Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Mechatronics, Electrical Power, and Vehicular Technology International Journal of Robotics and Control Systems
Edwin Romeroso Arboleda, Edwin Romeroso
Cavite State University
Control & Systems Engineering Electrical & Electronics Engineering

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

Found 2 Documents
Search

 1 
Development of a Low-Cost Electronic Wheelchair with Obstacle Avoidance Feature Arboleda, Edwin Romeroso; Alegre, Mary Christine Tumambing; Idica, Kathleen Felix
Journal of Mechatronics, Electrical Power and Vehicular Technology Vol 6, No 2 (2015)
Publisher : Research Centre for Electrical Power and Mechatronics, Indonesian Istitutes of Sciences

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (493.136 KB) | DOI: 10.14203/j.mev.2015.v6.89-96

Abstract

A low-cost electronic wheelchair was designed and developed which can perform the similar functions and features as a commercially available wheelchair. It also provides obstacle avoidance capability as added value. The electronic wheelchair was  realized by modification of a lightweight manual wheelchair. It uses two electric motors each of 320 W 24 V DC, 5-24 VDC 6 A H-bride drivers, and a 12 V 17 Ah rechargeable lead acid battery. It equipped with switches, joystick, infrared sensors and ultrasonic sensors. A GizduinoAtMega 328 microcontroller is used to read and interpret commands. User’s acceptance evaluation results shows that the developed low-cost wheelchair is able to receive and interpret commands provided by the joystick, detect if a person  is seated on it, navigate to avoid obstacles as well as to detect edge and stairs. Technical evaluation result shows that on a flat surface it could move at the speed of around 39.9 m/minute without load and 32 m/minute with 80 kg load. At 10 degrees inclined surface, the maximum weight limit is 30 kg with the speed of 12 m/minute. At 20 degrees inclined surface, the maximum weight limit is 10 kg with the speed of 3 m/minute. Regarding cost, it is just a fraction of a cost compared to the commercially available model. Therefore, the developed wheelchair offers an option for potential users who cannot afford to buy the commercially available one.
Efficient Detection Classifiers for Genetically-Modified Golden Rice Via Machine Learning Gutierrez, Joshua Balistoy; Arboleda, Edwin Romeroso
International Journal of Robotics and Control Systems Vol 5, No 2 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i1.1686

Abstract

Rice is a staple food for over half of the global population, especially in the Philippines. However, traditional rice lacks essential micronutrients like vitamin A, contributing to widespread Vitamin A Deficiency (VAD). Golden Rice was developed to combat VAD, and this is biofortified with beta-carotene, a precursor of Vitamin A. However, concerns about cross-contamination, food safety, and ethics have emerged. Current GMO detection methods, such as PCR and ELISA, are not ideal for large-scale or on-site use since these are intended to be performed inside laboratory and requires technical expertise.  This study presents a novel machine learning (ML)-based approach for the detection of genetically modified Golden Rice using RGB image data and several classification models as an efficient, rapid, non-destructive method to detect GMO Golden Rice. Two datasets of rice images (340 samples of GMO Golden Rice and 340 samples of Traditional Rice) were processed and split for training and testing (80-20 ratio). This study found that WEKA's Random Tree and MATLAB's Trilayered Neural Network achieved 100% accuracy in detecting GMO Golden Rice, with the fastest computational efficiency in their respective platforms. Additional metrics, such as Precision and Recall, further verified the robustness of these classifiers.  This research lays the foundation for developing portable, field-deployable detection tools to empower farmers and regulators while enhancing consumer trust in GMO labeling. Furthermore, the application of ML to GMO rice detection opens new possibilities for biofortified crop monitoring. Future work may explore integrating additional rice features and GMO varieties, validating the results, and expanding this methodology to other GMO rice variants and hybrid varieties to further enhance detection accuracy and scalability.
Co-Authors Gutierrez, Joshua Balistoy Kathleen Felix Idica, Kathleen Felix Mary Christine Tumambing Alegre, Mary Christine Tumambing