The current paper describes a compact, long-reading-range tag antenna for radio frequency identification (RFID) applications in the UHF (ultra-high frequency) band, operating at 915 MHz. The antenna's miniaturized design is achieved through the utilization of the meandering line technique. A T-matching structure matches the chip impedance to that of the antenna. Polytetrafluoroethylene, or PTFE, is used as the substrate for fabrication. The UHF tag's physical dimensions are 44.4 × 14.4 × 0.8 mm³. This antenna was first designed, simulated and then optimized by software CST-MWS (Computer Simulation Technology-Microwave Studio) before being fabri-cated. The Measured reflection coefficient at 915.5 MHz is approximately -24 dB, exhibiting a bandwidth of 7,9 MHz (911.5 MHz-919.4 MHz). The proposed tag is shown to have gain of 1.56 dB and radiation efficiency of 90% at the resonant frequency of 915 MHz. Its long-reading-range at 915 MHz is roughly 18.41 m, for an EIRP of 4 W. The measured results closely align with the simulated ones.

Aiming at the current distributed array subarray optimization design and DOA estimation problem, a robust and effective distributed array subarray optimization method is proposed, and a discrete quantum electromagnetic field optimization algorithm is designed to quickly solve the resulting objective function to obtain the optimal subarray structure. Then, based on this array structure, the infinite-norm exponential kernel maximum likelihood method is utilized for direction of arrival (DOA) estimation. The simulation results show that the proposed method can still be effective in the case of impulsive noise, small snapshots and low signal-to-noise ratio, which further verifies that the proposed method can obtain a better subarray layout and superior DOA estimates.

This article presents an 8 × 8 Multiple-Input Multiple-Output (MIMO) antenna system that operates in two frequency bands: 3.4-3.8 GHz and 10.5-14.0 GHz. The core element of this antenna system is a rectangular patch with a line slot. To assess the diversity performance of this MIMO antenna, various parameters like S-Parameters, Envelope Correlation Coefficient (ECC), Mean Effective Gain (MEG), Channel Capacity Loss (CCL), Total Active Reflection Coefficient (TARC), and Channel Capacity (CC) were used. The study demonstrates a strong alignment between measurement and simulation results. The article thoroughly analyzes the simulated and measured performances of the lower band (LB) (3.4-3.8 GHz) and upper band (UB) (10.5-14.0 GHz). In the LB, the measured values for parameters such as reflection coefficient, mutual coupling coefficient, TARC, total efficiency, ECC, MEGi-MEGj, CCL, and CC all surpass or equal specific benchmarks. Specifically, these values are better than -6 dB, 10 dB, -11 dB, 56%, 0.15, 0.83 dB, 0.275 bps/Hz, and 38 bps/Hz, respectively. For the UB, the values are also quite favorable, exceeding or closely matching key criteria: -6 dB, 10 dB, -5 dB, 40%, 0.2, 1.6 dB, 0.55 bps/Hz, and 33.6 bps/Hz. These findings suggest that the intended MIMO antenna meets the necessary conditions for both the LB and UB regions. As a result, it appears to be a promising choice for applications in 5G mobile networks and satellite communications, including Direct Broadcast Satellite (DBS) and Fixed Satellite Services (FSS).

In order to achieve the optimization objectives of low torque ripple, high torque and high efficiency, this paper proposes a multi-objective optimization strategy based on genetic algorithms optimization BP neural network (GA-BP) combined with non-dominated sorting genetic algorithm (NSGA-II) and applies it to the multi-objective optimization design of an external rotor ferrite-assisted synchronous reluctance motor (ERFa-SynRM). Firstly, the preliminary design and selection of ERFa-SynRM structure are carried out. Secondly, a comprehensive sensitivity analysis is presented on the extent to which the design variables affect the optimization objectives. Following this, a high-precision prediction model is constructed by GA-BP neural network, and NSGA-II is applied to global optimization of the prediction model. Finally, the electromagnetic performances of the motor before and after the optimization are compared by the finite element analysis (FEA) software. Compared with the initial motor, the average torque and efficiency of the optimized motor are improved, and the torque ripple is reduced by 54.9%, which verifies the effectiveness of the multi-objective optimization design method.

This paper focuses on optimizing the radiation pattern of sparse array antennas using reinforcement learning, with many algorithms. The paper aims to leverage Proximal Policy Optimization's (PPO's) advantages in optimization and its effectiveness in handling stochastic transitions and rewards to achieve a reduced number of elements while maintaining desired signal performance and minimizing unnecessary side lobe signals. By removing a few of the antennas using reinforcement learning and PPO optimization, the same results as a complete array have been obtained. The anticipated outcomes of this research hold the promise of significantly enhancing the effectiveness and utility of sparse array antennas in communication systems.

This paper details the design and realization of a high-isolation multiple-input-multiple-output (MIMO) antenna tailored for fifth-generation (5G) wireless applications. The antenna consists of a 2-element array, with each unit being a patch antenna loaded with six uniformly sized complementary split-ring resonators (CSRRs). These CSRRs are strategically etched to minimize the antenna's overall size. In addition, the fragment-type split ring resonators (SRRs) are horizontally positioned between the antenna units to further improve isolation. The placement and structure of these fragment-type SRRs are optimized through a combined use of High-Frequency Structure Simulator (HFSS) and genetic algorithm (GA) techniques, which enables significant isolation levels exceeding -40 dB between antenna units. The proposed MIMO antenna operates within the 5G C-band with a -10 dB bandwidth ranging from 4.84 to 5.00 GHz, while the isolation at 4.9 GHz improves from 14.73 dB to 42.88 dB. Moreover, the maximum Envelope Correlation Coefficient is 0.002, and the antenna dimensions are 50 mm × 44 mm × 1.6 mm. Antenna samples are fabricated using wet etching on an FR4 substrate. The measured and simulated values are found to be in good agreement. Compared to the traditional antenna design method, which relies on parameters sweeping, the algorithmic approach used in this paper significantly enhances both the design's effectiveness and efficiency.

A CPW-fed flexible fractal shape circular ring patch (FSCRP) antenna is presented in this paper and operates at ISM band for biomedical applications. The proposed antenna operates at 2.46 GHz both in free space and on a human hand. This antenna functions within a 10 dB impedance bandwidth of 390 MHz (2.38 GHz to 2.77 GHz) in free space and 800 MHz (2.04 GHz to 2.84 GHz) on human hand structure with a reflection coefficient of -33.9 dB and -36.97 dB respectively. The circular shape fractal structure operates the antenna with circular polarization, and a 3 dB axial ratio of 170 MHz (2.4 GHz to 2.57 GHz) has been observed. The proposed antenna can be used in Implantable Medical Devices (IMDs) for biotelemetry applications. The simulated and measured results for the proposed FSCRP antenna are also presented in this paper.

Radio frequency energy harvesting (RF-EH), which uses an ultra-wideband (UWB) antenna, is the best substitute for traditional batteries for continuously powering sensor networks. The UWB antenna helps to receive the ambient radio frequency energy that radiates from communication applications for harvesting purposes to power devices or recharge batteries. A novel aspect of this design is the use of dual antenna ports with four ``A" shaped in radiating patches and ground plane, which permits the harvester to completely utilize all accessible frequency bands. The design analysis of a compact dual-port (2 × 1) ultra-wideband multiple-input multiple-output (UWB-MIMO) antenna based on four ``A" shaped and shared ground plane for RF energy harvesting in the band of 2.3-21.7 GHz is presented. The proposed antenna has been implemented on a Rogers RT 5880 substrate with a size of 39 mm × 30 mm, a thickness of 0.8 mm, and a dielectric constant of 2.2. It achieves S₁₁ ≤ -10 dB at (2.3-21.7) GHz and a maximum peak gain of 10.29 dB at 20.53 GHz. The proposed antenna is designed and simulated with ANSYS HFSS and fabricated. The results of simulation and measurement of the proposed antenna are in good agreement, and the antenna achieves bandwidth of 2.3–20 GHz that supports radio frequency energy harvesting in addition to UWB applications across satellite, Wi-Fi, Wi-Max, and mobile applications.

In order to solve the issues of large computation and control performance affected by motor parameters in the conventional model predictive torque control (MPTC) of permanent magnet synchronous motors (PMSMs), a robust model predictive torque control strategy with online parameter identification based on an improved differential evolution extended Kalman filter (IDEEKF-RMPTC) is proposed To begin with, and a steady-state voltage vector at the next time is obtained through a low-pass filter and used as the reference voltage vector to select the alternative voltage vector. The parameter robustness of the PMSM system is enhanced, and the computational effort is reduced. In addition, an improved differential evacuation algorithm for the extended Kalman filter (EKF) is designed, and the system noise matrix Q and the measurement noise matrix R of the EKF are optimized. The estimation error is reduced; the stability of the system is enhanced; and the accuracy of the identification of the motor parameters is improved. Finally, the computational effort of the system is effectively reduced by the proposed IDEEKF-RMPTC strategy, and the parameter robustness of the PMSM drive system under parameter mismatch conditions is enhanced which are proved by the experimental results.

This article presents a miniaturized dual layer angularly stable quadband frequency selective surface (FSS) for shielding applications. The shield consists of four metallic square rings on a thin FR4 substrate of relative permittivity 4.4 and thickness 0.5 mm with two rings on top layer and other two rings in the bottom layer. The dimension of the shielding unit cell is 0.2λ × 0.2λ, for the lowest frequency. These shields have been analyzed in both planar and conformal configurations. The equivalent circuit models as well as analytical model are determined. The shield exhibits quad band band stop characteristics with transmission zeros at 5 GHz (4.3-5.8 GHz), 6.6 GHz (6.3-6.8 GHz), 8.3 GHz (7-8.8 GHz) and 15 GHz (11-17 GHz). These bands find their application in shielding upper WLAN band, sub 6 GHz 5G band C/Ku band for satellite communication. The proposed FSS prototype is fabricated and tested for shield effectiveness in an anechoic chamber. The proposed FSS design offers stable angular response up to 60˚ for planar and geometry. Simulated and measured transmission coefficients are in good agreement and hence well suited for shielding applications. As the structure is fourfold symmetric, it exhibits polarization insensitive and angular stability in all four bands.

In this paper, we present our measurements about 3D printable microwave absorber materials. First, we determined the electromagnetic parameters of the material using different measurement techniques, whose some examples we present. Knowing the material parameters, a geometry for a 3D printable absorber was selected, and simulations were performed to optimise the geometry from X-band (8.2 GHz to 12.4 GHz) to Ka-band (26.5 GHz to 40 GHz). Pieces of absorbers were 3D printed using the optimised dimensions and were mounted to a metallic corner reflector as test subject. The corner reflector camouflaged in this way was then measured in an anechoic chamber, and measurements with and without the 3D printed absorbers are compared. We found good agreement between the measurements and simulations and found the structure and the material we used as usable candidates for the reduction of the radar cross section of an object.

The advancement of wireless communication technology demands antennas that can achieve significant gain while functioning across diverse frequency ranges. Numerous studies have aimed to enhance the gain and radiation properties of such antennas. However, when these antennas operate near the human body, their performance regarding return loss, gain, radiation pattern, and specific absorption rate (SAR) are influenced by the interaction and absorption of human tissue. To enhance overall antenna performance, artificial magnetic conductor (AMC) surfaces have been introduced. Numerous studies have been conducted to improve antenna performance through the use of AMC surfaces. This paper proposes a coplanar waveguide (CPW)-fed wearable antenna integrated with an AMC array. The integrated antenna is expected to operate at both 2.45 GHz and 5.5 GHz, making it suitable for applications in wireless local area networks (WLAN) and wireless body area networks (WBAN). The study focuses on the benefits of the integrated antenna, highlighting advantages such as improved gain and lowered SAR in comparison to the antenna alone. These improvements are validated through both simulated and measured outcomes. This antenna, featuring a simple feed structure, low cost, and ease of fabrication, is a promising option for wearable medical applications.

A phase-only and amplitude-phase genetic algorithm (GA) has been investigated to restore the array pattern of a 4 × 2 planar array in the presence of centre-elements phase malfunctioning. A single and double adjacent antenna elements are considered for phase malfunctioning. The new array weights for functioning antenna elements are computed with GA to restore the value of array peak gain and sidelobe level (SLL). The simulation results, which are verified with measurements, indicated that complete recovery of array pattern without SLL constraint in the presence of malfunctioning elements was possible with the phase-only GA weights. It is shown that the uncorrected pattern can also be compensated for main beam scanning with phase-only GA weights. However, pattern compensation with SLL constraint is not possible using the phase-only GA weights. Therefore, amplitude-phase GA weights are estimated to restore the peak gain and the desired SLL simultaneously at the cost of widening the main beam. A prototype of X-band 4 × 2 microstrip patch array controlled through X-band phaser evaluation boards was used in the in-house anechoic chamber measurements facility to validate the full-wave HFSS simulation results.

In this paper, a novel measurement matrix construction method based on adaptive cross-approximation (ACA) is proposed to improve the performance of the compressive sensing-based method of moments (CS-MoM) for analyzing electromagnetic scattering problems. ACA is based on a weight scheme and is able to recognize the rows and columns that contribute significantly to the matrix. Thus, the object is divided into multiple blocks, and the impedance matrix is partitioned into near-field and far-field groups to establish the condition for applying ACA. Then, the row indexes are extracted from the group with the highest number of ACA recognized rows in the far-field groups of each block. Finally, by combining all row indexes to extract the impedance matrix, a lower-dimensional and deterministic measurement matrix is constructed, thereby improving computational efficiency. Numerical simulation results validate the accuracy and effectiveness of the proposed method.

To address challenges such as low starting torque and inaccurate position estimation in traditional sensorless control methods for switched reluctance motors (SRMs), this paper proposes a sensorless control strategy suitable for the startup and low-to-medium speed operation of multiphase SRMs. Firstly, an improved inductance calculation model for the pulse injection region is proposed based on the electromagnetic characteristics of SRM. Secondly, leveraging the results of the inductance model calculation, a three-phase commutation rule is designed to enhance the starting capability. Lastly, an adaptive angle tuning (AAT) module is devised to improve the phase commutation width, and the pulse injection region is optimized through a dynamic inductance threshold method. The efficacy of the proposed method was validated through simulations conducted on a prototype six-phase 12/10 SRM.

Improving the efficiency of electrical machine is an important way to reduce carbon emissions. Accurate calculation and measurement of iron loss is an important part of improving efficiency of electrical machine. Therefore, how to accurately calculate and optimize the device structure to reduce iron loss has become a research focus. In this study, the influence of power supply, motor structure, ferromagnetic material, manufacturing processes and multiphysics on the motor iron loss is discussed and summarized. Then, the classification and summary of the existing iron loss models are discussed, and shortcomings and the future research direction are suggested. In addition, several induction motor efficiency measurement standards are described, and the defects and improvement direction of efficiency measurement of converter-fed motor are discussed. The contents discussed and summarized in this study can be helpful to engineers engaged in high efficiency motor design and motor driving algorithm development.

This article presents a groundbreaking approach to osteoporosis detection and monitoring by integrating a new wearable monopole antenna design with advanced machine learning algorithm (neural network). Inspired by the intricate pattern of a Christmas snowflake, the system utilizes UWB electromagnetic waves and bone attenuation analysis for compact, noninvasive, and highly accurate bone health assessment. Fabricated entirely from textile materials, the antenna features remarkable performance metrics, including an impedance bandwidth of 4.9 to 12.6 GHz and a reflection coefficient consistently below -10 dB, within a compact form factor of 41.9 mm × 29.2 mm. Experimental validation and comparative studies demonstrate the effectiveness of this approach in precisely classifying osteoporosis levels, achieving an outstanding accuracy rate of 87%. This study signifies a significant advancement in osteoporosis detection and diagnosis, combining state-of-the-art antenna technology with advanced machine learning techniques. The developed system holds promise for early detection and personalized monitoring of osteoporosis, contributing to improved healthcare outcomes and enhanced quality of life for individuals at risk of bone-related diseases.

The paper presents a model of an open resonator exhibiting a single high-Q eigen oscillation within a one-octave frequency band. The resonator is synthesized by integrating a diffraction radiation antenna, which comprises a segment of a dielectric waveguide above a metal substrate with a diffraction grating, into a system of flat reflectors aligned parallel to the wave fronts of surface and bulk waves generated by the antenna. A pulse response with an amplitude-frequency characteristic featuring one pronounced resonant maximum, which corresponds to an eigen oscillation with Q factor exceeding 10⁴, has been achieved in the proposed system. The optical length of the resonator exceeds the wavelength of the working oscillation by over 50 times. The feasibility of tuning the resonator via moving both the mirrors and the diffraction grating is demonstrated. The proposed model holds promise for applications in the development of solid-state and quantum radiation sources operating in the microwave and higher frequency ranges.

The integration of machine learning (ML) regression models in predicting the parameters of metamaterial antennas significantly reduces the design time required for optimizing antenna performance compared to traditional simulation tools. Metamaterial antennas, known for overcoming the bandwidth constraints of small antennas, benefit greatly from these advanced predictive models. This study applies and evaluates four ML regression models - Extra Trees, Random Forest, XGBoost, and CatBoost - to predict key antenna parameters such as S₁₁, gain, and bandwidth. Each model's performance is assessed using metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), R-squared (R2), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE) across different training and testing set configurations (30%, 50%, and 70%). The Extra Trees model achieves the best performance for predicting gain, with an R2 of 0.9990, MAE of 0.0069, MSE of 0.0002, RMSE of 0.0145, and MAPE of 0.3106. Feature importance analysis reveals that specific features, such as pr and p0 for gain and Ya and Xa for bandwidth, are critical in the predictive models. These findings highlight the potential of ML methods to improve the efficiency and accuracy of metamaterial antenna design.

Radon-Fourier transform (RFT) is able to effectively overcome the coupling between the range cell migration (RCM) effect and Doppler modulation by searching along range and velocity dimensions jointly for the moving target, which depends on envelope alignment and Doppler phase compensation. However, without effective clutter suppression, clutter would also be intergraded via RFT. Thus, the adaptive RFT (ARFT) has been proposed to clutter suppression by introducing an optimal filter weight, which is determined from the clutter's covariance matrix as well as the steering vector for the moving target with the consideration of RCM effect. Nevertheless, the ARFT needs to address the difficulty for real implementation, i.e., computational complexity is too high to a large number of pulse samples. It is known that to obtain the inversion the sample covariance matrix (R_cn^-1) is order M³, i.e., O(M³), in which $M$ is the order of the matrix. It is the most complexity consumed step in ARFT. In this paper, we propose a modified adaptive RFT (MARFT) method to obtain R_cn^-1 with recursive computation, which takes the complexity order M², i.e., O(M²). Simulations show that the proposed method has the same clutter suppression results as the conventional ARFT method, where the computational complexity is much lower.