Abstracts:Understanding how different virtual reality (VR) game genres modulate physiological arousal is crucial for designing emotionally adaptive immersive systems. This study introduces a novel experimental framework combining high-resolution Skin Conductance Response (SCR) data and neural predictive modeling to compare emotional activation across horror, skill-based, and exercise VR games. Using Galvanic Skin Response (GSR) sensors, we recorded phasic peaks in SCR from 25 university-aged participants during gameplay sessions with controlled exposure times and standardized transitions. However, given the minimal difference relative to the large variability, this observation should be considered preliminary and specific to the tested games and cohort. A feed-forward neural network was developed to forecast individual arousal levels based solely on genre-induced features, achieving strong predictive performance. This dual contribution empirical genre comparison and lightweight predictive modeling offers a scalable tool for integrating emotional responsiveness into VR systems without continuous biosignal monitoring. The findings not only advance the state of the art in affective computing but also open new avenues for therapeutic, educational, and entertainment applications grounded in physiological adaptation.

Abstracts:This paper describes the development of a bidirectional visible light communication system using a 5 mm red, green, and blue (RGB) light emitting diode (LED), which only serve as data transmitters and receivers but also function as power generators. A distinctive feature of the system is the implementation of a power divider using an RGB LED, which mitigates the complexity of the implementation of the optical transceiver and the collection of energy generated by the LED. The primary objective is to model a visible light communication identification system (VLC-ID) that is capable of operating efficiently in access applications by leveraging the ability of RGB LEDs to perform multiple functions simultaneously. To achieve this, the system employs OOK modulation and capacitor voltage accumulation. The research adopts an experimental approach, evaluating the bit error rate and the voltage accumulated by the system to demonstrate the viability and efficiency of the proposed model for access systems based on visible light communication technology.

Abstracts:This article presents a novel structural test strategy for a single MOSFET (Metal-Oxide-Semiconductor Field-Effect Transistor) source designed for Fast Field-Cycling Nuclear Magnetic Resonance (FFC-NMR) systems. The proposed methodology enables in-field fault detection during idle intervals or before experiment initiation, a critical step to ensure the reliability and validity of the experimental outcomes. The circuit under test is divided into two sections: low-power and high-power. Each one is evaluated using tailored analog testing techniques: OBT (Oscillation-Based Test) and direct current testing are applied to the low-power section, while transient analysis with DTW (Dynamic Time Warping) is used for fault detection in the high-power section. This approach achieves high fault coverage 93.7% for the low-power section and 100% for the high-power section without requiring complex signal processing. The effectiveness of the method is validated through simulation studies complement-ed by experimental fault injection on a scaled-down prototype. The results demonstrate that this test strategy significantly enhances system reliability, offering a valuable contribution to the development of more robust and maintainable FFC-NMR instrumentation for scientific and industrial applications.

Abstracts:This study investigates the design and performance of millimeter-wave (mm-Wave) Multiple-Input Multiple-Output (MIMO) antennas for fifth-generation (5G) applications, with a particular focus on the consequences of incorporating a ring resonator within the antenna system. This study compares two design variations, one with a ring resonator, and one without to assess their impact on enhancing the antenna's performance characteristics. The research employs five machine learning algorithms, namely, Decision Tree, Random Forest, K-Nearest Neighbors (KNN), XG-Boost, and Gradient Boosting Regressor (GBR), to estimate return loss. Among these, the Random Forest algorithm demonstrates superior performance in terms of accuracy, Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and R-squared metrics. The proposed MIMO antenna system shows better performance in Envelope Correlation Coefficient (ECC), Diversity Gain (DG), Channel Capacity Loss (CCL) and Total Active Reflection Coefficient (TARC). The results indicate that including a ring resonator in the antenna design significantly improves the antenna's performance, and machine learning algorithms, particularly Random Forest, can effectively predict and optimize critical parameters for antenna design in 5G applications.

Abstracts:Thanks to recent advances in artificial intelligence, interest in autonomous mobile systems has increased, and consequently, the development and validation of advanced control schemes for them has also seen a rise. This work introduces a two-layer neuro-adaptive compensation control scheme designed to address the trajectory tracking problem for an omnidirectional wheeled mobile robot equipped with four independent Mecanum wheels. The two-layer artificial neural network is used to compensate for the unknown dynamics of the mobile robot; the filtered error technique is used to obtain the weights of the artificial neural network. This approach does not require offline training. A key contribution of this approach is the integration of a novel auxiliary signal to provide robustness, particularly in non-ideal scenarios. This robust term effectively bounds the disturbance commonly encountered in such control approaches. A significant advantage of this approach is its independence from precise knowledge of plant parameters or the overall plant dynamics. Experimental results demonstrate the effectiveness of the proposed controller in achieving desired performance for the 4-wheeled omnidirectional mobile robot.

Abstracts:Automatic Modulation Recognition (AMR) is a fundamental capability for Unmanned Aerial Vehicle (UAV) communication systems in sixth-generation (6G) wireless networks. It enables UAVs to intelligently identify and track received signals, supporting reliable connectivity under dynamic environments. In practical UAV applications, AMR methods must achieve high recognition accuracy with minimal computational complexity, since UAV platforms operate under strict constraints in storage, memory, and processing power. While recent Deep Learning (DL)-based solutions have advanced AMR performance, most prioritize accuracy at the cost of significantly larger models and higher computational demands. Conversely, lightweight models often lack the accuracy required for real-time deployment, limiting their practical utility. To overcome these limitations, this paper presents a novel Super Light Convolutional Neural Network (SLCNN) for AMR. Unlike conventional models, SLCNN em-ploys a carefully optimized architecture with fewer convolutional layers, smaller filters, and pooling operations, combined with Gaussian noise and dropout for robust generalization. This design strategy reduces model size and inference time while preserving high accuracy. The proposed SLCNN was evaluated on the HisarMod 2019.1 dataset and validated across RML 2016.10a, 2016.10b, and 2018.01a datasets. Experimental comparisons with Convolutional Long Short-Term Memory Deep Neural Network (CLNN), Long Short-Term Memory, Gated Recurrent Unit, and Residual Network highlight that SLCNN achieves superior results, attaining 98.50% classification accuracy with significantly reduced computational cost. Furthermore, deployment on the NVIDIA Jetson Orin Nano demonstrates real-time suitability, confirming the models effectiveness for UAV-based 6G wireless networks.

Abstracts:Electrical substations must keep their critical task of providing the power grid safe, reliable, protected, and manageable electricity flow within the context of increased digitalization in the power sector. On one hand, digital substation automation systems enable novel capabilities and functions, but on the other hand utilities must effectively manage such transformation while keeping their assets in operation. This paper presents an overview of how substations of different epochs are designed, operated and maintained in a practice-centered context of a Brazilian transmission utility. Drawing from a comparative case study based on a theoretical classification model, three high-voltage substations with different degrees of digitalization are analyzed regarding their automation system's design, features, lifetime upgrades, and future implications. The study shows that real-world conditions present challenges to operators and utilities retrofit existing substations in modernization efforts. There is a strong tendency to digitalize substation automation systems based on the IEC 61850, but the implementation of a process bus is still not widespread. Contrasting the cases with the academic literature reveals there are still areas that require further development to be competitively implemented by utilities, such as the usage of low-power instrument transformers, and that utilities must actively prepare to leverage the long-term benefits of those installations. Particularly in Brazil, extensive upcoming investment in such facilities are expected. As such, this study contributes to the understanding and discussion of the role of digital technologies in substations and their relationship to the professional practice of transmission utilities.

Abstracts:Solar PV arrays are susceptible to various faults, such as hotspots, cracks, and Potential Induced Degradation, which can impair efficiency and longevity. Traditional fault detection methods are time-intensive and limited in accuracy, especially for large-scale installations. This paper proposes an Adaptive Attention Pyramid Network (AAPN) for accurate and efficient fault detection in PV modules. AAPN integrates depthwise separable convolutions, squeeze-and-excitation blocks, and adaptive attention mechanisms to achieve high accuracy in identifying fault types across different classification complexities. Extensive experimentation on a comprehensive dataset of infrared PV images, organized into 12 fault classes, demonstrated AAPNs high classification accuracy of up to 96% in binary and 92% in 12-class classification scenarios. The proposed model is tested using an infrared solar module dataset for 2-class, 8- class, 11-class, and 12-class fault categories. Its effectiveness is compared with 69 existing deep-learning models for various fault classes. An ablation study was conducted to evaluate the impact of different architectural components, such as depthwise separable convolutions and squeeze-and-excitation blocks, on the models performance, showing an optimal trade-off between accuracy and computational efficiency. The proposed architecture model is very lightweight, utilizing only 0.8 million parameters. Its effective balance between high accuracies and low parameter utilization makes it highly suitable for deployment on drone-based edge devices, facilitating on-site real-time PV fault monitoring, maintenance, and detection. Additionally, the model has been successfully implemented on the Google Coral Edge TPU, achieving 40.2 ms inference time per image, confirming its efficiency and suitability for real-time applications in resource-constrained environments.

Abstracts:This article proposes high step-up dual-switch Luo non-isolated DC-DC converter with fault-tolerant capability for critical load applications. The proposed converter constitutes more advantages, which including increased voltage gains with a reduced duty cycle, common grounding between the source and load, low component count and lower voltage and current stress. In addition, it offers reconfiguration capability and decreases the power handling capability by devices, thereby enhancing overall converter efficiency. The performance characteristics of the proposed converter are analyzed in continuous current mode (CCM), with a comprehensive discussion of its features. The proposed converter experimental results are validated at 400 W output power for operational effectiveness and feasibility.

Abstracts:The increasing integration of Distributed Energy Resources (DERs) in power distribution networks demands accurate system modeling and reliable power flow analysis. This paper presents a structured methodology for modeling a real Electrical Distribution System (EDS) using OpenDSS, applied to the Federal University of Lavras (UFLA), Brazil. Due to the lack of georeferenced data, the method combines satellite-based geolocation with load characterization from real measurements and statistical distributions. The model supports deterministic and time-series power flow simulations under three conditions: without DERs, with a 1.2 MWp photovoltaic plant, and with additional power factor correction. To achieve this, an incremental algorithm is proposed to determine the optimal size of a fixed capacitor bank at the feeder, improving the power factor without violating constraints. Results showed that DER integration reverses power flow, increases losses, and reduces the power factor, which also becomes variable and highly dependent on photovoltaic generation, but is improved by the proposed algorithm. This methodology enables effective, simplified modeling and analysis of real-world EDSs.