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3. Time series analysis of duty cycle induced randomness in thermal lens systemMohanachandran Nair Sindhu Swapna, 2020, original scientific article Abstract: 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 anti-persistent 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.
Keywords: Time series analysis
Fractal analysis
Photothermal lens spectroscopy
Fractal dimension
Hurst exponent
Sample entropy
Published in RUNG: 05.07.2022; Views: 1882; Downloads: 0
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5. Neural net pattern recognition based auscultation of croup cough and pertussis using phase portrait featuresMohanachandran Nair Sindhu Swapna, 2021, original scientific article Abstract: 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.
Keywords: Croup cough
Pertussis
Fractal dimension
Phase portrait
Sample entropy
Machine learning techniques
Published in RUNG: 04.07.2022; Views: 1718; Downloads: 0
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6. Fractal and time-series analyses based rhonchi and bronchial auscultation: A machine learning approachMohanachandran 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: 2012; Downloads: 0
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7. Time series and fractal analyses of wheezing : a novel approachMohanachandran 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: 1954; Downloads: 0
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8. Downscaling of sample entropy of nanofluids by carbon allotropes : a thermal lens studyMohanachandran Nair Sindhu Swapna, Vimal Raj, S. Sreejyothi, K. Satheesh Kumar, Sankaranarayana Iyer Sankararaman, 2020, original scientific article Abstract: The work reported in this paper is the first attempt to delineate the molecular or particle dynamics from the thermal lens signal of carbon allotropic nanofluids (CANs), employing time series and fractal analyses. The nanofluids of multi-walled carbon nanotubes and graphene are prepared in base fluid, coconut oil, at low volume fraction and are subjected to thermal lens study. We have studied the thermal diffusivity and refractive index variations of the medium by analyzing the thermal lens (TL) signal. By segmenting the TL signal, the complex dynamics
involved during its evolution is investigated through the phase portrait, fractal dimension, Hurst exponent, and sample entropy using time series and fractal analyses. The study also explains how the increase of the photothermal energy turns a system into stochastic and anti-persistent. The sample entropy (S) and refractive index analyses of the TL signal by segmenting into five regions reveal the evolution of S with the increase of enthalpy. The lowering of S in CAN along with its thermal diffusivity (50%–57% below) as a result of heat-trapping suggests
the technique of downscaling sample entropy of the base fluid using carbon allotropes and thereby opening a novel method of improving the efficiency of thermal systems.
Keywords: carbon allotropic nanofluids, time series, entropy, MWCNT, thermal lens signal
Published in RUNG: 30.06.2022; Views: 1917; Downloads: 0
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9. Soot effected sample entropy minimization in nanofluid for thermal system design : a thermal lens studyMohanachandran Nair Sindhu Swapna, Vimal Raj, K. Satheesh Kumar, Sankaranarayana Iyer Sankararaman, 2020, original scientific article Abstract: The present work suggests a method of improving the thermal system efficiency, through entropy minimisation,
and unveils the mechanism involved by analysing the molecular/particle dynamics in soot nanofluids (SNFs)
using the time series, power spectrum, and wavelet analyses of the thermal lens signal (TLS). The photothermal
energy deposition in the SNF lowers the refractive index due to the temperature rise. It triggers the particle dynamics that are investigated by segmenting the TLS and analysing the refractive index, phase portrait, fractal dimension (D), Hurst exponent (H), and sample entropy (SampEn). The wavelet analysis gives information about
the relation between the entropy and the frequency components. When the phase portrait analysis reflects the
complex dynamics from region 1 to 2 for all the samples, the SampEn analysis supports it. The decreasing
value of D (from 1.59 of the base fluid to 1.55 and 1.52) and the SampEn (from 1.11 of the base fluid to 0.385
and 0.699) with the incorporation of diesel and camphor soot, indicate its ability to lower the complexity, randomness, and entropy. The increase of SampEn with photothermal energy deposition suggests its relation to
the thermodynamic entropy (S). The lowering of thermal diffusivity value of the base fluid from
1.4 × 10−7 m2/s to 1.1 × 10−7 and 0.5 × 10−7 m2
/s upon diesel and camphor soot incorporation suggests the
heat-trapping and reduced molecular dynamics in heat dissipation.
Keywords: soot, entropy, thermal system, photothermal, time series, nanofluid, fractal
Published in RUNG: 30.06.2022; Views: 1924; Downloads: 0
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10. Nonlinear time series and principal component analyses: Potential diagnostic tools for COVID-19 auscultationMohanachandran 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: 2254; Downloads: 0
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