Enhanced Sliding Discrete Fourier Transform (eSDFT) With Error-Bound Control for Real-Time Parallel Processing
Issued Date
2025-01-01
Resource Type
Scopus ID
2-s2.0-105030470974
Journal Title
2025 Asia Pacific Signal and Information Processing Association Annual Summit and Conference Apsipa ASC 2025
Start Page
1218
End Page
1222
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SCOPUS
Bibliographic Citation
2025 Asia Pacific Signal and Information Processing Association Annual Summit and Conference Apsipa ASC 2025 (2025) , 1218-1222
Suggested Citation
Arnin J., Kahani D., Conway B.A. Enhanced Sliding Discrete Fourier Transform (eSDFT) With Error-Bound Control for Real-Time Parallel Processing. 2025 Asia Pacific Signal and Information Processing Association Annual Summit and Conference Apsipa ASC 2025 (2025) , 1218-1222. 1222. doi:10.1109/APSIPAASC65261.2025.11249289 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/115481
Title
Enhanced Sliding Discrete Fourier Transform (eSDFT) With Error-Bound Control for Real-Time Parallel Processing
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Abstract
This study presents the enhanced Sliding Discrete Fourier Transform (DFT), a novel method for real-time frequency analysis optimized for multi-core platforms. Traditional SDFT approaches suffer from error accumulation and inefficiencies in parallel processing. The proposed eSDFT introduces a boundederror recursive formulation with m-sample shift updates, enabling efficient, scalable computation across CPU cores. A parallel update strategy ensures low-latency processing without compromising spectral accuracy. Complexity analysis reveals a significant reduction in operations per sample, while experiments on synthetic and real signals demonstrate that eSDFT achieves a speedup of up to 4 times compared to FFTW for N=8192, with negligible degradation in accuracy. The method is well-suited for high-throughput applications such as biomedical signal processing and vibration monitoring, offering both robustness and performance in constrained environments.