Removing Noise from Noisy Signal Data within Principal Component Analysis Framework

Mehmet Cevri

doi:10.29130/dubited.1649830

EN TR

Removing Noise from Noisy Signal Data within Principal Component Analysis Framework

Abstract

The separation of noise from data represents one of the fundamental problems in signal processing. Principal component analysis (PCA) is a multivariate statistical technique that is employed in all scientific disciplines for the identification of patterns in data and the compression of data by reducing the size without significant loss of information. This paper concerns the removal of noise from noisy sinusoidal data using PCA. The aim is to achieve this by focusing on the separation of noise from signal data without estimating the parameters of sinusoidal signals. To this end, a code was developed in the Mathematica programming language, with modifications of its algorithm then being assessed on data derived from a number of noisy signals. The effectiveness of PCA was assessed by using the mean square error (MSE) values in relation to the variation in signal-to-noise ratio (SNR). The simulation results obtained demonstrate the effectiveness of PCA in removing noise from noisy sinusoidal signals.

Keywords

Principal component analysis, sinusoidal, dimension reduction, optimization

Supporting Institution

This work is part of the project number 41895 that was submitted to Istanbul University's Scientific Projects Coordination Unit for support.

Ethical Statement

The conducted research is not related to either human or animal use.

Temel Bileşenler Analizi Çerçevesinde Gürültülü Sinyal Verilerinden Gürültünün Giderilmesi

Abstract

Gürültünün verilerden ayrılması, sinyal işlemenin temel problemlerinden birini temsil etmektedir. Temel bileşen analizi (PCA), verilerdeki örüntülerin tanımlanması ve önemli bilgi kaybı olmadan boyutun küçültülerek verilerin sıkıştırılması için tüm bilimsel disiplinlerde kullanılan çok değişkenli bir istatistiksel tekniktir. Bu makale, PCA kullanarak gürültülü sinüzoidal verilerden gürültünün giderilmesi ile ilgilenmektedir. Amaç, sinüzoidal sinyallerin parametrelerini tahmin etmeden sinyal verilerinden gürültünün ayrılmasına odaklanarak bunu başarmaktır. Bunun için, Mathematica programlama dilinde bir kod geliştirilmiş ve algoritmasının modifikasyonları daha sonra bir dizi gürültülü sinyalden elde edilen veriler üzerinde değerlendirilmiştir. PCA'nın etkinliği, sinyal-gürültü oranındaki (SNR) değişime bağlı olarak ortalama kare hata (MSE) değerleri kullanılarak değerlendirilmiştir. Elde edilen simülasyon sonuçları, PCA'nın gürültülü sinüzoidal sinyallerdeki gürültüyü gidermedeki etkinliğini göstermektedir.

Keywords

Temel bileşenler analizi, sinüzoidal, boyut azaltma, optimizasyon

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Details

Primary Language

English

Subjects

Classification Algorithms, Electrical Engineering (Other)

Journal Section

Research Article

Authors

Mehmet Cevri ^*
0000-0002-7388-4412
Türkiye

Publication Date

July 31, 2025

Submission Date

March 2, 2025

Acceptance Date

June 12, 2025

Published in Issue

Year 2025 Volume: 13 Number: 3

DOI

https://doi.org/10.29130/dubited.1649830

IZ

https://izlik.org/JA65TY99NN

APA

Cevri, M. (2025). Removing Noise from Noisy Signal Data within Principal Component Analysis Framework. Duzce University Journal of Science and Technology, 13(3), 1371-1384. https://doi.org/10.29130/dubited.1649830

AMA

1.Cevri M. Removing Noise from Noisy Signal Data within Principal Component Analysis Framework. DUBİTED. 2025;13(3):1371-1384. doi:10.29130/dubited.1649830

Chicago

Cevri, Mehmet. 2025. “Removing Noise from Noisy Signal Data Within Principal Component Analysis Framework”. Duzce University Journal of Science and Technology 13 (3): 1371-84. https://doi.org/10.29130/dubited.1649830.

EndNote

Cevri M (July 1, 2025) Removing Noise from Noisy Signal Data within Principal Component Analysis Framework. Duzce University Journal of Science and Technology 13 3 1371–1384.

IEEE

[1]M. Cevri, “Removing Noise from Noisy Signal Data within Principal Component Analysis Framework”, DUBİTED, vol. 13, no. 3, pp. 1371–1384, July 2025, doi: 10.29130/dubited.1649830.

ISNAD

Cevri, Mehmet. “Removing Noise from Noisy Signal Data Within Principal Component Analysis Framework”. Duzce University Journal of Science and Technology 13/3 (July 1, 2025): 1371-1384. https://doi.org/10.29130/dubited.1649830.

JAMA

1.Cevri M. Removing Noise from Noisy Signal Data within Principal Component Analysis Framework. DUBİTED. 2025;13:1371–1384.

MLA

Cevri, Mehmet. “Removing Noise from Noisy Signal Data Within Principal Component Analysis Framework”. Duzce University Journal of Science and Technology, vol. 13, no. 3, July 2025, pp. 1371-84, doi:10.29130/dubited.1649830.

Vancouver

1.Mehmet Cevri. Removing Noise from Noisy Signal Data within Principal Component Analysis Framework. DUBİTED. 2025 Jul. 1;13(3):1371-84. doi:10.29130/dubited.1649830

Removing Noise from Noisy Signal Data within Principal Component Analysis Framework

Abstract

Keywords

Supporting Institution

Ethical Statement

Temel Bileşenler Analizi Çerçevesinde Gürültülü Sinyal Verilerinden Gürültünün Giderilmesi

Abstract

Keywords

References

Details

Primary Language

Subjects

Journal Section

Authors

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

Cite