JournalTOCs

Fluctuation and Noise Letters
Journal Prestige (SJR): 0.27

Citation Impact (citeScore): 1
Number of Followers: 0

Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0219-4775 - ISSN (Online) 1793-6780
Published by World Scientific

[120 journals]

Measurement on the National Competitive Advantage Based on Two-Mode MRIO
Network: Taking Asian, European and African Countries along the Belt and
Road for Example
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  Authors: Yanni Wang, Wen Chen, Lizhi Xing
  Abstract: Fluctuation and Noise Letters, Ahead of Print.
  For the Belt and Road Initiative, “extensive consultation, joint contribution and shared benefits” is the basic principle, and the competitiveness heterogeneity is an important condition for cooperation. Based on the complex network theory, this paper constructs a complex network model of global value chain (GVC) division of labor system by using the Multi-Regional Input-Output (MRIO) table and reveals the variation trend of competitiveness of industrial sectors and economies on the GVC network by the National Competitive Advantage Index (NCAI). The results verify the effectiveness of BRI and help countries along the BRI route to explore their comparative advantages and cooperation prospects with other countries. The research also provides a direction and reference for China to better implement the Initiative.
  Citation: Fluctuation and Noise Letters
  PubDate: 2022-05-11T07:00:00Z
  DOI: 10.1142/S0219477522500377
Comparison of Price-Volume Correlation for Some Cryptocurrencies Based on
MF-ADCCA
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  Authors: Yan Yan, Wei Shao, Jian Wang
  Abstract: Fluctuation and Noise Letters, Ahead of Print.
  In this paper, we explored the price-volume cross-correlation asymmetric multifractal properties of some cryptocurrencies using multifractal asymmetric detrended cross-correlation analysis (MF-ADCCA). Bitcoin, Ethereum, Tether, Binance Coin, Cardano, and Dogecoin are examples of cryptocurrencies. The empirical results reveal that the price-volume cross-correlations of all six cryptocurrencies display anti-persistent, multifractal, and asymmetric features, although the degree of these characteristics differs depending on the cryptocurrencies and market trends. Specifically, the anti-persistence multifractal of the price-volume cross-correlation is significantly stronger in the upward market than in the downward market for Bitcoin and Ethereum; for the remaining four cryptocurrencies, the opposite result is obtained. Furthermore, Dogecoin price-volume cross-correlation exhibits the most marked anti-persistence and the most significant asymmetric multifractality among the six cryptocurrencies analyzed.
  Citation: Fluctuation and Noise Letters
  PubDate: 2022-05-07T07:00:00Z
  DOI: 10.1142/S0219477522500407
Asymmetric Multifractal Cross-Correlations Between Economic Policy
Uncertainty and Agricultural Futures Prices
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  Authors: You-Shuai Feng, Yang Li, Bao-Ming Cao
  Abstract: Fluctuation and Noise Letters, Ahead of Print.
  This paper investigates the fluctuation characteristics and asymmetry of cross-correlations between economic policy uncertainty (EPU) and agricultural futures prices in China and the US by using the multifractal detrended cross-correlation analysis (MF-X-DFA) and multifractal asymmetric detrended cross-correlation analysis (MF-ADCCA). We find that the multifractal cross-correlations exist between EPU and agricultural futures prices, and the cross-correlations are anti-persistent and asymmetric. The anti-persistent cross-correlations in China are all stronger than those in the US. The multifractal degree of cross-correlation between EPU and soybean futures price is lower in China than in the US, while the multifractal degree of cross-correlation between EPU and soybean meal, soybean oil or corn futures price is higher in China than in the US. Moreover, China’s soybean futures price is more susceptible to the upward and downward trends in China’s EPU, while the US soybean meal, soybean oil, and corn futures prices are more susceptible to them in the US EPU.
  Citation: Fluctuation and Noise Letters
  PubDate: 2022-04-22T07:00:00Z
  DOI: 10.1142/S0219477522500353
Robustness of Detrended Cross-correlation Analysis Method Under Outliers
Observations
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  Authors: Zouhaier Dhifaoui
  Abstract: Fluctuation and Noise Letters, Ahead of Print.
  The computation of the bivariate Hurst exponent constitutes an important technique to test the power-law cross-correlation of time series. For this objective, the detrended cross-correlation analysis (DCCA) method represents the most used one. In this paper, we prove the robustness of the DCCA method, where the trend is estimated using the polynomial fitting, to estimate the bivariate Hurst exponent when time series are corrupted by outliers observations. On the other hand, we give the exact polynomial order and a regression region for computing a detrended cross-correlation function to obtain a least square estimator of bivariate Hurst exponent. Our theoretical results are shown by a simulation study on a two-fractional Gaussian noise process corrupted by outliers observations. Additionally, our results are applied to financial time series. The empirical findings results are accompanied by interpretations.
  Citation: Fluctuation and Noise Letters
  PubDate: 2022-04-21T07:00:00Z
  DOI: 10.1142/S0219477522500390
Multivariate Regression with Stable Errors Using Order Statistics
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  Authors: Reza Alizadeh Noughabi, Adel Mohammadpour
  Abstract: Fluctuation and Noise Letters, Ahead of Print.
  In this paper, we investigate a multivariate regression model with multivariate heavy-tailed stable errors. Since in heavy-tailed data, especially in multivariate stable distributions, some moments do not exist; classical multivariate regression methods do not perform well. We suggest using an effective property of the existence of some moments of order statistics stable distribution. We propose a method for trimming the data set using this property. Then, we estimate the regression coefficients based on the rest of the ordered data. We calculate the trimmed data set based on the error’s tail index and skewness parameters. Also, we analytically compute the bias and variance of the introduced estimators of the regression parameters. Finally, we study the performance of the proposed methods with ordinary least squares via a simulation study and a real data set.
  Citation: Fluctuation and Noise Letters
  PubDate: 2022-04-18T07:00:00Z
  DOI: 10.1142/S0219477522500298
Persistence Analysis of a Stochastic Single Species Population Model with
Allee Effect
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  Authors: Chaoqun Xu, Guohua Li
  Abstract: Fluctuation and Noise Letters, Ahead of Print.
  This paper considers a stochastic single species population model with the Allee effect. We mainly analyze the persistence of population, and obtain the strictly critical conditions of persistence and extinction of population: When [math], the population will go extinct in probability; when [math], the stochastic model possesses a unique stationary distribution which implies that the population is permanent. The obtained results are finally verified through numerical simulations.
  Citation: Fluctuation and Noise Letters
  PubDate: 2022-04-18T07:00:00Z
  DOI: 10.1142/S0219477522500328
Exhibition of Noise-Aided Stochastic Resonance by Discontinuity Detectors
in Smartphone Images
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  Authors: Deepak Dhillon, Rajlaxmi Chouhan
  Abstract: Fluctuation and Noise Letters, Ahead of Print.
  The use of smartphone cameras for capturing photographs has seen an exponential growth in the last decade. The noise present in these photographs significantly deviates from the popular Independently Identically Distributed (i.i.d.) Additive White Gaussian Noise (AWGN) noise, and thus the conclusions drawn from simulated-AWGN cannot be directly applied to the smartphone’s true noise. This paper is the first reporting of the exhibition of Stochastic Resonance (SR) or noise-induced threshold-crossing in three genres of discontinuity detectors used in image processing — corner detector, line detector, and edge detector for real-world smartphone images. For the images under investigation, the performance of these detectors is quantified w.r.t. parameters representing intrinsic noise. Observations suggest that all these detectors inherently exhibit the phenomenon of SR due to the fundamental assistance offered by controlled amount of noise in crossing detector thresholds. The manifestations of SR — constant parameter value with varying noise and varying parameter value with constant noise — are demonstrated to exhibit SR in each of the three detectors.
  Citation: Fluctuation and Noise Letters
  PubDate: 2022-04-06T07:00:00Z
  DOI: 10.1142/S0219477522500389
Novel Results for Markov Jump 2D Interfered Digital Filters with
State-Delay and Saturation Nonlinearities
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  Authors: Mani Kant Kumar
  Abstract: Fluctuation and Noise Letters, Ahead of Print.
  Existing results on [math] stability analysis problems for 2D digital filters are limited to external interference and saturation arithmetic. Unfortunately, developed results so far are not sufficient to tackle Markovian jumping parameters (MJPs) and state-delay. Such stability problem for 2D digital filters under MJPs, saturation arithmetic, state-delay and external interference has not been considered in the existing literature. In this paper, novel [math] performance analysis criterion is first proposed for 2D interfered digital filters in which state-delay, saturation nonlinearities and MJPs are considered. The mathematical model of the underlying 2D system is described by the Roesser model. Furthermore, improved [math] performance analysis criterion over the existing result is also obtained for 2D interfered digital filters with saturation nonlinearities only. At the end, two examples are employed to illustrate the effectiveness of the devised stability results.
  Citation: Fluctuation and Noise Letters
  PubDate: 2022-03-23T07:00:00Z
  DOI: 10.1142/S0219477522500365
Efficiency and Long-Range Correlation in G-20 Stock Indexes: A Sliding
Windows Approach
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  Authors: E. F. Guedes, R. P. C. Santos, L. H. R. Figueredo, P. A. da Silva, R. M. T. S. Dias, G. F. Zebende
  Abstract: Fluctuation and Noise Letters, Ahead of Print.
  This paper aims to analyze whether the financial crises of the past 20 years have reduced efficiency, in its weak form, in 19 stock markets belonging to the 20 most developed economies (G-20). The sample period comprises the period from 2 January 2000 to 5 February 2021 with the respective financial crises, namely, Dot-com, Argentina, Subprime, Sovereign debt, China stock market crash (2015–2016), UK’s withdrawal from the European Union and the global pandemic of 2020. The results highlight that most markets show signs of (in)efficiency in each sliding window (1000 days), that is, they show asymmetries and non-Gaussian distributions, and [math]. These findings suggest that the random walk hypothesis is rejected in certain markets, which has implications for investors, since some returns may be expected, creating arbitrage and abnormal profit opportunities, contrary to the random walk and informational efficiency hypotheses. The results found also open room for market regulators to take steps to ensure better informational data across international financial markets.
  Citation: Fluctuation and Noise Letters
  PubDate: 2022-03-16T07:00:00Z
  DOI: 10.1142/S021947752250033X
Automatic Brain Tumor Detection and Classification Based on IoT and
Machine Learning Techniques
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  Authors: Revathi Sundarasekar, Ahilan Appathurai
  Abstract: Fluctuation and Noise Letters, Ahead of Print.
  Brain tumor detection, segmentation, and classification are essential in clinical diagnosis and efficient treatment. Researchers have recently shown a greater interest in attaining accurate brain tumor categorization using the Internet of Things (IoT) and machine learning. The rigidity of tumor classification and segmentation in magnetic resonance imaging is due to large data and indistinct boundaries. Hence, in this study, Machine Learning assisted Automatic Brain Tumor Detection Framework (MLABTDF) has been proposed using IoT. Our study includes establishing a deep convolutional neural network (DCNN) for spotting brain tumors from magnetic resonance imageries. This article accommodated technologies of the IoT for helping brain treatment specialists in identifying the need to make surgeries contingent on MR images. The standard medical image dataset has been gathered and experimentally examined to validate the accuracy, efficiency, specificity, sensitivity, optimum automatic recognition for non-tumor and tumor regions, and the model’s error rate utilizing statistical construction. This study pays its ability in brain irregularity recognition and analysis in the healthcare sector without humanoid intermediation. Compared to other systems, the experimental results show that the recommended MLABTDF model improves efficiency by 95.7%, segmentation and classification accuracy by 99.9%, specificity by 97.3%, sensitivity by 96.4%, optimal automatic detection by 93.4%, Matthews correlation coefficient ratio by 97.1% and error rate by 10.2%.
  Citation: Fluctuation and Noise Letters
  PubDate: 2022-03-02T08:00:00Z
  DOI: 10.1142/S0219477522500304
Nearest Neighbor Optimal Smooth Denoising Dynamic Classification Method
for Financial Time Series
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  Authors: Bing Liu, Chengli Zheng, Huanhuan Cheng
  Abstract: Fluctuation and Noise Letters, Ahead of Print.
  In view of the problem of excessive noise in financial time series, this paper proposes a nearest neighbor dynamic time warping classification method for financial time series based on the optimal smooth denoising model (osdDTW2). First, the optimal smooth denoising model is improved, and then the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method is used to decompose the time series signal. Then, the improved optimal smooth denoising model is used to construct a low-pass filter to do the denoising of the time series. After being denoised, the time series are aligned by dynamic time warping (DTW), Finally, the nearest neighbor method is used for classification. This paper also uses the UCR datasets to verify the effectiveness of the proposed method and applies it method to financial time series classification. The experimental results suggest that osdDTW2 ([math]) can improve the effectivness of the benchmark algorithm (DTW) to some extent.
  Citation: Fluctuation and Noise Letters
  PubDate: 2022-02-28T08:00:00Z
  DOI: 10.1142/S0219477522500341
Statistical Random Number Generator Attack Against the
Kirchhoff-Law-Johnson-Noise (KLJN) Secure Key Exchange Protocol
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  Authors: Christiana Chamon, Shahriar Ferdous, Laszlo B. Kish
  Abstract: Fluctuation and Noise Letters, Ahead of Print.
  This paper introduces and demonstrates four new statistical attacks against the Kirchhoff-Law-Johnson-Noise (KLJN) secure key exchange scheme. The attacks utilize compromised random number generators (RNGs) at Alice’s/Bob’s site(s). The case of partial correlations between Alice’s/Bob’s and Eve’s probing noises is explored, that is, Eve’s knowledge of Alice’s and Bob’s noises is limited but not zero. We explore the bilateral situation where Eve has partial knowledge of Alice’s and Bob’s RNGs. It is shown that in this situation Eve can crack the secure key bit by taking the highest cross-correlation between her probing noises and the measured voltage noise in the wire. She can also crack the secure key bit by taking the highest cross-correlation between her noise voltages and her evaluation of Alice’s/Bob’s noise voltages. We then explore the unilateral situation in which Eve has partial knowledge of only Alice’s RNG thus only those noises (of Alice and Eve) are correlated. In this situation, Eve can still crack the secure key bit, but for sufficiently low error probability, she needs to use the whole bit exchange period for the attack. The security of the KLJN key exchange scheme, similarly to other protocols, necessitates that the RNG outputs are truly random for Eve.
  Citation: Fluctuation and Noise Letters
  PubDate: 2022-02-25T08:00:00Z
  DOI: 10.1142/S0219477522500274
Analysis of Symmetrical and Unsymmetrical Faults Using the EEMD and
Scale-Dependent Intrinsic Entropies
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  Authors: Wei-Tai Hsu, Chia-Wei Huang
  Abstract: Fluctuation and Noise Letters, Ahead of Print.
  The rapid and accurate diagnosis of power grid faults plays a vital role in speeding up the process of accident handling and system recovery and ensuring the safe operation of the power system. This paper proposes to apply the ensemble empirical mode decomposition (EEMD) method and scale-related intrinsic entropy to diagnose the type of fault for the transmission line. First, the electrical data collected by the power system is decomposed by using the EEMD method. Then by eliminating some intrinsic mode functions, the signal is reconstructed by inspecting the correlation coefficient. Finally, the complexity of the reconstructed signal is calculated by using the scale-dependent intrinsic entropy. Since the scale-dependent intrinsic entropy reflects the complexity of one-dimensional time series, it is susceptible to signal changes. The complexity is helpful in the power system for fault signal analysis. The results show the combined method’s effectiveness and practicability through failure analysis using the IEEE 14-bus system as the simulation model.
  Citation: Fluctuation and Noise Letters
  PubDate: 2022-02-16T08:00:00Z
  DOI: 10.1142/S0219477522500316
The Impact of Polysilicon Gate Doping on the Low Frequency Noise of MOS
Transistors
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  Authors: Eleftherios G. Ioannidis, Friedrich P. Leisenberger, Karl Rohracher, Rainer Minixhofer
  Abstract: Fluctuation and Noise Letters, Ahead of Print.
  New results are presented for the low frequency noise (LFN) characterization of N-MOS and P-MOS from a standard CMOS technology node. The impact of n[math] and p[math] polysilicon gate doping on the LFN for N-MOS and P-MOS devices has been investigated. The results demonstrate that the higher p[math] poly doping of the P-MOS improves the noise performance up to a factor of six. The N-MOS device with higher n[math] poly doping shows no significant change in the LFN level. It is interesting to note that the effective Coulomb scattering prefactor [math] increased for both devices.
  Citation: Fluctuation and Noise Letters
  PubDate: 2022-02-10T08:00:00Z
  DOI: 10.1142/S0219477522500286
[math]: A New Approach to Measure Contagion Effect on Financial Crisis
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  Authors: E. F. Guedes, A. P. N. de Castro, A. M. da Silva Filho, G. F. Zebende
  Abstract: Fluctuation and Noise Letters, Ahead of Print.
  In this paper, we implemented a new approach of measuring contagion effect on financial crisis based on the Detrended Multiple Cross-Correlation Coefficient, [math], with a statistical test to assess its significance. Our study is restricted to the particular case in which three stock indexes are analyzed at the same time, with the results being divided into simulated and empirical cases. The simulated case was important to present the probability distribution function of [math] and [math], respectively, as well as confidence intervals for [math]. The empirical case presents [math] and [math] for fourteen stock market indexes in the subprime crisis. With these applications, our study defines contagion effect on the financial system where crisis effect was perceived. In general, our results show the statistical significance of [math], while measure of contagion effect depends on the size of the series and the time scale evaluated.
  Citation: Fluctuation and Noise Letters
  PubDate: 2022-02-07T08:00:00Z
  DOI: 10.1142/S0219477522500262
More on the Reference-Grounding-Based Search in Noise-Based Logic
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  Authors: Walter C. Daugherity, Laszlo B. Kish
  Abstract: Fluctuation and Noise Letters, Ahead of Print.
  We point out that the exponentially fast, grounding-based search scheme in noise-based logic works mostly on core superpositions. When the superposition contains elements that are outputs of logic gate operations, the search result can be erroneous, because grounding of a reference bit can change a logic function too. Adding superpositions with a search bit of inverted signal amplitude sign (sign inversion instead of grounding) can fix the problem in special cases, but a general solution is yet to be found. Note that because phonebooks are core superpositions, the original search algorithm remains valid for phonebook lookups, for both name and number search, including fractions of names or numbers.
  Citation: Fluctuation and Noise Letters
  PubDate: 2021-12-18T08:00:00Z
  DOI: 10.1142/S0219477522500237
From Cold Resistor to Secure Key Exchanger
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  Authors: Jiaao Song, Laszlo B. Kish
  Abstract: Fluctuation and Noise Letters, Ahead of Print.
  Utilizing a formerly published cold resistor circuitry, a secure key exchange system is conceived and explored. A circuit realization of the system is constructed and simulated. Similar to the Pao-Lo key exchanger, this system is secure in the steady-state limit but crackable in the transient situations.
  Citation: Fluctuation and Noise Letters
  PubDate: 2021-12-16T08:00:00Z
  DOI: 10.1142/S0219477522500225
Quantum Entanglement of Free Particles
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  Authors: Roumen Tsekov
  Abstract: Fluctuation and Noise Letters, Ahead of Print.
  In this paper, the Schrödinger equation is solved for many free particles and their quantum entanglement is studied via correlation analysis. Converting the Schrödinger equation in the Madelung hydrodynamic-like form, the quantum mechanics is extended to open quantum systems by adding Ohmic friction forces. The dissipative evolution confirms the correlation decay over time, but a new integral of motion is discovered, being appropriate for storing everlasting quantum information.
  Citation: Fluctuation and Noise Letters
  PubDate: 2021-12-06T08:00:00Z
  DOI: 10.1142/S0219477522500249
How Statistically Significant is the DMCA Coefficient'
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  Authors: Everaldo Freitas Guedes
  Abstract: Fluctuation and Noise Letters, Ahead of Print.
  In this paper, we proposed a statistical test for the Detrending Moving-Average Cross-Correlation Coefficient ([math]). With this methodology, it is possible to evaluate the statistical significance of [math] for different confidence levels. The test was applied to financial market and climatological data. Findings on this research show that rejection or non-rejection of the null hypothesis of [math] depends on the size [math] of the series and the moving average window length [math] evaluated. Our findings also show a behavioral pattern in the critical values of [math]. Fixing the size of the series [math], as the size of the moving average window length [math] increases, the critical values tend to increase.
  Citation: Fluctuation and Noise Letters
  PubDate: 2021-12-03T08:00:00Z
  DOI: 10.1142/S0219477522500213
Effective Technique for Noise Removal and Emotion Recognition in Speech
Signals Using Cat Swarm Optimized Spiking Neural Networks
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  Authors: C. Revathy, R. Sureshbabu
  Abstract: Fluctuation and Noise Letters, Ahead of Print.
  Speech processing is one of the required fields in digital signal processing that helps in processing the speech signals. The speech process is utilized in different fields such as emotion recognition, virtual assistants, voice identification, etc. Among the various applications, emotion recognition is one of the critical areas because it is used to recognize people’s exact emotions and eliminate physiological issues. Several researchers utilize signal processing and machine learning techniques together to find the exact human emotions. However, they fail to attain their feelings with less computational complexity and high accuracy. This paper introduces the intelligent computational technique called cat swarm optimized spiking neural network (CSSPNN). Initially, the emotional speech signal is collected from the Toronto emotional speech set (TESS) dataset, which is then processed by applying a wavelet approach to extract the features. The derived features are further examined using the defined classifier CSSPNN, which recognizes human emotions due to the effective training and learning process. Finally, the proficiency of the system is determined using experimental results and discussions. The proposed system recognizes the speech emotions up to 99.3% accuracy compared to recurrent neural networks (RNNs), deep neural networks (DNNs) and deep shallow neural networks (DSNNs).
  Citation: Fluctuation and Noise Letters
  PubDate: 2021-11-27T08:00:00Z
  DOI: 10.1142/S0219477522500195
Nonlinearity Attack Against the Kichhoff–Law–Johnson-Noise (KLJN)
Secure Key Exchange Protocol
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  Authors: Christiana Chamon, Laszlo B. Kish
  Abstract: Fluctuation and Noise Letters, Ahead of Print.
  This paper introduces a new attack against the Kirchhoff–Law–Johnson-Noise (KLJN) secure key exchange scheme. The attack is based on the nonlinearity of the noise generators. We explore the effect of total distortion ([math]) at the second order ([math]), third order ([math]) and a combination of the second and third orders ([math]) on the security of the KLJN scheme. It is demonstrated that as little as 1% results in a notable power flow along the information channel, which leads to a significant information leak. We also show that decreasing the effective temperature (that is, the wire voltage) and, in this way reducing nonlinearity, results in the KLJN scheme approaching perfect security.
  Citation: Fluctuation and Noise Letters
  PubDate: 2021-11-20T08:00:00Z
  DOI: 10.1142/S0219477522500201
Forecasting Monthly Rainfall using Bio-Inspired Artificial Algae Deep
Learning Network
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  Authors: A. Kala, S. Ganesh Vaidyanathan
  Abstract: Fluctuation and Noise Letters, Ahead of Print.
  Rainfall forecasting is the most critical and challenging task because of its dependence on different climatic and weather parameters. Hence, robust and accurate rainfall forecasting models need to be created by applying various machine learning and deep learning approaches. Several automatic systems were created to predict the weather, but it depends on the type of weather pattern, season and location, which leads in maximizing the processing time. Therefore, in this work, significant artificial algae long short-term memory (LSTM) deep learning network is introduced to forecast the monthly rainfall. During this process, Homogeneous Indian Monthly Rainfall Data Set (1871–2016) is utilized to collect the rainfall information. The gathered information is computed with the help of an LSTM approach, which is able to process the time series data and predict the dependency between the data effectively. The most challenging phase of LSTM training process is finding optimal network parameters such as weight and bias. For obtaining the optimal parameters, one of the Meta heuristic bio-inspired algorithms called Artificial Algae Algorithm (AAA) is used. The forecasted rainfall for the testing dataset is compared with the existing models. The forecasted results exhibit superiority of our model over the state-of-the-art models for forecasting Indian Monsoon rainfall. The LSTM model combined with AAA predicts the monsoon from June–September accurately.
  Citation: Fluctuation and Noise Letters
  PubDate: 2021-11-06T07:00:00Z
  DOI: 10.1142/S0219477522500183
Acoustical Study of a Lossy Gradient-Based Sonic Crystal Using Acoustic
Beamforming
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  Authors: Debasish Panda, Amiya Ranjan Mohanty
  Abstract: Fluctuation and Noise Letters, Ahead of Print.
  Sonic crystals (SCs) are unique periodic structures designed to attenuate acoustic waves in tunable frequency bands known as bandgaps. Though previous works on conventional uniform SCs show good insertion loss (IL) inside the bandgaps, this work is focused on widening their bandgaps and achieving better IL inside the bandgaps by using a gradient-based sonic crystal (GBSC). The GBSC applies property gradient to the conventional SC array by varying its basic properties, i.e., the distance between the scatterers/resonators (lattice constant), and resonator dimensions between the columns and hence the name GBSC. The design of the GBSC is backed by the results of acoustic beamforming experiments conducted over the uniform SCs of hollow scatterers and Helmholtz resonators (HRs) having two-dimensional (2D) periodicity prepared by using Polyvinyl chloride (PVC) pipes without any property gradient and their respective 2D finite element (FE) studies. The experimental and FE simulation results of the uniform SCs were found to be in good agreement and therefore, the GBSC was modeled and analyzed using FE method considering the viscothermal losses inside the resonators. The results indicated that the property gradient improves both Bragg scattering and Helmholtz resonance compared to that of the uniform SCs and therefore, the GBSC exhibits wider attenuation gaps and higher attenuation levels. An array of 30 microphones was used to conduct acoustic beamforming experiments on the uniform SCs. Beamforming was found to be an advanced and fast method to perform quick measurements on the SCs.
  Citation: Fluctuation and Noise Letters
  PubDate: 2021-10-29T07:00:00Z
  DOI: 10.1142/S0219477522500171
Signal-to-Noise Ratio Enhancement by Accumulation of Signal and Noise
along the Spectrum
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  Authors: Igor Lebedev, Elena Dmitriyeva, Ekaterina Bondar, Sayora Ibraimova, Anastasiya Fedosimova, Abzal Temiraliev
  Abstract: Fluctuation and Noise Letters, Ahead of Print.
  This work presents a method for background removal and signal-to-noise ratio enhancement by an accumulation of signal and noise along analyzed spectrum. In this case, the signals are accumulated, and noise, due to its chaotic nature, is suppressed. The method is applied to analyze spectra obtained on DRON-6 diffractometer for study of the crystal structure of thin tin dioxide films produced by sol–gel technology and deposited on a glass substrate. The standard analysis of the crystallographic planes of the samples under study is practically impossible due to the high noise level and the negative influence of the background from the glass substrate. The proposed method transformed the initial spectrum, which cannot be analyzed, into an informative spectrum: the background signal from the substrate is correctly subtracted and the noise decreases by 10 times. To check for possible signal distortion due to accumulation signal along the spectrum, an analysis of simulated spectra was carried out. The onset of the transition of an amorphous state to a crystalline structure of SnO2 is investigated. The crystalline structure of SnO2 thin films depending on the annealing temperature is studied.
  Citation: Fluctuation and Noise Letters
  PubDate: 2021-10-15T07:00:00Z
  DOI: 10.1142/S021947752250016X
Rolling Bearing Fault Diagnosis by Aperiodic Stochastic Resonance Under
Variable Speed Conditions
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  Authors: Xuzhu Zhuang, Chen Yang, Jianhua Yang, Chengjin Wu, Zhen Shan, Tao Gong
  Abstract: Fluctuation and Noise Letters, Ahead of Print.
  The fault characteristic of rolling bearings under variable speed condition is a typical non-stationary stochastic signal. It is difficult to extract due to the interference of strong background noise makes the applicability of traditional noise reduction methods less. In this paper, an aperiodic stochastic resonance (ASR) method is proposed to study the fault diagnosis of rolling bearings under variable speed conditions. Based on numerical simulation, the effect of noise intensity and damping coefficient on the ASR of the second-order underdamped system is discussed, and an appropriate damping coefficient is found to reach the optimal ASR. The proposed method enhances the fault characteristic information of bearing fault simulation signal. Corresponding to rising-stationary and the stationary-declining running conditions, the method is verified by both simulated and experimental signals. It provides reference for fault diagnosis under variable speed condition.
  Citation: Fluctuation and Noise Letters
  PubDate: 2021-10-14T07:00:00Z
  DOI: 10.1142/S0219477522500158
Complexity-Entropy Causality Plane Analysis of Air Pollution Series
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  Authors: Fang Ma, Qingju Fan, Guang Ling
  Abstract: Fluctuation and Noise Letters, Ahead of Print.
  In this study, we explore the subtle temporal structure of environmental data using symbolic information-theory approach. The newly developed multivariate multiscale permutation entropy and complexity-entropy causality plane methodology are applied to the six pollutants data recorded in Beijing during 2013–2016, which is a powerful tool to discriminate nonlinear deterministic and stochastic dynamics. The obtained results showed that pollutant series exhibit significant randomness and a lower level of predictability in spring and summer, and more temporal correlations in winter and fall. In addition, surrogate analysis is implemented to avoid biased conclusion. We also define the relative complexity measure of multivariate series to reflect the complexity of a system. The highest relative complexity in winter is in line with the physical behavior of the pollution phenomenon.
  Citation: Fluctuation and Noise Letters
  PubDate: 2021-10-13T07:00:00Z
  DOI: 10.1142/S0219477522500110
Regression with Stable Errors Based on Order Statistics
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  Authors: Reza Alizadeh Noughabi, Adel Mohammadpour
  Abstract: Fluctuation and Noise Letters, Ahead of Print.
  Classical regression approaches are not robust when errors are heavy-tailed or asymmetric. That may be due to the non-existence of the mean or variance of the error distribution. Estimation based on trimmed data, which ignored outlier or leverage points, has an old history and frequently used. This procedure chooses fixed cut-off points. In this work, we use this idea recently applied for initial estimates of regression coefficients with heavy-tailed stable errors. We propose an effective procedure to calculate the cut-off points based on the tail index and skewness parameters of errors. We use the property of the existence of some moments of stable distribution order statistics. Data are trimmed based on ordered residuals of a least square regression. However, the trimmed data’s optimal number is determined based on the number of error order statistics whose variance exists. Then, we use the rest of the ordered data to estimate the regression coefficients. Based on these order statistics’ joint distribution, we analytically compute the bias and variance of the introduced estimator of regression parameters that was impossible for regression with stable errors.
  Citation: Fluctuation and Noise Letters
  PubDate: 2021-10-04T07:00:00Z
  DOI: 10.1142/S0219477522500146
Analysis of Fluctuation Patterns in Emotional States Using Electrodermal
Activity Signals and Improved Symbolic Aggregate Approximation
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  Authors: Yedukondala Rao Veeranki, Nagarajan Ganapathy, Ramakrishnan Swaminathan
  Abstract: Fluctuation and Noise Letters, Ahead of Print.
  Analysis of fluctuations in electrodermal activity (EDA) signals is widely preferred for emotion recognition. In this work, an attempt has been made to determine the patterns of fluctuations in EDA signals for various emotional states using improved symbolic aggregate approximation. For this, the EDA is obtained from a publicly available online database. The EDA is decomposed into phasic components and divided into equal segments. Each segment is transformed into a piecewise aggregate approximation (PAA). These approximations are discretized using 11 time-domain features to obtain symbolic sequences. Shannon entropy is extracted from each PAA-based symbolic sequence using varied symbol size [math] and window length [math]. Three machine-learning algorithms, namely Naive Bayes, support vector machine and rotation forest, are used for the classification. The results show that the proposed approach is able to determine the patterns of fluctuations for various emotional states in EDA signals. PAA features, namely maximum amplitude and chaos, significantly identify the subtle fluctuations in EDA and transforms them in symbolic sequences. The optimal values of [math] and [math] yield the highest performance. The rotation forest is accurate (F-[math] and 60.02% for arousal and valence dimensions) in classifying various emotional states. The proposed approach can capture the patterns of fluctuations for varied-length signals. Particularly, the support vector machine yields the highest performance for a lower length of signals. Thus, it appears that the proposed method might be utilized to analyze various emotional states in both normal and clinical settings.
  Citation: Fluctuation and Noise Letters
  PubDate: 2021-09-29T07:00:00Z
  DOI: 10.1142/S0219477522500134
Detrended Correlogram Method for Non-Stationary Time-Series Analysis
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  Authors: G. F. Zebende, E. F. Guedes
  Abstract: Fluctuation and Noise Letters, Ahead of Print.
  A correlogram is a statistical tool that is used to check time-series memory by computing the auto-correlation coefficient as a function of the time lag. If the time-series has no memory, then the auto-correlation must be close to zero for any time lag, otherwise if there is a memory, then the auto-correlations must be significantly different from zero. Therefore, based on the robust detrended cross-correlation coefficient, [math], we propose the detrended correlogram method in this paper, which will be tested for some time-series (simulated and empirical). This new statistical tool is able to visualize a complete map of the auto-correlation for many time lags and time-scales, and can therefore analyze the memory effect for any time-series.
  Citation: Fluctuation and Noise Letters
  PubDate: 2021-09-23T07:00:00Z
  DOI: 10.1142/S0219477522500122