This paper introduces an innovative design and analysis of a magneto-electric dipole antenna exhibiting high-gain, ultra-wideband operation, and stable radiation characteristics in the 60-GHz mm-wave band. Furthermore, the trapped printed gap waveguide (TPGW) technology is presented as a low-cost, minimal-loss, and low-dispersion guiding structure to feed the proposed antenna. The antenna covers a relative matching bandwidth of over 33.33% from 50 to 70 GHz with a maximum gain up to 8 dBi. In addition, the antenna is integrated with a perforated dielectric substrate layer lens on the antenna’s broadside location, enhancing the gain by an average of 3 dB along its entire operational bandwidth. Moreover, an efficient approach for designing a large ME dipole antenna array and its corporate feeding network is presented. Both ME-dipole sub-arrays and the out-of-phase power divider with WR-15 standard interface are designed and studied separately, where a systematic design procedure is presented to obtain initial design parameters. A 2 × 2 planar antenna array is designed and implemented, featuring proper integration between the radiating elements and a differentially fed wide-bandwidth TPGW power divider. Then, the operation of the individual components has been assessed using simulation and measurements. Furthermore, an in-depth mathematical analysis is presented to investigate the potential resonance conditions arising from disparities in complementary components. Consequently, a proposed solution is provided to break the resonance loop and shield the two opposing sub-arrays. The 2 × 2 array of ME-dipoles has overall dimensions of 1.6 1.4 and demonstrates an impedance bandwidth (– 10 dB) exceeding 33.33 at 60 GHz, with a peak gain of over 18 dBi.

Frequency-Selective Surfaces (FSSs) are structures designed to selectively transmit or reflect electromagnetic waves, making them essential for applications requiring precise control over frequency bands and wave propagation characteristics. However, traditional FSS designs face challenges such as fixed geometries, limited scalability, and poor bandwidth efficiency, often requiring compromises between size reduction and performance. To address these limitations, this work introduces the use of Stepped Impedance Resonators (SIRs) to synthesize miniaturized FSS structures with four-legged elements (FLEs). By combining transmission line theory, SIR equations, and parallel coplanar stripline models, an innovative synthesis method is proposed, enabling precise control over spurious frequencies and resulting in a 54% reduction in unit-cell size without sacrificing performance. This approach significantly enhances the feasibility of compact FSS applications. To further improve performance, an arrow-bending technique was introduced to reduce the coupling between adjacent cells, yielding a 30% improvement in isolation. Three distinct surface designs have been fabricated and tested under both normal incidence and oblique angles for TE and TM modes. These designs include the SIR-based FSS cell, an enhanced design featuring arrow bending, and a reverse arrow formation intended to reduce edge effects between adjacent cells. Additionally, measurements demonstrate excellent performance stability, with tolerance maintained for incident angles up to 60◦ . Experimental validation confirms effective blocking at 10 GHz and highlights the robustness of the design across varying incident angles. Prototypes fabricated from the miniaturized FSS elements show excellent agreement with simulations, underscoring the potential of this method for advanced applications in communications, radar, and electromagnetic shielding.

This work presents a novel dual-polarized antenna array tailored for Internet of Things (IoT) applications, specifically designed to operate in the millimeter-wave (mm-wave) spectrum within the frequency range of 30–60 GHz. Leveraging printed ridge gap waveguide (PRGW) technology, the antenna ensures robust performance by eliminating parasitic radiation from the feed network, thus significantly enhancing the reliability and efficiency required by IoT communication systems, particularly for smart cities, autonomous vehicles, and high-speed sensor networks. The proposed antenna achieves superior radiation characteristics through a cross-shaped magneto-electric (ME) dipole backed by an artificial magnetic conductor (AMC) cavity and electromagnetic bandgap (EBG) structures. These features suppress surface waves, reduce edge diffraction, and minimize back-lobe emissions, enabling stable, high-quality IoT connectivity. The antenna demonstrates a wide impedance bandwidth of 24% centered at 30 GHz and exceptional isolation exceeding 40 dB, ensuring interference-free dual-polarized operation crucial for densely populated IoT environments. Fabrication and testing validate the design, consistently achieving a gain of approximately 13.88 dBi across the operational bandwidth. The antenna’s performance effectively addresses the critical requirements of emerging IoT systems, including ultra-high data throughput, reduced latency, and robust wireless connectivity, essential for real-time applications such as healthcare monitoring, vehicular communication, and smart infrastructure.

One of the main causes of failure in Insulated Gate Bipolar Transistor (IGBT) modules used in high-power conversion applications is thermal-stress-induced degradation. In this paper, an experimental testing setup for thermal stress and real-time degradation monitoring, as well as a deep neural network (DNN)-based lifetime prediction of IGBT modules under thermo-electrically stressed inverter operation, is proposed. A two-level SVPWM inverter is implemented to create a hybrid power cycling test platform that imposes well-defined junction-temperature swings representative of real-world operation by combining controlled electrical loading and active induction heating with water cooling. Throughout the aging process, on-state voltage and module temperature are constantly monitored to identify degradation precursors associated with thermo-mechanical fatigue. A physics-based Coffin–Manson lifetime model is fitted using failure datasets to characterize temperature-dependent lifetime behavior. An offline deep neural network (DNN) is trained on degradation trajectories derived from on-state collector–emitter voltage (V_ce,on) to predict remaining useful lifetime. This approach uses partial degradation histories for accurate early-life prediction. The proposed DNN model for competitive and computationally efficient lifetime prediction is validated experimentally on several IGBT modules under different thermal stresses, and its accuracy is compared with other prediction methods.