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21.
22.
Evolution of fractal dimension in pulsed laser deposited MoO3 film with ablation time and annealing temperature
Mohanachandran Nair Sindhu Swapna, 2021, original scientific article

Abstract: The multifractal analysis is a potential method for assessing thin flm surface morphology and its changes due to diferent deposition conditions and post-deposition treatments. In this work, the multifractal analysis is carried out to understand the surface morphology—root mean square (RMS) surface roughness—of nanostructured MoO3 flms prepared by pulsed laser deposition technique by varying the ablation time and post-deposition annealing. The XRD analysis shows the evolution of crystalline nature with annealing temperature. The XRD pattern of all the annealed flms shows the characteristic peak of the orthorhombic MoO3 phase. The FESEM and AFM analysis reveals the morphological modifcation with ablation time and annealing temperature. The multifractal analysis of the AFM images shows that the box—counting, information and correlation dimension varies with the annealing temperature. The study also reveals the inverse relation between the fractal dimension and the RMS surface roughness due to the annealing induced particle size variation and reorientation. The fractal dimension’s evolution in the pulsed laser deposited MoO3 flm with ablation time and annealing temperature is also investigated. Thus, the study reveals the potential of multifractal analysis in the thin flm surface characterizatio
Keywords: Multifractal analysis · Pulsed laser deposition · Molybdenum oxide · Atomic force microscopy · Fractal dimension
Published in RUNG: 04.07.2022; Views: 1140; Downloads: 0
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23.
Investigation of Fractality and variation of fractal dimension in germinating seed
Mohanachandran Nair Sindhu Swapna, SREEJYOTHI S, Sankararaman S, 2020, original scientific article

Abstract: The fractal analysis has now been recognized as a potential mathematical tool in analyzing complex structures. The present work reports not only the fractal nature of Vigna radiata seed analyzed with the help of Field Emission Scanning Electron Microscopic images but also the variation of fractal dimension (FD) in a germinating seed. The variation of FD during germination in different media—water, salt, and diesel soot with carbon nanoparticles (CNPs)—is studied using the box-counting technique. The study is the first report of the fractality of seed. Irrespective of the media, the FD attains a maximum value on the day of germination and decreases after that. The time (T) for achieving maximum FD varies with the nature of stress. In the study, when the CNPs of diesel soot lower the T value, the salt raises the T value with respect to the control set. The Fourier Transform Infrared analysis of the seeds germinating in different media shows an increased rate of protein formation during the initial stage of germination and a steady state after that. In conjunction with the literature, the variation in the amino nitrogen, soluble nucleotide—RNA, and protein content of the seed during the initial days of germination gets reflected in its FD.
Keywords: fractal analysis, seed germination, Vigna radiata
Published in RUNG: 04.07.2022; Views: 1150; Downloads: 0
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24.
Fractal and time-series analyses based rhonchi and bronchial auscultation: A machine learning approach
Mohanachandran Nair Sindhu Swapna, 2022, original scientific article

Abstract: Objectives: The present work reports the study of 34 rhonchi (RB) and Bronchial Breath (BB) signals employing machine learning techniques, timefrequency, fractal, and non-linear time-series analyses. Methods: The timefrequency analyses and the complexity in the dynamics of airflow in BB and RB are studied using both Power Spectral Density (PSD) features and non-linear measures. For accurate prediction of these signals, PSD and nonlinear measures are fed as input attributes to various machine learning models. Findings: The spectral analyses reveal fewer, low-intensity frequency components along with its overtones in the intermittent and rapidly damping RB signal. The complexity in the dynamics of airflow in BB and RB is investigated through the fractal dimension, Hurst exponent, phase portrait, maximal Lyapunov exponent, and sample entropy values. The greater value of entropy for the RB signal provides an insight into the internal morphology of the airways containing mucous and other obstructions. The Principal Component Analysis (PCA) employs PSD features, and Linear Discriminant Analysis (LDA) along with Pattern Recognition Neural Network (PRNN) uses non-linear measures for predicting BB and RB. Signal classification based on phase portrait features evaluates the multidimensional aspects of signal intensities, whereas that based on PSD features considers mere signal intensities. The principal components in PCA cover about 86.5% of the overall variance of the data class, successfully distinguishing BB and RB signals. LDA and PRNN that use nonlinear time-series parameters identify and predict RB and BB signals with 100% accuracy, sensitivity, specificity, and precision. Novelty: The study divulges the potential of non-linear measures and PSD features in classifying these signals enabling its application to be extended for low-cost, non-invasive COVID-19 detection and real-time health monitoring.
Keywords: lung signal, fractal analysis, sample entropy, non­linear time­series, machine learning techniques
Published in RUNG: 30.06.2022; Views: 1252; Downloads: 0
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25.
Complex network-based cough signal analysis for digital auscultation: a machine learning approach
Mohanachandran Nair Sindhu Swapna, 2022, original scientific article

Abstract: The paper proposes a novel approach to bring out the potential of complex networks based on graph theory to unwrap the hidden characteristics of cough signals, croup (BC), and pertussis (PS). The spectral and complex network analyses of 48 cough sounds are utilized for understanding the airflow through the infected respiratory tract. Among the different phases of the cough sound time-domain signals of BC and PS – expulsive (X), intermediate (I), and voiced (V) - the phase ‘I’ is noisy in BC due to improper glottal functioning. The spectral analyses reveal high-frequency components in both cough signals with an additional high-intense low-frequency spread in BC. The complex network features created by the correlation mapping approach, like number of edges (E), graph density (G), transitivity (), degree centrality (D), average path length (L), and number of components () distinguishes BC and PS. The higher values of E, G, and for BC indicate its musical nature through the strong correlation between the signal segments and the presence of high-intense low-frequency components in BC, unlike that in PS. The values of D, L, and discriminate BC and PS in terms of the strength of the correlation between the nodes within them. The linear discriminant analysis (LDA) and quadratic support vector machine (QSVM) classifies BC and PS, with greater accuracy of 94.11% for LDA. The proposed work opens up the potentiality of employing complex networks for cough sound analysis, which is vital in the current scenario of COVID-19.
Keywords: Complex network analysis, Auscultation, Croup cough, Pertussis Spectral analysis, Machine learning techniques
Published in RUNG: 30.06.2022; Views: 1266; Downloads: 0
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26.
Time series and fractal analyses of wheezing : a novel approach
Mohanachandran Nair Sindhu Swapna, Ammini Renjini, Vimal Raj, S. Sreejyothi, Sankaranarayana Iyer Sankararaman, 2020, original scientific article

Abstract: Since the outbreak of the pandemic Coronavirus Disease 2019, the world is in search of novel non-invasive methods for safer and early detection of lung diseases. The pulmonary pathological symptoms refected through the lung sound opens a possibility of detection through auscultation and of employing spectral, fractal, nonlinear time series and principal component analyses. Thirty-fve signals of vesicular and expiratory wheezing breath sound, subjected to spectral analyses shows a clear distinction in terms of time duration, intensity, and the number of frequency components. An investigation of the dynamics of air molecules during respiration using phase portrait, Lyapunov exponent, sample entropy, fractal dimension, and Hurst exponent helps in understanding the degree of complexity arising due to the presence of mucus secretions and constrictions in the respiratory airways. The feature extraction of the power spectral density data and the application of principal component analysis helps in distinguishing vesicular and expiratory wheezing and thereby, giving a ray of hope in accomplishing an early detection of pulmonary diseases through sound signal analysis.
Keywords: auscultation, wheeze, fractals, nonlinear time series analysis, sample entropy
Published in RUNG: 30.06.2022; Views: 1179; Downloads: 0
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27.
Nonlinear time series and principal component analyses: Potential diagnostic tools for COVID-19 auscultation
Mohanachandran Nair Sindhu Swapna, RAJ VIMAL, RENJINI A, SREEJYOTHI S, SANKARARMAN S, 2020, original scientific article

Abstract: The development of novel digital auscultation techniques has become highly significant in the context of the outburst of the pandemic COVID 19. The present work reports the spectral, nonlinear time series, fractal, and complexity analysis of vesicular (VB) and bronchial (BB) breath signals. The analysis is carried out with 37 breath sound signals. The spectral analysis brings out the signatures of VB and BB through the power spectral density plot and wavelet scalogram. The dynamics of airflow through the respiratory tract during VB and BB are investigated using the nonlinear time series and complexity analyses in terms of the phase portrait, fractal dimension, Hurst exponent, and sample entropy. The higher degree of chaoticity in BB relative to VB is unwrapped through the maximal Lyapunov exponent. The principal component analysis helps in classifying VB and BB sound signals through the feature extraction from the power spectral density data. The method proposed in the present work is simple, cost-effective, and sensitive, with a far-reaching potential of addressing and diagnosing the current issue of COVID 19 through lung auscultation.
Keywords: Breath sound analysis, Fractal dimension, Nonlinear time series analysis, Sample entropy, Hurst exponent, Principal component analysis
Published in RUNG: 28.06.2022; Views: 1423; Downloads: 0
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28.
Unravelling the potential of phase portrait in the auscultation of mitral valve dysfunction
Mohanachandran Nair Sindhu Swapna, SREEJYOTHI S, RENJINI A, RAJ VIMAL, SANKARARAMAN SANKARANARAYANA IYER, 2021, original scientific article

Abstract: The manuscript elucidates the potential of phase portrait, fast Fourier transform, wavelet, and time-series analyses of the heart murmur (HM) of normal (healthy) and mitral regurgitation (MR) in the diagnosis of valve-related cardiovascular diseases. The temporal evolution study of phase portrait and the entropy analyses of HM unveil the valve dysfunctioninduced haemodynamics. A tenfold increase in sample entropy in MR from that of normal indicates the valve dysfunction. The occurrence of a large number of frequency components between lub and dub in MR, compared to the normal, is substantiated through the spectral analyses. The machine learning techniques, K-nearest neighbour, support vector machine, and principal component analyses give 100% predictive accuracy. Thus, the study suggests a surrogate method of auscultation of HM that can be employed cost-effectively in rural health centres.
Keywords: phase portrait, auscultation, mitral valve dysfunction, heart murmur, nonlinear time series analysis
Published in RUNG: 28.06.2022; Views: 1112; Downloads: 0
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29.
Characterization of a karst aquifer in the recharge area of Malenščica and Unica springs based on spatial and temporal variations of natural tracers
Blaž Kogovšek, 2022, doctoral dissertation

Abstract: The aim of the present study is to characterize and improve the still insufficient knowledge of the recharge processes that have an important influence on the flow and solute transport in karst aquifers and thus also on the quantity and quality of karst water sources. A binary karst aquifer in the recharge area of the Malenščica and Unica springs, which covers an area of about 820 km2 in SW Slovenia, was selected as the study area. A dense monitoring network was established at 20 observation points (six springs, four ponors, seven water-active caves and three surface streams) for simultaneous monitoring of the hydrological characteristics and the physicochemical properties of the water, the so-called natural tracers. Data-loggers were installed to measure water pressure, temperature and conductivity. During selected storm events, samples were taken for chemical and microbiological analyses and discharge measurements were made. The meteorological and hydrological data of the Slovenian Environment Agency complemented the extensive dataset. Collected data allowed the analysis and comparison of the spatial and temporal variations of the natural tracers under different hydrological conditions. Frequent discharge measurements allowed the generation of rating curves and proved to be a crucial element for understanding the hydraulic processes that determine the functioning of this system. The calculation of the water budget allowed an assessment of the proportion of autogenic and allogenic recharge of the springs and a quantitative estimate of autogenic recharge under different hydrological conditions. The hydrological analysis, i.e. the flow duration curve, the hydrograph separation techniques and the recession analysis, revealed that the Malenščica spring has a higher storage capacity, a greater proportion of autogenic recharge, especially at low-flow, and a slower recession than the Unica spring. This was also confirmed by correlation and spectral analyses, which were also used to investigate the relationships between discharges at ponors and springs. However, the results of the cross-correlation analysis showed hardly any difference between the two springs and in this case proved to be unsuitable for studying the influence of allogenic recharge. Instead, partial cross-correlation analysis was used to control the input parameters of effective precipitation and discharge of one of the sinking streams to determine the contribution of the other sinking stream to the observed spring. The results confirmed differences in allogenic recharge of the Unica and Malenščica springs. Hysteresis analysis has been applied as a complementary method to time series analysis and represents an improved approach to the characterization of the karst hydrological system. The hydraulic approach to the construction of hysteresis enabled a detailed analysis of allogenic and autogenic water interaction and its influence on the Malenščica and Unica springs under different hydrological conditions. Narrow shapes of the hysteresis indicate a direct hydraulic connection between the ponor and the spring and thus a well-developed drainage system. Any deviation towards a convex or concave shape indicates a less developed, more matrix-related drainage system or the influence of other recharge sources. Analysis of physicochemical hysteretic function of individual locations confirmed the differences in the recharge characteristics of the two springs. Compared to the Unica spring, the Malenščica spring has specific recharge characteristics that result in lower vulnerability to the effects of the sinking streams. A greater proportion of autogenic recharge in the initial phase of the storm event is important, as it allows for a time delay of the possible negative effects of the sinking stream. However, possible pollution from the area of autogenic recharge can have strong negative effects, as in this initial phase with low discharges the dilution effect is negligible.
Keywords: karst aquifer, dynamics of natural tracers, storm events, discharge measurements, time series analysis, hysteresis, Unica spring, Malenščica spring
Published in RUNG: 01.03.2022; Views: 2080; Downloads: 94
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