Fluctuation and Noise Letters

When the electromyography (EMG) signal is acquired from muscles in the torso, the electrocardiography (ECG) signal coming from heart activity can interfere. As a result, the EMG signal can be contaminated during data collection. In this paper, a technique based on discrete stationary wavelet transform (DSWT) is proposed to remove ECG interference from the EMG signal while taking into account the signal-to-noise ratio (SNR). The contaminated EMG signal is decomposed using 5-level DSWT with the Symlet wavelet function. The coefficients for levels 4 and 5, which are contaminated by ECG, are set to zero when their absolute values are less than or equal to a threshold determined for each SNR level. A clean EMG signal can then be obtained by inverse DSWT mapping of the new thresholded coefficients. We evaluated the performance of the proposed algorithm using simulated EMG contaminated with both simulated and real ECG signals, at 9 SNR levels from $-$20 to 20dB with 5dB increments. The performance based on mean absolute error, correlation coefficient and relative error shows that the DSWT method is better than a high-pass filter.

In this paper, we investigate the impact of random dopant fluctuation (RDF) on the statistical variations in negative capacitance MOSFETs (NCFETs) through a device simulation coupled with the Landau–Khalatnikov (LK) equation. Compact models for feedback mechanisms that are based on the internal gate voltage amplification in NCFETs are proposed. The results show that internal voltage amplification plays a decisive role in performance improvement of device variability. Further, our simulation study demonstrates that owing to the feedback mechanism, the dispersions of the performance parameters in NCFETs exhibit different statistical distribution characteristics compared to their MOSFET counterparts. Our study may provide further insight regarding device and/or circuit designs utilizing NCFETs.

The Trigonometric Box-Cox ARMA Trend Seasonal (TBATS) model has been designed to estimate complex cyclical patterns (e.g., weeks within years) in time series data. This paper seeks to evaluate its applicability to educational data, daily school attendance in particular. Attendance rates in four high schools are analyzed over a ten year period using TBATS to illustrate the presence of both weekly and annual patterns in three of the schools and only weekly patterns in the fourth. The model features are explicated and it is shown how the estimation of weekly and annual cycles enhances the description of the data and improves our understanding of how the assessment of endogenous variability contributes to our understanding of daily high school attendance behavior. R script is provided in an appendix.

A class of spiking neuronal models with threshold 2 is considered. It is defined by a set of conditions typical for basic threshold-type models, such as the leaky integrate-and-fire (LIF) or the binding neuron model, and also for some artificial neurons. A neuron is stimulated with a Poisson stream of excitatory impulses. Each output impulse is conveyed through the feedback line to the neuron input after finite delay $\mathrm{\Delta }$. This impulse is identical to those delivered from the input stream. We have obtained a general relation allowing calculating exactly the probability density function (PDF) $p(t)$ for distribution of the first passage time of crossing the threshold, which is the distribution of output interspike intervals (ISI) values for this neuron. The calculation is based on known PDF $p^0(t)$ for that same neuron without feedback, intensity of the input stream $\lambda$ and properties of the feedback line. Also, we derive exact relation for calculating the moments of $p(t)$ based on known moments of $p^0(t)$.

The obtained general expression for $p(t)$ is checked numerically using Monte Carlo simulation for the case of LIF model. The course of $p(t)$ has a $\delta$-function-type peculiarity. This fact contributes to the discussion about the possibility to model neuronal activity with Poisson process, supporting the “no” answer.

Analysis of surface quality of machined workpiece is an important issue in machining of materials. For this purpose, scientists analyze how the texture of machined surface changes due to different conditions. Machine vibration is one of the factors that highly affects the surface quality of machined surface. In this research, we analyze the relation between machine vibration and surface quality of machined workpiece. For this purpose, we employ fractal theory and analyze how the complex structure of machined surface changes with the complex structure of machine vibration signal in case of variations of machining parameters, namely, depth of cut, feed rate and spindle speed, in milling operation. Based on the results, variations of surface quality of machined workpiece are related with the variations of complexity of machine vibration signal. The method of analysis employed in this research can be applied to other machining operations in order to find the relation between machine vibration and surface quality of machined workpiece.

This paper aims to explore the Lévy noise-induced effects in underdamped asymmetric bistable system. Lévy noise is generated by Janicki–Weron algorithm which is different from the usual Gaussian noise. The numerical solutions of system equation are obtained by the fourth-order stochastic Runge–Kutta algorithm. Then the quasi-steady-state probability density (QSPD) is obtained by solving the equation of system, and the stochastic resonance (SR) is determined by the classical measure of signal-to-noise ratio (SNR). The influence of various parameters of the Lévy noise and the system parameters on QSPD and SNR is discussed. Noise-induced transitions occur by varying the parameters of the Lévy noise and the driven system. Moreover, within certain limits, the larger value of the stability index $\alpha$ of Lévy noise, signal amplitude $A$, and the absolute values of asymmetric parameter $r$ can give rise to the SR phenomenon. On the contrary, the larger values of skewness parameters $\beta$ of Lévy noise and damping parameter $\gamma$ further weaken the occurrence of the SR phenomenon in the given system.

Acoustic feedback is the main problem associated with (broadband) active noise control (ANC) in feed-forward configuration. To solve this problem, auxiliary noise is injected into the ANC system to have accurate model of the feedback path. The conventional approaches substantially model the feedback path at the cost of degradation in the noise reduction performance (NRP) and vice versa. Here, we propose a fuzzy logic-based gain scheduling (of auxiliary noise) for online feedback path modeling and neutralization (FBPMN). The fuzzy controller computes the instantaneous gain for auxiliary noise based on two inputs, the first input gives information about modeling status of FBPMN filter while the second input gives information regarding variance of the disturbance signal for the adaptive FBPMN filter. Furthermore, variable step-size (VSS) is used for the adaptation of FBPMN filter that uses a larger value of step-size during modeling (phase) of FBPMN filter for fast adaptation and a smaller value when the feedback path has been (adequately) modeled. Fuzzy gain scheduler together with VSS in FBPMN filter improves both the NRP and feedback path modeling characteristics. Moreover, the proposed method does not require initial offline modeling of the feedback path thereby demonstrating complete online operation. To make the system computationally efficient, a look-up table-based approach is also proposed in this paper. Simulations results are presented to demonstrate the effectiveness of the proposed method.

This work involves the application of a non-linear method, multifractal detrended fluctuation analysis (MFDFA), to describe fish sound data recorded from the open waters of two major estuarine systems. Applying MFDFA, the second-order Hurst exponent $h(q=2)$ values are found to be $1.10\pm 0.03$ and $1.43\pm 0.02$ for the fish families Batrachoididae (common name: Toadfish) and Sciaenidae (common name: Croakers, drums), respectively. The generalized Hurst exponent $h(q)$-related width parameters $\mathrm{\Delta }h(q)$ are found to be $1.05\pm 0.08$ and $0.94\pm 0.06$, respectively, for toadfish and Sciaenidae vocalizations, implying greater heterogeneity and multifractal characteristics. The results suggest that the Sciaenidae fish calls are smoother in comparison with Batrachoididae. Clustering of multifractal spectrum-related parameters with respect to toadfish and Sciaenidae vocalization characteristics is observed in this analyses.

Singular Spectrum Analysis (SSA) is an increasingly popular time series filtering and forecasting technique. Owing to its widespread applications in a variety of fields, there is a growing interest towards improving its forecasting capabilities. As such, this paper takes into consideration the Recurrent forecasting approach in SSA (SSA-R) and presents a new mechanism for improving the accuracy of forecasts attainable via this method. The proposed Recurrent SSA-R approach is referred to as Weighted SSA-R (W:SSA-R), and we propose using a weighting algorithm for weigthing the coefficients of the Linear Recurrent Relation (LRR). The performance of forecasts from the W:SSA-R approach are compared with forecasts from the established SSA-R approach. We exploit real data and various simulated time series for the comparison, so as to provide the reader with more conclusive findings. Our results confirm that the W:SSA-R approach can provide comparatively more accurate forecasts and is indeed a viable solution for improving forecasts by SSA.

Physical layer key generation exploiting inherent channel randomness is an open research area in securing the networks with resource constraint nodes; therefore reduction of numerical computation is desirable to save battery power. However, the correlated components due to colored noise also affect the system performance. In this work, we consider the correlated colored noise components along with the additive white Gaussian noise (AWGN) in the wireless channel and analyze the effect of these correlated components on the system performance. We further propose a hybrid averaging and dimensionality reduction (AvDR), based received signal strength (RSS) preprocessing which is the combination of moving window averaging (Av) and principal component analysis (PCA) as dimensionality reduction technique (DR) to improve the system performance. Further, the system performance was evaluated by numerical simulations, and it is observed that the same improvement in system performance is achieved by generating keys from a fewer number of points selected after PCA as compared to processing all the points. Picking a few of the points in the data sequence instead of all reduces the total number of numerical calculations and saves system power, which is the primary requirement of resource constraint networks like the IoT.