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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, original scientific article

Abstract: 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.
Found in: ključnih besedah
Summary of found: ...MWCNT, thermal lens, fractals, nonlinear time series, phase portrait, sample entropy...
Keywords: MWCNT, thermal lens, fractals, nonlinear time series, phase portrait, sample entropy
Published: 28.06.2022; Views: 132; Downloads: 0
.pdf Fulltext (3,59 MB)

2.
Unravelling the potential of phase portrait in the auscultation of mitral valve dysfunction
SANKARARAMAN SANKARANARAYANA IYER, RAJ VIMAL, RENJINI A, SREEJYOTHI S, SWAPNA MOHANACHANDRAN NAIR SINDHU, 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.
Found in: ključnih besedah
Summary of found: ...phase portrait,auscultation, mitral valve dysfunction, heart murmur, nonlinear time series analysis... ...phase portrait, fast Fourier transform, wavelet, and time-series analyses of the heart murmur (HM) of...
Keywords: phase portrait, auscultation, mitral valve dysfunction, heart murmur, nonlinear time series analysis
Published: 28.06.2022; Views: 97; Downloads: 0
.pdf Fulltext (2,02 MB)

3.
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, 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.
Found in: ključnih besedah
Keywords: Breath sound analysis, Fractal dimension, Nonlinear time series analysis, Sample entropy, Hurst exponent, Principal component analysis
Published: 28.06.2022; Views: 122; Downloads: 0
.pdf Fulltext (2,73 MB)

4.
Time series and fractal analyses of wheezing
Sankaranarayana Iyer Sankararaman, S. Sreejyothi, Vimal Raj, A. Renjini, Mohanachandran Nair Sindhu Swapna, 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.
Found in: ključnih besedah
Summary of found: ...through auscultation and of employing spectral, fractal, nonlinear time series and principal component analyses. Thirty-fve...
Keywords: auscultation, wheeze, fractals, nonlinear time series analysis, sample entropy
Published: 30.06.2022; Views: 103; Downloads: 0
.pdf Fulltext (2,46 MB)

5.
6.
Nonlinear signal processing, spectral, and fractal based stridor auscultation: A machine learning approach
SWAPNA MOHANACHANDRAN NAIR SINDHU, 2022, original scientific article

Found in: ključnih besedah
Summary of found: ...Breath sound, fractal, linear discriminant analysis, nonlinear time series, stridor....
Keywords: Breath sound, fractal, linear discriminant analysis, nonlinear time series, stridor.
Published: 06.07.2022; Views: 125; Downloads: 0
.pdf Fulltext (3,34 MB)

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