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3. Time series and mel frequency analyses of wet and dry cough signals : a neural net classificationAmmini Renjini, Mohanachandran Nair Sindhu Swapna, K. Satheesh Kumar, Sankaranarayana Iyer Sankararaman, 2023, izvirni znanstveni članek Ključne besede: time series, mel frequency, cough signal, wet cough, dry cough, phase portrait, mel coefficients, fractal dimension, neural network
Objavljeno v RUNG: 29.09.2023; Ogledov: 1651; Prenosov: 6
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4. Markov chain : a novel tool for electronic ripple analysisVijayan Vijesh, K. Satheesh Kumar, Mohanachandran Nair Sindhu Swapna, Sankaranarayana Iyer Sankararaman, 2022, izvirni znanstveni članek Ključne besede: complex network, Markov chain, rectifier, time series, ripple
Objavljeno v RUNG: 29.11.2022; Ogledov: 1796; Prenosov: 0
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8. Time series analysis of duty cycle induced randomness in thermal lens systemMohanachandran Nair Sindhu Swapna, 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 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.
Ključne besede: Time series analysis
Fractal analysis
Photothermal lens spectroscopy
Fractal dimension
Hurst exponent
Sample entropy
Objavljeno v RUNG: 05.07.2022; Ogledov: 2108; Prenosov: 0
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9. Phase Portrait for High Fidelity Feature Extraction and Classification: A Surrogate ApproachMohanachandran Nair Sindhu Swapna, 2020, izvirni znanstveni članek Opis: This paper proposes a novel surrogate method of classification of breath sound signals for auscultation through the principal component
analysis (PCA), extracting the features of a phase portrait. The nonlinear parameters of the phase portrait like the Lyapunov exponent, the
sample entropy, the fractal dimension, and the Hurst exponent help in understanding the degree of complexity arising due to the turbulence
of air molecules in the airways of the lungs. Thirty-nine breath sound signals of bronchial breath (BB) and pleural rub (PR) are studied
through spectral, fractal, and phase portrait analyses. The fast Fourier transform and wavelet analyses show a lesser number of high-intense,
low-frequency components in PR, unlike BB. The fractal dimension and sample entropy values for PR are, respectively, 1.772 and 1.041, while
those for BB are 1.801 and 1.331, respectively. This study reveals that the BB signal is more complex and random, as evidenced by the fractal
dimension and sample entropy values. The signals are classified by PCA based on the features extracted from the power spectral density (PSD)
data and the features of the phase portrait. The PCA based on the features of the phase portrait considers the temporal correlation of the signal
amplitudes and that based on the PSD data considers only the signal amplitudes, suggesting that the former method is better than the latter
as it reflects the multidimensional aspects of the signal. This appears in the PCA-based classification as 89.6% for BB, a higher variance than
the 80.5% for the PR signal, suggesting the higher fidelity of the phase portrait-based classification.
Ključne besede: Phase Portrait, time series, feature extraction, pleural rub
Objavljeno v RUNG: 05.07.2022; Ogledov: 1893; Prenosov: 0
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