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All Journal IAES International Journal of Robotics and Automation (IJRA) Bulletin of Electrical Engineering and Informatics
Yusof, Ashaari
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Automotive Engineering Electrical & Electronics Engineering

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Remote procedure call communication and control of autonomous mobile robot for indoor smart waste monitoring Yusof, Ashaari; Man, Abdullah; Ibrahim, Azmi; Husni Zai, Mohamed Ashraf; Hossen, Md. Jakir
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i1.pp89-98

Abstract

The integration of autonomous mobile robots (AMRs) and Internet of Things (IoT) technology has revolutionized various industries, including smart waste management (SWM). In this paper, the implementation of a customized remote procedure call (RPC) methodology was successfully demonstrated. This methodology facilitated control and monitoring of AMRs for smart indoor waste management to collect and dispose waste, monitor bin threshold levels and report relevant parameters to a cloud-based platform. Key operational parameters from the AMR and the smart bins via assembled user smart dashboard ensures seamless user monitoring for indoor waste management. Our findings underscore the relevance of RPC in advancing smart waste management technologies, contributing to operational efficiency and sustainability.
Real-time object detection and XAI-based activation map visualization using YOLOv8s Yusof, Ashaari; Hishamuddin, Muhammad; Hossen, Md. Jakir
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.9765

Abstract

This study introduced a methodology for real-time object detection and interpretability using YOLOv8s, trained on the MS common objects in context (COCO) dataset. The system captured live webcam footage, processes frames resized to 640×384, and applies YOLOv8s to detect objects with bounding boxes, labels, and confidence scores. YOLOv8s architecture comprising a CSPDarknet53-based backbone, neck, and head ensures efficient feature extraction and accurate detection. To enhance model transparency, activation map generation is implemented by attaching forward hooks to intermediate convolutional layers. Feature maps are captured during the forward pass, averaged, normalized, and resized to match the original image dimensions. This visualization highlights regions influencing the model’s predictions, aligning with explainable artificial intelligence (XAI) principles. Experimental results demonstrate high detection accuracy and effective interpretability in indoor environments, making the framework suitable for robotics applications requiring both precision and transparency. The proposed method offers a practical and explainable solution for real-time scene understanding in intelligent systems.
Co-Authors Hishamuddin, Muhammad Hossen, Md. Jakir Husni Zai, Mohamed Ashraf Ibrahim, Azmi Man, Abdullah