This paper presents a parametric study of square loop and square slot frequency selective surfaces (FSSs) aimed at their equivalent circuit (EC) model optimization. Consideration was given to their physical attributes, i.e., the unit cell dimensions and spacing, substrate thickness and dielectric properties, for several frequencies and plane wave incident angles. Correlation analysis and evaluation of the influence of physical related input parameters on the FSS performance are presented. Subsequent optimization factor for the square loop classical EC model is analyzed, and a novel EC model formulation for the square slot FSS is proposed. The performance of the proposed EC model was assessed against results obtained from appropriate electromagnetic (EM) simulations, based on a root-mean-square error (RMSE) criteria. Results demonstrate the validity of the optimized EC model, in which good estimations of the frequency response of FSS structures were obtained. Significant reduction of the resonant frequency offsets, in the order of 650 (from 910 to 260) and 460 (770 to 310) MHz, was obtained for square loops and square slots, respectively. The models were further validated against measurements performed on two physical FSS prototypes inside an anechoic chamber at 2.4 GHz. Relatively good agreement was obtained between measurements of real FSS prototypes and results obtained with the EC model. Finally, this work is sought to provide the necessary refinement of elementary models for further studies with more complex and novel FSS structures.

A novel two-dimensional (2-D) beamsteering transmitarray inspired on frequency selective surfaces is proposed in this paper. Individual phase range is applied to each transmitarray element and thus enabling the steering of the main lobe of an original antenna pattern in both elevation and azimuth planes (2-D-beamsteering). This has been demonstrated on a 5 × 5 unit-cell stacked structure, loaded with surface mount capacitors, coupled to the aperture of a standard gain horn antenna. Additionally, a complete parametric study based on electromagnetic (EM) simulations using CST Microwave Studio is presented to evaluate and characterize unit-cell parameters and the behavior of the array with various numbers of stacked layers and separation distances. Antenna beamsteering with ranges up to Az = 25° and El = 25° are achieved by means of EM simulations and validated against experimental results performed on a physical prototype at 5.3 GHz. The comparisons yield positive results and clearly demonstrate the potential and limitations of using such structures for beamsteering purposes.

We introduce an innovative 3-D mechanically tunable frequency selective surface (FSS), which is inspired by the classical flat square slot FSS. The proposal improves the performance of classical 2-D FSS designs, and it also represents a novel method of achieving mechanical frequency tuning, despite other 3-D designs that consist of a collection of stacked 3-D layers exist. In our proposal, the rotation of an inner element provides tuning capability to the squared cell structure, consisting of metallic grids with a movable inner element. An aluminum prototype was built, which can be tuned from 2.4 to 4 GHz, and also compared its measured performance and numerical simulations. Some characteristics of the proposed structure are the rejection level at main polarization, up to 20 dB, and the maximum frequency sweep of approximately 50% of the fundamental frequency. The prototype showed a stable frequency response for angles of incidence up to 45°. Since results are in good agreement with simulations, we provide parametric equations to design 3-D structures at desired frequencies.

This communication presents a hybrid frequency selective surface (FSS) and rectenna design for wireless power harvesting. To this extent, the center of the original FSS structure acts as a probe-fed patch antenna and an additional layer was added to the rear of FSS structure to mimic the required antenna ground plane, while minimizing its impact on FSS performance. The proposed hybrid solution yields a bandpass frequency response at GSM 900-MHz frequency band, with band reject behavior at 2.4 GHz and, on the other hand, act as a energy harvesting system through the antenna element at the 2.4-GHz ISM band, e.g., for recycling ambient Wi-Fi energy. The developed prototype, consisting of an array of 12 unit cells, was able to power a 56-mW LED at a 2-Hz rate from an incident power level of -10 dBm, resulting in an average output power of 300 μW and an unit cell RF-DC conversion efficiency of 50%.

This study proposes a frequency selective surface (FSS) design to be used in Wi‐Fi shielding applications as either a band reject or band pass dual‐band single‐layer filter. The proposed design consists of a combination of basic elements, that is, ring loops/slots, and is tuned at both 2.4 and 5.2 GHz Wi‐Fi frequency bands. It has a relatively stable frequency response in the aforementioned Wi‐Fi bands for incidence angles ranging from 0° to 45°. Both band reject and band pass designs are presented, along with their unit cell dimensions. Simulation and model validation through measurements demonstrate the performance of the proposed FSS design. Active variants are also proposed and briefly evaluated, in simulation environment, which should allow for applications where an on–off switching is desired at 2.4 and 5.2 GHz Wi‐Fi bands.

Intelligent approaches in sports using IoT devices to gather data, attempting to optimize athlete’s training and performance, are cutting edge research. Synergies between recent wearable hardware and wireless communication strategies, together with the advances in intelligent algorithms, which are able to perform online pattern recognition and classification with seamless results, are at the front line of high-performance sports coaching. In this work, an intelligent data analytics system for swimmer performance is proposed. The system includes (i) pre-processing of raw signals; (ii) feature representation of wearable sensors and biosensors; (iii) online recognition of the swimming style and turns; and (iv) post-analysis of the performance for coaching decision support, including stroke counting and average speed. The system is supported by wearable inertial (AHRS) and biosensors (heart rate and pulse oximetry) placed on a swimmer’s body. Radio-frequency links are employed to communicate with the heart rate sensor and the station in the vicinity of the swimming pool, where analytics is carried out. Experiments were carried out in a real training setup, including 10 athletes aged 15 to 17 years. This scenario resulted in a set of circa 8000 samples. The experimental results show that the proposed system for intelligent swimming analytics with wearable sensors effectively yields immediate feedback to coaches and swimmers based on real-time data analysis. The best result was achieved with a Random Forest classifier with a macro-averaged $F_1$ of 95.02%. The benefit of the proposed framework was demonstrated by effectively supporting coaches while monitoring the training of several swimmers.