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This paper presents a proof-of-concept of a versatile Power Quality (PQ) analyser for tracking the voltage supply in industrial and residential sectors. It implements 2D Higher-Order Statistics (HOS) to assess voltage quality, based more on the sinusoidal waveform than on power fluctuations. Beyond the second-order parameters and permissible deviations regulated by the norm, EN 50160, the two-dimensional traces and probability density functions, along with a previously studied differential index, manage to identify different states of the electrical grid. Waveforms were measured in the wall plugs of a public building. In regard to analysing reliability and voltage waveform, the results corroborate that incorporating skewness and kurtosis indicators improves the characterisation, as well as extracting the customers' supply behaviour under normal and anomalous operations. The instrument showed good behaviour in site characterisation, and the implemented method was considered as a probabilistic approach for the risk assessment of an installation. The prototype was tested in the facilities of a public building of the university, being able to detect deviations in 10 s traces of 3.9% in variance and 0.6% in kurtosis. [ABSTRACT FROM AUTHOR]

Abstract Background Physical exercise is an important method for both the physical and mental health of the senior population. However, excessive exertion can lead to increased risks of falls, severe injuries, and diminished quality of life. Therefore, simple and effective methods for fatigue monitoring during exercise are highly desirable, particularly in community settings. The purpose of this study was to explore the possibility of real-time detection of exercise-induced fatigue using surface Electromyogram (sEMG) features, including the kurtosis and skewness of the Probability Density Function (PDF) in the community settings to solve the issues of low sensitivity and high computational complexity of commonly used sEMG features. Methods sEMG signals from six forearm muscles were recorded during hand grip tasks at 20% maximal voluntary contraction (MVC) task-to-failure contractions from 30 healthy community-dwelling elders at their respective community centers. PDF shape features of the sEMG, namely kurtosis and skewness, were computed from 25 s of non-fatigue stable phase and 25 s of fatigue data for comparison. Statistical tests were conducted to compare and test for the significance of these features. We further proposed a novel fatigue indicator, Temporal-Mean-Kurtosis (TMK) of channel-averaged kurtosis, to detect fatigue with relatively low computational complexity and adequate sensitivity in community settings. ANOVA and post-hoc analyses were performed to examine the performance of TMK. Results Statistically significant differences were found between the non-fatigue period and the fatigue period for both kurtosis and skewness, with increasing values when approaching fatigue. TMK was shown to be sensitive in detecting fatigue with respect to time with lower computational complexity than the Sample Entropy. Conclusion This study investigated PDF shape features of sEMG signals during a handgrip exercise to identify muscle fatigue in older adults in community experiments. Results revealed significant changes in kurtosis upon fatigue, indicating that PDF shape features were suitable convenient detectors of muscle fatigue in community experiments. The proposed indicator, TMK, showed potential sensitivity in tracking muscle fatigue over time in community-based settings with limited computational complexity, highlighting the promise of sEMG’s PDF features in detecting muscle fatigue among the elderly.

Background: Physical exercise is an important method for both the physical and mental health of the senior population. However, excessive exertion can lead to increased risks of falls, severe injuries, and diminished quality of life. Therefore, simple and effective methods for fatigue monitoring during exercise are highly desirable, particularly in community settings. The purpose of this study was to explore the possibility of real-time detection of exercise-induced fatigue using surface Electromyogram (sEMG) features, including the kurtosis and skewness of the Probability Density Function (PDF) in the community settings to solve the issues of low sensitivity and high computational complexity of commonly used sEMG features. Methods: sEMG signals from six forearm muscles were recorded during hand grip tasks at 20% maximal voluntary contraction (MVC) task-to-failure contractions from 30 healthy community-dwelling elders at their respective community centers. PDF shape features of the sEMG, namely kurtosis and skewness, were computed from 25 s of non-fatigue stable phase and 25 s of fatigue data for comparison. Statistical tests were conducted to compare and test for the significance of these features. We further proposed a novel fatigue indicator, Temporal-Mean-Kurtosis (TMK) of channel-averaged kurtosis, to detect fatigue with relatively low computational complexity and adequate sensitivity in community settings. ANOVA and post-hoc analyses were performed to examine the performance of TMK. Results: Statistically significant differences were found between the non-fatigue period and the fatigue period for both kurtosis and skewness, with increasing values when approaching fatigue. TMK was shown to be sensitive in detecting fatigue with respect to time with lower computational complexity than the Sample Entropy. Conclusion: This study investigated PDF shape features of sEMG signals during a handgrip exercise to identify muscle fatigue in older adults in community experiments. Results revealed significant changes in kurtosis upon fatigue, indicating that PDF shape features were suitable convenient detectors of muscle fatigue in community experiments. The proposed indicator, TMK, showed potential sensitivity in tracking muscle fatigue over time in community-based settings with limited computational complexity, highlighting the promise of sEMG's PDF features in detecting muscle fatigue among the elderly. [ABSTRACT FROM AUTHOR]

The global burden of atrial fibrillation (AFIB) is constantly increasing, and its early detection is still a challenge for public health and motivates researchers to improve methods for automatic AFIB prediction and management. This work proposes higher-order spectra analysis, especially the bispectrum of electrocardiogram (ECG) signals combined with the convolution neural network (CNN) for AFIB detection. Like other biomedical signals, ECG is non-stationary, non-linear, and non-Gaussian in nature, so the spectra of higher-order cumulants, in this case, bispectra, preserve valuable features. The two-dimensional (2D) bispectrum images were applied as input for the two CNN architectures with the output AFIB vs. no-AFIB: the pre-trained modified GoogLeNet and the proposed CNN called AFIB-NET. The MIT-BIH Atrial Fibrillation Database (AFDB) was used to evaluate the performance of the proposed methodology. AFIB-NET detected atrial fibrillation with a sensitivity of 95.3%, a specificity of 93.7%, and an area under the receiver operating characteristic (ROC) of 98.3%, while for GoogLeNet results for sensitivity and specificity were equal to 96.7%, 82%, respectively, and the area under ROC was equal to 96.7%. According to preliminary studies, bispectrum images as input to 2D CNN can be successfully used for AFIB rhythm detection. [ABSTRACT FROM AUTHOR]

Since a 1932 work from von Neumann, it has been considered that if two statistical mixtures are represented by the same density operator ρ, they should, in fact, be considered as the same mixture. In a 1970 paper, Zeh introduced a thought experiment with neutron spins, and suggested that, in that experiment, the density operator could not tell the whole story. Since then, no consensus has emerged yet, and controversies on the subject still presently develop. In his 1995 book, speaking of the use of the density operator, Peres spoke of a von Neumann postulate. In this paper, keeping the random variable used by von Neumann in his treatment of statistical mixtures, but also considering higher-order moments of this random variable, it is established that the two mixtures imagined by Zeh, with the same ρ, should however be distinguished. We show that the rejection of that postulate, installed on statistical mixtures for historical reasons, does not affect the general use of ρ, e.g., in quantum statistical mechanics, and the von Neumann entropy keeps its own interest and even helps clarifying that confusing consequence of the postulate identified by Peres.

In recent years, sub-synchronous oscillation accidents caused by wind power integration have received extensive attention. The recorded constant-amplitude waveforms can be induced by either linear or nonlinear oscillation mechanisms. Hence, the nonlinear behavior needs to be distinguished prior to choosing the analysis method. Since the 1960s, the higher-order statistics (HOS) theory has become a powerful tool for the detection of nonlinear behavior (DNB) in production quality control wherein it has mainly been applied to mechanical condition monitoring and fault diagnosis. This study focuses on the hard limiters of the voltage source converter (VSC) control systems in the wind farms and attempts to detect the nonlinear behavior caused by bi- or uni-Iateral saturation hard limiting using the HOS analysis. First, the conventional describing function is extended to obtain the detailed frequency domain information on the bi- and uni-Iateral saturation hard limiting. Furthermore, the bi- and tri-spectra are introduced as the HOS, which are extended into bi- and tri-coherence spectra to eliminate the effects of the linear parts on the harmonic characteristics of hard limiting in the VSC control system, respectively. The effectiveness of the HOS in the DNB and the classification of the hard-limiting types is proven, and its detailed derivation and estimation procedure is presented. Finally, the quadratic and cubic phase coupling in the signals is illustrated, and the performance of the proposed method is evaluated and discussed.

Quantum mechanics and hence quantum information processing methods widely use two types of states, namely (deterministic-coefficient) pure states and statistical mixtures. Density operators can be associated with them. We here address a third type of states, whose ket coefficients are random variables, as opposed to the deterministic coefficients of usual pure states. We therefore call them Random-Coefficient Pure States, or RCPS. We define physical setups that yield RCPS. We analyze the properties of RCPS and show that they contain much richer information than the density operator and mean of observables that we associate with them , because that operator only exploits the second-order statistics of the random state coefficients, whereas their higher-order statistics contain additional information. That information can be accessed in practice with the multiple-preparation procedure that we propose for RCPS, by using second-order and higher-order statistics of associated random probabilities of measurement outcomes. Exploiting these higher-order statistics yields a very general approach to advanced quantum information processing. We illustrate its relevance with a generic quantum parameter estimation problem related to quantum process tomography , especially considering its blind/unsupervised version. We show that this problem cannot be solved by using only the density operator ρ of an RCPS and the associated mean value T r (ρ A ^) of the operator A ^ corresponding to the considered physical quantity. We solve it by exploiting a fourth-order statistical parameter of state coefficients, in addition to second-order statistics. Numerical tests validate this result and show that the proposed method yields accurate parameter estimation for the considered number of state preparations. [ABSTRACT FROM AUTHOR]

This research studies the problem of a joint capacity/capacity reliability analysis of the multiuser multi-input multioutput (MIMO) system functioning in the presence of generalized multipath fading. The study presents the derived results of the closed-form analytical statistical description of the ergodic sum-rate capacity, the capacity reliability and the capacity's higher-order statistics in the case of complex Nakagami-m distributed channel transmission coefficients. A numerical verification of the derived expressions was performed, and it demonstrated excellent correspondence with the simulation. The system performance was evaluated with the help of a numerical analysis of the joint first- and second-order statistics description, depending on the channel and system parameters. The results demonstrated several peculiarities, e.g., the existence of a specific extremum of the capacity reliability for small-sized MIMO systems, its opposite behavior (in terms of the varying number of antenna elements) for heavy and light fading, and the existing asymptotic regions of the system and the channel parameters. [ABSTRACT FROM AUTHOR]

The goal of domain-generalized person reidentification (DG-ReID) is to train a model in the source domain and apply it directly to unknown target domains for specific pedestrian retrieval. Existing methods rely primarily on low-order statistics (such as the mean, standard deviation, or variance), thereby ensuring the stability of the source domain data distribution for model training. However, such methods underperform when the data follow a non-Gaussian distribution, thereby reducing the generalization ability of the model on unseen target domains. To address this issue, this study proposes an instance normalization-based skewness regularity (INSR) framework that uses high-order statistics (skewness and high-order moments) to measure the skewness and regularity of the data distribution. Such measures allow further learning of the morphological features (skewness degree, trait of data near the mean, etc.) of complex data distributions for DG-ReID. Specifically, the proposed framework first extracts the skewness and third-order moments from the source domains, which provide more features (high-order moments, variance, etc.) to characterize the data distribution. Subsequently, a batch normalization-like operation was implemented to project the data into a new feature space with zero mean and unit variance, enhancing model adaption and accuracy. Extensive experiments were conducted on small-scale (VIPeR, PRID, GRID, and i-LIDS) and large-scale (Market-1501, DukeMTMC-reID, CUHK03, MSMT17) public datasets using two different protocols, demonstrating that the proposed INSR framework significantly outperforms other state-of-the-art counterparts for DG-ReID. [ABSTRACT FROM AUTHOR]

This article presents a unique dataset, from a public building, of voltage data, acquired using a hybrid measurement solution that combines PythonTM for acquisition and GrafanaTM for results representation. This study aims to benefit communities, by demonstrating how to achieve more efficient energy management. The study outlines how to obtain a more realistic vision of the quality of the supply, that is oriented to the monitoring of the state of the network; this should allow for better understanding, which should in turn enable the optimization of the operation and maintenance of power systems. Our work focused on frequency and higher order statistical estimators which, combined with exploratory data analysis techniques, improved the characterization of the shape of the stress signal. These techniques and data, together with the acquisition and monitoring system, present a unique combination of low-cost measurement solutions, which have the underlying benefit of contributing to industrial benchmarking. Our study proposes an effective and versatile system, which can do acquisition, statistical analysis, database management and results representation in less than a second. The system offers a wide variety of graphs to present the results of the analysis, so that the user can observe them and identify, with relative ease, any anomalies in the supply which could damage the sensitive equipment of the correspondent installation. It is a system, therefore, that not only provides information about the power quality, but also significantly contributes to the safety and maintenance of the installation. This system can be practically realized, subject to the availability of internet access. [ABSTRACT FROM AUTHOR]

Statistical properties of signals play an essential role in designing algorithms for Direction of Arrival (DoA) estimation. Many times, signals are assumed to be circular, and circular moments are used thereof. Practically, non-circular signals exist and provide extra statistical information in terms of pseudo/non-circular moments/cumulants which can be used to improve the performance of any parameter estimation algorithm. This paper uses the 2qth-order non-circular cumulants along with circular cumulants to solve the underdetermined non-Gaussian non-circular system. A generalized method based on higher-order non-circularity has been proposed to design the physical array such that the corresponding virtual array provides the larger degree of freedoms than existing arrays. The weight function, an important metric, to measure the stability of the corresponding virtual array, has also been evaluated analytically. Numerical simulation demonstrates performance gain due to the proposed array structure. [ABSTRACT FROM AUTHOR]

This paper proposes an easy-to-implement method for detecting and assessing two of the most frequent PQ (Power Quality) problems: voltage sags and swells. These can affect sensitive equipment such as computers, programmable logic controllers, contactors, etc. Therefore, it is of great interest to implement it in any laboratory, not only for protection reasons but also as a safeguard for claims against the supply company. Thanks to the actual context, in which it is possible to manage big volumes of data, connect multiple devices with IoT (Internet of Things), etc., it is feasible and of great interest to monitor the voltage at specific points of the network. This makes it possible to detect voltage sags and swells and diagnose which points are more prone to this type of problems. For the detection of sags and swells, a program written in Python is in charge of crawling all the files in the database and target those RMS values that fall outside the established limits. Compared to LabVIEW, which might have been the most logical alternative, being the acquisition hardware from the same company (National Instruments), Python has a higher computational performance and is also free of charge, unlike LabVIEW. Thanks to the libraries available in Python, it allows a hardware control close to what is possible using LabVIEW. Implemented in MATLAB, the ITIC (Information Technology Industry Council) power acceptability curve reflects the impact of these power quality disturbances in electrical power systems. The results showed that the combined action of Python and MATLAB performed well on a conventional desktop computer.

This article presents a unique dataset, from a public building, of voltage data, acquired using a hybrid measurement solution that combines PythonTM for acquisition and GrafanaTM for results representation. This study aims to benefit communities, by demonstrating how to achieve more efficient energy management. The study outlines how to obtain a more realistic vision of the quality of the supply, that is oriented to the monitoring of the state of the network; this should allow for better understanding, which should in turn enable the optimization of the operation and maintenance of power systems. Our work focused on frequency and higher order statistical estimators which, combined with exploratory data analysis techniques, improved the characterization of the shape of the stress signal. These techniques and data, together with the acquisition and monitoring system, present a unique combination of low-cost measurement solutions, which have the underlying benefit of contributing to industrial benchmarking. Our study proposes an effective and versatile system, which can do acquisition, statistical analysis, database management and results representation in less than a second. The system offers a wide variety of graphs to present the results of the analysis, so that the user can observe them and identify, with relative ease, any anomalies in the supply which could damage the sensitive equipment of the correspondent installation. It is a system, therefore, that not only provides information about the power quality, but also significantly contributes to the safety and maintenance of the installation. This system can be practically realized, subject to the availability of internet access.

Epilepsy is a persistent neurological disorder characterized by epileptic seizures. Medication is often unable to regulate epileptic episodes, and surgery is the only cure for epilepsy. Therefore, localizing the epileptic zone is crucial for effective epileptic surgery. This surgical area can be identified through the presence of cerebral intracranial focal electroencephalogram (FEEG) signals. This article proposes a mixed approach based on higher-order statistics (HOS) with sensitivity analysis and residual wavelet transform (RWT). The sensitivity analysis method examines only the portions of the brain signal dominated by transient and burst events, and measuring a set of frequencies associated with underlying nonlinear dynamics. The suggested approach assesses the brain output in two distinct spaces based on these frequency benchmarks. The RWT investigates transient and impulsive changes in a non-stationary time series by evaluating it in time-scale space. Additionally, the HOS explores nonlinear dynamics in higher-dimensional space without loss of generality. The data are then mapped and ranked using a data-reduction method, LSDA (locality sensitivity discrimination analysis). The pertinent and delicate information was decoded using a deep learning method based on bidirectional long-short term memory (bi-LSTM). The results reveal that the presented algorithm has yielded a better discrimination accuracy of 99.76% on the Bern Barcelona EEG database. The proposed algorithm enables a clinician to locate the epileptogenic zone, which is essential for effective brain surgery. [Display omitted] • The Sensitivity analysis method measures a set of frequencies which maximize the probability of seizures onset. • EEG signals are assess in the RWT and HOS spaces on these frequencies benchmark. • The RWT investigate the transient and impulsive changes of a non-stationary time series by evaluating it in time-scale space. • While, the HOS is introduced to explore the non-linear dynamics in higher-dimensional space without loss of generality. • The examined data have been mapped and ranked according to the likelihood of discrimination using a data-reduction method, namely, locality sensitivity discrimination analysis (LSDA). • A bi-LSTM based deep learning method is used to analyze Bern Barcelona EEG database. [ABSTRACT FROM AUTHOR]

In geostatistics, available conditional information is typically categorized as either hard (with no uncertainty) or soft (associated with an uncertainty) data. 2-point based Gaussian (kriging) simulation methods have since their inception been able to account for hard and soft data. In contrast, multiple-point statistical (MPS) simulation methods, that allow quantifying more complex phenomena than Gaussian methods, have focused mostly on conditioning to hard data. The most widely used method for accounting for soft data is to consider only co-located soft data. Here we define conditional information data through a probability distribution and show how a general probabilistic solution, in the form of a posterior probability distribution, can be obtained, combining conditional categorical information with information from a categorical training image. We demonstrate how to compute the conditional distribution, as needed by most MPS-based sequential simulation algorithms, that allows accounting for both co- and non-co-located hard and soft data. Examples are provided comparing the direct computation of conditional statistics to using more computational demanding rejection sampling. In order to reduce the significant computational demands, a distance-based measure is proposed to increase the entropy of conditional data, based on the distance to the point being estimated and simulated. • Ignoring non-co-located soft data leads to artefacts and loss of information. • Using a preferential path with co-locations soft data conditioning increases the accuracy of MPS. • All non-co-locational conditioning data can in principle be accounted for using MPS sequential simulation. • Accounting for correlated co-locational data with MPS is still an unsolved problem. [ABSTRACT FROM AUTHOR]

• An analysis method of response higher-order moment spectra is proposed. • The expressions of response higher-order moment spectra are deduced. • The response of harmonic excitation was used to calculate response moment spectra. • The effectiveness of proposed method is demonstrated by other methods. In the classical random vibration analysis, the frequency-domain analysis method, especially the time-frequency domain analysis method based on the frequency domain analysis method, is the most commonly analysis method. However, the time-frequency domain analysis method is challenging when dealing with multiple non-stationary non-Gaussian excitations. In this paper, an accurate and efficient auxiliary harmonic excitation generalized method for higher-order analysis of linear structures under multiple non-stationary non-Gaussian excitations is proposed. Firstly, the expression for the higher-order moment spectrum of response is theoretically deduced by the mode superposition method, in which the time-varying generalized impulse response function and the time-varying generalized frequency response function are introduced. Secondly, according to the relationship between the response of harmonic excitation and the time-varying generalized frequency response function, the expression for the high-order moment spectrum of response is reconstructed in terms of the response of harmonic excitation and the higher-order moment spectrum matrix of the excitations, in which the response of the time-varying auxiliary harmonic excitation can be evaluated efficiently based on the time-domain explicit formulation method. Finally, two examples are investigated to verify the accuracy and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

• A novel model for the accurate representation of high-impedance faults (HIFs) is developed, involving the analysis of five different contact surfaces and considering the variation of the model parameters. • Application of higher-order statistics (HOS) based on the calculation of cumulants to extract characteristics of electrical signals associated with the simulated events in the distribution network, with focus on high-impedance faults. • Use of a multilayer perceptron (MLP) artificial neural network (ANN) for the recognition of patterns associated with each type of event, aiming to classifying them accurately, with particular interest in high-impedance faults. This work presents a novel method for the classification of high-impedance faults (HIFs) in power distribution networks based on the association of higher-order statistics (HOS) and a multilayer perceptron (MLP) artificial neural network (ANN). An alternative model is developed to represent the HIF phenomenon considering five different contact surfaces with the ground. A broad analysis comprising six types of typical events that occur in distribution networks is performed in the Alternative Transient Program (ATP) software, including several conditions such as: normal operation; single-phase faults; two-phase faults; three-phase faults; energization of transformers and capacitor banks; switching of inductive loads; as well as faults involving five modeled surfaces. HOS is combined with Fisher's discriminant ratio (FDR) to extract the best characteristics. At the end, the MLP-type ANN is used to recognize the specific patterns of each event aiming to identify each event accurately, especially HIFs. The obtained results demonstrate that the proposed technique proves to be a reliable and accurate tool, achieving classification hits above 98%. [ABSTRACT FROM AUTHOR]