Effect of Static Bifurcation on Logical Stochastic Resonance in a Symmetric Bistable System

In previous research works, logical stochastic resonance (LSR) was reported to frequently occur in an asymmetric bistable system, where the bias parameter is the key factor to make the LSR appear. In this work, we investigate the effect of different anharmonic periodic signals on the pitchfork and saddle-node bifurcations in a symmetric bistable system. We focus on the relationship between the static bifurcation and LSR. We use both numerical and circuit simulations to analyze some interesting phenomena. Like the bias parameter, some anharmonic periodic signals also break the symmetry of the symmetric bistable system and lead to the saddle-node bifurcation. The anharmonic periodic signal with a constant term in its expanded Fourier series induces a reliable LSR in the symmetric bistable system. The key factor of LSR is the saddle-node bifurcation which implies the asymmetry of the system. Here, we replace the bias parameter by choosing an anharmonic periodic signal and make the LSR occur in a different way.

Searching for Diamagnetic Blob Accretion in the 74 day K2 Observation of V2400 Ophiuchi

Since its discovery in 1995, V2400 Ophiuchi (V2400 Oph) has stood apart from most known intermediate polar cataclysmic variables due to its proposed magnetic field strength (9–27 MG) and diskless accretion. To date, the exact accretion mechanism of the system is still unknown, and standard accretion models fail to accurately predict the peculiar behavior of its light curve. We present the K2 Campaign 11 light curve of V2400 Oph recording 74.19 days of photometric data cadenced at 1 minute. The light curve is dominated by aperiodic flickering and quasiperiodic oscillations, which make the beat and spin signals inconspicuous on short timescales. Notably, a log–log full power spectrum shows a break frequency at ∼102 cycles d−1 similar to some disk-fed systems. Through power-spectral analysis, the beat and spin periods are measured as 1003.4 ± 0.2 s and 927.7 ± 0.1 s, respectively. A power spectrum of the entire K2 observation demonstrates beat period dominance. However, time-resolved power spectra reveal a strong dependence between observation length and the dominant frequency of the light curve. For short observations (2–12 hr) the beat, spin, or first beat harmonic can be observed as the dominant periodic signal. Such incoherence and variability indicate a dynamical accretion system more complex than current intermediate polar theories can explain. We propose that a diamagnetic blob accretion model may serve as a plausible explanation for the accretion mechanism.

Mass-ratio and Magnetic Flux Dependence of Modulated Accretion from Circumbinary Disks

Accreting supermassive binary black holes (SMBBHs) are potential multimessenger sources because they emit both gravitational-wave and electromagnetic (EM) radiation. Past work has shown that their EM output may be periodically modulated by an asymmetric density distribution in the circumbinary disk, often called an “overdensity” or “lump;” this modulation could possibly be used to identify a source as a binary. We explore the sensitivity of the overdensity to SMBBH mass ratio and magnetic flux through the accretion disk. We find that the relative amplitude of the overdensity and its associated EM periodic signal both degrade with diminishing mass ratio, vanishing altogether somewhere between 1:2 and 1:5. Greater magnetization also weakens the lump and any modulation of the light output. We develop a model to describe how lump formation results from internal stress degrading faster in the lump region than it can be rejuvenated through accretion inflow, and predicts a threshold value in specific internal stress below which lump formation should occur and which all our lump-forming simulations satisfy. Thus, detection of such a modulation would provide a constraint on both mass ratio and magnetic flux piercing the accretion flow.

Development of Time Delay Estimation Algorithm Using Fuzzy Based Optimized Iterative Unscented Kalman Filter

In radar-based applications, Time Delay Estimation (TDE) is an essential criterion. Because of non-stationary behaviour, estimating the time delay between two turbulent signals is difficult. Existing delay estimation methods such as the cross correlation technique are restricted to stationary signals. The non-stationary signals are either fractal or periodic signal. The accuracy of this method is more reliable for fractal signals than for periodic signals. With a cost function at hand it is sensible to check whether the state correction results in a cost decrease in the first place, new parameter is optimized using Fuzzy Elephant Herding Optimization (FEHO). Further this paper incorporates ADAM based neural network (ADAM-NN) model for efficient time delay estimation. The study resulted in significant improvement upto 21.5% in estimating the time delay when compared with conventional methods.

Avoiding non-linearity of optically pumped magnetometer MEG within an actively shielded two-layer mu-metal room

Some optically pumped magnetometer (OPM) sensors available for biomagnetic investigations have a linear range limited to +- 1 nT due to the specific properties of their open loop operation. In a two-layer magnetically shielded room of type Ak3b/Vacoshield Advanced with an external active compensation we studied how much sensor movement is allowed until amplitudes exceed the linearity range. Intentional movements were performed by a subject wearing an OPM-MEG sensor array. It was found that movements of 8 cm did yield non-linear amplitudes, but a reduction of the movement in half already preserves linearity. Despite movements, the heartbeat was found to generate a periodic signal, although the generating mechanism could not be identified so far.

Symmetric Projection Attractor Reconstruction: Sex Differences in the ECG

Background: The electrocardiogram (ECG) is a key tool in patient management. Automated ECG analysis supports clinical decision-making, but traditional fiducial point identification discards much of the time-series data that captures the morphology of the whole waveform. Our Symmetric Projection Attractor Reconstruction (SPAR) method uses all the available data to provide a new visualization and quantification of the morphology and variability of any approximately periodic signal. We therefore applied SPAR to ECG signals to ascertain whether this more detailed investigation of ECG morphology adds clinical value.Methods: Our aim was to demonstrate the accuracy of the SPAR method in discriminating between two biologically distinct groups. As sex has been shown to influence the waveform appearance, we investigated sex differences in normal sinus rhythm ECGs. We applied the SPAR method to 9,007 10 second 12-lead ECG recordings from Physionet, which comprised; Dataset 1: 104 subjects (40% female), Dataset 2: 8,903 subjects (54% female).Results: SPAR showed clear visual differences between female and male ECGs (Dataset 1). A stacked machine learning model achieved a cross-validation sex classification accuracy of 86.3% (Dataset 2) and an unseen test accuracy of 91.3% (Dataset 1). The mid-precordial leads performed best in classification individually, but the highest overall accuracy was achieved with all 12 leads. Classification accuracy was highest for young adults and declined with older age.Conclusions: SPAR allows quantification of the morphology of the ECG without the need to identify conventional fiducial points, whilst utilizing of all the data reduces inadvertent bias. By intuitively re-visualizing signal morphology as two-dimensional images, SPAR accurately discriminated ECG sex differences in a small dataset. We extended the approach to a machine learning classification of sex for a larger dataset, and showed that the SPAR method provided a means of visualizing the similarities of subjects given the same classification. This proof-of-concept study therefore provided an implementation of SPAR using existing data and showed that subtle differences in the ECG can be amplified by the attractor. SPAR's supplementary analysis of ECG morphology may enhance conventional automated analysis in clinically important datasets, and improve patient stratification and risk management.

Quick Estimation of Periodic Signal Parameters from One-bit Measurements

<div> <div> <div> <p>Estimation of periodic signals, based on quantized data, is a topic of general interest in the area of instrumentation and measurement. While several methods are available, new applications require low-power, low-complexity, and adequate estimation accuracy. In this paper, we consider the simplest possible quantization, that is binary quantization, and describe a technique to estimate the parameters of a sampled periodic signal, using a fast algorithm. By neglecting the possibility that the sampling process is triggered by some signal-derived event, sampling is assumed to be asynchronous, that is the ratio between the signal and the sampling periods is defined to be an irrational number. To preserve enough information at the quantizer output, additive Gaussian input noise is assumed as the information encoding mechanism. With respect to published techniques addressing the same problem, the proposed approach does not rely on the numerical estimation of the maximum likelihood function, but provides solutions that are very closed to this estimate. At the same time, since the main estimator is based on matrix inversion, it proves to be less time-consuming than the numerical maximization of the likelihood function, especially when solving problems with a large number of parameters. The estimation procedure is described in detail and validated using both simulation and experimental results. The estimator performance limitations are also highlighted. </p> </div> </div> </div>

Quick Estimation of Periodic Signal Parameters from One-bit Measurements

<div> <div> <div> <p>Estimation of periodic signals, based on quantized data, is a topic of general interest in the area of instrumentation and measurement. While several methods are available, new applications require low-power, low-complexity, and adequate estimation accuracy. In this paper, we consider the simplest possible quantization, that is binary quantization, and describe a technique to estimate the parameters of a sampled periodic signal, using a fast algorithm. By neglecting the possibility that the sampling process is triggered by some signal-derived event, sampling is assumed to be asynchronous, that is the ratio between the signal and the sampling periods is defined to be an irrational number. To preserve enough information at the quantizer output, additive Gaussian input noise is assumed as the information encoding mechanism. With respect to published techniques addressing the same problem, the proposed approach does not rely on the numerical estimation of the maximum likelihood function, but provides solutions that are very closed to this estimate. At the same time, since the main estimator is based on matrix inversion, it proves to be less time-consuming than the numerical maximization of the likelihood function, especially when solving problems with a large number of parameters. The estimation procedure is described in detail and validated using both simulation and experimental results. The estimator performance limitations are also highlighted. </p> </div> </div> </div>

A simple time-delay memristor and its application in 2D HR neuron model

Since the concept of memristor was proposed, the memristor, as the fourth-generation electronic component, has attracted great attention from researchers. Memristors can be used not only for nonvolatile memory but also to mimic the behavior of nervous system. The inherent nonlinearity of memristor makes it valuable in nonlinear circuits. Since it is still difficult to realize the commercial application of memristors at present, designing suitable memristor models can play a guiding role in practical application. This work proposes a novel memristor model with a time-delay state variable. The proposed memristor cannot only generate pinched hysteresis loop under periodic signal excitation, but also can generate chaotic current or even hyperchaotic current under DC voltage. The nonlinear dynamics of the proposed time-delay memristor are studied by phase portraits, bifurcation diagram and Lyapunov exponents. Furthermore, the proposed memristor is used in the HR neuron model to study neuronal electrical activities with electromagnetic induction. Multiple firing patterns of the memristive neuron can be generated such as periodic, bursting and chaotic. Finally, the memristor emulator circuit and the HR neuron model circuit are designed and simulated by Pspice.