Introduction: Satellites in geodesy receive and transport important information. Among those, satellites with Low Earth Orbit (LEO), which are at altitudes less than 1000 km, have a significant role in the advancement of geophysical sciences such as earth’, s potential field. Many parameters have an impact on the precision and accuracy of their information. Atmospheric friction is one of the most principal forces on satellites, which may cause deviation and falling of satellite on a short period. From the beginning of aerospace missions, many efforts have been done to determine atmospheric friction by geodesists, e. g., empirical models of atmosphere neutral density. Because of the complex nature of atmosphere behavior and also data limitations, these models may have low accuracy. So, there is a need for methods to improve the accuracy of empirical models by means of combining observations of atmospheric density to predict its future state. Materials & Methods: Along with the extension of computer science, new reliable algorithms have been introduced which are able to predict a time series,Artificial Intelligent (AI) and Neural Networks (NN) are the best of these methods. These simple algorithms are inspirations of the human brain and its ability to learn and have been used in many different scientific fields. In these techniques without any requirement for constructing complex modeling, the relation between input and output will be provided only using weight and bias vectors during the training procedure. Simple Neural Networks are memoryless meaning that the value of time-series in previous can’, t be used for predicting the future value of time series and therefore some important dependency of signal values with time will be lost. A Recurrent Neural Network (RNN) has been implemented to overcome this issue. RNN’, s can store some important information of the values of the time series in the previous steps in a chain-like structure and using this information for predicting the next value of time series that will improve the accuracy of Prediction. In this study, the Long Short-Term Memory (LSTM) Neural Network which is a kind of Recurrent Neural Network’, s has been implemented to predict the scale for correcting atmospheric density of numerical models. The data of Grace Accelerometer observation in the 6 first month of the year 2014 have been used for training the LSTM for univariate training. Also, the LSTM has been trained in multi-variants mode once with using the coefficient of atmospheric Correction expansion up to degree 2 and once with using sun geomagnetic information along with information of k_p index. Results & Discussion: After training the LSTM network, by using the estimated parameters of the model, the zero degrees coefficient of harmonic expansion for a scale factor of correcting atmospheric density has been predicted in periods of 7, 14, 30, 60, and 90 days. The results of the univariate model show that the lower RMSE (Root Mean Square Error) is obtained about 0. 054 in the period of Prediction of about 14 days. Also, the results show that the multi-variants model with input data of sun geomagnetic information and k_p index has lower RMSE values in considered Prediction periods compared to the other modes and the lowest RMSE is about 0. 03 and belongs to the Prediction of about 7 days. For evaluation of LSTM parameters in the obtained results, the Predictions have been implemented with various Window sizes. The results show that by increasing windows size, the RMSE of the Prediction will be reduced and the lowest RMSE was for Prediction of 7 days with a window size of about 90 days. For the purpose of more evaluation, with the predicted atmospheric densities Correction coefficient, the orbit of GRACE satellites has been propagated and the calculated position and velocity of satellites have been compared with the real orbit data. The results show that the lower RMSE will be provided with the Prediction of 7 days with an RMSE for position and velocity of about 50 meters and 0. 15 m/s respectively. Conclusion: In this study, due to the complex nature of the atmosphere, the LSTM Neural Network has been used for modeling and predict the zero-order scale for correcting atmospheric densities harmonic expansion. For training the network, the data of Grace Satellites Accelerometer in the 180 days of the year 2014 have been used. The LSTM has been in univariate and multi-variant models. In the multi-variants model, once with using the coefficient of atmospheric Correction expansion up to degree two and once with using sun geomagnetic information along with information of k_p index the network have been trained. The period of Prediction was considered of about 7, 14, 30, 60, and 90 days. The results show that the LSTM is capable to predict the Correction coefficient in considered periods with a mean RMSE of about 0. 05 for zero-order degree. Also, the results show that the lowest RMSE was for the 7 and 14 days of Prediction and by increasing the window size of LSTM the RMSE will be decreased. The results of calculating the position of GRACE satellites position and velocity using predicted Correction coefficients with real data show that the lowest RMSE was for Prediction of 7 days for implemented method.

In this paper, a new mechanism is proposed to transform the structural modeling elements of the UML class diagram and Object-Z specifications into each other. A set of bidirectional rules is defined to transform the mentioned elements into each other. Bidirectional transformation of the UML class diagram, as one of the most useful diagrams of UML, and Object-Z specifications into each other prepares the ground for the use of the unique advantages of both formal and visual modeling methods. The feasibility of the proposed approach is evaluated using the multi-lift case study. The results of conducting the multi-lift case study show that the proposed mechanism is feasible.

This paper presents an accurate Differential Global Positioning System (DGPS) using multi-layered Neural Networks (NNs) based on the Back Propagation (BP) and Imperialistic Competition Algorithm (ICA) in order to predict the DGPS Corrections for accurate positioning. Simulation results allowed us to optimize the NN performance in term of residual mean square error. We compare results obtained by the NN technique with BP and ICA. Results show a good improvement obtained by the application of the NN trained by the ICA. The experimental results on measurement data demonstrate that the Prediction total RMS error using NN trained by the ICA learning algorithm are 0.8273 and 0.7143 m, before and after selective availability, respectively.

Day by day demand for Object tracing is increasing because of the huge scope in real-time applications. Object tracing is one of the difficult issues in the computer vision and video processing field. Nowadays, Object tracing is a common problem in many applications specifically video footage, traffic management, video indexing, machine learning, artificial intelligence, and many other related fields. In this paper, the Enhanced Method of Object Tracing Using Extended Kalman Filter via Binary Search Algorithm is proposed. Initially, the background subtraction method was used for merge sort and binary search algorithm to identify moving Objects from the video. Merge sort is to divide the regions and conquer the algorithm that arranges the region in ascending order. After sorting, the binary search algorithm detects the position of noise in sorted frames and then the next step extended the Kalman Filter algorithm used to predict the moving Object. The proposed methodology is linear about the valuation of mean and covariance parameters. Finally, the proposed work considered less time as compared to the state of art methods while tacking the moving Objects. Its shows less absolute error and less Object tracing error while evaluating the proposed work.

In spatial analysis, the variogram function that determines the spatial correlation structure of the data is usually unknown and it should be estimated by observations. Although there are several estimation methods for variogram parameters, limited number of observations produces in much uncertainty in variogram parameter estimates and finally non-precision of the spatial Prediction. In this paper, precision measures of the weighted least squares estimates of variogram parameters are determined by separate block bootstrap method. Next, these estimates are corrected using precision measures. Then, it is shown by cross-validation method that using a variogram with corrected parameter estimates with result in increasing spatial Prediction precision.

Photometry is a well-known method for 3D reconstruction of Objects using images taken in different lighting conditions. In this method, by knowing the light sources' direction, the normal vectors of the surface are recovered in a dense grid through the intensities recorded in the captured images. Each normal vector is then converted to the height difference in two orthogonal directions, and the simultaneous estimation of the heights for the dense grid is done by solving a system of linear, overdetermined and inconsistent equations. The miss-alignment of the coordinate system represents normal vectors and the dense grid frame of 3D reconstruction causes a systematic error in the estimation of the gridded heights map. Photometric self-calibration methods for determining the light sources’ direction are one of the causes of miss-alignments in Object and surface normal vectors coordinate systems. In this paper, a sequential and iterative process is proposed to estimate and perform an appropriate rotation to the surface normal vectors. In each iteration of this method, a portion of the necessary rotation is identified in order to parallelize of the two Object coordinate systems and surface normal vectors through fitting a geometric transformation to the estimated residuals of the 3D reconstruction process. The results of using the proposed method in various experiments have demonstrated a noticeable improvement in the precision and accuracy of 3D reconstruction.