Mohsen Javaherian

Relying on the quantum tunnelling concept and Maxwell–Boltzmann–Gibbs statistics, Gamow shows that the star-burning process happens at temperatures comparable to a critical value, called the Gamow temperature (T) and less than the prediction of the classical framework. In order to highlight the role of the equipartition theorem in the Gamow argument, a thermal length scale is defined, and then the effects of non-extensivity on the Gamow temperature have been investigated by focusing on the Tsallis and Kaniadakis statistics. The results attest that while the Gamow temperature decreases in the framework of Kaniadakis statistics, it can be bigger or smaller than T when Tsallis statistics are employed.

With the advent of new high-resolution instruments for detecting and studying radio galaxies with different morphologies, the need for the use of automatic classification methods is undeniable. Here, we focused on the morphological-based classification of radio galaxies known as Fanaroff–Riley (FR) type I and type II via supervised machine-learning approaches. Galaxy images with a resolution of 5″ at 1.4 GHz provided by the Faint Images of the Radio Sky at Twenty centimeters (FIRST) survey are employed. The radial Zernike polynomials are exploited to extract image moments. Then, the rotation, translation, and scale-invariant moments of images are used to form a training set (65% of the radio galaxy sample) and a test set (the remaining 35%). The classes of the test set are determined by two classifiers: a support vector machine and a twin support vector machine (TWSVM). In addition the genetic algorithm is employed to optimize the length of moment series and to find the optimum values of the parameters of the classifiers. The labels of outputs are compared to identify the best performance classifier. To do this the confidence level of classifications is estimated by four different metrics: precision, recall, F1 score, and accuracy. All tests show that implementing TWSVM with the radial basis function as a kernel achieves a confidence level of more than 95% in grouping galaxies.

The first gravitational-wave (GW) signal was detected in the year 2015 indicating tiny distortions of spacetime caused by accelerated masses. We focused on the GW signals consisting of a peak GW strain of 1.0 × 1 0 − 21 that shows merging pairs of large masses. We applied the generalized entropy known as multiscale entropy to the GW interval time series recorded by different observatories (H1, L1, and V1). This enables us to investigate the behavior of entropies on different scales as a method of studying complexity and organization. We found that the entropies of GW interval data with similar physical properties make the identical manner in different scales. Moreover, the results reveal that the signals collected by each observatory have approximately a similar trend in the multiscale analysis results. According to our findings, although different signals have different values for short-range correlations, the long-range correlations are not noticeable in most of them.

Aims: The statistics of the photospheric granulation pattern are investigated using continuum images observed by Solar Dynamic Observatory (SDO)/Helioseismic and Magnetic Imager (HMI) taken at 6713 Å. Methods: The supergranular boundaries can be extracted by tracking photospheric velocity plasma flows. The local ball-tracking method is employed to apply on the HMI data gathered over the years 2011-2015 to estimate the boundaries of the cells. The edge sharpening techniques are exerted on the output of ball-tracking to precisely identify the cells borders. To study the fractal dimensionality (FD) of supergranulation, the box counting method is used. Results: We found that both the size and eccentricity follow the log-normal distributions with peak values about 330 Mm^2 and 0.85, respectively. The five-year mean value of the cells number appeared in half-hour sequences is obtained to be about 60 ± 6 within an area of 350^"×350^". The cells orientation distribution presents t...

In the last century, the health of human beings has been affected by the industrial developments. Among some problems which jeopardize human health, we must point to environmental pollution by making noise produced by artificial machineries like cars, buses, motorcycles, airplanes, etc. Thus, we decided to study noise pollution in Zanjan. Here, we investigated the noise pollution of Zanjan Province to provide the rates of Noise levels through the city during hours of different days. After collecting and analyzing data, we compared the final results of commercial, residential, and commercial-residential regions with prepared standards .The noise pollution rate of all regions was compared with the standards defined for the country. The most effective factors on noise pollution were identified and its control methods were introduced. Finally, the noise pollution map of different regions are extracted that can help to identify the best locations for health cares, marketing, etc.

Solar coronal loops represent the variety of fast, intermediate, and slow normal mode oscillations. In this study, the transverse oscillations of the loops with a few-minutes period and also with damping caused by the resonant absorption were analyzed using extreme ultraviolet (EUV) images of the Sun. We employed the 171 Å data recorded by Solar Dynamic Observatory (SDO)/Atmospheric Imaging Assembly (AIA) to analyze the parameters of coronal loop oscillations such as period, damping time, loop length, and loop width. For the loop observed on 11 October 2013, the period and the damping of this loop are obtained to be 19 and 70 minutes, respectively. The damping quality, the ratio of the damping time to the period, is computed about 3.6. The period and damping time for the extracted loop recorded on 22 January 2013 are about 81 and 6.79 minutes, respectively. The damping quality is also computed as 12. It can be concluded that the damping of the transverse oscillations of the loops is...

We investigate the characteristics of the solar flares complex network. The limited predictability, non-linearity, and self-organized criticality of the flares allow us to study systems of flares in the field of the complex systems. Both the occurrence time and the location of flares detected from January 1, 2006 to July 21, 2016 are used to design the growing flares network. The solar surface is divided into cells with equal areas. The cells, which include flare(s), are considered as nodes of the network. The related links are equivalent to sympathetic flaring. The extracted features present that the network of flares follows quantitative measures of complexity. The power-law nature of the connectivity distribution with a degree exponent greater than three reveals that flares form a scale-free and small-world network. The great value of the clustering coefficient, small characteristic path length, and slowly change of the diameter are all characteristics of the flares network. We s...

In this study, we propose methods for the automatic detection of photospheric features (bright points and granules) from ultra-violet (UV) radiation, using a feature-based classifier. The methods use quiet-Sun observations at 214 nm and 525 nm images taken by Sunrise on 9 June 2009. The function of region growing and mean shift procedure are applied to segment the bright points (BPs) and granules, respectively. Zernike moments of each region are computed. The Zernike moments of BPs, granules, and other features are distinctive enough to be separated using a support vector machine (SVM) classifier. The size distribution of BPs can be fitted with a power-law slope -1.5. The peak value of granule sizes is found to be about 0.5 arcsec^2. The mean value of the filling factor of BPs is 0.01, and for granules it is 0.51. There is a critical scale for granules so that small granules with sizes smaller than 2.5 arcsec^2 cover a wide range of brightness, while the brightness of large granules...

Magnetic elements of the solar surface are studied in magnetograms recorded with the high-resolution Solar Dynamics Observatory / Helioseismic and Magnetic Imager . To extract some statistical and physical properties of these elements (e.g., filling factors, magnetic flux, size, lifetimes), the Yet Another Feature Tracking Algorithm (YAFTA), a region-based method, is employed. An area with 400^"×400^" was selected to investigate the magnetic characteristics during the year 2011. The correlation coefficient between filling factors of negative and positive polarities is 0.51. A broken power law fit was applied to the frequency distribution of size and flux. Exponents of the power-law distributions for sizes smaller and greater than 16 arcsec^2 were found to be -2.24 and -4.04, respectively. The exponents of power-law distributions for fluxes smaller and greater than 2.63×10^19 Mx were found to be -2.11 and -2.51, respectively. The relationship between the size (S) and flux (...

The solar corona is the origin of very dynamic events that are mostly produced in active regions (AR) and coronal holes (CH). The exact location of these large-scale features can be determined by applying image-processing approaches to extreme-ultraviolet (EUV) data. We here investigate the problem of segmentation of solar EUV images into ARs, CHs, and quiet-Sun (QS) images in a firm Bayesian way. On the basis of Bayes' rule, we need to obtain both prior and likelihood models. To find the prior model of an image, we used a Potts model in non-local mode. To construct the likelihood model, we combined a mixture of a Markov-Gauss model and non-local means. After estimating labels and hyperparameters with the Gibbs estimator, cellular learning automata were employed to determine the label of each pixel. We applied the proposed method to a Solar Dynamics Observatory/ Atmospheric Imaging Assembly (SDO/AIA) dataset recorded during 2011 and found that the mean value of the filling facto...

In this study, we use three kinds of clustering methods based on c-means, k-means, and fuzzy c-means (FCM) algorithms to segment solar ultraviolet (UV) images. The methods are applied on a sequence of quiet-Sun photospheric observations at 525 nm images taken by Sunrise on 9 June 2009. The comparison between these three algorithms represents a little bit differences in extraction of physical parameters (filling factors, brightness fluctuations, size distribution, etc.) from images. On the basis of FCM algorithm, the mean value of granule sizes is found to be about 1.8 arcsec 2 (0.85 Mm 2). Granules with sizes smaller than 2.8 arcsec 2 cover a wide range of brightness, while larger granules approaches a particular value. Granules may have lifetimes less than 10 minutes in this part of the Sun. Investigation of local fractal dimension of photospheric images shows that granulation pattern are approximately scale free in some resolutions.

Feature subset selection is a substantial problem in the field of data classification tasks. The purpose of feature subset selection is a mechanism to find efficient subset retrieved from original datasets to increase both efficiency and accuracy rate and reduce the costs of data classification. Working on high-dimensional datasets with a very large number of predictive attributes while the number of instances is presented in a low volume needs to be employed techniques to select an optimal feature subset. In this paper, a hybrid method is proposed for efficient subset selection in high-dimensional datasets. The proposed algorithm runs filter-wrapper algorithms in two phases. The symmetrical uncertainty (SU) criterion is exploited to weight features in filter phase for discriminating the classes. In wrapper phase, both FICA (Fuzzy Imperialist Competitive Algorithm) and IWSSr (Incremental Wrapper Subset Selection with replacement) in weighted feature space are executed to find relevan...

Here, we analyze magnetic elements of the solar active regions (ARs) observed in the line-of-sight magnetograms (the 6173 Å FeI line) recorded with the Solar Dynamics Observatory (SDO)/Hel\-ioseismic and Magnetic Imager (HMI). The Yet Another Feature Tracking Algorithm (YAFTA}) was employed to analyze the statistical properties of these features (e.g., filling factor, magnetic flux, and lifetime). Magnetic features were extracted from the areas of 180 o ×180 o inside the flaring AR (NOAA 12443) for November 3-5, 2015 and non-flaring AR (NOAA 12446) for November 4-6, 2015. The mean filling factor of polarities was found to be about 0.49 for the flaring AR, while this value was 0.08 for the non-flaring AR. Time series of the filling factors of the negative and positive polarities for the flaring AR showed anti-correlated behavior (with the Pearson value of-0.80). However, there was a strong positive correlation (with the Pearson value of 0.95) for the non-flaring AR. A power-law funct...

The Earth's atmosphere is an environment replete with particles of different sizes with various refractive indices which affect the light radiation traveling through it. The Mie scattering theory is one of the well-known light scattering techniques applicable to modeling of electromagnetic scattering from tiny atmospheric particles or aerosols floating in the air or within the clouds. In this study, the scattering characteristics of atmospheric particles are investigated for a wide range of particle types and particle sizes within the framework of Mie's theory. The scattering and back-scattering coefficients are calculated and it is observed that the maximum scattering occurs for particle sizes comparable to the radiation wavelength while the spherical particles with diameters much greater than the wavelength scatter the least. The calculations were carried out in the MATLAB environment and the results demonstrate that the scattering anisotropy has a direct relation with dia...

The solar corona is the origin of very dynamic events, mostly produced in active regions (AR) and coronal holes (CH). Determining the exact location of these large-scale features can be done by image processing approaches applied to extreme ultraviolet data. This paper tackles the problem of segmentation of solar EUV images into ARs, CHs, and QS (quiet Sun) in a firm Bayesian way. On the basis of Bayes' rule, we need to obtain both prior and likelihood models. To find the prior model of an image, we use a Potts model in non-local mode. To construct the likelihood model, we combine a mixture of Markov-Gauss model and non-local means. After estimating labels and hyperparameters by using Gibbs estimator, cellular learning automata are employed to find the label of each pixel. By applying the proposed method on a Solar Dynamics Observatory/ Atmospheric Imaging Assembly (SDO/AIA) dataset recorded during the year 2011, the mean value of the filling factor of ARs is 0.032, and for CHs ...

Here, we analyzed magnetic elements of the solar active regions (ARs) observed in the line-of-sight magnetograms (the 6173 Å Fe I line) recorded with the Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI). The Yet Another Feature Tracking Algorithm (YAFTA) was employed to extract the statistical properties of these features (e.g. filling factor, magnetic flux, and lifetime) within the areas of 180′′ × 180′′ inside the flaring AR (NOAA 12443) and the non-flaring AR (NOAA 12446) for 3 to 5 November 2015 and for 4 to 6 November 2015, respectively. The mean filling factor of polarities was obtained to be about 0.49 for the flaring AR; this value was 0.08 for the non-flaring AR. Time series of the filling factors of the negative and positive polarities for the flaring AR showed anti-correlation (with the Pearson value of -0.80); while for the non-flaring AR, there was the strong positive correlation (with the Pearson value of 0.95). A power-law function was fitted to...

The first gravitational-wave (GW) signal was detected in the year 2015 indicating tiny distortions of spacetime caused by accelerated masses. We focused on the GW signals consisting of a peak GW strain of that shows merging pairs of large masses. We applied the generalized entropy known as multiscale entropy to the GW interval time series recorded by different observatories (H1, L1, and V1). This enables us to investigate the behavior of entropies on different scales as a method of studying complexity and organization. We found that the entropies of GW interval data with similar physical properties make the identical manner in different scales. Moreover, the results reveal that the signals collected by each observatory have approximately a similar trend in the multiscale analysis results. According to our findings, although different signals have different values for short-range correlations, the long-range correlations are not noticeable in most of them.

Solar coronal loops represent the variety of fast, intermediate, and slow normal mode oscillations. In this study, the transverse oscillations of the loops with a few-minutes period and also with damping caused by the resonant absorption were analyzed using extreme ultraviolet (EUV) images of the Sun. We employed the 171 $\AA$ data recorded by Solar Dynamic Observatory (SDO)/Atmospheric Imaging Assembly (AIA) to analyze the parameters of coronal loop oscillations such as period, damping time, loop length, and loop width. For the loop observed on 11 October 2013, the period and the damping of this loop are obtained to be 19 and 70 minutes, respectively. The damping quality, the ratio of the damping time to the period, is computed about 3.6. The period and damping time for the extracted loop recorded on 22 January 2013 are about 81 and 6.79 minutes, respectively. The damping quality is also computed as 12. It can be concluded that the damping of the transverse oscillations of the loop...

The Earth’s atmosphere is an environment replete with particles of different sizes with various refractive indices which affect the light radiation traveling through it. The Mie scattering theory is one of the well-known light scattering techniques applicable to modeling of electromagnetic scattering from tiny atmospheric particles or aerosols floating in the air or within the clouds. In this study, the scattering characteristics of atmospheric particles are investigated for a wide range of particle types and particle sizes within the framework of Mie’s theory. The scattering and back-scattering coefficients are calculated and it is observed that the maximum scattering occurs for particle sizes comparable to the radiation wavelength while the spherical particles with diameters much greater than the wavelength scatter the least. The calculations were carried out in the MATLAB environment and the results demonstrate that the scattering anisotropy has a direct relation with diameter of...

Solar coronal loops represent variety of fast, intermediate, and slow normal mode oscillations. In this study, the transverse oscillations of a coronal loop observed on 11 October, 2013 are analyzed using the extreme ultra-violet (EUV) images of the Sun. Employing the 171 Å solar images recorded by the Solar Dynamic Observatory (SDO)/Atmospheric Imaging Assembly (AIA), we extracted the oscillation parameters such as period, damping time, loop length, and the loop width. The period and the damping of this loop are obtained to be 19± 1 and 70± 1 minutes, respectively. Also, the damping quality, the ratio of the damping time to the period, is obtained to be 3.6. Therefore, we conclude that the damping of the transverse oscillation of this loop is in the strong damping regime. It is suggested that the resonant absorption would be a well suitable candidate for the damping mechanism of the studied loop.

In this study, we propose methods for the automatic detection of photospheric features (bright points and granules) from ultra-violet (UV) radiation, using a feature-based classifier. The methods use quiet-Sun observations at 214 nm and 525 nm images taken by Sunrise on 9 June 2009. The function of region growing and mean shift procedure are applied to segment the bright points (BPs) and granules, respectively. Zernike moments of each region are computed. The Zernike moments of BPs, granules, and other features are distinctive enough to be separated using a support vector machine (SVM) classifier.The size distribution of BPs can be fitted with a power-law slope −1.5. The peak value of granule sizes is found to be about 0.5 arcsec2. The mean value of the filling factor of BPs is 0.01, and for granules it is 0.51. There is a critical scale for granules so that small granules with sizes smaller than 2.5 arcsec2 cover a wide range of brightness, while the brightness of large granules ap...

Magnetic elements of the solar surface are studied (using the 6173 Å Fe i line) in magnetograms recorded with the high-resolution Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI). To extract some statistical and physical properties of these elements (e.g. filling factors, magnetic flux, size, and lifetimes), we employed the region-based method called Yet Another Feature Tracking Algorithm (YAFTA). An area of 400″×400″$400^{\prime\prime}\times400^{\prime\prime}$ was selected to investigate the magnetic characteristics in 2011. The correlation coefficient between filling factors of negative and positive polarities is 0.51. A broken power-law fit was applied to the frequency distribution of size and flux. Exponents of the power-law distributions for sizes smaller and greater than 16arcsec2$16~\mbox{arcsec}^{2}$ were found to be −2.24 and −4.04, respectively. The exponents of power-law distributions for fluxes lower and greater than 2.63×1019Mx$2.63\times 10^{19}~...

In this study, we use three kinds of clustering methods based on c-means, k means, and fuzzy c-means (FCM) algorithms to segment solar ultra-violet (UV) images. The methods are applied on a sequence of quiet-Sun photospheric observations at 525 nm images taken by Sunrise on 9 June 2009. The comparison between these three algorithms represents a little bit differences in extraction of physical parameters (filling factors, brightness fluctuations, size distribution, etc.) from images. On the basis of FCM algorithm, the mean value of granule sizes is found to be about 1.8 arcsec (0.85 Mm). Granules with sizes smaller than 2.8 arcsec cover a wide range of brightness, while larger granules approaches a particular value. Granules may have lifetimes less than 10 minutes in this part of the Sun. Investigation of local fractal dimension of photospheric images shows that granulation pattern are approximately scale free in some resolutions.

Magnetic elements of the solar surface are studied in magnetograms recorded with the high-resolution Solar Dynamics Observatory / Helioseismic and Magnetic Imager . To extract some statistical and physical properties of these elements (e.g., filling factors, magnetic flux, size, lifetimes), the Yet Another Feature Tracking Algorithm (YAFTA), a region-based method, is employed. An area with 400$^{\prime\prime}\times$400$^{\prime\prime}$ was selected to investigate the magnetic characteristics during the year 2011. The correlation coefficient between filling factors of negative and positive polarities is 0.51. A broken power law fit was applied to the frequency distribution of size and flux. Exponents of the power-law distributions for sizes smaller and greater than 16 arcsec$^2$ were found to be -2.24 and -4.04, respectively. The exponents of power$-$law distributions for fluxes smaller and greater than 2.63$\times$10$^{19}$ Mx were found to be -2.11 and -2.51, respectively. The rela...

In the last century, the health of human beings has been affected by the industrial developments. Among some problems which jeopardize human health, we must point to environmental pollution by making noise produced by artificial machineries like cars, buses, motorcycles, airplanes, etc. Thus, we decided to study noise pollution in Zanjan. Here, we investigated the noise pollution of Zanjan Province to provide the rates of Noise levels through the city during hours of different days. After collecting and analyzing data, we compared the final results of commercial, residential, and commercial – residential regions with prepared standards .The noise pollution rate of all regions was compared with the standards defined for the country. The most effective factors on noise pollution were identified and its control methods were introduced. Finally, the noise pollution map of different regions are extracted that can help to identify the best locations for health cares, marketing, etc.

DESCRIPTION In this study, we use three kinds of clustering methods based on c-means,k-means, and fuzzy c-means (FCM) algorithms to segment solar ultra-violet (UV) images. The methods are applied on a sequence of quiet-Sun photospheric observations at 525 nm images taken by Sunrise on 9 June 2009. The comparison between these three algorithms represent a little bit differences in extraction of physical parameters (filling factors, brightness fluctuations, size distribution, etc.) from images. On the basis of FCM algorithm, the mean value of granule sizes is found to be about 1.8 arcsec2 (0.85 Mm^2). Granules with sizes smaller than 2.8 arcsec^2 cover a wide range of brightness, while larger granules approach a particular value. Granules may have lifetimes less than 10 minutes in this part of the Sun. Investigation of local fractal dimension of photospheric images shows that granulation pattern is approximately scale free in some resolutions.

In this study, we use three kinds of clustering methods based on c-means, k-means, and fuzzy c-means (FCM) algorithms to segment solar ultraviolet (UV) images. The methods are applied on a sequence of quiet-Sun photospheric observations at 525 nm images taken by Sunrise on 9 June 2009. The comparison between these three algorithms represents a little bit differences in extraction of physical parameters (filling factors, brightness fluctuations, size distribution, etc.) from images. On the basis of FCM algorithm, the mean value of granule sizes is found to be about 1.8 arcsec 2 (0.85 Mm 2). Granules with sizes smaller than 2.8 arcsec 2 cover a wide range of brightness, while larger granules approaches a particular value. Granules may have lifetimes less than 10 minutes in this part of the Sun. Investigation of local fractal dimension of photospheric images shows that granulation pattern are approximately scale free in some resolutions.

Feature subset selection is a substantial problem in the field of data classification tasks. The purpose of feature subset selection is a mechanism to find efficient subset retrieved from original datasets to increase both efficiency and accuracy rate and reduce the costs of data classification. Working on high-dimensional datasets with a very large number of predictive attributes while the number of instances is presented in a low volume needs to be employed techniques to select an optimal feature subset. In this paper, a hybrid method is proposed for efficient subset selection in high-dimensional datasets. The proposed algorithm runs filter-wrapper algorithms in two phases. The symmetrical uncertainty (SU) criterion is exploited to weight features in filter phase for discriminating the classes. In wrapper phase, both FICA (Fuzzy Imperialist Competitive Algorithm) and IWSSr (Incremental Wrapper Subset Selection with replacement) in weighted feature space are executed to find relevant attributes. The new scheme is successfully applied on ten standard high-dimensional datasets, especially within the field of biosciences and medicine, where the number of features compared to the number of samples is large, inducing a severe curse of dimensionality problem. This method has been assessed by applying on ten standard high-dimensional datasets. The comparison between the results of our method and other algorithms and using non-parametric statistical test (Friedman test) confirms that our method has the most accuracy rate and it is also able to achieve to the efficient compact subset.