Articles published in Volume 13

Volume 13

Enhanced Performance of E-STATCOM Using Fuzzy Logic Controller for Grid-Connected Wind Systems Under Dynamic Fault Conditions

This Paper introduces an advanced approach to enhancing the efficiency of grid-connected wind cogeneration systems by replacing conventional proportional-integral (PI) controllers with fuzzy logic controllers (FLC) for the Voltage Source Converter (VSC). Integrating renewable energy sources into power grids poses several challenges, particularly in maintaining the stability of the electrical distribution network (EDN). To address these issues, this work employs points of common coupling (CCP) and two-level converters with Dual Active Bridge (DAB) technology to integrate storage modules effectively.

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Design and Implementation of Direct Torque Control of IPMSM Drive using Three-Level Inverter and FPGA for Electric Vehicle

This study introduces the design and implementation for Direct Torque Control (DTC) of Field Programmable Gate Array (FPGA) based Interior Permanent Magnet Synchronous Motor (IPMSM) with Space Vector Pulse Width Modulation (SVPWM) configuration inverter for Electrical Vehicle (EV). In contrast to conventional DTC, a new DTC algorithm of drive system is proposed that endowed unified torque/flux responses with precise control levels of multiple voltage vectors. The proposed drive unified inverter states for obtaining faster dynamic torque. Consequently, the DTC scheme inspected with suppresses steady state torque ripples. The main design features of the IPMSM drive are self-regulated flux and hardware implementation for EV.

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Binary Gravitational Search Based Algorithm for Optimal DG and Capacitor Allocation Along with Network Reconfiguration in Radial Distribution Systems

The Binary Gravitational Search Algorithm (BGSA) is a multi-purpose optimization technique presented in this research that may be used to identify the best capacity, position DG modules and capacitor groups, and reconfigure networks in distribution systems. The objective function has six performance indices: section load ability, voltage deviation, voltage stability index, dynamic and sensitive losses, and balancing current index. The optimization problem's objective function takes into account both the relevance of each indication and its combination.

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Hybrid Design of Phase Frequency Detector Applied in Phase Locked Loop to Eliminate Dead and Blind Zones

This paper presents a hybrid design and simulation of a Phase Frequency Detector (PFD) which eliminates the effects of the blind and the dead zones for a charge-pump phase-locked loop (CP_PLL). These parameters limit the detection range of the PLL since they occur at zero and 2π phase differences respectively. Two XOR gates have been added to the conventional D-Flip-flop PFD to eliminate both parameters simultaneously. The proposed system has been simulated using Multisim 14.3 and tested using a 10 MHz input frequency. The simulation results show the system is free of dead and blind zones.

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Transfer Efficiency Enhancement using Double Negative Metamaterial in Wireless Power Transfer System

Recently, there have been a lot of inventions and development in the field of wireless power transfer (WPT), which has increased the need for WPT systems with high power transfer efficiency (PTE) and longer transmission distances for end users. However, several of the presently accessible WPT systems exhibit restricted PTE and transmission range as a result of their utilization of inductive coupling. In addition, the PTE experiences a significant decline as the separation between the transmitter and receiver coils grows while employing this methodology. Hence, this study presents a proposal for the design of inductive WPT using metamaterials (MTMs) to improve PTE through the manipulation of magnetic field refraction.

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Modelling and Simulation of Multi-Level Inverters Utilizing Alternate Phase Disposition (APOD) PWM Modulation in MATLAB/Simulink

In recent times, the growing demand for enhanced industrial applications and the rise of Electric Vehicles (EVs) have led to a requirement for higher power equipment. Certain applications, such as medium voltage motor drives and utility systems, now demand the utilization of medium voltage and megawatt drivers. To address these needs, the concept of multi-level inverter topologies has been introduced, particularly for medium and high-power applications. The evolution of multilevel converters, starting with three levels to achieve elevated power levels, has given rise to various topologies.

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Investigation of Discontinuous PWM Schemes for Power Loss Minimization in SiC Three-Phase Inverter

SiC inverters are considered highly attractive in power electronic applications such as electric vehicles, industrial motor drives, and photovoltaic (PV) systems. These SiC-based inverters present greater thermal conductivity, enabling operation at higher temperatures, frequencies, and voltages with reduced losses. This study utilizes the PLECS platform to analyze and simulate power losses in SiC inverter under two decided assumptions.

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Impact on Breakdown Voltage for AlGaN Channel E-HEMT Device used with the DC Boost Converter Circuit

In this paper, the impact on breakdown performance is demonstrated by the coupled operation of the E-mode AlGaN Channel HEMT for DC Boost Converter Circuit. CAD optimization of individual devices on the process and E-mode HEMT device level affects the circuit performance DC Boost Converter Circuit. The field plate length of Fp=2.7 µm results in the steady current at the voltage of VBV=790 V, whereas the field plate length of 3.6 µm results breakdown voltage of more than 1k volts. Circuit voltages at various nodes, Current in HEMT and SBD for switching cycle, and also the power dissipation is evaluated for two various doping concentrations 3E15(/cm3) and 9E15(/cm3).

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A Tiered Control Strategy for Energy Management in PEMFC Hybrid Systems for Distributed Power and Vehicle Applications

This paper presents an optimized energy configuration using a polymer electrolyte membrane fuel cell (PEMFC) as the primary power source, paired with a battery and supercapacitor for storage. This hybrid system, designed for modern distributed generation and next-gen fuel cell vehicles, achieves energy balance through DC bus voltage regulation. The supercapacitor, with high power density and fast response, stabilizes the DC bus voltage, while the battery, valued for its high energy density, continually recharges the supercapacitor. The fuel cell, with a slower response, ensures the battery remains charged.

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Design and Analysis of a Transformer-Integrated Multilevel Inverters for Solar Energy Systems

This work presents the design and implementation of a transformer-integrated multilevel inverter for photovoltaic (PV) power applications. The proposed topology combines the benefits of multilevel inverters and transformer integration to enhance efficiency, reliability, and power quality in PV systems. Traditional multilevel inverters often encounter issues such as complex control schemes, high component count, and significant harmonic distortion. This innovative design addresses these challenges by incorporating a transformer, which simplifies the control strategy and provides galvanic isolation, improving system safety and grid compatibility. The system uses a PV array as the input supply, directly generating DC power, which is then converted to AC through the multilevel inverter

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PI Backstepping Control of a Surface-Mounted Permanent Magnet Synchronous Motors

Backstepping control is a systematic technique for stabilizing nonlinear systems, particularly Permanent Magnet Synchronous Motors (PMSMs), by addressing their coupled electrical and mechanical dynamics. This Lyapunov-based approach allows for the design of control laws in a step-by-step manner, enhancing stability and performance under uncertainties. This paper presents a comprehensive evaluation of the PI Backstepping (PI-BS) Controller for speed regulation of PMSMs, showing significant improvements in dynamic performance, stability, and disturbance rejection compared to the Gain-Scheduled PI (GSPI) Controller. The control design focuses on rotor speed regulation through q-axis current, ensuring global asymptotic stability via Lyapunov criteria.

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Deep Learning-Driven Behavioral Analysis for Real-Time Threat Detection and Classification in Network Traffic

With the evolution of digital spaces, cyber threats now evolve to more complex forms, requiring innovative solutions for real-time intrusion detection and classification for network traffic. Cybersecurity is also critical for building resilient infrastructure, which is one of the goals of the United Nations, which emphasizes secure and sustainable digital ecosystems. This research proposes a framework powered by deep learning that employs an enhanced fully connected neural network (EFNN) to analyze behavior and detect threats. The proposed algorithm, Enhanced Fully Connected Neural Network-Based Threat Detection (EFNN-TD), fuses advanced data preprocessing with FCBF-based feature selection and SMOTE-based handling of class imbalance.

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Advanced Artificial Intelligence Techniques for Fault Distance Prediction in Optical Fibres

It is necessary to estimate the value of distances to faults in the optical fibres for proper functioning and fault diagnosis of the fibre optic systems. This research proposes a comparison of result outcomes within numerous categories of machine learning algorithms such as Decision Tree, Random Forest, Gradient Boosting, XGBoost, LightGBM, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) on a new and emerging Fibre Optic Fault Distance Dataset. Given dataset contains sequenced OTDR signatures corresponding to different types as well as positions of the faults. The data then went through data pre-processing and was separated into training and test sets where models were trained from 80% of the data and tested on 20%.

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A Switched Capacitor – Inductor High Gain DC-DC Converter for Solar PV Applications

A new switched capacitor-inductor high-voltage gain DC-DC boost converter is presented in this work. A switched-inductor cell is used at input side of the suggested converter to lessen input current source ripples, which is a crucial issue in PV systems for high-reliability applications. To further increase voltage-gain and reduced voltage stress across the converter's power switches, a switched-capacitor cell is employed at the output side of the converter. This is a critical component in applications like to extended lifespan of the PV panel and other suggested converter parts, especially semiconductor devices. To validate the efficacy of the presented DC-DC converter, extensive simulations are conducted by using PSIM simulation tools.

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A Novel Performance Probability Model for Capacity Assessment of Communication Channels in 5G Wireless Mobile Networks

Capacity assessment of communication channels in 5G wireless mobile networks is essential for optimizing wireless networks for data-intensive applications. Capacity assessment can help determine optimal channel conditions by considering the effects of various physical characteristics such as noise, interference, spectrum limitation, and subscriber density. Additionally, capacity assessment can help identify and characterize the critical technical challenges likely to limit the performance of a wireless network and identify areas for improvement. The novel Performance Probability Model (PPM) for capacity assessment of communication channels in 5G wireless mobile networks is a powerful tool for accurately predicting the future performance of 5G networks. It considers various performance factors such as interference levels, data rates, transmission range, and available spectrum.

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Enhancing Solar Energy Efficiency Integrating Neural Networks with Maximum Power Point Tracking Systems

This paper investigates the integration of artificial neural networks (ANN) into Maximum Power Point Tracking (MPPT) systems to enhance the efficiency and performance of solar photovoltaic (PV) systems. Conventional MPPT controllers, such as Perturb and Observe (P&O) and Incremental Conductance, often face challenges in maintaining optimal efficiency under rapidly changing environmental conditions. To address these challenges, this study proposes an innovative approach leveraging the adaptive learning capabilities of ANN. Extensive datasets, including solar irradiance, temperature, and power output under various conditions, were collected to design and train the ANN model. The ANN architecture features a multilayer structure with advanced activation functions, enabling effective handling of the nonlinear behavior of solar PV systems.

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Ultra-High Photosensitivity of Nb₂O₅/Ge Prepared via Direct Current Reactive Magnetron Sputtering Technique

The primary objective of this research is to fabricate and evaluate Nb₂O₅ thin films prepared via DC (direct current) reactive magnetron sputtering at target powers of 25 W, 50 W, and 75 W, deposited on quartz substrates and Ge wafers. The structural and morphological characteristics of the fabricated Nb₂O₅ thin films were analyzed using XRD (X-ray diffraction) and FE-SEM (field emission scanning electron microscopy), while their electrical and optical properties were characterized using UV-Vis spectrophotometry and I-V (current-voltage) tests. XRD results confirmed a natural polycrystalline structure with a hexagonal lattice, while FE-SEM imaging revealed uniform deposition and strong dependence of nanostructure size and configuration on deposition parameters. EDS (Energy-Dispersive Spectroscopy) analysis showed an increase in Nb content with higher sputtering power.

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Precise Models for Power Loss Analysis in a 15-level Asymmetric Reduced Switch Inverter

In the examination of power converters, power losses are the most important metrics, and since they are approximated well enough, they have a considerable influence on both economic and technical evaluations. In comparison to high-switching frequency modulation, the purpose of this article is to demonstrate that switching and conduction losses, which are both types of power losses, are much lower in low-frequency switching modulation. Phase Disposition (PD), a pulse width modulation (PWM) method that is based on high-frequency multi-carriers, and Selective Harmonic Elimination Pulse Width Modulation (SHEPWM), which operates at a fundamental switching frequency, are the two switching modulation techniques that will be utilized in this investigation. The objective of this study is to evaluate the power losses that occur in an asymmetric multi-level energy converter that has fifteen levels of reduced switches. To determine the switching losses that occur in multi-level inverters, a MATLAB Simulink model has been constructed. The PLECS software was used to construct the thermal model of the recommended inverter, which would further facilitate in-depth research. A comparison of the switching losses and conduction losses of the proposed inverter system is carried out by this study via the use of the PLECS thermal model and MATLAB simulation.

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Smart Meter Utilizing Fuzzy Logic and IoT

Smart systems are utilized for home applications to enhance the protection reliability and cost economy. The main challenges of a smart system are time process, decision reliability, fault classification, and the ability to connect with the Internet of Things (IoT). In this proposal, a smart system power meter will be designed using artificial intelligence (AI) and IoT. The fuzzy logic with the microcontroller and the internet application are system parameters. Results show that the cost and fault of the power are successfully classified and discovered via fuzzy logic to notify the customer about the situations of home power. The system manages the home power cost and protects the home’s devices from damage.

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Simulation and Modeling of Position Control for an X-Configuration Quadcopter Using PID Controller

Quadcopters are gaining popularity in UAV platforms within the control community because of their intricate dynamics and significant potential in outside applications, owing to their advantages over conventional aerial vehicles. This work introduces the mathematical modeling of a basic quadcopter using an X-configuration and simulates its target position using MATLAB. A Proportional-Integral-Derivative (PID) controller is designed for controlling the position and stabilizing the attitude of the quadcopter. The quadcopter's underactuated nature allows this PID controller to maneuver the vehicle in three dimensions and adjust the yaw angle to the required values while stabilizing the pitch and roll angles. T

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Implementation of Pass Transistor Logic and C2MOS Linear Feedback Shift Register (LFSR) Circuit using FPGA and PSpice

In this paper, a 4-bit Linear Feedback Shift Register (LFSR) is implemented based on a well-designed architecture combining using Pass-Transistor (PT) and Clock Complementary Metal Oxide Semiconductor (C2MOS) logic techniques. The D-flip flop registers and the XOR gates are the main parts of the propose LFSR. Number of transistors along with the speed of LFSR were positively enhanced since the exploited logic design techniques tends to blend the flavor of NMOS and PMOS devices. The PSpice and Field Programmable Gate Array (FPGA) based on Hardware Description Language (HDL) are the two different LFSR implementation environments. It has been observed that LFSR performance was effectively improved in terms of size and speed. Therefore, paper’s main aim refers to decreasing in number of transistors as well as speeding up LFSR circuit. A minimum clock time of 5ns was recorded under clearly correct LFSR output patterns.

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Enhanced Luo Converter for Low Component Stress in DC-DC Power Conversion for Fuel Cell Powered BLDC Motor Drive

Fuel cell powered BLDC motor drive for electric vehicle is the cleaner energy conversion solution with higher efficiency. DC-DC power conversion in this drive plays a significant role in adoptability, efficiency and reliability of the overall drive. This paper presents an enhanced Luo converter for increased voltage gain with additional multiplier stage and reduced capacitor voltage stress owing to reduced clamping voltage. T

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Implementation of DC-DC Boost & Luo Converters for Photovoltaic Applications

Renewables are eco-friendly, of them solar photovoltaic (PV) systems are growing in popularity as a means to directly transform solar radiation into electricity. Installing a PV array is simple, and the ongoing decline in the price of PV modules provides a support as renewable energy source. As the globe continues to deplete its fossil fuel reserves, discussions about alternative, renewable energy sources are heating up. Due to its quiet operation, low maintenance requirements, and lack of emissions, the photovoltaic (PV) power system is quickly rising to the top of the renewable energy industry.

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Performance Improvement of PMSM Using PID and GA-PID Controllers

A permanent magnet synchronous motor (PMSM) is widely used in AC servo drives because of its high-power density and high torque for industrial applications, with a wide range of applications. The Permanent Magnet Synchronous Motor is modeled, and simulation is used in MATLAB's Simulink. After representing the motor mathematically with the transfer function according to characteristics suitable for applications similar to the proposed characteristics. This paper proposes using PID to improve the performance of PMSM. Then, the genetic algorithm, an optimization method, is used to adjust the P, I, and D parameters. Simulation tests are conducted for an open and closed system circuit without control and with control. The outcomes are contrasted with conventional PID controller tuning by genetic algorithm.

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Extending the Hopf Bifurcation Limit Using Power System Stabilizer in a Two Area Power System Having Huge Industrial Loads

he phenomenon of Hopf bifurcation is observed in several engineering and non-engineering domains which use both linear and nonlinear dynamic models. One parameter and two parameter variations of set of Differential Algebraic Equations of Kundur two area system was explored in this manuscript assisted with eight different cases to have detailed insight into Hopf bifurcation by modelling the load buses with industrial loads.

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Adaptive Predictive Control with Non-Integral Voltage Monitoring for Enhanced Shunt Hybrid Active Power Filters

This paper presents a hybrid control strategy that integrates Adaptive Predictive Deadbeat Current Control with a Non-Integral AC Capacitor Voltage Monitoring Method to enhance the performance and reliability of Shunt Hybrid Active Power Filters (SHAPFs). The proposed approach combines the precision and fast dynamic response of predictive deadbeat control for harmonic compensation with a novel non-integral method for calculating the AC capacitor voltage, mitigating the risk of overvoltage without the accumulation of errors typical in integral-based methods. Simulation results demonstrate significant improvements over traditional methods. The proposed approach reduces Total Harmonic Distortion (THD) from 21% to 2.8%, achieving a improvement over conventional PI-based controllers.

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Data Mining in Power System Fault Identification using Artificial Intelligence

This paper presents a power system fault identification method by simultaneously applying the Kendall and Spearman correlation coefficients for feature selection, combined with an Artificial Neural Network (ANN) to enhance accuracy and optimize training time. Experimental results indicate that Kendall demonstrates superior performance in handling nonlinear data and mitigating the impact of outliers, leading to more optimal fault identification outcomes. Backpropagation Neural Network (BPNN), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN) models are trained on datasets after feature selection using both correlation coefficients.

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Performance Evaluation of Hybrid Chaotic and Permutation Schemes for Image Transmission Based MC-CDMA

The demand for more reliable and efficient multimedia data transfer through wireless communications channels is escalating. However, multimedia data, such as images, suffers significantly from wireless channel effects, including interference, fading, and burst errors. The Multi-Carrier Code Division Multiple Access (MC-CDMA) technology is regarded as the most efficient method for data transfer across wireless networks, supporting multiple users simultaneously without requiring an expansion of the frequency band. This paper employs permutation and hybrid chaotic techniques to demonstrate image transmission performance over the MC-CDMA network.

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Optimizing Configurable Logic Blocks with Advanced Error-Resilient Circuits for Low-Power FPGA Systems

This paper aims at enhancing configurable logic blocks (CLBs) in FPGA systems through incorporating more complex error-tolerant circuits and power control strategies. The architecture of the Presented FPGA considered in this study has been designed using MATLAB simulation and is tailored for low power consumption and high reliability. Power management is another feature implemented in the system through Dynamic Voltage Scaling (DVS) to improve electrical power usage essentially by 20%-25% at low load”. The fault tolerance is implemented through incorporating ECC and TMR into CLBs to render the system capable of tolerating with faults and still work efficiently.

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Performance Driven Outlier Detection in Health-Care Data: A Hybrid Approach Using Dual-Feature Optimization and Segmentation Techniques

The healthcare sector is a domain where the implementation of human-centered design approaches and concepts can significantly impact well-being and patient care. Delivering superior medical care necessitates a profound comprehension of an individual's desires, encounters, and interests. This study examined the quantitative evaluation and utilization of MRI scans for preoperative conditions of the brain, lungs, and heart. However, identifying these intricate compositions is a formidable challenge. Traditional diagnostic methods are laborious and rely heavily on the clinical expertise of radiologists. This research proposes a non-invasive automatic diagnosis system for diseases utilizing hybrid deep learning approaches, specifically LSTM & PSO (Long Short-Term Memory & Particle Swarm Optimization), to improve the efficiency of outlier detection.

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Application of LSTM and GRU Neural Networks in Forecasting the Power Output of Wind Power Plant

This paper proposes the application of artificial intelligence to forecast the generation capacity of wind power plants by processing data through noise reduction and filtering. It subsequently employs Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks for training, testing, and evaluation. Processing the initial data will help minimize noise and reduce the data space. The study focuses on preprocessing methods and selecting the appropriate neural network between LSTM and GRU. The initial data processing will assess the similarity through the Spearman rank correlation coefficient. The data used in the paper is taken from local wind turbines.

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Numerical Simulation on Charge Transport in Polyethylene with Field-Dependent Parameters Under DC Electric Field

During the past few years, the use of HVDC cables has increased exponentially. However, the accumulation of space charges within insulating materials remains a major challenge. Understanding the mechanisms governing this phenomenon is key to improving HVDC performance. This goal is often achieved through numerical simulations. Therefore, it is imperative that they are performed efficiently. In this work, a bipolar charge transport (BCT) model is used to offer a physical description of space charge behavior in low-density polyethylene (LDPE) under a high DC electric field. This model includes injection, migration, trapping, dettraping and recombination charges with parameters dependent on the electric field such as mobility, trapping, and recombination.

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Analysis of Copper and Iron Loss Interactions in a 15 kW Three-Phase Induction Motor under Variable Operating Conditions

This research presents an innovative contribution to the field of induction motor efficiency optimization by analyzing the complex interactions between copper and iron losses in a 15 kW three-phase induction motor under variable operating conditions (10–60 Hz).

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An Improved UFLD-V2 Lane Line Recognition Method

Lane line recognition remains a crucial component of autonomous driving, particularly under complex scenarios involving illumination changes and occlusions. This paper presents a structurally efficient and robust improvement of the UFLD-V2 architecture, designed for real-time and reliable lane detection. The proposed method integrates three lightweight yet complementary components: (1) Res2Net, replacing the original ResNet backbone, enhances multi-scale feature extraction and inference efficiency through reparameterization; (2) an Efficient Multi-scale Attention (EMA) module captures fine-grained contextual details across varying scene complexities; and (3) the Simple Attention Module (SimAM) is applied in the segmentation head to suppress background noise and improve localization accuracy.

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Real-Time Traffic Light Optimization Using Yolov9 and Length-Based Metrics

The Indian traffic control system faces lots of difficulties due to the increasing volume of vehicles, ineffective systems for traffic administration during peak hours, and the frequent need for manual intervention due to the inadequate performance of traffic signals in managing heavy traffic flow. Traditional traffic lights in India have defined timings for each lane, which frequently cause longer traffic jams in lanes with more traffic. This study presents an intelligent traffic control system that incorporates the YOLOv9 model for real-time traffic length prediction and intelligently allocates green, red, and orange signal timings. YOLOv9 builds a bounding box that allows it to compute vehicle density precisely by enclosing the initial and final cars in every frame.

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A Lightweight CNN Architecture for Efficient Brain Tumor Detection in MRI Scans

The intricate morphology of brain tumors poses significant diagnostic challenges in MRI interpretation. While AI-driven systems offer potential for automation, balancing accuracy with computational efficiency remains critical for clinical adoption. This work introduces a lightweight convolutional neural network optimized for brain tumor detection and classification in MRI scans. The architecture’s design emphasizes a systematic exploration of layer-ordering strategies, with experiments revealing that batch normalization in post-activation mode (Post-BN) outperforms Pre-BN in training stability and classification accuracy.

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Tunable Triple-Notched Ultra-Wideband Bandpass Filter for Efficient In-Band and Out-of-Band Interference Mitigation

This study analyzes the growing need for effective in-band and out-of-band interference mitigation in ultra-wideband (UWB) communication systems. We present a novel microstrip bandpass filter (BPF) with changeable triple-notched bands that preserves a large passband and a higher stopband. The filter comprises a multimode resonator (MMR) architecture that incorporates a hollow T-shaped structure that generates two transmission zeros at the passband boundaries, thereby boosting selectivity.

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Design of Fractal Antenna For S-Band and X-Band Applications

A fractal antenna that can be used for S-band and X-band applications is proposed in this paper. The antenna is able to be easily integrated into naval radar systems. The antenna is made up of fractal structures that are cross-shaped and organized in stairwell-like repetitions. It is a two-port antenna that receives its feed via coaxial cable. Meta materials are integrated into the design to achieve bidirectional gain and reduce mutual coupling.

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