Long-range correlations in time series generated by time-fractional diffusion: A numerical study

NASA Astrophysics Data System (ADS)

Barbieri, Davide; Vivoli, Alessandro

2005-09-01

Time series models showing power law tails in autocorrelation functions are common in econometrics. A special non-Markovian model for such kind of time series is provided by the random walk introduced by Gorenflo et al. as a discretization of time fractional diffusion. The time series so obtained are analyzed here from a numerical point of view in terms of autocorrelations and covariance matrices.

Using Exponential Smoothing to Specify Intervention Models for Interrupted Time Series.

ERIC Educational Resources Information Center

Mandell, Marvin B.; Bretschneider, Stuart I.

1984-01-01

The authors demonstrate how exponential smoothing can play a role in the identification of the intervention component of an interrupted time-series design model that is analogous to the role that the sample autocorrelation and partial autocorrelation functions serve in the identification of the noise portion of such a model. (Author/BW)

Aging Wiener-Khinchin theorem and critical exponents of 1/f^{β} noise.

PubMed

Leibovich, N; Dechant, A; Lutz, E; Barkai, E

2016-11-01

The power spectrum of a stationary process may be calculated in terms of the autocorrelation function using the Wiener-Khinchin theorem. We here generalize the Wiener-Khinchin theorem for nonstationary processes and introduce a time-dependent power spectrum 〈S_{t_{m}}(ω)〉 where t_{m} is the measurement time. For processes with an aging autocorrelation function of the form 〈I(t)I(t+τ)〉=t^{Υ}ϕ_{EA}(τ/t), where ϕ_{EA}(x) is a nonanalytic function when x is small, we find aging 1/f^{β} noise. Aging 1/f^{β} noise is characterized by five critical exponents. We derive the relations between the scaled autocorrelation function and these exponents. We show that our definition of the time-dependent spectrum retains its interpretation as a density of Fourier modes and discuss the relation to the apparent infrared divergence of 1/f^{β} noise. We illustrate our results for blinking-quantum-dot models, single-file diffusion, and Brownian motion in a logarithmic potential.

An asymptotic theory for cross-correlation between auto-correlated sequences and its application on neuroimaging data.

PubMed

Zhou, Yunyi; Tao, Chenyang; Lu, Wenlian; Feng, Jianfeng

2018-04-20

Functional connectivity is among the most important tools to study brain. The correlation coefficient, between time series of different brain areas, is the most popular method to quantify functional connectivity. Correlation coefficient in practical use assumes the data to be temporally independent. However, the time series data of brain can manifest significant temporal auto-correlation. A widely applicable method is proposed for correcting temporal auto-correlation. We considered two types of time series models: (1) auto-regressive-moving-average model, (2) nonlinear dynamical system model with noisy fluctuations, and derived their respective asymptotic distributions of correlation coefficient. These two types of models are most commonly used in neuroscience studies. We show the respective asymptotic distributions share a unified expression. We have verified the validity of our method, and shown our method exhibited sufficient statistical power for detecting true correlation on numerical experiments. Employing our method on real dataset yields more robust functional network and higher classification accuracy than conventional methods. Our method robustly controls the type I error while maintaining sufficient statistical power for detecting true correlation in numerical experiments, where existing methods measuring association (linear and nonlinear) fail. In this work, we proposed a widely applicable approach for correcting the effect of temporal auto-correlation on functional connectivity. Empirical results favor the use of our method in functional network analysis. Copyright © 2018. Published by Elsevier B.V.

An empirical analysis of the distribution of the duration of overshoots in a stationary gaussian stochastic process

NASA Technical Reports Server (NTRS)

Parrish, R. S.; Carter, M. C.

1974-01-01

This analysis utilizes computer simulation and statistical estimation. Realizations of stationary gaussian stochastic processes with selected autocorrelation functions are computer simulated. Analysis of the simulated data revealed that the mean and the variance of a process were functionally dependent upon the autocorrelation parameter and crossing level. Using predicted values for the mean and standard deviation, by the method of moments, the distribution parameters was estimated. Thus, given the autocorrelation parameter, crossing level, mean, and standard deviation of a process, the probability of exceeding the crossing level for a particular length of time was calculated.

A new estimator method for GARCH models

NASA Astrophysics Data System (ADS)

Onody, R. N.; Favaro, G. M.; Cazaroto, E. R.

2007-06-01

The GARCH (p, q) model is a very interesting stochastic process with widespread applications and a central role in empirical finance. The Markovian GARCH (1, 1) model has only 3 control parameters and a much discussed question is how to estimate them when a series of some financial asset is given. Besides the maximum likelihood estimator technique, there is another method which uses the variance, the kurtosis and the autocorrelation time to determine them. We propose here to use the standardized 6th moment. The set of parameters obtained in this way produces a very good probability density function and a much better time autocorrelation function. This is true for both studied indexes: NYSE Composite and FTSE 100. The probability of return to the origin is investigated at different time horizons for both Gaussian and Laplacian GARCH models. In spite of the fact that these models show almost identical performances with respect to the final probability density function and to the time autocorrelation function, their scaling properties are, however, very different. The Laplacian GARCH model gives a better scaling exponent for the NYSE time series, whereas the Gaussian dynamics fits better the FTSE scaling exponent.

Diffusion and viscosity of liquid tin: Green-Kubo relationship-based calculations from molecular dynamics simulations

NASA Astrophysics Data System (ADS)

Mouas, Mohamed; Gasser, Jean-Georges; Hellal, Slimane; Grosdidier, Benoît; Makradi, Ahmed; Belouettar, Salim

2012-03-01

Molecular dynamics (MD) simulations of liquid tin between its melting point and 1600 °C have been performed in order to interpret and discuss the ionic structure. The interactions between ions are described by a new accurate pair potential built within the pseudopotential formalism and the linear response theory. The calculated structure factor that reflects the main information on the local atomic order in liquids is compared to diffraction measurements. Having some confidence in the ability of this pair potential to give a good representation of the atomic structure, we then focused our attention on the investigation of the atomic transport properties through the MD computations of the velocity autocorrelation function and stress autocorrelation function. Using the Green-Kubo formula (for the first time to our knowledge for liquid tin) we determine the macroscopic transport properties from the corresponding microscopic time autocorrelation functions. The selfdiffusion coefficient and the shear viscosity as functions of temperature are found to be in good agreement with the experimental data.

Periodicity in the autocorrelation function as a mechanism for regularly occurring zero crossings or extreme values of a Gaussian process.

PubMed

Wilson, Lorna R M; Hopcraft, Keith I

2017-12-01

The problem of zero crossings is of great historical prevalence and promises extensive application. The challenge is to establish precisely how the autocorrelation function or power spectrum of a one-dimensional continuous random process determines the density function of the intervals between the zero crossings of that process. This paper investigates the case where periodicities are incorporated into the autocorrelation function of a smooth process. Numerical simulations, and statistics about the number of crossings in a fixed interval, reveal that in this case the zero crossings segue between a random and deterministic point process depending on the relative time scales of the periodic and nonperiodic components of the autocorrelation function. By considering the Laplace transform of the density function, we show that incorporating correlation between successive intervals is essential to obtaining accurate results for the interval variance. The same method enables prediction of the density function tail in some regions, and we suggest approaches for extending this to cover all regions. In an ever-more complex world, the potential applications for this scale of regularity in a random process are far reaching and powerful.

Periodicity in the autocorrelation function as a mechanism for regularly occurring zero crossings or extreme values of a Gaussian process

NASA Astrophysics Data System (ADS)

Wilson, Lorna R. M.; Hopcraft, Keith I.

2017-12-01

The problem of zero crossings is of great historical prevalence and promises extensive application. The challenge is to establish precisely how the autocorrelation function or power spectrum of a one-dimensional continuous random process determines the density function of the intervals between the zero crossings of that process. This paper investigates the case where periodicities are incorporated into the autocorrelation function of a smooth process. Numerical simulations, and statistics about the number of crossings in a fixed interval, reveal that in this case the zero crossings segue between a random and deterministic point process depending on the relative time scales of the periodic and nonperiodic components of the autocorrelation function. By considering the Laplace transform of the density function, we show that incorporating correlation between successive intervals is essential to obtaining accurate results for the interval variance. The same method enables prediction of the density function tail in some regions, and we suggest approaches for extending this to cover all regions. In an ever-more complex world, the potential applications for this scale of regularity in a random process are far reaching and powerful.

Dangers and uses of cross-correlation in analyzing time series in perception, performance, movement, and neuroscience: The importance of constructing transfer function autoregressive models.

PubMed

Dean, Roger T; Dunsmuir, William T M

2016-06-01

Many articles on perception, performance, psychophysiology, and neuroscience seek to relate pairs of time series through assessments of their cross-correlations. Most such series are individually autocorrelated: they do not comprise independent values. Given this situation, an unfounded reliance is often placed on cross-correlation as an indicator of relationships (e.g., referent vs. response, leading vs. following). Such cross-correlations can indicate spurious relationships, because of autocorrelation. Given these dangers, we here simulated how and why such spurious conclusions can arise, to provide an approach to resolving them. We show that when multiple pairs of series are aggregated in several different ways for a cross-correlation analysis, problems remain. Finally, even a genuine cross-correlation function does not answer key motivating questions, such as whether there are likely causal relationships between the series. Thus, we illustrate how to obtain a transfer function describing such relationships, informed by any genuine cross-correlations. We illustrate the confounds and the meaningful transfer functions by two concrete examples, one each in perception and performance, together with key elements of the R software code needed. The approach involves autocorrelation functions, the establishment of stationarity, prewhitening, the determination of cross-correlation functions, the assessment of Granger causality, and autoregressive model development. Autocorrelation also limits the interpretability of other measures of possible relationships between pairs of time series, such as mutual information. We emphasize that further complexity may be required as the appropriate analysis is pursued fully, and that causal intervention experiments will likely also be needed.

Skewness, long-time memory, and non-stationarity: Application to leverage effect in financial time series

NASA Astrophysics Data System (ADS)

Roman, H. E.; Porto, M.; Dose, C.

2008-10-01

We analyze daily log-returns data for a set of 1200 stocks, taken from US stock markets, over a period of 2481 trading days (January 1996-November 2005). We estimate the degree of non-stationarity in daily market volatility employing a polynomial fit, used as a detrending function. We find that the autocorrelation function of absolute detrended log-returns departs strongly from the corresponding original data autocorrelation function, while the observed leverage effect depends only weakly on trends. Such effect is shown to occur when both skewness and long-time memory are simultaneously present. A fractional derivative random walk model is discussed yielding a quantitative agreement with the empirical results.

Studies in astronomical time series analysis. III - Fourier transforms, autocorrelation functions, and cross-correlation functions of unevenly spaced data

NASA Technical Reports Server (NTRS)

Scargle, Jeffrey D.

1989-01-01

This paper develops techniques to evaluate the discrete Fourier transform (DFT), the autocorrelation function (ACF), and the cross-correlation function (CCF) of time series which are not evenly sampled. The series may consist of quantized point data (e.g., yes/no processes such as photon arrival). The DFT, which can be inverted to recover the original data and the sampling, is used to compute correlation functions by means of a procedure which is effectively, but not explicitly, an interpolation. The CCF can be computed for two time series not even sampled at the same set of times. Techniques for removing the distortion of the correlation functions caused by the sampling, determining the value of a constant component to the data, and treating unequally weighted data are also discussed. FORTRAN code for the Fourier transform algorithm and numerical examples of the techniques are given.

Employing the Hilbert-Huang Transform to analyze observed natural complex signals: Calm wind meandering cases

NASA Astrophysics Data System (ADS)

Martins, Luis Gustavo Nogueira; Stefanello, Michel Baptistella; Degrazia, Gervásio Annes; Acevedo, Otávio Costa; Puhales, Franciano Scremin; Demarco, Giuliano; Mortarini, Luca; Anfossi, Domenico; Roberti, Débora Regina; Costa, Felipe Denardin; Maldaner, Silvana

2016-11-01

In this study we analyze natural complex signals employing the Hilbert-Huang spectral analysis. Specifically, low wind meandering meteorological data are decomposed into turbulent and non turbulent components. These non turbulent movements, responsible for the absence of a preferential direction of the horizontal wind, provoke negative lobes in the meandering autocorrelation functions. The meandering characteristic time scales (meandering periods) are determined from the spectral peak provided by the Hilbert-Huang marginal spectrum. The magnitudes of the temperature and horizontal wind meandering period obtained agree with the results found from the best fit of the heuristic meandering autocorrelation functions. Therefore, the new method represents a new procedure to evaluate meandering periods that does not employ mathematical expressions to represent observed meandering autocorrelation functions.

A Possible Application of Coherent Light Scattering on Biological Fluids

NASA Astrophysics Data System (ADS)

Chicea, Dan; Chicea, Liana Maria

2007-04-01

Human urine from both healthy patients and patients with different diseases was used as scattering medium in a coherent light scattering experiment. The time variation of the light intensity in the far field speckle image was acquired using a data acquisition system on a PC and a time series resulted for each sample. The autocorrelation function for each sample was calculated and the autocorrelation time was determined. The same samples were analyzed in a medical laboratory using the standard procedure. We found so far that the autocorrelation time is differently modified by the presence of pus, albumin, urobilin and sediments. The results suggest a fast procedure that can be used as laboratory test to detect the presence not of each individual component in suspensions but of big conglomerates as albumin, cylinders, oxalate crystals.

Velocity autocorrelation function in supercooled liquids: Long-time tails and anomalous shear-wave propagation.

PubMed

Peng, H L; Schober, H R; Voigtmann, Th

2016-12-01

Molecular dynamic simulations are performed to reveal the long-time behavior of the velocity autocorrelation function (VAF) by utilizing the finite-size effect in a Lennard-Jones binary mixture. Whereas in normal liquids the classical positive t^{-3/2} long-time tail is observed, we find in supercooled liquids a negative tail. It is strongly influenced by the transfer of the transverse current wave across the period boundary. The t^{-5/2} decay of the negative long-time tail is confirmed in the spectrum of VAF. Modeling the long-time transverse current within a generalized Maxwell model, we reproduce the negative long-time tail of the VAF, but with a slower algebraic t^{-2} decay.

Characteristic measurement for femtosecond laser pulses using a GaAs PIN photodiode as a two-photon photovoltaic receiver

NASA Astrophysics Data System (ADS)

Chen, Junbao; Xia, Wei; Wang, Ming

2017-06-01

Photodiodes that exhibit a two-photon absorption effect within the spectral communication band region can be useful for building an ultra-compact autocorrelator for the characteristic inspection of optical pulses. In this work, we develop an autocorrelator for measuring the temporal profile of pulses at 1550 nm from an erbium-doped fiber laser based on the two-photon photovoltaic (TPP) effect in a GaAs PIN photodiode. The temporal envelope of the autocorrelation function contains two symmetrical temporal side lobes due to the third order dispersion of the laser pulses. Moreover, the joint time-frequency distribution of the dispersive pulses and the dissimilar two-photon response spectrum of GaAs and Si result in different delays for the appearance of the temporal side lobes. Compared with Si, GaAs displays a greater sensitivity for pulse shape reconstruction at 1550 nm, benefiting from the higher signal-to-noise ratio of the side lobes and the more centralized waveform of the autocorrelation trace. We also measure the pulse width using the GaAs PIN photodiode, and the resolution of the measured full width at half maximum of the TPP autocorrelation trace is 0.89 fs, which is consistent with a conventional second-harmonic generation crystal autocorrelator. The GaAs PIN photodiode is shown to be highly suitable for real-time second-order autocorrelation measurements of femtosecond optical pulses. It is used both for the generation and detection of the autocorrelation signal, allowing the construction of a compact and inexpensive intensity autocorrelator.

Data Analysis Methods for Synthetic Polymer Mass Spectrometry: Autocorrelation

PubMed Central

Wallace, William E.; Guttman, Charles M.

2002-01-01

Autocorrelation is shown to be useful in describing the periodic patterns found in high- resolution mass spectra of synthetic polymers. Examples of this usefulness are described for a simple linear homopolymer to demonstrate the method fundamentals, a condensation polymer to demonstrate its utility in understanding complex spectra with multiple repeating patterns on different mass scales, and a condensation copolymer to demonstrate how it can elegantly and efficiently reveal unexpected phenomena. It is shown that using autocorrelation to determine where the signal devolves into noise can be useful in determining molecular mass distributions of synthetic polymers, a primary focus of the NIST synthetic polymer mass spectrometry effort. The appendices describe some of the effects of transformation from time to mass space when time-of-flight mass separation is used, as well as the effects of non-trivial baselines on the autocorrelation function. PMID:27446716

An Overdetermined System for Improved Autocorrelation Based Spectral Moment Estimator Performance

NASA Technical Reports Server (NTRS)

Keel, Byron M.

1996-01-01

Autocorrelation based spectral moment estimators are typically derived using the Fourier transform relationship between the power spectrum and the autocorrelation function along with using either an assumed form of the autocorrelation function, e.g., Gaussian, or a generic complex form and applying properties of the characteristic function. Passarelli has used a series expansion of the general complex autocorrelation function and has expressed the coefficients in terms of central moments of the power spectrum. A truncation of this series will produce a closed system of equations which can be solved for the central moments of interest. The autocorrelation function at various lags is estimated from samples of the random process under observation. These estimates themselves are random variables and exhibit a bias and variance that is a function of the number of samples used in the estimates and the operational signal-to-noise ratio. This contributes to a degradation in performance of the moment estimators. This dissertation investigates the use autocorrelation function estimates at higher order lags to reduce the bias and standard deviation in spectral moment estimates. In particular, Passarelli's series expansion is cast in terms of an overdetermined system to form a framework under which the application of additional autocorrelation function estimates at higher order lags can be defined and assessed. The solution of the overdetermined system is the least squares solution. Furthermore, an overdetermined system can be solved for any moment or moments of interest and is not tied to a particular form of the power spectrum or corresponding autocorrelation function. As an application of this approach, autocorrelation based variance estimators are defined by a truncation of Passarelli's series expansion and applied to simulated Doppler weather radar returns which are characterized by a Gaussian shaped power spectrum. The performance of the variance estimators determined from a closed system is shown to improve through the application of additional autocorrelation lags in an overdetermined system. This improvement is greater in the narrowband spectrum region where the information is spread over more lags of the autocorrelation function. The number of lags needed in the overdetermined system is a function of the spectral width, the number of terms in the series expansion, the number of samples used in estimating the autocorrelation function, and the signal-to-noise ratio. The overdetermined system provides a robustness to the chosen variance estimator by expanding the region of spectral widths and signal-to-noise ratios over which the estimator can perform as compared to the closed system.

Spectra of empirical autocorrelation matrices: A random-matrix-theory-inspired perspective

NASA Astrophysics Data System (ADS)

Jamali, Tayeb; Jafari, G. R.

2015-07-01

We construct an autocorrelation matrix of a time series and analyze it based on the random-matrix theory (RMT) approach. The autocorrelation matrix is capable of extracting information which is not easily accessible by the direct analysis of the autocorrelation function. In order to provide a precise conclusion based on the information extracted from the autocorrelation matrix, the results must be first evaluated. In other words they need to be compared with some sort of criterion to provide a basis for the most suitable and applicable conclusions. In the context of the present study, the criterion is selected to be the well-known fractional Gaussian noise (fGn). We illustrate the applicability of our method in the context of stock markets. For the former, despite the non-Gaussianity in returns of the stock markets, a remarkable agreement with the fGn is achieved.

Asymptotic neutron scattering laws for anomalously diffusing quantum particles

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kneller, Gerald R.; Université d’Orléans, Chateau de la Source-Ave. du Parc Floral, 45067 Orléans; Synchrotron-SOLEIL, L’Orme de Merisiers, 91192 Gif-sur-Yvette

2016-07-28

The paper deals with a model-free approach to the analysis of quasielastic neutron scattering intensities from anomalously diffusing quantum particles. All quantities are inferred from the asymptotic form of their time-dependent mean square displacements which grow ∝t{sup α}, with 0 ≤ α < 2. Confined diffusion (α = 0) is here explicitly included. We discuss in particular the intermediate scattering function for long times and the Fourier spectrum of the velocity autocorrelation function for small frequencies. Quantum effects enter in both cases through the general symmetry properties of quantum time correlation functions. It is shown that the fractional diffusion constantmore » can be expressed by a Green-Kubo type relation involving the real part of the velocity autocorrelation function. The theory is exact in the diffusive regime and at moderate momentum transfers.« less

MATLAB-Based Program for Teaching Autocorrelation Function and Noise Concepts

ERIC Educational Resources Information Center

Jovanovic Dolecek, G.

2012-01-01

An attractive MATLAB-based tool for teaching the basics of autocorrelation function and noise concepts is presented in this paper. This tool enhances traditional in-classroom lecturing. The demonstrations of the tool described here highlight the description of the autocorrelation function (ACF) in a general case for wide-sense stationary (WSS)…

Correlation range in a supercooled liquid via Green-Kubo expression for viscosity, local atomic stresses, and MD simulations

NASA Astrophysics Data System (ADS)

Levashov, Valentin A.; Egami, Takeshi; Morris, James R.

2009-03-01

We present a new approach to the issue of correlation range in supercooled liquids based on Green-Kubo expression for viscosity. The integrand of this expression is the average stress-stress autocorrelation function. This correlation function could be rewritten in terms of correlations among local atomic stresses at different times and distances. The features of the autocorrelation function decay with time depend on temperature and correlation range. Through this approach we can study the development of spatial correlation with time, thus directly addressing the question of dynamic heterogeneity. We performed MD simulations on a single component system of particles interacting through short range pair potential. Our results indicate that even above the crossover temperature correlations extend well beyond the nearest neighbors. Surprisingly we found that the system size effects exist even on relatively large systems. We also address the role of diffusion in decay of stress-stress correlation function.

Autocorrelation-based time synchronous averaging for condition monitoring of planetary gearboxes in wind turbines

NASA Astrophysics Data System (ADS)

Ha, Jong M.; Youn, Byeng D.; Oh, Hyunseok; Han, Bongtae; Jung, Yoongho; Park, Jungho

2016-03-01

We propose autocorrelation-based time synchronous averaging (ATSA) to cope with the challenges associated with the current practice of time synchronous averaging (TSA) for planet gears in planetary gearboxes of wind turbine (WT). An autocorrelation function that represents physical interactions between the ring, sun, and planet gears in the gearbox is utilized to define the optimal shape and range of the window function for TSA using actual kinetic responses. The proposed ATSA offers two distinctive features: (1) data-efficient TSA processing and (2) prevention of signal distortion during the TSA process. It is thus expected that an order analysis with the ATSA signals significantly improves the efficiency and accuracy in fault diagnostics of planet gears in planetary gearboxes. Two case studies are presented to demonstrate the effectiveness of the proposed method: an analytical signal from a simulation and a signal measured from a 2 kW WT testbed. It can be concluded from the results that the proposed method outperforms conventional TSA methods in condition monitoring of the planetary gearbox when the amount of available stationary data is limited.

Possible Noise Nature of Elsässer Variable z- in Highly Alfvénic Solar Wind Fluctuations

NASA Astrophysics Data System (ADS)

Wang, X.; Tu, C.-Y.; He, J.-S.; Wang, L.-H.; Yao, S.; Zhang, L.

2018-01-01

It has been a long-standing debate on the nature of Elsässer variable z- observed in the solar wind fluctuations. It is widely believed that z- represents inward propagating Alfvén waves and interacts nonlinearly with z+ (outward propagating Alfvén waves) to generate energy cascade. However, z- variations sometimes show a feature of convective structures. Here we present a new data analysis on autocorrelation functions of z- in order to get some definite information on its nature. We find that there is usually a large drop on the z- autocorrelation function when the solar wind fluctuations are highly Alfvénic. The large drop observed by Helios 2 spacecraft near 0.3 AU appears at the first nonzero time lag τ = 81 s, where the value of the autocorrelation coefficient drops to 25%-65% of that at τ = 0 s. Beyond the first nonzero time lag, the autocorrelation coefficient decreases gradually to zero. The drop of z- correlation function also appears in the Wind observations near 1 AU. These features of the z- correlation function may suggest that z- fluctuations consist of two components: high-frequency white noise and low-frequency pseudo structures, which correspond to flat and steep parts of z- power spectrum, respectively. This explanation is confirmed by doing a simple test on an artificial time series, which is obtained from the superposition of a random data series on its smoothed sequence. Our results suggest that in highly Alfvénic fluctuations, z- may not contribute importantly to the interactions with z+ to produce energy cascade.

Autocorrelation exponent of conserved spin systems in the scaling regime following a critical quench.

PubMed

Sire, Clément

2004-09-24

We study the autocorrelation function of a conserved spin system following a quench at the critical temperature. Defining the correlation length L(t) approximately t(1/z), we find that for times t' and t satisfying L(t')infinity limit, we show that lambda(')(c)=d+2 and phi=z/2. We give a heuristic argument suggesting that this result is, in fact, valid for any dimension d and spin vector dimension n. We present numerical simulations for the conserved Ising model in d=1 and d=2, which are fully consistent with the present theory.

Sign reversals of the output autocorrelation function for the stochastic Bernoulli-Verhulst equation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lumi, N., E-mail: Neeme.Lumi@tlu.ee; Mankin, R., E-mail: Romi.Mankin@tlu.ee

2015-10-28

We consider a stochastic Bernoulli-Verhulst equation as a model for population growth processes. The effect of fluctuating environment on the carrying capacity of a population is modeled as colored dichotomous noise. Relying on the composite master equation an explicit expression for the stationary autocorrelation function (ACF) of population sizes is found. On the basis of this expression a nonmonotonic decay of the ACF by increasing lag-time is shown. Moreover, in a certain regime of the noise parameters the ACF demonstrates anticorrelation as well as related sign reversals at some values of the lag-time. The conditions for the appearance of thismore » highly unexpected effect are also discussed.« less

The calculation of transport properties in quantum liquids using the maximum entropy numerical analytic continuation method: Application to liquid para-hydrogen

PubMed Central

Rabani, Eran; Reichman, David R.; Krilov, Goran; Berne, Bruce J.

2002-01-01

We present a method based on augmenting an exact relation between a frequency-dependent diffusion constant and the imaginary time velocity autocorrelation function, combined with the maximum entropy numerical analytic continuation approach to study transport properties in quantum liquids. The method is applied to the case of liquid para-hydrogen at two thermodynamic state points: a liquid near the triple point and a high-temperature liquid. Good agreement for the self-diffusion constant and for the real-time velocity autocorrelation function is obtained in comparison to experimental measurements and other theoretical predictions. Improvement of the methodology and future applications are discussed. PMID:11830656

The Superstatistical Nature and Interoccurrence Time of Atmospheric Mercury Concentration Fluctuations

EPA Science Inventory

The probability density function (PDF) of the time intervals between subsequent extreme events in atmospheric Hg0 concentration data series from different latitudes has been investigated. The Hg0 dynamic possesses a long-term memory autocorrelation function. Above a fixed thresh...

A statistical physics view of pitch fluctuations in the classical music from Bach to Chopin: evidence for scaling.

PubMed

Liu, Lu; Wei, Jianrong; Zhang, Huishu; Xin, Jianhong; Huang, Jiping

2013-01-01

Because classical music has greatly affected our life and culture in its long history, it has attracted extensive attention from researchers to understand laws behind it. Based on statistical physics, here we use a different method to investigate classical music, namely, by analyzing cumulative distribution functions (CDFs) and autocorrelation functions of pitch fluctuations in compositions. We analyze 1,876 compositions of five representative classical music composers across 164 years from Bach, to Mozart, to Beethoven, to Mendelsohn, and to Chopin. We report that the biggest pitch fluctuations of a composer gradually increase as time evolves from Bach time to Mendelsohn/Chopin time. In particular, for the compositions of a composer, the positive and negative tails of a CDF of pitch fluctuations are distributed not only in power laws (with the scale-free property), but also in symmetry (namely, the probability of a treble following a bass and that of a bass following a treble are basically the same for each composer). The power-law exponent decreases as time elapses. Further, we also calculate the autocorrelation function of the pitch fluctuation. The autocorrelation function shows a power-law distribution for each composer. Especially, the power-law exponents vary with the composers, indicating their different levels of long-range correlation of notes. This work not only suggests a way to understand and develop music from a viewpoint of statistical physics, but also enriches the realm of traditional statistical physics by analyzing music.

Optimal periodic binary codes of lengths 28 to 64

NASA Technical Reports Server (NTRS)

Tyler, S.; Keston, R.

1980-01-01

Results from computer searches performed to find repeated binary phase coded waveforms with optimal periodic autocorrelation functions are discussed. The best results for lengths 28 to 64 are given. The code features of major concern are where (1) the peak sidelobe in the autocorrelation function is small and (2) the sum of the squares of the sidelobes in the autocorrelation function is small.

Damage detection and isolation via autocorrelation: a step toward passive sensing

NASA Astrophysics Data System (ADS)

Chang, Y. S.; Yuan, F. G.

2018-03-01

Passive sensing technique may eliminate the need of expending power from actuators and thus provide a means of developing a compact and simple structural health monitoring system. More importantly, it may provide a solution for monitoring the aircraft subjected to environmental loading from air flow during operation. In this paper, a non-contact auto-correlation based technique is exploited as a feasibility study for passive sensing application to detect damage and isolate the damage location. Its theoretical basis bears some resemblance to reconstructing Green's function from diffusive wavefield through cross-correlation. Localized high pressure air from air compressor are randomly and continuously applied on the one side surface of the aluminum panels through the air blow gun. A laser Doppler vibrometer (LDV) was used to scan a 90 mm × 90 mm area to create a 6 × 6 2D-array signals from the opposite side of the panels. The scanned signals were auto-correlated to reconstruct a "selfimpulse response" (or Green's function). The premise for stably reconstructing the accurate Green's function requires long sensing times. For a 609.6 mm × 609.6 mm flat aluminum panel, the sensing times roughly at least four seconds is sufficient to establish converged Green's function through correlation. For the integral stiffened aluminum panel, the geometrical features of the panel expedite the formation of the diffusive wavefield and thus shorten the sensing times. The damage is simulated by gluing a magnet onto the panels. Reconstructed Green's functions (RGFs) are used for damage detection and damage isolation based on an imaging condition with mean square deviation of the RGFs from the pristine and the damaged structure and the results are shown in color maps. The auto-correlation based technique is shown to consistently detect the simulated damage, image and isolate the damage in the structure subjected to high pressure air excitation. This technique may be transformed into passive sensing applied on the aircraft during operation.

Monte Carlo errors with less errors

NASA Astrophysics Data System (ADS)

Wolff, Ulli; Alpha Collaboration

2004-01-01

We explain in detail how to estimate mean values and assess statistical errors for arbitrary functions of elementary observables in Monte Carlo simulations. The method is to estimate and sum the relevant autocorrelation functions, which is argued to produce more certain error estimates than binning techniques and hence to help toward a better exploitation of expensive simulations. An effective integrated autocorrelation time is computed which is suitable to benchmark efficiencies of simulation algorithms with regard to specific observables of interest. A Matlab code is offered for download that implements the method. It can also combine independent runs (replica) allowing to judge their consistency.

Subfemtosecond quantum nuclear dynamics in water isotopomers.

PubMed

Rao, B Jayachander; Varandas, A J C

2015-05-21

Subfemtosecond quantum dynamics studies of all water isotopomers in the X̃ (2)B1 and à (2)A1 electronic states of the cation formed by Franck-Condon ionization of the neutral ground electronic state are reported. Using the ratio of the autocorrelation functions for the isotopomers as obtained from the solution of the time-dependent Schrödinger equation in a grid representation, high-order harmonic generation signals are calculated as a function of time. The results are found to be in agreement with the available experimental findings and with our earlier study for D2O(+)/H2O(+). Maxima are predicted in the autocorrelation function ratio at various times. Their origin and occurrence is explained by calculating expectation values of the bond lengths and bond angle of the water isotopomers as a function of time. The values so calculated for the (2)B1 and (2)A1 electronic states of the cation show quasiperiodic oscillations that can be associated with the time at which the nuclear wave packet reaches the minima of the potential energy surface, there being responsible for the peaks in the HHG signals.

Integrated autocorrelator based on superconducting nanowires.

PubMed

Sahin, Döndü; Gaggero, Alessandro; Hoang, Thang Ba; Frucci, Giulia; Mattioli, Francesco; Leoni, Roberto; Beetz, Johannes; Lermer, Matthias; Kamp, Martin; Höfling, Sven; Fiore, Andrea

2013-05-06

We demonstrate an integrated autocorrelator based on two superconducting single-photon detectors patterned on top of a GaAs ridge waveguide. This device enables the on-chip measurement of the second-order intensity correlation function g(2)(τ). A polarization-independent device quantum efficiency in the 1% range is reported, with a timing jitter of 88 ps at 1300 nm. g(2)(τ) measurements of continuous-wave and pulsed laser excitations are demonstrated with no measurable crosstalk within our measurement accuracy.

Fluctuation analysis of proficient and dysgraphic handwriting in children

NASA Astrophysics Data System (ADS)

Rosenblum, S.; Roman, H. E.

2009-03-01

We analyze handwriting records from several school children with the aim of characterizing the fluctuating behavior of the writing speed. It will be concluded that remarkable differences exist between proficient and dysgraphic handwritings which were unknown so far. It is shown that in the case of proficient handwriting, the variations in handwriting speed are strongly autocorrelated within times corresponding to the completion of a single character or letter, while become uncorrelated at longer times. In the case of dysgraphia, such correlations persist on longer time scales and the autocorrelation function seems to display algebraic time decay, indicating the presence of strong anomalies in the handwriting process. Applications of the results in educational/clinical programs are envisaged.

Boundary versus bulk behavior of time-dependent correlation functions in one-dimensional quantum systems

NASA Astrophysics Data System (ADS)

Eliëns, I. S.; Ramos, F. B.; Xavier, J. C.; Pereira, R. G.

2016-05-01

We study the influence of reflective boundaries on time-dependent responses of one-dimensional quantum fluids at zero temperature beyond the low-energy approximation. Our analysis is based on an extension of effective mobile impurity models for nonlinear Luttinger liquids to the case of open boundary conditions. For integrable models, we show that boundary autocorrelations oscillate as a function of time with the same frequency as the corresponding bulk autocorrelations. This frequency can be identified as the band edge of elementary excitations. The amplitude of the oscillations decays as a power law with distinct exponents at the boundary and in the bulk, but boundary and bulk exponents are determined by the same coupling constant in the mobile impurity model. For nonintegrable models, we argue that the power-law decay of the oscillations is generic for autocorrelations in the bulk, but turns into an exponential decay at the boundary. Moreover, there is in general a nonuniversal shift of the boundary frequency in comparison with the band edge of bulk excitations. The predictions of our effective field theory are compared with numerical results obtained by time-dependent density matrix renormalization group (tDMRG) for both integrable and nonintegrable critical spin-S chains with S =1 /2 , 1, and 3 /2 .

Assessment of drug-induced arrhythmic risk using limit cycle and autocorrelation analysis of human iPSC-cardiomyocyte contractility

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kirby, R. Jason

2016-08-15

Cardiac safety assays incorporating label-free detection of human stem-cell derived cardiomyocyte contractility provide human relevance and medium throughput screening to assess compound-induced cardiotoxicity. In an effort to provide quantitative analysis of the large kinetic datasets resulting from these real-time studies, we applied bioinformatic approaches based on nonlinear dynamical system analysis, including limit cycle analysis and autocorrelation function, to systematically assess beat irregularity. The algorithms were integrated into a software program to seamlessly generate results for 96-well impedance-based data. Our approach was validated by analyzing dose- and time-dependent changes in beat patterns induced by known proarrhythmic compounds and screening a cardiotoxicitymore » library to rank order compounds based on their proarrhythmic potential. We demonstrate a strong correlation for dose-dependent beat irregularity monitored by electrical impedance and quantified by autocorrelation analysis to traditional manual patch clamp potency values for hERG blockers. In addition, our platform identifies non-hERG blockers known to cause clinical arrhythmia. Our method provides a novel suite of medium-throughput quantitative tools for assessing compound effects on cardiac contractility and predicting compounds with potential proarrhythmia and may be applied to in vitro paradigms for pre-clinical cardiac safety evaluation. - Highlights: • Impedance-based monitoring of human iPSC-derived cardiomyocyte contractility • Limit cycle analysis of impedance data identifies aberrant oscillation patterns. • Nonlinear autocorrelation function quantifies beat irregularity. • Identification of hERG and non-hERG inhibitors with known risk of arrhythmia • Automated software processes limit cycle and autocorrelation analyses of 96w data.« less

Real-time autocorrelator for fluorescence correlation spectroscopy based on graphical-processor-unit architecture: method, implementation, and comparative studies

NASA Astrophysics Data System (ADS)

Laracuente, Nicholas; Grossman, Carl

2013-03-01

We developed an algorithm and software to calculate autocorrelation functions from real-time photon-counting data using the fast, parallel capabilities of graphical processor units (GPUs). Recent developments in hardware and software have allowed for general purpose computing with inexpensive GPU hardware. These devices are more suited for emulating hardware autocorrelators than traditional CPU-based software applications by emphasizing parallel throughput over sequential speed. Incoming data are binned in a standard multi-tau scheme with configurable points-per-bin size and are mapped into a GPU memory pattern to reduce time-expensive memory access. Applications include dynamic light scattering (DLS) and fluorescence correlation spectroscopy (FCS) experiments. We ran the software on a 64-core graphics pci card in a 3.2 GHz Intel i5 CPU based computer running Linux. FCS measurements were made on Alexa-546 and Texas Red dyes in a standard buffer (PBS). Software correlations were compared to hardware correlator measurements on the same signals. Supported by HHMI and Swarthmore College

Spatial and temporal changes in the structure of groundwater nitrate concentration time series (1935 1999) as demonstrated by autoregressive modelling

NASA Astrophysics Data System (ADS)

Jones, A. L.; Smart, P. L.

2005-08-01

Autoregressive modelling is used to investigate the internal structure of long-term (1935-1999) records of nitrate concentration for five karst springs in the Mendip Hills. There is a significant short term (1-2 months) positive autocorrelation at three of the five springs due to the availability of sufficient nitrate within the soil store to maintain concentrations in winter recharge for several months. The absence of short term (1-2 months) positive autocorrelation in the other two springs is due to the marked contrast in land use between the limestone and swallet parts of the catchment, rapid concentrated recharge from the latter causing short term switching in the dominant water source at the spring and thus fluctuating nitrate concentrations. Significant negative autocorrelation is evident at lags varying from 4 to 7 months through to 14-22 months for individual springs, with positive autocorrelation at 19-20 months at one site. This variable timing is explained by moderation of the exhaustion effect in the soil by groundwater storage, which gives longer residence times in large catchments and those with a dominance of diffuse flow. The lags derived from autoregressive modelling may therefore provide an indication of average groundwater residence times. Significant differences in the structure of the autocorrelation function for successive 10-year periods are evident at Cheddar Spring, and are explained by the effect the ploughing up of grasslands during the Second World War and increased fertiliser usage on available nitrogen in the soil store. This effect is moderated by the influence of summer temperatures on rates of mineralization, and of both summer and winter rainfall on the timing and magnitude of nitrate leaching. The pattern of nitrate leaching also appears to have been perturbed by the 1976 drought.

Microfluidic volumetric flow determination using optical coherence tomography speckle: An autocorrelation approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

De Pretto, Lucas R., E-mail: lucas.de.pretto@usp.br; Nogueira, Gesse E. C.; Freitas, Anderson Z.

2016-04-28

Functional modalities of Optical Coherence Tomography (OCT) based on speckle analysis are emerging in the literature. We propose a simple approach to the autocorrelation of OCT signal to enable volumetric flow rate differentiation, based on decorrelation time. Our results show that this technique could distinguish flows separated by 3 μl/min, limited by the acquisition speed of the system. We further perform a B-scan of gradient flow inside a microchannel, enabling the visualization of the drag effect on the walls.

Time-scale effects on the gain-loss asymmetry in stock indices

NASA Astrophysics Data System (ADS)

Sándor, Bulcsú; Simonsen, Ingve; Nagy, Bálint Zsolt; Néda, Zoltán

2016-08-01

The gain-loss asymmetry, observed in the inverse statistics of stock indices is present for logarithmic return levels that are over 2 % , and it is the result of the non-Pearson-type autocorrelations in the index. These non-Pearson-type correlations can be viewed also as functionally dependent daily volatilities, extending for a finite time interval. A generalized time-window shuffling method is used to show the existence of such autocorrelations. Their characteristic time scale proves to be smaller (less than 25 trading days) than what was previously believed. It is also found that this characteristic time scale has decreased with the appearance of program trading in the stock market transactions. Connections with the leverage effect are also established.

Dynamical analyses of the time series for three foreign exchange rates

NASA Astrophysics Data System (ADS)

Kim, Sehyun; Kim, Soo Yong; Jung, Jae-Won; Kim, Kyungsik

2012-05-01

In this study, we investigate the multifractal properties of three foreign exchange rates (USD-KRW, USD-JPY, and EUR-USD) that are quoted with different economic scales. We estimate and analyze both the generalized Hurst exponent and the autocorrelation function in three foreign exchange rates. The USD-KRW is shown to have the strongest of the Hurst exponents when compared with the other two foreign exchange rates. In particular, the autocorrelation function of the USD-KRW has the largest memory behavior among three foreign exchange rates. It also exhibits a long-memory property in the first quarter, more than those in the other quarters.

Development of a local size hierarchy causes regular spacing of trees in an even-aged Abies forest: analyses using spatial autocorrelation and the mark correlation function.

PubMed

Suzuki, Satoshi N; Kachi, Naoki; Suzuki, Jun-Ichirou

2008-09-01

During the development of an even-aged plant population, the spatial distribution of individuals often changes from a clumped pattern to a random or regular one. The development of local size hierarchies in an Abies forest was analysed for a period of 47 years following a large disturbance in 1959. In 1980 all trees in an 8 x 8 m plot were mapped and their height growth after the disturbance was estimated. Their mortality and growth were then recorded at 1- to 4-year intervals between 1980 and 2006. Spatial distribution patterns of trees were analysed by the pair correlation function. Spatial correlations between tree heights were analysed with a spatial autocorrelation function and the mark correlation function. The mark correlation function was able to detect a local size hierarchy that could not be detected by the spatial autocorrelation function alone. The small-scale spatial distribution pattern of trees changed from clumped to slightly regular during the 47 years. Mortality occurred in a density-dependent manner, which resulted in regular spacing between trees after 1980. The spatial autocorrelation and mark correlation functions revealed the existence of tree patches consisting of large trees at the initial stage. Development of a local size hierarchy was detected within the first decade after the disturbance, although the spatial autocorrelation was not negative. Local size hierarchies that developed persisted until 2006, and the spatial autocorrelation became negative at later stages (after about 40 years). This is the first study to detect local size hierarchies as a prelude to regular spacing using the mark correlation function. The results confirm that use of the mark correlation function together with the spatial autocorrelation function is an effective tool to analyse the development of a local size hierarchy of trees in a forest.

Intrinsic autocorrelation time of picoseconds for thermal noise in water.

PubMed

Zhu, Zhi; Sheng, Nan; Wan, Rongzheng; Fang, Haiping

2014-10-02

Whether thermal noise is colored or white is of fundamental importance. In conventional theory, thermal noise is usually treated as white noise so that there are no directional transportations in the asymmetrical systems without external inputs, since only the colored fluctuations with appropriate autocorrelation time length can lead to directional transportations in the asymmetrical systems. Here, on the basis of molecular dynamics simulations, we show that the autocorrelation time length of thermal noise in water is ~10 ps at room temperature, which indicates that thermal noise is not white in the molecular scale while thermal noise can be reasonably assumed as white in macro- and meso-scale systems. The autocorrelation time length of thermal noise is intrinsic, since the value is almost unchanged for different temperature coupling methods. Interestingly, the autocorrelation time of thermal noise is correlated with the lifetime of hydrogen bonds, suggesting that the finite autocorrelation time length of thermal noise mainly comes from the finite lifetime of the interactions between neighboring water molecules.

Time correlation functions of simple liquids: A new insight on the underlying dynamical processes

NASA Astrophysics Data System (ADS)

Garberoglio, Giovanni; Vallauri, Renzo; Bafile, Ubaldo

2018-05-01

Extensive molecular dynamics simulations of liquid sodium have been carried out to evaluate correlation functions of several dynamical quantities. We report the results of a novel analysis of the longitudinal and transverse correlation functions obtained by evaluating directly their self- and distinct contributions at different wavevectors k. It is easily recognized that the self-contribution remains close to its k → 0 limit, which turns out to be exactly the autocorrelation function of the single particle velocity. The wavevector dependence of the longitudinal and transverse spectra and their self- and distinct parts is also presented. By making use of the decomposition of the velocity autocorrelation spectrum in terms of longitudinal and transverse parts, our analysis is able to recognize the effect of different dynamical processes in different frequency ranges.

Probabilistic density function method for nonlinear dynamical systems driven by colored noise.

PubMed

Barajas-Solano, David A; Tartakovsky, Alexandre M

2016-05-01

We present a probability density function (PDF) method for a system of nonlinear stochastic ordinary differential equations driven by colored noise. The method provides an integrodifferential equation for the temporal evolution of the joint PDF of the system's state, which we close by means of a modified large-eddy-diffusivity (LED) closure. In contrast to the classical LED closure, the proposed closure accounts for advective transport of the PDF in the approximate temporal deconvolution of the integrodifferential equation. In addition, we introduce the generalized local linearization approximation for deriving a computable PDF equation in the form of a second-order partial differential equation. We demonstrate that the proposed closure and localization accurately describe the dynamics of the PDF in phase space for systems driven by noise with arbitrary autocorrelation time. We apply the proposed PDF method to analyze a set of Kramers equations driven by exponentially autocorrelated Gaussian colored noise to study nonlinear oscillators and the dynamics and stability of a power grid. Numerical experiments show the PDF method is accurate when the noise autocorrelation time is either much shorter or longer than the system's relaxation time, while the accuracy decreases as the ratio of the two timescales approaches unity. Similarly, the PDF method accuracy decreases with increasing standard deviation of the noise.

Method to manage integration error in the Green-Kubo method.

PubMed

Oliveira, Laura de Sousa; Greaney, P Alex

2017-02-01

The Green-Kubo method is a commonly used approach for predicting transport properties in a system from equilibrium molecular dynamics simulations. The approach is founded on the fluctuation dissipation theorem and relates the property of interest to the lifetime of fluctuations in its thermodynamic driving potential. For heat transport, the lattice thermal conductivity is related to the integral of the autocorrelation of the instantaneous heat flux. A principal source of error in these calculations is that the autocorrelation function requires a long averaging time to reduce remnant noise. Integrating the noise in the tail of the autocorrelation function becomes conflated with physically important slow relaxation processes. In this paper we present a method to quantify the uncertainty on transport properties computed using the Green-Kubo formulation based on recognizing that the integrated noise is a random walk, with a growing envelope of uncertainty. By characterizing the noise we can choose integration conditions to best trade off systematic truncation error with unbiased integration noise, to minimize uncertainty for a given allocation of computational resources.

Method to manage integration error in the Green-Kubo method

NASA Astrophysics Data System (ADS)

Oliveira, Laura de Sousa; Greaney, P. Alex

2017-02-01

The Green-Kubo method is a commonly used approach for predicting transport properties in a system from equilibrium molecular dynamics simulations. The approach is founded on the fluctuation dissipation theorem and relates the property of interest to the lifetime of fluctuations in its thermodynamic driving potential. For heat transport, the lattice thermal conductivity is related to the integral of the autocorrelation of the instantaneous heat flux. A principal source of error in these calculations is that the autocorrelation function requires a long averaging time to reduce remnant noise. Integrating the noise in the tail of the autocorrelation function becomes conflated with physically important slow relaxation processes. In this paper we present a method to quantify the uncertainty on transport properties computed using the Green-Kubo formulation based on recognizing that the integrated noise is a random walk, with a growing envelope of uncertainty. By characterizing the noise we can choose integration conditions to best trade off systematic truncation error with unbiased integration noise, to minimize uncertainty for a given allocation of computational resources.

Pattern, growth, and aging in aggregation kinetics of a Vicsek-like active matter model

NASA Astrophysics Data System (ADS)

Das, Subir K.

2017-01-01

Via molecular dynamics simulations, we study kinetics in a Vicsek-like phase-separating active matter model. Quantitative results, for isotropic bicontinuous pattern, are presented on the structure, growth, and aging. These are obtained via the two-point equal-time density-density correlation function, the average domain length, and the two-time density autocorrelation function. Both the correlation functions exhibit basic scaling properties, implying self-similarity in the pattern dynamics, for which the average domain size exhibits a power-law growth in time. The equal-time correlation has a short distance behavior that provides reasonable agreement between the corresponding structure factor tail and the Porod law. The autocorrelation decay is a power-law in the average domain size. Apart from these basic similarities, the overall quantitative behavior of the above-mentioned observables is found to be vastly different from those of the corresponding passive limit of the model which also undergoes phase separation. The functional forms of these have been quantified. An exceptionally rapid growth in the active system occurs due to fast coherent motion of the particles, mean-squared-displacements of which exhibit multiple scaling regimes, including a long time ballistic one.

Narayanaswamy's 1971 aging theory and material time

NASA Astrophysics Data System (ADS)

Dyre, Jeppe C.

2015-09-01

The Bochkov-Kuzovlev nonlinear fluctuation-dissipation theorem is used to derive Narayanaswamy's phenomenological theory of physical aging, in which this highly nonlinear phenomenon is described by a linear material-time convolution integral. A characteristic property of the Narayanaswamy aging description is material-time translational invariance, which is here taken as the basic assumption of the derivation. It is shown that only one possible definition of the material time obeys this invariance, namely, the square of the distance travelled from a configuration of the system far back in time. The paper concludes with suggestions for computer simulations that test for consequences of material-time translational invariance. One of these is the "unique-triangles property" according to which any three points on the system's path form a triangle such that two side lengths determine the third; this is equivalent to the well-known triangular relation for time-autocorrelation functions of aging spin glasses [L. F. Cugliandolo and J. Kurchan, J. Phys. A: Math. Gen. 27, 5749 (1994)]. The unique-triangles property implies a simple geometric interpretation of out-of-equilibrium time-autocorrelation functions, which extends to aging a previously proposed framework for such functions in equilibrium [J. C. Dyre, e-print arXiv:cond-mat/9712222 (1997)].

Diffraction and geometrical optical transfer functions: calculation time comparison

NASA Astrophysics Data System (ADS)

Díaz, José Antonio; Mahajan, Virendra N.

2017-08-01

In a recent paper, we compared the diffraction and geometrical optical transfer functions (OTFs) of an optical imaging system, and showed that the GOTF approximates the DOTF within 10% when a primary aberration is about two waves or larger [Appl. Opt., 55, 3241-3250 (2016)]. In this paper, we determine and compare the times to calculate the DOTF by autocorrelation or digital autocorrelation of the pupil function, and by a Fourier transform (FT) of the point-spread function (PSF); and the GOTF by a FT of the geometrical PSF and its approximation, the spot diagram. Our starting point for calculating the DOTF is the wave aberrations of the system in its pupil plane, and the ray aberrations in the image plane for the GOTF. The numerical results for primary aberrations and a typical imaging system show that the direct integrations are slow, but the calculation of the DOTF by a FT of the PSF is generally faster than the GOTF calculation by a FT of the spot diagram.

From fast fluorescence imaging to molecular diffusion law on live cell membranes in a commercial microscope.

PubMed

Di Rienzo, Carmine; Gratton, Enrico; Beltram, Fabio; Cardarelli, Francesco

2014-10-09

It has become increasingly evident that the spatial distribution and the motion of membrane components like lipids and proteins are key factors in the regulation of many cellular functions. However, due to the fast dynamics and the tiny structures involved, a very high spatio-temporal resolution is required to catch the real behavior of molecules. Here we present the experimental protocol for studying the dynamics of fluorescently-labeled plasma-membrane proteins and lipids in live cells with high spatiotemporal resolution. Notably, this approach doesn't need to track each molecule, but it calculates population behavior using all molecules in a given region of the membrane. The starting point is a fast imaging of a given region on the membrane. Afterwards, a complete spatio-temporal autocorrelation function is calculated correlating acquired images at increasing time delays, for example each 2, 3, n repetitions. It is possible to demonstrate that the width of the peak of the spatial autocorrelation function increases at increasing time delay as a function of particle movement due to diffusion. Therefore, fitting of the series of autocorrelation functions enables to extract the actual protein mean square displacement from imaging (iMSD), here presented in the form of apparent diffusivity vs average displacement. This yields a quantitative view of the average dynamics of single molecules with nanometer accuracy. By using a GFP-tagged variant of the Transferrin Receptor (TfR) and an ATTO488 labeled 1-palmitoyl-2-hydroxy-sn-glycero-3-phosphoethanolamine (PPE) it is possible to observe the spatiotemporal regulation of protein and lipid diffusion on µm-sized membrane regions in the micro-to-milli-second time range.

Quantitative fluorescence correlation spectroscopy on DNA in living cells

NASA Astrophysics Data System (ADS)

Hodges, Cameron; Kafle, Rudra P.; Meiners, Jens-Christian

2017-02-01

FCS is a fluorescence technique conventionally used to study the kinetics of fluorescent molecules in a dilute solution. Being a non-invasive technique, it is now drawing increasing interest for the study of more complex systems like the dynamics of DNA or proteins in living cells. Unlike an ordinary dye solution, the dynamics of macromolecules like proteins or entangled DNA in crowded environments is often slow and subdiffusive in nature. This in turn leads to longer residence times of the attached fluorophores in the excitation volume of the microscope and artifacts from photobleaching abound that can easily obscure the signature of the molecular dynamics of interest and make quantitative analysis challenging.We discuss methods and procedures to make FCS applicable to quantitative studies of the dynamics of DNA in live prokaryotic and eukaryotic cells. The intensity autocorrelation is computed function from weighted arrival times of the photons on the detector that maximizes the information content while simultaneously correcting for the effect of photobleaching to yield an autocorrelation function that reflects only the underlying dynamics of the sample. This autocorrelation function in turn is used to calculate the mean square displacement of the fluorophores attached to DNA. The displacement data is more amenable to further quantitative analysis than the raw correlation functions. By using a suitable integral transform of the mean square displacement, we can then determine the viscoelastic moduli of the DNA in its cellular environment. The entire analysis procedure is extensively calibrated and validated using model systems and computational simulations.

Sub-femtosecond quantum dynamics of the strong-field ionization of water to the X ̃(2)B1 and Ã(2)A1 states of the cation.

PubMed

Jayachander Rao, B; Varandas, A J C

2015-03-07

Motivated by recent efforts to achieve sub-femtosecond structural resolution in various molecular systems, we have performed a femtosecond quantum dynamics study of the water cation in the X ̃(2)B1 and Ã(2)A1 electronic states. Autocorrelation functions for H2O(+) and D2O(+) are calculated for such electronic states by solving numerically the time-dependent Schrödinger equation. From the ratio of the squared autocorrelation functions of D2O(+) and H2O(+), the high-order harmonic generation signals are calculated. Substantial vibrational dynamics is found in the Ã(2)A1 state as compared to the one in X ̃(2)B1, which supports recent experimental findings of Farrell et al., Phys. Rev. Lett., 2011, 107, 083001. Maxima in the above ratio are also predicted at ∼1.1 fs and ∼1.6 fs for the X ̃(2)B1 and Ã(2)A1 states, respectively. The expectation values of the positions of the atoms in H2O(+) as a function of time reveal a strong excitation of the bending mode in the Ã(2)A1 state, which explains the observed vibrational dynamics. The peaks in the ratios of the squared autocorrelation functions are also explained in terms of the evolving geometries of the water cation.

NMR spin-rotation relaxation and diffusion of methane

NASA Astrophysics Data System (ADS)

Singer, P. M.; Asthagiri, D.; Chapman, W. G.; Hirasaki, G. J.

2018-05-01

The translational diffusion-coefficient and the spin-rotation contribution to the 1H NMR relaxation rate for methane (CH4) are investigated using MD (molecular dynamics) simulations, over a wide range of densities and temperatures, spanning the liquid, supercritical, and gas phases. The simulated diffusion-coefficients agree well with measurements, without any adjustable parameters in the interpretation of the simulations. A minimization technique is developed to compute the angular velocity for non-rigid spherical molecules, which is used to simulate the autocorrelation function for spin-rotation interactions. With increasing diffusivity, the autocorrelation function shows increasing deviations from the single-exponential decay predicted by the Langevin theory for rigid spheres, and the deviations are quantified using inverse Laplace transforms. The 1H spin-rotation relaxation rate derived from the autocorrelation function using the "kinetic model" agrees well with measurements in the supercritical/gas phase, while the relaxation rate derived using the "diffusion model" agrees well with measurements in the liquid phase. 1H spin-rotation relaxation is shown to dominate over the MD-simulated 1H-1H dipole-dipole relaxation at high diffusivity, while the opposite is found at low diffusivity. At high diffusivity, the simulated spin-rotation correlation time agrees with the kinetic collision time for gases, which is used to derive a new expression for 1H spin-rotation relaxation, without any adjustable parameters.

Determination of modulation transfer function of a printer by measuring the autocorrelation of the transmission function of a printed Ronchi grating

DOE Office of Scientific and Technical Information (OSTI.GOV)

Madanipour, Khosro; Tavassoly, Mohammad T

2009-02-01

We show theoretically and verify experimentally that the modulation transfer function (MTF) of a printing system can be determined by measuring the autocorrelation of a printed Ronchi grating. In practice, two similar Ronchi gratings are printed on two transparencies and the transparencies are superimposed with parallel grating lines. Then, the gratings are uniformly illuminated and the transmitted light from a large section is measured versus the displacement of one grating with respect to the other in a grating pitch interval. This measurement provides the required autocorrelation function for determination of the MTF.

Role of internal motions and molecular geometry on the NMR relaxation of hydrocarbons

NASA Astrophysics Data System (ADS)

Singer, P. M.; Asthagiri, D.; Chen, Z.; Valiya Parambathu, A.; Hirasaki, G. J.; Chapman, W. G.

2018-04-01

The role of internal motions and molecular geometry on 1H NMR relaxation rates in liquid-state hydrocarbons is investigated using MD (molecular dynamics) simulations of the autocorrelation functions for intramolecular and intermolecular 1H-1H dipole-dipole interactions. The effects of molecular geometry and internal motions on the functional form of the autocorrelation functions are studied by comparing symmetric molecules such as neopentane and benzene to corresponding straight-chain alkanes n-pentane and n-hexane, respectively. Comparison of rigid versus flexible molecules shows that internal motions cause the intramolecular and intermolecular correlation-times to get significantly shorter, and the corresponding relaxation rates to get significantly smaller, especially for longer-chain n-alkanes. Site-by-site simulations of 1H's across the chains indicate significant variations in correlation times and relaxation rates across the molecule, and comparison with measurements reveals insights into cross-relaxation effects. Furthermore, the simulations reveal new insights into the relative strength of intramolecular versus intermolecular relaxation as a function of internal motions, as a function of molecular geometry, and on a site-by-site basis across the chain.

Using the thermal Gaussian approximation for the Boltzmann operator in semiclassical initial value time correlation functions.

PubMed

Liu, Jian; Miller, William H

2006-12-14

The thermal Gaussian approximation (TGA) recently developed by Frantsuzov et al. [Chem. Phys. Lett. 381, 117 (2003)] has been demonstrated to be a practical way for approximating the Boltzmann operator exp(-betaH) for multidimensional systems. In this paper the TGA is combined with semiclassical (SC) initial value representations (IVRs) for thermal time correlation functions. Specifically, it is used with the linearized SC-IVR (LSC-IVR, equivalent to the classical Wigner model), and the "forward-backward semiclassical dynamics" approximation developed by Shao and Makri [J. Phys. Chem. A 103, 7753 (1999); 103, 9749 (1999)]. Use of the TGA with both of these approximate SC-IVRs allows the oscillatory part of the IVR to be integrated out explicitly, providing an extremely simple result that is readily applicable to large molecular systems. Calculation of the force-force autocorrelation for a strongly anharmonic oscillator demonstrates its accuracy, and calculation of the velocity autocorrelation function (and thus the diffusion coefficient) of liquid neon demonstrates its applicability.

Momentum conserving Brownian dynamics propagator for complex soft matter fluids

DOE Office of Scientific and Technical Information (OSTI.GOV)

Padding, J. T.; Briels, W. J.

2014-12-28

We present a Galilean invariant, momentum conserving first order Brownian dynamics scheme for coarse-grained simulations of highly frictional soft matter systems. Friction forces are taken to be with respect to moving background material. The motion of the background material is described by locally averaged velocities in the neighborhood of the dissolved coarse coordinates. The velocity variables are updated by a momentum conserving scheme. The properties of the stochastic updates are derived through the Chapman-Kolmogorov and Fokker-Planck equations for the evolution of the probability distribution of coarse-grained position and velocity variables, by requiring the equilibrium distribution to be a stationary solution.more » We test our new scheme on concentrated star polymer solutions and find that the transverse current and velocity time auto-correlation functions behave as expected from hydrodynamics. In particular, the velocity auto-correlation functions display a long time tail in complete agreement with hydrodynamics.« less

Narayanaswamy’s 1971 aging theory and material time

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dyre, Jeppe C., E-mail: dyre@ruc.dk

2015-09-21

The Bochkov-Kuzovlev nonlinear fluctuation-dissipation theorem is used to derive Narayanaswamy’s phenomenological theory of physical aging, in which this highly nonlinear phenomenon is described by a linear material-time convolution integral. A characteristic property of the Narayanaswamy aging description is material-time translational invariance, which is here taken as the basic assumption of the derivation. It is shown that only one possible definition of the material time obeys this invariance, namely, the square of the distance travelled from a configuration of the system far back in time. The paper concludes with suggestions for computer simulations that test for consequences of material-time translational invariance.more » One of these is the “unique-triangles property” according to which any three points on the system’s path form a triangle such that two side lengths determine the third; this is equivalent to the well-known triangular relation for time-autocorrelation functions of aging spin glasses [L. F. Cugliandolo and J. Kurchan, J. Phys. A: Math. Gen. 27, 5749 (1994)]. The unique-triangles property implies a simple geometric interpretation of out-of-equilibrium time-autocorrelation functions, which extends to aging a previously proposed framework for such functions in equilibrium [J. C. Dyre, e-print arXiv:cond-mat/9712222 (1997)].« less

Geomagnetic storm under laboratory conditions: randomized experiment

NASA Astrophysics Data System (ADS)

Gurfinkel, Yu I.; Vasin, A. L.; Pishchalnikov, R. Yu; Sarimov, R. M.; Sasonko, M. L.; Matveeva, T. A.

2017-10-01

The influence of the previously recorded geomagnetic storm (GS) on human cardiovascular system and microcirculation has been studied under laboratory conditions. Healthy volunteers in lying position were exposed under two artificially created conditions: quiet (Q) and storm (S). The Q regime playbacks a noise-free magnetic field (MF) which is closed to the natural geomagnetic conditions on Moscow's latitude. The S regime playbacks the initially recorded 6-h geomagnetic storm which is repeated four times sequentially. The cardiovascular response to the GS impact was assessed by measuring capillary blood velocity (CBV) and blood pressure (BP) and by the analysis of the 24-h ECG recording. A storm-to-quiet ratio for the cardio intervals (CI) and the heart rate variability (HRV) was introduced in order to reveal the average over group significant differences of HRV. An individual sensitivity to the GS was estimated using the autocorrelation function analysis of the high-frequency (HF) part of the CI spectrum. The autocorrelation analysis allowed for detection a group of subjects of study which autocorrelation functions (ACF) react differently in the Q and S regimes of exposure.

Geomagnetic storm under laboratory conditions: randomized experiment.

PubMed

Gurfinkel, Yu I; Vasin, A L; Pishchalnikov, R Yu; Sarimov, R M; Sasonko, M L; Matveeva, T A

2018-04-01

The influence of the previously recorded geomagnetic storm (GS) on human cardiovascular system and microcirculation has been studied under laboratory conditions. Healthy volunteers in lying position were exposed under two artificially created conditions: quiet (Q) and storm (S). The Q regime playbacks a noise-free magnetic field (MF) which is closed to the natural geomagnetic conditions on Moscow's latitude. The S regime playbacks the initially recorded 6-h geomagnetic storm which is repeated four times sequentially. The cardiovascular response to the GS impact was assessed by measuring capillary blood velocity (CBV) and blood pressure (BP) and by the analysis of the 24-h ECG recording. A storm-to-quiet ratio for the cardio intervals (CI) and the heart rate variability (HRV) was introduced in order to reveal the average over group significant differences of HRV. An individual sensitivity to the GS was estimated using the autocorrelation function analysis of the high-frequency (HF) part of the CI spectrum. The autocorrelation analysis allowed for detection a group of subjects of study which autocorrelation functions (ACF) react differently in the Q and S regimes of exposure.

Forecasting coconut production in the Philippines with ARIMA model

NASA Astrophysics Data System (ADS)

Lim, Cristina Teresa

2015-02-01

The study aimed to depict the situation of the coconut industry in the Philippines for the future years applying Autoregressive Integrated Moving Average (ARIMA) method. Data on coconut production, one of the major industrial crops of the country, for the period of 1990 to 2012 were analyzed using time-series methods. Autocorrelation (ACF) and partial autocorrelation functions (PACF) were calculated for the data. Appropriate Box-Jenkins autoregressive moving average model was fitted. Validity of the model was tested using standard statistical techniques. The forecasting power of autoregressive moving average (ARMA) model was used to forecast coconut production for the eight leading years.

Statistical properties of a filtered Poisson process with additive random noise: distributions, correlations and moment estimation

NASA Astrophysics Data System (ADS)

Theodorsen, A.; E Garcia, O.; Rypdal, M.

2017-05-01

Filtered Poisson processes are often used as reference models for intermittent fluctuations in physical systems. Such a process is here extended by adding a noise term, either as a purely additive term to the process or as a dynamical term in a stochastic differential equation. The lowest order moments, probability density function, auto-correlation function and power spectral density are derived and used to identify and compare the effects of the two different noise terms. Monte-Carlo studies of synthetic time series are used to investigate the accuracy of model parameter estimation and to identify methods for distinguishing the noise types. It is shown that the probability density function and the three lowest order moments provide accurate estimations of the model parameters, but are unable to separate the noise types. The auto-correlation function and the power spectral density also provide methods for estimating the model parameters, as well as being capable of identifying the noise type. The number of times the signal crosses a prescribed threshold level in the positive direction also promises to be able to differentiate the noise type.

Using PPI network autocorrelation in hierarchical multi-label classification trees for gene function prediction.

PubMed

Stojanova, Daniela; Ceci, Michelangelo; Malerba, Donato; Dzeroski, Saso

2013-09-26

Ontologies and catalogs of gene functions, such as the Gene Ontology (GO) and MIPS-FUN, assume that functional classes are organized hierarchically, that is, general functions include more specific ones. This has recently motivated the development of several machine learning algorithms for gene function prediction that leverages on this hierarchical organization where instances may belong to multiple classes. In addition, it is possible to exploit relationships among examples, since it is plausible that related genes tend to share functional annotations. Although these relationships have been identified and extensively studied in the area of protein-protein interaction (PPI) networks, they have not received much attention in hierarchical and multi-class gene function prediction. Relations between genes introduce autocorrelation in functional annotations and violate the assumption that instances are independently and identically distributed (i.i.d.), which underlines most machine learning algorithms. Although the explicit consideration of these relations brings additional complexity to the learning process, we expect substantial benefits in predictive accuracy of learned classifiers. This article demonstrates the benefits (in terms of predictive accuracy) of considering autocorrelation in multi-class gene function prediction. We develop a tree-based algorithm for considering network autocorrelation in the setting of Hierarchical Multi-label Classification (HMC). We empirically evaluate the proposed algorithm, called NHMC (Network Hierarchical Multi-label Classification), on 12 yeast datasets using each of the MIPS-FUN and GO annotation schemes and exploiting 2 different PPI networks. The results clearly show that taking autocorrelation into account improves the predictive performance of the learned models for predicting gene function. Our newly developed method for HMC takes into account network information in the learning phase: When used for gene function prediction in the context of PPI networks, the explicit consideration of network autocorrelation increases the predictive performance of the learned models. Overall, we found that this holds for different gene features/ descriptions, functional annotation schemes, and PPI networks: Best results are achieved when the PPI network is dense and contains a large proportion of function-relevant interactions.

First-passage problems: A probabilistic dynamic analysis for degraded structures

NASA Technical Reports Server (NTRS)

Shiao, Michael C.; Chamis, Christos C.

1990-01-01

Structures subjected to random excitations with uncertain system parameters degraded by surrounding environments (a random time history) are studied. Methods are developed to determine the statistics of dynamic responses, such as the time-varying mean, the standard deviation, the autocorrelation functions, and the joint probability density function of any response and its derivative. Moreover, the first-passage problems with deterministic and stationary/evolutionary random barriers are evaluated. The time-varying (joint) mean crossing rate and the probability density function of the first-passage time for various random barriers are derived.

Computation and analysis of the transverse current autocorrelation function, Ct(k,t), for small wave vectors: A molecular-dynamics study for a Lennard-Jones fluid

NASA Astrophysics Data System (ADS)

Vogelsang, R.; Hoheisel, C.

1987-02-01

Molecular-dynamics (MD) calculations are reported for three thermodynamic states of a Lennard-Jones fluid. Systems of 2048 particles and 105 integration steps were used. The transverse current autocorrelation function, Ct(k,t), has been determined for wave vectors of the range 0.5<||k||σ<1.5. Ct(k,t) was fitted by hydrodynamic-type functions. The fits returned k-dependent decay times and shear viscosities which showed a systematic behavior as a function of k. Extrapolation to the hydrodynamic region at k=0 gave shear viscosity coefficients in good agreement with direct Green-Kubo results obtained in previous work. The two-exponential model fit for the memory function proposed by other authors does not provide a reasonable description of the MD results, as the fit parameters show no systematic wave-vector dependence, although the Ct(k,t) functions are somewhat better fitted. Similarly, the semiempirical interpolation formula for the decay time based on the viscoelastic concept proposed by Akcasu and Daniels fails to reproduce the correct k dependence for the wavelength range investigated herein.

Autocorrelation of the susceptible-infected-susceptible process on networks

NASA Astrophysics Data System (ADS)

Liu, Qiang; Van Mieghem, Piet

2018-06-01

In this paper, we focus on the autocorrelation of the susceptible-infected-susceptible (SIS) process on networks. The N -intertwined mean-field approximation (NIMFA) is applied to calculate the autocorrelation properties of the exact SIS process. We derive the autocorrelation of the infection state of each node and the fraction of infected nodes both in the steady and transient states as functions of the infection probabilities of nodes. Moreover, we show that the autocorrelation can be used to estimate the infection and curing rates of the SIS process. The theoretical results are compared with the simulation of the exact SIS process. Our work fully utilizes the potential of the mean-field method and shows that NIMFA can indeed capture the autocorrelation properties of the exact SIS process.

Statistical regularities of Carbon emission trading market: Evidence from European Union allowances

NASA Astrophysics Data System (ADS)

Zheng, Zeyu; Xiao, Rui; Shi, Haibo; Li, Guihong; Zhou, Xiaofeng

2015-05-01

As an emerging financial market, the trading value of carbon emission trading market has definitely increased. In recent years, the carbon emission allowances have already become a way of investment. They are bought and sold not only by carbon emitters but also by investors. In this paper, we analyzed the price fluctuations of the European Union allowances (EUA) futures in European Climate Exchange (ECX) market from 2007 to 2011. The symmetric and power-law probability density function of return time series was displayed. We found that there are only short-range correlations in price changes (return), while long-range correlations in the absolute of price changes (volatility). Further, detrended fluctuation analysis (DFA) approach was applied with focus on long-range autocorrelations and Hurst exponent. We observed long-range power-law autocorrelations in the volatility that quantify risk, and found that they decay much more slowly than the autocorrelation of return time series. Our analysis also showed that the significant cross correlations exist between return time series of EUA and many other returns. These cross correlations exist in a wide range of fields, including stock markets, energy concerned commodities futures, and financial futures. The significant cross-correlations between energy concerned futures and EUA indicate the physical relationship between carbon emission and energy production process. Additionally, the cross-correlations between financial futures and EUA indicate that the speculation behavior may become an important factor that can affect the price of EUA. Finally we modeled the long-range volatility time series of EUA with a particular version of the GARCH process, and the result also suggests long-range volatility autocorrelations.

Sources of variation in Landsat autocorrelation

NASA Technical Reports Server (NTRS)

Craig, R. G.; Labovitz, M. L.

1980-01-01

Analysis of sixty-four scan lines representing diverse conditions across satellites, channels, scanners, locations and cloud cover confirms that Landsat data are autocorrelated and consistently follow an Arima (1,0,1) pattern. The AR parameter varies significantly with location and the MA coefficient with cloud cover. Maximum likelihood classification functions are considerably in error unless this autocorrelation is compensated for in sampling.

Stochastic modelling of the monthly average maximum and minimum temperature patterns in India 1981-2015

NASA Astrophysics Data System (ADS)

Narasimha Murthy, K. V.; Saravana, R.; Vijaya Kumar, K.

2018-04-01

The paper investigates the stochastic modelling and forecasting of monthly average maximum and minimum temperature patterns through suitable seasonal auto regressive integrated moving average (SARIMA) model for the period 1981-2015 in India. The variations and distributions of monthly maximum and minimum temperatures are analyzed through Box plots and cumulative distribution functions. The time series plot indicates that the maximum temperature series contain sharp peaks in almost all the years, while it is not true for the minimum temperature series, so both the series are modelled separately. The possible SARIMA model has been chosen based on observing autocorrelation function (ACF), partial autocorrelation function (PACF), and inverse autocorrelation function (IACF) of the logarithmic transformed temperature series. The SARIMA (1, 0, 0) × (0, 1, 1)12 model is selected for monthly average maximum and minimum temperature series based on minimum Bayesian information criteria. The model parameters are obtained using maximum-likelihood method with the help of standard error of residuals. The adequacy of the selected model is determined using correlation diagnostic checking through ACF, PACF, IACF, and p values of Ljung-Box test statistic of residuals and using normal diagnostic checking through the kernel and normal density curves of histogram and Q-Q plot. Finally, the forecasting of monthly maximum and minimum temperature patterns of India for the next 3 years has been noticed with the help of selected model.

Accounting for Non-Gaussian Sources of Spatial Correlation in Parametric Functional Magnetic Resonance Imaging Paradigms II: A Method to Obtain First-Level Analysis Residuals with Uniform and Gaussian Spatial Autocorrelation Function and Independent and Identically Distributed Time-Series.

PubMed

Gopinath, Kaundinya; Krishnamurthy, Venkatagiri; Lacey, Simon; Sathian, K

2018-02-01

In a recent study Eklund et al. have shown that cluster-wise family-wise error (FWE) rate-corrected inferences made in parametric statistical method-based functional magnetic resonance imaging (fMRI) studies over the past couple of decades may have been invalid, particularly for cluster defining thresholds less stringent than p < 0.001; principally because the spatial autocorrelation functions (sACFs) of fMRI data had been modeled incorrectly to follow a Gaussian form, whereas empirical data suggest otherwise. Hence, the residuals from general linear model (GLM)-based fMRI activation estimates in these studies may not have possessed a homogenously Gaussian sACF. Here we propose a method based on the assumption that heterogeneity and non-Gaussianity of the sACF of the first-level GLM analysis residuals, as well as temporal autocorrelations in the first-level voxel residual time-series, are caused by unmodeled MRI signal from neuronal and physiological processes as well as motion and other artifacts, which can be approximated by appropriate decompositions of the first-level residuals with principal component analysis (PCA), and removed. We show that application of this method yields GLM residuals with significantly reduced spatial correlation, nearly Gaussian sACF and uniform spatial smoothness across the brain, thereby allowing valid cluster-based FWE-corrected inferences based on assumption of Gaussian spatial noise. We further show that application of this method renders the voxel time-series of first-level GLM residuals independent, and identically distributed across time (which is a necessary condition for appropriate voxel-level GLM inference), without having to fit ad hoc stochastic colored noise models. Furthermore, the detection power of individual subject brain activation analysis is enhanced. This method will be especially useful for case studies, which rely on first-level GLM analysis inferences.

Autocorrelated process control: Geometric Brownian Motion approach versus Box-Jenkins approach

NASA Astrophysics Data System (ADS)

Salleh, R. M.; Zawawi, N. I.; Gan, Z. F.; Nor, M. E.

2018-04-01

Existing of autocorrelation will bring a significant effect on the performance and accuracy of process control if the problem does not handle carefully. When dealing with autocorrelated process, Box-Jenkins method will be preferred because of the popularity. However, the computation of Box-Jenkins method is too complicated and challenging which cause of time-consuming. Therefore, an alternative method which known as Geometric Brownian Motion (GBM) is introduced to monitor the autocorrelated process. One real case of furnace temperature data is conducted to compare the performance of Box-Jenkins and GBM methods in monitoring autocorrelation process. Both methods give the same results in terms of model accuracy and monitoring process control. Yet, GBM is superior compared to Box-Jenkins method due to its simplicity and practically with shorter computational time.

Coherence solution for bidirectional reflectance distributions of surfaces with wavelength-scale statistics.

PubMed

Hoover, Brian G; Gamiz, Victor L

2006-02-01

The scalar bidirectional reflectance distribution function (BRDF) due to a perfectly conducting surface with roughness and autocorrelation width comparable with the illumination wavelength is derived from coherence theory on the assumption of a random reflective phase screen and an expansion valid for large effective roughness. A general quadratic expansion of the two-dimensional isotropic surface autocorrelation function near the origin yields representative Cauchy and Gaussian BRDF solutions and an intermediate general solution as the sum of an incoherent component and a nonspecular coherent component proportional to an integral of the plasma dispersion function in the complex plane. Plots illustrate agreement of the derived general solution with original bistatic BRDF data due to a machined aluminum surface, and comparisons are drawn with previously published data in the examination of variations with incident angle, roughness, illumination wavelength, and autocorrelation coefficients in the bistatic and monostatic geometries. The general quadratic autocorrelation expansion provides a BRDF solution that smoothly interpolates between the well-known results of the linear and parabolic approximations.

Single-molecule fluorescence detection: autocorrelation criterion and experimental realization with phycoerythrin.

PubMed Central

Peck, K; Stryer, L; Glazer, A N; Mathies, R A

1989-01-01

A theory for single-molecule fluorescence detection is developed and then used to analyze data from subpicomolar solutions of B-phycoerythrin (PE). The distribution of detected counts is the convolution of a Poissonian continuous background with bursts arising from the passage of individual fluorophores through the focused laser beam. The autocorrelation function reveals single-molecule events and provides a criterion for optimizing experimental parameters. The transit time of fluorescent molecules through the 120-fl imaged volume was 800 microseconds. The optimal laser power (32 mW at 514.5 nm) gave an incident intensity of 1.8 x 10(23) photons.cm-2.s-1, corresponding to a mean time of 1.1 ns between absorptions. The mean incremental count rate was 1.5 per 100 microseconds for PE monomers and 3.0 for PE dimers above a background count rate of 1.0. The distribution of counts and the autocorrelation function for 200 fM monomer and 100 fM dimer demonstrate that single-molecule detection was achieved. At this concentration, the mean occupancy was 0.014 monomer molecules in the probed volume. A hard-wired version of this detection system was used to measure the concentration of PE down to 1 fM. This single-molecule counter is 3 orders of magnitude more sensitive than conventional fluorescence detection systems. PMID:2726766

Holographic Measurements of Fuel Droplet Diffusion in Isotropic Turbulence

NASA Astrophysics Data System (ADS)

Gopalan, Balaji

2005-11-01

High-speed digital holographic cinematography was used to investigate the diffusion of slightly buoyant fuel droplets in locally isotropic turbulence. High turbulence levels with a weak mean velocity was generated at the center of a tank by four rotating grids. 0.3-1.5mm droplets were injected here and tracked using in-line holography. To obtain all three components of velocity, we simultaneously recorded holograms of the central 37x37x37 mm^3 volume from two perpendicular directions. These were numerically reconstructed to provide focused images of the droplets. An automated code was developed to identify the 3-D droplet trajectories from the two views, and then calculate time series of their velocity. After subtracting the local mean fluid velocity, the time series were used to obtain the 3-D Lagrangian autocorrelation function of droplet velocity. Averaging over many trajectories provided the autocorrelation functions as a function of direction and droplet sizes. As expected, the correlation was higher in the vertical direction due to the effect of gravity. Data analysis is still in progress.

Spatial autocorrelation of radiation measured by the Earth Radiation Budget Experiment: Scene inhomogeneity and reciprocity violation

NASA Technical Reports Server (NTRS)

Davies, Roger

1994-01-01

The spatial autocorrelation functions of broad-band longwave and shortwave radiances measured by the Earth Radiation Budget Experiment (ERBE) are analyzed as a function of view angle in an investigation of the general effects of scene inhomogeneity on radiation. For nadir views, the correlation distance of the autocorrelation function is about 900 km for longwave radiance and about 500 km for shortwave radiance, consistent with higher degrees of freedom in shortwave reflection. Both functions rise monotonically with view angle, but there is a substantial difference in the relative angular dependence of the shortwave and longwave functions, especially for view angles less than 50 deg. In this range, the increase with angle of the longwave functions is found to depend only on the expansion of pixel area with angle, whereas the shortwave functions show an additional dependence on angle that is attributed to the occlusion of inhomogeneities by cloud height variations. Beyond a view angle of about 50 deg, both longwave and shortwave functions appear to be affected by cloud sides. The shortwave autocorrelation functions do not satisfy the principle of directional reciprocity, thereby proving that the average scene is horizontally inhomogeneous over the scale of an ERBE pixel (1500 sq km). Coarse stratification of the measurements by cloud amount, however, indicates that the average cloud-free scene does satisfy directional reciprocity on this scale.

Scaling analysis and model estimation of solar corona index

NASA Astrophysics Data System (ADS)

Ray, Samujjwal; Ray, Rajdeep; Khondekar, Mofazzal Hossain; Ghosh, Koushik

2018-04-01

A monthly average solar green coronal index time series for the period from January 1939 to December 2008 collected from NOAA (The National Oceanic and Atmospheric Administration) has been analysed in this paper in perspective of scaling analysis and modelling. Smoothed and de-noising have been done using suitable mother wavelet as a pre-requisite. The Finite Variance Scaling Method (FVSM), Higuchi method, rescaled range (R/S) and a generalized method have been applied to calculate the scaling exponents and fractal dimensions of the time series. Autocorrelation function (ACF) is used to find autoregressive (AR) process and Partial autocorrelation function (PACF) has been used to get the order of AR model. Finally a best fit model has been proposed using Yule-Walker Method with supporting results of goodness of fit and wavelet spectrum. The results reveal an anti-persistent, Short Range Dependent (SRD), self-similar property with signatures of non-causality, non-stationarity and nonlinearity in the data series. The model shows the best fit to the data under observation.

Controlling for seasonal patterns and time varying confounders in time-series epidemiological models: a simulation study.

PubMed

Perrakis, Konstantinos; Gryparis, Alexandros; Schwartz, Joel; Le Tertre, Alain; Katsouyanni, Klea; Forastiere, Francesco; Stafoggia, Massimo; Samoli, Evangelia

2014-12-10

An important topic when estimating the effect of air pollutants on human health is choosing the best method to control for seasonal patterns and time varying confounders, such as temperature and humidity. Semi-parametric Poisson time-series models include smooth functions of calendar time and weather effects to control for potential confounders. Case-crossover (CC) approaches are considered efficient alternatives that control seasonal confounding by design and allow inclusion of smooth functions of weather confounders through their equivalent Poisson representations. We evaluate both methodological designs with respect to seasonal control and compare spline-based approaches, using natural splines and penalized splines, and two time-stratified CC approaches. For the spline-based methods, we consider fixed degrees of freedom, minimization of the partial autocorrelation function, and general cross-validation as smoothing criteria. Issues of model misspecification with respect to weather confounding are investigated under simulation scenarios, which allow quantifying omitted, misspecified, and irrelevant-variable bias. The simulations are based on fully parametric mechanisms designed to replicate two datasets with different mortality and atmospheric patterns. Overall, minimum partial autocorrelation function approaches provide more stable results for high mortality counts and strong seasonal trends, whereas natural splines with fixed degrees of freedom perform better for low mortality counts and weak seasonal trends followed by the time-season-stratified CC model, which performs equally well in terms of bias but yields higher standard errors. Copyright © 2014 John Wiley & Sons, Ltd.

DETAILED DATA ANALYSIS OF ECHO I, ECHO II AND MOON REFLECTED SIGNALS. VOLUME 2. AUTOCORRELATION FUNCTIONS OF ECHO II REFLECTED SIGNALS,

DTIC Science & Technology

techniques is presented. Two methods for linearizing the data are given. An expression for the specular-to-spattered power ratio is derived, and the inverse ... transform of the autocorrelation function is discussed. The surface roughness of the reflector, the discrete fading rates, and fading frequencies

Characterizing the functional MRI response using Tikhonov regularization.

PubMed

Vakorin, Vasily A; Borowsky, Ron; Sarty, Gordon E

2007-09-20

The problem of evaluating an averaged functional magnetic resonance imaging (fMRI) response for repeated block design experiments was considered within a semiparametric regression model with autocorrelated residuals. We applied functional data analysis (FDA) techniques that use a least-squares fitting of B-spline expansions with Tikhonov regularization. To deal with the noise autocorrelation, we proposed a regularization parameter selection method based on the idea of combining temporal smoothing with residual whitening. A criterion based on a generalized chi(2)-test of the residuals for white noise was compared with a generalized cross-validation scheme. We evaluated and compared the performance of the two criteria, based on their effect on the quality of the fMRI response. We found that the regularization parameter can be tuned to improve the noise autocorrelation structure, but the whitening criterion provides too much smoothing when compared with the cross-validation criterion. The ultimate goal of the proposed smoothing techniques is to facilitate the extraction of temporal features in the hemodynamic response for further analysis. In particular, these FDA methods allow us to compute derivatives and integrals of the fMRI signal so that fMRI data may be correlated with behavioral and physiological models. For example, positive and negative hemodynamic responses may be easily and robustly identified on the basis of the first derivative at an early time point in the response. Ultimately, these methods allow us to verify previously reported correlations between the hemodynamic response and the behavioral measures of accuracy and reaction time, showing the potential to recover new information from fMRI data. 2007 John Wiley & Sons, Ltd

Thermal noise in confined fluids.

PubMed

Sanghi, T; Aluru, N R

2014-11-07

In this work, we discuss a combined memory function equation (MFE) and generalized Langevin equation (GLE) approach (referred to as MFE/GLE formulation) to characterize thermal noise in confined fluids. Our study reveals that for fluids confined inside nanoscale geometries, the correlation time and the time decay of the autocorrelation function of the thermal noise are not significantly different across the confinement. We show that it is the strong cross-correlation of the mean force with the molecular velocity that gives rise to the spatial anisotropy in the velocity-autocorrelation function of the confined fluids. Further, we use the MFE/GLE formulation to extract the thermal force a fluid molecule experiences in a MD simulation. Noise extraction from MD simulation suggests that the frequency distribution of the thermal force is non-Gaussian. Also, the frequency distribution of the thermal force near the confining surface is found to be different in the direction parallel and perpendicular to the confinement. We also use the formulation to compute the noise correlation time of water confined inside a (6,6) carbon-nanotube (CNT). It is observed that inside the (6,6) CNT, in which water arranges itself in a highly concerted single-file arrangement, the correlation time of thermal noise is about an order of magnitude higher than that of bulk water.

Thermal noise in confined fluids

NASA Astrophysics Data System (ADS)

Sanghi, T.; Aluru, N. R.

2014-11-01

In this work, we discuss a combined memory function equation (MFE) and generalized Langevin equation (GLE) approach (referred to as MFE/GLE formulation) to characterize thermal noise in confined fluids. Our study reveals that for fluids confined inside nanoscale geometries, the correlation time and the time decay of the autocorrelation function of the thermal noise are not significantly different across the confinement. We show that it is the strong cross-correlation of the mean force with the molecular velocity that gives rise to the spatial anisotropy in the velocity-autocorrelation function of the confined fluids. Further, we use the MFE/GLE formulation to extract the thermal force a fluid molecule experiences in a MD simulation. Noise extraction from MD simulation suggests that the frequency distribution of the thermal force is non-Gaussian. Also, the frequency distribution of the thermal force near the confining surface is found to be different in the direction parallel and perpendicular to the confinement. We also use the formulation to compute the noise correlation time of water confined inside a (6,6) carbon-nanotube (CNT). It is observed that inside the (6,6) CNT, in which water arranges itself in a highly concerted single-file arrangement, the correlation time of thermal noise is about an order of magnitude higher than that of bulk water.

Crustal thickness across the Trans-European Suture Zone from ambient noise autocorrelations

NASA Astrophysics Data System (ADS)

Becker, G.; Knapmeyer-Endrun, B.

2018-02-01

We derive autocorrelations from ambient seismic noise to image the reflectivity of the subsurface and to extract the Moho depth beneath the stations for two different data sets in Central Europe. The autocorrelations are calculated by smoothing the spectrum of the data in order to suppress high amplitude, narrow-band signals of industrial origin, applying a phase autocorrelation algorithm and time-frequency domain phase-weighted stacking. The stacked autocorrelation results are filtered and analysed predominantly in the frequency range of 1-2 Hz. Moho depth is automatically picked inside uncertainty windows obtained from prior information. The processing scheme we developed is applied to data from permanent seismic stations located in different geological provinces across Europe, with varying Moho depths between 25 and 50 km, and to the mainly short period temporary PASSEQ stations along seismic profile POLONAISE P4. The autocorrelation results are spatially and temporarily stable, but show a clear correlation with the existence of cultural noise. On average, a minimum of six months of data is needed to obtain stable results. The obtained Moho depth results are in good agreement with the subsurface model provided by seismic profiling, receiver function estimates and the European Moho depth map. In addition to extracting the Moho depth, it is possible to identify an intracrustal layer along the profile, again closely matching the seismic model. For more than half of the broad-band stations, another change in reflectivity within the mantle is observed and can be correlated with the lithosphere-asthenosphere boundary to the west and a mid-lithospheric discontinuity beneath the East European Craton. With the application of the developed autocorrelation processing scheme to different stations with varying crustal thicknesses, it is shown that Moho depth can be extracted independent of subsurface structure, when station coverage is low, when no strong seismic sources are present, and when only limited amounts of data are available.

Temporal and spatiotemporal autocorrelation of daily concentrations of Alnus, Betula, and Corylus pollen in Poland.

PubMed

Nowosad, J; Stach, A; Kasprzyk, I; Grewling, Ł; Latałowa, M; Puc, M; Myszkowska, D; Weryszko-Chmielewska, E; Piotrowska-Weryszko, K; Chłopek, K; Majkowska-Wojciechowska, B; Uruska, A

The aim of the study was to determine the characteristics of temporal and space-time autocorrelation of pollen counts of Alnus , Betula , and Corylus in the air of eight cities in Poland. Daily average pollen concentrations were monitored over 8 years (2001-2005 and 2009-2011) using Hirst-designed volumetric spore traps. The spatial and temporal coherence of data was investigated using the autocorrelation and cross-correlation functions. The calculation and mathematical modelling of 61 correlograms were performed for up to 25 days back. The study revealed an association between temporal variations in Alnus , Betula , and Corylus pollen counts in Poland and three main groups of factors such as: (1) air mass exchange after the passage of a single weather front (30-40 % of pollen count variation); (2) long-lasting factors (50-60 %); and (3) random factors, including diurnal variations and measurements errors (10 %). These results can help to improve the quality of forecasting models.

Geometrical optical transfer function: is it worth calculating?

PubMed

Díaz, José A; Mahajan, Virendra N

2017-10-01

In this paper, we explore the merit of calculating the geometrical optical transfer function (GOTF) in optical design by comparing the time to calculate it with the time to calculate the diffraction optical transfer function (DOTF). We determine the DOTF by numerical integration of the pupil function autocorrelation (that reduces to an integration of a complex exponential of the aberration difference function), 2D digital autocorrelation of the pupil function, and the Fourier transform (FT) of the point-spread function (PSF); and we determine the GOTF by the FT of the geometrical PSF (that reduces to an integration over the pupil plane of a complex exponential that is a scalar product of the spatial frequency and transverse ray aberration vectors) and the FT of the spot diagram. Our starting point for calculating the DOTF is the wave aberrations of the system in its pupil plane, and the transverse ray aberrations in the image plane for the GOTF. Numerical results for primary aberrations and some typical imaging systems show that the direct numerical integrations are slow, but the GOTF calculation by a FT of the spot diagram is two or even three times slower than the DOTF calculation by an FT of the PSF, depending on the aberration. We conclude that the calculation of GOTF is, at best, an approximation of the DOTF and only for large aberrations; GOTF does not offer any advantage in the optical design process, and hence negates its utility.

Time to burn: Modeling wildland arson as an autoregressive crime function

Treesearch

Jeffrey P. Prestemon; David T. Butry

2005-01-01

Six Poisson autoregressive models of order p [PAR(p)] of daily wildland arson ignition counts are estimated for five locations in Florida (1994-2001). In addition, a fixed effects time-series Poisson model of annual arson counts is estimated for all Florida counties (1995-2001). PAR(p) model estimates reveal highly significant arson ignition autocorrelation, lasting up...

Advanced Sine Wave Modulation of Continuous Wave Laser System for Atmospheric CO2 Differential Absorption Measurements

NASA Technical Reports Server (NTRS)

Campbell, Joel F.; Lin, Bing; Nehrir, Amin R.

2014-01-01

NASA Langley Research Center in collaboration with ITT Exelis have been experimenting with Continuous Wave (CW) laser absorption spectrometer (LAS) as a means of performing atmospheric CO2 column measurements from space to support the Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS) mission.Because range resolving Intensity Modulated (IM) CW lidar techniques presented here rely on matched filter correlations, autocorrelation properties without side lobes or other artifacts are highly desirable since the autocorrelation function is critical for the measurements of lidar return powers, laser path lengths, and CO2 column amounts. In this paper modulation techniques are investigated that improve autocorrelation properties. The modulation techniques investigated in this paper include sine waves modulated by maximum length (ML) sequences in various hardware configurations. A CW lidar system using sine waves modulated by ML pseudo random noise codes is described, which uses a time shifting approach to separate channels and make multiple, simultaneous online/offline differential absorption measurements. Unlike the pure ML sequence, this technique is useful in hardware that is band pass filtered as the IM sine wave carrier shifts the main power band. Both amplitude and Phase Shift Keying (PSK) modulated IM carriers are investigated that exibit perfect autocorrelation properties down to one cycle per code bit. In addition, a method is presented to bandwidth limit the ML sequence based on a Gaussian filter implemented in terms of Jacobi theta functions that does not seriously degrade the resolution or introduce side lobes as a means of reducing aliasing and IM carrier bandwidth.

Autocorrelations of stellar light and mass at z˜ 0 and ˜1: from SDSS to DEEP2

NASA Astrophysics Data System (ADS)

Li, Cheng; White, Simon D. M.; Chen, Yanmei; Coil, Alison L.; Davis, Marc; De Lucia, Gabriella; Guo, Qi; Jing, Y. P.; Kauffmann, Guinevere; Willmer, Christopher N. A.; Zhang, Wei

2012-01-01

We present measurements of projected autocorrelation functions wp(rp) for the stellar mass of galaxies and for their light in the U, B and V bands, using data from the third data release of the DEEP2 Galaxy Redshift Survey and the final data release of the Sloan Digital Sky Survey (SDSS). We investigate the clustering bias of stellar mass and light by comparing these to projected autocorrelations of dark matter estimated from the Millennium Simulations (MS) at z= 1 and 0.07, the median redshifts of our galaxy samples. All of the autocorrelation and bias functions show systematic trends with spatial scale and waveband which are impressively similar at the two redshifts. This shows that the well-established environmental dependence of stellar populations in the local Universe is already in place at z= 1. The recent MS-based galaxy formation simulation of Guo et al. reproduces the scale-dependent clustering of luminosity to an accuracy better than 30 per cent in all bands and at both redshifts, but substantially overpredicts mass autocorrelations at separations below about 2 Mpc. Further comparison of the shapes of our stellar mass bias functions with those predicted by the model suggests that both the SDSS and DEEP2 data prefer a fluctuation amplitude of σ8˜ 0.8 rather than the σ8= 0.9 assumed by the MS.

Testing Pairwise Association between Spatially Autocorrelated Variables: A New Approach Using Surrogate Lattice Data

PubMed Central

Deblauwe, Vincent; Kennel, Pol; Couteron, Pierre

2012-01-01

Background Independence between observations is a standard prerequisite of traditional statistical tests of association. This condition is, however, violated when autocorrelation is present within the data. In the case of variables that are regularly sampled in space (i.e. lattice data or images), such as those provided by remote-sensing or geographical databases, this problem is particularly acute. Because analytic derivation of the null probability distribution of the test statistic (e.g. Pearson's r) is not always possible when autocorrelation is present, we propose instead the use of a Monte Carlo simulation with surrogate data. Methodology/Principal Findings The null hypothesis that two observed mapped variables are the result of independent pattern generating processes is tested here by generating sets of random image data while preserving the autocorrelation function of the original images. Surrogates are generated by matching the dual-tree complex wavelet spectra (and hence the autocorrelation functions) of white noise images with the spectra of the original images. The generated images can then be used to build the probability distribution function of any statistic of association under the null hypothesis. We demonstrate the validity of a statistical test of association based on these surrogates with both actual and synthetic data and compare it with a corrected parametric test and three existing methods that generate surrogates (randomization, random rotations and shifts, and iterative amplitude adjusted Fourier transform). Type I error control was excellent, even with strong and long-range autocorrelation, which is not the case for alternative methods. Conclusions/Significance The wavelet-based surrogates are particularly appropriate in cases where autocorrelation appears at all scales or is direction-dependent (anisotropy). We explore the potential of the method for association tests involving a lattice of binary data and discuss its potential for validation of species distribution models. An implementation of the method in Java for the generation of wavelet-based surrogates is available online as supporting material. PMID:23144961

Studies of the micromorphology of sputtered TiN thin films by autocorrelation techniques

NASA Astrophysics Data System (ADS)

Smagoń, Kamil; Stach, Sebastian; Ţălu, Ştefan; Arman, Ali; Achour, Amine; Luna, Carlos; Ghobadi, Nader; Mardani, Mohsen; Hafezi, Fatemeh; Ahmadpourian, Azin; Ganji, Mohsen; Grayeli Korpi, Alireza

2017-12-01

Autocorrelation techniques are crucial tools for the study of the micromorphology of surfaces: They provide the description of anisotropic properties and the identification of repeated patterns on the surface, facilitating the comparison of samples. In the present investigation, some fundamental concepts of these techniques including the autocorrelation function and autocorrelation length have been reviewed and applied in the study of titanium nitride thin films by atomic force microscopy (AFM). The studied samples were grown on glass substrates by reactive magnetron sputtering at different substrate temperatures (from 25 {}°C to 400 {}°C , and their micromorphology was studied by AFM. The obtained AFM data were analyzed using MountainsMap Premium software obtaining the correlation function, the structure of isotropy and the spatial parameters according to ISO 25178 and EUR 15178N. These studies indicated that the substrate temperature during the deposition process is an important parameter to modify the micromorphology of sputtered TiN thin films and to find optimized surface properties. For instance, the autocorrelation length exhibited a maximum value for the sample prepared at a substrate temperature of 300 {}°C , and the sample obtained at 400 {}°C presented a maximum angle of the direction of the surface structure.

A method to identify differential expression profiles of time-course gene data with Fourier transformation.

PubMed

Kim, Jaehee; Ogden, Robert Todd; Kim, Haseong

2013-10-18

Time course gene expression experiments are an increasingly popular method for exploring biological processes. Temporal gene expression profiles provide an important characterization of gene function, as biological systems are both developmental and dynamic. With such data it is possible to study gene expression changes over time and thereby to detect differential genes. Much of the early work on analyzing time series expression data relied on methods developed originally for static data and thus there is a need for improved methodology. Since time series expression is a temporal process, its unique features such as autocorrelation between successive points should be incorporated into the analysis. This work aims to identify genes that show different gene expression profiles across time. We propose a statistical procedure to discover gene groups with similar profiles using a nonparametric representation that accounts for the autocorrelation in the data. In particular, we first represent each profile in terms of a Fourier basis, and then we screen out genes that are not differentially expressed based on the Fourier coefficients. Finally, we cluster the remaining gene profiles using a model-based approach in the Fourier domain. We evaluate the screening results in terms of sensitivity, specificity, FDR and FNR, compare with the Gaussian process regression screening in a simulation study and illustrate the results by application to yeast cell-cycle microarray expression data with alpha-factor synchronization.The key elements of the proposed methodology: (i) representation of gene profiles in the Fourier domain; (ii) automatic screening of genes based on the Fourier coefficients and taking into account autocorrelation in the data, while controlling the false discovery rate (FDR); (iii) model-based clustering of the remaining gene profiles. Using this method, we identified a set of cell-cycle-regulated time-course yeast genes. The proposed method is general and can be potentially used to identify genes which have the same patterns or biological processes, and help facing the present and forthcoming challenges of data analysis in functional genomics.

Fetal source extraction from magnetocardiographic recordings by dependent component analysis

NASA Astrophysics Data System (ADS)

de Araujo, Draulio B.; Kardec Barros, Allan; Estombelo-Montesco, Carlos; Zhao, Hui; Roque da Silva Filho, A. C.; Baffa, Oswaldo; Wakai, Ronald; Ohnishi, Noboru

2005-10-01

Fetal magnetocardiography (fMCG) has been extensively reported in the literature as a non-invasive, prenatal technique that can be used to monitor various functions of the fetal heart. However, fMCG signals often have low signal-to-noise ratio (SNR) and are contaminated by strong interference from the mother's magnetocardiogram signal. A promising, efficient tool for extracting signals, even under low SNR conditions, is blind source separation (BSS), or independent component analysis (ICA). Herein we propose an algorithm based on a variation of ICA, where the signal of interest is extracted using a time delay obtained from an autocorrelation analysis. We model the system using autoregression, and identify the signal component of interest from the poles of the autocorrelation function. We show that the method is effective in removing the maternal signal, and is computationally efficient. We also compare our results to more established ICA methods, such as FastICA.

Diffusion in shear flow

NASA Astrophysics Data System (ADS)

Dufty, J. W.

1984-09-01

Diffusion of a tagged particle in a fluid with uniform shear flow is described. The continuity equation for the probability density describing the position of the tagged particle is considered. The diffusion tensor is identified by expanding the irreversible part of the probability current to first order in the gradient of the probability density, but with no restriction on the shear rate. The tensor is expressed as the time integral of a nonequilibrium autocorrelation function for the velocity of the tagged particle in its local fluid rest frame, generalizing the Green-Kubo expression to the nonequilibrium state. The tensor is evaluated from results obtained previously for the velocity autocorrelation function that are exact for Maxwell molecules in the Boltzmann limit. The effects of viscous heating are included and the dependence on frequency and shear rate is displayed explicitly. The mode-coupling contributions to the frequency and shear-rate dependent diffusion tensor are calculated.

Ring polymer dynamics in curved spaces

NASA Astrophysics Data System (ADS)

Wolf, S.; Curotto, E.

2012-07-01

We formulate an extension of the ring polymer dynamics approach to curved spaces using stereographic projection coordinates. We test the theory by simulating the particle in a ring, {T}^1, mapped by a stereographic projection using three potentials. Two of these are quadratic, and one is a nonconfining sinusoidal model. We propose a new class of algorithms for the integration of the ring polymer Hamilton equations in curved spaces. These are designed to improve the energy conservation of symplectic integrators based on the split operator approach. For manifolds, the position-position autocorrelation function can be formulated in numerous ways. We find that the position-position autocorrelation function computed from configurations in the Euclidean space {R}^2 that contains {T}^1 as a submanifold has the best statistical properties. The agreement with exact results obtained with vector space methods is excellent for all three potentials, for all values of time in the interval simulated, and for a relatively broad range of temperatures.

Probabilistic density function method for nonlinear dynamical systems driven by colored noise

DOE Office of Scientific and Technical Information (OSTI.GOV)

Barajas-Solano, David A.; Tartakovsky, Alexandre M.

2016-05-01

We present a probability density function (PDF) method for a system of nonlinear stochastic ordinary differential equations driven by colored noise. The method provides an integro-differential equation for the temporal evolution of the joint PDF of the system's state, which we close by means of a modified Large-Eddy-Diffusivity-type closure. Additionally, we introduce the generalized local linearization (LL) approximation for deriving a computable PDF equation in the form of the second-order partial differential equation (PDE). We demonstrate the proposed closure and localization accurately describe the dynamics of the PDF in phase space for systems driven by noise with arbitrary auto-correlation time.more » We apply the proposed PDF method to the analysis of a set of Kramers equations driven by exponentially auto-correlated Gaussian colored noise to study the dynamics and stability of a power grid.« less

Commercial counterboard for 10 ns software correlator for photon and fluorescence correlation spectroscopy.

PubMed

Molteni, Matteo; Ferri, Fabio

2016-11-01

A 10 ns time resolution, multi-tau software correlator, capable of computing simultaneous autocorrelation (A-A, B-B) and cross (A-B) correlation functions at count rates up to ∼10 MHz, with no data loss, has been developed in LabVIEW and C++ by using the National Instrument timer/counterboard (NI PCIe-6612) and a fast Personal Computer (PC) (Intel Core i7-4790 Processor 3.60 GHz ). The correlator works by using two algorithms: for large lag times (τ ≳ 1 μs), a classical time-mode scheme, based on the measure of the number of pulses per time interval, is used; differently, for τ ≲ 1 μs a photon-mode (PM) scheme is adopted and the correlation function is retrieved from the sequence of the photon arrival times. Single auto- and cross-correlation functions can be processed online in full real time up to count rates of ∼1.8 MHz and ∼1.2 MHz, respectively. Two autocorrelation (A-A, B-B) and a cross correlation (A-B) functions can be simultaneously processed in full real time only up to count rates of ∼750 kHz. At higher count rates, the online processing takes place in a delayed modality, but with no data loss. When tested with simulated correlation data and latex spheres solutions, the overall performances of the correlator appear to be comparable with those of commercial hardware correlators, but with several nontrivial advantages related to its flexibility, low cost, and easy adaptability to future developments of PC and data acquisition technology.

Commercial counterboard for 10 ns software correlator for photon and fluorescence correlation spectroscopy

NASA Astrophysics Data System (ADS)

Molteni, Matteo; Ferri, Fabio

2016-11-01

A 10 ns time resolution, multi-tau software correlator, capable of computing simultaneous autocorrelation (A-A, B-B) and cross (A-B) correlation functions at count rates up to ˜10 MHz, with no data loss, has been developed in LabVIEW and C++ by using the National Instrument timer/counterboard (NI PCIe-6612) and a fast Personal Computer (PC) (Intel Core i7-4790 Processor 3.60 GHz ). The correlator works by using two algorithms: for large lag times (τ ≳ 1 μs), a classical time-mode scheme, based on the measure of the number of pulses per time interval, is used; differently, for τ ≲ 1 μs a photon-mode (PM) scheme is adopted and the correlation function is retrieved from the sequence of the photon arrival times. Single auto- and cross-correlation functions can be processed online in full real time up to count rates of ˜1.8 MHz and ˜1.2 MHz, respectively. Two autocorrelation (A-A, B-B) and a cross correlation (A-B) functions can be simultaneously processed in full real time only up to count rates of ˜750 kHz. At higher count rates, the online processing takes place in a delayed modality, but with no data loss. When tested with simulated correlation data and latex spheres solutions, the overall performances of the correlator appear to be comparable with those of commercial hardware correlators, but with several nontrivial advantages related to its flexibility, low cost, and easy adaptability to future developments of PC and data acquisition technology.

The calculation of the viscosity from the autocorrelation function using molecular and atomic stress tensors

NASA Astrophysics Data System (ADS)

Cui, S. T.

The stress-stress correlation function and the viscosity of a united-atom model of liquid decane are studied by equilibrium molecular dynamics simulation using two different formalisms for the stress tensor: the atomic and the molecular formalisms. The atomic and molecular correlation functions show dramatic difference in short-time behaviour. The integrals of the two correlation functions, however, become identical after a short transient period whichis significantly shorter than the rotational relaxation time of the molecule. Both reach the same plateau value in a time period corresponding to this relaxation time. These results provide a convenient guide for the choice of the upper integral time limit in calculating the viscosity by the Green-Kubo formula.

Using simulations and data to evaluate mean sensitivity (ζ) as a useful statistic in dendrochronology

Treesearch

Andrew G. Bunn; Esther Jansma; Mikko Korpela; Robert D. Westfall; James Baldwin

2013-01-01

Mean sensitivity (ζ) continues to be used in dendrochronology despite a literature that shows it to be of questionable value in describing the properties of a time series. We simulate first-order autoregressive models with known parameters and show that ζ is a function of variance and autocorrelation of a time series. We then use 500 random tree-ring...

Autocorrelation of location estimates and the analysis of radiotracking data

USGS Publications Warehouse

Otis, D.L.; White, Gary C.

1999-01-01

The wildlife literature has been contradictory about the importance of autocorrelation in radiotracking data used for home range estimation and hypothesis tests of habitat selection. By definition, the concept of a home range involves autocorrelated movements, but estimates or hypothesis tests based on sampling designs that predefine a time frame of interest, and that generate representative samples of an animal's movement during this time frame, should not be affected by length of the sampling interval and autocorrelation. Intensive sampling of the individual's home range and habitat use during the time frame of the study leads to improved estimates for the individual, but use of location estimates as the sample unit to compare across animals is pseudoreplication. We therefore recommend against use of habitat selection analysis techniques that use locations instead of individuals as the sample unit. We offer a general outline for sampling designs for radiotracking studies.

Performance of signal-to-noise ratio estimation for scanning electron microscope using autocorrelation Levinson-Durbin recursion model.

PubMed

Sim, K S; Lim, M S; Yeap, Z X

2016-07-01

A new technique to quantify signal-to-noise ratio (SNR) value of the scanning electron microscope (SEM) images is proposed. This technique is known as autocorrelation Levinson-Durbin recursion (ACLDR) model. To test the performance of this technique, the SEM image is corrupted with noise. The autocorrelation function of the original image and the noisy image are formed. The signal spectrum based on the autocorrelation function of image is formed. ACLDR is then used as an SNR estimator to quantify the signal spectrum of noisy image. The SNR values of the original image and the quantified image are calculated. The ACLDR is then compared with the three existing techniques, which are nearest neighbourhood, first-order linear interpolation and nearest neighbourhood combined with first-order linear interpolation. It is shown that ACLDR model is able to achieve higher accuracy in SNR estimation. © 2016 The Authors Journal of Microscopy © 2016 Royal Microscopical Society.

Correlation time and diffusion coefficient imaging: application to a granular flow system.

PubMed

Caprihan, A; Seymour, J D

2000-05-01

A parametric method for spatially resolved measurements for velocity autocorrelation functions, R(u)(tau) = , expressed as a sum of exponentials, is presented. The method is applied to a granular flow system of 2-mm oil-filled spheres rotated in a half-filled horizontal cylinder, which is an Ornstein-Uhlenbeck process with velocity autocorrelation function R(u)(tau) = e(- ||tau ||/tau(c)), where tau(c) is the correlation time and D = tau(c) is the diffusion coefficient. The pulsed-field-gradient NMR method consists of applying three different gradient pulse sequences of varying motion sensitivity to distinguish the range of correlation times present for particle motion. Time-dependent apparent diffusion coefficients are measured for these three sequences and tau(c) and D are then calculated from the apparent diffusion coefficient images. For the cylinder rotation rate of 2.3 rad/s, the axial diffusion coefficient at the top center of the free surface was 5.5 x 10(-6) m(2)/s, the correlation time was 3 ms, and the velocity fluctuation or granular temperature was 1.8 x 10(-3) m(2)/s(2). This method is also applicable to study transport in systems involving turbulence and porous media flows. Copyright 2000 Academic Press.

Inferential Precision in Single-Case Time-Series Data Streams: How Well Does the EM Procedure Perform When Missing Observations Occur in Autocorrelated Data?

PubMed Central

Smith, Justin D.; Borckardt, Jeffrey J.; Nash, Michael R.

2013-01-01

The case-based time-series design is a viable methodology for treatment outcome research. However, the literature has not fully addressed the problem of missing observations with such autocorrelated data streams. Mainly, to what extent do missing observations compromise inference when observations are not independent? Do the available missing data replacement procedures preserve inferential integrity? Does the extent of autocorrelation matter? We use Monte Carlo simulation modeling of a single-subject intervention study to address these questions. We find power sensitivity to be within acceptable limits across four proportions of missing observations (10%, 20%, 30%, and 40%) when missing data are replaced using the Expectation-Maximization Algorithm, more commonly known as the EM Procedure (Dempster, Laird, & Rubin, 1977).This applies to data streams with lag-1 autocorrelation estimates under 0.80. As autocorrelation estimates approach 0.80, the replacement procedure yields an unacceptable power profile. The implications of these findings and directions for future research are discussed. PMID:22697454

Rapid and stable measurement of respiratory rate from Doppler radar signals using time domain autocorrelation model.

PubMed

Sun, Guanghao; Matsui, Takemi

2015-01-01

Noncontact measurement of respiratory rate using Doppler radar will play a vital role in future clinical practice. Doppler radar remotely monitors the tiny chest wall movements induced by respiration activity. The most competitive advantage of this technique is to allow users fully unconstrained with no biological electrode attachments. However, the Doppler radar, unlike other contact-type sensors, is easily affected by the random body movements. In this paper, we proposed a time domain autocorrelation model to process the radar signals for rapid and stable estimation of the respiratory rate. We tested the autocorrelation model on 8 subjects in laboratory, and compared the respiratory rates detected by noncontact radar with reference contact-type respiratory effort belt. Autocorrelation model showed the effects of reducing the random body movement noise added to Doppler radar's respiration signals. Moreover, the respiratory rate can be rapidly calculated from the first main peak in the autocorrelation waveform within 10 s.

a Study of the Concentration Dependence of Macromolecular Diffusion Using Photon Correlation Spectroscopy.

NASA Astrophysics Data System (ADS)

Marlowe, Robert Lloyd

The dynamic light scattering technique of photon correlation spectroscopy has been used to investigate the dependence of the mutual diffusion coefficient of a macromolecular system upon concentration. The first part of the research was devoted to the design and construction of a single-clipping autocorrelator based on newly-developed integrated circuits. The resulting 128 channel instrument can perform real time autocorrelation for sample time intervals >(, )10 (mu)s, and batch processed autocorrelation for intervals down to 3 (mu)s. An improved design for a newer, all-digital autocorrelator is given. Homodyne light scattering experiments were then undertaken on monodisperse solutions of polystyrene spheres. The single-mode TEM(,oo) beam of an argon-ion laser ((lamda) = 5145 (ANGSTROM)) was used as the light source; all solutions were studied at room temperature. The scattering angle was varied from 30(DEGREES) to 110(DEGREES). Excellent agreement with the manufacturer's specification for the particle size was obtained from the photon correlation studies. Finally, aqueous solutions of the globular protein ovalbumin, ranging in concentration from 18.9 to 244.3 mg/ml, were illuminated under the same conditions of temperature and wavelength as before; the homodyne scattered light was detected at a fixed scattering angle of 30(DEGREES). The single-clipped photocount autocorrelation function was analyzed using the homodyne exponential integral method of Meneely et al. The resulting diffusion coefficients showed a general linear dependence upon concentration, as predicted by the generalized Stokes-Einstein equation. However, a clear peak in the data was evident at c (TURNEQ) 100 mg/ml, which could not be explained on the basis of a non -interacting particle theory. A semi-quantitative approach based on the Debye-Huckel theory of electrostatic interactions is suggested as the probable cause for the peak's rise, and an excluded volume effect for its decline.

The method of trend analysis of parameters time series of gas-turbine engine state

NASA Astrophysics Data System (ADS)

Hvozdeva, I.; Myrhorod, V.; Derenh, Y.

2017-10-01

This research substantiates an approach to interval estimation of time series trend component. The well-known methods of spectral and trend analysis are used for multidimensional data arrays. The interval estimation of trend component is proposed for the time series whose autocorrelation matrix possesses a prevailing eigenvalue. The properties of time series autocorrelation matrix are identified.

Spectrum sensing algorithm based on autocorrelation energy in cognitive radio networks

NASA Astrophysics Data System (ADS)

Ren, Shengwei; Zhang, Li; Zhang, Shibing

2016-10-01

Cognitive radio networks have wide applications in the smart home, personal communications and other wireless communication. Spectrum sensing is the main challenge in cognitive radios. This paper proposes a new spectrum sensing algorithm which is based on the autocorrelation energy of signal received. By taking the autocorrelation energy of the received signal as the statistics of spectrum sensing, the effect of the channel noise on the detection performance is reduced. Simulation results show that the algorithm is effective and performs well in low signal-to-noise ratio. Compared with the maximum generalized eigenvalue detection (MGED) algorithm, function of covariance matrix based detection (FMD) algorithm and autocorrelation-based detection (AD) algorithm, the proposed algorithm has 2 11 dB advantage.

Time-series network analysis of civil aviation in Japan (1985-2005)

NASA Astrophysics Data System (ADS)

Michishita, Ryo; Xu, Bing; Yamada, Ikuho

2008-10-01

Due to the airline deregulation in 1985, a series of new airport developments in the 1990s and 2000s, and the reorganization of airline companies in the 2000s, Japan's air passenger transportation has been dramatically altered in the last two decades in many ways. In this paper, the authors examine how the network and flow structures of domestic air passenger transportation in Japan have geographically changed since 1985. For this purpose, passenger flow data in 1985, 1995, and 2005 were extracted from the Air Transportation Statistical Survey conducted by the Ministry of Land, Infrastructure and Transport, Japan. First, national and regional hub airports are identified via dominant flow and hub function analysis. Then the roles of the hub airports and individual connections over the network are examined with respect to their spatial and network autocorrelations. Spatial and network autocorrelations were evaluated both globally and locally using Moran's I and LISA statistics. The passenger flow data were first examined as a whole and then divided into 3 airline-based categories. Dominant flow and hub function enabled us to detect the hub airports. Structural processes of the hub-and-spoke network were confirmed in each airline through spatial autocorrelation analysis. Network autocorrelation analysis showed that all airlines ingeniously optimized their networks by connecting their routes with large numbers of passengers to other routes with large numbers of passengers, and routes with small numbers of passengers to other routes with small numbers of passengers. The effects of political events and the changes in the strategies of each airline on the whole networks were strongly reflected in the results of this study.

Partial correlation properties of pseudonoise /PN/ codes in noncoherent synchronization/detection schemes

NASA Technical Reports Server (NTRS)

Cartier, D. E.

1976-01-01

This concise paper considers the effect on the autocorrelation function of a pseudonoise (PN) code when the acquisition scheme only integrates coherently over part of the code and then noncoherently combines these results. The peak-to-null ratio of the effective PN autocorrelation function is shown to degrade to the square root of n, where n is the number of PN symbols over which coherent integration takes place.

Non-Markovian properties and multiscale hidden Markovian network buried in single molecule time series

NASA Astrophysics Data System (ADS)

Sultana, Tahmina; Takagi, Hiroaki; Morimatsu, Miki; Teramoto, Hiroshi; Li, Chun-Biu; Sako, Yasushi; Komatsuzaki, Tamiki

2013-12-01

We present a novel scheme to extract a multiscale state space network (SSN) from single-molecule time series. The multiscale SSN is a type of hidden Markov model that takes into account both multiple states buried in the measurement and memory effects in the process of the observable whenever they exist. Most biological systems function in a nonstationary manner across multiple timescales. Combined with a recently established nonlinear time series analysis based on information theory, a simple scheme is proposed to deal with the properties of multiscale and nonstationarity for a discrete time series. We derived an explicit analytical expression of the autocorrelation function in terms of the SSN. To demonstrate the potential of our scheme, we investigated single-molecule time series of dissociation and association kinetics between epidermal growth factor receptor (EGFR) on the plasma membrane and its adaptor protein Ash/Grb2 (Grb2) in an in vitro reconstituted system. We found that our formula successfully reproduces their autocorrelation function for a wide range of timescales (up to 3 s), and the underlying SSNs change their topographical structure as a function of the timescale; while the corresponding SSN is simple at the short timescale (0.033-0.1 s), the SSN at the longer timescales (0.1 s to ˜3 s) becomes rather complex in order to capture multiscale nonstationary kinetics emerging at longer timescales. It is also found that visiting the unbound form of the EGFR-Grb2 system approximately resets all information of history or memory of the process.

Analysis of data from NASA B-57B gust gradient program

NASA Technical Reports Server (NTRS)

Frost, W.; Lin, M. C.; Chang, H. P.; Ringnes, E.

1985-01-01

Statistical analysis of the turbulence measured in flight 6 of the NASA B-57B over Denver, Colorado, from July 7 to July 23, 1982 included the calculations of average turbulence parameters, integral length scales, probability density functions, single point autocorrelation coefficients, two point autocorrelation coefficients, normalized autospectra, normalized two point autospectra, and two point cross sectra for gust velocities. The single point autocorrelation coefficients were compared with the theoretical model developed by von Karman. Theoretical analyses were developed which address the effects spanwise gust distributions, using two point spatial turbulence correlations.

Imaging the Lower Crust and Moho Beneath Long Beach, CA Using Autocorrelations

NASA Astrophysics Data System (ADS)

Clayton, R. W.

2017-12-01

Three-dimensional images of the lower crust and Moho in a 10x10 km region beneath Long Beach, CA are constructed from autocorrelations of ambient noise. The results show the Moho at a depth of 15 km at the coast and dipping at 45 degrees inland to a depth of 25 km. The shape of the Moho interface is irregular in both the coast perpendicular and parallel directions. The lower crust appears as a zone of enhanced reflectivity with numerous small-scale structures. The autocorrelations are constructed from virtual source gathers that were computed from the dense Long Beach array that were used in the Lin et al (2013) study. All near zero-offset traces within a 200 m disk are stacked to produce a single autocorrelation at that point. The stack typically is over 50-60 traces. To convert the auto correlation to reflectivity as in Claerbout (1968), the noise source autocorrelation, which is estimated as the average of all autocorrelations is subtracted from each trace. The subsurface image is then constructed with a 0.1-2 Hz filter and AGC scaling. The main features of the image are confirmed with broadband receiver functions from the LASSIE survey (Ma et al, 2016). The use of stacked autocorrelations extends ambient noise into the lower crust.

Time series analyses of hydrological parameter variations and their correlations at a coastal area in Busan, South Korea

NASA Astrophysics Data System (ADS)

Chung, Sang Yong; Senapathi, Venkatramanan; Sekar, Selvam; Kim, Tae Hyung

2018-02-01

Monitoring and time-series analysis of the hydrological parameters electrical conductivity (EC), water pressure, precipitation and tide were carried out, to understand the characteristics of the parameter variations and their correlations at a coastal area in Busan, South Korea. The monitoring data were collected at a sharp interface between freshwater and saline water at the depth of 25 m below ground. Two well-logging profiles showed that seawater intrusion has largely expanded (progressed inland), and has greatly affected the groundwater quality in a coastal aquifer of tuffaceous sedimentary rock over a 9-year period. According to the time series analyses, the periodograms of the hydrological parameters present very similar trends to the power spectral densities (PSD) of the hydrological parameters. Autocorrelation functions (ACF) and partial autocorrelation functions (PACF) of the hydrological parameters were produced to evaluate their self-correlations. The ACFs of all hydrologic parameters showed very good correlation over the entire time lag, but the PACF revealed that the correlations were good only at time lag 1. Crosscorrelation functions (CCF) were used to evaluate the correlations between the hydrological parameters and the characteristics of seawater intrusion in the coastal aquifer system. The CCFs showed that EC had a close relationship with water pressure and precipitation rather than tide. The CCFs of water pressure with tide and precipitation were in inverse proportion, and the CCF of water pressure with precipitation was larger than that with tide.

Carbonaceous aerosol at two rural locations in New York State: Characterization and behavior

NASA Astrophysics Data System (ADS)

Sunder Raman, Ramya; Hopke, Philip K.; Holsen, Thomas M.

2008-06-01

Fine particle samples were collected to determine the chemical constituents in PM2.5 at two rural background sites (Potsdam and Stockton, N. Y.) in the northeastern United States from November 2002 to August 2005. Samples were collected every third day for 24 h with a speciation network sampler. The measured carbonaceous species included thermal-optical organic carbon (OC), elemental carbon (EC), pyrolytic carbon (OP), black carbon (BC), and water-soluble, short-chain (WSSC) organic acids. Concentration time series, autocorrelations, and seasonal variations of the carbonaceous species were examined. During this multiyear period, the contributions of the total carbon (OC + EC) to the measured fine particle mass were 31.2% and 31.1% at Potsdam and Stockton, respectively. The average sum of the WSSC acids carbon accounted for approximately 2.5% of the organic carbon at Potsdam and 3.0% at Stockton. At Potsdam, the seasonal differences in the autocorrelation function (ACF) and partial autocorrelation function (PACF) values for carbonaceous species suggest that secondary formation may be an important contributor to the observed concentrations of species likely to be secondary in origin, particularly during the photochemically active time of the year (May to October). This study also investigated the relationships between carbonaceous species to better understand the behavior of carbonaceous aerosol and to assess the contribution of secondary organic carbon (SOC) to the total organic carbon mass (the EC tracer method was used to estimate SOC). At Potsdam the average SOC contribution to total OC varied between 66% and 72%, while at Stockton it varied between 58% and 64%.

Inferential precision in single-case time-series data streams: how well does the em procedure perform when missing observations occur in autocorrelated data?

PubMed

Smith, Justin D; Borckardt, Jeffrey J; Nash, Michael R

2012-09-01

The case-based time-series design is a viable methodology for treatment outcome research. However, the literature has not fully addressed the problem of missing observations with such autocorrelated data streams. Mainly, to what extent do missing observations compromise inference when observations are not independent? Do the available missing data replacement procedures preserve inferential integrity? Does the extent of autocorrelation matter? We use Monte Carlo simulation modeling of a single-subject intervention study to address these questions. We find power sensitivity to be within acceptable limits across four proportions of missing observations (10%, 20%, 30%, and 40%) when missing data are replaced using the Expectation-Maximization Algorithm, more commonly known as the EM Procedure (Dempster, Laird, & Rubin, 1977). This applies to data streams with lag-1 autocorrelation estimates under 0.80. As autocorrelation estimates approach 0.80, the replacement procedure yields an unacceptable power profile. The implications of these findings and directions for future research are discussed. Copyright © 2011. Published by Elsevier Ltd.

The use of spatio-temporal correlation to forecast critical transitions

NASA Astrophysics Data System (ADS)

Karssenberg, Derek; Bierkens, Marc F. P.

2010-05-01

Complex dynamical systems may have critical thresholds at which the system shifts abruptly from one state to another. Such critical transitions have been observed in systems ranging from the human body system to financial markets and the Earth system. Forecasting the timing of critical transitions before they are reached is of paramount importance because critical transitions are associated with a large shift in dynamical regime of the system under consideration. However, it is hard to forecast critical transitions, because the state of the system shows relatively little change before the threshold is reached. Recently, it was shown that increased spatio-temporal autocorrelation and variance can serve as alternative early warning signal for critical transitions. However, thus far these second order statistics have not been used for forecasting in a data assimilation framework. Here we show that the use of spatio-temporal autocorrelation and variance in the state of the system reduces the uncertainty in the predicted timing of critical transitions compared to classical approaches that use the value of the system state only. This is shown by assimilating observed spatio-temporal autocorrelation and variance into a dynamical system model using a Particle Filter. We adapt a well-studied distributed model of a logistically growing resource with a fixed grazing rate. The model describes the transition from an underexploited system with high resource biomass to overexploitation as grazing pressure crosses the critical threshold, which is a fold bifurcation. To represent limited prior information, we use a large variance in the prior probability distributions of model parameters and the system driver (grazing rate). First, we show that the rate of increase in spatio-temporal autocorrelation and variance prior to reaching the critical threshold is relatively consistent across the uncertainty range of the driver and parameter values used. This indicates that an increase in spatio-temporal autocorrelation and variance are consistent predictors of a critical transition, even under the condition of a poorly defined system. Second, we perform data assimilation experiments using an artificial exhaustive data set generated by one realization of the model. To mimic real-world sampling, an observational data set is created from this exhaustive data set. This is done by sampling on a regular spatio-temporal grid, supplemented by sampling locations at a short distance. Spatial and temporal autocorrelation in this observational data set is calculated for different spatial and temporal separation (lag) distances. To assign appropriate weights to observations (here, autocorrelation values and variance) in the Particle Filter, the covariance matrix of the error in these observations is required. This covariance matrix is estimated using Monte Carlo sampling, selecting a different random position of the sampling network relative to the exhaustive data set for each realization. At each update moment in the Particle Filter, observed autocorrelation values are assimilated into the model and the state of the model is updated. Using this approach, it is shown that the use of autocorrelation reduces the uncertainty in the forecasted timing of a critical transition compared to runs without data assimilation. The performance of the use of spatial autocorrelation versus temporal autocorrelation depends on the timing and number of observational data. This study is restricted to a single model only. However, it is becoming increasingly clear that spatio-temporal autocorrelation and variance can be used as early warning signals for a large number of systems. Thus, it is expected that spatio-temporal autocorrelation and variance are valuable in data assimilation frameworks in a large number of dynamical systems.

Coarse-grained modeling of polyethylene melts: Effect on dynamics

DOE PAGES

Peters, Brandon L.; Salerno, K. Michael; Agrawal, Anupriya; ...

2017-05-23

The distinctive viscoelastic behavior of polymers results from a coupled interplay of motion on multiple length and time scales. Capturing the broad time and length scales of polymer motion remains a challenge. Using polyethylene (PE) as a model macromolecule, we construct coarse-grained (CG) models of PE with three to six methyl groups per CG bead and probe two critical aspects of the technique: pressure corrections required after iterative Boltzmann inversion (IBI) to generate CG potentials that match the pressure of reference fully atomistic melt simulations and the transferability of CG potentials across temperatures. While IBI produces nonbonded pair potentials thatmore » give excellent agreement between the atomistic and CG pair correlation functions, the resulting pressure for the CG models is large compared with the pressure of the atomistic system. We find that correcting the potential to match the reference pressure leads to nonbonded interactions with much deeper minima and slightly smaller effective bead diameter. However, simulations with potentials generated by IBI and pressure-corrected IBI result in similar mean-square displacements (MSDs) and stress autocorrelation functions G( t) for PE melts. While the time rescaling factor required to match CG and atomistic models is the same for pressure- and non-pressure-corrected CG models, it strongly depends on temperature. Furthermore, transferability was investigated by comparing the MSDs and stress autocorrelation functions for potentials developed at different temperatures.« less

Coarse-grained modeling of polyethylene melts: Effect on dynamics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Peters, Brandon L.; Salerno, K. Michael; Agrawal, Anupriya

The distinctive viscoelastic behavior of polymers results from a coupled interplay of motion on multiple length and time scales. Capturing the broad time and length scales of polymer motion remains a challenge. Using polyethylene (PE) as a model macromolecule, we construct coarse-grained (CG) models of PE with three to six methyl groups per CG bead and probe two critical aspects of the technique: pressure corrections required after iterative Boltzmann inversion (IBI) to generate CG potentials that match the pressure of reference fully atomistic melt simulations and the transferability of CG potentials across temperatures. While IBI produces nonbonded pair potentials thatmore » give excellent agreement between the atomistic and CG pair correlation functions, the resulting pressure for the CG models is large compared with the pressure of the atomistic system. We find that correcting the potential to match the reference pressure leads to nonbonded interactions with much deeper minima and slightly smaller effective bead diameter. However, simulations with potentials generated by IBI and pressure-corrected IBI result in similar mean-square displacements (MSDs) and stress autocorrelation functions G( t) for PE melts. While the time rescaling factor required to match CG and atomistic models is the same for pressure- and non-pressure-corrected CG models, it strongly depends on temperature. Furthermore, transferability was investigated by comparing the MSDs and stress autocorrelation functions for potentials developed at different temperatures.« less

Anomalous diffusion due to the non-Markovian process of the dust particle velocity in complex plasmas

NASA Astrophysics Data System (ADS)

Ghannad, Z.; Hakimi Pajouh, H.

2017-12-01

In this work, the motion of a dust particle under the influence of the random force due to dust charge fluctuations is considered as a non-Markovian stochastic process. Memory effects in the velocity process of the dust particle are studied. A model is developed based on the fractional Langevin equation for the motion of the dust grain. The fluctuation-dissipation theorem for the dust grain is derived from this equation. The mean-square displacement and the velocity autocorrelation function of the dust particle are obtained in terms of the Mittag-Leffler functions. Their asymptotic behavior and the dust particle temperature due to charge fluctuations are studied in the long-time limit. As an interesting result, it is found that the presence of memory effects in the velocity process of the dust particle as a non-Markovian process can cause an anomalous diffusion in dusty plasmas. In this case, the velocity autocorrelation function of the dust particle has a power-law decay like t - α - 2, where the exponent α take values 0 < α < 1.

First-principles calculation of entropy for liquid metals.

PubMed

Desjarlais, Michael P

2013-12-01

We demonstrate the accurate calculation of entropies and free energies for a variety of liquid metals using an extension of the two-phase thermodynamic (2PT) model based on a decomposition of the velocity autocorrelation function into gas-like (hard sphere) and solid-like (harmonic) subsystems. The hard sphere model for the gas-like component is shown to give systematically high entropies for liquid metals as a direct result of the unphysical Lorentzian high-frequency tail. Using a memory function framework we derive a generally applicable velocity autocorrelation and frequency spectrum for the diffusive component which recovers the low-frequency (long-time) behavior of the hard sphere model while providing for realistic short-time coherence and high-frequency tails to the spectrum. This approach provides a significant increase in the accuracy of the calculated entropies for liquid metals and is compared to ambient pressure data for liquid sodium, aluminum, gallium, tin, and iron. The use of this method for the determination of melt boundaries is demonstrated with a calculation of the high-pressure bcc melt boundary for sodium. With the significantly improved accuracy available with the memory function treatment for softer interatomic potentials, the 2PT model for entropy calculations should find broader application in high energy density science, warm dense matter, planetary science, geophysics, and material science.

First-principles calculation of entropy for liquid metals

NASA Astrophysics Data System (ADS)

Desjarlais, Michael P.

2013-12-01

We demonstrate the accurate calculation of entropies and free energies for a variety of liquid metals using an extension of the two-phase thermodynamic (2PT) model based on a decomposition of the velocity autocorrelation function into gas-like (hard sphere) and solid-like (harmonic) subsystems. The hard sphere model for the gas-like component is shown to give systematically high entropies for liquid metals as a direct result of the unphysical Lorentzian high-frequency tail. Using a memory function framework we derive a generally applicable velocity autocorrelation and frequency spectrum for the diffusive component which recovers the low-frequency (long-time) behavior of the hard sphere model while providing for realistic short-time coherence and high-frequency tails to the spectrum. This approach provides a significant increase in the accuracy of the calculated entropies for liquid metals and is compared to ambient pressure data for liquid sodium, aluminum, gallium, tin, and iron. The use of this method for the determination of melt boundaries is demonstrated with a calculation of the high-pressure bcc melt boundary for sodium. With the significantly improved accuracy available with the memory function treatment for softer interatomic potentials, the 2PT model for entropy calculations should find broader application in high energy density science, warm dense matter, planetary science, geophysics, and material science.

Comparing phase-sensitive and phase-insensitive echolocation target images using a monaural audible sonar.

PubMed

Kuc, Roman

2018-04-01

This paper describes phase-sensitive and phase-insensitive processing of monaural echolocation waveforms to generate target maps. Composite waveforms containing both the emission and echoes are processed to estimate the target impulse response using an audible sonar. Phase-sensitive processing yields the composite signal envelope, while phase-insensitive processing that starts with the composite waveform power spectrum yields the envelope of the autocorrelation function. Analysis and experimental verification show that multiple echoes form an autocorrelation function that produces near-range phantom-reflector artifacts. These artifacts interfere with true target echoes when the first true echo occurs at a time that is less than the total duration of the target echoes. Initial comparison of phase-sensitive and phase-insensitive maps indicates that both display important target features, indicating that phase is not vital. A closer comparison illustrates the improved resolution of phase-sensitive processing, the near-range phantom-reflectors produced by phase-insensitive processing, and echo interference and multiple reflection artifacts that were independent of the processing.

Mesoscopic fluctuations and intermittency in aging dynamics

NASA Astrophysics Data System (ADS)

Sibani, P.

2006-01-01

Mesoscopic aging systems are characterized by large intermittent noise fluctuations. In a record dynamics scenario (Sibani P. and Dall J., Europhys. Lett., 64 (2003) 8) these events, quakes, are treated as a Poisson process with average αln (1 + t/tw), where t is the observation time, tw is the age and α is a parameter. Assuming for simplicity that quakes constitute the only source of de-correlation, we present a model for the probability density function (PDF) of the configuration autocorrelation function. Beside α, the model has the average quake size 1/q as a parameter. The model autocorrelation PDF has a Gumbel-like shape, which approaches a Gaussian for large t/tw and becomes sharply peaked in the thermodynamic limit. Its average and variance, which are given analytically, depend on t/tw as a power law and a power law with a logarithmic correction, respectively. Most predictions are in good agreement with data from the literature and with the simulations of the Edwards-Anderson spin-glass carried out as a test.

Method and apparatus for in-situ characterization of energy storage and energy conversion devices

DOEpatents

Christophersen, Jon P [Idaho Falls, ID; Motloch, Chester G [Idaho Falls, ID; Morrison, John L [Butte, MT; Albrecht, Weston [Layton, UT

2010-03-09

Disclosed are methods and apparatuses for determining an impedance of an energy-output device using a random noise stimulus applied to the energy-output device. A random noise signal is generated and converted to a random noise stimulus as a current source correlated to the random noise signal. A bias-reduced response of the energy-output device to the random noise stimulus is generated by comparing a voltage at the energy-output device terminal to an average voltage signal. The random noise stimulus and bias-reduced response may be periodically sampled to generate a time-varying current stimulus and a time-varying voltage response, which may be correlated to generate an autocorrelated stimulus, an autocorrelated response, and a cross-correlated response. Finally, the autocorrelated stimulus, the autocorrelated response, and the cross-correlated response may be combined to determine at least one of impedance amplitude, impedance phase, and complex impedance.

Constant-Envelope Waveform Design for Optimal Target-Detection and Autocorrelation Performances

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sen, Satyabrata

2013-01-01

We propose an algorithm to directly synthesize in time-domain a constant-envelope transmit waveform that achieves the optimal performance in detecting an extended target in the presence of signal-dependent interference. This approach is in contrast to the traditional indirect methods that synthesize the transmit signal following the computation of the optimal energy spectral density. Additionally, we aim to maintain a good autocorrelation property of the designed signal. Therefore, our waveform design technique solves a bi-objective optimization problem in order to simultaneously improve the detection and autocorrelation performances, which are in general conflicting in nature. We demonstrate this compromising characteristics of themore » detection and autocorrelation performances with numerical examples. Furthermore, in the absence of the autocorrelation criterion, our designed signal is shown to achieve a near-optimum detection performance.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Davies, R.

The spatial autocorrelation functions of broad-band longwave and shortwave radiances measured by the Earth Radiation Budget Experiment (ERBE) are analyzed as a function of view angle in an investigation of the general effects of scene inhomogeneity on radiation. For nadir views, the correlation distance of the autocorrelation function is about 900 km for longwave radiance and about 500 km for shortwave radiance, consistent with higher degrees of freedom in shortwave reflection. Both functions rise monotonically with view angle, but there is a substantial difference in the relative angular dependence of the shortwave and longwave functions, especially for view angles lessmore » than 50 deg. In this range, the increase with angle of the longwave functions is found to depend only on the expansion of pixel area with angle, whereas the shortwave functions show an additional dependence on angle that is attributed to the occlusion of inhomogeneities by cloud height variations. Beyond a view angle of about 50 deg, both longwave and shortwave functions appear to be affected by cloud sides. The shortwave autocorrelation functions do not satisfy the principle of directional reciprocity, thereby proving that the average scene is horizontally inhomogeneous over the scale of an ERBE pixel (1500 sq km). Coarse stratification of the measurements by cloud amount, however, indicates that the average cloud-free scene does satisfy directional reciprocity on this scale.« less

A method to identify differential expression profiles of time-course gene data with Fourier transformation

PubMed Central

2013-01-01

Background Time course gene expression experiments are an increasingly popular method for exploring biological processes. Temporal gene expression profiles provide an important characterization of gene function, as biological systems are both developmental and dynamic. With such data it is possible to study gene expression changes over time and thereby to detect differential genes. Much of the early work on analyzing time series expression data relied on methods developed originally for static data and thus there is a need for improved methodology. Since time series expression is a temporal process, its unique features such as autocorrelation between successive points should be incorporated into the analysis. Results This work aims to identify genes that show different gene expression profiles across time. We propose a statistical procedure to discover gene groups with similar profiles using a nonparametric representation that accounts for the autocorrelation in the data. In particular, we first represent each profile in terms of a Fourier basis, and then we screen out genes that are not differentially expressed based on the Fourier coefficients. Finally, we cluster the remaining gene profiles using a model-based approach in the Fourier domain. We evaluate the screening results in terms of sensitivity, specificity, FDR and FNR, compare with the Gaussian process regression screening in a simulation study and illustrate the results by application to yeast cell-cycle microarray expression data with alpha-factor synchronization. The key elements of the proposed methodology: (i) representation of gene profiles in the Fourier domain; (ii) automatic screening of genes based on the Fourier coefficients and taking into account autocorrelation in the data, while controlling the false discovery rate (FDR); (iii) model-based clustering of the remaining gene profiles. Conclusions Using this method, we identified a set of cell-cycle-regulated time-course yeast genes. The proposed method is general and can be potentially used to identify genes which have the same patterns or biological processes, and help facing the present and forthcoming challenges of data analysis in functional genomics. PMID:24134721

Decorrelation Times of Photospheric Fields and Flows

NASA Technical Reports Server (NTRS)

Welsch, B. T.; Kusano, K.; Yamamoto, T. T.; Muglach, K.

2012-01-01

We use autocorrelation to investigate evolution in flow fields inferred by applying Fourier Local Correlation Tracking (FLCT) to a sequence of high-resolution (0.3 "), high-cadence (approx = 2 min) line-of-sight magnetograms of NOAA active region (AR) 10930 recorded by the Narrowband Filter Imager (NFI) of the Solar Optical Telescope (SOT) aboard the Hinode satellite over 12 - 13 December 2006. To baseline the timescales of flow evolution, we also autocorrelated the magnetograms, at several spatial binnings, to characterize the lifetimes of active region magnetic structures versus spatial scale. Autocorrelation of flow maps can be used to optimize tracking parameters, to understand tracking algorithms f susceptibility to noise, and to estimate flow lifetimes. Tracking parameters varied include: time interval Delta t between magnetogram pairs tracked, spatial binning applied to the magnetograms, and windowing parameter sigma used in FLCT. Flow structures vary over a range of spatial and temporal scales (including unresolved scales), so tracked flows represent a local average of the flow over a particular range of space and time. We define flow lifetime to be the flow decorrelation time, tau . For Delta t > tau, tracking results represent the average velocity over one or more flow lifetimes. We analyze lifetimes of flow components, divergences, and curls as functions of magnetic field strength and spatial scale. We find a significant trend of increasing lifetimes of flow components, divergences, and curls with field strength, consistent with Lorentz forces partially governing flows in the active photosphere, as well as strong trends of increasing flow lifetime and decreasing magnitudes with increases in both spatial scale and Delta t.

Green-Kubo relation for viscosity tested using experimental data for a two-dimensional dusty plasma

NASA Astrophysics Data System (ADS)

Feng, Yan; Goree, J.; Liu, Bin; Cohen, E. G. D.

2011-10-01

The theoretical Green-Kubo relation for viscosity is tested using experimentally obtained data. In a dusty plasma experiment, micron-sized dust particles are introduced into a partially ionized argon plasma, where they become negatively charged. They are electrically levitated to form a single-layer Wigner crystal, which is subsequently melted using laser heating. In the liquid phase, these dust particles experience interparticle electric repulsion, laser heating, and friction from the ambient neutral argon gas, and they can be considered to be in a nonequilibrium steady state. Direct measurements of the positions and velocities of individual dust particles are then used to obtain a time series for an off-diagonal element of the stress tensor and its time autocorrelation function. This calculation also requires the interparticle potential, which was not measured experimentally but was obtained using a Debye-Hückel-type model with experimentally determined parameters. Integrating the autocorrelation function over time yields the viscosity for shearing motion among dust particles. The viscosity so obtained is found to agree with results from a previous experiment using a hydrodynamical Navier-Stokes equation. This comparison serves as a test of the Green-Kubo relation for viscosity. Our result is also compared to the predictions of several simulations.

Floquet spin states in graphene under ac-driven spin-orbit interaction

NASA Astrophysics Data System (ADS)

López, A.; Sun, Z. Z.; Schliemann, J.

2012-05-01

We study the role of periodically driven time-dependent Rashba spin-orbit coupling (RSOC) on a monolayer graphene sample. After recasting the originally 4×4 system of dynamical equations as two time-reversal related two-level problems, the quasienergy spectrum and the related dynamics are investigated via various techniques and approximations. In the static case, the system is gapped at the Dirac point. The rotating wave approximation (RWA) applied to the driven system unphysically preserves this feature, while the Magnus-Floquet approach as well as a numerically exact evaluation of the Floquet equation show that this gap is dynamically closed. In addition, a sizable oscillating pattern of the out-of-plane spin polarization is found in the driven case for states that are completely unpolarized in the static limit. Evaluation of the autocorrelation function shows that the original uniform interference pattern corresponding to time-independent RSOC gets distorted. The resulting structure can be qualitatively explained as a consequence of the transitions induced by the ac driving among the static eigenstates, i.e., these transitions modulate the relative phases that add up to give the quantum revivals of the autocorrelation function. Contrary to the static case, in the driven scenario, quantum revivals (suppressions) are correlated to spin-up (down) phases.

Autocorrelation Function for Monitoring the Gap between The Steel Plates During Laser Welding

NASA Astrophysics Data System (ADS)

Mrna, Libor; Hornik, Petr

Proper alignment of the plates prior to laser welding represents an important factor that determines the quality of the resulting weld. A gap between the plates in a butt or overlap joint affects the oscillations of the keyhole and the surrounding weld pool. We present an experimental study of the butt and overlap welds with the artificial gap of the different thickness of the plates. The welds were made on a 2 kW fiber laser machine for the steel plates and the various welding parameters settings. The eigenfrequency of the keyhole oscillations and its changes were determined from the light emissions of the plasma plume using an autocorrelation function. As a result, we describe the relations between the autocorrelation characteristics, the thickness of the gap between plates and the weld geometry.

Diagnostic System Based on the Human AUDITORY-BRAIN Model for Measuring Environmental NOISE—AN Application to Railway Noise

NASA Astrophysics Data System (ADS)

SAKAI, H.; HOTEHAMA, T.; ANDO, Y.; PRODI, N.; POMPOLI, R.

2002-02-01

Measurements of railway noise were conducted by use of a diagnostic system of regional environmental noise. The system is based on the model of the human auditory-brain system. The model consists of the interplay of autocorrelators and an interaural crosscorrelator acting on the pressure signals arriving at the ear entrances, and takes into account the specialization of left and right human cerebral hemispheres. Different kinds of railway noise were measured through binaural microphones of a dummy head. To characterize the railway noise, physical factors, extracted from the autocorrelation functions (ACF) and interaural crosscorrelation function (IACF) of binaural signals, were used. The factors extracted from ACF were (1) energy represented at the origin of the delay, Φ (0), (2) effective duration of the envelope of the normalized ACF, τe, (3) the delay time of the first peak, τ1, and (4) its amplitude,ø1 . The factors extracted from IACF were (5) IACC, (6) interaural delay time at which the IACC is defined, τIACC, and (7) width of the IACF at the τIACC,WIACC . The factor Φ (0) can be represented as a geometrical mean of energies at both ears as listening level, LL.

A longitudinal model for disease progression was developed and applied to multiple sclerosis

PubMed Central

Lawton, Michael; Tilling, Kate; Robertson, Neil; Tremlett, Helen; Zhu, Feng; Harding, Katharine; Oger, Joel; Ben-Shlomo, Yoav

2015-01-01

Objectives To develop a model of disease progression using multiple sclerosis (MS) as an exemplar. Study Design and Settings Two observational cohorts, the University of Wales MS (UoWMS), UK (1976), and British Columbia MS (BCMS) database, Canada (1980), with longitudinal disability data [the Expanded Disability Status Scale (EDSS)] were used; individuals potentially eligible for MS disease-modifying drugs treatments, but who were unexposed, were selected. Multilevel modeling was used to estimate the EDSS trajectory over time in one data set and validated in the other; challenges addressed included the choice and function of time axis, complex observation-level variation, adjustments for MS relapses, and autocorrelation. Results The best-fitting model for the UoWMS cohort (404 individuals, and 2,290 EDSS observations) included a nonlinear function of time since onset. Measurement error decreased over time and ad hoc methods reduced autocorrelation and the effect of relapse. Replication within the BCMS cohort (978 individuals and 7,335 EDSS observations) led to a model with similar time (years) coefficients, time [0.22 (95% confidence interval {CI}: 0.19, 0.26), 0.16 (95% CI: 0.10, 0.22)] and log time [−0.13 (95% CI: −0.39, 0.14), −0.15 (95% CI: −0.70, 0.40)] for BCMS and UoWMS, respectively. Conclusion It is possible to develop robust models of disability progression for chronic disease. However, explicit validation is important given the complex methodological challenges faced. PMID:26071892

3D Time-lapse Imaging and Quantification of Mitochondrial Dynamics

NASA Astrophysics Data System (ADS)

Sison, Miguel; Chakrabortty, Sabyasachi; Extermann, Jérôme; Nahas, Amir; James Marchand, Paul; Lopez, Antonio; Weil, Tanja; Lasser, Theo

2017-02-01

We present a 3D time-lapse imaging method for monitoring mitochondrial dynamics in living HeLa cells based on photothermal optical coherence microscopy and using novel surface functionalization of gold nanoparticles. The biocompatible protein-based biopolymer coating contains multiple functional groups which impart better cellular uptake and mitochondria targeting efficiency. The high stability of the gold nanoparticles allows continuous imaging over an extended time up to 3000 seconds without significant cell damage. By combining temporal autocorrelation analysis with a classical diffusion model, we quantify mitochondrial dynamics and cast these results into 3D maps showing the heterogeneity of diffusion parameters across the whole cell volume.

Spatial design and strength of spatial signal: Effects on covariance estimation

USGS Publications Warehouse

Irvine, Kathryn M.; Gitelman, Alix I.; Hoeting, Jennifer A.

2007-01-01

In a spatial regression context, scientists are often interested in a physical interpretation of components of the parametric covariance function. For example, spatial covariance parameter estimates in ecological settings have been interpreted to describe spatial heterogeneity or “patchiness” in a landscape that cannot be explained by measured covariates. In this article, we investigate the influence of the strength of spatial dependence on maximum likelihood (ML) and restricted maximum likelihood (REML) estimates of covariance parameters in an exponential-with-nugget model, and we also examine these influences under different sampling designs—specifically, lattice designs and more realistic random and cluster designs—at differing intensities of sampling (n=144 and 361). We find that neither ML nor REML estimates perform well when the range parameter and/or the nugget-to-sill ratio is large—ML tends to underestimate the autocorrelation function and REML produces highly variable estimates of the autocorrelation function. The best estimates of both the covariance parameters and the autocorrelation function come under the cluster sampling design and large sample sizes. As a motivating example, we consider a spatial model for stream sulfate concentration.

Statistical process control of mortality series in the Australian and New Zealand Intensive Care Society (ANZICS) adult patient database: implications of the data generating process.

PubMed

Moran, John L; Solomon, Patricia J

2013-05-24

Statistical process control (SPC), an industrial sphere initiative, has recently been applied in health care and public health surveillance. SPC methods assume independent observations and process autocorrelation has been associated with increase in false alarm frequency. Monthly mean raw mortality (at hospital discharge) time series, 1995-2009, at the individual Intensive Care unit (ICU) level, were generated from the Australia and New Zealand Intensive Care Society adult patient database. Evidence for series (i) autocorrelation and seasonality was demonstrated using (partial)-autocorrelation ((P)ACF) function displays and classical series decomposition and (ii) "in-control" status was sought using risk-adjusted (RA) exponentially weighted moving average (EWMA) control limits (3 sigma). Risk adjustment was achieved using a random coefficient (intercept as ICU site and slope as APACHE III score) logistic regression model, generating an expected mortality series. Application of time-series to an exemplar complete ICU series (1995-(end)2009) was via Box-Jenkins methodology: autoregressive moving average (ARMA) and (G)ARCH ((Generalised) Autoregressive Conditional Heteroscedasticity) models, the latter addressing volatility of the series variance. The overall data set, 1995-2009, consisted of 491324 records from 137 ICU sites; average raw mortality was 14.07%; average(SD) raw and expected mortalities ranged from 0.012(0.113) and 0.013(0.045) to 0.296(0.457) and 0.278(0.247) respectively. For the raw mortality series: 71 sites had continuous data for assessment up to or beyond lag40 and 35% had autocorrelation through to lag40; and of 36 sites with continuous data for ≥ 72 months, all demonstrated marked seasonality. Similar numbers and percentages were seen with the expected series. Out-of-control signalling was evident for the raw mortality series with respect to RA-EWMA control limits; a seasonal ARMA model, with GARCH effects, displayed white-noise residuals which were in-control with respect to EWMA control limits and one-step prediction error limits (3SE). The expected series was modelled with a multiplicative seasonal autoregressive model. The data generating process of monthly raw mortality series at the ICU level displayed autocorrelation, seasonality and volatility. False-positive signalling of the raw mortality series was evident with respect to RA-EWMA control limits. A time series approach using residual control charts resolved these issues.

Autocorrelation and cross-correlation in time series of homicide and attempted homicide

NASA Astrophysics Data System (ADS)

Machado Filho, A.; da Silva, M. F.; Zebende, G. F.

2014-04-01

We propose in this paper to establish the relationship between homicides and attempted homicides by a non-stationary time-series analysis. This analysis will be carried out by Detrended Fluctuation Analysis (DFA), Detrended Cross-Correlation Analysis (DCCA), and DCCA cross-correlation coefficient, ρ(n). Through this analysis we can identify a positive cross-correlation between homicides and attempted homicides. At the same time, looked at from the point of view of autocorrelation (DFA), this analysis can be more informative depending on time scale. For short scale (days), we cannot identify auto-correlations, on the scale of weeks DFA presents anti-persistent behavior, and for long time scales (n>90 days) DFA presents a persistent behavior. Finally, the application of this new type of statistical analysis proved to be efficient and, in this sense, this paper can contribute to a more accurate descriptive statistics of crime.

Bounds of memory strength for power-law series.

PubMed

Guo, Fangjian; Yang, Dan; Yang, Zimo; Zhao, Zhi-Dan; Zhou, Tao

2017-05-01

Many time series produced by complex systems are empirically found to follow power-law distributions with different exponents α. By permuting the independently drawn samples from a power-law distribution, we present nontrivial bounds on the memory strength (first-order autocorrelation) as a function of α, which are markedly different from the ordinary ±1 bounds for Gaussian or uniform distributions. When 1<α≤3, as α grows bigger, the upper bound increases from 0 to +1 while the lower bound remains 0; when α>3, the upper bound remains +1 while the lower bound descends below 0. Theoretical bounds agree well with numerical simulations. Based on the posts on Twitter, ratings of MovieLens, calling records of the mobile operator Orange, and the browsing behavior of Taobao, we find that empirical power-law-distributed data produced by human activities obey such constraints. The present findings explain some observed constraints in bursty time series and scale-free networks and challenge the validity of measures such as autocorrelation and assortativity coefficient in heterogeneous systems.

Bounds of memory strength for power-law series

NASA Astrophysics Data System (ADS)

Guo, Fangjian; Yang, Dan; Yang, Zimo; Zhao, Zhi-Dan; Zhou, Tao

2017-05-01

Many time series produced by complex systems are empirically found to follow power-law distributions with different exponents α . By permuting the independently drawn samples from a power-law distribution, we present nontrivial bounds on the memory strength (first-order autocorrelation) as a function of α , which are markedly different from the ordinary ±1 bounds for Gaussian or uniform distributions. When 1 <α ≤3 , as α grows bigger, the upper bound increases from 0 to +1 while the lower bound remains 0; when α >3 , the upper bound remains +1 while the lower bound descends below 0. Theoretical bounds agree well with numerical simulations. Based on the posts on Twitter, ratings of MovieLens, calling records of the mobile operator Orange, and the browsing behavior of Taobao, we find that empirical power-law-distributed data produced by human activities obey such constraints. The present findings explain some observed constraints in bursty time series and scale-free networks and challenge the validity of measures such as autocorrelation and assortativity coefficient in heterogeneous systems.

Copulas and time series with long-ranged dependencies.

PubMed

Chicheportiche, Rémy; Chakraborti, Anirban

2014-04-01

We review ideas on temporal dependencies and recurrences in discrete time series from several areas of natural and social sciences. We revisit existing studies and redefine the relevant observables in the language of copulas (joint laws of the ranks). We propose that copulas provide an appropriate mathematical framework to study nonlinear time dependencies and related concepts-like aftershocks, Omori law, recurrences, and waiting times. We also critically argue, using this global approach, that previous phenomenological attempts involving only a long-ranged autocorrelation function lacked complexity in that they were essentially monoscale.

Dynamical properties of a tumor growth system in the presence of immunization and colored cross-correlated noises

NASA Astrophysics Data System (ADS)

Jia, Zheng-Lin; Mei, Dong-Cheng

2010-05-01

We investigate the effects of the noise parameters and immunization strength β on the dynamical properties of a tumor growth system with both immunization and colored cross-correlated noises. The analytical expressions for the associated relaxation time TC and the normalized correlation function C(s) are derived by means of the projection operator method. The results indicate that: (i) TC as a function of the multiplicative noise intensity α shows resonance-like behavior, i.e. the curves of TC versus α exhibit a single-peak structure and its peak position changes with increasing correlation strength between noises λ, the autocorrelation time of multiplicative noise τ1, the autocorrelation time of additive noise τ2 and the cross-correlation time τ3. This behavior can be understood in terms of the noise-enhanced stability effect and the influence of the memory effects on it. (ii) The increasing λ, τ1, τ2 and the additive noise intensity D slow down the fluctuation decay of the tumor population, whereas the increasing τ3 and β speed it up. (iii) C(s) increases as λ, τ1, τ2 and β increase, while it decreases with τ3 increasing. Our study shows that the effects of some noise parameters on tumor growth can be modified due to the presence of the immunization effect.

Modeling continuous seismic velocity changes due to ground shaking in Chile

NASA Astrophysics Data System (ADS)

Gassenmeier, Martina; Richter, Tom; Sens-Schönfelder, Christoph; Korn, Michael; Tilmann, Frederik

2015-04-01

In order to investigate temporal seismic velocity changes due to earthquake related processes and environmental forcing, we analyze 8 years of ambient seismic noise recorded by the Integrated Plate Boundary Observatory Chile (IPOC) network in northern Chile between 18° and 25° S. The Mw 7.7 Tocopilla earthquake in 2007 and the Mw 8.1 Iquique earthquake in 2014 as well as numerous smaller events occurred in this area. By autocorrelation of the ambient seismic noise field, approximations of the Green's functions are retrieved. The recovered function represents backscattered or multiply scattered energy from the immediate neighborhood of the station. To detect relative changes of the seismic velocities we apply the stretching method, which compares individual autocorrelation functions to stretched or compressed versions of a long term averaged reference autocorrelation function. We use time windows in the coda of the autocorrelations, that contain scattered waves which are highly sensitive to minute changes in the velocity. At station PATCX we observe seasonal changes in seismic velocity as well as temporary velocity reductions in the frequency range of 4-6 Hz. The seasonal changes can be attributed to thermal stress changes in the subsurface related to variations of the atmospheric temperature. This effect can be modeled well by a sine curve and is subtracted for further analysis of short term variations. Temporary velocity reductions occur at the time of ground shaking usually caused by earthquakes and are followed by a recovery. We present an empirical model that describes the seismic velocity variations based on continuous observations of the local ground acceleration. Our hypothesis is that not only the shaking of earthquakes provokes velocity drops, but any small vibrations continuously induce minor velocity variations that are immediately compensated by healing in the steady state. We show that the shaking effect is accumulated over time and best described by the integrated envelope of the ground acceleration over 1 day which is the discretization interval of the velocity measurements. In our model the amplitude of the velocity reduction as well as the recovery time are proportional to the size of the excitation. This model with the two free scaling parameters for the shaking induced velocity variation fits the data in remarkable detail. Additionally, a linear trend is observed that might be related to a recovery process from one or more earthquakes before our measurement period. For the Tocopilla earthquake in 2007 and the Iquique earthquake in 2014 velocity reductions are also observed at other stations of the IPOC network. However, a clear relationship between the ground shaking and the induced velocity reductions is not visible at other stations. We attribute the outstanding sensitivity of PATCX to ground shaking to the special geological setting of the station, where the material consists of relatively loose conglomerate with high pore volume.

New approaches for calculating Moran's index of spatial autocorrelation.

PubMed

Chen, Yanguang

2013-01-01

Spatial autocorrelation plays an important role in geographical analysis; however, there is still room for improvement of this method. The formula for Moran's index is complicated, and several basic problems remain to be solved. Therefore, I will reconstruct its mathematical framework using mathematical derivation based on linear algebra and present four simple approaches to calculating Moran's index. Moran's scatterplot will be ameliorated, and new test methods will be proposed. The relationship between the global Moran's index and Geary's coefficient will be discussed from two different vantage points: spatial population and spatial sample. The sphere of applications for both Moran's index and Geary's coefficient will be clarified and defined. One of theoretical findings is that Moran's index is a characteristic parameter of spatial weight matrices, so the selection of weight functions is very significant for autocorrelation analysis of geographical systems. A case study of 29 Chinese cities in 2000 will be employed to validate the innovatory models and methods. This work is a methodological study, which will simplify the process of autocorrelation analysis. The results of this study will lay the foundation for the scaling analysis of spatial autocorrelation.

Asymmetric multiscale multifractal analysis of wind speed signals

NASA Astrophysics Data System (ADS)

Zhang, Xiaonei; Zeng, Ming; Meng, Qinghao

We develop a new method called asymmetric multiscale multifractal analysis (A-MMA) to explore the multifractality and asymmetric autocorrelations of the signals with a variable scale range. Three numerical experiments are provided to demonstrate the effectiveness of our approach. Then, the proposed method is applied to investigate multifractality and asymmetric autocorrelations of difference sequences between wind speed fluctuations with uptrends or downtrends. The results show that these sequences appear to be far more complex and contain abundant fractal dynamics information. Through analyzing the Hurst surfaces of nine difference sequences, we found that all series exhibit multifractal properties and multiscale structures. Meanwhile, the asymmetric autocorrelations are observed in all variable scale ranges and the asymmetry results are of good consistency within a certain spatial range. The sources of multifractality and asymmetry in nine difference series are further discussed using the corresponding shuffled series and surrogate series. We conclude that the multifractality of these series is due to both long-range autocorrelation and broad probability density function, but the major source of multifractality is long-range autocorrelation, and the source of asymmetry is affected by the spatial distance.

Very High-Frequency (VHF) ionospheric scintillation fading measurements at Lima, Peru

NASA Technical Reports Server (NTRS)

Blank, H. A.; Golden, T. S.

1972-01-01

During the spring equinox of 1970, scintillating signals at VHF (136.4 MHz) were observed at Lima, Peru. The transmission originated from ATS 3 and was observed through a pair of antennas spaced 1200 feet apart on an east-west baseline. The empirical data were digitized, reduced, and analyzed. The results include amplitude probability density and distribution functions, time autocorrelation functions, cross correlation functions for the spaced antennas, and appropriate spectral density functions. Results show estimates of the statistics of the ground diffraction pattern to gain insight into gross ionospheric irregularity size, and irregularity velocity in the antenna planes.

Cross-section fluctuations in chaotic scattering systems.

PubMed

Ericson, Torleif E O; Dietz, Barbara; Richter, Achim

2016-10-01

Exact analytical expressions for the cross-section correlation functions of chaotic scattering systems have hitherto been derived only under special conditions. The objective of the present article is to provide expressions that are applicable beyond these restrictions. The derivation is based on a statistical model of Breit-Wigner type for chaotic scattering amplitudes which has been shown to describe the exact analytical results for the scattering (S)-matrix correlation functions accurately. Our results are given in the energy and in the time representations and apply in the whole range from isolated to overlapping resonances. The S-matrix contributions to the cross-section correlations are obtained in terms of explicit irreducible and reducible correlation functions. Consequently, the model can be used for a detailed exploration of the key features of the cross-section correlations and the underlying physical mechanisms. In the region of isolated resonances, the cross-section correlations contain a dominant contribution from the self-correlation term. For narrow states the self-correlations originate predominantly from widely spaced states with exceptionally large partial width. In the asymptotic region of well-overlapping resonances, the cross-section autocorrelation functions are given in terms of the S-matrix autocorrelation functions. For inelastic correlations, in particular, the Ericson fluctuations rapidly dominate in that region. Agreement with known analytical and experimental results is excellent.

Prediction of hourly PM2.5 using a space-time support vector regression model

NASA Astrophysics Data System (ADS)

Yang, Wentao; Deng, Min; Xu, Feng; Wang, Hang

2018-05-01

Real-time air quality prediction has been an active field of research in atmospheric environmental science. The existing methods of machine learning are widely used to predict pollutant concentrations because of their enhanced ability to handle complex non-linear relationships. However, because pollutant concentration data, as typical geospatial data, also exhibit spatial heterogeneity and spatial dependence, they may violate the assumptions of independent and identically distributed random variables in most of the machine learning methods. As a result, a space-time support vector regression model is proposed to predict hourly PM2.5 concentrations. First, to address spatial heterogeneity, spatial clustering is executed to divide the study area into several homogeneous or quasi-homogeneous subareas. To handle spatial dependence, a Gauss vector weight function is then developed to determine spatial autocorrelation variables as part of the input features. Finally, a local support vector regression model with spatial autocorrelation variables is established for each subarea. Experimental data on PM2.5 concentrations in Beijing are used to verify whether the results of the proposed model are superior to those of other methods.

Investment Dynamics with Natural Expectations.

PubMed

Fuster, Andreas; Hebert, Benjamin; Laibson, David

2010-01-01

We study an investment model in which agents have the wrong beliefs about the dynamic properties of fundamentals. Specifically, we assume that agents underestimate the rate of mean reversion. The model exhibits the following six properties: (i) Beliefs are excessively optimistic in good times and excessively pessimistic in bad times. (ii) Asset prices are too volatile. (iii) Excess returns are negatively autocorrelated. (iv) High levels of corporate profits predict negative future excess returns. (v) Real economic activity is excessively volatile; the economy experiences amplified investment cycles. (vi) Corporate profits are positively autocorrelated in the short run and negatively autocorrelated in the medium run. The paper provides an illustrative model of animal spirits, amplified business cycles, and excess volatility.

Investment Dynamics with Natural Expectations*

PubMed Central

Fuster, Andreas; Hebert, Benjamin; Laibson, David

2012-01-01

We study an investment model in which agents have the wrong beliefs about the dynamic properties of fundamentals. Specifically, we assume that agents underestimate the rate of mean reversion. The model exhibits the following six properties: (i) Beliefs are excessively optimistic in good times and excessively pessimistic in bad times. (ii) Asset prices are too volatile. (iii) Excess returns are negatively autocorrelated. (iv) High levels of corporate profits predict negative future excess returns. (v) Real economic activity is excessively volatile; the economy experiences amplified investment cycles. (vi) Corporate profits are positively autocorrelated in the short run and negatively autocorrelated in the medium run. The paper provides an illustrative model of animal spirits, amplified business cycles, and excess volatility. PMID:23243469

Numerical analysis for finite-range multitype stochastic contact financial market dynamic systems

NASA Astrophysics Data System (ADS)

Yang, Ge; Wang, Jun; Fang, Wen

2015-04-01

In an attempt to reproduce and study the dynamics of financial markets, a random agent-based financial price model is developed and investigated by the finite-range multitype contact dynamic system, in which the interaction and dispersal of different types of investment attitudes in a stock market are imitated by viruses spreading. With different parameters of birth rates and finite-range, the normalized return series are simulated by Monte Carlo simulation method and numerical studied by power-law distribution analysis and autocorrelation analysis. To better understand the nonlinear dynamics of the return series, a q-order autocorrelation function and a multi-autocorrelation function are also defined in this work. The comparisons of statistical behaviors of return series from the agent-based model and the daily historical market returns of Shanghai Composite Index and Shenzhen Component Index indicate that the proposed model is a reasonable qualitative explanation for the price formation process of stock market systems.

A study of stationarity in time series by using wavelet transform

NASA Astrophysics Data System (ADS)

Dghais, Amel Abdoullah Ahmed; Ismail, Mohd Tahir

2014-07-01

In this work the core objective is to apply discrete wavelet transform (DWT) functions namely Haar, Daubechies, Symmlet, Coiflet and discrete approximation of the meyer wavelets in non-stationary financial time series data from US stock market (DJIA30). The data consists of 2048 daily data of closing index starting from December 17, 2004 until October 23, 2012. From the unit root test the results show that the data is non stationary in the level. In order to study the stationarity of a time series, the autocorrelation function (ACF) is used. Results indicate that, Haar function is the lowest function to obtain noisy series as compared to Daubechies, Symmlet, Coiflet and discrete approximation of the meyer wavelets. In addition, the original data after decomposition by DWT is less noisy series than decomposition by DWT for return time series.

General simulation algorithm for autocorrelated binary processes.

PubMed

Serinaldi, Francesco; Lombardo, Federico

2017-02-01

The apparent ubiquity of binary random processes in physics and many other fields has attracted considerable attention from the modeling community. However, generation of binary sequences with prescribed autocorrelation is a challenging task owing to the discrete nature of the marginal distributions, which makes the application of classical spectral techniques problematic. We show that such methods can effectively be used if we focus on the parent continuous process of beta distributed transition probabilities rather than on the target binary process. This change of paradigm results in a simulation procedure effectively embedding a spectrum-based iterative amplitude-adjusted Fourier transform method devised for continuous processes. The proposed algorithm is fully general, requires minimal assumptions, and can easily simulate binary signals with power-law and exponentially decaying autocorrelation functions corresponding, for instance, to Hurst-Kolmogorov and Markov processes. An application to rainfall intermittency shows that the proposed algorithm can also simulate surrogate data preserving the empirical autocorrelation.

A Bayesian Approach for Summarizing and Modeling Time-Series Exposure Data with Left Censoring.

PubMed

Houseman, E Andres; Virji, M Abbas

2017-08-01

Direct reading instruments are valuable tools for measuring exposure as they provide real-time measurements for rapid decision making. However, their use is limited to general survey applications in part due to issues related to their performance. Moreover, statistical analysis of real-time data is complicated by autocorrelation among successive measurements, non-stationary time series, and the presence of left-censoring due to limit-of-detection (LOD). A Bayesian framework is proposed that accounts for non-stationary autocorrelation and LOD issues in exposure time-series data in order to model workplace factors that affect exposure and estimate summary statistics for tasks or other covariates of interest. A spline-based approach is used to model non-stationary autocorrelation with relatively few assumptions about autocorrelation structure. Left-censoring is addressed by integrating over the left tail of the distribution. The model is fit using Markov-Chain Monte Carlo within a Bayesian paradigm. The method can flexibly account for hierarchical relationships, random effects and fixed effects of covariates. The method is implemented using the rjags package in R, and is illustrated by applying it to real-time exposure data. Estimates for task means and covariates from the Bayesian model are compared to those from conventional frequentist models including linear regression, mixed-effects, and time-series models with different autocorrelation structures. Simulations studies are also conducted to evaluate method performance. Simulation studies with percent of measurements below the LOD ranging from 0 to 50% showed lowest root mean squared errors for task means and the least biased standard deviations from the Bayesian model compared to the frequentist models across all levels of LOD. In the application, task means from the Bayesian model were similar to means from the frequentist models, while the standard deviations were different. Parameter estimates for covariates were significant in some frequentist models, but in the Bayesian model their credible intervals contained zero; such discrepancies were observed in multiple datasets. Variance components from the Bayesian model reflected substantial autocorrelation, consistent with the frequentist models, except for the auto-regressive moving average model. Plots of means from the Bayesian model showed good fit to the observed data. The proposed Bayesian model provides an approach for modeling non-stationary autocorrelation in a hierarchical modeling framework to estimate task means, standard deviations, quantiles, and parameter estimates for covariates that are less biased and have better performance characteristics than some of the contemporary methods. Published by Oxford University Press on behalf of the British Occupational Hygiene Society 2017.

Monitoring autocorrelated process: A geometric Brownian motion process approach

NASA Astrophysics Data System (ADS)

Li, Lee Siaw; Djauhari, Maman A.

2013-09-01

Autocorrelated process control is common in today's modern industrial process control practice. The current practice of autocorrelated process control is to eliminate the autocorrelation by using an appropriate model such as Box-Jenkins models or other models and then to conduct process control operation based on the residuals. In this paper we show that many time series are governed by a geometric Brownian motion (GBM) process. Therefore, in this case, by using the properties of a GBM process, we only need an appropriate transformation and model the transformed data to come up with the condition needs in traditional process control. An industrial example of cocoa powder production process in a Malaysian company will be presented and discussed to illustrate the advantages of the GBM approach.

Synchronous scattering and diffraction from gold nanotextured surfaces with structure factors

NASA Astrophysics Data System (ADS)

Gu, Min-Jhong; Lee, Ming-Tsang; Huang, Chien-Hsun; Wu, Chi-Chun; Chen, Yu-Bin

2018-05-01

Synchronous scattering and diffraction were demonstrated using reflectance from gold nanotextured surfaces at oblique (θi = 15° and 60°) incidence of wavelength λ = 405 nm. Two samples of unique auto-correlation functions were cost-effectively fabricated. Multiple structure factors of their profiles were confirmed with Fourier expansions. Bi-directional reflectance function (BRDF) from these samples provided experimental proofs. On the other hand, standard deviation of height and unique auto-correlation function of each sample were used to generate surfaces numerically. Comparing their BRDF with those of totally random rough surfaces further suggested that structure factors in profile could reduce specular reflection more than totally random roughness.

An empirical analysis of the distribution of overshoots in a stationary Gaussian stochastic process

NASA Technical Reports Server (NTRS)

Carter, M. C.; Madison, M. W.

1973-01-01

The frequency distribution of overshoots in a stationary Gaussian stochastic process is analyzed. The primary processes involved in this analysis are computer simulation and statistical estimation. Computer simulation is used to simulate stationary Gaussian stochastic processes that have selected autocorrelation functions. An analysis of the simulation results reveals a frequency distribution for overshoots with a functional dependence on the mean and variance of the process. Statistical estimation is then used to estimate the mean and variance of a process. It is shown that for an autocorrelation function, the mean and the variance for the number of overshoots, a frequency distribution for overshoots can be estimated.

An Expert System for the Evaluation of Cost Models

DTIC Science & Technology

1990-09-01

contrast to the condition of equal error variance, called homoscedasticity. (Reference: Applied Linear Regression Models by John Neter - page 423...normal. (Reference: Applied Linear Regression Models by John Neter - page 125) Click Here to continue -> Autocorrelation Click Here for the index - Index...over time. Error terms correlated over time are said to be autocorrelated or serially correlated. (REFERENCE: Applied Linear Regression Models by John

Time-series analysis of delta13C from tree rings. I. Time trends and autocorrelation.

PubMed

Monserud, R A; Marshall, J D

2001-09-01

Univariate time-series analyses were conducted on stable carbon isotope ratios obtained from tree-ring cellulose. We looked for the presence and structure of autocorrelation. Significant autocorrelation violates the statistical independence assumption and biases hypothesis tests. Its presence would indicate the existence of lagged physiological effects that persist for longer than the current year. We analyzed data from 28 trees (60-85 years old; mean = 73 years) of western white pine (Pinus monticola Dougl.), ponderosa pine (Pinus ponderosa Laws.), and Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco var. glauca) growing in northern Idaho. Material was obtained by the stem analysis method from rings laid down in the upper portion of the crown throughout each tree's life. The sampling protocol minimized variation caused by changing light regimes within each tree. Autoregressive moving average (ARMA) models were used to describe the autocorrelation structure over time. Three time series were analyzed for each tree: the stable carbon isotope ratio (delta(13)C); discrimination (delta); and the difference between ambient and internal CO(2) concentrations (c(a) - c(i)). The effect of converting from ring cellulose to whole-leaf tissue did not affect the analysis because it was almost completely removed by the detrending that precedes time-series analysis. A simple linear or quadratic model adequately described the time trend. The residuals from the trend had a constant mean and variance, thus ensuring stationarity, a requirement for autocorrelation analysis. The trend over time for c(a) - c(i) was particularly strong (R(2) = 0.29-0.84). Autoregressive moving average analyses of the residuals from these trends indicated that two-thirds of the individual tree series contained significant autocorrelation, whereas the remaining third were random (white noise) over time. We were unable to distinguish between individuals with and without significant autocorrelation beforehand. Significant ARMA models were all of low order, with either first- or second-order (i.e., lagged 1 or 2 years, respectively) models performing well. A simple autoregressive (AR(1)), model was the most common. The most useful generalization was that the same ARMA model holds for each of the three series (delta(13)C, delta, c(a) - c(i)) for an individual tree, if the time trend has been properly removed for each series. The mean series for the two pine species were described by first-order ARMA models (1-year lags), whereas the Douglas-fir mean series were described by second-order models (2-year lags) with negligible first-order effects. Apparently, the process of constructing a mean time series for a species preserves an underlying signal related to delta(13)C while canceling some of the random individual tree variation. Furthermore, the best model for the overall mean series (e.g., for a species) cannot be inferred from a consensus of the individual tree model forms, nor can its parameters be estimated reliably from the mean of the individual tree parameters. Because two-thirds of the individual tree time series contained significant autocorrelation, the normal assumption of a random structure over time is unwarranted, even after accounting for the time trend. The residuals of an appropriate ARMA model satisfy the independence assumption, and can be used to make hypothesis tests.

The time series approach to short term load forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hagan, M.T.; Behr, S.M.

The application of time series analysis methods to load forecasting is reviewed. It is shown than Box and Jenkins time series models, in particular, are well suited to this application. The logical and organized procedures for model development using the autocorrelation function make these models particularly attractive. One of the drawbacks of these models is the inability to accurately represent the nonlinear relationship between load and temperature. A simple procedure for overcoming this difficulty is introduced, and several Box and Jenkins models are compared with a forecasting procedure currently used by a utility company.

Effect of endotoxin on ventilation and breath variability: role of cyclooxygenase pathway.

PubMed

Preas, H L; Jubran, A; Vandivier, R W; Reda, D; Godin, P J; Banks, S M; Tobin, M J; Suffredini, A F

2001-08-15

To evaluate the effects of endotoxemia on respiratory controller function, 12 subjects were randomized to receive endotoxin or saline; six also received ibuprofen, a cyclooxygenase inhibitor, and six received placebo. Administration of endotoxin produced fever, increased respiratory frequency, decreased inspiratory time, and widened alveolar-arterial oxygen tension gradient (all p < or = 0.001); these responses were blocked by ibuprofen. Independent of ibuprofen, endotoxin produced dyspnea, and it increased fractional inspiratory time, minute ventilation, and mean inspiratory flow (all p < or = 0.025). Endotoxin altered the autocorrelative behavior of respiratory frequency by increasing its autocorrelation coefficient at a lag of one breath, the number of breath lags with significant serial correlations, and its correlated fraction (all p < 0.05); these responses were blocked by ibuprofen. Changes in correlated behavior of respiratory frequency were related to changes in arterial carbon dioxide tension (r = 0.86; p < 0.03). Endotoxin decreased the oscillatory fraction of inspiratory time in both the placebo (p < 0.05) and ibuprofen groups (p = 0.06). In conclusion, endotoxin produced increases in respiratory motor output and dyspnea independent of fever and symptoms, and it curtailed the freedom to vary respiratory timing-a response that appears to be mediated by the cyclooxygenase pathway.

Queues with Dropping Functions and General Arrival Processes

PubMed Central

Chydzinski, Andrzej; Mrozowski, Pawel

2016-01-01

In a queueing system with the dropping function the arriving customer can be denied service (dropped) with the probability that is a function of the queue length at the time of arrival of this customer. The potential applicability of such mechanism is very wide due to the fact that by choosing the shape of this function one can easily manipulate several performance characteristics of the queueing system. In this paper we carry out analysis of the queueing system with the dropping function and a very general model of arrival process—the model which includes batch arrivals and the interarrival time autocorrelation, and allows for fitting the actual shape of the interarrival time distribution and its moments. For such a system we obtain formulas for the distribution of the queue length and the overall customer loss ratio. The analytical results are accompanied with numerical examples computed for several dropping functions. PMID:26943171

A recursive linear predictive vocoder

NASA Astrophysics Data System (ADS)

Janssen, W. A.

1983-12-01

A non-real time 10 pole recursive autocorrelation linear predictive coding vocoder was created for use in studying effects of recursive autocorrelation on speech. The vocoder is composed of two interchangeable pitch detectors, a speech analyzer, and speech synthesizer. The time between updating filter coefficients is allowed to vary from .125 msec to 20 msec. The best quality was found using .125 msec between each update. The greatest change in quality was noted when changing from 20 msec/update to 10 msec/update. Pitch period plots for the center clipping autocorrelation pitch detector and simplified inverse filtering technique are provided. Plots of speech into and out of the vocoder are given. Formant versus time three dimensional plots are shown. Effects of noise on pitch detection and formants are shown. Noise effects the voiced/unvoiced decision process causing voiced speech to be re-constructed as unvoiced.

Calling depths of baleen whales from single sensor data: development of an autocorrelation method using multipath localization.

PubMed

Valtierra, Robert D; Glynn Holt, R; Cholewiak, Danielle; Van Parijs, Sofie M

2013-09-01

Multipath localization techniques have not previously been applied to baleen whale vocalizations due to difficulties in application to tonal vocalizations. Here it is shown that an autocorrelation method coupled with the direct reflected time difference of arrival localization technique can successfully resolve location information. A derivation was made to model the autocorrelation of a direct signal and its overlapping reflections to illustrate that an autocorrelation may be used to extract reflection information from longer duration signals containing a frequency sweep, such as some calls produced by baleen whales. An analysis was performed to characterize the difference in behavior of the autocorrelation when applied to call types with varying parameters (sweep rate, call duration). The method's feasibility was tested using data from playback transmissions to localize an acoustic transducer at a known depth and location. The method was then used to estimate the depth and range of a single North Atlantic right whale (Eubalaena glacialis) and humpback whale (Megaptera novaeangliae) from two separate experiments.

Retrieval of P wave Basin Response from Autocorrelation of Seismic Noise-Jakarta, Indonesia

NASA Astrophysics Data System (ADS)

Saygin, E.; Cummins, P. R.; Lumley, D. E.

2016-12-01

Indonesia's capital city, Jakarta, is home to a very large (over 10 million), vulnerable population and is proximate to known active faults, as well as to the subduction of Australian plate, which has a megathrust at abut 300 km distance, as well as intraslab seismicity extending to directly beneath the city. It is also located in a basin filled with a thick layer of unconsolidated and poorly consolidated sediment, which increases the seismic hazard the city is facing. Therefore, the information on the seismic velocity structure of the basin is crucial for increasing our knowledge of the seismic risk. We undertook a passive deployment of broadband seismographs throughout the city over a 3-month interval in 2013-2014, recording ambient seismic noise at over 90 sites for intervals of 1 month or more. Here we consider autocorrelations of the vertical component of the continuously recorded seismic wavefield across this dense network to image the shallow P wave velocity structure of Jakarta, Indonesia. Unlike the surface wave Green's functions used in ambient noise tomography, the vertical-component autocorrelograms are dominated by body wave energy that is potentially sensitive to sharp velocity contrasts, which makes them useful in seismic imaging. Results show autocorrelograms at different seismic stations with travel time variations that largely reflect changes in sediment thickness across the basin. We also confirm the validity our interpretation of the observed autocorrelation waveforms by conducting 2D finite difference full waveform numerical modeling for randomly distributed seismic sources to retrieve the reflection response through autocorrelation.

Statistical, time series, and fractal analysis of full stretch of river Yamuna (India) for water quality management.

PubMed

Parmar, Kulwinder Singh; Bhardwaj, Rashmi

2015-01-01

River water is a major resource of drinking water on earth. Management of river water is highly needed for surviving. Yamuna is the main river of India, and monthly variation of water quality of river Yamuna, using statistical methods have been compared at different sites for each water parameters. Regression, correlation coefficient, autoregressive integrated moving average (ARIMA), box-Jenkins, residual autocorrelation function (ACF), residual partial autocorrelation function (PACF), lag, fractal, Hurst exponent, and predictability index have been estimated to analyze trend and prediction of water quality. Predictive model is useful at 95% confidence limits and all water parameters reveal platykurtic curve. Brownian motion (true random walk) behavior exists at different sites for BOD, AMM, and total Kjeldahl nitrogen (TKN). Quality of Yamuna River water at Hathnikund is good, declines at Nizamuddin, Mazawali, Agra D/S, and regains good quality again at Juhikha. For all sites, almost all parameters except potential of hydrogen (pH), water temperature (WT) crosses the prescribed limits of World Health Organization (WHO)/United States Environmental Protection Agency (EPA).

On the non-stationary generalized Langevin equation

NASA Astrophysics Data System (ADS)

Meyer, Hugues; Voigtmann, Thomas; Schilling, Tanja

2017-12-01

In molecular dynamics simulations and single molecule experiments, observables are usually measured along dynamic trajectories and then averaged over an ensemble ("bundle") of trajectories. Under stationary conditions, the time-evolution of such averages is described by the generalized Langevin equation. By contrast, if the dynamics is not stationary, it is not a priori clear which form the equation of motion for an averaged observable has. We employ the formalism of time-dependent projection operator techniques to derive the equation of motion for a non-equilibrium trajectory-averaged observable as well as for its non-stationary auto-correlation function. The equation is similar in structure to the generalized Langevin equation but exhibits a time-dependent memory kernel as well as a fluctuating force that implicitly depends on the initial conditions of the process. We also derive a relation between this memory kernel and the autocorrelation function of the fluctuating force that has a structure similar to a fluctuation-dissipation relation. In addition, we show how the choice of the projection operator allows us to relate the Taylor expansion of the memory kernel to data that are accessible in MD simulations and experiments, thus allowing us to construct the equation of motion. As a numerical example, the procedure is applied to Brownian motion initialized in non-equilibrium conditions and is shown to be consistent with direct measurements from simulations.

Reflection Response of the Parnaíba Basin (NE Brazil) from Seismic Ambient Noise Autocorrelation Functions

NASA Astrophysics Data System (ADS)

Julià, Jordi; Schimmel, Martin; Cedraz, Victória

2017-04-01

Reflected-wave interferometry relies on the recording of transient seismic signals from random wavefields located beneath recording stations. Under vertical incidence, the recordings contain the full transmission response, which includes the direct wave as well as multiple reverberations from seismic discontinuities located between the wavefields and the receiver. It has been shown that, under those assumptions, the reflection response of the medium can be recovered from the autocorrelation function (ACF) of the transmission response at a given receiver, as if the wavefields had originated themselves at the free surface. This passive approach to seismic reflection profiling has the obvious advantage of being low-cost and non-invasive when compared to its active-source counterpart, and it has been successfully utilized in other sedimentary basins worldwide. In this paper we evaluate the ability of the autocorrelation of ambient seismic noise recorded in the Parnaíba basin - a large Paleozoic basin in NE Brazil - to recover the reflection response of the basin. The dataset was acquired by the Universidade Federal do Rio Grande do Norte during 2015 and 2016 under the Parnaíba Basin Analysis Project (PBAP), a multi-disciplinary and multi-institutional effort funded by BP Energy do Brasil aimed at improving our current understanding of the architecture of this cratonic basin. The dataset consists of about 1 year of continuous ground motion data from 10 short-period, 3-component stations located in the central portion of the basin. The stations were co-located with an existing (active-source) seismic reflection profile that was shot in 2012, making a linear array of about 100 km in aperture and about 10 km inter-station spacing. To develop the autocorrelation at a given station we considered the vertical component of ground motion only, which should result in the P-wave response. The vertical recordings were first split into 10 min-long windows, demeaned, de-trended, re-sampled, and band-pass filtered between 8 and 16 Hz before autocorrelation, and then stacked with phase-weighting to enhance coherency of the retrieved signal. The ACFs show coherent signal is recovered at lag times between 0.5 and 2 s, which we interpret as P- and S-wave energy reflected on top of an intra-sedimentary discontinuity. Our results are consistent, to first-order, with a previously developed active-source reflection response of the basin.

The Chaotic Light Curves of Accreting Black Holes

NASA Technical Reports Server (NTRS)

Kazanas, Demosthenes

2007-01-01

We present model light curves for accreting Black Hole Candidates (BHC) based on a recently developed model of these sources. According to this model, the observed light curves and aperiodic variability of BHC are due to a series of soft photon injections at random (Poisson) intervals and the stochastic nature of the Comptonization process in converting these soft photons to the observed high energy radiation. The additional assumption of our model is that the Comptonization process takes place in an extended but non-uniform hot plasma corona surrounding the compact object. We compute the corresponding Power Spectral Densities (PSD), autocorrelation functions, time skewness of the light curves and time lags between the light curves of the sources at different photon energies and compare our results to observation. Our model reproduces the observed light curves well, in that it provides good fits to their overall morphology (as manifest by the autocorrelation and time skewness) and also to their PSDs and time lags, by producing most of the variability power at time scales 2 a few seconds, while at the same time allowing for shots of a few msec in duration, in accordance with observation. We suggest that refinement of this type of model along with spectral and phase lag information can be used to probe the structure of this class of high energy sources.

Comprehensive comparisons of geodesic acoustic mode characteristics and dynamics between Tore Supra experiments and gyrokinetic simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Storelli, A., E-mail: alexandre.storelli@lpp.polytechnique.fr; Vermare, L.; Hennequin, P.

2015-06-15

In a dedicated collisionality scan in Tore Supra, the geodesic acoustic mode (GAM) is detected and identified with the Doppler backscattering technique. Observations are compared to the results of a simulation with the gyrokinetic code GYSELA. We found that the GAM frequency in experiments is lower than predicted by simulation and theory. Moreover, the disagreement is higher in the low collisionality scenario. Bursts of non harmonic GAM oscillations have been characterized with filtering techniques, such as the Hilbert-Huang transform. When comparing this dynamical behaviour between experiments and simulation, the probability density function of GAM amplitude and the burst autocorrelation timemore » are found to be remarkably similar. In the simulation, where the radial profile of GAM frequency is continuous, we observed a phenomenon of radial phase mixing of the GAM oscillations, which could influence the burst autocorrelation time.« less

Rainfall statistics, stationarity, and climate change.

PubMed

Sun, Fubao; Roderick, Michael L; Farquhar, Graham D

2018-03-06

There is a growing research interest in the detection of changes in hydrologic and climatic time series. Stationarity can be assessed using the autocorrelation function, but this is not yet common practice in hydrology and climate. Here, we use a global land-based gridded annual precipitation (hereafter P ) database (1940-2009) and find that the lag 1 autocorrelation coefficient is statistically significant at around 14% of the global land surface, implying nonstationary behavior (90% confidence). In contrast, around 76% of the global land surface shows little or no change, implying stationary behavior. We use these results to assess change in the observed P over the most recent decade of the database. We find that the changes for most (84%) grid boxes are within the plausible bounds of no significant change at the 90% CI. The results emphasize the importance of adequately accounting for natural variability when assessing change. Copyright © 2018 the Author(s). Published by PNAS.

Rainfall statistics, stationarity, and climate change

NASA Astrophysics Data System (ADS)

Sun, Fubao; Roderick, Michael L.; Farquhar, Graham D.

2018-03-01

There is a growing research interest in the detection of changes in hydrologic and climatic time series. Stationarity can be assessed using the autocorrelation function, but this is not yet common practice in hydrology and climate. Here, we use a global land-based gridded annual precipitation (hereafter P) database (1940–2009) and find that the lag 1 autocorrelation coefficient is statistically significant at around 14% of the global land surface, implying nonstationary behavior (90% confidence). In contrast, around 76% of the global land surface shows little or no change, implying stationary behavior. We use these results to assess change in the observed P over the most recent decade of the database. We find that the changes for most (84%) grid boxes are within the plausible bounds of no significant change at the 90% CI. The results emphasize the importance of adequately accounting for natural variability when assessing change.

New Approaches for Calculating Moran’s Index of Spatial Autocorrelation

PubMed Central

Chen, Yanguang

2013-01-01

Spatial autocorrelation plays an important role in geographical analysis; however, there is still room for improvement of this method. The formula for Moran’s index is complicated, and several basic problems remain to be solved. Therefore, I will reconstruct its mathematical framework using mathematical derivation based on linear algebra and present four simple approaches to calculating Moran’s index. Moran’s scatterplot will be ameliorated, and new test methods will be proposed. The relationship between the global Moran’s index and Geary’s coefficient will be discussed from two different vantage points: spatial population and spatial sample. The sphere of applications for both Moran’s index and Geary’s coefficient will be clarified and defined. One of theoretical findings is that Moran’s index is a characteristic parameter of spatial weight matrices, so the selection of weight functions is very significant for autocorrelation analysis of geographical systems. A case study of 29 Chinese cities in 2000 will be employed to validate the innovatory models and methods. This work is a methodological study, which will simplify the process of autocorrelation analysis. The results of this study will lay the foundation for the scaling analysis of spatial autocorrelation. PMID:23874592

Source localization of narrow band signals in multipath environments, with application to marine mammals

NASA Astrophysics Data System (ADS)

Valtierra, Robert Daniel

Passive acoustic localization has benefited from many major developments and has become an increasingly important focus point in marine mammal research. Several challenges still remain. This work seeks to address several of these challenges such as tracking the calling depths of baleen whales. In this work, data from an array of widely spaced Marine Acoustic Recording Units (MARUs) was used to achieve three dimensional localization by combining the methods Time Difference of Arrival (TDOA) and Direct-Reflected Time Difference of Arrival (DRTD) along with a newly developed autocorrelation technique. TDOA was applied to data for two dimensional (latitude and longitude) localization and depth was resolved using DRTD. Previously, DRTD had been limited to pulsed broadband signals, such as sperm whale or dolphin echolocation, where individual direct and reflected signals are separated in time. Due to the length of typical baleen whale vocalizations, individual multipath signal arrivals can overlap making time differences of arrival difficult to resolve. This problem can be solved using an autocorrelation, which can extract reflection information from overlapping signals. To establish this technique, a derivation was made to model the autocorrelation of a direct signal and its overlapping reflection. The model was exploited to derive performance limits allowing for prediction of the minimum resolvable direct-reflected time difference for a known signal type. The dependence on signal parameters (sweep rate, call duration) was also investigated. The model was then verified using both recorded and simulated data from two analysis cases for North Atlantic right whales (NARWs, Eubalaena glacialis) and humpback whales (Megaptera noveaengliae). The newly developed autocorrelation technique was then combined with DRTD and tested using data from playback transmissions to localize an acoustic transducer at a known depth and location. The combined DRTD-autocorrelation methods enabled calling depth and range estimations of a vocalizing NARW and humpback whale in two separate cases. The DRTD-autocorrelation method was then combined with TDOA to create a three dimensional track of a NARW in the Stellwagen Bank National Marine Sanctuary. Results from these experiments illustrated the potential of the combined methods to successfully resolve baleen calling depths in three dimensions.

Communication: Symmetrical quasi-classical analysis of linear optical spectroscopy

NASA Astrophysics Data System (ADS)

Provazza, Justin; Coker, David F.

2018-05-01

The symmetrical quasi-classical approach for propagation of a many degree of freedom density matrix is explored in the context of computing linear spectra. Calculations on a simple two state model for which exact results are available suggest that the approach gives a qualitative description of peak positions, relative amplitudes, and line broadening. Short time details in the computed dipole autocorrelation function result in exaggerated tails in the spectrum.

Molecular dynamic simulations of N2-broadened methane line shapes and comparison with experiments

NASA Astrophysics Data System (ADS)

Le, Tuong; Doménech, José-Luis; Lepère, Muriel; Tran, Ha

2017-03-01

Absorption spectra of methane transitions broadened by nitrogen have been calculated for the first time using classical molecular dynamic simulations. For that, the time evolution of the auto-correlation function of the dipole moment vector, assumed along a C-H axis, was computed using an accurate site-site intermolecular potential for CH4-N2. Quaternion coordinates were used to treat the rotation of the molecules. A requantization procedure was applied to the classical rotation and spectra were then derived as the Fourier-Laplace transform of the auto-correlation function. These computed spectra were compared with experimental ones recorded with a tunable diode laser and a difference-frequency laser spectrometer. Specifically, nine isolated methane lines broadened by nitrogen, belonging to various vibrational bands and having rotational quantum numbers J from 0 to 9, were measured at room temperature and at several pressures from 20 to 945 mbar. Comparisons between measured and calculated spectra were made through their fits using the Voigt profile. The results show that ab initio calculated spectra reproduce with very high fidelity non-Voigt effects on the measurements and that classical molecular dynamic simulations can be used to predict spectral shapes of isolated lines of methane perturbed by nitrogen.

Characterization, parameter estimation, and aircraft response statistics of atmospheric turbulence

NASA Technical Reports Server (NTRS)

Mark, W. D.

1981-01-01

A nonGaussian three component model of atmospheric turbulence is postulated that accounts for readily observable features of turbulence velocity records, their autocorrelation functions, and their spectra. Methods for computing probability density functions and mean exceedance rates of a generic aircraft response variable are developed using nonGaussian turbulence characterizations readily extracted from velocity recordings. A maximum likelihood method is developed for optimal estimation of the integral scale and intensity of records possessing von Karman transverse of longitudinal spectra. Formulas for the variances of such parameter estimates are developed. The maximum likelihood and least-square approaches are combined to yield a method for estimating the autocorrelation function parameters of a two component model for turbulence.

An investigation of turbulent transport in the extreme lower atmosphere

NASA Technical Reports Server (NTRS)

Koper, C. A., Jr.; Sadeh, W. Z.

1975-01-01

A model in which the Lagrangian autocorrelation is expressed by a domain integral over a set of usual Eulerian autocorrelations acquired concurrently at all points within a turbulence box is proposed along with a method for ascertaining the statistical stationarity of turbulent velocity by creating an equivalent ensemble to investigate the flow in the extreme lower atmosphere. Simultaneous measurements of turbulent velocity on a turbulence line along the wake axis were carried out utilizing a longitudinal array of five hot-wire anemometers remotely operated. The stationarity test revealed that the turbulent velocity is approximated as a realization of a weakly self-stationary random process. Based on the Lagrangian autocorrelation it is found that: (1) large diffusion time predominated; (2) ratios of Lagrangian to Eulerian time and spatial scales were smaller than unity; and, (3) short and long diffusion time scales and diffusion spatial scales were constrained within their Eulerian counterparts.

Microchannel plate cross-talk mitigation for spatial autocorrelation measurements

NASA Astrophysics Data System (ADS)

Lipka, Michał; Parniak, Michał; Wasilewski, Wojciech

2018-05-01

Microchannel plates (MCP) are the basis for many spatially resolved single-particle detectors such as ICCD or I-sCMOS cameras employing image intensifiers (II), MCPs with delay-line anodes for the detection of cold gas particles or Cherenkov radiation detectors. However, the spatial characterization provided by an MCP is severely limited by cross-talk between its microchannels, rendering MCP and II ill-suited for autocorrelation measurements. Here, we present a cross-talk subtraction method experimentally exemplified for an I-sCMOS based measurement of pseudo-thermal light second-order intensity autocorrelation function at the single-photon level. The method merely requires a dark counts measurement for calibration. A reference cross-correlation measurement certifies the cross-talk subtraction. While remaining universal for MCP applications, the presented cross-talk subtraction, in particular, simplifies quantum optical setups. With the possibility of autocorrelation measurements, the signal needs no longer to be divided into two camera regions for a cross-correlation measurement, reducing the experimental setup complexity and increasing at least twofold the simultaneously employable camera sensor region.

Elephants in space and time

Treesearch

Samuel A. Cushman; Michael Chase; Curtice Griffin

2005-01-01

Autocorrelation in animal movements can be both a serious nuisance to analysis and a source of valuable information about the scale and patterns of animal behavior, depending on the question and the techniques employed. In this paper we present an approach to analyzing the patterns of autocorrelation in animal movements that provides a detailed picture of seasonal...

Numerical analysis for finite-range multitype stochastic contact financial market dynamic systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Ge; Wang, Jun; Fang, Wen, E-mail: fangwen@bjtu.edu.cn

In an attempt to reproduce and study the dynamics of financial markets, a random agent-based financial price model is developed and investigated by the finite-range multitype contact dynamic system, in which the interaction and dispersal of different types of investment attitudes in a stock market are imitated by viruses spreading. With different parameters of birth rates and finite-range, the normalized return series are simulated by Monte Carlo simulation method and numerical studied by power-law distribution analysis and autocorrelation analysis. To better understand the nonlinear dynamics of the return series, a q-order autocorrelation function and a multi-autocorrelation function are also definedmore » in this work. The comparisons of statistical behaviors of return series from the agent-based model and the daily historical market returns of Shanghai Composite Index and Shenzhen Component Index indicate that the proposed model is a reasonable qualitative explanation for the price formation process of stock market systems.« less

DS/LPI autocorrelation detection in noise plus random-tone interference. [Direct Sequence Low-Probabilty of Intercept

NASA Technical Reports Server (NTRS)

Hinedi, S.; Polydoros, A.

1988-01-01

The authors present and analyze a frequency-noncoherent two-lag autocorrelation statistic for the wideband detection of random BPSK signals in noise-plus-random-multitone interference. It is shown that this detector is quite robust to the presence or absence of interference and its specific parameter values, contrary to the case of an energy detector. The rule assumes knowledge of the data rate and the active scenario under H0. It is concluded that the real-time autocorrelation domain and its samples (lags) are a viable approach for detecting random signals in dense environments.

Analysis of stochastic characteristics of the Benue River flow process

NASA Astrophysics Data System (ADS)

Otache, Martins Y.; Bakir, Mohammad; Li, Zhijia

2008-05-01

Stochastic characteristics of the Benue River streamflow process are examined under conditions of data austerity. The streamflow process is investigated for trend, non-stationarity and seasonality for a time period of 26 years. Results of trend analyses with Mann-Kendall test show that there is no trend in the annual mean discharges. Monthly flow series examined with seasonal Kendall test indicate the presence of positive change in the trend for some months, especially the months of August, January, and February. For the stationarity test, daily and monthly flow series appear to be stationary whereas at 1%, 5%, and 10% significant levels, the stationarity alternative hypothesis is rejected for the annual flow series. Though monthly flow appears to be stationary going by this test, because of high seasonality, it could be said to exhibit periodic stationarity based on the seasonality analysis. The following conclusions are drawn: (1) There is seasonality in both the mean and variance with unimodal distribution. (2) Days with high mean also have high variance. (3) Skewness coefficients for the months within the dry season period are greater than those of the wet season period, and seasonal autocorrelations for streamflow during dry season are generally larger than those of the wet season. Precisely, they are significantly different for most of the months. (4) The autocorrelation functions estimated “over time” are greater in the absolute value for data that have not been deseasonalised but were initially normalised by logarithmic transformation only, while autocorrelation functions for i = 1, 2, ..., 365 estimated “over realisations” have their coefficients significantly different from other coefficients.

Strong Clustering of Lyman Break Galaxies around Luminous Quasars at Z ˜ 4

NASA Astrophysics Data System (ADS)

García-Vergara, Cristina; Hennawi, Joseph F.; Barrientos, L. Felipe; Rix, Hans-Walter

2017-10-01

In the standard picture of structure formation, the first massive galaxies are expected to form at the highest peaks of the density field, which constitute the cores of massive proto-clusters. Luminous quasars (QSOs) at z ˜ 4 are the most strongly clustered population known, and should thus reside in massive dark matter halos surrounded by large overdensities of galaxies, implying a strong QSO-galaxy cross-correlation function. We observed six z ˜ 4 QSO fields with VLT/FORS, exploiting a novel set of narrow-band filters custom designed to select Lyman Break Galaxies (LBGs) in a thin redshift slice of {{Δ }}z˜ 0.3, mitigating the projection effects that have limited the sensitivity of previous searches for galaxies around z≳ 4 QSOs. We find that LBGs are strongly clustered around QSOs, and present the first measurement of the QSO-LBG cross-correlation function at z ˜ 4, on scales of 0.1≲ R≲ 9 {h}-1 {Mpc} (comoving). Assuming a power-law form for the cross-correlation function ξ ={(r/{r}0{QG})}γ , we measure {r}0{QG}={8.83}-1.51+1.39 {h}-1 {Mpc} for a fixed slope of γ =2.0. This result is in agreement with the expected cross-correlation length deduced from measurements of the QSO and LBG auto-correlation function, and assuming a deterministic bias model. We also measure a strong auto-correlation of LBGs in our QSO fields, finding {r}0{GG}={21.59}-1.69+1.72 {h}-1 {Mpc} for a fixed slope of γ =1.5, which is ˜4 times larger than the LBG auto-correlation length in blank fields, providing further evidence that QSOs reside in overdensities of LBGs. Our results qualitatively support a picture where luminous QSOs inhabit exceptionally massive ({M}{halo}> {10}12 {M}⊙ ) dark matter halos at z ˜ 4.

Role of initial state and final quench temperature on aging properties in phase-ordering kinetics.

PubMed

Corberi, Federico; Villavicencio-Sanchez, Rodrigo

2016-05-01

We study numerically the two-dimensional Ising model with nonconserved dynamics quenched from an initial equilibrium state at the temperature T_{i}≥T_{c} to a final temperature T_{f} below the critical one. By considering processes initiating both from a disordered state at infinite temperature T_{i}=∞ and from the critical configurations at T_{i}=T_{c} and spanning the range of final temperatures T_{f}∈[0,T_{c}[ we elucidate the role played by T_{i} and T_{f} on the aging properties and, in particular, on the behavior of the autocorrelation C and of the integrated response function χ. Our results show that for any choice of T_{f}, while the autocorrelation function exponent λ_{C} takes a markedly different value for T_{i}=∞ [λ_{C}(T_{i}=∞)≃5/4] or T_{i}=T_{c} [λ_{C}(T_{i}=T_{c})≃1/8] the response function exponents are unchanged. Supported by the outcome of the analytical solution of the solvable spherical model we interpret this fact as due to the different contributions provided to autocorrelation and response by the large-scale properties of the system. As changing T_{f} is considered, although this is expected to play no role in the large-scale and long-time properties of the system, we show important effects on the quantitative behavior of χ. In particular, data for quenches to T_{f}=0 are consistent with a value of the response function exponent λ_{χ}=1/2λ_{C}(T_{i}=∞)=5/8 different from the one [λ_{χ}∈(0.5-0.56)] found in a wealth of previous numerical determinations in quenches to finite final temperatures. This is interpreted as due to important preasymptotic corrections associated to T_{f}>0.

Statistical process control of mortality series in the Australian and New Zealand Intensive Care Society (ANZICS) adult patient database: implications of the data generating process

PubMed Central

2013-01-01

Background Statistical process control (SPC), an industrial sphere initiative, has recently been applied in health care and public health surveillance. SPC methods assume independent observations and process autocorrelation has been associated with increase in false alarm frequency. Methods Monthly mean raw mortality (at hospital discharge) time series, 1995–2009, at the individual Intensive Care unit (ICU) level, were generated from the Australia and New Zealand Intensive Care Society adult patient database. Evidence for series (i) autocorrelation and seasonality was demonstrated using (partial)-autocorrelation ((P)ACF) function displays and classical series decomposition and (ii) “in-control” status was sought using risk-adjusted (RA) exponentially weighted moving average (EWMA) control limits (3 sigma). Risk adjustment was achieved using a random coefficient (intercept as ICU site and slope as APACHE III score) logistic regression model, generating an expected mortality series. Application of time-series to an exemplar complete ICU series (1995-(end)2009) was via Box-Jenkins methodology: autoregressive moving average (ARMA) and (G)ARCH ((Generalised) Autoregressive Conditional Heteroscedasticity) models, the latter addressing volatility of the series variance. Results The overall data set, 1995-2009, consisted of 491324 records from 137 ICU sites; average raw mortality was 14.07%; average(SD) raw and expected mortalities ranged from 0.012(0.113) and 0.013(0.045) to 0.296(0.457) and 0.278(0.247) respectively. For the raw mortality series: 71 sites had continuous data for assessment up to or beyond lag40 and 35% had autocorrelation through to lag40; and of 36 sites with continuous data for ≥ 72 months, all demonstrated marked seasonality. Similar numbers and percentages were seen with the expected series. Out-of-control signalling was evident for the raw mortality series with respect to RA-EWMA control limits; a seasonal ARMA model, with GARCH effects, displayed white-noise residuals which were in-control with respect to EWMA control limits and one-step prediction error limits (3SE). The expected series was modelled with a multiplicative seasonal autoregressive model. Conclusions The data generating process of monthly raw mortality series at the ICU level displayed autocorrelation, seasonality and volatility. False-positive signalling of the raw mortality series was evident with respect to RA-EWMA control limits. A time series approach using residual control charts resolved these issues. PMID:23705957

Real time correlation function in a single phase space integral beyond the linearized semiclassical initial value representation.

PubMed

Liu, Jian; Miller, William H

2007-06-21

It is shown how quantum mechanical time correlation functions [defined, e.g., in Eq. (1.1)] can be expressed, without approximation, in the same form as the linearized approximation of the semiclassical initial value representation (LSC-IVR), or classical Wigner model, for the correlation function [cf. Eq. (2.1)], i.e., as a phase space average (over initial conditions for trajectories) of the Wigner functions corresponding to the two operators. The difference is that the trajectories involved in the LSC-IVR evolve classically, i.e., according to the classical equations of motion, while in the exact theory they evolve according to generalized equations of motion that are derived here. Approximations to the exact equations of motion are then introduced to achieve practical methods that are applicable to complex (i.e., large) molecular systems. Four such methods are proposed in the paper--the full Wigner dynamics (full WD) and the second order WD based on "Wigner trajectories" [H. W. Lee and M. D. Scully, J. Chem. Phys. 77, 4604 (1982)] and the full Donoso-Martens dynamics (full DMD) and the second order DMD based on "Donoso-Martens trajectories" [A. Donoso and C. C. Martens, Phys. Rev. Lett. 8722, 223202 (2001)]--all of which can be viewed as generalizations of the original LSC-IVR method. Numerical tests of the four versions of this new approach are made for two anharmonic model problems, and for each the momentum autocorrelation function (i.e., operators linear in coordinate or momentum operators) and the force autocorrelation function (nonlinear operators) have been calculated. These four new approximate treatments are indeed seen to be significant improvements to the original LSC-IVR approximation.

Generalized Langevin dynamics of a nanoparticle using a finite element approach: Thermostating with correlated noise

NASA Astrophysics Data System (ADS)

Uma, B.; Swaminathan, T. N.; Ayyaswamy, P. S.; Eckmann, D. M.; Radhakrishnan, R.

2011-09-01

A direct numerical simulation (DNS) procedure is employed to study the thermal motion of a nanoparticle in an incompressible Newtonian stationary fluid medium with the generalized Langevin approach. We consider both the Markovian (white noise) and non-Markovian (Ornstein-Uhlenbeck noise and Mittag-Leffler noise) processes. Initial locations of the particle are at various distances from the bounding wall to delineate wall effects. At thermal equilibrium, the numerical results are validated by comparing the calculated translational and rotational temperatures of the particle with those obtained from the equipartition theorem. The nature of the hydrodynamic interactions is verified by comparing the velocity autocorrelation functions and mean square displacements with analytical results. Numerical predictions of wall interactions with the particle in terms of mean square displacements are compared with analytical results. In the non-Markovian Langevin approach, an appropriate choice of colored noise is required to satisfy the power-law decay in the velocity autocorrelation function at long times. The results obtained by using non-Markovian Mittag-Leffler noise simultaneously satisfy the equipartition theorem and the long-time behavior of the hydrodynamic correlations for a range of memory correlation times. The Ornstein-Uhlenbeck process does not provide the appropriate hydrodynamic correlations. Comparing our DNS results to the solution of an one-dimensional generalized Langevin equation, it is observed that where the thermostat adheres to the equipartition theorem, the characteristic memory time in the noise is consistent with the inherent time scale of the memory kernel. The performance of the thermostat with respect to equilibrium and dynamic properties for various noise schemes is discussed.

Autocorrelation structure of convective rainfall in semiarid-arid climate derived from high-resolution X-Band radar estimates

NASA Astrophysics Data System (ADS)

Marra, Francesco; Morin, Efrat

2018-02-01

Small scale rainfall variability is a key factor driving runoff response in fast responding systems, such as mountainous, urban and arid catchments. In this paper, the spatial-temporal autocorrelation structure of convective rainfall is derived with extremely high resolutions (60 m, 1 min) using estimates from an X-Band weather radar recently installed in a semiarid-arid area. The 2-dimensional spatial autocorrelation of convective rainfall fields and the temporal autocorrelation of point-wise and distributed rainfall fields are examined. The autocorrelation structures are characterized by spatial anisotropy, correlation distances 1.5-2.8 km and rarely exceeding 5 km, and time-correlation distances 1.8-6.4 min and rarely exceeding 10 min. The observed spatial variability is expected to negatively affect estimates from rain gauges and microwave links rather than satellite and C-/S-Band radars; conversely, the temporal variability is expected to negatively affect remote sensing estimates rather than rain gauges. The presented results provide quantitative information for stochastic weather generators, cloud-resolving models, dryland hydrologic and agricultural models, and multi-sensor merging techniques.

Electron Beam Transport in the Ionosphere - Energy Deposition and Optical Emissions Based upon the Combined Effects of Plasma Turbulence and Particle-Particle Interactions.

DTIC Science & Technology

1982-02-01

function of both E, and an auto- correlation time :. We choose to replace E, by an expression containing v, the velocity spread of the beam...f’K or eEL ArGC - ’ (5) where E,_ is now the perpendicular component of the turbulent E field and , is the time int-erval for a coherent interaction...the auto-correlation time ). Equation (5) is the basis for our random walk model for wave particle interactions. It can also be derived using the tX

feets: feATURE eXTRACTOR for tIME sERIES

NASA Astrophysics Data System (ADS)

Cabral, Juan; Sanchez, Bruno; Ramos, Felipe; Gurovich, Sebastián; Granitto, Pablo; VanderPlas, Jake

2018-06-01

feets characterizes and analyzes light-curves from astronomical photometric databases for modelling, classification, data cleaning, outlier detection and data analysis. It uses machine learning algorithms to determine the numerical descriptors that characterize and distinguish the different variability classes of light-curves; these range from basic statistical measures such as the mean or standard deviation to complex time-series characteristics such as the autocorrelation function. The library is not restricted to the astronomical field and could also be applied to any kind of time series. This project is a derivative work of FATS (ascl:1711.017).

Interferometric Near-Infrared Spectroscopy (iNIRS) for determination of optical and dynamical properties of turbid media

PubMed Central

Borycki, Dawid; Kholiqov, Oybek; Chong, Shau Poh; Srinivasan, Vivek J.

2016-01-01

We introduce and implement interferometric near-infrared spectroscopy (iNIRS), which simultaneously extracts optical and dynamical properties of turbid media through analysis of a spectral interference fringe pattern. The spectral interference fringe pattern is measured using a Mach-Zehnder interferometer with a frequency-swept narrow linewidth laser. Fourier analysis of the detected signal is used to determine time-of-flight (TOF)-resolved intensity, which is then analyzed over time to yield TOF-resolved intensity autocorrelations. This approach enables quantification of optical properties, which is not possible in conventional, continuous-wave near-infrared spectroscopy (NIRS). Furthermore, iNIRS quantifies scatterer motion based on TOF-resolved autocorrelations, which is a feature inaccessible by well-established diffuse correlation spectroscopy (DCS) techniques. We prove this by determining TOF-resolved intensity and temporal autocorrelations for light transmitted through diffusive fluid phantoms with optical thicknesses of up to 55 reduced mean free paths (approximately 120 scattering events). The TOF-resolved intensity is used to determine optical properties with time-resolved diffusion theory, while the TOF-resolved intensity autocorrelations are used to determine dynamics with diffusing wave spectroscopy. iNIRS advances the capabilities of diffuse optical methods and is suitable for in vivo tissue characterization. Moreover, iNIRS combines NIRS and DCS capabilities into a single modality. PMID:26832264

Virtual sensor models for real-time applications

NASA Astrophysics Data System (ADS)

Hirsenkorn, Nils; Hanke, Timo; Rauch, Andreas; Dehlink, Bernhard; Rasshofer, Ralph; Biebl, Erwin

2016-09-01

Increased complexity and severity of future driver assistance systems demand extensive testing and validation. As supplement to road tests, driving simulations offer various benefits. For driver assistance functions the perception of the sensors is crucial. Therefore, sensors also have to be modeled. In this contribution, a statistical data-driven sensor-model, is described. The state-space based method is capable of modeling various types behavior. In this contribution, the modeling of the position estimation of an automotive radar system, including autocorrelations, is presented. For rendering real-time capability, an efficient implementation is presented.

Diffusion in Deterministic Interacting Lattice Systems

NASA Astrophysics Data System (ADS)

Medenjak, Marko; Klobas, Katja; Prosen, Tomaž

2017-09-01

We study reversible deterministic dynamics of classical charged particles on a lattice with hard-core interaction. It is rigorously shown that the system exhibits three types of transport phenomena, ranging from ballistic, through diffusive to insulating. By obtaining an exact expressions for the current time-autocorrelation function we are able to calculate the linear response transport coefficients, such as the diffusion constant and the Drude weight. Additionally, we calculate the long-time charge profile after an inhomogeneous quench and obtain diffusive profilewith the Green-Kubo diffusion constant. Exact analytical results are corroborated by Monte Carlo simulations.

Non-isotropic noise correlation in PET data reconstructed by FBP but not by OSEM demonstrated using auto-correlation function.

PubMed

Razifar, Pasha; Lubberink, Mark; Schneider, Harald; Långström, Bengt; Bengtsson, Ewert; Bergström, Mats

2005-05-13

BACKGROUND: Positron emission tomography (PET) is a powerful imaging technique with the potential of obtaining functional or biochemical information by measuring distribution and kinetics of radiolabelled molecules in a biological system, both in vitro and in vivo. PET images can be used directly or after kinetic modelling to extract quantitative values of a desired physiological, biochemical or pharmacological entity. Because such images are generally noisy, it is essential to understand how noise affects the derived quantitative values. A pre-requisite for this understanding is that the properties of noise such as variance (magnitude) and texture (correlation) are known. METHODS: In this paper we explored the pattern of noise correlation in experimentally generated PET images, with emphasis on the angular dependence of correlation, using the autocorrelation function (ACF). Experimental PET data were acquired in 2D and 3D acquisition mode and reconstructed by analytical filtered back projection (FBP) and iterative ordered subsets expectation maximisation (OSEM) methods. The 3D data was rebinned to a 2D dataset using FOurier REbinning (FORE) followed by 2D reconstruction using either FBP or OSEM. In synthetic images we compared the ACF results with those from covariance matrix. The results were illustrated as 1D profiles and also visualized as 2D ACF images. RESULTS: We found that the autocorrelation images from PET data obtained after FBP were not fully rotationally symmetric or isotropic if the object deviated from a uniform cylindrical radioactivity distribution. In contrast, similar autocorrelation images obtained after OSEM reconstruction were isotropic even when the phantom was not circular. Simulations indicated that the noise autocorrelation is non-isotropic in images created by FBP when the level of noise in projections is angularly variable. Comparison between 1D cross profiles on autocorrelation images obtained by FBP reconstruction and covariance matrices produced almost identical results in a simulation study. CONCLUSION: With asymmetric radioactivity distribution in PET, reconstruction using FBP, in contrast to OSEM, generates images in which the noise correlation is non-isotropic when the noise magnitude is angular dependent, such as in objects with asymmetric radioactivity distribution. In this respect, iterative reconstruction is superior since it creates isotropic noise correlations in the images.

The Use of Time Series Analysis and t Tests with Serially Correlated Data Tests.

ERIC Educational Resources Information Center

Nicolich, Mark J.; Weinstein, Carol S.

1981-01-01

Results of three methods of analysis applied to simulated autocorrelated data sets with an intervention point (varying in autocorrelation degree, variance of error term, and magnitude of intervention effect) are compared and presented. The three methods are: t tests; maximum likelihood Box-Jenkins (ARIMA); and Bayesian Box Jenkins. (Author/AEF)

Molecular dynamics test of the Brownian description of Na(+) motion in water

NASA Technical Reports Server (NTRS)

Wilson, M. A.; Pohorille, A.; Pratt, L. R.

1985-01-01

The present paper provides the results of molecular dynamics calculations on a Na(+) ion in aqueous solution. Attention is given to the sodium-oxygen and sodium-hydrogen radial distribution functions, the velocity autocorrelation function for the Na(+) ion, the autocorrelation function of the force on the stationary ion, and the accuracy of Brownian motion assumptions which are basic to hydrodynamic models of ion dyanmics in solution. It is pointed out that the presented calculations provide accurate data for testing theories of ion dynamics in solution. The conducted tests show that it is feasible to calculate Brownian friction constants for ions in aqueous solutions. It is found that for Na(+) under the considered conditions the Brownian mobility is in error by only 60 percent.

Polarization-correlation study of biotissue multifractal structure

NASA Astrophysics Data System (ADS)

Olar, O. I.; Ushenko, A. G.

2003-09-01

This paper presents the results of polarization-correlation study of multifractal collagen structure of physiologically normal and pathologically changed tissues of women"s reproductive sphere and skin. The technique of polarization selection of coherent images of biotissues with further determination of their autocorrelation functions and spectral densities is suggested. The correlation-optical criteria of early diagnostics of appearance of pathological changes in the cases of myometry (forming the germ of fibromyoma) and skin (psoriasis) are determined. This study is directed to investigate the possibilities of recognition of pathological changes of biotissue morphological structure by determining the polarization-dependent autocorrelation functions (ACF) and corresponding spectral densities of tissue coherent images.

Polarization-correlation investigation of biotissue multifractal structure and diagnostics of its pathological change

NASA Astrophysics Data System (ADS)

Angelsky, Oleg V.; Pishak, Vasyl P.; Ushenko, Alexander G.; Burkovets, Dimitry N.; Pishak, Olga V.

2001-05-01

The paper presents the results of polarization-correlation investigation of multifractal collagen structure of physiologically normal and pathologically changed tissues of women's reproductive sphere and of skin. The technique of polarization selection of coherent biotissues' images followed by determination of their autocorrelation functions and spectral densities is suggested. The correlation- optical criteria of early diagnostics of pathological changes' appearance of myometry (forming of the germ of fibromyoma) and of skin (psoriasis) are determined. The present paper examines the possibilities of diagnostics of pathological changes of biotissues' morphological structure by means of determining the polarizationally filtered autocorrelation functions (ACF) and corresponding spectral densities of their coherent images.

Increasing market efficiency in the stock markets

NASA Astrophysics Data System (ADS)

Yang, Jae-Suk; Kwak, Wooseop; Kaizoji, Taisei; Kim, In-Mook

2008-01-01

We study the temporal evolutions of three stock markets; Standard and Poor's 500 index, Nikkei 225 Stock Average, and the Korea Composite Stock Price Index. We observe that the probability density function of the log-return has a fat tail but the tail index has been increasing continuously in recent years. We have also found that the variance of the autocorrelation function, the scaling exponent of the standard deviation, and the statistical complexity decrease, but that the entropy density increases as time goes over time. We introduce a modified microscopic spin model and simulate the model to confirm such increasing and decreasing tendencies in statistical quantities. These findings indicate that these three stock markets are becoming more efficient.

Assessing the significance of global and local correlations under spatial autocorrelation: a nonparametric approach.

PubMed

Viladomat, Júlia; Mazumder, Rahul; McInturff, Alex; McCauley, Douglas J; Hastie, Trevor

2014-06-01

We propose a method to test the correlation of two random fields when they are both spatially autocorrelated. In this scenario, the assumption of independence for the pair of observations in the standard test does not hold, and as a result we reject in many cases where there is no effect (the precision of the null distribution is overestimated). Our method recovers the null distribution taking into account the autocorrelation. It uses Monte-Carlo methods, and focuses on permuting, and then smoothing and scaling one of the variables to destroy the correlation with the other, while maintaining at the same time the initial autocorrelation. With this simulation model, any test based on the independence of two (or more) random fields can be constructed. This research was motivated by a project in biodiversity and conservation in the Biology Department at Stanford University. © 2014, The International Biometric Society.

Understanding the determinants of volatility clustering in terms of stationary Markovian processes

NASA Astrophysics Data System (ADS)

Miccichè, S.

2016-11-01

Volatility is a key variable in the modeling of financial markets. The most striking feature of volatility is that it is a long-range correlated stochastic variable, i.e. its autocorrelation function decays like a power-law τ-β for large time lags. In the present work we investigate the determinants of such feature, starting from the empirical observation that the exponent β of a certain stock's volatility is a linear function of the average correlation of such stock's volatility with all other volatilities. We propose a simple approach consisting in diagonalizing the cross-correlation matrix of volatilities and investigating whether or not the diagonalized volatilities still keep some of the original volatility stylized facts. As a result, the diagonalized volatilities result to share with the original volatilities either the power-law decay of the probability density function and the power-law decay of the autocorrelation function. This would indicate that volatility clustering is already present in the diagonalized un-correlated volatilities. We therefore present a parsimonious univariate model based on a non-linear Langevin equation that well reproduces these two stylized facts of volatility. The model helps us in understanding that the main source of volatility clustering, once volatilities have been diagonalized, is that the economic forces driving volatility can be modeled in terms of a Smoluchowski potential with logarithmic tails.

[Characteristics of temporal-spatial differentiation in landscape pattern vulnerability in Nansihu Lake wetland, China.

PubMed

Liang, Jia Xin; Li, Xin Ju

2018-02-01

With remote sensing images from 1985, 2000 Lantsat 5 TM and 2015 Lantsat 8 OLI as data sources, we tried to select the suitable research scale and examine the temporal-spatial diffe-rentiation with such scale in the Nansihu Lake wetland by using landscape pattern vulnerability index constructed by sensitivity index and adaptability index, and combined with space statistics such as semivariogram and spatial autocorrelation. The results showed that 1 km × 1 km equidistant grid was the suitable research scale, which could eliminate the influence of spatial heterogeneity induced by random factors. From 1985 to 2015, the landscape pattern vulnerability in the Nansihu Lake wetland deteriorated gradually. The high-risk area of landscape pattern vulnerability dramatically expanded with time. The spatial heterogeneity of landscape pattern vulnerability increased, and the influence of non-structural factors on landscape pattern vulnerability strengthened. Spatial variability affected by spatial autocorrelation slightly weakened. Landscape pattern vulnerability had strong general spatial positive correlation, with the significant form of spatial agglomeration. The positive spatial autocorrelation continued to increase and the phenomenon of spatial concentration was more and more obvious over time. The local autocorrelation mainly based on high-high accumulation zone and low-low accumulation zone had stronger spatial autocorrelation among neighboring space units. The high-high accumulation areas showed the strongest level of significance, and the significant level of low-low accumulation zone increased with time. Natural factors, such as temperature and precipitation, affected water-level and landscape distribution, and thus changed the landscape patterns vulnerability of Nansihu Lake wetland. The dominant driver for the deterioration of landscape patterns vulnerability was human activities, including social economy activity and policy system.

Program for the analysis of time series. [by means of fast Fourier transform algorithm

NASA Technical Reports Server (NTRS)

Brown, T. J.; Brown, C. G.; Hardin, J. C.

1974-01-01

A digital computer program for the Fourier analysis of discrete time data is described. The program was designed to handle multiple channels of digitized data on general purpose computer systems. It is written, primarily, in a version of FORTRAN 2 currently in use on CDC 6000 series computers. Some small portions are written in CDC COMPASS, an assembler level code. However, functional descriptions of these portions are provided so that the program may be adapted for use on any facility possessing a FORTRAN compiler and random-access capability. Properly formatted digital data are windowed and analyzed by means of a fast Fourier transform algorithm to generate the following functions: (1) auto and/or cross power spectra, (2) autocorrelations and/or cross correlations, (3) Fourier coefficients, (4) coherence functions, (5) transfer functions, and (6) histograms.

Image correlation based method for the analysis of collagen fibers patterns

NASA Astrophysics Data System (ADS)

Rosa, Ramon G. T.; Pratavieira, Sebastião.; Kurachi, Cristina

2015-06-01

The collagen fibers are one of the most important structural proteins in skin, being responsible for its strength and flexibility. It is known that their properties, like fibers density, ordination and mean diameter can be affected by several skin conditions, what makes these properties a good parameter to be used on the diagnosis and evaluation of skin aging, cancer, healing, among other conditions. There is, however, a need for methods capable of analyzing quantitatively the organization patterns of these fibers. To address this need, we developed a method based on the autocorrelation function of the images that allows the construction of vector field plots of the fibers directions and does not require any kind of curve fitting or optimization. The analyzed images were obtained through Second Harmonic Generation Imaging Microscopy. This paper presents a concise review on the autocorrelation function and some of its applications to image processing, details the developed method and the results obtained through the analysis of hystopathological slides of landrace porcine skin. The method has high accuracy on the determination of the fibers direction and presents high performance. We look forward to perform further studies keeping track of different skin conditions over time.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shumway, R.H.; McQuarrie, A.D.

Robust statistical approaches to the problem of discriminating between regional earthquakes and explosions are developed. We compare linear discriminant analysis using descriptive features like amplitude and spectral ratios with signal discrimination techniques using the original signal waveforms and spectral approximations to the log likelihood function. Robust information theoretic techniques are proposed and all methods are applied to 8 earthquakes and 8 mining explosions in Scandinavia and to an event from Novaya Zemlya of unknown origin. It is noted that signal discrimination approaches based on discrimination information and Renyi entropy perform better in the test sample than conventional methods based onmore » spectral ratios involving the P and S phases. Two techniques for identifying the ripple-firing pattern for typical mining explosions are proposed and shown to work well on simulated data and on several Scandinavian earthquakes and explosions. We use both cepstral analysis in the frequency domain and a time domain method based on the autocorrelation and partial autocorrelation functions. The proposed approach strips off underlying smooth spectral and seasonal spectral components corresponding to the echo pattern induced by two simple ripple-fired models. For two mining explosions, a pattern is identified whereas for two earthquakes, no pattern is evident.« less

Measurement of Regional Environmental Noise by Use of a Pc-Based System. A Application to the Noise Near Airport ``G. Marconi'' in Bologna

NASA Astrophysics Data System (ADS)

Sakai, H.; Sato, S.; Prodi, N.; Pompoli, R.

2001-03-01

Measurements of aircraft noise were made at the airport "G. Marconi" in Bologna by using a measurement system for regional environmental noise. The system is based on the model of the human auditory-brain system, which is based on the interplay of autocorrelators and an interaural cross-correlator acting on the pressure signals arriving at the ear entrances, and takes into account the specialization of left and right human cerebral hemispheres (see reference [8]). Measurements were taken through dual microphones at ear entrances of a dummy head. The aircraft noise was characterized with the following physical factors calculated from the autocorrelation function (ACF) and interaural cross-correlation function (IACF) for binaural signals. From the ACF analysis, (1) energy represented at the origin of delay,Φ (0), (2) effective duration of the envelope of the normalized ACF, τe, (3) the delay time of the first peak, τ1, and (4) its amplitude, φ1were extracted. From the IACF analysis, (5) IACC, (6) interaural delay time at which the IACC is defined, τIACC, and (7) width of the IACF at the τIACC, WIACCwere extracted. The factorΦ (0) can be represented as the geometrical mean of the energies at both ears. A noise source may be identified by these factors as timbre.

Auto-correlation in the motor/imaginary human EEG signals: A vision about the FDFA fluctuations.

PubMed

Zebende, Gilney Figueira; Oliveira Filho, Florêncio Mendes; Leyva Cruz, Juan Alberto

2017-01-01

In this paper we analyzed, by the FDFA root mean square fluctuation (rms) function, the motor/imaginary human activity produced by a 64-channel electroencephalography (EEG). We utilized the Physionet on-line databank, a publicly available database of human EEG signals, as a standardized reference database for this study. Herein, we report the use of detrended fluctuation analysis (DFA) method for EEG analysis. We show that the complex time series of the EEG exhibits characteristic fluctuations depending on the analyzed channel in the scalp-recorded EEG. In order to demonstrate the effectiveness of the proposed technique, we analyzed four distinct channels represented here by F332, F637 (frontal region of the head) and P349, P654 (parietal region of the head). We verified that the amplitude of the FDFA rms function is greater for the frontal channels than for the parietal. To tabulate this information in a better way, we define and calculate the difference between FDFA (in log scale) for the channels, thus defining a new path for analysis of EEG signals. Finally, related to the studied EEG signals, we obtain the auto-correlation exponent, αDFA by DFA method, that reveals self-affinity at specific time scale. Our results shows that this strategy can be applied to study the human brain activity in EEG processing.

Frequency-feature based antistrong-disturbance signal processing method and system for vortex flowmeter with single sensor

NASA Astrophysics Data System (ADS)

Xu, Ke-Jun; Luo, Qing-Lin; Wang, Gang; Liu, San-Shan; Kang, Yi-Bo

2010-07-01

Digital signal processing methods have been applied to vortex flowmeter for extracting the useful information from noisy output of the vortex flow sensor. But these approaches are unavailable when the power of the mechanical vibration noise is larger than that of the vortex flow signal. In order to solve this problem, an antistrong-disturbance signal processing method is proposed based on frequency features of the vortex flow signal and mechanical vibration noise for the vortex flowmeter with single sensor. The frequency bandwidth of the vortex flow signal is different from that of the mechanical vibration noise. The autocorrelation function can represent bandwidth features of the signal and noise. The output of the vortex flow sensor is processed by the spectrum analysis, filtered by bandpass filters, and calculated by autocorrelation function at the fixed delaying time and at τ =0 to obtain ratios. The frequency corresponding to the minimal ratio is regarded as the vortex flow frequency. With an ultralow-power microcontroller, a digital signal processing system is developed to implement the antistrong-disturbance algorithm, and at the same time to ensure low-power and two-wire mode for meeting the requirement of process instrumentation. The water flow-rate calibration and vibration test experiments are conducted, and the experimental results show that both the algorithm and system are effective.

Frequency-feature based antistrong-disturbance signal processing method and system for vortex flowmeter with single sensor.

PubMed

Xu, Ke-Jun; Luo, Qing-Lin; Wang, Gang; Liu, San-Shan; Kang, Yi-Bo

2010-07-01

Digital signal processing methods have been applied to vortex flowmeter for extracting the useful information from noisy output of the vortex flow sensor. But these approaches are unavailable when the power of the mechanical vibration noise is larger than that of the vortex flow signal. In order to solve this problem, an antistrong-disturbance signal processing method is proposed based on frequency features of the vortex flow signal and mechanical vibration noise for the vortex flowmeter with single sensor. The frequency bandwidth of the vortex flow signal is different from that of the mechanical vibration noise. The autocorrelation function can represent bandwidth features of the signal and noise. The output of the vortex flow sensor is processed by the spectrum analysis, filtered by bandpass filters, and calculated by autocorrelation function at the fixed delaying time and at tau=0 to obtain ratios. The frequency corresponding to the minimal ratio is regarded as the vortex flow frequency. With an ultralow-power microcontroller, a digital signal processing system is developed to implement the antistrong-disturbance algorithm, and at the same time to ensure low-power and two-wire mode for meeting the requirement of process instrumentation. The water flow-rate calibration and vibration test experiments are conducted, and the experimental results show that both the algorithm and system are effective.

An improved portmanteau test for autocorrelated errors in interrupted time-series regression models.

PubMed

Huitema, Bradley E; McKean, Joseph W

2007-08-01

A new portmanteau test for autocorrelation among the errors of interrupted time-series regression models is proposed. Simulation results demonstrate that the inferential properties of the proposed Q(H-M) test statistic are considerably more satisfactory than those of the well known Ljung-Box test and moderately better than those of the Box-Pierce test. These conclusions generally hold for a wide variety of autoregressive (AR), moving averages (MA), and ARMA error processes that are associated with time-series regression models of the form described in Huitema and McKean (2000a, 2000b).

High Frequency Sampling of TTL Pulses on a Raspberry Pi for Diffuse Correlation Spectroscopy Applications.

PubMed

Tivnan, Matthew; Gurjar, Rajan; Wolf, David E; Vishwanath, Karthik

2015-08-12

Diffuse Correlation Spectroscopy (DCS) is a well-established optical technique that has been used for non-invasive measurement of blood flow in tissues. Instrumentation for DCS includes a correlation device that computes the temporal intensity autocorrelation of a coherent laser source after it has undergone diffuse scattering through a turbid medium. Typically, the signal acquisition and its autocorrelation are performed by a correlation board. These boards have dedicated hardware to acquire and compute intensity autocorrelations of rapidly varying input signal and usually are quite expensive. Here we show that a Raspberry Pi minicomputer can acquire and store a rapidly varying time-signal with high fidelity. We show that this signal collected by a Raspberry Pi device can be processed numerically to yield intensity autocorrelations well suited for DCS applications. DCS measurements made using the Raspberry Pi device were compared to those acquired using a commercial hardware autocorrelation board to investigate the stability, performance, and accuracy of the data acquired in controlled experiments. This paper represents a first step toward lowering the instrumentation cost of a DCS system and may offer the potential to make DCS become more widely used in biomedical applications.

High Frequency Sampling of TTL Pulses on a Raspberry Pi for Diffuse Correlation Spectroscopy Applications

PubMed Central

Tivnan, Matthew; Gurjar, Rajan; Wolf, David E.; Vishwanath, Karthik

2015-01-01

Diffuse Correlation Spectroscopy (DCS) is a well-established optical technique that has been used for non-invasive measurement of blood flow in tissues. Instrumentation for DCS includes a correlation device that computes the temporal intensity autocorrelation of a coherent laser source after it has undergone diffuse scattering through a turbid medium. Typically, the signal acquisition and its autocorrelation are performed by a correlation board. These boards have dedicated hardware to acquire and compute intensity autocorrelations of rapidly varying input signal and usually are quite expensive. Here we show that a Raspberry Pi minicomputer can acquire and store a rapidly varying time-signal with high fidelity. We show that this signal collected by a Raspberry Pi device can be processed numerically to yield intensity autocorrelations well suited for DCS applications. DCS measurements made using the Raspberry Pi device were compared to those acquired using a commercial hardware autocorrelation board to investigate the stability, performance, and accuracy of the data acquired in controlled experiments. This paper represents a first step toward lowering the instrumentation cost of a DCS system and may offer the potential to make DCS become more widely used in biomedical applications. PMID:26274961

The time-frequency method of signal analysis in internal combustion engine diagnostics

NASA Astrophysics Data System (ADS)

Avramchuk, V. S.; Kazmin, V. P.; Faerman, V. A.; Le, V. T.

2017-01-01

The paper presents the results of the study of applicability of time-frequency correlation functions to solving the problems of internal combustion engine fault diagnostics. The proposed methods are theoretically justified and experimentally tested. In particular, the method’s applicability is illustrated by the example of specially generated signals that simulate the vibration of an engine both during the normal operation and in the case of a malfunction in the system supplying fuel to the cylinders. This method was confirmed during an experiment with an automobile internal combustion engine. The study offers the main findings of the simulation and the experiment and highlights certain characteristic features of time-frequency autocorrelation functions that allow one to identify malfunctions in an engine’s cylinder. The possibility in principle of using time-frequency correlation functions in function testing of the internal combustion engine is demonstrated. The paper’s conclusion proposes further research directions including the application of the method to diagnosing automobile gearboxes.

A longitudinal model for functional connectivity networks using resting-state fMRI.

PubMed

Hart, Brian; Cribben, Ivor; Fiecas, Mark

2018-06-04

Many neuroimaging studies collect functional magnetic resonance imaging (fMRI) data in a longitudinal manner. However, the current fMRI literature lacks a general framework for analyzing functional connectivity (FC) networks in fMRI data obtained from a longitudinal study. In this work, we build a novel longitudinal FC model using a variance components approach. First, for all subjects' visits, we account for the autocorrelation inherent in the fMRI time series data using a non-parametric technique. Second, we use a generalized least squares approach to estimate 1) the within-subject variance component shared across the population, 2) the baseline FC strength, and 3) the FC's longitudinal trend. Our novel method for longitudinal FC networks seeks to account for the within-subject dependence across multiple visits, the variability due to the subjects being sampled from a population, and the autocorrelation present in fMRI time series data, while restricting the number of parameters in order to make the method computationally feasible and stable. We develop a permutation testing procedure to draw valid inference on group differences in the baseline FC network and change in FC over longitudinal time between a set of patients and a comparable set of controls. To examine performance, we run a series of simulations and apply the model to longitudinal fMRI data collected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Overall, we found no difference in the global FC network between Alzheimer's disease patients and healthy controls, but did find differing local aging patterns in the FC between the left hippocampus and the posterior cingulate cortex. Copyright © 2018 Elsevier Inc. All rights reserved.

Non-equilibrium dynamics from RPMD and CMD.

PubMed

Welsch, Ralph; Song, Kai; Shi, Qiang; Althorpe, Stuart C; Miller, Thomas F

2016-11-28

We investigate the calculation of approximate non-equilibrium quantum time correlation functions (TCFs) using two popular path-integral-based molecular dynamics methods, ring-polymer molecular dynamics (RPMD) and centroid molecular dynamics (CMD). It is shown that for the cases of a sudden vertical excitation and an initial momentum impulse, both RPMD and CMD yield non-equilibrium TCFs for linear operators that are exact for high temperatures, in the t = 0 limit, and for harmonic potentials; the subset of these conditions that are preserved for non-equilibrium TCFs of non-linear operators is also discussed. Furthermore, it is shown that for these non-equilibrium initial conditions, both methods retain the connection to Matsubara dynamics that has previously been established for equilibrium initial conditions. Comparison of non-equilibrium TCFs from RPMD and CMD to Matsubara dynamics at short times reveals the orders in time to which the methods agree. Specifically, for the position-autocorrelation function associated with sudden vertical excitation, RPMD and CMD agree with Matsubara dynamics up to O(t 4 ) and O(t 1 ), respectively; for the position-autocorrelation function associated with an initial momentum impulse, RPMD and CMD agree with Matsubara dynamics up to O(t 5 ) and O(t 2 ), respectively. Numerical tests using model potentials for a wide range of non-equilibrium initial conditions show that RPMD and CMD yield non-equilibrium TCFs with an accuracy that is comparable to that for equilibrium TCFs. RPMD is also used to investigate excited-state proton transfer in a system-bath model, and it is compared to numerically exact calculations performed using a recently developed version of the Liouville space hierarchical equation of motion approach; again, similar accuracy is observed for non-equilibrium and equilibrium initial conditions.

High-Responsivity Graphene-Boron Nitride Photodetector and Autocorrelator in a Silicon Photonic Integrated Circuit.

PubMed

Shiue, Ren-Jye; Gao, Yuanda; Wang, Yifei; Peng, Cheng; Robertson, Alexander D; Efetov, Dmitri K; Assefa, Solomon; Koppens, Frank H L; Hone, James; Englund, Dirk

2015-11-11

Graphene and other two-dimensional (2D) materials have emerged as promising materials for broadband and ultrafast photodetection and optical modulation. These optoelectronic capabilities can augment complementary metal-oxide-semiconductor (CMOS) devices for high-speed and low-power optical interconnects. Here, we demonstrate an on-chip ultrafast photodetector based on a two-dimensional heterostructure consisting of high-quality graphene encapsulated in hexagonal boron nitride. Coupled to the optical mode of a silicon waveguide, this 2D heterostructure-based photodetector exhibits a maximum responsivity of 0.36 A/W and high-speed operation with a 3 dB cutoff at 42 GHz. From photocurrent measurements as a function of the top-gate and source-drain voltages, we conclude that the photoresponse is consistent with hot electron mediated effects. At moderate peak powers above 50 mW, we observe a saturating photocurrent consistent with the mechanisms of electron-phonon supercollision cooling. This nonlinear photoresponse enables optical on-chip autocorrelation measurements with picosecond-scale timing resolution and exceptionally low peak powers.

Autocorrelation analysis for the unbiased determination of power-law exponents in single-quantum-dot blinking.

PubMed

Houel, Julien; Doan, Quang T; Cajgfinger, Thomas; Ledoux, Gilles; Amans, David; Aubret, Antoine; Dominjon, Agnès; Ferriol, Sylvain; Barbier, Rémi; Nasilowski, Michel; Lhuillier, Emmanuel; Dubertret, Benoît; Dujardin, Christophe; Kulzer, Florian

2015-01-27

We present an unbiased and robust analysis method for power-law blinking statistics in the photoluminescence of single nanoemitters, allowing us to extract both the bright- and dark-state power-law exponents from the emitters' intensity autocorrelation functions. As opposed to the widely used threshold method, our technique therefore does not require discriminating the emission levels of bright and dark states in the experimental intensity timetraces. We rely on the simultaneous recording of 450 emission timetraces of single CdSe/CdS core/shell quantum dots at a frame rate of 250 Hz with single photon sensitivity. Under these conditions, our approach can determine ON and OFF power-law exponents with a precision of 3% from a comparison to numerical simulations, even for shot-noise-dominated emission signals with an average intensity below 1 photon per frame and per quantum dot. These capabilities pave the way for the unbiased, threshold-free determination of blinking power-law exponents at the microsecond time scale.

Errors in radial velocity variance from Doppler wind lidar

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, H.; Barthelmie, R. J.; Doubrawa, P.

A high-fidelity lidar turbulence measurement technique relies on accurate estimates of radial velocity variance that are subject to both systematic and random errors determined by the autocorrelation function of radial velocity, the sampling rate, and the sampling duration. Our paper quantifies the effect of the volumetric averaging in lidar radial velocity measurements on the autocorrelation function and the dependence of the systematic and random errors on the sampling duration, using both statistically simulated and observed data. For current-generation scanning lidars and sampling durations of about 30 min and longer, during which the stationarity assumption is valid for atmospheric flows, themore » systematic error is negligible but the random error exceeds about 10%.« less

Pathogenic changes of dispersion and contrast of coherent images of biotissues

NASA Astrophysics Data System (ADS)

Pishak, Olga V.

2002-02-01

The paper presents the results of polarization-correlation investigation of multifractal collagen structure of physiologically normal and pathologically changed tissues of women's reproductive sphere and of skin. The technique of polarization selection of coherent biotissues' images with the following determination of their autocorrelation functions and spectral densities is suggested. The correlation-optical criteria of early diagnostics of pathological changes' appearance of myometry (forming of the germ of fibromyoma) and of skin(psoriasis) are determined. The suggested paper is directed to investigation of the possibilities of pathological changes of biotissues' morphological structure by means of determining the polarizationally filtered autocorrelation functions (ACF) and corresponding spectral densities of their coherent images.

Errors in radial velocity variance from Doppler wind lidar

DOE PAGES

Wang, H.; Barthelmie, R. J.; Doubrawa, P.; ...

2016-08-29

A high-fidelity lidar turbulence measurement technique relies on accurate estimates of radial velocity variance that are subject to both systematic and random errors determined by the autocorrelation function of radial velocity, the sampling rate, and the sampling duration. Our paper quantifies the effect of the volumetric averaging in lidar radial velocity measurements on the autocorrelation function and the dependence of the systematic and random errors on the sampling duration, using both statistically simulated and observed data. For current-generation scanning lidars and sampling durations of about 30 min and longer, during which the stationarity assumption is valid for atmospheric flows, themore » systematic error is negligible but the random error exceeds about 10%.« less

Scilab software package for the study of dynamical systems

NASA Astrophysics Data System (ADS)

Bordeianu, C. C.; Beşliu, C.; Jipa, Al.; Felea, D.; Grossu, I. V.

2008-05-01

This work presents a new software package for the study of chaotic flows and maps. The codes were written using Scilab, a software package for numerical computations providing a powerful open computing environment for engineering and scientific applications. It was found that Scilab provides various functions for ordinary differential equation solving, Fast Fourier Transform, autocorrelation, and excellent 2D and 3D graphical capabilities. The chaotic behaviors of the nonlinear dynamics systems were analyzed using phase-space maps, autocorrelation functions, power spectra, Lyapunov exponents and Kolmogorov-Sinai entropy. Various well known examples are implemented, with the capability of the users inserting their own ODE. Program summaryProgram title: Chaos Catalogue identifier: AEAP_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEAP_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 885 No. of bytes in distributed program, including test data, etc.: 5925 Distribution format: tar.gz Programming language: Scilab 3.1.1 Computer: PC-compatible running Scilab on MS Windows or Linux Operating system: Windows XP, Linux RAM: below 100 Megabytes Classification: 6.2 Nature of problem: Any physical model containing linear or nonlinear ordinary differential equations (ODE). Solution method: Numerical solving of ordinary differential equations. The chaotic behavior of the nonlinear dynamical system is analyzed using Poincaré sections, phase-space maps, autocorrelation functions, power spectra, Lyapunov exponents and Kolmogorov-Sinai entropies. Restrictions: The package routines are normally able to handle ODE systems of high orders (up to order twelve and possibly higher), depending on the nature of the problem. Running time: 10 to 20 seconds for problems that do not involve Lyapunov exponents calculation; 60 to 1000 seconds for problems that involve high orders ODE and Lyapunov exponents calculation.

iQIST v0.7: An open source continuous-time quantum Monte Carlo impurity solver toolkit

NASA Astrophysics Data System (ADS)

Huang, Li

2017-12-01

In this paper, we present a new version of the iQIST software package, which is capable of solving various quantum impurity models by using the hybridization expansion (or strong coupling expansion) continuous-time quantum Monte Carlo algorithm. In the revised version, the software architecture is completely redesigned. New basis (intermediate representation or singular value decomposition representation) for the single-particle and two-particle Green's functions is introduced. A lot of useful physical observables are added, such as the charge susceptibility, fidelity susceptibility, Binder cumulant, and autocorrelation time. Especially, we optimize measurement for the two-particle Green's functions. Both the particle-hole and particle-particle channels are supported. In addition, the block structure of the two-particle Green's functions is exploited to accelerate the calculation. Finally, we fix some known bugs and limitations. The computational efficiency of the code is greatly enhanced.

Structural connectome topology relates to regional BOLD signal dynamics in the mouse brain

NASA Astrophysics Data System (ADS)

Sethi, Sarab S.; Zerbi, Valerio; Wenderoth, Nicole; Fornito, Alex; Fulcher, Ben D.

2017-04-01

Brain dynamics are thought to unfold on a network determined by the pattern of axonal connections linking pairs of neuronal elements; the so-called connectome. Prior work has indicated that structural brain connectivity constrains pairwise correlations of brain dynamics ("functional connectivity"), but it is not known whether inter-regional axonal connectivity is related to the intrinsic dynamics of individual brain areas. Here we investigate this relationship using a weighted, directed mesoscale mouse connectome from the Allen Mouse Brain Connectivity Atlas and resting state functional MRI (rs-fMRI) time-series data measured in 184 brain regions in eighteen anesthetized mice. For each brain region, we measured degree, betweenness, and clustering coefficient from weighted and unweighted, and directed and undirected versions of the connectome. We then characterized the univariate rs-fMRI dynamics in each brain region by computing 6930 time-series properties using the time-series analysis toolbox, hctsa. After correcting for regional volume variations, strong and robust correlations between structural connectivity properties and rs-fMRI dynamics were found only when edge weights were accounted for, and were associated with variations in the autocorrelation properties of the rs-fMRI signal. The strongest relationships were found for weighted in-degree, which was positively correlated to the autocorrelation of fMRI time series at time lag τ = 34 s (partial Spearman correlation ρ = 0.58 ), as well as a range of related measures such as relative high frequency power (f > 0.4 Hz: ρ = - 0.43 ). Our results indicate that the topology of inter-regional axonal connections of the mouse brain is closely related to intrinsic, spontaneous dynamics such that regions with a greater aggregate strength of incoming projections display longer timescales of activity fluctuations.

Coherent light depolarization by multiple scattering media and tissues: some fundamentals and applications

NASA Astrophysics Data System (ADS)

Zimnyakov, Dmitry A.; Tuchin, Valery V.; Yodh, Arjun G.; Mishin, Alexey A.; Peretochkin, Igor S.

1998-04-01

Relationships between decorrelation and depolarization of coherent light scattered by disordered media are examined by using the conception of the photon paths distribution functions. Analysis of behavior of the autocorrelation functions of the scattered field fluctuations and their polarization properties allows us to introduce generalized parameter of scattering media such as specific correlation time. Determination of specific correlation time has been carried out for phantom scattering media (water suspensions of polystyrene spheres). Results of statistical, correlation and polarization analysis of static and dynamic speckle patterns carried out in the experiments with human sclera with artificially controlled optical transmittance are presented. Some possibilities of applications of such polarization- correlation technique for monitoring and visualization of non- single scattering tissue structures are discussed.

Fluctuation correlation models for receptor immobilization

NASA Astrophysics Data System (ADS)

Fourcade, B.

2017-12-01

Nanoscale dynamics with cycles of receptor diffusion and immobilization by cell-external-or-internal factors is a key process in living cell adhesion phenomena at the origin of a plethora of signal transduction pathways. Motivated by modern correlation microscopy approaches, the receptor correlation functions in physical models based on diffusion-influenced reaction is studied. Using analytical and stochastic modeling, this paper focuses on the hybrid regime where diffusion and reaction are not truly separable. The time receptor autocorrelation functions are shown to be indexed by different time scales and their asymptotic expansions are given. Stochastic simulations show that this analysis can be extended to situations with a small number of molecules. It is also demonstrated that this analysis applies when receptor immobilization is coupled to environmental noise.

How cosmic microwave background correlations at large angles relate to mass autocorrelations in space

NASA Technical Reports Server (NTRS)

Blumenthal, George R.; Johnston, Kathryn V.

1994-01-01

The Sachs-Wolfe effect is known to produce large angular scale fluctuations in the cosmic microwave background radiation (CMBR) due to gravitational potential fluctuations. We show how the angular correlation function of the CMBR can be expressed explicitly in terms of the mass autocorrelation function xi(r) in the universe. We derive analytic expressions for the angular correlation function and its multipole moments in terms of integrals over xi(r) or its second moment, J(sub 3)(r), which does not need to satisfy the sort of integral constraint that xi(r) must. We derive similar expressions for bulk flow velocity in terms of xi and J(sub 3). One interesting result that emerges directly from this analysis is that, for all angles theta, there is a substantial contribution to the correlation function from a wide range of distance r and that radial shape of this contribution does not vary greatly with angle.

Density fingering in spatially modulated Hele-Shaw cells

DOE Office of Scientific and Technical Information (OSTI.GOV)

Toth, Tamara; Horvath, Dezso; Toth, Agota

Density fingering of the chlorite-tetrathionate reaction has been studied experimentally in a periodically heterogeneous Hele-Shaw cell where the heterogeneity is introduced in the form of spatial modulation of gap width along the front. Depending on the spatial wavelength, gap width, and chemical composition, three types of cellular structures have been observed. The initial evolution is characterized by dispersion curves, while the long time behavior is described by the change in the autocorrelation function of the front profile and in the mixing length of the patterns.

The Effect of Alcohol on Emotional Inertia: A Test of Alcohol Myopia

PubMed Central

Fairbairn, Catharine E.; Sayette, Michael A.

2017-01-01

Alcohol Myopia (AM) has emerged as one of the most widely-researched theories of alcohol’s effects on emotional experience. Given this theory’s popularity it is notable that a central tenet of AM has not been tested—namely, that alcohol creates a myopic focus on the present moment, limiting the extent to which the present is permeated by emotions derived from prior experience. We aimed to test the impact of alcohol on moment-to-moment fluctuations in affect, applying advances in emotion assessment and statistical analysis to test this aspect of AM without drawing the attention of participants to their own emotional experiences. We measured emotional fluctuations using autocorrelation, a statistic borrowed from time-series analysis measuring the correlation between successive observations in time. High emotion autocorrelation is termed “emotional inertia” and linked to negative mood outcomes. Seven-hundred-twenty social drinkers consumed alcohol, placebo, or control beverages in groups of three over a 36-min group formation task. We indexed affect using the Duchenne smile, recorded continuously during the interaction (34.9 million video frames) according to Paul Ekman’s Facial Action Coding System. Autocorrelation of Duchenne smiling emerged as the most consistent predictor of self-reported mood and social bonding when compared with Duchenne smiling mean, standard deviation, and linear trend. Alcohol reduced affective autocorrelation, and autocorrelation mediated the link between alcohol and self-reported mood and social outcomes. Findings suggest that alcohol enhances our ability to freely enjoy the present moment untethered by past experience and highlight the importance of emotion dynamics in research examining affective correlates of psychopathology. PMID:24016015

Decay of the supersonic turbulent wakes from micro-ramps

NASA Astrophysics Data System (ADS)

Sun, Z.; Schrijer, F. F. J.; Scarano, F.; van Oudheusden, B. W.

2014-02-01

The wakes resulting from micro-ramps immersed in a supersonic turbulent boundary layer at Ma = 2.0 are investigated by means of particle image velocimetry. Two micro-ramps are investigated with height of 60% and 80% of the undisturbed boundary layer, respectively. The measurement domain is placed at the symmetry plane of the ramp and encompasses the range from 10 to 32 ramp heights downstream of the ramp. The decay of the flow field properties is evaluated in terms of time-averaged and root-mean-square (RMS) statistics. In the time-averaged flow field, the recovery from the imparted momentum deficit and the decay of upwash motion are analyzed. The RMS fluctuations of the velocity components exhibit strong anisotropy at the most upstream location and develop into a more isotropic regime downstream. The self-similarity properties of velocity components and fluctuation components along wall-normal direction are followed. The investigation of the unsteady large scale motion is carried out by means of snapshot analysis and by a statistical approach based on the spatial auto-correlation function. The Kelvin-Helmholtz (K-H) instability at the upper shear layer is observed to develop further with the onset of vortex pairing. The average distance between vortices is statistically estimated using the spatial auto-correlation. A marked transition with the wavelength increase is observed across the pairing regime. The K-H instability, initially observed only at the upper shear layer also begins to appear in the lower shear layer as soon as the wake is elevated sufficiently off the wall. The auto-correlation statistics confirm the coherence of counter-rotating vortices from the upper and lower sides, indicating the formation of vortex rings downstream of the pairing region.

Generalizability Assessment of Autocorrelated Direct Observation Data: The Applicability of the Tiao-Tan Method and Alternative.

ERIC Educational Resources Information Center

Suen, Hoi K.; And Others

The applicability is explored of the Bayesian random-effect analysis of variance (ANOVA) model developed by G. C. Tiao and W. Y. Tan (1966) and a method suggested by H. K. Suen and P. S. Lee (1987) for the generalizability analysis of autocorrelated data. According to Tiao and Tan, if time series data could be described as a first-order…

The Error Structure of the SMAP Single and Dual Channel Soil Moisture Retrievals

NASA Astrophysics Data System (ADS)

Dong, Jianzhi; Crow, Wade T.; Bindlish, Rajat

2018-01-01

Knowledge of the temporal error structure for remotely sensed surface soil moisture retrievals can improve our ability to exploit them for hydrologic and climate studies. This study employs a triple collocation analysis to investigate both the total variance and temporal autocorrelation of errors in Soil Moisture Active and Passive (SMAP) products generated from two separate soil moisture retrieval algorithms, the vertically polarized brightness temperature-based single-channel algorithm (SCA-V, the current baseline SMAP algorithm) and the dual-channel algorithm (DCA). A key assumption made in SCA-V is that real-time vegetation opacity can be accurately captured using only a climatology for vegetation opacity. Results demonstrate that while SCA-V generally outperforms DCA, SCA-V can produce larger total errors when this assumption is significantly violated by interannual variability in vegetation health and biomass. Furthermore, larger autocorrelated errors in SCA-V retrievals are found in areas with relatively large vegetation opacity deviations from climatological expectations. This implies that a significant portion of the autocorrelated error in SCA-V is attributable to the violation of its vegetation opacity climatology assumption and suggests that utilizing a real (as opposed to climatological) vegetation opacity time series in the SCA-V algorithm would reduce the magnitude of autocorrelated soil moisture retrieval errors.

Ecosystem functional assessment based on the "optical type" concept and self-similarity patterns: An application using MODIS-NDVI time series autocorrelation

NASA Astrophysics Data System (ADS)

Huesca, Margarita; Merino-de-Miguel, Silvia; Eklundh, Lars; Litago, Javier; Cicuéndez, Victor; Rodríguez-Rastrero, Manuel; Ustin, Susan L.; Palacios-Orueta, Alicia

2015-12-01

Remote sensing (RS) time series are an excellent operative source for information about the land surface across several scales and different levels of landscape heterogeneity. Ustin and Gamon (2010) proposed the new concept of "optical types" (OT), meaning "optically distinguishable functional types", as a way to better understand remote sensing signals related to the actual functional behavior of species that share common physiognomic forms but differ in functionality. Whereas the OT approach seems to be promising and consistent with ecological theory as a way to monitor vegetation derived from RS, it received little implementation. This work presents a method for implementing the OT concept for efficient monitoring of ecosystems based on RS time series. We propose relying on an ecosystem's repetitive pattern in the temporal domain (self-similarity) to assess its dynamics. Based on this approach, our main hypothesis is that distinct dynamics are intrinsic to a specific OT. Self-similarity level in the temporal domain within a broadleaf forest class was quantitatively assessed using the auto-correlation function (ACF), from statistical time series analysis. A vector comparison classification method, spectral angle mapper, and principal component analysis were used to identify general patterns related to forest dynamics. Phenological metrics derived from MODIS NDVI time series using the TIMESAT software, together with information from the National Forest Map were used to explain the different dynamics found. Results showed significant and highly stable self-similarity patterns in OTs that corresponded to forests under non-moisture-limited environments with an adaptation strategy based on a strong phenological synchrony with climate seasonality. These forests are characterized by dense closed canopy deciduous forests associated with high productivity and low biodiversity in terms of dominant species. Forests in transitional areas were associated with patterns of less temporal stability probably due to mixtures of different adaptation strategies (i.e., deciduous, marcescent and evergreen species) and higher functional diversity related to climate variability at long and short terms. A less distinct seasonality and even a double season appear in the OT of the broadleaf Mediterranean forest characterized by an open canopy dominated by evergreen-sclerophyllous formations. Within this forest, understory and overstory dynamics maximize functional diversity resulting in contrasting traits adapted to summer drought, winter frosts, and high precipitation variability.

Burst and inter-burst duration statistics as empirical test of long-range memory in the financial markets

NASA Astrophysics Data System (ADS)

Gontis, V.; Kononovicius, A.

2017-10-01

We address the problem of long-range memory in the financial markets. There are two conceptually different ways to reproduce power-law decay of auto-correlation function: using fractional Brownian motion as well as non-linear stochastic differential equations. In this contribution we address this problem by analyzing empirical return and trading activity time series from the Forex. From the empirical time series we obtain probability density functions of burst and inter-burst duration. Our analysis reveals that the power-law exponents of the obtained probability density functions are close to 3 / 2, which is a characteristic feature of the one-dimensional stochastic processes. This is in a good agreement with earlier proposed model of absolute return based on the non-linear stochastic differential equations derived from the agent-based herding model.

Generalized Green's function molecular dynamics for canonical ensemble simulations

NASA Astrophysics Data System (ADS)

Coluci, V. R.; Dantas, S. O.; Tewary, V. K.

2018-05-01

The need of small integration time steps (˜1 fs) in conventional molecular dynamics simulations is an important issue that inhibits the study of physical, chemical, and biological systems in real timescales. Additionally, to simulate those systems in contact with a thermal bath, thermostating techniques are usually applied. In this work, we generalize the Green's function molecular dynamics technique to allow simulations within the canonical ensemble. By applying this technique to one-dimensional systems, we were able to correctly describe important thermodynamic properties such as the temperature fluctuations, the temperature distribution, and the velocity autocorrelation function. We show that the proposed technique also allows the use of time steps one order of magnitude larger than those typically used in conventional molecular dynamics simulations. We expect that this technique can be used in long-timescale molecular dynamics simulations.

Structure of the North Anatolian Fault Zone from the Auto-Correlation of Ambient Seismic Noise Recorded at a Dense Seismometer Array

NASA Astrophysics Data System (ADS)

Taylor, D. G.; Rost, S.; Houseman, G.

2015-12-01

In recent years the technique of cross-correlating the ambient seismic noise wavefield at two seismometers to reconstruct empirical Green's Functions for the determination of Earth structure has been a powerful tool to study the Earth's interior without earthquake or man-made sources. However, far less attention has been paid to using auto-correlations of seismic noise to reveal body wave reflections from interfaces in the subsurface. In principle, the Green's functions thus derived should be comparable to the Earth's impulse response to a co-located source and receiver. We use data from a dense seismic array (Dense Array for Northern Anatolia - DANA) deployed across the northern branch of the North Anatolian Fault Zone (NAFZ) in the region of the 1999 magnitude 7.6 Izmit earthquake in western Turkey. The NAFZ is a major strike-slip system that extends ~1200 km across northern Turkey and continues to pose a high level of seismic hazard, in particular to the mega-city of Istanbul. We construct reflection images for the entire crust and upper mantle over the ~35 km by 70 km footprint of the 70-station DANA array. Using auto-correlations of vertical and horizontal components of ground motion, both P- and S-wave velocity information can be retrieved from the wavefield to constrain crustal structure further to established methods. We show that clear P-wave reflections from the crust-mantle boundary (Moho) can be retrieved using the autocorrelation technique, indicating topography on the Moho on horizontal scales of less than 10 km. Offsets in crustal structure can be identified that seem to be correlated with the surface expression of the fault zone in the region. The combined analysis of auto-correlations using vertical and horizontal components will lead to further insight into the fault zone structure throughout the crust and upper mantle.

Spatial statistical network models for stream and river temperature in New England, USA

NASA Astrophysics Data System (ADS)

Detenbeck, Naomi E.; Morrison, Alisa C.; Abele, Ralph W.; Kopp, Darin A.

2016-08-01

Watershed managers are challenged by the need for predictive temperature models with sufficient accuracy and geographic breadth for practical use. We described thermal regimes of New England rivers and streams based on a reduced set of metrics for the May-September growing season (July or August median temperature, diurnal rate of change, and magnitude and timing of growing season maximum) chosen through principal component analysis of 78 candidate metrics. We then developed and assessed spatial statistical models for each of these metrics, incorporating spatial autocorrelation based on both distance along the flow network and Euclidean distance between points. Calculation of spatial autocorrelation based on travel or retention time in place of network distance yielded tighter-fitting Torgegrams with less scatter but did not improve overall model prediction accuracy. We predicted monthly median July or August stream temperatures as a function of median air temperature, estimated urban heat island effect, shaded solar radiation, main channel slope, watershed storage (percent lake and wetland area), percent coarse-grained surficial deposits, and presence or maximum depth of a lake immediately upstream, with an overall root-mean-square prediction error of 1.4 and 1.5°C, respectively. Growing season maximum water temperature varied as a function of air temperature, local channel slope, shaded August solar radiation, imperviousness, and watershed storage. Predictive models for July or August daily range, maximum daily rate of change, and timing of growing season maximum were statistically significant but explained a much lower proportion of variance than the above models (5-14% of total).

Statistical assessment of optical phase fluctuations through turbulent mixing layers

NASA Astrophysics Data System (ADS)

Gardner, Patrick J.; Roggemann, Michael C.; Welsh, Byron M.; Bowersox, Rodney D.

1995-09-01

A lateral shearing interferometer is used to measure the slope of perturbed wavefronts after propagating through turbulent shear flows. This provides a two-dimensional flow visualization technique which is nonintrusive. The slope measurements are used to reconstruct the phase of the turbulence-corrupted wave front. Experiments were performed on a plane shear mixing layer of helium and nitrogen gas at fixed velocities, for five locations in the flow development. The two gases, having a density ratio of approximately seven, provide an effective means of simulating compressible shear layers. Statistical autocorrelation functions and structure functions are computed on the reconstructed phase maps. The autocorrelation function results indicate that the turbulence-induced phase fluctuations are not wide-sense stationary. The structure functions exhibit statistical homogeneity, indicating the phase fluctuation are stationary in first increments. However, the turbulence-corrupted phase is not isotropic. A five-thirds power law is shown to fit one-dimensional, orthogonal slices of the structure function, with scaling coefficients related to the location in the flow.

Reliabilities of Intraindividual Variability Indicators with Autocorrelated Longitudinal Data: Implications for Longitudinal Study Designs.

PubMed

Du, Han; Wang, Lijuan

2018-04-23

Intraindividual variability can be measured by the intraindividual standard deviation ([Formula: see text]), intraindividual variance ([Formula: see text]), estimated hth-order autocorrelation coefficient ([Formula: see text]), and mean square successive difference ([Formula: see text]). Unresolved issues exist in the research on reliabilities of intraindividual variability indicators: (1) previous research only studied conditions with 0 autocorrelations in the longitudinal responses; (2) the reliabilities of [Formula: see text] and [Formula: see text] have not been studied. The current study investigates reliabilities of [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and the intraindividual mean, with autocorrelated longitudinal data. Reliability estimates of the indicators were obtained through Monte Carlo simulations. The impact of influential factors on reliabilities of the intraindividual variability indicators is summarized, and the reliabilities are compared across the indicators. Generally, all the studied indicators of intraindividual variability were more reliable with a more reliable measurement scale and more assessments. The reliabilities of [Formula: see text] were generally lower than those of [Formula: see text] and [Formula: see text], the reliabilities of [Formula: see text] were usually between those of [Formula: see text] and [Formula: see text] unless the scale reliability was large and/or the interindividual standard deviation in autocorrelation coefficients was large, and the reliabilities of the intraindividual mean were generally the highest. An R function is provided for planning longitudinal studies to ensure sufficient reliabilities of the intraindividual indicators are achieved.

Calculation of power spectrums from digital time series with missing data points

NASA Technical Reports Server (NTRS)

Murray, C. W., Jr.

1980-01-01

Two algorithms are developed for calculating power spectrums from the autocorrelation function when there are missing data points in the time series. Both methods use an average sampling interval to compute lagged products. One method, the correlation function power spectrum, takes the discrete Fourier transform of the lagged products directly to obtain the spectrum, while the other, the modified Blackman-Tukey power spectrum, takes the Fourier transform of the mean lagged products. Both techniques require fewer calculations than other procedures since only 50% to 80% of the maximum lags need be calculated. The algorithms are compared with the Fourier transform power spectrum and two least squares procedures (all for an arbitrary data spacing). Examples are given showing recovery of frequency components from simulated periodic data where portions of the time series are missing and random noise has been added to both the time points and to values of the function. In addition the methods are compared using real data. All procedures performed equally well in detecting periodicities in the data.

Bayesian Estimates of Autocorrelations in Single-Case Designs

ERIC Educational Resources Information Center

Shadish, William R.; Rindskopf, David M.; Hedges, Larry V.; Sullivan, Kristynn J.

2012-01-01

Researchers in the single-case design tradition have debated the size and importance of the observed autocorrelations in those designs. All of the past estimates of the autocorrelation in that literature have taken the observed autocorrelation estimates as the data to be used in the debate. However, estimates of the autocorrelation are subject to…

Emission from quantum-dot high-β microcavities: transition from spontaneous emission to lasing and the effects of superradiant emitter coupling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kreinberg, Sören; Chow, Weng W.; Wolters, Janik

Measured and calculated results are presented for the emission properties of a new class of emitters operating in the cavity quantum electrodynamics regime. The structures are based on high-finesse GaAs/AlAs micropillar cavities, each with an active medium consisting of a layer of InGaAs quantum dots (QDs) and the distinguishing feature of having a substantial fraction of spontaneous emission channeled into one cavity mode (high β-factor). This paper demonstrates that the usual criterion for lasing with a conventional (low β-factor) cavity, that is, a sharp non-linearity in the input–output curve accompanied by noticeable linewidth narrowing, has to be reinforced by themore » equal-time second-order photon autocorrelation function to confirm lasing. The article also shows that the equal-time second-order photon autocorrelation function is useful for recognizing superradiance, a manifestation of the correlations possible in high-β microcavities operating with QDs. In terms of consolidating the collected data and identifying the physics underlying laser action, both theory and experiment suggest a sole dependence on intracavity photon number. Evidence for this assertion comes from all our measured and calculated data on emission coherence and fluctuation, for devices ranging from light-emitting diodes (LEDs) and cavity-enhanced LEDs to lasers, lying on the same two curves: one for linewidth narrowing versus intracavity photon number and the other for g( 2)(0) versus intracavity photon number.« less

Emission from quantum-dot high-β microcavities: transition from spontaneous emission to lasing and the effects of superradiant emitter coupling

DOE PAGES

Kreinberg, Sören; Chow, Weng W.; Wolters, Janik; ...

2017-02-28

Measured and calculated results are presented for the emission properties of a new class of emitters operating in the cavity quantum electrodynamics regime. The structures are based on high-finesse GaAs/AlAs micropillar cavities, each with an active medium consisting of a layer of InGaAs quantum dots (QDs) and the distinguishing feature of having a substantial fraction of spontaneous emission channeled into one cavity mode (high β-factor). This paper demonstrates that the usual criterion for lasing with a conventional (low β-factor) cavity, that is, a sharp non-linearity in the input–output curve accompanied by noticeable linewidth narrowing, has to be reinforced by themore » equal-time second-order photon autocorrelation function to confirm lasing. The article also shows that the equal-time second-order photon autocorrelation function is useful for recognizing superradiance, a manifestation of the correlations possible in high-β microcavities operating with QDs. In terms of consolidating the collected data and identifying the physics underlying laser action, both theory and experiment suggest a sole dependence on intracavity photon number. Evidence for this assertion comes from all our measured and calculated data on emission coherence and fluctuation, for devices ranging from light-emitting diodes (LEDs) and cavity-enhanced LEDs to lasers, lying on the same two curves: one for linewidth narrowing versus intracavity photon number and the other for g( 2)(0) versus intracavity photon number.« less

Effect of confounding variables on hemodynamic response function estimation using averaging and deconvolution analysis: An event-related NIRS study.

PubMed

Aarabi, Ardalan; Osharina, Victoria; Wallois, Fabrice

2017-07-15

Slow and rapid event-related designs are used in fMRI and functional near-infrared spectroscopy (fNIRS) experiments to temporally characterize the brain hemodynamic response to discrete events. Conventional averaging (CA) and the deconvolution method (DM) are the two techniques commonly used to estimate the Hemodynamic Response Function (HRF) profile in event-related designs. In this study, we conducted a series of simulations using synthetic and real NIRS data to examine the effect of the main confounding factors, including event sequence timing parameters, different types of noise, signal-to-noise ratio (SNR), temporal autocorrelation and temporal filtering on the performance of these techniques in slow and rapid event-related designs. We also compared systematic errors in the estimates of the fitted HRF amplitude, latency and duration for both techniques. We further compared the performance of deconvolution methods based on Finite Impulse Response (FIR) basis functions and gamma basis sets. Our results demonstrate that DM was much less sensitive to confounding factors than CA. Event timing was the main parameter largely affecting the accuracy of CA. In slow event-related designs, deconvolution methods provided similar results to those obtained by CA. In rapid event-related designs, our results showed that DM outperformed CA for all SNR, especially above -5 dB regardless of the event sequence timing and the dynamics of background NIRS activity. Our results also show that periodic low-frequency systemic hemodynamic fluctuations as well as phase-locked noise can markedly obscure hemodynamic evoked responses. Temporal autocorrelation also affected the performance of both techniques by inducing distortions in the time profile of the estimated hemodynamic response with inflated t-statistics, especially at low SNRs. We also found that high-pass temporal filtering could substantially affect the performance of both techniques by removing the low-frequency components of HRF profiles. Our results emphasize the importance of characterization of event timing, background noise and SNR when estimating HRF profiles using CA and DM in event-related designs. Copyright © 2017 Elsevier Inc. All rights reserved.

Capturing the effect of [PF3(C2F5)3]-vs. [PF6]-, flexible anion vs. rigid, and scaled charge vs. unit on the transport properties of [bmim]+-based ionic liquids: a comparative MD study.

PubMed

Kowsari, Mohammad H; Ebrahimi, Soraya

2018-05-16

Comprehensive molecular dynamics simulations are performed to study the average single-particle dynamics and the transport properties of 1-butyl-3-methylimidazolium hexafluorophosphate, [bmim][PF6], and 1-butyl-3-methylimidazolium tris(pentafluoroethyl)trifluorophosphate, [bmim][FAP], ionic liquids (ILs) at 400 K. We applied one of the most widely used nonpolarizable all-atom force fields for ILs, both with the original unit (±1) charges on each ion and with the partial charges uniformly scaled to 80-85%, taking into account the average polarizability and tracing the experimentally compatible transport properties. In all simulations, [bmim]+ was considered to be flexible, while the effect of a flexible vs. rigid structure of the anions and the effect of two applied charge sets on the calculated properties were separately investigated in detail. The simulation results showed that replacing [PF6]- with [FAP]-, considering anion flexibility, and applying the charge-scaled model significantly enhanced the ionic self-diffusion, ionic conductivity, inverse viscosity, and hyper anion preference (HAP). Both of the calculated self-diffusion coefficients from the long-time linear slope of the mean-square displacement (MSD) and from the integration of the velocity autocorrelation function (VACF) for the centers of mass of the ions were used for evaluation of the ionic transference number, HAP, ideal Nernst-Einstein ionic conductivity (σNE), and the Stokes-Einstein viscosity. In addition, for quantification of the degree of complicated ionic association (known as the Nernst-Einstein deviation parameter, Δ) and ionicity phenomena in the two studied ILs, the ionic conductivity was determined more rigorously by the Green-Kubo integral of the electric-current autocorrelation function (ECACF), and then the σGK/σNE ratio was evaluated. It was found that the correlated motion of the (cationanion) neighbors in [bmim][FAP] is smaller than in [bmim][PF6]. The relaxation times of the normalized reorientational autocorrelation functions were computed to gain a deep, molecular-level insight into the rotational motion of the ions. The geometric shape of the ion is a key factor in determining its reorientational dynamics. [bmim]+ shows faster translational and slower rotational dynamics in contrast to [PF6]-.

Lithium ion dynamics in Li2S+GeS2+GeO2 glasses studied using (7)Li NMR field-cycling relaxometry and line-shape analysis.

PubMed

Gabriel, Jan; Petrov, Oleg V; Kim, Youngsik; Martin, Steve W; Vogel, Michael

2015-09-01

We use (7)Li NMR to study the ionic jump motion in ternary 0.5Li2S+0.5[(1-x)GeS2+xGeO2] glassy lithium ion conductors. Exploring the "mixed glass former effect" in this system led to the assumption of a homogeneous and random variation of diffusion barriers in this system. We exploit that combining traditional line-shape analysis with novel field-cycling relaxometry, it is possible to measure the spectral density of the ionic jump motion in broad frequency and temperature ranges and, thus, to determine the distribution of activation energies. Two models are employed to parameterize the (7)Li NMR data, namely, the multi-exponential autocorrelation function model and the power-law waiting times model. Careful evaluation of both of these models indicates a broadly inhomogeneous energy landscape for both the single (x=0.0) and the mixed (x=0.1) network former glasses. The multi-exponential autocorrelation function model can be well described by a Gaussian distribution of activation barriers. Applicability of the methods used and their sensitivity to microscopic details of ionic motion are discussed. Copyright © 2015 Elsevier Inc. All rights reserved.

Field test comparison of an autocorrelation technique for determining grain size using a digital 'beachball' camera versus traditional methods

USGS Publications Warehouse

Barnard, P.L.; Rubin, D.M.; Harney, J.; Mustain, N.

2007-01-01

This extensive field test of an autocorrelation technique for determining grain size from digital images was conducted using a digital bed-sediment camera, or 'beachball' camera. Using 205 sediment samples and >1200 images from a variety of beaches on the west coast of the US, grain size ranging from sand to granules was measured from field samples using both the autocorrelation technique developed by Rubin [Rubin, D.M., 2004. A simple autocorrelation algorithm for determining grain size from digital images of sediment. Journal of Sedimentary Research, 74(1): 160-165.] and traditional methods (i.e. settling tube analysis, sieving, and point counts). To test the accuracy of the digital-image grain size algorithm, we compared results with manual point counts of an extensive image data set in the Santa Barbara littoral cell. Grain sizes calculated using the autocorrelation algorithm were highly correlated with the point counts of the same images (r2 = 0.93; n = 79) and had an error of only 1%. Comparisons of calculated grain sizes and grain sizes measured from grab samples demonstrated that the autocorrelation technique works well on high-energy dissipative beaches with well-sorted sediment such as in the Pacific Northwest (r2 ??? 0.92; n = 115). On less dissipative, more poorly sorted beaches such as Ocean Beach in San Francisco, results were not as good (r2 ??? 0.70; n = 67; within 3% accuracy). Because the algorithm works well compared with point counts of the same image, the poorer correlation with grab samples must be a result of actual spatial and vertical variability of sediment in the field; closer agreement between grain size in the images and grain size of grab samples can be achieved by increasing the sampling volume of the images (taking more images, distributed over a volume comparable to that of a grab sample). In all field tests the autocorrelation method was able to predict the mean and median grain size with ???96% accuracy, which is more than adequate for the majority of sedimentological applications, especially considering that the autocorrelation technique is estimated to be at least 100 times faster than traditional methods.

Modified Exponential Weighted Moving Average (EWMA) Control Chart on Autocorrelation Data

NASA Astrophysics Data System (ADS)

Herdiani, Erna Tri; Fandrilla, Geysa; Sunusi, Nurtiti

2018-03-01

In general, observations of the statistical process control are assumed to be mutually independence. However, this assumption is often violated in practice. Consequently, statistical process controls were developed for interrelated processes, including Shewhart, Cumulative Sum (CUSUM), and exponentially weighted moving average (EWMA) control charts in the data that were autocorrelation. One researcher stated that this chart is not suitable if the same control limits are used in the case of independent variables. For this reason, it is necessary to apply the time series model in building the control chart. A classical control chart for independent variables is usually applied to residual processes. This procedure is permitted provided that residuals are independent. In 1978, Shewhart modification for the autoregressive process was introduced by using the distance between the sample mean and the target value compared to the standard deviation of the autocorrelation process. In this paper we will examine the mean of EWMA for autocorrelation process derived from Montgomery and Patel. Performance to be investigated was investigated by examining Average Run Length (ARL) based on the Markov Chain Method.

Testing algorithms for critical slowing down

NASA Astrophysics Data System (ADS)

Cossu, Guido; Boyle, Peter; Christ, Norman; Jung, Chulwoo; Jüttner, Andreas; Sanfilippo, Francesco

2018-03-01

We present the preliminary tests on two modifications of the Hybrid Monte Carlo (HMC) algorithm. Both algorithms are designed to travel much farther in the Hamiltonian phase space for each trajectory and reduce the autocorrelations among physical observables thus tackling the critical slowing down towards the continuum limit. We present a comparison of costs of the new algorithms with the standard HMC evolution for pure gauge fields, studying the autocorrelation times for various quantities including the topological charge.

A new radial strain and strain rate estimation method using autocorrelation for carotid artery

NASA Astrophysics Data System (ADS)

Ye, Jihui; Kim, Hoonmin; Park, Jongho; Yeo, Sunmi; Shim, Hwan; Lim, Hyungjoon; Yoo, Yangmo

2014-03-01

Atherosclerosis is a leading cause of cardiovascular disease. The early diagnosis of atherosclerosis is of clinical interest since it can prevent any adverse effects of atherosclerotic vascular diseases. In this paper, a new carotid artery radial strain estimation method based on autocorrelation is presented. In the proposed method, the strain is first estimated by the autocorrelation of two complex signals from the consecutive frames. Then, the angular phase from autocorrelation is converted to strain and strain rate and they are analyzed over time. In addition, a 2D strain image over region of interest in a carotid artery can be displayed. To evaluate the feasibility of the proposed radial strain estimation method, radiofrequency (RF) data of 408 frames in the carotid artery of a volunteer were acquired by a commercial ultrasound system equipped with a research package (V10, Samsung Medison, Korea) by using a L5-13IS linear array transducer. From in vivo carotid artery data, the mean strain estimate was -0.1372 while its minimum and maximum values were -2.961 and 0.909, respectively. Moreover, the overall strain estimates are highly correlated with the reconstructed M-mode trace. Similar results were obtained from the estimation of the strain rate change over time. These results indicate that the proposed carotid artery radial strain estimation method is useful for assessing the arterial wall's stiffness noninvasively without increasing the computational complexity.

Bulk viscosity of the Lennard-Jones fluid for a wide range of states computed by equilibrium molecular dynamics

NASA Astrophysics Data System (ADS)

Hoheisel, C.; Vogelsang, R.; Schoen, M.

1987-12-01

Accurate data for the bulk viscosity ηv have been obtained by molecular dynamics calculations. Many thermodynamic states of the Lennard-Jones fluid were considered. The Green-Kubo integrand of ηv is analyzed in terms of partial correlation functions constituting the total one. These partial functions behave rather differently from those found for the shear viscosity or the thermal conductivity. Generally the total autocorrelation function of ηv shows a steeper initial decay and a more pronounced long time form than those of the shear viscosity or the thermal conductivity. For states near transition to solid phases, like the pseudotriple point of argon, the Green-Kubo integrand of ηv has a significantly longer ranged time behavior than that of the shear viscosity. Hence, for the latter states, a systematic error is expected for ηv using equilibrium molecular dynamics for its computation.

nMoldyn: a program package for a neutron scattering oriented analysis of molecular dynamics simulations.

PubMed

Róg, T; Murzyn, K; Hinsen, K; Kneller, G R

2003-04-15

We present a new implementation of the program nMoldyn, which has been developed for the computation and decomposition of neutron scattering intensities from Molecular Dynamics trajectories (Comp. Phys. Commun 1995, 91, 191-214). The new implementation extends the functionality of the original version, provides a much more convenient user interface (both graphical/interactive and batch), and can be used as a tool set for implementing new analysis modules. This was made possible by the use of a high-level language, Python, and of modern object-oriented programming techniques. The quantities that can be calculated by nMoldyn are the mean-square displacement, the velocity autocorrelation function as well as its Fourier transform (the density of states) and its memory function, the angular velocity autocorrelation function and its Fourier transform, the reorientational correlation function, and several functions specific to neutron scattering: the coherent and incoherent intermediate scattering functions with their Fourier transforms, the memory function of the coherent scattering function, and the elastic incoherent structure factor. The possibility to compute memory function is a new and powerful feature that allows to relate simulation results to theoretical studies. Copyright 2003 Wiley Periodicals, Inc. J Comput Chem 24: 657-667, 2003

Contribution to viscosity from the structural relaxation via the atomic scale Green-Kubo stress correlation function.

PubMed

Levashov, V A

2017-11-14

We studied the connection between the structural relaxation and viscosity for a binary model of repulsive particles in the supercooled liquid regime. The used approach is based on the decomposition of the macroscopic Green-Kubo stress correlation function into the correlation functions between the atomic level stresses. Previously we used the approach to study an iron-like single component system of particles. The role of vibrational motion has been addressed through the demonstration of the relationship between viscosity and the shear waves propagating over large distances. In our previous considerations, however, we did not discuss the role of the structural relaxation. Here we suggest that the contribution to viscosity from the structural relaxation can be taken into account through the consideration of the contribution from the atomic stress auto-correlation term only. This conclusion, however, does not mean that only the auto-correlation term represents the contribution to viscosity from the structural relaxation. Previously the role of the structural relaxation for viscosity has been addressed through the considerations of the transitions between inherent structures and within the mode-coupling theory by other authors. In the present work, we study the structural relaxation through the considerations of the parent liquid and the atomic level stress correlations in it. The comparison with the results obtained on the inherent structures also is made. Our current results suggest, as our previous observations, that in the supercooled liquid regime, the vibrational contribution to viscosity extends over the times that are much larger than the Einstein's vibrational period and much larger than the times that it takes for the shear waves to propagate over the model systems. Besides addressing the atomic level shear stress correlations, we also studied correlations between the atomic level pressure elements.

On the wrong inference of long-range correlations in climate data; the case of the solar and volcanic forcing over the Tropical Pacific

NASA Astrophysics Data System (ADS)

Varotsos, Costas A.; Efstathiou, Maria N.

2017-05-01

A substantial weakness of several climate studies on long-range dependence is the conclusion of long-term memory of the climate conditions, without considering it necessary to establish the power-law scaling and to reject a simple exponential decay of the autocorrelation function. We herewith show one paradigmatic case, where a strong long-range dependence could be wrongly inferred from incomplete data analysis. We firstly apply the DFA method on the solar and volcanic forcing time series over the tropical Pacific, during the past 1000 years and the results obtained show that a statistically significant straight line fit to the fluctuation function in a log-log representation is revealed with slope higher than 0.5, which wrongly may be assumed as an indication of persistent long-range correlations in the time series. We argue that the long-range dependence cannot be concluded just from this straight line fit, but it requires the fulfilment of the two additional prerequisites i.e. reject the exponential decay of the autocorrelation function and establish the power-law scaling. In fact, the investigation of the validity of these prerequisites showed that the DFA exponent higher than 0.5 does not justify the existence of persistent long-range correlations in the temporal evolution of the solar and volcanic forcing during last millennium. In other words, we show that empirical analyses, based on these two prerequisites must not be considered as panacea for a direct proof of scaling, but only as evidence that the scaling hypothesis is plausible. We also discuss the scaling behaviour of solar and volcanic forcing data based on the Haar tool, which recently proved its ability to reliably detect the existence of the scaling effect in climate series.

Contribution to viscosity from the structural relaxation via the atomic scale Green-Kubo stress correlation function

NASA Astrophysics Data System (ADS)

Levashov, V. A.

2017-11-01

We studied the connection between the structural relaxation and viscosity for a binary model of repulsive particles in the supercooled liquid regime. The used approach is based on the decomposition of the macroscopic Green-Kubo stress correlation function into the correlation functions between the atomic level stresses. Previously we used the approach to study an iron-like single component system of particles. The role of vibrational motion has been addressed through the demonstration of the relationship between viscosity and the shear waves propagating over large distances. In our previous considerations, however, we did not discuss the role of the structural relaxation. Here we suggest that the contribution to viscosity from the structural relaxation can be taken into account through the consideration of the contribution from the atomic stress auto-correlation term only. This conclusion, however, does not mean that only the auto-correlation term represents the contribution to viscosity from the structural relaxation. Previously the role of the structural relaxation for viscosity has been addressed through the considerations of the transitions between inherent structures and within the mode-coupling theory by other authors. In the present work, we study the structural relaxation through the considerations of the parent liquid and the atomic level stress correlations in it. The comparison with the results obtained on the inherent structures also is made. Our current results suggest, as our previous observations, that in the supercooled liquid regime, the vibrational contribution to viscosity extends over the times that are much larger than the Einstein's vibrational period and much larger than the times that it takes for the shear waves to propagate over the model systems. Besides addressing the atomic level shear stress correlations, we also studied correlations between the atomic level pressure elements.

High-Responsivity Graphene–Boron Nitride Photodetector and Autocorrelator in a Silicon Photonic Integrated Circuit

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shiue, Ren-Jye; Gao, Yuanda; Wang, Yifei

2015-11-11

Graphene and other two-dimensional (2D) materials have emerged as promising materials for broadband and ultrafast photodetection and optical modulation. These optoelectronic capabilities can augment complementary metal–oxide–semiconductor (CMOS) devices for high-speed and low-power optical interconnects. Here, we demonstrate an on-chip ultrafast photodetector based on a two-dimensional heterostructure consisting of high-quality graphene encapsulated in hexagonal boron nitride. Coupled to the optical mode of a silicon waveguide, this 2D heterostructure-based photodetector exhibits a maximum responsivity of 0.36 A/W and high-speed operation with a 3 dB cutoff at 42 GHz. From photocurrent measurements as a function of the top-gate and source-drain voltages, we concludemore » that the photoresponse is consistent with hot electron mediated effects. At moderate peak powers above 50 mW, we observe a saturating photocurrent consistent with the mechanisms of electron–phonon supercollision cooling. This nonlinear photoresponse enables optical on-chip autocorrelation measurements with picosecond-scale timing resolution and exceptionally low peak powers.« less

Autocorrelation factors and intelligibility of Japanese monosyllables in individuals with sensorineural hearing loss.

PubMed

Shimokura, Ryota; Akasaka, Sakie; Nishimura, Tadashi; Hosoi, Hiroshi; Matsui, Toshie

2017-02-01

Some Japanese monosyllables contain consonants that are not easily discernible for individuals with sensorineural hearing loss. However, the acoustic features that make these monosyllables difficult to discern have not been clearly identified. Here, this study used the autocorrelation function (ACF), which can capture temporal features of signals, to clarify the factors influencing speech intelligibility. For each monosyllable, five factors extracted from the ACF [Φ(0): total energy; τ 1 and ϕ 1 : delay time and amplitude of the maximum peak; τ e : effective duration; W ϕ (0) : spectral centroid], voice onset time, speech intelligibility index, and loudness level were compared with the percentage of correctly perceived articulations (144 ears) obtained by 50 Japanese vowel and consonant-vowel monosyllables produced by one female speaker. Results showed that median effective duration [(τ e ) med ] was strongly correlated with the percentage of correctly perceived articulations of the consonants (r = 0.87, p < 0.01). (τ e ) med values were computed by running ACFs with the time lag at which the magnitude of the logarithmic-ACF envelope had decayed to -10 dB. Effective duration is a measure of temporal pattern persistence, i.e., the duration over which the waveform maintains a stable pattern. The authors postulate that low recognition ability is related to degraded perception of temporal fluctuation patterns.

Taylor dispersion of colloidal particles in narrow channels

NASA Astrophysics Data System (ADS)

Sané, Jimaan; Padding, Johan T.; Louis, Ard A.

2015-09-01

We use a mesoscopic particle-based simulation technique to study the classic convection-diffusion problem of Taylor dispersion for colloidal discs in confined flow. When the disc diameter becomes non-negligible compared to the diameter of the pipe, there are important corrections to the original Taylor picture. For example, the colloids can flow more rapidly than the underlying fluid, and their Taylor dispersion coefficient is decreased. For narrow pipes, there are also further hydrodynamic wall effects. The long-time tails in the velocity autocorrelation functions are altered by the Poiseuille flow.

Modified Beer-Lambert law for blood flow.

PubMed

Baker, Wesley B; Parthasarathy, Ashwin B; Busch, David R; Mesquita, Rickson C; Greenberg, Joel H; Yodh, A G

2014-11-01

We develop and validate a Modified Beer-Lambert law for blood flow based on diffuse correlation spectroscopy (DCS) measurements. The new formulation enables blood flow monitoring from temporal intensity autocorrelation function data taken at single or multiple delay-times. Consequentially, the speed of the optical blood flow measurement can be substantially increased. The scheme facilitates blood flow monitoring of highly scattering tissues in geometries wherein light propagation is diffusive or non-diffusive, and it is particularly well-suited for utilization with pressure measurement paradigms that employ differential flow signals to reduce contributions of superficial tissues.

Relaxation and collective excitations of cluster nano-plasmas

NASA Astrophysics Data System (ADS)

Reinholz, Heidi; Röpke, Gerd; Broda, Ingrid; Morozov, Igor; Bystryi, Roman; Lavrinenko, Yaroslav

2018-01-01

Nano-plasmas produced, for example, in clusters after short-pulse laser irradiation, can show collective excitations, as derived from the time evolution of fluctuations in thermodynamic equilibrium. Molecular dynamical simulations are performed for various cluster sizes. New data are obtained for the minimum value of the stationary cluster charge. The bi-local autocorrelation function gives the spatial structure of the eigenmodes, for which energy eigenvalues are obtained. By varying the cluster size, starting from a few-particle cluster, the emergence of macroscopic properties such as collective excitations is shown.

Moho depth across the Trans-European Suture Zone from ambient vibration autocorrelations

NASA Astrophysics Data System (ADS)

Becker, Gesa; Knapmeyer-Endrun, Brigitte

2017-04-01

In 2018 the InSight mission to Mars will deploy a seismic station on the planet. This seismic station will consist of a three-component very broadband seismic sensor and a collocated three-component short period seismometer. Single station methods are therefore needed to extract information from the data and learn more about the interior structure of Mars. One potential method is the extraction of reflected phases from autocorrelations. Here autocorrelations are derived from ambient seismic noise to make the most of the data expected, as seismicity on Mars is likely less abundant than on Earth. These autocorrelations are calculated using a phase autocorrelation algorithm and time-frequency domain phase-weighted stacking as the main processing steps in addition to smoothing the spectrum of the data with a short term-long term average algorithm. Afterward the obtained results are filtered and analyzed in the frequency range of 1-2 Hz. The developed processing scheme is applied to data from permanent seismic stations located in different geological provinces across Europe, i.e. the Upper Rhine Graben, Central European Platform, Bohemian Massif, Northern German and Polish Basin, and the East European Craton, with varying Moho depths between 25-50 km. These crustal thicknesses are comparable to various estimates for Mars, therefore providing a good reference and indication of resolvability for Moho depths that might be encountered at the landing site. Changes in reflectivity can be observed in the calculated autocorrelations. The lag times of these changes are converted into depths with the help of available velocity information (EPcrust and local models for Poland and the Czech Republic, respectively) and the results are compared to existing information on Moho depths, which show good agreement. The results are temporarily stable, but show a clear correlation with the existence of cultural noise. Based on the closely located broadband and short period stations of the GERESS-array, it is shown that the processing scheme is also applicable to short period stations. Subsequently it is applied to the mainly short period and temporary stations of the PASSEQ network along the seismic profile POLONAISE P4, running from Eastern Germany to Lithuania crossing the Trans-European Suture Zone.

Establishing the diffuse correlation spectroscopy signal relationship with blood flow.

PubMed

Boas, David A; Sakadžić, Sava; Selb, Juliette; Farzam, Parisa; Franceschini, Maria Angela; Carp, Stefan A

2016-07-01

Diffuse correlation spectroscopy (DCS) measurements of blood flow rely on the sensitivity of the temporal autocorrelation function of diffusively scattered light to red blood cell (RBC) mean square displacement (MSD). For RBCs flowing with convective velocity [Formula: see text], the autocorrelation is expected to decay exponentially with [Formula: see text], where [Formula: see text] is the delay time. RBCs also experience shear-induced diffusion with a diffusion coefficient [Formula: see text] and an MSD of [Formula: see text]. Surprisingly, experimental data primarily reflect diffusive behavior. To provide quantitative estimates of the relative contributions of convective and diffusive movements, we performed Monte Carlo simulations of light scattering through tissue of varying vessel densities. We assumed laminar vessel flow profiles and accounted for shear-induced diffusion effects. In agreement with experimental data, we found that diffusive motion dominates the correlation decay for typical DCS measurement parameters. Furthermore, our model offers a quantitative relationship between the RBC diffusion coefficient and absolute tissue blood flow. We thus offer, for the first time, theoretical support for the empirically accepted ability of the DCS blood flow index ([Formula: see text]) to quantify tissue perfusion. We find [Formula: see text] to be linearly proportional to blood flow, but with a proportionality modulated by the hemoglobin concentration and the average blood vessel diameter.

Aging in freely evolving granular gas with impact velocity dependent coefficient of restitution

NASA Astrophysics Data System (ADS)

Kumari, Shikha; Ahmad, Syed Rashid

2018-05-01

The evolution of granular system is governed by the concept of coefficient of restitution that gives a relationship between normal component of relative velocities before and after collision. Most of the studies consider a simplified collision model where particles interact through coefficient of restitution which is a constant while in reality, the coefficient of restitution must be a variable that depends on the impact velocity of colliding particles. In this work, we have considered the aging in the velocity autocorrelation function, A(τw, τ) for a granular gas of realistic particles interacting through coefficient of restitution that is depending on impact velocity. Molecular dynamics simulation is used to study granular gas that is evolving freely in absence of any external force. From the simulation results, we observe that A(τw, τ) depends explicitly on waiting time τw and collision time τ. Initially, the function decays exponentially but as the waiting time increases the decay of function becomes slow due to correlations that emerge in velocity field.

The Superstatistical Nature and Interoccurrence Time of Atmospheric Mercury Concentration Fluctuations

NASA Astrophysics Data System (ADS)

Carbone, F.; Bruno, A. G.; Naccarato, A.; De Simone, F.; Gencarelli, C. N.; Sprovieri, F.; Hedgecock, I. M.; Landis, M. S.; Skov, H.; Pfaffhuber, K. A.; Read, K. A.; Martin, L.; Angot, H.; Dommergue, A.; Magand, O.; Pirrone, N.

2018-01-01

The probability density function (PDF) of the time intervals between subsequent extreme events in atmospheric Hg0 concentration data series from different latitudes has been investigated. The Hg0 dynamic possesses a long-term memory autocorrelation function. Above a fixed threshold Q in the data, the PDFs of the interoccurrence time of the Hg0 data are well described by a Tsallis q-exponential function. This PDF behavior has been explained in the framework of superstatistics, where the competition between multiple mesoscopic processes affects the macroscopic dynamics. An extensive parameter μ, encompassing all possible fluctuations related to mesoscopic phenomena, has been identified. It follows a χ2 distribution, indicative of the superstatistical nature of the overall process. Shuffling the data series destroys the long-term memory, the distributions become independent of Q, and the PDFs collapse on to the same exponential distribution. The possible central role of atmospheric turbulence on extreme events in the Hg0 data is highlighted.

Simulation of Near-Edge X-ray Absorption Fine Structure with Time-Dependent Equation-of-Motion Coupled-Cluster Theory.

PubMed

Nascimento, Daniel R; DePrince, A Eugene

2017-07-06

An explicitly time-dependent (TD) approach to equation-of-motion (EOM) coupled-cluster theory with single and double excitations (CCSD) is implemented for simulating near-edge X-ray absorption fine structure in molecular systems. The TD-EOM-CCSD absorption line shape function is given by the Fourier transform of the CCSD dipole autocorrelation function. We represent this transform by its Padé approximant, which provides converged spectra in much shorter simulation times than are required by the Fourier form. The result is a powerful framework for the blackbox simulation of broadband absorption spectra. K-edge X-ray absorption spectra for carbon, nitrogen, and oxygen in several small molecules are obtained from the real part of the absorption line shape function and are compared with experiment. The computed and experimentally obtained spectra are in good agreement; the mean unsigned error in the predicted peak positions is only 1.2 eV. We also explore the spectral signatures of protonation in these molecules.

Exploring fractal behaviour of blood oxygen saturation in preterm babies

NASA Astrophysics Data System (ADS)

Zahari, Marina; Hui, Tan Xin; Zainuri, Nuryazmin Ahmat; Darlow, Brian A.

2017-04-01

Recent evidence has been emerging that oxygenation instability in preterm babies could lead to an increased risk of retinal injury such as retinopathy of prematurity. There is a potential that disease severity could be better understood using nonlinear methods for time series data such as fractal theories [1]. Theories on fractal behaviours have been employed by researchers in various disciplines who were motivated to look into the behaviour or structure of irregular fluctuations in temporal data. In this study, an investigation was carried out to examine whether fractal behaviour could be detected in blood oxygen time series. Detection for the presence of fractals in oxygen data of preterm infants was performed using the methods of power spectrum, empirical probability distribution function and autocorrelation function. The results from these fractal identification methods indicate the possibility that these data exhibit fractal nature. Subsequently, a fractal framework for future research was suggested for oxygen time series.

Models for discrete-time self-similar vector processes with application to network traffic

NASA Astrophysics Data System (ADS)

Lee, Seungsin; Rao, Raghuveer M.; Narasimha, Rajesh

2003-07-01

The paper defines self-similarity for vector processes by employing the discrete-time continuous-dilation operation which has successfully been used previously by the authors to define 1-D discrete-time stochastic self-similar processes. To define self-similarity of vector processes, it is required to consider the cross-correlation functions between different 1-D processes as well as the autocorrelation function of each constituent 1-D process in it. System models to synthesize self-similar vector processes are constructed based on the definition. With these systems, it is possible to generate self-similar vector processes from white noise inputs. An important aspect of the proposed models is that they can be used to synthesize various types of self-similar vector processes by choosing proper parameters. Additionally, the paper presents evidence of vector self-similarity in two-channel wireless LAN data and applies the aforementioned systems to simulate the corresponding network traffic traces.

Hotspot detection using image pattern recognition based on higher-order local auto-correlation

NASA Astrophysics Data System (ADS)

Maeda, Shimon; Matsunawa, Tetsuaki; Ogawa, Ryuji; Ichikawa, Hirotaka; Takahata, Kazuhiro; Miyairi, Masahiro; Kotani, Toshiya; Nojima, Shigeki; Tanaka, Satoshi; Nakagawa, Kei; Saito, Tamaki; Mimotogi, Shoji; Inoue, Soichi; Nosato, Hirokazu; Sakanashi, Hidenori; Kobayashi, Takumi; Murakawa, Masahiro; Higuchi, Tetsuya; Takahashi, Eiichi; Otsu, Nobuyuki

2011-04-01

Below 40nm design node, systematic variation due to lithography must be taken into consideration during the early stage of design. So far, litho-aware design using lithography simulation models has been widely applied to assure that designs are printed on silicon without any error. However, the lithography simulation approach is very time consuming, and under time-to-market pressure, repetitive redesign by this approach may result in the missing of the market window. This paper proposes a fast hotspot detection support method by flexible and intelligent vision system image pattern recognition based on Higher-Order Local Autocorrelation. Our method learns the geometrical properties of the given design data without any defects as normal patterns, and automatically detects the design patterns with hotspots from the test data as abnormal patterns. The Higher-Order Local Autocorrelation method can extract features from the graphic image of design pattern, and computational cost of the extraction is constant regardless of the number of design pattern polygons. This approach can reduce turnaround time (TAT) dramatically only on 1CPU, compared with the conventional simulation-based approach, and by distributed processing, this has proven to deliver linear scalability with each additional CPU.

0.1 Trend analysis of δ18O composition of precipitation in Germany: Combining Mann-Kendall trend test and ARIMA models to correct for higher order serial correlation

NASA Astrophysics Data System (ADS)

Klaus, Julian; Pan Chun, Kwok; Stumpp, Christine

2015-04-01

Spatio-temporal dynamics of stable oxygen (18O) and hydrogen (2H) isotopes in precipitation can be used as proxies for changing hydro-meteorological and regional and global climate patterns. While spatial patterns and distributions gained much attention in recent years the temporal trends in stable isotope time series are rarely investigated and our understanding of them is still limited. These might be a result of a lack of proper trend detection tools and effort for exploring trend processes. Here we make use of an extensive data set of stable isotope in German precipitation. In this study we investigate temporal trends of δ18O in precipitation at 17 observation station in Germany between 1978 and 2009. For that we test different approaches for proper trend detection, accounting for first and higher order serial correlation. We test if significant trends in the isotope time series based on different models can be observed. We apply the Mann-Kendall trend tests on the isotope series, using general multiplicative seasonal autoregressive integrate moving average (ARIMA) models which account for first and higher order serial correlations. With the approach we can also account for the effects of temperature, precipitation amount on the trend. Further we investigate the role of geographic parameters on isotope trends. To benchmark our proposed approach, the ARIMA results are compared to a trend-free prewhiting (TFPW) procedure, the state of the art method for removing the first order autocorrelation in environmental trend studies. Moreover, we explore whether higher order serial correlations in isotope series affects our trend results. The results show that three out of the 17 stations have significant changes when higher order autocorrelation are adjusted, and four stations show a significant trend when temperature and precipitation effects are considered. Significant trends in the isotope time series are generally observed at low elevation stations (≤315 m a.s.l.). Higher order autoregressive processes are important in the isotope time series analysis. Our results show that the widely used trend analysis with only the first order autocorrelation adjustment may not adequately take account of the high order autocorrelated processes in the stable isotope series. The investigated time series analysis method including higher autocorrelation and external climate variable adjustments is shown to be a better alternative.

Time series analysis of influenza incidence in Chinese provinces from 2004 to 2011

PubMed Central

Song, Xin; Xiao, Jun; Deng, Jiang; Kang, Qiong; Zhang, Yanyu; Xu, Jinbo

2016-01-01

Abstract Influenza as a severe infectious disease has caused catastrophes throughout human history, and every pandemic of influenza has produced a great social burden. We compiled monthly data of influenza incidence from all provinces and autonomous regions in mainland China from January 2004 to December 2011, comprehensively evaluated and classified these data, and then randomly selected 4 provinces with higher incidence (Hebei, Gansu, Guizhou, and Hunan), 2 provinces with median incidence (Tianjin and Henan), 1 province with lower incidence (Shandong), using time series analysis to construct an ARIMA model, which is based on the monthly incidence from 2004 to 2011 as the training set. We exerted the X-12-ARIMA procedure for modeling due to the seasonality these data implied. Autocorrelation function (ACF), partial autocorrelation function (PACF), and automatic model selection were to determine the order of the model parameters. The optimal model was decided by a nonseasonal and seasonal moving average test. Finally, we applied this model to predict the monthly incidence of influenza in 2012 as the test set, and the simulated incidence was compared with the observed incidence to evaluate the model's validity by the criterion of both percentage variability in regression analyses (R2) and root mean square error (RMSE). It is conceivable that SARIMA (0,1,1)(0,1,1)12 could simultaneously forecast the influenza incidence of the Hebei Province, Guizhou Province, Henan Province, and Shandong Province; SARIMA (1,0,0)(0,1,1)12 could forecast the influenza incidence in Gansu Province; SARIMA (3,1,1)(0,1,1)12 could forecast the influenza incidence in Tianjin City; and SARIMA (0,1,1)(0,0,1)12 could forecast the influenza incidence in Hunan Province. Time series analysis is a good tool for prediction of disease incidence. PMID:27367989

Building a three-dimensional model of CYP2C9 inhibition using the Autocorrelator: an autonomous model generator.

PubMed

Lardy, Matthew A; Lebrun, Laurie; Bullard, Drew; Kissinger, Charles; Gobbi, Alberto

2012-05-25

In modern day drug discovery campaigns, computational chemists have to be concerned not only about improving the potency of molecules but also reducing any off-target ADMET activity. There are a plethora of antitargets that computational chemists may have to consider. Fortunately many antitargets have crystal structures deposited in the PDB. These structures are immediately useful to our Autocorrelator: an automated model generator that optimizes variables for building computational models. This paper describes the use of the Autocorrelator to construct high quality docking models for cytochrome P450 2C9 (CYP2C9) from two publicly available crystal structures. Both models result in strong correlation coefficients (R² > 0.66) between the predicted and experimental determined log(IC₅₀) values. Results from the two models overlap well with each other, converging on the same scoring function, deprotonated charge state, and predicted the binding orientation for our collection of molecules.

Comparison of two fractal interpolation methods

NASA Astrophysics Data System (ADS)

Fu, Yang; Zheng, Zeyu; Xiao, Rui; Shi, Haibo

2017-03-01

As a tool for studying complex shapes and structures in nature, fractal theory plays a critical role in revealing the organizational structure of the complex phenomenon. Numerous fractal interpolation methods have been proposed over the past few decades, but they differ substantially in the form features and statistical properties. In this study, we simulated one- and two-dimensional fractal surfaces by using the midpoint displacement method and the Weierstrass-Mandelbrot fractal function method, and observed great differences between the two methods in the statistical characteristics and autocorrelation features. From the aspect of form features, the simulations of the midpoint displacement method showed a relatively flat surface which appears to have peaks with different height as the fractal dimension increases. While the simulations of the Weierstrass-Mandelbrot fractal function method showed a rough surface which appears to have dense and highly similar peaks as the fractal dimension increases. From the aspect of statistical properties, the peak heights from the Weierstrass-Mandelbrot simulations are greater than those of the middle point displacement method with the same fractal dimension, and the variances are approximately two times larger. When the fractal dimension equals to 1.2, 1.4, 1.6, and 1.8, the skewness is positive with the midpoint displacement method and the peaks are all convex, but for the Weierstrass-Mandelbrot fractal function method the skewness is both positive and negative with values fluctuating in the vicinity of zero. The kurtosis is less than one with the midpoint displacement method, and generally less than that of the Weierstrass-Mandelbrot fractal function method. The autocorrelation analysis indicated that the simulation of the midpoint displacement method is not periodic with prominent randomness, which is suitable for simulating aperiodic surface. While the simulation of the Weierstrass-Mandelbrot fractal function method has strong periodicity, which is suitable for simulating periodic surface.

Sample size calculation for stepped wedge and other longitudinal cluster randomised trials.

PubMed

Hooper, Richard; Teerenstra, Steven; de Hoop, Esther; Eldridge, Sandra

2016-11-20

The sample size required for a cluster randomised trial is inflated compared with an individually randomised trial because outcomes of participants from the same cluster are correlated. Sample size calculations for longitudinal cluster randomised trials (including stepped wedge trials) need to take account of at least two levels of clustering: the clusters themselves and times within clusters. We derive formulae for sample size for repeated cross-section and closed cohort cluster randomised trials with normally distributed outcome measures, under a multilevel model allowing for variation between clusters and between times within clusters. Our formulae agree with those previously described for special cases such as crossover and analysis of covariance designs, although simulation suggests that the formulae could underestimate required sample size when the number of clusters is small. Whether using a formula or simulation, a sample size calculation requires estimates of nuisance parameters, which in our model include the intracluster correlation, cluster autocorrelation, and individual autocorrelation. A cluster autocorrelation less than 1 reflects a situation where individuals sampled from the same cluster at different times have less correlated outcomes than individuals sampled from the same cluster at the same time. Nuisance parameters could be estimated from time series obtained in similarly clustered settings with the same outcome measure, using analysis of variance to estimate variance components. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Modified Beer-Lambert law for blood flow

NASA Astrophysics Data System (ADS)

Baker, Wesley B.; Parthasarathy, Ashwin B.; Busch, David R.; Mesquita, Rickson C.; Greenberg, Joel H.; Yodh, A. G.

2015-03-01

The modified Beer-Lambert law is among the most widely used approaches for analysis of near-infrared spectroscopy (NIRS) reflectance signals for measurements of tissue blood volume and oxygenation. Briefly, the modified Beer-Lambert paradigm is a scheme to derive changes in tissue optical properties based on continuous-wave (CW) diffuse optical intensity measurements. In its simplest form, the scheme relates differential changes in light transmission (in any geometry) to differential changes in tissue absorption. Here we extend this paradigm to the measurement of tissue blood flow by diffuse correlation spectroscopy (DCS). In the new approach, differential changes of the intensity temporal auto-correlation function at a single delay-time are related to differential changes in blood flow. The key theoretical results for measurement of blood flow changes in any tissue geometry are derived, and we demonstrate the new method to monitor cerebral blood flow in a pig under conditions wherein the semi-infinite geometry approximation is fairly good. Specifically, the drug dinitrophenol was injected in the pig to induce a gradual 200% increase in cerebral blood flow, as measured with MRI velocity flow mapping and by DCS. The modified Beer-Lambert law for flow accurately recovered these flow changes using only a single delay-time in the intensity auto-correlation function curve. The scheme offers increased DCS measurement speed of blood flow. Further, the same techniques using the modified Beer-Lambert law to filter out superficial tissue effects in NIRS measurements of deep tissues can be applied to the DCS modified Beer-Lambert law for blood flow monitoring of deep tissues.

Identification of Piecewise Linear Uniform Motion Blur

NASA Astrophysics Data System (ADS)

Patanukhom, Karn; Nishihara, Akinori

A motion blur identification scheme is proposed for nonlinear uniform motion blurs approximated by piecewise linear models which consist of more than one linear motion component. The proposed scheme includes three modules that are a motion direction estimator, a motion length estimator and a motion combination selector. In order to identify the motion directions, the proposed scheme is based on a trial restoration by using directional forward ramp motion blurs along different directions and an analysis of directional information via frequency domain by using a Radon transform. Autocorrelation functions of image derivatives along several directions are employed for estimation of the motion lengths. A proper motion combination is identified by analyzing local autocorrelation functions of non-flat component of trial restored results. Experimental examples of simulated and real world blurred images are given to demonstrate a promising performance of the proposed scheme.

Autocorrelated residuals in inverse modelling of soil hydrological processes: a reason for concern or something that can safely be ignored?

NASA Astrophysics Data System (ADS)

Scharnagl, Benedikt; Durner, Wolfgang

2013-04-01

Models are inherently imperfect because they simplify processes that are themselves imperfectly known and understood. Moreover, the input variables and parameters needed to run a model are typically subject to various sources of error. As a consequence of these imperfections, model predictions will always deviate from corresponding observations. In most applications in soil hydrology, these deviations are clearly not random but rather show a systematic structure. From a statistical point of view, this systematic mismatch may be a reason for concern because it violates one of the basic assumptions made in inverse parameter estimation: the assumption of independence of the residuals. But what are the consequences of simply ignoring the autocorrelation in the residuals, as it is current practice in soil hydrology? Are the parameter estimates still valid even though the statistical foundation they are based on is partially collapsed? Theory and practical experience from other fields of science have shown that violation of the independence assumption will result in overconfident uncertainty bounds and that in some cases it may lead to significantly different optimal parameter values. In our contribution, we present three soil hydrological case studies, in which the effect of autocorrelated residuals on the estimated parameters was investigated in detail. We explicitly accounted for autocorrelated residuals using a formal likelihood function that incorporates an autoregressive model. The inverse problem was posed in a Bayesian framework, and the posterior probability density function of the parameters was estimated using Markov chain Monte Carlo simulation. In contrast to many other studies in related fields of science, and quite surprisingly, we found that the first-order autoregressive model, often abbreviated as AR(1), did not work well in the soil hydrological setting. We showed that a second-order autoregressive, or AR(2), model performs much better in these applications, leading to parameter and uncertainty estimates that satisfy all the underlying statistical assumptions. For theoretical reasons, these estimates are deemed more reliable than those estimates based on the neglect of autocorrelation in the residuals. In compliance with theory and results reported in the literature, our results showed that parameter uncertainty bounds were substantially wider if autocorrelation in the residuals was explicitly accounted for, and also the optimal parameter vales were slightly different in this case. We argue that the autoregressive model presented here should be used as a matter of routine in inverse modeling of soil hydrological processes.

Rising relative fluctuation as a warning indicator of discontinuous transitions in symbiotic metapopulations

NASA Astrophysics Data System (ADS)

Lumi, Neeme; Laas, Katrin; Mankin, Romi

2015-11-01

The long-time limit behavior of the stochastic Lotka-Volterra model of a symbiotic metapopulation subjected to generalized Verhulst self-regulation is considered. The influence of a time-variable environment on the carrying capacities of subpopulations is modeled as a periodic deterministic part and a symmetric dichotomous noise. Relying on the mean-field approach it is established that at certain parameter regimes the mean field (average subpopulations size) exhibits hysteresis in respect to the noise correlation time, manifested in the appearance of colored-noise-induced discontinuous transitions. Especially, it is shown that the relative fluctuation of the subpopulation sizes exhibits accelerated increase prior to abrupt transitions of the metapopulation state. Moreover, in certain cases the autocorrelation function of the population sizes demonstrates anticorrelation at some values of the lag time.

Anisotropic dielectric properties of two-dimensional matrix in pseudo-spin ferroelectric system

NASA Astrophysics Data System (ADS)

Kim, Se-Hun

2016-10-01

The anisotropic dielectric properties of a two-dimensional (2D) ferroelectric system were studied using the statistical calculation of the pseudo-spin Ising Hamiltonian model. It is necessary to delay the time for measurements of the observable and the independence of the new spin configuration under Monte Carlo sampling, in which the thermal equilibrium state depends on the temperature and size of the system. The autocorrelation time constants of the normalized relaxation function were determined by taking temperature and 2D lattice size into account. We discuss the dielectric constants of a two-dimensional ferroelectric system by using the Metropolis method in view of the Slater-Takagi defect energies.

Environmental drivers of spatial variation in whole-tree transpiration in an aspen-dominated upland-to-wetland forest gradient

NASA Astrophysics Data System (ADS)

Loranty, Michael M.; Mackay, D. Scott; Ewers, Brent E.; Adelman, Jonathan D.; Kruger, Eric L.

2008-02-01

Assumed representative center-of-stand measurements are typical inputs to models that scale forest transpiration to stand and regional extents. These inputs do not consider gradients in transpiration at stand boundaries or along moisture gradients and therefore potentially bias the large-scale estimates. We measured half-hourly sap flux (JS) for 173 trees in a spatially explicit cyclic sampling design across a topographically controlled gradient between a forested wetland and upland forest in northern Wisconsin. Our analyses focused on three dominant species in the site: quaking aspen (Populus tremuloides Michx), speckled alder (Alnus incana (DuRoi) Spreng), and white cedar (Thuja occidentalis L.). Sapwood area (AS) was used to scale JS to whole tree transpiration (EC). Because spatial patterns imply underlying processes, geostatistical analyses were employed to quantify patterns of spatial autocorrelation across the site. A simple Jarvis type model parameterized using a Monte Carlo sampling approach was used to simulate EC (EC-SIM). EC-SIM was compared with observed EC(EC-OBS) and found to reproduce both the temporal trends and spatial variance of canopy transpiration. EC-SIM was then used to examine spatial autocorrelation as a function of environmental drivers. We found no spatial autocorrelation in JS across the gradient from forested wetland to forested upland. EC was spatially autocorrelated and this was attributed to spatial variation in AS which suggests species spatial patterns are important for understanding spatial estimates of transpiration. However, the range of autocorrelation in EC-SIM decreased linearly with increasing vapor pressure deficit, implying that consideration of spatial variation in the sensitivity of canopy stomatal conductance to D is also key to accurately scaling up transpiration in space.

An approach for generating trajectory-based dynamics which conserves the canonical distribution in the phase space formulation of quantum mechanics. II. Thermal correlation functions.

PubMed

Liu, Jian; Miller, William H

2011-03-14

We show the exact expression of the quantum mechanical time correlation function in the phase space formulation of quantum mechanics. The trajectory-based dynamics that conserves the quantum canonical distribution-equilibrium Liouville dynamics (ELD) proposed in Paper I is then used to approximately evaluate the exact expression. It gives exact thermal correlation functions (of even nonlinear operators, i.e., nonlinear functions of position or momentum operators) in the classical, high temperature, and harmonic limits. Various methods have been presented for the implementation of ELD. Numerical tests of the ELD approach in the Wigner or Husimi phase space have been made for a harmonic oscillator and two strongly anharmonic model problems, for each potential autocorrelation functions of both linear and nonlinear operators have been calculated. It suggests ELD can be a potentially useful approach for describing quantum effects for complex systems in condense phase.

The influence of autocorrelation in signature extraction: An example from a geobotanical investigation of Cotter Basin, Montana

NASA Technical Reports Server (NTRS)

Labovitz, M. L.; Masuoka, E. J. (Principal Investigator)

1981-01-01

The presence of positive serial correlation (autocorrelation) in remotely sensed data results in an underestimate of the variance-covariance matrix when calculated using contiguous pixels. This underestimate produces an inflation in F statistics. For a set of Thematic Mapper Simulator data (TMS), used to test the ability to discriminate a known geobotanical anomaly from its background, the inflation in F statistics related to serial correlation is between 7 and 70 times. This means that significance tests of means of the spectral bands initially appear to suggest that the anomalous site is very different in spectral reflectance and emittance from its background sites. However, this difference often disappears and is always dramatically reduced when compared to frequency distributions of test statistics produced by the comparison of simulated training sets possessing equal means, but which are composed of autocorrelated observations.

Intrinsic Multi-Scale Dynamic Behaviors of Complex Financial Systems.

PubMed

Ouyang, Fang-Yan; Zheng, Bo; Jiang, Xiong-Fei

2015-01-01

The empirical mode decomposition is applied to analyze the intrinsic multi-scale dynamic behaviors of complex financial systems. In this approach, the time series of the price returns of each stock is decomposed into a small number of intrinsic mode functions, which represent the price motion from high frequency to low frequency. These intrinsic mode functions are then grouped into three modes, i.e., the fast mode, medium mode and slow mode. The probability distribution of returns and auto-correlation of volatilities for the fast and medium modes exhibit similar behaviors as those of the full time series, i.e., these characteristics are rather robust in multi time scale. However, the cross-correlation between individual stocks and the return-volatility correlation are time scale dependent. The structure of business sectors is mainly governed by the fast mode when returns are sampled at a couple of days, while by the medium mode when returns are sampled at dozens of days. More importantly, the leverage and anti-leverage effects are dominated by the medium mode.

Modeling long correlation times using additive binary Markov chains: Applications to wind generation time series.

PubMed

Weber, Juliane; Zachow, Christopher; Witthaut, Dirk

2018-03-01

Wind power generation exhibits a strong temporal variability, which is crucial for system integration in highly renewable power systems. Different methods exist to simulate wind power generation but they often cannot represent the crucial temporal fluctuations properly. We apply the concept of additive binary Markov chains to model a wind generation time series consisting of two states: periods of high and low wind generation. The only input parameter for this model is the empirical autocorrelation function. The two-state model is readily extended to stochastically reproduce the actual generation per period. To evaluate the additive binary Markov chain method, we introduce a coarse model of the electric power system to derive backup and storage needs. We find that the temporal correlations of wind power generation, the backup need as a function of the storage capacity, and the resting time distribution of high and low wind events for different shares of wind generation can be reconstructed.

Modeling long correlation times using additive binary Markov chains: Applications to wind generation time series

NASA Astrophysics Data System (ADS)

Weber, Juliane; Zachow, Christopher; Witthaut, Dirk

2018-03-01

Wind power generation exhibits a strong temporal variability, which is crucial for system integration in highly renewable power systems. Different methods exist to simulate wind power generation but they often cannot represent the crucial temporal fluctuations properly. We apply the concept of additive binary Markov chains to model a wind generation time series consisting of two states: periods of high and low wind generation. The only input parameter for this model is the empirical autocorrelation function. The two-state model is readily extended to stochastically reproduce the actual generation per period. To evaluate the additive binary Markov chain method, we introduce a coarse model of the electric power system to derive backup and storage needs. We find that the temporal correlations of wind power generation, the backup need as a function of the storage capacity, and the resting time distribution of high and low wind events for different shares of wind generation can be reconstructed.

A comparison of chronologies from tree rings

Treesearch

Kurt H. Riitters

1990-01-01

Forty-five-year ring width index chronologies were estimated by five mean-value functions applied to 183 ring width series from four similar sites. The effects of autocorrelation on the comparisons among mean-value functions were explored by fitting box-Jenkins models to individual-tree index services prior to pooling (prewhitening), and to the pooled chronologies...

Spot auto-focusing and spot auto-stigmation methods with high-definition auto-correlation function in high-resolution TEM.

PubMed

Isakozawa, Shigeto; Fuse, Taishi; Amano, Junpei; Baba, Norio

2018-04-01

As alternatives to the diffractogram-based method in high-resolution transmission electron microscopy, a spot auto-focusing (AF) method and a spot auto-stigmation (AS) method are presented with a unique high-definition auto-correlation function (HD-ACF). The HD-ACF clearly resolves the ACF central peak region in small amorphous-thin-film images, reflecting the phase contrast transfer function. At a 300-k magnification for a 120-kV transmission electron microscope, the smallest areas used are 64 × 64 pixels (~3 nm2) for the AF and 256 × 256 pixels for the AS. A useful advantage of these methods is that the AF function has an allowable accuracy even for a low s/n (~1.0) image. A reference database on the defocus dependency of the HD-ACF by the pre-acquisition of through-focus amorphous-thin-film images must be prepared to use these methods. This can be very beneficial because the specimens are not limited to approximations of weak phase objects but can be extended to objects outside such approximations.

PERIODIC AUTOREGRESSIVE-MOVING AVERAGE (PARMA) MODELING WITH APPLICATIONS TO WATER RESOURCES.

USGS Publications Warehouse

Vecchia, A.V.

1985-01-01

Results involving correlation properties and parameter estimation for autogressive-moving average models with periodic parameters are presented. A multivariate representation of the PARMA model is used to derive parameter space restrictions and difference equations for the periodic autocorrelations. Close approximation to the likelihood function for Gaussian PARMA processes results in efficient maximum-likelihood estimation procedures. Terms in the Fourier expansion of the parameters are sequentially included, and a selection criterion is given for determining the optimal number of harmonics to be included. Application of the techniques is demonstrated through analysis of a monthly streamflow time series.

Modified Beer-Lambert law for blood flow

PubMed Central

Baker, Wesley B.; Parthasarathy, Ashwin B.; Busch, David R.; Mesquita, Rickson C.; Greenberg, Joel H.; Yodh, A. G.

2014-01-01

We develop and validate a Modified Beer-Lambert law for blood flow based on diffuse correlation spectroscopy (DCS) measurements. The new formulation enables blood flow monitoring from temporal intensity autocorrelation function data taken at single or multiple delay-times. Consequentially, the speed of the optical blood flow measurement can be substantially increased. The scheme facilitates blood flow monitoring of highly scattering tissues in geometries wherein light propagation is diffusive or non-diffusive, and it is particularly well-suited for utilization with pressure measurement paradigms that employ differential flow signals to reduce contributions of superficial tissues. PMID:25426330

Numerical tests of local scale invariance in ageing q-state Potts models

NASA Astrophysics Data System (ADS)

Lorenz, E.; Janke, W.

2007-01-01

Much effort has been spent over the last years to achieve a coherent theoretical description of ageing as a non-linear dynamics process. Long supposed to be a consequence of the slow dynamics of glassy systems only, ageing phenomena could also be identified in the phase-ordering kinetics of simple ferromagnets. As a phenomenological approach Henkel et al. developed a group of local scale transformations under which two-time autocorrelation and response functions should transform covariantly. This work is to extend previous numerical tests of the predicted scaling functions for the Ising model by Monte Carlo simulations of two-dimensional q-state Potts models with q=3 and 8, which, in equilibrium, undergo temperature-driven phase transitions of second and first order, respectively.

Statistical spatial properties of speckle patterns generated by multiple laser beams

DOE Office of Scientific and Technical Information (OSTI.GOV)

Le Cain, A.; Sajer, J. M.; Riazuelo, G.

2011-08-15

This paper investigates hot spot characteristics generated by the superposition of multiple laser beams. First, properties of speckle statistics are studied in the context of only one laser beam by computing the autocorrelation function. The case of multiple laser beams is then considered. In certain conditions, it is shown that speckles have an ellipsoidal shape. Analytical expressions of hot spot radii generated by multiple laser beams are derived and compared to numerical estimates made from the autocorrelation function. They are also compared to numerical simulations performed within the paraxial approximation. Excellent agreement is found for the speckle width as wellmore » as for the speckle length. Application to the speckle patterns generated in the Laser MegaJoule configuration in the zone where all the beams overlap is presented. Influence of polarization on the size of the speckles as well as on their abundance is studied.« less

Quantifying uncertainty in soot volume fraction estimates using Bayesian inference of auto-correlated laser-induced incandescence measurements

NASA Astrophysics Data System (ADS)

Hadwin, Paul J.; Sipkens, T. A.; Thomson, K. A.; Liu, F.; Daun, K. J.

2016-01-01

Auto-correlated laser-induced incandescence (AC-LII) infers the soot volume fraction (SVF) of soot particles by comparing the spectral incandescence from laser-energized particles to the pyrometrically inferred peak soot temperature. This calculation requires detailed knowledge of model parameters such as the absorption function of soot, which may vary with combustion chemistry, soot age, and the internal structure of the soot. This work presents a Bayesian methodology to quantify such uncertainties. This technique treats the additional "nuisance" model parameters, including the soot absorption function, as stochastic variables and incorporates the current state of knowledge of these parameters into the inference process through maximum entropy priors. While standard AC-LII analysis provides a point estimate of the SVF, Bayesian techniques infer the posterior probability density, which will allow scientists and engineers to better assess the reliability of AC-LII inferred SVFs in the context of environmental regulations and competing diagnostics.

The Clustering of High-redshift (2.9 ≤ z ≤ 5.1) Quasars in SDSS Stripe 82

NASA Astrophysics Data System (ADS)

Timlin, John D.; Ross, Nicholas P.; Richards, Gordon T.; Myers, Adam D.; Pellegrino, Andrew; Bauer, Franz E.; Lacy, Mark; Schneider, Donald P.; Wollack, Edward J.; Zakamska, Nadia L.

2018-05-01

We present a measurement of the two-point autocorrelation function of photometrically selected high-z quasars over ∼100 deg2 on the Sloan Digital Sky Survey Stripe 82 field. Selection is performed using three machine-learning algorithms in a six-dimensional optical/mid-infrared color space. Optical data from the Sloan Digital Sky Survey are combined with overlapping deep mid-infrared data from the Spitzer IRAC Equatorial Survey and the Spitzer-HETDEX Exploratory Large-Area survey. Our selection algorithms are trained on the colors of known high-z quasars. The selected quasar sample consists of 1378 objects and contains both spectroscopically confirmed quasars and photometrically selected quasar candidates. These objects span a redshift range of 2.9 ≤ z ≤ 5.1 and are generally fainter than i = 20.2, a regime that has lacked sufficient number density to perform autocorrelation function measurements of photometrically classified quasars. We compute the angular correlation function of these data, marginally detecting quasar clustering. We fit a single power law with an index of δ = 1.39 ± 0.618 and amplitude of θ 0 = 0.‧71 ± 0.‧546 . A dark matter model is fit to the angular correlation function to estimate the linear bias. At the average redshift of our survey (< z> =3.38), the bias is b = 6.78 ± 1.79. Using this bias, we calculate a characteristic dark matter halo mass of 1.70–9.83× {10}12{h}-1 {M}ȯ . Our bias estimate suggests that quasar feedback intermittently shuts down the accretion of gas onto the central supermassive black hole at early times. If confirmed, these results hint at a level of luminosity dependence in the clustering of quasars at high-z.

Quantum state reconstruction and photon number statistics for low dimensional semiconductor opto-electronic devices

NASA Astrophysics Data System (ADS)

Böhm, Fabian; Grosse, Nicolai B.; Kolarczik, Mirco; Herzog, Bastian; Achtstein, Alexander; Owschimikow, Nina; Woggon, Ulrike

2017-09-01

Quantum state tomography and the reconstruction of the photon number distribution are techniques to extract the properties of a light field from measurements of its mean and fluctuations. These techniques are particularly useful when dealing with macroscopic or mesoscopic systems, where a description limited to the second order autocorrelation soon becomes inadequate. In particular, the emission of nonclassical light is expected from mesoscopic quantum dot systems strongly coupled to a cavity or in systems with large optical nonlinearities. We analyze the emission of a quantum dot-semiconductor optical amplifier system by quantifying the modifications of a femtosecond laser pulse propagating through the device. Using a balanced detection scheme in a self-heterodyning setup, we achieve precise measurements of the quadrature components and their fluctuations at the quantum noise limit1. We resolve the photon number distribution and the thermal-to-coherent evolution in the photon statistics of the emission. The interferometric detection achieves a high sensitivity in the few photon limit. From our data, we can also reconstruct the second order autocorrelation function with higher precision and time resolution compared with classical Hanbury Brown-Twiss experiments.

On the microscopic fluctuations driving the NMR relaxation of quadrupolar ions in water

DOE Office of Scientific and Technical Information (OSTI.GOV)

Carof, Antoine; Salanne, Mathieu; Rotenberg, Benjamin, E-mail: benjamin.rotenberg@upmc.fr

Nuclear Magnetic Resonance (NMR) relaxation is sensitive to the local structure and dynamics around the probed nuclei. The Electric Field Gradient (EFG) is the key microscopic quantity to understand the NMR relaxation of quadrupolar ions, such as {sup 7}Li{sup +}, {sup 23}Na{sup +}, {sup 25}Mg{sup 2+}, {sup 35}Cl{sup −}, {sup 39}K{sup +}, or {sup 133}Cs{sup +}. Using molecular dynamics simulations, we investigate the statistical and dynamical properties of the EFG experienced by alkaline, alkaline Earth, and chloride ions at infinite dilution in water. Specifically, we analyze the effect of the ionic charge and size on the distribution of the EFGmore » tensor and on the multi-step decay of its auto-correlation function. The main contribution to the NMR relaxation time arises from the slowest mode, with a characteristic time on the picosecond time scale. The first solvation shell of the ion plays a dominant role in the fluctuations of the EFG, all the more that the ion radius is small and its charge is large. We propose an analysis based on a simplified charge distribution around the ion, which demonstrates that the auto-correlation of the EFG, hence the NMR relaxation time, reflects primarily the collective translational motion of water molecules in the first solvation shell of the cations. Our findings provide a microscopic route to the quantitative interpretation of NMR relaxation measurements and open the way to the design of improved analytical theories for NMR relaxation for small ionic solutes, which should focus on water density fluctuations around the ion.« less

Correlations in magnitude series to assess nonlinearities: Application to multifractal models and heartbeat fluctuations.

PubMed

Bernaola-Galván, Pedro A; Gómez-Extremera, Manuel; Romance, A Ramón; Carpena, Pedro

2017-09-01

The correlation properties of the magnitude of a time series are associated with nonlinear and multifractal properties and have been applied in a great variety of fields. Here we have obtained the analytical expression of the autocorrelation of the magnitude series (C_{|x|}) of a linear Gaussian noise as a function of its autocorrelation (C_{x}). For both, models and natural signals, the deviation of C_{|x|} from its expectation in linear Gaussian noises can be used as an index of nonlinearity that can be applied to relatively short records and does not require the presence of scaling in the time series under study. In a model of artificial Gaussian multifractal signal we use this approach to analyze the relation between nonlinearity and multifractallity and show that the former implies the latter but the reverse is not true. We also apply this approach to analyze experimental data: heart-beat records during rest and moderate exercise. For each individual subject, we observe higher nonlinearities during rest. This behavior is also achieved on average for the analyzed set of 10 semiprofessional soccer players. This result agrees with the fact that other measures of complexity are dramatically reduced during exercise and can shed light on its relationship with the withdrawal of parasympathetic tone and/or the activation of sympathetic activity during physical activity.

Anisotropy of the Earth's inner inner core from autocorrelations of earthquake coda in China Regional Seismic Networks

NASA Astrophysics Data System (ADS)

Xia, H.; Song, X.; Wang, T.

2014-12-01

The Earth's inner core possesses strong cylindrical anisotropy with the fast symmetry axis parallel to the rotation axis. However, recent study has suggested that the inner part of the inner core has a fast symmetry axis near the equator with a different form of anisotropy from the outer part (Wang et al., this session). To confirm the observation, we use data from dense seismic arrays of the China Regional Seismic Networks. We perform autocorrelation (ACC) of the coda after major earthquakes (Mw>=7.0) at each station and then stack the ACCs at each cluster of stations. The PKIKP2 and PKIIKP2 phases (round-trip phase from the Earth's surface reflections) can be clearly extracted from the stacked empirical Green's functions. We observe systematic variation of the differential times between PKIKP2 and PKIIKP2 phases, which are sensitive to the bulk anisotropy of the inner core. The differential times show large variations with both latitudes and longitudes, even though our ray paths are not polar (with our stations at mid-range latitudes of about 20 to 45 degrees). The observations cannot be explained by an averaged anisotropy model with the fast axis along the rotation axis. The pattern appears consistent with an inner inner core that has a fast axis near the equator.

Correlations in magnitude series to assess nonlinearities: Application to multifractal models and heartbeat fluctuations

NASA Astrophysics Data System (ADS)

Bernaola-Galván, Pedro A.; Gómez-Extremera, Manuel; Romance, A. Ramón; Carpena, Pedro

2017-09-01

The correlation properties of the magnitude of a time series are associated with nonlinear and multifractal properties and have been applied in a great variety of fields. Here we have obtained the analytical expression of the autocorrelation of the magnitude series (C|x |) of a linear Gaussian noise as a function of its autocorrelation (Cx). For both, models and natural signals, the deviation of C|x | from its expectation in linear Gaussian noises can be used as an index of nonlinearity that can be applied to relatively short records and does not require the presence of scaling in the time series under study. In a model of artificial Gaussian multifractal signal we use this approach to analyze the relation between nonlinearity and multifractallity and show that the former implies the latter but the reverse is not true. We also apply this approach to analyze experimental data: heart-beat records during rest and moderate exercise. For each individual subject, we observe higher nonlinearities during rest. This behavior is also achieved on average for the analyzed set of 10 semiprofessional soccer players. This result agrees with the fact that other measures of complexity are dramatically reduced during exercise and can shed light on its relationship with the withdrawal of parasympathetic tone and/or the activation of sympathetic activity during physical activity.

Theory of acoustic design of opera house and a design proposal

NASA Astrophysics Data System (ADS)

Ando, Yoichi

2004-05-01

First of all, the theory of subjective preference for sound fields based on the model of auditory-brain system is briefly mentioned. It consists of the temporal factors and spatial factors associated with the left and right cerebral hemispheres, respectively. The temporal criteria are the initial time delay gap between the direct sound and the first Reflection (Dt1) and the subsequent reverberation time (Tsub). These preferred conditions are related to the minimum value of effective duration of the running autocorrelation function of source signals (te)min. The spatial criteria are binaural listening level (LL) and the IACC, which may be extracted from the interaural crosscorrelation function. In the opera house, there are two different kind of sound sources, i.e., the vocal source of relatively short values of (te)min in the stage and the orchestra music of long values of (te)min in the pit. For these sources, a proposal is made here.

Fractional Brownian motion and the critical dynamics of zipping polymers.

PubMed

Walter, J-C; Ferrantini, A; Carlon, E; Vanderzande, C

2012-03-01

We consider two complementary polymer strands of length L attached by a common-end monomer. The two strands bind through complementary monomers and at low temperatures form a double-stranded conformation (zipping), while at high temperature they dissociate (unzipping). This is a simple model of DNA (or RNA) hairpin formation. Here we investigate the dynamics of the strands at the equilibrium critical temperature T=T(c) using Monte Carlo Rouse dynamics. We find that the dynamics is anomalous, with a characteristic time scaling as τ∼L(2.26(2)), exceeding the Rouse time ∼L(2.18). We investigate the probability distribution function, velocity autocorrelation function, survival probability, and boundary behavior of the underlying stochastic process. These quantities scale as expected from a fractional Brownian motion with a Hurst exponent H=0.44(1). We discuss similarities to and differences from unbiased polymer translocation.

Incorporating spatial autocorrelation into species distribution models alters forecasts of climate-mediated range shifts.

PubMed

Crase, Beth; Liedloff, Adam; Vesk, Peter A; Fukuda, Yusuke; Wintle, Brendan A

2014-08-01

Species distribution models (SDMs) are widely used to forecast changes in the spatial distributions of species and communities in response to climate change. However, spatial autocorrelation (SA) is rarely accounted for in these models, despite its ubiquity in broad-scale ecological data. While spatial autocorrelation in model residuals is known to result in biased parameter estimates and the inflation of type I errors, the influence of unmodeled SA on species' range forecasts is poorly understood. Here we quantify how accounting for SA in SDMs influences the magnitude of range shift forecasts produced by SDMs for multiple climate change scenarios. SDMs were fitted to simulated data with a known autocorrelation structure, and to field observations of three mangrove communities from northern Australia displaying strong spatial autocorrelation. Three modeling approaches were implemented: environment-only models (most frequently applied in species' range forecasts), and two approaches that incorporate SA; autologistic models and residuals autocovariate (RAC) models. Differences in forecasts among modeling approaches and climate scenarios were quantified. While all model predictions at the current time closely matched that of the actual current distribution of the mangrove communities, under the climate change scenarios environment-only models forecast substantially greater range shifts than models incorporating SA. Furthermore, the magnitude of these differences intensified with increasing increments of climate change across the scenarios. When models do not account for SA, forecasts of species' range shifts indicate more extreme impacts of climate change, compared to models that explicitly account for SA. Therefore, where biological or population processes induce substantial autocorrelation in the distribution of organisms, and this is not modeled, model predictions will be inaccurate. These results have global importance for conservation efforts as inaccurate forecasts lead to ineffective prioritization of conservation activities and potentially to avoidable species extinctions. © 2014 John Wiley & Sons Ltd.

Disentangling the effects of forage, social rank, and risk on movement autocorrelation of elephants using Fourier and wavelet analyses

PubMed Central

Wittemyer, George; Polansky, Leo; Douglas-Hamilton, Iain; Getz, Wayne M.

2008-01-01

The internal state of an individual—as it relates to thirst, hunger, fear, or reproductive drive—can be inferred by referencing points on its movement path to external environmental and sociological variables. Using time-series approaches to characterize autocorrelative properties of step-length movements collated every 3 h for seven free-ranging African elephants, we examined the influence of social rank, predation risk, and seasonal variation in resource abundance on periodic properties of movement. The frequency domain methods of Fourier and wavelet analyses provide compact summaries of temporal autocorrelation and show both strong diurnal and seasonal based periodicities in the step-length time series. This autocorrelation is weaker during the wet season, indicating random movements are more common when ecological conditions are good. Periodograms of socially dominant individuals are consistent across seasons, whereas subordinate individuals show distinct differences diverging from that of dominants during the dry season. We link temporally localized statistical properties of movement to landscape features and find that diurnal movement correlation is more common within protected wildlife areas, and multiday movement correlations found among lower ranked individuals are typically outside of protected areas where predation risks are greatest. A frequency-related spatial analysis of movement-step lengths reveal that rest cycles related to the spatial distribution of critical resources (i.e., forage and water) are responsible for creating the observed patterns. Our approach generates unique information regarding the spatial-temporal interplay between environmental and individual characteristics, providing an original approach for understanding the movement ecology of individual animals and the spatial organization of animal populations. PMID:19060207

Vibrationally resolved photoelectron spectra of lower diamondoids: A time-dependent approach

NASA Astrophysics Data System (ADS)

Xiong, Tao; Włodarczyk, Radosław; Gallandi, Lukas; Körzdörfer, Thomas; Saalfrank, Peter

2018-01-01

Vibrationally resolved lowest-energy bands of the photoelectron spectra (PES) of adamantane, diamantane, and urotropine were simulated by a time-dependent correlation function approach within the harmonic approximation. Geometries and normal modes for neutral and cationic molecules were obtained from B3LYP hybrid density functional theory (DFT). It is shown that the simulated spectra reproduce the experimentally observed vibrational finestructure (or its absence) quite well. Origins of the finestructure are discussed and related to recurrences of autocorrelation functions and dominant vibrations. Remaining quantitative and qualitative errors of the DFT-derived PES spectra refer to (i) an overall redshift by ˜0.5 eV and (ii) the absence of satellites in the high-energy region of the spectra. The former error is shown to be due to the neglect of many-body corrections to ordinary Kohn-Sham methods, while the latter has been argued to be due to electron-nuclear couplings beyond the Born-Oppenheimer approximation [Gali et al., Nat. Commun. 7, 11327 (2016)].

Noninvasive observation of skeletal muscle contraction using near-infrared time-resolved reflectance and diffusing-wave spectroscopy

NASA Astrophysics Data System (ADS)

Belau, Markus; Ninck, Markus; Hering, Gernot; Spinelli, Lorenzo; Contini, Davide; Torricelli, Alessandro; Gisler, Thomas

2010-09-01

We introduce a method for noninvasively measuring muscle contraction in vivo, based on near-infrared diffusing-wave spectroscopy (DWS). The method exploits the information about time-dependent shear motions within the contracting muscle that are contained in the temporal autocorrelation function g(1)(τ,t) of the multiply scattered light field measured as a function of lag time, τ, and time after stimulus, t. The analysis of g(1)(τ,t) measured on the human M. biceps brachii during repetitive electrical stimulation, using optical properties measured with time-resolved reflectance spectroscopy, shows that the tissue dynamics giving rise to the speckle fluctuations can be described by a combination of diffusion and shearing. The evolution of the tissue Cauchy strain e(t) shows a strong correlation with the force, indicating that a significant part of the shear observed with DWS is due to muscle contraction. The evolution of the DWS decay time shows quantitative differences between the M. biceps brachii and the M. gastrocnemius, suggesting that DWS allows to discriminate contraction of fast- and slow-twitch muscle fibers.

Statistical models and time series forecasting of sulfur dioxide: a case study Tehran.

PubMed

Hassanzadeh, S; Hosseinibalam, F; Alizadeh, R

2009-08-01

This study performed a time-series analysis, frequency distribution and prediction of SO(2) levels for five stations (Pardisan, Vila, Azadi, Gholhak and Bahman) in Tehran for the period of 2000-2005. Most sites show a quite similar characteristic with highest pollution in autumn-winter time and least pollution in spring-summer. The frequency distributions show higher peaks at two residential sites. The potential for SO(2) problems is high because of high emissions and the close geographical proximity of the major industrial and urban centers. The ACF and PACF are nonzero for several lags, indicating a mixed (ARMA) model, then at Bahman station an ARMA model was used for forecasting SO(2). The partial autocorrelations become close to 0 after about 5 lags while the autocorrelations remain strong through all the lags shown. The results proved that ARMA (2,2) model can provides reliable, satisfactory predictions for time series.

Extension of the spatial autocorrelation (SPAC) method to mixed-component correlations of surface waves

USGS Publications Warehouse

Haney, Matthew M.; Mikesell, T. Dylan; van Wijk, Kasper; Nakahara, Hisashi

2012-01-01

Using ambient seismic noise for imaging subsurface structure dates back to the development of the spatial autocorrelation (SPAC) method in the 1950s. We present a theoretical analysis of the SPAC method for multicomponent recordings of surface waves to determine the complete 3 × 3 matrix of correlations between all pairs of three-component motions, called the correlation matrix. In the case of isotropic incidence, when either Rayleigh or Love waves arrive from all directions with equal power, the only non-zero off-diagonal terms in the matrix are the vertical–radial (ZR) and radial–vertical (RZ) correlations in the presence of Rayleigh waves. Such combinations were not considered in the development of the SPAC method. The method originally addressed the vertical–vertical (ZZ), RR and TT correlations, hence the name spatial autocorrelation. The theoretical expressions we derive for the ZR and RZ correlations offer additional ways to measure Rayleigh wave dispersion within the SPAC framework. Expanding on the results for isotropic incidence, we derive the complete correlation matrix in the case of generally anisotropic incidence. We show that the ZR and RZ correlations have advantageous properties in the presence of an out-of-plane directional wavefield compared to ZZ and RR correlations. We apply the results for mixed-component correlations to a data set from Akutan Volcano, Alaska and find consistent estimates of Rayleigh wave phase velocity from ZR compared to ZZ correlations. This work together with the recently discovered connections between the SPAC method and time-domain correlations of ambient noise provide further insights into the retrieval of surface wave Green’s functions from seismic noise.

Spectral density mapping at multiple magnetic fields suitable for 13C NMR relaxation studies

NASA Astrophysics Data System (ADS)

Kadeřávek, Pavel; Zapletal, Vojtěch; Fiala, Radovan; Srb, Pavel; Padrta, Petr; Přecechtělová, Jana Pavlíková; Šoltésová, Mária; Kowalewski, Jozef; Widmalm, Göran; Chmelík, Josef; Sklenář, Vladimír; Žídek, Lukáš

2016-05-01

Standard spectral density mapping protocols, well suited for the analysis of 15N relaxation rates, introduce significant systematic errors when applied to 13C relaxation data, especially if the dynamics is dominated by motions with short correlation times (small molecules, dynamic residues of macromolecules). A possibility to improve the accuracy by employing cross-correlated relaxation rates and on measurements taken at several magnetic fields has been examined. A suite of protocols for analyzing such data has been developed and their performance tested. Applicability of the proposed protocols is documented in two case studies, spectral density mapping of a uniformly labeled RNA hairpin and of a selectively labeled disaccharide exhibiting highly anisotropic tumbling. Combination of auto- and cross-correlated relaxation data acquired at three magnetic fields was applied in the former case in order to separate effects of fast motions and conformational or chemical exchange. An approach using auto-correlated relaxation rates acquired at five magnetic fields, applicable to anisotropically moving molecules, was used in the latter case. The results were compared with a more advanced analysis of data obtained by interpolation of auto-correlated relaxation rates measured at seven magnetic fields, and with the spectral density mapping of cross-correlated relaxation rates. The results showed that sufficiently accurate values of auto- and cross-correlated spectral density functions at zero and 13C frequencies can be obtained from data acquired at three magnetic fields for uniformly 13C -labeled molecules with a moderate anisotropy of the rotational diffusion tensor. Analysis of auto-correlated relaxation rates at five magnetic fields represents an alternative for molecules undergoing highly anisotropic motions.

Switchgrass and Miscanthus Biomass and Theoretical Ethanol Production from Reclaimed Mine Lands in West Virginia

NASA Astrophysics Data System (ADS)

Scagline, Steffany M.

Near infrared stimulation or Low Level Laser Therapy (LLLT) is an innovative technique shown to effect the microvasculature hemodynamics. The aim of this study is to use Diffused Correlation Spectroscopy (DCS) to evaluate the physiological effects of LLLT on blood perfusion. This study is divided into two parts: the fist part is the development of DCS system and the second part is investigating the effects of LLLT on biological tissue. DCS is an emerging non-invasive technique to probe deep tissue hemodynamics. DCS uses time-averaged intensity autocorrelation function for the fluctuations caused due to the moving scatterers (RBCs) in biological tissue. We present a software based autocorrelator system to complete the acquisition and processing parts. We conducted validation studies on an intralipid phantom and human forearm. Both the studies proved smooth decay curves which help in getting a better curve fitting and as a result more accurate blood flow index (BFI). We show that the software based autocorrelation system can be an alternative to the conventional hardware based correlators in DCS systems with benefits such as flexibility in raw photon count data processing and low cost. The objective of the second part of this study is evaluating how a single session of LLLT alters the hemodynamics in the microvasculature. We performed an experiment where the subjects forearm was stimulated with LLLT and the corresponding changes were recorded using DCS system. The results obtained shows significant hemodynamic changes in response to LLLT with a 95%confidence interval. The results in this study indicate that LLLT could lead to the development of non-invasive technique to help in rehabilitation and performance-enhancing of healthy humans.

A Comparison of Weights Matrices on Computation of Dengue Spatial Autocorrelation

NASA Astrophysics Data System (ADS)

Suryowati, K.; Bekti, R. D.; Faradila, A.

2018-04-01

Spatial autocorrelation is one of spatial analysis to identify patterns of relationship or correlation between locations. This method is very important to get information on the dispersal patterns characteristic of a region and linkages between locations. In this study, it applied on the incidence of Dengue Hemorrhagic Fever (DHF) in 17 sub districts in Sleman, Daerah Istimewa Yogyakarta Province. The link among location indicated by a spatial weight matrix. It describe the structure of neighbouring and reflects the spatial influence. According to the spatial data, type of weighting matrix can be divided into two types: point type (distance) and the neighbourhood area (contiguity). Selection weighting function is one determinant of the results of the spatial analysis. This study use queen contiguity based on first order neighbour weights, queen contiguity based on second order neighbour weights, and inverse distance weights. Queen contiguity first order and inverse distance weights shows that there is the significance spatial autocorrelation in DHF, but not by queen contiguity second order. Queen contiguity first and second order compute 68 and 86 neighbour list

Forty-photon-per-pulse spectral phase retrieval by shaper-assisted modified interferometric field autocorrelation.

PubMed

Hsu, Chen-Shao; Chiang, Hsin-Chien; Chuang, Hsiu-Po; Huang, Chen-Bin; Yang, Shang-Da

2011-07-15

We retrieve the spectral phase of 400 fs pulses at 1560 nm with 5.2 aJ coupled pulse energy (40 photons) by the modified interferometric field autocorrelation method, using a pulse shaper and a 5 cm long periodically poled lithium niobate waveguide. The carrier-envelope phase control of the shaper can reduce the fringe density of the interferometric trace and permits longer lock-in time constants, achieving a sensitivity of 2.7×10(-9) mW(2) (40 times better than the previous record for self-referenced nonlinear pulse measurement). The high stability of the pulse shaper allows for accurate and reproducible measurements of complicated spectral phases. © 2011 Optical Society of America

Single-particle structure determination by correlations of snapshot X-ray diffraction patterns (CXIDB ID 20)

DOE Data Explorer

Starodub, D.

2013-03-25

This deposition includes the diffraction images generated by the paired polystyrene spheres in random orientations. These images were used to determine and phase the single particle diffraction volume from their autocorrelation functions.

Removal of a hazardous heavy metal from aqueous solution using functionalized graphene and boron nitride nanosheets: Insights from simulations.

PubMed

Azamat, Jafar; Sattary, Batoul Shirforush; Khataee, Alireza; Joo, Sang Woo

2015-09-01

A computer simulation was performed to investigate the removal of Zn(2+) as a heavy metal from aqueous solution using the functionalized pore of a graphene nanosheet and boron nitride nanosheet (BNNS). The simulated systems were comprised of a graphene nanosheet or BNNS with a functionalized pore containing an aqueous ionic solution of zinc chloride. In order to remove heavy metal from an aqueous solution using the functionalized pore of a graphene nanosheet and BNNS, an external voltage was applied along the z-axis of the simulated box. For the selective removal of zinc ions, the pores of graphene and BNNS were functionalized by passivating each atom at the pore edge with appropriate atoms. For complete analysis systems, we calculated the potential of the mean force of ions, the radial distribution function of ion-water, the residence time of ions, the hydrogen bond, and the autocorrelation function of the hydrogen bond. Copyright © 2015 Elsevier Inc. All rights reserved.

Geostatistical Prediction of Microbial Water Quality Throughout a Stream Network Using Meteorology, Land Cover, and Spatiotemporal Autocorrelation.

PubMed

Holcomb, David A; Messier, Kyle P; Serre, Marc L; Rowny, Jakob G; Stewart, Jill R

2018-06-25

Predictive modeling is promising as an inexpensive tool to assess water quality. We developed geostatistical predictive models of microbial water quality that empirically modeled spatiotemporal autocorrelation in measured fecal coliform (FC) bacteria concentrations to improve prediction. We compared five geostatistical models featuring different autocorrelation structures, fit to 676 observations from 19 locations in North Carolina's Jordan Lake watershed using meteorological and land cover predictor variables. Though stream distance metrics (with and without flow-weighting) failed to improve prediction over the Euclidean distance metric, incorporating temporal autocorrelation substantially improved prediction over the space-only models. We predicted FC throughout the stream network daily for one year, designating locations "impaired", "unimpaired", or "unassessed" if the probability of exceeding the state standard was ≥90%, ≤10%, or >10% but <90%, respectively. We could assign impairment status to more of the stream network on days any FC were measured, suggesting frequent sample-based monitoring remains necessary, though implementing spatiotemporal predictive models may reduce the number of concurrent sampling locations required to adequately assess water quality. Together, these results suggest that prioritizing sampling at different times and conditions using geographically sparse monitoring networks is adequate to build robust and informative geostatistical models of water quality impairment.

Bridging the gulf between correlated random walks and Lévy walks: autocorrelation as a source of Lévy walk movement patterns.

PubMed

Reynolds, Andy M

2010-12-06

For many years, the dominant conceptual framework for describing non-oriented animal movement patterns has been the correlated random walk (CRW) model in which an individual's trajectory through space is represented by a sequence of distinct, independent randomly oriented 'moves'. It has long been recognized that the transformation of an animal's continuous movement path into a broken line is necessarily arbitrary and that probability distributions of move lengths and turning angles are model artefacts. Continuous-time analogues of CRWs that overcome this inherent shortcoming have appeared in the literature and are gaining prominence. In these models, velocities evolve as a Markovian process and have exponential autocorrelation. Integration of the velocity process gives the position process. Here, through a simple scaling argument and through an exact analytical analysis, it is shown that autocorrelation inevitably leads to Lévy walk (LW) movement patterns on timescales less than the autocorrelation timescale. This is significant because over recent years there has been an accumulation of evidence from a variety of experimental and theoretical studies that many organisms have movement patterns that can be approximated by LWs, and there is now intense debate about the relative merits of CRWs and LWs as representations of non-orientated animal movement patterns.

Statistical anisotropy in free turbulence for mixing layers at high Reynolds numbers

NASA Astrophysics Data System (ADS)

Gardner, Patrick J.; Roggemann, Michael C.; Welsh, Byron M.; Bowersox, Rodney D.; Luke, Theodore E.

1996-08-01

A lateral shearing interferometer was used to measure the slope of perturbed wave fronts after propagating through free turbulent mixing layers. Shearing interferometers provide a two-dimensional flow visualization that is nonintrusive. Slope measurements were used to reconstruct the phase of the turbulence-corrupted wave front. The random phase fluctuations induced by the mixing layer were captured in a large ensemble of wave-front measurements. Experiments were performed on an unbounded, plane shear mixing layer of helium and nitrogen gas at fixed velocities and high Reynolds numbers for six locations in the flow development. Statistical autocorrelation functions and structure functions were computed on the reconstructed phase maps. The autocorrelation function results indicated that the turbulence-induced phase fluctuations were not wide-sense stationary. The structure functions exhibited statistical homogeneity, indicating that the phase fluctuations were stationary in first increments. However, the turbulence-corrupted phase was not isotropic. A five-thirds power law is shown to fit orthogonal slices of the structure function, analogous to the Kolmogorov model for isotropic turbulence. Strehl ratios were computed from the phase structure functions and compared with classical estimates that assume isotropy. The isotropic models are shown to overestimate the optical degradation by nearly 3 orders of magnitude compared with the structure function calculations.

Scaling and efficiency determine the irreversible evolution of a market

PubMed Central

Baldovin, F.; Stella, A. L.

2007-01-01

In setting up a stochastic description of the time evolution of a financial index, the challenge consists in devising a model compatible with all stylized facts emerging from the analysis of financial time series and providing a reliable basis for simulating such series. Based on constraints imposed by market efficiency and on an inhomogeneous-time generalization of standard simple scaling, we propose an analytical model which accounts simultaneously for empirical results like the linear decorrelation of successive returns, the power law dependence on time of the volatility autocorrelation function, and the multiscaling associated to this dependence. In addition, our approach gives a justification and a quantitative assessment of the irreversible character of the index dynamics. This irreversibility enters as a key ingredient in a novel simulation strategy of index evolution which demonstrates the predictive potential of the model.

A reanalysis of "Two types of asynchronous activity in networks of excitatory and inhibitory spiking neurons".

PubMed

Engelken, Rainer; Farkhooi, Farzad; Hansel, David; van Vreeswijk, Carl; Wolf, Fred

2016-01-01

Neuronal activity in the central nervous system varies strongly in time and across neuronal populations. It is a longstanding proposal that such fluctuations generically arise from chaotic network dynamics. Various theoretical studies predict that the rich dynamics of rate models operating in the chaotic regime can subserve circuit computation and learning. Neurons in the brain, however, communicate via spikes and it is a theoretical challenge to obtain similar rate fluctuations in networks of spiking neuron models. A recent study investigated spiking balanced networks of leaky integrate and fire (LIF) neurons and compared their dynamics to a matched rate network with identical topology, where single unit input-output functions were chosen from isolated LIF neurons receiving Gaussian white noise input. A mathematical analogy between the chaotic instability in networks of rate units and the spiking network dynamics was proposed. Here we revisit the behavior of the spiking LIF networks and these matched rate networks. We find expected hallmarks of a chaotic instability in the rate network: For supercritical coupling strength near the transition point, the autocorrelation time diverges. For subcritical coupling strengths, we observe critical slowing down in response to small external perturbations. In the spiking network, we found in contrast that the timescale of the autocorrelations is insensitive to the coupling strength and that rate deviations resulting from small input perturbations rapidly decay. The decay speed even accelerates for increasing coupling strength. In conclusion, our reanalysis demonstrates fundamental differences between the behavior of pulse-coupled spiking LIF networks and rate networks with matched topology and input-output function. In particular there is no indication of a corresponding chaotic instability in the spiking network.

Fluid-fluid interfacial mobility from random walks

NASA Astrophysics Data System (ADS)

Barclay, Paul L.; Lukes, Jennifer R.

2017-12-01

Dual control volume grand canonical molecular dynamics is used to perform the first calculation of fluid-fluid interfacial mobilities. The mobility is calculated from one-dimensional random walks of the interface by relating the diffusion coefficient to the interfacial mobility. Three different calculation methods are employed: one using the interfacial position variance as a function of time, one using the mean-squared interfacial displacement, and one using the time-autocorrelation of the interfacial velocity. The mobility is calculated for two liquid-liquid interfaces and one liquid-vapor interface to examine the robustness of the methods. Excellent agreement between the three calculation methods is shown for all the three interfaces, indicating that any of them could be used to calculate the interfacial mobility.

Study of trabecular bone microstructure using spatial autocorrelation analysis

NASA Astrophysics Data System (ADS)

Wald, Michael J.; Vasilic, Branimir; Saha, Punam K.; Wehrli, Felix W.

2005-04-01

The spatial autocorrelation analysis method represents a powerful, new approach to quantitative characterization of structurally quasi-periodic anisotropic materials such as trabecular bone (TB). The method is applicable to grayscale images and thus does not require any preprocessing, such as segmentation which is difficult to achieve in the limited resolution regime of in vivo imaging. The 3D autocorrelation function (ACF) can be efficiently calculated using the Fourier transform. The resulting trabecular thickness and spacing measurements are robust to the presence of noise and produce values within the expected range as determined by other methods from μCT and μMRI datasets. TB features found from the ACF are shown to correlate well with those determined by the Fuzzy Distance transform (FDT) in the transverse plane, i.e. the plane orthogonal to bone"s major axis. The method is further shown to be applicable to in-vivo μMRI data. Using the ACF, we examine data acquired in a previous study aimed at evaluating the structural implications of male hypogonadism characterized by testosterone deficiency and reduced bone mass. Specifically, we consider the hypothesis that eugonadal and hypogonadal men differ in the anisotropy of their trabecular networks. The analysis indicates a significant difference in trabecular bone thickness and longitudinal spacing between the control group and the testosterone deficient group. We conclude that spatial autocorrelation analysis is able to characterize the 3D structure and anisotropy of trabecular bone and provides new insight into the structural changes associated with osteoporotic trabecular bone loss.

Old document image segmentation using the autocorrelation function and multiresolution analysis

NASA Astrophysics Data System (ADS)

Mehri, Maroua; Gomez-Krämer, Petra; Héroux, Pierre; Mullot, Rémy

2013-01-01

Recent progress in the digitization of heterogeneous collections of ancient documents has rekindled new challenges in information retrieval in digital libraries and document layout analysis. Therefore, in order to control the quality of historical document image digitization and to meet the need of a characterization of their content using intermediate level metadata (between image and document structure), we propose a fast automatic layout segmentation of old document images based on five descriptors. Those descriptors, based on the autocorrelation function, are obtained by multiresolution analysis and used afterwards in a specific clustering method. The method proposed in this article has the advantage that it is performed without any hypothesis on the document structure, either about the document model (physical structure), or the typographical parameters (logical structure). It is also parameter-free since it automatically adapts to the image content. In this paper, firstly, we detail our proposal to characterize the content of old documents by extracting the autocorrelation features in the different areas of a page and at several resolutions. Then, we show that is possible to automatically find the homogeneous regions defined by similar indices of autocorrelation without knowledge about the number of clusters using adapted hierarchical ascendant classification and consensus clustering approaches. To assess our method, we apply our algorithm on 316 old document images, which encompass six centuries (1200-1900) of French history, in order to demonstrate the performance of our proposal in terms of segmentation and characterization of heterogeneous corpus content. Moreover, we define a new evaluation metric, the homogeneity measure, which aims at evaluating the segmentation and characterization accuracy of our methodology. We find a 85% of mean homogeneity accuracy. Those results help to represent a document by a hierarchy of layout structure and content, and to define one or more signatures for each page, on the basis of a hierarchical representation of homogeneous blocks and their topology.

Structure of the North Anatolian Fault Zone from the Autocorrelation of Ambient Seismic Noise

NASA Astrophysics Data System (ADS)

Taylor, George; Rost, Sebastian; Houseman, Gregory

2016-04-01

In recent years the technique of cross-correlating the ambient seismic noise wavefield at two seismometers to reconstruct empirical Green's Functions for the determination of Earth structure has been a powerful tool to study the Earth's interior without earthquakes or man-made sources. However, far less attention has been paid to using auto-correlations of seismic noise to reveal body wave reflections from interfaces in the subsurface. In principle, the Green's functions thus derived should be comparable to the Earth's impulse response to a co-located source and receiver. We use data from a dense seismic array (Dense Array for Northern Anatolia - DANA) deployed across the northern branch of the North Anatolian Fault Zone (NAFZ) in the region of the 1999 magnitude 7.6 Izmit earthquake in western Turkey. The NAFZ is a major strike-slip system that extends ~1200 km across northern Turkey and continues to pose a high level of seismic hazard, in particular to the mega-city of Istanbul. We construct body wave images for the entire crust and the shallow upper mantle over the ~35 km by 70 km footprint of the 70-station DANA array. Using autocorrelations of the vertical component of ground motion, P-wave reflections can be retrieved from the wavefield to constrain crustal structure. We show that clear P-wave reflections from the crust-mantle boundary (Moho) can be retrieved using the autocorrelation technique, indicating topography on the Moho on horizontal scales of less than 10 km. Offsets in crustal structure can be identified that seem to be correlated with the surface expression of the northern branch of the fault zone, indicating that the NAFZ reaches the upper mantle as a narrow structure. The southern branch has a less clear effect on crustal structure. We also see evidence of several discontinuities in the mid-crust in addition to an upper mantle reflector that we interpret to represent the Hales discontinuity.

Time Series Analysis of the Quasar PKS 1749+096

NASA Astrophysics Data System (ADS)

Lam, Michael T.; Balonek, T. J.

2011-01-01

Multiple timescales of variability are observed in quasars at a variety of wavelengths, the nature of which is not fully understood. In 2007 and 2008, the quasar 1749+096 underwent two unprecedented optical outbursts, reaching a brightness never before seen in our twenty years of monitoring. Much lower level activity had been seen prior to these two outbursts. We present an analysis of the timescales of variability over the two regimes using a variety of statistical techniques. An IDL software package developed at Colgate University over the summer of 2010, the Quasar User Interface (QUI), provides effective computation of four time series functions for analyzing underlying trends present in generic, discretely sampled data sets. Using the Autocorrelation Function, Structure Function, and Power Spectrum, we are able to quickly identify possible variability timescales. QUI is also capable of computing the Cross-Correlation Function for comparing variability at different wavelengths. We apply these algorithms to 1749+096 and present our analysis of the timescales for this object. Funding for this project was received from Colgate University, the Justus and Jayne Schlichting Student Research Fund, and the NASA / New York Space Grant.

Continuous-Time Random Walk with multi-step memory: an application to market dynamics

NASA Astrophysics Data System (ADS)

Gubiec, Tomasz; Kutner, Ryszard

2017-11-01

An extended version of the Continuous-Time Random Walk (CTRW) model with memory is herein developed. This memory involves the dependence between arbitrary number of successive jumps of the process while waiting times between jumps are considered as i.i.d. random variables. This dependence was established analyzing empirical histograms for the stochastic process of a single share price on a market within the high frequency time scale. Then, it was justified theoretically by considering bid-ask bounce mechanism containing some delay characteristic for any double-auction market. Our model appeared exactly analytically solvable. Therefore, it enables a direct comparison of its predictions with their empirical counterparts, for instance, with empirical velocity autocorrelation function. Thus, the present research significantly extends capabilities of the CTRW formalism. Contribution to the Topical Issue "Continuous Time Random Walk Still Trendy: Fifty-year History, Current State and Outlook", edited by Ryszard Kutner and Jaume Masoliver.

Dynamics of DNA breathing: weak noise analysis, finite time singularity, and mapping onto the quantum Coulomb problem.

PubMed

Fogedby, Hans C; Metzler, Ralf

2007-12-01

We study the dynamics of denaturation bubbles in double-stranded DNA on the basis of the Poland-Scheraga model. We show that long time distributions for the survival of DNA bubbles and the size autocorrelation function can be derived from an asymptotic weak noise approach. In particular, below the melting temperature the bubble closure corresponds to a noisy finite time singularity. We demonstrate that the associated Fokker-Planck equation is equivalent to a quantum Coulomb problem. Below the melting temperature, the bubble lifetime is associated with the continuum of scattering states of the repulsive Coulomb potential; at the melting temperature, the Coulomb potential vanishes and the underlying first exit dynamics exhibits a long time power law tail; above the melting temperature, corresponding to an attractive Coulomb potential, the long time dynamics is controlled by the lowest bound state. Correlations and finite size effects are discussed.

Reversible geminate recombination of hydrogen-bonded water molecule pair

NASA Astrophysics Data System (ADS)

Markovitch, Omer; Agmon, Noam

2008-08-01

The (history independent) autocorrelation function for a hydrogen-bonded water molecule pair, calculated from classical molecular dynamics trajectories of liquid water, exhibits a t-3/2 asymptotic tail. Its whole time dependence agrees quantitatively with the solution for reversible diffusion-influenced geminate recombination derived by Agmon and Weiss [J. Chem. Phys. 91, 6937 (1989)]. Agreement with diffusion theory is independent of the precise definition of the bound state. Given the water self-diffusion constant, this theory enables us to determine the dissociation and bimolecular recombination rate parameters for a water dimer. (The theory is indispensable for obtaining the bimolecular rate coefficient.) Interestingly, the activation energies obtained from the temperature dependence of these rate coefficients are similar, rather than differing by the hydrogen-bond (HB) strength. This suggests that recombination requires displacing another water molecule, which meanwhile occupied the binding site. Because these activation energies are about twice the HB strength, cleavage of two HBs may be required to allow pair separation. The autocorrelation function without the HB angular restriction yields a recombination rate coefficient that is larger than that for rebinding to all four tetrahedral water sites (with angular restrictions), suggesting the additional participation of interstitial sites. Following dissociation, the probability of the pair to be unbound but within the reaction sphere rises more slowly than expected, possibly because binding to the interstitial sites delays pair separation. An extended diffusion model, which includes an additional binding site, can account for this behavior.

Dynamics at Intermediate Time Scales and Management of Ecological Populations

DTIC Science & Technology

2017-05-10

thinking about the importance of transients is to recognize the importance of serial autocorrelation in time of forcing terms over realistic ecological time...rich areas helps produce divergent home range responses bet - ween individuals from difference age classes. This model has broad applications for

Real-Time Gait Cycle Parameter Recognition Using a Wearable Accelerometry System

PubMed Central

Yang, Che-Chang; Hsu, Yeh-Liang; Shih, Kao-Shang; Lu, Jun-Ming

2011-01-01

This paper presents the development of a wearable accelerometry system for real-time gait cycle parameter recognition. Using a tri-axial accelerometer, the wearable motion detector is a single waist-mounted device to measure trunk accelerations during walking. Several gait cycle parameters, including cadence, step regularity, stride regularity and step symmetry can be estimated in real-time by using autocorrelation procedure. For validation purposes, five Parkinson’s disease (PD) patients and five young healthy adults were recruited in an experiment. The gait cycle parameters among the two subject groups of different mobility can be quantified and distinguished by the system. Practical considerations and limitations for implementing the autocorrelation procedure in such a real-time system are also discussed. This study can be extended to the future attempts in real-time detection of disabling gaits, such as festinating or freezing of gait in PD patients. Ambulatory rehabilitation, gait assessment and personal telecare for people with gait disorders are also possible applications. PMID:22164019

On a method to detect long-latency excitations and inhibitions of single hand muscle motoneurons in man.

PubMed

Awiszus, F; Feistner, H; Schäfer, S S

1991-01-01

The peri-stimulus-time histogram (PSTH) analysis of stimulus-related neuronal spike train data is usually regarded as a method to detect stimulus-induced excitations or inhibitions. However, for a fairly regularly discharging neuron such as the human alpha-motoneuron, long-latency modulations of a PSTH are difficult to interpret as PSTH modulations can also occur as a consequence of a modulated neuronal autocorrelation. The experiments reported here were made (i) to investigate the extent to which a PSTH of a human hand-muscle motoneuron may be contaminated by features of the autocorrelation and (ii) to develop methods that display the motoneuronal excitations and inhibitions without such contamination. Responses of 29 single motor units to electrical ulnar nerve stimulation below motor threshold were investigated in the first dorsal interosseous muscle of three healthy volunteers using an experimental protocol capable of demonstrating the presence of autocorrelative modulations in the neuronal response. It was found for all units that the PSTH as well as the cumulative sum (CUSUM) derived from these responses were severely affected by the presence of autocorrelative features. On the other hand, calculating the CUSUM in a slightly modified form yielded--for all units investigated--a neuronal output feature sensitive only to motoneuronal excitations and inhibitions induced by the afferent volley. The price that has to be paid to arrive at such a modified CUSUM (mCUSUM) was a high computational effort prohibiting the on-line availability of this output feature during the experiment. It was found, however, that an interspike interval superposition plot (IISP)--easily obtainable during the experiment--is also free of autocorrelative features.(ABSTRACT TRUNCATED AT 250 WORDS)

The "long tail" of the protein tumbling correlation function: observation by (1)H NMR relaxometry in a wide frequency and concentration range.

PubMed

Roos, Matthias; Hofmann, Marius; Link, Susanne; Ott, Maria; Balbach, Jochen; Rössler, Ernst; Saalwächter, Kay; Krushelnitsky, Alexey

2015-12-01

Inter-protein interactions in solution affect the auto-correlation function of Brownian tumbling not only in terms of a simple increase of the correlation time, they also lead to the appearance of a weak slow component ("long tail") of the correlation function due to a slowly changing local anisotropy of the microenvironment. The conventional protocol of correlation time estimation from the relaxation rate ratio R1/R2 assumes a single-component tumbling correlation function, and thus can provide incorrect results as soon as the "long tail" is of relevance. This effect, however, has been underestimated in many instances. In this work we present a detailed systematic study of the tumbling correlation function of two proteins, lysozyme and bovine serum albumin, at different concentrations and temperatures using proton field-cycling relaxometry combined with R1ρ and R2 measurements. Unlike high-field NMR relaxation methods, these techniques enable a detailed study of dynamics on a time scale longer than the normal protein tumbling correlation time and, thus, a reliable estimate of the parameters of the "long tail". In this work we analyze the concentration dependence of the intensity and correlation time of the slow component and perform simulations of high-field (15)N NMR relaxation data demonstrating the importance of taking the "long tail" in the analysis into account.

Improvement of photon correlation spectroscopy method for measuring nanoparticle size by using attenuated total reflectance.

PubMed

Krishtop, Victor; Doronin, Ivan; Okishev, Konstantin

2012-11-05

Photon correlation spectroscopy is an effective method for measuring nanoparticle sizes and has several advantages over alternative methods. However, this method suffers from a disadvantage in that its measuring accuracy reduces in the presence of convective flows of fluid containing nanoparticles. In this paper, we propose a scheme based on attenuated total reflectance in order to reduce the influence of convection currents. The autocorrelation function for the light-scattering intensity was found for this case, and it was shown that this method afforded a significant decrease in the time required to measure the particle sizes and an increase in the measuring accuracy.

Anomalous law of cooling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lapas, Luciano C., E-mail: luciano.lapas@unila.edu.br; Ferreira, Rogelma M. S., E-mail: rogelma.maria@gmail.com; Rubí, J. Miguel, E-mail: mrubi@ub.edu

2015-03-14

We analyze the temperature relaxation phenomena of systems in contact with a thermal reservoir that undergoes a non-Markovian diffusion process. From a generalized Langevin equation, we show that the temperature is governed by a law of cooling of the Newton’s law type in which the relaxation time depends on the velocity autocorrelation and is then characterized by the memory function. The analysis of the temperature decay reveals the existence of an anomalous cooling in which the temperature may oscillate. Despite this anomalous behavior, we show that the variation of entropy remains always positive in accordance with the second law ofmore » thermodynamics.« less

Under-reported data analysis with INAR-hidden Markov chains.

PubMed

Fernández-Fontelo, Amanda; Cabaña, Alejandra; Puig, Pedro; Moriña, David

2016-11-20

In this work, we deal with correlated under-reported data through INAR(1)-hidden Markov chain models. These models are very flexible and can be identified through its autocorrelation function, which has a very simple form. A naïve method of parameter estimation is proposed, jointly with the maximum likelihood method based on a revised version of the forward algorithm. The most-probable unobserved time series is reconstructed by means of the Viterbi algorithm. Several examples of application in the field of public health are discussed illustrating the utility of the models. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Fractal analysis of phasic laser images of the myocardium for the purpose of diagnostics of acute coronary insufficiency

NASA Astrophysics Data System (ADS)

Wanchuliak, O. Y.; Bachinskyi, V. T.

2011-09-01

In this work on the base of Mueller-matrix description of optical anisotropy, the possibility of monitoring of time changes of myocardium tissue birefringence, has been considered. The optical model of polycrystalline networks of myocardium is suggested. The results of investigating the interrelation between the values correlation (correlation area, asymmetry coefficient and autocorrelation function excess) and fractal (dispersion of logarithmic dependencies of power spectra) parameters are presented. They characterize the distributions of Mueller matrix elements in the points of laser images of myocardium histological sections. The criteria of differentiation of death coming reasons are determined.

Protein oligomerization monitored by fluorescence fluctuation spectroscopy: Self-assembly of Rubisco activase

USDA-ARS?s Scientific Manuscript database

A methodology is presented to characterize complex protein assembly pathways by fluorescence correlation spectroscopy. We have derived the total autocorrelation function describing the behavior of mixtures of labeled and unlabeled protein under equilibrium conditions. Our modeling approach allows us...

Statistical characteristics of MST radar echoes and its interpretation

NASA Technical Reports Server (NTRS)

Woodman, Ronald F.

1989-01-01

Two concepts of fundamental importance are reviewed: the autocorrelation function and the frequency power spectrum. In addition, some turbulence concepts, the relationship between radar signals and atmospheric medium statistics, partial reflection, and the characteristics of noise and clutter interference are discussed.

A geostatistical state-space model of animal densities for stream networks.

PubMed

Hocking, Daniel J; Thorson, James T; O'Neil, Kyle; Letcher, Benjamin H

2018-06-21

Population dynamics are often correlated in space and time due to correlations in environmental drivers as well as synchrony induced by individual dispersal. Many statistical analyses of populations ignore potential autocorrelations and assume that survey methods (distance and time between samples) eliminate these correlations, allowing samples to be treated independently. If these assumptions are incorrect, results and therefore inference may be biased and uncertainty under-estimated. We developed a novel statistical method to account for spatio-temporal correlations within dendritic stream networks, while accounting for imperfect detection in the surveys. Through simulations, we found this model decreased predictive error relative to standard statistical methods when data were spatially correlated based on stream distance and performed similarly when data were not correlated. We found that increasing the number of years surveyed substantially improved the model accuracy when estimating spatial and temporal correlation coefficients, especially from 10 to 15 years. Increasing the number of survey sites within the network improved the performance of the non-spatial model but only marginally improved the density estimates in the spatio-temporal model. We applied this model to Brook Trout data from the West Susquehanna Watershed in Pennsylvania collected over 34 years from 1981 - 2014. We found the model including temporal and spatio-temporal autocorrelation best described young-of-the-year (YOY) and adult density patterns. YOY densities were positively related to forest cover and negatively related to spring temperatures with low temporal autocorrelation and moderately-high spatio-temporal correlation. Adult densities were less strongly affected by climatic conditions and less temporally variable than YOY but with similar spatio-temporal correlation and higher temporal autocorrelation. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Photon statistics and polarization correlations at telecommunications wavelengths from a warm atomic ensemble.

PubMed

Willis, R T; Becerra, F E; Orozco, L A; Rolston, S L

2011-07-18

We present measurements of the polarization correlation and photon statistics of photon pairs that emerge from a laser-pumped warm rubidium vapor cell. The photon pairs occur at 780 nm and 1367 nm and are polarization entangled. We measure the autocorrelation of each of the generated fields as well as the cross-correlation function, and observe a strong violation of the two-beam Cauchy-Schwartz inequality. We evaluate the performance of the system as source of heralded single photons at a telecommunication wavelength. We measure the heralded autocorrelation and see that coincidences are suppressed by a factor of ≈ 20 from a Poissonian source at a generation rate of 1500 s(-1), a heralding efficiency of 10%, and a narrow spectral width.

Low Streamflow Forcasting using Minimum Relative Entropy

NASA Astrophysics Data System (ADS)

Cui, H.; Singh, V. P.

2013-12-01

Minimum relative entropy spectral analysis is derived in this study, and applied to forecast streamflow time series. Proposed method extends the autocorrelation in the manner that the relative entropy of underlying process is minimized so that time series data can be forecasted. Different prior estimation, such as uniform, exponential and Gaussian assumption, is taken to estimate the spectral density depending on the autocorrelation structure. Seasonal and nonseasonal low streamflow series obtained from Colorado River (Texas) under draught condition is successfully forecasted using proposed method. Minimum relative entropy determines spectral of low streamflow series with higher resolution than conventional method. Forecasted streamflow is compared to the prediction using Burg's maximum entropy spectral analysis (MESA) and Configurational entropy. The advantage and disadvantage of each method in forecasting low streamflow is discussed.

ASSESSMENT OF SPATIAL AUTOCORRELATION IN EMPIRICAL MODELS IN ECOLOGY

EPA Science Inventory

Statistically assessing ecological models is inherently difficult because data are autocorrelated and this autocorrelation varies in an unknown fashion. At a simple level, the linking of a single species to a habitat type is a straightforward analysis. With some investigation int...

Demonstration of a real-time interferometer as a bunch-length monitor in a high-current electron beam accelerator.

PubMed

Thangaraj, J; Andonian, G; Thurman-Keup, R; Ruan, J; Johnson, A S; Lumpkin, A; Santucci, J; Maxwell, T; Murokh, A; Ruelas, M; Ovodenko, A

2012-04-01

A real-time interferometer (RTI) has been developed to monitor the bunch length of an electron beam in an accelerator. The RTI employs spatial autocorrelation, reflective optics, and a fast response pyro-detector array to obtain a real-time autocorrelation trace of the coherent radiation from an electron beam thus providing the possibility of online bunch-length diagnostics. A complete RTI system has been commissioned at the A0 photoinjector facility to measure sub-mm bunches at 13 MeV. Bunch length variation (FWHM) between 0.8 ps (~0.24 mm) and 1.5 ps (~0.45 mm) has been measured and compared with a Martin-Puplett interferometer and a streak camera. The comparisons show that RTI is a viable, complementary bunch length diagnostic for sub-mm electron bunches. © 2012 American Institute of Physics

Joint Seasonal ARMA Approach for Modeling of Load Forecast Errors in Planning Studies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hafen, Ryan P.; Samaan, Nader A.; Makarov, Yuri V.

2014-04-14

To make informed and robust decisions in the probabilistic power system operation and planning process, it is critical to conduct multiple simulations of the generated combinations of wind and load parameters and their forecast errors to handle the variability and uncertainty of these time series. In order for the simulation results to be trustworthy, the simulated series must preserve the salient statistical characteristics of the real series. In this paper, we analyze day-ahead load forecast error data from multiple balancing authority locations and characterize statistical properties such as mean, standard deviation, autocorrelation, correlation between series, time-of-day bias, and time-of-day autocorrelation.more » We then construct and validate a seasonal autoregressive moving average (ARMA) model to model these characteristics, and use the model to jointly simulate day-ahead load forecast error series for all BAs.« less

Demonstration of a real-time interferometer as a bunch-lenght monitor in a high-current electron beam accelerator

DOE Office of Scientific and Technical Information (OSTI.GOV)

Thangaraj, J.; Thurman-Keup, R.; Ruan, J.

2012-03-01

A real-time interferometer (RTI) has been developed to monitor the bunch length of an electron beam in an accelerator. The RTI employs spatial autocorrelation, reflective optics, and a fast response pyro-detector array to obtain a real-time autocorrelation trace of the coherent radiation from an electron beam thus providing the possibility of online bunch-length diagnostics. A complete RTI system has been commissioned at the A0 photoinjector facility to measure sub-mm bunches at 13 MeV. Bunch length variation (FWHM) between 0.8 ps (-0.24 mm) and 1.5 ps (-0.45 mm) has been measured and compared with a Martin-Puplett interferometer and a streak camera.more » The comparisons show that RTI is a viable, complementary bunch length diagnostic for sub-mm electron bunches.« less

Demonstration of a real-time interferometer as a bunch-length monitor in a high-current electron beam accelerator

DOE Office of Scientific and Technical Information (OSTI.GOV)

Thangaraj, J.; Thurman-Keup, R.; Ruan, J.

2012-04-15

A real-time interferometer (RTI) has been developed to monitor the bunch length of an electron beam in an accelerator. The RTI employs spatial autocorrelation, reflective optics, and a fast response pyro-detector array to obtain a real-time autocorrelation trace of the coherent radiation from an electron beam thus providing the possibility of online bunch-length diagnostics. A complete RTI system has been commissioned at the A0 photoinjector facility to measure sub-mm bunches at 13 MeV. Bunch length variation (FWHM) between 0.8 ps ({approx}0.24 mm) and 1.5 ps ({approx}0.45 mm) has been measured and compared with a Martin-Puplett interferometer and a streak camera.more » The comparisons show that RTI is a viable, complementary bunch length diagnostic for sub-mm electron bunches.« less

Correlation Time of Ocean Ambient Noise Intensity in San Diego Bay and Target Recognition in Acoustic Daylight Images

NASA Astrophysics Data System (ADS)

Wadsworth, Adam J.

A method for passively detecting and imaging underwater targets using ambient noise as the sole source of illumination (named acoustic daylight) was successfully implemented in the form of the Acoustic Daylight Ocean Noise Imaging System (ADONIS). In a series of imaging experiments conducted in San Diego Bay, where the dominant source of high-frequency ambient noise is snapping shrimp, a large quantity of ambient noise intensity data was collected with the ADONIS (Epifanio, 1997). In a subset of the experimental data sets, fluctuations of time-averaged ambient noise intensity exhibited a diurnal pattern consistent with the increase in frequency of shrimp snapping near dawn and dusk. The same subset of experimental data is revisited here and the correlation time is estimated and analysed for sequences of ambient noise data several minutes in length, with the aim of detecting possible periodicities or other trends in the fluctuation of the shrimp-dominated ambient noise field. Using videos formed from sequences of acoustic daylight images along with other experimental information, candidate segments of static-configuration ADONIS raw ambient noise data were isolated. For each segment, the normalized intensity auto-correlation closely resembled the delta function, the auto-correlation of white noise. No intensity fluctuation patterns at timescales smaller than a few minutes were discernible, suggesting that the shrimp do not communicate, synchronise, or exhibit any periodicities in their snapping. Also presented here is a ADONIS-specific target recognition algorithm based on principal component analysis, along with basic experimental results using a database of acoustic daylight images.

A 3D Monte Carlo model of radiation affecting cells, and its application to neuronal cells and GCR irradiation

NASA Astrophysics Data System (ADS)

Ponomarev, Artem; Sundaresan, Alamelu; Kim, Angela; Vazquez, Marcelo E.; Guida, Peter; Kim, Myung-Hee; Cucinotta, Francis A.

A 3D Monte Carlo model of radiation transport in matter is applied to study the effect of heavy ion radiation on human neuronal cells. Central nervous system effects, including cognitive impairment, are suspected from the heavy ion component of galactic cosmic radiation (GCR) during space missions. The model can count, for instance, the number of direct hits from ions, which will have the most affect on the cells. For comparison, the remote hits, which are received through δ-rays from the projectile traversing space outside the volume of the cell, are also simulated and their contribution is estimated. To simulate tissue effects from irradiation, cellular matrices of neuronal cells, which were derived from confocal microscopy, were simulated in our model. To produce this realistic model of the brain tissue, image segmentation was used to identify cells in the images of cells cultures. The segmented cells were inserted pixel by pixel into the modeled physical space, which represents a volume of interacting cells with periodic boundary conditions (PBCs). PBCs were used to extrapolate the model results to the macroscopic tissue structures. Specific spatial patterns for cell apoptosis are expected from GCR, as heavy ions produce concentrated damage along their trajectories. The apoptotic cell patterns were modeled based on the action cross sections for apoptosis, which were estimated from the available experimental data. The cell patterns were characterized with an autocorrelation function, which values are higher for non-random cell patterns, and the values of the autocorrelation function were compared for X rays and Fe ion irradiations. The autocorrelation function indicates the directionality effects present in apoptotic neuronal cells from GCR.

Off-equilibrium sphaleron transitions in the Glasma

DOE PAGES

Mace, Mark; Schlichting, Soren; Venugopalan, Raju

2016-04-28

We perform the first, to our knowledge, classical-statistical real time lattice simulations of topological transitions in the nonequilibrium glasma of weakly coupled but highly occupied gauge fields created immediately after the collision of ultrarelativistic nuclei. Simplifying our description by employing SU(2) gauge fields, and neglecting their longitudinal expansion, we find that the rate of topological transitions is initially strongly enhanced relative to the thermal sphaleron transition rate and decays with time during the thermalization process. Qualitative features of the time dependence of this nonequilibrium transition rate can be understood when expressed in terms of the magnetic screening length, which wemore » also extract nonperturbatively. Furthermore, a detailed investigation of auto-correlation functions of the Chern-Simons number (N CS) reveals non-Markovian features of the evolution distinct from previous simulations of non-Abelian plasmas in thermal equilibrium.« less

Optical coherence tomography speckle decorrelation for detecting cell death

NASA Astrophysics Data System (ADS)

Farhat, Golnaz; Mariampillai, Adrian; Yang, Victor X. D.; Czarnota, Gregory J.; Kolios, Michael C.

2011-03-01

We present a dynamic light scattering technique applied to optical coherence tomography (OCT) for detecting changes in intracellular motion caused by cellular reorganization during apoptosis. We have validated our method by measuring Brownian motion in microsphere suspensions and comparing the measured values to those derived based on particle diffusion calculated using the Einstein-Stokes equation. Autocorrelations of OCT signal intensities acquired from acute myeloid leukemia cells as a function of treatment time demonstrated a significant drop in the decorrelation time after 24 hours of cisplatin treatment. This corresponded with nuclear fragmentation and irregular cell shape observed in histological sections. A similar analysis conducted with multicellular tumor spheroids indicated a shorter decorrelation time in the spheroid core relative to its edges. The spheroid core corresponded to a region exhibiting signs of cell death in histological sections and increased backscatter intensity in OCT images.

An advanced analysis and modelling the air pollutant concentration temporal dynamics in atmosphere of the industrial cities: Odessa city

NASA Astrophysics Data System (ADS)

Buyadzhi, V. V.; Glushkov, A. V.; Khetselius, O. Yu; Ternovsky, V. B.; Serga, I. N.; Bykowszczenko, N.

2017-10-01

Results of analysis and modelling the air pollutant (dioxide of nitrogen) concentration temporal dynamics in atmosphere of the industrial city Odessa are presented for the first time and based on computing by nonlinear methods of the chaos and dynamical systems theories. A chaotic behaviour is discovered and investigated. To reconstruct the corresponding strange chaotic attractor, the time delay and embedding dimension are computed. The former is determined by the methods of autocorrelation function and average mutual information, and the latter is calculated by means of correlation dimension method and algorithm of false nearest neighbours. It is shown that low-dimensional chaos exists in the nitrogen dioxide concentration time series under investigation. Further, the Lyapunov’s exponents spectrum, Kaplan-Yorke dimension and Kolmogorov entropy are computed.

Intrinsic Multi-Scale Dynamic Behaviors of Complex Financial Systems

PubMed Central

Ouyang, Fang-Yan; Zheng, Bo; Jiang, Xiong-Fei

2015-01-01

The empirical mode decomposition is applied to analyze the intrinsic multi-scale dynamic behaviors of complex financial systems. In this approach, the time series of the price returns of each stock is decomposed into a small number of intrinsic mode functions, which represent the price motion from high frequency to low frequency. These intrinsic mode functions are then grouped into three modes, i.e., the fast mode, medium mode and slow mode. The probability distribution of returns and auto-correlation of volatilities for the fast and medium modes exhibit similar behaviors as those of the full time series, i.e., these characteristics are rather robust in multi time scale. However, the cross-correlation between individual stocks and the return-volatility correlation are time scale dependent. The structure of business sectors is mainly governed by the fast mode when returns are sampled at a couple of days, while by the medium mode when returns are sampled at dozens of days. More importantly, the leverage and anti-leverage effects are dominated by the medium mode. PMID:26427063

Seismic Structure of Perth Basin (Australia) and surroundings from Passive Seismic Deployments

NASA Astrophysics Data System (ADS)

Issa, N.; Saygin, E.; Lumley, D. E.; Hoskin, T. E.

2016-12-01

We image the subsurface structure of Perth Basin, Western Australia and surroundings by using ambient seismic noise data from 14 seismic stations recently deployed by University of Western Australia (UWA) and other available permanent stations from Geoscience Australia seismic network and the Australian Seismometers in Schools program. Each of these 14 UWA seismic stations comprises a broadband sensor and a high fidelity 3-component 10 Hz geophone, recording in tandem at 250 Hz and 1000 Hz. The other stations used in this study are equipped with short period and broadband sensors. In addition, one shallow borehole station is operated with eight 3 component geophones at depths of between 2 and 44 m. The network is deployed to characterize natural seismicity in the basin and to try and identify any microseismic activity across Darling Fault Zone (DFZ), bounding the basin to the east. The DFZ stretches to approximately 1000 km north-south in Western Australia, and is one of the longest fault zones on the earth with a limited number of detected earthquakes. We use seismic noise cross- and auto-correlation methods to map seismic velocity perturbations across the basin and the transition from DFZ to the basin. Retrieved Green's functions are stable and show clear dispersed waveforms. Travel times of the surface wave Green's functions from noise cross-correlations are inverted with a two-step probabilistic framework to map the absolute shear wave velocities as a function of depth. The single station auto-correlations from the seismic noise yields P wave reflectivity under each station, marking the major discontinuities. Resulting images show the shear velocity perturbations across the region. We also quantify the variation of ambient seismic noise at different depths in the near surface using the geophones in the shallow borehole array.

Dynamic speckle interferometry of microscopic processes in solid state and thin biological objects

NASA Astrophysics Data System (ADS)

Vladimirov, A. P.

2015-08-01

Modernized theory of dynamic speckle interferometry is considered. It is shown that the time-average radiation intensity has the parameters characterizing the wave phase changes. It also brings forward an expression for time autocorrelation function of the radiation intensity. It is shown that with the vanishing averaging time value the formulas transform to the prior expressions. The results of experiments with high-cycle material fatigue and cell metabolism analysis conducted using the time-averaging technique are discussed. Good reproducibility of the results is demonstrated. It is specified that the upgraded technique allows analyzing accumulation of fatigue damage, detecting the crack start moment and determining its growth velocity with uninterrupted cyclic load. It is also demonstrated that in the experiments with a cell monolayer the technique allows studying metabolism change both in an individual cell and in a group of cells.

A model-free characterization of recurrences in stationary time series

NASA Astrophysics Data System (ADS)

Chicheportiche, Rémy; Chakraborti, Anirban

2017-05-01

Study of recurrences in earthquakes, climate, financial time-series, etc. is crucial to better forecast disasters and limit their consequences. Most of the previous phenomenological studies of recurrences have involved only a long-ranged autocorrelation function, and ignored the multi-scaling properties induced by potential higher order dependencies. We argue that copulas is a natural model-free framework to study non-linear dependencies in time series and related concepts like recurrences. Consequently, we arrive at the facts that (i) non-linear dependences do impact both the statistics and dynamics of recurrence times, and (ii) the scaling arguments for the unconditional distribution may not be applicable. Hence, fitting and/or simulating the intertemporal distribution of recurrence intervals is very much system specific, and cannot actually benefit from universal features, in contrast to the previous claims. This has important implications in epilepsy prognosis and financial risk management applications.

Detecting dynamical changes in time series by using the Jensen Shannon divergence

NASA Astrophysics Data System (ADS)

Mateos, D. M.; Riveaud, L. E.; Lamberti, P. W.

2017-08-01

Most of the time series in nature are a mixture of signals with deterministic and random dynamics. Thus the distinction between these two characteristics becomes important. Distinguishing between chaotic and aleatory signals is difficult because they have a common wide band power spectrum, a delta like autocorrelation function, and share other features as well. In general, signals are presented as continuous records and require to be discretized for being analyzed. In this work, we introduce different schemes for discretizing and for detecting dynamical changes in time series. One of the main motivations is to detect transitions between the chaotic and random regime. The tools here used here originate from the Information Theory. The schemes proposed are applied to simulated and real life signals, showing in all cases a high proficiency for detecting changes in the dynamics of the associated time series.

[Correlation coefficient-based classification method of hydrological dependence variability: With auto-regression model as example].

PubMed

Zhao, Yu Xi; Xie, Ping; Sang, Yan Fang; Wu, Zi Yi

2018-04-01

Hydrological process evaluation is temporal dependent. Hydrological time series including dependence components do not meet the data consistency assumption for hydrological computation. Both of those factors cause great difficulty for water researches. Given the existence of hydrological dependence variability, we proposed a correlationcoefficient-based method for significance evaluation of hydrological dependence based on auto-regression model. By calculating the correlation coefficient between the original series and its dependence component and selecting reasonable thresholds of correlation coefficient, this method divided significance degree of dependence into no variability, weak variability, mid variability, strong variability, and drastic variability. By deducing the relationship between correlation coefficient and auto-correlation coefficient in each order of series, we found that the correlation coefficient was mainly determined by the magnitude of auto-correlation coefficient from the 1 order to p order, which clarified the theoretical basis of this method. With the first-order and second-order auto-regression models as examples, the reasonability of the deduced formula was verified through Monte-Carlo experiments to classify the relationship between correlation coefficient and auto-correlation coefficient. This method was used to analyze three observed hydrological time series. The results indicated the coexistence of stochastic and dependence characteristics in hydrological process.

Principal Components of Recurrence Quantification Analysis of EMG

DTIC Science & Technology

2001-10-25

Springer, 1981, pp. 366-381. 4. M. Fraser and H. L. Swinney, “ Independent coordinates for strange attractors from mutual information ,” Phys. Rev. A...autocorrelation function of s(n), although it has also been argued that the first local minimum of the auto mutual information function is more appropriate [4...recordings from a given subject. T was taken as the lag corresponding to the first minimum of the auto mutual information function, calculated as

Real-time micro-vibration multi-spot synchronous measurement within a region based on heterodyne interference

NASA Astrophysics Data System (ADS)

Lan, Ma; Xiao, Wen; Chen, Zonghui; Hao, Hongliang; Pan, Feng

2018-01-01

Real-time micro-vibration measurement is widely used in engineering applications. It is very difficult for traditional optical detection methods to achieve real-time need in a relatively high frequency and multi-spot synchronous measurement of a region at the same time,especially at the nanoscale. Based on the method of heterodyne interference, an experimental system of real-time measurement of micro - vibration is constructed to satisfy the demand in engineering applications. The vibration response signal is measured by combing optical heterodyne interferometry and a high-speed CMOS-DVR image acquisition system. Then, by extracting and processing multiple pixels at the same time, four digital demodulation technique are implemented to simultaneously acquire the vibrating velocity of the target from the recorded sequences of images. Different kinds of demodulation algorithms are analyzed and the results show that these four demodulation algorithms are suitable for different interference signals. Both autocorrelation algorithm and cross-correlation algorithm meet the needs of real-time measurements. The autocorrelation algorithm demodulates the frequency more accurately, while the cross-correlation algorithm is more accurate in solving the amplitude.

Model Identification of Integrated ARMA Processes

ERIC Educational Resources Information Center

Stadnytska, Tetiana; Braun, Simone; Werner, Joachim

2008-01-01

This article evaluates the Smallest Canonical Correlation Method (SCAN) and the Extended Sample Autocorrelation Function (ESACF), automated methods for the Autoregressive Integrated Moving-Average (ARIMA) model selection commonly available in current versions of SAS for Windows, as identification tools for integrated processes. SCAN and ESACF can…

Physics behind the oscillation of pressure tensor autocorrelation function for nanocolloidal dispersions.

PubMed

Wang, Tao; Wang, Xinwei; Luo, Zhongyang; Cen, Kefa

2008-08-01

In this work, extensive equilibrium molecular dynamics simulations are conducted to explore the physics behind the oscillation of pressure tensor autocorrelation function (PTACF) for nanocolloidal dispersions, which leads to strong instability in viscosity calculation. By reducing the particle size and density, we find the intensity of the oscillation decreases while the frequency of the oscillation becomes higher. Careful analysis of the relationship between the oscillation and nanoparticle characteristics reveals that the stress wave scattering/reflection at the particle-liquid interface plays a critical role in PTACF oscillation while the Brownian motion/vibration of solid particles has little effect. Our modeling proves that it is practical to eliminate the PTACF oscillation through suppressing the acoustic mismatch at the solid-liquid interface by designing special nanoparticle materials. It is also found when the particle size is comparable with the wavelength of the stress wave, diffraction of stress wave happens at the interface. Such effect substantially reduces the PTACF oscillation and improves the stability of viscosity calculation.

On the origin of the soft X-ray background. [in cosmological observations

NASA Technical Reports Server (NTRS)

Wang, Q. D.; Mccray, Richard

1993-01-01

The angular autocorrelation function and spectrum of the soft X-ray background is studied below a discrete source detection limit, using two deep images from the Rosat X-ray satellite. The average spectral shape of pointlike sources, which account for 40 to 60 percent of the background intensity, is determined by using the autocorrelation function. The background spectrum, in the 0.5-0.9 keV band (M band), is decomposed into a pointlike source component characterized by a power law and a diffuse component represented by a two-temperature plasma. These pointlike sources cannot contribute more than 60 percent of the X-ray background intensity in the M band without exceeding the total observed flux in the R7 band. Spectral analysis has shown that the local soft diffuse component, although dominating the background intensity at energies not greater than 0.3 keV, contributes only a small fraction of the M band background intensity. The diffuse component may represent an important constituent of the interstellar or intergalactic medium.

Reexamination of relaxation of spins due to a magnetic field gradient: Identity of the Redfield and Torrey theories

DOE Office of Scientific and Technical Information (OSTI.GOV)

Golub, R.; Rohm, Ryan M.; Swank, C. M.

2011-02-15

There is an extensive literature on magnetic-gradient-induced spin relaxation. Cates, Schaefer, and Happer, in a seminal publication, have solved the problem in the regime where diffusion theory (the Torrey equation) is applicable using an expansion of the density matrix in diffusion equation eigenfunctions and angular momentum tensors. McGregor has solved the problem in the same regime using a slightly more general formulation using the Redfield theory formulated in terms of the autocorrelation function of the fluctuating field seen by the spins and calculating the correlation functions using the diffusion-theory Green's function. The results of both calculations were shown to agreemore » for a special case. In the present work, we show that the eigenfunction expansion of the Torrey equation yields the expansion of the Green's function for the diffusion equation, thus showing the identity of this approach with that of the Redfield theory. The general solution can also be obtained directly from the Torrey equation for the density matrix. Thus, the physical content of the Redfield and Torrey approaches are identical. We then introduce a more general expression for the position autocorrelation function of particles moving in a closed cell, extending the range of applicability of the theory.« less

Dispersion relation for electromagnetic propagation in stochastic dielectric and magnetic helical photonic crystals

NASA Astrophysics Data System (ADS)

Avendaño, Carlos G.; Reyes, Arturo

2017-03-01

We theoretically study the dispersion relation for axially propagating electromagnetic waves throughout a one-dimensional helical structure whose pitch and dielectric and magnetic properties are spatial random functions with specific statistical characteristics. In the system of coordinates rotating with the helix, by using a matrix formalism, we write the set of differential equations that governs the expected value of the electromagnetic field amplitudes and we obtain the corresponding dispersion relation. We show that the dispersion relation depends strongly on the noise intensity introduced in the system and the autocorrelation length. When the autocorrelation length increases at fixed fluctuation and when the fluctuation augments at fixed autocorrelation length, the band gap widens and the attenuation coefficient of electromagnetic waves propagating in the random medium gets larger. By virtue of the degeneracy in the imaginary part of the eigenvalues associated with the propagating modes, the random medium acts as a filter for circularly polarized electromagnetic waves, in which only the propagating backward circularly polarized wave can propagate with no attenuation. Our results are valid for any kind of dielectric and magnetic structures which possess a helical-like symmetry such as cholesteric and chiral smectic-C liquid crystals, structurally chiral materials, and stressed cholesteric elastomers.

Water Quality Sensing and Spatio-Temporal Monitoring Structure with Autocorrelation Kernel Methods.

PubMed

Vizcaíno, Iván P; Carrera, Enrique V; Muñoz-Romero, Sergio; Cumbal, Luis H; Rojo-Álvarez, José Luis

2017-10-16

Pollution on water resources is usually analyzed with monitoring campaigns, which consist of programmed sampling, measurement, and recording of the most representative water quality parameters. These campaign measurements yields a non-uniform spatio-temporal sampled data structure to characterize complex dynamics phenomena. In this work, we propose an enhanced statistical interpolation method to provide water quality managers with statistically interpolated representations of spatial-temporal dynamics. Specifically, our proposal makes efficient use of the a priori available information of the quality parameter measurements through Support Vector Regression (SVR) based on Mercer's kernels. The methods are benchmarked against previously proposed methods in three segments of the Machángara River and one segment of the San Pedro River in Ecuador, and their different dynamics are shown by statistically interpolated spatial-temporal maps. The best interpolation performance in terms of mean absolute error was the SVR with Mercer's kernel given by either the Mahalanobis spatial-temporal covariance matrix or by the bivariate estimated autocorrelation function. In particular, the autocorrelation kernel provides with significant improvement of the estimation quality, consistently for all the six water quality variables, which points out the relevance of including a priori knowledge of the problem.

Water Quality Sensing and Spatio-Temporal Monitoring Structure with Autocorrelation Kernel Methods

PubMed Central

Vizcaíno, Iván P.; Muñoz-Romero, Sergio; Cumbal, Luis H.

2017-01-01

Pollution on water resources is usually analyzed with monitoring campaigns, which consist of programmed sampling, measurement, and recording of the most representative water quality parameters. These campaign measurements yields a non-uniform spatio-temporal sampled data structure to characterize complex dynamics phenomena. In this work, we propose an enhanced statistical interpolation method to provide water quality managers with statistically interpolated representations of spatial-temporal dynamics. Specifically, our proposal makes efficient use of the a priori available information of the quality parameter measurements through Support Vector Regression (SVR) based on Mercer’s kernels. The methods are benchmarked against previously proposed methods in three segments of the Machángara River and one segment of the San Pedro River in Ecuador, and their different dynamics are shown by statistically interpolated spatial-temporal maps. The best interpolation performance in terms of mean absolute error was the SVR with Mercer’s kernel given by either the Mahalanobis spatial-temporal covariance matrix or by the bivariate estimated autocorrelation function. In particular, the autocorrelation kernel provides with significant improvement of the estimation quality, consistently for all the six water quality variables, which points out the relevance of including a priori knowledge of the problem. PMID:29035333

Prediction of the Length of Upcoming Solar Cycles

NASA Astrophysics Data System (ADS)

Kakad, Bharati; Kakad, Amar; Ramesh, Durbha Sai

2017-12-01

The forecast of solar cycle (SC) characteristics is crucial particularly for several space-based missions. In the present study, we propose a new model for predicting the length of the SC. The model uses the information of the width of an autocorrelation function that is derived from the daily sunspot data for each SC. We tested the model on Versions 1 and 2 of the daily international sunspot number data for SCs 10 - 24. We found that the autocorrelation width Aw n of SC n during the second half of its ascending phase correlates well with the modified length that is defined as T_{cy}^{n+2} - Tan. Here T_{cy}^{n+2} and T_{ a}n are the length and ascent time of SCs n+2 and n, respectively. The estimated correlation coefficient between the model parameters is 0.93 (0.91) for Version 1 (Version 2) sunspot series. The standard errors in the observed and predicted lengths of the SCs for Version 1 and Version 2 data are 0.38 and 0.44 years, respectively. The advantage of the proposed model is that the predictions of the length of the upcoming two SCs ( i.e., n+1, n+2) are readily available at the time of the peak of SC n. The present model gives a forecast of 11.01, 10.52, and 11.91 years (11.01, 12.20, and 11.68 years) for the length of SCs 24, 25, and 26, respectively, for Version 1 (Version 2).

Correlation lifetimes of quiet and magnetic granulation from the SOUP instrument on Spacelab 2

NASA Astrophysics Data System (ADS)

Title, A.; Tarbell, T.; Topka, K.; Acton, L.; Duncan, D.; Ferguson, S.; Finch, M.; Frank, Z.; Kelly, G.; Lindgren, R.; Morrill, M.; Pope, T.; Reeves, R.; Rehse, R.; Shine, R.; Simon, G.; Harvey, J.; Leibacher, J.; Livingston, W.; November, L.; Zirker, J.

The time sequences of diffraction limited granulation images obtained by the Solar Optical Universal Polarimeter on Spacelab 2 are presented. The uncorrection autocorrelation limetime in magnetic regions is dominated by the 5-min oscillation. The removal of this oscillation causes the autocorrelation lifetime to increase by more than a factor of 2. The results suggest that a significant fraction of granule lifetimes are terminated by nearby explosions. Horizontal displacements and transverse velocities in the intensity field are measured. Lower limits to the lifetime in the quiet and magnetic sun are set at 440 s and 950 s, respectively.

Correlation lifetimes of quiet and magnetic granulation from the SOUP instrument on Spacelab 2. [Solar Optical Universal Polarimeter

NASA Technical Reports Server (NTRS)

Title, A.; Tarbell, T.; Topka, K.; Acton, L.; Duncan, D.

1988-01-01

The time sequences of diffraction limited granulation images obtained by the Solar Optical Universal Polarimeter on Spacelab 2 are presented. The uncorrection autocorrelation limetime in magnetic regions is dominated by the 5-min oscillation. The removal of this oscillation causes the autocorrelation lifetime to increase by more than a factor of 2. The results suggest that a significant fraction of granule lifetimes are terminated by nearby explosions. Horizontal displacements and transverse velocities in the intensity field are measured. Lower limits to the lifetime in the quiet and magnetic sun are set at 440 s and 950 s, respectively.

A generalization of random matrix theory and its application to statistical physics.

PubMed

Wang, Duan; Zhang, Xin; Horvatic, Davor; Podobnik, Boris; Eugene Stanley, H

2017-02-01

To study the statistical structure of crosscorrelations in empirical data, we generalize random matrix theory and propose a new method of cross-correlation analysis, known as autoregressive random matrix theory (ARRMT). ARRMT takes into account the influence of auto-correlations in the study of cross-correlations in multiple time series. We first analytically and numerically determine how auto-correlations affect the eigenvalue distribution of the correlation matrix. Then we introduce ARRMT with a detailed procedure of how to implement the method. Finally, we illustrate the method using two examples taken from inflation rates for air pressure data for 95 US cities.

Quantifying the uncertainty introduced by discretization and time-averaging in two-fluid model predictions

DOE PAGES

Syamlal, Madhava; Celik, Ismail B.; Benyahia, Sofiane

2017-07-12

The two-fluid model (TFM) has become a tool for the design and troubleshooting of industrial fluidized bed reactors. To use TFM for scale up with confidence, the uncertainty in its predictions must be quantified. Here, we study two sources of uncertainty: discretization and time-averaging. First, we show that successive grid refinement may not yield grid-independent transient quantities, including cross-section–averaged quantities. Successive grid refinement would yield grid-independent time-averaged quantities on sufficiently fine grids. A Richardson extrapolation can then be used to estimate the discretization error, and the grid convergence index gives an estimate of the uncertainty. Richardson extrapolation may not workmore » for industrial-scale simulations that use coarse grids. We present an alternative method for coarse grids and assess its ability to estimate the discretization error. Second, we assess two methods (autocorrelation and binning) and find that the autocorrelation method is more reliable for estimating the uncertainty introduced by time-averaging TFM data.« less

Robust estimation of fetal heart rate from US Doppler signals

NASA Astrophysics Data System (ADS)

Voicu, Iulian; Girault, Jean-Marc; Roussel, Catherine; Decock, Aliette; Kouame, Denis

2010-01-01

Introduction: In utero, Monitoring of fetal wellbeing or suffering is today an open challenge, due to the high number of clinical parameters to be considered. An automatic monitoring of fetal activity, dedicated for quantifying fetal wellbeing, becomes necessary. For this purpose and in a view to supply an alternative for the Manning test, we used an ultrasound multitransducer multigate Doppler system. One important issue (and first step in our investigation) is the accurate estimation of fetal heart rate (FHR). An estimation of the FHR is obtained by evaluating the autocorrelation function of the Doppler signals for ills and healthiness foetus. However, this estimator is not enough robust since about 20% of FHR are not detected in comparison to a reference system. These non detections are principally due to the fact that the Doppler signal generated by the fetal moving is strongly disturbed by the presence of others several Doppler sources (mother' s moving, pseudo breathing, etc.). By modifying the existing method (autocorrelation method) and by proposing new time and frequency estimators used in the audio' s domain, we reduce to 5% the probability of non-detection of the fetal heart rate. These results are really encouraging and they enable us to plan the use of automatic classification techniques in order to discriminate between healthy and in suffering foetus.

Modeling Seasonal Influenza Transmission and Its Association with Climate Factors in Thailand Using Time-Series and ARIMAX Analyses.

PubMed

Chadsuthi, Sudarat; Iamsirithaworn, Sopon; Triampo, Wannapong; Modchang, Charin

2015-01-01

Influenza is a worldwide respiratory infectious disease that easily spreads from one person to another. Previous research has found that the influenza transmission process is often associated with climate variables. In this study, we used autocorrelation and partial autocorrelation plots to determine the appropriate autoregressive integrated moving average (ARIMA) model for influenza transmission in the central and southern regions of Thailand. The relationships between reported influenza cases and the climate data, such as the amount of rainfall, average temperature, average maximum relative humidity, average minimum relative humidity, and average relative humidity, were evaluated using cross-correlation function. Based on the available data of suspected influenza cases and climate variables, the most appropriate ARIMA(X) model for each region was obtained. We found that the average temperature correlated with influenza cases in both central and southern regions, but average minimum relative humidity played an important role only in the southern region. The ARIMAX model that includes the average temperature with a 4-month lag and the minimum relative humidity with a 2-month lag is the appropriate model for the central region, whereas including the minimum relative humidity with a 4-month lag results in the best model for the southern region.

Optical binding of two microparticles levitated in vacuum

NASA Astrophysics Data System (ADS)

Arita, Yoshihiko; Wright, Ewan M.; Dholakia, Kishan

2017-04-01

Optical binding refers to an optically mediated inter-particle interaction that creates new equilibrium positions for closely spaced particles [1-5]. Optical binding of mesoscopic particles levitated in vacuum can pave the way towards the realisation of a large scale quantum bound array in cavity-optomechanics [6-9]. Recently we have demonstrated trapping and rotation of two mesoscopic particles in vacuum using a spatial-light-modulator-based approach to trap more than one particle, induce controlled rotation of individual particles, and mediate interparticle separation [10]. By trapping and rotating two vaterite particles, we observe intensity modulation of the scattered light at the sum and difference frequencies with respect to the individual rotation rates. This first demonstration of optical interference between two microparticles in vacuum has lead to a platform to explore optical binding. Here we demonstrate for the first time optically bound two microparticles mediated by light scattering in vacuum. We investigate autocorrelations between the two normal modes of oscillation, which are determined by the centre-of-mass and the relative positions of the two-particle system. In situ determination of the optical restoring force acting on the bound particles are based on measurement of the oscillation frequencies of the autocorrelation functions of the two normal modes, thereby providing a powerful and original platform to explore multiparticle entanglement in cavity-optomechanics.

Glutamate receptor-channel gating. Maximum likelihood analysis of gigaohm seal recordings from locust muscle.

PubMed Central

Bates, S E; Sansom, M S; Ball, F G; Ramsey, R L; Usherwood, P N

1990-01-01

Gigaohm recordings have been made from glutamate receptor channels in excised, outside-out patches of collagenase-treated locust muscle membrane. The channels in the excised patches exhibit the kinetic state switching first seen in megaohm recordings from intact muscle fibers. Analysis of channel dwell time distributions reveals that the gating mechanism contains at least four open states and at least four closed states. Dwell time autocorrelation function analysis shows that there are at least three gateways linking the open states of the channel with the closed states. A maximum likelihood procedure has been used to fit six different gating models to the single channel data. Of these models, a cooperative model yields the best fit, and accurately predicts most features of the observed channel gating kinetics. PMID:1696510

Separability of spatiotemporal spectra of image sequences. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Eckert, Michael P.; Buchsbaum, Gershon; Watson, Andrew B.

1992-01-01

The spatiotemporal power spectrum was calculated of 14 image sequences in order to determine the degree to which the spectra are separable in space and time, and to assess the validity of the commonly used exponential correlation model found in the literature. The spectrum was expanded by a Singular Value Decomposition into a sum of separable terms and an index was defined of spatiotemporal separability as the fraction of the signal energy that can be represented by the first (largest) separable term. All spectra were found to be highly separable with an index of separability above 0.98. The power spectra of the sequences were well fit by a separable model. The power spectrum model corresponds to a product of exponential autocorrelation functions separable in space and time.

Langevin Dynamics Deciphers the Motility Pattern of Swimming Parasites

NASA Astrophysics Data System (ADS)

Zaburdaev, Vasily; Uppaluri, Sravanti; Pfohl, Thomas; Engstler, Markus; Friedrich, Rudolf; Stark, Holger

2011-05-01

The parasite African trypanosome swims in the bloodstream of mammals and causes the highly dangerous human sleeping sickness. Cell motility is essential for the parasite’s survival within the mammalian host. We present an analysis of the random-walk pattern of a swimming trypanosome. From experimental time-autocorrelation functions for the direction of motion we identify two relaxation times that differ by an order of magnitude. They originate from the rapid deformations of the cell body and a slower rotational diffusion of the average swimming direction. Velocity fluctuations are athermal and increase for faster cells whose trajectories are also straighter. We demonstrate that such a complex dynamics is captured by two decoupled Langevin equations that decipher the complex trajectory pattern by referring it to the microscopic details of cell behavior.

The evolving interaction of low-frequency earthquakes during transient slip.

PubMed

Frank, William B; Shapiro, Nikolaï M; Husker, Allen L; Kostoglodov, Vladimir; Gusev, Alexander A; Campillo, Michel

2016-04-01

Observed along the roots of seismogenic faults where the locked interface transitions to a stably sliding one, low-frequency earthquakes (LFEs) primarily occur as event bursts during slow slip. Using an event catalog from Guerrero, Mexico, we employ a statistical analysis to consider the sequence of LFEs at a single asperity as a point process, and deduce the level of time clustering from the shape of its autocorrelation function. We show that while the plate interface remains locked, LFEs behave as a simple Poisson process, whereas they become strongly clustered in time during even the smallest slow slip, consistent with interaction between different LFE sources. Our results demonstrate that bursts of LFEs can result from the collective behavior of asperities whose interaction depends on the state of the fault interface.

Quantifying time-of-flight-resolved optical field dynamics in turbid media with interferometric near-infrared spectroscopy (iNIRS) (Conference Presentation)

NASA Astrophysics Data System (ADS)

Borycki, Dawid; Kholiqov, Oybek; Zhou, Wenjun; Srinivasan, Vivek J.

2017-03-01

Sensing and imaging methods based on the dynamic scattering of coherent light, including laser speckle, laser Doppler, and diffuse correlation spectroscopy quantify scatterer motion using light intensity (speckle) fluctuations. The underlying optical field autocorrelation (OFA), rather than being measured directly, is typically inferred from the intensity autocorrelation (IA) through the Siegert relationship, by assuming that the scattered field obeys Gaussian statistics. In this work, we demonstrate interferometric near-infrared spectroscopy (iNIRS) for measurement of time-of-flight (TOF) resolved field and intensity autocorrelations in fluid tissue phantoms and in vivo. In phantoms, we find a breakdown of the Siegert relationship for short times-of-flight due to a contribution from static paths whose optical field does not decorrelate over experimental time scales, and demonstrate that eliminating such paths by polarization gating restores the validity of the Siegert relationship. Inspired by these results, we developed a method, called correlation gating, for separating the OFA into static and dynamic components. Correlation gating enables more precise quantification of tissue dynamics. To prove this, we show that iNIRS and correlation gating can be applied to measure cerebral hemodynamics of the nude mouse in vivo using dynamically scattered (ergodic) paths and not static (non-ergodic) paths, which may not be impacted by blood. More generally, correlation gating, in conjunction with TOF resolution, enables more precise separation of diffuse and non-diffusive contributions to OFA than is possible with TOF resolution alone. Finally, we show that direct measurements of OFA are statistically more efficient than indirect measurements based on IA.

Modeling multivariate time series on manifolds with skew radial basis functions.

PubMed

Jamshidi, Arta A; Kirby, Michael J

2011-01-01

We present an approach for constructing nonlinear empirical mappings from high-dimensional domains to multivariate ranges. We employ radial basis functions and skew radial basis functions for constructing a model using data that are potentially scattered or sparse. The algorithm progresses iteratively, adding a new function at each step to refine the model. The placement of the functions is driven by a statistical hypothesis test that accounts for correlation in the multivariate range variables. The test is applied on training and validation data and reveals nonstatistical or geometric structure when it fails. At each step, the added function is fit to data contained in a spatiotemporally defined local region to determine the parameters--in particular, the scale of the local model. The scale of the function is determined by the zero crossings of the autocorrelation function of the residuals. The model parameters and the number of basis functions are determined automatically from the given data, and there is no need to initialize any ad hoc parameters save for the selection of the skew radial basis functions. Compactly supported skew radial basis functions are employed to improve model accuracy, order, and convergence properties. The extension of the algorithm to higher-dimensional ranges produces reduced-order models by exploiting the existence of correlation in the range variable data. Structure is tested not just in a single time series but between all pairs of time series. We illustrate the new methodologies using several illustrative problems, including modeling data on manifolds and the prediction of chaotic time series.

Quantifying temporal change in biodiversity: challenges and opportunities

PubMed Central

Dornelas, Maria; Magurran, Anne E.; Buckland, Stephen T.; Chao, Anne; Chazdon, Robin L.; Colwell, Robert K.; Curtis, Tom; Gaston, Kevin J.; Gotelli, Nicholas J.; Kosnik, Matthew A.; McGill, Brian; McCune, Jenny L.; Morlon, Hélène; Mumby, Peter J.; Øvreås, Lise; Studeny, Angelika; Vellend, Mark

2013-01-01

Growing concern about biodiversity loss underscores the need to quantify and understand temporal change. Here, we review the opportunities presented by biodiversity time series, and address three related issues: (i) recognizing the characteristics of temporal data; (ii) selecting appropriate statistical procedures for analysing temporal data; and (iii) inferring and forecasting biodiversity change. With regard to the first issue, we draw attention to defining characteristics of biodiversity time series—lack of physical boundaries, uni-dimensionality, autocorrelation and directionality—that inform the choice of analytic methods. Second, we explore methods of quantifying change in biodiversity at different timescales, noting that autocorrelation can be viewed as a feature that sheds light on the underlying structure of temporal change. Finally, we address the transition from inferring to forecasting biodiversity change, highlighting potential pitfalls associated with phase-shifts and novel conditions. PMID:23097514

Spatial autocorrelation in growth of undisturbed natural pine stands across Georgia

Treesearch

Raymond L. Czaplewski; Robin M. Reich; William A. Bechtold

1994-01-01

Moran's I statistic measures the spatial autocorrelation in a random variable measured at discrete locations in space. Permutation procedures test the null hypothesis that the observed Moran's I value is no greater than that expected by chance. The spatial autocorrelation of gross basal area increment is analyzed for undisturbed, naturally regenerated stands...

Propagation of mechanical waves through a stochastic medium with spherical symmetry

NASA Astrophysics Data System (ADS)

Avendaño, Carlos G.; Reyes, J. Adrián

2018-01-01

We theoretically analyze the propagation of outgoing mechanical waves through an infinite isotropic elastic medium possessing spherical symmetry whose Lamé coefficients and density are spatial random functions characterized by well-defined statistical parameters. We derive the differential equation that governs the average displacement for a system whose properties depend on the radial coordinate. We show that such an equation is an extended version of the well-known Bessel differential equation whose perturbative additional terms contain coefficients that depend directly on the squared noise intensities and the autocorrelation lengths in an exponential decay fashion. We numerically solve the second order differential equation for several values of noise intensities and autocorrelation lengths and compare the corresponding displacement profiles with that of the exact analytic solution for the case of absent inhomogeneities.

Brownian dynamics and dynamic Monte Carlo simulations of isotropic and liquid crystal phases of anisotropic colloidal particles: a comparative study.

PubMed

Patti, Alessandro; Cuetos, Alejandro

2012-07-01

We report on the diffusion of purely repulsive and freely rotating colloidal rods in the isotropic, nematic, and smectic liquid crystal phases to probe the agreement between Brownian and Monte Carlo dynamics under the most general conditions. By properly rescaling the Monte Carlo time step, being related to any elementary move via the corresponding self-diffusion coefficient, with the acceptance rate of simultaneous trial displacements and rotations, we demonstrate the existence of a unique Monte Carlo time scale that allows for a direct comparison between Monte Carlo and Brownian dynamics simulations. To estimate the validity of our theoretical approach, we compare the mean square displacement of rods, their orientational autocorrelation function, and the self-intermediate scattering function, as obtained from Brownian dynamics and Monte Carlo simulations. The agreement between the results of these two approaches, even under the condition of heterogeneous dynamics generally observed in liquid crystalline phases, is excellent.

Comparison of single-ion molecular dynamics in common solvents

NASA Astrophysics Data System (ADS)

Muralidharan, A.; Pratt, L. R.; Chaudhari, M. I.; Rempe, S. B.

2018-06-01

Laying a basis for molecularly specific theory for the mobilities of ions in solutions of practical interest, we report a broad survey of velocity autocorrelation functions (VACFs) of Li+ and PF6- ions in water, ethylene carbonate, propylene carbonate, and acetonitrile solutions. We extract the memory function, γ(t), which characterizes the random forces governing the mobilities of ions. We provide comparisons controlling for the effects of electrolyte concentration and ion-pairing, van der Waals attractive interactions, and solvent molecular characteristics. For the heavier ion (PF6-), velocity relaxations are all similar: negative tail relaxations for the VACF and a clear second relaxation for γ (t ), observed previously also for other molecular ions and with n-pentanol as the solvent. For the light Li+ ion, short time-scale oscillatory behavior masks simple, longer time-scale relaxation of γ (t ). But the corresponding analysis of the solventberg Li+(H2O)4 does conform to the standard picture set by all the PF6- results.

Average-atom model for two-temperature states and ionic transport properties of aluminum in the warm dense matter regime

NASA Astrophysics Data System (ADS)

Hou, Yong; Fu, Yongsheng; Bredow, Richard; Kang, Dongdong; Redmer, Ronald; Yuan, Jianmin

2017-03-01

The average-atom model combined with the hyper-netted chain approximation is an efficient tool for electronic and ionic structure calculations for warm dense matter. Here we generalize this method in order to describe non-equilibrium states with different electron and ion temperature as produced in laser-matter interactions on ultra-short time scales. In particular, the electron-ion and ion-ion correlation effects are considered when calculating the electron structure. We derive an effective ion-ion pair-potential using the electron densities in the framework of temperature-depended density functional theory. Using this ion-ion potential we perform molecular dynamics simulations in order to determine the ionic transport properties such as the ionic diffusion coefficient and the shear viscosity through the ionic velocity autocorrelation functions.

Correlation functions for Hermitian many-body systems: Necessary conditions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brown, E.B.

1994-02-01

Lee [Phys. Rev. B 47, 8293 (1993)] has shown that the odd-numbered derivatives of the Kubo autocorrelation function vanish at [ital t]=0. We show that this condition is based on a more general property of nondiagonal Kubo correlation functions. This general property provides that certain functional forms (e.g., simple exponential decay) are not admissible for any symmetric or antisymmetric Kubo correlation function in a Hermitian many-body system. Lee's result emerges as a special case of this result. Applications to translationally invariant systems and systems with rotational symmetries are also demonstrated.

Spatial Autocorrelation Approaches to Testing Residuals from Least Squares Regression.

PubMed

Chen, Yanguang

2016-01-01

In geo-statistics, the Durbin-Watson test is frequently employed to detect the presence of residual serial correlation from least squares regression analyses. However, the Durbin-Watson statistic is only suitable for ordered time or spatial series. If the variables comprise cross-sectional data coming from spatial random sampling, the test will be ineffectual because the value of Durbin-Watson's statistic depends on the sequence of data points. This paper develops two new statistics for testing serial correlation of residuals from least squares regression based on spatial samples. By analogy with the new form of Moran's index, an autocorrelation coefficient is defined with a standardized residual vector and a normalized spatial weight matrix. Then by analogy with the Durbin-Watson statistic, two types of new serial correlation indices are constructed. As a case study, the two newly presented statistics are applied to a spatial sample of 29 China's regions. These results show that the new spatial autocorrelation models can be used to test the serial correlation of residuals from regression analysis. In practice, the new statistics can make up for the deficiencies of the Durbin-Watson test.

Comparison of Kasai Autocorrelation and Maximum Likelihood Estimators for Doppler Optical Coherence Tomography

PubMed Central

Chan, Aaron C.; Srinivasan, Vivek J.

2013-01-01

In optical coherence tomography (OCT) and ultrasound, unbiased Doppler frequency estimators with low variance are desirable for blood velocity estimation. Hardware improvements in OCT mean that ever higher acquisition rates are possible, which should also, in principle, improve estimation performance. Paradoxically, however, the widely used Kasai autocorrelation estimator’s performance worsens with increasing acquisition rate. We propose that parametric estimators based on accurate models of noise statistics can offer better performance. We derive a maximum likelihood estimator (MLE) based on a simple additive white Gaussian noise model, and show that it can outperform the Kasai autocorrelation estimator. In addition, we also derive the Cramer Rao lower bound (CRLB), and show that the variance of the MLE approaches the CRLB for moderate data lengths and noise levels. We note that the MLE performance improves with longer acquisition time, and remains constant or improves with higher acquisition rates. These qualities may make it a preferred technique as OCT imaging speed continues to improve. Finally, our work motivates the development of more general parametric estimators based on statistical models of decorrelation noise. PMID:23446044

Statistical microeconomics and commodity prices: theory and empirical results.

PubMed

Baaquie, Belal E

2016-01-13

A review is made of the statistical generalization of microeconomics by Baaquie (Baaquie 2013 Phys. A 392, 4400-4416. (doi:10.1016/j.physa.2013.05.008)), where the market price of every traded commodity, at each instant of time, is considered to be an independent random variable. The dynamics of commodity market prices is given by the unequal time correlation function and is modelled by the Feynman path integral based on an action functional. The correlation functions of the model are defined using the path integral. The existence of the action functional for commodity prices that was postulated to exist in Baaquie (Baaquie 2013 Phys. A 392, 4400-4416. (doi:10.1016/j.physa.2013.05.008)) has been empirically ascertained in Baaquie et al. (Baaquie et al. 2015 Phys. A 428, 19-37. (doi:10.1016/j.physa.2015.02.030)). The model's action functionals for different commodities has been empirically determined and calibrated using the unequal time correlation functions of the market commodity prices using a perturbation expansion (Baaquie et al. 2015 Phys. A 428, 19-37. (doi:10.1016/j.physa.2015.02.030)). Nine commodities drawn from the energy, metal and grain sectors are empirically studied and their auto-correlation for up to 300 days is described by the model to an accuracy of R(2)>0.90-using only six parameters. © 2015 The Author(s).

Spatio-temporal wildland arson crime functions

Treesearch

David T. Butry; Jeffrey P. Prestemon

2005-01-01

Wildland arson creates damages to structures and timber and affects the health and safety of people living in rural and wildland urban interface areas. We develop a model that incorporates temporal autocorrelations and spatial correlations in wildland arson ignitions in Florida. A Poisson autoregressive model of order p, or PAR(p)...

On the linearity of tracer bias around voids

NASA Astrophysics Data System (ADS)

Pollina, Giorgia; Hamaus, Nico; Dolag, Klaus; Weller, Jochen; Baldi, Marco; Moscardini, Lauro

2017-07-01

The large-scale structure of the Universe can be observed only via luminous tracers of the dark matter. However, the clustering statistics of tracers are biased and depend on various properties, such as their host-halo mass and assembly history. On very large scales, this tracer bias results in a constant offset in the clustering amplitude, known as linear bias. Towards smaller non-linear scales, this is no longer the case and tracer bias becomes a complicated function of scale and time. We focus on tracer bias centred on cosmic voids, I.e. depressions of the density field that spatially dominate the Universe. We consider three types of tracers: galaxies, galaxy clusters and active galactic nuclei, extracted from the hydrodynamical simulation Magneticum Pathfinder. In contrast to common clustering statistics that focus on auto-correlations of tracers, we find that void-tracer cross-correlations are successfully described by a linear bias relation. The tracer-density profile of voids can thus be related to their matter-density profile by a single number. We show that it coincides with the linear tracer bias extracted from the large-scale auto-correlation function and expectations from theory, if sufficiently large voids are considered. For smaller voids we observe a shift towards higher values. This has important consequences on cosmological parameter inference, as the problem of unknown tracer bias is alleviated up to a constant number. The smallest scales in existing data sets become accessible to simpler models, providing numerous modes of the density field that have been disregarded so far, but may help to further reduce statistical errors in constraining cosmology.

Assessment of sacrococcygeal pressure ulcers using diffuse correlation spectroscopy

NASA Astrophysics Data System (ADS)

Diaz, David; Lafontant, Alec; Neidrauer, Michael; Weingarten, Michael S.; DiMaria-Ghalili, Rose Ann; Fried, Guy W.; Rece, Julianne; Lewin, Peter A.; Zubkov, Leonid

2016-03-01

Microcirculation is essential for proper supply of oxygen and nutritive substances to the biological tissue and the removal of waste products of metabolism. The determination of microcirculatory blood flow (mBF) is therefore of substantial interest to clinicians for assessing tissue health; particularly in pressure ulceration and suspected deep tissue injury. The goal of this pilot clinical study was to assess deep-tissue pressure ulceration by non-invasively measuring mBF using Diffuse Correlation Spectroscopy (DCS). DCS provides information about the flow of red blood cells in the capillary network by measuring the temporal autocorrelation function of scattering light intensity. A novel optical probe was developed in order to obtain measurements under the load of the subject's body as pressure is applied (ischemia) and then released (reperfusion) on sacrococcygeal tissue in a hospital bed. Prior to loading measurements, baseline readings of the sacral region were obtained by measuring the subjects in a side-lying position. DCS measurements from the sacral region of twenty healthy volunteers have been compared to those of two patients who initially had similar non-blanchable redness. The temporal autocorrelation function of scattering light intensity of the patient whose redness later disappeared was similar to that of the average healthy subject. The second patient, whose redness developed into an advanced pressure ulcer two weeks later, had a substantial decrease in blood flow while under the loading position compared to healthy subjects. Preliminary results suggest the developed system may potentially predict whether non-blanchable redness will manifest itself as advanced ulceration or dissipate over time.

Preliminary rotor wake measurements with a laser velocimeter

NASA Technical Reports Server (NTRS)

Hoad, D. R.; Rhodes, D. B.; Meyers, J. F.

1983-01-01

A laser velocimeter (LV) was used to determine rotor wake characteristics. The effect of various fuselage widths and rotor-fuselage spacings on time averaged and detailed time dependent rotor wake velocity characteristics was defined. Definition of time dependent velocity characteristics was attempted with the LV by associating a rotor azimuth position with each velocity measurement. Results were discouraging in that no apparent time dependent velocity characteristics could be discerned from the LV measurements. Since the LV is a relatively new instrument in the rotor wake measurement field, the cause of this lack of periodicity is as important as the basic research objectives. An attempt was made to identify the problem by simulated acquisition of LV-type data for a predicted rotor wake velocity time history. Power spectral density and autocorrelation function estimation techniques were used to substantiate the conclusion that the primary cause of the lack of time dependent velocity characteristics was the nonstationary flow condition generated by the periodic turbulence level that currently exists in the open throat configuration of the wind tunnel.

The Effects of Autocorrelation on the Curve-of-Factors Growth Model

ERIC Educational Resources Information Center

Murphy, Daniel L.; Beretvas, S. Natasha; Pituch, Keenan A.

2011-01-01

This simulation study examined the performance of the curve-of-factors model (COFM) when autocorrelation and growth processes were present in the first-level factor structure. In addition to the standard curve-of factors growth model, 2 new models were examined: one COFM that included a first-order autoregressive autocorrelation parameter, and a…

Amplitude death and synchronized states in nonlinear time-delay systems coupled through mean-field diffusion

NASA Astrophysics Data System (ADS)

Banerjee, Tanmoy; Biswas, Debabrata

2013-12-01

We explore and experimentally demonstrate the phenomena of amplitude death (AD) and the corresponding transitions through synchronized states that lead to AD in coupled intrinsic time-delayed hyperchaotic oscillators interacting through mean-field diffusion. We identify a novel synchronization transition scenario leading to AD, namely transitions among AD, generalized anticipatory synchronization (GAS), complete synchronization (CS), and generalized lag synchronization (GLS). This transition is mediated by variation of the difference of intrinsic time-delays associated with the individual systems and has no analogue in non-delayed systems or coupled oscillators with coupling time-delay. We further show that, for equal intrinsic time-delays, increasing coupling strength results in a transition from the unsynchronized state to AD state via in-phase (complete) synchronized states. Using Krasovskii-Lyapunov theory, we derive the stability conditions that predict the parametric region of occurrence of GAS, GLS, and CS; also, using a linear stability analysis, we derive the condition of occurrence of AD. We use the error function of proper synchronization manifold and a modified form of the similarity function to provide the quantitative support to GLS and GAS. We demonstrate all the scenarios in an electronic circuit experiment; the experimental time-series, phase-plane plots, and generalized autocorrelation function computed from the experimental time series data are used to confirm the occurrence of all the phenomena in the coupled oscillators.

Map LineUps: Effects of spatial structure on graphical inference.

PubMed

Beecham, Roger; Dykes, Jason; Meulemans, Wouter; Slingsby, Aidan; Turkay, Cagatay; Wood, Jo

2017-01-01

Fundamental to the effective use of visualization as an analytic and descriptive tool is the assurance that presenting data visually provides the capability of making inferences from what we see. This paper explores two related approaches to quantifying the confidence we may have in making visual inferences from mapped geospatial data. We adapt Wickham et al.'s 'Visual Line-up' method as a direct analogy with Null Hypothesis Significance Testing (NHST) and propose a new approach for generating more credible spatial null hypotheses. Rather than using as a spatial null hypothesis the unrealistic assumption of complete spatial randomness, we propose spatially autocorrelated simulations as alternative nulls. We conduct a set of crowdsourced experiments (n=361) to determine the just noticeable difference (JND) between pairs of choropleth maps of geographic units controlling for spatial autocorrelation (Moran's I statistic) and geometric configuration (variance in spatial unit area). Results indicate that people's abilities to perceive differences in spatial autocorrelation vary with baseline autocorrelation structure and the geometric configuration of geographic units. These results allow us, for the first time, to construct a visual equivalent of statistical power for geospatial data. Our JND results add to those provided in recent years by Klippel et al. (2011), Harrison et al. (2014) and Kay & Heer (2015) for correlation visualization. Importantly, they provide an empirical basis for an improved construction of visual line-ups for maps and the development of theory to inform geospatial tests of graphical inference.

Low-Dimensional Chaos in an Instance of Epilepsy

NASA Astrophysics Data System (ADS)

Babloyantz, A.; Destexhe, A.

1986-05-01

Using a time series obtained from the electroencephalogram recording of a human epileptic seizure, we show the existence of a chaotic attractor, the latter being the direct consequence of the deterministic nature of brain activity. This result is compared with other attractors seen in normal human brain dynamics. A sudden jump is observed between the dimensionalities of these brain attractors 4.05 ± 0.05 for deep sleep) and the very low dimensionality of the epileptic state (2.05 ± 0.09). The evaluation of the autocorrelation function and of the largest Lyapunov exponent allows us to sharpen further the main features of underlying dynamics. Possible implications in biological and medical research are briefly discussed.

Revisiting long-range dependence in annual precipitation

NASA Astrophysics Data System (ADS)

Iliopoulou, Theano; Papalexiou, Simon Michael; Markonis, Yannis; Koutsoyiannis, Demetris

2018-01-01

Long-range dependence (LRD), the so-called Hurst-Kolmogorov behaviour, is considered to be an intrinsic characteristic of most natural processes. This behaviour manifests itself by the prevalence of slowly decaying autocorrelation function and questions the Markov assumption, often habitually employed in time series analysis. Herein, we investigate the dependence structure of annual rainfall using a large set, comprising more than a thousand stations worldwide of length 100 years or more, as well as a smaller number of paleoclimatic reconstructions covering the last 12,000 years. Our findings suggest weak long-term persistence for instrumental data (average H = 0.59), which becomes stronger with scale, i.e. in the paleoclimatic reconstructions (average H = 0.75).

Glassy behaviour in simple kinetically constrained models: topological networks, lattice analogues and annihilation-diffusion

NASA Astrophysics Data System (ADS)

Sherrington, David; Davison, Lexie; Buhot, Arnaud; Garrahan, Juan P.

2002-02-01

We report a study of a series of simple model systems with only non-interacting Hamiltonians, and hence simple equilibrium thermodynamics, but with constrained dynamics of a type initially suggested by foams and idealized covalent glasses. We demonstrate that macroscopic dynamical features characteristic of real and more complex model glasses, such as two-time decays in energy and auto-correlation functions, arise from the dynamics and we explain them qualitatively and quantitatively in terms of annihilation-diffusion concepts and theory. The comparison is with strong glasses. We also consider fluctuation-dissipation relations and demonstrate subtleties of interpretation. We find no FDT breakdown when the correct normalization is chosen.

An effective chaos-geometric computational approach to analysis and prediction of evolutionary dynamics of the environmental systems: Atmospheric pollution dynamics

NASA Astrophysics Data System (ADS)

Buyadzhi, V. V.; Glushkov, A. V.; Khetselius, O. Yu; Bunyakova, Yu Ya; Florko, T. A.; Agayar, E. V.; Solyanikova, E. P.

2017-10-01

The present paper concerns the results of computational studying dynamics of the atmospheric pollutants (dioxide of nitrogen, sulphur etc) concentrations in an atmosphere of the industrial cities (Odessa) by using the dynamical systems and chaos theory methods. A chaotic behaviour in the nitrogen dioxide and sulphurous anhydride concentration time series at several sites of the Odessa city is numerically investigated. As usually, to reconstruct the corresponding attractor, the time delay and embedding dimension are needed. The former is determined by the methods of autocorrelation function and average mutual information, and the latter is calculated by means of a correlation dimension method and algorithm of false nearest neighbours. Further, the Lyapunov’s exponents spectrum, Kaplan-Yorke dimension and Kolmogorov entropy are computed. It has been found an existence of a low-D chaos in the time series of the atmospheric pollutants concentrations.

Nature of self-diffusion in two-dimensional fluids

NASA Astrophysics Data System (ADS)

Choi, Bongsik; Han, Kyeong Hwan; Kim, Changho; Talkner, Peter; Kidera, Akinori; Lee, Eok Kyun

2017-12-01

Self-diffusion in a two-dimensional simple fluid is investigated by both analytical and numerical means. We investigate the anomalous aspects of self-diffusion in two-dimensional fluids with regards to the mean square displacement, the time-dependent diffusion coefficient, and the velocity autocorrelation function (VACF) using a consistency equation relating these quantities. We numerically confirm the consistency equation by extensive molecular dynamics simulations for finite systems, corroborate earlier results indicating that the kinematic viscosity approaches a finite, non-vanishing value in the thermodynamic limit, and establish the finite size behavior of the diffusion coefficient. We obtain the exact solution of the consistency equation in the thermodynamic limit and use this solution to determine the large time asymptotics of the mean square displacement, the diffusion coefficient, and the VACF. An asymptotic decay law of the VACF resembles the previously known self-consistent form, 1/(t\\sqrt{{ln}t}), however with a rescaled time.

Factors Influencing Army Accessions.

DTIC Science & Technology

1982-12-01

partial autocorrelations were examined for significant lags or a recognizable pattern such as a damped exponential or a sine wave. The TSP prugrams...decreasing function indicating nonstation- *arity or a very long sine wave where only a small portion of the wave is plotted. The partial...plot of the raw data appeared (Appendix E-1) to be either the middle of a long sine wave or a linearly decreasing function. This pattern is recognized

Efficiencies of joint non-local update moves in Monte Carlo simulations of coarse-grained polymers

NASA Astrophysics Data System (ADS)

Austin, Kieran S.; Marenz, Martin; Janke, Wolfhard

2018-03-01

In this study four update methods are compared in their performance in a Monte Carlo simulation of polymers in continuum space. The efficiencies of the update methods and combinations thereof are compared with the aid of the autocorrelation time with a fixed (optimal) acceptance ratio. Results are obtained for polymer lengths N = 14, 28 and 42 and temperatures below, at and above the collapse transition. In terms of autocorrelation, the optimal acceptance ratio is approximately 0.4. Furthermore, an overview of the step sizes of the update methods that correspond to this optimal acceptance ratio is given. This shall serve as a guide for future studies that rely on efficient computer simulations.

LETTER TO THE EDITOR: Exhaustive search for low-autocorrelation binary sequences

NASA Astrophysics Data System (ADS)

Mertens, S.

1996-09-01

Binary sequences with low autocorrelations are important in communication engineering and in statistical mechanics as ground states of the Bernasconi model. Computer searches are the main tool in the construction of such sequences. Owing to the exponential size 0305-4470/29/18/005/img1 of the configuration space, exhaustive searches are limited to short sequences. We discuss an exhaustive search algorithm with run-time characteristic 0305-4470/29/18/005/img2 and apply it to compile a table of exact ground states of the Bernasconi model up to N = 48. The data suggest F > 9 for the optimal merit factor in the limit 0305-4470/29/18/005/img3.

Support for equatorial anisotropy of Earth's inner-inner core from seismic interferometry at low latitudes

NASA Astrophysics Data System (ADS)

Wang, Tao; Song, Xiaodong

2018-03-01

Anisotropy of Earth's inner core provides a key role to understand its evolution and the Earth's magnetic field. Recently, using autocorrelations from earthquake's coda, we found an equatorial anisotropy of the inner-inner core (IIC), in apparent contrast to the polar anisotropy of the outer-inner core (OIC). To reduce the influence of the polar anisotropy and reduce possible contaminations from the large Fresnel zone of the PKIKP2 and PKIIKP2 phases at low frequencies, we processed coda noise of large earthquakes (10,000-40,000 s after magnitude ≥7.0) from stations at low latitudes (within ±35°) during 1990-2013. Using a number of improved procedures of both autocorrelation and cross-correlation, we extracted 52 array-stacked high-quality empirical Green's functions (EGFs), an increase of over 60% from our previous study. The high-quality data allow us to measure the relative arrival times by automatic waveform cross correlation. The results show large variation (∼10.9 s) in the differential times between the PKIKP2 and PKIIKP2 phases. The estimated influence of the Fresnel zone is insignificant (<1.1 s), compared to the observed data variation and measurement uncertainty. The observed time residuals match very well previous IIC model with a quasi-equatorial fast axis (near Central America and the Southeast Asia) and the spatial pattern from the low-latitude measurements is similar to the previous global dataset, including the fast axis and two low-velocity open rings, thus providing further support for the equatorial anisotropy model of the IIC. Speculations for the shift of the fast axis between the OIC and the IIC include: change of deformation regimes during the inner core history, change of geomagnetic field, and a proto-inner core.

Scaling analysis of stock markets

NASA Astrophysics Data System (ADS)

Bu, Luping; Shang, Pengjian

2014-06-01

In this paper, we apply the detrended fluctuation analysis (DFA), local scaling detrended fluctuation analysis (LSDFA), and detrended cross-correlation analysis (DCCA) to investigate correlations of several stock markets. DFA method is for the detection of long-range correlations used in time series. LSDFA method is to show more local properties by using local scale exponents. DCCA method is a developed method to quantify the cross-correlation of two non-stationary time series. We report the results of auto-correlation and cross-correlation behaviors in three western countries and three Chinese stock markets in periods 2004-2006 (before the global financial crisis), 2007-2009 (during the global financial crisis), and 2010-2012 (after the global financial crisis) by using DFA, LSDFA, and DCCA method. The findings are that correlations of stocks are influenced by the economic systems of different countries and the financial crisis. The results indicate that there are stronger auto-correlations in Chinese stocks than western stocks in any period and stronger auto-correlations after the global financial crisis for every stock except Shen Cheng; The LSDFA shows more comprehensive and detailed features than traditional DFA method and the integration of China and the world in economy after the global financial crisis; When it turns to cross-correlations, it shows different properties for six stock markets, while for three Chinese stocks, it reaches the weakest cross-correlations during the global financial crisis.

A simple autocorrelation algorithm for determining grain size from digital images of sediment

USGS Publications Warehouse

Rubin, D.M.

2004-01-01

Autocorrelation between pixels in digital images of sediment can be used to measure average grain size of sediment on the bed, grain-size distribution of bed sediment, and vertical profiles in grain size in a cross-sectional image through a bed. The technique is less sensitive than traditional laboratory analyses to tails of a grain-size distribution, but it offers substantial other advantages: it is 100 times as fast; it is ideal for sampling surficial sediment (the part that interacts with a flow); it can determine vertical profiles in grain size on a scale finer than can be sampled physically; and it can be used in the field to provide almost real-time grain-size analysis. The technique can be applied to digital images obtained using any source with sufficient resolution, including digital cameras, digital video, or underwater digital microscopes (for real-time grain-size mapping of the bed). ?? 2004, SEPM (Society for Sedimentary Geology).

Parallel auto-correlative statistics with VTK.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pebay, Philippe Pierre; Bennett, Janine Camille

2013-08-01

This report summarizes existing statistical engines in VTK and presents both the serial and parallel auto-correlative statistics engines. It is a sequel to [PT08, BPRT09b, PT09, BPT09, PT10] which studied the parallel descriptive, correlative, multi-correlative, principal component analysis, contingency, k-means, and order statistics engines. The ease of use of the new parallel auto-correlative statistics engine is illustrated by the means of C++ code snippets and algorithm verification is provided. This report justifies the design of the statistics engines with parallel scalability in mind, and provides scalability and speed-up analysis results for the autocorrelative statistics engine.

False alarms: How early warning signals falsely predict abrupt sea ice loss

NASA Astrophysics Data System (ADS)

Wagner, Till J. W.; Eisenman, Ian

2016-04-01

Uncovering universal early warning signals for critical transitions has become a coveted goal in diverse scientific disciplines, ranging from climate science to financial mathematics. There has been a flurry of recent research proposing such signals, with increasing autocorrelation and increasing variance being among the most widely discussed candidates. A number of studies have suggested that increasing autocorrelation alone may suffice to signal an impending transition, although some others have questioned this. Here we consider variance and autocorrelation in the context of sea ice loss in an idealized model of the global climate system. The model features no bifurcation, nor increased rate of retreat, as the ice disappears. Nonetheless, the autocorrelation of summer sea ice area is found to increase in a global warming scenario. The variance, by contrast, decreases. A simple physical mechanism is proposed to explain the occurrence of increasing autocorrelation but not variance when there is no approaching bifurcation. Additionally, a similar mechanism is shown to allow an increase in both indicators with no physically attainable bifurcation. This implies that relying on autocorrelation and variance as early warning signals can raise false alarms in the climate system, warning of "tipping points" that are not actually there.

Experimental evaluation of fluctuating density and radiated noise from a high temperature jet

NASA Technical Reports Server (NTRS)

Massier, P. F.; Parthasarathy, S. P.; Cuffel, R. F.

1973-01-01

An experimental investigation has been conducted to characterize the fluctuating density within a high-temperature (1100 K) subsonic jet and to characterize by the noise radiated to the surroundings. Cross correlations obtained by introducing time delay to the signals detected from spatially separated crossed laser beams set up as a Schlieren system were used to determine radial and axial distributions of the convection velocity of the moving noise sources (eddies). In addition, the autocorrelation of the fluctuating density was evaluated in the moving frame of reference of the eddies. Also, the autocorrelation of the radiated noise in the moving reference frame was evaluated from cross correlations by introducing time delay to the signals detected by spatially separated pairs of microphones. Radial distributions of the mean velocity were obtained from measurements of the stagnation temperature, and stagnation and static pressures with the use of probes.

Evaporation of nanoscale water on a uniformly complete wetting surface at different temperatures.

PubMed

Guo, Yuwei; Wan, Rongzheng

2018-05-03

The evaporation of nanoscale water films on surfaces affects many processes in nature and industry. Using molecular dynamics (MD) simulations, we show the evaporation of a nanoscale water film on a uniformly complete wetting surface at different temperatures. With the increase in temperature, the growth of the water evaporation rate becomes slow. Analyses show that the hydrogen bond (H-bond) lifetimes and orientational autocorrelation times of the outermost water film decrease slowly with the increase in temperature. Compared to a thicker water film, the H-bond lifetimes and orientational autocorrelation times of a monolayer water film are much slower. This suggests that the lower evaporation rate of the monolayer water film on a uniformly complete wetting surface may be caused by the constriction of the water rotation due to the substrate. This finding may be helpful for controlling nanoscale water evaporation within a certain range of temperatures.

An experimental investigation of a three dimensional wall jet. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Catalano, G. D.

1977-01-01

One and two point statistical properties are measured in the flow fields of a coflowing turbulent jet. Two different confining surfaces (one flat, one with large curvature) are placed adjacent to the lip of the circular nozzle; and the resultant effects on the flow field are determined. The one point quantities measured include mean velocities, turbulent intensities, velocity and concentration autocorrelations and power spectral densities, and intermittencies. From the autocorrelation curves, the Taylor microscale and the integral length scale are calculated. Two point quantities measured include velocity and concentration space-time correlations and pressure velocity correlations. From the velocity space-time correlations, iso-correlation contours are constructed along with the lines of maximum maximorum. These lines allow a picture of the flow pattern to be determined. The pressures monitored in the pressure velocity correlations are measured both in the flow field and at the surface of the confining wall(s).

Intensity autocorrelation measurements of frequency combs in the terahertz range

NASA Astrophysics Data System (ADS)

Benea-Chelmus, Ileana-Cristina; Rösch, Markus; Scalari, Giacomo; Beck, Mattias; Faist, Jérôme

2017-09-01

We report on direct measurements of the emission character of quantum cascade laser based frequency combs, using intensity autocorrelation. Our implementation is based on fast electro-optic sampling, with a detection spectral bandwidth matching the emission bandwidth of the comb laser, around 2.5 THz. We find the output of these frequency combs to be continuous even in the locked regime, but accompanied by a strong intensity modulation. Moreover, with our record temporal resolution of only few hundreds of femtoseconds, we can resolve correlated intensity modulation occurring on time scales as short as the gain recovery time, about 4 ps. By direct comparison with pulsed terahertz light originating from a photoconductive emitter, we demonstrate the peculiar emission pattern of these lasers. The measurement technique is self-referenced and ultrafast, and requires no reconstruction. It will be of significant importance in future measurements of ultrashort pulses from quantum cascade lasers.

Parameter Uncertainty for Aircraft Aerodynamic Modeling using Recursive Least Squares

NASA Technical Reports Server (NTRS)

Grauer, Jared A.; Morelli, Eugene A.

2016-01-01

A real-time method was demonstrated for determining accurate uncertainty levels of stability and control derivatives estimated using recursive least squares and time-domain data. The method uses a recursive formulation of the residual autocorrelation to account for colored residuals, which are routinely encountered in aircraft parameter estimation and change the predicted uncertainties. Simulation data and flight test data for a subscale jet transport aircraft were used to demonstrate the approach. Results showed that the corrected uncertainties matched the observed scatter in the parameter estimates, and did so more accurately than conventional uncertainty estimates that assume white residuals. Only small differences were observed between batch estimates and recursive estimates at the end of the maneuver. It was also demonstrated that the autocorrelation could be reduced to a small number of lags to minimize computation and memory storage requirements without significantly degrading the accuracy of predicted uncertainty levels.

Conditional Poisson models: a flexible alternative to conditional logistic case cross-over analysis.

PubMed

Armstrong, Ben G; Gasparrini, Antonio; Tobias, Aurelio

2014-11-24

The time stratified case cross-over approach is a popular alternative to conventional time series regression for analysing associations between time series of environmental exposures (air pollution, weather) and counts of health outcomes. These are almost always analyzed using conditional logistic regression on data expanded to case-control (case crossover) format, but this has some limitations. In particular adjusting for overdispersion and auto-correlation in the counts is not possible. It has been established that a Poisson model for counts with stratum indicators gives identical estimates to those from conditional logistic regression and does not have these limitations, but it is little used, probably because of the overheads in estimating many stratum parameters. The conditional Poisson model avoids estimating stratum parameters by conditioning on the total event count in each stratum, thus simplifying the computing and increasing the number of strata for which fitting is feasible compared with the standard unconditional Poisson model. Unlike the conditional logistic model, the conditional Poisson model does not require expanding the data, and can adjust for overdispersion and auto-correlation. It is available in Stata, R, and other packages. By applying to some real data and using simulations, we demonstrate that conditional Poisson models were simpler to code and shorter to run than are conditional logistic analyses and can be fitted to larger data sets than possible with standard Poisson models. Allowing for overdispersion or autocorrelation was possible with the conditional Poisson model but when not required this model gave identical estimates to those from conditional logistic regression. Conditional Poisson regression models provide an alternative to case crossover analysis of stratified time series data with some advantages. The conditional Poisson model can also be used in other contexts in which primary control for confounding is by fine stratification.

Transitional analysis of organic thin color filter layers in displays during baking process using multi-speckle diffusing wave spectroscopy

NASA Astrophysics Data System (ADS)

Park, Baek Sung; Hyung, Kyung Hee; Oh, Gwi Jeong; Jung, Hyun Wook

2018-02-01

The color filter (CF) is one of the key components for improving the performance of TV displays such as liquid crystal display (LCD) and white organic light emitting diodes (WOLED). The profile defects like undercut during the fine fabrication processes for CF layers are inevitably generated through the UV exposure and development processes, however, these can be controlled through the baking process. In order to resolve the profile defects of CF layers, in this study, the real-time dynamic changes of CF layers are monitored during the baking process by changing components such as polymeric binder and acrylate. The motion of pigment particles in CF layers during baking is quantitatively interpreted using multi-speckle diffusing wave spectroscopy (MSDWS), in terms of the autocorrelation function and the characteristic time of α-relaxation.

Pitch-angle diffusion of electrons through growing and propagating along a magnetic field electromagnetic wave in Earth's radiation belts

DOE Office of Scientific and Technical Information (OSTI.GOV)

Choi, C.-R., E-mail: crchoi@kaist.ac.kr; Dokgo, K.; Min, K.-W.

The diffusion of electrons via a linearly polarized, growing electromagnetic (EM) wave propagating along a uniform magnetic field is investigated. The diffusion of electrons that interact with the growing EM wave is investigated through the autocorrelation function of the parallel electron acceleration in several tens of electron gyration timescales, which is a relatively short time compared with the bounce time of electrons between two mirror points in Earth's radiation belts. Furthermore, the pitch-angle diffusion coefficient is derived for the resonant and non-resonant electrons, and the effect of the wave growth on the electron diffusion is discussed. The results can bemore » applied to other problems related to local acceleration or the heating of electrons in space plasmas, such as in the radiation belts.« less

An integrative time-varying frequency detection and channel sounding method for dynamic plasma sheath

NASA Astrophysics Data System (ADS)

Shi, Lei; Yao, Bo; Zhao, Lei; Liu, Xiaotong; Yang, Min; Liu, Yanming

2018-01-01

The plasma sheath-surrounded hypersonic vehicle is a dynamic and time-varying medium and it is almost impossible to calculate time-varying physical parameters directly. The in-fight detection of the time-varying degree is important to understand the dynamic nature of the physical parameters and their effect on re-entry communication. In this paper, a constant envelope zero autocorrelation (CAZAC) sequence based on time-varying frequency detection and channel sounding method is proposed to detect the plasma sheath electronic density time-varying property and wireless channel characteristic. The proposed method utilizes the CAZAC sequence, which has excellent autocorrelation and spread gain characteristics, to realize dynamic time-varying detection/channel sounding under low signal-to-noise ratio in the plasma sheath environment. Theoretical simulation under a typical time-varying radio channel shows that the proposed method is capable of detecting time-variation frequency up to 200 kHz and can trace the channel amplitude and phase in the time domain well under -10 dB. Experimental results conducted in the RF modulation discharge plasma device verified the time variation detection ability in practical dynamic plasma sheath. Meanwhile, nonlinear phenomenon of dynamic plasma sheath on communication signal is observed thorough channel sounding result.

Signal-to-noise ratio enhancement on SEM images using a cubic spline interpolation with Savitzky-Golay filters and weighted least squares error.

PubMed

Kiani, M A; Sim, K S; Nia, M E; Tso, C P

2015-05-01

A new technique based on cubic spline interpolation with Savitzky-Golay smoothing using weighted least squares error filter is enhanced for scanning electron microscope (SEM) images. A diversity of sample images is captured and the performance is found to be better when compared with the moving average and the standard median filters, with respect to eliminating noise. This technique can be implemented efficiently on real-time SEM images, with all mandatory data for processing obtained from a single image. Noise in images, and particularly in SEM images, are undesirable. A new noise reduction technique, based on cubic spline interpolation with Savitzky-Golay and weighted least squares error method, is developed. We apply the combined technique to single image signal-to-noise ratio estimation and noise reduction for SEM imaging system. This autocorrelation-based technique requires image details to be correlated over a few pixels, whereas the noise is assumed to be uncorrelated from pixel to pixel. The noise component is derived from the difference between the image autocorrelation at zero offset, and the estimation of the corresponding original autocorrelation. In the few test cases involving different images, the efficiency of the developed noise reduction filter is proved to be significantly better than those obtained from the other methods. Noise can be reduced efficiently with appropriate choice of scan rate from real-time SEM images, without generating corruption or increasing scanning time. © 2015 The Authors Journal of Microscopy © 2015 Royal Microscopical Society.

A Search for Quasi-periodic Oscillations in the Blazar 1ES 1959+650

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Xiao-Pan; Luo, Yu-Hui; Yang, Hai-Yan

We have searched quasi-periodic oscillations (QPOs) in the 15 GHz light curve of the BL Lac object 1ES 1959+650 monitored by the Owens Valley Radio Observatory 40 m telescope during the period from 2008 January to 2016 February, using the Lomb–Scargle Periodogram, power spectral density (PSD), discrete autocorrelation function, and phase dispersion minimization (PDM) techniques. The red noise background has been established via the PSD method, and no QPO can be derived at the 3 σ confidence level accounting for the impact of the red noise variability. We conclude that the light curve of 1ES 1959+650 can be explained bymore » a stochastic red noise process that contributes greatly to the total observed variability amplitude, dominates the power spectrum, causes spurious bumps and wiggles in the autocorrelation function and can result in the variance of the folded light curve decreasing toward lower temporal frequencies when few-cycle, sinusoid-like patterns are present. Moreover, many early supposed periodicity claims for blazar light curves need to be reevaluated assuming red noise.« less

Sound quality characteristics of refrigerator noise in real living environments with relation to psychoacoustical and autocorrelation function parameters.

PubMed

Sato, Shin-ichi; You, Jin; Jeon, Jin Yong

2007-07-01

Psychoacoustical and autocorrelation function (ACF) parameters were employed to describe the temporal fluctuations of refrigerator noise during starting, transition into/from the stationary phase and termination of operation. The temporal fluctuations of refrigerator noise include a click at start-up, followed by a rapid increase in volume, a change of pitch, and termination of the operation. Subjective evaluations of the noise of 24 different refrigerators were conducted in a real living environment. The relationship between objective measures and perceived noisiness was examined by multiple regression analysis. Sound quality indices were developed based on psychoacoustical and ACF parameters. The psychoacoustical parameters found to be important for evaluating noisiness in the stationary phase were loudness and roughness. The relationship between noisiness and ACF parameters shows that sound energy and its fluctuations are important for evaluating noisiness. Also, refrigerator sounds that had a fluctuation of pitch were rated as more annoying. The tolerance level for the starting phase of refrigerator noise was found to be 33 dBA, which is the level where 65% of the participants in the subjective tests were satisfied.

DYNAMICS OF SELF-GRAVITY WAKES IN DENSE PLANETARY RINGS. I. PITCH ANGLE

DOE Office of Scientific and Technical Information (OSTI.GOV)

Michikoshi, Shugo; Kokubo, Eiichiro; Fujii, Akihiko

2015-10-20

We investigate the dynamics of self-gravity wakes in dense planetary rings. In particular, we examine how the pitch angles of self-gravity wakes depend on ring parameters using N-body simulations. We calculate the pitch angles using the two-dimensional autocorrelation function of the ring surface density. We obtain the pitch angles for the inner and outer parts of the autocorrelation function separately. We confirm that the pitch angles are 15°–30° for reasonable ring parameters, which are consistent with previous studies. We find that the inner pitch angle increases with the Saturnicentric distance, while it barely depends on the optical depth and themore » restitution coefficient of ring particles. The increase of the inner pitch angle with the Saturnicentric distance is consistent with the observations of the A ring. The outer pitch angle does not have a clear dependence on any ring parameters and is about 10°–15°. This value is consistent with the pitch angle of spiral arms in collisionless systems.« less

Geographical distribution of shear wave anisotropy within marine sediments in the northwestern Pacific

NASA Astrophysics Data System (ADS)

Tonegawa, Takashi; Fukao, Yoshio; Fujie, Gou; Takemura, Shunsuke; Takahashi, Tsutomu; Kodaira, Shuichi

2015-12-01

In the northwestern Pacific, the elastic properties of marine sediments, including P-wave velocities ( Vp) and S wave velocities ( Vs), have recently been constrained by active seismic surveys. However, information on S anisotropy associated with the alignments of fractures and fabric remains elusive. To obtain such information, we used ambient noise records observed by ocean-bottom seismometers at 254 sites in the northwestern Pacific to calculate the auto-correlation functions for the S reflection retrieval from the top of the basement. For these S reflections, we measured differential travel times and polarized directions to reveal the potential geographical systematic distribution of S anisotropy. As a result, the observed differential times between fast and slow axes were at most 0.05 s. The fast polarization axes tend to align in the trench-parallel direction in the outer rise region. In particular, their directions changed systematically in accordance with the direction of the trench axis, which changes sharply across the junction of the Kuril and Japan trenches. We consider that a contributing factor for the obtained S anisotropy within marine sediments in the outer rise region is primarily aligned fractures due to the tensional stresses associated with the bending of the Pacific Plate. Moreover, numerical simulations conducted by using the three-dimensional (3D) finite difference method for isotropic and anisotropic media indicates that the successful extraction of S anisotropic information from the S reflection observed in this study is obtained from near-vertically propagating S waves due to extremely low Vs within marine sediments. In addition, we conducted an additional numerical simulation with a realistic velocity model to confirm whether S reflections below the basement can be extracted or not. The resultant auto-correlation function shows only S reflections from the top of the basement. It appears that such near-vertically propagating S waves obscure S reflections from interfaces below the basement.

Density of states and dynamical crossover in a dense fluid revealed by exponential mode analysis of the velocity autocorrelation function

NASA Astrophysics Data System (ADS)

Bellissima, S.; Neumann, M.; Guarini, E.; Bafile, U.; Barocchi, F.

2017-01-01

Extending a preceding study of the velocity autocorrelation function (VAF) in a simulated Lennard-Jones fluid [Phys. Rev. E 92, 042166 (2015), 10.1103/PhysRevE.92.042166] to cover higher-density and lower-temperature states, we show that the recently demonstrated multiexponential expansion method allows for a full account and understanding of the basic dynamical processes encompassed by a fundamental quantity as the VAF. In particular, besides obtaining evidence of a persisting long-time tail, we assign specific and unambiguous physical meanings to groups of exponential modes related to the longitudinal and transverse collective dynamics, respectively. We have made this possible by consistently introducing the interpretation of the VAF frequency spectrum as a global density of states in fluids, generalizing a solid-state concept, and by giving to specific spectral components, obtained through the VAF exponential expansion, the corresponding meaning of partial densities of states relative to specific dynamical processes. The clear identification of a high-frequency oscillation of the VAF with the near-top excitation frequency in the dispersion curve of acoustic waves is a neat example of the power of the method. As for the transverse mode contribution, its analysis turns out to be particularly important, because the multiexponential expansion reveals a transition marking the onset of propagating excitations when the density is increased beyond a threshold value. While this finding agrees with the recent literature debating the issue of dynamical crossover boundaries, such as the one identified with the Frenkel line, we can add detailed information on the modes involved in this specific process in the domains of both time and frequency. This will help obtain a still missing full account of transverse dynamics, in both its nonpropagating and propagating aspects which are linked through dynamical transitions depending on both the thermodynamic states and the excitation wave vectors.

Stochastic approaches for time series forecasting of boron: a case study of Western Turkey.

PubMed

Durdu, Omer Faruk

2010-10-01

In the present study, a seasonal and non-seasonal prediction of boron concentrations time series data for the period of 1996-2004 from Büyük Menderes river in western Turkey are addressed by means of linear stochastic models. The methodology presented here is to develop adequate linear stochastic models known as autoregressive integrated moving average (ARIMA) and multiplicative seasonal autoregressive integrated moving average (SARIMA) to predict boron content in the Büyük Menderes catchment. Initially, the Box-Whisker plots and Kendall's tau test are used to identify the trends during the study period. The measurements locations do not show significant overall trend in boron concentrations, though marginal increasing and decreasing trends are observed for certain periods at some locations. ARIMA modeling approach involves the following three steps: model identification, parameter estimation, and diagnostic checking. In the model identification step, considering the autocorrelation function (ACF) and partial autocorrelation function (PACF) results of boron data series, different ARIMA models are identified. The model gives the minimum Akaike information criterion (AIC) is selected as the best-fit model. The parameter estimation step indicates that the estimated model parameters are significantly different from zero. The diagnostic check step is applied to the residuals of the selected ARIMA models and the results indicate that the residuals are independent, normally distributed, and homoscadastic. For the model validation purposes, the predicted results using the best ARIMA models are compared to the observed data. The predicted data show reasonably good agreement with the actual data. The comparison of the mean and variance of 3-year (2002-2004) observed data vs predicted data from the selected best models show that the boron model from ARIMA modeling approaches could be used in a safe manner since the predicted values from these models preserve the basic statistics of observed data in terms of mean. The ARIMA modeling approach is recommended for predicting boron concentration series of a river.

The temporal representation of the delay of dynamic iterated rippled noise with positive and negative gain by single units in the ventral cochlear nucleus.

PubMed

Sayles, Mark; Winter, Ian Michael

2007-09-26

Spike trains were recorded from single units in the ventral cochlear nucleus of the anaesthetised guinea-pig in response to dynamic iterated rippled noise with positive and negative gain. The short-term running waveform autocorrelation functions of these stimuli show peaks at integer multiples of the time-varying delay when the gain is +1, and troughs at odd-integer multiples and peaks at even-integer multiples of the time-varying delay when the gain is -1. In contrast, the short-term autocorrelation of the Hilbert envelope shows peaks at integer multiples of the time-varying delay for both positive and negative gain stimuli. A running short-term all-order interspike interval analysis demonstrates the ability of single units to represent the modulated pitch contour in their short-term interval statistics. For units with low best frequency (approximate < or = 1.1 kHz) the temporal discharge pattern reflected the waveform fine structure regardless of unit classification (Primary-like, Chopper). For higher best frequency units the pattern of response varied according to unit type. Chopper units with best frequency approximate > or = 1.1 kHz responded to envelope modulation; showing no difference between their response to stimuli with positive and negative gain. Primary-like units with best frequencies in the range 1-3 kHz were still able to represent the difference in the temporal fine structure between dynamic rippled noise with positive and negative gain. No unit with a best frequency above 3 kHz showed a response to the temporal fine structure. Chopper units in this high frequency group showed significantly greater representation of envelope modulation relative to primary-like units with the same range of best frequencies. These results show that at the level of the cochlear nucleus there exists sufficient information in the time domain to represent the time-varying pitch associated with dynamic iterated rippled noise.

Statistical Approach To Extraction Of Texture In SAR

NASA Technical Reports Server (NTRS)

Rignot, Eric J.; Kwok, Ronald

1992-01-01

Improved statistical method of extraction of textural features in synthetic-aperture-radar (SAR) images takes account of effects of scheme used to sample raw SAR data, system noise, resolution of radar equipment, and speckle. Treatment of speckle incorporated into overall statistical treatment of speckle, system noise, and natural variations in texture. One computes speckle auto-correlation function from system transfer function that expresses effect of radar aperature and incorporates range and azimuth resolutions.

Why Non-Equilibrium is Different

NASA Astrophysics Data System (ADS)

Dorfman, J. Robert; Kirkpatrick, Theodore R.; Sengers, Jan V.

The 1970 paper, "Decay of the Velocity Correlation Function" [Phys. Rev. A1, 18 (1970), see also Phys. Rev. Lett. 18, 988, (1967)] by Berni Alder and Tom Wainwright, demonstrated, by means of computer simulations, that the velocity autocorrelation function for a particle moving diffusively in a gas of hard disks decays algebraically in time as t-1, and as t-3/2 for a gas of hard spheres. These decays appear in non-equilibrium fluids and have no counterpart in fluids in thermodynamic equilibrium. The work of Alder and Wainwright stimulated theorists to find explanations for these "long time tails" using kinetic theory or a mesoscopic mode-coupling theory. This paper has had a profound influence on our understanding of the non-equilibrium properties of fluid systems. Here we discuss the kinetic origins of the long time tails, the microscopic foundations of modecoupling theory, and the implications of these results for the physics of fluids. We also mention applications of the long time tails and mode-coupling theory to other, seemingly unrelated, fields of physics. We are honored to dedicate this short review to Berni Alder on the occasion of his 90th birthday!

Effects of autocorrelation upon LANDSAT classification accuracy. [Richmond, Virginia and Denver, Colorado

NASA Technical Reports Server (NTRS)

Craig, R. G. (Principal Investigator)

1983-01-01

Richmond, Virginia and Denver, Colorado were study sites in an effort to determine the effect of autocorrelation on the accuracy of a parallelopiped classifier of LANDSAT digital data. The autocorrelation was assumed to decay to insignificant levels when sampled at distances of at least ten pixels. Spectral themes developed using blocks of adjacent pixels, and using groups of pixels spaced at least 10 pixels apart were used. Effects of geometric distortions were minimized by using only pixels from the interiors of land cover sections. Accuracy was evaluated for three classes; agriculture, residential and "all other"; both type 1 and type 2 errors were evaluated by means of overall classification accuracy. All classes give comparable results. Accuracy is approximately the same in both techniques; however, the variance in accuracy is significantly higher using the themes developed from autocorrelated data. The vectors of mean spectral response were nearly identical regardless of sampling method used. The estimated variances were much larger when using autocorrelated pixels.

Negligible influence of spatial autocorrelation in the assessment of fire effects in a mixed conifer forest

USGS Publications Warehouse

van Mantgem, P.J.; Schwilk, D.W.

2009-01-01

Fire is an important feature of many forest ecosystems, although the quantification of its effects is compromised by the large scale at which fire occurs and its inherent unpredictability. A recurring problem is the use of subsamples collected within individual burns, potentially resulting in spatially autocorrelated data. Using subsamples from six different fires (and three unburned control areas) we show little evidence for strong spatial autocorrelation either before or after burning for eight measures of forest conditions (both fuels and vegetation). Additionally, including a term for spatially autocorrelated errors provided little improvement for simple linear models contrasting the effects of early versus late season burning. While the effects of spatial autocorrelation should always be examined, it may not always greatly influence assessments of fire effects. If high patch scale variability is common in Sierra Nevada mixed conifer forests, even following more than a century of fire exclusion, treatments designed to encourage further heterogeneity in forest conditions prior to the reintroduction of fire will likely be unnecessary.

A novel hybrid ensemble learning paradigm for tourism forecasting

NASA Astrophysics Data System (ADS)

Shabri, Ani

2015-02-01

In this paper, a hybrid forecasting model based on Empirical Mode Decomposition (EMD) and Group Method of Data Handling (GMDH) is proposed to forecast tourism demand. This methodology first decomposes the original visitor arrival series into several Intrinsic Model Function (IMFs) components and one residual component by EMD technique. Then, IMFs components and the residual components is forecasted respectively using GMDH model whose input variables are selected by using Partial Autocorrelation Function (PACF). The final forecasted result for tourism series is produced by aggregating all the forecasted results. For evaluating the performance of the proposed EMD-GMDH methodologies, the monthly data of tourist arrivals from Singapore to Malaysia are used as an illustrative example. Empirical results show that the proposed EMD-GMDH model outperforms the EMD-ARIMA as well as the GMDH and ARIMA (Autoregressive Integrated Moving Average) models without time series decomposition.

Ballistic aggregation in systems of inelastic particles: Cluster growth, structure, and aging

NASA Astrophysics Data System (ADS)

Paul, Subhajit; Das, Subir K.

2017-07-01

We study far-from-equilibrium dynamics in models of freely cooling granular gas and ballistically aggregating compact clusters. For both the cases, from event-driven molecular dynamics simulations, we have presented detailed results on structure and dynamics in space dimensions d =1 and 2. Via appropriate analyses it has been confirmed that the ballistic aggregation mechanism applies in d =1 granular gases as well. Aging phenomena for this mechanism, in both the dimensions, have been studied via the two-time density autocorrelation function. This quantity is demonstrated to exhibit scaling property similar to that in the standard phase transition kinetics. The corresponding functional forms have been quantified and the outcomes have been discussed in connection with the structural properties. Our results on aging establish a more complete equivalence between the granular gas and the ballistic aggregation models in d =1 .

Early Warning Signals for Abrupt Change Raise False Alarm During Sea Ice Loss

NASA Astrophysics Data System (ADS)

Wagner, T. J. W.; Eisenman, I.

2015-12-01

Uncovering universal early warning signals for critical transitions has become a coveted goal in diverse scientific disciplines, ranging from climate science to financial mathematics. There has been a flurry of recent research proposing such signals, with increasing autocorrelation and increasing variance being among the most widely discussed candidates. A number of studies have suggested that increasing autocorrelation alone may suffice to signal an impending transition, although some others have questioned this. Here, we consider variance and autocorrelation in the context of sea ice loss in an idealized model of the global climate system. The model features no bifurcation, nor increased rate of retreat, as the ice disappears. Nonetheless, the autocorrelation of summer sea ice area is found to increase with diminishing sea ice cover in a global warming scenario. The variance, by contrast, decreases. A simple physical mechanism is proposed to explain the occurrence of increasing autocorrelation but not variance in the model when there is no approaching bifurcation. Additionally, a similar mechanism is shown to allow an increase in both indicators with no physically attainable bifurcation. This implies that relying on autocorrelation and variance as early warning signals can raise false alarms in the climate system, warning of "tipping points" that are not actually there.

Distributed intrusion monitoring system with fiber link backup and on-line fault diagnosis functions

NASA Astrophysics Data System (ADS)

Xu, Jiwei; Wu, Huijuan; Xiao, Shunkun

2014-12-01

A novel multi-channel distributed optical fiber intrusion monitoring system with smart fiber link backup and on-line fault diagnosis functions was proposed. A 1× N optical switch was intelligently controlled by a peripheral interface controller (PIC) to expand the fiber link from one channel to several ones to lower the cost of the long or ultra-long distance intrusion monitoring system and also to strengthen the intelligent monitoring link backup function. At the same time, a sliding window auto-correlation method was presented to identify and locate the broken or fault point of the cable. The experimental results showed that the proposed multi-channel system performed well especially whenever any a broken cable was detected. It could locate the broken or fault point by itself accurately and switch to its backup sensing link immediately to ensure the security system to operate stably without a minute idling. And it was successfully applied in a field test for security monitoring of the 220-km-length national borderline in China.

Climate reddening increases the chance of critical transitions

NASA Astrophysics Data System (ADS)

van der Bolt, Bregje; van Nes, Egbert H.; Bathiany, Sebastian; Vollebregt, Marlies E.; Scheffer, Marten

2018-06-01

Climate change research often focuses on trends in the mean and variance. However, analyses of palaeoclimatic and contemporary dynamics reveal that climate memory — as measured for instance by temporal autocorrelation — may also change substantially over time. Here, we show that elevated temporal autocorrelation in climatic variables should be expected to increase the chance of critical transitions in climate-sensitive systems with tipping points. We demonstrate that this prediction is consistent with evidence from forests, coral reefs, poverty traps, violent conflict and ice sheet instability. In each example, the duration of anomalous dry or warm events elevates chances of invoking a critical transition. Understanding the effects of climate variability thus requires research not only on variance, but also on climate memory.

Distributions of Autocorrelated First-Order Kinetic Outcomes: Illness Severity

PubMed Central

Englehardt, James D.

2015-01-01

Many complex systems produce outcomes having recurring, power law-like distributions over wide ranges. However, the form necessarily breaks down at extremes, whereas the Weibull distribution has been demonstrated over the full observed range. Here the Weibull distribution is derived as the asymptotic distribution of generalized first-order kinetic processes, with convergence driven by autocorrelation, and entropy maximization subject to finite positive mean, of the incremental compounding rates. Process increments represent multiplicative causes. In particular, illness severities are modeled as such, occurring in proportion to products of, e.g., chronic toxicant fractions passed by organs along a pathway, or rates of interacting oncogenic mutations. The Weibull form is also argued theoretically and by simulation to be robust to the onset of saturation kinetics. The Weibull exponential parameter is shown to indicate the number and widths of the first-order compounding increments, the extent of rate autocorrelation, and the degree to which process increments are distributed exponential. In contrast with the Gaussian result in linear independent systems, the form is driven not by independence and multiplicity of process increments, but by increment autocorrelation and entropy. In some physical systems the form may be attracting, due to multiplicative evolution of outcome magnitudes towards extreme values potentially much larger and smaller than control mechanisms can contain. The Weibull distribution is demonstrated in preference to the lognormal and Pareto I for illness severities versus (a) toxicokinetic models, (b) biologically-based network models, (c) scholastic and psychological test score data for children with prenatal mercury exposure, and (d) time-to-tumor data of the ED01 study. PMID:26061263

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids.

PubMed

Dahlberg, Jerry; Tkacik, Peter T; Mullany, Brigid; Fleischhauer, Eric; Shahinian, Hossein; Azimi, Farzad; Navare, Jayesh; Owen, Spencer; Bisel, Tucker; Martin, Tony; Sholar, Jodie; Keanini, Russell G

2017-12-04

An analog, macroscopic method for studying molecular-scale hydrodynamic processes in dense gases and liquids is described. The technique applies a standard fluid dynamic diagnostic, particle image velocimetry (PIV), to measure: i) velocities of individual particles (grains), extant on short, grain-collision time-scales, ii) velocities of systems of particles, on both short collision-time- and long, continuum-flow-time-scales, iii) collective hydrodynamic modes known to exist in dense molecular fluids, and iv) short- and long-time-scale velocity autocorrelation functions, central to understanding particle-scale dynamics in strongly interacting, dense fluid systems. The basic system is composed of an imaging system, light source, vibrational sensors, vibrational system with a known media, and PIV and analysis software. Required experimental measurements and an outline of the theoretical tools needed when using the analog technique to study molecular-scale hydrodynamic processes are highlighted. The proposed technique provides a relatively straightforward alternative to photonic and neutron beam scattering methods traditionally used in molecular hydrodynamic studies.

Spin relaxation in geometrically frustrated pyrochlores

NASA Astrophysics Data System (ADS)

Dunsiger, Sarah Ruth

This thesis describes muSR experiments which focus on systems where the magnetic ions occupy the vertices of edge or corner sharing triangular units, in particular the pyrochlores A2B2O7. The scientific interest in pyrochlores is based on the fact that they display novel magnetic behaviour at low temperatures due to geometrical frustration. The ground state of these systems is sensitively dependent on such factors as the range of the spin-spin interactions, disorder, anisotropy, thermal and quantum fluctuations. For example, Y2Mo2O7 shows many features reminiscent of a conventional spin glass, even though this material has nominally zero chemical disorder. It is found that the muon spin polarisation obeys a time-field scaling relation which indicates that the spin-spin autocorrelation function has a power law form in time, in stark contrast with the exponential form often assumed for conventional magnets above their transition temperature. Gd2Ti2O7 shows long range order, but only at a temperature much lower than its Curie-Weiss temperature, a signature of a frustrated system. In the paramagnetic regime, it is well described by an isotropic Heisenberg Hamiltonian with nearest neighbour couplings in the presence of a Zeeman interaction, from which the spin-spin autocorrelation function may be calculated as a power series in time. The muon spin relaxation rate decreases with magnetic field as the Zeeman energy becomes comparable with the exchange coupling between Gd spins. Thus, an independent measure of the exchange coupling or equivalently the Gd spin fluctuation rate is extracted. By contrast, Tb2Ti2O7 has been identified as a type of cooperative paramagnet. Short range correlations develop below 50 K. However, there is no long range ordering down to very low temperatures (0.075 K). The Tb3+ ion is subject to strong crystal electric field effects: point charge calculations indicate that this system is Ising like at low temperatures. Thus this system may be analogous to water ice, a system theoretically predicted to have finite entropy at zero temperature. It is possible to qualitatively explain the unusual changes in T1-1 as a function of applied magnetic field which are also observed using muSR.

Spatial Autocorrelation Approaches to Testing Residuals from Least Squares Regression

PubMed Central

Chen, Yanguang

2016-01-01

In geo-statistics, the Durbin-Watson test is frequently employed to detect the presence of residual serial correlation from least squares regression analyses. However, the Durbin-Watson statistic is only suitable for ordered time or spatial series. If the variables comprise cross-sectional data coming from spatial random sampling, the test will be ineffectual because the value of Durbin-Watson’s statistic depends on the sequence of data points. This paper develops two new statistics for testing serial correlation of residuals from least squares regression based on spatial samples. By analogy with the new form of Moran’s index, an autocorrelation coefficient is defined with a standardized residual vector and a normalized spatial weight matrix. Then by analogy with the Durbin-Watson statistic, two types of new serial correlation indices are constructed. As a case study, the two newly presented statistics are applied to a spatial sample of 29 China’s regions. These results show that the new spatial autocorrelation models can be used to test the serial correlation of residuals from regression analysis. In practice, the new statistics can make up for the deficiencies of the Durbin-Watson test. PMID:26800271

The Azimuth Structure of Nuclear Collisions — I

NASA Astrophysics Data System (ADS)

Trainor, Thomas A.; Kettler, David T.

We describe azimuth structure commonly associated with elliptic and directed flow in the context of 2D angular autocorrelations for the purpose of precise separation of so-called nonflow (mainly minijets) from flow. We extend the Fourier-transform description of azimuth structure to include power spectra and autocorrelations related by the Wiener-Khintchine theorem. We analyze several examples of conventional flow analysis in that context and question the relevance of reaction plane estimation to flow analysis. We introduce the 2D angular autocorrelation with examples from data analysis and describe a simulation exercise which demonstrates precise separation of flow and nonflow using the 2D autocorrelation method. We show that an alternative correlation measure based on Pearson's normalized covariance provides a more intuitive measure of azimuth structure.

Developmental nicotine exposure adversely effects respiratory patterning in the barbiturate anesthetized neonatal rat.

PubMed

Barreda, Santiago; Kidder, Ian J; Mudery, Jordan A; Bailey, E Fiona

2015-03-01

Neonates at risk for sudden infant death syndrome (SIDS) are hospitalized for cardiorespiratory monitoring however, monitoring is costly and generates large quantities of averaged data that serve as poor predictors of infant risk. In this study we used a traditional autocorrelation function (ACF) testing its suitability as a tool to detect subtle alterations in respiratory patterning in vivo. We applied the ACF to chest wall motion tracings obtained from rat pups in the period corresponding to the mid-to-end of the third trimester of human pregnancy. Pups were drawn from two groups: nicotine-exposed and saline-exposed at each age (i.e., P7, P8, P9, and P10). Respiratory-related motions of the chest wall were recorded in room air and in response to an arousal stimulus (FIO2 14%). The autocorrelation function was used to determine measures of breathing rate and respiratory patterning. Unlike alternative tools such as Poincare plots that depict an averaged difference in a measure breath to breath, the ACF when applied to a digitized chest wall trace yields an instantaneous sample of data points that can be used to compare (data) points at the same time in the next breath or in any subsequent number of breaths. The moment-to-moment evaluation of chest wall motion detected subtle differences in respiratory pattern in rat pups exposed to nicotine in utero and aged matched saline-exposed peers. The ACF can be applied online as well as to existing data sets and requires comparatively short sampling windows (∼2 min). As shown here, the ACF could be used to identify factors that precipitate or minimize instability and thus, offers a quantitative measure of risk in vulnerable populations. Copyright © 2015 Elsevier B.V. All rights reserved.

Effects of Ionic Dependence of DNA Persistence Length on the DNA Condensation at Room Temperature

NASA Astrophysics Data System (ADS)

Mao, Wei; Liu, Yan-Hui; Hu, Lin; Xu, Hou-Qiang

2016-05-01

DNA persistence length is a key parameter for quantitative interpretation of the conformational properties of DNA and related to the bending rigidity of DNA. A series of experiments pointed out that, in the DNA condensation process by multivalent cations, the condensed DNA takes elongated coil or compact globule states and the population of the compact globule states increases with an increase in ionic concentration. At the same time, single molecule experiments carried out in solution with multivalent cations (such as spermidine, spermine) indicated that DNA persistence length strongly depends on the ionic concentration. In order to revolve the effects of ionic concentration dependence of persistence length on DNA condensation, a model including the ionic concentration dependence of persistence length and strong correlation of multivalent cation on DNA is provided. The autocorrelation function of the tangent vectors is found as an effective way to detect the ionic concentration dependence of toroidal conformations. With an increase in ion concentration, the first periodic oscillation contained in the autocorrelation function shifts, the number of segment contained in the first periodic oscillation decreases gradually. According to the experiments, the average long-axis length is defined to estimate the ionic concentration dependence of condensation process further. The relation between long-axis length and ionic concentration matches the experimental results qualitatively. Supported by National Natural Science Foundation of China under Grant Nos. 11047022, 11204045, 11464004 and 31360215; The Research Foundation from Ministry of Education of China (212152), Guizhou Provincial Tracking Key Program of Social Development (SY20123089, SZ20113069); The General Financial Grant from the China Postdoctoral Science Foundation (2014M562341); The Research Foundation for Young University Teachers from Guizhou University (201311); The West Light Foundation (2015) and College Innovation Talent Team of Guizhou Province, (2014) 32

Communication: Probing anomalous diffusion in frequency space

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stachura, Sławomir; Synchrotron Soleil, L’Orme de Merisiers, 91192 Gif-sur-Yvette; Kneller, Gerald R., E-mail: gerald.kneller@cnrs-orleans.fr

Anomalous diffusion processes are usually detected by analyzing the time-dependent mean square displacement of the diffusing particles. The latter evolves asymptotically as W(t) ∼ 2D{sub α}t{sup α}, where D{sub α} is the fractional diffusion constant and 0 < α < 2. In this article we show that both D{sub α} and α can also be extracted from the low-frequency Fourier spectrum of the corresponding velocity autocorrelation function. This offers a simple method for the interpretation of quasielastic neutron scattering spectra from complex (bio)molecular systems, in which subdiffusive transport is frequently encountered. The approach is illustrated and validated by analyzing molecularmore » dynamics simulations of molecular diffusion in a lipid POPC bilayer.« less

Asymptotic Dynamics of Self-driven Vehicles in a Closed Boundary

NASA Astrophysics Data System (ADS)

Lee, Chi-Lun; Huang, Chia-Ling

2011-08-01

We study the asymptotic dynamics of self-driven vehicles in a loop using a car-following model with the consideration of volume exclusions. In particular, we derive the dynamical steady states for the single-cluster case and obtain the corresponding fundamental diagrams, exhibiting two branches representative of entering and leaving the jam, respectively. By simulations we find that the speed average over all vehicles eventually reaches the same value, regardless of final clustering states. The autocorrelation functions for overall speed average and single-vehicle speed are studied, each revealing a unique time scale. We also discuss the role of noises in vehicular accelerations. Based on our observations we give trial definitions about the degree of chaoticity for general self-driven many-body systems.

Image correlation and sampling study

NASA Technical Reports Server (NTRS)

Popp, D. J.; Mccormack, D. S.; Sedwick, J. L.

1972-01-01

The development of analytical approaches for solving image correlation and image sampling of multispectral data is discussed. Relevant multispectral image statistics which are applicable to image correlation and sampling are identified. The general image statistics include intensity mean, variance, amplitude histogram, power spectral density function, and autocorrelation function. The translation problem associated with digital image registration and the analytical means for comparing commonly used correlation techniques are considered. General expressions for determining the reconstruction error for specific image sampling strategies are developed.

Linking snowflake microstructure to multi-frequency radar observations

NASA Astrophysics Data System (ADS)

Leinonen, J.; Moisseev, D.; Nousiainen, T.

2013-04-01

Spherical or spheroidal particle shape models are commonly used to calculate numerically the radar backscattering properties of aggregate snowflakes. A more complicated and computationally intensive approach is to use detailed models of snowflake structure together with numerical scattering models that can operate on arbitrary particle shapes. Recent studies have shown that there can be significant differences between the results of these approaches. In this paper, an analytical model, based on the Rayleigh-Gans scattering theory, is formulated to explain this discrepancy in terms of the effect of discrete ice crystals that constitute the snowflake. The ice crystals cause small-scale inhomogeneities whose effects can be understood through the density autocorrelation function of the particle mass, which the Rayleigh-Gans theory connects to the function that gives the radar reflectivity as a function of frequency. The derived model is a weighted sum of two Gaussian functions. A term that corresponds to the average shape of the particle, similar to that given by the spheroidal shape model, dominates at low frequencies. At high frequencies, that term vanishes and is gradually replaced by the effect of the ice crystal monomers. The autocorrelation-based description of snowflake microstructure appears to be sufficient for multi-frequency radar studies. The link between multi-frequency radar observations and the particle microstructure can thus be used to infer particle properties from the observations.

Dynamic frailty models based on compound birth-death processes.

PubMed

Putter, Hein; van Houwelingen, Hans C

2015-07-01

Frailty models are used in survival analysis to model unobserved heterogeneity. They accommodate such heterogeneity by the inclusion of a random term, the frailty, which is assumed to multiply the hazard of a subject (individual frailty) or the hazards of all subjects in a cluster (shared frailty). Typically, the frailty term is assumed to be constant over time. This is a restrictive assumption and extensions to allow for time-varying or dynamic frailties are of interest. In this paper, we extend the auto-correlated frailty models of Henderson and Shimakura and of Fiocco, Putter and van Houwelingen, developed for longitudinal count data and discrete survival data, to continuous survival data. We present a rigorous construction of the frailty processes in continuous time based on compound birth-death processes. When the frailty processes are used as mixtures in models for survival data, we derive the marginal hazards and survival functions and the marginal bivariate survival functions and cross-ratio function. We derive distributional properties of the processes, conditional on observed data, and show how to obtain the maximum likelihood estimators of the parameters of the model using a (stochastic) expectation-maximization algorithm. The methods are applied to a publicly available data set. © The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Reliable Viscosity Calculation from Equilibrium Molecular Dynamics Simulations: A Time Decomposition Method.

PubMed

Zhang, Yong; Otani, Akihito; Maginn, Edward J

2015-08-11

Equilibrium molecular dynamics is often used in conjunction with a Green-Kubo integral of the pressure tensor autocorrelation function to compute the shear viscosity of fluids. This approach is computationally expensive and is subject to a large amount of variability because the plateau region of the Green-Kubo integral is difficult to identify unambiguously. Here, we propose a time decomposition approach for computing the shear viscosity using the Green-Kubo formalism. Instead of one long trajectory, multiple independent trajectories are run and the Green-Kubo relation is applied to each trajectory. The averaged running integral as a function of time is fit to a double-exponential function with a weighting function derived from the standard deviation of the running integrals. Such a weighting function minimizes the uncertainty of the estimated shear viscosity and provides an objective means of estimating the viscosity. While the formal Green-Kubo integral requires an integration to infinite time, we suggest an integration cutoff time tcut, which can be determined by the relative values of the running integral and the corresponding standard deviation. This approach for computing the shear viscosity can be easily automated and used in computational screening studies where human judgment and intervention in the data analysis are impractical. The method has been applied to the calculation of the shear viscosity of a relatively low-viscosity liquid, ethanol, and relatively high-viscosity ionic liquid, 1-n-butyl-3-methylimidazolium bis(trifluoromethane-sulfonyl)imide ([BMIM][Tf2N]), over a range of temperatures. These test cases show that the method is robust and yields reproducible and reliable shear viscosity values.

Dissipative particle dynamics study of velocity autocorrelation function and self-diffusion coefficient in terms of interaction potential strength

NASA Astrophysics Data System (ADS)

Zohravi, Elnaz; Shirani, Ebrahim; Pishevar, Ahmadreza; Karimpour, Hossein

2018-07-01

This research focuses on numerically investigating the self-diffusion coefficient and velocity autocorrelation function (VACF) of a dissipative particle dynamics (DPD) fluid as a function of the conservative interaction strength. Analytic solutions to VACF and self-diffusion coefficients in DPD were obtained by many researchers in some restricted cases including ideal gases, without the account of conservative force. As departure from the ideal gas conditions are accentuated with increasing the relative proportion of conservative force, it is anticipated that the VACF should gradually deviate from its normally expected exponentially decay. This trend is confirmed through numerical simulations and an expression in terms of the conservative force parameter, density and temperature is proposed for the self-diffusion coefficient. As it concerned the VACF, the equivalent Langevin equation describing Brownian motion of particles with a harmonic potential is adapted to the problem and reveals an exponentially decaying oscillatory pattern influenced by the conservative force parameter, dissipative parameter and temperature. Although the proposed model for obtaining the self-diffusion coefficient with consideration of the conservative force could not be verified due to computational complexities, nonetheless the Arrhenius dependency of the self-diffusion coefficient to temperature and pressure permits to certify our model over a definite range of DPD parameters.

Zero Autocorrelation Waveforms: A Doppler Statistic and Multifunction Problems

DTIC Science & Technology

2006-01-01

by ANSI Std Z39-18 It is natural to refer to A as the ambiguity function of u, since in the usual setting on the real line R, the analogue ambiguity...Doppler statistic |Cu,uek(j)| is excellent and provable for detecting deodorized Doppler frequency shift [11] (see Fig. 2). Also, if one graphs only

Time-to-space mapping of femtosecond pulses.

PubMed

Nuss, M C; Li, M; Chiu, T H; Weiner, A M; Partovi, A

1994-05-01

We report time-to-space mapping of femtosecond light pulses in a temporal holography setup. By reading out a temporal hologram of a short optical pulse with a continuous-wave diode laser, we accurately convert temporal pulse-shape information into a spatial pattern that can be viewed with a camera. We demonstrate real-time acquisition of electric-field autocorrelation and cross correlation of femtosecond pulses with this technique.

Geary autocorrelation and DCCA coefficient: Application to predict apoptosis protein subcellular localization via PSSM

NASA Astrophysics Data System (ADS)

Liang, Yunyun; Liu, Sanyang; Zhang, Shengli

2017-02-01

Apoptosis is a fundamental process controlling normal tissue homeostasis by regulating a balance between cell proliferation and death. Predicting subcellular location of apoptosis proteins is very helpful for understanding its mechanism of programmed cell death. Prediction of apoptosis protein subcellular location is still a challenging and complicated task, and existing methods mainly based on protein primary sequences. In this paper, we propose a new position-specific scoring matrix (PSSM)-based model by using Geary autocorrelation function and detrended cross-correlation coefficient (DCCA coefficient). Then a 270-dimensional (270D) feature vector is constructed on three widely used datasets: ZD98, ZW225 and CL317, and support vector machine is adopted as classifier. The overall prediction accuracies are significantly improved by rigorous jackknife test. The results show that our model offers a reliable and effective PSSM-based tool for prediction of apoptosis protein subcellular localization.

Generalized moment analysis of magnetic field correlations for accumulations of spherical and cylindrical magnetic pertubers

NASA Astrophysics Data System (ADS)

Kurz, Felix; Kampf, Thomas; Buschle, Lukas; Schlemmer, Heinz-Peter; Bendszus, Martin; Heiland, Sabine; Ziener, Christian

2016-12-01

In biological tissue, an accumulation of similarly shaped objects with a susceptibility difference to the surrounding tissue generates a local distortion of the external magnetic field in magnetic resonance imaging. It induces stochastic field fluctuations that characteristically influence proton spin diffusion in the vicinity of these magnetic perturbers. The magnetic field correlation that is associated with such local magnetic field inhomogeneities can be expressed in the form of a dynamic frequency autocorrelation function that is related to the time evolution of the measured magnetization. Here, an eigenfunction expansion for two simple magnetic perturber shapes, that of spheres and cylinders, is considered for restricted spin diffusion in a simple model geometry. Then, the concept of generalized moment analysis, an approximation technique that is applied in the study of (non-)reactive processes that involve Brownian motion, allows to provide analytical expressions for the correlation function for different exponential decay forms. Results for the biexponential decay for both spherical and cylindrical magnetized objects are derived and compared with the frequently used (less accurate) monoexponential decay forms. They are in asymptotic agreement with the numerically exact value of the correlation function for long and short times.

Seismic Wave Velocity in the Subducted Oceanic Crust from Autocorrelation of Tectonic Tremor Signals

NASA Astrophysics Data System (ADS)

Ducellier, A.; Creager, K.

2017-12-01

Hydration and dehydration of minerals in subduction zones play a key role in the geodynamic processes that generate seismicity and that allow tectonic plates to subduct. Detecting the presence of water in the subducted plate is thus crucial to better understand the seismogenesis and the consequent seismic hazard. A landward dipping, low velocity layer has been detected in most subduction zones. In Cascadia, this low velocity zone is characterized by a low S-wave velocity and a very high Poisson's ratio, which has been interpreted as high pore-fluid pressure in the upper half part of the subducted oceanic crust. Most previous studies were based on seismic reflection imaging, receiver function analysis, or body wave tomography, with seismic sources located far from the low velocity zone. In contrast, the sources of the tectonic tremors generated during Episodic Tremor and Slip (ETS) events are located on the plate boundary. As the sources of the tremors are much closer to the low velocity zone, seismic waves recorded during ETS events should illuminate the area with greater precision. Most methods to detect and locate tectonic tremors and low-frequency earthquakes are based on the cross correlation of seismic signals; either signals at the same station for different events, or the same event at different stations. We use the autocorrelation of the seismic signal recorded by eight arrays of stations, located in the Olympic Peninsula, Washington. Each tremor, assumed to be on the plate boundary, generates a direct wave and reflected and converted waves from both the strong shear-wave velocity contrast in the mid-oceanic crust, and from the Moho of the subducted oceanic crust. The time lag between the arrivals of these different waves at a seismic station corresponds to a peak of amplitude on the autocorrelation signals. Using the time lags observed for different locations of the tremor source, we intend to invert for the seismic wave velocity of the subducted oceanic crust under the arrays. Identifying zones with lower S-wave velocity and a high Poisson's ratio will then help detecting the presence of water in the subducted oceanic crust. Our ultimate goal is contributing to a better understanding of the mechanism of ETS and subduction zone processes.

Daily mortality and air pollutants: findings from Köln, Germany.

PubMed

Spix, C; Wichmann, H E

1996-04-01

For the APHEA study, the short term effects of air pollutants on human health were investigated in a comparable way in various European cities. Daily mortality was used as one of the health effects indicators. This report aims to demonstrate the steps in epidemiological model building in this type of time series analysis aimed at detecting short term effects under a poisson distribution assumption and shows the tools for decision making. In addition, it assesses the impact of these steps on the pollution effect estimates. Köln, Germany, is a city of one million inhabitants. It is densely populated with a warm, humid, unfavourable climate and a high traffic density. In previous studies, smog episodes were found to increase mortality and higher sulphur dioxide (SO2) levels were connected with increases in the number of episodes of croup. Daily total mortality was obtained for 1975-85. SO2, total suspended particulates, and nitrogen dioxide (NO2) data were available from two to five stations for the city area, and size fractionated PM7 data from a neighbouring city. The main tools were time series plots of the raw data, predicted and residual data, the partial autocorrelation function and periodogram of the residuals, cross correlations of prefiltered series, plots of categorised influences, chi 2 statistics of influences and sensitivity analyses taking overdispersion and autocorrelation into account. With regard to model building, it is concluded that seasonal and epidemic correction are the most important steps. The actual model chosen depends very much on the properties of the data set. For the pollution effect estimates, trend, season, and epidemic corrections are important to avoid overestimation of the effect, while an appropriate short term meterology influence correction model may actually prevent underestimation. When the model leaves very little over-dispersion and autocorrelation in the residuals, which indicates a good fit, correction for them has consequently little impact. Given the model, most of the range of SO2 values (5th centile to 95th centile) led to a 3-4% increase in mortality (significant), particulates led to a 2% increase (borderline significant, less data than for SO2), and NO2 had no relationship with mortality (measurements possibly not representative of actual exposure). Effects were usually delayed by a day.

Daily mortality and air pollutants: findings from Köln, Germany.

PubMed Central

Spix, C; Wichmann, H E

1996-01-01

STUDY OBJECTIVE AND DESIGN: For the APHEA study, the short term effects of air pollutants on human health were investigated in a comparable way in various European cities. Daily mortality was used as one of the health effects indicators. This report aims to demonstrate the steps in epidemiological model building in this type of time series analysis aimed at detecting short term effects under a poisson distribution assumption and shows the tools for decision making. In addition, it assesses the impact of these steps on the pollution effect estimates. SETTING: Köln, Germany, is a city of one million inhabitants. It is densely populated with a warm, humid, unfavourable climate and a high traffic density. In previous studies, smog episodes were found to increase mortality and higher sulphur dioxide (SO2) levels were connected with increases in the number of episodes of croup. PARTICIPANTS, MATERIALS AND METHODS: Daily total mortality was obtained for 1975-85. SO2, total suspended particulates, and nitrogen dioxide (NO2) data were available from two to five stations for the city area, and size fractionated PM7 data from a neighbouring city. The main tools were time series plots of the raw data, predicted and residual data, the partial autocorrelation function and periodogram of the residuals, cross correlations of prefiltered series, plots of categorised influences, chi 2 statistics of influences and sensitivity analyses taking overdispersion and autocorrelation into account. RESULTS AND CONCLUSIONS: With regard to model building, it is concluded that seasonal and epidemic correction are the most important steps. The actual model chosen depends very much on the properties of the data set. For the pollution effect estimates, trend, season, and epidemic corrections are important to avoid overestimation of the effect, while an appropriate short term meterology influence correction model may actually prevent underestimation. When the model leaves very little over-dispersion and autocorrelation in the residuals, which indicates a good fit, correction for them has consequently little impact. Given the model, most of the range of SO2 values (5th centile to 95th centile) led to a 3-4% increase in mortality (significant), particulates led to a 2% increase (borderline significant, less data than for SO2), and NO2 had no relationship with mortality (measurements possibly not representative of actual exposure). Effects were usually delayed by a day. PMID:8758225

Self-rated health: patterns in the journeys of patients with multi-morbidity and frailty.

PubMed

Martin, Carmel Mary

2014-12-01

Self-rated health (SRH) is a single measure predictor of hospital utilization and health outcomes in epidemiological studies. There have been few studies of SRH in patient journeys in clinical settings. Reduced resilience to stressors, reflected by SRH, exposes older people (complex systems) to the risk of hospitalization. It is proposed that SRH reflects rather than predicts deteriorations and hospital use; with low SRH autocorrelation in time series. The aim was to investigate SRH fluctuations in regular outbound telephone calls (average biweekly) to patients by Care Guides. Descriptive case study using quantitative autoregressive techniques and qualitative case analysis on SRH time series. Fourteen participants were randomly selected from the Patient Journey Record System (PaJR) database. The PaJR database recorded 198 consecutively sampled older multi-morbid patients journeys in three primary care settings. Analysis consisted of triangulation of SRH (0 very poor - 6 excellent) patterns from three analyses: SRH graduations associations with service utilization; time series modelling (autocorrelation, and step ahead forecast); and qualitative categorization of deteriorations. Fourteen patients reported mean SRH 2.84 (poor-fair) in 818 calls over 13 ± 6.4 months of follow-up. In 24% calls, SRH was poor-fair and significantly associated with hospital use. SRH autocorrelation was low in 14 time series (-0.11 to 0.26) with little difference (χ(2)  = 6.46, P = 0.91) among them. Fluctuations between better and worse health were very common and poor health was associated with hospital use. It is not clear why some patients continued on a downward trajectory, whereas others who destabilized appeared to completely recover, and even improved over time. SRH reflects an individual's complex health trajectory, but as a single measure does not predict when and how deteriorations will occur in this study. Individual patients appear to behave as complex adaptive systems. The dynamics of SRH and its influences in destabilizations warrant further research. © 2014 John Wiley & Sons, Ltd.

Elastic moduli of a Brownian colloidal glass former

NASA Astrophysics Data System (ADS)

Fritschi, S.; Fuchs, M.

2018-01-01

The static, dynamic and flow-dependent shear moduli of a binary mixture of Brownian hard disks are studied by an event-driven molecular dynamics simulation. Thereby, the emergence of rigidity close to the glass transition encoded in the static shear modulus G_∞ is accessed by three methods. Results from shear stress auto-correlation functions, elastic dispersion relations, and the elastic response to strain deformations upon the start-up of shear flow are compared. This enables one to sample the time-dependent shear modulus G(t) consistently over several decades in time. By that a very precise specification of the glass transition point and of G_∞ is feasible. Predictions by mode coupling theory of a finite shear modulus at the glass transition, of α-scaling in fluid states close to the transition, and of shear induced decay in yielding glass states are tested and broadly verified.

Stochastic analysis of pitch angle scattering of charged particles by transverse magnetic waves

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lemons, Don S.; Liu Kaijun; Winske, Dan

2009-11-15

This paper describes a theory of the velocity space scattering of charged particles in a static magnetic field composed of a uniform background field and a sum of transverse, circularly polarized, magnetic waves. When that sum has many terms the autocorrelation time required for particle orbits to become effectively randomized is small compared with the time required for the particle velocity distribution to change significantly. In this regime the deterministic equations of motion can be transformed into stochastic differential equations of motion. The resulting stochastic velocity space scattering is described, in part, by a pitch angle diffusion rate that ismore » a function of initial pitch angle and properties of the wave spectrum. Numerical solutions of the deterministic equations of motion agree with the theory at all pitch angles, for wave energy densities up to and above the energy density of the uniform field, and for different wave spectral shapes.« less

Self-consistent molecular dynamics calculation of diffusion in higher n-alkanes.

PubMed

Kondratyuk, Nikolay D; Norman, Genri E; Stegailov, Vladimir V

2016-11-28

Diffusion is one of the key subjects of molecular modeling and simulation studies. However, there is an unresolved lack of consistency between Einstein-Smoluchowski (E-S) and Green-Kubo (G-K) methods for diffusion coefficient calculations in systems of complex molecules. In this paper, we analyze this problem for the case of liquid n-triacontane. The non-conventional long-time tails of the velocity autocorrelation function (VACF) are found for this system. Temperature dependence of the VACF tail decay exponent is defined. The proper inclusion of the long-time tail contributions to the diffusion coefficient calculation results in the consistency between G-K and E-S methods. Having considered the major factors influencing the precision of the diffusion rate calculations in comparison with experimental data (system size effects and force field parameters), we point to hydrogen nuclear quantum effects as, presumably, the last obstacle to fully consistent n-alkane description.

Producing coherent excitations in pumped Mott antiferromagnetic insulators

NASA Astrophysics Data System (ADS)

Wang, Yao; Claassen, Martin; Moritz, B.; Devereaux, T. P.

2017-12-01

Nonequilibrium dynamics in correlated materials has attracted attention due to the possibility of characterizing, tuning, and creating complex ordered states. To understand the photoinduced microscopic dynamics, especially the linkage under realistic pump conditions between transient states and remnant elementary excitations, we performed nonperturbative simulations of various time-resolved spectroscopies. We used the Mott antiferromagnetic insulator as a model platform. The transient dynamics of multiparticle excitations can be attributed to the interplay between Floquet virtual states and a modification of the density of states, in which interactions induce a spectral weight transfer. Using an autocorrelation of the time-dependent spectral function, we show that resonance of the virtual states with the upper Hubbard band in the Mott insulator provides the route towards manipulating the electronic distribution and modifying charge and spin excitations. Our results link transient dynamics to the nature of many-body excitations and provide an opportunity to design nonequilibrium states of matter via tuned laser pulses.

Power spectral measurements of clear-air turbulence to long wavelengths for altitudes up to 14,000 meters

NASA Technical Reports Server (NTRS)

Murrow, H. N.; Mccain, W. E.; Rhyne, R. H.

1982-01-01

Measurements of three components of clear air atmospheric turbulence were made with an airplane incorporating a special instrumentation system to provide accurate data resolution to wavelengths of approximately 12,500 m (40,000 ft). Flight samplings covered an altitude range from approximately 500 to 14,000 m (1500 to 46,000 ft) in various meteorological conditions. Individual autocorrelation functions and power spectra for the three turbulence components from 43 data runs taken primarily from mountain wave and jet stream encounters are presented. The flight location (Eastern or Western United States), date, time, run length, intensity level (standard deviation), and values of statistical degrees of freedom for each run are provided in tabular form. The data presented should provide adequate information for detailed meteorological correlations. Some time histories which contain predominant low frequency wave motion are also presented.

Effect of helicity on the correlation time of large scales in turbulent flows

NASA Astrophysics Data System (ADS)

Cameron, Alexandre; Alexakis, Alexandros; Brachet, Marc-Étienne

2017-11-01

Solutions of the forced Navier-Stokes equation have been conjectured to thermalize at scales larger than the forcing scale, similar to an absolute equilibrium obtained for the spectrally truncated Euler equation. Using direct numeric simulations of Taylor-Green flows and general-periodic helical flows, we present results on the probability density function, energy spectrum, autocorrelation function, and correlation time that compare the two systems. In the case of highly helical flows, we derive an analytic expression describing the correlation time for the absolute equilibrium of helical flows that is different from the E-1 /2k-1 scaling law of weakly helical flows. This model predicts a new helicity-based scaling law for the correlation time as τ (k ) ˜H-1 /2k-1 /2 . This scaling law is verified in simulations of the truncated Euler equation. In simulations of the Navier-Stokes equations the large-scale modes of forced Taylor-Green symmetric flows (with zero total helicity and large separation of scales) follow the same properties as absolute equilibrium including a τ (k ) ˜E-1 /2k-1 scaling for the correlation time. General-periodic helical flows also show similarities between the two systems; however, the largest scales of the forced flows deviate from the absolute equilibrium solutions.

Temporal pattern and memory in sediment transport in an experimental step-pool channel

NASA Astrophysics Data System (ADS)

Saletti, Matteo; Molnar, Peter; Zimmermann, André; Hassan, Marwan A.; Church, Michael; Burlando, Paolo

2015-04-01

In this work we study the complex dynamics of sediment transport and bed morphology in steep streams, using a dataset of experiments performed in a steep flume with natural sediment. High-resolution (1 sec) time series of sediment transport were measured for individual size classes at the outlet of the flume for different combinations of sediment input rates, discharges, and flume slopes. The data show that the relation between instantaneous discharge and sediment transport exhibits large variability on different levels. After dividing the time series into segments of constant water discharge, we quantify the statistical properties of transport rates by fitting the data with a Generalized Extreme Value distribution, whose 3 parameters are related to the average sediment flux. We analyze separately extreme events of transport rate in terms of their fractional composition; if only events of high magnitude are considered, coarse grains become the predominant component of the total sediment yield. We quantify the memory in grain size dependent sediment transport with variance scaling and autocorrelation analyses; more specifically, we study how the variance changes with different aggregation scales and how the autocorrelation coefficient changes with different time lags. Our results show that there is a tendency to an infinite memory regime in transport rate signals, which is limited by the intermittency of the largest fractions. Moreover, the structure of memory is both grain size-dependent and magnitude-dependent: temporal autocorrelation is stronger for small grain size fractions and when the average sediment transport rate is large. The short-term memory in coarse grain transport increases with temporal aggregation and this reveals the importance of the sampling frequency of bedload transport rates in natural streams, especially for large fractions.

An autocorrelation method to detect low frequency earthquakes within tremor

USGS Publications Warehouse

Brown, J.R.; Beroza, G.C.; Shelly, D.R.

2008-01-01

Recent studies have shown that deep tremor in the Nankai Trough under western Shikoku consists of a swarm of low frequency earthquakes (LFEs) that occur as slow shear slip on the down-dip extension of the primary seismogenic zone of the plate interface. The similarity of tremor in other locations suggests a similar mechanism, but the absence of cataloged low frequency earthquakes prevents a similar analysis. In this study, we develop a method for identifying LFEs within tremor. The method employs a matched-filter algorithm, similar to the technique used to infer that tremor in parts of Shikoku is comprised of LFEs; however, in this case we do not assume the origin times or locations of any LFEs a priori. We search for LFEs using the running autocorrelation of tremor waveforms for 6 Hi-Net stations in the vicinity of the tremor source. Time lags showing strong similarity in the autocorrelation represent either repeats, or near repeats, of LFEs within the tremor. We test the method on an hour of Hi-Net recordings of tremor and demonstrates that it extracts both known and previously unidentified LFEs. Once identified, we cross correlate waveforms to measure relative arrival times and locate the LFEs. The results are able to explain most of the tremor as a swarm of LFEs and the locations of newly identified events appear to fill a gap in the spatial distribution of known LFEs. This method should allow us to extend the analysis of Shelly et al. (2007a) to parts of the Nankai Trough in Shikoku that have sparse LFE coverage, and may also allow us to extend our analysis to other regions that experience deep tremor, but where LFEs have not yet been identified. Copyright 2008 by the American Geophysical Union.

Dynamical Resilience Indicators in Time Series of Self-Rated Health Correspond to Frailty Levels in Older Adults.

PubMed

Gijzel, Sanne M W; van de Leemput, Ingrid A; Scheffer, Marten; Roppolo, Mattia; Olde Rikkert, Marcel G M; Melis, René J F

2017-07-01

We currently still lack valid methods to dynamically measure resilience for stressors before the appearance of adverse health outcomes that hamper well-being. Quantifying an older adult's resilience in an early stage would aid complex decision-making in health care. Translating complex dynamical systems theory to humans, we hypothesized that three dynamical indicators of resilience (variance, temporal autocorrelation, and cross-correlation) in time series of self-rated physical, mental, and social health were associated with frailty levels in older adults. We monitored self-rated physical, mental, and social health during 100 days using daily visual analogue scale questions in 22 institutionalized older adults (mean age 84.0, SD: 5.9 years). Frailty was determined by the Survey of Health, Ageing and Retirement in Europe (SHARE) frailty index. The resilience indicators (variance, temporal autocorrelation, and cross-correlation) were calculated using multilevel models. The self-rated health time series of frail elderly exhibited significantly elevated variance in the physical, mental, and social domain, as well as significantly stronger cross-correlations between all three domains, as compared to the nonfrail group (all P < 0.001). Temporal autocorrelation was not significantly associated with frailty. We found supporting evidence for two out of three hypothesized resilience indicators to be related to frailty levels in older adults. By mirroring the dynamical resilience indicators to a frailty index, we delivered a first empirical base to validate and quantify the construct of systemic resilience in older adults in a dynamic way. © The Author 2017. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Indexed semi-Markov process for wind speed modeling.

NASA Astrophysics Data System (ADS)

Petroni, F.; D'Amico, G.; Prattico, F.

2012-04-01

The increasing interest in renewable energy leads scientific research to find a better way to recover most of the available energy. Particularly, the maximum energy recoverable from wind is equal to 59.3% of that available (Betz law) at a specific pitch angle and when the ratio between the wind speed in output and in input is equal to 1/3. The pitch angle is the angle formed between the airfoil of the blade of the wind turbine and the wind direction. Old turbine and a lot of that actually marketed, in fact, have always the same invariant geometry of the airfoil. This causes that wind turbines will work with an efficiency that is lower than 59.3%. New generation wind turbines, instead, have a system to variate the pitch angle by rotating the blades. This system able the wind turbines to recover, at different wind speed, always the maximum energy, working in Betz limit at different speed ratios. A powerful system control of the pitch angle allows the wind turbine to recover better the energy in transient regime. A good stochastic model for wind speed is then needed to help both the optimization of turbine design and to assist the system control to predict the value of the wind speed to positioning the blades quickly and correctly. The possibility to have synthetic data of wind speed is a powerful instrument to assist designer to verify the structures of the wind turbines or to estimate the energy recoverable from a specific site. To generate synthetic data, Markov chains of first or higher order are often used [1,2,3]. In particular in [1] is presented a comparison between a first-order Markov chain and a second-order Markov chain. A similar work, but only for the first-order Markov chain, is conduced by [2], presenting the probability transition matrix and comparing the energy spectral density and autocorrelation of real and synthetic wind speed data. A tentative to modeling and to join speed and direction of wind is presented in [3], by using two models, first-order Markov chain with different number of states, and Weibull distribution. All this model use Markov chains to generate synthetic wind speed time series but the search for a better model is still open. Approaching this issue, we applied new models which are generalization of Markov models. More precisely we applied semi-Markov models to generate synthetic wind speed time series. In a previous work we proposed different semi-Markov models, showing their ability to reproduce the autocorrelation structures of wind speed data. In that paper we showed also that the autocorrelation is higher with respect to the Markov model. Unfortunately this autocorrelation was still too small compared to the empirical one. In order to overcome the problem of low autocorrelation, in this paper we propose an indexed semi-Markov model. More precisely we assume that wind speed is described by a discrete time homogeneous semi-Markov process. We introduce a memory index which takes into account the periods of different wind activities. With this model the statistical characteristics of wind speed are faithfully reproduced. The wind is a very unstable phenomenon characterized by a sequence of lulls and sustained speeds, and a good wind generator must be able to reproduce such sequences. To check the validity of the predictive semi-Markovian model, the persistence of synthetic winds were calculated, then averaged and computed. The model is used to generate synthetic time series for wind speed by means of Monte Carlo simulations and the time lagged autocorrelation is used to compare statistical properties of the proposed models with those of real data and also with a time series generated though a simple Markov chain. [1] A. Shamshad, M.A. Bawadi, W.M.W. Wan Hussin, T.A. Majid, S.A.M. Sanusi, First and second order Markov chain models for synthetic generation of wind speed time series, Energy 30 (2005) 693-708. [2] H. Nfaoui, H. Essiarab, A.A.M. Sayigh, A stochastic Markov chain model for simulating wind speed time series at Tangiers, Morocco, Renewable Energy 29 (2004) 1407-1418. [3] F. Youcef Ettoumi, H. Sauvageot, A.-E.-H. Adane, Statistical bivariate modeling of wind using first-order Markov chain and Weibull distribution, Renewable Energy 28 (2003) 1787-1802.

Stochastic modelling of intermittent fluctuations in the scrape-off layer: Correlations, distributions, level crossings, and moment estimation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Garcia, O. E., E-mail: odd.erik.garcia@uit.no; Kube, R.; Theodorsen, A.

A stochastic model is presented for intermittent fluctuations in the scrape-off layer of magnetically confined plasmas. The fluctuations in the plasma density are modeled by a super-position of uncorrelated pulses with fixed shape and duration, describing radial motion of blob-like structures. In the case of an exponential pulse shape and exponentially distributed pulse amplitudes, predictions are given for the lowest order moments, probability density function, auto-correlation function, level crossings, and average times for periods spent above and below a given threshold level. Also, the mean squared errors on estimators of sample mean and variance for realizations of the process bymore » finite time series are obtained. These results are discussed in the context of single-point measurements of fluctuations in the scrape-off layer, broad density profiles, and implications for plasma–wall interactions due to the transient transport events in fusion grade plasmas. The results may also have wide applications for modelling fluctuations in other magnetized plasmas such as basic laboratory experiments and ionospheric irregularities.« less

Correlations of turbidity to suspended-sediment concentration in the Toutle River Basin, near Mount St. Helens, Washington, 2010-11

USGS Publications Warehouse

Uhrich, Mark A.; Kolasinac, Jasna; Booth, Pamela L.; Fountain, Robert L.; Spicer, Kurt R.; Mosbrucker, Adam R.

2014-01-01

Researchers at the U.S. Geological Survey, Cascades Volcano Observatory, investigated alternative methods for the traditional sample-based sediment record procedure in determining suspended-sediment concentration (SSC) and discharge. One such sediment-surrogate technique was developed using turbidity and discharge to estimate SSC for two gaging stations in the Toutle River Basin near Mount St. Helens, Washington. To provide context for the study, methods for collecting sediment data and monitoring turbidity are discussed. Statistical methods used include the development of ordinary least squares regression models for each gaging station. Issues of time-related autocorrelation also are evaluated. Addition of lagged explanatory variables was used to account for autocorrelation in the turbidity, discharge, and SSC data. Final regression model equations and plots are presented for the two gaging stations. The regression models support near-real-time estimates of SSC and improved suspended-sediment discharge records by incorporating continuous instream turbidity. Future use of such models may potentially lower the costs of sediment monitoring by reducing time it takes to collect and process samples and to derive a sediment-discharge record.

Time series clustering analysis of health-promoting behavior

NASA Astrophysics Data System (ADS)

Yang, Chi-Ta; Hung, Yu-Shiang; Deng, Guang-Feng

2013-10-01

Health promotion must be emphasized to achieve the World Health Organization goal of health for all. Since the global population is aging rapidly, ComCare elder health-promoting service was developed by the Taiwan Institute for Information Industry in 2011. Based on the Pender health promotion model, ComCare service offers five categories of health-promoting functions to address the everyday needs of seniors: nutrition management, social support, exercise management, health responsibility, stress management. To assess the overall ComCare service and to improve understanding of the health-promoting behavior of elders, this study analyzed health-promoting behavioral data automatically collected by the ComCare monitoring system. In the 30638 session records collected for 249 elders from January, 2012 to March, 2013, behavior patterns were identified by fuzzy c-mean time series clustering algorithm combined with autocorrelation-based representation schemes. The analysis showed that time series data for elder health-promoting behavior can be classified into four different clusters. Each type reveals different health-promoting needs, frequencies, function numbers and behaviors. The data analysis result can assist policymakers, health-care providers, and experts in medicine, public health, nursing and psychology and has been provided to Taiwan National Health Insurance Administration to assess the elder health-promoting behavior.

Artificial fingerprint recognition by using optical coherence tomography with autocorrelation analysis.

PubMed

Cheng, Yezeng; Larin, Kirill V

2006-12-20

Fingerprint recognition is one of the most widely used methods of biometrics. This method relies on the surface topography of a finger and, thus, is potentially vulnerable for spoofing by artificial dummies with embedded fingerprints. In this study, we applied the optical coherence tomography (OCT) technique to distinguish artificial materials commonly used for spoofing fingerprint scanning systems from the real skin. Several artificial fingerprint dummies made from household cement and liquid silicone rubber were prepared and tested using a commercial fingerprint reader and an OCT system. While the artificial fingerprints easily spoofed the commercial fingerprint reader, OCT images revealed the presence of them at all times. We also demonstrated that an autocorrelation analysis of the OCT images could be potentially used in automatic recognition systems.

Artificial fingerprint recognition by using optical coherence tomography with autocorrelation analysis

NASA Astrophysics Data System (ADS)

Cheng, Yezeng; Larin, Kirill V.

2006-12-01

Fingerprint recognition is one of the most widely used methods of biometrics. This method relies on the surface topography of a finger and, thus, is potentially vulnerable for spoofing by artificial dummies with embedded fingerprints. In this study, we applied the optical coherence tomography (OCT) technique to distinguish artificial materials commonly used for spoofing fingerprint scanning systems from the real skin. Several artificial fingerprint dummies made from household cement and liquid silicone rubber were prepared and tested using a commercial fingerprint reader and an OCT system. While the artificial fingerprints easily spoofed the commercial fingerprint reader, OCT images revealed the presence of them at all times. We also demonstrated that an autocorrelation analysis of the OCT images could be potentially used in automatic recognition systems.

Detection of Subtle Hydromechanical Medium Changes Caused By a Small-Magnitude Earthquake Swarm in NE Brazil

NASA Astrophysics Data System (ADS)

D'Hour, V.; Schimmel, M.; Do Nascimento, A. F.; Ferreira, J. M.; Lima Neto, H. C.

2016-04-01

Ambient noise correlation analyses are largely used in seismology to map heterogeneities and to monitor the temporal evolution of seismic velocity changes associated mostly with stress field variations and/or fluid movements. Here we analyse a small earthquake swarm related to a main mR 3.7 intraplate earthquake in North-East of Brazil to study the corresponding post-seismic effects on the medium. So far, post-seismic effects have been observed mainly for large magnitude events. In our study, we show that we were able to detect localized structural changes even for a small earthquake swarm in an intraplate setting. Different correlation strategies are presented and their performances are also shown. We compare the classical auto-correlation with and without pre-processing, including 1-bit normalization and spectral whitening, and the phase auto-correlation. The worst results were obtained for the pre-processed data due to the loss of waveform details. The best results were achieved with the phase cross-correlation which is amplitude unbiased and sensitive to small amplitude changes as long as there exist waveform coherence superior to other unrelated signals and noise. The analysis of 6 months of data using phase auto-correlation and cross-correlation resulted in the observation of a progressive medium change after the major recorded event. The progressive medium change is likely related to the swarm activity through opening new path ways for pore fluid diffusion. We further observed for the auto-correlations a lag time frequency-dependent change which likely indicates that the medium change is localized in depth. As expected, the main change is observed along the fault.

Preliminary Field Demonstration of Passive Radio Sounding Using the Sun as a Signal for Echo Detection

NASA Astrophysics Data System (ADS)

Peters, S. T.; Schroeder, D. M.; Romero-Wolf, A.; Haynes, M.

2017-12-01

The Radar for Icy Moon Exploration (RIME) and the Radar for Europa Assessment and Sounding: Ocean to Near-surface (REASON) have been identified as potential candidates for the implementation of passive sounding as additional observing modes for the ESA and NASA missions to Ganymede and Europa. Recent work has shown the theoretical potential for Jupiter's decametric radiation to be used as a source for passive radio sounding of its icy moons. We are further developing and adapting this geophysical approach for use in terrestrial glaciology. Here, we present results from preliminary field testing of a prototype passive radio sounder from cliffs along the California coast. This includes both using a Lloyd's mirror to measure the Sun's direct path and its reflection off the ocean's surface and exploiting autocorrelation to detect the delay time of the echo. This is the first in-situ demonstration of the autocorrelation-based passive-sounding approach using an astronomical white noise signal. We also discuss preliminary field tests on rougher terrestrial and subglacial surfaces, including at Store Glacier in Greenland. Additionally, we present modeling and experimental results that demonstrate the feasibility of applying presumming approaches to the autocorrelations to achieve coherent gain from an inherently random signal. We note that while recording with wider bandwidths and greater delays places fundamental limits on the Lloyd's mirror approach, our new autocorrelation method has no such limitation. Furthermore, we show how achieving wide bandwidths via spectral-stitching methods allows us to obtain a finer range resolution than given by the receiver's instantaneous bandwidth. Finally, we discuss the potential for this technique to eliminate the need for active transmitters in certain types of ice sounding experiments, thereby reducing the complexity, power consumption, and cost of systems and observations.

Downlink Probability Density Functions for EOS-McMurdo Sound

NASA Technical Reports Server (NTRS)

Christopher, P.; Jackson, A. H.

1996-01-01

The visibility times and communication link dynamics for the Earth Observations Satellite (EOS)-McMurdo Sound direct downlinks have been studied. The 16 day EOS periodicity may be shown with the Goddard Trajectory Determination System (GTDS) and the entire 16 day period should be simulated for representative link statistics. We desire many attributes of the downlink, however, and a faster orbital determination method is desirable. We use the method of osculating elements for speed and accuracy in simulating the EOS orbit. The accuracy of the method of osculating elements is demonstrated by closely reproducing the observed 16 day Landsat periodicity. An autocorrelation function method is used to show the correlation spike at 16 days. The entire 16 day record of passes over McMurdo Sound is then used to generate statistics for innage time, outage time, elevation angle, antenna angle rates, and propagation loss. The levation angle probability density function is compared with 1967 analytic approximation which has been used for medium to high altitude satellites. One practical result of this comparison is seen to be the rare occurrence of zenith passes. The new result is functionally different than the earlier result, with a heavy emphasis on low elevation angles. EOS is one of a large class of sun synchronous satellites which may be downlinked to McMurdo Sound. We examine delay statistics for an entire group of sun synchronous satellites ranging from 400 km to 1000 km altitude. Outage probability density function results are presented three dimensionally.

Simulation-based power calculation for designing interrupted time series analyses of health policy interventions.

PubMed

Zhang, Fang; Wagner, Anita K; Ross-Degnan, Dennis

2011-11-01

Interrupted time series is a strong quasi-experimental research design to evaluate the impacts of health policy interventions. Using simulation methods, we estimated the power requirements for interrupted time series studies under various scenarios. Simulations were conducted to estimate the power of segmented autoregressive (AR) error models when autocorrelation ranged from -0.9 to 0.9 and effect size was 0.5, 1.0, and 2.0, investigating balanced and unbalanced numbers of time periods before and after an intervention. Simple scenarios of autoregressive conditional heteroskedasticity (ARCH) models were also explored. For AR models, power increased when sample size or effect size increased, and tended to decrease when autocorrelation increased. Compared with a balanced number of study periods before and after an intervention, designs with unbalanced numbers of periods had less power, although that was not the case for ARCH models. The power to detect effect size 1.0 appeared to be reasonable for many practical applications with a moderate or large number of time points in the study equally divided around the intervention. Investigators should be cautious when the expected effect size is small or the number of time points is small. We recommend conducting various simulations before investigation. Copyright © 2011 Elsevier Inc. All rights reserved.

Ultralow-Power Digital Correlator for Microwave Polarimetry

NASA Technical Reports Server (NTRS)

Piepmeier, Jeffrey R.; Hass, K. Joseph

2004-01-01

A recently developed high-speed digital correlator is especially well suited for processing readings of a passive microwave polarimeter. This circuit computes the autocorrelations of, and the cross-correlations among, data in four digital input streams representing samples of in-phase (I) and quadrature (Q) components of two intermediate-frequency (IF) signals, denoted A and B, that are generated in heterodyne reception of two microwave signals. The IF signals arriving at the correlator input terminals have been digitized to three levels (-1,0,1) at a sampling rate up to 500 MHz. Two bits (representing sign and magnitude) are needed to represent the instantaneous datum in each input channel; hence, eight bits are needed to represent the four input signals during any given cycle of the sampling clock. The accumulation (integration) time for the correlation is programmable in increments of 2(exp 8) cycles of the sampling clock, up to a maximum of 2(exp 24) cycles. The basic functionality of the correlator is embodied in 16 correlation slices, each of which contains identical logic circuits and counters (see figure). The first stage of each correlation slice is a logic gate that computes one of the desired correlations (for example, the autocorrelation of the I component of A or the negative of the cross-correlation of the I component of A and the Q component of B). The sampling of the output of the logic gate output is controlled by the sampling-clock signal, and an 8-bit counter increments in every clock cycle when the logic gate generates output. The most significant bit of the 8-bit counter is sampled by a 16-bit counter with a clock signal at 2(exp 8) the frequency of the sampling clock. The 16-bit counter is incremented every time the 8-bit counter rolls over.

Vortex relaxation in type-II superconductors following current quenches

NASA Astrophysics Data System (ADS)

Chaturvedi, Harsh; Assi, Hiba; Dobramysl, Ulrich; Pleimling, Michel; Täuber, Uwe

2015-03-01

The mixed phase in type-II superconductors is characterized by the presence of mutually repulsive magnetic flux lines that are driven by external currents and pinned by point-like or extended material defects. We represent the disordered vortex system in the London limit by an elastic directed line model, whose relaxational dynamics we investigate numerically by means of Langevin Molecular Dynamics. We specifically study the effects of sudden changes of the driving current on the time evolution of the mean flux line gyration radius and the associated transverse displacement correlation functions. Upon quenching from the moving into the pinned glassy phase, we observe algebraically slow relaxation. The associated two-time height-autocorrelations display broken time translation invariance and can be described by a simple aging scaling form, albeit with non-universal scaling exponents. Research supported by the U.S. Department of Energy, Office of Basic Energy Sciences, Division of Materials Sciences and Engineering under Award DE-FG02-09ER46613.

Nature of self-diffusion in two-dimensional fluids

DOE Office of Scientific and Technical Information (OSTI.GOV)

Choi, Bongsik; Han, Kyeong Hwan; Kim, Changho

Self-diffusion in a two-dimensional simple fluid is investigated by both analytical and numerical means. We investigate the anomalous aspects of self-diffusion in two-dimensional fluids with regards to the mean square displacement, the time-dependent diffusion coefficient, and the velocity autocorrelation function (VACF) using a consistency equation relating these quantities. Here, we numerically confirm the consistency equation by extensive molecular dynamics simulations for finite systems, corroborate earlier results indicating that the kinematic viscosity approaches a finite, non-vanishing value in the thermodynamic limit, and establish the finite size behavior of the diffusion coefficient. We obtain the exact solution of the consistency equation in the thermodynamic limit and use this solution to determine the large time asymptotics of the mean square displacement, the diffusion coefficient, and the VACF. An asymptotic decay law of the VACF resembles the previously known self-consistent form, 1/(more » $$t\\sqrt{In t)}$$ however with a rescaled time.« less

Broadband boundary effects on Brownian motion.

PubMed

Mo, Jianyong; Simha, Akarsh; Raizen, Mark G

2015-12-01

Brownian motion of particles in confined fluids is important for many applications, yet the effects of the boundary over a wide range of time scales are still not well understood. We report high-bandwidth, comprehensive measurements of Brownian motion of an optically trapped micrometer-sized silica sphere in water near an approximately flat wall. At short distances we observe anisotropic Brownian motion with respect to the wall. We find that surface confinement not only occurs in the long time scale diffusive regime but also in the short time scale ballistic regime, and the velocity autocorrelation function of the Brownian particle decays faster than that of a particle in bulk fluid. Furthermore, at low frequencies the thermal force loses its color due to the reflected flow from the no-slip boundary. The power spectrum of the thermal force on the particle near a no-slip boundary becomes flat at low frequencies. This detailed understanding of boundary effects on Brownian motion opens a door to developing a 3D microscope using particles as remote sensors.

Detrended fluctuation analysis of non-stationary cardiac beat-to-beat interval of sick infants

NASA Astrophysics Data System (ADS)

Govindan, Rathinaswamy B.; Massaro, An N.; Al-Shargabi, Tareq; Niforatos Andescavage, Nickie; Chang, Taeun; Glass, Penny; du Plessis, Adre J.

2014-11-01

We performed detrended fluctuation analysis (DFA) of cardiac beat-to-beat intervals (RRis) collected from sick newborn infants over 1-4 day periods. We calculated four different metrics from the DFA fluctuation function: the DFA exponents αL (>40 beats up to one-fourth of the record length), αs (15-30 beats), root-mean-square (RMS) fluctuation on a short-time scale (20-50 beats), and RMS fluctuation on a long-time scale (110-150 beats). Except αL , all metrics clearly distinguished two groups of newborn infants (favourable vs. adverse) with well-characterized outcomes. However, the RMS fluctuations distinguished the two groups more consistently over time compared to αS . Furthermore, RMS distinguished the RRi of the two groups earlier compared to the DFA exponent. In all the three measures, the favourable outcome group displayed higher values, indicating a higher magnitude of (auto-)correlation and variability, thus normal physiology, compared to the adverse outcome group.

Nature of self-diffusion in two-dimensional fluids

DOE PAGES

Choi, Bongsik; Han, Kyeong Hwan; Kim, Changho; ...

2017-12-18

Self-diffusion in a two-dimensional simple fluid is investigated by both analytical and numerical means. We investigate the anomalous aspects of self-diffusion in two-dimensional fluids with regards to the mean square displacement, the time-dependent diffusion coefficient, and the velocity autocorrelation function (VACF) using a consistency equation relating these quantities. Here, we numerically confirm the consistency equation by extensive molecular dynamics simulations for finite systems, corroborate earlier results indicating that the kinematic viscosity approaches a finite, non-vanishing value in the thermodynamic limit, and establish the finite size behavior of the diffusion coefficient. We obtain the exact solution of the consistency equation in the thermodynamic limit and use this solution to determine the large time asymptotics of the mean square displacement, the diffusion coefficient, and the VACF. An asymptotic decay law of the VACF resembles the previously known self-consistent form, 1/(more » $$t\\sqrt{In t)}$$ however with a rescaled time.« less

Characterizing the kinetics of suspended cylindrical particles by polarization measurements

NASA Astrophysics Data System (ADS)

Liao, Ran; Ou, Xueheng; Ma, Hui

2015-09-01

Polarization has promising potential to retrieve the information of the steady samples, such as tissues. However, for the fast changing sample such as the suspended algae in the water, the kinetics of the particles also influence the scattered polarization. The present paper will show our recent results to extract the information about the kinetics of the suspended cylindrical particles by polarization measurements. The sample is the aqueous suspension of the glass fibers stirred by a magnetic stirrer. We measure the scattered polarization of the fibers by use of a simultaneous polarization measurement system and obtain the time series of two orthogonal polarization components. By use of correlation analysis, we obtain the time parameters from the auto-correlation functions of the polarization components, and observe the changes with the stirring speeds. Results show that these time parameters indicate the immigration of the fibers. After discussion, we find that they may further characterize the kinetics, including the translation and rotation, of the glass fibers in the fluid field.