nils-herrmann/clean_scirepeval huggingface dataset:
Learning Sparse Representation Using Iterative Subspace Identification

In this paper, we introduce the iterative subspace identification (ISI) algorithm for learning subspaces in which the data may live. Our subspace identification method differs from currently available method in its ability to infer the dimension of the subspaces from the data without prior knowledge. The learned subspaces can be combined to produce a data driven overcomplete dictionary with good sparseness and generalizability qualities, or can be directly exploited in applications where block sparseness is needed. We describe the ISI algorithm and a complementary optimization method. We demonstrate the ability of the proposed method to produce sparse representations comparable to those achieved with the K-SVD algorithm, but with less than one eighth the training time. Furthermore, the computation savings allows us to develop a shift-tolerant training procedure. We also illustrate its benefits in underdetermined blind source separation of audio, where performance is directly impacted by the sparseness of the representation.
Personalized music distribution

New media distribution channels have created a strong need for digital media personalization that helps both users and producers get the most of media products. We concentrate on the customized music delivery issue. We propose an approach for constructing a sequence of tracks that satisfies user requirements and at the same time optimally exploits music catalogs. An optimal solution consists of examining all possible tracks enumeration in the database. This is clearly a combinatorial NP-hard problem. We use vector space concepts to formulate the constrained sequence retrieval problem as an integer program. Our experiments demonstrate the power of our approximation to the original problem in reducing the search space and producing valid solutions.
Haptic interface design considerations for scrub nurse robots in microsurgery

We present results from our research into assistive robots for highly unstructured environments. The concrete type of robot we studied is a robotic scrub nurse (RSN) for microsurgery. In microsurgery the surgeon works behind a microscope and must maintain field of view as much as possible. The task of managing and delivering instruments to the surgeon rests with the scrub nurse. During our attempt to design a robot which could automate this delicate but repetitive task we have identified the robot interface to instruments and human subjects as the essential component and have made that the central topic of our work. Special attention was paid to the robot-human interface at the haptic level. To that end we designed and evaluated a shape conforming grasp mechanism to act as a soft touch interface constantly adapting to the human touch. Such approach is uncommon in the robotic industry where per- task adapter-style exchangeable grasping mechanisms are mounted on the robot for each run. While industrial robots deal with few instruments and must lift heavy weights, this problem domain contains hundreds of instruments and prohibits any type of crushing or cutting movements. Our results show that the application of universal soft touch grippers is justified in environments where humans interface robots.
Secure watermark detection with nonparametric decision boundaries

In this paper we will address the problem of constructing a nonparameteric decision boundary for watermark detection. Most current watermarking algorithms have a parametric decision boundary that can be undone if the pirate has unlimited access to the detector. In this work we propose a fractal decision boundary which can not be estimated. That boundary is obtained by processing the decision boundary corresponding to the underlying watermarking algorithm. The performance of the new technique is essentially similar to any watermarking algorithm from which it is derived.
A novel QRS complex detection on ECG with motion artifact during exercise

We present a novel QRS complex detection scheme from ECG with motion artifact. The algorithm relies on subspace learning and template matching. QRS complex detection during exercise is a challenging problem because multiple artifacts affect the ECG measurement. Motion artifact is considered to be the main disturbance added to the measurement during exercise. To deal with the problem, we train a dictionary to represent motion artifact using information from a tri-axis accelerometer, and then remove the artifact contribution from noisy ECG measurements. We select the GCC-PHAT filter for efficient QRS detection on the denoised ECG measurements. We show that the proposed algorithm has appreciably higher motion artifact reduction capability and lower computational complexity than competing algorithms. It is therefore a preferred alternative for implementation in mobile health monitoring systems.
Simplified Ranging Systems Using Discrete Wavelet Decomposition

We employ the concepts and tools of the discrete wavelet transform (DWT) to encapsulate a body of previous research on efficiently analyzable phase-coded waveforms for use in ranging applications. This DWT perspective enables the direct synthesis of half-rate implementations which are suitable for high-speed applications. We introduce and study a set of sequences, based on a classical Welti construction, which are both DWT-decomposable and exhibit a zero autocorrelation zone property under cyclic extension. We propose a general extension technique which extends the zero correlation width while retaining the same simple receiver filter, and show that this technique is appropriate for many of the sequences in our construction. Finally, we propose a transmit waveform construction which allows a further reduction of two in the receiver hardware complexity. We discuss the resulting tradeoffs between receiver complexity and transmit power.
Low complexity tracking for long term monitoring of heart sounds

This paper describes a novel framework for tracking heart sounds. For a given heart sound component (e.g. S1) candidate features (e.g. peaks) are mapped to states and assigned appropriate local scores. A low complexity dynamic programming based algorithm is developed to select the largest most consistent feature from the candidate features. This algorithm is also capable of detecting multiple simultaneous features for improved noise immunity. The proposed algorithm has linear complexity with time and cubic complexity with the number of features to be tracked. Data from six swine balloon-occlusion studies are used for preliminary testing of the algorithm. The S1 amplitude and heart sound ejection time (HSET) are found to be statistically significantly correlated with changes in systolic performance due to ischemia induction.
A Bayesian Approach to Adaptive Detection in Nonhomogeneous Environments

We consider the adaptive detection of a signal of interest embedded in colored noise, when the environment is nonhomogeneous, i.e., when the training samples used for adaptation do not share the same covariance matrix as the vector under test. A Bayesian framework is proposed where the covariance matrices of the primary and the secondary data are assumed to be random, with some appropriate joint distribution. The prior distributions of these matrices require a rough knowledge about the environment. This provides a flexible, yet simple, knowledge-aided model where the degree of nonhomogeneity can be tuned through some scalar variables. Within this framework, an approximate generalized likelihood ratio test is formulated. Accordingly, two Bayesian versions of the adaptive matched filter are presented, where the conventional maximum likelihood estimate of the primary data covariance matrix is replaced either by its minimum mean-square error estimate or by its maximum a posteriori estimate. Two detectors require generating samples distributed according to the joint posterior distribution of primary and secondary data covariance matrices. This is achieved through the use of a Gibbs sampling strategy. Numerical simulations illustrate the performances of these detectors, and compare them with those of the conventional adaptive matched filter.
Linear unmixing of hyperspectral images using a scaled gradient method

This paper addresses the problem of linear unmixing for hyperspectral imagery. This problem can be formulated as a linear regression problem whose regression coefficients (abundances) satisfy sum-toone and positivity constraints. Two scaled gradient iterative methods are proposed for estimating the abundances of the linear mixing model. The first method is obtained by including a normalization step in the scaled gradient method. The second method inspired by the fully constrained least squares algorithm includes the sum-to-one constraint in the observation model with an appropriate weighting parameter. Simulations on synthetic data illustrate the performance of these algorithms.
Estimating the Polarization Degree of Polarimetric Images using Maximum Likelihood Methods

This paper shows that the joint distribution of polarimetric intensity images is a multivariate gamma distribution in the case of coherent illumination with fully developed speckle. The parameters of this gamma distribution can be estimated according to the maximum likelihood (ML) principle. Different estimators depending on the number of available polarimetric images are studied. These estimators provide different ways of estimating the degree of polarization (DoP) associated to each pixel of the image. A performance comparison with estimators based on methods of moments shows the interest of the ML method for estimating the DoP of polarimetric images.
Labeling skin tissues in ultrasound images using a generalized Rayleigh mixture model

This paper addresses the problem of estimating the statistical distribution of multiple-tissue non-stationary ultrasound images of skin. The distribution of multiple-tissue images is modeled as a finite mixture of Heavy-Tailed Rayleigh distributions. An original Bayesian algorithm combined with a Markov chain Monte Carlo method is then derived to jointly estimate the mixture parameters and a label vector associating each voxel to a tissue. Precisely, a hybrid Metropolis-within-Gibbs sampler is proposed to draw samples that are asymptotically distributed according to the posterior distribution of the Bayesian model. These samples are then used to compute the Bayesian estimators of the model parameters. Simulation results are conducted on synthetic data to illustrate the performance of the proposed estimation strategy. The method is then successfully applied to the detection of an in-vivo skin lesion in a high frequency 3D ultrasound image.
A hierarchical Bayesian model for frame representation

In many signal processing problems, it may be fruitful to represent the signal under study in a redundant linear decomposition called a frame. If a probabilistic approach is adopted, it becomes then necessary to estimate the hyper-parameters characterizing the probability distribution of the frame coefficients. This problem is difficult since in general, the frame synthesis operator is not bijective and consequently, the frame coefficients are not directly observable. In this work, a hierarchical Bayesian model is introduced to solve this problem. A hybrid MCMC algorithm is subsequently proposed to sample from the derived posterior distribution. We show that through classical Bayesian estimators, this algorithm allows us to determine these hyper-parameters, as well as the frame coefficients in applications to image denoising with uniform noise.
Extended constrained Viterbi algorithm for AIS signals received by satellite

This paper addresses the problem of error correction of AIS messages by using the a priori knowledge of some information in the messages. Indeed, the AIS recommendation sets a unique value or a range of values for certain fields in the messages. Moreover, the physics can limit the range of fields, such as the speed of the vessel or its position (given the position of the receiver). The repetition of the messages gives also some information. Indeed, the evolution of the ship position is limited between messages and the ship ID is known. The constrained demodulation algorithm presented in this article is an evolution of the constrained Viterbi algorithm (C-VA). It is based on a modified Viterbi algorithm that allows the constraints to be considered in order to correct transmission errors by using some new registers in the state variables. The constraints can be either a single value or a range of values for the message fields. Simulation results illustrate the algorithm performance in terms of bit error rate and packet error rate. The performance of the proposed algorithm is 2 dB better than that obtained with the receiver without constraints.
Bayesian Estimation of Covariance Matrices in Non-Homogeneous Environments

In many applications, it is required to detect, from a primary vector, the presence of a signal of interest embedded in noise with unknown statistics. We consider a situation where the training samples used to infer the noise statistics do not share the same covariance matrix as the vector under test. A Bayesian model is proposed where the covariance matrices of the primary and the secondary data are assumed to be random, with some appropriate joint distribution. The prior distributions of these matrices reflect a rough knowledge about the environment. Within this framework, the minimum mean-square error (MMSE) estimator and the maximum a posteriori (MAP) estimator of the primary data covariance matrix are derived. A Gibbs sampling strategy is presented for the implementation of the MMSE estimator. Numerical simulations illustrate the performances of these estimators and compare them with those of the sample covariance matrix estimator.
On an iterative method for direction of arrival estimation using multiple frequencies

We develop a method for the estimation of the location of sources from measurements at multiple frequencies, including wideband measurements, recorded by a linear array of sensors. We employ interpolation matrices to address unequal sampling at different frequencies and make use of the Kronecker theorem to cast the nonlinear least squares problem associated with direction of arrival estimation into an optimization problem in the space of sequences generating Hankel matrices of fixed rank.We then obtain approximate solutions to this problem using the alternating direction method of multipliers. The resulting algorithm is simple and easy to implement. We provide numerical simulations that illustrate its excellent practical performance, significantly outperforming subspace-based methods both at low and high signal-to-noise ratio.
Parameter Estimation for Peaky Altimetric Waveforms

Much attention has been recently devoted to the analysis of coastal altimetric waveforms. When approaching the coast, altimetric waveforms are sometimes corrupted by peaks caused by high reflective areas inside the illuminated land surfaces or by the modification of the sea state close to the shoreline. This paper introduces a new parametric model for these peaky altimetric waveforms. This model assumes that the received altimetric waveform is the sum of a Brown echo and an asymmetric Gaussian peak. The asymmetric Gaussian peak is parameterized by a location, an amplitude, a width, and an asymmetry coefficient. A maximum-likelihood estimator is studied to estimate the Brown plus peak model parameters. The Cramér-Rao lower bounds of the model parameters are then derived providing minimum variances for any unbiased estimator, i.e., a reference in terms of estimation error. The performance of the proposed model and the resulting estimation strategy are evaluated via many simulations conducted on synthetic and real data. Results obtained in this paper show that the proposed model can be used to retrack efficiently standard oceanic Brown echoes as well as coastal echoes corrupted by symmetric or asymmetric Gaussian peaks. Thus, the Brown with Gaussian peak model is useful for analyzing altimetric measurements closer to the coast.
Partial CRC-assisted error correction of AIS signals received by satellite

This paper deals with the demodulation of automatic identification system (AIS) signals received by a satellite. More precisely, an error correction algorithm is presented, whose computational complexity is reduced with respect to that of a previously considered approach. This latter approach makes use of the cyclic redundancy check (CRC) of a message as redundancy, in order to correct transmission errors. In this paper, the CRC is also considered as a correction tool, but only a part of it is used for that purpose; the remaining part is only used as an error detection means. This novel approach allows the decoding performance to be adapted to the noise power, and provides a reduction of the computational complexity. Simulation results obtained with and without complexity optimization are presented and compared in the context of the AIS system.
High-resolution spectral image reconstruction based on compressed data fusion

Compressive spectral imagers drastically reduce the number of sampled pixels by performing linear combinations of coded spectral information. However, compressing information with simultaneously high spatial and high spectral resolutions demands expensive high-resolution sensors. This work introduces a model allowing compressive data from high spatial/low spectral and low spatial/high spectral resolution sensors to be fused. The sensing matrix of this model is designed carefully to be incoherent with the dictionary associated with the unknown image. Based on this model, the compressive fusion process is formulated as an inverse problem that minimizes an objective function defined as the sum of a quadratic data fidelity term and smoothness and sparsity regularization penalties.
Bayesian sparse image reconstruction for MRFM

In this paper, we propose a Bayesian model and a Monte Carlo Markov chain (MCMC) algorithm for reconstructing images that consist of only few non-zero pixels. An appropriate distribution that promotes sparsity is proposed as prior distribution for the pixel values. The hyperparameters involved in the modeling are also assigned prior distributions, resulting in a hierarchical model. A Gibbs sampler allows us to draw samples distributed according the full posterior of interest. These samples are then used to approximate standard maximum a posteriori (MAP) estimator. By conducting some simulations, we show that the proposed estimator clearly outperforms previous estimators proposed in the literature.
Bounds for Estimation of Covariance Matrices From Heterogeneous Samples

This correspondence derives lower bounds on the mean-square error (MSE) for the estimation of a covariance matrix \mbi Mp, using samples \mbi Zk,k=1,...,K, whose covariance matrices \mbi Mk are randomly distributed around \mbi Mp. This framework can be encountered e.g., in a radar system operating in a nonhomogeneous environment, when it is desired to estimate the covariance matrix of a range cell under test, using training samples from adjacent cells, and the noise is nonhomogeneous between the cells. We consider two different assumptions for \mbi Mp. First, we assume that \mbi Mp is a deterministic and unknown matrix, and we derive the Cramer-Rao bound for its estimation. In a second step, we assume that \mbi Mp is a random matrix, with some prior distribution, and we derive the Bayesian bound under this hypothesis.
Library-based linear unmixing for hyperspectral imagery via reversible jump MCMC sampling

This paper studies a semi-supervised algorithm for linear hyperspectral unmixing. The proposed unmixing method assumes that the pure material spectra denoted as endmembers belong to a library that is a priori available. However, the number and the nature of endmembers appearing in the pixel are not known a priori, resulting in a model selection problem. This paper proposes to handle this model selection problem within a fully Bayesian framework. First, appropriate distributions are elected as prior distributions for the unknown parameters. Particularly, a distribution defined on a simplex is chosen as prior for an appropriate partial abundance vector to ensure the positivity and the sum-to-one constraints of the mixing coefficients. Due to the complexity of the posterior distribution, a reversible jump Markov chain Monte Carlo algorithm is proposed to estimate the number and the nature of the macroscopic materials, as well as their respective proportions in the pixel. The accuracy of the proposed method is illustrated by simulations on synthetic hyperspectral data.
Sampling jitter detection using higher-order statistics

The spectrum of a signal subjected, to sampling jitter can be significantly different from the spectrum of the same signal sampled without jitter. The first part of the paper shows that the spectrum of a continuous Gaussian signal can be reconstructed from a combined use of the sampled (with jitter) signal second and fourth-order statistics. This spectral reconstruction is then used to detect the presence or absence of jitter in a sampled signal. A likelihood ratio detector based on the spectral corrective term is studied. It gives a reference to which suboptimal detectors can be compared.
Semi-Supervised Linear Spectral Unmixing Using a Hierarchical Bayesian Model for Hyperspectral Imagery

This paper proposes a hierarchical Bayesian model that can be used for semi-supervised hyperspectral image unmixing. The model assumes that the pixel reflectances result from linear combinations of pure component spectra contaminated by an additive Gaussian noise. The abundance parameters appearing in this model satisfy positivity and additivity constraints. These constraints are naturally expressed in a Bayesian context by using appropriate abundance prior distributions. The posterior distributions of the unknown model parameters are then derived. A Gibbs sampler allows one to draw samples distributed according to the posteriors of interest and to estimate the unknown abundances. An extension of the algorithm is finally studied for mixtures with unknown numbers of spectral components belonging to a know library. The performance of the different unmixing strategies is evaluated via simulations conducted on synthetic and real data.
Estimating the Intrinsic Dimension of Hyperspectral Images Using a Noise-Whitened Eigengap Approach

Linear mixture models are commonly used to represent a hyperspectral data cube as linear combinations of endmember spectra. However, determining the number of endmembers for images embedded in noise is a crucial task. This paper proposes a fully automatic approach for estimating the number of endmembers in hyperspectral images. The estimation is based on recent results of random matrix theory related to the so-called spiked population model. More precisely, we study the gap between successive eigenvalues of the sample covariance matrix constructed from high-dimensional noisy samples. The resulting estimation strategy is fully automatic and robust to correlated noise owing to the consideration of a noise-whitening step. This strategy is validated on both synthetic and real images. The experimental results are very promising and show the accuracy of this algorithm with respect to state-of-the-art algorithms.