The emergence of embedded systems is driven by technological evolutions that reshape the general conception about smart connectivity, modularity, or intelligent computing. Such trends suppose technology matures and versatile interconnection-communication abilities where the cost-benefit case becomes obvious. This paper presents a specially developed embedded system that fulfills most of the above expectations. The heart of this hardware topology is an FPGA-based Genesys Virtex-5 ready-to-use development board. This development system has been interfaced to a power electronic module specially conceived to drive twin DC motors. Obviously, both open- and closed-loop motor control is allowed by this electronic configuration. The software layer of the embedded system was designed by using last generation technology based on a MicroBlaze soft-core processor. All development steps have been gradually unfolded under main frame of the Xilinx EDK toolkit. As result, a compact and modularly embedded system has been obtained, well suited for a wide range of industrial servo control or automotive applications. Such developments may serve as useful guidance for future high performance and real-time computing embedded systems implementation.
Inverter technology plays a critical role in converting DC power to AC power for various applications, including renewable energy systems and portable devices. This study addresses the development of an Arduino Nano-based inverter and evaluates its performance compared to the commercial EGS002 inverter under no load, resistive, inductive, and capacitive load conditions. The methods involved waveform analysis and electrical parameter measurements, including output voltage, input and output currents, frequency, and duty cycle, to assess stability and efficiency. Results demonstrate that the Arduino Nano inverter maintains stable output voltages near the nominal 220 V AC with consistent sinusoidal waveforms across all load types. Under no-load conditions, it exhibited a lower input current of 1.091 A compared to 1.230 A for the EGS002, indicating higher efficiency. For the resistive load, both devices delivered output voltages around 192–195 V with comparable input currents near 4.070 A. Under inductive load, the Arduino Nano maintained a Vrms of 207.49 V and input current of 2.462 A, closely matching the EGS002. Capacitive load testing showed stable voltage output near 204 V with minimal waveform distortion for both inverters. This research concludes that the Arduino Nano inverter is a viable, cost-effective alternative to commercial inverters, capable of reliable performance under diverse load conditions. The novelty lies in demonstrating that a low-cost microcontroller platform can achieve competitive inverter performance, supporting flexible and efficient power conversion solutions.
Finite Control Set Model Predictive Control (FCS-MPC) is widely used in DC motor drive systems due to its fast dynamic response and simple control structure. However, conventional FCS-MPC often leads to excessive armature current and torque peaks during startup and transient operating conditions, which may exceed the motor’s rated limits and cause thermal stress or damage to the motor windings.in addition, the voltage stagnation phenomenon can degrade control performance and dynamic behavior. This paper presents an Enhanced Finite Control Set Model Predictive Control (EFCS-MPC) strategy for DC motor drives supplied by an H-bridge converter, that explicitly incorporates physical constraints, an extended finite voltage set, and a dominant speed-tracking term to predict future system behavior for each admissible switching state, and an optimized cost function is used to select the optimal control action directly applied to the converter. The proposed approach EFCS-MPC effectively limits the armature current, improves reference tracking accuracy, and significantly reduces steady-state speed and current ripple while preserving fast transient response, that enhances operational safety suitable for practical high-performance DC motor drive applications.
The Mobile Ad Hoc Networks (MANETs) are dynamic and infrastructure-less wireless networks where the devices are resource constrained and the topology changes rapidly. In the development of routing protocols in MANETs, it is very important to optimize parameters such as bandwidth, hop count and reduce energy usage. This paper introduces a new multi-objective routing protocol (NMORP) that will improve the performance and efficiency of MANET. The proposed protocol employs a composite cost scheme that considers a variety of metrics (energy, bandwidth, hop count) to guide routing choices in heterogeneous networks. We deploy NMORP in OMNeT++/INET and make comparisons to the conventional Destination-Sequenced Distance Vector (DSDV) protocol. The simulation outcome demonstrates that NMORP is superior to DSDV: network lifetime increases by a factor of up to 41%, throughput increases by approximately 32%, the end-to-end delay is minimized by up to 90%, and Packet Delivery Ratio (PDR) becomes more than 98% in certain situations. These advances indicate that NMORP can efficiently use its resources and provide credible data transfer across different conditions, which outlines the significance of multi-metric routing plans in the next-generation MANET design.
This paper presents the design and development of a functional ionic propulsion prototype system based on the electrohydrodynamic (EHD) principle, with the main objective of understanding and evaluating the mechanism responsible for ionic wind generation. The study does not focus on the actual motion of bodies, but rather on the fundamental physical phenomenon underlying the ionic propulsion and on identifying the design parameters that influence the system performance. To this end, several emitter-collector configurations were designed and tested using High-Voltage power supplies in the 10-15 kV range. The experimental measurements showed that the velocity of the ionized air varies as a function of electro geometry and electric field intensity. The result confirms the operation of the prototype and highlights several critical factors involved in the ionic wind generation including the distance between the electrodes, their shape material, as well as the stability of corona discharges. Fortis reason the paper can be regarded as an important preliminary step toward the future development of ionic propulsion system for practical applications, providing a useful experimental basis for geometry optimization and for improving the efficiency of EHD devices.
During the manufacturing and evaluation phases of mechanical components, an efficient, straightforward method for measuring thickness in real time is required. Consequently, to identify a method for measuring thickness during the manufacturing phase, this work aims to develop a reliable approach based on Eddy current (EC) for accurate and simple thickness measurement of flat conductive surfaces during the manufacturing stage. The EC technique used is considered a non-destructive evaluation (NDE) method. A probe impedance is developed to detect local changes in the tested material, including physical or geometric features. The experimental setup, specifically designed for this approach, included a solenoidal coil and an impedance meter (LCR), both controlled by a LabVIEW program. The validation tests confirmed the method's robustness and suitability for non-destructive evaluation of lightweight aluminium structures, which are widely used in aerospace and automotive industries.
In this paper, a GA-based method is proposed as a strategy to adjust system parameters under M-QAM-OFDM systems for the aim of enhancing performance, particularly in dynamic wireless environments. The traditional method makes use of predetermined subcarrier, modulation order, and cyclic prefix length parameters which often result in suboptimal Bit Error Rate (BER) performance under varying channel conditions. Therefore, this work has investigated whether the GA-based method can dynamically adjust these system parameters based on adapted modulation order to reduce the Bit Error Rate (BER) while maintaining spectral efficiency. Computer-simulated findings show notable improvements in BER performance, data rate, and power consumption with optimized parameters, underscoring the benefits of GA-based optimization in enhancing the robustness and efficiency of next-generation wireless communication systems.
The purpose of this research is to examine the dynamic performance of a squirrel cage induction generator (SCIG)-based wind turbine connected to the power grid. It focuses on managing voltage instability resulting from the reactive power demand inherent in SCIG along with various grid disturbances. Three compensation strategies were designed, compared, and studied, including no compensation system, a system with fixed capacitor bank, and a system using a Static Synchronous Compensator (STATCOM). The model is subjected to different perturbations, including wind speed changes, a sudden load application, and both symmetrical and asymmetrical fault scenarios. The results indicate that, although the fixed capacitor bank provides enhanced reactive power support when the system is at the single pre-set operating point, it is unable to respond effectively to rapid system changes, causing significant voltage sags as well as overcompensation. On the other hand, the STATCOM effectively maintains the point of common coupling (PCC) voltage close to the reference of 1.0 pu by continuously injecting or absorbing reactive power. The ability of STATCOM to support low-voltage ride-through (LVRT) demonstrates its superior performance and suitability for enhancing wind energy grid integration.
In this study we are delving into the behavior of oil under high voltage stress. We tested the oil using an OTS60SX voltage breakdown tester and used three standards: D1816, IEC 60156 and D877. The results show that the oil breakdown phenomena causes a variation in breakdown voltage depending on the standard and the oil contamination, mostly moisture and particles in the oil. We used computer simulations to see what cannot be observed in practical terms at such a small scale, which is the electric field distribution, electrostatic potential and temperature inside the oil. The simulations show that the electric field is not uniform in this insulating medium and it gets stronger near the electrode edges. This causes mechanical and chemical stress which leads to partial discharge and formation of conductive pathways that, by the same token, lead to hot spots and Joule heating combined with micro-discharges. This leads us to believe that electrical, thermal and fluid dynamic processes are highly linked. The results coincide with the simulations and by that we can conclude that the dielectric breakdown of oil is actually a series of complex interactions between the electric field, corona discharge, temperature and impurities. This helps us consolidate how we view transformer oil breakdown and gives a fresh insight into the phenomena.
The paper presents an extensive analysis of thermal management systems (BTMS – Battery Thermal Management System) for Li-Ion accumulators used in various stationary electrical energy storage applications, with emphasis placed on their integration into photovoltaic systems. The most used thermal management solutions are presented, liquid cooling, PCM (phase change materials), hybrid systems, as well as their impact in terms of energy efficiency and the lifespan of the accumulators. The paper represents an important preliminary phase for the study and development of a thermal management system for Li-Ion storage batteries, providing a theoretical and experimental basis for the design and future testing of cooling solutions.
In recent years, along with the development of artificial intelligence (AI), AI-integrated unmanned aerial vehicles (UAVs), UAV-AI have become a potential solution for intelligent transportation systems. However, UAV-AI systems face challenges such as small vehicle size, obstacles, high vehicle density, etc. In addition, practical UAV-AI systems require a balance between accuracy and computational cost. This paper presents a comprehensive evaluation framework and deployment guidelines for UAV-AI-based intelligent transportation applications, using the latest object detection and tracking models. Techniques based on a combination of the YOLO model with the DeepSORT algorithm or the YOLO model with the SAHI technique were tested in various scenarios using traffic video data obtained from UAVs. The results showed that the vehicle count calculation achieved an accuracy of approximately 95.1%-96.9%, and the vehicle detection reliability in the best-case scenario was approximately 0.73-0.89. Furthermore, the study was conducted within the context of a UAV platform with a payload of approximately 24 kg, allowing for discussion of the possibility of deploying AI models on UAV systems capable of carrying more powerful sensors and computing devices in the future. Finally, the analyses and discussions are presented along with proposed potential research directions.
This paper explores the design and evaluation of microstrip attenuators manufactured using PCB technology on FR4 substrates. Different materials are used to get the best performance over a frequency range of 1 to 18 GHz.The tested materials include graphite powder, graphite-silicon carbide (GSC) composites, and thin GSC films. We analysed the attenuators' performance in terms of S-parameters, concentrating on attenuation and reflection. We compared the experimental results with simulations, which demonstrated good agreement and validated the robustness of the used numerical models. Graphite, while effective at high frequencies, shows limitations at lower frequencies, whereas GSC provides stable and efficient attenuation across a wide frequency range. The study also reveals effective impedance matching with S11 values exceeding 20 dB for all tested materials. The designed attenuator successfully integrates these materials, providing a solid foundation for future optimisation of attenuator designs.
Multiprocessing systems are essential for delivering the performance and responsiveness required by modern and real-time applications. These systems depend heavily on task assignments and scheduling mechanisms to make sure efficient use of computational resources and adherence to restrict timing constraints. Task or job allocation strategies directly impact system throughput, energy efficiency, and deadline compliance, particularly under dynamic and heterogeneous workloads. As real-time tasks increasingly exhibit variability, context sensitivity, and criticality, there is a growing need for adaptive task management techniques that respond intelligently to changing system conditions. In this paper we propose a focused direction for enhancing task modeling, assignment, and scheduling in real-time multiprocessing systems. The approach integrates context-aware decision-making, criticality-sensitive prioritization, and adaptive scheduling to align task execution with system-level performance goals. Core performance metrics such as response time, deadline miss rate, and processor load variance are identified as key indicators of effectiveness. Experimental findings demonstrate that advanced models, including Integer Linear Programming and Deep Learning-based schedulers, significantly outperform traditional approaches in reliability, efficiency, and load balancing. These findings support the development of more intelligent and robust task management frameworks for next-generation real-time systems.
Integrated Krakatau Observatory Networks (IKON) is designed as a monitoring and observation system by which the processed data collected from mount Anak Krakatau area is utilized for a near-end tsunami early warning system (T-EWS) in Sunda Strait. The IKON composes of several specific purpose of wireless sensor network (WSN) such as water level sensors, under water pressure sensors, seismic sensors, etc. The WSNs are located separately in the specific area at mount Anak Krakatau and surrounding islands and sea. The collected data are transmitted wirelessly to the main hub called Sink Node. To be able to send the data, a long-range radio frequency module is required. This work studied the capability of LoRA RF module to be implemented as WSN infrastructure for IKON. There are two tests which have been done, they are functional testing and performance testing. On functional testing, the system runs well which means the system can send and receive the packet. Performance testing is carried out to study device module performance based on QoS considering the variation of distance and the packet size. The test result shows that the module can send packets up to 6.01 Km with a 128-byte packet. However, the total packet loss ratio for the measurement in real environment reach 38.51%. Based on the results, it is concluded that this kind of LoRA can be used for IKON, but further adjustments and setting are required for better reliability and durability of data transmission.
As we rely more and more on renewable energy, we need to come up with better ways to make photovoltaic (PV) systems work better, even when they face problems like partial shading. This article looks into how genetic algorithms (GAs) can be used to improve maximum power point tracking (MPPT), which is a key method for getting the most energy out of photovoltaic (PV) systems. Conventional MPPT methods often struggle to identify the global maximum power point under partial shading, leading to suboptimal performance. GAs, inspired by biological evolution, offer a promising solution by dynamically adapting to changing conditions. This study examines the application of GAs to MPPT, highlighting their ability to navigate complex voltage-power curves and achieve higher energy yields. We show that GA-enhanced MPPT is better at making PV systems more efficient overall through simulations and comparisons. These findings underscore the importance of sophisticated MPPT methods in advancing the sustainability and efficacy of solar energy technologies.
In spite of latest remarkable technological achievements reached in electronic devices and converters development, faults in power electronics remains unavoidable. Even using modern gate-driven power devices that ensure high performance electronic commutation, the reliability criteria represent major design paradigm for developers. In a vast majority of applications, the hardware redundancy represents one of the best solutions to achieve high level fault-tolerance in power electronics. Starting from the above remarks, this paper presents a specially conceived converter for servomotors that operate in a twin hardware topology to reach high reliability and fault-free operation. In a first step, the specific mathematical model of the converter is presented that considers several faulty states which may occur during its functioning cycle. This model has been tested then through computer simulations. To experiment with the power converter operation in real conditions, a digital control system has been interfaced suitable to generate various operation regimes and conditions. The heart of this digital control system is BigPIC6 embedded development board that represents a versatile microcontroller-based platform suitable for a wide range of control and automation applications. By using this hardware topology, specially developed software has been implemented to experiment with the twin power converter operation under various test conditions. These tests prove the proper operation of the entire test platform and confirm the viability of the twin power converter topology for high reliability applications. Such solutions are mostly preferred in industrial environments where faults or damages are not avoided.
Renewable energy sources are mainly used today to supply end-users cleaner and efficient electrical power. Wind is the most promised Renewable energy source, since is considered as the best cost-effective one. The wind park under study is consisted of by 31 wind turbines of 1.3 MW, having total nameplate power 41 MW, located in mountain Rodopi-1 site/Greece. The estimated amount of power for the same time/year, are compared with the actual one for a period of 10 years, 2011-2020. We compared the actual to estimated outcomes of each time/year to check which of 3 models is achieving the greater estimating accuracy. The prototype approach of our work lies on two facts, first, the given data are 100% actual, incorporating all losses of wind power produced/transmitted and second, we use 3 different models using the same data/time for estimating/predicting the wind power. The models used are Persistence, Multiple Regression Analysis and Adapted Neuro-Fuzzy Inference System. For the anytime estimation/prediction of power production of the Wind turbines we have used 120 recorded monthly wind speeds, air densities, gusts and power production, referred to the period 2011-2020. These data come from 2 sources, from 31 SCADA systems giving 100% actual information per 15 min and from nearby stations of National Meteo Service. The comparisons showed that the actual and estimated power were influenced by 34.8% of technical features of Wind turbines and by 66.2% by 3 variables, average wind speed (64.3%), wind density (12.8%) and Weibull Contribution of wind (22.9%). The most accurate estimation among 3 models was found the Adapted Neuro-Fuzzy Inference System one, having, simultaneously, the less power production uncertainty.
This study presents a finite element analysis of an electrical contact using ANSYS, focusing on both mechanical and thermal behavior. A silver–copper contact model was analyzed under loading to assess stress, deformation, and strain energy distributions. Results show localized mechanical hotspots that may lead to fatigue. Thermal simulations, incorporating Joule heating, revealed temperature peaks up to 109.9°C, overlapping with mechanical stress zones. These critical areas indicate coupled failure risks. The analysis highlights the value of simulation for identifying design weaknesses and improving contact reliability.
Co-articulation is a phenomenon in speech production where the articulatory movements for a speech sound are influenced by the surrounding sounds and their movements. The proposed work quantitatively evaluates co-articulation in VCV (Vowel-Consonant-Vowel) sequences using real-time MRI videos, focusing on the consonants /p/, /t/, and /k/ followed by the vowels /a/, /e/, and /u/. The analysis examines air-tissue boundaries to measure co-articulation and observe articulatory variations across different phonetic environments. Three methods are employed: Euclidean distance, dynamic time warping, and correlation coefficients. The results indicate that the consonant /k/ shows more symmetry in its articulation compared to /p/ and /t/, regardless of the surrounding vowels. The study suggests that depending on the consonant and vowel context the co-articulation in VCV sequences tends to vary and exhibits asymmetric nature.
One of the problems in post-stroke rehabilitation is the low level of effectiveness of the supporting system caused by a lack of feedback to the patient. This problem can be improved by increasing the immersive and interactive levels of the system using an interactive system. This study utilized a system with integrated Virtual Reality (VR), haptic feedback, and Electroencephalograph (EEG). As in the previous study, a VR system can provide immersive visual feedback to the user in real-time. Also, haptic feedback can be used to pay attention to the user without disturbing the visual feedback. A BCI system has also been used as an interactive input to stimulate the user's brain nerves. This study has been carried out to integrate these three systems to work in real time. Thus, it is a single system for post-stroke rehabilitation. The study has also found that a system that integrates VR, haptic, and BCI can meet the critical factors of an interactive system: presence, immersion, interactivity, satisfaction, and usefulness.
