@inproceedings{491e58687b814d67a3bb9b3d076b7f18,
title = "Enhancing Tsunami Warnings with Low-Rank Matrix Estimation for Signal Reconstruction",
abstract = "This study aims to improve signal reconstruction for tsunami early warning systems by utilizing low-rank matrix estimation to handle sparse underwater acoustic data. The objectives are to measure signal sparsity using the Gini Index and to compare the effectiveness of three reconstruction methods: Orthogonal Least Square (OLS), Orthogonal Matching Pursuit (OMP), and L1 Norm. The methods involve processing acoustic data from underwater experiments and evaluating reconstruction success based on error magnitude, computation time, and cosine similarity. Results indicate that the L1 Norm method outperforms OLS and OMP in terms of lower error rates and faster computation times. The findings have significant implications for enhancing the reliability and timeliness of tsunami early warnings, with potential applications in other fields such as medical imaging and wireless communications.",
keywords = "L1 Norm, OLS, OMP, low rank matrix, reconstruction",
author = "Setianingsih, {Dhenok Ayu} and Dhany Arifianto and Suyanto and Liana, {Margiasih Putri}",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 3rd International Conference on Creative Communication and Innovative Technology, ICCIT 2024 ; Conference date: 07-08-2024 Through 08-08-2024",
year = "2024",
doi = "10.1109/ICCIT62134.2024.10701263",
language = "English",
series = "2024 3rd International Conference on Creative Communication and Innovative Technology, ICCIT 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2024 3rd International Conference on Creative Communication and Innovative Technology, ICCIT 2024",
address = "United States",
}