مقالات رزومه دکتر مهدی جلالی

In this article, a novel geometry for the multiple input multiple output (MIMO) array slot antenna with directive radiation patterns for WLAN (IEEE 802.11ac) & ITU applications is proposed. By using a two sub-element topology, a combined novel element is obtained. The proposed MIMO array slot antenna illustrates beneficences in terms of small size with physical dimensions of 8.5λh×8.5λh and relatively high front-to-back ratio without using reflectors and directors with approximately 20dB F/B. Therefore, a compact MIMO antenna array with high isolation among those the combined elements are obtained. The practicality of the proposed MIMO is validated by Simulated and measured results.

This article reviews some of the smart antennas used in Eyewear, which can receive frequencies of 700-960 MHz and 2.7-1.7 GHz, as well as Wi-Fi 5G (5.18-5.85 GHz) and Wi-Fi 6e (5.925 -7.125 GHz) to be used in wireless Eyewear glasses. Since the antennas of Eyewear devices that work in 4G, 5G cellular communication standard and Wi-Fi 6 internal wireless communication is a new category, first a general study for antenna bandwidth in different locations is done. Given that, these antennas are designed as mutual elements (coupled) and are mostly placed on FR4 or polycarbonate substrate. Then the MIMO antennas used in these glasses are investigated and gain, efficiency and SAR studies are performed along with different modes. In order to obtain a good adaptation to the use cases, the human head is always considered in the simulations. After that, antenna designs with suitable dielectric cover and frame are suggested.

In recent years, image annotation is one of the active research topics. In this article, a semi-supervised cooperative clustering technique is proposed for image annotation. Clustering methods are very popular because they do not require annotations. In order to achieve the highest efficiency, the clustering results of six systems with different color space and similarity criteria are cooperatively combined with the majority vote. When the number of votes for an image is low, relevant feedback is used to annotate it. One of the most important parts of the image retrieval system and clustering algorithm is determining the appropriate similarity criteria between images. Nowadays, the linear similarity criterion is mostly used to determine the similarity between images, but the nonlinear models can have much better performance due to their proximity to the human vision system, for this purpose, the KMRBF nonlinear similarity criterion is used to simulate vision. Humans and improvement of recovery results are suggested. Experiments on the Corel image database and satellite images show that the proposed method has good performance. According to the results obtained in the satellite image database, the YIQ color space has a higher accuracy (82.5%). Also, the three color spaces CIELab, HSV and YIQ have higher efficiency, because in these color spaces, luminance is separated from chrominance and these color spaces are closer to the human vision system.

In this paper, for the classification of images, the observed cooperative classification method is proposed with the aim of reducing the semantic vacuum. Because most classification methods are sensitive to cluster centers at initialization, the algorithm converges optimally locally if the quantification is not done correctly. It is also very difficult to combine the results of the classification due to the fact that the labels of the work centers are not clear. Supervised semi-classification is used to solve these problems. To achieve the highest performance, the system classification results are combined with different color space and similarity criteria with multiple features as a semi-supervised cooperative. When the number of features is effective, the relevant feedback is used for its semi-regulatory classification. One of the most important parts of an image retrieval system and a classification algorithm is to determine the appropriate similarity between images. In this study, two methods of classification of cases have been used, which include k-NN and PNN classification, which according to the results in all proposed methods, a better response from k-NN classification than PNN has been observed. The proposed algorithm is very suitable for classifying large image databases due to the reduction of time complexity. Also, in image retrieval in different color spaces and using different similarity criteria, different classifications are obtained. Better results can be achieved if the classification results are combined.

A broadband circularly polarized MIMO antenna array with a 2×4 feed network was proposed for C-band application. Different unique techniques were utilized in the proposed array to enhance the antenna characteristics, such as gain, 3dB Axial Ratio Bandwidth (ARBW), impedance tuning, and ruinous mutual coupling effects. For a better improvement of circular polarization features, a 2*4 MIMO Sequentially Rotated (MIMO-SR) feed network was used to achieve broader 3dB ARBW. Besides, the MIMO feature of the feed network could control the left- and right-handed CP, respectively. Moreover, electromagnetic band-gap structure was applied to enhance the overall performance of antenna array. Ultimately, specific forms of slot and slit structures were applied onto the top layer of MIMO feed network that provided a high isolation between the radiating elements and array network. The extracted measured results illustrated an impedance bandwidth of 4.6-7.3 GHz at port 1 and 4.25-7.2 GHz at port 2 for VWSR

In this paper, we first simulate ML, MUSIC, Root-MUSIC algorithms and space smoothing in noisy and fiddling environments, and then these algorithms are compared in terms of estimating the accuracy of the angles received from the two sources. By presenting the simulation results of four algorithms in the form of tables that are based on mean, deviation from mean, variance of estimated angles and the percentage of successful experiments, it is observed that Root-MUSIC and MUSIC algorithms in terms of estimation and Detecting close angles is more accurate. In these two methods, the angles close to each other, up to about one degree, can be detected and separated. In addition, simulation results show that spatial smoothing algorithm is the most effective way to identify coherent resources.