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Clues and traces of the universe's origin and its developmental process are deeply buried in galaxy shapes and formations. Automated galaxies classification from their images is complicated due to the faintness of the galaxy images, conflicting bright background stars, and image noise. For this purpose, the current work proposes a novel logically structured modular algorithm that analyses galaxy morphological raw brightness data to automatically detect galaxy visual center, region, and classification. First, a novel selective brightness threshold is employed to eliminate the effect of bright background stars on detecting galaxy visual centers. Second, a galaxy region detection technique is developed. Finally, a novel technique based on galaxy brightness variation patterns is employed for galaxies classification. The current work has been tested with a run on a collection of 1000 galaxies from the EFIGI catalog. Results demonstrated a success rate of 97.2% in galaxies classification with an average processing time of 0.37 s per galaxy. The high success rates and the low processing time proved the efficiency of the proposed work.

Membership of open star clusters or Galactic open clusters is very important roles to study the formation and evaluation of gravitationally bound system. In the field of an image, the relative x and y coordinate positions of each star with respect to all the other stars are adapted. Therefore, in this paper, a new method for the determination open star cluster membership based on Fuzzy C-means Clustering algorithm is proposed. In the fuzzy clustering algorithm, a data point may belong to several clusters with different degree of memberships. Therefore, the membership values for a data point will represents the degree to which that point belongs to a particular cluster. The proposed method allows to efficiently discriminating the cluster membership from the field stars. The membership probabilities have been calculated and compared to those obtained by the other methods. To validate the method, we applied it on Berkeley 39 and NGC 188 open star clusters, where the membership stars in these clusters are obtained. The membership probability of stars clusters is assigned through the approach provides number of probable members.

The Virgo cluster of galaxies is of great importance to study the development of the universe due to its close distance from the earth as well as being the center of the local super cluster. The problem that faces Virgo cluster studies is that it shares the same right ascension (RA) and Declination (DEC) ranges with large number of background as well as foreground galaxies. This study aims to geometrically and statistically estimate Virgo cluster membership. The study employs Virgo cluster data, prepared by Harvard University. The radial velocity (RV) data of the Virgo cluster were treated and employed in exchange of missing galaxies’ third dimension, taking advantage of their proportionality. The data were treated by K-means algorithm, using Matlab 2014, and visual and logical exclusion of extremity galaxies to determine the rational center of the Virgo galaxies cluster. Results were presented, compared and discussed. Finally distances of galaxies from the Virgo cluster center were employed along with normal probability distribution characteristics to identify the most probable Virgo cluster members from the range of Virgo cluster of galaxies. The results showed that out of 17,466 objects surveyed in Virgo galaxy range, only few of galaxies were estimated to be genuine Virgo members.

Artificial neural network modelling has proven effective in impressively wide range of scientific disciplines. To obtain better quality of astronomical images the signatures of transient artifacts such as cosmic rays, satellite trails and scattered cosmic light must be removed. In this paper we have developed an Artificial Neural Network (ANN) method which detection cosmic ray and removing it from the space CCD astronomical images. The method has been trained tested with a conjugate gradient training algorithm using observational data from an astronomical image. This method of detecting and removing cosmic ray from astronomical images is quite general. Our method effectively removes cosmic rays while leaving almost all the image data untouched. Keywords: Artificial neural network; Cosmic rays; astronomical CCD images

The development of automated morphological classification schemes for galaxies is important to study the formation and subsequent evolution of galaxies in our universe. This paper proposed a new machine learning method for classifying three types of galaxies image (Hubble types) namely elliptical, lenticulars, and spirals. The proposed approach consists of three stages: In the first stage, the features are extracted from the galaxies image by using Gegenbauer moments that have several properties like scale, rotation, and invariant. However, not all these extract features are relevant; therefore, in the second stage, a swarm algorithm called artificial bee colony (ABC) is used as feature selection method, where ABC has a small number of parameters and its fast convergence to the global solution. The third stage is used to evaluate the performance of the selected features in the classification of galaxies image through using the support vector machine as a classifier. The experimental results are performed based on a sample from EFIGI catalog, and the results show the high performance of the proposed method.

Classification of edge-on galaxies is important to astronomical studies due to our Milky Way galaxy being an edge-on galaxy. Edge-on galaxies pose a problem to classification due to their less overall brightness levels and smaller numbers of pixels. In the current work, a novel technique for the classification of edge-on galaxies has been developed. This technique is based on the mathematical treatment of galaxy brightness data from their images. A special treatment for galaxies’ brightness data is developed to enhance faint galaxies and eliminate adverse effects of high brightness backgrounds as well as adverse effects of background bright stars. A novel slimness weighting factor is developed to classify edge-on galaxies based on their slimness. The technique has the capacity to be optimized for different catalogs with different brightness levels. In the current work, the developed technique is optimized for the EFIGI catalog and is trained using a set of 1800 galaxies from this catalog. Upon classification of the full set of 4458 galaxies from the EFIGI catalog, an accuracy of 97.5% has been achieved, with an average processing time of about 0.26 seconds per galaxy on an average laptop.

The development of automated morphological classification schemes can successfully distinguish between morphological types of galaxies and can be used for studies of the formation and subsequent evolution of galaxies in our universe. In this paper, we present a new automated machine supervised learning astronomical classification scheme based on the Nonnegative Matrix Factorization algorithm. This scheme is making distinctions between all types roughly corresponding to Hubble types such as elliptical, lenticulars, spiral, and irregular galaxies. The proposed algorithm is performed on two examples with different number of image (small dataset contains 110 image and large dataset contains 700 images). The experimental results show that galaxy images from EFIGI catalog can be classified automatically with an accuracy of ∼93% for small and ∼92% for large number. These results are in good agreement when compared with the visual classifications.

In this work we present JHKs near-infrared photometric study for the poorly studied open star clusters Alessi 16, Chupina1, Juchert-Saloran1 and Patchick 78 based on 2MASS data. Astrometry and photometric parameters have been calculated using the Redial Density Profile (RDP) and Color Magnitude Diagrams (CMDs) fittings. Radius, distance, reddening, age, Luminosity Function (LF), Mass Function (MF), total mass and relaxation time have been calculated from the analysis of 2MASS data. The stellar memberships have been determined using a new method.

Wind power is the energy generated through the flow of air on the wind turbines and convert their mechanical power into electrical power. Wind energy is a plentiful renewable energy source and is widely distributed with low cost of electricity generation from available resources. Wind speed prediction has a significant role in wind energy production. The presented work provides a new approach to predict and compute wind speed and forecasts stochastic wind power in an astronomical site based on the model of Adaptive Neuro-Fuzzy Inference System (ANFIS). A computed prediction output (wind speed) based on (ANFIS) algorithm is compared with original data obtained from the same location. The predicted wind speed and the power produced from the wind energy have been calculated and evaluated to assess the proposed model. This model has been used to predict the weather for availability of green energy in future times. We introduce the simulation results that show the performance of the proposed model in predicting wind speed and power generated in renewable energy systems.

In the present study, we have used Deep CCD images of the extremely young open star cluster NGC 6611, up to a limiting magnitude of V ∼ 22.86 mag in V, R and I passbands. The resulting color-magnitude V; (V–I) diagram as well as their radial density profiles has been determined. Using 2MASS data, we confirmed the consistency between the 2MASS photometry, by fitting isochrones, the extinction E(V–I) = 0.530 ± 0.04 mag, E(J–H) = 0.31 ± 0.02, from the color magnitude diagram the cluster distance =2.2 ± 0.21 kpc and age = 3.6 Myr, based on the fitting of theoretical stellar isochrones of solar metallicity Z = 0.019. The distance modulus of the cluster is estimated at 12.3. The radial stellar density profiles and the cluster center have been determined by two methods. The core and cluster radii are determined from the radial stellar density profiles. Only about 40% of the cluster members are present in the core region. The cluster luminosity function has been calculated. The mass function slope of the entire cluster is ∼−0.67 ± 0.12. The effects of mass segregation, most probably due to dynamical evolution, have been observed in the cluster.

In the field of galaxies images, the relative coordinate positions of each star with respect to all the other stars are adapted. Therefore the membership of star cluster will be adapted by two basic criterions, one for geometric membership and other for physical (photometric) membership. So in this paper, we presented a new method for the determination of open cluster membership based on K-means clustering algorithm. This algorithm allows us to efficiently discriminate the cluster membership from the field stars. To validate the method we applied it on NGC 188 and NGC 2266, membership stars in these clusters have been obtained. The color-magnitude diagram of the membership stars is significantly clearer and shows a well-defined main sequence and a red giant branch in NGC 188, which allows us to better constrain the cluster members and estimate their physical parameters. The membership probabilities have been calculated and compared to those obtained by the other methods. The results show that the K-means clustering algorithm can effectively select probable member stars in space without any assumption about the spatial distribution of stars in cluster or field. The similarity of our results is in a good agreement with results derived by previous works.

In modern astronomy with the advent of astronomical imaging technology developments and the increased capacity of digital storage, lead to the production of photographic atlases of data which need to be processed autonomously. Galaxies morphology is an important topic to understand questions concerning the evolution and formation of galaxies and their content. In this work, morphological classification of galaxies is presented using a new method based on Non-Negative matrix factorization for images of galaxies in the Zsolt frei Catalog. The algorithm is trained using manually classified images of elliptical, spiral and lenticular galaxies. Experimental results show that galaxy images from Zsolt Frei catalog can be classified automatically with an accuracy of 93 percent compared to classifications carried out by other authors and manually classified. General Terms Pattern recognition; image classification.

An overview of automated astronomical facilities (especially in Egypt) is presented. Control of motors speed used to open and close the dome and mirror shutters due to weather station data. The fuzzy logic control introduced as a new control system for the Kottamia astronomical dome’s shutters and mirror’s shutter due to weather station data. The real operating of Kottamia dome and mirror shutters have been studied to kipping its operation in good performance with weather changes. The weather station data transfer to the fuzzy control system simultaneously. Fuzzy system used to control the opening or closed operation of the dome’s shutters and mirror’s shutter due to the online homogenous environmental weather station data. In this paper, wind speed, humidity, rain rate, and dust particle are used as an input parameter occurs and the top shutter, bottom shatter and mirror shutter are used as an output parameters reaction.

Variations of light curves for space objects are investigated. Optical observations and photometric measurements for small space debris on highly elliptical orbits (HEO) and geostationary orbits (GEO) are used to determine their orbital parameters. Light curves of small space debris with various area-to-mass ratios and orbital characteristics are discussed. Tracking of some objects shows very rapid brightness variations related to perturbations of the orbital parameters. Changes in brightness and equatorial coordinates of the studied objects are found in observational data. Our results allow improving the accuracy of space debris orbital elements.

Galaxies’ studies play an important role in the astronomic. Accurate classification of these galaxies enables scientists to understand the formation and evolution of the Universe. During the last decades, there have been several methods applied to classify the galaxy images. However, these methods encounter three big challenges. First, most existing methods converted the color images of galaxies into gray images which result in losing the essential color information. Second, the utilized feature selection methods, that used to remove the irrelevant features, may be stuck at the attractive local point. Third, using an irrelevant classifier could lead to decrease the classification accuracy. In this paper, a new algorithm is proposed to classify color images of galaxies. In this algorithm, highly accurate non-redundant color features are extracted from the color images of galaxies by using the quaternion polar complex exponential transform moments (QPCET). The quaternion representation deals with a color image in a holistic way which keeps the correlation between components and then successfully represents the color images. The QPCET moments are highly accurate, noise resistant, and numerically stable. Moreover, these moments are invariants with respect to rotation, scaling and translation (RST). These characteristics assure the excellency of the extracted color features. The Stochastic Fractal Search (SFS) has a very high ability to avoid the stuck at local point. Its binary version is utilized to select the most appropriate features which improve the classification process. The Extreme Machine learning (EML) is used to classify the color images of galaxies using the selected color features. Experiments are performed with the well-known datasets of galaxies (EFIGI catalog), where the proposed algorithm achieved high classification rate. The obtained results clearly show that the proposed method outperformed all existing galaxies classification methods.

This paper is an extended version of "Deep Galaxy: Classification of Galaxies based on Deep Convolutional Neural Networks". In this paper, a robust deep convolutional neural network architecture for galaxy morphology classification is presented. A galaxy can be classified based on its features into one of three categories (Elliptical, Spiral, or Irregular) according to the Hubble galaxy morphology classification from 1926. The proposed convolutional neural network architecture consists of 8 layers, including one main convolutional layer for feature ex-traction with 96 filters and two principle fully connected layers for classification. The architecture is trained over 4238 images and achieved a 97.772% testing accuracy. In this version, "Deep Galaxy V2", an augmentation process is applied to the training data to overcome the overfitting problem and make the proposed architecture more robust and immune to memorizing the training data. A comparative result is present, and the testing accuracy was compared with those of other related works. The proposed architecture outperformed the other related works in terms of its testing accuracy.

The cuckoo search algorithm (CS) is a simple and effective global optimization algorithm. It has been applied to solve a wide range of real-world optimization problem. In this paper, an improved Cuckoo Search Algorithm (ICS) is presented for determining the age and relative contribution of different stellar populations in galaxies. The results indicate that the proposed method performs better than, or at least comparable to state-of- the-art method from literature when considering the quality of the solutions obtained. The proposed algorithm will be applied to integrated color of galaxy NGC 3384. Simulation results further demonstrate the proposed method is very effective. The study revealed that cuckoo search can successfully be applied to a wide range of stellar population and space optimization problems.

In this paper we present astrophysical parameters of the very old open star cluster Berkeley 39 using 2MASS JHK data and observation of BV CCD photometry (V ~ 21.5 mag) in Johnson-Cousin filters. We have derived reddening, distance, metallicity and age etc. using two color diagrams in near-infrared and optical bands. We have found EB-V = 0.099 ± 0.02, EJ-H = 0.018 ± 0.001 and EJ-Ks = 0.016 ± 0.001 mag, and the distance modulus of (m-M) 0 = 13.24 ± 0.03 mag, which give a distance of 4.2 ± 0.04 Kpc, and the age of the cluster is 5.6 Gyr with metallicity; Z = 0.008 (obtained from fitting of the observed CMDs with Padova iso-chrones). The radius of the cluster obtained from the analysis of 2MASS data is equal 9.0 ± 0.13 arc minutes; it reflects that the cluster have large radius with new cluster center. Luminosity and mass functions have been investigated as well as the dynamical relaxation time of the cluster Berkeley 39.

This paper presents a new approach for the automatic detection of galaxy morphology from datasets based on an image-retrieval approach. Currently, there are several classification methods proposed to detect galaxy types within an image. However, in some situations, the aim is not only to determine the type of galaxy within the queried image, but also to determine the most similar images for query image. Therefore, this paper proposes an image-retrieval method to detect the type of galaxies within an image and return with the most similar image. The proposed method consists of two stages, in the first stage, a set of features is extracted based on shape, color and texture descriptors, then a binary sine cosine algorithm selects the most relevant features. In the second stage, the similarity between the features of the queried galaxy image and the features of other galaxy images is computed. Our experiments were performed using the EFIGI catalogue, which contains about 5000 galaxies images with different types (edge-on spiral, spiral, elliptical and irregular). We demonstrate that our proposed approach has better performance compared with the particle swarm optimization (PSO) and genetic algorithm (GA) methods.

Forecasting the weather in future won the attention of many researchers in different countries around the world. In this work a robust hybrid technique was applied to forecast rainfall by combining Particle Swarm Optimization based Learning Automata (PSOLA) and Multi-Layer Perceptron (MLP) which is popular kind used in Feed Forward Neural Network (FFNN). The proposed technique solves the flaw of PSO through implementing it in two steps, first step construct ANN - MLP and second step generate optimal weights by (PSOLA) based on four weather parameters. These parameters are Minimum temperature (Min temperature), Maximum temperature (Max temperature), Humidity, and Wind Speed as input parameters whereas rain rate parameter considered as output. Trained MLP to predict rainfall based on weights driven from the second phase. The simulation result of the hybrid technique represents that Mean Square Error (MSE) of testing dataset is 0.9.

Structural and Optical Characteristics for Gold Thin Films Deposited on Polymer Substrates Gold nanostructures are receiving great interest due to their wide applications in many fields, biomedical, nonlinear optical device, aerospace and space science technology. In this research, fabrication technique for gold thin films deposited onto polypropylene, as polymer substrates is discussed. Synthesis of gold nanoparticles and thin film deposition technique are carried out in the experimental setup initiated at Nano- NRIAG Unit (NNU). For gold nanoparticles, phase and particle size distributions have been determined by transmission electron microscopy (TEM). Optical properties have been characterized by UV-Vis spectroscopy. For gold/polypropylene (film/substrate) systems, immersion method is used to localize gold nanoparticles on polymer substrate. Thin film growth is performed with different intensities and particle sizes. The spectral analysis and properties of the formed nano films are performed using the CCD image processing to characterize the nanocomposite and surface structures. Two samples with low and heavy particle concentrations are considered in these analyses. The obtained results are morphologically analyzed.

Galaxy classification has an important role in understanding the formation of galaxies and the evaluation of our universe. Most of the machine learning methods were used to improve galaxy image classification. However, these methods suffer from some limitations, such as getting stuck in local point and slow convergence. Therefore, an alternative method to enhance the performance of galaxy images classification and avoid the limitations of other methods is proposed. The proposed method for galaxy classification (called BSOMFOG) is based on an improvement in the brain storm optimization (BSO) through combining it with the moth flame optimization (MFO). In this modified version of BSO (called BSOMFO), the MFO algorithm works as a local search operator to enhance the exploitation ability of BSO. The performance of the BSOMFO algorithm is compared against other algorithms through two experiments. In the first one, a set of 15 global optimization problems is used to evaluate the ability of the BSOMFO algorithm to find the solution for these problems. Meanwhile, in the second experiment, the BSOMFO is included in the BSOMFOG framework to improve the classification of the galaxy images into three classes, namely, spiral, lenticular, and elliptical. BSOMFOG consists of three phases: the first phase is to extract the shape, color, and texture features from the galaxy images, while the second phase used the BSOMFO algorithm to select the relevant features from the extracted features. The last phase is to evaluate the selected features through classification using the k -nearest neighbor classifier. The experimental results show that the BSOMFO algorithm provides better results than the traditional BSO algorithm and other metaheuristic algorithms to solve the optimization problem. Moreover, it makes the proposed BSOMFOG framework improves the classification accuracy (∼97 % ) for galaxy images, and its general purpose makes it suitable for automatic classification of galaxies.

When numerical simulations are used for determining the age and contribution of different stellar populations in the integrated colour of a galaxy some problems were encountered. In this paper, a modified flower pollination algorithm (MFPA) is proposed for determining the age and relative contribution of different stellar populations of galaxies. The results show that the proposed algorithm can search efficiently through the very large space of the possible ages for the different integrated colour of galaxies. The proposed algorithm will be applied to an integrated colour of galaxy NGC 3384. The numerical results and statistical analysis show that the proposed algorithm performs significantly better than a previously used genetic algorithm (Attia et al., 2005) and cuckoo search (Abdel-Baset et al., 2015). The study revealed that the proposed algorithm can successfully be applied to a wide range of stellar population and space optimisation problems.

We present a new method for determining the age and relative contri- bution of different stellar populations in galaxies based on the genetic algorithm. We apply this method to the barred spiral galaxy NGC 3384, using CCD images in U, B, V , R and I bands. This analysis indicates that the galaxy NGC 3384 is mainly inhabited by old stellar population (age > 10 9 yr). Some problems were encountered when numerical simulations are used for determining the contribution of different stellar populations in the integrated color of a galaxy. The results show that the proposed genetic algorithm can search efficiently through the very large space of the possible ages.

In this paper, we are going to introduce the Kottamia Astronomical Observatory, KAO, to the astronomical community. The current status of the telescope together with the available instrumentations is described. An upgrade stage including a new optical system and a computer controlling of both the telescope and dome are achieved. The specifications of a set of CCD cameras for direct imaging and spectroscopy are given. A grating spectrograph is recently gifted to KAO from Okayama Astrophysical Observatory, OAO, of the National Astronomical Observatories in Japan. This spectrograph is successfully tested and installed at the F/18 Cassegrain focus of the KAO 74 ′′ telescope.