Recurrence Quantification Analysis Technique Research Articles

A new intrusion detection system based on using non-linear statistical analysis and features selection techniques

The increase in the number of connected devices to the Internet and Internet of Things (IoT) development accompanied a massive increase in the number and types of attacks. Most IoT devices have security vulnerabilities due to their limited computing and storage capabilities and specific protocols. Thus, there is essential to build Intrusion Detection Systems (IDSs) that can detect these threats that affect smart applications. Researchers examined different data mining techniques, statistical analysis techniques, and machine learning (ML) algorithms. In this paper, we propose a novel approach for building an anomaly-based intrusion detection system based on using a non-linear statistical analysis technique called recurrence quantification analysis (RQA). Our approach uses RQA to identify abnormal behavior in an individual feature extracted from a series of packets rather than inspecting the packet's payload. The proposed procedure implies finding the minimum number of features, applying the RQA technique to each effective feature separately, and applying different ML algorithms to classify the RQA measurements resulting from each effective feature. The system's performance was evaluated based on the accuracy and F-score using the UNSW-NB15 dataset. Results show our proposed approach's effectiveness in discovering hidden characteristics in the underlying series of an individual feature that leads to identifying different attacks. Besides, the proposed approach outperforms most previous works using one feature.

On the Post Hoc Explainability of Optimized Self-Organizing Reservoir Network for Action Recognition.

This work proposes a novel unsupervised self-organizing network, called the Self-Organizing Convolutional Echo State Network (SO-ConvESN), for learning node centroids and interconnectivity maps compatible with the deterministic initialization of Echo State Network (ESN) input and reservoir weights, in the context of human action recognition (HAR). To ensure stability and echo state property in the reservoir, Recurrent Plots (RPs) and Recurrence Quantification Analysis (RQA) techniques are exploited for explainability and characterization of the reservoir dynamics and hence tuning ESN hyperparameters. The optimized self-organizing reservoirs are cascaded with a Convolutional Neural Network (CNN) to ensure that the activation of internal echo state representations (ESRs) echoes similar topological qualities and temporal features of the input time-series, and the CNN efficiently learns the dynamics and multiscale temporal features from the ESRs for action recognition. The hyperparameter optimization (HPO) algorithms are additionally adopted to optimize the CNN stage in SO-ConvESN. Experimental results on the HAR problem using several publicly available 3D-skeleton-based action datasets demonstrate the showcasing of the RPs and RQA technique in examining the explainability of reservoir dynamics for designing stable self-organizing reservoirs and the usefulness of implementing HPOs in SO-ConvESN for the HAR task. The proposed SO-ConvESN exhibits competitive recognition accuracy.

Nonlinear dynamic analysis of daily rainfall variability across the UK from 1989 to 2018

Proper understanding of rainfall variability is of essential importance for meteorological and hydrological modelling. This study examines whether a variety of nonlinear dynamic concepts can yield additional insights in the rainfall variability in the UK. Daily rainfall data recorded over a 30-yr period (1989–2018) from 32 meteorological stations across the UK are analysed using recurrence plots, recurrence quantification analysis techniques and phase space reconstructions. The results of this analysis underscore the complex processes and variability that are associated with the UK’s rainfall data. The existence of chaos in UK rainfall data was identified, and the extent of complexity in rainfall dynamics exhibited apparent geographic variability. In general, rainfall dynamics associated with stations on the Eastern coast and in central England tend to be more complex than those at other regions. Rainfall dynamics in Western and Northern Scotland were found to reveal marked long-term variability. Moreover, likewise to the rainfall total, seasonal variability in rainfall dynamics was also evident.

RECURRENCE QUANTIFICATION ANALYSIS OF ELECTROOCULOGRAPHY SIGNAL TO A CONTROL QUESTION TEST: A NEW APPROACH FOR THE DETECTION OF DECEPTION

This study aimed to evaluate a lie-detection system by nonlinear analysis of electrooculography (EOG) signals in the polygraph test. The physiological signals such as photoplethysmography signal, electrodermal response, respiratory changes as well as EOG signal were recorded based on a Control Question Test (CQT). Three psychophysiological signals were evaluated based on the extracted features in the seven-position numerical scoring. The dynamics of EOG signals in subjects that had a total negative score were analyzed by recurrence quantification analysis (RQA). The six values of RQA were calculated to analyze the EOG signals in relevant questions compared to other questions. A one-way ANOVA with multiple comparisons was performed to evaluate the extracted variables in different questions. Eleven subjects had a total score of [Formula: see text]2 and less, so the EOG signals of these subjects were evaluated. Recurrence plots (RPs) of EOG signals showed clear differences in the two types of questions. The recurrence quantification analysis of vertical EOG signal indicated that [Formula: see text] and determinism (DET) values decreased significantly for relevant questions compared to other questions. Moreover, a significant decrease was observed in all RQA parameters except RR for the horizontal EOG signal. The differences of EOG signals in relevant questions observed using RPs and RQA were possibly related to the underlying changes in rapid eye movement due to the stress. The results of this study illustrate that the RQA technique is well suited to analyze the EOG signals in the detection of deception.

Nonlinear Processing of Wrist Pulse Signals to Distinguish Diabetic and Non-Diabetic Subjects

In pulse diagnosis, the pulse signals obtained at wrist have been used for analysis of certain diseases in ancient systems of medicine in which the practitioner feels the pulse of the subject by placing his three fingers on the subject’s wrist at three distinct radial pulse point locations. The preliminary studies show that there are many conventional linear techniques applied to analyze the wrist pulse signals and less focus on non-linear techniques. Hence, the main aim of this research is to apply Recurrence Plot and Recurrence Quantification Analysis (RQA), a nonlinear technique to analyze the wrist pulse signals for distinguishing between diabetic and non-diabetic subjects. Wrist pulse signals from 32 subjects were recorded during the morning hours and were analyzed using RQA techniques. The results show significant differences in the RQA parameters of the wrist pulse signals as they are obtained from the recurrences occurring in the phase space plots of the wrist pulse signals. It was found that parameters like entropy, divergence and average diagonal line length showed significant variations for diabetic and non diabetic subjects. Therefore, it can be concluded that RQA parameters can be used effectively to identify diabetic and non diabetic subjects and thus may be applied on the wrist pulse signals for early detection of various diseases.

Nonlinear Signal Processing Method Detects Emotional Changes Induced by Indian Classical Music

Music is one of the major activities that alters the emotional experience of a person. Musical processing in the brain is a complex process involving coordination between various areas of the brain. There are less number of studies that focus on analyzing brain responses due to music using modern signal processing techniques. This research aims to apply a nonlinear signal processing technique i.e. the Recurrence Quantification Analysis (RQA) technique to analyze the brain correlates of happy and sad music conditions while listening to happy and sad ragas of North Indian Classical Music (NICM). EEG signals from 20 different subjects are acquired while listening to excerpts of raga elaboration phases of NICM. Along with behavioural ratings, the signals were analyzed using the Recurrence Quantification Analysis technique. The results showed significant differences in the recurrence plot and recurrence parameters extracted from the frontal and fronto temporal regions in the right and left hemispheres of the brain. Therefore, from the results, it can be concluded that RQA parameters can detect emotional changes due to happy and sad music conditions.

Vibration fault detection of fuel pump using Recurrence Quantification Analysis

Abstract This research is aimed at investigating the use of a novel technique for monitoring non-linear transient accelerometer data using Recurrence Quantification Analysis (RQA) for use in auxiliary aircraft hardware health monitoring. Identification of premature failure using the RQA technique is explored and investigated for its use in preempting hardware failure of auxiliary fuel pump and forming a new basis for a Mean Time Before Failure (MTBF) for these devices. Authors assume that use Recurrence quantification analysis its combination with traditional methods could indicate early warning of incoming faults and more precise set of MTBF. The question whether the development of this technique is applicable across other aircraft parts may be offered.

Multifractal and recurrence behaviors of continuum percolation-based financial price dynamics

The modeling of financial price fluctuations is always a hot research aspect that many researchers are concerned about. A financial price model based on two-dimensional continuum percolation system, which is one of the most important statistical physics systems, is introduced in this work. In the model, the fluctuations of stock price changes are assumed to be attributed to the market information interactions among the traders, and the percolation cluster is taken to represent the traders holding the same investment attitude. Then, multifractal detrended fluctuation analysis method is adopted to study the multifractal behaviors of simulation data with different parameter sets. Finally, the recurrence plot and recurrence quantification analysis techniques are applied to investigate the complex determinism dynamics hidden in the simulated stock returns from the price model, as well as in their different intrinsic mode functions (IMFs) decomposed from the empirical mode decomposition method. Abundant and distinctive recurrence behaviors can be observed among returns and IMFs time series. In the meanwhile, the corresponding behaviors of the Chinese Shanghai Composite Index is studied for comparison.

Investigating the fluctuations in tool vibration during GFRP drilling through recurrence quantification analysis

Glass fiber-reinforced polymers (GFRPs) are a unique class of materials in machining because unlike metals, they are not homogeneous. Given the inhomogeneous and anisotropic nature of composite materials, their machining behavior differs in many respects from that of metal machining. With metallic materials, the cutting force is uniform during drilling. Given GFRPs, drill bits are subject to variable cutting forces, whose responses to machining vary significantly. During GFRP drilling, heavy vibration is undesirable and results in rapid tool wear. The cutting condition can be controlled effectively to avoid heavy vibration by applying the appropriate cutting parameters. Hence, the most suitable cutting condition must be selected for proper process control. This study investigates the influence of vibration as a result of changes in cutting condition, drill diameter, and the number of holes. This influence is continuously monitored by the vibration signals generated during GFRP drilling with a TiN/TiALN-coated carbide drill bit. Experiments were conducted on a computer numerical control milling machine. The input parameters used for various cutting conditions were spindle speed and feed. The vibration signal was measured with an accelerometer, and its fluctuations were examined by a non-linear technique called recurrence quantification analysis (RQA). The influence of these fluctuations on vibration and their effect on tool life were also determined using RQA parameters. These parameters include percent recurrence, percent determinism, and percent laminarity. They have been used to study the effect of continuous fluctuations in vibration signals during drilling. This study confirmed that the RQA technique has potential for use in the investigation of a dynamical system.

Recurrence quantification analysis of simulations of near-marginal dissipative-trapped-electron-mode turbulence

Recurrence quantification analysis (RQA) is a powerful tool to study dynamical systems and to help us understand and characterize the underlying physics when a transition occurs. The idea is based on the fact that, given sufficiently long time lapses, every dynamical system returns to states arbitrarily close to those it had in the past. This fundamental property of dynamical systems is called recurrence. In this work, we analyze, using the RQA technique, the recurrence properties of time series obtained from a series of numerical simulations of a dissipative-trapped-electron-mode (DTEM) turbulence model in near-marginal conditions where a transition in the nature of turbulent transport was observed as a subdominant diffusive channel strength is increased from zero [J. A. Mier et al., Phys. Plasmas 15, 112301 (2008)]. The results of the RQA analysis clearly show that the degree of determinism and complexity of the dynamics closely follows the degree of non-diffusiveness in the observed transport.

Recurrence quantification analysis of sustained sub-maximal grip contractions in patients with various metabolic muscle disorders

Recurrence quantification analysis (RQA) was used to analyse force signals during sustained sub-maximal grip contraction (SSGC) of three types of patients suffering from a metabolic muscle disorder (glycogen storage disease type III (GSD III), glycogen storage disease type V (GSD V) and mitochondrial myopathies (MITO)) compared to control subjects. Recurrence plots (RP) of patients showed clear non-uniformity, in comparison to control subjects who displayed quasi-periodic patterns. Quantitative analysis of the RP showed significant differences between patients with metabolic disorders and the control group for four RQA parameters. The results showed that the SSGC signals of patients had decreased L max, which indicated more chaotic patterns. In addition the deterministic component of the signals was less complex for patients than for controls. The differences of SSGC signal observed using RP and RQA were possibly related to the underlying changes in metabolism of muscle fibres due to the disease. Results of this study illustrate that the RQA technique is well suited to analyse sustained grip-force signals.

Investigation on feasibility of recurrence quantification analysis for detecting flank wear in face milling

Flank wear is a critical phenomenon which has direct impact on quality of surface finish, dimensional precision and ultimately cost of the finished product. In any metal cutting operation, cutting tool wear estimation will help in identifying tool state, which is a critical factor in productivity. In this paper, the vibration signals are used for detecting flank wear in face milling .The vibration signals are analyzed using a novel non linear technique called recurrence quantification analysis (RQA). RQA includes time delay and dimensions embedding process so as to reconstruct the time series data of vibration signal, to obtain better information of the changes in nonlinear dynamics underlying the milling process. An investigation of this technique was carried out to see its capability in detecting flank wear in face milling. Experiments were conducted on universal milling machine using AISI H11 steel as work material. The investigation proved that RQA technique has a good potential in detecting flank wear in face milling. The RQA parameters such as percent recurrence (REC), trapping time (TT), percent laminarity (LAM) and entropy (ENT), and also the recurrence plots color patterns for different flank wear, can be used in detecting insert wear in face milling. Keywords: milling, flank wear, recurrence plot, recurrence quantification analysis.

Using recurrence plot analysis to distinguish between endogenous and exogenous stock market crashes

Recurrence Plots are graphical tools based on Phase Space Reconstruction. Recurrence Quantification Analysis (RQA) is a statistical quantification of RPs. RP and RQA are good at working with non-stationarity and noisy data, in detecting changes in data behavior, in particular in detecting breaks, like a phase transition and in informing about other dynamic properties of a time series. Endogenous Stock Market Crashes have been modeled as phase changes in recent times. Motivated by this, we have used RP and RQA techniques for detecting critical regimes preceding an endogenous crash seen as a phase transition and hence give an estimation of the initial bubble time. We have used a new method for computing RQA measures with confidence intervals. We have also used the techniques on a known exogenous crash to see if the RP reveals a different story or not. The analysis is made on Nifty, Hong Kong AOI and Dow Jones Industrial Average, taken over a time span of about 3 years for the endogenous crashes. Then the RPs of all time series have been observed, compared and discussed. All the time series have been first transformed into the classical momentum divided by the maximum Xmax of the time series over the time window which is considered in the specific analysis. RPs have been plotted for each time series, and RQA variables have been computed on different epochs. Our studies reveal that, in the case of an endogenous crash, we have been able to identify the bubble, while in the case of exogenous crashes the plots do not show any such pattern, thus helping us in identifying such crashes.

Recurrence Quantification Analysis of Sustained Sub-maximal Grip Force in Patients with Glycogen Storage Disease type III

Recurrence Quantification Analysis (RQA) was used to analyse sustained sub-maximal grip force of patients with Glycogen storage disease type III (GSD III) and control subjects. Recurrence plots (RP) of GSD III patients showed clear disruptions in comparison to control subjects. GSD III patients had more chaotic patterns that were less complex than those of control subjects. The RP of GSD III patients were less reliable than those of the controls, with changes between states occurring more often. The changes observed between the groups were not related to the fatigue induced by the experiment. The RQA technique is well suited for use with sustained grip-force signals.

Comparison of recurrence plot features of swallowing and breath sounds

Nonlinear dynamics theory has been a tool of choice in the analysis of physiological signals and systems. This paper presents a novel approach in the analysis of tracheal swallowing and breath sounds based on nonlinear dynamics theory. As the tracheal sound signal is nonstationary, the signal was studied based on the recurrence quantification analysis (RQA) method, which is a useful technique in the analysis of nonstationary and noisy signals. Tracheal sound recordings of 15 healthy and 9 dysphagic subjects were studied. The multidimensional state space trajectory of each signal was reconstructed using Taken’s method of delays. The reconstructed trajectories were analyzed by the RQA technique. The preliminary results suggested that some recurrence parameters were appreciably different between swallowing and breath sounds. In order to confirm discriminating ability of the parameters, an automated method for extraction of swallowing sounds in the records of the tracheal swallowing and breath sounds was investigated. The swallowing sound extraction results were validated by manual inspection of the simultaneously recorded airflow signal and spectrogram of the sounds and also by auditory means. Experimental results proved that the investigated method more accurately detected the boundaries of swallowing sounds than methods proposed previously. Swallowing sound detection may be employed in a system for automated swallowing assessment and diagnosis of swallowing disorders (dysphagia) by acoustical means.