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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics
Nik Syahrim Nik Anwar
Universiti Teknikal Malaysia Melaka
Computer Science & IT Electrical & Electronics Engineering

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Stereo matching based on absolute differences for multiple objects detection Rostam Affendi Hamzah; Melvin Gan Yeou Wei; Nik Syahrim Nik Anwar
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 1: February 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i1.9185

Abstract

This article presents a new algorithm for object detection using stereo camera system. The problem to get an accurate object detion using stereo camera is the imprecise of matching process between two scenes with the same viewpoint. Hence, this article aims to reduce the incorrect matching pixel with four stages. This new algorithm is the combination of continuous process of matching cost computation, aggregation, optimization and filtering. The first stage is matching cost computation to acquire preliminary result using an absolute differences method. Then the second stage known as aggregation step uses a guided filter with fixed window support size. After that, the optimization stage uses winner-takes-all (WTA) approach which selects the smallest matching differences value and normalized it to the disparity level. The last stage in the framework uses a bilateral filter. It is effectively further decrease the error on the disparity map which contains information of object detection and locations. The proposed work produces low errors (i.e., 12.11% and 14.01% nonocc and all errors) based on the KITTI dataset and capable to perform much better compared with before the proposed framework and competitive with some newly available methods.
Improved no-line-of-sight static and dynamic sensor data classification using KNN algorithm with PLS model Enoch Adama Jiya; Ilesanmi B. Oluwafemi; Francis Ayoleke Ibikunle; Nik Syahrim Nik Anwar
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.v15i2.9437

Abstract

The rising cases of structural collapses across the world have aggravated the problem of finding people under rubble as part of search and rescue (SAR) effort. The conventional search techniques, namely drill operation and the use of dog searching, are usually slow, labour-intensive and unsuccessful in difficult debris setting. Radar systems, though non-invasive, are limited by attenuation and multipath interference in non-line-of-sight (NLOS) environments. The proposed research will contribute to the improvement of victim recognition by creating an advanced machine learning (ML) model that will operate in the most challenging environmental settings. It suggests a modular prediction model combining both the K-nearest neighbor (KNN) and partial least squares (PLS) to extract features and reduce the dimensions. The procedure includes the derivation of essential signal characteristics, dataset validation, PLS application to get limited and discriminative feature amounts, and KNN classification under conditions of both fixed and dynamic conditions. Experimental findings indicate classification scores of 87.87 and 75.70 respectively in case of static and dynamic data. These results validate the practicability of the suggested solution in enhancing the forecasting accuracy during NLOS circumstances and emphasize its possible use in enhancing quicker, more dependable, and evidence-based official choices during the actual SAR operations.
Co-Authors Enoch Adama Jiya Francis Ayoleke Ibikunle Ilesanmi B. Oluwafemi Melvin Gan Yeou Wei Rostam Affendi Hamzah