Chaotic Dynamics

We present and compare three generically applicable signal processing methods for periodic orbit quantization via harmonic inversion of semiclassical recurrence functions. In a first step of each method, a band-limited decimated periodic orbit signal is obtained by analytical frequency windowing of the periodic orbit sum. In a second step, the frequencies and amplitudes of the decimated signal are determined by either Decimated Linear Predictor, Decimated Pade Approximant, or Decimated Signal Diagonalization. These techniques, which would have been numerically unstable without the windowing, provide numerically more accurate semiclassical spectra than does the filter-diagonalization method.

A simple technique for decoding an unknown modulated chaotic time-series is presented. We point out that, by fitting a polynomial model to the modulated chaotic signal, the error in the fit gives sufficient information to decode the modulating signal. For analog implementation, a lowpass filter can be used for fitting. This method is simple and easy to implement in hardware.

Scattering on the energy shell is viewed here as the relation between the bound states of the Hamiltonian, restricted to sections on leads that are asymptotically independent, far away from the interaction region. The decomposition is achieved by sectioning this region and adding new leads, thus generating two new scatterers. So a resonant scatterer, whose $\bS$-matrix has sharp energy peaks, can be resolved into a pair of scatterers with smooth energy dependence. The resonant behaviour is concentrated in a spectral determinant obtained from a dissipative section map. The semiclassical limit of this theory coincides with the orbit resummation previously proposed by Georgeot and Prange. A numerical example for a semiseparable scatterer is investigated, revealing the accurate portrayal of the Wigner time delay by the spectral determinant.

We describe the dynamical behavior found in numerical solutions of the Vector Complex Ginzburg-Landau equation in parameter values where plane waves are stable. Topological defects in the system are responsible for a rich behavior. At low coupling between the vector components, a {\sl frozen} phase is found, whereas a {\sl gas-like} phase appears at higher coupling. The transition is a consequence of a defect unbinding phenomena. Entropy functions display a characteristic behavior around the transition.

For controlling periodic orbits with delayed feedback methods the periodicity has to be known a priori. We propose a simple scheme, how to detect the period of orbits from properties of the control signal, at least if a periodic but nonvanishing signal is observed. We analytically derive a simple expression relating the delay, the control amplitude, and the unknown period. Thus, the latter can be computed from experimentally accessible quantities. Our findings are confirmed by numerical simulations and electronic circuit experiments

We study the deterministic diffusion coefficient of the two-dimensional periodic Lorentz gas as a function of the density of scatterers. Results obtained from computer simulations are compared to the analytical approximation of Machta and Zwanzig [Phys.Rev.Lett. 50, 1959 (1983)] showing that their argument is only correct in the limit of high densities. We discuss how the Machta-Zwanzig argument, which is based on treating diffusion as a Markovian hopping process on a lattice, can be corrected systematically by including microscopic correlations. We furthermore show that, on a fine scale, the diffusion coefficient is a non-trivial function of the density. We finally argue that, on a coarse scale and for lower densities, the diffusion coefficient exhibits a Boltzmann-like behavior, whereas for very high densities it crosses over to a regime which can be understood qualitatively by the Machta-Zwanzig approximation.

We propose an informal test for stationarity in a time series which checks for the compatibility of nonlinear approximations to the dynamics made in different segments of the sequence. The segments are compared directly, rather than via statistical parameters. The approach provides detailed information about episodes with similar dynamics during the measurement period. Thus physically relevant changes in the dynamics can be followed.

We explain why aliasing can be detected in a generic temporally-sampled stationary signal process. We then define a concept of stationarity that makes sense for single waveforms. (This is done without assuming that the waveform is a sample path of some underlying stochastic process.) We show how to use this concept to detect aliasing in sampled waveforms. The constraint that must be satisfied to make detection of aliasing possible is shown to be fairly unrestrictive. We use simple harmonic signals to elucidate the method. We then demonstrate that the method works for continuous-spectrum signals---specifically, for time series from the Lorenz and Rossler systems. Finally we explain how the method might permit the recovery of additional information about Fourier components outside the Nyquist band.

We suggest an algorithm for determining the proper delay time and the minimum embedding dimension for Takens' delay-time embedding procedure. This method resorts to the rate of change of the spatial distribution of points on a reconstructed attractor with respect to the delay time, and can be successfully applied to a noisy time series which is too noisy to be discriminated from a sutructureless noisy time series by means of the correlation integral, and also indicates that the proper delay time depends on the embedding dimension.

Nowadays there is no universally accepted definition of quantum chaos. In this paper we review and critically discuss different approaches to the subject, such as Quantum Chaology and the Random Matrix Theory. Then we analyze the problem of dynamical chaos and the time scales associated with chaos suppression in quantum mechanics. Summary: 1. Introduction 2. Quantum Chaology and Spectral Statistics 3. From Poisson to GOE Transition: Comparison with Experimental Data 3.1 Atomic Nuclei 3.2 The Hydrogen Atom in the Strong Magnetic Field 4. Quantum Chaos and Field Theory 5. Alternative Approaches to Quantum Chaos 6. Dynamical Quantum Chaos and Time Scales 6.1 Mean-Field Approximation and Dynamical Chaos 7. Conclusions