Chiral metamaterials (MTMs), a new class of MTMs, provide a simpler solution for obtaining negative refraction and dynamic electromagnetic response. Many research groups have been studying chiral MTMs asking for strong chiral behaviors such as optical activity and circular dichroism. These studies have generally focused on the properties of a large chirality and circular dichroism. However, small and constant chirality over a certain frequency band have not been queried so far in the literature. In fact, this area is mostly ignored by the researchers. This study numerically and experimentally focuses on small and constant chirality with a new-generation planar chiral MTM based on rectangular split ring resonators (SRRs), in details by using two different geometries. We have successfully obtained small chirality values by using the proposed designs, which can be easily integrated into many interesting applications such as transmission and antireflection filters or polarization control devices.

This paper presents a system based on artificial neural network (ANN) that predicts ground plane design for desired radiation properties of a monopole antenna with operation band of 4.8 to 5 GHz. The operating frequency can be adapted to any other frequency regimes. Initially, a 180 × 180 mm2 ground plane, which is composed of a copper layer, is designed and integrated to a radiative pole that creates monopole antenna configuration. The ground plane is divided into 18 rows and 18 columns as 18 × 18 matrix so that each unit cell has a square shape having 10 mm side length. Moreover, 152 different ground plane configurations are created by using logic 1 s and 0 s. Multi‐layered feed forward ANN is used along with Scale Conjugate Gradient learning algorithm to design ground plane of the monopole antenna. Simulated 152 random ground plane arrays and obtained radiation patterns are used to train ANN for the ground plane design. If a user wants to manipulate radiation, artificial neural network gives the optimum ground plane design for the desired radiation direction and gain with 91.03% accuracy. Finally, one test antenna is fabricated and experimentally measured to support the results of the simulated one. The proposed ANN model approach can be easily used for antenna applications in the antenna industry.

In this manuscript, a resonator layer is presented for the purpose of reducing the mutual coupling effect between each antenna element of a cross dipole antenna. In design processes, an artificial neural network approach was used for various resonator designs. In the operating frequency band of 2.2–2.7 GHz, 48 different 6 × 6 resonator layers were created and integrated into the cross dipole antenna to reduce transmission and improve isolation between each antenna elements. Moreover, when training an artificial neural network in the Matlab program, 48 different resonator layers were used with the return losses and transmission values of cross dipole antenna elements. After training process, eight unknown resonator designs were tested and accurate results were obtained. Finally, one of the resonator planes, which was obtained from the artificial neural network, was fabricated and experimentally tested, then an accurate result was obtained. This study provides a good solution, especially for improving isolation in multiport antenna systems, using an artificial neural network approach.

Bu çalışmada, PCB (Printed Circuit Board - Baskı Devre) dairesel mikroşerit anten kullanılarak etanol+su ve metanol+su karışımlarının miktarlarını algılayabilecek bir algılayıcı tasarlanmıştır. Antenin alt zemini üzerinde tam merkezde açılan dairesel bir boşluk ile birlikte sistemin 3 GHz frekansında çalışabilmesi için optimizasyon yapılmış ve bunun sonucunda geometrik değerler elde edilmiştir. Analizler gerçekleştirilirken dairesel boşluğa, alt iletken yapıya temas etmeden sıvı karışım numunesinin yerleştirilmesi sağlanmış ve rezonans frekansının değişiminin test altındaki numunelerin karışım yüzdeleriyle ilişkisi ortaya konmuştur. Rezonans frekansı etanol su karışımı için 44 MHz’lik bir algılama bandı sunmakta ve bu aralık metanol su karışımı için yaklaşık 38 MHz civarında olmaktadır. Rezonans frekanslarında oluşan değişimler doğrusala çok yakın olup, hem etanol hem de metanol karışımlarının yüzdelerini çok yüksek hassasiyetle ve gerçek zamanlı olarak belirlemeye imkan sağlamaktadır. Tasarlanan anten ve algılayıcı yapı hem düşük profile, düşük maliyete sahip olup hem de yüksek hassasiyetli ve eş zamanlı ölçüm kapasitesine sahiptir.

This study presents the design and optimization of a microstrip monopole antenna for 5G sub‐6 GHz applications, employing a deep learning‐based surrogate model combined with honeybee mating optimization (HBMO). The studied antenna structure employs air via arrays, intended to enhance antenna performance, including improved impedance matching and increased bandwidth. It is important to note that, unlike conventional antennas, the proposed design does not include a fully enclosed metallic cavity similar to a substrate integrated waveguide (SIW) antenna designs. A sensitivity analysis was conducted to assess the impact of these parameters, emphasizing the need for optimal tuning. To generate training and test datasets efficiently, Latin hypercube sampling (LHS) was used. A convolutional neural network (CNN) surrogate model was trained, outperforming other machine learning (ML) algorithms in predictive accuracy and generalization. The proposed CNN‐HBMO framework reduced computational costs by minimizing the need for expensive electromagnetic (EM) simulations, enabling rapid design space exploration. The optimized antenna was fabricated and validated through experimental measurements, achieving 2–3 dBi gain and 𝑆 ₁₁ < −10 dB across the 2.7–5.2 GHz band. Compared to existing designs, the proposed antenna offers a compact size (34 × 34 mm) with competitive performance, making it suitable for multi‐band 5G applications.