Search

Various policies to suppress Covid-19 cases have been implemented by the government since Covid-19 first appeared in Indonesia in March 2020. The island of Java has become one of the government's focuses in suppressing the Covid-19 cases, considering that the island of Java is central in Indonesia. Six provinces on the island of Java are also the provinces with the highest cases. DKI Jakarta, West Java, and Central Java are the three provinces with the most cases. The spread of Covid-19 cases on the island of Java is possible to occur spatially, given the easy access to mobility. One of the measuring tools in Statistics that can be used to see the correlation between locations is the Moran's Index method. This method considers the global spatial autocorrelation. This study aims to measure the spatial correlation and map the government policies' results in Covid-19 cases. This study looks at the spatial autocorrelation between provinces on Java Island based on data confirmed positive for Covid-19 from January 11st to October 31st, 2021. The data obtained are grouped based on the date of government policies related to extensive and micro-scale social restrictions; namely, there are 26 observations. The result is that four observations have positive spatial autocorrelation, and the rest have negative autocorrelation. The spatial influence between provinces on Java Island globally, which correlates with more than 50%, occurred in 12 observations (extension of PPKM, micro PPKM, and emergency PPKM). [ABSTRACT FROM AUTHOR]

Several methods of processing microtremor data have been developed, including Spatial Autocorrelation or Spatially Averaged Coherency Spectra (SPAC) Method. Various types of array shapes in this method such as linear-shaped, L-shaped, triangle-shaped, and circular-shaped array. Among them, the triangle array is the most common. Due to some limitations on the field during the survey, sometimes the triangle array is more difficult to deploy where only L shape array can be done. The purpose of this study is aimed to evaluate the L-shaped array in estimating shear wave velocity structure. Data acquisition was conducted next to the micro-seismic monitoring station of the Experimental Power Reactor (RDE) Site, South Tangerang, Indonesia. An L-shaped array with a combination of different array sizes and the azimuth angle is performed. The phase velocity dispersion curves and shear wave velocity curves of L-shaped arrays with the same array size and different azimuth coincide quite well with those of L-shaped arrays with the same different azimuth and array size. It observed that the different combinations in terms of array size and azimuth produce similar shear wave velocity profiles. Based on the results of the analysis, an L-shaped array can be utilized in order to estimate the shear wave velocity structure. [ABSTRACT FROM AUTHOR]

A probit regression model is a regression with categorical dependent variable in dichotomy or binary form. The dependent variable value of the probit regression states the probability of certain issue. In some cases, the application of this model considers the influence of area (spatial effect). The dependency tendency to the close regions is known as autocorrelation in spatial data. Due to this matter, the parameter estimations of ordinary least square (OLS) method can not be used thus it is substituted by the simulation technique, which is a method in randomly raising data. The method includes direct and indirect simulations. The latter has Markov Chain Monte Carlo (MCMC) method with Gibbs Sampling algorithm, which is the order in conducting certain distributed random data sampling by understanding the required distribution. In this case, the beta binomial distribution is applied. A data simulation with Gibbs Sampling algorithm can be conducted by knowing the required distributions of each variable used for R software beforehand. This research purposes to define the parameter estimation value of spatial probit regression by applying MCMC and Gibbs sampling methods with R software. The results show that the parameter estimations of spatial bivariate probit regression model by simulating through Gibbs sampling algorithm (R software), in which β̂ is the independent variable parameter and ρ̂ is the spatial lag autoregressive coefficient. The simulation with the first-value determination rise result data β = (0, 1, -1), ρ = 0.7 deciding n = 400, 10, and k = 6 show the estimations for β̂ = (0.01205, 0.98709, -0.9675) and β̂ = 0.68523. [ABSTRACT FROM AUTHOR]

This paper discusses fixed-b asymptotic distribution models which have been developed for panel data suffered by the problems of autocorrelation and heteroscedasticity. The fixed-b asymptotic distribution models can be used to infer the parameter of interest. For the purpose of inference the Bartlett Kernel Fixed-b Critical Values have been utilized. This technique is applied to analyze drop-out rates for the elementary and junior high school students in Central Java. The results showed that the drop-out rates in Central Java Province are significantly affected by morbidity rates, GRDP, and gross enrolment rates. [ABSTRACT FROM AUTHOR]

For the past decades, tourism has become one of the key growth sectors in ASEAN and has proven resilient during economic challenges globally. Due to the economic importance of the tourism industry, this study examines the relationship between tourism demand of ASEAN-4 countries and exchange rate, income level, relative price and substitute relative price by using static panel data. This study also looks into the impact of social factors such as political stability and corruption on tourism demand for ASEAN-4 countries. Based on Hausman test statistic, fixed effect estimator model is preferred. Robustness checking is applied to solve for autocorrelation and heteroskedasticity problems. The results showed that exchange rate, income level, relative price and substitute relative price have expected sign following economic theory. As expected, both the political stability and corruption level have positive impact on the number of tourist arrivals to the ASEAN-4 countries while terrorism did not have the expected negative impact. While leprosy has a significant impact on the number of tourist arrivals, surprisingly, the finding found that malaria does not have a significant impact on the number of tourist arrivals to the ASEAN-4 countries. [ABSTRACT FROM AUTHOR]

Conventional control charts are considered satisfied enough to monitor the observations that are assumed to be independent and identically distributed (IID). Nevertheless, in real industry environment, the process data exhibits some serial dependence or autocorrelation in which the IID assumption is violated. With the presence of autocorrelation, the control limits of the control charts should be loosened since the tight control limits can decrease the in-control average run length (ARL); thus, lead to a higher false alarm rate. This paper aims to compare the performance and investigate the relative effectiveness of three control charts: modified Shewhart (mShewhart), modified EWMA (mEWMA), and ARMAST charts, based on the original observations when the process data follows ARMA( 1,1) model. The parameters of those charts are manipulated to give the in-control ARL of 370. The simulation results shows that the mShewhart chart is not completely robust to the deviation from IID assumption for small mean shifts. Although the mEWMA chart is very good at detecting small mean shift, the optimal ARMAST chart outperforms the mEWMA chart when there is autocorrelation in the process. In addition, the ARMAST chart also surpasses the mShewart in monitoring large mean shift. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper focuses on a parameter-driven count time series model with three different distributions. We provide a brief description of the first order autoregressive, AR(1) latent process. We consider the first four central moments of each models that are mean, variance, skewness and kurtosis. Next, the autocovariance and autocorrelation functions for each models are derived. We outline and discuss the possible directions of future research. [ABSTRACT FROM AUTHOR]

Normality of the error terms in regression models is one of the basic assumptions in the applied regression analysis. Therefore, testing for normality of the error terms constitutes one of the most important steps of regression model verification and validation. Failure to assess non-normality of the error terms may lead to incorrect results of usual statistical inference techniques such as t-test or F-test. Within the applied regression analysis there is a frequent problem of the presence of autocorrelation and conditional heteroscedasticity of the error terms. Under both autocorrelation and heteroscedasticity, the usual OLS estimators are still unbiased, linear and asymptotically normally distributed, however, no longer have the minimum variance property among all linear unbiased estimators. Therefore, the aim of this paper is to present and discuss normality testing of the error terms with presence of autocorrelation and conditional heteroscedasticity. To explore the power of selected classical tests and robust tests for normality, we perform simulation study. [ABSTRACT FROM AUTHOR]

Normality of the error terms in regression models is one of the basic assumptions in the applied regression analysis. Therefore, testing for normality of the error terms constitutes one of the most important steps of regression model verification and validation. Failure to assess non-normality of the error terms may lead to incorrect results of usual statistical inference techniques such as t-test or F-test. Within the applied regression analysis there is a frequent problem of the presence of autocorrelation and conditional heteroscedasticity of the error terms. Under both autocorrelation and heteroscedasticity, the usual OLS estimators are still unbiased, linear and asymptotically normally distributed, however, no longer have the minimum variance property among all linear unbiased estimators. Therefore, the aim of this paper is to present and discuss normality testing of the error terms with presence of autocorrelation and conditional heteroscedasticity. To explore the power of selected classical tests and robust tests for normality, we perform simulation study. [ABSTRACT FROM AUTHOR]

Experimental results have determined the mesostrain characteristics of aluminum in a plane stress-strain state. Tensor components of plastic deformations are defined for grains in a plane of a rolled aluminum sheet. The intensity of principal mesostrains is calculated. The correlation between deformations of individual parts is estimated by the Znormalized autocorrelation functions. Strain distribution laws are formulated. Parameters of stress-strained grains are indicated. [ABSTRACT FROM AUTHOR]

Although bulk waves have served as the industry standard in nondestructive evaluation for many years, guided waves (Lamb waves in plates) have become the focus of many current research efforts because they are able to interrogate larger areas of a structure in less time. Despite this advantage, guided waves also have characteristics that obfuscate data interpretation. The first property of guided waves that complicates analysis is their dispersive nature: their wave speed is a function of frequency. The second is that they are multimodal: they propagate as multiple symmetric and antisymmetric modes. Using pulse-compression techniques and a priori calculations of theoretical dispersion curves, the dispersive matched filter attempts to take advantage of these otherwise undesirable characteristics by maximizing the autocorrelation for only one mode, ideally increasing both the signal-to-noise ratio and time-resolution of ultrasonic guided wave measurements. In this research, the responses from broadband chirp excitations are recorded from a sparse transducer array after propagation through an aluminum plate containing no damage and simulated damage. Dispersive matched filtering is applied to the measurements and localization images are generated using the delay-and-sum method. Imaging results are compared to those obtained with narrowband tone burst excitations in terms of their ability to detect and localize the different scatterers. Results show that the dispersive matched filter notably improves the quality of the localization images. [ABSTRACT FROM AUTHOR]

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 this highly unexpected effect are also discussed. [ABSTRACT FROM AUTHOR]

Molecular dynamics simulations are used to study the pair distribution and dynamic correlations in liquid Sn near the melting point. The properties like structure factor, velocity autocorrelation function, its spectral density, and selfdiffusion coefficient are calculated with a pair potential obtained from a pseudopotential originally developed for the solid state. Good agreement with the experiment is observed for the structure factor and the computed velocity autocorrelation functions compare favorably with the results obtained by other theoretical calculations, showing the transferability of the pseudopotential used from solid to liquid environment in the case of liquid Sn. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In recent years, modeling in long memory properties or fractionally integrated processes in stochastic volatility has been applied in the financial time series. A time series with structural breaks can generate a strong persistence in the autocorrelation function, which is an observed behaviour of a long memory process. This paper considers the structural break of data in order to determine true long memory time series data. Unlike usual short memory models for log volatility, the fractional Ornstein-Uhlenbeck process is neither a Markovian process nor can it be easily transformed into a Markovian process. This makes the likelihood evaluation and parameter estimation for the long memory stochastic volatility (LMSV) model challenging tasks. The drift and volatility parameters of the fractional Ornstein-Unlenbeck model are estimated separately using the least square estimator (lse) and quadratic generalized variations (qgv) method respectively. Finally, the empirical distribution of unobserved volatility is estimated using the particle filtering with sequential important sampling-resampling (SIR) method. The mean square error (MSE) between the estimated and empirical volatility indicates that the performance of the model towards the index prices of FTSE Bursa Malaysia KLCI is fairly well. [ABSTRACT FROM AUTHOR]

The IceCube neutrino observatory built in the antarctic ice offers unique opportunities in studying high energy neutrino emission from Galactic and extragalactic sources. Detecting such neutrino emission could give valuable information about the origin of cosmic rays. Recently, first evidence for a diffuse astrophysical neutrino signal in the PeV range was found with IceCube. There is no clear point source signal yet, and advanced analysis methods are being developed in order to improve the sensitivity to point sources and resolve the diffuse signal into point sources. Other searches like a energy-dependent likelihood scan were already performed on IceCube data. In this paper, we report a complementary search for multiple neutrino point sources from three years of IceCube data, performing an autocorrelation test on the full sky. This analysis is aiming for various faint sources at unknown positions and energy spectra in the sky. The result obtained from this search is consistant with a background-only case and shows an underfluctuation. Here, we present limits using the ansatz of the small-scale anisotropy search. [ABSTRACT FROM AUTHOR]

Terahertz signal is generated from Low temperature gallium arsenide photoconductive dipole antennas (gap =5μm, length = 20μm) by focusing 15 fs laser pulses and applying 12V DC across it. Terahertz intensity is detected by Pyroelectric detector (THZ1.5MB-USB). The collinear autocorrelation arrangement provides dynamic fringes which are allowed to be incident on photoconductive antennas to study the variation in terahertz intensity with respect to delay between laser pulses. Interestingly, the profile of THz intensity variation was similar to interferometric autocorrelation signal of laser pulses. The THz power attenuation with its propagation distance in atmosphere was measured. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A key assumption in traditional statistical process control (SPC) technique is based on the requirement that observations or time series data are normally and independently distributed. The presences of a serial autocorrelation results in a number of problems, including an increase in the type I error rate and thereby increase the expected number of false alarm in the process observation. However, the independency assumption is often violated in practice due to the influence of serial correlation in the observation. Therefore, the aim of this paper is to demonstrate with the hospital admission data, the influence of serial correlation on the statistical control charts. The trend free pre-whitening (TFPW) method has been used and applied as an alternative method to obtain residuals series which are statistically uncorrelated to each other. In this study, a data set of daily hospital admission for respiratory and cardiovascular diseases was used from the period of 1 January 2009 to 31 December 2009 (365 days). Result showed that TFPW method is an easy and useful method in removing the influence of serial correlation from the hospital admission data. It can be concluded that statistical control chart based on residual series perform better compared to original hospital admission series which influenced by the effects of serial correlation data. [ABSTRACT FROM AUTHOR]

In real life we always come across autocorrelated multivariate process where the present process is related to the previous process. This type of process can be modeled using the traditional multivariate time series models and then the process control can be conducted based on the residual which becomes univariate in nature. However, in this paper, we show that many time series are governed by a geometric Brownian motion (GBM) process. In this case, instead of time series modeling, we only need an appropriate transformation to come up with the condition required in the traditional multivariate process control. Therefore, under GBM process, traditional multivariate process control can be used on the transformed time series data. A real industrial example will be given to illustrate the advantage of the proposed method. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

An algorithm is described that is intended to estimate and remove spurious influences from the surface temperature record at a meteorological station, which may be due to changes in the location of the station or in its environment, or in the method used to make measurements, and which are unrelated to climate change, similarly to [1]. The estimate of these influences is based on a comparison of non-parametric decompositions of the target series with series measured at other stations in a neighborhood of the target series. The uncertainty of the estimated spurious artifacts is determined using a statistical bootstrap method that accounts for temporal correlation structure beyond what is expected from seasonal effects. Our computer-intensive bootstrap procedure lends itself readily to parallelization, which makes the algorithm practicable for large collections of stations. The role that the proposed procedure may play in practice is contingent on the results of large-scale testing, still under way, using historical data. [ABSTRACT FROM AUTHOR]

We describe several statistical methods to detect possible change-points in a time series of values of surface temperature measured at a meteorological station, and to assess the statistical significance of such changes, taking into account the natural variability of the measured values, and the autocorrelations between them. These methods serve to determine whether the record may suffer from biases unrelated to the climate signal, hence whether there may be a need for adjustments as considered by M. J. Menne and C. N. Williams (2009) "Homogenization of Temperature Series via Pairwise Comparisons", Journal of Climate 22 (7), 1700-1717. We also review methods to characterize patterns of seasonality (seasonal decomposition using monthly medians or robust local regression), and explain the role they play in the imputation of missing values, and in enabling robust decompositions of the measured values into a seasonal component, a possible climate signal, and a station-specific remainder. The methods for change-point detection that we describe include statistical process control, wavelet multi-resolution analysis, adaptive weights smoothing, and a Bayesian procedure, all of which are applicable to single station records. [ABSTRACT FROM AUTHOR]

A replica of one of the 1660 Cherenkov detectors used in the ground array of the Pierre Auger Cosmic Ray Observatory in Argentina has been constructed on the roof of the VATLY astrophysics laboratory in Ha Noi (Viet Nam). We report on measurements of low amplitude signals using the detector to study event pairs occurring within a small time window. The data include time autocorrelation and charge distributions. [ABSTRACT FROM AUTHOR]

We describe a new designed multispeckle correlation spectroscopy (MSCS) setup which uses a CCD-camera as optical detector. Due to the simultaneous detection of many coherence areas (speckles) we get a much better statistic than in standard dynamic light scattering (DLS) measurements. Furthermore the intermediate scattering function (ISF) for nonergodic samples can be determined from one measurement. A special feature of our setup is the direct imaging of the scattering volume on the CCD-camera. Therefore every speckle can be connected to its origin scattering volume. This space-resolution gives us the possibility to probe the particle dynamics at different sample positions. Further, we present frequency distributions of the intensity autocorrelation functions for a fluid and a metastable sample. [ABSTRACT FROM AUTHOR]

Non-equilibrium relaxation of Coulomb glass in disordered thin films is investigated by kinetic Monte Carlo simulation. We numerically confirm aging phenomena in the autocorrelation function C(t,tW) in a quasi-two-dimensional system with finite thickness. The characterization of electron diffusion in these systems is also discussed. [ABSTRACT FROM AUTHOR]

In this work we develop an analytical approach for calculation of the all-order interspike interval density (AOISID), show its connection with the autocorrelation function, and try to explain the discovered resemblance of AOISID to the power spectrum of the same spike train. [ABSTRACT FROM AUTHOR]

When applied to signals defined on fractal sets, the classical autocorrelation function has generally been exploited through its power law properties, the main hypothesis being that the exponent involved in this power law is uniquely defined. In this paper, we show that different power laws can likely be retrieved for the same signal. This non uniqueness turns out to be associated to the uncertainty in determination of the exponent value. To avoid such degeneracy, we propose to use a generalized form of the autocorrelation function, a version of which we have previously introduced in the context of characterization of fractal sets. [ABSTRACT FROM AUTHOR]

The problem of the dynamics of speckles in the image plane of the object, caused by random movements of scattering centers is solved. We consider three cases: 1) during the observation the points move at random, but constant speeds, and 2) the relative displacement of any pair of points is a continuous random process, and 3) the motion of the centers is the sum of a deterministic movement and random displacement. For the cases 1) and 2) the characteristics of temporal and spectral autocorrelation function of the radiation intensity can be used for determining of individually and the average relative displacement of the centers, their dispersion and the relaxation time. For the case 3) is showed that under certain conditions, the optical signal contains a periodic component, the number of periods is proportional to the derivations of the deterministic displacements. The results of experiments conducted to test and application of theory are given. [ABSTRACT FROM AUTHOR]

This paper studies the problem of three-dimensional wave scattering by an elastic inclusion with quadratic nonlinearity in an otherwise linear elastic medium. Due to the nonlinearity of the inclusion, second order wave appears in the scattered field. Under the incidence of a plane longitudinal wave, solution to the scattered second order field is derived explicitly in terms of the Green's function. A far field approximation of the scattered field is also obtained. The results of far field show that the scattered second harmonic field consists of a longitudinal spherical wave and a shear spherical wave. Furthermore, it is found that the amplitude of the forward scattered field is proportional to the acoustic nonlinearity parameter β averaged over the volume of the inclusion, and the amplitude of backscattered field is proportional to a spatially weighted average of β. Finally, a method is described to nondestructively obtain the statistics of the spatial variation of β over the inclusion such as the mean, the variance and autocorrelation length. [ABSTRACT FROM AUTHOR]

An efficient and flexible algorithm for generating random fields on the sphere is presented. The spectral coefficients result from the Gauss-Markov process. Their spherical harmonics expansion creates a random Gaussian field on the sphere with prescribed mean and variance. A fortran code for the spherical harmonics expansion is described. [ABSTRACT FROM AUTHOR]

This work addresses the problem of testing the significance of the slope of a linear trend with and without an eventual seasonal effect. It is assumed that the error term follows an AR(1), and that the autoregressive parameter is unknown. The autoregressive parameter is obtained through some competing estimators, namely, a parametric version, a modified Kendall's correlation coefficient, and another non-parametric counter part developed earlier in the context of the state space models. The accuracy of the estimation of this parameter is also analyzed. The performance of the tests is done taking the three estimators simultaneously and is compared through a Monte Carlo simulation study under different assumptions. The study is extended in order to compare the slopes of two or more periods in the same time series. [ABSTRACT FROM AUTHOR]

The Telescope Array (TA) experiment detects air showers induced by extremely high energy cosmic rays in the middle west of Utah, USA. The TA experiment consists of two different types of detector such as atmospheric fluorescence detectors (FDs) and surface array particle detectors (SDs). The SD observation started in March 2008. This paper describes the search for the small scale clusters from 850-days TA SD data by auto-correlation method for several energy thresholds. As a result, there is no indication of small scale clusters by TA SD. [ABSTRACT FROM AUTHOR]

We formulated reduction in visibility in the x-ray Talbot interferometer by the autocorrelation function of spatial fluctuations of a wavefront caused by unresolvable micron-size structures in a sample. The experimental results for several isotropic samples were well fitted by our formula with a simple model having three parameters characterizing the wavefront fluctuations: variance, correlation length, and the Hurst exponent. The Hurst exponent is relevant to the shapes of the microstructures, while the correlation length can be interpreted to their average size. Thus our results opens a new way to analyze microstructures in a sample. [ABSTRACT FROM AUTHOR]

We've analyzed the Fermi high time resolution GRB light curves' autocorrelation function looking for gravitational lens effects. The theoretical amplification-time delay function was applied with a photon count S/N approximation to determine the optimal photon binning. We conclude that during the lens signal search among the Fermi data the usual linearity based techniques (e.g. matched filters, FIR filtering, Bayes blocks, autocorrelation function) are sub-optimal. Only the short and very bright GRBs can be the plausible candidates of these detection techniques. [ABSTRACT FROM AUTHOR]

The general conditions for the weak and strong regimes of incompressible magnetohydrodynamic turbulence are derived and studied in the framework of the direct interaction approximation. It is shown that in the framework of the weak turbulence theory, the autocorrelation and cascade timescales are always of the same order of magnitude. This means that, contrary to the general belief, any model of turbulence which implies a large number of collisions among wave packets for an efficient energy cascade (such as the Iroshnikov-Kraichnan model), does not represent a model of weak turbulence. [ABSTRACT FROM AUTHOR]

This paper studies the one-dimensional wave propagation in an elastic medium with spatially non-uniform quadratic nonlinearity. Two problems are solved analytically. One is for a time-harmonic wave propagating in a half-space where the displacement is prescribed on the surface of the half-space. It is found that spatial non-uniformity of the material nonlinearity causes backscattering of the second order harmonic, which when combined with the forward propagating waves generates a standing wave in steady-state wave motion. The second problem solved is the reflection from and transmission through a layer of finite thickness embedded in an otherwise linearly elastic medium of infinite extent, where it is assumed that the layer has a spatially non-uniform quadratic nonlinearity. The results show that the transmission coefficient for the second order harmonic is proportional to the spatial average of the nonlinearity across the thickness of the layer, independent of the spatial distribution of the nonlinearity. On the other hand, the coefficient of reflection is proportional to a weighted average of the nonlinearity across the layer thickness. The weight function in this weighted average is related to the propagating phase, thus making the coefficient of reflection dependent on the spatial distribution of the nonlinearity. Finally, the paper concludes with some discussions on how to use the reflected and transmitted second harmonic waves to evaluate the variance and autocorrelation length of nonlinear parameter β when the nonlinearity distribution in the layer is a stochastic process. [ABSTRACT FROM AUTHOR]

A mode locked Titanium sapphire Ti:Al2O3 oscillator energized by Diode pumped solid state laser at 532 nm was developed and characterized. The oscillator was aligned based on z-folder cavity. The stability of the output was monitor via the oscilloscope. The mode locked frequency was operated at 75 MHz. The power was detected by high-speed photodiode and digitized on a broadband powermeter. The pulse duration was directly measured via autocorellator. A beam profiler was utilized to record the dynamic expansion of the beam along the Rayleigh region. The average output power of the laser was found to be as 260 mW corresponding the input power of 4 W. The energy of the femtopulse is 3.57 nJ with pulse duration of 50 fs. The focused intensity is achieved almost 4×108 W/cm2 with M2∼2. [ABSTRACT FROM AUTHOR]

We derive the conditions on laser energy and photon number under which a short strong laser pulse excites a collective nuclear mode. We use the Giant Dipole Resonance as a representative example, and a random-matrix description of the fine-structure states and perturbation theory as tools. We identify the relevant observable as the nuclear time-decay function. That function is the Fourier transform of the autocorrelation function of the associated scattering matrix and contains information not otherwise available. We evaluate that function in specific cases and show that it may deviate significantly from an exponential. [ABSTRACT FROM AUTHOR]

Most of physical experiments are usually described as repeated measurements of some random variables. Experimental data registered by on-line computers form time series of outcomes. The frequencies of different outcomes are compared with the probabilities provided by the algorithms of quantum theory (QT). In spite of statistical predictions of QT a claim was made that it provided the most complete description of the data and of the underlying physical phenomena. This claim could be easily rejected if some fine structures, averaged out in the standard descriptive statistical analysis, were found in time series of experimental data. To search for these structures one has to use more subtle statistical tools which were developed to study time series produced by various stochastic processes. In this talk we review some of these tools. As an example we show how the standard descriptive statistical analysis of the data is unable to reveal a fine structure in a simulated sample of AR (2) stochastic process. We emphasize once again that the violation of Bell inequalities gives no information on the completeness or the non locality of QT. The appropriate way to test the completeness of quantum theory is to search for fine structures in time series of the experimental data by means of the purity tests or by studying the autocorrelation and partial autocorrelation functions. [ABSTRACT FROM AUTHOR]

We report nonlinear multiphoton processes in atoms and molecules by intense high harmonics and their applications to attosecond pulse characterization. Phase matched high harmonics by a loosely focusing geometry produce highly focusable intensity with fully spatiotemporal coherence, which is sufficient to induce nonlinear optical phenomena in the extreme ultraviolet and soft x-ray (XUV) region. With this XUV coherent light source, two-photon double ionization in He is demonstrated with 42-eV high harmonic photons. On the other hand, when intense high harmonics around 20 eV is subjected to N2 molecules, occurrence of Coulomb explosion following to two-photon double ionization is observed in attosecond temporal precision. Taking advantage of larger cross section of two-photon ionization in molecules, we successfully perform the interferometric autocorrelation of an attosecond pulse train with the ion signals produced by Coulomb explosion of nitrogen molecules. The result reveals the phase relation between attosecond pulses in the train. [ABSTRACT FROM AUTHOR]

Explosive sources are widely used as sound sources in underwater acoustics experiments. Unfortunately, the resulting direct pulse is often corrupted by bubble pulses, which has an undesired influence on the observation and its applications. Canceling these bubble pulses will greatly enhance the quality of the observation and will be helpful for its applications. To cancel the bubble pulse, an adaptive filter, updated from an adaptive least squares algorithm in order to model the physical transmission from the explosive source to the receiver, is proposed. The auto-correlation functions of both the original field trial data and the filtered results show the bubble pulses in the observation can be effectively cancelled. The normal modes of signal propagation can be reconstructed from the filtered results with an adaptive time-frequency representation. The estimated time delay of different normal modes is further applied in geo-acoustic inversion. [ABSTRACT FROM AUTHOR]

Using number 35 lubricants, air and running water as experimental mediums, high-speed camera is applied to get dynamic images of the 6 typical flows for the oil-air-water three-phase flow in vertical upward pipe. Grayscale average values of each frame images are extracted to form the time series. Based on the values, the chaotic characteristic is analyzed. The autocorrelation function is applied to calculate the delay time, and simultaneously the attractor and chaos associated and HURST Exponent are also used to analyze the formation mechanisms of the flow patterns for the oil-air-water three-phase flow. The experimental result draws to the conclusion: the image grayscale signals of the oil-air-water three-phase flow in vertical upward pipe have chaotic characteristic, by which the several typical flow patterns of three-phase flow can be fully represented. [ABSTRACT FROM AUTHOR]

Atomic Force Microscopy (AFM) is a widely used tool for nanoscale surface analysis. Dynamic atomic force microscopy (tapping or non-contact mode) consists of a vibrating microcantilever with a nanoscale tip that interacts with a sample surface via short- and long-range intermolecular forces. Due to the need of high precision measurements, the interaction forces between the nanotip and the specimen must be carefully examined and analyzed in order to best evaluate the nonlinear dynamical response of the AFM system. Recent research on AFM systems has focused on detailed numerical analysis such that possible chaotic regions can be well defined. In this paper, nonlinear time-series analysis methods (NTSA) are applied to analyze signals generated by a numerical single-degree-of-freedom lumped-model of a dynamic-mode AFM. The signal characteristics will provide useful insight of the nonlinear system response leading in future work, to the evaluation of deterministic uncertainty in real-time AFM measurements that is being introduced because of these complex phenomena. The confirmation of chaos existence and the prediction of the regions where chaotic motion is possible remain crucial for the quality of AFM images. The methods used for the system analysis include Autocorrelation Function, Power Spectral Density Analysis, Phase-space analysis, Poincare Maps and Lyapunov Indicator. All the above computational techniques can then be applied to experimental AFM data in order to find system parameters and regimes, which ensure stable motions across the sample surface scanning process. [ABSTRACT FROM AUTHOR]

We report analysis of temperature fluctuations in turbulent thermal convection in a cylindrical container of aspect ratio 4. Temperature fluctuations are measured at the opposite ends of a cell diameter 4 cm inboard of the sidewall and at the cell centre in the central horizontal plane of the apparatus, for Rayleigh numbers (Ra) ranging from 1.043×109 to 6.5×1011. For these Ra, experimental conditions obey Boussinesq approximation. Autocorrelation and cross correlation have been used to analyze the data. The period of about 33 s found by autocorrelation for Ra = 1.043×109, indicates the advection of plumes by a large-scale coherent wind, tilted along the diagonal, which encompasses the entire container. In good agreement with autocorrelation, cross-correlation between sidewall signals in the central horizontal, but at opposite ends of a cell diameter, show a strong long-time correlation indicative of a well ordered and robust mean wind. While no clear correlation exists between the sidewall fluctuations and those at the centre which corroborates the lack of any strong mean wind at the centre. Cross correlation of parts (5 minutes each) of the same signals, instead of the whole, has shown that about 20% of the time there is correlation between side and center sensors. The velocity of mean wind for this Ra has been calculated to be about 3.19 cm/s. Possible mean wind trajectory has also been drawn. For Ra = 6.5×1011 there is no correlation between any set of temperature signals, indicating that the mean wind is no longer coherent over the entire container. [ABSTRACT FROM AUTHOR]

Restoration of digital mammograms, as a pre-processing tool, using deconvolution procedures, is analysed. It implies, knowing Modulation Transfer Function (MTF) of the mammography device and the estimation of the mammogram's noise. Wiener filter is used, as the most objective in mammograms restoration by deconvolution. Using MATLAB program the deconvolution procedures are conducted in two ways with different level of approximation. The first method approximates the noise/signal power ratio by a constant and the second method uses autocorrelation functions of the noise and signals. Abilities and limitations of the methods are analysed and checked by using the raw images of the bar-pattern due to better visualisation of the obtained results. The raw images are obtained at a Computed Radiography (CR) mammography device in Clinical Centre in Podgorica. It is found that quality of the restored image highly depends of knowledge of type and magnitude of noise. In the both methods spatial resolution of the restored images are improved, but much better in a case where autocorrelation functions of the noise and signal are used. This procedure is proposed as an objective pre-processing tool to put back imperfectness of mammography devices. [ABSTRACT FROM AUTHOR]

In this work we present a review of main statistical properties of speckle patterns and accomplish a comparative study of the more used methods for analysis and extraction of information from optical grainy. The first and second order space-time statistics are dicussed in an overview perspective. The biospeckle phenomenon has detailed attention, specially in its application on monitoring of activity in tissues. The main techniques used to obtain information from speckle patterns are presented, with special prominence to autocorrelation function, co-occurrence matrices, Fujii's method, Briers' contrast and spatial and temporal contrast analisys (LASCA and LASTCA). An incipient method for analysis, based on the study of sucessive correlations contrast, is introduced. Numerical simulations, using diferent probability density functions for velocities of scatterers, were made with two objectives: to test the analysis methods and to give subsidies for interpretation of in vivo results. Vegetable and animal tissues are investigated, achieving the monitoring of senescence process and vascularization maps on leaves, the accompaniment of fungi contamined fruits, the mapping of activity in flowers and the analisys of healing in rats subjected to abdominal surgery. Experiments using the biospeckle phenomenon in microscopy are carried out. At last, it is evaluated the potentiality of biospeckle as diagnosis tool in chronic vein ulcer cared with low intensity laser therapy and the better analysis methods for each kind of tissue are pointed. [ABSTRACT FROM AUTHOR]

We show how Dynamic Sound Scattering (the acoustical analog of Dynamic Light Scattering) can be used to measure the average velocity profile and the particle velocity variance of neutrally buoyant suspensions in the Couette geometry. The motion of the particles was determined from the temporal autocorrelation function of the scattered field fluctuations in a single speckle spot. In contrast to theories that assume isotropic particle velocity fluctuations, it is shown that “suspension temperature” (defined as the average kinetic energy contained in the particle velocity fluctuations) is a tensor, whose components have different magnitudes. For particle volume fractions up to 0.4, components of the fluctuations parallel to the flow and in the vertical direction are much larger than in the radial direction. For higher concentrations the fluctuation anisotropy decreases. [ABSTRACT FROM AUTHOR]