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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Shahrul Nizam Yaakob
Universiti Malaysia Perlis
Computer Science & IT Electrical & Electronics Engineering

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Improving the iterative back projection estimation through Lorentzian sharp infinite symmetrical filter Amir Nazren Abdul Rahim; Shahrul Nizam Yaakob; Lee Yeng Seng; Mohd Wafi Nasrudin; Iszaidy Ismail
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp2539-2552

Abstract

This study proposed an enhancement technique for improvising the estimation technique in iterative back projection (IBP) by using the Lorentzian error function with a sharp infinite symmetrical filter (SISEF). The IBP estimation is an iteratively based error correction that can minimize the error reconstruction significantly. However, the IBP has a drawback in that it suffers from jaggy and ringing artifacts as a result of the iterative reconstruction method and the absence of edge guidance. Furthermore, because the IBP estimator tended to oscillate at the same solution frequently, numerous iterations were required. Therefore, this study proposed edge enhancement to enhance the estimator by using the combination of the IBP with Lorentzian SISEF to produce a finer high-resolution output image. As a result, the SISEF is used to improvise the estimator by providing high accuracy of edge detail information for enhancing the edge image. At the same time, the Lorentzian error norm helps to increase the robustness of the IBP algorithm from contamination of additional noise and the ringing artifacts.
Accuracy and Stability Trade-off in RSSI Window Aggregation for MLP-Based Wi-Fi Indoor Positioning Wahyu Arthanugraha; Abdul Halim Ismail; Shahrul Nizam Yaakob
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 3: September 2026
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i3.8256

Abstract

Wi-Fi RSSI fingerprinting is practical for indoor positioning, but short-term signal fluctuations can reduce classification consistency. This study evaluates how fixed RSSI window size affects the accuracy, stability, and sample availability of an MLP-based indoor positioning system. RSSI from three Access Points was collected at 45 ordered corridor positions. Non-overlapping windows with n=1,3,5,7,9,11,15,21 and 29 were represented by the mean and standard deviation of three RSSI channels. The MLP was assessed using stratified 5-fold cross-validation with supplementary order-blocked validation. Relative to the n=1 baseline Accuracy of 68.24%, n=7 achieved 90.60 ± 0.71% Accuracy, 95.08 ± 1.31% Top-2 Accuracy, 0.9047 ± 0.0060 Macro-F1, and 0.4319 ± 0.1364 Ordinal Error. Larger windows achieved higher mean Accuracy but sharply reduced window-level samples and increased fold-to-fold variability. Order-blocked validation produced more conservative estimates and shifted the strongest performance region toward n=9 to n=11. These findings show that fixed-window selection should consider predictive performance, evaluation stability, sample availability, and validation design rather than maximum Accuracy alone.
Co-Authors Abdul Halim Ismail Amir Nazren Abdul Rahim Iszaidy Ismail Lee Yeng Seng Mohd Wafi Nasrudin Wahyu Arthanugraha