Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal IAES International Journal of Robotics and Automation (IJRA) IAES International Journal of Artificial Intelligence (IJ-AI)
Yee Kit Chan
Multimedia University
Automotive Engineering Computer Science & IT Electrical & Electronics Engineering Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Tree diameter at breast height measurement based on computer vision Mohamad Razmil Abdul Rahman; Ishak Suleiman; Mohammed Al Haek; Yee Kit Chan
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i2.pp458-472

Abstract

Diameter at breast height (DBH) is a crucial metric in forestry, serving as a key input for estimating timber volumes and biomass, assessing forest health, and aiding in biodiversity and climate change studies. However, traditional measurement methods practiced today are time-consuming and labour-intensive, while many advanced methods introduced in recent years require high upfront costs, limiting wide adoption by small-scale institutions and projects. This research paper aims to explore innovative approaches to DBH measurement that balance accuracy with cost-effectiveness, ultimately contributing to the broader goals of sustainability and environmental protection. In this paper, the authors propose an automated DBH measurement method, extracting the value from smartphone RGB images through the utilization of computer vision techniques and mathematical algorithms. By incorporating tree distance data in Phase 3 of the study, the proposed method achieved accuracy comparable to manual tape measurements while significantly reducing the time and resources required for fieldwork. Specifically, 74 out of 143 trees (51.7%) had an estimated DBH that fell within 1 cm of the actual measurements, resulting in an absolute mean error (MAE) of 1.10 cm, root mean square error (RMSE) of 1.80 cm, and relative root mean square error (RRMSE) of 6.0%. Thus, this hybrid approach offers a promising solution for forestry applications, enhancing both the efficiency and accessibility of DBH data collection.
Robust UAV localization of ground sensors in urban environments via path loss refinement and geometric selection Ahmed M. A. A. Elngar; Heng Siong Lim; Yee Kit Chan; Yaser Awadh Bakhuraisa; Ida Wahidah
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp412-428

Abstract

Localizing ground sensors with unmanned aerial vehicles (UAVs) in dense urban environments is challenging because multipath and non-line-of-sight (NLoS) propagation distorts path loss (PL) measurements. This paper proposes a two-stage UAV localization framework that refines PL data and selects geometrically stable waypoint subsets before position estimation. In stage 1, PL samples are spatially smoothed by averaging measurements at neighboring UAV waypoints to reduce localized fluctuations. In stage 2, waypoint subsets are filtered using non-collinearity and non-adjacency constraints, and sensor positions are estimated using weighted least squares (WLS) and particle swarm optimization (PSO), with final estimates averaged across valid subsets. Wireless InSite ray-tracing simulations show that the framework reduces mean absolute error (MAE) from over 150 m to approximately 8.5 m. The proposed approach improves the practicality of UAV-assisted localization for urban internet of things (IoT) sensor deployments.
Co-Authors Ahmed M. A. A. Elngar Heng Siong Lim Ida Wahidah Hamzah Ishak Suleiman Mohamad Razmil Abdul Rahman Mohammed Al Haek Yaser Awadh Bakhuraisa