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p-Index From 2021 - 2026
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This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Journal of Applied Engineering and Technological Science (JAETS)
Duc-Tan Tran
Phenikaa University
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Develop algorithms to determine the status of car drivers using built-in accelerometer and GBDT Thi Thu Nguyen; Phuc Thinh Doan; Anh-Ngoc Le; Kolla Bhanu Prakash; Subrata Chowdhury; Duc-Nghia Tran; Duc-Tan Tran
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i1.pp785-792

Abstract

In this paper, we introduce a mobile application called CarSafe, in which data from the acceleration sensor integrated on smartphones is exploited to come up with an efficient classification algorithm. Two statuses, "Driving" or "Not driving," are monitored in the real-time manner. It enables automatic actions to help the driver safer. Also, from these data, our software can detect the crash situation. The software will then automatically send messages with the user's location to their emergency departments for timely assistance. The application will also issue the same alert if it detects a driver of a vehicle driving too long. The algorithm's quality is assessed through an average accuracy of 96.5%, which is better than the previous work (i.e., 93%).
Improvements on the performance of subcarrier multiplexing/wavelength division multiplexing based radio over fiber system Duc-Tan Tran; Ninh Trung Bui
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i2.pp1439-1449

Abstract

Radio over fiber (RoF) techniques are good candidates to create the backbone of the next generation of wireless networks. Many parameters affect RoF communications such as amplified spontaneous emission noise (ASE), four-wave mixing nonlinearity (FWM), the modulation, channel spacing, switching voltage, and phase shifter. In this paper, we propose an improved model of RoF communication systems using subcarrier multiplexing/wavelength division multiplexing (SCM/WDM) technique with unequal channel spacing and 1-km Erbium-doped fiber amplifier (EDFA). Simulation results confirmed that we could obtain the lowest bit error rate and noises when the EDFA is placed at 1 km from the transmitter by using optical single-sideband (OSSB) modulation at frequencies 193.1 THz, 193.2 THz, 193.35 THz, and 193.6 THz.
A study on agricultural engineering equipment in South Tamilnadu using linear regression Chandrakumar Thangavel; Ramya Thangavel; Karthik Chandran; Gunnam Suryanarayana; Subrata Chowdhury; Nguyen Duc Uyen; Thi-Thu Nguyen; Duc-Tan Tran
Bulletin of Electrical Engineering and Informatics Vol 11, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i3.3325

Abstract

Economic growth in India purely depends on the Indian agricultural sector. In developing countries, the mechanization of agriculture plays a vital role in productivity. The research focuses on identifying which farmers in South Tamilnadu mostly use agricultural machinery. In this paper, we have taken farmer names and mobile numbers, choice of implement requirement into consideration by collecting the real data through DBT portal (https://agrimachinery.nic.in). This research work deals with five southern districts in Tamilnadu, namely Dindigul, Madurai, Theni, Ramnad, and Virudhunagar, in which we have predicted which machinery is suitable for that area. The linear regression model was used in this research by testing and training the dataset in all five data frames to get efficient results. Prediction of each data frame reveals the efficient working of the particular machinery for that specific area due to the different geographical features.
Students’ Activeness Measure in Moodle Learning Management System Using Machine Learning Chandrakumar Thangavel; Valliammai S E; Amritha P. P; Karthik Chandran; Subrata Chowdhury; Nguyen Thi Thu; Bo Quoc Bao; Duc-Tan Tran; Duc-Nghia Tran; Do Quang Trang
Journal of Applied Engineering and Technological Science (JAETS) Vol. 6 No. 1 (2024): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v6i1.6128

Abstract

Due to COVID-19, the need for online education has increased worldwide, prompting students to shift from traditional learning methods to online platforms as guided by higher education departments. Higher learning institutes are focused on developing constructive online learning platforms. This research aims to measure students’ academic performance on an online learning platform – Moodle Learning Management System (LMS) – using machine learning techniques. Moodle LMS, a popular free and open-source system, has seen significant growth since the COVID-19 lockdown. Many researchers have analyzed student performance in online learning, yet there remains a need to predict academic outcomes effectively. In this study, data were collected from a higher learning institute in Tamil Nadu, and linear regression was applied to predict students' final course outcomes. The analysis, based on students' activity in Moodle LMS across both theory and laboratory courses, helps faculty identify students at risk of failing and adjust instructional methods and assignments accordingly. This approach aims to reduce failure rates by providing timely warnings and encouraging students to improve their engagement with LMS resources.
Evaluating random–Nyquist sampling ratios in combined compressed sensing magnetic resonance imaging Duc Khanh Pham; Duc-Tan Tran; Anh Quang Tran
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10333

Abstract

Compressed sensing (CS) has been widely applied in magnetic resonance imaging (MRI) to accelerate the image acquisition without significantly reducing its image quality. In Cartesian MRI, acquisition time can be reduced by skipping phase-encoding steps for faster data acquisition. However, the balance between random under-sampling and Nyquist sampling at the k-space center strongly determines image quality. In this study, we systematically evaluate the impact of different random-to-Nyquist sampling ratios for both single-coil (CS-MRI) and multi-coil (CS-pMRI) reconstructions. Simulation results reveal that dense Nyquist sampling around the k-space center is essential for maintaining image fidelity, whereas reconstruction quality deteriorates sharply when random sampling exceeds approximately 60% of the total under-sampled data. Moreover, CS-pMRI consistently outperforms CS-MRI under equivalent under-sampling factors, benefiting from additional coil sensitivity information that improves resilience against aliasing and noise. These findings provide practical guidelines for hybrid under-sampling design, emphasizing that sufficient Nyquist sampling coverage of central k-space is crucial for achieving high-quality reconstructions while enabling high acceleration in CS-MRI.
Efficient Road Surface Classification on Low-Cost Devices Using Vehicle Vibration Data Cong Ngo Van; Duc-Nghia Tran; Thu Bui Thi; Vu Duong Tung; Pham Quang Huy; Manh Tuyen Vi; Duc-Tan Tran
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/2afgj009

Abstract

During road traffic operations, pavement quality directly affects safety, vehicle operating costs, and pavement maintenance activities. Traditional inspection methods are often costly and time-consuming, and they cannot provide continuous data on pavement conditions. This study aims to develop an efficient road-surface classification system capable of real-time operation on low-cost hardware devices. The system uses vibration data collected from vehicles in motion to identify and classify road types with high accuracy and optimized performance. The proposed system employs inertial sensors mounted on vehicles to acquire accelerometer and gyroscope signals and then extracts time-domain statistical features from these signals. To address the main challenge of deploying an effective recognition model in a resource-constrained computing environment, the paper proposes a hybrid feature selection algorithm that combines filter and wrapper methods. This algorithm leverages the fast-processing speed of filter methods and the effective feature selection capability of wrapper methods. The selected feature set is then evaluated using three machine learning models: Random Forest (RF), Gradient Boosting (GBM), and XGBoost. The classification task focuses on three real-world pavement types: smooth asphalt (with less than 10 years of service), degraded asphalt (with more than 15 years of service), and cement concrete pavement. Experimental results show that the proposed feature selection algorithm and classification models achieve high classification performance and fast execution speed. The system attains accuracy higher than 0.95 while reducing computational cost. These findings confirm the feasibility of deploying road-surface classification systems on low-cost devices for real-time pavement monitoring and highlight the importance of appropriate feature selection in balancing system accuracy and performance.
VRACE-VANET : Fuzzy-based Relaible Adaptive Clustering Approach For Connectivity Enhancement J. Naskath; Subir Gupta; Duc-Tan Tran; Nguyen Canh Minh
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i2.9444

Abstract

Vehicular Ad Hoc Networks (VANETs) play an important role in ensuring reliable communication in Intelligent Transportation Systems (ITS). This helps to improve efficient transportation services for vehicles. However, several existing clustering methods such as mobility based and weighted clustering algorithms, which face challenges in maintaining stability in clusters. This issue is further pronounced in environments where there is high vehicle mobility and periodic changes in network structure. Therefore, to overcome these drawbacks, this study proposes Vehicular Reliable Adaptive Clustering Environment (VRACE), an adaptive clustering method based on a fuzzy approach. This incorporates queuing theory to improve the cluster stability and communication efficiency of the network. This method selects the cluster heads based on several factors such as relative mobility, direction of vehicles, link quality, travel direction and vehicle speed. Estimating these factors allows the structure to make adaptive decisions suitable for dynamic vehicular environments. This system was evaluated through simulation under different vehicle density scenarios using SUMO and NS2.  The proposed method improves overall network performance by showing approximately 14% increase in cluster lifetime, 2.5% higher throughput, 4.3% improvement in packet delivery ratio (PDR) and 22.5% reduction in end-to-end delay. These findings indicate that VRACE can support reliable communication in dense and rapidly changing vehicular networks.
Co-Authors Amritha P. P Anh Quang Tran Anh-Ngoc Le Bo Quoc Bao Chandrakumar Thangavel Chandrakumar Thangavel Cong Ngo Van Do Quang Trang Duc Khanh Pham Duc-Nghia Tran Duc-Nghia Tran Gunnam Suryanarayana J. Naskath Karthik Chandran Kolla Bhanu Prakash Manh Tuyen Vi Nguyen Canh Minh Nguyen Duc Uyen Nguyen Thi Thu Ninh Trung Bui Pham Quang Huy Phuc Thinh Doan Ramya Thangavel Subir Gupta Subrata Chowdhury Subrata Chowdhury Subrata Chowdhury Thi Thu Nguyen Thi-Thu Nguyen Thu Bui Thi Valliammai S E Vu Duong Tung