AI-Enabled Smart Bus System for Modern Urban Mobility
The rapid urbanization and increasing demand for efficient public transportation systems necessitate the adoption of intelligent and automated solutions to overcome the limitations of conventional transport management. Traditional bus systems, which rely on manual ticketing and human supervision, often suffer from issues such as revenue leakage, lack of transparency, inefficient passenger monitoring, and security vulnerabilities. This research presents an AI-Enabled Smart Bus System for Modern Urban Mobility that integrates Internet of Things (IoT), cloud computing, and machine learning techniques to enhance operational efficiency, safety, and user experience. The proposed system utilizes an ESP32 microcontroller as the central processing unit, interfaced with RFID-based smart card technology for contactless ticketing, IR sensors for accurate passenger counting, GPS module for real-time vehicle tracking, and ESP32-CAM for surveillance and unauthorized access detection. Passenger data is continuously collected and transmitted to a cloud-based platform for real-time monitoring and storage. Machine learning algorithms implemented in Python are employed for anomaly detection, fraud identification, and passenger demand prediction, enabling data-driven decision-making and optimized route management. Furthermore, an auxiliary Arduino Uno module is incorporated to handle seat selection and stop request operations, thereby improving system responsiveness and reducing computational load. The proposed framework ensures high accuracy, reduced human intervention, enhanced security, and efficient resource utilization. The results demonstrate that the system is scalable, cost-effective, and well-suited for deployment in smart city environments, contributing to sustainable and intelligent urban transportation systems.
Copyright (c) 2026 Journal of Electronics and Informatics
Construction Site Worker’s Safety Detection System
The construction sites are one of the most dangerous workplaces, due to the presence of machinery, tall buildings, and unsafe practices at work. One of the main factors leading to accidents is the incorrect use of personal protective equipment (PPE) like safety helmets, safety glasses, safety footwear, and reflective jackets by employees. Besides, there are cases of undetected health problems that lead to injuries. Traditional approaches to safety monitoring involve human inspection and periodical examination of workers, which are time-consuming, prone to errors, and do not work effectively on large-scale construction sites. The goal of this research is the development of an intelligent worker safety detection algorithm for the smart construction site that would incorporate machine vision–based detection of PPEs and Internet of Things (IoT) technologies for health monitoring. The algorithm detects safety helmet and reflective jacket using machine vision based on deep learning algorithms, while body temperature is detected using a DS18B20 sensor interfaced with an ESP32 microcontroller. A decision-making mechanism confirms safety of the conditions and sends a warning message in case any violation or abnormal situation is observed.
Aqua Sense: Smart IoT System for Water Usage and Quality Monitoring
Water scarcity and water quality degradation are becoming significant global issues due to rapid urbanization and improper water management practices. This paper proposes a smart IoT-based system named Aqua Sense, which can be used for real-time monitoring of water consumption and water quality in water tanks, cities, and commercial areas. In the proposed system, water flow, water level, pH, TDS, and turbidity sensors are connected to an ESP32 microcontroller for real-time monitoring of water parameters. A threshold-based approach and an alert counter are used for efficient water parameter monitoring. Moreover, motor control is also included in the proposed system for efficient water management, which can prevent water overflow and motor dry running. All the operations of the proposed system are stored in a cloud database, which can be accessed remotely using a mobile or web-based application. The experimental results of the proposed system show efficient water parameter monitoring, water savings, and increased awareness of water consumption. The proposed system can be used as a cost-effective, efficient, and reliable solution for water management systems.
Intelligent Accident Prevention and Alert System Using Sensors and Machine Learning
Road accidents are one of the primary causes of death around the globe, and these can be avoided if timely actions are taken by the driver of the vehicle. This paper proposes a system known as the Intelligent Accident Prevention and Alert System, which makes use of various sensors and machine learning algorithms to predict potential hazards on the road in real time. This system utilizes data from various sensors such as accelerometers, gyroscopes, GPS, proximity sensors, and driver sensors. This data is fed into a feature extraction module, where a random forest algorithm is utilized to classify the level of risk of an accident occurring. Based on this, an alert signal is sent to the driver, enabling them to take timely action. A feedback mechanism has been included in the system to ensure dynamic risk assessment. The performance of the proposed system is evaluated through a scenario-based simulated dataset, and the results show an accuracy of 93.5%, thereby proving the efficiency and reliability of the proposed system in detecting hazardous driving conditions. The proposed system also provides timely alerts, thereby enhancing the efficiency and effectiveness of accident prevention. The results prove that the proposed system offers an efficient and effective solution for accident prevention in real-world applications.
Intelligent Accident Prevention System (IAPAS)
AI-Powered Real-Time Diagnosis of Neuromuscular Disorders Using EMG and Gait Analysis
The proposed project aims to develop an online diagnostic system that can optimally integrate gait analysis and electromyography (EMG) signal processing to diagnose neuromuscular and neurodegenerative diseases in patients. The proposed system can utilize computer vision to detect abnormal gait patterns in patients. The abnormal gait patterns can include abnormal stride patterns, shuffling gait, freezing gait, and balance problems. The proposed system can utilize an EMG sensor to detect muscle activity in patients. The EMG sensor can be connected to an ESP8266 module to send muscle activity data wirelessly to a computer system using a WebSocket server. An online dashboard can be created to visualize muscle activity and gait patterns in patients. The proposed system can be an affordable and mobile AI-based system that can aid in early disease diagnosis in patients and provide better accessibility to healthcare services.
High-Data-Retention 6T SRAM with Dual Sleep-Transistor Power-Gating and Duty-Cycle Aware Energy Analysis for Low-Power IoT Application
Static Random Access Memory (SRAM) is the main contributor to standby power dissipation in duty-cycled Internet of Things (IoT) systems. The paper presents a 6 transistor SRAM cell that has low leakage and dual sleep transistor-based power gating. The memory cell is mode aware and supports different modes of operation. The header PMOS and footer NMOS sleep transistors have been used to minimize leakage and retain stored information during extended periods of sleep. The proposed memory architecture is validated through 45 nm CMOS models and simulations in LT spice. The memory is able to achieve picoampere levels of supply current in deep sleep mode. A duty cycle aware analysis of energy dissipation is also presented. The analysis shows that the proposed memory is an effective solution for ultra-low-power memory in IoT systems.
Power Electronic and Embedded System Co-Design for Scalable Smart Energy Network Optimization
The existing electrical infrastructure changed into digital, distributed and renewable-based methods develop smart energy networks required for constant interaction of power devices with advanced control systems. The usage of electric cars, distributed energy resources and energy storage increased to allow two-way transfer of power, varied load response provide challenges to coordination, efficiency conversion and scalability operation. This study explains technological system for combining power electronic converters and embedded system architecture for scalable smart energy network optimization. The advanced converter architecture, temperature management, hardware optimization algorithms and integrated sensor interface provide effective operations of distributed energy systems. The embedded control platforms, communication infrastructure units and supervisory energy management techniques are the examples of multilayer architectural model. This research shows integrated hardware-software designs improve system ability for effective power regulation and scalability of distributed energy resource implementations. The detailed design data develops robust and smart power electronic infrastructures for next-generation smart energy networks.
Quantum Semiconductor Devices: Current Progress and Future Trends
Modern electronics are frequently used in quantum semiconductor devices. The devices are extremely small and their behavior cannot be described by traditional theories. The impacts include quantum confinement and tunneling. Several new quantum devices have been proposed in the past, including an examination of their theory and design. Additionally, new modeling methods are being developed. Atomic-scale production has been shown to be constantly increasing. This effort addresses several issues at the same time. As a result, the device's dependability remains a challenge, produce variations have an impact on performance, cooling and thermal management are not easy to perform. This study reviews recent achievements in quantum semiconductor devices. It discusses device characteristics, modeling and production methods. It also identifies current disadvantages and unsolved challenges that minimize future effort are addressed in detail.
Study on Thin-Film Transistor and Circuit Design Using Artificial Intelligence
This paper reviews the detailed inspection of the current situation of thin-film transistors (TFT) and circuit design using AI, its widespread applications, and the future changes that are about to take place in this field. In liquid crystal display (LCD) systems, thin-film transistors have bridged the gap between the technologies used in this application and have been employed in a wide variety of areas, such as flexible electronics, biological sensing, and integrated IoT platforms. The focus of the paper is on the areas where TFT is heading rather than those it has already traversed. A close look at the sectors reveals that high-resolution displays, where TFT is integrated, result from some vibrant circuits made on flexible substrates, which would not be possible without TFT. Artificial intelligence can be applied to predict and enhance manufacturing efficiency and to estimate costs more accurately. Integrating TFT thin-film transistors with AI integrated circuits in future electronic devices will represent a significant advancement in the field. The AI built-in circuit type augmented by thin-film transistors would unlock visionary progress in entrenched high-tech areas and provide a unique and innovative gateway for businesses and researchers.
Analysis of Learning-Based Methods for Intelligent Edge Computing
Edge Computing is recognized as an important computing paradigm that helps in the processing of data as well as the making of decisions near the user, hence reducing the need to rely on cloud infrastructure. Edge Computing is of high importance in fields that are sensitive to latency, such as smart vehicles, IoT services, health monitoring, UAVs, and smart cities. Edge Computing environments face various issues, such as limited computational power, network variability, mobility, and the need to process high volumes of diverse tasks. These issues in edge computing are difficult to handle using traditional methods of optimization. Deep Reinforcement Learning (DRL) algorithms are significantly used in edge computing systems as an intelligent decision-making tool that can optimize decisions through interactions with the edge computing system. This review paper presents a comprehensive analysis of recent studies and representative research that apply DRL techniques to edge computing systems. The reviewed papers cover various applications such as vehicular edge computing, IoT-based multi-access edge computing, UAV-assisted edge computing, collaboration between fog and cloud computing, and blockchain and federated learning-based edge computing systems. The discussion on DRL applications encompasses task offloading, resource management, workload management, caching, and mobility management. A comparative analysis discusses the most used DRL algorithms and their applications along with performance improvements. Additionally, this literature review highlights some of the main drawbacks of DRL, such as training complexity, scalability, privacy, and deployment, and identifies areas that require improvement to create safe and efficient DRL-based edge computing solutions.
An Area Efficient Lifting Wavelet for ECG R-peak Detection Applications
Noise removal is a vital pre-processing step in wearable ECG devices for accurate arrhythmia detection. This paper proposes a hardware-efficient, multiplier-less FPGA architecture for ECG denoising using a lifting-based wavelet transform. A universal thresholding function with soft thresholding enhances signal quality, while a modified lifting-based DWT eliminates multipliers and simplifies computation. An optimized median calculation and thresholding method remove the need for comparators in VLSI design. ECG data from the MIT-BIH databases validate the approach, achieving an SNR improvement of 7.4 dB and an MSE of 0.0206. FPGA implementation on the Nexys 4 DDR board demonstrates low hardware usage and a high operating frequency of 166 MHz, outperforming existing designs.
EdgeSNN-RT: Low-Overhead Spiking Simulation on Embedded GPUs for On-Device Intelligence
EdgeSNN-RT proposes a Python-managed, GPU-accelerated spiking neural network simulation method optimized for edge platforms, integrating their definition, execution, and, critically, low-cost host-device synchronization. A dense spike bitfield logging tool groups spikes on the device, obviating the need for one-timestep communication, yielding up to 10x cost savings for logging spikes. Experiments conducted for full-scale cortical microcircuit simulation and long-term conditioning tasks with large time horizons range across variant models of diversified, consumer-class, and larger-scale, heterogeneous GPUs, from the low-power, as-specified, 15W Jetson Xavier NX, to measure the latency, kernel execution time, and interpretative overheads of their execution in the real-time regime specifically for the purposes of the challenge. The system supports real-time or faster simulation execution for today’s leading-class GPUs, and, for shared-memory architectures of embedded platforms, preserves their leading performance with direct, low-level control, NumPy direct views to the managed buffers, and an events-based representation of plasticity within a convenient API. On-device inference and learning of spiking neural networks for real-world tasks will become feasible for networks of the described architecture based on this work, mapping telemetry, memory representation, and scheduling approaches to fit within limitations imposed by embedded platforms, supplanting today's restricted assumptions about direct, one-step execution communication and/or awaiting improvements in physical implementation technology for general programmed computation targets from desktop workstations back toward rudimentary, network-edge platforms in an industry led by programmability for applied innovation.
Copyright (c) 2025 Journal of Electronics and Informatics
Bioinformatics-Driven Automation in Hydroponics: Nutrient Management System and Growth Prediction Model
Effective nutrient management is needed for hydroponic farming. Herein, a bioinformatics-driven automation system that incorporates growth prediction models, automated dosing of nutrients, and real-time analysis of sensor data for optimal plant growth is introduced. For control and monitoring in real-time, a microcontroller is coupled with calibrated TDS, pH, and EC sensors. TDS differentials are used to predict nutrient uptake, and a pH- and EC-based regression model is employed to predict plant height. The technique was validated using a range of plants like tomatoes, lettuce, and beans. Without relying on IoT infrastructure, the outcome is higher nutrient efficiency and accurate height prediction, offering a scalable and cost-effective alternative to existing systems.
Optimized Double Tail Comparator based ADC for High Speed and Low Power Applications
Dynamic regenerative comparators are being used to increase speed and power efficiency in response to the need for ultra-low-power, area-efficient, and high-speed analog-to-digital converters. This study suggests using two transistors to create a low power double-tail comparator for the Flash Analog-to-Digital Converter. Designing a comparator and thermometer code to binary code converter at low supply voltage and low power dissipation is a challenging task in the creation of a low power Flash ADC. The primary goal is to construct comparators in order to achieve a high-speed Flash ADC. We should design each comparator to have a lower total power consumption because the comparators in Flash ADC are power-hungry components. Here, a low-power dynamic comparator is being designed. Next, we used a 2:1 MUX based on pass transistor logic to construct an encoder block. We have constructed a 5-bit Flash ADC using these components. To lower the Flash ADC's power consumption, a comparator and encoder are implemented utilizing dynamic CMOS circuitry. The Tanner EDA back-end tool is used to run the simulation.
Enhancement of Underwater Images using Texture Distribution Mapping Method
In either situation, underwater vision is affected by low light, diffraction, and color aberrations that lead to image distortion. To prevent this, we implemented a Texture Distribution Mapping Approach for undersea image improvement using MATLAB. Under this framework, we will enhance texture distribution, contrast, and visibility in general by controlling the most relevant properties of texture in the underwater environment. The TDM process is incremental: it involves pre-processing to eliminate noise and color distortion, followed by enhancement using another method for texture improvement. By enhancing texture mapping onto the image, we achieve a balance of visibility in high-texture regions, resulting in low-texture regions and better clarity and coloring. The method was compared against real underwater image data sets and tested for performance using quality measures like UCIQE (Underwater Color Image Quality Evaluation) and UIQM (Underwater Image Quality Measure). Experimental results and MATLAB implementation of the proposed algorithm exhibited stunning improvements in image quality and luminosity enhancements in texture compared to baseline methods, especially in challenging water conditions.
Performance Analysis of High-Speed Parallel Prefix Adders for Arithmetic Applications
Research on affordable adder architectures has been spurred by the need for high-performance arithmetic in digital and embedded signal processors. Carry propagation affects speed, area, and power consumption by limiting two-operand addition, a fundamental operation in these systems. Because of their versatility in modifying particular performance metrics, Parallel Prefix Adders (PPAs) represent a viable substitute. The performance of various PPAs, including Kogge-Stone, Sparse Kogge-Stone, Spanning Tree, Brent-Kung, Han-Carlson, and Ladner-Fischer, on a Virtex-7 FPGA with Xilinx Vivado 2022.2 is investigated in this paper. The findings imply that each PPA design performs well in its own way and that no single design can offer the best balance of power, latency, and area. By taking performance parameter trade-offs into account, the research helps designers choose the best PPA for their design needs.
Digital Signal Processors (DSP). Parallel Prefix Adders (PPAs)
FPGA Based 32-Bit Hybrid Ripple Ling Carry Adder
The 32-bit Hybrid Ripple Ling carry adder uses a Ling-based parallel prefix adder for the upper 16 bits and a ripple-carry adder (RCA) for the lower 16 bits to optimize performance, power, and area efficiency for VLSI designs. While the Ling adder speeds up carry propagation for larger bits, the RCA minimizes area and power for smaller bit-widths. This hybrid structure offers high-speed, low-power operation while saving 12% power, 30%–40% area, and a worst-case delay of 0.182 ns in a 28 nm process. The adder employs a Ling-based parallel prefix topology for the higher-order bits. A re-expression of the carry-lookahead technique is used to construct ling adders, which reduce the complexity of the carry-propagation process and allow for a higher speed carry calculation. The Ling adder's parallel prefix character suggests that multiple bits' carry signals are computed in parallel, significantly reducing the total delay. For the higher-order bits, where the carry chain length would normally be the dominant critical path, this speedup is especially important. The hybrid adder increases the global speed of the addition operation by reducing the critical path by using a Ling adder for the upper bits. The Xilinx Zynq-7000 SoC is used for the implementation.
Development of a Hybrid Energy Storage System using Batteries and Supercapacitors
The development of hybrid energy storage systems (HESS), which combine batteries and supercapacitors, has accelerated due to the need for dependable and efficient energy storage. Batteries have a high energy density, but their lifespan and charge/discharge rates are limited. Supercapacitors, on the other hand, have a lower energy capacity but offer long life and quick charging. This paper presents a low-cost, practical HESS for real-time energy delivery that uses inverter output control, supercapacitor buffering, and voltage regulation based on the LM317. Improved efficiency (up to 96%), thermal stability, and voltage regulation under variable load conditions are confirmed by experimental results. Applications for the system include solar-integrated energy units, UPS systems, and EVs.
Power Flow Analysis for the Steel Industry
Significant equipment like electric arc furnaces (EAFs) and ladle refining furnaces (LRFs) are among the highly dynamic and nonlinear electrical loads found in steel production facilities like Surana Industry. Reactive power imbalance, harmonic distortion, and voltage instability are just a few of the significant power quality issues caused by these complex loads. Because of this, accurate modeling and thorough analysis of these complex power systems are essential to ensuring operational dependability and maintaining power quality. The current literature tends to emphasize mostly steady-state conditions that are frequently not verified empirically with respect to actual operating data, despite the fact that the Electrical Transient Analyzer Program (ETAP) is a common tool used for load flow analysis in industry applications. With an emphasis on accurately simulating the real system behavior under various operating regimes, this paper provides a thorough power flow analysis of the Surana Steel Industry using ETAP. Two 100 MVA, 220/110 kV transformers step down the 110 kV supply that powers the plant, which is drawn from the Chikkasagur substation. The primary loads—a 35-ton rolling mill, a 4 MW EAF, and an LRF are supplemented with a captive power generation system and a harmonic filter to improve power quality and reliability. Under five different operating conditions with different load levels and generator configurations, key performance metrics such as voltage regulation, reactive power flow, harmonic suppression, and generator dynamic response are thoroughly assessed. The power system of the Surana Steel Industry is accurately simulated by the analytical method employed here. Even though a real-time comparison with SCADA data was not attempted, the ETAP model was painstakingly built with detailed equipment specs, typical operating conditions, and performance patterns actually observed at the facility. Accurate simulation of industrial environments, including the complex, unbalanced, and nonlinear loads present in steel mills, was made possible by this all-encompassing approach. Thus, this study shows that when appropriately configured and backed by extensive empirical data, ETAP has a considerable ability to accurately model these difficult environments. These results offer practical information that can be used right away for large-scale industrial power system planning, assessment, and enhancement.
Enhanced Fault Detection for FPGA-based Elliptic Curve Cryptography
For secure communications in particular, elliptic curve cryptography relies heavily on Elliptic Curve Scalar Multiplication (ECSM). The window approach is perfect for FPGA-based solutions since it increases ECSM efficiency. Verifying the accuracy of calculations made by hardware or software is essential to spotting possible mistakes. In this study, we introduce new defect detection strategies for scalar multiplication using the window method, which has not been studied in detail. Our strategy entails creating intricate algorithms and implementations to successfully minimize both transient and permanent faults. We validate our method's correctness by simulating a failure model, proving its dependability and broad error coverage in our assessments.
Elliptic Curve Scalar Multiplication (ECSM)
Secured E-Banking using Multi-Identity based Fully Homomorphic Encryption
In today's digital landscape, ensuring the security of e-banking platforms is crucial for preventing unauthorized access and safeguarding sensitive financial information. This project employs Fully Homomorphic Encryption (FHE), a significant advancement in cryptography that enables the processing of encrypted data without the need for decryption, thereby preserving privacy throughout the transaction. A pivotal aspect of the system is its encrypted authentication mechanism, which protects login credentials, including usernames and passwords. Additionally, the platform introduces a multi-identity authentication model, allowing users to engage with the system under various secure roles. By integrating FHE with a multi-identity framework, the system provides robust protection against cyber threats.
Fiber Optic Network Innovations to Bypass the Red Sea Route
This ambitious project seeks to establish a robust, high-speed fiber optic network that will connect Europe with South Asia through a terrestrial route spanning Saudi Arabia, Iraq, Turkey, Bulgaria, Hungary, and Austria. The project presents a strategic alternative to traditional maritime internet cables, which often traverse underwater routes from Europe to South Asia. By relying on a land-based network, this initiative aims to mitigate some of the risks associated with undersea cables, such as disruptions caused by natural disasters, shipping accidents, or geopolitical tensions in maritime zones. One of the key components of this project is infrastructure development. This involves laying extensive fiber optic cables, building data centers, and ensuring that the network is both secure and scalable. Unlike the challenges faced by undersea networks, land-based infrastructure allows for easier maintenance, potential expansions, and faster upgrades to meet future demands for higher data speeds and increased bandwidth. Technology integration plays a crucial role as well. Advanced networking technologies like 5G, edge computing, and cloud services can be integrated into this fiber optic network, enabling the efficient handling of vast amounts of data and enhancing overall performance. The deployment of state-of-the-art technologies will also help in reducing latency, improving user experience, and fostering innovation in the region.
A Comparative Study on Hashing Algorithms for Data Integrity and Efficiency
In recent years, the widespread availability of image editing tools has led to the proliferation of phony and manipulated photos on the Internet and social media. Various techniques have been developed to detect image forgery and identify altered or fabricated regions, with a growing emphasis on deep learning (DL) methods. This study explores recent advances in DL-based forgery detection algorithms, focusing on the detection of copy-move and splicing attacks two of the most common image tampering techniques. Additionally, the challenges posed by DeepFake-generated content, which often mimics splicing manipulation, are discussed. The study also compares hashing algorithms (SHA-256, CRC32, Random Projection Hashing, and Count-Min Sketch) for use in data integrity, similarity searches, and frequency estimation. Finally, recommendations for selecting suitable algorithms and hybrid approaches are provided to enhance image authentication and large-scale data analysis.
Underground Cable Fault Analysis by Distance Calculation and Location Detection using Microcontroller and GPS
In today’s power distribution systems, underground cables are preferred for their safety and tidy appearance. However, detecting faults in these cables can be slow, costly, and technically challenging. This study introduces a smart fault detection and location system that uses the PIC16F887 microcontroller as its core. It sends a low DC voltage through the cable and uses voltage and current sensors to monitor for irregularities. Based on the changes in readings, the system estimates the fault distance using Ohm’s Law. To pinpoint the exact location, a GPS module is included. The fault details are shown on an LCD and also transmitted through GSM for SMS alerts. Additionally, the ESP8266 Wi-Fi module allows the data to be shared online for remote monitoring. This solution provides faster and more accurate fault detection, reducing repair time and improving the reliability of underground cable systems.
BMS Control of UPS for Hybrid Energy System using IoT
This study explores the integration of renewable energy sources, namely wind and solar power, into a home power backup system. In the event of a main power outage, an uninterruptible power supply (UPS) automatically activates to provide electricity to the household. As the UPS operates, its battery gradually depletes. When the battery reaches a low charge threshold, the system initiates charging through renewable energy sources. Once the battery is fully recharged, any surplus energy is redirected to the electrical grid. The system incorporates IoT (Internet of Things) technology using an ESP32 microcontroller to monitor and display real-time data on the battery’s State of Charge (SOC) and State of Health (SOH), enabling users to track system performance and anticipate maintenance requirements.
Performance Analysis V2G Technology in Electric Vehicles
The increasing adoption of electric vehicles (EVs) has paved the way for innovative energy management solutions, particularly through bi-directional energy transfer systems. Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) technologies enable EVs to not only consume energy from the grid but also supply energy back to it, creating a dynamic interaction between EVs and the power grid. This bi-directional capability holds significant potential for enhancing grid stability, supporting renewable energy integration, and providing economic benefits to EV owners. However, the implementation of such systems requires robust design, efficient control strategies, and thorough analysis to ensure optimal performance and reliability. This study addresses these challenges by proposing a bi-directional battery system for EVs, designed and simulated using MATLAB Simulink, to evaluate its performance in both V2G and G2V modes. The proposed system integrates a lithium-ion battery pack, a bi-directional DC-AC converter, and a control system to manage energy transfer between the EV battery and the power grid. The MATLAB Simulink model includes detailed components such as the battery block with SOC estimation, the bi-directional converter, and a three-phase grid interface. Simulation results demonstrate that the system achieves an energy transfer efficiency of 92% in both V2G and G2V modes, effectively maintains the battery SOC within the 20%-80% range, and ensures stable grid synchronization under varying load conditions. The findings highlight the system's potential to support grid stability and renewable energy integration while addressing challenges such as battery degradation and grid infrastructure requirements.
Sensor Less BLDC Drive System for Conveyor Applications
The research aims to design and implement a sensorless Brushless DC (BLDC) motor optimized for conveyor applications. Typically, BLDC motors rely on sensors for rotor position detection to determine the rotor's location, which can increase system complexity and maintenance costs, among other drawbacks. This research explores the application of sensorless techniques, specifically back electromotive force (back-EMF) detection, to estimate the rotor position without the need for physical sensors. The proposed system comprises a step-down transformer, a bridge rectifier, an electronic commutator, and an STM32F103C8T6 microcontroller. By integrating advanced control algorithms, this system seeks to improve the efficiency, reliability, and cost-effectiveness of conveyor operations. The designed hardware for the sensorless BLDC drive system will be tested and verified for its robustness under various load conditions, demonstrating its potential applicability in industrial conveyor systems.
Design and Performance Optimization in Wireless Power Transfer System
Wireless Power Transfer (WPT) is one of the contactless energy transmission technologies that does not require physical connectors, and it improves efficiency in various applications such as electric vehicles, consumer electronics, and medical devices. In this article, the issue of optimizing the design and performance of an inductive WPT system based on LCC compensation has been proposed for better power transfer efficiency. The system topology consists of a high-frequency H-bridge inverter, coupling coils, and an AC-DC stage for efficient energy transfer. The LCC compensation network used in the system will reduce the impedance mismatch and maximize the coupling coefficient for improving transmission efficiency with larger air gaps. The simulation results correlate with the theoretical model. The proposed design shows an effective result. Thus, the optimized WPT system provides an energy transfer efficiency of 81.7%, affirming its feasibility for sustainable and high-performance applications.
Advanced PLC-SCADA System for Toxic Solvent Mixing Process Control and Monitoring
Industries that work with dangerous solvents struggle with control of level, flow, and consistency as a result of the high toxicity of these solvents. This study, aims to solve these problems, using a Programmable Logic Controller (PLC) equipped with SCADA. The study objective is to develop and deploy an intelligent monitoring and controlling system for toxic solvent mixing processes based on PLC and WinCC. This system is designed for pharmaceutical, chemical, food, and beverage industries. It contains sensors for flow rate measurement and temperature control, as well as actuator control, which allow managing processes with high precision while minimizing risk exposure and involvement of people. The system has both automatic and manual Clean-In-Place (CIP) hygiene procedures. The system also has visualization and alarming features that guarantee control of processes flow, and WinCC enhances product quality and process effectiveness while assuring operator safety and cleanliness of equipment. This system is designed in a way that makes it easily adaptable, flexible, and allows integration with other system.
Fault-Resilient Brent-Kung Adder Design Using Advanced Built-in Self-Test (BIST) Architecture
In modern digital systems, ensuring both high performance and reliability is essential, especially in fault-sensitive environments. This research introduces the design and implementation of a fault-tolerant Brent-Kung adder, integrated with an advanced Built-In Self-Test (BIST) framework. The Brent-Kung adder, known for its efficient carry propagation and speed optimization, is augmented with BIST techniques to enhance its reliability and testability in digital systems. A Linear Feedback Shift Register (LFSR) is used to produce pseudo-random test patterns, while a Multiple Input Signature Register (MISR) compresses the adder’s output into a compact signature for fault detection. The design is carried out in Verilog and synthesized using Xilinx Vivado 2019.1 to evaluate performance metrics, including area utilization, speed, and fault coverage. By combining the Brent-Kung adder's high-speed characteristics with a robust BIST framework, the research achieves an effective balance between performance and fault detection. This approach provides a promising solution for applications that require both computational efficiency and increased reliability in fault-sensitive environments.
Multiple Input Signature Register (MISR)