nils-herrmann/clean_scirepeval huggingface dataset:
Nonminimum Phase Identification Based on Higher Order Spectrum Slices

A new family of methods, named phase estimation using polyspectrum slices (PEP), for the reconstruction of the Fourier phase of a complex linear time invariant (LTI) system excited by a white non-Gaussian input is proposed. More precisely, we propose two subfamilies of methods, the q-PEP (q ges 3) and (q1,q2)-PEP (q2 > q1 ges 3) algorithms. The q-PEP methods exploit the best two-dimensional (2-D) slice of the data qth-order spectrum. The originality of the (q1,q2 )-PEP methods consists of exploiting simultaneously one 1-D slice of the q1th-order spectrum and one 2-D slice of the q2th-order spectrum. These new algorithms are easy both to implement and to use. Moreover, the asymptotic unbiasedness and consistency of these methods are demonstrated. Eventually, computer simulations show that the PEP algorithms exhibit in general better performances than classical methods especially for band-limited systems.
Using SVD and analyzing wavelets for the analysis and the segmentation of digital subtraction angiography images

The authors introduce the singular value decomposition transform and present an application to intraframe, interframe and intersequence analysis for two image sequences resulting from a binocular angiographic system. It is shown that the second singular value follows the heart's cyclic behavior, which allows two angiographic sequences to be postsynchronized without electrocardiogram signal recording. Pyramid wavelet transforms and their application to digital subtraction angiography images are addressed. A novel analyzing wavelet acting like a matched filter is discussed.<<ETX>>
Detection and localization of complex SEEG patterns in epileptic seizures using time-frequency analysis

The authors applied signal detection techniques based on time-frequency signal analysis on depth EEG recordings in order to detect and to localize some patterns in the time-frequency plane. This paper deals with a particular pattern representing a discharge activity in the ictal phase. Beside the detection techniques using fixed-kernel Time-Frequency Representation (Auto TFR and Cross TFR) presented here, a new technique using Signal-Dependent TFR is proposed and a comparison of results is given.
Iterative PET Image Reconstruction Using Translation Invariant Wavelet Transform

The present work describes a Bayesian maximum a posteriori (MAP) method using a statistical multiscale wavelet prior model. Rather than using the orthogonal discrete wavelet transform (DWT), this prior is built on the translation invariant wavelet transform (TIWT). The statistical modeling of wavelet coefficients relies on the generalized Gaussian distribution. Image reconstruction is performed in spatial domain with a fast block sequential iteration algorithm. We study theoretically the TIWT MAP method by analyzing the Hessian of the prior function to provide some insights on noise and resolution properties of image reconstruction. We adapt the key concept of local shift invariance and explore how the TIWT MAP algorithm behaves with different scales. It is also shown that larger support wavelet filters do not offer better performance in contrast recovery studies. These theoretical developments are confirmed through simulation studies. The results show that the proposed method is more attractive than other MAP methods using either the conventional Gibbs prior or the DWT-based wavelet prior.
Delta waves differently modulate high frequency components of EEG oscillations in various unconsciousness levels

In this paper we investigate the modulation properties of high frequency EEG activities by delta waves during various depth of anesthesia. We show that slow and fast delta waves (0-2 Hz and 2-4 Hz respectively) and high frequency components of the EEG (8-20 Hz) are correlated with each other and there is a kind of phase locking between them that varies with depth of anesthesia. Our analyses show that maximum amplitudes of high frequency components of the EEG signal are appeared in different phases of slow and fast delta waves when the concentration of Desflurane and Propofol anesthetic agents varies in a patient. There are some slight differences in using slow and fast components of delta waves. For instance, when depth of anesthesia changes, biphasic responses of the EEG have more influences on results of the fast delta wave method. In addition, this method obtains more robust and less noisy results compared with the slow delta wave method. Since phase angle between fast EEG oscillations and delta waves indicates the status of information processing in the brain and it changes in various unconsciousness levels, it may improve the performance of other classic methods of determining depth of anesthesia.
Blind source separation methods applied to synthesized polysomnographic recordings: a comparative study

Blind Source Separation (BSS) problems, under the assumption of static mixture, were extensively explored from the theoretical point of view. Powerful algorithms are now at hand to deal with many concrete BSS applications. Nevertheless, the performances of BSS methods, for a given biomedical application, are rarely investigated. The aim of this paper is to perform quantitative comparisons between various well-known BSS techniques. To do so, synthetic data, reproducing real polysomnographic recordings, are considered.
Quaternion softmax classifier

For the feature extraction of red-blue-green (RGB) colour images, researchers usually deal with R, G and B channels separately to obtain three feature vectors, and then combine them together to obtain a long real feature vector. This approach does not exploit the relationships between the three channels of the colour images. Recently, attention has been paid to quaternion features, which take the relationships between channels into consideration and seem to be more suitable for representing colour images. However, there are only a few quaternion classifiers for dealing with quaternion features. To meet this requirement, a new quaternion classifier, namely, the quaternion softmax classifier is proposed, which is an extended version of the conventional softmax classifier generally defined in the complex (or real) domain. The proposed quaternion softmax classifier is applied to two of the most common quaternion features, that is, the quaternion principal components analysis feature and the colour image pixel feature. The experimental results show that the proposed method performs better than the quaternion back propagation neural network in terms of accuracy and convergence rate.
A Novel Classification Method for Prediction of Rectal Bleeding in Prostate Cancer Radiotherapy Based on a Semi-Nonnegative ICA of 3D Planned Dose Distributions

The understanding of dose/side-effects relationships in prostate cancer radiotherapy is crucial to define appropriate individual's constraints for the therapy planning. Most of the existing methods to predict side-effects do not fully exploit the rich spatial information conveyed by the three-dimensional planned dose distributions. We propose a new classification method for three-dimensional individuals' doses, based on a new semi-nonnegative ICA algorithm to identify patients at risk of presenting rectal bleeding from a population treated for prostate cancer. The method first determines two bases of vectors from the population data: the two bases span vector subspaces, which characterize patients with and without rectal bleeding, respectively. The classification is then achieved by calculating the distance of a given patient to the two subspaces. The results, obtained on a cohort of 87 patients (at two year follow-up) treated with radiotherapy, showed high performance in terms of sensitivity and specificity.
Tensor decomposition exploiting structural constraints for brain source imaging

The separation of Electroencephalography (EEG) sources is a typical application of tensor decompositions in biomedical engineering. The objective of most approaches studied in the literature consists in providing separate spatial maps and time signatures for the identified sources. However, for some applications, a precise localization of each source is required. To achieve this, a two-step approach has been proposed. The idea of this approach is to separate the sources using the canonical polyadic decomposition in the first step and to employ the results of the tensor decomposition to estimate distributed sources in the second step, using the so-called disk algorithm. In this paper, we propose to combine the tensor decomposition and the source localization in a single step. To this end, we directly impose structural constraints, which are based on a priori information on the possible source locations, on the factor matrix of spatial characteristics. The resulting optimization problem is solved using the alternating direction method of multipliers, which is incorporated in the alternating least squares tensor decomposition algorithm. Realistic simulations with epileptic EEG data confirm that the proposed single-step source localization approach outperforms the previously developed two-step approach.
A generalized modified split-radix FFT algorithm for N=q×2m and its applications

This paper presents a generalized odd factor modified split-radix-2/4 Fast Fourier transform (FFT) algorithm for computing the DFT of length-N = q×2<sup>m</sup>, where q is a positive odd integer. Compared with former algorithms for N = q×2<sup>m</sup>, the proposed algorithm reduces the computation complexity of DFT from O(4Nlog<sub>2</sub>(N/q)) to O((34/9)Nlog<sub>2</sub>(N/q)) for complex input. The proposed algorithm can be applied to some international wireless standards where the length of DFT is not a power of two, and to some international speech and audio standards where the modified discrete cosine transform (MDCT) with length-N = q×2<sup>m</sup>, q = 5, 15 is used.
Color image recovery via quaternion matrix completion

In this paper, we present a new method for recovering the color image in a low sample ratio by solving a nuclear norm minimization optimization problem in quaternion number field. This problem is converted to an equivalent semi-definite programming optimization problem, the latter is then solved by solver package based on primal-dual interior point algorithms. Different choices of the measurement matrix including the real sample matrix, complex sample matrix and quaternion sample matrix are used in the experiments to test its effect on the recovered images. Numerical results show that the proposed method achieves good performance.
Fast Radix-3 Algorithm for the Generalized Discrete Hartley Transform of Type II

We present a new fast radix-3 algorithm for the computation of the length-<i>N</i> generalized discrete Hartley transform of type-II (GDHT-II), where <i>N</i> = 3<sup>m</sup>, <i>m</i> ≥ 2. Then we apply this algorithm to the direct computation of length-<i>N</i> GDHT-II coefficients when given three adjacent length-<i>N</i>/3 GDHT-II coefficients. The computational complexity of the proposed method is lower than that of the traditional approach for length <i>N</i> ≥ 9. The arithmetic operations can be saved from 19% to 29% for <i>N</i> = 3<sup>m</sup> varying from 9 to 243 and from 17% to 29% for <i>N</i> = 3×2<sup>m</sup> varying from 12 to 384. Furthermore, the new approach can be easily implemented.
New Fast Algorithm for Modulated Complex Lapped Transform With Sine Windowing Function

A novel algorithm for fast computation of the modulated complex lapped transform (MCLT) with sine windowing function is presented. For the MCLT of length-2M input data sequence, the proposed algorithm is based on computing a length-2M type-II generalized discrete Hartley transform. Comparison with existing algorithms shows that the proposed method achieves the minimal number of arithmetic operations.
Early diagnosis of NAFLD-NASH transition using mid infrared spectroscopy.

Non-alcoholic fatty liver disease (NAFLD) is defined as an excessive accumulation of fat in the liver in the absence of excessive drinking of alcohol. Initially considered as benign and self-limited, NAFLD may progress to the malignant stage of non-alcoholic steatohepatitis (NASH) characterized by degenerate hepatocellular ballooning and lobular inflammation. NASH can lead to hepatic fibrosis and ultimately to cirrhosis and hepatocellular carcinoma. Unfortunately, the transition from NAFLD to NASH is difficult to detect so far. In this paper, we propose to evaluate the characterization of NASH using mid infrared fiber evanescent wave spectroscopy on blood serum. We used an heuristic variable selection method and a generalized linear model to classify NAFLD and NASH spectra. The obtained results proved that this technique is a promising non-invasive and simple diagnosis tool for NASH.
CP decomposition of semi-nonnegative semi-symmetric tensors based on QR matrix factorization

The problem of Canonical Polyadic (CP) decomposition of semi-nonnegative semi-symmetric three-way arrays is often encountered in Independent Component Analysis (ICA), where the cumulant of a nonnegative mixing process is frequently involved, such as the Magnetic Resonance Spectroscopy (MRS). We propose a new method, called JDQR+, to solve such a problem. The nonnegativity constraint is imposed by means of a square change of variable. Then the high-dimensional optimization problem is decomposed into several sequential rational subproblems using QR matrix factorization. A numerical experiment on simulated arrays emphasizes its good performance. A BSS application on MRS data confirms the validity and improvement of the proposed method.
Tensor object classification via multilinear discriminant analysis network

This paper proposes an multilinear discriminant analysis network (MLDANet) for the recognition of multidimensional objects, knows as tensor objects. The MLDANet is a variation of linear discriminant analysis network (LDANet) and principal component analysis network (PCANet), both of which are the recently proposed deep learning algorithms. The MLDANet consists of three parts: 1) The encoder learned by MLDA from tensor data. 2) Features maps obtained from decoder. 3) The use of binary hashing and histogram for feature pooling. A learning algorithm for MLDANet is described. Evaluations on UCF11 database indicate that the proposed MLDANet outperforms the PCANet, LDANet, MPCA+LDA, and MLDA in terms of classification for tensor objects.
Reconstruct the compressively sensed complex-valued terahertz data through BFGS method

In this paper, we present a new algorithm to reconstruct the compressively sensed complex-valued terahertz data by solving a ℓ1-norm minimization problem. The approach is based on an improved quasi-Newton algorithm. We use the discrete cosine transform and discrete wavelet transform respectively as the sparsity-promotion basis for the complex-value data. Application of the proposed method to both synthetic and real THz data shows that the proposed method achieves better performance compared to the existing methods.
A low cost and reliable Polak-Ribiere conjugate gradient deflation ICA algorithm for real signals

In this paper a new more robust version of a classical deflation independent component analysis algorithm (namely DelLR) which was introduced by Delfosse and Loubaton[1] for real context, is implemented. The new version uses one of the nonlinear conjugate gradient optimization methods, Polak-Ribiere. We compared the new algorithm with the classical one and the FastICA technique in order to propose a performance comparison of different deflation based ICA algorithms in terms of estimation accuracy and numerical complexity on different signals, audio and images. We show that the new algorithm is faster and has higher quality of separation and a lower computational cost than others.
ECG denoising using mutual information based classification of IMFs and interval thresholding

The Electrocardiogram (ECG) is widely used for diagnosis of heart diseases. Therefore, the quality of information extracted from the ECG has a vital role. In real recordings, ECG is corrupted by artifacts such as prolonged repolarization, respiration, changes of electrode position, muscle contraction, and power line interface. In this paper, a denoising technique for ECG signals based on Empirical Mode Decomposition (EMD) is proposed. We use Ensemble Empirical Mode Decomposition (EEMD) to overcome the limitations of EMD. Moreover, to overcome the limitations of thresholding methods we use the combination of mutual information and two EMD based interval thresholding approaches. Our new method is evaluated on ECG signals available in MIT-BIH database. This method is compared with two EEMD based interval thresholding methods. The results show that our proposed method has a better Signal to Noise Ratio improvement (SNRimp) and a lower Mean Square Error (MSE) than the other two methods.
3A-EMD: A generalized approach for monovariate and multivariate EMD

EMD is an emerging topic in signal processing research and is applied in various practical fields. Its recent extension to multivariate signals, motivated by the need to jointly analyze multi-channel signals, is an active topic of research. However, all the existing extensions specifically hold either mono-, bi- or tri-variate signals or require multiple projections that complexify the original process. In this communication, a novel EMD approach called 3A-EMD is proposed. It is essentially based on the redefinition of the mean envelope operator and allows, under certain conditions, a straightforward decomposition of monovariate and multivariate signals without any change in the core of the algorithm. A comparative study with classical monovariate and bivariate methods is presented and shows the competitiveness of 3A-EMD. A trivariate decomposition is also given to illustrate the extension of the proposed algorithm to any signal dimension, D>2.
Mixed-Radix Algorithm for the Computation of Forward and Inverse MDCTs

The modified discrete cosine transform (MDCT) and inverse MDCT (IMDCT) are two of the most computationally intensive operations in MPEG audio coding standards. A new mixed-radix algorithm for efficiently computing the MDCT/IMDCT is presented. The proposed mixed-radix MDCT algorithm is composed of two recursive algorithms. The first algorithm, called the radix-2 decimation-in-frequency algorithm, is obtained by decomposing an N-point MDCT into two MDCTs with the length N/2. The second algorithm, called the radix-3 decimation-in-time algorithm, is obtained by decomposing an N -point MDCT into three MDCTs with the length N/3. Since the proposed MDCT algorithm is also expressed in the form of a simple sparse matrix factorization, the corresponding IMDCT algorithm can be easily derived by simply transposing the matrix factorization. Comparison of the proposed algorithm with some existing ones shows that our proposed algorithm is more suitable for parallel implementation and particularly suitable for the layer III of MPEG-1 and MPEG-2 audio encoding and decoding. Moreover, the proposed algorithm can be easily extended to the multidimensional case by using the vector-radix method.
Affine Covariant Features for Fisheye Distortion Local Modeling

Perspective cameras are the most popular imaging sensors used in computer vision. However, many application fields, including automotive, surveillance, and robotics, require the use of wide angle cameras (e.g., fisheye), which allow to acquire a larger portion of the scene using a single device at the cost of the introduction of noticeable radial distortion in the images. Affine covariant feature detectors have proved successful in a variety of computer vision applications, including object recognition, image registration, and visual search. Moreover, their robustness to a series of variabilities related to both the scene and the image acquisition process has been thoroughly studied in the literature. In this paper, we investigate their effectiveness on fisheye images providing both theoretical and experimental analyses. As theoretical outcome, we show that the inherently non-linear radial distortion can be locally approximated by linear functions with a reasonably small error. The experimental analysis builds on Mikolajczyk's benchmark to assess the robustness of three popular affine region detectors (i.e., maximally stable extremal regions, and Harris and Hessian affine region detectors), with respect to different variabilities as well as to radial distortion. To support the evaluations, we rely on the Oxford data set and introduce a novel benchmark data set comprising 50 images depicting different scene categories. Experiments are carried out on rectilinear images to which radial distortion is artificially added, and on real-world images acquired using fisheye lenses. Our analysis points out that affine region detectors can be effectively employed directly on fisheye images and that the radial distortion is locally modeled as an additional affine variability.
Red-eyes removal through cluster based Linear Discriminant Analysis

Red-eye artifact is a well-known problem in digital photography. Since the large diffusion of mobile devices with embedded camera and flashgun, automatic detection and correction of red-eyes have become an important task. In this paper we describe a technique that makes use of three steps to identify and correct red-eyes. First, red-eye candidates are extracted from the input image by using simple color segmentation coupled with geometrical constraints. A set of linear discriminant classifiers is then learned on the clustered patches space, and hence employed to distinguish between eyes and non-eyes patches. The proposed cluster-based Linear Discriminant Analysis is used to deal with the multi-modally nature of the input space. The third step of the pipeline is devoted to artifacts correction through de-saturation and brightness reduction. Experimental results on a large dataset of images demonstrate the effectiveness of the pro- posed pipeline that outperforms other existing solutions in terms of hit rates maximization, false positives reduction and ad-hoc quality measure.
Recognizing Personal Contexts from Egocentric Images

Wearable cameras can gather first-person images of the environment, opening new opportunities for the development of systems able to assist the users in their daily life. This paper studies the problem of recognizing personal contexts from images acquired by wearable devices, which finds useful applications in daily routine analysis and stress monitoring. To assess the influence of different device-specific features, such as the Field Of View and the wearing modality, a dataset of five personal contexts is acquired using four different devices. We propose a benchmark classification pipeline which combines a one-class classifier to detect the negative samples (i.e., images not representing any of the personal contexts under analysis) with a classic one-vs-one multi-class classifier to discriminate among the contexts. Several experiments are designed to compare the performances of many state-of-the-art representations for object and scene classification when used with data acquired by different wearable devices.
Neurofuzzy segmentation of microarray images

In this paper we propose a novel microarray segmentation strategy to separate background and foreground signals in microarray images making use of a neurofuzzy processing pipeline. In particular a Kohonen Self Organizing Map followed by a Fuzzy K-Mean classifier are employed to properly manage critical cases like saturated spot and spike noise. To speed up the overall process a Hilbert sampling is performed together with an ad-hoc analysis of statistical distribution of signals. Experiments confirm the validity of the proposed technique both in terms of measured and visual inspection quality.