195 research outputs found

    Generalized chronotaxic systems: time-dependent oscillatory dynamics stable under continuous perturbation

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    Chronotaxic systems represent deterministic nonautonomous oscillatory systems which are capable of resisting continuous external perturbations while having a complex time-dependent dynamics. Until their recent introduction in \emph{Phys. Rev. Lett.} \textbf{111}, 024101 (2013) chronotaxic systems had often been treated as stochastic, inappropriately, and the deterministic component had been ignored. While the previous work addressed the case of the decoupled amplitude and phase dynamics, in this paper we develop a generalized theory of chronotaxic systems where such decoupling is not required. The theory presented is based on the concept of a time-dependent point attractor or a driven steady state and on the contraction theory of dynamical systems. This simplifies the analysis of chronotaxic systems and makes possible the identification of chronotaxic systems with time-varying parameters. All types of chronotaxic dynamics are classified and their properties are discussed using the nonautonomous Poincar\'e oscillator as an example. We demonstrate that these types differ in their transient dynamics towards a driven steady state and according to their response to external perturbations. Various possible realizations of chronotaxic systems are discussed, including systems with temporal chronotaxicity and interacting chronotaxic systems.Comment: 9 pages, 8 figure

    On the extraction of instantaneous frequencies from ridges in time-frequency representations of signals

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    The extraction of oscillatory components and their properties from different time-frequency representations, such as windowed Fourier transform and wavelet transform, is an important topic in signal processing. The first step in this procedure is to find an appropriate ridge curve: a sequence of amplitude peak positions (ridge points), corresponding to the component of interest. This is not a trivial issue, and the optimal method for extraction is still not settled or agreed. We discuss and develop procedures that can be used for this task and compare their performance on both simulated and real data. In particular, we propose a method which, in contrast to many other approaches, is highly adaptive so that it does not need any parameter adjustment for the signal to be analysed. Being based on dynamic path optimization and fixed point iteration, the method is very fast, and its superior accuracy is also demonstrated. In addition, we investigate the advantages and drawbacks that synchrosqueezing offers in relation to curve extraction. The codes used in this work are freely available for download.Comment: 13 pages, 7 figures, plus 4 supplementary figure

    Application of an Instrumented Tracer in an Abrasion Mill for Rock Abrasion Studies

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    One of research fields in studying dynamics of gravel-bed rivers is the interaction between sediment particles in motion and incision rates in rock-bottom river reaches. This natural phenomenon of rock abrasion was studied in a laboratory in a Dubree-type abrasion (tumbling) mill with the diameter of 711 mm, using different mixtures of fluvial sediments as abrasive media. A set of rock plates of different lithologies was fixed to the inside mill wall to evaluate rock abrasion by moving sediment particles. The dynamics of the abrasion process of the rock plates was studied by a spherical instrumented tracer with the diameter of 99 mm. This paper describes our solution to the problem of recognizing and differentiating between impacts of the instrumented tracer with different bodies: sediment particles, rock plates, soft lining of the mill and steel side plates of the mill. For this purpose, the signal analysis of measured 3D accelerations of the instrumented tracer gave sufficient information to recognize the tribological surrounding and sufficiently describe the intensity of the abrasion process (number and amplitudes of contact forces). An effective and computationally inexpensive algorithm for automatic impact recognition and evaluation was developed, based on time domain analysis. Furthermore, the frequency domain analysis gave a method for discriminating different signals. Both mentioned methods allow us to classify all recorded signals into groups based on similarity of measurement conditions

    A Tutorial on Time-Evolving Dynamical Bayesian Inference

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    In view of the current availability and variety of measured data, there is an increasing demand for powerful signal processing tools that can cope successfully with the associated problems that often arise when data are being analysed. In practice many of the data-generating systems are not only time-variable, but also influenced by neighbouring systems and subject to random fluctuations (noise) from their environments. To encompass problems of this kind, we present a tutorial about the dynamical Bayesian inference of time-evolving coupled systems in the presence of noise. It includes the necessary theoretical description and the algorithms for its implementation. For general programming purposes, a pseudocode description is also given. Examples based on coupled phase and limit-cycle oscillators illustrate the salient features of phase dynamics inference. State domain inference is illustrated with an example of coupled chaotic oscillators. The applicability of the latter example to secure communications based on the modulation of coupling functions is outlined. MatLab codes for implementation of the method, as well as for the explicit examples, accompany the tutorial.Comment: Matlab codes can be found on http://py-biomedical.lancaster.ac.uk

    Inference of Time-Evolving Coupled Dynamical Systems in the Presence of Noise

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    A new method is introduced for analysis of interactions between time-dependent coupled oscillators, based on the signals they generate. It distinguishes unsynchronized dynamics from noise-induced phase slips, and enables the evolution of the coupling functions and other parameters to be followed. It is based on phase dynamics, with Bayesian inference of the time-evolving parameters achieved by shaping the prior densities to incorporate knowledge of previous samples. The method is tested numerically and applied to reveal and quantify the time-varying nature of cardiorespiratory interactions.Comment: 5 pages, 3 figures, accepted for Physical Review Letter
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