Repozitorij Univerze v Novi Gorici

Iskanje po repozitoriju
A+ | A- | Pomoč | SLO | ENG

Iskalni niz: išči po
išči po
išči po
išči po
* po starem in bolonjskem študiju

Opcije:
  Ponastavi


21 - 30 / 65
Na začetekNa prejšnjo stran1234567Na naslednjo stranNa konec
21.
Time series analysis of duty cycle induced randomness in thermal lens system
Mohanachandran 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: 1119; Prenosov: 0
Gradivo ima več datotek! Več...

22.
Phase Portrait for High Fidelity Feature Extraction and Classification: A Surrogate Approach
Mohanachandran 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: 1020; Prenosov: 0
Gradivo ima več datotek! Več...

23.
Speckle interferometric investigation of argon pressure-induced surface roughness modifications in RF-sputtered MoO[sub]3 film
S. Soumya, R. Arun Kumar, S. Sreejyothi, Vimal Raj, Mohanachandran Nair Sindhu Swapna, Sankaranarayana Iyer Sankararaman, 2021, izvirni znanstveni članek

Opis: Film quality analysis is of more considerable signifcance due to its diversifed applications in various felds of technology. The present work reports the speckle interferometric analysis of the argon pressure-induced surface roughness modifcations of RF sputtered MoO3 flms. The paper suggests a new method of surface quality analysis of thin flms through a parameter δ, which is the diference between the initial and fnal inertia moment values in the study of the thermal-induced dynamic speckle pattern. The limitations of root mean square surface roughness analysis of the atomic force microscopic image of the flms is also exemplifed. The research suggests that argon pressure plays a vital role in the surface property of RF sputtered flms and also that the dynamic speckle analysis can give precise information about the quality of flms. The contour plot of particle displacement vector under thermal stress, suggests the degree of uniformity in the distribution of particles in the flm.
Ključne besede: speckle pattern interferometry, time history of speckle pattern, cross correlation, inertia moment
Objavljeno v RUNG: 04.07.2022; Ogledov: 1114; Prenosov: 0
Gradivo ima več datotek! Več...

24.
25.
Fractal and time-series analyses based rhonchi and bronchial auscultation: A machine learning approach
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.
Ključne besede: lung signal, fractal analysis, sample entropy, non­linear time­series, machine learning techniques
Objavljeno v RUNG: 30.06.2022; Ogledov: 1253; Prenosov: 0
Gradivo ima več datotek! Več...

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, 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.
Ključne besede: auscultation, wheeze, fractals, nonlinear time series analysis, sample entropy
Objavljeno v RUNG: 30.06.2022; Ogledov: 1182; Prenosov: 0
Gradivo ima več datotek! Več...

27.
Downscaling of sample entropy of nanofluids by carbon allotropes : a thermal lens study
Mohanachandran Nair Sindhu Swapna, Vimal Raj, S. Sreejyothi, K. Satheesh Kumar, Sankaranarayana Iyer Sankararaman, 2020, izvirni znanstveni članek

Opis: 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.
Ključne besede: carbon allotropic nanofluids, time series, entropy, MWCNT, thermal lens signal
Objavljeno v RUNG: 30.06.2022; Ogledov: 1177; Prenosov: 0
Gradivo ima več datotek! Več...

28.
Soot effected sample entropy minimization in nanofluid for thermal system design : a thermal lens study
Mohanachandran Nair Sindhu Swapna, Vimal Raj, K. Satheesh Kumar, Sankaranarayana Iyer Sankararaman, 2020, izvirni znanstveni članek

Opis: 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.
Ključne besede: soot, entropy, thermal system, photothermal, time series, nanofluid, fractal
Objavljeno v RUNG: 30.06.2022; Ogledov: 1115; Prenosov: 0
Gradivo ima več datotek! Več...

29.
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, 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.
Ključne besede: Breath sound analysis, Fractal dimension, Nonlinear time series analysis, Sample entropy, Hurst exponent, Principal component analysis
Objavljeno v RUNG: 28.06.2022; Ogledov: 1425; Prenosov: 0
Gradivo ima več datotek! Več...

30.
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, izvirni znanstveni članek

Opis: 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.
Ključne besede: phase portrait, auscultation, mitral valve dysfunction, heart murmur, nonlinear time series analysis
Objavljeno v RUNG: 28.06.2022; Ogledov: 1114; Prenosov: 0
Gradivo ima več datotek! Več...

Iskanje izvedeno v 0.06 sek.
Na vrh