Higher-order Statistical Analysis Research Articles

A multivariate and wavelet-based analysis on the wall pressure fluctuations for propellers in ground effect

This study, conducted at the University of Bristol’s aeroacoustic facility, explores the aeroacoustic characteristics of a 10-inch APC propeller in pusher configuration within ground effect conditions. Tested at a rotational speed of 7000 r/min, the propeller’s performance was evaluated at varying distances from the ground. Wall pressure fluctuations near the propeller were measured at eight radial points, complemented by far-field acoustic measurements. Consistent with previous research, distinctive thrust and torque behaviours were observed when operating both in and out of ground effect. Notably, a significant noise increase of approximately 5 dB was observed on the ground’s reflected side, while a reduction of about 14 dB was detected on the shielded side. Higher-order statistical analysis such as skewness and kurtosis revealed substantial effects of tip vortex impingement in ground effect conditions. This study applies two-point statistics and the Corcos model to analyze propeller-induced wall pressure fluctuations. The coherence function effectively captures the intricate dynamics of pressure variations across different spatial locations and frequencies. This approach offers novel insights into the behaviour of boundary layers and flow-induced pressures in the context of propellers operating in the ground effect. Wavelet decomposition was utilized to differentiate the influences of the boundary layer and the acoustic field. This comprehensive analysis highlights the intricate dynamics of propeller operation in ground effect, contributing valuable insights to the field of aeroacoustic research.

Numerical investigation of triadic interactions during wave propagation over a submerged bar using a fully nonlinear numerical wave tank

This study models wave propagation over a submerged trapezoidal bar problem with a Higher Order Boundary Element Method (HOBEM) based on a two-dimensional fully nonlinear numerical wave tank (NWT). A Mixed Eulerian-Lagrangian technique is implemented, and the NWT is validated using the experimental results available from the existing literature. A numerical investigation is conducted on the individual wave harmonics as it propagates across the numerical wave tank over the submerged bar. The amplitude spectrum is obtained from the time histories of wave elevation across the tank at various locations over the trapezoidal bar and compared with the experimental results. The redistribution of the energy spectrum across the domain is recorded as the regular long wave propagates and undergoes deformations because of shoaling followed by de-shoaling. This phenomenon is investigated using a higher-order statistical analysis method viz. Bispectrum. The mechanics of the redistribution of energy among the harmonics is quantified through bispectrum analysis. Further, regular and irregular wave propagation over the submerged bar in the presence of the following and opposing currents is simulated. The effect of current is studied using the amplitude spectrum and the harmonic interaction for each case is compared using bispectrum analysis.

Monofractality in the Solar Wind at Electron Scales: Insights from Kinetic Alfvén Waves Turbulence.

The breakdown of scale invariance in turbulent flows, known as multifractal scaling, is considered a cornerstone of turbulence. In solar wind turbulence, a monofractal behavior can be observed at electron scales, in contrast to larger scales where multifractality always prevails. Why scale invariance appears at electron scales is a challenging theoretical puzzle with important implications for understanding solar wind heating and acceleration. We investigate this long-standing problem using direct numerical simulations of three-dimensional electron reduced magnetohydrodynamics. Both weak and strong kinetic Alfvén waves turbulence regimes are studied in the balanced case. After recovering the expected theoretical predictions for the magnetic spectra, a higher-order multiscale statistical analysis is performed. This study reveals a striking difference between the two regimes, with the emergence of monofractality only in weak turbulence, whereas strong turbulence is multifractal. This result, combined with recent studies, shows the relevance of collisionless weak KAW turbulence to describe the solar wind at electron scales.

Estymacja parametrów asymetrycznych procesów niegaussowskich o średniej kroczącej metodą maksymalizacji wielomianu stochastycznego PMM

In this paper consider is the application of the Polynomial Maximization Method PMM to find estimates of the parameters of non-Gaussian Moving Average model. This approach is adaptive and is based on the analysis of higher-order statistics. The case of asymmetry of distributions of Moving Average of the stochastic processes is also considered. It is shown that the asymptotic variance of estimates of the Polynomial Maximization Method (2nd order) have such analytical expressions, whose allow to finding estimates and analyzing their uncertainties. Above approach can be significantly less than the variance of the classic estimates based on minimizing the Conditional Sum of Squares or Maximum Likelihood (in the Gaussian case). The increase of accuracy depends on the values of the coefficient’s asymmetry and the kurtosis of residuals. The results of statistical modeling by the Monte Carlo Method confirm the effectiveness of the proposed approach.

Ballastless track mortar layer void detection by high-order statistical analysis of axle box acceleration

A scheme based on high-order statistical analysis of vertical axle box acceleration (ABA) is proposed to locate and identify mortar voids in ballastless tracks quickly. The ABA signal near a void area contains abnormal non-Gaussian components, but are easily buried by random noise. A 4th order cumulative time-varying kurtosis difference (TKD-cum4) method is designed to locate void areas. Twenty-four edge-type and concealed-type mortar void cases are identified and classified by bispectral analysis of ABA signal segments. The measured and simulated data verify the proposed scheme's CAM void location and recognition effect. The results show that the maximum void area location error along the railway line is 0.137 m. The recognition accuracy of unidirectional void level is 92%. The simultaneous recognition rate of void type and damage degree is nearly 90%. The good noise resistance of the proposed detection scheme is verified.

SOFFLFM: Super-resolution optical fluctuation Fourier light-field microscopy

Fourier light-field microscopy (FLFM) uses a microlens array (MLA) to segment the Fourier plane of the microscopic objective lens to generate multiple two-dimensional perspective views, thereby reconstructing the three-dimensional (3D) structure of the sample using 3D deconvolution calculation without scanning. However, the resolution of FLFM is still limited by diffraction, and furthermore, it is dependent on the aperture division. In order to improve its resolution, a super-resolution optical fluctuation Fourier light-field microscopy (SOFFLFM) was proposed here, in which the super-resolution optical fluctuation imaging (SOFI) with the ability of super-resolution was introduced into FLFM. SOFFLFM uses higher-order cumulants statistical analysis on an image sequence collected by FLFM, and then carries out 3D deconvolution calculation to reconstruct the 3D structure of the sample. The theoretical basis of SOFFLFM on improving resolution was explained and then verified with the simulations. Simulation results demonstrated that SOFFLFM improved the lateral and axial resolution by more than [Formula: see text] and 2 times in the second- and fourth-order accumulations, compared with that of FLFM.

Ballastless Track Mortar Layer Void Detection by High-Order Statistical Analysis of Axle Box Acceleration

Ballastless Track Mortar Layer Void Detection by High-Order Statistical Analysis of Axle Box Acceleration

Nonlinear Channel Estimation for Minimally Sampled Baseband OFDM Systems

The orthogonal frequency-division multiplexing (OFDM) is a widely used modulation scheme in modern digital communication systems. An OFDM signal can be susceptible to channel nonlinearities due to its high peak-to-average power ratio (PAR). This makes estimation and compensation of nonlinear channels particularly vital for OFDM systems. In this paper, we consider identifying cubically nonlinear channels in baseband OFDM systems using minimally sampled input and output signals. Unlike nonlinear channels in bandpass OFDM systems, which can be modeled by a complex baseband equivalent Volterra series with only odd order terms, the nonlinear channel in a baseband OFDM system requires a real Volterra series with both even and odd order terms. The minimally sampled input and output signals of the baseband OFDM system are used to estimate the frequency-domain Volterra kernels of the nonlinear channel. Due to the possible spectral regrowth caused by the channel nonlinearities, the minimally sampled output signal could be aliased. Nevertheless, by conducting higher-order statistical analyses on the OFDM signals, we show that closed form expressions of the Volterra kernels can still be derived. As a result of the closed form expressions, a simple estimate for the Volterra kernels is attained. Furthermore, the derived Volterra kernel estimate is shown to achieve the minimum mean square error (MMSE). Therefore, the proposed approach indeed provides an efficient yet accurate way to estimate nonlinear channels in baseband OFDM systems.

Effect of corporate social responsibility on consumer retention for FMCG industries: a deep learning analysis of professional students of Odisha

Corporate social responsibility (CSR) represents a form of international private business self-regulation that contributes to the social aims of a charitable, activist, or philanthropic nature through helping ethically based practices. The benefits from CSR like generating better public image, enhance in community support, enhance in market share, enhance in the satisfaction of the consumer, enhance in the exports, offering better quality services and products, and enhance in the productivity is not considered as the perception level consumers. This paper analyzes the effect of CSR activities on the fast moving consumer goods (FMCG) industries on the basis of the retention of the consumers of FMCG products. Initially, a questionnaire is prepared that consists of five categories. Each category is composed of more than ten questions. The information related to the questionnaire is obtained in the view of the professional students from Odisha. The customers are requested to fill the exact information as much as possible. Next, a higher-order statistical analysis is considered for realizing the behaviour of the categories. Additionally, the usage of deep learning called Optimized RNN (O-RNN) is used for predicting the customer retention in maintaining the CSR on FMCG industries. The improvement in the traditional RNN is done by optimizing the hidden neurons by the improved form of Sea Lion Optimization Algorithm (slno) called Modified slno (M-slno) with the intention of minimizing the error among the actual and the predicted outcome, hence it is called as O-RNN. The retention of the customers regarding the effect of CSR activities on FMCG companies and the benefits from CSR is investigated using the O-RNN prediction analysis against several traditional deep learning models as well as different statistical measures.

Comparative Study between Healthy Young and Elderly Subjects: Higher-Order Statistical Parameters as Indices of Vocal Aging and Sex

The objective of this study was to test higher-order statistical (HOS) parameters for the classification of young and elderly voice signals and identify gender- and age-related differences through HOS analysis. This study was based on data from 116 subjects (58 females and 58 males) extracted from the Saarbruecken voice database. In the gender analysis, the same number of voice samples were analyzed for each sex. Further, we conducted experiments on the voices of elderly people using gender analysis. Finally, we reviewed the standards and reference models to reduce sex and gender bias. The acoustic parameters were extracted from young and elderly voice signals using Praat and a time–frequency analysis program (TF32). Additionally, we investigated the gender- and age-related differences in HOS parameters. Young and elderly voice signals significantly differed in normalized skewness (p = 0.005) in women and normalized kurtosis (p = 0.011) in men. Therefore, normalized skewness is a useful parameter for distinguishing between young and elderly female voices, and normalized kurtosis is essential for distinguishing between young and elderly male voices. We will continue to investigate parameters that represent important information in elderly voice signals.

Fault diagnosis of rolling element bearings based on cascaded combination top-hat product filter and Teager energy spectrum

The fault features of rolling bearings are always difficult to detect due to the complex operating environment, especially when the fault is in the incipient stage with weak symptoms. For fault detection, this paper proposes a signal processing method based on cascaded combination top-hat product filter and Teager energy spectrum (CCTHPF-TES). First, combining the morphological top-hat operation and cascaded morphological operators, four cascaded combination morphological top-hat operators are presented for extracting the fault features in the vibration signal. Subsequently, by comprehensively investigating the filter characteristics of the proposed operators, a cascaded combination top-hat product operator (CCTHPO) is constructed to further highlight the fault feature information. Moreover, a high-order statistical analysis tool called TES is employed to further improve the detection ability of CCTHPO. Finally, an autocorrelation kurtosis criterion is defined to automatically determine the optimal output of CCTHPF-TES without prior knowledge of faults. Experimental data obtained from the bearing life-cycle test and engineering application are applied to testify the availability of CCTHPF-TES. The results show the superiority of the proposed CCTHPF-TES in bearing fault detection.

Individual identification of electronic equipment based on electromagnetic fingerprint characteristics

With the rapid development of communication and computer, the individual identification technology of communication equipment has been brought to many application scenarios. The identification of the same type of electronic equipment is of considerable significance, whether it is the identification of friend or foe in military applications, identity determination, radio spectrum management in civil applications, equipment fault diagnosis, and so on. Because of the limited-expression ability of the traditional electromagnetic signal representation methods in the face of complex signals, a new method of individual identification of the same equipment of communication equipment based on deep learning is proposed. The contents of this paper include the following aspects: (1) Considering the shortcomings of deep learning in processing small sample data, this paper provides a universal and robust feature template for signal data. This paper constructs a relatively complete signal template library from multiple perspectives, such as time domain and transform domain features, combined with high-order statistical analysis. Based on the inspiration of the image texture feature, characteristics of amplitude histogram of signal and the signal amplitude co-occurrence matrix (SACM) are proposed in this paper. These signal features can be used as a signal fingerprint template for individual identification. (2) Considering the limitation of the recognition rate of a single classifier, using the integrated classifier has achieved better generalization ability. The final average accuracy of 5 NRF24LE1 modules is up to 98% and solved the problem of individual identification of the same equipment of communication equipment under the condition of the small sample, low signal-to-noise ratio.

Signal Processing Techniques to Detect Centrifugal Compressors Instabilities in Large Volume Power Plants

Abstract This paper shows signal processing techniques applied to experimental data obtained from a T100 microturbine connected with different volume sizes. This experimental activity was conducted by means of the test rig developed at the University of Genoa for hybrid systems emulation. However, these results can be extended to all advanced cycles in which a microturbine is connected with additional external components which lead to an increase of the plant volume size. Since in this case a 100 kW microturbine was used, the volume was located between the heat recovery unit outlet and the combustor inlet like in the typical cases related to small size plants. A modular vessel was used to perform and to compare the tests with different volume sizes. The main results reported in this paper are related to rotating stall and surge operations. This analysis was carried out to extend the knowledge about these risk conditions: the systems equipped with large volume size connected to the machine present critical issues related to surge and stall prevention, especially during transient operations toward low mass flowrate working conditions. Investigations conducted on acoustic and vibrational measurements can provide interesting diagnostic and predictive solutions by means of suitable instability quantifiers which are extracted from microphone and accelerometer data signals. Hence, different possible tools for rotating stall and incipient surge identification were developed through the use of different signal processing techniques, such as wavelet analysis and higher order statistics analysis (HOSA) methods. Indeed, these advanced techniques are necessary to maximize all the information conveyed by acquired signals, particularly in those environments in which measured physical quantities are hidden by strong noise, including both broadband background one (i.e., typical random noise) but also uninteresting components associated with the signal of interest. For instance, in complex coupled physical systems like the one it is meant to be studied, which do not satisfy the hypothesis of linear and Gaussian processes inside them, it is reasonable to exploit these kinds of tools, instead of the classical fast Fourier transform (FFT) technique by itself, which is mainly adapt for linear systems periodic analysis. The proposed techniques led to the definition of a quantitative indicator, the sum of all autobispectrum components modulus in the subsynchronous range, which was proven to be reliable in predicting unstable operation. This can be used as an input for diagnostic systems for early surge detection. Furthermore, the presented methods will allow the definition of some new features complementary with the ones obtainable from conventional techniques, in order to improve control systems reliability and to avoid false positives.

Forecasting the Impact of Social Media Advertising among College Students using Higher Order Statistical Functions

Nowadays, social media has emerged as one of the activity among the users on their day-to-day life activity. This research work plans to develop a statistical review that concerns on social media advertising among college students from diverse universities. The review analysis on social media advertising is given under six sections such as (i) Personal Profile; (ii) Usage; (iii) Assessment; (iv) Higher Order statistics like Community, Connectedness, Openness, Dependence and Participation; (v) Trustworthiness such as Trust, Perceived value and Perceived risk; and (vi) Towards advertisement which involves attitude towards advertisement, response towards advertisement and purchase intension. The initial stage is on questionnaire preparation based on social media networking. The records stated in the questionnaire are intimately taken about the usage of social media sites and the advertisement on social media networking. In the second stage, the planned questionnaire is distributed over college students from diverse universities. The entire questions are made mandatory in this questionnaire and subsequent to this, the students from various universities are demanded to fill up their response to this questionnaire. These responses from the students are then taken for analysis purpose. In this research work, the analysis is performed on the basis of higher-order statistical analysis that favorably concerned on correlation coefficients and entropy. This in turns helps to determine the correlation and entropy among the response towards the social media network.

Texture Based Classification of Malaria Parasites from Giemsa-Stained Thin Blood Films

Quantification of parasitemia is an important part of a microscopic malaria diagnosis. Giemsa-stained thin blood smear is the gold standard method for detecting malaria parasite enumeration. However, manual counting reveals the limitations of human inconsistency and fatigue, as well as the unreliability of accuracy and non-reproducibility. Inaccurate parasitemia affects clinical diagnosis and therapeutic procedure. Automated quantification is therefore useful to improve the performance of quantifying parasite density. In this paper, the texture-based classification approach is investigated. The methods consist of the following processes: pre-processing, segmentation, feature extraction and the classification of erythrocytes. The pre-processing is applied for image conversion and enhancement. The segmentation combines local adaptive thresholding, morphological process and watershed transform to extract red blood cells, separate touching and overlapping cells. Texture analysis is performed to establish parameters obtained from first-order, second-order and higher-order statistical analysis and wavelet transform. Two feature selection approaches, the sequential forward selection method and sequential backward elimination method, integrated with a support vector machine classifier are examined to obtain the optimal feature set for identifying the Plasmodium falciparum stages. We found that gray-level co-occurrence matrices based textural features were highly selected. The optimal feature set selected by the sequential forward selection yields lesser number of features and tends to give a higher degree of accuracy than the feature set selected by sequential backward elimination. The proposed method produces 98.87% accuracy for binary classification, 99.56% accuracy for ring stage classification, and 99.48% accuracy for tropozoite stage classification. Grey-level co-occurrence matrices based texture analysis is the dominant method compared to first-order and higher-order statistical texture analysis as well as wavelet transform.

Digital image forensic approach based on the second-order statistical analysis of CFA artifacts

The truthfulness of digital images can be evaluated by investigating the CFA artifacts introduced due to the interpolation process of the image acquisition phase. In this paper, an image tampering detection technique is proposed by exposing the CFA artifacts in difference domain through the higher-order statistical analysis based on the Markov transition probability matrix (MTPM). Firstly, the given image is re-interpolated with most commonly used four Bayer CFA patterns. The re-interpolation process is performed by using bilinear interpolation scheme for simplicity purpose. Then, the difference between the given image and its re-interpolated versions is evaluated to analyze the CFA inconsistencies. The target difference image is selected corresponding to the maximum sum which is further processed to evaluate the MTPM based second-order statistical feature. The recommended approach is assessed on different images from UCID dataset and various social networking websites based on scalar-based and SVM/machine learning based forensic detectors. The experiment results confirm that the projected method offers improved efficiency in comparison to the existing techniques based on different forgery scenarios.

The role of IRT in the archaeometric study of ancient glass through XRF and FORS

The process of degradation of archaeological glass subjected to centuries of burial can be of great relevance: typical consequence of degradation in the original vitreous material is rainbow-like iridescence due to chemical alteration of surface layers, salts formation and ion migration. The research presented in this paper is focused on the study of a collection of Roman glass (I - II century A.D.) held by the Museo Civico Etnografico Archeologico Fanchini of Oleggio, Italy. Chemical characterization (namely flux, chromophores and opacifiers determination) has been performed by a combination of X-Ray Fluorescence (XRF) and Fibre Optics Reflectance Spectroscopy (FORS) in the UV–Vis-NIR region. Conservation conditions have been studied and degraded areas have been mapped through Infrared thermography (IRT). IRT is a non-invasive method typically used to measure the apparent temperature of objects and represent it as pseudo-colour images. In the present work, we demonstrate the feasibility of IRT for identifying and mapping glass substrate defects due to ageing of glass, assuming that they may be considered as thermal anomalies. Thermogram sequences have been processed by high order statistical analysis, which is particularly suitable since it is based on automated processes where the output is a single representative image. The use of a thermal camera allows furthermore to perform remote analysis in areas hardly reachable in a fast and effective way.

Fluctuation X-ray scattering from nanorods in solution reveals weak temperature-dependent orientational ordering.

Higher-order statistical analysis of X-ray scattering from dilute solutions of polydisperse goethite nanorods was performed and revealed structural information which is inaccessible by conventional small-angle scattering. For instance, a pronounced temperature dependence of the correlated scattering from suspension was observed. The higher-order scattering terms deviate from those expected for a perfectly isotropic distribution of particle orientations, demonstrating that the method can reveal faint orientational order in apparently disordered systems. The observation of correlated scattering from polydisperse particle solutions is also encouraging for future free-electron laser experiments aimed at extracting high-resolution structural information from systems with low particle heterogeneity.

Mueller-matrix-based polarization imaging and quantitative assessment of optically anisotropic polycrystalline networks.

We introduce a Mueller-matrix imaging polarization-based approach for the quantitative digital screening of the polycrystalline structure of fibrillary-based biological tissues in vitro. The morphometric evaluation of histological sections of myocardium was performed utilizing the high-order statistical moments calculated based on the spatial distribution of linear and circular birefringence and dichroism obtained experimentally. We demonstrate that spatial distributions of phase of light and optical anisotropy of scattering inherent to fibrillar networks of myocardium at different necrotic stages can be effectively used as a quantitative marker of stages of myosin fibril degradation. Processing the images of phase of light scattered in biological tissues with high order statistical analysis provides a functional tool for the quantitative characterization of necrotic conditions of the myocardium.

Computational multivariate modelling of electrical activity of the porcine uterus during spontaneous and hormone-induced oestrus.

What is the central question of this study? Does oestrous cycle synchronization influence myoelectrical activity of porcine myometrium? What is the main finding and its importance? Exogenous hormones used to synchronize oestrus in pigs altered myoelectrical activity, which was effectively modelled. Higher-order multivariate statistic modelling provided evidence of similar activity in both types of oestrus, but a larger order of EMG signals during induced oestrus. Higher-order statistical analysis of the probabilistic model suggests the beginning of the early follicular phase and the mid-luteal phase to be most important in evaluation of the natural patterns of myoelectrical activity. Higher-order multivariate cumulants are more informative than classical statistics in characterization of myoelectrical activity changes in porcine myometrium. In pig production units, control of the oestrous cycle and synchronization of ovulation have become routine herd management procedures. During the oestrous cycle, in both induced and spontaneous conditions, the ovaries and the uterus undergo hormone-dominated physiological changes, which are consistent with the hypothesis that there is a functional role of uterine contractions in promoting fertilization. We have used electromyography to determine whether the use of exogenous hormones, such as equine chorionic gonadotrophin and human chorionic gonadotrophin, which have the potential to control the timing of ovulation in female pigs, changes the multivariate relationships between parameters of electrical bursts and modulates the patterns of myoelectrical activity. We used the mathematical approach of higher-order multivariate cumulants in complex modelling of the myometrial electrical activity. The experiment was conducted on 12 mature Polish Landrace sows, and uterine activity was recorded during both spontaneous and induced oestrous cycles. The burst parameters were determined using six features in the time domain and, after Fast Fourier transformation, in the frequency domain. Evaluation of myoelectrical activity patterns was conducted based on classical univariate statistical methods and multivariate probabilistic modelling. The classical statistical approach indicated weaker myoelectrical activity after hormonal stimulation, whereas the higher-order multivariate statistical model showed evidence of similar status of activity and a larger order of signals during induced oestrus. Routine oestrous cycle synchronization affects the multivariate probabilistic model of myometrial electrical activity.