Iskanje po repozitoriju

A+ | A- | | SLO | ENG

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: 1279; Prenosov: 0
Gradivo ima več datotek! Več...

Opis: The process of functionalization of metals in natural carbon matrices has become an important area of research due to its improved properties and applications. Carbon materials possessing photoluminescence (PL) properties find a wide range of applications in photonics. Among the various carbon materials available in nature, cellulose has critical importance since it is the most abundant and wide-spread biopolymer on Earth, and also, the important component in plants’ skeleton. In the present work, the functionalized carbonaceous material is prepared by the hydrothermal treatment of natural cellulosic source Aloe Vera and the metallic element sodium is properly incorporated into it by adding sodium borohydride to observe the fluorescence emission changes. The incorporation of metal ions in the carbon matrix leads to structural modifications and properties as evidenced by field emission scanning electron microscopy, Energy dispersive spectroscopy, X-ray dot mapping, X-ray Photoelectron spectroscopy, and X-ray diffraction analysis. The optical emission characteristics are studied using Photoluminescence spectroscopy, CIE plot, power spectrum, color purity, and quantum yield. The excitation wavelength dependent photoluminescence emission mechanism shown by the carbon–metal incorporated products obtained from the cellulosic raw materials makes them suitable for biomedical and biosensing applications because of the non-toxic and eco-friendly nature.
Ključne besede: Fluorescent emission, sodium carbide, cellulose, carbon matrix
Objavljeno v RUNG: 30.06.2022; Ogledov: 1097; Prenosov: 0
Gradivo ima več datotek! Več...

Opis: Today diesel engines are used worldwide for various applications and very importantly in transportation. Hydrocarbons are the most widespread precursors among carbon sources employed in the production of carbon nanotubes (CNTs). The aging of internal combustion engine is an important parameter in deciding the carbon emission and particulate matter due to incomplete combustion of fuel. In the present work, an attempt has been made for the effective utilization of the aged engines for potential applicationapplications in fuel cells and nanoelectronics. To analyze the impact of aging, the particulate matter rich in carbon content areis collected from diesel engines of different ages. The soot with CNTs is purified by the liquid phase oxidation method and analyzed by Field Emission Scanning Electron Microscopy, High-Resolution Transmission Electron Microscopy, Energy Dispersive Spectroscopy, UV-Visible spectroscopy, Raman spectroscopy and Thermogravimetric analysis. The SEM image contains self-similar patterns probing fractal analysis. The fractal dimensions of the samples are determined by the box counting method. We could find a greater amount of single-walled carbon nanotubes (SWCNTs) in the particulate matter emitted by aged diesel engines and thereby giving information about the combustion efficiency of the engine. The SWCNT rich sample finds a wide range of applications in nanoelectronics and thereby pointing a potential use of these aged engines.
Ključne besede: Fractals, internal combustion engine, efficiency, soot, carbon nanoparticle
Objavljeno v RUNG: 30.06.2022; Ogledov: 1200; Prenosov: 0
Gradivo ima več datotek! Več...

Opis: The present work is the first report of producing blue light emission from phosphor free and low-cost material—the diesel soot from the internal combustion engines (ICEs). The structural morphology is analyzed by field emission scanning electron microscopy and high-resolution transmission electron microscopy. The optical characterization is done by recording UV–visible spectrum and photoluminescent Spectrum. The CIE plot and the power spectrum for the sample show blue emission. This is further verified by collecting diesel soot from the ICE of different year of make. A visual confirmation of blue emission is obtained by exciting the sample with UV laser. The presence of various allotropic forms of carbon in the sample is identified by x-ray diffraction, Fourier transform infrared and Raman spectroscopic analysis.
Ključne besede: blue light emitter, diesel soot, photoluminescence, CIE plot
Objavljeno v RUNG: 30.06.2022; Ogledov: 1093; Prenosov: 0
Gradivo ima več datotek! Več...

Opis: Fractal analysis has emerged as a potential analytical tool in almost all branches of science and technology. The paper is the first report of using fractal dimension as a surrogate technique for estimating particle size. A regression equation is set connecting the soot particle size and fractal dimension, after investigating the Field Emission Scanning Electron Microscopic (FESEM) images of carbonaceous soot from five different sources. Since the fractal dimension is an invariant property under the scale transformation, an ordinary photograph of the soot should also yield the same fractal dimension. This enables one to determine the average size of the soot particles, using the regression equation, by calculating the fractal dimension from the photograph. Hence, instead of frequent measurement of average particle size from FESEM, this technique of estimating the particle size from the fractal dimension of the soot photograph, is found to be a potentially cost-effective and non-contact method.
Ključne besede: fractals, FESEM, carbon nanoparticles, particle size, box-counting
Objavljeno v RUNG: 30.06.2022; Ogledov: 1165; Prenosov: 0
Gradivo ima več datotek! Več...