The LLC series resonant converter has emerged as a solution to applications requiring power conversion with isolation, reduced volume and high efficiency, such as PV systems and EV chargers. However, the LLC resonant converter is limited in power, so it requires a multi-phase configuration in order to provide higher currents. This configuration connects the outputs of two or more LLC converters in parallel, increasing the output current but introducing imbalance and circulating currents due to the mismatch and tolerance values of components in each resonant tank. This paper proposes a simple PI control scheme to compensate the current imbalance and eliminate circulating currents generated when several LLC resonant converters are connected in parallel. Unlike reported current sharing methods, the proposed control scheme is based on multiple current control loops operating independently, using the switching frequency of each parallel-connected unit as a degree of freedom of the overall converter. The proposed control scheme has been successfully validated under simulations and experimental assessment, implementing two resonant tanks with ±5% tolerance of parameters, providing excellent steady-state and transient performance.

Accurate turbidity classification is essential for maintaining water quality in various contexts, from drinking water to industrial processes. Traditional turbidimeters face challenges, including interference from colored substances, particle shape and size variations, and the need for regular calibration and maintenance. This paper implements a convolutional neural network (CNN) to classify water samples based on their turbidity levels. The dataset consisted of images captured under controlled laboratory conditions, with turbidity levels measured using a 2100P Portable Turbidimeter. The CNN achieved a classification accuracy of 97.00% in laboratory settings. When tested on real-world water body samples, the model maintained an accuracy of 85.00%. The results demonstrate that deep learning can effectively classify turbidity levels, offering a promising solution to overcome the limitations of traditional methods. The study highlights the potential of CNNs for accurate and efficient turbidity measurement, balancing accuracy with practical applicability in field conditions.

This study presents a micro-scale hybrid wind turbine that integrates a Savonius rotor with a Helical Darrieus rotor, aiming to enhance energy conversion efficiency and adaptability for decentralized renewable energy generation. The hybrid design leverages the high torque generation of the Savonius rotor and the aerodynamic efficiency of the Helical Darrieus rotor. Computational analyses using CFD simulations and experimental validation with a 3D-printed prototype in a closed wind tunnel were conducted at speeds ranging from 3 to 8 m/s. The results demonstrate that the hybrid turbine achieves a power coefficient of 0.26 at an optimal tip-speed ratio of 2.7, marking a 180% improvement over standalone Savonius rotors. The hybridization process mitigates the low-speed inefficiencies of the Savonius rotor. It compensates for the high-speed limitations of the Darrieus rotor, resulting in a turbine capable of operating efficiently over a wider range of wind speeds. This balanced integration maximizes energy harvesting and improves adaptability to varying wind conditions, achieving balanced and synergistic performance.

Wind turbines are a solution for sustainable energy, significantly reducing carbon emissions and fostering a circular economy for more cost-effective and cleaner power generation, in line with worldwide environmental aspirations. In this context, this research aims to explore a novel two-stage, twisted-blade micro-Vertical-Axis Wind Turbine ($\mathsf{\mu }$-VAWT)alternative inspired by the Savonius Rotor (SR). This investigation utilizes the $\kappa -\omega$ SST turbulence model to explore the power coefficient ($C_P$) and torque coefficient ($C_T$), finding $C_P$ values ranging from 0.02 to 0.08 across the turbine by altering the free stream velocity (V). $C_T$ analysis further delves into four specific sections, highlighting areas of particular interest. These results are validated by examining velocity contours, pressure contours, and streamlines in four horizontal sections, demonstrating that the proposed turbine model exhibits minimal torque fluctuation. Moreover, the analysis of vertical wind streamlines illustrates very low interference with various wind turbine proposals, underscoring the turbine’s efficiency and potential for integration into diverse wind energy projects.

The proliferation of radio frequency (RF) devices in contemporary society, especially in the fields of smart homes, Internet of Things (IoT) gadgets, and smartphones, underscores the urgent need for robust identification methods to strengthen cybersecurity. This paper delves into the realms of RF fingerprint (RFF) based on applying the Jensen-Shannon divergence (JSD) to the statistical distribution of noise in RF signals to identify Bluetooth devices. Thus, through a detailed case study, Bluetooth RF noise taken at 5 Gsps from different devices is explored. A noise model is considered to extract a unique, universal, permanent, permanent, collectable, and robust statistical RFF that identifies each Bluetooth device. Then, the different JSD noise signals provided by Bluetooth devices are contrasted with the statistical RFF of all devices and a membership resolution is declared. The study shows that this way of identifying Bluetooth devices based on RFF allows one to discern between devices of the same make and model, achieving 99.5% identification effectiveness. By leveraging statistical RFFs extracted from noise in RF signals emitted by devices, this research not only contributes to the advancement of the field of implicit device authentication systems based on wireless communication but also provides valuable insights into the practical implementation of RF identification techniques, which could be useful in forensic processes.

This paper presents a detailed study of equivalent circuit models for series-parallel configurations of piezoelectric transducers used in Energy Harvesting (EH) applications. Optimizing these configurations is essential for enhancing the efficiency and performance of Piezoelectric Energy Harvesting (PEH) systems, which are increasingly employed to power small devices and sensors. The effectiveness of series-parallel configurations is demonstrated by their ability to improve the performance of the PEH system by accurately capturing system behavior across varying frequencies and load resistances. The proposed model offers a robust framework for designing and optimizing EH systems, improving the accuracy and performance of piezoelectric transducers in both series and parallel configurations. The key contribution of this work is the enhanced equivalent circuit models for PEHs, which incorporate series, parallel, and series-parallel configurations while decoupling mechanical and electrical. The models are validated through experimentation, providing a practical solution with tunable parameters that align theoretical predictions with observed performance.