nils-herrmann/clean_scirepeval huggingface dataset:
Oriented radial distribution on depth data: Application to the detection of end-effectors

End-effectors are considered to be the main topological extremities of a given 3D body. Even if the nature of such body is not restricted, this paper focuses on the human body case. Detection of human extremities is a key issue in the human motion capture domain, being needed to initialize and update the tracker. Therefore, the effectiveness of human motion capture systems usually depends on the reliability of the obtained end-effectors. The increasing accuracy, low cost and easy installation of depth cameras has opened the door to new strategies to overcome the body pose estimation problem. With the objective of detecting the head, hands and feet of a human body, we propose a new local feature computed from depth data, which gives an idea of its curvature and prominence. Such feature is weighted depending on recent detections, providing also a temporal dimension. Based on this feature, some end-effector candidate blobs are obtained and classified into head, hands and feet according to three probabilistic descriptors.
The PEP Approach: A New Family of Methods Solving the Phase Estimation Problem

Knowledge of the q-th (q > 2) order spectrum of a linear non-Gaussian process allows to reconstruct both the magnitude and the phase of the corresponding input sequence. We propose in this paper a new family of phase retrieval algorithms, based on higher order spectra, named PEP (phase estimation using polyspectra) These new algorithms are easier to implement and use. Moreover, computer simulations show that among them, the 4-PEP and the (3.4)-PEP algorithms exhibit good performances facing classical methods especially for bandlimited systems
MomentsNet: A simple learning-free method for binary image recognition

In this paper, we propose a new simple and learning-free deep learning network named MomentsNet, whose convolution layer, nonlinear processing layer and pooling layer are constructed by Moments kernels, binary hashing and block-wise histogram, respectively. Twelve typical moments (including geometrical moment, Zernike moment, Tchebichef moment, etc.) are used to construct the MomentsNet whose recognition performance for binary image is studied. The results reveal that MomentsNet has better recognition performance than its corresponding moments in almost all cases and ZernikeNet achieves the best recognition performance among MomentsNet constructed by twelve moments. ZernikeNet also shows better recognition performance on a binary image database than that of PCANet, which is a learning-based deep learning network.
Analysis of endocardial acceleration during intraoperative optimization of cardiac resynchronization therapy

Cardiac resynchronization therapy (CRT) is the therapy of choice for selected patients suffering from drug-refractory congestive heart failure and presenting an interventricular desynchronization. CRT is delivered by an implantable biventricular pacemaker, which stimulates the right atrium and both ventricles at specific timings. The optimization and personalization of this therapy requires to quantify both the electrical and the mechanical cardiac functions during the intraoperative and postoperative phases. The objective of this paper is to evaluate the feasibility of the calculation of features extracted from endocardial acceleration (EA) signals and the potential utility of these features for the intraoperative optimization of CRT. Endocardial intraoperative data from one patient are analyzed for 33 different pacing configurations, including changes in the atrio-ventricular and inter-ventricular delays and different ventricular stimulation sites. The main EA features are extracted for each pacing configuration and analyzed so as to estimate the intra-configuration and inter-configuration variability. Results show the feasibility of the proposed approach and suggest the potential utility of EA for intraoperative monitoring of the cardiac function and defining optimal, adaptive pacing configurations.
Direct Computation of Type-II Discrete Hartley Transform

We present in this letter an efficient direct method for the computation of a length-N type-II generalized discrete Hartley transform (GDHT) when given two adjacent length-N/2 GDHT coefficients. The computational complexity of the proposed method is lower than that of the traditional approach for length Nges8. The arithmetic operations can be saved from 16% to 24% for N varying from 16 to 64. Furthermore, the new approach can be easily implemented
HRV complexity as a diagnostic tool for late onset sepsis in sick premature infants

In this paper, the objective was to investigate the heart rate variability in two selected groups of premature infants (sepsis vs non-sepsis). We studied the RR interval series not only by linear methods - time domain and frequency domain, but also by non-linear methods - chaos theory and information theory, in order to find the optimal parameters to distinguish sepsis premature infants from non-sepsis ones. The results show that indexes of information theory are useful parameters for the diagnosis of late neonatal infection in premature infants with recurrent apnea-bradycardia.
Representation of SEEG signals using time-frequency signatures

This work focuses on the analysis of stereoelectroencephalographic (SEEG) signals recorded with depth electrodes on epileptic patients. This analysis provides major information on interactions between brain structures during seizures. Considering the non stationary nature of these signals, time-frequency representations are appropriate for this analysis. The study of the time-frequency contents of SEEG signals allows to extract classes of time-frequency signatures that are characteristic of different phases of a seizure. Even if the considered classes do not form a complete dictionary of signatures, they allow to describe the evolution of the seizures.
Various Ways to Compute the Continuous-Discrete Extended Kalman Filter

The Extended Kalman Filter (EKF) is a very popular tool dealing with state estimation. Its continuous-discrete version (CD-EKF) estimates the state trajectory of continuous-time nonlinear models, whose internal state is described by a stochastic differential equation and which is observed through a noisy nonlinear form of the sampled state. The prediction step of the CD-EKF leads to solve a differential equation that cannot be generally solved in a closed form. This technical note presents an overview of the numerical methods, including recent works, usually implemented to approximate this filter. Comparisons of theses methods on two different nonlinear models are finally presented. The first one is the Van der Pol oscillator which is widely used as a benchmark. The second one is a neuronal population model. This more original model is used to simulate EEG activity of the cortex. Experiments showed better stability properties of implementations for which the positivity of the prediction matrix is guaranteed.
Ica: a potential tool for bci systems

Several studies dealing with independent component analysis (ICA)-based brain-computer interface (BCI) systems have been reported. Most of them have only explored a limited number of ICA methods, mainly FastICA and INFOMAX. The aim of this article is to help the BCI community researchers, especially those who are not familiar with ICA techniques, to choose an appropriate ICA method. For this purpose, the concept of ICA is reviewed and different measures of statistical independence are reported. Then, the application of these measures is illustrated through a brief description of the widely used algorithms in the ICA community, namely SOBI, COM2, JADE, ICAR, FastICA, and INFOMAX. The implementation of these techniques in the BCI field is also explained. Finally, a comparative study of these algorithms, conducted on simulated electroencephalography (EEG) data, shows that an appropriate selection of an ICA algorithm may significantly improve the capabilities of BCI systems.
A Novel Split-Radix Fast Algorithm for 2-D Discrete Hartley Transform

This paper presents a fast split-radix- (2&times;2)/(8&times;8) algorithm for computing the 2-D discrete Hartley transform (DHT) of length N ×N with N = q · 2 m, where q is an odd integer. The proposed algorithm decomposes an N × N DHT into one N /2 × N /2 DHT and 48 N /8 × N /8 DHTs. It achieves an efficient reduction on the number of arithmetic operations, data transfers and twiddle factors compared to the split-radix-(2×2)/(4×4) algorithm. Moreover, the characteristic of expression in simple matrices leads to an easy implementation of the algorithm. If implementing the above two algorithms with fully parallel structure in hardware, it seems that the proposed algorithm can decrease the area complexity compared to the split-radix-(2×2)/(4×4) algorithm, but requires a little more time complexity. An application of the proposed algorithm to 2-D medical image compression is also provided.
Low rank canonical polyadic decomposition of tensors based on group sparsity

A new and robust method for low rank Canonical Polyadic (CP) decomposition of tensors is introduced in this paper. The proposed method imposes the Group Sparsity of the coefficients of each Loading (GSL) matrix under orthonormal subspace. By this way, the low rank CP decomposition problem is solved without any knowledge of the true rank and without using any nuclear norm regularization term, which generally leads to computationally prohibitive iterative optimization for large-scale data. Our GSL-CP technique can be then implemented using only an upper bound of the rank. It is compared in terms of performance with classical methods, which require to know exactly the rank of the tensor. Numerical simulated experiments with noisy tensors and results on fluorescence data show the advantages of the proposed GSL-CP method in comparison with classical algorithms.
Analysis of cardiac micro-acceleration signals for the estimation of systolic and diastolic time intervals in cardiac resynchronization therapy

Heart sounds, measured via cardiac micro-acceleration (CMA), carry valuable information about the mechanical and hemodynamic function of the heart. The aim of the present study is to estimate systolic and diastolic time intervals from CMA features, extracted by segmentation of the first two heart sounds, i.e. onset, maximum energy and end instants. CMA, ECG and echocardiographic audio signals were acquired simultaneously on 60 patients under different hemodynamic conditions. Linear models, fitted between CMA features and echo timings, were evaluated with their correlation coefficient, model error and coefficient stability. Models for mitral valve closing, aortic valve closing and opening instants showed satisfactory results, whereas the estimation of the opening instant of the mitral valve was more difficult. This work suggests the potential utility of CMA for monitoring cardiac function and defining optimal, adaptive pacing configurations.
Nonnegative Joint Diagonalization by Congruence Based on LU Matrix Factorization

In this letter, a new algorithm for joint diagonalization of a set of matrices by congruence is proposed to compute the nonnegative joint diagonalizer. The nonnegativity constraint is imposed by means of a square change of variables. Then we formulate the high-dimensional optimization problem into several sequential polynomial subproblems using LU matrix factorization. Numerical experiments on simulated matrices emphasize the advantages of the proposed method, especially in the case of degeneracies such as for low SNR values and a small number of matrices. An illustration of blind separation of nuclear magnetic resonance spectroscopy confirms the validity and improvement of the proposed method.
Designing a planar vector field to investigate the role of a slow variable in an enhanced mean-field model during general anesthesia

Local mean-field models (MFMs) describe regional brain activities by some connected differential equations. In an overall view, constituting variables of these differential equations can be divided to very fast, fast and slow variables. In this article we propose a method that can be used to determine role of a slow variable in behavior of MFMs. Very fast variables can be adiabatically removed from the equations. Isoclines of fast and slow variables and their corresponding vector field can provide valuable information about model behavior and role of the slow variable in it. The vector field of our interested MFM that is an enhanced MFM designed specially for general anesthesia, is a 3D field (one slow and two fast variables) and it is not so convenient for visually inspecting the role of the slow variable in this model. To afford this problem we design a 2D (planar) vector filed that only considers the slow variable and one of the fast variables
Feasibility of blind source separation methods for the denoising of dense-array EEG

High-density electroencephalographic recordings have recently been proved to bring useful information during the pre-surgical evaluation of patients suffering from drug-resistant epilepsy. However, these recordings can be particularly obscured by noise and artifacts. This paper focuses on the denoising of dense-array EEG data (e.g. 257 channels) contaminated with muscle artifacts. In this context, we compared the efficiency of several Independent Component Analysis (ICA) methods, namely SOBI, SOBIrob, PICA, InfoMax, two different implementations of FastICA, COM2, ERICA, and SIMBEC, as well as that of Canonical Correlation Analysis (CCA). We evaluated the performance using the Normalized Mean Square Error (NMSE) criterion and calculated the numerical complexity. Quantitative results obtained on realistic simulated data show that some of the ICA methods as well as CCA can properly remove muscular artifacts from dense-array EEG.
Improved radix-3 decimation-in-frequency algorithm for the fast computation of forward and inverse MDCT

Shu et al. recently proposed an efficient radix-3 decimation-in-Frequency (DIF) algorithm for the fast computation of forward and inverse modified discrete cosine transform (MDCT). Their algorithm decomposes the MDCT of a sequence with length N = 2×3m and the IMDCT of a sequence with length N = 4×3m into three N/3-point MDCTs and IMDCTs, respectively. In this paper, we present a refinement of their approach. Comparison with Shu's algorithm shows that the number of arithmetic operations can be reduced from 17.5% to 20.5% for MDCT and from 23.1% to 27.9% for IMDCT when N is varying from 36 to 324.
Reliable gradient search directions for kurtosis-based deflationary ICA: Application to physiological signal processing

Efficient gradient search directions for the optimisation of the kurtosis-based deflationary RobustICA algorithm in the case of real-valued data are proposed in this paper. The proposed scheme employs, in the gradient-like algorithm typically used to optimise the considered kurtosis-based objective function, search directions computed from a more reliable approximation of the negentropy than the kurtosis. The proposed scheme inherits the exact line search of the conventional RobustICA for which a good convergence property through a given direction is guaranteed. The efficiency of the proposed scheme is evaluated in terms of estimation quality, the execution time and the iterations count as a function of the number of used sensors and for different signal to noise ratios in the contexts of non-invasive epileptic ElectroEncephaloGraphic (EEG) and Magnetic Resonance Spectroscopic (MRS) analysis. The obtained results show that the proposed approach offer the best estimation performance/iterations count and execution time trade-off, especially in the case of high number of sensors.
Block term decomposition with rank estimation using group sparsity

In this paper, we propose a new rank-(L, L, 1) Block Term Decomposition (BTD) method. Contrarily to classical techniques, the proposed method estimates also the number of terms and the rank-(L, L, 1) of each term from an overestimated initialization of them. This is achieved by using Group Sparsity of the Loading (GSL) matrices. Numerical experiments with noisy tensors show the good behavior of GSL-BTD and its robustness with respect to the presence of noise in comparison with classical methods. Experiments on epileptic signals confirm its efficiency in practical contexts.
Nonnegative compression for Semi-Nonnegative Independent Component Analysis

In many Independent Component Analysis (ICA) problems the mixing matrix is nonnegative while the sources are unconstrained, giving rise to what we call hereafter the Semi-Nonnegative ICA (SN-ICA) problems. Exploiting the nonnegativity property can improve the ICA result. Besides, in some practical applications, the dimension of the observation space must be reduced. However, the classical dimension compression procedure, such as prewhitening, breaks the nonnegativity property of the compressed mixing matrix. In this paper, we introduce a new nonnegative compression method, which guarantees the nonnegativity of the compressed mixing matrix. Simulation results show its fast convergence property. An illustration of Blind Source Separation (BSS) of Magnetic Resonance Spectroscopy (MRS) data confirms the validity of the proposed method.
On joint diagonalization of cumulant matrices for independent component analysis of MRS and EEG signals

An extension of the original implementation of JADE, named eJADE(1) hereafter, was proposed in 2001 to perform independent component analysis for any combination of statistical orders greater than or equal to three. More precisely, eJADE(1) relies on the joint diagonalization of a set of several cumulant matrices corresponding to different matrix slices of one or several higher order cumulant tensors. An efficient way, without lose of statistical information, of reducing the number of third and fourth order cumulant matrices to be jointly diagonalized is proposed in this paper. The resulting approach, named eJADE3,4(2), can be interpreted as an improvement of the eJADE3,4(1) method. A performance comparison with classical methods is conducted in the context of MRS and EEG signals showing the good behavior of our technique.
Interictal EEG spike detection: a new framework based on wavelet transform

The paper deals with EEG spikes detection based on wavelet representation. A multilevel structure, which locates the temporal segments where abnormal events occur, is described. The methodology retained for the automatic adjustment of the thresholds is presented. The tests are performed both on simulated and real EEG data and the performances of the proposed detector are compared to another one proposed in the literature.<<ETX>>
Comparing wavelet transforms for recognizing cardiac patterns

The authors' study made use of wavelet transforms to describe and recognize isolated cardiac beats. The choice of the wavelet family as well as the selection of the analyzing function into these families are discussed. The criterion used in the first case was the correct classification rate, and in the second case, the correlation coefficient between the original pattern and the reconstructed one. Two types of description have been considered-the energy-based representation and the extrema distribution estimated at each decomposition level-and their quality has been assessed by using principal component analysis. Their capability of discrimination between normal, premature ventricular contraction, and ischemic beats has been studied by means of linear discriminant analysis. This work leads also, for the problem at hand, to the identification of the most relevant resolution levels.
Low cost and efficient kurtosis-based deflationary ICA method: application to MRS sources separation problem.

Improving the execution time and the numerical complexity of the well-known kurtosis-based maximization method, the RobustICA, is investigated in this paper. A Newton-based scheme is proposed and compared to the conventional RobustICA method. A new implementation using the nonlinear Conjugate Gradient one is investigated also. Regarding the Newton approach, an exact computation of the Hessian of the considered cost function is provided. The proposed approaches and the considered implementations inherit the global plane search of the initial RobustICA method for which a better convergence speed for a given direction is still guaranteed. Numerical results on Magnetic Resonance Spectroscopy (MRS) source separation show the efficiency of the proposed approaches notably the quasi-Newton one using the BFGS method.
Comparison of three spike detectors dedicated to single unit action potentials of the auditory nerve

This paper compares three methods for the detection of single unit action potentials in auditory nerve. The detector structures are similar consisting of a filtering procedure in the first stage and a decision rule in the second stage. The detection accuracy of each detector is characterized by the couple probability of a true detection vs. rates of false detection with synthetic data. The performance comparison between detectors shows that the detector using a band-pass finite-impulse-response filter with complex coefficients offers the best performance. This observation was especially evident for low signal to noise ratios. This finding is confirmed with real data and leads us to revise the protocol of spike detection in auditory nerve.
Turning Tangent Empirical Mode Decomposition: A Framework for Mono- and Multivariate Signals

A novel empirical mode decomposition (EMD) algorithm, called 2T-EMD, for both mono- and multivariate signals is proposed in this correspondence. It differs from the other approaches by its computational lightness and its algorithmic simplicity. The method is essentially based on a redefinition of the signal mean envelope, computed thanks to new characteristic points, which offers the possibility to decompose multivariate signals without any projection. The scope of application of the novel algorithm is specified, and a comparison of the 2T-EMD technique with classical methods is performed on various simulated mono- and multivariate signals. The monovariate behaviour of the proposed method on noisy signals is then validated by decomposing a fractional Gaussian noise and an application to real life EEG data is finally presented.
Application of the matching pursuit method for the time-scale transformation of the phonocardiogram

A time-scale transformation technique is developed for the phonocardiogram (PCG) based on the matching pursuit method. This method decomposes a signal into a series of time-frequency atoms by using an iterative process. Each atom is the product of a Gaussian window function with a sine wave representing its temporal and spectral properties, respectively. The modification of the time-scale of each atom can be performed without any perceptible change in its spectral components. Therefore, a time-scaled (expanded or compressed) PCG can be derived by the summation of the time-scaled atoms. The technique has been tested on a pathological PCG containing three heart sounds and a murmur. The results showed that the technique is suitable for the time scale transformation of both the transient property of the heart sounds and the more complex random property of the murmurs.