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International Journal of Electrical and Computer Engineering
Published by Institute of Advanced Engineering and Science
ISSN : 20888708     EISSN : 27222578     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal of Electrical and Computer Engineering (IJECE, ISSN: 2088-8708, a SCOPUS indexed Journal, SNIP: 1.001; SJR: 0.296; CiteScore: 0.99; SJR & CiteScore Q2 on both of the Electrical & Electronics Engineering, and Computer Science) is the official publication of the Institute of Advanced Engineering and Science (IAES). The journal is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 6,301 Documents
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Real-time paddy grain drying and monitoring system using long range-internet of things Hiendro, Ayong; Syaifurrahman, Syaifurrahman; Wigyarianto, F. Trias Pontia; Husin, Fitriah
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp448-454

Abstract

Grain drying environmental parameters are an important issue throughout the paddy grain production process. A real-time monitoring system requires rapid, online, and accurate measurement results. In the paddy grain drying process, the heated air velocity, temperature, relative humidity, and moisture content have to be carefully monitored and maintained to ensure product quality and safety. This study aimed to propose a real-time paddy grain drying and monitoring system using a long-range internet of things (LoRa-IoT). The real-time monitoring system consisted of sensors, LoRa, and IoT platforms. The LoRa end node and gateway were utilized as a wireless radio communication platform of IoT for long-distance signal transmission. From the experiment, the gateway received data from the end node at a distance of 2 km with a time on air (ToA) of 981 ms. As a result, the proposed monitoring system succeeded in measuring and recording the heated air velocity, temperature, and relative humidity data during the paddy grain drying process from 25% moisture content down to 14%. Regarding moisture content, the accuracy of real-time monitoring information was confirmed with a direct measurement method, resulting in a root mean square error (RMSE) of 6.17%.
Transmission line sag and magnetic field analysis with sag parabolic equations and Biot-Savart law Ahsan, Matiullah; Baharom, Md Nor Ramdon; Zainal, Zainab; Hassan, Omar Abu; Hanim, Faridah; Kamarudin, Saufi; Abd Rahman, Rahisham; Yousof, Mohd Fairouz Mohd; Jamail, Nor Akmal Mohd; Othman, Nordiana Azlin
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp76-88

Abstract

This study presents a novel approach to enhance the precision of calculating sag and magnetic fields beneath overhead transmission lines. The Biot-Savart law is integrated with parabolic equations to assign multiple magnetic field sources to each conductor, resulting in improved prediction accuracy. Addressing oversimplifications in traditional models improves the analysis of transmission lines in real-world scenarios. An analysis was performed using MATLAB and Simulink to validate the effectiveness and broad applicability of different configurations. The results demonstrated a significant improvement in precision compared to traditional methods, indicating that this approach has the potential to establish new benchmarks in the field. This methodology makes significant contributions to electromagnetic studies, offering engineers a reliable tool for designing transmission systems that are both safer and more efficient. This advancement in electrical engineering greatly improves transmission network performance by enhancing sag and magnetic field prediction accuracy. This aids in better maintenance planning and reduces outage risks, resulting in more efficient operations and improved overall performance.
Predictive modeling for healthcare worker well-being with cloud computing and machine learning for stress management Sudha, Muthukathan Rajendran; Malini, Gnanamuthu Bai Hema; Sankar, Rangasamy; Mythily, Murugaaboopathy; Kumaresh, Piskala Sathiyamurthy; Varadarajan, Mageshkumar Naarayanasamy; Sujatha, Shanmugam
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp1218-1228

Abstract

This paper provides a new method for stress management-focused predictive modeling of healthcare workers' well-being via cloud computing and machine learning. The need for proactive measures to track and assist healthcare workers' mental health is highlighted by the rising expectations placed on them. Using various data sources, our system compiles information from surveys, social media, electronic health records, and wearable devices into a single location for analysis. Predictive models that predict healthcare workers' stress levels and well-being are developed using gradient boosting, a strong machine learning (ML) technique. This work is suitable for gradient boosting due to its resilience to overfitting and capacity to process many kinds of data. Healthcare organizations may improve the health of their employees by using our technology to detect stress patterns and identify the causes of that stress. It can use specific treatments and support systems to alleviate that stress. Widespread adoption and real-time monitoring are made possible by the scalability, flexibility, and accessibility of cloud computing infrastructure. This method shows promise in the direction of proactive solutions driven by data for controlling the stress of healthcare workers and improving their general well-being.
Tomato plant disease prediction system with a new framework SSMAN using advanced deep learning techniques Sivalingam, Saravanan Madderi; Badabagni, Lakshmi Devi
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp940-948

Abstract

Agriculture plays a pivotal role in India's economy, and the timely detection of plant infections is essential to safeguard crops and prevent further spread of diseases. The conventional approach involves manual inspection of plant leaves to identify the specific type of disease, a task typically carried out by farmers or plant pathologists. In previous studies, you only look once (YOLO) and faster region-based convolutional neural network (R-CNN), machine learning algorithms were applied to datasets for detecting objects on tomato leaves which includes a total of images 2403 and got accuracies of 86 and 82 percent. In this paper, a deep convolutional neural network (DCNN) model proposed with a new framework separate, shift, and merge based AlexNet50 algorithm (SSMAN) is used to predict the disease at an earlier stage with higher accuracy. Among various pre-trained deep models, AlexNet emerges as the top performer, achieving the highest accuracy in disease classification. SSMAN can address anomalies in images by employing a class decomposition approach to scrutinize class boundaries. AlexNet exhibits a notable accuracy of 98.30% in successfully identifying tomato leaf diseases from images, with pre-trained new framework, superior to the original AlexNet architecture as well as traditional classification methods with other algorithms.
Modelling and controlling outputs of nonlinear systems using feedback Bahadirova, Gulnaz; Tasbolatuly, Nurbolat; Akanova, Akerke; Muratova, Gulzhan; Sadykova, Anar
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp540-549

Abstract

This study aimed to analyze methods for modeling and controlling the output of nonlinear systems using feedback, analytical methods, mathematical modeling, and differential equation theory. Key findings include the mathematical characterization of equations and the analysis of system stability and asymptotic behavior. The study explored various methods for addressing problems in nonlinear systems, emphasizing the importance of identifying effective solutions. The research highlights the significance of developing effective approaches to solving complex problems involving nonlinear systems. Feedback is essential for controlling and correcting dynamic processes in systems with nonlinearities. The study’s key finding is the mathematical characterization of equations describing nonlinear systems, providing insight into system structure and behavior under different parameters. Analyzing stability and asymptotic behavior allows for assessing system reliability and predicting long-term stability. This study contributes to the scientific understanding and development of methods for modeling and controlling nonlinear systems using feedback.
Estimation of the required number of nodes of a university cloud virtualization cluster Akhmetov, Bakhytzhan; Lakhno, Valery; Oshanova, Nurzhamal; Alimseitova, Zhuldyz; Bereke, Madina; Izbasova, Nurgul
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp580-591

Abstract

When designing a virtual desktop infrastructure (VDI) for a university or inter-university cloud, developers must overcome many complex technical challenges. One of these tasks is estimating the required number of virtualization cluster nodes. Such nodes host virtual machines for users. These virtual machines can be used by students and teachers to complete academic assignments or research work. Another task that arises in the VDI design process is the problem of algorithmizing the placement of virtual machines in a computer network. In this case, optimal placement of virtual machines will reduce the number of computer nodes without affecting functionality. And this, ultimately, helps to reduce the cost of such a solution, which is important for educational institutions. The article proposes a model for estimating the required number of virtualization cluster nodes. The proposed model is based on a combined approach, which involves jointly solving the problem of optimal packaging and finding the configuration of server platforms of a private university cloud using a genetic algorithm. The model introduced in this research is universal. It can be used in the design of university cloud systems for different purposes-for example, educational systems or inter-university scientific laboratory management systems.
Enhanced global navigation satellite system signal processing using field programmable gate array and system-on-chip based software receivers Kh, Chetna Devi; Rao, Malode Vishwanatha Panduranga
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp480-492

Abstract

This paper presents a new approach to improving global navigation satellite system (GNSS) signal processing by using baseband processing techniques on field-programmable gate array (FPGA) platforms and a system-on-chip (SoC)-based GNSS software receiver. By leveraging the flexibility and computational power of FPGAs and the integration capabilities of SoC platforms, the method significantly enhances signal acquisition, tracking accuracy, and overall system performance. The integration of the ADFMCOMMS3-EBZ RF front end with the Zynq 7000 SoC board, along with high-speed parallel I/O and serial peripheral interface (SPI) for data management and configuration, enables efficient processing of high-speed signals. The study also explores wavelet transform techniques, such as the discrete wavelet transform (DWT), to improve filtering and noise reduction in GNSS signals. The results show that the proposed baseband processing algorithm for GNSS software-defined radio (SDR) reduces acquisition time and enhances tracking accuracy compared to traditional personal computer (PC)-based systems. Additionally, the SoC-based receiver is more energy-efficient and uses fewer resources. Comparative analysis shows that the proposed method provides more received samples, fewer dropped samples, and a lower data loss rate, confirming its effectiveness in boosting GNSS signal processing reliability and efficiency.
Control of an aquaponic system to improve the yield of gray tilapia and lettuce cultivation Gamarra, Juan Herber Grados; Jimenez, Santiago Linder Rubiños; Olga, Rojas Salazar Arcelia; Gallegos, Eduardo Nelson Chávez; Jimenez, Linett Angélica Velasquez; Rivera, Robert Julio Contreras; Perez, Mario Alberto Garcia
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp505-519

Abstract

Water quality assessment presents challenges, primarily the paucity of available data and ongoing system maintenance. This research develops an automated monitoring and control of water quality parameters in aquaponic systems with internet of thing (IoT) technology. Proper fish feeding management is important, which is why the fish were fed at 12:00, 16:00 and 07:00. The most significant relative error recorded during the validation of the DS18B20, PH-4502C, SEN0244, SEN0237-A, SEN0189 and DFR0300 sensors is 5.0%. The maximum standard deviation between the mentioned sensors was 1.96, and the highest coefficient of variation reached 7.24%. Before the installation of the aquaponic system, the specific growth rate (SGR) of fish was 4.89±0.17% and after implementing the automated aquaponics system, the SGR of fish increased to 6.21±0.24%. The feed conversion ratio values of the fish, both before and after the installation of the control system, were 1.98±0.14% and 1.53±0.09%, respectively. In addition, an improvement in plant growth was observed, evidenced by the difference in the values of height, number of leaves, leaf length, and weight of the plants before and after the installation of the control system, which was 7.74 cm, 5 leaves, 5.6 cm, and 41.6 g respectively.
Optimizing power consumption in novel electrical design for single ended comparator circuit Zghoul, Fadi Nessir; Migdadi, Wafaa; Al-Mistarihi, Mamoun
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp208-223

Abstract

Contemporary society electronic technology has evolved into a pivotal component across various facets of our lives. Its indispensability is particularly evident in the advancement of medical, agricultural, industrial, and other sectors. As this technology continues to play a crucial role, optimizing its performance in terms of speed, accuracy, and energy consumption becomes paramount. This paper introduces a novel electrical design for the threshold inverter quantization comparator circuit aiming to meet the evolving demands of modern electronic applications. The proposed design enhances the classic threshold inverter quantization comparator’s performance by significantly reducing its power consumption. Through rigorous mathematical analysis and simulation results it is demonstrated that the proposed comparator design achieves a remarkable 50% reduction in power consumption compared to the conventional threshold inverter quantization comparator. Subsequently the newly devised design is applied to the construction of a 4-bit flash analog-to-digital converter using 0.35 μm complementary metal–oxide–semiconductor (CMOS) technology.
Design of a road marking violation detection system at railway level crossings Susilawati, Helfy; Nurpadillah, Sifa; Sediono, Wahju; Nurdin, Agung Ihwan
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp883-893

Abstract

When a train passed through a railway-level crossing, a common phenomenon was that many vehicles attempted to overtake others by crossing into lanes designated for oncoming traffic, resulting in both roads becoming congested with motorized vehicles. At that time, no system was in place to enforce penalties for violating road markings at level crossings. Therefore, a system capable of detecting such violations when trains pass through was needed. The designed system utilized a Raspberry Pi 4, a webcam, and an ultrasonic sensor. The single shot detector (SSD) method was employed for vehicle classification. The optical character recognition (OCR) method was used for character recognition on license plates. The research involved object detection at level crossings using varied objects (cars and motorcycles) with license plates categorized into two types: white background plates with black numbers and black background plates with white numbers. Based on the research results, turning on the webcam when the bar opened and closed using an ultrasonic sensor got an average error of 0.573% and 0.582%. The system could distinguish objects with an average recognition delay of 0.554 seconds and 0.702 seconds for car and motorbike objects. Regarding number plate detection, the success rate of character recognition stood at 64.45%.

Page 584 of 631 | Total Record : 6301

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