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28,188 Article Results

Minimizing the switching losses in the SiC MOSFET by using buried oxide

10.11591/ijape.v14.i3.pp613-619
Ali Hlal Mutlaq , Sura Hamad Faraj , Majeed Rashid Zaidan , Ghanim Thiab Hasan , Ahmed Saad Names
For optimizing the efficiency of the power switching devices, it is important to reduce the switching power losses. One method to minimize the switching power losses is to reduce the gate drain charge (QGD). In this paper, a 1.2 kV SiC MOSFET device with a buried oxide has been proposed to minimize QGD. The proposed design has been conducted by using the TCAD simulation program. The on-resistance (Ron,sp), QGD have been measured and analyzed based on the width and thickness of the buried oxide layer and compared with the measurement of traditional SiC MOSFET. The obtained results indicate that the QGD of 1.2 kV SiC MOSFET with buried oxide with WBO of 0.25 μm and TBO of 0.3 μm was reduced to about 31.3% which mean a minimize of power losses. The comparison results indicate that the proposed device with a buried oxide layer can be effectively used as an optimum solution for minimizing the power switching losses.
Volume: 14
Issue: 3
Page: 613-619
Publish at: 2025-09-01

OFF-grid efficiency evaluation of an inverter dependent on solar PV generator in Iraq

10.11591/ijape.v14.i3.pp761-768
Bilal Abdullah Nasir , Kutaiba Khalaf Khaleel , Mohammed Ahmed Khalaf
The solar photovoltaic (PV) inverter weighted efficiency is more precise and favorable as it mainly deems the inverter output power properties when exposed to disparate solar PV irradiance. The European metrical efficiency (𝜂𝐸𝑈𝑅𝑂), presently, is the bulk broadly admissible in inverter efficiency calculation. This is due to, historically, the European countries have been the biggest exporters and spent of solar PV inverters everywhere in the world. The European efficiency (𝜂𝐸𝑈𝑅𝑂) is a concluded metric relying on a standardized European irradiance profile. However, the rendition weightings embedded in this metric may not be fully representative or appropriate for photovoltaic inverters deployed in regions characterized by different climatic conditions, particularly in equatorial and subtropical environments. Accordingly, this study aims to validate the proposed assumption and develop a novel metrical efficiency equation for inverters operating in the Iraqi climate, specifically Baghdad city, relying on the IEC 61683:1999 criterion and the inverter load-duration curve. The proposed formula, validated with field data from an SMA-SB-4000-TL inverter, estimated the energy outcome of a 5.0 kW off-grid SPV system in Baghdad with a 2% deviation from measured values. These results validate the use of η_EURO tailored to Baghdad conditions as a reliable alternative to 𝜂𝐸𝑈𝑅𝑂 or 𝜂𝑀𝐴𝑋. This enhances the accuracy of system energy yield estimation, investment return calculations, and payback period assessment for solar PV systems.
Volume: 14
Issue: 3
Page: 761-768
Publish at: 2025-09-01

Localization and mapping of autonomous wheel mobile robot using Google cartographer

10.11591/ijra.v14i3.pp322-331
Qory Hidayati , Novendra Setyawan , Amrul Faruq , Muhammad Irfan , Nur Kasan , Fitri Yakub
COVID-19 has become a world concern because of the spread and number of cases that have befallen the world. Medical workers are the first exposed group because they have direct contact with patients. So, a vehicle is needed to replace tasks such as logistics, delivery, and patient waste transportation. An autonomous wheeled mobile robot (AWMR) is a wheeled robot capable of moving freely from one place to another. AWMR is required to have good navigation and trajectory control skills. The purpose of this study is to develop an AWMR navigation system model based on the simultaneous localization and mapping (SLAM) algorithm, accurately in a dynamic environment. With this research, developing a good navigation and trajectory method for AWMR, in the future, it can be applied to produce an AWMR platform for multipurpose. This research was conducted in two stages of development. The first year is the research that is currently being carried out, focused on sensor modeling, designing SLAM-based navigation models, and making navigation system testbeds. This research produces a trajectory navigation and control system that can be implemented on an AWMR platform for the purposes of logistics, transportation, and patient waste in hospitals.
Volume: 14
Issue: 3
Page: 322-331
Publish at: 2025-09-01

Robotic mist bath wheelchair: innovations in automated body drying and sanitization for improved patient hygiene

10.11591/ijra.v14i3.pp301-310
Vijay Mahadeo Mane , Harshal Ambadas Durge , Chin-Shiuh Shieh , Rajesh Dey , Rupali Atul Mahajan , Siddharth Bhorge
This paper presents the development and evaluation of the robotic mist bath wheelchair (MBWC), a multifunctional assistive device designed to enhance hygiene and comfort for individuals with limited mobility. The MBWC integrates mist-based bathing, automated sanitization, and warm air-drying into a compact, wheelchair-mounted system suitable for home and clinical settings. Experimental evaluations demonstrated effective temperature maintenance and a 30% reduction in bathing time compared to conventional methods. User trials with 20 participants indicated a 92% satisfaction rate, reflecting improvements in hygiene, comfort, and operational ease. MBWC provides a cost-effective, hygienic alternative to traditional bathing methods, addressing critical challenges in eldercare and rehabilitation environments.
Volume: 14
Issue: 3
Page: 301-310
Publish at: 2025-09-01

Faraid distribution calculation using AI-based Quranic chatbot

10.11591/ijra.v14i3.pp393-406
Iman Hafizi Md Zin , Nur Farraliza Mansor , Norizan Mat Diah , Shakirah Hashim , Mastura Mansor
Faraid, Islamic inheritance law, refers to that aspect of Shariah law which is not properly understood and has created issues and impediments in the distribution of estates. This paper discusses the development of an AI-based Quranic chatbot to be used by the public to learn the Faraid rules and automate calculations of inheritance distribution. The chatbot has been developed using natural language processing and a rule-based algorithm, which intends to search and get an accurate interpretation from the user queries, retrieve relevant verses of the Quran, and compute the share of inheritance according to the established Islamic jurisprudence. Fuzzy match identifies and corrects variation in queries, enhancing user interaction, ensuring that it appears more intuitive and accessible. The system processes user input regarding heirs of the deceased, estate value, and debts, and applies Faraid rules to generate accurate distribution results that happen to be web-based platforms of this chatbot. It intends to link traditional Islamic knowledge with modern digital solutions, bringing Faraid calculations closer, more comfortable, faster, and transparent. Through rigorous tests and user feedback will prove above, revealing the chatbot’s potential in understanding the application of Islamic inheritance law and promoting digital engagement in all these through Quranic teachings.
Volume: 14
Issue: 3
Page: 393-406
Publish at: 2025-09-01

SCADA system in water storage tanks with NI vision LabVIEW

10.11591/ijra.v14i3.pp381-392
Kartika Kartika , Misriana Misriana , M. Fathan Naqi , Asran Asran , Misbahul Jannah , Arnawan Hasibuan , Suryati Suryati
Advances in technology have driven the need for efficient water management systems. This study presents a SCADA-based water management system that integrates LabVIEW and Arduino to monitor and regulate water levels and flow rates in a storage tank. The system uses an HC-SRF04 ultrasonic sensor for water level measurement with 99.77% accuracy and an HX710 pressure sensor, which achieves 98.54% accuracy. The LabVIEW interface displays real-time data, giving users an intuitive view of system performance. A proportional integral derivative (PID) algorithm optimizes the water pump through pulse width modulation (PWM), achieving water flow rate control. The Ziegler-Nichols method tunes the PID parameters to Kp = 16.59, Ti = 1.102, and Td = 0.2755. This tuning ensures the system maintains a consistent target flow rate of 4 liters per minute (L/min) with minimal variation. Initial testing showed a 2.5% overshoot but stabilized at the desired flow rate within 10 seconds, indicating effective control. This SCADA system reduces water and energy waste by enabling continuous real-time monitoring and control. The system provides accurate data through a LabVIEW interface, ensuring effective and informed operational decisions. This robust solution supports efficient water management for industrial and environmental applications, contributing to sustainability and resource optimization.
Volume: 14
Issue: 3
Page: 381-392
Publish at: 2025-09-01

Robot Gaussian-historical relocalization: inertial measurement unit-LiDAR likelihood field matching

10.11591/ijra.v14i3.pp438-450
Ye-Ming Shen , Min Kang , Jia-Qiang Yang , Zhong-Hou Cai
Robot localization is a foundational technology for autonomous navigation, enabling task execution and adaptation to dynamic environments. However, failure to return to the correct pose after power loss or sudden displacement (the “kidnapping” problem) can lead to critical system failures. Existing methods often suffer from slow relocalization, high computational cost, and poor robustness to dynamic obstacles. We propose a novel inertial measurement unit (IMU)-LiDAR fusion relocalization framework based on Gaussian historical constraints and adaptive likelihood field matching. By incorporating IMU-derived yaw constraints and modeling historical poses within a 3σ Gaussian region, our method effectively narrows the LiDAR search space. Curvature and normal vector-based feature extraction reduces point cloud volume by 50–70%, while dynamic obstacle filtering via multi-frame differencing and neighborhood validation enhances robustness. An adaptive spiral search strategy further refines pose estimation. Compared to ORB-SLAM3 and adaptive Monte Carlo localization (AMCL), our method maintains comparable accuracy while significantly reducing relocalization time and CPU usage. Experimental results show a relocalization success rate of 84%, average time of 1.68 seconds, and CPU usage of 38.4%, demonstrating high efficiency and robustness in dynamic environments.
Volume: 14
Issue: 3
Page: 438-450
Publish at: 2025-09-01

An Internet of Things based mobile-controlled robot with emergency parking system

10.11591/ijra.v14i3.pp370-380
Abdul Kareem , Varuna Kumara , Vishwanath Madhava Shervegar , Karthik S. Shetty , Manvith Devadig , Mahammad Shamma , Kiran Maheshappa
This paper presents an Internet of Things (IoT) based mobile-controlled car with an emergency parking system that integrates advanced functionalities to enhance safety and user convenience, utilizing the ESP32 microcontroller as its core. The system allows users to control the car remotely via a mobile application, leveraging Wi-Fi connectivity for seamless communication. Key features include LED indicators for various operations such as reversing, left and right turns, and brake activation, ensuring clear signaling in real-time. The innovative emergency parking system detects obstacles or emergencies using sensors and halts the vehicle automatically, reducing the risk of accidents. The car's lightweight, energy-efficient design, combined with the versatility of the ESP32, ensures a responsive and reliable operation. Additionally, the system provides an intuitive user interface through the mobile app, enabling precise control and real-time feedback. The proposed system is faster in response compared to the existing systems. Moreover, the proposed system consumes less energy, and hence, it uses the battery more efficiently, extending the time of operation. Lower power consumption ensures longer operation time, reducing the need for frequent charging and making the system more practical. This paper demonstrates the integration of IoT and embedded systems to create a smart vehicle solution suitable for various applications, including robotics, automation, and personal transport. Its cost-effectiveness and scalability make it a viable choice for both hobbyists and developers.
Volume: 14
Issue: 3
Page: 370-380
Publish at: 2025-09-01

Transforming images into words: optical character recognition solutions for image text extraction

10.11591/ijai.v14.i4.pp3412-3420
Jyoti Wadmare , Sunita Patil , Dakshita Kolte , Kapil Bhatia , Palak Desai , Ganesh Wadmare
Optical character recognition (OCR) tool is a boon and greatest advancement in today’s emerging technology which has proven its remarkability in recent years by making it easier for humans to convert the textual information in images or physical documents into text data making it useful for analysis, automation processes and improvised productivity for different purposes. This paper presents the designing, development and implementation of a novel OCR tool aiming at text extraction and recognition tasks. The tool incorporates advanced techniques such as computer vision and natural language processing (NLP) which offer powerful performance for various document types. The performance of the tool is subject to metrics like analysis, accuracy, speed, and document format compatibility. The developed OCR tool provides an accuracy of 98.8% upon execution providing a character error rate of 2.4% and word error rate (WER) of 2.8%. OCR tool finds its applications in document digitization, personal identification, archival of valuable documents, processing of invoices, and other documents. OCR tool holds an immense amount of value for researchers, practitioners and many organizations which seek effective techniques for relevant and accurate text extraction and recognition tasks.
Volume: 14
Issue: 4
Page: 3412-3420
Publish at: 2025-08-01

Optimized pap-smear image enhancement: hybrid Perona-Malik diffusion filter-CLAHE using spider monkey optimization

10.11591/ijai.v14.i4.pp2765-2775
Ach Khozaimi , Isnani Darti , Wuryansari Muharini Kusumawinahyu , Syaiful Anam
Pap-smear image quality is crucial for cervical cancer detection. This study introduces an optimized hybrid approach that combines the Perona-Malik diffusion (PMD) filter with contrast-limited adaptive histogram equalization (CLAHE) to enhance pap-smear image quality. The PMD filter reduces the image noise, whereas CLAHE improves the image contrast. The hybrid method was optimized using spider monkey optimization (SMO PMD-CLAHE). Blind/reference-less image spatial quality evaluator (BRISQUE) and contrast enhancement-based image quality (CEIQ) are the new objective functions for the PMD filter and CLAHE optimization, respectively. The simulations were conducted using the SIPaKMeD dataset. The results indicate that SMO outperforms state-of-the-art methods in optimizing the PMD filter and CLAHE. The proposed method achieved an average effective measure of enhancement (EME) of 5.45, root mean square (RMS) contrast of 60.45, Michelson’s contrast (MC) of 0.995, and entropy of 6.80. This approach offers a new perspective for improving pap-smear image quality.
Volume: 14
Issue: 4
Page: 2765-2775
Publish at: 2025-08-01

Modified zero-reference deep curve estimation for contrast quality enhancement in face recognition

10.11591/ijai.v14.i4.pp3274-3286
Muhammad Kahfi Aulia , Dyah Aruming Tyas
Face recognition systems remain challenged by variable lighting conditions. While zero-reference deep curve estimation (Zero-DCE) effectively enhances low-light images, it frequently induces overexposure in normal- and high-brightness scenarios. This study introduces modified Zero-DCE combined with three established enhancement techniques: contrast stretching (CS), contrast limited adaptive histogram equalization (CLAHE), and brightness preserving dynamic histogram equalization (BPDHE). Evaluations employed the extended Yale face database B and face recognition technology (FERET) datasets, with 10 representative samples assessed using the blind/referenceless image spatial quality evaluator (BRISQUE) metric. Modified Zero-DCE with BPDHE produced optimal enhancement quality, achieving a mean BRISQUE score of 16.018. On the extended Yale face database B, visual geometry group 16 (VGG16) integrated with modified Zero-DCE and CLAHE attained 83.65% recognition accuracy, representing a 6.08-percentage-point improvement over conventional Zero-DCE. For the 200-subject FERET subset, residual network 50 (ResNet50) with modified Zero-DCE and CLAHE achieved 67.41% accuracy. Notably, standard Zero-DCE with CLAHE demonstrated superior robustness in extremely low-light conditions, highlighting the illumination-dependent performance characteristics of these enhancement approaches.
Volume: 14
Issue: 4
Page: 3274-3286
Publish at: 2025-08-01

Artificial neural network based load flow analysis of radial distribution system in Kurdistan region

10.11591/ijeecs.v39.i2.pp761-773
Warda Hussein Ali , Dana O. Qader , Mohamed A. Hussein
Today electric energy is the most commonly used source in the world. Power flow (load flow) analysis is conciderd as the backbone of any power system analysis and design; they have a great necessity for operating systems, future planning, fault analysis, and contingency analysis. For better utilization of electrical power, off-line modeling and simulation of power systems using powerful software are essential and significant task especially in developing countries and regions. Therefore, this paper performs a comparison study of conventional and non-conventional load flow techniques for a 24-Bus radial distribution system in the governorate of Sulaymaniyah. The conventional power flow techniques include the Newton-Raphson (NR), and Gauss-Seidel (GS) techniques, while the nonconventional load flow technique utilizes the artificial neural network (ANN). Modeling, simulation, and analysis of the 24-Bus feeder are performed using MATPOWER simulation tool. The MATPOWER and neural network techniques are implemented independently, and it has been proved that ANN model efficiently estimated the power flow analysis for the system mentioned above, the high regression values of nearly 0.999 indicates that the ANN model can be used as an efficient tool to perform power flow analysis.
Volume: 39
Issue: 2
Page: 761-773
Publish at: 2025-08-01

A curvilinear-based approach for sign-to-text conversion of Kannada deaf sign language

10.11591/ijeecs.v39.i2.pp1337-1349
Shantappa G Gollagi , Mahantesh Laddi , Suhas G K , Kalyan Devappa Bamane , Sulbha Yadav
This research addresses the challenge of translating Kannada sign language into text to improve communication for the deaf community. Existing methods, primarily shape-based approaches, often fail to accurately imprisonment the complexity of hand gestures, leading to reduced translation accuracy. This study proposes a curvilinear-based approach that leverages peak curvature features and contour evolution techniques to overcome these limitations. This method enhances the recognition and interpretation of sign language gestures while reducing processing overhead. Experimental results demonstrate that the proposed system significantly outperforms traditional methods, achieving higher precision and recall rates. The enhanced system provides a reliable solution for improving accessibility and communication for the deaf community. This research represents a significant step toward developing more inclusive digital communication tools, with future work focused on real-time processing and extending the system to other regional sign languages.
Volume: 39
Issue: 2
Page: 1337-1349
Publish at: 2025-08-01

A deep learning-based framework for automatic detection of COVID-19 using chest X-ray and CT-scan images

10.11591/ijai.v14.i4.pp3192-3200
Sivanagireddy Kalli , Bukka Narendra Kumar , Saggurthi Jagadeesh , Kushagari Chandramouli Ravi Kumar
COVID-19 has profoundly impacted global public health, underscoring the need for rapid detection methods. Radiography and radiologic imaging, especially chest X-rays, enable swift diagnosis of infected individuals. This study delves into leveraging machine learning to identify COVID-19 from X-ray images. By gathering a dataset of 9,000 chest X-rays and CT scans from public resources, meticulously vetted by board-licensed radiologists to confirm COVID-19 presence, the research sets a robust foundation. However, further validation is essential expanding datasets to encompass enough COVID-19 cases enhances convolutional neural network (CNN) accuracy. Among various machine learning techniques, deep learning excels in identifying distinct patterns on imaging characteristics discernible in chest radiographs of COVID-19 patients. Yet, extensive validation across diverse datasets and clinical trials is crucial to ensure the robustness and generalizability of these models. The conversation extends into complexities, including ethical considerations around patient privacy and integrating intelligent tech into clinical workflows. Collaborating closely with healthcare professionals ensures this technology complements the established diagnostic approach. Despite the potential to detect COVID-19 using chest X-ray imaging findings, thorough research and validation, alongside ethical deliberations, are vital before implementing it in the healthcare field. The results show that the proposed model achieved classification accuracy and F1 score of 96% and 98%, respectively, for the X-ray images.
Volume: 14
Issue: 4
Page: 3192-3200
Publish at: 2025-08-01

Analysis and modeling of a pneumatic artificial muscle system

10.11591/ijeecs.v39.i2.pp874-884
Vinh-Phuc Tran , Nhut-Thanh Tran , Chi-Ngon Nguyen , Chanh-Nghiem Nguyen
Hysteresis is a common challenge in achieving precise position control of pneumatic artificial muscles (PAMs). Accurate modeling of this phenomenon is essential for the development of efficient PAM control systems. This study evaluates four mathematical models for modeling PAM dynamics: Nonlinear AutoRegressive with eXogenous inputs (NARX), BoxJenkins (BJ), Prandtl-Ishlinskii (PI), and second-order underdamped system and one zero (P2UZ). To assess the effectiveness of these models, experiments were conducted with reference input signals of varying amplitudes. The accuracy and goodness of fit of these models were evaluated based on root mean square error (RMSE) and coefficient of determination. Results show that the P2UZ model achieved the highest fitness (97.15%) and the lowest RMSE (1.80 mm), followed closely by the NARX model with 96.83% fitness and an RMSE of 1.90 mm. The PI and BJ models demonstrated lower performance, with the BJ model showing the lowest fitness (90.79%) and the highest RMSE (3.25 mm). These findings provide valuable insights for improving PAM control and PAM-based automation systems by highlighting the strengths and limitations of each model.
Volume: 39
Issue: 2
Page: 874-884
Publish at: 2025-08-01
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