Detecting synchronization of self-sustained oscillators by external driving with varying frequency

We propose a method for detecting the presence of synchronization of self-sustained oscillator by external driving with linearly varying frequency. The method is based on a continuous wavelet transform of the signals of self-sustained oscillator and external force and allows one to distinguish the case of true synchronization from the case of spurious synchronization caused by linear mixing of the signals. We apply the method to driven van der Pol oscillator and to experimental data of human heart rate variability and respiration.Comment: 9 pages, 7 figure

A novel approach for nonlinearity detection in vibrating systems

This paper proposes a novel approach for nonlinearity detection in vibrating systems. The approach is developed based on a new concept recently proposed by the author known as nonlinear output frequency response functions (NOFRFs) and the properties of the NOFRFs for nonlinear systems with multiple degrees of freedom (mdof). The results of numerical simulation studies verify the effectiveness of the approach. Nonlinear components often represent faults in practical mdof systems including beams. The proposed approach therefore has significant potential in the fault diagnosis of practical mdof engineering systems and structures

Approximate entropy as an indicator of non-linearity in self paced voluntary finger movement EEG

This study investigates the indications of non-linear dynamic structures in electroencephalogram signals. The iterative amplitude adjusted surrogate data method along with seven non-linear test statistics namely the third order autocorrelation, asymmetry due to time reversal, delay vector variance method, correlation dimension, largest Lyapunov exponent, non-linear prediction error and approximate entropy has been used for analysing the EEG data obtained during self paced voluntary finger-movement. The results have demonstrated that there are clear indications of non-linearity in the EEG signals. However the rejection of the null hypothesis of non-linearity rate varied based on different parameter settings demonstrating significance of embedding dimension and time lag parameters for capturing underlying non-linear dynamics in the signals. Across non-linear test statistics, the highest degree of non-linearity was indicated by approximate entropy (APEN) feature regardless of the parameter settings

Laser writing of individual atomic defects in a crystal with near-unity yield

Atomic defects in wide band gap materials show great promise for development of a new generation of quantum information technologies, but have been hampered by the inability to produce and engineer the defects in a controlled way. The nitrogen-vacancy (NV) color center in diamond is one of the foremost candidates, with single defects allowing optical addressing of electron spin and nuclear spin degrees of freedom with potential for applications in advanced sensing and computing. Here we demonstrate a method for the deterministic writing of individual NV centers at selected locations with high positioning accuracy using laser processing with online fluorescence feedback. This method provides a new tool for the fabrication of engineered materials and devices for quantum technologies and offers insight into the diffusion dynamics of point defects in solids.Comment: 16 pages, 8 figure

Nonlinear softening as a predictive precursor to climate tipping

Approaching a dangerous bifurcation, from which a dynamical system such as the Earth's climate will jump (tip) to a different state, the current stable state lies within a shrinking basin of attraction. Persistence of the state becomes increasingly precarious in the presence of noisy disturbances. We consider an underlying potential, as defined theoretically for a saddle-node fold and (via averaging) for a Hopf bifurcation. Close to a stable state, this potential has a parabolic form; but approaching a jump it becomes increasingly dominated by softening nonlinearities. If we have already detected a decrease in the linear decay rate, nonlinear information allows us to estimate the propensity for early tipping due to noise. We argue that one needs to extract information about the nonlinear features (a "softening") of the underlying potential from the time series to judge the probability and timing of tipping. This analysis is the logical next step if one has detected a decrease of the linear decay rate. If there is no discernable trend in the linear analysis, nonlinear softening is even more important in showing the proximity to tipping. After extensive normal form calibration studies, we check two geological time series from paleo-climate tipping events for softening of the underlying well. For the ending of the last ice age, where we find no convincing linear precursor, we identify a statistically significant nonlinear softening towards increasing temperature. The analysis has thus successfully detected a warning of the imminent tipping event.Comment: 22 pages, 11 figures, changed title back, corrected smaller mistakes, updated reference

An Online Unsupervised Structural Plasticity Algorithm for Spiking Neural Networks

In this article, we propose a novel Winner-Take-All (WTA) architecture employing neurons with nonlinear dendrites and an online unsupervised structural plasticity rule for training it. Further, to aid hardware implementations, our network employs only binary synapses. The proposed learning rule is inspired by spike time dependent plasticity (STDP) but differs for each dendrite based on its activation level. It trains the WTA network through formation and elimination of connections between inputs and synapses. To demonstrate the performance of the proposed network and learning rule, we employ it to solve two, four and six class classification of random Poisson spike time inputs. The results indicate that by proper tuning of the inhibitory time constant of the WTA, a trade-off between specificity and sensitivity of the network can be achieved. We use the inhibitory time constant to set the number of subpatterns per pattern we want to detect. We show that while the percentage of successful trials are 92%, 88% and 82% for two, four and six class classification when no pattern subdivisions are made, it increases to 100% when each pattern is subdivided into 5 or 10 subpatterns. However, the former scenario of no pattern subdivision is more jitter resilient than the later ones.Comment: 11 pages, 10 figures, journa

A simple method for detecting chaos in nature

Chaos, or exponential sensitivity to small perturbations, appears everywhere in nature. Moreover, chaos is predicted to play diverse functional roles in living systems. A method for detecting chaos from empirical measurements should therefore be a key component of the biologist's toolkit. But, classic chaos-detection tools are highly sensitive to measurement noise and break down for common edge cases, making it difficult to detect chaos in domains, like biology, where measurements are noisy. However, newer tools promise to overcome these limitations. Here, we combine several such tools into an automated processing pipeline, and show that our pipeline can detect the presence (or absence) of chaos in noisy recordings, even for difficult edge cases. As a first-pass application of our pipeline, we show that heart rate variability is not chaotic as some have proposed, and instead reflects a stochastic process in both health and disease. Our tool is easy-to-use and freely available