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Far East Journal of Electronics and Communications

📢 Latest Update: New special issue call for papers on "Emerging Technologies in Research" - Submit by March 31, 2025

📢 Latest Update: New special issue call for papers on "Emerging Technologies in Research" - Submit by March 31, 2025

Volume 28, Issue 1

Volume 28, Issue 1 - $2024Current Issue

Volume 28 Issue 1 Cover

Issue Details:

Volume 28 Issue 1
Published:Invalid Date

Editorial: Advancing Interdisciplinary Research in the Digital Age

Welcome to the 2024 issue of Far East Journal of Electronics and Communications. This issue showcases the remarkable breadth and depth of contemporary research across multiple disciplines. From cutting-edge applications of machine learning in climate science to the revolutionary potential of quantum computing in drug discovery, our featured articles demonstrate the power of interdisciplinary collaboration in addressing global challenges.

We are particularly excited to present research that bridges traditional academic boundaries, reflecting our journal's commitment to fostering innovation through cross-disciplinary dialogue. The integration of artificial intelligence with environmental science, the application of blockchain technology to supply chain management, and the convergence of urban planning with smart city technologies exemplify the transformative potential of collaborative research.

As we continue to navigate an era of rapid technological advancement and global challenges, the research presented in this issue offers both insights and solutions that will shape our future. We thank our authors, reviewers, and editorial board members for their continued dedication to advancing knowledge and promoting scientific excellence.

Professor Bal S. Virdee
Editor-in-Chief
Far East Journal of Electronics and Communications

Articles in This Issue

Showing 9 of 9 articles
Research PaperID: FJEC1280030

Adaptive cruise control of vehicle with visual feedback of a webcam

Hou-Tsan Lee

The proposed adaptive cruise control system of vehicle guidance is based on the visual feedback from the webcam mounted on the vehicle. The road lines are identified first with the help of image processing techniques based on Hugh transform. Following, the tracking system is also developed based on some simple control algorithms to make the vehicles running inside the drivable area with or without a car in front within a safety speed limit. The computing of these control algorithms can be operated at a control center which connects the vehicles via WiFi communication system or directly on the microprocessor built in the vehicles to exchange the image information and control commands. Furthermore, the control center can monitor all the vehicles in some certain area for further applications such as dynamically planning the routes for all the vehicles to avoid congestion. To simplify the experimental setup, the drivable area is confined to a superhighway with only cars allowed on the road. The experimental results are given to demonstrate the effectiveness of the proposed control system. Received: August 10, 2025 Accepted: August 28, 2025 DOI: https://dx.doi.org/10.17654/0973700626002

adaptive cruise controlvisual-based controlvehicle control.
3,704 views
1,201 downloads

Contributors:

 Hou-Tsan Lee
Research PaperID: FJEC1280042

AI-enhanced human-machine collaboration in long-term care: A mixed-methods study on service efficiency and quality improvement

Yih-Chang Chen, Chia-Ching Lin

The global demographic transition toward an aging population presents unprecedented challenges for long-term care systems, with critical workforce shortages affecting $92 \%$ of nursing homes and $70 \%$ of assisted living facilities. This mixed-methods study investigates the effectiveness of AI-enhanced human-machine collaboration in improving long-term care service efficiency and quality. Following PRISMA and STROBE guidelines, we conducted a systematic review of 105 studies and controlled trials across 218 facilities ( 94 intervention, 124 control) over 18 months. The AI-enhanced system analyzed 150 daily clinical data points per patient, providing real-time alerts for condition changes, fall risk assessment, and medication monitoring. Results demonstrated significant improvements in $89 \%$ of quality measures, including a $9 \%$ reduction in major falls ( $p=0.034$ ), 22\% decrease in ADL dependency ( $p DOI: https://dx.doi.org/10.17654/0973700626007 Received: October 18, 2025 Accepted: November 3, 2025;

human-machine collaborationartificial intelligenceelderly carehealthcare efficiencycare qualitylong-term care
3,966 views
1,261 downloads

Contributors:

 Yih-Chang Chen
,
 Chia-Ching Lin
Research PaperID: FJEC1280010

Artificial Intelligence - Driven anomaly detection in energy systems

Chidi Ukamaka Betrand, Chinwe Gilean Onukwugha, Nneka Martina Oragba, Douglas Allswell Kelechi, Ihechiluru Chinwe Ugbor

Enormous amounts of data are being produced everyday by sub-meters and smart sensors installed in residential buildings. If leveraged properly, that data could assist end-users, energy producers and utility companies in detecting anomalous power consumption and understanding the causes of each anomaly. This research focuses on the use of AI for anomaly detection in energy systems, specifically targeting Central Processing Unit (CPU) and Graphic Processing Unit (GPU) overheating in energy systems. With the increasing complexity and reliance on energy-consuming devices, overheating can significantly affect system performance and energy efficiency. This research proposes an artificial intelligence driven model integrated into the task scheduler of a system to monitor CPU and GPU temperature levels. When abnormal temperature thresholds are detected, the system promptly alerts the user, preventing potential damage and ensuring optimal performance. The methodology follows a structured approach which is the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework, using Python programming and leveraging task scheduling for real-time monitoring. The results highlight the model’s accuracy in detecting anomalies, providing timely alerts, and preventing overheating events. The anomaly detection system improves energy management by identifying potential risks before they escalate, demonstrating its ability to optimize system efficiency, reduce energy waste, and improve decision-making regarding system and sustainability. Received: June 25, 2025 Accepted: July 23, 2025  DOI: https://doi.org/10.17654/0973700625001

emissionanomaly detectionenergy systemartificial intelligencetemprature
3,413 views
908 downloads

Contributors:

 Chidi Ukamaka Betrand
,
 Chinwe Gilean Onukwugha
,
 Nneka Martina Oragba
,
 Douglas Allswell Kelechi
,
 Ihechiluru Chinwe Ugbor
Research PaperID: fjec-00000039

Characterization of maternal and fetal heart rates signals for improved telemetry operation

Frank A. Ibikunle, Ebenezer A. Ajayi, Bright C. Unaegbu

The importance of telecommunication systems in the medical field is immense especially as it relates to monitoring of cardiovascular conditions as well as taking the heart beat rate of expectant mothers and that of the fetus. Fetal monitoring during pregnancy enables the physician to diagnose and monitor pathological conditions especially asphyxia. The Electrocardiogram (ECG) is the simplest non-inversive diagnostic method used to solve various heart diseases. In this study, the use of Poisson probabilistic algorithm is employed to predict R-R intervals (a valid and reliable assessment of the time between two successive heartbeats, measured in milliseconds, and it is the most crucial for a scientific and practical use of HRV) in both maternal and fetal ECG signals for a set of 72 ECG heart rates for both the mother and her fetus. The application of Poisson technique has demonstrated promising results in error rates and better monitoring accuracy. 72 ECG signals for a certain R-R timing ranging from 0.66 to 0.99 in seconds were done, and ECG monitoring for important performance metrics, such as throughput, packet loss, error rate, and energy consumption was recorded. Using the Poisson forecast, a ‘P’ error amplitude ranging from 0.7735 eV to 1.305 eV for an R-R timing of 0.66 to 0.99 sec was obtained. From the results, the eV error amplitude proves to be more error prone from the ECG graphs obtained when compared with that of the actual data for Fetal Electrocardiogram (FECG) and Mother Electrocardiogram (MECG). The results from the research work compete favourable when compared with the wireless network error rate, throughput, energy consumption and energy efficiency, with and without the Poisson forecast. The proposed model in the work was also compared with an energy efficient wireless network system that applied Poisson algorithm to substantiate the effectiveness and accuracy of our system. DOI: https://dx.doi.org/10.17654/0973700626006 Received: October 3, 2025 Accepted: October 16, 2025

electrocardiogramthroughputBER diagnosticWSNamplitude adaptivenon-inversive+1 more
4,045 views
1,329 downloads

Contributors:

 Frank A. Ibikunle
,
 Ebenezer A. Ajayi
,
 Bright C. Unaegbu
Research PaperID: FJEC1280037

From knowing to feeling: Cultivating social work empathy through embodied experience in virtual reality

Yih-Chang Chen, Chia-Ching Lin

Acknowledging the constraints inherent in conventional teaching approaches for cultivating empathy, the present study sought to assess the efficacy of an immersive Virtual Reality (VR) training intervention tailored for social work students. Utilizing a quasi-experimental design with pre-test and post-test measures alongside a control group, the research involved 82 participants enrolled in social work programs. The experimental cohort  participated in a 15-minute immersive VR simulation portraying a high-risk family scenario, whereas the control cohort  engaged with an equivalent text-based case study. Empathy levels were quantified using the Interpersonal Reactivity Index (IRI). Analysis of Covariance (ANCOVA) indicated that participants in the VR condition exhibited significantly greater enhancements in Perspective-Taking (PT) and Empathic Concern (EC) relative to those in the control condition  Additionally, the degree of experienced presence within the VR environment was positively associated with an increase in empathy. These results offer compelling empirical evidence supporting the integration of VR as an effective pedagogical instrument, promoting an embodied cognition framework within social work education and providing a validated module for the development of innovative curricular designs. Received: September 20, 2025 Accepted: October 7, 2025 DOI: https://dx.doi.org/10.17654/0973700626004

Virtual Reality (VR)empathy trainingimmersive learningsocial work educationperspective-taking
4,049 views
1,263 downloads

Contributors:

 Yih-Chang Chen
,
 Chia-Ching Lin
Research PaperID: FJEC1280011

Gamification and adaptive gamification in MOOCs: A systematic literature review

A. Papadimitriou

The purpose of this study is to present current research on gamification in MOOCs. These approaches aim to enhance the effectiveness of MOOCs by addressing challenges such as low completion rates, high dropout rates, low participant engagement, feelings of isolation, motivational issues, inadequate collaboration among participants, etc. To address the problem, rigorous papers were collected on gamification and adaptive gamification in MOOCs. The findings were categorized based on each paper’s specific challenges, providing valuable insights for researchers and MOOC designers in creating new courses for further study. The analysis indicated that both gamified and adaptive gamified MOOCs can have a positive impact on distance education. The incorporation of gamification elements into these courses significantly enhances their effectiveness by overcoming the limitations associated with traditional MOOCs. Distance education can offer an improved learning experience by implementing gamification and adaptive gamification. This review aims to assist researchers and practitioners involved in gamified and adaptive gamified MOOCs in avoiding or minimizing these challenges and managing them systematically. Additionally, it emphasizes the significance of gamification in enhancing the effectiveness of MOOCs. Received: July 7, 2025 Accepted: July 23, 2025 DOI: https://doi.org/10.17654/0973700625002

MOOCsdistance educationgamificationadaptive gamification
3,505 views
942 downloads

Contributors:

 A. Papadimitriou
Research PaperID: FJEC1280012

Noise reduction and signal path loss analysis of FM transmission in hilly terrain of Nepal: Case study of Dolakha

Diwakar Paudel

This paper investigates the effects of terrain-induced signal loss on FM radio transmission for community radio stations operating in the hilly regions of Nepal, with a specific focus on Dolakha district. Using terrain profiling through Google Earth and signal modeling with Free Space Path Loss (FSPL) along with knife-edge diffraction loss assumptions, we simulate the received signal strength at various distances from a 100 MHz FM transmitter in Charikot. The results demonstrate critical signal degradation in NLoS areas due to terrain obstructions. Based on the findings, repeater stations and directional antennas are recommended to optimize signal delivery in shadowed zones. Received: July 7, 2025 Accepted: July 23, 2025 DOI: https://dx.doi.org/10.17654/0973700626001

wireless propagationterrain modelingcommunity radioknife-edge diffractionDolakhaFM signal loss+1 more
3,353 views
973 downloads

Contributors:

 Diwakar Paudel
Research PaperID: FJEC1280032

Optimum synthesis of nonuniformly excited and unequally spaced antenna arrays

Mohammad Khalaj-Amirhosseini

Nonuniformly Excited and Unequally Spaced Arrays (NEUSA) are synthesized optimally. These general type of linear antenna arrays are synthesized to have maximum directivity and minimum Amplitude Dynamic Range (ADR) for a specified sidelobe level (SLL). For this purpose, both positions of antennas and the excitation currents of antennas are considered variable and are optimized simultaneously. A relation is obtained for calculating directivity of NEUSAs versus their amplitude and positions. The presented method is investigated by a comprehensive example. The optimum synthesized arrays have both higher directivity and lower amplitude dynamic range than both equally spaced arrays and uniformly excited arrays. Received: August 28, 2025 Accepted: September 4, 2025 DOI: https://dx.doi.org/10.17654/0973700626003

Nonuniformly Excited and Unequally Spaced Arrays (NEUSA)maximum directivityspecified sidelobe level
3,622 views
1,166 downloads

Contributors:

 Mohammad Khalaj-Amirhosseini
Research PaperID: FJEC1280028

Phase-only synthesis of linear and planar arrays using auto correlation matching method

Mohammad Khalaj-Amirhosseini

We present autocorrelation matching method as a phase-only synthesis method to design power pattern of both linear and planar antenna arrays. Equating the autocorrelation coefficients of an array having a presumed amplitude of antennas to those of a previously designed amplitude-phase array forms the basis of this method. Considering certain examples, the effectiveness of the proposed method for both linear and planar arrays has been verified. Received: August 4, 2025 Revised: September 5, 2025 Accepted: October 16, 2025 DOI: https://dx.doi.org/10.17654/0973700626005

autocorrelation matching methodantenna array synthesisphase-only synthesis.
3,415 views
1,093 downloads

Contributors:

 Mohammad Khalaj-Amirhosseini