In the coming years, major transportation sectors will be electrified, in which more accessible and easy solutions for charging Electric Vehicles (EVs) become vital. Wireless charging is considered one of the best and easiest methods to charge EVs anywhere, even during the driving of cars. To get accurate charging power rates while charging wirelessly, advanced mathematical models are needed to accurately present the charging power. Such models must take into consideration all the coils parameters, shapes, the used compensation topology, and if the system is static or dynamic. This paper presents a comprehensive analysis of the wireless charging systems adapted for EVs by stating the most common charging topologies, and architectures, and by concentrating on the corresponding mathematical models used to calculate the given electrical power as a function of the EV’s situation on streets and its speed. Then, it is possible to evaluate the EV autonomy, and an accurate approximation can be determined when EV is on a wireless charging road. Even with the citation and comparison between the two studied models, this paper opens some perspectives for energy transmitter tools and tries to explain how renewable energy can help the deployment of this technology.

The main objective of this work is to propose an approach for improving the performance of miniaturized microstrip patch antennas (MPAs) that are loaded with a thin film consisting of a high relative permittivity material. The method uses a thin film to decrease the antenna’s resonance frequency while keeping the antenna’s patch dimensions. For the enhancement of the antenna’s performance with a thin film, the dimensions of the patch of the designed antenna are optimized utilizing genetic algorithms (GAs). The resonance frequency of the microstrip patch antenna was changed from 5.8 GHz to 4.0 GHz, and the area of the proposed antenna was minimized by around 60%, especially in comparison to a conventional antenna alone without thin film. Most of the performances of the proposed antenna such as the return loss, bandwidth, and voltage standing wave ratio (VSWR) were improved.

The article presents a Co-planar Waveguide (CPW) fed antenna of a low-profile, simple geometry, and compact size operating at the dual band for ISM and WLAN applications for 5G communication devices. The antenna has a small size of 30 mm × 18 mm × 0.79 mm and is realized using Rogers RT/Duroid 5880 substrate. The proposed dual-band antenna contains a CPW feedline along with the triangular patch. Later on, various stubs are loaded to obtain optimal results. The proposed antenna offers a dual band at 2.4 and 5.4 GHz while covering the impedance bandwidths of 2.25–2.8 GHz for ISM and 5.45–5.65 GHz for WLAN applications, respectively. The proposed antenna design is studied and analyzed using the Electromagnetic (EM) High-Frequency Structure Simulator (HFSSv9) tool, and a hardware prototype is fabricated to verify the simulated results. As the antenna is intended for on-body applications, therefore, Specific Absorption Rate (SAR) analysis is carried out to investigate the Electromagnetic effects of the antenna on the human body. Moreover, a comparison between the proposed dual-band antenna and other relevant works in the literature is presented. The results and comparison of the proposed work with other literary works validate that the proposed dual-band antenna is suitable for future 5G devices working in Industrial, Scientific, Medical (ISM), and Wireless Local Area Network (WLAN) bands.

In this paper, battery electric vehicle (BEV) controllers are smartly tuned with particle swarm optimization (PSO) and genetic algorithm (GA) to ensure good speed regulation. Intelligent tuning is ensured with a proposed and well-defined cost function that aims to satisfy the design requirements in terms of minimum overshoot, fast response, and tolerable steady state input. Two proposed cost functions are formulated for both simple speed input and for driving cycles. The BEV is controlled with the field oriented control technique (FOC), and it is driven by a permanent magnet synchronous motor (PMSM). An efficient control scheme based on FOC is built using a simplified closed loop control system including BEV components such as regulators, inverter, traction machine, and sensors. Simulation results show that the optimum controller gains obtained by intelligent tuning have resulted in satisfactory BEV performance that sustains the harsh environmental conditions. Robustness tests against BEV parameter changes and environmental parameter variations confirmed the effectiveness of intelligent tuning methods.

The complexity of sensor signal patterns in healthcare, coupled with the variability of physiological data present significant challenges in developing reliable diagnostic and monitoring. While machine learning (ML) has greatly advanced sensor-based health analysis, its decision-making processes often lack transparency, raising concerns about reliability and clinical adoption. This review explores the role of Explainable Artificial Intelligence (XAI) in enhancing the interpretability of ML models for sensor signal analysis, particularly in applications such as pressure sensors, wearable inertial sensors, imaging sensors (MRI, CT, X-ray), ECG, EEG, and wearable health tracking. A systematic literature review was conducted across multiple databases to identify studies applying XAI techniques to sensor-based health monitoring. The review categorizes nine trending XAI methods used to interpret ML-driven analyses of biosignals, evaluating their advantages and limitations in different healthcare scenarios. The findings emphasize the importance of transparency in ML-driven sensor analysis, which is critical for building trust and real-time clinical decisionmaking and wearable healthcare applications. Despite its potential, XAI for sensor signals faces challenges related to model scalability, real-time processing, and clinician interpretability. The review identifies key research gaps in integrating XAI into sensor-based healthcare systems, emphasizing the need for robust validation methods and user-friendly explanations. Ultimately, XAI offers a promising path toward revolutionizing sensor-driven health monitoring, though further advancements are necessary to fully integrate explainability into real-world clinical and assistive applications.

This study describes the design and implementation of a small printed ultra-wideband (UWB) antenna for smart electronic systems with on-demand adjustable notching properties. A contiguous sub-band between 3–4.1 GHz, 4.45–6.5 GHz, or for both bands concurrently, can be mitigated by the antenna. Numerous technologies and applications, including WiMAX, Wi-Fi, ISMA, WLAN, and sub-6 GHz, primarily utilize these band segments remitted by the UWB. The upper notch band is implemented by inserting an open-ended stub with the partial ground plane; the lower notch band functionality is obtained by etching a U-shaped slot from the radiating structure. The basic UWB mode is then changed to a UWB mode, with a single or dual notch band, using two diodes to achieve reconfigurability. The antenna has a physically compact size of 17 × 23 mm² and a quasi-omnidirectional maximum gain of 4.9 dBi, along with a high efficiency of more than 80%, according to both simulation and measurement data. A significant bandwidth in the UWB region is also demonstrated by the proposed design, with a fractional bandwidth of 180% in relation to the 5.2 GHz center frequency. Regarding compactness, consistent gain, and programmable notch features, the proposed antenna outperforms the antennas described in the literature. In addition to these benefits, the antenna’s compact size makes it simple to incorporate into small electronic devices and enables producers to build many antennas without complications.