The development of miniature antennas for bio-medical applications has attracted the attention of many researchers in the last years. In this letter, we provide a miniature antenna for the RFID tag for identifying patients in African and European hospitals. The proposed antenna is designed on a flexible silicon substrate with a relative dielectric constant of 11.9 and a thickness of 1.6mm. An in-depth study of the proposed wearable antenna was made in free space and on human tissue. The achieved results showed good performance in terms of miniaturization, bandwidth, impedance matching and, reading distance. The presented tag antenna is designed and simulated by using CST-MWS solver and the results were validated by HFSS and both results are in good agreement.

This paper presents a novel design and analysis of a unit cell for reconfigurable reflective intelligent surfaces for 5G wireless communications. The design is based on the use of Reconfigurable Intelligent Meta-Surfaces (RIMS) to manipulate electromagnetic waves by shifting the phase of the incident signal. The unit cell consists of a double negative metamaterial structure consisting of a modified split ring combined with a thin wire called “MSRR”, both on the same side of an FR-4 substrate. This cell is equipped with two PIN diodes to switch the refractive index of the incident electromagnetic wave. The refractive index of the proposed structure can be controlled between -2 and 5 and operates around the 3.5 GHz frequency of the sub-6 GHz band intended for 5G applications. Through this, the research addresses the key challenge of RIS surface design and control, offering low realization cost and easy control of the RIS surface to monitor user movement in real-time to improve signal quality. This advance has great potential for 5G applications.

In this paper, we present a new dual band metamaterial printed antenna for radio frequency identification applications. The proposed antenna consists of two L-shaped slot in the radiating element for dual band operation and a complementary split ring resonator etched from the ground plane for size miniaturization. This antenna is designed and optimized by CST microwave studio on FR-4 substrate with thickness of 1.6 mm, dielectric constant of 4.4 and tangent loss of 0.025. A microstrip line with characteristic impedance of 50 ohms is used to feed this antenna. A prototype of the proposed antenna is fabricated to validate the simulation results. The measured and simulated results are in good agreement.

Smart greenhouses (SGHs) are controlled-environment agricultural systems that leverage digital technologies to optimize crop production and resource management. In particular, recent advances in artificial intelligence (AI) and the Internet of Things (IoT) have enabled the development of intelligent monitoring, predictive modeling, and automated decision-support systems within these environments. Against this backdrop, this comprehensive review synthesizes over 130 studies published between 2020 and 2025, with a focus on AI-driven monitoring, predictive modeling, and decision-support frameworks in SGH environments. More specifically, key application domains include microclimate regulation, crop growth assessment, disease and pest detection, yield estimation, and robotic harvesting. Moreover, particular attention is given to the interplay between AI methodologies and their data sources, encompassing IoT sensor networks, RGB, multispectral, and hyperspectral imaging, as well as multimodal data-fusion approaches. In addition, publicly available datasets, model architectures, and performance metrics are consolidated to support reproducibility and cross-study comparison. Nevertheless, persistent challenges are critically discussed, including data heterogeneity, limited model generalization across sites, interpretability constraints, and practical barriers to deployment. Finally, emerging research directions are identified, notably multimodal learning, edge-AI integration, standardized benchmarks, and scalable system architectures, with the overarching objective of guiding the development of robust, sustainable, and operationally feasible AI-enabled SGH systems.

In this paper, a novel design of metamaterial passive tag antenna for radio frequency identification operating in the UHF band is proposed. The design of the proposed tag antenna included a meandering technique to match the antenna with the tag chip and a complementary split ring resonator to reduce the antenna size. An advanced Photopaper substrate having a permittivity of 3.3, loss tangent of 0.04 and thickness of 0.25mm is used to design this tag antenna. The dimensions of the proposed tag antenna are optimized by using ADS. The total size of this circuit is 84.6 × 28mm ² . The performance of the proposed tag antenna is evaluated and analyzed based on gain, return loss, matching input impedance and read range. The simulated reflection coefficient is about -35dB at 915MHz, the impedance bandwidth is about 32.8MHz, gain is 1.8dB, and the read range is about 6.2 m.

This paper presents a novel approach to design a compact miniature coplanar band-pass filter by using rectangular split ring resonator. This proposed circuit is designed for the Industrial, Scientific and, Medical (ISM) frequency band applications at 2.4 GHz. At the first stage, a metamaterial resonator is designed and simulated in a TEM waveguide to verifiy its electromagnetic proprieties around the desired frequency bands. At the second stage, a band pass filter is designed using the proposed metamaterial resonator. Many parametric studies are realized to investigate the effect and influence of some resonator parameters on the proposed BPF performances. ADS Agilent and CST-MWS solvers are used in order to verify the simulated results. The circuit frequency responses show an excellent insertion loss and good return loss in the passband.