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1.
Thermal Lensing of Multi-Walled Carbon Nanotube Solutions as Heat-Transfer Nanofluids
SANKARARAMAN SANKARANARAYANA IYER, CABRERA HUMBERTO, RAJ VIMAL, Swapna Mohanachandran Nair Sindhu, 2021, izvirni znanstveni članek

Opis: This paper unwraps nanofluids’ particle dynamics with multi-walled carbon nanotubes (MWCNTs) in base fluids such as acetone, water, and ethylene glycol. Having confirmed the morphology and structure of the MWCNTs by field emission scanning electron microscopy, X-ray diffraction, and Raman spectroscopic analyses, the nanofluids are prepared in three different concentrations. The nonzero absorbance at the laser wavelength, revealed through the UV−visible spectrum, makes the thermal diffusivity study of the sample by the sensitive nondestructive single beam thermal lens (TL) technique possible. The TL signal analysis by time series and fractal techniques divulges the complex particle dynamics, through phase portrait, sample entropy, fractal dimension, and Hurst exponent. The study unveils the effect of the amount of nanoparticles and the viscosity of the medium on thermal diffusivity and particle dynamics. The observed inverse relation between thermal diffusivity and viscosity is in good agreement with the Sankar−Swapna model. The complexity of particle dynamics in MWCNT nanofluids reflected through sample entropy, and fractal dimension shows an inverse relation to the base fluid’s viscosity. This paper investigates the role of viscosity of the base fluid on particle dynamics and thermal diffusivity of the nanofluid to explore its applicability in various thermal systems, thereby suggesting a method to tune the sample entropy through proper selection of base fluid.
Najdeno v: ključnih besedah
Povzetek najdenega: ...makes the thermal diffusivity study of the sample by the sensitive nondestructive single beam thermal... ...complex particle dynamics, through phase portrait, sample entropy, fractal dimension, and Hurst exponent. The study...
Ključne besede: MWCNT, thermal lens, fractals, nonlinear time series, phase portrait, sample entropy
Objavljeno: 28.06.2022; Ogledov: 315; Prenosov: 0
.pdf Polno besedilo (3,59 MB)

2.
Nonlinear time series and principal component analyses: Potential diagnostic tools for COVID-19 auscultation
SANKARARMAN S, SREEJYOTHI S, RENJINI A, RAJ VIMAL, Swapna Mohanachandran Nair Sindhu, 2020, izvirni znanstveni članek

Opis: 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.
Najdeno v: ključnih besedah
Povzetek najdenega: ...phase portrait, fractal dimension, Hurst exponent, and sample entropy. The higher degree of chaoticity in BB...
Ključne besede: Breath sound analysis, Fractal dimension, Nonlinear time series analysis, Sample entropy, Hurst exponent, Principal component analysis
Objavljeno: 28.06.2022; Ogledov: 267; Prenosov: 0
.pdf Polno besedilo (2,73 MB)

3.
Time series and fractal analyses of wheezing
Sankaranarayana Iyer Sankararaman, S. Sreejyothi, Vimal Raj, Ammini Renjini, Mohanachandran Nair Sindhu Swapna, 2020, izvirni znanstveni članek

Opis: 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.
Najdeno v: ključnih besedah
Povzetek najdenega: ...during respiration using phase portrait, Lyapunov exponent, sample entropy, fractal dimension, and Hurst exponent helps...
Ključne besede: auscultation, wheeze, fractals, nonlinear time series analysis, sample entropy
Objavljeno: 30.06.2022; Ogledov: 236; Prenosov: 0
.pdf Polno besedilo (2,46 MB)

4.
Fractal and time-series analyses based rhonchi and bronchial auscultation: A machine learning approach
SWAPNA MOHANACHANDRAN NAIR SINDHU SWAPNA,, 2022, izvirni znanstveni članek

Opis: 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.
Najdeno v: ključnih besedah
Povzetek najdenega: ...Hurst exponent, phase portrait, maximal Lyapunov exponent, and sample entropy values. The greater value of entropy for...
Ključne besede: lung signal, fractal analysis, sample entropy, non­linear time­series, machine learning techniques
Objavljeno: 30.06.2022; Ogledov: 308; Prenosov: 0
.pdf Polno besedilo (1,50 MB)

5.
Neural net pattern recognition based auscultation of croup cough and pertussis using phase portrait features
Swapna Mohanachandran Nair Sindhu, 2021, izvirni znanstveni članek

Opis: Cough signal analysis for understanding the pathological condition has become important from the outset of the exigency posed by the epidemic COVID-19. The present work suggests a surrogate approach for the classification of cough signals - croup cough (CC) and pertussis (PT) – based on spectral, fractal, and nonlinear time-series techniques. The spectral analysis of CC reveals the presence of more frequency components in the short duration cough sound compared to PT. The musical nature of CC is unveiled not only through the spectral analysis but also through the phase portrait features – sample entropy (S), maximal Lyapunov exponent (L), and Hurst exponent (Hb). The modifications in the internal morphology of the respiratory tract, giving rise to more frequency components associated with the complex airflow dynamics, get staged through the higher fractal dimension of CC. Among the two supervised classification tools, cubic KNN (CKNN) and neural net pattern recognition (NNPR), used for classifying the CC and PT signals based on nonlinear time series parameters, NNPR is found better. Thus, the study opens the possibility of identification of pulmonary pathological conditions through cough sound signal analysis.
Najdeno v: ključnih besedah
Povzetek najdenega: ...also through the phase portrait features – sample entropy (S), maximal Lyapunov exponent (L), and...
Ključne besede: Croup cough Pertussis Fractal dimension Phase portrait Sample entropy Machine learning techniques
Objavljeno: 04.07.2022; Ogledov: 183; Prenosov: 0
.pdf Polno besedilo (5,42 MB)

6.
Unwrapping the laser beam quality through nonlinear time series and fractal analyses: A surrogate approach
Swapna Mohanachandran Nair Sindhu, 2021, izvirni znanstveni članek

Najdeno v: ključnih besedah
Povzetek najdenega: ...Laser beam quality Sample entropy Fractal dimension Nonlinear time series analysis...
Ključne besede: Laser beam quality Sample entropy Fractal dimension Nonlinear time series analysis Hurst exponent Fractal analysis
Objavljeno: 04.07.2022; Ogledov: 276; Prenosov: 0
.pdf Polno besedilo (4,23 MB)

7.
Time series analysis of duty cycle induced randomness in thermal lens system
Swapna Mohanachandran Nair Sindhu, 2020, izvirni znanstveni članek

Opis: The present work employs time series analysis, a proven powerful mathematical tool, for investigating the complex molecular dynamics of the thermal lens (TL) system induced by the duty cycle (C) variation. For intensity modulation, TL spectroscopy commonly uses optical choppers. The TL formation involves complex molecular dynamics that vary with the input photothermal energy, which is implemented by varying the duty cycle of the chopper. The molecular dynamics is studied from the fractal dimension (D), phase portrait, sample entropy (S), and Hurst exponent (H) for different duty cycles. The increasing value of C is found to increase D and S, indicating that the system is becoming complex and less deterministic, as evidenced by the phase portrait analysis. The value of H less than 0.5 conforms the evolution of the TL system to more antipersistent nature with C. The increasing value of C increases the enthalpy of the system that appears as an increase in full width at half maximum of the refractive index profile. Thus the study establishes that the sample entropy and thermodynamic entropy are directly related.
Najdeno v: ključnih besedah
Povzetek najdenega: ...from the fractal dimension (D), phase portrait, sample entropy (S), and Hurst exponent (H) for different...
Ključne besede: Time series analysis Fractal analysis Photothermal lens spectroscopy Fractal dimension Hurst exponent Sample entropy
Objavljeno: 05.07.2022; Ogledov: 212; Prenosov: 0
.pdf Polno besedilo (1,30 MB)

8.
Temporal evolution of sample entropy in thermal lens system
Vimal Raj, Mohanachandran Nair Sindhu Swapna, K. Satheesh Kumar, Sankaranarayana Iyer Sankararaman, 2020, izvirni znanstveni članek

Najdeno v: ključnih besedah
Ključne besede: photothermal, thermal lens, time series, sample entropy
Objavljeno: 05.07.2022; Ogledov: 278; Prenosov: 0

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