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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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Journal : Bulletin of Electrical Engineering and Informatics

 1 
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.
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.
Co-Authors Amritha P. P Anh Quang Tran Anh-Ngoc Le Bo Quoc Bao Chandrakumar Thangavel Chandrakumar Thangavel Do Quang Trang Duc Khanh Pham Duc-Nghia Tran Duc-Nghia Tran Gunnam Suryanarayana Karthik Chandran Kolla Bhanu Prakash Nguyen Duc Uyen Nguyen Thi Thu Ninh Trung Bui Phuc Thinh Doan Ramya Thangavel Subrata Chowdhury Subrata Chowdhury Subrata Chowdhury Thi Thu Nguyen Thi-Thu Nguyen Valliammai S E