Majlesi Journal of Electrical Engineering
The scope of Majlesi Journal of Electrical Engineering (MJEE) ranges from mathematical foundation to practical engineering design in all areas of electrical engineering. The editorial board is international and original unpublished papers are welcome worldwide. The journal is devoted primarily to research papers; however, extremely high-quality survey and tutorial papers can also be considered for publication. MJEE is a member of the Committee on Publication Ethics (COPE) and complies with the highest ethical standards in accordance with ethical laws. All submitted manuscripts are to be checked for similarity through iThenticate or Turnitin to ensure originality and then rigorously peer-reviewed by international expert reviewers. Keywords: Electrical Engineering, Power, Control, Telecommunication, Biological Engineering, Mechatronic Engineering Journal DOI: 10.57647/mjee Journal Abbreviation: Majlesi J Electr Eng Journal Title Acronym: MJEE Owner organisation: Islamic Azad University-Isfahan Branch Publisher: Islamic Azad University-Isfahan Branch, Co-publishing: OICC Press, UK Keywords: Electrical Engineering, Power, Control, Telecommunications, Bioelectrical Engineering, Mechatronic Engineering MJEE accepts only high-quality papers that include both theoretical and experimental results. Authors of papers lacking experimental results must provide a valid justification; otherwise, the paper will be rejected immediately. © Author(s) All articles are licensed under the CC BY 4.0, Latest News: We would like to inform all authors and contributors that, effective 1st October 2025, an Article Processing Charge (APC) will apply to all manuscripts submitted on or after this date that are accepted for publication in the Majlesi Journal of Electrical Engineering (MJEE).
A Novel Switched Capacitor Multilevel Inverter for Renewable Energy Application
This paper introduces a novel single-phase five-level switched-capacitor multilevel inverter (5L-SCMLI) topology distinguished by self-voltage balancing and intrinsic voltage enhancement capabilities. Without an extra DC-DC boost converter or transformer, the proposed topology (PT) provides a voltage gain factor of two. The PT produces a five-level output voltage utilizing only eight switches and two capacitors, hence offering a more compact, cost-effective, and efficient design. The proposed design implements the PWM technique for the smooth operation of the PT. A comprehensive comparison with the current SCMLI design highlights the PT benefits. To assess the usefulness of the proposed methodology, extensive simulations and experimental investigations have been executed to confirm the effectiveness of PT. The PT stands out as the most cost-effective design at only 73.44 USD.
Copyright (c) 2026 Prayag Chandra Barik, Kasinath Jena, Krishna Kumar Gupta, Debashish Mishra (Author) https://creativecommons.org/licenses/by/4.0
Hexagonal Microstrip MIMO Antenna with Defected Ground Structures for ISM/IoT Application
In this manuscript, an original multiple-input multiple-output (MIMO) array antenna is proposed. The proposed array consists of two mirror-image elements placed adjacently. The proposed MIMO antenna is designed on the Rogers 4003C substrate with dimensions of 54 × 26 × 0.508 mm3. The recommended MIMO antenna exhibits a fractional impedance bandwidth of 13.79% (5.4-6.2 GHz), and achieves an element isolation greater than 22 dB. The proposed MIMO array demonstrates a gain of 5 dB with an efficiency of 95% at 5.8 GHz. Additionally, the MIMO array has a channel capacity loss less of than 0.2 b/s/Hz, an envelope correlation coefficient of ≤ 0.02, a diversity gains of ≥ 9.96 dB, and a mean effective gain ratio between elements of 1 dB. Due to its optimal operating frequency, high efficiency, and compact size, this antenna is an appropriate candidate for ISM/IoT applications.
Copyright (c) 2026 Farzaneh Talebi, Pejman Rezaei, Sina Kiani (Author) https://creativecommons.org/licenses/by/4.0
Interpretable Epileptic Seizure Detection Using Entropy and Gram Matrix Features in Beta–Gamma Bands
Epileptic seizure detection using EEG is vital for clinical diagnosis. This paper proposes a novel, interpretable framework for automated seizure classification based on entropy features from Beta (12–30 Hz) and Gamma (30–60 Hz) bands. The Discrete Legendre Transform (DLT) decomposes EEG signals, enabling extraction of four entropy measures: Approximate, Permutation, Sample, and Fuzzy Entropy. To capture inter-feature dependencies, Gram Matrix modeling is applied, yielding four global descriptors (trace, Frobenius norm, max eigenvalue, determinant), fused into a 12-dimensional feature vector. Evaluated with SVM and ProCRC classifiers on two datasets, Bonn (interictal D vs. ictal E) and independent Hauz Khas (HK, interictal vs. ictal), the method achieves 100% accuracy on Bonn and 97.5% on HK under 60–40 stratified split, and 99.5% (Bonn) and 97% (HK) under 10-fold cross-validation. Performance surpasses multiple state-of-the-art methods, including deep and hybrid models, while remaining fully interpretable, lightweight, and free of deep learning, image conversion, or GPU dependency. The framework’s transparency, computational frugality, and cross-dataset robustness make it highly suitable for portable, real-world clinical deployment in resource-limited settings. This work confirms that high diagnostic accuracy and interpretability are not mutually exclusive in seizure detection.
Probabilistic collaborative representation
Copyright (c) 2026 Shiva Rasaei Salari, Hamed Erfankhah (Author) https://creativecommons.org/licenses/by/4.0
A Real-Time Driver Drowsiness Detection Method Using a Hybrid of Deep Learning and Fuzzy Logic
Driver drowsiness contributes to approximately 21% of traffic accidents. Three deep neural networks were trained, with ResNet50 achieving the best performance: 99.74% training accuracy, 99.62% validation accuracy, an average test F1-score of 0.99, no overfitting, and real-time inference at 0.023 seconds per frame on an RTX 3060 laptop GPU with 6GB RAM. Following drowsiness detection, a fuzzy inference system integrating Eye Aspect Ratio (EAR) and Mouth Aspect Ratio (MAR) is applied to determine the drowsiness severity. The system was tested with an infrared camera on 453 volunteers from various Middle Eastern ethnic groups under different lighting conditions. A local database was compiled from these tests, recording 31 misclassifications and achieving an overall accuracy of 93.16%. The findings demonstrate that the system performs reliably under both day and night conditions and across diverse ethnicities, supporting its suitability for integration into advanced driver-assistance systems (ADAS).
Copyright (c) 2026 Amir Sohrabinezhad, Reza Sabbaghi-Nadooshan, Nasser Talebi, Roohollah Barzamini, Fardad Farokhi (Author) https://creativecommons.org/licenses/by/4.0
A Novel Quadratic High Step-Up Converter Featuring Low Ripple Input Current and Lossless Snubber
This paper introduces a high step-up DC–DC converter that combines a quadratic boost architecture with a flyback stage and incorporates a novel lossless snubber network to enhance efficiency and voltage stress performance. The converter achieves a substantial step-up ratio increase with diminished switch over-voltage, while maintaining soft-switching operation for all semiconductor devices. Consequently, transition-related losses are suppressed and diode reverse-recovery is mitigated. The leakage inductance energy is efficiently captured by the snubber and redirected to the output, contributing to overall performance enhancement. The circuit design is simplified due to the ground-referenced single power switch, which facilitates easier control and gate driving. Moreover, the low input-current ripple renders the converter configuration highly suitable for photovoltaic energy systems. A comprehensive theoretical analysis is conducted, and a 140 W prototype has been built. The experimental results validate the proposed analysis and indicate a 4% efficiency improvement over conventional hard-switched equivalents.
Copyright (c) 2026 Fatemeh Omrani, Majid Delshad, Bahador Fani (Author) https://creativecommons.org/licenses/by/4.0
Efficiency Improvement of MEMS-based Solar Cells by Optimizing the Structure and Dimensions of Piezoelectric-based Vertical Nanowires
The global energy crisis and environmental challenges have drawn increasing attention from researchers toward the development of renewable energy technologies, particularly solar energy. Among the emerging technologies in this field, Micro-Electro-Mechanical Systems (MEMS) have gained considerable interest in recent years due to the relatively low efficiency of conventional photovoltaic modules. MEMS-based technologies offer a wide range of applications, including light control, thermal management, and enhancement of optical properties. Another technology that has recently attracted attention in solar energy research is piezoelectric technology. The integration of piezoelectric materials enables the generation of electrical energy from mechanical stresses induced by temperature differences across distinct surfaces, thereby enhancing the energy output and overall efficiency of solar cells. This article describes the structure of a hybrid solar cell based on MEMS technology, the use of vertical nanowires, and the piezoelectric properties of materials. It can be reached to higher efficiency by choosing suitable materials and changing some structural parameters. The proposed structure consists of a two-layer triangular cantilever (aluminum and silicon oxide). The piezoelectric layer is made arranging a different structure of nanowires and increasing the area covered by nanowires. Despite previous works present only one structure, this paper tries to present and compare different structures for more flexible structure selection. The simulation results using COMSOL software, show improved performance compared to similar researches. Based on the results, using the proposed structure, a variable voltage with a maximum value of 1.15 volts and the best efficiency of 46.11% (in a special structure) is obtained.
Copyright (c) 2026 Amir Saljooghi, Hadi Saghafi, Mohammad Rouhollah Yazdani , Mohammadali Abbasian (Author) https://creativecommons.org/licenses/by/4.0
Adaptive Control of Freedom Upper-Limb Exoskeleton Robots Using Extreme Learning Machine and Terminal Sliding Mode
This paper presents a novel Adaptive Dynamic Terminal Sliding Mode Controller (ADTSMC) with an exponential reaching law, specifically designed for precise position control of 5-Degree of Freedom (DOF) upper-limb exoskeleton robots. The proposed control strategy addresses critical challenges in precision, stability, and adaptability, introducing several key innovations. Firstly, the incorporation of a Terminal Sliding Mode Surface (TSMS) ensures finite-time convergence of system states, significantly enhancing tracking performance. Secondly, the integration of Dynamic Second-Order Sliding Mode Control (DSMC) with an exponential reaching law mitigates chattering, providing smoother and more reliable control inputs. Thirdly, an Extreme Learning Machine (ELM) is utilized to optimize controller parameters in real-time, offering robustness against uncertainties and variations in system dynamics while managing the complex nonlinearities of upper-limb exoskeletons. The ELM helps maintain stable learning and avoids issues such as gradient vanishing. The stability of the closed-loop system is rigorously validated using a Lyapunov candidate function, ensuring reliable operation under varying conditions. Simulation results show that the proposed adaptive DSMC outperforms existing methods in terms of convergence speed, tracking accuracy, and chattering suppression, demonstrating a significant improvement in performance, with reductions of approximately 74.3% in performance indices.
Copyright (c) 2026 Mohammed Mahfoudi, Ismail Angri, Abdellah Najid, Mohammed Fattah, Moulhime El Bekkali (Author) https://creativecommons.org/licenses/by/4.0
Intelligent Electronic Devices Placement for Reconfiguration of Active Distribution Networks Considering Reliability and Protection
Utilizing distributed generations (DGs) in distribution networks offers numerous technical and economic benefits. However, DGs can also negatively impact the network by causing protection miscoordination and potential protection scheme failures. Accurate fault location and rapid fault clearance are essential to maintain system stability and ensure continuous load supply. Intelligent Electronic Devices (IEDs) enable fast communication among network components through Generic Object-Oriented Substation Events (GOOSE) messaging. Nevertheless, their deployment is limited due to high investment, installation, and infrastructure costs. This paper investigates the optimal allocation of IEDs alongside DGs to enhance distribution system reliability and reduce power losses. Network reconfiguration is also incorporated to achieve efficient system operation. Two objective functions—power losses and the Energy Not Supplied (ENS) index—are optimized using the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm. Considering ENS in addition to losses allows the operator to achieve higher reliability levels. The Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) method is then employed to select an optimal trade-off solution between the objectives. The proposed approach is tested on the IEEE 33-bus standard distribution system. Simulation results demonstrate a substantial reduction in power losses and a significant improvement in network reliability through ENS minimization.
Multi objective particle swarm optimization
Technique for order preference by similarity to the ideal solution
Copyright (c) 2026 Ashkan Soufizadeh, Vahid Talavat, Sadjad Galvani, Behruz Tousi (Author) https://creativecommons.org/licenses/by/4.0
Optimal Sizing and Cost Reduction of Grid Tied Photovoltaic Battery Systems Using Artificial Protozoa Optimization
This work focuses on optimal sizing of rooftop photovoltaic (PV) systems and battery energy storage systems (BESS) for grid-connected houses (GCHs) to minimize electricity costs under flat and time-of-use (TOU) rate structures. The research employs Artificial Protozoa Optimization (APO) algorithm to create a cost-effective optimization model, considering grid constraints, solar and load profiles, component costs, and degradation effects. Deep analyses explore the impact of electricity rate variations and grid limitations on system sizing and expenses. A rule-based energy organization system is used to optimize power flow amongst PV, BESS, load, and grid connections. The study compares Particle Swarm Optimization (PSO) and Artificial Protozoa Optimization (APO) algorithms across different tariff scenarios: flat-flat, TOU-flat, flat-TOU, and TOU-TOU. APO consistently outperforms PSO, achieving lower net present cost (NPC) and cost of energy (COE) while optimizing system power output and storage capacity. In various pricing scenarios, the APO algorithm consistently outperformed PSO, achieving significantly lower Net NPC and COE with optimized PV and BESS configurations. APO proved more effective in minimizing costs across TOU-TOU, flat-flat, and TOU-flat scenarios These findings highlight APO’s superior performance in cost and efficiency optimization for grid-linked solar PV and BESS approaches.
Copyright (c) 2026 Devesh Jaiswal, Monika Mittal, Vikas Mittal (Author) https://creativecommons.org/licenses/by/4.0
Enhancement of Cluster based Routing Protocol for Wireless Mesh Networks with Modified Chicken Swarm Optimization
Wireless Mesh Networks (WMNs) offer a promising approach to pervasive communication with efficient network coverage using minimal infrastructure. However, current WMN routing protocols are inefficient due to high energy consumption. This is caused by limited battery life of nodes, uneven distribution of traffic (load imbalance), and long data transmission distances, all of which shorten network lifetime. This paper proposes a new routing protocol for WMNs called Modified Chicken Swarm Optimization-based Efficient Cluster Head Selection (MCSO-ECHS). MCSO-ECHS leverages the network gateway to select optimal Cluster Heads (CHs). An objective function, considering both residual energy and node distance, is used for CH selection. The MCSO algorithm then refines the selection process to ensure balanced energy consumption among these energy-constrained nodes. This approach prolongs the network lifetime and improves overall energy efficiency. Simulation results demonstrate that MCSO-ECHS outperforms existing protocols in terms of energy consumption, network lifetime, throughput, end-to-end delay and packet delivery ratio, significantly enhancing the energy efficiency of WMNs.
Copyright (c) 2025 Damilare O. Akande, James S. Osunniyi, Zacheaus K. Adeyemo, Ayobami. O. Fawole (Author) https://creativecommons.org/licenses/by/4.0
A 16-Level Digital Trimmable CMOS 65nm Bandgap Reference with Process, Voltage, and Temperature Stability
A 65nm CMOS trimmable bandgap reference (BGR) circuit is developed to deliver stable reference voltages of 0.65V, with a supply voltage range of 1.5V to 3.6V. A key contribution is the incorporation of a 16-level 4-bit digital trimming circuit, allowing fine adjustment of the reference voltage. The trimming circuit employs a decoder and multiplexer structure to select different points along a resistor ladder, enabling post-manufacturing voltage tuning to compensate for process variations. This methodology ensures precise control of the output voltage, improving accuracy and reliability under varying conditions. The design aims to achieve a phase margin greater than 45°, a temperature coefficient below 2.5mV, and low current consumption under 6µA across Process, Voltage & Temperature (PVT) variations. The workflow includes calculating MOSFET and passive component parameters, designing the schematic of a single-stage operational amplifier and the trimmable BGR circuit, followed by pre-layout simulations. The pre-layout results show a DC gain of 60.88dB and a phase margin of 63° for the op-amp. The temperature coefficient for the 0.65V reference voltage trimmable BGR is 1.67mV across Process, Voltage & Temperature (PVT), with current consumption remaining below 6µA. Considering the degraded circuit performance caused by the process & manufacturing deviation, the addition of the trimming circuit significantly enhances the design's adaptability, ensuring it meets the performance targets even in the presence of manufacturing variations which allow us trim the affected output voltage back to desired voltage where in this work we set it at 0.65V.
Copyright (c) 2025 Guo Li Ng, Jagadheswaran Rajendran, Selvakumar Mariappan, Norhamizah Idros, Asrulnizam Abdul Manaf, Narendra Kumar, Arokia Nathan (Author) https://creativecommons.org/licenses/by/4.0
Optimizing Network Performance in Mobile Data Offloading Using Cooperative IEEE 802.11 MAC Protocol and Deep Reinforcement Learning
With the exponential growth of mobile data traffic and the increasing demand for latency-sensitive applications, wireless networks face critical resource management challenges. In particular, heterogeneous architectures such as LTE and Wi-Fi necessitate intelligent and adaptive offloading mechanisms to ensure efficiency and Quality of Service (QoS). This paper exploits Deep Reinforcement Learning (DRL) and cooperative communication within the MAC layer of the IEEE 802.11n standard to design an adaptive and scalable framework for data offloading in wireless networks. The proposed model employs the Partially Observable Markov Decision Process (POMDP) structure to optimally select the communication path and the type of communication (direct or multi-hop) in real-time. The proposed learning algorithm, which leverages the Deep Q-Learning structure and the Policy Improvement mechanism, demonstrates significant performance gains compared to conventional methods. Simulation results indicate that the average cumulative reward achieved by the DRL algorithm is 3.4, a substantial improvement in decision-making effectiveness compared to 2.2 for Q-Learning and 0.7 for Heuristic methods. Moreover, the energy efficiency of the proposed method improved by 87% compared to CoopMAC, and the throughput increased by 18%. These results establish the proposed framework as an effective and low-power solution for implementation in Fifth-Generation (5G) network architectures.
Copyright (c) 2025 Nabeel Abdolrazagh Yaseen Alrashedi, Rasool Sadeghi, Wael Hussein Zayer Al-Lamy, Mehdi Hamidkhani, Reihaneh Khorsand (Author) https://creativecommons.org/licenses/by/4.0
Modeling and Bandgap Optimization of Multijunction Solar Cells for High-Efficiency Photovoltaic Performance
Global energy use is rising yearly, resulting in a higher demand. Power plants must generate more electricity, thereby placing significant stress on the electrical grid. Solar engineering is a promising solution to this challenge. However, traditional silicon-based solar cells face inherent limitations. These cells rely on a single material, which restricts their power conversion efficiency by only capturing a very specific range of the sunlight spectrum.This study explores a novel concept in solar cell design that utilizes a multijunction approach, employing various semiconductor materials to create multiple p-n junctions within the cells. By strategically arranging these materials according to their energy bandgaps, the aim is to maximize the capture of the sunlight spectrum for enhanced power conversion and energy output, ultimately improving efficiency. The research was conducted using MATLAB software to focus on mathematical modeling. By analyzing designs from single junctions up to quintuple junctions and utilizing materials such as Germanium Sulfide, Gallium Arsenide, and Germanium, the study revealed that increasing the number of material layers significantly enhanced the performance of the solar cells. Notably, the quintuple junction solar cell demonstrated the highest efficiency, achieving 26.87% more efficiency compared to single junction cells, which recorded an efficiency of 42.63%.
Copyright (c) 2025 Muhammad Aliff Hafeez Bin Azman, Sim Yi Sy Yi, Kah Haw Law, Mohd Abdul Talib Mat Yusoh, Md Nor Ramdon Bin Baharom, Alvin John Lim Meng Siang, Nur Sabrina Binti Azli Murzami, Khalid Aboubakr Hasan Mousay (Author) https://creativecommons.org/licenses/by/4.0
Modelling and Analysis of a Hybrid Photovoltaic-Thermoelectric Generator System using a Relative Humidity Adaptive Maximum Power Point Tracking Algorithm
As the need for green energy intensifies, hybrid energy systems have emerged as promising solutions to improve power generation efficiency and reliability. Among these, Photovoltaic Thermoelectric Generator (PV-TEG) hybrid systems have attracted significant attention due to their ability to harness both solar irradiance and waste heat. Despite the progress in PV-TEG hybrid technologies, environmental factors such as relative humidity (RH) are often overlooked in system modelling and performance analysis. The present research develops a novel maximum power point tracking (MPPT) algorithm for a hybrid PV-TEG system integrating a humidity derating factor (HDF) and examines it in SIMULINK under diverse scenarios of solar irradiance, ambient temperature, and RH. Linear Regression is used to analyse corelations among the performance parameters of the hybrid system. Simulation results show that at 90% RH, the efficiency of the standalone PV system drops by 7%, while the same is improved by 12% in the PV-TEG hybrid model. The proposed humidity-adaptive MPPT algorithm based on Perturb & Observe framework achieves convergence within 0.03 seconds per iteration with a total simulation runtime of 12.4 seconds for a full 24-hour environmental profile. The findings highlight the ability of the proposed PV-TEG system to sustain stable voltage and power output during environmental fluctuations; and underscore the importance of incorporating HDF in MPPT control.
Copyright (c) 2025 Pallavi Roy, Bipul Kumar Talukdar, Sandip Bordoloi, Bani Kanta Talukdar (Author) https://creativecommons.org/licenses/by/4.0
A Systematic Review on Fabry-Perot Antennas for Advanced CubeSat Missions
CubeSats are a developing disruptive technology with a broad range of applications, including moon missions supplied by some international space agencies. They have motivated the scientific community to focus on this sector with various technologies aimed to be more suitable for advanced CubeSat and deep space missions. These CubeSat capabilities can be enhanced by designing a suitable new antenna design, which has the potential to significantly reduce manufacturing costs and energy consumption. This research investigates how to develop Fabry-Perot antenna designs and their use for advanced CubeSat missions. In order to identify the best CubeSat application for each design examined in this work, tens of Fabry-Perot antenna designs put forth by the scientific community are evaluated for their geometrical, mechanical, and electrical appropriateness for an advanced CubeSat mission. This study provides comprehensive data to examine the current status of Fabry-Perot antennas and if they are suitable for CubeSats. Furthermore, we offer a collection of design-based analytical models that could be applied to various upcoming advanced CubeSat projects at academic institutions or in collaboration with private or national space agencies.
Copyright (c) 2025 Fouad Omari, Boutaina Benhmimou, Nancy Gupta, Khalid x Khalid El Khadiri, Niamat Hussain, Rachid Ahl Laamara, Mohamed El Bakkali (Author) https://creativecommons.org/licenses/by/4.0
Implementation a 10 MW Grid-Connected Solar Photovoltaic System in India
This study presents the design, optimization and performance analysis of a 10 MWp grid-connected solar photovoltaic (PV) systemin North India. A sequential approach is applied for SPV system design using PVSyst software, following by execution and feasibility assessment. Two PV technologies Hanwa (13.41 MWp) and Waaree (1.505 MWp) modules are modelled using PVSyst meteorological data to ensure accurate performance estimation. The optimized system comprises 47,140 modules arranged in strings of 20 modules, achieving a nominal capacity of 14.92 MWp and occupying 75 acres with a total module area of 92,144 m². Total annual energy production is 22,651.4 MWh. The performance ratio (PR) ranges from 72.6% to 75.7%, the Capacity Utilization Factor (CUF) varies between 14.4% and 20.3%. System and array capture losses are 0.46 kWh/kWp and 1.44 kWh/kWp respectively. The key performance indicators like Gross Monthly Energy Yield (1,887.62 MWh/month), Daily Energy Yield (62.06 MWh/day), Array Yield (1,518 kWh/kWp/year), and Final Yield (1,518 kWh/kWp/year) are analyzed. The novelty of this study lies in its experimental validation using a dual-string setup monitored through real-time SCADA data, which effectively quantified soiling impacts showing an average daily degradation of 0.7% in uncleaned modules and confirmed the accuracy of simulation results.
Copyright (c) 2025 Sandeep K Singh, Rajib K Mandal, Prabhat R Sarkar, Faizan A Khan (Author) https://creativecommons.org/licenses/by/4.0
High Accuracy Dual Split Ring Resonator - Defected Ground Structure based Microwave Sensor for Material Characterization
Microwave sensors have grown in popularity in recent years because of their contactless sensing capability, real-time detection capability, measurement, accuracy, ease of manufacture and robustness. They have become one of the primary choices in smart sensing applications. However, some of their key limitations, such as accuracy, sensitivity, and selectivity, might be regarded as limiting their utilization and application range. Thus, this project proposed to design and develop a high-accuracy microwave sensor for material characterization. This microwave sensor uses Defected Ground Structure (DGS) for enhancing sensor accuracy in determining dielectric characteristics of material under test (MUT). The sensor achieved high accuracy with a percentage error of 0.56% to 1.86% for the tested MUT, demonstrating reliable precision. The DGS significantly enhances performance, optimizing efficiency and compactness while reducing transmission losses on cost-effective substrates like FR4. Its high Q-factor of 595 enables detecting small dielectric constant variation.
Copyright (c) 2025 Mohamad Harris Misran, Maizatul Alice Meor Said, Mohd Azlishah Othman, Siti Normi Zabri, Eliyana Ruslan, Noor Azwan Shairi, Zahriladha Zakaria, Shadia Suhaimi, Mohd Zahid Idris (Author) https://creativecommons.org/licenses/by/4.0
A CMOS 65nm On-Chip RF Inductor with Hybrid Spiral-Meander Layout Optimization and Ferroelectric Stack-Up Integration for Q-Factor Enhancement
On-chip inductors are crucial for enhancing the functionality of microelectronic devices, particularly in wireless communication. Still, traditional designs often lead to increased chip size and reduced performance due to their bulkiness and integration challenges. By utilizing an electromagnetic (EM) simulation tool, this research presents a novel on-chip inductor design optimized for 65 nm CMOS technology, achieving an inductance of 3.2 nH with a Q-factor of 15.31 at 1.7 GHz. The primary contributor to the Q-factor enhancement is the layout optimization techniques, which improve the Q-factor by approximately 21%. Additionally, further improvements in inductance and Q-factor are achieved by integrating Nickel Zinc Cobalt-based ferroelectric liquid materials into the simulation stack-up layer. This material stack-up serves as a secondary contributor, enhancing the magnetic properties of the inductor and increasing the Q-factor to 15.83 which is an additional 6% improvement over the proposed design. The proposed layout optimization and material innovations demonstrate the potential for achieving higher inductance values and Q-factors, effectively addressing the limitations of conventional methods. The results highlight the feasibility of incorporating advanced materials in on-chip inductor design, paving the way for next-generation RF integrated circuits that meet the growing demands for miniaturization and energy efficiency in modern electronics.
Copyright (c) 2025 Manibharathee Muniandy, Selvakumar Mariappan, Jagadheswaran Rajendran, Norhamizah Idros, Asrulnizam Abdul Manaf, Narendra Kumar, Arokia Nathan (Author) https://creativecommons.org/licenses/by/4.0
Design of 65nm 6T SRAM Using Improved Sense Amplifiers and Write Driver Circuits
Designing high-speed 6T SRAM for efficient read and write operations poses a significant challenge for circuit designers. In this paper, we propose a 65nm 6T SRAM architecture using sense amplifiers and write driver circuits to enhance the read and write performance. The sense amplifier helps the reading process go faster and the reading data be more stable. The write driver is designed with a symmetrical structure to reduce the write delay. In addition, the control circuit performs the checking process to synchronize read operations, optimize latency without interruption. The simulation result shows that the read delay and write delay are 58.66ps and 79.67ps, respectively. These delays outperform most of the other study.
Copyright (c) 2025 Quang Vinh Truong, Tuan Dat Tieu (Author) https://creativecommons.org/licenses/by/4.0
An Improved Deep Mask Region-based Convolutional Neural Network for the Detection of Power Transmission Insulator Defects
In the power industry, accurate and real-time defect detection in power transmission lines is crucial to reducing accidents and maintenance costs. Although various image processing and AI-based methods have been proposed using unmanned arial vehicles (UAV) images, their efficiency significantly drops under complex environmental conditions, such as high color or texture diversity in backgrounds. To address this challenge, this paper presents an improved version of the mask region-based convolutional neural network (R-CNN) network by redefining the architecture of the head and classification sections to enhance detection accuracy in complex scenarios. The proposed model introduces a novel rhombus-structured fully connected layer in the classification branch for better feature encoding and decoding, alongside a drop-out layer in the bounding box detection branch to prevent overfitting. Additionally, ResNet-101 is employed as the backbone, and transfer learning is used to optimize training and reduce computational complexity. Experimental evaluations demonstrate that the improved Mask R-CNN achieves a mean average precision (mAP) of 92.07% and an accuracy of 92.50% in detecting power transmission line defects, outperforming existing methods. Furthermore, the proposed approach helps lower the cost and time required for fault detection and maintenance, making it a practical solution for real-world power transmission system inspections.
Mask region-based convolutional neural network
Copyright (c) 2025 Haider Abdulzahra Saad Alsaide, Mohammadreza Soltanaghaei, Wael Hussein Zayer Al-Lami, Razieh Asgarnezhad (Author) https://creativecommons.org/licenses/by/4.0
Direct-Written AgNP Electrodes in All-Solution-Processed Low-Voltage Organic Thin Film Transistors Employing High-k PVP Dielectric
Researchers have explored various fabrication methods for organic devices to meet the growing demand for printed electronics and wearables. Inkjet printing has been widely used for deposition of solution-processable materials at low temperatures, making it ideal for flexible electronics. However, nozzle clogging and strict ink and substrate requirements limit its commercialization for OTFTs. Besides, the practical applications of OTFT devices are limited by their high operating voltages. To address these limitations, this study proposes a simple, solution-based fabrication method for developing low-voltage OTFTs on Silicon substrates. A novel direct-write printing technique was utilized to deposit the source/drain electrodes at temperatures below 150 °C in ambient conditions without experiencing nozzle clogging issues, while a spin-coating method was employed for the deposition of TIPS-pentacene semiconducting and high-k PVP dielectric layers. Remarkably, the fabricated OTFT achieved a channel length of 120 µm with saturation mobility of 4.49 × 10-1 cm2/Vs, a threshold voltage of –1.5 V, an On/Off current ratio of 108, and a subthreshold swing of 66.8 mV/decade, operating below –5 V. The integration of direct-write printing with a high-k dielectric layer offers a new approach for fabricating OTFTs and other organic devices at lower temperatures, making it suitable for flexible electronics.
Copyright (c) 2025 Nur Syahadah Yusof, Mohamed Fauzi Packeer Mohamed, Muammar Mohamad Isa, Norhawati Ahmad, Khatijah Aisha Yaacob, Mohd Hendra Hairi (Author) https://creativecommons.org/licenses/by/4.0
Integrated Sensing and Communication-Assisted Orthogonal Time Frequency Space in a Non-Guard Band-Orthogonal Multiple Access Downlink Architecture for Vehicle-to-Infrastructure Networks
Orthogonal time frequency space (OTFS) modulation has emerged as a robust solution for high-mobility wireless communication and radar sensing by leveraging the delay-Doppler (DD) domain. This paper tackles challenges in resource allocation and multi-user interference (MUI) suppression in integrated sensing and communication (ISAC)-enabled vehicle-to-infrastructure (V2I) networks. We leverage a non-guard band-orthogonal multiple access (non-GB-OMA) scheme using interleaved DD-domain resource allocation with zero padding (ZP), thereby eliminating guard bands and reducing MUI. A two-dimensional (2D) correlation-based radar sensing method with an adaptive threshold detects user terminals (UTs), enabling UT-aware downlink resource allocation, channel estimation, and data detection. Channel estimation is treated as a sparse signal recovery problem, solved via the subspace pursuit (SP) algorithm using structured pilot patterns, a dedicated frame structure, and a specific external phase matrix. Furthermore, to combat high peak-to-average power ratio (PAPR) in OTFS systems, we employ low-power Zadoff-chu (Zch) and quadrature amplitude modulation (QAM) pilot sequences that maintain orthogonality and accuracy. Eventually, a two-stage feedback mechanism adapts the block linear-minimum mean square error (BL-MMSE) detector for data recovery. The proposed framework effectively manages PAPR, inter-symbol interference (ISI), and MUI, enhancing V2I communication reliability and efficiency under high mobility. Simulation results demonstrate improved SNR and BER performance.
Copyright (c) 2025 Ghasem Saeidi, Hamid Saeedi-Sourck (Author) https://creativecommons.org/licenses/by/4.0
A review on Machine Learning Approaches for Plant Disease Diagnosis
In recent years, the identification and classification of plant and crop diseases have significantly increased due to the use of machine learning (ML) algorithms. This rise is attributed to the remarkable precision and potential of ML methods in automating plant disease identification. This comprehensive analysis examines various ML approaches and image processing techniques used to identify and classify diseases such as false smut, bacterial leaf blight, brown leaf spot, leaf scald, rice blast, rice tungro, sheath blight, and stem rot also we embed the rice crop major disease, the disease caused by, symptoms of diseases, factors that trigger the disease and in what stage the disease affect the crops. Here we register the common issues in generalization of models, crop disease diagnosis process stages. We explore advanced ML techniques as well as conventional ML methods like random forest (RF), naive bayes (NB), decision tree (DT), k-nearest neighbors (KNN) and support vector machine (SVM). A tabular summary of the results of these methods is provided in this paper. This article also discusses about the challenges and limitations associated with applying ML methods to detect plant disease, offering insights into the constraints practitioners face. Our goal is to provide a thorough overview of the latest developments in machine learning algorithms for crop disease diagnostics by integrating existing research. Ultimately, this study enhances understanding of the diseases, methodologies, challenges, and future directions for using ML in sustainable agriculture.
Copyright (c) 2025 Vijayan Subramanian, Chiranji Lal Chowdhary (Author) https://creativecommons.org/licenses/by/4.0
Optimal Sizing and Analysis of Budget-Constrained Solar PV-Grid Systems
The global transition to sustainable energy has increased the adoption of solar photovoltaic (PV) systems, but high initial costs remain a barrier in developing nations like Nigeria. This study evaluates the optimal sizing and feasibility of a budget-constrained solar PV system to supplement grid power for a suburban grocery store. Using HOMER software, six locations in Nigeria were simulated within a $1,600 budget. The optimal design included a 1.5 kW solar PV system, two 12V 200AH batteries, and a 2.5 kW inverter, costing $1,554. Ughelli achieved the lowest cost of energy (CoE) at $0.0712/kWh and a net present cost (NPC) of $4,039, while Talata Mafara recorded the highest CoE at $0.0731/kWh and an NPC of $4,414, underscoring the need for site-specific designs. Northern locations (Talata Mafara, Dikwa, Karu) showed higher solar contributions (32.3%, 32.1%, 30.4%), while southern sites (Ughelli, Ilaro, Amuzu) had lower contributions (26.5%, 27.4%, 28.6%). Despite optimization, unmet energy demand persisted, particularly in southern regions. Excess electricity generation highlights opportunities for improved energy management through enhanced battery storage and demand-side strategies.
Copyright (c) 2025 Joseph Y. Oricha, Shereefdeen O. Sanni, Olumoroti Ikotun, Sikiru M. Abdullahi, Hillary U. Ezea (Author) https://creativecommons.org/licenses/by/4.0
Accuracy Improvement of Detecting the Effect of Alcohol Consumption on Brain Structure in MRI Images using Deep Learning Methods
This paper proposes a deep convolution neural network model for detecting the effects of alcohol consumption in brain MRI images. The main objective of the research is to simulate and identify the structural and functional changes of the brain caused by alcohol consumption. In this research, MRI images were first pre-processed using SPM software to prepare for model training. Then, a CNN model was designed and trained to detect the effects of alcohol consumption. Experiments showed that by increasing the number of IPAKs, setting the learning rate to 0.001, and using a convolution window size of 3×3, the best performance was achieved. The experimental results indicate a precision of 95.85%, a recall rate of 95%, and an F1 criterion of 96%, which indicates the excellent performance of the model. This research can be used as an effective tool for automatically detecting the effects of alcohol consumption in MRI images in medical and clinical research and can help increase the accuracy and speed of diagnosis in clinical processes. The results could also be useful in studies related to brain disorders caused by drug and alcohol use.
Copyright (c) 2025 Esmaeil Khalili, Mehran Emadi (Author) https://creativecommons.org/licenses/by/4.0
Brain Tumor Detection through Image Fusion Using Enhanced Dual Channel Pulse Coupled Neural Networks and Grey Wolf Optimizer
Image fusion is particularly crucial for diagnostic imaging in medical applications such as radiation therapy and image-guided surgeries. Medical image fusion seeks to improve diagnostic accuracy by preserving important characteristics and features from the individual pictures in the combined image. This study introduces a novel fusion methodology for MRI and CT medical imaging by decomposing the source images as base and detail layers using a novel three-scale decomposition strategy that employs Gaussian and Guided filters. Gaussian curvature directs the guided filtering procedure for each source image. The base layers are fused using the Proposed Grey Wolf Optimization algorithm (PGWO), which contains an objective function designed to maximize entropy, edge strength, and standard deviation. In order to integrate the detail layers, the activity level information is simultaneously determined using the Enhanced Dual Channel PCNN. To evaluate the effectiveness of the proposed method, thirty slices of seven different types of medical images from various sources were analyzed and compared both visually and statistically with existing approaches. According to experimental data, the suggested approach performs better than traditional approaches in terms of both objective metrics and qualitative image quality. Quantitative findings show notable advancements over current techniques: Standard deviation rises from 15.5 to 32.7%, spatial frequency from 38.2 to 70.5%, mutual information from 42.8 to 62.9%, edge strength from 37.4 to 61.9%, structural similarity index from 37.8 to 43.8%, and image entropy from 12 to 18%.
Enhanced Dual Channel Pulse Coupled Neural Networks
Copyright (c) 2025 Ratna Kumari Jogi, Usha Rani Macigi (Author) https://creativecommons.org/licenses/by/4.0
FPGA-Accelerated Bird Detection Algorithm for Paddy Farmers
Paddy farmers face significant challenges from bird pests, particularly species like pipits and sparrows, which can reduce yields by up to 70%, especially during the grain-filling stage, leading to substantial economic losses. Traditional methods of pest control, such as physical barriers, scare tactics, and chemical deterrents, are often inefficient and labour-intensive. To address this issue, this research develops an artificial intelligence (AI)-based bird detection system to protect paddy fields. The solution involves using Field-Programmable Gate Array (FPGA)-accelerated object detection models to accurately identify bird activity in real time, aligning with journal requirements for first-time abbreviation usage. The system integrates a notification mechanism via Telegram to alert farmers immediately, allowing swift manual action. The research employed the detr- resnet50 model for its precision and high confidence in detection, running on both Central Processing Unit (CPU) and FPGA configurations to optimize performance. The results showed significant improvements in latency and frames per second (FPS) when using FPGA acceleration, demonstrating effective real-time bird detection capabilities. The system’s implementation enhanced crop protection, promoted eco-friendly practices, and improved overall farming efficiency by reducing manual surveillance and providing valuable data for long-term pest management strategies. Key quantitative findings showed that FPGA acceleration improved FPS by over 200% compared to CPU performance.
Copyright (c) 2025 Muhamad Ili Firdaus Mohamad Sa’ad, Rizalafande Che Ismail, Siti Zarina Md Naziri, Mohd Nazrin Md Isa, Ahmad Husni Mohd Shapri (Author) https://creativecommons.org/licenses/by/4.0
A New ZVS Multi-input Converter with Modular Auxiliary Circuit and Low Voltage Stress for Renewable Energy Applications
Renewable energy sources like wind and solar power experience output voltage fluctuations due to changing weather conditions. To maintain a stable power supply, integrating multiple input sources is essential. A Multi-Input Converter (MIC) provides a more efficient solution by reducing the need for numerous passive components, which in turn minimizes cost, size, and weight compared to separate converters. This study introduces a dual-input boost converter with zero voltage switching (ZVS), utilizing a single auxiliary circuit to enable soft switching for all semiconductor elements. This design not only improves efficiency but also retains the advantages of multi-input converters. Additionally, a voltage multiplier is incorporated to enhance the voltage conversion ratio, achieving higher voltage gains. The theoretical analysis of the proposed converter is validated through experimental results, with efficiency measurements demonstrating a 3% improvement over conventional hard-switching designs.
Copyright (c) 2026 Mehdi Khodadadian Zaghmari, Majid Delshad, Mohammadali Abbasian, Mohammad Rouhollah Yazdani (Author) https://creativecommons.org/licenses/by/4.0
Towards Omni-Font Optical Character Recognition (OCR) for Persian script using the YOLO object detection model
Optical Character Recognition (OCR), especially for scripts with complex structures like Persian script, faces significant challenges in interpreting nuanced characters and contextual variations. This study provides a straightforward and scalable approach to developing omni-font OCR systems. A synthetic dataset incorporating words, numbers, punctuation marks, mathematic symbols, and whitespace characters is developed to evaluate YOLO's capability in detecting 70 characters across 15 formal, informal, and handwritten-style fonts. The proposed method for detection of regular space and non-breaking space characters achieved high-precision results that may eliminate the need for a separate word detection stage in an OCR system. In another investigation, we attempted to detect characters from an unseen font by training the model on a batch of other fonts. A formal font such as “B Nazanin” is near-completely detectable without being directly included in the model’s training with a batch of fourteen other fonts. For a handwritten-style font such as “MRT_Sayeh-1”, the mean detectability increases from 54%, when training the model with other single fonts, to 80% when a batch of fourteen other fonts is used. Overall, this study demonstrates that object detection-based OCR models have the potential for Omni-Font text recognition through expanded datasets and advancements in deep learning.
Copyright (c) 2025 Mojtaba Gandomkar, Sahar Khoramipour (Author) https://creativecommons.org/licenses/by/4.0
Photovoltaic Array Fault Detection Using Ensemble Learning-Based Technique
Fault detection in the photovoltaic array aims to ensure a stable and continuous power supply. Detecting faults in photovoltaic arrays is challenging because normal and faulty conditions can sometimes exhibit similar characteristics. This paper presents an approach for photovoltaic array fault detection using an ensemble learning-based technique. A 3.2 kW MATLAB-Simulink photovoltaic array model is developed, and the fault characteristics of short circuit faults, line-ground faults, and hot spot faults are analyzed to identify the most suitable measurements for effective fault detection and classification. It is observed that photovoltaic array measurements such as voltage, current, power, rate of change of voltage and current over time (dV/dt and dI/dt), and change in power to voltage and current (dP/dV and dP/dI) exhibit distinct fault characteristics, making them valuable for accurate fault discrimination. The proposed model is trained using the selected photovoltaic array measurements, and its effectiveness is validated through a testing dataset, with performance indices derived from the confusion matrix. The proposed technique achieves a promising fault detection accuracy of 99.72%. Additionally, the performance of the proposed technique is evaluated and compared with other artificial intelligence-based techniques. The results demonstrate that the proposed method outperforms these alternatives.
Copyright (c) 2025 Anshul Shekhar, M. Senthil Kumar (Author) https://creativecommons.org/licenses/by/4.0