Academic Journal
Low-latency stage-adaptive cascade architecture for real time non-stationary noise filtering.
| Τίτλος: | Low-latency stage-adaptive cascade architecture for real time non-stationary noise filtering. |
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| Συγγραφείς: | Han-Trong T; School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi, Vietnam., Van TB; School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi, Vietnam., Minh QH; School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi, Vietnam., Do Trung A; Department of Science and Technology Management and International Cooperation, Posts and Telecommunications Institute of Technology, Hanoi, Vietnam. |
| Πηγή: | PloS one [PLoS One] 2026 Jul 22; Vol. 21 (7), pp. e0354022. Date of Electronic Publication: 2026 Jul 22 (Print Publication: 2026). |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: San Francisco, CA : Public Library of Science |
| Ιατρικοί όροι (MeSH): | Algorithms* , Signal Processing, Computer-Assisted*, Signal-To-Noise Ratio ; Computer Simulation ; Least-Squares Analysis |
| Περίληψη: | In many real-time measurement and monitoring systems, the quality of acquired signals is often severely degraded by complex environmental noise sources with non-stationary properties, rendering analysis, important feature extraction, and decision-making unreliable. This study proposes a multi-stage adaptive denoising architecture based on the least mean square (LMS) algorithm, in which the number of filter stages and the step size are automatically adjusted according to error statistics, the remaining correlation between the residual and the reference signal, and the real-time signal-to-noise ratio (SNR) of the signal. The stopping mechanism is determined by a two-tailed Fisher-z correlation test, with effective sample size correction in the presence of autocorrelation and modulation based on SNR, to ensure the stability of the adaptive system against non-stationary noise. The filter is evaluated on simulated signal datasets and real-world measured data. Compared with the conventional LMS filter configuration under the tested simulated conditions, the proposed architecture reduces mean squared error (MSE) by 38-82% and mean absolute error (MAE) by 15-45%, while improving both SNR and peak signal-to-noise ratio (PSNR). The execution time of the proposed method is approximately 3.5-4 times lower than that of the fixed-threshold method under the tested settings. These results indicate that the proposed method can improve the trade-off between denoising performance and computational efficiency, showing potential for low-latency implementation on resource-constrained devices. (Copyright: © 2026 Han-Trong et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.) |
| Competing Interests: | The authors have declared that no competing interests exist. |
| References: | Biomed Signal Process Control. 2024 Aug;94:106235. (PMID: 39846001) Sensors (Basel). 2025 Oct 28;25(21):. (PMID: 41228852) Sensors (Basel). 2025 Aug 13;25(16):. (PMID: 40871880) Biomed Res Int. 2021 Feb 24;2021:7565398. (PMID: 33681379) Br J Math Stat Psychol. 2022 Feb;75(1):1-22. (PMID: 33934346) Comput Biol Med. 2025 Oct;197(Pt A):110850. (PMID: 40907259) Front Artif Intell. 2024 Nov 13;7:1434022. (PMID: 39605951) |
| Entry Date(s): | Date Created: 20260722 Date Completed: 20260722 Latest Revision: 20260813 |
| Update Code: | 20260814 |
| PubMed Central ID: | PMC13390872 |
| DOI: | 10.1371/journal.pone.0354022 |
| PMID: | 42485427 |
| Βάση Δεδομένων: | MEDLINE |
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