نشریه صنایع الکترونیک
سال ششم شماره 3 (پیاپی 22، پاییز 1394)

A novel technique for acquiring high aperture efficiency in dual reflectarray antennas (DRA) is presented. This technique is performed by using a shaped beam sub-reflectarray which gives optimum illumination on the main reflectarray aperture. Applying this technique resolves the inherent deficiency of reflectarrays; which is the impossibility of controlling elements amplitude. In this regard, it has been shown that using this feature, the aperture efficiency of a classic dual reflectarray antenna can be enhanced significantly by optimally shaping the sub-reflectarray radiation pattern. Theoretical analysis demonstrates an improvement of about 12 % in antenna aperture efficiency comparing with a conventional dual reflectarray antenna.

The orthogonal frequency division multiplexing systems are efficient method for data transfer with high rate in most of the communication systems of today due to resistance against the errors arising from multipath of dispersion environment and decreasing the inter symbol errors. In these systems, it is necessary to use the blind methods to estimate the time and frequency offset for achieving the highest spectral productivity and the optimal use of bandwidth., Therefore, in this paper a blind symbol synchronization algorithm for OFDM systems is proposed and a new function timing based on the iteration cyclic prefix (CP) is provided; So that the maximum of the function, irrespective of channel conditions necessary to correct the offset time. This function in AWGN channels have a unbiased estimate of the time offset, According to the simulation results are also shown in a 1000 iterations, The proposed algorithm over existing algorithms based on cyclic prefix especially in frequency selective fading channels much stronger and more effective.

The purpose of this paper is design of a three axis attitude control algorithm for sample remote sensing satellite. According to the orbital and physical characteristics of sample satellite, three attitude control algorithms are designed. These algorithms can simultaneously control the satellite’s attitude and its rotational rate in the presence of parametric uncertainties, unknown and nonlinear dynamics and variable disturbances. The proposed structure provides the possibility of designing reduced order control system and increases stability and robustness of system against the uncertainties. The first algorithm is inverse dynamic based control which is nonlinear and static control method. Second algorithm is designed based on adaptive control principal. This way has the capability to control satellite in the presence of uncertainties. In this method, when the value of angular velocity is large, the magnitude of control effort is high. This property results in increased power consumption and actuator saturation. On the other hand, the adaptive control algorithm in some conditions becomes unstable. So using this method is only suitable for pointing and imaging mode.Thus,in the structure of supervisory adaptive control, a high level supervisor which works based on fuzzy logic determines the contribution of low level controllers according to the characteristics of them. In this control algorithm, accuracy is higher than the tow algorithms that were introduced above, while controlling torque will be less. This approach avoid the saturation of actuators while have the advantages of other two algorithms are presented as well.

In this paper, a new algorithm based on three steps has been proposed for segmentation of SAR images. In the first segmentation step, the input SAR satellite image is proposed using CLA and the difference between the gray levels of pixels in each region of SAR image is reduced and a basic segmented image is produced which is used as an input image to the second step of the algorithm. In the second step which is an expletive segmentation step, a threshold is selected as a gray level distance between the pixels of each region. If the gray level distance between two adjacent pixels is smaller than, then these two pixels are related to same region and will take the same label. After labeling the regions, the average of the same labeled pixels of gray level is considered as gray level indicator of each region. Theresholding is the last stage of the proposed algorithm in segmentation process for identification of regions. In this step, all of the same regions, take the similar label. The proposed algorithm is evaluated on two types of SAR images: the simulated SAR images and the real SAR images. The results show that the proposed method has less error rate and higher accuracy in comparison with other methods for segmentation of SAR images.

By increasing spatial resolution of remote sensing images, it is possible to apply spatial information in the classification. This leads to improve the accuracy of multi-spectral images classification. One of the methods for incorporation spatial information is un-supervised segmentation which can be implemented through Expectation Maximization(EM) clustering and connected-component labeling. But this clustering method which always is trapped in local optimum. Therefore, a new algorithm is proposed that can solve the mentioned problem and has better performance in multi-spectral images classification. In order to form a spatial-spectral classifier, first a pixel-wised classifier is applied. Then, after using the LPP approach for dimension reduction, the results of pixel -wised classifier and segmentation map, obtained from proposed method, are combined via majority voting. The results of simulation indicate that the presented spectral-spatial classifier leads to so considerable improvement that the accuracy and validity of classification have reached to 88.68% and 80.8%, respectively.

Head pose estimation is the main step of the face recognition process. Finding a method which can be fully automated and capable of responding to different poses in all aspects, has created a challenge for this topic. Compared to face detection and recognition, which have been the primary focus of face-related vision research, the head pose estimation is an inter processing step during the implementation of programs in machine vision systems. In this paper, a new algorithm is presented which first the face region is extracted from 3D head image and then the feature vectors that strongly influence the human perception of head pose and these are extremely salient cues regarding the orientation of the head are extracted. Finally, the extracted vectors are trained as input to the classifiers for comparing and differentiating features. In this paper, the Support vector Machines (SVM), the Radial Basis Function neural network classifier (RBF) and the K-nearest neighbor (KNN) methods were used to train feature vectors and data classification.The algorithm was tested on Frav3D and Gavab databases. Finally the experimental results of 98.48 percent of correct estimates which obtained from KNN method, indicate considerable enhancement compared to previous methods.

In recent years, Plagiarism has been easier through increasing development of internet and online papers. Plagiarism is to reuse or copy a text without referencing to the original author. Plagiarism or fraud in schools and universities will be a stimulating factor for researchers. If plagiarism was not identified correctly, cheaters and Plagiarists could get results that are not deserved.This paper presents a method based on the semantic role labeling (SRL) and Genetic Algorithm (GA). The Proposed method works on English texts. Results of the experiments on PAN-PC-9 corpus demonstrate that the proposed method improves values of evaluation parameters such as recall, precision and F-measure, comparing with previous approaches in plagiarism detection.