This work delves into advancements in wireless power transfer (WPT) and radiofrequency (RF) energy harvesting (EH), focusing on on-demand beamforming and wide-dynamic power range technologies. These innovations promise significant improvements in efficiency and adaptability for wireless energy systems. For transmitting RF power, on-demand beamforming enhances power delivery precision by accurately targeting specific devices, minimizing energy waste, and maximizing received power. This technology is particularly useful in dynamic environments with constantly changing device positions, ensuring stable power levels and effective real-time power transfer, such as for mobile device charging. For receiving RF power, wide-dynamic power range implementation allows EH and WPT systems to adjust power output across a broad spectrum, optimizing energy use and extending device lifespan. This capability supports scalability, accommodating devices with varied power needs, also enabling new applications in consumer electronics, healthcare, smart homes, and cities, and enhancing energy management in smart infrastructures. Additionally, this study explores three-dimensional (3D)-printable antennas and RF circuitries for battery-free applications. The versatility of 3D printing allows the creation of complex, efficient, and customizable RF components, fostering innovative battery-free solutions. Integrating on-demand beamforming and wide-dynamic power range technologies in EH systems promise improved energy transfer efficiencies, reduced losses, and sustainable, cost-effective wireless power systems.

Development of large-scale quantum computing systems will require radio frequency (RF) and microwave technologies operating reliably at cryogenic temperatures down to tens of milli-Kelvin (mK). The quantum bits in the most promising quantum computing technologies such as the superconducting quantum computing are designed using principles of microwave engineering and operated using microwave signals. The control, readout, and coupling of qubits are implemented using a network of microwave components operating at various temperature stages. To ensure reliable operation of quantum computing systems, it is critical to ensure optimal performance of these microwave components and qubits at their respective operating temperatures, which can be as low as mK temperatures. It is, therefore, critical to understand the microwave characteristics of waveforms, components, circuits, networks, and systems at cryogenic temperatures. The UK’s National Physical Laboratory (NPL) is focussed on developing new microwave measurement capabilities through the UK’s National Quantum Technologies Programme to address various microwave test and measurement challenges in quantum computing. This includes the development of various measurement capabilities to characterize the microwave performance of quantum and microwave devices and substrate materials at cryogenic temperatures. This paper summarizes the roadmap of activities at NPL to address these microwave metrology challenges in quantum computing.

This paper presents a methodology designed to leverage multitemporal sequences of synthetic aperture radar (SAR) and multispectral data and automatically extract urban changes. The approach compares results using different radar and optical sensors, describing the advantages and drawbacks of using SAR data from the COnstellation of small Satellites for the Mediterranean basin Observation (COSMO)/SkyMed, SAtélite Argentino de Observación COn Microondas (SAOCOM), and Sentinel-1 constellations, as well as nighttime light data or Sentinel-2 images. Multiple indexes obtained from multispectral data are compared, too, and results obtained by an unsupervised clustering procedure are analyzed. The results show that using different datasets it is possible to obtain consistent results about different types of changes in urban areas (e.g., demolition, development, and densification) with different levels of spatial details.

Adrenal gland-induced hypertension, also known as secondary hypertension, is a medical condition caused by an underlying adrenal pathology, most typically adrenocortical adenomas. Current clinical practices involve pharmacotherapy or surgical resection to treat adrenal gland diseases that cause hypertension. However, due to the limitations of these treatment options, microwave ablation (MWA) has emerged as a promising minimally invasive alternative. An accurate understanding of the dielectric properties of adrenal glands would support the further development and optimization of MWA technology for treating adrenal tumors. Only a few studies have examined the dielectric properties of both human and animal adrenal glands, and the sample sizes of these studies have been relatively small. Therefore, further dielectric data of human and animal adrenal glands are warranted. This paper presents the ex vivo dielectric properties of the ovine adrenal glands (medulla and cortex) and summarizes the published literature on dielectric data of adrenal glands from porcine, bovine, ovine, and human samples in the microwave frequency range to analyze the consistency and reliability of the reported data. The dielectric properties of the ovine adrenal glands (N = 8) were measured using an open-ended coaxial probe measurement technique at frequencies ranging from 0.5 to 8.5 GHz. This study also investigated the temperature-dependent dielectric properties of the ovine adrenal medulla ranging from 37 to 64°C at frequencies ranging from 0.5 to 8.5 GHz. The dielectric properties of the ovine adrenal medulla measured in this study were found to be consistent with the literature. Moreover, the review suggests that variations exist in the dielectric properties of the adrenal medulla and cortex among species. The study also found that the dielectric properties of the adrenal medulla decrease with increasing temperature, similar to other tissues for which temperature-dependent dielectric data have been reported. This summary of dielectric data of adrenal glands and the temperature-dependent dielectric properties of the ovine adrenal medulla will accelerate the development of MWA technologies for hypertension treatment.

This article presents the design of a dielectric lens antenna that utilizes the concept of a stepped Fresnel lens for focusing electromagnetic millimeter waves. Based on the quasi-optical properties of these waves, a Cartesian Oval is optimized and employed as a focusing lens. Multiple such lenses are combined to two different Fresnel-based lens antennas. We survey these newly designed lens antennas and compare them with a focusing lens antenna based on a Cartesian oval and a far-field lens antenna. Simulations and measurements with a frequency-modulated continuous-wave (FMCW) radar validate the effectiveness of the new design, demonstrating an even improved focus size while significantly reducing the size and weight of the lens antenna by up to 53% and by nearly 48 %, respectively. Additionally, the Fresnel-based lens antennas reveal a frequency dependency, enabling frequency-based steering of the focal length over a wide relative tuning range of 177%, which we thoroughly investigate for various bandwidths and center frequencies.

Radar absorption structures made of an active frequency selective surfaces (AFSS) have enormous potential in the aviation, naval, and other industries. In this research paper, a systematic review (SR) is carried out in the field of the AFSS to bring uncertainties, obstacles, challenges, classifications, applications, and design issues that arrive in the development of the sub-6 GHz architecture. To bias the AFSS component, as per the signal requirements, a unique set of circuits (PIN diode) is required, with ON and OFF state and a transmission zone. The bandwidth of which is determined by the bias voltage supplied. It can behave as a complicated hybrid impedance structure by providing ON and OFF biasing voltage to a PIN diode embodied in an FSS structure. Higher manufacturing costs of AFSS components, more significant complexities involved, a large amount of power consumption, and reactive impedance losses are some common limitations faced while implementing and designing an AFSS. Many envisioned problems are corrected with the AFSS design, current or creative implementations, and processing parameters are investigated progressively. It implies that new AFSSs will be an alternative to regular FSSs in the future. This paper is based on Kitchenham’s three-phase review procedure and supplements it with results, views, and recommendations from other leading experts in the field.

In this study, a novel, cost-effective miniaturized tag antenna was developed for applications on the human body. To achieve impedance matching with the complex conjugate impedance of the Monza-4 tag chip (7.17–j74.22 Ω at 915 MHz), the proposed structure was configured by coarsely tuning the positions of vias and fine-tuning the small gaps of its coupled patches. For further reducing the profile and dimensions of the antenna, a design technique based on the three-dimensional dipole antenna current distribution was used. The proposed antenna configuration was not only miniaturized but also achieved a long stable reading distance (>5.0 m) and a wide impedance bandwidth of 71 MHz or 7.65% (covering the ultrahigh frequency radio frequency identification ranges in most regions), regardless of the location of the tag on the human body. Experiments were conducted to validate the simulated results, and adequate agreement was found between the simulated results and the measured results.

This paper proposes an air-filled substrate integrated waveguide (AFSIW) bandpass filter with a miniaturized non-resonant node (NRN). The NRN structure is introduced between the three resonators, and its size is smaller than the resonator size, which can realize the NRN structure’s miniaturization and reduce the model’s size. The NRN size of this filter is 41% of the NRN size of the existing AFSIW filter. This filter also introduces a transmission zero (TZ) above the passband. The measured results show that the filter’s center frequency is 20.73 GHz, and the bandwidth is 0.86 GHz. The insertion loss in the passband is 0.95 dB, and the return loss is better than 23 dB. Due to the TZ in the upper stopband, the AFSIW filter obtained good selectivity.

Radar-based hand gesture recognition is a potential noncontact human–machine interaction technique. To enhance the recognition performance of hand gesture, a multidomain fusion-based recognition method using frequency-modulated continuous wave radar is proposed in this article. The received raw echo data of gestures is preprocessed to obtain the range–time matrix, Doppler–time matrix, and range–Doppler–frame tensor. The obtained three-domain radar data corresponding to each gesture are input into the three-channel convolutional neural network for feature extraction. In particular, the extracted features from three-domain data are fused with learnable weight matrices to obtain the final gesture classification results. The experimental results have shown that the classification accuracy of the proposed multidomain fusion network based on learning weight matrix-based fusion is 98.45%, which improves the classification performance compared with the classic average-based fusion and concatenation fusion.

The main interest in using synthetic aperture radar (SAR) technology in automotive scenarios is that arbitrarily long arrays can be synthesized by exploiting the natural motion of the ego vehicle, enabling finer azimuth resolution and improved detection. All of this is achieved without increasing the hardware complexity in terms of the number of physical antennas. In this paper, we start by discussing the application of SAR imaging in the automotive environment from both theoretical and experimental perspectives. We proceed by describing an efficient processing workflow and we derive the rough number of operations required to focus an image proving the real-time imaging capability of the system. The experimental results are based on open road data acquired using an eight-channel radar at 77 GHz, considering side-looking SAR and forward SAR. The results confirm the idea that SAR imaging can be successfully and routinely used for high-resolution mapping of urban environments in the near future.

Frequency-modulated continuous-wave radar systems profit from increasing the absolute bandwidths of the generated frequency chirps to improve range resolution. As the relative bandwidth of SiGe-voltage-controlled oscillators (VCOs) is limited to about 80%, increasing the center frequency fundamentally or via frequency multiplication is the most direct way to increase that absolute bandwidth. However, as some applications require penetration depth, which dramatically decreases with frequency, other solutions are necessary. Therefore, state-of-the-art concepts rely on the down-conversion of generated frequency chirps via two separately stabilized frequency sources. This article implements a novel architecture, offering relative bandwidths of >100% within a single phase-locked loop (PLL). Therefore, two VCOs at different center frequencies are fed into a down-conversion mixer, whose output is directly stabilized via that PLL with one loop filter generating both tuning voltages. Those circuit blocks can be summarized as one equivalent VCO, offering a higher relative bandwidth and a significantly more linear tuning curve. Thereby, a solution to limited relative bandwidths with high VCO gain variation of single VCO synthesizers is offered while substantially reducing the hardware and implementation effort compared to the state-of-the-art.

Liquid crystal microwave phased arrays (LC-MPAs) are regarded as an ideal approach to realize compact antennas owing to their advantages in cost, size, weight, and power consumption. However, the shortcoming in low radiation deflection efficiency has been one of LC-MPAs’ main application limitations. To optimize the steering performance of LC-MPAs, it is essential to model the channel imperfections and compensate for the phase errors. In this paper, a phase error estimation model is built by training a neural network to establish a nonlinear relationship between the near-field phase error and the far-field pattern, hence realizing fast calibration for LC-MPAs within several measured patterns. Simulations and experiments on a 64-channel, two-dimensional planar antenna were conducted to validate this method. The results show that this method offers precise phase error estimations of 3.58° on average, realizes a fast calibration process with several field-measured radiation patterns, and improves the performances of the LC-MPA by approximately 4%–10% in deflection efficiency at different steering angles.

This paper presents a low-profile six-beam antenna implemented by a compact two-layer 6 × 6 beamforming network (BFN) and a 6 × 2 slot antenna in substrate integrated waveguide (SIW) technology. The main components of the proposed 6 × 6 BFN are 3 × 3 multi-aperture couplers, interlayer hybrid couplers, and several phase shifters which are embedded on two microwave substrates. The proposed antenna has been designed, simulated, and fabricated for the frequency range of 28–32 GHz. The size of this antenna is 82 × 31.8 × 0.787 mm3, which can be a suitable choice for 5G applications due to its compact dimensions compared to similar works. Prototype testing shows that the proposed structure presents a stable beamforming performance both in simulation and measurement with good agreement. The antenna generates six radiation beams in directions ±9°, ±30°, and ±54° with good return losses and isolations.

This article presents the design of a novel sigma slotted circular monopole antenna for wireless body area network applications. The design is proceeded with a sigma-shaped slot etched on the circular radiator with Rogers 4003C substrate. The size of the proposed antenna is 30 × 30 × 1 mm3. With partial ground plane, the bandwidth characteristics of the antenna have been enhanced. The designed antenna covers 2–10 GHz of the ultra wideband, thereby providing more than 106% bandwidth. The antenna exhibits high gain values of 1.95, 3.23, 3.46, and 5.95 dB at 2.45, 3.45, 5.8, and 9.5 GHz, respectively. The proposed design operates on off-body mode at 2.45 GHz and on-body mode at 5.8 GHz, thus acting as a dual-mode antenna. To ensure the health and safety measure, specific absorption rate analysis was done for both low and high values of input power and also with Cartesian and cylindrical human arm models. Bending analysis was also done. The time domain parameter group delay was also analyzed for the proposed antenna for on- and off-body scenarios. The prototype was fabricated, and validation of simulation results was done for free space and on-body scenarios.

This paper proposes a triple-band fractal slot antenna with enhanced gain operating at frequencies of 2.7, 3.5, and 4.8 GHz for microwave wireless power transmission. The antenna achieved more than 3 dB gain by preserving the symmetrical defected reflector structure behind it at a length of 0.54λ/4, where λ is the wavelength in free space at 2.7 GHz. A 50 Ω micro-strip line coupled at 66 mm length helps feed the triple band slot antenna. The purpose of a triple-band rectifier circuit with an impedance network is to convert the radio frequency (RF) signal into direct current. The measurements reveal that the antenna’s enhanced gain is 8.8 dB, and the rectifier’s highest RF signal to direct current conversion efficiency is 74.3%. For the combined configuration unit of measure, the antenna is assimilated into the rectifier via back-to-back 50 Ω Sub Miniature Version A (SMA) connectors. The highest recorded efficiency of 49.67% was obtained for the rectenna at optimum values of 1 kΩ resistive load and −5 dBm power input. It has been revealed that the rectifier-integrated antenna presented is effective for low-input RF energy capture and power transmission.

In this paper, a mode-matching procedure is used to prove for the first time the dispersionless behavior of glide symmetry (GS) in corrugated metasurface waveguides. Depending on their field symmetry, the Floquet harmonics have a different impact onto the frequency dispersion, which is reduced with GS when the gap between the surfaces is small. Indeed, the glide-symmetric waveguide is shown to have the same effective propagation features as a scaled mirror-symmetric waveguide with half the period and a doubled gap. We propose closed-form formulas for the effective refractive index of the glide-symmetric waveguide in the first Brillouin zone, having a small groove compared to the period. The closed-form expressions analytically prove the dispersionless behavior of GS.

Single-ended and balanced 90–120 GHz microstrip power amplifier MMICs have been designed for cost-sensitive 5G and 6G backhaul in a commercial 6-inch, 0.1-µm GaAs process. At 108 GHz, measured output power is 20.4 and 22.5 dBm, respectively. At 120 GHz, measured output is 12.6 and 17.4 dBm, respectively. This is the highest reported for GaAs, among the highest reported to date for microstrip MMIC amplifiers at these frequencies and competitive with more expensive InP and GaN processes. Measurement is compared with simulation.

The surface deformation of the main reflector in a large radio telescope is closely related to its working efficiency, which is important for some astronomical science studies. Here, we present a deep learning-based surface deformation recovery framework using non-interferometric intensity measurements as input. The recurrent convolutional neural network (RCNN) is developed to establish the inverse mapping relationship between the surface deformation of the main reflector and the intensity images at the aperture plane and at a near-field plane. Meanwhile, a physical forward propagation model is adopted to generate a large amount of data for pre-training in a computationally efficient manner. Then, the inverse mapping relationship is adjusted and improved by transfer learning using experimental data, which achieves a 15-fold reduction in the number of training image sets required, which is helpful to facilitate the practical application of deep learning in this field. In addition, the RCNN model can be trained as a denoiser, and it is robust to the axial positioning error of the measuring points. It is also promising to extend this method to the study of adaptive optics.