Fluctuation and Noise Letters

This study addresses a new question regarding the security of the Kirchhoff-Law-Johnson-Noise (KLJN) scheme compromised by DC sources at Alice and Bob: What is the impact of these parasitic sources on active attacks, such as the man-in-the-middle (MITM) attack, or the current injection attack if Alice and Bod did not eliminate these sources? The surprising answer is that the parasitic DC sources actually increase the security of the system because, in the case of the MITM attack, they make easier to uncover the eavesdropping than the ideal situation. In some of the cases, Eve can fix this deficiency but then the problem gets reduced to the original MITM attack to which the KLJN scheme is immune, it is already proven earlier.

In this paper, the dynamical complexity of FHN neuron system under the co-excitation of correlated noises and periodic signals is studied by means of information theory measures. Based on the definition of statistical complexity and normalized Shannon entropy, as well as the Bandt–Pompe algorithm, the total statistical complexity and the total normalized Shannon entropy of the FHN neuron system are calculated. Because the potential function of the system is asymmetric, we also calculate the statistical complexity and the normalized Shannon entropy in two different potential wells respectively. Moreover, the effects of additive noise intensity, multiplicative noise intensity, noise correlation time, cross-correlation strength and amplitude on dynamical complexity are analyzed.

In this paper, we propose Jensen–Shannon divergence (JSD) based on horizontal visibility graph (HVG) to measure the time series irreversibility for both stationary and non-stationary series efficiently. Numerical simulations are first conducted to show the validity of the proposed method and then empirical applications to the financial time series and traffic time series are investigated. It can be found that JSD shows better robustness than Kullback–Leibler divergence (KLD) on quantifying time series irreversibility and correctly distinguishes the different type of simulated series. For the empirical analysis, JSD based on HVG is able to detect the significant time irreversibility of stock indices and reveal the relationship between different stock indices. JSD results show the time irreversibility of speed time series for different detectors and present better accuracy and robustness than KLD. The hierarchical clustering based on their behavior of time irreversibility obtained by JSD classifies the detectors into four groups.

A multi-microphone array speech enhancement method using Generalized Sidelobe Canceller (GSC) beamforming with Combined Postfilter (CP) and Sparse Non-negative Matrix Factorization (SNMF) is proposed in this paper. GSC beamforming with CP and SNMF is implemented to reduce directional noise, diffuse noise, residual noise and to separate interferences in adverse environment. In this paper, the directional noise is reduced using GSC beamforming, whereas the diffuse noise in each subband is reduced with a combined postfilter using Unconstrained Frequency domain Normalized Least Mean Square (UFNLMS) algorithm. Finally, the residual noise at the output of CP is eliminated by SNMF which optimizes the noise. The performance of the proposed method is evaluated using parameters like PESQ, SSNR, STOI, SDR and LSD. The noise reduction for four and eight microphones is compared and illustrated in spectrograms. The proposed method shows better performance in terms of intelligibility and quality when compared to the existing methods in adverse environments.

Traffic noise barriers are one of the most important ways of environmental noise pollution control. Profiled barriers are one of the most successful noise control screens. A simple mathematical model representation of the zones behind rigid and absorbent barriers with the highest insertion loss using the destructive effect of the indirect path via barrier image is introduced. The results are compared with the results of a verified two-dimensional (2D) BEM in a wide field behind those barriers. Field measurements for the model near a highway in a megacity were also tested. A very good agreement between the results has been achieved. In this method, effective height is used for any different profile barrier. Since the highway dominant noise frequency was found to be at 125 Hz 1/3-octave band center frequency, the presented model in this study showed that the best place for installing a T-shaped barrier is 10.5m from the target receiver. The introduced model is very simple and fast and could be used for choosing the best location of profiled barriers to achieve the highest performance with no additional cost.

Surface electromyography (sEMG) is a non-invasive technique to assess the electrical activity of contracting skeletal muscles. sEMG-based muscle fatigue detection plays a key role in sports medicine, ergonomics and rehabilitation. These signals are random, multicomponent, nonlinear and the degree of fluctuations is higher in dynamic contractions. Hence, the extraction of reliable biomarkers remains a challenging task. In this work, an attempt has been made to differentiate non-fatigue, and fatigue conditions using nonlinear techniques, namely, binary and weighted Visibility Graph (VG) features. For this, signals are recorded from the biceps brachii muscle of 52 healthy adult volunteers. These signals are preprocessed, and the contractions associated with the non-fatigue and fatigue conditions are segmented. The graph transformation is performed, and first-order and second-order statistics, along with entropy measures, are extracted from the degree distribution. Parametric and non-parametric machine learning methods are applied for the classification. The results show that the proposed VG approach is able to capture the fluctuations of the signals in non-fatigue and fatigue conditions. Further, all extracted features exhibit a significant difference with $p$ $<0.05$. Maximum accuracy of 89.1% is achieved with information gain selected features and extreme learning machines classifier. Additionally, weighted VG features perform better than the binary version with a difference in the accuracy of 5%. It appears that the proposed approach could be used in real-time implementation for the monitoring of muscle fatigue conditions.

In this paper, the performance of compressive sensing (CS)-based technique for speech enhancement has been studied and results analyzed with recovery algorithms as a comparison of their performances. This is done for several recovery algorithms such as matching pursuit, orthogonal matching pursuit, stage-wise orthogonal matching pursuit, compressive sampling matching pursuit and generalized orthogonal matching pursuit. Performances of all these greedy algorithms were compared for speech enhancement. The evaluation of results has been carried out using objective measures (perceptual evaluation of speech quality, log-likelihood ratio, weighted spectral slope distance and segmental signal-to-noise ratio), simulation time and composite objective measures (signal distortion C${}_{SIG}$, background intrusiveness C${}_{BAK}$ and overall quality C${}_{OVL})$. Results showed that the CS-based technique using generalized orthogonal matching pursuit algorithm yields better performance than the other recovery algorithms in terms of speech quality and distortion.

We utilize the multifractal detrended cross-correlation analysis (MF-DCCA) to investigate the cross-correlations between the US economic policy uncertainty (EPU) and US stock markets in the framework of Fractal Market Hypothesis (FMH). The data contain daily closing values of EPU, and the returns of Dow Jones Industrial Average Index (DJI), S&P 500 index (GSPC) and NASDAQ Composite Index (IXIC). Our empirical results show that changes in EPU and fluctuations in the US stock markets interact in a nonlinear way. Furthermore, there exists significant multifractality in the cross-correlations between EPU and stock markets. The cross-correlations exhibit dynamics and are affected by major international events. We capture the underlying mechanisms such as multifractality and nonlinear relation that dominate EPU-US stock markets nexus by means of FMH. The findings add a new dimension to the existing literature, and are important for market participants to adjust investment decisions.

Analysis of fluctuations of uterine contractions under varied gestational ages is clinically significant. In this work, fluctuations associated with Preterm pregnancies are analyzed. For this, uterine electromyography (uEMG) signals in Preterm condition with varied gestational ages are considered. The signals are subjected to Adaptive Fractal Analysis (AFA) where a global trend is identified using overlapping windows of varying orders. The signal is detrended and fluctuation function is estimated. Hurst exponent features are extracted and analyzed statistically. Results show that AFA is able to characterize the variations in the fluctuations of Preterm uEMG signals. The feature values are distinct and vary with gestational age. Coefficient of variation is observed to be low, indicating that these features could handle inter-subject variability of Preterm signals. As early diagnosis of premature delivery is imperative for timely medical intervention and treatment, it appears that the proposed features could aid in determining the changes in uterine muscle contractions in Preterm condition.

Spectrum seems to be the lifeblood of wireless communication, which is of high demand as the traffic doubles every year. This increasing demand drives towards 5G NR, which is expected to support 100 folds increase in mobile devices, gigabit user data rate, ultra-low latency, high traffic and ultra-reliability. Since most of the available spectrum has been saturated, new methods that make use of spectrum in efficient manner must be considered. Through flexible 5G NR framework, 5G is aimed to utilize shared/unlicensed spectrum. Cognitive radio is the key enabler for operating through shared/unlicensed spectrum. In this situation, network interference caused by secondary users (SU) in accessing the unlicensed band for cognitive radio can be overcome by implementation of efficient resource management techniques. The interference issue among SU can be minimized to a large extent by efficiently allocating the available spectrum. Fuzzy Analytic hierarchy process (FAHP) seems to be an appropriate solution for spectrum allocation among SU without creating interference among themselves. The mathematical model proves that FAHP allocated the spectrum to the best SU.