Autocorrelation Function Analysis Research Articles

Atomic arrangement and mechanical properties of chemical-vapor-deposited amorphous boron

Amorphous boron can be synthesized by chemical vapor deposition (CVD) onto a tungsten wire substrate, and this core-shell fiber has been widely used in high-performance composites due to its superior mechanical properties. Although the amorphous boron coating makes a significant contribution to the high fracture strength, its mechanical properties have not been studied rigorously due to its thin thickness and strong adhesion to the substrate. Furthermore, the medium-range atomic ordering of CVD amorphous boron has not been clearly understood, and the determination of the closest crystalline structure has been a challenge. In this study, high-resolution transmission electron microscopy (HRTEM), nanoindentation, and in-situ micropillar compression were performed to investigate the atomic arrangement and mechanical properties. Electron diffraction and autocorrelation function analysis revealed α-rhombohedral boron ordering as the closest crystalline structure. Micropillar compression displayed near-ideal yield strength (~13 GPa), but nanoindentation a relatively moderate Young's modulus (~320 GPa), leading to a modulus of resilience (2.64 × 108 J/m3) unprecedentedly higher than most advanced engineering materials. Our structural and mechanical data will be discussed in terms of lattice point spacing and the influence of surface defects on fracture strength, respectively. Our results will be potentially useful to improve mechanical properties of amorphous boron core-shell fibers and related composites.

Drought index prediction using advanced fuzzy logic model: Regional case study over Kumaon in India.

A new version of the fuzzy logic model, called the co-active neuro fuzzy inference system (CANFIS), is introduced for predicting standardized precipitation index (SPI). Multiple scales of drought information at six meteorological stations located in Uttarakhand State, India, are used. Different lead times of SPI were computed for prediction, including 1, 3, 6, 9, 12, and 24 months, with inputs abstracted by autocorrelation function (ACF) and partial-ACF (PACF) analysis at 5% significance level. The proposed CANFIS model was validated against two models: classical artificial intelligence model (e.g., multilayer perceptron neural network (MLPNN)) and regression model (e.g., multiple linear regression (MLR)). Several performance evaluation metrices (root mean square error, Nash-Sutcliffe efficiency, coefficient of correlation, and Willmott index), and graphical visualizations (scatter plot and Taylor diagram) were computed for the evaluation of model performance. Results indicated that the CANFIS model predicted the SPI better than the other models and prediction results were different for different meteorological stations. The proposed model can build a reliable expert intelligent system for predicting meteorological drought at multi-time scales and decision making for remedial schemes to cope with meteorological drought at the study stations and can help to maintain sustainable water resources management.

Time dependence of quantum correlation functions.

In the past few years, the exponential expansion analysis of time autocorrelation functions has provided profound insight into the leading microscopic processes driving the atomic-scale dynamics and has made it possible to highlight the presence and the role of various relaxation channels through which the fundamental correlation functions decay with time. Here we apply this method to the determination of the full time dependence of a correlation function c(t) in a quantum system at nonzero temperature, by making explicit its relationship with its Kubo transform c_{K}(t), which in some cases can be approximately computed with the presently available quantum simulation techniques. We obtain an exact expression for c(t) in terms of the exponential modes that describe the time behavior of c_{K}(t). The relative importance of the various modes in determining the overall shape of c(t) can then be studied in detail. This work extends to the full time domain the results of a previous paper [Guarini et al., Phys. Rev. Lett. 123, 135301 (2019)PRLTAO0031-900710.1103/PhysRevLett.123.135301], in which we employed the same method to calculate the zero time value of the velocity autocorrelation function, to obtain a microscopic description of the quantum mean kinetic energy in a fluid. In particular, we show that the decay constants and the frequencies of the dominant microscopic modes of c(t) are the same as those of c_{K}(t), but the dynamics of the quantum system also contains an additional term decaying on a time scale determined solely by temperature of the system.

DETECTING SYSTEMATIC AND RANDOM COMPONENT OF SURFACE ROUGHNESS SIGNAL

The solution of the problem of separating the initial one-dimensional signal into two components – systematic and random – has an extremely wide practical application not only in the theory of information and communication (and related disciplines), but also in mechanical engineering disciplines. For example, mechanical engineering technology being a science discipline includes the teaching about the surface quality of the machined parts and researching the surface roughness after machining these parts by cutting and grinding. The paper shows that the theoretical and actual values of roughness parameters differ significantly (up to 20 times) due to the influence of a random component that is present in the roughness signal together with a systematic component. It is necessary to identify the share of each of these components in the specified surface quality parameters in accordance with the method proposed in the paper. This method allows detecting the systematic and random components of the signal and is based on the analysis of the signal autocorrelation function. Practical examples of this analysis are considered in detail for milled surface profilogram obtained experimentally. Both milling, which creates irregularities on the machined surface, and measurement of these irregularities are performed on modern CNC equipment: machining center 500V/5 and computer measuring station T8000, respectively. The developed and shown by examples signal separation technique is also applicable in other fields of science, technology and manufacturing. For example, when determining the signal to noise ratio in the theory of information and communication, in the field of telecommunications and telemetry, radio engineering, etc.

Noise and bias in optical coherence tomography intensity signal decorrelation.

Functional optical coherence tomography (OCT) imaging based on the decorrelation of the intensity signal has been used extensively in angiography and is finding use in flowmetry and therapy monitoring. In this work, we present a rigorous analysis of the autocorrelation function, introduce the concepts of contrast bias, statistical bias and variability, and identify the optimal definition of the second-order autocorrelation function (ACF) g (2) to improve its estimation from limited data. We benchmark different averaging strategies in reducing statistical bias and variability. We also developed an analytical correction for the noise contributions to the decorrelation of the ACF in OCT that extends the signal-to-noise ratio range in which ACF analysis can be used. We demonstrate the use of all the tools developed in the experimental determination of the lateral speckle size depth dependence in a rotational endoscopic probe with low NA, and we show the ability to more accurately determine the rotational speed of an endoscopic probe to implement NURD detection. We finally present g (2)-based angiography of the finger nailbed, demonstrating the improved results from noise correction and the optimal bias mitigation strategies.

АВТОКОРРЕЛЯЦИОННЫЙ АНАЛИЗ КАРДИОИНТЕРВАЛОГРАММ В РАЗРЕЗЕ ТРЕХ ГРУПП ПАЦИЕНТОВ С РАСШИРЕННЫМ АНАЛИЗОМ ПЕРВИЧНОЙ ОБРАБОТКИ ДАННЫХ

Purpose of the study. To develop a method for analyzing the seasonal and cyclic components of R-R intervals by estimating the autocorrelation function (ACF). Material and methods. The study consists of conducting a practical analysis of ACF as an application to R-R intervals, divided into three groups: all patients are patients with atrial fibrillation. The first group had tachycardia; the second had bradycardia; the third group - are patients without specific features. It should be noted that the age of the patients, according to which the analysis was carried out, is from 40 years and older. Results. The module developed at VBA (Visual Basic for Applications) enables to apply the methods of primary processing of the studied data, to create the ACF with its graphic representation for rather large values of the lag variable τ=1,100 . Samples of 1000 values were examined. The most optimal data cleaning methods have been selected to get rid of excessive noise. A detailed analysis of ACF was carried out in the three groups of patients. Impressive research statistics have been compiled. Conclusions. In people with signs of tachycardia, all ACF ratios were not significant. In cases with severe tachycardia, ACF coefficients were positive, and had a decreasing trend. In the series with signs of bradycardia, almost all ACF coefficients were significant. ACF decreased with increasing lag τ. It can be concluded that in this group of time series there is a marked constant linear trend. In the group of patients with normal heart rate dynamics, insignificant ACF coefficients were obtained, which indicates the absence of any tendency. The coefficients were concentrated along the axis of the lag, had both positive and negative values. The oscillating process indicates only a strong stochastic component of the studied data.

Адаптация радиолинии КИС по скорости передачи информации в условиях помех

In modern command and measurement systems (CMS), phase-shift keyed signals are widespread, which, with relatively simple methods of formation, have a low level of side lobes of the autocorrelation function (ACF). Also, in recent years, a number of studies have been carried out on the implementation of the principle of constructing the CMS, based on the adaptation of processing algorithms to the properties of useful signals and interference. On the basis of this principle, an algorithm for adapting the CMS radio link has been developed, which makes it possible to analyze information channels at various speeds and automatically connect the selected channel, which is an urgent task for controlling low-orbit spacecraft. The article proposes a receiver scheme with parallel reception of several information channels with different information transfer rates. The analysis of autocorrelation and cross-correlation functions made it possible to conclude that the data obtained do not contradict the assumptions about the possibility of implementing an adaptive receiver with the choice of an information channel in terms of speed. It is shown that the use of such a receiving device with an algorithm for selecting and switching information channels according to the reception rate provides a significant gain in information transmission time in comparison with traditional reception and switching of speeds by commands in conditions of complex interference stop.

Computational Implementation and Asymptotic Statistical Performance Analysis of Range Frequency Autocorrelation Function for Radar High-Speed Target Detection

The range frequency autocorrelation function (RFAF) based algorithm is proposed for radar target detection and motion parameter estimation in our previous work. In the RFAF-based method, the symmetric autocorrelation function (SAF) is constructed with respect to the range frequency, and three dimensional (slow time, range frequency, and shift frequency) energy accumulation can be completed coherently. In this article, as a further study of the RFAF-based method, the fast implementation and detailed asymptotic statistical performance analyses of RFAF are studied. First, on the basis of the frequency circular convolution theorem, the fast implementation is proposed. Then, to evaluate the anti-noise performance and estimation accuracy, the output signal to noise ratio (SNR) and asymptotic mean square errors (AMSEs) of estimated parameters are derived in closed forms. Theoretical analyses and numerical simulation results reveal that the RFAF-based method outperforms several state-of-the-art algorithms in terms of anti-noise performance and parameter estimation accuracy, and its computational efficiency is high.

Testing thermal conductivity models with equilibrium molecular dynamics simulations of the one-component plasma.

Equilibrium molecular dynamics simulations are used to calculate the thermal conductivity of the one-component plasma via the Green-Kubo formalism over a broad range of Coulomb coupling strength, 0.1≤Γ≤180. These simulations address previous discrepancies between computations using equilibrium versus nonequilibrium methods. Analysis of heat flux autocorrelation functions show that very long (6×10^{5}ω_{p}^{-1}) time series are needed to reduce the noise level to allow ≲2% accuracy. The simulations provide accurate data for Γ≲1. This enables a test of the traditional Landau-Spitzer theory, which is found to agree with the simulations for Γ≲0.3. It also enables tests of theories to address moderate and strong Coulomb coupling. Two are found to provide accurate extensions to the moderate coupling regime of Γ≲10, but none are accurate in the Γ≳10 regime where potential energy transport and coupling between mass flow and stress dominate thermal conduction.

Chaotic characteristic analysis of network traffic time series at different time scales

Abstract Characteristic analysis of network traffic time series is very meaningful for network traffic prediction. The dynamic behavior of network traffic time series is an external manifestation under the combined action of complex non-linear and multi-scale phenomena. Based on the chaotic theory, the chaotic characteristics of network traffic time series collected at different time scales are analyzed and discussed. Firstly, power spectral density analysis and autocorrelation function analysis are introduced. The results show that the power spectral density of network traffic has continuous broad spectrum, which qualitatively explains the chaotic behavior of network traffic. The value of autocorrelation function of network traffic decreases with time delay, which shows its short-term predictability. Calculations on scaling exponent show that network traffic is scale-free at different time scales. Then, Hurst index of network traffic is calculated, and the 0-1 test algorithm for chaos is used to calculate the incremental growth rate of network traffic time series. The variation of chaotic characteristics of network traffic at different time scales is discussed. Further, the phase space of network traffic time series at different time scales is reconstructed, and the correlation dimension, largest Lyapunov exponent, and Kolmogorov entropy are calculated respectively. These chaotic identification indexes are used to analyze the chaotic characteristics of network traffic and their variation with time scales. The results show that the relationship between the time scale and the non-linear characteristics of network traffic time series is not obvious. The incremental growth rate of network traffic time series has no obvious change with the increase of time scale. There is no clear relationship between the change of time scale and embedding dimension and delay time. The largest Lyapunov exponent show that the network traffic at different time scales has different maximum predictable time. At the same time, Kolmogorov entropy increase with the increase of time scale, which means that the chaotic characteristics of network traffic time series become stronger and the predictability becomes worse with the time scale.

ReadPTU: a python library to analyse time tagged time resolved data

readPTU is a python package designed to analyze time-correlated single-photon counting data. The use of the library promotes the storage of the complete time arrival information of the photons and full flexibility in post-processing data for analysis. The library supports the computation of time resolved signal with external triggers and second order autocorrelation function analysis can be performed using multiple algorithms that provide the user with different trade-offs with regards to speed and accuracy. Additionally, a thresholding algorithm to perform time post-selection is also available. The library has been designed with performance and extensibility in mind to allow future users to implement support for additional file extensions and algorithms without having to deal with low level details. We demonstrate the performance of readPTU by analyzing the second-order autocorrelation function of the resonance fluorescence from a single quantum dot in a two-dimensional semiconductor.

Non-stationary analysis of molecule capture and translocation in nanopore arrays.

Analytical formulas for the ON- and OFF-time distributions as well as for the autocorrelation function were derived for the case of single molecule translocation through nanopore arrays. The obtained time-dependent expressions describe very well experimentally recorded statistics of DNA translocations through an array of solid state nanopores, which allows us to extract molecule and system related physical parameters from the experimental traces. The necessity of non-stationary analysis as opposite to the steady-state approximation has been vindicated for the molecule capture process, where different time-dependent regimes were identified. A long tail in the distribution of translocation times has been rationalized invoking Markov jumps, where a possible sequential ordering of events was elucidated through autocorrelation function analysis.

How Water Permutes the Structural Organization and Microscopic Dynamics of Cholinium Glycinate Biocompatible Ionic Liquid.

We investigate the structural organization and microscopic dynamics of aqueous cholinium glycinate ([Ch][Gly]), a biocompatible ionic liquid (IL), by employing all-atom molecular dynamics simulations. Herein, we observe the effect of water content on the molecular-level arrangement of ions in the IL-water mixture through simulated X-ray scattering structure function, their partial components, and real-space correlation functions. The study reveals the presence of a principal peak in the total structure function of the neat [Ch][Gly] IL at around q = 1.4 Å-1. The corresponding correlation tends to decrease and shifts toward shorter length scales with increase in the water content. It is found that the principal peak mainly originates from the correlations between counter ions. Hydrogen bond analysis reveals that water molecules compete with the anions to form hydrogen bond with the hydroxyl hydrogen of cation. Concomitantly, strong hydrogen bonding is also observed between [Gly]- anion and water, which depreciates with the increasing hydration level. Hydrogen-bond autocorrelation function analysis manifests that average lifetimes of different possible hydrogen bonds decrease with increase in mole fraction of water. The mobilities of the ions are also significantly affected by water, showing a nonlinear increase with the increasing water content. The [Gly]- anion is found to show faster dynamics on the addition of water as compared to [Ch]+ cation.

The analysis of partial autocorrelation function in predicting maximum wind speed

The stationary and non-stationary testing of the data set can be done using a plot analysis of the Partial Autocorrelation Function of the data, by viewing the maximum number of the expected value of Partial Autocorrelation. The Autocorrelation Function (ACF) is a function that shows the correlation between the observation of the t-time and the observation at the previous time. The autocorrelation function shows the autocorrelation coefficient, which is the correlation measurement of the observations at different times. Data taken from Statistics Indonesia -known in Indonesia as BPS (Badan Pusat Statistik)- contains the information about the maximum wind speed by month at the Paotere station in 2008 - 2017 in Makassar City. By using the data, the maximum wind speed for the next 12 months will be estimated.ie from January 2017 to December 2018. The results obtained, forecasting is done with 12 leads period ahead with 95% confidence interval.

Inherentness of Non-stationarity in Solar Wind

Abstract Most studies of turbulence in the solar wind invoke stationarity as a working hypothesis. Unfortunately, this concept is difficult to verify in practice. To investigate the validity of the weak stationarity assumption we consider magnetic field measurements made by the WIND satellite and study the properties of the autocorrelation function (ACF), which is a classical gauge for characteristic times or scales. We find that the ACF suffers from a high variance, which precludes the routine interpretation of correlation times and scales. In addition, the ACF fails to converge toward a constant function, even when considering the longest available intervals of either fast or slow solar wind. The reasons behind this lack of convergence are better understood by considering the power spectral density (PSD) of the magnetic field and analyzing synthetic data that exhibit the same PSD. Interestingly, we find evidence for an f −1 scaling at low frequencies in both fast and slow solar winds. These results, together with the theoretical properties of processes with f −γ scaling all point to the non-stationary behavior of the solar wind, in particular for scales that correspond to the inertial range. They also impose strong constraints on the applicability of ACF analysis as a tool for characterizing statistical properties of solar wind turbulence.

Short term memory efficient pore pressure prediction via Bayesian neural networks at Bering Sea slope of IODP expedition 323

Pore pressure (PP) study can provide insightful information about evolution history and/or geological process taking place over a region. Conventional methods, mostly are of deterministic, and they do not allow considering underlying variability and uncertainty. Here we implement Bayesian neural networks (BNN) optimized by Scaled Conjugate Gradient (SCG) and Hybrid Monte Carlo (HMC) approach to model the PP and estimate the uncertainty in prediction from well log data of well U1343E located at Bering sea slope region of the IODP Expedition 323. In the first step, to create representative samples of well log and corresponding PP samples, Eaton’s and porosity methods are employed to estimate PP empirically from well log data (e.g., gamma ray, sonic velocity, bulk density and sonic derived porosity). In the second step, in total 357 representative samples are used to build a statistical model in Bayesian framework to model the PP against depth. Prior to actual data analysis, we conducted a series of experiments combining with auto-correlation function (ACF) and/or partial autocorrelation function (PACF) analysis to fix network structure (e.g., input lag, number of hidden node) and the bounds of network hyper-parameter. In contrast to previous approach, we seek to develop a mechanism which allows to explore the link between past PP and/or well log history and present PP under rapid sedimentation rate and changing environment. The model exhibits excellent performance between predicted and computed PP with Pearson's correlation coefficient, reduction of error (RE) (RESCG-BNN ∼ 0.99; REHMC-BNN ∼ 0.99), and index of agreement (IA) (IASCG-BNN ∼ 0.99; IAHMC-BNN ∼ 0.98). Comparison based on coefficient of determination, R2, it is obtained that BNN produced superior results than the conventional artificial neural networks (ANNs). Moreover, at 530 mbsf (meter below sea floor), abrupt PP change could be linked to the transition from Pliocene to Pleistocene. The approach used here, could be useful to identify overpressure zones (OPZ) and to understand the role of past PP/well log to present PP history in many other complex geo-environmental applications.

Research and Analysis of Autocorrelation Functions of Code Sequences Formed on the Basis of Monocyclic Quasi-Orthogonal Matrices

Introduction: Barker codes representing binary sequences (codes) of finite lengths 2, 3, 4, 5, 7, 11 and 13 are widely used in solving the problem of increasing the noise immunity of radar channels. However, the code sequences for n > 13 are unknown. Sequences derived from quasi-orthogonal Mersenne matrices also have not been used for these purposes.Purpose: Studying the ways to compress a complex modulated signal by Mersenne sequences obtained from the first rows of a monocyclic quasi-orthogonal Mersenne matrix, as an alternative to Barker codes.Results:It has been found out that the characteristics of autocorrelation functions for Mersenne codes 3, 7 and 11 exceed those for Barker codes. This is a basis for ensuring greater noise immunity of probing signals in radar channels, as well as for increasing the probability of their correct detection, proving the expediency of their application for amplitude and phase modulation of radio signals.Practical relevance:The obtained results allow you to increase the compression characteristics in radar systems when solving the problem of detecting targets under noise and interference. The wide application of Barker codes of length 3, 7 and 11 in digital data transmission systems provides a special interest in similar Mersenne codes when implementing noise-resistant data transmission in radio channels in a complex electromagnetic environment. Discussion: An unresolved problem is the non-symmetry of elements in a coding Mersenne sequence. This problem can be solved either by special synthesis of a phase-modulated signal or by finding new approaches to their compression.

Tungsten self-organization nanowires prepared by DC magnetron sputtering

Tungsten nanowires were deposited by DC magnetron sputtering onto vicinal sapphire substrates with a specific geometry in which the entrance angle (solid angle) of the vapor beam relative to the substrate was modified. Through 2D auto-correlation function analysis, suitable conditions to obtain a nanoscale corrugated surface with a high-order correlation were determined concerning the annealing procedure (1300 °C for 5 h) of the sapphire substrates with miscut angle of 0.3°. Depending on the entrance angle of the tungsten vapor beam, different nanoparticles and/or nanowires were systematically produced under controlled conditions. AFM images, from the samples deposited with an entrance angle lower or equal to the miscut angle (0.3°) on the annealed sapphire substrates have shown homogeneous, uniform and isolated tungsten nanowires.

Detection of Bursts from FRB 121102 with the Effelsberg 100 m Radio Telescope at 5 GHz and the Role of Scintillation

Abstract FRB 121102, the only repeating fast radio burst (FRB) known to date, was discovered at 1.4 GHz and shortly after the discovery of its repeating nature, detected up to 2.4 GHz. Here, we present three bursts detected with the 100 m Effelsberg radio telescope at 4.85 GHz. All three bursts exhibited frequency structure on broad and narrow frequency scales. Using an autocorrelation function analysis, we measured a characteristic bandwidth of the small-scale structure of 6.4 ± 1.6 MHz, which is consistent with the diffractive scintillation bandwidth for this line of sight through the Galactic interstellar medium (ISM) predicted by the NE2001 model. These were the only detections in a campaign totaling 22 hr in 10 observing epochs spanning five months. The observed burst detection rate within this observation was inconsistent with a Poisson process with a constant average occurrence rate; three bursts arrived in the final 0.3 hr of a 2 hr observation on 2016 August 20. We therefore observed a change in the rate of detectable bursts during this observation, and we argue that boosting by diffractive interstellar scintillations may have played a role in the detectability. Understanding whether changes in the detection rate of bursts from FRB 121102 observed at other radio frequencies and epochs are also a product of propagation effects, such as scintillation boosting by the Galactic ISM or plasma lensing in the host galaxy, or an intrinsic property of the burst emission will require further observations.

New Possibilities Provided by the Analysis of the Molecular Velocity Autocorrelation Function in Liquids

Long-time tails of the molecular velocity autocorrelation function (VACF) in liquid argon at temperatures higher and lower than the spinodal temperature have been analyzed. By considering the time dependence of the VACF, the self-diffusion and shear viscosity coefficients, and the Maxwell relaxation time are determined, as well as their changes when crossing the spinodal. It is shown that the characteristic changes in the temperature dependences of the indicated kinetic coefficients allow the spinodal position to be determined with a high accuracy. A possibility toapply the proposed method to other low-molecular liquids is considered. As an example, nitrogen and oxygen are used, for which the averaged potential of intermolecular interaction has the Lennard-Jones form.