Estimation Of Absorption Coefficient Research Articles

Combination of cryo-mediated and salt-assisted liquid-phase exfoliation for GaSe nanosheets preparation with absorption coefficient estimation via Raman signal attenuation

In this study, a novel preparation method combining cryo-mediated method and salt-assisted liquid-phase exfoliation is presented for the preparation of Gallium Selenide (GaSe) nanosheets and the absorption coefficient are estimated by the Raman signal attenuation method on silicon substrates.It was found that Potassium citrate (C6H5K3O7·H2O) as an accelerator increased the lateral size of GaSe nanosheets, while employing liquid nitrogen as a cryo-mediation significantly reduced the surface roughness of the nanosheets. Furthermore, a correlation exists between the duration of cryo-pretreatment and the thickness distribution of GaSe nanosheets.Using the absorption overlayer model, the study experimentally determined more accurate absorption coefficient of 2D GaSe nanosheets by analyzing the changes in Raman signal intensity on a silicon substrate induced by GaSe nanosheets with varying thicknesses, resulting in an average absorption coefficient of 5.68×104 cm−1.This study introduces a novel approach to prepare GaSe and other two-dimensional (2D) material nanosheets with large size and low roughness, based on which the absorption coefficient of GaSe nanosheets are also determined in a more precise way.

A comparative study on hydrocarbon detection using cepstrum–based methods

Amplitude anomaly caused by a high–frequency component is attenuated more rapidly than a low–frequency component for a seismic trace in sediments susceptible to gas influence and is always used as a direct indicator of hydrocarbons. Recently, cepstrum–based hydrocarbon detection methods have become an important exploration pathway for detecting amplitude anomalies in the gas–bearing formation more sensitively and accurately. In this study, we compared three cepstrum–based hydrocarbon detection methods. We compared their fluid identification abilities in gas reservoirs and the noise robustness. They were further compared with the traditional absorption coefficient estimation method. We also indicate how the choice of the selection of the sliding–window length influences the performance of the three cepstrum–based hydrocarbon detection methods. Model test results and real data applications show that for thick gas reservoirs, the hydrocarbon detection methods using the Berthil–based cepstrum and the wavelet–based cepstrum can better discriminate the upper and lower interfaces than the hydrocarbon detection method using the Fourier–based cepstrum. For thin gas reservoirs, the hydrocarbon detection method using the wavelet–based cepstrum can identify the lower interface of the gas–bearing reservoir more obviously than the upper interface. The hydrocarbon detection method using the Berthil cepstrum cannot identify the upper and lower interfaces of the gas–bearing reservoir, but it shows higher temporal and spatial resolution compared to the hydrocarbon detection method using the Fourier–based cepstrum. For noise robustness, the hydrocarbon detection method using the wavelet–based cepstrum has the strongest noise robustness property, and the hydrocarbon detection method using the Fourier–based cepstrum is the most sensitive method to the noise. For time–consumption, the hydrocarbon detection method using the wavelet–based cepstrum is the most time–consuming, and the hydrocarbon detection method using the Fourier–based cepstrum is the fastest.

Wintertime Diurnal Variation in Absorption Coefficient of Brown Carbon Associated with the Molecular Marker of Levoglucosan

This study investigated the aerosol particle properties and light absorption properties of brown carbon (BrC) by utilizing a seven-wavelength aethalometer, and analyzed NH4+, NO3−, SO42−, K+, K, organic carbon, elemental carbon, levoglucosan, and mannosan in PM2.5. The research was conducted in a rural area of Jeonnam, South Korea, during the winter season. In addition, the dithiothreitol assay-oxidative potential normalized to 9,10-phenanthrenequinone (QDTT-OP) was investigated throughout the study period. The absorption coefficient was found to be 2.6 to 5.6 times higher at 370 nm compared to 880 nm, suggesting the presence of light-absorbing substances in addition to black carbon (BC) particles. The estimated absorption coefficient of BrC370 was 29.9% of the total light absorption coefficient at 370 nm. Furthermore, BrC370 exhibited a strong affinity with levoglucosan while showing a weak correlation with K+, confirming the suitability of levoglucosan as a tracer for biomass burning. The QDTT-OP was 5.3 nM m−3, and highly correlated with the carbonaceous components levoglucosan and mannosan, suggesting a relatively high contribution of biomass combustion emissions to oxidative potential. Further research should be conducted to assess the health risks associated with future PM2.5 exposure related to biomass burning in the atmosphere.

Moving Beyond Simulation: Data-Driven Quantitative Photoacoustic Imaging Using Tissue-Mimicking Phantoms.

Accurate measurement of optical absorption coefficients from photoacoustic imaging (PAI) data would enable direct mapping of molecular concentrations, providing vital clinical insight. The ill-posed nature of the problem of absorption coefficient recovery has prohibited PAI from achieving this goal in living systems due to the domain gap between simulation and experiment. To bridge this gap, we introduce a collection of experimentally well-characterised imaging phantoms and their digital twins. This first-of-a-kind phantom data set enables supervised training of a U-Net on experimental data for pixel-wise estimation of absorption coefficients. We show that training on simulated data results in artefacts and biases in the estimates, reinforcing the existence of a domain gap between simulation and experiment. Training on experimentally acquired data, however, yielded more accurate and robust estimates of optical absorption coefficients. We compare the results to fluence correction with a Monte Carlo model from reference optical properties of the materials, which yields a quantification error of approximately 20%. Application of the trained U-Nets to a blood flow phantom demonstrated spectral biases when training on simulated data, while application to a mouse model highlighted the ability of both learning-based approaches to recover the depth-dependent loss of signal intensity. We demonstrate that training on experimental phantoms can restore the correlation of signal amplitudes measured in depth. While the absolute quantification error remains high and further improvements are needed, our results highlight the promise of deep learning to advance quantitative PAI.

Modeling of light absorption in self-assembled truncated conical quantum dot structures

Quantum Dots have shown a significant potential as a top candidate for infrared photodetection at higher temperatures. In the presented work, a theoretical model for estimating the coefficient of optical absorption of self-assembled truncated conical quantum dot is developed. This model considers both bound-to-continuum and bound-to-bound absorption mechanisms that increase the accuracy of the absorption coefficient estimation. The developed model is based on estimating the bound states by diagonalizing the Hamiltonian matrix, where the density of states is computed using the Non-Equilibrium Greens function and the effective mass theory to obtain the unbound states. The kinetic equation of Green’s function is solved numerically by finite difference method. Besides, the effects of quantum dot size, height, aspect ratio, and density on the coefficient of the optical absorption are investigated. The results of the developed model are contrasted with those of other alternative QD structures where the truncated conical QD structure results in a higher absorption coefficient in infrared range than semispherical and conical QD structures.

PM2.5 carbonaceous components and mineral dust at a COALESCE network site - Bhopal, India: Assessing the impacts of COVID-19 lockdowns on site-specific optical properties

Thermal elemental carbon (EC), optical black carbon (BC), organic carbon (OC), mineral dust (MD), and 7-wavelength optical attenuation of 24-hour ambient PM2.5 samples were measured/estimated at a regionally representative site (Bhopal, central India) during a business-as-usual year (2019) and the COVID-19 lockdowns year (2020). This dataset was used to estimate the influence of emissions source reductions on the optical properties of light-absorbing aerosols. During the lockdown period, the concentration of EC, OC, BC880 nm, and PM2.5 increased by 70 % ± 25 %, 74 % ± 20 %, 91 % ± 6 %, and 34 % ± 24 %, respectively, while MD concentration decreased by 32 % ± 30 %, compared to the same time period in 2019. Also, during the lockdown period, the estimated absorption coefficient (babs) and mass absorption cross-section (MAC) values of Brown Carbon (BrC) at 405 nm were higher (42 % ± 20 % and 16 % ± 7 %, respectively), while these quantities for MD, i.e., babs-MD and MACMD values were lower (19 % ± 9 % and 16 % ± 10 %), compared to the corresponding period during 2019. Also, babs-BC-808 (115 % ± 6 %) and MACBC-808 (69 % ± 45 %) values increased during the lockdown period compared with the corresponding period during 2019. It is hypothesized that although anthropogenic emissions (chiefly industrial and vehicular) reduced drastically during the lockdown period compared to the business-as-usual period, an increase in the values of optical properties (babs and MAC) and concentrations of BC and BrC, were likely due to the increased local and regional biomass burning emissions during this period. This hypothesis is supported by the CBPF (Conditional Bivariate Probability Function) and PSCF (Potential Source Contribution Function) analyses for BC and BrC.

Machine-learning-based estimation of absorption coefficients from transfer functions modeled by equivalent sources

In the field of room acoustics, it is important to find the absorption coefficients of the wall surface, which is a boundary condition for modeling the room acoustic field. However, it is not easy to measure the acoustic impedances of the entire room because it requires many measurement points near the wall surface. Recently, a method to estimate the acoustic impedance and absorption coefficients by using both measurement and simulation methods has been proposed. However, a large number of measurement points are required to obtain sufficient estimation accuracy. In this study, we proposed estimation method of the sound absorption coefficients using machine learning with virtually increasing the number of microphones. First, the transfer functions at the virtual microphones are obtained from small number of transfer functions based on the sound field modeling by sparse equivalent sources. Then, the both transfer functions at the virtual and real microphones are used as the training data for machine learning. To evaluate estimation accuracy of the proposed method, we conducted the two-dimensional simulation experiments based on the boundary element method.

Quantum mechanics-based seismic energy absorption analysis for hydrocarbon detection

SUMMARYSeismic attenuation has a considerable impact on resolution reduction and the increase in the dominant frequency period of seismic data. The absorption coefficient estimates, which measure inelastic attenuation, provide a deep understanding of the medium property changes in different geological settings. Conventional absorption coefficient estimation technologies always use time–frequency methods for seismic energy absorption analysis. However, despite continuing efforts to improve the absorption coefficient estimation, the limitation of the time–frequency methods still causes insufficient accuracy of the attenuation estimates, imposing major challenges in oil and gas hydrate exploration. In this study, a quantum mechanics-based seismic absorption coefficient estimation method was proposed for hydrocarbon detection. The seismic data were first projected on a specific basis constructed using the resolution of the Schrödinger equation. Seismic energy absorption analysis was then conducted in the potential-wave function domain. Finally, the quantum absorption coefficient estimates are given by the procedure after using a logarithmic operation and the least-squares fitting method. We examined the merits of these methods using model and field data. The gas reservoir was accurately targeted, which demonstrates that the proposed method has great potential for hydrocarbon detection.

Absorption Coefficient and Chlorophyll Concentration of Oceanic Waters Estimated from Band Difference of Satellite-Measured Remote Sensing Reflectance

Absorption coefficient and chlorophyll concentration ( Chl ) are important optical and biological properties of the aquatic environment, which can be estimated from the spectrum of water color, commonly measured by the remote sensing reflectance ( R rs ). In this study, we extended the band-difference scheme for Chl of oceanic waters developed a decade ago to the estimation of absorption coefficient at 440 nm ( a (440)). As demonstrated earlier for the estimation of Chl , a (440) product from the band difference of R rs showed much smoother spatial pattern than that from a semianalytical algorithm. More importantly, it is found that the upper limit of using band difference of R rs can be extended from −0.0005 sr −1 (the upper limit set a decade ago for the estimation of Chl ) to ~0.0005 sr −1 (corresponding to a (440) ~0.08 m −1 ), which covers ~91% of the global ocean. We further converted a (440) to Chl based on the “Case-1” water assumption and found that the standard Chl product of oligotrophic waters ( Chl ~ 0.1 mg/m 3 ) distributed by NASA is generally ~20% higher than Chl converted from a (440), possibly a result of different datasets used to determine the algorithm coefficients. These results not only extended the application of the band-difference scheme for more oceanic waters but also highlighted the need of more accurate field measurements of Chl and R rs in oligotrophic oceans in order to minimize the discrepancies observed in satellite Chl products derived using the same algorithm concept but different empirical approaches.

Design of Hybrid Waveguide Structures for High-Efficiency Integrated Optical Superconducting Single Photon Detectors On Ti:LiNbO3Waveguides

A configuration of integrated optical superconducting single photon detectors on lithium niobate substrates based on conventional titanium in-diffused waveguides with hybrid waveguide structures for enhancing the detector efficiency is proposed and analyzed. The sensing element in the form of a meander-like superconducting nanowire from niobium nitride is covered with an additional hybrid waveguide from a dielectric material with a higher refractive index than the LiNbO3 substrate to increase the light absorption. A special mode converter in the region free from a superconducting nanostructure is used to excite a localized mode with the maximum absorption coefficient that strongly interacts with the superconductive nanostructure. Silicon and titanium dioxide are considered and compared as suitable materials for the hybrid waveguide structures. Silicon based structures give a higher light concentration and, hence, a higher absorption coefficient. However, they are very demanding to the technological accuracy. Titanium dioxide structures potentially can be produced by the standard thin-film deposition technique and contact photolithography. The estimated absorption coefficient of 1.31 dB/μm is four orders of magnitude higher than that for the detectors with a standard titanium in-diffused waveguide, and a high transformation mode efficiency opens a way for fabrication of integrated optical superconducting single photon detectors with the efficiency comparable with other competing material platforms.

Machine Learning Algorithms for Chromophoric Dissolved Organic Matter (CDOM) Estimation Based on Landsat 8 Images

Chromophoric dissolved organic matter (CDOM) is crucial in the biogeochemical cycle and carbon cycle of aquatic environments. However, in inland waters, remotely sensed estimates of CDOM remain challenging due to the low optical signal of CDOM and complex optical conditions. Therefore, developing efficient, practical and robust models to estimate CDOM absorption coefficient in inland waters is essential for successful water environment monitoring and management. We examined and improved different machine learning algorithms using extensive CDOM measurements and Landsat 8 images covering different trophic states to develop the robust CDOM estimation model. The algorithms were evaluated via 111 Landsat 8 images and 1708 field measurements covering CDOM light absorption coefficient a(254) from 2.64 to 34.04 m−1. Overall, the four machine learning algorithms achieved more than 70% accuracy for CDOM absorption coefficient estimation. Based on model training, validation and the application on Landsat 8 OLI images, we found that the Gaussian process regression (GPR) had higher stability and estimation accuracy (R2 = 0.74, mean relative error (MRE) = 22.2%) than the other models. The estimation accuracy and MRE were R2 = 0.75 and MRE = 22.5% for backpropagation (BP) neural network, R2 = 0.71 and MRE = 24.4% for random forest regression (RFR) and R2 = 0.71 and MRE = 24.4% for support vector regression (SVR). In contrast, the best three empirical models had estimation accuracies of R2 less than 0.56. The model accuracies applied to Landsat images of Lake Qiandaohu (oligo-mesotrophic state) were better than those of Lake Taihu (eutrophic state) because of the more complex optical conditions in eutrophic lakes. Therefore, machine learning algorithms have great potential for CDOM monitoring in inland waters based on large datasets. Our study demonstrates that machine learning algorithms are available to map CDOM spatial-temporal patterns in inland waters.

Simulation study for cross-sectional absorption distribution in turbid medium using spatially resolved backscattered light with lateral scanning and one-dimensional solution of nonlinear inverse problem

For a practical technique of cross-sectional imaging of animal bodies, we developed a new method using spatially resolved backscattered light. This method is based on the solution of the one-dimensional nonlinear inverse problem, and on the lateral scan of the source–detector pair along the object surface. Using this method, unknown variables in inverse problems can be reduced more greatly than when using conventional methods. A stable solution for the inverse problem becomes possible. The possibility of using the proposed method was assessed using simulation analysis. The results verified that cross-sectional imaging from several to 10 millimeter depths is possible for animal tissue. This analysis clarified the specific spatial resolution and accuracy in the estimated absorption coefficient. Distortionless imaging was confirmed. Results suggest that the proposed method represents new options as a stable and practical method for biological cross-sectional imaging.

Diffuse reflectance and machine learning techniques to differentiate colorectal cancer ex vivo.

In this study, we used machine learning techniques to reconstruct the wavelength dependence of the absorption coefficient of human normal and pathological colorectal mucosa tissues. Using only diffuse reflectance spectra from the ex vivo mucosa tissues as input to algorithms, several approaches were tried before obtaining good matching between the generated absorption coefficients and the ones previously calculated for the mucosa tissues from invasive experimental spectral measurements. Considering the optimized match for the results generated with the multilayer perceptron regression method, we were able to identify differentiated accumulation of lipofuscin in the absorption coefficient spectra of both mucosa tissues as we have done before with the corresponding results calculated directly from invasive measurements. Considering the random forest regressor algorithm, the estimated absorption coefficient spectra almost matched the ones previously calculated. By subtracting the absorption of lipofuscin from these spectra, we obtained similar hemoglobin ratios at 410/550 nm: 18.9-fold/9.3-fold for the healthy mucosa and 46.6-fold/24.2-fold for the pathological mucosa, while from direct calculations, those ratios were 19.7-fold/10.1-fold for the healthy mucosa and 33.1-fold/17.3-fold for the pathological mucosa. The higher values obtained in this study indicate a higher blood content in the pathological samples used to measure the diffuse reflectance spectra. In light of such accuracy and sensibility to the presence of hidden absorbers, with a different accumulation between healthy and pathological tissues, good perspectives become available to develop minimally invasive spectroscopy methods for in vivo early detection and monitoring of colorectal cancer.

Spectral attenuation coefficients from measurements of light transmission in bare ice on the Greenland Ice Sheet

Abstract. Light transmission into bare glacial ice affects surface energy balance, biophotochemistry, and light detection and ranging (lidar) laser elevation measurements but has not previously been reported for the Greenland Ice Sheet. We present measurements of spectral transmittance at 350–900 nm in bare glacial ice collected at a field site in the western Greenland ablation zone (67.15∘ N, 50.02∘ W). Empirical irradiance attenuation coefficients at 350–750 nm are ∼ 0.9–8.0 m−1 for ice at 12–124 cm depth. The absorption minimum is at ∼ 390–397 nm, in agreement with snow transmission measurements in Antarctica and optical mapping of deep ice at the South Pole. From 350–530 nm, our empirical attenuation coefficients are nearly 1 order of magnitude larger than theoretical values for optically pure ice. The estimated absorption coefficient at 400 nm suggests the ice volume contained a light-absorbing particle concentration equivalent to ∼ 1–2 parts per billion (ppb) of black carbon, which is similar to pre-industrial values found in remote polar snow. The equivalent mineral dust concentration is ∼ 300–600 ppb, which is similar to values for Northern Hemisphere warm periods with low aeolian activity inferred from ice cores. For a layer of quasi-granular white ice (weathering crust) extending from the surface to ∼ 10 cm depth, attenuation coefficients are 1.5 to 4 times larger than for deeper bubbly ice. Owing to higher attenuation in this layer of near-surface granular ice, optical penetration depth at 532 nm is 14 cm (20 %) lower than asymptotic attenuation lengths for optically pure bubbly ice. In addition to the traditional concept of light scattering on air bubbles, our results imply that the granular near-surface ice microstructure of weathering crust is an important control on radiative transfer in bare ice on the Greenland Ice Sheet ablation zone, and we provide new values of flux attenuation, absorption, and scattering coefficients to support model development and validation.

Determination of the absorption coefficient of radar waves in solid earth media

In traditional ground penetrating radar (GPR) field data processing, e.g. horizontal stacking and migration, the wave propagating phase velocity is commonly used, while field magnitude decay is neglected. We derived the scalar wave equation of an electric vector field or magnetic vector field component and proved that the solution of this equation is the product of a decaying exponential function factor and a solution factor, which satisfies the pure wave equation. The waveform decomposition allows the evaluation of the absorption coefficient using the amplitudes of GPR waves. A meaningful method for absorption coefficient estimation in the time domain was introduced and tests with synthesized data demonstrated that this method is simple and effective. This method can simplify the process of absorption estimation and has a practical application value. Therefore, it is not only important for GPR data processing but may also yield important information on ground electrical properties.

Effects of optical variables in a single integrating sphere system on estimation of scattering properties of turbid media

This research was aimed at determining the effect of optical variables for a single integrating sphere system, coupled with the inverse adding-doubling method, on estimating the reduced scattering coefficient ( μ s ′ ) of turbid media. Integrating sphere measurements were performed on a total of 36 liquid samples and two solid samples. The results demonstrated that the system had good accuracy, with average errors for estimating absorption coefficient μa (pure absorption) and μ s ′ (pure scattering) of liquid samples being 15.0% and 4.7% over 550–900 nm, compared with the collimated transmittance and empirical equation, respectively. The system also had good reproducibility for μa and μ s ′ , with the coefficients of variation lower than 4%. Sensitivity analysis revealed that the detectable values of μa and μ s ′ were around 0.02 cm−1 and 4.5 cm−1. Entrance and detector port diameters, standard reflectivity and anisotropy factor had a negligible effect on estimating μ s ′ ( μ s ′ (2.8%–35.3%). Hence these variables should be carefully chosen or accurately measured before optical property measurements are carried out. Application examples of the μ s ′ estimation for apple skin and flesh further validated the system performance, indicating that appropriate selection of optical variables could improve the estimation of μ s ′ .

Sequences of Sub-Microsecond Laser Pulses for Material Processing: Modeling of Coupled Gas Dynamics and Heat Transfer

Multipulse laser processing of materials is promising because of the additional possibilities to control the thickness of the treated and the heat-affected zones and the energy efficiency. To study the physics of mutual interaction of pulses at high repetition rate, a model is proposed where heat transfer in the target and gas-dynamics of vapor and ambient gas are coupled by the gas-dynamic boundary conditions of evaporation/condensation. Numerical calculations are accomplished for a substrate of an austenitic steel subjected to a 300 ns single pulse of CO2 laser and a sequence of the similar pulses with lower intensity and 10 μs inter-pulse separation assuring approximately the same thermal impact on the target. It is revealed that the pulses of the sequence interact due to heat accumulation in the target but they cannot interact through the gas phase. Evaporation is considerably more intensive at the single-pulse processing. The vapor is slightly ionized and absorbs the infrared laser radiation by inverse bremsstrahlung. The estimated absorption coefficient and the optical thickness of the vapor domain are considerably greater for the single-pulse regime. The absorption initiates optical breakdown and the ignition of plasma shielding the target from laser radiation. The multipulse laser processing can be applied to avoid plasma ignition.

Synthesis of Silver Nanowires with Controllable Diameter and Simple Tool to Evaluate their Diameter, Concentration and Yield

AbstractSimple solvothermal method was used to synthesize of uniform silver nanowires with the controllable diameter (17, 20, 35, 70 and 100 nm). It is the first report on the solvothermal synthesis of ultra‐thin silver nanowires (17 nm) with the high aspect ratio (>1000). Knowing silver nanowires diameter, length and yield after synthesis are a critical step to classify them for the final goal. Up to now, although the length of nanowires can be measured with the optical microscope, but to estimate the diameter, costly high‐resolution microscopic techniques are necessary. Herein, UV/Vis spectroscopy as an easy and cost‐effective tool is introduced to evaluate the diameter and yield of nanowires. For this purpose, after synthesis and purification of silver different nanowires; from their UV/Vis spectra, a new equation for evaluation nanowires diameter was extracted. In addition, for the first time, absorption coefficients (ϵ) of nanowires with different diameter thickness were reported and a new equation to estimate absorption coefficients to calculate concentration (mg/ml) and yield of silver nanowires was extracted. These equations have good agreement with real data and are independent of the length of nanowires.

Acoustic measurement of additive manufactured concentric tube reverse flow resonators

This study investigates acoustic properties of additive manufactured (AM) concentric tube reverse flow (RF) resonators where acoustic attenuation with smaller sample length is achieved by increasing effective travel length of acoustic waves within a structure. The theoretical modeling based on narrow tube theory has been proposed to predict absorption coefficient, while numerical estimation of absorption coefficient is carried out using finite element method (FEM). The predicted theoretical and numerical results are found to be in good agreement with measured results in impedance tube, thus demonstrating its potential in achieving low frequency attenuation with compact size structures.

Detecting cerebrovascular changes in the brain caused by hypertension in atrial fibrillation group using acoustocerebrography.

Acoustocerebrography is a novel, non-invasive, transcranial ultrasonic diagnostic method based on the transmission of multispectral ultrasound signals propagating through the brain tissue. Dedicated signal processing enables the estimation of absorption coefficient, frequency-dependent attenuation, speed of sound and tissue elasticity. Hypertension and atrial fibrillation are well known factors correlated with white matter lesions, intracerebral hemorrhage and cryptogenic stroke numbers. The aim of this study was to compare the acoustocerebrography signal in the brains of asymptomatic atrial fibrillation patients with and without hypertension. The study included 97 asymptomatic patients (40 female and 57 male, age 66.26 ± 6.54 years) who were clinically monitored for atrial fibrillation. The patients were divided into two groups: group I (patients with hypertension) n = 75, and group II (patients without hypertension) n = 22. Phase and amplitude of all spectral components for the received signals from the brain path were extracted and compared to the phase and amplitude of the transmitted pulse. Next, the time of flight and the attenuation of each frequency component were calculated. Additionally, a fast Fourier transformation was performed and its features were extracted. After introducing a machine learning technique, the ROC plot of differentiations between group I and group II with an AUC of 0.958 (sensitivity 0.99 and specificity 0.968) was obtained. It can be assumed that the significant difference in the acoustocerebrography signals in patients with hypertension is due to changes in the brain tissue, and it allows for the differentiating of high-risk patients with asymptomatic atrial fibrillation and hypertension.