Glossary-HDSC/OWP

Science.gov Websites

Glossary Precipitation Frequency Data Server GIS Grids Maps Time Series Temporals Documents Probable provides a measure of the average time between years (and not events) in which a particular value is RECCURENCE INTERVAL). ANNUAL MAXIMUM SERIES (AMS) - Time series of the largest precipitation amounts in a

Long-term memory and volatility clustering in high-frequency price changes

NASA Astrophysics Data System (ADS)

oh, Gabjin; Kim, Seunghwan; Eom, Cheoljun

2008-02-01

We studied the long-term memory in diverse stock market indices and foreign exchange rates using Detrended Fluctuation Analysis (DFA). For all high-frequency market data studied, no significant long-term memory property was detected in the return series, while a strong long-term memory property was found in the volatility time series. The possible causes of the long-term memory property were investigated using the return data filtered by the AR(1) model, reflecting the short-term memory property, the GARCH(1,1) model, reflecting the volatility clustering property, and the FIGARCH model, reflecting the long-term memory property of the volatility time series. The memory effect in the AR(1) filtered return and volatility time series remained unchanged, while the long-term memory property diminished significantly in the volatility series of the GARCH(1,1) filtered data. Notably, there is no long-term memory property, when we eliminate the long-term memory property of volatility by the FIGARCH model. For all data used, although the Hurst exponents of the volatility time series changed considerably over time, those of the time series with the volatility clustering effect removed diminish significantly. Our results imply that the long-term memory property of the volatility time series can be attributed to the volatility clustering observed in the financial time series.

Nonlinear model updating applied to the IMAC XXXII Round Robin benchmark system

NASA Astrophysics Data System (ADS)

Kurt, Mehmet; Moore, Keegan J.; Eriten, Melih; McFarland, D. Michael; Bergman, Lawrence A.; Vakakis, Alexander F.

2017-05-01

We consider the application of a new nonlinear model updating strategy to a computational benchmark system. The approach relies on analyzing system response time series in the frequency-energy domain by constructing both Hamiltonian and forced and damped frequency-energy plots (FEPs). The system parameters are then characterized and updated by matching the backbone branches of the FEPs with the frequency-energy wavelet transforms of experimental and/or computational time series. The main advantage of this method is that no nonlinearity model is assumed a priori, and the system model is updated solely based on simulation and/or experimental measured time series. By matching the frequency-energy plots of the benchmark system and its reduced-order model, we show that we are able to retrieve the global strongly nonlinear dynamics in the frequency and energy ranges of interest, identify bifurcations, characterize local nonlinearities, and accurately reconstruct time series. We apply the proposed methodology to a benchmark problem, which was posed to the system identification community prior to the IMAC XXXII (2014) and XXXIII (2015) Conferences as a "Round Robin Exercise on Nonlinear System Identification". We show that we are able to identify the parameters of the non-linear element in the problem with a priori knowledge about its position.

Allan deviation analysis of financial return series

NASA Astrophysics Data System (ADS)

Hernández-Pérez, R.

2012-05-01

We perform a scaling analysis for the return series of different financial assets applying the Allan deviation (ADEV), which is used in the time and frequency metrology to characterize quantitatively the stability of frequency standards since it has demonstrated to be a robust quantity to analyze fluctuations of non-stationary time series for different observation intervals. The data used are opening price daily series for assets from different markets during a time span of around ten years. We found that the ADEV results for the return series at short scales resemble those expected for an uncorrelated series, consistent with the efficient market hypothesis. On the other hand, the ADEV results for absolute return series for short scales (first one or two decades) decrease following approximately a scaling relation up to a point that is different for almost each asset, after which the ADEV deviates from scaling, which suggests that the presence of clustering, long-range dependence and non-stationarity signatures in the series drive the results for large observation intervals.

Evaluation of Hydrologic and Meteorological Impacts on Dengue Fever Incidences in Southern Taiwan using Time- Frequency Method

NASA Astrophysics Data System (ADS)

Tsai, Christina; Yeh, Ting-Gu

2017-04-01

Extreme weather events are occurring more frequently as a result of climate change. Recently dengue fever has become a serious issue in southern Taiwan. It may have characteristic temporal scales that can be identified. Some researchers have hypothesized that dengue fever incidences are related to climate change. This study applies time-frequency analysis to time series data concerning dengue fever and hydrologic and meteorological variables. Results of three time-frequency analytical methods - the Hilbert Huang transform (HHT), the Wavelet Transform (WT) and the Short Time Fourier Transform (STFT) are compared and discussed. A more effective time-frequency analysis method will be identified to analyze relevant time series data. The most influential time scales of hydrologic and meteorological variables that are associated with dengue fever are determined. Finally, the linkage between hydrologic/meteorological factors and dengue fever incidences can be established.

The Fourier decomposition method for nonlinear and non-stationary time series analysis.

PubMed

Singh, Pushpendra; Joshi, Shiv Dutt; Patney, Rakesh Kumar; Saha, Kaushik

2017-03-01

for many decades, there has been a general perception in the literature that Fourier methods are not suitable for the analysis of nonlinear and non-stationary data. In this paper, we propose a novel and adaptive Fourier decomposition method (FDM), based on the Fourier theory, and demonstrate its efficacy for the analysis of nonlinear and non-stationary time series. The proposed FDM decomposes any data into a small number of 'Fourier intrinsic band functions' (FIBFs). The FDM presents a generalized Fourier expansion with variable amplitudes and variable frequencies of a time series by the Fourier method itself. We propose an idea of zero-phase filter bank-based multivariate FDM (MFDM), for the analysis of multivariate nonlinear and non-stationary time series, using the FDM. We also present an algorithm to obtain cut-off frequencies for MFDM. The proposed MFDM generates a finite number of band-limited multivariate FIBFs (MFIBFs). The MFDM preserves some intrinsic physical properties of the multivariate data, such as scale alignment, trend and instantaneous frequency. The proposed methods provide a time-frequency-energy (TFE) distribution that reveals the intrinsic structure of a data. Numerical computations and simulations have been carried out and comparison is made with the empirical mode decomposition algorithms.

Detection and characterization of lightning-based sources using continuous wavelet transform: application to audio-magnetotellurics

NASA Astrophysics Data System (ADS)

Larnier, H.; Sailhac, P.; Chambodut, A.

2018-01-01

Atmospheric electromagnetic waves created by global lightning activity contain information about electrical processes of the inner and the outer Earth. Large signal-to-noise ratio events are particularly interesting because they convey information about electromagnetic properties along their path. We introduce a new methodology to automatically detect and characterize lightning-based waves using a time-frequency decomposition obtained through the application of continuous wavelet transform. We focus specifically on three types of sources, namely, atmospherics, slow tails and whistlers, that cover the frequency range 10 Hz to 10 kHz. Each wave has distinguishable characteristics in the time-frequency domain due to source shape and dispersion processes. Our methodology allows automatic detection of each type of event in the time-frequency decomposition thanks to their specific signature. Horizontal polarization attributes are also recovered in the time-frequency domain. This procedure is first applied to synthetic extremely low frequency time-series with different signal-to-noise ratios to test for robustness. We then apply it on real data: three stations of audio-magnetotelluric data acquired in Guadeloupe, oversea French territories. Most of analysed atmospherics and slow tails display linear polarization, whereas analysed whistlers are elliptically polarized. The diversity of lightning activity is finally analysed in an audio-magnetotelluric data processing framework, as used in subsurface prospecting, through estimation of the impedance response functions. We show that audio-magnetotelluric processing results depend mainly on the frequency content of electromagnetic waves observed in processed time-series, with an emphasis on the difference between morning and afternoon acquisition. Our new methodology based on the time-frequency signature of lightning-induced electromagnetic waves allows automatic detection and characterization of events in audio-magnetotelluric time-series, providing the means to assess quality of response functions obtained through processing.

Statistical properties and time-frequency analysis of temperature, salinity and turbidity measured by the MAREL Carnot station in the coastal waters of Boulogne-sur-Mer (France)

NASA Astrophysics Data System (ADS)

Kbaier Ben Ismail, Dhouha; Lazure, Pascal; Puillat, Ingrid

2016-10-01

In marine sciences, many fields display high variability over a large range of spatial and temporal scales, from seconds to thousands of years. The longer recorded time series, with an increasing sampling frequency, in this field are often nonlinear, nonstationary, multiscale and noisy. Their analysis faces new challenges and thus requires the implementation of adequate and specific methods. The objective of this paper is to highlight time series analysis methods already applied in econometrics, signal processing, health, etc. to the environmental marine domain, assess advantages and inconvenients and compare classical techniques with more recent ones. Temperature, turbidity and salinity are important quantities for ecosystem studies. The authors here consider the fluctuations of sea level, salinity, turbidity and temperature recorded from the MAREL Carnot system of Boulogne-sur-Mer (France), which is a moored buoy equipped with physico-chemical measuring devices, working in continuous and autonomous conditions. In order to perform adequate statistical and spectral analyses, it is necessary to know the nature of the considered time series. For this purpose, the stationarity of the series and the occurrence of unit-root are addressed with the Augmented-Dickey Fuller tests. As an example, the harmonic analysis is not relevant for temperature, turbidity and salinity due to the nonstationary condition, except for the nearly stationary sea level datasets. In order to consider the dominant frequencies associated to the dynamics, the large number of data provided by the sensors should enable the estimation of Fourier spectral analysis. Different power spectra show a complex variability and reveal an influence of environmental factors such as tides. However, the previous classical spectral analysis, namely the Blackman-Tukey method, requires not only linear and stationary data but also evenly-spaced data. Interpolating the time series introduces numerous artifacts to the data. The Lomb-Scargle algorithm is adapted to unevenly-spaced data and is used as an alternative. The limits of the method are also set out. It was found that beyond 50% of missing measures, few significant frequencies are detected, several seasonalities are no more visible, and even a whole range of high frequency disappears progressively. Furthermore, two time-frequency decomposition methods, namely wavelets and Hilbert-Huang Transformation (HHT), are applied for the analysis of the entire dataset. Using the Continuous Wavelet Transform (CWT), some properties of the time series are determined. Then, the inertial wave and several low-frequency tidal waves are identified by the application of the Empirical Mode Decomposition (EMD). Finally, EMD based Time Dependent Intrinsic Correlation (TDIC) analysis is applied to consider the correlation between two nonstationary time series.

A Multitaper, Causal Decomposition for Stochastic, Multivariate Time Series: Application to High-Frequency Calcium Imaging Data.

PubMed

Sornborger, Andrew T; Lauderdale, James D

2016-11-01

Neural data analysis has increasingly incorporated causal information to study circuit connectivity. Dimensional reduction forms the basis of most analyses of large multivariate time series. Here, we present a new, multitaper-based decomposition for stochastic, multivariate time series that acts on the covariance of the time series at all lags, C ( τ ), as opposed to standard methods that decompose the time series, X ( t ), using only information at zero-lag. In both simulated and neural imaging examples, we demonstrate that methods that neglect the full causal structure may be discarding important dynamical information in a time series.

Transition Icons for Time-Series Visualization and Exploratory Analysis.

PubMed

Nickerson, Paul V; Baharloo, Raheleh; Wanigatunga, Amal A; Manini, Todd M; Tighe, Patrick J; Rashidi, Parisa

2018-03-01

The modern healthcare landscape has seen the rapid emergence of techniques and devices that temporally monitor and record physiological signals. The prevalence of time-series data within the healthcare field necessitates the development of methods that can analyze the data in order to draw meaningful conclusions. Time-series behavior is notoriously difficult to intuitively understand due to its intrinsic high-dimensionality, which is compounded in the case of analyzing groups of time series collected from different patients. Our framework, which we call transition icons, renders common patterns in a visual format useful for understanding the shared behavior within groups of time series. Transition icons are adept at detecting and displaying subtle differences and similarities, e.g., between measurements taken from patients receiving different treatment strategies or stratified by demographics. We introduce various methods that collectively allow for exploratory analysis of groups of time series, while being free of distribution assumptions and including simple heuristics for parameter determination. Our technique extracts discrete transition patterns from symbolic aggregate approXimation representations, and compiles transition frequencies into a bag of patterns constructed for each group. These transition frequencies are normalized and aligned in icon form to intuitively display the underlying patterns. We demonstrate the transition icon technique for two time-series datasets-postoperative pain scores, and hip-worn accelerometer activity counts. We believe transition icons can be an important tool for researchers approaching time-series data, as they give rich and intuitive information about collective time-series behaviors.

Applications of physical methods in high-frequency futures markets

NASA Astrophysics Data System (ADS)

Bartolozzi, M.; Mellen, C.; Chan, F.; Oliver, D.; Di Matteo, T.; Aste, T.

2007-12-01

In the present work we demonstrate the application of different physical methods to high-frequency or tick-bytick financial time series data. In particular, we calculate the Hurst exponent and inverse statistics for the price time series taken from a range of futures indices. Additionally, we show that in a limit order book the relaxation times of an imbalanced book state with more demand or supply can be described by stretched exponential laws analogous to those seen in many physical systems.

Defense Applications of Signal Processing

DTIC Science & Technology

1999-08-27

class of multiscale autoregressive moving average (MARMA) processes. These are generalisations of ARMA models in time series analysis , and they contain...including the two theoretical sinusoidal components. Analysis of the amplitude and frequency time series provided some novel insight into the real...communication channels, underwater acoustic signals, radar systems , economic time series and biomedical signals [7]. The alpha stable (aS) distribution has

Probe-Independent EEG Assessment of Mental Workload in Pilots

DTIC Science & Technology

2015-05-18

Teager Energy Operator - Frequency Modulated Component - z- score 10.94 17.46 10 Hurst Exponent - Discrete Second Order Derivative 7.02 17.06 D. Best...Teager Energy Operator– Frequency Modulated Component – Z-score 45. Line Length – Time Series 46. Line Length – Time Series – Z-score 47. Hurst Exponent ...Discrete Second Order Derivative 48. Hurst Exponent – Wavelet Based Adaptation 49. Hurst Exponent – Rescaled Range 50. Hurst Exponent – Discrete

Dimension reduction of frequency-based direct Granger causality measures on short time series.

PubMed

Siggiridou, Elsa; Kimiskidis, Vasilios K; Kugiumtzis, Dimitris

2017-09-01

The mainstream in the estimation of effective brain connectivity relies on Granger causality measures in the frequency domain. If the measure is meant to capture direct causal effects accounting for the presence of other observed variables, as in multi-channel electroencephalograms (EEG), typically the fit of a vector autoregressive (VAR) model on the multivariate time series is required. For short time series of many variables, the estimation of VAR may not be stable requiring dimension reduction resulting in restricted or sparse VAR models. The restricted VAR obtained by the modified backward-in-time selection method (mBTS) is adapted to the generalized partial directed coherence (GPDC), termed restricted GPDC (RGPDC). Dimension reduction on other frequency based measures, such the direct directed transfer function (dDTF), is straightforward. First, a simulation study using linear stochastic multivariate systems is conducted and RGPDC is favorably compared to GPDC on short time series in terms of sensitivity and specificity. Then the two measures are tested for their ability to detect changes in brain connectivity during an epileptiform discharge (ED) from multi-channel scalp EEG. It is shown that RGPDC identifies better than GPDC the connectivity structure of the simulated systems, as well as changes in the brain connectivity, and is less dependent on the free parameter of VAR order. The proposed dimension reduction in frequency measures based on VAR constitutes an appropriate strategy to estimate reliably brain networks within short-time windows. Copyright © 2017 Elsevier B.V. All rights reserved.

New Results in Magnitude and Sign Correlations in Heartbeat Fluctuations for Healthy Persons and Congestive Heart Failure (CHF) Patients

NASA Astrophysics Data System (ADS)

Diosdado, A. Muñoz; Cruz, H. Reyes; Hernández, D. Bueno; Coyt, G. Gálvez; González, J. Arellanes

2008-08-01

Heartbeat fluctuations exhibit temporal structure with fractal and nonlinear features that reflect changes in the neuroautonomic control. In this work we have used the detrended fluctuation analysis (DFA) to analyze heartbeat (RR) intervals of 54 healthy subjects and 40 patients with congestive heart failure during 24 hours; we separate time series for sleep and wake phases. We observe long-range correlations in time series of healthy persons and CHF patients. However, the correlations for CHF patients are weaker than the correlations for healthy persons; this fact has been reported by Ashkenazy et al. [1] but with a smaller group of subjects. In time series of CHF patients there is a crossover, it means that the correlations for high and low frequencies are different, but in time series of healthy persons there are not crossovers even if they are sleeping. These crossovers are more pronounced for CHF patients in the sleep phase. We decompose the heartbeat interval time series into magnitude and sign series, we know that these kinds of signals can exhibit different time organization for the magnitude and sign and the magnitude series relates to nonlinear properties of the original time series, while the sign series relates to the linear properties. Magnitude series are long-range correlated, while the sign series are anticorrelated. Newly, the correlations for healthy persons are different that the correlations for CHF patients both for magnitude and sign time series. In the paper of Ashkenazy et al. they proposed the empirical relation: αsign≈1/2(αoriginal+αmagnitude) for the short-range regime (high frequencies), however, we have found a different relation that in our calculations is valid for short and long-range regime: αsign≈1/4(αoriginal+αmagnitude).

Accuracy of time-domain and frequency-domain methods used to characterize catchment transit time distributions

NASA Astrophysics Data System (ADS)

Godsey, S. E.; Kirchner, J. W.

2008-12-01

The mean residence time - the average time that it takes rainfall to reach the stream - is a basic parameter used to characterize catchment processes. Heterogeneities in these processes lead to a distribution of travel times around the mean residence time. By examining this travel time distribution, we can better predict catchment response to contamination events. A catchment system with shorter residence times or narrower distributions will respond quickly to contamination events, whereas systems with longer residence times or longer-tailed distributions will respond more slowly to those same contamination events. The travel time distribution of a catchment is typically inferred from time series of passive tracers (e.g., water isotopes or chloride) in precipitation and streamflow. Variations in the tracer concentration in streamflow are usually damped compared to those in precipitation, because precipitation inputs from different storms (with different tracer signatures) are mixed within the catchment. Mathematically, this mixing process is represented by the convolution of the travel time distribution and the precipitation tracer inputs to generate the stream tracer outputs. Because convolution in the time domain is equivalent to multiplication in the frequency domain, it is relatively straightforward to estimate the parameters of the travel time distribution in either domain. In the time domain, the parameters describing the travel time distribution are typically estimated by maximizing the goodness of fit between the modeled and measured tracer outputs. In the frequency domain, the travel time distribution parameters can be estimated by fitting a power-law curve to the ratio of precipitation spectral power to stream spectral power. Differences between the methods of parameter estimation in the time and frequency domain mean that these two methods may respond differently to variations in data quality, record length and sampling frequency. Here we evaluate how well these two methods of travel time parameter estimation respond to different sources of uncertainty and compare the methods to one another. We do this by generating synthetic tracer input time series of different lengths, and convolve these with specified travel-time distributions to generate synthetic output time series. We then sample both the input and output time series at various sampling intervals and corrupt the time series with realistic error structures. Using these 'corrupted' time series, we infer the apparent travel time distribution, and compare it to the known distribution that was used to generate the synthetic data in the first place. This analysis allows us to quantify how different record lengths, sampling intervals, and error structures in the tracer measurements affect the apparent mean residence time and the apparent shape of the travel time distribution.

On the maximum-entropy/autoregressive modeling of time series

NASA Technical Reports Server (NTRS)

Chao, B. F.

1984-01-01

The autoregressive (AR) model of a random process is interpreted in the light of the Prony's relation which relates a complex conjugate pair of poles of the AR process in the z-plane (or the z domain) on the one hand, to the complex frequency of one complex harmonic function in the time domain on the other. Thus the AR model of a time series is one that models the time series as a linear combination of complex harmonic functions, which include pure sinusoids and real exponentials as special cases. An AR model is completely determined by its z-domain pole configuration. The maximum-entropy/autogressive (ME/AR) spectrum, defined on the unit circle of the z-plane (or the frequency domain), is nothing but a convenient, but ambiguous visual representation. It is asserted that the position and shape of a spectral peak is determined by the corresponding complex frequency, and the height of the spectral peak contains little information about the complex amplitude of the complex harmonic functions.

Quantifying evolutionary dynamics from variant-frequency time series

NASA Astrophysics Data System (ADS)

Khatri, Bhavin S.

2016-09-01

From Kimura’s neutral theory of protein evolution to Hubbell’s neutral theory of biodiversity, quantifying the relative importance of neutrality versus selection has long been a basic question in evolutionary biology and ecology. With deep sequencing technologies, this question is taking on a new form: given a time-series of the frequency of different variants in a population, what is the likelihood that the observation has arisen due to selection or neutrality? To tackle the 2-variant case, we exploit Fisher’s angular transformation, which despite being discovered by Ronald Fisher a century ago, has remained an intellectual curiosity. We show together with a heuristic approach it provides a simple solution for the transition probability density at short times, including drift, selection and mutation. Our results show under that under strong selection and sufficiently frequent sampling these evolutionary parameters can be accurately determined from simulation data and so they provide a theoretical basis for techniques to detect selection from variant or polymorphism frequency time-series.

Quantifying evolutionary dynamics from variant-frequency time series.

PubMed

Khatri, Bhavin S

2016-09-12

From Kimura's neutral theory of protein evolution to Hubbell's neutral theory of biodiversity, quantifying the relative importance of neutrality versus selection has long been a basic question in evolutionary biology and ecology. With deep sequencing technologies, this question is taking on a new form: given a time-series of the frequency of different variants in a population, what is the likelihood that the observation has arisen due to selection or neutrality? To tackle the 2-variant case, we exploit Fisher's angular transformation, which despite being discovered by Ronald Fisher a century ago, has remained an intellectual curiosity. We show together with a heuristic approach it provides a simple solution for the transition probability density at short times, including drift, selection and mutation. Our results show under that under strong selection and sufficiently frequent sampling these evolutionary parameters can be accurately determined from simulation data and so they provide a theoretical basis for techniques to detect selection from variant or polymorphism frequency time-series.

Frequency domain system identification of helicopter rotor dynamics incorporating models with time periodic coefficients

NASA Astrophysics Data System (ADS)

Hwang, Sunghwan

1997-08-01

One of the most prominent features of helicopter rotor dynamics in forward flight is the periodic coefficients in the equations of motion introduced by the rotor rotation. The frequency response characteristics of such a linear time periodic system exhibits sideband behavior, which is not the case for linear time invariant systems. Therefore, a frequency domain identification methodology for linear systems with time periodic coefficients was developed, because the linear time invariant theory cannot account for sideband behavior. The modulated complex Fourier series was introduced to eliminate the smearing effect of Fourier series expansions of exponentially modulated periodic signals. A system identification theory was then developed using modulated complex Fourier series expansion. Correlation and spectral density functions were derived using the modulated complex Fourier series expansion for linear time periodic systems. Expressions of the identified harmonic transfer function were then formulated using the spectral density functions both with and without additive noise processes at input and/or output. A procedure was developed to identify parameters of a model to match the frequency response characteristics between measured and estimated harmonic transfer functions by minimizing an objective function defined in terms of the trace of the squared frequency response error matrix. Feasibility was demonstrated by the identification of the harmonic transfer function and parameters for helicopter rigid blade flapping dynamics in forward flight. This technique is envisioned to satisfy the needs of system identification in the rotating frame, especially in the context of individual blade control. The technique was applied to the coupled flap-lag-inflow dynamics of a rigid blade excited by an active pitch link. The linear time periodic technique results were compared with the linear time invariant technique results. Also, the effect of noise processes and initial parameter guess on the identification procedure were investigated. To study the effect of elastic modes, a rigid blade with a trailing edge flap excited by a smart actuator was selected and system parameters were successfully identified, but with some expense of computational storage and time. Conclusively, the linear time periodic technique substantially improved the identified parameter accuracy compared to the linear time invariant technique. Also, the linear time periodic technique was robust to noises and initial guess of parameters. However, an elastic mode of higher frequency relative to the system pumping frequency tends to increase the computer storage requirement and computing time.

SPA- STATISTICAL PACKAGE FOR TIME AND FREQUENCY DOMAIN ANALYSIS

NASA Technical Reports Server (NTRS)

Brownlow, J. D.

1994-01-01

The need for statistical analysis often arises when data is in the form of a time series. This type of data is usually a collection of numerical observations made at specified time intervals. Two kinds of analysis may be performed on the data. First, the time series may be treated as a set of independent observations using a time domain analysis to derive the usual statistical properties including the mean, variance, and distribution form. Secondly, the order and time intervals of the observations may be used in a frequency domain analysis to examine the time series for periodicities. In almost all practical applications, the collected data is actually a mixture of the desired signal and a noise signal which is collected over a finite time period with a finite precision. Therefore, any statistical calculations and analyses are actually estimates. The Spectrum Analysis (SPA) program was developed to perform a wide range of statistical estimation functions. SPA can provide the data analyst with a rigorous tool for performing time and frequency domain studies. In a time domain statistical analysis the SPA program will compute the mean variance, standard deviation, mean square, and root mean square. It also lists the data maximum, data minimum, and the number of observations included in the sample. In addition, a histogram of the time domain data is generated, a normal curve is fit to the histogram, and a goodness-of-fit test is performed. These time domain calculations may be performed on both raw and filtered data. For a frequency domain statistical analysis the SPA program computes the power spectrum, cross spectrum, coherence, phase angle, amplitude ratio, and transfer function. The estimates of the frequency domain parameters may be smoothed with the use of Hann-Tukey, Hamming, Barlett, or moving average windows. Various digital filters are available to isolate data frequency components. Frequency components with periods longer than the data collection interval are removed by least-squares detrending. As many as ten channels of data may be analyzed at one time. Both tabular and plotted output may be generated by the SPA program. This program is written in FORTRAN IV and has been implemented on a CDC 6000 series computer with a central memory requirement of approximately 142K (octal) of 60 bit words. This core requirement can be reduced by segmentation of the program. The SPA program was developed in 1978.

Biogeochemical control of marine productivity in the Mediterranean Sea during the last 50 years

PubMed Central

Macias, Diego; Garcia-Gorriz, Elisa; Piroddi, Chiara; Stips, Adolf

2014-01-01

The temporal dynamics of biogeochemical variables derived from a coupled 3-D model of the Mediterranean Sea are evaluated for the last 50 years (1960–2010) against independent data on fisheries catch per unit effort (CPUE) for the same time period. Concordant patterns are found in the time series of all of the biological variables (from the model and from fisheries statistics), with low values at the beginning of the series, a later increase, with maximum levels reached at the end of the 1990s, and a posterior stabilization. Spectral analysis of the annual biological time series reveals coincident low-frequency signals in all of them. The first, more energetic signal peaks around the year 2000, while the second, less energetic signal peaks near 1982. Almost identical low-frequency signals are found in the nutrient loads of the rivers and in the integrated nutrient levels in the surface marine ecosystem. Nitrate concentration shows a maximum level in 1998, with a later stabilization to present-day values, coincident with the first low-frequency signal found in the biological series. Phosphate shows maximum concentrations around 1982 and a posterior sharp decline, in concordance with the second low-frequency signal observed in the biological series. That result seems to indicate that the control of marine productivity (plankton to fish) in the Mediterranean is principally mediated through bottom-up processes that could be traced back to the characteristics of riverine discharges. The high sensitivity of CPUE time series to environmental conditions might be another indicator of the overexploitation of this marine ecosystem. Key Points Biogeochemical evolution of the Mediterranean over the past 50 years River nutrient loads drive primary and secondary productions Strong link between low trophic levels and fisheries PMID:26180286

Simulation of Ground Winds Time Series for the NASA Crew Launch Vehicle (CLV)

NASA Technical Reports Server (NTRS)

Adelfang, Stanley I.

2008-01-01

Simulation of wind time series based on power spectrum density (PSD) and spectral coherence models for ground wind turbulence is described. The wind models, originally developed for the Shuttle program, are based on wind measurements at the NASA 150-m meteorological tower at Cape Canaveral, FL. The current application is for the design and/or protection of the CLV from wind effects during on-pad exposure during periods from as long as days prior to launch, to seconds or minutes just prior to launch and seconds after launch. The evaluation of vehicle response to wind will influence the design and operation of constraint systems for support of the on-pad vehicle. Longitudinal and lateral wind component time series are simulated at critical vehicle locations. The PSD model for wind turbulence is a function of mean wind speed, elevation and temporal frequency. Integration of the PSD equation over a selected frequency range yields the variance of the time series to be simulated. The square root of the PSD defines a low-pass filter that is applied to adjust the components of the Fast Fourier Transform (FFT) of Gaussian white noise. The first simulated time series near the top of the launch vehicle is the inverse transform of the adjusted FFT. Simulation of the wind component time series at the nearest adjacent location (and all other succeeding next nearest locations) is based on a model for the coherence between winds at two locations as a function of frequency and separation distance, where the adjacent locations are separated vertically and/or horizontally. The coherence function is used to calculate a coherence weighted FFT of the wind at the next nearest location, given the FFT of the simulated time series at the previous location and the essentially incoherent FFT of the wind at the selected location derived a priori from the PSD model. The simulated time series at each adjacent location is the inverse Fourier transform of the coherence weighted FFT. For a selected design case, the equations, the process and the simulated time series at multiple vehicle stations are presented.

Unveiling signatures of interdecadal climate changes by Hilbert analysis

NASA Astrophysics Data System (ADS)

Zappalà, Dario; Barreiro, Marcelo; Masoller, Cristina

2017-04-01

A recent study demonstrated that, in a class of networks of oscillators, the optimal network reconstruction from dynamics is obtained when the similarity analysis is performed not on the original dynamical time series, but on transformed series obtained by Hilbert transform. [1] That motivated us to use Hilbert transform to study another kind of (in a broad sense) "oscillating" series, such as the series of temperature. Actually, we found that Hilbert analysis of SAT (Surface Air Temperature) time series uncovers meaningful information about climate and is therefore a promising tool for the study of other climatological variables. [2] In this work we analysed a large dataset of SAT series, performing Hilbert transform and further analysis with the goal of finding signs of climate change during the analysed period. We used the publicly available ERA-Interim dataset, containing reanalysis data. [3] In particular, we worked on daily SAT time series, from year 1979 to 2015, in 16380 points arranged over a regular grid on the Earth surface. From each SAT time series we calculate the anomaly series and also, by using the Hilbert transform, we calculate the instantaneous amplitude and instantaneous frequency series. Our first approach is to calculate the relative variation: the difference between the average value on the last 10 years and the average value on the first 10 years, divided by the average value over all the analysed period. We did this calculations on our transformed series: frequency and amplitude, both with average values and standard deviation values. Furthermore, to have a comparison with an already known analysis methods, we did these same calculations on the anomaly series. We plotted these results as maps, where the colour of each site indicates the value of its relative variation. Finally, to gain insight in the interpretation of our results over real SAT data, we generated synthetic sinusoidal series with various levels of additive noise. By applying Hilbert analysis to the synthetic data, we uncovered a clear trend between mean amplitude and mean frequency: as the noise level grows, the amplitude increases while the frequency decreases. Research funded in part by AGAUR (Generalitat de Catalunya), EU LINC project (Grant No. 289447) and Spanish MINECO (FIS2015-66503-C3-2-P).

Orbital forced frequencies in the 975000 year pollen record from Tenagi Philippon (Greece)

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

Mommersteeg, H.J.P.M.; Young, R.; Wijmstra, T.A.

Frequency analysis was applied to different time series obtained from the 975 ka pollen record of Tenagi Philippon (Macedonia, Greece). These time series are characteristic of different vegetation types related to specific climatic conditions. Time control of the 196 m deep core was based on 11 finite {sup 14}C dates in the upper 17 m, magnetostratigraphy and correlation with the marine oxygen isotope stratigraphy. Maximum entropy spectrum analyses and thomson multi-taper spectrum analysis were applied using the complete time series. Periods of 95-99, 40-45. 24.0-25.5 and 19-21 ka which can be related to orbital forcing, as well as periods ofmore » about 68, 30 ka and of about 15.5, 13.5, 12 and 10.5 ka were detected. The detected periods of about 68, 30 ka and 16, 14, 12, 10.5 ka are likely to be harmonics and combination tones of the periods related to orbital forcing. The period of around 30 ka is possibly a secondary peak of obliquity. To study the stability of the detected periods through time, analysis with a moving window was employed. Signals in the eccentricity band were detected clearly during the last 650 ka. In the precession band, detected periods of about 24 ka show an increase in amplitude during the last 650 ka. The evolution of orbital frequencies during the last 1.0 Ma is in general agreement with the results of other marine and continental time series. Time series related to different climatic settings showed a different response to orbital forcing. Time series of vegetational elements sensitive to changes in net precipitation were forced in the precession and obliquity bands. Changes in precession caused changes in the monsoon system, which indirectly had a strong influence on the climatic history of Greece. Time series of vegetational elements which are more indicative of changes in annual temperature are forced in the eccentricity band. 54 refs., 12 figs., 3 tabs.« less

Wavelet-based group and phase velocity measurements: Method

NASA Astrophysics Data System (ADS)

Yang, H. Y.; Wang, W. W.; Hung, S. H.

2016-12-01

Measurements of group and phase velocities of surface waves are often carried out by applying a series of narrow bandpass or stationary Gaussian filters localized at specific frequencies to wave packets and estimating the corresponding arrival times at the peak envelopes and phases of the Fourier spectra. However, it's known that seismic waves are inherently nonstationary and not well represented by a sum of sinusoids. Alternatively, a continuous wavelet transform (CWT) which decomposes a time series into a family of wavelets, translated and scaled copies of a generally fast oscillating and decaying function known as the mother wavelet, is capable of retaining localization in both the time and frequency domain and well-suited for the time-frequency analysis of nonstationary signals. Here we develop a wavelet-based method to measure frequency-dependent group and phase velocities, an essential dataset used in crust and mantle tomography. For a given time series, we employ the complex morlet wavelet to obtain the scalogram of amplitude modulus |Wg| and phase φ on the time-frequency plane. The instantaneous frequency (IF) is then calculated by taking the derivative of phase with respect to time, i.e., (1/2π)dφ(f, t)/dt. Time windows comprising strong energy arrivals to be measured can be identified by those IFs close to the frequencies with the maximum modulus and varying smoothly and monotonically with time. The respective IFs in each selected time window are further interpolated to yield a smooth branch of ridge points or representative IFs at which the arrival time, tridge(f), and phase, φridge(f), after unwrapping and correcting cycle skipping based on a priori knowledge of the possible velocity range, are determined for group and phase velocity estimation. We will demonstrate our measurement method using both ambient noise cross correlation functions and multi-mode surface waves from earthquakes. The obtained dispersion curves will be compared with those by a conventional narrow bandpass method.

Finite-element time-domain modeling of electromagnetic data in general dispersive medium using adaptive Padé series

NASA Astrophysics Data System (ADS)

Cai, Hongzhu; Hu, Xiangyun; Xiong, Bin; Zhdanov, Michael S.

2017-12-01

The induced polarization (IP) method has been widely used in geophysical exploration to identify the chargeable targets such as mineral deposits. The inversion of the IP data requires modeling the IP response of 3D dispersive conductive structures. We have developed an edge-based finite-element time-domain (FETD) modeling method to simulate the electromagnetic (EM) fields in 3D dispersive medium. We solve the vector Helmholtz equation for total electric field using the edge-based finite-element method with an unstructured tetrahedral mesh. We adopt the backward propagation Euler method, which is unconditionally stable, with semi-adaptive time stepping for the time domain discretization. We use the direct solver based on a sparse LU decomposition to solve the system of equations. We consider the Cole-Cole model in order to take into account the frequency-dependent conductivity dispersion. The Cole-Cole conductivity model in frequency domain is expanded using a truncated Padé series with adaptive selection of the center frequency of the series for early and late time. This approach can significantly increase the accuracy of FETD modeling.

The matrix exponential in transient structural analysis

NASA Technical Reports Server (NTRS)

Minnetyan, Levon

1987-01-01

The primary usefulness of the presented theory is in the ability to represent the effects of high frequency linear response with accuracy, without requiring very small time steps in the analysis of dynamic response. The matrix exponential contains a series approximation to the dynamic model. However, unlike the usual analysis procedure which truncates the high frequency response, the approximation in the exponential matrix solution is in the time domain. By truncating the series solution to the matrix exponential short, the solution is made inaccurate after a certain time. Yet, up to that time the solution is extremely accurate, including all high frequency effects. By taking finite time increments, the exponential matrix solution can compute the response very accurately. Use of the exponential matrix in structural dynamics is demonstrated by simulating the free vibration response of multi degree of freedom models of cantilever beams.

Intrinsic vs. spurious long-range memory in high-frequency records of environmental radioactivity - Critical re-assessment and application to indoor 222Rn concentrations from Coimbra, Portugal

NASA Astrophysics Data System (ADS)

Donner, Reik V.; Potirakis, Stelios M.; Barbosa, Susana M.; Matos, Jose A. O.

2015-04-01

The presence or absence of long-range correlations in environmental radioactivity fluctuations has recently attracted considerable interest. Among a multiplicity of practically relevant applications, identifying and disentangling the environmental factors controlling the variable concentrations of the radioactive noble gas Radon is important for estimating its effect on human health and the efficiency of possible measures for reducing the corresponding exposition. In this work, we present a critical re-assessment of a multiplicity of complementary methods that have been previously applied for evaluating the presence of long-range correlations and fractal scaling in environmental Radon variations with a particular focus on the specific properties of the underlying time series. As an illustrative case study, we subsequently re-analyze two high-frequency records of indoor Radon concentrations from Coimbra, Portugal, each of which spans several months of continuous measurements at a high temporal resolution of five minutes. Our results reveal that at the study site, Radon concentrations exhibit complex multi-scale dynamics with qualitatively different properties at different time-scales: (i) essentially white noise in the high-frequency part (up to time-scales of about one hour), (ii) spurious indications of a non-stationary, apparently long-range correlated process (at time scales between hours and one day) arising from marked periodic components probably related to tidal frequencies, and (iii) low-frequency variability indicating a true long-range dependent process, which might be dominated by a response to meteorological drivers. In the presence of such multi-scale variability, common estimators of long-range memory in time series are necessarily prone to fail if applied to the raw data without previous separation of time-scales with qualitatively different dynamics. We emphasize that similar properties can be found in other types of geophysical time series (for example, tide gauge records), calling for a careful application of time series analysis tools when studying such data.

Methods for Counting High-Frequency Repeat Victimizations in the National Crime Victimization Survey. Technical Series Report. NCJ 237308

ERIC Educational Resources Information Center

Lauritsen, Janet L.; Owens, Jennifer Gatewood; Planty, Michael; Rand, Michael R.; Truman, Jennifer L.

2012-01-01

Examines the nature and extent of series victimization in the National Crime Victimization Survey (NCVS). This technical report assesses the general patterns of victims' responses to being asked, "How many times did this type of incident occur?" and provides data on how reports of high-frequency repeat victimizations have changed over…

Modelling of Vortex-Induced Loading on a Single-Blade Installation Setup

NASA Astrophysics Data System (ADS)

Skrzypiński, Witold; Gaunaa, Mac; Heinz, Joachim

2016-09-01

Vortex-induced integral loading fluctuations on a single suspended blade at various inflow angles were modeled in the presents work by means of stochastic modelling methods. The reference time series were obtained by 3D DES CFD computations carried out on the DTU 10MW reference wind turbine blade. In the reference time series, the flapwise force component, Fx, showed both higher absolute values and variation than the chordwise force component, Fz, for every inflow angle considered. For this reason, the present paper focused on modelling of the Fx and not the Fz whereas Fz would be modelled using exactly the same procedure. The reference time series were significantly different, depending on the inflow angle. This made the modelling of all the time series with a single and relatively simple engineering model challenging. In order to find model parameters, optimizations were carried out, based on the root-mean-square error between the Single-Sided Amplitude Spectra of the reference and modelled time series. In order to model well defined frequency peaks present at certain inflow angles, optimized sine functions were superposed on the stochastically modelled time series. The results showed that the modelling accuracy varied depending on the inflow angle. None the less, the modelled and reference time series showed a satisfactory general agreement in terms of their visual and frequency characteristics. This indicated that the proposed method is suitable to model loading fluctuations on suspended blades.

Long-term, high-frequency water quality monitoring in an agricultural catchment: insights from spectral analysis

NASA Astrophysics Data System (ADS)

Aubert, Alice; Kirchner, James; Faucheux, Mikael; Merot, Philippe; Gascuel-Odoux, Chantal

2013-04-01

The choice of sampling frequency is a key issue in the design and operation of environmental observatories. The choice of sampling frequency creates a spectral window (or temporal filter) that highlights some timescales and processes, and de-emphasizes others (1). New online measurement technologies can monitor surface water quality almost continuously, allowing the creation of very rich time series. The question of how best to analyze such detailed temporal datasets is an important issue in environmental monitoring. In the present work, we studied water quality data from the AgrHys long-term hydrological observatory (located at Kervidy-Naizin, Western France) sampled at daily and 20-minute time scales. Manual sampling has provided 12 years of daily measurements of nitrate, dissolved organic carbon (DOC), chloride and sulfate (2), and 3 years of daily measurements of about 30 other solutes. In addition, a UV-spectrometry probe (Spectrolyser) provides one year of 20-minute measurements for nitrate and DOC. Spectral analysis of the daily water quality time series reveals that our intensively farmed catchment exhibits universal 1/f scaling (power spectrum slope of -1) for a large number of solutes, confirming and extending the earlier discovery of universal 1/f scaling in the relatively pristine Plynlimon catchment (3). 1/f time series confound conventional methods for assessing the statistical significance of trends. Indeed, conventional methods assume that there is a clear separation of scales between the signal (the trend line) and the noise (the scatter around the line). This is not true for 1/f noise, since it overestimates the occurrence of significant trends. Our results raise the possibility that 1/f scaling is widespread in water quality time series, thus posing fundamental challenges to water quality trend analysis. Power spectra of the 20-minute nitrate and DOC time series show 1/f scaling at frequencies below 1/day, consistent with the longer-term daily measurements. At higher frequencies, however, the spectra steepen to a slope of -2, indicating that at sub-daily time scales the concentration time series become relatively smooth. However, at time scales shorter than 2-3 hours, the spectra flatten to a slope near zero (white noise), reflecting analytical noise in the measurement probe. This result demonstrates that measuring water quality dynamics at high frequencies also requires high measurement precision, because as measurements are taken closer and closer together in time, the real-world differences that must be measured between adjacent measurements become smaller and smaller. Our results highlight the importance of quantifying the spectral properties of analytical noise in environmental measurements, to identify frequency ranges where measurements could be dominated by analytical noise instead of real-world signals. 1. Kirchner, J.W., Feng, X., Neal, C., Robson, A.J., 2004. The fine structure of water-quality dynamics: the (high-frequency) wave of the future. Hydrological Processes, 18(7): 1353-1359 2. Aubert, A.H. et al., 2012. The chemical signature of a livestock farming catchment: synthesis from a high-frequency multi-element long term monitoring. HESSD, 9(8): 9715 - 9741 3. Kirchner, J.W. and Neal, C., 2013. Universal fractal scaling in water quality dynamics across the periodic table. Manuscript in review.

Discriminant Analysis of Time Series in the Presence of Within-Group Spectral Variability.

PubMed

Krafty, Robert T

2016-07-01

Many studies record replicated time series epochs from different groups with the goal of using frequency domain properties to discriminate between the groups. In many applications, there exists variation in cyclical patterns from time series in the same group. Although a number of frequency domain methods for the discriminant analysis of time series have been explored, there is a dearth of models and methods that account for within-group spectral variability. This article proposes a model for groups of time series in which transfer functions are modeled as stochastic variables that can account for both between-group and within-group differences in spectra that are identified from individual replicates. An ensuing discriminant analysis of stochastic cepstra under this model is developed to obtain parsimonious measures of relative power that optimally separate groups in the presence of within-group spectral variability. The approach possess favorable properties in classifying new observations and can be consistently estimated through a simple discriminant analysis of a finite number of estimated cepstral coefficients. Benefits in accounting for within-group spectral variability are empirically illustrated in a simulation study and through an analysis of gait variability.

Spectral analysis of hydrological time series of a river basin in southern Spain

NASA Astrophysics Data System (ADS)

Luque-Espinar, Juan Antonio; Pulido-Velazquez, David; Pardo-Igúzquiza, Eulogio; Fernández-Chacón, Francisca; Jiménez-Sánchez, Jorge; Chica-Olmo, Mario

2016-04-01

Spectral analysis has been applied with the aim to determine the presence and statistical significance of climate cycles in data series from different rainfall, piezometric and gauging stations located in upper Genil River Basin. This river starts in Sierra Nevada Range at 3,480 m a.s.l. and is one of the most important rivers of this region. The study area has more than 2.500 km2, with large topographic differences. For this previous study, we have used more than 30 rain data series, 4 piezometric data series and 3 data series from gauging stations. Considering a monthly temporal unit, the studied period range from 1951 to 2015 but most of the data series have some lacks. Spectral analysis is a methodology widely used to discover cyclic components in time series. The time series is assumed to be a linear combination of sinusoidal functions of known periods but of unknown amplitude and phase. The amplitude is related with the variance of the time series, explained by the oscillation at each frequency (Blackman and Tukey, 1958, Bras and Rodríguez-Iturbe, 1985, Chatfield, 1991, Jenkins and Watts, 1968, among others). The signal component represents the structured part of the time series, made up of a small number of embedded periodicities. Then, we take into account the known result for the one-sided confidence band of the power spectrum estimator. For this study, we established confidence levels of <90%, 90%, 95%, and 99%. Different climate signals have been identified: ENSO, QBO, NAO, Sun Spot cycles, as well as others related to sun activity, but the most powerful signals correspond to the annual cycle, followed by the 6 month and NAO cycles. Nevertheless, significant differences between rain data series and piezometric/flow data series have been pointed out. In piezometric data series and flow data series, ENSO and NAO signals could be stronger than others with high frequencies. The climatic peaks in lower frequencies in rain data are smaller and the confidence level too. On the other hand, the most important influence on groundwater resources and river flows are NAO, Sun Spot, ENSO and annual cycle. Acknowledgments: This research has been partially supported by the IMPADAPT project (CGL2013-48424-C2-1-R) with Spanish MINECO funds and Junta de Andalucía (Group RNM122).

The Fourier decomposition method for nonlinear and non-stationary time series analysis

PubMed Central

Joshi, Shiv Dutt; Patney, Rakesh Kumar; Saha, Kaushik

2017-01-01

for many decades, there has been a general perception in the literature that Fourier methods are not suitable for the analysis of nonlinear and non-stationary data. In this paper, we propose a novel and adaptive Fourier decomposition method (FDM), based on the Fourier theory, and demonstrate its efficacy for the analysis of nonlinear and non-stationary time series. The proposed FDM decomposes any data into a small number of ‘Fourier intrinsic band functions’ (FIBFs). The FDM presents a generalized Fourier expansion with variable amplitudes and variable frequencies of a time series by the Fourier method itself. We propose an idea of zero-phase filter bank-based multivariate FDM (MFDM), for the analysis of multivariate nonlinear and non-stationary time series, using the FDM. We also present an algorithm to obtain cut-off frequencies for MFDM. The proposed MFDM generates a finite number of band-limited multivariate FIBFs (MFIBFs). The MFDM preserves some intrinsic physical properties of the multivariate data, such as scale alignment, trend and instantaneous frequency. The proposed methods provide a time–frequency–energy (TFE) distribution that reveals the intrinsic structure of a data. Numerical computations and simulations have been carried out and comparison is made with the empirical mode decomposition algorithms. PMID:28413352

Effect of noise and filtering on largest Lyapunov exponent of time series associated with human walking.

PubMed

Mehdizadeh, Sina; Sanjari, Mohammad Ali

2017-11-07

This study aimed to determine the effect of added noise, filtering and time series length on the largest Lyapunov exponent (LyE) value calculated for time series obtained from a passive dynamic walker. The simplest passive dynamic walker model comprising of two massless legs connected by a frictionless hinge joint at the hip was adopted to generate walking time series. The generated time series was used to construct a state space with the embedding dimension of 3 and time delay of 100 samples. The LyE was calculated as the exponential rate of divergence of neighboring trajectories of the state space using Rosenstein's algorithm. To determine the effect of noise on LyE values, seven levels of Gaussian white noise (SNR=55-25dB with 5dB steps) were added to the time series. In addition, the filtering was performed using a range of cutoff frequencies from 3Hz to 19Hz with 2Hz steps. The LyE was calculated for both noise-free and noisy time series with different lengths of 6, 50, 100 and 150 strides. Results demonstrated a high percent error in the presence of noise for LyE. Therefore, these observations suggest that Rosenstein's algorithm might not perform well in the presence of added experimental noise. Furthermore, findings indicated that at least 50 walking strides are required to calculate LyE to account for the effect of noise. Finally, observations support that a conservative filtering of the time series with a high cutoff frequency might be more appropriate prior to calculating LyE. Copyright © 2017 Elsevier Ltd. All rights reserved.

Characterizing time series: when Granger causality triggers complex networks

NASA Astrophysics Data System (ADS)

Ge, Tian; Cui, Yindong; Lin, Wei; Kurths, Jürgen; Liu, Chong

2012-08-01

In this paper, we propose a new approach to characterize time series with noise perturbations in both the time and frequency domains by combining Granger causality and complex networks. We construct directed and weighted complex networks from time series and use representative network measures to describe their physical and topological properties. Through analyzing the typical dynamical behaviors of some physical models and the MIT-BIHMassachusetts Institute of Technology-Beth Israel Hospital. human electrocardiogram data sets, we show that the proposed approach is able to capture and characterize various dynamics and has much potential for analyzing real-world time series of rather short length.

Bispectral Inversion: The Construction of a Time Series from Its Bispectrum

DTIC Science & Technology

1988-04-13

take the inverse transform . Since the goal is to compute a time series given its bispectrum, it would also be nice to stay entirely in the frequency...domain and be able to go directly from the bispectrum to the Fourier transform of the time series without the need to inverse transform continuous...the picture. The approximations arise from representing the bicovariance, which is the inverse transform of a continuous function, by the inverse disrte

Ocean time-series near Bermuda: Hydrostation S and the US JGOFS Bermuda Atlantic time-series study

NASA Technical Reports Server (NTRS)

Michaels, Anthony F.; Knap, Anthony H.

1992-01-01

Bermuda is the site of two ocean time-series programs. At Hydrostation S, the ongoing biweekly profiles of temperature, salinity and oxygen now span 37 years. This is one of the longest open-ocean time-series data sets and provides a view of decadal scale variability in ocean processes. In 1988, the U.S. JGOFS Bermuda Atlantic Time-series Study began a wide range of measurements at a frequency of 14-18 cruises each year to understand temporal variability in ocean biogeochemistry. On each cruise, the data range from chemical analyses of discrete water samples to data from electronic packages of hydrographic and optics sensors. In addition, a range of biological and geochemical rate measurements are conducted that integrate over time-periods of minutes to days. This sampling strategy yields a reasonable resolution of the major seasonal patterns and of decadal scale variability. The Sargasso Sea also has a variety of episodic production events on scales of days to weeks and these are only poorly resolved. In addition, there is a substantial amount of mesoscale variability in this region and some of the perceived temporal patterns are caused by the intersection of the biweekly sampling with the natural spatial variability. In the Bermuda time-series programs, we have added a series of additional cruises to begin to assess these other sources of variation and their impacts on the interpretation of the main time-series record. However, the adequate resolution of higher frequency temporal patterns will probably require the introduction of new sampling strategies and some emerging technologies such as biogeochemical moorings and autonomous underwater vehicles.

A spectral analysis of team dynamics and tactics in Brazilian football.

PubMed

Moura, Felipe Arruda; Martins, Luiz Eduardo Barreto; Anido, Ricardo O; Ruffino, Paulo Régis C; Barros, Ricardo M L; Cunha, Sergio Augusto

2013-01-01

The purposes of this study were to characterise the total space covered and the distances between players within teams over ten Brazilian First Division Championship matches. Filmed recordings, combined with a tracking system, were used to obtain the trajectories of the players (n = 277), before and after half-time. The team surface area (the area of the convex hull formed by the positions of the players) and spread (the Frobenius norm of the distance-between-player matrix) were calculated as functions of time. A Fast Fourier Transform (FFT) was applied to each time series. The median frequency was then calculated. The results of the surface area time series median frequencies for the first half (0.63 ± 0.10 cycles · min⁻¹) were significantly greater (P < 0.01) than the second-half values (0.47 ± 0.14 cycles · min⁻¹). Similarly, the spread variable median frequencies for the first half (0.60 ± 0.14 cycles · min⁻¹) were significantly greater (P < 0.01) than the second-half values (0.46 ± 0.16 cycles · min⁻¹). The median frequencies allowed the characterisation of the time series oscillations that represent the speed at which players distribute and then compact their team formation during a match. This analysis can provide insights that allow coaches to better control the team organisation on the pitch.

Radio Frequency Ultrasound Time Series Signal Analysis to Evaluate High-intensity Focused Ultrasound Lesion Formation Status in Tissue.

PubMed

Mobasheri, Saeedeh; Behnam, Hamid; Rangraz, Parisa; Tavakkoli, Jahan

2016-01-01

High-intensity focused ultrasound (HIFU) is a novel treatment modality used by scientists and clinicians in the recent decades. This modality has had a great and significant success as a noninvasive surgery technique applicable in tissue ablation therapy and cancer treatment. In this study, radio frequency (RF) ultrasound signals were acquired and registered in three stages of before, during, and after HIFU exposures. Different features of RF time series signals including the sum of amplitude spectrum in the four quarters of the frequency range, the slope, and intercept of the best-fit line to the entire power spectrum and the Shannon entropy were utilized to distinguish between the HIFU-induced thermal lesion and the normal tissue. We also examined the RF data, frame by frame to identify exposure effects on the formation and characteristics of a HIFU thermal lesion at different time steps throughout the treatment. The results obtained showed that the spectrum frequency quarters and the slope and intercept of the best fit line to the entire power spectrum both increased two times during the HIFU exposures. The Shannon entropy, however, decreased after the exposures. In conclusion, different characteristics of RF time series signal possess promising features that can be used to characterize ablated and nonablated tissues and to distinguish them from each other in a quasi-quantitative fashion.

A Methodology for the Parametric Reconstruction of Non-Steady and Noisy Meteorological Time Series

NASA Astrophysics Data System (ADS)

Rovira, F.; Palau, J. L.; Millán, M.

2009-09-01

Climatic and meteorological time series often show some persistence (in time) in the variability of certain features. One could regard annual, seasonal and diurnal time variability as trivial persistence in the variability of some meteorological magnitudes (as, e.g., global radiation, air temperature above surface, etc.). In these cases, the traditional Fourier transform into frequency space will show the principal harmonics as the components with the largest amplitude. Nevertheless, meteorological measurements often show other non-steady (in time) variability. Some fluctuations in measurements (at different time scales) are driven by processes that prevail on some days (or months) of the year but disappear on others. By decomposing a time series into time-frequency space through the continuous wavelet transformation, one is able to determine both the dominant modes of variability and how those modes vary in time. This study is based on a numerical methodology to analyse non-steady principal harmonics in noisy meteorological time series. This methodology combines both the continuous wavelet transform and the development of a parametric model that includes the time evolution of the principal and the most statistically significant harmonics of the original time series. The parameterisation scheme proposed in this study consists of reproducing the original time series by means of a statistically significant finite sum of sinusoidal signals (waves), each defined by using the three usual parameters: amplitude, frequency and phase. To ensure the statistical significance of the parametric reconstruction of the original signal, we propose a standard statistical t-student analysis of the confidence level of the amplitude in the parametric spectrum for the different wave components. Once we have assured the level of significance of the different waves composing the parametric model, we can obtain the statistically significant principal harmonics (in time) of the original time series by using the Fourier transform of the modelled signal. Acknowledgements The CEAM Foundation is supported by the Generalitat Valenciana and BANCAIXA (València, Spain). This study has been partially funded by the European Commission (FP VI, Integrated Project CIRCE - No. 036961) and by the Ministerio de Ciencia e Innovación, research projects "TRANSREG” (CGL2007-65359/CLI) and "GRACCIE” (CSD2007-00067, Program CONSOLIDER-INGENIO 2010).

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.

Daily water and sediment discharges from selected rivers of the eastern United States; a time-series modeling approach

USGS Publications Warehouse

Fitzgerald, Michael G.; Karlinger, Michael R.

1983-01-01

Time-series models were constructed for analysis of daily runoff and sediment discharge data from selected rivers of the Eastern United States. Logarithmic transformation and first-order differencing of the data sets were necessary to produce second-order, stationary time series and remove seasonal trends. Cyclic models accounted for less than 42 percent of the variance in the water series and 31 percent in the sediment series. Analysis of the apparent oscillations of given frequencies occurring in the data indicates that frequently occurring storms can account for as much as 50 percent of the variation in sediment discharge. Components of the frequency analysis indicate that a linear representation is reasonable for the water-sediment system. Models that incorporate lagged water discharge as input prove superior to univariate techniques in modeling and prediction of sediment discharges. The random component of the models includes errors in measurement and model hypothesis and indicates no serial correlation. An index of sediment production within or between drain-gage basins can be calculated from model parameters.

Analysis and stochastic modelling of Intensity-Duration-Frequency relationship from 88 years of 10 min rainfall data in North Spain

NASA Astrophysics Data System (ADS)

Delgado, Oihane; Campo-Bescós, Miguel A.; López, J. Javier

2017-04-01

Frequently, when we are trying to solve certain hydrological engineering problems, it is often necessary to know rain intensity values related to a specific probability or return period, T. Based on analyses of extreme rainfall events at different time scale aggregation, we can deduce the relationships among Intensity-Duration-Frequency (IDF), that are widely used in hydraulic infrastructure design. However, the lack of long time series of rainfall intensities for smaller time periods, minutes or hours, leads to use mathematical expressions to characterize and extend these curves. One way to deduce them is through the development of synthetic rainfall time series generated from stochastic models, which is evaluated in this work. From recorded accumulated rainfall time series every 10 min in the pluviograph of Igueldo (San Sebastian, Spain) for the time period between 1927-2005, their homogeneity has been checked and possible statistically significant increasing or decreasing trends have also been shown. Subsequently, two models have been calibrated: Bartlett-Lewis and Markov chains models, which are based on the successions of storms, composed for a series of rainfall events, separated by a short interval of time each. Finally, synthetic ten-minute rainfall time series are generated, which allow to estimate detailed IDF curves and compare them with the estimated IDF based on the recorded data.

Cardiovascular response to acute stress in freely moving rats: time-frequency analysis.

PubMed

Loncar-Turukalo, Tatjana; Bajic, Dragana; Japundzic-Zigon, Nina

2008-01-01

Spectral analysis of cardiovascular series is an important tool for assessing the features of the autonomic control of the cardiovascular system. In this experiment Wistar rats ecquiped with intraarterial catheter for blood pressure (BP) recording were exposed to stress induced by blowing air. The problem of non stationary data was overcomed applying the Smoothed Pseudo Wigner Villle (SPWV) time-frequency distribution. Spectral analysis was done before stress, during stress, immediately after stress and later in recovery. The spectral indices were calculated for both systolic blood pressure (SBP) and pulse interval (PI) series. The time evolution of spectral indices showed perturbed sympathovagal balance.

Analysis of crude oil markets with improved multiscale weighted permutation entropy

NASA Astrophysics Data System (ADS)

Niu, Hongli; Wang, Jun; Liu, Cheng

2018-03-01

Entropy measures are recently extensively used to study the complexity property in nonlinear systems. Weighted permutation entropy (WPE) can overcome the ignorance of the amplitude information of time series compared with PE and shows a distinctive ability to extract complexity information from data having abrupt changes in magnitude. Improved (or sometimes called composite) multi-scale (MS) method possesses the advantage of reducing errors and improving the accuracy when applied to evaluate multiscale entropy values of not enough long time series. In this paper, we combine the merits of WPE and improved MS to propose the improved multiscale weighted permutation entropy (IMWPE) method for complexity investigation of a time series. Then it is validated effective through artificial data: white noise and 1 / f noise, and real market data of Brent and Daqing crude oil. Meanwhile, the complexity properties of crude oil markets are explored respectively of return series, volatility series with multiple exponents and EEMD-produced intrinsic mode functions (IMFs) which represent different frequency components of return series. Moreover, the instantaneous amplitude and frequency of Brent and Daqing crude oil are analyzed by the Hilbert transform utilized to each IMF.

Multi-frequency complex network from time series for uncovering oil-water flow structure.

PubMed

Gao, Zhong-Ke; Yang, Yu-Xuan; Fang, Peng-Cheng; Jin, Ning-De; Xia, Cheng-Yi; Hu, Li-Dan

2015-02-04

Uncovering complex oil-water flow structure represents a challenge in diverse scientific disciplines. This challenge stimulates us to develop a new distributed conductance sensor for measuring local flow signals at different positions and then propose a novel approach based on multi-frequency complex network to uncover the flow structures from experimental multivariate measurements. In particular, based on the Fast Fourier transform, we demonstrate how to derive multi-frequency complex network from multivariate time series. We construct complex networks at different frequencies and then detect community structures. Our results indicate that the community structures faithfully represent the structural features of oil-water flow patterns. Furthermore, we investigate the network statistic at different frequencies for each derived network and find that the frequency clustering coefficient enables to uncover the evolution of flow patterns and yield deep insights into the formation of flow structures. Current results present a first step towards a network visualization of complex flow patterns from a community structure perspective.

A new approach for measuring power spectra and reconstructing time series in active galactic nuclei

NASA Astrophysics Data System (ADS)

Li, Yan-Rong; Wang, Jian-Min

2018-05-01

We provide a new approach to measure power spectra and reconstruct time series in active galactic nuclei (AGNs) based on the fact that the Fourier transform of AGN stochastic variations is a series of complex Gaussian random variables. The approach parametrizes a stochastic series in frequency domain and transforms it back to time domain to fit the observed data. The parameters and their uncertainties are derived in a Bayesian framework, which also allows us to compare the relative merits of different power spectral density models. The well-developed fast Fourier transform algorithm together with parallel computation enables an acceptable time complexity for the approach.

A Kalman Filter Clock Algorithm for Use in the Presence of Flicker Frequency Modulation Noise

DTIC Science & Technology

2004-09-01

40, S335-S341. [5] P. M. Harris, J. A. Davis, M. G. Cox, and S. L. Shemar, 2003, “ Least - squares analysis of time series data and its application to two - way satellite time and frequency transfer measurements ,” Metrologia

Using BiSON to detect solar internal g-modes

NASA Astrophysics Data System (ADS)

Kuszlewicz, J.; Davies, G. R.; Chaplin, W. J.

2015-09-01

The unambiguous detection of individual solar internal g modes continues to elude us. With the aid of new additions to calibration procedures, as well as updated methods to combine multi-site time series more effectively, the noise and signal detection threshold levels in the low-frequency domain (where the g modes are expected to be found) have been greatly improved. In the BiSON 23-year dataset these levels now rival those of GOLF, and with much greater frequency resolution available, due to the long time series, there is an opportunity to place more constraints on the upper limits of individual g mode amplitudes. Here we detail recent work dedicated to the challenges of observing low-frequency oscillations using a ground-based network, including the role of the window function as well as the effect of calibration on the low frequency domain.

Study of the Effect of Temporal Sampling Frequency on DSCOVR Observations Using the GEOS-5 Nature Run Results. Part II; Cloud Coverage

NASA Technical Reports Server (NTRS)

Holdaway, Daniel; Yang, Yuekui

2016-01-01

This is the second part of a study on how temporal sampling frequency affects satellite retrievals in support of the Deep Space Climate Observatory (DSCOVR) mission. Continuing from Part 1, which looked at Earth's radiation budget, this paper presents the effect of sampling frequency on DSCOVR-derived cloud fraction. The output from NASA's Goddard Earth Observing System version 5 (GEOS-5) Nature Run is used as the "truth". The effect of temporal resolution on potential DSCOVR observations is assessed by subsampling the full Nature Run data. A set of metrics, including uncertainty and absolute error in the subsampled time series, correlation between the original and the subsamples, and Fourier analysis have been used for this study. Results show that, for a given sampling frequency, the uncertainties in the annual mean cloud fraction of the sunlit half of the Earth are larger over land than over ocean. Analysis of correlation coefficients between the subsamples and the original time series demonstrates that even though sampling at certain longer time intervals may not increase the uncertainty in the mean, the subsampled time series is further and further away from the "truth" as the sampling interval becomes larger and larger. Fourier analysis shows that the simulated DSCOVR cloud fraction has underlying periodical features at certain time intervals, such as 8, 12, and 24 h. If the data is subsampled at these frequencies, the uncertainties in the mean cloud fraction are higher. These results provide helpful insights for the DSCOVR temporal sampling strategy.

Transient electromagnetic scattering by a radially uniaxial dielectric sphere: Debye series, Mie series and ray tracing methods

NASA Astrophysics Data System (ADS)

Yazdani, Mohsen

Transient electromagnetic scattering by a radially uniaxial dielectric sphere is explored using three well-known methods: Debye series, Mie series, and ray tracing theory. In the first approach, the general solutions for the impulse and step responses of a uniaxial sphere are evaluated using the inverse Laplace transformation of the generalized Mie series solution. Following high frequency scattering solution of a large uniaxial sphere, the Mie series summation is split into the high frequency (HF) and low frequency terms where the HF term is replaced by its asymptotic expression allowing a significant reduction in computation time of the numerical Bromwich integral. In the second approach, the generalized Debye series for a radially uniaxial dielectric sphere is introduced and the Mie series coefficients are replaced by their equivalent Debye series formulations. The results are then applied to examine the transient response of each individual Debye term allowing the identification of impulse returns in the transient response of the uniaxial sphere. In the third approach, the ray tracing theory in a uniaxial sphere is investigated to evaluate the propagation path as well as the arrival time of the ordinary and extraordinary returns in the transient response of the uniaxial sphere. This is achieved by extracting the reflection and transmission angles of a plane wave obliquely incident on the radially oriented air-uniaxial and uniaxial-air boundaries, and expressing the phase velocities as well as the refractive indices of the ordinary and extraordinary waves in terms of the incident angle, optic axis and propagation direction. The results indicate a satisfactory agreement between Debye series, Mie series and ray tracing methods.

Surface Layer Flux Processes During Cloud Intermittency and Advection above a Middle Rio Grande Riparian Forest, New Mexico

NASA Astrophysics Data System (ADS)

Cleverly, J. R.; Prueger, J.; Cooper, D. I.; Hipps, L.; Eichinger, W.

2002-12-01

An intensive field campaign was undertaken to bring together state-of-the-art methodologies for investigating surface layer physical characteristics over a desert riparian forest. Three-dimensional sonic eddy covariance (3SEC), LIDAR, SODAR, Radiosonde, one-dimensional propeller eddy covariance (1PEC), heat dissipation sap flux, and leaf gas exchange were simultaneously in use 13 -- 21 June 1999 at Bosque del Apache National Wildlife Refuge (NWR) in New Mexico. A one hour period of intense advection was identified by /line{v} >> 0 and /line{u} = 0, indicating that wind direction was transverse to the riparian corridor. The period of highest /line{v} was 1400 h on 20 June; this hour experienced intermittent cloud cover and enhanced mesoscale forcing of surface fluxes. High-frequency (20 Hz) time series of u, v, w, q, θ , and T were collected for spectral, cospectral, and wavelet analyses. These time series analyses illustrate scales at which processes co-occur. At high frequencies (> 0.015 Hz), /line{T' q'} > 0, and (KH)/ (KW) = 1. At low frequencies, however, /line{T' q'} < 0, and (KH)/(KW) !=q 1. Under these transient conditions, frequencies below 0.015 Hz are associated with advection. While power cospectra are useful in associating processes at certain frequencies, further analysis must be performed to determine whether such examples of aphasia are localized to transient events or constant through time. Continuous wavelet transformation (CWT) sacrifices localization in frequency space for localization in time. Mother wavelets were evaluated, and Daubechies order 10 wavelet was found to reduce red noise and leakage near the spectral gap. The spectral gap is a frequency domain between synoptic and turbulent scales. Low frequency turbulent structures near the spectral gap in the time series of /line{T' q'}, /line{w' T'}, and /line{w' q'} followed a perturbation--relaxation pattern to cloud cover. Further cloud cover in the same hour did not produce the low frequency variation associated with mesoscale forcing. Two dimensional vertical LIDAR scans of eddy structure explains the observed frequency response patterns. Insight into the temporal progression of homeostatic processes in the surface layer will provide resources for water managers to better predict ET.

The Cross-Wavelet Transform and Analysis of Quasi-periodic Behavior in the Pearson-Readhead VLBI Survey Sources

NASA Astrophysics Data System (ADS)

Kelly, Brandon C.; Hughes, Philip A.; Aller, Hugh D.; Aller, Margo F.

2003-07-01

We introduce an algorithm for applying a cross-wavelet transform to analysis of quasi-periodic variations in a time series and introduce significance tests for the technique. We apply a continuous wavelet transform and the cross-wavelet algorithm to the Pearson-Readhead VLBI survey sources using data obtained from the University of Michigan 26 m paraboloid at observing frequencies of 14.5, 8.0, and 4.8 GHz. Thirty of the 62 sources were chosen to have sufficient data for analysis, having at least 100 data points for a given time series. Of these 30 sources, a little more than half exhibited evidence for quasi-periodic behavior in at least one observing frequency, with a mean characteristic period of 2.4 yr and standard deviation of 1.3 yr. We find that out of the 30 sources, there were about four timescales for every 10 time series, and about half of those sources showing quasi-periodic behavior repeated the behavior in at least one other observing frequency.

Dynamic Cross-Entropy.

PubMed

Aur, Dorian; Vila-Rodriguez, Fidel

2017-01-01

Complexity measures for time series have been used in many applications to quantify the regularity of one dimensional time series, however many dynamical systems are spatially distributed multidimensional systems. We introduced Dynamic Cross-Entropy (DCE) a novel multidimensional complexity measure that quantifies the degree of regularity of EEG signals in selected frequency bands. Time series generated by discrete logistic equations with varying control parameter r are used to test DCE measures. Sliding window DCE analyses are able to reveal specific period doubling bifurcations that lead to chaos. A similar behavior can be observed in seizures triggered by electroconvulsive therapy (ECT). Sample entropy data show the level of signal complexity in different phases of the ictal ECT. The transition to irregular activity is preceded by the occurrence of cyclic regular behavior. A significant increase of DCE values in successive order from high frequencies in gamma to low frequencies in delta band reveals several phase transitions into less ordered states, possible chaos in the human brain. To our knowledge there are no reliable techniques able to reveal the transition to chaos in case of multidimensional times series. In addition, DCE based on sample entropy appears to be robust to EEG artifacts compared to DCE based on Shannon entropy. The applied technique may offer new approaches to better understand nonlinear brain activity. Copyright © 2016 Elsevier B.V. All rights reserved.

Time series analysis of the developed financial markets' integration using visibility graphs

NASA Astrophysics Data System (ADS)

Zhuang, Enyu; Small, Michael; Feng, Gang

2014-09-01

A time series representing the developed financial markets' segmentation from 1973 to 2012 is studied. The time series reveals an obvious market integration trend. To further uncover the features of this time series, we divide it into seven windows and generate seven visibility graphs. The measuring capabilities of the visibility graphs provide means to quantitatively analyze the original time series. It is found that the important historical incidents that influenced market integration coincide with variations in the measured graphical node degree. Through the measure of neighborhood span, the frequencies of the historical incidents are disclosed. Moreover, it is also found that large "cycles" and significant noise in the time series are linked to large and small communities in the generated visibility graphs. For large cycles, how historical incidents significantly affected market integration is distinguished by density and compactness of the corresponding communities.

A high-fidelity weather time series generator using the Markov Chain process on a piecewise level

NASA Astrophysics Data System (ADS)

Hersvik, K.; Endrerud, O.-E. V.

2017-12-01

A method is developed for generating a set of unique weather time-series based on an existing weather series. The method allows statistically valid weather variations to take place within repeated simulations of offshore operations. The numerous generated time series need to share the same statistical qualities as the original time series. Statistical qualities here refer mainly to the distribution of weather windows available for work, including durations and frequencies of such weather windows, and seasonal characteristics. The method is based on the Markov chain process. The core new development lies in how the Markov Process is used, specifically by joining small pieces of random length time series together rather than joining individual weather states, each from a single time step, which is a common solution found in the literature. This new Markov model shows favorable characteristics with respect to the requirements set forth and all aspects of the validation performed.

Scaling behavior of EEG amplitude and frequency time series across sleep stages

NASA Astrophysics Data System (ADS)

Kantelhardt, Jan W.; Tismer, Sebastian; Gans, Fabian; Schumann, Aicko Y.; Penzel, Thomas

2015-10-01

We study short-term and long-term persistence properties (related with auto-correlations) of amplitudes and frequencies of EEG oscillations in 176 healthy subjects and 40 patients during nocturnal sleep. The amplitudes show scaling from 2 to 500 seconds (depending on the considered band) with large fluctuation exponents during (nocturnal) wakefulness (0.73-0.83) and small ones during deep sleep (0.50-0.69). Light sleep is similar to deep sleep, while REM sleep (0.64-0.76) is closer to wakefulness except for the EEG γ band. Some of the frequency time series also show long-term scaling, depending on the selected bands and stages. Only minor deviations are seen for patients with depression, anxiety, or Parkinson's disease.

Multiscale Poincaré plots for visualizing the structure of heartbeat time series.

PubMed

Henriques, Teresa S; Mariani, Sara; Burykin, Anton; Rodrigues, Filipa; Silva, Tiago F; Goldberger, Ary L

2016-02-09

Poincaré delay maps are widely used in the analysis of cardiac interbeat interval (RR) dynamics. To facilitate visualization of the structure of these time series, we introduce multiscale Poincaré (MSP) plots. Starting with the original RR time series, the method employs a coarse-graining procedure to create a family of time series, each of which represents the system's dynamics in a different time scale. Next, the Poincaré plots are constructed for the original and the coarse-grained time series. Finally, as an optional adjunct, color can be added to each point to represent its normalized frequency. We illustrate the MSP method on simulated Gaussian white and 1/f noise time series. The MSP plots of 1/f noise time series reveal relative conservation of the phase space area over multiple time scales, while those of white noise show a marked reduction in area. We also show how MSP plots can be used to illustrate the loss of complexity when heartbeat time series from healthy subjects are compared with those from patients with chronic (congestive) heart failure syndrome or with atrial fibrillation. This generalized multiscale approach to Poincaré plots may be useful in visualizing other types of time series.

Sensitivity analysis of machine-learning models of hydrologic time series

NASA Astrophysics Data System (ADS)

O'Reilly, A. M.

2017-12-01

Sensitivity analysis traditionally has been applied to assessing model response to perturbations in model parameters, where the parameters are those model input variables adjusted during calibration. Unlike physics-based models where parameters represent real phenomena, the equivalent of parameters for machine-learning models are simply mathematical "knobs" that are automatically adjusted during training/testing/verification procedures. Thus the challenge of extracting knowledge of hydrologic system functionality from machine-learning models lies in their very nature, leading to the label "black box." Sensitivity analysis of the forcing-response behavior of machine-learning models, however, can provide understanding of how the physical phenomena represented by model inputs affect the physical phenomena represented by model outputs.As part of a previous study, hybrid spectral-decomposition artificial neural network (ANN) models were developed to simulate the observed behavior of hydrologic response contained in multidecadal datasets of lake water level, groundwater level, and spring flow. Model inputs used moving window averages (MWA) to represent various frequencies and frequency-band components of time series of rainfall and groundwater use. Using these forcing time series, the MWA-ANN models were trained to predict time series of lake water level, groundwater level, and spring flow at 51 sites in central Florida, USA. A time series of sensitivities for each MWA-ANN model was produced by perturbing forcing time-series and computing the change in response time-series per unit change in perturbation. Variations in forcing-response sensitivities are evident between types (lake, groundwater level, or spring), spatially (among sites of the same type), and temporally. Two generally common characteristics among sites are more uniform sensitivities to rainfall over time and notable increases in sensitivities to groundwater usage during significant drought periods.

Synthesis of Natural Electric and Magnetic Time Series Using Impulse Responses of Inter-station Transfer Functions and a Reference

NASA Astrophysics Data System (ADS)

Wang, H.; Cheng, J.

2017-12-01

A method to Synthesis natural electric and magnetic Time series is proposed whereby the time series of local site are derived using an Impulse Response and a reference (STIR). The method is based on the assumption that the external source of magnetic fields are uniform, and the electric and magnetic fields acquired at the surface satisfy a time-independent linear relation in frequency domain.According to the convolution theorem, we can synthesize natural electric and magnetic time series using the impulse responses of inter-station transfer functions with a reference. Applying this method, two impulse responses need to be estimated: the quasi-MT impulse response tensor and the horizontal magnetic impulse response tensor. These impulse response tensors relate the local horizontal electric and magnetic components with the horizontal magnetic components at a reference site, respectively. Some clean segments of times series are selected to estimate impulse responses by using least-square (LS) method. STIR is similar with STIN (Wang, 2017), but STIR does not need to estimate the inter-station transfer functions, and the synthesized data are more accurate in high frequency, where STIN fails when the inter-station transfer functions are contaminated severely. A test with good quality of MT data shows that synthetic time-series are similar to natural electric and magnetic time series. For contaminated AMT example, when this method is used to remove noise present at the local site, the scatter of MT sounding curves are clear reduced, and the data quality are improved. *This work is funded by National Key R&D Program of China(2017YFC0804105),National Natural Science Foundation of China (41604064, 51574250), State Key Laboratory of Coal Resources and Safe Mining ,China University of Mining & Technology,(SKLCRSM16DC09)

High-frequency signal and noise estimates of CSR GRACE RL04

NASA Astrophysics Data System (ADS)

Bonin, Jennifer A.; Bettadpur, Srinivas; Tapley, Byron D.

2012-12-01

A sliding window technique is used to create daily-sampled Gravity Recovery and Climate Experiment (GRACE) solutions with the same background processing as the official CSR RL04 monthly series. By estimating over shorter time spans, more frequent solutions are made using uncorrelated data, allowing for higher frequency resolution in addition to daily sampling. Using these data sets, high-frequency GRACE errors are computed using two different techniques: assuming the GRACE high-frequency signal in a quiet area of the ocean is the true error, and computing the variance of differences between multiple high-frequency GRACE series from different centers. While the signal-to-noise ratios prove to be sufficiently high for confidence at annual and lower frequencies, at frequencies above 3 cycles/year the signal-to-noise ratios in the large hydrological basins looked at here are near 1.0. Comparisons with the GLDAS hydrological model and high frequency GRACE series developed at other centers confirm CSR GRACE RL04's poor ability to accurately and reliably measure hydrological signal above 3-9 cycles/year, due to the low power of the large-scale hydrological signal typical at those frequencies compared to the GRACE errors.

Scaling analysis of bilateral hand tremor movements in essential tremor patients.

PubMed

Blesic, S; Maric, J; Dragasevic, N; Milanovic, S; Kostic, V; Ljubisavljevic, Milos

2011-08-01

Recent evidence suggests that the dynamic-scaling behavior of the time-series of signals extracted from separate peaks of tremor spectra may reveal existence of multiple independent sources of tremor. Here, we have studied dynamic characteristics of the time-series of hand tremor movements in essential tremor (ET) patients using the detrended fluctuation analysis method. Hand accelerometry was recorded with (500 g) and without weight loading under postural conditions in 25 ET patients and 20 normal subjects. The time-series comprising peak-to-peak (PtP) intervals were extracted from regions around the first three main frequency components of power spectra (PwS) of the recorded tremors. The data were compared between the load and no-load condition on dominant (related to tremor severity) and non-dominant tremor side and with the normal (physiological) oscillations in healthy subjects. Our analysis shows that, in ET, the dynamic characteristics of the main frequency component of recorded tremors exhibit scaling behavior. Furthermore, they show that the two main components of ET tremor frequency spectra, otherwise indistinguishable without load, become significantly different after inertial loading and that they differ between the tremor sides (related to tremor severity). These results show that scaling, a time-domain analysis, helps revealing tremor features previously not revealed by frequency-domain analysis and suggest that distinct oscillatory central circuits may generate the tremor in ET patients.

Testing for Granger Causality in the Frequency Domain: A Phase Resampling Method.

PubMed

Liu, Siwei; Molenaar, Peter

2016-01-01

This article introduces phase resampling, an existing but rarely used surrogate data method for making statistical inferences of Granger causality in frequency domain time series analysis. Granger causality testing is essential for establishing causal relations among variables in multivariate dynamic processes. However, testing for Granger causality in the frequency domain is challenging due to the nonlinear relation between frequency domain measures (e.g., partial directed coherence, generalized partial directed coherence) and time domain data. Through a simulation study, we demonstrate that phase resampling is a general and robust method for making statistical inferences even with short time series. With Gaussian data, phase resampling yields satisfactory type I and type II error rates in all but one condition we examine: when a small effect size is combined with an insufficient number of data points. Violations of normality lead to slightly higher error rates but are mostly within acceptable ranges. We illustrate the utility of phase resampling with two empirical examples involving multivariate electroencephalography (EEG) and skin conductance data.

VizieR Online Data Catalog: AR Sco VLA radio observations (Stanway+, 2018)

NASA Astrophysics Data System (ADS)

Stanway, E. R.; Marsh, T. R.; Chote, P.; Gaensicke, B. T.; Steeghs, D.; Wheatley, P. J.

2018-02-01

Time series VLA radio observations were undertaken of the highly variable white dwarf binary AR Scorpii. These were analysed for periodicity, spectral behaviour and other characteristics. Here we present time series data in the Stokes I parameter at three frequencies. These were centred at 1.5GHz (1GHz bandwidth), 5GHz (2GHz bandwidth) and 9GHz (2GHz bandwidth). The AR Sco binary is unresolved at these frequencies. In the case of the 1.5GHz data, fluxes have been deconvolved with those of a neighbouring object. (3 data files).

Simulation Study Using a New Type of Sample Variance

NASA Technical Reports Server (NTRS)

Howe, D. A.; Lainson, K. J.

1996-01-01

We evaluate with simulated data a new type of sample variance for the characterization of frequency stability. The new statistic (referred to as TOTALVAR and its square root TOTALDEV) is a better predictor of long-term frequency variations than the present sample Allan deviation. The statistical model uses the assumption that a time series of phase or frequency differences is wrapped (periodic) with overall frequency difference removed. We find that the variability at long averaging times is reduced considerably for the five models of power-law noise commonly encountered with frequency standards and oscillators.

Monitoring of tissue ablation using time series of ultrasound RF data.

PubMed

Imani, Farhad; Wu, Mark Z; Lasso, Andras; Burdette, Everett C; Daoud, Mohammad; Fitchinger, Gabor; Abolmaesumi, Purang; Mousavi, Parvin

2011-01-01

This paper is the first report on the monitoring of tissue ablation using ultrasound RF echo time series. We calcuate frequency and time domain features of time series of RF echoes from stationary tissue and transducer, and correlate them with ablated and non-ablated tissue properties. We combine these features in a nonlinear classification framework and demonstrate up to 99% classification accuracy in distinguishing ablated and non-ablated regions of tissue, in areas as small as 12mm2 in size. We also demonstrate significant improvement of ablated tissue classification using RF time series compared to the conventional approach of using single RF scan lines. The results of this study suggest RF echo time series as a promising approach for monitoring ablation, and capturing the changes in the tissue microstructure as a result of heat-induced necrosis.

Network Analyses for Space-Time High Frequency Wind Data

NASA Astrophysics Data System (ADS)

Laib, Mohamed; Kanevski, Mikhail

2017-04-01

Recently, network science has shown an important contribution to the analysis, modelling and visualization of complex time series. Numerous existing methods have been proposed for constructing networks. This work studies spatio-temporal wind data by using networks based on the Granger causality test. Furthermore, a visual comparison is carried out with several frequencies of data and different size of moving window. The main attention is paid to the temporal evolution of connectivity intensity. The Hurst exponent is applied on the provided time series in order to explore if there is a long connectivity memory. The results explore the space time structure of wind data and can be applied to other environmental data. The used dataset presents a challenging case study. It consists of high frequency (10 minutes) wind data from 120 measuring stations in Switzerland, for a time period of 2012-2013. The distribution of stations covers different geomorphological zones and elevation levels. The results are compared with the Person correlation network as well.

Detection and characterization of cultural noise sources in magnetotelluric data: individual and joint analysis of the polarization attributes of the electric and magnetic field time-series in the time-frequency domain

NASA Astrophysics Data System (ADS)

Escalas, M.; Queralt, P.; Ledo, J.; Marcuello, A.

2012-04-01

Magnetotelluric (MT) method is a passive electromagnetic technique, which is currently used to characterize sites for the geological storage of CO2. These later ones are usually located nearby industrialized, urban or farming areas, where man-made electromagnetic (EM) signals contaminate the MT data. The identification and characterization of the artificial EM sources which generate the so-called "cultural noise" is an important challenge to obtain the most reliable results with the MT method. The polarization attributes of an EM signal (tilt angle, ellipticity and phase difference between its orthogonal components) are related to the character of its source. In a previous work (Escalas et al. 2011), we proposed a method to distinguish natural signal from cultural noise in the raw MT data. It is based on the polarization analysis of the MT time-series in the time-frequency domain, using a wavelet scheme. We developed an algorithm to implement the method, and was tested with both synthetic and field data. In 2010, we carried out a controlled-source electromagnetic (CSEM) experiment in the Hontomín site (the Research Laboratory on Geological Storage of CO2 in Spain). MT time-series were contaminated at different frequencies with the signal emitted by a controlled artificial EM source: two electric dipoles (1 km long, arranged in North-South and East-West directions). The analysis with our algorithm of the electric field time-series acquired in this experiment was successful: the polarization attributes of both the natural and artificial signal were obtained in the time-frequency domain, highlighting their differences. The processing of the magnetic field time-series acquired in the Hontomín experiment has been done in the present work. This new analysis of the polarization attributes of the magnetic field data has provided additional information to detect the contribution of the artificial source in the measured data. Moreover, the joint analysis of the polarization attributes of the electric and magnetic field has been crucial to fully characterize the properties and the location of the noise source. Escalas, M., Queralt, P., Ledo, J., Marcuello, A., 2011. Identification of cultural noise sources in magnetotelluric data: estimating polarization attributes in the time-frequency domain using wavelet analysis. Geophysical Research Abstracts Vol. 13, EGU2011-6085. EGU General Assembly 2011.

Method of estimating pulse response using an impedance spectrum

DOEpatents

Morrison, John L; Morrison, William H; Christophersen, Jon P; Motloch, Chester G

2014-10-21

Electrochemical Impedance Spectrum data are used to predict pulse performance of an energy storage device. The impedance spectrum may be obtained in-situ. A simulation waveform includes a pulse wave with a period greater than or equal to the lowest frequency used in the impedance measurement. Fourier series coefficients of the pulse train can be obtained. The number of harmonic constituents in the Fourier series are selected so as to appropriately resolve the response, but the maximum frequency should be less than or equal to the highest frequency used in the impedance measurement. Using a current pulse as an example, the Fourier coefficients of the pulse are multiplied by the impedance spectrum at corresponding frequencies to obtain Fourier coefficients of the voltage response to the desired pulse. The Fourier coefficients of the response are then summed and reassembled to obtain the overall time domain estimate of the voltage using the Fourier series analysis.

High-frequency measurements of aeolian saltation flux: Field-based methodology and applications

NASA Astrophysics Data System (ADS)

Martin, Raleigh L.; Kok, Jasper F.; Hugenholtz, Chris H.; Barchyn, Thomas E.; Chamecki, Marcelo; Ellis, Jean T.

2018-02-01

Aeolian transport of sand and dust is driven by turbulent winds that fluctuate over a broad range of temporal and spatial scales. However, commonly used aeolian transport models do not explicitly account for such fluctuations, likely contributing to substantial discrepancies between models and measurements. Underlying this problem is the absence of accurate sand flux measurements at the short time scales at which wind speed fluctuates. Here, we draw on extensive field measurements of aeolian saltation to develop a methodology for generating high-frequency (up to 25 Hz) time series of total (vertically-integrated) saltation flux, namely by calibrating high-frequency (HF) particle counts to low-frequency (LF) flux measurements. The methodology follows four steps: (1) fit exponential curves to vertical profiles of saltation flux from LF saltation traps, (2) determine empirical calibration factors through comparison of LF exponential fits to HF number counts over concurrent time intervals, (3) apply these calibration factors to subsamples of the saltation count time series to obtain HF height-specific saltation fluxes, and (4) aggregate the calibrated HF height-specific saltation fluxes into estimates of total saltation fluxes. When coupled to high-frequency measurements of wind velocity, this methodology offers new opportunities for understanding how aeolian saltation dynamics respond to variability in driving winds over time scales from tens of milliseconds to days.

Microbial oceanography and the Hawaii Ocean Time-series programme.

PubMed

Karl, David M; Church, Matthew J

2014-10-01

The Hawaii Ocean Time-series (HOT) programme has been tracking microbial and biogeochemical processes in the North Pacific Subtropical Gyre since October 1988. The near-monthly time series observations have revealed previously undocumented phenomena within a temporally dynamic ecosystem that is vulnerable to climate change. Novel microorganisms, genes and unexpected metabolic pathways have been discovered and are being integrated into our evolving ecological paradigms. Continued research, including higher-frequency observations and at-sea experimentation, will help to provide a comprehensive scientific understanding of microbial processes in the largest biome on Earth.

Option pricing from wavelet-filtered financial series

NASA Astrophysics Data System (ADS)

de Almeida, V. T. X.; Moriconi, L.

2012-10-01

We perform wavelet decomposition of high frequency financial time series into large and small time scale components. Taking the FTSE100 index as a case study, and working with the Haar basis, it turns out that the small scale component defined by most (≃99.6%) of the wavelet coefficients can be neglected for the purpose of option premium evaluation. The relevance of the hugely compressed information provided by low-pass wavelet-filtering is related to the fact that the non-gaussian statistical structure of the original financial time series is essentially preserved for expiration times which are larger than just one trading day.

Spontaneous Swallowing Frequency [Has Potential to] Identify Dysphagia in Acute Stroke

PubMed Central

Carnaby, Giselle D; Sia, Isaac; Khanna, Anna; Waters, Michael

2014-01-01

Background and Purpose Spontaneous swallowing frequency has been described as an index of dysphagia in various health conditions. This study evaluated the potential of spontaneous swallow frequency analysis as a screening protocol for dysphagia in acute stroke. Methods In a cohort of 63 acute stroke cases swallow frequency rates (swallows per minute: SPM) were compared to stroke and swallow severity indices, age, time from stroke to assessment, and consciousness level. Mean differences in SPM were compared between patients with vs. without clinically significant dysphagia. ROC analysis was used to identify the optimal threshold in SPM which was compared to a validated clinical dysphagia examination for identification of dysphagia cases. Time series analysis was employed to identify the minimally adequate time period to complete spontaneous swallow frequency analysis. Results SPM correlated significantly with stroke and swallow severity indices but not with age, time from stroke onset, or consciousness level. Patients with dysphagia demonstrated significantly lower SPM rates. SPM differed by dysphagia severity. ROC analysis yielded a threshold of SPM ≤ 0.40 which identified dysphagia (per the criterion referent) with 0.96 sensitivity, 0.68 specificity, and 0.96 negative predictive value. Time series analysis indicated that a 5 to 10 minute sampling window was sufficient to calculate spontaneous swallow frequency to identify dysphagia cases in acute stroke. Conclusions Spontaneous swallowing frequency presents high potential to screen for dysphagia in acute stroke without the need for trained, available personnel. PMID:24149008

Spontaneous swallowing frequency has potential to identify dysphagia in acute stroke.

PubMed

Crary, Michael A; Carnaby, Giselle D; Sia, Isaac; Khanna, Anna; Waters, Michael F

2013-12-01

Spontaneous swallowing frequency has been described as an index of dysphagia in various health conditions. This study evaluated the potential of spontaneous swallow frequency analysis as a screening protocol for dysphagia in acute stroke. In a cohort of 63 acute stroke cases, swallow frequency rates (swallows per minute [SPM]) were compared with stroke and swallow severity indices, age, time from stroke to assessment, and consciousness level. Mean differences in SPM were compared between patients with versus without clinically significant dysphagia. Receiver operating characteristic curve analysis was used to identify the optimal threshold in SPM, which was compared with a validated clinical dysphagia examination for identification of dysphagia cases. Time series analysis was used to identify the minimally adequate time period to complete spontaneous swallow frequency analysis. SPM correlated significantly with stroke and swallow severity indices but not with age, time from stroke onset, or consciousness level. Patients with dysphagia demonstrated significantly lower SPM rates. SPM differed by dysphagia severity. Receiver operating characteristic curve analysis yielded a threshold of SPM≤0.40 that identified dysphagia (per the criterion referent) with 0.96 sensitivity, 0.68 specificity, and 0.96 negative predictive value. Time series analysis indicated that a 5- to 10-minute sampling window was sufficient to calculate spontaneous swallow frequency to identify dysphagia cases in acute stroke. Spontaneous swallowing frequency presents high potential to screen for dysphagia in acute stroke without the need for trained, available personnel.

Identifying the scale-dependent motifs in atmospheric surface layer by ordinal pattern analysis

NASA Astrophysics Data System (ADS)

Li, Qinglei; Fu, Zuntao

2018-07-01

Ramp-like structures in various atmospheric surface layer time series have been long studied, but the presence of motifs with the finer scale embedded within larger scale ramp-like structures has largely been overlooked in the reported literature. Here a novel, objective and well-adapted methodology, the ordinal pattern analysis, is adopted to study the finer-scaled motifs in atmospheric boundary-layer (ABL) time series. The studies show that the motifs represented by different ordinal patterns take clustering properties and 6 dominated motifs out of the whole 24 motifs account for about 45% of the time series under particular scales, which indicates the higher contribution of motifs with the finer scale to the series. Further studies indicate that motif statistics are similar for both stable conditions and unstable conditions at larger scales, but large discrepancies are found at smaller scales, and the frequencies of motifs "1234" and/or "4321" are a bit higher under stable conditions than unstable conditions. Under stable conditions, there are great changes for the occurrence frequencies of motifs "1234" and "4321", where the occurrence frequencies of motif "1234" decrease from nearly 24% to 4.5% with the scale factor increasing, and the occurrence frequencies of motif "4321" change nonlinearly with the scale increasing. These great differences of dominated motifs change with scale can be taken as an indicator to quantify the flow structure changes under different stability conditions, and motif entropy can be defined just by only 6 dominated motifs to quantify this time-scale independent property of the motifs. All these results suggest that the defined scale of motifs with the finer scale should be carefully taken into consideration in the interpretation of turbulence coherent structures.

[Gene method for inconsistent hydrological frequency calculation. I: Inheritance, variability and evolution principles of hydrological genes].

PubMed

Xie, Ping; Wu, Zi Yi; Zhao, Jiang Yan; Sang, Yan Fang; Chen, Jie

2018-04-01

A stochastic hydrological process is influenced by both stochastic and deterministic factors. A hydrological time series contains not only pure random components reflecting its inheri-tance characteristics, but also deterministic components reflecting variability characteristics, such as jump, trend, period, and stochastic dependence. As a result, the stochastic hydrological process presents complicated evolution phenomena and rules. To better understand these complicated phenomena and rules, this study described the inheritance and variability characteristics of an inconsistent hydrological series from two aspects: stochastic process simulation and time series analysis. In addition, several frequency analysis approaches for inconsistent time series were compared to reveal the main problems in inconsistency study. Then, we proposed a new concept of hydrological genes origined from biological genes to describe the inconsistent hydrolocal processes. The hydrologi-cal genes were constructed using moments methods, such as general moments, weight function moments, probability weight moments and L-moments. Meanwhile, the five components, including jump, trend, periodic, dependence and pure random components, of a stochastic hydrological process were defined as five hydrological bases. With this method, the inheritance and variability of inconsistent hydrological time series were synthetically considered and the inheritance, variability and evolution principles were fully described. Our study would contribute to reveal the inheritance, variability and evolution principles in probability distribution of hydrological elements.

Detection of anomalous signals in temporally correlated data (Invited)

NASA Astrophysics Data System (ADS)

Langbein, J. O.

2010-12-01

Detection of transient tectonic signals in data obtained from large geodetic networks requires the ability to detect signals that are both temporally and spatially coherent. In this report I will describe a modification to an existing method that estimates both the coefficients of temporally correlated noise model and an efficient filter based on the noise model. This filter, when applied to the original time-series, effectively whitens (or flattens) the power spectrum. The filtered data provide the means to calculate running averages which are then used to detect deviations from the background trends. For large networks, time-series of signal-to-noise ratio (SNR) can be easily constructed since, by filtering, each of the original time-series has been transformed into one that is closer to having a Gaussian distribution with a variance of 1.0. Anomalous intervals may be identified by counting the number of GPS sites for which the SNR exceeds a specified value. For example, during one time interval, if there were 5 out of 20 time-series with SNR>2, this would be considered anomalous; typically, one would expect at 95% confidence that there would be at least 1 out of 20 time-series with an SNR>2. For time intervals with an anomalously large number of high SNR, the spatial distribution of the SNR is mapped to identify the location of the anomalous signal(s) and their degree of spatial clustering. Estimating the filter that should be used to whiten the data requires modification of the existing methods that employ maximum likelihood estimation to determine the temporal covariance of the data. In these methods, it is assumed that the noise components in the data are a combination of white, flicker and random-walk processes and that they are derived from three different and independent sources. Instead, in this new method, the covariance matrix is constructed assuming that only one source is responsible for the noise and that source can be represented as a white-noise random-number generator convolved with a filter whose spectral properties are frequency (f) independent at its highest frequencies, 1/f at the middle frequencies, and 1/f2 at the lowest frequencies. For data sets with no gaps in their time-series, construction of covariance and inverse covariance matrices is extremely efficient. Application of the above algorithm to real data potentially involves several iterations as small, tectonic signals of interest are often indistinguishable from background noise. Consequently, simply plotting the time-series of each GPS site is used to identify the largest outliers and signals independent of their cause. Any analysis of the background noise levels must factor in these other signals while the gross outliers need to be removed.

Time delay spectrum conditioner

DOEpatents

Greiner, Norman R.

1980-01-01

A device for delaying specified frequencies of a multiple frequency laser beam. The device separates the multiple frequency beam into a series of spatially separated single frequency beams. The propagation distance of the single frequency beam is subsequently altered to provide the desired delay for each specific frequency. Focusing reflectors can be utilized to provide a simple but nonadjustable system or, flat reflectors with collimating and focusing optics can be utilized to provide an adjustable system.

The local properties of ocean surface waves by the phase-time method

NASA Technical Reports Server (NTRS)

Huang, Norden E.; Long, Steven R.; Tung, Chi-Chao; Donelan, Mark A.; Yuan, Yeli; Lai, Ronald J.

1992-01-01

A new approach using phase information to view and study the properties of frequency modulation, wave group structures, and wave breaking is presented. The method is applied to ocean wave time series data and a new type of wave group (containing the large 'rogue' waves) is identified. The method also has the capability of broad applications in the analysis of time series data in general.

Pi2 detection using Empirical Mode Decomposition (EMD)

NASA Astrophysics Data System (ADS)

Mieth, Johannes Z. D.; Frühauff, Dennis; Glassmeier, Karl-Heinz

2017-04-01

Empirical Mode Decomposition has been used as an alternative method to wavelet transformation to identify onset times of Pi2 pulsations in data sets of the Scandinavian Magnetometer Array (SMA). Pi2 pulsations are magnetohydrodynamic waves occurring during magnetospheric substorms. Almost always Pi2 are observed at substorm onset in mid to low latitudes on Earth's nightside. They are fed by magnetic energy release caused by dipolarization processes. Their periods lie between 40 to 150 seconds. Usually, Pi2 are detected using wavelet transformation. Here, Empirical Mode Decomposition (EMD) is presented as an alternative approach to the traditional procedure. EMD is a young signal decomposition method designed for nonlinear and non-stationary time series. It provides an adaptive, data driven, and complete decomposition of time series into slow and fast oscillations. An optimized version using Monte-Carlo-type noise assistance is used here. By displaying the results in a time-frequency space a characteristic frequency modulation is observed. This frequency modulation can be correlated with the onset of Pi2 pulsations. A basic algorithm to find the onset is presented. Finally, the results are compared to classical wavelet-based analysis. The use of different SMA stations furthermore allows the spatial analysis of Pi2 onset times. EMD mostly finds application in the fields of engineering and medicine. This work demonstrates the applicability of this method to geomagnetic time series.

Computer Operations Study of Reservoir Operations for Six Mississippi River Headwaters Dams. Appendix A.

DTIC Science & Technology

1982-06-01

p*A C.._ _ __ _ _ A, d.tibutiou is unhimta 4 iit 84~ L0 TABLE OF CONTENTS APPENDIX SCOPE OF WORK B MERGE AND COST PROGRAM DOCUMENTATION C FATSCO... PROGRAM TO COMPUTE TIME SERIES FREQUENCY RELATIONSHIPS D HEC-DSS - TIME SERIES DATA FILE MANAGEMENT SYSTEM E PLAN 1 -TIM SERIES DATA PLOTS AND ANNUAL...University of Minnesota, utilized an early version of the Hydrologic Engineering * Center’s (HEC) EEC-5c Computer Program . EEC is a Corps of Engineers

Filter-based multiscale entropy analysis of complex physiological time series.

PubMed

Xu, Yuesheng; Zhao, Liang

2013-08-01

Multiscale entropy (MSE) has been widely and successfully used in analyzing the complexity of physiological time series. We reinterpret the averaging process in MSE as filtering a time series by a filter of a piecewise constant type. From this viewpoint, we introduce filter-based multiscale entropy (FME), which filters a time series to generate multiple frequency components, and then we compute the blockwise entropy of the resulting components. By choosing filters adapted to the feature of a given time series, FME is able to better capture its multiscale information and to provide more flexibility for studying its complexity. Motivated by the heart rate turbulence theory, which suggests that the human heartbeat interval time series can be described in piecewise linear patterns, we propose piecewise linear filter multiscale entropy (PLFME) for the complexity analysis of the time series. Numerical results from PLFME are more robust to data of various lengths than those from MSE. The numerical performance of the adaptive piecewise constant filter multiscale entropy without prior information is comparable to that of PLFME, whose design takes prior information into account.

Nonlinear time-series analysis of current signal in cathodic contact glow discharge electrolysis

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

Allagui, Anis, E-mail: aallagui@sharjah.ac.ae; Abdelkareem, Mohammad Ali; Rojas, Andrea Espinel

In the standard two-electrode configuration employed in electrolytic process, when the control dc voltage is brought to a critical value, the system undergoes a transition from conventional electrolysis to contact glow discharge electrolysis (CGDE), which has also been referred to as liquid-submerged micro-plasma, glow discharge plasma electrolysis, electrode effect, electrolytic plasma, etc. The light-emitting process is associated with the development of an irregular and erratic current time-series which has been arbitrarily labelled as “random,” and thus dissuaded further research in this direction. Here, we examine the current time-series signals measured in cathodic CGDE configuration in a concentrated KOH solution atmore » different dc bias voltages greater than the critical voltage. We show that the signals are, in fact, not random according to the NIST SP. 800-22 test suite definition. We also demonstrate that post-processing low-pass filtered sequences requires less time than the native as-measured sequences, suggesting a superposition of low frequency chaotic fluctuations and high frequency behaviors (which may be produced by more than one possible source of entropy). Using an array of nonlinear time-series analyses for dynamical systems, i.e., the computation of largest Lyapunov exponents and correlation dimensions, and re-construction of phase portraits, we found that low-pass filtered datasets undergo a transition from quasi-periodic to chaotic to quasi-hyper-chaotic behavior, and back again to chaos when the voltage controlling-parameter is increased. The high frequency part of the signals is discussed in terms of highly nonlinear turbulent motion developed around the working electrode.« less

Conditional Spectral Analysis of Replicated Multiple Time Series with Application to Nocturnal Physiology.

PubMed

Krafty, Robert T; Rosen, Ori; Stoffer, David S; Buysse, Daniel J; Hall, Martica H

2017-01-01

This article considers the problem of analyzing associations between power spectra of multiple time series and cross-sectional outcomes when data are observed from multiple subjects. The motivating application comes from sleep medicine, where researchers are able to non-invasively record physiological time series signals during sleep. The frequency patterns of these signals, which can be quantified through the power spectrum, contain interpretable information about biological processes. An important problem in sleep research is drawing connections between power spectra of time series signals and clinical characteristics; these connections are key to understanding biological pathways through which sleep affects, and can be treated to improve, health. Such analyses are challenging as they must overcome the complicated structure of a power spectrum from multiple time series as a complex positive-definite matrix-valued function. This article proposes a new approach to such analyses based on a tensor-product spline model of Cholesky components of outcome-dependent power spectra. The approach exibly models power spectra as nonparametric functions of frequency and outcome while preserving geometric constraints. Formulated in a fully Bayesian framework, a Whittle likelihood based Markov chain Monte Carlo (MCMC) algorithm is developed for automated model fitting and for conducting inference on associations between outcomes and spectral measures. The method is used to analyze data from a study of sleep in older adults and uncovers new insights into how stress and arousal are connected to the amount of time one spends in bed.

A new method for reconstruction of solar irradiance

NASA Astrophysics Data System (ADS)

Privalsky, Victor

2018-07-01

The purpose of this research is to show how time series should be reconstructed using an example with the data on total solar irradiation (TSI) of the Earth and on sunspot numbers (SSN) since 1749. The traditional approach through regression equation(s) is designed for time-invariant vectors of random variables and is not applicable to time series, which present random functions of time. The autoregressive reconstruction (ARR) method suggested here requires fitting a multivariate stochastic difference equation to the target/proxy time series. The reconstruction is done through the scalar equation for the target time series with the white noise term excluded. The time series approach is shown to provide a better reconstruction of TSI than the correlation/regression method. A reconstruction criterion is introduced which allows one to define in advance the achievable level of success in the reconstruction. The conclusion is that time series, including the total solar irradiance, cannot be reconstructed properly if the data are not treated as sample records of random processes and analyzed in both time and frequency domains.

Dual Fractal Dimension and Long-Range Correlation of Chinese Stock Prices

NASA Astrophysics Data System (ADS)

Chen, Chaoshi; Wang, Lei

2012-03-01

The recently developed modified inverse random midpoint displacement (mIRMD) and conventional detrended fluctuation analysis (DFA) algorithms are used to analyze the tick-by-tick high-frequency time series of Chinese A-share stock prices and indexes. A dual-fractal structure with a crossover at about 10 min is observed. The majority of the selected time series show visible persistence within this time threshold, but approach a random walk on a longer time scale. The phenomenon is found to be industry-dependent, i.e., the crossover is much more prominent for stocks belonging to cyclical industries than for those belonging to noncyclical (defensive) industries. We have also shown that the sign series show a similar dual-fractal structure, while like generally found, the magnitude series show a much longer time persistence.

Laboratory and Modeling Studies of Insect Swarms

DTIC Science & Technology

2016-03-10

and measuring the response. These novel methods allowed us for the first time to characterize precisely properties of the swarm at the group level... Time series for a randomly chosen pair as well as its continuous wavelet transform (CWT; bottom panel). Nearly all of the power in the signal for... based time -frequency analysis to identify such transient interactions, as long as they modified the frequency structure of the insect flight

Two-Way Satellite Time Transfer Between USNO and PTB

DTIC Science & Technology

2005-08-01

Observatory 3450 Massachusetts Ave. NW Washington, DC 20392, USA Abstract—Two completely independent two-way time and frequency transfer ( TWSTFT ...for the realization of TAI. The X- band data are provided as a backup. To reach the full potential of TWSTFT , especially for time scale comparisons...ns for both links were achieved. A change of the TWSTFT transmission frequencies or satellite changes in general cause discontinuities in the series

System for generating pluralities of optical pulses with predetermined frequencies in a temporally and spatially overlapped relationship

DOEpatents

Meyerhofer, David D.; Schmid, Ansgar W.; Chuang, Yung-ho

1992-01-01

Ultra short (pico second and shorter) laser pulses having components of different frequency which are overlapped coherently in space and with a predetermined constant relationship in time, are generated and may be used in applications where plural spectrally separate, time-synchronized pulses are needed as in wave-length resolved spectroscopy and spectral pump probe measurements for characterization of materials. A Chirped Pulse Amplifier (CPA), such as a regenerative amplifier, which provides amplified, high intensity pulses at the output thereof which have the same spatial intensity profile, is used to process a series of chirped pulses, each with a different central frequency (the desired frequencies contained in the output pulses). Each series of chirped pulses is obtained from a single chirped pulse by spectral windowing with a mask in a dispersive expansion stage ahead of the laser amplifier. The laser amplifier amplifies the pulses and provides output pulses with like spatial and temporal profiles. A compression stage then compresses the amplified pulses. All the individual pulses of different frequency, which originated in each single chirped pulse, are compressed and thereby coherently overlapped in space and time. The compressed pulses may be used for the foregoing purposes and other purposes wherien pulses having a plurality of discrete frequency components are required.

System for generating pluralities of optical pulses with predetermined frequencies in a temporally and spatially overlapped relationship

DOEpatents

Meyerhofer, D.D.; Schmid, A.W.; Chuang, Y.

1992-03-10

Ultrashort (pico second and shorter) laser pulses having components of different frequency which are overlapped coherently in space and with a predetermined constant relationship in time, are generated and may be used in applications where plural spectrally separate, time-synchronized pulses are needed as in wave-length resolved spectroscopy and spectral pump probe measurements for characterization of materials. A Chirped Pulse Amplifier (CPA), such as a regenerative amplifier, which provides amplified, high intensity pulses at the output thereof which have the same spatial intensity profile, is used to process a series of chirped pulses, each with a different central frequency (the desired frequencies contained in the output pulses). Each series of chirped pulses is obtained from a single chirped pulse by spectral windowing with a mask in a dispersive expansion stage ahead of the laser amplifier. The laser amplifier amplifies the pulses and provides output pulses with like spatial and temporal profiles. A compression stage then compresses the amplified pulses. All the individual pulses of different frequency, which originated in each single chirped pulse, are compressed and thereby coherently overlapped in space and time. The compressed pulses may be used for the foregoing purposes and other purposes wherien pulses having a plurality of discrete frequency components are required. 4 figs.

Temporal Evolution of Chromospheric Oscillations in Flaring Regions: A Pilot Study

NASA Astrophysics Data System (ADS)

Monsue, T.; Hill, F.; Stassun, K. G.

2016-10-01

We have analyzed Hα intensity images obtained at a 1 minute cadence with the Global Oscillation Network Group (GONG) system to investigate the properties of oscillations in the 0-8 mHz frequency band at the location and time of strong M- and X-class flares. For each of three subregions within two flaring active regions, we extracted time series from multiple distinct positions, including the flare core and quieter surrounding areas. The time series were analyzed with a moving power-map analysis to examine power as a function of frequency and time. We find that, in the flare core of all three subregions, the low-frequency power (˜1-2 mHz) is substantially enhanced immediately prior to and after the flare, and that power at all frequencies up to 8 mHz is depleted at flare maximum. This depletion is both frequency- and time-dependent, which probably reflects the changing depths visible during the flare in the bandpass of the filter. These variations are not observed outside the flare cores. The depletion may indicate that acoustic energy is being converted into thermal energy at flare maximum, while the low-frequency enhancement may arise from an instability in the chromosphere and provide an early warning of the flare onset. Dark lanes of reduced wave power are also visible in the power maps, which may arise from the interaction of the acoustic waves and the magnetic field.

TEMPORAL EVOLUTION OF CHROMOSPHERIC OSCILLATIONS IN FLARING REGIONS: A PILOT STUDY

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

Monsue, T.; Stassun, K. G.; Hill, F., E-mail: teresa.monsue@vanderbilt.edu, E-mail: keivan.stassun@vanderbilt.edu, E-mail: hill@email.noao.edu

2016-10-01

We have analyzed H α intensity images obtained at a 1 minute cadence with the Global Oscillation Network Group (GONG) system to investigate the properties of oscillations in the 0–8 mHz frequency band at the location and time of strong M- and X-class flares. For each of three subregions within two flaring active regions, we extracted time series from multiple distinct positions, including the flare core and quieter surrounding areas. The time series were analyzed with a moving power-map analysis to examine power as a function of frequency and time. We find that, in the flare core of all threemore » subregions, the low-frequency power (∼1–2 mHz) is substantially enhanced immediately prior to and after the flare, and that power at all frequencies up to 8 mHz is depleted at flare maximum. This depletion is both frequency- and time-dependent, which probably reflects the changing depths visible during the flare in the bandpass of the filter. These variations are not observed outside the flare cores. The depletion may indicate that acoustic energy is being converted into thermal energy at flare maximum, while the low-frequency enhancement may arise from an instability in the chromosphere and provide an early warning of the flare onset. Dark lanes of reduced wave power are also visible in the power maps, which may arise from the interaction of the acoustic waves and the magnetic field.« less

Detecting chaos in irregularly sampled time series.

PubMed

Kulp, C W

2013-09-01

Recently, Wiebe and Virgin [Chaos 22, 013136 (2012)] developed an algorithm which detects chaos by analyzing a time series' power spectrum which is computed using the Discrete Fourier Transform (DFT). Their algorithm, like other time series characterization algorithms, requires that the time series be regularly sampled. Real-world data, however, are often irregularly sampled, thus, making the detection of chaotic behavior difficult or impossible with those methods. In this paper, a characterization algorithm is presented, which effectively detects chaos in irregularly sampled time series. The work presented here is a modification of Wiebe and Virgin's algorithm and uses the Lomb-Scargle Periodogram (LSP) to compute a series' power spectrum instead of the DFT. The DFT is not appropriate for irregularly sampled time series. However, the LSP is capable of computing the frequency content of irregularly sampled data. Furthermore, a new method of analyzing the power spectrum is developed, which can be useful for differentiating between chaotic and non-chaotic behavior. The new characterization algorithm is successfully applied to irregularly sampled data generated by a model as well as data consisting of observations of variable stars.

Assessing the quality of rainfall data when aiming to achieve flood resilience

NASA Astrophysics Data System (ADS)

Hoang, C. T.; Tchiguirinskaia, I.; Schertzer, D.; Lovejoy, S.

2012-04-01

A new EU Floods Directive entered into force five years ago. This Directive requires Member States to coordinate adequate measures to reduce flood risk. European flood management systems require reliable rainfall statistics, e.g. the Intensity-duration-Frequency curves for shorter and shorter durations and for a larger and larger range of return periods. Preliminary studies showed that the number of floods was lower when using low time resolution data of high intensity rainfall events, compared to estimates obtained with the help of higher time resolution data. These facts suggest that a particular attention should be paid to the rainfall data quality in order to adequately investigate flood risk aiming to achieve flood resilience. The potential consequences of changes in measuring and recording techniques have been somewhat discussed in the literature with respect to a possible introduction of artificial inhomogeneities in time series. In this paper, we discuss how to detect another artificiality: most of the rainfall time series have a lower recording frequency than that is assumed, furthermore the effective high-frequency limit often depends on the recording year due to algorithm changes. This question is particularly important for operational hydrology, because an error on the effective recording high frequency introduces biases in the corresponding statistics. In this direction, we developed a first version of a SERQUAL procedure to automatically detect the effective time resolution of highly mixed data. Being applied to the 166 rainfall time series in France, the SERQUAL procedure has detected that most of them have an effective hourly resolution, rather than a 5 minutes resolution. Furthermore, series having an overall 5 minute resolution do not have it for all years. These results raise serious concerns on how to benchmark stochastic rainfall models at a sub-hourly resolution, which are particularly desirable for operational hydrology. Therefore, database quality must be checked before use. Due to the fact that the multiple scales and possible scaling behaviour of hydrological data are particularly important for many applications, including flood resilience research, this paper first investigates the sensitivity of the scaling estimates and methods to the deficit of short duration rainfall data, and consequently propose a few simple criteria for a reliable evaluation of the data quality. Then we showed that our procedure SERQUAL enable us to extract high quality sub-series from longer time series that will be much more reliable to calibrate and/or validate short duration quantiles and hydrological models.

Assessing multiscale complexity of short heart rate variability series through a model-based linear approach

NASA Astrophysics Data System (ADS)

Porta, Alberto; Bari, Vlasta; Ranuzzi, Giovanni; De Maria, Beatrice; Baselli, Giuseppe

2017-09-01

We propose a multiscale complexity (MSC) method assessing irregularity in assigned frequency bands and being appropriate for analyzing the short time series. It is grounded on the identification of the coefficients of an autoregressive model, on the computation of the mean position of the poles generating the components of the power spectral density in an assigned frequency band, and on the assessment of its distance from the unit circle in the complex plane. The MSC method was tested on simulations and applied to the short heart period (HP) variability series recorded during graded head-up tilt in 17 subjects (age from 21 to 54 years, median = 28 years, 7 females) and during paced breathing protocols in 19 subjects (age from 27 to 35 years, median = 31 years, 11 females) to assess the contribution of time scales typical of the cardiac autonomic control, namely in low frequency (LF, from 0.04 to 0.15 Hz) and high frequency (HF, from 0.15 to 0.5 Hz) bands to the complexity of the cardiac regulation. The proposed MSC technique was compared to a traditional model-free multiscale method grounded on information theory, i.e., multiscale entropy (MSE). The approach suggests that the reduction of HP variability complexity observed during graded head-up tilt is due to a regularization of the HP fluctuations in LF band via a possible intervention of sympathetic control and the decrement of HP variability complexity observed during slow breathing is the result of the regularization of the HP variations in both LF and HF bands, thus implying the action of physiological mechanisms working at time scales even different from that of respiration. MSE did not distinguish experimental conditions at time scales larger than 1. Over a short time series MSC allows a more insightful association between cardiac control complexity and physiological mechanisms modulating cardiac rhythm compared to a more traditional tool such as MSE.

Extreme events in total ozone over Arosa - Part 1: Application of extreme value theory

NASA Astrophysics Data System (ADS)

Rieder, H. E.; Staehelin, J.; Maeder, J. A.; Peter, T.; Ribatet, M.; Davison, A. C.; Stübi, R.; Weihs, P.; Holawe, F.

2010-10-01

In this study ideas from extreme value theory are for the first time applied in the field of stratospheric ozone research, because statistical analysis showed that previously used concepts assuming a Gaussian distribution (e.g. fixed deviations from mean values) of total ozone data do not adequately address the structure of the extremes. We show that statistical extreme value methods are appropriate to identify ozone extremes and to describe the tails of the Arosa (Switzerland) total ozone time series. In order to accommodate the seasonal cycle in total ozone, a daily moving threshold was determined and used, with tools from extreme value theory, to analyse the frequency of days with extreme low (termed ELOs) and high (termed EHOs) total ozone at Arosa. The analysis shows that the Generalized Pareto Distribution (GPD) provides an appropriate model for the frequency distribution of total ozone above or below a mathematically well-defined threshold, thus providing a statistical description of ELOs and EHOs. The results show an increase in ELOs and a decrease in EHOs during the last decades. The fitted model represents the tails of the total ozone data set with high accuracy over the entire range (including absolute monthly minima and maxima), and enables a precise computation of the frequency distribution of ozone mini-holes (using constant thresholds). Analyzing the tails instead of a small fraction of days below constant thresholds provides deeper insight into the time series properties. Fingerprints of dynamical (e.g. ENSO, NAO) and chemical features (e.g. strong polar vortex ozone loss), and major volcanic eruptions, can be identified in the observed frequency of extreme events throughout the time series. Overall the new approach to analysis of extremes provides more information on time series properties and variability than previous approaches that use only monthly averages and/or mini-holes and mini-highs.

Extreme events in total ozone over Arosa - Part 1: Application of extreme value theory

NASA Astrophysics Data System (ADS)

Rieder, H. E.; Staehelin, J.; Maeder, J. A.; Peter, T.; Ribatet, M.; Davison, A. C.; Stübi, R.; Weihs, P.; Holawe, F.

2010-05-01

In this study ideas from extreme value theory are for the first time applied in the field of stratospheric ozone research, because statistical analysis showed that previously used concepts assuming a Gaussian distribution (e.g. fixed deviations from mean values) of total ozone data do not adequately address the structure of the extremes. We show that statistical extreme value methods are appropriate to identify ozone extremes and to describe the tails of the Arosa (Switzerland) total ozone time series. In order to accommodate the seasonal cycle in total ozone, a daily moving threshold was determined and used, with tools from extreme value theory, to analyse the frequency of days with extreme low (termed ELOs) and high (termed EHOs) total ozone at Arosa. The analysis shows that the Generalized Pareto Distribution (GPD) provides an appropriate model for the frequency distribution of total ozone above or below a mathematically well-defined threshold, thus providing a statistical description of ELOs and EHOs. The results show an increase in ELOs and a decrease in EHOs during the last decades. The fitted model represents the tails of the total ozone data set with high accuracy over the entire range (including absolute monthly minima and maxima), and enables a precise computation of the frequency distribution of ozone mini-holes (using constant thresholds). Analyzing the tails instead of a small fraction of days below constant thresholds provides deeper insight into the time series properties. Fingerprints of dynamical (e.g. ENSO, NAO) and chemical features (e.g. strong polar vortex ozone loss), and major volcanic eruptions, can be identified in the observed frequency of extreme events throughout the time series. Overall the new approach to analysis of extremes provides more information on time series properties and variability than previous approaches that use only monthly averages and/or mini-holes and mini-highs.

Amplitude variations in the sdBV star PG 1605+072: Another beating time scale?

NASA Astrophysics Data System (ADS)

Pereira, T. M. D.; Lopes, I. P.

2004-10-01

PG 1605+072 has an unique and complex oscillation spectrum amongst the pulsating members of the EC 14026 stars. It has the longest periods and the richest, most puzzling frequency spectrum. We present a quantitative analysis of the photometric time-series obtained at 1-m telescope of the South African Astronomical Observatory. Thirteen oscillation parameters, frequencies, amplitudes and initial phases were determined from a 45 h time-series. Our work confirm previous observational results. The observed frequencies are within a difference smaller than 2.7% of the theoretical values, and less than 0.1% of other previous studies. We also infer the existence of variation of a periodicity of 4-5 days on the amplitude of the observed modes, similar to the yearly time-scale variation found by previous studies. Furthermore, we found a new frequency of 2133 μ Hz which has not been previously reported, its origin being yet unclear. Based on observations obtained at the South African Astronomical Observatory (SAAO). This research was supported by a grant from Fundação da Ciência e Tecnologia, grant No. PESO/P/PRO/40142/2000.

European Climate and Pinot Noir Grape-Harvest Dates in Burgundy, since the 17th Century

NASA Astrophysics Data System (ADS)

Tourre, Y. M.

2011-12-01

Time-series of growing season air temperature anomalies in the Parisian region and of 'Pinot Noir' grape-harvest dates (GHD) in Burgundy (1676-2004) are analyzed in the frequency-domain. Variability of both time-series display three significant frequency-bands (peaks significant at the 5% level) i.e., a low-frequency band (multi-decadal) with a 25-year peak period; a 3-to-8 year band period (inter-annual) with a 3.1-year peak period; and a 2-to-3 year band period (quasi-biennial) with a 2.4-year peak period. Joint sea surface temperature/sea level pressure (SST/SLP) empirical orthogonal functions (EOF) analyses during the 20th century, along with spatio-temporal patterns for the above frequency-bands are presented. It is found that SST anomalies display early significant spatial SST patterns in the North Atlantic Ocean (air temperature lagging by 6 months) similar to those obtained from EOF analyses. It is thus proposed that the robust power spectra for the above frequency-bands could be linked with Atlantic climate variability metrics modulating Western European climate i.e., 1) the global Multi-decadal Oscillation (MDO) with its Atlantic Multi-decadal Oscillation (AMO) footprint; 2) the Atlantic Inter-Annual (IA) fluctuations; and 3) the Atlantic Quasi-Biennial (QB) fluctuations, respectively. Moreover these specific Western European climate signals have effects on ecosystem health and can be perceived as contributors to the length of the growing season and the timing of GHD in Burgundy. Thus advance knowledge on the evolution and phasing of the above climate fluctuations become important elements for viticulture and wine industry management. It is recognized that anthropogenic effects could have modified time-series patterns presented here, particularly since the mid 1980s.

Evaluation of Bayesian estimation of a hidden continuous-time Markov chain model with application to threshold violation in water-quality indicators

USGS Publications Warehouse

Deviney, Frank A.; Rice, Karen; Brown, Donald E.

2012-01-01

Natural resource managers require information concerning  the frequency, duration, and long-term probability of occurrence of water-quality indicator (WQI) violations of defined thresholds. The timing of these threshold crossings often is hidden from the observer, who is restricted to relatively infrequent observations. Here, a model for the hidden process is linked with a model for the observations, and the parameters describing duration, return period, and long-term probability of occurrence are estimated using Bayesian methods. A simulation experiment is performed to evaluate the approach under scenarios based on the equivalent of a total monitoring period of 5-30 years and an observation frequency of 1-50 observations per year. Given constant threshold crossing rate, accuracy and precision of parameter estimates increased with longer total monitoring period and more-frequent observations. Given fixed monitoring period and observation frequency, accuracy and precision of parameter estimates increased with longer times between threshold crossings. For most cases where the long-term probability of being in violation is greater than 0.10, it was determined that at least 600 observations are needed to achieve precise estimates.  An application of the approach is presented using 22 years of quasi-weekly observations of acid-neutralizing capacity from Deep Run, a stream in Shenandoah National Park, Virginia. The time series also was sub-sampled to simulate monthly and semi-monthly sampling protocols. Estimates of the long-term probability of violation were unbiased despite sampling frequency; however, the expected duration and return period were over-estimated using the sub-sampled time series with respect to the full quasi-weekly time series.

One nanosecond time synchronization using series and GPS

NASA Technical Reports Server (NTRS)

Buennagel, A. A.; Spitzmesser, D. J.; Young, L. E.

1983-01-01

Subnanosecond time sychronization between two remote rubidium frequency standards is verified by a traveling clock comparison. Using a novel, code ignorant Global Positioning System (GPS) receiver developed at JPL, the SERIES geodetic baseline measurement system is applied to establish the offset between the 1 Hz. outputs of the remote standards. Results of the two intercomparison experiments to date are presented as well as experimental details.

Application of the Allan Variance to Time Series Analysis in Astrometry and Geodesy: A Review.

PubMed

Malkin, Zinovy

2016-04-01

The Allan variance (AVAR) was introduced 50 years ago as a statistical tool for assessing the frequency standards deviations. For the past decades, AVAR has increasingly been used in geodesy and astrometry to assess the noise characteristics in geodetic and astrometric time series. A specific feature of astrometric and geodetic measurements, as compared with clock measurements, is that they are generally associated with uncertainties; thus, an appropriate weighting should be applied during data analysis. In addition, some physically connected scalar time series naturally form series of multidimensional vectors. For example, three station coordinates time series X, Y, and Z can be combined to analyze 3-D station position variations. The classical AVAR is not intended for processing unevenly weighted and/or multidimensional data. Therefore, AVAR modifications, namely weighted AVAR (WAVAR), multidimensional AVAR (MAVAR), and weighted multidimensional AVAR (WMAVAR), were introduced to overcome these deficiencies. In this paper, a brief review is given of the experience of using AVAR and its modifications in processing astrogeodetic time series.

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

Ferraioli, Luigi; Hueller, Mauro; Vitale, Stefano

The scientific objectives of the LISA Technology Package experiment on board of the LISA Pathfinder mission demand accurate calibration and validation of the data analysis tools in advance of the mission launch. The level of confidence required in the mission outcomes can be reached only by intensively testing the tools on synthetically generated data. A flexible procedure allowing the generation of a cross-correlated stationary noise time series was set up. A multichannel time series with the desired cross-correlation behavior can be generated once a model for a multichannel cross-spectral matrix is provided. The core of the procedure comprises a noisemore » coloring, multichannel filter designed via a frequency-by-frequency eigendecomposition of the model cross-spectral matrix and a subsequent fit in the Z domain. The common problem of initial transients in a filtered time series is solved with a proper initialization of the filter recursion equations. The noise generator performance was tested in a two-dimensional case study of the closed-loop LISA Technology Package dynamics along the two principal degrees of freedom.« less

Array magnetics modal analysis for the DIII-D tokamak based on localized time-series modelling

DOE PAGES

Olofsson, K. Erik J.; Hanson, Jeremy M.; Shiraki, Daisuke; ...

2014-07-14

Here, time-series analysis of magnetics data in tokamaks is typically done using block-based fast Fourier transform methods. This work presents the development and deployment of a new set of algorithms for magnetic probe array analysis. The method is based on an estimation technique known as stochastic subspace identification (SSI). Compared with the standard coherence approach or the direct singular value decomposition approach, the new technique exhibits several beneficial properties. For example, the SSI method does not require that frequencies are orthogonal with respect to the timeframe used in the analysis. Frequencies are obtained directly as parameters of localized time-series models.more » The parameters are extracted by solving small-scale eigenvalue problems. Applications include maximum-likelihood regularized eigenmode pattern estimation, detection of neoclassical tearing modes, including locked mode precursors, and automatic clustering of modes, and magnetics-pattern characterization of sawtooth pre- and postcursors, edge harmonic oscillations and fishbones.« less

Study of the Effect of Temporal Sampling Frequency on DSCOVR Observations Using the GEOS-5 Nature Run Results (Part I): Earths Radiation Budget

NASA Technical Reports Server (NTRS)

Holdaway, Daniel; Yang, Yuekui

2016-01-01

Satellites always sample the Earth-atmosphere system in a finite temporal resolution. This study investigates the effect of sampling frequency on the satellite-derived Earth radiation budget, with the Deep Space Climate Observatory (DSCOVR) as an example. The output from NASA's Goddard Earth Observing System Version 5 (GEOS-5) Nature Run is used as the truth. The Nature Run is a high spatial and temporal resolution atmospheric simulation spanning a two-year period. The effect of temporal resolution on potential DSCOVR observations is assessed by sampling the full Nature Run data with 1-h to 24-h frequencies. The uncertainty associated with a given sampling frequency is measured by computing means over daily, monthly, seasonal and annual intervals and determining the spread across different possible starting points. The skill with which a particular sampling frequency captures the structure of the full time series is measured using correlations and normalized errors. Results show that higher sampling frequency gives more information and less uncertainty in the derived radiation budget. A sampling frequency coarser than every 4 h results in significant error. Correlations between true and sampled time series also decrease more rapidly for a sampling frequency less than 4 h.

[Analysis of time domain and frequency domain heart rate variability in fighter pilot before and after upright tilt].

PubMed

Wang, L; Wu, L; Ji, G; Zhang, X; Chen, T; Wang, L

1998-12-01

Effects of upright tilt on mechanism of autonomic nervous regulation of cardiovascular system and characteristics of heart rate variability (HRV) were observed in sixty healthy male pilots. Relation between time domain and frequency domain indexes of short-time HRV (5 min) were analysed before and after upright tilt. The results showed that there are significant difference in short time HRV parameters before and after upright tilt. Significant relationship was formed between time domain and frequency domain indexes of HRV. It suggests that time domain and frequency domain HRV analysis is capable of revealing certain informations embedded in a short series of R-R intervals and can help to evaluate the status of autonomic regulation of cardiovascular function in pilots.

Polycrystalline diamond RF MOSFET with MoO3 gate dielectric

NASA Astrophysics Data System (ADS)

Ren, Zeyang; Zhang, Jinfeng; Zhang, Jincheng; Zhang, Chunfu; Chen, Dazheng; Quan, Rudai; Yang, Jiayin; Lin, Zhiyu; Hao, Yue

2017-12-01

We report the radio frequency characteristics of the diamond metal-oxide-semiconductor field effect transistor with MoO3 gate dielectric for the first time. The device with 2-μm gate length was fabricated on high quality polycrystalline diamond. The maximum drain current of 150 mA/mm at VGS = -5 V and the maximum transconductance of 27 mS/mm were achieved. The extrinsic cutoff frequency of 1.2 GHz and the maximum oscillation frequency of 1.9 GHz have been measured. The moderate frequency characteristics are attributed to the moderate transconductance limited by the series resistance along the channel. We expect that the frequency characteristics of the device can be improved by increasing the magnitude of gm, or fundamentally decreasing the gate-controlled channel resistance and series resistance along the channel, and down-scaling the gate length.

Hilbert-Huang spectral analysis for characterizing the intrinsic time-scales of variability in decennial time-series of surface solar radiation

NASA Astrophysics Data System (ADS)

Bengulescu, Marc; Blanc, Philippe; Wald, Lucien

2016-04-01

An analysis of the variability of the surface solar irradiance (SSI) at different local time-scales is presented in this study. Since geophysical signals, such as long-term measurements of the SSI, are often produced by the non-linear interaction of deterministic physical processes that may also be under the influence of non-stationary external forcings, the Hilbert-Huang transform (HHT), an adaptive, noise-assisted, data-driven technique, is employed to extract locally - in time and in space - the embedded intrinsic scales at which a signal oscillates. The transform consists of two distinct steps. First, by means of the Empirical Mode Decomposition (EMD), the time-series is "de-constructed" into a finite number - often small - of zero-mean components that have distinct temporal scales of variability, termed hereinafter the Intrinsic Mode Functions (IMFs). The signal model of the components is an amplitude modulation - frequency modulation (AM - FM) one, and can also be thought of as an extension of a Fourier series having both time varying amplitude and frequency. Following the decomposition, Hilbert spectral analysis is then employed on the IMFs, yielding a time-frequency-energy representation that portrays changes in the spectral contents of the original data, with respect to time. As measurements of surface solar irradiance may possibly be contaminated by the manifestation of different type of stochastic processes (i.e. noise), the identification of real, physical processes from this background of random fluctuations is of interest. To this end, an adaptive background noise null hypothesis is assumed, based on the robust statistical properties of the EMD when applied to time-series of different classes of noise (e.g. white, red or fractional Gaussian). Since the algorithm acts as an efficient constant-Q dyadic, "wavelet-like", filter bank, the different noise inputs are decomposed into components having the same spectral shape, but that are translated to the next lower octave in the spectral domain. Thus, when the sampling step is increased, the spectral shape of IMFs cannot remain at its original position, due to the new lower Nyquist frequency, and is instead pushed toward the lower scaled frequency. Based on these features, the identification of potential signals within the data should become possible without any prior knowledge of the background noises. When applying the above outlined procedure to decennial time-series of surface solar irradiance, only the component that has an annual time-scale of variability is shown to have statistical properties that diverge from those of noise. Nevertheless, the noise-like components are not completely devoid of information, as it is found that their AM components have a non-null rank correlation coefficient with the annual mode, i.e. the background noise intensity seems to be modulated by the seasonal cycle. The findings have possible implications on the modelling and forecast of the surface solar irradiance, by discriminating its deterministic from its quasi-stochastic constituents, at distinct local time-scales.

Generalized Hurst exponent and multifractal function of original and translated texts mapped into frequency and length time series

NASA Astrophysics Data System (ADS)

Ausloos, M.

2012-09-01

A nonlinear dynamics approach can be used in order to quantify complexity in written texts. As a first step, a one-dimensional system is examined: two written texts by one author (Lewis Carroll) are considered, together with one translation into an artificial language (i.e., Esperanto) are mapped into time series. Their corresponding shuffled versions are used for obtaining a baseline. Two different one-dimensional time series are used here: one based on word lengths (LTS), the other on word frequencies (FTS). It is shown that the generalized Hurst exponent h(q) and the derived f(α) curves of the original and translated texts show marked differences. The original texts are far from giving a parabolic f(α) function, in contrast to the shuffled texts. Moreover, the Esperanto text has more extreme values. This suggests cascade model-like, with multiscale time-asymmetric features as finally written texts. A discussion of the difference and complementarity of mapping into a LTS or FTS is presented. The FTS f(α) curves are more opened than the LTS ones.

Generalized Hurst exponent and multifractal function of original and translated texts mapped into frequency and length time series.

PubMed

Ausloos, M

2012-09-01

A nonlinear dynamics approach can be used in order to quantify complexity in written texts. As a first step, a one-dimensional system is examined: two written texts by one author (Lewis Carroll) are considered, together with one translation into an artificial language (i.e., Esperanto) are mapped into time series. Their corresponding shuffled versions are used for obtaining a baseline. Two different one-dimensional time series are used here: one based on word lengths (LTS), the other on word frequencies (FTS). It is shown that the generalized Hurst exponent h(q) and the derived f(α) curves of the original and translated texts show marked differences. The original texts are far from giving a parabolic f(α) function, in contrast to the shuffled texts. Moreover, the Esperanto text has more extreme values. This suggests cascade model-like, with multiscale time-asymmetric features as finally written texts. A discussion of the difference and complementarity of mapping into a LTS or FTS is presented. The FTS f(α) curves are more opened than the LTS ones.

Land Use and Land Cover Change Dynamics across the Brazilian Amazon: Insights from Extensive Time-Series Analysis of Remote Sensing Data

PubMed Central

Carreiras, João M. B.; Jones, Joshua; Lucas, Richard M.; Gabriel, Cristina

2014-01-01

Throughout the Amazon region, the age of forests regenerating on previously deforested land is determined, in part, by the periods of active land use prior to abandonment and the frequency of reclearance of regrowth, both of which can be quantified by comparing time-series of Landsat sensor data. Using these time-series of near annual data from 1973–2011 for an area north of Manaus (in Amazonas state), from 1984–2010 for south of Santarém (Pará state) and 1984–2011 near Machadinho d’Oeste (Rondônia state), the changes in the area of primary forest, non-forest and secondary forest were documented from which the age of regenerating forests, periods of active land use and the frequency of forest reclearance were derived. At Manaus, and at the end of the time-series, over 50% of regenerating forests were older than 16 years, whilst at Santarém and Machadinho d’Oeste, 57% and 41% of forests respectively were aged 6–15 years, with the remainder being mostly younger forests. These differences were attributed to the time since deforestation commenced but also the greater frequencies of reclearance of forests at the latter two sites with short periods of use in the intervening periods. The majority of clearance for agriculture was also found outside of protected areas. The study suggested that a) the history of clearance and land use should be taken into account when protecting deforested land for the purpose of restoring both tree species diversity and biomass through natural regeneration and b) a greater proportion of the forested landscape should be placed under protection, including areas of regrowth. PMID:25099362

Land use and land cover change dynamics across the Brazilian Amazon: insights from extensive time-series analysis of remote sensing data.

PubMed

Carreiras, João M B; Jones, Joshua; Lucas, Richard M; Gabriel, Cristina

2014-01-01

Throughout the Amazon region, the age of forests regenerating on previously deforested land is determined, in part, by the periods of active land use prior to abandonment and the frequency of reclearance of regrowth, both of which can be quantified by comparing time-series of Landsat sensor data. Using these time-series of near annual data from 1973-2011 for an area north of Manaus (in Amazonas state), from 1984-2010 for south of Santarém (Pará state) and 1984-2011 near Machadinho d'Oeste (Rondônia state), the changes in the area of primary forest, non-forest and secondary forest were documented from which the age of regenerating forests, periods of active land use and the frequency of forest reclearance were derived. At Manaus, and at the end of the time-series, over 50% of regenerating forests were older than 16 years, whilst at Santarém and Machadinho d'Oeste, 57% and 41% of forests respectively were aged 6-15 years, with the remainder being mostly younger forests. These differences were attributed to the time since deforestation commenced but also the greater frequencies of reclearance of forests at the latter two sites with short periods of use in the intervening periods. The majority of clearance for agriculture was also found outside of protected areas. The study suggested that a) the history of clearance and land use should be taken into account when protecting deforested land for the purpose of restoring both tree species diversity and biomass through natural regeneration and b) a greater proportion of the forested landscape should be placed under protection, including areas of regrowth.

Radon anomalies: When are they possible to be detected?

NASA Astrophysics Data System (ADS)

Passarelli, Luigi; Woith, Heiko; Seyis, Cemil; Nikkhoo, Mehdi; Donner, Reik

2017-04-01

Records of the Radon noble gas in different environments like soil, air, groundwater, rock, caves, and tunnels, typically display cyclic variations including diurnal (S1), semidiurnal (S2) and seasonal components. But there are also cases where theses cycles are absent. Interestingly, radon emission can also be affected by transient processes, which inhibit or enhance the radon carrying process at the surface. This results in transient changes in the radon emission rate, which are superimposed on the low and high frequency cycles. The complexity in the spectral contents of the radon time-series makes any statistical analysis aiming at understanding the physical driving processes a challenging task. In the past decades there have been several attempts to relate changes in radon emission rate with physical triggering processes such as earthquake occurrence. One of the problems in this type of investigation is to objectively detect anomalies in the radon time-series. In the present work, we propose a simple and objective statistical method for detecting changes in the radon emission rate time-series. The method uses non-parametric statistical tests (e.g., Kolmogorov-Smirnov) to compare empirical distributions of radon emission rate by sequentially applying various time window to the time-series. The statistical test indicates whether two empirical distributions of data originate from the same distribution at a desired significance level. We test the algorithm on synthetic data in order to explore the sensitivity of the statistical test to the sample size. We successively apply the test to six radon emission rate recordings from stations located around the Marmara Sea obtained within the MARsite project (MARsite has received funding from the European Union's Seventh Programme for research, technological development and demonstration under grant agreement No 308417). We conclude that the test performs relatively well on identify transient changes in the radon emission rate, but the results are strongly dependent on the length of the time window and/or type of frequency filtering. More importantly, when raw time-series contain cyclic components (e.g. seasonal or diurnal variation), the quest of anomalies related to transients becomes meaningless. We conclude that an objective identification of transient changes can be performed only after filtering the raw time-series for the physically meaningful frequency content.

A nonlinear generalization of the Savitzky-Golay filter and the quantitative analysis of saccades

PubMed Central

Dai, Weiwei; Selesnick, Ivan; Rizzo, John-Ross; Rucker, Janet; Hudson, Todd

2017-01-01

The Savitzky-Golay (SG) filter is widely used to smooth and differentiate time series, especially biomedical data. However, time series that exhibit abrupt departures from their typical trends, such as sharp waves or steps, which are of physiological interest, tend to be oversmoothed by the SG filter. Hence, the SG filter tends to systematically underestimate physiological parameters in certain situations. This article proposes a generalization of the SG filter to more accurately track abrupt deviations in time series, leading to more accurate parameter estimates (e.g., peak velocity of saccadic eye movements). The proposed filtering methodology models a time series as the sum of two component time series: a low-frequency time series for which the conventional SG filter is well suited, and a second time series that exhibits instantaneous deviations (e.g., sharp waves, steps, or more generally, discontinuities in a higher order derivative). The generalized SG filter is then applied to the quantitative analysis of saccadic eye movements. It is demonstrated that (a) the conventional SG filter underestimates the peak velocity of saccades, especially those of small amplitude, and (b) the generalized SG filter estimates peak saccadic velocity more accurately than the conventional filter. PMID:28813566

A nonlinear generalization of the Savitzky-Golay filter and the quantitative analysis of saccades.

PubMed

Dai, Weiwei; Selesnick, Ivan; Rizzo, John-Ross; Rucker, Janet; Hudson, Todd

2017-08-01

The Savitzky-Golay (SG) filter is widely used to smooth and differentiate time series, especially biomedical data. However, time series that exhibit abrupt departures from their typical trends, such as sharp waves or steps, which are of physiological interest, tend to be oversmoothed by the SG filter. Hence, the SG filter tends to systematically underestimate physiological parameters in certain situations. This article proposes a generalization of the SG filter to more accurately track abrupt deviations in time series, leading to more accurate parameter estimates (e.g., peak velocity of saccadic eye movements). The proposed filtering methodology models a time series as the sum of two component time series: a low-frequency time series for which the conventional SG filter is well suited, and a second time series that exhibits instantaneous deviations (e.g., sharp waves, steps, or more generally, discontinuities in a higher order derivative). The generalized SG filter is then applied to the quantitative analysis of saccadic eye movements. It is demonstrated that (a) the conventional SG filter underestimates the peak velocity of saccades, especially those of small amplitude, and (b) the generalized SG filter estimates peak saccadic velocity more accurately than the conventional filter.

Collaborative Research with Chinese, Indian, Filipino and North European Research Organizations on Infectious Disease Epidemics.

PubMed

Sumi, Ayako; Kobayashi, Nobumichi

2017-01-01

In this report, we present a short review of applications of time series analysis, which consists of spectral analysis based on the maximum entropy method in the frequency domain and the least squares method in the time domain, to the incidence data of infectious diseases. This report consists of three parts. First, we present our results obtained by collaborative research on infectious disease epidemics with Chinese, Indian, Filipino and North European research organizations. Second, we present the results obtained with the Japanese infectious disease surveillance data and the time series numerically generated from a mathematical model, called the susceptible/exposed/infectious/recovered (SEIR) model. Third, we present an application of the time series analysis to pathologic tissues to examine the usefulness of time series analysis for investigating the spatial pattern of pathologic tissue. It is anticipated that time series analysis will become a useful tool for investigating not only infectious disease surveillance data but also immunological and genetic tests.

Radar/Sonar and Time Series Analysis

DTIC Science & Technology

1991-04-08

Fourier and Likelihood Analysis in NMR Spectroscopy .......... David Brillinger and Reinhold Kaiser Resampling Techniques for Stationary Time-series... Meyer The parabolic Fock theory foi a convex dielectric Georgia Tech. scatterer Abstract. This talk deals with a high frequency as) mptotic m~thod for...Malesky Inst. of Physics, Moscow Jun 11 - Jun 15 Victor P. Maslov MIEIM, USSR May 29 - Jun 15 Robert P. Meyer University of Wisconsin Jun 11 - Jun 15

Multiresolution analysis of Bursa Malaysia KLCI time series

NASA Astrophysics Data System (ADS)

Ismail, Mohd Tahir; Dghais, Amel Abdoullah Ahmed

2017-05-01

In general, a time series is simply a sequence of numbers collected at regular intervals over a period. Financial time series data processing is concerned with the theory and practice of processing asset price over time, such as currency, commodity data, and stock market data. The primary aim of this study is to understand the fundamental characteristics of selected financial time series by using the time as well as the frequency domain analysis. After that prediction can be executed for the desired system for in sample forecasting. In this study, multiresolution analysis which the assist of discrete wavelet transforms (DWT) and maximal overlap discrete wavelet transform (MODWT) will be used to pinpoint special characteristics of Bursa Malaysia KLCI (Kuala Lumpur Composite Index) daily closing prices and return values. In addition, further case study discussions include the modeling of Bursa Malaysia KLCI using linear ARIMA with wavelets to address how multiresolution approach improves fitting and forecasting results.

Testing for nonlinearity in non-stationary physiological time series.

PubMed

Guarín, Diego; Delgado, Edilson; Orozco, Álvaro

2011-01-01

Testing for nonlinearity is one of the most important preprocessing steps in nonlinear time series analysis. Typically, this is done by means of the linear surrogate data methods. But it is a known fact that the validity of the results heavily depends on the stationarity of the time series. Since most physiological signals are non-stationary, it is easy to falsely detect nonlinearity using the linear surrogate data methods. In this document, we propose a methodology to extend the procedure for generating constrained surrogate time series in order to assess nonlinearity in non-stationary data. The method is based on the band-phase-randomized surrogates, which consists (contrary to the linear surrogate data methods) in randomizing only a portion of the Fourier phases in the high frequency domain. Analysis of simulated time series showed that in comparison to the linear surrogate data method, our method is able to discriminate between linear stationarity, linear non-stationary and nonlinear time series. Applying our methodology to heart rate variability (HRV) records of five healthy patients, we encountered that nonlinear correlations are present in this non-stationary physiological signals.

Performance of vegetation indices from Landsat time series in deforestation monitoring

NASA Astrophysics Data System (ADS)

Schultz, Michael; Clevers, Jan G. P. W.; Carter, Sarah; Verbesselt, Jan; Avitabile, Valerio; Quang, Hien Vu; Herold, Martin

2016-10-01

The performance of Landsat time series (LTS) of eight vegetation indices (VIs) was assessed for monitoring deforestation across the tropics. Three sites were selected based on differing remote sensing observation frequencies, deforestation drivers and environmental factors. The LTS of each VI was analysed using the Breaks For Additive Season and Trend (BFAST) Monitor method to identify deforestation. A robust reference database was used to evaluate the performance regarding spatial accuracy, sensitivity to observation frequency and combined use of multiple VIs. The canopy cover sensitive Normalized Difference Fraction Index (NDFI) was the most accurate. Among those tested, wetness related VIs (Normalized Difference Moisture Index (NDMI) and the Tasselled Cap wetness (TCw)) were spatially more accurate than greenness related VIs (Normalized Difference Vegetation Index (NDVI) and Tasselled Cap greenness (TCg)). When VIs were fused on feature level, spatial accuracy was improved and overestimation of change reduced. NDVI and NDFI produced the most robust results when observation frequency varies.

Russian State Time and Earth Rotation Service: Observations, Eop Series, Prediction

NASA Astrophysics Data System (ADS)

Kaufman, M.; Pasynok, S.

2010-01-01

Russian State Time, Frequency and Earth Rotation Service provides the official EOP data and time for use in scientific, technical and metrological works in Russia. The observations of GLONASS and GPS on 30 stations in Russia, and also the Russian and worldwide observations data of VLBI (35 stations) and SLR (20 stations) are used now. To these three series of EOP the data calculated in two other Russian analysis centers are added: IAA (VLBI, GPS and SLR series) and MCC (SLR). Joint processing of these 7 series is carried out every day (the operational EOP data for the last day and the predicted values for 50 days). The EOP values are weekly refined and systematic errors of every individual series are corrected. The combined results become accessible on the VNIIFTRI server (ftp.imvp.ru) approximately at 6h UT daily.

Statistical Frequency-Dependent Analysis of Trial-to-Trial Variability in Single Time Series by Recurrence Plots.

PubMed

Tošić, Tamara; Sellers, Kristin K; Fröhlich, Flavio; Fedotenkova, Mariia; Beim Graben, Peter; Hutt, Axel

2015-01-01

For decades, research in neuroscience has supported the hypothesis that brain dynamics exhibits recurrent metastable states connected by transients, which together encode fundamental neural information processing. To understand the system's dynamics it is important to detect such recurrence domains, but it is challenging to extract them from experimental neuroscience datasets due to the large trial-to-trial variability. The proposed methodology extracts recurrent metastable states in univariate time series by transforming datasets into their time-frequency representations and computing recurrence plots based on instantaneous spectral power values in various frequency bands. Additionally, a new statistical inference analysis compares different trial recurrence plots with corresponding surrogates to obtain statistically significant recurrent structures. This combination of methods is validated by applying it to two artificial datasets. In a final study of visually-evoked Local Field Potentials in partially anesthetized ferrets, the methodology is able to reveal recurrence structures of neural responses with trial-to-trial variability. Focusing on different frequency bands, the δ-band activity is much less recurrent than α-band activity. Moreover, α-activity is susceptible to pre-stimuli, while δ-activity is much less sensitive to pre-stimuli. This difference in recurrence structures in different frequency bands indicates diverse underlying information processing steps in the brain.

Statistical Frequency-Dependent Analysis of Trial-to-Trial Variability in Single Time Series by Recurrence Plots

PubMed Central

Tošić, Tamara; Sellers, Kristin K.; Fröhlich, Flavio; Fedotenkova, Mariia; beim Graben, Peter; Hutt, Axel

2016-01-01

For decades, research in neuroscience has supported the hypothesis that brain dynamics exhibits recurrent metastable states connected by transients, which together encode fundamental neural information processing. To understand the system's dynamics it is important to detect such recurrence domains, but it is challenging to extract them from experimental neuroscience datasets due to the large trial-to-trial variability. The proposed methodology extracts recurrent metastable states in univariate time series by transforming datasets into their time-frequency representations and computing recurrence plots based on instantaneous spectral power values in various frequency bands. Additionally, a new statistical inference analysis compares different trial recurrence plots with corresponding surrogates to obtain statistically significant recurrent structures. This combination of methods is validated by applying it to two artificial datasets. In a final study of visually-evoked Local Field Potentials in partially anesthetized ferrets, the methodology is able to reveal recurrence structures of neural responses with trial-to-trial variability. Focusing on different frequency bands, the δ-band activity is much less recurrent than α-band activity. Moreover, α-activity is susceptible to pre-stimuli, while δ-activity is much less sensitive to pre-stimuli. This difference in recurrence structures in different frequency bands indicates diverse underlying information processing steps in the brain. PMID:26834580

Nonstationary frequency analysis for the trivariate flood series of the Weihe River

NASA Astrophysics Data System (ADS)

Jiang, Cong; Xiong, Lihua

2016-04-01

Some intensive human activities such as water-soil conservation can significantly alter the natural hydrological processes of rivers. In this study, the effect of the water-soil conservation on the trivariate flood series from the Weihe River located in the Northwest China is investigated. The annual maxima daily discharge, annual maxima 3-day flood volume and annual maxima 5-day flood volume are chosen as the study data and used to compose the trivariate flood series. The nonstationarities in both the individual univariate flood series and the corresponding antecedent precipitation series generating the flood events are examined by the Mann-Kendall trend test. It is found that all individual univariate flood series present significant decreasing trend, while the antecedent precipitation series can be treated as stationary. It indicates that the increase of the water-soil conservation land area has altered the rainfall-runoff relationship of the Weihe basin, and induced the nonstationarities in the three individual univariate flood series. The time-varying moments model based on the Pearson type III distribution is applied to capture the nonstationarities in the flood frequency distribution with the water-soil conservation land area introduced as the explanatory variable of the flood distribution parameters. Based on the analysis for each individual univariate flood series, the dependence structure among the three univariate flood series are investigated by the time-varying copula model also with the water-soil conservation land area as the explanatory variable of copula parameters. The results indicate that the dependence among the trivariate flood series is enhanced by the increase of water-soil conservation land area.

Detection of main tidal frequencies using least squares harmonic estimation method

NASA Astrophysics Data System (ADS)

Mousavian, R.; Hossainali, M. Mashhadi

2012-11-01

In this paper the efficiency of the method of Least Squares Harmonic Estimation (LS-HE) for detecting the main tidal frequencies is investigated. Using this method, the tidal spectrum of the sea level data is evaluated at two tidal stations: Bandar Abbas in south of Iran and Workington on the eastern coast of the UK. The amplitudes of the tidal constituents at these two tidal stations are not the same. Moreover, in contrary to the Workington station, the Bandar Abbas tidal record is not an equispaced time series. Therefore, the analysis of the hourly tidal observations in Bandar Abbas and Workington can provide a reasonable insight into the efficiency of this method for analyzing the frequency content of tidal time series. Furthermore, applying the method of Fourier transform to the Workington tidal record provides an independent source of information for evaluating the tidal spectrum proposed by the LS-HE method. According to the obtained results, the spectrums of these two tidal records contain the components with the maximum amplitudes among the expected ones in this time span and some new frequencies in the list of known constituents. In addition, in terms of frequencies with maximum amplitude; the power spectrums derived from two aforementioned methods are the same. These results demonstrate the ability of LS-HE for identifying the frequencies with maximum amplitude in both tidal records.

Adventures in Modern Time Series Analysis: From the Sun to the Crab Nebula and Beyond

NASA Technical Reports Server (NTRS)

Scargle, Jeffrey

2014-01-01

With the generation of long, precise, and finely sampled time series the Age of Digital Astronomy is uncovering and elucidating energetic dynamical processes throughout the Universe. Fulfilling these opportunities requires data effective analysis techniques rapidly and automatically implementing advanced concepts. The Time Series Explorer, under development in collaboration with Tom Loredo, provides tools ranging from simple but optimal histograms to time and frequency domain analysis for arbitrary data modes with any time sampling. Much of this development owes its existence to Joe Bredekamp and the encouragement he provided over several decades. Sample results for solar chromospheric activity, gamma-ray activity in the Crab Nebula, active galactic nuclei and gamma-ray bursts will be displayed.

Revising time series of the Elbe river discharge for flood frequency determination at gauge Dresden

NASA Astrophysics Data System (ADS)

Bartl, S.; Schümberg, S.; Deutsch, M.

2009-11-01

The German research programme RIsk MAnagment of eXtreme flood events has accomplished the improvement of regional hazard assessment for the large rivers in Germany. Here we focused on the Elbe river at its gauge Dresden, which belongs to the oldest gauges in Europe with officially available daily discharge time series beginning on 1 January 1890. The project on the one hand aimed to extend and to revise the existing time series, and on the other hand to examine the variability of the Elbe river discharge conditions on a greater time scale. Therefore one major task were the historical searches and the examination of the retrieved documents and the contained information. After analysing this information the development of the river course and the discharge conditions were discussed. Using the provided knowledge, in an other subproject, a historical hydraulic model was established. Its results then again were used here. A further purpose was the determining of flood frequency based on all pre-processed data. The obtained knowledge about historical changes was also used to get an idea about possible future variations under climate change conditions. Especially variations in the runoff characteristic of the Elbe river over the course of the year were analysed. It succeeded to obtain a much longer discharge time series which contain fewer errors and uncertainties. Hence an optimized regional hazard assessment was realised.

GSC 02505-00411: A new delta Sct star in the field of RZ LMi

NASA Astrophysics Data System (ADS)

Ishioka, R.; Kokumbaeva, R.

2017-04-01

We present the time series analysis of CCD photometry from ``EAST'' Zeiss-1000 telescope at Tien-Shan Astronomical Observatory (Almaty, Kazakhstan) for GSC 02505-00411. GSC 02505-00411 is a new multi-frequency delta Scuti variable with a primary frequency of 43.84 c/d.

Continuous time transfer using GPS carrier phase.

PubMed

Dach, Rolf; Schildknecht, Thomas; Springer, Tim; Dudle, Gregor; Prost, Leon

2002-11-01

The Astronomical Institute of the University of Berne is hosting one of the Analysis Centers (AC) of the International GPS Service (IGS). A network of a few GPS stations in Europe and North America is routinely analyzed for time transfer purposes, using the carrier phase observations. This work is done in the framework of a joint project with the Swiss Federal Office of Metrology and Accreditation (METAS). The daily solutions are computed independently. The resulting time transfer series show jumps of up to 1 ns at the day boundaries. A method to concatenate the daily time transfer solutions to a continuous series was developed. A continuous time series is available for a time span of more than 4 mo. The results were compared with the time transfer results from other techniques such as two-way satellite time and frequency transfer. This concatenation improves the results obtained in a daily computing scheme because a continuous time series better reflects the characteristics of continuously working clocks.

A general theory on frequency and time-frequency analysis of irregularly sampled time series based on projection methods - Part 2: Extension to time-frequency analysis

NASA Astrophysics Data System (ADS)

Lenoir, Guillaume; Crucifix, Michel

2018-03-01

Geophysical time series are sometimes sampled irregularly along the time axis. The situation is particularly frequent in palaeoclimatology. Yet, there is so far no general framework for handling the continuous wavelet transform when the time sampling is irregular. Here we provide such a framework. To this end, we define the scalogram as the continuous-wavelet-transform equivalent of the extended Lomb-Scargle periodogram defined in Part 1 of this study (Lenoir and Crucifix, 2018). The signal being analysed is modelled as the sum of a locally periodic component in the time-frequency plane, a polynomial trend, and a background noise. The mother wavelet adopted here is the Morlet wavelet classically used in geophysical applications. The background noise model is a stationary Gaussian continuous autoregressive-moving-average (CARMA) process, which is more general than the traditional Gaussian white and red noise processes. The scalogram is smoothed by averaging over neighbouring times in order to reduce its variance. The Shannon-Nyquist exclusion zone is however defined as the area corrupted by local aliasing issues. The local amplitude in the time-frequency plane is then estimated with least-squares methods. We also derive an approximate formula linking the squared amplitude and the scalogram. Based on this property, we define a new analysis tool: the weighted smoothed scalogram, which we recommend for most analyses. The estimated signal amplitude also gives access to band and ridge filtering. Finally, we design a test of significance for the weighted smoothed scalogram against the stationary Gaussian CARMA background noise, and provide algorithms for computing confidence levels, either analytically or with Monte Carlo Markov chain methods. All the analysis tools presented in this article are available to the reader in the Python package WAVEPAL.

Possible signatures of dissipation from time-series analysis techniques using a turbulent laboratory magnetohydrodynamic plasma

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

Schaffner, D. A.; Brown, M. R.; Rock, A. B.

The frequency spectrum of magnetic fluctuations as measured on the Swarthmore Spheromak Experiment is broadband and exhibits a nearly Kolmogorov 5/3 scaling. It features a steepening region which is indicative of dissipation of magnetic fluctuation energy similar to that observed in fluid and magnetohydrodynamic turbulence systems. Two non-spectrum based time-series analysis techniques are implemented on this data set in order to seek other possible signatures of turbulent dissipation beyond just the steepening of fluctuation spectra. Presented here are results for the flatness, permutation entropy, and statistical complexity, each of which exhibits a particular character at spectral steepening scales which canmore » then be compared to the behavior of the frequency spectrum.« less

Scaling laws from geomagnetic time series

USGS Publications Warehouse

Voros, Z.; Kovacs, P.; Juhasz, A.; Kormendi, A.; Green, A.W.

1998-01-01

The notion of extended self-similarity (ESS) is applied here for the X - component time series of geomagnetic field fluctuations. Plotting nth order structure functions against the fourth order structure function we show that low-frequency geomagnetic fluctuations up to the order n = 10 follow the same scaling laws as MHD fluctuations in solar wind, however, for higher frequencies (f > l/5[h]) a clear departure from the expected universality is observed for n > 6. ESS does not allow to make an unambiguous statement about the non triviality of scaling laws in "geomagnetic" turbulence. However, we suggest to use higher order moments as promising diagnostic tools for mapping the contributions of various remote magnetospheric sources to local observatory data. Copyright 1998 by the American Geophysical Union.

Deconvolution of time series in the laboratory

NASA Astrophysics Data System (ADS)

John, Thomas; Pietschmann, Dirk; Becker, Volker; Wagner, Christian

2016-10-01

In this study, we present two practical applications of the deconvolution of time series in Fourier space. First, we reconstruct a filtered input signal of sound cards that has been heavily distorted by a built-in high-pass filter using a software approach. Using deconvolution, we can partially bypass the filter and extend the dynamic frequency range by two orders of magnitude. Second, we construct required input signals for a mechanical shaker in order to obtain arbitrary acceleration waveforms, referred to as feedforward control. For both situations, experimental and theoretical approaches are discussed to determine the system-dependent frequency response. Moreover, for the shaker, we propose a simple feedback loop as an extension to the feedforward control in order to handle nonlinearities of the system.

What explains rare and conspicuous colours in a snail? A test of time-series data against models of drift, migration or selection.

PubMed

Johannesson, K; Butlin, R K

2017-01-01

It is intriguing that conspicuous colour morphs of a prey species may be maintained at low frequencies alongside cryptic morphs. Negative frequency-dependent selection by predators using search images ('apostatic selection') is often suggested without rejecting alternative explanations. Using a maximum likelihood approach we fitted predictions from models of genetic drift, migration, constant selection, heterozygote advantage or negative frequency-dependent selection to time-series data of colour frequencies in isolated populations of a marine snail (Littorina saxatilis), re-established with perturbed colour morph frequencies and followed for >20 generations. Snails of conspicuous colours (white, red, banded) are naturally rare in the study area (usually <10%) but frequencies were manipulated to levels of ~50% (one colour per population) in 8 populations at the start of the experiment in 1992. In 2013, frequencies had declined to ~15-45%. Drift alone could not explain these changes. Migration could not be rejected in any population, but required rates much higher than those recorded. Directional selection was rejected in three populations in favour of balancing selection. Heterozygote advantage and negative frequency-dependent selection could not be distinguished statistically, although overall the results favoured the latter. Populations varied idiosyncratically as mild or variable colour selection (3-11%) interacted with demographic stochasticity, and the overall conclusion was that multiple mechanisms may contribute to maintaining the polymorphisms.

Detecting and characterizing high-frequency oscillations in epilepsy: a case study of big data analysis

NASA Astrophysics Data System (ADS)

Huang, Liang; Ni, Xuan; Ditto, William L.; Spano, Mark; Carney, Paul R.; Lai, Ying-Cheng

2017-01-01

We develop a framework to uncover and analyse dynamical anomalies from massive, nonlinear and non-stationary time series data. The framework consists of three steps: preprocessing of massive datasets to eliminate erroneous data segments, application of the empirical mode decomposition and Hilbert transform paradigm to obtain the fundamental components embedded in the time series at distinct time scales, and statistical/scaling analysis of the components. As a case study, we apply our framework to detecting and characterizing high-frequency oscillations (HFOs) from a big database of rat electroencephalogram recordings. We find a striking phenomenon: HFOs exhibit on-off intermittency that can be quantified by algebraic scaling laws. Our framework can be generalized to big data-related problems in other fields such as large-scale sensor data and seismic data analysis.

76 FR 70165 - Proposed Collection, Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2011-11-10

... vacancies, labor hires, and labor separations. As the monthly JOLTS time series grow longer, their value in... ensure that requested data can be provided in the desired format, reporting burden (time and financial... businesses and organizations. Total Total Average time Estimated Affected public respondents Frequency...

Extended causal modeling to assess Partial Directed Coherence in multiple time series with significant instantaneous interactions.

PubMed

Faes, Luca; Nollo, Giandomenico

2010-11-01

The Partial Directed Coherence (PDC) and its generalized formulation (gPDC) are popular tools for investigating, in the frequency domain, the concept of Granger causality among multivariate (MV) time series. PDC and gPDC are formalized in terms of the coefficients of an MV autoregressive (MVAR) model which describes only the lagged effects among the time series and forsakes instantaneous effects. However, instantaneous effects are known to affect linear parametric modeling, and are likely to occur in experimental time series. In this study, we investigate the impact on the assessment of frequency domain causality of excluding instantaneous effects from the model underlying PDC evaluation. Moreover, we propose the utilization of an extended MVAR model including both instantaneous and lagged effects. This model is used to assess PDC either in accordance with the definition of Granger causality when considering only lagged effects (iPDC), or with an extended form of causality, when we consider both instantaneous and lagged effects (ePDC). The approach is first evaluated on three theoretical examples of MVAR processes, which show that the presence of instantaneous correlations may produce misleading profiles of PDC and gPDC, while ePDC and iPDC derived from the extended model provide here a correct interpretation of extended and lagged causality. It is then applied to representative examples of cardiorespiratory and EEG MV time series. They suggest that ePDC and iPDC are better interpretable than PDC and gPDC in terms of the known cardiovascular and neural physiologies.

Nonlinear data-driven identification of polymer electrolyte membrane fuel cells for diagnostic purposes: A Volterra series approach

NASA Astrophysics Data System (ADS)

Ritzberger, D.; Jakubek, S.

2017-09-01

In this work, a data-driven identification method, based on polynomial nonlinear autoregressive models with exogenous inputs (NARX) and the Volterra series, is proposed to describe the dynamic and nonlinear voltage and current characteristics of polymer electrolyte membrane fuel cells (PEMFCs). The structure selection and parameter estimation of the NARX model is performed on broad-band voltage/current data. By transforming the time-domain NARX model into a Volterra series representation using the harmonic probing algorithm, a frequency-domain description of the linear and nonlinear dynamics is obtained. With the Volterra kernels corresponding to different operating conditions, information from existing diagnostic tools in the frequency domain such as electrochemical impedance spectroscopy (EIS) and total harmonic distortion analysis (THDA) are effectively combined. Additionally, the time-domain NARX model can be utilized for fault detection by evaluating the difference between measured and simulated output. To increase the fault detectability, an optimization problem is introduced which maximizes this output residual to obtain proper excitation frequencies. As a possible extension it is shown, that by optimizing the periodic signal shape itself that the fault detectability is further increased.

The short time Fourier transform and local signals

NASA Astrophysics Data System (ADS)

Okumura, Shuhei

In this thesis, I examine the theoretical properties of the short time discrete Fourier transform (STFT). The STFT is obtained by applying the Fourier transform by a fixed-sized, moving window to input series. We move the window by one time point at a time, so we have overlapping windows. I present several theoretical properties of the STFT, applied to various types of complex-valued, univariate time series inputs, and their outputs in closed forms. In particular, just like the discrete Fourier transform, the STFT's modulus time series takes large positive values when the input is a periodic signal. One main point is that a white noise time series input results in the STFT output being a complex-valued stationary time series and we can derive the time and time-frequency dependency structure such as the cross-covariance functions. Our primary focus is the detection of local periodic signals. I present a method to detect local signals by computing the probability that the squared modulus STFT time series has consecutive large values exceeding some threshold after one exceeding observation following one observation less than the threshold. We discuss a method to reduce the computation of such probabilities by the Box-Cox transformation and the delta method, and show that it works well in comparison to the Monte Carlo simulation method.

Fault Diagnosis from Raw Sensor Data Using Deep Neural Networks Considering Temporal Coherence.

PubMed

Zhang, Ran; Peng, Zhen; Wu, Lifeng; Yao, Beibei; Guan, Yong

2017-03-09

Intelligent condition monitoring and fault diagnosis by analyzing the sensor data can assure the safety of machinery. Conventional fault diagnosis and classification methods usually implement pretreatments to decrease noise and extract some time domain or frequency domain features from raw time series sensor data. Then, some classifiers are utilized to make diagnosis. However, these conventional fault diagnosis approaches suffer from the expertise of feature selection and they do not consider the temporal coherence of time series data. This paper proposes a fault diagnosis model based on Deep Neural Networks (DNN). The model can directly recognize raw time series sensor data without feature selection and signal processing. It also takes advantage of the temporal coherence of the data. Firstly, raw time series training data collected by sensors are used to train the DNN until the cost function of DNN gets the minimal value; Secondly, test data are used to test the classification accuracy of the DNN on local time series data. Finally, fault diagnosis considering temporal coherence with former time series data is implemented. Experimental results show that the classification accuracy of bearing faults can get 100%. The proposed fault diagnosis approach is effective in recognizing the type of bearing faults.

Fault Diagnosis from Raw Sensor Data Using Deep Neural Networks Considering Temporal Coherence

PubMed Central

Zhang, Ran; Peng, Zhen; Wu, Lifeng; Yao, Beibei; Guan, Yong

2017-01-01

Intelligent condition monitoring and fault diagnosis by analyzing the sensor data can assure the safety of machinery. Conventional fault diagnosis and classification methods usually implement pretreatments to decrease noise and extract some time domain or frequency domain features from raw time series sensor data. Then, some classifiers are utilized to make diagnosis. However, these conventional fault diagnosis approaches suffer from the expertise of feature selection and they do not consider the temporal coherence of time series data. This paper proposes a fault diagnosis model based on Deep Neural Networks (DNN). The model can directly recognize raw time series sensor data without feature selection and signal processing. It also takes advantage of the temporal coherence of the data. Firstly, raw time series training data collected by sensors are used to train the DNN until the cost function of DNN gets the minimal value; Secondly, test data are used to test the classification accuracy of the DNN on local time series data. Finally, fault diagnosis considering temporal coherence with former time series data is implemented. Experimental results show that the classification accuracy of bearing faults can get 100%. The proposed fault diagnosis approach is effective in recognizing the type of bearing faults. PMID:28282936

How one might miss early warning signals of critical transitions in time series data: A systematic study of two major currency pairs

PubMed Central

Ciamarra, Massimo Pica; Cheong, Siew Ann

2018-01-01

There is growing interest in the use of critical slowing down and critical fluctuations as early warning signals for critical transitions in different complex systems. However, while some studies found them effective, others found the opposite. In this paper, we investigated why this might be so, by testing three commonly used indicators: lag-1 autocorrelation, variance, and low-frequency power spectrum at anticipating critical transitions in the very-high-frequency time series data of the Australian Dollar-Japanese Yen and Swiss Franc-Japanese Yen exchange rates. Besides testing rising trends in these indicators at a strict level of confidence using the Kendall-tau test, we also required statistically significant early warning signals to be concurrent in the three indicators, which must rise to appreciable values. We then found for our data set the optimum parameters for discovering critical transitions, and showed that the set of critical transitions found is generally insensitive to variations in the parameters. Suspecting that negative results in the literature are the results of low data frequencies, we created time series with time intervals over three orders of magnitude from the raw data, and tested them for early warning signals. Early warning signals can be reliably found only if the time interval of the data is shorter than the time scale of critical transitions in our complex system of interest. Finally, we compared the set of time windows with statistically significant early warning signals with the set of time windows followed by large movements, to conclude that the early warning signals indeed provide reliable information on impending critical transitions. This reliability becomes more compelling statistically the more events we test. PMID:29538373

How one might miss early warning signals of critical transitions in time series data: A systematic study of two major currency pairs.

PubMed

Wen, Haoyu; Ciamarra, Massimo Pica; Cheong, Siew Ann

2018-01-01

There is growing interest in the use of critical slowing down and critical fluctuations as early warning signals for critical transitions in different complex systems. However, while some studies found them effective, others found the opposite. In this paper, we investigated why this might be so, by testing three commonly used indicators: lag-1 autocorrelation, variance, and low-frequency power spectrum at anticipating critical transitions in the very-high-frequency time series data of the Australian Dollar-Japanese Yen and Swiss Franc-Japanese Yen exchange rates. Besides testing rising trends in these indicators at a strict level of confidence using the Kendall-tau test, we also required statistically significant early warning signals to be concurrent in the three indicators, which must rise to appreciable values. We then found for our data set the optimum parameters for discovering critical transitions, and showed that the set of critical transitions found is generally insensitive to variations in the parameters. Suspecting that negative results in the literature are the results of low data frequencies, we created time series with time intervals over three orders of magnitude from the raw data, and tested them for early warning signals. Early warning signals can be reliably found only if the time interval of the data is shorter than the time scale of critical transitions in our complex system of interest. Finally, we compared the set of time windows with statistically significant early warning signals with the set of time windows followed by large movements, to conclude that the early warning signals indeed provide reliable information on impending critical transitions. This reliability becomes more compelling statistically the more events we test.

CFAVC scheme for high frequency series resonant inverter-fed domestic induction heating system

NASA Astrophysics Data System (ADS)

Nagarajan, Booma; Reddy Sathi, Rama

2016-01-01

This article presents the investigations on the constant frequency asymmetric voltage cancellation control in the AC-AC resonant converter-fed domestic induction heating system. Conventional fixed frequency control techniques used in the high frequency converters lead to non-zero voltage switching operation and reduced output power. The proposed control technique produces higher output power than the conventional fixed-frequency control strategies. In this control technique, zero-voltage-switching operation is maintained during different duty cycle operation for reduction in the switching losses. Complete analysis of the induction heating power supply system with asymmetric voltage cancellation control is discussed in this article. Simulation and experimental study on constant frequency asymmetric voltage cancellation (CFAVC)-controlled full bridge series resonant inverter is performed. Time domain simulation results for the open and closed loop of the system are obtained using MATLAB simulation tool. The simulation results prove the control of voltage and power in a wide range. PID controller-based closed loop control system achieves the voltage regulation of the proposed system for the step change in load. Hardware implementation of the system under CFAVC control is done using the embedded controller. The simulation and experimental results validate the performance of the CFAVC control technique for series resonant-based induction cooking system.

Narrow bandwidth detection of vibration signature using fiber lasers

DOEpatents

Moore, Sean; Soh, Daniel B.S.

2018-05-08

The various technologies presented herein relate to extracting a portion of each pulse in a series of pulses reflected from a target to facilitate determination of a Doppler-shifted frequency for each pulse and, subsequently, a vibration frequency for the series of pulses. Each pulse can have a square-wave configuration, whereby each pulse can be time-gated to facilitate discarding the leading edge and the trailing edge (and associated non-linear effects) of each pulse and accordingly, capture of the central portion of the pulse from which the Doppler-shifted frequency, and ultimately, the vibration frequency of the target can be determined. Determination of the vibration velocity facilitates identification of the target being in a state of motion. The plurality of pulses can be formed from a laser beam (e.g., a continuous wave), the laser beam having a narrow bandwidth.

Series resonant converter with auxiliary winding turns: analysis, design and implementation

NASA Astrophysics Data System (ADS)

Lin, Bor-Ren

2018-05-01

Conventional series resonant converters have researched and applied for high-efficiency power units due to the benefit of its low switching losses. The main problems of series resonant converters are wide frequency variation and high circulating current. Thus, resonant converter is limited at narrow input voltage range and large input capacitor is normally adopted in commercial power units to provide the minimum hold-up time requirement when AC power is off. To overcome these problems, the resonant converter with auxiliary secondary windings are presented in this paper to achieve high voltage gain at low input voltage case such as hold-up time duration when utility power is off. Since the high voltage gain is used at low input voltage cased, the frequency variation of the proposed converter compared to the conventional resonant converter is reduced. Compared to conventional resonant converter, the hold-up time in the proposed converter is more than 40ms. The larger magnetising inductance of transformer is used to reduce the circulating current losses. Finally, a laboratory prototype is constructed and experiments are provided to verify the converter performance.

Time series, periodograms, and significance

NASA Astrophysics Data System (ADS)

Hernandez, G.

1999-05-01

The geophysical literature shows a wide and conflicting usage of methods employed to extract meaningful information on coherent oscillations from measurements. This makes it difficult, if not impossible, to relate the findings reported by different authors. Therefore, we have undertaken a critical investigation of the tests and methodology used for determining the presence of statistically significant coherent oscillations in periodograms derived from time series. Statistical significance tests are only valid when performed on the independent frequencies present in a measurement. Both the number of possible independent frequencies in a periodogram and the significance tests are determined by the number of degrees of freedom, which is the number of true independent measurements, present in the time series, rather than the number of sample points in the measurement. The number of degrees of freedom is an intrinsic property of the data, and it must be determined from the serial coherence of the time series. As part of this investigation, a detailed study has been performed which clearly illustrates the deleterious effects that the apparently innocent and commonly used processes of filtering, de-trending, and tapering of data have on periodogram analysis and the consequent difficulties in the interpretation of the statistical significance thus derived. For the sake of clarity, a specific example of actual field measurements containing unevenly-spaced measurements, gaps, etc., as well as synthetic examples, have been used to illustrate the periodogram approach, and pitfalls, leading to the (statistical) significance tests for the presence of coherent oscillations. Among the insights of this investigation are: (1) the concept of a time series being (statistically) band limited by its own serial coherence and thus having a critical sampling rate which defines one of the necessary requirements for the proper statistical design of an experiment; (2) the design of a critical test for the maximum number of significant frequencies which can be used to describe a time series, while retaining intact the variance of the test sample; (3) a demonstration of the unnecessary difficulties that manipulation of the data brings into the statistical significance interpretation of said data; and (4) the resolution and correction of the apparent discrepancy in significance results obtained by the use of the conventional Lomb-Scargle significance test, when compared with the long-standing Schuster-Walker and Fisher tests.

Some Applications of Fourier's Great Discovery for Beginners

ERIC Educational Resources Information Center

Kraftmakher, Yaakov

2012-01-01

Nearly two centuries ago, Fourier discovered that any periodic function of period T can be presented as a sum of sine waveforms of frequencies equal to an integer times the fundamental frequency [omega] = 2[pi]/T (Fourier's series). It is impossible to overestimate the importance of Fourier's discovery, and all physics or engineering students…

Time Series Discord Detection in Medical Data using a Parallel Relational Database

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

Woodbridge, Diane; Rintoul, Mark Daniel; Wilson, Andrew T.

Recent advances in sensor technology have made continuous real-time health monitoring available in both hospital and non-hospital settings. Since data collected from high frequency medical sensors includes a huge amount of data, storing and processing continuous medical data is an emerging big data area. Especially detecting anomaly in real time is important for patients’ emergency detection and prevention. A time series discord indicates a subsequence that has the maximum difference to the rest of the time series subsequences, meaning that it has abnormal or unusual data trends. In this study, we implemented two versions of time series discord detection algorithmsmore » on a high performance parallel database management system (DBMS) and applied them to 240 Hz waveform data collected from 9,723 patients. The initial brute force version of the discord detection algorithm takes each possible subsequence and calculates a distance to the nearest non-self match to find the biggest discords in time series. For the heuristic version of the algorithm, a combination of an array and a trie structure was applied to order time series data for enhancing time efficiency. The study results showed efficient data loading, decoding and discord searches in a large amount of data, benefiting from the time series discord detection algorithm and the architectural characteristics of the parallel DBMS including data compression, data pipe-lining, and task scheduling.« less

Time Series Discord Detection in Medical Data using a Parallel Relational Database [PowerPoint

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

Woodbridge, Diane; Wilson, Andrew T.; Rintoul, Mark Daniel

Recent advances in sensor technology have made continuous real-time health monitoring available in both hospital and non-hospital settings. Since data collected from high frequency medical sensors includes a huge amount of data, storing and processing continuous medical data is an emerging big data area. Especially detecting anomaly in real time is important for patients’ emergency detection and prevention. A time series discord indicates a subsequence that has the maximum difference to the rest of the time series subsequences, meaning that it has abnormal or unusual data trends. In this study, we implemented two versions of time series discord detection algorithmsmore » on a high performance parallel database management system (DBMS) and applied them to 240 Hz waveform data collected from 9,723 patients. The initial brute force version of the discord detection algorithm takes each possible subsequence and calculates a distance to the nearest non-self match to find the biggest discords in time series. For the heuristic version of the algorithm, a combination of an array and a trie structure was applied to order time series data for enhancing time efficiency. The study results showed efficient data loading, decoding and discord searches in a large amount of data, benefiting from the time series discord detection algorithm and the architectural characteristics of the parallel DBMS including data compression, data pipe-lining, and task scheduling.« less

Internal margin assessment using cine MRI analysis of deglutition in head and neck cancer radiotherapy

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

Paulson, Eric S.; Bradley, Julie A.; Wang Dian

2011-04-15

Purpose: Intensity-modulated radiation therapy (IMRT) is a promising treatment modality for patients with head and neck cancer (HNC). The dose distributions from IMRT are static and, thus, are unable to account for variations and/or uncertainties in the relationship between the patient (region being treated) and the beam. Organ motion comprises one such source of this uncertainty, introduced by physiological variation in the position, size, and shape of organs during treatment. In the head and neck, the predominant source of this variation arises from deglutition (swallowing). The purpose of this study was to investigate whether cinematographic MRI (cine MRI) could bemore » used to determine asymmetric (nonuniform) internal margin (IM) components of tumor planning target volumes based on the actual deglutition-induced tumor displacement. Methods: Five head and neck cancer patients were set up in treatment position on a 3 T MRI scanner. Two time series of single-slice, sagittal, cine images were acquired using a 2D FLASH sequence. The first time series was a 12.8 min scan designed to capture the frequency and duration of deglutition in the treatment position. The second time series was a short, 15 s scan designed to capture the displacement of deglutition in the treatment position. Deglutition frequency and mean swallow duration were estimated from the long time series acquisition. Swallowing and resting (nonswallowing) events were identified on the short time series acquisition and displacement was estimated based on contours of gross tumor volume (GTV) generated at each time point of a particular event. A simple linear relationship was derived to estimate 1D asymmetric IMs in the presence of resting- and deglutition-induced displacement. Results: Deglutition was nonperiodic, with frequency and duration ranging from 2.89-24.18 mHz and from 3.86 to 6.10 s, respectively. The deglutition frequency and mean duration were found to vary among patients. Deglutition-induced maximal GTV displacements ranged from 0.00 to 28.36 mm with mean and standard deviation of 4.72{+-}3.18, 3.70{+-}2.81, 2.75{+-}5.24, and 10.40{+-}10.76 mm in the A, P, I, and S directions, respectively. Resting-induced maximal GTV displacement ranged from 0.00 to 5.59 mm with mean and standard deviation of 3.01{+-}1.80, 1.25{+-}1.10, 3.23+2.20, and 2.47{+-}1.11 mm in the A, P, I, and S directions, respectively. For both resting and swallowing states, displacement along the S-I direction dominated displacement along the A-P direction. The calculated IMs were dependent on deglutition frequency, ranging from 3.28-4.37 mm for the lowest deglutition frequency patient to 3.76-6.43 mm for the highest deglutition frequency patient. A statistically significant difference was detected between IMs calculated for P and S directions (p=0.0018). Conclusions: Cine MRI is able to capture tumor motion during deglutition. Swallowing events can be demarcated by MR signal intensity changes caused by anatomy containing fully relaxed spins that move medially into the imaging plane during deglutition. Deglutition is nonperiodic and results in dynamic changes in the tumor position. Deglutition-induced displacements are larger and more variable than resting displacements. The nonzero mean maximum resting displacement indicates that some tumor motion occurs even when the patient is not swallowing. Asymmetric IMs, derived from deglutition frequency, duration, and directional displacement, should be employed to account for tumor motion in HNC RT.« less

A time-frequency analysis method to obtain stable estimates of magnetotelluric response function based on Hilbert-Huang transform

NASA Astrophysics Data System (ADS)

Cai, Jianhua

2017-05-01

The time-frequency analysis method represents signal as a function of time and frequency, and it is considered a powerful tool for handling arbitrary non-stationary time series by using instantaneous frequency and instantaneous amplitude. It also provides a possible alternative to the analysis of the non-stationary magnetotelluric (MT) signal. Based on the Hilbert-Huang transform (HHT), a time-frequency analysis method is proposed to obtain stable estimates of the magnetotelluric response function. In contrast to conventional methods, the response function estimation is performed in the time-frequency domain using instantaneous spectra rather than in the frequency domain, which allows for imaging the response parameter content as a function of time and frequency. The theory of the method is presented and the mathematical model and calculation procedure, which are used to estimate response function based on HHT time-frequency spectrum, are discussed. To evaluate the results, response function estimates are compared with estimates from a standard MT data processing method based on the Fourier transform. All results show that apparent resistivities and phases, which are calculated from the HHT time-frequency method, are generally more stable and reliable than those determined from the simple Fourier analysis. The proposed method overcomes the drawbacks of the traditional Fourier methods, and the resulting parameter minimises the estimation bias caused by the non-stationary characteristics of the MT data.

Was That Assumption Necessary? Reconsidering Boundary Conditions for Analytical Solutions to Estimate Streambed Fluxes

NASA Astrophysics Data System (ADS)

Luce, Charles H.; Tonina, Daniele; Applebee, Ralph; DeWeese, Timothy

2017-11-01

Two common refrains about using the one-dimensional advection diffusion equation to estimate fluid fluxes and thermal conductivity from temperature time series in streambeds are that the solution assumes that (1) the surface boundary condition is a sine wave or nearly so, and (2) there is no gradient in mean temperature with depth. Although the mathematical posing of the problem in the original solution to the problem might lead one to believe these constraints exist, the perception that they are a source of error is a fallacy. Here we develop a mathematical proof demonstrating the equivalence of the solution as developed based on an arbitrary (Fourier integral) surface temperature forcing when evaluated at a single given frequency versus that derived considering a single frequency from the beginning. The implication is that any single frequency can be used in the frequency-domain solutions to estimate thermal diffusivity and 1-D fluid flux in streambeds, even if the forcing has multiple frequencies. This means that diurnal variations with asymmetric shapes or gradients in the mean temperature with depth are not actually assumptions, and deviations from them should not cause errors in estimates. Given this clarification, we further explore the potential for using information at multiple frequencies to augment the information derived from time series of temperature.

Frequency Analysis of Modis Ndvi Time Series for Determining Hotspot of Land Degradation in Mongolia

NASA Astrophysics Data System (ADS)

Nasanbat, E.; Sharav, S.; Sanjaa, T.; Lkhamjav, O.; Magsar, E.; Tuvdendorj, B.

2018-04-01

This study examines MODIS NDVI satellite imagery time series can be used to determine hotspot of land degradation area in whole Mongolia. The trend statistical analysis of Mann-Kendall was applied to a 16-year MODIS NDVI satellite imagery record, based on 16-day composited temporal data (from May to September) for growing seasons and from 2000 to 2016. We performed to frequency analysis that resulting NDVI residual trend pattern would enable successful determined of negative and positive changes in photo synthetically health vegetation. Our result showed that negative and positive values and generated a map of significant trends. Also, we examined long-term of meteorological parameters for the same period. The result showed positive and negative NDVI trends concurred with land cover types change representing an improve or a degrade in vegetation, respectively. Also, integrated the climate parameters which were precipitation and air temperature changes in the same time period seem to have had an affecting on huge NDVI trend area. The time series trend analysis approach applied successfully determined hotspot of an improvement and a degraded area due to land degradation and desertification.

Time-frequency featured co-movement between the stock and prices of crude oil and gold

NASA Astrophysics Data System (ADS)

Huang, Shupei; An, Haizhong; Gao, Xiangyun; Huang, Xuan

2016-02-01

The nonlinear relationships among variables caused by the hidden frequency information complicate the time series analysis. To shed more light on this nonlinear issue, we examine their relationships in joint time-frequency domain with multivariate framework, and the analyses in the time domain and frequency domain serve as comparisons. The daily Brent oil prices, London gold fixing price and Shanghai Composite index from January 1991 to September 2014 are adopted as example. First, they have long-term cointegration relationship in time domain from holistic perspective. Second, the Granger causality tests in different frequency bands are heterogeneous. Finally, the comparison between results from wavelet coherence and multiple wavelet coherence in the joint time-frequency domain indicates that in the high (1-14 days) and medium frequency (14-128 days) bands, the combination of Brent and gold prices has stronger correlation with the stock. In the low frequency band (256-512 days), year 2003 is the structure broken point before which Brent and oil are ideal choice for hedging the risk of the stock market. Thus, this paper offers more details between the Chinese stock market and the commodities markets of crude oil and gold, which suggests that the decisions for different time and frequencies should consider the corresponding benchmark information.

The effect of sampling rate and anti-aliasing filters on high-frequency response spectra

USGS Publications Warehouse

Boore, David M.; Goulet, Christine

2013-01-01

The most commonly used intensity measure in ground-motion prediction equations is the pseudo-absolute response spectral acceleration (PSA), for response periods from 0.01 to 10 s (or frequencies from 0.1 to 100 Hz). PSAs are often derived from recorded ground motions, and these motions are usually filtered to remove high and low frequencies before the PSAs are computed. In this article we are only concerned with the removal of high frequencies. In modern digital recordings, this filtering corresponds at least to an anti-aliasing filter applied before conversion to digital values. Additional high-cut filtering is sometimes applied both to digital and to analog records to reduce high-frequency noise. Potential errors on the short-period (high-frequency) response spectral values are expected if the true ground motion has significant energy at frequencies above that of the anti-aliasing filter. This is especially important for areas where the instrumental sample rate and the associated anti-aliasing filter corner frequency (above which significant energy in the time series is removed) are low relative to the frequencies contained in the true ground motions. A ground-motion simulation study was conducted to investigate these effects and to develop guidance for defining the usable bandwidth for high-frequency PSA. The primary conclusion is that if the ratio of the maximum Fourier acceleration spectrum (FAS) to the FAS at a frequency fsaa corresponding to the start of the anti-aliasing filter is more than about 10, then PSA for frequencies above fsaa should be little affected by the recording process, because the ground-motion frequencies that control the response spectra will be less than fsaa . A second topic of this article concerns the resampling of the digital acceleration time series to a higher sample rate often used in the computation of short-period PSA. We confirm previous findings that sinc-function interpolation is preferred to the standard practice of using linear time interpolation for the resamplin

Causality Analysis of Neural Connectivity: Critical Examination of Existing Methods and Advances of New Methods

PubMed Central

Hu, Sanqing; Dai, Guojun; Worrell, Gregory A.; Dai, Qionghai; Liang, Hualou

2012-01-01

Granger causality (GC) is one of the most popular measures to reveal causality influence of time series and has been widely applied in economics and neuroscience. Especially, its counterpart in frequency domain, spectral GC, as well as other Granger-like causality measures have recently been applied to study causal interactions between brain areas in different frequency ranges during cognitive and perceptual tasks. In this paper, we show that: 1) GC in time domain cannot correctly determine how strongly one time series influences the other when there is directional causality between two time series, and 2) spectral GC and other Granger-like causality measures have inherent shortcomings and/or limitations because of the use of the transfer function (or its inverse matrix) and partial information of the linear regression model. On the other hand, we propose two novel causality measures (in time and frequency domains) for the linear regression model, called new causality and new spectral causality, respectively, which are more reasonable and understandable than GC or Granger-like measures. Especially, from one simple example, we point out that, in time domain, both new causality and GC adopt the concept of proportion, but they are defined on two different equations where one equation (for GC) is only part of the other (for new causality), thus the new causality is a natural extension of GC and has a sound conceptual/theoretical basis, and GC is not the desired causal influence at all. By several examples, we confirm that new causality measures have distinct advantages over GC or Granger-like measures. Finally, we conduct event-related potential causality analysis for a subject with intracranial depth electrodes undergoing evaluation for epilepsy surgery, and show that, in the frequency domain, all measures reveal significant directional event-related causality, but the result from new spectral causality is consistent with event-related time–frequency power spectrum activity. The spectral GC as well as other Granger-like measures are shown to generate misleading results. The proposed new causality measures may have wide potential applications in economics and neuroscience. PMID:21511564

Modeling climate change impacts on combined sewer overflow using synthetic precipitation time series.

PubMed

Bendel, David; Beck, Ferdinand; Dittmer, Ulrich

2013-01-01

In the presented study climate change impacts on combined sewer overflows (CSOs) in Baden-Wuerttemberg, Southern Germany, were assessed based on continuous long-term rainfall-runoff simulations. As input data, synthetic rainfall time series were used. The applied precipitation generator NiedSim-Klima accounts for climate change effects on precipitation patterns. Time series for the past (1961-1990) and future (2041-2050) were generated for various locations. Comparing the simulated CSO activity of both periods we observe significantly higher overflow frequencies for the future. Changes in overflow volume and overflow duration depend on the type of overflow structure. Both values will increase at simple CSO structures that merely divide the flow, whereas they will decrease when the CSO structure is combined with a storage tank. However, there is a wide variation between the results of different precipitation time series (representative for different locations).

Time Series Data Analysis of Wireless Sensor Network Measurements of Temperature.

PubMed

Bhandari, Siddhartha; Bergmann, Neil; Jurdak, Raja; Kusy, Branislav

2017-05-26

Wireless sensor networks have gained significant traction in environmental signal monitoring and analysis. The cost or lifetime of the system typically depends on the frequency at which environmental phenomena are monitored. If sampling rates are reduced, energy is saved. Using empirical datasets collected from environmental monitoring sensor networks, this work performs time series analyses of measured temperature time series. Unlike previous works which have concentrated on suppressing the transmission of some data samples by time-series analysis but still maintaining high sampling rates, this work investigates reducing the sampling rate (and sensor wake up rate) and looks at the effects on accuracy. Results show that the sampling period of the sensor can be increased up to one hour while still allowing intermediate and future states to be estimated with interpolation RMSE less than 0.2 °C and forecasting RMSE less than 1 °C.

On the Inference of Functional Circadian Networks Using Granger Causality

PubMed Central

Pourzanjani, Arya; Herzog, Erik D.; Petzold, Linda R.

2015-01-01

Being able to infer one way direct connections in an oscillatory network such as the suprachiastmatic nucleus (SCN) of the mammalian brain using time series data is difficult but crucial to understanding network dynamics. Although techniques have been developed for inferring networks from time series data, there have been no attempts to adapt these techniques to infer directional connections in oscillatory time series, while accurately distinguishing between direct and indirect connections. In this paper an adaptation of Granger Causality is proposed that allows for inference of circadian networks and oscillatory networks in general called Adaptive Frequency Granger Causality (AFGC). Additionally, an extension of this method is proposed to infer networks with large numbers of cells called LASSO AFGC. The method was validated using simulated data from several different networks. For the smaller networks the method was able to identify all one way direct connections without identifying connections that were not present. For larger networks of up to twenty cells the method shows excellent performance in identifying true and false connections; this is quantified by an area-under-the-curve (AUC) 96.88%. We note that this method like other Granger Causality-based methods, is based on the detection of high frequency signals propagating between cell traces. Thus it requires a relatively high sampling rate and a network that can propagate high frequency signals. PMID:26413748

Systematic comparisons between PRISM version 1.0.0, BAP, and CSMIP ground-motion processing

USGS Publications Warehouse

Kalkan, Erol; Stephens, Christopher

2017-02-23

A series of benchmark tests was run by comparing results of the Processing and Review Interface for Strong Motion data (PRISM) software version 1.0.0 to Basic Strong-Motion Accelerogram Processing Software (BAP; Converse and Brady, 1992), and to California Strong Motion Instrumentation Program (CSMIP) processing (Shakal and others, 2003, 2004). These tests were performed by using the MatLAB implementation of PRISM, which is equivalent to its public release version in Java language. Systematic comparisons were made in time and frequency domains of records processed in PRISM and BAP, and in CSMIP, by using a set of representative input motions with varying resolutions, frequency content, and amplitudes. Although the details of strong-motion records vary among the processing procedures, there are only minor differences among the waveforms for each component and within the frequency passband common to these procedures. A comprehensive statistical evaluation considering more than 1,800 ground-motion components demonstrates that differences in peak amplitudes of acceleration, velocity, and displacement time series obtained from PRISM and CSMIP processing are equal to or less than 4 percent for 99 percent of the data, and equal to or less than 2 percent for 96 percent of the data. Other statistical measures, including the Euclidian distance (L2 norm) and the windowed root mean square level of processed time series, also indicate that both processing schemes produce statistically similar products.

Fading channel simulator

DOEpatents

Argo, Paul E.; Fitzgerald, T. Joseph

1993-01-01

Fading channel effects on a transmitted communication signal are simulated with both frequency and time variations using a channel scattering function to affect the transmitted signal. A conventional channel scattering function is converted to a series of channel realizations by multiplying the square root of the channel scattering function by a complex number of which the real and imaginary parts are each independent variables. The two-dimensional inverse-FFT of this complex-valued channel realization yields a matrix of channel coefficients that provide a complete frequency-time description of the channel. The transmitted radio signal is segmented to provide a series of transmitted signal and each segment is subject to FFT to generate a series of signal coefficient matrices. The channel coefficient matrices and signal coefficient matrices are then multiplied and subjected to inverse-FFT to output a signal representing the received affected radio signal. A variety of channel scattering functions can be used to characterize the response of a transmitter-receiver system to such atmospheric effects.

Traveltime delay relative to the maximum energy of the wave train for dispersive tsunamis propagating across the Pacific Ocean: the case of 2010 and 2015 Chilean Tsunamis

NASA Astrophysics Data System (ADS)

Poupardin, A.; Heinrich, P.; Hébert, H.; Schindelé, F.; Jamelot, A.; Reymond, D.; Sugioka, H.

2018-05-01

This paper evaluates the importance of frequency dispersion in the propagation of recent trans-Pacific tsunamis. Frequency dispersion induces a time delay for the most energetic waves, which increases for long propagation distances and short source dimensions. To calculate this time delay, propagation of tsunamis is simulated and analyzed from spectrograms of time-series at specific gauges in the Pacific Ocean. One- and two-dimensional simulations are performed by solving either shallow water or Boussinesq equations and by considering realistic seismic sources. One-dimensional sensitivity tests are first performed in a constant-depth channel to study the influence of the source width. Two-dimensional tests are then performed in a simulated Pacific Ocean with a 4000-m constant depth and by considering tectonic sources of 2010 and 2015 Chilean earthquakes. For these sources, both the azimuth and the distance play a major role in the frequency dispersion of tsunamis. Finally, simulations are performed considering the real bathymetry of the Pacific Ocean. Multiple reflections, refractions as well as shoaling of waves result in much more complex time series for which the effects of the frequency dispersion are hardly discernible. The main point of this study is to evaluate frequency dispersion in terms of traveltime delays by calculating spectrograms for a time window of 6 hours after the arrival of the first wave. Results of the spectral analysis show that the wave packets recorded by pressure and tide sensors in the Pacific Ocean seem to be better reproduced by the Boussinesq model than the shallow water model and approximately follow the theoretical dispersion relationship linking wave arrival times and frequencies. Additionally, a traveltime delay is determined above which effects of frequency dispersion are considered to be significant in terms of maximum surface elevations.

Dynamic Granger causality based on Kalman filter for evaluation of functional network connectivity in fMRI data

PubMed Central

Havlicek, Martin; Jan, Jiri; Brazdil, Milan; Calhoun, Vince D.

2015-01-01

Increasing interest in understanding dynamic interactions of brain neural networks leads to formulation of sophisticated connectivity analysis methods. Recent studies have applied Granger causality based on standard multivariate autoregressive (MAR) modeling to assess the brain connectivity. Nevertheless, one important flaw of this commonly proposed method is that it requires the analyzed time series to be stationary, whereas such assumption is mostly violated due to the weakly nonstationary nature of functional magnetic resonance imaging (fMRI) time series. Therefore, we propose an approach to dynamic Granger causality in the frequency domain for evaluating functional network connectivity in fMRI data. The effectiveness and robustness of the dynamic approach was significantly improved by combining a forward and backward Kalman filter that improved estimates compared to the standard time-invariant MAR modeling. In our method, the functional networks were first detected by independent component analysis (ICA), a computational method for separating a multivariate signal into maximally independent components. Then the measure of Granger causality was evaluated using generalized partial directed coherence that is suitable for bivariate as well as multivariate data. Moreover, this metric provides identification of causal relation in frequency domain, which allows one to distinguish the frequency components related to the experimental paradigm. The procedure of evaluating Granger causality via dynamic MAR was demonstrated on simulated time series as well as on two sets of group fMRI data collected during an auditory sensorimotor (SM) or auditory oddball discrimination (AOD) tasks. Finally, a comparison with the results obtained from a standard time-invariant MAR model was provided. PMID:20561919

International and Domestic Business Cycles as Dynamics of a Network of Networks

NASA Astrophysics Data System (ADS)

Ikeda, Yuichi; Iyetomi, Hiroshi; Aoyama, Hideaki; Yoshikawa, Hiroshi

2014-03-01

Synchronization in business cycles has attracted economists and physicists as self-organization in the time domain. From a different point of view, international and domestic business cycles are also interesting as dynamics of a network of networks or a multi-level network. In this paper, we analyze the Indices of Industrial Production monthly time-series in Japan from January 1988 to December 2007 to develop a deeper understanding of domestic business cycles. The frequency entrainment and the partial phase locking were observed for the 16 sectors to be direct evidence of synchronization. We also showed that the information of the economic shock is carried by the phase time-series. The common shock and individual shocks are separated using phase time-series. The former dominates the economic recession in all of 1992, 1998 and 2001. In addition to the above analysis, we analyze the quarterly GDP time series for Australia, Canada, France, Italy, the United Kingdom, and the United States from Q2 1960 to Q1 2010 in order to clarify its origin. We find frequency entrainment and partial phase locking. Furthermore, a coupled limit-cycle oscillator model is developed to explain the mechanism of synchronization. In this model, the interaction due to international trade is interpreted as the origin of the synchronization. The obtained results suggest that the business cycle may be described as a dynamics of the multi-level coupled oscillators exposed to random individual shocks.

Application of the Hilbert-Huang Transform to Financial Data

NASA Technical Reports Server (NTRS)

Huang, Norden

2005-01-01

A paper discusses the application of the Hilbert-Huang transform (HHT) method to time-series financial-market data. The method was described, variously without and with the HHT name, in several prior NASA Tech Briefs articles and supporting documents. To recapitulate: The method is especially suitable for analyzing time-series data that represent nonstationary and nonlinear phenomena including physical phenomena and, in the present case, financial-market processes. The method involves the empirical mode decomposition (EMD), in which a complicated signal is decomposed into a finite number of functions, called "intrinsic mode functions" (IMFs), that admit well-behaved Hilbert transforms. The HHT consists of the combination of EMD and Hilbert spectral analysis. The local energies and the instantaneous frequencies derived from the IMFs through Hilbert transforms can be used to construct an energy-frequency-time distribution, denoted a Hilbert spectrum. The instant paper begins with a discussion of prior approaches to quantification of market volatility, summarizes the HHT method, then describes the application of the method in performing time-frequency analysis of mortgage-market data from the years 1972 through 2000. Filtering by use of the EMD is shown to be useful for quantifying market volatility.

Statistical approaches for studying the wave climate of crossing-sea states

NASA Astrophysics Data System (ADS)

Barbariol, Francesco; Portilla, Jesus; Benetazzo, Alvise; Cavaleri, Luigi; Sclavo, Mauro; Carniel, Sandro

2017-04-01

Surface waves are an important feature of the world's oceans and seas. Their role in the air-sea exchanges is well recognized, together with their effects on the upper ocean and lower atmosphere dynamics. Physical processes involving surface waves contribute in driving the Earth's climate that, while experiencing changes at global and regional scales, in turn affects the surface waves climate over the oceans. The assessment of the wave climate at specific locations of the ocean is fruitful for many research fields in marine and atmospheric sciences and also for the human activities in the marine environment. Very often, wind generated waves (wind-sea) and one or more swell systems occur simultaneously, depending on the complexity of the atmospheric conditions that force the waves. Therefore, a wave climate assessed from the statistical analysis of long time series of integral wave parameters, can hardly say something about the frequency of occurrence of the so-called crossing-seas, as well as of their features. Directional wave spectra carry such information but proper statistical methods to analyze them are needed. In this respect, in order to identify the crossing sea states within the spectral time series and to assess their frequency of occurrence we exploit two advanced statistical techniques. First, we apply the Spectral Partitioning, a well-established method based on a two-step partitioning of the spectrum that allows to identify the individual wave systems and to compute their probability of occurrence in the frequency/direction space. Then, we use the Self-Organizing Maps, an unsupervised neural network algorithm that quantize the time series by autonomously identifying an arbitrary (small) number of wave spectra representing the whole wave climate, each with its frequency of occurrence. This method has been previously applied to time series of wave parameters and for the first time is applied to directional wave spectra. We analyze the wave climate of one of the most severe regions of the Mediterranean Sea, between north-west Sardinia and the Gulf of Lion, where quite often wave systems coming from different directions superpose. Time series for the analysis is taken from the ERA-Interim Reanalysis dataset, which provides global directional wave spectra at 1° resolution, starting from 1979 up to the present. Results from the two techniques are shown to be consistent, and their comparison points out the contribution that each technique can provide for a more detailed interpretation of the wave climate.

Spectral-decomposition techniques for the identification of periodic and anomalous phenomena in radon time-series.

NASA Astrophysics Data System (ADS)

Crockett, R. G. M.; Perrier, F.; Richon, P.

2009-04-01

Building on independent investigations by research groups at both IPGP, France, and the University of Northampton, UK, hourly-sampled radon time-series of durations exceeding one year have been investigated for periodic and anomalous phenomena using a variety of established and novel techniques. These time-series have been recorded in locations having no routine human behaviour and thus are effectively free of significant anthropogenic influences. With regard to periodic components, the long durations of these time-series allow, in principle, very high frequency resolutions for established spectral-measurement techniques such as Fourier and maximum-entropy. However, as has been widely observed, the stochastic nature of radon emissions from rocks and soils, coupled with sensitivity to a wide variety influences such as temperature, wind-speed and soil moisture-content has made interpretation of the results obtained by such techniques very difficult, with uncertain results, in many cases. We here report developments in the investigation of radon-time series for periodic and anomalous phenomena using spectral-decomposition techniques. These techniques, in variously separating ‘high', ‘middle' and ‘low' frequency components, effectively ‘de-noise' the data by allowing components of interest to be isolated from others which (might) serve to obscure weaker information-containing components. Once isolated, these components can be investigated using a variety of techniques. Whilst this is very much work in early stages of development, spectral decomposition methods have been used successfully to indicate the presence of diurnal and sub-diurnal cycles in radon concentration which we provisionally attribute to tidal influences. Also, these methods have been used to enhance the identification of short-duration anomalies, attributable to a variety of causes including, for example, earthquakes and rapid large-magnitude changes in weather conditions. Keywords: radon; earthquakes; tidal-influences; anomalies; time series; spectral-decomposition.

Solar-Terrestrial Coupling Evidenced by Periodic Behavior in Geomagnetic Indexes and the Infrared Energy Budget of the Thermosphere

NASA Technical Reports Server (NTRS)

Mlynczak, Martin G.; Martin-Torres, F. Javier; Mertens, Christopher J.; Marshall, B. Thomas; Thompson, R. Earl; Kozyra, Janet U.; Remsberg, Ellis E.; Gordley, Larry L.; Russell, James M.; Woods, Thomas

2008-01-01

We examine time series of the daily global power (W) radiated by carbon dioxide (at 15 microns) and by nitric oxide (at 5.3 microns) from the Earth s thermosphere between 100 km and 200 km altitude. Also examined is a time series of the daily absorbed solar ultraviolet power in the same altitude region in the wavelength span 0 to 175 nm. The infrared data are derived from the SABER instrument and the solar data are derived from the SEE instrument, both on the NASA TIMED satellite. The time series cover nearly 5 years from 2002 through 2006. The infrared and solar time series exhibit a decrease in radiated and absorbed power consistent with the declining phase of the current 11-year solar cycle. The infrared time series also exhibits high frequency variations that are not evident in the solar power time series. Spectral analysis shows a statistically significant 9-day periodicity in the infrared data but not in the solar data. A very strong 9-day periodicity is also found to exist in the time series of daily A(sub p) and K(sub p) geomagnetic indexes. These 9-day periodicities are linked to the recurrence of coronal holes on the Sun. These results demonstrate a direct coupling between the upper atmosphere of the Sun and the infrared energy budget of the thermosphere.

Statistical analysis of low level atmospheric turbulence

NASA Technical Reports Server (NTRS)

Tieleman, H. W.; Chen, W. W. L.

1974-01-01

The statistical properties of low-level wind-turbulence data were obtained with the model 1080 total vector anemometer and the model 1296 dual split-film anemometer, both manufactured by Thermo Systems Incorporated. The data obtained from the above fast-response probes were compared with the results obtained from a pair of Gill propeller anemometers. The digitized time series representing the three velocity components and the temperature were each divided into a number of blocks, the length of which depended on the lowest frequency of interest and also on the storage capacity of the available computer. A moving-average and differencing high-pass filter was used to remove the trend and the low frequency components in the time series. The calculated results for each of the anemometers used are represented in graphical or tabulated form.

Mitigation of intra-channel nonlinearities using a frequency-domain Volterra series equalizer.

PubMed

Guiomar, Fernando P; Reis, Jacklyn D; Teixeira, António L; Pinto, Armando N

2012-01-16

We address the issue of intra-channel nonlinear compensation using a Volterra series nonlinear equalizer based on an analytical closed-form solution for the 3rd order Volterra kernel in frequency-domain. The performance of the method is investigated through numerical simulations for a single-channel optical system using a 20 Gbaud NRZ-QPSK test signal propagated over 1600 km of both standard single-mode fiber and non-zero dispersion shifted fiber. We carry on performance and computational effort comparisons with the well-known backward propagation split-step Fourier (BP-SSF) method. The alias-free frequency-domain implementation of the Volterra series nonlinear equalizer makes it an attractive approach to work at low sampling rates, enabling to surpass the maximum performance of BP-SSF at 2× oversampling. Linear and nonlinear equalization can be treated independently, providing more flexibility to the equalization subsystem. The parallel structure of the algorithm is also a key advantage in terms of real-time implementation.

An Educational Laboratory Virtual Instrumentation Suite Assisted Experiment for Studying Fundamentals of Series Resistance-Inductance-Capacitance Circuit

ERIC Educational Resources Information Center

Rana, K. P. S.; Kumar, Vineet; Mendiratta, Jatin

2017-01-01

One of the most elementary concepts in freshmen Electrical Engineering subject comprises the Resistance-Inductance-Capacitance (RLC) circuit fundamentals, that is, their time and frequency domain responses. For a beginner, generally, it is difficult to understand and appreciate the step and the frequency responses, particularly the resonance. This…

On the Prony series representation of stretched exponential relaxation

NASA Astrophysics Data System (ADS)

Mauro, John C.; Mauro, Yihong Z.

2018-09-01

Stretched exponential relaxation is a ubiquitous feature of homogeneous glasses. The stretched exponential decay function can be derived from the diffusion-trap model, which predicts certain critical values of the fractional stretching exponent, β. In practical implementations of glass relaxation models, it is computationally convenient to represent the stretched exponential function as a Prony series of simple exponentials. Here, we perform a comprehensive mathematical analysis of the Prony series approximation of the stretched exponential relaxation, including optimized coefficients for certain critical values of β. The fitting quality of the Prony series is analyzed as a function of the number of terms in the series. With a sufficient number of terms, the Prony series can accurately capture the time evolution of the stretched exponential function, including its "fat tail" at long times. However, it is unable to capture the divergence of the first-derivative of the stretched exponential function in the limit of zero time. We also present a frequency-domain analysis of the Prony series representation of the stretched exponential function and discuss its physical implications for the modeling of glass relaxation behavior.

A combinatorial filtering method for magnetotelluric time-series based on Hilbert-Huang transform

NASA Astrophysics Data System (ADS)

Cai, Jianhua

2014-11-01

Magnetotelluric (MT) time-series are often contaminated with noise from natural or man-made processes. A substantial improvement is possible when the time-series are presented as clean as possible for further processing. A combinatorial method is described for filtering of MT time-series based on the Hilbert-Huang transform that requires a minimum of human intervention and leaves good data sections unchanged. Good data sections are preserved because after empirical mode decomposition the data are analysed through hierarchies, morphological filtering, adaptive threshold and multi-point smoothing, allowing separation of noise from signals. The combinatorial method can be carried out without any assumption about the data distribution. Simulated data and the real measured MT time-series from three different regions, with noise caused by baseline drift, high frequency noise and power-line contribution, are processed to demonstrate the application of the proposed method. Results highlight the ability of the combinatorial method to pick out useful signals, and the noise is suppressed greatly so that their deleterious influence is eliminated for the MT transfer function estimation.

Time-frequency analysis of functional optical mammographic images

NASA Astrophysics Data System (ADS)

Barbour, Randall L.; Graber, Harry L.; Schmitz, Christoph H.; Tarantini, Frank; Khoury, Georges; Naar, David J.; Panetta, Thomas F.; Lewis, Theophilus; Pei, Yaling

2003-07-01

We have introduced working technology that provides for time-series imaging of the hemoglobin signal in large tissue structures. In this study we have explored our ability to detect aberrant time-frequency responses of breast vasculature for subjects with Stage II breast cancer at rest and in response to simple provocations. The hypothesis being explored is that time-series imaging will be sensitive to the known structural and functional malformations of the tumor vasculature. Mammographic studies were conducted using an adjustable hemisheric measuring head containing 21 source and 21 detector locations (441 source-detector pairs). Simultaneous dual-wavelength studies were performed at 760 and 830 nm at a framing rate of ~2.7 Hz. Optical measures were performed on women lying prone with the breast hanging in a pendant position. Two class of measures were performed: (1) 20- minute baseline measure wherein the subject was at rest; (2) provocation studies wherein the subject was asked to perform some simple breathing maneuvers. Collected data were analyzed to identify the time-frequency structure and central tendencies of the detector responses and those of the image time series. Imaging data were generated using the Normalized Difference Method (Pei et al., Appl. Opt. 40, 5755-5769, 2001). Results obtained clearly document three classes of anomalies when compared to the normal contralateral breast. 1) Breast tumors exhibit altered oxygen supply/demand imbalance in response to an oxidative challenge (breath hold). 2) The vasomotor response of the tumor vasculature is mainly depressed and exhibits an altered modulation. 3) The affected area of the breast wherein the altered vasomotor signature is seen extends well beyond the limits of the tumor itself.

On the Coriolis effect in acoustic waveguides.

PubMed

Wegert, Henry; Reindl, Leonard M; Ruile, Werner; Mayer, Andreas P

2012-05-01

Rotation of an elastic medium gives rise to a shift of frequency of its acoustic modes, i.e., the time-period vibrations that exist in it. This frequency shift is investigated by applying perturbation theory in the regime of small ratios of the rotation velocity and the frequency of the acoustic mode. In an expansion of the relative frequency shift in powers of this ratio, upper bounds are derived for the first-order and the second-order terms. The derivation of the theoretical upper bounds of the first-order term is presented for linear vibration modes as well as for stable nonlinear vibrations with periodic time dependence that can be represented by a Fourier series.

PRECISION TIME-DELAY CIRCUIT

DOEpatents

Creveling, R.

1959-03-17

A tine-delay circuit which produces a delay time in d. The circuit a capacitor, an te back resistance, connected serially with the anode of the diode going to ground. At the start of the time delay a negative stepfunction is applied to the series circuit and initiates a half-cycle transient oscillatory voltage terminated by a transient oscillatory voltage of substantially higher frequency. The output of the delay circuit is taken at the junction of the inductor and diode where a sudden voltage rise appears after the initiation of the higher frequency transient oscillations.

Enhancement to Non-Contacting Stress Measurement of Blade Vibration Frequency

NASA Technical Reports Server (NTRS)

Platt, Michael; Jagodnik, John

2011-01-01

A system for turbo machinery blade vibration has been developed that combines time-of-arrival sensors for blade vibration amplitude measurement and radar sensors for vibration frequency and mode identification. The enabling technology for this continuous blade monitoring system is the radar sensor, which provides a continuous time series of blade displacement over a portion of a revolution. This allows the data reduction algorithms to directly calculate the blade vibration frequency and to correctly identify the active modes of vibration. The work in this project represents a significant enhancement in the mode identification and stress calculation accuracy in non-contacting stress measurement system (NSMS) technology when compared to time-of-arrival measurements alone.

Frequency distribution of causal connectivity in rat sensorimotor network: resting-state fMRI analyses.

PubMed

Shim, Woo H; Baek, Kwangyeol; Kim, Jeong Kon; Chae, Yongwook; Suh, Ji-Yeon; Rosen, Bruce R; Jeong, Jaeseung; Kim, Young R

2013-01-01

Resting-state functional MRI (fMRI) has emerged as an important method for assessing neural networks, enabling extensive connectivity analyses between multiple brain regions. Among the analysis techniques proposed, partial directed coherence (PDC) provides a promising tool to unveil causal connectivity networks in the frequency domain. Using the MRI time series obtained from the rat sensorimotor system, we applied PDC analysis to determine the frequency-dependent causality networks. In particular, we compared in vivo and postmortem conditions to establish the statistical significance of directional PDC values. Our results demonstrate that two distinctive frequency populations drive the causality networks in rat; significant, high-frequency causal connections clustered in the range of 0.2-0.4 Hz, and the frequently documented low-frequency connections <0.15 Hz. Frequency-dependence and directionality of the causal connection are characteristic between sensorimotor regions, implying the functional role of frequency bands to transport specific resting-state signals. In particular, whereas both intra- and interhemispheric causal connections between heterologous sensorimotor regions are robust over all frequency levels, the bilaterally homologous regions are interhemispherically linked mostly via low-frequency components. We also discovered a significant, frequency-independent, unidirectional connection from motor cortex to thalamus, indicating dominant cortical inputs to the thalamus in the absence of external stimuli. Additionally, to address factors underlying the measurement error, we performed signal simulations and revealed that the interactive MRI system noise alone is a likely source of the inaccurate PDC values. This work demonstrates technical basis for the PDC analysis of resting-state fMRI time series and the presence of frequency-dependent causality networks in the sensorimotor system.

Statistical Analysis of Categorical Time Series of Atmospheric Elementary Circulation Mechanisms - Dzerdzeevski Classification for the Northern Hemisphere

PubMed Central

Brenčič, Mihael

2016-01-01

Northern hemisphere elementary circulation mechanisms, defined with the Dzerdzeevski classification and published on a daily basis from 1899–2012, are analysed with statistical methods as continuous categorical time series. Classification consists of 41 elementary circulation mechanisms (ECM), which are assigned to calendar days. Empirical marginal probabilities of each ECM were determined. Seasonality and the periodicity effect were investigated with moving dispersion filters and randomisation procedure on the ECM categories as well as with the time analyses of the ECM mode. The time series were determined as being non-stationary with strong time-dependent trends. During the investigated period, periodicity interchanges with periods when no seasonality is present. In the time series structure, the strongest division is visible at the milestone of 1986, showing that the atmospheric circulation pattern reflected in the ECM has significantly changed. This change is result of the change in the frequency of ECM categories; before 1986, the appearance of ECM was more diverse, and afterwards fewer ECMs appear. The statistical approach applied to the categorical climatic time series opens up new potential insight into climate variability and change studies that have to be performed in the future. PMID:27116375

Statistical Analysis of Categorical Time Series of Atmospheric Elementary Circulation Mechanisms - Dzerdzeevski Classification for the Northern Hemisphere.

PubMed

Brenčič, Mihael

2016-01-01

Northern hemisphere elementary circulation mechanisms, defined with the Dzerdzeevski classification and published on a daily basis from 1899-2012, are analysed with statistical methods as continuous categorical time series. Classification consists of 41 elementary circulation mechanisms (ECM), which are assigned to calendar days. Empirical marginal probabilities of each ECM were determined. Seasonality and the periodicity effect were investigated with moving dispersion filters and randomisation procedure on the ECM categories as well as with the time analyses of the ECM mode. The time series were determined as being non-stationary with strong time-dependent trends. During the investigated period, periodicity interchanges with periods when no seasonality is present. In the time series structure, the strongest division is visible at the milestone of 1986, showing that the atmospheric circulation pattern reflected in the ECM has significantly changed. This change is result of the change in the frequency of ECM categories; before 1986, the appearance of ECM was more diverse, and afterwards fewer ECMs appear. The statistical approach applied to the categorical climatic time series opens up new potential insight into climate variability and change studies that have to be performed in the future.

Estimating serial correlation and self-similarity in financial time series-A diversification approach with applications to high frequency data

NASA Astrophysics Data System (ADS)

Gerlich, Nikolas; Rostek, Stefan

2015-09-01

We derive a heuristic method to estimate the degree of self-similarity and serial correlation in financial time series. Especially, we propagate the use of a tailor-made selection of different estimation techniques that are used in various fields of time series analysis but until now have not consequently found their way into the finance literature. Following the idea of portfolio diversification, we show that considerable improvements with respect to robustness and unbiasedness can be achieved by using a basket of estimation methods. With this methodological toolbox at hand, we investigate real market data to show that noticeable deviations from the assumptions of constant self-similarity and absence of serial correlation occur during certain periods. On the one hand, this may shed a new light on seemingly ambiguous scientific findings concerning serial correlation of financial time series. On the other hand, a proven time-changing degree of self-similarity may help to explain high-volatility clusters of stock price indices.

Dynamical Analysis and Visualization of Tornadoes Time Series

PubMed Central

2015-01-01

In this paper we analyze the behavior of tornado time-series in the U.S. from the perspective of dynamical systems. A tornado is a violently rotating column of air extending from a cumulonimbus cloud down to the ground. Such phenomena reveal features that are well described by power law functions and unveil characteristics found in systems with long range memory effects. Tornado time series are viewed as the output of a complex system and are interpreted as a manifestation of its dynamics. Tornadoes are modeled as sequences of Dirac impulses with amplitude proportional to the events size. First, a collection of time series involving 64 years is analyzed in the frequency domain by means of the Fourier transform. The amplitude spectra are approximated by power law functions and their parameters are read as an underlying signature of the system dynamics. Second, it is adopted the concept of circular time and the collective behavior of tornadoes analyzed. Clustering techniques are then adopted to identify and visualize the emerging patterns. PMID:25790281

Dynamical analysis and visualization of tornadoes time series.

PubMed

Lopes, António M; Tenreiro Machado, J A

2015-01-01

In this paper we analyze the behavior of tornado time-series in the U.S. from the perspective of dynamical systems. A tornado is a violently rotating column of air extending from a cumulonimbus cloud down to the ground. Such phenomena reveal features that are well described by power law functions and unveil characteristics found in systems with long range memory effects. Tornado time series are viewed as the output of a complex system and are interpreted as a manifestation of its dynamics. Tornadoes are modeled as sequences of Dirac impulses with amplitude proportional to the events size. First, a collection of time series involving 64 years is analyzed in the frequency domain by means of the Fourier transform. The amplitude spectra are approximated by power law functions and their parameters are read as an underlying signature of the system dynamics. Second, it is adopted the concept of circular time and the collective behavior of tornadoes analyzed. Clustering techniques are then adopted to identify and visualize the emerging patterns.

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.

Wavelet Statistical Analysis of Low-Latitude Geomagnetic Measurements

NASA Astrophysics Data System (ADS)

Papa, A. R.; Akel, A. F.

2009-05-01

Following previous works by our group (Papa et al., JASTP, 2006), where we analyzed a series of records acquired at the Vassouras National Geomagnetic Observatory in Brazil for the month of October 2000, we introduced a wavelet analysis for the same type of data and for other periods. It is well known that wavelets allow a more detailed study in several senses: the time window for analysis can be drastically reduced if compared to other traditional methods (Fourier, for example) and at the same time allow an almost continuous accompaniment of both amplitude and frequency of signals as time goes by. This advantage brings some possibilities for potentially useful forecasting methods of the type also advanced by our group in previous works (see for example, Papa and Sosman, JASTP, 2008). However, the simultaneous statistical analysis of both time series (in our case amplitude and frequency) is a challenging matter and is in this sense that we have found what we consider our main goal. Some possible trends for future works are advanced.

Stochastic Gabor reflectivity and acoustic impedance inversion

NASA Astrophysics Data System (ADS)

Hariri Naghadeh, Diako; Morley, Christopher Keith; Ferguson, Angus John

2018-02-01

To delineate subsurface lithology to estimate petrophysical properties of a reservoir, it is possible to use acoustic impedance (AI) which is the result of seismic inversion. To change amplitude to AI, removal of wavelet effects from the seismic signal in order to get a reflection series, and subsequently transforming those reflections to AI, is vital. To carry out seismic inversion correctly it is important to not assume that the seismic signal is stationary. However, all stationary deconvolution methods are designed following that assumption. To increase temporal resolution and interpretation ability, amplitude compensation and phase correction are inevitable. Those are pitfalls of stationary reflectivity inversion. Although stationary reflectivity inversion methods are trying to estimate reflectivity series, because of incorrect assumptions their estimations will not be correct, but may be useful. Trying to convert those reflection series to AI, also merging with the low frequency initial model, can help us. The aim of this study was to apply non-stationary deconvolution to eliminate time variant wavelet effects from the signal and to convert the estimated reflection series to the absolute AI by getting bias from well logs. To carry out this aim, stochastic Gabor inversion in the time domain was used. The Gabor transform derived the signal’s time-frequency analysis and estimated wavelet properties from different windows. Dealing with different time windows gave an ability to create a time-variant kernel matrix, which was used to remove matrix effects from seismic data. The result was a reflection series that does not follow the stationary assumption. The subsequent step was to convert those reflections to AI using well information. Synthetic and real data sets were used to show the ability of the introduced method. The results highlight that the time cost to get seismic inversion is negligible related to general Gabor inversion in the frequency domain. Also, obtaining bias could help the method to estimate reliable AI. To justify the effect of random noise on deterministic and stochastic inversion results, a stationary noisy trace with signal-to-noise ratio equal to 2 was used. The results highlight the inability of deterministic inversion in dealing with a noisy data set even using a high number of regularization parameters. Also, despite the low level of signal, stochastic Gabor inversion not only can estimate correctly the wavelet’s properties but also, because of bias from well logs, the inversion result is very close to the real AI. Comparing deterministic and introduced inversion results on a real data set shows that low resolution results, especially in the deeper parts of seismic sections using deterministic inversion, creates significant reliability problems for seismic prospects, but this pitfall is solved completely using stochastic Gabor inversion. The estimated AI using Gabor inversion in the time domain is much better and faster than general Gabor inversion in the frequency domain. This is due to the extra number of windows required to analyze the time-frequency information and also the amount of temporal increment between windows. In contrast, stochastic Gabor inversion can estimate trustable physical properties close to the real characteristics. Applying to a real data set could give an ability to detect the direction of volcanic intrusion and the ability of lithology distribution delineation along the fan. Comparing the inversion results highlights the efficiency of stochastic Gabor inversion to delineate lateral lithology changes because of the improved frequency content and zero phasing of the final inversion volume.

Characterization of time dynamical evolution of electroencephalographic epileptic records

NASA Astrophysics Data System (ADS)

Rosso, Osvaldo A.; Mairal, María. Liliana

2002-09-01

Since traditional electrical brain signal analysis is mostly qualitative, the development of new quantitative methods is crucial for restricting the subjectivity in the study of brain signals. These methods are particularly fruitful when they are strongly correlated with intuitive physical concepts that allow a better understanding of the brain dynamics. The processing of information by the brain is reflected in dynamical changes of the electrical activity in time, frequency, and space. Therefore, the concomitant studies require methods capable of describing the qualitative variation of the signal in both time and frequency. The entropy defined from the wavelet functions is a measure of the order/disorder degree present in a time series. In consequence, this entropy evaluates over EEG time series gives information about the underlying dynamical process in the brain, more specifically of the synchrony of the group cells involved in the different neural responses. The total wavelet entropy results independent of the signal energy and becomes a good tool for detecting dynamical changes in the system behavior. In addition the total wavelet entropy has advantages over the Lyapunov exponents, because it is parameter free and independent of the stationarity of the time series. In this work we compared the results of the time evolution of the chaoticity (Lyapunov exponent as a function of time) with the corresponding time evolution of the total wavelet entropy in two different EEG records, one provide by depth electrodes and other by scalp ones.

The spectra and periodograms of anti-correlated discrete fractional Gaussian noise.

PubMed

Raymond, G M; Percival, D B; Bassingthwaighte, J B

2003-05-01

Discrete fractional Gaussian noise (dFGN) has been proposed as a model for interpreting a wide variety of physiological data. The form of actual spectra of dFGN for frequencies near zero varies as f(1-2H), where 0 < H < 1 is the Hurst coefficient; however, this form for the spectra need not be a good approximation at other frequencies. When H approaches zero, dFGN spectra exhibit the 1 - 2H power-law behavior only over a range of low frequencies that is vanishingly small. When dealing with a time series of finite length drawn from a dFGN process with unknown H, practitioners must deal with estimated spectra in lieu of actual spectra. The most basic spectral estimator is the periodogram. The expected value of the periodogram for dFGN with small H also exhibits non-power-law behavior. At the lowest Fourier frequencies associated with a time series of N values sampled from a dFGN process, the expected value of the periodogram for H approaching zero varies as f(0) rather than f(1-2H). For finite N and small H, the expected value of the periodogram can in fact exhibit a local power-law behavior with a spectral exponent of 1 - 2H at only two distinct frequencies.

Flood Frequency Analysis For Partial Duration Series In Ganjiang River Basin

NASA Astrophysics Data System (ADS)

zhangli, Sun; xiufang, Zhu; yaozhong, Pan

2016-04-01

Accurate estimation of flood frequency is key to effective, nationwide flood damage abatement programs. The partial duration series (PDS) method is widely used in hydrologic studies because it considers all events above a certain threshold level as compared to the annual maximum series (AMS) method, which considers only the annual maximum value. However, the PDS has a drawback in that it is difficult to define the thresholds and maintain an independent and identical distribution of the partial duration time series; this drawback is discussed in this paper. The Ganjiang River is the seventh largest tributary of the Yangtze River, the longest river in China. The Ganjiang River covers a drainage area of 81,258 km2 at the Wanzhou hydrologic station as the basin outlet. In this work, 56 years of daily flow data (1954-2009) from the Wanzhou station were used to analyze flood frequency, and the Pearson-III model was employed as the hydrologic probability distribution. Generally, three tasks were accomplished: (1) the threshold of PDS by percentile rank of daily runoff was obtained; (2) trend analysis of the flow series was conducted using PDS; and (3) flood frequency analysis was conducted for partial duration flow series. The results showed a slight upward trend of the annual runoff in the Ganjiang River basin. The maximum flow with a 0.01 exceedance probability (corresponding to a 100-year flood peak under stationary conditions) was 20,000 m3/s, while that with a 0.1 exceedance probability was 15,000 m3/s. These results will serve as a guide to hydrological engineering planning, design, and management for policymakers and decision makers associated with hydrology.

Investigation of SIS Up-Converters for Use in Multi-pixel Receivers

NASA Astrophysics Data System (ADS)

Uzawa, Yoshinori; Kojima, Takafumi; Shan, Wenlei; Gonzalez, Alvaro; Kroug, Matthias

2018-02-01

We propose the use of SIS junctions as a frequency up-converter based on quasiparticle mixing in frequency division multiplexing circuits for multi-pixel heterodyne receivers. Our theoretical calculation showed that SIS junctions have the potential to achieve positive gain and low-noise characteristics in the frequency up-conversion process at local oscillator (LO) frequencies larger than the voltage scale of the dc nonlinearity of the SIS junction. We experimentally observed up-conversion gain in a mixer with four-series Nb-based SIS junctions at the LO frequency of 105 GHz for the first time.

Feature extraction across individual time series observations with spikes using wavelet principal component analysis.

PubMed

Røislien, Jo; Winje, Brita

2013-09-20

Clinical studies frequently include repeated measurements of individuals, often for long periods. We present a methodology for extracting common temporal features across a set of individual time series observations. In particular, the methodology explores extreme observations within the time series, such as spikes, as a possible common temporal phenomenon. Wavelet basis functions are attractive in this sense, as they are localized in both time and frequency domains simultaneously, allowing for localized feature extraction from a time-varying signal. We apply wavelet basis function decomposition of individual time series, with corresponding wavelet shrinkage to remove noise. We then extract common temporal features using linear principal component analysis on the wavelet coefficients, before inverse transformation back to the time domain for clinical interpretation. We demonstrate the methodology on a subset of a large fetal activity study aiming to identify temporal patterns in fetal movement (FM) count data in order to explore formal FM counting as a screening tool for identifying fetal compromise and thus preventing adverse birth outcomes. Copyright © 2013 John Wiley & Sons, Ltd.

A time domain frequency-selective multivariate Granger causality approach.

PubMed

Leistritz, Lutz; Witte, Herbert

2016-08-01

The investigation of effective connectivity is one of the major topics in computational neuroscience to understand the interaction between spatially distributed neuronal units of the brain. Thus, a wide variety of methods has been developed during the last decades to investigate functional and effective connectivity in multivariate systems. Their spectrum ranges from model-based to model-free approaches with a clear separation into time and frequency range methods. We present in this simulation study a novel time domain approach based on Granger's principle of predictability, which allows frequency-selective considerations of directed interactions. It is based on a comparison of prediction errors of multivariate autoregressive models fitted to systematically modified time series. These modifications are based on signal decompositions, which enable a targeted cancellation of specific signal components with specific spectral properties. Depending on the embedded signal decomposition method, a frequency-selective or data-driven signal-adaptive Granger Causality Index may be derived.

Application of time series analysis on molecular dynamics simulations of proteins: a study of different conformational spaces by principal component analysis.

PubMed

Alakent, Burak; Doruker, Pemra; Camurdan, Mehmet C

2004-09-08

Time series analysis is applied on the collective coordinates obtained from principal component analysis of independent molecular dynamics simulations of alpha-amylase inhibitor tendamistat and immunity protein of colicin E7 based on the Calpha coordinates history. Even though the principal component directions obtained for each run are considerably different, the dynamics information obtained from these runs are surprisingly similar in terms of time series models and parameters. There are two main differences in the dynamics of the two proteins: the higher density of low frequencies and the larger step sizes for the interminima motions of colicin E7 than those of alpha-amylase inhibitor, which may be attributed to the higher number of residues of colicin E7 and/or the structural differences of the two proteins. The cumulative density function of the low frequencies in each run conforms to the expectations from the normal mode analysis. When different runs of alpha-amylase inhibitor are projected on the same set of eigenvectors, it is found that principal components obtained from a certain conformational region of a protein has a moderate explanation power in other conformational regions and the local minima are similar to a certain extent, while the height of the energy barriers in between the minima significantly change. As a final remark, time series analysis tools are further exploited in this study with the motive of explaining the equilibrium fluctuations of proteins. Copyright 2004 American Institute of Physics

Application of time series analysis on molecular dynamics simulations of proteins: A study of different conformational spaces by principal component analysis

NASA Astrophysics Data System (ADS)

Alakent, Burak; Doruker, Pemra; Camurdan, Mehmet C.

2004-09-01

Time series analysis is applied on the collective coordinates obtained from principal component analysis of independent molecular dynamics simulations of α-amylase inhibitor tendamistat and immunity protein of colicin E7 based on the Cα coordinates history. Even though the principal component directions obtained for each run are considerably different, the dynamics information obtained from these runs are surprisingly similar in terms of time series models and parameters. There are two main differences in the dynamics of the two proteins: the higher density of low frequencies and the larger step sizes for the interminima motions of colicin E7 than those of α-amylase inhibitor, which may be attributed to the higher number of residues of colicin E7 and/or the structural differences of the two proteins. The cumulative density function of the low frequencies in each run conforms to the expectations from the normal mode analysis. When different runs of α-amylase inhibitor are projected on the same set of eigenvectors, it is found that principal components obtained from a certain conformational region of a protein has a moderate explanation power in other conformational regions and the local minima are similar to a certain extent, while the height of the energy barriers in between the minima significantly change. As a final remark, time series analysis tools are further exploited in this study with the motive of explaining the equilibrium fluctuations of proteins.

A hybrid-domain approach for modeling climate data time series

NASA Astrophysics Data System (ADS)

Wen, Qiuzi H.; Wang, Xiaolan L.; Wong, Augustine

2011-09-01

In order to model climate data time series that often contain periodic variations, trends, and sudden changes in mean (mean shifts, mostly artificial), this study proposes a hybrid-domain (HD) algorithm, which incorporates a time domain test and a newly developed frequency domain test through an iterative procedure that is analogue to the well known backfitting algorithm. A two-phase competition procedure is developed to address the confounding issue between modeling periodic variations and mean shifts. A variety of distinctive features of climate data time series, including trends, periodic variations, mean shifts, and a dependent noise structure, can be modeled in tandem using the HD algorithm. This is particularly important for homogenization of climate data from a low density observing network in which reference series are not available to help preserve climatic trends and long-term periodic variations, preventing them from being mistaken as artificial shifts. The HD algorithm is also powerful in estimating trend and periodicity in a homogeneous data time series (i.e., in the absence of any mean shift). The performance of the HD algorithm (in terms of false alarm rate and hit rate in detecting shifts/cycles, and estimation accuracy) is assessed via a simulation study. Its power is further illustrated through its application to a few climate data time series.

Sequential visibility-graph motifs

NASA Astrophysics Data System (ADS)

Iacovacci, Jacopo; Lacasa, Lucas

2016-04-01

Visibility algorithms transform time series into graphs and encode dynamical information in their topology, paving the way for graph-theoretical time series analysis as well as building a bridge between nonlinear dynamics and network science. In this work we introduce and study the concept of sequential visibility-graph motifs, smaller substructures of n consecutive nodes that appear with characteristic frequencies. We develop a theory to compute in an exact way the motif profiles associated with general classes of deterministic and stochastic dynamics. We find that this simple property is indeed a highly informative and computationally efficient feature capable of distinguishing among different dynamics and robust against noise contamination. We finally confirm that it can be used in practice to perform unsupervised learning, by extracting motif profiles from experimental heart-rate series and being able, accordingly, to disentangle meditative from other relaxation states. Applications of this general theory include the automatic classification and description of physical, biological, and financial time series.

Transmission of linear regression patterns between time series: From relationship in time series to complex networks

NASA Astrophysics Data System (ADS)

Gao, Xiangyun; An, Haizhong; Fang, Wei; Huang, Xuan; Li, Huajiao; Zhong, Weiqiong; Ding, Yinghui

2014-07-01

The linear regression parameters between two time series can be different under different lengths of observation period. If we study the whole period by the sliding window of a short period, the change of the linear regression parameters is a process of dynamic transmission over time. We tackle fundamental research that presents a simple and efficient computational scheme: a linear regression patterns transmission algorithm, which transforms linear regression patterns into directed and weighted networks. The linear regression patterns (nodes) are defined by the combination of intervals of the linear regression parameters and the results of the significance testing under different sizes of the sliding window. The transmissions between adjacent patterns are defined as edges, and the weights of the edges are the frequency of the transmissions. The major patterns, the distance, and the medium in the process of the transmission can be captured. The statistical results of weighted out-degree and betweenness centrality are mapped on timelines, which shows the features of the distribution of the results. Many measurements in different areas that involve two related time series variables could take advantage of this algorithm to characterize the dynamic relationships between the time series from a new perspective.

Transmission of linear regression patterns between time series: from relationship in time series to complex networks.

PubMed

Gao, Xiangyun; An, Haizhong; Fang, Wei; Huang, Xuan; Li, Huajiao; Zhong, Weiqiong; Ding, Yinghui

2014-07-01

The linear regression parameters between two time series can be different under different lengths of observation period. If we study the whole period by the sliding window of a short period, the change of the linear regression parameters is a process of dynamic transmission over time. We tackle fundamental research that presents a simple and efficient computational scheme: a linear regression patterns transmission algorithm, which transforms linear regression patterns into directed and weighted networks. The linear regression patterns (nodes) are defined by the combination of intervals of the linear regression parameters and the results of the significance testing under different sizes of the sliding window. The transmissions between adjacent patterns are defined as edges, and the weights of the edges are the frequency of the transmissions. The major patterns, the distance, and the medium in the process of the transmission can be captured. The statistical results of weighted out-degree and betweenness centrality are mapped on timelines, which shows the features of the distribution of the results. Many measurements in different areas that involve two related time series variables could take advantage of this algorithm to characterize the dynamic relationships between the time series from a new perspective.

Search for Correlated Fluctuations in the Beta+ Decay of Na-22

NASA Astrophysics Data System (ADS)

Silverman, M. P.; Strange, W.

2008-10-01

Claims for a ``cosmogenic'' force that correlates otherwise independent stochastic events have been made for at least 10 years, based largely on visual inspection of time series of histograms whose shapes were interpreted as suggestive of recurrent patterns with semi-diurnal, diurnal, and monthly periods. Building on our earlier work to test randomness of different nuclear decay processes, we have searched for correlations in the time-series of coincident positron-electron annihilations deriving from beta+ decay of Na-22. Disintegrations were counted within a narrow time window over a period of 7 days, leading to a time series of more than 1 million events. Statistical tests were performed on the raw time series, its correlation function, and its Fourier transform to search for cyclic correlations indicative of quantum-mechanical violating deviations from Poisson statistics. The time series was then partitioned into a sequence of 167 ``bags'' each of 8192 events. A histogram was made of the events of each bag, where contiguous frequency classes differed by a single count. The chronological sequence of histograms was then tested for correlations within classes. In all cases the results of the tests were in accord with statistical control, giving no evidence of correlated fluctuations.

Quantifying Selection with Pool-Seq Time Series Data.

PubMed

Taus, Thomas; Futschik, Andreas; Schlötterer, Christian

2017-11-01

Allele frequency time series data constitute a powerful resource for unraveling mechanisms of adaptation, because the temporal dimension captures important information about evolutionary forces. In particular, Evolve and Resequence (E&R), the whole-genome sequencing of replicated experimentally evolving populations, is becoming increasingly popular. Based on computer simulations several studies proposed experimental parameters to optimize the identification of the selection targets. No such recommendations are available for the underlying parameters selection strength and dominance. Here, we introduce a highly accurate method to estimate selection parameters from replicated time series data, which is fast enough to be applied on a genome scale. Using this new method, we evaluate how experimental parameters can be optimized to obtain the most reliable estimates for selection parameters. We show that the effective population size (Ne) and the number of replicates have the largest impact. Because the number of time points and sequencing coverage had only a minor effect, we suggest that time series analysis is feasible without major increase in sequencing costs. We anticipate that time series analysis will become routine in E&R studies. © The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

Analysis of DE-1 PWI electric field data

NASA Technical Reports Server (NTRS)

Weimer, Daniel

1994-01-01

The measurement of low frequency electric field oscillations may be accomplished with the Plasma Wave Instrument (PWI) on DE 1. Oscillations at a frequency around 1 Hz are below the range of the conventional plasma wave receivers, but they can be detected by using a special processing of the quasi-static electric field data. With this processing it is also possible to determine if the electric field oscillations are predominately parallel or perpendicular to the ambient magnetic field. The quasi-static electric field in the DE 1 spin/orbit plane is measured with a long-wire 'double probe'. This antenna is perpendicular to the satellite spin axis, which in turn is approximately perpendicular to the geomagnetic field in the polar magnetosphere. The electric field data are digitally sampled at a frequency of 16 Hz. The measured electric field signal, which has had phase reversals introduced by the rotating antenna, is multiplied by the sine of the rotation angle between the antenna and the magnetic field. This is called the 'perpendicular' signal. The measured time series is also multiplied with the cosine of the angle to produce a separate 'parallel' signal. These two separate time series are then processed to determine the frequency power spectrum.

Volterra-series-based nonlinear system modeling and its engineering applications: A state-of-the-art review

NASA Astrophysics Data System (ADS)

Cheng, C. M.; Peng, Z. K.; Zhang, W. M.; Meng, G.

2017-03-01

Nonlinear problems have drawn great interest and extensive attention from engineers, physicists and mathematicians and many other scientists because most real systems are inherently nonlinear in nature. To model and analyze nonlinear systems, many mathematical theories and methods have been developed, including Volterra series. In this paper, the basic definition of the Volterra series is recapitulated, together with some frequency domain concepts which are derived from the Volterra series, including the general frequency response function (GFRF), the nonlinear output frequency response function (NOFRF), output frequency response function (OFRF) and associated frequency response function (AFRF). The relationship between the Volterra series and other nonlinear system models and nonlinear problem solving methods are discussed, including the Taylor series, Wiener series, NARMAX model, Hammerstein model, Wiener model, Wiener-Hammerstein model, harmonic balance method, perturbation method and Adomian decomposition. The challenging problems and their state of arts in the series convergence study and the kernel identification study are comprehensively introduced. In addition, a detailed review is then given on the applications of Volterra series in mechanical engineering, aeroelasticity problem, control engineering, electronic and electrical engineering.

47 CFR 73.49 - AM transmission system fencing requirements.

Code of Federal Regulations, 2010 CFR

2010-10-01

.... Antenna towers having radio frequency potential at the base (series fed, folded unipole, and insulated... be provided to each antenna tower base for meter reading and maintenance purposes at all times...

47 CFR 73.49 - AM transmission system fencing requirements.

Code of Federal Regulations, 2011 CFR

2011-10-01

.... Antenna towers having radio frequency potential at the base (series fed, folded unipole, and insulated... be provided to each antenna tower base for meter reading and maintenance purposes at all times...

47 CFR 73.49 - AM transmission system fencing requirements.

Code of Federal Regulations, 2012 CFR

2012-10-01

.... Antenna towers having radio frequency potential at the base (series fed, folded unipole, and insulated... be provided to each antenna tower base for meter reading and maintenance purposes at all times...

An intensive time-series evaluation of the effectiveness of cognitive behaviour therapy for hoarding disorder: a 2-year prospective study.

PubMed

Pollock, Lisa; Kellett, Stephen; Totterdell, Peter

2014-01-01

To intensively evaluate the effectiveness of cognitive-behavioural therapy (CBT) for Hoarding Disorder. An ABC with extended follow-up N=1 single-case experimental design (SCED) measured discard incidence/frequency/volume and associated cognitions, behaviours and emotions in a 644-day time series. Following a 4-week baseline (A), CBT was initially delivered via out-patient sessions (B) and then out-patient sessions plus domiciliary visits (C). Total treatment duration was 45 sessions (65 weeks) and follow-up was 4 sessions over 23 weeks. There was a significant increase in frequency and volume of discard, with a reliable and clinically significant reduction in hoarding. The addition of domiciliary visits did not significantly improve discard ability. The clinical utility of domiciliary visits whilst treating of hoarding is discussed and study limitations noted.

Electromagnetic pulse propagation in dispersive planar dielectrics.

PubMed

Moten, K; Durney, C H; Stockham, T G

1989-01-01

The responses of a plane-wave pulse train irradiating a lossy dispersive dielectric half-space are investigated. The incident pulse train is expressed as a Fourier series with summing done by the inverse fast Fourier transform. The Fourier series technique is adopted to avoid the many difficulties often encountered in finding the inverse Fourier transform when transform analyses are used. Calculations are made for propagation in pure water, and typical waveforms inside the dielectric half-space are presented. Higher harmonics are strongly attenuated, resulting in a single continuous sinusoidal waveform at the frequency of the fundamental depth in the material. The time-averaged specific absorption rate (SAR) for pulse-train propagation is shown to be the sum of the time-averaged SARs of the individual harmonic components of the pulse train. For the same average power, calculated SARs reveal that pulse trains generally penetrate deeper than carrier-frequency continuous waves but not deeper than continuous waves at frequencies approaching the fundamental of the pulse train. The effects of rise time on the propagating pulse train in the dielectrics are shown and explained. Since most practical pulsed systems are very limited in bandwidth, no pronounced differences between their response and continuous wave (CW) response would be expected. Typical results for pulse-train propagation in arrays of dispersive planar dielectric slabs are presented. Expressing the pulse train as a Fourier series provides a practical way of interpreting the dispersion characteristics from the spectral point of view.

Linear and nonlinear frequency- and time-domain spectroscopy with multiple frequency combs.

PubMed

Bennett, Kochise; Rouxel, Jeremy R; Mukamel, Shaul

2017-09-07

Two techniques that employ equally spaced trains of optical pulses to map an optical high frequency into a low frequency modulation of the signal that can be detected in real time are compared. The development of phase-stable optical frequency combs has opened up new avenues to metrology and spectroscopy. The ability to generate a series of frequency spikes with precisely controlled separation permits a fast, highly accurate sampling of the material response. Recently, pairs of frequency combs with slightly different repetition rates have been utilized to down-convert material susceptibilities from the optical to microwave regime where they can be recorded in real time. We show how this one-dimensional dual comb technique can be extended to multiple dimensions by using several combs. We demonstrate how nonlinear susceptibilities can be quickly acquired using this technique. In a second class of techniques, sequences of ultrafast mode locked laser pulses are used to recover pathways of interactions contributing to nonlinear susceptibilities by using a photo-acoustic modulation varying along the sequences. We show that these techniques can be viewed as a time-domain analog of the multiple frequency comb scheme.

New approaches to some methodological problems of meteor science

NASA Technical Reports Server (NTRS)

Meisel, David D.

1987-01-01

Several low cost approaches to continuous radioscatter monitoring of the incoming meteor flux are described. Preliminary experiments were attempted using standard time frequency stations WWVH and CHU (on frequencies near 15 MHz) during nighttime hours. Around-the-clock monitoring using the international standard aeronautical beacon frequency of 75 MHz was also attempted. The techniques are simple and can be managed routinely by amateur astronomers with relatively little technical expertise. Time series analysis can now be performed using relatively inexpensive microcomputers. Several algorithmic approaches to the analysis of meteor rates are discussed. Methods of obtaining optimal filter predictions of future meteor flux are also discussed.

Stability Estimation of ABWR on the Basis of Noise Analysis

NASA Astrophysics Data System (ADS)

Furuya, Masahiro; Fukahori, Takanori; Mizokami, Shinya; Yokoya, Jun

In order to investigate the stability of a nuclear reactor core with an oxide mixture of uranium and plutonium (MOX) fuel installed, channel stability and regional stability tests were conducted with the SIRIUS-F facility. The SIRIUS-F facility was designed and constructed to provide a highly accurate simulation of thermal-hydraulic (channel) instabilities and coupled thermalhydraulics-neutronics instabilities of the Advanced Boiling Water Reactors (ABWRs). A real-time simulation was performed by modal point kinetics of reactor neutronics and fuel-rod thermal conduction on the basis of a measured void fraction in a reactor core section of the facility. A time series analysis was performed to calculate decay ratio and resonance frequency from a dominant pole of a transfer function by applying auto regressive (AR) methods to the time-series of the core inlet flow rate. Experiments were conducted with the SIRIUS-F facility, which simulates ABWR with MOX fuel installed. The variations in the decay ratio and resonance frequency among the five common AR methods are within 0.03 and 0.01 Hz, respectively. In this system, the appropriate decay ratio and resonance frequency can be estimated on the basis of the Yule-Walker method with the model order of 30.

Forecasting volcanic air pollution in Hawaii: Tests of time series models

NASA Astrophysics Data System (ADS)

Reikard, Gordon

2012-12-01

Volcanic air pollution, known as vog (volcanic smog) has recently become a major issue in the Hawaiian islands. Vog is caused when volcanic gases react with oxygen and water vapor. It consists of a mixture of gases and aerosols, which include sulfur dioxide and other sulfates. The source of the volcanic gases is the continuing eruption of Mount Kilauea. This paper studies predicting vog using statistical methods. The data sets include time series for SO2 and SO4, over locations spanning the west, south and southeast coasts of Hawaii, and the city of Hilo. The forecasting models include regressions and neural networks, and a frequency domain algorithm. The most typical pattern for the SO2 data is for the frequency domain method to yield the most accurate forecasts over the first few hours, and at the 24 h horizon. The neural net places second. For the SO4 data, the results are less consistent. At two sites, the neural net generally yields the most accurate forecasts, except at the 1 and 24 h horizons, where the frequency domain technique wins narrowly. At one site, the neural net and the frequency domain algorithm yield comparable errors over the first 5 h, after which the neural net dominates. At the remaining site, the frequency domain method is more accurate over the first 4 h, after which the neural net achieves smaller errors. For all the series, the average errors are well within one standard deviation of the actual data at all the horizons. However, the errors also show irregular outliers. In essence, the models capture the central tendency of the data, but are less effective in predicting the extreme events.

Investigating the Small-Scale Spatial Variabilty of Precipitable Water Vapor by Adding Single-Frequency Receivers into an Existing Dual-Frequency Receiver Network

NASA Astrophysics Data System (ADS)

Krietemeyer, Andreas; ten Veldhuis, Marie-claire; van de Giesen, Nick

2017-04-01

Exploiting GNSS signal delays is one possibility to obtain Precipitable Water Vapor (PWV) estimates in the atmosphere. The technique is well known since the early 1990s and by now an established method in the meteorological community. The data is crucial for weather forecasting and its assimilation into numerical weather forecasting models is a topic of ongoing research. However, the spatial resolution of ground based GNSS receivers is usually low, in the order of tens of kilometres. Since severe weather events such as convective storms can be concentrated in spatial extent, existing GNSS networks are often not sufficient to retrieve small scale PWV fluctuations and need to be densified. For economic reasons, the use of low-cost single-frequency receivers is a promising solution. In this study, we will deploy a network of single-frequency receivers to densify an existing dual-frequency network in order to investigate the spatial and temporal PWV variations. We demonstrate a test network consisting of four single-frequency receivers in the Rotterdam area (Netherlands). In order to eliminate the delay caused by the ionosphere, the Satellite-specific Epoch-differenced Ionospheric Delay model (SEID) is applied, using a surrounding dual-frequency network distributed over a radius of approximately 25 km. With the synthesized L2 frequency, the tropospheric delays are estimated using the Precise Point Positioning (PPP) strategy and International GNSS Service (IGS) final orbits. The PWV time series are validated by a comparison of a collocated single-frequency and a dual-frequency receiver. The time series themselves form the basis for potential further studies like data assimilation into numerical weather models and GNSS tomography to study the impact of the increased spatial resolution on local heavy rain forecast.

Documentation of a spreadsheet for time-series analysis and drawdown estimation

USGS Publications Warehouse

Halford, Keith J.

2006-01-01

Drawdowns during aquifer tests can be obscured by barometric pressure changes, earth tides, regional pumping, and recharge events in the water-level record. These stresses can create water-level fluctuations that should be removed from observed water levels prior to estimating drawdowns. Simple models have been developed for estimating unpumped water levels during aquifer tests that are referred to as synthetic water levels. These models sum multiple time series such as barometric pressure, tidal potential, and background water levels to simulate non-pumping water levels. The amplitude and phase of each time series are adjusted so that synthetic water levels match measured water levels during periods unaffected by an aquifer test. Differences between synthetic and measured water levels are minimized with a sum-of-squares objective function. Root-mean-square errors during fitting and prediction periods were compared multiple times at four geographically diverse sites. Prediction error equaled fitting error when fitting periods were greater than or equal to four times prediction periods. The proposed drawdown estimation approach has been implemented in a spreadsheet application. Measured time series are independent so that collection frequencies can differ and sampling times can be asynchronous. Time series can be viewed selectively and magnified easily. Fitting and prediction periods can be defined graphically or entered directly. Synthetic water levels for each observation well are created with earth tides, measured time series, moving averages of time series, and differences between measured and moving averages of time series. Selected series and fitting parameters for synthetic water levels are stored and drawdowns are estimated for prediction periods. Drawdowns can be viewed independently and adjusted visually if an anomaly skews initial drawdowns away from 0. The number of observations in a drawdown time series can be reduced by averaging across user-defined periods. Raw or reduced drawdown estimates can be copied from the spreadsheet application or written to tab-delimited ASCII files.

Improved analysis of ground vibrations produced by man-made sources.

PubMed

Ainalis, Daniel; Ducarne, Loïc; Kaufmann, Olivier; Tshibangu, Jean-Pierre; Verlinden, Olivier; Kouroussis, Georges

2018-03-01

Man-made sources of ground vibration must be carefully monitored in urban areas in order to ensure that structural damage and discomfort to residents is prevented or minimised. The research presented in this paper provides a comparative evaluation of various methods used to analyse a series of tri-axial ground vibration measurements generated by rail, road, and explosive blasting. The first part of the study is focused on comparing various techniques to estimate the dominant frequency, including time-frequency analysis. The comparative evaluation of the various methods to estimate the dominant frequency revealed that, depending on the method used, there can be significant variation in the estimates obtained. A new and improved analysis approach using the continuous wavelet transform was also presented, using the time-frequency distribution to estimate the localised dominant frequency and peak particle velocity. The technique can be used to accurately identify the level and frequency content of a ground vibration signal as it varies with time, and identify the number of times the threshold limits of damage are exceeded. Copyright © 2017 Elsevier B.V. All rights reserved.

Atmospheric circulation patterns associated to the variability of River Ammer floods: evidence from observed and proxy data

NASA Astrophysics Data System (ADS)

Rimbu, N.; Czymzik, M.; Ionita, M.; Lohmann, G.; Brauer, A.

2015-09-01

The relationship between the frequency of River Ammer floods (southern Germany) and atmospheric circulation variability is investigated based on observational Ammer discharge data back to 1926 and a flood layer time series from varved sediments of the downstream Lake Ammersee for the pre-instrumental period back to 1766. A composite analysis reveals that, at synoptic time scales, observed River Ammer floods are associated with enhanced moisture transport from the Atlantic Ocean and the Mediterranean towards the Ammer region, a pronounced trough over Western Europe as well as enhanced potential vorticity at upper levels. We argue that this synoptic scale configuration can trigger heavy precipitation and floods in the Ammer region. Interannual to multidecadal increases in flood frequency as recorded in the instrumental discharge record are associated to a wave-train pattern extending from the North Atlantic to western Asia with a prominent negative center over western Europe. A similar atmospheric circulation pattern is associated to increases in flood layer frequency in the Lake Ammersee sediment record during the pre-instrumental period. We argue that the complete flood layer time-series from Lake Ammersee sediments covering the last 5500 years, contains information about atmospheric circulation variability on inter-annual to millennial time-scales.

Statistical properties of Fourier-based time-lag estimates

NASA Astrophysics Data System (ADS)

Epitropakis, A.; Papadakis, I. E.

2016-06-01

Context. The study of X-ray time-lag spectra in active galactic nuclei (AGN) is currently an active research area, since it has the potential to illuminate the physics and geometry of the innermost region (I.e. close to the putative super-massive black hole) in these objects. To obtain reliable information from these studies, the statistical properties of time-lags estimated from data must be known as accurately as possible. Aims: We investigated the statistical properties of Fourier-based time-lag estimates (I.e. based on the cross-periodogram), using evenly sampled time series with no missing points. Our aim is to provide practical "guidelines" on estimating time-lags that are minimally biased (I.e. whose mean is close to their intrinsic value) and have known errors. Methods: Our investigation is based on both analytical work and extensive numerical simulations. The latter consisted of generating artificial time series with various signal-to-noise ratios and sampling patterns/durations similar to those offered by AGN observations with present and past X-ray satellites. We also considered a range of different model time-lag spectra commonly assumed in X-ray analyses of compact accreting systems. Results: Discrete sampling, binning and finite light curve duration cause the mean of the time-lag estimates to have a smaller magnitude than their intrinsic values. Smoothing (I.e. binning over consecutive frequencies) of the cross-periodogram can add extra bias at low frequencies. The use of light curves with low signal-to-noise ratio reduces the intrinsic coherence, and can introduce a bias to the sample coherence, time-lag estimates, and their predicted error. Conclusions: Our results have direct implications for X-ray time-lag studies in AGN, but can also be applied to similar studies in other research fields. We find that: a) time-lags should be estimated at frequencies lower than ≈ 1/2 the Nyquist frequency to minimise the effects of discrete binning of the observed time series; b) smoothing of the cross-periodogram should be avoided, as this may introduce significant bias to the time-lag estimates, which can be taken into account by assuming a model cross-spectrum (and not just a model time-lag spectrum); c) time-lags should be estimated by dividing observed time series into a number, say m, of shorter data segments and averaging the resulting cross-periodograms; d) if the data segments have a duration ≳ 20 ks, the time-lag bias is ≲15% of its intrinsic value for the model cross-spectra and power-spectra considered in this work. This bias should be estimated in practise (by considering possible intrinsic cross-spectra that may be applicable to the time-lag spectra at hand) to assess the reliability of any time-lag analysis; e) the effects of experimental noise can be minimised by only estimating time-lags in the frequency range where the sample coherence is larger than 1.2/(1 + 0.2m). In this range, the amplitude of noise variations caused by measurement errors is smaller than the amplitude of the signal's intrinsic variations. As long as m ≳ 20, time-lags estimated by averaging over individual data segments have analytical error estimates that are within 95% of the true scatter around their mean, and their distribution is similar, albeit not identical, to a Gaussian.

Combining nutation and surface gravity observations to estimate the Earth's core and inner core resonant frequencies

NASA Astrophysics Data System (ADS)

Ziegler, Yann; Lambert, Sébastien; Rosat, Séverine; Nurul Huda, Ibnu; Bizouard, Christian

2017-04-01

Nutation time series derived from very long baseline interferometry (VLBI) and time varying surface gravity data recorded by superconducting gravimeters (SG) have long been used separately to assess the Earth's interior via the estimation of the free core and inner core resonance effects on nutation or tidal gravity. The results obtained from these two techniques have been shown recently to be consistent, making relevant the combination of VLBI and SG observables and the estimation of Earth's interior parameters in a single inversion. We present here the intermediate results of the ongoing project of combining nutation and surface gravity time series to improve estimates of the Earth's core and inner core resonant frequencies. We use VLBI nutation time series spanning 1984-2016 derived by the International VLBI Service for geodesy and astrometry (IVS) as the result of a combination of inputs from various IVS analysis centers, and surface gravity data from about 15 SG stations. We address here the resonance model used for describing the Earth's interior response to tidal excitation, the data preparation consisting of the error recalibration and amplitude fitting for nutation data, and processing of SG time-varying gravity to remove any gaps, spikes, steps and other disturbances, followed by the tidal analysis with the ETERNA 3.4 software package, the preliminary estimates of the resonant periods, and the correlations between parameters.

Frequency analysis via the method of moment functionals

NASA Technical Reports Server (NTRS)

Pearson, A. E.; Pan, J. Q.

1990-01-01

Several variants are presented of a linear-in-parameters least squares formulation for determining the transfer function of a stable linear system at specified frequencies given a finite set of Fourier series coefficients calculated from transient nonstationary input-output data. The basis of the technique is Shinbrot's classical method of moment functionals using complex Fourier based modulating functions to convert a differential equation model on a finite time interval into an algebraic equation which depends linearly on frequency-related parameters.

Assessing co-regulation of directly linked genes in biological networks using microarray time series analysis.

PubMed

Del Sorbo, Maria Rosaria; Balzano, Walter; Donato, Michele; Draghici, Sorin

2013-11-01

Differential expression of genes detected with the analysis of high throughput genomic experiments is a commonly used intermediate step for the identification of signaling pathways involved in the response to different biological conditions. The impact analysis was the first approach for the analysis of signaling pathways involved in a certain biological process that was able to take into account not only the magnitude of the expression change of the genes but also the topology of signaling pathways including the type of each interactions between the genes. In the impact analysis, signaling pathways are represented as weighted directed graphs with genes as nodes and the interactions between genes as edges. Edges weights are represented by a β factor, the regulatory efficiency, which is assumed to be equal to 1 in inductive interactions between genes and equal to -1 in repressive interactions. This study presents a similarity analysis between gene expression time series aimed to find correspondences with the regulatory efficiency, i.e. the β factor as found in a widely used pathway database. Here, we focused on correlations among genes directly connected in signaling pathways, assuming that the expression variations of upstream genes impact immediately downstream genes in a short time interval and without significant influences by the interactions with other genes. Time series were processed using three different similarity metrics. The first metric is based on the bit string matching; the second one is a specific application of the Dynamic Time Warping to detect similarities even in presence of stretching and delays; the third one is a quantitative comparative analysis resulting by an evaluation of frequency domain representation of time series: the similarity metric is the correlation between dominant spectral components. These three approaches are tested on real data and pathways, and a comparison is performed using Information Retrieval benchmark tools, indicating the frequency approach as the best similarity metric among the three, for its ability to detect the correlation based on the correspondence of the most significant frequency components. Copyright © 2013. Published by Elsevier Ireland Ltd.

System and method for constructing filters for detecting signals whose frequency content varies with time

DOEpatents

Qian, S.; Dunham, M.E.

1996-11-12

A system and method are disclosed for constructing a bank of filters which detect the presence of signals whose frequency content varies with time. The present invention includes a novel system and method for developing one or more time templates designed to match the received signals of interest and the bank of matched filters use the one or more time templates to detect the received signals. Each matched filter compares the received signal x(t) with a respective, unique time template that has been designed to approximate a form of the signals of interest. The robust time domain template is assumed to be of the order of w(t)=A(t)cos(2{pi}{phi}(t)) and the present invention uses the trajectory of a joint time-frequency representation of x(t) as an approximation of the instantaneous frequency function {phi}{prime}(t). First, numerous data samples of the received signal x(t) are collected. A joint time frequency representation is then applied to represent the signal, preferably using the time frequency distribution series. The joint time-frequency transformation represents the analyzed signal energy at time t and frequency f, P(t,f), which is a three-dimensional plot of time vs. frequency vs. signal energy. Then P(t,f) is reduced to a multivalued function f(t), a two dimensional plot of time vs. frequency, using a thresholding process. Curve fitting steps are then performed on the time/frequency plot, preferably using Levenberg-Marquardt curve fitting techniques, to derive a general instantaneous frequency function {phi}{prime}(t) which best fits the multivalued function f(t). Integrating {phi}{prime}(t) along t yields {phi}{prime}(t), which is then inserted into the form of the time template equation. A suitable amplitude A(t) is also preferably determined. Once the time template has been determined, one or more filters are developed which each use a version or form of the time template. 7 figs.

A geodetic matched filter search for slow slip with application to the Mexico subduction zone

NASA Astrophysics Data System (ADS)

Rousset, B.; Campillo, M.; Lasserre, C.; Frank, W. B.; Cotte, N.; Walpersdorf, A.; Socquet, A.; Kostoglodov, V.

2017-12-01

Since the discovery of slow slip events, many methods have been successfully applied to model obvious transient events in geodetic time series, such as the widely used network strain filter. Independent seismological observations of tremors or low-frequency earthquakes and repeating earthquakes provide evidence of low-amplitude slow deformation but do not always coincide with clear occurrences of transient signals in geodetic time series. Here we aim to extract the signal corresponding to slow slips hidden in the noise of GPS time series, without using information from independent data sets. We first build a library of synthetic slow slip event templates by assembling a source function with Green's functions for a discretized fault. We then correlate the templates with postprocessed GPS time series. Once the events have been detected in time, we estimate their duration T and magnitude Mw by modeling a weighted stack of GPS time series. An analysis of synthetic time series shows that this method is able to resolve the correct timing, location, T, and Mw of events larger than Mw 6 in the context of the Mexico subduction zone. Applied on a real data set of 29 GPS time series in the Guerrero area from 2005 to 2014, this technique allows us to detect 28 transient events from Mw 6.3 to 7.2 with durations that range from 3 to 39 days. These events have a dominant recurrence time of 40 days and are mainly located at the downdip edges of the Mw>7.5 slow slip events.

A geodetic matched-filter search for slow slip with application to the Mexico subduction zone

NASA Astrophysics Data System (ADS)

Rousset, B.; Campillo, M.; Lasserre, C.; Frank, W.; Cotte, N.; Walpersdorf, A.; Socquet, A.; Kostoglodov, V.

2017-12-01

Since the discovery of slow slip events, many methods have been successfully applied to model obvious transient events in geodetic time series, such as the widely used network strain filter. Independent seismological observations of tremors or low frequency earthquakes and repeating earthquakes provide evidence of low amplitude slow deformation but do not always coincide with clear occurrences of transient signals in geodetic time series. Here, we aim to extract the signal corresponding to slow slips hidden in the noise of GPS time series, without using information from independent datasets. We first build a library of synthetic slow slip event templates by assembling a source function with Green's functions for a discretized fault. We then correlate the templates with post-processed GPS time series. Once the events have been detected in time, we estimate their duration T and magnitude Mw by modelling a weighted stack of GPS time series. An analysis of synthetic time series shows that this method is able to resolve the correct timing, location, T and Mw of events larger than Mw 6.0 in the context of the Mexico subduction zone. Applied on a real data set of 29 GPS time series in the Guerrero area from 2005 to 2014, this technique allows us to detect 28 transient events from Mw 6.3 to 7.2 with durations that range from 3 to 39 days. These events have a dominant recurrence time of 40 days and are mainly located at the down dip edges of the Mw > 7.5 SSEs.

A cluster merging method for time series microarray with production values.

PubMed

Chira, Camelia; Sedano, Javier; Camara, Monica; Prieto, Carlos; Villar, Jose R; Corchado, Emilio

2014-09-01

A challenging task in time-course microarray data analysis is to cluster genes meaningfully combining the information provided by multiple replicates covering the same key time points. This paper proposes a novel cluster merging method to accomplish this goal obtaining groups with highly correlated genes. The main idea behind the proposed method is to generate a clustering starting from groups created based on individual temporal series (representing different biological replicates measured in the same time points) and merging them by taking into account the frequency by which two genes are assembled together in each clustering. The gene groups at the level of individual time series are generated using several shape-based clustering methods. This study is focused on a real-world time series microarray task with the aim to find co-expressed genes related to the production and growth of a certain bacteria. The shape-based clustering methods used at the level of individual time series rely on identifying similar gene expression patterns over time which, in some models, are further matched to the pattern of production/growth. The proposed cluster merging method is able to produce meaningful gene groups which can be naturally ranked by the level of agreement on the clustering among individual time series. The list of clusters and genes is further sorted based on the information correlation coefficient and new problem-specific relevant measures. Computational experiments and results of the cluster merging method are analyzed from a biological perspective and further compared with the clustering generated based on the mean value of time series and the same shape-based algorithm.

Crop Frequency Mapping for Land Use Intensity Estimation During Three Decades

NASA Astrophysics Data System (ADS)

Schmidt, Michael; Tindall, Dan

2016-08-01

Crop extent and frequency maps are an important input to inform the debate around land value and competitive land uses, food security and sustainability of agricultural practices. Such spatial datasets are likely to support decisions on natural resource management, planning and policy. The complete Landsat Time Series (LTS) archive for 23 Landsat footprints in western Queensland from 1987 to 2015 was used in a multi-temporal mapping approach. Spatial, spectral and temporal information were combined in multiple crop-modelling steps, supported by on ground training data sampled across space and time for the classes Crop and No-Crop. Temporal information within summer and winter growing seasons for each year were summarised, and combined with various vegetation indices and band ratios computed from a mid-season spectral-composite image. All available temporal information was spatially aggregated to the scale of image segments in the mid- season composite for each growing season and used to train a random forest classifier for a Crop and No- Crop classification. Validation revealed that the predictive accuracy varied by growing season and region to be within k = 0.88 to 0.97 and are thus suitable for mapping current and historic cropping activity. Crop frequency maps were produced for all regions at different time intervals. The crop frequency maps were validated separately with a historic crop information time series. Different land use intensities and conversions e.g. from agricultural to pastures are apparent and potential drivers of these conversions are discussed.

Comparison of data transformation procedures to enhance topographical accuracy in time-series analysis of the human EEG.

PubMed

Hauk, O; Keil, A; Elbert, T; Müller, M M

2002-01-30

We describe a methodology to apply current source density (CSD) and minimum norm (MN) estimation as pre-processing tools for time-series analysis of single trial EEG data. The performance of these methods is compared for the case of wavelet time-frequency analysis of simulated gamma-band activity. A reasonable comparison of CSD and MN on the single trial level requires regularization such that the corresponding transformed data sets have similar signal-to-noise ratios (SNRs). For region-of-interest approaches, it should be possible to optimize the SNR for single estimates rather than for the whole distributed solution. An effective implementation of the MN method is described. Simulated data sets were created by modulating the strengths of a radial and a tangential test dipole with wavelets in the frequency range of the gamma band, superimposed with simulated spatially uncorrelated noise. The MN and CSD transformed data sets as well as the average reference (AR) representation were subjected to wavelet frequency-domain analysis, and power spectra were mapped for relevant frequency bands. For both CSD and MN, the influence of noise can be sufficiently suppressed by regularization to yield meaningful information, but only MN represents both radial and tangential dipole sources appropriately as single peaks. Therefore, when relating wavelet power spectrum topographies to their neuronal generators, MN should be preferred.

Normalization of time-series satellite reflectance data to a standard sun-target-sensor geometry using a semi-empirical model

NASA Astrophysics Data System (ADS)

Zhao, Yongguang; Li, Chuanrong; Ma, Lingling; Tang, Lingli; Wang, Ning; Zhou, Chuncheng; Qian, Yonggang

2017-10-01

Time series of satellite reflectance data have been widely used to characterize environmental phenomena, describe trends in vegetation dynamics and study climate change. However, several sensors with wide spatial coverage and high observation frequency are usually designed to have large field of view (FOV), which cause variations in the sun-targetsensor geometry in time-series reflectance data. In this study, on the basis of semiempirical kernel-driven BRDF model, a new semi-empirical model was proposed to normalize the sun-target-sensor geometry of remote sensing image. To evaluate the proposed model, bidirectional reflectance under different canopy growth conditions simulated by Discrete Anisotropic Radiative Transfer (DART) model were used. The semi-empirical model was first fitted by using all simulated bidirectional reflectance. Experimental result showed a good fit between the bidirectional reflectance estimated by the proposed model and the simulated value. Then, MODIS time-series reflectance data was normalized to a common sun-target-sensor geometry by the proposed model. The experimental results showed the proposed model yielded good fits between the observed and estimated values. The noise-like fluctuations in time-series reflectance data was also reduced after the sun-target-sensor normalization process.

Efficient detection of a CW signal with a linear frequency drift

NASA Technical Reports Server (NTRS)

Swarztrauber, Paul N.; Bailey, David H.

1989-01-01

An efficient method is presented for the detection of a continuous wave (CW) signal with a frequency drift that is linear in time. Signals of this type occur in transmissions between any two locations that are accelerating relative to one another, e.g., transmissions from the Voyager spacecraft. We assume that both the frequency and the drift are unknown. We also assume that the signal is weak compared to the Gaussian noise. The signal is partitioned into subsequences whose discrete Fourier transforms provide a sequence of instantaneous spectra at equal time intervals. These spectra are then accumulated with a shift that is proportional to time. When the shift is equal to the frequency drift, the signal to noise ratio increases and detection occurs. Here, we show how to compute these accumulations for many shifts in an efficient manner using a variety of Fast Fourier Transformations (FFT). Computing time is proportional to L log L where L is the length of the time series.

Propagation Impact on Modern HF (High Frequency) Communications System Design

DTIC Science & Technology

1986-03-01

received SNR is maximised and interference avoided. As a general principle, system availability and reliability should be improved by the use of...LECTURE SERIES No. 145 propagation Impact on Modern HF Communications System Design. NORTH ATLANTIC TREATY ORGANIZATION gS ^, DISTRIBUTION ...civil and military communities for high frequency communications. It will discuss concepts of real time channel evaluation , system design, as well as

Multivariate stochastic analysis for Monthly hydrological time series at Cuyahoga River Basin

NASA Astrophysics Data System (ADS)

zhang, L.

2011-12-01

Copula has become a very powerful statistic and stochastic methodology in case of the multivariate analysis in Environmental and Water resources Engineering. In recent years, the popular one-parameter Archimedean copulas, e.g. Gumbel-Houggard copula, Cook-Johnson copula, Frank copula, the meta-elliptical copula, e.g. Gaussian Copula, Student-T copula, etc. have been applied in multivariate hydrological analyses, e.g. multivariate rainfall (rainfall intensity, duration and depth), flood (peak discharge, duration and volume), and drought analyses (drought length, mean and minimum SPI values, and drought mean areal extent). Copula has also been applied in the flood frequency analysis at the confluences of river systems by taking into account the dependence among upstream gauge stations rather than by using the hydrological routing technique. In most of the studies above, the annual time series have been considered as stationary signal which the time series have been assumed as independent identically distributed (i.i.d.) random variables. But in reality, hydrological time series, especially the daily and monthly hydrological time series, cannot be considered as i.i.d. random variables due to the periodicity existed in the data structure. Also, the stationary assumption is also under question due to the Climate Change and Land Use and Land Cover (LULC) change in the fast years. To this end, it is necessary to revaluate the classic approach for the study of hydrological time series by relaxing the stationary assumption by the use of nonstationary approach. Also as to the study of the dependence structure for the hydrological time series, the assumption of same type of univariate distribution also needs to be relaxed by adopting the copula theory. In this paper, the univariate monthly hydrological time series will be studied through the nonstationary time series analysis approach. The dependence structure of the multivariate monthly hydrological time series will be studied through the copula theory. As to the parameter estimation, the maximum likelihood estimation (MLE) will be applied. To illustrate the method, the univariate time series model and the dependence structure will be determined and tested using the monthly discharge time series of Cuyahoga River Basin.

Small-signal model for the series resonant converter

NASA Technical Reports Server (NTRS)

King, R. J.; Stuart, T. A.

1985-01-01

The results of a previous discrete-time model of the series resonant dc-dc converter are reviewed and from these a small signal dynamic model is derived. This model is valid for low frequencies and is based on the modulation of the diode conduction angle for control. The basic converter is modeled separately from its output filter to facilitate the use of these results for design purposes. Experimental results are presented.

Do We Really Need Sinusoidal Surface Temperatures to Apply Heat Tracing Techniques to Estimate Streambed Fluid Fluxes?

NASA Astrophysics Data System (ADS)

Luce, C. H.; Tonina, D.; Applebee, R.; DeWeese, T.

2017-12-01

Two common refrains about using the one-dimensional advection diffusion equation to estimate fluid fluxes, thermal conductivity, or bed surface elevation from temperature time series in streambeds are that the solution assumes that 1) the surface boundary condition is a sine wave or nearly so, and 2) there is no gradient in mean temperature with depth. Concerns on these subjects are phrased in various ways, including non-stationarity in frequency, amplitude, or phase. Although the mathematical posing of the original solution to the problem might lead one to believe these constraints exist, the perception that they are a source of error is a fallacy. Here we re-derive the inverse solution of the 1-D advection-diffusion equation starting with an arbitrary surface boundary condition for temperature. In doing so, we demonstrate the frequency-independence of the solution, meaning any single frequency can be used in the frequency-domain solutions to estimate thermal diffusivity and 1-D fluid flux in streambeds, even if the forcing has multiple frequencies. This means that diurnal variations with asymmetric shapes, gradients in the mean temperature with depth, or `non-stationary' amplitude and frequency (or phase) do not actually represent violations of assumptions, and they should not cause errors in estimates when using one of the suite of existing solution methods derived based on a single frequency. Misattribution of errors to these issues constrains progress on solving real sources of error. Numerical and physical experiments are used to verify this conclusion and consider the utility of information at `non-standard' frequencies and multiple frequencies to augment the information derived from time series of temperature.

Coupling Poisson rectangular pulse and multiplicative microcanonical random cascade models to generate sub-daily precipitation timeseries

NASA Astrophysics Data System (ADS)

Pohle, Ina; Niebisch, Michael; Müller, Hannes; Schümberg, Sabine; Zha, Tingting; Maurer, Thomas; Hinz, Christoph

2018-07-01

To simulate the impacts of within-storm rainfall variabilities on fast hydrological processes, long precipitation time series with high temporal resolution are required. Due to limited availability of observed data such time series are typically obtained from stochastic models. However, most existing rainfall models are limited in their ability to conserve rainfall event statistics which are relevant for hydrological processes. Poisson rectangular pulse models are widely applied to generate long time series of alternating precipitation events durations and mean intensities as well as interstorm period durations. Multiplicative microcanonical random cascade (MRC) models are used to disaggregate precipitation time series from coarse to fine temporal resolution. To overcome the inconsistencies between the temporal structure of the Poisson rectangular pulse model and the MRC model, we developed a new coupling approach by introducing two modifications to the MRC model. These modifications comprise (a) a modified cascade model ("constrained cascade") which preserves the event durations generated by the Poisson rectangular model by constraining the first and last interval of a precipitation event to contain precipitation and (b) continuous sigmoid functions of the multiplicative weights to consider the scale-dependency in the disaggregation of precipitation events of different durations. The constrained cascade model was evaluated in its ability to disaggregate observed precipitation events in comparison to existing MRC models. For that, we used a 20-year record of hourly precipitation at six stations across Germany. The constrained cascade model showed a pronounced better agreement with the observed data in terms of both the temporal pattern of the precipitation time series (e.g. the dry and wet spell durations and autocorrelations) and event characteristics (e.g. intra-event intermittency and intensity fluctuation within events). The constrained cascade model also slightly outperformed the other MRC models with respect to the intensity-frequency relationship. To assess the performance of the coupled Poisson rectangular pulse and constrained cascade model, precipitation events were stochastically generated by the Poisson rectangular pulse model and then disaggregated by the constrained cascade model. We found that the coupled model performs satisfactorily in terms of the temporal pattern of the precipitation time series, event characteristics and the intensity-frequency relationship.

Sidebands alongside the diurnal frequencies of radon time series in a simulation experiment -- an indication for a direct association with the earth-sun system

NASA Astrophysics Data System (ADS)

Steinitz, Gideon; Sturrock, Peter A.; Piatibratova, Oksana; Kotlarsky, Peter

2015-04-01

A radon simulation experiment using a confined mode is operating at GSI since 2007 at a time resolution of 15-minutes [1]. The nuclear radiation from radon in the confined air is measured using internal alpha and gamma sensors, and external gamma sensors. Detailed analysis [1, 2] demonstrated that the variation patterns cannot be ascribed to local environmental influences. On the other hand the specific features and relation led to the suggestion that a component in solar radiation is driving the signals. Prominent periodicities dominate the variation in the annual and diurnal frequency bands. The primary periodicity in the diurnal band has a frequency of 1 CPD (S1). Significant multiples occur at 2 CPD (S2), 3 CPD (S3) and also at 4 CPD (S4). The S2 and S3 constituents are clearly observed in the time domain in addition to the primary S1 periodicity. The measured signal is detrended by removing the large annual variation. Spectral analysis (FFT) of the residual time series reveals sidebands (Sb) alongside and on both sides of the S1 frequency in the time series of the alpha and gamma sensors. The lower sideband (LSb) occurs at a frequency close to the astronomical sidereal frequency (0.9972696 CPD). The upper sideband (USb) occurs at a symmetric frequency relative to S1. The four sensors (alpha and gamma)exhibit the LSb, S1, and USb at the following frequencies (CPD): Gamma-C: 0.99739; 0.99989; 1.00275 Gamma-W: 0.99717; 0.99986; 1.00257 Alpha-H: 0.99710; 0.99992; 1.00269 Alpha-L: 0.99719; 0.99991 Multiples of LSb and USb are observed around the S1 periodicity. Similar features of Sb and multiples occur also around S2, S3, and S4. The development of the specific Sb around the diurnal periodicities may be attributed to a driver composed of two waveforms having periodicities of 1 day and 365.25 days, which interacts in a non-linear mode with radon inside the confined volume. The pattern of the alpha and gamma emission of the decaying radon is reflecting this non-linear interaction. The observed patterns of diurnal periodicities together with the associated Sb and their multiples can be demonstrated by statistical simulation using polynomial combinations of these sinusoidal waveforms. Notwithstanding, at this stage the identification of the underlying physical and geophysical processes remains open. The observation of sidebands around S1 at the specific periodicities indicates that the periodic signals in the radon time series of the experiment are directly related to the cyclic rotational relations in the earth-sun system. This in turn is an independent confirmation of the notion that these signals are influenced by a component in solar radiation [1, 2]. 1. Steinitz, G., Piatibratova, O., Kotlarsky, P., 2011. Possible effect of solar tides on radon signals. Journal of Environmental Radioactivity, 102, 749-765. doi: 10.1016/j.jenvrad.2011.04.002. 2. Sturrock, P.A., Steinitz, G., Fischbach, E., Javorsek, D. and Jenkins, J.H., 2012. Analysis of Gamma Radiation from a Radon Source: Indications of a Solar Influence. Astroparticle Physics, 36/1, 18-26.

Linear and nonlinear trending and prediction for AVHRR time series data

NASA Technical Reports Server (NTRS)

Smid, J.; Volf, P.; Slama, M.; Palus, M.

1995-01-01

The variability of AVHRR calibration coefficient in time was analyzed using algorithms of linear and non-linear time series analysis. Specifically we have used the spline trend modeling, autoregressive process analysis, incremental neural network learning algorithm and redundancy functional testing. The analysis performed on available AVHRR data sets revealed that (1) the calibration data have nonlinear dependencies, (2) the calibration data depend strongly on the target temperature, (3) both calibration coefficients and the temperature time series can be modeled, in the first approximation, as autonomous dynamical systems, (4) the high frequency residuals of the analyzed data sets can be best modeled as an autoregressive process of the 10th degree. We have dealt with a nonlinear identification problem and the problem of noise filtering (data smoothing). The system identification and filtering are significant problems for AVHRR data sets. The algorithms outlined in this study can be used for the future EOS missions. Prediction and smoothing algorithms for time series of calibration data provide a functional characterization of the data. Those algorithms can be particularly useful when calibration data are incomplete or sparse.

Time-Frequency Analyses of Tide-Gauge Sensor Data

PubMed Central

Erol, Serdar

2011-01-01

The real world phenomena being observed by sensors are generally non-stationary in nature. The classical linear techniques for analysis and modeling natural time-series observations are inefficient and should be replaced by non-linear techniques of whose theoretical aspects and performances are varied. In this manner adopting the most appropriate technique and strategy is essential in evaluating sensors’ data. In this study, two different time-series analysis approaches, namely least squares spectral analysis (LSSA) and wavelet analysis (continuous wavelet transform, cross wavelet transform and wavelet coherence algorithms as extensions of wavelet analysis), are applied to sea-level observations recorded by tide-gauge sensors, and the advantages and drawbacks of these methods are reviewed. The analyses were carried out using sea-level observations recorded at the Antalya-II and Erdek tide-gauge stations of the Turkish National Sea-Level Monitoring System. In the analyses, the useful information hidden in the noisy signals was detected, and the common features between the two sea-level time series were clarified. The tide-gauge records have data gaps in time because of issues such as instrumental shortcomings and power outages. Concerning the difficulties of the time-frequency analysis of data with voids, the sea-level observations were preprocessed, and the missing parts were predicted using the neural network method prior to the analysis. In conclusion the merits and limitations of the techniques in evaluating non-stationary observations by means of tide-gauge sensors records were documented and an analysis strategy for the sequential sensors observations was presented. PMID:22163829

Time-frequency analyses of tide-gauge sensor data.

PubMed

Erol, Serdar

2011-01-01

The real world phenomena being observed by sensors are generally non-stationary in nature. The classical linear techniques for analysis and modeling natural time-series observations are inefficient and should be replaced by non-linear techniques of whose theoretical aspects and performances are varied. In this manner adopting the most appropriate technique and strategy is essential in evaluating sensors' data. In this study, two different time-series analysis approaches, namely least squares spectral analysis (LSSA) and wavelet analysis (continuous wavelet transform, cross wavelet transform and wavelet coherence algorithms as extensions of wavelet analysis), are applied to sea-level observations recorded by tide-gauge sensors, and the advantages and drawbacks of these methods are reviewed. The analyses were carried out using sea-level observations recorded at the Antalya-II and Erdek tide-gauge stations of the Turkish National Sea-Level Monitoring System. In the analyses, the useful information hidden in the noisy signals was detected, and the common features between the two sea-level time series were clarified. The tide-gauge records have data gaps in time because of issues such as instrumental shortcomings and power outages. Concerning the difficulties of the time-frequency analysis of data with voids, the sea-level observations were preprocessed, and the missing parts were predicted using the neural network method prior to the analysis. In conclusion the merits and limitations of the techniques in evaluating non-stationary observations by means of tide-gauge sensors records were documented and an analysis strategy for the sequential sensors observations was presented.

Short-term prediction of rain attenuation level and volatility in Earth-to-Satellite links at EHF band

NASA Astrophysics Data System (ADS)

de Montera, L.; Mallet, C.; Barthès, L.; Golé, P.

2008-08-01

This paper shows how nonlinear models originally developed in the finance field can be used to predict rain attenuation level and volatility in Earth-to-Satellite links operating at the Extremely High Frequencies band (EHF, 20 50 GHz). A common approach to solving this problem is to consider that the prediction error corresponds only to scintillations, whose variance is assumed to be constant. Nevertheless, this assumption does not seem to be realistic because of the heteroscedasticity of error time series: the variance of the prediction error is found to be time-varying and has to be modeled. Since rain attenuation time series behave similarly to certain stocks or foreign exchange rates, a switching ARIMA/GARCH model was implemented. The originality of this model is that not only the attenuation level, but also the error conditional distribution are predicted. It allows an accurate upper-bound of the future attenuation to be estimated in real time that minimizes the cost of Fade Mitigation Techniques (FMT) and therefore enables the communication system to reach a high percentage of availability. The performance of the switching ARIMA/GARCH model was estimated using a measurement database of the Olympus satellite 20/30 GHz beacons and this model is shown to outperform significantly other existing models. The model also includes frequency scaling from the downlink frequency to the uplink frequency. The attenuation effects (gases, clouds and rain) are first separated with a neural network and then scaled using specific scaling factors. As to the resulting uplink prediction error, the error contribution of the frequency scaling step is shown to be larger than that of the downlink prediction, indicating that further study should focus on improving the accuracy of the scaling factor.

Estimating the effective spatial resolution of an AVHRR time series

USGS Publications Warehouse

Meyer, D.J.

1996-01-01

A method is proposed to estimate the spatial degradation of geometrically rectified AVHRR data resulting from misregistration and off-nadir viewing, and to infer the cumulative effect of these degradations over time. Misregistrations are measured using high resolution imagery as a geometric reference, and pixel sizes are computed directly from satellite zenith angles. The influence or neighbouring features on a nominal 1 km by 1 km pixel over a given site is estimated from the above information, and expressed as a spatial distribution whose spatial frequency response is used to define an effective field-of-view (EFOV) for a time series. In a demonstration of the technique applied to images from the Conterminous U.S. AVHRR data set, an EFOV of 3·1km in the east-west dimension and 19 km in the north-south dimension was estimated for a time series accumulated over a grasslands test site.

PULSE SYNTHESIZING GENERATOR

DOEpatents

Kerns, Q.A.

1963-08-01

>An electronlc circuit for synthesizing electrical current pulses having very fast rise times includes several sinewave generators tuned to progressively higher harmonic frequencies with signal amplitudes and phases selectable according to the Fourier series of the waveform that is to be synthesized. Phase control is provided by periodically triggering the generators at precisely controlled times. The outputs of the generators are combined in a coaxial transmission line. Any frequency-dependent delays that occur in the transmission line can be readily compensated for so that the desired signal wave shape is obtained at the output of the line. (AEC)

Detection of bifurcations in noisy coupled systems from multiple time series

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

Williamson, Mark S., E-mail: m.s.williamson@exeter.ac.uk; Lenton, Timothy M.

We generalize a method of detecting an approaching bifurcation in a time series of a noisy system from the special case of one dynamical variable to multiple dynamical variables. For a system described by a stochastic differential equation consisting of an autonomous deterministic part with one dynamical variable and an additive white noise term, small perturbations away from the system's fixed point will decay slower the closer the system is to a bifurcation. This phenomenon is known as critical slowing down and all such systems exhibit this decay-type behaviour. However, when the deterministic part has multiple coupled dynamical variables, themore » possible dynamics can be much richer, exhibiting oscillatory and chaotic behaviour. In our generalization to the multi-variable case, we find additional indicators to decay rate, such as frequency of oscillation. In the case of approaching a homoclinic bifurcation, there is no change in decay rate but there is a decrease in frequency of oscillations. The expanded method therefore adds extra tools to help detect and classify approaching bifurcations given multiple time series, where the underlying dynamics are not fully known. Our generalisation also allows bifurcation detection to be applied spatially if one treats each spatial location as a new dynamical variable. One may then determine the unstable spatial mode(s). This is also something that has not been possible with the single variable method. The method is applicable to any set of time series regardless of its origin, but may be particularly useful when anticipating abrupt changes in the multi-dimensional climate system.« less

Detection of bifurcations in noisy coupled systems from multiple time series

NASA Astrophysics Data System (ADS)

Williamson, Mark S.; Lenton, Timothy M.

2015-03-01

We generalize a method of detecting an approaching bifurcation in a time series of a noisy system from the special case of one dynamical variable to multiple dynamical variables. For a system described by a stochastic differential equation consisting of an autonomous deterministic part with one dynamical variable and an additive white noise term, small perturbations away from the system's fixed point will decay slower the closer the system is to a bifurcation. This phenomenon is known as critical slowing down and all such systems exhibit this decay-type behaviour. However, when the deterministic part has multiple coupled dynamical variables, the possible dynamics can be much richer, exhibiting oscillatory and chaotic behaviour. In our generalization to the multi-variable case, we find additional indicators to decay rate, such as frequency of oscillation. In the case of approaching a homoclinic bifurcation, there is no change in decay rate but there is a decrease in frequency of oscillations. The expanded method therefore adds extra tools to help detect and classify approaching bifurcations given multiple time series, where the underlying dynamics are not fully known. Our generalisation also allows bifurcation detection to be applied spatially if one treats each spatial location as a new dynamical variable. One may then determine the unstable spatial mode(s). This is also something that has not been possible with the single variable method. The method is applicable to any set of time series regardless of its origin, but may be particularly useful when anticipating abrupt changes in the multi-dimensional climate system.

Intrinsic vs. spurious long-range memory in high-frequency records of environmental radioactivity. Critical re-assessment and application to indoor 222Rn concentrations from Coimbra, Portugal

NASA Astrophysics Data System (ADS)

Donner, R. V.; Potirakis, S. M.; Barbosa, S. M.; Matos, J. A. O.; Pereira, A. J. S. C.; Neves, L. J. P. F.

2015-05-01

The presence or absence of long-range correlations in the environmental radioactivity fluctuations has recently attracted considerable interest. Among a multiplicity of practically relevant applications, identifying and disentangling the environmental factors controlling the variable concentrations of the radioactive noble gas radon is important for estimating its effect on human health and the efficiency of possible measures for reducing the corresponding exposition. In this work, we present a critical re-assessment of a multiplicity of complementary methods that have been previously applied for evaluating the presence of long-range correlations and fractal scaling in environmental radon variations with a particular focus on the specific properties of the underlying time series. As an illustrative case study, we subsequently re-analyze two high-frequency records of indoor radon concentrations from Coimbra, Portugal, each of which spans several weeks of continuous measurements at a high temporal resolution of five minutes.Our results reveal that at the study site, radon concentrations exhibit complex multi-scale dynamics with qualitatively different properties at different time-scales: (i) essentially white noise in the high-frequency part (up to time-scales of about one hour), (ii) spurious indications of a non-stationary, apparently long-range correlated process (at time scales between some hours and one day) arising from marked periodic components, and (iii) low-frequency variability indicating a true long-range dependent process. In the presence of such multi-scale variability, common estimators of long-range memory in time series are prone to fail if applied to the raw data without previous separation of time-scales with qualitatively different dynamics.

One-year delayed effect of fog on malaria transmission: a time-series analysis in the rain forest area of Mengla County, south-west China

PubMed Central

Tian, Linwei; Bi, Yan; Ho, Suzanne C; Liu, Wenjie; Liang, Song; Goggins, William B; Chan, Emily YY; Zhou, Shuisen; Sung, Joseph JY

2008-01-01

Background Malaria is a major public health burden in the tropics with the potential to significantly increase in response to climate change. Analyses of data from the recent past can elucidate how short-term variations in weather factors affect malaria transmission. This study explored the impact of climate variability on the transmission of malaria in the tropical rain forest area of Mengla County, south-west China. Methods Ecological time-series analysis was performed on data collected between 1971 and 1999. Auto-regressive integrated moving average (ARIMA) models were used to evaluate the relationship between weather factors and malaria incidence. Results At the time scale of months, the predictors for malaria incidence included: minimum temperature, maximum temperature, and fog day frequency. The effect of minimum temperature on malaria incidence was greater in the cool months than in the hot months. The fog day frequency in October had a positive effect on malaria incidence in May of the following year. At the time scale of years, the annual fog day frequency was the only weather predictor of the annual incidence of malaria. Conclusion Fog day frequency was for the first time found to be a predictor of malaria incidence in a rain forest area. The one-year delayed effect of fog on malaria transmission may involve providing water input and maintaining aquatic breeding sites for mosquitoes in vulnerable times when there is little rainfall in the 6-month dry seasons. These findings should be considered in the prediction of future patterns of malaria for similar tropical rain forest areas worldwide. PMID:18565224

Detection of Geomagnetic Pulsations of the Earth Using GPS-TEC Data

NASA Astrophysics Data System (ADS)

Koroglu, Ozan; Arikan, Feza; Köroǧlu, Meltem; Sabri Ozkazanc, Yakup

2016-07-01

The magnetosphere of the Earth is made up of both magnetic fields and plasma. In this layer, plasma waves propagate as Ultra Low Frequency (ULF) waves having mHz scale frequencies. ULF waves are produced due to complicated solar-geomagnetic interactions. In the literature, these ULF waves are defined as pulsations. The geomagnetic pulsations are classified into main two groups as continuous pulsations (Pc) and irregular pulsations (Pi). These pulsations can be determined by ionospheric parameters due to the complex lithosphere-ionosphere-magnetosphere coupling processes. Total Electron Content (TEC) is one of the most important parameters for investigating the variability of ionosphere. Global Positioning System (GPS) provides a cost-effective means for estimating TEC from GPS satellite orbital height of 20,000 km to the ground based receivers. Therefore, the time series of GPS-TEC inherently contains the above mentioned ULF waves. In this study, time series analysis of GPS-TEC is carried out by applying periodogram method to the mid-latitude annual TEC data. After the analysis of GPS-TEC data obtained for GPS stations located in Central Europe and Turkey for 2011, it is observed that some of the fundamental frequencies that are indicators of Pc waves, diurnal and semi-diurnal periodicity and earth-free oscillations can be identified. These results will be used in determination of low frequency trend structure of magnetosphere and ionosphere. Further investigation of remaining relatively low magnitude frequencies, all Pi and Pc can be identified by using time and frequency domain techniques such as wavelet analysis. This study is supported by the joint TUBITAK 115E915 and joint TUBITAK114E092 and AS CR 14/001 projects.

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

Effect of train carbody's parameters on vertical bending stiffness performance

NASA Astrophysics Data System (ADS)

Yang, Guangwu; Wang, Changke; Xiang, Futeng; Xiao, Shoune

2016-10-01

Finite element analysis(FEA) and modal test are main methods to give the first-order vertical bending vibration frequency of train carbody at present, but they are inefficiency and waste plenty of time. Based on Timoshenko beam theory, the bending deformation, moment of inertia and shear deformation are considered. Carbody is divided into some parts with the same length, and it's stiffness is calculated with series principle, it's cross section area, moment of inertia and shear shape coefficient is equivalent by segment length, and the fimal corrected first-order vertical bending vibration frequency analytical formula is deduced. There are 6 simple carbodies and 1 real carbody as examples to test the formula, all analysis frequencies are very close to their FEA frequencies, and especially for the real carbody, the error between analysis and experiment frequency is 0.75%. Based on the analytic formula, sensitivity analysis of the real carbody's design parameters is done, and some main parameters are found. The series principle of carbody stiffness is introduced into Timoshenko beam theory to deduce a formula, which can estimate the first-order vertical bending vibration frequency of carbody quickly without traditional FEA method and provide a reference to design engineers.

Real-Time Monitoring of Psychotherapeutic Processes: Concept and Compliance

PubMed Central

Schiepek, Günter; Aichhorn, Wolfgang; Gruber, Martin; Strunk, Guido; Bachler, Egon; Aas, Benjamin

2016-01-01

Objective: The feasibility of a high-frequency real-time monitoring approach to psychotherapy is outlined and tested for patients' compliance to evaluate its integration to everyday practice. Criteria concern the ecological momentary assessment, the assessment of therapy-related cognitions and emotions, equidistant time sampling, real-time nonlinear time series analysis, continuous participative process control by client and therapist, and the application of idiographic (person-specific) surveys. Methods: The process-outcome monitoring is technically realized by an internet-based device for data collection and data analysis, the Synergetic Navigation System. Its feasibility is documented by a compliance study on 151 clients treated in an inpatient and a day-treatment clinic. Results: We found high compliance rates (mean: 78.3%, median: 89.4%) amongst the respondents, independent of the severity of symptoms or the degree of impairment. Compared to other diagnoses, the compliance rate was lower in the group diagnosed with personality disorders. Conclusion: The results support the feasibility of high-frequency monitoring in routine psychotherapy settings. Daily collection of psychological surveys allows for the assessment of highly resolved, equidistant time series data which gives insight into the nonlinear qualities of therapeutic change processes (e.g., pattern transitions, critical instabilities). PMID:27199837

Parallels among the ``music scores'' of solar cycles, space weather and Earth's climate

NASA Astrophysics Data System (ADS)

Kolláth, Zoltán; Oláh, Katalin; van Driel-Gesztelyi, Lidia

2012-07-01

Solar variability and its effects on the physical variability of our (space) environment produces complex signals. In the indicators of solar activity at least four independent cyclic components can be identified, all of them with temporal variations in their timescales. Time-frequency distributions (see Kolláth & Oláh 2009) are perfect tools to disclose the ``music scores'' in these complex time series. Special features in the time-frequency distributions, like frequency splitting, or modulations on different timescales provide clues, which can reveal similar trends among different indices like sunspot numbers, interplanetary magnetic field strength in the Earth's neighborhood and climate data. On the pseudo-Wigner Distribution (PWD) the frequency splitting of all the three main components (the Gleissberg and Schwabe cycles, and an ~5.5 year signal originating from cycle asymmetry, i.e. the Waldmeier effect) can be identified as a ``bubble'' shaped structure after 1950. The same frequency splitting feature can also be found in the heliospheric magnetic field data and the microwave radio flux.

Climate Change: A New Metric to Measure Changes in the Frequency of Extreme Temperatures using Record Data

NASA Technical Reports Server (NTRS)

Munasinghe, L.; Jun, T.; Rind, D. H.

2012-01-01

Consensus on global warming is the result of multiple and varying lines of evidence, and one key ramification is the increase in frequency of extreme climate events including record high temperatures. Here we develop a metric- called "record equivalent draws" (RED)-based on record high (low) temperature observations, and show that changes in RED approximate changes in the likelihood of extreme high (low) temperatures. Since we also show that this metric is independent of the specifics of the underlying temperature distributions, RED estimates can be aggregated across different climates to provide a genuinely global assessment of climate change. Using data on monthly average temperatures across the global landmass we find that the frequency of extreme high temperatures increased 10-fold between the first three decades of the last century (1900-1929) and the most recent decade (1999-2008). A more disaggregated analysis shows that the increase in frequency of extreme high temperatures is greater in the tropics than in higher latitudes, a pattern that is not indicated by changes in mean temperature. Our RED estimates also suggest concurrent increases in the frequency of both extreme high and extreme low temperatures during 2002-2008, a period when we observe a plateauing of global mean temperature. Using daily extreme temperature observations, we find that the frequency of extreme high temperatures is greater in the daily minimum temperature time-series compared to the daily maximum temperature time-series. There is no such observable difference in the frequency of extreme low temperatures between the daily minimum and daily maximum.

Instantaneous frequency time analysis of physiology signals: The application of pregnant women’s radial artery pulse signals

NASA Astrophysics Data System (ADS)

Su, Zhi-Yuan; Wang, Chuan-Chen; Wu, Tzuyin; Wang, Yeng-Tseng; Tang, Feng-Cheng

2008-01-01

This study used the Hilbert-Huang transform, a recently developed, instantaneous frequency-time analysis, to analyze radial artery pulse signals taken from women in their 36th week of pregnancy and after pregnancy. The acquired instantaneous frequency-time spectrum (Hilbert spectrum) is further compared with the Morlet wavelet spectrum. Results indicate that the Hilbert spectrum is especially suitable for analyzing the time series of non-stationary radial artery pulse signals since, in the Hilbert-Huang transform, signals are decomposed into different mode functions in accordance with signal’s local time scale. Therefore, the Hilbert spectrum contains more detailed information than the Morlet wavelet spectrum. From the Hilbert spectrum, we can see that radial artery pulse signals taken from women in their 36th week of pregnancy and after pregnancy have different patterns. This approach could be applied to facilitate non-invasive diagnosis of fetus’ physiological signals in the future.

Time-frequency analysis of band-limited EEG with BMFLC and Kalman filter for BCI applications

PubMed Central

2013-01-01

Background Time-Frequency analysis of electroencephalogram (EEG) during different mental tasks received significant attention. As EEG is non-stationary, time-frequency analysis is essential to analyze brain states during different mental tasks. Further, the time-frequency information of EEG signal can be used as a feature for classification in brain-computer interface (BCI) applications. Methods To accurately model the EEG, band-limited multiple Fourier linear combiner (BMFLC), a linear combination of truncated multiple Fourier series models is employed. A state-space model for BMFLC in combination with Kalman filter/smoother is developed to obtain accurate adaptive estimation. By virtue of construction, BMFLC with Kalman filter/smoother provides accurate time-frequency decomposition of the bandlimited signal. Results The proposed method is computationally fast and is suitable for real-time BCI applications. To evaluate the proposed algorithm, a comparison with short-time Fourier transform (STFT) and continuous wavelet transform (CWT) for both synthesized and real EEG data is performed in this paper. The proposed method is applied to BCI Competition data IV for ERD detection in comparison with existing methods. Conclusions Results show that the proposed algorithm can provide optimal time-frequency resolution as compared to STFT and CWT. For ERD detection, BMFLC-KF outperforms STFT and BMFLC-KS in real-time applicability with low computational requirement. PMID:24274109

Frequency adaptation in controlled stochastic resonance utilizing delayed feedback method: two-pole approximation for response function.

PubMed

Tutu, Hiroki

2011-06-01

Stochastic resonance (SR) enhanced by time-delayed feedback control is studied. The system in the absence of control is described by a Langevin equation for a bistable system, and possesses a usual SR response. The control with the feedback loop, the delay time of which equals to one-half of the period (2π/Ω) of the input signal, gives rise to a noise-induced oscillatory switching cycle between two states in the output time series, while its average frequency is just smaller than Ω in a small noise regime. As the noise intensity D approaches an appropriate level, the noise constructively works to adapt the frequency of the switching cycle to Ω, and this changes the dynamics into a state wherein the phase of the output signal is entrained to that of the input signal from its phase slipped state. The behavior is characterized by power loss of the external signal or response function. This paper deals with the response function based on a dichotomic model. A method of delay-coordinate series expansion, which reduces a non-Markovian transition probability flux to a series of memory fluxes on a discrete delay-coordinate system, is proposed. Its primitive implementation suggests that the method can be a potential tool for a systematic analysis of SR phenomenon with delayed feedback loop. We show that a D-dependent behavior of poles of a finite Laplace transform of the response function qualitatively characterizes the structure of the power loss, and we also show analytical results for the correlation function and the power spectral density.

Long-range prediction of the low-frequency mode in the low-level Indian monsoon circulation with a simple statistical method

NASA Astrophysics Data System (ADS)

Chen, Tsing-Chang; Yen, Ming-Cheng; Wu, Kuang-Der; Ng, Thomas

1992-08-01

The time evolution of the Indian monsoon is closely related to locations of the northward migrating monsoon troughs and ridges which can be well depicted with the 30 60day filtered 850-mb streamfunction. Thus, long-range forecasts of the large-scale low-level monsoon can be obtained from those of the filtered 850-mb streamfunction. These long-range forecasts were made in this study in terms of the Auto Regressive (AR) Moving-Average process. The historical series of the AR model were constructed with the 30 60day filtered 850-mb streamfunction [˜ψ (850mb)] time series of 4months. However, the phase of the last low-frequency cycle in the ˜ψ (850mb) time series can be skewed by the bandpass filtering. To reduce this phase skewness, a simple scheme is introduced. With this phase modification of the filtered 850-mb streamfunction, we performed the pilot forecast experiments of three summers with the AR forecast process. The forecast errors in the positions of the northward propagating monsoon troughs and ridges at Day 20 are generally within the range of 12days behind the observed, except in some extreme cases.

Time series analysis of ozone data in Isfahan

NASA Astrophysics Data System (ADS)

Omidvari, M.; Hassanzadeh, S.; Hosseinibalam, F.

2008-07-01

Time series analysis used to investigate the stratospheric ozone formation and decomposition processes. Different time series methods are applied to detect the reason for extreme high ozone concentrations for each season. Data was convert into seasonal component and frequency domain, the latter has been evaluated by using the Fast Fourier Transform (FFT), spectral analysis. The power density spectrum estimated from the ozone data showed peaks at cycle duration of 22, 20, 36, 186, 365 and 40 days. According to seasonal component analysis most fluctuation was in 1999 and 2000, but the least fluctuation was in 2003. The best correlation between ozone and sun radiation was found in 2000. Other variables which are not available cause to this fluctuation in the 1999 and 2001. The trend of ozone is increasing in 1999 and is decreasing in other years.

Inferring the relative resilience of alternative states

USGS Publications Warehouse

Angeler, David G.; Allen, Craig R.; Rojo, Carmen; Alvarez-Cobelas, Miguel; Rodrigo, Maria A.; Sanchez-Carrillo, Salvador

2013-01-01

Ecological systems may occur in alternative states that differ in ecological structures, functions and processes. Resilience is the measure of disturbance an ecological system can absorb before changing states. However, how the intrinsic structures and processes of systems that characterize their states affects their resilience remains unclear. We analyzed time series of phytoplankton communities at three sites in a floodplain in central Spain to assess the dominant frequencies or “temporal scales” in community dynamics and compared the patterns between a wet and a dry alternative state. The identified frequencies and cross-scale structures are expected to arise from positive feedbacks that are thought to reinforce processes in alternative states of ecological systems and regulate emergent phenomena such as resilience. Our analyses show a higher species richness and diversity but lower evenness in the dry state. Time series modeling revealed a decrease in the importance of short-term variability in the communities, suggesting that community dynamics slowed down in the dry relative to the wet state. The number of temporal scales at which community dynamics manifested, and the explanatory power of time series models, was lower in the dry state. The higher diversity, reduced number of temporal scales and the lower explanatory power of time series models suggest that species dynamics tended to be more stochastic in the dry state. From a resilience perspective our results highlight a paradox: increasing species richness may not necessarily enhance resilience. The loss of cross-scale structure (i.e. the lower number of temporal scales) in community dynamics across sites suggests that resilience erodes during drought. Phytoplankton communities in the dry state are therefore likely less resilient than in the wet state. Our case study demonstrates the potential of time series modeling to assess attributes that mediate resilience. The approach is useful for assessing resilience of alternative states across ecological and other complex systems.

Acute Positive Effects of Exercise on Center-of-Pressure Fluctuations During Quiet Standing in Middle-Aged and Elderly Women.

PubMed

Fukusaki, Chiho; Masani, Kei; Miyasaka, Maya; Nakazawa, Kimitaka

2016-01-01

Acute effects of exercise on postural stability have been studied with a focus on fatigue. This study investigated the acute effects of moderate-intensity exercise on center-of-pressure (COP) fluctuation measures in middle-aged and elderly women. Thirty-five healthy women volunteered: 18 women performed a moderate aquatic exercise session for 80 minutes and 17 remained calm in a sitting position for the same duration. Center-of-pressure fluctuations during quiet standing were recorded for 60 seconds with eyes open and closed before and after the exercise and sitting tasks. The time- and frequency-domain measures of the COP time series were calculated. The frequency-domain measures were also calculated for the COP velocity time series. According to 2-way analysis of variance and paired t-tests with a Bonferroni's correction, mean velocity of COP fluctuations, mean velocity of COP fluctuations in the medial-lateral (ML) direction, and total power of the COP velocity time series in the ML direction exhibited significant reductions after 1 session of exercise. These results indicated that a moderate-intensity aquatic exercise decreased COP velocity, counteracting age-related and fatigue-inducing postural deterioration. Therefore, we concluded that a single session of moderate-intensity aquatic exercise has acute positive effects on postural stability in middle-aged and elderly women.

Discriminating low frequency components from long range persistent fluctuations in daily atmospheric temperature variability

NASA Astrophysics Data System (ADS)

Lanfredi, M.; Simoniello, T.; Cuomo, V.; Macchiato, M.

2009-02-01

This study originated from recent results reported in literature, which support the existence of long-range (power-law) persistence in atmospheric temperature fluctuations on monthly and inter-annual scales. We investigated the results of Detrended Fluctuation Analysis (DFA) carried out on twenty-two historical daily time series recorded in Europe in order to evaluate the reliability of such findings in depth. More detailed inspections emphasized systematic deviations from power-law and high statistical confidence for functional form misspecification. Rigorous analyses did not support scale-free correlation as an operative concept for Climate modelling, as instead suggested in literature. In order to understand the physical implications of our results better, we designed a bivariate Markov process, parameterised on the basis of the atmospheric observational data by introducing a slow dummy variable. The time series generated by this model, analysed both in time and frequency domains, tallied with the real ones very well. They accounted for both the deceptive scaling found in literature and the correlation details enhanced by our analysis. Our results seem to evidence the presence of slow fluctuations from another climatic sub-system such as ocean, which inflates temperature variance up to several months. They advise more precise re-analyses of temperature time series before suggesting dynamical paradigms useful for Climate modelling and for the assessment of Climate Change.

Discriminating low frequency components from long range persistent fluctuations in daily atmospheric temperature variability

NASA Astrophysics Data System (ADS)

Lanfredi, M.; Simoniello, T.; Cuomo, V.; Macchiato, M.

2009-07-01

This study originated from recent results reported in literature, which support the existence of long-range (power-law) persistence in atmospheric temperature fluctuations on monthly and inter-annual scales. We investigated the results of Detrended Fluctuation Analysis (DFA) carried out on twenty-two historical daily time series recorded in Europe in order to evaluate the reliability of such findings in depth. More detailed inspections emphasized systematic deviations from power-law and high statistical confidence for functional form misspecification. Rigorous analyses did not support scale-free correlation as an operative concept for Climate modelling, as instead suggested in literature. In order to understand the physical implications of our results better, we designed a bivariate Markov process, parameterised on the basis of the atmospheric observational data by introducing a slow dummy variable. The time series generated by this model, analysed both in time and frequency domains, tallied with the real ones very well. They accounted for both the deceptive scaling found in literature and the correlation details enhanced by our analysis. Our results seem to evidence the presence of slow fluctuations from another climatic sub-system such as ocean, which inflates temperature variance up to several months. They advise more precise re-analyses of temperature time series before suggesting dynamical paradigms useful for Climate modelling and for the assessment of Climate Change.

System and method for constructing filters for detecting signals whose frequency content varies with time

DOEpatents

Qian, Shie; Dunham, Mark E.

1996-01-01

A system and method for constructing a bank of filters which detect the presence of signals whose frequency content varies with time. The present invention includes a novel system and method for developing one or more time templates designed to match the received signals of interest and the bank of matched filters use the one or more time templates to detect the received signals. Each matched filter compares the received signal x(t) with a respective, unique time template that has been designed to approximate a form of the signals of interest. The robust time domain template is assumed to be of the order of w(t)=A(t)cos{2.pi..phi.(t)} and the present invention uses the trajectory of a joint time-frequency representation of x(t) as an approximation of the instantaneous frequency function {.phi.'(t). First, numerous data samples of the received signal x(t) are collected. A joint time frequency representation is then applied to represent the signal, preferably using the time frequency distribution series (also known as the Gabor spectrogram). The joint time-frequency transformation represents the analyzed signal energy at time t and frequency .function., P(t,f), which is a three-dimensional plot of time vs. frequency vs. signal energy. Then P(t,f) is reduced to a multivalued function f(t), a two dimensional plot of time vs. frequency, using a thresholding process. Curve fitting steps are then performed on the time/frequency plot, preferably using Levenberg-Marquardt curve fitting techniques, to derive a general instantaneous frequency function .phi.'(t) which best fits the multivalued function f(t), a trajectory of the joint time-frequency domain representation of x(t). Integrating .phi.'(t) along t yields .phi.(t), which is then inserted into the form of the time template equation. A suitable amplitude A(t) is also preferably determined. Once the time template has been determined, one or more filters are developed which each use a version or form of the time template.

Vibration Sensor Data Denoising Using a Time-Frequency Manifold for Machinery Fault Diagnosis

PubMed Central

He, Qingbo; Wang, Xiangxiang; Zhou, Qiang

2014-01-01

Vibration sensor data from a mechanical system are often associated with important measurement information useful for machinery fault diagnosis. However, in practice the existence of background noise makes it difficult to identify the fault signature from the sensing data. This paper introduces the time-frequency manifold (TFM) concept into sensor data denoising and proposes a novel denoising method for reliable machinery fault diagnosis. The TFM signature reflects the intrinsic time-frequency structure of a non-stationary signal. The proposed method intends to realize data denoising by synthesizing the TFM using time-frequency synthesis and phase space reconstruction (PSR) synthesis. Due to the merits of the TFM in noise suppression and resolution enhancement, the denoised signal would have satisfactory denoising effects, as well as inherent time-frequency structure keeping. Moreover, this paper presents a clustering-based statistical parameter to evaluate the proposed method, and also presents a new diagnostic approach, called frequency probability time series (FPTS) spectral analysis, to show its effectiveness in fault diagnosis. The proposed TFM-based data denoising method has been employed to deal with a set of vibration sensor data from defective bearings, and the results verify that for machinery fault diagnosis the method is superior to two traditional denoising methods. PMID:24379045

Correlation and Stacking of Relative Paleointensity and Oxygen Isotope Data

NASA Astrophysics Data System (ADS)

Lurcock, P. C.; Channell, J. E.; Lee, D.

2012-12-01

The transformation of a depth-series into a time-series is routinely implemented in the geological sciences. This transformation often involves correlation of a depth-series to an astronomically calibrated time-series. Eyeball tie-points with linear interpolation are still regularly used, although these have the disadvantages of being non-repeatable and not based on firm correlation criteria. Two automated correlation methods are compared: the simulated annealing algorithm (Huybers and Wunsch, 2004) and the Match protocol (Lisiecki and Lisiecki, 2002). Simulated annealing seeks to minimize energy (cross-correlation) as "temperature" is slowly decreased. The Match protocol divides records into intervals, applies penalty functions that constrain accumulation rates, and minimizes the sum of the squares of the differences between two series while maintaining the data sequence in each series. Paired relative paleointensity (RPI) and oxygen isotope records, such as those from IODP Site U1308 and/or reference stacks such as LR04 and PISO, are warped using known warping functions, and then the un-warped and warped time-series are correlated to evaluate the efficiency of the correlation methods. Correlations are performed in tandem to simultaneously optimize RPI and oxygen isotope data. Noise spectra are introduced at differing levels to determine correlation efficiency as noise levels change. A third potential method, known as dynamic time warping, involves minimizing the sum of distances between correlated point pairs across the whole series. A "cost matrix" between the two series is analyzed to find a least-cost path through the matrix. This least-cost path is used to nonlinearly map the time/depth of one record onto the depth/time of another. Dynamic time warping can be expanded to more than two dimensions and used to stack multiple time-series. This procedure can improve on arithmetic stacks, which often lose coherent high-frequency content during the stacking process.

TIMESERIESSTREAMING.VI: LabVIEW program for reliable data streaming of large analog time series

NASA Astrophysics Data System (ADS)

Czerwinski, Fabian; Oddershede, Lene B.

2011-02-01

With modern data acquisition devices that work fast and very precise, scientists often face the task of dealing with huge amounts of data. These need to be rapidly processed and stored onto a hard disk. We present a LabVIEW program which reliably streams analog time series of MHz sampling. Its run time has virtually no limitation. We explicitly show how to use the program to extract time series from two experiments: For a photodiode detection system that tracks the position of an optically trapped particle and for a measurement of ionic current through a glass capillary. The program is easy to use and versatile as the input can be any type of analog signal. Also, the data streaming software is simple, highly reliable, and can be easily customized to include, e.g., real-time power spectral analysis and Allan variance noise quantification. Program summaryProgram title: TimeSeriesStreaming.VI Catalogue identifier: AEHT_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEHT_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.: 250 No. of bytes in distributed program, including test data, etc.: 63 259 Distribution format: tar.gz Programming language: LabVIEW ( http://www.ni.com/labview/) Computer: Any machine running LabVIEW 8.6 or higher Operating system: Windows XP and Windows 7 RAM: 60-360 Mbyte Classification: 3 Nature of problem: For numerous scientific and engineering applications, it is highly desirable to have an efficient, reliable, and flexible program to perform data streaming of time series sampled with high frequencies and possibly for long time intervals. This type of data acquisition often produces very large amounts of data not easily streamed onto a computer hard disk using standard methods. Solution method: This LabVIEW program is developed to directly stream any kind of time series onto a hard disk. Due to optimized timing and usage of computational resources, such as multicores and protocols for memory usage, this program provides extremely reliable data acquisition. In particular, the program is optimized to deal with large amounts of data, e.g., taken with high sampling frequencies and over long time intervals. The program can be easily customized for time series analyses. Restrictions: Only tested in Windows-operating LabVIEW environments, must use TDMS format, acquisition cards must be LabVIEW compatible, driver DAQmx installed. Running time: As desirable: microseconds to hours

Correlated errors in geodetic time series: Implications for time-dependent deformation

USGS Publications Warehouse

Langbein, J.; Johnson, H.

1997-01-01

Analysis of frequent trilateration observations from the two-color electronic distance measuring networks in California demonstrate that the noise power spectra are dominated by white noise at higher frequencies and power law behavior at lower frequencies. In contrast, Earth scientists typically have assumed that only white noise is present in a geodetic time series, since a combination of infrequent measurements and low precision usually preclude identifying the time-correlated signature in such data. After removing a linear trend from the two-color data, it becomes evident that there are primarily two recognizable types of time-correlated noise present in the residuals. The first type is a seasonal variation in displacement which is probably a result of measuring to shallow surface monuments installed in clayey soil which responds to seasonally occurring rainfall; this noise is significant only for a small fraction of the sites analyzed. The second type of correlated noise becomes evident only after spectral analysis of line length changes and shows a functional relation at long periods between power and frequency of and where f is frequency and ?? ??? 2. With ?? = 2, this type of correlated noise is termed random-walk noise, and its source is mainly thought to be small random motions of geodetic monuments with respect to the Earth's crust, though other sources are possible. Because the line length changes in the two-color networks are measured at irregular intervals, power spectral techniques cannot reliably estimate the level of I//" noise. Rather, we also use here a maximum likelihood estimation technique which assumes that there are only two sources of noise in the residual time series (white noise and randomwalk noise) and estimates the amount of each. From this analysis we find that the random-walk noise level averages about 1.3 mm/Vyr and that our estimates of the white noise component confirm theoretical limitations of the measurement technique. In addition, the seasonal noise can be as large as 3 mm in amplitude but typically is less than 0.5 mm. Because of the presence of random-walk noise in these time series, modeling and interpretation of the geodetic data must account for this source of error. By way of example we show that estimating the time-varying strain tensor (a form of spatial averaging) from geodetic data having both random-walk and white noise error components results in seemingly significant variations in the rate of strain accumulation; spatial averaging does reduce the size of both noise components but not their relative influence on the resulting strain accumulation model. Copyright 1997 by the American Geophysical Union.

Photon time-interval statistics applied to the analysis of laser heterodyne signal with photon counter

NASA Astrophysics Data System (ADS)

Liu, Lisheng; Zhang, Heyong; Guo, Jin; Zhao, Shuai; Wang, Tingfeng

2012-08-01

In this paper, we report a mathematical derivation of probability density function (PDF) of time-interval between two successive photoelectrons of the laser heterodyne signal, and give a confirmation of the theoretical result by both numerical simulation and an experiment. The PDF curve of the beat signal displays a series of fluctuations, the period and amplitude of which are respectively determined by the beat frequency and the mixing efficiency. The beat frequency is derived from the frequency of fluctuations accordingly when the PDF curve is measured. This frequency measurement method still works while the traditional Fast Fourier Transform (FFT) algorithm hardly derives the correct peak value of the beat frequency in the condition that we detect 80 MHz beat signal with 8 Mcps (counts per-second) photons count rate, and this indicates an advantage of the PDF method.

Life stress on the Roman limes in continental Croatia.

PubMed

Slaus, M; Pećina-Slaus, N; Brkić, H

2004-01-01

The purpose of the paper is to analyze and compare the demographic profiles and disease frequencies between a skeletal series from Zmajevac, a settlement on the Danubian limes, and a composite "non-limes" skeletal series consisting of human osteological remains from three large urban settlements to the west of the limes; roman Mursa (modern Osijek), Cibalae (Vinkovci) and Certissia (Strbinci). To determine if life stresses were different in settlements on the limes the age and sex distribution in Zmajevac was compared to the composite "non-limes" series. All skeletons were also analyzed for the presence of dental pathology, dental enamel hypoplasia, cribra orbitalia, trauma, and physical stress. Data collected from the skeletal series show that, with the exception of some indicators of physical stress, no significant differences in quality of life is evident. Both series are characterized by an under-representation of subadults from the youngest age category and by similar average adult male and female ages at death. In Zmajevac the average ages at death for adult males and females were 40.0 and 39.0 years respectively, in the composite "non-limes" series 37.4 years for both males and females. The frequencies of dental disease, subadult stress indicators, and trauma are similar in both series. The only consistent difference between the two series is noted in the frequencies of skeletal markers of physical stress, in particular the frequencies of vertebral osteoarthritis and Schmorl's defects. Total male and total female vertebral osteoarthritis frequencies in the two series are significantly different, as is the difference in total male frequencies of Schmorl's defects. Young adult males in the Zmajevac series seem to have been experiencing particularly heavy physical strain on the vertebral column. They exhibit significantly higher frequencies of both vertebral osteoarthritis and Schmorl's defects than young adult males from the composite non-limes series.

Laser pulse coded signal frequency measuring device based on DSP and CPLD

NASA Astrophysics Data System (ADS)

Zhang, Hai-bo; Cao, Li-hua; Geng, Ai-hui; Li, Yan; Guo, Ru-hai; Wang, Ting-feng

2011-06-01

Laser pulse code is an anti-jamming measures used in semi-active laser guided weapons. On account of the laser-guided signals adopting pulse coding mode and the weak signal processing, it need complex calculations in the frequency measurement process according to the laser pulse code signal time correlation to meet the request in optoelectronic countermeasures in semi-active laser guided weapons. To ensure accurately completing frequency measurement in a short time, it needed to carry out self-related process with the pulse arrival time series composed of pulse arrival time, calculate the signal repetition period, and then identify the letter type to achieve signal decoding from determining the time value, number and rank number in a signal cycle by Using CPLD and DSP for signal processing chip, designing a laser-guided signal frequency measurement in the pulse frequency measurement device, improving the signal processing capability through the appropriate software algorithms. In this article, we introduced the principle of frequency measurement of the device, described the hardware components of the device, the system works and software, analyzed the impact of some system factors on the accuracy of the measurement. The experimental results indicated that this system improve the accuracy of the measurement under the premise of volume, real-time, anti-interference, low power of the laser pulse frequency measuring device. The practicality of the design, reliability has been demonstrated from the experimental point of view.

Notched-noise embedded frequency specific chirps for objective audiometry using auditory brainstem responses

PubMed Central

Corona-Strauss, Farah I.; Schick, Bernhard; Delb, Wolfgang; Strauss, Daniel J.

2012-01-01

It has been shown recently that chirp-evoked auditory brainstem responses (ABRs) show better performance than click stimulations, especially at low intensity levels. In this paper we present the development, test, and evaluation of a series of notched-noise embedded frequency specific chirps. ABRs were collected in healthy young control subjects using the developed stimuli. Results of the analysis of the corresponding ABRs using a time-scale phase synchronization stability (PSS) measure are also reported. The resultant wave V amplitude and latency measures showed a similar behavior as for values reported in literature. The PSS of frequency specific chirp-evoked ABRs reflected the presence of the wave V for all stimulation intensities. The scales that resulted in higher PSS are in line with previous findings, where ABRs evoked by broadband chirps were analyzed, and which stated that low frequency channels are better for the recognition and analysis of chirp-evoked ABRs. We conclude that the development and test of the series of notched-noise embedded frequency specific chirps allowed the assessment of frequency specific ABRs, showing an identifiable wave V for different intensity levels. Future work may include the development of a faster automatic recognition scheme for these frequency specific ABRs. PMID:26557336

Extracting oscillation frequencies from sparse spectra: Fourier analysis

NASA Astrophysics Data System (ADS)

Jerzykiewicz, M.

2008-12-01

I begin by explaining the properties of spectral windows of time-series data. Emphasis is on data obtained at a single geographic longitude, but ground-based multi-longitude cam- paigns and space missions such as MOST and Hipparcos are not entirely neglected. In the second section, I consider the Fourier transform of time-series data and the procedure of pre-whitening. Sect. 3 is devoted to the pioneers of the subject. In Sect. 4, I suggest how to avoid pitfalls in the practice of periodogram-analysing variable-stars observations. In the last section, I venture an opinion.

Methodology for time-domain estimation of storm time geoelectric fields using the 3-D magnetotelluric response tensors

USGS Publications Warehouse

Kelbert, Anna; Balch, Christopher; Pulkkinen, Antti; Egbert, Gary D; Love, Jeffrey J.; Rigler, E. Joshua; Fujii, Ikuko

2017-01-01

Geoelectric fields at the Earth's surface caused by magnetic storms constitute a hazard to the operation of electric power grids and related infrastructure. The ability to estimate these geoelectric fields in close to real time and provide local predictions would better equip the industry to mitigate negative impacts on their operations. Here we report progress toward this goal: development of robust algorithms that convolve a magnetic storm time series with a frequency domain impedance for a realistic three-dimensional (3-D) Earth, to estimate the local, storm time geoelectric field. Both frequency domain and time domain approaches are presented and validated against storm time geoelectric field data measured in Japan. The methods are then compared in the context of a real-time application.

Methodology for time-domain estimation of storm time geoelectric fields using the 3-D magnetotelluric response tensors

NASA Astrophysics Data System (ADS)

Kelbert, Anna; Balch, Christopher C.; Pulkkinen, Antti; Egbert, Gary D.; Love, Jeffrey J.; Rigler, E. Joshua; Fujii, Ikuko

2017-07-01

Geoelectric fields at the Earth's surface caused by magnetic storms constitute a hazard to the operation of electric power grids and related infrastructure. The ability to estimate these geoelectric fields in close to real time and provide local predictions would better equip the industry to mitigate negative impacts on their operations. Here we report progress toward this goal: development of robust algorithms that convolve a magnetic storm time series with a frequency domain impedance for a realistic three-dimensional (3-D) Earth, to estimate the local, storm time geoelectric field. Both frequency domain and time domain approaches are presented and validated against storm time geoelectric field data measured in Japan. The methods are then compared in the context of a real-time application.

Nonlinear analysis and dynamic structure in the energy market

NASA Astrophysics Data System (ADS)

Aghababa, Hajar

This research assesses the dynamic structure of the energy sector of the aggregate economy in the context of nonlinear mechanisms. Earlier studies have focused mainly on the price of the energy products when detecting nonlinearities in time series data of the energy market, and there is little mention of the production side of the market. Moreover, there is a lack of exploration about the implication of high dimensionality and time aggregation when analyzing the market's fundamentals. This research will address these gaps by including the quantity side of the market in addition to the price and by systematically incorporating various frequencies for sample sizes in three essays. The goal of this research is to provide an inclusive and exhaustive examination of the dynamics in the energy markets. The first essay begins with the application of statistical techniques, and it incorporates the most well-known univariate tests for nonlinearity with distinct power functions over alternatives and tests different null hypotheses. It utilizes the daily spot price observations on five major products in the energy market. The results suggest that the time series daily spot prices of the energy products are highly nonlinear in their nature. They demonstrate apparent evidence of general nonlinear serial dependence in each individual series, as well as nonlinearity in the first, second, and third moments of the series. The second essay examines the underlying mechanism of crude oil production and identifies the nonlinear structure of the production market by utilizing various monthly time series observations of crude oil production: the U.S. field, Organization of the Petroleum Exporting Countries (OPEC), non-OPEC, and the world production of crude oil. The finding implies that the time series data of the U.S. field, OPEC, and the world production of crude oil exhibit deep nonlinearity in their structure and are generated by nonlinear mechanisms. However, the dynamics of the non-OPEC production time series data does not reveal signs of nonlinearity. The third essay explores nonlinear structure in the case of high dimensionality of the observations, different frequencies of sample sizes, and division of the samples into sub-samples. It systematically examines the robustness of the inference methods at various levels of time aggregation by employing daily spot prices on crude oil for 26 years as well as monthly spot price index on crude oil for 41 years. The daily and monthly samples are divided into sub-samples as well. All the tests detect strong evidence of nonlinear structure in the daily spot price of crude oil; whereas in monthly observations the evidence of nonlinear dependence is less dramatic, indicating that the nonlinear serial dependence will not be as intense when the time aggregation increase in time series observations.

Intermolecular dynamics of substitued benzene and cyclohexane liquids, studied by femtosecond nonlinear-optical polarization spectroscopy

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

Chang, Y.J.; Castner, E.W. Jr.

Femtosecond time-resolved optical-heterodyne detected Raman-induced Kerr effect spectroscopy (OHD-RIKES) is shown to be a powerful and comprehensive tool for studying the intermolecular dynamics occurring in liquids. The observed dynamics include both the underdamped or coherent inertial motions, and the longer time scale diffusive relaxation. The inertial dynamics include phonon-like intermolecular vibrations, intermolecular collisions, and librational caging motions. Data are presented and analyzed for a series of five liquids: cyclohexane, methylcyclohexane, toluene, benzyl alcohol, and benzonitrile, listed in order of increasing polarity. We explore the effects of aromaticity (e.g., methylcyclohexane vs toluene), symmetry reduction (cyclohexane vs methylcyclohexane), and substitution effects (e.g.,more » substituted benzene series) on the ultrafast intermolecular dynamics, for a group of molecular liquids of similar size and volume. We analyze the intermolecular dynamics in both the time and frequency domains by means of Fourier transformations. When Fourier-transformed into the frequency domain, the OHD-RIKES ultrafast transients of the intermolecular dynamics can be directly compared with the frequency domain spectra obtained from the far-infrared absorption and depolarized Raman techniques. This is done using the Gaussian librational caging model of Lynden-Bell and Steele, which results in a power-law scaling relation between dipole and polarizability time correlation functions. 122 refs., 7 figs., 7 tabs.« less

Confounding environmental colour and distribution shape leads to underestimation of population extinction risk.

PubMed

Fowler, Mike S; Ruokolainen, Lasse

2013-01-01

The colour of environmental variability influences the size of population fluctuations when filtered through density dependent dynamics, driving extinction risk through dynamical resonance. Slow fluctuations (low frequencies) dominate in red environments, rapid fluctuations (high frequencies) in blue environments and white environments are purely random (no frequencies dominate). Two methods are commonly employed to generate the coloured spatial and/or temporal stochastic (environmental) series used in combination with population (dynamical feedback) models: autoregressive [AR(1)] and sinusoidal (1/f) models. We show that changing environmental colour from white to red with 1/f models, and from white to red or blue with AR(1) models, generates coloured environmental series that are not normally distributed at finite time-scales, potentially confounding comparison with normally distributed white noise models. Increasing variability of sample Skewness and Kurtosis and decreasing mean Kurtosis of these series alter the frequency distribution shape of the realised values of the coloured stochastic processes. These changes in distribution shape alter patterns in the probability of single and series of extreme conditions. We show that the reduced extinction risk for undercompensating (slow growing) populations in red environments previously predicted with traditional 1/f methods is an artefact of changes in the distribution shapes of the environmental series. This is demonstrated by comparison with coloured series controlled to be normally distributed using spectral mimicry. Changes in the distribution shape that arise using traditional methods lead to underestimation of extinction risk in normally distributed, red 1/f environments. AR(1) methods also underestimate extinction risks in traditionally generated red environments. This work synthesises previous results and provides further insight into the processes driving extinction risk in model populations. We must let the characteristics of known natural environmental covariates (e.g., colour and distribution shape) guide us in our choice of how to best model the impact of coloured environmental variation on population dynamics.

A general theory on frequency and time-frequency analysis of irregularly sampled time series based on projection methods - Part 1: Frequency analysis

NASA Astrophysics Data System (ADS)

Lenoir, Guillaume; Crucifix, Michel

2018-03-01

We develop a general framework for the frequency analysis of irregularly sampled time series. It is based on the Lomb-Scargle periodogram, but extended to algebraic operators accounting for the presence of a polynomial trend in the model for the data, in addition to a periodic component and a background noise. Special care is devoted to the correlation between the trend and the periodic component. This new periodogram is then cast into the Welch overlapping segment averaging (WOSA) method in order to reduce its variance. We also design a test of significance for the WOSA periodogram, against the background noise. The model for the background noise is a stationary Gaussian continuous autoregressive-moving-average (CARMA) process, more general than the classical Gaussian white or red noise processes. CARMA parameters are estimated following a Bayesian framework. We provide algorithms that compute the confidence levels for the WOSA periodogram and fully take into account the uncertainty in the CARMA noise parameters. Alternatively, a theory using point estimates of CARMA parameters provides analytical confidence levels for the WOSA periodogram, which are more accurate than Markov chain Monte Carlo (MCMC) confidence levels and, below some threshold for the number of data points, less costly in computing time. We then estimate the amplitude of the periodic component with least-squares methods, and derive an approximate proportionality between the squared amplitude and the periodogram. This proportionality leads to a new extension for the periodogram: the weighted WOSA periodogram, which we recommend for most frequency analyses with irregularly sampled data. The estimated signal amplitude also permits filtering in a frequency band. Our results generalise and unify methods developed in the fields of geosciences, engineering, astronomy and astrophysics. They also constitute the starting point for an extension to the continuous wavelet transform developed in a companion article (Lenoir and Crucifix, 2018). All the methods presented in this paper are available to the reader in the Python package WAVEPAL.

Dynamical density delay maps: simple, new method for visualising the behaviour of complex systems

PubMed Central

2014-01-01

Background Physiologic signals, such as cardiac interbeat intervals, exhibit complex fluctuations. However, capturing important dynamical properties, including nonstationarities may not be feasible from conventional time series graphical representations. Methods We introduce a simple-to-implement visualisation method, termed dynamical density delay mapping (“D3-Map” technique) that provides an animated representation of a system’s dynamics. The method is based on a generalization of conventional two-dimensional (2D) Poincaré plots, which are scatter plots where each data point, x(n), in a time series is plotted against the adjacent one, x(n + 1). First, we divide the original time series, x(n) (n = 1,…, N), into a sequence of segments (windows). Next, for each segment, a three-dimensional (3D) Poincaré surface plot of x(n), x(n + 1), h[x(n),x(n + 1)] is generated, in which the third dimension, h, represents the relative frequency of occurrence of each (x(n),x(n + 1)) point. This 3D Poincaré surface is then chromatised by mapping the relative frequency h values onto a colour scheme. We also generate a colourised 2D contour plot from each time series segment using the same colourmap scheme as for the 3D Poincaré surface. Finally, the original time series graph, the colourised 3D Poincaré surface plot, and its projection as a colourised 2D contour map for each segment, are animated to create the full “D3-Map.” Results We first exemplify the D3-Map method using the cardiac interbeat interval time series from a healthy subject during sleeping hours. The animations uncover complex dynamical changes, such as transitions between states, and the relative amount of time the system spends in each state. We also illustrate the utility of the method in detecting hidden temporal patterns in the heart rate dynamics of a patient with atrial fibrillation. The videos, as well as the source code, are made publicly available. Conclusions Animations based on density delay maps provide a new way of visualising dynamical properties of complex systems not apparent in time series graphs or standard Poincaré plot representations. Trainees in a variety of fields may find the animations useful as illustrations of fundamental but challenging concepts, such as nonstationarity and multistability. For investigators, the method may facilitate data exploration. PMID:24438439

Ultrasonic RF time series for early assessment of the tumor response to chemotherapy.

PubMed

Lin, Qingguang; Wang, Jianwei; Li, Qing; Lin, Chunyi; Guo, Zhixing; Zheng, Wei; Yan, Cuiju; Li, Anhua; Zhou, Jianhua

2018-01-05

Ultrasound radio-frequency (RF) time series have been shown to carry tissue typing information. To evaluate the potential of RF time series for early prediction of tumor response to chemotherapy, 50MCF-7 breast cancer-bearing nude mice were randomized to receive cisplatin and paclitaxel (treatment group; n = 26) or sterile saline (control group; n = 24). Sequential ultrasound imaging was performed on days 0, 3, 6, and 8 of treatment to simultaneously collect B-mode images and RF data. Six RF time series features, slope, intercept, S1, S2, S3 , and S4 , were extracted during RF data analysis and contrasted with microstructural tumor changes on histopathology. Chemotherapy administration reduced tumor growth relative to control on days 6 and 8. Compared with day 0, intercept, S1 , and S2 were increased while slope was decreased on days 3, 6, and 8 in the treatment group. Compared with the control group, intercept, S1, S2, S3 , and S4 were increased, and slope was decreased, on days 3, 6, and 8 in the treatment group. Tumor cell density decreased significantly in the latter on day 3. We conclude that ultrasonic RF time series analysis provides a simple way to noninvasively assess the early tumor response to chemotherapy.

Quantifying new water fractions and water age distributions using ensemble hydrograph separation

NASA Astrophysics Data System (ADS)

Kirchner, James

2017-04-01

Catchment transit times are important controls on contaminant transport, weathering rates, and runoff chemistry. Recent theoretical studies have shown that catchment transit time distributions are nonstationary, reflecting the temporal variability in precipitation forcing, the structural heterogeneity of catchments themselves, and the nonlinearity of the mechanisms controlling storage and transport in the subsurface. The challenge of empirically estimating these nonstationary transit time distributions in real-world catchments, however, has only begun to be explored. Long, high-frequency tracer time series are now becoming available, creating new opportunities to study how rainfall becomes streamflow on timescales of minutes to days following the onset of precipitation. Here I show that the conventional formula used for hydrograph separation can be converted into an equivalent linear regression equation that quantifies the fraction of current rainfall in streamflow across ensembles of precipitation events. These ensembles can be selected to represent different discharge ranges, different precipitation intensities, or different levels of antecedent moisture, thus quantifying how the fraction of "new water" in streamflow varies with forcings such as these. I further show how this approach can be generalized to empirically determine the contributions of precipitation inputs to streamflow across a range of time lags. In this way the short-term tail of the transit time distribution can be directly quantified for an ensemble of precipitation events. Benchmark testing with a simple, nonlinear, nonstationary catchment model demonstrates that this approach quantitatively measures the short tail of the transit time distribution for a wide range of catchment response characteristics. In combination with reactive tracer time series, this approach can potentially be extended to measure short-term chemical reaction rates at the catchment scale. High-frequency tracer time series from several experimental catchments will be used to demonstrate the utility of the new approach outlined here.

Multiscale characterization and prediction of monsoon rainfall in India using Hilbert-Huang transform and time-dependent intrinsic correlation analysis

NASA Astrophysics Data System (ADS)

Adarsh, S.; Reddy, M. Janga

2017-07-01

In this paper, the Hilbert-Huang transform (HHT) approach is used for the multiscale characterization of All India Summer Monsoon Rainfall (AISMR) time series and monsoon rainfall time series from five homogeneous regions in India. The study employs the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) for multiscale decomposition of monsoon rainfall in India and uses the Normalized Hilbert Transform and Direct Quadrature (NHT-DQ) scheme for the time-frequency characterization. The cross-correlation analysis between orthogonal modes of All India monthly monsoon rainfall time series and that of five climate indices such as Quasi Biennial Oscillation (QBO), El Niño Southern Oscillation (ENSO), Sunspot Number (SN), Atlantic Multi Decadal Oscillation (AMO), and Equatorial Indian Ocean Oscillation (EQUINOO) in the time domain showed that the links of different climate indices with monsoon rainfall are expressed well only for few low-frequency modes and for the trend component. Furthermore, this paper investigated the hydro-climatic teleconnection of ISMR in multiple time scales using the HHT-based running correlation analysis technique called time-dependent intrinsic correlation (TDIC). The results showed that both the strength and nature of association between different climate indices and ISMR vary with time scale. Stemming from this finding, a methodology employing Multivariate extension of EMD and Stepwise Linear Regression (MEMD-SLR) is proposed for prediction of monsoon rainfall in India. The proposed MEMD-SLR method clearly exhibited superior performance over the IMD operational forecast, M5 Model Tree (MT), and multiple linear regression methods in ISMR predictions and displayed excellent predictive skill during 1989-2012 including the four extreme events that have occurred during this period.

Do regional methods really help reduce uncertainties in flood frequency analyses?

NASA Astrophysics Data System (ADS)

Cong Nguyen, Chi; Payrastre, Olivier; Gaume, Eric

2013-04-01

Flood frequency analyses are often based on continuous measured series at gauge sites. However, the length of the available data sets is usually too short to provide reliable estimates of extreme design floods. To reduce the estimation uncertainties, the analyzed data sets have to be extended either in time, making use of historical and paleoflood data, or in space, merging data sets considered as statistically homogeneous to build large regional data samples. Nevertheless, the advantage of the regional analyses, the important increase of the size of the studied data sets, may be counterbalanced by the possible heterogeneities of the merged sets. The application and comparison of four different flood frequency analysis methods to two regions affected by flash floods in the south of France (Ardèche and Var) illustrates how this balance between the number of records and possible heterogeneities plays in real-world applications. The four tested methods are: (1) a local statistical analysis based on the existing series of measured discharges, (2) a local analysis valuating the existing information on historical floods, (3) a standard regional flood frequency analysis based on existing measured series at gauged sites and (4) a modified regional analysis including estimated extreme peak discharges at ungauged sites. Monte Carlo simulations are conducted to simulate a large number of discharge series with characteristics similar to the observed ones (type of statistical distributions, number of sites and records) to evaluate to which extent the results obtained on these case studies can be generalized. These two case studies indicate that even small statistical heterogeneities, which are not detected by the standard homogeneity tests implemented in regional flood frequency studies, may drastically limit the usefulness of such approaches. On the other hand, these result show that the valuation of information on extreme events, either historical flood events at gauged sites or estimated extremes at ungauged sites in the considered region, is an efficient way to reduce uncertainties in flood frequency studies.

A method for generating high resolution satellite image time series

NASA Astrophysics Data System (ADS)

Guo, Tao

2014-10-01

There is an increasing demand for satellite remote sensing data with both high spatial and temporal resolution in many applications. But it still is a challenge to simultaneously improve spatial resolution and temporal frequency due to the technical limits of current satellite observation systems. To this end, much R&D efforts have been ongoing for years and lead to some successes roughly in two aspects, one includes super resolution, pan-sharpen etc. methods which can effectively enhance the spatial resolution and generate good visual effects, but hardly preserve spectral signatures and result in inadequate analytical value, on the other hand, time interpolation is a straight forward method to increase temporal frequency, however it increase little informative contents in fact. In this paper we presented a novel method to simulate high resolution time series data by combing low resolution time series data and a very small number of high resolution data only. Our method starts with a pair of high and low resolution data set, and then a spatial registration is done by introducing LDA model to map high and low resolution pixels correspondingly. Afterwards, temporal change information is captured through a comparison of low resolution time series data, and then projected onto the high resolution data plane and assigned to each high resolution pixel according to the predefined temporal change patterns of each type of ground objects. Finally the simulated high resolution data is generated. A preliminary experiment shows that our method can simulate a high resolution data with a reasonable accuracy. The contribution of our method is to enable timely monitoring of temporal changes through analysis of time sequence of low resolution images only, and usage of costly high resolution data can be reduces as much as possible, and it presents a highly effective way to build up an economically operational monitoring solution for agriculture, forest, land use investigation, environment and etc. applications.

Maximum likelihood method for estimating airplane stability and control parameters from flight data in frequency domain

NASA Technical Reports Server (NTRS)

Klein, V.

1980-01-01

A frequency domain maximum likelihood method is developed for the estimation of airplane stability and control parameters from measured data. The model of an airplane is represented by a discrete-type steady state Kalman filter with time variables replaced by their Fourier series expansions. The likelihood function of innovations is formulated, and by its maximization with respect to unknown parameters the estimation algorithm is obtained. This algorithm is then simplified to the output error estimation method with the data in the form of transformed time histories, frequency response curves, or spectral and cross-spectral densities. The development is followed by a discussion on the equivalence of the cost function in the time and frequency domains, and on advantages and disadvantages of the frequency domain approach. The algorithm developed is applied in four examples to the estimation of longitudinal parameters of a general aviation airplane using computer generated and measured data in turbulent and still air. The cost functions in the time and frequency domains are shown to be equivalent; therefore, both approaches are complementary and not contradictory. Despite some computational advantages of parameter estimation in the frequency domain, this approach is limited to linear equations of motion with constant coefficients.

Synchronous radio-frequency FM signal generator using direct digital synthesizers

NASA Astrophysics Data System (ADS)

Arablu, Masoud; Kafashi, Sajad; Smith, Stuart T.

2018-04-01

A novel Radio-Frequency Frequency-Modulated (RF-FM) signal generation method is introduced and a prototype circuit developed to evaluate its functionality and performance. The RF-FM signal generator uses a modulated, voltage-controlled time delay to correspondingly modulate the phase of a 10 MHz sinusoidal reference signal. This modulated reference signal is, in turn, used to clock a Direct Digital Synthesizer (DDS) circuit resulting in an FM signal at its output. The modulating signal that is input to the voltage-controlled time delay circuit is generated by another DDS that is synchronously clocked by the same 10 MHz sine wave signal before modulation. As a consequence, all of the digital components are timed from a single sine wave oscillator that forms the basis of all timing. The resultant output signal comprises a center, or carrier, frequency plus a series of phase-synchronized sidebands having exact integer harmonic frequency separation. In this study, carrier frequencies ranging from 10 MHz to 70 MHz are generated with modulation frequencies ranging from 10 kHz to 300 kHz. The captured spectra show that the FM signal characteristics, amplitude and phase, of the sidebands and the modulation depth are consistent with the Jacobi-Anger expansion for modulated harmonic signals.

Multitaper Spectral Analysis and Wavelet Denoising Applied to Helioseismic Data

NASA Technical Reports Server (NTRS)

Komm, R. W.; Gu, Y.; Hill, F.; Stark, P. B.; Fodor, I. K.

1999-01-01

Estimates of solar normal mode frequencies from helioseismic observations can be improved by using Multitaper Spectral Analysis (MTSA) to estimate spectra from the time series, then using wavelet denoising of the log spectra. MTSA leads to a power spectrum estimate with reduced variance and better leakage properties than the conventional periodogram. Under the assumption of stationarity and mild regularity conditions, the log multitaper spectrum has a statistical distribution that is approximately Gaussian, so wavelet denoising is asymptotically an optimal method to reduce the noise in the estimated spectra. We find that a single m-upsilon spectrum benefits greatly from MTSA followed by wavelet denoising, and that wavelet denoising by itself can be used to improve m-averaged spectra. We compare estimates using two different 5-taper estimates (Stepian and sine tapers) and the periodogram estimate, for GONG time series at selected angular degrees l. We compare those three spectra with and without wavelet-denoising, both visually, and in terms of the mode parameters estimated from the pre-processed spectra using the GONG peak-fitting algorithm. The two multitaper estimates give equivalent results. The number of modes fitted well by the GONG algorithm is 20% to 60% larger (depending on l and the temporal frequency) when applied to the multitaper estimates than when applied to the periodogram. The estimated mode parameters (frequency, amplitude and width) are comparable for the three power spectrum estimates, except for modes with very small mode widths (a few frequency bins), where the multitaper spectra broadened the modest compared with the periodogram. We tested the influence of the number of tapers used and found that narrow modes at low n values are broadened to the extent that they can no longer be fit if the number of tapers is too large. For helioseismic time series of this length and temporal resolution, the optimal number of tapers is less than 10.

Serial Founder Effects During Range Expansion: A Spatial Analog of Genetic Drift

PubMed Central

Slatkin, Montgomery; Excoffier, Laurent

2012-01-01

Range expansions cause a series of founder events. We show that, in a one-dimensional habitat, these founder events are the spatial analog of genetic drift in a randomly mating population. The spatial series of allele frequencies created by successive founder events is equivalent to the time series of allele frequencies in a population of effective size ke, the effective number of founders. We derive an expression for ke in a discrete-population model that allows for local population growth and migration among established populations. If there is selection, the net effect is determined approximately by the product of the selection coefficients and the number of generations between successive founding events. We use the model of a single population to compute analytically several quantities for an allele present in the source population: (i) the probability that it survives the series of colonization events, (ii) the probability that it reaches a specified threshold frequency in the last population, and (iii) the mean and variance of the frequencies in each population. We show that the analytic theory provides a good approximation to simulation results. A consequence of our approximation is that the average heterozygosity of neutral alleles decreases by a factor of 1 – 1/(2ke) in each new population. Therefore, the population genetic consequences of surfing can be predicted approximately by the effective number of founders and the effective selection coefficients, even in the presence of migration among populations. We also show that our analytic results are applicable to a model of range expansion in a continuously distributed population. PMID:22367031

Serial founder effects during range expansion: a spatial analog of genetic drift.

PubMed

Slatkin, Montgomery; Excoffier, Laurent

2012-05-01

Range expansions cause a series of founder events. We show that, in a one-dimensional habitat, these founder events are the spatial analog of genetic drift in a randomly mating population. The spatial series of allele frequencies created by successive founder events is equivalent to the time series of allele frequencies in a population of effective size ke, the effective number of founders. We derive an expression for ke in a discrete-population model that allows for local population growth and migration among established populations. If there is selection, the net effect is determined approximately by the product of the selection coefficients and the number of generations between successive founding events. We use the model of a single population to compute analytically several quantities for an allele present in the source population: (i) the probability that it survives the series of colonization events, (ii) the probability that it reaches a specified threshold frequency in the last population, and (iii) the mean and variance of the frequencies in each population. We show that the analytic theory provides a good approximation to simulation results. A consequence of our approximation is that the average heterozygosity of neutral alleles decreases by a factor of 1-1/(2ke) in each new population. Therefore, the population genetic consequences of surfing can be predicted approximately by the effective number of founders and the effective selection coefficients, even in the presence of migration among populations. We also show that our analytic results are applicable to a model of range expansion in a continuously distributed population.

Volcanic eruptions and solar activity

NASA Technical Reports Server (NTRS)

Stothers, Richard B.

1989-01-01

The historical record of large volcanic eruptions from 1500 to 1980 is subjected to detailed time series analysis. In two weak but probably statistically significant periodicities of about 11 and 80 yr, the frequency of volcanic eruptions increases (decreases) slightly around the times of solar minimum (maximum). Time series analysis of the volcanogenic acidities in a deep ice core from Greenland reveals several very long periods ranging from about 80 to about 350 yr which are similar to the very slow solar cycles previously detected in auroral and C-14 records. Solar flares may cause changes in atmospheric circulation patterns that abruptly alter the earth's spin. The resulting jolt probably triggers small earthquakes which affect volcanism.

Intradaily variability of water quality in a shallow tidal lagoon: Mechanisms and implications

USGS Publications Warehouse

Lucas, L.V.; Sereno, D.M.; Burau, J.R.; Schraga, T.S.; Lopez, C.B.; Stacey, M.T.; Parchevsky, K.V.; Parchevsky, V.P.

2006-01-01

Although surface water quality and its underlying processes vary over time scales ranging from seconds to decades, they have historically been studied at the lower (weekly to interannual) frequencies. The aim of this study was to investigate intradaily variability of three water quality parameters in a small freshwater tidal lagoon (Mildred Island, California). High frequency time series of specific conductivity, water temperature, and chlorophyll a at two locations within the habitat were analyzed in conjunction with supporting hydrodynamic, meteorological, biological, and spatial mapping data. All three constituents exhibited large amplitude intradaily (e.g., semidiurnal tidal and diurnal) oscillations, and periodicity varied across constituents, space, and time. Like other tidal embayments, this habitat is influenced by several processes with distinct periodicities including physical controls, such as tides, solar radiation, and wind, and biological controls, such as photosynthesis, growth, and grazing. A scaling approach was developed to estimate individual process contributions to the observed variability. Scaling results were generally consistent with observations and together with detailed examination of time series and time derivatives, revealed specific mechanisms underlying the observed periodicities, including interactions between the tidal variability, heating, wind, and biology. The implications for monitoring were illustrated through subsampling of the data set. This exercise demonstrated how quantities needed by scientists and managers (e.g., mean or extreme concentrations) may be misrepresented by low frequency data and how short-duration high frequency measurements can aid in the design and interpretation of temporally coarser sampling programs. The dispersive export of chlorophyll a from the habitat exhibited a fortnightly variability corresponding to the modulation of semidiurnal tidal currents with the diurnal cycle of phytoplankton variability, demonstrating how high frequency interactions can govern long-term trends. Process identification, as through the scaling analysis here, can help us anticipate changes in system behavior and adapt our own interactions with the system. ?? 2006 Estuarine Research Federation.

The magnetic tides of Honolulu

USGS Publications Warehouse

Love, Jeffrey J.; Rigler, Erin Joshua

2013-01-01

We review the phenomenon of time-stationary, periodic quiet-time geomagnetic tides. These are generated by the ionospheric and oceanic dynamos, and, to a lesser-extent, by the quiet-time magnetosphere, and they are affected by currents induced in the Earth's electrically conducting interior. We examine historical time series of hourly magnetic-vector measurements made at the Honolulu observatory. We construct high-resolution, frequency-domain Lomb-periodogram and maximum-entropy power spectra that reveal a panorama of stationary harmonics across periods from 0.1 to 10000.0-d, including harmonics that result from amplitude and phase modulation. We identify solar-diurnal tides and their annual and solar-cycle sideband modulations, lunar semi-diurnal tides and their solar-diurnal sidebands, and tides due to precession of lunar eccentricity and nodes. We provide evidence that a method intended for separating the ionospheric and oceanic dynamo signals by midnight subsampling of observatory data time series is prone to frequency-domain aliasing. The tidal signals we summarize in this review can be used to test our fundamental understanding of the dynamics of the quiet-time ionosphere and magnetosphere, induction in the ocean and in the electrically conducting interior of the Earth, and they are useful for defining a quiet-time baseline against which magnetospheric-storm intensity is measured.

Modeling time-dependent corrosion fatigue crack propagation in 7000 series aluminum alloys

NASA Technical Reports Server (NTRS)

Mason, Mark E.; Gangloff, Richard P.

1994-01-01

Stress corrosion cracking and corrosion fatigue experiments were conducted with the susceptible S-L orientation of AA7075-T651, immersed in acidified and inhibited NaCl solution, to provide a basis for incorporating environmental effects into fatigue crack propagation life prediction codes such as NASA FLAGRO. This environment enhances da/dN by five to ten-fold compared to fatigue in moist air. Time-based crack growth rates from quasi-static load experiments are an order of magnitude too small for accurate linear superposition prediction of da/dN for loading frequencies above 0.001 Hz. Alternate methods of establishing da/dt, based on rising-load or ripple-load-enhanced crack tip strain rate, do not increase da/dt and do not improve linear superposition. Corrosion fatigue is characterized by two regimes of frequency dependence; da/dN is proportional to f(exp -1) below 0.001 Hz and to F(exp 0) to F(exp -0.1) for higher frequencies. Da/dN increases mildly both with increasing hold-time at K(sub max) and with increasing rise-time for a range of loading waveforms. The mild time-dependence is due to cycle-time-dependent corrosion fatigue growth. This behavior is identical for S-L nd L-T crack orientations. The frequency response of environmental fatigue in several 7000 series alloys is variable and depends on undefined compositional or microstructural variables. Speculative explanations are based on the effect of Mg on occluded crack chemistry and embritting hydrogen uptake, or on variable hydrogen diffusion in the crack tip process zone. Cracking in the 7075/NaCl system is adequately described for life prediction by linear superposition for prolonged load-cycle periods, and by a time-dependent upper bound relationship between da/dN and delta K for moderate loading times.

Errors in the estimation of approximate entropy and other recurrence-plot-derived indices due to the finite resolution of RR time series.

PubMed

García-González, Miguel A; Fernández-Chimeno, Mireya; Ramos-Castro, Juan

2009-02-01

An analysis of the errors due to the finite resolution of RR time series in the estimation of the approximate entropy (ApEn) is described. The quantification errors in the discrete RR time series produce considerable errors in the ApEn estimation (bias and variance) when the signal variability or the sampling frequency is low. Similar errors can be found in indices related to the quantification of recurrence plots. An easy way to calculate a figure of merit [the signal to resolution of the neighborhood ratio (SRN)] is proposed in order to predict when the bias in the indices could be high. When SRN is close to an integer value n, the bias is higher than when near n - 1/2 or n + 1/2. Moreover, if SRN is close to an integer value, the lower this value, the greater the bias is.

Shapes of Magnetically Controlled Electron Density Structures in the Dayside Martian Ionosphere

NASA Astrophysics Data System (ADS)

Diéval, C.; Kopf, A. J.; Wild, J. A.

2018-05-01

Nonhorizontal localized electron density structures associated with regions of near-radial crustal magnetic fields are routinely detected via radar oblique echoes on the dayside of Mars with the ionospheric sounding mode of the Mars Advanced Radar for Subsurface and Ionospheric Sounding (MARSIS) radar onboard Mars Express. Previous studies mostly investigated these structures at a fixed plasma frequency and assumed that the larger apparent altitude of the structures compared to the normal surrounding ionosphere implied that they are bulges. However, the signal is subjected to dispersion when it propagates through the plasma, so interpretations based on the apparent altitude should be treated with caution. We go further by investigating the frequency dependence (i.e., the altitude dependence) of the shape of 48 density structure events, using time series of MARSIS electron density profiles corrected for signal dispersion. Four possible simplest shapes are detected in these time series, which can give oblique echoes: bulges, dips, downhill slopes, and uphill slopes. The altitude differences between the density structures and their edges are, in absolute value, larger at low frequency (high altitude) than at high frequency (low altitude), going from a few tens of kilometers to a few kilometers as frequency increases. Bulges dominate in numbers in most of the frequency range. Finally, the geographical extension of the density structures covers a wide range of crustal magnetic fields orientations, with near-vertical fields toward their center and near-horizontal fields toward their edges, as expected. Transport processes are suggested to be a key driver for these density structures.

Frequency-domain nonlinear regression algorithm for spectral analysis of broadband SFG spectroscopy.

PubMed

He, Yuhan; Wang, Ying; Wang, Jingjing; Guo, Wei; Wang, Zhaohui

2016-03-01

The resonant spectral bands of the broadband sum frequency generation (BB-SFG) spectra are often distorted by the nonresonant portion and the lineshapes of the laser pulses. Frequency domain nonlinear regression (FDNLR) algorithm was proposed to retrieve the first-order polarization induced by the infrared pulse and to improve the analysis of SFG spectra through simultaneous fitting of a series of time-resolved BB-SFG spectra. The principle of FDNLR was presented, and the validity and reliability were tested by the analysis of the virtual and measured SFG spectra. The relative phase, dephasing time, and lineshapes of the resonant vibrational SFG bands can be retrieved without any preset assumptions about the SFG bands and the incident laser pulses.

Analysis of Realized Volatility in Two Trading Sessionsof the Japanese Stock Market

NASA Astrophysics Data System (ADS)

Takaishi, T.; Chen, T. T.; Zheng, Z.

We analyze realized volatilities constructedusing high-frequency stock data on the Tokyo Stock Exchange. In order to avoid non-trading hours issue in volatility calculations we define two realized volatilities calculated separately in the two trading sessions of the Tokyo Stock Exchange, i.e. morning and afternoon sessions. After calculating the realized volatilities at various sampling frequencies we evaluate the bias from the microstructure noise as a function of sampling frequency. Taking account of the bias to realized volatility we examine returns standardized by realized volatilities and confirm that price returns on the Tokyo Stock Exchange are described approximately by Gaussian time series with time-varying volatility, i.e. consistent with a mixture of distributions hypothesis.

Estimation of respiratory rhythm during night sleep using a bio-radar

NASA Astrophysics Data System (ADS)

Tataraidze, Alexander; Anishchenko, Lesya; Alekhin, Maksim; Korostovtseva, Lyudmila; Sviryaev, Yurii

2014-05-01

An assessment of bio-radiolocation monitoring of respiratory rhythm during sleep is given. Full-night respiratory inductance plethysmography (RIP) and bio-radiolocation (BRL) records were collected simultaneously in a sleep laboratory. Polysomnography data from 5 subjects without sleep breathing disorders were used. A multi-frequency bioradar with step frequency modulation was applied. It has 8 operating frequencies ranging from 3.6 to 4.0 GHz. BRL data are recorded in two quadratures. Respiratory cycles were detected in time domain. Obtained data was used for the evaluation of correlation between BRL and RIP respiration rate estimates. Strong correlation between corresponding time series was revealed. BRL method is reliably implemented for estimation of respiratory rhythm and respiratory rate variability during full night sleep.

Ball Milling Assisted Solvent and Catalyst Free Synthesis of Benzimidazoles and Their Derivatives.

PubMed

El-Sayed, Taghreed H; Aboelnaga, Asmaa; Hagar, Mohamed

2016-08-24

Benzoic acid and o-phenylenediamine efficiently reacted under the green solvent-free Ball Milling method. Several reaction parameters were investigated such as rotation frequency; milling balls weight and milling time. The optimum reaction condition was milling with 56.6 g weight of balls at 20 Hz frequency for one hour milling time. The study was extended for synthesis of a series of benzimidazol-2-one or benzimidazol-2-thione using different aldehydes; carboxylic acids; urea; thiourea or ammonium thiocyanate with o-phenylenediamine. Moreover; the alkylation of benzimidazolone or benzimidazolthione using ethyl chloroacetate was also studied.

Periodicity and Multi-scale Analysis of Runoff and Sediment Load in the Wulanghe River, Jinsha River

NASA Astrophysics Data System (ADS)

Chen, Yiming

2018-01-01

Based on the annual runoff and sediment data (1959-2014 ) of Zongguantian hydrological station, time-frequency wavelet transform characteristics and their periodic rules of high and low flow alternating change were analyzed in multi-time scales by the Morlet continue wavelet transformation (CWT). It is concluded that the primary periods of runoff and sediment load time series of the high and low annual flow in the different time scales were 12-year, 3-year and 26-year, 18-year, 13-year, 5-year, respectively, and predicted that the major variant trend of the two time series would been gradually decreasing and been in the high flow period around 8-year (from 2014 to 2022) and 10-year (from 2014 to 2020).

Potential Biological and Ecological Effects of Flickering Artificial Light

PubMed Central

Inger, Richard; Bennie, Jonathan; Davies, Thomas W.; Gaston, Kevin J.

2014-01-01

Organisms have evolved under stable natural lighting regimes, employing cues from these to govern key ecological processes. However, the extent and density of artificial lighting within the environment has increased recently, causing widespread alteration of these regimes. Indeed, night-time electric lighting is known significantly to disrupt phenology, behaviour, and reproductive success, and thence community composition and ecosystem functioning. Until now, most attention has focussed on effects of the occurrence, timing, and spectral composition of artificial lighting. Little considered is that many types of lamp do not produce a constant stream of light but a series of pulses. This flickering light has been shown to have detrimental effects in humans and other species. Whether a species is likely to be affected will largely be determined by its visual temporal resolution, measured as the critical fusion frequency. That is the frequency at which a series of light pulses are perceived as a constant stream. Here we use the largest collation to date of critical fusion frequencies, across a broad range of taxa, to demonstrate that a significant proportion of species can detect such flicker in widely used lamps. Flickering artificial light thus has marked potential to produce ecological effects that have not previously been considered. PMID:24874801

Multiperiodic pulsations in the Be stars NW Serpentis and V1446 Aquilae

NASA Astrophysics Data System (ADS)

Gutiérrez-Soto, J.; Fabregat, J.; Suso, J.; Suárez, J. C.; Moya, A.; Garrido, R.; Hubert, A.-M.; Floquet, M.; Neiner, C.; Frémat, Y.

2007-09-01

Aims:We present accurate photometric time series of two Be stars: NW Ser and V1446 Aql. Both stars were observed at the Observatorio de Sierra Nevada (Granada) in July 2003 with an automatic four-channel Strömgren photometer. We also present a preliminary theoretical study showing that the periodic variations exhibited by these stars can be due to pulsation. Methods: An exhaustive Fourier analysis together with a least-square fitting has been carried out on the time series for all four Strömgren bands. Several independent frequencies and non-periodic trends explain most of the variance. A theoretical non-adiabatic code applied to stellar models for these stars shows that g-modes are unstable. Results: Both stars show rapid variations in amplitude, probably due to a beating phenomenon. Four significant frequencies have been detected for each star. Comparison of the observed amplitude ratios for each pulsational frequency with those calculated from theoretical pulsation codes allows us to estimate the pulsation modes associated with the different detected frequencies. NW Ser seems also to show unstable p-modes and thus could be one of the newly discovered β Cephei and SPB hybrid stars. Further spectroscopic observations are planned to study the stability of the detected frequencies. Tables A.1 and A.2 are only available in electronic form at the CDS via anonymous ftp to cdsarc.u-strasbg.fr(130.79.128.5) or via http://cdsweb.u-strasbg.fr/cgi-bin/qcat?J/A+A/472/565

Visibility graph analysis on quarterly macroeconomic series of China based on complex network theory

NASA Astrophysics Data System (ADS)

Wang, Na; Li, Dong; Wang, Qiwen

2012-12-01

The visibility graph approach and complex network theory provide a new insight into time series analysis. The inheritance of the visibility graph from the original time series was further explored in the paper. We found that degree distributions of visibility graphs extracted from Pseudo Brownian Motion series obtained by the Frequency Domain algorithm exhibit exponential behaviors, in which the exponential exponent is a binomial function of the Hurst index inherited in the time series. Our simulations presented that the quantitative relations between the Hurst indexes and the exponents of degree distribution function are different for different series and the visibility graph inherits some important features of the original time series. Further, we convert some quarterly macroeconomic series including the growth rates of value-added of three industry series and the growth rates of Gross Domestic Product series of China to graphs by the visibility algorithm and explore the topological properties of graphs associated from the four macroeconomic series, namely, the degree distribution and correlations, the clustering coefficient, the average path length, and community structure. Based on complex network analysis we find degree distributions of associated networks from the growth rates of value-added of three industry series are almost exponential and the degree distributions of associated networks from the growth rates of GDP series are scale free. We also discussed the assortativity and disassortativity of the four associated networks as they are related to the evolutionary process of the original macroeconomic series. All the constructed networks have “small-world” features. The community structures of associated networks suggest dynamic changes of the original macroeconomic series. We also detected the relationship among government policy changes, community structures of associated networks and macroeconomic dynamics. We find great influences of government policies in China on the changes of dynamics of GDP and the three industries adjustment. The work in our paper provides a new way to understand the dynamics of economic development.

Nonlinear Prediction Model for Hydrologic Time Series Based on Wavelet Decomposition

NASA Astrophysics Data System (ADS)

Kwon, H.; Khalil, A.; Brown, C.; Lall, U.; Ahn, H.; Moon, Y.

2005-12-01

Traditionally forecasting and characterizations of hydrologic systems is performed utilizing many techniques. Stochastic linear methods such as AR and ARIMA and nonlinear ones such as statistical learning theory based tools have been extensively used. The common difficulty to all methods is the determination of sufficient and necessary information and predictors for a successful prediction. Relationships between hydrologic variables are often highly nonlinear and interrelated across the temporal scale. A new hybrid approach is proposed for the simulation of hydrologic time series combining both the wavelet transform and the nonlinear model. The present model employs some merits of wavelet transform and nonlinear time series model. The Wavelet Transform is adopted to decompose a hydrologic nonlinear process into a set of mono-component signals, which are simulated by nonlinear model. The hybrid methodology is formulated in a manner to improve the accuracy of a long term forecasting. The proposed hybrid model yields much better results in terms of capturing and reproducing the time-frequency properties of the system at hand. Prediction results are promising when compared to traditional univariate time series models. An application of the plausibility of the proposed methodology is provided and the results conclude that wavelet based time series model can be utilized for simulating and forecasting of hydrologic variable reasonably well. This will ultimately serve the purpose of integrated water resources planning and management.

Smoothing Polymer Surfaces by Solvent-Vapor Exposure

NASA Astrophysics Data System (ADS)

Anthamatten, Mitchell

2003-03-01

Ultra-smooth polymer surfaces are of great importance in a large body of technical applications such as optical coatings, supermirrors, waveguides, paints, and fusion targets. We are investigating a simple approach to controlling surface roughness: by temporarily swelling the polymer with solvent molecules. As the solvent penetrates into the polymer, its viscosity is lowered, and surface tension forces drive surface flattening. To investigate sorption kinetics and surface-smoothing phenomena, a series of vapor-deposited poly(amic acid) films were exposed to dimethyl sulfoxide vapors. During solvent exposure, the surface topology was continuously monitored using light interference microscopy. The resulting power spectra indicate that high-frequency defects smooth faster than low-frequency defects. This frequency dependence was studied by depositing polymer films onto a series of 2D sinusoidal surfaces and performing smoothing experiments. Results show that the amplitudes of the sinusoidal surfaces decay exponentially with solvent exposure time, and the exponential decay constants are proportional to surface frequency. This work was performed under the auspices of the U.S. Department of Energy by the University of California Lawrence Livermore National Laboratory under contract No. W-7405-Eng-48.

Observatory geoelectric fields induced in a two-layer lithosphere during magnetic storms

USGS Publications Warehouse

Love, Jeffrey J.; Swidinsky, Andrei

2015-01-01

We report on the development and validation of an algorithm for estimating geoelectric fields induced in the lithosphere beneath an observatory during a magnetic storm. To accommodate induction in three-dimensional lithospheric electrical conductivity, we analyze a simple nine-parameter model: two horizontal layers, each with uniform electrical conductivity properties given by independent distortion tensors. With Laplace transformation of the induction equations into the complex frequency domain, we obtain a transfer function describing induction of observatory geoelectric fields having frequency-dependent polarization. Upon inverse transformation back to the time domain, the convolution of the corresponding impulse-response function with a geomagnetic time series yields an estimated geoelectric time series. We obtain an optimized set of conductivity parameters using 1-s resolution geomagnetic and geoelectric field data collected at the Kakioka, Japan, observatory for five different intense magnetic storms, including the October 2003 Halloween storm; our estimated geoelectric field accounts for 93% of that measured during the Halloween storm. This work demonstrates the need for detailed modeling of the Earth’s lithospheric conductivity structure and the utility of co-located geomagnetic and geoelectric monitoring.

DynPeak: An Algorithm for Pulse Detection and Frequency Analysis in Hormonal Time Series

PubMed Central

Vidal, Alexandre; Zhang, Qinghua; Médigue, Claire; Fabre, Stéphane; Clément, Frédérique

2012-01-01

The endocrine control of the reproductive function is often studied from the analysis of luteinizing hormone (LH) pulsatile secretion by the pituitary gland. Whereas measurements in the cavernous sinus cumulate anatomical and technical difficulties, LH levels can be easily assessed from jugular blood. However, plasma levels result from a convolution process due to clearance effects when LH enters the general circulation. Simultaneous measurements comparing LH levels in the cavernous sinus and jugular blood have revealed clear differences in the pulse shape, the amplitude and the baseline. Besides, experimental sampling occurs at a relatively low frequency (typically every 10 min) with respect to LH highest frequency release (one pulse per hour) and the resulting LH measurements are noised by both experimental and assay errors. As a result, the pattern of plasma LH may be not so clearly pulsatile. Yet, reliable information on the InterPulse Intervals (IPI) is a prerequisite to study precisely the steroid feedback exerted on the pituitary level. Hence, there is a real need for robust IPI detection algorithms. In this article, we present an algorithm for the monitoring of LH pulse frequency, basing ourselves both on the available endocrinological knowledge on LH pulse (shape and duration with respect to the frequency regime) and synthetic LH data generated by a simple model. We make use of synthetic data to make clear some basic notions underlying our algorithmic choices. We focus on explaining how the process of sampling affects drastically the original pattern of secretion, and especially the amplitude of the detectable pulses. We then describe the algorithm in details and perform it on different sets of both synthetic and experimental LH time series. We further comment on how to diagnose possible outliers from the series of IPIs which is the main output of the algorithm. PMID:22802933

Constant strain rate and peri-implant bone modeling: an in vivo longitudinal micro-CT analysis.

PubMed

De Smet, Els; Jaecques, Siegfried V N; Wevers, Martine; Sloten, Jos Vander; Naert, Ignace E

2013-06-01

Strain, frequency, loading time, and strain rate, among others, determine mechanical parameters in osteogenic loading. We showed a significant osteogenic effect on bone mass (BM) by daily peri-implant loading at 1.600µε.s(-1) after 4 weeks. To study the peri-implant osteogenic effect of frequency and strain in the guinea pig tibia by in vivo longitudinal micro-computed tomography (CT) analysis. One week after implant installation in both hind limb tibiae, one implant was loaded daily for 10' during 4 weeks, while the other served as control. Frequencies (3, 10, and 30Hz) and strains varied alike in the three series to keep the strain rate constant at 1.600µε.s(-1) . In vivo micro-CT scans were taken of both tibiae: 1 week after implantation but before loading (v1) and after 2 (v2) and 4 weeks (v3) of loading as well as postmortem (pm). BM (BM (%) bone-occupied area fraction) was calculated as well as the difference between test and control sides (delta BM) RESULTS: All implants (n=78) were clinically stable at 4 weeks. Significant increase in BM was measured between v1 and v2 (p<.0001) and between v1 and v3 (p<.0001). A significant positive effect of loading on delta BM was observed in the distal peri-implant marrow 500 Region of Interest already 2 weeks after loading (p=.01) and was significantly larger (11%) in series 1 compared with series 2 (p=.006) and 3 (p=.016). Within the constraints of constant loading time and strain rate, the effect of early implant loading on the peri-implant bone is strongly dependent on strain and frequency. This cortical bone model has shown to be most sensitive for high force loading at low frequency. © 2011 Wiley Periodicals, Inc.

Novel Covariance-Based Neutrality Test of Time-Series Data Reveals Asymmetries in Ecological and Economic Systems

PubMed Central

Burby, Joshua W.; Lacker, Daniel

2016-01-01

Systems as diverse as the interacting species in a community, alleles at a genetic locus, and companies in a market are characterized by competition (over resources, space, capital, etc) and adaptation. Neutral theory, built around the hypothesis that individual performance is independent of group membership, has found utility across the disciplines of ecology, population genetics, and economics, both because of the success of the neutral hypothesis in predicting system properties and because deviations from these predictions provide information about the underlying dynamics. However, most tests of neutrality are weak, based on static system properties such as species-abundance distributions or the number of singletons in a sample. Time-series data provide a window onto a system’s dynamics, and should furnish tests of the neutral hypothesis that are more powerful to detect deviations from neutrality and more informative about to the type of competitive asymmetry that drives the deviation. Here, we present a neutrality test for time-series data. We apply this test to several microbial time-series and financial time-series and find that most of these systems are not neutral. Our test isolates the covariance structure of neutral competition, thus facilitating further exploration of the nature of asymmetry in the covariance structure of competitive systems. Much like neutrality tests from population genetics that use relative abundance distributions have enabled researchers to scan entire genomes for genes under selection, we anticipate our time-series test will be useful for quick significance tests of neutrality across a range of ecological, economic, and sociological systems for which time-series data are available. Future work can use our test to categorize and compare the dynamic fingerprints of particular competitive asymmetries (frequency dependence, volatility smiles, etc) to improve forecasting and management of complex adaptive systems. PMID:27689714

Flicker Noise in GNSS Station Position Time Series: How much is due to Crustal Loading Deformations?

NASA Astrophysics Data System (ADS)

Rebischung, P.; Chanard, K.; Metivier, L.; Altamimi, Z.

2017-12-01

The presence of colored noise in GNSS station position time series was detected 20 years ago. It has been shown since then that the background spectrum of non-linear GNSS station position residuals closely follows a power-law process (known as flicker noise, 1/f noise or pink noise), with some white noise taking over at the highest frequencies. However, the origin of the flicker noise present in GNSS station position time series is still unclear. Flicker noise is often described as intrinsic to the GNSS system, i.e. due to errors in the GNSS observations or in their modeling, but no such error source has been identified so far that could explain the level of observed flicker noise, nor its spatial correlation.We investigate another possible contributor to the observed flicker noise, namely real crustal displacements driven by surface mass transports, i.e. non-tidal loading deformations. This study is motivated by the presence of power-law noise in the time series of low-degree (≤ 40) and low-order (≤ 12) Stokes coefficients observed by GRACE - power-law noise might also exist at higher degrees and orders, but obscured by GRACE observational noise. By comparing GNSS station position time series with loading deformation time series derived from GRACE gravity fields, both with their periodic components removed, we therefore assess whether GNSS and GRACE both plausibly observe the same flicker behavior of surface mass transports / loading deformations. Taking into account GRACE observability limitations, we also quantify the amount of flicker noise in GNSS station position time series that could be explained by such flicker loading deformations.

A scalable database model for multiparametric time series: a volcano observatory case study

NASA Astrophysics Data System (ADS)

Montalto, Placido; Aliotta, Marco; Cassisi, Carmelo; Prestifilippo, Michele; Cannata, Andrea

2014-05-01

The variables collected by a sensor network constitute a heterogeneous data source that needs to be properly organized in order to be used in research and geophysical monitoring. With the time series term we refer to a set of observations of a given phenomenon acquired sequentially in time. When the time intervals are equally spaced one speaks of period or sampling frequency. Our work describes in detail a possible methodology for storage and management of time series using a specific data structure. We designed a framework, hereinafter called TSDSystem (Time Series Database System), in order to acquire time series from different data sources and standardize them within a relational database. The operation of standardization provides the ability to perform operations, such as query and visualization, of many measures synchronizing them using a common time scale. The proposed architecture follows a multiple layer paradigm (Loaders layer, Database layer and Business Logic layer). Each layer is specialized in performing particular operations for the reorganization and archiving of data from different sources such as ASCII, Excel, ODBC (Open DataBase Connectivity), file accessible from the Internet (web pages, XML). In particular, the loader layer performs a security check of the working status of each running software through an heartbeat system, in order to automate the discovery of acquisition issues and other warning conditions. Although our system has to manage huge amounts of data, performance is guaranteed by using a smart partitioning table strategy, that keeps balanced the percentage of data stored in each database table. TSDSystem also contains modules for the visualization of acquired data, that provide the possibility to query different time series on a specified time range, or follow the realtime signal acquisition, according to a data access policy from the users.

A multidisciplinary database for geophysical time series management

NASA Astrophysics Data System (ADS)

Montalto, P.; Aliotta, M.; Cassisi, C.; Prestifilippo, M.; Cannata, A.

2013-12-01

The variables collected by a sensor network constitute a heterogeneous data source that needs to be properly organized in order to be used in research and geophysical monitoring. With the time series term we refer to a set of observations of a given phenomenon acquired sequentially in time. When the time intervals are equally spaced one speaks of period or sampling frequency. Our work describes in detail a possible methodology for storage and management of time series using a specific data structure. We designed a framework, hereinafter called TSDSystem (Time Series Database System), in order to acquire time series from different data sources and standardize them within a relational database. The operation of standardization provides the ability to perform operations, such as query and visualization, of many measures synchronizing them using a common time scale. The proposed architecture follows a multiple layer paradigm (Loaders layer, Database layer and Business Logic layer). Each layer is specialized in performing particular operations for the reorganization and archiving of data from different sources such as ASCII, Excel, ODBC (Open DataBase Connectivity), file accessible from the Internet (web pages, XML). In particular, the loader layer performs a security check of the working status of each running software through an heartbeat system, in order to automate the discovery of acquisition issues and other warning conditions. Although our system has to manage huge amounts of data, performance is guaranteed by using a smart partitioning table strategy, that keeps balanced the percentage of data stored in each database table. TSDSystem also contains modules for the visualization of acquired data, that provide the possibility to query different time series on a specified time range, or follow the realtime signal acquisition, according to a data access policy from the users.

Rapid response of a hydrologic system to volcanic activity: Masaya volcano, Nicaragua

USGS Publications Warehouse

Pearson, S.C.P.; Connor, C.B.; Sanford, W.E.

2008-01-01

Hydrologic systems change in response to volcanic activity, and in turn may be sensitive indicators of volcanic activity. Here we investigate the coupled nature of magmatic and hydrologic systems using continuous multichannel time series of soil temperature collected on the flanks of Masaya volcano, Nicaragua, one of the most active volcanoes in Central America. The soil temperatures were measured in a low-temperature fumarole field located 3.5 km down the flanks of the volcano. Analysis of these time series reveals that they respond extremely rapidly, on a time scale of minutes, to changes in volcanic activity also manifested at the summit vent. These rapid temperature changes are caused by increased flow of water vapor through flank fumaroles during volcanism. The soil temperature response, ~5 °C, is repetitive and complex, with as many as 13 pulses during a single volcanic episode. Analysis of the frequency spectrum of these temperature time series shows that these anomalies are characterized by broad frequency content during volcanic activity. They are thus easily distinguished from seasonal trends, diurnal variations, or individual rainfall events, which triggered rapid transient increases in temperature during 5% of events. We suggest that the mechanism responsible for the distinctive temperature signals is rapid change in pore pressure in response to magmatism, a response that can be enhanced by meteoric water infiltration. Monitoring of distal fumaroles can therefore provide insight into coupled volcanic-hydrologic-meteorologic systems, and has potential as an inexpensive monitoring tool.

Magnetic field shift due to mechanical vibration in functional magnetic resonance imaging.

PubMed

Foerster, Bernd U; Tomasi, Dardo; Caparelli, Elisabeth C

2005-11-01

Mechanical vibrations of the gradient coil system during readout in echo-planar imaging (EPI) can increase the temperature of the gradient system and alter the magnetic field distribution during functional magnetic resonance imaging (fMRI). This effect is enhanced by resonant modes of vibrations and results in apparent motion along the phase encoding direction in fMRI studies. The magnetic field drift was quantified during EPI by monitoring the resonance frequency interleaved with the EPI acquisition, and a novel method is proposed to correct the apparent motion. The knowledge on the frequency drift over time was used to correct the phase of the k-space EPI dataset. Since the resonance frequency changes very slowly over time, two measurements of the resonance frequency, immediately before and after the EPI acquisition, are sufficient to remove the field drift effects from fMRI time series. The frequency drift correction method was tested "in vivo" and compared to the standard image realignment method. The proposed method efficiently corrects spurious motion due to magnetic field drifts during fMRI. (c) 2005 Wiley-Liss, Inc.

Uncovering the genetic signature of quantitative trait evolution with replicated time series data.

PubMed

Franssen, S U; Kofler, R; Schlötterer, C

2017-01-01

The genetic architecture of adaptation in natural populations has not yet been resolved: it is not clear to what extent the spread of beneficial mutations (selective sweeps) or the response of many quantitative trait loci drive adaptation to environmental changes. Although much attention has been given to the genomic footprint of selective sweeps, the importance of selection on quantitative traits is still not well studied, as the associated genomic signature is extremely difficult to detect. We propose 'Evolve and Resequence' as a promising tool, to study polygenic adaptation of quantitative traits in evolving populations. Simulating replicated time series data we show that adaptation to a new intermediate trait optimum has three characteristic phases that are reflected on the genomic level: (1) directional frequency changes towards the new trait optimum, (2) plateauing of allele frequencies when the new trait optimum has been reached and (3) subsequent divergence between replicated trajectories ultimately leading to the loss or fixation of alleles while the trait value does not change. We explore these 3 phase characteristics for relevant population genetic parameters to provide expectations for various experimental evolution designs. Remarkably, over a broad range of parameters the trajectories of selected alleles display a pattern across replicates, which differs both from neutrality and directional selection. We conclude that replicated time series data from experimental evolution studies provide a promising framework to study polygenic adaptation from whole-genome population genetics data.

Precipitation extremes on multiple timescales - Bartlett-Lewis rectangular pulse model and intensity-duration-frequency curves

NASA Astrophysics Data System (ADS)

Ritschel, Christoph; Ulbrich, Uwe; Névir, Peter; Rust, Henning W.

2017-12-01

For several hydrological modelling tasks, precipitation time series with a high (i.e. sub-daily) resolution are indispensable. The data are, however, not always available, and thus model simulations are used to compensate. A canonical class of stochastic models for sub-daily precipitation are Poisson cluster processes, with the original Bartlett-Lewis (OBL) model as a prominent representative. The OBL model has been shown to well reproduce certain characteristics found in observations. Our focus is on intensity-duration-frequency (IDF) relationships, which are of particular interest in risk assessment. Based on a high-resolution precipitation time series (5 min) from Berlin-Dahlem, OBL model parameters are estimated and IDF curves are obtained on the one hand directly from the observations and on the other hand from OBL model simulations. Comparing the resulting IDF curves suggests that the OBL model is able to reproduce the main features of IDF statistics across several durations but cannot capture rare events (here an event with a return period larger than 1000 years on the hourly timescale). In this paper, IDF curves are estimated based on a parametric model for the duration dependence of the scale parameter in the generalized extreme value distribution; this allows us to obtain a consistent set of curves over all durations. We use the OBL model to investigate the validity of this approach based on simulated long time series.

Detrended Cross Correlation Analysis: a new way to figure out the underlying cause of global warming

NASA Astrophysics Data System (ADS)

Hazra, S.; Bera, S. K.

2016-12-01

Analysing non-stationary time series is a challenging task in earth science, seismology, solar physics, climate, biology, finance etc. Most of the cases external noise like oscillation, high frequency noise, low frequency noise in different scales lead to erroneous result. Many statistical methods are proposed to find the correlation between two non-stationary time series. N. Scafetta and B. J. West, Phys. Rev. Lett. 90, 248701 (2003), reported a strong relationship between solar flare intermittency (SFI) and global temperature anomalies (GTA) using diffusion entropy analysis. It has been recently shown that detrended cross correlation analysis (DCCA) is better technique to remove the effects of any unwanted signal as well as local and periodic trend. Thus DCCA technique is more suitable to find the correlation between two non-stationary time series. By this technique, correlation coefficient at different scale can be estimated. Motivated by this here we have applied a new DCCA technique to find the relationship between SFI and GTA. We have also applied this technique to find the relationship between GTA and carbon di-oxide density, GTA and methane density on earth atmosphere. In future we will try to find the relationship between GTA and aerosols present in earth atmosphere, water vapour density on earth atmosphere, ozone depletion etc. This analysis will help us for better understanding about the reason behind global warming

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.

Study of spectro-temporal variation in paleo-climatic marine proxy records using wavelet transformations

NASA Astrophysics Data System (ADS)

Pandey, Chhavi P.

2017-10-01

Wavelet analysis is a powerful mathematical and computational tool to study periodic phenomena in time series particu-larly in the presence of potential frequency changes in time. Continuous wavelet transformation (CWT) provides localised spectral information of the analysed dataset and in particular useful to study multiscale, nonstationary processes occurring over finite spatial and temporal domains. In the present work, oxygen-isotope ratio from the plantonic foraminifera species (viz. Globigerina bul-loides and Globigerinoides ruber) acquired from the broad central plateau of the Maldives ridge situated in south-eastern Arabian sea have been used as climate proxy. CWT of the time series generated using both the biofacies indicate spectro-temporal varia-tion of the natural climatic cycles. The dominant period resembles to the period of Milankovitch glacial-interglacial cycle. Apart from that, various other cycles are present in the time series. The results are in good agreement with the astronomical theory of paleoclimates and can provide better visualisation of Indian summer monsoon in the context of climate change.

Assessing the Transient Gust Response of a Representative Ship Airwake using Proper Orthogonal Decomposition

DTIC Science & Technology

Velocimetry system was then used to acquire flow field data across a series of three horizontal planes spanning from 0.25 to 1.5 times the ship hangar height...included six separate data points at gust-frequency referenced Strouhal numbers ranging from 0.430 to1.474. A 725-Hertz time -resolved Particle Image

Is Solar Variability Reflected in the Nile River?

NASA Technical Reports Server (NTRS)

Ruzmaikin, Alexander; Feynman, Joan; Yung, Yuk L.

2006-01-01

We investigate the possibility that solar variability influences North African climate by using annual records of the water level of the Nile collected in 622-1470 A.D. The time series of these records are nonstationary, in that the amplitudes and frequencies of the quasi-periodic variations are time-dependent. We apply the Empirical Mode Decomposition technique especially designed to deal with such time series. We identify two characteristic timescales in the records that may be linked to solar variability: a period of about 88 years and one exceeding 200 years. We show that these timescales are present in the number of auroras reported per decade in the Northern Hemisphere at the same time. The 11-year cycle is seen in the Nile's high-water level variations, but it is damped in the low-water anomalies. We suggest a possible physical link between solar variability and the low-frequency variations of the Nile water level. This link involves the influence of solar variability on the atmospheric Northern Annual Mode and on its North Atlantic Ocean and Indian Ocean patterns that affect the rainfall over the sources of the Nile in eastern equatorial Africa.

Wavelet-based analysis of circadian behavioral rhythms.

PubMed

Leise, Tanya L

2015-01-01

The challenging problems presented by noisy biological oscillators have led to the development of a great variety of methods for accurately estimating rhythmic parameters such as period and amplitude. This chapter focuses on wavelet-based methods, which can be quite effective for assessing how rhythms change over time, particularly if time series are at least a week in length. These methods can offer alternative views to complement more traditional methods of evaluating behavioral records. The analytic wavelet transform can estimate the instantaneous period and amplitude, as well as the phase of the rhythm at each time point, while the discrete wavelet transform can extract the circadian component of activity and measure the relative strength of that circadian component compared to those in other frequency bands. Wavelet transforms do not require the removal of noise or trend, and can, in fact, be effective at removing noise and trend from oscillatory time series. The Fourier periodogram and spectrogram are reviewed, followed by descriptions of the analytic and discrete wavelet transforms. Examples illustrate application of each method and their prior use in chronobiology is surveyed. Issues such as edge effects, frequency leakage, and implications of the uncertainty principle are also addressed. © 2015 Elsevier Inc. All rights reserved.

A heuristic method for identifying chaos from frequency content.

PubMed

Wiebe, R; Virgin, L N

2012-03-01

The sign of the largest Lyapunov exponent is the fundamental indicator of chaos in a dynamical system. However, although the extraction of Lyapunov exponents can be accomplished with (necessarily noisy) the experimental data, this is still a relatively data-intensive and sensitive endeavor. This paper presents an alternative pragmatic approach to identifying chaos using response frequency characteristics and extending the concept of the spectrogram. The method is shown to work well on both experimental and simulated time series.

Limitations to Dual Frequency Ionosphere Corrections for Frequency Switched K-Q-Band Observations with the VLBA

NASA Technical Reports Server (NTRS)

Lanyi, Gabor; Gordon, David; Sovers, Ojars J.

2004-01-01

A series of VLBA experiments were carried out at K and Q bands for astrometry and imaging within the KQ VLBI Survey Collaboration. The paired K and Q observations of each source are separated by approximately 3 minutes of time. We investigate the delay effect of the ionosphere between K and Q bands involving the interscan separation. This differential delay effect is intermixed with the differential fluctuation effect of the troposphere.

Basic Auditory Processing and Developmental Dyslexia in Chinese

ERIC Educational Resources Information Center

Wang, Hsiao-Lan Sharon; Huss, Martina; Hamalainen, Jarmo A.; Goswami, Usha

2012-01-01

The present study explores the relationship between basic auditory processing of sound rise time, frequency, duration and intensity, phonological skills (onset-rime and tone awareness, sound blending, RAN, and phonological memory) and reading disability in Chinese. A series of psychometric, literacy, phonological, auditory, and character…

Perceptual Audio Hashing Functions

NASA Astrophysics Data System (ADS)

Özer, Hamza; Sankur, Bülent; Memon, Nasir; Anarım, Emin

2005-12-01

Perceptual hash functions provide a tool for fast and reliable identification of content. We present new audio hash functions based on summarization of the time-frequency spectral characteristics of an audio document. The proposed hash functions are based on the periodicity series of the fundamental frequency and on singular-value description of the cepstral frequencies. They are found, on one hand, to perform very satisfactorily in identification and verification tests, and on the other hand, to be very resilient to a large variety of attacks. Moreover, we address the issue of security of hashes and propose a keying technique, and thereby a key-dependent hash function.

Volterra Series Approach for Nonlinear Aeroelastic Response of 2-D Lifting Surfaces

NASA Technical Reports Server (NTRS)

Silva, Walter A.; Marzocca, Piergiovanni; Librescu, Liviu

2001-01-01

The problem of the determination of the subcritical aeroelastic response and flutter instability of nonlinear two-dimensional lifting surfaces in an incompressible flow-field via Volterra series approach is addressed. The related aeroelastic governing equations are based upon the inclusion of structural nonlinearities, of the linear unsteady aerodynamics and consideration of an arbitrary time-dependent external pressure pulse. Unsteady aeroelastic nonlinear kernels are determined, and based on these, frequency and time histories of the subcritical aeroelastic response are obtained, and in this context the influence of geometric nonlinearities is emphasized. Conclusions and results displaying the implications of the considered effects are supplied.

Harmonic oscillators and resonance series generated by a periodic unstable classical orbit

NASA Technical Reports Server (NTRS)

Kazansky, A. K.; Ostrovsky, Valentin N.

1995-01-01

The presence of an unstable periodic classical orbit allows one to introduce the decay time as a purely classical magnitude: inverse of the Lyapunov index which characterizes the orbit instability. The Uncertainty Relation gives the corresponding resonance width which is proportional to the Planck constant. The more elaborate analysis is based on the parabolic equation method where the problem is effectively reduced to the multidimensional harmonic oscillator with the time-dependent frequency. The resonances form series in the complex energy plane which is equidistant in the direction perpendicular to the real axis. The applications of the general approach to various problems in atomic physics are briefly exposed.

Spectral estimation—What is new? What is next?

NASA Astrophysics Data System (ADS)

Tary, Jean Baptiste; Herrera, Roberto Henry; Han, Jiajun; van der Baan, Mirko

2014-12-01

Spectral estimation, and corresponding time-frequency representation for nonstationary signals, is a cornerstone in geophysical signal processing and interpretation. The last 10-15 years have seen the development of many new high-resolution decompositions that are often fundamentally different from Fourier and wavelet transforms. These conventional techniques, like the short-time Fourier transform and the continuous wavelet transform, show some limitations in terms of resolution (localization) due to the trade-off between time and frequency localizations and smearing due to the finite size of the time series of their template. Well-known techniques, like autoregressive methods and basis pursuit, and recently developed techniques, such as empirical mode decomposition and the synchrosqueezing transform, can achieve higher time-frequency localization due to reduced spectral smearing and leakage. We first review the theory of various established and novel techniques, pointing out their assumptions, adaptability, and expected time-frequency localization. We illustrate their performances on a provided collection of benchmark signals, including a laughing voice, a volcano tremor, a microseismic event, and a global earthquake, with the intention to provide a fair comparison of the pros and cons of each method. Finally, their outcomes are discussed and possible avenues for improvements are proposed.

Smoothing analysis of slug tests data for aquifer characterization at laboratory scale

NASA Astrophysics Data System (ADS)

Aristodemo, Francesco; Ianchello, Mario; Fallico, Carmine

2018-07-01

The present paper proposes a smoothing analysis of hydraulic head data sets obtained by means of different slug tests introduced in a confined aquifer. Laboratory experiments were performed through a 3D large-scale physical model built at the University of Calabria. The hydraulic head data were obtained by a pressure transducer placed in the injection well and subjected to a processing operation to smooth out the high-frequency noise occurring in the recorded signals. The adopted smoothing techniques working in time, frequency and time-frequency domain are the Savitzky-Golay filter modeled by third-order polynomial, the Fourier Transform and two types of Wavelet Transform (Mexican hat and Morlet). The performances of the filtered time series of the hydraulic heads for different slug volumes and measurement frequencies were statistically analyzed in terms of optimal fitting of the classical Cooper's equation. For practical purposes, the hydraulic heads smoothed by the involved techniques were used to determine the hydraulic conductivity of the aquifer. The energy contents and the frequency oscillations of the hydraulic head variations in the aquifer were exploited in the time-frequency domain by means of Wavelet Transform as well as the non-linear features of the observed hydraulic head oscillations around the theoretical Cooper's equation.

Performance enhancement in a semi-autonomous confined microsociety

NASA Technical Reports Server (NTRS)

Brady, J. V.; Bernstein, D. J.; Foltin, R. W.; Nellis, M. J.

1988-01-01

Research in a continuously programmed human experimental laboratory has been directed toward identifying, defining, and expanding generalized knowledge concerning motivational factors within the structure of human behavioral repertoires that maintain and enhance performance. Participants (in groups of three) engaged in a series of repetitive work activities (e.g., word sorting and rug-hooking) for extended periods each day, while living continuously in a residential laboratory. Other parts of the day were spent either interacting socially with other participants or engaging in individual recreational activities. The percentage of time devoted to the various work tasks provided the basis for selecting one activity that occurred with high frequency and one with low frequency. Performance of the low-frequency activity was then required in order to gain access to the high-frequency activity. Under such contingencies, time devoted to the original low-frequency activity increased greatly, and the participants consistently did more than the required amount of the low-frequency work than was necessary to restore access to the restricted work activity. The theoretical significance of these findings resides in the clear demonstration that a time-based model of value applies as well to the enhancement of work-like performance as it does to voluntarily selected or preferred recreational activities.

From heavy-tailed to exponential distribution of interevent time in cellphone top-up behavior

NASA Astrophysics Data System (ADS)

Wang, Peng; Ma, Qiang

2017-05-01

Cellphone top-up is a kind of activities, to a great extent, driven by individual consumption rather than personal interest and this behavior should be stable in common sense. However, our researches find there are heavy-tails both in interevent time distribution and purchase frequency distribution at the global level. Moreover, we find both memories of interevent time and unit price series are negative, which is different from previous bursty activities. We divide individuals into five groups according to the purchase frequency and the average unit price respectively. Then, the group analysis shows some significant heterogeneity in this behavior. On one hand, we obtain only the individuals with high purchase frequency have the heavy-tailed nature in interevent time distribution. On the contrary, the negative memory is only caused by low purchase-frequency individuals without burstiness. On the other hand, the individuals with different preferential price also have different power-law exponents at the group level and there is no data collapse after rescaling between these distributions. Our findings produce the evidence for the significant heterogeneity of human activity in many aspects.

Experimental studies of the overshoot and undershoot in pulse-modulated radio-frequency atmospheric discharge

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

Huo, W. G.; Li, R. M.; Shi, J. J.

The overshoot and undershoot of the applied voltage on the electrodes, the discharge current, and radio frequency (RF) power were observed at the initial phase of pulse-modulated (PM) RF atmospheric pressure discharges, but factors influencing the overshoot and undershoot have not been fully elucidated. In this paper, the experimental studies were performed to seek the reasons for the overshoot and undershoot. The experimental results show that the overshoot and undershoot are associated with the pulse frequency, the rise time of pulse signal, and the series capacitor C{sub s} in the inversely L-shaped matching network. In the case of a highmore » RF power discharge, these overshoot and undershoot become serious when shortening the rise time of a pulse signal (5 ns) or operating at a moderate pulse frequency (500 Hz or 1 kHz).« less

Linear and nonlinear mechanical properties of a series of epoxy resins

NASA Technical Reports Server (NTRS)

Curliss, D. B.; Caruthers, J. M.

1987-01-01

The linear viscoelastic properties have been measured for a series of bisphenol-A-based epoxy resins cured with the diamine DDS. The linear viscoelastic master curves were constructed via time-temperature superposition of frequency dependent G-prime and G-double-prime isotherms. The G-double-prime master curves exhibited two sub-Tg transitions. Superposition of isotherms in the glass-to-rubber transition (i.e., alpha) and the beta transition at -60 C was achieved by simple horizontal shifts in the log frequency axis; however, in the region between alpha and beta, superposition could not be effected by simple horizontal shifts along the log frequency axis. The different temperature dependency of the alpha and beta relaxation mechanisms causes a complex response of G-double-prime in the so called alpha-prime region. A novel numerical procedure has been developed to extract the complete relaxation spectra and its temperature dependence from the G-prime and G-double-prime isothermal data in the alpha-prime region.

Detection of frequency-mode-shift during thermoacoustic combustion oscillations in a staged aircraft engine model combustor

NASA Astrophysics Data System (ADS)

Kobayashi, Hiroaki; Gotoda, Hiroshi; Tachibana, Shigeru; Yoshida, Seiji

2017-12-01

We conduct an experimental study using time series analysis based on symbolic dynamics to detect a precursor of frequency-mode-shift during thermoacoustic combustion oscillations in a staged aircraft engine model combustor. With increasing amount of the main fuel, a significant shift in the dominant frequency-mode occurs in noisy periodic dynamics, leading to a notable increase in oscillation amplitudes. The sustainment of noisy periodic dynamics during thermoacoustic combustion oscillations is clearly shown by the multiscale complexity-entropy causality plane in terms of statistical complexity. A modified version of the permutation entropy allows us to detect a precursor of the frequency-mode-shift before the amplification of pressure fluctuations.

Influence of skin peeling procedure in allergic contact dermatitis.

PubMed

Kim, Jung Eun; Park, Hyun Jeong; Cho, Baik Kee; Lee, Jun Young

2008-03-01

The prevalence of allergic contact dermatitis in patients who have previously undergone skin peeling has been rarely studied. We compared the frequency of positive patch test (PT) reactions in a patient group with a history of peeling, to that of a control group with no history of peeling. The Korean standard series and cosmetic series were performed on a total of 262 patients. 62 patients had previously undergone peeling and 200 patients did not. The frequency of positive PT reactions on Korean standard series was significantly higher in the peeling group compared with that of the control group (P < 0.05, chi-square test). However, the most commonly identified allergens were mostly cosmetic-unrelated allergens. The frequency of positive PT reactions on cosmetic series in the peeling group was higher than that of the control group, but lacked statistical significance. The frequency (%) of positive PT reactions on cosmetic series in the high-frequency peel group was higher than that of the low-frequency group, but lacked statistical significance. It appears peeling may not generally affect the development of contact sensitization. Further work is required focusing on the large-scale prospective studies by performing a PT before and after peeling.

Climate signal detected in sub-fossil and living oak trees data. An analysis of signal frequency components

NASA Astrophysics Data System (ADS)

Constantin, Nechita; Francisca, Chiriloaei; Maria, Radoane; Ionel, Popa; Nicoae, Radoane

2016-04-01

This study is focused on analysis the frequency components of the signal detected in living and sub-fossil tree ring series from different time periods. The investigation is oriented to analyze signal frequency components (low and high) of the two categories of trees. The interpretation technique of tree ring width is the instrument most often used to elaborate past climatic reconstructions. The annual resolution, but also, the high capacity of trees to accumulate climatic information are attributes which confer to palaeo-environmental reconstructions the biggest credibility. The main objective of the study refers to the evaluation of climatic signal characteristics, both present day climate and palaeo-climate (last 7000 years BP). Modern dendrochronological methods were applied on 350 samples of sub-fossil trees and 400 living trees. The subfossil trunks were sampled from different fluvial environments (Siret, Suceava, Moldova). Their age was determined using radiocarbon, varying from under 100 years to almost 7000 years BP. The subfossil tree species investigated were Quercus, Alnus, Ulmus. Considering living trees, these were identified on eastern part of Romania, in different actual physico-geographical conditions. The studied living tree species consisted in Quercus species (robur and petraea). Each site was investigated regarding stress factors of the sampled tree. The working methods were applied to the total wood series, both late and early, to detect intra-annual level climate information. Each series has been tested to separate individual trees with climatic signal of other trees with different signals (noises determined by competition between individuals or site stress, or anthropic impact). Comparing dendrochronological series (sub-fossil and living trees) we want to identify what significant causes determined the difference in the signal frequencies. Especially, the human interventions registered in the last 2 centuries will be evaluated by these different types of signal in the tree rings. In order to evaluate this aspect we used time series which were standardized to avoid the non-climatic signal. This type of investigation is the first of its kind to Eastern Europe, an area so large (over 50 000 km2) and a high number of sites and individuals studied (about 1000). The obtained results will help us to understand the palaeo-environment evolution in the last Holocene and when human intervention has been really significant.

The cross wavelet and wavelet coherence analysis of spatio-temporal rainfall-groundwater system in Pingtung plain, Taiwan

NASA Astrophysics Data System (ADS)

Lin, Yuan-Chien; Yu, Hwa-Lung

2013-04-01

The increasing frequency and intensity of extreme rainfall events has been observed recently in Taiwan. Particularly, Typhoon Morakot, Typhoon Fanapi, and Typhoon Megi consecutively brought record-breaking intensity and magnitude of rainfalls to different locations of Taiwan in these two years. However, records show the extreme rainfall events did not elevate the amount of annual rainfall accordingly. Conversely, the increasing frequency of droughts has also been occurring in Taiwan. The challenges have been confronted by governmental agencies and scientific communities to come up with effective adaptation strategies for natural disaster reduction and sustainable environment establishment. Groundwater has long been a reliable water source for a variety of domestic, agricultural, and industrial uses because of its stable quantity and quality. In Taiwan, groundwater accounts for the largest proportion of all water resources for about 40%. This study plans to identify and quantify the nonlinear relationship between precipitation and groundwater recharge, find the non-stationary time-frequency relations between the variations of rainfall and groundwater levels to understand the phase difference of time series. Groundwater level data and over-50-years hourly rainfall records obtained from 20 weather stations in Pingtung Plain, Taiwan has been collected. Extract the space-time pattern by EOF method, which is a decomposition of a signal or data set in terms of orthogonal basis functions determined from the data for both time series and spatial patterns, to identify the important spatial pattern of groundwater recharge and using cross wavelet and wavelet coherence method to identify the relationship between rainfall and groundwater levels. Results show that EOF method can specify the spatial-temporal patterns which represents certain geological characteristics and other mechanisms of groundwater, and the wavelet coherence method can identify general correlation between rainfall and groundwater signal at low frequency and high frequency relationship at some certain extreme rainfall events. Keywords: extreme rainfall, groundwater, EOF, wavelet coherence

ULFEM time series analysis package

USGS Publications Warehouse

Karl, Susan M.; McPhee, Darcy K.; Glen, Jonathan M. G.; Klemperer, Simon L.

2013-01-01

This manual describes how to use the Ultra-Low-Frequency ElectroMagnetic (ULFEM) software package. Casual users can read the quick-start guide and will probably not need any more information than this. For users who may wish to modify the code, we provide further description of the routines.

Mapping Wetlands of Dongting Lake in China Using Landsat and SENTINEL-1 Time Series at 30M

NASA Astrophysics Data System (ADS)

Xing, L.; Tang, X.; Wang, H.; Fan, W.; Gao, X.

2018-04-01

Mapping and monitoring wetlands of Dongting lake using optical sensor data has been limited by cloud cover, and open access Sentinal-1 C-band data could provide cloud-free SAR images with both have high spatial and temporal resolution, which offer new opportunities for monitoring wetlands. In this study, we combined optical data and SAR data to map wetland of Dongting Lake reserves in 2016. Firstly, we generated two monthly composited Landsat land surface reflectance, NDVI, NDWI, TC-Wetness time series and Sentinel-1 (backscattering coefficient for VH and VV) time series. Secondly, we derived surface water body with two monthly frequencies based on the threshold method using the Sentinel-1 time series. Then the permanent water and seasonal water were separated by the submergence ratio. Other land cover types were identified based on SVM classifier using Landsat time series. Results showed that (1) the overall accuracies and kappa coefficients were above 86.6 % and 0.8. (3) Natural wetlands including permanent water body (14.8 %), seasonal water body (34.6 %), and permanent marshes (10.9 %) were the main land cover types, accounting for 60.3 % of the three wetland reserves. Human-made wetlands, such as rice fields, accounted 34.3 % of the total area. Generally, this study proposed a new flowchart for wetlands mapping in Dongting lake by combining multi-source remote sensing data, and the use of the two-monthly composited optical time series effectively made up the missing data due to the clouds and increased the possibility of precise wetlands classification.

Trimming algorithm of frequency modulation for CIAE-230 MeV proton superconducting synchrocyclotron model cavity

NASA Astrophysics Data System (ADS)

Li, Pengzhan; Zhang, Tianjue; Ji, Bin; Hou, Shigang; Guo, Juanjuan; Yin, Meng; Xing, Jiansheng; Lv, Yinlong; Guan, Fengping; Lin, Jun

2017-01-01

A new project, the 230 MeV proton superconducting synchrocyclotron for cancer therapy, was proposed at CIAE in 2013. A model cavity is designed to verify the frequency modulation trimming algorithm featuring a half-wave structure and eight sets of rotating blades for 1 kHz frequency modulation. Based on the electromagnetic (EM) field distribution analysis of the model cavity, the variable capacitor works as a function of time and the frequency can be written in Maclaurin series. Curve fitting is applied for theoretical frequency and original simulation frequency. The second-order fitting excels at the approximation given its minimum variance. Constant equivalent inductance is considered as an important condition in the calculation. The equivalent parameters of theoretical frequency can be achieved through this conversion. Then the trimming formula for rotor blade outer radius is found by discretization in time domain. Simulation verification has been performed and the results show that the calculation radius with minus 0.012 m yields an acceptable result. The trimming amendment in the time range of 0.328-0.4 ms helps to reduce the frequency error to 0.69% in Simulation C with an increment of 0.075 mm/0.001 ms, which is half of the error in Simulation A (constant radius in 0.328-0.4 ms). The verification confirms the feasibility of the trimming algorithm for synchrocyclotron frequency modulation.

New multi-site observations of the delta Scuti stars BS and BT Cancri. Results of the STEPHI VII campaign on the Praesepe cluster

NASA Astrophysics Data System (ADS)

Hernandez, M. M.; Michel, E.; Belmonte, J. A.; Jiang, S. Y.; Alvarez, M.; Chevreton, M.; Paparo, M.; Kjeldsen, H.; Bauduin, D.; Fromage, J.; Goupil, M. J.; Li, Z. P.; Liu, Y. Y.; Mangeney, A.; Massacrier, G.; Ringot, O.; Cortes, T. Roca; Servan, B.; Vidal, I.

1998-09-01

New observations of BS and BT Cnc were performed during the STEPHI VII campaign in 1996 February. An overall run of 115 hours of data was collected. Different methods have been used to analyse the time series, bringing the detection of 2 frequencies for BT Cnc and 3 frequencies for BS Cnc above the 99% confidence level. A comparison with the literature reveals that the dominant mode of BT Cnc ( ~ 9.8 c/d) has kept excited over tens of years, while the secondary modes have not been the same all the time. However, during the very last years the two frequencies detected by us have been the only ones noticeably excited in the spectrum.

Spectral of electrocardiographic RR intervals to indicate atrial fibrillation

NASA Astrophysics Data System (ADS)

Nuryani, Nuryani; Satrio Nugroho, Anto

2017-11-01

Atrial fibrillation is a serious heart diseases, which is associated on the risk of death, and thus an early detection of atrial fibrillation is necessary. We have investigated spectral pattern of electrocardiogram in relation to atrial fibrillation. The utilized feature of electrocardiogram is RR interval. RR interval is the time interval between a two-consecutive R peaks. A series of RR intervals in a time segment is converted to a signal with a frequency domain. The frequency components are investigated to find the components which significantly associate to atrial fibrillation. A segment is defined as atrial fibrillation or normal segments by considering a defined number of atrial fibrillation RR in the segment. Using clinical data of 23 patients with atrial fibrillation, we find that the frequency components could be used to indicate atrial fibrillation.

Advanced Space Shuttle simulation model

NASA Technical Reports Server (NTRS)

Tatom, F. B.; Smith, S. R.

1982-01-01

A non-recursive model (based on von Karman spectra) for atmospheric turbulence along the flight path of the shuttle orbiter was developed. It provides for simulation of instantaneous vertical and horizontal gusts at the vehicle center-of-gravity, and also for simulation of instantaneous gusts gradients. Based on this model the time series for both gusts and gust gradients were generated and stored on a series of magnetic tapes, entitled Shuttle Simulation Turbulence Tapes (SSTT). The time series are designed to represent atmospheric turbulence from ground level to an altitude of 120,000 meters. A description of the turbulence generation procedure is provided. The results of validating the simulated turbulence are described. Conclusions and recommendations are presented. One-dimensional von Karman spectra are tabulated, while a discussion of the minimum frequency simulated is provided. The results of spectral and statistical analyses of the SSTT are presented.

Comparison of environmental forcings affecting suspended sediments variability in two macrotidal, highly-turbid estuaries

NASA Astrophysics Data System (ADS)

Jalón-Rojas, Isabel; Schmidt, Sabine; Sottolichio, Aldo

2017-11-01

The relative contribution of environmental forcing frequencies on turbidity variability is, for the first time, quantified at seasonal and multiannual time scales in tidal estuarine systems. With a decade of high-frequency, multi-site turbidity monitoring, the two nearby, macrotidal and highly-turbid Gironde and Loire estuaries (west France) are excellent natural laboratories for this purpose. Singular Spectrum Analyses, combined with Lomb-Scargle periodograms and Wavelet Transforms, were applied to the continuous multiannual turbidity time series. Frequencies of the main environmental factors affecting turbidity were identified: hydrological regime (high versus low river discharges), river flow variability, tidal range, tidal cycles, and turbulence. Their relative influences show similar patterns in both estuaries and depend on the estuarine region (lower or upper estuary) and the time scale (multiannual or seasonal). On the multiannual time scale, the relative contribution of tidal frequencies (tidal cycles and range) to turbidity variability decreases up-estuary from 68% to 47%, while the influence of river flow frequencies increases from 3% to 42%. On the seasonal time scale, the relative influence of forcings frequencies remains almost constant in the lower estuary, dominated by tidal frequencies (60% and 30% for tidal cycles and tidal range, respectively); in the upper reaches, it is variable depending on hydrological regime, even if tidal frequencies are responsible for up 50% of turbidity variance. These quantifications show the potential of combined spectral analyses to compare the behavior of suspended sediment in tidal estuaries throughout the world and to evaluate long-term changes in environmental forcings, especially in a context of global change. The relevance of this approach to compare nearby and overseas systems and to support management strategies is discussed (e.g., selection of effective operation frequencies/regions, prediction of the most affected regions by the implementation of operational management plans).

The Generic Structure Potential of Science Nonfiction Selections in Four Basal Reading Series, Grades One and Two

ERIC Educational Resources Information Center

Anthony, Angela Beckman

2009-01-01

Basal reading series are used in a majority of classrooms in the United States. The purpose of this study was to examine the frequency of fiction and nonfiction genres included in four recently published first and second grade basal reading series and to compare the frequencies to studies of older basal reading series. Based on the work of…

NASA atomic hydrogen standards program: An update

NASA Technical Reports Server (NTRS)

Reinhardt, V. S.; Kaufmann, D. C.; Adams, W. A.; Deluca, J. J.; Soucy, J. L.

1976-01-01

Comparisons are made between the NP series and the NX series of hydrogen masers. A field operable hydrogen maser (NR series) is also described. Atomic hydrogen primary frequency standards are in development stages. Standards are being developed for a hydrogen beam frequency standard and for a concertina hydrogen maser.

Driving factors of interactions between the exchange rate market and the commodity market: A wavelet-based complex network perspective

NASA Astrophysics Data System (ADS)

Wen, Shaobo; An, Haizhong; Chen, Zhihua; Liu, Xueyong

2017-08-01

In traditional econometrics, a time series must be in a stationary sequence. However, it usually shows time-varying fluctuations, and it remains a challenge to execute a multiscale analysis of the data and discover the topological characteristics of conduction in different scales. Wavelet analysis and complex networks in physical statistics have special advantages in solving these problems. We select the exchange rate variable from the Chinese market and the commodity price index variable from the world market as the time series of our study. We explore the driving factors behind the behavior of the two markets and their topological characteristics in three steps. First, we use the Kalman filter to find the optimal estimation of the relationship between the two markets. Second, wavelet analysis is used to extract the scales of the relationship that are driven by different frequency wavelets. Meanwhile, we search for the actual economic variables corresponding to different frequency wavelets. Finally, a complex network is used to search for the transfer characteristics of the combination of states driven by different frequency wavelets. The results show that statistical physics have a unique advantage over traditional econometrics. The Chinese market has time-varying impacts on the world market: it has greater influence when the world economy is stable and less influence in times of turmoil. The process of forming the state combination is random. Transitions between state combinations have a clustering feature. Based on these characteristics, we can effectively reduce the information burden on investors and correctly respond to the government's policy mix.

Detecting Selection on Temporal and Spatial Scales: A Genomic Time-Series Assessment of Selective Responses to Devil Facial Tumor Disease

PubMed Central

Brüniche-Olsen, Anna; Austin, Jeremy J.; Jones, Menna E.; Holland, Barbara R.; Burridge, Christopher P.

2016-01-01

Detecting loci under selection is an important task in evolutionary biology. In conservation genetics detecting selection is key to investigating adaptation to the spread of infectious disease. Loci under selection can be detected on a spatial scale, accounting for differences in demographic history among populations, or on a temporal scale, tracing changes in allele frequencies over time. Here we use these two approaches to investigate selective responses to the spread of an infectious cancer—devil facial tumor disease (DFTD)—that since 1996 has ravaged the Tasmanian devil (Sarcophilus harrisii). Using time-series ‘restriction site associated DNA’ (RAD) markers from populations pre- and post DFTD arrival, and DFTD free populations, we infer loci under selection due to DFTD and investigate signatures of selection that are incongruent among methods, populations, and times. The lack of congruence among populations influenced by DFTD with respect to inferred loci under selection, and the direction of that selection, fail to implicate a consistent selective role for DFTD. Instead genetic drift is more likely driving the observed allele frequency changes over time. Our study illustrates the importance of applying methods with different performance optima e.g. accounting for population structure and background selection, and assessing congruence of the results. PMID:26930198

We should be using nonlinear indices when relating heart-rate dynamics to cognition and mood

PubMed Central

Young, Hayley; Benton, David

2015-01-01

Both heart rate (HR) and brain functioning involve the integrated output of a multitude of regulatory mechanisms, that are not quantified adequately by linear approximations such as means and standard deviations. It was therefore considered whether non-linear measures of HR complexity are more strongly associated with cognition and mood. Whilst resting, the inter-beat (R-R) time series of twenty-one males and twenty-four females were measured for five minutes. The data were summarised using time, frequency and nonlinear complexity measures. Attention, memory, reaction times, mood and cortisol levels were assessed. Nonlinear HR indices captured additional information, enabling a greater percentage of the variance in behaviour to be explained. On occasions non-linear indices were related to aspects for behaviour, for example focused attention and cortisol production, when time or frequency indices were not. These effects were sexually dimorphic with HR complexity being more strongly associated with the behaviour of females. It was concluded that nonlinear rather than linear methods of summarizing the HR times series offers a novel way of relating brain functioning and behaviour. It should be considered whether non-linear measures of HR complexity can be used as a biomarker of the integrated functioning of the brain. PMID:26565560

Genome-wide Selective Sweeps in Natural Bacterial Populations Revealed by Time-series Metagenomics

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

Chan, Leong-Keat; Bendall, Matthew L.; Malfatti, Stephanie

2014-06-18

Multiple evolutionary models have been proposed to explain the formation of genetically and ecologically distinct bacterial groups. Time-series metagenomics enables direct observation of evolutionary processes in natural populations, and if applied over a sufficiently long time frame, this approach could capture events such as gene-specific or genome-wide selective sweeps. Direct observations of either process could help resolve how distinct groups form in natural microbial assemblages. Here, from a three-year metagenomic study of a freshwater lake, we explore changes in single nucleotide polymorphism (SNP) frequencies and patterns of gene gain and loss in populations of Chlorobiaceae and Methylophilaceae. SNP analyses revealedmore » substantial genetic heterogeneity within these populations, although the degree of heterogeneity varied considerably among closely related, co-occurring Methylophilaceae populations. SNP allele frequencies, as well as the relative abundance of certain genes, changed dramatically over time in each population. Interestingly, SNP diversity was purged at nearly every genome position in one of the Chlorobiaceae populations over the course of three years, while at the same time multiple genes either swept through or were swept from this population. These patterns were consistent with a genome-wide selective sweep, a process predicted by the ‘ecotype model’ of diversification, but not previously observed in natural populations.« less

Genome-wide Selective Sweeps in Natural Bacterial Populations Revealed by Time-series Metagenomics

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

Chan, Leong-Keat; Bendall, Matthew L.; Malfatti, Stephanie

2014-05-12

Multiple evolutionary models have been proposed to explain the formation of genetically and ecologically distinct bacterial groups. Time-series metagenomics enables direct observation of evolutionary processes in natural populations, and if applied over a sufficiently long time frame, this approach could capture events such as gene-specific or genome-wide selective sweeps. Direct observations of either process could help resolve how distinct groups form in natural microbial assemblages. Here, from a three-year metagenomic study of a freshwater lake, we explore changes in single nucleotide polymorphism (SNP) frequencies and patterns of gene gain and loss in populations of Chlorobiaceae and Methylophilaceae. SNP analyses revealedmore » substantial genetic heterogeneity within these populations, although the degree of heterogeneity varied considerably among closely related, co-occurring Methylophilaceae populations. SNP allele frequencies, as well as the relative abundance of certain genes, changed dramatically over time in each population. Interestingly, SNP diversity was purged at nearly every genome position in one of the Chlorobiaceae populations over the course of three years, while at the same time multiple genes either swept through or were swept from this population. These patterns were consistent with a genome-wide selective sweep, a process predicted by the ecotype model? of diversification, but not previously observed in natural populations.« less

Real-time volcano monitoring using GNSS single-frequency receivers

NASA Astrophysics Data System (ADS)

Lee, Seung-Woo; Yun, Sung-Hyo; Kim, Do Hyeong; Lee, Dukkee; Lee, Young J.; Schutz, Bob E.

2015-12-01

We present a real-time volcano monitoring strategy that uses the Global Navigation Satellite System (GNSS), and we examine the performance of the strategy by processing simulated and real data and comparing the results with published solutions. The cost of implementing the strategy is reduced greatly by using single-frequency GNSS receivers except for one dual-frequency receiver that serves as a base receiver. Positions of the single-frequency receivers are computed relative to the base receiver on an epoch-by-epoch basis using the high-rate double-difference (DD) GNSS technique, while the position of the base station is fixed to the values obtained with a deferred-time precise point positioning technique and updated on a regular basis. Since the performance of the single-frequency high-rate DD technique depends on the conditions of the ionosphere over the monitoring area, the ionospheric total electron content is monitored using the dual-frequency data from the base receiver. The surface deformation obtained with the high-rate DD technique is eventually processed by a real-time inversion filter based on the Mogi point source model. The performance of the real-time volcano monitoring strategy is assessed through a set of tests and case studies, in which the data recorded during the 2007 eruption of Kilauea and the 2005 eruption of Augustine are processed in a simulated real-time mode. The case studies show that the displacement time series obtained with the strategy seem to agree with those obtained with deferred-time, dual-frequency approaches at the level of 10-15 mm. Differences in the estimated volume change of the Mogi source between the real-time inversion filter and previously reported works were in the range of 11 to 13% of the maximum volume changes of the cases examined.

Controlled generation of a single Trichel pulse and a series of single Trichel pulses in air

NASA Astrophysics Data System (ADS)

Mizeraczyk, Jerzy; Berendt, Artur; Akishev, Yuri

2018-04-01

In this paper, a simple method for the controlled generation of a single Trichel pulse or a series of single Trichel pulses of a regulated repetition frequency in air is proposed. The concept of triggering a single Trichel pulse or a series of such pulses is based on the precise controlling the voltage inception of the negative corona, which can be accomplished through the use of a ramp voltage pulse or a series of such pulses with properly chosen ramp voltage pulse parameters (rise and fall times, and ramp voltage pulse repetition frequency). The proposal has been tested in experiments using a needle-to-plate electrode arrangement in air, and reproducible Trichel pulses (single or in a series) were obtained by triggering them with an appropriately designed voltage waveform. The proposed method and results obtained have been qualitatively analysed. The analysis provides guidance for designing the voltage ramp pulse in respect of the generation of a single Trichel pulse or a series of single Trichel pulses. The controlled generation of a single Trichel pulse or a series of such pulses would be a helpful research tool for the refined studies of the fundamental processes in a negative corona discharge in a single- (air is an example) and multi-phase gaseous fluids. The controlled generation of a single Trichel pulse or a series of Trichel pulses can also be attractive for those corona treatments which need manipulation of the electric charge and heat portions delivered by the Trichel pulses to the object.

10Be in late deglacial climate simulated by ECHAM5-HAM - Part 2: Isolating the solar signal from 10Be deposition

NASA Astrophysics Data System (ADS)

Heikkilä, U.; Shi, X.; Phipps, S. J.; Smith, A. M.

2013-10-01

This study investigates the effect of deglacial climate on the deposition of the solar proxy 10Be globally, and at two specific locations, the GRIP site at Summit, Central Greenland, and the Law Dome site in coastal Antarctica. The deglacial climate is represented by three 30 yr time slice simulations of 10 000 BP (years before present = 1950 CE), 11 000 BP and 12 000 BP, compared with a preindustrial control simulation. The model used is the ECHAM5-HAM atmospheric aerosol-climate model, driven with sea surface temperatures and sea ice cover simulated using the CSIRO Mk3L coupled climate system model. The focus is on isolating the 10Be production signal, driven by solar variability, from the weather or climate driven noise in the 10Be deposition flux during different stages of climate. The production signal varies on lower frequencies, dominated by the 11yr solar cycle within the 30 yr time scale of these experiments. The climatic noise is of higher frequencies. We first apply empirical orthogonal functions (EOF) analysis to global 10Be deposition on the annual scale and find that the first principal component, consisting of the spatial pattern of mean 10Be deposition and the temporally varying solar signal, explains 64% of the variability. The following principal components are closely related to those of precipitation. Then, we apply ensemble empirical decomposition (EEMD) analysis on the time series of 10Be deposition at GRIP and at Law Dome, which is an effective method for adaptively decomposing the time series into different frequency components. The low frequency components and the long term trend represent production and have reduced noise compared to the entire frequency spectrum of the deposition. The high frequency components represent climate driven noise related to the seasonal cycle of e.g. precipitation and are closely connected to high frequencies of precipitation. These results firstly show that the 10Be atmospheric production signal is preserved in the deposition flux to surface even during climates very different from today's both in global data and at two specific locations. Secondly, noise can be effectively reduced from 10Be deposition data by simply applying the EOF analysis in the case of a reasonably large number of available data sets, or by decomposing the individual data sets to filter out high-frequency fluctuations.

The French Atlantic Littoral and the Massif Armoricain

NASA Technical Reports Server (NTRS)

Verger, F. (Principal Investigator); Monget, J. M.; Scanvic, J. Y.

1976-01-01

The author has identified the following significant results. Diachronic use of LANDSAT data time series will in time allow statistical study of submersion frequencies in tidal areas. This is an essential element of coastal geomorphology and of coastal zone management being particularly useful in siting shellfish farms. Maps are being obtained at useable scales and simple, user oriented legends which can be used for coastal planning.

Hierarchical structure of the energy landscape of proteins revisited by time series analysis. II. Investigation of explicit solvent effects

NASA Astrophysics Data System (ADS)

Alakent, Burak; Camurdan, Mehmet C.; Doruker, Pemra

2005-10-01

Time series analysis tools are employed on the principal modes obtained from the Cα trajectories from two independent molecular-dynamics simulations of α-amylase inhibitor (tendamistat). Fluctuations inside an energy minimum (intraminimum motions), transitions between minima (interminimum motions), and relaxations in different hierarchical energy levels are investigated and compared with those encountered in vacuum by using different sampling window sizes and intervals. The low-frequency low-indexed mode relationship, established in vacuum, is also encountered in water, which shows the reliability of the important dynamics information offered by principal components analysis in water. It has been shown that examining a short data collection period (100ps) may result in a high population of overdamped modes, while some of the low-frequency oscillations (<10cm-1) can be captured in water by using a longer data collection period (1200ps). Simultaneous analysis of short and long sampling window sizes gives the following picture of the effect of water on protein dynamics. Water makes the protein lose its memory: future conformations are less dependent on previous conformations due to the lowering of energy barriers in hierarchical levels of the energy landscape. In short-time dynamics (<10ps), damping factors extracted from time series model parameters are lowered. For tendamistat, the friction coefficient in the Langevin equation is found to be around 40-60cm-1 for the low-indexed modes, compatible with literature. The fact that water has increased the friction and that on the other hand has lubrication effect at first sight contradicts. However, this comes about because water enhances the transitions between minima and forces the protein to reduce its already inherent inability to maintain oscillations observed in vacuum. Some of the frequencies lower than 10cm-1 are found to be overdamped, while those higher than 20cm-1 are slightly increased. As for the long-time dynamics in water, it is found that random-walk motion is maintained for approximately 200ps (about five times of that in vacuum) in the low-indexed modes, showing the lowering of energy barriers between the higher-level minima.

Approaches to quantifying long-term continental shelf sediment transport with an example from the Northern California STRESS mid-shelf site

NASA Astrophysics Data System (ADS)

Harris, Courtney K.; Wiberg, Patricia L.

1997-09-01

Modeling shelf sediment transport rates and bed reworking depths is problematic when the wave and current forcing conditions are not precisely known, as is usually the case when long-term sedimentation patterns are of interest. Two approaches to modeling sediment transport under such circumstances are considered. The first relies on measured or simulated time series of flow conditions to drive model calculations. The second approach uses as model input probability distribution functions of bottom boundary layer flow conditions developed from wave and current measurements. Sediment transport rates, frequency of bed resuspension by waves and currents, and bed reworking calculated using the two methods are compared at the mid-shelf STRESS (Sediment TRansport on Shelves and Slopes) site on the northern California continental shelf. Current, wave and resuspension measurements at the site are used to generate model inputs and test model results. An 11-year record of bottom wave orbital velocity, calculated from surface wave spectra measured by the National Data Buoy Center (NDBC) Buoy 46013 and verified against bottom tripod measurements, is used to characterize the frequency and duration of wave-driven transport events and to estimate the joint probability distribution of wave orbital velocity and period. A 109-day record of hourly current measurements 10 m above bottom is used to estimate the probability distribution of bottom boundary layer current velocity at this site and to develop an auto-regressive model to simulate current velocities for times when direct measurements of currents are not available. Frequency of transport, the maximum volume of suspended sediment, and average flux calculated using measured wave and simulated current time series agree well with values calculated using measured time series. A probabilistic approach is more amenable to calculations over time scales longer than existing wave records, but it tends to underestimate net transport because it does not capture the episodic nature of transport events. Both methods enable estimates to be made of the uncertainty in transport quantities that arise from an incomplete knowledge of the specific timing of wave and current conditions. 1997 Elsevier Science Ltd

Time-frequency analysis of stimulus frequency otoacoustic emissions and their changes with efferent stimulation in guinea pigs

NASA Astrophysics Data System (ADS)

Berezina-Greene, Maria A.; Guinan, John J.

2015-12-01

To aid in understanding their origin, stimulus frequency otoacoustic emissions (SFOAEs) were measured at a series of tone frequencies using the suppression method, both with and without stimulation of medial olivocochlear (MOC) efferents, in anesthetized guinea pigs. Time-frequency analysis showed SFOAE energy peaks in 1-3 delay components throughout the measured frequency range (0.5-12 kHz). One component's delay usually coincided with the phase-gradient delay. When multiple delay components were present, they were usually near SFOAE dips. Below 2 kHz, SFOAE delays were shorter than predicted from mechanical measurements. With MOC stimulation, SFOAE amplitude was decreased at most frequencies, but was sometimes enhanced, and all SFOAE delay components were affected. The MOC effects and an analysis of model data suggest that the multiple SFOAE delay components arise at the edges of the traveling-wave peak, not far basal of the peak. Comparisons with published guinea-pig neural data suggest that the short latencies of low-frequency SFOAEs may arise from coherent reflection from an organ-of-Corti motion that has a shorter group delay than the traveling wave.

Comparative analysis of time-scaling properties about water pH in Poyang Lake Inlet and Outlet on the basis of fractal methods.

PubMed

Shi, K; Liu, C Q; Huang, Z W; Zhang, B; Su, Y

2010-01-01

Detrended fluctuation analysis (DFA) and multifractal methods are applied to the time-scaling properties analysis of water pH series in Poyang Lake Inlet and Outlet in China. The results show that these pH series are characterised by long-term memory and multifractal scaling, and these characteristics have obvious differences between the Lake Inlet and Outlet. The comparison results suggest that monofractal and multifractal parameters can be quantitative dynamical indexes reflecting the capability of anti-acidification of Poyang Lake. Furthermore, we investigated the frequency-size distribution of pH series in Poyang Lake Inlet and Outlet. Our findings suggest that water pH is an example of a self-organised criticality (SOC) process. The results show that it is different SOC behaviours that result in the differences of power-law relations between pH series in Poyang Lake Inlet and Outlet. This work can be helpful to improvement of modelling of lake water quality.

Topological data analysis of financial time series: Landscapes of crashes

NASA Astrophysics Data System (ADS)

Gidea, Marian; Katz, Yuri

2018-02-01

We explore the evolution of daily returns of four major US stock market indices during the technology crash of 2000, and the financial crisis of 2007-2009. Our methodology is based on topological data analysis (TDA). We use persistence homology to detect and quantify topological patterns that appear in multidimensional time series. Using a sliding window, we extract time-dependent point cloud data sets, to which we associate a topological space. We detect transient loops that appear in this space, and we measure their persistence. This is encoded in real-valued functions referred to as a 'persistence landscapes'. We quantify the temporal changes in persistence landscapes via their Lp-norms. We test this procedure on multidimensional time series generated by various non-linear and non-equilibrium models. We find that, in the vicinity of financial meltdowns, the Lp-norms exhibit strong growth prior to the primary peak, which ascends during a crash. Remarkably, the average spectral density at low frequencies of the time series of Lp-norms of the persistence landscapes demonstrates a strong rising trend for 250 trading days prior to either dotcom crash on 03/10/2000, or to the Lehman bankruptcy on 09/15/2008. Our study suggests that TDA provides a new type of econometric analysis, which complements the standard statistical measures. The method can be used to detect early warning signals of imminent market crashes. We believe that this approach can be used beyond the analysis of financial time series presented here.

Simulating extreme low-discharge events for the Rhine using a stochastic model

NASA Astrophysics Data System (ADS)

Macian-Sorribes, Hector; Mens, Marjolein; Schasfoort, Femke; Diermanse, Ferdinand; Pulido-Velazquez, Manuel

2017-04-01

The specific features of hydrological droughts make them more difficult to be analysed than other water-related phenomena: longer time scales (months to several years) so less historical events are available, and the drought severity and associate damage depends on a combination of variables with no clear prevalence (e.g., total water deficit, maximum deficit and duration). As part of drought risk analysis, which aims to provide insight into the variability of hydrological conditions and associated socio-economic impacts, long synthetic time series should therefore be developed. In this contribution, we increase the length of the available inflow time series using stochastic autoregressive modelling. This enhancement could improve the characterization of the extreme range and can define extreme droughts with similar periods of return but different patterns that can lead to distinctly different damages. The methodology consists of: 1) fitting an autoregressive model (AR, ARMA…) to the available records; 2) generating extended time series (thousands of years); 3) performing a frequency analysis with different characteristic variables (total, deficit, maximum deficit and so on); and 4) selecting extreme drought events associated with different characteristic variables and return periods. The methodology was applied to the Rhine river discharge at location Lobith, where the Rhine enters The Netherlands. A monthly ARMA(1,1) autoregressive model with seasonally varying parameters was fitted and successfully validated to the historical records available since year 1901. The maximum monthly deficit with respect to a threshold value of 1800 m3/s and the average discharge for a given time span in m3/s were chosen as indicators to identify drought periods. A synthetic series of 10,000 years of discharges was generated using the validated ARMA model. Two time spans were considered in the analysis: the whole calendar year and the half-year period between April and September (the summer half year, where water demands are highest). Frequency analysis was performed for both indicators and time spans for the generated time series and the historical records. The comparison between observed and generated series showed that the ARMA model provides a good reproduction of the maximum deficits and total discharges, especially for the summer half-year period. The resulting synthetic series are therefore considered credible. These synthetic series, with its wealth of information, can then be used as inputs for the damage assessment models, together with information on precipitation deficits, in order to estimate the risk that lower inflows can have on the urban, the agricultural, the shipping sector and so on. This will help in associating economic losses and periods of return, as well as for estimating how droughts with similar periods of return but different patterns can lead to different damages. ACKNOWLEDGEMENT This study has been supported by the European Union's Horizon 2020 research and innovation programme under the IMPREX project (grant agreement no: 641.811), and by the Climate-KIC Pioneers into Practice Program supported by the European Union's EIT.

Handling the satellite inter-frequency biases in triple-frequency observations

NASA Astrophysics Data System (ADS)

Zhao, Lewen; Ye, Shirong; Song, Jia

2017-04-01

The new generation of GNSS satellites, including BDS, Galileo, modernized GPS, and GLONASS, transmit navigation sdata at more frequencies. Multi-frequency signals open new prospects for precise positioning, but satellite code and phase inter-frequency biases (IFB) induced by the third frequency need to be handled. Satellite code IFB can be corrected using products estimated by different strategies, the theoretical and numerical compatibility of these methods need to be proved. Furthermore, a new type of phase IFB, which changes with the relative sun-spacecraft-earth geometry, has been observed. It is necessary to investigate the cause and possible impacts of phase Time-variant IFB (TIFB). Therefore, we present systematic analysis to illustrate the relevancy between satellite clocks and phase TIFB, and compare the handling strategies of the code and phase IFB in triple-frequency positioning. First, the un-differenced L1/L2 satellite clock corrections considering the hardware delays are derived. And IFB induced by the dual-frequency satellite clocks to triple-frequency PPP model is detailed. The analysis shows that estimated satellite clocks actually contain the time-variant phase hardware delays, which can be compensated in L1/L2 ionosphere-free combinations. However, the time-variant hardware delays will lead to TIFB if the third frequency is used. Then, the methods used to correct the code and phase IFB are discussed. Standard point positioning (SPP) and precise point positioning (PPP) using BDS observations are carried out to validate the improvement of different IFB correction strategies. Experiments show that code IFB derived from DCB or geometry-free and ionosphere-free combination show an agreement of 0.3 ns for all satellites. Positioning results and error distribution with two different code IFB correcting strategies achieve similar tendency, which shows their substitutability. The original and wavelet filtered phase TIFB long-term series show significant periodical characteristic for most GEO and IGSO satellites, with the magnitude varies between - 5 cm and 5 cm. Finally, BDS L1/L3 kinematic PPP is conducted with code IFB corrected with DCB combinations, and TIFB corrected with filtered series. Results show that the IFB corrected L1/L3 PPP can achieve comparable convergence and positioning accuracy as L1/L2 combinations in static and kinematic mode.

Optimal Selection of Time Series Coefficients for Wrist Myoelectric Control Based on Intramuscular Recordings

DTIC Science & Technology

2001-10-25

considered static or invariant because the spectral behavior of EMG data is dependent on the specific muscle , contraction level, and limb function. However...produced at the onset of the muscle contraction . Because the units with lower conduction velocity (lower frequency components) are recruited first, the

Spectroscopic mode identification of γ Doradus stars

NASA Astrophysics Data System (ADS)

Brunsden, E.; Pollard, K. R.; Cottrell, P. L.; Wright, D. J.; Cat, P. De

2017-09-01

The g-mode pulsations in γ Doradus stars are identified using time-series colour photometry and high-resolution spectroscopy. For 22 class members the pulsational frequencies and modes are compared. Ground-based spectroscopic and photometric results show good agreement. The prevalence of (1, |1|) modes is noted and examined.

A 0.8-4.2 GHz monolithic all-digital PLL based frequency synthesizer for wireless communications

NASA Astrophysics Data System (ADS)

Yuanxin, Zhao; Yuanpei, Gao; Wei, Li; Ning, Li; Junyan, Ren

2015-01-01

A 0.8-4.2 GHz monolithic all-digital PLL based frequency synthesizer for wireless communications is successfully realized by the 130 nm CMOS process. A series of novel methods are proposed in this paper. Two band DCOs with high frequency resolution are utilized to cover the frequency band of interest, which is as wide as 2.5 to 5 GHz. An overflow counter is proposed to prevent the “pulse-swallowing” phenomenon so as to significantly reduce the locking time. A NTW-clamp digital module is also proposed to prevent the overflow of the loop control word. A modified programmable divider is presented to prevent the failure operation at the boundary. The measurement results show that the output frequency range of this frequency synthesizer is 0.8-4.2 GHz. The locking time achieves a reduction of 84% at 2.68 GHz. The best in-band and out-band phase noise performances have reached -100 dBc/Hz, and -125 dBc/Hz respectively. The lowest reference spur is -58 dBc.

Visualization of synchronization of the uterine contraction signals: running cross-correlation and wavelet running cross-correlation methods.

PubMed

Oczeretko, Edward; Swiatecka, Jolanta; Kitlas, Agnieszka; Laudanski, Tadeusz; Pierzynski, Piotr

2006-01-01

In physiological research, we often study multivariate data sets, containing two or more simultaneously recorded time series. The aim of this paper is to present the cross-correlation and the wavelet cross-correlation methods to assess synchronization between contractions in different topographic regions of the uterus. From a medical point of view, it is important to identify time delays between contractions, which may be of potential diagnostic significance in various pathologies. The cross-correlation was computed in a moving window with a width corresponding to approximately two or three contractions. As a result, the running cross-correlation function was obtained. The propagation% parameter assessed from this function allows quantitative description of synchronization in bivariate time series. In general, the uterine contraction signals are very complicated. Wavelet transforms provide insight into the structure of the time series at various frequencies (scales). To show the changes of the propagation% parameter along scales, a wavelet running cross-correlation was used. At first, the continuous wavelet transforms as the uterine contraction signals were received and afterwards, a running cross-correlation analysis was conducted for each pair of transformed time series. The findings show that running functions are very useful in the analysis of uterine contractions.

A new data-driven model for post-transplant antibody dynamics in high risk kidney transplantation.

PubMed

Zhang, Yan; Briggs, David; Lowe, David; Mitchell, Daniel; Daga, Sunil; Krishnan, Nithya; Higgins, Robert; Khovanova, Natasha

2017-02-01

The dynamics of donor specific human leukocyte antigen antibodies during early stage after kidney transplantation are of great clinical interest as these antibodies are considered to be associated with short and long term clinical outcomes. The limited number of antibody time series and their diverse patterns have made the task of modelling difficult. Focusing on one typical post-transplant dynamic pattern with rapid falls and stable settling levels, a novel data-driven model has been developed for the first time. A variational Bayesian inference method has been applied to select the best model and learn its parameters for 39 time series from two groups of graft recipients, i.e. patients with and without acute antibody-mediated rejection (AMR) episodes. Linear and nonlinear dynamic models of different order were attempted to fit the time series, and the third order linear model provided the best description of the common features in both groups. Both deterministic and stochastic parameters are found to be significantly different in the AMR and no-AMR groups showing that the time series in the AMR group have significantly higher frequency of oscillations and faster dissipation rates. This research may potentially lead to better understanding of the immunological mechanisms involved in kidney transplantation. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Geomagnetic field declination: from decadal to centennial scales

NASA Astrophysics Data System (ADS)

Dobrica, Venera; Demetrescu, Crisan; Mandea, Mioara

2018-04-01

Declination annual mean time series longer than 1 century provided by 24 geomagnetic observatories worldwide, together with 5 Western European reconstructed declination series over the last 4 centuries, have been analyzed in terms of the frequency constituents of the secular variation at inter-decadal and sub-centennial timescales of 20-35 and 70-90 years. Observatory and reconstructed time series have been processed by several types of filtering, namely Hodrick-Prescott, running averages, and Butterworth. The Hodrick-Prescott filtering allows us to separate a quasi-oscillation at a decadal timescale, which is assumed to be related to external variations and called the 11-year constituent, from a long-term trend. The latter has been decomposed into two other oscillations called inter-decadal and sub-centennial constituents by applying a Butterworth filtering with cutoffs at 30 and 73 years, respectively. The analysis shows that the generally accepted geomagnetic jerks occur around extrema in the time derivative of the trend and coincide with extrema in the time derivative of the 11-year constituent. The sub-centennial constituent is traced back to 1600 in the five 400-year-long time series and seems to be a major constituent of the secular variation, geomagnetic jerks included.

MEM spectral analysis for predicting influenza epidemics in Japan.

PubMed

Sumi, Ayako; Kamo, Ken-ichi

2012-03-01

The prediction of influenza epidemics has long been the focus of attention in epidemiology and mathematical biology. In this study, we tested whether time series analysis was useful for predicting the incidence of influenza in Japan. The method of time series analysis we used consists of spectral analysis based on the maximum entropy method (MEM) in the frequency domain and the nonlinear least squares method in the time domain. Using this time series analysis, we analyzed the incidence data of influenza in Japan from January 1948 to December 1998; these data are unique in that they covered the periods of pandemics in Japan in 1957, 1968, and 1977. On the basis of the MEM spectral analysis, we identified the periodic modes explaining the underlying variations of the incidence data. The optimum least squares fitting (LSF) curve calculated with the periodic modes reproduced the underlying variation of the incidence data. An extension of the LSF curve could be used to predict the incidence of influenza quantitatively. Our study suggested that MEM spectral analysis would allow us to model temporal variations of influenza epidemics with multiple periodic modes much more effectively than by using the method of conventional time series analysis, which has been used previously to investigate the behavior of temporal variations in influenza data.

Brain-computer interface using wavelet transformation and naïve bayes classifier.

PubMed

Bassani, Thiago; Nievola, Julio Cesar

2010-01-01

The main purpose of this work is to establish an exploratory approach using electroencephalographic (EEG) signal, analyzing the patterns in the time-frequency plane. This work also aims to optimize the EEG signal analysis through the improvement of classifiers and, eventually, of the BCI performance. In this paper a novel exploratory approach for data mining of EEG signal based on continuous wavelet transformation (CWT) and wavelet coherence (WC) statistical analysis is introduced and applied. The CWT allows the representation of time-frequency patterns of the signal's information content by WC qualiatative analysis. Results suggest that the proposed methodology is capable of identifying regions in time-frequency spectrum during the specified task of BCI. Furthermore, an example of a region is identified, and the patterns are classified using a Naïve Bayes Classifier (NBC). This innovative characteristic of the process justifies the feasibility of the proposed approach to other data mining applications. It can open new physiologic researches in this field and on non stationary time series analysis.

Passive microwave sensing of soil moisture content: Soil bulk density and surface roughness

NASA Technical Reports Server (NTRS)

Wang, J. R.

1982-01-01

Microwave radiometric measurements over bare fields of different surface roughnesses were made at the frequencies of 1.4 GHz, 5 GHz, and 10.7 GHz to study the frequency dependence as well as the possible time variation of surface roughness. The presence of surface roughness was found to increase the brightness temperature of soils and reduce the slope of regression between brightness temperature and soil moisture content. The frequency dependence of the surface roughness effect was relatively weak when compared with that of the vegetation effect. Radiometric time series observation over a given field indicated that field surface roughness might gradually diminish with time, especially after a rainfall or irrigation. This time variation of surface roughness served to enhance the uncertainty in remote soil moisture estimate by microwave radiometry. Three years of radiometric measurements over a test site revealed a possible inconsistency in the soil bulk density determination, which turned out to be an important factor in the interpretation of radiometric data.

Rank-frequency distributions of Romanian words

NASA Astrophysics Data System (ADS)

Cocioceanu, Adrian; Raportaru, Carina Mihaela; Nicolin, Alexandru I.; Jakimovski, Dragan

2017-12-01

The calibration of voice biometrics solutions requires detailed analyses of spoken texts and in this context we investigate by computational means the rank-frequency distributions of Romanian words and word series to determine the most common words and word series of the language. To this end, we have constructed a corpus of approximately 2.5 million words and then determined that the rank-frequency distributions of the Romanian words, as well as series of two, and three subsequent words, obey the celebrated Zipf law.

Fundamental and subharmonic excitation for an oscillator with several tunneling diodes in series

NASA Technical Reports Server (NTRS)

Boric-Lubecke, Olga; Pan, Dee-Son; Itoh, Tatsuo

1995-01-01

Connecting several tunneling diodes in series shows promise as a method for increasing the output power of these devices as millimeter-wave oscillators. However, due to the negative differential resistance (NDR) region in the dc I-V curve of a single tunneling diode, a circuit using several devices connected in series, and biased simultaneously in the NDR region, is dc unstable. Because of this instability, an oscillator with several tunneling diodes in series has a demanding excitation condition. Excitation using an externally applied RF signal is one approach to solving this problem. This is experimentally demonstrated using an RF source, both with frequency close to as well as with frequency considerably lower than the oscillation frequency. Excitation by an RF (radio frequency) source with a frequency as low as one sixth of the oscillation frequency was demonstrated in a proof-of-principle experiment at 2 GHz, for an oscillator with two tunnel diodes connected in series. Strong harmonics of the oscillation signal were generated as a result of the highly nonlinear dc I-V curve of the tunnel diode and a large signal oscillator design. Third harmonic output power comparable to that of the fundamental was observed in one oscillator circuit. If submillimeter wave resonant-tunneling diodes (RTD's) are used instead of tunnel diodes, this harmonic output may be useful for generating signals at frequencies well into the terahertz range.

NeuroRhythmics: software for analyzing time-series measurements of saltatory movements in neuronal processes.

PubMed

Kerlin, Aaron M; Lindsley, Tara A

2008-08-15

Time-lapse imaging of living neurons both in vivo and in vitro has revealed that the growth of axons and dendrites is highly dynamic and characterized by alternating periods of extension and retraction. These growth dynamics are associated with important features of neuronal development and are differentially affected by experimental treatments, but the underlying cellular mechanisms are poorly understood. NeuroRhythmics was developed to semi-automate specific quantitative tasks involved in analysis of two-dimensional time-series images of processes that exhibit saltatory elongation. This software provides detailed information on periods of growth and nongrowth that it identifies by transitions in elongation (i.e. initiation time, average rate, duration) and information regarding the overall pattern of saltatory growth (i.e. time of pattern onset, frequency of transitions, relative time spent in a state of growth vs. nongrowth). Plots and numeric output are readily imported into other applications. The user has the option to specify criteria for identifying transitions in growth behavior, which extends the potential application of the software to neurons of different types or developmental stage and to other time-series phenomena that exhibit saltatory dynamics. NeuroRhythmics will facilitate mechanistic studies of periodic axonal and dendritic growth in neurons.

Frequency-Dependent Modulation of Regional Synchrony in the Human Brain by Eyes Open and Eyes Closed Resting-States.

PubMed

Song, Xiaopeng; Zhou, Shuqin; Zhang, Yi; Liu, Yijun; Zhu, Huaiqiu; Gao, Jia-Hong

2015-01-01

The eyes-open (EO) and eyes-closed (EC) states have differential effects on BOLD-fMRI signal dynamics, affecting both the BOLD oscillation frequency of a single voxel and the regional homogeneity (ReHo) of several neighboring voxels. To explore how the two resting-states modulate the local synchrony through different frequency bands, we decomposed the time series of each voxel into several components that fell into distinct frequency bands. The ReHo in each of the bands was calculated and compared between the EO and EC conditions. The cross-voxel correlations between the mean frequency and the overall ReHo of each voxel's original BOLD series in different brain areas were also calculated and compared between the two states. Compared with the EC state, ReHo decreased with EO in a wide frequency band of 0.01-0.25 Hz in the bilateral thalamus, sensorimotor network, and superior temporal gyrus, while ReHo increased significantly in the band of 0-0.01 Hz in the primary visual cortex, and in a higher frequency band of 0.02-0.1 Hz in the higher order visual areas. The cross-voxel correlations between the frequency and overall ReHo were negative in all the brain areas but varied from region to region. These correlations were stronger with EO in the visual network and the default mode network. Our results suggested that different frequency bands of ReHo showed different sensitivity to the modulation of EO-EC states. The better spatial consistency between the frequency and overall ReHo maps indicated that the brain might adopt a stricter frequency-dependent configuration with EO than with EC.

Large-scale vegetation responses to terrestrial moisture storage changes

NASA Astrophysics Data System (ADS)

Andrew, Robert L.; Guan, Huade; Batelaan, Okke

2017-09-01

The normalised difference vegetation index (NDVI) is a useful tool for studying vegetation activity and ecosystem performance at a large spatial scale. In this study we use the Gravity Recovery and Climate Experiment (GRACE) total water storage (TWS) estimates to examine temporal variability of the NDVI across Australia. We aim to demonstrate a new method that reveals the moisture dependence of vegetation cover at different temporal resolutions. Time series of monthly GRACE TWS anomalies are decomposed into different temporal frequencies using a discrete wavelet transform and analysed against time series of the NDVI anomalies in a stepwise regression. The results show that combinations of different frequencies of decomposed GRACE TWS data explain NDVI temporal variations better than raw GRACE TWS alone. Generally, the NDVI appears to be more sensitive to interannual changes in water storage than shorter changes, though grassland-dominated areas are sensitive to higher-frequencies of water-storage changes. Different types of vegetation, defined by areas of land use type, show distinct differences in how they respond to the changes in water storage, which is generally consistent with our physical understanding. This unique method provides useful insight into how the NDVI is affected by changes in water storage at different temporal scales across land use types.

Detection of the significant geomagnetic field signals in the interannual variations of Length-of-Day using wavelet method

NASA Astrophysics Data System (ADS)

Liu, Genyou; Duan, Pengshuo; Hao, Xiaoguang; Hu, Xiaogang

2015-04-01

The previous studies indicated that the most of the interannual variations in Length-Of-Day (LOD) could be explained by the joint effects of ENSO (EI Nino-Southern Oscillations) and QBO (Quasi-Biennial Oscillation) phenomenon in the atmosphere. Due to the limit of the used methods, those results cannot give the 'time-frequency' coherence spectrum between ENSO and LOD, and cannot indicate in which specific periods the weak coherence occurred and difficult to give the reliable reason. This paper uses Daubechies wavelet with 10 order vanishing moment to analyze the LOD monthly time series from 1962 to 2011. Based on cross-wavelet and wavelet coherence methods, the analysis of the time-frequency correlations between ENSO and LOD series (1962-2011) on the 1.3~10.7 year scales is given. We have extracted and reconstructed the LOD signals on 1.3~10.7year scales. The result shows that there is obvious weak coherence on both biennial and 5~8 year scales after 1982 relative to before 1982. According to the previous works, the biennial weak coherence is due to QBO, but the weak coherence on 5~8 year scales cannot be interpreted by the effects of ENSO and QBO. In this study, the Geomagnetic field signals (can be characterized as Aa index) are introduced, we have further extracted and reconstructed the LOD, ENSO and Aa signals in 5-8.0 year band using wavelet packet analysis. Through analyzing the standardized series of the three signals, we found a linear time-frequency formula among the original observation series: LOD(t,f) =αENSO(t,f) +βAa(t,f). This study indicates that the LOD signals on 5.3~8.0 year scales can be expressed in term of linear combination of ENSO and Aa signals. Especially after 1982, the contributions of ENSO and Aa to LOD respectively reach about 0.95ms and 1.0ms.The results also imply that there is an obvious Geomagnetic field signal in interannual variations of LOD. Furthermore, after considering the geomagnetic field signal correction, the Pearson correlation coefficient between LOD and ENSO will increase from 0.51 to 0.98. Consequently, we can conclude that the weak coherence after 1982 on 5.3-8.0 year scales between LOD and ENSO is mainly due to the disturbance of Aa signal, and the observed LOD series is the result of the interaction between ENSO and geomagnetic field signals.

THE NANOGRAV NINE-YEAR DATA SET: OBSERVATIONS, ARRIVAL TIME MEASUREMENTS, AND ANALYSIS OF 37 MILLISECOND PULSARS

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

Arzoumanian, Zaven; Brazier, Adam; Chatterjee, Shami

2015-11-01

We present high-precision timing observations spanning up to nine years for 37 millisecond pulsars monitored with the Green Bank and Arecibo radio telescopes as part of the North American Nanohertz Observatory for Gravitational Waves (NANOGrav) project. We describe the observational and instrumental setups used to collect the data, and methodology applied for calculating pulse times of arrival; these include novel methods for measuring instrumental offsets and characterizing low signal-to-noise ratio timing results. The time of arrival data are fit to a physical timing model for each source, including terms that characterize time-variable dispersion measure and frequency-dependent pulse shape evolution. Inmore » conjunction with the timing model fit, we have performed a Bayesian analysis of a parameterized timing noise model for each source, and detect evidence for excess low-frequency, or “red,” timing noise in 10 of the pulsars. For 5 of these cases this is likely due to interstellar medium propagation effects rather than intrisic spin variations. Subsequent papers in this series will present further analysis of this data set aimed at detecting or limiting the presence of nanohertz-frequency gravitational wave signals.« less

Improved Transient and Steady-State Performances of Series Resonant ZCS High-Frequency Inverter-Coupled Voltage Multiplier Converter with Dual Mode PFM Control Scheme

NASA Astrophysics Data System (ADS)

Chu, Enhui; Gamage, Laknath; Ishitobi, Manabu; Hiraki, Eiji; Nakaoka, Mutsuo

The A variety of switched-mode high voltage DC power supplies using voltage-fed type or current-fed type high-frequency transformer resonant inverters using MOS gate bipolar power transistors; IGBTs have been recently developed so far for a medical-use X-ray high power generator. In general, the high voltage high power X-ray generator using voltage-fed high frequency inverter with a high voltage transformer link has to meet some performances such as (i) short rising period in start transient of X-ray tube voltage (ii) no overshoot transient response in tube voltage, (iii) minimized voltage ripple in periodic steady-state under extremely wide load variations and filament heater current fluctuation conditions of the X-ray tube. This paper presents two lossless inductor snubber-assisted series resonant zero current soft switching high-frequency inverter using a diode-capacitor ladder type voltage multiplier called Cockcroft-Walton circuit, which is effectively implemented for a high DC voltage X-ray power generator. This DC high voltage generator which incorporates pulse frequency modulated series resonant inverter using IGBT power module packages is based on the operation principle of zero current soft switching commutation scheme under discontinuous resonant current and continuous resonant current transition modes. This series capacitor compensated for transformer resonant power converter with a high frequency transformer linked voltage boost multiplier can efficiently work a novel selectively-changed dual mode PFM control scheme in order to improve the start transient and steady-state response characteristics and can completely achieve stable zero current soft switching commutation tube filament current dependent for wide load parameter setting values with the aid of two lossless inductor snubbers. It is proved on the basis of simulation and experimental results in which a simple and low cost control implementation based on selectively-changed dual-mode PFM for high-voltage X-ray DC-DC power converter with a voltage multiplier strategy has some specified voltage pattern tracking voltage response performances under rapid rising time and no overshoot in start transient tube voltage as well as the minimized steady-state voltage ripple in tube voltage.

Transient deformation from daily GPS displacement time series: postseismic deformation, ETS and evolving strain rates

NASA Astrophysics Data System (ADS)

Bock, Y.; Fang, P.; Moore, A. W.; Kedar, S.; Liu, Z.; Owen, S. E.; Glasscoe, M. T.

2016-12-01

Detection of time-dependent crustal deformation relies on the availability of accurate surface displacements, proper time series analysis to correct for secular motion, coseismic and non-tectonic instrument offsets, periodic signatures at different frequencies, and a realistic estimate of uncertainties for the parameters of interest. As part of the NASA Solid Earth Science ESDR System (SESES) project, daily displacement time series are estimated for about 2500 stations, focused on tectonic plate boundaries and having a global distribution for accessing the terrestrial reference frame. The "combined" time series are optimally estimated from independent JPL GIPSY and SIO GAMIT solutions, using a consistent set of input epoch-date coordinates and metadata. The longest time series began in 1992; more than 30% of the stations have experienced one or more of 35 major earthquakes with significant postseismic deformation. Here we present three examples of time-dependent deformation that have been detected in the SESES displacement time series. (1) Postseismic deformation is a fundamental time-dependent signal that indicates a viscoelastic response of the crust/mantle lithosphere, afterslip, or poroelastic effects at different spatial and temporal scales. It is critical to identify and estimate the extent of postseismic deformation in both space and time not only for insight into the crustal deformation and earthquake cycles and their underlying physical processes, but also to reveal other time-dependent signals. We report on our database of characterized postseismic motions using a principal component analysis to isolate different postseismic processes. (2) Starting with the SESES combined time series and applying a time-dependent Kalman filter, we examine episodic tremor and slow slip (ETS) in the Cascadia subduction zone. We report on subtle slip details, allowing investigation of the spatiotemporal relationship between slow slip transients and tremor and their underlying physical mechanisms. (3) We present evolving strain dilatation and shear rates based on the SESES velocities for regional subnetworks as a metric for assigning earthquake probabilities and detection of possible time-dependent deformation related to underlying physical processes.

Methods for removal of unwanted signals from gravity time-series: Comparison using linear techniques complemented with analysis of system dynamics

NASA Astrophysics Data System (ADS)

Valencio, Arthur; Grebogi, Celso; Baptista, Murilo S.

2017-10-01

The presence of undesirable dominating signals in geophysical experimental data is a challenge in many subfields. One remarkable example is surface gravimetry, where frequencies from Earth tides correspond to time-series fluctuations up to a thousand times larger than the phenomena of major interest, such as hydrological gravity effects or co-seismic gravity changes. This work discusses general methods for the removal of unwanted dominating signals by applying them to 8 long-period gravity time-series of the International Geodynamics and Earth Tides Service, equivalent to the acquisition from 8 instruments in 5 locations representative of the network. We compare three different conceptual approaches for tide removal: frequency filtering, physical modelling, and data-based modelling. Each approach reveals a different limitation to be considered depending on the intended application. Vestiges of tides remain in the residues for the modelling procedures, whereas the signal was distorted in different ways by the filtering and data-based procedures. The linear techniques employed were power spectral density, spectrogram, cross-correlation, and classical harmonics decomposition, while the system dynamics was analysed by state-space reconstruction and estimation of the largest Lyapunov exponent. Although the tides could not be completely eliminated, they were sufficiently reduced to allow observation of geophysical events of interest above the 10 nm s-2 level, exemplified by a hydrology-related event of 60 nm s-2. The implementations adopted for each conceptual approach are general, so that their principles could be applied to other kinds of data affected by undesired signals composed mainly by periodic or quasi-periodic components.

Pediatric emotional dysregulation and behavioral disruptiveness treated with hypnosis: a time-series design.

PubMed

Iglesias, Alex; Iglesias, Adam

2014-01-01

A case of pediatric oppositional defiant disorder (ODD) with concomitant emotional dysregulation and secondary behavioral disruptiveness was treated with hypnosis by means of the hypnotic hold, a method adapted by the authors. An A-B-A-B time-series design with multiple replications was employed to measure the relationship of the hypnotic treatment to the dependent measure: episodes of emotional dysregulation with accompanying behavioral disruptiveness. The findings indicated a statistically significant relationship between the degree of change from phase to phase and the treatment. Follow-up at 6 months indicated a significant reduction of the frequency of targeted episodes of emotional dysregulation and behavioral disruptiveness at home.

A time series analysis of the rabies control programme in Chile.

PubMed Central

Ernst, S. N.; Fabrega, F.

1989-01-01

The classical time series decomposition method was used to compare the temporal pattern of rabies in Chile before and after the implementation of the control programme. In the years 1950-60, a period without control measures, rabies showed an increasing trend, a seasonal excess of cases in November and December and a cyclic behaviour with outbreaks occurring every 5 years. During 1961-1970 and 1971-86, a 26-year period that includes two different phases of the rabies programme which started in 1961, there was a general decline in the incidence of rabies. The seasonality disappeared when the disease reached a low frequency level and the cyclical component was not evident. PMID:2606167

A field data assessment of contemporary models of beach cusp formation

USGS Publications Warehouse

Allen, J.R.; Psuty, N.P.; Bauer, B.O.; Carter, R.W.G.

1996-01-01

Cusp formation was observed during an instrumented, daily profiled, time series of a reflective beach in Canaveral National Seashore, Florida on January 5, 1988. The monitored cusp embayment formed by erosion of the foreshore and the cusp series had a mean spacing of approximately 28 m. During this time, inshore fluid flows were dominated by two standing edge waves at frequencies of 0.06 Hz (primary) and 0.035 Hz (secondary) whereas incident waves were broadbanded at 0.12-0.16 Hz. Directly measured flows (and indirectly estimated swash excursion) data support both the standing wave subharmonic model and the self-organization model of cusp formation in this study.

On pads and filters: Processing strong-motion data

USGS Publications Warehouse

Boore, D.M.

2005-01-01

Processing of strong-motion data in many cases can be as straightforward as filtering the acceleration time series and integrating to obtain velocity and displacement. To avoid the introduction of spurious low-frequency noise in quantities derived from the filtered accelerations, however, care must be taken to append zero pads of adequate length to the beginning and end of the segment of recorded data. These padded sections of the filtered acceleration need to be retained when deriving velocities, displacements, Fourier spectra, and response spectra. In addition, these padded and filtered sections should also be included in the time series used in the dynamic analysis of structures and soils to ensure compatibility with the filtered accelerations.

Random walker in temporally deforming higher-order potential forces observed in a financial crisis.

PubMed

Watanabe, Kota; Takayasu, Hideki; Takayasu, Misako

2009-11-01

Basic peculiarities of market price fluctuations are known to be well described by a recently developed random-walk model in a temporally deforming quadratic potential force whose center is given by a moving average of past price traces [M. Takayasu, T. Mizuno, and H. Takayasu, Physica A 370, 91 (2006)]. By analyzing high-frequency financial time series of exceptional events, such as bubbles and crashes, we confirm the appearance of higher-order potential force in the markets. We show statistical significance of its existence by applying the information criterion. This time series analysis is expected to be applied widely for detecting a nonstationary symptom in random phenomena.

Aeroelastic Response of Nonlinear Wing Section By Functional Series Technique

NASA Technical Reports Server (NTRS)

Marzocca, Piergiovanni; Librescu, Liviu; Silva, Walter A.

2000-01-01

This paper addresses the problem of the determination of the subcritical aeroelastic response and flutter instability of nonlinear two-dimensional lifting surfaces in an incompressible flow-field via indicial functions and Volterra series approach. The related aeroelastic governing equations are based upon the inclusion of structural and damping nonlinearities in plunging and pitching, of the linear unsteady aerodynamics and consideration of an arbitrary time-dependent external pressure pulse. Unsteady aeroelastic nonlinear kernels are determined, and based on these, frequency and time histories of the subcritical aeroelastic response are obtained, and in this context the influence of the considered nonlinearities is emphasized. Conclusions and results displaying the implications of the considered effects are supplied.

Aeroelastic Response of Nonlinear Wing Section by Functional Series Technique

NASA Technical Reports Server (NTRS)

Silva, Walter A.; Marzocca, Piergiovanni

2001-01-01

This paper addresses the problem of the determination of the subcritical aeroelastic response and flutter instability of nonlinear two-dimensional lifting surfaces in an incompressible flow-field via indicial functions and Volterra series approach. The related aeroelastic governing equations are based upon the inclusion of structural and damping nonlinearities in plunging and pitching, of the linear unsteady aerodynamics and consideration of an arbitrary time-dependent external pressure pulse. Unsteady aeroelastic nonlinear kernels are determined, and based on these, frequency and time histories of the subcritical aeroelastic response are obtained, and in this context the influence of the considered nonlinearities is emphasized. Conclusions and results displaying the implications of the considered effects are supplied.

Ocean rogue waves and their phase space dynamics in the limit of a linear interference model.

PubMed

Birkholz, Simon; Brée, Carsten; Veselić, Ivan; Demircan, Ayhan; Steinmeyer, Günter

2016-10-12

We reanalyse the probability for formation of extreme waves using the simple model of linear interference of a finite number of elementary waves with fixed amplitude and random phase fluctuations. Under these model assumptions no rogue waves appear when less than 10 elementary waves interfere with each other. Above this threshold rogue wave formation becomes increasingly likely, with appearance frequencies that may even exceed long-term observations by an order of magnitude. For estimation of the effective number of interfering waves, we suggest the Grassberger-Procaccia dimensional analysis of individual time series. For the ocean system, it is further shown that the resulting phase space dimension may vary, such that the threshold for rogue wave formation is not always reached. Time series analysis as well as the appearance of particular focusing wind conditions may enable an effective forecast of such rogue-wave prone situations. In particular, extracting the dimension from ocean time series allows much more specific estimation of the rogue wave probability.

Polar motion excitation analysis due to global continental water redistribution

NASA Astrophysics Data System (ADS)

Fernandez, L.; Schuh, H.

2006-10-01

We present the results obtained when studying the hydrological excitation of the Earth‘s wobble due to global redistribution of continental water storage. This work was performed in two steps. First, we computed the hydrological angular momentum (HAM) time series based on the global hydrological model LaD (Land Dynamics model) for the period 1980 till 2004. Then, we compared the effectiveness of this excitation by analysing the residuals of the geodetic time series after removing atmospheric and oceanic contributions with the respective hydrological ones. The emphasis was put on low frequency variations. We also present a comparison of HAM time series from LaD with respect to that one from a global model based on the assimilated soil moisture and snow accumulation data from NCEP/NCAR (The National Center for Environmental Prediction/The National Center for Atmospheric Research) reanalysis. Finally, we evaluate the performance of LaD model in closing the polar motion budget at seasonal periods in comparison with the NCEP and the Land Data Assimilation System (LDAS) models.

Spectral decompositions of multiple time series: a Bayesian non-parametric approach.

PubMed

Macaro, Christian; Prado, Raquel

2014-01-01

We consider spectral decompositions of multiple time series that arise in studies where the interest lies in assessing the influence of two or more factors. We write the spectral density of each time series as a sum of the spectral densities associated to the different levels of the factors. We then use Whittle's approximation to the likelihood function and follow a Bayesian non-parametric approach to obtain posterior inference on the spectral densities based on Bernstein-Dirichlet prior distributions. The prior is strategically important as it carries identifiability conditions for the models and allows us to quantify our degree of confidence in such conditions. A Markov chain Monte Carlo (MCMC) algorithm for posterior inference within this class of frequency-domain models is presented.We illustrate the approach by analyzing simulated and real data via spectral one-way and two-way models. In particular, we present an analysis of functional magnetic resonance imaging (fMRI) brain responses measured in individuals who participated in a designed experiment to study pain perception in humans.

Ocean rogue waves and their phase space dynamics in the limit of a linear interference model

PubMed Central

Birkholz, Simon; Brée, Carsten; Veselić, Ivan; Demircan, Ayhan; Steinmeyer, Günter

2016-01-01

We reanalyse the probability for formation of extreme waves using the simple model of linear interference of a finite number of elementary waves with fixed amplitude and random phase fluctuations. Under these model assumptions no rogue waves appear when less than 10 elementary waves interfere with each other. Above this threshold rogue wave formation becomes increasingly likely, with appearance frequencies that may even exceed long-term observations by an order of magnitude. For estimation of the effective number of interfering waves, we suggest the Grassberger-Procaccia dimensional analysis of individual time series. For the ocean system, it is further shown that the resulting phase space dimension may vary, such that the threshold for rogue wave formation is not always reached. Time series analysis as well as the appearance of particular focusing wind conditions may enable an effective forecast of such rogue-wave prone situations. In particular, extracting the dimension from ocean time series allows much more specific estimation of the rogue wave probability. PMID:27731411

Oscillation spectrum of WASP-33 from the MOST photometry

NASA Astrophysics Data System (ADS)

Mkrtichian, David

2015-08-01

We present results of extended continuous time series photometry of the Delta Scuti type pulsating exoplanet host star WASP-33 obtained in two seasons (2011 and 2013) with the MOST space telescope. Our frequency analysis yealds rich, low-amplitude multi-frequency spectrum of oscillation modes. We discuss possible resonances between the orbiital period of the planet and frequencies of the oscillation modes. We present results of our measurements of planets orbital O-C variations and analyze possible existence of invisible planets in the system. We review recent results of the high-resolution spectroscopic campaign on WASP-33 and confirm the retrograde orbital motion of the planet WASP-33b.

Time-evolution of photon heat current through series coupled two mesoscopic Josephson junction devices

NASA Astrophysics Data System (ADS)

Lu, Wen-Ting; Zhao, Hong-Kang; Wang, Jian

2018-03-01

Photon heat current tunneling through a series coupled two mesoscopic Josephson junction (MJJ) system biased by dc voltages has been investigated by employing the nonequilibrium Green’s function approach. The time-oscillating photon heat current is contributed by the superposition of different current branches associated with the frequencies of MJJs ω j (j = 1, 2). Nonlinear behaviors are exhibited to be induced by the self-inductance, Coulomb interaction, and interference effect relating to the coherent transport of Cooper pairs in the MJJs. Time-oscillating pumping photon heat current is generated in the absence of temperature difference, while it becomes zero after time-average. The combination of ω j and Coulomb interactions in the MJJs determines the concrete heat current configuration. As the external and intrinsic frequencies ω j and ω 0 of MJJs match some specific combinations, resonant photon heat current exhibits sinusoidal behaviors with large amplitudes. Symmetric and asymmetric evolutions versus time t with respect to ω 1 t and ω 2 t are controlled by the applied dc voltages of V 1 and V 2. The dc photon heat current formula is a special case of the general time-dependent heat current formula when the bias voltages are settled to zero. The Aharonov-Bohm effect has been investigated, and versatile oscillation structures of photon heat current can be achieved by tuning the magnetic fluxes threading through separating MJJs.

Towards a novel look on low-frequency climate reconstructions

NASA Astrophysics Data System (ADS)

Kamenik, Christian; Goslar, Tomasz; Hicks, Sheila; Barnekow, Lena; Huusko, Antti

2010-05-01

Information on low-frequency (millennial to sub-centennial) climate change is often derived from sedimentary archives, such as peat profiles or lake sediments. Usually, these archives have non-annual and varying time resolution. Their dating is mainly based on radionuclides, which provide probabilistic age-depth relationships with complex error structures. Dating uncertainties impede the interpretation of sediment-based climate reconstructions. They complicate the calculation of time-dependent rates. In most cases, they make any calibration in time impossible. Sediment-based climate proxies are therefore often presented as a single, best-guess time series without proper calibration and error estimation. Errors along time and dating errors that propagate into the calculation of time-dependent rates are neglected. Our objective is to overcome the aforementioned limitations by using a 'swarm' or 'ensemble' of reconstructions instead of a single best-guess. The novelty of our approach is to take into account age-depth uncertainties by permuting through a large number of potential age-depth relationships of the archive of interest. For each individual permutation we can then calculate rates, calibrate proxies in time, and reconstruct the climate-state variable of interest. From the resulting swarm of reconstructions, we can derive realistic estimates of even complex error structures. The likelihood of reconstructions is visualized by a grid of two-dimensional kernels that take into account probabilities along time and the climate-state variable of interest simultaneously. For comparison and regional synthesis, likelihoods can be scored against other independent climate time series.

Complex demodulation in VLBI estimation of high frequency Earth rotation components

NASA Astrophysics Data System (ADS)

Böhm, S.; Brzeziński, A.; Schuh, H.

2012-12-01

The spectrum of high frequency Earth rotation variations contains strong harmonic signal components mainly excited by ocean tides along with much weaker non-harmonic fluctuations driven by irregular processes like the diurnal thermal tides in the atmosphere and oceans. In order to properly investigate non-harmonic phenomena a representation in time domain is inevitable. We present a method, operating in time domain, which is easily applicable within Earth rotation estimation from Very Long Baseline Interferometry (VLBI). It enables the determination of diurnal and subdiurnal variations, and is still effective with merely diurnal parameter sampling. The features of complex demodulation are used in an extended parameterization of polar motion and universal time which was implemented into a dedicated version of the Vienna VLBI Software VieVS. The functionality of the approach was evaluated by comparing amplitudes and phases of harmonic variations at tidal periods (diurnal/semidiurnal), derived from demodulated Earth rotation parameters (ERP), estimated from hourly resolved VLBI ERP time series and taken from a recently published VLBI ERP model to the terms of the conventional model for ocean tidal effects in Earth rotation recommended by the International Earth Rotation and Reference System Service (IERS). The three sets of tidal terms derived from VLBI observations extensively agree among each other within the three-sigma level of the demodulation approach, which is below 6 μas for polar motion and universal time. They also coincide in terms of differences to the IERS model, where significant deviations primarily for several major tidal terms are apparent. An additional spectral analysis of the as well estimated demodulated ERP series of the ter- and quarterdiurnal frequency bands did not reveal any significant signal structure. The complex demodulation applied in VLBI parameter estimation could be demonstrated a suitable procedure for the reliable reproduction of high frequency Earth rotation components and thus represents a qualified tool for future studies of irregular geophysical signals in ERP measured by space geodetic techniques.

Noise/spike detection in phonocardiogram signal as a cyclic random process with non-stationary period interval.

PubMed

Naseri, H; Homaeinezhad, M R; Pourkhajeh, H

2013-09-01

The major aim of this study is to describe a unified procedure for detecting noisy segments and spikes in transduced signals with a cyclic but non-stationary periodic nature. According to this procedure, the cycles of the signal (onset and offset locations) are detected. Then, the cycles are clustered into a finite number of groups based on appropriate geometrical- and frequency-based time series. Next, the median template of each time series of each cluster is calculated. Afterwards, a correlation-based technique is devised for making a comparison between a test cycle feature and the associated time series of each cluster. Finally, by applying a suitably chosen threshold for the calculated correlation values, a segment is prescribed to be either clean or noisy. As a key merit of this research, the procedure can introduce a decision support for choosing accurately orthogonal-expansion-based filtering or to remove noisy segments. In this paper, the application procedure of the proposed method is comprehensively described by applying it to phonocardiogram (PCG) signals for finding noisy cycles. The database consists of 126 records from several patients of a domestic research station acquired by a 3M Littmann(®) 3200, 4KHz sampling frequency electronic stethoscope. By implementing the noisy segments detection algorithm with this database, a sensitivity of Se=91.41% and a positive predictive value, PPV=92.86% were obtained based on physicians assessments. Copyright © 2013 Elsevier Ltd. All rights reserved.

Kalman filters for assimilating near-surface observations in the Richards equation - Part 2: A dual filter approach for simultaneous retrieval of states and parameters

NASA Astrophysics Data System (ADS)

Medina, H.; Romano, N.; Chirico, G. B.

2012-12-01

We present a dual Kalman Filter (KF) approach for retrieving states and parameters controlling soil water dynamics in a homogenous soil column by using near-surface state observations. The dual Kalman filter couples a standard KF algorithm for retrieving the states and an unscented KF algorithm for retrieving the parameters. We examine the performance of the dual Kalman Filter applied to two alternative state-space formulations of the Richards equation, respectively differentiated by the type of variable employed for representing the states: either the soil water content (θ) or the soil matric pressure head (h). We use a synthetic time-series series of true states and noise corrupted observations and a synthetic time-series of meteorological forcing. The performance analyses account for the effect of the input parameters, the observation depth and the assimilation frequency as well as the relationship between the retrieved states and the assimilated variables. We show that the identifiability of the parameters is strongly conditioned by several factors, such as the initial guess of the unknown parameters, the wet or dry range of the retrieved states, the boundary conditions, as well as the form (h-based or θ-based) of the state-space formulation. State identifiability is instead efficient even with a relatively coarse time-resolution of the assimilated observation. The accuracy of the retrieved states exhibits limited sensitivity to the observation depth and the assimilation frequency.

Concentration-discharge relationships to understand the interplay between hydrological and biogeochemical processes: insights from data analysis and numerical experiments in headwater catchments.

NASA Astrophysics Data System (ADS)

De Dreuzy, J. R.; Marçais, J.; Moatar, F.; Minaudo, C.; Courtois, Q.; Thomas, Z.; Longuevergne, L.; Pinay, G.

2017-12-01

Integration of hydrological and biogeochemical processes led to emerging patterns at the catchment scale. Monitoring in rivers reflects the aggregation of these effects. While discharge time series have been measured for decades, high frequency water quality monitoring in rivers now provides prominent measurements to characterize the interplay between hydrological and biogeochemical processes, especially to infer the processes that happen in the heterogeneous subsurface. However, we still lack frameworks to relate observed patterns to specific processes, because of the "organized complexity" of hydrological systems. Indeed, it is unclear what controls, for example, patterns in concentration-discharge (C/Q) relationships due to non-linear processes and hysteresis effects. Here we develop a non-intensive process-based model to test how the integration of different landforms (i.e. geological heterogeneities and structures, topographical features) with different biogeochemical reactivity assumptions (e.g. reactive zone locations) can shape the overall water quality time series. With numerical experiments, we investigate typical patterns in high frequency C/Q relationships. In headwater basins, we found that typical hysteretic patterns in C/Q relationships observed in data time series can be attributed to differences in water and solute locations stored across the hillslope. At the catchment scale though, these effects tend to average out by integrating contrasted hillslopes' landforms. Together these results suggest that information contained in headwater water quality monitoring can be used to understand how hydrochemical processes determine downstream conditions.

A comparative analysis of spectral exponent estimation techniques for 1/fβ processes with applications to the analysis of stride interval time series

PubMed Central

Schaefer, Alexander; Brach, Jennifer S.; Perera, Subashan; Sejdić, Ervin

2013-01-01

Background The time evolution and complex interactions of many nonlinear systems, such as in the human body, result in fractal types of parameter outcomes that exhibit self similarity over long time scales by a power law in the frequency spectrum S(f) = 1/fβ. The scaling exponent β is thus often interpreted as a “biomarker” of relative health and decline. New Method This paper presents a thorough comparative numerical analysis of fractal characterization techniques with specific consideration given to experimentally measured gait stride interval time series. The ideal fractal signals generated in the numerical analysis are constrained under varying lengths and biases indicative of a range of physiologically conceivable fractal signals. This analysis is to complement previous investigations of fractal characteristics in healthy and pathological gait stride interval time series, with which this study is compared. Results The results of our analysis showed that the averaged wavelet coefficient method consistently yielded the most accurate results. Comparison with Existing Methods: Class dependent methods proved to be unsuitable for physiological time series. Detrended fluctuation analysis as most prevailing method in the literature exhibited large estimation variances. Conclusions The comparative numerical analysis and experimental applications provide a thorough basis for determining an appropriate and robust method for measuring and comparing a physiologically meaningful biomarker, the spectral index β. In consideration of the constraints of application, we note the significant drawbacks of detrended fluctuation analysis and conclude that the averaged wavelet coefficient method can provide reasonable consistency and accuracy for characterizing these fractal time series. PMID:24200509

A comparative analysis of spectral exponent estimation techniques for 1/f(β) processes with applications to the analysis of stride interval time series.

PubMed

Schaefer, Alexander; Brach, Jennifer S; Perera, Subashan; Sejdić, Ervin

2014-01-30

The time evolution and complex interactions of many nonlinear systems, such as in the human body, result in fractal types of parameter outcomes that exhibit self similarity over long time scales by a power law in the frequency spectrum S(f)=1/f(β). The scaling exponent β is thus often interpreted as a "biomarker" of relative health and decline. This paper presents a thorough comparative numerical analysis of fractal characterization techniques with specific consideration given to experimentally measured gait stride interval time series. The ideal fractal signals generated in the numerical analysis are constrained under varying lengths and biases indicative of a range of physiologically conceivable fractal signals. This analysis is to complement previous investigations of fractal characteristics in healthy and pathological gait stride interval time series, with which this study is compared. The results of our analysis showed that the averaged wavelet coefficient method consistently yielded the most accurate results. Class dependent methods proved to be unsuitable for physiological time series. Detrended fluctuation analysis as most prevailing method in the literature exhibited large estimation variances. The comparative numerical analysis and experimental applications provide a thorough basis for determining an appropriate and robust method for measuring and comparing a physiologically meaningful biomarker, the spectral index β. In consideration of the constraints of application, we note the significant drawbacks of detrended fluctuation analysis and conclude that the averaged wavelet coefficient method can provide reasonable consistency and accuracy for characterizing these fractal time series. Copyright © 2013 Elsevier B.V. All rights reserved.

Epileptic seizure classification of EEG time-series using rational discrete short-time fourier transform.

PubMed

Samiee, Kaveh; Kovács, Petér; Gabbouj, Moncef

2015-02-01

A system for epileptic seizure detection in electroencephalography (EEG) is described in this paper. One of the challenges is to distinguish rhythmic discharges from nonstationary patterns occurring during seizures. The proposed approach is based on an adaptive and localized time-frequency representation of EEG signals by means of rational functions. The corresponding rational discrete short-time Fourier transform (DSTFT) is a novel feature extraction technique for epileptic EEG data. A multilayer perceptron classifier is fed by the coefficients of the rational DSTFT in order to separate seizure epochs from seizure-free epochs. The effectiveness of the proposed method is compared with several state-of-art feature extraction algorithms used in offline epileptic seizure detection. The results of the comparative evaluations show that the proposed method outperforms competing techniques in terms of classification accuracy. In addition, it provides a compact representation of EEG time-series.

Analytically exploiting noise correlations inside the feedback loop to improve locked-oscillator performance.

PubMed

Sastrawan, J; Jones, C; Akhalwaya, I; Uys, H; Biercuk, M J

2016-08-01

We introduce concepts from optimal estimation to the stabilization of precision frequency standards limited by noisy local oscillators. We develop a theoretical framework casting various measures for frequency standard variance in terms of frequency-domain transfer functions, capturing the effects of feedback stabilization via a time series of Ramsey measurements. Using this framework, we introduce an optimized hybrid predictive feedforward measurement protocol that employs results from multiple past measurements and transfer-function-based calculations of measurement covariance to improve the accuracy of corrections within the feedback loop. In the presence of common non-Markovian noise processes these measurements will be correlated in a calculable manner, providing a means to capture the stochastic evolution of the local oscillator frequency during the measurement cycle. We present analytic calculations and numerical simulations of oscillator performance under competing feedback schemes and demonstrate benefits in both correction accuracy and long-term oscillator stability using hybrid feedforward. Simulations verify that in the presence of uncompensated dead time and noise with significant spectral weight near the inverse cycle time predictive feedforward outperforms traditional feedback, providing a path towards developing a class of stabilization software routines for frequency standards limited by noisy local oscillators.

Period and phase comparisons of near-decadal oscillations in solar, geomagnetic, and cosmic ray time series

NASA Astrophysics Data System (ADS)

Juckett, David A.

2001-09-01

A more complete understanding of the periodic dynamics of the Sun requires continued exploration of non-11-year oscillations in addition to the benchmark 11-year sunspot cycle. In this regard, several solar, geomagnetic, and cosmic ray time series were examined to identify common spectral components and their relative phase relationships. Several non-11-year oscillations were identified within the near-decadal range with periods of ~8, 10, 12, 15, 18, 22, and 29 years. To test whether these frequency components were simply low-level noise or were related to a common source, the phases were extracted for each component in each series. The phases were nearly identical across the solar and geomagnetic series, while the corresponding components in four cosmic ray surrogate series exhibited inverted phases, similar to the known phase relationship with the 11-year sunspot cycle. Cluster analysis revealed that this pattern was unlikely to occur by chance. It was concluded that many non-11-year oscillations truly exist in the solar dynamical environment and that these contribute to the complex variations observed in geomagnetic and cosmic ray time series. Using the different energy sensitivities of the four cosmic ray surrogate series, a preliminary indication of the relative intensities of the various solar-induced oscillations was observed. It provides evidence that many of the non-11-year oscillations result from weak interplanetary magnetic field/solar wind oscillations that originate from corresponding variations in the open-field regions of the Sun.

Preliminary comparative assessment of PM10 hourly measurement results from new monitoring stations type using stochastic and exploratory methodology and models

NASA Astrophysics Data System (ADS)

Czechowski, Piotr Oskar; Owczarek, Tomasz; Badyda, Artur; Majewski, Grzegorz; Rogulski, Mariusz; Ogrodnik, Paweł

2018-01-01

The paper presents selected preliminary stage key issues proposed extended equivalence measurement results assessment for new portable devices - the comparability PM10 concentration results hourly series with reference station measurement results with statistical methods. In article presented new portable meters technical aspects. The emphasis was placed on the comparability the results using the stochastic and exploratory methods methodology concept. The concept is based on notice that results series simple comparability in the time domain is insufficient. The comparison of regularity should be done in three complementary fields of statistical modeling: time, frequency and space. The proposal is based on model's results of five annual series measurement results new mobile devices and WIOS (Provincial Environmental Protection Inspectorate) reference station located in Nowy Sacz city. The obtained results indicate both the comparison methodology completeness and the high correspondence obtained new measurements results devices with reference.

On the dynamical behaviour of low-frequency earthquake swarms prior to a dome collapse of Soufrière Hill volcano, Montserrat

NASA Astrophysics Data System (ADS)

Hammer, C.; Neuberg, J. W.

2009-03-01

A series of low-frequency earthquake swarms prior to a dome collapse on Soufrière Hills volcano, Montserrat, are investigated with the emphasis on event rate and amplitude behaviour. In a single swarm, the amplitudes of consecutive events tend to increase with time, while the rate of event occurrence accelerates initially and then decelerates toward the end of the swarm. However, when consecutive swarms are considered, the average event rates seem to follow the material failure law, and the time of the dome collapse can be successfully estimated using the inverse event rate. These patterns in amplitude and event rate are interpreted as fluctuations in magma ascent velocity, which result in both the generation of low-frequency events as well as cyclic ground deformation accompanying the swarm activity.

The French Atlantic Littoral and the Massif Armoricain. [Bay of Biscay, France and Spain

NASA Technical Reports Server (NTRS)

Verger, F. (Principal Investigator); Monget, J. M.; Scanvic, J. Y.

1976-01-01

The author has identified the following significant results. Diachronic use of LANDSAT data time series will in time allow study of statistically submerged frequencies in tidal areas. This is an essential element of coastal geomorphology and of coastal zone management, being particularly useful in siting shellfish farms. Maps at useable scales and simple user oriented legends should become an essential document for coastal planning agencies.

Dielectric and structural characterisation of chalcogenide glasses via terahertz time-domain spectroscopy

NASA Astrophysics Data System (ADS)

Ravagli, A.; Naftaly, M.; Craig, C.; Weatherby, E.; Hewak, D. W.

2017-07-01

Terahertz time-domain spectroscopy (THz TDS) was used to investigate a series of chalcogenide glasses. In particular, the dielectric properties at terahertz frequencies were determined and correlated with the glass composition. The experimental results showed a strong relationship between the dielectric properties and the polarizability of the glasses studied. A new explanation based on the coordination number of the metallic cations was proposed to understand these observations.

A time-series method for automated measurement of changes in mitotic and interphase duration from time-lapse movies.

PubMed

Sigoillot, Frederic D; Huckins, Jeremy F; Li, Fuhai; Zhou, Xiaobo; Wong, Stephen T C; King, Randall W

2011-01-01

Automated time-lapse microscopy can visualize proliferation of large numbers of individual cells, enabling accurate measurement of the frequency of cell division and the duration of interphase and mitosis. However, extraction of quantitative information by manual inspection of time-lapse movies is too time-consuming to be useful for analysis of large experiments. Here we present an automated time-series approach that can measure changes in the duration of mitosis and interphase in individual cells expressing fluorescent histone 2B. The approach requires analysis of only 2 features, nuclear area and average intensity. Compared to supervised learning approaches, this method reduces processing time and does not require generation of training data sets. We demonstrate that this method is as sensitive as manual analysis in identifying small changes in interphase or mitotic duration induced by drug or siRNA treatment. This approach should facilitate automated analysis of high-throughput time-lapse data sets to identify small molecules or gene products that influence timing of cell division.

Two Point Space-Time Correlation of Density Fluctuations Measured in High Velocity Free Jets

NASA Technical Reports Server (NTRS)

Panda, Jayanta

2006-01-01

Two-point space-time correlations of air density fluctuations in unheated, fully-expanded free jets at Mach numbers M(sub j) = 0.95, 1.4, and 1.8 were measured using a Rayleigh scattering based diagnostic technique. The molecular scattered light from two small probe volumes of 1.03 mm length was measured for a completely non-intrusive means of determining the turbulent density fluctuations. The time series of density fluctuations were analyzed to estimate the integral length scale L in a moving frame of reference and the convective Mach number M(sub c) at different narrow Strouhal frequency (St) bands. It was observed that M(sub c) and the normalized moving frame length scale L*St/D, where D is the jet diameter, increased with Strouhal frequency before leveling off at the highest resolved frequency. Significant differences were observed between data obtained from the lip shear layer and the centerline of the jet. The wave number frequency transform of the correlation data demonstrated progressive increase in the radiative part of turbulence fluctuations with increasing jet Mach number.

A laboratory validation study of the time-lapse oscillatory pumping test concept for leakage detection in geological repositories

NASA Astrophysics Data System (ADS)

Sun, A. Y.; Islam, A.; Lu, J.

2017-12-01

Time-lapse oscillatory pumping test (OPT) has been introduced recently as a pressure-based monitoring technique for detecting potential leakage in geologic repositories. By routinely conducting OPT at a number of pulsing frequencies, a site operator may identify the potential anomalies in the frequency domain, alleviating the ambiguity caused by reservoir noise and improving the signal-to-noise ratio. Building on previous theoretical and field studies, this work performed a series of laboratory experiments to validate the concept of time-lapse OPT using a custom made, stainless steel tank under relatively high pressures ( 120psi). The experimental configuration simulates a miniature geologic storage repository consisting of three layers (i.e., injection zone, caprock, and above-zone aquifer). Results show that leakage in the injection zone led to deviations in the power spectrum of observed pressure data, and the amplitude of which also increases with decreasing pulsing frequencies. The experimental results were further analyzed by developing a 3D flow model, using which the model parameters were estimated through frequency domain inversion.

Radiation safety in the cardiac catheterization lab: A time series quality improvement initiative.

PubMed

Abuzeid, Wael; Abunassar, Joseph; Leis, Jerome A; Tang, Vicky; Wong, Brian; Ko, Dennis T; Wijeysundera, Harindra C

Interventional cardiologists have one of the highest annual radiation exposures yet systems of care that promote radiation safety in cardiac catheterization labs are lacking. This study sought to reduce the frequency of radiation exposure, for PCI procedures, above 1.5Gy in labs utilizing a Phillips system at our local institution by 40%, over a 12-month period. We performed a time series study to assess the impact of different interventions on the frequency of radiation exposure above 1.5Gy. Process measures were percent of procedures where collimation and magnification were used and percent of completion of online educational modules. Balancing measures were the mean number of cases performed and mean fluoroscopy time. Information sessions, online modules, policies and posters were implemented followed by the introduction of a new lab with a novel software (AlluraClarity©) to reduce radiation dose. There was a significant reduction (91%, p<0.05) in the frequency of radiation exposure above 1.5Gy after utilizing a novel software (AlluraClarity©) in a new Phillips lab. Process measures of use of collimation (95.0% to 98.0%), use of magnification (20.0% to 14.0%) and completion of online modules (62%) helped track implementation. The mean number of cases performed and mean fluoroscopy time did not change significantly. While educational strategies had limited impact on reducing radiation exposure, implementing a novel software system provided the most effective means of reducing radiation exposure. Crown Copyright © 2017. Published by Elsevier Inc. All rights reserved.

[Gene method for inconsistent hydrological frequency calculation. 2: Diagnosis system of hydrological genes and method of hydrological moment genes with inconsistent characters].

PubMed

Xie, Ping; Zhao, Jiang Yan; Wu, Zi Yi; Sang, Yan Fang; Chen, Jie; Li, Bin Bin; Gu, Hai Ting

2018-04-01

The analysis of inconsistent hydrological series is one of the major problems that should be solved for engineering hydrological calculation in changing environment. In this study, the diffe-rences of non-consistency and non-stationarity were analyzed from the perspective of composition of hydrological series. The inconsistent hydrological phenomena were generalized into hydrological processes with inheritance, variability and evolution characteristics or regulations. Furthermore, the hydrological genes were identified following the theory of biological genes, while their inheritance bases and variability bases were determined based on composition of hydrological series under diffe-rent time scales. To identify and test the components of hydrological genes, we constructed a diagnosis system of hydrological genes. With the P-3 distribution as an example, we described the process of construction and expression of the moment genes to illustrate the inheritance, variability and evolution principles of hydrological genes. With the annual minimum 1-month runoff series of Yunjinghong station in Lancangjiang River basin as an example, we verified the feasibility and practicability of hydrological gene theory for the calculation of inconsistent hydrological frequency. The results showed that the method could be used to reveal the evolution of inconsistent hydrological series. Therefore, it provided a new research pathway for engineering hydrological calculation in changing environment and an essential reference for the assessment of water security.

Time series analysis of reported cases of hand, foot, and mouth disease from 2010 to 2013 in Wuhan, China.

PubMed

Chen, Banghua; Sumi, Ayako; Toyoda, Shin'ichi; Hu, Quan; Zhou, Dunjin; Mise, Keiji; Zhao, Junchan; Kobayashi, Nobumichi

2015-11-03

Hand, foot, and mouth disease (HFMD) is an infectious disease caused by a group of enteroviruses, including Coxsackievirus A16 (CVA16) and Enterovirus A71 (EV-A71). In recent decades, Asian countries have experienced frequent and widespread HFMD outbreaks, with deaths predominantly among children. In several Asian countries, epidemics usually peak in the late spring/early summer, with a second small peak in late autumn/early winter. We investigated the possible underlying association between the seasonality of HFMD epidemics and meteorological variables, which could improve our ability to predict HFMD epidemics. We used a time series analysis composed of a spectral analysis based on the maximum entropy method (MEM) in the frequency domain and the nonlinear least squares method in the time domain. The time series analysis was applied to three kinds of monthly time series data collected in Wuhan, China, where high-quality surveillance data for HFMD have been collected: (i) reported cases of HFMD, (ii) reported cases of EV-A71 and CVA16 detected in HFMD patients, and (iii) meteorological variables. In the power spectral densities for HFMD and EV-A71, the dominant spectral lines were observed at frequency positions corresponding to 1-year and 6-month cycles. The optimum least squares fitting (LSF) curves calculated for the 1-year and 6-month cycles reproduced the bimodal cycles that were clearly observed in the HFMD and EV-A71 data. The peak months on the LSF curves for the HFMD data were consistent with those for the EV-A71 data. The risk of infection was relatively high at 10 °C ≤ t < 15 °C (t, temperature [°C]) and 15 °C ≤ t < 20 °C, and peaked at 20 °C ≤ t < 25 °C. In this study, the HFMD infections occurring in Wuhan showed two seasonal peaks, in summer (June) and winter (November or December). The results obtained with a time series analysis suggest that the bimodal seasonal peaks in HFMD epidemics are attributable to EV-A71 epidemics. Our results suggest that controlling the spread of EV-A71 infections when the temperature is approximately 20-25 °C should be considered to prevent HFMD infections in Wuhan, China.

A Millennial-length Reconstruction of the Western Pacific Pattern with Associated Paleoclimate

NASA Astrophysics Data System (ADS)

Wright, W. E.; Guan, B. T.; Wei, K.

2010-12-01

The Western Pacific Pattern (WP) is a lesser known 500 hPa pressure pattern similar to the NAO or PNA. As defined, the poles of the WP index are centered on 60°N over the Kamchatka peninsula and the neighboring Pacific and on 32.5°N over the western north Pacific. However, the area of influence for the southern half of the dipole includes a wide swath from East Asia, across Taiwan, through the Philippine Sea, to the western north Pacific. Tree rings of Taiwanese Chamaecyparis obtusa var. formosana in this extended region show significant correlation with the WP, and with local temperature. The WP is also significantly correlated with atmospheric temperatures over Taiwan, especially at 850hPa and 700 hPa, pressure levels that bracket the tree site. Spectral analysis indicates that variations in the WP occur at relatively high frequency, with most power at less than 5 years. Simple linear regression against high frequency variants of the tree-ring chronology yielded the most significant correlation coefficients. Two reconstructions are presented. The first uses a tree-ring time series produced as the first intrinsic mode function (IMF) from an Ensemble Empirical Mode Decomposition (EEMD), based on the Hilbert-Huang Transform. The significance of the regression using the EEMD-derived time series was much more significant than time series produced using traditional high pass filtering. The second also uses the first IMF of a tree-ring time series, but the dataset was first sorted and partitioned at a specified quantile prior to EEMD decomposition, with the mean of the partitioned data forming the input to the EEMD. The partitioning was done to filter out the less climatically sensitive tree rings, a common problem with shade tolerant trees. Time series statistics indicate that the first reconstruction is reliable to 1241 of the Common Era. Reliability of the second reconstruction is dependent on the development of statistics related to the quantile partitioning, and the consequent reduction in sample depth. However, the correlation coefficients from regressions over the instrumental period greatly exceed those from any other method of chronology generation, and so the technique holds promise. Additional atmospheric parameters having significant correlations against the WPO and tree ring time series with similar spatial patterns are also presented. These include vertical wind shear (850hPa-700hPa) over the northern Philippines and the Philippine Sea, surface Omega and 850hPa v-winds over the East China Sea, Japan and Taiwan. Possible links to changes in the subtropical jet stream will also be discussed.

Space-Time Localization of Plasma Turbulence Using Multiple Spacecraft Radio Links

NASA Technical Reports Server (NTRS)

Armstrong, John W.; Estabrook, Frank B.

2011-01-01

Space weather is described as the variability of solar wind plasma that can disturb satellites and systems and affect human space exploration. Accurate prediction requires information of the heliosphere inside the orbit of the Earth. However, for predictions using remote sensing, one needs not only plane-of-sky position but also range information the third spatial dimension to show the distance to the plasma disturbances and thus when they might propagate or co-rotate to create disturbances at the orbit of the Earth. Appropriately processed radio signals from spacecraft having communications lines-of-sight passing through the inner heliosphere can be used for this spacetime localization of plasma disturbances. The solar plasma has an electron density- and radio-wavelength-dependent index of refraction. An approximately monochromatic wave propagating through a thin layer of plasma turbulence causes a geometrical-optics phase shift proportional to the electron density at the point of passage, the radio wavelength, and the thickness of the layer. This phase shift is the same for a wave propagating either up or down through the layer at the point of passage. This attribute can be used for space-time localization of plasma irregularities. The transfer function of plasma irregularities to the observed time series depends on the Doppler tracking mode. When spacecraft observations are in the two-way mode (downlink radio signal phase-locked to an uplink radio transmission), plasma fluctuations have a two-pulse response in the Doppler. In the two-way mode, the Doppler time series y2(t) is the difference between the frequency of the downlink signal received and the frequency of a ground reference oscillator. A plasma blob localized at a distance x along the line of sight perturbs the phase on both the up and down link, giving rise to two events in the two-way tracking time series separated by a time lag depending the blob s distance from the Earth: T2-2x/c, where T2 is the two-way time-of-flight of radio waves to/from the spacecraft and c is the speed of light. In some tracking situations, more information is available. For example, with the 5-link Cassini radio system, the plasma contribution to the up and down links, y(sub up)(t) and y(sub dn)(t), can be computed separately. The times series y(sub up)(t) and y(sub dn)(t) respond to a localized plasma blob with one event in each time series. These events are also separated in time by T2-2x/c. By cross-correlating the up and down link Doppler time series, the time separation of the plasma events can be measured and hence the plasma blob s distance from the Earth determined. Since the plane-of-sky position is known, this technique allows localization of plasma events in time and three space dimensions.

Novel covariance-based neutrality test of time-series data reveals asymmetries in ecological and economic systems

DOE PAGES

Washburne, Alex D.; Burby, Joshua W.; Lacker, Daniel; ...

2016-09-30

Systems as diverse as the interacting species in a community, alleles at a genetic locus, and companies in a market are characterized by competition (over resources, space, capital, etc) and adaptation. Neutral theory, built around the hypothesis that individual performance is independent of group membership, has found utility across the disciplines of ecology, population genetics, and economics, both because of the success of the neutral hypothesis in predicting system properties and because deviations from these predictions provide information about the underlying dynamics. However, most tests of neutrality are weak, based on static system properties such as species-abundance distributions or themore » number of singletons in a sample. Time-series data provide a window onto a system’s dynamics, and should furnish tests of the neutral hypothesis that are more powerful to detect deviations from neutrality and more informative about to the type of competitive asymmetry that drives the deviation. Here, we present a neutrality test for time-series data. We apply this test to several microbial time-series and financial time-series and find that most of these systems are not neutral. Our test isolates the covariance structure of neutral competition, thus facilitating further exploration of the nature of asymmetry in the covariance structure of competitive systems. Much like neutrality tests from population genetics that use relative abundance distributions have enabled researchers to scan entire genomes for genes under selection, we anticipate our time-series test will be useful for quick significance tests of neutrality across a range of ecological, economic, and sociological systems for which time-series data are available. Here, future work can use our test to categorize and compare the dynamic fingerprints of particular competitive asymmetries (frequency dependence, volatility smiles, etc) to improve forecasting and management of complex adaptive systems.« less

Novel covariance-based neutrality test of time-series data reveals asymmetries in ecological and economic systems

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

Washburne, Alex D.; Burby, Joshua W.; Lacker, Daniel

Systems as diverse as the interacting species in a community, alleles at a genetic locus, and companies in a market are characterized by competition (over resources, space, capital, etc) and adaptation. Neutral theory, built around the hypothesis that individual performance is independent of group membership, has found utility across the disciplines of ecology, population genetics, and economics, both because of the success of the neutral hypothesis in predicting system properties and because deviations from these predictions provide information about the underlying dynamics. However, most tests of neutrality are weak, based on static system properties such as species-abundance distributions or themore » number of singletons in a sample. Time-series data provide a window onto a system’s dynamics, and should furnish tests of the neutral hypothesis that are more powerful to detect deviations from neutrality and more informative about to the type of competitive asymmetry that drives the deviation. Here, we present a neutrality test for time-series data. We apply this test to several microbial time-series and financial time-series and find that most of these systems are not neutral. Our test isolates the covariance structure of neutral competition, thus facilitating further exploration of the nature of asymmetry in the covariance structure of competitive systems. Much like neutrality tests from population genetics that use relative abundance distributions have enabled researchers to scan entire genomes for genes under selection, we anticipate our time-series test will be useful for quick significance tests of neutrality across a range of ecological, economic, and sociological systems for which time-series data are available. Here, future work can use our test to categorize and compare the dynamic fingerprints of particular competitive asymmetries (frequency dependence, volatility smiles, etc) to improve forecasting and management of complex adaptive systems.« less

Flood frequency analysis for a braided river catchment in New Zealand: Comparing annual maximum and partial duration series with varying record lengths

NASA Astrophysics Data System (ADS)

Nagy, B. K.; Mohssen, M.; Hughey, K. F. D.

2017-04-01

This study addresses technical questions concerning the use of the partial duration series (PDS) within the domain of flood frequency analysis. The recurring questions which often prevent the standardised use of the PDS are peak independence and threshold selection. This paper explores standardised approaches to peak and threshold selection to produce PDS samples with differing average annual exceedances, using six theoretical probability distributions. The availability of historical annual maximum (AMS) data (1930-1966) in addition to systemic AMS data (1967-2015) enables a unique comparison between the performance of the PDS sample and the systemic AMS sample. A recently derived formula for the translation of the PDS into the annual domain, simplifying the use of the PDS, is utilised in an applied case study for the first time. Overall, the study shows that PDS sampling returns flood magnitudes similar to those produced by AMS series utilising historical data and thus the use of the PDS should be preferred in cases where historical flood data is unavailable.

The Chernobyl accident, congenital anomalies and other reproductive outcomes.

PubMed

Little, J

1993-04-01

Studies of the association between the Chernobyl accident in April 1986 and reproductive outcome, with particular reference to congenital anomalies, are reviewed. All of the studies so far have been based on the detection of a change in frequency over time. An increased frequency of trisomy 21 in the former West Berlin in January 1987, and increases in the frequency of neural tube defects in several small hospital-based series in Turkey, are not confirmed in larger and more representative series in Europe. No clear changes in the prevalence at birth of anomalies which might be associated with the accident are apparent in Byelorussia or the Ukraine, the republics with the highest exposure to fallout. However, these data are difficult to interpret as the methods of acquisition have not been described and they have not yet been reported in full. Thus, there is no consistent evidence of a detrimental physical effect of the Chernobyl accident on congenital anomalies. This is also the case for other measured outcomes of pregnancy. There is evidence of indirect effects--an increase in induced abortions substantial enough to show as a reduction in total births, due to anxieties created. Data are not available on the reproductive outcomes of women pregnant at the time of the accident who were evacuated from the 30 km zone of immediate contamination, of workers in the plant at the time of the accident or of decontamination workers. Moreover, no data are available from several of the other countries closest to the Chernobyl area.

Microelectromechanical filter formed from parallel-connected lattice networks of contour-mode resonators

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

Wojciechowski, Kenneth E; Olsson, III, Roy H; Ziaei-Moayyed, Maryam

2013-07-30

A microelectromechanical (MEM) filter is disclosed which has a plurality of lattice networks formed on a substrate and electrically connected together in parallel. Each lattice network has a series resonant frequency and a shunt resonant frequency provided by one or more contour-mode resonators in the lattice network. Different types of contour-mode resonators including single input, single output resonators, differential resonators, balun resonators, and ring resonators can be used in MEM filter. The MEM filter can have a center frequency in the range of 10 MHz-10 GHz, with a filter bandwidth of up to about 1% when all of the latticemore » networks have the same series resonant frequency and the same shunt resonant frequency. The filter bandwidth can be increased up to about 5% by using unique series and shunt resonant frequencies for the lattice networks.« less

FREQUENCY DISTRIBUTIONS AND SPATIAL ANALYSIS OF FINE PARTICLE MEASUREMENTS IN ST. LOUIS DURING THE REGIONAL AIR POLLUTION STUDY/REGIONAL AIR MONITORING SYSTEM

EPA Science Inventory

Community, time-series epidemiology typically uses either 24-hour integrated particulate matter (PM) concentrations averaged across several monitors in a city or data obtained at a central monitoring site to relate PM concentrations to human health effects. If 24-hour integrated...

Atmospheric dust events in Central Asia: Relationship to wind, soil type, and land use

USDA-ARS?s Scientific Manuscript database

Xinjiang Province is one of the most important source regions of atmospheric dust in China. Spatial-temporal characteristics of dust events in the region were investigated by time series analysis of annual dust event frequency and meteorological data collected at 101 stations in Xinjiang Province fr...

Exploratory Causal Analysis in Bivariate Time Series Data

NASA Astrophysics Data System (ADS)

McCracken, James M.

Many scientific disciplines rely on observational data of systems for which it is difficult (or impossible) to implement controlled experiments and data analysis techniques are required for identifying causal information and relationships directly from observational data. This need has lead to the development of many different time series causality approaches and tools including transfer entropy, convergent cross-mapping (CCM), and Granger causality statistics. In this thesis, the existing time series causality method of CCM is extended by introducing a new method called pairwise asymmetric inference (PAI). It is found that CCM may provide counter-intuitive causal inferences for simple dynamics with strong intuitive notions of causality, and the CCM causal inference can be a function of physical parameters that are seemingly unrelated to the existence of a driving relationship in the system. For example, a CCM causal inference might alternate between ''voltage drives current'' and ''current drives voltage'' as the frequency of the voltage signal is changed in a series circuit with a single resistor and inductor. PAI is introduced to address both of these limitations. Many of the current approaches in the times series causality literature are not computationally straightforward to apply, do not follow directly from assumptions of probabilistic causality, depend on assumed models for the time series generating process, or rely on embedding procedures. A new approach, called causal leaning, is introduced in this work to avoid these issues. The leaning is found to provide causal inferences that agree with intuition for both simple systems and more complicated empirical examples, including space weather data sets. The leaning may provide a clearer interpretation of the results than those from existing time series causality tools. A practicing analyst can explore the literature to find many proposals for identifying drivers and causal connections in times series data sets, but little research exists of how these tools compare to each other in practice. This work introduces and defines exploratory causal analysis (ECA) to address this issue along with the concept of data causality in the taxonomy of causal studies introduced in this work. The motivation is to provide a framework for exploring potential causal structures in time series data sets. ECA is used on several synthetic and empirical data sets, and it is found that all of the tested time series causality tools agree with each other (and intuitive notions of causality) for many simple systems but can provide conflicting causal inferences for more complicated systems. It is proposed that such disagreements between different time series causality tools during ECA might provide deeper insight into the data than could be found otherwise.

On the influence of solar activity on the mid-latitude sporadic E layer

NASA Astrophysics Data System (ADS)

Pezzopane, Michael; Pignalberi, Alessio; Pietrella, Marco

2015-09-01

To investigate the influence of solar cycle variability on the sporadic E layer (Es), hourly measurements of the critical frequency of the Es ordinary mode of propagation, foEs, and of the blanketing frequency of the Es layer, fbEs, recorded from January 1976 to December 2009 at the Rome (Italy) ionospheric station (41.8° N, 12.5° E), were examined. The results are: (1) a high positive correlation between the F10.7 solar index and foEs as well as between F10.7 and fbEs, both for the whole data set and for each solar cycle separately, the correlation between F10.7 and fbEs being much higher than the one between F10.7 and foEs; (2) a decreasing long-term trend of the F10.7, foEs and fbEs time series, with foEs decreasing more rapidly than F10.7 and fbEs; (3) clear and statistically significant peaks at 11 years in the foEs and fbEs time series, inferred from Lomb-Scargle periodograms.

Novel approaches to estimating the turbulent kinetic energy dissipation rate from low- and moderate-resolution velocity fluctuation time series

NASA Astrophysics Data System (ADS)

Wacławczyk, Marta; Ma, Yong-Feng; Kopeć, Jacek M.; Malinowski, Szymon P.

2017-11-01

In this paper we propose two approaches to estimating the turbulent kinetic energy (TKE) dissipation rate, based on the zero-crossing method by Sreenivasan et al. (1983). The original formulation requires a fine resolution of the measured signal, down to the smallest dissipative scales. However, due to finite sampling frequency, as well as measurement errors, velocity time series obtained from airborne experiments are characterized by the presence of effective spectral cutoffs. In contrast to the original formulation the new approaches are suitable for use with signals originating from airborne experiments. The suitability of the new approaches is tested using measurement data obtained during the Physics of Stratocumulus Top (POST) airborne research campaign as well as synthetic turbulence data. They appear useful and complementary to existing methods. We show the number-of-crossings-based approaches respond differently to errors due to finite sampling and finite averaging than the classical power spectral method. Hence, their application for the case of short signals and small sampling frequencies is particularly interesting, as it can increase the robustness of turbulent kinetic energy dissipation rate retrieval.

Examination of time series through randomly broken windows. [space observations from earth

NASA Technical Reports Server (NTRS)

Sturrock, P. A.

1980-01-01

The effect of irregular interruption of data collection (the breaking of the window function) on the spectrum determination of a time series is investigated. It is assumed that there is a uniform probability p that each interval of length tau, of the total interval of length T = tau, yields no data. For the linear case (Fourier transform) it is found that the noise to signal ratio has a (one sigma) value less than epsilon if N exceeds p(-1) (1-p) epsilon (-2). For the quadratic case, the same requirement is met by the less restrictive requirement that N exceed p(-1) (1-p) epsilon (-1). It appears that, if four observatories spaced around the Earth were to operate for 25 days, each for six hours a day (N = 100), and if the probability of cloud cover at any site on any day is 20% (p = 0.8), the r.m.s. noise to signal ratio is 0.25% for frequencies displaced from a sharp strong signal by 15 micro Hz. The noise to signal ratio drops off rapidly if the frequency offset exceeds 15 micro Hz.

A Novel Multilevel-SVD Method to Improve Multistep Ahead Forecasting in Traffic Accidents Domain.

PubMed

Barba, Lida; Rodríguez, Nibaldo

2017-01-01

Here is proposed a novel method for decomposing a nonstationary time series in components of low and high frequency. The method is based on Multilevel Singular Value Decomposition (MSVD) of a Hankel matrix. The decomposition is used to improve the forecasting accuracy of Multiple Input Multiple Output (MIMO) linear and nonlinear models. Three time series coming from traffic accidents domain are used. They represent the number of persons with injuries in traffic accidents of Santiago, Chile. The data were continuously collected by the Chilean Police and were weekly sampled from 2000:1 to 2014:12. The performance of MSVD is compared with the decomposition in components of low and high frequency of a commonly accepted method based on Stationary Wavelet Transform (SWT). SWT in conjunction with the Autoregressive model (SWT + MIMO-AR) and SWT in conjunction with an Autoregressive Neural Network (SWT + MIMO-ANN) were evaluated. The empirical results have shown that the best accuracy was achieved by the forecasting model based on the proposed decomposition method MSVD, in comparison with the forecasting models based on SWT.

A Novel Multilevel-SVD Method to Improve Multistep Ahead Forecasting in Traffic Accidents Domain

PubMed Central

Rodríguez, Nibaldo

2017-01-01

Here is proposed a novel method for decomposing a nonstationary time series in components of low and high frequency. The method is based on Multilevel Singular Value Decomposition (MSVD) of a Hankel matrix. The decomposition is used to improve the forecasting accuracy of Multiple Input Multiple Output (MIMO) linear and nonlinear models. Three time series coming from traffic accidents domain are used. They represent the number of persons with injuries in traffic accidents of Santiago, Chile. The data were continuously collected by the Chilean Police and were weekly sampled from 2000:1 to 2014:12. The performance of MSVD is compared with the decomposition in components of low and high frequency of a commonly accepted method based on Stationary Wavelet Transform (SWT). SWT in conjunction with the Autoregressive model (SWT + MIMO-AR) and SWT in conjunction with an Autoregressive Neural Network (SWT + MIMO-ANN) were evaluated. The empirical results have shown that the best accuracy was achieved by the forecasting model based on the proposed decomposition method MSVD, in comparison with the forecasting models based on SWT. PMID:28261267

Concurrent identification of aero-acoustic scattering and noise sources at a flow duct singularity in low Mach number flow

NASA Astrophysics Data System (ADS)

Sovardi, Carlo; Jaensch, Stefan; Polifke, Wolfgang

2016-09-01

A numerical method to concurrently characterize both aeroacoustic scattering and noise sources at a duct singularity is presented. This approach combines Large Eddy Simulation (LES) with techniques of System Identification (SI): In a first step, a highly resolved LES with external broadband acoustic excitation is carried out. Subsequently, time series data extracted from the LES are post-processed by means of SI to model both acoustic propagation and noise generation. The present work studies the aero-acoustic characteristics of an orifice placed in a duct at low flow Mach numbers with the "LES-SI" method. Parametric SI based on the Box-Jenkins mathematical structure is employed, with a prediction error approach that utilizes correlation analysis of the output residuals to avoid overfitting. Uncertainties of model parameters due to the finite length of times series are quantified in terms of confidence intervals. Numerical results for acoustic scattering matrices and power spectral densities of broad-band noise are validated against experimental measurements over a wide range of frequencies below the cut-off frequency of the duct.

Quest for secondary μSR signals for Fe3O4 using MaxEnt: a Verwey phase transition study.

NASA Astrophysics Data System (ADS)

Boekema, C.; Colebaugh, A.; Lee, A.-L.; Lin, I.; Cabot, A.; Morante, C.

Most muon-spin rotation (μSR) time series for magnetite (Fe3O4) have been interpreted in terms of one μSR frequency signal. Its Fourier transform appears to confirm this internal magnetic field. Yet many time series show a beat pattern, strongly suggesting a second signal with a close-by frequency. We are searching for secondary signals in zero-field Fe3O4 μ SR data using Maximum Entropy, a recently developed technique more sensitive than curve fitting and/or Fourier transformation. There is also another dilemma namely: the upper signal found for Fe3O4 has a local magnetic field larger than the maximum allowable vectorial sum of external and internal contributions. However, the (non)occurrence of secondary signals may shed light on the nature of the Verwey phase transition and its precursors in the Fe3O4 Mott-Wigner glass between Tv (123 K) and twice Tv (247 K). Research supported by LANL-DOE, SETI-NASA, SJSU & AFC.

Study of a sample of faint Be stars in the exofield of CoRoT. II. Pulsation and outburst events: Time series analysis of photometric variations

NASA Astrophysics Data System (ADS)

Semaan, T.; Hubert, A. M.; Zorec, J.; Gutiérrez-Soto, J.; Frémat, Y.; Martayan, C.; Fabregat, J.; Eggenberger, P.

2018-06-01

Context. The class of Be stars are the epitome of rapid rotators in the main sequence. These stars are privileged candidates for studying the incidence of rotation on the stellar internal structure and on non-radial pulsations. Pulsations are considered possible mechanisms to trigger mass-ejection phenomena required to build up the circumstellar disks of Be stars. Aims: Time series analyses of the light curves of 15 faint Be stars observed with the CoRoT satellite were performed to obtain the distribution of non-radial pulsation (NRP) frequencies in their power spectra at epochs with and without light outbursts and to discriminate pulsations from rotation-related photometric variations. Methods: Standard Fourier techniques were employed to analyze the CoRoT light curves. Fundamental parameters corrected for rapid-rotation effects were used to study the power spectrum as a function of the stellar location in the instability domains of the Hertzsprung-Russell (H-R) diagram. Results: Frequencies are concentrated in separate groups as predicted for g-modes in rapid B-type rotators, except for the two stars that are outside the H-R instability domain. In five objects the variations in the power spectrum are correlated with the time-dependent outbursts characteristics. Time-frequency analysis showed that during the outbursts the amplitudes of stable main frequencies within 0.03 c d-1 intervals strongly change, while transients and/or frequencies of low amplitude appear separated or not separated from the stellar frequencies. The frequency patterns and activities depend on evolution phases: (i) the average separations between groups of frequencies are larger in the zero-age main sequence (ZAMS) than in the terminal age main sequence (TAMS) and are the largest in the middle of the MS phase; (ii) a poor frequency spectrum with f ≲ 1 cd-1 of low amplitude characterizes the stars beyond the TAMS; and (iii) outbursts are seen in stars hotter than B4 spectral type and in the second half of the MS. Conclusions: The two main frequency groups are separated by δf = (1.24 ± 0.28) × frot in agreement with models of prograde sectoral g-modes (m = -1, -2) of intermediate-mass rapid rotators. The changes of amplitudes of individual frequencies and the presence of transients correlated with the outburst events deserve further studies of physical conditions in the subatmospheric layers to establish the relationship between pulsations and sporadic mass-ejection events. Tables 7 to 22 are only available at the CDS via anonymous ftp to http://cdsarc.u-strasbg.fr (ftp://130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/613/A70

Wavelet-based time series bootstrap model for multidecadal streamflow simulation using climate indicators

NASA Astrophysics Data System (ADS)

Erkyihun, Solomon Tassew; Rajagopalan, Balaji; Zagona, Edith; Lall, Upmanu; Nowak, Kenneth

2016-05-01

A model to generate stochastic streamflow projections conditioned on quasi-oscillatory climate indices such as Pacific Decadal Oscillation (PDO) and Atlantic Multi-decadal Oscillation (AMO) is presented. Recognizing that each climate index has underlying band-limited components that contribute most of the energy of the signals, we first pursue a wavelet decomposition of the signals to identify and reconstruct these features from annually resolved historical data and proxy based paleoreconstructions of each climate index covering the period from 1650 to 2012. A K-Nearest Neighbor block bootstrap approach is then developed to simulate the total signal of each of these climate index series while preserving its time-frequency structure and marginal distributions. Finally, given the simulated climate signal time series, a K-Nearest Neighbor bootstrap is used to simulate annual streamflow series conditional on the joint state space defined by the simulated climate index for each year. We demonstrate this method by applying it to simulation of streamflow at Lees Ferry gauge on the Colorado River using indices of two large scale climate forcings: Pacific Decadal Oscillation (PDO) and Atlantic Multi-decadal Oscillation (AMO), which are known to modulate the Colorado River Basin (CRB) hydrology at multidecadal time scales. Skill in stochastic simulation of multidecadal projections of flow using this approach is demonstrated.

Solar modulation of flood frequency in Central Europe during spring and summer on inter-annual to millennial time-scales

NASA Astrophysics Data System (ADS)

Czymzik, M.; Muscheler, R.; Brauer, A.

2015-10-01

Solar influences on climate variability are one of the most controversially discussed topics in climate research. We analyze solar forcing of flood frequency in Central Europe on inter-annual to millennial time-scales using daily discharge data of River Ammer (southern Germany) back to AD 1926 and revisiting the 5500 year flood layer time-series from varved sediments of the downstream Lake Ammersee. Flood frequency in the discharge record is significantly correlated to changes in solar activity during solar cycles 16-23 (r = -0.47, p < 0.0001, n = 73). Flood layer frequency (n = 1501) in the sediment record depicts distinct multi-decadal variability and significant correlations to 10Be fluxes from a Greenland ice core (r = 0.45, p < 0.0001) and 14C production rates (r =0.36, p < 0.0001), proxy records of solar activity. Flood frequency is higher when solar activity is reduced. These correlations between flood frequency and solar activity might provide empirical support for the solar top-down mechanism expected to modify the mid-latitude storm tracks over Europe by model studies. A lag of flood frequency responses in the Ammer discharge record to changes in solar activity of about one to three years could be explained by a modelled ocean-atmosphere feedback delaying the atmospheric reaction to solar activity variations up to a few years.

Complexity in congestive heart failure: A time-frequency approach

NASA Astrophysics Data System (ADS)

Banerjee, Santo; Palit, Sanjay K.; Mukherjee, Sayan; Ariffin, MRK; Rondoni, Lamberto

2016-03-01

Reconstruction of phase space is an effective method to quantify the dynamics of a signal or a time series. Various phase space reconstruction techniques have been investigated. However, there are some issues on the optimal reconstructions and the best possible choice of the reconstruction parameters. This research introduces the idea of gradient cross recurrence (GCR) and mean gradient cross recurrence density which shows that reconstructions in time frequency domain preserve more information about the dynamics than the optimal reconstructions in time domain. This analysis is further extended to ECG signals of normal and congestive heart failure patients. By using another newly introduced measure—gradient cross recurrence period density entropy, two classes of aforesaid ECG signals can be classified with a proper threshold. This analysis can be applied to quantifying and distinguishing biomedical and other nonlinear signals.

Comparison of ITRF2014 station coordinate input time series of DORIS, VLBI and GNSS

NASA Astrophysics Data System (ADS)

Tornatore, Vincenza; Tanır Kayıkçı, Emine; Roggero, Marco

2016-12-01

In this paper station coordinate time series from three space geodesy techniques that have contributed to the realization of the International Terrestrial Reference Frame 2014 (ITRF2014) are compared. In particular the height component time series extracted from official combined intra-technique solutions submitted for ITRF2014 by DORIS, VLBI and GNSS Combination Centers have been investigated. The main goal of this study is to assess the level of agreement among these three space geodetic techniques. A novel analytic method, modeling time series as discrete-time Markov processes, is presented and applied to the compared time series. The analysis method has proven to be particularly suited to obtain quasi-cyclostationary residuals which are an important property to carry out a reliable harmonic analysis. We looked for common signatures among the three techniques. Frequencies and amplitudes of the detected signals have been reported along with their percentage of incidence. Our comparison shows that two of the estimated signals, having one-year and 14 days periods, are common to all the techniques. Different hypotheses on the nature of the signal having a period of 14 days are presented. As a final check we have compared the estimated velocities and their standard deviations (STD) for the sites that co-located the VLBI, GNSS and DORIS stations, obtaining a good agreement among the three techniques both in the horizontal (1.0 mm/yr mean STD) and in the vertical (0.7 mm/yr mean STD) component, although some sites show larger STDs, mainly due to lack of data, different data spans or noisy observations.

(abstract) A Comparison Between Measurements of the F-layer Critical Frequency and Values Derived from the PRISM Adjustment Algorithm Applied to Total Electron Content Data in the Equatorial Region

NASA Technical Reports Server (NTRS)

Mannucci, A. J.; Anderson, D. N.; Abdu, A. M.

1994-01-01

The Parametrized Real-Time Ionosphere Specification Model (PRISM) is a global ionospheric specification model that can incorporate real-time data to compute accurate electron density profiles. Time series of computed and measured data are compared in this paper. This comparison can be used to suggest methods of optimizing the PRISM adjustment algorithm for TEC data obtained at low altitudes.

Are changes in drinking related to changes in cannabis use among Swedish adolescents? A time-series analysis for the period 1989-2016.

PubMed

Gripe, Isabella; Danielsson, Anna-Karin; Ramstedt, Mats

2018-04-21

To examine if changes in alcohol consumption are associated with changes in cannabis use among Swedish adolescents in a period of diverging trends, and to investigate if cannabis and alcohol act as complements or substitutes. Data comprise a nationally representative annual school survey of alcohol and drug habits among Swedish 9th-grade students (aged 15-16 years) covering years 1989-2016 (n = 149 603). Alcohol and cannabis use were measured concurrently and alcohol consumption was measured in litres of 100% alcohol per year. Frequency of cannabis use was transformed into a mean using category mid-points. Autoregressive integrated moving average (ARIMA) time-series analysis was used to estimate the association between cannabis and alcohol use. To elucidate changes in the association during the study period, two subperiods (2000-16 and 1989-99) were analysed. There was a positive and statistically significant association between changes in alcohol consumption and changes in frequency of cannabis use among cannabis users for the period 1989-2016. A 1-litre increase in mean alcohol consumption was associated with a 0.28 increase in frequency of cannabis use (P = 0.010). The corresponding increase for the period 1989-99 was 0.52 (P = 0.003). When restricting the analysis to 2000-16, the association was not statistically significant (P = 0.735). When analysing all adolescents we found no statistically significant association between changes in alcohol consumption and changes in frequency of cannabis use. From 1989 to 2016 there appears to be a positive association between alcohol and cannabis consumption among Swedish adolescents who use cannabis. This association seems to have become weaker over time, suggesting that alcohol and cannabis are neither substitutes nor complements among Swedish adolescents and that the recent decline in youth drinking is not associated with the increase in frequency of cannabis use. © 2018 Society for the Study of Addiction.

Method of frequency dependent correlations: investigating the variability of total solar irradiance

NASA Astrophysics Data System (ADS)

Pelt, J.; Käpylä, M. J.; Olspert, N.

2017-04-01

Context. This paper contributes to the field of modeling and hindcasting of the total solar irradiance (TSI) based on different proxy data that extend further back in time than the TSI that is measured from satellites. Aims: We introduce a simple method to analyze persistent frequency-dependent correlations (FDCs) between the time series and use these correlations to hindcast missing historical TSI values. We try to avoid arbitrary choices of the free parameters of the model by computing them using an optimization procedure. The method can be regarded as a general tool for pairs of data sets, where correlating and anticorrelating components can be separated into non-overlapping regions in frequency domain. Methods: Our method is based on low-pass and band-pass filtering with a Gaussian transfer function combined with de-trending and computation of envelope curves. Results: We find a major controversy between the historical proxies and satellite-measured targets: a large variance is detected between the low-frequency parts of targets, while the low-frequency proxy behavior of different measurement series is consistent with high precision. We also show that even though the rotational signal is not strongly manifested in the targets and proxies, it becomes clearly visible in FDC spectrum. A significant part of the variability can be explained by a very simple model consisting of two components: the original proxy describing blanketing by sunspots, and the low-pass-filtered curve describing the overall activity level. The models with the full library of the different building blocks can be applied to hindcasting with a high level of confidence, Rc ≈ 0.90. The usefulness of these models is limited by the major target controversy. Conclusions: The application of the new method to solar data allows us to obtain important insights into the different TSI modeling procedures and their capabilities for hindcasting based on the directly observed time intervals.

Local regression type methods applied to the study of geophysics and high frequency financial data

NASA Astrophysics Data System (ADS)

Mariani, M. C.; Basu, K.

2014-09-01

In this work we applied locally weighted scatterplot smoothing techniques (Lowess/Loess) to Geophysical and high frequency financial data. We first analyze and apply this technique to the California earthquake geological data. A spatial analysis was performed to show that the estimation of the earthquake magnitude at a fixed location is very accurate up to the relative error of 0.01%. We also applied the same method to a high frequency data set arising in the financial sector and obtained similar satisfactory results. The application of this approach to the two different data sets demonstrates that the overall method is accurate and efficient, and the Lowess approach is much more desirable than the Loess method. The previous works studied the time series analysis; in this paper our local regression models perform a spatial analysis for the geophysics data providing different information. For the high frequency data, our models estimate the curve of best fit where data are dependent on time.

Experimental demonstration of deep frequency modulation interferometry.

PubMed

Isleif, Katharina-Sophie; Gerberding, Oliver; Schwarze, Thomas S; Mehmet, Moritz; Heinzel, Gerhard; Cervantes, Felipe Guzmán

2016-01-25

Experiments for space and ground-based gravitational wave detectors often require a large dynamic range interferometric position readout of test masses with 1 pm/√Hz precision over long time scales. Heterodyne interferometer schemes that achieve such precisions are available, but they require complex optical set-ups, limiting their scalability for multiple channels. This article presents the first experimental results on deep frequency modulation interferometry, a new technique that combines sinusoidal laser frequency modulation in unequal arm length interferometers with a non-linear fit algorithm. We have tested the technique in a Michelson and a Mach-Zehnder Interferometer topology, respectively, demonstrated continuous phase tracking of a moving mirror and achieved a performance equivalent to a displacement sensitivity of 250 pm/Hz at 1 mHz between the phase measurements of two photodetectors monitoring the same optical signal. By performing time series fitting of the extracted interference signals, we measured that the linearity of the laser frequency modulation is on the order of 2% for the laser source used.

Using Coupled Groundwater-Surface Water Models to Simulate Eco-Regional Differences in Climate Change Impacts on Hydrological Drought Regimes in British Columbia

NASA Astrophysics Data System (ADS)

Dierauer, J. R.; Allen, D. M.

2016-12-01

Climate change is expected to lead to an increase in extremes, including daily maximum temperatures, heat waves, and meteorological droughts, which will likely result in shifts in the hydrological drought regime (i.e. the frequency, timing, duration, and severity of drought events). While many studies have used hydrologic models to simulate climate change impacts on water resources, only a small portion of these studies have analyzed impacts on low flows and/or hydrological drought. This study is the first to use a fully coupled groundwater-surface water (gw-sw) model to study climate change impacts on hydrological drought. Generic catchment-scale gw-sw models were created for each of the six major eco-regions in British Columbia using the MIKE-SHE/MIKE-11 modelling code. Daily precipitation and temperature time series downscaled using bias-correction spatial disaggregation for the simulated period of 1950-2100 were obtained from the Pacific Climate Institute Consortium (PCIC). Streamflow and groundwater drought events were identified from the simulated time series for each catchment model using the moving window quantile threshold. The frequency, timing, duration, and severity of drought events were compared between the reference period (1961-2000) and two future time periods (2031-2060, 2071-2100). Results show how hydrological drought regimes across the different British Columbia eco-regions will be impacted by climate change.

Multipixel frequency-domain imaging of spontaneous canine breast disease using fluorescent contrast agents

NASA Astrophysics Data System (ADS)

Reynolds, Jeffery S.; Thompson, Alan B.; Troy, Tamara L.; Mayer, Ralf H.; Waters, David J.; Sevick-Muraca, Eva M.

1999-07-01

In this paper we demonstrate the ability to detect the frequency-domain fluorescent signal from the contrast agent indocyanine green within the mammary chain of dogs with spontaneous mammary tumors. We use a gain-modulated image intensifier to rapidly capture multi-pixel images of the fluorescent modulation amplitude, modulation phase, and average intensity signals. Excitation is provided by a 100 MHz amplitude-modulated, 780 nm laser diode. Time series images of the uptake and clearance of the contrast agent in the diseased tissue are also presented.

Analysis of Synchronization Phenomena in Broadband Signals with Nonlinear Excitable Media

NASA Astrophysics Data System (ADS)

Chernihovskyi, Anton; Elger, Christian E.; Lehnertz, Klaus

2009-12-01

We apply the method of frequency-selective excitation waves in excitable media to characterize synchronization phenomena in interacting complex dynamical systems by measuring coincidence rates of induced excitations. We relax the frequency-selectivity of excitable media and demonstrate two applications of the method to signals with broadband spectra. Findings obtained from analyzing time series of coupled chaotic oscillators as well as electroencephalographic (EEG) recordings from an epilepsy patient indicate that this method can provide an alternative and complementary way to estimate the degree of phase synchronization in noisy signals.

Frequency Domain Analysis of Narx Neural Networks

NASA Astrophysics Data System (ADS)

Chance, J. E.; Worden, K.; Tomlinson, G. R.

1998-06-01

A method is proposed for interpreting the behaviour of NARX neural networks. The correspondence between time-delay neural networks and Volterra series is extended to the NARX class of networks. The Volterra kernels, or rather, their Fourier transforms, are obtained via harmonic probing. In the same way that the Volterra kernels generalize the impulse response to non-linear systems, the Volterra kernel transforms can be viewed as higher-order analogues of the Frequency Response Functions commonly used in Engineering dynamics; they can be interpreted in much the same way.

Noise and complexity in human postural control: interpreting the different estimations of entropy.

PubMed

Rhea, Christopher K; Silver, Tobin A; Hong, S Lee; Ryu, Joong Hyun; Studenka, Breanna E; Hughes, Charmayne M L; Haddad, Jeffrey M

2011-03-17

Over the last two decades, various measures of entropy have been used to examine the complexity of human postural control. In general, entropy measures provide information regarding the health, stability and adaptability of the postural system that is not captured when using more traditional analytical techniques. The purpose of this study was to examine how noise, sampling frequency and time series length influence various measures of entropy when applied to human center of pressure (CoP) data, as well as in synthetic signals with known properties. Such a comparison is necessary to interpret data between and within studies that use different entropy measures, equipment, sampling frequencies or data collection durations. The complexity of synthetic signals with known properties and standing CoP data was calculated using Approximate Entropy (ApEn), Sample Entropy (SampEn) and Recurrence Quantification Analysis Entropy (RQAEn). All signals were examined at varying sampling frequencies and with varying amounts of added noise. Additionally, an increment time series of the original CoP data was examined to remove long-range correlations. Of the three measures examined, ApEn was the least robust to sampling frequency and noise manipulations. Additionally, increased noise led to an increase in SampEn, but a decrease in RQAEn. Thus, noise can yield inconsistent results between the various entropy measures. Finally, the differences between the entropy measures were minimized in the increment CoP data, suggesting that long-range correlations should be removed from CoP data prior to calculating entropy. The various algorithms typically used to quantify the complexity (entropy) of CoP may yield very different results, particularly when sampling frequency and noise are different. The results of this study are discussed within the context of the neural noise and loss of complexity hypotheses.

VHF Scintillation in an Artificially Heated Ionosphere

NASA Astrophysics Data System (ADS)

Suszcynsky, D. M.; Layne, J.; Light, M. E.; Pigue, M. J.; Rivera, L.

2017-12-01

As part of an ongoing project to characterize very-high-frequency (VHF) radio wave propagation through structured ionospheres, Los Alamos National Laboratory has been conducting a set of experiments to measure the scintillation effects of VHF transmissions under a variety of ionospheric conditions. Previous work (see 2015 Fall AGU poster by D. Suszcynsky et al.) measured the S4 index and ionospheric coherence bandwidth in the 32 - 44 MHz frequency range under naturally scintillated conditions in the equatorial region at Kwajalein Atoll during three separate campaigns centered on the 2014 and 2015 equinoxes. In this paper, we will present preliminary results from the February and September, 2017 High Altitude Auroral Research Project (HAARP) Experimental Campaigns where we are attempting to make these measurements under more controlled conditions using the HAARP ionospheric heater in a twisted-beam mode. Two types of measurements are made by transmitting VHF signals through the heated ionospheric volume to the Radio Frequency Propagation (RFProp) satellite experiment. The S4 scintillation index is determined by measuring the power fluctuations of a 135-MHz continuous wave signal and the ionospheric coherence bandwidth is simultaneously determined by measuring the delay spread of a frequency-modulated continuous wave (FMCW) signal in the 130 - 140 MHz frequency range. Additionally, a spatial Fourier transform of the CW time series is used to calculate the irregularity spectral density function. Finally, the temporal evolution of the time series is used to characterize spread-Doppler clutter effects arising from preferential ray paths to the satellite due to refraction off of isolated density irregularities. All results are compared to theory and scaled for comparison to the 32 - 44 MHz Kwajalein measurements.

Conditional adaptive Bayesian spectral analysis of nonstationary biomedical time series.

PubMed

Bruce, Scott A; Hall, Martica H; Buysse, Daniel J; Krafty, Robert T

2018-03-01

Many studies of biomedical time series signals aim to measure the association between frequency-domain properties of time series and clinical and behavioral covariates. However, the time-varying dynamics of these associations are largely ignored due to a lack of methods that can assess the changing nature of the relationship through time. This article introduces a method for the simultaneous and automatic analysis of the association between the time-varying power spectrum and covariates, which we refer to as conditional adaptive Bayesian spectrum analysis (CABS). The procedure adaptively partitions the grid of time and covariate values into an unknown number of approximately stationary blocks and nonparametrically estimates local spectra within blocks through penalized splines. CABS is formulated in a fully Bayesian framework, in which the number and locations of partition points are random, and fit using reversible jump Markov chain Monte Carlo techniques. Estimation and inference averaged over the distribution of partitions allows for the accurate analysis of spectra with both smooth and abrupt changes. The proposed methodology is used to analyze the association between the time-varying spectrum of heart rate variability and self-reported sleep quality in a study of older adults serving as the primary caregiver for their ill spouse. © 2017, The International Biometric Society.

Variability of suspended-sediment concentration at tidal to annual time scales in San Francisco Bay, USA

USGS Publications Warehouse

Schoellhamer, D.H.

2002-01-01

Singular spectrum analysis for time series with missing data (SSAM) was used to reconstruct components of a 6-yr time series of suspended-sediment concentration (SSC) from San Francisco Bay. Data were collected every 15 min and the time series contained missing values that primarily were due to sensor fouling. SSAM was applied in a sequential manner to calculate reconstructed components with time scales of variability that ranged from tidal to annual. Physical processes that controlled SSC and their contribution to the total variance of SSC were (1) diurnal, semidiurnal, and other higher frequency tidal constituents (24%), (2) semimonthly tidal cycles (21%), (3) monthly tidal cycles (19%), (4) semiannual tidal cycles (12%), and (5) annual pulses of sediment caused by freshwater inflow, deposition, and subsequent wind-wave resuspension (13%). Of the total variance 89% was explained and subtidal variability (65%) was greater than tidal variability (24%). Processes at subtidal time scales accounted for more variance of SSC than processes at tidal time scales because sediment accumulated in the water column and the supply of easily erodible bed sediment increased during periods of increased subtidal energy. This large range of time scales that each contained significant variability of SSC and associated contaminants can confound design of sampling programs and interpretation of resulting data.

Alternating current (AC) iontophoretic transport across human epidermal membrane: effects of AC frequency and amplitude.

PubMed

Yan, Guang; Xu, Qingfang; Anissimov, Yuri G; Hao, Jinsong; Higuchi, William I; Li, S Kevin

2008-03-01

As a continuing effort to understand the mechanisms of alternating current (AC) transdermal iontophoresis and the iontophoretic transport pathways in the stratum corneum (SC), the objectives of the present study were to determine the interplay of AC frequency, AC voltage, and iontophoretic transport of ionic and neutral permeants across human epidermal membrane (HEM) and use AC as a means to characterize the transport pathways. Constant AC voltage iontophoresis experiments were conducted with HEM in 0.10 M tetraethyl ammonium pivalate (TEAP). AC frequencies ranging from 0.0001 to 25 Hz and AC applied voltages of 0.5 and 2.5 V were investigated. Tetraethyl ammonium (TEA) and arabinose (ARA) were the ionic and neutral model permeants, respectively. In data analysis, the logarithm of the permeability coefficients of HEM for the model permeants was plotted against the logarithm of the HEM electrical resistance for each AC condition. As expected, linear correlations between the logarithms of permeability coefficients and the logarithms of resistances of HEM were observed, and the permeability data were first normalized and then compared at the same HEM electrical resistance using these correlations. Transport enhancement of the ionic permeant was significantly larger than that of the neutral permeant during AC iontophoresis. The fluxes of the ionic permeant during AC iontophoresis of 2.5 V in the frequency range from 5 to 1,000 Hz were relatively constant and were approximately 4 times over those of passive transport. When the AC frequency decreased from 5 to 0.001 Hz at 2.5 V, flux enhancement increased to around 50 times over passive transport. While the AC frequency for achieving the full effect of iontophoretic enhancement at low AC frequency was lower than anticipated, the frequency for approaching passive diffusion transport at high frequency was higher than expected from the HEM morphology. These observations are consistent with a transport model of multiple barriers in series and the previous hypothesis that the iontophoresis pathways across HEM under AC behave like a series of reservoirs interconnected by short pore pathways.

Study of the human postural control system during quiet standing using detrended fluctuation analysis

NASA Astrophysics Data System (ADS)

Teresa Blázquez, M.; Anguiano, Marta; de Saavedra, Fernando Arias; Lallena, Antonio M.; Carpena, Pedro

2009-05-01

The detrended fluctuation analysis is used to study the behavior of different time series obtained from the trajectory of the center of pressure, the output of the activity of the human postural control system. The results suggest that these trajectories present two different regimes in their scaling properties: persistent (for high frequencies, short-range time scale) to antipersistent (for low frequencies, long-range time scale) behaviors. The similitude between the results obtained for the measurements, done with both eyes open and eyes closed, indicate either that the visual system may be disregarded by the postural control system while maintaining the quiet standing, or that the control mechanisms associated with each type of information (visual, vestibular and somatosensory) cannot be disentangled with the type of analysis performed here.

Stabilization of Taylor-Couette flow due to time-periodic outer cylinder oscillation

NASA Technical Reports Server (NTRS)

Murray, B. T.; Mcfadden, G. B.; Coriell, S. R.

1990-01-01

The linear stability of circular Couette flow between concentric infinite cylinders is considered for the case when the inner cylinder is rotated at a constant angular velocity and the outer cylinder is driven sinusoidally in time with zero mean rotation. This configuration was studied experimentally by Walsh and Donnelly. The critical Reynolds numbers calculated from linear stability theory agree well with the experimental values, except at large modulation amplitudes and small frequencies. The theoretical values are obtained using Floquet theory implemented in two distinct approaches: a truncated Fourier series representation in time, and a fundamental solution matrix based on a Chebyshev pseudospectral representation in space. For large amplitude, low frequency modulation, the linear eigenfunctions are temporally complex, consisting of a quiescent region followed by rapid change in the perturbed flow velocities.

Preliminary evaluation of the potential of tree-ring cellulose content as a novel supplementary proxy in dendroclimatology

NASA Astrophysics Data System (ADS)

Ziehmer, Malin M.; Nicolussi, Kurt; Schlüchter, Christian; Leuenberger, Markus

2018-02-01

Cellulose content (CC (%)) in tree rings is usually utilised as a tool to control the quality of the α-cellulose extraction from tree rings in the preparation of stable-isotope analysis in wooden tissues. Reported amounts of CC (%) are often limited to mean values per tree. For the first time, CC (%) series from two high-Alpine species, Larix decidua Mill. (European Larch, LADE) and Pinus cembra L. (Swiss stone pine, PICE) are investigated in modern wood samples and Holocene wood remains from the Early and mid-Holocene. Modern CC (%) series reveal a species-specific low-frequency trend independent of their sampling site over the past 150 years. Climate-cellulose relationships illustrate the ability of CC (%) to record temperature in both species but for slightly different periods within the growing season. The Holocene CC (%) series illustrate diverging low-frequency trends in both species, independent of sampling site characteristics (latitude, longitude and elevation). Moreover, potential age trends are not apparent in the two coniferous species. The arithmetic mean of CC (%) series in the Early and mid-Holocene indicate low CC (%) succeeding cold events. In conclusion, CC (%) in tree rings show high potential to be established as novel supplementary proxy in dendroclimatology.

Unexpected series of regular frequency spacing of δ Scuti stars in the non-asymptotic regime - I. The methodology

DOE PAGES

Paparo, M.; Benko, J. M.; Hareter, M.; ...

2016-05-11

In this study, a sequence search method was developed to search the regular frequency spacing in δ Scuti stars through visual inspection and an algorithmic search. We searched for sequences of quasi-equally spaced frequencies, containing at least four members per sequence, in 90 δ Scuti stars observed by CoRoT. We found an unexpectedly large number of independent series of regular frequency spacing in 77 δ Scuti stars (from one to eight sequences) in the non-asymptotic regime. We introduce the sequence search method presenting the sequences and echelle diagram of CoRoT 102675756 and the structure of the algorithmic search. Four sequencesmore » (echelle ridges) were found in the 5–21 d –1 region where the pairs of the sequences are shifted (between 0.5 and 0.59 d –1) by twice the value of the estimated rotational splitting frequency (0.269 d –1). The general conclusions for the whole sample are also presented in this paper. The statistics of the spacings derived by the sequence search method, by FT (Fourier transform of the frequencies), and the statistics of the shifts are also compared. In many stars more than one almost equally valid spacing appeared. The model frequencies of FG Vir and their rotationally split components were used to formulate the possible explanation that one spacing is the large separation while the other is the sum of the large separation and the rotational frequency. In CoRoT 102675756, the two spacings (2.249 and 1.977 d –1) are in better agreement with the sum of a possible 1.710 d –1 large separation and two or one times, respectively, the value of the rotational frequency.« less

Unexpected series of regular frequency spacing of δ Scuti stars in the non-asymptotic regime - I. The methodology

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

Paparo, M.; Benko, J. M.; Hareter, M.

In this study, a sequence search method was developed to search the regular frequency spacing in δ Scuti stars through visual inspection and an algorithmic search. We searched for sequences of quasi-equally spaced frequencies, containing at least four members per sequence, in 90 δ Scuti stars observed by CoRoT. We found an unexpectedly large number of independent series of regular frequency spacing in 77 δ Scuti stars (from one to eight sequences) in the non-asymptotic regime. We introduce the sequence search method presenting the sequences and echelle diagram of CoRoT 102675756 and the structure of the algorithmic search. Four sequencesmore » (echelle ridges) were found in the 5–21 d –1 region where the pairs of the sequences are shifted (between 0.5 and 0.59 d –1) by twice the value of the estimated rotational splitting frequency (0.269 d –1). The general conclusions for the whole sample are also presented in this paper. The statistics of the spacings derived by the sequence search method, by FT (Fourier transform of the frequencies), and the statistics of the shifts are also compared. In many stars more than one almost equally valid spacing appeared. The model frequencies of FG Vir and their rotationally split components were used to formulate the possible explanation that one spacing is the large separation while the other is the sum of the large separation and the rotational frequency. In CoRoT 102675756, the two spacings (2.249 and 1.977 d –1) are in better agreement with the sum of a possible 1.710 d –1 large separation and two or one times, respectively, the value of the rotational frequency.« less

Time-dependent scaling patterns in high frequency financial data

NASA Astrophysics Data System (ADS)

Nava, Noemi; Di Matteo, Tiziana; Aste, Tomaso

2016-10-01

We measure the influence of different time-scales on the intraday dynamics of financial markets. This is obtained by decomposing financial time series into simple oscillations associated with distinct time-scales. We propose two new time-varying measures of complexity: 1) an amplitude scaling exponent and 2) an entropy-like measure. We apply these measures to intraday, 30-second sampled prices of various stock market indices. Our results reveal intraday trends where different time-horizons contribute with variable relative amplitudes over the course of the trading day. Our findings indicate that the time series we analysed have a non-stationary multifractal nature with predominantly persistent behaviour at the middle of the trading session and anti-persistent behaviour at the opening and at the closing of the session. We demonstrate that these patterns are statistically significant, robust, reproducible and characteristic of each stock market. We argue that any modelling, analytics or trading strategy must take into account these non-stationary intraday scaling patterns.

75 FR 5692 - Airworthiness Directives; The Boeing Company Model 747-200C and -200F Series Airplanes

Federal Register 2010, 2011, 2012, 2013, 2014

2010-02-04

... all Model 747-200C and -200F series airplanes. This AD requires a high frequency eddy current (HFEC... on July 6, 2009 (74 FR 31894). That NPRM proposed to require a high frequency eddy current inspection..., whichever occurs later: Do an open-hole high frequency eddy current (HFEC) inspection of all the fastener...

Passive microwave sensing of soil moisture content - The effects of soil bulk density and surface roughness

NASA Technical Reports Server (NTRS)

Wang, J. R.

1983-01-01

Microwave radiometric measurements over bare fields of different surface roughness were made at frequencies of 1.4 GHz, 5 GHz, and 10.7 GHz to study the frequency dependence, as well as the possible time variation, of surface roughness. An increase in surface roughness was found to increase the brightness temperature of soils and reduce the slope of regression between brightness temperature and soil moisture content. The frequency dependence of the surface roughness effect was relatively weak when compared with that of the vegetation effect. Radiometric time-series observations over a given field indicate that field surface roughness might gradually diminish with time, especially after a rainfall or irrigation. The variation of surface roughness increases the uncertainty of remote soil moisture estimates by microwave radiometry. Three years of radiometric measurements over a test site revealed a possible inconsistency in the soil bulk density determination, which is an important factor in the interpretation of radiometric data.

Polarization-resolved time-delay signatures of chaos induced by FBG-feedback in VCSEL.

PubMed

Zhong, Zhu-Qiang; Li, Song-Sui; Chan, Sze-Chun; Xia, Guang-Qiong; Wu, Zheng-Mao

2015-06-15

Polarization-resolved chaotic emission intensities from a vertical-cavity surface-emitting laser (VCSEL) subject to feedback from a fiber Bragg grating (FBG) are numerically investigated. Time-delay (TD) signatures of the feedback are examined through various means including self-correlations of intensity time-series of individual polarizations, cross-correlation of intensities time-series between both polarizations, and permutation entropies calculated for the individual polarizations. The results show that the TD signatures can be clearly suppressed by selecting suitable operation parameters such as the feedback strength, FBG bandwidth, and Bragg frequency. Also, in the operational parameter space, numerical maps of TD signatures and effective bandwidths are obtained, which show regions of chaotic signals with both wide bandwidths and weak TD signatures. Finally, by comparing with a VCSEL subject to feedback from a mirror, the VCSEL subject to feedback from the FBG generally shows better concealment of the TD signatures with similar, or even wider, bandwidths.

Coherent changes of multifractal properties of continuous acoustic emission at failure of heterogeneous materials

NASA Astrophysics Data System (ADS)

Panteleev, Ivan; Bayandin, Yuriy; Naimark, Oleg

2017-12-01

This work performs a correlation analysis of the statistical properties of continuous acoustic emission recorded in different parts of marble and fiberglass laminate samples under quasi-static deformation. A spectral coherent measure of time series, which is a generalization of the squared coherence spectrum on a multidimensional series, was chosen. The spectral coherent measure was estimated in a sliding time window for two parameters of the acoustic emission multifractal singularity spectrum: the spectrum width and the generalized Hurst exponent realizing the maximum of the singularity spectrum. It is shown that the preparation of the macrofracture focus is accompanied by the synchronization (coherent behavior) of the statistical properties of acoustic emission in allocated frequency intervals.

EDDIE Seismology: Introductory spectral analysis for undergraduates

NASA Astrophysics Data System (ADS)

Soule, D. C.; Gougis, R.; O'Reilly, C.

2016-12-01

We present a spectral seismology lesson in which students use spectral analysis to describe the frequency of seismic arrivals based on a conceptual presentation of waveforms and filters. The goal is for students to surpass basic waveform terminology and relate a time domain signals to their conjugates in the frequency domain. Although seismology instruction commonly engages students in analysis of authentic seismological data, this is less true for lower-level undergraduate seismology instruction due to coding barriers to many seismological analysis tasks. To address this, our module uses Seismic Canvas (Kroeger, 2015; https://seiscode.iris.washington.edu/projects/seismiccanvas), a graphically interactive application for accessing, viewing and analyzing waveform data, which we use to plot earthquake data in the time domain. Once students are familiarized with the general components of the waveform (i.e. frequency, wavelength, amplitude and period), they use Seismic Canvas to transform the data into the frequency domain. Bypassing the mathematics of Fourier Series allows focus on conceptual understanding by plotting and manipulating seismic data in both time and frequency domains. Pre/post-tests showed significant improvements in students' use of seismograms and spectrograms to estimate the frequency content of the primary wave, which demonstrated students' understanding of frequency and how data on the spectrogram and seismogram are related. Students were also able to identify the time and frequency of the largest amplitude arrival, indicating understanding of amplitude and use of a spectrogram as an analysis tool. Students were also asked to compare plots of raw data and the same data filtered with a high-pass filter, and identify the filter used to create the second plot. Students demonstrated an improved understanding of how frequency content can be removed from a signal in the spectral domain.

Phonon vibrational frequencies of all single-wall carbon nanotubes at the lambda point: reduced matrix calculations.

PubMed

Wang, Yufang; Wu, Yanzhao; Feng, Min; Wang, Hui; Jin, Qinghua; Ding, Datong; Cao, Xuewei

2008-12-01

With a simple method-the reduced matrix method, we simplified the calculation of the phonon vibrational frequencies according to SWNTs structure and their phonon symmetric property and got the dispersion properties of all SWNTs at Gamma point in Brillouin zone, whose diameters lie between 0.6 and 2.5 nm. The calculating time is shrunk about 2-4 orders. A series of the dependent relationships between the diameters of SWNTs and the frequencies of Raman and IR active modes are given. Several fine structures including "glazed tile" structures in omega approximately d figures are found, which might predict a certain macro-quantum phenomenon of the phonons in SWNTs.

High Frequency, Long Time Series Measurements from the Bermuda Testbed Mooring in Support of SIMBIOS. Chapter 8

NASA Technical Reports Server (NTRS)

Dickey, Tommy; Dobeck, Laura; Sigurdson, David; Zedler, Sarah; Manov, Derek; Yu, Xuri

2001-01-01

It has been recognized that optical moorings are important platforms for the validation of Sea-Viewing Wide Field-of-view Sensor (SeaWiFS). It was recommended that optical moorings be maintained in order to: (1) provide long-term time series comparisons between in situ and SeaWIFS measurements of normalized water-leaving radiance; (2) develop and test algorithms for pigment biomass and phytoplankton primary productivity; and (3) provide long-term, virtually continuous in situ observations which can be used to determine and optimize the accuracy of derived satellite products. These applications require the use of in situ radiometers for long periods of time to evaluate and correct for inherent satellite undersampling (aliasing and biasing) and degradation of satellite color sensors (e.g., drifts as experienced by the Coastal Zone Color Scanner). The Bermuda Testbed Mooring (BTM) program was initiated in 1994 at a site located about 80km southeast of Bermuda in waters of about 4530 m depth. In August 1997, with NASA's support, we started to provide the Sensor Intercomparison and Merger for Biological and Interdisciplinary Oceanic Studies (SIMBIOS) program with large volumes of high frequency, long-term time-series bio-optical data from the BTM for SeaWiFS satellite ocean color groundtruthing and algorithm development. This NASA supported portion of the BTM activity spanned three years and covered five BTM deployments. During these three years, the quality of radiometric data has improved dramatically. Excellent agreement between BTM moored data and both SeaWiFS and nearby ship profile radiometric data demonstrate that technical advances in the moored optical observations have reduced the major difficulties that moored platforms face: biofouling and less frequent calibration.

Trend Detection and Bivariate Frequency Analysis for Nonstrationary Rainfall Data

NASA Astrophysics Data System (ADS)

Joo, K.; Kim, H.; Shin, J. Y.; Heo, J. H.

2017-12-01

Multivariate frequency analysis has been developing for hydro-meteorological data such as rainfall, flood, and drought. Particularly, the copula has been used as a useful tool for multivariate probability model which has no limitation on deciding marginal distributions. The time-series rainfall data can be characterized to rainfall event by inter-event time definition (IETD) and each rainfall event has a rainfall depth and rainfall duration. In addition, nonstationarity in rainfall event has been studied recently due to climate change and trend detection of rainfall event is important to determine the data has nonstationarity or not. With the rainfall depth and duration of a rainfall event, trend detection and nonstationary bivariate frequency analysis has performed in this study. 62 stations from Korea Meteorological Association (KMA) over 30 years of hourly recorded data used in this study and the suitability of nonstationary copula for rainfall event has examined by the goodness-of-fit test.

Detecting time-specific differences between temporal nonlinear curves: Analyzing data from the visual world paradigm

PubMed Central

Oleson, Jacob J; Cavanaugh, Joseph E; McMurray, Bob; Brown, Grant

2015-01-01

In multiple fields of study, time series measured at high frequencies are used to estimate population curves that describe the temporal evolution of some characteristic of interest. These curves are typically nonlinear, and the deviations of each series from the corresponding curve are highly autocorrelated. In this scenario, we propose a procedure to compare the response curves for different groups at specific points in time. The method involves fitting the curves, performing potentially hundreds of serially correlated tests, and appropriately adjusting the overall alpha level of the tests. Our motivating application comes from psycholinguistics and the visual world paradigm. We describe how the proposed technique can be adapted to compare fixation curves within subjects as well as between groups. Our results lead to conclusions beyond the scope of previous analyses. PMID:26400088

Real time high frequency monitoring of water quality in river streams using a UV-visible spectrometer: interest, limits and consequences for monitoring strategies

NASA Astrophysics Data System (ADS)

Faucheux, Mikaël; Fovet, Ophélie; Gruau, Gérard; Jaffrézic, Anne; Petitjean, Patrice; Gascuel-Odoux, Chantal; Ruiz, Laurent

2013-04-01

Stream water chemistry is highly variable in space and time, therefore high frequency water quality measurement methods are likely to lead to conceptual advances in the hydrological sciences. Sub-daily data on water quality improve the characterization of pollutant sources and pathways during flood events as well as during long-term periods [1]. However, real time, high frequency monitoring devices needs to be properly calibrated and validated in real streams. This study analyses data from in situ monitoring of a stream water quality. During two hydrological years (2010-11, 2011-12), a submersible UV-visible spectrometer (Scan Spectrolyser) was used for surface water quality measurement at the outlet of a headwater catchment located at Kervidy-Naizin, Western France (AgrHys long-term hydrological observatory, http://www.inra.fr/ore_agrhys/). The spectrometer is reagentless and equipped with an auto-cleaning system. It allows real time, in situ and high frequency (20 min) measurements and uses a multiwavelengt spectral (200-750 nm) for simultaneous measurement of nitrate, dissolved organic carbon (DOC) and total suspended solids (TSS). A global calibration based on a PLS (Partial Least Squares) regression is provided by the manufacturer as default configuration of the UV-visible spectrometer. We carried out a local calibration of the spectrometer based on nitrates and DOC concentrations analysed in the laboratory from daily manual sampling and sub-daily automatic sampling of flood events. TSS results are compared with 15 min turbidity records from a continuous turdidimeter (Ponsel). The results show a good correlation between laboratory data and spectrometer data both during basis flows periods and flood events. However, the local calibration gives better results than the global one. Nutrient fluxes estimates based on high and different low frequency time series (daily to monthly) are compared to discuss the implication for environmental monitoring strategies. Such monitoring methods can therefore be interesting for designing monitoring strategy of environmental observatory and provide dense time series likely to highlight patterns or trends using appropriate approaches such as spectral analysis [2]. 1. Wade, A.J. et al., HESS Discuss., 2012. 9(5), p.6458- 6506. 2. Aubert, A. et al., submitted to EGU 2013-4745 vol. 15.

Evidence in Support of the Independent Channel Model Describing the Sensorimotor Control of Human Stance Using a Humanoid Robot

PubMed Central

Pasma, Jantsje H.; Assländer, Lorenz; van Kordelaar, Joost; de Kam, Digna; Mergner, Thomas; Schouten, Alfred C.

2018-01-01

The Independent Channel (IC) model is a commonly used linear balance control model in the frequency domain to analyze human balance control using system identification and parameter estimation. The IC model is a rudimentary and noise-free description of balance behavior in the frequency domain, where a stable model representation is not guaranteed. In this study, we conducted firstly time-domain simulations with added noise, and secondly robot experiments by implementing the IC model in a real-world robot (PostuRob II) to test the validity and stability of the model in the time domain and for real world situations. Balance behavior of seven healthy participants was measured during upright stance by applying pseudorandom continuous support surface rotations. System identification and parameter estimation were used to describe the balance behavior with the IC model in the frequency domain. The IC model with the estimated parameters from human experiments was implemented in Simulink for computer simulations including noise in the time domain and robot experiments using the humanoid robot PostuRob II. Again, system identification and parameter estimation were used to describe the simulated balance behavior. Time series, Frequency Response Functions, and estimated parameters from human experiments, computer simulations, and robot experiments were compared with each other. The computer simulations showed similar balance behavior and estimated control parameters compared to the human experiments, in the time and frequency domain. Also, the IC model was able to control the humanoid robot by keeping it upright, but showed small differences compared to the human experiments in the time and frequency domain, especially at high frequencies. We conclude that the IC model, a descriptive model in the frequency domain, can imitate human balance behavior also in the time domain, both in computer simulations with added noise and real world situations with a humanoid robot. This provides further evidence that the IC model is a valid description of human balance control. PMID:29615886

Evidence in Support of the Independent Channel Model Describing the Sensorimotor Control of Human Stance Using a Humanoid Robot.

PubMed

Pasma, Jantsje H; Assländer, Lorenz; van Kordelaar, Joost; de Kam, Digna; Mergner, Thomas; Schouten, Alfred C

2018-01-01

The Independent Channel (IC) model is a commonly used linear balance control model in the frequency domain to analyze human balance control using system identification and parameter estimation. The IC model is a rudimentary and noise-free description of balance behavior in the frequency domain, where a stable model representation is not guaranteed. In this study, we conducted firstly time-domain simulations with added noise, and secondly robot experiments by implementing the IC model in a real-world robot (PostuRob II) to test the validity and stability of the model in the time domain and for real world situations. Balance behavior of seven healthy participants was measured during upright stance by applying pseudorandom continuous support surface rotations. System identification and parameter estimation were used to describe the balance behavior with the IC model in the frequency domain. The IC model with the estimated parameters from human experiments was implemented in Simulink for computer simulations including noise in the time domain and robot experiments using the humanoid robot PostuRob II. Again, system identification and parameter estimation were used to describe the simulated balance behavior. Time series, Frequency Response Functions, and estimated parameters from human experiments, computer simulations, and robot experiments were compared with each other. The computer simulations showed similar balance behavior and estimated control parameters compared to the human experiments, in the time and frequency domain. Also, the IC model was able to control the humanoid robot by keeping it upright, but showed small differences compared to the human experiments in the time and frequency domain, especially at high frequencies. We conclude that the IC model, a descriptive model in the frequency domain, can imitate human balance behavior also in the time domain, both in computer simulations with added noise and real world situations with a humanoid robot. This provides further evidence that the IC model is a valid description of human balance control.

Estimating soil hydraulic properties from soil moisture time series by inversion of a dual-permeability model

NASA Astrophysics Data System (ADS)

Dalla Valle, Nicolas; Wutzler, Thomas; Meyer, Stefanie; Potthast, Karin; Michalzik, Beate

2017-04-01

Dual-permeability type models are widely used to simulate water fluxes and solute transport in structured soils. These models contain two spatially overlapping flow domains with different parameterizations or even entirely different conceptual descriptions of flow processes. They are usually able to capture preferential flow phenomena, but a large set of parameters is needed, which are very laborious to obtain or cannot be measured at all. Therefore, model inversions are often used to derive the necessary parameters. Although these require sufficient input data themselves, they can use measurements of state variables instead, which are often easier to obtain and can be monitored by automated measurement systems. In this work we show a method to estimate soil hydraulic parameters from high frequency soil moisture time series data gathered at two different measurement depths by inversion of a simple one dimensional dual-permeability model. The model uses an advection equation based on the kinematic wave theory to describe the flow in the fracture domain and a Richards equation for the flow in the matrix domain. The soil moisture time series data were measured in mesocosms during sprinkling experiments. The inversion consists of three consecutive steps: First, the parameters of the water retention function were assessed using vertical soil moisture profiles in hydraulic equilibrium. This was done using two different exponential retention functions and the Campbell function. Second, the soil sorptivity and diffusivity functions were estimated from Boltzmann-transformed soil moisture data, which allowed the calculation of the hydraulic conductivity function. Third, the parameters governing flow in the fracture domain were determined using the whole soil moisture time series. The resulting retention functions were within the range of values predicted by pedotransfer functions apart from very dry conditions, where all retention functions predicted lower matrix potentials. The diffusivity function predicted values of a similar range as shown in other studies. Overall, the model was able to emulate soil moisture time series for low measurement depths, but deviated increasingly at larger depths. This indicates that some of the model parameters are not constant throughout the profile. However, overall seepage fluxes were still predicted correctly. In the near future we will apply the inversion method to lower frequency soil moisture data from different sites to evaluate the model's ability to predict preferential flow seepage fluxes at the field scale.

Variability of rainfall over Lake Kariba catchment area in the Zambezi river basin, Zimbabwe

NASA Astrophysics Data System (ADS)

Muchuru, Shepherd; Botai, Joel O.; Botai, Christina M.; Landman, Willem A.; Adeola, Abiodun M.

2016-04-01

In this study, average monthly and annual rainfall totals recorded for the period 1970 to 2010 from a network of 13 stations across the Lake Kariba catchment area of the Zambezi river basin were analyzed in order to characterize the spatial-temporal variability of rainfall across the catchment area. In the analysis, the data were subjected to intervention and homogeneity analysis using the Cumulative Summation (CUSUM) technique and step change analysis using rank-sum test. Furthermore, rainfall variability was characterized by trend analysis using the non-parametric Mann-Kendall statistic. Additionally, the rainfall series were decomposed and the spectral characteristics derived using Cross Wavelet Transform (CWT) and Wavelet Coherence (WC) analysis. The advantage of using the wavelet-based parameters is that they vary in time and can therefore be used to quantitatively detect time-scale-dependent correlations and phase shifts between rainfall time series at various localized time-frequency scales. The annual and seasonal rainfall series were homogeneous and demonstrated no apparent significant shifts. According to the inhomogeneity classification, the rainfall series recorded across the Lake Kariba catchment area belonged to category A (useful) and B (doubtful), i.e., there were zero to one and two absolute tests rejecting the null hypothesis (at 5 % significance level), respectively. Lastly, the long-term variability of the rainfall series across the Lake Kariba catchment area exhibited non-significant positive and negative trends with coherent oscillatory modes that are constantly locked in phase in the Morlet wavelet space.

Impacts of Non-Stationarity in Climate on Flood Intensity-Duration-Frequency: Case Studies in Mountainous Areas with Snowmelt

NASA Astrophysics Data System (ADS)

Hou, Z.; Ren, H.; Sun, N.; Leung, L. R.; Liu, Y.; Coleman, A. M.; Skaggs, R.; Wigmosta, M. S.

2017-12-01

Hydrologic engineering design usually involves intensity-duration-frequency (IDF) analysis for calculating runoff from a design storm of specified precipitation frequency and duration using event-based hydrologic rainfall-runoff models. Traditionally, the procedure assumes climate stationarity and neglects snowmelt-driven runoff contribution to floods. In this study, we used high resolution climate simulations to provide inputs to the physics-based Distributed Hydrology Soil and Vegetation Model (DHSVM) to determine the spatially distributed precipitation and snowmelt available for runoff. Climate model outputs were extracted around different mountainous field sites in Colorado and California. IDF curves were generated at each numerical grid of DHSVM based on the simulated precipitation, temperature, and available water for runoff. Quantitative evaluation of trending and stationarity tests were conducted to identify (quasi-)stationary time periods for reliable IDF analysis. The impact of stationarity was evaluated by comparing the derived IDF attributes with respect to time windows of different length and level of stationarity. Spatial mapping of event return-period was performed for various design storms, and spatial mapping of event intensity was performed for given duration and return periods. IDF characteristics were systematically compared (historical vs RCP4.5 vs RCP8.5) using annual maximum series vs partial duration series data with the goal of providing reliable IDF analyses to support hydrologic engineering design.

Econophysics — complex correlations and trend switchings in financial time series

NASA Astrophysics Data System (ADS)

Preis, T.

2011-03-01

This article focuses on the analysis of financial time series and their correlations. A method is used for quantifying pattern based correlations of a time series. With this methodology, evidence is found that typical behavioral patterns of financial market participants manifest over short time scales, i.e., that reactions to given price patterns are not entirely random, but that similar price patterns also cause similar reactions. Based on the investigation of the complex correlations in financial time series, the question arises, which properties change when switching from a positive trend to a negative trend. An empirical quantification by rescaling provides the result that new price extrema coincide with a significant increase in transaction volume and a significant decrease in the length of corresponding time intervals between transactions. These findings are independent of the time scale over 9 orders of magnitude, and they exhibit characteristics which one can also find in other complex systems in nature (and in physical systems in particular). These properties are independent of the markets analyzed. Trends that exist only for a few seconds show the same characteristics as trends on time scales of several months. Thus, it is possible to study financial bubbles and their collapses in more detail, because trend switching processes occur with higher frequency on small time scales. In addition, a Monte Carlo based simulation of financial markets is analyzed and extended in order to reproduce empirical features and to gain insight into their causes. These causes include both financial market microstructure and the risk aversion of market participants.

High-resolution time series of Pseudomonas aeruginosa gene expression and rhamnolipid secretion through growth curve synchronization.

PubMed

van Ditmarsch, Dave; Xavier, João B

2011-06-17

Online spectrophotometric measurements allow monitoring dynamic biological processes with high-time resolution. Contrastingly, numerous other methods require laborious treatment of samples and can only be carried out offline. Integrating both types of measurement would allow analyzing biological processes more comprehensively. A typical example of this problem is acquiring quantitative data on rhamnolipid secretion by the opportunistic pathogen Pseudomonas aeruginosa. P. aeruginosa cell growth can be measured by optical density (OD600) and gene expression can be measured using reporter fusions with a fluorescent protein, allowing high time resolution monitoring. However, measuring the secreted rhamnolipid biosurfactants requires laborious sample processing, which makes this an offline measurement. Here, we propose a method to integrate growth curve data with endpoint measurements of secreted metabolites that is inspired by a model of exponential cell growth. If serial diluting an inoculum gives reproducible time series shifted in time, then time series of endpoint measurements can be reconstructed using calculated time shifts between dilutions. We illustrate the method using measured rhamnolipid secretion by P. aeruginosa as endpoint measurements and we integrate these measurements with high-resolution growth curves measured by OD600 and expression of rhamnolipid synthesis genes monitored using a reporter fusion. Two-fold serial dilution allowed integrating rhamnolipid measurements at a ~0.4 h-1 frequency with high-time resolved data measured at a 6 h-1 frequency. We show how this simple method can be used in combination with mutants lacking specific genes in the rhamnolipid synthesis or quorum sensing regulation to acquire rich dynamic data on P. aeruginosa virulence regulation. Additionally, the linear relation between the ratio of inocula and the time-shift between curves produces high-precision measurements of maximum specific growth rates, which were determined with a precision of ~5.4%. Growth curve synchronization allows integration of rich time-resolved data with endpoint measurements to produce time-resolved quantitative measurements. Such data can be valuable to unveil the dynamic regulation of virulence in P. aeruginosa. More generally, growth curve synchronization can be applied to many biological systems thus helping to overcome a key obstacle in dynamic regulation: the scarceness of quantitative time-resolved data.

Allan Variance Computed in Space Domain: Definition and Application to InSAR Data to Characterize Noise and Geophysical Signal.

PubMed

Cavalié, Olivier; Vernotte, François

2016-04-01

The Allan variance was introduced 50 years ago for analyzing the stability of frequency standards. In addition to its metrological interest, it may be also considered as an estimator of the large trends of the power spectral density (PSD) of frequency deviation. For instance, the Allan variance is able to discriminate different types of noise characterized by different power laws in the PSD. The Allan variance was also used in other fields than time and frequency metrology: for more than 20 years, it has been used in accelerometry, geophysics, geodesy, astrophysics, and even finances. However, it seems that up to now, it has been exclusively applied for time series analysis. We propose here to use the Allan variance on spatial data. Interferometric synthetic aperture radar (InSAR) is used in geophysics to image ground displacements in space [over the synthetic aperture radar (SAR) image spatial coverage] and in time thanks to the regular SAR image acquisitions by dedicated satellites. The main limitation of the technique is the atmospheric disturbances that affect the radar signal while traveling from the sensor to the ground and back. In this paper, we propose to use the Allan variance for analyzing spatial data from InSAR measurements. The Allan variance was computed in XY mode as well as in radial mode for detecting different types of behavior for different space-scales, in the same way as the different types of noise versus the integration time in the classical time and frequency application. We found that radial Allan variance is the more appropriate way to have an estimator insensitive to the spatial axis and we applied it on SAR data acquired over eastern Turkey for the period 2003-2011. Spatial Allan variance allowed us to well characterize noise features, classically found in InSAR such as phase decorrelation producing white noise or atmospheric delays, behaving like a random walk signal. We finally applied the spatial Allan variance to an InSAR time series to detect when the geophysical signal, here the ground motion, emerges from the noise.

High salmon density and low discharge create periodic hypoxia in coastal rivers

Treesearch

Christopher J. Sergeant; J. Ryan Bellmore; Casey McConnell; Jonathan W. Moore

2017-01-01

Dissolved oxygen (DO) is essential to the survival of almost all aquatic organisms. Here, we examine the possibility that abundant Pacific salmon (Oncorhynchus spp.) and low streamflow combine to create hypoxic events in coastal rivers. Using high-frequency DO time series from two similar watersheds in southeastern Alaska, we summarize DO regimes...

Breaks in MODIS time series portend vegetation change: verification using long-term data in an arid grassland ecosystem

USDA-ARS?s Scientific Manuscript database

Frequency and severity of extreme climatic events are forecast to increase in the 21st century. Predicting how managed ecosystems may respond to climatic extremes is intensified by uncertainty associated with knowing when, where, and how long effects of the extreme events will be manifest in the eco...

A data-driven approach for denoising GNSS position time series

NASA Astrophysics Data System (ADS)

Li, Yanyan; Xu, Caijun; Yi, Lei; Fang, Rongxin

2017-12-01

Global navigation satellite system (GNSS) datasets suffer from common mode error (CME) and other unmodeled errors. To decrease the noise level in GNSS positioning, we propose a new data-driven adaptive multiscale denoising method in this paper. Both synthetic and real-world long-term GNSS datasets were employed to assess the performance of the proposed method, and its results were compared with those of stacking filtering, principal component analysis (PCA) and the recently developed multiscale multiway PCA. It is found that the proposed method can significantly eliminate the high-frequency white noise and remove the low-frequency CME. Furthermore, the proposed method is more precise for denoising GNSS signals than the other denoising methods. For example, in the real-world example, our method reduces the mean standard deviation of the north, east and vertical components from 1.54 to 0.26, 1.64 to 0.21 and 4.80 to 0.72 mm, respectively. Noise analysis indicates that for the original signals, a combination of power-law plus white noise model can be identified as the best noise model. For the filtered time series using our method, the generalized Gauss-Markov model is the best noise model with the spectral indices close to - 3, indicating that flicker walk noise can be identified. Moreover, the common mode error in the unfiltered time series is significantly reduced by the proposed method. After filtering with our method, a combination of power-law plus white noise model is the best noise model for the CMEs in the study region.

Observer aging and long-term avian survey data quality

PubMed Central

Farmer, Robert G; Leonard, Marty L; Mills Flemming, Joanna E; Anderson, Sean C

2014-01-01

Long-term wildlife monitoring involves collecting time series data, often using the same observers over multiple years. Aging-related changes to these observers may be an important, under-recognized source of error that can bias management decisions. In this study, we used data from two large, independent bird surveys, the Atlas of the Breeding Birds of Ontario (“OBBA”) and the North American Breeding Bird Survey (“BBS”), to test for age-related observer effects in long-term time series of avian presence and abundance. We then considered the effect of such aging phenomena on current population trend estimates. We found significantly fewer detections among older versus younger observers for 13 of 43 OBBA species, and declines in detection as an observer ages for 4 of 6 vocalization groups comprising 59 of 64 BBS species. Consistent with hearing loss influencing this pattern, we also found evidence for increasingly severe detection declines with increasing call frequency among nine high-pitched bird species (OBBA); however, there were also detection declines at other frequencies, suggesting important additional effects of aging, independent of hearing loss. We lastly found subtle, significant relationships between some species' published population trend estimates and (1) their corresponding vocalization frequency (n ≥ 22 species) and (2) their estimated declines in detectability among older observers (n = 9 high-frequency, monotone species), suggesting that observer aging can negatively bias long-term monitoring data for some species in part through hearing loss effects. We recommend that survey designers and modelers account for observer age where possible. PMID:25360286

Implementation of optimal trajectory control of series resonant converter

NASA Technical Reports Server (NTRS)

Oruganti, Ramesh; Yang, James J.; Lee, Fred C.

1987-01-01

Due to the presence of a high-frequency LC tank circuit, the dynamics of a resonant converter are unpredictable. There is often a large surge of tank energy during transients. Using state-plane analysis technique, an optimal trajectory control utilizing the desired solution trajectory as the control law was previously proposed for the series resonant converters. The method predicts the fastest response possible with minimum energy surge in the resonant tank. The principle of the control and its experimental implementation are described here. The dynamics of the converter are shown to be close to time-optimal.

Effect of the Matching Circuit on the Electromechanical Characteristics of Sandwiched Piezoelectric Transducers.

PubMed

Lin, Shuyu; Xu, Jie

2017-02-10

The input electrical impedance behaves as a capacitive when a piezoelectric transducer is excited near its resonance frequency. In order to increase the energy transmission efficiency, a series or parallel inductor should be used to compensate the capacitive impedance of the piezoelectric transducer. In this paper, the effect of the series matching inductor on the electromechanical characteristics of the piezoelectric transducer is analyzed. The dependency of the resonance/anti-resonance frequency, the effective electromechanical coupling coefficient, the electrical quality factor and the electro-acoustical efficiency on the matching inductor is obtained. It is shown that apart from compensating the capacitive impedance of the piezoelectric transducer, the series matching inductor can also change the electromechanical characteristics of the piezoelectric transducer. When series matching inductor is increased, the resonance frequency is decreased and the anti-resonance unchanged; the effective electromechanical coupling coefficient is increased. For the electrical quality factor and the electroacoustic efficiency, the dependency on the matching inductor is different when the transducer is operated at the resonance and the anti-resonance frequency. The electromechanical characteristics of the piezoelectric transducer with series matching inductor are measured. It is shown that the theoretically predicted relationship between the electromechanical characteristics and the series matching inductor is in good agreement with the experimental results.