skin lentigo. The proposed Bayesian approach takes advantage of the distribution of the multiplicative speckle noise affecting the true reflectivity of these images and of appropriate priors for the unknown model rameters. A Markov chain Monte Carlo (MCMC) algorithm is proposed to jointly estimate the model parameters and the image of true reflectivity while classifying images according to the distribution of their reflectivity. Precisely, a Metropolis-within-Gibbs sampler is investigated to sample the posterior distribution of the Bayesian model associated with RCM images and to build estimators of its parameters, including labels indicating the class of each RCM image. The resulting algorithm is applied to synthetic data and to real images from a clinical study containing healthy and lentigo patients.
On the use of cyclostationary filters to transmit information

Linear periodic time-varying filters are often introduced today in telecommunications. They spread the spectrum and can be used for scrambling, multi-user access or channel modeling. Recently, the authors have defined linear cyclostationary filters. In particular, this generalization has permitted us to take into account the random parameters of a transmission channel. This paper defines a new case of linear cyclostationary filter where information is included in the filter. We first recall the definition of linear periodic and linear cyclostationary filters. The paper presents then particular cases of these filters based on clock change. Thus, we introduce modulated periodic clock change. This filter can be used to transmit simultaneously an analog and a digital signal. We present the reconstruction method for the initial signals. We obtain reconstruction results in the case of the simultaneous transmission of analog and binary information.
Bivariate Gamma Distributions for Multisensor Sar Images

This paper addresses the problem of estimating the parameters of a family of bivariate gamma distributions whose margins have different shape parameters. These distributions are interesting to detect changes in two synthetic radar aperture (SAR) images acquired by different sensors and having different numbers of looks. The estimators based on the maximum likelihood method and the method of moments are studied for these distributions. An application to change detection is finally discussed.
Bayesian fusion of multispectral and hyperspectral images using a block coordinate descent method

This paper studies a new Bayesian optimization algorithm for fusing hyperspectral and multispectral images. The hyperspectral image is supposed to be obtained by blurring and subsampling a high spatial and high spectral target image. The multispectral image is modeled as a spectral mixing version of the target image. By introducing appropriate priors for parameters and hyperparameters, the fusion problem is formulated within a Bayesian estimation framework, which is very convenient to model the noise and the target image. The high spatial resolution hyperspectral image is then inferred from its posterior distribution. To compute the Bayesian maximum a posteriori estimator associated with this posterior, an alternating direction method of multipliers within block coordinate descent algorithm is proposed. Simulation results demonstrate the efficiency of the proposed fusion method when compared with several state-of-the-art fusion techniques.
An improved regularized particle filter for GPS/INS integration

Hybridization techniques receive a renewed interest due to recent navigation systems such as Galileo. Hybridization takes advantage of the complementarity of different sensor types to increase navigation performance. This study focuses on the integration of the Global Positioning System (GPS) and the inertial navigation systems (INS). GPS allows to compensate for the long term drift of INS estimates, while aided INS provide a solution in case of GPS signal blocking. The GPS/INS coupling is performed by a nonlinear filtering approach whereby GPS measurements are used to correct INS estimates. However, a classical particle filter is bound to diverge due to the dynamics of the unknown parameters. Indeed, the state model has a small process noise and is exponentially unstable. The regularized particle filter presented in N. Oudjane et al. (2000) allows to overcome this limitation at the expense of an increased estimation variance. This study proposes to introduce a Metropolis-Hastings step to accept/reject the particles updated by the regularization process. The method is shown to prevent the degeneracy without introducing additional noise on the estimates.
Covariance Matrix Estimation With Heterogeneous Samples

We consider the problem of estimating the covariance matrix Mp of an observation vector, using heterogeneous training samples, i.e., samples whose covariance matrices are not exactly Mp. More precisely, we assume that the training samples can be clustered into K groups, each one containing Lk, snapshots sharing the same covariance matrix Mk. Furthermore, a Bayesian approach is proposed in which the matrices Mk. are assumed to be random with some prior distribution. We consider two different assumptions for Mp. In a fully Bayesian framework, Mp is assumed to be random with a given prior distribution. Under this assumption, we derive the minimum mean-square error (MMSE) estimator of Mp which is implemented using a Gibbs-sampling strategy. Moreover, a simpler scheme based on a weighted sample covariance matrix (SCM) is also considered. The weights minimizing the mean square error (MSE) of the estimated covariance matrix are derived. Furthermore, we consider estimators based on colored or diagonal loading of the weighted SCM, and we determine theoretically the optimal level of loading. Finally, in order to relax the a priori assumptions about the covariance matrix Mp, the second part of the paper assumes that this matrix is deterministic and derives its maximum-likelihood estimator. Numerical simulations are presented to illustrate the performance of the different estimation schemes.
Unsupervised Bayesian analysis of gene expression patterns

In this paper we introduce a new method for analyzing expression patterns from high throughput and complex data such as gene expression microarrays. These microarrays are collected under different conditions such as time, phenotype and treatment. The proposed method uses a Bayesian matrix decomposition, called Bayesian linear unmixing (BLU), to extract a set of characteristic gene signatures, or factors, and a set of coefficients, factor scores, that specify the relative contribution of each signature to a specific sample. BLU is related to Bayesian factor analysis but differs in an important respect: BLU constrains the factor loadings to be non-negative and the factor scores to be probability distributions over the factors. Thus BLU reduces the multiplexing of genes into different factors and can enhance interpretability of the factor loadings and factor scores. The unsupervised version of BLU presented in this paper also provides estimates of the number of factors. We illustrate the application of BLU to bioinformatics by analyzing gene expression microarray datasets.
Classification of GMSK signals with different bandwidths

This paper studies a Bayesian classifier which recognizes Gaussian minimum shift keying (GMSK) modulated signals with different bandwiths. We focus on identifying two different GMSK signals with BT = 0.25 and BT = 0.5 standardized by the consultative committee for space data system (CCSDS) for future space missions. The main idea of the proposed classifier is to compute the posterior probability of the observation sequence given each possible model by a modified Baum-Welch (BW) algorithm. The received GMSK signals are then classified according to the maximum a posteriori (MAP) rule.
Detection of multiplicative noise in stationary random processes using second- and higher order statistics

This paper addresses the problem of detecting the presence of colored multiplicative noise, when the information process can be modeled as a parametric ARMA process. For the case of zero-mean multiplicative noise, a cumulant based suboptimal detector is studied. This detector tests the nullity of a specific cumulant slice. A second detector is developed when the multiplicative noise is nonzero mean. This detector consists of filtering the data by an estimated AR filter. Cumulants of the residual data are then shown to be well suited to the detection problem. Theoretical expressions for the asymptotic probability of detection are given. Simulation-derived finite-sample ROC curves are shown for different sets of model parameters.
Detection of extra solar planets using parametric modeling

We present an algorithm for the detection of extra-solar planets by occultation on the satellite COROT. Under a high flux assumption, the signal is modeled as an autoregressive process having equal mean and variance. A transit of a planet in front of a star will produce an abrupt jump in the mean/variance of the process. The Neyman-Pearson detector is derived when the abrupt change parameters are known. The theoretical distribution of the test statistic is obtained allowing the computation of the ROC curves. The generalized likelihood ratio detector is then studied for the practical case were the change parameters are unknown. This detector requires the maximum likelihood estimates of the parameters. ROC curves are then determined using computer simulations.
Estimation of the Degree of Polarization for Hybrid/Compact and Linear Dual-Pol SAR Intensity Images: Principles and Applications

Analysis and comparison of linear and hybrid/compact dual-polarization (dual-pol) synthetic aperture radar (SAR) imagery have gained a wholly new importance in the last few years, in particular, with the advent of new spaceborne SARs such as the Japanese ALOS PALSAR, the Canadian RADARSAT-2, and the German TerraSAR-X. Compact polarimetry, hybrid dual-pol, and quad-pol modes are newly promoted in the literature for future SAR missions. In this paper, we investigate and compare different hybrid/compact and linear dual-pol modes in terms of the estimation of the degree of polarization (DoP). The DoP has long been recognized as one of the most important parameters characterizing a partially polarized electromagnetic wave. It can be effectively used to characterize the information content of SAR data. We study and compare the information content of the intensity data provided by different hybrid/compact and linear dual-pol SAR modes. For this purpose, we derive the joint distribution of multilook SAR intensity images. We use this distribution to derive the maximum likelihood and moment-based estimators of the DoP in hybrid/compact and linear dual-pol modes. We evaluate and compare the performance of these estimators for different modes on both synthetic and real data, which are acquired by RADARSAT-2 spaceborne and NASA/JPL airborne SAR systems, over various terrain types such as urban, vegetation, and ocean.
Bayesian linear unmixing of hyperspectral images corrupted by colored Gaussian noise with unknown covariance matrix

This paper addresses the problem of unmixing hyperspectral images contamined by additive colored noise. Each pixel of the image is modeled as a linear combination of pure materials (denoted as end-members) corrupted by an additive zero mean Gaussian noise sequence with unknown covariance matrix. Appropriate priors are defined ensuring positivity and additivity constraints on the mixture coefficients (denoted as abundances). These coefficients as well as the noise covariance matrix are then estimated from their joint posterior distribution. A Gibbs sampling strategy generates abundances and noise covariance matrices distributed according to the joint posterior. These samples are then averaged for minimum mean square error estimation.
MCMC Sampling for Joint Segmentation of Wind Speed and Direction

The problem of detecting changes in wind speed and direction is considered. Bayesian priors, with various degrees of certainty, are used to represent relationships between the two time series. Segmentation is then conducted using a hierarchical Bayesian model that accounts for correlations between the wind speed and direction. A Gibbs sampling strategy overcomes the computational complexity of the hierarchical model and is used to estimate the unknown parameters and hyperparameters. The performance of the proposed algorithm is illustrated with results obtained with synthetic data.