The paper presents the results of a novel high-gain dual-band magneto-electric dipole (MED) antenna. The antenna comprises two pairs of horizontal metal plates of different heights that are excited by a Γ-shaped feedline structure. The antenna is entirely made of metal plates. Compared to traditional MED antennas the proposed design exhibits dual-band operation with higher radiation gain and whose frequency ratio can be modified by simply adjusting the heights of the two magneto-electric dipole segments. This feature is necessary for cellular base-station applications. The operation and characteristics of the antenna are validated by measurements. The measured results confirm the proposed antenna achieves fractional bandwidths of 13.31% (801.9-916.2 MHz) and 19.76% (1710.7-2085.7 MHz) for S11 ≤-10 dB. It has stable unidirectional radiation patterns and optimum radiation gains of 9.2 dBi and 7.8 dBi at the first and second operating bands, respectively.

A compact printed dipole antenna using an integrated balun is introduced in this work, which is achieved three frequency bands by using C-shaped resonators. The total dimension of the proposed antenna is 42 × 72 mm 2 , and it covers the frequency bands of 1. 62-1.88, 2.36-2.81, and 5.15-6.16 GHz with S 11 < −10 dB. Furthermore, the measured peak gains of the antenna in three frequency bands are equal to 8.52, 9.46, and 6.81 dB, respectively. Dipole antennas with integrated baluns have a main frequency band based on the length of the dipole arms. Nevertheless, in the presented design, two additional frequency bands have been created for the antenna, by producing suitable magnetic couplings between the C-shaped resonators and the Γ-shaped feed line innovatively, which has a better radiation behavior, unlike traditional triple-band antennas. An equivalent circuit of the introduced antenna has been investigated and afterward, the optimized antenna has been fabricated and tested.

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Astrodaucus persicus is a potential source of valuable and natural larvicidal compounds against malaria vector, An. stephensi and can be used in mosquitoes control programs as an alternative to synthetic insecticides.

It has been a long time since we use magnetic resonance imaging (MRI) to detect brain diseases and many useful techniques have been developed for this task. However, there is still a potential for further improvement of classification of brain diseases in order to be sure of the results. In this research we presented, for the first time, a non-linear feature extraction method from the MRI sub-images that are obtained from the three levels of the two-dimensional Dual tree complex wavelet transform (2D DT-CWT) in order to classify multiple brain disease. After extracting the non-linear features from the sub-images, we used the spectral regression discriminant analysis (SRDA) algorithm to reduce the classifying features. Instead of using the deep neural networks that are computationally expensive, we proposed the Hybrid RBF network that uses the k-means and recursive least squares (RLS) algorithm simultaneously in its structure for classification. To evaluate the performance of RBF networks with hybrid learning algorithms, we classify nine brain diseases based on MRI processing using these networks, and compare the results with the previously presented classifiers including, supporting vector machines (SVM) and K-nearest neighbour (KNN). Comprehensive comparisons are made with the recently proposed cases by extracting various types and numbers of features. Our aim in this paper is to reduce the complexity and improve the classifying results with the hybrid RBF classifier and the results showed 100 percent classification accuracy in both the two class and the multiple classification of brain diseases in 8 and 10 classes. In this paper, we provided a low computational and precise method for brain MRI disease classification. the results show that the proposed method is not only accurate but also computationally reasonable.