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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science
Sarah Rafil Hashim
Al-Zahraa University for Women
Computer Science & IT Electrical & Electronics Engineering

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Increasing radiation power in half width microstrip leaky wave antenna by using slots technique Muhannad Kaml Abdulhameed; Sarah Rafil Hashim; Noor Kamil Abdalhameed; Ahmed Jamal Abdullah Al-Gburi
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i1.pp392-398

Abstract

The radiation power in the endfire is decreased while the main beam of half substrate integrated waveguide scan from broadside to endfire in a forward. The design of half-width microstrip leaky-wave antenna (HW-MLWA) has been presented in this work to increase the power radiation near endfire by using the slots technique in the radiation element. This slot leads to a decrease the cross-polarization. The proposed design comprises one element of HW-MLWA with repeated meandered square slots in the radiation element. One aspect of this antenna is generated by using a half substrate integrated waveguide with a full tapered feed line. The proposed antenna was terminated by load of 50 Ω, and feed on the other end of the antenna. Finally, the suggested design is simulated and acceptable results were found. The released gain is increased from 10.6 dBi to 12 dBi at 4.3 GHz. This design is suitable for unmanned aerial vehicle UAVs at C band application.
A unique deep-learning-based model with chest x-ray image for diagnosing COVID-19 Alyaa Mahdi Al-khafagy; Sarah Rafil Hashim; Rusul Ali Enad
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 2: November 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i2.pp1147-1154

Abstract

Later innovative advancements cleared the way for deep learning-based methods to be used in the therapeutic field due to its exactness for the detection and localization of different illnesses. Recently, the coronavirus widespread has put a parcel of weight on the health framework all around the world. Reverse Transcription- Polymerase Chain Reaction test and medical envisioning are both possible and effective techniques to determine the coronavirus infection. Since coronavirus is highly infection and Reverse Transcription- Polymerase Chain Reaction is time-consuming, determination utilizing a chest X-ray to early diagnosing the infection is considered secure in different situations. A preprocessing step is done first to balance classes inside the dataset and increase the training data. A deep learning-based method is proposed in this study to determine some human lung infections and classify coronavirus from other non-coronavirus diseases accordingly. The proposed model is used for multi-class classification which is more complicated than binary classification especially in the medical image due to the inter classes' large similarity. The proposed procedure effectively classifies four classes that incorporate coronavirus, lung opacity, normal lung, and viral pneumonia with an accuracy of 97.5 %. The proposed strategy appears excellent in terms of accuracy when compared with later strategies.
Co-Authors Ahmed Jamal Abdullah Al-Gburi Alyaa Mahdi Al-khafagy Muhannad Kaml Abdulhameed Noor Kamil Abdalhameed Rusul Ali Enad