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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Nik Anwar, Nik Syahrim
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Electrical & Electronics Engineering

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Improvement of horizontal streak on disparity map thru parameter optimization for stereo vision algorithm Yeou Wei, Melvin Gan; Hamzah, Rostam Affendi; Nik Anwar, Nik Syahrim; Herman, Adi Irwan; Jamil Alsayaydeh, Jamil Abedalrahim
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1886-1894

Abstract

In this paper, an improved local based stereo vision disparity map (SVDM) algorithm is proposed. The proposed local based SVDM algorithm include four stages and they are matching cost computation, cost aggregation disparity optimization and disparity refinement. The matching cost computation started by combining pixel to pixel matching techniques, which are absolute difference (AD) and gradient matching (GM) in producing the initial disparity map. Next, the cost aggregation uses minimum spanning tree (MST) segmentation, which equipped with edge preserving properties and noise filtering. Then, disparity optimization uses local approach with winner-take-all (WTA) technique. At the final stage, disparity refinement uses bilateral filter (BF) with weighted median (WM), which can improve the disparity map through noise removing and edges preserving. Then, the research continues to optimize the proposed local based SVDM algorithm through parameters optimization in obtaining the final disparity map. Here, multiple parameters from the proposed SVDM algorithm are manipulated and they are constant values for GM and several constant parameters in BF. By selecting the optimum parameter values, the performance of the proposed SVDM algorithm increased, especially robustness towards the horizontal streaks.
Improved no-line-of-sight static and dynamic sensor data classification using KNN algorithm with PLS model Jiya, Enoch Adama; B. Oluwafemi, Ilesanmi; Ibikunle, Francis Ayoleke; Nik Anwar, Nik Syahrim
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 B. Oluwafemi, Ilesanmi Hamzah, Rostam Affendi Herman, Adi Irwan Ibikunle, Francis Ayoleke Jamil Alsayaydeh, Jamil Abedalrahim Jiya, Enoch Adama Yeou Wei, Melvin Gan