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📢 Latest Update: New special issue call for papers on "Emerging Technologies in Research" - Submit by March 31, 2025

📢 Latest Update: New special issue call for papers on "Emerging Technologies in Research" - Submit by March 31, 2025

December 15, 2022

Volume 26, Issue 1 - $2022

Volume 26 Issue 1 Cover

Issue Details:

Volume 26 Issue 1
Published:Invalid Date

Editorial: December 15, 2022

Welcome to the 2022 issue of Far East Journal of Electronics and Communications. This issue showcases the remarkable breadth and depth of contemporary research across multiple disciplines. From cutting-edge applications of machine learning in climate science to the revolutionary potential of quantum computing in drug discovery, our featured articles demonstrate the power of interdisciplinary collaboration in addressing global challenges.

We are particularly excited to present research that bridges traditional academic boundaries, reflecting our journal's commitment to fostering innovation through cross-disciplinary dialogue. The integration of artificial intelligence with environmental science, the application of blockchain technology to supply chain management, and the convergence of urban planning with smart city technologies exemplify the transformative potential of collaborative research.

As we continue to navigate an era of rapid technological advancement and global challenges, the research presented in this issue offers both insights and solutions that will shape our future. We thank our authors, reviewers, and editorial board members for their continued dedication to advancing knowledge and promoting scientific excellence.

Professor Bal S. Virdee
Editor-in-Chief
Far East Journal of Electronics and Communications

Articles in This Issue

Showing 2 of 2 articles
Research PaperID: FJEC1261508

CIRCULARLY POLARIZED METAMATERIAL PATCH ANTENNA CIRCUITRY FOR MODERN APPLICATIONS

Marwah Haleem, Taha A. Elwi

The proposed antenna structure is designed for modern wireless communication systems. The antenna structure is consistent with twelve-unit metamaterial (MTM) unit cells. Therefore, the antenna size is miniaturized effectively to 30 × 40mm2 which is equivalently about where is the free space wavelength at 2.7GHz. This is achieved by conducting the use of Hilbert shape MTM structure with T-resonator induction structure. The antenna structure is printed on a single side substrate to cover the frequency bands from 2.7GHz to 3.7GHz and 5.4GHz to 5.6GHz. Such antenna is found to provide a maximum gain of 2.2dBi at first and the second band of interest. Next, proposed antenna is found to be circularly polarized at 3.3GHz and 5.6GHz. The proposed antenna performance is simulated numerically using CST MWS software package with all design methodology that is chosen to arrive to the optimal performance. Then, the optimal antenna design is tested numerically using HFSS software package for validation. Finally, an excellent agreement is achieved between the two conducted software results.

twelve-unit metamaterial (MTM) unit cellsHilbert shape MTM structureT-resonator induction structure
1,382 views
423 downloads
DOI:
10.17654/0973700622003

Contributors:

 Marwah Haleem
,
 Taha A. Elwi
Research PaperID: FJEC1261507

TRAINING PI-SIGMA NEURAL NETWORK USING DOUBLE REGULARIZATION

Khidir Shaib Mohamed, Osman Abdalla Adam Osman, Khalid Makin, Mohammed Nour A., D. S. Muntasir Suhail

Traditional regularization parameters such as L1 and L2 are added to the cost function for neural network learning to improve learning ability and generate sparsity in the solution. L2 regularization adds the squared value of the weights to the cost function, whereas L1 regularization adds the absolute value of the weights. This study proposes an online gradient method with a novel double regularization (OGDr) for enhancing the learning ability of pi-sigma neural networks (PSNNs). The L1 and L2 regularization methods are combined in the double regularization method, which is frequently used in several machine learning frameworks. To improve the suggested method’s performance learning ability, we applied the XOR problem, parity problem, Gabor function problem, and sonar benchmark challenge. The numerical examples of cases, OGL1, and OGL2 were compared. The OGDr has a good learning accuracy, according to numerical statistics. In addition, unlike OGL1 and OGL2, the error decreases monotonically, and the gradient of the error function approaches zero throughout learning. Received: September 8, 2022 Accepted: October 29, 2022

online gradient methodpi-sigma neural networksdouble regularizationL2 regularization.
1,642 views
455 downloads
DOI:
10.17654/0973700622002

Contributors:

 Khidir Shaib Mohamed
,
 Osman Abdalla Adam Osman
,
 Khalid Makin
,
 Mohammed Nour A.
,
 D. S. Muntasir Suhail

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