Parametric, nonparametric and parametric modelling of a chaotic circuit time series

The determination of a differential equation underlying a measured time series is a frequently arising task in nonlinear time series analysis. In the validation of a proposed model one often faces the dilemma that it is hard to decide whether possible discrepancies between the time series and model output are caused by an inappropriate model or by bad estimates of parameters in a correct type of model, or both. We propose a combination of parametric modelling based on Bock's multiple shooting algorithm and nonparametric modelling based on optimal transformations as a strategy to test proposed models and if rejected suggest and test new ones. We exemplify this strategy on an experimental time series from a chaotic circuit where we obtain an extremely accurate reconstruction of the observed attractor.Comment: 19 pages, 8 Fig

NAV op de bres voor eiwitrijke gewassen

Artikel n.a.v. rapport ' Perspectieven van sojavervanging in voer : op zoek naar Europese alternatieven voor soja ' PPO -project 325011960

Confluence reduction for Markov automata

Markov automata are a novel formalism for specifying systems exhibiting nondeterminism, probabilistic choices and Markovian rates. Recently, the process algebra MAPA was introduced to efficiently model such systems. As always, the state space explosion threatens the analysability of the models generated by such specifications. We therefore introduce confluence reduction for Markov automata, a powerful reduction technique to keep these models small. We define the notion of confluence directly on Markov automata, and discuss how to syntactically detect confluence on the MAPA language as well. That way, Markov automata generated by MAPA specifications can be reduced on-the-fly while preserving divergence-sensitive branching bisimulation. Three case studies demonstrate the significance of our approach, with reductions in analysis time up to an order of magnitude

Selective low concentration ammonia sensing in a microfluidic lab-on-a-chip

In the medical community, there is a considerable interest in a diagnostic breath analyzer for ammonia that is selectively enough to measure in exhaled air and small enough for the small volumes available in such an application. An indirect measurement system for low gaseous ammonia concentrations has been miniaturized and integrated on a chip in order to reach this goal. The detection limit of the system was calculated to be 1.1 parts per billion (ppb). The response time was determined to be 1.6 min with a gas How of 50 ml/min. The required gas volume for one measurement is therefore sufficiently small, although sampling assistance is required for breath analysis. The selectivity of the system is sufficient to measure ammonia concentrations in the low-ppb range. The system is even sufficiently selective to be used in environments that contain elevated carbon dioxide levels, like exhaled air. The lower ammonia concentration expected in diagnostic breath analysis applications, 50 ppb, was demonstrated to be detectable

Tempting long-memory - on the interpretation of DFA results

We study the inference of long-range correlations by means of Detrended Fluctuation Analysis (DFA) and argue that power-law scaling of the fluctuation function and thus long-memory may not be assumed a priori but have to be established. This requires the investigation of the local slopes. We account for the variability characteristic for stochastic processes by calculating empirical confidence regions. Comparing a long-memory with a short-memory model shows that the inference of long-range correlations from a finite amount of data by means of DFA is not specific. We remark that scaling cannot be concluded from a straight line fit to the fluctuation function in a log-log representation. Furthermore, we show that a local slope larger than α=0.5 for large scales does not necessarily imply long-memory. We also demonstrate, that it is not valid to conclude from a finite scaling region of the fluctuation function to an equivalent scaling region of the autocorrelation function. Finally, we review DFA results for the Prague temperature data set and show that long-range correlations cannot not be concluded unambiguously

Cessation of X-ray Pulsation of GX 1+4

We report results from our weekly monitoring campaign on the X-ray pulsar GX 1+4 with the {\em Rossi X-ray Timing Explorer} satellite. The spin-down trend of GX 1+4 was continuing, with the pulsar being at its longest period ever measured (about 138.7 s). At the late stage of the campaign, the source entered an extended faint state, when its X-ray (2-60 keV) flux decreased significantly to an average level of $\sim 3 \times 10^{-10} ergs cm^{-2} s^{-1}$. It was highly variable in the faint state; the flux dropped to as low as $\sim 3 \times 10^{-11} ergs cm^{-2} s^{-1}$. In several observations during this period, the X-ray pulsation became undetectable. We can, therefore, conclude conservatively that the pulsed fraction, which is normally $\gtrsim$ 70% (peak-to-peak), must have decreased drastically in those cases. This is very similar to what was observed of GX 1+4 in 1996 when it became similarly faint in X-ray. In fact, the flux at which the cessation of X-ray pulsation first occurred is nearly the same as it was in 1996. We suggest that we have, once again, observed the propeller effect in GX 1+4, a phenomenon that is predicted by theoretical models of accreting X-ray pulsars.Comment: 13 pages, 9 figures (available at http://www.physics.purdue.edu/~cui/ftp/cuifigs.tar.gz). To appear in Ap

Linear and nonlinear time series analysis of the black hole candidate Cygnus X-1

We analyze the variability in the X-ray lightcurves of the black hole candidate Cygnus X-1 by linear and nonlinear time series analysis methods. While a linear model describes the over-all second order properties of the observed data well, surrogate data analysis reveals a significant deviation from linearity. We discuss the relation between shot noise models usually applied to analyze these data and linear stochastic autoregressive models. We debate statistical and interpretational issues of surrogate data testing for the present context. Finally, we suggest a combination of tools from linear andnonlinear time series analysis methods as a procedure to test the predictions of astrophysical models on observed data.Comment: 15 pages, to appear in Phys. Rev.

Phase synchronization from noisy univariate signals

We present methods for detecting phase synchronization of two unidirectionally coupled, self-sustained noisy oscillators from a signal of the driven oscillator alone. One method detects soft, another hard phase locking. Both are applied to the problem of detecting phase synchronization in von Karman vortex flow meters.Comment: 4 pages, 4 figure

Correlated X-ray and Optical Variability in Mkn 509

We present results of a 3 year monitoring campaign of the Seyfert 1 galaxy Markarian 509, using X-ray data from the Rossi X-ray Timing Explorer (RXTE) and optical data taken by the SMARTS consortium. Both light curves show significant variations, and are strongly correlated with the optical flux leading the X-ray flux by 15 days. The X-ray power spectrum shows a steep high-frequency slope of -2.0, breaking to a slope of -1.0 at at timescale of 34 days. The lag from optical to X-ray emission is most likely caused by variations in the accretion disk propagating inward.Comment: 13 pages, 3 figures. Accepted for publication in the Astrophysical Journa