Search Results (16)

We investigate whether it is possible to distinguish chaotic time series from random time series using network theory. In this perspective, we selected four methods to generate graphs from time series: the natural, the horizontal, the limited penetrable horizontal visibility graph, and the phase space reconstruction method. These methods claim that the distinction of chaos from randomness is possible by studying the degree distribution of the generated graphs. We evaluated these methods by computing the results for chaotic time series from the 2D Torus Automorphisms, the chaotic Lorenz system, and a random sequence derived from the normal distribution. Although the results confirm previous studies, we found that the distinction of chaos from randomness is not generally possible in the context of the above methodologies. Full article

The classification of time series using machine learning (ML) analysis and entropy-based features is an urgent task for the study of nonlinear signals in the fields of finance, biology and medicine, including EEG analysis and Brain–Computer Interfacing. As several entropy measures exist, the problem is assessing the effectiveness of entropies used as features for the ML classification of nonlinear dynamics of time series. We propose a method, called global efficiency (GEFMCC), for assessing the effectiveness of entropy features using several chaotic mappings. GEFMCC is a fitness function for optimizing the type and parameters of entropies for time series classification problems. We analyze fuzzy entropy (FuzzyEn) and neural network entropy (NNetEn) for four discrete mappings, the logistic map, the sine map, the Planck map, and the two-memristor-based map, with a base length time series of 300 elements. FuzzyEn has greater GEFMCC in the classification task compared to NNetEn. However, NNetEn classification efficiency is higher than FuzzyEn for some local areas of the time series dynamics. The results of using horizontal visibility graphs (HVG) instead of the raw time series demonstrate the GEFMCC decrease after HVG time series transformation. However, the GEFMCC increases after applying the HVG for some local areas of time series dynamics. The scientific community can use the results to explore the efficiency of the entropy-based classification of time series in “The Entropy Universe”. An implementation of the algorithms in Python is presented. Full article

The analysis of textures is an important task in image processing and computer vision because it provides significant data for image retrieval, synthesis, segmentation, and classification. Automatic texture recognition is difficult, however, and necessitates advanced computational techniques due to the complexity and diversity of natural textures. This paper presents a method for classifying textures using graphs; specifically, natural and horizontal visibility graphs. The related image natural visibility graph (INVG) and image horizontal visibility graph (IHVG) are used to obtain features for classifying textures. These features are the clustering coefficient and the degree distribution. The suggested outcomes show that the aforementioned technique outperforms traditional ones and even comes close to matching the performance of convolutional neural networks (CNNs). Classifiers such as the support vector machine (SVM), K-nearest neighbor (KNN), decision tree (DT), and random forest (RF) are utilized for the categorization. The suggested method is tested on well-known image datasets like the Brodatz texture and the Salzburg texture image (STex) datasets. The results are positive, showing the potential of graph methods for texture classification. Full article

The analysis in view is proposed to investigate the impacts of entropy in the peristaltically flown Ree–Eyring fluid under the stress of a normally imposed uniform magnetic field in a non-uniform symmetric channel of varying thickness. The administering equations of the present flow problem are switched into the non-dimensional form and then reduced by the availing of long wavelengths and creeping flow regime restrictions. The analytical treatment for the developed problem is performed to attain closed-form solutions which are further displayed as graphs of velocity, pressure, temperature, and entropy distribution. The trapping phenomenon has also been an area of our current examination. The role of relevant pronounced parameters such as the Brinkmann number, Hartmann number, and Ree–Eyring parameter for throwing vivid impacts are also concerned. It has been inferred that both the Brinkmann number and Ree–Eyring parameter with rising values inflate temperature and entropy profiles. The velocity profile shows the symmetric nature due to the horizontally assumed symmetric channel of varying thickness. The circulation of streamlines and bolus formations is visibly reduced in response to the increasing Hartmann number. Full article

We focus on characterizing the high-energy emission mechanisms of blazars by analyzing the variability in the radio band of the light curves of more than a thousand sources. We are interested in assigning complexity parameters to these sources, modeling the time series of the light curves with the method of the Horizontal Visibility Graph (HVG), which allows us to obtain properties from degree distributions, such as a characteristic exponent to describe its stochasticity and the Kullback–Leibler Divergence (KLD), presenting a new perspective to the methods commonly used to study Active Galactic Nuclei (AGN). We contrast these parameters with the excess variance, which is an astronomical measurement of variability in light curves; at the same time, we use the spectral classification of the sources. While it is not possible to find significant correlations with the excess variance, the degree distributions extracted from the network are detecting differences related to the spectral classification of blazars. These differences suggest a chaotic behavior in the time series for the BL Lac sources and a correlated stochastic behavior in the time series for the FSRQ sources. Our results show that complex networks may be a valuable alternative tool to study AGNs according to the variability of their energy output. Full article

The climatological characterization of variables allows us to understand the average behavior of atmospheric conditions, detect extremes and fluctuations, and the relationships of variables with geographic physical factors; it is presented as another aide for weather forecasting. The aim of this research is characterizing the behavior of the meteorological variables at the Antonio Maceo International Airport in Santiago de Cuba for the period 2017–2021. Antonio Maceo International Airport has the particularity of being located in a complex relief, exposed to marked breeze influences, and a significant number of wind shear pilot reports. The characterization was based on the concepts and graphs of descriptive statistics. The mean monthly distribution of the variables: temperature, relative humidity, atmospheric pressure, and wind speed and direction was obtained and is represented. The distributions of the maximum monthly and annual accumulations, the cloud cover, and the ranges of the reduction of the horizontal and vertical visibility were analyzed. Full article

Solar magnetic activity exhibits a complex nonlinear behavior, but its dynamic process has not been fully understood. As the complex network technique can better capture the dynamics of nonlinear system, the visibility graphs (VG), the horizontal visibility graphs (HVG), and the limited penetrable visibility graphs (LPVG) are applied to implement the mapping of sunspot relative numbers in the northern and southern hemispheres. The results show that these three methods can capture important information of nonlinear dynamics existing in the long-term hemispheric sunspot activity. In the presentation of the results, the network degree sequence of the HVG method changes preferentially to the original data series as well as the VG and the LPVG, while both the VG and the LPVG slightly lag behind the original time series, which provides some new ideas for the nonlinear dynamics of the hemispheric asymmetry in the two hemispheres. Meanwhile, the use of statistical feature-skewness values and complex network visibility graphs can yield some complementary information for mutual verification. Full article

Websites are one of the most frequently used communication environments, and creating sustainable web designs should be an objective for all companies. Ensuring high usability is proving to be one of the main contributors to sustainable web design, reducing usage time, eliminating frustration and increasing satisfaction and retention. The present paper studies the usability of different website landing pages, seeking to identify the elements, structures and designs that increase usability. The study analyzed the behavior of 22 participants during their interaction with five different landing pages while they performed three tasks on the webpage and freely viewed each page for one minute. The stimuli were represented by five different banking websites, each of them presenting the task content in a different mode (text, image, symbol, graph, etc.).; the data obtained from the eye tracker (fixations location, order and duration, saccades, revisits of the same element, etc.), together with the data from the applied survey lead to interesting conclusions: the top, center and right sides of the webpage attract the most attention; the use of pictures depicting persons increase visibility; the scanpaths follow a vertical and horizontal direction; numerical data should be presented through graphs or tables. Even if a user's past experience influences their experience on a website, we show that the design of the webpage itself has a greater influence on webpage usability. Full article

The transformation of time series into complex networks through visibility graphs is an innovative way to study time-based events. In this work, we use visibility graphs to transform IOTA and IoTeX price volatility time series into complex networks. Our aim is twofold: first, to better understand the markets of the two most capitalised Internet of Things (IoT) platforms at the time of writing. IOTA runs on a public directed acyclic graph (DAG) and IoTeX on a blockchain. Second, to suggest how 5G can improve information security in these two key IoT platforms. The analysis of the networks created by the natural and horizontal visibility graphs shows, first, that both IOTA and IoTeX are still at their infancy in their development, with IoTex seemingly developing faster. Second, both IoT tokens form communities in a hierarchical structure, and third, 5G can accelerate their development. We use intentional risk management as a lever to understand the impact of 5G on IOTA and IoTeX. Our results lead us to provide a set of design recommendations that contribute to improving information security in future 5G-based IoT implementations. Full article

Anthropogenic impulsive sound sources with high intensity are a threat to marine life and it is crucial to keep them under control to preserve the biodiversity of marine ecosystems. Underwater explosions are one of the representatives of these impulsive sound sources, and existing detection techniques are generally based on monitoring the pressure level as well as some frequency-related features. In this paper, we propose a complementary approach to the underwater explosion detection problem through assessing the arrow of time. The arrow of time of the pressure waves coming from underwater explosions conveys information about the complex characteristics of the nonlinear physical processes taking place as a consequence of the explosion to some extent. We present a thorough review of the characterization of arrows of time in time-series, and then provide specific details regarding their applications in passive acoustic monitoring. Visibility graph-based metrics, specifically the direct horizontal visibility graph of the instantaneous phase, have the best performance when assessing the arrow of time in real explosions compared to similar acoustic events of different kinds. The proposed technique has been validated in both simulations and real underwater explosions. Full article

Remarkable progress in the development of modeling methods for indoor spaces has been made in recent years with a focus on the reconstruction of complex environments, such as multi-room and multi-level buildings. Existing methods represent indoor structure models as a combination of several sub-spaces, which are constructed by room segmentation or horizontal slicing approach that divide the multi-room or multi-level building environments into several segments. In this study, we propose an automatic reconstruction method of multi-level indoor spaces with unique models, including inter-room and inter-floor connections from point cloud and trajectory. We construct structural points from registered point cloud and extract piece-wise planar segments from the structural points. Then, a three-dimensional space decomposition is conducted and water-tight meshes are generated with energy minimization using graph cut algorithm. The data term of the energy function is expressed as a difference in visibility between each decomposed space and trajectory. The proposed method allows modeling of indoor spaces in complex environments, such as multi-room, room-less, and multi-level buildings. The performance of the proposed approach is evaluated for seven indoor space datasets. Full article

One of the fundamental open questions in plasma physics is the role of non-thermal particles distributions in poorly collisional plasma environments, a system that is commonly found throughout the Universe, e.g., the solar wind and the Earth’s magnetosphere correspond to natural plasma physics laboratories in which turbulent phenomena can be studied. Our study perspective is born from the method of Horizontal Visibility Graph (HVG) that has been developed in the last years to analyze time series avoiding the tedium and the high computational cost that other methods offer. Here, we build a complex network based on directed HVG technique applied to magnetic field fluctuations time series obtained from Particle In Cell (PIC) simulations of a magnetized collisionless plasma to distinguish the degree distributions and calculate the Kullback–Leibler Divergence (KLD) as a measure of relative entropy of data sets produced by processes that are not in equilibrium. First, we analyze the connectivity probability distribution for the undirected version of HVG finding how the Kappa distribution for low values of $\kappa$ tends to be an uncorrelated time series, while the Maxwell–Boltzmann distribution shows a correlated stochastic processes behavior. Subsequently, we investigate the degree of temporary irreversibility of magnetic fluctuations that are self-generated by the plasma, comparing the case of a thermal plasma (described by a Maxwell–Botzmann velocity distribution function) with non-thermal Kappa distributions. We have shown that the KLD associated to the HVG is able to distinguish the level of reversibility that is associated to the thermal equilibrium in the plasma, because the dissipative degree of the system increases as the value of $\kappa$ parameter decreases and the distribution function departs from the Maxwell–Boltzmann equilibrium. Full article

Emotion recognition, as a challenging and active research area, has received considerable awareness in recent years. In this study, an attempt was made to extract complex network features from electroencephalogram (EEG) signals for emotion recognition. We proposed a novel method of constructing forward weighted horizontal visibility graphs (FWHVG) and backward weighted horizontal visibility graphs (BWHVG) based on angle measurement. The two types of complex networks were used to extract network features. Then, the two feature matrices were fused into a single feature matrix to classify EEG signals. The average emotion recognition accuracies based on complex network features of proposed method in the valence and arousal dimension were 97.53% and 97.75%. The proposed method achieved classification accuracies of 98.12% and 98.06% for valence and arousal when combined with time-domain features. Full article

The last decades have been successively warmer at the Earth’s surface. An increasing interest in climate variability is appearing, and many research works have investigated the main effects on different climate variables. Some of them apply complex networks approaches to explore the spatial relation between distinct grid points or stations. In this work, the authors investigate whether topological properties change over several years. To this aim, we explore the application of the horizontal visibility graph (HVG) approach which maps a time series into a complex network. Data used in this study include a 60-year period of daily mean temperature anomalies in several stations over the Iberian Peninsula (Spain). Average degree, degree distribution exponent, and global clustering coefficient were analyzed. Interestingly, results show that they agree on a lack of significant trends, unlike annual mean values of anomalies, which present a characteristic upward trend. The main conclusions obtained are that complex networks structures and nonlinear features, such as weak correlations, appear not to be affected by rising temperatures derived from global climate conditions. Furthermore, different locations present a similar behavior and the intrinsic nature of these signals seems to be well described by network parameters. Full article

Remote sensing of vegetation has largely been revolving around the measurement of passive or active electromagnetic radiation of the top of the canopy. Nevertheless, plants hold a vertical structure and different processes and intensities take place within a plant organism depending on the environmental conditions. One of the main inputs for photosynthesis is photosynthetic active radiation (PAR) and a few studies have taken into account the effect of the qualitative and quantitative changes of the available PAR within the plants canopies. Mostly large plants (trees, shrubs) are affected by this phenomena, while signs of it could be observed in dense monocultures, too. Lake Balaton is a large lake with 12 km² dense reed stands, some of which have been suffering from reed die-back; consequently, the reed density and stress condition exhibit a vertical PAR variability within the canopy due to the structure and condition of the plants but also a horizontal variability attributed to the reedbed’s heterogeneous density. In this study we investigate the expression of photosynthetic and spectroscopic parameters in different PAR conditions. We concentrate on chlorophyll fluorescence as this is an early-stage indicator of stress manifestation in plants. We first investigate how these parameters differ across leaf samples which are exposed to a higher degree of PAR variability due to their vertical position in the reed culm (sun and shade leaves). In the second part, we concentrate on how the same parameters exhibit in reed patches of different densities. We then look into hyperspectral regions through graphs of coefficient of determination and associate the former with the physiological parameters. We report on the large variability found from measurements taken at different parts of the canopy and the association with spectral regions in the visible and near-infrared domain. We find that at low irradiance plants increase their acclimation to low light conditions. Plant density at Phragmites stands affects the vertical light attenuation and consequently the photophysiological response of basal leaves. Moreover, the hyperspectral response from the sun and shade leaves has been found to differ; charts of the coefficient of determination indicate that the spectral region around the red-edge inflection point for each case of sun and shade leaves correlate strongly with ETR_max and α. When analysing the data cumulatively, independent of their vertical position within the stand, we found correlations of R² = 0.65 (band combination 696 and 651) and R² = 0.61 (band combination 636 and 642) for the ETR_max and α, respectively. Full article