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Opis: Fingerprint recognition is the most popular biometric identification method currently employed in security applications. Though there are several optical methods for fingerprint analysis based on intensity recording, phase recording alone can give the precise result. Interference and diffraction phenomenon are capable of recording phase variations. In the present method diffraction patterns corresponding to the fingerprints are generated and the spacing between the ridges is calculated. The spacing between ridges is found to be in agreement with the value measured using the software ImageJ. The fingerprint impressions of more than thirty-five persons are recorded and analyzed using the statistical tools in Matlab.The fingerprints are also subjected to fractal analysis. The program could successfully identify a fingerprint from the database. The present work is the first report of fingerprint analysis from diffraction pattern
Ključne besede: Diffraction, Skew, Kurtosis, Fractals
Objavljeno v RUNG: 30.06.2022; Ogledov: 1105; Prenosov: 0
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Opis: Graphene has emerged as a potential material in various scientific disciplines, ranging from material science, engineering, and more recently biomedicine. The paper describes the investigation of the presence of graphene and graphene oxide (GO) in the carbon soot of internal combustion diesel engines. The UV-Visible, Fourier transform infrared (FTIR), X- ray diffraction (XRD), Photoluminescent (PL) and Raman spectroscopic analysis of the sample provided a conclusive evidence of the formation of graphene and GO. The Field Emission Scanning Electron Microscopy (FESEM) and Energy Dispersive Spectrum (EDX) of the sample show carbon nanoparticles (CNPs) of size less than 50nm. The High -Resolution Transmission Electron Microscopy (HR-TEM) analysis confirmed the formation of graphene sheets with carbon nanospheres attached to it. The study reveals the possible exploitation of the diesel soot for potential applications in science and technology
Ključne besede: Graphene, Graphene oxide, Carbon nanoparticles, Combustion, Carbon nanotube
Objavljeno v RUNG: 30.06.2022; Ogledov: 1277; Prenosov: 0
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Opis: The paper proposes a novel approach to bring out the potential of complex networks based on graph theory to unwrap the hidden characteristics of cough signals, croup (BC), and pertussis (PS). The spectral and complex network analyses of 48 cough sounds are utilized for understanding the airflow through the infected respiratory tract. Among the different phases of the cough sound time-domain signals of BC and PS – expulsive (X), intermediate (I), and voiced (V) - the phase ‘I’ is noisy in BC due to improper glottal functioning. The spectral analyses reveal high-frequency components in both cough signals with an additional high-intense low-frequency spread in BC. The complex network features created by the correlation mapping approach, like number of edges (E), graph density (G), transitivity (), degree centrality (D), average path length (L), and number of components () distinguishes BC and PS. The higher values of E, G, and for BC indicate its musical nature through the strong correlation between the signal segments and the presence of high-intense low-frequency components in BC, unlike that in PS. The values of D, L, and discriminate BC and PS in terms of the strength of the correlation between the nodes within them. The linear discriminant analysis (LDA) and quadratic support vector machine (QSVM) classifies BC and PS, with greater accuracy of 94.11% for LDA. The proposed work opens up the potentiality of employing complex networks for cough sound analysis, which is vital in the current scenario of COVID-19.
Ključne besede: Complex network analysis, Auscultation, Croup cough, Pertussis Spectral analysis, Machine learning techniques
Objavljeno v RUNG: 30.06.2022; Ogledov: 1406; Prenosov: 0
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Ključne besede: dental composites, thermal lens spectrometry, SiO2 and BaSO4 leachates
Objavljeno v RUNG: 30.06.2022; Ogledov: 1301; Prenosov: 0
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Opis: The paper proposes a graph-theoretical approach to auscultation, bringing out the potential of graph features in classifying the bioacoustics signals. The complex network analysis of the bioacoustics signals - vesicular (VE) and bronchial (BR) breath sound - of 48 healthy persons are carried out for understanding the airflow dynamics during respiration. The VE and BR are classified by the machine learning techniques extracting the graph features – the number of edges (E), graph density (D), transitivity (T), degree centrality (Dcg) and eigenvector centrality (Ecg). The higher value of E, D, and T in BR indicates the temporally correlated airflow through the wider tracheobronchial tract resulting in sustained high-intense low-frequencies. The frequency spread and high-frequencies in VE, arising due to the less correlated airflow through the narrow segmental bronchi and lobar, appears as a lower value for E, D, and T. The lower values of Dcg and Ecg justify the inferences from the spectral and other graph parameters. The study proposes a methodology in remote auscultation that can be employed in the current scenario of COVID-19.
Ključne besede: Bioacoustic signal, Graph theory, Complex network, Lung auscultation
Objavljeno v RUNG: 30.06.2022; Ogledov: 1279; Prenosov: 0
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