Nurhan Türker Tokan

This study presents a reconfigurable frequency-selective surface (R-FSS) designed to dynamically switch between WLAN, WiMAX, and sub-6 GHz band frequencies. The electronic switching mechanism of this R-FSS is controlled in real-time using PIN-diodes. Depending on the activation state of these diodes, the structure operates in three distinct modes. Among the three modes, one exhibits polarization-stable wideband suppression, whereas the other two demonstrate polarization selectivity by interchanging between the dual-narrow and single-wide stopband regimes under orthogonal polarizations. The design is described with an equivalent-circuit model, corroborated by full-wave electromagnetic simulations, and validated through measurements of a fabricated prototype. This reconfigurability allows the proposed structure to operate across WLAN, sub-6 GHz, and WiMAX frequency ranges either with two narrow stopbands or with a single-wide stopband, while providing polarization selectivity for frequency-selective applications.

This letter presents a multimode, miniaturized frequency-reconfigurable frequency selective surface (MR-FSS) capable of switching between passbands centered at approximately 1.9 GHz, 4.9 GHz, and 5.8 GHz, as well as stopbands at approximately 2.4 GHz, 3.5 GHz, and 5.8 GHz. By changing the state of the RF PIN diodes, the proposed MR-FSS provides flexible polarization control and can operate in either a bandstop mode or a band-pass mode. This enables it to function as a reflective or transparent structure. The proposed design involves metallic patterns imprinted on both sides of a singlelayer RO5880 substrate, with RF-PIN diodes placed on both the top and bottom sides to achieve the desired multi-mode switching functionality. With multi-band reconfigurability and polarization selectivity, the proposed miniaturized R-FSS enables electromagnetic shielding and dynamic frequency control for 5G systems, IoT networks, and military radar applications.

In this work, a planar microwave angular displacement sensor featuring concentric and precisely aligned stator and rotor components for accurate angular position measurement is presented. The sensor operates using a dual-band architecture incorporating two concentric loop resonators to enhance angular sensing performance. The structure is realized on two FR-4 printed circuit boards (PCBs), each measuring 40 mm × 40 mm with a thickness of 1.6 mm. The PCBs are arranged such that the conductive layers containing the microstrip patterns face each other, ensuring strong electromagnetic coupling and precise concentric alignment. The rotor consists of a split-ring resonator patterned on the underside of the upper PCB and is placed concentrically within a circular aperture on the top layer. The stator comprises all regions outside the circular apertures on both PCBs. The lower PCB includes a straight microstrip feed line magnetically coupled to a closed-loop primary resonator. The split-ring resonator on the rotor is positioned directly above the primary resonator, facilitating strong mutual coupling. Simulation results indicate resonances at 2.17 GHz and 3.57 GHz, demonstrating dynamic angular sensing capability over a 0° to 90° rotation range. Full-wave electromagnetic simulations reveal absolute angular sensitivities of 0.83 MHz/° and 0.40 MHz/° at the respective resonant frequencies. The findings indicate that the proposed sensor design holds significant potential for industrial applications requiring precise angular displacement measurements.

Terahertz (THz) antennas are among the critical components required for enabling the transition to sixth-generation (6G) wireless networks. Although research on THz antennas for 6G communication systems has garnered significant attention, a standardized antenna design has yet to be established. This study introduces the modeling of a full-metal transverse slotted waveguide antenna (TSWA) for 6G and beyond. The proposed antenna operates across the upper regions of the V-band and the entire W-band. Designed and simulated using widely adopted full-wave analysis tools, the antenna achieves a peak gain of 17 dBi and a total efficiency exceeding 90% within the band. Additionally, it exhibits pattern-reconfigurable capabilities, enabling main lobe beam steering between 5° and 68° with low side lobe levels. Simulations are conducted to assess the power handling capability (PHC) of the antenna, including both the peak (PPHC) and average (APHC) values. The results indicate that the antenna can handle 17 W of APHC within the W-band and 3.4 W across the 60–160 GHz range. Furthermore, corona discharge and multipaction analyses are performed to evaluate the antenna’s power handling performance under extreme operating conditions. These features make the proposed TSWA a strong candidate for high-performance space applications, 6G communication systems, and beyond.

The quick and effective detection of humans trapped under debris is crucial in search and rescue operations. This study explores the use of antennas operating within the 150–650 MHz frequency range to identify human respiration and movement under building wreckage. A debris model consisting of construction materials was generated at the laboratory, and attenuation characteristics were observed to set ideal operating frequencies. Time-dependent transmission coefficient data were collected over 20 s and processed using short-time Fourier transform, wavelet transform, and empirical mode decomposition for time-frequency analysis. To enhance signal clarity, denoising techniques were applied before the radar signals were categorized into three classes: empty debris, human respiration, and human movement. Generative adversarial networks augmented environmental noise data to enrich training datasets comprising nine subsets. Deep learning models, including temporal convolutional networks, long short-term memory, and convolutional neural networks, were employed for classification. Hyperparameter optimization via random search further refined model performance. Results indicate that the convolutional neural networks using short-time Fourier transform data consistently achieved the highest classification accuracy across subsets. These findings demonstrate the potential of combining radar with deep learning for reliable human detection under debris, advancing rescue efforts in disaster scenarios.

A small-sized reflectarray antenna array with compact production specifications is designed and fabricated for high-speed Ka-band communication systems. In the design phase, firstly, the reflection characteristics of unit cells used in the reflective surface are obtained by the full wave computational analysis tool, CST Microwave Studio. Secondly, an aperture efficiency analysis is carried out to determine the physical size of the reflectarray and the distances between the feeding antenna and the individual unit cells. Then, the entire reflectarray antenna is analyzed by array theory to obtain geometrical dimensions to be used in the fabrication phase. These results are verified by CST Microwave Studio and similar fabrication guidelines are obtained for both TE and TM polarizations. In the fabrication phase, the carefully tailored design parameters of the unit cells are used to build the antenna and measure important parameters such as radiation patterns, gain, cross polarization levels and S11 parameters, which agree with the results obtained in the design phase. The proposed reflectarray antenna makes it possible to support dual-polarized multi-beams in the range 18-20 GHz with stable gain behavior, which makes it possible to use it in high speed 5G satellite communication systems.

Detecting live humans in buildings that have collapsed due to disasters and identifying their condition of health is of great importance for search and rescue operations. Although various methods have been used for this purpose, there are still critical challenges to ensure accurate and rapid life-saving operations. Immediate detection of the presence of living humans under debris combined with the assessment of their vital signs is a crucial factor. This research endeavors to introduce a previously unexplored method: the use of artificial neural network-based techniques to detect human respiration under building debris by generating novel simulation-derived electromagnetic data. To achieve this, a realistic three-dimensional debris model was integrated into an electromagnetic simulation program and complemented by a phantom simulating anterior–posterior body movements indicative of respiration. Measurements of magnitude and phase between 150 and 650 MHz were performed under different conditions. Using surrogate models based on artificial neural networks, noise with different signal-to-noise ratios within the selected frequencies was introduced. These models were used to perform two different steps. Firstly, the presence of respiration of living humans trapped under debris was successfully detected with a success rate of 99.97%. Secondly, the difficult task of classifying the respiration patterns of the human was accomplished with an impressive accuracy of 99.69%, providing a solid basis for proof of concept.

In this paper, a reconfigurable Multiple-Input Multiple-Output (MIMO) antenna array is presented for 5G portable devices. The proposed array consists of four radiating elements and an Electromagnetic Band Gap (EBG) structure. Planar monopole radiating elements are employed in the array with Coplanar Waveguide Ports (CWPs). Each CWP is grounded on one side to a reflecting L-shaped structure that has an effect of improving the antenna’s directivity. It is shown that by inductively connecting Minkowski fractal structure of $1^{st}$ order to the radiating element, the impedance matching is improved that results in enhancement in the array’s bandwidth performance. The EBG structure is used to provide the isolation between antenna elements in the MIMO array. The fractal structure is connected to the L-shaped reflector through four photosensitive light dependent resistor (LDR) switches. The effect of various LDR switching configurations on the performance of the antenna is investigated. The proposed array provides a novel performance in terms of S-parameters with enhancements in the radiation properties. Such enhancements

A dual-band, dual-polarized frequency selective surface (FSS)-backed multilayer reflectarray antenna is designed for 5G high-speed satellites operating at Ka-band uplink and downlink frequencies (20/30 GHz). A reflectarray antenna system consists of two reflectarrays that are separated from each other by an FSS layer that behaves as a planar bandpass filter at Ka-band satellite uplink frequencies. Each reflectarray antenna is designed with dual-polarized unit cells. In order to achieve a uniform phase distribution across the reflectarray surface, physical dimensions and positions of the unit cells with a fixed periodicity are carefully chosen. The FSS conductor is etched to the bottom layer of the 30 GHz reflectarray substrate to save cost and weight. The reflectarray performance is analyzed by using CST Microwave Studio and array theory. A prototype is fabricated, and the results are experimentally verified. The gain of the reflectarray is measured as 21.13 dBi and 26.94 dBi at 20 and 30 GHz, respectively. A crosspol level of more than 35 dB is observed at both frequencies. The simulated and measured results show that the proposed reflectarray is suitable for high-speed Ka-band satellites.

High‐performance frequency selective surfaces (FSSs) have gained attention for their spatial filtering characteristics in 5G communication systems. In this work, we propose an efficient and accurate design methodology for the FSS. Three different artificial neural network methods (ANN) are employed, and their performances are compared for analysis and synthesis purposes. Results show that GRNN has the highest performance for both training and test phase of ANN based FSS analysis and synthesis. A novel, compact, low‐profile triple band FSS unit cell is introduced, and the working mechanism is described. By applying ANN based design procedure, the unit cell dimensions to resonate at the 5G mm‐wave frequency band is extracted. A unit cell with the extracted physical dimensions is simulated with a full‐wave analysis tool. The simulation results show that the FSS has the filtering feature at the predetermined mm‐wave frequencies of the 5G communication. The prototype of the FSS is fabricated, as well. The simulations are verified experimentally with measurement results. The results show that proposed ANN based analysis and synthesis method can be an effective tool for the design of FSS band‐pass filter for 5G applications.