nils-herrmann/clean_scirepeval huggingface dataset:
Enhancement of 250-MHz quantitative acoustic-microscopy data using a single-image super-resolution method

Scanning acoustic microscopy (SAM) is a well-accepted imaging modality for forming quantitative, two-dimensional maps of acoustic properties of soft tissues at microscopic scales. The quantitative maps formed using our custom SAM system using a 250-MHz single-element transducer have a nominal resolution of 7 µm, which is insufficient for some investigations. To enhance spatial resolution, a SAM system operating at even higher frequencies could be designed, but associated costs and experimental difficulties are challenging. Therefore, the objective of this study is to evaluate the potential of super-resolution (SR) image processing to enhance the spatial resolution of quantitative maps in SAM. To the best of our knowledge, this is the first attempt at using post-processing, image-enhancement techniques in SAM. Results of realistic simulations and experimental data acquired from a standard resolution test pattern confirm the improved spatial resolution and the potential value of using SR in SAM.
Sparse representation of migrating targets in low PRF wideband radar

In wideband (high range resolution) radar, moving targets migrate along the range during the coherent processing interval (CPI). In this paper, we propose to use the information brought by this migration to alleviate the velocity ambiguity that occurs in low pulse repetition frequency (PRF) mode. More specifically, a Bayesian algorithm is implemented to give a sparse representation of the received signal while unfolding properly the range-velocity map. Though computationally intensive, the algorithm demonstrates the possibility of designing a non-ambiguous radar mode with a single PRF in case of a multi-target scenario.
Bayesian Orthogonal Component Analysis for Sparse Representation

This paper addresses the problem of identifying a lower dimensional space where observed data can be sparsely represented. This undercomplete dictionary learning task can be formulated as a blind separation problem of sparse sources linearly mixed with an unknown orthogonal mixing matrix. This issue is formulated in a Bayesian framework. First, the unknown sparse sources are modeled as Bernoulli-Gaussian processes. To promote sparsity, a weighted mixture of an atom at zero and a Gaussian distribution is proposed as prior distribution for the unobserved sources. A noninformative prior distribution defined on an appropriate Stiefel manifold is elected for the mixing matrix. The Bayesian inference on the unknown parameters is conducted using a Markov chain Monte Carlo (MCMC) method. A partially collapsed Gibbs sampler is designed to generate samples asymptotically distributed according to the joint posterior distribution of the unknown model parameters and hyperparameters. These samples are then used to approximate the joint maximum a posteriori estimator of the sources and mixing matrix. Simulations conducted on synthetic data are reported to illustrate the performance of the method for recovering sparse representations. An application to sparse coding on undercomplete dictionary is finally investigated.
Data-driven online variational filtering in wireless sensor networks

In this paper, a data-driven extension of the variational algorithm is proposed. Based on a few selected sensors, target tracking is performed distributively without any information about the observation model. Tracking under such conditions is possible if one exploits the information collected from extra inter-sensor RSSI measurements. The target tracking problem is formulated as a kernel matrix completion problem. A probabilistic kernel regression is then proposed that yields a Gaussian likelihood function. The likelihood is used to derive an efficient and accelerated version of the variational filter without resorting to Monte Carlo integration. The proposed data-driven algorithm is, by construction, robust to observation model deviations and adapted to non-stationary environments.
Shape classification of altimetric signals using anomaly detection and bayes decision rule

This paper addresses the problem of classifying altimetric signals according to their shapes. The proposed classifier is divided into three steps. A one-class support vector machine method is first used to isolate the large amount of Brown-like echoes from others signals which are considered as outliers. The second step extracts pertinent features from the the remaining echoes (which cannot be well described by the Brown model). These features are projected onto discriminant axes using linear discriminant analysis. The final step classifies the projected feature vectors using a standard Bayesian classifier. The proposed three step classification strategy is evaluated on supervised real altimetric echoes.
A Bernoulli-Gaussian model for gene factor analysis

This paper investigates a Bayesian model and a Markov chain Monte Carlo (MCMC) algorithm for gene factor analysis. Each sample in the dataset is decomposed as a linear combination of characteristic gene signatures (also referred to as factors) following a linear mixing model. To enforce the sparsity of the relative contribution (called factor score) of each gene signature to a specific sample, constrained Bernoulli-Gaussian distributions are elected as prior distributions for these factor scores. This distribution allows one to ensure non-negativity and full-additivity constraints for the scores that are interpreted as concentrations. The complexity of the resulting Bayesian estimators is alleviated by using a Gibbs sampler which generates samples distributed according to the posterior distribution of interest. These samples are then used to approximate the standard maximum a posteriori (MAP) or minimum mean square error (MMSE) estimators. The accuracy of the proposed Bayesian method is illustrated by simulations conducted on synthetic and real data.
A New Frequency Estimation Method for Equally and Unequally Spaced Data

Spectral estimation is an important classical problem that has received considerable attention in the signal processing literature. In this contribution, we propose a novel method for estimating the parameters of sums of complex exponentials embedded in additive noise from regularly or irregularly spaced samples. The method relies on Kronecker's theorem for Hankel operators, which enables us to formulate the nonlinear least squares problem associated with the spectral estimation problem in terms of a rank constraint on an appropriate Hankel matrix. This matrix is generated by sequences approximating the underlying sum of complex exponentials. Unequally spaced sampling is accounted for through a proper choice of interpolation matrices. The resulting optimization problem is then cast in a form that is suitable for using the alternating direction method of multipliers (ADMM). The method can easily include either a nuclear norm or a finite rank constraint for limiting the number of complex exponentials. The usage of a finite rank constraint makes, in contrast to the nuclear norm constraint, the method heuristic in the sense that the problem is non-convex and convergence to a global minimum can not be guaranteed. However, we provide a large set of numerical experiments that indicate that usage of the finite rank constraint nevertheless makes the method converge to minima close to the global minimum for reasonably high signal to noise ratios, hence essentially yielding maximum-likelihood parameter estimates. Moreover, the method does not seem to be particularly sensitive to initialization and performs substantially better than standard subspace-based methods.
An adaptive detection of spread targets in locally Gaussian ground clutter using a long integraton time

This paper deals with the problem of detecting a collision target in ground clutter, using a long integration time. A single reception channel being available, classical space time adaptive processing (STAP) cannot be used. After range processing, ground clutter can be modeled as a known interference subspace in the Doppler domain depending on its radial and orthoradial speeds. We exploit this a priori knowledge to perform an adpative detection of a collision target supposed to lie in a known and different subspace. A GLRT detector is first derived for known clutter covariance matrix. Then, the unknown covariance matrix is adaptively estimated from the projection of the data onto the modeled clutter subspace, and is plugged in the GLRT to form a suboptimal detector. The proposed scheme can be viewed as a synthetic STAP, for which the space domain is replaced by a clutter orthoradial information and longer integration 
time.
A multivariate statistical model for multiple images acquired by homogeneous or heterogeneous sensors

This paper introduces a new statistical model for homogeneous images acquired by the same kind of sensor (e.g., two optical images) and heterogeneous images acquired by different sensors (e.g., optical and synthetic aperture radar (SAR) images). The proposed model assumes that each image pixel is distributed according to a mixture of multi-dimensional distributions depending on the noise properties and on the transformation between the actual scene and the image intensities. The parameters of this new model can be estimated by the classical expectation-maximization algorithm. The estimated parameters are finally used to learn the relationships between the different images. This information can be used in many image processing applications, particularly those requiring a similarity measure (e.g., change detection or registration). Simulation results on synthetic and real images show the potential of the proposed model. A brief application to change detection between optical and SAR images is finally investigated.
Unmixing hyperspectral images using a normal compositional model and MCMC methods

This paper studies a new unmixing algorithm for hyperspectral images. Each pixel of the image is modeled as a linear combination of endmembers which are supposed to be random in order to model uncertainties regarding their knowledge. More precisely, endmembers are modeled as Gaussian vectors with known means (resulting from an endmember extraction algorithm such as the famous N-FINDR or VCA algorithm). This paper proposes to estimate the mixture coefficients (referred to as abundances) using a Bayesian algorithm. Suitable priors are assigned to the abundances in order to satisfy positivity and additivity constraints whereas a conjugate prior is chosen for the variance. The computational complexity of the resulting Bayesian estimators is alleviated by constructing an hybrid Gibbs algorithm to generate abundance and variance samples distributed according to the posterior distribution of the unknown parameters. The associated hyperparameter is also generated. The performance of the proposed methodology is evaluated thanks to simulation results conducted on synthetic and real images.
Stochastic Analysis of the LMS Algorithm for System Identification With Subspace Inputs

This paper studies the behavior of the low-rank least mean squares (LMS) adaptive algorithm for the general case in which the input transformation may not capture the exact input subspace. It is shown that the Independence Theory and the independent additive noise model are not applicable to this case. A new theoretical model for the weight mean and fluctuation behaviors is developed which incorporates the correlation between successive data vectors (as opposed to the Independence Theory model). The new theory is applied to a network echo cancellation scheme which uses partial-Haar input vector transformations. Comparison of the new model predictions with Monte Carlo simulations shows good-to-excellent agreement, certainly much better than predicted by the Independence Theory based model available in the literature.
A Particle Filtering Approach for Joint Detection/Estimation of Multipath Effects on GPS Measurements

Multipath propagation causes major impairments to global positioning system (GPS) based navigation. Multipath results in biased GPS measurements, hence inaccurate position estimates. In this paper, multipath effects are considered as abrupt changes affecting the navigation system. A multiple model formulation is proposed whereby the changes are represented by a discrete valued process. The detection of the errors induced by multipath is handled by a Rao-Blackwellized particle filter (RBPF). The RBPF estimates the indicator process jointly with the navigation states and multipath biases. The interest of this approach is its ability to integrate a priori constraints about the propagation environment. The detection is improved by using information from near future GPS measurements at the particle filter (PF) sampling step. A computationally modest delayed sampling is developed, which is based on a minimal duration assumption for multipath effects. Finally, the standard PF resampling stage is modified to include an hypothesis test based decision step
An affine combination of two NLMS adaptive filters - Transient mean-square analysis

This paper studies the statistical behavior of an affine combination of the outputs of two NLMS adaptive filters that simultaneously adapt using the same white Gaussian inputs. The behaviors of the mean optimal mixing parameter and combined mean-square deviation (MSD) are studied for a time-varying unknown channel. Simulation results are presented for the optimal unrealizable affine combiner and for a practical combining scheme. With proper design, the practical combiner scheme yields an overall MSD that is usually less than the MSD of either filter.
Detection and estimation of multiplicative jumps using the continuous wavelet transform

This paper addresses the problem of multiplicative jump detection and estimation in the time-scale plane. The signature is derived for a multiplicative jump in the time-scale plane. The signature allows the study of two wavelet based detectors. The first detector computes the continuous wavelet transform (CWT) correlation with 2D signature. The second detector sums fixed scale slices of the CWT. For comparison purpose, the optimal Neyman-Pearson detector (NPD) is then derived. The NPD requires a priori knowledge of the jump and signal parameters. A sub-optimal detector is presented which replaces these parameters by appropriate estimates. The performance of this detector is studied.
Residual Component Analysis of Hyperspectral Images—Application to Joint Nonlinear Unmixing and Nonlinearity Detection

This paper presents a nonlinear mixing model for joint hyperspectral image unmixing and nonlinearity detection. The proposed model assumes that the pixel reflectances are linear combinations of known pure spectral components corrupted by an additional nonlinear term, affecting the end members and contaminated by an additive Gaussian noise. A Markov random field is considered for nonlinearity detection based on the spatial structure of the nonlinear terms. The observed image is segmented into regions where nonlinear terms, if present, share similar statistical properties. A Bayesian algorithm is proposed to estimate the parameters involved in the model yielding a joint nonlinear unmixing and nonlinearity detection algorithm. The performance of the proposed strategy is first evaluated on synthetic data. Simulations conducted with real data show the accuracy of the proposed unmixing and nonlinearity detection strategy for the analysis of hyperspectral images.
Unmixing multitemporal hyperspectral images with variability: An online algorithm

Hyperspectral unmixing consists in determining the reference spectral signatures composing a hyperspectral image and their relative abundance fractions in each pixel. In practice, the identified signatures may be affected by a significant spectral variability resulting for instance from the temporal evolution of the imaged scene. This phenomenon can be accounted for by using a perturbed linear mixing model. This paper studies an online estimation algorithm for the parameters of this extended linear mixing model. This algorithm is of interest for the practical applications where the size of the hyper-spectral images precludes the use of batch procedures. The performance of the proposed method is evaluated on synthetic data.
Improving synthetic aperture radar detection using the automatic identification system

This paper studies a maritime vessel detection method based on the fusion of data obtained from two different sensors, namely a synthetic aperture radar (SAR) and an automatic identification system (AIS) embedded in a satellite. In this work we propose a detector that uses the vessel position provided by the AIS system to improve the radar detection performance. The problem is handled by a generalized likelihood ratio test leading to a detector whose test statistics has a simple closed form expression. The distribution of the test statistics under the hypotheses is also determined, allowing theoretical and simulated receiver operational characteristics (ROCs) to be compared. Our results indicate that the proposed method improves detection performance and motivates the joint use of raw radar data with AIS demodulated information for ship detection.
Hyperspectral Unmixing With Spectral Variability Using a Perturbed Linear Mixing Model

Given a mixed hyperspectral data set, linear unmixing aims at estimating the reference spectral signatures composing the data-referred to as endmembers-their abundance fractions and their number. In practice, the identified endmembers can vary spectrally within a given image and can thus be construed as variable instances of reference endmembers. Ignoring this variability induces estimation errors that are propagated into the unmixing procedure. To address this issue, endmember variability estimation consists of estimating the reference spectral signatures from which the estimated endmembers have been derived as well as their variability with respect to these references. This paper introduces a new linear mixing model that explicitly accounts for spatial and spectral endmember variabilities. The parameters of this model can be estimated using an optimization algorithm based on the alternating direction method of multipliers. The performance of the proposed unmixing method is evaluated on synthetic and real data. A comparison with state-of-the-art algorithms designed to model and estimate endmember variability allows the interest of the proposed unmixing solution to be appreciated.
Nonlinear Unmixing of Hyperspectral Images: Models and Algorithms

When considering the problem of unmixing hyperspectral images, most of the literature in the geoscience and image processing areas relies on the widely used linear mixing model (LMM). However, the LMM may be not valid, and other nonlinear models need to be considered, for instance, when there are multiscattering effects or intimate interactions. Consequently, over the last few years, several significant contributions have been proposed to overcome the limitations inherent in the LMM. In this article, we present an overview of recent advances in nonlinear unmixing modeling.
Multi-subject joint parcellation detection estimation in functional MRI

fMRI experiments are usually conducted over a population of interest for investigating brain activity across different regions stimuli and objects. Multi-subject analysis proceeds in two steps, intra-subject analysis is performed sequentially on each individual and then group-level analysis is addressed to report significant results at the population level. This paper considers an existing Joint Parcellation Detection Estimation (JPDE) model which performs joint hemodynamic parcellation, brain dynamics estimation and evoked activity detection. The hierarchy of the JPDE model is extended for multi-subject analysis in order to perform group-level parcellation. Then, the corresponding underlying dynamics is estimated in each parcel while the detection and estimation steps are iterated over each individual. Validation on synthetic and real fMRI data shows its robustness in inferring the group-level parcellation and the corresponding hemodynamic profiles.
High resolution optical and sar image fusion for road database updating

This paper addresses the issue of cartographic database creation or updating using high resolution SAR and optical images. It proposes a processing chain to create or update road databases in urban environment. The approach is composed of two steps. First, if a database is available, the presence of each database object is checked in the images. Then, we verify if road hypotheses extracted from images should be included in the database. These two steps are conducted by extracting relevant features from the images in the neighborhood of the considered object. The object removal/inclusion in the database is based on a score obtained by the fusion of features in the framework of Dempster-Shafer evidence theory.
A method for 3D direction of arrival estimation for general arrays using multiple frequencies

We develop a novel high-resolution method for the estimation of the direction of incidence of energy emitted by sources in 3D space from wideband measurements collected by a planar array of sensors. We make use of recent generalizations of Kronecker's theorem and formulate the direction of arrival estimation problem as an optimization problem in the space of sequences generating so called “general domain Hankel matrices” of fixed rank. The unequal sampling at different wavelengths is handled by using appropriate interpolation operators. The algorithm is operational for general array geometries (i.e., not restricted to, e.g., rectangular arrays) and for equidistantly as well as unequally spaced receivers. Numerical simulations for different sensor arrays and various signal-to-noise ratios are provided and demonstrate its excellent performance.
Adaptive signal processing by particle filters and discounting of old measurements

In adaptive signal processing the principle of exponentially weighted recursive least-squares plays a major role in developing various estimation algorithms. It is based on the concept of discounting of old measurements and allows for better performance in problems with time-varying signals and signals in nonstationary noise. We show how this concept can be combined with the Bayesian methodology. We propose that the discounting of old measurements within the Bayesian framework be implemented by employing particle filters. The main idea is presented by way of a simple example. The methodology is very attractive and can be used in a very wide range of scenarios including ones that involve highly nonlinear models and non-Gaussian noise.
Band selection in RKHS for fast nonlinear unmixing of hyperspectral images

The profusion of spectral bands generated by the acquisition process of hyperspectral images generally leads to high computational costs. Such difficulties arise in particular with nonlinear unmixing methods, which are naturally more complex than linear ones. This complexity, associated with the high redundancy of information within the complete set of bands, make the search of band selection algorithms relevant. With this work, we propose a band selection strategy in reproducing kernel Hilbert spaces that allows to drastically reduce the processing time required by nonlinear unmixing techniques. Simulation results show a complexity reduction of two orders of magnitude without compromising unmixing performance.