In this paper, new expressions for the effective radius and fringing capacitance have been derived to predict accurately the resonant frequency of the two-layered circular microstrip patch antenna. These expressions are obtained based on genetic algorithm and the data base is generated using moment method (MOM). The proposed model is very simple, fast, and valid for an entire range of permittivities and thicknesses of two-layered substrate. The present model has been validated by comparing our numerical results obtained for the resonant frequencies with measurements. Finaly, the effect of the two-layered substrate on the resonant charateristics of the circular microstrip patch antenna has been presented.

This paper deals with the robust power control of a grid-connected brushless doubly-fed induction generator (BDFIG) driven by the variable speed wind turbine. With the using of a super twisting algorithm which is a high-order sliding mode controller (HOSMC). This approach guarantees both the dynamic performance and the same robustness as traditional first order (SMC) algorithm and reduces the chattering phenomenon, which is the biggest disadvantage in the implementation of this technique. The developed algorithm relies on the decoupling control by implementing the strategy of oriented grid flux vector control. In order to enhance the desired performances, an attempt is made by controlling the generated stator active and reactive powers in a linear and decoupled manner to ensure the global asymptotical stability, HOSMC approach is implemented. Therefore, an optimal operation of the BDFIG in sub-synchronous operation is used in addition to the stator power flows where the stator power factor is kept in a unity. The suggested method is examined with the Matlab/Simulink software. The performances and the feasibility of the designed control are illustrated by simulation results.

Recently, the identification of an individual race has become an important research topic in face recognition systems, especially in large-scale face images. In this paper, we propose a new large-scale race classification method which combines Local Binary Pattern (LBP) and Logistic Regression (LR) on Spark framework. LBP is used to extract features from facial images, while spark’s logistic regression is used as a classifier to improve the accuracy and speedup the classification system. The race recognition method is performed on Spark framework to process, in a parallel way, a large scale of data. The evaluation of our proposed method has been performed on two large face image datasets CAS-PEAL and Color FERET. Two major races were considered for this work, including Asian and Non-Asian races. As a result, we achieve the highest race classification accuracy (99.99%) compared to Linear SVM, Naive Bayesian (NB), Random Forest(RF), and Decision Tree (DT) Spark’s classifiers. Our method is compared against different state-of-the-art methods on race classification, the obtained results show that our approach is more efficient in terms of accuracy and processing time.

In this work, we propose a secure version of ElGamal public key cryptosystem, and prove that it is semantically secure assuming the hardness of what we call the two-dimensional decisional Diffie-Hellman (2DDDH) problem, this cryptosystem is distinguished by the speed of encryption and decryption processes and by its resistance to active adversaries. Since the 2DDDH problem is harder than the decisional Diffie-Hellman (DDH) problem (as it will be seen), one may conclude that our model reinforces the exchange security compared to the existed cryptosystems falling within the same context, also we discuss the difficult problems that guarantee its security.