2025, Vol. 6, Issue 2, Part A
Signal processing algorithms for noise reduction in high-dimensional systems
Author(s): Ramesh Kumar Thapa, Sita Pradhan and Anil Raj Shrestha
Abstract: High-dimensional systems are increasingly common in fields such as remote sensing, communications, and biomedical signal processing, where data typically comprises multivariate signals with interdependent components. These systems often suffer from noise contamination, which can significantly reduce the fidelity of signal interpretation. Traditional noise-reduction techniques, such as Wiener filtering or wavelet-based methods, frequently fail to perform optimally when applied to high-dimensional data due to challenges associated with high variance, noise correlation, and large computational demands [1-5]. In this article, we review state-of-the-art signal-processing algorithms for noise reduction specifically designed for high-dimensional systems. We focus on dimensionality reduction techniques like Principal Component Analysis (PCA), Independent Component Analysis (ICA), and kernel-based approaches, which have shown promise in improving signal fidelity while reducing noise [6, 7]. Furthermore, we introduce a novel hybrid algorithm that combines dimensionality reduction with adaptive filtering, hypothesizing that this integration will yield better results compared to conventional methods that rely solely on adaptive filtering. The objective is to demonstrate that by reducing dimensionality before applying adaptive filtering, the SNR (signal-to-noise ratio) can be significantly improved. Simulation results on high-dimensional datasets in remote sensing and biomedical signal applications confirm that the proposed hybrid approach outperforms baseline techniques, showing higher robustness and efficiency in both noise suppression and computational load [8-11]. This research highlights key contributions in the field, focusing on the advantages of incorporating dimensionality reduction as a preprocessing stage in high-dimensional signal processing [12-15]. Future work may involve fine-tuning these algorithms for real-time applications in various domains.
DOI: 10.22271/27084531.2025.v6.i2a.104
Pages: 61-65 | Views: 83 | Downloads: 45
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How to cite this article:
Ramesh Kumar Thapa, Sita Pradhan, Anil Raj Shrestha. Signal processing algorithms for noise reduction in high-dimensional systems. Int J Res Circuits Devices Syst 2025;6(2):61-65. DOI: 10.22271/27084531.2025.v6.i2a.104
