This paper presents a compact, low-cost dual-band antenna design with low specific absorption rate (SAR) for Wireless Body Area Networks (WBAN) and Industrial, Scientific, and Medical (ISM) applications. The proposed antenna operates at 2.4 GHz with linear polarization and 5.8 GHz with circular polarization, fabricated on a 1.52 mm thick RF35 dielectric substrate (28 $$\times $$ 40 mm). The design features a coplanar waveguide (CPW) feed integrated with substrate-integrated waveguide (SIW) technology to enhance bandwidth and return loss characteristics. The antenna has broad bandwidths of 0.5 GHz with a 2.4 GHz center and 1 GHz with a 5.8 GHz center. To mitigate back radiation and reduce SAR, a 4 $$\times $$ 4 electromagnetic bandgap (EBG) structure was incorporated, resulting in significant performance improvements. The EBG implementation increased the maximum gain from 3.66 to 8.56 dB at 2.4 GHz and from 3.97 to 10 dB at 5.8 GHz. Additionally, SAR values decreased from 7.34 to 1.62 W/kg for 1 g of tissue at 2.4 GHz and from 2.18 to 0.91 W/kg at 5.8 GHz. Prototype measurements confirm simulation results, demonstrating the antenna’s suitability for WBAN and wearable ISM applications, including sensor networks and Wi-Fi devices. The design offers advantages of independent band tuning, circular polarization at the higher frequencies, and compliance with safety standards for human exposure to electromagnetic fields.

The widespread availability of consumer drones has introduced new challenges related to safety, security, and privacy, as these platforms are increasingly misused in sensitive or restricted areas., existing counter-drone technologies–such as radar, optical tracking, and multi-sensor fusion–offer reliable performance but are often prohibitively expensive and impractical for large-scale civilian deployment. This work presents a low-cost framework for real-time drone detection and classification that leverages the radio frequency (RF) emissions exchanged between drones and their controllers. The system is built on a software-defined radio (SDR) platform (USRP B210), which captures RF signals and converts them into spectrograms for analysis using deep learning. A labeled dataset of drone and non-drone signals was developed to train and evaluate detection models. Two state-of-the-art architectures, YOLOv5 and Faster R-CNN, were adapted to this task, with evaluation under varying signal-to-noise ratio (SNR) conditions. Results demonstrate that the proposed system achieves high detection accuracy and robustness even in noisy environments, highlighting its potential as a scalable and practical solution for RF-based drone monitoring.

RF signals are widely used in various applications such as telecommunications, wireless communication systems, and radar systems. These signals can be manipulated using phase shifters that adjust the signal's phase. This adjustment is essential for beam shaping, signal cancellation, and frequency synthesis in antenna arrays. By controlling the phase of the RF signal, phase shifters help manipulate electromagnetic waves for various applications. Therefore, as Gallo points out, phase shifters are essential for manipulating and controlling high-frequency signals. This manipulation and control is essential to improving the performance of wireless communication and radar systems and can improve signal reception and transmission.The study examines different types of phase shifters, conducts a comparative analysis of different phase shifter topologies and technologies, and highlights their respective advantages and limitations in applications. In addition, the review includes a specific study of liquid metal phase shifters. Finally, the article outlines future research directions for liquid metal phase shifters, It emphasizes the need for innovative design strategies to keep pace with the evolving wireless communications and telecommunications fields. Therefore, this article can serve as a reference for the milestones in RF phase shifter research.

In communication applications, the radiation pattern of conformal antenna arrays is a critical design consideration. By electronically steering the main radiating beam in both azimuth and elevation, a hemispherical coverage can be achieved, enabling a pencil-shaped radiation pattern. This paper presents a neural network-based synthesis approach for adaptive beamforming in conical and cylindrical conformal antenna arrays, including three-dimensional array synthesis examples. The array factor (AF) is derived by adjusting the antenna array’s dimensions using generalized analytical techniques, and key performance metrics such as sidelobe level (SLL), half-power beamwidth (HPBW), and directivity are evaluated. Mutual coupling effects are neglected, assuming isotropic radiators. The proposed array demonstrates significant improvements over existing conical, coaxial cylindrical, and cylindrical configurations in terms of size, 3D scanning capability, HPBW, SLL, and directivity. Using MATLAB simulations, we apply neural network-based beamforming to conformal arrays, focusing on cylindrical, coaxial cylindrical, and conical geometries. The neural network effectively learns the radiation characteristics of these arrays and demonstrates strong performance. Building on prior work, we examine three new scenarios: A uniform circular array (UCA) mounted on an empty coated cylinder, a concentric circular array (CCA) on a multilayered coated cylinder and a conical array formed by stacking uniform circular arrays with linearly decreasing diameters along the z-axis. The proposed method is validated through numerical examples and can be extended to other 3D array geometries, such as spherical configurations or aperiodic conformal phased arrays.